DataFrameGroupBy.idxmax(axis=None, skipna=True, numeric_only=False)[source]#

Return index of first occurrence of maximum over requested axis.

NA/null values are excluded.

axis{{0 or ‘index’, 1 or ‘columns’}}, default None

The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise. If axis is not provided, grouper’s axis is used.

Changed in version 2.0.0.

skipnabool, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA.

numeric_onlybool, default False

Include only float, int or boolean data.

New in version 1.5.0.


Indexes of maxima along the specified axis.

  • If the row/column is empty

See also


Return index of the maximum element.


This method is the DataFrame version of ndarray.argmax.


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