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#
# 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.
#
"""
A wrapper for GroupedData to behave like pandas GroupBy.
"""
from abc import ABCMeta, abstractmethod
import inspect
from collections import defaultdict, namedtuple
from functools import partial
from itertools import product
from typing import (
Any,
Callable,
Dict,
Generic,
Iterator,
Mapping,
List,
Optional,
Sequence,
Set,
Tuple,
Type,
Union,
cast,
TYPE_CHECKING,
)
import warnings
import pandas as pd
from pandas.api.types import is_number, is_hashable, is_list_like # type: ignore[attr-defined]
from pandas.core.common import _builtin_table # type: ignore[attr-defined]
from pyspark.sql import Column, DataFrame as SparkDataFrame, Window, functions as F
from pyspark.sql.types import (
BooleanType,
DataType,
DoubleType,
NumericType,
StructField,
StructType,
StringType,
)
from pyspark import pandas as ps # For running doctests and reference resolution in PyCharm.
from pyspark.pandas._typing import Axis, FrameLike, Label, Name
from pyspark.pandas.typedef import infer_return_type, DataFrameType, ScalarType, SeriesType
from pyspark.pandas.frame import DataFrame
from pyspark.pandas.internal import (
InternalField,
InternalFrame,
HIDDEN_COLUMNS,
NATURAL_ORDER_COLUMN_NAME,
SPARK_INDEX_NAME_FORMAT,
SPARK_DEFAULT_SERIES_NAME,
SPARK_INDEX_NAME_PATTERN,
)
from pyspark.pandas.missing.groupby import (
MissingPandasLikeDataFrameGroupBy,
MissingPandasLikeSeriesGroupBy,
)
from pyspark.pandas.series import Series, first_series
from pyspark.pandas.spark import functions as SF
from pyspark.pandas.config import get_option
from pyspark.pandas.correlation import (
compute,
CORRELATION_VALUE_1_COLUMN,
CORRELATION_VALUE_2_COLUMN,
CORRELATION_CORR_OUTPUT_COLUMN,
CORRELATION_COUNT_OUTPUT_COLUMN,
)
from pyspark.pandas.utils import (
align_diff_frames,
is_name_like_tuple,
is_name_like_value,
name_like_string,
same_anchor,
scol_for,
verify_temp_column_name,
log_advice,
)
from pyspark.pandas.spark.utils import as_nullable_spark_type, force_decimal_precision_scale
from pyspark.pandas.exceptions import DataError
if TYPE_CHECKING:
from pyspark.pandas.window import RollingGroupby, ExpandingGroupby, ExponentialMovingGroupby
# to keep it the same as pandas
NamedAgg = namedtuple("NamedAgg", ["column", "aggfunc"])
class GroupBy(Generic[FrameLike], metaclass=ABCMeta):
"""
:ivar _psdf: The parent dataframe that is used to perform the groupby
:type _psdf: DataFrame
:ivar _groupkeys: The list of keys that will be used to perform the grouping
:type _groupkeys: List[Series]
"""
def __init__(
self,
psdf: DataFrame,
groupkeys: List[Series],
as_index: bool,
dropna: bool,
column_labels_to_exclude: Set[Label],
agg_columns_selected: bool,
agg_columns: List[Series],
):
self._psdf = psdf
self._groupkeys = groupkeys
self._as_index = as_index
self._dropna = dropna
self._column_labels_to_exclude = column_labels_to_exclude
self._agg_columns_selected = agg_columns_selected
self._agg_columns = agg_columns
@property
def _groupkeys_scols(self) -> List[Column]:
return [s.spark.column for s in self._groupkeys]
@property
def _agg_columns_scols(self) -> List[Column]:
return [s.spark.column for s in self._agg_columns]
@abstractmethod
def _apply_series_op(
self,
op: Callable[["SeriesGroupBy"], Series],
should_resolve: bool = False,
numeric_only: bool = False,
) -> FrameLike:
pass
@abstractmethod
def _handle_output(
self, psdf: DataFrame, agg_column_names: Optional[List[str]] = None
) -> FrameLike:
pass
# TODO: Series support is not implemented yet.
# TODO: not all arguments are implemented comparing to pandas' for now.
def aggregate(
self,
func_or_funcs: Optional[Union[str, List[str], Dict[Name, Union[str, List[str]]]]] = None,
*args: Any,
**kwargs: Any,
) -> DataFrame:
"""Aggregate using one or more operations over the specified axis.
Parameters
----------
func_or_funcs : dict, str or list
a dict mapping from column name (string) to
aggregate functions (string or list of strings).
Returns
-------
Series or DataFrame
The return can be:
* Series : when DataFrame.agg is called with a single function
* DataFrame : when DataFrame.agg is called with several functions
Return Series or DataFrame.
Notes
-----
`agg` is an alias for `aggregate`. Use the alias.
See Also
--------
pyspark.pandas.Series.groupby
pyspark.pandas.DataFrame.groupby
Examples
--------
>>> df = ps.DataFrame({'A': [1, 1, 2, 2],
... 'B': [1, 2, 3, 4],
... 'C': [0.362, 0.227, 1.267, -0.562]},
... columns=['A', 'B', 'C'])
>>> df
A B C
0 1 1 0.362
1 1 2 0.227
2 2 3 1.267
3 2 4 -0.562
Different aggregations per column
>>> aggregated = df.groupby('A').agg({'B': 'min', 'C': 'sum'})
>>> aggregated[['B', 'C']].sort_index() # doctest: +NORMALIZE_WHITESPACE
B C
A
1 1 0.589
2 3 0.705
>>> aggregated = df.groupby('A').agg({'B': ['min', 'max']})
>>> aggregated.sort_index() # doctest: +NORMALIZE_WHITESPACE
B
min max
A
1 1 2
2 3 4
>>> aggregated = df.groupby('A').agg('min')
>>> aggregated.sort_index() # doctest: +NORMALIZE_WHITESPACE
B C
A
1 1 0.227
2 3 -0.562
>>> aggregated = df.groupby('A').agg(['min', 'max'])
>>> aggregated.sort_index() # doctest: +NORMALIZE_WHITESPACE
B C
min max min max
A
1 1 2 0.227 0.362
2 3 4 -0.562 1.267
To control the output names with different aggregations per column, pandas-on-Spark
also supports 'named aggregation' or nested renaming in .agg. It can also be
used when applying multiple aggregation functions to specific columns.
>>> aggregated = df.groupby('A').agg(b_max=ps.NamedAgg(column='B', aggfunc='max'))
>>> aggregated.sort_index() # doctest: +NORMALIZE_WHITESPACE
b_max
A
1 2
2 4
>>> aggregated = df.groupby('A').agg(b_max=('B', 'max'), b_min=('B', 'min'))
>>> aggregated.sort_index() # doctest: +NORMALIZE_WHITESPACE
b_max b_min
A
1 2 1
2 4 3
>>> aggregated = df.groupby('A').agg(b_max=('B', 'max'), c_min=('C', 'min'))
>>> aggregated.sort_index() # doctest: +NORMALIZE_WHITESPACE
b_max c_min
A
1 2 0.227
2 4 -0.562
"""
# I think current implementation of func and arguments in pandas-on-Spark for aggregate
# is different than pandas, later once arguments are added, this could be removed.
if func_or_funcs is None and kwargs is None:
raise ValueError("No aggregation argument or function specified.")
relabeling = func_or_funcs is None and is_multi_agg_with_relabel(**kwargs)
if relabeling:
(
func_or_funcs,
columns,
order,
) = normalize_keyword_aggregation( # type: ignore[assignment]
kwargs
)
if not isinstance(func_or_funcs, (str, list)):
if not isinstance(func_or_funcs, dict) or not all(
is_name_like_value(key)
and (
isinstance(value, str)
or isinstance(value, list)
and all(isinstance(v, str) for v in value)
)
for key, value in func_or_funcs.items()
):
raise ValueError(
"aggs must be a dict mapping from column name "
"to aggregate functions (string or list of strings)."
)
else:
agg_cols = [col.name for col in self._agg_columns]
func_or_funcs = {col: func_or_funcs for col in agg_cols}
psdf: DataFrame = DataFrame(
GroupBy._spark_groupby(self._psdf, func_or_funcs, self._groupkeys)
)
if self._dropna:
psdf = DataFrame(
psdf._internal.with_new_sdf(
psdf._internal.spark_frame.dropna(
subset=psdf._internal.index_spark_column_names
)
)
)
if not self._as_index:
index_cols = psdf._internal.column_labels
should_drop_index = set(
i for i, gkey in enumerate(self._groupkeys) if gkey._psdf is not self._psdf
)
if len(should_drop_index) > 0:
drop = not any(
[
isinstance(func_or_funcs[gkey.name], list)
for gkey in self._groupkeys
if gkey.name in func_or_funcs
]
)
psdf = psdf.reset_index(level=should_drop_index, drop=drop)
if len(should_drop_index) < len(self._groupkeys):
psdf = psdf.reset_index()
index_cols = [c for c in psdf._internal.column_labels if c not in index_cols]
if relabeling:
psdf = psdf[pd.Index(index_cols + list(order))]
psdf.columns = pd.Index([c[0] for c in index_cols] + list(columns))
if relabeling and self._as_index:
psdf = psdf[order]
psdf.columns = columns # type: ignore[assignment]
return psdf
agg = aggregate
@staticmethod
def _spark_groupby(
psdf: DataFrame,
func: Mapping[Name, Union[str, List[str]]],
groupkeys: Sequence[Series] = (),
) -> InternalFrame:
groupkey_names = [SPARK_INDEX_NAME_FORMAT(i) for i in range(len(groupkeys))]
groupkey_scols = [s.spark.column.alias(name) for s, name in zip(groupkeys, groupkey_names)]
multi_aggs = any(isinstance(v, list) for v in func.values())
reordered = []
data_columns = []
column_labels = []
for key, value in func.items():
label = key if is_name_like_tuple(key) else (key,)
if len(label) != psdf._internal.column_labels_level:
raise TypeError("The length of the key must be the same as the column label level.")
for aggfunc in [value] if isinstance(value, str) else value:
column_label = tuple(list(label) + [aggfunc]) if multi_aggs else label
column_labels.append(column_label)
data_col = name_like_string(column_label)
data_columns.append(data_col)
col_name = psdf._internal.spark_column_name_for(label)
if aggfunc == "nunique":
reordered.append(
F.expr("count(DISTINCT `{0}`) as `{1}`".format(col_name, data_col))
)
# Implement "quartiles" aggregate function for ``describe``.
elif aggfunc == "quartiles":
reordered.append(
F.expr(
"percentile_approx(`{0}`, array(0.25, 0.5, 0.75)) as `{1}`".format(
col_name, data_col
)
)
)
else:
reordered.append(
F.expr("{1}(`{0}`) as `{2}`".format(col_name, aggfunc, data_col))
)
sdf = psdf._internal.spark_frame.select(groupkey_scols + psdf._internal.data_spark_columns)
sdf = sdf.groupby(*groupkey_names).agg(*reordered)
return InternalFrame(
spark_frame=sdf,
index_spark_columns=[scol_for(sdf, col) for col in groupkey_names],
index_names=[psser._column_label for psser in groupkeys],
index_fields=[
psser._internal.data_fields[0].copy(name=name)
for psser, name in zip(groupkeys, groupkey_names)
],
column_labels=column_labels,
data_spark_columns=[scol_for(sdf, col) for col in data_columns],
)
def count(self) -> FrameLike:
"""
Compute count of group, excluding missing values.
See Also
--------
pyspark.pandas.Series.groupby
pyspark.pandas.DataFrame.groupby
Examples
--------
>>> df = ps.DataFrame({'A': [1, 1, 2, 1, 2],
... 'B': [np.nan, 2, 3, 4, 5],
... 'C': [1, 2, 1, 1, 2]}, columns=['A', 'B', 'C'])
>>> df.groupby('A').count().sort_index() # doctest: +NORMALIZE_WHITESPACE
B C
A
1 2 3
2 2 2
"""
return self._reduce_for_stat_function(F.count)
def first(self, numeric_only: Optional[bool] = False, min_count: int = -1) -> FrameLike:
"""
Compute first of group values.
.. versionadded:: 3.3.0
Parameters
----------
numeric_only : bool, default False
Include only float, int, boolean columns. If None, will attempt to use
everything, then use only numeric data.
.. versionadded:: 3.4.0
min_count : int, default -1
The required number of valid values to perform the operation. If fewer
than ``min_count`` non-NA values are present the result will be NA.
.. versionadded:: 3.4.0
See Also
--------
pyspark.pandas.Series.groupby
pyspark.pandas.DataFrame.groupby
Examples
--------
>>> df = ps.DataFrame({"A": [1, 2, 1, 2], "B": [True, False, False, True],
... "C": [3, 3, 4, 4], "D": ["a", "b", "a", "a"]})
>>> df
A B C D
0 1 True 3 a
1 2 False 3 b
2 1 False 4 a
3 2 True 4 a
>>> df.groupby("A").first().sort_index()
B C D
A
1 True 3 a
2 False 3 b
Include only float, int, boolean columns when set numeric_only True.
>>> df.groupby("A").first(numeric_only=True).sort_index()
B C
A
1 True 3
2 False 3
>>> df.groupby("D").first().sort_index()
A B C
D
a 1 True 3
b 2 False 3
>>> df.groupby("D").first(min_count=3).sort_index()
A B C
D
a 1.0 True 3.0
b NaN None NaN
"""
if not isinstance(min_count, int):
raise TypeError("min_count must be integer")
return self._reduce_for_stat_function(
lambda col: F.first(col, ignorenulls=True),
accepted_spark_types=(NumericType, BooleanType) if numeric_only else None,
min_count=min_count,
)
def last(self, numeric_only: Optional[bool] = False, min_count: int = -1) -> FrameLike:
"""
Compute last of group values.
.. versionadded:: 3.3.0
Parameters
----------
numeric_only : bool, default False
Include only float, int, boolean columns. If None, will attempt to use
everything, then use only numeric data.
.. versionadded:: 3.4.0
min_count : int, default -1
The required number of valid values to perform the operation. If fewer
than ``min_count`` non-NA values are present the result will be NA.
.. versionadded:: 3.4.0
See Also
--------
pyspark.pandas.Series.groupby
pyspark.pandas.DataFrame.groupby
Examples
--------
>>> df = ps.DataFrame({"A": [1, 2, 1, 2], "B": [True, False, False, True],
... "C": [3, 3, 4, 4], "D": ["a", "a", "b", "a"]})
>>> df
A B C D
0 1 True 3 a
1 2 False 3 a
2 1 False 4 b
3 2 True 4 a
>>> df.groupby("A").last().sort_index()
B C D
A
1 False 4 b
2 True 4 a
Include only float, int, boolean columns when set numeric_only True.
>>> df.groupby("A").last(numeric_only=True).sort_index()
B C
A
1 False 4
2 True 4
>>> df.groupby("D").last().sort_index()
A B C
D
a 2 True 4
b 1 False 4
>>> df.groupby("D").last(min_count=3).sort_index()
A B C
D
a 2.0 True 4.0
b NaN None NaN
"""
if not isinstance(min_count, int):
raise TypeError("min_count must be integer")
return self._reduce_for_stat_function(
lambda col: F.last(col, ignorenulls=True),
accepted_spark_types=(NumericType, BooleanType) if numeric_only else None,
min_count=min_count,
)
def max(self, numeric_only: Optional[bool] = False, min_count: int = -1) -> FrameLike:
"""
Compute max of group values.
.. versionadded:: 3.3.0
Parameters
----------
numeric_only : bool, default False
Include only float, int, boolean columns. If None, will attempt to use
everything, then use only numeric data.
.. versionadded:: 3.4.0
min_count : bool, default -1
The required number of valid values to perform the operation. If fewer
than min_count non-NA values are present the result will be NA.
.. versionadded:: 3.4.0
See Also
--------
pyspark.pandas.Series.groupby
pyspark.pandas.DataFrame.groupby
Examples
--------
>>> df = ps.DataFrame({"A": [1, 2, 1, 2], "B": [True, False, False, True],
... "C": [3, 4, 3, 4], "D": ["a", "a", "b", "a"]})
>>> df.groupby("A").max().sort_index()
B C D
A
1 True 3 b
2 True 4 a
Include only float, int, boolean columns when set numeric_only True.
