pyspark.sql.DataFrame.withColumnsRenamed¶

DataFrame.withColumnsRenamed(colsMap: Dict[str, str]) → pyspark.sql.dataframe.DataFrame[source]¶

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

New in version 3.4.0: Added support for multiple columns renaming

Parameters

colsMapdict: a dict of existing column names and corresponding desired column names. Currently, only a single map is supported.

Returns

DataFrame: DataFrame with renamed columns.

See also

withColumnRenamed()

Notes

Support Spark Connect

Examples

>>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
>>> df = df.withColumns({'age2': df.age + 2, 'age3': df.age + 3})
>>> df.withColumnsRenamed({'age2': 'age4', 'age3': 'age5'}).show()
+---+-----+----+----+
|age| name|age4|age5|
+---+-----+----+----+
|  2|Alice|   4|   5|
|  5|  Bob|   7|   8|
+---+-----+----+----+

pyspark.sql.DataFrame.withColumnRenamed

pyspark.sql.DataFrame.withMetadata