```{r}
#| label: setup
suppressPackageStartupMessages({
library(reticulate)
library(ggformula)
})
theme_set(theme_bw())
```There and back again
Including R and Python in Quarto
Basic back and forth between R and Python
- Include
library(reticulate)in your document. That package does the connecting between R and Python.
To access an R object named
objectin Python, user.object.To access a Python object named
objectin R, usepy$object.Do some double checking to make sure the object coversion yield what you are expecting.
Simple Example
```{r}
#| label: some-r
x <- 5
``````{python}
#| label: python-calling-r
print(r.x)
y = r.x * 2
```5.0```{r}
#| label: r-calling-python
py$y
```[1] 10More interesting Example
```{r}
#| label: r-penguins
library(palmerpenguins)
data(penguins)
penguins |> reactable::reactable()
str(penguins)
```tibble [344 × 8] (S3: tbl_df/tbl/data.frame)
$ species : Factor w/ 3 levels "Adelie","Chinstrap",..: 1 1 1 1 1 1 1 1 1 1 ...
$ island : Factor w/ 3 levels "Biscoe","Dream",..: 3 3 3 3 3 3 3 3 3 3 ...
$ bill_length_mm : num [1:344] 39.1 39.5 40.3 NA 36.7 39.3 38.9 39.2 34.1 42 ...
$ bill_depth_mm : num [1:344] 18.7 17.4 18 NA 19.3 20.6 17.8 19.6 18.1 20.2 ...
$ flipper_length_mm: int [1:344] 181 186 195 NA 193 190 181 195 193 190 ...
$ body_mass_g : int [1:344] 3750 3800 3250 NA 3450 3650 3625 4675 3475 4250 ...
$ sex : Factor w/ 2 levels "female","male": 2 1 1 NA 1 2 1 2 NA NA ...
$ year : int [1:344] 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 ...We can access penguins in Python, but what sort of thing will it be? Let’s do some snooping.
```{python}
#| label: python-penguins
import pandas as pd
penguins = r.penguins
type(penguins)
```<class 'pandas.core.frame.DataFrame'>dir()
dir() is another useful function we can use to snoop information about a Python object, but the output is often long, so let’s spend a little time with dir() to make it more manageable. Since the result of dir() is a Python object, we can inspect dir(penguins) to see what it is.
```{python}
#| label: what-does-dir-return
type(dir(penguins))
```<class 'list'>It’s a list. Let’s see how long the list is.
```{python}
#| label: how-long-is-dir
len(dir(penguins))
```448We could print all those values, but let’s just do some of them.
```{python}
#| label: culling-dir
dir(penguins)[0:10]
```['T', '_AXIS_LEN', '_AXIS_ORDERS', '_AXIS_TO_AXIS_NUMBER', '_HANDLED_TYPES', '__abs__', '__add__', '__and__', '__annotations__', '__array__']Now let’s look only the ones without underscores.
```{python}
[item for item in dir(penguins) if not "_" in item]
```['T', 'abs', 'add', 'agg', 'aggregate', 'align', 'all', 'any', 'apply', 'applymap', 'asfreq', 'asof', 'assign', 'astype', 'at', 'attrs', 'axes', 'backfill', 'bfill', 'bool', 'boxplot', 'clip', 'columns', 'combine', 'compare', 'copy', 'corr', 'corrwith', 'count', 'cov', 'cummax', 'cummin', 'cumprod', 'cumsum', 'describe', 'diff', 'div', 'divide', 'dot', 'drop', 'droplevel', 'dropna', 'dtypes', 'duplicated', 'empty', 'eq', 'equals', 'eval', 'ewm', 'expanding', 'explode', 'ffill', 'fillna', 'filter', 'first', 'flags', 'floordiv', 'ge', 'get', 'groupby', 'gt', 'head', 'hist', 'iat', 'idxmax', 'idxmin', 'iloc', 'index', 'info', 'insert', 'interpolate', 'isetitem', 'isin', 'island', 'isna', 'isnull', 'items', 'iterrows', 'itertuples', 'join', 'keys', 'kurt', 'kurtosis', 'last', 'le', 'loc', 'lt', 'map', 'mask', 'max', 'mean', 'median', 'melt', 'merge', 'min', 'mod', 'mode', 'mul', 'multiply', 'ndim', 'ne', 'nlargest', 'notna', 'notnull', 'nsmallest', 'nunique', 'pad', 'pipe', 'pivot', 'plot', 'pop', 'pow', 'prod', 'product', 'quantile', 'query', 'radd', 'rank', 'rdiv', 'reindex', 'rename', 'replace', 'resample', 'rfloordiv', 'rmod', 'rmul', 'rolling', 'round', 'rpow', 'rsub', 'rtruediv', 'sample', 'sem', 'sex', 'shape', 'shift', 'size', 'skew', 'species', 'squeeze', 'stack', 'std', 'style', 'sub', 'subtract', 'sum', 'swapaxes', 'swaplevel', 'tail', 'take', 'transform', 'transpose', 'truediv', 'truncate', 'unstack', 'update', 'values', 'var', 'where', 'xs', 'year']Clearly we have some things to learn about pandas data frames, but some of these sound familiar enough that we could just try them out:
```{python}
#| label: inspecting-penguins
penguins.shape
penguins.head(5)
penguins.tail(2)
penguins.ndim
penguins.species
type(penguins.species)
# We can guess that there is also a bill_length_mm (which we removed from our list)
penguins.bill_length_mm
type(penguins.bill_length_mm)
```(344, 8)
species island bill_length_mm ... body_mass_g sex year
0 Adelie Torgersen 39.1 ... 3750 male 2007
1 Adelie Torgersen 39.5 ... 3800 female 2007
2 Adelie Torgersen 40.3 ... 3250 female 2007
3 Adelie Torgersen NaN ... -2147483648 NaN 2007
4 Adelie Torgersen 36.7 ... 3450 female 2007
[5 rows x 8 columns]
species island bill_length_mm ... body_mass_g sex year
342 Chinstrap Dream 50.8 ... 4100 male 2009
343 Chinstrap Dream 50.2 ... 3775 female 2009
[2 rows x 8 columns]
2
0 Adelie
1 Adelie
2 Adelie
3 Adelie
4 Adelie
...
339 Chinstrap
340 Chinstrap
341 Chinstrap
342 Chinstrap
343 Chinstrap
Name: species, Length: 344, dtype: category
Categories (3, object): ['Adelie', 'Chinstrap', 'Gentoo']
<class 'pandas.core.series.Series'>
0 39.1
1 39.5
2 40.3
3 NaN
4 36.7
...
339 55.8
340 43.5
341 49.6
342 50.8
343 50.2
Name: bill_length_mm, Length: 344, dtype: float64
<class 'pandas.core.series.Series'>help()
The help() function provides another type of information: documentation. This can also be long. Open the block below to see what we get from help(penguins).
Show results of
help(penguins)
```{python}
help(penguins)
```Help on DataFrame in module pandas.core.frame object:
class DataFrame(pandas.core.generic.NDFrame, pandas.core.arraylike.OpsMixin)
| DataFrame(data=None, index: 'Axes | None' = None, columns: 'Axes | None' = None, dtype: 'Dtype | None' = None, copy: 'bool | None' = None) -> 'None'
|
| Two-dimensional, size-mutable, potentially heterogeneous tabular data.
|
| Data structure also contains labeled axes (rows and columns).
| Arithmetic operations align on both row and column labels. Can be
| thought of as a dict-like container for Series objects. The primary
| pandas data structure.
|
| Parameters
| ----------
| data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame
| Dict can contain Series, arrays, constants, dataclass or list-like objects. If
| data is a dict, column order follows insertion-order. If a dict contains Series
| which have an index defined, it is aligned by its index. This alignment also
| occurs if data is a Series or a DataFrame itself. Alignment is done on
| Series/DataFrame inputs.
|
| If data is a list of dicts, column order follows insertion-order.
|
| index : Index or array-like
| Index to use for resulting frame. Will default to RangeIndex if
| no indexing information part of input data and no index provided.
| columns : Index or array-like
| Column labels to use for resulting frame when data does not have them,
| defaulting to RangeIndex(0, 1, 2, ..., n). If data contains column labels,
| will perform column selection instead.
| dtype : dtype, default None
| Data type to force. Only a single dtype is allowed. If None, infer.
| copy : bool or None, default None
| Copy data from inputs.
| For dict data, the default of None behaves like ``copy=True``. For DataFrame
| or 2d ndarray input, the default of None behaves like ``copy=False``.
| If data is a dict containing one or more Series (possibly of different dtypes),
| ``copy=False`` will ensure that these inputs are not copied.
|
| .. versionchanged:: 1.3.0
|
| See Also
| --------
| DataFrame.from_records : Constructor from tuples, also record arrays.
| DataFrame.from_dict : From dicts of Series, arrays, or dicts.
| read_csv : Read a comma-separated values (csv) file into DataFrame.
| read_table : Read general delimited file into DataFrame.
| read_clipboard : Read text from clipboard into DataFrame.
|
| Notes
| -----
| Please reference the :ref:`User Guide <basics.dataframe>` for more information.
|
| Examples
| --------
| Constructing DataFrame from a dictionary.
|
| >>> d = {'col1': [1, 2], 'col2': [3, 4]}
| >>> df = pd.DataFrame(data=d)
| >>> df
| col1 col2
| 0 1 3
| 1 2 4
|
| Notice that the inferred dtype is int64.
|
| >>> df.dtypes
| col1 int64
| col2 int64
| dtype: object
|
| To enforce a single dtype:
|
| >>> df = pd.DataFrame(data=d, dtype=np.int8)
| >>> df.dtypes
| col1 int8
| col2 int8
| dtype: object
|
| Constructing DataFrame from a dictionary including Series:
|
| >>> d = {'col1': [0, 1, 2, 3], 'col2': pd.Series([2, 3], index=[2, 3])}
| >>> pd.DataFrame(data=d, index=[0, 1, 2, 3])
| col1 col2
| 0 0 NaN
| 1 1 NaN
| 2 2 2.0
| 3 3 3.0
|
| Constructing DataFrame from numpy ndarray:
|
| >>> df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
| ... columns=['a', 'b', 'c'])
| >>> df2
| a b c
| 0 1 2 3
| 1 4 5 6
| 2 7 8 9
|
| Constructing DataFrame from a numpy ndarray that has labeled columns:
|
| >>> data = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],
| ... dtype=[("a", "i4"), ("b", "i4"), ("c", "i4")])
| >>> df3 = pd.DataFrame(data, columns=['c', 'a'])
| ...
| >>> df3
| c a
| 0 3 1
| 1 6 4
| 2 9 7
|
| Constructing DataFrame from dataclass:
|
| >>> from dataclasses import make_dataclass
| >>> Point = make_dataclass("Point", [("x", int), ("y", int)])
| >>> pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])
| x y
| 0 0 0
| 1 0 3
| 2 2 3
|
| Constructing DataFrame from Series/DataFrame:
|
| >>> ser = pd.Series([1, 2, 3], index=["a", "b", "c"])
| >>> df = pd.DataFrame(data=ser, index=["a", "c"])
| >>> df
| 0
| a 1
| c 3
|
| >>> df1 = pd.DataFrame([1, 2, 3], index=["a", "b", "c"], columns=["x"])
| >>> df2 = pd.DataFrame(data=df1, index=["a", "c"])
| >>> df2
| x
| a 1
| c 3
|
| Method resolution order:
| DataFrame
| pandas.core.generic.NDFrame
| pandas.core.base.PandasObject
| pandas.core.accessor.DirNamesMixin
| pandas.core.indexing.IndexingMixin
| pandas.core.arraylike.OpsMixin
| builtins.object
|
| Methods defined here:
|
| __arrow_c_stream__(self, requested_schema=None)
| Export the pandas DataFrame as an Arrow C stream PyCapsule.
|
| This relies on pyarrow to convert the pandas DataFrame to the Arrow
| format (and follows the default behaviour of ``pyarrow.Table.from_pandas``
| in its handling of the index, i.e. store the index as a column except
| for RangeIndex).
| This conversion is not necessarily zero-copy.
|
| Parameters
| ----------
| requested_schema : PyCapsule, default None
| The schema to which the dataframe should be casted, passed as a
| PyCapsule containing a C ArrowSchema representation of the
| requested schema.
|
| Returns
| -------
| PyCapsule
|
| __dataframe__(self, nan_as_null: 'bool' = False, allow_copy: 'bool' = True) -> 'DataFrameXchg'
| Return the dataframe interchange object implementing the interchange protocol.
|
| Parameters
| ----------
| nan_as_null : bool, default False
| `nan_as_null` is DEPRECATED and has no effect. Please avoid using
| it; it will be removed in a future release.
| allow_copy : bool, default True
| Whether to allow memory copying when exporting. If set to False
| it would cause non-zero-copy exports to fail.
|
| Returns
| -------
| DataFrame interchange object
| The object which consuming library can use to ingress the dataframe.
|
| Notes
| -----
| Details on the interchange protocol:
| https://data-apis.org/dataframe-protocol/latest/index.html
|
| Examples
| --------
| >>> df_not_necessarily_pandas = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
| >>> interchange_object = df_not_necessarily_pandas.__dataframe__()
| >>> interchange_object.column_names()
| Index(['A', 'B'], dtype='object')
| >>> df_pandas = (pd.api.interchange.from_dataframe
| ... (interchange_object.select_columns_by_name(['A'])))
| >>> df_pandas
| A
| 0 1
| 1 2
|
| These methods (``column_names``, ``select_columns_by_name``) should work
| for any dataframe library which implements the interchange protocol.
|
| __dataframe_consortium_standard__(self, *, api_version: 'str | None' = None) -> 'Any'
| Provide entry point to the Consortium DataFrame Standard API.
|
| This is developed and maintained outside of pandas.
| Please report any issues to https://github.com/data-apis/dataframe-api-compat.
|
| __divmod__(self, other) -> 'tuple[DataFrame, DataFrame]'
|
| __getitem__(self, key)
|
| __init__(self, data=None, index: 'Axes | None' = None, columns: 'Axes | None' = None, dtype: 'Dtype | None' = None, copy: 'bool | None' = None) -> 'None'
| Initialize self. See help(type(self)) for accurate signature.
|
| __len__(self) -> 'int'
| Returns length of info axis, but here we use the index.
|
| __matmul__(self, other: 'AnyArrayLike | DataFrame') -> 'DataFrame | Series'
| Matrix multiplication using binary `@` operator.
|
| __rdivmod__(self, other) -> 'tuple[DataFrame, DataFrame]'
|
| __repr__(self) -> 'str'
| Return a string representation for a particular DataFrame.
|
| __rmatmul__(self, other) -> 'DataFrame'
| Matrix multiplication using binary `@` operator.
|
| __setitem__(self, key, value) -> 'None'
|
| add(self, other, axis: 'Axis' = 'columns', level=None, fill_value=None) -> 'DataFrame'
| Get Addition of dataframe and other, element-wise (binary operator `add`).
|
| Equivalent to ``dataframe + other``, but with support to substitute a fill_value
| for missing data in one of the inputs. With reverse version, `radd`.
|
| Among flexible wrappers (`add`, `sub`, `mul`, `div`, `floordiv`, `mod`, `pow`) to
| arithmetic operators: `+`, `-`, `*`, `/`, `//`, `%`, `**`.
|
| Parameters
| ----------
| other : scalar, sequence, Series, dict or DataFrame
| Any single or multiple element data structure, or list-like object.
| axis : {0 or 'index', 1 or 'columns'}
| Whether to compare by the index (0 or 'index') or columns.
| (1 or 'columns'). For Series input, axis to match Series index on.
| level : int or label
| Broadcast across a level, matching Index values on the
| passed MultiIndex level.
| fill_value : float or None, default None
| Fill existing missing (NaN) values, and any new element needed for
| successful DataFrame alignment, with this value before computation.
| If data in both corresponding DataFrame locations is missing
| the result will be missing.
|
| Returns
| -------
| DataFrame
| Result of the arithmetic operation.
|
| See Also
| --------
| DataFrame.add : Add DataFrames.
| DataFrame.sub : Subtract DataFrames.
| DataFrame.mul : Multiply DataFrames.
| DataFrame.div : Divide DataFrames (float division).
| DataFrame.truediv : Divide DataFrames (float division).
| DataFrame.floordiv : Divide DataFrames (integer division).
| DataFrame.mod : Calculate modulo (remainder after division).
| DataFrame.pow : Calculate exponential power.
|
| Notes
| -----
| Mismatched indices will be unioned together.
|
| Examples
| --------
| >>> df = pd.DataFrame({'angles': [0, 3, 4],
| ... 'degrees': [360, 180, 360]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> df
| angles degrees
| circle 0 360
| triangle 3 180
| rectangle 4 360
|
| Add a scalar with operator version which return the same
| results.
|
| >>> df + 1
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| >>> df.add(1)
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| Divide by constant with reverse version.
|
| >>> df.div(10)
| angles degrees
| circle 0.0 36.0
| triangle 0.3 18.0
| rectangle 0.4 36.0
|
| >>> df.rdiv(10)
| angles degrees
| circle inf 0.027778
| triangle 3.333333 0.055556
| rectangle 2.500000 0.027778
|
| Subtract a list and Series by axis with operator version.
|
| >>> df - [1, 2]
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub([1, 2], axis='columns')
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
| ... axis='index')
| angles degrees
| circle -1 359
| triangle 2 179
| rectangle 3 359
|
| Multiply a dictionary by axis.
|
| >>> df.mul({'angles': 0, 'degrees': 2})
| angles degrees
| circle 0 720
| triangle 0 360
| rectangle 0 720
|
| >>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
| angles degrees
| circle 0 0
| triangle 6 360
| rectangle 12 1080
|
| Multiply a DataFrame of different shape with operator version.
|
| >>> other = pd.DataFrame({'angles': [0, 3, 4]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> other
| angles
| circle 0
| triangle 3
| rectangle 4
|
| >>> df * other
| angles degrees
| circle 0 NaN
| triangle 9 NaN
| rectangle 16 NaN
|
| >>> df.mul(other, fill_value=0)
| angles degrees
| circle 0 0.0
| triangle 9 0.0
| rectangle 16 0.0
|
| Divide by a MultiIndex by level.
|
| >>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
| ... 'degrees': [360, 180, 360, 360, 540, 720]},
| ... index=[['A', 'A', 'A', 'B', 'B', 'B'],
| ... ['circle', 'triangle', 'rectangle',
| ... 'square', 'pentagon', 'hexagon']])
| >>> df_multindex
| angles degrees
| A circle 0 360
| triangle 3 180
| rectangle 4 360
| B square 4 360
| pentagon 5 540
| hexagon 6 720
|
| >>> df.div(df_multindex, level=1, fill_value=0)
| angles degrees
| A circle NaN 1.0
| triangle 1.0 1.0
| rectangle 1.0 1.0
| B square 0.0 0.0
| pentagon 0.0 0.0
| hexagon 0.0 0.0
|
| agg = aggregate(self, func=None, axis: 'Axis' = 0, *args, **kwargs)
|
| aggregate(self, func=None, axis: 'Axis' = 0, *args, **kwargs)
| Aggregate using one or more operations over the specified axis.
|
| Parameters
| ----------
| func : function, str, list or dict
| Function to use for aggregating the data. If a function, must either
| work when passed a DataFrame or when passed to DataFrame.apply.
|
| Accepted combinations are:
|
| - function
| - string function name
| - list of functions and/or function names, e.g. ``[np.sum, 'mean']``
| - dict of axis labels -> functions, function names or list of such.
| axis : {0 or 'index', 1 or 'columns'}, default 0
| If 0 or 'index': apply function to each column.
| If 1 or 'columns': apply function to each row.
| *args
| Positional arguments to pass to `func`.
| **kwargs
| Keyword arguments to pass to `func`.
|
| Returns
| -------
| scalar, Series or DataFrame
|
| The return can be:
|
| * scalar : when Series.agg is called with single function
| * Series : when DataFrame.agg is called with a single function
| * DataFrame : when DataFrame.agg is called with several functions
|
| See Also
| --------
| DataFrame.apply : Perform any type of operations.
| DataFrame.transform : Perform transformation type operations.
| pandas.DataFrame.groupby : Perform operations over groups.
| pandas.DataFrame.resample : Perform operations over resampled bins.
| pandas.DataFrame.rolling : Perform operations over rolling window.
| pandas.DataFrame.expanding : Perform operations over expanding window.
| pandas.core.window.ewm.ExponentialMovingWindow : Perform operation over exponential
| weighted window.
|
| Notes
| -----
| The aggregation operations are always performed over an axis, either the
| index (default) or the column axis. This behavior is different from
| `numpy` aggregation functions (`mean`, `median`, `prod`, `sum`, `std`,
| `var`), where the default is to compute the aggregation of the flattened
| array, e.g., ``numpy.mean(arr_2d)`` as opposed to
| ``numpy.mean(arr_2d, axis=0)``.
|
| `agg` is an alias for `aggregate`. Use the alias.
|
| Functions that mutate the passed object can produce unexpected
| behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
| for more details.
|
| A passed user-defined-function will be passed a Series for evaluation.
|
| Examples
| --------
| >>> df = pd.DataFrame([[1, 2, 3],
| ... [4, 5, 6],
| ... [7, 8, 9],
| ... [np.nan, np.nan, np.nan]],
| ... columns=['A', 'B', 'C'])
|
| Aggregate these functions over the rows.
|
| >>> df.agg(['sum', 'min'])
| A B C
| sum 12.0 15.0 18.0
| min 1.0 2.0 3.0
|
| Different aggregations per column.
|
| >>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})
| A B
| sum 12.0 NaN
| min 1.0 2.0
| max NaN 8.0
|
| Aggregate different functions over the columns and rename the index of the resulting
| DataFrame.
|
| >>> df.agg(x=('A', 'max'), y=('B', 'min'), z=('C', 'mean'))
| A B C
| x 7.0 NaN NaN
| y NaN 2.0 NaN
| z NaN NaN 6.0
|
| Aggregate over the columns.
|
| >>> df.agg("mean", axis="columns")
| 0 2.0
| 1 5.0
| 2 8.0
| 3 NaN
| dtype: float64
|
| all(self, axis: 'Axis | None' = 0, bool_only: 'bool' = False, skipna: 'bool' = True, **kwargs) -> 'Series | bool'
| Return whether all elements are True, potentially over an axis.
|
| Returns True unless there at least one element within a series or
| along a Dataframe axis that is False or equivalent (e.g. zero or
| empty).
|
| Parameters
| ----------
| axis : {0 or 'index', 1 or 'columns', None}, default 0
| Indicate which axis or axes should be reduced. For `Series` this parameter
| is unused and defaults to 0.
|
| * 0 / 'index' : reduce the index, return a Series whose index is the
| original column labels.
| * 1 / 'columns' : reduce the columns, return a Series whose index is the
| original index.
| * None : reduce all axes, return a scalar.
|
| bool_only : bool, default False
| Include only boolean columns. Not implemented for Series.
| skipna : bool, default True
| Exclude NA/null values. If the entire row/column is NA and skipna is
| True, then the result will be True, as for an empty row/column.
| If skipna is False, then NA are treated as True, because these are not
| equal to zero.
| **kwargs : any, default None
| Additional keywords have no effect but might be accepted for
| compatibility with NumPy.
|
| Returns
| -------
| Series or DataFrame
| If level is specified, then, DataFrame is returned; otherwise, Series
| is returned.
|
| See Also
| --------
| Series.all : Return True if all elements are True.
| DataFrame.any : Return True if one (or more) elements are True.
|
| Examples
| --------
| **Series**
|
| >>> pd.Series([True, True]).all()
| True
| >>> pd.Series([True, False]).all()
| False
| >>> pd.Series([], dtype="float64").all()
| True
| >>> pd.Series([np.nan]).all()
| True
| >>> pd.Series([np.nan]).all(skipna=False)
| True
|
| **DataFrames**
|
| Create a dataframe from a dictionary.
|
| >>> df = pd.DataFrame({'col1': [True, True], 'col2': [True, False]})
| >>> df
| col1 col2
| 0 True True
| 1 True False
|
| Default behaviour checks if values in each column all return True.
|
| >>> df.all()
| col1 True
| col2 False
| dtype: bool
|
| Specify ``axis='columns'`` to check if values in each row all return True.
|
| >>> df.all(axis='columns')
| 0 True
| 1 False
| dtype: bool
|
| Or ``axis=None`` for whether every value is True.
|
| >>> df.all(axis=None)
| False
|
| any(self, *, axis: 'Axis | None' = 0, bool_only: 'bool' = False, skipna: 'bool' = True, **kwargs) -> 'Series | bool'
| Return whether any element is True, potentially over an axis.
|
| Returns False unless there is at least one element within a series or
| along a Dataframe axis that is True or equivalent (e.g. non-zero or
| non-empty).
|
| Parameters
| ----------
| axis : {0 or 'index', 1 or 'columns', None}, default 0
| Indicate which axis or axes should be reduced. For `Series` this parameter
| is unused and defaults to 0.
|
| * 0 / 'index' : reduce the index, return a Series whose index is the
| original column labels.
| * 1 / 'columns' : reduce the columns, return a Series whose index is the
| original index.
| * None : reduce all axes, return a scalar.
|
| bool_only : bool, default False
| Include only boolean columns. Not implemented for Series.
| skipna : bool, default True
| Exclude NA/null values. If the entire row/column is NA and skipna is
| True, then the result will be False, as for an empty row/column.
| If skipna is False, then NA are treated as True, because these are not
| equal to zero.
| **kwargs : any, default None
| Additional keywords have no effect but might be accepted for
| compatibility with NumPy.
|
| Returns
| -------
| Series or DataFrame
| If level is specified, then, DataFrame is returned; otherwise, Series
| is returned.
|
| See Also
| --------
| numpy.any : Numpy version of this method.
| Series.any : Return whether any element is True.
| Series.all : Return whether all elements are True.
| DataFrame.any : Return whether any element is True over requested axis.
| DataFrame.all : Return whether all elements are True over requested axis.
|
| Examples
| --------
| **Series**
|
| For Series input, the output is a scalar indicating whether any element
| is True.
|
| >>> pd.Series([False, False]).any()
| False
| >>> pd.Series([True, False]).any()
| True
| >>> pd.Series([], dtype="float64").any()
| False
| >>> pd.Series([np.nan]).any()
| False
| >>> pd.Series([np.nan]).any(skipna=False)
| True
|
| **DataFrame**
|
| Whether each column contains at least one True element (the default).
|
| >>> df = pd.DataFrame({"A": [1, 2], "B": [0, 2], "C": [0, 0]})
| >>> df
| A B C
| 0 1 0 0
| 1 2 2 0
|
| >>> df.any()
| A True
| B True
| C False
| dtype: bool
|
| Aggregating over the columns.
|
| >>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]})
| >>> df
| A B
| 0 True 1
| 1 False 2
|
| >>> df.any(axis='columns')
| 0 True
| 1 True
| dtype: bool
|
| >>> df = pd.DataFrame({"A": [True, False], "B": [1, 0]})
| >>> df
| A B
| 0 True 1
| 1 False 0
|
| >>> df.any(axis='columns')
| 0 True
| 1 False
| dtype: bool
|
| Aggregating over the entire DataFrame with ``axis=None``.
|
| >>> df.any(axis=None)
| True
|
| `any` for an empty DataFrame is an empty Series.
|
| >>> pd.DataFrame([]).any()
| Series([], dtype: bool)
|
| apply(self, func: 'AggFuncType', axis: 'Axis' = 0, raw: 'bool' = False, result_type: "Literal['expand', 'reduce', 'broadcast'] | None" = None, args=(), by_row: "Literal[False, 'compat']" = 'compat', engine: "Literal['python', 'numba']" = 'python', engine_kwargs: 'dict[str, bool] | None' = None, **kwargs)
| Apply a function along an axis of the DataFrame.
|
| Objects passed to the function are Series objects whose index is
| either the DataFrame's index (``axis=0``) or the DataFrame's columns
| (``axis=1``). By default (``result_type=None``), the final return type
| is inferred from the return type of the applied function. Otherwise,
| it depends on the `result_type` argument.
|
| Parameters
| ----------
| func : function
| Function to apply to each column or row.
| axis : {0 or 'index', 1 or 'columns'}, default 0
| Axis along which the function is applied:
|
| * 0 or 'index': apply function to each column.
| * 1 or 'columns': apply function to each row.
|
| raw : bool, default False
| Determines if row or column is passed as a Series or ndarray object:
|
| * ``False`` : passes each row or column as a Series to the
| function.
| * ``True`` : the passed function will receive ndarray objects
| instead.
| If you are just applying a NumPy reduction function this will
| achieve much better performance.
|
| result_type : {'expand', 'reduce', 'broadcast', None}, default None
| These only act when ``axis=1`` (columns):
|
| * 'expand' : list-like results will be turned into columns.
| * 'reduce' : returns a Series if possible rather than expanding
| list-like results. This is the opposite of 'expand'.
| * 'broadcast' : results will be broadcast to the original shape
| of the DataFrame, the original index and columns will be
| retained.
|
| The default behaviour (None) depends on the return value of the
| applied function: list-like results will be returned as a Series
| of those. However if the apply function returns a Series these
| are expanded to columns.
| args : tuple
| Positional arguments to pass to `func` in addition to the
| array/series.
| by_row : False or "compat", default "compat"
| Only has an effect when ``func`` is a listlike or dictlike of funcs
| and the func isn't a string.
| If "compat", will if possible first translate the func into pandas
| methods (e.g. ``Series().apply(np.sum)`` will be translated to
| ``Series().sum()``). If that doesn't work, will try call to apply again with
| ``by_row=True`` and if that fails, will call apply again with
| ``by_row=False`` (backward compatible).
| If False, the funcs will be passed the whole Series at once.
|
| .. versionadded:: 2.1.0
|
| engine : {'python', 'numba'}, default 'python'
| Choose between the python (default) engine or the numba engine in apply.
|
| The numba engine will attempt to JIT compile the passed function,
| which may result in speedups for large DataFrames.
| It also supports the following engine_kwargs :
|
| - nopython (compile the function in nopython mode)
| - nogil (release the GIL inside the JIT compiled function)
| - parallel (try to apply the function in parallel over the DataFrame)
|
| Note: Due to limitations within numba/how pandas interfaces with numba,
| you should only use this if raw=True
|
| Note: The numba compiler only supports a subset of
| valid Python/numpy operations.
|
| Please read more about the `supported python features
| <https://numba.pydata.org/numba-doc/dev/reference/pysupported.html>`_
| and `supported numpy features
| <https://numba.pydata.org/numba-doc/dev/reference/numpysupported.html>`_
| in numba to learn what you can or cannot use in the passed function.
|
| .. versionadded:: 2.2.0
|
| engine_kwargs : dict
| Pass keyword arguments to the engine.
| This is currently only used by the numba engine,
| see the documentation for the engine argument for more information.
| **kwargs
| Additional keyword arguments to pass as keywords arguments to
| `func`.
|
| Returns
| -------
| Series or DataFrame
| Result of applying ``func`` along the given axis of the
| DataFrame.
|
| See Also
| --------
| DataFrame.map: For elementwise operations.
| DataFrame.aggregate: Only perform aggregating type operations.
| DataFrame.transform: Only perform transforming type operations.
|
| Notes
| -----
| Functions that mutate the passed object can produce unexpected
| behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
| for more details.
|
| Examples
| --------
| >>> df = pd.DataFrame([[4, 9]] * 3, columns=['A', 'B'])
| >>> df
| A B
| 0 4 9
| 1 4 9
| 2 4 9
|
| Using a numpy universal function (in this case the same as
| ``np.sqrt(df)``):
|
| >>> df.apply(np.sqrt)
| A B
| 0 2.0 3.0
| 1 2.0 3.0
| 2 2.0 3.0
|
| Using a reducing function on either axis
|
| >>> df.apply(np.sum, axis=0)
| A 12
| B 27
| dtype: int64
|
| >>> df.apply(np.sum, axis=1)
| 0 13
| 1 13
| 2 13
| dtype: int64
|
| Returning a list-like will result in a Series
|
| >>> df.apply(lambda x: [1, 2], axis=1)
| 0 [1, 2]
| 1 [1, 2]
| 2 [1, 2]
| dtype: object
|
| Passing ``result_type='expand'`` will expand list-like results
| to columns of a Dataframe
|
| >>> df.apply(lambda x: [1, 2], axis=1, result_type='expand')
| 0 1
| 0 1 2
| 1 1 2
| 2 1 2
|
| Returning a Series inside the function is similar to passing
| ``result_type='expand'``. The resulting column names
| will be the Series index.
|
| >>> df.apply(lambda x: pd.Series([1, 2], index=['foo', 'bar']), axis=1)
| foo bar
| 0 1 2
| 1 1 2
| 2 1 2
|
| Passing ``result_type='broadcast'`` will ensure the same shape
| result, whether list-like or scalar is returned by the function,
| and broadcast it along the axis. The resulting column names will
| be the originals.
|
| >>> df.apply(lambda x: [1, 2], axis=1, result_type='broadcast')
| A B
| 0 1 2
| 1 1 2
| 2 1 2
|
| applymap(self, func: 'PythonFuncType', na_action: 'NaAction | None' = None, **kwargs) -> 'DataFrame'
| Apply a function to a Dataframe elementwise.
|
| .. deprecated:: 2.1.0
|
| DataFrame.applymap has been deprecated. Use DataFrame.map instead.
|
| This method applies a function that accepts and returns a scalar
| to every element of a DataFrame.
|
| Parameters
| ----------
| func : callable
| Python function, returns a single value from a single value.
| na_action : {None, 'ignore'}, default None
| If 'ignore', propagate NaN values, without passing them to func.
| **kwargs
| Additional keyword arguments to pass as keywords arguments to
| `func`.
|
| Returns
| -------
| DataFrame
| Transformed DataFrame.
|
| See Also
| --------
| DataFrame.apply : Apply a function along input axis of DataFrame.
| DataFrame.map : Apply a function along input axis of DataFrame.
| DataFrame.replace: Replace values given in `to_replace` with `value`.
|
| Examples
| --------
| >>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])
| >>> df
| 0 1
| 0 1.000 2.120
| 1 3.356 4.567
|
| >>> df.map(lambda x: len(str(x)))
| 0 1
| 0 3 4
| 1 5 5
|
| assign(self, **kwargs) -> 'DataFrame'
| Assign new columns to a DataFrame.
|
| Returns a new object with all original columns in addition to new ones.
| Existing columns that are re-assigned will be overwritten.
|
| Parameters
| ----------
| **kwargs : dict of {str: callable or Series}
| The column names are keywords. If the values are
| callable, they are computed on the DataFrame and
| assigned to the new columns. The callable must not
| change input DataFrame (though pandas doesn't check it).
| If the values are not callable, (e.g. a Series, scalar, or array),
| they are simply assigned.
|
| Returns
| -------
| DataFrame
| A new DataFrame with the new columns in addition to
| all the existing columns.
|
| Notes
| -----
| Assigning multiple columns within the same ``assign`` is possible.
| Later items in '\*\*kwargs' may refer to newly created or modified
| columns in 'df'; items are computed and assigned into 'df' in order.
|
| Examples
| --------
| >>> df = pd.DataFrame({'temp_c': [17.0, 25.0]},
| ... index=['Portland', 'Berkeley'])
| >>> df
| temp_c
| Portland 17.0
| Berkeley 25.0
|
| Where the value is a callable, evaluated on `df`:
|
| >>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
| temp_c temp_f
| Portland 17.0 62.6
| Berkeley 25.0 77.0
|
| Alternatively, the same behavior can be achieved by directly
| referencing an existing Series or sequence:
|
| >>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)
| temp_c temp_f
| Portland 17.0 62.6
| Berkeley 25.0 77.0
|
| You can create multiple columns within the same assign where one
| of the columns depends on another one defined within the same assign:
|
| >>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,
| ... temp_k=lambda x: (x['temp_f'] + 459.67) * 5 / 9)
| temp_c temp_f temp_k
| Portland 17.0 62.6 290.15
| Berkeley 25.0 77.0 298.15
|
| boxplot = boxplot_frame(self: 'DataFrame', column=None, by=None, ax=None, fontsize: 'int | None' = None, rot: 'int' = 0, grid: 'bool' = True, figsize: 'tuple[float, float] | None' = None, layout=None, return_type=None, backend=None, **kwargs) from pandas.plotting._core
| Make a box plot from DataFrame columns.
|
| Make a box-and-whisker plot from DataFrame columns, optionally grouped
| by some other columns. A box plot is a method for graphically depicting
| groups of numerical data through their quartiles.
| The box extends from the Q1 to Q3 quartile values of the data,
| with a line at the median (Q2). The whiskers extend from the edges
| of box to show the range of the data. By default, they extend no more than
| `1.5 * IQR (IQR = Q3 - Q1)` from the edges of the box, ending at the farthest
| data point within that interval. Outliers are plotted as separate dots.
|
| For further details see
| Wikipedia's entry for `boxplot <https://en.wikipedia.org/wiki/Box_plot>`_.
|
| Parameters
| ----------
| column : str or list of str, optional
| Column name or list of names, or vector.
| Can be any valid input to :meth:`pandas.DataFrame.groupby`.
| by : str or array-like, optional
| Column in the DataFrame to :meth:`pandas.DataFrame.groupby`.
| One box-plot will be done per value of columns in `by`.
| ax : object of class matplotlib.axes.Axes, optional
| The matplotlib axes to be used by boxplot.
| fontsize : float or str
| Tick label font size in points or as a string (e.g., `large`).
| rot : float, default 0
| The rotation angle of labels (in degrees)
| with respect to the screen coordinate system.
| grid : bool, default True
| Setting this to True will show the grid.
| figsize : A tuple (width, height) in inches
| The size of the figure to create in matplotlib.
| layout : tuple (rows, columns), optional
| For example, (3, 5) will display the subplots
| using 3 rows and 5 columns, starting from the top-left.
| return_type : {'axes', 'dict', 'both'} or None, default 'axes'
| The kind of object to return. The default is ``axes``.
|
| * 'axes' returns the matplotlib axes the boxplot is drawn on.
| * 'dict' returns a dictionary whose values are the matplotlib
| Lines of the boxplot.
| * 'both' returns a namedtuple with the axes and dict.
| * when grouping with ``by``, a Series mapping columns to
| ``return_type`` is returned.
|
| If ``return_type`` is `None`, a NumPy array
| of axes with the same shape as ``layout`` is returned.
| backend : str, default None
| Backend to use instead of the backend specified in the option
| ``plotting.backend``. For instance, 'matplotlib'. Alternatively, to
| specify the ``plotting.backend`` for the whole session, set
| ``pd.options.plotting.backend``.
|
| **kwargs
| All other plotting keyword arguments to be passed to
| :func:`matplotlib.pyplot.boxplot`.
|
| Returns
| -------
| result
| See Notes.
|
| See Also
| --------
| pandas.Series.plot.hist: Make a histogram.
| matplotlib.pyplot.boxplot : Matplotlib equivalent plot.
|
| Notes
| -----
| The return type depends on the `return_type` parameter:
|
| * 'axes' : object of class matplotlib.axes.Axes
| * 'dict' : dict of matplotlib.lines.Line2D objects
| * 'both' : a namedtuple with structure (ax, lines)
|
| For data grouped with ``by``, return a Series of the above or a numpy
| array:
|
| * :class:`~pandas.Series`
| * :class:`~numpy.array` (for ``return_type = None``)
|
| Use ``return_type='dict'`` when you want to tweak the appearance
| of the lines after plotting. In this case a dict containing the Lines
| making up the boxes, caps, fliers, medians, and whiskers is returned.
|
| Examples
| --------
|
| Boxplots can be created for every column in the dataframe
| by ``df.boxplot()`` or indicating the columns to be used:
|
| .. plot::
| :context: close-figs
|
| >>> np.random.seed(1234)
| >>> df = pd.DataFrame(np.random.randn(10, 4),
| ... columns=['Col1', 'Col2', 'Col3', 'Col4'])
| >>> boxplot = df.boxplot(column=['Col1', 'Col2', 'Col3']) # doctest: +SKIP
|
| Boxplots of variables distributions grouped by the values of a third
| variable can be created using the option ``by``. For instance:
|
| .. plot::
| :context: close-figs
|
| >>> df = pd.DataFrame(np.random.randn(10, 2),
| ... columns=['Col1', 'Col2'])
| >>> df['X'] = pd.Series(['A', 'A', 'A', 'A', 'A',
| ... 'B', 'B', 'B', 'B', 'B'])
| >>> boxplot = df.boxplot(by='X')
|
| A list of strings (i.e. ``['X', 'Y']``) can be passed to boxplot
| in order to group the data by combination of the variables in the x-axis:
|
| .. plot::
| :context: close-figs
|
| >>> df = pd.DataFrame(np.random.randn(10, 3),
| ... columns=['Col1', 'Col2', 'Col3'])
| >>> df['X'] = pd.Series(['A', 'A', 'A', 'A', 'A',
| ... 'B', 'B', 'B', 'B', 'B'])
| >>> df['Y'] = pd.Series(['A', 'B', 'A', 'B', 'A',
| ... 'B', 'A', 'B', 'A', 'B'])
| >>> boxplot = df.boxplot(column=['Col1', 'Col2'], by=['X', 'Y'])
|
| The layout of boxplot can be adjusted giving a tuple to ``layout``:
|
| .. plot::
| :context: close-figs
|
| >>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X',
| ... layout=(2, 1))
|
| Additional formatting can be done to the boxplot, like suppressing the grid
| (``grid=False``), rotating the labels in the x-axis (i.e. ``rot=45``)
| or changing the fontsize (i.e. ``fontsize=15``):
|
| .. plot::
| :context: close-figs
|
| >>> boxplot = df.boxplot(grid=False, rot=45, fontsize=15) # doctest: +SKIP
|
| The parameter ``return_type`` can be used to select the type of element
| returned by `boxplot`. When ``return_type='axes'`` is selected,
| the matplotlib axes on which the boxplot is drawn are returned:
|
| >>> boxplot = df.boxplot(column=['Col1', 'Col2'], return_type='axes')
| >>> type(boxplot)
| <class 'matplotlib.axes._axes.Axes'>
|
| When grouping with ``by``, a Series mapping columns to ``return_type``
| is returned:
|
| >>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X',
| ... return_type='axes')
| >>> type(boxplot)
| <class 'pandas.core.series.Series'>
|
| If ``return_type`` is `None`, a NumPy array of axes with the same shape
| as ``layout`` is returned:
|
| >>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X',
| ... return_type=None)
| >>> type(boxplot)
| <class 'numpy.ndarray'>
|
| combine(self, other: 'DataFrame', func: 'Callable[[Series, Series], Series | Hashable]', fill_value=None, overwrite: 'bool' = True) -> 'DataFrame'
| Perform column-wise combine with another DataFrame.
|
| Combines a DataFrame with `other` DataFrame using `func`
| to element-wise combine columns. The row and column indexes of the
| resulting DataFrame will be the union of the two.
|
| Parameters
| ----------
| other : DataFrame
| The DataFrame to merge column-wise.
| func : function
| Function that takes two series as inputs and return a Series or a
| scalar. Used to merge the two dataframes column by columns.
| fill_value : scalar value, default None
| The value to fill NaNs with prior to passing any column to the
| merge func.
| overwrite : bool, default True
| If True, columns in `self` that do not exist in `other` will be
| overwritten with NaNs.
|
| Returns
| -------
| DataFrame
| Combination of the provided DataFrames.
|
| See Also
| --------
| DataFrame.combine_first : Combine two DataFrame objects and default to
| non-null values in frame calling the method.
|
| Examples
| --------
| Combine using a simple function that chooses the smaller column.
|
| >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
| >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
| >>> take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2
| >>> df1.combine(df2, take_smaller)
| A B
| 0 0 3
| 1 0 3
|
| Example using a true element-wise combine function.
|
| >>> df1 = pd.DataFrame({'A': [5, 0], 'B': [2, 4]})
| >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
| >>> df1.combine(df2, np.minimum)
| A B
| 0 1 2
| 1 0 3
|
| Using `fill_value` fills Nones prior to passing the column to the
| merge function.
|
| >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
| >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
| >>> df1.combine(df2, take_smaller, fill_value=-5)
| A B
| 0 0 -5.0
| 1 0 4.0
|
| However, if the same element in both dataframes is None, that None
| is preserved
|
| >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
| >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [None, 3]})
| >>> df1.combine(df2, take_smaller, fill_value=-5)
| A B
| 0 0 -5.0
| 1 0 3.0
|
| Example that demonstrates the use of `overwrite` and behavior when
| the axis differ between the dataframes.
|
| >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
| >>> df2 = pd.DataFrame({'B': [3, 3], 'C': [-10, 1], }, index=[1, 2])
| >>> df1.combine(df2, take_smaller)
| A B C
| 0 NaN NaN NaN
| 1 NaN 3.0 -10.0
| 2 NaN 3.0 1.0
|
| >>> df1.combine(df2, take_smaller, overwrite=False)
| A B C
| 0 0.0 NaN NaN
| 1 0.0 3.0 -10.0
| 2 NaN 3.0 1.0
|
| Demonstrating the preference of the passed in dataframe.
|
| >>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1], }, index=[1, 2])
| >>> df2.combine(df1, take_smaller)
| A B C
| 0 0.0 NaN NaN
| 1 0.0 3.0 NaN
| 2 NaN 3.0 NaN
|
| >>> df2.combine(df1, take_smaller, overwrite=False)
| A B C
| 0 0.0 NaN NaN
| 1 0.0 3.0 1.0
| 2 NaN 3.0 1.0
|
| combine_first(self, other: 'DataFrame') -> 'DataFrame'
| Update null elements with value in the same location in `other`.
|
| Combine two DataFrame objects by filling null values in one DataFrame
| with non-null values from other DataFrame. The row and column indexes
| of the resulting DataFrame will be the union of the two. The resulting
| dataframe contains the 'first' dataframe values and overrides the
| second one values where both first.loc[index, col] and
| second.loc[index, col] are not missing values, upon calling
| first.combine_first(second).
|
| Parameters
| ----------
| other : DataFrame
| Provided DataFrame to use to fill null values.
|
| Returns
| -------
| DataFrame
| The result of combining the provided DataFrame with the other object.
|
| See Also
| --------
| DataFrame.combine : Perform series-wise operation on two DataFrames
| using a given function.
|
| Examples
| --------
| >>> df1 = pd.DataFrame({'A': [None, 0], 'B': [None, 4]})
| >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
| >>> df1.combine_first(df2)
| A B
| 0 1.0 3.0
| 1 0.0 4.0
|
| Null values still persist if the location of that null value
| does not exist in `other`
|
| >>> df1 = pd.DataFrame({'A': [None, 0], 'B': [4, None]})
| >>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1]}, index=[1, 2])
| >>> df1.combine_first(df2)
| A B C
| 0 NaN 4.0 NaN
| 1 0.0 3.0 1.0
| 2 NaN 3.0 1.0
|
| compare(self, other: 'DataFrame', align_axis: 'Axis' = 1, keep_shape: 'bool' = False, keep_equal: 'bool' = False, result_names: 'Suffixes' = ('self', 'other')) -> 'DataFrame'
| Compare to another DataFrame and show the differences.
|
| Parameters
| ----------
| other : DataFrame
| Object to compare with.
|
| align_axis : {0 or 'index', 1 or 'columns'}, default 1
| Determine which axis to align the comparison on.
|
| * 0, or 'index' : Resulting differences are stacked vertically
| with rows drawn alternately from self and other.
| * 1, or 'columns' : Resulting differences are aligned horizontally
| with columns drawn alternately from self and other.
|
| keep_shape : bool, default False
| If true, all rows and columns are kept.
| Otherwise, only the ones with different values are kept.
|
| keep_equal : bool, default False
| If true, the result keeps values that are equal.
| Otherwise, equal values are shown as NaNs.
|
| result_names : tuple, default ('self', 'other')
| Set the dataframes names in the comparison.
|
| .. versionadded:: 1.5.0
|
| Returns
| -------
| DataFrame
| DataFrame that shows the differences stacked side by side.
|
| The resulting index will be a MultiIndex with 'self' and 'other'
| stacked alternately at the inner level.
|
| Raises
| ------
| ValueError
| When the two DataFrames don't have identical labels or shape.
|
| See Also
| --------
| Series.compare : Compare with another Series and show differences.
| DataFrame.equals : Test whether two objects contain the same elements.
|
| Notes
| -----
| Matching NaNs will not appear as a difference.
|
| Can only compare identically-labeled
| (i.e. same shape, identical row and column labels) DataFrames
|
| Examples
| --------
| >>> df = pd.DataFrame(
| ... {
| ... "col1": ["a", "a", "b", "b", "a"],
| ... "col2": [1.0, 2.0, 3.0, np.nan, 5.0],
| ... "col3": [1.0, 2.0, 3.0, 4.0, 5.0]
| ... },
| ... columns=["col1", "col2", "col3"],
| ... )
| >>> df
| col1 col2 col3
| 0 a 1.0 1.0
| 1 a 2.0 2.0
| 2 b 3.0 3.0
| 3 b NaN 4.0
| 4 a 5.0 5.0
|
| >>> df2 = df.copy()
| >>> df2.loc[0, 'col1'] = 'c'
| >>> df2.loc[2, 'col3'] = 4.0
| >>> df2
| col1 col2 col3
| 0 c 1.0 1.0
| 1 a 2.0 2.0
| 2 b 3.0 4.0
| 3 b NaN 4.0
| 4 a 5.0 5.0
|
| Align the differences on columns
|
| >>> df.compare(df2)
| col1 col3
| self other self other
| 0 a c NaN NaN
| 2 NaN NaN 3.0 4.0
|
| Assign result_names
|
| >>> df.compare(df2, result_names=("left", "right"))
| col1 col3
| left right left right
| 0 a c NaN NaN
| 2 NaN NaN 3.0 4.0
|
| Stack the differences on rows
|
| >>> df.compare(df2, align_axis=0)
| col1 col3
| 0 self a NaN
| other c NaN
| 2 self NaN 3.0
| other NaN 4.0
|
| Keep the equal values
|
| >>> df.compare(df2, keep_equal=True)
| col1 col3
| self other self other
| 0 a c 1.0 1.0
| 2 b b 3.0 4.0
|
| Keep all original rows and columns
|
| >>> df.compare(df2, keep_shape=True)
| col1 col2 col3
| self other self other self other
| 0 a c NaN NaN NaN NaN
| 1 NaN NaN NaN NaN NaN NaN
| 2 NaN NaN NaN NaN 3.0 4.0
| 3 NaN NaN NaN NaN NaN NaN
| 4 NaN NaN NaN NaN NaN NaN
|
| Keep all original rows and columns and also all original values
|
| >>> df.compare(df2, keep_shape=True, keep_equal=True)
| col1 col2 col3
| self other self other self other
| 0 a c 1.0 1.0 1.0 1.0
| 1 a a 2.0 2.0 2.0 2.0
| 2 b b 3.0 3.0 3.0 4.0
| 3 b b NaN NaN 4.0 4.0
| 4 a a 5.0 5.0 5.0 5.0
|
| corr(self, method: 'CorrelationMethod' = 'pearson', min_periods: 'int' = 1, numeric_only: 'bool' = False) -> 'DataFrame'
| Compute pairwise correlation of columns, excluding NA/null values.
|
| Parameters
| ----------
| method : {'pearson', 'kendall', 'spearman'} or callable
| Method of correlation:
|
| * pearson : standard correlation coefficient
| * kendall : Kendall Tau correlation coefficient
| * spearman : Spearman rank correlation
| * callable: callable with input two 1d ndarrays
| and returning a float. Note that the returned matrix from corr
| will have 1 along the diagonals and will be symmetric
| regardless of the callable's behavior.
| min_periods : int, optional
| Minimum number of observations required per pair of columns
| to have a valid result. Currently only available for Pearson
| and Spearman correlation.
| numeric_only : bool, default False
| Include only `float`, `int` or `boolean` data.
|
| .. versionadded:: 1.5.0
|
| .. versionchanged:: 2.0.0
| The default value of ``numeric_only`` is now ``False``.
|
| Returns
| -------
| DataFrame
| Correlation matrix.
|
| See Also
| --------
| DataFrame.corrwith : Compute pairwise correlation with another
| DataFrame or Series.
| Series.corr : Compute the correlation between two Series.
|
| Notes
| -----
| Pearson, Kendall and Spearman correlation are currently computed using pairwise complete observations.
|
| * `Pearson correlation coefficient <https://en.wikipedia.org/wiki/Pearson_correlation_coefficient>`_
| * `Kendall rank correlation coefficient <https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient>`_
| * `Spearman's rank correlation coefficient <https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient>`_
|
| Examples
| --------
| >>> def histogram_intersection(a, b):
| ... v = np.minimum(a, b).sum().round(decimals=1)
| ... return v
| >>> df = pd.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],
| ... columns=['dogs', 'cats'])
| >>> df.corr(method=histogram_intersection)
| dogs cats
| dogs 1.0 0.3
| cats 0.3 1.0
|
| >>> df = pd.DataFrame([(1, 1), (2, np.nan), (np.nan, 3), (4, 4)],
| ... columns=['dogs', 'cats'])
| >>> df.corr(min_periods=3)
| dogs cats
| dogs 1.0 NaN
| cats NaN 1.0
|
| corrwith(self, other: 'DataFrame | Series', axis: 'Axis' = 0, drop: 'bool' = False, method: 'CorrelationMethod' = 'pearson', numeric_only: 'bool' = False) -> 'Series'
| Compute pairwise correlation.
|
| Pairwise correlation is computed between rows or columns of
| DataFrame with rows or columns of Series or DataFrame. DataFrames
| are first aligned along both axes before computing the
| correlations.
|
| Parameters
| ----------
| other : DataFrame, Series
| Object with which to compute correlations.
| axis : {0 or 'index', 1 or 'columns'}, default 0
| The axis to use. 0 or 'index' to compute row-wise, 1 or 'columns' for
| column-wise.
| drop : bool, default False
| Drop missing indices from result.
| method : {'pearson', 'kendall', 'spearman'} or callable
| Method of correlation:
|
| * pearson : standard correlation coefficient
| * kendall : Kendall Tau correlation coefficient
| * spearman : Spearman rank correlation
| * callable: callable with input two 1d ndarrays
| and returning a float.
|
| numeric_only : bool, default False
| Include only `float`, `int` or `boolean` data.
|
| .. versionadded:: 1.5.0
|
| .. versionchanged:: 2.0.0
| The default value of ``numeric_only`` is now ``False``.
|
| Returns
| -------
| Series
| Pairwise correlations.
|
| See Also
| --------
| DataFrame.corr : Compute pairwise correlation of columns.
|
| Examples
| --------
| >>> index = ["a", "b", "c", "d", "e"]
| >>> columns = ["one", "two", "three", "four"]
| >>> df1 = pd.DataFrame(np.arange(20).reshape(5, 4), index=index, columns=columns)
| >>> df2 = pd.DataFrame(np.arange(16).reshape(4, 4), index=index[:4], columns=columns)
| >>> df1.corrwith(df2)
| one 1.0
| two 1.0
| three 1.0
| four 1.0
| dtype: float64
|
| >>> df2.corrwith(df1, axis=1)
| a 1.0
| b 1.0
| c 1.0
| d 1.0
| e NaN
| dtype: float64
|
| count(self, axis: 'Axis' = 0, numeric_only: 'bool' = False)
| Count non-NA cells for each column or row.
|
| The values `None`, `NaN`, `NaT`, ``pandas.NA`` are considered NA.
|
| Parameters
| ----------
| axis : {0 or 'index', 1 or 'columns'}, default 0
| If 0 or 'index' counts are generated for each column.
| If 1 or 'columns' counts are generated for each row.
| numeric_only : bool, default False
| Include only `float`, `int` or `boolean` data.
|
| Returns
| -------
| Series
| For each column/row the number of non-NA/null entries.
|
| See Also
| --------
| Series.count: Number of non-NA elements in a Series.
| DataFrame.value_counts: Count unique combinations of columns.
| DataFrame.shape: Number of DataFrame rows and columns (including NA
| elements).
| DataFrame.isna: Boolean same-sized DataFrame showing places of NA
| elements.
|
| Examples
| --------
| Constructing DataFrame from a dictionary:
|
| >>> df = pd.DataFrame({"Person":
| ... ["John", "Myla", "Lewis", "John", "Myla"],
| ... "Age": [24., np.nan, 21., 33, 26],
| ... "Single": [False, True, True, True, False]})
| >>> df
| Person Age Single
| 0 John 24.0 False
| 1 Myla NaN True
| 2 Lewis 21.0 True
| 3 John 33.0 True
| 4 Myla 26.0 False
|
| Notice the uncounted NA values:
|
| >>> df.count()
| Person 5
| Age 4
| Single 5
| dtype: int64
|
| Counts for each **row**:
|
| >>> df.count(axis='columns')
| 0 3
| 1 2
| 2 3
| 3 3
| 4 3
| dtype: int64
|
| cov(self, min_periods: 'int | None' = None, ddof: 'int | None' = 1, numeric_only: 'bool' = False) -> 'DataFrame'
| Compute pairwise covariance of columns, excluding NA/null values.
|
| Compute the pairwise covariance among the series of a DataFrame.
| The returned data frame is the `covariance matrix
| <https://en.wikipedia.org/wiki/Covariance_matrix>`__ of the columns
| of the DataFrame.
|
| Both NA and null values are automatically excluded from the
| calculation. (See the note below about bias from missing values.)
| A threshold can be set for the minimum number of
| observations for each value created. Comparisons with observations
| below this threshold will be returned as ``NaN``.
|
| This method is generally used for the analysis of time series data to
| understand the relationship between different measures
| across time.
|
| Parameters
| ----------
| min_periods : int, optional
| Minimum number of observations required per pair of columns
| to have a valid result.
|
| ddof : int, default 1
| Delta degrees of freedom. The divisor used in calculations
| is ``N - ddof``, where ``N`` represents the number of elements.
| This argument is applicable only when no ``nan`` is in the dataframe.
|
| numeric_only : bool, default False
| Include only `float`, `int` or `boolean` data.
|
| .. versionadded:: 1.5.0
|
| .. versionchanged:: 2.0.0
| The default value of ``numeric_only`` is now ``False``.
|
| Returns
| -------
| DataFrame
| The covariance matrix of the series of the DataFrame.
|
| See Also
| --------
| Series.cov : Compute covariance with another Series.
| core.window.ewm.ExponentialMovingWindow.cov : Exponential weighted sample
| covariance.
| core.window.expanding.Expanding.cov : Expanding sample covariance.
| core.window.rolling.Rolling.cov : Rolling sample covariance.
|
| Notes
| -----
| Returns the covariance matrix of the DataFrame's time series.
| The covariance is normalized by N-ddof.
|
| For DataFrames that have Series that are missing data (assuming that
| data is `missing at random
| <https://en.wikipedia.org/wiki/Missing_data#Missing_at_random>`__)
| the returned covariance matrix will be an unbiased estimate
| of the variance and covariance between the member Series.
|
| However, for many applications this estimate may not be acceptable
| because the estimate covariance matrix is not guaranteed to be positive
| semi-definite. This could lead to estimate correlations having
| absolute values which are greater than one, and/or a non-invertible
| covariance matrix. See `Estimation of covariance matrices
| <https://en.wikipedia.org/w/index.php?title=Estimation_of_covariance_
| matrices>`__ for more details.
|
| Examples
| --------
| >>> df = pd.DataFrame([(1, 2), (0, 3), (2, 0), (1, 1)],
| ... columns=['dogs', 'cats'])
| >>> df.cov()
| dogs cats
| dogs 0.666667 -1.000000
| cats -1.000000 1.666667
|
| >>> np.random.seed(42)
| >>> df = pd.DataFrame(np.random.randn(1000, 5),
| ... columns=['a', 'b', 'c', 'd', 'e'])
| >>> df.cov()
| a b c d e
| a 0.998438 -0.020161 0.059277 -0.008943 0.014144
| b -0.020161 1.059352 -0.008543 -0.024738 0.009826
| c 0.059277 -0.008543 1.010670 -0.001486 -0.000271
| d -0.008943 -0.024738 -0.001486 0.921297 -0.013692
| e 0.014144 0.009826 -0.000271 -0.013692 0.977795
|
| **Minimum number of periods**
|
| This method also supports an optional ``min_periods`` keyword
| that specifies the required minimum number of non-NA observations for
| each column pair in order to have a valid result:
|
| >>> np.random.seed(42)
| >>> df = pd.DataFrame(np.random.randn(20, 3),
| ... columns=['a', 'b', 'c'])
| >>> df.loc[df.index[:5], 'a'] = np.nan
| >>> df.loc[df.index[5:10], 'b'] = np.nan
| >>> df.cov(min_periods=12)
| a b c
| a 0.316741 NaN -0.150812
| b NaN 1.248003 0.191417
| c -0.150812 0.191417 0.895202
|
| cummax(self, axis: 'Axis | None' = None, skipna: 'bool' = True, *args, **kwargs)
| Return cumulative maximum over a DataFrame or Series axis.
|
| Returns a DataFrame or Series of the same size containing the cumulative
| maximum.
|
| Parameters
| ----------
| axis : {0 or 'index', 1 or 'columns'}, default 0
| The index or the name of the axis. 0 is equivalent to None or 'index'.
| For `Series` this parameter is unused and defaults to 0.
| skipna : bool, default True
| Exclude NA/null values. If an entire row/column is NA, the result
| will be NA.
| *args, **kwargs
| Additional keywords have no effect but might be accepted for
| compatibility with NumPy.
|
| Returns
| -------
| Series or DataFrame
| Return cumulative maximum of Series or DataFrame.
|
| See Also
| --------
| core.window.expanding.Expanding.max : Similar functionality
| but ignores ``NaN`` values.
| DataFrame.max : Return the maximum over
| DataFrame axis.
| DataFrame.cummax : Return cumulative maximum over DataFrame axis.
| DataFrame.cummin : Return cumulative minimum over DataFrame axis.
| DataFrame.cumsum : Return cumulative sum over DataFrame axis.
| DataFrame.cumprod : Return cumulative product over DataFrame axis.
|
| Examples
| --------
| **Series**
|
| >>> s = pd.Series([2, np.nan, 5, -1, 0])
| >>> s
| 0 2.0
| 1 NaN
| 2 5.0
| 3 -1.0
| 4 0.0
| dtype: float64
|
| By default, NA values are ignored.
|
| >>> s.cummax()
| 0 2.0
| 1 NaN
| 2 5.0
| 3 5.0
| 4 5.0
| dtype: float64
|
| To include NA values in the operation, use ``skipna=False``
|
| >>> s.cummax(skipna=False)
| 0 2.0
| 1 NaN
| 2 NaN
| 3 NaN
| 4 NaN
| dtype: float64
|
| **DataFrame**
|
| >>> df = pd.DataFrame([[2.0, 1.0],
| ... [3.0, np.nan],
| ... [1.0, 0.0]],
| ... columns=list('AB'))
| >>> df
| A B
| 0 2.0 1.0
| 1 3.0 NaN
| 2 1.0 0.0
|
| By default, iterates over rows and finds the maximum
| in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
|
| >>> df.cummax()
| A B
| 0 2.0 1.0
| 1 3.0 NaN
| 2 3.0 1.0
|
| To iterate over columns and find the maximum in each row,
| use ``axis=1``
|
| >>> df.cummax(axis=1)
| A B
| 0 2.0 2.0
| 1 3.0 NaN
| 2 1.0 1.0
|
| cummin(self, axis: 'Axis | None' = None, skipna: 'bool' = True, *args, **kwargs)
| Return cumulative minimum over a DataFrame or Series axis.
|
| Returns a DataFrame or Series of the same size containing the cumulative
| minimum.
|
| Parameters
| ----------
| axis : {0 or 'index', 1 or 'columns'}, default 0
| The index or the name of the axis. 0 is equivalent to None or 'index'.
| For `Series` this parameter is unused and defaults to 0.
| skipna : bool, default True
| Exclude NA/null values. If an entire row/column is NA, the result
| will be NA.
| *args, **kwargs
| Additional keywords have no effect but might be accepted for
| compatibility with NumPy.
|
| Returns
| -------
| Series or DataFrame
| Return cumulative minimum of Series or DataFrame.
|
| See Also
| --------
| core.window.expanding.Expanding.min : Similar functionality
| but ignores ``NaN`` values.
| DataFrame.min : Return the minimum over
| DataFrame axis.
| DataFrame.cummax : Return cumulative maximum over DataFrame axis.
| DataFrame.cummin : Return cumulative minimum over DataFrame axis.
| DataFrame.cumsum : Return cumulative sum over DataFrame axis.
| DataFrame.cumprod : Return cumulative product over DataFrame axis.
|
| Examples
| --------
| **Series**
|
| >>> s = pd.Series([2, np.nan, 5, -1, 0])
| >>> s
| 0 2.0
| 1 NaN
| 2 5.0
| 3 -1.0
| 4 0.0
| dtype: float64
|
| By default, NA values are ignored.
|
| >>> s.cummin()
| 0 2.0
| 1 NaN
| 2 2.0
| 3 -1.0
| 4 -1.0
| dtype: float64
|
| To include NA values in the operation, use ``skipna=False``
|
| >>> s.cummin(skipna=False)
| 0 2.0
| 1 NaN
| 2 NaN
| 3 NaN
| 4 NaN
| dtype: float64
|
| **DataFrame**
|
| >>> df = pd.DataFrame([[2.0, 1.0],
| ... [3.0, np.nan],
| ... [1.0, 0.0]],
| ... columns=list('AB'))
| >>> df
| A B
| 0 2.0 1.0
| 1 3.0 NaN
| 2 1.0 0.0
|
| By default, iterates over rows and finds the minimum
| in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
|
| >>> df.cummin()
| A B
| 0 2.0 1.0
| 1 2.0 NaN
| 2 1.0 0.0
|
| To iterate over columns and find the minimum in each row,
| use ``axis=1``
|
| >>> df.cummin(axis=1)
| A B
| 0 2.0 1.0
| 1 3.0 NaN
| 2 1.0 0.0
|
| cumprod(self, axis: 'Axis | None' = None, skipna: 'bool' = True, *args, **kwargs)
| Return cumulative product over a DataFrame or Series axis.
|
| Returns a DataFrame or Series of the same size containing the cumulative
| product.
|
| Parameters
| ----------
| axis : {0 or 'index', 1 or 'columns'}, default 0
| The index or the name of the axis. 0 is equivalent to None or 'index'.
| For `Series` this parameter is unused and defaults to 0.
| skipna : bool, default True
| Exclude NA/null values. If an entire row/column is NA, the result
| will be NA.
| *args, **kwargs
| Additional keywords have no effect but might be accepted for
| compatibility with NumPy.
|
| Returns
| -------
| Series or DataFrame
| Return cumulative product of Series or DataFrame.
|
| See Also
| --------
| core.window.expanding.Expanding.prod : Similar functionality
| but ignores ``NaN`` values.
| DataFrame.prod : Return the product over
| DataFrame axis.
| DataFrame.cummax : Return cumulative maximum over DataFrame axis.
| DataFrame.cummin : Return cumulative minimum over DataFrame axis.
| DataFrame.cumsum : Return cumulative sum over DataFrame axis.
| DataFrame.cumprod : Return cumulative product over DataFrame axis.
|
| Examples
| --------
| **Series**
|
| >>> s = pd.Series([2, np.nan, 5, -1, 0])
| >>> s
| 0 2.0
| 1 NaN
| 2 5.0
| 3 -1.0
| 4 0.0
| dtype: float64
|
| By default, NA values are ignored.
|
| >>> s.cumprod()
| 0 2.0
| 1 NaN
| 2 10.0
| 3 -10.0
| 4 -0.0
| dtype: float64
|
| To include NA values in the operation, use ``skipna=False``
|
| >>> s.cumprod(skipna=False)
| 0 2.0
| 1 NaN
| 2 NaN
| 3 NaN
| 4 NaN
| dtype: float64
|
| **DataFrame**
|
| >>> df = pd.DataFrame([[2.0, 1.0],
| ... [3.0, np.nan],
| ... [1.0, 0.0]],
| ... columns=list('AB'))
| >>> df
| A B
| 0 2.0 1.0
| 1 3.0 NaN
| 2 1.0 0.0
|
| By default, iterates over rows and finds the product
| in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
|
| >>> df.cumprod()
| A B
| 0 2.0 1.0
| 1 6.0 NaN
| 2 6.0 0.0
|
| To iterate over columns and find the product in each row,
| use ``axis=1``
|
| >>> df.cumprod(axis=1)
| A B
| 0 2.0 2.0
| 1 3.0 NaN
| 2 1.0 0.0
|
| cumsum(self, axis: 'Axis | None' = None, skipna: 'bool' = True, *args, **kwargs)
| Return cumulative sum over a DataFrame or Series axis.
|
| Returns a DataFrame or Series of the same size containing the cumulative
| sum.
|
| Parameters
| ----------
| axis : {0 or 'index', 1 or 'columns'}, default 0
| The index or the name of the axis. 0 is equivalent to None or 'index'.
| For `Series` this parameter is unused and defaults to 0.
| skipna : bool, default True
| Exclude NA/null values. If an entire row/column is NA, the result
| will be NA.
| *args, **kwargs
| Additional keywords have no effect but might be accepted for
| compatibility with NumPy.
|
| Returns
| -------
| Series or DataFrame
| Return cumulative sum of Series or DataFrame.
|
| See Also
| --------
| core.window.expanding.Expanding.sum : Similar functionality
| but ignores ``NaN`` values.
| DataFrame.sum : Return the sum over
| DataFrame axis.
| DataFrame.cummax : Return cumulative maximum over DataFrame axis.
| DataFrame.cummin : Return cumulative minimum over DataFrame axis.
| DataFrame.cumsum : Return cumulative sum over DataFrame axis.
| DataFrame.cumprod : Return cumulative product over DataFrame axis.
|
| Examples
| --------
| **Series**
|
| >>> s = pd.Series([2, np.nan, 5, -1, 0])
| >>> s
| 0 2.0
| 1 NaN
| 2 5.0
| 3 -1.0
| 4 0.0
| dtype: float64
|
| By default, NA values are ignored.
|
| >>> s.cumsum()
| 0 2.0
| 1 NaN
| 2 7.0
| 3 6.0
| 4 6.0
| dtype: float64
|
| To include NA values in the operation, use ``skipna=False``
|
| >>> s.cumsum(skipna=False)
| 0 2.0
| 1 NaN
| 2 NaN
| 3 NaN
| 4 NaN
| dtype: float64
|
| **DataFrame**
|
| >>> df = pd.DataFrame([[2.0, 1.0],
| ... [3.0, np.nan],
| ... [1.0, 0.0]],
| ... columns=list('AB'))
| >>> df
| A B
| 0 2.0 1.0
| 1 3.0 NaN
| 2 1.0 0.0
|
| By default, iterates over rows and finds the sum
| in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
|
| >>> df.cumsum()
| A B
| 0 2.0 1.0
| 1 5.0 NaN
| 2 6.0 1.0
|
| To iterate over columns and find the sum in each row,
| use ``axis=1``
|
| >>> df.cumsum(axis=1)
| A B
| 0 2.0 3.0
| 1 3.0 NaN
| 2 1.0 1.0
|
| diff(self, periods: 'int' = 1, axis: 'Axis' = 0) -> 'DataFrame'
| First discrete difference of element.
|
| Calculates the difference of a DataFrame element compared with another
| element in the DataFrame (default is element in previous row).
|
| Parameters
| ----------
| periods : int, default 1
| Periods to shift for calculating difference, accepts negative
| values.
| axis : {0 or 'index', 1 or 'columns'}, default 0
| Take difference over rows (0) or columns (1).
|
| Returns
| -------
| DataFrame
| First differences of the Series.
|
| See Also
| --------
| DataFrame.pct_change: Percent change over given number of periods.
| DataFrame.shift: Shift index by desired number of periods with an
| optional time freq.
| Series.diff: First discrete difference of object.
|
| Notes
| -----
| For boolean dtypes, this uses :meth:`operator.xor` rather than
| :meth:`operator.sub`.
| The result is calculated according to current dtype in DataFrame,
| however dtype of the result is always float64.
|
| Examples
| --------
|
| Difference with previous row
|
| >>> df = pd.DataFrame({'a': [1, 2, 3, 4, 5, 6],
| ... 'b': [1, 1, 2, 3, 5, 8],
| ... 'c': [1, 4, 9, 16, 25, 36]})
| >>> df
| a b c
| 0 1 1 1
| 1 2 1 4
| 2 3 2 9
| 3 4 3 16
| 4 5 5 25
| 5 6 8 36
|
| >>> df.diff()
| a b c
| 0 NaN NaN NaN
| 1 1.0 0.0 3.0
| 2 1.0 1.0 5.0
| 3 1.0 1.0 7.0
| 4 1.0 2.0 9.0
| 5 1.0 3.0 11.0
|
| Difference with previous column
|
| >>> df.diff(axis=1)
| a b c
| 0 NaN 0 0
| 1 NaN -1 3
| 2 NaN -1 7
| 3 NaN -1 13
| 4 NaN 0 20
| 5 NaN 2 28
|
| Difference with 3rd previous row
|
| >>> df.diff(periods=3)
| a b c
| 0 NaN NaN NaN
| 1 NaN NaN NaN
| 2 NaN NaN NaN
| 3 3.0 2.0 15.0
| 4 3.0 4.0 21.0
| 5 3.0 6.0 27.0
|
| Difference with following row
|
| >>> df.diff(periods=-1)
| a b c
| 0 -1.0 0.0 -3.0
| 1 -1.0 -1.0 -5.0
| 2 -1.0 -1.0 -7.0
| 3 -1.0 -2.0 -9.0
| 4 -1.0 -3.0 -11.0
| 5 NaN NaN NaN
|
| Overflow in input dtype
|
| >>> df = pd.DataFrame({'a': [1, 0]}, dtype=np.uint8)
| >>> df.diff()
| a
| 0 NaN
| 1 255.0
|
| div = truediv(self, other, axis: 'Axis' = 'columns', level=None, fill_value=None) -> 'DataFrame'
|
| divide = truediv(self, other, axis: 'Axis' = 'columns', level=None, fill_value=None) -> 'DataFrame'
|
| dot(self, other: 'AnyArrayLike | DataFrame') -> 'DataFrame | Series'
| Compute the matrix multiplication between the DataFrame and other.
|
| This method computes the matrix product between the DataFrame and the
| values of an other Series, DataFrame or a numpy array.
|
| It can also be called using ``self @ other``.
|
| Parameters
| ----------
| other : Series, DataFrame or array-like
| The other object to compute the matrix product with.
|
| Returns
| -------
| Series or DataFrame
| If other is a Series, return the matrix product between self and
| other as a Series. If other is a DataFrame or a numpy.array, return
| the matrix product of self and other in a DataFrame of a np.array.
|
| See Also
| --------
| Series.dot: Similar method for Series.
|
| Notes
| -----
| The dimensions of DataFrame and other must be compatible in order to
| compute the matrix multiplication. In addition, the column names of
| DataFrame and the index of other must contain the same values, as they
| will be aligned prior to the multiplication.
|
| The dot method for Series computes the inner product, instead of the
| matrix product here.
|
| Examples
| --------
| Here we multiply a DataFrame with a Series.
|
| >>> df = pd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
| >>> s = pd.Series([1, 1, 2, 1])
| >>> df.dot(s)
| 0 -4
| 1 5
| dtype: int64
|
| Here we multiply a DataFrame with another DataFrame.
|
| >>> other = pd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]])
| >>> df.dot(other)
| 0 1
| 0 1 4
| 1 2 2
|
| Note that the dot method give the same result as @
|
| >>> df @ other
| 0 1
| 0 1 4
| 1 2 2
|
| The dot method works also if other is an np.array.
|
| >>> arr = np.array([[0, 1], [1, 2], [-1, -1], [2, 0]])
| >>> df.dot(arr)
| 0 1
| 0 1 4
| 1 2 2
|
| Note how shuffling of the objects does not change the result.
|
| >>> s2 = s.reindex([1, 0, 2, 3])
| >>> df.dot(s2)
| 0 -4
| 1 5
| dtype: int64
|
| drop(self, labels: 'IndexLabel | None' = None, *, axis: 'Axis' = 0, index: 'IndexLabel | None' = None, columns: 'IndexLabel | None' = None, level: 'Level | None' = None, inplace: 'bool' = False, errors: 'IgnoreRaise' = 'raise') -> 'DataFrame | None'
| Drop specified labels from rows or columns.
|
| Remove rows or columns by specifying label names and corresponding
| axis, or by directly specifying index or column names. When using a
| multi-index, labels on different levels can be removed by specifying
| the level. See the :ref:`user guide <advanced.shown_levels>`
| for more information about the now unused levels.
|
| Parameters
| ----------
| labels : single label or list-like
| Index or column labels to drop. A tuple will be used as a single
| label and not treated as a list-like.
| axis : {0 or 'index', 1 or 'columns'}, default 0
| Whether to drop labels from the index (0 or 'index') or
| columns (1 or 'columns').
| index : single label or list-like
| Alternative to specifying axis (``labels, axis=0``
| is equivalent to ``index=labels``).
| columns : single label or list-like
| Alternative to specifying axis (``labels, axis=1``
| is equivalent to ``columns=labels``).
| level : int or level name, optional
| For MultiIndex, level from which the labels will be removed.
| inplace : bool, default False
| If False, return a copy. Otherwise, do operation
| in place and return None.
| errors : {'ignore', 'raise'}, default 'raise'
| If 'ignore', suppress error and only existing labels are
| dropped.
|
| Returns
| -------
| DataFrame or None
| Returns DataFrame or None DataFrame with the specified
| index or column labels removed or None if inplace=True.
|
| Raises
| ------
| KeyError
| If any of the labels is not found in the selected axis.
|
| See Also
| --------
| DataFrame.loc : Label-location based indexer for selection by label.
| DataFrame.dropna : Return DataFrame with labels on given axis omitted
| where (all or any) data are missing.
| DataFrame.drop_duplicates : Return DataFrame with duplicate rows
| removed, optionally only considering certain columns.
| Series.drop : Return Series with specified index labels removed.
