# dask.dataframe.groupby.DataFrameGroupBy.idxmax

DataFrameGroupBy.idxmax(split_every=None, split_out=1, shuffle_method=None, axis=<no_default>, skipna=True, numeric_only=<no_default>)

  Return index of first occurrence of maximum over requested axis.

  This docstring was copied from pandas.core.frame.DataFrame.idxmax.

  Some inconsistencies with the Dask version may exist.

  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.

      Added in version 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