How to Replace Strings in a Spark DataFrame Column Using PySpark?

Replacing strings in a Spark DataFrame column using PySpark can be efficiently performed with the help of functions from the `pyspark.sql` module. The `regexp_replace` function is particularly useful for this purpose as it allows to replace the strings in a column based on regular expressions.

Example

Suppose we have a DataFrame `df` with a column `column_name` containing some strings, and we want to replace the substring “old_value” with “new_value”. We can use `regexp_replace` from `pyspark.sql.functions` to achieve this.

Step-by-Step Explanation

1. Initialize PySpark


# Initialize the PySpark session
from pyspark.sql import SparkSession

spark = SparkSession.builder \
    .appName("Replace Strings Example") \
    .getOrCreate()

2. Create DataFrame

We create a sample DataFrame with some sample data that includes strings we want to replace.


from pyspark.sql import Row

# Creating sample data
data = [
    Row(id=1, column_name='old_value is in here'),
    Row(id=2, column_name='another old_value is here'),
    Row(id=3, column_name='no replacement needed'),
]

# Creating DataFrame
df = spark.createDataFrame(data)
df.show(truncate=False)

+---+-------------------------+
|id |column_name              |
+---+-------------------------+
|1  |old_value is in here     |
|2  |another old_value is here|
|3  |no replacement needed    |
+---+-------------------------+

3. Import Required Function

We import the `regexp_replace` function which allows us to replace patterns in the column.


from pyspark.sql.functions import regexp_replace

4. Apply the `regexp_replace` Function

We use the `regexp_replace` function to replace the substring “old_value” with “new_value” in the `column_name` column.


# Replacing string old_value with new_value in column_name
df_replaced = df.withColumn('column_name', regexp_replace('column_name', 'old_value', 'new_value'))

# Show the transformed DataFrame
df_replaced.show(truncate=False)

+---+-------------------------+
|id |column_name              |
+---+-------------------------+
|1  |new_value is in here     |
|2  |another new_value is here|
|3  |no replacement needed    |
+---+-------------------------+

Conclusion

As demonstrated, the `regexp_replace` function in PySpark provides a simple and effective way to replace substrings within a DataFrame column. The example illustrates replacing “old_value” with “new_value” in a sample DataFrame.

About Editorial Team

Our Editorial Team is made up of tech enthusiasts who are highly skilled in Apache Spark, PySpark, and Machine Learning. They are also proficient in Python, Pandas, R, Hive, PostgreSQL, Snowflake, and Databricks. They aren't just experts; they are passionate teachers. They are dedicated to making complex data concepts easy to understand through engaging and simple tutorials with examples.

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