pyspark.sql.DataFrameWriter.save

DataFrameWriter.save(path: Optional[str] = None, format: Optional[str] = None, mode: Optional[str] = None, partitionBy: Union[str, List[str], None] = None, **options: OptionalPrimitiveType) → None[source]

Saves the contents of the DataFrame to a data source.

The data source is specified by the format and a set of options. If format is not specified, the default data source configured by spark.sql.sources.default will be used.

New in version 1.4.0.

Changed in version 3.4.0: Supports Spark Connect.

Parameters
pathstr, optional

the path in a Hadoop supported file system

formatstr, optional

the format used to save

modestr, optional

specifies the behavior of the save operation when data already exists.

  • append: Append contents of this DataFrame to existing data.

  • overwrite: Overwrite existing data.

  • ignore: Silently ignore this operation if data already exists.

  • error or errorifexists (default case): Throw an exception if data already exists.

partitionBylist, optional

names of partitioning columns

**optionsdict

all other string options

Examples

Write a DataFrame into a JSON file and read it back.

>>> import tempfile
>>> with tempfile.TemporaryDirectory() as d:
...     # Write a DataFrame into a JSON file
...     spark.createDataFrame(
...         [{"age": 100, "name": "Hyukjin Kwon"}]
...     ).write.mode("overwrite").format("json").save(d)
...
...     # Read the JSON file as a DataFrame.
...     spark.read.format('json').load(d).show()
+---+------------+
|age|        name|
+---+------------+
|100|Hyukjin Kwon|
+---+------------+
pyspark.sql.DataFrameWriter.partitionBy pyspark.sql.DataFrameWriter.saveAsTable