withColumnRenamed

Returns a new DataFrame by renaming an existing column. This is a no-op if the schema doesn't contain the given column name.

Syntax

withColumnRenamed(existing: str, new: str)

Parameters

Parameter	Type	Description
existing	str	The name of the existing column to be renamed.
new	str	The new name to be assigned to the column.

Parameter	Type	Description
existing	str	The name of the existing column to be renamed.
new	str	The new name to be assigned to the column.

Returns

DataFrame: A new DataFrame with renamed column.

Examples

Python

df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])

df.withColumnRenamed("age", "age2").show()
# +----+-----+
# |age2| name|
# +----+-----+
# |   2|Alice|
# |   5|  Bob|
# +----+-----+

df.withColumnRenamed("non_existing", "new_name").show()
# +---+-----+
# |age| name|
# +---+-----+
# |  2|Alice|
# |  5|  Bob|
# +---+-----+

df.withColumnRenamed("age", "age2").withColumnRenamed("name", "name2").show()
# +----+-----+
# |age2|name2|
# +----+-----+
# |   2|Alice|
# |   5|  Bob|
# +----+-----+