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# The ASF licenses this file to You under the Apache License, Version 2.0
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import sys
from pyspark.sql.column import Column, _to_seq
from pyspark.sql.dataframe import DataFrame
from pyspark.sql.pandas.group_ops import PandasGroupedOpsMixin
from pyspark.sql.types import StructType, StructField, IntegerType, StringType
__all__ = ["GroupedData"]
def dfapi(f):
def _api(self):
name = f.__name__
jdf = getattr(self._jgd, 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, *cols):
name = f.__name__
jdf = getattr(self._jgd, name)(_to_seq(self.sql_ctx._sc, cols))
return DataFrame(jdf, self.sql_ctx)
_api.__name__ = f.__name__
_api.__doc__ = f.__doc__
return _api
[docs]class GroupedData(PandasGroupedOpsMixin):
"""
A set of methods for aggregations on a :class:`DataFrame`,
created by :func:`DataFrame.groupBy`.
.. versionadded:: 1.3
"""
def __init__(self, jgd, df):
self._jgd = jgd
self._df = df
self.sql_ctx = df.sql_ctx
[docs] def agg(self, *exprs):
"""Compute aggregates and returns the result as a :class:`DataFrame`.
The available aggregate functions can be:
1. built-in aggregation functions, such as `avg`, `max`, `min`, `sum`, `count`
2. group aggregate pandas UDFs, created with :func:`pyspark.sql.functions.pandas_udf`
.. note:: There is no partial aggregation with group aggregate UDFs, i.e.,
a full shuffle is required. Also, 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.
.. seealso:: :func:`pyspark.sql.functions.pandas_udf`
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.
.. versionadded:: 1.3.0
Parameters
----------
exprs : dict
a dict mapping from column name (string) to aggregate functions (string),
or a list of :class:`Column`.
Notes
-----
Built-in aggregation functions and group aggregate pandas UDFs cannot be mixed
in a single call to this function.
Examples
--------
>>> gdf = df.groupBy(df.name)
>>> sorted(gdf.agg({"*": "count"}).collect())
[Row(name='Alice', count(1)=1), Row(name='Bob', count(1)=1)]
>>> from pyspark.sql import functions as F
>>> sorted(gdf.agg(F.min(df.age)).collect())
[Row(name='Alice', min(age)=2), Row(name='Bob', min(age)=5)]
>>> from pyspark.sql.functions import pandas_udf, PandasUDFType
>>> @pandas_udf('int', PandasUDFType.GROUPED_AGG) # doctest: +SKIP
... def min_udf(v):
... return v.min()
>>> sorted(gdf.agg(min_udf(df.age)).collect()) # doctest: +SKIP
[Row(name='Alice', min_udf(age)=2), Row(name='Bob', min_udf(age)=5)]
"""
assert exprs, "exprs should not be empty"
if len(exprs) == 1 and isinstance(exprs[0], dict):
jdf = self._jgd.agg(exprs[0])
else:
# Columns
assert all(isinstance(c, Column) for c in exprs), "all exprs should be Column"
jdf = self._jgd.agg(exprs[0]._jc,
_to_seq(self.sql_ctx._sc, [c._jc for c in exprs[1:]]))
return DataFrame(jdf, self.sql_ctx)
[docs] @dfapi
def count(self):
"""Counts the number of records for each group.
.. versionadded:: 1.3.0
Examples
--------
>>> sorted(df.groupBy(df.age).count().collect())
[Row(age=2, count=1), Row(age=5, count=1)]
"""
[docs] @df_varargs_api
def mean(self, *cols):
"""Computes average values for each numeric columns for each group.
:func:`mean` is an alias for :func:`avg`.
.. versionadded:: 1.3.0
Parameters
----------
cols : str
column names. Non-numeric columns are ignored.
Examples
--------
>>> 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)]
"""
[docs] @df_varargs_api
def avg(self, *cols):
"""Computes average values for each numeric columns for each group.
:func:`mean` is an alias for :func:`avg`.
.. versionadded:: 1.3.0
Parameters
----------
cols : str
column names. Non-numeric columns are ignored.
Examples
--------
>>> 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)]
"""
[docs] @df_varargs_api
def max(self, *cols):
"""Computes the max value for each numeric columns for each group.
.. versionadded:: 1.3.0
Examples
--------
>>> df.groupBy().max('age').collect()
[Row(max(age)=5)]
>>> df3.groupBy().max('age', 'height').collect()
[Row(max(age)=5, max(height)=85)]
"""
[docs] @df_varargs_api
def min(self, *cols):
"""Computes the min value for each numeric column for each group.
.. versionadded:: 1.3.0
Parameters
----------
cols : str
column names. Non-numeric columns are ignored.
Examples
--------
>>> df.groupBy().min('age').collect()
[Row(min(age)=2)]
>>> df3.groupBy().min('age', 'height').collect()
[Row(min(age)=2, min(height)=80)]
"""
[docs] @df_varargs_api
def sum(self, *cols):
"""Computes the sum for each numeric columns for each group.
.. versionadded:: 1.3.0
Parameters
----------
cols : str
column names. Non-numeric columns are ignored.
Examples
--------
>>> df.groupBy().sum('age').collect()
[Row(sum(age)=7)]
>>> df3.groupBy().sum('age', 'height').collect()
[Row(sum(age)=7, sum(height)=165)]
"""
[docs] def pivot(self, pivot_col, values=None):
"""
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.
.. versionadded:: 1.6.0
Parameters
----------
pivot_col : str
Name of the column to pivot.
values : list, optional
List of values that will be translated to columns in the output DataFrame.
Examples
--------
# Compute the sum of earnings for each year by course with each course as a separate 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)]
# Or without specifying column values (less efficient)
>>> df4.groupBy("year").pivot("course").sum("earnings").collect()
[Row(year=2012, Java=20000, dotNET=15000), Row(year=2013, Java=30000, dotNET=48000)]
>>> df5.groupBy("sales.year").pivot("sales.course").sum("sales.earnings").collect()
[Row(year=2012, Java=20000, dotNET=15000), Row(year=2013, Java=30000, dotNET=48000)]
"""
if values is None:
jgd = self._jgd.pivot(pivot_col)
else:
jgd = self._jgd.pivot(pivot_col, values)
return GroupedData(jgd, self._df)
def _test():
import doctest
from pyspark.sql import Row, SparkSession
import pyspark.sql.group
globs = pyspark.sql.group.__dict__.copy()
spark = SparkSession.builder\
.master("local[4]")\
.appName("sql.group tests")\
.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())]))
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()
globs['df5'] = sc.parallelize([
Row(training="expert", sales=Row(course="dotNET", year=2012, earnings=10000)),
Row(training="junior", sales=Row(course="Java", year=2012, earnings=20000)),
Row(training="expert", sales=Row(course="dotNET", year=2012, earnings=5000)),
Row(training="junior", sales=Row(course="dotNET", year=2013, earnings=48000)),
Row(training="expert", sales=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)
spark.stop()
if failure_count:
sys.exit(-1)
if __name__ == "__main__":
_test()