+ ŜiNa0t$Rt^RIHt^RIHtHtHtHtH t H t ^RI t ^RI H t Ht^RIHtHtHtHtHtHtHtHtHtHt^RIt^RIt^RIHtHt^RI H!t!^RI"H#uH$t%^RI&H't'^R I(H)t)H*t*H+t+H,t,H-t-H.t.H/t/H0t0H1t1^R I2H3t4^R I5H6t6H7t7H8t8^R I9H:t:^R I;Ht>H?t?^RI@HAtAHBtBHCtCHDtDHEtEHFtFHGtGHHtHHItIHJtJHKtKHLtLHMtM^RINHOtOHPtPHQtQ^RIRHStSHTtT^RIUHVtV^RIWHXtXHYtYHZtZH[t[H\t\H]t]^RI^H_t_^RI`Hata^RIbHctcHdtd^RIeHfuHgth^RIiHjtj^RIkHltl^RImHntnHotoHptp^RIqHrtr^RIsHtttHutu^RIvHwtwHxtxHyty^RIzH{t{^RI|H}t}^RI~Ht^R IHtHtHt]'d-^R!IHt^R"IHt^R#I(HtHtHt^R$IHt^R%IHt^R&IHtHtHtR'tR(tR)tR*t]!R+R,]c44t]]],,]].]3,,]]].]3,,,] ]]3,,tR-]R.&!R/R0]c]d].,]t4t]!R1]lR27t!R3R4]].,4tR9R5R6lltR7R8ltR#):a Provide the groupby split-apply-combine paradigm. Define the GroupBy class providing the base-class of operations. The SeriesGroupBy and DataFrameGroupBy sub-class (defined in pandas.core.groupby.generic) expose these user-facing objects to provide specific functionality. ) annotations)CallableHashableIterableIteratorMappingSequenceN)partialwraps) TYPE_CHECKING ConcatenateLiteralSelf TypeAliasTypeVarUnioncastfinaloverload) Timestamplib)rank_1d)NA) AnyArrayLike ArrayLikeDtypeObj IndexLabelIntervalClosedTypeNDFrameTPositionalIndexer RandomStatenpt)function)AbstractMethodError DataErrorPandas4Warning)cache_readonly)find_stack_level)coerce_indexer_dtypeensure_dtype_can_hold_na) is_bool is_bool_dtypeis_float_dtype is_hashable is_integeris_integer_dtype is_list_likeis_numeric_dtypeis_object_dtype is_scalaris_string_dtypeneeds_i8_conversion pandas_dtype)isnana_value_for_dtypenotna) algorithmssample)executor)ArrowExtensionArrayBaseMaskedArrayExtensionArray FloatingArray IntegerArray SparseArray) StringDtype)ArrowStringArray) PandasObjectSelectionMixin) DataFrame)NDFrame)basenumba_ops) get_grouper)GroupByIndexingMixinGroupByNthSelector)Index MultiIndex default_index)ensure_block_shape)Series)get_group_index_sorter)get_jit_argumentsmaybe_use_numbaprepare_function_arguments) BaseOffset) Timedelta)AnyPT) BaseIndexer) Resampler)ExpandingGroupbyExponentialMovingWindowGroupbyRollingGroupbya Compute {fname} of group values. Parameters ---------- numeric_only : bool, default {no} Include only float, int, boolean columns. .. versionchanged:: 2.0.0 numeric_only no longer accepts ``None``. min_count : int, default {mc} 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. engine : str, default None {e} * ``'cython'`` : Runs rolling apply through C-extensions from cython. * ``'numba'`` : Runs rolling apply through JIT compiled code from numba. Only available when ``raw`` is set to ``True``. * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba`` engine_kwargs : dict, default None {ek} * For ``'cython'`` engine, there are no accepted ``engine_kwargs`` * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil`` and ``parallel`` dictionary keys. The values must either be ``True`` or ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is ``{{'nopython': True, 'nogil': False, 'parallel': False}}`` and will be applied to both the ``func`` and the ``apply`` groupby aggregation. Returns ------- Series or DataFrame Computed {fname} of values within each group. See Also -------- SeriesGroupBy.min : Return the min of the group values. DataFrameGroupBy.min : Return the min of the group values. SeriesGroupBy.max : Return the max of the group values. DataFrameGroupBy.max : Return the max of the group values. SeriesGroupBy.sum : Return the sum of the group values. DataFrameGroupBy.sum : Return the sum of the group values. Examples -------- {example} aV Compute {fname} of group values. Parameters ---------- numeric_only : bool, default {no} Include only float, int, boolean columns. .. versionchanged:: 2.0.0 numeric_only no longer accepts ``None``. min_count : int, default {mc} 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. skipna : bool, default {s} Exclude NA/null values. If the entire group is NA and ``skipna`` is ``True``, the result will be NA. .. versionchanged:: 3.0.0 engine : str, default None {e} * ``'cython'`` : Runs rolling apply through C-extensions from cython. * ``'numba'`` : Runs rolling apply through JIT compiled code from numba. Only available when ``raw`` is set to ``True``. * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba`` engine_kwargs : dict, default None {ek} * For ``'cython'`` engine, there are no accepted ``engine_kwargs`` * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil`` and ``parallel`` dictionary keys. The values must either be ``True`` or ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is ``{{'nopython': True, 'nogil': False, 