Resolving the Column Object is Not Callable Error in PySpark Using the when Function

Learn how to effectively replace column values in PySpark with the correct usage of the `when` function and avoid common errors.
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Resolving the Column Object is Not Callable Error in PySpark Using the when Function

Dealing with errors in any programming language can often be frustrating, especially when they seem cryptic at first glance. If you're working with PySpark, you might encounter a common error when attempting to modify column values using the when function. The error message "TypeError: 'Column' object is not callable" usually indicates that there's a syntax issue in your transformation code. In this guide, we’ll explore a scenario that leads to this error and provide a clear solution for replacing illegal names in a DataFrame.

The Problem

Suppose you have a DataFrame with names, and you want to replace any names that are not part of a specified list with the string "INVALID". Here’s a simple representation of the DataFrame and the list of legal names:

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

You also have a list of valid names:

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

The goal is to replace any name not in legal_names with "INVALID". A common approach might look like the following script:

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

However, running this will produce the following error message:

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

So, what went wrong? Let's break it down.

The Cause of the Error

The error occurs because of a typo in your code: otherwhise is incorrectly spelled. The correct function to use is otherwise. Since Python interprets the term otherwhise as a non-existing attribute, it leads to the Column object not being callable error.

The Solution

To resolve this issue, we need to make a minor adjustment to our original code. Here’s the corrected version that addresses the typo:

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

Breaking Down the Code

withColumn: This method creates a new column or replaces an existing one in the DataFrame.

F.when(condition, value): This function checks if the condition is true. If it is, it returns the specified value.

F.col("name").isin(*legal_names): This checks if the value in the name column is included in the list of legal_names. The asterisk (*) unpacks the list, allowing isin to properly evaluate each name.

otherwise(F.lit('INVALID')): This function specifies what to return when the condition is false.

Output

After applying the corrected code, your DataFrame will look like this:

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

Conclusion

Errors are an unavoidable part of programming, but they can also be excellent learning opportunities. Understanding and resolving the "TypeError: 'Column' object is not callable" error in PySpark requires careful attention to detail in your code. By ensuring that you use the correct function names and syntax, you can successfully replace column values and avoid frustration.

Now, go ahead and confidently transform your DataFrame in PySpark without the fear of running into this particular error again!