The cummsum() function of a DataFrame object is used to obtain the cumulative sum over its axis.
Note: Axis here simply represents the row and column of the DataFrame. An axis with a value of
0indicates the axes running vertically downwards across a row, while a value of1indicates the axes running horizontally across a column.
DataFrame.cumsum(axis=None, skipna=True, *args, **kwargs)
axis: This represents the name for the row ( designated as 0 or 'index') or the column (designated as 1 or columns) axis.skipna: This takes a boolean value indicating if null values are to be excluded or not. This is an optional parameter.args, **kwargs: These keywords have no effect but may be accepted for compatibility with NumPy. These are optional.This function returns a Series or DataFrame object showing the cumulative maximum in the axis.
# A code to illustrate the cumsum() function in Pandas# importing the pandas libraryimport pandas as pd# creating a dataframedf = pd.DataFrame([[5,10,4,15,3],[1,7,5,9,0.5],[3,11,13,14,12]],columns=list('ABCDE'))# printing the dataframeprint(df)# obtaining the cummulative sum vertically across rowsprint(df.cumsum(axis="index"))# obtaining the cummulative sum horizontally over columnsprint(df.cumsum(axis="columns"))
pandas library.df.df.cumsum() function to obtain the cumulative maximum values running downwards across the rows (axis 0). We print the result to the console.cumsum() function to obtain the cumulative maximum values running horizontally across columns (axis 1). We print the result to the console.