'parallel': False}}`` and will be applied to both the ``func`` and the ``apply`` groupby aggregation. Returns ------- Series or DataFrame Computed {fname} of values within each group. See Also -------- SeriesGroupBy.min : Return the min of the group values. DataFrameGroupBy.min : Return the min of the group values. SeriesGroupBy.max : Return the max of the group values. DataFrameGroupBy.max : Return the max of the group values. SeriesGroupBy.sum : Return the sum of the group values. DataFrameGroupBy.sum : Return the sum of the group values. Examples -------- {example} a* Apply a ``func`` with arguments to this %(klass)s object and return its result. Use `.pipe` when you want to improve readability by chaining together functions that expect Series, DataFrames, GroupBy or Resampler objects. Instead of writing >>> h = lambda x, arg2, arg3: x + 1 - arg2 * arg3 >>> g = lambda x, arg1: x * 5 / arg1 >>> f = lambda x: x ** 4 >>> df = pd.DataFrame([["a", 4], ["b", 5]], columns=["group", "value"]) >>> h(g(f(df.groupby('group')), arg1=1), arg2=2, arg3=3) # doctest: +SKIP You can write >>> (df.groupby('group') ... .pipe(f) ... .pipe(g, arg1=1) ... .pipe(h, arg2=2, arg3=3)) # doctest: +SKIP which is much more readable. Parameters ---------- func : callable or tuple of (callable, str) Function to apply to this %(klass)s object or, alternatively, a `(callable, data_keyword)` tuple where `data_keyword` is a string indicating the keyword of `callable` that expects the %(klass)s object. *args : iterable, optional Positional arguments passed into `func`. **kwargs : dict, optional A dictionary of keyword arguments passed into `func`. Returns ------- %(klass)s The original object with the function `func` applied. See Also -------- Series.pipe : Apply a function with arguments to a series. DataFrame.pipe: Apply a function with arguments to a dataframe. apply : Apply function to each group instead of to the full %(klass)s object. Notes ----- See more `here `_ Examples -------- %(examples)s a Call function producing a same-indexed %(klass)s on each group. Returns a %(klass)s having the same indexes as the original object filled with the transformed values. Parameters ---------- func : function, str Function to apply to each group. See the Notes section below for requirements. Accepted inputs are: - String - Python function - Numba JIT function with ``engine='numba'`` specified. Only passing a single function is supported with this engine. If the ``'numba'`` engine is chosen, the function must be a user defined function with ``values`` and ``index`` as the first and second arguments respectively in the function signature. Each group's index will be passed to the user defined function and optionally available for use. If a string is chosen, then it needs to be the name of the groupby method you want to use. *args Positional arguments to pass to func. engine : str, default None * ``'cython'`` : Runs the function through C-extensions from cython. * ``'numba'`` : Runs the function through JIT compiled code from numba. * ``None`` : Defaults to ``'cython'`` or the global setting ``compute.use_numba`` engine_kwargs : dict, default None * For ``'cython'`` engine, there are no accepted ``engine_kwargs`` * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil`` and ``parallel`` dictionary keys. The values must either be ``True`` or ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is ``{'nopython': True, 'nogil': False, 'parallel': False}`` and will be applied to the function **kwargs Keyword arguments to be passed into func. Returns ------- %(klass)s %(klass)s with the same indexes as the original object filled with transformed values. See Also -------- %(klass)s.groupby.apply : Apply function ``func`` group-wise and combine the results together. %(klass)s.groupby.aggregate : Aggregate using one or more operations. %(klass)s.transform : Call ``func`` on self producing a %(klass)s with the same axis shape as self. Notes ----- Each group is endowed the attribute 'name' in case you need to know which group you are working on. The current implementation imposes three requirements on f: * f must return a value that either has the same shape as the input subframe or can be broadcast to the shape of the input subframe. For example, if `f` returns a scalar it will be broadcast to have the same shape as the input subframe. * if this is a DataFrame, f must support application column-by-column in the subframe. If f also supports application to the entire subframe, then a fast path is used starting from the