[SPARK-20396][SQL][PySpark] groupby().apply() with pandas udf

python/pyspark/sql/dataframe.py

@@ -1227,7 +1227,7 @@ def groupBy(self, *cols):
         """
         jgd = self._jdf.groupBy(self._jcols(*cols))
         from pyspark.sql.group import GroupedData
-        return GroupedData(jgd, self.sql_ctx)
+        return GroupedData(jgd, self)
 
     @since(1.4)
     def rollup(self, *cols):
@@ -1248,7 +1248,7 @@ def rollup(self, *cols):
         """
         jgd = self._jdf.rollup(self._jcols(*cols))
         from pyspark.sql.group import GroupedData
-        return GroupedData(jgd, self.sql_ctx)
+        return GroupedData(jgd, self)
 
     @since(1.4)
     def cube(self, *cols):
@@ -1271,7 +1271,7 @@ def cube(self, *cols):
         """
         jgd = self._jdf.cube(self._jcols(*cols))
         from pyspark.sql.group import GroupedData
-        return GroupedData(jgd, self.sql_ctx)
+        return GroupedData(jgd, self)
 
     @since(1.3)
     def agg(self, *exprs):

python/pyspark/sql/functions.py

@@ -2058,7 +2058,7 @@ def __init__(self, func, returnType, name=None, vectorized=False):
         self._name = name or (
             func.__name__ if hasattr(func, '__name__')
             else func.__class__.__name__)
-        self._vectorized = vectorized
+        self.vectorized = vectorized
 
     @property
     def returnType(self):
@@ -2090,7 +2090,7 @@ def _create_judf(self):
         wrapped_func = _wrap_function(sc, self.func, self.returnType)
         jdt = spark._jsparkSession.parseDataType(self.returnType.json())
         judf = sc._jvm.org.apache.spark.sql.execution.python.UserDefinedPythonFunction(
-            self._name, wrapped_func, jdt, self._vectorized)
+            self._name, wrapped_func, jdt, self.vectorized)
         return judf
 
     def __call__(self, *cols):
@@ -2118,8 +2118,10 @@ def wrapper(*args):
         wrapper.__name__ = self._name
         wrapper.__module__ = (self.func.__module__ if hasattr(self.func, '__module__')
                               else self.func.__class__.__module__)
+
         wrapper.func = self.func
         wrapper.returnType = self.returnType
+        wrapper.vectorized = self.vectorized
 
         return wrapper
 
@@ -2129,8 +2131,12 @@ def _create_udf(f, returnType, vectorized):
     def _udf(f, returnType=StringType(), vectorized=vectorized):
         if vectorized:
             import inspect
-            if len(inspect.getargspec(f).args) == 0:
-                raise NotImplementedError("0-parameter pandas_udfs are not currently supported")
+            argspec = inspect.getargspec(f)
+            if len(argspec.args) == 0 and argspec.varargs is None:
+                raise ValueError(
+                    "0-arg pandas_udfs are not supported. "
+                    "Instead, create a 1-arg pandas_udf and ignore the arg in your function."
+                )
         udf_obj = UserDefinedFunction(f, returnType, vectorized=vectorized)
         return udf_obj._wrapped()
 
@@ -2146,7 +2152,7 @@ def _udf(f, returnType=StringType(), vectorized=vectorized):
 
 @since(1.3)
 def udf(f=None, returnType=StringType()):
-    """Creates a :class:`Column` expression representing a user defined function (UDF).
+    """Creates a user defined function (UDF).
 
     .. note:: The user-defined functions must be deterministic. Due to optimization,
         duplicate invocations may be eliminated or the function may even be invoked more times than
@@ -2181,30 +2187,70 @@ def udf(f=None, returnType=StringType()):
 @since(2.3)
 def pandas_udf(f=None, returnType=StringType()):
     """
-    Creates a :class:`Column` expression representing a user defined function (UDF) that accepts
-    `Pandas.Series` as input arguments and outputs a `Pandas.Series` of the same length.
+    Creates a vectorized user defined function (UDF).
 
-    :param f: python function if used as a standalone function
+    :param f: user-defined function. A python function if used as a standalone function
     :param returnType: a :class:`pyspark.sql.types.DataType` object
 
-    >>> from pyspark.sql.types import IntegerType, StringType
-    >>> slen = pandas_udf(lambda s: s.str.len(), IntegerType())
-    >>> @pandas_udf(returnType=StringType())
-    ... def to_upper(s):
-    ...     return s.str.upper()
-    ...
-    >>> @pandas_udf(returnType="integer")
-    ... def add_one(x):
-    ...     return x + 1
-    ...
-    >>> df = spark.createDataFrame([(1, "John Doe", 21)], ("id", "name", "age"))
-    >>> df.select(slen("name").alias("slen(name)"), to_upper("name"), add_one("age")) \\
-    ...     .show()  # doctest: +SKIP
-    +----------+--------------+------------+
-    |slen(name)|to_upper(name)|add_one(age)|
-    +----------+--------------+------------+
-    |         8|      JOHN DOE|          22|
-    +----------+--------------+------------+
+    The user-defined function can define one of the following transformations:
+
+    1. One or more `pandas.Series` -> A `pandas.Series`
+
+       This udf is used with :meth:`pyspark.sql.DataFrame.withColumn` and
+       :meth:`pyspark.sql.DataFrame.select`.
+       The returnType should be a primitive data type, e.g., `DoubleType()`.
+       The length of the returned `pandas.Series` must be of the same as the input `pandas.Series`.
+
+       >>> from pyspark.sql.types import IntegerType, StringType
+       >>> slen = pandas_udf(lambda s: s.str.len(), IntegerType())
+       >>> @pandas_udf(returnType=StringType())
+       ... def to_upper(s):
+       ...     return s.str.upper()
+       ...
+       >>> @pandas_udf(returnType="integer")
+       ... def add_one(x):
+       ...     return x + 1
+       ...
+       >>> df = spark.createDataFrame([(1, "John Doe", 21)], ("id", "name", "age"))
+       >>> df.select(slen("name").alias("slen(name)"), to_upper("name"), add_one("age")) \\
+       ...     .show()  # doctest: +SKIP
+       +----------+--------------+------------+
+       |slen(name)|to_upper(name)|add_one(age)|
+       +----------+--------------+------------+
+       |         8|      JOHN DOE|          22|
+       +----------+--------------+------------+
+
+    2. A `pandas.DataFrame` -> A `pandas.DataFrame`
+
+       This udf is only used with :meth:`pyspark.sql.GroupedData.apply`.
+       The returnType should be a :class:`StructType` describing the schema of the returned
+       `pandas.DataFrame`.
+
+       >>> df = spark.createDataFrame(
+       ...     [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
+       ...     ("id", "v"))
+       >>> @pandas_udf(returnType=df.schema)
+       ... 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|
+       +---+-------------------+
+
+       .. note:: This type of udf cannot be used with functions such as `withColumn` or `select`
+                 because it defines a `DataFrame` transformation rather than a `Column`
+                 transformation.
+
+       .. seealso:: :meth:`pyspark.sql.GroupedData.apply`
+
+    .. note:: The user-defined function must be deterministic.
     """
     return _create_udf(f, returnType=returnType, vectorized=True)
 

