Скачать или смотреть How to Get the Required DataFrame After PySpark Pivot?

Discover how to achieve your desired DataFrame after performing a pivot in PySpark with our step-by-step guide.
---
This video is based on the question https://stackoverflow.com/q/71408404/ asked by the user 'Afzal Abdul Azeez' ( https://stackoverflow.com/u/12613652/ ) and on the answer https://stackoverflow.com/a/71408780/ provided by the user 'David דודו Markovitz' ( https://stackoverflow.com/u/6336479/ ) 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: How to get required dataframe after pyspark pivot?

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.
---
Introduction

When working with large datasets in Apache Spark, data transformations often require some specialized operations to get results in a desired format. One common operation is to use a pivot table to summarize and reorganize data. However, sometimes after executing a pivot, you may find that the resulting DataFrame doesn't perfectly match your expectations.

In this guide, we will explore a real-world scenario with a PySpark DataFrame and explain how to achieve the required DataFrame layout after performing a pivot operation.

The Problem

Assume we have the following Spark DataFrame containing user data:

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

When we pivot this DataFrame using the following code:

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

The result is:

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

But the desired output looks slightly different, specifically for the ram entries where it should aggregate the user counts together, resulting in:

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

The Solution

To achieve the desired DataFrame, we will need to calculate the minimum pDate for each unique name prior to performing the pivot operation. This can be done using window functions in PySpark. Here’s how it's done:

Step 1: Import the Required Libraries

First, you need to import the necessary libraries:

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

Step 2: Create the Initial DataFrame

If you haven't already created the initial DataFrame, you can do so as follows:

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

Step 3: Calculate the Minimum pDate

Next, you need to calculate the minimum pDate for each name:

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

Step 4: Perform the Pivot

Once you have the minimum dates, you can group by both name and min_pDate, and then perform the pivot:

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

Step 5: Display the Result

Finally, you can display the resulting DataFrame:

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

Resulting DataFrame

After executing the above code, your DataFrame will look like this:

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

Conclusion

Using the above steps, you can transform your DataFrame to match your requirements after a pivot operation in PySpark. By calculating the min_pDate with window functions prior to pivoting, you can ensure accurate aggregation of data showing counts correctly across the desired dimensions.

We hope that this guide helps you in your data transformation tasks! If you have any questions or need further assistance, feel free to reach out.