spark-instrumented-optimizer/python/pyspark/sql/pandas/group_ops.py

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import sys
import warnings

from pyspark import since
from pyspark.rdd import PythonEvalType
from pyspark.sql.column import Column
from pyspark.sql.dataframe import DataFrame


class PandasGroupedOpsMixin(object):
    """
    Min-in for pandas grouped operations. Currently, only :class:`GroupedData`
    can use this class.
    """

    @since(2.3)
    def apply(self, udf):
        """
        It is an alias of :meth:`pyspark.sql.GroupedData.applyInPandas`; however, it takes a
        :meth:`pyspark.sql.functions.pandas_udf` whereas
        :meth:`pyspark.sql.GroupedData.applyInPandas` takes a Python native function.

        .. note:: It is preferred to use :meth:`pyspark.sql.GroupedData.applyInPandas` over this
            API. This API will be deprecated in the future releases.

        :param udf: a grouped map user-defined function returned by
            :func:`pyspark.sql.functions.pandas_udf`.

        >>> from pyspark.sql.functions import pandas_udf, PandasUDFType
        >>> df = spark.createDataFrame(
        ...     [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
        ...     ("id", "v"))
        >>> @pandas_udf("id long, v double", PandasUDFType.GROUPED_MAP)  # doctest: +SKIP
        ... def normalize(pdf):
        ...     v = pdf.v
        ...     return pdf.assign(v=(v - v.mean()) / v.std())
        >>> df.groupby("id").apply(normalize).show()  # doctest: +SKIP
        +---+-------------------+
        | id|                  v|
        +---+-------------------+
        |  1|-0.7071067811865475|
        |  1| 0.7071067811865475|
        |  2|-0.8320502943378437|
        |  2|-0.2773500981126146|
        |  2| 1.1094003924504583|
        +---+-------------------+

        .. seealso:: :meth:`pyspark.sql.functions.pandas_udf`

        """
        # Columns are special because hasattr always return True
        if isinstance(udf, Column) or not hasattr(udf, 'func') \
                or udf.evalType != PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF:
            raise ValueError("Invalid udf: the udf argument must be a pandas_udf of type "
                             "GROUPED_MAP.")

        warnings.warn(
            "It is preferred to use 'applyInPandas' over this "
            "API. This API will be deprecated in the future releases. See SPARK-28264 for "
            "more details.", UserWarning)

        return self.applyInPandas(udf.func, schema=udf.returnType)

    @since(3.0)
    def applyInPandas(self, func, schema):
        """
        Maps each group of the current :class:`DataFrame` using a pandas udf and returns the result
        as a `DataFrame`.

        The function should take a `pandas.DataFrame` and return another
        `pandas.DataFrame`. For each group, all columns are passed together as a `pandas.DataFrame`
        to the user-function and the returned `pandas.DataFrame` are combined as a
        :class:`DataFrame`.

        The returned `pandas.DataFrame` can be of arbitrary length and its schema must match the
        returnType of the pandas udf.

        .. note:: This function requires a full shuffle. All the data of a group will be loaded
            into memory, so the user should be aware of the potential OOM risk if data is skewed
            and certain groups are too large to fit in memory.

        :param func: a Python native function that takes a `pandas.DataFrame`, and outputs a
            `pandas.DataFrame`.
        :param schema: the return type of the `func` in PySpark. The value can be either a
            :class:`pyspark.sql.types.DataType` object or a DDL-formatted type string.

        .. note:: Experimental

        >>> from pyspark.sql.functions import pandas_udf, PandasUDFType
        >>> df = spark.createDataFrame(
        ...     [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
        ...     ("id", "v"))
        >>> def normalize(pdf):
        ...     v = pdf.v
        ...     return pdf.assign(v=(v - v.mean()) / v.std())
        >>> df.groupby("id").applyInPandas(normalize, schema="id long, v double").show()
        ... # doctest: +SKIP
        +---+-------------------+
        | id|                  v|
        +---+-------------------+
        |  1|-0.7071067811865475|
        |  1| 0.7071067811865475|
        |  2|-0.8320502943378437|
        |  2|-0.2773500981126146|
        |  2| 1.1094003924504583|
        +---+-------------------+

        .. seealso:: :meth:`pyspark.sql.functions.pandas_udf`

        """
        from pyspark.sql import GroupedData
        from pyspark.sql.functions import pandas_udf, PandasUDFType

        assert isinstance(self, GroupedData)

        udf = pandas_udf(
            func, returnType=schema, functionType=PandasUDFType.GROUPED_MAP)
        df = self._df
        udf_column = udf(*[df[col] for col in df.columns])
        jdf = self._jgd.flatMapGroupsInPandas(udf_column._jc.expr())
        return DataFrame(jdf, self.sql_ctx)

    @since(3.0)
    def cogroup(self, other):
        """
        Cogroups this group with another group so that we can run cogrouped operations.

        See :class:`CoGroupedData` for the operations that can be run.
        """
        from pyspark.sql import GroupedData

        assert isinstance(self, GroupedData)

        return PandasCogroupedOps(self, other)


class PandasCogroupedOps(object):
    """
    A logical grouping of two :class:`GroupedData`,
    created by :func:`GroupedData.cogroup`.