>>> df.groupby("A").max(numeric_only=True).sort_index()
B C
A
1 True 3
2 True 4
>>> df.groupby("D").max().sort_index()
A B C
D
a 2 True 4
b 1 False 3
>>> df.groupby("D").max(min_count=3).sort_index()
A B C
D
a 2.0 True 4.0
b NaN None NaN
"""
if not isinstance(min_count, int):
raise TypeError("min_count must be integer")
return self._reduce_for_stat_function(
F.max,
accepted_spark_types=(NumericType, BooleanType) if numeric_only else None,
min_count=min_count,
)
def mean(self, numeric_only: Optional[bool] = False) -> FrameLike:
"""
Compute mean of groups, excluding missing values.
Parameters
----------
numeric_only : bool, default False
Include only float, int, boolean columns.
.. versionadded:: 3.4.0
.. versionchanged:: 4.0.0
Returns
-------
pyspark.pandas.Series or pyspark.pandas.DataFrame
See Also
--------
pyspark.pandas.Series.groupby
pyspark.pandas.DataFrame.groupby
Examples
--------
>>> df = ps.DataFrame({'A': [1, 1, 2, 1, 2],
... 'B': [np.nan, 2, 3, 4, 5],
... 'C': [1, 2, 1, 1, 2],
... 'D': [True, False, True, False, True]})
Groupby one column and return the mean of the remaining columns in
each group.
>>> df.groupby('A').mean().sort_index() # doctest: +NORMALIZE_WHITESPACE
B C D
A
1 3.0 1.333333 0.333333
2 4.0 1.500000 1.000000
"""
self._validate_agg_columns(numeric_only=numeric_only, function_name="median")
return self._reduce_for_stat_function(
F.mean, accepted_spark_types=(NumericType,), bool_to_numeric=True
)
# TODO: 'q' accepts list like type
def quantile(self, q: float = 0.5, accuracy: int = 10000) -> FrameLike:
"""
Return group values at the given quantile.
.. versionadded:: 3.4.0
Parameters
----------
q : float, default 0.5 (50% quantile)
Value between 0 and 1 providing the quantile to compute.
accuracy : int, optional
Default accuracy of approximation. Larger value means better accuracy.
The relative error can be deduced by 1.0 / accuracy.
This is a panda-on-Spark specific parameter.
Returns
-------
pyspark.pandas.Series or pyspark.pandas.DataFrame
Return type determined by caller of GroupBy object.
Notes
-----
`quantile` in pandas-on-Spark are using distributed percentile approximation
algorithm unlike pandas, the result might be different with pandas, also
`interpolation` parameter is not supported yet.
See Also
--------
pyspark.pandas.Series.quantile
pyspark.pandas.DataFrame.quantile
pyspark.sql.functions.percentile_approx
Examples
--------
>>> df = ps.DataFrame([
... ['a', 1], ['a', 2], ['a', 3],
... ['b', 1], ['b', 3], ['b', 5]
... ], columns=['key', 'val'])
Groupby one column and return the quantile of the remaining columns in
each group.
>>> df.groupby('key').quantile()
val
key
a 2.0
b 3.0
"""
if is_list_like(q):
raise NotImplementedError("q doesn't support for list like type for now")
if not is_number(q):
raise TypeError("must be real number, not %s" % type(q).__name__)
if not 0 <= q <= 1:
raise ValueError("'q' must be between 0 and 1. Got '%s' instead" % q)
if any(isinstance(_agg_col.spark.data_type, BooleanType) for _agg_col in self._agg_columns):
warnings.warn(
f"Allowing bool dtype in {self.__class__.__name__}.quantile is deprecated "
"and will raise in a future version, matching the Series/DataFrame behavior. "
"Cast to uint8 dtype before calling quantile instead.",
FutureWarning,
)
return self._reduce_for_stat_function(
lambda col: F.percentile_approx(col.cast(DoubleType()), q, accuracy),
accepted_spark_types=(NumericType, BooleanType),
bool_to_numeric=True,
)
def min(self, numeric_only: Optional[bool] = False, min_count: int = -1) -> FrameLike:
"""
Compute min of group values.
.. versionadded:: 3.3.0
Parameters
----------
numeric_only : bool, default False
Include only float, int, boolean columns. If None, will attempt to use
everything, then use only numeric data.
.. versionadded:: 3.4.0
min_count : bool, default -1
The required number of valid values to perform the operation. If fewer
than min_count non-NA values are present the result will be NA.
.. versionadded:: 3.4.0
See Also
--------
pyspark.pandas.Series.groupby
pyspark.pandas.DataFrame.groupby
Examples
--------
>>> df = ps.DataFrame({"A": [1, 2, 1, 2], "B": [True, False, False, True],
... "C": [3, 4, 3, 4], "D": ["a", "a", "b", "a"]})
>>> df.groupby("A").min().sort_index()
B C D
A
1 False 3 a
2 False 4 a
Include only float, int, boolean columns when set numeric_only True.
>>> df.groupby("A").min(numeric_only=True).sort_index()
B C
A
1 False 3
2 False 4
>>> df.groupby("D").min().sort_index()
A B C
D
a 1 False 3
b 1 False 3
>>> df.groupby("D").min(min_count=3).sort_index()
A B C
D
a 1.0 False 3.0
b NaN None NaN
"""
if not isinstance(min_count, int):
raise TypeError("min_count must be integer")
return self._reduce_for_stat_function(
F.min,
accepted_spark_types=(NumericType, BooleanType) if numeric_only else None,
min_count=min_count,
)
# TODO: sync the doc.
def std(self, ddof: int = 1) -> FrameLike:
"""
Compute standard deviation of groups, excluding missing values.
.. versionadded:: 3.3.0
Parameters
----------
ddof : int, default 1
Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
where N represents the number of elements.
.. versionchanged:: 3.4.0
Supported including arbitary integers.
Examples
--------
>>> df = ps.DataFrame({"A": [1, 2, 1, 2], "B": [True, False, False, True],
... "C": [3, 4, 3, 4], "D": ["a", "b", "b", "a"]})
>>> df.groupby("A").std()
B C
A
1 0.707107 0.0
2 0.707107 0.0
See Also
--------
pyspark.pandas.Series.groupby
pyspark.pandas.DataFrame.groupby
"""
if not isinstance(ddof, int):
raise TypeError("ddof must be integer")
# Raise the TypeError when all aggregation columns are of unaccepted data types
any_accepted = any(
isinstance(_agg_col.spark.data_type, (NumericType, BooleanType))
for _agg_col in self._agg_columns
)
if not any_accepted:
raise TypeError(
"Unaccepted data types of aggregation columns; numeric or bool expected."
)
def std(col: Column) -> Column:
return SF.stddev(col, ddof)
return self._reduce_for_stat_function(
std,
accepted_spark_types=(NumericType,),
bool_to_numeric=True,
)
def sum(self, numeric_only: bool = False, min_count: int = 0) -> FrameLike:
"""
Compute sum of group values
.. versionadded:: 3.3.0
Parameters
----------
numeric_only : bool, default False
Include only float, int, boolean columns.
.. versionadded:: 3.4.0
.. versionchanged:: 4.0.0
min_count : int, default 0
The required number of valid values to perform the operation.
If fewer than min_count non-NA values are present the result will be NA.
.. versionadded:: 3.4.0
Examples
--------
>>> df = ps.DataFrame({"A": [1, 2, 1, 2], "B": [True, False, False, True],
... "C": [3, 4, 3, 4], "D": ["a", "a", "b", "a"]})
>>> df.groupby("A").sum().sort_index()
B C D
A
1 1 6 ab
2 1 8 aa
>>> df.groupby("D").sum().sort_index()
A B C
D
a 5 2 11
b 1 0 3
>>> df.groupby("D").sum(min_count=3).sort_index()
A B C
D
a 5.0 2.0 11.0
b NaN NaN NaN
Notes
-----
There is a behavior difference between pandas-on-Spark and pandas:
* when there is a non-numeric aggregation column, it will be ignored
even if `numeric_only` is False.
See Also
--------
pyspark.pandas.Series.groupby
pyspark.pandas.DataFrame.groupby
"""
if numeric_only is not None and not isinstance(numeric_only, bool):
raise TypeError("numeric_only must be None or bool")
if not isinstance(min_count, int):
raise TypeError("min_count must be integer")
if numeric_only is not None and not numeric_only:
unsupported = [
col.name
for col in self._agg_columns
if not isinstance(col.spark.data_type, (NumericType, BooleanType, StringType))
]
if len(unsupported) > 0:
log_advice(
"GroupBy.sum() can only support numeric, bool and string columns even if"
f"numeric_only=False, skip unsupported columns: {unsupported}"
)
return self._reduce_for_stat_function(
F.sum,
accepted_spark_types=(NumericType, BooleanType, StringType),
bool_to_numeric=True,
min_count=min_count,
)
# TODO: sync the doc.
def var(self, ddof: int = 1, numeric_only: bool = False) -> FrameLike:
"""
Compute variance of groups, excluding missing values.
.. versionadded:: 3.3.0
Parameters
----------
ddof : int, default 1
Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
where N represents the number of elements.
.. versionchanged:: 3.4.0
Supported including arbitary integers.
numeric_only : bool, default False
Include only float, int, boolean columns.
.. versionadded:: 4.0.0
Examples
--------
>>> df = ps.DataFrame({"A": [1, 2, 1, 2], "B": [True, False, False, True],
... "C": [3, 4, 3, 4], "D": ["a", "b", "b", "a"]})
>>> df.groupby("A").var()
B C
A
1 0.5 0.0
2 0.5 0.0
See Also
--------
pyspark.pandas.Series.groupby
pyspark.pandas.DataFrame.groupby
"""
if not isinstance(ddof, int):
raise TypeError("ddof must be integer")
def var(col: Column) -> Column:
return SF.var(col, ddof)
return self._reduce_for_stat_function(
var,
accepted_spark_types=(NumericType,),
bool_to_numeric=True,
numeric_only=numeric_only,
)
def skew(self) -> FrameLike:
"""
Compute skewness of groups, excluding missing values.
.. versionadded:: 3.4.0
Examples
--------
>>> df = ps.DataFrame({"A": [1, 2, 1, 1], "B": [True, False, False, True],
... "C": [3, 4, 3, 4], "D": ["a", "b", "b", "a"]})
>>> df.groupby("A").skew()
B C
A
1 -1.732051 1.732051
2 NaN NaN
See Also
--------
pyspark.pandas.Series.groupby
pyspark.pandas.DataFrame.groupby
"""
return self._reduce_for_stat_function(
SF.skew,
accepted_spark_types=(NumericType,),
bool_to_numeric=True,
)
def sem(self, ddof: int = 1) -> FrameLike:
"""
Compute standard error of the mean of groups, excluding missing values.
.. versionadded:: 3.4.0
Parameters
----------
ddof : int, default 1
Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
where N represents the number of elements.
Examples
--------
>>> df = ps.DataFrame({"A": [1, 2, 1, 1], "B": [True, False, False, True],
... "C": [3, None, 3, 4], "D": ["a", "b", "b", "a"]})
>>> df.groupby("A").sem()
B C
A
1 0.333333 0.333333
2 NaN NaN
>>> df.groupby("D").sem(ddof=1)
A B C
D
a 0.0 0.0 0.5
b 0.5 0.0 NaN
>>> df.B.groupby(df.A).sem()
A
1 0.333333
2 NaN
Name: B, dtype: float64
See Also
--------
pyspark.pandas.Series.sem
pyspark.pandas.DataFrame.sem
"""
if not isinstance(ddof, int):
raise TypeError("ddof must be integer")
# Raise the TypeError when all aggregation columns are of unaccepted data types
any_accepted = any(
isinstance(_agg_col.spark.data_type, (NumericType, BooleanType))
for _agg_col in self._agg_columns
)
if not any_accepted:
raise TypeError(
"Unaccepted data types of aggregation columns; numeric or bool expected."
)
def sem(col: Column) -> Column:
return SF.stddev(col, ddof) / F.sqrt(F.count(col))
return self._reduce_for_stat_function(
sem,
accepted_spark_types=(NumericType, BooleanType),
bool_to_numeric=True,
)
# TODO: 1, 'n' accepts list and slice; 2, implement 'dropna' parameter
def nth(self, n: int) -> FrameLike:
"""
Take the nth row from each group.
.. versionadded:: 3.4.0
Parameters
----------
n : int
A single nth value for the row
Returns
-------
Series or DataFrame
Notes
-----
There is a behavior difference between pandas-on-Spark and pandas:
* when there is no aggregation column, and `n` not equal to 0 or -1,
the returned empty dataframe may have an index with different lenght `__len__`.
Examples
--------
>>> import numpy as np
>>> df = ps.DataFrame({'A': [1, 1, 2, 1, 2],
... 'B': [np.nan, 2, 3, 4, 5]}, columns=['A', 'B'])
>>> g = df.groupby('A')
>>> g.nth(0)
A B
0 1 NaN
2 2 3.0
>>> g.nth(1)
A B
1 1 2.0
4 2 5.0
>>> g.nth(-1)
A B
3 1 4.0
4 2 5.0
See Also
--------
pyspark.pandas.Series.groupby
pyspark.pandas.DataFrame.groupby
"""
if isinstance(n, slice) or is_list_like(n):
raise NotImplementedError("n doesn't support slice or list for now")
if not isinstance(n, int):
raise TypeError("Invalid index %s" % type(n).__name__)
groupkey_names: List[str] = [str(groupkey.name) for groupkey in self._groupkeys]
psdf = self._psdf
internal = psdf._internal
sdf = internal.spark_frame
if len(psdf._internal.column_labels) > 0:
window1 = Window.partitionBy(*groupkey_names).orderBy(NATURAL_ORDER_COLUMN_NAME)
tmp_row_number_col = verify_temp_column_name(sdf, "__tmp_row_number_col__")
if n >= 0:
sdf = (
psdf._internal.spark_frame.withColumn(
tmp_row_number_col, F.row_number().over(window1)
)
.where(F.col(tmp_row_number_col) == n + 1)
.drop(tmp_row_number_col)
)
else:
window2 = Window.partitionBy(*groupkey_names).rowsBetween(
Window.unboundedPreceding, Window.unboundedFollowing
)
tmp_group_size_col = verify_temp_column_name(sdf, "__tmp_group_size_col__")
sdf = (
psdf._internal.spark_frame.withColumn(
tmp_group_size_col, F.count(F.lit(0)).over(window2)
)
.withColumn(tmp_row_number_col, F.row_number().over(window1))
.where(F.col(tmp_row_number_col) == F.col(tmp_group_size_col) + 1 + n)
.drop(tmp_group_size_col, tmp_row_number_col)
)
else:
sdf = sdf.select(*groupkey_names).distinct()
agg_columns = []
if not self._agg_columns_selected:
for psser in self._groupkeys:
agg_columns.append(psser)
for psser in self._agg_columns:
agg_columns.append(psser)
internal = InternalFrame(
spark_frame=sdf,
index_spark_columns=[scol_for(sdf, col) for col in internal.index_spark_column_names],
index_names=internal.index_names,
index_fields=internal.index_fields,
data_spark_columns=[
scol_for(sdf, psser._internal.data_spark_column_names[0]) for psser in agg_columns
],
column_labels=[psser._column_label for psser in agg_columns],
data_fields=[psser._internal.data_fields[0] for psser in agg_columns],
column_label_names=self._psdf._internal.column_label_names,
)
agg_column_names = (
[str(agg_column.name) for agg_column in self._agg_columns]
if self._agg_columns_selected
else None
)
return self._prepare_return(DataFrame(internal), agg_column_names=agg_column_names)
def prod(self, numeric_only: bool = False, min_count: int = 0) -> FrameLike:
"""
Compute prod of groups.
.. versionadded:: 3.4.0
Parameters
----------
numeric_only : bool, default False
Include only float, int, boolean columns.
.. versionchanged:: 4.0.0
min_count : int, default 0
The required number of valid values to perform the operation.
If fewer than min_count non-NA values are present the result will be NA.
Returns
-------
Series or DataFrame
Computed prod of values within each group.
See Also
--------
pyspark.pandas.Series.groupby
pyspark.pandas.DataFrame.groupby
Examples
--------
>>> import numpy as np
>>> df = ps.DataFrame(
... {
... "A": [1, 1, 2, 1, 2],
... "B": [np.nan, 2, 3, 4, 5],
... "C": [1, 2, 1, 1, 2],
... "D": [True, False, True, False, True],
... }
... )
Groupby one column and return the prod of the remaining columns in
each group.
>>> df.groupby('A').prod().sort_index()
B C D
A
1 8.0 2 0
2 15.0 2 1
>>> df.groupby('A').prod(min_count=3).sort_index()
B C D
A
1 NaN 2.0 0.0
2 NaN NaN NaN
"""
if not isinstance(min_count, int):
raise TypeError("min_count must be integer")
self._validate_agg_columns(numeric_only=numeric_only, function_name="prod")
return self._reduce_for_stat_function(
lambda col: SF.product(col, True),
accepted_spark_types=(NumericType, BooleanType),
bool_to_numeric=True,
min_count=min_count,
)
def all(self, skipna: bool = True) -> FrameLike:
"""
Returns True if all values in the group are truthful, else False.
