pandas.core.groupby.DataFrameGroupBy.idxmax#

DataFrameGroupBy.idxmax(skipna=True, numeric_only=False)[source]#

Return index of first occurrence of maximum in each group.

Parameters:

skipnabool, default True: Exclude NA values.
numeric_onlybool, default False: Include only float, int or boolean data.

Added in version 1.5.0.

Returns:

DataFrame: Indexes of maxima in each column according to the group.

Raises:

ValueError

If a column is empty or skipna=False and any value is NA.

See also

Series.idxmax: Return index of the maximum element.
DataFrame.idxmax: Indexes of maxima along the specified axis.

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],
...         "food_type": ["meat", "plant", "meat"],
...     },
...     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 according to the group.

>>> df.groupby("food_type").idxmax()
                consumption   co2_emissions
food_type
animal                 Beef            Beef
plant        Wheat Products  Wheat Products

pandas.core.groupby.DataFrameGroupBy.idxmax#

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