How to rename multiple columns in Pyspark

The withColumnRenamed() method is used to rename an existing column. The method returns a new DataFrame with the newly named column. Multiple columns in a DataFrame can be renamed by chaining the withColumnRenamed() method for each column.

Syntax

DataFrame.withColumnRenamed(existing, new)

Parameters

existing: This is the name of the existing column.
new: This is the new name to be given to the existing column.

Return value

A new DataFrame is generated with the renamed columns.

Code example

Let’s look at the code below:

import pyspark
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('edpresso').getOrCreate()
data = [("James","Smith","USA","CA"),
    ("Michael","Rose","USA","NY"),
    ("Robert","Williams","USA","CA"),
    ("Maria","Jones","USA","FL")
  ]
columns = ["firstname","lastname","country","state"]
df = spark.createDataFrame(data = data, schema = columns)
print("Original dataframe:")
df.show(truncate=False)
new_df = df.withColumnRenamed("firstname", "First-Name") \
          .withColumnRenamed("lastname", "Last-Name") \
          .withColumnRenamed("country", "Country")
print("Renamed dataframe:")
new_df.show(truncate=False)

Code explanation

Lines 1–2: We import the pyspark and SparkSession.
Line 4: A spark session named edpresso is created.
Lines 6–10: We define data for the DataFrame.
Line 12: The names of the DataFrame’s columns are defined.
Line 13: A DataFrame is created using the createDataframe() method.
Line 15: The original DataFrame is printed.
Lines 17-19: Multiple columns of DataFrame are renamed by chaining the withColumnRenamed() method.
Line 23: The new DataFrame with new column names is printed.

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