|
| Examples
| --------
| >>> df = pd.DataFrame(np.arange(12).reshape(3, 4),
| ... columns=['A', 'B', 'C', 'D'])
| >>> df
| A B C D
| 0 0 1 2 3
| 1 4 5 6 7
| 2 8 9 10 11
|
| Drop columns
|
| >>> df.drop(['B', 'C'], axis=1)
| A D
| 0 0 3
| 1 4 7
| 2 8 11
|
| >>> df.drop(columns=['B', 'C'])
| A D
| 0 0 3
| 1 4 7
| 2 8 11
|
| Drop a row by index
|
| >>> df.drop([0, 1])
| A B C D
| 2 8 9 10 11
|
| Drop columns and/or rows of MultiIndex DataFrame
|
| >>> midx = pd.MultiIndex(levels=[['llama', 'cow', 'falcon'],
| ... ['speed', 'weight', 'length']],
| ... codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
| ... [0, 1, 2, 0, 1, 2, 0, 1, 2]])
| >>> df = pd.DataFrame(index=midx, columns=['big', 'small'],
| ... data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
| ... [250, 150], [1.5, 0.8], [320, 250],
| ... [1, 0.8], [0.3, 0.2]])
| >>> df
| big small
| llama speed 45.0 30.0
| weight 200.0 100.0
| length 1.5 1.0
| cow speed 30.0 20.0
| weight 250.0 150.0
| length 1.5 0.8
| falcon speed 320.0 250.0
| weight 1.0 0.8
| length 0.3 0.2
|
| Drop a specific index combination from the MultiIndex
| DataFrame, i.e., drop the combination ``'falcon'`` and
| ``'weight'``, which deletes only the corresponding row
|
| >>> df.drop(index=('falcon', 'weight'))
| big small
| llama speed 45.0 30.0
| weight 200.0 100.0
| length 1.5 1.0
| cow speed 30.0 20.0
| weight 250.0 150.0
| length 1.5 0.8
| falcon speed 320.0 250.0
| length 0.3 0.2
|
| >>> df.drop(index='cow', columns='small')
| big
| llama speed 45.0
| weight 200.0
| length 1.5
| falcon speed 320.0
| weight 1.0
| length 0.3
|
| >>> df.drop(index='length', level=1)
| big small
| llama speed 45.0 30.0
| weight 200.0 100.0
| cow speed 30.0 20.0
| weight 250.0 150.0
| falcon speed 320.0 250.0
| weight 1.0 0.8
|
| drop_duplicates(self, subset: 'Hashable | Sequence[Hashable] | None' = None, *, keep: 'DropKeep' = 'first', inplace: 'bool' = False, ignore_index: 'bool' = False) -> 'DataFrame | None'
| Return DataFrame with duplicate rows removed.
|
| Considering certain columns is optional. Indexes, including time indexes
| are ignored.
|
| Parameters
| ----------
| subset : column label or sequence of labels, optional
| Only consider certain columns for identifying duplicates, by
| default use all of the columns.
| keep : {'first', 'last', ``False``}, default 'first'
| Determines which duplicates (if any) to keep.
|
| - 'first' : Drop duplicates except for the first occurrence.
| - 'last' : Drop duplicates except for the last occurrence.
| - ``False`` : Drop all duplicates.
|
| inplace : bool, default ``False``
| Whether to modify the DataFrame rather than creating a new one.
| ignore_index : bool, default ``False``
| If ``True``, the resulting axis will be labeled 0, 1, …, n - 1.
|
| Returns
| -------
| DataFrame or None
| DataFrame with duplicates removed or None if ``inplace=True``.
|
| See Also
| --------
| DataFrame.value_counts: Count unique combinations of columns.
|
| Examples
| --------
| Consider dataset containing ramen rating.
|
| >>> df = pd.DataFrame({
| ... 'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
| ... 'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
| ... 'rating': [4, 4, 3.5, 15, 5]
| ... })
| >>> df
| brand style rating
| 0 Yum Yum cup 4.0
| 1 Yum Yum cup 4.0
| 2 Indomie cup 3.5
| 3 Indomie pack 15.0
| 4 Indomie pack 5.0
|
| By default, it removes duplicate rows based on all columns.
|
| >>> df.drop_duplicates()
| brand style rating
| 0 Yum Yum cup 4.0
| 2 Indomie cup 3.5
| 3 Indomie pack 15.0
| 4 Indomie pack 5.0
|
| To remove duplicates on specific column(s), use ``subset``.
|
| >>> df.drop_duplicates(subset=['brand'])
| brand style rating
| 0 Yum Yum cup 4.0
| 2 Indomie cup 3.5
|
| To remove duplicates and keep last occurrences, use ``keep``.
|
| >>> df.drop_duplicates(subset=['brand', 'style'], keep='last')
| brand style rating
| 1 Yum Yum cup 4.0
| 2 Indomie cup 3.5
| 4 Indomie pack 5.0
|
| dropna(self, *, axis: 'Axis' = 0, how: 'AnyAll | lib.NoDefault' = <no_default>, thresh: 'int | lib.NoDefault' = <no_default>, subset: 'IndexLabel | None' = None, inplace: 'bool' = False, ignore_index: 'bool' = False) -> 'DataFrame | None'
| Remove missing values.
|
| See the :ref:`User Guide <missing_data>` for more on which values are
| considered missing, and how to work with missing data.
|
| Parameters
| ----------
| axis : {0 or 'index', 1 or 'columns'}, default 0
| Determine if rows or columns which contain missing values are
| removed.
|
| * 0, or 'index' : Drop rows which contain missing values.
| * 1, or 'columns' : Drop columns which contain missing value.
|
| Only a single axis is allowed.
|
| how : {'any', 'all'}, default 'any'
| Determine if row or column is removed from DataFrame, when we have
| at least one NA or all NA.
|
| * 'any' : If any NA values are present, drop that row or column.
| * 'all' : If all values are NA, drop that row or column.
|
| thresh : int, optional
| Require that many non-NA values. Cannot be combined with how.
| subset : column label or sequence of labels, optional
| Labels along other axis to consider, e.g. if you are dropping rows
| these would be a list of columns to include.
| inplace : bool, default False
| Whether to modify the DataFrame rather than creating a new one.
| ignore_index : bool, default ``False``
| If ``True``, the resulting axis will be labeled 0, 1, …, n - 1.
|
| .. versionadded:: 2.0.0
|
| Returns
| -------
| DataFrame or None
| DataFrame with NA entries dropped from it or None if ``inplace=True``.
|
| See Also
| --------
| DataFrame.isna: Indicate missing values.
| DataFrame.notna : Indicate existing (non-missing) values.
| DataFrame.fillna : Replace missing values.
| Series.dropna : Drop missing values.
| Index.dropna : Drop missing indices.
|
| Examples
| --------
| >>> df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],
| ... "toy": [np.nan, 'Batmobile', 'Bullwhip'],
| ... "born": [pd.NaT, pd.Timestamp("1940-04-25"),
| ... pd.NaT]})
| >>> df
| name toy born
| 0 Alfred NaN NaT
| 1 Batman Batmobile 1940-04-25
| 2 Catwoman Bullwhip NaT
|
| Drop the rows where at least one element is missing.
|
| >>> df.dropna()
| name toy born
| 1 Batman Batmobile 1940-04-25
|
| Drop the columns where at least one element is missing.
|
| >>> df.dropna(axis='columns')
| name
| 0 Alfred
| 1 Batman
| 2 Catwoman
|
| Drop the rows where all elements are missing.
|
| >>> df.dropna(how='all')
| name toy born
| 0 Alfred NaN NaT
| 1 Batman Batmobile 1940-04-25
| 2 Catwoman Bullwhip NaT
|
| Keep only the rows with at least 2 non-NA values.
|
| >>> df.dropna(thresh=2)
| name toy born
| 1 Batman Batmobile 1940-04-25
| 2 Catwoman Bullwhip NaT
|
| Define in which columns to look for missing values.
|
| >>> df.dropna(subset=['name', 'toy'])
| name toy born
| 1 Batman Batmobile 1940-04-25
| 2 Catwoman Bullwhip NaT
|
| duplicated(self, subset: 'Hashable | Sequence[Hashable] | None' = None, keep: 'DropKeep' = 'first') -> 'Series'
| Return boolean Series denoting duplicate rows.
|
| Considering certain columns is optional.
|
| Parameters
| ----------
| subset : column label or sequence of labels, optional
| Only consider certain columns for identifying duplicates, by
| default use all of the columns.
| keep : {'first', 'last', False}, default 'first'
| Determines which duplicates (if any) to mark.
|
| - ``first`` : Mark duplicates as ``True`` except for the first occurrence.
| - ``last`` : Mark duplicates as ``True`` except for the last occurrence.
| - False : Mark all duplicates as ``True``.
|
| Returns
| -------
| Series
| Boolean series for each duplicated rows.
|
| See Also
| --------
| Index.duplicated : Equivalent method on index.
| Series.duplicated : Equivalent method on Series.
| Series.drop_duplicates : Remove duplicate values from Series.
| DataFrame.drop_duplicates : Remove duplicate values from DataFrame.
|
| Examples
| --------
| Consider dataset containing ramen rating.
|
| >>> df = pd.DataFrame({
| ... 'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
| ... 'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
| ... 'rating': [4, 4, 3.5, 15, 5]
| ... })
| >>> df
| brand style rating
| 0 Yum Yum cup 4.0
| 1 Yum Yum cup 4.0
| 2 Indomie cup 3.5
| 3 Indomie pack 15.0
| 4 Indomie pack 5.0
|
| By default, for each set of duplicated values, the first occurrence
| is set on False and all others on True.
|
| >>> df.duplicated()
| 0 False
| 1 True
| 2 False
| 3 False
| 4 False
| dtype: bool
|
| By using 'last', the last occurrence of each set of duplicated values
| is set on False and all others on True.
|
| >>> df.duplicated(keep='last')
| 0 True
| 1 False
| 2 False
| 3 False
| 4 False
| dtype: bool
|
| By setting ``keep`` on False, all duplicates are True.
|
| >>> df.duplicated(keep=False)
| 0 True
| 1 True
| 2 False
| 3 False
| 4 False
| dtype: bool
|
| To find duplicates on specific column(s), use ``subset``.
|
| >>> df.duplicated(subset=['brand'])
| 0 False
| 1 True
| 2 False
| 3 True
| 4 True
| dtype: bool
|
| eq(self, other, axis: 'Axis' = 'columns', level=None) -> 'DataFrame'
| Get Equal to of dataframe and other, element-wise (binary operator `eq`).
|
| Among flexible wrappers (`eq`, `ne`, `le`, `lt`, `ge`, `gt`) to comparison
| operators.
|
| Equivalent to `==`, `!=`, `<=`, `<`, `>=`, `>` with support to choose axis
| (rows or columns) and level for comparison.
|
| Parameters
| ----------
| other : scalar, sequence, Series, or DataFrame
| Any single or multiple element data structure, or list-like object.
| axis : {0 or 'index', 1 or 'columns'}, default 'columns'
| Whether to compare by the index (0 or 'index') or columns
| (1 or 'columns').
| level : int or label
| Broadcast across a level, matching Index values on the passed
| MultiIndex level.
|
| Returns
| -------
| DataFrame of bool
| Result of the comparison.
|
| See Also
| --------
| DataFrame.eq : Compare DataFrames for equality elementwise.
| DataFrame.ne : Compare DataFrames for inequality elementwise.
| DataFrame.le : Compare DataFrames for less than inequality
| or equality elementwise.
| DataFrame.lt : Compare DataFrames for strictly less than
| inequality elementwise.
| DataFrame.ge : Compare DataFrames for greater than inequality
| or equality elementwise.
| DataFrame.gt : Compare DataFrames for strictly greater than
| inequality elementwise.
|
| Notes
| -----
| Mismatched indices will be unioned together.
| `NaN` values are considered different (i.e. `NaN` != `NaN`).
|
| Examples
| --------
| >>> df = pd.DataFrame({'cost': [250, 150, 100],
| ... 'revenue': [100, 250, 300]},
| ... index=['A', 'B', 'C'])
| >>> df
| cost revenue
| A 250 100
| B 150 250
| C 100 300
|
| Comparison with a scalar, using either the operator or method:
|
| >>> df == 100
| cost revenue
| A False True
| B False False
| C True False
|
| >>> df.eq(100)
| cost revenue
| A False True
| B False False
| C True False
|
| When `other` is a :class:`Series`, the columns of a DataFrame are aligned
| with the index of `other` and broadcast:
|
| >>> df != pd.Series([100, 250], index=["cost", "revenue"])
| cost revenue
| A True True
| B True False
| C False True
|
| Use the method to control the broadcast axis:
|
| >>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
| cost revenue
| A True False
| B True True
| C True True
| D True True
|
| When comparing to an arbitrary sequence, the number of columns must
| match the number elements in `other`:
|
| >>> df == [250, 100]
| cost revenue
| A True True
| B False False
| C False False
|
| Use the method to control the axis:
|
| >>> df.eq([250, 250, 100], axis='index')
| cost revenue
| A True False
| B False True
| C True False
|
| Compare to a DataFrame of different shape.
|
| >>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
| ... index=['A', 'B', 'C', 'D'])
| >>> other
| revenue
| A 300
| B 250
| C 100
| D 150
|
| >>> df.gt(other)
| cost revenue
| A False False
| B False False
| C False True
| D False False
|
| Compare to a MultiIndex by level.
|
| >>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
| ... 'revenue': [100, 250, 300, 200, 175, 225]},
| ... index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
| ... ['A', 'B', 'C', 'A', 'B', 'C']])
| >>> df_multindex
| cost revenue
| Q1 A 250 100
| B 150 250
| C 100 300
| Q2 A 150 200
| B 300 175
| C 220 225
|
| >>> df.le(df_multindex, level=1)
| cost revenue
| Q1 A True True
| B True True
| C True True
| Q2 A False True
| B True False
| C True False
|
| eval(self, expr: 'str', *, inplace: 'bool' = False, **kwargs) -> 'Any | None'
| Evaluate a string describing operations on DataFrame columns.
|
| Operates on columns only, not specific rows or elements. This allows
| `eval` to run arbitrary code, which can make you vulnerable to code
| injection if you pass user input to this function.
|
| Parameters
| ----------
| expr : str
| The expression string to evaluate.
| inplace : bool, default False
| If the expression contains an assignment, whether to perform the
| operation inplace and mutate the existing DataFrame. Otherwise,
| a new DataFrame is returned.
| **kwargs
| See the documentation for :func:`eval` for complete details
| on the keyword arguments accepted by
| :meth:`~pandas.DataFrame.query`.
|
| Returns
| -------
| ndarray, scalar, pandas object, or None
| The result of the evaluation or None if ``inplace=True``.
|
| See Also
| --------
| DataFrame.query : Evaluates a boolean expression to query the columns
| of a frame.
| DataFrame.assign : Can evaluate an expression or function to create new
| values for a column.
| eval : Evaluate a Python expression as a string using various
| backends.
|
| Notes
| -----
| For more details see the API documentation for :func:`~eval`.
| For detailed examples see :ref:`enhancing performance with eval
| <enhancingperf.eval>`.
|
| Examples
| --------
| >>> df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})
| >>> df
| A B
| 0 1 10
| 1 2 8
| 2 3 6
| 3 4 4
| 4 5 2
| >>> df.eval('A + B')
| 0 11
| 1 10
| 2 9
| 3 8
| 4 7
| dtype: int64
|
| Assignment is allowed though by default the original DataFrame is not
| modified.
|
| >>> df.eval('C = A + B')
| A B C
| 0 1 10 11
| 1 2 8 10
| 2 3 6 9
| 3 4 4 8
| 4 5 2 7
| >>> df
| A B
| 0 1 10
| 1 2 8
| 2 3 6
| 3 4 4
| 4 5 2
|
| Multiple columns can be assigned to using multi-line expressions:
|
| >>> df.eval(
| ... '''
| ... C = A + B
| ... D = A - B
| ... '''
| ... )
| A B C D
| 0 1 10 11 -9
| 1 2 8 10 -6
| 2 3 6 9 -3
| 3 4 4 8 0
| 4 5 2 7 3
|
| explode(self, column: 'IndexLabel', ignore_index: 'bool' = False) -> 'DataFrame'
| Transform each element of a list-like to a row, replicating index values.
|
| Parameters
| ----------
| column : IndexLabel
| Column(s) to explode.
| For multiple columns, specify a non-empty list with each element
| be str or tuple, and all specified columns their list-like data
| on same row of the frame must have matching length.
|
| .. versionadded:: 1.3.0
| Multi-column explode
|
| ignore_index : bool, default False
| If True, the resulting index will be labeled 0, 1, …, n - 1.
|
| Returns
| -------
| DataFrame
| Exploded lists to rows of the subset columns;
| index will be duplicated for these rows.
|
| Raises
| ------
| ValueError :
| * If columns of the frame are not unique.
| * If specified columns to explode is empty list.
| * If specified columns to explode have not matching count of
| elements rowwise in the frame.
|
| See Also
| --------
| DataFrame.unstack : Pivot a level of the (necessarily hierarchical)
| index labels.
| DataFrame.melt : Unpivot a DataFrame from wide format to long format.
| Series.explode : Explode a DataFrame from list-like columns to long format.
|
| Notes
| -----
| This routine will explode list-likes including lists, tuples, sets,
| Series, and np.ndarray. The result dtype of the subset rows will
| be object. Scalars will be returned unchanged, and empty list-likes will
| result in a np.nan for that row. In addition, the ordering of rows in the
| output will be non-deterministic when exploding sets.
|
| Reference :ref:`the user guide <reshaping.explode>` for more examples.
|
| Examples
| --------
| >>> df = pd.DataFrame({'A': [[0, 1, 2], 'foo', [], [3, 4]],
| ... 'B': 1,
| ... 'C': [['a', 'b', 'c'], np.nan, [], ['d', 'e']]})
| >>> df
| A B C
| 0 [0, 1, 2] 1 [a, b, c]
| 1 foo 1 NaN
| 2 [] 1 []
| 3 [3, 4] 1 [d, e]
|
| Single-column explode.
|
| >>> df.explode('A')
| A B C
| 0 0 1 [a, b, c]
| 0 1 1 [a, b, c]
| 0 2 1 [a, b, c]
| 1 foo 1 NaN
| 2 NaN 1 []
| 3 3 1 [d, e]
| 3 4 1 [d, e]
|
| Multi-column explode.
|
| >>> df.explode(list('AC'))
| A B C
| 0 0 1 a
| 0 1 1 b
| 0 2 1 c
| 1 foo 1 NaN
| 2 NaN 1 NaN
| 3 3 1 d
| 3 4 1 e
|
| floordiv(self, other, axis: 'Axis' = 'columns', level=None, fill_value=None) -> 'DataFrame'
| Get Integer division of dataframe and other, element-wise (binary operator `floordiv`).
|
| Equivalent to ``dataframe // other``, but with support to substitute a fill_value
| for missing data in one of the inputs. With reverse version, `rfloordiv`.
|
| Among flexible wrappers (`add`, `sub`, `mul`, `div`, `floordiv`, `mod`, `pow`) to
| arithmetic operators: `+`, `-`, `*`, `/`, `//`, `%`, `**`.
|
| Parameters
| ----------
| other : scalar, sequence, Series, dict or DataFrame
| Any single or multiple element data structure, or list-like object.
| axis : {0 or 'index', 1 or 'columns'}
| Whether to compare by the index (0 or 'index') or columns.
| (1 or 'columns'). For Series input, axis to match Series index on.
| level : int or label
| Broadcast across a level, matching Index values on the
| passed MultiIndex level.
| fill_value : float or None, default None
| Fill existing missing (NaN) values, and any new element needed for
| successful DataFrame alignment, with this value before computation.
| If data in both corresponding DataFrame locations is missing
| the result will be missing.
|
| Returns
| -------
| DataFrame
| Result of the arithmetic operation.
|
| See Also
| --------
| DataFrame.add : Add DataFrames.
| DataFrame.sub : Subtract DataFrames.
| DataFrame.mul : Multiply DataFrames.
| DataFrame.div : Divide DataFrames (float division).
| DataFrame.truediv : Divide DataFrames (float division).
| DataFrame.floordiv : Divide DataFrames (integer division).
| DataFrame.mod : Calculate modulo (remainder after division).
| DataFrame.pow : Calculate exponential power.
|
| Notes
| -----
| Mismatched indices will be unioned together.
|
| Examples
| --------
| >>> df = pd.DataFrame({'angles': [0, 3, 4],
| ... 'degrees': [360, 180, 360]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> df
| angles degrees
| circle 0 360
| triangle 3 180
| rectangle 4 360
|
| Add a scalar with operator version which return the same
| results.
|
| >>> df + 1
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| >>> df.add(1)
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| Divide by constant with reverse version.
|
| >>> df.div(10)
| angles degrees
| circle 0.0 36.0
| triangle 0.3 18.0
| rectangle 0.4 36.0
|
| >>> df.rdiv(10)
| angles degrees
| circle inf 0.027778
| triangle 3.333333 0.055556
| rectangle 2.500000 0.027778
|
| Subtract a list and Series by axis with operator version.
|
| >>> df - [1, 2]
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub([1, 2], axis='columns')
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
| ... axis='index')
| angles degrees
| circle -1 359
| triangle 2 179
| rectangle 3 359
|
| Multiply a dictionary by axis.
|
| >>> df.mul({'angles': 0, 'degrees': 2})
| angles degrees
| circle 0 720
| triangle 0 360
| rectangle 0 720
|
| >>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
| angles degrees
| circle 0 0
| triangle 6 360
| rectangle 12 1080
|
| Multiply a DataFrame of different shape with operator version.
|
| >>> other = pd.DataFrame({'angles': [0, 3, 4]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> other
| angles
| circle 0
| triangle 3
| rectangle 4
|
| >>> df * other
| angles degrees
| circle 0 NaN
| triangle 9 NaN
| rectangle 16 NaN
|
| >>> df.mul(other, fill_value=0)
| angles degrees
| circle 0 0.0
| triangle 9 0.0
| rectangle 16 0.0
|
| Divide by a MultiIndex by level.
|
| >>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
| ... 'degrees': [360, 180, 360, 360, 540, 720]},
| ... index=[['A', 'A', 'A', 'B', 'B', 'B'],
| ... ['circle', 'triangle', 'rectangle',
| ... 'square', 'pentagon', 'hexagon']])
| >>> df_multindex
| angles degrees
| A circle 0 360
| triangle 3 180
| rectangle 4 360
| B square 4 360
| pentagon 5 540
| hexagon 6 720
|
| >>> df.div(df_multindex, level=1, fill_value=0)
| angles degrees
| A circle NaN 1.0
| triangle 1.0 1.0
| rectangle 1.0 1.0
| B square 0.0 0.0
| pentagon 0.0 0.0
| hexagon 0.0 0.0
|
| ge(self, other, axis: 'Axis' = 'columns', level=None) -> 'DataFrame'
| Get Greater than or equal to of dataframe and other, element-wise (binary operator `ge`).
|
| Among flexible wrappers (`eq`, `ne`, `le`, `lt`, `ge`, `gt`) to comparison
| operators.
|
| Equivalent to `==`, `!=`, `<=`, `<`, `>=`, `>` with support to choose axis
| (rows or columns) and level for comparison.
|
| Parameters
| ----------
| other : scalar, sequence, Series, or DataFrame
| Any single or multiple element data structure, or list-like object.
| axis : {0 or 'index', 1 or 'columns'}, default 'columns'
| Whether to compare by the index (0 or 'index') or columns
| (1 or 'columns').
| level : int or label
| Broadcast across a level, matching Index values on the passed
| MultiIndex level.
|
| Returns
| -------
| DataFrame of bool
| Result of the comparison.
|
| See Also
| --------
| DataFrame.eq : Compare DataFrames for equality elementwise.
| DataFrame.ne : Compare DataFrames for inequality elementwise.
| DataFrame.le : Compare DataFrames for less than inequality
| or equality elementwise.
| DataFrame.lt : Compare DataFrames for strictly less than
| inequality elementwise.
| DataFrame.ge : Compare DataFrames for greater than inequality
| or equality elementwise.
| DataFrame.gt : Compare DataFrames for strictly greater than
| inequality elementwise.
|
| Notes
| -----
| Mismatched indices will be unioned together.
| `NaN` values are considered different (i.e. `NaN` != `NaN`).
|
| Examples
| --------
| >>> df = pd.DataFrame({'cost': [250, 150, 100],
| ... 'revenue': [100, 250, 300]},
| ... index=['A', 'B', 'C'])
| >>> df
| cost revenue
| A 250 100
| B 150 250
| C 100 300
|
| Comparison with a scalar, using either the operator or method:
|
| >>> df == 100
| cost revenue
| A False True
| B False False
| C True False
|
| >>> df.eq(100)
| cost revenue
| A False True
| B False False
| C True False
|
| When `other` is a :class:`Series`, the columns of a DataFrame are aligned
| with the index of `other` and broadcast:
|
| >>> df != pd.Series([100, 250], index=["cost", "revenue"])
| cost revenue
| A True True
| B True False
| C False True
|
| Use the method to control the broadcast axis:
|
| >>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
| cost revenue
| A True False
| B True True
| C True True
| D True True
|
| When comparing to an arbitrary sequence, the number of columns must
| match the number elements in `other`:
|
| >>> df == [250, 100]
| cost revenue
| A True True
| B False False
| C False False
|
| Use the method to control the axis:
|
| >>> df.eq([250, 250, 100], axis='index')
| cost revenue
| A True False
| B False True
| C True False
|
| Compare to a DataFrame of different shape.
|
| >>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
| ... index=['A', 'B', 'C', 'D'])
| >>> other
| revenue
| A 300
| B 250
| C 100
| D 150
|
| >>> df.gt(other)
| cost revenue
| A False False
| B False False
| C False True
| D False False
|
| Compare to a MultiIndex by level.
|
| >>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
| ... 'revenue': [100, 250, 300, 200, 175, 225]},
| ... index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
| ... ['A', 'B', 'C', 'A', 'B', 'C']])
| >>> df_multindex
| cost revenue
| Q1 A 250 100
| B 150 250
| C 100 300
| Q2 A 150 200
| B 300 175
| C 220 225
|
| >>> df.le(df_multindex, level=1)
| cost revenue
| Q1 A True True
| B True True
| C True True
| Q2 A False True
| B True False
| C True False
|
| groupby(self, by=None, axis: 'Axis | lib.NoDefault' = <no_default>, level: 'IndexLabel | None' = None, as_index: 'bool' = True, sort: 'bool' = True, group_keys: 'bool' = True, observed: 'bool | lib.NoDefault' = <no_default>, dropna: 'bool' = True) -> 'DataFrameGroupBy'
| Group DataFrame using a mapper or by a Series of columns.
|
| A groupby operation involves some combination of splitting the
| object, applying a function, and combining the results. This can be
| used to group large amounts of data and compute operations on these
| groups.
|
| Parameters
| ----------
| by : mapping, function, label, pd.Grouper or list of such
| Used to determine the groups for the groupby.
| If ``by`` is a function, it's called on each value of the object's
| index. If a dict or Series is passed, the Series or dict VALUES
| will be used to determine the groups (the Series' values are first
| aligned; see ``.align()`` method). If a list or ndarray of length
| equal to the selected axis is passed (see the `groupby user guide
| <https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#splitting-an-object-into-groups>`_),
| the values are used as-is to determine the groups. A label or list
| of labels may be passed to group by the columns in ``self``.
| Notice that a tuple is interpreted as a (single) key.
| axis : {0 or 'index', 1 or 'columns'}, default 0
| Split along rows (0) or columns (1). For `Series` this parameter
| is unused and defaults to 0.
|
| .. deprecated:: 2.1.0
|
| Will be removed and behave like axis=0 in a future version.
| For ``axis=1``, do ``frame.T.groupby(...)`` instead.
|
| level : int, level name, or sequence of such, default None
| If the axis is a MultiIndex (hierarchical), group by a particular
| level or levels. Do not specify both ``by`` and ``level``.
| as_index : bool, default True
| Return object with group labels as the
| index. Only relevant for DataFrame input. as_index=False is
| effectively "SQL-style" grouped output. This argument has no effect
| on filtrations (see the `filtrations in the user guide
| <https://pandas.pydata.org/docs/dev/user_guide/groupby.html#filtration>`_),
| such as ``head()``, ``tail()``, ``nth()`` and in transformations
| (see the `transformations in the user guide
| <https://pandas.pydata.org/docs/dev/user_guide/groupby.html#transformation>`_).
| sort : bool, default True
| Sort group keys. Get better performance by turning this off.
| Note this does not influence the order of observations within each
| group. Groupby preserves the order of rows within each group. If False,
| the groups will appear in the same order as they did in the original DataFrame.
| This argument has no effect on filtrations (see the `filtrations in the user guide
| <https://pandas.pydata.org/docs/dev/user_guide/groupby.html#filtration>`_),
| such as ``head()``, ``tail()``, ``nth()`` and in transformations
| (see the `transformations in the user guide
| <https://pandas.pydata.org/docs/dev/user_guide/groupby.html#transformation>`_).
|
| .. versionchanged:: 2.0.0
|
| Specifying ``sort=False`` with an ordered categorical grouper will no
| longer sort the values.
|
| group_keys : bool, default True
| When calling apply and the ``by`` argument produces a like-indexed
| (i.e. :ref:`a transform <groupby.transform>`) result, add group keys to
| index to identify pieces. By default group keys are not included
| when the result's index (and column) labels match the inputs, and
| are included otherwise.
|
| .. versionchanged:: 1.5.0
|
| Warns that ``group_keys`` will no longer be ignored when the
| result from ``apply`` is a like-indexed Series or DataFrame.
| Specify ``group_keys`` explicitly to include the group keys or
| not.
|
| .. versionchanged:: 2.0.0
|
| ``group_keys`` now defaults to ``True``.
|
| observed : bool, default False
| This only applies if any of the groupers are Categoricals.
| If True: only show observed values for categorical groupers.
| If False: show all values for categorical groupers.
|
| .. deprecated:: 2.1.0
|
| The default value will change to True in a future version of pandas.
|
| dropna : bool, default True
| If True, and if group keys contain NA values, NA values together
| with row/column will be dropped.
| If False, NA values will also be treated as the key in groups.
|
| Returns
| -------
| pandas.api.typing.DataFrameGroupBy
| Returns a groupby object that contains information about the groups.
|
| See Also
| --------
| resample : Convenience method for frequency conversion and resampling
| of time series.
|
| Notes
| -----
| See the `user guide
| <https://pandas.pydata.org/pandas-docs/stable/groupby.html>`__ for more
| detailed usage and examples, including splitting an object into groups,
| iterating through groups, selecting a group, aggregation, and more.
|
| Examples
| --------
| >>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
| ... 'Parrot', 'Parrot'],
| ... 'Max Speed': [380., 370., 24., 26.]})
| >>> df
| Animal Max Speed
| 0 Falcon 380.0
| 1 Falcon 370.0
| 2 Parrot 24.0
| 3 Parrot 26.0
| >>> df.groupby(['Animal']).mean()
| Max Speed
| Animal
| Falcon 375.0
| Parrot 25.0
|
| **Hierarchical Indexes**
|
| We can groupby different levels of a hierarchical index
| using the `level` parameter:
|
| >>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
| ... ['Captive', 'Wild', 'Captive', 'Wild']]
| >>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
| >>> df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},
| ... index=index)
| >>> df
| Max Speed
| Animal Type
| Falcon Captive 390.0
| Wild 350.0
| Parrot Captive 30.0
| Wild 20.0
| >>> df.groupby(level=0).mean()
| Max Speed
| Animal
| Falcon 370.0
| Parrot 25.0
| >>> df.groupby(level="Type").mean()
| Max Speed
| Type
| Captive 210.0
| Wild 185.0
|
| We can also choose to include NA in group keys or not by setting
| `dropna` parameter, the default setting is `True`.
|
| >>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
| >>> df = pd.DataFrame(l, columns=["a", "b", "c"])
|
| >>> df.groupby(by=["b"]).sum()
| a c
| b
| 1.0 2 3
| 2.0 2 5
|
| >>> df.groupby(by=["b"], dropna=False).sum()
| a c
| b
| 1.0 2 3
| 2.0 2 5
| NaN 1 4
|
| >>> l = [["a", 12, 12], [None, 12.3, 33.], ["b", 12.3, 123], ["a", 1, 1]]
| >>> df = pd.DataFrame(l, columns=["a", "b", "c"])
|
| >>> df.groupby(by="a").sum()
| b c
| a
| a 13.0 13.0
| b 12.3 123.0
|
| >>> df.groupby(by="a", dropna=False).sum()
| b c
| a
| a 13.0 13.0
| b 12.3 123.0
| NaN 12.3 33.0
|
| When using ``.apply()``, use ``group_keys`` to include or exclude the
| group keys. The ``group_keys`` argument defaults to ``True`` (include).
|
| >>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
| ... 'Parrot', 'Parrot'],
| ... 'Max Speed': [380., 370., 24., 26.]})
| >>> df.groupby("Animal", group_keys=True)[['Max Speed']].apply(lambda x: x)
| Max Speed
| Animal
| Falcon 0 380.0
| 1 370.0
| Parrot 2 24.0
| 3 26.0
|
| >>> df.groupby("Animal", group_keys=False)[['Max Speed']].apply(lambda x: x)
| Max Speed
| 0 380.0
| 1 370.0
| 2 24.0
| 3 26.0
|
| gt(self, other, axis: 'Axis' = 'columns', level=None) -> 'DataFrame'
| Get Greater than of dataframe and other, element-wise (binary operator `gt`).
|
| Among flexible wrappers (`eq`, `ne`, `le`, `lt`, `ge`, `gt`) to comparison
| operators.
|
| Equivalent to `==`, `!=`, `<=`, `<`, `>=`, `>` with support to choose axis
| (rows or columns) and level for comparison.
|
| Parameters
| ----------
| other : scalar, sequence, Series, or DataFrame
| Any single or multiple element data structure, or list-like object.
| axis : {0 or 'index', 1 or 'columns'}, default 'columns'
| Whether to compare by the index (0 or 'index') or columns
| (1 or 'columns').
| level : int or label
| Broadcast across a level, matching Index values on the passed
| MultiIndex level.
|
| Returns
| -------
| DataFrame of bool
| Result of the comparison.
|
| See Also
| --------
| DataFrame.eq : Compare DataFrames for equality elementwise.
| DataFrame.ne : Compare DataFrames for inequality elementwise.
| DataFrame.le : Compare DataFrames for less than inequality
| or equality elementwise.
| DataFrame.lt : Compare DataFrames for strictly less than
| inequality elementwise.
| DataFrame.ge : Compare DataFrames for greater than inequality
| or equality elementwise.
| DataFrame.gt : Compare DataFrames for strictly greater than
| inequality elementwise.
|
| Notes
| -----
| Mismatched indices will be unioned together.
| `NaN` values are considered different (i.e. `NaN` != `NaN`).
|
| Examples
| --------
| >>> df = pd.DataFrame({'cost': [250, 150, 100],
| ... 'revenue': [100, 250, 300]},
| ... index=['A', 'B', 'C'])
| >>> df
| cost revenue
| A 250 100
| B 150 250
| C 100 300
|
| Comparison with a scalar, using either the operator or method:
|
| >>> df == 100
| cost revenue
| A False True
| B False False
| C True False
|
| >>> df.eq(100)
| cost revenue
| A False True
| B False False
| C True False
|
| When `other` is a :class:`Series`, the columns of a DataFrame are aligned
| with the index of `other` and broadcast:
|
| >>> df != pd.Series([100, 250], index=["cost", "revenue"])
| cost revenue
| A True True
| B True False
| C False True
|
| Use the method to control the broadcast axis:
|
| >>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
| cost revenue
| A True False
| B True True
| C True True
| D True True
|
| When comparing to an arbitrary sequence, the number of columns must
| match the number elements in `other`:
|
| >>> df == [250, 100]
| cost revenue
| A True True
| B False False
| C False False
|
| Use the method to control the axis:
|
| >>> df.eq([250, 250, 100], axis='index')
| cost revenue
| A True False
| B False True
| C True False
|
| Compare to a DataFrame of different shape.
|
| >>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
| ... index=['A', 'B', 'C', 'D'])
| >>> other
| revenue
| A 300
| B 250
| C 100
| D 150
|
| >>> df.gt(other)
| cost revenue
| A False False
| B False False
| C False True
| D False False
|
| Compare to a MultiIndex by level.
|
| >>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
| ... 'revenue': [100, 250, 300, 200, 175, 225]},
| ... index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
| ... ['A', 'B', 'C', 'A', 'B', 'C']])
| >>> df_multindex
| cost revenue
| Q1 A 250 100
| B 150 250
| C 100 300
| Q2 A 150 200
| B 300 175
| C 220 225
|
| >>> df.le(df_multindex, level=1)
| cost revenue
| Q1 A True True
| B True True
| C True True
| Q2 A False True
| B True False
| C True False
|
| hist = hist_frame(data: 'DataFrame', column: 'IndexLabel | None' = None, by=None, grid: 'bool' = True, xlabelsize: 'int | None' = None, xrot: 'float | None' = None, ylabelsize: 'int | None' = None, yrot: 'float | None' = None, ax=None, sharex: 'bool' = False, sharey: 'bool' = False, figsize: 'tuple[int, int] | None' = None, layout: 'tuple[int, int] | None' = None, bins: 'int | Sequence[int]' = 10, backend: 'str | None' = None, legend: 'bool' = False, **kwargs) from pandas.plotting._core
| Make a histogram of the DataFrame's columns.
|
| A `histogram`_ is a representation of the distribution of data.
| This function calls :meth:`matplotlib.pyplot.hist`, on each series in
| the DataFrame, resulting in one histogram per column.
|
| .. _histogram: https://en.wikipedia.org/wiki/Histogram
|
| Parameters
| ----------
| data : DataFrame
| The pandas object holding the data.
| column : str or sequence, optional
| If passed, will be used to limit data to a subset of columns.
| by : object, optional
| If passed, then used to form histograms for separate groups.
| grid : bool, default True
| Whether to show axis grid lines.
| xlabelsize : int, default None
| If specified changes the x-axis label size.
| xrot : float, default None
| Rotation of x axis labels. For example, a value of 90 displays the
| x labels rotated 90 degrees clockwise.
| ylabelsize : int, default None
| If specified changes the y-axis label size.
| yrot : float, default None
| Rotation of y axis labels. For example, a value of 90 displays the
| y labels rotated 90 degrees clockwise.
| ax : Matplotlib axes object, default None
| The axes to plot the histogram on.
| sharex : bool, default True if ax is None else False
| In case subplots=True, share x axis and set some x axis labels to
| invisible; defaults to True if ax is None otherwise False if an ax
| is passed in.
| Note that passing in both an ax and sharex=True will alter all x axis
| labels for all subplots in a figure.
| sharey : bool, default False
| In case subplots=True, share y axis and set some y axis labels to
| invisible.
| figsize : tuple, optional
| The size in inches of the figure to create. Uses the value in
| `matplotlib.rcParams` by default.
| layout : tuple, optional
| Tuple of (rows, columns) for the layout of the histograms.
| bins : int or sequence, default 10
| Number of histogram bins to be used. If an integer is given, bins + 1
| bin edges are calculated and returned. If bins is a sequence, gives
| bin edges, including left edge of first bin and right edge of last
| bin. In this case, bins is returned unmodified.
|
| backend : str, default None
| Backend to use instead of the backend specified in the option
| ``plotting.backend``. For instance, 'matplotlib'. Alternatively, to
| specify the ``plotting.backend`` for the whole session, set
| ``pd.options.plotting.backend``.
|
| legend : bool, default False
| Whether to show the legend.
|
| **kwargs
| All other plotting keyword arguments to be passed to
| :meth:`matplotlib.pyplot.hist`.
|
| Returns
| -------
| matplotlib.AxesSubplot or numpy.ndarray of them
|
| See Also
| --------
| matplotlib.pyplot.hist : Plot a histogram using matplotlib.
|
| Examples
| --------
| This example draws a histogram based on the length and width of
| some animals, displayed in three bins
|
| .. plot::
| :context: close-figs
|
| >>> data = {'length': [1.5, 0.5, 1.2, 0.9, 3],
| ... 'width': [0.7, 0.2, 0.15, 0.2, 1.1]}
| >>> index = ['pig', 'rabbit', 'duck', 'chicken', 'horse']
| >>> df = pd.DataFrame(data, index=index)
| >>> hist = df.hist(bins=3)
|
| idxmax(self, axis: 'Axis' = 0, skipna: 'bool' = True, numeric_only: 'bool' = False) -> 'Series'
| Return index of first occurrence of maximum over requested axis.
|
| NA/null values are excluded.
|
| Parameters
| ----------
| axis : {0 or 'index', 1 or 'columns'}, default 0
| The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
| skipna : bool, default True
| Exclude NA/null values. If an entire row/column is NA, the result
| will be NA.
| numeric_only : bool, default False
| Include only `float`, `int` or `boolean` data.
|
| .. versionadded:: 1.5.0
|
| Returns
| -------
| Series
| Indexes of maxima along the specified axis.
|
| Raises
| ------
| ValueError
| * If the row/column is empty
|
| See Also
| --------
| Series.idxmax : Return index of the maximum element.
|
| Notes
| -----
| This method is the DataFrame version of ``ndarray.argmax``.
|
| Examples
| --------
| Consider a dataset containing food consumption in Argentina.
|
| >>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],
| ... 'co2_emissions': [37.2, 19.66, 1712]},
| ... index=['Pork', 'Wheat Products', 'Beef'])
|
| >>> df
| consumption co2_emissions
| Pork 10.51 37.20
| Wheat Products 103.11 19.66
| Beef 55.48 1712.00
|
| By default, it returns the index for the maximum value in each column.
|
| >>> df.idxmax()
| consumption Wheat Products
| co2_emissions Beef
| dtype: object
|
| To return the index for the maximum value in each row, use ``axis="columns"``.
|
| >>> df.idxmax(axis="columns")
| Pork co2_emissions
| Wheat Products consumption
| Beef co2_emissions
| dtype: object
|
| idxmin(self, axis: 'Axis' = 0, skipna: 'bool' = True, numeric_only: 'bool' = False) -> 'Series'
| Return index of first occurrence of minimum over requested axis.
|
| NA/null values are excluded.
|
| Parameters
| ----------
| axis : {0 or 'index', 1 or 'columns'}, default 0
| The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
| skipna : bool, default True
| Exclude NA/null values. If an entire row/column is NA, the result
| will be NA.
| numeric_only : bool, default False
| Include only `float`, `int` or `boolean` data.
|
| .. versionadded:: 1.5.0
|
| Returns
| -------
| Series
| Indexes of minima along the specified axis.
|
| Raises
| ------
| ValueError
| * If the row/column is empty
|
| See Also
| --------
| Series.idxmin : Return index of the minimum element.
|
| Notes
| -----
| This method is the DataFrame version of ``ndarray.argmin``.
|
| Examples
| --------
| Consider a dataset containing food consumption in Argentina.
|
| >>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],
| ... 'co2_emissions': [37.2, 19.66, 1712]},
| ... index=['Pork', 'Wheat Products', 'Beef'])
|
| >>> df
| consumption co2_emissions
| Pork 10.51 37.20
| Wheat Products 103.11 19.66
| Beef 55.48 1712.00
|
| By default, it returns the index for the minimum value in each column.
|
| >>> df.idxmin()
| consumption Pork
| co2_emissions Wheat Products
| dtype: object
|
| To return the index for the minimum value in each row, use ``axis="columns"``.
|
| >>> df.idxmin(axis="columns")
| Pork consumption
| Wheat Products co2_emissions
| Beef consumption
| dtype: object
|
| info(self, verbose: 'bool | None' = None, buf: 'WriteBuffer[str] | None' = None, max_cols: 'int | None' = None, memory_usage: 'bool | str | None' = None, show_counts: 'bool | None' = None) -> 'None'
| Print a concise summary of a DataFrame.
|
| This method prints information about a DataFrame including
| the index dtype and columns, non-null values and memory usage.
|
| Parameters
| ----------
| verbose : bool, optional
| Whether to print the full summary. By default, the setting in
| ``pandas.options.display.max_info_columns`` is followed.
| buf : writable buffer, defaults to sys.stdout
| Where to send the output. By default, the output is printed to
| sys.stdout. Pass a writable buffer if you need to further process
| the output.
| max_cols : int, optional
| When to switch from the verbose to the truncated output. If the
| DataFrame has more than `max_cols` columns, the truncated output
| is used. By default, the setting in
| ``pandas.options.display.max_info_columns`` is used.
| memory_usage : bool, str, optional
| Specifies whether total memory usage of the DataFrame
| elements (including the index) should be displayed. By default,
| this follows the ``pandas.options.display.memory_usage`` setting.
|
| True always show memory usage. False never shows memory usage.
| A value of 'deep' is equivalent to "True with deep introspection".
| Memory usage is shown in human-readable units (base-2
| representation). Without deep introspection a memory estimation is
| made based in column dtype and number of rows assuming values
| consume the same memory amount for corresponding dtypes. With deep
| memory introspection, a real memory usage calculation is performed
| at the cost of computational resources. See the
| :ref:`Frequently Asked Questions <df-memory-usage>` for more
| details.
| show_counts : bool, optional
| Whether to show the non-null counts. By default, this is shown
| only if the DataFrame is smaller than
| ``pandas.options.display.max_info_rows`` and
| ``pandas.options.display.max_info_columns``. A value of True always
| shows the counts, and False never shows the counts.
|
| Returns
| -------
| None
| This method prints a summary of a DataFrame and returns None.
|
| See Also
| --------
| DataFrame.describe: Generate descriptive statistics of DataFrame
| columns.
| DataFrame.memory_usage: Memory usage of DataFrame columns.
|
| Examples
| --------
| >>> int_values = [1, 2, 3, 4, 5]
| >>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
| >>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
| >>> df = pd.DataFrame({"int_col": int_values, "text_col": text_values,
| ... "float_col": float_values})
| >>> df
| int_col text_col float_col
| 0 1 alpha 0.00
| 1 2 beta 0.25
| 2 3 gamma 0.50
| 3 4 delta 0.75
| 4 5 epsilon 1.00
|
| Prints information of all columns:
|
| >>> df.info(verbose=True)
| <class 'pandas.core.frame.DataFrame'>
| RangeIndex: 5 entries, 0 to 4
| Data columns (total 3 columns):
| # Column Non-Null Count Dtype
| --- ------ -------------- -----
| 0 int_col 5 non-null int64
| 1 text_col 5 non-null object
| 2 float_col 5 non-null float64
| dtypes: float64(1), int64(1), object(1)
| memory usage: 248.0+ bytes
|
| Prints a summary of columns count and its dtypes but not per column
| information:
|
| >>> df.info(verbose=False)
| <class 'pandas.core.frame.DataFrame'>
| RangeIndex: 5 entries, 0 to 4
| Columns: 3 entries, int_col to float_col
| dtypes: float64(1), int64(1), object(1)
| memory usage: 248.0+ bytes
|
| Pipe output of DataFrame.info to buffer instead of sys.stdout, get
| buffer content and writes to a text file:
|
| >>> import io
| >>> buffer = io.StringIO()
| >>> df.info(buf=buffer)
| >>> s = buffer.getvalue()
| >>> with open("df_info.txt", "w",
| ... encoding="utf-8") as f: # doctest: +SKIP
| ... f.write(s)
| 260
|
| The `memory_usage` parameter allows deep introspection mode, specially
| useful for big DataFrames and fine-tune memory optimization:
|
| >>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
| >>> df = pd.DataFrame({
| ... 'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6),
| ... 'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6),
| ... 'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)
| ... })
| >>> df.info()
| <class 'pandas.core.frame.DataFrame'>
| RangeIndex: 1000000 entries, 0 to 999999
| Data columns (total 3 columns):
| # Column Non-Null Count Dtype
| --- ------ -------------- -----
| 0 column_1 1000000 non-null object
| 1 column_2 1000000 non-null object
| 2 column_3 1000000 non-null object
| dtypes: object(3)
| memory usage: 22.9+ MB
|
| >>> df.info(memory_usage='deep')
| <class 'pandas.core.frame.DataFrame'>
| RangeIndex: 1000000 entries, 0 to 999999
| Data columns (total 3 columns):
| # Column Non-Null Count Dtype
| --- ------ -------------- -----
| 0 column_1 1000000 non-null object
| 1 column_2 1000000 non-null object
| 2 column_3 1000000 non-null object
| dtypes: object(3)
| memory usage: 165.9 MB
|
| insert(self, loc: 'int', column: 'Hashable', value: 'Scalar | AnyArrayLike', allow_duplicates: 'bool | lib.NoDefault' = <no_default>) -> 'None'
| Insert column into DataFrame at specified location.
|
| Raises a ValueError if `column` is already contained in the DataFrame,
| unless `allow_duplicates` is set to True.
|
| Parameters
| ----------
| loc : int
| Insertion index. Must verify 0 <= loc <= len(columns).
| column : str, number, or hashable object
| Label of the inserted column.
| value : Scalar, Series, or array-like
| Content of the inserted column.
| allow_duplicates : bool, optional, default lib.no_default
| Allow duplicate column labels to be created.
|
| See Also
| --------
| Index.insert : Insert new item by index.
|
| Examples
| --------
| >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
| >>> df
| col1 col2
| 0 1 3
| 1 2 4
| >>> df.insert(1, "newcol", [99, 99])
| >>> df
| col1 newcol col2
| 0 1 99 3
| 1 2 99 4
| >>> df.insert(0, "col1", [100, 100], allow_duplicates=True)
| >>> df
| col1 col1 newcol col2
| 0 100 1 99 3
| 1 100 2 99 4
|
| Notice that pandas uses index alignment in case of `value` from type `Series`:
|
| >>> df.insert(0, "col0", pd.Series([5, 6], index=[1, 2]))
| >>> df
| col0 col1 col1 newcol col2
| 0 NaN 100 1 99 3
| 1 5.0 100 2 99 4
|
| isetitem(self, loc, value) -> 'None'
| Set the given value in the column with position `loc`.
|
| This is a positional analogue to ``__setitem__``.
|
| Parameters
| ----------
| loc : int or sequence of ints
| Index position for the column.
| value : scalar or arraylike
| Value(s) for the column.
|
| Notes
| -----
| ``frame.isetitem(loc, value)`` is an in-place method as it will
| modify the DataFrame in place (not returning a new object). In contrast to
| ``frame.iloc[:, i] = value`` which will try to update the existing values in
| place, ``frame.isetitem(loc, value)`` will not update the values of the column
| itself in place, it will instead insert a new array.
|
| In cases where ``frame.columns`` is unique, this is equivalent to
| ``frame[frame.columns[i]] = value``.
|
| isin(self, values: 'Series | DataFrame | Sequence | Mapping') -> 'DataFrame'
| Whether each element in the DataFrame is contained in values.
|
| Parameters
| ----------
| values : iterable, Series, DataFrame or dict
| The result will only be true at a location if all the
| labels match. If `values` is a Series, that's the index. If
| `values` is a dict, the keys must be the column names,
| which must match. If `values` is a DataFrame,
| then both the index and column labels must match.
|
| Returns
| -------
| DataFrame
| DataFrame of booleans showing whether each element in the DataFrame
| is contained in values.
|
| See Also
| --------
| DataFrame.eq: Equality test for DataFrame.
| Series.isin: Equivalent method on Series.
| Series.str.contains: Test if pattern or regex is contained within a
| string of a Series or Index.
|
| Examples
| --------
| >>> df = pd.DataFrame({'num_legs': [2, 4], 'num_wings': [2, 0]},
| ... index=['falcon', 'dog'])
| >>> df
| num_legs num_wings
| falcon 2 2
| dog 4 0
|
| When ``values`` is a list check whether every value in the DataFrame
| is present in the list (which animals have 0 or 2 legs or wings)
|
| >>> df.isin([0, 2])
| num_legs num_wings
| falcon True True
| dog False True
|
| To check if ``values`` is *not* in the DataFrame, use the ``~`` operator:
|
| >>> ~df.isin([0, 2])
| num_legs num_wings
| falcon False False
| dog True False
|
| When ``values`` is a dict, we can pass values to check for each
| column separately:
|
| >>> df.isin({'num_wings': [0, 3]})
| num_legs num_wings
| falcon False False
| dog False True
|
| When ``values`` is a Series or DataFrame the index and column must
| match. Note that 'falcon' does not match based on the number of legs
| in other.
|
| >>> other = pd.DataFrame({'num_legs': [8, 3], 'num_wings': [0, 2]},
| ... index=['spider', 'falcon'])
| >>> df.isin(other)
| num_legs num_wings
| falcon False True
| dog False False
|
| isna(self) -> 'DataFrame'
| Detect missing values.
|
| Return a boolean same-sized object indicating if the values are NA.
| NA values, such as None or :attr:`numpy.NaN`, gets mapped to True
| values.
| Everything else gets mapped to False values. Characters such as empty
| strings ``''`` or :attr:`numpy.inf` are not considered NA values
| (unless you set ``pandas.options.mode.use_inf_as_na = True``).
|
| Returns
| -------
| DataFrame
| Mask of bool values for each element in DataFrame that
| indicates whether an element is an NA value.
|
| See Also
| --------
| DataFrame.isnull : Alias of isna.
| DataFrame.notna : Boolean inverse of isna.
| DataFrame.dropna : Omit axes labels with missing values.
| isna : Top-level isna.
|
| Examples
| --------
| Show which entries in a DataFrame are NA.
|
| >>> df = pd.DataFrame(dict(age=[5, 6, np.nan],
| ... born=[pd.NaT, pd.Timestamp('1939-05-27'),
| ... pd.Timestamp('1940-04-25')],
| ... name=['Alfred', 'Batman', ''],
| ... toy=[None, 'Batmobile', 'Joker']))
| >>> df
| age born name toy
| 0 5.0 NaT Alfred None
| 1 6.0 1939-05-27 Batman Batmobile
| 2 NaN 1940-04-25 Joker
|
| >>> df.isna()
| age born name toy
| 0 False True False True
| 1 False False False False
| 2 True False False False
|
| Show which entries in a Series are NA.
|
| >>> ser = pd.Series([5, 6, np.nan])
| >>> ser
| 0 5.0
| 1 6.0
| 2 NaN
| dtype: float64
|
| >>> ser.isna()
| 0 False
| 1 False
| 2 True
| dtype: bool
|
| isnull(self) -> 'DataFrame'
| DataFrame.isnull is an alias for DataFrame.isna.
|
| Detect missing values.
|
| Return a boolean same-sized object indicating if the values are NA.
| NA values, such as None or :attr:`numpy.NaN`, gets mapped to True
| values.
| Everything else gets mapped to False values. Characters such as empty
| strings ``''`` or :attr:`numpy.inf` are not considered NA values
| (unless you set ``pandas.options.mode.use_inf_as_na = True``).
|
| Returns
| -------
| DataFrame
| Mask of bool values for each element in DataFrame that
| indicates whether an element is an NA value.
|
| See Also
| --------
| DataFrame.isnull : Alias of isna.
| DataFrame.notna : Boolean inverse of isna.
| DataFrame.dropna : Omit axes labels with missing values.
| isna : Top-level isna.
|
| Examples
| --------
| Show which entries in a DataFrame are NA.
|
| >>> df = pd.DataFrame(dict(age=[5, 6, np.nan],
| ... born=[pd.NaT, pd.Timestamp('1939-05-27'),
| ... pd.Timestamp('1940-04-25')],
| ... name=['Alfred', 'Batman', ''],
| ... toy=[None, 'Batmobile', 'Joker']))
| >>> df
| age born name toy
| 0 5.0 NaT Alfred None
| 1 6.0 1939-05-27 Batman Batmobile
| 2 NaN 1940-04-25 Joker
|
| >>> df.isna()
| age born name toy
| 0 False True False True
| 1 False False False False
| 2 True False False False
|
| Show which entries in a Series are NA.
|
| >>> ser = pd.Series([5, 6, np.nan])
| >>> ser
| 0 5.0
| 1 6.0
| 2 NaN
| dtype: float64
|
| >>> ser.isna()
| 0 False
| 1 False
| 2 True
| dtype: bool
|
| items(self) -> 'Iterable[tuple[Hashable, Series]]'
| Iterate over (column name, Series) pairs.
|
| Iterates over the DataFrame columns, returning a tuple with
| the column name and the content as a Series.
|
| Yields
| ------
| label : object
| The column names for the DataFrame being iterated over.
| content : Series
| The column entries belonging to each label, as a Series.
|
| See Also
| --------
| DataFrame.iterrows : Iterate over DataFrame rows as
| (index, Series) pairs.
| DataFrame.itertuples : Iterate over DataFrame rows as namedtuples
| of the values.
|
| Examples
| --------
| >>> df = pd.DataFrame({'species': ['bear', 'bear', 'marsupial'],
| ... 'population': [1864, 22000, 80000]},
| ... index=['panda', 'polar', 'koala'])
| >>> df
| species population
| panda bear 1864
| polar bear 22000
| koala marsupial 80000
| >>> for label, content in df.items():
| ... print(f'label: {label}')
| ... print(f'content: {content}', sep='\n')
| ...
| label: species
| content:
| panda bear
| polar bear
| koala marsupial
| Name: species, dtype: object
| label: population
| content:
| panda 1864
| polar 22000
| koala 80000
| Name: population, dtype: int64
|
| iterrows(self) -> 'Iterable[tuple[Hashable, Series]]'
| Iterate over DataFrame rows as (index, Series) pairs.
|
| Yields
| ------
| index : label or tuple of label
| The index of the row. A tuple for a `MultiIndex`.
| data : Series
| The data of the row as a Series.
|
| See Also
| --------
| DataFrame.itertuples : Iterate over DataFrame rows as namedtuples of the values.
| DataFrame.items : Iterate over (column name, Series) pairs.
|
| Notes
| -----
| 1. Because ``iterrows`` returns a Series for each row,
| it does **not** preserve dtypes across the rows (dtypes are
| preserved across columns for DataFrames).
|
| To preserve dtypes while iterating over the rows, it is better
| to use :meth:`itertuples` which returns namedtuples of the values
| and which is generally faster than ``iterrows``.
|
| 2. You should **never modify** something you are iterating over.
| This is not guaranteed to work in all cases. Depending on the
| data types, the iterator returns a copy and not a view, and writing
| to it will have no effect.
|
| Examples
| --------
|
| >>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])
| >>> row = next(df.iterrows())[1]
| >>> row
| int 1.0
| float 1.5
| Name: 0, dtype: float64
| >>> print(row['int'].dtype)
| float64
| >>> print(df['int'].dtype)
| int64
|
| itertuples(self, index: 'bool' = True, name: 'str | None' = 'Pandas') -> 'Iterable[tuple[Any, ...]]'
| Iterate over DataFrame rows as namedtuples.
|
| Parameters
| ----------
| index : bool, default True
| If True, return the index as the first element of the tuple.
| name : str or None, default "Pandas"
| The name of the returned namedtuples or None to return regular
| tuples.
|
| Returns
| -------
| iterator
| An object to iterate over namedtuples for each row in the
| DataFrame with the first field possibly being the index and
| following fields being the column values.
|
| See Also
| --------
| DataFrame.iterrows : Iterate over DataFrame rows as (index, Series)
| pairs.
| DataFrame.items : Iterate over (column name, Series) pairs.
|
| Notes
| -----
| The column names will be renamed to positional names if they are
| invalid Python identifiers, repeated, or start with an underscore.
|
| Examples
| --------
| >>> df = pd.DataFrame({'num_legs': [4, 2], 'num_wings': [0, 2]},
| ... index=['dog', 'hawk'])
| >>> df
| num_legs num_wings
| dog 4 0
| hawk 2 2
| >>> for row in df.itertuples():
| ... print(row)
| ...
| Pandas(Index='dog', num_legs=4, num_wings=0)
| Pandas(Index='hawk', num_legs=2, num_wings=2)
|
| By setting the `index` parameter to False we can remove the index
| as the first element of the tuple:
|
| >>> for row in df.itertuples(index=False):
| ... print(row)
| ...
| Pandas(num_legs=4, num_wings=0)
| Pandas(num_legs=2, num_wings=2)
|
| With the `name` parameter set we set a custom name for the yielded
| namedtuples:
|
| >>> for row in df.itertuples(name='Animal'):
| ... print(row)
| ...
| Animal(Index='dog', num_legs=4, num_wings=0)
| Animal(Index='hawk', num_legs=2, num_wings=2)
|
| join(self, other: 'DataFrame | Series | Iterable[DataFrame | Series]', on: 'IndexLabel | None' = None, how: 'MergeHow' = 'left', lsuffix: 'str' = '', rsuffix: 'str' = '', sort: 'bool' = False, validate: 'JoinValidate | None' = None) -> 'DataFrame'
| Join columns of another DataFrame.
|
| Join columns with `other` DataFrame either on index or on a key
| column. Efficiently join multiple DataFrame objects by index at once by
| passing a list.
|
| Parameters
| ----------
| other : DataFrame, Series, or a list containing any combination of them
| Index should be similar to one of the columns in this one. If a
| Series is passed, its name attribute must be set, and that will be
| used as the column name in the resulting joined DataFrame.
| on : str, list of str, or array-like, optional
| Column or index level name(s) in the caller to join on the index
| in `other`, otherwise joins index-on-index. If multiple
| values given, the `other` DataFrame must have a MultiIndex. Can
| pass an array as the join key if it is not already contained in
| the calling DataFrame. Like an Excel VLOOKUP operation.
| how : {'left', 'right', 'outer', 'inner', 'cross'}, default 'left'
| How to handle the operation of the two objects.
|
| * left: use calling frame's index (or column if on is specified)
| * right: use `other`'s index.
| * outer: form union of calling frame's index (or column if on is
| specified) with `other`'s index, and sort it lexicographically.
| * inner: form intersection of calling frame's index (or column if
| on is specified) with `other`'s index, preserving the order
| of the calling's one.
| * cross: creates the cartesian product from both frames, preserves the order
| of the left keys.
| lsuffix : str, default ''
| Suffix to use from left frame's overlapping columns.
| rsuffix : str, default ''
| Suffix to use from right frame's overlapping columns.
| sort : bool, default False
| Order result DataFrame lexicographically by the join key. If False,
| the order of the join key depends on the join type (how keyword).
| validate : str, optional
| If specified, checks if join is of specified type.
|
| * "one_to_one" or "1:1": check if join keys are unique in both left
| and right datasets.
| * "one_to_many" or "1:m": check if join keys are unique in left dataset.
| * "many_to_one" or "m:1": check if join keys are unique in right dataset.
| * "many_to_many" or "m:m": allowed, but does not result in checks.
|
| .. versionadded:: 1.5.0
|
| Returns
| -------
| DataFrame
| A dataframe containing columns from both the caller and `other`.
|
| See Also
| --------
| DataFrame.merge : For column(s)-on-column(s) operations.
|
| Notes
| -----
| Parameters `on`, `lsuffix`, and `rsuffix` are not supported when
| passing a list of `DataFrame` objects.
|
| Examples
| --------
| >>> df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],
| ... 'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})
|
| >>> df
| key A
| 0 K0 A0
| 1 K1 A1
| 2 K2 A2
| 3 K3 A3
| 4 K4 A4
| 5 K5 A5
|
| >>> other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],
| ... 'B': ['B0', 'B1', 'B2']})
|
| >>> other
| key B
| 0 K0 B0
| 1 K1 B1
| 2 K2 B2
|
| Join DataFrames using their indexes.
|
| >>> df.join(other, lsuffix='_caller', rsuffix='_other')
| key_caller A key_other B
| 0 K0 A0 K0 B0
| 1 K1 A1 K1 B1
| 2 K2 A2 K2 B2
| 3 K3 A3 NaN NaN
| 4 K4 A4 NaN NaN
| 5 K5 A5 NaN NaN
|
| If we want to join using the key columns, we need to set key to be
| the index in both `df` and `other`. The joined DataFrame will have
| key as its index.
|
| >>> df.set_index('key').join(other.set_index('key'))
| A B
| key
| K0 A0 B0
| K1 A1 B1
| K2 A2 B2
| K3 A3 NaN
| K4 A4 NaN
| K5 A5 NaN
|
| Another option to join using the key columns is to use the `on`
| parameter. DataFrame.join always uses `other`'s index but we can use
| any column in `df`. This method preserves the original DataFrame's
| index in the result.
|
| >>> df.join(other.set_index('key'), on='key')
| key A B
| 0 K0 A0 B0
| 1 K1 A1 B1
| 2 K2 A2 B2
| 3 K3 A3 NaN
| 4 K4 A4 NaN
| 5 K5 A5 NaN
|
| Using non-unique key values shows how they are matched.
|
| >>> df = pd.DataFrame({'key': ['K0', 'K1', 'K1', 'K3', 'K0', 'K1'],
| ... 'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})
|
| >>> df
| key A
| 0 K0 A0
| 1 K1 A1
| 2 K1 A2
| 3 K3 A3
| 4 K0 A4
| 5 K1 A5
|
| >>> df.join(other.set_index('key'), on='key', validate='m:1')
| key A B
| 0 K0 A0 B0
| 1 K1 A1 B1
| 2 K1 A2 B1
| 3 K3 A3 NaN
| 4 K0 A4 B0
| 5 K1 A5 B1
|
| kurt(self, axis: 'Axis | None' = 0, skipna: 'bool' = True, numeric_only: 'bool' = False, **kwargs)
| Return unbiased kurtosis over requested axis.
|
| Kurtosis obtained using Fisher's definition of
| kurtosis (kurtosis of normal == 0.0). Normalized by N-1.
|
| Parameters
| ----------
| axis : {index (0), columns (1)}
| Axis for the function to be applied on.
| For `Series` this parameter is unused and defaults to 0.
|
| For DataFrames, specifying ``axis=None`` will apply the aggregation
| across both axes.
|
| .. versionadded:: 2.0.0
|
| skipna : bool, default True
| Exclude NA/null values when computing the result.
| numeric_only : bool, default False
| Include only float, int, boolean columns. Not implemented for Series.
|
| **kwargs
| Additional keyword arguments to be passed to the function.
|
| Returns
| -------
| Series or scalar
|
| Examples
| --------
| >>> s = pd.Series([1, 2, 2, 3], index=['cat', 'dog', 'dog', 'mouse'])
| >>> s
| cat 1
| dog 2
| dog 2
| mouse 3
| dtype: int64
| >>> s.kurt()
| 1.5
|
| With a DataFrame
|
| >>> df = pd.DataFrame({'a': [1, 2, 2, 3], 'b': [3, 4, 4, 4]},
| ... index=['cat', 'dog', 'dog', 'mouse'])
| >>> df
| a b
| cat 1 3
| dog 2 4
| dog 2 4
| mouse 3 4
| >>> df.kurt()
| a 1.5
| b 4.0
| dtype: float64
|
| With axis=None
|
| >>> df.kurt(axis=None).round(6)
| -0.988693
|
| Using axis=1
|
| >>> df = pd.DataFrame({'a': [1, 2], 'b': [3, 4], 'c': [3, 4], 'd': [1, 2]},
| ... index=['cat', 'dog'])
| >>> df.kurt(axis=1)
| cat -6.0
| dog -6.0
| dtype: float64
|
| kurtosis = kurt(self, axis: 'Axis | None' = 0, skipna: 'bool' = True, numeric_only: 'bool' = False, **kwargs)
|
| le(self, other, axis: 'Axis' = 'columns', level=None) -> 'DataFrame'
| Get Less than or equal to of dataframe and other, element-wise (binary operator `le`).
|
| Among flexible wrappers (`eq`, `ne`, `le`, `lt`, `ge`, `gt`) to comparison
| operators.
|
| Equivalent to `==`, `!=`, `<=`, `<`, `>=`, `>` with support to choose axis
| (rows or columns) and level for comparison.
|
| Parameters
| ----------
| other : scalar, sequence, Series, or DataFrame
| Any single or multiple element data structure, or list-like object.
| axis : {0 or 'index', 1 or 'columns'}, default 'columns'
| Whether to compare by the index (0 or 'index') or columns
| (1 or 'columns').
| level : int or label
| Broadcast across a level, matching Index values on the passed
| MultiIndex level.
|
| Returns
| -------
| DataFrame of bool
| Result of the comparison.
|
| See Also
| --------
| DataFrame.eq : Compare DataFrames for equality elementwise.
| DataFrame.ne : Compare DataFrames for inequality elementwise.
| DataFrame.le : Compare DataFrames for less than inequality
| or equality elementwise.
| DataFrame.lt : Compare DataFrames for strictly less than
| inequality elementwise.
| DataFrame.ge : Compare DataFrames for greater than inequality
| or equality elementwise.
| DataFrame.gt : Compare DataFrames for strictly greater than
| inequality elementwise.
|
| Notes
| -----
| Mismatched indices will be unioned together.
| `NaN` values are considered different (i.e. `NaN` != `NaN`).
|
| Examples
| --------
| >>> df = pd.DataFrame({'cost': [250, 150, 100],
| ... 'revenue': [100, 250, 300]},
| ... index=['A', 'B', 'C'])
| >>> df
| cost revenue
| A 250 100
| B 150 250
| C 100 300
|
| Comparison with a scalar, using either the operator or method:
|
| >>> df == 100
| cost revenue
| A False True
| B False False
| C True False
|
| >>> df.eq(100)
| cost revenue
| A False True
| B False False
| C True False
|
| When `other` is a :class:`Series`, the columns of a DataFrame are aligned
| with the index of `other` and broadcast:
|
| >>> df != pd.Series([100, 250], index=["cost", "revenue"])
| cost revenue
| A True True
| B True False
| C False True
|
| Use the method to control the broadcast axis:
|
| >>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
| cost revenue
| A True False
| B True True
| C True True
| D True True
|
| When comparing to an arbitrary sequence, the number of columns must
| match the number elements in `other`:
|
| >>> df == [250, 100]
| cost revenue
| A True True
| B False False
| C False False
|
| Use the method to control the axis:
|
| >>> df.eq([250, 250, 100], axis='index')
| cost revenue
| A True False
| B False True
| C True False
|
| Compare to a DataFrame of different shape.
|
| >>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
| ... index=['A', 'B', 'C', 'D'])
| >>> other
| revenue
| A 300
| B 250
| C 100
| D 150
|
| >>> df.gt(other)
| cost revenue
| A False False
| B False False
| C False True
| D False False
|
| Compare to a MultiIndex by level.
|
| >>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
| ... 'revenue': [100, 250, 300, 200, 175, 225]},
| ... index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
| ... ['A', 'B', 'C', 'A', 'B', 'C']])
| >>> df_multindex
| cost revenue
| Q1 A 250 100
| B 150 250
| C 100 300
| Q2 A 150 200
| B 300 175
| C 220 225
|
| >>> df.le(df_multindex, level=1)
| cost revenue
| Q1 A True True
| B True True
| C True True
| Q2 A False True
| B True False
| C True False
|
| lt(self, other, axis: 'Axis' = 'columns', level=None) -> 'DataFrame'
| Get Less than of dataframe and other, element-wise (binary operator `lt`).
|
| Among flexible wrappers (`eq`, `ne`, `le`, `lt`, `ge`, `gt`) to comparison
| operators.
|
| Equivalent to `==`, `!=`, `<=`, `<`, `>=`, `>` with support to choose axis
| (rows or columns) and level for comparison.
|
| Parameters
| ----------
| other : scalar, sequence, Series, or DataFrame
| Any single or multiple element data structure, or list-like object.
| axis : {0 or 'index', 1 or 'columns'}, default 'columns'
| Whether to compare by the index (0 or 'index') or columns
| (1 or 'columns').
| level : int or label
| Broadcast across a level, matching Index values on the passed
| MultiIndex level.
|
| Returns
| -------
| DataFrame of bool
| Result of the comparison.
|
| See Also
| --------
| DataFrame.eq : Compare DataFrames for equality elementwise.
| DataFrame.ne : Compare DataFrames for inequality elementwise.
| DataFrame.le : Compare DataFrames for less than inequality
| or equality elementwise.
| DataFrame.lt : Compare DataFrames for strictly less than
| inequality elementwise.
| DataFrame.ge : Compare DataFrames for greater than inequality
| or equality elementwise.
| DataFrame.gt : Compare DataFrames for strictly greater than
| inequality elementwise.
|
| Notes
| -----
| Mismatched indices will be unioned together.
| `NaN` values are considered different (i.e. `NaN` != `NaN`).
|
| Examples
| --------
| >>> df = pd.DataFrame({'cost': [250, 150, 100],
| ... 'revenue': [100, 250, 300]},
| ... index=['A', 'B', 'C'])
| >>> df
| cost revenue
| A 250 100
| B 150 250
| C 100 300
|
| Comparison with a scalar, using either the operator or method:
|
| >>> df == 100
| cost revenue
| A False True
| B False False
| C True False
|
| >>> df.eq(100)
| cost revenue
| A False True
| B False False
| C True False
|
| When `other` is a :class:`Series`, the columns of a DataFrame are aligned
| with the index of `other` and broadcast:
|
| >>> df != pd.Series([100, 250], index=["cost", "revenue"])
| cost revenue
| A True True
| B True False
| C False True
|
| Use the method to control the broadcast axis:
|
| >>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
| cost revenue
| A True False
| B True True
| C True True
| D True True
|
| When comparing to an arbitrary sequence, the number of columns must
| match the number elements in `other`:
|
| >>> df == [250, 100]
| cost revenue
| A True True
| B False False
| C False False
|
| Use the method to control the axis:
|
| >>> df.eq([250, 250, 100], axis='index')
| cost revenue
| A True False
| B False True
| C True False
|
| Compare to a DataFrame of different shape.
|
| >>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
| ... index=['A', 'B', 'C', 'D'])
| >>> other
| revenue
| A 300
| B 250
| C 100
| D 150
|
| >>> df.gt(other)
| cost revenue
| A False False
| B False False
| C False True
| D False False
|
| Compare to a MultiIndex by level.
|
| >>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
| ... 'revenue': [100, 250, 300, 200, 175, 225]},
| ... index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
| ... ['A', 'B', 'C', 'A', 'B', 'C']])
| >>> df_multindex
| cost revenue
| Q1 A 250 100
| B 150 250
| C 100 300
| Q2 A 150 200
| B 300 175
| C 220 225
|
| >>> df.le(df_multindex, level=1)
| cost revenue
| Q1 A True True
| B True True
| C True True
| Q2 A False True
| B True False
| C True False
|
| map(self, func: 'PythonFuncType', na_action: 'str | None' = None, **kwargs) -> 'DataFrame'
| Apply a function to a Dataframe elementwise.
|
| .. versionadded:: 2.1.0
|
| DataFrame.applymap was deprecated and renamed to DataFrame.map.
|
| This method applies a function that accepts and returns a scalar
| to every element of a DataFrame.
|
| Parameters
| ----------
| func : callable
| Python function, returns a single value from a single value.
| na_action : {None, 'ignore'}, default None
| If 'ignore', propagate NaN values, without passing them to func.
| **kwargs
| Additional keyword arguments to pass as keywords arguments to
| `func`.
|
| Returns
| -------
| DataFrame
| Transformed DataFrame.
|
| See Also
| --------
| DataFrame.apply : Apply a function along input axis of DataFrame.
| DataFrame.replace: Replace values given in `to_replace` with `value`.
| Series.map : Apply a function elementwise on a Series.
|
| Examples
| --------
| >>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])
| >>> df
| 0 1
| 0 1.000 2.120
| 1 3.356 4.567
|
| >>> df.map(lambda x: len(str(x)))
| 0 1
| 0 3 4
| 1 5 5
|
| Like Series.map, NA values can be ignored:
|
| >>> df_copy = df.copy()
| >>> df_copy.iloc[0, 0] = pd.NA
| >>> df_copy.map(lambda x: len(str(x)), na_action='ignore')
| 0 1
| 0 NaN 4
| 1 5.0 5
|
| It is also possible to use `map` with functions that are not
| `lambda` functions:
|
| >>> df.map(round, ndigits=1)
| 0 1
| 0 1.0 2.1
| 1 3.4 4.6
|
| Note that a vectorized version of `func` often exists, which will
| be much faster. You could square each number elementwise.
|
| >>> df.map(lambda x: x**2)
| 0 1
| 0 1.000000 4.494400
| 1 11.262736 20.857489
|
| But it's better to avoid map in that case.
|
| >>> df ** 2
| 0 1
| 0 1.000000 4.494400
| 1 11.262736 20.857489
|
| max(self, axis: 'Axis | None' = 0, skipna: 'bool' = True, numeric_only: 'bool' = False, **kwargs)
| Return the maximum of the values over the requested axis.
|
| If you want the *index* of the maximum, use ``idxmax``. This is the equivalent of the ``numpy.ndarray`` method ``argmax``.
|
| Parameters
| ----------
| axis : {index (0), columns (1)}
| Axis for the function to be applied on.
| For `Series` this parameter is unused and defaults to 0.
|
| For DataFrames, specifying ``axis=None`` will apply the aggregation
| across both axes.
|
| .. versionadded:: 2.0.0
|
| skipna : bool, default True
| Exclude NA/null values when computing the result.
| numeric_only : bool, default False
| Include only float, int, boolean columns. Not implemented for Series.
|
| **kwargs
| Additional keyword arguments to be passed to the function.
|
| Returns
| -------
| Series or scalar
|
| See Also
| --------
| Series.sum : Return the sum.
| Series.min : Return the minimum.
| Series.max : Return the maximum.
| Series.idxmin : Return the index of the minimum.
| Series.idxmax : Return the index of the maximum.
| DataFrame.sum : Return the sum over the requested axis.
| DataFrame.min : Return the minimum over the requested axis.
| DataFrame.max : Return the maximum over the requested axis.
| DataFrame.idxmin : Return the index of the minimum over the requested axis.
| DataFrame.idxmax : Return the index of the maximum over the requested axis.
|
| Examples
| --------
| >>> idx = pd.MultiIndex.from_arrays([
| ... ['warm', 'warm', 'cold', 'cold'],
| ... ['dog', 'falcon', 'fish', 'spider']],
| ... names=['blooded', 'animal'])
| >>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
| >>> s
| blooded animal
| warm dog 4
| falcon 2
| cold fish 0
| spider 8
| Name: legs, dtype: int64
|
| >>> s.max()
| 8
|
| mean(self, axis: 'Axis | None' = 0, skipna: 'bool' = True, numeric_only: 'bool' = False, **kwargs)
| Return the mean of the values over the requested axis.
|
| Parameters
| ----------
| axis : {index (0), columns (1)}
| Axis for the function to be applied on.
| For `Series` this parameter is unused and defaults to 0.
|
| For DataFrames, specifying ``axis=None`` will apply the aggregation
| across both axes.
|
| .. versionadded:: 2.0.0
|
| skipna : bool, default True
| Exclude NA/null values when computing the result.
| numeric_only : bool, default False
| Include only float, int, boolean columns. Not implemented for Series.
|
| **kwargs
| Additional keyword arguments to be passed to the function.
|
| Returns
| -------
| Series or scalar
|
| Examples
| --------
| >>> s = pd.Series([1, 2, 3])
| >>> s.mean()
| 2.0
|
| With a DataFrame
|
| >>> df = pd.DataFrame({'a': [1, 2], 'b': [2, 3]}, index=['tiger', 'zebra'])
| >>> df
| a b
| tiger 1 2
| zebra 2 3
| >>> df.mean()
| a 1.5
| b 2.5
| dtype: float64
|
| Using axis=1
|
| >>> df.mean(axis=1)
| tiger 1.5
| zebra 2.5
| dtype: float64
|
| In this case, `numeric_only` should be set to `True` to avoid
| getting an error.
|
| >>> df = pd.DataFrame({'a': [1, 2], 'b': ['T', 'Z']},
| ... index=['tiger', 'zebra'])
| >>> df.mean(numeric_only=True)
| a 1.5
| dtype: float64
|
| median(self, axis: 'Axis | None' = 0, skipna: 'bool' = True, numeric_only: 'bool' = False, **kwargs)
| Return the median of the values over the requested axis.
|
| Parameters
| ----------
| axis : {index (0), columns (1)}
| Axis for the function to be applied on.
| For `Series` this parameter is unused and defaults to 0.
|
| For DataFrames, specifying ``axis=None`` will apply the aggregation
| across both axes.
|
| .. versionadded:: 2.0.0
|
| skipna : bool, default True
| Exclude NA/null values when computing the result.
