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# pandas.Series.max

Series.max(*, axis=0, skipna=True, numeric_only=False, **kwargs)

  Return the maximum of the values over the requested axis.

  If you want the index of the maximum, use `idxmax`. This is the equivalent of the `numpy.ndarray` method `argmax`.

  Parameters:
    axis{index (0)}

      Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

      For DataFrames, specifying `axis=None` will apply the aggregation across both axes.

      Added in version 2.0.0.

    skipna bool, default True

      Exclude NA/null values when computing the result.

    numeric_only bool, default False

      Include only float, int, boolean columns.

    **kwargs

      Additional keyword arguments have no effect but might be accepted for compatibility with NumPy. See NumPy compatibility kwargs for more.

  Returns:
    scalar

      The maximum of the values in the Series.

  See also

  `numpy.max`

    Equivalent numpy function for arrays.

  `Series.min`

    Return the minimum.

  `Series.max`

    Return the maximum.

  `Series.idxmin`

    Return the index of the minimum.

  `Series.idxmax`

    Return the index of the maximum.

  `DataFrame.min`

    Return the minimum over the requested axis.

  `DataFrame.max`

    Return the maximum over the requested axis.

  `DataFrame.idxmin`

    Return the index of the minimum over the requested axis.

  `DataFrame.idxmax`

    Return the index of the maximum over the requested axis.

  Examples

      >>> idx = pd.MultiIndex.from_arrays(
      ...     [["warm", "warm", "cold", "cold"], ["dog", "falcon", "fish", "spider"]],
      ...     names=["blooded", "animal"],
      ... )
      >>> s = pd.Series([4, 2, 0, 8], name="legs", index=idx)
      >>> s
      blooded  animal
      warm     dog       4
               falcon    2
      cold     fish      0
               spider    8
      Name: legs, dtype: int64

      >>> s.max()
      8