Parameters
----------
skipna : bool, default True
Flag to ignore NA(nan/null) values during truth testing.
See Also
--------
pyspark.pandas.Series.groupby
pyspark.pandas.DataFrame.groupby
Examples
--------
>>> df = ps.DataFrame({'A': [1, 1, 2, 2, 3, 3, 4, 4, 5, 5],
... 'B': [True, True, True, False, False,
... False, None, True, None, False]},
... columns=['A', 'B'])
>>> df
A B
0 1 True
1 1 True
2 2 True
3 2 False
4 3 False
5 3 False
6 4 None
7 4 True
8 5 None
9 5 False
>>> df.groupby('A').all().sort_index() # doctest: +NORMALIZE_WHITESPACE
B
A
1 True
2 False
3 False
4 True
5 False
>>> df.groupby('A').all(skipna=False).sort_index() # doctest: +NORMALIZE_WHITESPACE
B
A
1 True
2 False
3 False
4 False
5 False
"""
groupkey_names = [SPARK_INDEX_NAME_FORMAT(i) for i in range(len(self._groupkeys))]
internal, _, sdf = self._prepare_reduce(groupkey_names)
psdf: DataFrame = DataFrame(internal)
def sfun(scol: Column, scol_type: DataType) -> Column:
if isinstance(scol_type, NumericType) or skipna:
# np.nan takes no effect to the result; None takes no effect if `skipna`
all_col = F.min(F.coalesce(scol.cast("boolean"), F.lit(True)))
else:
# Take None as False when not `skipna`
all_col = F.min(F.when(scol.isNull(), F.lit(False)).otherwise(scol.cast("boolean")))
return all_col
if len(psdf._internal.column_labels) > 0:
stat_exprs = []
for label in psdf._internal.column_labels:
psser = psdf._psser_for(label)
stat_exprs.append(
sfun(
psser._dtype_op.nan_to_null(psser).spark.column, psser.spark.data_type
).alias(psser._internal.data_spark_column_names[0])
)
sdf = sdf.groupby(*groupkey_names).agg(*stat_exprs)
else:
sdf = sdf.select(*groupkey_names).distinct()
internal = internal.copy(
spark_frame=sdf,
index_spark_columns=[scol_for(sdf, col) for col in groupkey_names],
data_spark_columns=[scol_for(sdf, col) for col in internal.data_spark_column_names],
data_fields=None,
)
return self._prepare_return(DataFrame(internal))
# TODO: skipna should be implemented.
def any(self) -> FrameLike:
"""
Returns True if any value in the group is truthful, else False.
See Also
--------
pyspark.pandas.Series.groupby
pyspark.pandas.DataFrame.groupby
Examples
--------
>>> df = ps.DataFrame({'A': [1, 1, 2, 2, 3, 3, 4, 4, 5, 5],
... 'B': [True, True, True, False, False,
... False, None, True, None, False]},
... columns=['A', 'B'])
>>> df
A B
0 1 True
1 1 True
2 2 True
3 2 False
4 3 False
5 3 False
6 4 None
7 4 True
8 5 None
9 5 False
>>> df.groupby('A').any().sort_index() # doctest: +NORMALIZE_WHITESPACE
B
A
1 True
2 True
3 False
4 True
5 False
"""
return self._reduce_for_stat_function(
lambda col: F.max(F.coalesce(col.cast("boolean"), F.lit(False)))
)
# TODO: groupby multiply columns should be implemented.
def size(self) -> Series:
"""
Compute group sizes.
See Also
--------
pyspark.pandas.Series.groupby
pyspark.pandas.DataFrame.groupby
Examples
--------
>>> df = ps.DataFrame({'A': [1, 2, 2, 3, 3, 3],
... 'B': [1, 1, 2, 3, 3, 3]},
... columns=['A', 'B'])
>>> df
A B
0 1 1
1 2 1
2 2 2
3 3 3
4 3 3
5 3 3
>>> df.groupby('A').size().sort_index()
A
1 1
2 2
3 3
dtype: int64
>>> df.groupby(['A', 'B']).size().sort_index()
A B
1 1 1
2 1 1
2 1
3 3 3
dtype: int64
For Series,
>>> df.B.groupby(df.A).size().sort_index()
A
1 1
2 2
3 3
Name: B, dtype: int64
>>> df.groupby(df.A).B.size().sort_index()
A
1 1
2 2
3 3
Name: B, dtype: int64
"""
groupkeys = self._groupkeys
groupkey_names = [SPARK_INDEX_NAME_FORMAT(i) for i in range(len(groupkeys))]
groupkey_scols = [s.spark.column.alias(name) for s, name in zip(groupkeys, groupkey_names)]
sdf = self._psdf._internal.spark_frame.select(
groupkey_scols + self._psdf._internal.data_spark_columns
)
sdf = sdf.groupby(*groupkey_names).count()
internal = InternalFrame(
spark_frame=sdf,
index_spark_columns=[scol_for(sdf, col) for col in groupkey_names],
index_names=[psser._column_label for psser in groupkeys],
index_fields=[
psser._internal.data_fields[0].copy(name=name)
for psser, name in zip(groupkeys, groupkey_names)
],
column_labels=[None],
data_spark_columns=[scol_for(sdf, "count")],
)
return first_series(DataFrame(internal))
def diff(self, periods: int = 1) -> FrameLike:
"""
First discrete difference of element.
Calculates the difference of a DataFrame element compared with another element in the
DataFrame group (default is the element in the same column of the previous row).
Parameters
----------
periods : int, default 1
Periods to shift for calculating difference, accepts negative values.
Returns
-------
diffed : DataFrame or Series
See Also
--------
pyspark.pandas.Series.groupby
pyspark.pandas.DataFrame.groupby
Examples
--------
>>> df = ps.DataFrame({'a': [1, 2, 3, 4, 5, 6],
... 'b': [1, 1, 2, 3, 5, 8],
... 'c': [1, 4, 9, 16, 25, 36]}, columns=['a', 'b', 'c'])
>>> df
a b c
0 1 1 1
1 2 1 4
2 3 2 9
3 4 3 16
4 5 5 25
5 6 8 36
>>> df.groupby(['b']).diff().sort_index()
a c
0 NaN NaN
1 1.0 3.0
2 NaN NaN
3 NaN NaN
4 NaN NaN
5 NaN NaN
Difference with previous column in a group.
>>> df.groupby(['b'])['a'].diff().sort_index()
0 NaN
1 1.0
2 NaN
3 NaN
4 NaN
5 NaN
Name: a, dtype: float64
"""
return self._apply_series_op(
lambda sg: sg._psser._diff(periods, part_cols=sg._groupkeys_scols), should_resolve=True
)
def cumcount(self, ascending: bool = True) -> Series:
"""
Number each item in each group from 0 to the length of that group - 1.
Essentially this is equivalent to
.. code-block:: python
self.apply(lambda x: pd.Series(np.arange(len(x)), x.index))
Parameters
----------
ascending : bool, default True
If False, number in reverse, from length of group - 1 to 0.
Returns
-------
Series
Sequence number of each element within each group.
Examples
--------
>>> df = ps.DataFrame([['a'], ['a'], ['a'], ['b'], ['b'], ['a']],
... columns=['A'])
>>> df
A
0 a
1 a
2 a
3 b
4 b
5 a
>>> df.groupby('A').cumcount().sort_index()
0 0
1 1
2 2
3 0
4 1
5 3
dtype: int64
>>> df.groupby('A').cumcount(ascending=False).sort_index()
0 3
1 2
2 1
3 1
4 0
5 0
dtype: int64
"""
ret = (
self._groupkeys[0]
.rename()
.spark.transform(lambda _: F.lit(0))
._cum(F.count, True, part_cols=self._groupkeys_scols, ascending=ascending)
- 1
)
internal = ret._internal.resolved_copy
return first_series(DataFrame(internal))
def cummax(self) -> FrameLike:
"""
Cumulative max for each group.
Returns
-------
Series or DataFrame
See Also
--------
Series.cummax
DataFrame.cummax
Examples
--------
>>> df = ps.DataFrame(
... [[1, None, 4], [1, 0.1, 3], [1, 20.0, 2], [4, 10.0, 1]],
... columns=list('ABC'))
>>> df
A B C
0 1 NaN 4
1 1 0.1 3
2 1 20.0 2
3 4 10.0 1
By default, iterates over rows and finds the sum in each column.
>>> df.groupby("A").cummax().sort_index()
B C
0 NaN 4
1 0.1 4
2 20.0 4
3 10.0 1
It works as below in Series.
>>> df.C.groupby(df.A).cummax().sort_index()
0 4
1 4
2 4
3 1
Name: C, dtype: int64
"""
return self._apply_series_op(
lambda sg: sg._psser._cum(F.max, True, part_cols=sg._groupkeys_scols),
should_resolve=True,
numeric_only=True,
)
def cummin(self) -> FrameLike:
"""
Cumulative min for each group.
Returns
-------
Series or DataFrame
See Also
--------
Series.cummin
DataFrame.cummin
Examples
--------
>>> df = ps.DataFrame(
... [[1, None, 4], [1, 0.1, 3], [1, 20.0, 2], [4, 10.0, 1]],
... columns=list('ABC'))
>>> df
A B C
0 1 NaN 4
1 1 0.1 3
2 1 20.0 2
3 4 10.0 1
By default, iterates over rows and finds the sum in each column.
>>> df.groupby("A").cummin().sort_index()
B C
0 NaN 4
1 0.1 3
2 0.1 2
3 10.0 1
It works as below in Series.
>>> df.B.groupby(df.A).cummin().sort_index()
0 NaN
1 0.1
2 0.1
3 10.0
Name: B, dtype: float64
"""
return self._apply_series_op(
lambda sg: sg._psser._cum(F.min, True, part_cols=sg._groupkeys_scols),
should_resolve=True,
numeric_only=True,
)
def cumprod(self) -> FrameLike:
"""
Cumulative product for each group.
Returns
-------
Series or DataFrame
See Also
--------
Series.cumprod
DataFrame.cumprod
Examples
--------
>>> df = ps.DataFrame(
... [[1, None, 4], [1, 0.1, 3], [1, 20.0, 2], [4, 10.0, 1]],
... columns=list('ABC'))
>>> df
A B C
0 1 NaN 4
1 1 0.1 3
2 1 20.0 2
3 4 10.0 1
By default, iterates over rows and finds the sum in each column.
>>> df.groupby("A").cumprod().sort_index()
B C
0 NaN 4
1 0.1 12
2 2.0 24
3 10.0 1
It works as below in Series.
>>> df.B.groupby(df.A).cumprod().sort_index()
0 NaN
1 0.1
2 2.0
3 10.0
Name: B, dtype: float64
"""
return self._apply_series_op(
lambda sg: sg._psser._cumprod(True, part_cols=sg._groupkeys_scols),
should_resolve=True,
numeric_only=True,
)
def cumsum(self) -> FrameLike:
"""
Cumulative sum for each group.
Returns
-------
Series or DataFrame
See Also
--------
Series.cumsum
DataFrame.cumsum
Examples
--------
>>> df = ps.DataFrame(
... [[1, None, 4], [1, 0.1, 3], [1, 20.0, 2], [4, 10.0, 1]],
... columns=list('ABC'))
>>> df
A B C
0 1 NaN 4
1 1 0.1 3
2 1 20.0 2
3 4 10.0 1
By default, iterates over rows and finds the sum in each column.
>>> df.groupby("A").cumsum().sort_index()
B C
0 NaN 4
1 0.1 7
2 20.1 9
3 10.0 1
It works as below in Series.
>>> df.B.groupby(df.A).cumsum().sort_index()
0 NaN
1 0.1
2 20.1
3 10.0
Name: B, dtype: float64
"""
return self._apply_series_op(
lambda sg: sg._psser._cumsum(True, part_cols=sg._groupkeys_scols),
should_resolve=True,
numeric_only=True,
)
def apply(self, func: Callable, *args: Any, **kwargs: Any) -> Union[DataFrame, Series]:
"""
Apply function `func` group-wise and combine the results together.
The function passed to `apply` must take a DataFrame as its first
argument and return a DataFrame. `apply` will
then take care of combining the results back together into a single
dataframe. `apply` is therefore a highly flexible
grouping method.
While `apply` is a very flexible method, its downside is that
using it can be quite a bit slower than using more specific methods
like `agg` or `transform`. pandas-on-Spark offers a wide range of method that will
be much faster than using `apply` for their specific purposes, so try to
use them before reaching for `apply`.
.. note:: this API executes the function once to infer the type which is
potentially expensive, for instance, when the dataset is created after
aggregations or sorting.
To avoid this, specify return type in ``func``, for instance, as below:
>>> def pandas_div(x) -> ps.DataFrame[int, [float, float]]:
... return x[['B', 'C']] / x[['B', 'C']]
If the return type is specified, the output column names become
`c0, c1, c2 ... cn`. These names are positionally mapped to the returned
DataFrame in ``func``.
To specify the column names, you can assign them in a NumPy compound type style
as below:
>>> def pandas_div(x) -> ps.DataFrame[("index", int), [("a", float), ("b", float)]]:
... return x[['B', 'C']] / x[['B', 'C']]
>>> pdf = pd.DataFrame({'B': [1.], 'C': [3.]})
>>> def plus_one(x) -> ps.DataFrame[
... (pdf.index.name, pdf.index.dtype), zip(pdf.columns, pdf.dtypes)]:
... return x[['B', 'C']] / x[['B', 'C']]
.. note:: the dataframe within ``func`` is actually a pandas dataframe. Therefore,
any pandas API within this function is allowed.
Parameters
----------
func : callable
A callable that takes a DataFrame as its first argument, and
returns a dataframe.
*args
Positional arguments to pass to func.
**kwargs
Keyword arguments to pass to func.
Returns
-------
applied : DataFrame or Series
See Also
--------
aggregate : Apply aggregate function to the GroupBy object.
DataFrame.apply : Apply a function to a DataFrame.
Series.apply : Apply a function to a Series.
Examples
--------
>>> df = ps.DataFrame({'A': 'a a b'.split(),
... 'B': [1, 2, 3],
... 'C': [4, 6, 5]}, columns=['A', 'B', 'C'])
>>> g = df.groupby('A')
Notice that ``g`` has two groups, ``a`` and ``b``.
Calling `apply` in various ways, we can get different grouping results:
Below the functions passed to `apply` takes a DataFrame as
its argument and returns a DataFrame. `apply` combines the result for
each group together into a new DataFrame:
>>> def plus_min(x):
... return x + x.min()
>>> g.apply(plus_min).sort_index() # doctest: +SKIP
A B C
0 aa 2 8
1 aa 3 10
2 bb 6 10
>>> g.apply(sum).sort_index() # doctest: +NORMALIZE_WHITESPACE
A B C
A
a aa 3 10
b b 3 5
>>> g.apply(len).sort_index() # doctest: +NORMALIZE_WHITESPACE
A
a 2
b 1
dtype: int64
You can specify the type hint and prevent schema inference for better performance.
>>> def pandas_div(x) -> ps.DataFrame[int, [float, float]]:
... return x[['B', 'C']] / x[['B', 'C']]
>>> g.apply(pandas_div).sort_index() # doctest: +SKIP
c0 c1
0 1.0 1.0
1 1.0 1.0
2 1.0 1.0
>>> def pandas_div(x) -> ps.DataFrame[("index", int), [("f1", float), ("f2", float)]]:
... return x[['B', 'C']] / x[['B', 'C']]
>>> g.apply(pandas_div).sort_index() # doctest: +SKIP
f1 f2
index
0 1.0 1.0
1 1.0 1.0
2 1.0 1.0
In case of Series, it works as below.
>>> def plus_max(x) -> ps.Series[int]:
... return x + x.max()
>>> df.B.groupby(df.A).apply(plus_max).sort_index() # doctest: +SKIP
0 6
1 3
2 4
Name: B, dtype: int64
>>> def plus_min(x):
... return x + x.min()
>>> df.B.groupby(df.A).apply(plus_min).sort_index() # doctest: +SKIP
0 2
1 3
2 6
Name: B, dtype: int64
You can also return a scalar value as an aggregated value of the group:
>>> def plus_length(x) -> int:
... return len(x)
>>> df.B.groupby(df.A).apply(plus_length).sort_index() # doctest: +SKIP
0 1
1 2
Name: B, dtype: int64
The extra arguments to the function can be passed as below.
