pyspark.sql.DataFrame.withColumns¶

DataFrame.withColumns(*colsMap: Dict[str, pyspark.sql.column.Column]) → pyspark.sql.dataframe.DataFrame[source]¶

Returns a new DataFrame by adding multiple columns or replacing the existing columns that have the same names.

The colsMap is a map of column name and column, the column must only refer to attributes supplied by this Dataset. It is an error to add columns that refer to some other Dataset.

New in version 3.3.0: Added support for multiple columns adding

Changed in version 3.4.0: Supports Spark Connect.

Parameters

colsMapdict: a dict of column name and Column. Currently, only a single map is supported.

Returns

DataFrame: DataFrame with new or replaced columns.

Examples

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

pyspark.sql.DataFrame.withColumn pyspark.sql.DataFrame.withColumnRenamed