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# pandas.DataFrame.idxmin

DataFrame.idxmin(axis=0, skipna=True, numeric_only=False)

  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.

  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: str

  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: str