    .. note:: Experimental

    .. versionadded:: 3.0
    """

    def __init__(self, gd1, gd2):
        self._gd1 = gd1
        self._gd2 = gd2
        self.sql_ctx = gd1.sql_ctx

    @since(3.0)
    def applyInPandas(self, func, schema):
        """
        Applies a function to each cogroup using pandas and returns the result
        as a `DataFrame`.

        The function should take two `pandas.DataFrame`\\s and return another
        `pandas.DataFrame`.  For each side of the cogroup, all columns are passed together as a
        `pandas.DataFrame` to the user-function and the returned `pandas.DataFrame` are combined as
        a :class:`DataFrame`.

        The returned `pandas.DataFrame` can be of arbitrary length and its schema must match the
        returnType of the pandas udf.

        .. note:: This function requires a full shuffle. All the data of a cogroup will be loaded
            into memory, so the user should be aware of the potential OOM risk if data is skewed
            and certain groups are too large to fit in memory.

        .. note:: Experimental

        :param func: a Python native function that takes two `pandas.DataFrame`\\s, and
            outputs a `pandas.DataFrame`, or that takes one tuple (grouping keys) and two
            pandas ``DataFrame``s, and outputs a pandas ``DataFrame``.
        :param schema: the return type of the `func` in PySpark. The value can be either a
            :class:`pyspark.sql.types.DataType` object or a DDL-formatted type string.

        >>> from pyspark.sql.functions import pandas_udf, PandasUDFType
        >>> df1 = spark.createDataFrame(
        ...     [(20000101, 1, 1.0), (20000101, 2, 2.0), (20000102, 1, 3.0), (20000102, 2, 4.0)],
        ...     ("time", "id", "v1"))
        >>> df2 = spark.createDataFrame(
        ...     [(20000101, 1, "x"), (20000101, 2, "y")],
        ...     ("time", "id", "v2"))
        >>> def asof_join(l, r):
        ...     return pd.merge_asof(l, r, on="time", by="id")
        >>> df1.groupby("id").cogroup(df2.groupby("id")).applyInPandas(
        ...     asof_join, schema="time int, id int, v1 double, v2 string"
        ... ).show()  # doctest: +SKIP
        +--------+---+---+---+
        |    time| id| v1| v2|
        +--------+---+---+---+
        |20000101|  1|1.0|  x|
        |20000102|  1|3.0|  x|
        |20000101|  2|2.0|  y|
        |20000102|  2|4.0|  y|
        +--------+---+---+---+

        Alternatively, the user can define a function that takes three arguments.  In this case,
        the grouping key(s) will be passed as the first argument and the data will be passed as the
        second and third arguments.  The grouping key(s) will be passed as a tuple of numpy data
        types, e.g., `numpy.int32` and `numpy.float64`. The data will still be passed in as two
        `pandas.DataFrame` containing all columns from the original Spark DataFrames.

        >>> def asof_join(k, l, r):
        ...     if k == (1,):
        ...         return pd.merge_asof(l, r, on="time", by="id")
        ...     else:
        ...         return pd.DataFrame(columns=['time', 'id', 'v1', 'v2'])
        >>> df1.groupby("id").cogroup(df2.groupby("id")).applyInPandas(
        ...     asof_join, "time int, id int, v1 double, v2 string").show()  # doctest: +SKIP
        +--------+---+---+---+
        |    time| id| v1| v2|
        +--------+---+---+---+
        |20000101|  1|1.0|  x|
        |20000102|  1|3.0|  x|
        +--------+---+---+---+

        .. seealso:: :meth:`pyspark.sql.functions.pandas_udf`

        """
        from pyspark.sql.pandas.functions import pandas_udf

        udf = pandas_udf(
            func, returnType=schema, functionType=PythonEvalType.SQL_COGROUPED_MAP_PANDAS_UDF)
        all_cols = self._extract_cols(self._gd1) + self._extract_cols(self._gd2)
        udf_column = udf(*all_cols)
        jdf = self._gd1._jgd.flatMapCoGroupsInPandas(self._gd2._jgd, udf_column._jc.expr())
        return DataFrame(jdf, self.sql_ctx)

    @staticmethod
    def _extract_cols(gd):
        df = gd._df
        return [df[col] for col in df.columns]


def _test():
    import doctest
    from pyspark.sql import SparkSession
    import pyspark.sql.pandas.group_ops
    globs = pyspark.sql.pandas.group_ops.__dict__.copy()
    spark = SparkSession.builder\
        .master("local[4]")\
        .appName("sql.pandas.group tests")\
        .getOrCreate()
    globs['spark'] = spark
    (failure_count, test_count) = doctest.testmod(
        pyspark.sql.pandas.group_ops, globs=globs,
        optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE | doctest.REPORT_NDIFF)
    spark.stop()
    if failure_count:
        sys.exit(-1)


if __name__ == "__main__":
    _test()