pyspark.sql.DataFrame.mapInPandas¶

DataFrame.mapInPandas(func: PandasMapIterFunction, schema: Union[pyspark.sql.types.StructType, str], barrier: bool = False) → DataFrame¶

Maps an iterator of batches in the current DataFrame using a Python native function that takes and outputs a pandas DataFrame, and returns the result as a DataFrame.

The function should take an iterator of pandas.DataFrames and return another iterator of pandas.DataFrames. All columns are passed together as an iterator of pandas.DataFrames to the function and the returned iterator of pandas.DataFrames are combined as a DataFrame. Each pandas.DataFrame size can be controlled by spark.sql.execution.arrow.maxRecordsPerBatch. The size of the function’s input and output can be different.

New in version 3.0.0.

Changed in version 3.4.0: Supports Spark Connect.

Parameters

funcfunction: a Python native function that takes an iterator of pandas.DataFrames, and outputs an iterator of pandas.DataFrames.
schemapyspark.sql.types.DataType or str: the return type of the func in PySpark. The value can be either a pyspark.sql.types.DataType object or a DDL-formatted type string.
barrierbool, optional, default True: Use barrier mode execution.

See also

pyspark.sql.functions.pandas_udf

Notes

This API is experimental

Examples

>>> from pyspark.sql.functions import pandas_udf
>>> df = spark.createDataFrame([(1, 21), (2, 30)], ("id", "age"))
>>> def filter_func(iterator):
...     for pdf in iterator:
...         yield pdf[pdf.id == 1]
...
>>> df.mapInPandas(filter_func, df.schema).show()  
+---+---+
| id|age|
+---+---+
|  1| 21|
+---+---+

Set barrier to True to force the mapInPandas stage running in the barrier mode, it ensures all Python workers in the stage will be launched concurrently.

>>> df.mapInPandas(filter_func, df.schema, barrier=True).show()  
+---+---+
| id|age|
+---+---+
|  1| 21|
+---+---+

pyspark.sql.DataFrame.localCheckpoint

pyspark.sql.DataFrame.mapInArrow