# # 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 similar to pandas GroupBy. """ from abc import ABCMeta, abstractmethod import sys import inspect from collections import OrderedDict, namedtuple from distutils.version import LooseVersion from functools import partial from itertools import product from typing import ( Any, Callable, Dict, Generic, Iterator, Mapping, List, Optional, Sequence, Set, Tuple, Union, cast, TYPE_CHECKING, ) import warnings import pandas as pd from pandas.api.types import is_hashable, is_list_like if LooseVersion(pd.__version__) >= LooseVersion("1.3.0"): from pandas.core.common import _builtin_table else: from pandas.core.base import SelectionMixin _builtin_table = SelectionMixin._builtin_table from pyspark.sql import Column, DataFrame as SparkDataFrame, Window, functions as F from pyspark.sql.types import ( # noqa: F401 DataType, FloatType, 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, ) 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.utils import ( align_diff_frames, is_name_like_tuple, is_name_like_value, name_like_string, same_anchor, scol_for, verify_temp_column_name, ) 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 # noqa: F401 (SPARK-34943) # 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 _cleanup_and_return(self, psdf: DataFrame) -> 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(kwargs) # type: ignore 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 = OrderedDict([(col, func_or_funcs) for col in agg_cols]) psdf = DataFrame( GroupBy._spark_groupby(self._psdf, func_or_funcs, self._groupkeys) ) # type: DataFrame 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: 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() if relabeling: psdf = psdf[order] psdf.columns = columns 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], ) [docs] 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, only_numeric=False) # TODO: We should fix See Also when Series implementation is finished. [docs] def first(self) -> FrameLike: """ Compute first of group values. See Also -------- pyspark.pandas.Series.groupby pyspark.pandas.DataFrame.groupby """ return self._reduce_for_stat_function(F.first, only_numeric=False) [docs] def last(self) -> FrameLike: """ Compute last of group values. See Also -------- pyspark.pandas.Series.groupby pyspark.pandas.DataFrame.groupby """ return self._reduce_for_stat_function( lambda col: F.last(col, ignorenulls=True), only_numeric=False ) [docs] def max(self) -> FrameLike: """ Compute max of group values. See Also -------- pyspark.pandas.Series.groupby pyspark.pandas.DataFrame.groupby """ return self._reduce_for_stat_function(F.max, only_numeric=False) # TODO: examples should be updated. [docs] def mean(self) -> FrameLike: """ Compute mean of groups, excluding missing values. 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]}, columns=['A', 'B', 'C']) 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 A 1 3.0 1.333333 2 4.0 1.500000 """ return self._reduce_for_stat_function(F.mean, only_numeric=True) [docs] def min(self) -> FrameLike: """ Compute min of group values. See Also -------- pyspark.pandas.Series.groupby pyspark.pandas.DataFrame.groupby """ return self._reduce_for_stat_function(F.min, only_numeric=False) # TODO: sync the doc. [docs] def std(self, ddof: int = 1) -> FrameLike: """ Compute standard deviation of groups, excluding missing values. Parameters ---------- ddof : int, default 1 Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. See Also -------- pyspark.pandas.Series.groupby pyspark.pandas.DataFrame.groupby """ assert ddof in (0, 1) return self._reduce_for_stat_function( F.stddev_pop if ddof == 0 else F.stddev_samp, only_numeric=True ) [docs] def sum(self) -> FrameLike: """ Compute sum of group values See Also -------- pyspark.pandas.Series.groupby pyspark.pandas.DataFrame.groupby """ return self._reduce_for_stat_function(F.sum, only_numeric=True) # TODO: sync the doc. [docs] def var(self, ddof: int = 1) -> FrameLike: """ Compute variance of groups, excluding missing values. Parameters ---------- ddof : int, default 1 Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. See Also -------- pyspark.pandas.Series.groupby pyspark.pandas.DataFrame.groupby """ assert ddof in (0, 1) return self._reduce_for_stat_function( F.var_pop if ddof == 0 else F.var_samp, only_numeric=True ) # TODO: skipna should be implemented. [docs] def all(self) -> FrameLike: """ Returns True if all values in the group are 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').all().sort_index() # doctest: +NORMALIZE_WHITESPACE B A 1 True 2 False 3 False 4 True 5 False """ return self._reduce_for_stat_function( lambda col: F.min(F.coalesce(col.cast("boolean"), SF.lit(True))), only_numeric=False ) # TODO: skipna should be implemented. [docs] 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"), SF.lit(False))), only_numeric=False ) # TODO: groupby multiply columns should be implemented. [docs] 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)) [docs] 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 ) [docs] 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 _: SF.lit(0)) ._cum(F.count, True, part_cols=self._groupkeys_scols, ascending=ascending) - 1 ) internal = ret._internal.resolved_copy return first_series(DataFrame(internal)) [docs] 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, ) [docs] 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, ) [docs] 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, ) [docs] 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, ) [docs] 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[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 pandas friendly style as below: >>> def pandas_div(x) -> ps.DataFrame["a": float, "b": float]: ... return x[['B', 'C']] / x[['B', 'C']] >>> pdf = pd.DataFrame({'B': [1.], 'C': [3.]