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Скачать или смотреть How to Split the Text in a PySpark Column Using a Delimiter

  • vlogize
  • 2025-03-29
  • 1
How to Split the Text in a PySpark Column Using a Delimiter
How to split the text in a pyspark column using a delimiter?dataframepyspark
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Описание к видео How to Split the Text in a PySpark Column Using a Delimiter

Learn how to easily split text in a PySpark DataFrame column using a delimiter, with a detailed example, best practices, and tips for effective usage.
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This video is based on the question https://stackoverflow.com/q/73613950/ asked by the user 'Marcos Dias' ( https://stackoverflow.com/u/15363250/ ) and on the answer https://stackoverflow.com/a/73614128/ provided by the user 'walking' ( https://stackoverflow.com/u/3102035/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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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.

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How to Split the Text in a PySpark Column Using a Delimiter

Working with data can often throw some curveballs our way, especially when it comes to cleaning and formatting. One common task in data preprocessing is splitting text in a column based on a specific delimiter. In this guide, we will tackle how to split the text in a PySpark DataFrame column using a delimiter, specifically focusing on the example of product prices combined with currency codes.

Understanding the Problem

Suppose you have a PySpark DataFrame containing product prices displayed together with their currency code. The common format here might look like 10|USD, where 10 is the price, and USD indicates the currency. For ease of analysis, you may want to separate these two elements into distinct columns. Here's a brief glance at what our DataFrame looks like:

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

As per your requirement, you want the numeric price part (e.g., 10, 19.9, etc.) while discarding the |USD part.

The Solution

The key function for achieving this in PySpark is the split function. However, we need to be cautious about the delimiter we use. The pipe character | holds special meaning in regex (it represents logical OR). Therefore, to correctly split the string, we need to escape the pipe character.

Step-by-Step Instructions

Here’s how to perform the split operation effectively:

Import Necessary Libraries: First, ensure you have imported the necessary functions from PySpark.

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

Create Your DataFrame: If you haven't already, create your DataFrame products_price as shown:

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

Use the Split Function: Apply the split function on the price column. Be sure to escape the pipe symbol with a backslash (\). This way, the regular expression interprets it as a literal character.

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

View the Results: Finally, check the transformed DataFrame to see your new column with only the price value.

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

Expected Output

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

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

Conclusion

By utilizing the split function in PySpark and properly managing reserved symbols in regex, you can efficiently separate data within a column. Following this guide, you now have the tools to tidy up your DataFrames, leading to better analysis and insights. Happy coding!

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