>>> def calculation(x, y, z) -> int:
... return len(x) + y * z
>>> df.B.groupby(df.A).apply(calculation, 5, z=10).sort_index() # doctest: +SKIP
0 51
1 52
Name: B, dtype: int64
"""
if not callable(func):
raise TypeError("%s object is not callable" % type(func).__name__)
spec = inspect.getfullargspec(func)
return_sig = spec.annotations.get("return", None)
should_infer_schema = return_sig is None
should_retain_index = should_infer_schema
is_series_groupby = isinstance(self, SeriesGroupBy)
psdf = self._psdf
if self._agg_columns_selected:
agg_columns = self._agg_columns
else:
agg_columns = [
psdf._psser_for(label)
for label in psdf._internal.column_labels
if label not in self._column_labels_to_exclude
]
psdf, groupkey_labels, groupkey_names = GroupBy._prepare_group_map_apply(
psdf, self._groupkeys, agg_columns
)
if is_series_groupby:
name = psdf.columns[-1]
pandas_apply = _builtin_table.get(func, func)
else:
f = _builtin_table.get(func, func)
def pandas_apply(pdf: pd.DataFrame, *a: Any, **k: Any) -> Any:
return f(pdf.drop(groupkey_names, axis=1), *a, **k)
should_return_series = False
if should_infer_schema:
# Here we execute with the first 1000 to get the return type.
log_advice(
"If the type hints is not specified for `groupby.apply`, "
"it is expensive to infer the data type internally."
)
limit = get_option("compute.shortcut_limit")
# Ensure sampling rows >= 2 to make sure apply's infer schema is accurate
# See related: https://github.com/pandas-dev/pandas/issues/46893
sample_limit = limit + 1 if limit else 2
pdf = psdf.head(sample_limit)._to_internal_pandas()
groupkeys = [
pdf[groupkey_name].rename(psser.name)
for groupkey_name, psser in zip(groupkey_names, self._groupkeys)
]
grouped = pdf.groupby(groupkeys)
if is_series_groupby:
pser_or_pdf = grouped[name].apply(pandas_apply, *args, **kwargs)
else:
pser_or_pdf = grouped.apply(pandas_apply, *args, **kwargs)
psser_or_psdf = ps.from_pandas(pser_or_pdf.infer_objects())
if len(pdf) <= limit:
if isinstance(psser_or_psdf, ps.Series) and is_series_groupby:
psser_or_psdf = psser_or_psdf.rename(cast(SeriesGroupBy, self)._psser.name)
return cast(Union[Series, DataFrame], psser_or_psdf)
if len(grouped) <= 1:
with warnings.catch_warnings():
warnings.simplefilter("always")
warnings.warn(
"The amount of data for return type inference might not be large enough. "
"Consider increasing an option `compute.shortcut_limit`."
)
if isinstance(psser_or_psdf, Series):
should_return_series = True
psdf_from_pandas = psser_or_psdf._psdf
else:
psdf_from_pandas = cast(DataFrame, psser_or_psdf)
index_fields = [
field.normalize_spark_type() for field in psdf_from_pandas._internal.index_fields
]
data_fields = [
field.normalize_spark_type() for field in psdf_from_pandas._internal.data_fields
]
return_schema = StructType([field.struct_field for field in index_fields + data_fields])
else:
return_type = infer_return_type(func)
if not is_series_groupby and isinstance(return_type, SeriesType):
raise TypeError(
"Series as a return type hint at frame groupby is not supported "
"currently; however got [%s]. Use DataFrame type hint instead." % return_sig
)
if isinstance(return_type, DataFrameType):
data_fields = return_type.data_fields
return_schema = return_type.spark_type
index_fields = return_type.index_fields
should_retain_index = len(index_fields) > 0
psdf_from_pandas = None
else:
should_return_series = True
dtype = cast(Union[SeriesType, ScalarType], return_type).dtype
spark_type = cast(Union[SeriesType, ScalarType], return_type).spark_type
if is_series_groupby:
data_fields = [
InternalField(
dtype=dtype, struct_field=StructField(name=name, dataType=spark_type)
)
]
else:
data_fields = [
InternalField(
dtype=dtype,
struct_field=StructField(
name=SPARK_DEFAULT_SERIES_NAME, dataType=spark_type
),
)
]
return_schema = StructType([field.struct_field for field in data_fields])
def pandas_groupby_apply(pdf: pd.DataFrame) -> pd.DataFrame:
if is_series_groupby:
pdf_or_ser = pdf.groupby(groupkey_names)[name].apply(pandas_apply, *args, **kwargs)
else:
pdf_or_ser = pdf.groupby(groupkey_names).apply(pandas_apply, *args, **kwargs)
if should_return_series and isinstance(pdf_or_ser, pd.DataFrame):
pdf_or_ser = pdf_or_ser.stack()
if not isinstance(pdf_or_ser, pd.DataFrame):
return pd.DataFrame(pdf_or_ser)
else:
return pdf_or_ser
sdf = GroupBy._spark_group_map_apply(
psdf,
pandas_groupby_apply,
[psdf._internal.spark_column_for(label) for label in groupkey_labels],
return_schema,
retain_index=should_retain_index,
)
if should_retain_index:
# If schema is inferred, we can restore indexes too.
if psdf_from_pandas is not None:
internal = psdf_from_pandas._internal.with_new_sdf(
spark_frame=sdf, index_fields=index_fields, data_fields=data_fields
)
else:
index_names: Optional[List[Optional[Tuple[Any, ...]]]] = None
index_spark_columns = [
scol_for(sdf, index_field.struct_field.name) for index_field in index_fields
]
if not any(
[
SPARK_INDEX_NAME_PATTERN.match(index_field.struct_field.name)
for index_field in index_fields
]
):
index_names = [(index_field.struct_field.name,) for index_field in index_fields]
internal = InternalFrame(
spark_frame=sdf,
index_names=index_names,
index_spark_columns=index_spark_columns,
index_fields=index_fields,
data_fields=data_fields,
)
else:
# Otherwise, it loses index.
internal = InternalFrame(
spark_frame=sdf, index_spark_columns=None, data_fields=data_fields
)
if should_return_series:
psser = first_series(DataFrame(internal))
if is_series_groupby:
psser = psser.rename(cast(SeriesGroupBy, self)._psser.name)
return psser
else:
return DataFrame(internal)
# TODO: implement 'dropna' parameter
def filter(self, func: Callable[[FrameLike], FrameLike]) -> FrameLike:
"""
Return a copy of a DataFrame excluding elements from groups that
do not satisfy the boolean criterion specified by func.
Parameters
----------
f : function
Function to apply to each subframe. Should return True or False.
dropna : Drop groups that do not pass the filter. True by default;
if False, groups that evaluate False are filled with NaNs.
Returns
-------
filtered : DataFrame or Series
Notes
-----
Each subframe is endowed the attribute 'name' in case you need to know
which group you are working on.
Examples
--------
>>> df = ps.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
... 'foo', 'bar'],
... 'B' : [1, 2, 3, 4, 5, 6],
... 'C' : [2.0, 5., 8., 1., 2., 9.]}, columns=['A', 'B', 'C'])
>>> grouped = df.groupby('A')
>>> grouped.filter(lambda x: x['B'].mean() > 3.)
A B C
1 bar 2 5.0
3 bar 4 1.0
5 bar 6 9.0
>>> df.B.groupby(df.A).filter(lambda x: x.mean() > 3.)
1 2
3 4
5 6
Name: B, dtype: int64
"""
if not callable(func):
raise TypeError("%s object is not callable" % type(func).__name__)
is_series_groupby = isinstance(self, SeriesGroupBy)
psdf = self._psdf
if self._agg_columns_selected:
agg_columns = self._agg_columns
else:
agg_columns = [
psdf._psser_for(label)
for label in psdf._internal.column_labels
if label not in self._column_labels_to_exclude
]
data_schema = (
psdf[agg_columns]._internal.resolved_copy.spark_frame.drop(*HIDDEN_COLUMNS).schema
)
psdf, groupkey_labels, groupkey_names = GroupBy._prepare_group_map_apply(
psdf, self._groupkeys, agg_columns
)
if is_series_groupby:
def pandas_filter(pdf: pd.DataFrame) -> pd.DataFrame:
return pd.DataFrame(pdf.groupby(groupkey_names)[pdf.columns[-1]].filter(func))
else:
f = _builtin_table.get(func, func)
def wrapped_func(pdf: pd.DataFrame) -> pd.DataFrame:
return f(pdf.drop(groupkey_names, axis=1))
def pandas_filter(pdf: pd.DataFrame) -> pd.DataFrame:
return pdf.groupby(groupkey_names).filter(wrapped_func).drop(groupkey_names, axis=1)
sdf = GroupBy._spark_group_map_apply(
psdf,
pandas_filter,
[psdf._internal.spark_column_for(label) for label in groupkey_labels],
data_schema,
retain_index=True,
)
psdf = DataFrame(self._psdf[agg_columns]._internal.with_new_sdf(sdf))
if is_series_groupby:
return cast(FrameLike, first_series(psdf))
else:
return cast(FrameLike, psdf)
@staticmethod
def _prepare_group_map_apply(
psdf: DataFrame, groupkeys: List[Series], agg_columns: List[Series]
) -> Tuple[DataFrame, List[Label], List[str]]:
groupkey_labels: List[Label] = [
verify_temp_column_name(psdf, "__groupkey_{}__".format(i))
for i in range(len(groupkeys))
]
psdf = psdf[[s.rename(label) for s, label in zip(groupkeys, groupkey_labels)] + agg_columns]
groupkey_names = [label if len(label) > 1 else label[0] for label in groupkey_labels]
return DataFrame(psdf._internal.resolved_copy), groupkey_labels, groupkey_names
@staticmethod
def _spark_group_map_apply(
psdf: DataFrame,
func: Callable[[pd.DataFrame], pd.DataFrame],
groupkeys_scols: List[Column],
return_schema: StructType,
retain_index: bool,
) -> SparkDataFrame:
output_func = GroupBy._make_pandas_df_builder_func(psdf, func, return_schema, retain_index)
sdf = psdf._internal.spark_frame.drop(*HIDDEN_COLUMNS)
return sdf.groupby(*groupkeys_scols).applyInPandas(output_func, return_schema)
@staticmethod
def _make_pandas_df_builder_func(
psdf: DataFrame,
func: Callable[[pd.DataFrame], pd.DataFrame],
return_schema: StructType,
retain_index: bool,
) -> Callable[[pd.DataFrame], pd.DataFrame]:
"""
Creates a function that can be used inside the pandas UDF. This function can construct
the same pandas DataFrame as if the pandas-on-Spark DataFrame is collected to driver side.
The index, column labels, etc. are re-constructed within the function.
"""
from pyspark.sql.utils import is_timestamp_ntz_preferred
arguments_for_restore_index = psdf._internal.arguments_for_restore_index
prefer_timestamp_ntz = is_timestamp_ntz_preferred()
def rename_output(pdf: pd.DataFrame) -> pd.DataFrame:
pdf = InternalFrame.restore_index(pdf.copy(), **arguments_for_restore_index)
pdf = func(pdf)
# If schema should be inferred, we don't restore the index. pandas seems to restore
# the index in some cases.
# When Spark output type is specified, without executing it, we don't know
# if we should restore the index or not. For instance, see the example in
# https://github.com/databricks/koalas/issues/628.
pdf, _, _, _, _ = InternalFrame.prepare_pandas_frame(
pdf, retain_index=retain_index, prefer_timestamp_ntz=prefer_timestamp_ntz
)
# Just positionally map the column names to given schema's.
pdf.columns = return_schema.names
return pdf
return rename_output
def rank(self, method: str = "average", ascending: bool = True) -> FrameLike:
"""
Provide the rank of values within each group.
Parameters
----------
method : {'average', 'min', 'max', 'first', 'dense'}, default 'average'
* average: average rank of group
* min: lowest rank in group
* max: highest rank in group
* first: ranks assigned in order they appear in the array
* dense: like 'min', but rank always increases by 1 between groups
ascending : boolean, default True
False for ranks by high (1) to low (N)
Returns
-------
DataFrame with ranking of values within each group
Examples
--------
>>> df = ps.DataFrame({
... 'a': [1, 1, 1, 2, 2, 2, 3, 3, 3],
... 'b': [1, 2, 2, 2, 3, 3, 3, 4, 4]}, columns=['a', 'b'])
>>> df
a b
0 1 1
1 1 2
2 1 2
3 2 2
4 2 3
5 2 3
6 3 3
7 3 4
8 3 4
>>> df.groupby("a").rank().sort_index()
b
0 1.0
1 2.5
2 2.5
3 1.0
4 2.5
5 2.5
6 1.0
7 2.5
8 2.5
>>> df.b.groupby(df.a).rank(method='max').sort_index()
0 1.0
1 3.0
2 3.0
3 1.0
4 3.0
5 3.0
6 1.0
7 3.0
8 3.0
Name: b, dtype: float64
"""
return self._apply_series_op(
lambda sg: sg._psser._rank(method, ascending, part_cols=sg._groupkeys_scols),
should_resolve=True,
)
def idxmax(self, skipna: bool = True) -> FrameLike:
"""
Return index of first occurrence of maximum over requested axis in group.
NA/null values are excluded.
Parameters
----------
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result will be NA.
See Also
--------
Series.idxmax
DataFrame.idxmax
pyspark.pandas.Series.groupby
pyspark.pandas.DataFrame.groupby
Examples
--------
>>> df = ps.DataFrame({'a': [1, 1, 2, 2, 3],
... 'b': [1, 2, 3, 4, 5],
... 'c': [5, 4, 3, 2, 1]}, columns=['a', 'b', 'c'])
>>> df.groupby(['a'])['b'].idxmax().sort_index() # doctest: +NORMALIZE_WHITESPACE
a
1 1
2 3
3 4
Name: b, dtype: int64
>>> df.groupby(['a']).idxmax().sort_index() # doctest: +NORMALIZE_WHITESPACE
b c
a
1 1 0
2 3 2
3 4 4
"""
if self._psdf._internal.index_level != 1:
raise ValueError("idxmax only support one-level index now")
groupkey_names = ["__groupkey_{}__".format(i) for i in range(len(self._groupkeys))]
sdf = self._psdf._internal.spark_frame
for s, name in zip(self._groupkeys, groupkey_names):
sdf = sdf.withColumn(name, s.spark.column)
index = self._psdf._internal.index_spark_column_names[0]
stat_exprs = []
for psser, scol in zip(self._agg_columns, self._agg_columns_scols):
name = psser._internal.data_spark_column_names[0]
if skipna:
order_column = scol.desc_nulls_last()
else:
order_column = scol.desc_nulls_first()
window = Window.partitionBy(*groupkey_names).orderBy(
order_column, NATURAL_ORDER_COLUMN_NAME
)
sdf = sdf.withColumn(
name, F.when(F.row_number().over(window) == 1, scol_for(sdf, index)).otherwise(None)
)
stat_exprs.append(F.max(scol_for(sdf, name)).alias(name))
sdf = sdf.groupby(*groupkey_names).agg(*stat_exprs)
internal = InternalFrame(
spark_frame=sdf,
index_spark_columns=[scol_for(sdf, col) for col in groupkey_names],
index_names=[psser._column_label for psser in self._groupkeys],
index_fields=[
psser._internal.data_fields[0].copy(name=name)
for psser, name in zip(self._groupkeys, groupkey_names)
],
column_labels=[psser._column_label for psser in self._agg_columns],
data_spark_columns=[
scol_for(sdf, psser._internal.data_spark_column_names[0])
for psser in self._agg_columns
],
)
return self._handle_output(DataFrame(internal))
def idxmin(self, skipna: bool = True) -> FrameLike:
"""
Return index of first occurrence of minimum over requested axis in group.
NA/null values are excluded.
Parameters
----------
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result will be NA.