second chunk. * f must not mutate groups. Mutation is not supported and may produce unexpected results. See :ref:`gotchas.udf-mutation` for more details. When using ``engine='numba'``, there will be no "fall back" behavior internally. The group data and group index will be passed as numpy arrays to the JITed user defined function, and no alternative execution attempts will be tried. The resulting dtype will reflect the return value of the passed ``func``, see the examples below. .. versionchanged:: 2.0.0 When using ``.transform`` on a grouped DataFrame and the transformation function returns a DataFrame, pandas now aligns the result's index with the input's index. You can call ``.to_numpy()`` on the result of the transformation function to avoid alignment. Examples -------- %(example)sc8]tRtRtRtRRltRtRRltRtR #) GroupByPlotiz= Class implementing the .plot attribute for groupby objects. c V^8dQhRRRR/#)groupbyGroupByreturnNone)formats"d/Users/mibo/.openclaw/workspace/.venv-ak/lib/python3.14/site-packages/pandas/core/groupby/groupby.py __annotate__GroupByPlot.__annotate__s   D c WnR#N_groupby)selfrfs&&rl__init__GroupByPlot.__init__s roc aaVV3RlpRVnVPPW0PP4#)c(<VP!S/SB#rq)plot)rtargskwargss&rlfGroupByPlot.__call__..fs99d-f- -rory)__name__rs_python_apply_general _selected_obj)rtrzr{r|s&jl rl__call__GroupByPlot.__call__s0 . }}221mm6Q6QRRrocV^8dQhRR/#renamestrrj)rks"rlrmrnsroc aaVV3RlpV#)cx<aaVVV3RlpSPPVSPP4#)c<<\VPS4!S/SB#rq)getattrry)rtrzr{rs&rlr|0GroupByPlot.__getattr__..attr..fstyy$/@@@ro)rsrr)rzr{r|rrtsjl rlattr%GroupByPlot.__getattr__..attrs, A==66q$--:U:UV Vrorj)rtrrsff rl __getattr__GroupByPlot.__getattr__s W  rorrN) r~ __module__ __qualname____firstlineno____doc__rurr__static_attributes__rjrorlrcrcs Srorcr _KeysArgTypec]tRtRt$]P 0R$m,tR]R&RtR]R&RtR ]R&R ]R&] R R l4t ] R Rl4t ] ] RRl44t ] ] RRl44t] ] RRl44t] R4t] ]R44t] RRl4t]RRl4t]RRl4tRRlt] RR l4t] R!R"l4tR#tR#)% BaseGroupByi group_keyskeyslevelops.BaseGrouper_grouperN_KeysArgType | NoneIndexLabel | NoneboolcV^8dQhRR/#rerhintrj)rks"rlrmBaseGroupBy.__annotate__%%%roc .VPP#rqrngroupsrts&rl__len__BaseGroupBy.__len__s}}$$$rocV^8dQhRR/#)rerhrrj)rks"rlrmrs%%#%roc ,\PV4#rq)object__repr__rs&rlrBaseGroupBy.__repr__st$$rocV^8dQhRR/#)rerhzdict[Hashable, Index]rj)rks"rlrmrsI$I$-I$roc >\VP\4'di\VP4^8XdO\P !RVP^, RVP^, R2\ \4R7VPP#)a Dict {group name -> group labels}. This property provides a dictionary representation of the groupings formed during a groupby operation, where each key represents a unique group value from the specified column(s), and each value is a list of index labels that belong to that group. See Also -------- core.groupby.DataFrameGroupBy.get_group : Retrieve group from a ``DataFrameGroupBy`` object with provided name. core.groupby.SeriesGroupBy.get_group : Retrieve group from a ``SeriesGroupBy`` object with provided name. core.resample.Resampler.get_group : Retrieve group from a ``Resampler`` object with provided name. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b"] >>> ser = pd.Series([1, 2, 3], index=lst) >>> ser a 1 a 2 b 3 dtype: int64 >>> ser.groupby(level=0).groups {'a': ['a', 'a'], 'b': ['b']} For DataFrameGroupBy: >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]] >>> df = pd.DataFrame(data, columns=["a", "b", "c"]) >>> df a b c 0 1 2 3 1 1 5 6 2 7 8 9 >>> df.groupby(by="a").groups {1: [0, 1], 7: [2]} For Resampler: >>> ser = pd.Series( ... [1, 2, 3, 4], ... index=pd.DatetimeIndex( ... ["2023-01-01", "2023-01-15", "2023-02-01", "2023-02-15"] ... ), ... ) >>> ser 2023-01-01 1 2023-01-15 2 2023-02-01 3 2023-02-15 4 dtype: int64 >>> ser.resample("MS").groups {Timestamp('2023-01-01 00:00:00'): np.int64(2), Timestamp('2023-02-01 00:00:00'): np.int64(4)} zWIn a future version, the keys of `groups` will be a tuple with a single element, e.g. (z,) , instead of a scalar, e.g. z, when grouping by a list with a single element. Use ``df.groupby(by='a').groups`` instead of ``df.groupby(by=['a']).groups`` to avoid this warning) stacklevel) isinstancerlistlenwarningswarnr%r'rgroupsrs&rlrBaseGroupBy.groupssB dii & &3tyy>Q+> MM66:iil^D--1YYq\N;SS +- }}###rocV^8dQhRR/#rrj)rks"rlrmr*rroc .VPP#rqrrs&rlrBaseGroupBy.ngroups(s}}$$$rocV^8dQhRR/#)rerhz$dict[Hashable, npt.NDArray[np.intp]]rj)rks"rlrmr/sC%C%=C%roc .VPP#)a Dict {group name -> group indices}. The dictionary keys represent the group labels (e.g., timestamps for a time-based resampling operation), and the values are arrays of integer positions indicating where the elements of each group are located in the original data. This property is particularly useful when working with resampled data, as it provides insight into how the original time-series data has been grouped. See Also -------- core.groupby.DataFrameGroupBy.indices : Provides a mapping of group rows to positions of the elements. core.groupby.SeriesGroupBy.indices : Provides a mapping of group rows to positions of the elements. core.resample.Resampler.indices : Provides a mapping of group rows to positions of the elements. