pandas.core.groupby.DataFrameGroupBy.idxmin#

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

Return index of first occurrence of minimum 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 minima in each column according to the group.

Raises:

ValueError

When there are no valid values for a group. Then can happen if:

There is an unobserved group and observed=False.

All values for a group are NA.

Some values for a group are NA and skipna=False.

Changed in version 3.0.0: Previously if all values for a group are NA or some values for a group are NA and skipna=False, this method would return NA. Now it raises instead.

See also

Series.idxmin: Return index of the minimum element.
DataFrame.idxmin: Indexes of minima along the specified axis.

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],
...         "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 minimum value in each column according to the group.

>>> df.groupby("food_type").idxmin()
                consumption   co2_emissions
food_type
animal                 Pork            Pork
plant        Wheat Products  Wheat Products

pandas.core.groupby.DataFrameGroupBy.idxmin#

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