pyspark.sql.DataFrameReader.json#

DataFrameReader.json(path, schema=None, primitivesAsString=None, prefersDecimal=None, allowComments=None, allowUnquotedFieldNames=None, allowSingleQuotes=None, allowNumericLeadingZero=None, allowBackslashEscapingAnyCharacter=None, mode=None, columnNameOfCorruptRecord=None, dateFormat=None, timestampFormat=None, multiLine=None, allowUnquotedControlChars=None, lineSep=None, samplingRatio=None, dropFieldIfAllNull=None, encoding=None, locale=None, pathGlobFilter=None, recursiveFileLookup=None, modifiedBefore=None, modifiedAfter=None, allowNonNumericNumbers=None, useUnsafeRow=None)[source]#

Loads JSON files and returns the results as a DataFrame.

JSON Lines (newline-delimited JSON) is supported by default. For JSON (one record per file), set the multiLine parameter to true.

If the schema parameter is not specified, this function goes through the input once to determine the input schema.

New in version 1.4.0.

Changed in version 3.4.0: Supports Spark Connect.

Changed in version 4.2.0: Supports DataFrame input.

Parameters

pathstr, list, RDD, or DataFrame: string represents path to the JSON dataset, or a list of paths, or RDD of Strings storing JSON objects, or a DataFrame with a single string column containing JSON strings.
schemapyspark.sql.types.StructType or str, optional: an optional pyspark.sql.types.StructType for the input schema or a DDL-formatted string (For example col0 INT, col1 DOUBLE).

Other Parameters

Extra options: For the extra options, refer to Data Source Option for the version you use.

Examples

Example 1: Write a DataFrame into a JSON file and read it back.

>>> import tempfile
>>> with tempfile.TemporaryDirectory(prefix="json1") as d:
...     # Write a DataFrame into a JSON file
...     spark.createDataFrame(
...         [{"age": 100, "name": "Hyukjin"}]
...     ).write.mode("overwrite").format("json").save(d)
...
...     # Read the JSON file as a DataFrame.
...     spark.read.json(d).show()
+---+-------+
|age|   name|
+---+-------+
|100|Hyukjin|
+---+-------+

Example 2: Read JSON from multiple files in a directory

>>> from tempfile import TemporaryDirectory
>>> with TemporaryDirectory(prefix="json2") as d1, TemporaryDirectory(prefix="json3") as d2:
...     # Write a DataFrame into a JSON file
...     spark.createDataFrame(
...         [{"age": 30, "name": "Bob"}]
...     ).write.mode("overwrite").format("json").save(d1)
...
...     # Read the JSON files as a DataFrame.
...     spark.createDataFrame(
...         [{"age": 25, "name": "Alice"}]
...     ).write.mode("overwrite").format("json").save(d2)
...     spark.read.json([d1, d2]).show()
+---+-----+
|age| name|
+---+-----+
| 25|Alice|
| 30|  Bob|
+---+-----+

Example 3: Read JSON with a custom schema

>>> import tempfile
>>> with tempfile.TemporaryDirectory(prefix="json4") as d:
...     # Write a DataFrame into a JSON file
...     spark.createDataFrame(
...        [{"age": 30, "name": "Bob"}]
...     ).write.mode("overwrite").format("json").save(d)
...     custom_schema = "name STRING, age INT"
...     spark.read.json(d, schema=custom_schema).show()
+----+---+
|name|age|
+----+---+
| Bob| 30|
+----+---+

Example 4: Parse JSON from a DataFrame with a single string column.

>>> json_df = spark.createDataFrame(
...     [('{"name": "Alice", "age": 25}',), ('{"name": "Bob", "age": 30}',)],
...     schema="value STRING",
... )
>>> spark.read.json(json_df).sort("name").show()
+---+-----+
|age| name|
+---+-----+
| 25|Alice|
| 30|  Bob|
+---+-----+