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b"] >>> ser = pd.Series([1, 2, 3], index=lst) >>> ser a 1 a 2 b 3 dtype: int64 >>> ser.groupby(level=0).indices {'a': array([0, 1]), 'b': array([2])} For DataFrameGroupBy: >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]] >>> df = pd.DataFrame( ... data, columns=["a", "b", "c"], index=["owl", "toucan", "eagle"] ... ) >>> df a b c owl 1 2 3 toucan 1 5 6 eagle 7 8 9 >>> df.groupby(by=["a"]).indices {np.int64(1): array([0, 1]), np.int64(7): array([2])} For Resampler: >>> ser = pd.Series( ... [1, 2, 3, 4], ... index=pd.DatetimeIndex( ... ["2023-01-01", "2023-01-15", "2023-02-01", "2023-02-15"] ... ), ... ) >>> ser 2023-01-01 1 2023-01-15 2 2023-02-01 3 2023-02-15 4 dtype: int64 >>> ser.resample("MS").indices defaultdict(, {Timestamp('2023-01-01 00:00:00'): [0, 1], Timestamp('2023-02-01 00:00:00'): [2, 3]}) )rindicesrs&rlrBaseGroupBy.indices-sJ}}$$$roc  ~aRo\V4'd+VPP\P.4#\ V\ 4'd-\ ;QJd.RV4FNK 5M !RV44p\VP4^8d \\VP44pMRp\ V\ 4'd\ V\ 4'gRp\V4h\V4\V48XgVPV,#V3RlV4p\ ;QJd.R\WQRR 74FNK 5M!R\WQRR 744pMS!V4pV!V4pVPPV.4# \dpRp\T4ThRp?ii;i) zL Safe get multiple indices, translate keys for datelike to underlying repr. c\V\P4'dR#\V\P4'dR#R#)c\V4#rq)rkeys&rl?BaseGroupBy._get_index..get_converter..s9S>roc,\V4P#rq)rasm8rs&rlrrs9S>#6#6rocV#rqrjrs&rlrrs3ro)rdatetimenp datetime64)ss&rl get_converter-BaseGroupBy._get_index..get_converter{s:!X..//11Ar}}--66&&roc3h"TF(p\V4'd\PMTxK* R#5irq)r7rnan).0comps& rl )BaseGroupBy._get_index..s!IDD4::47Ds02Nz>> h = lambda x, arg2, arg3: x + 1 - arg2 * arg3 >>> g = lambda x, arg1: x * 5 / arg1 >>> f = lambda x: x**4 >>> df = pd.DataFrame([["a", 4], ["b", 5]], columns=["group", "value"]) >>> h(g(f(df.groupby("group")), arg1=1), arg2=2, arg3=3) # doctest: +SKIP You can write >>> ( ... df.groupby("group").pipe(f).pipe(g, arg1=1).pipe(h, arg2=2, arg3=3) ... ) # doctest: +SKIP which is much more readable. Parameters ---------- func : callable or tuple of (callable, str) Function to apply to this GroupBy object or, alternatively, a `(callable, data_keyword)` tuple where `data_keyword` is a string indicating the keyword of `callable` that expects the GroupBy object. *args : iterable, optional Positional arguments passed into `func`. **kwargs : dict, optional A dictionary of keyword arguments passed into `func`. Returns ------- GroupBy The return type of `func`. See Also -------- Series.pipe : Apply a function with arguments to a series. DataFrame.pipe : Apply a function with arguments to a dataframe. apply : Apply function to each group instead of to the full GroupBy object. Notes ----- See more `here `_ Examples -------- >>> df = pd.DataFrame({"A": "a b a b".split(), "B": [1, 2, 3, 4]}) >>> df A B 0 a 1 1 b 2 2 a 3 3 b 4 To get the difference between each groups maximum and minimum value in one pass, you can do >>> df.groupby("A").pipe(lambda x: x.max() - x.min()) B A a 2 b 2 )comrrs&&*,rlrrsVxx4T4V44rocV^8dQhRR/#rerhDataFrame | Seriesrj)rks"rlrmrs\-\-!3\-roc VPpVPp\V4'd\V4^8Xg"\V4'dL\V4^8Xd<\ V\ 4'd\V4^8Xd V^,pM \ V4hVPV4p\V4'g \ V4hVPPV,#)a" Construct DataFrame from group with provided name. Parameters ---------- name : object The name of the group to get as a DataFrame. Returns ------- Series or DataFrame Get the respective Series or DataFrame corresponding to the group provided. See Also -------- DataFrameGroupBy.groups: Dictionary representation of the groupings formed during a groupby operation. DataFrameGroupBy.indices: Provides a mapping of group rows to positions of the elements. SeriesGroupBy.groups: Dictionary representation of the groupings formed during a groupby operation. SeriesGroupBy.indices: Provides a mapping of group rows to positions of the elements. