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DOC: move idxmin and idxmax docs from core/shared_docs.py to core/frame.py #59735

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Sep 6, 2024
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DOC: move idxmin and idxmax docs from core/shared_docs.py to core/frame.py #59735

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144 changes: 142 additions & 2 deletions pandas/core/frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12745,10 +12745,80 @@ def nunique(self, axis: Axis = 0, dropna: bool = True) -> Series:
"""
return self.apply(Series.nunique, axis=axis, dropna=dropna)

@doc(_shared_docs["idxmin"], numeric_only_default="False")
def 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 the entire DataFrame is NA,
or if ``skipna=False`` and there is an NA value, this method
will raise a ``ValueError``.
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
"""
axis = self._get_axis_number(axis)

if self.empty and len(self.axes[axis]):
Expand Down Expand Up @@ -12782,10 +12852,80 @@ def idxmin(
final_result = data._constructor_sliced(result, index=data._get_agg_axis(axis))
return final_result.__finalize__(self, method="idxmin")

@doc(_shared_docs["idxmax"], numeric_only_default="False")
def 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 the entire DataFrame is NA,
or if ``skipna=False`` and there is an NA value, this method
will raise a ``ValueError``.
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
"""
axis = self._get_axis_number(axis)

if self.empty and len(self.axes[axis]):
Expand Down
130 changes: 0 additions & 130 deletions pandas/core/shared_docs.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -649,133 +649,3 @@
3 3 d e
4 4 e e
"""

_shared_docs["idxmin"] = """
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 the entire Series is NA, or if ``skipna=False``
and there is an NA value, this method will raise a ``ValueError``.
numeric_only : bool, default {numeric_only_default}
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
"""

_shared_docs["idxmax"] = """
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 the entire Series is NA, or if ``skipna=False``
and there is an NA value, this method will raise a ``ValueError``.
numeric_only : bool, default {numeric_only_default}
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
"""
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