| numeric_only : bool, default False
| Include only float, int, boolean columns. Not implemented for Series.
|
| **kwargs
| Additional keyword arguments to be passed to the function.
|
| Returns
| -------
| Series or scalar
|
| Examples
| --------
| >>> s = pd.Series([1, 2, 3])
| >>> s.median()
| 2.0
|
| With a DataFrame
|
| >>> df = pd.DataFrame({'a': [1, 2], 'b': [2, 3]}, index=['tiger', 'zebra'])
| >>> df
| a b
| tiger 1 2
| zebra 2 3
| >>> df.median()
| a 1.5
| b 2.5
| dtype: float64
|
| Using axis=1
|
| >>> df.median(axis=1)
| tiger 1.5
| zebra 2.5
| dtype: float64
|
| In this case, `numeric_only` should be set to `True`
| to avoid getting an error.
|
| >>> df = pd.DataFrame({'a': [1, 2], 'b': ['T', 'Z']},
| ... index=['tiger', 'zebra'])
| >>> df.median(numeric_only=True)
| a 1.5
| dtype: float64
|
| melt(self, id_vars=None, value_vars=None, var_name=None, value_name: 'Hashable' = 'value', col_level: 'Level | None' = None, ignore_index: 'bool' = True) -> 'DataFrame'
| Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
|
| This function is useful to massage a DataFrame into a format where one
| or more columns are identifier variables (`id_vars`), while all other
| columns, considered measured variables (`value_vars`), are "unpivoted" to
| the row axis, leaving just two non-identifier columns, 'variable' and
| 'value'.
|
| Parameters
| ----------
| id_vars : scalar, tuple, list, or ndarray, optional
| Column(s) to use as identifier variables.
| value_vars : scalar, tuple, list, or ndarray, optional
| Column(s) to unpivot. If not specified, uses all columns that
| are not set as `id_vars`.
| var_name : scalar, default None
| Name to use for the 'variable' column. If None it uses
| ``frame.columns.name`` or 'variable'.
| value_name : scalar, default 'value'
| Name to use for the 'value' column, can't be an existing column label.
| col_level : scalar, optional
| If columns are a MultiIndex then use this level to melt.
| ignore_index : bool, default True
| If True, original index is ignored. If False, the original index is retained.
| Index labels will be repeated as necessary.
|
| Returns
| -------
| DataFrame
| Unpivoted DataFrame.
|
| See Also
| --------
| melt : Identical method.
| pivot_table : Create a spreadsheet-style pivot table as a DataFrame.
| DataFrame.pivot : Return reshaped DataFrame organized
| by given index / column values.
| DataFrame.explode : Explode a DataFrame from list-like
| columns to long format.
|
| Notes
| -----
| Reference :ref:`the user guide <reshaping.melt>` for more examples.
|
| Examples
| --------
| >>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
| ... 'B': {0: 1, 1: 3, 2: 5},
| ... 'C': {0: 2, 1: 4, 2: 6}})
| >>> df
| A B C
| 0 a 1 2
| 1 b 3 4
| 2 c 5 6
|
| >>> df.melt(id_vars=['A'], value_vars=['B'])
| A variable value
| 0 a B 1
| 1 b B 3
| 2 c B 5
|
| >>> df.melt(id_vars=['A'], value_vars=['B', 'C'])
| A variable value
| 0 a B 1
| 1 b B 3
| 2 c B 5
| 3 a C 2
| 4 b C 4
| 5 c C 6
|
| The names of 'variable' and 'value' columns can be customized:
|
| >>> df.melt(id_vars=['A'], value_vars=['B'],
| ... var_name='myVarname', value_name='myValname')
| A myVarname myValname
| 0 a B 1
| 1 b B 3
| 2 c B 5
|
| Original index values can be kept around:
|
| >>> df.melt(id_vars=['A'], value_vars=['B', 'C'], ignore_index=False)
| A variable value
| 0 a B 1
| 1 b B 3
| 2 c B 5
| 0 a C 2
| 1 b C 4
| 2 c C 6
|
| If you have multi-index columns:
|
| >>> df.columns = [list('ABC'), list('DEF')]
| >>> df
| A B C
| D E F
| 0 a 1 2
| 1 b 3 4
| 2 c 5 6
|
| >>> df.melt(col_level=0, id_vars=['A'], value_vars=['B'])
| A variable value
| 0 a B 1
| 1 b B 3
| 2 c B 5
|
| >>> df.melt(id_vars=[('A', 'D')], value_vars=[('B', 'E')])
| (A, D) variable_0 variable_1 value
| 0 a B E 1
| 1 b B E 3
| 2 c B E 5
|
| memory_usage(self, index: 'bool' = True, deep: 'bool' = False) -> 'Series'
| Return the memory usage of each column in bytes.
|
| The memory usage can optionally include the contribution of
| the index and elements of `object` dtype.
|
| This value is displayed in `DataFrame.info` by default. This can be
| suppressed by setting ``pandas.options.display.memory_usage`` to False.
|
| Parameters
| ----------
| index : bool, default True
| Specifies whether to include the memory usage of the DataFrame's
| index in returned Series. If ``index=True``, the memory usage of
| the index is the first item in the output.
| deep : bool, default False
| If True, introspect the data deeply by interrogating
| `object` dtypes for system-level memory consumption, and include
| it in the returned values.
|
| Returns
| -------
| Series
| A Series whose index is the original column names and whose values
| is the memory usage of each column in bytes.
|
| See Also
| --------
| numpy.ndarray.nbytes : Total bytes consumed by the elements of an
| ndarray.
| Series.memory_usage : Bytes consumed by a Series.
| Categorical : Memory-efficient array for string values with
| many repeated values.
| DataFrame.info : Concise summary of a DataFrame.
|
| Notes
| -----
| See the :ref:`Frequently Asked Questions <df-memory-usage>` for more
| details.
|
| Examples
| --------
| >>> dtypes = ['int64', 'float64', 'complex128', 'object', 'bool']
| >>> data = dict([(t, np.ones(shape=5000, dtype=int).astype(t))
| ... for t in dtypes])
| >>> df = pd.DataFrame(data)
| >>> df.head()
| int64 float64 complex128 object bool
| 0 1 1.0 1.0+0.0j 1 True
| 1 1 1.0 1.0+0.0j 1 True
| 2 1 1.0 1.0+0.0j 1 True
| 3 1 1.0 1.0+0.0j 1 True
| 4 1 1.0 1.0+0.0j 1 True
|
| >>> df.memory_usage()
| Index 128
| int64 40000
| float64 40000
| complex128 80000
| object 40000
| bool 5000
| dtype: int64
|
| >>> df.memory_usage(index=False)
| int64 40000
| float64 40000
| complex128 80000
| object 40000
| bool 5000
| dtype: int64
|
| The memory footprint of `object` dtype columns is ignored by default:
|
| >>> df.memory_usage(deep=True)
| Index 128
| int64 40000
| float64 40000
| complex128 80000
| object 180000
| bool 5000
| dtype: int64
|
| Use a Categorical for efficient storage of an object-dtype column with
| many repeated values.
|
| >>> df['object'].astype('category').memory_usage(deep=True)
| 5244
|
| merge(self, right: 'DataFrame | Series', how: 'MergeHow' = 'inner', on: 'IndexLabel | AnyArrayLike | None' = None, left_on: 'IndexLabel | AnyArrayLike | None' = None, right_on: 'IndexLabel | AnyArrayLike | None' = None, left_index: 'bool' = False, right_index: 'bool' = False, sort: 'bool' = False, suffixes: 'Suffixes' = ('_x', '_y'), copy: 'bool | None' = None, indicator: 'str | bool' = False, validate: 'MergeValidate | None' = None) -> 'DataFrame'
| Merge DataFrame or named Series objects with a database-style join.
|
| A named Series object is treated as a DataFrame with a single named column.
|
| The join is done on columns or indexes. If joining columns on
| columns, the DataFrame indexes *will be ignored*. Otherwise if joining indexes
| on indexes or indexes on a column or columns, the index will be passed on.
| When performing a cross merge, no column specifications to merge on are
| allowed.
|
| .. warning::
|
| If both key columns contain rows where the key is a null value, those
| rows will be matched against each other. This is different from usual SQL
| join behaviour and can lead to unexpected results.
|
| Parameters
| ----------
| right : DataFrame or named Series
| Object to merge with.
| how : {'left', 'right', 'outer', 'inner', 'cross'}, default 'inner'
| Type of merge to be performed.
|
| * left: use only keys from left frame, similar to a SQL left outer join;
| preserve key order.
| * right: use only keys from right frame, similar to a SQL right outer join;
| preserve key order.
| * outer: use union of keys from both frames, similar to a SQL full outer
| join; sort keys lexicographically.
| * inner: use intersection of keys from both frames, similar to a SQL inner
| join; preserve the order of the left keys.
| * cross: creates the cartesian product from both frames, preserves the order
| of the left keys.
| on : label or list
| Column or index level names to join on. These must be found in both
| DataFrames. If `on` is None and not merging on indexes then this defaults
| to the intersection of the columns in both DataFrames.
| left_on : label or list, or array-like
| Column or index level names to join on in the left DataFrame. Can also
| be an array or list of arrays of the length of the left DataFrame.
| These arrays are treated as if they are columns.
| right_on : label or list, or array-like
| Column or index level names to join on in the right DataFrame. Can also
| be an array or list of arrays of the length of the right DataFrame.
| These arrays are treated as if they are columns.
| left_index : bool, default False
| Use the index from the left DataFrame as the join key(s). If it is a
| MultiIndex, the number of keys in the other DataFrame (either the index
| or a number of columns) must match the number of levels.
| right_index : bool, default False
| Use the index from the right DataFrame as the join key. Same caveats as
| left_index.
| sort : bool, default False
| Sort the join keys lexicographically in the result DataFrame. If False,
| the order of the join keys depends on the join type (how keyword).
| suffixes : list-like, default is ("_x", "_y")
| A length-2 sequence where each element is optionally a string
| indicating the suffix to add to overlapping column names in
| `left` and `right` respectively. Pass a value of `None` instead
| of a string to indicate that the column name from `left` or
| `right` should be left as-is, with no suffix. At least one of the
| values must not be None.
| copy : bool, default True
| If False, avoid copy if possible.
|
| .. note::
| The `copy` keyword will change behavior in pandas 3.0.
| `Copy-on-Write
| <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
| will be enabled by default, which means that all methods with a
| `copy` keyword will use a lazy copy mechanism to defer the copy and
| ignore the `copy` keyword. The `copy` keyword will be removed in a
| future version of pandas.
|
| You can already get the future behavior and improvements through
| enabling copy on write ``pd.options.mode.copy_on_write = True``
| indicator : bool or str, default False
| If True, adds a column to the output DataFrame called "_merge" with
| information on the source of each row. The column can be given a different
| name by providing a string argument. The column will have a Categorical
| type with the value of "left_only" for observations whose merge key only
| appears in the left DataFrame, "right_only" for observations
| whose merge key only appears in the right DataFrame, and "both"
| if the observation's merge key is found in both DataFrames.
|
| validate : str, optional
| If specified, checks if merge is of specified type.
|
| * "one_to_one" or "1:1": check if merge keys are unique in both
| left and right datasets.
| * "one_to_many" or "1:m": check if merge keys are unique in left
| dataset.
| * "many_to_one" or "m:1": check if merge keys are unique in right
| dataset.
| * "many_to_many" or "m:m": allowed, but does not result in checks.
|
| Returns
| -------
| DataFrame
| A DataFrame of the two merged objects.
|
| See Also
| --------
| merge_ordered : Merge with optional filling/interpolation.
| merge_asof : Merge on nearest keys.
| DataFrame.join : Similar method using indices.
|
| Examples
| --------
| >>> df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
| ... 'value': [1, 2, 3, 5]})
| >>> df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],
| ... 'value': [5, 6, 7, 8]})
| >>> df1
| lkey value
| 0 foo 1
| 1 bar 2
| 2 baz 3
| 3 foo 5
| >>> df2
| rkey value
| 0 foo 5
| 1 bar 6
| 2 baz 7
| 3 foo 8
|
| Merge df1 and df2 on the lkey and rkey columns. The value columns have
| the default suffixes, _x and _y, appended.
|
| >>> df1.merge(df2, left_on='lkey', right_on='rkey')
| lkey value_x rkey value_y
| 0 foo 1 foo 5
| 1 foo 1 foo 8
| 2 bar 2 bar 6
| 3 baz 3 baz 7
| 4 foo 5 foo 5
| 5 foo 5 foo 8
|
| Merge DataFrames df1 and df2 with specified left and right suffixes
| appended to any overlapping columns.
|
| >>> df1.merge(df2, left_on='lkey', right_on='rkey',
| ... suffixes=('_left', '_right'))
| lkey value_left rkey value_right
| 0 foo 1 foo 5
| 1 foo 1 foo 8
| 2 bar 2 bar 6
| 3 baz 3 baz 7
| 4 foo 5 foo 5
| 5 foo 5 foo 8
|
| Merge DataFrames df1 and df2, but raise an exception if the DataFrames have
| any overlapping columns.
|
| >>> df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False))
| Traceback (most recent call last):
| ...
| ValueError: columns overlap but no suffix specified:
| Index(['value'], dtype='object')
|
| >>> df1 = pd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2]})
| >>> df2 = pd.DataFrame({'a': ['foo', 'baz'], 'c': [3, 4]})
| >>> df1
| a b
| 0 foo 1
| 1 bar 2
| >>> df2
| a c
| 0 foo 3
| 1 baz 4
|
| >>> df1.merge(df2, how='inner', on='a')
| a b c
| 0 foo 1 3
|
| >>> df1.merge(df2, how='left', on='a')
| a b c
| 0 foo 1 3.0
| 1 bar 2 NaN
|
| >>> df1 = pd.DataFrame({'left': ['foo', 'bar']})
| >>> df2 = pd.DataFrame({'right': [7, 8]})
| >>> df1
| left
| 0 foo
| 1 bar
| >>> df2
| right
| 0 7
| 1 8
|
| >>> df1.merge(df2, how='cross')
| left right
| 0 foo 7
| 1 foo 8
| 2 bar 7
| 3 bar 8
|
| min(self, axis: 'Axis | None' = 0, skipna: 'bool' = True, numeric_only: 'bool' = False, **kwargs)
| Return the minimum of the values over the requested axis.
|
| If you want the *index* of the minimum, use ``idxmin``. This is the equivalent of the ``numpy.ndarray`` method ``argmin``.
|
| Parameters
| ----------
| axis : {index (0), columns (1)}
| Axis for the function to be applied on.
| For `Series` this parameter is unused and defaults to 0.
|
| For DataFrames, specifying ``axis=None`` will apply the aggregation
| across both axes.
|
| .. versionadded:: 2.0.0
|
| skipna : bool, default True
| Exclude NA/null values when computing the result.
| numeric_only : bool, default False
| Include only float, int, boolean columns. Not implemented for Series.
|
| **kwargs
| Additional keyword arguments to be passed to the function.
|
| Returns
| -------
| Series or scalar
|
| See Also
| --------
| Series.sum : Return the sum.
| Series.min : Return the minimum.
| Series.max : Return the maximum.
| Series.idxmin : Return the index of the minimum.
| Series.idxmax : Return the index of the maximum.
| DataFrame.sum : Return the sum over the requested axis.
| DataFrame.min : Return the minimum over the requested axis.
| DataFrame.max : Return the maximum over the requested axis.
| DataFrame.idxmin : Return the index of the minimum over the requested axis.
| DataFrame.idxmax : Return the index of the maximum over the requested axis.
|
| Examples
| --------
| >>> idx = pd.MultiIndex.from_arrays([
| ... ['warm', 'warm', 'cold', 'cold'],
| ... ['dog', 'falcon', 'fish', 'spider']],
| ... names=['blooded', 'animal'])
| >>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
| >>> s
| blooded animal
| warm dog 4
| falcon 2
| cold fish 0
| spider 8
| Name: legs, dtype: int64
|
| >>> s.min()
| 0
|
| mod(self, other, axis: 'Axis' = 'columns', level=None, fill_value=None) -> 'DataFrame'
| Get Modulo of dataframe and other, element-wise (binary operator `mod`).
|
| Equivalent to ``dataframe % other``, but with support to substitute a fill_value
| for missing data in one of the inputs. With reverse version, `rmod`.
|
| Among flexible wrappers (`add`, `sub`, `mul`, `div`, `floordiv`, `mod`, `pow`) to
| arithmetic operators: `+`, `-`, `*`, `/`, `//`, `%`, `**`.
|
| Parameters
| ----------
| other : scalar, sequence, Series, dict or DataFrame
| Any single or multiple element data structure, or list-like object.
| axis : {0 or 'index', 1 or 'columns'}
| Whether to compare by the index (0 or 'index') or columns.
| (1 or 'columns'). For Series input, axis to match Series index on.
| level : int or label
| Broadcast across a level, matching Index values on the
| passed MultiIndex level.
| fill_value : float or None, default None
| Fill existing missing (NaN) values, and any new element needed for
| successful DataFrame alignment, with this value before computation.
| If data in both corresponding DataFrame locations is missing
| the result will be missing.
|
| Returns
| -------
| DataFrame
| Result of the arithmetic operation.
|
| See Also
| --------
| DataFrame.add : Add DataFrames.
| DataFrame.sub : Subtract DataFrames.
| DataFrame.mul : Multiply DataFrames.
| DataFrame.div : Divide DataFrames (float division).
| DataFrame.truediv : Divide DataFrames (float division).
| DataFrame.floordiv : Divide DataFrames (integer division).
| DataFrame.mod : Calculate modulo (remainder after division).
| DataFrame.pow : Calculate exponential power.
|
| Notes
| -----
| Mismatched indices will be unioned together.
|
| Examples
| --------
| >>> df = pd.DataFrame({'angles': [0, 3, 4],
| ... 'degrees': [360, 180, 360]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> df
| angles degrees
| circle 0 360
| triangle 3 180
| rectangle 4 360
|
| Add a scalar with operator version which return the same
| results.
|
| >>> df + 1
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| >>> df.add(1)
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| Divide by constant with reverse version.
|
| >>> df.div(10)
| angles degrees
| circle 0.0 36.0
| triangle 0.3 18.0
| rectangle 0.4 36.0
|
| >>> df.rdiv(10)
| angles degrees
| circle inf 0.027778
| triangle 3.333333 0.055556
| rectangle 2.500000 0.027778
|
| Subtract a list and Series by axis with operator version.
|
| >>> df - [1, 2]
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub([1, 2], axis='columns')
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
| ... axis='index')
| angles degrees
| circle -1 359
| triangle 2 179
| rectangle 3 359
|
| Multiply a dictionary by axis.
|
| >>> df.mul({'angles': 0, 'degrees': 2})
| angles degrees
| circle 0 720
| triangle 0 360
| rectangle 0 720
|
| >>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
| angles degrees
| circle 0 0
| triangle 6 360
| rectangle 12 1080
|
| Multiply a DataFrame of different shape with operator version.
|
| >>> other = pd.DataFrame({'angles': [0, 3, 4]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> other
| angles
| circle 0
| triangle 3
| rectangle 4
|
| >>> df * other
| angles degrees
| circle 0 NaN
| triangle 9 NaN
| rectangle 16 NaN
|
| >>> df.mul(other, fill_value=0)
| angles degrees
| circle 0 0.0
| triangle 9 0.0
| rectangle 16 0.0
|
| Divide by a MultiIndex by level.
|
| >>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
| ... 'degrees': [360, 180, 360, 360, 540, 720]},
| ... index=[['A', 'A', 'A', 'B', 'B', 'B'],
| ... ['circle', 'triangle', 'rectangle',
| ... 'square', 'pentagon', 'hexagon']])
| >>> df_multindex
| angles degrees
| A circle 0 360
| triangle 3 180
| rectangle 4 360
| B square 4 360
| pentagon 5 540
| hexagon 6 720
|
| >>> df.div(df_multindex, level=1, fill_value=0)
| angles degrees
| A circle NaN 1.0
| triangle 1.0 1.0
| rectangle 1.0 1.0
| B square 0.0 0.0
| pentagon 0.0 0.0
| hexagon 0.0 0.0
|
| mode(self, axis: 'Axis' = 0, numeric_only: 'bool' = False, dropna: 'bool' = True) -> 'DataFrame'
| Get the mode(s) of each element along the selected axis.
|
| The mode of a set of values is the value that appears most often.
| It can be multiple values.
|
| Parameters
| ----------
| axis : {0 or 'index', 1 or 'columns'}, default 0
| The axis to iterate over while searching for the mode:
|
| * 0 or 'index' : get mode of each column
| * 1 or 'columns' : get mode of each row.
|
| numeric_only : bool, default False
| If True, only apply to numeric columns.
| dropna : bool, default True
| Don't consider counts of NaN/NaT.
|
| Returns
| -------
| DataFrame
| The modes of each column or row.
|
| See Also
| --------
| Series.mode : Return the highest frequency value in a Series.
| Series.value_counts : Return the counts of values in a Series.
|
| Examples
| --------
| >>> df = pd.DataFrame([('bird', 2, 2),
| ... ('mammal', 4, np.nan),
| ... ('arthropod', 8, 0),
| ... ('bird', 2, np.nan)],
| ... index=('falcon', 'horse', 'spider', 'ostrich'),
| ... columns=('species', 'legs', 'wings'))
| >>> df
| species legs wings
| falcon bird 2 2.0
| horse mammal 4 NaN
| spider arthropod 8 0.0
| ostrich bird 2 NaN
|
| By default, missing values are not considered, and the mode of wings
| are both 0 and 2. Because the resulting DataFrame has two rows,
| the second row of ``species`` and ``legs`` contains ``NaN``.
|
| >>> df.mode()
| species legs wings
| 0 bird 2.0 0.0
| 1 NaN NaN 2.0
|
| Setting ``dropna=False`` ``NaN`` values are considered and they can be
| the mode (like for wings).
|
| >>> df.mode(dropna=False)
| species legs wings
| 0 bird 2 NaN
|
| Setting ``numeric_only=True``, only the mode of numeric columns is
| computed, and columns of other types are ignored.
|
| >>> df.mode(numeric_only=True)
| legs wings
| 0 2.0 0.0
| 1 NaN 2.0
|
| To compute the mode over columns and not rows, use the axis parameter:
|
| >>> df.mode(axis='columns', numeric_only=True)
| 0 1
| falcon 2.0 NaN
| horse 4.0 NaN
| spider 0.0 8.0
| ostrich 2.0 NaN
|
| mul(self, other, axis: 'Axis' = 'columns', level=None, fill_value=None) -> 'DataFrame'
| Get Multiplication of dataframe and other, element-wise (binary operator `mul`).
|
| Equivalent to ``dataframe * other``, but with support to substitute a fill_value
| for missing data in one of the inputs. With reverse version, `rmul`.
|
| Among flexible wrappers (`add`, `sub`, `mul`, `div`, `floordiv`, `mod`, `pow`) to
| arithmetic operators: `+`, `-`, `*`, `/`, `//`, `%`, `**`.
|
| Parameters
| ----------
| other : scalar, sequence, Series, dict or DataFrame
| Any single or multiple element data structure, or list-like object.
| axis : {0 or 'index', 1 or 'columns'}
| Whether to compare by the index (0 or 'index') or columns.
| (1 or 'columns'). For Series input, axis to match Series index on.
| level : int or label
| Broadcast across a level, matching Index values on the
| passed MultiIndex level.
| fill_value : float or None, default None
| Fill existing missing (NaN) values, and any new element needed for
| successful DataFrame alignment, with this value before computation.
| If data in both corresponding DataFrame locations is missing
| the result will be missing.
|
| Returns
| -------
| DataFrame
| Result of the arithmetic operation.
|
| See Also
| --------
| DataFrame.add : Add DataFrames.
| DataFrame.sub : Subtract DataFrames.
| DataFrame.mul : Multiply DataFrames.
| DataFrame.div : Divide DataFrames (float division).
| DataFrame.truediv : Divide DataFrames (float division).
| DataFrame.floordiv : Divide DataFrames (integer division).
| DataFrame.mod : Calculate modulo (remainder after division).
| DataFrame.pow : Calculate exponential power.
|
| Notes
| -----
| Mismatched indices will be unioned together.
|
| Examples
| --------
| >>> df = pd.DataFrame({'angles': [0, 3, 4],
| ... 'degrees': [360, 180, 360]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> df
| angles degrees
| circle 0 360
| triangle 3 180
| rectangle 4 360
|
| Add a scalar with operator version which return the same
| results.
|
| >>> df + 1
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| >>> df.add(1)
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| Divide by constant with reverse version.
|
| >>> df.div(10)
| angles degrees
| circle 0.0 36.0
| triangle 0.3 18.0
| rectangle 0.4 36.0
|
| >>> df.rdiv(10)
| angles degrees
| circle inf 0.027778
| triangle 3.333333 0.055556
| rectangle 2.500000 0.027778
|
| Subtract a list and Series by axis with operator version.
|
| >>> df - [1, 2]
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub([1, 2], axis='columns')
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
| ... axis='index')
| angles degrees
| circle -1 359
| triangle 2 179
| rectangle 3 359
|
| Multiply a dictionary by axis.
|
| >>> df.mul({'angles': 0, 'degrees': 2})
| angles degrees
| circle 0 720
| triangle 0 360
| rectangle 0 720
|
| >>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
| angles degrees
| circle 0 0
| triangle 6 360
| rectangle 12 1080
|
| Multiply a DataFrame of different shape with operator version.
|
| >>> other = pd.DataFrame({'angles': [0, 3, 4]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> other
| angles
| circle 0
| triangle 3
| rectangle 4
|
| >>> df * other
| angles degrees
| circle 0 NaN
| triangle 9 NaN
| rectangle 16 NaN
|
| >>> df.mul(other, fill_value=0)
| angles degrees
| circle 0 0.0
| triangle 9 0.0
| rectangle 16 0.0
|
| Divide by a MultiIndex by level.
|
| >>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
| ... 'degrees': [360, 180, 360, 360, 540, 720]},
| ... index=[['A', 'A', 'A', 'B', 'B', 'B'],
| ... ['circle', 'triangle', 'rectangle',
| ... 'square', 'pentagon', 'hexagon']])
| >>> df_multindex
| angles degrees
| A circle 0 360
| triangle 3 180
| rectangle 4 360
| B square 4 360
| pentagon 5 540
| hexagon 6 720
|
| >>> df.div(df_multindex, level=1, fill_value=0)
| angles degrees
| A circle NaN 1.0
| triangle 1.0 1.0
| rectangle 1.0 1.0
| B square 0.0 0.0
| pentagon 0.0 0.0
| hexagon 0.0 0.0
|
| multiply = mul(self, other, axis: 'Axis' = 'columns', level=None, fill_value=None) -> 'DataFrame'
|
| ne(self, other, axis: 'Axis' = 'columns', level=None) -> 'DataFrame'
| Get Not equal to of dataframe and other, element-wise (binary operator `ne`).
|
| Among flexible wrappers (`eq`, `ne`, `le`, `lt`, `ge`, `gt`) to comparison
| operators.
|
| Equivalent to `==`, `!=`, `<=`, `<`, `>=`, `>` with support to choose axis
| (rows or columns) and level for comparison.
|
| Parameters
| ----------
| other : scalar, sequence, Series, or DataFrame
| Any single or multiple element data structure, or list-like object.
| axis : {0 or 'index', 1 or 'columns'}, default 'columns'
| Whether to compare by the index (0 or 'index') or columns
| (1 or 'columns').
| level : int or label
| Broadcast across a level, matching Index values on the passed
| MultiIndex level.
|
| Returns
| -------
| DataFrame of bool
| Result of the comparison.
|
| See Also
| --------
| DataFrame.eq : Compare DataFrames for equality elementwise.
| DataFrame.ne : Compare DataFrames for inequality elementwise.
| DataFrame.le : Compare DataFrames for less than inequality
| or equality elementwise.
| DataFrame.lt : Compare DataFrames for strictly less than
| inequality elementwise.
| DataFrame.ge : Compare DataFrames for greater than inequality
| or equality elementwise.
| DataFrame.gt : Compare DataFrames for strictly greater than
| inequality elementwise.
|
| Notes
| -----
| Mismatched indices will be unioned together.
| `NaN` values are considered different (i.e. `NaN` != `NaN`).
|
| Examples
| --------
| >>> df = pd.DataFrame({'cost': [250, 150, 100],
| ... 'revenue': [100, 250, 300]},
| ... index=['A', 'B', 'C'])
| >>> df
| cost revenue
| A 250 100
| B 150 250
| C 100 300
|
| Comparison with a scalar, using either the operator or method:
|
| >>> df == 100
| cost revenue
| A False True
| B False False
| C True False
|
| >>> df.eq(100)
| cost revenue
| A False True
| B False False
| C True False
|
| When `other` is a :class:`Series`, the columns of a DataFrame are aligned
| with the index of `other` and broadcast:
|
| >>> df != pd.Series([100, 250], index=["cost", "revenue"])
| cost revenue
| A True True
| B True False
| C False True
|
| Use the method to control the broadcast axis:
|
| >>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
| cost revenue
| A True False
| B True True
| C True True
| D True True
|
| When comparing to an arbitrary sequence, the number of columns must
| match the number elements in `other`:
|
| >>> df == [250, 100]
| cost revenue
| A True True
| B False False
| C False False
|
| Use the method to control the axis:
|
| >>> df.eq([250, 250, 100], axis='index')
| cost revenue
| A True False
| B False True
| C True False
|
| Compare to a DataFrame of different shape.
|
| >>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
| ... index=['A', 'B', 'C', 'D'])
| >>> other
| revenue
| A 300
| B 250
| C 100
| D 150
|
| >>> df.gt(other)
| cost revenue
| A False False
| B False False
| C False True
| D False False
|
| Compare to a MultiIndex by level.
|
| >>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
| ... 'revenue': [100, 250, 300, 200, 175, 225]},
| ... index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
| ... ['A', 'B', 'C', 'A', 'B', 'C']])
| >>> df_multindex
| cost revenue
| Q1 A 250 100
| B 150 250
| C 100 300
| Q2 A 150 200
| B 300 175
| C 220 225
|
| >>> df.le(df_multindex, level=1)
| cost revenue
| Q1 A True True
| B True True
| C True True
| Q2 A False True
| B True False
| C True False
|
| nlargest(self, n: 'int', columns: 'IndexLabel', keep: 'NsmallestNlargestKeep' = 'first') -> 'DataFrame'
| Return the first `n` rows ordered by `columns` in descending order.
|
| Return the first `n` rows with the largest values in `columns`, in
| descending order. The columns that are not specified are returned as
| well, but not used for ordering.
|
| This method is equivalent to
| ``df.sort_values(columns, ascending=False).head(n)``, but more
| performant.
|
| Parameters
| ----------
| n : int
| Number of rows to return.
| columns : label or list of labels
| Column label(s) to order by.
| keep : {'first', 'last', 'all'}, default 'first'
| Where there are duplicate values:
|
| - ``first`` : prioritize the first occurrence(s)
| - ``last`` : prioritize the last occurrence(s)
| - ``all`` : keep all the ties of the smallest item even if it means
| selecting more than ``n`` items.
|
| Returns
| -------
| DataFrame
| The first `n` rows ordered by the given columns in descending
| order.
|
| See Also
| --------
| DataFrame.nsmallest : Return the first `n` rows ordered by `columns` in
| ascending order.
| DataFrame.sort_values : Sort DataFrame by the values.
| DataFrame.head : Return the first `n` rows without re-ordering.
|
| Notes
| -----
| This function cannot be used with all column types. For example, when
| specifying columns with `object` or `category` dtypes, ``TypeError`` is
| raised.
|
| Examples
| --------
| >>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,
| ... 434000, 434000, 337000, 11300,
| ... 11300, 11300],
| ... 'GDP': [1937894, 2583560 , 12011, 4520, 12128,
| ... 17036, 182, 38, 311],
| ... 'alpha-2': ["IT", "FR", "MT", "MV", "BN",
| ... "IS", "NR", "TV", "AI"]},
| ... index=["Italy", "France", "Malta",
| ... "Maldives", "Brunei", "Iceland",
| ... "Nauru", "Tuvalu", "Anguilla"])
| >>> df
| population GDP alpha-2
| Italy 59000000 1937894 IT
| France 65000000 2583560 FR
| Malta 434000 12011 MT
| Maldives 434000 4520 MV
| Brunei 434000 12128 BN
| Iceland 337000 17036 IS
| Nauru 11300 182 NR
| Tuvalu 11300 38 TV
| Anguilla 11300 311 AI
|
| In the following example, we will use ``nlargest`` to select the three
| rows having the largest values in column "population".
|
| >>> df.nlargest(3, 'population')
| population GDP alpha-2
| France 65000000 2583560 FR
| Italy 59000000 1937894 IT
| Malta 434000 12011 MT
|
| When using ``keep='last'``, ties are resolved in reverse order:
|
| >>> df.nlargest(3, 'population', keep='last')
| population GDP alpha-2
| France 65000000 2583560 FR
| Italy 59000000 1937894 IT
| Brunei 434000 12128 BN
|
| When using ``keep='all'``, the number of element kept can go beyond ``n``
| if there are duplicate values for the smallest element, all the
| ties are kept:
|
| >>> df.nlargest(3, 'population', keep='all')
| population GDP alpha-2
| France 65000000 2583560 FR
| Italy 59000000 1937894 IT
| Malta 434000 12011 MT
| Maldives 434000 4520 MV
| Brunei 434000 12128 BN
|
| However, ``nlargest`` does not keep ``n`` distinct largest elements:
|
| >>> df.nlargest(5, 'population', keep='all')
| population GDP alpha-2
| France 65000000 2583560 FR
| Italy 59000000 1937894 IT
| Malta 434000 12011 MT
| Maldives 434000 4520 MV
| Brunei 434000 12128 BN
|
| To order by the largest values in column "population" and then "GDP",
| we can specify multiple columns like in the next example.
|
| >>> df.nlargest(3, ['population', 'GDP'])
| population GDP alpha-2
| France 65000000 2583560 FR
| Italy 59000000 1937894 IT
| Brunei 434000 12128 BN
|
| notna(self) -> 'DataFrame'
| Detect existing (non-missing) values.
|
| Return a boolean same-sized object indicating if the values are not NA.
| Non-missing values get mapped to True. Characters such as empty
| strings ``''`` or :attr:`numpy.inf` are not considered NA values
| (unless you set ``pandas.options.mode.use_inf_as_na = True``).
| NA values, such as None or :attr:`numpy.NaN`, get mapped to False
| values.
|
| Returns
| -------
| DataFrame
| Mask of bool values for each element in DataFrame that
| indicates whether an element is not an NA value.
|
| See Also
| --------
| DataFrame.notnull : Alias of notna.
| DataFrame.isna : Boolean inverse of notna.
| DataFrame.dropna : Omit axes labels with missing values.
| notna : Top-level notna.
|
| Examples
| --------
| Show which entries in a DataFrame are not NA.
|
| >>> df = pd.DataFrame(dict(age=[5, 6, np.nan],
| ... born=[pd.NaT, pd.Timestamp('1939-05-27'),
| ... pd.Timestamp('1940-04-25')],
| ... name=['Alfred', 'Batman', ''],
| ... toy=[None, 'Batmobile', 'Joker']))
| >>> df
| age born name toy
| 0 5.0 NaT Alfred None
| 1 6.0 1939-05-27 Batman Batmobile
| 2 NaN 1940-04-25 Joker
|
| >>> df.notna()
| age born name toy
| 0 True False True False
| 1 True True True True
| 2 False True True True
|
| Show which entries in a Series are not NA.
|
| >>> ser = pd.Series([5, 6, np.nan])
| >>> ser
| 0 5.0
| 1 6.0
| 2 NaN
| dtype: float64
|
| >>> ser.notna()
| 0 True
| 1 True
| 2 False
| dtype: bool
|
| notnull(self) -> 'DataFrame'
| DataFrame.notnull is an alias for DataFrame.notna.
|
| Detect existing (non-missing) values.
|
| Return a boolean same-sized object indicating if the values are not NA.
| Non-missing values get mapped to True. Characters such as empty
| strings ``''`` or :attr:`numpy.inf` are not considered NA values
| (unless you set ``pandas.options.mode.use_inf_as_na = True``).
| NA values, such as None or :attr:`numpy.NaN`, get mapped to False
| values.
|
| Returns
| -------
| DataFrame
| Mask of bool values for each element in DataFrame that
| indicates whether an element is not an NA value.
|
| See Also
| --------
| DataFrame.notnull : Alias of notna.
| DataFrame.isna : Boolean inverse of notna.
| DataFrame.dropna : Omit axes labels with missing values.
| notna : Top-level notna.
|
| Examples
| --------
| Show which entries in a DataFrame are not NA.
|
| >>> df = pd.DataFrame(dict(age=[5, 6, np.nan],
| ... born=[pd.NaT, pd.Timestamp('1939-05-27'),
| ... pd.Timestamp('1940-04-25')],
| ... name=['Alfred', 'Batman', ''],
| ... toy=[None, 'Batmobile', 'Joker']))
| >>> df
| age born name toy
| 0 5.0 NaT Alfred None
| 1 6.0 1939-05-27 Batman Batmobile
| 2 NaN 1940-04-25 Joker
|
| >>> df.notna()
| age born name toy
| 0 True False True False
| 1 True True True True
| 2 False True True True
|
| Show which entries in a Series are not NA.
|
| >>> ser = pd.Series([5, 6, np.nan])
| >>> ser
| 0 5.0
| 1 6.0
| 2 NaN
| dtype: float64
|
| >>> ser.notna()
| 0 True
| 1 True
| 2 False
| dtype: bool
|
| nsmallest(self, n: 'int', columns: 'IndexLabel', keep: 'NsmallestNlargestKeep' = 'first') -> 'DataFrame'
| Return the first `n` rows ordered by `columns` in ascending order.
|
| Return the first `n` rows with the smallest values in `columns`, in
| ascending order. The columns that are not specified are returned as
| well, but not used for ordering.
|
| This method is equivalent to
| ``df.sort_values(columns, ascending=True).head(n)``, but more
| performant.
|
| Parameters
| ----------
| n : int
| Number of items to retrieve.
| columns : list or str
| Column name or names to order by.
| keep : {'first', 'last', 'all'}, default 'first'
| Where there are duplicate values:
|
| - ``first`` : take the first occurrence.
| - ``last`` : take the last occurrence.
| - ``all`` : keep all the ties of the largest item even if it means
| selecting more than ``n`` items.
|
| Returns
| -------
| DataFrame
|
| See Also
| --------
| DataFrame.nlargest : Return the first `n` rows ordered by `columns` in
| descending order.
| DataFrame.sort_values : Sort DataFrame by the values.
| DataFrame.head : Return the first `n` rows without re-ordering.
|
| Examples
| --------
| >>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,
| ... 434000, 434000, 337000, 337000,
| ... 11300, 11300],
| ... 'GDP': [1937894, 2583560 , 12011, 4520, 12128,
| ... 17036, 182, 38, 311],
| ... 'alpha-2': ["IT", "FR", "MT", "MV", "BN",
| ... "IS", "NR", "TV", "AI"]},
| ... index=["Italy", "France", "Malta",
| ... "Maldives", "Brunei", "Iceland",
| ... "Nauru", "Tuvalu", "Anguilla"])
| >>> df
| population GDP alpha-2
| Italy 59000000 1937894 IT
| France 65000000 2583560 FR
| Malta 434000 12011 MT
| Maldives 434000 4520 MV
| Brunei 434000 12128 BN
| Iceland 337000 17036 IS
| Nauru 337000 182 NR
| Tuvalu 11300 38 TV
| Anguilla 11300 311 AI
|
| In the following example, we will use ``nsmallest`` to select the
| three rows having the smallest values in column "population".
|
| >>> df.nsmallest(3, 'population')
| population GDP alpha-2
| Tuvalu 11300 38 TV
| Anguilla 11300 311 AI
| Iceland 337000 17036 IS
|
| When using ``keep='last'``, ties are resolved in reverse order:
|
| >>> df.nsmallest(3, 'population', keep='last')
| population GDP alpha-2
| Anguilla 11300 311 AI
| Tuvalu 11300 38 TV
| Nauru 337000 182 NR
|
| When using ``keep='all'``, the number of element kept can go beyond ``n``
| if there are duplicate values for the largest element, all the
| ties are kept.
|
| >>> df.nsmallest(3, 'population', keep='all')
| population GDP alpha-2
| Tuvalu 11300 38 TV
| Anguilla 11300 311 AI
| Iceland 337000 17036 IS
| Nauru 337000 182 NR
|
| However, ``nsmallest`` does not keep ``n`` distinct
| smallest elements:
|
| >>> df.nsmallest(4, 'population', keep='all')
| population GDP alpha-2
| Tuvalu 11300 38 TV
| Anguilla 11300 311 AI
| Iceland 337000 17036 IS
| Nauru 337000 182 NR
|
| To order by the smallest values in column "population" and then "GDP", we can
| specify multiple columns like in the next example.
|
| >>> df.nsmallest(3, ['population', 'GDP'])
| population GDP alpha-2
| Tuvalu 11300 38 TV
| Anguilla 11300 311 AI
| Nauru 337000 182 NR
|
| nunique(self, axis: 'Axis' = 0, dropna: 'bool' = True) -> 'Series'
| Count number of distinct elements in specified axis.
|
| Return Series with number of distinct elements. Can ignore NaN
| values.
|
| Parameters
| ----------
| axis : {0 or 'index', 1 or 'columns'}, default 0
| The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for
| column-wise.
| dropna : bool, default True
| Don't include NaN in the counts.
|
| Returns
| -------
| Series
|
| See Also
| --------
| Series.nunique: Method nunique for Series.
| DataFrame.count: Count non-NA cells for each column or row.
|
| Examples
| --------
| >>> df = pd.DataFrame({'A': [4, 5, 6], 'B': [4, 1, 1]})
| >>> df.nunique()
| A 3
| B 2
| dtype: int64
|
| >>> df.nunique(axis=1)
| 0 1
| 1 2
| 2 2
| dtype: int64
|
| pivot(self, *, columns, index=<no_default>, values=<no_default>) -> 'DataFrame'
| Return reshaped DataFrame organized by given index / column values.
|
| Reshape data (produce a "pivot" table) based on column values. Uses
| unique values from specified `index` / `columns` to form axes of the
| resulting DataFrame. This function does not support data
| aggregation, multiple values will result in a MultiIndex in the
| columns. See the :ref:`User Guide <reshaping>` for more on reshaping.
|
| Parameters
| ----------
| columns : str or object or a list of str
| Column to use to make new frame's columns.
| index : str or object or a list of str, optional
| Column to use to make new frame's index. If not given, uses existing index.
| values : str, object or a list of the previous, optional
| Column(s) to use for populating new frame's values. If not
| specified, all remaining columns will be used and the result will
| have hierarchically indexed columns.
|
| Returns
| -------
| DataFrame
| Returns reshaped DataFrame.
|
| Raises
| ------
| ValueError:
| When there are any `index`, `columns` combinations with multiple
| values. `DataFrame.pivot_table` when you need to aggregate.
|
| See Also
| --------
| DataFrame.pivot_table : Generalization of pivot that can handle
| duplicate values for one index/column pair.
| DataFrame.unstack : Pivot based on the index values instead of a
| column.
| wide_to_long : Wide panel to long format. Less flexible but more
| user-friendly than melt.
|
| Notes
| -----
| For finer-tuned control, see hierarchical indexing documentation along
| with the related stack/unstack methods.
|
| Reference :ref:`the user guide <reshaping.pivot>` for more examples.
|
| Examples
| --------
| >>> df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two',
| ... 'two'],
| ... 'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
| ... 'baz': [1, 2, 3, 4, 5, 6],
| ... 'zoo': ['x', 'y', 'z', 'q', 'w', 't']})
| >>> df
| foo bar baz zoo
| 0 one A 1 x
| 1 one B 2 y
| 2 one C 3 z
| 3 two A 4 q
| 4 two B 5 w
| 5 two C 6 t
|
| >>> df.pivot(index='foo', columns='bar', values='baz')
| bar A B C
| foo
| one 1 2 3
| two 4 5 6
|
| >>> df.pivot(index='foo', columns='bar')['baz']
| bar A B C
| foo
| one 1 2 3
| two 4 5 6
|
| >>> df.pivot(index='foo', columns='bar', values=['baz', 'zoo'])
| baz zoo
| bar A B C A B C
| foo
| one 1 2 3 x y z
| two 4 5 6 q w t
|
| You could also assign a list of column names or a list of index names.
|
| >>> df = pd.DataFrame({
| ... "lev1": [1, 1, 1, 2, 2, 2],
| ... "lev2": [1, 1, 2, 1, 1, 2],
| ... "lev3": [1, 2, 1, 2, 1, 2],
| ... "lev4": [1, 2, 3, 4, 5, 6],
| ... "values": [0, 1, 2, 3, 4, 5]})
| >>> df
| lev1 lev2 lev3 lev4 values
| 0 1 1 1 1 0
| 1 1 1 2 2 1
| 2 1 2 1 3 2
| 3 2 1 2 4 3
| 4 2 1 1 5 4
| 5 2 2 2 6 5
|
| >>> df.pivot(index="lev1", columns=["lev2", "lev3"], values="values")
| lev2 1 2
| lev3 1 2 1 2
| lev1
| 1 0.0 1.0 2.0 NaN
| 2 4.0 3.0 NaN 5.0
|
| >>> df.pivot(index=["lev1", "lev2"], columns=["lev3"], values="values")
| lev3 1 2
| lev1 lev2
| 1 1 0.0 1.0
| 2 2.0 NaN
| 2 1 4.0 3.0
| 2 NaN 5.0
|
| A ValueError is raised if there are any duplicates.
|
| >>> df = pd.DataFrame({"foo": ['one', 'one', 'two', 'two'],
| ... "bar": ['A', 'A', 'B', 'C'],
| ... "baz": [1, 2, 3, 4]})
| >>> df
| foo bar baz
| 0 one A 1
| 1 one A 2
| 2 two B 3
| 3 two C 4
|
| Notice that the first two rows are the same for our `index`
| and `columns` arguments.
|
| >>> df.pivot(index='foo', columns='bar', values='baz')
| Traceback (most recent call last):
| ...
| ValueError: Index contains duplicate entries, cannot reshape
|
| pivot_table(self, values=None, index=None, columns=None, aggfunc: 'AggFuncType' = 'mean', fill_value=None, margins: 'bool' = False, dropna: 'bool' = True, margins_name: 'Level' = 'All', observed: 'bool | lib.NoDefault' = <no_default>, sort: 'bool' = True) -> 'DataFrame'
| Create a spreadsheet-style pivot table as a DataFrame.
|
| The levels in the pivot table will be stored in MultiIndex objects
| (hierarchical indexes) on the index and columns of the result DataFrame.
|
| Parameters
| ----------
| values : list-like or scalar, optional
| Column or columns to aggregate.
| index : column, Grouper, array, or list of the previous
| Keys to group by on the pivot table index. If a list is passed,
| it can contain any of the other types (except list). If an array is
| passed, it must be the same length as the data and will be used in
| the same manner as column values.
| columns : column, Grouper, array, or list of the previous
| Keys to group by on the pivot table column. If a list is passed,
| it can contain any of the other types (except list). If an array is
| passed, it must be the same length as the data and will be used in
| the same manner as column values.
| aggfunc : function, list of functions, dict, default "mean"
| If a list of functions is passed, the resulting pivot table will have
| hierarchical columns whose top level are the function names
| (inferred from the function objects themselves).
| If a dict is passed, the key is column to aggregate and the value is
| function or list of functions. If ``margin=True``, aggfunc will be
| used to calculate the partial aggregates.
| fill_value : scalar, default None
| Value to replace missing values with (in the resulting pivot table,
| after aggregation).
| margins : bool, default False
| If ``margins=True``, special ``All`` columns and rows
| will be added with partial group aggregates across the categories
| on the rows and columns.
| dropna : bool, default True
| Do not include columns whose entries are all NaN. If True,
| rows with a NaN value in any column will be omitted before
| computing margins.
| margins_name : str, default 'All'
| Name of the row / column that will contain the totals
| when margins is True.
| observed : bool, default False
| This only applies if any of the groupers are Categoricals.
| If True: only show observed values for categorical groupers.
| If False: show all values for categorical groupers.
|
| .. deprecated:: 2.2.0
|
| The default value of ``False`` is deprecated and will change to
| ``True`` in a future version of pandas.
|
| sort : bool, default True
| Specifies if the result should be sorted.
|
| .. versionadded:: 1.3.0
|
| Returns
| -------
| DataFrame
| An Excel style pivot table.
|
| See Also
| --------
| DataFrame.pivot : Pivot without aggregation that can handle
| non-numeric data.
| DataFrame.melt: Unpivot a DataFrame from wide to long format,
| optionally leaving identifiers set.
| wide_to_long : Wide panel to long format. Less flexible but more
| user-friendly than melt.
|
| Notes
| -----
| Reference :ref:`the user guide <reshaping.pivot>` for more examples.
|
| Examples
| --------
| >>> df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
| ... "bar", "bar", "bar", "bar"],
| ... "B": ["one", "one", "one", "two", "two",
| ... "one", "one", "two", "two"],
| ... "C": ["small", "large", "large", "small",
| ... "small", "large", "small", "small",
| ... "large"],
| ... "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
| ... "E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
| >>> df
| A B C D E
| 0 foo one small 1 2
| 1 foo one large 2 4
| 2 foo one large 2 5
| 3 foo two small 3 5
| 4 foo two small 3 6
| 5 bar one large 4 6
| 6 bar one small 5 8
| 7 bar two small 6 9
| 8 bar two large 7 9
|
| This first example aggregates values by taking the sum.
|
| >>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
| ... columns=['C'], aggfunc="sum")
| >>> table
| C large small
| A B
| bar one 4.0 5.0
| two 7.0 6.0
| foo one 4.0 1.0
| two NaN 6.0
|
| We can also fill missing values using the `fill_value` parameter.
|
| >>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
| ... columns=['C'], aggfunc="sum", fill_value=0)
| >>> table
| C large small
| A B
| bar one 4 5
| two 7 6
| foo one 4 1
| two 0 6
|
| The next example aggregates by taking the mean across multiple columns.
|
| >>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
| ... aggfunc={'D': "mean", 'E': "mean"})
| >>> table
| D E
| A C
| bar large 5.500000 7.500000
| small 5.500000 8.500000
| foo large 2.000000 4.500000
| small 2.333333 4.333333
|
| We can also calculate multiple types of aggregations for any given
| value column.
|
| >>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
| ... aggfunc={'D': "mean",
| ... 'E': ["min", "max", "mean"]})
| >>> table
| D E
| mean max mean min
| A C
| bar large 5.500000 9 7.500000 6
| small 5.500000 9 8.500000 8
| foo large 2.000000 5 4.500000 4
| small 2.333333 6 4.333333 2
|
| pop(self, item: 'Hashable') -> 'Series'
| Return item and drop from frame. Raise KeyError if not found.
|
| Parameters
| ----------
| item : label
| Label of column to be popped.
|
| Returns
| -------
| Series
|
| Examples
| --------
| >>> df = pd.DataFrame([('falcon', 'bird', 389.0),
| ... ('parrot', 'bird', 24.0),
| ... ('lion', 'mammal', 80.5),
| ... ('monkey', 'mammal', np.nan)],
| ... columns=('name', 'class', 'max_speed'))
| >>> df
| name class max_speed
| 0 falcon bird 389.0
| 1 parrot bird 24.0
| 2 lion mammal 80.5
| 3 monkey mammal NaN
|
| >>> df.pop('class')
| 0 bird
| 1 bird
| 2 mammal
| 3 mammal
| Name: class, dtype: object
|
| >>> df
| name max_speed
| 0 falcon 389.0
| 1 parrot 24.0
| 2 lion 80.5
| 3 monkey NaN
|
| pow(self, other, axis: 'Axis' = 'columns', level=None, fill_value=None) -> 'DataFrame'
| Get Exponential power of dataframe and other, element-wise (binary operator `pow`).
|
| Equivalent to ``dataframe ** other``, but with support to substitute a fill_value
| for missing data in one of the inputs. With reverse version, `rpow`.
|
| Among flexible wrappers (`add`, `sub`, `mul`, `div`, `floordiv`, `mod`, `pow`) to
| arithmetic operators: `+`, `-`, `*`, `/`, `//`, `%`, `**`.
|
| Parameters
| ----------
| other : scalar, sequence, Series, dict or DataFrame
| Any single or multiple element data structure, or list-like object.
| axis : {0 or 'index', 1 or 'columns'}
| Whether to compare by the index (0 or 'index') or columns.
| (1 or 'columns'). For Series input, axis to match Series index on.
| level : int or label
| Broadcast across a level, matching Index values on the
| passed MultiIndex level.
| fill_value : float or None, default None
| Fill existing missing (NaN) values, and any new element needed for
| successful DataFrame alignment, with this value before computation.
| If data in both corresponding DataFrame locations is missing
| the result will be missing.
|
| Returns
| -------
| DataFrame
| Result of the arithmetic operation.
|
| See Also
| --------
| DataFrame.add : Add DataFrames.
| DataFrame.sub : Subtract DataFrames.
| DataFrame.mul : Multiply DataFrames.
| DataFrame.div : Divide DataFrames (float division).
| DataFrame.truediv : Divide DataFrames (float division).
| DataFrame.floordiv : Divide DataFrames (integer division).
| DataFrame.mod : Calculate modulo (remainder after division).
| DataFrame.pow : Calculate exponential power.
|
| Notes
| -----
| Mismatched indices will be unioned together.
|
| Examples
| --------
| >>> df = pd.DataFrame({'angles': [0, 3, 4],
| ... 'degrees': [360, 180, 360]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> df
| angles degrees
| circle 0 360
| triangle 3 180
| rectangle 4 360
|
| Add a scalar with operator version which return the same
| results.
|
| >>> df + 1
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| >>> df.add(1)
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| Divide by constant with reverse version.
|
| >>> df.div(10)
| angles degrees
| circle 0.0 36.0
| triangle 0.3 18.0
| rectangle 0.4 36.0
|
| >>> df.rdiv(10)
| angles degrees
| circle inf 0.027778
| triangle 3.333333 0.055556
| rectangle 2.500000 0.027778
|
| Subtract a list and Series by axis with operator version.
|
| >>> df - [1, 2]
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub([1, 2], axis='columns')
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
| ... axis='index')
| angles degrees
| circle -1 359
| triangle 2 179
| rectangle 3 359
|
| Multiply a dictionary by axis.
|
| >>> df.mul({'angles': 0, 'degrees': 2})
| angles degrees
| circle 0 720
| triangle 0 360
| rectangle 0 720
|
| >>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
| angles degrees
| circle 0 0
| triangle 6 360
| rectangle 12 1080
|
| Multiply a DataFrame of different shape with operator version.
|
| >>> other = pd.DataFrame({'angles': [0, 3, 4]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> other
| angles
| circle 0
| triangle 3
| rectangle 4
|
| >>> df * other
| angles degrees
| circle 0 NaN
| triangle 9 NaN
| rectangle 16 NaN
|
| >>> df.mul(other, fill_value=0)
| angles degrees
| circle 0 0.0
| triangle 9 0.0
| rectangle 16 0.0
|
| Divide by a MultiIndex by level.
|
| >>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
| ... 'degrees': [360, 180, 360, 360, 540, 720]},
| ... index=[['A', 'A', 'A', 'B', 'B', 'B'],
| ... ['circle', 'triangle', 'rectangle',
| ... 'square', 'pentagon', 'hexagon']])
| >>> df_multindex
| angles degrees
| A circle 0 360
| triangle 3 180
| rectangle 4 360
| B square 4 360
| pentagon 5 540
| hexagon 6 720
|
| >>> df.div(df_multindex, level=1, fill_value=0)
| angles degrees
| A circle NaN 1.0
| triangle 1.0 1.0
| rectangle 1.0 1.0
| B square 0.0 0.0
| pentagon 0.0 0.0
| hexagon 0.0 0.0
|
| prod(self, axis: 'Axis | None' = 0, skipna: 'bool' = True, numeric_only: 'bool' = False, min_count: 'int' = 0, **kwargs)
| Return the product of the values over the requested axis.
|
| Parameters
| ----------
| axis : {index (0), columns (1)}
| Axis for the function to be applied on.
| For `Series` this parameter is unused and defaults to 0.
|
| .. warning::
|
| The behavior of DataFrame.prod with ``axis=None`` is deprecated,
| in a future version this will reduce over both axes and return a scalar
| To retain the old behavior, pass axis=0 (or do not pass axis).
|
| .. versionadded:: 2.0.0
|
| skipna : bool, default True
| Exclude NA/null values when computing the result.
| numeric_only : bool, default False
| Include only float, int, boolean columns. Not implemented for Series.
|
| min_count : int, default 0
| The required number of valid values to perform the operation. If fewer than
| ``min_count`` non-NA values are present the result will be NA.
| **kwargs
| Additional keyword arguments to be passed to the function.
|
| Returns
| -------
| Series or scalar
|
| See Also
| --------
| Series.sum : Return the sum.
| Series.min : Return the minimum.
| Series.max : Return the maximum.
| Series.idxmin : Return the index of the minimum.
| Series.idxmax : Return the index of the maximum.
| DataFrame.sum : Return the sum over the requested axis.
| DataFrame.min : Return the minimum over the requested axis.
| DataFrame.max : Return the maximum over the requested axis.
| DataFrame.idxmin : Return the index of the minimum over the requested axis.
| DataFrame.idxmax : Return the index of the maximum over the requested axis.
|
| Examples
| --------
| By default, the product of an empty or all-NA Series is ``1``
|
| >>> pd.Series([], dtype="float64").prod()
| 1.0
|
| This can be controlled with the ``min_count`` parameter
|
| >>> pd.Series([], dtype="float64").prod(min_count=1)
| nan
|
| Thanks to the ``skipna`` parameter, ``min_count`` handles all-NA and
| empty series identically.
|
| >>> pd.Series([np.nan]).prod()
| 1.0
|
| >>> pd.Series([np.nan]).prod(min_count=1)
| nan
|
| product = prod(self, axis: 'Axis | None' = 0, skipna: 'bool' = True, numeric_only: 'bool' = False, min_count: 'int' = 0, **kwargs)
|
| quantile(self, q: 'float | AnyArrayLike | Sequence[float]' = 0.5, axis: 'Axis' = 0, numeric_only: 'bool' = False, interpolation: 'QuantileInterpolation' = 'linear', method: "Literal['single', 'table']" = 'single') -> 'Series | DataFrame'
| Return values at the given quantile over requested axis.
|
| Parameters
| ----------
| q : float or array-like, default 0.5 (50% quantile)
| Value between 0 <= q <= 1, the quantile(s) to compute.
| axis : {0 or 'index', 1 or 'columns'}, default 0
| Equals 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
| numeric_only : bool, default False
| Include only `float`, `int` or `boolean` data.
|
| .. versionchanged:: 2.0.0
| The default value of ``numeric_only`` is now ``False``.
|
| interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
| This optional parameter specifies the interpolation method to use,
| when the desired quantile lies between two data points `i` and `j`:
|
| * linear: `i + (j - i) * fraction`, where `fraction` is the
| fractional part of the index surrounded by `i` and `j`.
| * lower: `i`.
| * higher: `j`.
| * nearest: `i` or `j` whichever is nearest.
| * midpoint: (`i` + `j`) / 2.
| method : {'single', 'table'}, default 'single'
| Whether to compute quantiles per-column ('single') or over all columns
| ('table'). When 'table', the only allowed interpolation methods are
| 'nearest', 'lower', and 'higher'.
|
| Returns
| -------
| Series or DataFrame
|
| If ``q`` is an array, a DataFrame will be returned where the
| index is ``q``, the columns are the columns of self, and the
| values are the quantiles.
| If ``q`` is a float, a Series will be returned where the
| index is the columns of self and the values are the quantiles.
|
| See Also
| --------
| core.window.rolling.Rolling.quantile: Rolling quantile.
| numpy.percentile: Numpy function to compute the percentile.
|
| Examples
| --------
| >>> df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),
| ... columns=['a', 'b'])
| >>> df.quantile(.1)
| a 1.3
| b 3.7
| Name: 0.1, dtype: float64
| >>> df.quantile([.1, .5])
| a b
| 0.1 1.3 3.7
| 0.5 2.5 55.0
|
| Specifying `method='table'` will compute the quantile over all columns.
|
| >>> df.quantile(.1, method="table", interpolation="nearest")
| a 1
| b 1
| Name: 0.1, dtype: int64
| >>> df.quantile([.1, .5], method="table", interpolation="nearest")
| a b
| 0.1 1 1
| 0.5 3 100
|
| Specifying `numeric_only=False` will also compute the quantile of
| datetime and timedelta data.
|
| >>> df = pd.DataFrame({'A': [1, 2],
| ... 'B': [pd.Timestamp('2010'),
| ... pd.Timestamp('2011')],
| ... 'C': [pd.Timedelta('1 days'),
| ... pd.Timedelta('2 days')]})
| >>> df.quantile(0.5, numeric_only=False)
| A 1.5
| B 2010-07-02 12:00:00
| C 1 days 12:00:00
| Name: 0.5, dtype: object
|
| query(self, expr: 'str', *, inplace: 'bool' = False, **kwargs) -> 'DataFrame | None'
| Query the columns of a DataFrame with a boolean expression.
|
| Parameters
| ----------
| expr : str
| The query string to evaluate.
|
| You can refer to variables
| in the environment by prefixing them with an '@' character like
| ``@a + b``.
|
| You can refer to column names that are not valid Python variable names
| by surrounding them in backticks. Thus, column names containing spaces
| or punctuations (besides underscores) or starting with digits must be
| surrounded by backticks. (For example, a column named "Area (cm^2)" would
| be referenced as ```Area (cm^2)```). Column names which are Python keywords
| (like "list", "for", "import", etc) cannot be used.
|
| For example, if one of your columns is called ``a a`` and you want
| to sum it with ``b``, your query should be ```a a` + b``.
|
| inplace : bool
| Whether to modify the DataFrame rather than creating a new one.
| **kwargs
| See the documentation for :func:`eval` for complete details
| on the keyword arguments accepted by :meth:`DataFrame.query`.
|
| Returns
| -------
| DataFrame or None
| DataFrame resulting from the provided query expression or
| None if ``inplace=True``.
|
| See Also
| --------
| eval : Evaluate a string describing operations on
| DataFrame columns.
| DataFrame.eval : Evaluate a string describing operations on
| DataFrame columns.
|
| Notes
| -----
| The result of the evaluation of this expression is first passed to
| :attr:`DataFrame.loc` and if that fails because of a
| multidimensional key (e.g., a DataFrame) then the result will be passed
| to :meth:`DataFrame.__getitem__`.
|
| This method uses the top-level :func:`eval` function to
| evaluate the passed query.
|
| The :meth:`~pandas.DataFrame.query` method uses a slightly
| modified Python syntax by default. For example, the ``&`` and ``|``
| (bitwise) operators have the precedence of their boolean cousins,
| :keyword:`and` and :keyword:`or`. This *is* syntactically valid Python,
| however the semantics are different.
|
| You can change the semantics of the expression by passing the keyword
| argument ``parser='python'``. This enforces the same semantics as
| evaluation in Python space. Likewise, you can pass ``engine='python'``
| to evaluate an expression using Python itself as a backend. This is not
| recommended as it is inefficient compared to using ``numexpr`` as the
| engine.
|
| The :attr:`DataFrame.index` and
| :attr:`DataFrame.columns` attributes of the
| :class:`~pandas.DataFrame` instance are placed in the query namespace
| by default, which allows you to treat both the index and columns of the
| frame as a column in the frame.
| The identifier ``index`` is used for the frame index; you can also
| use the name of the index to identify it in a query. Please note that
| Python keywords may not be used as identifiers.
|
| For further details and examples see the ``query`` documentation in
| :ref:`indexing <indexing.query>`.
|
| *Backtick quoted variables*
|
| Backtick quoted variables are parsed as literal Python code and
| are converted internally to a Python valid identifier.
| This can lead to the following problems.
|
| During parsing a number of disallowed characters inside the backtick
| quoted string are replaced by strings that are allowed as a Python identifier.
| These characters include all operators in Python, the space character, the
| question mark, the exclamation mark, the dollar sign, and the euro sign.
| For other characters that fall outside the ASCII range (U+0001..U+007F)
| and those that are not further specified in PEP 3131,
| the query parser will raise an error.
| This excludes whitespace different than the space character,
| but also the hashtag (as it is used for comments) and the backtick
| itself (backtick can also not be escaped).
|
| In a special case, quotes that make a pair around a backtick can
| confuse the parser.
| For example, ```it's` > `that's``` will raise an error,
| as it forms a quoted string (``'s > `that'``) with a backtick inside.
|
| See also the Python documentation about lexical analysis
| (https://docs.python.org/3/reference/lexical_analysis.html)
| in combination with the source code in :mod:`pandas.core.computation.parsing`.
|
| Examples
| --------
| >>> df = pd.DataFrame({'A': range(1, 6),
| ... 'B': range(10, 0, -2),
| ... 'C C': range(10, 5, -1)})
| >>> df
| A B C C
| 0 1 10 10
| 1 2 8 9
| 2 3 6 8
| 3 4 4 7
| 4 5 2 6
| >>> df.query('A > B')
| A B C C
| 4 5 2 6
|
| The previous expression is equivalent to
|
| >>> df[df.A > df.B]
| A B C C
| 4 5 2 6
|
| For columns with spaces in their name, you can use backtick quoting.
|
| >>> df.query('B == `C C`')
| A B C C
| 0 1 10 10
|
| The previous expression is equivalent to
|
| >>> df[df.B == df['C C']]
| A B C C
| 0 1 10 10
|
| radd(self, other, axis: 'Axis' = 'columns', level=None, fill_value=None) -> 'DataFrame'
| Get Addition of dataframe and other, element-wise (binary operator `radd`).
|
| Equivalent to ``other + dataframe``, but with support to substitute a fill_value
| for missing data in one of the inputs. With reverse version, `add`.
|
| Among flexible wrappers (`add`, `sub`, `mul`, `div`, `floordiv`, `mod`, `pow`) to
| arithmetic operators: `+`, `-`, `*`, `/`, `//`, `%`, `**`.
|
| Parameters
| ----------
| other : scalar, sequence, Series, dict or DataFrame
| Any single or multiple element data structure, or list-like object.
| axis : {0 or 'index', 1 or 'columns'}
| Whether to compare by the index (0 or 'index') or columns.
| (1 or 'columns'). For Series input, axis to match Series index on.
| level : int or label
| Broadcast across a level, matching Index values on the
| passed MultiIndex level.
| fill_value : float or None, default None
| Fill existing missing (NaN) values, and any new element needed for
| successful DataFrame alignment, with this value before computation.
| If data in both corresponding DataFrame locations is missing
| the result will be missing.
|
| Returns
| -------
| DataFrame
| Result of the arithmetic operation.
|
| See Also
| --------
| DataFrame.add : Add DataFrames.
| DataFrame.sub : Subtract DataFrames.
| DataFrame.mul : Multiply DataFrames.
| DataFrame.div : Divide DataFrames (float division).
| DataFrame.truediv : Divide DataFrames (float division).
| DataFrame.floordiv : Divide DataFrames (integer division).
| DataFrame.mod : Calculate modulo (remainder after division).
| DataFrame.pow : Calculate exponential power.
|
| Notes
| -----
| Mismatched indices will be unioned together.
|
| Examples
| --------
| >>> df = pd.DataFrame({'angles': [0, 3, 4],
| ... 'degrees': [360, 180, 360]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> df
| angles degrees
| circle 0 360
| triangle 3 180
| rectangle 4 360
|
| Add a scalar with operator version which return the same
| results.
|
| >>> df + 1
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| >>> df.add(1)
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| Divide by constant with reverse version.
|
| >>> df.div(10)
| angles degrees
| circle 0.0 36.0
| triangle 0.3 18.0
| rectangle 0.4 36.0
|
| >>> df.rdiv(10)
| angles degrees
| circle inf 0.027778
| triangle 3.333333 0.055556
| rectangle 2.500000 0.027778
|
| Subtract a list and Series by axis with operator version.
|
| >>> df - [1, 2]
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub([1, 2], axis='columns')
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
| ... axis='index')
| angles degrees
| circle -1 359
| triangle 2 179
| rectangle 3 359
|
| Multiply a dictionary by axis.
|
| >>> df.mul({'angles': 0, 'degrees': 2})
| angles degrees
| circle 0 720
| triangle 0 360
| rectangle 0 720
|
| >>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
| angles degrees
| circle 0 0
| triangle 6 360
| rectangle 12 1080
|
| Multiply a DataFrame of different shape with operator version.
|
| >>> other = pd.DataFrame({'angles': [0, 3, 4]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> other
| angles
| circle 0
| triangle 3
| rectangle 4
|
| >>> df * other
| angles degrees
| circle 0 NaN
| triangle 9 NaN
| rectangle 16 NaN
|
| >>> df.mul(other, fill_value=0)
| angles degrees
| circle 0 0.0
| triangle 9 0.0
| rectangle 16 0.0
|
| Divide by a MultiIndex by level.
|
| >>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
| ... 'degrees': [360, 180, 360, 360, 540, 720]},
| ... index=[['A', 'A', 'A', 'B', 'B', 'B'],
| ... ['circle', 'triangle', 'rectangle',
| ... 'square', 'pentagon', 'hexagon']])
| >>> df_multindex
| angles degrees
| A circle 0 360
| triangle 3 180
| rectangle 4 360
| B square 4 360
| pentagon 5 540
| hexagon 6 720
|
| >>> df.div(df_multindex, level=1, fill_value=0)
| angles degrees
| A circle NaN 1.0
| triangle 1.0 1.0
| rectangle 1.0 1.0
| B square 0.0 0.0
| pentagon 0.0 0.0
| hexagon 0.0 0.0
|
| rdiv = rtruediv(self, other, axis: 'Axis' = 'columns', level=None, fill_value=None) -> 'DataFrame'
|
| reindex(self, labels=None, *, index=None, columns=None, axis: 'Axis | None' = None, method: 'ReindexMethod | None' = None, copy: 'bool | None' = None, level: 'Level | None' = None, fill_value: 'Scalar | None' = nan, limit: 'int | None' = None, tolerance=None) -> 'DataFrame'
| Conform DataFrame to new index with optional filling logic.
|
| Places NA/NaN in locations having no value in the previous index. A new object
| is produced unless the new index is equivalent to the current one and
| ``copy=False``.
|
| Parameters
| ----------
|
| labels : array-like, optional
| New labels / index to conform the axis specified by 'axis' to.
| index : array-like, optional
| New labels for the index. Preferably an Index object to avoid
| duplicating data.
| columns : array-like, optional
| New labels for the columns. Preferably an Index object to avoid
| duplicating data.
| axis : int or str, optional
| Axis to target. Can be either the axis name ('index', 'columns')
| or number (0, 1).
| method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}
| Method to use for filling holes in reindexed DataFrame.
| Please note: this is only applicable to DataFrames/Series with a
| monotonically increasing/decreasing index.
|
| * None (default): don't fill gaps
| * pad / ffill: Propagate last valid observation forward to next
| valid.
| * backfill / bfill: Use next valid observation to fill gap.
| * nearest: Use nearest valid observations to fill gap.
|
| copy : bool, default True
| Return a new object, even if the passed indexes are the same.
|
| .. note::
| The `copy` keyword will change behavior in pandas 3.0.
| `Copy-on-Write
| <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
| will be enabled by default, which means that all methods with a
| `copy` keyword will use a lazy copy mechanism to defer the copy and
| ignore the `copy` keyword. The `copy` keyword will be removed in a
| future version of pandas.
|
| You can already get the future behavior and improvements through
| enabling copy on write ``pd.options.mode.copy_on_write = True``
| level : int or name
| Broadcast across a level, matching Index values on the
| passed MultiIndex level.
| fill_value : scalar, default np.nan
| Value to use for missing values. Defaults to NaN, but can be any
| "compatible" value.
| limit : int, default None
| Maximum number of consecutive elements to forward or backward fill.
| tolerance : optional
| Maximum distance between original and new labels for inexact
| matches. The values of the index at the matching locations most
| satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
|
| Tolerance may be a scalar value, which applies the same tolerance
| to all values, or list-like, which applies variable tolerance per
| element. List-like includes list, tuple, array, Series, and must be
| the same size as the index and its dtype must exactly match the
| index's type.
|
| Returns
| -------
| DataFrame with changed index.
|
| See Also
| --------
| DataFrame.set_index : Set row labels.
| DataFrame.reset_index : Remove row labels or move them to new columns.
| DataFrame.reindex_like : Change to same indices as other DataFrame.
|
| Examples
| --------
| ``DataFrame.reindex`` supports two calling conventions
|
| * ``(index=index_labels, columns=column_labels, ...)``
| * ``(labels, axis={'index', 'columns'}, ...)``
|
| We *highly* recommend using keyword arguments to clarify your
| intent.
|
| Create a dataframe with some fictional data.
|
| >>> index = ['Firefox', 'Chrome', 'Safari', 'IE10', 'Konqueror']
| >>> df = pd.DataFrame({'http_status': [200, 200, 404, 404, 301],
| ... 'response_time': [0.04, 0.02, 0.07, 0.08, 1.0]},
| ... index=index)
| >>> df
| http_status response_time
| Firefox 200 0.04
| Chrome 200 0.02
| Safari 404 0.07
| IE10 404 0.08
| Konqueror 301 1.00
|
| Create a new index and reindex the dataframe. By default
| values in the new index that do not have corresponding
| records in the dataframe are assigned ``NaN``.
|
| >>> new_index = ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10',
| ... 'Chrome']
| >>> df.reindex(new_index)
| http_status response_time
| Safari 404.0 0.07
| Iceweasel NaN NaN
| Comodo Dragon NaN NaN
| IE10 404.0 0.08
| Chrome 200.0 0.02
|
| We can fill in the missing values by passing a value to
| the keyword ``fill_value``. Because the index is not monotonically
| increasing or decreasing, we cannot use arguments to the keyword
| ``method`` to fill the ``NaN`` values.
|
| >>> df.reindex(new_index, fill_value=0)
| http_status response_time
| Safari 404 0.07
| Iceweasel 0 0.00
| Comodo Dragon 0 0.00
| IE10 404 0.08
| Chrome 200 0.02
|
| >>> df.reindex(new_index, fill_value='missing')
| http_status response_time
| Safari 404 0.07
| Iceweasel missing missing
| Comodo Dragon missing missing
| IE10 404 0.08
| Chrome 200 0.02
|
| We can also reindex the columns.
|
| >>> df.reindex(columns=['http_status', 'user_agent'])
| http_status user_agent
| Firefox 200 NaN
| Chrome 200 NaN
| Safari 404 NaN
| IE10 404 NaN
| Konqueror 301 NaN
|
| Or we can use "axis-style" keyword arguments
|
| >>> df.reindex(['http_status', 'user_agent'], axis="columns")
| http_status user_agent
| Firefox 200 NaN
| Chrome 200 NaN
| Safari 404 NaN
| IE10 404 NaN
| Konqueror 301 NaN
|
| To further illustrate the filling functionality in
| ``reindex``, we will create a dataframe with a
| monotonically increasing index (for example, a sequence
| of dates).
|
| >>> date_index = pd.date_range('1/1/2010', periods=6, freq='D')
| >>> df2 = pd.DataFrame({"prices": [100, 101, np.nan, 100, 89, 88]},
| ... index=date_index)
| >>> df2
| prices
| 2010-01-01 100.0
| 2010-01-02 101.0
| 2010-01-03 NaN
| 2010-01-04 100.0
| 2010-01-05 89.0
| 2010-01-06 88.0
|
| Suppose we decide to expand the dataframe to cover a wider
| date range.
|
| >>> date_index2 = pd.date_range('12/29/2009', periods=10, freq='D')
| >>> df2.reindex(date_index2)
| prices
| 2009-12-29 NaN
| 2009-12-30 NaN
| 2009-12-31 NaN
| 2010-01-01 100.0
| 2010-01-02 101.0
| 2010-01-03 NaN
| 2010-01-04 100.0
| 2010-01-05 89.0
| 2010-01-06 88.0
| 2010-01-07 NaN
|
| The index entries that did not have a value in the original data frame
| (for example, '2009-12-29') are by default filled with ``NaN``.
| If desired, we can fill in the missing values using one of several
| options.
|
| For example, to back-propagate the last valid value to fill the ``NaN``
| values, pass ``bfill`` as an argument to the ``method`` keyword.
|
| >>> df2.reindex(date_index2, method='bfill')
| prices
| 2009-12-29 100.0
| 2009-12-30 100.0
| 2009-12-31 100.0
| 2010-01-01 100.0
| 2010-01-02 101.0
| 2010-01-03 NaN
| 2010-01-04 100.0
| 2010-01-05 89.0
| 2010-01-06 88.0
| 2010-01-07 NaN
|
| Please note that the ``NaN`` value present in the original dataframe
| (at index value 2010-01-03) will not be filled by any of the
| value propagation schemes. This is because filling while reindexing
| does not look at dataframe values, but only compares the original and
| desired indexes. If you do want to fill in the ``NaN`` values present
| in the original dataframe, use the ``fillna()`` method.
|
| See the :ref:`user guide <basics.reindexing>` for more.
|
| rename(self, mapper: 'Renamer | None' = None, *, index: 'Renamer | None' = None, columns: 'Renamer | None' = None, axis: 'Axis | None' = None, copy: 'bool | None' = None, inplace: 'bool' = False, level: 'Level | None' = None, errors: 'IgnoreRaise' = 'ignore') -> 'DataFrame | None'
| Rename columns or index labels.
|
| Function / dict values must be unique (1-to-1). Labels not contained in
| a dict / Series will be left as-is. Extra labels listed don't throw an
| error.
|
| See the :ref:`user guide <basics.rename>` for more.
|
| Parameters
| ----------
| mapper : dict-like or function
| Dict-like or function transformations to apply to
| that axis' values. Use either ``mapper`` and ``axis`` to
| specify the axis to target with ``mapper``, or ``index`` and
| ``columns``.
| index : dict-like or function
| Alternative to specifying axis (``mapper, axis=0``
| is equivalent to ``index=mapper``).
| columns : dict-like or function
| Alternative to specifying axis (``mapper, axis=1``
| is equivalent to ``columns=mapper``).
| axis : {0 or 'index', 1 or 'columns'}, default 0
| Axis to target with ``mapper``. Can be either the axis name
| ('index', 'columns') or number (0, 1). The default is 'index'.
| copy : bool, default True
| Also copy underlying data.
|
| .. note::
| The `copy` keyword will change behavior in pandas 3.0.
| `Copy-on-Write
| <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
| will be enabled by default, which means that all methods with a
| `copy` keyword will use a lazy copy mechanism to defer the copy and
| ignore the `copy` keyword. The `copy` keyword will be removed in a
| future version of pandas.
|
| You can already get the future behavior and improvements through
| enabling copy on write ``pd.options.mode.copy_on_write = True``
| inplace : bool, default False
| Whether to modify the DataFrame rather than creating a new one.
| If True then value of copy is ignored.
| level : int or level name, default None
| In case of a MultiIndex, only rename labels in the specified
| level.
