pyspark.sql.functions.transform_values#
- pyspark.sql.functions.transform_values(col, f)[source]#
Applies a function to every key-value pair in a map and returns a map with the results of those applications as the new values for the pairs.
New in version 3.1.0.
Changed in version 3.4.0: Supports Spark Connect.
- Parameters
- col
Column
or str name of column or expression
- ffunction
a binary function
(k: Column, v: Column) -> Column...
Can use methods ofColumn
, functions defined inpyspark.sql.functions
and ScalaUserDefinedFunctions
. PythonUserDefinedFunctions
are not supported (SPARK-27052).
- col
- Returns
Column
a new map of enties where new values were calculated by applying given function to each key value argument.
Examples
>>> df = spark.createDataFrame([(1, {"IT": 10.0, "SALES": 2.0, "OPS": 24.0})], ("id", "data")) >>> row = df.select(transform_values( ... "data", lambda k, v: when(k.isin("IT", "OPS"), v + 10.0).otherwise(v) ... ).alias("new_data")).head() >>> sorted(row["new_data"].items()) [('IT', 20.0), ('OPS', 34.0), ('SALES', 2.0)]