python/pyspark/sql/group.py

@@ -54,9 +54,10 @@ class GroupedData(object):
     .. versionadded:: 1.3
     """
 
-    def __init__(self, jgd, sql_ctx):
+    def __init__(self, jgd, df):
         self._jgd = jgd
-        self.sql_ctx = sql_ctx
+        self._df = df
+        self.sql_ctx = df.sql_ctx
 
     @ignore_unicode_prefix
     @since(1.3)
@@ -170,7 +171,7 @@ def sum(self, *cols):
     @since(1.6)
     def pivot(self, pivot_col, values=None):
         """
-        Pivots a column of the current [[DataFrame]] and perform the specified aggregation.
+        Pivots a column of the current :class:`DataFrame` and perform the specified aggregation.
         There are two versions of pivot function: one that requires the caller to specify the list
         of distinct values to pivot on, and one that does not. The latter is more concise but less
         efficient, because Spark needs to first compute the list of distinct values internally.
@@ -192,7 +193,85 @@ def pivot(self, pivot_col, values=None):
             jgd = self._jgd.pivot(pivot_col)
         else:
             jgd = self._jgd.pivot(pivot_col, values)
-        return GroupedData(jgd, self.sql_ctx)
+        return GroupedData(jgd, self._df)
+
+    @since(2.3)
+    def apply(self, udf):
+        """
+        Maps each group of the current :class:`DataFrame` using a pandas udf and returns the result
+        as a `DataFrame`.
+
+        The user-defined 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`s 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.
+
+        This function does not support partial aggregation, and requires shuffling all the data in
+        the :class:`DataFrame`.
+
+        :param udf: A function object returned by :meth:`pyspark.sql.functions.pandas_udf`
+
+        >>> from pyspark.sql.functions import pandas_udf
+        >>> df = spark.createDataFrame(
+        ...     [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
+        ...     ("id", "v"))
+        >>> @pandas_udf(returnType=df.schema)
+        ... 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`
+
+        """
+        from pyspark.sql.functions import pandas_udf
+
+        # Columns are special because hasattr always return True
+        if isinstance(udf, Column) or not hasattr(udf, 'func') or not udf.vectorized:
+            raise ValueError("The argument to apply must be a pandas_udf")
+        if not isinstance(udf.returnType, StructType):
+            raise ValueError("The returnType of the pandas_udf must be a StructType")
+
+        df = self._df
+        func = udf.func
+        returnType = udf.returnType
+
+        # The python executors expects the function to use pd.Series as input and output
+        # So we to create a wrapper function that turns that to a pd.DataFrame before passing
+        # down to the user function, then turn the result pd.DataFrame back into pd.Series
+        columns = df.columns
+
+        def wrapped(*cols):
+            from pyspark.sql.types import to_arrow_type
+            import pandas as pd
+            result = func(pd.concat(cols, axis=1, keys=columns))
+            if not isinstance(result, pd.DataFrame):
+                raise TypeError("Return type of the user-defined function should be "
+                                "Pandas.DataFrame, but is {}".format(type(result)))
+            if not len(result.columns) == len(returnType):
+                raise RuntimeError(
+                    "Number of columns of the returned Pandas.DataFrame "
+                    "doesn't match specified schema. "
+                    "Expected: {} Actual: {}".format(len(returnType), len(result.columns)))
+            arrow_return_types = (to_arrow_type(field.dataType) for field in returnType)
+            return [(result[result.columns[i]], arrow_type)
+                    for i, arrow_type in enumerate(arrow_return_types)]
+
+        wrapped_udf_obj = pandas_udf(wrapped, returnType)
+        udf_column = wrapped_udf_obj(*[df[col] for col in df.columns])
+        jdf = self._jgd.flatMapGroupsInPandas(udf_column._jc.expr())
+        return DataFrame(jdf, self.sql_ctx)
 
 
 def _test():
@@ -206,6 +285,7 @@ def _test():
         .getOrCreate()
     sc = spark.sparkContext
     globs['sc'] = sc
+    globs['spark'] = spark
     globs['df'] = sc.parallelize([(2, 'Alice'), (5, 'Bob')]) \
         .toDF(StructType([StructField('age', IntegerType()),
                           StructField('name', StringType())]))