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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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:
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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.
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Create Your DataFrame: If you haven't already, create your DataFrame products_price as shown:
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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.
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View the Results: Finally, check the transformed DataFrame to see your new column with only the price value.
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Expected Output
After executing the above commands, your DataFrame will look like this:
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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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