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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 `schema` should be a :class:`StructType` describing the schema of the returned
`pandas.DataFrame`. The column labels of the returned `pandas.DataFrame` must either match
the field names in the defined schema if specified as strings, or match the
field data types by position if not strings, e.g. integer indices.
The length of the returned `pandas.DataFrame` can be arbitrary.
: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.
>>> import pandas as pd # doctest: +SKIP
>>> from pyspark.sql.functions import pandas_udf, ceil
>>> df = spark.createDataFrame(
... [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
... ("id", "v")) # doctest: +SKIP
>>> 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|
+---+-------------------+
Alternatively, the user can pass a function that takes two arguments.
In this case, the grouping key(s) will be passed as the first argument and the data will
be passed as the second argument. 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 a `pandas.DataFrame` containing all columns from the original Spark DataFrame.
This is useful when the user does not want to hardcode grouping key(s) in the function.
>>> df = spark.createDataFrame(
... [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
... ("id", "v")) # doctest: +SKIP
>>> def mean_func(key, pdf):
... # key is a tuple of one numpy.int64, which is the value
... # of 'id' for the current group
... return pd.DataFrame([key + (pdf.v.mean(),)])
>>> df.groupby('id').applyInPandas(
... mean_func, schema="id long, v double").show() # doctest: +SKIP
+---+---+
| id| v|
+---+---+
| 1|1.5|
| 2|6.0|
+---+---+
>>> def sum_func(key, pdf):
... # key is a tuple of two numpy.int64s, which is the values
... # of 'id' and 'ceil(df.v / 2)' for the current group
... return pd.DataFrame([key + (pdf.v.sum(),)])
>>> df.groupby(df.id, ceil(df.v / 2)).applyInPandas(
... sum_func, schema="id long, `ceil(v / 2)` long, v double").show() # doctest: +SKIP
+---+-----------+----+
| id|ceil(v / 2)| v|
+---+-----------+----+
| 2| 5|10.0|
| 1| 1| 3.0|
| 2| 3| 5.0|
| 2| 2| 3.0|
+---+-----------+----+
.. 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.
.. note:: If returning a new `pandas.DataFrame` constructed with a dictionary, it is
recommended to explicitly index the columns by name to ensure the positions are correct,
or alternatively use an `OrderedDict`.
For example, `pd.DataFrame({'id': ids, 'a': data}, columns=['id', 'a'])` or
`pd.DataFrame(OrderedDict([('id', ids), ('a', data)]))`.
.. note:: Experimental
.. 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)
[docs]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
[docs] @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 `schema` should be a :class:`StructType` describing the schema of the returned
`pandas.DataFrame`. The column labels of the returned `pandas.DataFrame` must either match
the field names in the defined schema if specified as strings, or match the
field data types by position if not strings, e.g. integer indices.
The length of the returned `pandas.DataFrame` can be arbitrary.
: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
>>> 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|
+--------+---+---+---+
.. 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:: If returning a new `pandas.DataFrame` constructed with a dictionary, it is
recommended to explicitly index the columns by name to ensure the positions are correct,
or alternatively use an `OrderedDict`.
For example, `pd.DataFrame({'id': ids, 'a': data}, columns=['id', 'a'])` or
`pd.DataFrame(OrderedDict([('id', ids), ('a', data)]))`.
.. note:: Experimental
.. 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()
