#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from pyspark import since
from pyspark.rdd import ignore_unicode_prefix
from pyspark.sql.column import Column, _to_seq, _to_java_column, _create_column_from_literal
from pyspark.sql.dataframe import DataFrame
from pyspark.sql.types import *
__all__ = ["GroupedData"]
def dfapi(f):
def _api(self):
name = f.__name__
jdf = getattr(self._jdf, name)()
return DataFrame(jdf, self.sql_ctx)
_api.__name__ = f.__name__
_api.__doc__ = f.__doc__
return _api
def df_varargs_api(f):
def _api(self, *args):
name = f.__name__
jdf = getattr(self._jdf, name)(_to_seq(self.sql_ctx._sc, args))
return DataFrame(jdf, self.sql_ctx)
_api.__name__ = f.__name__
_api.__doc__ = f.__doc__
return _api
class GroupedData(object):
"""
A set of methods for aggregations on a :class:`DataFrame`,
created by :func:`DataFrame.groupBy`.
.. note:: Experimental
.. versionadded:: 1.3
"""
def __init__(self, jdf, sql_ctx):
self._jdf = jdf
self.sql_ctx = sql_ctx
@ignore_unicode_prefix
@since(1.3)
def agg(self, *exprs):
"""Compute aggregates and returns the result as a :class:`DataFrame`.
The available aggregate functions are `avg`, `max`, `min`, `sum`, `count`.
If ``exprs`` is a single :class:`dict` mapping from string to string, then the key
is the column to perform aggregation on, and the value is the aggregate function.
Alternatively, ``exprs`` can also be a list of aggregate :class:`Column` expressions.
:param exprs: a dict mapping from column name (string) to aggregate functions (string),
or a list of :class:`Column`.
>>> gdf = df.groupBy(df.name)
>>> gdf.agg({"*": "count"}).collect()
[Row(name=u'Alice', count(1)=1), Row(name=u'Bob', count(1)=1)]
>>> from pyspark.sql import functions as F
>>> gdf.agg(F.min(df.age)).collect()
[Row(name=u'Alice', min(age)=2), Row(name=u'Bob', min(age)=5)]
"""
assert exprs, "exprs should not be empty"
if len(exprs) == 1 and isinstance(exprs[0], dict):
jdf = self._jdf.agg(exprs[0])
else:
# Columns
assert all(isinstance(c, Column) for c in exprs), "all exprs should be Column"
jdf = self._jdf.agg(exprs[0]._jc,
_to_seq(self.sql_ctx._sc, [c._jc for c in exprs[1:]]))
return DataFrame(jdf, self.sql_ctx)
@dfapi
@since(1.3)
def count(self):
"""Counts the number of records for each group.
>>> df.groupBy(df.age).count().collect()
[Row(age=2, count=1), Row(age=5, count=1)]
"""
@df_varargs_api
@since(1.3)
def mean(self, *cols):
"""Computes average values for each numeric columns for each group.
:func:`mean` is an alias for :func:`avg`.
:param cols: list of column names (string). Non-numeric columns are ignored.
>>> df.groupBy().mean('age').collect()
[Row(avg(age)=3.5)]
>>> df3.groupBy().mean('age', 'height').collect()
[Row(avg(age)=3.5, avg(height)=82.5)]
"""
@df_varargs_api
@since(1.3)
def avg(self, *cols):
"""Computes average values for each numeric columns for each group.
:func:`mean` is an alias for :func:`avg`.
:param cols: list of column names (string). Non-numeric columns are ignored.
>>> df.groupBy().avg('age').collect()
[Row(avg(age)=3.5)]
>>> df3.groupBy().avg('age', 'height').collect()
[Row(avg(age)=3.5, avg(height)=82.5)]
"""
@df_varargs_api
@since(1.3)
def max(self, *cols):
"""Computes the max value for each numeric columns for each group.
>>> df.groupBy().max('age').collect()
[Row(max(age)=5)]
>>> df3.groupBy().max('age', 'height').collect()
[Row(max(age)=5, max(height)=85)]
"""
@df_varargs_api
@since(1.3)
def min(self, *cols):
"""Computes the min value for each numeric column for each group.
:param cols: list of column names (string). Non-numeric columns are ignored.
>>> df.groupBy().min('age').collect()
[Row(min(age)=2)]
>>> df3.groupBy().min('age', 'height').collect()
[Row(min(age)=2, min(height)=80)]
"""
@df_varargs_api
@since(1.3)
def sum(self, *cols):
"""Compute the sum for each numeric columns for each group.
:param cols: list of column names (string). Non-numeric columns are ignored.
>>> df.groupBy().sum('age').collect()
[Row(sum(age)=7)]
>>> df3.groupBy().sum('age', 'height').collect()
[Row(sum(age)=7, sum(height)=165)]
"""
@since(1.6)
def pivot(self, pivot_col, *values):
"""Pivots a column of the current DataFrame and preform the specified aggregation.
:param pivot_col: Column to pivot
:param values: Optional list of values of pivotColumn that will be translated to columns in
the output data frame. If values are not provided the method with do an immediate call
to .distinct() on the pivot column.
>>> df4.groupBy("year").pivot("course", "dotNET", "Java").sum("earnings").collect()
[Row(year=2012, dotNET=15000, Java=20000), Row(year=2013, dotNET=48000, Java=30000)]
>>> df4.groupBy("year").pivot("course").sum("earnings").collect()
[Row(year=2012, Java=20000, dotNET=15000), Row(year=2013, Java=30000, dotNET=48000)]
"""
jgd = self._jdf.pivot(_to_java_column(pivot_col),
_to_seq(self.sql_ctx._sc, values, _create_column_from_literal))
return GroupedData(jgd, self.sql_ctx)
def _test():
import doctest
from pyspark.context import SparkContext
from pyspark.sql import Row, SQLContext
import pyspark.sql.group
globs = pyspark.sql.group.__dict__.copy()
sc = SparkContext('local[4]', 'PythonTest')
globs['sc'] = sc
globs['sqlContext'] = SQLContext(sc)
globs['df'] = sc.parallelize([(2, 'Alice'), (5, 'Bob')]) \
.toDF(StructType([StructField('age', IntegerType()),
StructField('name', StringType())]))
globs['df3'] = sc.parallelize([Row(name='Alice', age=2, height=80),
Row(name='Bob', age=5, height=85)]).toDF()
globs['df4'] = sc.parallelize([Row(course="dotNET", year=2012, earnings=10000),
Row(course="Java", year=2012, earnings=20000),
Row(course="dotNET", year=2012, earnings=5000),
Row(course="dotNET", year=2013, earnings=48000),
Row(course="Java", year=2013, earnings=30000)]).toDF()
(failure_count, test_count) = doctest.testmod(
pyspark.sql.group, globs=globs,
optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE | doctest.REPORT_NDIFF)
globs['sc'].stop()
if failure_count:
exit(-1)
if __name__ == "__main__":
_test()
