Spark Reference

Introduction to the startswith Function in PySpark

The startswith function in PySpark is a straightforward yet powerful tool for string manipulation. It allows you to check if a string column in a DataFrame starts with a specified prefix.

Syntax and Parameters

The startswith function adheres to a simple syntax:

  • Syntax: F.startswith(str, prefix)

  • Parameters:

    • str: The input string column to be checked.
    • prefix: The prefix against which the input string column is checked.

The function expects both str and prefix to be of STRING or BINARY type and returns a boolean value based on the comparison.

Examples of Using startswith in PySpark

To effectively utilize the startswith function in PySpark, let's look at some practical examples.

  1. Checking if a string starts with a specific prefix:
df.select(F.col("name").startswith("Mr").alias("is_mr")).show()
  1. Filtering rows based on the prefix of a string column:
df.filter(F.col("name").startswith("Dr")).show()
  1. Combining startswith with other conditions:
df.filter(F.col("name").startswith("Ms") & F.col("age") > 30).show()

These examples demonstrate the versatility of the startswith function in data manipulation and analysis tasks.

Common Use Cases

  • Data Filtering: Easily filter rows based on whether a string column starts with a certain prefix.
  • Data Validation: Implement validation rules that require checking the beginning of a string.
  • Text Data Preprocessing: Categorize or extract entries based on their starting pattern.

By following these guidelines and employing the startswith function thoughtfully, you can perform efficient string manipulation and analysis within your PySpark applications.