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b"] >>> ser = pd.Series([1, 2, 3], index=lst) >>> ser a 1 a 2 b 3 dtype: int64 >>> ser.groupby(level=0).get_group("a") a 1 a 2 dtype: int64 For DataFrameGroupBy: >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]] >>> df = pd.DataFrame( ... data, columns=["a", "b", "c"], index=["owl", "toucan", "eagle"] ... ) >>> df a b c owl 1 2 3 toucan 1 5 6 eagle 7 8 9 >>> df.groupby(by=["a"]).get_group((1,)) a b c owl 1 2 3 toucan 1 5 6 For Resampler: >>> ser = pd.Series( ... [1, 2, 3, 4], ... index=pd.DatetimeIndex( ... ["2023-01-01", "2023-01-15", "2023-02-01", "2023-02-15"] ... ), ... ) >>> ser 2023-01-01 1 2023-01-15 2 2023-02-01 3 2023-02-15 4 dtype: int64 >>> ser.resample("MS").get_group("2023-01-01") 2023-01-01 1 2023-01-15 2 dtype: int64 ) rrr0rrrrrriloc)rtrrrindss&& rl get_groupBaseGroupBy.get_groups\yy    CJ!O   3t9>$&&3t9>Awtn$t$4yy4. !!&&t,,rocV^8dQhRR/#)rerhz#Iterator[tuple[Hashable, NDFrameT]]rj)rks"rlrmr}sbb=broc VPpVPpVPPVP4p\ V4'd\ V4^8Xg'\V\4'd\ V4^8Xd RV4pV#)a Groupby iterator. This method provides an iterator over the groups created by the ``resample`` or ``groupby`` operation on the object. The method yields tuples where the first element is the label (group key) corresponding to each group or resampled bin, and the second element is the subset of the data that falls within that group or bin. Returns ------- Iterator Generator yielding a sequence of (name, subsetted object) for each group. See Also -------- Series.groupby : Group data by a specific key or column. DataFrame.groupby : Group DataFrame using mapper or by columns. DataFrame.resample : Resample a DataFrame. Series.resample : Resample a Series. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b"] >>> ser = pd.Series([1, 2, 3], index=lst) >>> ser a 1 a 2 b 3 dtype: int64 >>> for x, y in ser.groupby(level=0): ... print(f"{x}\n{y}\n") a a 1 a 2 dtype: int64 b b 3 dtype: int64 For DataFrameGroupBy: >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]] >>> df = pd.DataFrame(data, columns=["a", "b", "c"]) >>> df a b c 0 1 2 3 1 1 5 6 2 7 8 9 >>> for x, y in df.groupby(by=["a"]): ... print(f"{x}\n{y}\n") (1,) a b c 0 1 2 3 1 1 5 6 (7,) a b c 2 7 8 9 For Resampler: >>> ser = pd.Series( ... [1, 2, 3, 4], ... index=pd.DatetimeIndex( ... ["2023-01-01", "2023-01-15", "2023-02-01", "2023-02-15"] ... ), ... ) >>> ser 2023-01-01 1 2023-01-15 2 2023-02-01 3 2023-02-15 4 dtype: int64 >>> for x, y in ser.resample("MS"): ... print(f"{x}\n{y}\n") 2023-01-01 00:00:00 2023-01-01 1 2023-01-15 2 dtype: int64 2023-02-01 00:00:00 2023-02-01 3 2023-02-15 4 dtype: int64 c30"TF wrV3V3xK R#5irqrj)rrgroups& rlr'BaseGroupBy.__iter__..s?*#vuos) rrr get_iteratorrr0rrr)rtrrresults& rl__iter__BaseGroupBy.__iter__|sotyy ++D,>,>?   CJ!O tT " "s4yA~@?F rorj> rrsortrdropnagrouperas_indexobserved exclusionsr)r~rrrrE _hidden_attrs__annotations__rrrrrpropertyrrrrr&rrrrrrrrjrorlrrs\ .. 2 M $D $#E # % % % %  I$ I$V  % %  C% C%J 0* 0*d  & ) )K5Z \- \-| b brorOutputFrameOrSeries)boundc]tRtRt$RtR]R&R]R&]RR R ll4tR R lt]R Rl4t ]RRRll4t ]RRl4t ]RRRll4t ]RRRll4t RRRllt]RRl4tRRlt]RR/Rl4t]RR/Rl4tR R/R!R"llt]RR#R$ll4t]RR%R/R&R'lll4tR(R)lt]RR*R+ll4tRR,R-llt]R.RRR/R/l4t]R.RRR/R0l4t]R1R2l4t]R34t]RR4R5ll4t]]R6R7l44t]RR8R9ll4t ]RR:R;ll4t!]R<R=l4t"]RR>R?ll4t#]RR@RAll4t$]RRBRCll4t%]RRDREll4t&]RRFRGll4t']RRHRIll4t(]RJRKl4t)]RRLRMll4t*]RRNROll4t+]RRPRQll4t,]RRRRSll4t-]RRTRUll4t.]RRVRWll4t/]RXRYl4t0RRZR[llt1]R R/R\R]ll4t2]RR^R_ll4t3]RR`Rall4t4]RRbRcll4t5]RRdRell4t6]RRfRgll4t7]RRhRill4t8]]RjRkl44t9RRlRmllt:]RRnRoll4t;]RRpRqll4t<]RRrRsll4t=]RRtRull4t>]RRvRwll4t?]RRxRyll4t@]RRzR{ll4tA]RR|R}ll4tB]^R]CPR3R~Rll4tE]RRRll4tF]RRRll4tG]RRRll4tH]RRRll4tI]RRl4tJ]RRRll4tKRRRlltLRRltMRtNR#)rgi Class for grouping and aggregating relational data. See aggregate, transform, and apply functions on this object. It's easiest to use obj.groupby(...) to use GroupBy, but you can also do: :: grouped = groupby(obj, ...) Parameters ---------- obj : pandas object level : int, default None Level of MultiIndex groupings : list of Grouping objects Most users should ignore this exclusions : array-like, optional List of columns to exclude name : str Most users should ignore this Returns ------- **Attributes** groups : dict {group name -> group labels} len(grouped) : int Number of groups Notes ----- After grouping, see aggregate, apply, and transform functions. Here are some other brief notes about usage. When grouping by multiple groups, the result index will be a MultiIndex (hierarchical) by default. Iteration produces (key, group) tuples, i.e. chunking the data by group. So you can write code like: :: grouped = obj.groupby(keys) for key, group in grouped: # do something with the data Function calls on GroupBy, if not specially implemented, "dispatch" to the grouped data. So if you group a DataFrame and wish to invoke the std() method on each group, you can simply do: :: df.groupby(mapper).std() rather than :: df.groupby(mapper).aggregate(np.std) You can pass arguments to these "wrapped" functions, too. See the online documentation for full exposition on these topics and much more rrrrNFcHV^8dQhRRRRRRRRR R R RR R RR RR RR RR RR/ #)rerrrrrrrops.BaseGrouper | Nonerzfrozenset[Hashable] | None selectionrrrrrrrhrirj)rks"rlrmGroupBy.