# pandas.DataFrame.max

DataFrame.max(axis=None, skipna=None, level=None, numeric_only=None, **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), columns (1)}

      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 Series.

    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
    Series or DataFrame (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