How to Remove Non-English Words from a Column in PySpark

Learn how to effectively clean your PySpark DataFrame by removing non-English words and numeric values from a column with this easy-to-follow guide.
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How to Remove Non-English Words from a Column in PySpark

When working with large datasets in PySpark, cleaning your data is crucial for accurate analysis and reporting. One common task is to remove non-English words from a DataFrame column. This article will guide you through the necessary steps to achieve this, using a simple example of a DataFrame that contains English words mixed with non-English terms and numeric values.

The Problem

Consider the following sample DataFrame, where one of the columns, words, contains an array of words, some of which are non-English (or contain numeric values). Here’s how the DataFrame looks:

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

Objective

Our goal is to filter out any words that are not valid English words or contain numeric characters. This requires an understanding of how to manipulate DataFrames in PySpark effectively.

The Solution

Step 1: Setup Your Environment

Before you start, ensure that you have the necessary libraries installed. You’ll need PySpark as well as the Natural Language Toolkit (NLTK). If you haven't installed NLTK, you can do it using pip:

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Step 2: Import Required Libraries

You need to import the necessary PySpark functions and the NLTK library. Here is how you can do that:

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Initialize the lemmatizer:

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Step 3: Define the User-Defined Function (UDF)

The crux of the solution lies in creating a User-Defined Function (UDF) that will check each word against the NLTK corpus and return only valid English words. Here’s how you can define the UDF:

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

This function will go through each array of words and filter out any word that is not part of the English language according to the NLTK corpus.

Step 4: Apply the UDF to Your DataFrame

Now, you need to apply this UDF to your DataFrame to clean the words column. Here’s how you can do it:

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

This line of code transforms the words column by applying the remove_words function, effectively filtering out all non-English words and those that contain numbers.

Conclusion

By following these steps, you can efficiently remove non-English words from a column in your PySpark DataFrame. Data cleaning is a vital part of data processing, and utilizing the NLTK library alongside PySpark can significantly enhance your data quality.

Experiment with this technique and adapt it to your specific use cases, ensuring your dataset remains robust and analyzable.

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