__annotate__-s&O&O &O"&O! &O ( &O / &O%&O&O&O&O&O&O &Oroc  `W`n\V\4'gQ\V44hW0nWpnW nWnWnWn Vf\VVVVV VPR7wrEpWn Wn W@n V'd\V4VnR#\4VnR#)N)rrrr)rrrHtyperrrrrrrLrrr frozensetr) rtrrrrrrrrrrrs &&&&&&&&&&&&rlruGroupBy.__init__,s$#w''2c2'    $ ?'2!{{ ( $G!  3=)J/9;rocV^8dQhRR/#)rerrrj)rks"rlrmr Us   roc WP9d\PW4#WP9d W,#\ R\ V4P RV R24h)'z' object has no attribute ')_internal_names_setr__getattribute__rAttributeErrorr"r~)rtrs&&rlrGroupBy.__getattr__Us\ ++ +**46 6 88 : T ##$$?vQ G  rocV^8dQhRR/#rrj)rks"rlrmr `s#roc aaa\\VP4V4oVVV3RlpWnV\P 9dVP W@P4#V\P9pVP VVPVV'*R7pVPP'dV'dVPV4pV#)z.currieddsQ((( (ro) is_transformnot_indexed_same) rr"rr~rIplotting_methodsrrtransformation_kernelsrhas_dropped_na_set_result_index_ordered)rtrrzr{r0r2r r|s&&jl @rl _op_via_applyGroupBy._op_via_apply_s D223T : )   4(( (--g7I7IJ Jt::: ++   % %%!-- ,  == ' ' 'L33F;F roc V^8dQhRRRR/#)rer3rr2rj)rks"rlrmr s >?>?>? >?roc  `^RIHpVP'dyV'gqVP'dSVPP pVPP pVPPpV!V^VVVRR7pEMV!V^R7pEMV'gV!V^R7pVPPp VP'd$VPPp V R8gp W,p V P'dzVP^,PV 4'gR\P !V P"4p VPP%V 4wrVP'V ^R7pMVP)V ^R7pM V!V^R7pVP*P,^8XdVP*P.pM*\1VP24'dVP2pMRp\5V\64'd VeWnVP9VP*RR7#) concatF)axisrlevelsnamesrr?Nrfmethod)pandas.core.reshape.concatr>rrr result_indexr@rArindexridshas_duplicatesaxesequalsr:unique1d_valuesget_indexer_non_uniquetakereindexrndimrr-rrrS __finalize__)rtvaluesr3r2r>r group_levels group_namesr axlabelsmasktargetindexer_rs&&&& rl_concat_objectsGroupBy._concat_objectss 6 ???<}}}!]]77 #}}33 "mm11 #'%  Q/!F+F##))B{{{**|X   Q)>)>r)B)B#,,RZZ8#\\@@H W153F+F 88==A 88==D  ) )??DD ff % %$*:K""488I">>roc V^8dQhRRRR/#)rer rrhrj)rks"rlrmr s) roc VPPpVPP'd2VPP'gVP V^R7pV#\ VPPRR7pVP V^R7pVP^R7pVPP'd&VP\\V44^R7pVP V^R7pV#)r<rBFcopy) rrHr is_monotonicr6set_axisrO result_ilocs sort_indexrQrQr)rtr rHoriginal_positionss&& rlr7!GroupBy._set_result_index_ordereds == % % %dmm.J.J.J__U_3FM#4==#=#=EJ!3!<"""* == ' ' '^^M#e*$=A^FFQ/ roc$V^8dQhRRRRRR/#)rer Series | DataFrameqsnpt.NDArray[np.float64] | NonerhrGrj)rks"rlrmr s$&&(&.L& &roc  \V\4'dVP4p\VPP 4pVeEVP ^RV 2\P!V\V4\V4,44\\\VPP4VPP4RR74FwpwrVVf V^8XdVfRMRW4, ^, 2pWQP9gK=VfVP ^WV4KUVP ^V\\P !V\V44RR74K V#)Nlevel_TrrHFra)rrSto_framerr groupingsinsertrtile enumeraterreversedrAget_group_levelscolumnsrOrepeat)rtr rk n_groupingsrrlevs&&& rl_insert_inaxis_grouperGroupBy._insert_inaxis_groupers+ ff % %__&F$--112 > MMVK=)2772s6{c"g7M+N  #, ,,- ..0 # E;D|#a'BJ!+"5"9!:;>>):MM!T/MM!T53B1Hu+UV-# 0 roc V^8dQhRRRR/#)rer rjrkrlrj)rks"rlrmr s"""" +"roc VP'g>VPWR7pVP4p\\ V44VnV#VP PpVe \W24pW1nV#)z Wraps the output of GroupBy aggregations into the expected result. Parameters ---------- result : Series, DataFrame Returns ------- Series or DataFrame rk) rrz _consolidaterQrrHrrG_insert_quantile_level)rtr rkrHs&&& rl_wrap_aggregated_outputGroupBy._wrap_aggregated_outputss(}}}000?F((*F(V5FL MM..E~/u9 L roc$V^8dQhRRRRRR/#)rerTrr3rr2rj)rks"rlrmr *s*((( (  (roc \V4hrqr#)rtdatarTr3r2s&&&&&rl_wrap_applied_outputGroupBy._wrap_applied_output*s"$''rocV^8dQhRR/#)rerrGrj)rks"rlrmr 7s   roc TVPPpVPPpVPPpVP V^R7P 4pVP p\V\4'dh\VPP4^8d \R4hVPP^,PpVPV4pVP V4P 4p\P!WB4wrV V VV3#)r<rBz_Grouping with more than 1 grouping labels and a MultiIndex is not supported with engine='numba')rrre _sorted_idsrPto_numpyrHrrPrrpNotImplementedErrorrget_level_valuesrgenerate_slices) rtrr sorted_index sorted_ids sorted_data index_data group_keysorted_index_datastartsendss && rl _numba_prepGroupBy._numba_prep6s--''}}11 ]].. ii 1i5>>@ ZZ j* - -4==**+a/)H //277I#44Y?J&OOL9BBD**:?        roc$V^8dQhRRRRRR/#)rerr dtype_mappingzdict[np.dtype, Any] engine_kwargsdict[str, bool] | Nonerj)rks"rlrmr Qs()))+). )roc VP'g \R4hVPpVP^8XdTMVP 4p\ P !VVR3/\V4BpVPPpVPPp VPP!V3RVRV /VBp VPPV P^&VPWPR7p VP^8Xd%V P!R4p VP"V nV #VP$V nV #)zX Perform groupby with a standard numerical aggregation function (e.g. mean) with Numba. z^^I.F))FK "\\FN rorc  VPpVPPpVP^8XdTMVP 4pVP V4wrr\ P!V4\WV^R7wr4\ P!V3/\V4Bp V !V V VV \VP4.VO5!p V P\P!V4^R7p VP pVP^8Xd RVP"/pV P%4p MRVP/pVP&!V 3RV/VB#)a Perform groupby transform routine with the numba engine. This routine mimics the data splitting routine of the DataSplitter class to generate the indices of each group in the sorted data and then passes the data and indices into a Numba jitted function. num_required_argsrBrrvrH)rrrerRrorrJ validate_udfrWgenerate_numba_transform_funcrUrrvrPrargsortrHrravel _constructor)rtrrrzr{r index_sortingrrrrrnumba_transform_funcr rH result_kwargss&&$*, rl_transform_with_numbaGroupBy._transform_with_numba|s.