pyspark.sql.functions.from_json¶

pyspark.sql.functions.from_json(col, schema, options={})[source]¶

Parses a column containing a JSON string into a MapType with StringType as keys type, StructType or ArrayType with the specified schema. Returns null, in the case of an unparseable string.

New in version 2.1.0.

Parameters:

colColumn or str: string column in json format
schemaDataType or str: a StructType or ArrayType of StructType to use when parsing the json column.

Changed in version 2.3: the DDL-formatted string is also supported for schema.
optionsdict, optional: options to control parsing. accepts the same options as the json datasource

Examples

>>> from pyspark.sql.types import *
>>> data = [(1, '''{"a": 1}''')]
>>> schema = StructType([StructField("a", IntegerType())])
>>> df = spark.createDataFrame(data, ("key", "value"))
>>> df.select(from_json(df.value, schema).alias("json")).collect()
[Row(json=Row(a=1))]
>>> df.select(from_json(df.value, "a INT").alias("json")).collect()
[Row(json=Row(a=1))]
>>> df.select(from_json(df.value, "MAP<STRING,INT>").alias("json")).collect()
[Row(json={'a': 1})]
>>> data = [(1, '''[{"a": 1}]''')]
>>> schema = ArrayType(StructType([StructField("a", IntegerType())]))
>>> df = spark.createDataFrame(data, ("key", "value"))
>>> df.select(from_json(df.value, schema).alias("json")).collect()
[Row(json=[Row(a=1)])]
>>> schema = schema_of_json(lit('''{"a": 0}'''))
>>> df.select(from_json(df.value, schema).alias("json")).collect()
[Row(json=Row(a=None))]
>>> data = [(1, '''[1, 2, 3]''')]
>>> schema = ArrayType(IntegerType())
>>> df = spark.createDataFrame(data, ("key", "value"))
>>> df.select(from_json(df.value, schema).alias("json")).collect()
[Row(json=[1, 2, 3])]

pyspark.sql.functions.from_csv pyspark.sql.functions.from_unixtime