Скачать или смотреть Combining Multiple DataFrames in PySpark

Learn how to combine multiple DataFrames in PySpark with specific conditions using joins and union operations.
---
This video is based on the question https://stackoverflow.com/q/62714273/ asked by the user 'Shan' ( https://stackoverflow.com/u/10383926/ ) and on the answer https://stackoverflow.com/a/62714935/ provided by the user 'Som' ( https://stackoverflow.com/u/4758823/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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: PySpark combine two or more dataframe with condition

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Combining Multiple DataFrames in PySpark: A Step-by-Step Guide

Working with multiple DataFrames in PySpark can be tricky, especially when you want to merge them based on specific conditions. In this post, we will tackle a common scenario: merging multiple DataFrames in such a way that if certain conditions are met, we combine the rows; otherwise, we keep them separate. We will walk through the process step-by-step and provide code snippets to clarify each part of the solution.

Understanding the Problem

Suppose you have multiple Spark DataFrames df1, df2, and df3, each with the following schema:

X (float): This column holds some numerical values.

Y (float): Another numerical column.

id (String): A unique identifier for each row.

The Merging Conditions

You want to merge these DataFrames with the following rules:

If df1.X is equal to df2.X and df1.Y is equal to df2.Y, then concatenate df1.id and df2.id and present it as a single row in the resultant DataFrame.

If the above condition is not satisfied, include both as separate rows in the final DataFrame.

This poses the intriguing question: How can you achieve this using joins or lambda functions in PySpark? Let’s delve into the solution!

Step 1: Load the Test Data

First, you'll need to load the data into your Spark session. Here's how you can create sample DataFrames df1 and df2:

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

Expected Output:

df1:

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

df2:

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

Step 2: Merge Common and Uncommon Records

Next, we will merge the DataFrames using a combination of inner and anti-joins to manage both common and uncommon records efficiently:

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

Expected Output:

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

Step 3: Alternative Approach with Full Outer Join

If you prefer to use a full outer join instead of handling common and uncommon records separately, you can achieve the same results like this:

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

Expected Output:

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

Conclusion

Merging multiple DataFrames in PySpark can be streamlined by understanding your data and the relationships between DataFrames. By using joins effectively, you can create a comprehensive resultant DataFrame that represents your data accurately. Whether you choose to handle conditions via inner and left anti-joins or utilize a full outer join approach, both methods are powerful in their right.

Hopefully, this guide provides a clear path for merging DataFrames with specific conditions in PySpark. Happy coding!