pyspark.sql.functions.map_filter

pyspark.sql.functions.map_filter(col: ColumnOrName, f: Callable[[pyspark.sql.column.Column, pyspark.sql.column.Column], pyspark.sql.column.Column]) → pyspark.sql.column.Column

Returns a map whose key-value pairs satisfy a predicate.

Parameters

colColumn or str

name of column or expression

ffunction

a binary function (k: Column, v: Column) -> Column... Can use methods of Column, functions defined in pyspark.sql.functions and Scala UserDefinedFunctions. Python UserDefinedFunctions are not supported (SPARK-27052).

Returns

Column

Examples

>>> df = spark.createDataFrame([(1, {"foo": 42.0, "bar": 1.0, "baz": 32.0})], ("id", "data"))
>>> df.select(map_filter(
...     "data", lambda _, v: v > 30.0).alias("data_filtered")
... ).show(truncate=False)
+--------------------------+
|data_filtered             |
+--------------------------+
|{baz -> 32.0, foo -> 42.0}|
+--------------------------+

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