Python | Pandas dataframe.cummax()

Last Updated : 16 Nov, 2018
Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas dataframe.cummax() is used to find the cumulative maximum value over any axis. Each cell is populated with the maximum value seen so far.
Syntax: DataFrame.cummax(axis=None, skipna=True, *args, **kwargs) Parameters: axis : {index (0), columns (1)} skipna : Exclude NA/null values. If an entire row/column is NA, the result will be NA Returns: cummax : Series
Example #1: Use cummax() function to find the cumulative maximum value along the index axis. Python3 
# importing pandas as pd
import pandas as pd

# Creating the dataframe
df = pd.DataFrame({"A":[5, 3, 6, 4],
                   "B":[11, 2, 4, 3],
                   "C":[4, 3, 8, 5], 
                   "D":[5, 4, 2, 8]})

# Print the dataframe
df
Output : Now find the cumulative maximum value over the index axis Python3 1== 
# To find the cumulative max
df.cummax(axis = 0)
Output :   Example #2: Use cummax() function to find the cumulative maximum value along the column axis. Python3 
# importing pandas as pd
import pandas as pd

# Creating the dataframe
df = pd.DataFrame({"A":[5, 3, 6, 4],
                   "B":[11, 2, 4, 3],
                   "C":[4, 3, 8, 5], 
                   "D":[5, 4, 2, 8]})

# To find the cumulative max along column axis
df.cummax(axis = 1)
Output :   Example #3: Use cummax() function to find the cumulative maximum value along the index axis in a data frame with NaN value. Python3 
# importing pandas as pd
import pandas as pd

# Creating the dataframe
df = pd.DataFrame({"A":[5, 3, None, 4],
                   "B":[None, 2, 4, 3],
                   "C":[4, 3, 8, 5], 
                   "D":[5, 4, 2, None]})

# To find the cumulative max
df.cummax(axis = 0, skipna = True)
Output :
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