Mastering Pandas DataFrame Cumsum Method

Introduction

The Pandas DataFrame cumsum() method is used to calculate the cumulative sum over a DataFrame or Series axis. It returns a DataFrame or Series of the same size that contains the cumulative sum.

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Import the required libraries

In order to use the cumsum() method, we need to import the pandas library.

import pandas as pd

Create a DataFrame

Next, we need to create a DataFrame using the data we want to perform the cumulative sum on. For example:

df = pd.DataFrame({"A": [1, 2, 3, 4], "B": [5, 6, 7, 8]})

Calculate the cumulative sum over the index axis

To calculate the cumulative sum over the index axis, we can use the cumsum() method on the DataFrame. By default, the cumsum() method calculates the sum column-wise, so we need to specify the axis as 0.

result = df.cumsum(axis=0)

Calculate the cumulative sum over the column axis

To calculate the cumulative sum over the column axis, we can again use the cumsum() method on the DataFrame, but this time we specify the axis as 1.

result = df.cumsum(axis=1)

Handling null values in the DataFrame

If the DataFrame contains null values, by default the cumsum() method skips these values. However, we can change this behavior and include the null values in the cumulative sum calculation by specifying skipna=False.

result = df.cumsum(axis=0, skipna=False)

Summary

The cumsum() method in Pandas allows us to calculate the cumulative sum over a DataFrame or Series axis. It can be used to perform cumulative sum calculations over both the index and column axes. The method also provides the option to include or exclude null values from the calculation.

Pandas DataFrame Cumsum Method