See Also
--------
Series.idxmin
DataFrame.idxmin
pyspark.pandas.Series.groupby
pyspark.pandas.DataFrame.groupby
Examples
--------
>>> df = ps.DataFrame({'a': [1, 1, 2, 2, 3],
... 'b': [1, 2, 3, 4, 5],
... 'c': [5, 4, 3, 2, 1]}, columns=['a', 'b', 'c'])
>>> df.groupby(['a'])['b'].idxmin().sort_index() # doctest: +NORMALIZE_WHITESPACE
a
1 0
2 2
3 4
Name: b, dtype: int64
>>> df.groupby(['a']).idxmin().sort_index() # doctest: +NORMALIZE_WHITESPACE
b c
a
1 0 1
2 2 3
3 4 4
"""
if self._psdf._internal.index_level != 1:
raise ValueError("idxmin only support one-level index now")
groupkey_names = ["__groupkey_{}__".format(i) for i in range(len(self._groupkeys))]
sdf = self._psdf._internal.spark_frame
for s, name in zip(self._groupkeys, groupkey_names):
sdf = sdf.withColumn(name, s.spark.column)
index = self._psdf._internal.index_spark_column_names[0]
stat_exprs = []
for psser, scol in zip(self._agg_columns, self._agg_columns_scols):
name = psser._internal.data_spark_column_names[0]
if skipna:
order_column = scol.asc_nulls_last()
else:
order_column = scol.asc_nulls_first()
window = Window.partitionBy(*groupkey_names).orderBy(
order_column, NATURAL_ORDER_COLUMN_NAME
)
sdf = sdf.withColumn(
name, F.when(F.row_number().over(window) == 1, scol_for(sdf, index)).otherwise(None)
)
stat_exprs.append(F.max(scol_for(sdf, name)).alias(name))
sdf = sdf.groupby(*groupkey_names).agg(*stat_exprs)
internal = InternalFrame(
spark_frame=sdf,
index_spark_columns=[scol_for(sdf, col) for col in groupkey_names],
index_names=[psser._column_label for psser in self._groupkeys],
index_fields=[
psser._internal.data_fields[0].copy(name=name)
for psser, name in zip(self._groupkeys, groupkey_names)
],
column_labels=[psser._column_label for psser in self._agg_columns],
data_spark_columns=[
scol_for(sdf, psser._internal.data_spark_column_names[0])
for psser in self._agg_columns
],
)
return self._handle_output(DataFrame(internal))
def fillna(
self,
value: Optional[Any] = None,
method: Optional[str] = None,
axis: Optional[Axis] = None,
inplace: bool = False,
limit: Optional[int] = None,
) -> FrameLike:
"""Fill NA/NaN values in group.
Parameters
----------
value : scalar, dict, Series
Value to use to fill holes. alternately a dict/Series of values
specifying which value to use for each column.
DataFrame is not supported.
method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
Method to use for filling holes in reindexed Series pad / ffill: propagate last valid
observation forward to next valid backfill / bfill:
use NEXT valid observation to fill gap
.. deprecated:: 4.0.0
axis : {0 or `index`}
1 and `columns` are not supported.
.. deprecated:: 4.0.0
For axis=1, operate on the underlying object instead.
Otherwise the axis keyword is not necessary.
inplace : boolean, default False
Fill in place (do not create a new object)
limit : int, default None
If method is specified, this is the maximum number of consecutive NaN values to
forward/backward fill. In other words, if there is a gap with more than this number of
consecutive NaNs, it will only be partially filled. If method is not specified,
this is the maximum number of entries along the entire axis where NaNs will be filled.
Must be greater than 0 if not None
.. deprecated:: 4.0.0
Returns
-------
DataFrame
DataFrame with NA entries filled.
Examples
--------
>>> df = ps.DataFrame({
... 'A': [1, 1, 2, 2],
... 'B': [2, 4, None, 3],
... 'C': [None, None, None, 1],
... 'D': [0, 1, 5, 4]
... },
... columns=['A', 'B', 'C', 'D'])
>>> df
A B C D
0 1 2.0 NaN 0
1 1 4.0 NaN 1
2 2 NaN NaN 5
3 2 3.0 1.0 4
We can also propagate non-null values forward or backward in group.
>>> df.groupby(['A'])['B'].fillna(method='ffill').sort_index()
0 2.0
1 4.0
2 NaN
3 3.0
Name: B, dtype: float64
>>> df.groupby(['A']).fillna(method='bfill').sort_index()
B C D
0 2.0 NaN 0
1 4.0 NaN 1
2 3.0 1.0 5
3 3.0 1.0 4
"""
should_resolve = method is not None
if should_resolve:
warnings.warn(
"DataFrameGroupBy.fillna with 'method' is deprecated "
"and will raise in a future version. "
"Use DataFrameGroupBy.ffill() or DataFrameGroupBy.bfill() instead.",
FutureWarning,
)
return self._apply_series_op(
lambda sg: sg._psser._fillna(
value=value, method=method, axis=axis, limit=limit, part_cols=sg._groupkeys_scols
),
should_resolve=should_resolve,
)
def bfill(self, limit: Optional[int] = None) -> FrameLike:
"""
Synonym for `DataFrame.fillna()` with ``method=`bfill```.
Parameters
----------
axis : {0 or `index`}
1 and `columns` are not supported.
inplace : boolean, default False
Fill in place (do not create a new object)
limit : int, default None
If method is specified, this is the maximum number of consecutive NaN values to
forward/backward fill. In other words, if there is a gap with more than this number of
consecutive NaNs, it will only be partially filled. If method is not specified,
this is the maximum number of entries along the entire axis where NaNs will be filled.
Must be greater than 0 if not None
Returns
-------
DataFrame
DataFrame with NA entries filled.
Examples
--------
>>> df = ps.DataFrame({
... 'A': [1, 1, 2, 2],
... 'B': [2, 4, None, 3],
... 'C': [None, None, None, 1],
... 'D': [0, 1, 5, 4]
... },
... columns=['A', 'B', 'C', 'D'])
>>> df
A B C D
0 1 2.0 NaN 0
1 1 4.0 NaN 1
2 2 NaN NaN 5
3 2 3.0 1.0 4
Propagate non-null values backward.
>>> df.groupby(['A']).bfill().sort_index()
B C D
0 2.0 NaN 0
1 4.0 NaN 1
2 3.0 1.0 5
3 3.0 1.0 4
"""
return self.fillna(method="bfill", limit=limit)
def ffill(self, limit: Optional[int] = None) -> FrameLike:
"""
Synonym for `DataFrame.fillna()` with ``method=`ffill```.
Parameters
----------
axis : {0 or `index`}
1 and `columns` are not supported.
inplace : boolean, default False
Fill in place (do not create a new object)
limit : int, default None
If method is specified, this is the maximum number of consecutive NaN values to
forward/backward fill. In other words, if there is a gap with more than this number of
consecutive NaNs, it will only be partially filled. If method is not specified,
this is the maximum number of entries along the entire axis where NaNs will be filled.
Must be greater than 0 if not None
Returns
-------
DataFrame
DataFrame with NA entries filled.
Examples
--------
>>> df = ps.DataFrame({
... 'A': [1, 1, 2, 2],
... 'B': [2, 4, None, 3],
... 'C': [None, None, None, 1],
... 'D': [0, 1, 5, 4]
... },
... columns=['A', 'B', 'C', 'D'])
>>> df
A B C D
0 1 2.0 NaN 0
1 1 4.0 NaN 1
2 2 NaN NaN 5
3 2 3.0 1.0 4
Propagate non-null values forward.
>>> df.groupby(['A']).ffill().sort_index()
B C D
0 2.0 NaN 0
1 4.0 NaN 1
2 NaN NaN 5
3 3.0 1.0 4
"""
return self.fillna(method="ffill", limit=limit)
def _limit(self, n: int, asc: bool) -> FrameLike:
"""
Private function for tail and head.
"""
psdf = self._psdf
if self._agg_columns_selected:
agg_columns = self._agg_columns
else:
agg_columns = [
psdf._psser_for(label)
for label in psdf._internal.column_labels
if label not in self._column_labels_to_exclude
]
psdf, groupkey_labels, _ = GroupBy._prepare_group_map_apply(
psdf,
self._groupkeys,
agg_columns,
)
groupkey_scols = [psdf._internal.spark_column_for(label) for label in groupkey_labels]
sdf = psdf._internal.spark_frame
window = Window.partitionBy(*groupkey_scols)
# This part is handled differently depending on whether it is a tail or a head.
ordered_window = (
window.orderBy(F.col(NATURAL_ORDER_COLUMN_NAME).asc())
if asc
else window.orderBy(F.col(NATURAL_ORDER_COLUMN_NAME).desc())
)
if n >= 0:
tmp_row_num_col = verify_temp_column_name(sdf, "__row_number__")
sdf = (
sdf.withColumn(tmp_row_num_col, F.row_number().over(ordered_window))
.filter(F.col(tmp_row_num_col) <= n)
.drop(tmp_row_num_col)
)
else:
# Pandas supports Groupby positional indexing since v1.4.0
# https://pandas.pydata.org/docs/whatsnew/v1.4.0.html#groupby-positional-indexing
#
# To support groupby positional indexing, we need add a `__tmp_lag__` column to help
# us filtering rows before the specified offset row.
#
# For example for the dataframe:
# >>> df = ps.DataFrame([["g", "g0"],
# ... ["g", "g1"],
# ... ["g", "g2"],
# ... ["g", "g3"],
# ... ["h", "h0"],
# ... ["h", "h1"]], columns=["A", "B"])
# >>> df.groupby("A").head(-1)
#
# Below is a result to show the `__tmp_lag__` column for above df, the limit n is
# `-1`, the `__tmp_lag__` will be set to `0` in rows[:-1], and left will be set to
# `null`:
#
# >>> sdf.withColumn(tmp_lag_col, F.lag(F.lit(0), -1).over(ordered_window))
# +-----------------+--------------+---+---+-----------------+-----------+
# |__index_level_0__|__groupkey_0__| A| B|__natural_order__|__tmp_lag__|
# +-----------------+--------------+---+---+-----------------+-----------+
# | 0| g| g| g0| 0| 0|
# | 1| g| g| g1| 8589934592| 0|
# | 2| g| g| g2| 17179869184| 0|
# | 3| g| g| g3| 25769803776| null|
# | 4| h| h| h0| 34359738368| 0|
# | 5| h| h| h1| 42949672960| null|
# +-----------------+--------------+---+---+-----------------+-----------+
#
tmp_lag_col = verify_temp_column_name(sdf, "__tmp_lag__")
sdf = (
sdf.withColumn(tmp_lag_col, F.lag(F.lit(0), n).over(ordered_window))
.where(~F.isnull(F.col(tmp_lag_col)))
.drop(tmp_lag_col)
)
internal = psdf._internal.with_new_sdf(sdf)
return self._handle_output(DataFrame(internal).drop(groupkey_labels, axis=1))
def head(self, n: int = 5) -> FrameLike:
"""
Return first n rows of each group.
Returns
-------
DataFrame or Series
Examples
--------
>>> df = ps.DataFrame({'a': [1, 1, 1, 1, 2, 2, 2, 3, 3, 3],
... 'b': [2, 3, 1, 4, 6, 9, 8, 10, 7, 5],
... 'c': [3, 5, 2, 5, 1, 2, 6, 4, 3, 6]},
... columns=['a', 'b', 'c'],
... index=[7, 2, 4, 1, 3, 4, 9, 10, 5, 6])
>>> df
a b c
7 1 2 3
2 1 3 5
4 1 1 2
1 1 4 5
3 2 6 1
4 2 9 2
9 2 8 6
10 3 10 4
5 3 7 3
6 3 5 6
>>> df.groupby('a').head(2).sort_index()
a b c
2 1 3 5
3 2 6 1
4 2 9 2
5 3 7 3
7 1 2 3
10 3 10 4
>>> df.groupby('a')['b'].head(2).sort_index()
2 3
3 6
4 9
5 7
7 2
10 10
Name: b, dtype: int64
Supports Groupby positional indexing Since pandas on Spark 3.4 (with pandas 1.4+):
>>> df = ps.DataFrame([["g", "g0"],
... ["g", "g1"],
... ["g", "g2"],
... ["g", "g3"],
... ["h", "h0"],
... ["h", "h1"]], columns=["A", "B"])
>>> df.groupby("A").head(-1) # doctest: +SKIP
A B
0 g g0
1 g g1
2 g g2
4 h h0
"""
return self._limit(n, asc=True)
def tail(self, n: int = 5) -> FrameLike:
"""
Return last n rows of each group.
Similar to `.apply(lambda x: x.tail(n))`, but it returns a subset of rows from
the original DataFrame with original index and order preserved (`as_index` flag is ignored).
Does not work for negative values of n.
Returns
-------
DataFrame or Series
Examples
--------
>>> df = ps.DataFrame({'a': [1, 1, 1, 1, 2, 2, 2, 3, 3, 3],
... 'b': [2, 3, 1, 4, 6, 9, 8, 10, 7, 5],
... 'c': [3, 5, 2, 5, 1, 2, 6, 4, 3, 6]},
... columns=['a', 'b', 'c'],
... index=[7, 2, 3, 1, 3, 4, 9, 10, 5, 6])
>>> df
a b c
7 1 2 3
2 1 3 5
3 1 1 2
1 1 4 5
3 2 6 1
4 2 9 2
9 2 8 6
10 3 10 4
5 3 7 3
6 3 5 6
>>> df.groupby('a').tail(2).sort_index()
a b c
1 1 4 5
3 1 1 2
4 2 9 2
5 3 7 3
6 3 5 6
9 2 8 6
>>> df.groupby('a')['b'].tail(2).sort_index()
1 4
3 1
4 9
5 7
6 5
9 8
Name: b, dtype: int64
Supports Groupby positional indexing Since pandas on Spark 3.4 (with pandas 1.4+):
>>> df = ps.DataFrame([["g", "g0"],
... ["g", "g1"],
... ["g", "g2"],
... ["g", "g3"],
... ["h", "h0"],
... ["h", "h1"]], columns=["A", "B"])
>>> df.groupby("A").tail(-1) # doctest: +SKIP
A B
3 g g3
2 g g2
1 g g1
5 h h1
"""
return self._limit(n, asc=False)
def shift(self, periods: int = 1, fill_value: Optional[Any] = None) -> FrameLike:
"""
Shift each group by periods observations.
Parameters
----------
periods : integer, default 1
number of periods to shift
fill_value : optional
Returns
-------
Series or DataFrame
Object shifted within each group.
Examples
--------
>>> df = ps.DataFrame({
... 'a': [1, 1, 1, 2, 2, 2, 3, 3, 3],
... 'b': [1, 2, 2, 2, 3, 3, 3, 4, 4]}, columns=['a', 'b'])
>>> df
a b
0 1 1
1 1 2
2 1 2
3 2 2
4 2 3
5 2 3
6 3 3
7 3 4
8 3 4
>>> df.groupby('a').shift().sort_index() # doctest: +SKIP
b
0 NaN
1 1.0
2 2.0
3 NaN
4 2.0
5 3.0
6 NaN
7 3.0
8 4.0
>>> df.groupby('a').shift(periods=-1, fill_value=0).sort_index() # doctest: +SKIP
b
0 2
1 2
2 0
3 3
4 3
5 0
6 4
7 4
8 0
"""
return self._apply_series_op(
lambda sg: sg._psser._shift(periods, fill_value, part_cols=sg._groupkeys_scols),
should_resolve=True,
)
def transform(self, func: Callable[..., pd.Series], *args: Any, **kwargs: Any) -> FrameLike:
"""
Apply function column-by-column to the GroupBy object.
The function passed to `transform` must take a Series as its first
argument and return a Series. The given function is executed for
each series in each grouped data.
While `transform` is a very flexible method, its downside is that
using it can be quite a bit slower than using more specific methods
like `agg` or `transform`. pandas-on-Spark offers a wide range of method that will
be much faster than using `transform` for their specific purposes, so try to
use them before reaching for `transform`.
.. note:: this API executes the function once to infer the type which is
potentially expensive, for instance, when the dataset is created after
aggregations or sorting.
To avoid this, specify return type in ``func``, for instance, as below:
>>> def convert_to_string(x) -> ps.Series[str]:
... return x.apply("a string {}".format)
When the given function has the return type annotated, the original index of the
GroupBy object will be lost, and a default index will be attached to the result.
Please be careful about configuring the default index. See also `Default Index Type
<https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/options.html#default-index-type>`_.
.. note:: the series within ``func`` is actually a pandas series. Therefore,
any pandas API within this function is allowed.
Parameters
----------
func : callable
A callable that takes a Series as its first argument, and
returns a Series.
*args
Positional arguments to pass to func.
**kwargs
Keyword arguments to pass to func.
Returns
-------
applied : DataFrame
See Also
--------
aggregate : Apply aggregate function to the GroupBy object.
Series.apply : Apply a function to a Series.
Examples
--------
>>> df = ps.DataFrame({'A': [0, 0, 1],
... 'B': [1, 2, 3],
... 'C': [4, 6, 5]}, columns=['A', 'B', 'C'])
>>> g = df.groupby('A')
Notice that ``g`` has two groups, ``0`` and ``1``.
