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  * [ Resampling ](../resampling.html)
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  * [ Missing values ](../missing_value.html)

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  * [ API reference ](../index.html)
  * [ DataFrame ](../frame.html)
  * pandas.DataFrame.max 

#  pandas.DataFrame.max  #

DataFrame.  max  (  _ axis  =  0  _ , _ skipna  =  True  _ , _ numeric_only  =
False  _ , _ **  kwargs  _ )  [ [source]  ](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/frame.py#L11648-L11659) #

    

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.

New 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  ` ](pandas.Series.sum.html#pandas.Series.sum
"pandas.Series.sum")

    

Return the sum.

[ ` Series.min  ` ](pandas.Series.min.html#pandas.Series.min
"pandas.Series.min")

    

Return the minimum.

[ ` Series.max  ` ](pandas.Series.max.html#pandas.Series.max
"pandas.Series.max")

    

Return the maximum.

[ ` Series.idxmin  ` ](pandas.Series.idxmin.html#pandas.Series.idxmin
"pandas.Series.idxmin")

    

Return the index of the minimum.

[ ` Series.idxmax  ` ](pandas.Series.idxmax.html#pandas.Series.idxmax
"pandas.Series.idxmax")

    

Return the index of the maximum.

[ ` DataFrame.sum  ` ](pandas.DataFrame.sum.html#pandas.DataFrame.sum
"pandas.DataFrame.sum")

    

Return the sum over the requested axis.

[ ` DataFrame.min  ` ](pandas.DataFrame.min.html#pandas.DataFrame.min
"pandas.DataFrame.min")

    

Return the minimum over the requested axis.

` DataFrame.max  `

    

Return the maximum over the requested axis.

[ ` DataFrame.idxmin  ` ](pandas.DataFrame.idxmin.html#pandas.DataFrame.idxmin
"pandas.DataFrame.idxmin")

    

Return the index of the minimum over the requested axis.

[ ` DataFrame.idxmax  ` ](pandas.DataFrame.idxmax.html#pandas.DataFrame.idxmax
"pandas.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
    

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__ On this page

  * ` DataFrame.max()  `

[ __ Show Source ](../../_sources/reference/api/pandas.DataFrame.max.rst.txt)

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