(( 22 YY!^T262B2B22F/lD!1 !  &CC  %m4 &      O    RZZ 6Q?  99>#TYY/M\\^F& 5M  FuF FFroc  VPpVP^8XdTMVP4pVPV4wrxr\P !V4\ WV^R7wr4\P!V3/\V4Bp V !V V VV\VP4.VO5!p VPPp VP^8Xd RVP/pV P4p MRVP/pVP!V 3RV /VBpVP 'g+VP#V4p\%\V44VnV#)a Perform groupby aggregation routine with the numba engine. This routine mimics the data splitting routine of the DataSplitter class to generate the indices of each group in the sorted data and then passes the data and indices into a Numba jitted function. rrrvrH)rrRrorrJrrWgenerate_numba_agg_funcrUrrvrrGrrrrrzrQrH)rtrrrzr{rrrrrrnumba_agg_funcr rHrress&&$*, rl_aggregate_with_numbaGroupBy._aggregate_with_numbas6((YY!^T262B2B22F/lD!1 !  77  %m4       O     ** 99>#TYY/M\\^F& 5MEeE}E}}}--c2C%c#h/CI roinclude_groupsc V^8dQhRRRR/#)rerrrhrrj)rks"rlrmr s"cHcHcHHcHroc aaaV'd \R4h\S\4'dg\VS4'dF\ VS4p\ V4'd V!S/SB#S'g S'd\RS 24hV#\ RS R24hS'g S'd4\ S4'd\S4VVV3Rl4pM \R4hSpVPW`P4#)a 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, Series or scalar. ``apply`` will then take care of combining the results back together into a single dataframe or series. ``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 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``. Parameters ---------- func : callable A callable that takes a dataframe as its first argument, and returns a dataframe, a series or a scalar. In addition the callable may take positional and keyword arguments. *args : tuple Optional positional arguments to pass to ``func``. include_groups : bool, default False When True, will attempt to apply ``func`` to the groupings in the case that they are columns of the DataFrame. If this raises a TypeError, the result will be computed with the groupings excluded. When False, the groupings will be excluded when applying ``func``. .. versionadded:: 2.2.0 .. versionchanged:: 3.0.0 The default changed from True to False, and True is no longer allowed. **kwargs : dict Optional keyword arguments to pass to ``func``. Returns ------- Series or DataFrame A pandas object with the result of applying ``func`` to each group. See Also -------- pipe : Apply function to the full GroupBy object instead of to each group. aggregate : Apply aggregate function to the GroupBy object. transform : Apply function column-by-column to the GroupBy object. Series.apply : Apply a function to a Series. DataFrame.apply : Apply a function to each row or column of a DataFrame. Notes ----- The resulting dtype will reflect the return value of the passed ``func``, see the examples below. Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See :ref:`gotchas.udf-mutation` for more details. Examples -------- >>> df = pd.DataFrame({"A": "a a b".split(), "B": [1, 2, 3], "C": [4, 6, 5]}) >>> g1 = df.groupby("A", group_keys=False) >>> g2 = df.groupby("A", group_keys=True) Notice that ``g1`` and ``g2`` have two groups, ``a`` and ``b``, and only differ in their ``group_keys`` argument. Calling `apply` in various ways, we can get different grouping results: Example 1: below the function 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: >>> g1[["B", "C"]].apply(lambda x: x / x.sum()) B C 0 0.333333 0.4 1 0.666667 0.6 2 1.000000 1.0 In the above, the groups are not part of the index. We can have them included by using ``g2`` where ``group_keys=True``: >>> g2[["B", "C"]].apply(lambda x: x / x.sum()) B C A a 0 0.333333 0.4 1 0.666667 0.6 b 2 1.000000 1.0 Example 2: The function passed to `apply` takes a DataFrame as its argument and returns a Series. `apply` combines the result for each group together into a new DataFrame. The resulting dtype will reflect the return value of the passed ``func``. >>> g1[["B", "C"]].apply(lambda x: x.astype(float).max() - x.min()) B C A a 1.0 2.0 b 0.0 0.0 >>> g2[["B", "C"]].apply(lambda x: x.astype(float).max() - x.min()) B C A a 1.0 2.0 b 0.0 0.0 The ``group_keys`` argument has no effect here because the result is not like-indexed (i.e. :ref:`a transform `) when compared to the input. Example 3: The function passed to `apply` takes a DataFrame as its argument and returns a scalar. `apply` combines the result for each group together into a Series, including setting the index as appropriate: >>> g1.apply(lambda x: x.C.max() - x.B.min()) A a 5 b 2 dtype: int64 Example 4: The function passed to ``apply`` returns ``None`` for one of the group. This group is filtered from the result: >>> g1.apply(lambda x: None if x.iloc[0, 0] == 3 else x) B C 0 1 4 1 2 6 )include_groups=True is no longer allowed.z"Cannot pass arguments to property z$apply func should be callable, not 'r'c<S!V.SO5/SB#rqrj)grzrr{s&rlr|GroupBy.apply..fgs3D3F33roz6func must be a callable if args or kwargs are supplied) rrrhasattrrcallable TypeErrorr rr)rtrrrzr{rr|s&f$jl rlr GroupBy.applysN HI I dC tT""dD)C==///V$'I$%PQQ  "FtfA NOO V~~t44!LA))!