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# pandas.Index.argmax

Index.argmax(axis=None, skipna=True, *args, **kwargs)

  Return int position of the largest value in the Series.

  If the maximum is achieved in multiple locations, the first row position is returned.

  Parameters:
    axis None

      Unused. Parameter needed for compatibility with DataFrame.

    skipna bool, default True

      Exclude NA/null values. If the entire Series is NA, or if `skipna=False` and there is an NA value, this method will raise a `ValueError`.

    *args, **kwargs

      Additional arguments and keywords for compatibility with NumPy.

  Returns:
    int

      Row position of the maximum value.

  See also

  `Series.argmax`

    Return position of the maximum value.

  `Series.argmin`

    Return position of the minimum value.

  `numpy.ndarray.argmax`

    Equivalent method for numpy arrays.

  `Series.idxmax`

    Return index label of the maximum values.

  `Series.idxmin`

    Return index label of the minimum values.

  Examples

  Consider dataset containing cereal calories

      >>> s = pd.Series(
      ...     [100.0, 110.0, 120.0, 110.0],
      ...     index=[
      ...         "Corn Flakes",
      ...         "Almond Delight",
      ...         "Cinnamon Toast Crunch",
      ...         "Cocoa Puff",
      ...     ],
      ... )
      >>> s
      Corn Flakes              100.0
      Almond Delight           110.0
      Cinnamon Toast Crunch    120.0
      Cocoa Puff               110.0
      dtype: float64

      >>> s.argmax()
      np.int64(2)
      >>> s.argmin()
      np.int64(0)

  The maximum cereal calories is the third element and the minimum cereal calories is the first element, since series is zero-indexed.