Calling `transform` in various ways, we can get different grouping results:
Below the functions passed to `transform` takes a Series as
its argument and returns a Series. `transform` applies the function on each series
in each grouped data, and combine them into a new DataFrame:
>>> def convert_to_string(x) -> ps.Series[str]:
... return x.apply("a string {}".format)
>>> g.transform(convert_to_string) # doctest: +NORMALIZE_WHITESPACE
B C
0 a string 1 a string 4
1 a string 2 a string 6
2 a string 3 a string 5
>>> def plus_max(x) -> ps.Series[int]:
... return x + x.max()
>>> g.transform(plus_max) # doctest: +NORMALIZE_WHITESPACE
B C
0 3 10
1 4 12
2 6 10
You can omit the type hint and let pandas-on-Spark infer its type.
>>> def plus_min(x):
... return x + x.min()
>>> g.transform(plus_min) # doctest: +NORMALIZE_WHITESPACE
B C
0 2 8
1 3 10
2 6 10
In case of Series, it works as below.
>>> df.B.groupby(df.A).transform(plus_max)
0 3
1 4
2 6
Name: B, dtype: int64
>>> (df * -1).B.groupby(df.A).transform(abs)
0 1
1 2
2 3
Name: B, dtype: int64
You can also specify extra arguments to pass to the function.
>>> def calculation(x, y, z) -> ps.Series[int]:
... return x + x.min() + y + z
>>> g.transform(calculation, 5, z=20) # doctest: +NORMALIZE_WHITESPACE
B C
0 27 33
1 28 35
2 31 35
"""
if not callable(func):
raise TypeError("%s object is not callable" % type(func).__name__)
spec = inspect.getfullargspec(func)
return_sig = spec.annotations.get("return", None)
psdf, groupkey_labels, groupkey_names = GroupBy._prepare_group_map_apply(
self._psdf, self._groupkeys, agg_columns=self._agg_columns
)
def pandas_transform(pdf: pd.DataFrame) -> pd.DataFrame:
return pdf.groupby(groupkey_names).transform(func, *args, **kwargs)
should_infer_schema = return_sig is None
if should_infer_schema:
# Here we execute with the first 1000 to get the return type.
# If the records were less than 1000, it uses pandas API directly for a shortcut.
log_advice(
"If the type hints is not specified for `groupby.transform`, "
"it is expensive to infer the data type internally."
)
limit = get_option("compute.shortcut_limit")
pdf = psdf.head(limit + 1)._to_internal_pandas()
pdf = pdf.groupby(groupkey_names).transform(func, *args, **kwargs)
psdf_from_pandas: DataFrame = DataFrame(pdf)
return_schema = force_decimal_precision_scale(
as_nullable_spark_type(
psdf_from_pandas._internal.spark_frame.drop(*HIDDEN_COLUMNS).schema
)
)
if len(pdf) <= limit:
return self._handle_output(psdf_from_pandas)
sdf = GroupBy._spark_group_map_apply(
psdf,
pandas_transform,
[psdf._internal.spark_column_for(label) for label in groupkey_labels],
return_schema,
retain_index=True,
)
# If schema is inferred, we can restore indexes too.
internal = psdf_from_pandas._internal.with_new_sdf(
sdf,
index_fields=[
field.copy(nullable=True) for field in psdf_from_pandas._internal.index_fields
],
data_fields=[
field.copy(nullable=True) for field in psdf_from_pandas._internal.data_fields
],
)
else:
return_type = infer_return_type(func)
if not isinstance(return_type, SeriesType):
raise TypeError(
"Expected the return type of this function to be of Series type, "
"but found type {}".format(return_type)
)
dtype = return_type.dtype
spark_type = return_type.spark_type
data_fields = [
InternalField(dtype=dtype, struct_field=StructField(name=c, dataType=spark_type))
for c in psdf._internal.data_spark_column_names
if c not in groupkey_names
]
return_schema = StructType([field.struct_field for field in data_fields])
sdf = GroupBy._spark_group_map_apply(
psdf,
pandas_transform,
[psdf._internal.spark_column_for(label) for label in groupkey_labels],
return_schema,
retain_index=False,
)
# Otherwise, it loses index.
internal = InternalFrame(
spark_frame=sdf, index_spark_columns=None, data_fields=data_fields
)
return self._handle_output(DataFrame(internal))
def nunique(self, dropna: bool = True) -> FrameLike:
"""
Return DataFrame with number of distinct observations per group for each column.
Parameters
----------
dropna : boolean, default True
Don’t include NaN in the counts.
Returns
-------
nunique : DataFrame or Series
Examples
--------
>>> df = ps.DataFrame({'id': ['spam', 'egg', 'egg', 'spam',
... 'ham', 'ham'],
... 'value1': [1, 5, 5, 2, 5, 5],
... 'value2': list('abbaxy')}, columns=['id', 'value1', 'value2'])
>>> df
id value1 value2
0 spam 1 a
1 egg 5 b
2 egg 5 b
3 spam 2 a
4 ham 5 x
5 ham 5 y
>>> df.groupby('id').nunique().sort_index() # doctest: +SKIP
value1 value2
id
egg 1 1
ham 1 2
spam 2 1
>>> df.groupby('id')['value1'].nunique().sort_index() # doctest: +NORMALIZE_WHITESPACE
id
egg 1
ham 1
spam 2
Name: value1, dtype: int64
"""
if dropna:
def stat_function(col: Column) -> Column:
return F.countDistinct(col)
else:
def stat_function(col: Column) -> Column:
return F.countDistinct(col) + F.when(
F.count(F.when(col.isNull(), 1).otherwise(None)) >= 1, 1
).otherwise(0)
return self._reduce_for_stat_function(stat_function)
def rolling(
self, window: int, min_periods: Optional[int] = None
) -> "RollingGroupby[FrameLike]":
"""
Return an rolling grouper, providing rolling
functionality per group.
.. note:: 'min_periods' in pandas-on-Spark works as a fixed window size unlike pandas.
Unlike pandas, NA is also counted as the period. This might be changed
soon.
Parameters
----------
window : int, or offset
Size of the moving window.
This is the number of observations used for calculating the statistic.
Each window will be a fixed size.
min_periods : int, default 1
Minimum number of observations in window required to have a value
(otherwise result is NA).
See Also
--------
Series.groupby
DataFrame.groupby
"""
from pyspark.pandas.window import RollingGroupby
return RollingGroupby(self, window, min_periods=min_periods)
def expanding(self, min_periods: int = 1) -> "ExpandingGroupby[FrameLike]":
"""
Return an expanding grouper, providing expanding
functionality per group.
.. note:: 'min_periods' in pandas-on-Spark works as a fixed window size unlike pandas.
Unlike pandas, NA is also counted as the period. This might be changed
soon.
Parameters
----------
min_periods : int, default 1
Minimum number of observations in window required to have a value
(otherwise result is NA).
See Also
--------
Series.groupby
DataFrame.groupby
"""
from pyspark.pandas.window import ExpandingGroupby
return ExpandingGroupby(self, min_periods=min_periods)
# TODO: 'adjust', 'axis', 'method' parameter should be implemented.
def ewm(
self,
com: Optional[float] = None,
span: Optional[float] = None,
halflife: Optional[float] = None,
alpha: Optional[float] = None,
min_periods: Optional[int] = None,
ignore_na: bool = False,
) -> "ExponentialMovingGroupby[FrameLike]":
"""
Return an ewm grouper, providing ewm functionality per group.
.. note:: 'min_periods' in pandas-on-Spark works as a fixed window size unlike pandas.
Unlike pandas, NA is also counted as the period. This might be changed
soon.
.. versionadded:: 3.4.0
Parameters
----------
com : float, optional
Specify decay in terms of center of mass.
alpha = 1 / (1 + com), for com >= 0.
span : float, optional
Specify decay in terms of span.
alpha = 2 / (span + 1), for span >= 1.
halflife : float, optional
Specify decay in terms of half-life.
alpha = 1 - exp(-ln(2) / halflife), for halflife > 0.
alpha : float, optional
Specify smoothing factor alpha directly.
0 < alpha <= 1.
min_periods : int, default None
Minimum number of observations in window required to have a value
(otherwise result is NA).
ignore_na : bool, default False
Ignore missing values when calculating weights.
- When ``ignore_na=False`` (default), weights are based on absolute positions.
For example, the weights of :math:`x_0` and :math:`x_2` used in calculating
the final weighted average of [:math:`x_0`, None, :math:`x_2`] are
:math:`(1-\alpha)^2` and :math:`1` if ``adjust=True``, and
:math:`(1-\alpha)^2` and :math:`\alpha` if ``adjust=False``.
- When ``ignore_na=True``, weights are based
on relative positions. For example, the weights of :math:`x_0` and :math:`x_2`
used in calculating the final weighted average of
[:math:`x_0`, None, :math:`x_2`] are :math:`1-\alpha` and :math:`1` if
``adjust=True``, and :math:`1-\alpha` and :math:`\alpha` if ``adjust=False``.
"""
from pyspark.pandas.window import ExponentialMovingGroupby
return ExponentialMovingGroupby(
self,
com=com,
span=span,
halflife=halflife,
alpha=alpha,
min_periods=min_periods,
ignore_na=ignore_na,
)
def get_group(self, name: Union[Name, List[Name]]) -> FrameLike:
"""
Construct DataFrame from group with provided name.
Parameters
----------
name : object
The name of the group to get as a DataFrame.
Returns
-------
group : same type as obj
Examples
--------
>>> psdf = ps.DataFrame([('falcon', 'bird', 389.0),
... ('parrot', 'bird', 24.0),
... ('lion', 'mammal', 80.5),
... ('monkey', 'mammal', np.nan)],
... columns=['name', 'class', 'max_speed'],
... index=[0, 2, 3, 1])
>>> psdf
name class max_speed
0 falcon bird 389.0
2 parrot bird 24.0
3 lion mammal 80.5
1 monkey mammal NaN
>>> psdf.groupby("class").get_group("bird").sort_index()
name class max_speed
0 falcon bird 389.0
2 parrot bird 24.0
>>> psdf.groupby("class").get_group("mammal").sort_index()
name class max_speed
1 monkey mammal NaN
3 lion mammal 80.5
"""
groupkeys = self._groupkeys
if not is_hashable(name):
raise TypeError("unhashable type: '{}'".format(type(name).__name__))
elif len(groupkeys) > 1:
if not isinstance(name, tuple):
raise ValueError("must supply a tuple to get_group with multiple grouping keys")
if len(groupkeys) != len(name):
raise ValueError(
"must supply a same-length tuple to get_group with multiple grouping keys"
)
if not is_list_like(name):
name = [name]
cond = F.lit(True)
for groupkey, item in zip(groupkeys, name):
scol = groupkey.spark.column
cond = cond & (scol == item)
if self._agg_columns_selected:
internal = self._psdf._internal
spark_frame = internal.spark_frame.select(
internal.index_spark_columns + self._agg_columns_scols
).filter(cond)
internal = internal.copy(
spark_frame=spark_frame,
index_spark_columns=[
scol_for(spark_frame, col) for col in internal.index_spark_column_names
],
column_labels=[s._column_label for s in self._agg_columns],
data_spark_columns=[
scol_for(spark_frame, s._internal.data_spark_column_names[0])
for s in self._agg_columns
],
data_fields=[s._internal.data_fields[0] for s in self._agg_columns],
)
else:
internal = self._psdf._internal.with_filter(cond)
if internal.spark_frame.head() is None:
raise KeyError(name)
return self._handle_output(DataFrame(internal))
def median(self, numeric_only: bool = False, accuracy: int = 10000) -> FrameLike:
"""
Compute median of groups, excluding missing values.
For multiple groupings, the result index will be a MultiIndex
.. note:: Unlike pandas', the median in pandas-on-Spark is an approximated median based upon
approximate percentile computation because computing median across a large dataset
is extremely expensive.
Parameters
----------
numeric_only : bool, default False
Include only float, int, boolean columns.
.. versionadded:: 3.4.0
.. versionchanged:: 4.0.0
Returns
-------
Series or DataFrame
Median of values within each group.
Examples
--------
>>> psdf = ps.DataFrame({'a': [1., 1., 1., 1., 2., 2., 2., 3., 3., 3.],
... 'b': [2., 3., 1., 4., 6., 9., 8., 10., 7., 5.],
... 'c': [3., 5., 2., 5., 1., 2., 6., 4., 3., 6.]},
... columns=['a', 'b', 'c'],
... index=[7, 2, 4, 1, 3, 4, 9, 10, 5, 6])
>>> psdf
a b c
7 1.0 2.0 3.0
2 1.0 3.0 5.0
4 1.0 1.0 2.0
1 1.0 4.0 5.0
3 2.0 6.0 1.0
4 2.0 9.0 2.0
9 2.0 8.0 6.0
10 3.0 10.0 4.0
5 3.0 7.0 3.0
6 3.0 5.0 6.0
DataFrameGroupBy
>>> psdf.groupby('a').median().sort_index() # doctest: +NORMALIZE_WHITESPACE
b c
a
1.0 2.0 3.0
2.0 8.0 2.0
3.0 7.0 4.0
SeriesGroupBy
>>> psdf.groupby('a')['b'].median().sort_index()
a
1.0 2.0
2.0 8.0
3.0 7.0
Name: b, dtype: float64
"""
if not isinstance(accuracy, int):
raise TypeError(
"accuracy must be an integer; however, got [%s]" % type(accuracy).__name__
)
self._validate_agg_columns(numeric_only=numeric_only, function_name="median")
def stat_function(col: Column) -> Column:
return F.percentile_approx(col, 0.5, accuracy)
return self._reduce_for_stat_function(
stat_function,
accepted_spark_types=(NumericType,),
bool_to_numeric=True,
)
def _validate_agg_columns(self, numeric_only: Optional[bool], function_name: str) -> None:
"""Validate aggregation columns and raise an error or a warning following pandas."""
has_non_numeric = False
for _agg_col in self._agg_columns:
if not isinstance(_agg_col.spark.data_type, (NumericType, BooleanType)):
has_non_numeric = True
break
if has_non_numeric:
if isinstance(self, SeriesGroupBy):
raise TypeError("Only numeric aggregation column is accepted.")
if not numeric_only and has_non_numeric:
warnings.warn(
"Dropping invalid columns in DataFrameGroupBy.%s is deprecated. "
"In a future version, a TypeError will be raised. "
"Before calling .%s, select only columns which should be "
"valid for the function." % (function_name, function_name),
FutureWarning,
)
def _reduce_for_stat_function(
self,
sfun: Callable[[Column], Column],
accepted_spark_types: Optional[Tuple[Type[DataType], ...]] = None,
bool_to_numeric: bool = False,
**kwargs: Any,
) -> FrameLike:
"""Apply an aggregate function `sfun` per column and reduce to a FrameLike.
Parameters
----------
sfun : The aggregate function to apply per column.
accepted_spark_types: Accepted spark types of columns to be aggregated;
default None means all spark types are accepted.
bool_to_numeric: If True, boolean columns are converted to numeric columns, which
are accepted for all statistical functions regardless of
`accepted_spark_types`.
