Pyspark regex replace special characters

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To replace special characters in a column using regular expressions in PySpark, you can use the regexp_replace function. regexp_replace allows you to specify a column, a regular expression pattern, and a replacement string.

Here’s an example:

from pyspark.sql import SparkSession
from pyspark.sql.functions import regexp_replace
  
# Create a Spark session
spark = SparkSession.builder.getOrCreate()

# Sample data
data = [("John!Doe",), ("Jane@Doe",), ("John*Doe",)]

# Create a DataFrame
df = spark.createDataFrame(data, ["name"])

# Replace special characters in the "name" column
df = df.withColumn("name", regexp_replace("name", "[!@*]", ""))

# Show the updated DataFrame
df.show()

This code snippet creates a Spark session, sets up some sample data with special characters in the “name” column, and then uses regexp_replace to remove the special characters by specifying the regular expression pattern [!@*] (which matches any occurrence of !, @, or *) and the replacement string "" (which is an empty string).

The above code will output:

+------+
|  name|
+------+
|JohnDoe|
|JaneDoe|
|JohnDoe|
+------+

In this example, the special characters !, @, and * are removed from the “name” column using regexp_replace.

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