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import sys
from typing import Callable, List, Optional, TYPE_CHECKING, overload, Dict, Union, cast, Tuple
from py4j.java_gateway import JavaObject
from pyspark.sql.column import Column, _to_seq
from pyspark.sql.session import SparkSession
from pyspark.sql.dataframe import DataFrame
from pyspark.sql.pandas.group_ops import PandasGroupedOpsMixin
if TYPE_CHECKING:
from pyspark.sql._typing import LiteralType
__all__ = ["GroupedData"]
def dfapi(f: Callable[..., DataFrame]) -> Callable[..., DataFrame]:
def _api(self: "GroupedData") -> DataFrame:
name = f.__name__
jdf = getattr(self._jgd, name)()
return DataFrame(jdf, self.session)
_api.__name__ = f.__name__
_api.__doc__ = f.__doc__
return _api
def df_varargs_api(f: Callable[..., DataFrame]) -> Callable[..., DataFrame]:
def _api(self: "GroupedData", *cols: str) -> DataFrame:
name = f.__name__
jdf = getattr(self._jgd, name)(_to_seq(self.session._sc, cols))
return DataFrame(jdf, self.session)
_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:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
"""
def __init__(self, jgd: JavaObject, df: DataFrame):
self._jgd = jgd
self._df = df
self.session: SparkSession = df.sparkSession
def __repr__(self) -> str:
index = 26 # index to truncate string from the JVM side
jvm_string = self._jgd.toString()
if jvm_string is not None and len(jvm_string) > index and jvm_string[index] == "[":
return f"GroupedData{jvm_string[index:]}"
else:
return super().__repr__()
@overload
def agg(self, *exprs: Column) -> DataFrame:
...
@overload
def agg(self, __exprs: Dict[str, str]) -> DataFrame:
...
[docs] def agg(self, *exprs: Union[Column, Dict[str, str]]) -> DataFrame:
"""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
.. versionchanged:: 3.4.0
Supports Spark Connect.
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
--------
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.functions import pandas_udf, PandasUDFType
>>> df = spark.createDataFrame(
... [(2, "Alice"), (3, "Alice"), (5, "Bob"), (10, "Bob")], ["age", "name"])
>>> df.show()
+---+-----+
|age| name|
+---+-----+
| 2|Alice|
| 3|Alice|
| 5| Bob|
| 10| Bob|
+---+-----+
Group-by name, and count each group.
>>> df.groupBy(df.name)
GroupedData[grouping...: [name...], value: [age: bigint, name: string], type: GroupBy]
>>> df.groupBy(df.name).agg({"*": "count"}).sort("name").show()
+-----+--------+
| name|count(1)|
+-----+--------+
|Alice| 2|
| Bob| 2|
+-----+--------+
Group-by name, and calculate the minimum age.
>>> df.groupBy(df.name).agg(sf.min(df.age)).sort("name").show()
+-----+--------+
| name|min(age)|
+-----+--------+
|Alice| 2|
| Bob| 5|
+-----+--------+
Same as above but uses pandas UDF.
>>> @pandas_udf('int', PandasUDFType.GROUPED_AGG) # doctest: +SKIP
... def min_udf(v):
... return v.min()
...
>>> df.groupBy(df.name).agg(min_udf(df.age)).sort("name").show() # doctest: +SKIP
+-----+------------+
| name|min_udf(age)|
+-----+------------+
|Alice| 2|
| Bob| 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"
exprs = cast(Tuple[Column, ...], exprs)
jdf = self._jgd.agg(exprs[0]._jc, _to_seq(self.session._sc, [c._jc for c in exprs[1:]]))
return DataFrame(jdf, self.session)
[docs] @dfapi
def count(self) -> DataFrame:
"""Counts the number of records for each group.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Examples
--------
>>> df = spark.createDataFrame(
... [(2, "Alice"), (3, "Alice"), (5, "Bob"), (10, "Bob")], ["age", "name"])
>>> df.show()
+---+-----+
|age| name|
+---+-----+
| 2|Alice|
| 3|Alice|
| 5| Bob|
| 10| Bob|
+---+-----+
Group-by name, and count each group.
>>> df.groupBy(df.name).count().sort("name").show()
+-----+-----+
| name|count|
+-----+-----+
|Alice| 2|
| Bob| 2|
+-----+-----+
"""
[docs] @df_varargs_api
def mean(self, *cols: str) -> DataFrame:
"""Computes average values for each numeric columns for each group.
:func:`mean` is an alias for :func:`avg`.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
cols : str
column names. Non-numeric columns are ignored.
"""
[docs] @df_varargs_api
def avg(self, *cols: str) -> DataFrame:
"""Computes average values for each numeric columns for each group.
:func:`mean` is an alias for :func:`avg`.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
cols : str
column names. Non-numeric columns are ignored.
Examples
--------
>>> df = spark.createDataFrame([
... (2, "Alice", 80), (3, "Alice", 100),
... (5, "Bob", 120), (10, "Bob", 140)], ["age", "name", "height"])
>>> df.show()
+---+-----+------+
|age| name|height|
+---+-----+------+
| 2|Alice| 80|
| 3|Alice| 100|
| 5| Bob| 120|
| 10| Bob| 140|
+---+-----+------+
Group-by name, and calculate the mean of the age in each group.
>>> df.groupBy("name").avg('age').sort("name").show()
+-----+--------+
| name|avg(age)|
+-----+--------+
|Alice| 2.5|
| Bob| 7.5|
+-----+--------+
Calculate the mean of the age and height in all data.
