# pandas.Series.max

`Series.``max`(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

  Return the maximum of the values for 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.

  skipna : bool, default True

  > Exclude NA/null values when computing the result.

  level : int or level name, default None

  > If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar.

  numeric_only : bool, default None

  > Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

  **kwargs

  > Additional keyword arguments to be passed to the function.

  Returns: | scalar or Series (if level specified)

  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

  Max using level names, as well as indices.

      >>> s.max(level='blooded')
      blooded
      warm    4
      cold    8
      Name: legs, dtype: int64

      >>> s.max(level=0)
      blooded
      warm    4
      cold    8
      Name: legs, dtype: int64

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