| errors : {'ignore', 'raise'}, default 'ignore'
| If 'raise', raise a `KeyError` when a dict-like `mapper`, `index`,
| or `columns` contains labels that are not present in the Index
| being transformed.
| If 'ignore', existing keys will be renamed and extra keys will be
| ignored.
|
| Returns
| -------
| DataFrame or None
| DataFrame with the renamed axis labels or None if ``inplace=True``.
|
| Raises
| ------
| KeyError
| If any of the labels is not found in the selected axis and
| "errors='raise'".
|
| See Also
| --------
| DataFrame.rename_axis : Set the name of the axis.
|
| Examples
| --------
| ``DataFrame.rename`` supports two calling conventions
|
| * ``(index=index_mapper, columns=columns_mapper, ...)``
| * ``(mapper, axis={'index', 'columns'}, ...)``
|
| We *highly* recommend using keyword arguments to clarify your
| intent.
|
| Rename columns using a mapping:
|
| >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
| >>> df.rename(columns={"A": "a", "B": "c"})
| a c
| 0 1 4
| 1 2 5
| 2 3 6
|
| Rename index using a mapping:
|
| >>> df.rename(index={0: "x", 1: "y", 2: "z"})
| A B
| x 1 4
| y 2 5
| z 3 6
|
| Cast index labels to a different type:
|
| >>> df.index
| RangeIndex(start=0, stop=3, step=1)
| >>> df.rename(index=str).index
| Index(['0', '1', '2'], dtype='object')
|
| >>> df.rename(columns={"A": "a", "B": "b", "C": "c"}, errors="raise")
| Traceback (most recent call last):
| KeyError: ['C'] not found in axis
|
| Using axis-style parameters:
|
| >>> df.rename(str.lower, axis='columns')
| a b
| 0 1 4
| 1 2 5
| 2 3 6
|
| >>> df.rename({1: 2, 2: 4}, axis='index')
| A B
| 0 1 4
| 2 2 5
| 4 3 6
|
| reorder_levels(self, order: 'Sequence[int | str]', axis: 'Axis' = 0) -> 'DataFrame'
| Rearrange index levels using input order. May not drop or duplicate levels.
|
| Parameters
| ----------
| order : list of int or list of str
| List representing new level order. Reference level by number
| (position) or by key (label).
| axis : {0 or 'index', 1 or 'columns'}, default 0
| Where to reorder levels.
|
| Returns
| -------
| DataFrame
|
| Examples
| --------
| >>> data = {
| ... "class": ["Mammals", "Mammals", "Reptiles"],
| ... "diet": ["Omnivore", "Carnivore", "Carnivore"],
| ... "species": ["Humans", "Dogs", "Snakes"],
| ... }
| >>> df = pd.DataFrame(data, columns=["class", "diet", "species"])
| >>> df = df.set_index(["class", "diet"])
| >>> df
| species
| class diet
| Mammals Omnivore Humans
| Carnivore Dogs
| Reptiles Carnivore Snakes
|
| Let's reorder the levels of the index:
|
| >>> df.reorder_levels(["diet", "class"])
| species
| diet class
| Omnivore Mammals Humans
| Carnivore Mammals Dogs
| Reptiles Snakes
|
| reset_index(self, level: 'IndexLabel | None' = None, *, drop: 'bool' = False, inplace: 'bool' = False, col_level: 'Hashable' = 0, col_fill: 'Hashable' = '', allow_duplicates: 'bool | lib.NoDefault' = <no_default>, names: 'Hashable | Sequence[Hashable] | None' = None) -> 'DataFrame | None'
| Reset the index, or a level of it.
|
| Reset the index of the DataFrame, and use the default one instead.
| If the DataFrame has a MultiIndex, this method can remove one or more
| levels.
|
| Parameters
| ----------
| level : int, str, tuple, or list, default None
| Only remove the given levels from the index. Removes all levels by
| default.
| drop : bool, default False
| Do not try to insert index into dataframe columns. This resets
| the index to the default integer index.
| inplace : bool, default False
| Whether to modify the DataFrame rather than creating a new one.
| col_level : int or str, default 0
| If the columns have multiple levels, determines which level the
| labels are inserted into. By default it is inserted into the first
| level.
| col_fill : object, default ''
| If the columns have multiple levels, determines how the other
| levels are named. If None then the index name is repeated.
| allow_duplicates : bool, optional, default lib.no_default
| Allow duplicate column labels to be created.
|
| .. versionadded:: 1.5.0
|
| names : int, str or 1-dimensional list, default None
| Using the given string, rename the DataFrame column which contains the
| index data. If the DataFrame has a MultiIndex, this has to be a list or
| tuple with length equal to the number of levels.
|
| .. versionadded:: 1.5.0
|
| Returns
| -------
| DataFrame or None
| DataFrame with the new index or None if ``inplace=True``.
|
| See Also
| --------
| DataFrame.set_index : Opposite of reset_index.
| DataFrame.reindex : Change to new indices or expand indices.
| DataFrame.reindex_like : Change to same indices as other DataFrame.
|
| Examples
| --------
| >>> df = pd.DataFrame([('bird', 389.0),
| ... ('bird', 24.0),
| ... ('mammal', 80.5),
| ... ('mammal', np.nan)],
| ... index=['falcon', 'parrot', 'lion', 'monkey'],
| ... columns=('class', 'max_speed'))
| >>> df
| class max_speed
| falcon bird 389.0
| parrot bird 24.0
| lion mammal 80.5
| monkey mammal NaN
|
| When we reset the index, the old index is added as a column, and a
| new sequential index is used:
|
| >>> df.reset_index()
| index class max_speed
| 0 falcon bird 389.0
| 1 parrot bird 24.0
| 2 lion mammal 80.5
| 3 monkey mammal NaN
|
| We can use the `drop` parameter to avoid the old index being added as
| a column:
|
| >>> df.reset_index(drop=True)
| class max_speed
| 0 bird 389.0
| 1 bird 24.0
| 2 mammal 80.5
| 3 mammal NaN
|
| You can also use `reset_index` with `MultiIndex`.
|
| >>> index = pd.MultiIndex.from_tuples([('bird', 'falcon'),
| ... ('bird', 'parrot'),
| ... ('mammal', 'lion'),
| ... ('mammal', 'monkey')],
| ... names=['class', 'name'])
| >>> columns = pd.MultiIndex.from_tuples([('speed', 'max'),
| ... ('species', 'type')])
| >>> df = pd.DataFrame([(389.0, 'fly'),
| ... (24.0, 'fly'),
| ... (80.5, 'run'),
| ... (np.nan, 'jump')],
| ... index=index,
| ... columns=columns)
| >>> df
| speed species
| max type
| class name
| bird falcon 389.0 fly
| parrot 24.0 fly
| mammal lion 80.5 run
| monkey NaN jump
|
| Using the `names` parameter, choose a name for the index column:
|
| >>> df.reset_index(names=['classes', 'names'])
| classes names speed species
| max type
| 0 bird falcon 389.0 fly
| 1 bird parrot 24.0 fly
| 2 mammal lion 80.5 run
| 3 mammal monkey NaN jump
|
| If the index has multiple levels, we can reset a subset of them:
|
| >>> df.reset_index(level='class')
| class speed species
| max type
| name
| falcon bird 389.0 fly
| parrot bird 24.0 fly
| lion mammal 80.5 run
| monkey mammal NaN jump
|
| If we are not dropping the index, by default, it is placed in the top
| level. We can place it in another level:
|
| >>> df.reset_index(level='class', col_level=1)
| speed species
| class max type
| name
| falcon bird 389.0 fly
| parrot bird 24.0 fly
| lion mammal 80.5 run
| monkey mammal NaN jump
|
| When the index is inserted under another level, we can specify under
| which one with the parameter `col_fill`:
|
| >>> df.reset_index(level='class', col_level=1, col_fill='species')
| species speed species
| class max type
| name
| falcon bird 389.0 fly
| parrot bird 24.0 fly
| lion mammal 80.5 run
| monkey mammal NaN jump
|
| If we specify a nonexistent level for `col_fill`, it is created:
|
| >>> df.reset_index(level='class', col_level=1, col_fill='genus')
| genus speed species
| class max type
| name
| falcon bird 389.0 fly
| parrot bird 24.0 fly
| lion mammal 80.5 run
| monkey mammal NaN jump
|
| rfloordiv(self, other, axis: 'Axis' = 'columns', level=None, fill_value=None) -> 'DataFrame'
| Get Integer division of dataframe and other, element-wise (binary operator `rfloordiv`).
|
| Equivalent to ``other // dataframe``, but with support to substitute a fill_value
| for missing data in one of the inputs. With reverse version, `floordiv`.
|
| Among flexible wrappers (`add`, `sub`, `mul`, `div`, `floordiv`, `mod`, `pow`) to
| arithmetic operators: `+`, `-`, `*`, `/`, `//`, `%`, `**`.
|
| Parameters
| ----------
| other : scalar, sequence, Series, dict or DataFrame
| Any single or multiple element data structure, or list-like object.
| axis : {0 or 'index', 1 or 'columns'}
| Whether to compare by the index (0 or 'index') or columns.
| (1 or 'columns'). For Series input, axis to match Series index on.
| level : int or label
| Broadcast across a level, matching Index values on the
| passed MultiIndex level.
| fill_value : float or None, default None
| Fill existing missing (NaN) values, and any new element needed for
| successful DataFrame alignment, with this value before computation.
| If data in both corresponding DataFrame locations is missing
| the result will be missing.
|
| Returns
| -------
| DataFrame
| Result of the arithmetic operation.
|
| See Also
| --------
| DataFrame.add : Add DataFrames.
| DataFrame.sub : Subtract DataFrames.
| DataFrame.mul : Multiply DataFrames.
| DataFrame.div : Divide DataFrames (float division).
| DataFrame.truediv : Divide DataFrames (float division).
| DataFrame.floordiv : Divide DataFrames (integer division).
| DataFrame.mod : Calculate modulo (remainder after division).
| DataFrame.pow : Calculate exponential power.
|
| Notes
| -----
| Mismatched indices will be unioned together.
|
| Examples
| --------
| >>> df = pd.DataFrame({'angles': [0, 3, 4],
| ... 'degrees': [360, 180, 360]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> df
| angles degrees
| circle 0 360
| triangle 3 180
| rectangle 4 360
|
| Add a scalar with operator version which return the same
| results.
|
| >>> df + 1
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| >>> df.add(1)
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| Divide by constant with reverse version.
|
| >>> df.div(10)
| angles degrees
| circle 0.0 36.0
| triangle 0.3 18.0
| rectangle 0.4 36.0
|
| >>> df.rdiv(10)
| angles degrees
| circle inf 0.027778
| triangle 3.333333 0.055556
| rectangle 2.500000 0.027778
|
| Subtract a list and Series by axis with operator version.
|
| >>> df - [1, 2]
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub([1, 2], axis='columns')
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
| ... axis='index')
| angles degrees
| circle -1 359
| triangle 2 179
| rectangle 3 359
|
| Multiply a dictionary by axis.
|
| >>> df.mul({'angles': 0, 'degrees': 2})
| angles degrees
| circle 0 720
| triangle 0 360
| rectangle 0 720
|
| >>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
| angles degrees
| circle 0 0
| triangle 6 360
| rectangle 12 1080
|
| Multiply a DataFrame of different shape with operator version.
|
| >>> other = pd.DataFrame({'angles': [0, 3, 4]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> other
| angles
| circle 0
| triangle 3
| rectangle 4
|
| >>> df * other
| angles degrees
| circle 0 NaN
| triangle 9 NaN
| rectangle 16 NaN
|
| >>> df.mul(other, fill_value=0)
| angles degrees
| circle 0 0.0
| triangle 9 0.0
| rectangle 16 0.0
|
| Divide by a MultiIndex by level.
|
| >>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
| ... 'degrees': [360, 180, 360, 360, 540, 720]},
| ... index=[['A', 'A', 'A', 'B', 'B', 'B'],
| ... ['circle', 'triangle', 'rectangle',
| ... 'square', 'pentagon', 'hexagon']])
| >>> df_multindex
| angles degrees
| A circle 0 360
| triangle 3 180
| rectangle 4 360
| B square 4 360
| pentagon 5 540
| hexagon 6 720
|
| >>> df.div(df_multindex, level=1, fill_value=0)
| angles degrees
| A circle NaN 1.0
| triangle 1.0 1.0
| rectangle 1.0 1.0
| B square 0.0 0.0
| pentagon 0.0 0.0
| hexagon 0.0 0.0
|
| rmod(self, other, axis: 'Axis' = 'columns', level=None, fill_value=None) -> 'DataFrame'
| Get Modulo of dataframe and other, element-wise (binary operator `rmod`).
|
| Equivalent to ``other % dataframe``, but with support to substitute a fill_value
| for missing data in one of the inputs. With reverse version, `mod`.
|
| Among flexible wrappers (`add`, `sub`, `mul`, `div`, `floordiv`, `mod`, `pow`) to
| arithmetic operators: `+`, `-`, `*`, `/`, `//`, `%`, `**`.
|
| Parameters
| ----------
| other : scalar, sequence, Series, dict or DataFrame
| Any single or multiple element data structure, or list-like object.
| axis : {0 or 'index', 1 or 'columns'}
| Whether to compare by the index (0 or 'index') or columns.
| (1 or 'columns'). For Series input, axis to match Series index on.
| level : int or label
| Broadcast across a level, matching Index values on the
| passed MultiIndex level.
| fill_value : float or None, default None
| Fill existing missing (NaN) values, and any new element needed for
| successful DataFrame alignment, with this value before computation.
| If data in both corresponding DataFrame locations is missing
| the result will be missing.
|
| Returns
| -------
| DataFrame
| Result of the arithmetic operation.
|
| See Also
| --------
| DataFrame.add : Add DataFrames.
| DataFrame.sub : Subtract DataFrames.
| DataFrame.mul : Multiply DataFrames.
| DataFrame.div : Divide DataFrames (float division).
| DataFrame.truediv : Divide DataFrames (float division).
| DataFrame.floordiv : Divide DataFrames (integer division).
| DataFrame.mod : Calculate modulo (remainder after division).
| DataFrame.pow : Calculate exponential power.
|
| Notes
| -----
| Mismatched indices will be unioned together.
|
| Examples
| --------
| >>> df = pd.DataFrame({'angles': [0, 3, 4],
| ... 'degrees': [360, 180, 360]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> df
| angles degrees
| circle 0 360
| triangle 3 180
| rectangle 4 360
|
| Add a scalar with operator version which return the same
| results.
|
| >>> df + 1
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| >>> df.add(1)
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| Divide by constant with reverse version.
|
| >>> df.div(10)
| angles degrees
| circle 0.0 36.0
| triangle 0.3 18.0
| rectangle 0.4 36.0
|
| >>> df.rdiv(10)
| angles degrees
| circle inf 0.027778
| triangle 3.333333 0.055556
| rectangle 2.500000 0.027778
|
| Subtract a list and Series by axis with operator version.
|
| >>> df - [1, 2]
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub([1, 2], axis='columns')
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
| ... axis='index')
| angles degrees
| circle -1 359
| triangle 2 179
| rectangle 3 359
|
| Multiply a dictionary by axis.
|
| >>> df.mul({'angles': 0, 'degrees': 2})
| angles degrees
| circle 0 720
| triangle 0 360
| rectangle 0 720
|
| >>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
| angles degrees
| circle 0 0
| triangle 6 360
| rectangle 12 1080
|
| Multiply a DataFrame of different shape with operator version.
|
| >>> other = pd.DataFrame({'angles': [0, 3, 4]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> other
| angles
| circle 0
| triangle 3
| rectangle 4
|
| >>> df * other
| angles degrees
| circle 0 NaN
| triangle 9 NaN
| rectangle 16 NaN
|
| >>> df.mul(other, fill_value=0)
| angles degrees
| circle 0 0.0
| triangle 9 0.0
| rectangle 16 0.0
|
| Divide by a MultiIndex by level.
|
| >>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
| ... 'degrees': [360, 180, 360, 360, 540, 720]},
| ... index=[['A', 'A', 'A', 'B', 'B', 'B'],
| ... ['circle', 'triangle', 'rectangle',
| ... 'square', 'pentagon', 'hexagon']])
| >>> df_multindex
| angles degrees
| A circle 0 360
| triangle 3 180
| rectangle 4 360
| B square 4 360
| pentagon 5 540
| hexagon 6 720
|
| >>> df.div(df_multindex, level=1, fill_value=0)
| angles degrees
| A circle NaN 1.0
| triangle 1.0 1.0
| rectangle 1.0 1.0
| B square 0.0 0.0
| pentagon 0.0 0.0
| hexagon 0.0 0.0
|
| rmul(self, other, axis: 'Axis' = 'columns', level=None, fill_value=None) -> 'DataFrame'
| Get Multiplication of dataframe and other, element-wise (binary operator `rmul`).
|
| Equivalent to ``other * dataframe``, but with support to substitute a fill_value
| for missing data in one of the inputs. With reverse version, `mul`.
|
| Among flexible wrappers (`add`, `sub`, `mul`, `div`, `floordiv`, `mod`, `pow`) to
| arithmetic operators: `+`, `-`, `*`, `/`, `//`, `%`, `**`.
|
| Parameters
| ----------
| other : scalar, sequence, Series, dict or DataFrame
| Any single or multiple element data structure, or list-like object.
| axis : {0 or 'index', 1 or 'columns'}
| Whether to compare by the index (0 or 'index') or columns.
| (1 or 'columns'). For Series input, axis to match Series index on.
| level : int or label
| Broadcast across a level, matching Index values on the
| passed MultiIndex level.
| fill_value : float or None, default None
| Fill existing missing (NaN) values, and any new element needed for
| successful DataFrame alignment, with this value before computation.
| If data in both corresponding DataFrame locations is missing
| the result will be missing.
|
| Returns
| -------
| DataFrame
| Result of the arithmetic operation.
|
| See Also
| --------
| DataFrame.add : Add DataFrames.
| DataFrame.sub : Subtract DataFrames.
| DataFrame.mul : Multiply DataFrames.
| DataFrame.div : Divide DataFrames (float division).
| DataFrame.truediv : Divide DataFrames (float division).
| DataFrame.floordiv : Divide DataFrames (integer division).
| DataFrame.mod : Calculate modulo (remainder after division).
| DataFrame.pow : Calculate exponential power.
|
| Notes
| -----
| Mismatched indices will be unioned together.
|
| Examples
| --------
| >>> df = pd.DataFrame({'angles': [0, 3, 4],
| ... 'degrees': [360, 180, 360]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> df
| angles degrees
| circle 0 360
| triangle 3 180
| rectangle 4 360
|
| Add a scalar with operator version which return the same
| results.
|
| >>> df + 1
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| >>> df.add(1)
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| Divide by constant with reverse version.
|
| >>> df.div(10)
| angles degrees
| circle 0.0 36.0
| triangle 0.3 18.0
| rectangle 0.4 36.0
|
| >>> df.rdiv(10)
| angles degrees
| circle inf 0.027778
| triangle 3.333333 0.055556
| rectangle 2.500000 0.027778
|
| Subtract a list and Series by axis with operator version.
|
| >>> df - [1, 2]
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub([1, 2], axis='columns')
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
| ... axis='index')
| angles degrees
| circle -1 359
| triangle 2 179
| rectangle 3 359
|
| Multiply a dictionary by axis.
|
| >>> df.mul({'angles': 0, 'degrees': 2})
| angles degrees
| circle 0 720
| triangle 0 360
| rectangle 0 720
|
| >>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
| angles degrees
| circle 0 0
| triangle 6 360
| rectangle 12 1080
|
| Multiply a DataFrame of different shape with operator version.
|
| >>> other = pd.DataFrame({'angles': [0, 3, 4]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> other
| angles
| circle 0
| triangle 3
| rectangle 4
|
| >>> df * other
| angles degrees
| circle 0 NaN
| triangle 9 NaN
| rectangle 16 NaN
|
| >>> df.mul(other, fill_value=0)
| angles degrees
| circle 0 0.0
| triangle 9 0.0
| rectangle 16 0.0
|
| Divide by a MultiIndex by level.
|
| >>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
| ... 'degrees': [360, 180, 360, 360, 540, 720]},
| ... index=[['A', 'A', 'A', 'B', 'B', 'B'],
| ... ['circle', 'triangle', 'rectangle',
| ... 'square', 'pentagon', 'hexagon']])
| >>> df_multindex
| angles degrees
| A circle 0 360
| triangle 3 180
| rectangle 4 360
| B square 4 360
| pentagon 5 540
| hexagon 6 720
|
| >>> df.div(df_multindex, level=1, fill_value=0)
| angles degrees
| A circle NaN 1.0
| triangle 1.0 1.0
| rectangle 1.0 1.0
| B square 0.0 0.0
| pentagon 0.0 0.0
| hexagon 0.0 0.0
|
| round(self, decimals: 'int | dict[IndexLabel, int] | Series' = 0, *args, **kwargs) -> 'DataFrame'
| Round a DataFrame to a variable number of decimal places.
|
| Parameters
| ----------
| decimals : int, dict, Series
| Number of decimal places to round each column to. If an int is
| given, round each column to the same number of places.
| Otherwise dict and Series round to variable numbers of places.
| Column names should be in the keys if `decimals` is a
| dict-like, or in the index if `decimals` is a Series. Any
| columns not included in `decimals` will be left as is. Elements
| of `decimals` which are not columns of the input will be
| ignored.
| *args
| Additional keywords have no effect but might be accepted for
| compatibility with numpy.
| **kwargs
| Additional keywords have no effect but might be accepted for
| compatibility with numpy.
|
| Returns
| -------
| DataFrame
| A DataFrame with the affected columns rounded to the specified
| number of decimal places.
|
| See Also
| --------
| numpy.around : Round a numpy array to the given number of decimals.
| Series.round : Round a Series to the given number of decimals.
|
| Examples
| --------
| >>> df = pd.DataFrame([(.21, .32), (.01, .67), (.66, .03), (.21, .18)],
| ... columns=['dogs', 'cats'])
| >>> df
| dogs cats
| 0 0.21 0.32
| 1 0.01 0.67
| 2 0.66 0.03
| 3 0.21 0.18
|
| By providing an integer each column is rounded to the same number
| of decimal places
|
| >>> df.round(1)
| dogs cats
| 0 0.2 0.3
| 1 0.0 0.7
| 2 0.7 0.0
| 3 0.2 0.2
|
| With a dict, the number of places for specific columns can be
| specified with the column names as key and the number of decimal
| places as value
|
| >>> df.round({'dogs': 1, 'cats': 0})
| dogs cats
| 0 0.2 0.0
| 1 0.0 1.0
| 2 0.7 0.0
| 3 0.2 0.0
|
| Using a Series, the number of places for specific columns can be
| specified with the column names as index and the number of
| decimal places as value
|
| >>> decimals = pd.Series([0, 1], index=['cats', 'dogs'])
| >>> df.round(decimals)
| dogs cats
| 0 0.2 0.0
| 1 0.0 1.0
| 2 0.7 0.0
| 3 0.2 0.0
|
| rpow(self, other, axis: 'Axis' = 'columns', level=None, fill_value=None) -> 'DataFrame'
| Get Exponential power of dataframe and other, element-wise (binary operator `rpow`).
|
| Equivalent to ``other ** dataframe``, but with support to substitute a fill_value
| for missing data in one of the inputs. With reverse version, `pow`.
|
| Among flexible wrappers (`add`, `sub`, `mul`, `div`, `floordiv`, `mod`, `pow`) to
| arithmetic operators: `+`, `-`, `*`, `/`, `//`, `%`, `**`.
|
| Parameters
| ----------
| other : scalar, sequence, Series, dict or DataFrame
| Any single or multiple element data structure, or list-like object.
| axis : {0 or 'index', 1 or 'columns'}
| Whether to compare by the index (0 or 'index') or columns.
| (1 or 'columns'). For Series input, axis to match Series index on.
| level : int or label
| Broadcast across a level, matching Index values on the
| passed MultiIndex level.
| fill_value : float or None, default None
| Fill existing missing (NaN) values, and any new element needed for
| successful DataFrame alignment, with this value before computation.
| If data in both corresponding DataFrame locations is missing
| the result will be missing.
|
| Returns
| -------
| DataFrame
| Result of the arithmetic operation.
|
| See Also
| --------
| DataFrame.add : Add DataFrames.
| DataFrame.sub : Subtract DataFrames.
| DataFrame.mul : Multiply DataFrames.
| DataFrame.div : Divide DataFrames (float division).
| DataFrame.truediv : Divide DataFrames (float division).
| DataFrame.floordiv : Divide DataFrames (integer division).
| DataFrame.mod : Calculate modulo (remainder after division).
| DataFrame.pow : Calculate exponential power.
|
| Notes
| -----
| Mismatched indices will be unioned together.
|
| Examples
| --------
| >>> df = pd.DataFrame({'angles': [0, 3, 4],
| ... 'degrees': [360, 180, 360]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> df
| angles degrees
| circle 0 360
| triangle 3 180
| rectangle 4 360
|
| Add a scalar with operator version which return the same
| results.
|
| >>> df + 1
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| >>> df.add(1)
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| Divide by constant with reverse version.
|
| >>> df.div(10)
| angles degrees
| circle 0.0 36.0
| triangle 0.3 18.0
| rectangle 0.4 36.0
|
| >>> df.rdiv(10)
| angles degrees
| circle inf 0.027778
| triangle 3.333333 0.055556
| rectangle 2.500000 0.027778
|
| Subtract a list and Series by axis with operator version.
|
| >>> df - [1, 2]
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub([1, 2], axis='columns')
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
| ... axis='index')
| angles degrees
| circle -1 359
| triangle 2 179
| rectangle 3 359
|
| Multiply a dictionary by axis.
|
| >>> df.mul({'angles': 0, 'degrees': 2})
| angles degrees
| circle 0 720
| triangle 0 360
| rectangle 0 720
|
| >>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
| angles degrees
| circle 0 0
| triangle 6 360
| rectangle 12 1080
|
| Multiply a DataFrame of different shape with operator version.
|
| >>> other = pd.DataFrame({'angles': [0, 3, 4]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> other
| angles
| circle 0
| triangle 3
| rectangle 4
|
| >>> df * other
| angles degrees
| circle 0 NaN
| triangle 9 NaN
| rectangle 16 NaN
|
| >>> df.mul(other, fill_value=0)
| angles degrees
| circle 0 0.0
| triangle 9 0.0
| rectangle 16 0.0
|
| Divide by a MultiIndex by level.
|
| >>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
| ... 'degrees': [360, 180, 360, 360, 540, 720]},
| ... index=[['A', 'A', 'A', 'B', 'B', 'B'],
| ... ['circle', 'triangle', 'rectangle',
| ... 'square', 'pentagon', 'hexagon']])
| >>> df_multindex
| angles degrees
| A circle 0 360
| triangle 3 180
| rectangle 4 360
| B square 4 360
| pentagon 5 540
| hexagon 6 720
|
| >>> df.div(df_multindex, level=1, fill_value=0)
| angles degrees
| A circle NaN 1.0
| triangle 1.0 1.0
| rectangle 1.0 1.0
| B square 0.0 0.0
| pentagon 0.0 0.0
| hexagon 0.0 0.0
|
| rsub(self, other, axis: 'Axis' = 'columns', level=None, fill_value=None) -> 'DataFrame'
| Get Subtraction of dataframe and other, element-wise (binary operator `rsub`).
|
| Equivalent to ``other - dataframe``, but with support to substitute a fill_value
| for missing data in one of the inputs. With reverse version, `sub`.
|
| Among flexible wrappers (`add`, `sub`, `mul`, `div`, `floordiv`, `mod`, `pow`) to
| arithmetic operators: `+`, `-`, `*`, `/`, `//`, `%`, `**`.
|
| Parameters
| ----------
| other : scalar, sequence, Series, dict or DataFrame
| Any single or multiple element data structure, or list-like object.
| axis : {0 or 'index', 1 or 'columns'}
| Whether to compare by the index (0 or 'index') or columns.
| (1 or 'columns'). For Series input, axis to match Series index on.
| level : int or label
| Broadcast across a level, matching Index values on the
| passed MultiIndex level.
| fill_value : float or None, default None
| Fill existing missing (NaN) values, and any new element needed for
| successful DataFrame alignment, with this value before computation.
| If data in both corresponding DataFrame locations is missing
| the result will be missing.
|
| Returns
| -------
| DataFrame
| Result of the arithmetic operation.
|
| See Also
| --------
| DataFrame.add : Add DataFrames.
| DataFrame.sub : Subtract DataFrames.
| DataFrame.mul : Multiply DataFrames.
| DataFrame.div : Divide DataFrames (float division).
| DataFrame.truediv : Divide DataFrames (float division).
| DataFrame.floordiv : Divide DataFrames (integer division).
| DataFrame.mod : Calculate modulo (remainder after division).
| DataFrame.pow : Calculate exponential power.
|
| Notes
| -----
| Mismatched indices will be unioned together.
|
| Examples
| --------
| >>> df = pd.DataFrame({'angles': [0, 3, 4],
| ... 'degrees': [360, 180, 360]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> df
| angles degrees
| circle 0 360
| triangle 3 180
| rectangle 4 360
|
| Add a scalar with operator version which return the same
| results.
|
| >>> df + 1
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| >>> df.add(1)
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| Divide by constant with reverse version.
|
| >>> df.div(10)
| angles degrees
| circle 0.0 36.0
| triangle 0.3 18.0
| rectangle 0.4 36.0
|
| >>> df.rdiv(10)
| angles degrees
| circle inf 0.027778
| triangle 3.333333 0.055556
| rectangle 2.500000 0.027778
|
| Subtract a list and Series by axis with operator version.
|
| >>> df - [1, 2]
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub([1, 2], axis='columns')
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
| ... axis='index')
| angles degrees
| circle -1 359
| triangle 2 179
| rectangle 3 359
|
| Multiply a dictionary by axis.
|
| >>> df.mul({'angles': 0, 'degrees': 2})
| angles degrees
| circle 0 720
| triangle 0 360
| rectangle 0 720
|
| >>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
| angles degrees
| circle 0 0
| triangle 6 360
| rectangle 12 1080
|
| Multiply a DataFrame of different shape with operator version.
|
| >>> other = pd.DataFrame({'angles': [0, 3, 4]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> other
| angles
| circle 0
| triangle 3
| rectangle 4
|
| >>> df * other
| angles degrees
| circle 0 NaN
| triangle 9 NaN
| rectangle 16 NaN
|
| >>> df.mul(other, fill_value=0)
| angles degrees
| circle 0 0.0
| triangle 9 0.0
| rectangle 16 0.0
|
| Divide by a MultiIndex by level.
|
| >>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
| ... 'degrees': [360, 180, 360, 360, 540, 720]},
| ... index=[['A', 'A', 'A', 'B', 'B', 'B'],
| ... ['circle', 'triangle', 'rectangle',
| ... 'square', 'pentagon', 'hexagon']])
| >>> df_multindex
| angles degrees
| A circle 0 360
| triangle 3 180
| rectangle 4 360
| B square 4 360
| pentagon 5 540
| hexagon 6 720
|
| >>> df.div(df_multindex, level=1, fill_value=0)
| angles degrees
| A circle NaN 1.0
| triangle 1.0 1.0
| rectangle 1.0 1.0
| B square 0.0 0.0
| pentagon 0.0 0.0
| hexagon 0.0 0.0
|
| rtruediv(self, other, axis: 'Axis' = 'columns', level=None, fill_value=None) -> 'DataFrame'
| Get Floating division of dataframe and other, element-wise (binary operator `rtruediv`).
|
| Equivalent to ``other / dataframe``, but with support to substitute a fill_value
| for missing data in one of the inputs. With reverse version, `truediv`.
|
| Among flexible wrappers (`add`, `sub`, `mul`, `div`, `floordiv`, `mod`, `pow`) to
| arithmetic operators: `+`, `-`, `*`, `/`, `//`, `%`, `**`.
|
| Parameters
| ----------
| other : scalar, sequence, Series, dict or DataFrame
| Any single or multiple element data structure, or list-like object.
| axis : {0 or 'index', 1 or 'columns'}
| Whether to compare by the index (0 or 'index') or columns.
| (1 or 'columns'). For Series input, axis to match Series index on.
| level : int or label
| Broadcast across a level, matching Index values on the
| passed MultiIndex level.
| fill_value : float or None, default None
| Fill existing missing (NaN) values, and any new element needed for
| successful DataFrame alignment, with this value before computation.
| If data in both corresponding DataFrame locations is missing
| the result will be missing.
|
| Returns
| -------
| DataFrame
| Result of the arithmetic operation.
|
| See Also
| --------
| DataFrame.add : Add DataFrames.
| DataFrame.sub : Subtract DataFrames.
| DataFrame.mul : Multiply DataFrames.
| DataFrame.div : Divide DataFrames (float division).
| DataFrame.truediv : Divide DataFrames (float division).
| DataFrame.floordiv : Divide DataFrames (integer division).
| DataFrame.mod : Calculate modulo (remainder after division).
| DataFrame.pow : Calculate exponential power.
|
| Notes
| -----
| Mismatched indices will be unioned together.
|
| Examples
| --------
| >>> df = pd.DataFrame({'angles': [0, 3, 4],
| ... 'degrees': [360, 180, 360]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> df
| angles degrees
| circle 0 360
| triangle 3 180
| rectangle 4 360
|
| Add a scalar with operator version which return the same
| results.
|
| >>> df + 1
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| >>> df.add(1)
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| Divide by constant with reverse version.
|
| >>> df.div(10)
| angles degrees
| circle 0.0 36.0
| triangle 0.3 18.0
| rectangle 0.4 36.0
|
| >>> df.rdiv(10)
| angles degrees
| circle inf 0.027778
| triangle 3.333333 0.055556
| rectangle 2.500000 0.027778
|
| Subtract a list and Series by axis with operator version.
|
| >>> df - [1, 2]
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub([1, 2], axis='columns')
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
| ... axis='index')
| angles degrees
| circle -1 359
| triangle 2 179
| rectangle 3 359
|
| Multiply a dictionary by axis.
|
| >>> df.mul({'angles': 0, 'degrees': 2})
| angles degrees
| circle 0 720
| triangle 0 360
| rectangle 0 720
|
| >>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
| angles degrees
| circle 0 0
| triangle 6 360
| rectangle 12 1080
|
| Multiply a DataFrame of different shape with operator version.
|
| >>> other = pd.DataFrame({'angles': [0, 3, 4]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> other
| angles
| circle 0
| triangle 3
| rectangle 4
|
| >>> df * other
| angles degrees
| circle 0 NaN
| triangle 9 NaN
| rectangle 16 NaN
|
| >>> df.mul(other, fill_value=0)
| angles degrees
| circle 0 0.0
| triangle 9 0.0
| rectangle 16 0.0
|
| Divide by a MultiIndex by level.
|
| >>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
| ... 'degrees': [360, 180, 360, 360, 540, 720]},
| ... index=[['A', 'A', 'A', 'B', 'B', 'B'],
| ... ['circle', 'triangle', 'rectangle',
| ... 'square', 'pentagon', 'hexagon']])
| >>> df_multindex
| angles degrees
| A circle 0 360
| triangle 3 180
| rectangle 4 360
| B square 4 360
| pentagon 5 540
| hexagon 6 720
|
| >>> df.div(df_multindex, level=1, fill_value=0)
| angles degrees
| A circle NaN 1.0
| triangle 1.0 1.0
| rectangle 1.0 1.0
| B square 0.0 0.0
| pentagon 0.0 0.0
| hexagon 0.0 0.0
|
| select_dtypes(self, include=None, exclude=None) -> 'Self'
| Return a subset of the DataFrame's columns based on the column dtypes.
|
| Parameters
| ----------
| include, exclude : scalar or list-like
| A selection of dtypes or strings to be included/excluded. At least
| one of these parameters must be supplied.
|
| Returns
| -------
| DataFrame
| The subset of the frame including the dtypes in ``include`` and
| excluding the dtypes in ``exclude``.
|
| Raises
| ------
| ValueError
| * If both of ``include`` and ``exclude`` are empty
| * If ``include`` and ``exclude`` have overlapping elements
| * If any kind of string dtype is passed in.
|
| See Also
| --------
| DataFrame.dtypes: Return Series with the data type of each column.
|
| Notes
| -----
| * To select all *numeric* types, use ``np.number`` or ``'number'``
| * To select strings you must use the ``object`` dtype, but note that
| this will return *all* object dtype columns
| * See the `numpy dtype hierarchy
| <https://numpy.org/doc/stable/reference/arrays.scalars.html>`__
| * To select datetimes, use ``np.datetime64``, ``'datetime'`` or
| ``'datetime64'``
| * To select timedeltas, use ``np.timedelta64``, ``'timedelta'`` or
| ``'timedelta64'``
| * To select Pandas categorical dtypes, use ``'category'``
| * To select Pandas datetimetz dtypes, use ``'datetimetz'``
| or ``'datetime64[ns, tz]'``
|
| Examples
| --------
| >>> df = pd.DataFrame({'a': [1, 2] * 3,
| ... 'b': [True, False] * 3,
| ... 'c': [1.0, 2.0] * 3})
| >>> df
| a b c
| 0 1 True 1.0
| 1 2 False 2.0
| 2 1 True 1.0
| 3 2 False 2.0
| 4 1 True 1.0
| 5 2 False 2.0
|
| >>> df.select_dtypes(include='bool')
| b
| 0 True
| 1 False
| 2 True
| 3 False
| 4 True
| 5 False
|
| >>> df.select_dtypes(include=['float64'])
| c
| 0 1.0
| 1 2.0
| 2 1.0
| 3 2.0
| 4 1.0
| 5 2.0
|
| >>> df.select_dtypes(exclude=['int64'])
| b c
| 0 True 1.0
| 1 False 2.0
| 2 True 1.0
| 3 False 2.0
| 4 True 1.0
| 5 False 2.0
|
| sem(self, axis: 'Axis | None' = 0, skipna: 'bool' = True, ddof: 'int' = 1, numeric_only: 'bool' = False, **kwargs)
| Return unbiased standard error of the mean over requested axis.
|
| Normalized by N-1 by default. This can be changed using the ddof argument
|
| Parameters
| ----------
| axis : {index (0), columns (1)}
| For `Series` this parameter is unused and defaults to 0.
|
| .. warning::
|
| The behavior of DataFrame.sem with ``axis=None`` is deprecated,
| in a future version this will reduce over both axes and return a scalar
| To retain the old behavior, pass axis=0 (or do not pass axis).
|
| skipna : bool, default True
| Exclude NA/null values. If an entire row/column is NA, the result
| will be NA.
| ddof : int, default 1
| Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
| where N represents the number of elements.
| numeric_only : bool, default False
| Include only float, int, boolean columns. Not implemented for Series.
|
| Returns
| -------
| Series or DataFrame (if level specified)
|
| Examples
| --------
| >>> s = pd.Series([1, 2, 3])
| >>> s.sem().round(6)
| 0.57735
|
| With a DataFrame
|
| >>> df = pd.DataFrame({'a': [1, 2], 'b': [2, 3]}, index=['tiger', 'zebra'])
| >>> df
| a b
| tiger 1 2
| zebra 2 3
| >>> df.sem()
| a 0.5
| b 0.5
| dtype: float64
|
| Using axis=1
|
| >>> df.sem(axis=1)
| tiger 0.5
| zebra 0.5
| dtype: float64
|
| In this case, `numeric_only` should be set to `True`
| to avoid getting an error.
|
| >>> df = pd.DataFrame({'a': [1, 2], 'b': ['T', 'Z']},
| ... index=['tiger', 'zebra'])
| >>> df.sem(numeric_only=True)
| a 0.5
| dtype: float64
|
| set_axis(self, labels, *, axis: 'Axis' = 0, copy: 'bool | None' = None) -> 'DataFrame'
| Assign desired index to given axis.
|
| Indexes for column or row labels can be changed by assigning
| a list-like or Index.
|
| Parameters
| ----------
| labels : list-like, Index
| The values for the new index.
|
| axis : {0 or 'index', 1 or 'columns'}, default 0
| The axis to update. The value 0 identifies the rows. For `Series`
| this parameter is unused and defaults to 0.
|
| copy : bool, default True
| Whether to make a copy of the underlying data.
|
| .. note::
| The `copy` keyword will change behavior in pandas 3.0.
| `Copy-on-Write
| <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
| will be enabled by default, which means that all methods with a
| `copy` keyword will use a lazy copy mechanism to defer the copy and
| ignore the `copy` keyword. The `copy` keyword will be removed in a
| future version of pandas.
|
| You can already get the future behavior and improvements through
| enabling copy on write ``pd.options.mode.copy_on_write = True``
|
| Returns
| -------
| DataFrame
| An object of type DataFrame.
|
| See Also
| --------
| DataFrame.rename_axis : Alter the name of the index or columns.
|
| Examples
| --------
| >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
|
| Change the row labels.
|
| >>> df.set_axis(['a', 'b', 'c'], axis='index')
| A B
| a 1 4
| b 2 5
| c 3 6
|
| Change the column labels.
|
| >>> df.set_axis(['I', 'II'], axis='columns')
| I II
| 0 1 4
| 1 2 5
| 2 3 6
|
| set_index(self, keys, *, drop: 'bool' = True, append: 'bool' = False, inplace: 'bool' = False, verify_integrity: 'bool' = False) -> 'DataFrame | None'
| Set the DataFrame index using existing columns.
|
| Set the DataFrame index (row labels) using one or more existing
| columns or arrays (of the correct length). The index can replace the
| existing index or expand on it.
|
| Parameters
| ----------
| keys : label or array-like or list of labels/arrays
| This parameter can be either a single column key, a single array of
| the same length as the calling DataFrame, or a list containing an
| arbitrary combination of column keys and arrays. Here, "array"
| encompasses :class:`Series`, :class:`Index`, ``np.ndarray``, and
| instances of :class:`~collections.abc.Iterator`.
| drop : bool, default True
| Delete columns to be used as the new index.
| append : bool, default False
| Whether to append columns to existing index.
| inplace : bool, default False
| Whether to modify the DataFrame rather than creating a new one.
| verify_integrity : bool, default False
| Check the new index for duplicates. Otherwise defer the check until
| necessary. Setting to False will improve the performance of this
| method.
|
| Returns
| -------
| DataFrame or None
| Changed row labels or None if ``inplace=True``.
|
| See Also
| --------
| DataFrame.reset_index : Opposite of set_index.
| DataFrame.reindex : Change to new indices or expand indices.
| DataFrame.reindex_like : Change to same indices as other DataFrame.
|
| Examples
| --------
| >>> df = pd.DataFrame({'month': [1, 4, 7, 10],
| ... 'year': [2012, 2014, 2013, 2014],
| ... 'sale': [55, 40, 84, 31]})
| >>> df
| month year sale
| 0 1 2012 55
| 1 4 2014 40
| 2 7 2013 84
| 3 10 2014 31
|
| Set the index to become the 'month' column:
|
| >>> df.set_index('month')
| year sale
| month
| 1 2012 55
| 4 2014 40
| 7 2013 84
| 10 2014 31
|
| Create a MultiIndex using columns 'year' and 'month':
|
| >>> df.set_index(['year', 'month'])
| sale
| year month
| 2012 1 55
| 2014 4 40
| 2013 7 84
| 2014 10 31
|
| Create a MultiIndex using an Index and a column:
|
| >>> df.set_index([pd.Index([1, 2, 3, 4]), 'year'])
| month sale
| year
| 1 2012 1 55
| 2 2014 4 40
| 3 2013 7 84
| 4 2014 10 31
|
| Create a MultiIndex using two Series:
|
| >>> s = pd.Series([1, 2, 3, 4])
| >>> df.set_index([s, s**2])
| month year sale
| 1 1 1 2012 55
| 2 4 4 2014 40
| 3 9 7 2013 84
| 4 16 10 2014 31
|
| shift(self, periods: 'int | Sequence[int]' = 1, freq: 'Frequency | None' = None, axis: 'Axis' = 0, fill_value: 'Hashable' = <no_default>, suffix: 'str | None' = None) -> 'DataFrame'
| Shift index by desired number of periods with an optional time `freq`.
|
| When `freq` is not passed, shift the index without realigning the data.
| If `freq` is passed (in this case, the index must be date or datetime,
| or it will raise a `NotImplementedError`), the index will be
| increased using the periods and the `freq`. `freq` can be inferred
| when specified as "infer" as long as either freq or inferred_freq
| attribute is set in the index.
|
| Parameters
| ----------
| periods : int or Sequence
| Number of periods to shift. Can be positive or negative.
| If an iterable of ints, the data will be shifted once by each int.
| This is equivalent to shifting by one value at a time and
| concatenating all resulting frames. The resulting columns will have
| the shift suffixed to their column names. For multiple periods,
| axis must not be 1.
| freq : DateOffset, tseries.offsets, timedelta, or str, optional
| Offset to use from the tseries module or time rule (e.g. 'EOM').
| If `freq` is specified then the index values are shifted but the
| data is not realigned. That is, use `freq` if you would like to
| extend the index when shifting and preserve the original data.
| If `freq` is specified as "infer" then it will be inferred from
| the freq or inferred_freq attributes of the index. If neither of
| those attributes exist, a ValueError is thrown.
| axis : {0 or 'index', 1 or 'columns', None}, default None
| Shift direction. For `Series` this parameter is unused and defaults to 0.
| fill_value : object, optional
| The scalar value to use for newly introduced missing values.
| the default depends on the dtype of `self`.
| For numeric data, ``np.nan`` is used.
| For datetime, timedelta, or period data, etc. :attr:`NaT` is used.
| For extension dtypes, ``self.dtype.na_value`` is used.
| suffix : str, optional
| If str and periods is an iterable, this is added after the column
| name and before the shift value for each shifted column name.
|
| Returns
| -------
| DataFrame
| Copy of input object, shifted.
|
| See Also
| --------
| Index.shift : Shift values of Index.
| DatetimeIndex.shift : Shift values of DatetimeIndex.
| PeriodIndex.shift : Shift values of PeriodIndex.
|
| Examples
| --------
| >>> df = pd.DataFrame({"Col1": [10, 20, 15, 30, 45],
| ... "Col2": [13, 23, 18, 33, 48],
| ... "Col3": [17, 27, 22, 37, 52]},
| ... index=pd.date_range("2020-01-01", "2020-01-05"))
| >>> df
| Col1 Col2 Col3
| 2020-01-01 10 13 17
| 2020-01-02 20 23 27
| 2020-01-03 15 18 22
| 2020-01-04 30 33 37
| 2020-01-05 45 48 52
|
| >>> df.shift(periods=3)
| Col1 Col2 Col3
| 2020-01-01 NaN NaN NaN
| 2020-01-02 NaN NaN NaN
| 2020-01-03 NaN NaN NaN
| 2020-01-04 10.0 13.0 17.0
| 2020-01-05 20.0 23.0 27.0
|
| >>> df.shift(periods=1, axis="columns")
| Col1 Col2 Col3
| 2020-01-01 NaN 10 13
| 2020-01-02 NaN 20 23
| 2020-01-03 NaN 15 18
| 2020-01-04 NaN 30 33
| 2020-01-05 NaN 45 48
|
| >>> df.shift(periods=3, fill_value=0)
| Col1 Col2 Col3
| 2020-01-01 0 0 0
| 2020-01-02 0 0 0
| 2020-01-03 0 0 0
| 2020-01-04 10 13 17
| 2020-01-05 20 23 27
|
| >>> df.shift(periods=3, freq="D")
| Col1 Col2 Col3
| 2020-01-04 10 13 17
| 2020-01-05 20 23 27
| 2020-01-06 15 18 22
| 2020-01-07 30 33 37
| 2020-01-08 45 48 52
|
| >>> df.shift(periods=3, freq="infer")
| Col1 Col2 Col3
| 2020-01-04 10 13 17
| 2020-01-05 20 23 27
| 2020-01-06 15 18 22
| 2020-01-07 30 33 37
| 2020-01-08 45 48 52
|
| >>> df['Col1'].shift(periods=[0, 1, 2])
| Col1_0 Col1_1 Col1_2
| 2020-01-01 10 NaN NaN
| 2020-01-02 20 10.0 NaN
| 2020-01-03 15 20.0 10.0
| 2020-01-04 30 15.0 20.0
| 2020-01-05 45 30.0 15.0
|
| skew(self, axis: 'Axis | None' = 0, skipna: 'bool' = True, numeric_only: 'bool' = False, **kwargs)
| Return unbiased skew over requested axis.
|
| Normalized by N-1.
|
| Parameters
| ----------
| axis : {index (0), columns (1)}
| Axis for the function to be applied on.
| For `Series` this parameter is unused and defaults to 0.
|
| For DataFrames, specifying ``axis=None`` will apply the aggregation
| across both axes.
|
| .. versionadded:: 2.0.0
|
| skipna : bool, default True
| Exclude NA/null values when computing the result.
| numeric_only : bool, default False
| Include only float, int, boolean columns. Not implemented for Series.
|
| **kwargs
| Additional keyword arguments to be passed to the function.
|
| Returns
| -------
| Series or scalar
|
| Examples
| --------
| >>> s = pd.Series([1, 2, 3])
| >>> s.skew()
| 0.0
|
| With a DataFrame
|
| >>> df = pd.DataFrame({'a': [1, 2, 3], 'b': [2, 3, 4], 'c': [1, 3, 5]},
| ... index=['tiger', 'zebra', 'cow'])
| >>> df
| a b c
| tiger 1 2 1
| zebra 2 3 3
| cow 3 4 5
| >>> df.skew()
| a 0.0
| b 0.0
| c 0.0
| dtype: float64
|
| Using axis=1
|
| >>> df.skew(axis=1)
| tiger 1.732051
| zebra -1.732051
| cow 0.000000
| dtype: float64
|
| In this case, `numeric_only` should be set to `True` to avoid
| getting an error.
|
| >>> df = pd.DataFrame({'a': [1, 2, 3], 'b': ['T', 'Z', 'X']},
| ... index=['tiger', 'zebra', 'cow'])
| >>> df.skew(numeric_only=True)
| a 0.0
| dtype: float64
|
| sort_index(self, *, axis: 'Axis' = 0, level: 'IndexLabel | None' = None, ascending: 'bool | Sequence[bool]' = True, inplace: 'bool' = False, kind: 'SortKind' = 'quicksort', na_position: 'NaPosition' = 'last', sort_remaining: 'bool' = True, ignore_index: 'bool' = False, key: 'IndexKeyFunc | None' = None) -> 'DataFrame | None'
| Sort object by labels (along an axis).
|
| Returns a new DataFrame sorted by label if `inplace` argument is
| ``False``, otherwise updates the original DataFrame and returns None.
|
| Parameters
| ----------
| axis : {0 or 'index', 1 or 'columns'}, default 0
| The axis along which to sort. The value 0 identifies the rows,
| and 1 identifies the columns.
| level : int or level name or list of ints or list of level names
| If not None, sort on values in specified index level(s).
| ascending : bool or list-like of bools, default True
| Sort ascending vs. descending. When the index is a MultiIndex the
| sort direction can be controlled for each level individually.
| inplace : bool, default False
| Whether to modify the DataFrame rather than creating a new one.
| kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort'
| Choice of sorting algorithm. See also :func:`numpy.sort` for more
| information. `mergesort` and `stable` are the only stable algorithms. For
| DataFrames, this option is only applied when sorting on a single
| column or label.
| na_position : {'first', 'last'}, default 'last'
| Puts NaNs at the beginning if `first`; `last` puts NaNs at the end.
| Not implemented for MultiIndex.
| sort_remaining : bool, default True
| If True and sorting by level and index is multilevel, sort by other
| levels too (in order) after sorting by specified level.
| ignore_index : bool, default False
| If True, the resulting axis will be labeled 0, 1, …, n - 1.
| key : callable, optional
| If not None, apply the key function to the index values
| before sorting. This is similar to the `key` argument in the
| builtin :meth:`sorted` function, with the notable difference that
| this `key` function should be *vectorized*. It should expect an
| ``Index`` and return an ``Index`` of the same shape. For MultiIndex
| inputs, the key is applied *per level*.
|
| Returns
| -------
| DataFrame or None
| The original DataFrame sorted by the labels or None if ``inplace=True``.
|
| See Also
| --------
| Series.sort_index : Sort Series by the index.
| DataFrame.sort_values : Sort DataFrame by the value.
| Series.sort_values : Sort Series by the value.
|
| Examples
| --------
| >>> df = pd.DataFrame([1, 2, 3, 4, 5], index=[100, 29, 234, 1, 150],
| ... columns=['A'])
| >>> df.sort_index()
| A
| 1 4
| 29 2
| 100 1
| 150 5
| 234 3
|
| By default, it sorts in ascending order, to sort in descending order,
| use ``ascending=False``
|
| >>> df.sort_index(ascending=False)
| A
| 234 3
| 150 5
| 100 1
| 29 2
| 1 4
|
| A key function can be specified which is applied to the index before
| sorting. For a ``MultiIndex`` this is applied to each level separately.
|
| >>> df = pd.DataFrame({"a": [1, 2, 3, 4]}, index=['A', 'b', 'C', 'd'])
| >>> df.sort_index(key=lambda x: x.str.lower())
| a
| A 1
| b 2
| C 3
| d 4
|
| sort_values(self, by: 'IndexLabel', *, axis: 'Axis' = 0, ascending: 'bool | list[bool] | tuple[bool, ...]' = True, inplace: 'bool' = False, kind: 'SortKind' = 'quicksort', na_position: 'str' = 'last', ignore_index: 'bool' = False, key: 'ValueKeyFunc | None' = None) -> 'DataFrame | None'
| Sort by the values along either axis.
|
| Parameters
| ----------
| by : str or list of str
| Name or list of names to sort by.
|
| - if `axis` is 0 or `'index'` then `by` may contain index
| levels and/or column labels.
| - if `axis` is 1 or `'columns'` then `by` may contain column
| levels and/or index labels.
| axis : "{0 or 'index', 1 or 'columns'}", default 0
| Axis to be sorted.
| ascending : bool or list of bool, default True
| Sort ascending vs. descending. Specify list for multiple sort
| orders. If this is a list of bools, must match the length of
| the by.
| inplace : bool, default False
| If True, perform operation in-place.
| kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort'
| Choice of sorting algorithm. See also :func:`numpy.sort` for more
| information. `mergesort` and `stable` are the only stable algorithms. For
| DataFrames, this option is only applied when sorting on a single
| column or label.
| na_position : {'first', 'last'}, default 'last'
| Puts NaNs at the beginning if `first`; `last` puts NaNs at the
| end.
| ignore_index : bool, default False
| If True, the resulting axis will be labeled 0, 1, …, n - 1.
| key : callable, optional
| Apply the key function to the values
| before sorting. This is similar to the `key` argument in the
| builtin :meth:`sorted` function, with the notable difference that
| this `key` function should be *vectorized*. It should expect a
| ``Series`` and return a Series with the same shape as the input.
| It will be applied to each column in `by` independently.
|
| Returns
| -------
| DataFrame or None
| DataFrame with sorted values or None if ``inplace=True``.
|
| See Also
| --------
| DataFrame.sort_index : Sort a DataFrame by the index.
| Series.sort_values : Similar method for a Series.
|
| Examples
| --------
| >>> df = pd.DataFrame({
| ... 'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],
| ... 'col2': [2, 1, 9, 8, 7, 4],
| ... 'col3': [0, 1, 9, 4, 2, 3],
| ... 'col4': ['a', 'B', 'c', 'D', 'e', 'F']
| ... })
| >>> df
| col1 col2 col3 col4
| 0 A 2 0 a
| 1 A 1 1 B
| 2 B 9 9 c
| 3 NaN 8 4 D
| 4 D 7 2 e
| 5 C 4 3 F
|
| Sort by col1
|
| >>> df.sort_values(by=['col1'])
| col1 col2 col3 col4
| 0 A 2 0 a
| 1 A 1 1 B
| 2 B 9 9 c
| 5 C 4 3 F
| 4 D 7 2 e
| 3 NaN 8 4 D
|
| Sort by multiple columns
|
| >>> df.sort_values(by=['col1', 'col2'])
| col1 col2 col3 col4
| 1 A 1 1 B
| 0 A 2 0 a
| 2 B 9 9 c
| 5 C 4 3 F
| 4 D 7 2 e
| 3 NaN 8 4 D
|
| Sort Descending
|
| >>> df.sort_values(by='col1', ascending=False)
| col1 col2 col3 col4
| 4 D 7 2 e
| 5 C 4 3 F
| 2 B 9 9 c
| 0 A 2 0 a
| 1 A 1 1 B
| 3 NaN 8 4 D
|
| Putting NAs first
|
| >>> df.sort_values(by='col1', ascending=False, na_position='first')
| col1 col2 col3 col4
| 3 NaN 8 4 D
| 4 D 7 2 e
| 5 C 4 3 F
| 2 B 9 9 c
| 0 A 2 0 a
| 1 A 1 1 B
|
| Sorting with a key function
|
| >>> df.sort_values(by='col4', key=lambda col: col.str.lower())
| col1 col2 col3 col4
| 0 A 2 0 a
| 1 A 1 1 B
| 2 B 9 9 c
| 3 NaN 8 4 D
| 4 D 7 2 e
| 5 C 4 3 F
|
| Natural sort with the key argument,
| using the `natsort <https://github.com/SethMMorton/natsort>` package.
|
| >>> df = pd.DataFrame({
| ... "time": ['0hr', '128hr', '72hr', '48hr', '96hr'],
| ... "value": [10, 20, 30, 40, 50]
| ... })
| >>> df
| time value
| 0 0hr 10
| 1 128hr 20
| 2 72hr 30
| 3 48hr 40
| 4 96hr 50
| >>> from natsort import index_natsorted
| >>> df.sort_values(
| ... by="time",
| ... key=lambda x: np.argsort(index_natsorted(df["time"]))
| ... )
| time value
| 0 0hr 10
| 3 48hr 40
| 2 72hr 30
| 4 96hr 50
| 1 128hr 20
|
| stack(self, level: 'IndexLabel' = -1, dropna: 'bool | lib.NoDefault' = <no_default>, sort: 'bool | lib.NoDefault' = <no_default>, future_stack: 'bool' = False)
| Stack the prescribed level(s) from columns to index.
|
| Return a reshaped DataFrame or Series having a multi-level
| index with one or more new inner-most levels compared to the current
| DataFrame. The new inner-most levels are created by pivoting the
| columns of the current dataframe:
|
| - if the columns have a single level, the output is a Series;
| - if the columns have multiple levels, the new index
| level(s) is (are) taken from the prescribed level(s) and
| the output is a DataFrame.
|
| Parameters
| ----------
| level : int, str, list, default -1
| Level(s) to stack from the column axis onto the index
| axis, defined as one index or label, or a list of indices
| or labels.
| dropna : bool, default True
| Whether to drop rows in the resulting Frame/Series with
| missing values. Stacking a column level onto the index
| axis can create combinations of index and column values
| that are missing from the original dataframe. See Examples
| section.
| sort : bool, default True
| Whether to sort the levels of the resulting MultiIndex.
| future_stack : bool, default False
| Whether to use the new implementation that will replace the current
| implementation in pandas 3.0. When True, dropna and sort have no impact
| on the result and must remain unspecified. See :ref:`pandas 2.1.0 Release
| notes <whatsnew_210.enhancements.new_stack>` for more details.
|
| Returns
| -------
| DataFrame or Series
| Stacked dataframe or series.
|
| See Also
| --------
| DataFrame.unstack : Unstack prescribed level(s) from index axis
| onto column axis.
| DataFrame.pivot : Reshape dataframe from long format to wide
| format.
| DataFrame.pivot_table : Create a spreadsheet-style pivot table
| as a DataFrame.
|
| Notes
| -----
| The function is named by analogy with a collection of books
| being reorganized from being side by side on a horizontal
| position (the columns of the dataframe) to being stacked
| vertically on top of each other (in the index of the
| dataframe).
|
| Reference :ref:`the user guide <reshaping.stacking>` for more examples.
|
| Examples
| --------
| **Single level columns**
|
| >>> df_single_level_cols = pd.DataFrame([[0, 1], [2, 3]],
| ... index=['cat', 'dog'],
| ... columns=['weight', 'height'])
|
| Stacking a dataframe with a single level column axis returns a Series:
|
| >>> df_single_level_cols
| weight height
| cat 0 1
| dog 2 3
| >>> df_single_level_cols.stack(future_stack=True)
| cat weight 0
| height 1
| dog weight 2
| height 3
| dtype: int64
|
| **Multi level columns: simple case**
|
| >>> multicol1 = pd.MultiIndex.from_tuples([('weight', 'kg'),
| ... ('weight', 'pounds')])
| >>> df_multi_level_cols1 = pd.DataFrame([[1, 2], [2, 4]],
| ... index=['cat', 'dog'],
| ... columns=multicol1)
|
| Stacking a dataframe with a multi-level column axis:
|
| >>> df_multi_level_cols1
| weight
| kg pounds
| cat 1 2
| dog 2 4
| >>> df_multi_level_cols1.stack(future_stack=True)
| weight
| cat kg 1
| pounds 2
| dog kg 2
| pounds 4
|
| **Missing values**
|
| >>> multicol2 = pd.MultiIndex.from_tuples([('weight', 'kg'),
| ... ('height', 'm')])
| >>> df_multi_level_cols2 = pd.DataFrame([[1.0, 2.0], [3.0, 4.0]],
| ... index=['cat', 'dog'],
| ... columns=multicol2)
|
| It is common to have missing values when stacking a dataframe
| with multi-level columns, as the stacked dataframe typically
| has more values than the original dataframe. Missing values
| are filled with NaNs:
|
| >>> df_multi_level_cols2
| weight height
| kg m
| cat 1.0 2.0
| dog 3.0 4.0
| >>> df_multi_level_cols2.stack(future_stack=True)
| weight height
| cat kg 1.0 NaN
| m NaN 2.0
| dog kg 3.0 NaN
| m NaN 4.0
|
| **Prescribing the level(s) to be stacked**
|
| The first parameter controls which level or levels are stacked:
|
| >>> df_multi_level_cols2.stack(0, future_stack=True)
| kg m
| cat weight 1.0 NaN
| height NaN 2.0
| dog weight 3.0 NaN
| height NaN 4.0
| >>> df_multi_level_cols2.stack([0, 1], future_stack=True)
| cat weight kg 1.0
| height m 2.0
| dog weight kg 3.0
| height m 4.0
| dtype: float64
|
| std(self, axis: 'Axis | None' = 0, skipna: 'bool' = True, ddof: 'int' = 1, numeric_only: 'bool' = False, **kwargs)
| Return sample standard deviation over requested axis.
|
| Normalized by N-1 by default. This can be changed using the ddof argument.
|
| Parameters
| ----------
| axis : {index (0), columns (1)}
| For `Series` this parameter is unused and defaults to 0.
|
| .. warning::
|
| The behavior of DataFrame.std with ``axis=None`` is deprecated,
| in a future version this will reduce over both axes and return a scalar
| To retain the old behavior, pass axis=0 (or do not pass axis).
|
| skipna : bool, default True
| Exclude NA/null values. If an entire row/column is NA, the result
| will be NA.
| ddof : int, default 1
| Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
| where N represents the number of elements.
| numeric_only : bool, default False
| Include only float, int, boolean columns. Not implemented for Series.
|
| Returns
| -------
| Series or DataFrame (if level specified)
|
| Notes
| -----
| To have the same behaviour as `numpy.std`, use `ddof=0` (instead of the
| default `ddof=1`)
|
| Examples
| --------
| >>> df = pd.DataFrame({'person_id': [0, 1, 2, 3],
| ... 'age': [21, 25, 62, 43],
| ... 'height': [1.61, 1.87, 1.49, 2.01]}
| ... ).set_index('person_id')
| >>> df
| age height
| person_id
| 0 21 1.61
| 1 25 1.87
| 2 62 1.49
| 3 43 2.01
|
| The standard deviation of the columns can be found as follows:
|
| >>> df.std()
| age 18.786076
| height 0.237417
| dtype: float64
|
| Alternatively, `ddof=0` can be set to normalize by N instead of N-1:
|
| >>> df.std(ddof=0)
| age 16.269219
| height 0.205609
| dtype: float64
|
| sub(self, other, axis: 'Axis' = 'columns', level=None, fill_value=None) -> 'DataFrame'
| Get Subtraction of dataframe and other, element-wise (binary operator `sub`).
|
| Equivalent to ``dataframe - other``, but with support to substitute a fill_value
| for missing data in one of the inputs. With reverse version, `rsub`.
|
| Among flexible wrappers (`add`, `sub`, `mul`, `div`, `floordiv`, `mod`, `pow`) to
| arithmetic operators: `+`, `-`, `*`, `/`, `//`, `%`, `**`.
|
| Parameters
| ----------
| other : scalar, sequence, Series, dict or DataFrame
| Any single or multiple element data structure, or list-like object.
| axis : {0 or 'index', 1 or 'columns'}
| Whether to compare by the index (0 or 'index') or columns.
| (1 or 'columns'). For Series input, axis to match Series index on.
| level : int or label
| Broadcast across a level, matching Index values on the
| passed MultiIndex level.
| fill_value : float or None, default None
| Fill existing missing (NaN) values, and any new element needed for
| successful DataFrame alignment, with this value before computation.
| If data in both corresponding DataFrame locations is missing
| the result will be missing.
|
| Returns
| -------
| DataFrame
| Result of the arithmetic operation.
|
| See Also
| --------
| DataFrame.add : Add DataFrames.
| DataFrame.sub : Subtract DataFrames.
| DataFrame.mul : Multiply DataFrames.
| DataFrame.div : Divide DataFrames (float division).
| DataFrame.truediv : Divide DataFrames (float division).
| DataFrame.floordiv : Divide DataFrames (integer division).
| DataFrame.mod : Calculate modulo (remainder after division).
| DataFrame.pow : Calculate exponential power.
|
| Notes
| -----
| Mismatched indices will be unioned together.
|
| Examples
| --------
| >>> df = pd.DataFrame({'angles': [0, 3, 4],
| ... 'degrees': [360, 180, 360]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> df
| angles degrees
| circle 0 360
| triangle 3 180
| rectangle 4 360
|
| Add a scalar with operator version which return the same
| results.
|
| >>> df + 1
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| >>> df.add(1)
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| Divide by constant with reverse version.
|
| >>> df.div(10)
| angles degrees
| circle 0.0 36.0
| triangle 0.3 18.0
| rectangle 0.4 36.0
|
| >>> df.rdiv(10)
| angles degrees
| circle inf 0.027778
| triangle 3.333333 0.055556
| rectangle 2.500000 0.027778
|
| Subtract a list and Series by axis with operator version.
|
| >>> df - [1, 2]
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub([1, 2], axis='columns')
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
| ... axis='index')
| angles degrees
| circle -1 359
| triangle 2 179
| rectangle 3 359
|
| Multiply a dictionary by axis.
|
| >>> df.mul({'angles': 0, 'degrees': 2})
| angles degrees
| circle 0 720
| triangle 0 360
| rectangle 0 720
|
| >>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
| angles degrees
| circle 0 0
| triangle 6 360
| rectangle 12 1080
|
| Multiply a DataFrame of different shape with operator version.
|
| >>> other = pd.DataFrame({'angles': [0, 3, 4]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> other
| angles
| circle 0
| triangle 3
| rectangle 4
|
| >>> df * other
| angles degrees
| circle 0 NaN
| triangle 9 NaN
| rectangle 16 NaN
|
| >>> df.mul(other, fill_value=0)
| angles degrees
| circle 0 0.0
| triangle 9 0.0
| rectangle 16 0.0
|
| Divide by a MultiIndex by level.
|
| >>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
| ... 'degrees': [360, 180, 360, 360, 540, 720]},
| ... index=[['A', 'A', 'A', 'B', 'B', 'B'],
| ... ['circle', 'triangle', 'rectangle',
| ... 'square', 'pentagon', 'hexagon']])
| >>> df_multindex
| angles degrees
| A circle 0 360
| triangle 3 180
| rectangle 4 360
| B square 4 360
| pentagon 5 540
| hexagon 6 720
|
| >>> df.div(df_multindex, level=1, fill_value=0)
| angles degrees
| A circle NaN 1.0
| triangle 1.0 1.0
| rectangle 1.0 1.0
| B square 0.0 0.0
| pentagon 0.0 0.0
| hexagon 0.0 0.0
|
| subtract = sub(self, other, axis: 'Axis' = 'columns', level=None, fill_value=None) -> 'DataFrame'
|
| sum(self, axis: 'Axis | None' = 0, skipna: 'bool' = True, numeric_only: 'bool' = False, min_count: 'int' = 0, **kwargs)
| Return the sum of the values over the requested axis.
|
| This is equivalent to the method ``numpy.sum``.
|
| Parameters
| ----------
| axis : {index (0), columns (1)}
| Axis for the function to be applied on.
| For `Series` this parameter is unused and defaults to 0.
|
| .. warning::
|
| The behavior of DataFrame.sum with ``axis=None`` is deprecated,
| in a future version this will reduce over both axes and return a scalar
| To retain the old behavior, pass axis=0 (or do not pass axis).
|
| .. versionadded:: 2.0.0
|
| skipna : bool, default True
| Exclude NA/null values when computing the result.
| numeric_only : bool, default False
| Include only float, int, boolean columns. Not implemented for Series.
|
| min_count : int, default 0
| The required number of valid values to perform the operation. If fewer than
| ``min_count`` non-NA values are present the result will be NA.
| **kwargs
| Additional keyword arguments to be passed to the function.
|
| Returns
| -------
| Series or scalar
|
| See Also
| --------
| Series.sum : Return the sum.
| Series.min : Return the minimum.
| Series.max : Return the maximum.
| Series.idxmin : Return the index of the minimum.
| Series.idxmax : Return the index of the maximum.
| DataFrame.sum : Return the sum over the requested axis.
| DataFrame.min : Return the minimum over the requested axis.
| DataFrame.max : Return the maximum over the requested axis.
| DataFrame.idxmin : Return the index of the minimum over the requested axis.
| DataFrame.idxmax : Return the index of the maximum over the requested axis.
|
| Examples
| --------
| >>> idx = pd.MultiIndex.from_arrays([
| ... ['warm', 'warm', 'cold', 'cold'],
| ... ['dog', 'falcon', 'fish', 'spider']],
| ... names=['blooded', 'animal'])
| >>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
| >>> s
| blooded animal
| warm dog 4
| falcon 2
| cold fish 0
| spider 8
| Name: legs, dtype: int64
|
| >>> s.sum()
| 14
|
| By default, the sum of an empty or all-NA Series is ``0``.
|
| >>> pd.Series([], dtype="float64").sum() # min_count=0 is the default
| 0.0
|
| This can be controlled with the ``min_count`` parameter. For example, if
| you'd like the sum of an empty series to be NaN, pass ``min_count=1``.
|
| >>> pd.Series([], dtype="float64").sum(min_count=1)
| nan
|
| Thanks to the ``skipna`` parameter, ``min_count`` handles all-NA and
| empty series identically.
|
| >>> pd.Series([np.nan]).sum()
| 0.0
|
| >>> pd.Series([np.nan]).sum(min_count=1)
| nan
|
| swaplevel(self, i: 'Axis' = -2, j: 'Axis' = -1, axis: 'Axis' = 0) -> 'DataFrame'
| Swap levels i and j in a :class:`MultiIndex`.
|
| Default is to swap the two innermost levels of the index.
|
| Parameters
| ----------
| i, j : int or str
| Levels of the indices to be swapped. Can pass level name as string.
| axis : {0 or 'index', 1 or 'columns'}, default 0
| The axis to swap levels on. 0 or 'index' for row-wise, 1 or
| 'columns' for column-wise.
|
| Returns
| -------
| DataFrame
| DataFrame with levels swapped in MultiIndex.
|
| Examples
| --------
| >>> df = pd.DataFrame(
| ... {"Grade": ["A", "B", "A", "C"]},
| ... index=[
| ... ["Final exam", "Final exam", "Coursework", "Coursework"],
| ... ["History", "Geography", "History", "Geography"],
| ... ["January", "February", "March", "April"],
| ... ],
| ... )
| >>> df
| Grade
| Final exam History January A
| Geography February B
| Coursework History March A
| Geography April C
|
| In the following example, we will swap the levels of the indices.
| Here, we will swap the levels column-wise, but levels can be swapped row-wise
| in a similar manner. Note that column-wise is the default behaviour.
| By not supplying any arguments for i and j, we swap the last and second to
| last indices.
|
| >>> df.swaplevel()
| Grade
| Final exam January History A
| February Geography B
| Coursework March History A
| April Geography C
|
| By supplying one argument, we can choose which index to swap the last
| index with. We can for example swap the first index with the last one as
| follows.
|
| >>> df.swaplevel(0)
| Grade
| January History Final exam A
| February Geography Final exam B
| March History Coursework A
| April Geography Coursework C
|
| We can also define explicitly which indices we want to swap by supplying values
| for both i and j. Here, we for example swap the first and second indices.
|
| >>> df.swaplevel(0, 1)
| Grade
| History Final exam January A
| Geography Final exam February B
| History Coursework March A
| Geography Coursework April C
|
| to_dict(self, orient: "Literal['dict', 'list', 'series', 'split', 'tight', 'records', 'index']" = 'dict', *, into: 'type[MutableMappingT] | MutableMappingT' = <class 'dict'>, index: 'bool' = True) -> 'MutableMappingT | list[MutableMappingT]'
| Convert the DataFrame to a dictionary.
|
| The type of the key-value pairs can be customized with the parameters
| (see below).
|
| Parameters
| ----------
| orient : str {'dict', 'list', 'series', 'split', 'tight', 'records', 'index'}
| Determines the type of the values of the dictionary.
|
| - 'dict' (default) : dict like {column -> {index -> value}}
| - 'list' : dict like {column -> [values]}
| - 'series' : dict like {column -> Series(values)}
| - 'split' : dict like
| {'index' -> [index], 'columns' -> [columns], 'data' -> [values]}
| - 'tight' : dict like
| {'index' -> [index], 'columns' -> [columns], 'data' -> [values],
| 'index_names' -> [index.names], 'column_names' -> [column.names]}
| - 'records' : list like
| [{column -> value}, ... , {column -> value}]
| - 'index' : dict like {index -> {column -> value}}
|
| .. versionadded:: 1.4.0
| 'tight' as an allowed value for the ``orient`` argument
|
| into : class, default dict
| The collections.abc.MutableMapping subclass used for all Mappings
| in the return value. Can be the actual class or an empty
| instance of the mapping type you want. If you want a
| collections.defaultdict, you must pass it initialized.
|
| index : bool, default True
| Whether to include the index item (and index_names item if `orient`
| is 'tight') in the returned dictionary. Can only be ``False``
| when `orient` is 'split' or 'tight'.
|
| .. versionadded:: 2.0.0
|
| Returns
| -------
| dict, list or collections.abc.MutableMapping
| Return a collections.abc.MutableMapping object representing the
| DataFrame. The resulting transformation depends on the `orient`
| parameter.
|
| See Also
| --------
| DataFrame.from_dict: Create a DataFrame from a dictionary.
| DataFrame.to_json: Convert a DataFrame to JSON format.
|
| Examples
| --------
| >>> df = pd.DataFrame({'col1': [1, 2],
| ... 'col2': [0.5, 0.75]},
| ... index=['row1', 'row2'])
| >>> df
| col1 col2
| row1 1 0.50
| row2 2 0.75
| >>> df.to_dict()
| {'col1': {'row1': 1, 'row2': 2}, 'col2': {'row1': 0.5, 'row2': 0.75}}
|
| You can specify the return orientation.
|
| >>> df.to_dict('series')
| {'col1': row1 1
| row2 2
| Name: col1, dtype: int64,
| 'col2': row1 0.50
| row2 0.75
| Name: col2, dtype: float64}
|
| >>> df.to_dict('split')
| {'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
| 'data': [[1, 0.5], [2, 0.75]]}
|
| >>> df.to_dict('records')
| [{'col1': 1, 'col2': 0.5}, {'col1': 2, 'col2': 0.75}]
|
| >>> df.to_dict('index')
| {'row1': {'col1': 1, 'col2': 0.5}, 'row2': {'col1': 2, 'col2': 0.75}}
|
| >>> df.to_dict('tight')
| {'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
| 'data': [[1, 0.5], [2, 0.75]], 'index_names': [None], 'column_names': [None]}
|
| You can also specify the mapping type.
|
| >>> from collections import OrderedDict, defaultdict
| >>> df.to_dict(into=OrderedDict)
| OrderedDict([('col1', OrderedDict([('row1', 1), ('row2', 2)])),
| ('col2', OrderedDict([('row1', 0.5), ('row2', 0.75)]))])
|
| If you want a `defaultdict`, you need to initialize it:
|
| >>> dd = defaultdict(list)
| >>> df.to_dict('records', into=dd)
| [defaultdict(<class 'list'>, {'col1': 1, 'col2': 0.5}),
| defaultdict(<class 'list'>, {'col1': 2, 'col2': 0.75})]
|
| to_feather(self, path: 'FilePath | WriteBuffer[bytes]', **kwargs) -> 'None'
| Write a DataFrame to the binary Feather format.
|
| Parameters
| ----------
| path : str, path object, file-like object
| String, path object (implementing ``os.PathLike[str]``), or file-like
| object implementing a binary ``write()`` function. If a string or a path,
| it will be used as Root Directory path when writing a partitioned dataset.
| **kwargs :
| Additional keywords passed to :func:`pyarrow.feather.write_feather`.
| This includes the `compression`, `compression_level`, `chunksize`
| and `version` keywords.
|
| Notes
| -----
| This function writes the dataframe as a `feather file
| <https://arrow.apache.org/docs/python/feather.html>`_. Requires a default
| index. For saving the DataFrame with your custom index use a method that
| supports custom indices e.g. `to_parquet`.
|
| Examples
| --------
| >>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]])
| >>> df.to_feather("file.feather") # doctest: +SKIP
|
| to_gbq(self, destination_table: 'str', *, project_id: 'str | None' = None, chunksize: 'int | None' = None, reauth: 'bool' = False, if_exists: 'ToGbqIfexist' = 'fail', auth_local_webserver: 'bool' = True, table_schema: 'list[dict[str, str]] | None' = None, location: 'str | None' = None, progress_bar: 'bool' = True, credentials=None) -> 'None'
| Write a DataFrame to a Google BigQuery table.
|
| .. deprecated:: 2.2.0
|
| Please use ``pandas_gbq.to_gbq`` instead.
|
| This function requires the `pandas-gbq package
| <https://pandas-gbq.readthedocs.io>`__.
|
| See the `How to authenticate with Google BigQuery
| <https://pandas-gbq.readthedocs.io/en/latest/howto/authentication.html>`__
| guide for authentication instructions.
|
| Parameters
| ----------
| destination_table : str
| Name of table to be written, in the form ``dataset.tablename``.
| project_id : str, optional
| Google BigQuery Account project ID. Optional when available from
| the environment.
| chunksize : int, optional
| Number of rows to be inserted in each chunk from the dataframe.
| Set to ``None`` to load the whole dataframe at once.
| reauth : bool, default False
| Force Google BigQuery to re-authenticate the user. This is useful
| if multiple accounts are used.