>>> df.groupBy().avg('age', 'height').show()
+--------+-----------+
|avg(age)|avg(height)|
+--------+-----------+
| 5.0| 110.0|
+--------+-----------+
"""
[docs] @df_varargs_api
def max(self, *cols: str) -> DataFrame:
"""Computes the max value for each numeric columns for each group.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Examples
--------
>>> df = spark.createDataFrame([
... (2, "Alice", 80), (3, "Alice", 100),
... (5, "Bob", 120), (10, "Bob", 140)], ["age", "name", "height"])
>>> df.show()
+---+-----+------+
|age| name|height|
+---+-----+------+
| 2|Alice| 80|
| 3|Alice| 100|
| 5| Bob| 120|
| 10| Bob| 140|
+---+-----+------+
Group-by name, and calculate the max of the age in each group.
>>> df.groupBy("name").max("age").sort("name").show()
+-----+--------+
| name|max(age)|
+-----+--------+
|Alice| 3|
| Bob| 10|
+-----+--------+
Calculate the max of the age and height in all data.
>>> df.groupBy().max("age", "height").show()
+--------+-----------+
|max(age)|max(height)|
+--------+-----------+
| 10| 140|
+--------+-----------+
"""
[docs] @df_varargs_api
def min(self, *cols: str) -> DataFrame:
"""Computes the min value for each numeric column for each group.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
cols : str
column names. Non-numeric columns are ignored.
Examples
--------
>>> df = spark.createDataFrame([
... (2, "Alice", 80), (3, "Alice", 100),
... (5, "Bob", 120), (10, "Bob", 140)], ["age", "name", "height"])
>>> df.show()
+---+-----+------+
|age| name|height|
+---+-----+------+
| 2|Alice| 80|
| 3|Alice| 100|
| 5| Bob| 120|
| 10| Bob| 140|
+---+-----+------+
Group-by name, and calculate the min of the age in each group.
>>> df.groupBy("name").min("age").sort("name").show()
+-----+--------+
| name|min(age)|
+-----+--------+
|Alice| 2|
| Bob| 5|
+-----+--------+
Calculate the min of the age and height in all data.
>>> df.groupBy().min("age", "height").show()
+--------+-----------+
|min(age)|min(height)|
+--------+-----------+
| 2| 80|
+--------+-----------+
"""
[docs] @df_varargs_api
def sum(self, *cols: str) -> DataFrame:
"""Computes the sum for each numeric columns for each group.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
cols : str
column names. Non-numeric columns are ignored.
Examples
--------
>>> df = spark.createDataFrame([
... (2, "Alice", 80), (3, "Alice", 100),
... (5, "Bob", 120), (10, "Bob", 140)], ["age", "name", "height"])
>>> df.show()
+---+-----+------+
|age| name|height|
+---+-----+------+
| 2|Alice| 80|
| 3|Alice| 100|
| 5| Bob| 120|
| 10| Bob| 140|
+---+-----+------+
Group-by name, and calculate the sum of the age in each group.
>>> df.groupBy("name").sum("age").sort("name").show()
+-----+--------+
| name|sum(age)|
+-----+--------+
|Alice| 5|
| Bob| 15|
+-----+--------+
Calculate the sum of the age and height in all data.
>>> df.groupBy().sum("age", "height").show()
+--------+-----------+
|sum(age)|sum(height)|
+--------+-----------+
| 20| 440|
+--------+-----------+
"""
# TODO(SPARK-41746): SparkSession.createDataFrame does not support nested datatypes
[docs] def pivot(self, pivot_col: str, values: Optional[List["LiteralType"]] = None) -> "GroupedData":
"""
Pivots a column of the current :class:`DataFrame` and perform the specified aggregation.
There are two versions of the 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
.. versionchanged:: 3.4.0
Supports Spark Connect.
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
--------
>>> from pyspark.sql import Row
>>> df1 = spark.createDataFrame([
... 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),
... ])
>>> df1.show()
+------+----+--------+
|course|year|earnings|
+------+----+--------+
|dotNET|2012| 10000|
| Java|2012| 20000|
|dotNET|2012| 5000|
|dotNET|2013| 48000|
| Java|2013| 30000|
+------+----+--------+
>>> df2 = spark.createDataFrame([
... 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)),
... ]) # doctest: +SKIP
>>> df2.show() # doctest: +SKIP
+--------+--------------------+
|training| sales|
+--------+--------------------+
| expert|{dotNET, 2012, 10...|
| junior| {Java, 2012, 20000}|
| expert|{dotNET, 2012, 5000}|
| junior|{dotNET, 2013, 48...|
| expert| {Java, 2013, 30000}|
+--------+--------------------+
Compute the sum of earnings for each year by course with each course as a separate column
>>> df1.groupBy("year").pivot("course", ["dotNET", "Java"]).sum("earnings").show()
+----+------+-----+
|year|dotNET| Java|
+----+------+-----+
|2012| 15000|20000|
|2013| 48000|30000|
+----+------+-----+
Or without specifying column values (less efficient)
>>> df1.groupBy("year").pivot("course").sum("earnings").show()
+----+-----+------+
|year| Java|dotNET|
+----+-----+------+
|2012|20000| 15000|
|2013|30000| 48000|
+----+-----+------+
>>> df2.groupBy("sales.year").pivot("sales.course").sum("sales.earnings").show()
... # doctest: +SKIP
+----+-----+------+
|year| Java|dotNET|
+----+-----+------+
|2012|20000| 15000|
|2013|30000| 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() -> None:
import doctest
from pyspark.sql import SparkSession
import pyspark.sql.group
globs = pyspark.sql.group.__dict__.copy()
spark = SparkSession.builder.master("local[4]").appName("sql.group tests").getOrCreate()
globs["spark"] = spark
(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()