pandas.DataFrame.max#

DataFrame.max(*, axis=0, skipna=True, numeric_only=False, **kwargs)[source]#

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. 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.

skipnabool, default True

Exclude NA/null values when computing the result.

numeric_onlybool, default False

Include only float, int, boolean columns.

**kwargs

Additional keyword arguments to be passed to the function.

Returns:

Series or scalar: Value containing the calculation referenced in the description.

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