| if_exists : str, default 'fail'
| Behavior when the destination table exists. Value can be one of:
|
| ``'fail'``
| If table exists raise pandas_gbq.gbq.TableCreationError.
| ``'replace'``
| If table exists, drop it, recreate it, and insert data.
| ``'append'``
| If table exists, insert data. Create if does not exist.
| auth_local_webserver : bool, default True
| Use the `local webserver flow`_ instead of the `console flow`_
| when getting user credentials.
|
| .. _local webserver flow:
| https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_local_server
| .. _console flow:
| https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_console
|
| *New in version 0.2.0 of pandas-gbq*.
|
| .. versionchanged:: 1.5.0
| Default value is changed to ``True``. Google has deprecated the
| ``auth_local_webserver = False`` `"out of band" (copy-paste)
| flow
| <https://developers.googleblog.com/2022/02/making-oauth-flows-safer.html?m=1#disallowed-oob>`_.
| table_schema : list of dicts, optional
| List of BigQuery table fields to which according DataFrame
| columns conform to, e.g. ``[{'name': 'col1', 'type':
| 'STRING'},...]``. If schema is not provided, it will be
| generated according to dtypes of DataFrame columns. See
| BigQuery API documentation on available names of a field.
|
| *New in version 0.3.1 of pandas-gbq*.
| location : str, optional
| Location where the load job should run. See the `BigQuery locations
| documentation
| <https://cloud.google.com/bigquery/docs/dataset-locations>`__ for a
| list of available locations. The location must match that of the
| target dataset.
|
| *New in version 0.5.0 of pandas-gbq*.
| progress_bar : bool, default True
| Use the library `tqdm` to show the progress bar for the upload,
| chunk by chunk.
|
| *New in version 0.5.0 of pandas-gbq*.
| credentials : google.auth.credentials.Credentials, optional
| Credentials for accessing Google APIs. Use this parameter to
| override default credentials, such as to use Compute Engine
| :class:`google.auth.compute_engine.Credentials` or Service
| Account :class:`google.oauth2.service_account.Credentials`
| directly.
|
| *New in version 0.8.0 of pandas-gbq*.
|
| See Also
| --------
| pandas_gbq.to_gbq : This function in the pandas-gbq library.
| read_gbq : Read a DataFrame from Google BigQuery.
|
| Examples
| --------
| Example taken from `Google BigQuery documentation
| <https://cloud.google.com/bigquery/docs/samples/bigquery-pandas-gbq-to-gbq-simple>`_
|
| >>> project_id = "my-project"
| >>> table_id = 'my_dataset.my_table'
| >>> df = pd.DataFrame({
| ... "my_string": ["a", "b", "c"],
| ... "my_int64": [1, 2, 3],
| ... "my_float64": [4.0, 5.0, 6.0],
| ... "my_bool1": [True, False, True],
| ... "my_bool2": [False, True, False],
| ... "my_dates": pd.date_range("now", periods=3),
| ... }
| ... )
|
| >>> df.to_gbq(table_id, project_id=project_id) # doctest: +SKIP
|
| to_html(self, buf: 'FilePath | WriteBuffer[str] | None' = None, *, columns: 'Axes | None' = None, col_space: 'ColspaceArgType | None' = None, header: 'bool' = True, index: 'bool' = True, na_rep: 'str' = 'NaN', formatters: 'FormattersType | None' = None, float_format: 'FloatFormatType | None' = None, sparsify: 'bool | None' = None, index_names: 'bool' = True, justify: 'str | None' = None, max_rows: 'int | None' = None, max_cols: 'int | None' = None, show_dimensions: 'bool | str' = False, decimal: 'str' = '.', bold_rows: 'bool' = True, classes: 'str | list | tuple | None' = None, escape: 'bool' = True, notebook: 'bool' = False, border: 'int | bool | None' = None, table_id: 'str | None' = None, render_links: 'bool' = False, encoding: 'str | None' = None) -> 'str | None'
| Render a DataFrame as an HTML table.
|
| Parameters
| ----------
| buf : str, Path or StringIO-like, optional, default None
| Buffer to write to. If None, the output is returned as a string.
| columns : array-like, optional, default None
| The subset of columns to write. Writes all columns by default.
| col_space : str or int, list or dict of int or str, optional
| The minimum width of each column in CSS length units. An int is assumed to be px units..
| header : bool, optional
| Whether to print column labels, default True.
| index : bool, optional, default True
| Whether to print index (row) labels.
| na_rep : str, optional, default 'NaN'
| String representation of ``NaN`` to use.
| formatters : list, tuple or dict of one-param. functions, optional
| Formatter functions to apply to columns' elements by position or
| name.
| The result of each function must be a unicode string.
| List/tuple must be of length equal to the number of columns.
| float_format : one-parameter function, optional, default None
| Formatter function to apply to columns' elements if they are
| floats. This function must return a unicode string and will be
| applied only to the non-``NaN`` elements, with ``NaN`` being
| handled by ``na_rep``.
| sparsify : bool, optional, default True
| Set to False for a DataFrame with a hierarchical index to print
| every multiindex key at each row.
| index_names : bool, optional, default True
| Prints the names of the indexes.
| justify : str, default None
| How to justify the column labels. If None uses the option from
| the print configuration (controlled by set_option), 'right' out
| of the box. Valid values are
|
| * left
| * right
| * center
| * justify
| * justify-all
| * start
| * end
| * inherit
| * match-parent
| * initial
| * unset.
| max_rows : int, optional
| Maximum number of rows to display in the console.
| max_cols : int, optional
| Maximum number of columns to display in the console.
| show_dimensions : bool, default False
| Display DataFrame dimensions (number of rows by number of columns).
| decimal : str, default '.'
| Character recognized as decimal separator, e.g. ',' in Europe.
|
| bold_rows : bool, default True
| Make the row labels bold in the output.
| classes : str or list or tuple, default None
| CSS class(es) to apply to the resulting html table.
| escape : bool, default True
| Convert the characters <, >, and & to HTML-safe sequences.
| notebook : {True, False}, default False
| Whether the generated HTML is for IPython Notebook.
| border : int
| A ``border=border`` attribute is included in the opening
| `<table>` tag. Default ``pd.options.display.html.border``.
| table_id : str, optional
| A css id is included in the opening `<table>` tag if specified.
| render_links : bool, default False
| Convert URLs to HTML links.
| encoding : str, default "utf-8"
| Set character encoding.
|
| Returns
| -------
| str or None
| If buf is None, returns the result as a string. Otherwise returns
| None.
|
| See Also
| --------
| to_string : Convert DataFrame to a string.
|
| Examples
| --------
| >>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [4, 3]})
| >>> html_string = '''<table border="1" class="dataframe">
| ... <thead>
| ... <tr style="text-align: right;">
| ... <th></th>
| ... <th>col1</th>
| ... <th>col2</th>
| ... </tr>
| ... </thead>
| ... <tbody>
| ... <tr>
| ... <th>0</th>
| ... <td>1</td>
| ... <td>4</td>
| ... </tr>
| ... <tr>
| ... <th>1</th>
| ... <td>2</td>
| ... <td>3</td>
| ... </tr>
| ... </tbody>
| ... </table>'''
| >>> assert html_string == df.to_html()
|
| to_markdown(self, buf: 'FilePath | WriteBuffer[str] | None' = None, *, mode: 'str' = 'wt', index: 'bool' = True, storage_options: 'StorageOptions | None' = None, **kwargs) -> 'str | None'
| Print DataFrame in Markdown-friendly format.
|
| Parameters
| ----------
| buf : str, Path or StringIO-like, optional, default None
| Buffer to write to. If None, the output is returned as a string.
| mode : str, optional
| Mode in which file is opened, "wt" by default.
| index : bool, optional, default True
| Add index (row) labels.
|
| storage_options : dict, optional
| Extra options that make sense for a particular storage connection, e.g.
| host, port, username, password, etc. For HTTP(S) URLs the key-value pairs
| are forwarded to ``urllib.request.Request`` as header options. For other
| URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are
| forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more
| details, and for more examples on storage options refer `here
| <https://pandas.pydata.org/docs/user_guide/io.html?
| highlight=storage_options#reading-writing-remote-files>`_.
|
| **kwargs
| These parameters will be passed to `tabulate <https://pypi.org/project/tabulate>`_.
|
| Returns
| -------
| str
| DataFrame in Markdown-friendly format.
|
| Notes
| -----
| Requires the `tabulate <https://pypi.org/project/tabulate>`_ package.
|
| Examples
| --------
| >>> df = pd.DataFrame(
| ... data={"animal_1": ["elk", "pig"], "animal_2": ["dog", "quetzal"]}
| ... )
| >>> print(df.to_markdown())
| | | animal_1 | animal_2 |
| |---:|:-----------|:-----------|
| | 0 | elk | dog |
| | 1 | pig | quetzal |
|
| Output markdown with a tabulate option.
|
| >>> print(df.to_markdown(tablefmt="grid"))
| +----+------------+------------+
| | | animal_1 | animal_2 |
| +====+============+============+
| | 0 | elk | dog |
| +----+------------+------------+
| | 1 | pig | quetzal |
| +----+------------+------------+
|
| to_numpy(self, dtype: 'npt.DTypeLike | None' = None, copy: 'bool' = False, na_value: 'object' = <no_default>) -> 'np.ndarray'
| Convert the DataFrame to a NumPy array.
|
| By default, the dtype of the returned array will be the common NumPy
| dtype of all types in the DataFrame. For example, if the dtypes are
| ``float16`` and ``float32``, the results dtype will be ``float32``.
| This may require copying data and coercing values, which may be
| expensive.
|
| Parameters
| ----------
| dtype : str or numpy.dtype, optional
| The dtype to pass to :meth:`numpy.asarray`.
| copy : bool, default False
| Whether to ensure that the returned value is not a view on
| another array. Note that ``copy=False`` does not *ensure* that
| ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that
| a copy is made, even if not strictly necessary.
| na_value : Any, optional
| The value to use for missing values. The default value depends
| on `dtype` and the dtypes of the DataFrame columns.
|
| Returns
| -------
| numpy.ndarray
|
| See Also
| --------
| Series.to_numpy : Similar method for Series.
|
| Examples
| --------
| >>> pd.DataFrame({"A": [1, 2], "B": [3, 4]}).to_numpy()
| array([[1, 3],
| [2, 4]])
|
| With heterogeneous data, the lowest common type will have to
| be used.
|
| >>> df = pd.DataFrame({"A": [1, 2], "B": [3.0, 4.5]})
| >>> df.to_numpy()
| array([[1. , 3. ],
| [2. , 4.5]])
|
| For a mix of numeric and non-numeric types, the output array will
| have object dtype.
|
| >>> df['C'] = pd.date_range('2000', periods=2)
| >>> df.to_numpy()
| array([[1, 3.0, Timestamp('2000-01-01 00:00:00')],
| [2, 4.5, Timestamp('2000-01-02 00:00:00')]], dtype=object)
|
| to_orc(self, path: 'FilePath | WriteBuffer[bytes] | None' = None, *, engine: "Literal['pyarrow']" = 'pyarrow', index: 'bool | None' = None, engine_kwargs: 'dict[str, Any] | None' = None) -> 'bytes | None'
| Write a DataFrame to the ORC format.
|
| .. versionadded:: 1.5.0
|
| Parameters
| ----------
| path : str, file-like object or None, default None
| If a string, it will be used as Root Directory path
| when writing a partitioned dataset. By file-like object,
| we refer to objects with a write() method, such as a file handle
| (e.g. via builtin open function). If path is None,
| a bytes object is returned.
| engine : {'pyarrow'}, default 'pyarrow'
| ORC library to use.
| index : bool, optional
| If ``True``, include the dataframe's index(es) in the file output.
| If ``False``, they will not be written to the file.
| If ``None``, similar to ``infer`` the dataframe's index(es)
| will be saved. However, instead of being saved as values,
| the RangeIndex will be stored as a range in the metadata so it
| doesn't require much space and is faster. Other indexes will
| be included as columns in the file output.
| engine_kwargs : dict[str, Any] or None, default None
| Additional keyword arguments passed to :func:`pyarrow.orc.write_table`.
|
| Returns
| -------
| bytes if no path argument is provided else None
|
| Raises
| ------
| NotImplementedError
| Dtype of one or more columns is category, unsigned integers, interval,
| period or sparse.
| ValueError
| engine is not pyarrow.
|
| See Also
| --------
| read_orc : Read a ORC file.
| DataFrame.to_parquet : Write a parquet file.
| DataFrame.to_csv : Write a csv file.
| DataFrame.to_sql : Write to a sql table.
| DataFrame.to_hdf : Write to hdf.
|
| Notes
| -----
| * Before using this function you should read the :ref:`user guide about
| ORC <io.orc>` and :ref:`install optional dependencies <install.warn_orc>`.
| * This function requires `pyarrow <https://arrow.apache.org/docs/python/>`_
| library.
| * For supported dtypes please refer to `supported ORC features in Arrow
| <https://arrow.apache.org/docs/cpp/orc.html#data-types>`__.
| * Currently timezones in datetime columns are not preserved when a
| dataframe is converted into ORC files.
|
| Examples
| --------
| >>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [4, 3]})
| >>> df.to_orc('df.orc') # doctest: +SKIP
| >>> pd.read_orc('df.orc') # doctest: +SKIP
| col1 col2
| 0 1 4
| 1 2 3
|
| If you want to get a buffer to the orc content you can write it to io.BytesIO
|
| >>> import io
| >>> b = io.BytesIO(df.to_orc()) # doctest: +SKIP
| >>> b.seek(0) # doctest: +SKIP
| 0
| >>> content = b.read() # doctest: +SKIP
|
| to_parquet(self, path: 'FilePath | WriteBuffer[bytes] | None' = None, *, engine: "Literal['auto', 'pyarrow', 'fastparquet']" = 'auto', compression: 'str | None' = 'snappy', index: 'bool | None' = None, partition_cols: 'list[str] | None' = None, storage_options: 'StorageOptions | None' = None, **kwargs) -> 'bytes | None'
| Write a DataFrame to the binary parquet format.
|
| This function writes the dataframe as a `parquet file
| <https://parquet.apache.org/>`_. You can choose different parquet
| backends, and have the option of compression. See
| :ref:`the user guide <io.parquet>` for more details.
|
| Parameters
| ----------
| path : str, path object, file-like object, or None, default None
| String, path object (implementing ``os.PathLike[str]``), or file-like
| object implementing a binary ``write()`` function. If None, the result is
| returned as bytes. If a string or path, it will be used as Root Directory
| path when writing a partitioned dataset.
| engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto'
| Parquet library to use. If 'auto', then the option
| ``io.parquet.engine`` is used. The default ``io.parquet.engine``
| behavior is to try 'pyarrow', falling back to 'fastparquet' if
| 'pyarrow' is unavailable.
| compression : str or None, default 'snappy'
| Name of the compression to use. Use ``None`` for no compression.
| Supported options: 'snappy', 'gzip', 'brotli', 'lz4', 'zstd'.
| index : bool, default None
| If ``True``, include the dataframe's index(es) in the file output.
| If ``False``, they will not be written to the file.
| If ``None``, similar to ``True`` the dataframe's index(es)
| will be saved. However, instead of being saved as values,
| the RangeIndex will be stored as a range in the metadata so it
| doesn't require much space and is faster. Other indexes will
| be included as columns in the file output.
| partition_cols : list, optional, default None
| Column names by which to partition the dataset.
| Columns are partitioned in the order they are given.
| Must be None if path is not a string.
| storage_options : dict, optional
| Extra options that make sense for a particular storage connection, e.g.
| host, port, username, password, etc. For HTTP(S) URLs the key-value pairs
| are forwarded to ``urllib.request.Request`` as header options. For other
| URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are
| forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more
| details, and for more examples on storage options refer `here
| <https://pandas.pydata.org/docs/user_guide/io.html?
| highlight=storage_options#reading-writing-remote-files>`_.
|
| **kwargs
| Additional arguments passed to the parquet library. See
| :ref:`pandas io <io.parquet>` for more details.
|
| Returns
| -------
| bytes if no path argument is provided else None
|
| See Also
| --------
| read_parquet : Read a parquet file.
| DataFrame.to_orc : Write an orc file.
| DataFrame.to_csv : Write a csv file.
| DataFrame.to_sql : Write to a sql table.
| DataFrame.to_hdf : Write to hdf.
|
| Notes
| -----
| This function requires either the `fastparquet
| <https://pypi.org/project/fastparquet>`_ or `pyarrow
| <https://arrow.apache.org/docs/python/>`_ library.
|
| Examples
| --------
| >>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]})
| >>> df.to_parquet('df.parquet.gzip',
| ... compression='gzip') # doctest: +SKIP
| >>> pd.read_parquet('df.parquet.gzip') # doctest: +SKIP
| col1 col2
| 0 1 3
| 1 2 4
|
| If you want to get a buffer to the parquet content you can use a io.BytesIO
| object, as long as you don't use partition_cols, which creates multiple files.
|
| >>> import io
| >>> f = io.BytesIO()
| >>> df.to_parquet(f)
| >>> f.seek(0)
| 0
| >>> content = f.read()
|
| to_period(self, freq: 'Frequency | None' = None, axis: 'Axis' = 0, copy: 'bool | None' = None) -> 'DataFrame'
| Convert DataFrame from DatetimeIndex to PeriodIndex.
|
| Convert DataFrame from DatetimeIndex to PeriodIndex with desired
| frequency (inferred from index if not passed).
|
| Parameters
| ----------
| freq : str, default
| Frequency of the PeriodIndex.
| axis : {0 or 'index', 1 or 'columns'}, default 0
| The axis to convert (the index by default).
| copy : bool, default True
| If False then underlying input data is not copied.
|
| .. note::
| The `copy` keyword will change behavior in pandas 3.0.
| `Copy-on-Write
| <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
| will be enabled by default, which means that all methods with a
| `copy` keyword will use a lazy copy mechanism to defer the copy and
| ignore the `copy` keyword. The `copy` keyword will be removed in a
| future version of pandas.
|
| You can already get the future behavior and improvements through
| enabling copy on write ``pd.options.mode.copy_on_write = True``
|
| Returns
| -------
| DataFrame
| The DataFrame has a PeriodIndex.
|
| Examples
| --------
| >>> idx = pd.to_datetime(
| ... [
| ... "2001-03-31 00:00:00",
| ... "2002-05-31 00:00:00",
| ... "2003-08-31 00:00:00",
| ... ]
| ... )
|
| >>> idx
| DatetimeIndex(['2001-03-31', '2002-05-31', '2003-08-31'],
| dtype='datetime64[ns]', freq=None)
|
| >>> idx.to_period("M")
| PeriodIndex(['2001-03', '2002-05', '2003-08'], dtype='period[M]')
|
| For the yearly frequency
|
| >>> idx.to_period("Y")
| PeriodIndex(['2001', '2002', '2003'], dtype='period[Y-DEC]')
|
| to_records(self, index: 'bool' = True, column_dtypes=None, index_dtypes=None) -> 'np.rec.recarray'
| Convert DataFrame to a NumPy record array.
|
| Index will be included as the first field of the record array if
| requested.
|
| Parameters
| ----------
| index : bool, default True
| Include index in resulting record array, stored in 'index'
| field or using the index label, if set.
| column_dtypes : str, type, dict, default None
| If a string or type, the data type to store all columns. If
| a dictionary, a mapping of column names and indices (zero-indexed)
| to specific data types.
| index_dtypes : str, type, dict, default None
| If a string or type, the data type to store all index levels. If
| a dictionary, a mapping of index level names and indices
| (zero-indexed) to specific data types.
|
| This mapping is applied only if `index=True`.
|
| Returns
| -------
| numpy.rec.recarray
| NumPy ndarray with the DataFrame labels as fields and each row
| of the DataFrame as entries.
|
| See Also
| --------
| DataFrame.from_records: Convert structured or record ndarray
| to DataFrame.
| numpy.rec.recarray: An ndarray that allows field access using
| attributes, analogous to typed columns in a
| spreadsheet.
|
| Examples
| --------
| >>> df = pd.DataFrame({'A': [1, 2], 'B': [0.5, 0.75]},
| ... index=['a', 'b'])
| >>> df
| A B
| a 1 0.50
| b 2 0.75
| >>> df.to_records()
| rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
| dtype=[('index', 'O'), ('A', '<i8'), ('B', '<f8')])
|
| If the DataFrame index has no label then the recarray field name
| is set to 'index'. If the index has a label then this is used as the
| field name:
|
| >>> df.index = df.index.rename("I")
| >>> df.to_records()
| rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
| dtype=[('I', 'O'), ('A', '<i8'), ('B', '<f8')])
|
| The index can be excluded from the record array:
|
| >>> df.to_records(index=False)
| rec.array([(1, 0.5 ), (2, 0.75)],
| dtype=[('A', '<i8'), ('B', '<f8')])
|
| Data types can be specified for the columns:
|
| >>> df.to_records(column_dtypes={"A": "int32"})
| rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
| dtype=[('I', 'O'), ('A', '<i4'), ('B', '<f8')])
|
| As well as for the index:
|
| >>> df.to_records(index_dtypes="<S2")
| rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
| dtype=[('I', 'S2'), ('A', '<i8'), ('B', '<f8')])
|
| >>> index_dtypes = f"<S{df.index.str.len().max()}"
| >>> df.to_records(index_dtypes=index_dtypes)
| rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
| dtype=[('I', 'S1'), ('A', '<i8'), ('B', '<f8')])
|
| to_stata(self, path: 'FilePath | WriteBuffer[bytes]', *, convert_dates: 'dict[Hashable, str] | None' = None, write_index: 'bool' = True, byteorder: 'ToStataByteorder | None' = None, time_stamp: 'datetime.datetime | None' = None, data_label: 'str | None' = None, variable_labels: 'dict[Hashable, str] | None' = None, version: 'int | None' = 114, convert_strl: 'Sequence[Hashable] | None' = None, compression: 'CompressionOptions' = 'infer', storage_options: 'StorageOptions | None' = None, value_labels: 'dict[Hashable, dict[float, str]] | None' = None) -> 'None'
| Export DataFrame object to Stata dta format.
|
| Writes the DataFrame to a Stata dataset file.
| "dta" files contain a Stata dataset.
|
| Parameters
| ----------
| path : str, path object, or buffer
| String, path object (implementing ``os.PathLike[str]``), or file-like
| object implementing a binary ``write()`` function.
|
| convert_dates : dict
| Dictionary mapping columns containing datetime types to stata
| internal format to use when writing the dates. Options are 'tc',
| 'td', 'tm', 'tw', 'th', 'tq', 'ty'. Column can be either an integer
| or a name. Datetime columns that do not have a conversion type
| specified will be converted to 'tc'. Raises NotImplementedError if
| a datetime column has timezone information.
| write_index : bool
| Write the index to Stata dataset.
| byteorder : str
| Can be ">", "<", "little", or "big". default is `sys.byteorder`.
| time_stamp : datetime
| A datetime to use as file creation date. Default is the current
| time.
| data_label : str, optional
| A label for the data set. Must be 80 characters or smaller.
| variable_labels : dict
| Dictionary containing columns as keys and variable labels as
| values. Each label must be 80 characters or smaller.
| version : {114, 117, 118, 119, None}, default 114
| Version to use in the output dta file. Set to None to let pandas
| decide between 118 or 119 formats depending on the number of
| columns in the frame. Version 114 can be read by Stata 10 and
| later. Version 117 can be read by Stata 13 or later. Version 118
| is supported in Stata 14 and later. Version 119 is supported in
| Stata 15 and later. Version 114 limits string variables to 244
| characters or fewer while versions 117 and later allow strings
| with lengths up to 2,000,000 characters. Versions 118 and 119
| support Unicode characters, and version 119 supports more than
| 32,767 variables.
|
| Version 119 should usually only be used when the number of
| variables exceeds the capacity of dta format 118. Exporting
| smaller datasets in format 119 may have unintended consequences,
| and, as of November 2020, Stata SE cannot read version 119 files.
|
| convert_strl : list, optional
| List of column names to convert to string columns to Stata StrL
| format. Only available if version is 117. Storing strings in the
| StrL format can produce smaller dta files if strings have more than
| 8 characters and values are repeated.
| compression : str or dict, default 'infer'
| For on-the-fly compression of the output data. If 'infer' and 'path' is
| path-like, then detect compression from the following extensions: '.gz',
| '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2'
| (otherwise no compression).
| Set to ``None`` for no compression.
| Can also be a dict with key ``'method'`` set
| to one of {``'zip'``, ``'gzip'``, ``'bz2'``, ``'zstd'``, ``'xz'``, ``'tar'``} and
| other key-value pairs are forwarded to
| ``zipfile.ZipFile``, ``gzip.GzipFile``,
| ``bz2.BZ2File``, ``zstandard.ZstdCompressor``, ``lzma.LZMAFile`` or
| ``tarfile.TarFile``, respectively.
| As an example, the following could be passed for faster compression and to create
| a reproducible gzip archive:
| ``compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}``.
|
| .. versionadded:: 1.5.0
| Added support for `.tar` files.
|
| .. versionchanged:: 1.4.0 Zstandard support.
|
| storage_options : dict, optional
| Extra options that make sense for a particular storage connection, e.g.
| host, port, username, password, etc. For HTTP(S) URLs the key-value pairs
| are forwarded to ``urllib.request.Request`` as header options. For other
| URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are
| forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more
| details, and for more examples on storage options refer `here
| <https://pandas.pydata.org/docs/user_guide/io.html?
| highlight=storage_options#reading-writing-remote-files>`_.
|
| value_labels : dict of dicts
| Dictionary containing columns as keys and dictionaries of column value
| to labels as values. Labels for a single variable must be 32,000
| characters or smaller.
|
| .. versionadded:: 1.4.0
|
| Raises
| ------
| NotImplementedError
| * If datetimes contain timezone information
| * Column dtype is not representable in Stata
| ValueError
| * Columns listed in convert_dates are neither datetime64[ns]
| or datetime.datetime
| * Column listed in convert_dates is not in DataFrame
| * Categorical label contains more than 32,000 characters
|
| See Also
| --------
| read_stata : Import Stata data files.
| io.stata.StataWriter : Low-level writer for Stata data files.
| io.stata.StataWriter117 : Low-level writer for version 117 files.
|
| Examples
| --------
| >>> df = pd.DataFrame({'animal': ['falcon', 'parrot', 'falcon',
| ... 'parrot'],
| ... 'speed': [350, 18, 361, 15]})
| >>> df.to_stata('animals.dta') # doctest: +SKIP
|
| to_string(self, buf: 'FilePath | WriteBuffer[str] | None' = None, *, columns: 'Axes | None' = None, col_space: 'int | list[int] | dict[Hashable, int] | None' = None, header: 'bool | SequenceNotStr[str]' = True, index: 'bool' = True, na_rep: 'str' = 'NaN', formatters: 'fmt.FormattersType | None' = None, float_format: 'fmt.FloatFormatType | None' = None, sparsify: 'bool | None' = None, index_names: 'bool' = True, justify: 'str | None' = None, max_rows: 'int | None' = None, max_cols: 'int | None' = None, show_dimensions: 'bool' = False, decimal: 'str' = '.', line_width: 'int | None' = None, min_rows: 'int | None' = None, max_colwidth: 'int | None' = None, encoding: 'str | None' = None) -> 'str | None'
| Render a DataFrame to a console-friendly tabular output.
|
| Parameters
| ----------
| buf : str, Path or StringIO-like, optional, default None
| Buffer to write to. If None, the output is returned as a string.
| columns : array-like, optional, default None
| The subset of columns to write. Writes all columns by default.
| col_space : int, list or dict of int, optional
| The minimum width of each column. If a list of ints is given every integers corresponds with one column. If a dict is given, the key references the column, while the value defines the space to use..
| header : bool or list of str, optional
| Write out the column names. If a list of columns is given, it is assumed to be aliases for the column names.
| index : bool, optional, default True
| Whether to print index (row) labels.
| na_rep : str, optional, default 'NaN'
| String representation of ``NaN`` to use.
| formatters : list, tuple or dict of one-param. functions, optional
| Formatter functions to apply to columns' elements by position or
| name.
| The result of each function must be a unicode string.
| List/tuple must be of length equal to the number of columns.
| float_format : one-parameter function, optional, default None
| Formatter function to apply to columns' elements if they are
| floats. This function must return a unicode string and will be
| applied only to the non-``NaN`` elements, with ``NaN`` being
| handled by ``na_rep``.
| sparsify : bool, optional, default True
| Set to False for a DataFrame with a hierarchical index to print
| every multiindex key at each row.
| index_names : bool, optional, default True
| Prints the names of the indexes.
| justify : str, default None
| How to justify the column labels. If None uses the option from
| the print configuration (controlled by set_option), 'right' out
| of the box. Valid values are
|
| * left
| * right
| * center
| * justify
| * justify-all
| * start
| * end
| * inherit
| * match-parent
| * initial
| * unset.
| max_rows : int, optional
| Maximum number of rows to display in the console.
| max_cols : int, optional
| Maximum number of columns to display in the console.
| show_dimensions : bool, default False
| Display DataFrame dimensions (number of rows by number of columns).
| decimal : str, default '.'
| Character recognized as decimal separator, e.g. ',' in Europe.
|
| line_width : int, optional
| Width to wrap a line in characters.
| min_rows : int, optional
| The number of rows to display in the console in a truncated repr
| (when number of rows is above `max_rows`).
| max_colwidth : int, optional
| Max width to truncate each column in characters. By default, no limit.
| encoding : str, default "utf-8"
| Set character encoding.
|
| Returns
| -------
| str or None
| If buf is None, returns the result as a string. Otherwise returns
| None.
|
| See Also
| --------
| to_html : Convert DataFrame to HTML.
|
| Examples
| --------
| >>> d = {'col1': [1, 2, 3], 'col2': [4, 5, 6]}
| >>> df = pd.DataFrame(d)
| >>> print(df.to_string())
| col1 col2
| 0 1 4
| 1 2 5
| 2 3 6
|
| to_timestamp(self, freq: 'Frequency | None' = None, how: 'ToTimestampHow' = 'start', axis: 'Axis' = 0, copy: 'bool | None' = None) -> 'DataFrame'
| Cast to DatetimeIndex of timestamps, at *beginning* of period.
|
| Parameters
| ----------
| freq : str, default frequency of PeriodIndex
| Desired frequency.
| how : {'s', 'e', 'start', 'end'}
| Convention for converting period to timestamp; start of period
| vs. end.
| axis : {0 or 'index', 1 or 'columns'}, default 0
| The axis to convert (the index by default).
| copy : bool, default True
| If False then underlying input data is not copied.
|
| .. note::
| The `copy` keyword will change behavior in pandas 3.0.
| `Copy-on-Write
| <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
| will be enabled by default, which means that all methods with a
| `copy` keyword will use a lazy copy mechanism to defer the copy and
| ignore the `copy` keyword. The `copy` keyword will be removed in a
| future version of pandas.
|
| You can already get the future behavior and improvements through
| enabling copy on write ``pd.options.mode.copy_on_write = True``
|
| Returns
| -------
| DataFrame
| The DataFrame has a DatetimeIndex.
|
| Examples
| --------
| >>> idx = pd.PeriodIndex(['2023', '2024'], freq='Y')
| >>> d = {'col1': [1, 2], 'col2': [3, 4]}
| >>> df1 = pd.DataFrame(data=d, index=idx)
| >>> df1
| col1 col2
| 2023 1 3
| 2024 2 4
|
| The resulting timestamps will be at the beginning of the year in this case
|
| >>> df1 = df1.to_timestamp()
| >>> df1
| col1 col2
| 2023-01-01 1 3
| 2024-01-01 2 4
| >>> df1.index
| DatetimeIndex(['2023-01-01', '2024-01-01'], dtype='datetime64[ns]', freq=None)
|
| Using `freq` which is the offset that the Timestamps will have
|
| >>> df2 = pd.DataFrame(data=d, index=idx)
| >>> df2 = df2.to_timestamp(freq='M')
| >>> df2
| col1 col2
| 2023-01-31 1 3
| 2024-01-31 2 4
| >>> df2.index
| DatetimeIndex(['2023-01-31', '2024-01-31'], dtype='datetime64[ns]', freq=None)
|
| to_xml(self, path_or_buffer: 'FilePath | WriteBuffer[bytes] | WriteBuffer[str] | None' = None, *, index: 'bool' = True, root_name: 'str | None' = 'data', row_name: 'str | None' = 'row', na_rep: 'str | None' = None, attr_cols: 'list[str] | None' = None, elem_cols: 'list[str] | None' = None, namespaces: 'dict[str | None, str] | None' = None, prefix: 'str | None' = None, encoding: 'str' = 'utf-8', xml_declaration: 'bool | None' = True, pretty_print: 'bool | None' = True, parser: 'XMLParsers | None' = 'lxml', stylesheet: 'FilePath | ReadBuffer[str] | ReadBuffer[bytes] | None' = None, compression: 'CompressionOptions' = 'infer', storage_options: 'StorageOptions | None' = None) -> 'str | None'
| Render a DataFrame to an XML document.
|
| .. versionadded:: 1.3.0
|
| Parameters
| ----------
| path_or_buffer : str, path object, file-like object, or None, default None
| String, path object (implementing ``os.PathLike[str]``), or file-like
| object implementing a ``write()`` function. If None, the result is returned
| as a string.
| index : bool, default True
| Whether to include index in XML document.
| root_name : str, default 'data'
| The name of root element in XML document.
| row_name : str, default 'row'
| The name of row element in XML document.
| na_rep : str, optional
| Missing data representation.
| attr_cols : list-like, optional
| List of columns to write as attributes in row element.
| Hierarchical columns will be flattened with underscore
| delimiting the different levels.
| elem_cols : list-like, optional
| List of columns to write as children in row element. By default,
| all columns output as children of row element. Hierarchical
| columns will be flattened with underscore delimiting the
| different levels.
| namespaces : dict, optional
| All namespaces to be defined in root element. Keys of dict
| should be prefix names and values of dict corresponding URIs.
| Default namespaces should be given empty string key. For
| example, ::
|
| namespaces = {"": "https://example.com"}
|
| prefix : str, optional
| Namespace prefix to be used for every element and/or attribute
| in document. This should be one of the keys in ``namespaces``
| dict.
| encoding : str, default 'utf-8'
| Encoding of the resulting document.
| xml_declaration : bool, default True
| Whether to include the XML declaration at start of document.
| pretty_print : bool, default True
| Whether output should be pretty printed with indentation and
| line breaks.
| parser : {'lxml','etree'}, default 'lxml'
| Parser module to use for building of tree. Only 'lxml' and
| 'etree' are supported. With 'lxml', the ability to use XSLT
| stylesheet is supported.
| stylesheet : str, path object or file-like object, optional
| A URL, file-like object, or a raw string containing an XSLT
| script used to transform the raw XML output. Script should use
| layout of elements and attributes from original output. This
| argument requires ``lxml`` to be installed. Only XSLT 1.0
| scripts and not later versions is currently supported.
| compression : str or dict, default 'infer'
| For on-the-fly compression of the output data. If 'infer' and 'path_or_buffer' is
| path-like, then detect compression from the following extensions: '.gz',
| '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2'
| (otherwise no compression).
| Set to ``None`` for no compression.
| Can also be a dict with key ``'method'`` set
| to one of {``'zip'``, ``'gzip'``, ``'bz2'``, ``'zstd'``, ``'xz'``, ``'tar'``} and
| other key-value pairs are forwarded to
| ``zipfile.ZipFile``, ``gzip.GzipFile``,
| ``bz2.BZ2File``, ``zstandard.ZstdCompressor``, ``lzma.LZMAFile`` or
| ``tarfile.TarFile``, respectively.
| As an example, the following could be passed for faster compression and to create
| a reproducible gzip archive:
| ``compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}``.
|
| .. versionadded:: 1.5.0
| Added support for `.tar` files.
|
| .. versionchanged:: 1.4.0 Zstandard support.
|
| storage_options : dict, optional
| Extra options that make sense for a particular storage connection, e.g.
| host, port, username, password, etc. For HTTP(S) URLs the key-value pairs
| are forwarded to ``urllib.request.Request`` as header options. For other
| URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are
| forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more
| details, and for more examples on storage options refer `here
| <https://pandas.pydata.org/docs/user_guide/io.html?
| highlight=storage_options#reading-writing-remote-files>`_.
|
| Returns
| -------
| None or str
| If ``io`` is None, returns the resulting XML format as a
| string. Otherwise returns None.
|
| See Also
| --------
| to_json : Convert the pandas object to a JSON string.
| to_html : Convert DataFrame to a html.
|
| Examples
| --------
| >>> df = pd.DataFrame({'shape': ['square', 'circle', 'triangle'],
| ... 'degrees': [360, 360, 180],
| ... 'sides': [4, np.nan, 3]})
|
| >>> df.to_xml() # doctest: +SKIP
| <?xml version='1.0' encoding='utf-8'?>
| <data>
| <row>
| <index>0</index>
| <shape>square</shape>
| <degrees>360</degrees>
| <sides>4.0</sides>
| </row>
| <row>
| <index>1</index>
| <shape>circle</shape>
| <degrees>360</degrees>
| <sides/>
| </row>
| <row>
| <index>2</index>
| <shape>triangle</shape>
| <degrees>180</degrees>
| <sides>3.0</sides>
| </row>
| </data>
|
| >>> df.to_xml(attr_cols=[
| ... 'index', 'shape', 'degrees', 'sides'
| ... ]) # doctest: +SKIP
| <?xml version='1.0' encoding='utf-8'?>
| <data>
| <row index="0" shape="square" degrees="360" sides="4.0"/>
| <row index="1" shape="circle" degrees="360"/>
| <row index="2" shape="triangle" degrees="180" sides="3.0"/>
| </data>
|
| >>> df.to_xml(namespaces={"doc": "https://example.com"},
| ... prefix="doc") # doctest: +SKIP
| <?xml version='1.0' encoding='utf-8'?>
| <doc:data xmlns:doc="https://example.com">
| <doc:row>
| <doc:index>0</doc:index>
| <doc:shape>square</doc:shape>
| <doc:degrees>360</doc:degrees>
| <doc:sides>4.0</doc:sides>
| </doc:row>
| <doc:row>
| <doc:index>1</doc:index>
| <doc:shape>circle</doc:shape>
| <doc:degrees>360</doc:degrees>
| <doc:sides/>
| </doc:row>
| <doc:row>
| <doc:index>2</doc:index>
| <doc:shape>triangle</doc:shape>
| <doc:degrees>180</doc:degrees>
| <doc:sides>3.0</doc:sides>
| </doc:row>
| </doc:data>
|
| transform(self, func: 'AggFuncType', axis: 'Axis' = 0, *args, **kwargs) -> 'DataFrame'
| Call ``func`` on self producing a DataFrame with the same axis shape as self.
|
| Parameters
| ----------
| func : function, str, list-like or dict-like
| Function to use for transforming the data. If a function, must either
| work when passed a DataFrame or when passed to DataFrame.apply. If func
| is both list-like and dict-like, dict-like behavior takes precedence.
|
| Accepted combinations are:
|
| - function
| - string function name
| - list-like of functions and/or function names, e.g. ``[np.exp, 'sqrt']``
| - dict-like of axis labels -> functions, function names or list-like of such.
| axis : {0 or 'index', 1 or 'columns'}, default 0
| If 0 or 'index': apply function to each column.
| If 1 or 'columns': apply function to each row.
| *args
| Positional arguments to pass to `func`.
| **kwargs
| Keyword arguments to pass to `func`.
|
| Returns
| -------
| DataFrame
| A DataFrame that must have the same length as self.
|
| Raises
| ------
| ValueError : If the returned DataFrame has a different length than self.
|
| See Also
| --------
| DataFrame.agg : Only perform aggregating type operations.
| DataFrame.apply : Invoke function on a DataFrame.
|
| Notes
| -----
| Functions that mutate the passed object can produce unexpected
| behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
| for more details.
|
| Examples
| --------
| >>> df = pd.DataFrame({'A': range(3), 'B': range(1, 4)})
| >>> df
| A B
| 0 0 1
| 1 1 2
| 2 2 3
| >>> df.transform(lambda x: x + 1)
| A B
| 0 1 2
| 1 2 3
| 2 3 4
|
| Even though the resulting DataFrame must have the same length as the
| input DataFrame, it is possible to provide several input functions:
|
| >>> s = pd.Series(range(3))
| >>> s
| 0 0
| 1 1
| 2 2
| dtype: int64
| >>> s.transform([np.sqrt, np.exp])
| sqrt exp
| 0 0.000000 1.000000
| 1 1.000000 2.718282
| 2 1.414214 7.389056
|
| You can call transform on a GroupBy object:
|
| >>> df = pd.DataFrame({
| ... "Date": [
| ... "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05",
| ... "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05"],
| ... "Data": [5, 8, 6, 1, 50, 100, 60, 120],
| ... })
| >>> df
| Date Data
| 0 2015-05-08 5
| 1 2015-05-07 8
| 2 2015-05-06 6
| 3 2015-05-05 1
| 4 2015-05-08 50
| 5 2015-05-07 100
| 6 2015-05-06 60
| 7 2015-05-05 120
| >>> df.groupby('Date')['Data'].transform('sum')
| 0 55
| 1 108
| 2 66
| 3 121
| 4 55
| 5 108
| 6 66
| 7 121
| Name: Data, dtype: int64
|
| >>> df = pd.DataFrame({
| ... "c": [1, 1, 1, 2, 2, 2, 2],
| ... "type": ["m", "n", "o", "m", "m", "n", "n"]
| ... })
| >>> df
| c type
| 0 1 m
| 1 1 n
| 2 1 o
| 3 2 m
| 4 2 m
| 5 2 n
| 6 2 n
| >>> df['size'] = df.groupby('c')['type'].transform(len)
| >>> df
| c type size
| 0 1 m 3
| 1 1 n 3
| 2 1 o 3
| 3 2 m 4
| 4 2 m 4
| 5 2 n 4
| 6 2 n 4
|
| transpose(self, *args, copy: 'bool' = False) -> 'DataFrame'
| Transpose index and columns.
|
| Reflect the DataFrame over its main diagonal by writing rows as columns
| and vice-versa. The property :attr:`.T` is an accessor to the method
| :meth:`transpose`.
|
| Parameters
| ----------
| *args : tuple, optional
| Accepted for compatibility with NumPy.
| copy : bool, default False
| Whether to copy the data after transposing, even for DataFrames
| with a single dtype.
|
| Note that a copy is always required for mixed dtype DataFrames,
| or for DataFrames with any extension types.
|
| .. note::
| The `copy` keyword will change behavior in pandas 3.0.
| `Copy-on-Write
| <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
| will be enabled by default, which means that all methods with a
| `copy` keyword will use a lazy copy mechanism to defer the copy and
| ignore the `copy` keyword. The `copy` keyword will be removed in a
| future version of pandas.
|
| You can already get the future behavior and improvements through
| enabling copy on write ``pd.options.mode.copy_on_write = True``
|
| Returns
| -------
| DataFrame
| The transposed DataFrame.
|
| See Also
| --------
| numpy.transpose : Permute the dimensions of a given array.
|
| Notes
| -----
| Transposing a DataFrame with mixed dtypes will result in a homogeneous
| DataFrame with the `object` dtype. In such a case, a copy of the data
| is always made.
|
| Examples
| --------
| **Square DataFrame with homogeneous dtype**
|
| >>> d1 = {'col1': [1, 2], 'col2': [3, 4]}
| >>> df1 = pd.DataFrame(data=d1)
| >>> df1
| col1 col2
| 0 1 3
| 1 2 4
|
| >>> df1_transposed = df1.T # or df1.transpose()
| >>> df1_transposed
| 0 1
| col1 1 2
| col2 3 4
|
| When the dtype is homogeneous in the original DataFrame, we get a
| transposed DataFrame with the same dtype:
|
| >>> df1.dtypes
| col1 int64
| col2 int64
| dtype: object
| >>> df1_transposed.dtypes
| 0 int64
| 1 int64
| dtype: object
|
| **Non-square DataFrame with mixed dtypes**
|
| >>> d2 = {'name': ['Alice', 'Bob'],
| ... 'score': [9.5, 8],
| ... 'employed': [False, True],
| ... 'kids': [0, 0]}
| >>> df2 = pd.DataFrame(data=d2)
| >>> df2
| name score employed kids
| 0 Alice 9.5 False 0
| 1 Bob 8.0 True 0
|
| >>> df2_transposed = df2.T # or df2.transpose()
| >>> df2_transposed
| 0 1
| name Alice Bob
| score 9.5 8.0
| employed False True
| kids 0 0
|
| When the DataFrame has mixed dtypes, we get a transposed DataFrame with
| the `object` dtype:
|
| >>> df2.dtypes
| name object
| score float64
| employed bool
| kids int64
| dtype: object
| >>> df2_transposed.dtypes
| 0 object
| 1 object
| dtype: object
|
| truediv(self, other, axis: 'Axis' = 'columns', level=None, fill_value=None) -> 'DataFrame'
| Get Floating division of dataframe and other, element-wise (binary operator `truediv`).
|
| Equivalent to ``dataframe / other``, but with support to substitute a fill_value
| for missing data in one of the inputs. With reverse version, `rtruediv`.
|
| Among flexible wrappers (`add`, `sub`, `mul`, `div`, `floordiv`, `mod`, `pow`) to
| arithmetic operators: `+`, `-`, `*`, `/`, `//`, `%`, `**`.
|
| Parameters
| ----------
| other : scalar, sequence, Series, dict or DataFrame
| Any single or multiple element data structure, or list-like object.
| axis : {0 or 'index', 1 or 'columns'}
| Whether to compare by the index (0 or 'index') or columns.
| (1 or 'columns'). For Series input, axis to match Series index on.
| level : int or label
| Broadcast across a level, matching Index values on the
| passed MultiIndex level.
| fill_value : float or None, default None
| Fill existing missing (NaN) values, and any new element needed for
| successful DataFrame alignment, with this value before computation.
| If data in both corresponding DataFrame locations is missing
| the result will be missing.
|
| Returns
| -------
| DataFrame
| Result of the arithmetic operation.
|
| See Also
| --------
| DataFrame.add : Add DataFrames.
| DataFrame.sub : Subtract DataFrames.
| DataFrame.mul : Multiply DataFrames.
| DataFrame.div : Divide DataFrames (float division).
| DataFrame.truediv : Divide DataFrames (float division).
| DataFrame.floordiv : Divide DataFrames (integer division).
| DataFrame.mod : Calculate modulo (remainder after division).
| DataFrame.pow : Calculate exponential power.
|
| Notes
| -----
| Mismatched indices will be unioned together.
|
| Examples
| --------
| >>> df = pd.DataFrame({'angles': [0, 3, 4],
| ... 'degrees': [360, 180, 360]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> df
| angles degrees
| circle 0 360
| triangle 3 180
| rectangle 4 360
|
| Add a scalar with operator version which return the same
| results.
|
| >>> df + 1
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| >>> df.add(1)
| angles degrees
| circle 1 361
| triangle 4 181
| rectangle 5 361
|
| Divide by constant with reverse version.
|
| >>> df.div(10)
| angles degrees
| circle 0.0 36.0
| triangle 0.3 18.0
| rectangle 0.4 36.0
|
| >>> df.rdiv(10)
| angles degrees
| circle inf 0.027778
| triangle 3.333333 0.055556
| rectangle 2.500000 0.027778
|
| Subtract a list and Series by axis with operator version.
|
| >>> df - [1, 2]
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub([1, 2], axis='columns')
| angles degrees
| circle -1 358
| triangle 2 178
| rectangle 3 358
|
| >>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
| ... axis='index')
| angles degrees
| circle -1 359
| triangle 2 179
| rectangle 3 359
|
| Multiply a dictionary by axis.
|
| >>> df.mul({'angles': 0, 'degrees': 2})
| angles degrees
| circle 0 720
| triangle 0 360
| rectangle 0 720
|
| >>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
| angles degrees
| circle 0 0
| triangle 6 360
| rectangle 12 1080
|
| Multiply a DataFrame of different shape with operator version.
|
| >>> other = pd.DataFrame({'angles': [0, 3, 4]},
| ... index=['circle', 'triangle', 'rectangle'])
| >>> other
| angles
| circle 0
| triangle 3
| rectangle 4
|
| >>> df * other
| angles degrees
| circle 0 NaN
| triangle 9 NaN
| rectangle 16 NaN
|
| >>> df.mul(other, fill_value=0)
| angles degrees
| circle 0 0.0
| triangle 9 0.0
| rectangle 16 0.0
|
| Divide by a MultiIndex by level.
|
| >>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
| ... 'degrees': [360, 180, 360, 360, 540, 720]},
| ... index=[['A', 'A', 'A', 'B', 'B', 'B'],
| ... ['circle', 'triangle', 'rectangle',
| ... 'square', 'pentagon', 'hexagon']])
| >>> df_multindex
| angles degrees
| A circle 0 360
| triangle 3 180
| rectangle 4 360
| B square 4 360
| pentagon 5 540
| hexagon 6 720
|
| >>> df.div(df_multindex, level=1, fill_value=0)
| angles degrees
| A circle NaN 1.0
| triangle 1.0 1.0
| rectangle 1.0 1.0
| B square 0.0 0.0
| pentagon 0.0 0.0
| hexagon 0.0 0.0
|
| unstack(self, level: 'IndexLabel' = -1, fill_value=None, sort: 'bool' = True)
| Pivot a level of the (necessarily hierarchical) index labels.
|
| Returns a DataFrame having a new level of column labels whose inner-most level
| consists of the pivoted index labels.
|
| If the index is not a MultiIndex, the output will be a Series
| (the analogue of stack when the columns are not a MultiIndex).
|
| Parameters
| ----------
| level : int, str, or list of these, default -1 (last level)
| Level(s) of index to unstack, can pass level name.
| fill_value : int, str or dict
| Replace NaN with this value if the unstack produces missing values.
| sort : bool, default True
| Sort the level(s) in the resulting MultiIndex columns.
|
| Returns
| -------
| Series or DataFrame
|
| See Also
| --------
| DataFrame.pivot : Pivot a table based on column values.
| DataFrame.stack : Pivot a level of the column labels (inverse operation
| from `unstack`).
|
| Notes
| -----
| Reference :ref:`the user guide <reshaping.stacking>` for more examples.
|
| Examples
| --------
| >>> index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'),
| ... ('two', 'a'), ('two', 'b')])
| >>> s = pd.Series(np.arange(1.0, 5.0), index=index)
| >>> s
| one a 1.0
| b 2.0
| two a 3.0
| b 4.0
| dtype: float64
|
| >>> s.unstack(level=-1)
| a b
| one 1.0 2.0
| two 3.0 4.0
|
| >>> s.unstack(level=0)
| one two
| a 1.0 3.0
| b 2.0 4.0
|
| >>> df = s.unstack(level=0)
| >>> df.unstack()
| one a 1.0
| b 2.0
| two a 3.0
| b 4.0
| dtype: float64
|
| update(self, other, join: 'UpdateJoin' = 'left', overwrite: 'bool' = True, filter_func=None, errors: 'IgnoreRaise' = 'ignore') -> 'None'
| Modify in place using non-NA values from another DataFrame.
|
| Aligns on indices. There is no return value.
|
| Parameters
| ----------
| other : DataFrame, or object coercible into a DataFrame
| Should have at least one matching index/column label
| with the original DataFrame. If a Series is passed,
| its name attribute must be set, and that will be
| used as the column name to align with the original DataFrame.
| join : {'left'}, default 'left'
| Only left join is implemented, keeping the index and columns of the
| original object.
| overwrite : bool, default True
| How to handle non-NA values for overlapping keys:
|
| * True: overwrite original DataFrame's values
| with values from `other`.
| * False: only update values that are NA in
| the original DataFrame.
|
| filter_func : callable(1d-array) -> bool 1d-array, optional
| Can choose to replace values other than NA. Return True for values
| that should be updated.
| errors : {'raise', 'ignore'}, default 'ignore'
| If 'raise', will raise a ValueError if the DataFrame and `other`
| both contain non-NA data in the same place.
|
| Returns
| -------
| None
| This method directly changes calling object.
|
| Raises
| ------
| ValueError
| * When `errors='raise'` and there's overlapping non-NA data.
| * When `errors` is not either `'ignore'` or `'raise'`
| NotImplementedError
| * If `join != 'left'`
|
| See Also
| --------
| dict.update : Similar method for dictionaries.
| DataFrame.merge : For column(s)-on-column(s) operations.
|
| Examples
| --------
| >>> df = pd.DataFrame({'A': [1, 2, 3],
| ... 'B': [400, 500, 600]})
| >>> new_df = pd.DataFrame({'B': [4, 5, 6],
| ... 'C': [7, 8, 9]})
| >>> df.update(new_df)
| >>> df
| A B
| 0 1 4
| 1 2 5
| 2 3 6
|
| The DataFrame's length does not increase as a result of the update,
| only values at matching index/column labels are updated.
|
| >>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
| ... 'B': ['x', 'y', 'z']})
| >>> new_df = pd.DataFrame({'B': ['d', 'e', 'f', 'g', 'h', 'i']})
| >>> df.update(new_df)
| >>> df
| A B
| 0 a d
| 1 b e
| 2 c f
|
| >>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
| ... 'B': ['x', 'y', 'z']})
| >>> new_df = pd.DataFrame({'B': ['d', 'f']}, index=[0, 2])
| >>> df.update(new_df)
| >>> df
| A B
| 0 a d
| 1 b y
| 2 c f
|
| For Series, its name attribute must be set.
|
| >>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
| ... 'B': ['x', 'y', 'z']})
| >>> new_column = pd.Series(['d', 'e', 'f'], name='B')
| >>> df.update(new_column)
| >>> df
| A B
| 0 a d
| 1 b e
| 2 c f
|
| If `other` contains NaNs the corresponding values are not updated
| in the original dataframe.
|
| >>> df = pd.DataFrame({'A': [1, 2, 3],
| ... 'B': [400., 500., 600.]})
| >>> new_df = pd.DataFrame({'B': [4, np.nan, 6]})
| >>> df.update(new_df)
| >>> df
| A B
| 0 1 4.0
| 1 2 500.0
| 2 3 6.0
|
| value_counts(self, subset: 'IndexLabel | None' = None, normalize: 'bool' = False, sort: 'bool' = True, ascending: 'bool' = False, dropna: 'bool' = True) -> 'Series'
| Return a Series containing the frequency of each distinct row in the Dataframe.
|
| Parameters
| ----------
| subset : label or list of labels, optional
| Columns to use when counting unique combinations.
| normalize : bool, default False
| Return proportions rather than frequencies.
| sort : bool, default True
| Sort by frequencies when True. Sort by DataFrame column values when False.
| ascending : bool, default False
| Sort in ascending order.
| dropna : bool, default True
| Don't include counts of rows that contain NA values.
|
| .. versionadded:: 1.3.0
|
| Returns
| -------
| Series
|
| See Also
| --------
| Series.value_counts: Equivalent method on Series.
|
| Notes
| -----
| The returned Series will have a MultiIndex with one level per input
| column but an Index (non-multi) for a single label. By default, rows
| that contain any NA values are omitted from the result. By default,
| the resulting Series will be in descending order so that the first
| element is the most frequently-occurring row.
|
| Examples
| --------
| >>> df = pd.DataFrame({'num_legs': [2, 4, 4, 6],
| ... 'num_wings': [2, 0, 0, 0]},
| ... index=['falcon', 'dog', 'cat', 'ant'])
| >>> df
| num_legs num_wings
| falcon 2 2
| dog 4 0
| cat 4 0
| ant 6 0
|
| >>> df.value_counts()
| num_legs num_wings
| 4 0 2
| 2 2 1
| 6 0 1
| Name: count, dtype: int64
|
| >>> df.value_counts(sort=False)
| num_legs num_wings
| 2 2 1
| 4 0 2
| 6 0 1
| Name: count, dtype: int64
|
| >>> df.value_counts(ascending=True)
| num_legs num_wings
| 2 2 1
| 6 0 1
| 4 0 2
| Name: count, dtype: int64
|
| >>> df.value_counts(normalize=True)
| num_legs num_wings
| 4 0 0.50
| 2 2 0.25
| 6 0 0.25
| Name: proportion, dtype: float64
|
| With `dropna` set to `False` we can also count rows with NA values.
|
| >>> df = pd.DataFrame({'first_name': ['John', 'Anne', 'John', 'Beth'],
| ... 'middle_name': ['Smith', pd.NA, pd.NA, 'Louise']})
| >>> df
| first_name middle_name
| 0 John Smith
| 1 Anne <NA>
| 2 John <NA>
| 3 Beth Louise
|
| >>> df.value_counts()
| first_name middle_name
| Beth Louise 1
| John Smith 1
| Name: count, dtype: int64
|
| >>> df.value_counts(dropna=False)
| first_name middle_name
| Anne NaN 1
| Beth Louise 1
| John Smith 1
| NaN 1
| Name: count, dtype: int64
|
| >>> df.value_counts("first_name")
| first_name
| John 2
| Anne 1
| Beth 1
| Name: count, dtype: int64
|
| var(self, axis: 'Axis | None' = 0, skipna: 'bool' = True, ddof: 'int' = 1, numeric_only: 'bool' = False, **kwargs)
| Return unbiased variance over requested axis.
|
| Normalized by N-1 by default. This can be changed using the ddof argument.
|
| Parameters
| ----------
| axis : {index (0), columns (1)}
| For `Series` this parameter is unused and defaults to 0.
|
| .. warning::
|
| The behavior of DataFrame.var with ``axis=None`` is deprecated,
| in a future version this will reduce over both axes and return a scalar
| To retain the old behavior, pass axis=0 (or do not pass axis).
|
| skipna : bool, default True
| Exclude NA/null values. If an entire row/column is NA, the result
| will be NA.
| ddof : int, default 1
| Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
| where N represents the number of elements.
| numeric_only : bool, default False
| Include only float, int, boolean columns. Not implemented for Series.
|
| Returns
| -------
| Series or DataFrame (if level specified)
|
| Examples
| --------
| >>> df = pd.DataFrame({'person_id': [0, 1, 2, 3],
| ... 'age': [21, 25, 62, 43],
| ... 'height': [1.61, 1.87, 1.49, 2.01]}
| ... ).set_index('person_id')
| >>> df
| age height
| person_id
| 0 21 1.61
| 1 25 1.87
| 2 62 1.49
| 3 43 2.01
|
| >>> df.var()
| age 352.916667
| height 0.056367
| dtype: float64
|
| Alternatively, ``ddof=0`` can be set to normalize by N instead of N-1:
|
| >>> df.var(ddof=0)
| age 264.687500
| height 0.042275
| dtype: float64
|
| ----------------------------------------------------------------------
| Class methods defined here:
|
| from_dict(data: 'dict', orient: 'FromDictOrient' = 'columns', dtype: 'Dtype | None' = None, columns: 'Axes | None' = None) -> 'DataFrame'
| Construct DataFrame from dict of array-like or dicts.
|
| Creates DataFrame object from dictionary by columns or by index
| allowing dtype specification.
|
| Parameters
| ----------
| data : dict
| Of the form {field : array-like} or {field : dict}.
| orient : {'columns', 'index', 'tight'}, default 'columns'
| The "orientation" of the data. If the keys of the passed dict
| should be the columns of the resulting DataFrame, pass 'columns'
| (default). Otherwise if the keys should be rows, pass 'index'.
| If 'tight', assume a dict with keys ['index', 'columns', 'data',
| 'index_names', 'column_names'].
|
| .. versionadded:: 1.4.0
| 'tight' as an allowed value for the ``orient`` argument
|
| dtype : dtype, default None
| Data type to force after DataFrame construction, otherwise infer.
| columns : list, default None
| Column labels to use when ``orient='index'``. Raises a ValueError
| if used with ``orient='columns'`` or ``orient='tight'``.
|
| Returns
| -------
| DataFrame
|
| See Also
| --------
| DataFrame.from_records : DataFrame from structured ndarray, sequence
| of tuples or dicts, or DataFrame.
| DataFrame : DataFrame object creation using constructor.
| DataFrame.to_dict : Convert the DataFrame to a dictionary.
|
| Examples
| --------
| By default the keys of the dict become the DataFrame columns:
|
| >>> data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
| >>> pd.DataFrame.from_dict(data)
| col_1 col_2
| 0 3 a
| 1 2 b
| 2 1 c
| 3 0 d
|
| Specify ``orient='index'`` to create the DataFrame using dictionary
| keys as rows:
|
| >>> data = {'row_1': [3, 2, 1, 0], 'row_2': ['a', 'b', 'c', 'd']}
| >>> pd.DataFrame.from_dict(data, orient='index')
| 0 1 2 3
| row_1 3 2 1 0
| row_2 a b c d
|
| When using the 'index' orientation, the column names can be
| specified manually:
|
| >>> pd.DataFrame.from_dict(data, orient='index',
| ... columns=['A', 'B', 'C', 'D'])
| A B C D
| row_1 3 2 1 0
| row_2 a b c d
|
| Specify ``orient='tight'`` to create the DataFrame using a 'tight'
| format:
|
| >>> data = {'index': [('a', 'b'), ('a', 'c')],
| ... 'columns': [('x', 1), ('y', 2)],
| ... 'data': [[1, 3], [2, 4]],
| ... 'index_names': ['n1', 'n2'],
| ... 'column_names': ['z1', 'z2']}
| >>> pd.DataFrame.from_dict(data, orient='tight')
| z1 x y
| z2 1 2
| n1 n2
| a b 1 3
| c 2 4
|
| from_records(data, index=None, exclude=None, columns=None, coerce_float: 'bool' = False, nrows: 'int | None' = None) -> 'DataFrame'
| Convert structured or record ndarray to DataFrame.
|
| Creates a DataFrame object from a structured ndarray, sequence of
| tuples or dicts, or DataFrame.
|
| Parameters
| ----------
| data : structured ndarray, sequence of tuples or dicts, or DataFrame
| Structured input data.
|
| .. deprecated:: 2.1.0
| Passing a DataFrame is deprecated.
| index : str, list of fields, array-like
| Field of array to use as the index, alternately a specific set of
| input labels to use.
| exclude : sequence, default None
| Columns or fields to exclude.
| columns : sequence, default None
| Column names to use. If the passed data do not have names
| associated with them, this argument provides names for the
| columns. Otherwise this argument indicates the order of the columns
| in the result (any names not found in the data will become all-NA
| columns).
| coerce_float : bool, default False
| Attempt to convert values of non-string, non-numeric objects (like
| decimal.Decimal) to floating point, useful for SQL result sets.
| nrows : int, default None
| Number of rows to read if data is an iterator.
|
| Returns
| -------
| DataFrame
|
| See Also
| --------
| DataFrame.from_dict : DataFrame from dict of array-like or dicts.
| DataFrame : DataFrame object creation using constructor.
|
| Examples
| --------
| Data can be provided as a structured ndarray:
|
| >>> data = np.array([(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')],
| ... dtype=[('col_1', 'i4'), ('col_2', 'U1')])
| >>> pd.DataFrame.from_records(data)
| col_1 col_2
| 0 3 a
| 1 2 b
| 2 1 c
| 3 0 d
|
| Data can be provided as a list of dicts:
|
| >>> data = [{'col_1': 3, 'col_2': 'a'},
| ... {'col_1': 2, 'col_2': 'b'},
| ... {'col_1': 1, 'col_2': 'c'},
| ... {'col_1': 0, 'col_2': 'd'}]
| >>> pd.DataFrame.from_records(data)
| col_1 col_2
| 0 3 a
| 1 2 b
| 2 1 c
| 3 0 d
|
| Data can be provided as a list of tuples with corresponding columns:
|
| >>> data = [(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')]
| >>> pd.DataFrame.from_records(data, columns=['col_1', 'col_2'])
| col_1 col_2
| 0 3 a
| 1 2 b
| 2 1 c
| 3 0 d
|
| ----------------------------------------------------------------------
| Readonly properties defined here:
|
| T
| The transpose of the DataFrame.
|
| Returns
| -------
| DataFrame
| The transposed DataFrame.
|
| See Also
| --------
| DataFrame.transpose : Transpose index and columns.
|
| Examples
| --------
| >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
| >>> df
| col1 col2
| 0 1 3
| 1 2 4
|
| >>> df.T
| 0 1
| col1 1 2
| col2 3 4
|
| axes
| Return a list representing the axes of the DataFrame.
|
| It has the row axis labels and column axis labels as the only members.
| They are returned in that order.
|
| Examples
| --------
| >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
| >>> df.axes
| [RangeIndex(start=0, stop=2, step=1), Index(['col1', 'col2'],
| dtype='object')]
|
| shape
| Return a tuple representing the dimensionality of the DataFrame.
|
| See Also
| --------
| ndarray.shape : Tuple of array dimensions.
|
| Examples
| --------
| >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
| >>> df.shape
| (2, 2)
|
| >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4],
| ... 'col3': [5, 6]})
| >>> df.shape
| (2, 3)
|
| style
| Returns a Styler object.
|
| Contains methods for building a styled HTML representation of the DataFrame.
|
| See Also
| --------
| io.formats.style.Styler : Helps style a DataFrame or Series according to the
| data with HTML and CSS.
|
| Examples
| --------
| >>> df = pd.DataFrame({'A': [1, 2, 3]})
| >>> df.style # doctest: +SKIP
|
| Please see
| `Table Visualization <../../user_guide/style.ipynb>`_ for more examples.
|
| values
| Return a Numpy representation of the DataFrame.
|
| .. warning::
|
| We recommend using :meth:`DataFrame.to_numpy` instead.
|
| Only the values in the DataFrame will be returned, the axes labels
| will be removed.
|
| Returns
| -------
| numpy.ndarray
| The values of the DataFrame.
|
| See Also
| --------
| DataFrame.to_numpy : Recommended alternative to this method.
| DataFrame.index : Retrieve the index labels.
| DataFrame.columns : Retrieving the column names.
|
| Notes
| -----
| The dtype will be a lower-common-denominator dtype (implicit
| upcasting); that is to say if the dtypes (even of numeric types)
| are mixed, the one that accommodates all will be chosen. Use this
| with care if you are not dealing with the blocks.
|
| e.g. If the dtypes are float16 and float32, dtype will be upcast to
| float32. If dtypes are int32 and uint8, dtype will be upcast to
| int32. By :func:`numpy.find_common_type` convention, mixing int64
| and uint64 will result in a float64 dtype.
|
| Examples
| --------
| A DataFrame where all columns are the same type (e.g., int64) results
| in an array of the same type.
|
| >>> df = pd.DataFrame({'age': [ 3, 29],
| ... 'height': [94, 170],
| ... 'weight': [31, 115]})
| >>> df
| age height weight
| 0 3 94 31
| 1 29 170 115
| >>> df.dtypes
| age int64
| height int64
| weight int64
| dtype: object
| >>> df.values
| array([[ 3, 94, 31],
| [ 29, 170, 115]])
|
| A DataFrame with mixed type columns(e.g., str/object, int64, float32)
| results in an ndarray of the broadest type that accommodates these
| mixed types (e.g., object).
|
| >>> df2 = pd.DataFrame([('parrot', 24.0, 'second'),
| ... ('lion', 80.5, 1),
| ... ('monkey', np.nan, None)],
| ... columns=('name', 'max_speed', 'rank'))
| >>> df2.dtypes
| name object
| max_speed float64
| rank object
| dtype: object
| >>> df2.values
| array([['parrot', 24.0, 'second'],
| ['lion', 80.5, 1],
| ['monkey', nan, None]], dtype=object)
|
| ----------------------------------------------------------------------
| Data descriptors defined here:
|
| columns
| The column labels of the DataFrame.
|
| Examples
| --------
| >>> df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
| >>> df
| A B
| 0 1 3
| 1 2 4
| >>> df.columns
| Index(['A', 'B'], dtype='object')
|
| index
| The index (row labels) of the DataFrame.
|
| The index of a DataFrame is a series of labels that identify each row.
| The labels can be integers, strings, or any other hashable type. The index
| is used for label-based access and alignment, and can be accessed or
| modified using this attribute.
|
| Returns
| -------
| pandas.Index
| The index labels of the DataFrame.
|
| See Also
| --------
| DataFrame.columns : The column labels of the DataFrame.
| DataFrame.to_numpy : Convert the DataFrame to a NumPy array.
|
| Examples
| --------
| >>> df = pd.DataFrame({'Name': ['Alice', 'Bob', 'Aritra'],
| ... 'Age': [25, 30, 35],
| ... 'Location': ['Seattle', 'New York', 'Kona']},
| ... index=([10, 20, 30]))
| >>> df.index
| Index([10, 20, 30], dtype='int64')
|
| In this example, we create a DataFrame with 3 rows and 3 columns,
| including Name, Age, and Location information. We set the index labels to
| be the integers 10, 20, and 30. We then access the `index` attribute of the
| DataFrame, which returns an `Index` object containing the index labels.
|
| >>> df.index = [100, 200, 300]
| >>> df
| Name Age Location
| 100 Alice 25 Seattle
| 200 Bob 30 New York
| 300 Aritra 35 Kona
|
| In this example, we modify the index labels of the DataFrame by assigning
| a new list of labels to the `index` attribute. The DataFrame is then
| updated with the new labels, and the output shows the modified DataFrame.
|
| ----------------------------------------------------------------------
| Data and other attributes defined here:
|
| __annotations__ = {'_AXIS_ORDERS': "list[Literal['index', 'columns']]"...
|
| __pandas_priority__ = 4000
|
| plot = <class 'pandas.plotting._core.PlotAccessor'>
| Make plots of Series or DataFrame.
|
| Uses the backend specified by the
| option ``plotting.backend``. By default, matplotlib is used.
|
| Parameters
| ----------
| data : Series or DataFrame
| The object for which the method is called.
| x : label or position, default None
| Only used if data is a DataFrame.
| y : label, position or list of label, positions, default None
| Allows plotting of one column versus another. Only used if data is a
| DataFrame.
| kind : str
| The kind of plot to produce:
|
| - 'line' : line plot (default)
| - 'bar' : vertical bar plot
| - 'barh' : horizontal bar plot
| - 'hist' : histogram
| - 'box' : boxplot
| - 'kde' : Kernel Density Estimation plot
| - 'density' : same as 'kde'
| - 'area' : area plot
| - 'pie' : pie plot
| - 'scatter' : scatter plot (DataFrame only)
| - 'hexbin' : hexbin plot (DataFrame only)
| ax : matplotlib axes object, default None
| An axes of the current figure.
| subplots : bool or sequence of iterables, default False
| Whether to group columns into subplots:
|
| - ``False`` : No subplots will be used
| - ``True`` : Make separate subplots for each column.
| - sequence of iterables of column labels: Create a subplot for each
| group of columns. For example `[('a', 'c'), ('b', 'd')]` will
| create 2 subplots: one with columns 'a' and 'c', and one
| with columns 'b' and 'd'. Remaining columns that aren't specified
| will be plotted in additional subplots (one per column).
|
| .. versionadded:: 1.5.0
|
| sharex : bool, default True if ax is None else False
| In case ``subplots=True``, share x axis and set some x axis labels
| to invisible; defaults to True if ax is None otherwise False if
| an ax is passed in; Be aware, that passing in both an ax and
| ``sharex=True`` will alter all x axis labels for all axis in a figure.
| sharey : bool, default False
| In case ``subplots=True``, share y axis and set some y axis labels to invisible.
| layout : tuple, optional
| (rows, columns) for the layout of subplots.
| figsize : a tuple (width, height) in inches
| Size of a figure object.
| use_index : bool, default True
| Use index as ticks for x axis.
| title : str or list
| Title to use for the plot. If a string is passed, print the string
| at the top of the figure. If a list is passed and `subplots` is
| True, print each item in the list above the corresponding subplot.
| grid : bool, default None (matlab style default)
| Axis grid lines.
| legend : bool or {'reverse'}
| Place legend on axis subplots.
| style : list or dict
| The matplotlib line style per column.
| logx : bool or 'sym', default False
| Use log scaling or symlog scaling on x axis.
|
| logy : bool or 'sym' default False
| Use log scaling or symlog scaling on y axis.
|
| loglog : bool or 'sym', default False
| Use log scaling or symlog scaling on both x and y axes.
|
| xticks : sequence
| Values to use for the xticks.
| yticks : sequence
| Values to use for the yticks.
| xlim : 2-tuple/list
| Set the x limits of the current axes.
| ylim : 2-tuple/list
| Set the y limits of the current axes.
| xlabel : label, optional
| Name to use for the xlabel on x-axis. Default uses index name as xlabel, or the
| x-column name for planar plots.
|
| .. versionchanged:: 2.0.0
|
| Now applicable to histograms.
|
| ylabel : label, optional
| Name to use for the ylabel on y-axis. Default will show no ylabel, or the
| y-column name for planar plots.
|
| .. versionchanged:: 2.0.0
|
| Now applicable to histograms.
|
| rot : float, default None
| Rotation for ticks (xticks for vertical, yticks for horizontal
| plots).
| fontsize : float, default None
| Font size for xticks and yticks.
| colormap : str or matplotlib colormap object, default None
| Colormap to select colors from. If string, load colormap with that
| name from matplotlib.
| colorbar : bool, optional
| If True, plot colorbar (only relevant for 'scatter' and 'hexbin'
| plots).
| position : float
| Specify relative alignments for bar plot layout.
| From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5
| (center).
| table : bool, Series or DataFrame, default False
| If True, draw a table using the data in the DataFrame and the data
| will be transposed to meet matplotlib's default layout.
| If a Series or DataFrame is passed, use passed data to draw a
| table.
| yerr : DataFrame, Series, array-like, dict and str
| See :ref:`Plotting with Error Bars <visualization.errorbars>` for
| detail.
| xerr : DataFrame, Series, array-like, dict and str
| Equivalent to yerr.
| stacked : bool, default False in line and bar plots, and True in area plot
| If True, create stacked plot.
| secondary_y : bool or sequence, default False
| Whether to plot on the secondary y-axis if a list/tuple, which
| columns to plot on secondary y-axis.
| mark_right : bool, default True
| When using a secondary_y axis, automatically mark the column
| labels with "(right)" in the legend.
| include_bool : bool, default is False
| If True, boolean values can be plotted.
| backend : str, default None
| Backend to use instead of the backend specified in the option
| ``plotting.backend``. For instance, 'matplotlib'. Alternatively, to
| specify the ``plotting.backend`` for the whole session, set
| ``pd.options.plotting.backend``.
| **kwargs
| Options to pass to matplotlib plotting method.
|
| Returns
| -------
| :class:`matplotlib.axes.Axes` or numpy.ndarray of them
| If the backend is not the default matplotlib one, the return value
| will be the object returned by the backend.
|
| Notes
| -----
| - See matplotlib documentation online for more on this subject
| - If `kind` = 'bar' or 'barh', you can specify relative alignments
| for bar plot layout by `position` keyword.
| From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5
| (center)
|
| Examples
| --------
| For Series:
|
| .. plot::
| :context: close-figs
|
| >>> ser = pd.Series([1, 2, 3, 3])
| >>> plot = ser.plot(kind='hist', title="My plot")
|
| For DataFrame:
|
| .. plot::
| :context: close-figs
|
| >>> df = pd.DataFrame({'length': [1.5, 0.5, 1.2, 0.9, 3],
| ... 'width': [0.7, 0.2, 0.15, 0.2, 1.1]},
| ... index=['pig', 'rabbit', 'duck', 'chicken', 'horse'])
| >>> plot = df.plot(title="DataFrame Plot")
|
| For SeriesGroupBy:
|
| .. plot::
| :context: close-figs
|
| >>> lst = [-1, -2, -3, 1, 2, 3]
| >>> ser = pd.Series([1, 2, 2, 4, 6, 6], index=lst)
| >>> plot = ser.groupby(lambda x: x > 0).plot(title="SeriesGroupBy Plot")
|
| For DataFrameGroupBy:
|
| .. plot::
| :context: close-figs
|
| >>> df = pd.DataFrame({"col1" : [1, 2, 3, 4],
| ... "col2" : ["A", "B", "A", "B"]})
| >>> plot = df.groupby("col2").plot(kind="bar", title="DataFrameGroupBy Plot")
|
|
| sparse = <class 'pandas.core.arrays.sparse.accessor.SparseFrameAccesso...
| DataFrame accessor for sparse data.
|
| Examples
| --------
| >>> df = pd.DataFrame({"a": [1, 2, 0, 0],
| ... "b": [3, 0, 0, 4]}, dtype="Sparse[int]")
| >>> df.sparse.density
| 0.5
|
|
| ----------------------------------------------------------------------
| Methods inherited from pandas.core.generic.NDFrame:
|
| __abs__(self) -> 'Self'
|
| __array__(self, dtype: 'npt.DTypeLike | None' = None, copy: 'bool_t | None' = None) -> 'np.ndarray'
|
| __array_ufunc__(self, ufunc: 'np.ufunc', method: 'str', *inputs: 'Any', **kwargs: 'Any')
|
| __bool__ = __nonzero__(self) -> 'NoReturn'
|
| __contains__(self, key) -> 'bool_t'
| True if the key is in the info axis
|
| __copy__(self, deep: 'bool_t' = True) -> 'Self'
|
| __deepcopy__(self, memo=None) -> 'Self'
| Parameters
| ----------
| memo, default None
| Standard signature. Unused
|
| __delitem__(self, key) -> 'None'
| Delete item
|
| __finalize__(self, other, method: 'str | None' = None, **kwargs) -> 'Self'
| Propagate metadata from other to self.
|
| Parameters
| ----------
| other : the object from which to get the attributes that we are going
| to propagate
| method : str, optional
| A passed method name providing context on where ``__finalize__``
| was called.
|
| .. warning::
|
| The value passed as `method` are not currently considered
| stable across pandas releases.
|
| __getattr__(self, name: 'str')
| After regular attribute access, try looking up the name
| This allows simpler access to columns for interactive use.
|
| __getstate__(self) -> 'dict[str, Any]'
| Helper for pickle.
|
| __iadd__(self, other) -> 'Self'
|
| __iand__(self, other) -> 'Self'
|
| __ifloordiv__(self, other) -> 'Self'
|
| __imod__(self, other) -> 'Self'
|
| __imul__(self, other) -> 'Self'
|
| __invert__(self) -> 'Self'
|
| __ior__(self, other) -> 'Self'
|
| __ipow__(self, other) -> 'Self'
|
| __isub__(self, other) -> 'Self'
|
| __iter__(self) -> 'Iterator'
| Iterate over info axis.
|
| Returns
| -------
| iterator
| Info axis as iterator.
|
| Examples
| --------
| >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
| >>> for x in df:
| ... print(x)
| A
| B
|
| __itruediv__(self, other) -> 'Self'
|
| __ixor__(self, other) -> 'Self'
|
| __neg__(self) -> 'Self'
|
| __nonzero__(self) -> 'NoReturn'
|
| __pos__(self) -> 'Self'
|
| __round__(self, decimals: 'int' = 0) -> 'Self'
|
| __setattr__(self, name: 'str', value) -> 'None'
| After regular attribute access, try setting the name
| This allows simpler access to columns for interactive use.
|
| __setstate__(self, state) -> 'None'
|
| abs(self) -> 'Self'
| Return a Series/DataFrame with absolute numeric value of each element.
|
| This function only applies to elements that are all numeric.
