How to Split a Pandas DataFrame into Multiple DataFrames Based on Column Values

Learn how to effectively `split a Pandas DataFrame` into separate DataFrames based on specific column values, particularly focusing on occurrences of zeros.
---

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Split a Pandas dataframe into multiple dataframes based on the value of a column

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
How to Split a Pandas DataFrame into Multiple DataFrames Based on Column Values

If you're working with data analysis in Python using Pandas, you may encounter situations where you need to split a DataFrame into multiple smaller DataFrames based on values in one of its columns. A common scenario is splitting a DataFrame at specific entries, such as zeros in a rolling sum column. In this guide, we'll guide you through a step-by-step process of achieving this task.

The Problem

Let's say you have a Pandas DataFrame that looks something like this:

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

Your goal is to split this DataFrame into multiple smaller DataFrames wherever you encounter a zero in the Rolling_sum column. The expected result should give you three DataFrames as follows:

Expected Results

DataFrame 1:

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

DataFrame 2:

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

DataFrame 3:

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

The Solution

To achieve this split, we can utilize the cumsum method alongside the groupby functionality in Pandas. Below are the steps you need to follow:

Step 1: Create Your DataFrame

First, ensure you have your DataFrame set up as shown below:

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

Step 2: Apply cumsum and groupby

Now, use the following code snippet to split the DataFrame based on occurrences of zeros in the Rolling_sum:

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

Explanation of the Code

df['Rolling_sum'].ne(0): This checks each entry in the Rolling_sum column to see if it is not equal to zero.

df['Rolling_sum'].eq(0).cumsum(): This computes the cumulative sum wherever zeros occur, generating a unique grouping key for consecutive non-zero entries.

groupby(...): This method is then used to create groups based on these keys.

{x: y for x, y in ...}: This dictionary comprehension converts the grouped DataFrames into a dictionary format for easy access.

Resulting DataFrames

The variable d now contains your split DataFrames, where you can access them using d[0], d[1], and d[2] for the three individual DataFrames you wanted.

Conclusion

Splitting a DataFrame based on specific column values is a powerful technique when working with data in Python. The combination of cumsum() and groupby() offers a flexible and efficient solution to manage and analyze your data. Now you can easily modify this approach for different conditions as needed. Happy coding!