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#  pandas.DataFrame.cummax  #

DataFrame.  cummax  (  _ axis  =  None  _ , _ skipna  =  True  _ , _ *  args
_ , _ **  kwargs  _ )  [ [source]  ](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/frame.py#L11786-L11788) #

    

Return cumulative maximum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative
maximum.

Parameters  :

    

**axis** {0 or âindexâ, 1 or âcolumnsâ}, default 0

    

The index or the name of the axis. 0 is equivalent to None or âindexâ. For
Series  this parameter is unused and defaults to 0.

**skipna** bool, default True

    

Exclude NA/null values. If an entire row/column is NA, the result will be NA.

***args, **kwargs**

    

Additional keywords have no effect but might be accepted for compatibility
with NumPy.

Returns  :

    

Series or DataFrame

    

Return cumulative maximum of Series or DataFrame.

See also

[ ` core.window.expanding.Expanding.max  `
](pandas.core.window.expanding.Expanding.max.html#pandas.core.window.expanding.Expanding.max
"pandas.core.window.expanding.Expanding.max")

    

Similar functionality but ignores ` NaN  ` values.

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

    

Return the maximum over DataFrame axis.

` DataFrame.cummax  `

    

Return cumulative maximum over DataFrame axis.

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

    

Return cumulative minimum over DataFrame axis.

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

    

Return cumulative sum over DataFrame axis.

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

    

Return cumulative product over DataFrame axis.

Examples

**Series**

    
    
    >>> s = pd.Series([2, np.nan, 5, -1, 0])
    >>> s
    0    2.0
    1    NaN
    2    5.0
    3   -1.0
    4    0.0
    dtype: float64
    

By default, NA values are ignored.

    
    
    >>> s.cummax()
    0    2.0
    1    NaN
    2    5.0
    3    5.0
    4    5.0
    dtype: float64
    

To include NA values in the operation, use ` skipna=False  `

    
    
    >>> s.cummax(skipna=False)
    0    2.0
    1    NaN
    2    NaN
    3    NaN
    4    NaN
    dtype: float64
    

**DataFrame**

    
    
    >>> df = pd.DataFrame([[2.0, 1.0],
    ...                    [3.0, np.nan],
    ...                    [1.0, 0.0]],
    ...                   columns=list('AB'))
    >>> df
         A    B
    0  2.0  1.0
    1  3.0  NaN
    2  1.0  0.0
    

By default, iterates over rows and finds the maximum in each column. This is
equivalent to ` axis=None  ` or ` axis='index'  ` .

    
    
    >>> df.cummax()
         A    B
    0  2.0  1.0
    1  3.0  NaN
    2  3.0  1.0
    

To iterate over columns and find the maximum in each row, use ` axis=1  `

    
    
    >>> df.cummax(axis=1)
         A    B
    0  2.0  2.0
    1  3.0  NaN
    2  1.0  1.0
    

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  * ` DataFrame.cummax()  `

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

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