How to obtain the cumulative maximum over a DataFrame axis

Overview

We use the cummax() function of a DataFrame object to obtain the cumulative maximum over its axis.

Axis here represents the row and column of the DataFrame. The axis with a value of '0' indicates the axes that run vertically downwards across a row, while a value of '1' shows the axes that run horizontally across a column.

Syntax

The cummax() function takes the syntax shown below:

Parameters

The cummax() function takes the following parameter values:

axis: This represents the name for the row (designated as 0 or 'index') or the column (designated as 1 or columns) axis.
skipna (optional): This takes a Boolean value indicating if NA or null values are to be excluded or not.
args, **kwargs (optional): These are keywords that have no effect but may be accepted for compatibility with NumPy.

Return value

The cummax() function returns a Series or DataFrame object showing the cumulative maximum in the axis.

Example

# A code to illustrate the cummax() function in Pandas

# importing the pandas library
import pandas as pd

# creating a dataframe
df = pd.DataFrame([[5,10,4,15,3],
                   [1,7,5,9,0.5],
                   [3,11,13,14,12]],
                   columns=list('ABCDE'))
# printing the dataframe
print(df)

# obtaining the cummulative maximum vertically across rows
print(df.cummax(axis="index"))

# obtaining the cummulative maximum horizontally over columns
print(df.cummax(axis="columns"))