
        
        
            
                
                
                    df = pd.DataFrame({"A":[3,2,4],"B":[7,6,2],"C":[3,5,6]})
df
                
            
               A  B  C
0  3  7  3
1  2  6  5
2  4  2  6

Cumulative maximum of each column

To compute the cumulative maximum for each column:


        
        
            
                
                
                    df.cummax()
                
            
               A  B  C
0  3  7  3
1  3  7  5
2  4  7  6

Cumulative maximum of each row

To compute the cumulative maximum for each row:


        
        
            
                
                
                    df.cummax(axis=1)
                
            
               A  B  C
0  3  7  7
1  2  6  6
2  4  4  6

Dealing with missing values

Consider the following DataFrame with a missing value:


        
        
            
                
                
                    df = pd.DataFrame({"A":[5,pd.np.nan,3]})
df
                
            
               A
0  5.0
1  NaN
2  3.0

By default, skipna=True, which means that missing values are ignored:


        
        
            
                
                
                    df.cummax()   # skipna=True
                
            
               A
0  5.0
1  NaN
2  5.0

To take into account missing values:


        
        
            
                
                
                    df.cummax(skipna=False)
                
            
               A
0  5.0
1  NaN
2  NaN

Here, notice how we end up with a NaN after the first NaN.

Published by Isshin Inada

Edited by 0 others

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Official Pandas Documentation

https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.DataFrame.cummax.html

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