}) >>> def plus_one(x) -> ps.DataFrame[zip(pdf.columns, pdf.dtypes)]: ... return x[['B', 'C']] / x[['B', 'C']] 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://koalas.readthedocs.io/en/latest/user_guide/options.html#default-index-type>`_. .. 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: +NORMALIZE_WHITESPACE 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[float, float]: ... return x[['B', 'C']] / x[['B', 'C']] >>> g.apply(pandas_div).sort_index() # doctest: +NORMALIZE_WHITESPACE c0 c1 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[np.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() 0 2 1 3 2 6 Name: B, dtype: int64 You can also return a scalar value as a aggregated value of the group: >>> def plus_length(x) -> np.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) -> np.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 isinstance(func, Callable): # type: ignore 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 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. limit = get_option("compute.shortcut_limit") pdf = psdf.head(limit + 1)._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) 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 = cast(DataFrameType, return_type).fields return_schema = cast(DataFrameType, return_type).spark_type 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 not is_series_groupby and LooseVersion(pd.__version__) < LooseVersion("0.25"): # `groupby.apply` in pandas<0.25 runs the functions twice for the first group. # https://github.com/pandas-dev/pandas/pull/24748 should_skip_first_call = True def wrapped_func( df: Union[pd.DataFrame, pd.Series], *a: Any, **k: Any ) -> Union[pd.DataFrame, pd.Series]: nonlocal should_skip_first_call if should_skip_first_call: should_skip_first_call = False if should_return_series: return pd.Series() else: return pd.DataFrame() else: return pandas_apply(df, *a, **k) else: wrapped_func = pandas_apply if is_series_groupby: pdf_or_ser = pdf.groupby(groupkey_names)[name].apply(wrapped_func, *args, **kwargs) else: pdf_or_ser = pdf.groupby(groupkey_names).apply(wrapped_func, *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_infer_schema, ) if should_infer_schema: # If schema is inferred, we can restore indexes too. internal = psdf_from_pandas._internal.with_new_sdf( spark_frame=sdf, 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 [docs] 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 isinstance(func, Callable): # type: ignore 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 first_series(psdf) # type: ignore else: return psdf # type: ignore @staticmethod def _prepare_group_map_apply( psdf: DataFrame, groupkeys: List[Series], agg_columns: List[Series] ) -> Tuple[DataFrame, List[Label], List[str]]: groupkey_labels = [ verify_temp_column_name(psdf, "__groupkey_{}__".format(i)) for i in range(len(groupkeys)) ] # type: List[Label] 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. """ arguments_for_restore_index = psdf._internal.arguments_for_restore_index 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 index. pandas seems restoring # 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/pyspark.pandas/issues/628. pdf, _, _, _, _ = InternalFrame.prepare_pandas_frame(pdf, retain_index=retain_index) # Just positionally map the column names to given schema's. pdf.columns = return_schema.names return pdf return rename_output [docs] 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, ) # TODO: add axis parameter [docs] 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._cleanup_and_return(DataFrame(internal)) # TODO: add axis parameter [docs] 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._cleanup_and_return(DataFrame(internal)) [docs] 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 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 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 """ 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=(method is not None), ) [docs] 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) backfill = bfill [docs] 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) pad = ffill 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 tmp_col = verify_temp_column_name(sdf, "__row_number__") # This part is handled differently depending on whether it is a tail or a head. window = ( Window.partitionBy(*groupkey_scols).orderBy(F.col(NATURAL_ORDER_COLUMN_NAME).asc()) if asc else Window.partitionBy(*groupkey_scols).orderBy( F.col(NATURAL_ORDER_COLUMN_NAME).desc() ) ) sdf = ( sdf.withColumn(tmp_col, F.row_number().over(window)) .filter(F.col(tmp_col) <= n) .drop(tmp_col) ) internal = psdf._internal.with_new_sdf(sdf) return self._cleanup_and_return(DataFrame(internal).drop(groupkey_labels, axis=1)) [docs] 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 """ return self._limit(n, asc=True) [docs] 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 """ return self._limit(n, asc=False) [docs] 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, ) [docs] 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://koalas.readthedocs.io/en/latest/user_guide/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[np.