|
| Returns
| -------
| abs
| Series/DataFrame containing the absolute value of each element.
|
| See Also
| --------
| numpy.absolute : Calculate the absolute value element-wise.
|
| Notes
| -----
| For ``complex`` inputs, ``1.2 + 1j``, the absolute value is
| :math:`\sqrt{ a^2 + b^2 }`.
|
| Examples
| --------
| Absolute numeric values in a Series.
|
| >>> s = pd.Series([-1.10, 2, -3.33, 4])
| >>> s.abs()
| 0 1.10
| 1 2.00
| 2 3.33
| 3 4.00
| dtype: float64
|
| Absolute numeric values in a Series with complex numbers.
|
| >>> s = pd.Series([1.2 + 1j])
| >>> s.abs()
| 0 1.56205
| dtype: float64
|
| Absolute numeric values in a Series with a Timedelta element.
|
| >>> s = pd.Series([pd.Timedelta('1 days')])
| >>> s.abs()
| 0 1 days
| dtype: timedelta64[ns]
|
| Select rows with data closest to certain value using argsort (from
| `StackOverflow <https://stackoverflow.com/a/17758115>`__).
|
| >>> df = pd.DataFrame({
| ... 'a': [4, 5, 6, 7],
| ... 'b': [10, 20, 30, 40],
| ... 'c': [100, 50, -30, -50]
| ... })
| >>> df
| a b c
| 0 4 10 100
| 1 5 20 50
| 2 6 30 -30
| 3 7 40 -50
| >>> df.loc[(df.c - 43).abs().argsort()]
| a b c
| 1 5 20 50
| 0 4 10 100
| 2 6 30 -30
| 3 7 40 -50
|
| add_prefix(self, prefix: 'str', axis: 'Axis | None' = None) -> 'Self'
| Prefix labels with string `prefix`.
|
| For Series, the row labels are prefixed.
| For DataFrame, the column labels are prefixed.
|
| Parameters
| ----------
| prefix : str
| The string to add before each label.
| axis : {0 or 'index', 1 or 'columns', None}, default None
| Axis to add prefix on
|
| .. versionadded:: 2.0.0
|
| Returns
| -------
| Series or DataFrame
| New Series or DataFrame with updated labels.
|
| See Also
| --------
| Series.add_suffix: Suffix row labels with string `suffix`.
| DataFrame.add_suffix: Suffix column labels with string `suffix`.
|
| Examples
| --------
| >>> s = pd.Series([1, 2, 3, 4])
| >>> s
| 0 1
| 1 2
| 2 3
| 3 4
| dtype: int64
|
| >>> s.add_prefix('item_')
| item_0 1
| item_1 2
| item_2 3
| item_3 4
| dtype: int64
|
| >>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
| >>> df
| A B
| 0 1 3
| 1 2 4
| 2 3 5
| 3 4 6
|
| >>> df.add_prefix('col_')
| col_A col_B
| 0 1 3
| 1 2 4
| 2 3 5
| 3 4 6
|
| add_suffix(self, suffix: 'str', axis: 'Axis | None' = None) -> 'Self'
| Suffix labels with string `suffix`.
|
| For Series, the row labels are suffixed.
| For DataFrame, the column labels are suffixed.
|
| Parameters
| ----------
| suffix : str
| The string to add after each label.
| axis : {0 or 'index', 1 or 'columns', None}, default None
| Axis to add suffix on
|
| .. versionadded:: 2.0.0
|
| Returns
| -------
| Series or DataFrame
| New Series or DataFrame with updated labels.
|
| See Also
| --------
| Series.add_prefix: Prefix row labels with string `prefix`.
| DataFrame.add_prefix: Prefix column labels with string `prefix`.
|
| Examples
| --------
| >>> s = pd.Series([1, 2, 3, 4])
| >>> s
| 0 1
| 1 2
| 2 3
| 3 4
| dtype: int64
|
| >>> s.add_suffix('_item')
| 0_item 1
| 1_item 2
| 2_item 3
| 3_item 4
| dtype: int64
|
| >>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
| >>> df
| A B
| 0 1 3
| 1 2 4
| 2 3 5
| 3 4 6
|
| >>> df.add_suffix('_col')
| A_col B_col
| 0 1 3
| 1 2 4
| 2 3 5
| 3 4 6
|
| align(self, other: 'NDFrameT', join: 'AlignJoin' = 'outer', axis: 'Axis | None' = None, level: 'Level | None' = None, copy: 'bool_t | None' = None, fill_value: 'Hashable | None' = None, method: 'FillnaOptions | None | lib.NoDefault' = <no_default>, limit: 'int | None | lib.NoDefault' = <no_default>, fill_axis: 'Axis | lib.NoDefault' = <no_default>, broadcast_axis: 'Axis | None | lib.NoDefault' = <no_default>) -> 'tuple[Self, NDFrameT]'
| Align two objects on their axes with the specified join method.
|
| Join method is specified for each axis Index.
|
| Parameters
| ----------
| other : DataFrame or Series
| join : {'outer', 'inner', 'left', 'right'}, default 'outer'
| Type of alignment to be performed.
|
| * left: use only keys from left frame, preserve key order.
| * right: use only keys from right frame, preserve key order.
| * outer: use union of keys from both frames, sort keys lexicographically.
| * inner: use intersection of keys from both frames,
| preserve the order of the left keys.
|
| axis : allowed axis of the other object, default None
| Align on index (0), columns (1), or both (None).
| level : int or level name, default None
| Broadcast across a level, matching Index values on the
| passed MultiIndex level.
| copy : bool, default True
| Always returns new objects. If copy=False and no reindexing is
| required then original objects are returned.
|
| .. note::
| The `copy` keyword will change behavior in pandas 3.0.
| `Copy-on-Write
| <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
| will be enabled by default, which means that all methods with a
| `copy` keyword will use a lazy copy mechanism to defer the copy and
| ignore the `copy` keyword. The `copy` keyword will be removed in a
| future version of pandas.
|
| You can already get the future behavior and improvements through
| enabling copy on write ``pd.options.mode.copy_on_write = True``
| fill_value : scalar, default np.nan
| Value to use for missing values. Defaults to NaN, but can be any
| "compatible" value.
| method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
| Method to use for filling holes in reindexed Series:
|
| - pad / ffill: propagate last valid observation forward to next valid.
| - backfill / bfill: use NEXT valid observation to fill gap.
|
| .. deprecated:: 2.1
|
| limit : int, default None
| If method is specified, this is the maximum number of consecutive
| NaN values to forward/backward fill. In other words, if there is
| a gap with more than this number of consecutive NaNs, it will only
| be partially filled. If method is not specified, this is the
| maximum number of entries along the entire axis where NaNs will be
| filled. Must be greater than 0 if not None.
|
| .. deprecated:: 2.1
|
| fill_axis : {0 or 'index'} for Series, {0 or 'index', 1 or 'columns'} for DataFrame, default 0
| Filling axis, method and limit.
|
| .. deprecated:: 2.1
|
| broadcast_axis : {0 or 'index'} for Series, {0 or 'index', 1 or 'columns'} for DataFrame, default None
| Broadcast values along this axis, if aligning two objects of
| different dimensions.
|
| .. deprecated:: 2.1
|
| Returns
| -------
| tuple of (Series/DataFrame, type of other)
| Aligned objects.
|
| Examples
| --------
| >>> df = pd.DataFrame(
| ... [[1, 2, 3, 4], [6, 7, 8, 9]], columns=["D", "B", "E", "A"], index=[1, 2]
| ... )
| >>> other = pd.DataFrame(
| ... [[10, 20, 30, 40], [60, 70, 80, 90], [600, 700, 800, 900]],
| ... columns=["A", "B", "C", "D"],
| ... index=[2, 3, 4],
| ... )
| >>> df
| D B E A
| 1 1 2 3 4
| 2 6 7 8 9
| >>> other
| A B C D
| 2 10 20 30 40
| 3 60 70 80 90
| 4 600 700 800 900
|
| Align on columns:
|
| >>> left, right = df.align(other, join="outer", axis=1)
| >>> left
| A B C D E
| 1 4 2 NaN 1 3
| 2 9 7 NaN 6 8
| >>> right
| A B C D E
| 2 10 20 30 40 NaN
| 3 60 70 80 90 NaN
| 4 600 700 800 900 NaN
|
| We can also align on the index:
|
| >>> left, right = df.align(other, join="outer", axis=0)
| >>> left
| D B E A
| 1 1.0 2.0 3.0 4.0
| 2 6.0 7.0 8.0 9.0
| 3 NaN NaN NaN NaN
| 4 NaN NaN NaN NaN
| >>> right
| A B C D
| 1 NaN NaN NaN NaN
| 2 10.0 20.0 30.0 40.0
| 3 60.0 70.0 80.0 90.0
| 4 600.0 700.0 800.0 900.0
|
| Finally, the default `axis=None` will align on both index and columns:
|
| >>> left, right = df.align(other, join="outer", axis=None)
| >>> left
| A B C D E
| 1 4.0 2.0 NaN 1.0 3.0
| 2 9.0 7.0 NaN 6.0 8.0
| 3 NaN NaN NaN NaN NaN
| 4 NaN NaN NaN NaN NaN
| >>> right
| A B C D E
| 1 NaN NaN NaN NaN NaN
| 2 10.0 20.0 30.0 40.0 NaN
| 3 60.0 70.0 80.0 90.0 NaN
| 4 600.0 700.0 800.0 900.0 NaN
|
| asfreq(self, freq: 'Frequency', method: 'FillnaOptions | None' = None, how: "Literal['start', 'end'] | None" = None, normalize: 'bool_t' = False, fill_value: 'Hashable | None' = None) -> 'Self'
| Convert time series to specified frequency.
|
| Returns the original data conformed to a new index with the specified
| frequency.
|
| If the index of this Series/DataFrame is a :class:`~pandas.PeriodIndex`, the new index
| is the result of transforming the original index with
| :meth:`PeriodIndex.asfreq <pandas.PeriodIndex.asfreq>` (so the original index
| will map one-to-one to the new index).
|
| Otherwise, the new index will be equivalent to ``pd.date_range(start, end,
| freq=freq)`` where ``start`` and ``end`` are, respectively, the first and
| last entries in the original index (see :func:`pandas.date_range`). The
| values corresponding to any timesteps in the new index which were not present
| in the original index will be null (``NaN``), unless a method for filling
| such unknowns is provided (see the ``method`` parameter below).
|
| The :meth:`resample` method is more appropriate if an operation on each group of
| timesteps (such as an aggregate) is necessary to represent the data at the new
| frequency.
|
| Parameters
| ----------
| freq : DateOffset or str
| Frequency DateOffset or string.
| method : {'backfill'/'bfill', 'pad'/'ffill'}, default None
| Method to use for filling holes in reindexed Series (note this
| does not fill NaNs that already were present):
|
| * 'pad' / 'ffill': propagate last valid observation forward to next
| valid
| * 'backfill' / 'bfill': use NEXT valid observation to fill.
| how : {'start', 'end'}, default end
| For PeriodIndex only (see PeriodIndex.asfreq).
| normalize : bool, default False
| Whether to reset output index to midnight.
| fill_value : scalar, optional
| Value to use for missing values, applied during upsampling (note
| this does not fill NaNs that already were present).
|
| Returns
| -------
| Series/DataFrame
| Series/DataFrame object reindexed to the specified frequency.
|
| See Also
| --------
| reindex : Conform DataFrame to new index with optional filling logic.
|
| Notes
| -----
| To learn more about the frequency strings, please see `this link
| <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
|
| Examples
| --------
| Start by creating a series with 4 one minute timestamps.
|
| >>> index = pd.date_range('1/1/2000', periods=4, freq='min')
| >>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)
| >>> df = pd.DataFrame({'s': series})
| >>> df
| s
| 2000-01-01 00:00:00 0.0
| 2000-01-01 00:01:00 NaN
| 2000-01-01 00:02:00 2.0
| 2000-01-01 00:03:00 3.0
|
| Upsample the series into 30 second bins.
|
| >>> df.asfreq(freq='30s')
| s
| 2000-01-01 00:00:00 0.0
| 2000-01-01 00:00:30 NaN
| 2000-01-01 00:01:00 NaN
| 2000-01-01 00:01:30 NaN
| 2000-01-01 00:02:00 2.0
| 2000-01-01 00:02:30 NaN
| 2000-01-01 00:03:00 3.0
|
| Upsample again, providing a ``fill value``.
|
| >>> df.asfreq(freq='30s', fill_value=9.0)
| s
| 2000-01-01 00:00:00 0.0
| 2000-01-01 00:00:30 9.0
| 2000-01-01 00:01:00 NaN
| 2000-01-01 00:01:30 9.0
| 2000-01-01 00:02:00 2.0
| 2000-01-01 00:02:30 9.0
| 2000-01-01 00:03:00 3.0
|
| Upsample again, providing a ``method``.
|
| >>> df.asfreq(freq='30s', method='bfill')
| s
| 2000-01-01 00:00:00 0.0
| 2000-01-01 00:00:30 NaN
| 2000-01-01 00:01:00 NaN
| 2000-01-01 00:01:30 2.0
| 2000-01-01 00:02:00 2.0
| 2000-01-01 00:02:30 3.0
| 2000-01-01 00:03:00 3.0
|
| asof(self, where, subset=None)
| Return the last row(s) without any NaNs before `where`.
|
| The last row (for each element in `where`, if list) without any
| NaN is taken.
| In case of a :class:`~pandas.DataFrame`, the last row without NaN
| considering only the subset of columns (if not `None`)
|
| If there is no good value, NaN is returned for a Series or
| a Series of NaN values for a DataFrame
|
| Parameters
| ----------
| where : date or array-like of dates
| Date(s) before which the last row(s) are returned.
| subset : str or array-like of str, default `None`
| For DataFrame, if not `None`, only use these columns to
| check for NaNs.
|
| Returns
| -------
| scalar, Series, or DataFrame
|
| The return can be:
|
| * scalar : when `self` is a Series and `where` is a scalar
| * Series: when `self` is a Series and `where` is an array-like,
| or when `self` is a DataFrame and `where` is a scalar
| * DataFrame : when `self` is a DataFrame and `where` is an
| array-like
|
| See Also
| --------
| merge_asof : Perform an asof merge. Similar to left join.
|
| Notes
| -----
| Dates are assumed to be sorted. Raises if this is not the case.
|
| Examples
| --------
| A Series and a scalar `where`.
|
| >>> s = pd.Series([1, 2, np.nan, 4], index=[10, 20, 30, 40])
| >>> s
| 10 1.0
| 20 2.0
| 30 NaN
| 40 4.0
| dtype: float64
|
| >>> s.asof(20)
| 2.0
|
| For a sequence `where`, a Series is returned. The first value is
| NaN, because the first element of `where` is before the first
| index value.
|
| >>> s.asof([5, 20])
| 5 NaN
| 20 2.0
| dtype: float64
|
| Missing values are not considered. The following is ``2.0``, not
| NaN, even though NaN is at the index location for ``30``.
|
| >>> s.asof(30)
| 2.0
|
| Take all columns into consideration
|
| >>> df = pd.DataFrame({'a': [10., 20., 30., 40., 50.],
| ... 'b': [None, None, None, None, 500]},
| ... index=pd.DatetimeIndex(['2018-02-27 09:01:00',
| ... '2018-02-27 09:02:00',
| ... '2018-02-27 09:03:00',
| ... '2018-02-27 09:04:00',
| ... '2018-02-27 09:05:00']))
| >>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
| ... '2018-02-27 09:04:30']))
| a b
| 2018-02-27 09:03:30 NaN NaN
| 2018-02-27 09:04:30 NaN NaN
|
| Take a single column into consideration
|
| >>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
| ... '2018-02-27 09:04:30']),
| ... subset=['a'])
| a b
| 2018-02-27 09:03:30 30.0 NaN
| 2018-02-27 09:04:30 40.0 NaN
|
| astype(self, dtype, copy: 'bool_t | None' = None, errors: 'IgnoreRaise' = 'raise') -> 'Self'
| Cast a pandas object to a specified dtype ``dtype``.
|
| Parameters
| ----------
| dtype : str, data type, Series or Mapping of column name -> data type
| Use a str, numpy.dtype, pandas.ExtensionDtype or Python type to
| cast entire pandas object to the same type. Alternatively, use a
| mapping, e.g. {col: dtype, ...}, where col is a column label and dtype is
| a numpy.dtype or Python type to cast one or more of the DataFrame's
| columns to column-specific types.
| copy : bool, default True
| Return a copy when ``copy=True`` (be very careful setting
| ``copy=False`` as changes to values then may propagate to other
| pandas objects).
|
| .. note::
| The `copy` keyword will change behavior in pandas 3.0.
| `Copy-on-Write
| <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
| will be enabled by default, which means that all methods with a
| `copy` keyword will use a lazy copy mechanism to defer the copy and
| ignore the `copy` keyword. The `copy` keyword will be removed in a
| future version of pandas.
|
| You can already get the future behavior and improvements through
| enabling copy on write ``pd.options.mode.copy_on_write = True``
| errors : {'raise', 'ignore'}, default 'raise'
| Control raising of exceptions on invalid data for provided dtype.
|
| - ``raise`` : allow exceptions to be raised
| - ``ignore`` : suppress exceptions. On error return original object.
|
| Returns
| -------
| same type as caller
|
| See Also
| --------
| to_datetime : Convert argument to datetime.
| to_timedelta : Convert argument to timedelta.
| to_numeric : Convert argument to a numeric type.
| numpy.ndarray.astype : Cast a numpy array to a specified type.
|
| Notes
| -----
| .. versionchanged:: 2.0.0
|
| Using ``astype`` to convert from timezone-naive dtype to
| timezone-aware dtype will raise an exception.
| Use :meth:`Series.dt.tz_localize` instead.
|
| Examples
| --------
| Create a DataFrame:
|
| >>> d = {'col1': [1, 2], 'col2': [3, 4]}
| >>> df = pd.DataFrame(data=d)
| >>> df.dtypes
| col1 int64
| col2 int64
| dtype: object
|
| Cast all columns to int32:
|
| >>> df.astype('int32').dtypes
| col1 int32
| col2 int32
| dtype: object
|
| Cast col1 to int32 using a dictionary:
|
| >>> df.astype({'col1': 'int32'}).dtypes
| col1 int32
| col2 int64
| dtype: object
|
| Create a series:
|
| >>> ser = pd.Series([1, 2], dtype='int32')
| >>> ser
| 0 1
| 1 2
| dtype: int32
| >>> ser.astype('int64')
| 0 1
| 1 2
| dtype: int64
|
| Convert to categorical type:
|
| >>> ser.astype('category')
| 0 1
| 1 2
| dtype: category
| Categories (2, int32): [1, 2]
|
| Convert to ordered categorical type with custom ordering:
|
| >>> from pandas.api.types import CategoricalDtype
| >>> cat_dtype = CategoricalDtype(
| ... categories=[2, 1], ordered=True)
| >>> ser.astype(cat_dtype)
| 0 1
| 1 2
| dtype: category
| Categories (2, int64): [2 < 1]
|
| Create a series of dates:
|
| >>> ser_date = pd.Series(pd.date_range('20200101', periods=3))
| >>> ser_date
| 0 2020-01-01
| 1 2020-01-02
| 2 2020-01-03
| dtype: datetime64[ns]
|
| at_time(self, time, asof: 'bool_t' = False, axis: 'Axis | None' = None) -> 'Self'
| Select values at particular time of day (e.g., 9:30AM).
|
| Parameters
| ----------
| time : datetime.time or str
| The values to select.
| axis : {0 or 'index', 1 or 'columns'}, default 0
| For `Series` this parameter is unused and defaults to 0.
|
| Returns
| -------
| Series or DataFrame
|
| Raises
| ------
| TypeError
| If the index is not a :class:`DatetimeIndex`
|
| See Also
| --------
| between_time : Select values between particular times of the day.
| first : Select initial periods of time series based on a date offset.
| last : Select final periods of time series based on a date offset.
| DatetimeIndex.indexer_at_time : Get just the index locations for
| values at particular time of the day.
|
| Examples
| --------
| >>> i = pd.date_range('2018-04-09', periods=4, freq='12h')
| >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
| >>> ts
| A
| 2018-04-09 00:00:00 1
| 2018-04-09 12:00:00 2
| 2018-04-10 00:00:00 3
| 2018-04-10 12:00:00 4
|
| >>> ts.at_time('12:00')
| A
| 2018-04-09 12:00:00 2
| 2018-04-10 12:00:00 4
|
| backfill(self, *, axis: 'None | Axis' = None, inplace: 'bool_t' = False, limit: 'None | int' = None, downcast: 'dict | None | lib.NoDefault' = <no_default>) -> 'Self | None'
| Fill NA/NaN values by using the next valid observation to fill the gap.
|
| .. deprecated:: 2.0
|
| Series/DataFrame.backfill is deprecated. Use Series/DataFrame.bfill instead.
|
| Returns
| -------
| Series/DataFrame or None
| Object with missing values filled or None if ``inplace=True``.
|
| Examples
| --------
| Please see examples for :meth:`DataFrame.bfill` or :meth:`Series.bfill`.
|
| between_time(self, start_time, end_time, inclusive: 'IntervalClosedType' = 'both', axis: 'Axis | None' = None) -> 'Self'
| Select values between particular times of the day (e.g., 9:00-9:30 AM).
|
| By setting ``start_time`` to be later than ``end_time``,
| you can get the times that are *not* between the two times.
|
| Parameters
| ----------
| start_time : datetime.time or str
| Initial time as a time filter limit.
| end_time : datetime.time or str
| End time as a time filter limit.
| inclusive : {"both", "neither", "left", "right"}, default "both"
| Include boundaries; whether to set each bound as closed or open.
| axis : {0 or 'index', 1 or 'columns'}, default 0
| Determine range time on index or columns value.
| For `Series` this parameter is unused and defaults to 0.
|
| Returns
| -------
| Series or DataFrame
| Data from the original object filtered to the specified dates range.
|
| Raises
| ------
| TypeError
| If the index is not a :class:`DatetimeIndex`
|
| See Also
| --------
| at_time : Select values at a particular time of the day.
| first : Select initial periods of time series based on a date offset.
| last : Select final periods of time series based on a date offset.
| DatetimeIndex.indexer_between_time : Get just the index locations for
| values between particular times of the day.
|
| Examples
| --------
| >>> i = pd.date_range('2018-04-09', periods=4, freq='1D20min')
| >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
| >>> ts
| A
| 2018-04-09 00:00:00 1
| 2018-04-10 00:20:00 2
| 2018-04-11 00:40:00 3
| 2018-04-12 01:00:00 4
|
| >>> ts.between_time('0:15', '0:45')
| A
| 2018-04-10 00:20:00 2
| 2018-04-11 00:40:00 3
|
| You get the times that are *not* between two times by setting
| ``start_time`` later than ``end_time``:
|
| >>> ts.between_time('0:45', '0:15')
| A
| 2018-04-09 00:00:00 1
| 2018-04-12 01:00:00 4
|
| bfill(self, *, axis: 'None | Axis' = None, inplace: 'bool_t' = False, limit: 'None | int' = None, limit_area: "Literal['inside', 'outside'] | None" = None, downcast: 'dict | None | lib.NoDefault' = <no_default>) -> 'Self | None'
| Fill NA/NaN values by using the next valid observation to fill the gap.
|
| Parameters
| ----------
| axis : {0 or 'index'} for Series, {0 or 'index', 1 or 'columns'} for DataFrame
| Axis along which to fill missing values. For `Series`
| this parameter is unused and defaults to 0.
| inplace : bool, default False
| If True, fill in-place. Note: this will modify any
| other views on this object (e.g., a no-copy slice for a column in a
| DataFrame).
| limit : int, default None
| If method is specified, this is the maximum number of consecutive
| NaN values to forward/backward fill. In other words, if there is
| a gap with more than this number of consecutive NaNs, it will only
| be partially filled. If method is not specified, this is the
| maximum number of entries along the entire axis where NaNs will be
| filled. Must be greater than 0 if not None.
| limit_area : {`None`, 'inside', 'outside'}, default None
| If limit is specified, consecutive NaNs will be filled with this
| restriction.
|
| * ``None``: No fill restriction.
| * 'inside': Only fill NaNs surrounded by valid values
| (interpolate).
| * 'outside': Only fill NaNs outside valid values (extrapolate).
|
| .. versionadded:: 2.2.0
|
| downcast : dict, default is None
| A dict of item->dtype of what to downcast if possible,
| or the string 'infer' which will try to downcast to an appropriate
| equal type (e.g. float64 to int64 if possible).
|
| .. deprecated:: 2.2.0
|
| Returns
| -------
| Series/DataFrame or None
| Object with missing values filled or None if ``inplace=True``.
|
| Examples
| --------
| For Series:
|
| >>> s = pd.Series([1, None, None, 2])
| >>> s.bfill()
| 0 1.0
| 1 2.0
| 2 2.0
| 3 2.0
| dtype: float64
| >>> s.bfill(limit=1)
| 0 1.0
| 1 NaN
| 2 2.0
| 3 2.0
| dtype: float64
|
| With DataFrame:
|
| >>> df = pd.DataFrame({'A': [1, None, None, 4], 'B': [None, 5, None, 7]})
| >>> df
| A B
| 0 1.0 NaN
| 1 NaN 5.0
| 2 NaN NaN
| 3 4.0 7.0
| >>> df.bfill()
| A B
| 0 1.0 5.0
| 1 4.0 5.0
| 2 4.0 7.0
| 3 4.0 7.0
| >>> df.bfill(limit=1)
| A B
| 0 1.0 5.0
| 1 NaN 5.0
| 2 4.0 7.0
| 3 4.0 7.0
|
| bool(self) -> 'bool_t'
| Return the bool of a single element Series or DataFrame.
|
| .. deprecated:: 2.1.0
|
| bool is deprecated and will be removed in future version of pandas.
| For ``Series`` use ``pandas.Series.item``.
|
| This must be a boolean scalar value, either True or False. It will raise a
| ValueError if the Series or DataFrame does not have exactly 1 element, or that
| element is not boolean (integer values 0 and 1 will also raise an exception).
|
| Returns
| -------
| bool
| The value in the Series or DataFrame.
|
| See Also
| --------
| Series.astype : Change the data type of a Series, including to boolean.
| DataFrame.astype : Change the data type of a DataFrame, including to boolean.
| numpy.bool_ : NumPy boolean data type, used by pandas for boolean values.
|
| Examples
| --------
| The method will only work for single element objects with a boolean value:
|
| >>> pd.Series([True]).bool() # doctest: +SKIP
| True
| >>> pd.Series([False]).bool() # doctest: +SKIP
| False
|
| >>> pd.DataFrame({'col': [True]}).bool() # doctest: +SKIP
| True
| >>> pd.DataFrame({'col': [False]}).bool() # doctest: +SKIP
| False
|
| This is an alternative method and will only work
| for single element objects with a boolean value:
|
| >>> pd.Series([True]).item() # doctest: +SKIP
| True
| >>> pd.Series([False]).item() # doctest: +SKIP
| False
|
| clip(self, lower=None, upper=None, *, axis: 'Axis | None' = None, inplace: 'bool_t' = False, **kwargs) -> 'Self | None'
| Trim values at input threshold(s).
|
| Assigns values outside boundary to boundary values. Thresholds
| can be singular values or array like, and in the latter case
| the clipping is performed element-wise in the specified axis.
|
| Parameters
| ----------
| lower : float or array-like, default None
| Minimum threshold value. All values below this
| threshold will be set to it. A missing
| threshold (e.g `NA`) will not clip the value.
| upper : float or array-like, default None
| Maximum threshold value. All values above this
| threshold will be set to it. A missing
| threshold (e.g `NA`) will not clip the value.
| axis : {{0 or 'index', 1 or 'columns', None}}, default None
| Align object with lower and upper along the given axis.
| For `Series` this parameter is unused and defaults to `None`.
| inplace : bool, default False
| Whether to perform the operation in place on the data.
| *args, **kwargs
| Additional keywords have no effect but might be accepted
| for compatibility with numpy.
|
| Returns
| -------
| Series or DataFrame or None
| Same type as calling object with the values outside the
| clip boundaries replaced or None if ``inplace=True``.
|
| See Also
| --------
| Series.clip : Trim values at input threshold in series.
| DataFrame.clip : Trim values at input threshold in dataframe.
| numpy.clip : Clip (limit) the values in an array.
|
| Examples
| --------
| >>> data = {'col_0': [9, -3, 0, -1, 5], 'col_1': [-2, -7, 6, 8, -5]}
| >>> df = pd.DataFrame(data)
| >>> df
| col_0 col_1
| 0 9 -2
| 1 -3 -7
| 2 0 6
| 3 -1 8
| 4 5 -5
|
| Clips per column using lower and upper thresholds:
|
| >>> df.clip(-4, 6)
| col_0 col_1
| 0 6 -2
| 1 -3 -4
| 2 0 6
| 3 -1 6
| 4 5 -4
|
| Clips using specific lower and upper thresholds per column:
|
| >>> df.clip([-2, -1], [4, 5])
| col_0 col_1
| 0 4 -1
| 1 -2 -1
| 2 0 5
| 3 -1 5
| 4 4 -1
|
| Clips using specific lower and upper thresholds per column element:
|
| >>> t = pd.Series([2, -4, -1, 6, 3])
| >>> t
| 0 2
| 1 -4
| 2 -1
| 3 6
| 4 3
| dtype: int64
|
| >>> df.clip(t, t + 4, axis=0)
| col_0 col_1
| 0 6 2
| 1 -3 -4
| 2 0 3
| 3 6 8
| 4 5 3
|
| Clips using specific lower threshold per column element, with missing values:
|
| >>> t = pd.Series([2, -4, np.nan, 6, 3])
| >>> t
| 0 2.0
| 1 -4.0
| 2 NaN
| 3 6.0
| 4 3.0
| dtype: float64
|
| >>> df.clip(t, axis=0)
| col_0 col_1
| 0 9 2
| 1 -3 -4
| 2 0 6
| 3 6 8
| 4 5 3
|
| convert_dtypes(self, infer_objects: 'bool_t' = True, convert_string: 'bool_t' = True, convert_integer: 'bool_t' = True, convert_boolean: 'bool_t' = True, convert_floating: 'bool_t' = True, dtype_backend: 'DtypeBackend' = 'numpy_nullable') -> 'Self'
| Convert columns to the best possible dtypes using dtypes supporting ``pd.NA``.
|
| Parameters
| ----------
| infer_objects : bool, default True
| Whether object dtypes should be converted to the best possible types.
| convert_string : bool, default True
| Whether object dtypes should be converted to ``StringDtype()``.
| convert_integer : bool, default True
| Whether, if possible, conversion can be done to integer extension types.
| convert_boolean : bool, defaults True
| Whether object dtypes should be converted to ``BooleanDtypes()``.
| convert_floating : bool, defaults True
| Whether, if possible, conversion can be done to floating extension types.
| If `convert_integer` is also True, preference will be give to integer
| dtypes if the floats can be faithfully casted to integers.
| dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
| Back-end data type applied to the resultant :class:`DataFrame`
| (still experimental). Behaviour is as follows:
|
| * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
| (default).
| * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
| DataFrame.
|
| .. versionadded:: 2.0
|
| Returns
| -------
| Series or DataFrame
| Copy of input object with new dtype.
|
| See Also
| --------
| infer_objects : Infer dtypes of objects.
| to_datetime : Convert argument to datetime.
| to_timedelta : Convert argument to timedelta.
| to_numeric : Convert argument to a numeric type.
|
| Notes
| -----
| By default, ``convert_dtypes`` will attempt to convert a Series (or each
| Series in a DataFrame) to dtypes that support ``pd.NA``. By using the options
| ``convert_string``, ``convert_integer``, ``convert_boolean`` and
| ``convert_floating``, it is possible to turn off individual conversions
| to ``StringDtype``, the integer extension types, ``BooleanDtype``
| or floating extension types, respectively.
|
| For object-dtyped columns, if ``infer_objects`` is ``True``, use the inference
| rules as during normal Series/DataFrame construction. Then, if possible,
| convert to ``StringDtype``, ``BooleanDtype`` or an appropriate integer
| or floating extension type, otherwise leave as ``object``.
|
| If the dtype is integer, convert to an appropriate integer extension type.
|
| If the dtype is numeric, and consists of all integers, convert to an
| appropriate integer extension type. Otherwise, convert to an
| appropriate floating extension type.
|
| In the future, as new dtypes are added that support ``pd.NA``, the results
| of this method will change to support those new dtypes.
|
| Examples
| --------
| >>> df = pd.DataFrame(
| ... {
| ... "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")),
| ... "b": pd.Series(["x", "y", "z"], dtype=np.dtype("O")),
| ... "c": pd.Series([True, False, np.nan], dtype=np.dtype("O")),
| ... "d": pd.Series(["h", "i", np.nan], dtype=np.dtype("O")),
| ... "e": pd.Series([10, np.nan, 20], dtype=np.dtype("float")),
| ... "f": pd.Series([np.nan, 100.5, 200], dtype=np.dtype("float")),
| ... }
| ... )
|
| Start with a DataFrame with default dtypes.
|
| >>> df
| a b c d e f
| 0 1 x True h 10.0 NaN
| 1 2 y False i NaN 100.5
| 2 3 z NaN NaN 20.0 200.0
|
| >>> df.dtypes
| a int32
| b object
| c object
| d object
| e float64
| f float64
| dtype: object
|
| Convert the DataFrame to use best possible dtypes.
|
| >>> dfn = df.convert_dtypes()
| >>> dfn
| a b c d e f
| 0 1 x True h 10 <NA>
| 1 2 y False i <NA> 100.5
| 2 3 z <NA> <NA> 20 200.0
|
| >>> dfn.dtypes
| a Int32
| b string[python]
| c boolean
| d string[python]
| e Int64
| f Float64
| dtype: object
|
| Start with a Series of strings and missing data represented by ``np.nan``.
|
| >>> s = pd.Series(["a", "b", np.nan])
| >>> s
| 0 a
| 1 b
| 2 NaN
| dtype: object
|
| Obtain a Series with dtype ``StringDtype``.
|
| >>> s.convert_dtypes()
| 0 a
| 1 b
| 2 <NA>
| dtype: string
|
| copy(self, deep: 'bool_t | None' = True) -> 'Self'
| Make a copy of this object's indices and data.
|
| When ``deep=True`` (default), a new object will be created with a
| copy of the calling object's data and indices. Modifications to
| the data or indices of the copy will not be reflected in the
| original object (see notes below).
|
| When ``deep=False``, a new object will be created without copying
| the calling object's data or index (only references to the data
| and index are copied). Any changes to the data of the original
| will be reflected in the shallow copy (and vice versa).
|
| .. note::
| The ``deep=False`` behaviour as described above will change
| in pandas 3.0. `Copy-on-Write
| <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
| will be enabled by default, which means that the "shallow" copy
| is that is returned with ``deep=False`` will still avoid making
| an eager copy, but changes to the data of the original will *no*
| longer be reflected in the shallow copy (or vice versa). Instead,
| it makes use of a lazy (deferred) copy mechanism that will copy
| the data only when any changes to the original or shallow copy is
| made.
|
| You can already get the future behavior and improvements through
| enabling copy on write ``pd.options.mode.copy_on_write = True``
|
| Parameters
| ----------
| deep : bool, default True
| Make a deep copy, including a copy of the data and the indices.
| With ``deep=False`` neither the indices nor the data are copied.
|
| Returns
| -------
| Series or DataFrame
| Object type matches caller.
|
| Notes
| -----
| When ``deep=True``, data is copied but actual Python objects
| will not be copied recursively, only the reference to the object.
| This is in contrast to `copy.deepcopy` in the Standard Library,
| which recursively copies object data (see examples below).
|
| While ``Index`` objects are copied when ``deep=True``, the underlying
| numpy array is not copied for performance reasons. Since ``Index`` is
| immutable, the underlying data can be safely shared and a copy
| is not needed.
|
| Since pandas is not thread safe, see the
| :ref:`gotchas <gotchas.thread-safety>` when copying in a threading
| environment.
|
| When ``copy_on_write`` in pandas config is set to ``True``, the
| ``copy_on_write`` config takes effect even when ``deep=False``.
| This means that any changes to the copied data would make a new copy
| of the data upon write (and vice versa). Changes made to either the
| original or copied variable would not be reflected in the counterpart.
| See :ref:`Copy_on_Write <copy_on_write>` for more information.
|
| Examples
| --------
| >>> s = pd.Series([1, 2], index=["a", "b"])
| >>> s
| a 1
| b 2
| dtype: int64
|
| >>> s_copy = s.copy()
| >>> s_copy
| a 1
| b 2
| dtype: int64
|
| **Shallow copy versus default (deep) copy:**
|
| >>> s = pd.Series([1, 2], index=["a", "b"])
| >>> deep = s.copy()
| >>> shallow = s.copy(deep=False)
|
| Shallow copy shares data and index with original.
|
| >>> s is shallow
| False
| >>> s.values is shallow.values and s.index is shallow.index
| True
|
| Deep copy has own copy of data and index.
|
| >>> s is deep
| False
| >>> s.values is deep.values or s.index is deep.index
| False
|
| Updates to the data shared by shallow copy and original is reflected
| in both (NOTE: this will no longer be true for pandas >= 3.0);
| deep copy remains unchanged.
|
| >>> s.iloc[0] = 3
| >>> shallow.iloc[1] = 4
| >>> s
| a 3
| b 4
| dtype: int64
| >>> shallow
| a 3
| b 4
| dtype: int64
| >>> deep
| a 1
| b 2
| dtype: int64
|
| Note that when copying an object containing Python objects, a deep copy
| will copy the data, but will not do so recursively. Updating a nested
| data object will be reflected in the deep copy.
|
| >>> s = pd.Series([[1, 2], [3, 4]])
| >>> deep = s.copy()
| >>> s[0][0] = 10
| >>> s
| 0 [10, 2]
| 1 [3, 4]
| dtype: object
| >>> deep
| 0 [10, 2]
| 1 [3, 4]
| dtype: object
|
| **Copy-on-Write is set to true**, the shallow copy is not modified
| when the original data is changed:
|
| >>> with pd.option_context("mode.copy_on_write", True):
| ... s = pd.Series([1, 2], index=["a", "b"])
| ... copy = s.copy(deep=False)
| ... s.iloc[0] = 100
| ... s
| a 100
| b 2
| dtype: int64
| >>> copy
| a 1
| b 2
| dtype: int64
|
| describe(self, percentiles=None, include=None, exclude=None) -> 'Self'
| Generate descriptive statistics.
|
| Descriptive statistics include those that summarize the central
| tendency, dispersion and shape of a
| dataset's distribution, excluding ``NaN`` values.
|
| Analyzes both numeric and object series, as well
| as ``DataFrame`` column sets of mixed data types. The output
| will vary depending on what is provided. Refer to the notes
| below for more detail.
|
| Parameters
| ----------
| percentiles : list-like of numbers, optional
| The percentiles to include in the output. All should
| fall between 0 and 1. The default is
| ``[.25, .5, .75]``, which returns the 25th, 50th, and
| 75th percentiles.
| include : 'all', list-like of dtypes or None (default), optional
| A white list of data types to include in the result. Ignored
| for ``Series``. Here are the options:
|
| - 'all' : All columns of the input will be included in the output.
| - A list-like of dtypes : Limits the results to the
| provided data types.
| To limit the result to numeric types submit
| ``numpy.number``. To limit it instead to object columns submit
| the ``numpy.object`` data type. Strings
| can also be used in the style of
| ``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To
| select pandas categorical columns, use ``'category'``
| - None (default) : The result will include all numeric columns.
| exclude : list-like of dtypes or None (default), optional,
| A black list of data types to omit from the result. Ignored
| for ``Series``. Here are the options:
|
| - A list-like of dtypes : Excludes the provided data types
| from the result. To exclude numeric types submit
| ``numpy.number``. To exclude object columns submit the data
| type ``numpy.object``. Strings can also be used in the style of
| ``select_dtypes`` (e.g. ``df.describe(exclude=['O'])``). To
| exclude pandas categorical columns, use ``'category'``
| - None (default) : The result will exclude nothing.
|
| Returns
| -------
| Series or DataFrame
| Summary statistics of the Series or Dataframe provided.
|
| See Also
| --------
| DataFrame.count: Count number of non-NA/null observations.
| DataFrame.max: Maximum of the values in the object.
| DataFrame.min: Minimum of the values in the object.
| DataFrame.mean: Mean of the values.
| DataFrame.std: Standard deviation of the observations.
| DataFrame.select_dtypes: Subset of a DataFrame including/excluding
| columns based on their dtype.
|
| Notes
| -----
| For numeric data, the result's index will include ``count``,
| ``mean``, ``std``, ``min``, ``max`` as well as lower, ``50`` and
| upper percentiles. By default the lower percentile is ``25`` and the
| upper percentile is ``75``. The ``50`` percentile is the
| same as the median.
|
| For object data (e.g. strings or timestamps), the result's index
| will include ``count``, ``unique``, ``top``, and ``freq``. The ``top``
| is the most common value. The ``freq`` is the most common value's
| frequency. Timestamps also include the ``first`` and ``last`` items.
|
| If multiple object values have the highest count, then the
| ``count`` and ``top`` results will be arbitrarily chosen from
| among those with the highest count.
|
| For mixed data types provided via a ``DataFrame``, the default is to
| return only an analysis of numeric columns. If the dataframe consists
| only of object and categorical data without any numeric columns, the
| default is to return an analysis of both the object and categorical
| columns. If ``include='all'`` is provided as an option, the result
| will include a union of attributes of each type.
|
| The `include` and `exclude` parameters can be used to limit
| which columns in a ``DataFrame`` are analyzed for the output.
| The parameters are ignored when analyzing a ``Series``.
|
| Examples
| --------
| Describing a numeric ``Series``.
|
| >>> s = pd.Series([1, 2, 3])
| >>> s.describe()
| count 3.0
| mean 2.0
| std 1.0
| min 1.0
| 25% 1.5
| 50% 2.0
| 75% 2.5
| max 3.0
| dtype: float64
|
| Describing a categorical ``Series``.
|
| >>> s = pd.Series(['a', 'a', 'b', 'c'])
| >>> s.describe()
| count 4
| unique 3
| top a
| freq 2
| dtype: object
|
| Describing a timestamp ``Series``.
|
| >>> s = pd.Series([
| ... np.datetime64("2000-01-01"),
| ... np.datetime64("2010-01-01"),
| ... np.datetime64("2010-01-01")
| ... ])
| >>> s.describe()
| count 3
| mean 2006-09-01 08:00:00
| min 2000-01-01 00:00:00
| 25% 2004-12-31 12:00:00
| 50% 2010-01-01 00:00:00
| 75% 2010-01-01 00:00:00
| max 2010-01-01 00:00:00
| dtype: object
|
| Describing a ``DataFrame``. By default only numeric fields
| are returned.
|
| >>> df = pd.DataFrame({'categorical': pd.Categorical(['d', 'e', 'f']),
| ... 'numeric': [1, 2, 3],
| ... 'object': ['a', 'b', 'c']
| ... })
| >>> df.describe()
| numeric
| count 3.0
| mean 2.0
| std 1.0
| min 1.0
| 25% 1.5
| 50% 2.0
| 75% 2.5
| max 3.0
|
| Describing all columns of a ``DataFrame`` regardless of data type.
|
| >>> df.describe(include='all') # doctest: +SKIP
| categorical numeric object
| count 3 3.0 3
| unique 3 NaN 3
| top f NaN a
| freq 1 NaN 1
| mean NaN 2.0 NaN
| std NaN 1.0 NaN
| min NaN 1.0 NaN
| 25% NaN 1.5 NaN
| 50% NaN 2.0 NaN
| 75% NaN 2.5 NaN
| max NaN 3.0 NaN
|
| Describing a column from a ``DataFrame`` by accessing it as
| an attribute.
|
| >>> df.numeric.describe()
| count 3.0
| mean 2.0
| std 1.0
| min 1.0
| 25% 1.5
| 50% 2.0
| 75% 2.5
| max 3.0
| Name: numeric, dtype: float64
|
| Including only numeric columns in a ``DataFrame`` description.
|
| >>> df.describe(include=[np.number])
| numeric
| count 3.0
| mean 2.0
| std 1.0
| min 1.0
| 25% 1.5
| 50% 2.0
| 75% 2.5
| max 3.0
|
| Including only string columns in a ``DataFrame`` description.
|
| >>> df.describe(include=[object]) # doctest: +SKIP
| object
| count 3
| unique 3
| top a
| freq 1
|
| Including only categorical columns from a ``DataFrame`` description.
|
| >>> df.describe(include=['category'])
| categorical
| count 3
| unique 3
| top d
| freq 1
|
| Excluding numeric columns from a ``DataFrame`` description.
|
| >>> df.describe(exclude=[np.number]) # doctest: +SKIP
| categorical object
| count 3 3
| unique 3 3
| top f a
| freq 1 1
|
| Excluding object columns from a ``DataFrame`` description.
|
| >>> df.describe(exclude=[object]) # doctest: +SKIP
| categorical numeric
| count 3 3.0
| unique 3 NaN
| top f NaN
| freq 1 NaN
| mean NaN 2.0
| std NaN 1.0
| min NaN 1.0
| 25% NaN 1.5
| 50% NaN 2.0
| 75% NaN 2.5
| max NaN 3.0
|
| droplevel(self, level: 'IndexLabel', axis: 'Axis' = 0) -> 'Self'
| Return Series/DataFrame with requested index / column level(s) removed.
|
| Parameters
| ----------
| level : int, str, or list-like
| If a string is given, must be the name of a level
| If list-like, elements must be names or positional indexes
| of levels.
|
| axis : {0 or 'index', 1 or 'columns'}, default 0
| Axis along which the level(s) is removed:
|
| * 0 or 'index': remove level(s) in column.
| * 1 or 'columns': remove level(s) in row.
|
| For `Series` this parameter is unused and defaults to 0.
|
| Returns
| -------
| Series/DataFrame
| Series/DataFrame with requested index / column level(s) removed.
|
| Examples
| --------
| >>> df = pd.DataFrame([
| ... [1, 2, 3, 4],
| ... [5, 6, 7, 8],
| ... [9, 10, 11, 12]
| ... ]).set_index([0, 1]).rename_axis(['a', 'b'])
|
| >>> df.columns = pd.MultiIndex.from_tuples([
| ... ('c', 'e'), ('d', 'f')
| ... ], names=['level_1', 'level_2'])
|
| >>> df
| level_1 c d
| level_2 e f
| a b
| 1 2 3 4
| 5 6 7 8
| 9 10 11 12
|
| >>> df.droplevel('a')
| level_1 c d
| level_2 e f
| b
| 2 3 4
| 6 7 8
| 10 11 12
|
| >>> df.droplevel('level_2', axis=1)
| level_1 c d
| a b
| 1 2 3 4
| 5 6 7 8
| 9 10 11 12
|
| equals(self, other: 'object') -> 'bool_t'
| Test whether two objects contain the same elements.
|
| This function allows two Series or DataFrames to be compared against
| each other to see if they have the same shape and elements. NaNs in
| the same location are considered equal.
|
| The row/column index do not need to have the same type, as long
| as the values are considered equal. Corresponding columns and
| index must be of the same dtype.
|
| Parameters
| ----------
| other : Series or DataFrame
| The other Series or DataFrame to be compared with the first.
|
| Returns
| -------
| bool
| True if all elements are the same in both objects, False
| otherwise.
|
| See Also
| --------
| Series.eq : Compare two Series objects of the same length
| and return a Series where each element is True if the element
| in each Series is equal, False otherwise.
| DataFrame.eq : Compare two DataFrame objects of the same shape and
| return a DataFrame where each element is True if the respective
| element in each DataFrame is equal, False otherwise.
| testing.assert_series_equal : Raises an AssertionError if left and
| right are not equal. Provides an easy interface to ignore
| inequality in dtypes, indexes and precision among others.
| testing.assert_frame_equal : Like assert_series_equal, but targets
| DataFrames.
| numpy.array_equal : Return True if two arrays have the same shape
| and elements, False otherwise.
|
| Examples
| --------
| >>> df = pd.DataFrame({1: [10], 2: [20]})
| >>> df
| 1 2
| 0 10 20
|
| DataFrames df and exactly_equal have the same types and values for
| their elements and column labels, which will return True.
|
| >>> exactly_equal = pd.DataFrame({1: [10], 2: [20]})
| >>> exactly_equal
| 1 2
| 0 10 20
| >>> df.equals(exactly_equal)
| True
|
| DataFrames df and different_column_type have the same element
| types and values, but have different types for the column labels,
| which will still return True.
|
| >>> different_column_type = pd.DataFrame({1.0: [10], 2.0: [20]})
| >>> different_column_type
| 1.0 2.0
| 0 10 20
| >>> df.equals(different_column_type)
| True
|
| DataFrames df and different_data_type have different types for the
| same values for their elements, and will return False even though
| their column labels are the same values and types.
|
| >>> different_data_type = pd.DataFrame({1: [10.0], 2: [20.0]})
| >>> different_data_type
| 1 2
| 0 10.0 20.0
| >>> df.equals(different_data_type)
| False
|
| ewm(self, com: 'float | None' = None, span: 'float | None' = None, halflife: 'float | TimedeltaConvertibleTypes | None' = None, alpha: 'float | None' = None, min_periods: 'int | None' = 0, adjust: 'bool_t' = True, ignore_na: 'bool_t' = False, axis: 'Axis | lib.NoDefault' = <no_default>, times: 'np.ndarray | DataFrame | Series | None' = None, method: "Literal['single', 'table']" = 'single') -> 'ExponentialMovingWindow'
| Provide exponentially weighted (EW) calculations.
|
| Exactly one of ``com``, ``span``, ``halflife``, or ``alpha`` must be
| provided if ``times`` is not provided. If ``times`` is provided,
| ``halflife`` and one of ``com``, ``span`` or ``alpha`` may be provided.
|
| Parameters
| ----------
| com : float, optional
| Specify decay in terms of center of mass
|
| :math:`\alpha = 1 / (1 + com)`, for :math:`com \geq 0`.
|
| span : float, optional
| Specify decay in terms of span
|
| :math:`\alpha = 2 / (span + 1)`, for :math:`span \geq 1`.
|
| halflife : float, str, timedelta, optional
| Specify decay in terms of half-life
|
| :math:`\alpha = 1 - \exp\left(-\ln(2) / halflife\right)`, for
| :math:`halflife > 0`.
|
| If ``times`` is specified, a timedelta convertible unit over which an
| observation decays to half its value. Only applicable to ``mean()``,
| and halflife value will not apply to the other functions.
|
| alpha : float, optional
| Specify smoothing factor :math:`\alpha` directly
|
| :math:`0 < \alpha \leq 1`.
|
| min_periods : int, default 0
| Minimum number of observations in window required to have a value;
| otherwise, result is ``np.nan``.
|
| adjust : bool, default True
| Divide by decaying adjustment factor in beginning periods to account
| for imbalance in relative weightings (viewing EWMA as a moving average).
|
| - When ``adjust=True`` (default), the EW function is calculated using weights
| :math:`w_i = (1 - \alpha)^i`. For example, the EW moving average of the series
| [:math:`x_0, x_1, ..., x_t`] would be:
|
| .. math::
| y_t = \frac{x_t + (1 - \alpha)x_{t-1} + (1 - \alpha)^2 x_{t-2} + ... + (1 -
| \alpha)^t x_0}{1 + (1 - \alpha) + (1 - \alpha)^2 + ... + (1 - \alpha)^t}
|
| - When ``adjust=False``, the exponentially weighted function is calculated
| recursively:
|
| .. math::
| \begin{split}
| y_0 &= x_0\\
| y_t &= (1 - \alpha) y_{t-1} + \alpha x_t,
| \end{split}
| ignore_na : bool, default False
| Ignore missing values when calculating weights.
|
| - When ``ignore_na=False`` (default), weights are based on absolute positions.
| For example, the weights of :math:`x_0` and :math:`x_2` used in calculating
| the final weighted average of [:math:`x_0`, None, :math:`x_2`] are
| :math:`(1-\alpha)^2` and :math:`1` if ``adjust=True``, and
| :math:`(1-\alpha)^2` and :math:`\alpha` if ``adjust=False``.
|
| - When ``ignore_na=True``, weights are based
| on relative positions. For example, the weights of :math:`x_0` and :math:`x_2`
| used in calculating the final weighted average of
| [:math:`x_0`, None, :math:`x_2`] are :math:`1-\alpha` and :math:`1` if
| ``adjust=True``, and :math:`1-\alpha` and :math:`\alpha` if ``adjust=False``.
|
| axis : {0, 1}, default 0
| If ``0`` or ``'index'``, calculate across the rows.
|
| If ``1`` or ``'columns'``, calculate across the columns.
|
| For `Series` this parameter is unused and defaults to 0.
|
| times : np.ndarray, Series, default None
|
| Only applicable to ``mean()``.
|
| Times corresponding to the observations. Must be monotonically increasing and
| ``datetime64[ns]`` dtype.
|
| If 1-D array like, a sequence with the same shape as the observations.
|
| method : str {'single', 'table'}, default 'single'
| .. versionadded:: 1.4.0
|
| Execute the rolling operation per single column or row (``'single'``)
| or over the entire object (``'table'``).
|
| This argument is only implemented when specifying ``engine='numba'``
| in the method call.
|
| Only applicable to ``mean()``
|
| Returns
| -------
| pandas.api.typing.ExponentialMovingWindow
|
| See Also
| --------
| rolling : Provides rolling window calculations.
| expanding : Provides expanding transformations.
|
| Notes
| -----
| See :ref:`Windowing Operations <window.exponentially_weighted>`
| for further usage details and examples.
|
| Examples
| --------
| >>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
| >>> df
| B
| 0 0.0
| 1 1.0
| 2 2.0
| 3 NaN
| 4 4.0
|
| >>> df.ewm(com=0.5).mean()
| B
| 0 0.000000
| 1 0.750000
| 2 1.615385
| 3 1.615385
| 4 3.670213
| >>> df.ewm(alpha=2 / 3).mean()
| B
| 0 0.000000
| 1 0.750000
| 2 1.615385
| 3 1.615385
| 4 3.670213
|
| **adjust**
|
| >>> df.ewm(com=0.5, adjust=True).mean()
| B
| 0 0.000000
| 1 0.750000
| 2 1.615385
| 3 1.615385
| 4 3.670213
| >>> df.ewm(com=0.5, adjust=False).mean()
| B
| 0 0.000000
| 1 0.666667
| 2 1.555556
| 3 1.555556
| 4 3.650794
|
| **ignore_na**
|
| >>> df.ewm(com=0.5, ignore_na=True).mean()
| B
| 0 0.000000
| 1 0.750000
| 2 1.615385
| 3 1.615385
| 4 3.225000
| >>> df.ewm(com=0.5, ignore_na=False).mean()
| B
| 0 0.000000
| 1 0.750000
| 2 1.615385
| 3 1.615385
| 4 3.670213
|
| **times**
|
| Exponentially weighted mean with weights calculated with a timedelta ``halflife``
| relative to ``times``.
|
| >>> times = ['2020-01-01', '2020-01-03', '2020-01-10', '2020-01-15', '2020-01-17']
| >>> df.ewm(halflife='4 days', times=pd.DatetimeIndex(times)).mean()
| B
| 0 0.000000
| 1 0.585786
| 2 1.523889
| 3 1.523889
| 4 3.233686
|
| expanding(self, min_periods: 'int' = 1, axis: 'Axis | lib.NoDefault' = <no_default>, method: "Literal['single', 'table']" = 'single') -> 'Expanding'
| Provide expanding window calculations.
|
| Parameters
| ----------
| min_periods : int, default 1
| Minimum number of observations in window required to have a value;
| otherwise, result is ``np.nan``.
|
| axis : int or str, default 0
| If ``0`` or ``'index'``, roll across the rows.
|
| If ``1`` or ``'columns'``, roll across the columns.
|
| For `Series` this parameter is unused and defaults to 0.
|
| method : str {'single', 'table'}, default 'single'
| Execute the rolling operation per single column or row (``'single'``)
| or over the entire object (``'table'``).
|
| This argument is only implemented when specifying ``engine='numba'``
| in the method call.
|
| .. versionadded:: 1.3.0
|
| Returns
| -------
| pandas.api.typing.Expanding
|
| See Also
| --------
| rolling : Provides rolling window calculations.
| ewm : Provides exponential weighted functions.
|
| Notes
| -----
| See :ref:`Windowing Operations <window.expanding>` for further usage details
| and examples.
|
| Examples
| --------
| >>> df = pd.DataFrame({"B": [0, 1, 2, np.nan, 4]})
| >>> df
| B
| 0 0.0
| 1 1.0
| 2 2.0
| 3 NaN
| 4 4.0
|
| **min_periods**
|
| Expanding sum with 1 vs 3 observations needed to calculate a value.
|
| >>> df.expanding(1).sum()
| B
| 0 0.0
| 1 1.0
| 2 3.0
| 3 3.0
| 4 7.0
| >>> df.expanding(3).sum()
| B
| 0 NaN
| 1 NaN
| 2 3.0
| 3 3.0
| 4 7.0
|
| ffill(self, *, axis: 'None | Axis' = None, inplace: 'bool_t' = False, limit: 'None | int' = None, limit_area: "Literal['inside', 'outside'] | None" = None, downcast: 'dict | None | lib.NoDefault' = <no_default>) -> 'Self | None'
| Fill NA/NaN values by propagating the last valid observation to next valid.
|
| Parameters
| ----------
| axis : {0 or 'index'} for Series, {0 or 'index', 1 or 'columns'} for DataFrame
| Axis along which to fill missing values. For `Series`
| this parameter is unused and defaults to 0.
| inplace : bool, default False
| If True, fill in-place. Note: this will modify any
| other views on this object (e.g., a no-copy slice for a column in a
| DataFrame).
| limit : int, default None
| If method is specified, this is the maximum number of consecutive
| NaN values to forward/backward fill. In other words, if there is
| a gap with more than this number of consecutive NaNs, it will only
| be partially filled. If method is not specified, this is the
| maximum number of entries along the entire axis where NaNs will be
| filled. Must be greater than 0 if not None.
| limit_area : {`None`, 'inside', 'outside'}, default None
| If limit is specified, consecutive NaNs will be filled with this
| restriction.
|
| * ``None``: No fill restriction.
| * 'inside': Only fill NaNs surrounded by valid values
| (interpolate).
| * 'outside': Only fill NaNs outside valid values (extrapolate).
|
| .. versionadded:: 2.2.0
|
| downcast : dict, default is None
| A dict of item->dtype of what to downcast if possible,
| or the string 'infer' which will try to downcast to an appropriate
| equal type (e.g. float64 to int64 if possible).
|
| .. deprecated:: 2.2.0
|
| Returns
| -------
| Series/DataFrame or None
| Object with missing values filled or None if ``inplace=True``.
|
| Examples
| --------
| >>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],
| ... [3, 4, np.nan, 1],
| ... [np.nan, np.nan, np.nan, np.nan],
| ... [np.nan, 3, np.nan, 4]],
| ... columns=list("ABCD"))
| >>> df
| A B C D
| 0 NaN 2.0 NaN 0.0
| 1 3.0 4.0 NaN 1.0
| 2 NaN NaN NaN NaN
| 3 NaN 3.0 NaN 4.0
|
| >>> df.ffill()
| A B C D
| 0 NaN 2.0 NaN 0.0
| 1 3.0 4.0 NaN 1.0
| 2 3.0 4.0 NaN 1.0
| 3 3.0 3.0 NaN 4.0
|
| >>> ser = pd.Series([1, np.nan, 2, 3])
| >>> ser.ffill()
| 0 1.0
| 1 1.0
| 2 2.0
| 3 3.0
| dtype: float64
|
| fillna(self, value: 'Hashable | Mapping | Series | DataFrame | None' = None, *, method: 'FillnaOptions | None' = None, axis: 'Axis | None' = None, inplace: 'bool_t' = False, limit: 'int | None' = None, downcast: 'dict | None | lib.NoDefault' = <no_default>) -> 'Self | None'
| Fill NA/NaN values using the specified method.
|
| Parameters
| ----------
| value : scalar, dict, Series, or DataFrame
| Value to use to fill holes (e.g. 0), alternately a
| dict/Series/DataFrame of values specifying which value to use for
| each index (for a Series) or column (for a DataFrame). Values not
| in the dict/Series/DataFrame will not be filled. This value cannot
| be a list.
| method : {'backfill', 'bfill', 'ffill', None}, default None
| Method to use for filling holes in reindexed Series:
|
| * ffill: propagate last valid observation forward to next valid.
| * backfill / bfill: use next valid observation to fill gap.
|
| .. deprecated:: 2.1.0
| Use ffill or bfill instead.
|
| axis : {0 or 'index'} for Series, {0 or 'index', 1 or 'columns'} for DataFrame
| Axis along which to fill missing values. For `Series`
| this parameter is unused and defaults to 0.
| inplace : bool, default False
| If True, fill in-place. Note: this will modify any
| other views on this object (e.g., a no-copy slice for a column in a
| DataFrame).
| limit : int, default None
| If method is specified, this is the maximum number of consecutive
| NaN values to forward/backward fill. In other words, if there is
| a gap with more than this number of consecutive NaNs, it will only
| be partially filled. If method is not specified, this is the
| maximum number of entries along the entire axis where NaNs will be
| filled. Must be greater than 0 if not None.
| downcast : dict, default is None
| A dict of item->dtype of what to downcast if possible,
| or the string 'infer' which will try to downcast to an appropriate
| equal type (e.g. float64 to int64 if possible).
|
| .. deprecated:: 2.2.0
|
| Returns
| -------
| Series/DataFrame or None
| Object with missing values filled or None if ``inplace=True``.
|
| See Also
| --------
| ffill : Fill values by propagating the last valid observation to next valid.
| bfill : Fill values by using the next valid observation to fill the gap.
| interpolate : Fill NaN values using interpolation.
| reindex : Conform object to new index.
| asfreq : Convert TimeSeries to specified frequency.
|
| Examples
| --------
| >>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],
| ... [3, 4, np.nan, 1],
| ... [np.nan, np.nan, np.nan, np.nan],
| ... [np.nan, 3, np.nan, 4]],
| ... columns=list("ABCD"))
| >>> df
| A B C D
| 0 NaN 2.0 NaN 0.0
| 1 3.0 4.0 NaN 1.0
| 2 NaN NaN NaN NaN
| 3 NaN 3.0 NaN 4.0
|
| Replace all NaN elements with 0s.
|
| >>> df.fillna(0)
| A B C D
| 0 0.0 2.0 0.0 0.0
| 1 3.0 4.0 0.0 1.0
| 2 0.0 0.0 0.0 0.0
| 3 0.0 3.0 0.0 4.0
|
| Replace all NaN elements in column 'A', 'B', 'C', and 'D', with 0, 1,
| 2, and 3 respectively.
|
| >>> values = {"A": 0, "B": 1, "C": 2, "D": 3}
| >>> df.fillna(value=values)
| A B C D
| 0 0.0 2.0 2.0 0.0
| 1 3.0 4.0 2.0 1.0
| 2 0.0 1.0 2.0 3.0
| 3 0.0 3.0 2.0 4.0
|
| Only replace the first NaN element.
|
| >>> df.fillna(value=values, limit=1)
| A B C D
| 0 0.0 2.0 2.0 0.0
| 1 3.0 4.0 NaN 1.0
| 2 NaN 1.0 NaN 3.0
| 3 NaN 3.0 NaN 4.0
|
| When filling using a DataFrame, replacement happens along
| the same column names and same indices
|
| >>> df2 = pd.DataFrame(np.zeros((4, 4)), columns=list("ABCE"))
| >>> df.fillna(df2)
| A B C D
| 0 0.0 2.0 0.0 0.0
| 1 3.0 4.0 0.0 1.0
| 2 0.0 0.0 0.0 NaN
| 3 0.0 3.0 0.0 4.0
|
| Note that column D is not affected since it is not present in df2.
|
| filter(self, items=None, like: 'str | None' = None, regex: 'str | None' = None, axis: 'Axis | None' = None) -> 'Self'
| Subset the dataframe rows or columns according to the specified index labels.
|
| Note that this routine does not filter a dataframe on its
| contents. The filter is applied to the labels of the index.
|
| Parameters
| ----------
| items : list-like
| Keep labels from axis which are in items.
| like : str
| Keep labels from axis for which "like in label == True".
| regex : str (regular expression)
| Keep labels from axis for which re.search(regex, label) == True.
| axis : {0 or 'index', 1 or 'columns', None}, default None
| The axis to filter on, expressed either as an index (int)
| or axis name (str). By default this is the info axis, 'columns' for
| DataFrame. For `Series` this parameter is unused and defaults to `None`.
|
| Returns
| -------
| same type as input object
|
| See Also
| --------
| DataFrame.loc : Access a group of rows and columns
| by label(s) or a boolean array.
|
| Notes
| -----
| The ``items``, ``like``, and ``regex`` parameters are
| enforced to be mutually exclusive.
|
| ``axis`` defaults to the info axis that is used when indexing
| with ``[]``.
|
| Examples
| --------
| >>> df = pd.DataFrame(np.array(([1, 2, 3], [4, 5, 6])),
| ... index=['mouse', 'rabbit'],
| ... columns=['one', 'two', 'three'])
| >>> df
| one two three
| mouse 1 2 3
| rabbit 4 5 6
|
| >>> # select columns by name
| >>> df.filter(items=['one', 'three'])
| one three
| mouse 1 3
| rabbit 4 6
|
| >>> # select columns by regular expression
| >>> df.filter(regex='e$', axis=1)
| one three
| mouse 1 3
| rabbit 4 6
|
| >>> # select rows containing 'bbi'
| >>> df.filter(like='bbi', axis=0)
| one two three
| rabbit 4 5 6
|
| first(self, offset) -> 'Self'
| Select initial periods of time series data based on a date offset.
|
| .. deprecated:: 2.1
| :meth:`.first` is deprecated and will be removed in a future version.
| Please create a mask and filter using `.loc` instead.
|
| For a DataFrame with a sorted DatetimeIndex, this function can
| select the first few rows based on a date offset.
|
| Parameters
| ----------
| offset : str, DateOffset or dateutil.relativedelta
| The offset length of the data that will be selected. For instance,
| '1ME' will display all the rows having their index within the first month.
|
| Returns
| -------
| Series or DataFrame
| A subset of the caller.
|
| Raises
| ------
| TypeError
| If the index is not a :class:`DatetimeIndex`
|
| See Also
| --------
| last : Select final periods of time series based on a date offset.
| at_time : Select values at a particular time of the day.
| between_time : Select values between particular times of the day.
|
| Examples
| --------
| >>> i = pd.date_range('2018-04-09', periods=4, freq='2D')
| >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
| >>> ts
| A
| 2018-04-09 1
| 2018-04-11 2
| 2018-04-13 3
| 2018-04-15 4
|
| Get the rows for the first 3 days:
|
| >>> ts.first('3D')
| A
| 2018-04-09 1
| 2018-04-11 2
|
| Notice the data for 3 first calendar days were returned, not the first
| 3 days observed in the dataset, and therefore data for 2018-04-13 was
| not returned.
|
| first_valid_index(self) -> 'Hashable | None'
| Return index for first non-NA value or None, if no non-NA value is found.
|
| Returns
| -------
| type of index
|
| Examples
| --------
| For Series:
|
| >>> s = pd.Series([None, 3, 4])
| >>> s.first_valid_index()
| 1
| >>> s.last_valid_index()
| 2
|
| >>> s = pd.Series([None, None])
| >>> print(s.first_valid_index())
| None
| >>> print(s.last_valid_index())
| None
|
| If all elements in Series are NA/null, returns None.
|
| >>> s = pd.Series()
| >>> print(s.first_valid_index())
| None
| >>> print(s.last_valid_index())
| None
|
| If Series is empty, returns None.
|
| For DataFrame:
|
| >>> df = pd.DataFrame({'A': [None, None, 2], 'B': [None, 3, 4]})
| >>> df
| A B
| 0 NaN NaN
| 1 NaN 3.0
| 2 2.0 4.0
| >>> df.first_valid_index()
| 1
| >>> df.last_valid_index()
| 2
|
| >>> df = pd.DataFrame({'A': [None, None, None], 'B': [None, None, None]})
| >>> df
| A B
| 0 None None
| 1 None None
| 2 None None
| >>> print(df.first_valid_index())
| None
| >>> print(df.last_valid_index())
| None
|
| If all elements in DataFrame are NA/null, returns None.
|
| >>> df = pd.DataFrame()
| >>> df
| Empty DataFrame
| Columns: []
| Index: []
| >>> print(df.first_valid_index())
| None
| >>> print(df.last_valid_index())
| None
|
| If DataFrame is empty, returns None.
|
| get(self, key, default=None)
| Get item from object for given key (ex: DataFrame column).
|
| Returns default value if not found.
|
| Parameters
| ----------
| key : object
|
| Returns
| -------
| same type as items contained in object
|
| Examples
| --------
| >>> df = pd.DataFrame(
| ... [
| ... [24.3, 75.7, "high"],
| ... [31, 87.8, "high"],
| ... [22, 71.6, "medium"],
| ... [35, 95, "medium"],
| ... ],
| ... columns=["temp_celsius", "temp_fahrenheit", "windspeed"],
| ... index=pd.date_range(start="2014-02-12", end="2014-02-15", freq="D"),
| ... )
|
| >>> df
| temp_celsius temp_fahrenheit windspeed
| 2014-02-12 24.3 75.7 high
| 2014-02-13 31.0 87.8 high
| 2014-02-14 22.0 71.6 medium
| 2014-02-15 35.0 95.0 medium
|
| >>> df.get(["temp_celsius", "windspeed"])
| temp_celsius windspeed
| 2014-02-12 24.3 high
| 2014-02-13 31.0 high
| 2014-02-14 22.0 medium
| 2014-02-15 35.0 medium
|
| >>> ser = df['windspeed']
| >>> ser.get('2014-02-13')
| 'high'
|
| If the key isn't found, the default value will be used.
|
| >>> df.get(["temp_celsius", "temp_kelvin"], default="default_value")
| 'default_value'
|
| >>> ser.get('2014-02-10', '[unknown]')
| '[unknown]'
|
| head(self, n: 'int' = 5) -> 'Self'
| Return the first `n` rows.
|
| This function returns the first `n` rows for the object based
| on position. It is useful for quickly testing if your object
| has the right type of data in it.
|
| For negative values of `n`, this function returns all rows except
| the last `|n|` rows, equivalent to ``df[:n]``.
|
| If n is larger than the number of rows, this function returns all rows.
|
| Parameters
| ----------
| n : int, default 5
| Number of rows to select.
|
| Returns
| -------
| same type as caller
| The first `n` rows of the caller object.
|
| See Also
| --------
| DataFrame.tail: Returns the last `n` rows.
|
| Examples
| --------
| >>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',
| ... 'monkey', 'parrot', 'shark', 'whale', 'zebra']})
| >>> df
| animal
| 0 alligator
| 1 bee
| 2 falcon
| 3 lion
| 4 monkey
| 5 parrot
| 6 shark
| 7 whale
| 8 zebra
|
| Viewing the first 5 lines
|
| >>> df.head()
| animal
| 0 alligator
| 1 bee
| 2 falcon
| 3 lion
| 4 monkey
|
| Viewing the first `n` lines (three in this case)
|
| >>> df.head(3)
| animal
| 0 alligator
| 1 bee
| 2 falcon
|
| For negative values of `n`
|
| >>> df.head(-3)
| animal
| 0 alligator
| 1 bee
| 2 falcon
| 3 lion
| 4 monkey
| 5 parrot
|
| infer_objects(self, copy: 'bool_t | None' = None) -> 'Self'
| Attempt to infer better dtypes for object columns.
|
| Attempts soft conversion of object-dtyped
| columns, leaving non-object and unconvertible
| columns unchanged. The inference rules are the
| same as during normal Series/DataFrame construction.
|
| Parameters
| ----------
| copy : bool, default True
| Whether to make a copy for non-object or non-inferable columns
| or Series.
|
| .. note::
| The `copy` keyword will change behavior in pandas 3.0.
| `Copy-on-Write
| <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
| will be enabled by default, which means that all methods with a
| `copy` keyword will use a lazy copy mechanism to defer the copy and
| ignore the `copy` keyword. The `copy` keyword will be removed in a
| future version of pandas.
|
| You can already get the future behavior and improvements through
| enabling copy on write ``pd.options.mode.copy_on_write = True``
|
| Returns
| -------
| same type as input object
|
| See Also
| --------
| to_datetime : Convert argument to datetime.
| to_timedelta : Convert argument to timedelta.
| to_numeric : Convert argument to numeric type.
| convert_dtypes : Convert argument to best possible dtype.
|
| Examples
| --------
| >>> df = pd.DataFrame({"A": ["a", 1, 2, 3]})
| >>> df = df.iloc[1:]
| >>> df
| A
| 1 1
| 2 2
| 3 3
|
| >>> df.dtypes
| A object
| dtype: object
|
| >>> df.infer_objects().dtypes
| A int64
| dtype: object
|
| interpolate(self, method: 'InterpolateOptions' = 'linear', *, axis: 'Axis' = 0, limit: 'int | None' = None, inplace: 'bool_t' = False, limit_direction: "Literal['forward', 'backward', 'both'] | None" = None, limit_area: "Literal['inside', 'outside'] | None" = None, downcast: "Literal['infer'] | None | lib.NoDefault" = <no_default>, **kwargs) -> 'Self | None'
| Fill NaN values using an interpolation method.
|
| Please note that only ``method='linear'`` is supported for
| DataFrame/Series with a MultiIndex.
|
| Parameters
| ----------
| method : str, default 'linear'
| Interpolation technique to use. One of:
|
| * 'linear': Ignore the index and treat the values as equally
| spaced. This is the only method supported on MultiIndexes.
| * 'time': Works on daily and higher resolution data to interpolate
| given length of interval.
| * 'index', 'values': use the actual numerical values of the index.
| * 'pad': Fill in NaNs using existing values.
| * 'nearest', 'zero', 'slinear', 'quadratic', 'cubic',
| 'barycentric', 'polynomial': Passed to
| `scipy.interpolate.interp1d`, whereas 'spline' is passed to
| `scipy.interpolate.UnivariateSpline`. These methods use the numerical
| values of the index. Both 'polynomial' and 'spline' require that
| you also specify an `order` (int), e.g.
| ``df.interpolate(method='polynomial', order=5)``. Note that,
| `slinear` method in Pandas refers to the Scipy first order `spline`
| instead of Pandas first order `spline`.
| * 'krogh', 'piecewise_polynomial', 'spline', 'pchip', 'akima',
| 'cubicspline': Wrappers around the SciPy interpolation methods of
| similar names. See `Notes`.
| * 'from_derivatives': Refers to
| `scipy.interpolate.BPoly.from_derivatives`.
|
| axis : {{0 or 'index', 1 or 'columns', None}}, default None
| Axis to interpolate along. For `Series` this parameter is unused
| and defaults to 0.
| limit : int, optional
| Maximum number of consecutive NaNs to fill. Must be greater than
| 0.
| inplace : bool, default False
| Update the data in place if possible.
| limit_direction : {{'forward', 'backward', 'both'}}, Optional
| Consecutive NaNs will be filled in this direction.
|
| If limit is specified:
| * If 'method' is 'pad' or 'ffill', 'limit_direction' must be 'forward'.
| * If 'method' is 'backfill' or 'bfill', 'limit_direction' must be
| 'backwards'.
|
| If 'limit' is not specified:
| * If 'method' is 'backfill' or 'bfill', the default is 'backward'
| * else the default is 'forward'
|
| raises ValueError if `limit_direction` is 'forward' or 'both' and
| method is 'backfill' or 'bfill'.
| raises ValueError if `limit_direction` is 'backward' or 'both' and
| method is 'pad' or 'ffill'.
|
| limit_area : {{`None`, 'inside', 'outside'}}, default None
| If limit is specified, consecutive NaNs will be filled with this
| restriction.
|
| * ``None``: No fill restriction.
| * 'inside': Only fill NaNs surrounded by valid values
| (interpolate).
| * 'outside': Only fill NaNs outside valid values (extrapolate).
|
| downcast : optional, 'infer' or None, defaults to None
| Downcast dtypes if possible.
|
| .. deprecated:: 2.1.0
|
| ``**kwargs`` : optional
| Keyword arguments to pass on to the interpolating function.
|
| Returns
| -------
| Series or DataFrame or None
| Returns the same object type as the caller, interpolated at
| some or all ``NaN`` values or None if ``inplace=True``.
|
| See Also
| --------
| fillna : Fill missing values using different methods.
| scipy.interpolate.Akima1DInterpolator : Piecewise cubic polynomials
| (Akima interpolator).
| scipy.interpolate.BPoly.from_derivatives : Piecewise polynomial in the
| Bernstein basis.
| scipy.interpolate.interp1d : Interpolate a 1-D function.
| scipy.interpolate.KroghInterpolator : Interpolate polynomial (Krogh
| interpolator).
| scipy.interpolate.PchipInterpolator : PCHIP 1-d monotonic cubic
| interpolation.
| scipy.interpolate.CubicSpline : Cubic spline data interpolator.
|
| Notes
| -----
| The 'krogh', 'piecewise_polynomial', 'spline', 'pchip' and 'akima'
| methods are wrappers around the respective SciPy implementations of
| similar names. These use the actual numerical values of the index.
| For more information on their behavior, see the
| `SciPy documentation
| <https://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation>`__.
|
| Examples
| --------
| Filling in ``NaN`` in a :class:`~pandas.Series` via linear
| interpolation.
|
| >>> s = pd.Series([0, 1, np.nan, 3])
| >>> s
| 0 0.0
| 1 1.0
| 2 NaN
| 3 3.0
| dtype: float64
| >>> s.interpolate()
| 0 0.0
| 1 1.0
| 2 2.0
| 3 3.0
| dtype: float64
|
| Filling in ``NaN`` in a Series via polynomial interpolation or splines:
| Both 'polynomial' and 'spline' methods require that you also specify
| an ``order`` (int).
|
| >>> s = pd.Series([0, 2, np.nan, 8])
| >>> s.interpolate(method='polynomial', order=2)
| 0 0.000000
| 1 2.000000
| 2 4.666667
| 3 8.000000
| dtype: float64
|
| Fill the DataFrame forward (that is, going down) along each column
| using linear interpolation.
|
| Note how the last entry in column 'a' is interpolated differently,
| because there is no entry after it to use for interpolation.
| Note how the first entry in column 'b' remains ``NaN``, because there
| is no entry before it to use for interpolation.
|
| >>> df = pd.DataFrame([(0.0, np.nan, -1.0, 1.0),
| ... (np.nan, 2.0, np.nan, np.nan),
| ... (2.0, 3.0, np.nan, 9.0),
| ... (np.nan, 4.0, -4.0, 16.0)],
| ... columns=list('abcd'))
| >>> df
| a b c d
| 0 0.0 NaN -1.0 1.0
| 1 NaN 2.0 NaN NaN
| 2 2.0 3.0 NaN 9.0
| 3 NaN 4.0 -4.0 16.0
| >>> df.interpolate(method='linear', limit_direction='forward', axis=0)
| a b c d
| 0 0.0 NaN -1.0 1.0
| 1 1.0 2.0 -2.0 5.0
| 2 2.0 3.0 -3.0 9.0
| 3 2.0 4.0 -4.0 16.0
|
| Using polynomial interpolation.
