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Learn how to effectively remove decimal values from a mixed data type column in a PySpark DataFrame while keeping string values intact.
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How to Remove Decimal Values from a PySpark DataFrame Column

Working with data in PySpark often presents challenges, especially when dealing with mixed data types. One common issue arises when you have a DataFrame column containing both strings and numeric values that may sometimes include decimal points. In this post, we will explore how to remove decimal values from such a column while preserving the integrity of string values.

The Problem

Consider the following example from a PySpark DataFrame's Source_ids column:

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

As you can see, this column consists of a mix of strings and numeric values. Our goal is to eliminate any decimal parts from these numbers. After processing, the expected output should look like this:

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

The Challenge

Since the column contains both strings and numbers, it is not feasible to convert the entire column to a numeric type. Instead, we need a solution that allows us to filter out the decimal values while retaining the textual data intact.

The Solution

To achieve this, we can use the regexp_replace function provided by PySpark. This function allows us to perform regular expression operations on a DataFrame column, making it easier to manipulate the string data based on patterns.

Step-by-step Explanation

Import Required Function: First, make sure to import the necessary function from the PySpark SQL functions module.

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

Utilize regexp_replace: We will employ the regexp_replace function to find and remove the decimal part of the numeric values. The regular expression we will use is ..*$, which matches a dot followed by any characters until the end of the string.

Breakdown of the regular expression:

. : Matches the dot character.

.* : Matches any characters following the dot.

$ : Asserts that this match should be at the end of the string.

Implement the Transformation: You can apply the transformation directly to the Source_ids column of your DataFrame as shown below.

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

This line of code replaces every occurrence of the decimal point and all characters after it with an empty string.

Conclusion

By following these steps, you can effectively remove decimal values from a mixed data type column in a PySpark DataFrame. This method ensures that your string values remain unchanged while filtering out extraneous decimal portions of numeric entries.

Now you have a clean, well-formatted DataFrame that meets your requirements!

If you have any questions or need further clarification on handling DataFrames in PySpark, feel free to leave a comment below!