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[np.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 isinstance(func, Callable): # type: ignore 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. 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(pdf) # type: DataFrame 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._cleanup_and_return(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 = cast(SeriesType, return_type).dtype spark_type = cast(SeriesType, 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._cleanup_and_return(DataFrame(internal)) [docs] 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: stat_function = lambda col: F.countDistinct(col) else: stat_function = lambda col: ( 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, only_numeric=False) 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 in the near future. 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 in the near future. 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) [docs] 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 = SF.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._cleanup_and_return(DataFrame(internal)) [docs] def median(self, numeric_only: bool = True, 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 True Include only float, int, boolean columns. False is not supported. This parameter is mainly for pandas compatibility. 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__ ) stat_function = lambda col: F.percentile_approx(col, 0.5, accuracy) return self._reduce_for_stat_function(stat_function, only_numeric=numeric_only) def _reduce_for_stat_function( self, sfun: Callable[[Column], Column], only_numeric: bool ) -> FrameLike: groupkey_names = [SPARK_INDEX_NAME_FORMAT(i) for i in range(len(self._groupkeys))] groupkey_scols = [s.alias(name) for s, name in zip(self._groupkeys_scols, groupkey_names)] agg_columns = [ psser for psser in self._agg_columns if isinstance(psser.spark.data_type, NumericType) or not only_numeric ] sdf = self._psdf._internal.spark_frame.select( *groupkey_scols, *[psser.spark.column for psser in agg_columns] ) 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, ) psdf = DataFrame(internal) # type: DataFrame 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).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) 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: 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._cleanup_and_return(psdf) @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 are 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) # type: ignore 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(cast(SeriesGroupBy, 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 _cleanup_and_return(self, psdf: DataFrame) -> DataFrame: return psdf # TODO: Implement 'percentiles', 'include', and 'exclude' arguments. # TODO: Add ``DataFrame.select_dtypes`` to See Also when 'include' # and 'exclude' arguments are implemented. [docs] 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") 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) # type: ignore 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 _cleanup_and_return(self, psdf: DataFrame) -> Series: return first_series(psdf).rename().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 [docs] 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 [docs] 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 [docs] 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, 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')['B'].value_counts().sort_index() # doctest: +NORMALIZE_WHITESPACE A B 1 1 1 2 1 1 2 1 3 3 3 Name: B, dtype: int64 """ 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 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=[self._agg_columns[0]._column_label], data_spark_columns=[scol_for(sdf, agg_column)], ) return first_series(DataFrame(internal)) [docs] def unique(self) -> Series: """ Return unique values in group. Uniques are 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, only_numeric=False) 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 OrderedDict[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')}) (OrderedDict([('input', ['sum'])]), ['output'], [('input', 'sum')]) """ # this is due to python version issue, not sure the impact on pandas-on-Spark PY36 = sys.version_info >= (3, 6) if not PY36: kwargs = OrderedDict(sorted(kwargs.items())) # TODO(Py35): When we drop python 3.5, change this to defaultdict(list) aggspec = OrderedDict() # type: Dict[Union[Any, Tuple], List[str]] order = [] # type: 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()