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Series.max(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)[source]
This method returns the maximum of the values in the object.
If you want the index of the maximum, use idxmax. This is the equivalent of the numpy.ndarray method argmax.
axis : {index (0)}

skipna : boolean, 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 : boolean, default None

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

max : scalar or Series (if level specified)


MultiIndex series example of monthly rainfall

>>> index = pd.MultiIndex.from_product(
...     [['London', 'New York'], ['Jun', 'Jul', 'Aug']],
...     names=['city', 'month'])
>>> s = pd.Series([47, 35, 54, 112, 117, 113], index=index)
>>> s
city      month
London    Jun       47
          Jul       35
          Aug       54
New York  Jun      112
          Jul      117
          Aug      113
dtype: int64
>>> s.max()

Max using level names, as well as indices

>>> s.max(level='city')
London       54
New York    117
dtype: int64
>>> s.max(level=1)
Jun    112
Jul    117
Aug    113
dtype: int64
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