|
| >>> df['d'].interpolate(method='polynomial', order=2)
| 0 1.0
| 1 4.0
| 2 9.0
| 3 16.0
| Name: d, dtype: float64
|
| keys(self) -> 'Index'
| Get the 'info axis' (see Indexing for more).
|
| This is index for Series, columns for DataFrame.
|
| Returns
| -------
| Index
| Info axis.
|
| Examples
| --------
| >>> d = pd.DataFrame(data={'A': [1, 2, 3], 'B': [0, 4, 8]},
| ... index=['a', 'b', 'c'])
| >>> d
| A B
| a 1 0
| b 2 4
| c 3 8
| >>> d.keys()
| Index(['A', 'B'], dtype='object')
|
| last(self, offset) -> 'Self'
| Select final periods of time series data based on a date offset.
|
| .. deprecated:: 2.1
| :meth:`.last` is deprecated and will be removed in a future version.
| Please create a mask and filter using `.loc` instead.
|
| For a DataFrame with a sorted DatetimeIndex, this function
| selects the last few rows based on a date offset.
|
| Parameters
| ----------
| offset : str, DateOffset, dateutil.relativedelta
| The offset length of the data that will be selected. For instance,
| '3D' will display all the rows having their index within the last 3 days.
|
| Returns
| -------
| Series or DataFrame
| A subset of the caller.
|
| Raises
| ------
| TypeError
| If the index is not a :class:`DatetimeIndex`
|
| See Also
| --------
| first : Select initial periods of time series based on a date offset.
| at_time : Select values at a particular time of the day.
| between_time : Select values between particular times of the day.
|
| Notes
| -----
| .. deprecated:: 2.1.0
| Please create a mask and filter using `.loc` instead
|
| Examples
| --------
| >>> i = pd.date_range('2018-04-09', periods=4, freq='2D')
| >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
| >>> ts
| A
| 2018-04-09 1
| 2018-04-11 2
| 2018-04-13 3
| 2018-04-15 4
|
| Get the rows for the last 3 days:
|
| >>> ts.last('3D') # doctest: +SKIP
| A
| 2018-04-13 3
| 2018-04-15 4
|
| Notice the data for 3 last calendar days were returned, not the last
| 3 observed days in the dataset, and therefore data for 2018-04-11 was
| not returned.
|
| last_valid_index(self) -> 'Hashable | None'
| Return index for last non-NA value or None, if no non-NA value is found.
|
| Returns
| -------
| type of index
|
| Examples
| --------
| For Series:
|
| >>> s = pd.Series([None, 3, 4])
| >>> s.first_valid_index()
| 1
| >>> s.last_valid_index()
| 2
|
| >>> s = pd.Series([None, None])
| >>> print(s.first_valid_index())
| None
| >>> print(s.last_valid_index())
| None
|
| If all elements in Series are NA/null, returns None.
|
| >>> s = pd.Series()
| >>> print(s.first_valid_index())
| None
| >>> print(s.last_valid_index())
| None
|
| If Series is empty, returns None.
|
| For DataFrame:
|
| >>> df = pd.DataFrame({'A': [None, None, 2], 'B': [None, 3, 4]})
| >>> df
| A B
| 0 NaN NaN
| 1 NaN 3.0
| 2 2.0 4.0
| >>> df.first_valid_index()
| 1
| >>> df.last_valid_index()
| 2
|
| >>> df = pd.DataFrame({'A': [None, None, None], 'B': [None, None, None]})
| >>> df
| A B
| 0 None None
| 1 None None
| 2 None None
| >>> print(df.first_valid_index())
| None
| >>> print(df.last_valid_index())
| None
|
| If all elements in DataFrame are NA/null, returns None.
|
| >>> df = pd.DataFrame()
| >>> df
| Empty DataFrame
| Columns: []
| Index: []
| >>> print(df.first_valid_index())
| None
| >>> print(df.last_valid_index())
| None
|
| If DataFrame is empty, returns None.
|
| mask(self, cond, other=<no_default>, *, inplace: 'bool_t' = False, axis: 'Axis | None' = None, level: 'Level | None' = None) -> 'Self | None'
| Replace values where the condition is True.
|
| Parameters
| ----------
| cond : bool Series/DataFrame, array-like, or callable
| Where `cond` is False, keep the original value. Where
| True, replace with corresponding value from `other`.
| If `cond` is callable, it is computed on the Series/DataFrame and
| should return boolean Series/DataFrame or array. The callable must
| not change input Series/DataFrame (though pandas doesn't check it).
| other : scalar, Series/DataFrame, or callable
| Entries where `cond` is True are replaced with
| corresponding value from `other`.
| If other is callable, it is computed on the Series/DataFrame and
| should return scalar or Series/DataFrame. The callable must not
| change input Series/DataFrame (though pandas doesn't check it).
| If not specified, entries will be filled with the corresponding
| NULL value (``np.nan`` for numpy dtypes, ``pd.NA`` for extension
| dtypes).
| inplace : bool, default False
| Whether to perform the operation in place on the data.
| axis : int, default None
| Alignment axis if needed. For `Series` this parameter is
| unused and defaults to 0.
| level : int, default None
| Alignment level if needed.
|
| Returns
| -------
| Same type as caller or None if ``inplace=True``.
|
| See Also
| --------
| :func:`DataFrame.where` : Return an object of same shape as
| self.
|
| Notes
| -----
| The mask method is an application of the if-then idiom. For each
| element in the calling DataFrame, if ``cond`` is ``False`` the
| element is used; otherwise the corresponding element from the DataFrame
| ``other`` is used. If the axis of ``other`` does not align with axis of
| ``cond`` Series/DataFrame, the misaligned index positions will be filled with
| True.
|
| The signature for :func:`DataFrame.where` differs from
| :func:`numpy.where`. Roughly ``df1.where(m, df2)`` is equivalent to
| ``np.where(m, df1, df2)``.
|
| For further details and examples see the ``mask`` documentation in
| :ref:`indexing <indexing.where_mask>`.
|
| The dtype of the object takes precedence. The fill value is casted to
| the object's dtype, if this can be done losslessly.
|
| Examples
| --------
| >>> s = pd.Series(range(5))
| >>> s.where(s > 0)
| 0 NaN
| 1 1.0
| 2 2.0
| 3 3.0
| 4 4.0
| dtype: float64
| >>> s.mask(s > 0)
| 0 0.0
| 1 NaN
| 2 NaN
| 3 NaN
| 4 NaN
| dtype: float64
|
| >>> s = pd.Series(range(5))
| >>> t = pd.Series([True, False])
| >>> s.where(t, 99)
| 0 0
| 1 99
| 2 99
| 3 99
| 4 99
| dtype: int64
| >>> s.mask(t, 99)
| 0 99
| 1 1
| 2 99
| 3 99
| 4 99
| dtype: int64
|
| >>> s.where(s > 1, 10)
| 0 10
| 1 10
| 2 2
| 3 3
| 4 4
| dtype: int64
| >>> s.mask(s > 1, 10)
| 0 0
| 1 1
| 2 10
| 3 10
| 4 10
| dtype: int64
|
| >>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B'])
| >>> df
| A B
| 0 0 1
| 1 2 3
| 2 4 5
| 3 6 7
| 4 8 9
| >>> m = df % 3 == 0
| >>> df.where(m, -df)
| A B
| 0 0 -1
| 1 -2 3
| 2 -4 -5
| 3 6 -7
| 4 -8 9
| >>> df.where(m, -df) == np.where(m, df, -df)
| A B
| 0 True True
| 1 True True
| 2 True True
| 3 True True
| 4 True True
| >>> df.where(m, -df) == df.mask(~m, -df)
| A B
| 0 True True
| 1 True True
| 2 True True
| 3 True True
| 4 True True
|
| pad(self, *, axis: 'None | Axis' = None, inplace: 'bool_t' = False, limit: 'None | int' = None, downcast: 'dict | None | lib.NoDefault' = <no_default>) -> 'Self | None'
| Fill NA/NaN values by propagating the last valid observation to next valid.
|
| .. deprecated:: 2.0
|
| Series/DataFrame.pad is deprecated. Use Series/DataFrame.ffill instead.
|
| Returns
| -------
| Series/DataFrame or None
| Object with missing values filled or None if ``inplace=True``.
|
| Examples
| --------
| Please see examples for :meth:`DataFrame.ffill` or :meth:`Series.ffill`.
|
| pct_change(self, periods: 'int' = 1, fill_method: 'FillnaOptions | None | lib.NoDefault' = <no_default>, limit: 'int | None | lib.NoDefault' = <no_default>, freq=None, **kwargs) -> 'Self'
| Fractional change between the current and a prior element.
|
| Computes the fractional change from the immediately previous row by
| default. This is useful in comparing the fraction of change in a time
| series of elements.
|
| .. note::
|
| Despite the name of this method, it calculates fractional change
| (also known as per unit change or relative change) and not
| percentage change. If you need the percentage change, multiply
| these values by 100.
|
| Parameters
| ----------
| periods : int, default 1
| Periods to shift for forming percent change.
| fill_method : {'backfill', 'bfill', 'pad', 'ffill', None}, default 'pad'
| How to handle NAs **before** computing percent changes.
|
| .. deprecated:: 2.1
| All options of `fill_method` are deprecated except `fill_method=None`.
|
| limit : int, default None
| The number of consecutive NAs to fill before stopping.
|
| .. deprecated:: 2.1
|
| freq : DateOffset, timedelta, or str, optional
| Increment to use from time series API (e.g. 'ME' or BDay()).
| **kwargs
| Additional keyword arguments are passed into
| `DataFrame.shift` or `Series.shift`.
|
| Returns
| -------
| Series or DataFrame
| The same type as the calling object.
|
| See Also
| --------
| Series.diff : Compute the difference of two elements in a Series.
| DataFrame.diff : Compute the difference of two elements in a DataFrame.
| Series.shift : Shift the index by some number of periods.
| DataFrame.shift : Shift the index by some number of periods.
|
| Examples
| --------
| **Series**
|
| >>> s = pd.Series([90, 91, 85])
| >>> s
| 0 90
| 1 91
| 2 85
| dtype: int64
|
| >>> s.pct_change()
| 0 NaN
| 1 0.011111
| 2 -0.065934
| dtype: float64
|
| >>> s.pct_change(periods=2)
| 0 NaN
| 1 NaN
| 2 -0.055556
| dtype: float64
|
| See the percentage change in a Series where filling NAs with last
| valid observation forward to next valid.
|
| >>> s = pd.Series([90, 91, None, 85])
| >>> s
| 0 90.0
| 1 91.0
| 2 NaN
| 3 85.0
| dtype: float64
|
| >>> s.ffill().pct_change()
| 0 NaN
| 1 0.011111
| 2 0.000000
| 3 -0.065934
| dtype: float64
|
| **DataFrame**
|
| Percentage change in French franc, Deutsche Mark, and Italian lira from
| 1980-01-01 to 1980-03-01.
|
| >>> df = pd.DataFrame({
| ... 'FR': [4.0405, 4.0963, 4.3149],
| ... 'GR': [1.7246, 1.7482, 1.8519],
| ... 'IT': [804.74, 810.01, 860.13]},
| ... index=['1980-01-01', '1980-02-01', '1980-03-01'])
| >>> df
| FR GR IT
| 1980-01-01 4.0405 1.7246 804.74
| 1980-02-01 4.0963 1.7482 810.01
| 1980-03-01 4.3149 1.8519 860.13
|
| >>> df.pct_change()
| FR GR IT
| 1980-01-01 NaN NaN NaN
| 1980-02-01 0.013810 0.013684 0.006549
| 1980-03-01 0.053365 0.059318 0.061876
|
| Percentage of change in GOOG and APPL stock volume. Shows computing
| the percentage change between columns.
|
| >>> df = pd.DataFrame({
| ... '2016': [1769950, 30586265],
| ... '2015': [1500923, 40912316],
| ... '2014': [1371819, 41403351]},
| ... index=['GOOG', 'APPL'])
| >>> df
| 2016 2015 2014
| GOOG 1769950 1500923 1371819
| APPL 30586265 40912316 41403351
|
| >>> df.pct_change(axis='columns', periods=-1)
| 2016 2015 2014
| GOOG 0.179241 0.094112 NaN
| APPL -0.252395 -0.011860 NaN
|
| pipe(self, func: 'Callable[..., T] | tuple[Callable[..., T], str]', *args, **kwargs) -> 'T'
| Apply chainable functions that expect Series or DataFrames.
|
| Parameters
| ----------
| func : function
| Function to apply to the Series/DataFrame.
| ``args``, and ``kwargs`` are passed into ``func``.
| Alternatively a ``(callable, data_keyword)`` tuple where
| ``data_keyword`` is a string indicating the keyword of
| ``callable`` that expects the Series/DataFrame.
| *args : iterable, optional
| Positional arguments passed into ``func``.
| **kwargs : mapping, optional
| A dictionary of keyword arguments passed into ``func``.
|
| Returns
| -------
| the return type of ``func``.
|
| See Also
| --------
| DataFrame.apply : Apply a function along input axis of DataFrame.
| DataFrame.map : Apply a function elementwise on a whole DataFrame.
| Series.map : Apply a mapping correspondence on a
| :class:`~pandas.Series`.
|
| Notes
| -----
| Use ``.pipe`` when chaining together functions that expect
| Series, DataFrames or GroupBy objects.
|
| Examples
| --------
| Constructing a income DataFrame from a dictionary.
|
| >>> data = [[8000, 1000], [9500, np.nan], [5000, 2000]]
| >>> df = pd.DataFrame(data, columns=['Salary', 'Others'])
| >>> df
| Salary Others
| 0 8000 1000.0
| 1 9500 NaN
| 2 5000 2000.0
|
| Functions that perform tax reductions on an income DataFrame.
|
| >>> def subtract_federal_tax(df):
| ... return df * 0.9
| >>> def subtract_state_tax(df, rate):
| ... return df * (1 - rate)
| >>> def subtract_national_insurance(df, rate, rate_increase):
| ... new_rate = rate + rate_increase
| ... return df * (1 - new_rate)
|
| Instead of writing
|
| >>> subtract_national_insurance(
| ... subtract_state_tax(subtract_federal_tax(df), rate=0.12),
| ... rate=0.05,
| ... rate_increase=0.02) # doctest: +SKIP
|
| You can write
|
| >>> (
| ... df.pipe(subtract_federal_tax)
| ... .pipe(subtract_state_tax, rate=0.12)
| ... .pipe(subtract_national_insurance, rate=0.05, rate_increase=0.02)
| ... )
| Salary Others
| 0 5892.48 736.56
| 1 6997.32 NaN
| 2 3682.80 1473.12
|
| If you have a function that takes the data as (say) the second
| argument, pass a tuple indicating which keyword expects the
| data. For example, suppose ``national_insurance`` takes its data as ``df``
| in the second argument:
|
| >>> def subtract_national_insurance(rate, df, rate_increase):
| ... new_rate = rate + rate_increase
| ... return df * (1 - new_rate)
| >>> (
| ... df.pipe(subtract_federal_tax)
| ... .pipe(subtract_state_tax, rate=0.12)
| ... .pipe(
| ... (subtract_national_insurance, 'df'),
| ... rate=0.05,
| ... rate_increase=0.02
| ... )
| ... )
| Salary Others
| 0 5892.48 736.56
| 1 6997.32 NaN
| 2 3682.80 1473.12
|
| rank(self, axis: 'Axis' = 0, method: "Literal['average', 'min', 'max', 'first', 'dense']" = 'average', numeric_only: 'bool_t' = False, na_option: "Literal['keep', 'top', 'bottom']" = 'keep', ascending: 'bool_t' = True, pct: 'bool_t' = False) -> 'Self'
| Compute numerical data ranks (1 through n) along axis.
|
| By default, equal values are assigned a rank that is the average of the
| ranks of those values.
|
| Parameters
| ----------
| axis : {0 or 'index', 1 or 'columns'}, default 0
| Index to direct ranking.
| For `Series` this parameter is unused and defaults to 0.
| method : {'average', 'min', 'max', 'first', 'dense'}, default 'average'
| How to rank the group of records that have the same value (i.e. ties):
|
| * average: average rank of the group
| * min: lowest rank in the group
| * max: highest rank in the group
| * first: ranks assigned in order they appear in the array
| * dense: like 'min', but rank always increases by 1 between groups.
|
| numeric_only : bool, default False
| For DataFrame objects, rank only numeric columns if set to True.
|
| .. versionchanged:: 2.0.0
| The default value of ``numeric_only`` is now ``False``.
|
| na_option : {'keep', 'top', 'bottom'}, default 'keep'
| How to rank NaN values:
|
| * keep: assign NaN rank to NaN values
| * top: assign lowest rank to NaN values
| * bottom: assign highest rank to NaN values
|
| ascending : bool, default True
| Whether or not the elements should be ranked in ascending order.
| pct : bool, default False
| Whether or not to display the returned rankings in percentile
| form.
|
| Returns
| -------
| same type as caller
| Return a Series or DataFrame with data ranks as values.
|
| See Also
| --------
| core.groupby.DataFrameGroupBy.rank : Rank of values within each group.
| core.groupby.SeriesGroupBy.rank : Rank of values within each group.
|
| Examples
| --------
| >>> df = pd.DataFrame(data={'Animal': ['cat', 'penguin', 'dog',
| ... 'spider', 'snake'],
| ... 'Number_legs': [4, 2, 4, 8, np.nan]})
| >>> df
| Animal Number_legs
| 0 cat 4.0
| 1 penguin 2.0
| 2 dog 4.0
| 3 spider 8.0
| 4 snake NaN
|
| Ties are assigned the mean of the ranks (by default) for the group.
|
| >>> s = pd.Series(range(5), index=list("abcde"))
| >>> s["d"] = s["b"]
| >>> s.rank()
| a 1.0
| b 2.5
| c 4.0
| d 2.5
| e 5.0
| dtype: float64
|
| The following example shows how the method behaves with the above
| parameters:
|
| * default_rank: this is the default behaviour obtained without using
| any parameter.
| * max_rank: setting ``method = 'max'`` the records that have the
| same values are ranked using the highest rank (e.g.: since 'cat'
| and 'dog' are both in the 2nd and 3rd position, rank 3 is assigned.)
| * NA_bottom: choosing ``na_option = 'bottom'``, if there are records
| with NaN values they are placed at the bottom of the ranking.
| * pct_rank: when setting ``pct = True``, the ranking is expressed as
| percentile rank.
|
| >>> df['default_rank'] = df['Number_legs'].rank()
| >>> df['max_rank'] = df['Number_legs'].rank(method='max')
| >>> df['NA_bottom'] = df['Number_legs'].rank(na_option='bottom')
| >>> df['pct_rank'] = df['Number_legs'].rank(pct=True)
| >>> df
| Animal Number_legs default_rank max_rank NA_bottom pct_rank
| 0 cat 4.0 2.5 3.0 2.5 0.625
| 1 penguin 2.0 1.0 1.0 1.0 0.250
| 2 dog 4.0 2.5 3.0 2.5 0.625
| 3 spider 8.0 4.0 4.0 4.0 1.000
| 4 snake NaN NaN NaN 5.0 NaN
|
| reindex_like(self, other, method: "Literal['backfill', 'bfill', 'pad', 'ffill', 'nearest'] | None" = None, copy: 'bool_t | None' = None, limit: 'int | None' = None, tolerance=None) -> 'Self'
| Return an object with matching indices as other object.
|
| Conform the object to the same index on all axes. Optional
| filling logic, placing NaN in locations having no value
| in the previous index. A new object is produced unless the
| new index is equivalent to the current one and copy=False.
|
| Parameters
| ----------
| other : Object of the same data type
| Its row and column indices are used to define the new indices
| of this object.
| method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}
| Method to use for filling holes in reindexed DataFrame.
| Please note: this is only applicable to DataFrames/Series with a
| monotonically increasing/decreasing index.
|
| * None (default): don't fill gaps
| * pad / ffill: propagate last valid observation forward to next
| valid
| * backfill / bfill: use next valid observation to fill gap
| * nearest: use nearest valid observations to fill gap.
|
| copy : bool, default True
| Return a new object, even if the passed indexes are the same.
|
| .. note::
| The `copy` keyword will change behavior in pandas 3.0.
| `Copy-on-Write
| <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
| will be enabled by default, which means that all methods with a
| `copy` keyword will use a lazy copy mechanism to defer the copy and
| ignore the `copy` keyword. The `copy` keyword will be removed in a
| future version of pandas.
|
| You can already get the future behavior and improvements through
| enabling copy on write ``pd.options.mode.copy_on_write = True``
| limit : int, default None
| Maximum number of consecutive labels to fill for inexact matches.
| tolerance : optional
| Maximum distance between original and new labels for inexact
| matches. The values of the index at the matching locations must
| satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
|
| Tolerance may be a scalar value, which applies the same tolerance
| to all values, or list-like, which applies variable tolerance per
| element. List-like includes list, tuple, array, Series, and must be
| the same size as the index and its dtype must exactly match the
| index's type.
|
| Returns
| -------
| Series or DataFrame
| Same type as caller, but with changed indices on each axis.
|
| See Also
| --------
| DataFrame.set_index : Set row labels.
| DataFrame.reset_index : Remove row labels or move them to new columns.
| DataFrame.reindex : Change to new indices or expand indices.
|
| Notes
| -----
| Same as calling
| ``.reindex(index=other.index, columns=other.columns,...)``.
|
| Examples
| --------
| >>> df1 = pd.DataFrame([[24.3, 75.7, 'high'],
| ... [31, 87.8, 'high'],
| ... [22, 71.6, 'medium'],
| ... [35, 95, 'medium']],
| ... columns=['temp_celsius', 'temp_fahrenheit',
| ... 'windspeed'],
| ... index=pd.date_range(start='2014-02-12',
| ... end='2014-02-15', freq='D'))
|
| >>> df1
| temp_celsius temp_fahrenheit windspeed
| 2014-02-12 24.3 75.7 high
| 2014-02-13 31.0 87.8 high
| 2014-02-14 22.0 71.6 medium
| 2014-02-15 35.0 95.0 medium
|
| >>> df2 = pd.DataFrame([[28, 'low'],
| ... [30, 'low'],
| ... [35.1, 'medium']],
| ... columns=['temp_celsius', 'windspeed'],
| ... index=pd.DatetimeIndex(['2014-02-12', '2014-02-13',
| ... '2014-02-15']))
|
| >>> df2
| temp_celsius windspeed
| 2014-02-12 28.0 low
| 2014-02-13 30.0 low
| 2014-02-15 35.1 medium
|
| >>> df2.reindex_like(df1)
| temp_celsius temp_fahrenheit windspeed
| 2014-02-12 28.0 NaN low
| 2014-02-13 30.0 NaN low
| 2014-02-14 NaN NaN NaN
| 2014-02-15 35.1 NaN medium
|
| rename_axis(self, mapper: 'IndexLabel | lib.NoDefault' = <no_default>, *, index=<no_default>, columns=<no_default>, axis: 'Axis' = 0, copy: 'bool_t | None' = None, inplace: 'bool_t' = False) -> 'Self | None'
| Set the name of the axis for the index or columns.
|
| Parameters
| ----------
| mapper : scalar, list-like, optional
| Value to set the axis name attribute.
| index, columns : scalar, list-like, dict-like or function, optional
| A scalar, list-like, dict-like or functions transformations to
| apply to that axis' values.
| Note that the ``columns`` parameter is not allowed if the
| object is a Series. This parameter only apply for DataFrame
| type objects.
|
| Use either ``mapper`` and ``axis`` to
| specify the axis to target with ``mapper``, or ``index``
| and/or ``columns``.
| axis : {0 or 'index', 1 or 'columns'}, default 0
| The axis to rename. For `Series` this parameter is unused and defaults to 0.
| copy : bool, default None
| Also copy underlying data.
|
| .. note::
| The `copy` keyword will change behavior in pandas 3.0.
| `Copy-on-Write
| <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
| will be enabled by default, which means that all methods with a
| `copy` keyword will use a lazy copy mechanism to defer the copy and
| ignore the `copy` keyword. The `copy` keyword will be removed in a
| future version of pandas.
|
| You can already get the future behavior and improvements through
| enabling copy on write ``pd.options.mode.copy_on_write = True``
| inplace : bool, default False
| Modifies the object directly, instead of creating a new Series
| or DataFrame.
|
| Returns
| -------
| Series, DataFrame, or None
| The same type as the caller or None if ``inplace=True``.
|
| See Also
| --------
| Series.rename : Alter Series index labels or name.
| DataFrame.rename : Alter DataFrame index labels or name.
| Index.rename : Set new names on index.
|
| Notes
| -----
| ``DataFrame.rename_axis`` supports two calling conventions
|
| * ``(index=index_mapper, columns=columns_mapper, ...)``
| * ``(mapper, axis={'index', 'columns'}, ...)``
|
| The first calling convention will only modify the names of
| the index and/or the names of the Index object that is the columns.
| In this case, the parameter ``copy`` is ignored.
|
| The second calling convention will modify the names of the
| corresponding index if mapper is a list or a scalar.
| However, if mapper is dict-like or a function, it will use the
| deprecated behavior of modifying the axis *labels*.
|
| We *highly* recommend using keyword arguments to clarify your
| intent.
|
| Examples
| --------
| **Series**
|
| >>> s = pd.Series(["dog", "cat", "monkey"])
| >>> s
| 0 dog
| 1 cat
| 2 monkey
| dtype: object
| >>> s.rename_axis("animal")
| animal
| 0 dog
| 1 cat
| 2 monkey
| dtype: object
|
| **DataFrame**
|
| >>> df = pd.DataFrame({"num_legs": [4, 4, 2],
| ... "num_arms": [0, 0, 2]},
| ... ["dog", "cat", "monkey"])
| >>> df
| num_legs num_arms
| dog 4 0
| cat 4 0
| monkey 2 2
| >>> df = df.rename_axis("animal")
| >>> df
| num_legs num_arms
| animal
| dog 4 0
| cat 4 0
| monkey 2 2
| >>> df = df.rename_axis("limbs", axis="columns")
| >>> df
| limbs num_legs num_arms
| animal
| dog 4 0
| cat 4 0
| monkey 2 2
|
| **MultiIndex**
|
| >>> df.index = pd.MultiIndex.from_product([['mammal'],
| ... ['dog', 'cat', 'monkey']],
| ... names=['type', 'name'])
| >>> df
| limbs num_legs num_arms
| type name
| mammal dog 4 0
| cat 4 0
| monkey 2 2
|
| >>> df.rename_axis(index={'type': 'class'})
| limbs num_legs num_arms
| class name
| mammal dog 4 0
| cat 4 0
| monkey 2 2
|
| >>> df.rename_axis(columns=str.upper)
| LIMBS num_legs num_arms
| type name
| mammal dog 4 0
| cat 4 0
| monkey 2 2
|
| replace(self, to_replace=None, value=<no_default>, *, inplace: 'bool_t' = False, limit: 'int | None' = None, regex: 'bool_t' = False, method: "Literal['pad', 'ffill', 'bfill'] | lib.NoDefault" = <no_default>) -> 'Self | None'
| Replace values given in `to_replace` with `value`.
|
| Values of the Series/DataFrame are replaced with other values dynamically.
| This differs from updating with ``.loc`` or ``.iloc``, which require
| you to specify a location to update with some value.
|
| Parameters
| ----------
| to_replace : str, regex, list, dict, Series, int, float, or None
| How to find the values that will be replaced.
|
| * numeric, str or regex:
|
| - numeric: numeric values equal to `to_replace` will be
| replaced with `value`
| - str: string exactly matching `to_replace` will be replaced
| with `value`
| - regex: regexs matching `to_replace` will be replaced with
| `value`
|
| * list of str, regex, or numeric:
|
| - First, if `to_replace` and `value` are both lists, they
| **must** be the same length.
| - Second, if ``regex=True`` then all of the strings in **both**
| lists will be interpreted as regexs otherwise they will match
| directly. This doesn't matter much for `value` since there
| are only a few possible substitution regexes you can use.
| - str, regex and numeric rules apply as above.
|
| * dict:
|
| - Dicts can be used to specify different replacement values
| for different existing values. For example,
| ``{'a': 'b', 'y': 'z'}`` replaces the value 'a' with 'b' and
| 'y' with 'z'. To use a dict in this way, the optional `value`
| parameter should not be given.
| - For a DataFrame a dict can specify that different values
| should be replaced in different columns. For example,
| ``{'a': 1, 'b': 'z'}`` looks for the value 1 in column 'a'
| and the value 'z' in column 'b' and replaces these values
| with whatever is specified in `value`. The `value` parameter
| should not be ``None`` in this case. You can treat this as a
| special case of passing two lists except that you are
| specifying the column to search in.
| - For a DataFrame nested dictionaries, e.g.,
| ``{'a': {'b': np.nan}}``, are read as follows: look in column
| 'a' for the value 'b' and replace it with NaN. The optional `value`
| parameter should not be specified to use a nested dict in this
| way. You can nest regular expressions as well. Note that
| column names (the top-level dictionary keys in a nested
| dictionary) **cannot** be regular expressions.
|
| * None:
|
| - This means that the `regex` argument must be a string,
| compiled regular expression, or list, dict, ndarray or
| Series of such elements. If `value` is also ``None`` then
| this **must** be a nested dictionary or Series.
|
| See the examples section for examples of each of these.
| value : scalar, dict, list, str, regex, default None
| Value to replace any values matching `to_replace` with.
| For a DataFrame a dict of values can be used to specify which
| value to use for each column (columns not in the dict will not be
| filled). Regular expressions, strings and lists or dicts of such
| objects are also allowed.
|
| inplace : bool, default False
| If True, performs operation inplace and returns None.
| limit : int, default None
| Maximum size gap to forward or backward fill.
|
| .. deprecated:: 2.1.0
| regex : bool or same types as `to_replace`, default False
| Whether to interpret `to_replace` and/or `value` as regular
| expressions. Alternatively, this could be a regular expression or a
| list, dict, or array of regular expressions in which case
| `to_replace` must be ``None``.
| method : {'pad', 'ffill', 'bfill'}
| The method to use when for replacement, when `to_replace` is a
| scalar, list or tuple and `value` is ``None``.
|
| .. deprecated:: 2.1.0
|
| Returns
| -------
| Series/DataFrame
| Object after replacement.
|
| Raises
| ------
| AssertionError
| * If `regex` is not a ``bool`` and `to_replace` is not
| ``None``.
|
| TypeError
| * If `to_replace` is not a scalar, array-like, ``dict``, or ``None``
| * If `to_replace` is a ``dict`` and `value` is not a ``list``,
| ``dict``, ``ndarray``, or ``Series``
| * If `to_replace` is ``None`` and `regex` is not compilable
| into a regular expression or is a list, dict, ndarray, or
| Series.
| * When replacing multiple ``bool`` or ``datetime64`` objects and
| the arguments to `to_replace` does not match the type of the
| value being replaced
|
| ValueError
| * If a ``list`` or an ``ndarray`` is passed to `to_replace` and
| `value` but they are not the same length.
|
| See Also
| --------
| Series.fillna : Fill NA values.
| DataFrame.fillna : Fill NA values.
| Series.where : Replace values based on boolean condition.
| DataFrame.where : Replace values based on boolean condition.
| DataFrame.map: Apply a function to a Dataframe elementwise.
| Series.map: Map values of Series according to an input mapping or function.
| Series.str.replace : Simple string replacement.
|
| Notes
| -----
| * Regex substitution is performed under the hood with ``re.sub``. The
| rules for substitution for ``re.sub`` are the same.
| * Regular expressions will only substitute on strings, meaning you
| cannot provide, for example, a regular expression matching floating
| point numbers and expect the columns in your frame that have a
| numeric dtype to be matched. However, if those floating point
| numbers *are* strings, then you can do this.
| * This method has *a lot* of options. You are encouraged to experiment
| and play with this method to gain intuition about how it works.
| * When dict is used as the `to_replace` value, it is like
| key(s) in the dict are the to_replace part and
| value(s) in the dict are the value parameter.
|
| Examples
| --------
|
| **Scalar `to_replace` and `value`**
|
| >>> s = pd.Series([1, 2, 3, 4, 5])
| >>> s.replace(1, 5)
| 0 5
| 1 2
| 2 3
| 3 4
| 4 5
| dtype: int64
|
| >>> df = pd.DataFrame({'A': [0, 1, 2, 3, 4],
| ... 'B': [5, 6, 7, 8, 9],
| ... 'C': ['a', 'b', 'c', 'd', 'e']})
| >>> df.replace(0, 5)
| A B C
| 0 5 5 a
| 1 1 6 b
| 2 2 7 c
| 3 3 8 d
| 4 4 9 e
|
| **List-like `to_replace`**
|
| >>> df.replace([0, 1, 2, 3], 4)
| A B C
| 0 4 5 a
| 1 4 6 b
| 2 4 7 c
| 3 4 8 d
| 4 4 9 e
|
| >>> df.replace([0, 1, 2, 3], [4, 3, 2, 1])
| A B C
| 0 4 5 a
| 1 3 6 b
| 2 2 7 c
| 3 1 8 d
| 4 4 9 e
|
| >>> s.replace([1, 2], method='bfill')
| 0 3
| 1 3
| 2 3
| 3 4
| 4 5
| dtype: int64
|
| **dict-like `to_replace`**
|
| >>> df.replace({0: 10, 1: 100})
| A B C
| 0 10 5 a
| 1 100 6 b
| 2 2 7 c
| 3 3 8 d
| 4 4 9 e
|
| >>> df.replace({'A': 0, 'B': 5}, 100)
| A B C
| 0 100 100 a
| 1 1 6 b
| 2 2 7 c
| 3 3 8 d
| 4 4 9 e
|
| >>> df.replace({'A': {0: 100, 4: 400}})
| A B C
| 0 100 5 a
| 1 1 6 b
| 2 2 7 c
| 3 3 8 d
| 4 400 9 e
|
| **Regular expression `to_replace`**
|
| >>> df = pd.DataFrame({'A': ['bat', 'foo', 'bait'],
| ... 'B': ['abc', 'bar', 'xyz']})
| >>> df.replace(to_replace=r'^ba.$', value='new', regex=True)
| A B
| 0 new abc
| 1 foo new
| 2 bait xyz
|
| >>> df.replace({'A': r'^ba.$'}, {'A': 'new'}, regex=True)
| A B
| 0 new abc
| 1 foo bar
| 2 bait xyz
|
| >>> df.replace(regex=r'^ba.$', value='new')
| A B
| 0 new abc
| 1 foo new
| 2 bait xyz
|
| >>> df.replace(regex={r'^ba.$': 'new', 'foo': 'xyz'})
| A B
| 0 new abc
| 1 xyz new
| 2 bait xyz
|
| >>> df.replace(regex=[r'^ba.$', 'foo'], value='new')
| A B
| 0 new abc
| 1 new new
| 2 bait xyz
|
| Compare the behavior of ``s.replace({'a': None})`` and
| ``s.replace('a', None)`` to understand the peculiarities
| of the `to_replace` parameter:
|
| >>> s = pd.Series([10, 'a', 'a', 'b', 'a'])
|
| When one uses a dict as the `to_replace` value, it is like the
| value(s) in the dict are equal to the `value` parameter.
| ``s.replace({'a': None})`` is equivalent to
| ``s.replace(to_replace={'a': None}, value=None, method=None)``:
|
| >>> s.replace({'a': None})
| 0 10
| 1 None
| 2 None
| 3 b
| 4 None
| dtype: object
|
| When ``value`` is not explicitly passed and `to_replace` is a scalar, list
| or tuple, `replace` uses the method parameter (default 'pad') to do the
| replacement. So this is why the 'a' values are being replaced by 10
| in rows 1 and 2 and 'b' in row 4 in this case.
|
| >>> s.replace('a')
| 0 10
| 1 10
| 2 10
| 3 b
| 4 b
| dtype: object
|
| .. deprecated:: 2.1.0
| The 'method' parameter and padding behavior are deprecated.
|
| On the other hand, if ``None`` is explicitly passed for ``value``, it will
| be respected:
|
| >>> s.replace('a', None)
| 0 10
| 1 None
| 2 None
| 3 b
| 4 None
| dtype: object
|
| .. versionchanged:: 1.4.0
| Previously the explicit ``None`` was silently ignored.
|
| When ``regex=True``, ``value`` is not ``None`` and `to_replace` is a string,
| the replacement will be applied in all columns of the DataFrame.
|
| >>> df = pd.DataFrame({'A': [0, 1, 2, 3, 4],
| ... 'B': ['a', 'b', 'c', 'd', 'e'],
| ... 'C': ['f', 'g', 'h', 'i', 'j']})
|
| >>> df.replace(to_replace='^[a-g]', value='e', regex=True)
| A B C
| 0 0 e e
| 1 1 e e
| 2 2 e h
| 3 3 e i
| 4 4 e j
|
| If ``value`` is not ``None`` and `to_replace` is a dictionary, the dictionary
| keys will be the DataFrame columns that the replacement will be applied.
|
| >>> df.replace(to_replace={'B': '^[a-c]', 'C': '^[h-j]'}, value='e', regex=True)
| A B C
| 0 0 e f
| 1 1 e g
| 2 2 e e
| 3 3 d e
| 4 4 e e
|
| resample(self, rule, axis: 'Axis | lib.NoDefault' = <no_default>, closed: "Literal['right', 'left'] | None" = None, label: "Literal['right', 'left'] | None" = None, convention: "Literal['start', 'end', 's', 'e'] | lib.NoDefault" = <no_default>, kind: "Literal['timestamp', 'period'] | None | lib.NoDefault" = <no_default>, on: 'Level | None' = None, level: 'Level | None' = None, origin: 'str | TimestampConvertibleTypes' = 'start_day', offset: 'TimedeltaConvertibleTypes | None' = None, group_keys: 'bool_t' = False) -> 'Resampler'
| Resample time-series data.
|
| Convenience method for frequency conversion and resampling of time series.
| The object must have a datetime-like index (`DatetimeIndex`, `PeriodIndex`,
| or `TimedeltaIndex`), or the caller must pass the label of a datetime-like
| series/index to the ``on``/``level`` keyword parameter.
|
| Parameters
| ----------
| rule : DateOffset, Timedelta or str
| The offset string or object representing target conversion.
| axis : {0 or 'index', 1 or 'columns'}, default 0
| Which axis to use for up- or down-sampling. For `Series` this parameter
| is unused and defaults to 0. Must be
| `DatetimeIndex`, `TimedeltaIndex` or `PeriodIndex`.
|
| .. deprecated:: 2.0.0
| Use frame.T.resample(...) instead.
| closed : {'right', 'left'}, default None
| Which side of bin interval is closed. The default is 'left'
| for all frequency offsets except for 'ME', 'YE', 'QE', 'BME',
| 'BA', 'BQE', and 'W' which all have a default of 'right'.
| label : {'right', 'left'}, default None
| Which bin edge label to label bucket with. The default is 'left'
| for all frequency offsets except for 'ME', 'YE', 'QE', 'BME',
| 'BA', 'BQE', and 'W' which all have a default of 'right'.
| convention : {'start', 'end', 's', 'e'}, default 'start'
| For `PeriodIndex` only, controls whether to use the start or
| end of `rule`.
|
| .. deprecated:: 2.2.0
| Convert PeriodIndex to DatetimeIndex before resampling instead.
| kind : {'timestamp', 'period'}, optional, default None
| Pass 'timestamp' to convert the resulting index to a
| `DateTimeIndex` or 'period' to convert it to a `PeriodIndex`.
| By default the input representation is retained.
|
| .. deprecated:: 2.2.0
| Convert index to desired type explicitly instead.
|
| on : str, optional
| For a DataFrame, column to use instead of index for resampling.
| Column must be datetime-like.
| level : str or int, optional
| For a MultiIndex, level (name or number) to use for
| resampling. `level` must be datetime-like.
| origin : Timestamp or str, default 'start_day'
| The timestamp on which to adjust the grouping. The timezone of origin
| must match the timezone of the index.
| If string, must be one of the following:
|
| - 'epoch': `origin` is 1970-01-01
| - 'start': `origin` is the first value of the timeseries
| - 'start_day': `origin` is the first day at midnight of the timeseries
|
| - 'end': `origin` is the last value of the timeseries
| - 'end_day': `origin` is the ceiling midnight of the last day
|
| .. versionadded:: 1.3.0
|
| .. note::
|
| Only takes effect for Tick-frequencies (i.e. fixed frequencies like
| days, hours, and minutes, rather than months or quarters).
| offset : Timedelta or str, default is None
| An offset timedelta added to the origin.
|
| group_keys : bool, default False
| Whether to include the group keys in the result index when using
| ``.apply()`` on the resampled object.
|
| .. versionadded:: 1.5.0
|
| Not specifying ``group_keys`` will retain values-dependent behavior
| from pandas 1.4 and earlier (see :ref:`pandas 1.5.0 Release notes
| <whatsnew_150.enhancements.resample_group_keys>` for examples).
|
| .. versionchanged:: 2.0.0
|
| ``group_keys`` now defaults to ``False``.
|
| Returns
| -------
| pandas.api.typing.Resampler
| :class:`~pandas.core.Resampler` object.
|
| See Also
| --------
| Series.resample : Resample a Series.
| DataFrame.resample : Resample a DataFrame.
| groupby : Group Series/DataFrame by mapping, function, label, or list of labels.
| asfreq : Reindex a Series/DataFrame with the given frequency without grouping.
|
| Notes
| -----
| See the `user guide
| <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling>`__
| for more.
|
| To learn more about the offset strings, please see `this link
| <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects>`__.
|
| Examples
| --------
| Start by creating a series with 9 one minute timestamps.
|
| >>> index = pd.date_range('1/1/2000', periods=9, freq='min')
| >>> series = pd.Series(range(9), index=index)
| >>> series
| 2000-01-01 00:00:00 0
| 2000-01-01 00:01:00 1
| 2000-01-01 00:02:00 2
| 2000-01-01 00:03:00 3
| 2000-01-01 00:04:00 4
| 2000-01-01 00:05:00 5
| 2000-01-01 00:06:00 6
| 2000-01-01 00:07:00 7
| 2000-01-01 00:08:00 8
| Freq: min, dtype: int64
|
| Downsample the series into 3 minute bins and sum the values
| of the timestamps falling into a bin.
|
| >>> series.resample('3min').sum()
| 2000-01-01 00:00:00 3
| 2000-01-01 00:03:00 12
| 2000-01-01 00:06:00 21
| Freq: 3min, dtype: int64
|
| Downsample the series into 3 minute bins as above, but label each
| bin using the right edge instead of the left. Please note that the
| value in the bucket used as the label is not included in the bucket,
| which it labels. For example, in the original series the
| bucket ``2000-01-01 00:03:00`` contains the value 3, but the summed
| value in the resampled bucket with the label ``2000-01-01 00:03:00``
| does not include 3 (if it did, the summed value would be 6, not 3).
|
| >>> series.resample('3min', label='right').sum()
| 2000-01-01 00:03:00 3
| 2000-01-01 00:06:00 12
| 2000-01-01 00:09:00 21
| Freq: 3min, dtype: int64
|
| To include this value close the right side of the bin interval,
| as shown below.
|
| >>> series.resample('3min', label='right', closed='right').sum()
| 2000-01-01 00:00:00 0
| 2000-01-01 00:03:00 6
| 2000-01-01 00:06:00 15
| 2000-01-01 00:09:00 15
| Freq: 3min, dtype: int64
|
| Upsample the series into 30 second bins.
|
| >>> series.resample('30s').asfreq()[0:5] # Select first 5 rows
| 2000-01-01 00:00:00 0.0
| 2000-01-01 00:00:30 NaN
| 2000-01-01 00:01:00 1.0
| 2000-01-01 00:01:30 NaN
| 2000-01-01 00:02:00 2.0
| Freq: 30s, dtype: float64
|
| Upsample the series into 30 second bins and fill the ``NaN``
| values using the ``ffill`` method.
|
| >>> series.resample('30s').ffill()[0:5]
| 2000-01-01 00:00:00 0
| 2000-01-01 00:00:30 0
| 2000-01-01 00:01:00 1
| 2000-01-01 00:01:30 1
| 2000-01-01 00:02:00 2
| Freq: 30s, dtype: int64
|
| Upsample the series into 30 second bins and fill the
| ``NaN`` values using the ``bfill`` method.
|
| >>> series.resample('30s').bfill()[0:5]
| 2000-01-01 00:00:00 0
| 2000-01-01 00:00:30 1
| 2000-01-01 00:01:00 1
| 2000-01-01 00:01:30 2
| 2000-01-01 00:02:00 2
| Freq: 30s, dtype: int64
|
| Pass a custom function via ``apply``
|
| >>> def custom_resampler(arraylike):
| ... return np.sum(arraylike) + 5
| ...
| >>> series.resample('3min').apply(custom_resampler)
| 2000-01-01 00:00:00 8
| 2000-01-01 00:03:00 17
| 2000-01-01 00:06:00 26
| Freq: 3min, dtype: int64
|
| For DataFrame objects, the keyword `on` can be used to specify the
| column instead of the index for resampling.
|
| >>> d = {'price': [10, 11, 9, 13, 14, 18, 17, 19],
| ... 'volume': [50, 60, 40, 100, 50, 100, 40, 50]}
| >>> df = pd.DataFrame(d)
| >>> df['week_starting'] = pd.date_range('01/01/2018',
| ... periods=8,
| ... freq='W')
| >>> df
| price volume week_starting
| 0 10 50 2018-01-07
| 1 11 60 2018-01-14
| 2 9 40 2018-01-21
| 3 13 100 2018-01-28
| 4 14 50 2018-02-04
| 5 18 100 2018-02-11
| 6 17 40 2018-02-18
| 7 19 50 2018-02-25
| >>> df.resample('ME', on='week_starting').mean()
| price volume
| week_starting
| 2018-01-31 10.75 62.5
| 2018-02-28 17.00 60.0
|
| For a DataFrame with MultiIndex, the keyword `level` can be used to
| specify on which level the resampling needs to take place.
|
| >>> days = pd.date_range('1/1/2000', periods=4, freq='D')
| >>> d2 = {'price': [10, 11, 9, 13, 14, 18, 17, 19],
| ... 'volume': [50, 60, 40, 100, 50, 100, 40, 50]}
| >>> df2 = pd.DataFrame(
| ... d2,
| ... index=pd.MultiIndex.from_product(
| ... [days, ['morning', 'afternoon']]
| ... )
| ... )
| >>> df2
| price volume
| 2000-01-01 morning 10 50
| afternoon 11 60
| 2000-01-02 morning 9 40
| afternoon 13 100
| 2000-01-03 morning 14 50
| afternoon 18 100
| 2000-01-04 morning 17 40
| afternoon 19 50
| >>> df2.resample('D', level=0).sum()
| price volume
| 2000-01-01 21 110
| 2000-01-02 22 140
| 2000-01-03 32 150
| 2000-01-04 36 90
|
| If you want to adjust the start of the bins based on a fixed timestamp:
|
| >>> start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00'
| >>> rng = pd.date_range(start, end, freq='7min')
| >>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng)
| >>> ts
| 2000-10-01 23:30:00 0
| 2000-10-01 23:37:00 3
| 2000-10-01 23:44:00 6
| 2000-10-01 23:51:00 9
| 2000-10-01 23:58:00 12
| 2000-10-02 00:05:00 15
| 2000-10-02 00:12:00 18
| 2000-10-02 00:19:00 21
| 2000-10-02 00:26:00 24
| Freq: 7min, dtype: int64
|
| >>> ts.resample('17min').sum()
| 2000-10-01 23:14:00 0
| 2000-10-01 23:31:00 9
| 2000-10-01 23:48:00 21
| 2000-10-02 00:05:00 54
| 2000-10-02 00:22:00 24
| Freq: 17min, dtype: int64
|
| >>> ts.resample('17min', origin='epoch').sum()
| 2000-10-01 23:18:00 0
| 2000-10-01 23:35:00 18
| 2000-10-01 23:52:00 27
| 2000-10-02 00:09:00 39
| 2000-10-02 00:26:00 24
| Freq: 17min, dtype: int64
|
| >>> ts.resample('17min', origin='2000-01-01').sum()
| 2000-10-01 23:24:00 3
| 2000-10-01 23:41:00 15
| 2000-10-01 23:58:00 45
| 2000-10-02 00:15:00 45
| Freq: 17min, dtype: int64
|
| If you want to adjust the start of the bins with an `offset` Timedelta, the two
| following lines are equivalent:
|
| >>> ts.resample('17min', origin='start').sum()
| 2000-10-01 23:30:00 9
| 2000-10-01 23:47:00 21
| 2000-10-02 00:04:00 54
| 2000-10-02 00:21:00 24
| Freq: 17min, dtype: int64
|
| >>> ts.resample('17min', offset='23h30min').sum()
| 2000-10-01 23:30:00 9
| 2000-10-01 23:47:00 21
| 2000-10-02 00:04:00 54
| 2000-10-02 00:21:00 24
| Freq: 17min, dtype: int64
|
| If you want to take the largest Timestamp as the end of the bins:
|
| >>> ts.resample('17min', origin='end').sum()
| 2000-10-01 23:35:00 0
| 2000-10-01 23:52:00 18
| 2000-10-02 00:09:00 27
| 2000-10-02 00:26:00 63
| Freq: 17min, dtype: int64
|
| In contrast with the `start_day`, you can use `end_day` to take the ceiling
| midnight of the largest Timestamp as the end of the bins and drop the bins
| not containing data:
|
| >>> ts.resample('17min', origin='end_day').sum()
| 2000-10-01 23:38:00 3
| 2000-10-01 23:55:00 15
| 2000-10-02 00:12:00 45
| 2000-10-02 00:29:00 45
| Freq: 17min, dtype: int64
|
| rolling(self, window: 'int | dt.timedelta | str | BaseOffset | BaseIndexer', min_periods: 'int | None' = None, center: 'bool_t' = False, win_type: 'str | None' = None, on: 'str | None' = None, axis: 'Axis | lib.NoDefault' = <no_default>, closed: 'IntervalClosedType | None' = None, step: 'int | None' = None, method: 'str' = 'single') -> 'Window | Rolling'
| Provide rolling window calculations.
|
| Parameters
| ----------
| window : int, timedelta, str, offset, or BaseIndexer subclass
| Size of the moving window.
|
| If an integer, the fixed number of observations used for
| each window.
|
| If a timedelta, str, or offset, the time period of each window. Each
| window will be a variable sized based on the observations included in
| the time-period. This is only valid for datetimelike indexes.
| To learn more about the offsets & frequency strings, please see `this link
| <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
|
| If a BaseIndexer subclass, the window boundaries
| based on the defined ``get_window_bounds`` method. Additional rolling
| keyword arguments, namely ``min_periods``, ``center``, ``closed`` and
| ``step`` will be passed to ``get_window_bounds``.
|
| min_periods : int, default None
| Minimum number of observations in window required to have a value;
| otherwise, result is ``np.nan``.
|
| For a window that is specified by an offset, ``min_periods`` will default to 1.
|
| For a window that is specified by an integer, ``min_periods`` will default
| to the size of the window.
|
| center : bool, default False
| If False, set the window labels as the right edge of the window index.
|
| If True, set the window labels as the center of the window index.
|
| win_type : str, default None
| If ``None``, all points are evenly weighted.
|
| If a string, it must be a valid `scipy.signal window function
| <https://docs.scipy.org/doc/scipy/reference/signal.windows.html#module-scipy.signal.windows>`__.
|
| Certain Scipy window types require additional parameters to be passed
| in the aggregation function. The additional parameters must match
| the keywords specified in the Scipy window type method signature.
|
| on : str, optional
| For a DataFrame, a column label or Index level on which
| to calculate the rolling window, rather than the DataFrame's index.
|
| Provided integer column is ignored and excluded from result since
| an integer index is not used to calculate the rolling window.
|
| axis : int or str, default 0
| If ``0`` or ``'index'``, roll across the rows.
|
| If ``1`` or ``'columns'``, roll across the columns.
|
| For `Series` this parameter is unused and defaults to 0.
|
| .. deprecated:: 2.1.0
|
| The axis keyword is deprecated. For ``axis=1``,
| transpose the DataFrame first instead.
|
| closed : str, default None
| If ``'right'``, the first point in the window is excluded from calculations.
|
| If ``'left'``, the last point in the window is excluded from calculations.
|
| If ``'both'``, the no points in the window are excluded from calculations.
|
| If ``'neither'``, the first and last points in the window are excluded
| from calculations.
|
| Default ``None`` (``'right'``).
|
| step : int, default None
|
| .. versionadded:: 1.5.0
|
| Evaluate the window at every ``step`` result, equivalent to slicing as
| ``[::step]``. ``window`` must be an integer. Using a step argument other
| than None or 1 will produce a result with a different shape than the input.
|
| method : str {'single', 'table'}, default 'single'
|
| .. versionadded:: 1.3.0
|
| Execute the rolling operation per single column or row (``'single'``)
| or over the entire object (``'table'``).
|
| This argument is only implemented when specifying ``engine='numba'``
| in the method call.
|
| Returns
| -------
| pandas.api.typing.Window or pandas.api.typing.Rolling
| An instance of Window is returned if ``win_type`` is passed. Otherwise,
| an instance of Rolling is returned.
|
| See Also
| --------
| expanding : Provides expanding transformations.
| ewm : Provides exponential weighted functions.
|
| Notes
| -----
| See :ref:`Windowing Operations <window.generic>` for further usage details
| and examples.
|
| Examples
| --------
| >>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
| >>> df
| B
| 0 0.0
| 1 1.0
| 2 2.0
| 3 NaN
| 4 4.0
|
| **window**
|
| Rolling sum with a window length of 2 observations.
|
| >>> df.rolling(2).sum()
| B
| 0 NaN
| 1 1.0
| 2 3.0
| 3 NaN
| 4 NaN
|
| Rolling sum with a window span of 2 seconds.
|
| >>> df_time = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
| ... index=[pd.Timestamp('20130101 09:00:00'),
| ... pd.Timestamp('20130101 09:00:02'),
| ... pd.Timestamp('20130101 09:00:03'),
| ... pd.Timestamp('20130101 09:00:05'),
| ... pd.Timestamp('20130101 09:00:06')])
|
| >>> df_time
| B
| 2013-01-01 09:00:00 0.0
| 2013-01-01 09:00:02 1.0
| 2013-01-01 09:00:03 2.0
| 2013-01-01 09:00:05 NaN
| 2013-01-01 09:00:06 4.0
|
| >>> df_time.rolling('2s').sum()
| B
| 2013-01-01 09:00:00 0.0
| 2013-01-01 09:00:02 1.0
| 2013-01-01 09:00:03 3.0
| 2013-01-01 09:00:05 NaN
| 2013-01-01 09:00:06 4.0
|
| Rolling sum with forward looking windows with 2 observations.
|
| >>> indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=2)
| >>> df.rolling(window=indexer, min_periods=1).sum()
| B
| 0 1.0
| 1 3.0
| 2 2.0
| 3 4.0
| 4 4.0
|
| **min_periods**
|
| Rolling sum with a window length of 2 observations, but only needs a minimum of 1
| observation to calculate a value.
|
| >>> df.rolling(2, min_periods=1).sum()
| B
| 0 0.0
| 1 1.0
| 2 3.0
| 3 2.0
| 4 4.0
|
| **center**
|
| Rolling sum with the result assigned to the center of the window index.
|
| >>> df.rolling(3, min_periods=1, center=True).sum()
| B
| 0 1.0
| 1 3.0
| 2 3.0
| 3 6.0
| 4 4.0
|
| >>> df.rolling(3, min_periods=1, center=False).sum()
| B
| 0 0.0
| 1 1.0
| 2 3.0
| 3 3.0
| 4 6.0
|
| **step**
|
| Rolling sum with a window length of 2 observations, minimum of 1 observation to
| calculate a value, and a step of 2.
|
| >>> df.rolling(2, min_periods=1, step=2).sum()
| B
| 0 0.0
| 2 3.0
| 4 4.0
|
| **win_type**
|
| Rolling sum with a window length of 2, using the Scipy ``'gaussian'``
| window type. ``std`` is required in the aggregation function.
|
| >>> df.rolling(2, win_type='gaussian').sum(std=3)
| B
| 0 NaN
| 1 0.986207
| 2 2.958621
| 3 NaN
| 4 NaN
|
| **on**
|
| Rolling sum with a window length of 2 days.
|
| >>> df = pd.DataFrame({
| ... 'A': [pd.to_datetime('2020-01-01'),
| ... pd.to_datetime('2020-01-01'),
| ... pd.to_datetime('2020-01-02'),],
| ... 'B': [1, 2, 3], },
| ... index=pd.date_range('2020', periods=3))
|
| >>> df
| A B
| 2020-01-01 2020-01-01 1
| 2020-01-02 2020-01-01 2
| 2020-01-03 2020-01-02 3
|
| >>> df.rolling('2D', on='A').sum()
| A B
| 2020-01-01 2020-01-01 1.0
| 2020-01-02 2020-01-01 3.0
| 2020-01-03 2020-01-02 6.0
|
| sample(self, n: 'int | None' = None, frac: 'float | None' = None, replace: 'bool_t' = False, weights=None, random_state: 'RandomState | None' = None, axis: 'Axis | None' = None, ignore_index: 'bool_t' = False) -> 'Self'
| Return a random sample of items from an axis of object.
|
| You can use `random_state` for reproducibility.
|
| Parameters
| ----------
| n : int, optional
| Number of items from axis to return. Cannot be used with `frac`.
| Default = 1 if `frac` = None.
| frac : float, optional
| Fraction of axis items to return. Cannot be used with `n`.
| replace : bool, default False
| Allow or disallow sampling of the same row more than once.
| weights : str or ndarray-like, optional
| Default 'None' results in equal probability weighting.
| If passed a Series, will align with target object on index. Index
| values in weights not found in sampled object will be ignored and
| index values in sampled object not in weights will be assigned
| weights of zero.
| If called on a DataFrame, will accept the name of a column
| when axis = 0.
| Unless weights are a Series, weights must be same length as axis
| being sampled.
| If weights do not sum to 1, they will be normalized to sum to 1.
| Missing values in the weights column will be treated as zero.
| Infinite values not allowed.
| random_state : int, array-like, BitGenerator, np.random.RandomState, np.random.Generator, optional
| If int, array-like, or BitGenerator, seed for random number generator.
| If np.random.RandomState or np.random.Generator, use as given.
|
| .. versionchanged:: 1.4.0
|
| np.random.Generator objects now accepted
|
| axis : {0 or 'index', 1 or 'columns', None}, default None
| Axis to sample. Accepts axis number or name. Default is stat axis
| for given data type. For `Series` this parameter is unused and defaults to `None`.
| ignore_index : bool, default False
| If True, the resulting index will be labeled 0, 1, …, n - 1.
|
| .. versionadded:: 1.3.0
|
| Returns
| -------
| Series or DataFrame
| A new object of same type as caller containing `n` items randomly
| sampled from the caller object.
|
| See Also
| --------
| DataFrameGroupBy.sample: Generates random samples from each group of a
| DataFrame object.
| SeriesGroupBy.sample: Generates random samples from each group of a
| Series object.
| numpy.random.choice: Generates a random sample from a given 1-D numpy
| array.
|
| Notes
| -----
| If `frac` > 1, `replacement` should be set to `True`.
|
| Examples
| --------
| >>> df = pd.DataFrame({'num_legs': [2, 4, 8, 0],
| ... 'num_wings': [2, 0, 0, 0],
| ... 'num_specimen_seen': [10, 2, 1, 8]},
| ... index=['falcon', 'dog', 'spider', 'fish'])
| >>> df
| num_legs num_wings num_specimen_seen
| falcon 2 2 10
| dog 4 0 2
| spider 8 0 1
| fish 0 0 8
|
| Extract 3 random elements from the ``Series`` ``df['num_legs']``:
| Note that we use `random_state` to ensure the reproducibility of
| the examples.
|
| >>> df['num_legs'].sample(n=3, random_state=1)
| fish 0
| spider 8
| falcon 2
| Name: num_legs, dtype: int64
|
| A random 50% sample of the ``DataFrame`` with replacement:
|
| >>> df.sample(frac=0.5, replace=True, random_state=1)
| num_legs num_wings num_specimen_seen
| dog 4 0 2
| fish 0 0 8
|
| An upsample sample of the ``DataFrame`` with replacement:
| Note that `replace` parameter has to be `True` for `frac` parameter > 1.
|
| >>> df.sample(frac=2, replace=True, random_state=1)
| num_legs num_wings num_specimen_seen
| dog 4 0 2
| fish 0 0 8
| falcon 2 2 10
| falcon 2 2 10
| fish 0 0 8
| dog 4 0 2
| fish 0 0 8
| dog 4 0 2
|
| Using a DataFrame column as weights. Rows with larger value in the
| `num_specimen_seen` column are more likely to be sampled.
|
| >>> df.sample(n=2, weights='num_specimen_seen', random_state=1)
| num_legs num_wings num_specimen_seen
| falcon 2 2 10
| fish 0 0 8
|
| set_flags(self, *, copy: 'bool_t' = False, allows_duplicate_labels: 'bool_t | None' = None) -> 'Self'
| Return a new object with updated flags.
|
| Parameters
| ----------
| copy : bool, default False
| Specify if a copy of the object should be made.
|
| .. note::
| The `copy` keyword will change behavior in pandas 3.0.
| `Copy-on-Write
| <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
| will be enabled by default, which means that all methods with a
| `copy` keyword will use a lazy copy mechanism to defer the copy and
| ignore the `copy` keyword. The `copy` keyword will be removed in a
| future version of pandas.
|
| You can already get the future behavior and improvements through
| enabling copy on write ``pd.options.mode.copy_on_write = True``
| allows_duplicate_labels : bool, optional
| Whether the returned object allows duplicate labels.
|
| Returns
| -------
| Series or DataFrame
| The same type as the caller.
|
| See Also
| --------
| DataFrame.attrs : Global metadata applying to this dataset.
| DataFrame.flags : Global flags applying to this object.
|
| Notes
| -----
| This method returns a new object that's a view on the same data
| as the input. Mutating the input or the output values will be reflected
| in the other.
|
| This method is intended to be used in method chains.
|
| "Flags" differ from "metadata". Flags reflect properties of the
| pandas object (the Series or DataFrame). Metadata refer to properties
| of the dataset, and should be stored in :attr:`DataFrame.attrs`.
|
| Examples
| --------
| >>> df = pd.DataFrame({"A": [1, 2]})
| >>> df.flags.allows_duplicate_labels
| True
| >>> df2 = df.set_flags(allows_duplicate_labels=False)
| >>> df2.flags.allows_duplicate_labels
| False
|
| squeeze(self, axis: 'Axis | None' = None)
| Squeeze 1 dimensional axis objects into scalars.
|
| Series or DataFrames with a single element are squeezed to a scalar.
| DataFrames with a single column or a single row are squeezed to a
| Series. Otherwise the object is unchanged.
|
| This method is most useful when you don't know if your
| object is a Series or DataFrame, but you do know it has just a single
| column. In that case you can safely call `squeeze` to ensure you have a
| Series.
|
| Parameters
| ----------
| axis : {0 or 'index', 1 or 'columns', None}, default None
| A specific axis to squeeze. By default, all length-1 axes are
| squeezed. For `Series` this parameter is unused and defaults to `None`.
|
| Returns
| -------
| DataFrame, Series, or scalar
| The projection after squeezing `axis` or all the axes.
|
| See Also
| --------
| Series.iloc : Integer-location based indexing for selecting scalars.
| DataFrame.iloc : Integer-location based indexing for selecting Series.
| Series.to_frame : Inverse of DataFrame.squeeze for a
| single-column DataFrame.
|
| Examples
| --------
| >>> primes = pd.Series([2, 3, 5, 7])
|
| Slicing might produce a Series with a single value:
|
| >>> even_primes = primes[primes % 2 == 0]
| >>> even_primes
| 0 2
| dtype: int64
|
| >>> even_primes.squeeze()
| 2
|
| Squeezing objects with more than one value in every axis does nothing:
|
| >>> odd_primes = primes[primes % 2 == 1]
| >>> odd_primes
| 1 3
| 2 5
| 3 7
| dtype: int64
|
| >>> odd_primes.squeeze()
| 1 3
| 2 5
| 3 7
| dtype: int64
|
| Squeezing is even more effective when used with DataFrames.
|
| >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b'])
| >>> df
| a b
| 0 1 2
| 1 3 4
|
| Slicing a single column will produce a DataFrame with the columns
| having only one value:
|
| >>> df_a = df[['a']]
| >>> df_a
| a
| 0 1
| 1 3
|
| So the columns can be squeezed down, resulting in a Series:
|
| >>> df_a.squeeze('columns')
| 0 1
| 1 3
| Name: a, dtype: int64
|
| Slicing a single row from a single column will produce a single
| scalar DataFrame:
|
| >>> df_0a = df.loc[df.index < 1, ['a']]
| >>> df_0a
| a
| 0 1
|
| Squeezing the rows produces a single scalar Series:
|
| >>> df_0a.squeeze('rows')
| a 1
| Name: 0, dtype: int64
|
| Squeezing all axes will project directly into a scalar:
|
| >>> df_0a.squeeze()
| 1
|
| swapaxes(self, axis1: 'Axis', axis2: 'Axis', copy: 'bool_t | None' = None) -> 'Self'
| Interchange axes and swap values axes appropriately.
|
| .. deprecated:: 2.1.0
| ``swapaxes`` is deprecated and will be removed.
| Please use ``transpose`` instead.
|
| Returns
| -------
| same as input
|
| Examples
| --------
| Please see examples for :meth:`DataFrame.transpose`.
|
| tail(self, n: 'int' = 5) -> 'Self'
| Return the last `n` rows.
|
| This function returns last `n` rows from the object based on
| position. It is useful for quickly verifying data, for example,
| after sorting or appending rows.
|
| For negative values of `n`, this function returns all rows except
| the first `|n|` rows, equivalent to ``df[|n|:]``.
|
| If n is larger than the number of rows, this function returns all rows.
|
| Parameters
| ----------
| n : int, default 5
| Number of rows to select.
|
| Returns
| -------
| type of caller
| The last `n` rows of the caller object.
|
| See Also
| --------
| DataFrame.head : The first `n` rows of the caller object.
|
| Examples
| --------
| >>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',
| ... 'monkey', 'parrot', 'shark', 'whale', 'zebra']})
| >>> df
| animal
| 0 alligator
| 1 bee
| 2 falcon
| 3 lion
| 4 monkey
| 5 parrot
| 6 shark
| 7 whale
| 8 zebra
|
| Viewing the last 5 lines
|
| >>> df.tail()
| animal
| 4 monkey
| 5 parrot
| 6 shark
| 7 whale
| 8 zebra
|
| Viewing the last `n` lines (three in this case)
|
| >>> df.tail(3)
| animal
| 6 shark
| 7 whale
| 8 zebra
|
| For negative values of `n`
|
| >>> df.tail(-3)
| animal
| 3 lion
| 4 monkey
| 5 parrot
| 6 shark
| 7 whale
| 8 zebra
|
| take(self, indices, axis: 'Axis' = 0, **kwargs) -> 'Self'
| Return the elements in the given *positional* indices along an axis.
|
| This means that we are not indexing according to actual values in
| the index attribute of the object. We are indexing according to the
| actual position of the element in the object.
|
| Parameters
| ----------
| indices : array-like
| An array of ints indicating which positions to take.
| axis : {0 or 'index', 1 or 'columns', None}, default 0
| The axis on which to select elements. ``0`` means that we are
| selecting rows, ``1`` means that we are selecting columns.
| For `Series` this parameter is unused and defaults to 0.
| **kwargs
| For compatibility with :meth:`numpy.take`. Has no effect on the
| output.
|
| Returns
| -------
| same type as caller
| An array-like containing the elements taken from the object.
|
| See Also
| --------
| DataFrame.loc : Select a subset of a DataFrame by labels.
| DataFrame.iloc : Select a subset of a DataFrame by positions.
| numpy.take : Take elements from an array along an axis.
|
| Examples
| --------
| >>> df = pd.DataFrame([('falcon', 'bird', 389.0),
| ... ('parrot', 'bird', 24.0),
| ... ('lion', 'mammal', 80.5),
| ... ('monkey', 'mammal', np.nan)],
| ... columns=['name', 'class', 'max_speed'],
| ... index=[0, 2, 3, 1])
| >>> df
| name class max_speed
| 0 falcon bird 389.0
| 2 parrot bird 24.0
| 3 lion mammal 80.5
| 1 monkey mammal NaN
|
| Take elements at positions 0 and 3 along the axis 0 (default).
|
| Note how the actual indices selected (0 and 1) do not correspond to
| our selected indices 0 and 3. That's because we are selecting the 0th
| and 3rd rows, not rows whose indices equal 0 and 3.
|
| >>> df.take([0, 3])
| name class max_speed
| 0 falcon bird 389.0
| 1 monkey mammal NaN
|
| Take elements at indices 1 and 2 along the axis 1 (column selection).
|
| >>> df.take([1, 2], axis=1)
| class max_speed
| 0 bird 389.0
| 2 bird 24.0
| 3 mammal 80.5
| 1 mammal NaN
|
| We may take elements using negative integers for positive indices,
| starting from the end of the object, just like with Python lists.
|
| >>> df.take([-1, -2])
| name class max_speed
| 1 monkey mammal NaN
| 3 lion mammal 80.5
|
| to_clipboard(self, *, excel: 'bool_t' = True, sep: 'str | None' = None, **kwargs) -> 'None'
| Copy object to the system clipboard.
|
| Write a text representation of object to the system clipboard.
| This can be pasted into Excel, for example.
|
| Parameters
| ----------
| excel : bool, default True
| Produce output in a csv format for easy pasting into excel.
|
| - True, use the provided separator for csv pasting.
| - False, write a string representation of the object to the clipboard.
|
| sep : str, default ``'\t'``
| Field delimiter.
| **kwargs
| These parameters will be passed to DataFrame.to_csv.
|
| See Also
| --------
| DataFrame.to_csv : Write a DataFrame to a comma-separated values
| (csv) file.
| read_clipboard : Read text from clipboard and pass to read_csv.
|
| Notes
| -----
| Requirements for your platform.
|
| - Linux : `xclip`, or `xsel` (with `PyQt4` modules)
| - Windows : none
| - macOS : none
|
| This method uses the processes developed for the package `pyperclip`. A
| solution to render any output string format is given in the examples.
|
| Examples
| --------
| Copy the contents of a DataFrame to the clipboard.
|
| >>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C'])
|
| >>> df.to_clipboard(sep=',') # doctest: +SKIP
| ... # Wrote the following to the system clipboard:
| ... # ,A,B,C
| ... # 0,1,2,3
| ... # 1,4,5,6
|
| We can omit the index by passing the keyword `index` and setting
| it to false.
|
| >>> df.to_clipboard(sep=',', index=False) # doctest: +SKIP
| ... # Wrote the following to the system clipboard:
| ... # A,B,C
| ... # 1,2,3
| ... # 4,5,6
|
| Using the original `pyperclip` package for any string output format.
|
| .. code-block:: python
|
| import pyperclip
| html = df.style.to_html()
| pyperclip.copy(html)
|
| to_csv(self, path_or_buf: 'FilePath | WriteBuffer[bytes] | WriteBuffer[str] | None' = None, *, sep: 'str' = ',', na_rep: 'str' = '', float_format: 'str | Callable | None' = None, columns: 'Sequence[Hashable] | None' = None, header: 'bool_t | list[str]' = True, index: 'bool_t' = True, index_label: 'IndexLabel | None' = None, mode: 'str' = 'w', encoding: 'str | None' = None, compression: 'CompressionOptions' = 'infer', quoting: 'int | None' = None, quotechar: 'str' = '"', lineterminator: 'str | None' = None, chunksize: 'int | None' = None, date_format: 'str | None' = None, doublequote: 'bool_t' = True, escapechar: 'str | None' = None, decimal: 'str' = '.', errors: 'OpenFileErrors' = 'strict', storage_options: 'StorageOptions | None' = None) -> 'str | None'
| Write object to a comma-separated values (csv) file.
|
| Parameters
| ----------
| path_or_buf : str, path object, file-like object, or None, default None
| String, path object (implementing os.PathLike[str]), or file-like
| object implementing a write() function. If None, the result is
| returned as a string. If a non-binary file object is passed, it should
| be opened with `newline=''`, disabling universal newlines. If a binary
| file object is passed, `mode` might need to contain a `'b'`.
| sep : str, default ','
| String of length 1. Field delimiter for the output file.
| na_rep : str, default ''
| Missing data representation.
| float_format : str, Callable, default None
| Format string for floating point numbers. If a Callable is given, it takes
| precedence over other numeric formatting parameters, like decimal.
| columns : sequence, optional
| Columns to write.
| header : bool or list of str, default True
| Write out the column names. If a list of strings is given it is
| assumed to be aliases for the column names.
| index : bool, default True
| Write row names (index).
| index_label : str or sequence, or False, default None
| Column label for index column(s) if desired. If None is given, and
| `header` and `index` are True, then the index names are used. A
| sequence should be given if the object uses MultiIndex. If
| False do not print fields for index names. Use index_label=False
| for easier importing in R.
| mode : {'w', 'x', 'a'}, default 'w'
| Forwarded to either `open(mode=)` or `fsspec.open(mode=)` to control
| the file opening. Typical values include:
|
| - 'w', truncate the file first.
| - 'x', exclusive creation, failing if the file already exists.
| - 'a', append to the end of file if it exists.
|
| encoding : str, optional
| A string representing the encoding to use in the output file,
| defaults to 'utf-8'. `encoding` is not supported if `path_or_buf`
| is a non-binary file object.
| compression : str or dict, default 'infer'
| For on-the-fly compression of the output data. If 'infer' and 'path_or_buf' is
| path-like, then detect compression from the following extensions: '.gz',
| '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2'
| (otherwise no compression).
| Set to ``None`` for no compression.
| Can also be a dict with key ``'method'`` set
| to one of {``'zip'``, ``'gzip'``, ``'bz2'``, ``'zstd'``, ``'xz'``, ``'tar'``} and
| other key-value pairs are forwarded to
| ``zipfile.ZipFile``, ``gzip.GzipFile``,
| ``bz2.BZ2File``, ``zstandard.ZstdCompressor``, ``lzma.LZMAFile`` or
| ``tarfile.TarFile``, respectively.
| As an example, the following could be passed for faster compression and to create
| a reproducible gzip archive:
| ``compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}``.
|
| .. versionadded:: 1.5.0
| Added support for `.tar` files.
|
| May be a dict with key 'method' as compression mode
| and other entries as additional compression options if
| compression mode is 'zip'.
|
| Passing compression options as keys in dict is
| supported for compression modes 'gzip', 'bz2', 'zstd', and 'zip'.
| quoting : optional constant from csv module
| Defaults to csv.QUOTE_MINIMAL. If you have set a `float_format`
| then floats are converted to strings and thus csv.QUOTE_NONNUMERIC
| will treat them as non-numeric.
| quotechar : str, default '\"'
| String of length 1. Character used to quote fields.
| lineterminator : str, optional
| The newline character or character sequence to use in the output
| file. Defaults to `os.linesep`, which depends on the OS in which
| this method is called ('\\n' for linux, '\\r\\n' for Windows, i.e.).
|
| .. versionchanged:: 1.5.0
|
| Previously was line_terminator, changed for consistency with
| read_csv and the standard library 'csv' module.
|
| chunksize : int or None
| Rows to write at a time.
| date_format : str, default None
| Format string for datetime objects.
| doublequote : bool, default True
| Control quoting of `quotechar` inside a field.
| escapechar : str, default None
| String of length 1. Character used to escape `sep` and `quotechar`
| when appropriate.
| decimal : str, default '.'
| Character recognized as decimal separator. E.g. use ',' for
| European data.
| errors : str, default 'strict'
| Specifies how encoding and decoding errors are to be handled.
| See the errors argument for :func:`open` for a full list
| of options.
|
| storage_options : dict, optional
| Extra options that make sense for a particular storage connection, e.g.
| host, port, username, password, etc. For HTTP(S) URLs the key-value pairs
| are forwarded to ``urllib.request.Request`` as header options. For other
| URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are
| forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more
| details, and for more examples on storage options refer `here
| <https://pandas.pydata.org/docs/user_guide/io.html?
| highlight=storage_options#reading-writing-remote-files>`_.
|
| Returns
| -------
| None or str
| If path_or_buf is None, returns the resulting csv format as a
| string. Otherwise returns None.
|
| See Also
| --------
| read_csv : Load a CSV file into a DataFrame.
| to_excel : Write DataFrame to an Excel file.
|
| Examples
| --------
| Create 'out.csv' containing 'df' without indices
|
| >>> df = pd.DataFrame({'name': ['Raphael', 'Donatello'],
| ... 'mask': ['red', 'purple'],
| ... 'weapon': ['sai', 'bo staff']})
| >>> df.to_csv('out.csv', index=False) # doctest: +SKIP
|
| Create 'out.zip' containing 'out.csv'
|
| >>> df.to_csv(index=False)
| 'name,mask,weapon\nRaphael,red,sai\nDonatello,purple,bo staff\n'
| >>> compression_opts = dict(method='zip',
| ... archive_name='out.csv') # doctest: +SKIP
| >>> df.to_csv('out.zip', index=False,
| ... compression=compression_opts) # doctest: +SKIP
|
| To write a csv file to a new folder or nested folder you will first
| need to create it using either Pathlib or os:
|
| >>> from pathlib import Path # doctest: +SKIP
| >>> filepath = Path('folder/subfolder/out.csv') # doctest: +SKIP
| >>> filepath.parent.mkdir(parents=True, exist_ok=True) # doctest: +SKIP
| >>> df.to_csv(filepath) # doctest: +SKIP
|
| >>> import os # doctest: +SKIP
| >>> os.makedirs('folder/subfolder', exist_ok=True) # doctest: +SKIP
| >>> df.to_csv('folder/subfolder/out.csv') # doctest: +SKIP
|
| to_excel(self, excel_writer: 'FilePath | WriteExcelBuffer | ExcelWriter', *, sheet_name: 'str' = 'Sheet1', na_rep: 'str' = '', float_format: 'str | None' = None, columns: 'Sequence[Hashable] | None' = None, header: 'Sequence[Hashable] | bool_t' = True, index: 'bool_t' = True, index_label: 'IndexLabel | None' = None, startrow: 'int' = 0, startcol: 'int' = 0, engine: "Literal['openpyxl', 'xlsxwriter'] | None" = None, merge_cells: 'bool_t' = True, inf_rep: 'str' = 'inf', freeze_panes: 'tuple[int, int] | None' = None, storage_options: 'StorageOptions | None' = None, engine_kwargs: 'dict[str, Any] | None' = None) -> 'None'
| Write object to an Excel sheet.
|
| To write a single object to an Excel .xlsx file it is only necessary to
| specify a target file name. To write to multiple sheets it is necessary to
| create an `ExcelWriter` object with a target file name, and specify a sheet
| in the file to write to.
|
| Multiple sheets may be written to by specifying unique `sheet_name`.