"""
groupkey_names = [SPARK_INDEX_NAME_FORMAT(i) for i in range(len(self._groupkeys))]
internal, _, sdf = self._prepare_reduce(
groupkey_names, accepted_spark_types, bool_to_numeric
)
psdf: DataFrame = DataFrame(internal)
if len(psdf._internal.column_labels) > 0:
min_count = kwargs.get("min_count", 0)
stat_exprs = []
for label in psdf._internal.column_labels:
psser = psdf._psser_for(label)
input_scol = psser._dtype_op.nan_to_null(psser).spark.column
if sfun.__name__ == "sum" and isinstance(
psdf._internal.spark_type_for(label), StringType
):
input_scol_name = psser._internal.data_spark_column_names[0]
# Sort data with natural order column to ensure order of data
sorted_array = F.array_sort(
F.collect_list(F.struct(NATURAL_ORDER_COLUMN_NAME, input_scol))
)
# Using transform to extract strings
output_scol = F.concat_ws(
"", F.transform(sorted_array, lambda x: x.getField(input_scol_name))
)
else:
output_scol = sfun(input_scol)
if min_count > 0:
output_scol = F.when(
F.count(F.when(~F.isnull(input_scol), F.lit(0))) >= min_count, output_scol
)
stat_exprs.append(output_scol.alias(psser._internal.data_spark_column_names[0]))
sdf = sdf.groupby(*groupkey_names).agg(*stat_exprs)
else:
sdf = sdf.select(*groupkey_names).distinct()
internal = internal.copy(
spark_frame=sdf,
index_spark_columns=[scol_for(sdf, col) for col in groupkey_names],
data_spark_columns=[scol_for(sdf, col) for col in internal.data_spark_column_names],
data_fields=None,
)
psdf = DataFrame(internal)
return self._prepare_return(psdf)
def _prepare_return(
self, psdf: DataFrame, agg_column_names: Optional[List[str]] = None
) -> FrameLike:
if self._dropna:
psdf = DataFrame(
psdf._internal.with_new_sdf(
psdf._internal.spark_frame.dropna(
subset=psdf._internal.index_spark_column_names
)
)
)
if not self._as_index:
column_names = [column.name for column in self._agg_columns]
for groupkey in self._groupkeys:
if groupkey.name not in column_names:
warnings.warn(
"A grouping was used that is not in the columns of the DataFrame and so "
"was excluded from the result. "
"This grouping will be included in a future version. "
"Add the grouping as a column of the DataFrame to silence this warning.",
FutureWarning,
)
should_drop_index = set(
i for i, gkey in enumerate(self._groupkeys) if gkey._psdf is not self._psdf
)
if len(should_drop_index) > 0:
psdf = psdf.reset_index(level=should_drop_index, drop=True)
if len(should_drop_index) < len(self._groupkeys):
psdf = psdf.reset_index()
return self._handle_output(psdf, agg_column_names)
def _prepare_reduce(
self,
groupkey_names: List,
accepted_spark_types: Optional[Tuple[Type[DataType], ...]] = None,
bool_to_numeric: bool = False,
) -> Tuple[InternalFrame, List[Series], SparkDataFrame]:
groupkey_scols = [s.alias(name) for s, name in zip(self._groupkeys_scols, groupkey_names)]
agg_columns = []
for psser in self._agg_columns:
if bool_to_numeric and isinstance(psser.spark.data_type, BooleanType):
agg_columns.append(psser.astype(int))
elif (accepted_spark_types is None) or isinstance(
psser.spark.data_type, accepted_spark_types
):
agg_columns.append(psser)
sdf = self._psdf._internal.spark_frame.select(
*groupkey_scols,
*[psser.spark.column for psser in agg_columns],
NATURAL_ORDER_COLUMN_NAME,
)
internal = InternalFrame(
spark_frame=sdf,
index_spark_columns=[scol_for(sdf, col) for col in groupkey_names],
index_names=[psser._column_label for psser in self._groupkeys],
index_fields=[
psser._internal.data_fields[0].copy(name=name)
for psser, name in zip(self._groupkeys, groupkey_names)
],
data_spark_columns=[
scol_for(sdf, psser._internal.data_spark_column_names[0]) for psser in agg_columns
],
column_labels=[psser._column_label for psser in agg_columns],
data_fields=[psser._internal.data_fields[0] for psser in agg_columns],
column_label_names=self._psdf._internal.column_label_names,
)
return internal, agg_columns, sdf
@staticmethod
def _resolve_grouping_from_diff_dataframes(
psdf: DataFrame, by: List[Union[Series, Label]]
) -> Tuple[DataFrame, List[Series], Set[Label]]:
column_labels_level = psdf._internal.column_labels_level
column_labels = []
additional_pssers = []
additional_column_labels = []
tmp_column_labels = set()
for i, col_or_s in enumerate(by):
if isinstance(col_or_s, Series):
if col_or_s._psdf is psdf:
column_labels.append(col_or_s._column_label)
elif same_anchor(col_or_s, psdf):
temp_label = verify_temp_column_name(psdf, "__tmp_groupkey_{}__".format(i))
column_labels.append(temp_label)
additional_pssers.append(col_or_s.rename(temp_label))
additional_column_labels.append(temp_label)
else:
temp_label = verify_temp_column_name(
psdf,
tuple(
([""] * (column_labels_level - 1)) + ["__tmp_groupkey_{}__".format(i)]
),
)
column_labels.append(temp_label)
tmp_column_labels.add(temp_label)
elif isinstance(col_or_s, tuple):
psser = psdf[col_or_s]
if not isinstance(psser, Series):
raise ValueError(name_like_string(col_or_s))
column_labels.append(col_or_s)
else:
raise ValueError(col_or_s)
psdf = DataFrame(
psdf._internal.with_new_columns(
[psdf._psser_for(label) for label in psdf._internal.column_labels]
+ additional_pssers
)
)
def assign_columns(
psdf: DataFrame, this_column_labels: List[Label], that_column_labels: List[Label]
) -> Iterator[Tuple[Series, Label]]:
raise NotImplementedError(
"Duplicated labels with groupby() and "
"'compute.ops_on_diff_frames' option is not supported currently "
"Please use unique labels in series and frames."
)
for col_or_s, label in zip(by, column_labels):
if label in tmp_column_labels:
psser = col_or_s
psdf = align_diff_frames(
assign_columns,
psdf,
psser.rename(label),
fillna=False,
how="inner",
preserve_order_column=True,
)
tmp_column_labels |= set(additional_column_labels)
new_by_series = []
for col_or_s, label in zip(by, column_labels):
if label in tmp_column_labels:
psser = col_or_s
new_by_series.append(psdf._psser_for(label).rename(psser.name))
else:
new_by_series.append(psdf._psser_for(label))
return psdf, new_by_series, tmp_column_labels
@staticmethod
def _resolve_grouping(psdf: DataFrame, by: List[Union[Series, Label]]) -> List[Series]:
new_by_series = []
for col_or_s in by:
if isinstance(col_or_s, Series):
new_by_series.append(col_or_s)
elif isinstance(col_or_s, tuple):
psser = psdf[col_or_s]
if not isinstance(psser, Series):
raise ValueError(name_like_string(col_or_s))
new_by_series.append(psser)
else:
raise ValueError(col_or_s)
return new_by_series
class DataFrameGroupBy(GroupBy[DataFrame]):
@staticmethod
def _build(
psdf: DataFrame, by: List[Union[Series, Label]], as_index: bool, dropna: bool
) -> "DataFrameGroupBy":
if any(isinstance(col_or_s, Series) and not same_anchor(psdf, col_or_s) for col_or_s in by):
(
psdf,
new_by_series,
column_labels_to_exclude,
) = GroupBy._resolve_grouping_from_diff_dataframes(psdf, by)
else:
new_by_series = GroupBy._resolve_grouping(psdf, by)
column_labels_to_exclude = set()
return DataFrameGroupBy(
psdf,
new_by_series,
as_index=as_index,
dropna=dropna,
column_labels_to_exclude=column_labels_to_exclude,
)
def __init__(
self,
psdf: DataFrame,
by: List[Series],
as_index: bool,
dropna: bool,
column_labels_to_exclude: Set[Label],
agg_columns: List[Label] = None,
):
agg_columns_selected = agg_columns is not None
if agg_columns_selected:
for label in agg_columns:
if label in column_labels_to_exclude:
raise KeyError(label)
else:
agg_columns = [
label
for label in psdf._internal.column_labels
if not any(label == key._column_label and key._psdf is psdf for key in by)
and label not in column_labels_to_exclude
]
super().__init__(
psdf=psdf,
groupkeys=by,
as_index=as_index,
dropna=dropna,
column_labels_to_exclude=column_labels_to_exclude,
agg_columns_selected=agg_columns_selected,
agg_columns=[psdf[label] for label in agg_columns],
)
def __getattr__(self, item: str) -> Any:
if hasattr(MissingPandasLikeDataFrameGroupBy, item):
property_or_func = getattr(MissingPandasLikeDataFrameGroupBy, item)
if isinstance(property_or_func, property):
return property_or_func.fget(self)
else:
return partial(property_or_func, self)
return self.__getitem__(item)
def __getitem__(self, item: Any) -> GroupBy:
if self._as_index and is_name_like_value(item):
return SeriesGroupBy(
self._psdf._psser_for(item if is_name_like_tuple(item) else (item,)),
self._groupkeys,
dropna=self._dropna,
)
else:
if is_name_like_tuple(item):
item = [item]
elif is_name_like_value(item):
item = [(item,)]
else:
item = [i if is_name_like_tuple(i) else (i,) for i in item]
if not self._as_index:
groupkey_names = set(key._column_label for key in self._groupkeys)
for name in item:
if name in groupkey_names:
raise ValueError(
"cannot insert {}, already exists".format(name_like_string(name))
)
return DataFrameGroupBy(
self._psdf,
self._groupkeys,
as_index=self._as_index,
dropna=self._dropna,
column_labels_to_exclude=self._column_labels_to_exclude,
agg_columns=item,
)
def _apply_series_op(
self,
op: Callable[["SeriesGroupBy"], Series],
should_resolve: bool = False,
numeric_only: bool = False,
) -> DataFrame:
applied = []
for column in self._agg_columns:
applied.append(op(column.groupby(self._groupkeys)))
if numeric_only:
applied = [col for col in applied if isinstance(col.spark.data_type, NumericType)]
if not applied:
raise DataError("No numeric types to aggregate")
internal = self._psdf._internal.with_new_columns(applied, keep_order=False)
if should_resolve:
internal = internal.resolved_copy
return DataFrame(internal)
def _handle_output(
self, psdf: DataFrame, agg_column_names: Optional[List[str]] = None
) -> DataFrame:
if agg_column_names is not None:
return psdf[agg_column_names]
else:
return psdf
# TODO: Implement 'percentiles', 'include', and 'exclude' arguments.
# TODO: Add ``DataFrame.select_dtypes`` to See Also when 'include'
# and 'exclude' arguments are implemented.
def describe(self) -> DataFrame:
"""
Generate descriptive statistics that summarize the central tendency,
dispersion and shape of a dataset's distribution, excluding
``NaN`` values.
Analyzes both numeric and object series, as well
as ``DataFrame`` column sets of mixed data types. The output
will vary depending on what is provided. Refer to the notes
below for more detail.
.. note:: Unlike pandas, the percentiles in pandas-on-Spark are based upon
approximate percentile computation because computing percentiles
across a large dataset is extremely expensive.
Returns
-------
DataFrame
Summary statistics of the DataFrame provided.
See Also
--------
DataFrame.count
DataFrame.max
DataFrame.min
DataFrame.mean
DataFrame.std
Examples
--------
>>> df = ps.DataFrame({'a': [1, 1, 3], 'b': [4, 5, 6], 'c': [7, 8, 9]})
>>> df
a b c
0 1 4 7
1 1 5 8
2 3 6 9
Describing a ``DataFrame``. By default only numeric fields
are returned.
>>> described = df.groupby('a').describe()
>>> described.sort_index() # doctest: +NORMALIZE_WHITESPACE
b c
count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max
a
1 2.0 4.5 0.707107 4.0 4.0 4.0 5.0 5.0 2.0 7.5 0.707107 7.0 7.0 7.0 8.0 8.0
3 1.0 6.0 NaN 6.0 6.0 6.0 6.0 6.0 1.0 9.0 NaN 9.0 9.0 9.0 9.0 9.0
"""
for col in self._agg_columns:
if isinstance(col.spark.data_type, StringType):
raise NotImplementedError(
"DataFrameGroupBy.describe() doesn't support for string type for now"
)
psdf = self.aggregate(["count", "mean", "std", "min", "quartiles", "max"])
sdf = psdf._internal.spark_frame
agg_column_labels = [col._column_label for col in self._agg_columns]
formatted_percentiles = ["25%", "50%", "75%"]
# Split "quartiles" columns into first, second, and third quartiles.
for label in agg_column_labels:
quartiles_col = name_like_string(tuple(list(label) + ["quartiles"]))
for i, percentile in enumerate(formatted_percentiles):
sdf = sdf.withColumn(
name_like_string(tuple(list(label) + [percentile])),
scol_for(sdf, quartiles_col)[i],
)
sdf = sdf.drop(quartiles_col)
# Reorder columns lexicographically by agg column followed by stats.
stats = ["count", "mean", "std", "min"] + formatted_percentiles + ["max"]
column_labels = [tuple(list(label) + [s]) for label, s in product(agg_column_labels, stats)]
data_columns = map(name_like_string, column_labels)
# Reindex the DataFrame to reflect initial grouping and agg columns.
internal = psdf._internal.copy(
spark_frame=sdf,
column_labels=column_labels,
data_spark_columns=[scol_for(sdf, col) for col in data_columns],
data_fields=None,
)
# Cast columns to ``"float64"`` to match `pandas.DataFrame.groupby`.
return DataFrame(internal).astype("float64")
def corr(
self,
method: str = "pearson",
min_periods: int = 1,
numeric_only: bool = False,
) -> "DataFrame":
"""
Compute pairwise correlation of columns, excluding NA/null values.
.. versionadded:: 4.0.0
Parameters
----------
method : {'pearson', 'spearman', 'kendall'}
* pearson : standard correlation coefficient
* spearman : Spearman rank correlation
* kendall : Kendall Tau correlation coefficient
min_periods : int, default 1
Minimum number of observations in window required to have a value
(otherwise result is NA).
numeric_only : bool, default False
Include only `float`, `int` or `boolean` data.
Returns
-------
DataFrame
See Also
--------
DataFrame.corrwith
Series.corr
Notes
-----
1. Pearson, Kendall and Spearman correlation are currently computed using pairwise
complete observations.
2. The complexity of Kendall correlation is O(#row * #row), if the dataset is too
large, sampling ahead of correlation computation is recommended.
