Splitting a Dataframe into Multiple Dataframes with Pandas in Python

Learn how to split a dataframe into multiple smaller dataframes with a maximum of `n` rows using `Pandas` in Python.
---

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 one dataframe to multiple with maximum n rows for each in Python

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Splitting a Dataframe into Multiple Dataframes Using Pandas

In data analysis, you often encounter situations where you need to split a large dataframe into smaller pieces for easier handling or processing. If you're working with Python's Pandas library, you might find yourself asking: How can I split one dataframe into multiple ones with a maximum of n rows?

This guide will provide a step-by-step guide on how to save yourself from the hassle of managing large dataframes by splitting them into more manageable chunks.

The Problem

Suppose you have a dataframe that looks something like this:

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

And, you want to split this into several smaller dataframes, each containing a maximum of n rows (let’s say n=10).

Desired Output

The expected outcome of your operation would be:

DataFrame 1 (df1): Rows 0 to 9

DataFrame 2 (df2): Rows 10 to 19

DataFrame 3 (df3): Rows 20 to 25

The Solution

To achieve this in Python using Pandas, you'll use a neat method involving the groupby functionality. Here's how you can do it:

Step-by-Step Guide

Import Pandas: First, ensure that you have the Pandas library installed and import it.

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

Create Your Dataframe: If you haven't already created the initial dataframe, you can do so like this:

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

Split the Dataframe: Now, you can split the dataframe into smaller pieces. Use the following code:

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

In this line:

We group the dataframe by integer division of the index by n. This creates groups of dataframes, each holding at most n rows.

We use a list comprehension to create a list of these smaller dataframes.

Example Output

After running the code, you will have a list of dataframes where each dataframe contains at most 10 rows. You can access them like this:

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

Conclusion

Splitting a large dataframe into smaller, more manageable pieces can greatly facilitate your data processing tasks in Python using Pandas. By using the groupby method along with integer division, you can easily create multiple smaller dataframes without much hassle.

Now you have the tools to handle your data in chunks, making analysis and manipulation simpler and more efficient. Happy coding!