| With all data written to the file it is necessary to save the changes.
| Note that creating an `ExcelWriter` object with a file name that already
| exists will result in the contents of the existing file being erased.
|
| Parameters
| ----------
| excel_writer : path-like, file-like, or ExcelWriter object
| File path or existing ExcelWriter.
| sheet_name : str, default 'Sheet1'
| Name of sheet which will contain DataFrame.
| na_rep : str, default ''
| Missing data representation.
| float_format : str, optional
| Format string for floating point numbers. For example
| ``float_format="%.2f"`` will format 0.1234 to 0.12.
| columns : sequence or list of str, optional
| Columns to write.
| header : bool or list of str, default True
| Write out the column names. If a list of string is given it is
| assumed to be aliases for the column names.
| index : bool, default True
| Write row names (index).
| index_label : str or sequence, optional
| Column label for index column(s) if desired. If not specified, and
| `header` and `index` are True, then the index names are used. A
| sequence should be given if the DataFrame uses MultiIndex.
| startrow : int, default 0
| Upper left cell row to dump data frame.
| startcol : int, default 0
| Upper left cell column to dump data frame.
| engine : str, optional
| Write engine to use, 'openpyxl' or 'xlsxwriter'. You can also set this
| via the options ``io.excel.xlsx.writer`` or
| ``io.excel.xlsm.writer``.
|
| merge_cells : bool, default True
| Write MultiIndex and Hierarchical Rows as merged cells.
| inf_rep : str, default 'inf'
| Representation for infinity (there is no native representation for
| infinity in Excel).
| freeze_panes : tuple of int (length 2), optional
| Specifies the one-based bottommost row and rightmost column that
| is to be frozen.
| storage_options : dict, optional
| Extra options that make sense for a particular storage connection, e.g.
| host, port, username, password, etc. For HTTP(S) URLs the key-value pairs
| are forwarded to ``urllib.request.Request`` as header options. For other
| URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are
| forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more
| details, and for more examples on storage options refer `here
| <https://pandas.pydata.org/docs/user_guide/io.html?
| highlight=storage_options#reading-writing-remote-files>`_.
|
| .. versionadded:: 1.2.0
| engine_kwargs : dict, optional
| Arbitrary keyword arguments passed to excel engine.
|
| See Also
| --------
| to_csv : Write DataFrame to a comma-separated values (csv) file.
| ExcelWriter : Class for writing DataFrame objects into excel sheets.
| read_excel : Read an Excel file into a pandas DataFrame.
| read_csv : Read a comma-separated values (csv) file into DataFrame.
| io.formats.style.Styler.to_excel : Add styles to Excel sheet.
|
| Notes
| -----
| For compatibility with :meth:`~DataFrame.to_csv`,
| to_excel serializes lists and dicts to strings before writing.
|
| Once a workbook has been saved it is not possible to write further
| data without rewriting the whole workbook.
|
| Examples
| --------
|
| Create, write to and save a workbook:
|
| >>> df1 = pd.DataFrame([['a', 'b'], ['c', 'd']],
| ... index=['row 1', 'row 2'],
| ... columns=['col 1', 'col 2'])
| >>> df1.to_excel("output.xlsx") # doctest: +SKIP
|
| To specify the sheet name:
|
| >>> df1.to_excel("output.xlsx",
| ... sheet_name='Sheet_name_1') # doctest: +SKIP
|
| If you wish to write to more than one sheet in the workbook, it is
| necessary to specify an ExcelWriter object:
|
| >>> df2 = df1.copy()
| >>> with pd.ExcelWriter('output.xlsx') as writer: # doctest: +SKIP
| ... df1.to_excel(writer, sheet_name='Sheet_name_1')
| ... df2.to_excel(writer, sheet_name='Sheet_name_2')
|
| ExcelWriter can also be used to append to an existing Excel file:
|
| >>> with pd.ExcelWriter('output.xlsx',
| ... mode='a') as writer: # doctest: +SKIP
| ... df1.to_excel(writer, sheet_name='Sheet_name_3')
|
| To set the library that is used to write the Excel file,
| you can pass the `engine` keyword (the default engine is
| automatically chosen depending on the file extension):
|
| >>> df1.to_excel('output1.xlsx', engine='xlsxwriter') # doctest: +SKIP
|
| to_hdf(self, path_or_buf: 'FilePath | HDFStore', *, key: 'str', mode: "Literal['a', 'w', 'r+']" = 'a', complevel: 'int | None' = None, complib: "Literal['zlib', 'lzo', 'bzip2', 'blosc'] | None" = None, append: 'bool_t' = False, format: "Literal['fixed', 'table'] | None" = None, index: 'bool_t' = True, min_itemsize: 'int | dict[str, int] | None' = None, nan_rep=None, dropna: 'bool_t | None' = None, data_columns: 'Literal[True] | list[str] | None' = None, errors: 'OpenFileErrors' = 'strict', encoding: 'str' = 'UTF-8') -> 'None'
| Write the contained data to an HDF5 file using HDFStore.
|
| Hierarchical Data Format (HDF) is self-describing, allowing an
| application to interpret the structure and contents of a file with
| no outside information. One HDF file can hold a mix of related objects
| which can be accessed as a group or as individual objects.
|
| In order to add another DataFrame or Series to an existing HDF file
| please use append mode and a different a key.
|
| .. warning::
|
| One can store a subclass of ``DataFrame`` or ``Series`` to HDF5,
| but the type of the subclass is lost upon storing.
|
| For more information see the :ref:`user guide <io.hdf5>`.
|
| Parameters
| ----------
| path_or_buf : str or pandas.HDFStore
| File path or HDFStore object.
| key : str
| Identifier for the group in the store.
| mode : {'a', 'w', 'r+'}, default 'a'
| Mode to open file:
|
| - 'w': write, a new file is created (an existing file with
| the same name would be deleted).
| - 'a': append, an existing file is opened for reading and
| writing, and if the file does not exist it is created.
| - 'r+': similar to 'a', but the file must already exist.
| complevel : {0-9}, default None
| Specifies a compression level for data.
| A value of 0 or None disables compression.
| complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib'
| Specifies the compression library to be used.
| These additional compressors for Blosc are supported
| (default if no compressor specified: 'blosc:blosclz'):
| {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy',
| 'blosc:zlib', 'blosc:zstd'}.
| Specifying a compression library which is not available issues
| a ValueError.
| append : bool, default False
| For Table formats, append the input data to the existing.
| format : {'fixed', 'table', None}, default 'fixed'
| Possible values:
|
| - 'fixed': Fixed format. Fast writing/reading. Not-appendable,
| nor searchable.
| - 'table': Table format. Write as a PyTables Table structure
| which may perform worse but allow more flexible operations
| like searching / selecting subsets of the data.
| - If None, pd.get_option('io.hdf.default_format') is checked,
| followed by fallback to "fixed".
| index : bool, default True
| Write DataFrame index as a column.
| min_itemsize : dict or int, optional
| Map column names to minimum string sizes for columns.
| nan_rep : Any, optional
| How to represent null values as str.
| Not allowed with append=True.
| dropna : bool, default False, optional
| Remove missing values.
| data_columns : list of columns or True, optional
| List of columns to create as indexed data columns for on-disk
| queries, or True to use all columns. By default only the axes
| of the object are indexed. See
| :ref:`Query via data columns<io.hdf5-query-data-columns>`. for
| more information.
| Applicable only to format='table'.
| errors : str, default 'strict'
| Specifies how encoding and decoding errors are to be handled.
| See the errors argument for :func:`open` for a full list
| of options.
| encoding : str, default "UTF-8"
|
| See Also
| --------
| read_hdf : Read from HDF file.
| DataFrame.to_orc : Write a DataFrame to the binary orc format.
| DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
| DataFrame.to_sql : Write to a SQL table.
| DataFrame.to_feather : Write out feather-format for DataFrames.
| DataFrame.to_csv : Write out to a csv file.
|
| Examples
| --------
| >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]},
| ... index=['a', 'b', 'c']) # doctest: +SKIP
| >>> df.to_hdf('data.h5', key='df', mode='w') # doctest: +SKIP
|
| We can add another object to the same file:
|
| >>> s = pd.Series([1, 2, 3, 4]) # doctest: +SKIP
| >>> s.to_hdf('data.h5', key='s') # doctest: +SKIP
|
| Reading from HDF file:
|
| >>> pd.read_hdf('data.h5', 'df') # doctest: +SKIP
| A B
| a 1 4
| b 2 5
| c 3 6
| >>> pd.read_hdf('data.h5', 's') # doctest: +SKIP
| 0 1
| 1 2
| 2 3
| 3 4
| dtype: int64
|
| to_json(self, path_or_buf: 'FilePath | WriteBuffer[bytes] | WriteBuffer[str] | None' = None, *, orient: "Literal['split', 'records', 'index', 'table', 'columns', 'values'] | None" = None, date_format: 'str | None' = None, double_precision: 'int' = 10, force_ascii: 'bool_t' = True, date_unit: 'TimeUnit' = 'ms', default_handler: 'Callable[[Any], JSONSerializable] | None' = None, lines: 'bool_t' = False, compression: 'CompressionOptions' = 'infer', index: 'bool_t | None' = None, indent: 'int | None' = None, storage_options: 'StorageOptions | None' = None, mode: "Literal['a', 'w']" = 'w') -> 'str | None'
| Convert the object to a JSON string.
|
| Note NaN's and None will be converted to null and datetime objects
| will be converted to UNIX timestamps.
|
| Parameters
| ----------
| path_or_buf : str, path object, file-like object, or None, default None
| String, path object (implementing os.PathLike[str]), or file-like
| object implementing a write() function. If None, the result is
| returned as a string.
| orient : str
| Indication of expected JSON string format.
|
| * Series:
|
| - default is 'index'
| - allowed values are: {'split', 'records', 'index', 'table'}.
|
| * DataFrame:
|
| - default is 'columns'
| - allowed values are: {'split', 'records', 'index', 'columns',
| 'values', 'table'}.
|
| * The format of the JSON string:
|
| - 'split' : dict like {'index' -> [index], 'columns' -> [columns],
| 'data' -> [values]}
| - 'records' : list like [{column -> value}, ... , {column -> value}]
| - 'index' : dict like {index -> {column -> value}}
| - 'columns' : dict like {column -> {index -> value}}
| - 'values' : just the values array
| - 'table' : dict like {'schema': {schema}, 'data': {data}}
|
| Describing the data, where data component is like ``orient='records'``.
|
| date_format : {None, 'epoch', 'iso'}
| Type of date conversion. 'epoch' = epoch milliseconds,
| 'iso' = ISO8601. The default depends on the `orient`. For
| ``orient='table'``, the default is 'iso'. For all other orients,
| the default is 'epoch'.
| double_precision : int, default 10
| The number of decimal places to use when encoding
| floating point values. The possible maximal value is 15.
| Passing double_precision greater than 15 will raise a ValueError.
| force_ascii : bool, default True
| Force encoded string to be ASCII.
| date_unit : str, default 'ms' (milliseconds)
| The time unit to encode to, governs timestamp and ISO8601
| precision. One of 's', 'ms', 'us', 'ns' for second, millisecond,
| microsecond, and nanosecond respectively.
| default_handler : callable, default None
| Handler to call if object cannot otherwise be converted to a
| suitable format for JSON. Should receive a single argument which is
| the object to convert and return a serialisable object.
| lines : bool, default False
| If 'orient' is 'records' write out line-delimited json format. Will
| throw ValueError if incorrect 'orient' since others are not
| list-like.
| compression : str or dict, default 'infer'
| For on-the-fly compression of the output data. If 'infer' and 'path_or_buf' is
| path-like, then detect compression from the following extensions: '.gz',
| '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2'
| (otherwise no compression).
| Set to ``None`` for no compression.
| Can also be a dict with key ``'method'`` set
| to one of {``'zip'``, ``'gzip'``, ``'bz2'``, ``'zstd'``, ``'xz'``, ``'tar'``} and
| other key-value pairs are forwarded to
| ``zipfile.ZipFile``, ``gzip.GzipFile``,
| ``bz2.BZ2File``, ``zstandard.ZstdCompressor``, ``lzma.LZMAFile`` or
| ``tarfile.TarFile``, respectively.
| As an example, the following could be passed for faster compression and to create
| a reproducible gzip archive:
| ``compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}``.
|
| .. versionadded:: 1.5.0
| Added support for `.tar` files.
|
| .. versionchanged:: 1.4.0 Zstandard support.
|
| index : bool or None, default None
| The index is only used when 'orient' is 'split', 'index', 'column',
| or 'table'. Of these, 'index' and 'column' do not support
| `index=False`.
|
| indent : int, optional
| Length of whitespace used to indent each record.
|
| storage_options : dict, optional
| Extra options that make sense for a particular storage connection, e.g.
| host, port, username, password, etc. For HTTP(S) URLs the key-value pairs
| are forwarded to ``urllib.request.Request`` as header options. For other
| URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are
| forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more
| details, and for more examples on storage options refer `here
| <https://pandas.pydata.org/docs/user_guide/io.html?
| highlight=storage_options#reading-writing-remote-files>`_.
|
| mode : str, default 'w' (writing)
| Specify the IO mode for output when supplying a path_or_buf.
| Accepted args are 'w' (writing) and 'a' (append) only.
| mode='a' is only supported when lines is True and orient is 'records'.
|
| Returns
| -------
| None or str
| If path_or_buf is None, returns the resulting json format as a
| string. Otherwise returns None.
|
| See Also
| --------
| read_json : Convert a JSON string to pandas object.
|
| Notes
| -----
| The behavior of ``indent=0`` varies from the stdlib, which does not
| indent the output but does insert newlines. Currently, ``indent=0``
| and the default ``indent=None`` are equivalent in pandas, though this
| may change in a future release.
|
| ``orient='table'`` contains a 'pandas_version' field under 'schema'.
| This stores the version of `pandas` used in the latest revision of the
| schema.
|
| Examples
| --------
| >>> from json import loads, dumps
| >>> df = pd.DataFrame(
| ... [["a", "b"], ["c", "d"]],
| ... index=["row 1", "row 2"],
| ... columns=["col 1", "col 2"],
| ... )
|
| >>> result = df.to_json(orient="split")
| >>> parsed = loads(result)
| >>> dumps(parsed, indent=4) # doctest: +SKIP
| {
| "columns": [
| "col 1",
| "col 2"
| ],
| "index": [
| "row 1",
| "row 2"
| ],
| "data": [
| [
| "a",
| "b"
| ],
| [
| "c",
| "d"
| ]
| ]
| }
|
| Encoding/decoding a Dataframe using ``'records'`` formatted JSON.
| Note that index labels are not preserved with this encoding.
|
| >>> result = df.to_json(orient="records")
| >>> parsed = loads(result)
| >>> dumps(parsed, indent=4) # doctest: +SKIP
| [
| {
| "col 1": "a",
| "col 2": "b"
| },
| {
| "col 1": "c",
| "col 2": "d"
| }
| ]
|
| Encoding/decoding a Dataframe using ``'index'`` formatted JSON:
|
| >>> result = df.to_json(orient="index")
| >>> parsed = loads(result)
| >>> dumps(parsed, indent=4) # doctest: +SKIP
| {
| "row 1": {
| "col 1": "a",
| "col 2": "b"
| },
| "row 2": {
| "col 1": "c",
| "col 2": "d"
| }
| }
|
| Encoding/decoding a Dataframe using ``'columns'`` formatted JSON:
|
| >>> result = df.to_json(orient="columns")
| >>> parsed = loads(result)
| >>> dumps(parsed, indent=4) # doctest: +SKIP
| {
| "col 1": {
| "row 1": "a",
| "row 2": "c"
| },
| "col 2": {
| "row 1": "b",
| "row 2": "d"
| }
| }
|
| Encoding/decoding a Dataframe using ``'values'`` formatted JSON:
|
| >>> result = df.to_json(orient="values")
| >>> parsed = loads(result)
| >>> dumps(parsed, indent=4) # doctest: +SKIP
| [
| [
| "a",
| "b"
| ],
| [
| "c",
| "d"
| ]
| ]
|
| Encoding with Table Schema:
|
| >>> result = df.to_json(orient="table")
| >>> parsed = loads(result)
| >>> dumps(parsed, indent=4) # doctest: +SKIP
| {
| "schema": {
| "fields": [
| {
| "name": "index",
| "type": "string"
| },
| {
| "name": "col 1",
| "type": "string"
| },
| {
| "name": "col 2",
| "type": "string"
| }
| ],
| "primaryKey": [
| "index"
| ],
| "pandas_version": "1.4.0"
| },
| "data": [
| {
| "index": "row 1",
| "col 1": "a",
| "col 2": "b"
| },
| {
| "index": "row 2",
| "col 1": "c",
| "col 2": "d"
| }
| ]
| }
|
| to_latex(self, buf: 'FilePath | WriteBuffer[str] | None' = None, *, columns: 'Sequence[Hashable] | None' = None, header: 'bool_t | SequenceNotStr[str]' = True, index: 'bool_t' = True, na_rep: 'str' = 'NaN', formatters: 'FormattersType | None' = None, float_format: 'FloatFormatType | None' = None, sparsify: 'bool_t | None' = None, index_names: 'bool_t' = True, bold_rows: 'bool_t' = False, column_format: 'str | None' = None, longtable: 'bool_t | None' = None, escape: 'bool_t | None' = None, encoding: 'str | None' = None, decimal: 'str' = '.', multicolumn: 'bool_t | None' = None, multicolumn_format: 'str | None' = None, multirow: 'bool_t | None' = None, caption: 'str | tuple[str, str] | None' = None, label: 'str | None' = None, position: 'str | None' = None) -> 'str | None'
| Render object to a LaTeX tabular, longtable, or nested table.
|
| Requires ``\usepackage{{booktabs}}``. The output can be copy/pasted
| into a main LaTeX document or read from an external file
| with ``\input{{table.tex}}``.
|
| .. versionchanged:: 2.0.0
| Refactored to use the Styler implementation via jinja2 templating.
|
| Parameters
| ----------
| buf : str, Path or StringIO-like, optional, default None
| Buffer to write to. If None, the output is returned as a string.
| columns : list of label, optional
| The subset of columns to write. Writes all columns by default.
| header : bool or list of str, default True
| Write out the column names. If a list of strings is given,
| it is assumed to be aliases for the column names.
| index : bool, default True
| Write row names (index).
| na_rep : str, default 'NaN'
| Missing data representation.
| formatters : list of functions or dict of {{str: function}}, optional
| Formatter functions to apply to columns' elements by position or
| name. The result of each function must be a unicode string.
| List must be of length equal to the number of columns.
| float_format : one-parameter function or str, optional, default None
| Formatter for floating point numbers. For example
| ``float_format="%.2f"`` and ``float_format="{{:0.2f}}".format`` will
| both result in 0.1234 being formatted as 0.12.
| sparsify : bool, optional
| Set to False for a DataFrame with a hierarchical index to print
| every multiindex key at each row. By default, the value will be
| read from the config module.
| index_names : bool, default True
| Prints the names of the indexes.
| bold_rows : bool, default False
| Make the row labels bold in the output.
| column_format : str, optional
| The columns format as specified in `LaTeX table format
| <https://en.wikibooks.org/wiki/LaTeX/Tables>`__ e.g. 'rcl' for 3
| columns. By default, 'l' will be used for all columns except
| columns of numbers, which default to 'r'.
| longtable : bool, optional
| Use a longtable environment instead of tabular. Requires
| adding a \usepackage{{longtable}} to your LaTeX preamble.
| By default, the value will be read from the pandas config
| module, and set to `True` if the option ``styler.latex.environment`` is
| `"longtable"`.
|
| .. versionchanged:: 2.0.0
| The pandas option affecting this argument has changed.
| escape : bool, optional
| By default, the value will be read from the pandas config
| module and set to `True` if the option ``styler.format.escape`` is
| `"latex"`. When set to False prevents from escaping latex special
| characters in column names.
|
| .. versionchanged:: 2.0.0
| The pandas option affecting this argument has changed, as has the
| default value to `False`.
| encoding : str, optional
| A string representing the encoding to use in the output file,
| defaults to 'utf-8'.
| decimal : str, default '.'
| Character recognized as decimal separator, e.g. ',' in Europe.
| multicolumn : bool, default True
| Use \multicolumn to enhance MultiIndex columns.
| The default will be read from the config module, and is set
| as the option ``styler.sparse.columns``.
|
| .. versionchanged:: 2.0.0
| The pandas option affecting this argument has changed.
| multicolumn_format : str, default 'r'
| The alignment for multicolumns, similar to `column_format`
| The default will be read from the config module, and is set as the option
| ``styler.latex.multicol_align``.
|
| .. versionchanged:: 2.0.0
| The pandas option affecting this argument has changed, as has the
| default value to "r".
| multirow : bool, default True
| Use \multirow to enhance MultiIndex rows. Requires adding a
| \usepackage{{multirow}} to your LaTeX preamble. Will print
| centered labels (instead of top-aligned) across the contained
| rows, separating groups via clines. The default will be read
| from the pandas config module, and is set as the option
| ``styler.sparse.index``.
|
| .. versionchanged:: 2.0.0
| The pandas option affecting this argument has changed, as has the
| default value to `True`.
| caption : str or tuple, optional
| Tuple (full_caption, short_caption),
| which results in ``\caption[short_caption]{{full_caption}}``;
| if a single string is passed, no short caption will be set.
| label : str, optional
| The LaTeX label to be placed inside ``\label{{}}`` in the output.
| This is used with ``\ref{{}}`` in the main ``.tex`` file.
|
| position : str, optional
| The LaTeX positional argument for tables, to be placed after
| ``\begin{{}}`` in the output.
|
| Returns
| -------
| str or None
| If buf is None, returns the result as a string. Otherwise returns None.
|
| See Also
| --------
| io.formats.style.Styler.to_latex : Render a DataFrame to LaTeX
| with conditional formatting.
| DataFrame.to_string : Render a DataFrame to a console-friendly
| tabular output.
| DataFrame.to_html : Render a DataFrame as an HTML table.
|
| Notes
| -----
| As of v2.0.0 this method has changed to use the Styler implementation as
| part of :meth:`.Styler.to_latex` via ``jinja2`` templating. This means
| that ``jinja2`` is a requirement, and needs to be installed, for this method
| to function. It is advised that users switch to using Styler, since that
| implementation is more frequently updated and contains much more
| flexibility with the output.
|
| Examples
| --------
| Convert a general DataFrame to LaTeX with formatting:
|
| >>> df = pd.DataFrame(dict(name=['Raphael', 'Donatello'],
| ... age=[26, 45],
| ... height=[181.23, 177.65]))
| >>> print(df.to_latex(index=False,
| ... formatters={"name": str.upper},
| ... float_format="{:.1f}".format,
| ... )) # doctest: +SKIP
| \begin{tabular}{lrr}
| \toprule
| name & age & height \\
| \midrule
| RAPHAEL & 26 & 181.2 \\
| DONATELLO & 45 & 177.7 \\
| \bottomrule
| \end{tabular}
|
| to_pickle(self, path: 'FilePath | WriteBuffer[bytes]', *, compression: 'CompressionOptions' = 'infer', protocol: 'int' = 5, storage_options: 'StorageOptions | None' = None) -> 'None'
| Pickle (serialize) object to file.
|
| Parameters
| ----------
| path : str, path object, or file-like object
| String, path object (implementing ``os.PathLike[str]``), or file-like
| object implementing a binary ``write()`` function. File path where
| the pickled object will be stored.
| compression : str or dict, default 'infer'
| For on-the-fly compression of the output data. If 'infer' and 'path' is
| path-like, then detect compression from the following extensions: '.gz',
| '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2'
| (otherwise no compression).
| Set to ``None`` for no compression.
| Can also be a dict with key ``'method'`` set
| to one of {``'zip'``, ``'gzip'``, ``'bz2'``, ``'zstd'``, ``'xz'``, ``'tar'``} and
| other key-value pairs are forwarded to
| ``zipfile.ZipFile``, ``gzip.GzipFile``,
| ``bz2.BZ2File``, ``zstandard.ZstdCompressor``, ``lzma.LZMAFile`` or
| ``tarfile.TarFile``, respectively.
| As an example, the following could be passed for faster compression and to create
| a reproducible gzip archive:
| ``compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}``.
|
| .. versionadded:: 1.5.0
| Added support for `.tar` files.
| protocol : int
| Int which indicates which protocol should be used by the pickler,
| default HIGHEST_PROTOCOL (see [1]_ paragraph 12.1.2). The possible
| values are 0, 1, 2, 3, 4, 5. A negative value for the protocol
| parameter is equivalent to setting its value to HIGHEST_PROTOCOL.
|
| .. [1] https://docs.python.org/3/library/pickle.html.
|
| storage_options : dict, optional
| Extra options that make sense for a particular storage connection, e.g.
| host, port, username, password, etc. For HTTP(S) URLs the key-value pairs
| are forwarded to ``urllib.request.Request`` as header options. For other
| URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are
| forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more
| details, and for more examples on storage options refer `here
| <https://pandas.pydata.org/docs/user_guide/io.html?
| highlight=storage_options#reading-writing-remote-files>`_.
|
| See Also
| --------
| read_pickle : Load pickled pandas object (or any object) from file.
| DataFrame.to_hdf : Write DataFrame to an HDF5 file.
| DataFrame.to_sql : Write DataFrame to a SQL database.
| DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
|
| Examples
| --------
| >>> original_df = pd.DataFrame({"foo": range(5), "bar": range(5, 10)}) # doctest: +SKIP
| >>> original_df # doctest: +SKIP
| foo bar
| 0 0 5
| 1 1 6
| 2 2 7
| 3 3 8
| 4 4 9
| >>> original_df.to_pickle("./dummy.pkl") # doctest: +SKIP
|
| >>> unpickled_df = pd.read_pickle("./dummy.pkl") # doctest: +SKIP
| >>> unpickled_df # doctest: +SKIP
| foo bar
| 0 0 5
| 1 1 6
| 2 2 7
| 3 3 8
| 4 4 9
|
| to_sql(self, name: 'str', con, *, schema: 'str | None' = None, if_exists: "Literal['fail', 'replace', 'append']" = 'fail', index: 'bool_t' = True, index_label: 'IndexLabel | None' = None, chunksize: 'int | None' = None, dtype: 'DtypeArg | None' = None, method: "Literal['multi'] | Callable | None" = None) -> 'int | None'
| Write records stored in a DataFrame to a SQL database.
|
| Databases supported by SQLAlchemy [1]_ are supported. Tables can be
| newly created, appended to, or overwritten.
|
| Parameters
| ----------
| name : str
| Name of SQL table.
| con : sqlalchemy.engine.(Engine or Connection) or sqlite3.Connection
| Using SQLAlchemy makes it possible to use any DB supported by that
| library. Legacy support is provided for sqlite3.Connection objects. The user
| is responsible for engine disposal and connection closure for the SQLAlchemy
| connectable. See `here <https://docs.sqlalchemy.org/en/20/core/connections.html>`_.
| If passing a sqlalchemy.engine.Connection which is already in a transaction,
| the transaction will not be committed. If passing a sqlite3.Connection,
| it will not be possible to roll back the record insertion.
|
| schema : str, optional
| Specify the schema (if database flavor supports this). If None, use
| default schema.
| if_exists : {'fail', 'replace', 'append'}, default 'fail'
| How to behave if the table already exists.
|
| * fail: Raise a ValueError.
| * replace: Drop the table before inserting new values.
| * append: Insert new values to the existing table.
|
| index : bool, default True
| Write DataFrame index as a column. Uses `index_label` as the column
| name in the table. Creates a table index for this column.
| index_label : str or sequence, default None
| Column label for index column(s). If None is given (default) and
| `index` is True, then the index names are used.
| A sequence should be given if the DataFrame uses MultiIndex.
| chunksize : int, optional
| Specify the number of rows in each batch to be written at a time.
| By default, all rows will be written at once.
| dtype : dict or scalar, optional
| Specifying the datatype for columns. If a dictionary is used, the
| keys should be the column names and the values should be the
| SQLAlchemy types or strings for the sqlite3 legacy mode. If a
| scalar is provided, it will be applied to all columns.
| method : {None, 'multi', callable}, optional
| Controls the SQL insertion clause used:
|
| * None : Uses standard SQL ``INSERT`` clause (one per row).
| * 'multi': Pass multiple values in a single ``INSERT`` clause.
| * callable with signature ``(pd_table, conn, keys, data_iter)``.
|
| Details and a sample callable implementation can be found in the
| section :ref:`insert method <io.sql.method>`.
|
| Returns
| -------
| None or int
| Number of rows affected by to_sql. None is returned if the callable
| passed into ``method`` does not return an integer number of rows.
|
| The number of returned rows affected is the sum of the ``rowcount``
| attribute of ``sqlite3.Cursor`` or SQLAlchemy connectable which may not
| reflect the exact number of written rows as stipulated in the
| `sqlite3 <https://docs.python.org/3/library/sqlite3.html#sqlite3.Cursor.rowcount>`__ or
| `SQLAlchemy <https://docs.sqlalchemy.org/en/20/core/connections.html#sqlalchemy.engine.CursorResult.rowcount>`__.
|
| .. versionadded:: 1.4.0
|
| Raises
| ------
| ValueError
| When the table already exists and `if_exists` is 'fail' (the
| default).
|
| See Also
| --------
| read_sql : Read a DataFrame from a table.
|
| Notes
| -----
| Timezone aware datetime columns will be written as
| ``Timestamp with timezone`` type with SQLAlchemy if supported by the
| database. Otherwise, the datetimes will be stored as timezone unaware
| timestamps local to the original timezone.
|
| Not all datastores support ``method="multi"``. Oracle, for example,
| does not support multi-value insert.
|
| References
| ----------
| .. [1] https://docs.sqlalchemy.org
| .. [2] https://www.python.org/dev/peps/pep-0249/
|
| Examples
| --------
| Create an in-memory SQLite database.
|
| >>> from sqlalchemy import create_engine
| >>> engine = create_engine('sqlite://', echo=False)
|
| Create a table from scratch with 3 rows.
|
| >>> df = pd.DataFrame({'name' : ['User 1', 'User 2', 'User 3']})
| >>> df
| name
| 0 User 1
| 1 User 2
| 2 User 3
|
| >>> df.to_sql(name='users', con=engine)
| 3
| >>> from sqlalchemy import text
| >>> with engine.connect() as conn:
| ... conn.execute(text("SELECT * FROM users")).fetchall()
| [(0, 'User 1'), (1, 'User 2'), (2, 'User 3')]
|
| An `sqlalchemy.engine.Connection` can also be passed to `con`:
|
| >>> with engine.begin() as connection:
| ... df1 = pd.DataFrame({'name' : ['User 4', 'User 5']})
| ... df1.to_sql(name='users', con=connection, if_exists='append')
| 2
|
| This is allowed to support operations that require that the same
| DBAPI connection is used for the entire operation.
|
| >>> df2 = pd.DataFrame({'name' : ['User 6', 'User 7']})
| >>> df2.to_sql(name='users', con=engine, if_exists='append')
| 2
| >>> with engine.connect() as conn:
| ... conn.execute(text("SELECT * FROM users")).fetchall()
| [(0, 'User 1'), (1, 'User 2'), (2, 'User 3'),
| (0, 'User 4'), (1, 'User 5'), (0, 'User 6'),
| (1, 'User 7')]
|
| Overwrite the table with just ``df2``.
|
| >>> df2.to_sql(name='users', con=engine, if_exists='replace',
| ... index_label='id')
| 2
| >>> with engine.connect() as conn:
| ... conn.execute(text("SELECT * FROM users")).fetchall()
| [(0, 'User 6'), (1, 'User 7')]
|
| Use ``method`` to define a callable insertion method to do nothing
| if there's a primary key conflict on a table in a PostgreSQL database.
|
| >>> from sqlalchemy.dialects.postgresql import insert
| >>> def insert_on_conflict_nothing(table, conn, keys, data_iter):
| ... # "a" is the primary key in "conflict_table"
| ... data = [dict(zip(keys, row)) for row in data_iter]
| ... stmt = insert(table.table).values(data).on_conflict_do_nothing(index_elements=["a"])
| ... result = conn.execute(stmt)
| ... return result.rowcount
| >>> df_conflict.to_sql(name="conflict_table", con=conn, if_exists="append", method=insert_on_conflict_nothing) # doctest: +SKIP
| 0
|
| For MySQL, a callable to update columns ``b`` and ``c`` if there's a conflict
| on a primary key.
|
| >>> from sqlalchemy.dialects.mysql import insert
| >>> def insert_on_conflict_update(table, conn, keys, data_iter):
| ... # update columns "b" and "c" on primary key conflict
| ... data = [dict(zip(keys, row)) for row in data_iter]
| ... stmt = (
| ... insert(table.table)
| ... .values(data)
| ... )
| ... stmt = stmt.on_duplicate_key_update(b=stmt.inserted.b, c=stmt.inserted.c)
| ... result = conn.execute(stmt)
| ... return result.rowcount
| >>> df_conflict.to_sql(name="conflict_table", con=conn, if_exists="append", method=insert_on_conflict_update) # doctest: +SKIP
| 2
|
| Specify the dtype (especially useful for integers with missing values).
| Notice that while pandas is forced to store the data as floating point,
| the database supports nullable integers. When fetching the data with
| Python, we get back integer scalars.
|
| >>> df = pd.DataFrame({"A": [1, None, 2]})
| >>> df
| A
| 0 1.0
| 1 NaN
| 2 2.0
|
| >>> from sqlalchemy.types import Integer
| >>> df.to_sql(name='integers', con=engine, index=False,
| ... dtype={"A": Integer()})
| 3
|
| >>> with engine.connect() as conn:
| ... conn.execute(text("SELECT * FROM integers")).fetchall()
| [(1,), (None,), (2,)]
|
| to_xarray(self)
| Return an xarray object from the pandas object.
|
| Returns
| -------
| xarray.DataArray or xarray.Dataset
| Data in the pandas structure converted to Dataset if the object is
| a DataFrame, or a DataArray if the object is a Series.
|
| See Also
| --------
| DataFrame.to_hdf : Write DataFrame to an HDF5 file.
| DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
|
| Notes
| -----
| See the `xarray docs <https://xarray.pydata.org/en/stable/>`__
|
| Examples
| --------
| >>> df = pd.DataFrame([('falcon', 'bird', 389.0, 2),
| ... ('parrot', 'bird', 24.0, 2),
| ... ('lion', 'mammal', 80.5, 4),
| ... ('monkey', 'mammal', np.nan, 4)],
| ... columns=['name', 'class', 'max_speed',
| ... 'num_legs'])
| >>> df
| name class max_speed num_legs
| 0 falcon bird 389.0 2
| 1 parrot bird 24.0 2
| 2 lion mammal 80.5 4
| 3 monkey mammal NaN 4
|
| >>> df.to_xarray() # doctest: +SKIP
| <xarray.Dataset>
| Dimensions: (index: 4)
| Coordinates:
| * index (index) int64 32B 0 1 2 3
| Data variables:
| name (index) object 32B 'falcon' 'parrot' 'lion' 'monkey'
| class (index) object 32B 'bird' 'bird' 'mammal' 'mammal'
| max_speed (index) float64 32B 389.0 24.0 80.5 nan
| num_legs (index) int64 32B 2 2 4 4
|
| >>> df['max_speed'].to_xarray() # doctest: +SKIP
| <xarray.DataArray 'max_speed' (index: 4)>
| array([389. , 24. , 80.5, nan])
| Coordinates:
| * index (index) int64 0 1 2 3
|
| >>> dates = pd.to_datetime(['2018-01-01', '2018-01-01',
| ... '2018-01-02', '2018-01-02'])
| >>> df_multiindex = pd.DataFrame({'date': dates,
| ... 'animal': ['falcon', 'parrot',
| ... 'falcon', 'parrot'],
| ... 'speed': [350, 18, 361, 15]})
| >>> df_multiindex = df_multiindex.set_index(['date', 'animal'])
|
| >>> df_multiindex
| speed
| date animal
| 2018-01-01 falcon 350
| parrot 18
| 2018-01-02 falcon 361
| parrot 15
|
| >>> df_multiindex.to_xarray() # doctest: +SKIP
| <xarray.Dataset>
| Dimensions: (date: 2, animal: 2)
| Coordinates:
| * date (date) datetime64[ns] 2018-01-01 2018-01-02
| * animal (animal) object 'falcon' 'parrot'
| Data variables:
| speed (date, animal) int64 350 18 361 15
|
| truncate(self, before=None, after=None, axis: 'Axis | None' = None, copy: 'bool_t | None' = None) -> 'Self'
| Truncate a Series or DataFrame before and after some index value.
|
| This is a useful shorthand for boolean indexing based on index
| values above or below certain thresholds.
|
| Parameters
| ----------
| before : date, str, int
| Truncate all rows before this index value.
| after : date, str, int
| Truncate all rows after this index value.
| axis : {0 or 'index', 1 or 'columns'}, optional
| Axis to truncate. Truncates the index (rows) by default.
| For `Series` this parameter is unused and defaults to 0.
| copy : bool, default is True,
| Return a copy of the truncated section.
|
| .. note::
| The `copy` keyword will change behavior in pandas 3.0.
| `Copy-on-Write
| <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
| will be enabled by default, which means that all methods with a
| `copy` keyword will use a lazy copy mechanism to defer the copy and
| ignore the `copy` keyword. The `copy` keyword will be removed in a
| future version of pandas.
|
| You can already get the future behavior and improvements through
| enabling copy on write ``pd.options.mode.copy_on_write = True``
|
| Returns
| -------
| type of caller
| The truncated Series or DataFrame.
|
| See Also
| --------
| DataFrame.loc : Select a subset of a DataFrame by label.
| DataFrame.iloc : Select a subset of a DataFrame by position.
|
| Notes
| -----
| If the index being truncated contains only datetime values,
| `before` and `after` may be specified as strings instead of
| Timestamps.
|
| Examples
| --------
| >>> df = pd.DataFrame({'A': ['a', 'b', 'c', 'd', 'e'],
| ... 'B': ['f', 'g', 'h', 'i', 'j'],
| ... 'C': ['k', 'l', 'm', 'n', 'o']},
| ... index=[1, 2, 3, 4, 5])
| >>> df
| A B C
| 1 a f k
| 2 b g l
| 3 c h m
| 4 d i n
| 5 e j o
|
| >>> df.truncate(before=2, after=4)
| A B C
| 2 b g l
| 3 c h m
| 4 d i n
|
| The columns of a DataFrame can be truncated.
|
| >>> df.truncate(before="A", after="B", axis="columns")
| A B
| 1 a f
| 2 b g
| 3 c h
| 4 d i
| 5 e j
|
| For Series, only rows can be truncated.
|
| >>> df['A'].truncate(before=2, after=4)
| 2 b
| 3 c
| 4 d
| Name: A, dtype: object
|
| The index values in ``truncate`` can be datetimes or string
| dates.
|
| >>> dates = pd.date_range('2016-01-01', '2016-02-01', freq='s')
| >>> df = pd.DataFrame(index=dates, data={'A': 1})
| >>> df.tail()
| A
| 2016-01-31 23:59:56 1
| 2016-01-31 23:59:57 1
| 2016-01-31 23:59:58 1
| 2016-01-31 23:59:59 1
| 2016-02-01 00:00:00 1
|
| >>> df.truncate(before=pd.Timestamp('2016-01-05'),
| ... after=pd.Timestamp('2016-01-10')).tail()
| A
| 2016-01-09 23:59:56 1
| 2016-01-09 23:59:57 1
| 2016-01-09 23:59:58 1
| 2016-01-09 23:59:59 1
| 2016-01-10 00:00:00 1
|
| Because the index is a DatetimeIndex containing only dates, we can
| specify `before` and `after` as strings. They will be coerced to
| Timestamps before truncation.
|
| >>> df.truncate('2016-01-05', '2016-01-10').tail()
| A
| 2016-01-09 23:59:56 1
| 2016-01-09 23:59:57 1
| 2016-01-09 23:59:58 1
| 2016-01-09 23:59:59 1
| 2016-01-10 00:00:00 1
|
| Note that ``truncate`` assumes a 0 value for any unspecified time
| component (midnight). This differs from partial string slicing, which
| returns any partially matching dates.
|
| >>> df.loc['2016-01-05':'2016-01-10', :].tail()
| A
| 2016-01-10 23:59:55 1
| 2016-01-10 23:59:56 1
| 2016-01-10 23:59:57 1
| 2016-01-10 23:59:58 1
| 2016-01-10 23:59:59 1
|
| tz_convert(self, tz, axis: 'Axis' = 0, level=None, copy: 'bool_t | None' = None) -> 'Self'
| Convert tz-aware axis to target time zone.
|
| Parameters
| ----------
| tz : str or tzinfo object or None
| Target time zone. Passing ``None`` will convert to
| UTC and remove the timezone information.
| axis : {0 or 'index', 1 or 'columns'}, default 0
| The axis to convert
| level : int, str, default None
| If axis is a MultiIndex, convert a specific level. Otherwise
| must be None.
| copy : bool, default True
| Also make a copy of the underlying data.
|
| .. note::
| The `copy` keyword will change behavior in pandas 3.0.
| `Copy-on-Write
| <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
| will be enabled by default, which means that all methods with a
| `copy` keyword will use a lazy copy mechanism to defer the copy and
| ignore the `copy` keyword. The `copy` keyword will be removed in a
| future version of pandas.
|
| You can already get the future behavior and improvements through
| enabling copy on write ``pd.options.mode.copy_on_write = True``
|
| Returns
| -------
| Series/DataFrame
| Object with time zone converted axis.
|
| Raises
| ------
| TypeError
| If the axis is tz-naive.
|
| Examples
| --------
| Change to another time zone:
|
| >>> s = pd.Series(
| ... [1],
| ... index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']),
| ... )
| >>> s.tz_convert('Asia/Shanghai')
| 2018-09-15 07:30:00+08:00 1
| dtype: int64
|
| Pass None to convert to UTC and get a tz-naive index:
|
| >>> s = pd.Series([1],
| ... index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']))
| >>> s.tz_convert(None)
| 2018-09-14 23:30:00 1
| dtype: int64
|
| tz_localize(self, tz, axis: 'Axis' = 0, level=None, copy: 'bool_t | None' = None, ambiguous: 'TimeAmbiguous' = 'raise', nonexistent: 'TimeNonexistent' = 'raise') -> 'Self'
| Localize tz-naive index of a Series or DataFrame to target time zone.
|
| This operation localizes the Index. To localize the values in a
| timezone-naive Series, use :meth:`Series.dt.tz_localize`.
|
| Parameters
| ----------
| tz : str or tzinfo or None
| Time zone to localize. Passing ``None`` will remove the
| time zone information and preserve local time.
| axis : {0 or 'index', 1 or 'columns'}, default 0
| The axis to localize
| level : int, str, default None
| If axis ia a MultiIndex, localize a specific level. Otherwise
| must be None.
| copy : bool, default True
| Also make a copy of the underlying data.
|
| .. note::
| The `copy` keyword will change behavior in pandas 3.0.
| `Copy-on-Write
| <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
| will be enabled by default, which means that all methods with a
| `copy` keyword will use a lazy copy mechanism to defer the copy and
| ignore the `copy` keyword. The `copy` keyword will be removed in a
| future version of pandas.
|
| You can already get the future behavior and improvements through
| enabling copy on write ``pd.options.mode.copy_on_write = True``
| ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'
| When clocks moved backward due to DST, ambiguous times may arise.
| For example in Central European Time (UTC+01), when going from
| 03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at
| 00:30:00 UTC and at 01:30:00 UTC. In such a situation, the
| `ambiguous` parameter dictates how ambiguous times should be
| handled.
|
| - 'infer' will attempt to infer fall dst-transition hours based on
| order
| - bool-ndarray where True signifies a DST time, False designates
| a non-DST time (note that this flag is only applicable for
| ambiguous times)
| - 'NaT' will return NaT where there are ambiguous times
| - 'raise' will raise an AmbiguousTimeError if there are ambiguous
| times.
| nonexistent : str, default 'raise'
| A nonexistent time does not exist in a particular timezone
| where clocks moved forward due to DST. Valid values are:
|
| - 'shift_forward' will shift the nonexistent time forward to the
| closest existing time
| - 'shift_backward' will shift the nonexistent time backward to the
| closest existing time
| - 'NaT' will return NaT where there are nonexistent times
| - timedelta objects will shift nonexistent times by the timedelta
| - 'raise' will raise an NonExistentTimeError if there are
| nonexistent times.
|
| Returns
| -------
| Series/DataFrame
| Same type as the input.
|
| Raises
| ------
| TypeError
| If the TimeSeries is tz-aware and tz is not None.
|
| Examples
| --------
| Localize local times:
|
| >>> s = pd.Series(
| ... [1],
| ... index=pd.DatetimeIndex(['2018-09-15 01:30:00']),
| ... )
| >>> s.tz_localize('CET')
| 2018-09-15 01:30:00+02:00 1
| dtype: int64
|
| Pass None to convert to tz-naive index and preserve local time:
|
| >>> s = pd.Series([1],
| ... index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']))
| >>> s.tz_localize(None)
| 2018-09-15 01:30:00 1
| dtype: int64
|
| Be careful with DST changes. When there is sequential data, pandas
| can infer the DST time:
|
| >>> s = pd.Series(range(7),
| ... index=pd.DatetimeIndex(['2018-10-28 01:30:00',
| ... '2018-10-28 02:00:00',
| ... '2018-10-28 02:30:00',
| ... '2018-10-28 02:00:00',
| ... '2018-10-28 02:30:00',
| ... '2018-10-28 03:00:00',
| ... '2018-10-28 03:30:00']))
| >>> s.tz_localize('CET', ambiguous='infer')
| 2018-10-28 01:30:00+02:00 0
| 2018-10-28 02:00:00+02:00 1
| 2018-10-28 02:30:00+02:00 2
| 2018-10-28 02:00:00+01:00 3
| 2018-10-28 02:30:00+01:00 4
| 2018-10-28 03:00:00+01:00 5
| 2018-10-28 03:30:00+01:00 6
| dtype: int64
|
| In some cases, inferring the DST is impossible. In such cases, you can
| pass an ndarray to the ambiguous parameter to set the DST explicitly
|
| >>> s = pd.Series(range(3),
| ... index=pd.DatetimeIndex(['2018-10-28 01:20:00',
| ... '2018-10-28 02:36:00',
| ... '2018-10-28 03:46:00']))
| >>> s.tz_localize('CET', ambiguous=np.array([True, True, False]))
| 2018-10-28 01:20:00+02:00 0
| 2018-10-28 02:36:00+02:00 1
| 2018-10-28 03:46:00+01:00 2
| dtype: int64
|
| If the DST transition causes nonexistent times, you can shift these
| dates forward or backward with a timedelta object or `'shift_forward'`
| or `'shift_backward'`.
|
| >>> s = pd.Series(range(2),
| ... index=pd.DatetimeIndex(['2015-03-29 02:30:00',
| ... '2015-03-29 03:30:00']))
| >>> s.tz_localize('Europe/Warsaw', nonexistent='shift_forward')
| 2015-03-29 03:00:00+02:00 0
| 2015-03-29 03:30:00+02:00 1
| dtype: int64
| >>> s.tz_localize('Europe/Warsaw', nonexistent='shift_backward')
| 2015-03-29 01:59:59.999999999+01:00 0
| 2015-03-29 03:30:00+02:00 1
| dtype: int64
| >>> s.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1h'))
| 2015-03-29 03:30:00+02:00 0
| 2015-03-29 03:30:00+02:00 1
| dtype: int64
|
| where(self, cond, other=nan, *, inplace: 'bool_t' = False, axis: 'Axis | None' = None, level: 'Level | None' = None) -> 'Self | None'
| Replace values where the condition is False.
|
| Parameters
| ----------
| cond : bool Series/DataFrame, array-like, or callable
| Where `cond` is True, keep the original value. Where
| False, replace with corresponding value from `other`.
| If `cond` is callable, it is computed on the Series/DataFrame and
| should return boolean Series/DataFrame or array. The callable must
| not change input Series/DataFrame (though pandas doesn't check it).
| other : scalar, Series/DataFrame, or callable
| Entries where `cond` is False are replaced with
| corresponding value from `other`.
| If other is callable, it is computed on the Series/DataFrame and
| should return scalar or Series/DataFrame. The callable must not
| change input Series/DataFrame (though pandas doesn't check it).
| If not specified, entries will be filled with the corresponding
| NULL value (``np.nan`` for numpy dtypes, ``pd.NA`` for extension
| dtypes).
| inplace : bool, default False
| Whether to perform the operation in place on the data.
| axis : int, default None
| Alignment axis if needed. For `Series` this parameter is
| unused and defaults to 0.
| level : int, default None
| Alignment level if needed.
|
| Returns
| -------
| Same type as caller or None if ``inplace=True``.
|
| See Also
| --------
| :func:`DataFrame.mask` : Return an object of same shape as
| self.
|
| Notes
| -----
| The where method is an application of the if-then idiom. For each
| element in the calling DataFrame, if ``cond`` is ``True`` the
| element is used; otherwise the corresponding element from the DataFrame
| ``other`` is used. If the axis of ``other`` does not align with axis of
| ``cond`` Series/DataFrame, the misaligned index positions will be filled with
| False.
|
| The signature for :func:`DataFrame.where` differs from
| :func:`numpy.where`. Roughly ``df1.where(m, df2)`` is equivalent to
| ``np.where(m, df1, df2)``.
|
| For further details and examples see the ``where`` documentation in
| :ref:`indexing <indexing.where_mask>`.
|
| The dtype of the object takes precedence. The fill value is casted to
| the object's dtype, if this can be done losslessly.
|
| Examples
| --------
| >>> s = pd.Series(range(5))
| >>> s.where(s > 0)
| 0 NaN
| 1 1.0
| 2 2.0
| 3 3.0
| 4 4.0
| dtype: float64
| >>> s.mask(s > 0)
| 0 0.0
| 1 NaN
| 2 NaN
| 3 NaN
| 4 NaN
| dtype: float64
|
| >>> s = pd.Series(range(5))
| >>> t = pd.Series([True, False])
| >>> s.where(t, 99)
| 0 0
| 1 99
| 2 99
| 3 99
| 4 99
| dtype: int64
| >>> s.mask(t, 99)
| 0 99
| 1 1
| 2 99
| 3 99
| 4 99
| dtype: int64
|
| >>> s.where(s > 1, 10)
| 0 10
| 1 10
| 2 2
| 3 3
| 4 4
| dtype: int64
| >>> s.mask(s > 1, 10)
| 0 0
| 1 1
| 2 10
| 3 10
| 4 10
| dtype: int64
|
| >>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B'])
| >>> df
| A B
| 0 0 1
| 1 2 3
| 2 4 5
| 3 6 7
| 4 8 9
| >>> m = df % 3 == 0
| >>> df.where(m, -df)
| A B
| 0 0 -1
| 1 -2 3
| 2 -4 -5
| 3 6 -7
| 4 -8 9
| >>> df.where(m, -df) == np.where(m, df, -df)
| A B
| 0 True True
| 1 True True
| 2 True True
| 3 True True
| 4 True True
| >>> df.where(m, -df) == df.mask(~m, -df)
| A B
| 0 True True
| 1 True True
| 2 True True
| 3 True True
| 4 True True
|
| xs(self, key: 'IndexLabel', axis: 'Axis' = 0, level: 'IndexLabel | None' = None, drop_level: 'bool_t' = True) -> 'Self'
| Return cross-section from the Series/DataFrame.
|
| This method takes a `key` argument to select data at a particular
| level of a MultiIndex.
|
| Parameters
| ----------
| key : label or tuple of label
| Label contained in the index, or partially in a MultiIndex.
| axis : {0 or 'index', 1 or 'columns'}, default 0
| Axis to retrieve cross-section on.
| level : object, defaults to first n levels (n=1 or len(key))
| In case of a key partially contained in a MultiIndex, indicate
| which levels are used. Levels can be referred by label or position.
| drop_level : bool, default True
| If False, returns object with same levels as self.
|
| Returns
| -------
| Series or DataFrame
| Cross-section from the original Series or DataFrame
| corresponding to the selected index levels.
|
| See Also
| --------
| DataFrame.loc : Access a group of rows and columns
| by label(s) or a boolean array.
| DataFrame.iloc : Purely integer-location based indexing
| for selection by position.
|
| Notes
| -----
| `xs` can not be used to set values.
|
| MultiIndex Slicers is a generic way to get/set values on
| any level or levels.
| It is a superset of `xs` functionality, see
| :ref:`MultiIndex Slicers <advanced.mi_slicers>`.
|
| Examples
| --------
| >>> d = {'num_legs': [4, 4, 2, 2],
| ... 'num_wings': [0, 0, 2, 2],
| ... 'class': ['mammal', 'mammal', 'mammal', 'bird'],
| ... 'animal': ['cat', 'dog', 'bat', 'penguin'],
| ... 'locomotion': ['walks', 'walks', 'flies', 'walks']}
| >>> df = pd.DataFrame(data=d)
| >>> df = df.set_index(['class', 'animal', 'locomotion'])
| >>> df
| num_legs num_wings
| class animal locomotion
| mammal cat walks 4 0
| dog walks 4 0
| bat flies 2 2
| bird penguin walks 2 2
|
| Get values at specified index
|
| >>> df.xs('mammal')
| num_legs num_wings
| animal locomotion
| cat walks 4 0
| dog walks 4 0
| bat flies 2 2
|
| Get values at several indexes
|
| >>> df.xs(('mammal', 'dog', 'walks'))
| num_legs 4
| num_wings 0
| Name: (mammal, dog, walks), dtype: int64
|
| Get values at specified index and level
|
| >>> df.xs('cat', level=1)
| num_legs num_wings
| class locomotion
| mammal walks 4 0
|
| Get values at several indexes and levels
|
| >>> df.xs(('bird', 'walks'),
| ... level=[0, 'locomotion'])
| num_legs num_wings
| animal
| penguin 2 2
|
| Get values at specified column and axis
|
| >>> df.xs('num_wings', axis=1)
| class animal locomotion
| mammal cat walks 0
| dog walks 0
| bat flies 2
| bird penguin walks 2
| Name: num_wings, dtype: int64
|
| ----------------------------------------------------------------------
| Readonly properties inherited from pandas.core.generic.NDFrame:
|
| dtypes
| Return the dtypes in the DataFrame.
|
| This returns a Series with the data type of each column.
| The result's index is the original DataFrame's columns. Columns
| with mixed types are stored with the ``object`` dtype. See
| :ref:`the User Guide <basics.dtypes>` for more.
|
| Returns
| -------
| pandas.Series
| The data type of each column.
|
| Examples
| --------
| >>> df = pd.DataFrame({'float': [1.0],
| ... 'int': [1],
| ... 'datetime': [pd.Timestamp('20180310')],
| ... 'string': ['foo']})
| >>> df.dtypes
| float float64
| int int64
| datetime datetime64[ns]
| string object
| dtype: object
|
| empty
| Indicator whether Series/DataFrame is empty.
|
| True if Series/DataFrame is entirely empty (no items), meaning any of the
| axes are of length 0.
|
| Returns
| -------
| bool
| If Series/DataFrame is empty, return True, if not return False.
|
| See Also
| --------
| Series.dropna : Return series without null values.
| DataFrame.dropna : Return DataFrame with labels on given axis omitted
| where (all or any) data are missing.
|
| Notes
| -----
| If Series/DataFrame contains only NaNs, it is still not considered empty. See
| the example below.
|
| Examples
| --------
| An example of an actual empty DataFrame. Notice the index is empty:
|
| >>> df_empty = pd.DataFrame({'A' : []})
| >>> df_empty
| Empty DataFrame
| Columns: [A]
| Index: []
| >>> df_empty.empty
| True
|
| If we only have NaNs in our DataFrame, it is not considered empty! We
| will need to drop the NaNs to make the DataFrame empty:
|
| >>> df = pd.DataFrame({'A' : [np.nan]})
| >>> df
| A
| 0 NaN
| >>> df.empty
| False
| >>> df.dropna().empty
| True
|
| >>> ser_empty = pd.Series({'A' : []})
| >>> ser_empty
| A []
| dtype: object
| >>> ser_empty.empty
| False
| >>> ser_empty = pd.Series()
| >>> ser_empty.empty
| True
|
| flags
| Get the properties associated with this pandas object.
|
| The available flags are
|
| * :attr:`Flags.allows_duplicate_labels`
|
| See Also
| --------
| Flags : Flags that apply to pandas objects.
| DataFrame.attrs : Global metadata applying to this dataset.
|
| Notes
| -----
| "Flags" differ from "metadata". Flags reflect properties of the
| pandas object (the Series or DataFrame). Metadata refer to properties
| of the dataset, and should be stored in :attr:`DataFrame.attrs`.
|
| Examples
| --------
| >>> df = pd.DataFrame({"A": [1, 2]})
| >>> df.flags
| <Flags(allows_duplicate_labels=True)>
|
| Flags can be get or set using ``.``
|
| >>> df.flags.allows_duplicate_labels
| True
| >>> df.flags.allows_duplicate_labels = False
|
| Or by slicing with a key
|
| >>> df.flags["allows_duplicate_labels"]
| False
| >>> df.flags["allows_duplicate_labels"] = True
|
| ndim
| Return an int representing the number of axes / array dimensions.
|
| Return 1 if Series. Otherwise return 2 if DataFrame.
|
| See Also
| --------
| ndarray.ndim : Number of array dimensions.
|
| Examples
| --------
| >>> s = pd.Series({'a': 1, 'b': 2, 'c': 3})
| >>> s.ndim
| 1
|
| >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
| >>> df.ndim
| 2
|
| size
| Return an int representing the number of elements in this object.
|
| Return the number of rows if Series. Otherwise return the number of
| rows times number of columns if DataFrame.
|
| See Also
| --------
| ndarray.size : Number of elements in the array.
|
| Examples
| --------
| >>> s = pd.Series({'a': 1, 'b': 2, 'c': 3})
| >>> s.size
| 3
|
| >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
| >>> df.size
| 4
|
| ----------------------------------------------------------------------
| Data descriptors inherited from pandas.core.generic.NDFrame:
|
| attrs
| Dictionary of global attributes of this dataset.
|
| .. warning::
|
| attrs is experimental and may change without warning.
|
| See Also
| --------
| DataFrame.flags : Global flags applying to this object.
|
| Notes
| -----
| Many operations that create new datasets will copy ``attrs``. Copies
| are always deep so that changing ``attrs`` will only affect the
| present dataset. ``pandas.concat`` copies ``attrs`` only if all input
| datasets have the same ``attrs``.
|
| Examples
| --------
| For Series:
|
| >>> ser = pd.Series([1, 2, 3])
| >>> ser.attrs = {"A": [10, 20, 30]}
| >>> ser.attrs
| {'A': [10, 20, 30]}
|
| For DataFrame:
|
| >>> df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
| >>> df.attrs = {"A": [10, 20, 30]}
| >>> df.attrs
| {'A': [10, 20, 30]}
|
| ----------------------------------------------------------------------
| Data and other attributes inherited from pandas.core.generic.NDFrame:
|
| __array_priority__ = 1000
|
| ----------------------------------------------------------------------
| Methods inherited from pandas.core.base.PandasObject:
|
| __sizeof__(self) -> 'int'
| Generates the total memory usage for an object that returns
| either a value or Series of values
|
| ----------------------------------------------------------------------
| Methods inherited from pandas.core.accessor.DirNamesMixin:
|
| __dir__(self) -> 'list[str]'
| Provide method name lookup and completion.
|
| Notes
| -----
| Only provide 'public' methods.
|
| ----------------------------------------------------------------------
| Data descriptors inherited from pandas.core.accessor.DirNamesMixin:
|
| __dict__
| dictionary for instance variables
|
| __weakref__
| list of weak references to the object
|
| ----------------------------------------------------------------------
| Readonly properties inherited from pandas.core.indexing.IndexingMixin:
|
| at
| Access a single value for a row/column label pair.
|
| Similar to ``loc``, in that both provide label-based lookups. Use
| ``at`` if you only need to get or set a single value in a DataFrame
| or Series.
|
| Raises
| ------
| KeyError
| If getting a value and 'label' does not exist in a DataFrame or Series.
|
| ValueError
| If row/column label pair is not a tuple or if any label
| from the pair is not a scalar for DataFrame.
| If label is list-like (*excluding* NamedTuple) for Series.
|
| See Also
| --------
| DataFrame.at : Access a single value for a row/column pair by label.
| DataFrame.iat : Access a single value for a row/column pair by integer
| position.
| DataFrame.loc : Access a group of rows and columns by label(s).
| DataFrame.iloc : Access a group of rows and columns by integer
| position(s).
| Series.at : Access a single value by label.
| Series.iat : Access a single value by integer position.
| Series.loc : Access a group of rows by label(s).
| Series.iloc : Access a group of rows by integer position(s).
|
| Notes
| -----
| See :ref:`Fast scalar value getting and setting <indexing.basics.get_value>`
| for more details.
|
| Examples
| --------
| >>> df = pd.DataFrame([[0, 2, 3], [0, 4, 1], [10, 20, 30]],
| ... index=[4, 5, 6], columns=['A', 'B', 'C'])
| >>> df
| A B C
| 4 0 2 3
| 5 0 4 1
| 6 10 20 30
|
| Get value at specified row/column pair
|
| >>> df.at[4, 'B']
| 2
|
| Set value at specified row/column pair
|
| >>> df.at[4, 'B'] = 10
| >>> df.at[4, 'B']
| 10
|
| Get value within a Series
|
| >>> df.loc[5].at['B']
| 4
|
| iat
| Access a single value for a row/column pair by integer position.
|
| Similar to ``iloc``, in that both provide integer-based lookups. Use
| ``iat`` if you only need to get or set a single value in a DataFrame
| or Series.
|
| Raises
| ------
| IndexError
| When integer position is out of bounds.
|
| See Also
| --------
| DataFrame.at : Access a single value for a row/column label pair.
| DataFrame.loc : Access a group of rows and columns by label(s).
| DataFrame.iloc : Access a group of rows and columns by integer position(s).
|
| Examples
| --------
| >>> df = pd.DataFrame([[0, 2, 3], [0, 4, 1], [10, 20, 30]],
| ... columns=['A', 'B', 'C'])
| >>> df
| A B C
| 0 0 2 3
| 1 0 4 1
| 2 10 20 30
|
| Get value at specified row/column pair
|
| >>> df.iat[1, 2]
| 1
|
| Set value at specified row/column pair
|
| >>> df.iat[1, 2] = 10
| >>> df.iat[1, 2]
| 10
|
| Get value within a series
|
| >>> df.loc[0].iat[1]
| 2
|
| iloc
| Purely integer-location based indexing for selection by position.
|
| .. deprecated:: 2.2.0
|
| Returning a tuple from a callable is deprecated.
|
| ``.iloc[]`` is primarily integer position based (from ``0`` to
| ``length-1`` of the axis), but may also be used with a boolean
| array.
|
| Allowed inputs are:
|
| - An integer, e.g. ``5``.
| - A list or array of integers, e.g. ``[4, 3, 0]``.
| - A slice object with ints, e.g. ``1:7``.
| - A boolean array.
| - A ``callable`` function with one argument (the calling Series or
| DataFrame) and that returns valid output for indexing (one of the above).
| This is useful in method chains, when you don't have a reference to the
| calling object, but would like to base your selection on
| some value.
| - A tuple of row and column indexes. The tuple elements consist of one of the
| above inputs, e.g. ``(0, 1)``.
|
| ``.iloc`` will raise ``IndexError`` if a requested indexer is
| out-of-bounds, except *slice* indexers which allow out-of-bounds
| indexing (this conforms with python/numpy *slice* semantics).
|
| See more at :ref:`Selection by Position <indexing.integer>`.
|
| See Also
| --------
| DataFrame.iat : Fast integer location scalar accessor.
| DataFrame.loc : Purely label-location based indexer for selection by label.
| Series.iloc : Purely integer-location based indexing for
| selection by position.
|
| Examples
| --------
| >>> mydict = [{'a': 1, 'b': 2, 'c': 3, 'd': 4},
| ... {'a': 100, 'b': 200, 'c': 300, 'd': 400},
| ... {'a': 1000, 'b': 2000, 'c': 3000, 'd': 4000}]
| >>> df = pd.DataFrame(mydict)
| >>> df
| a b c d
| 0 1 2 3 4
| 1 100 200 300 400
| 2 1000 2000 3000 4000
|
| **Indexing just the rows**
|
| With a scalar integer.
|
| >>> type(df.iloc[0])
| <class 'pandas.core.series.Series'>
| >>> df.iloc[0]
| a 1
| b 2
| c 3
| d 4
| Name: 0, dtype: int64
|
| With a list of integers.
|
| >>> df.iloc[[0]]
| a b c d
| 0 1 2 3 4
| >>> type(df.iloc[[0]])
| <class 'pandas.core.frame.DataFrame'>
|
| >>> df.iloc[[0, 1]]
| a b c d
| 0 1 2 3 4
| 1 100 200 300 400
|
| With a `slice` object.
|
| >>> df.iloc[:3]
| a b c d
| 0 1 2 3 4
| 1 100 200 300 400
| 2 1000 2000 3000 4000
|
| With a boolean mask the same length as the index.
|
| >>> df.iloc[[True, False, True]]
| a b c d
| 0 1 2 3 4
| 2 1000 2000 3000 4000
|
| With a callable, useful in method chains. The `x` passed
| to the ``lambda`` is the DataFrame being sliced. This selects
| the rows whose index label even.
|
| >>> df.iloc[lambda x: x.index % 2 == 0]
| a b c d
| 0 1 2 3 4
| 2 1000 2000 3000 4000
|
| **Indexing both axes**
|
| You can mix the indexer types for the index and columns. Use ``:`` to
| select the entire axis.
|
| With scalar integers.
|
| >>> df.iloc[0, 1]
| 2
|
| With lists of integers.
|
| >>> df.iloc[[0, 2], [1, 3]]
| b d
| 0 2 4
| 2 2000 4000
|
| With `slice` objects.
|
| >>> df.iloc[1:3, 0:3]
| a b c
| 1 100 200 300
| 2 1000 2000 3000
|
| With a boolean array whose length matches the columns.
|
| >>> df.iloc[:, [True, False, True, False]]
| a c
| 0 1 3
| 1 100 300
| 2 1000 3000
|
| With a callable function that expects the Series or DataFrame.
|
| >>> df.iloc[:, lambda df: [0, 2]]
| a c
| 0 1 3
| 1 100 300
| 2 1000 3000
|
| loc
| Access a group of rows and columns by label(s) or a boolean array.
|
| ``.loc[]`` is primarily label based, but may also be used with a
| boolean array.
|
| Allowed inputs are:
|
| - A single label, e.g. ``5`` or ``'a'``, (note that ``5`` is
| interpreted as a *label* of the index, and **never** as an
| integer position along the index).
| - A list or array of labels, e.g. ``['a', 'b', 'c']``.
| - A slice object with labels, e.g. ``'a':'f'``.
|
| .. warning:: Note that contrary to usual python slices, **both** the
| start and the stop are included
|
| - A boolean array of the same length as the axis being sliced,
| e.g. ``[True, False, True]``.
| - An alignable boolean Series. The index of the key will be aligned before
| masking.
| - An alignable Index. The Index of the returned selection will be the input.
| - A ``callable`` function with one argument (the calling Series or
| DataFrame) and that returns valid output for indexing (one of the above)
|
| See more at :ref:`Selection by Label <indexing.label>`.
|
| Raises
| ------
| KeyError
| If any items are not found.
| IndexingError
| If an indexed key is passed and its index is unalignable to the frame index.
|
| See Also
| --------
| DataFrame.at : Access a single value for a row/column label pair.
| DataFrame.iloc : Access group of rows and columns by integer position(s).
| DataFrame.xs : Returns a cross-section (row(s) or column(s)) from the
| Series/DataFrame.
| Series.loc : Access group of values using labels.
|
| Examples
| --------
| **Getting values**
|
| >>> df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],
| ... index=['cobra', 'viper', 'sidewinder'],
| ... columns=['max_speed', 'shield'])
| >>> df
| max_speed shield
| cobra 1 2
| viper 4 5
| sidewinder 7 8
|
| Single label. Note this returns the row as a Series.
|
| >>> df.loc['viper']
| max_speed 4
| shield 5
| Name: viper, dtype: int64
|
| List of labels. Note using ``[[]]`` returns a DataFrame.
|
| >>> df.loc[['viper', 'sidewinder']]
| max_speed shield
| viper 4 5
| sidewinder 7 8
|
| Single label for row and column
|
| >>> df.loc['cobra', 'shield']
| 2
|
| Slice with labels for row and single label for column. As mentioned
| above, note that both the start and stop of the slice are included.
|
| >>> df.loc['cobra':'viper', 'max_speed']
| cobra 1
| viper 4
| Name: max_speed, dtype: int64
|
| Boolean list with the same length as the row axis
|
| >>> df.loc[[False, False, True]]
| max_speed shield
| sidewinder 7 8
|
| Alignable boolean Series:
|
| >>> df.loc[pd.Series([False, True, False],
| ... index=['viper', 'sidewinder', 'cobra'])]
| max_speed shield
| sidewinder 7 8
|
| Index (same behavior as ``df.reindex``)
|
| >>> df.loc[pd.Index(["cobra", "viper"], name="foo")]
| max_speed shield
| foo
| cobra 1 2
| viper 4 5
|
| Conditional that returns a boolean Series
|
| >>> df.loc[df['shield'] > 6]
| max_speed shield
| sidewinder 7 8
|
| Conditional that returns a boolean Series with column labels specified
|
| >>> df.loc[df['shield'] > 6, ['max_speed']]
| max_speed
| sidewinder 7
|
| Multiple conditional using ``&`` that returns a boolean Series
|
| >>> df.loc[(df['max_speed'] > 1) & (df['shield'] < 8)]
| max_speed shield
| viper 4 5
|
| Multiple conditional using ``|`` that returns a boolean Series
|
| >>> df.loc[(df['max_speed'] > 4) | (df['shield'] < 5)]
| max_speed shield
| cobra 1 2
| sidewinder 7 8
|
| Please ensure that each condition is wrapped in parentheses ``()``.
| See the :ref:`user guide<indexing.boolean>`
| for more details and explanations of Boolean indexing.
|
| .. note::
| If you find yourself using 3 or more conditionals in ``.loc[]``,
| consider using :ref:`advanced indexing<advanced.advanced_hierarchical>`.
|
| See below for using ``.loc[]`` on MultiIndex DataFrames.
|
| Callable that returns a boolean Series
|
| >>> df.loc[lambda df: df['shield'] == 8]
| max_speed shield
| sidewinder 7 8
|
| **Setting values**
|
| Set value for all items matching the list of labels
|
| >>> df.loc[['viper', 'sidewinder'], ['shield']] = 50
| >>> df
| max_speed shield
| cobra 1 2
| viper 4 50
| sidewinder 7 50
|
| Set value for an entire row
|
| >>> df.loc['cobra'] = 10
| >>> df
| max_speed shield
| cobra 10 10
| viper 4 50
| sidewinder 7 50
|
| Set value for an entire column
|
| >>> df.loc[:, 'max_speed'] = 30
| >>> df
| max_speed shield
| cobra 30 10
| viper 30 50
| sidewinder 30 50
|
| Set value for rows matching callable condition
|
| >>> df.loc[df['shield'] > 35] = 0
| >>> df
| max_speed shield
| cobra 30 10
| viper 0 0
| sidewinder 0 0
|
| Add value matching location
|
| >>> df.loc["viper", "shield"] += 5
| >>> df
| max_speed shield
| cobra 30 10
| viper 0 5
| sidewinder 0 0
|
| Setting using a ``Series`` or a ``DataFrame`` sets the values matching the
| index labels, not the index positions.
|
| >>> shuffled_df = df.loc[["viper", "cobra", "sidewinder"]]
| >>> df.loc[:] += shuffled_df
| >>> df
| max_speed shield
| cobra 60 20
| viper 0 10
| sidewinder 0 0
|
| **Getting values on a DataFrame with an index that has integer labels**
|
| Another example using integers for the index
|
| >>> df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],
| ... index=[7, 8, 9], columns=['max_speed', 'shield'])
| >>> df
| max_speed shield
| 7 1 2
| 8 4 5
| 9 7 8
|
| Slice with integer labels for rows. As mentioned above, note that both
| the start and stop of the slice are included.
|
| >>> df.loc[7:9]
| max_speed shield
| 7 1 2
| 8 4 5
| 9 7 8
|
| **Getting values with a MultiIndex**
|
| A number of examples using a DataFrame with a MultiIndex
|
| >>> tuples = [
| ... ('cobra', 'mark i'), ('cobra', 'mark ii'),
| ... ('sidewinder', 'mark i'), ('sidewinder', 'mark ii'),
| ... ('viper', 'mark ii'), ('viper', 'mark iii')
| ... ]
| >>> index = pd.MultiIndex.from_tuples(tuples)
| >>> values = [[12, 2], [0, 4], [10, 20],
| ... [1, 4], [7, 1], [16, 36]]
| >>> df = pd.DataFrame(values, columns=['max_speed', 'shield'], index=index)
| >>> df
| max_speed shield
| cobra mark i 12 2
| mark ii 0 4
| sidewinder mark i 10 20
| mark ii 1 4
| viper mark ii 7 1
| mark iii 16 36
|
| Single label. Note this returns a DataFrame with a single index.
|
| >>> df.loc['cobra']
| max_speed shield
| mark i 12 2
| mark ii 0 4
|
| Single index tuple. Note this returns a Series.
|
| >>> df.loc[('cobra', 'mark ii')]
| max_speed 0
| shield 4
| Name: (cobra, mark ii), dtype: int64
|
| Single label for row and column. Similar to passing in a tuple, this
| returns a Series.
|
| >>> df.loc['cobra', 'mark i']
| max_speed 12
| shield 2
| Name: (cobra, mark i), dtype: int64
|
| Single tuple. Note using ``[[]]`` returns a DataFrame.
|
| >>> df.loc[[('cobra', 'mark ii')]]
| max_speed shield
| cobra mark ii 0 4
|
| Single tuple for the index with a single label for the column
|
| >>> df.loc[('cobra', 'mark i'), 'shield']
| 2
|
| Slice from index tuple to single label
|
| >>> df.loc[('cobra', 'mark i'):'viper']
| max_speed shield
| cobra mark i 12 2
| mark ii 0 4
| sidewinder mark i 10 20
| mark ii 1 4
| viper mark ii 7 1
| mark iii 16 36
|
| Slice from index tuple to index tuple
|
| >>> df.loc[('cobra', 'mark i'):('viper', 'mark ii')]
| max_speed shield
| cobra mark i 12 2
| mark ii 0 4
| sidewinder mark i 10 20
| mark ii 1 4
| viper mark ii 7 1
|
| Please see the :ref:`user guide<advanced.advanced_hierarchical>`
| for more details and explanations of advanced indexing.
|
| ----------------------------------------------------------------------
| Methods inherited from pandas.core.arraylike.OpsMixin:
|
| __add__(self, other)
| Get Addition of DataFrame and other, column-wise.
|
| Equivalent to ``DataFrame.add(other)``.
|
| Parameters
| ----------
| other : scalar, sequence, Series, dict or DataFrame
| Object to be added to the DataFrame.
|
| Returns
| -------
| DataFrame
| The result of adding ``other`` to DataFrame.
|
| See Also
| --------
| DataFrame.add : Add a DataFrame and another object, with option for index-
| or column-oriented addition.
|
| Examples
| --------
| >>> df = pd.DataFrame({'height': [1.5, 2.6], 'weight': [500, 800]},
| ... index=['elk', 'moose'])
| >>> df
| height weight
| elk 1.5 500
| moose 2.6 800
|
| Adding a scalar affects all rows and columns.
|
| >>> df[['height', 'weight']] + 1.5
| height weight
| elk 3.0 501.5
| moose 4.1 801.5
|
| Each element of a list is added to a column of the DataFrame, in order.
|
| >>> df[['height', 'weight']] + [0.5, 1.5]
| height weight
| elk 2.0 501.5
| moose 3.1 801.5
|
| Keys of a dictionary are aligned to the DataFrame, based on column names;
| each value in the dictionary is added to the corresponding column.
|
| >>> df[['height', 'weight']] + {'height': 0.5, 'weight': 1.5}
| height weight
| elk 2.0 501.5
| moose 3.1 801.5
|
| When `other` is a :class:`Series`, the index of `other` is aligned with the
| columns of the DataFrame.
|
| >>> s1 = pd.Series([0.5, 1.5], index=['weight', 'height'])
| >>> df[['height', 'weight']] + s1
| height weight
| elk 3.0 500.5
| moose 4.1 800.5
|
| Even when the index of `other` is the same as the index of the DataFrame,
| the :class:`Series` will not be reoriented. If index-wise alignment is desired,
| :meth:`DataFrame.add` should be used with `axis='index'`.
|
| >>> s2 = pd.Series([0.5, 1.5], index=['elk', 'moose'])
| >>> df[['height', 'weight']] + s2
| elk height moose weight
| elk NaN NaN NaN NaN
| moose NaN NaN NaN NaN
|
| >>> df[['height', 'weight']].add(s2, axis='index')
| height weight
| elk 2.0 500.5
| moose 4.1 801.5
|
| When `other` is a :class:`DataFrame`, both columns names and the
| index are aligned.
|
| >>> other = pd.DataFrame({'height': [0.2, 0.4, 0.6]},
| ... index=['elk', 'moose', 'deer'])
| >>> df[['height', 'weight']] + other
| height weight
| deer NaN NaN
| elk 1.7 NaN
| moose 3.0 NaN
|
| __and__(self, other)
|
| __eq__(self, other)
| Return self==value.
|
| __floordiv__(self, other)
|
| __ge__(self, other)
| Return self>=value.
|
| __gt__(self, other)
| Return self>value.
|
| __le__(self, other)
| Return self<=value.
|
| __lt__(self, other)
| Return self<value.
|
| __mod__(self, other)
|
| __mul__(self, other)
|
| __ne__(self, other)
| Return self!=value.
|
| __or__(self, other)
| Return self|value.
|
| __pow__(self, other)
|
| __radd__(self, other)
|
| __rand__(self, other)
|
| __rfloordiv__(self, other)
|
| __rmod__(self, other)
|
| __rmul__(self, other)
|
| __ror__(self, other)
| Return value|self.
|
| __rpow__(self, other)
|
| __rsub__(self, other)
|
| __rtruediv__(self, other)
|
| __rxor__(self, other)
|
| __sub__(self, other)
|
| __truediv__(self, other)
|
| __xor__(self, other)
|
| ----------------------------------------------------------------------
| Data and other attributes inherited from pandas.core.arraylike.OpsMixin:
|
| __hash__ = NoneTab completion
In many (most) interactive environments for R and Python, tab completion (hitting the tab key when you are part-way through typing something) can be a very useful way to get a nudge in the right direction or to get access to more help.
As far as I can tell, tab completion does not work for Python inside Quarto Python chunks, but does (at least in a limited fashion) in the Python conosle that appears after you run some Python code. In some other environments (like VS Code) you can probably get more mileage out of Python tab completion.
Tip
Note: You can work with Quarto documents in VS code, you don’t have to be in RStudio for that. Just install the Quarto extension for VS code.