withColumnsRenamed

Returns a new DataFrame by renaming multiple columns. This is a no-op if the schema doesn't contain the given column names.

Syntax

withColumnsRenamed(colsMap: Dict[str, str])

Parameters

Parameter	Type	Description
colsMap	dict	A dict of existing column names and corresponding desired column names. Currently, only a single map is supported.

Returns

DataFrame: DataFrame with renamed columns.

Examples

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

df.withColumnsRenamed({"age": "age2"}).show()
# +----+-----+
# |age2| name|
# +----+-----+
# |   2|Alice|
# |   5|  Bob|
# +----+-----+

df.withColumnsRenamed({"age": "age2", "name": "name2"}).show()
# +----+-----+
# |age2|name2|
# +----+-----+
# |   2|Alice|
# |   5|  Bob|
# +----+-----+

df.withColumnsRenamed({"non_existing": "new_name"}).show()
# +---+-----+
# |age| name|
# +---+-----+
# |  2|Alice|
# |  5|  Bob|
# +---+-----+