Examples
--------
>>> df = ps.DataFrame(
... {"A": [0, 0, 0, 1, 1, 2], "B": [-1, 2, 3, 5, 6, 0], "C": [4, 6, 5, 1, 3, 0]},
... columns=["A", "B", "C"])
>>> df.groupby("A").corr()
B C
A
0 B 1.000000 0.720577
C 0.720577 1.000000
1 B 1.000000 1.000000
C 1.000000 1.000000
2 B NaN NaN
C NaN NaN
>>> df.groupby("A").corr(min_periods=2)
B C
A
0 B 1.000000 0.720577
C 0.720577 1.000000
1 B 1.000000 1.000000
C 1.000000 1.000000
2 B NaN NaN
C NaN NaN
>>> df.groupby("A").corr("spearman")
B C
A
0 B 1.0 0.5
C 0.5 1.0
1 B 1.0 1.0
C 1.0 1.0
2 B NaN NaN
C NaN NaN
>>> df.groupby("A").corr('kendall')
B C
A
0 B 1.000000 0.333333
C 0.333333 1.000000
1 B 1.000000 1.000000
C 1.000000 1.000000
2 B 1.000000 NaN
C NaN 1.000000
"""
if method not in ["pearson", "spearman", "kendall"]:
raise ValueError(f"Invalid method {method}")
groupkey_names: List[str] = [str(key.name) for key in self._groupkeys]
internal, agg_columns, sdf = self._prepare_reduce(
groupkey_names=groupkey_names,
accepted_spark_types=(NumericType, BooleanType) if numeric_only else None,
bool_to_numeric=False,
)
numeric_labels = [
label
for label in internal.column_labels
if isinstance(internal.spark_type_for(label), (NumericType, BooleanType))
]
numeric_scols: List[Column] = [
internal.spark_column_for(label).cast("double") for label in numeric_labels
]
numeric_col_names: List[str] = [name_like_string(label) for label in numeric_labels]
num_scols = len(numeric_scols)
sdf = internal.spark_frame
index_1_col_name = verify_temp_column_name(sdf, "__groupby_corr_index_1_temp_column__")
index_2_col_name = verify_temp_column_name(sdf, "__groupby_corr_index_2_temp_column__")
pair_scols: List[Column] = []
for i in range(0, num_scols):
for j in range(i, num_scols):
pair_scols.append(
F.struct(
F.lit(i).alias(index_1_col_name),
F.lit(j).alias(index_2_col_name),
numeric_scols[i].alias(CORRELATION_VALUE_1_COLUMN),
numeric_scols[j].alias(CORRELATION_VALUE_2_COLUMN),
)
)
sdf = sdf.select(*[F.col(key) for key in groupkey_names], *[F.inline(F.array(*pair_scols))])
sdf = compute(
sdf=sdf, groupKeys=groupkey_names + [index_1_col_name, index_2_col_name], method=method
)
if method == "kendall":
sdf = sdf.withColumn(
CORRELATION_CORR_OUTPUT_COLUMN,
F.when(F.col(index_1_col_name) == F.col(index_2_col_name), F.lit(1.0)).otherwise(
F.col(CORRELATION_CORR_OUTPUT_COLUMN)
),
)
sdf = sdf.withColumn(
CORRELATION_CORR_OUTPUT_COLUMN,
F.when(F.col(CORRELATION_COUNT_OUTPUT_COLUMN) < min_periods, F.lit(None)).otherwise(
F.col(CORRELATION_CORR_OUTPUT_COLUMN)
),
)
auxiliary_col_name = verify_temp_column_name(sdf, "__groupby_corr_auxiliary_temp_column__")
sdf = sdf.withColumn(
auxiliary_col_name,
F.explode(
F.when(
F.col(index_1_col_name) == F.col(index_2_col_name),
F.lit([0]),
).otherwise(F.lit([0, 1]))
),
).select(
*[F.col(key) for key in groupkey_names],
*[
F.when(F.col(auxiliary_col_name) == 0, F.col(index_1_col_name))
.otherwise(F.col(index_2_col_name))
.alias(index_1_col_name),
F.when(F.col(auxiliary_col_name) == 0, F.col(index_2_col_name))
.otherwise(F.col(index_1_col_name))
.alias(index_2_col_name),
F.col(CORRELATION_CORR_OUTPUT_COLUMN),
],
)
array_col_name = verify_temp_column_name(sdf, "__groupby_corr_array_temp_column__")
sdf = sdf.groupby(groupkey_names + [index_1_col_name]).agg(
F.array_sort(
F.collect_list(
F.struct(
F.col(index_2_col_name),
F.col(CORRELATION_CORR_OUTPUT_COLUMN),
)
)
).alias(array_col_name)
)
for i in range(0, num_scols):
sdf = sdf.withColumn(auxiliary_col_name, F.get(F.col(array_col_name), i)).withColumn(
numeric_col_names[i],
F.col(f"{auxiliary_col_name}.{CORRELATION_CORR_OUTPUT_COLUMN}"),
)
sdf = sdf.orderBy(groupkey_names + [index_1_col_name]) # type: ignore[arg-type]
sdf = sdf.select(
*[F.col(col) for col in groupkey_names + numeric_col_names],
*[
F.get(F.lit(numeric_col_names), F.col(index_1_col_name)).alias(auxiliary_col_name),
F.monotonically_increasing_id().alias(NATURAL_ORDER_COLUMN_NAME),
],
)
return DataFrame(
InternalFrame(
spark_frame=sdf,
index_spark_columns=[
scol_for(sdf, key) for key in groupkey_names + [auxiliary_col_name]
],
index_names=(
[psser._column_label for psser in self._groupkeys]
+ self._psdf._internal.index_names
),
column_labels=numeric_labels,
column_label_names=internal.column_label_names,
)
)
class SeriesGroupBy(GroupBy[Series]):
@staticmethod
def _build(
psser: Series, by: List[Union[Series, Label]], as_index: bool, dropna: bool
) -> "SeriesGroupBy":
if any(
isinstance(col_or_s, Series) and not same_anchor(psser, col_or_s) for col_or_s in by
):
psdf, new_by_series, _ = GroupBy._resolve_grouping_from_diff_dataframes(
psser.to_frame(), by
)
return SeriesGroupBy(
first_series(psdf).rename(psser.name),
new_by_series,
as_index=as_index,
dropna=dropna,
)
else:
new_by_series = GroupBy._resolve_grouping(psser._psdf, by)
return SeriesGroupBy(psser, new_by_series, as_index=as_index, dropna=dropna)
def __init__(self, psser: Series, by: List[Series], as_index: bool = True, dropna: bool = True):
if not as_index:
raise TypeError("as_index=False only valid with DataFrame")
super().__init__(
psdf=psser._psdf,
groupkeys=by,
as_index=True,
dropna=dropna,
column_labels_to_exclude=set(),
agg_columns_selected=True,
agg_columns=[psser],
)
self._psser = psser
def __getattr__(self, item: str) -> Any:
if hasattr(MissingPandasLikeSeriesGroupBy, item):
property_or_func = getattr(MissingPandasLikeSeriesGroupBy, item)
if isinstance(property_or_func, property):
return property_or_func.fget(self)
else:
return partial(property_or_func, self)
raise AttributeError(item)
def _apply_series_op(
self,
op: Callable[["SeriesGroupBy"], Series],
should_resolve: bool = False,
numeric_only: bool = False,
) -> Series:
if numeric_only and not isinstance(self._agg_columns[0].spark.data_type, NumericType):
raise DataError("No numeric types to aggregate")
psser = op(self)
if should_resolve:
internal = psser._internal.resolved_copy
return first_series(DataFrame(internal))
else:
return psser.copy()
def _handle_output(
self, psdf: DataFrame, agg_column_names: Optional[List[str]] = None
) -> Series:
if agg_column_names is not None:
return psdf[agg_column_names[0]].rename(self._psser.name)
else:
return first_series(psdf).rename(self._psser.name)
def agg(self, *args: Any, **kwargs: Any) -> None:
return MissingPandasLikeSeriesGroupBy.agg(self, *args, **kwargs)
def aggregate(self, *args: Any, **kwargs: Any) -> None:
return MissingPandasLikeSeriesGroupBy.aggregate(self, *args, **kwargs)
def size(self) -> Series:
return super().size().rename(self._psser.name)
size.__doc__ = GroupBy.size.__doc__
# TODO: add keep parameter
def nsmallest(self, n: int = 5) -> Series:
"""
Return the smallest `n` elements.
Parameters
----------
n : int
Number of items to retrieve.
See Also
--------
pyspark.pandas.Series.nsmallest
pyspark.pandas.DataFrame.nsmallest
Examples
--------
>>> df = ps.DataFrame({'a': [1, 1, 1, 2, 2, 2, 3, 3, 3],
... 'b': [1, 2, 2, 2, 3, 3, 3, 4, 4]}, columns=['a', 'b'])
>>> df.groupby(['a'])['b'].nsmallest(1).sort_index() # doctest: +NORMALIZE_WHITESPACE
a
1 0 1
2 3 2
3 6 3
Name: b, dtype: int64
"""
if self._psser._internal.index_level > 1:
raise ValueError("nsmallest do not support multi-index now")
groupkey_col_names = [SPARK_INDEX_NAME_FORMAT(i) for i in range(len(self._groupkeys))]
sdf = self._psser._internal.spark_frame.select(
*[scol.alias(name) for scol, name in zip(self._groupkeys_scols, groupkey_col_names)],
*[
scol.alias(SPARK_INDEX_NAME_FORMAT(i + len(self._groupkeys)))
for i, scol in enumerate(self._psser._internal.index_spark_columns)
],
self._psser.spark.column,
NATURAL_ORDER_COLUMN_NAME,
)
window = Window.partitionBy(*groupkey_col_names).orderBy(
scol_for(sdf, self._psser._internal.data_spark_column_names[0]).asc(),
NATURAL_ORDER_COLUMN_NAME,
)
temp_rank_column = verify_temp_column_name(sdf, "__rank__")
sdf = (
sdf.withColumn(temp_rank_column, F.row_number().over(window))
.filter(F.col(temp_rank_column) <= n)
.drop(temp_rank_column)
).drop(NATURAL_ORDER_COLUMN_NAME)
internal = InternalFrame(
spark_frame=sdf,
index_spark_columns=(
[scol_for(sdf, col) for col in groupkey_col_names]
+ [
scol_for(sdf, SPARK_INDEX_NAME_FORMAT(i + len(self._groupkeys)))
for i in range(self._psdf._internal.index_level)
]
),
index_names=(
[psser._column_label for psser in self._groupkeys]
+ self._psdf._internal.index_names
),
index_fields=(
[
psser._internal.data_fields[0].copy(name=name)
for psser, name in zip(self._groupkeys, groupkey_col_names)
]
+ [
field.copy(name=SPARK_INDEX_NAME_FORMAT(i + len(self._groupkeys)))
for i, field in enumerate(self._psdf._internal.index_fields)
]
),
column_labels=[self._psser._column_label],
data_spark_columns=[scol_for(sdf, self._psser._internal.data_spark_column_names[0])],
data_fields=[self._psser._internal.data_fields[0]],
)
return first_series(DataFrame(internal))
# TODO: add keep parameter
def nlargest(self, n: int = 5) -> Series:
"""
Return the first n rows ordered by columns in descending order in group.
Return the first n rows with the smallest values in columns, in descending order.
The columns that are not specified are returned as well, but not used for ordering.
Parameters
----------
n : int
Number of items to retrieve.
See Also
--------
pyspark.pandas.Series.nlargest
pyspark.pandas.DataFrame.nlargest
Examples
--------
>>> df = ps.DataFrame({'a': [1, 1, 1, 2, 2, 2, 3, 3, 3],
... 'b': [1, 2, 2, 2, 3, 3, 3, 4, 4]}, columns=['a', 'b'])
>>> df.groupby(['a'])['b'].nlargest(1).sort_index() # doctest: +NORMALIZE_WHITESPACE
a
1 1 2
2 4 3
3 7 4
Name: b, dtype: int64
"""
if self._psser._internal.index_level > 1:
raise ValueError("nlargest do not support multi-index now")
groupkey_col_names = [SPARK_INDEX_NAME_FORMAT(i) for i in range(len(self._groupkeys))]
sdf = self._psser._internal.spark_frame.select(
*[scol.alias(name) for scol, name in zip(self._groupkeys_scols, groupkey_col_names)],
*[
scol.alias(SPARK_INDEX_NAME_FORMAT(i + len(self._groupkeys)))
for i, scol in enumerate(self._psser._internal.index_spark_columns)
],
self._psser.spark.column,
NATURAL_ORDER_COLUMN_NAME,
)
window = Window.partitionBy(*groupkey_col_names).orderBy(
scol_for(sdf, self._psser._internal.data_spark_column_names[0]).desc(),
NATURAL_ORDER_COLUMN_NAME,
)
temp_rank_column = verify_temp_column_name(sdf, "__rank__")
sdf = (
sdf.withColumn(temp_rank_column, F.row_number().over(window))
.filter(F.col(temp_rank_column) <= n)
.drop(temp_rank_column)
).drop(NATURAL_ORDER_COLUMN_NAME)
internal = InternalFrame(
spark_frame=sdf,
index_spark_columns=(
[scol_for(sdf, col) for col in groupkey_col_names]
+ [
scol_for(sdf, SPARK_INDEX_NAME_FORMAT(i + len(self._groupkeys)))
for i in range(self._psdf._internal.index_level)
]
),
index_names=(
[psser._column_label for psser in self._groupkeys]
+ self._psdf._internal.index_names
),
index_fields=(
[
psser._internal.data_fields[0].copy(name=name)
for psser, name in zip(self._groupkeys, groupkey_col_names)
]
+ [
field.copy(name=SPARK_INDEX_NAME_FORMAT(i + len(self._groupkeys)))
for i, field in enumerate(self._psdf._internal.index_fields)
]
),
column_labels=[self._psser._column_label],
data_spark_columns=[scol_for(sdf, self._psser._internal.data_spark_column_names[0])],
data_fields=[self._psser._internal.data_fields[0]],
)
return first_series(DataFrame(internal))
# TODO: add bins, normalize parameter
def value_counts(
self, sort: Optional[bool] = None, ascending: Optional[bool] = None, dropna: bool = True
) -> Series:
"""
Compute group sizes.
Parameters
----------
sort : boolean, default None
Sort by frequencies.
ascending : boolean, default False
Sort in ascending order.
dropna : boolean, default True
Don't include counts of NaN.
See Also
--------
pyspark.pandas.Series.groupby
pyspark.pandas.DataFrame.groupby
Examples
--------
>>> df = ps.DataFrame({'A': [1, 2, 2, 3, 3, 3],
... 'B': [1, 1, 2, 3, 3, np.nan]},
... columns=['A', 'B'])
>>> df
A B
0 1 1.0
1 2 1.0
2 2 2.0
3 3 3.0
4 3 3.0
5 3 NaN
>>> df.groupby('A')['B'].value_counts().sort_index() # doctest: +NORMALIZE_WHITESPACE
A B
1 1.0 1
2 1.0 1
2.0 1
3 3.0 2
Name: count, dtype: int64
Don't include counts of NaN when dropna is False.
>>> df.groupby('A')['B'].value_counts(
... dropna=False).sort_index() # doctest: +NORMALIZE_WHITESPACE
A B
1 1.0 1
2 1.0 1
2.0 1
3 3.0 2
NaN 1
Name: count, dtype: int64
"""
warnings.warn(
"The resulting Series will have a fixed name of 'count' from 4.0.0.",
FutureWarning,
)
groupkeys = self._groupkeys + self._agg_columns
groupkey_names = [SPARK_INDEX_NAME_FORMAT(i) for i in range(len(groupkeys))]
groupkey_cols = [s.spark.column.alias(name) for s, name in zip(groupkeys, groupkey_names)]
sdf = self._psdf._internal.spark_frame
agg_column = self._agg_columns[0]._internal.data_spark_column_names[0]
sdf = sdf.groupby(*groupkey_cols).count().withColumnRenamed("count", agg_column)
if self._dropna:
_groupkey_column_names = groupkey_names[: len(self._groupkeys)]
sdf = sdf.dropna(subset=_groupkey_column_names)
if dropna:
_agg_columns_names = groupkey_names[len(self._groupkeys) :]
sdf = sdf.dropna(subset=_agg_columns_names)
if sort:
if ascending:
sdf = sdf.orderBy(scol_for(sdf, agg_column).asc())
else:
sdf = sdf.orderBy(scol_for(sdf, agg_column).desc())
internal = InternalFrame(
spark_frame=sdf,
index_spark_columns=[scol_for(sdf, col) for col in groupkey_names],
index_names=[psser._column_label for psser in groupkeys],
index_fields=[
psser._internal.data_fields[0].copy(name=name)
for psser, name in zip(groupkeys, groupkey_names)
],
column_labels=[("count",)],
data_spark_columns=[scol_for(sdf, agg_column)],
)
return first_series(DataFrame(internal))
def unique(self) -> Series:
"""
Return unique values in group.
Unique is returned in order of unknown. It does NOT sort.
See Also
--------
pyspark.pandas.Series.unique
pyspark.pandas.Index.unique
Examples
--------
>>> df = ps.DataFrame({'a': [1, 1, 1, 2, 2, 2, 3, 3, 3],
... 'b': [1, 2, 2, 2, 3, 3, 3, 4, 4]}, columns=['a', 'b'])
>>> df.groupby(['a'])['b'].unique().sort_index() # doctest: +SKIP
a
1 [1, 2]
2 [2, 3]
3 [3, 4]
Name: b, dtype: object
"""
return self._reduce_for_stat_function(F.collect_set)
def is_multi_agg_with_relabel(**kwargs: Any) -> bool:
"""
Check whether the kwargs pass to .agg look like multi-agg with relabling.
Parameters
----------
**kwargs : dict
Returns
-------
bool
Examples
--------
>>> is_multi_agg_with_relabel(a='max')
False
>>> is_multi_agg_with_relabel(a_max=('a', 'max'),
... a_min=('a', 'min'))
True
>>> is_multi_agg_with_relabel()
False
"""
if not kwargs:
return False
return all(isinstance(v, tuple) and len(v) == 2 for v in kwargs.values())
def normalize_keyword_aggregation(
kwargs: Dict[str, Tuple[Name, str]],
) -> Tuple[Dict[Name, List[str]], List[str], List[Tuple]]:
"""
Normalize user-provided kwargs.
Transforms from the new ``Dict[str, NamedAgg]`` style kwargs
to the old defaultdict[str, List[scalar]].
Parameters
----------
kwargs : dict
Returns
-------
aggspec : dict
The transformed kwargs.
columns : List[str]
The user-provided keys.
order : List[Tuple[str, str]]
Pairs of the input and output column names.
Examples
--------
>>> normalize_keyword_aggregation({'output': ('input', 'sum')})
(defaultdict(<class 'list'>, {'input': ['sum']}), ['output'], [('input', 'sum')])
"""
aggspec: Dict[Union[Any, Tuple], List[str]] = defaultdict(list)
order: List[Tuple] = []
columns, pairs = zip(*kwargs.items())
for column, aggfunc in pairs:
if column in aggspec:
aggspec[column].append(aggfunc)
else:
aggspec[column] = [aggfunc]
order.append((column, aggfunc))
# For MultiIndex, we need to flatten the tuple, e.g. (('y', 'A'), 'max') needs to be
# flattened to ('y', 'A', 'max'), it won't do anything on normal Index.
if isinstance(order[0][0], tuple):
order = [(*levs, method) for levs, method in order]
return aggspec, list(columns), order
def _test() -> None:
import os
import doctest
import sys
import numpy
from pyspark.sql import SparkSession
import pyspark.pandas.groupby
os.chdir(os.environ["SPARK_HOME"])
globs = pyspark.pandas.groupby.__dict__.copy()
globs["np"] = numpy
globs["ps"] = pyspark.pandas
spark = (
SparkSession.builder.master("local[4]")
.appName("pyspark.pandas.groupby tests")
.getOrCreate()
)
(failure_count, test_count) = doctest.testmod(
pyspark.pandas.groupby,
globs=globs,
optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE,
)
spark.stop()
if failure_count:
sys.exit(-1)
if __name__ == "__main__":
_test()