Learn how to effectively use PySpark to write a User-Defined Function (UDF) that filters out specific values from an array based on another column's input.
---
This video is based on the question https://stackoverflow.com/q/65610649/ asked by the user 'yanachen' ( https://stackoverflow.com/u/6407393/ ) and on the answer https://stackoverflow.com/a/65610686/ provided by the user 'mck' ( https://stackoverflow.com/u/14165730/ ) 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 how to write UDF using two columns
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.
---
Mastering PySpark: How to Create a UDF to Remove Values from Arrays Using Two Columns
In the world of data engineering, handling and transforming large datasets efficiently is key. One common challenge you may encounter is how to manipulate array-like structures within your data. Suppose you have a DataFrame containing arrays and you want to remove specific elements based on a corresponding column's value. In this guide, we will dive into how to create a User-Defined Function (UDF) in PySpark that addresses this problem. We will also explore a built-in function that offers a simpler solution.
Problem Introduction
Let’s say you have a dataset with an array column and a target value column. Your goal is to filter out the target value from the array. Here's what the data looks like:
seqtarget[a, b, c]c[h, j, s]j[w, x, a]a[o, b, e]cIn this example, you aim to create a new column that holds the array filtered by excluding the value specified in the target column. The expected output would be:
seqtargetfiltered[a, b, c]c[a, b][h, j, s]j[h, s][w, x, a]a[w, x][o, b, e]c[o, b, e]Simplest Solution: Using Built-in Functions
Before we start writing a custom UDF, there's a built-in function in PySpark that simplifies this task: array_remove. This function can directly remove a specified value from an array. Here’s how you can implement it:
[[See Video to Reveal this Text or Code Snippet]]
This snippet of code will give you the desired output with ease. Using built-in functions is often the best approach because of their efficiency and integration into the PySpark ecosystem.
Creating a User-Defined Function (UDF)
If you prefer or need to create a UDF for more complex logic or scenarios, here's how you can do that:
Step 1: Define the UDF
We will define a UDF that takes two columns (the array column and the target column) as inputs and filters out the target from the array. Here’s how the UDF can be written:
[[See Video to Reveal this Text or Code Snippet]]
Step 2: Apply the UDF
Once the UDF is defined, you can apply it to your DataFrame like this:
[[See Video to Reveal this Text or Code Snippet]]
Step 3: Review the Output
After executing the above code, you will receive the same output as before, as shown below:
seqtargetfiltered[a, b, c]c[a, b][h, j, s]j[h, s][w, x, a]a[w, x][o, b, e]c[o, b, e]Conclusion
Whether you choose to leverage built-in functions like array_remove or craft a custom UDF depends on your specific needs and complexity of data processing. While built-in functions are often preferable due to their optimized performance, UDFs provide greater flexibility for complex scenarios.
Mastering these tools within PySpark will enhance your data manipulation skills and help you handle large datasets more effectively. Happy coding!
Информация по комментариям в разработке