pyspark.sql.functions.to_json¶

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

Converts a column containing a StructType, ArrayType or a MapType into a JSON string. Throws an exception, in the case of an unsupported type.

New in version 2.1.0.

Parameters:

colColumn or str: name of column containing a struct, an array or a map.
optionsdict, optional: options to control converting. accepts the same options as the JSON datasource. Additionally the function supports the pretty option which enables pretty JSON generation.

Examples

>>> from pyspark.sql import Row
>>> from pyspark.sql.types import *
>>> data = [(1, Row(age=2, name='Alice'))]
>>> df = spark.createDataFrame(data, ("key", "value"))
>>> df.select(to_json(df.value).alias("json")).collect()
[Row(json='{"age":2,"name":"Alice"}')]
>>> data = [(1, [Row(age=2, name='Alice'), Row(age=3, name='Bob')])]
>>> df = spark.createDataFrame(data, ("key", "value"))
>>> df.select(to_json(df.value).alias("json")).collect()
[Row(json='[{"age":2,"name":"Alice"},{"age":3,"name":"Bob"}]')]
>>> data = [(1, {"name": "Alice"})]
>>> df = spark.createDataFrame(data, ("key", "value"))
>>> df.select(to_json(df.value).alias("json")).collect()
[Row(json='{"name":"Alice"}')]
>>> data = [(1, [{"name": "Alice"}, {"name": "Bob"}])]
>>> df = spark.createDataFrame(data, ("key", "value"))
>>> df.select(to_json(df.value).alias("json")).collect()
[Row(json='[{"name":"Alice"},{"name":"Bob"}]')]
>>> data = [(1, ["Alice", "Bob"])]
>>> df = spark.createDataFrame(data, ("key", "value"))
>>> df.select(to_json(df.value).alias("json")).collect()
[Row(json='["Alice","Bob"]')]

pyspark.sql.functions.to_date pyspark.sql.functions.to_timestamp