# dask.dataframe.Series.max

Series.max(axis=0, skipna=True, split_every=False, out=None, numeric_only=None)

  Return the maximum of the values over the requested axis.

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

  Some inconsistencies with the Dask version may exist.

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

  Parameters:
    axis{index (0), columns (1)}

      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. Not implemented for Series.

    **kwargs

      Additional keyword arguments to be passed to the function.

  Returns:
    Series or scalar

  See also

  `Series.sum`

    Return the sum.

  `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.sum`

    Return the sum over the requested axis.

  `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