pyspark.sql.functions.to_json¶
-
pyspark.sql.functions.
to_json
(col: ColumnOrName, options: Optional[Dict[str, str]] = None) → pyspark.sql.column.Column[source]¶ Converts a column containing a
StructType
,ArrayType
or aMapType
into a JSON string. Throws an exception, in the case of an unsupported type.New in version 2.1.0.
Changed in version 3.4.0: Supports Spark Connect.
- Parameters
- col
Column
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. See Data Source Option for the version you use. Additionally the function supports the pretty option which enables pretty JSON generation.
- col
- Returns
Column
JSON object as string column.
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"]')]