Скачать или смотреть Calculating Conditional Cumulative Sums in Pandas Dataframe

Learn how to compute conditional cumulative sums in a Pandas dataframe using multiple categorical columns. This step-by-step guide will walk you through the process with examples.
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Understanding Conditional Cumulative Sums in Pandas DataFrames

When working with data, particularly in data analysis using Python's Pandas library, you might find yourself needing to calculate cumulative sums under specific conditions. This technique is especially handy when you need to group your data by one or more categorical columns before summing. In this guide, we're going to explore how to achieve that step-by-step.

Problem Scenario

Let's imagine you have a Pandas DataFrame that includes several columns representing categorical data and values. Here’s a simplified breakdown of your DataFrame:

Column A: A category identifying group 'A0' or 'A1'.

Column B: Another categorical column.

Column C: An index or identifier.

Column D: The values you want to sum cumulatively.

Column E: Another category which, together with column A, will dictate how the sums accumulate.

The goal is to calculate a cumulative sum of column D grouped by columns A and E. We want to create a new column, let's call it column F, that reflects this cumulative sum.

Setting Up Your Data

First, let’s create our example DataFrame using a Python dictionary:

[[See Video to Reveal this Text or Code Snippet]]

Initial DataFrame

After executing the above code, your DataFrame (df2) will look as follows:

ABCDEA0B0010E0A0B1111E1A0B2212E0A0B3313E1A1B4414E0A1B5515E1A1B6616E0A1B7717E1A1B8818E0A1B9919E1Computing the Cumulative Sum

Now, we'll compute the cumulative sum of column D based on the unique combinations of columns A and E. This can be done with the following line of code:

[[See Video to Reveal this Text or Code Snippet]]

Detailed Explanation

groupby: This function allows you to group the data based on one or more columns. In this case, we are grouping by both 'A' and 'E'.

cumsum: After grouping, this function computes the cumulative sum of the values in column 'D' for each group.

When you run the above code, your DataFrame will now include the cumulative sums in column F, resulting in:

ABCDEFA0B0010E010A0B1111E111A0B2212E022A0B3313E124A1B4414E014A1B5515E115A1B6616E030A1B7717E132A1B8818E048A1B9919E151Conclusion

By following these simple steps, you can effectively calculate conditional cumulative sums in a Pandas DataFrame based on multiple categorical columns. This technique is invaluable for summarizing data and can be applied to various data analysis and reporting tasks.

Now you're equipped to manage and analyze your data with cumulative sums tailored to your needs!