-F-FGGroc 0V^8dQhRRRRRRRRR RR R /#) rer|rrrr3z bool | Noner2ris_aggrhrrj)rks"rlrmr usF+ + + !+ & +  +  +  + roc pVPPW4wrgVfTpVPVVVV4#)a Apply function f in python space Parameters ---------- f : callable Function to apply data : Series or DataFrame Data to apply f to not_indexed_same: bool, optional When specified, overrides the value of not_indexed_same. Apply behaves differently when the result index is equal to the input index, but this can be coincidental leading to value-dependent behavior. is_transform : bool, default False Indicator for whether the function is actually a transform and should not have group keys prepended. is_agg : bool, default False Indicator for whether the function is an aggregation. When the result is empty, we don't want to warn for this case. See _GroupBy._python_agg_general. Returns ------- Series or DataFrame data after applying f )rapply_groupwiser)rtr|rr3r2rrTmutateds&&&&&& rlrGroupBy._python_apply_generaltsFF--77@  #& ((       ronpfuncc(V^8dQhRRRRRRRR/#) re numeric_onlyr min_countraliasrrCallable | Nonerj)rks"rlrmr s2????  ?  ?roc pVP!RRVRVRVRV/VBpVPVPRR7#)howaltrrrfrCrj_cython_agg_generalrSr)rtrrrrr{r s&&&$$, rl _agg_generalGroupBy._agg_generals^))   &     ""488I">>roc ,V^8dQhRRRRRRRRR R/#) rerrrTrrRrrrrhrj)rks"rlrmr s4-9-9-9 )-914-9;C-9 -9roc pVfQhVP^8Xd\VRR7pMN\VPVPR7pVP ^,^8XgQhVP R ,pVPPWTRR7pTPp T \8XdTP\RR7pM3\T 4'd#T P4p T PYzR7p\!YsR 7# \d+pRT RTP R 2p \T4!T 4ThRp?ii;i) zV Fallback to pure-python aggregation if _cython_operation raises NotImplementedError. NFradtypeT)preserve_dtypezagg function failed [how->z,dtype->])rR)NNNr<)rRrSrGr\rshaperr agg_series Exceptionr"rastyper4construct_array_type_from_sequencerR) rtrrTrRrserr res_valuesrrrstring_array_clss &&&&& rl_agg_py_fallbackGroupBy._agg_py_fallbacks ;;! e,C6886<<8B88A;!# ##''$-C  *11#41PJ   F?#**6*>J U # #$99; )888QJ "*88# *.se8CII;aHCs)C.c ) *s5D D5 %D00D5c(V^8dQhRRRRRRRR/#) rerrrrrrrrrj)rks"rlrmr s211 11 1  1roc 6aaaaaa \V4'g \R4hSPVSR7o RVV VVVV3RllpS PV4pSP V4pSR9dSP VSSR,R7pSP V4p V #)z(numeric_only accepts only Boolean valuesrrc V^8dQhRRRR/#rerTrrhrj)rks"rlrm1GroupBy._cython_agg_general..__annotate__s  y Y roc8<SPP!RVS3RSP^, RS/SBpV# \d/SR9d\ T\ 4'dMSeSR9dhMi;iSfQhSP STSPSR7pT#) aggregater?r)rRranyall)rrstdsem)r_cython_operationrRrrrBr)rTr rrrr{rrts& rl array_func/GroupBy._cython_agg_general..array_funcs 88Q  (   & '  .(Z -L-L[C+G$G%H ? "?**3TYYC*PFMs49(A2# A21A2skipna)rridxminidxmax)r*r_get_data_to_aggregategrouped_reduce_wrap_agged_manager_wrap_idxmax_idxminr) rtrrrrr{rnew_mgrroutrs fff&fl @rlrGroupBy._cython_agg_generals|$$GH H** 3*O  6%%j1&&w/ & &**3Cx@P*QC**3/ roc V^8dQhRRRR/#)rerrrrrj)rks"rlrmr s((S((roc \V4hrqr)rtrrr{s&&&,rl_cython_transformGroupBy._cython_transforms !$''roenginec  \V\4'gVP!WV.VO5/VB#V\P9dRV R2p\ V4hV\P 9gV\P9dVe W%R&W5R&\W4!V/VB#VP'dVP!V.VO5RVRV/VB#\P!VRR4;_uu_4\P!VRVPP4;_uu_4VP!V.VO5RVRV/VBuuRRR4uuRRR4# +'giM;iRRR4R# +'giR#;i)r'z2' is not a valid function name for transform(name)Nr rrTr)rr_transform_generalrItransform_kernel_allowlistrcythonized_kernelsr5rr_reduction_kernel_transformr temp_setattrrobserved_grouper)rtrr rrzr{rs&&$$*, rl _transformGroupBy._transformsn$$$**4XXQWX X 88 8dVMNCS/ ! T,, ,8S8S0S!#)x *7'4&77 7}}}77(.>KOU   z488  z4==3Q3QRR77(.>KOUSR98RRR9888s$'5E)E 6 E) E E)) E: c B\P!VRR4;_uu_4VR9d0\\R,V4pVP!VR.VO5/VBpMVe W%R&W5R&\ W4!V/VBpRRR4VP X4# +'giL!;i)rTNr rr)rrrr _idxmax_idxminr_wrap_transform_fast_result)rtrr rrzr{r s&&$$*, rlr#GroupBy._reduction_kernel_transform:s  dJ 5 5++G$67>,,T4I$I&I%'-8$.;?+ ,d=f=6//776 5s AB B c V^8dQhRRRR/#)rer rrhrj)rks"rlrmr Qs(xroc VPpVPPpVPVPP^R7pVP P ^8XdK\P!VPV4pVPWBPVPR7pV#VPPV4pVP^Wc3/RR7pVPVP^R7pV#)z' Fast transform path for aggregations. rBrHrT) allow_dups)rrrIrQrGrrRr:take_ndrNrrHrrP_reindex_with_indexersrd)rtr rrIroutputnew_axs&& rlr#GroupBy._wrap_transform_fast_resultPs ''mm : :C 88==A $$V^^S9C%%c%JF \\&&s+F22A}3ERV2WF__SYYQ_7F roc r\V4^8Xd\P!.RR7pM*\P!\P!V44pV'd VP P V^R7pV#\P!\VP P4\R7pVPR4RWAP\4&\P!V.VP PR,O^N4PpVP P!V4pV#)r<int64rrBFTNN)rrarrayr concatenaterrPemptyrHrfillrrrrrr\where)rtrrfilteredrYs&&& rl _apply_filterGroupBy._apply_filterls w<1 hhr1GggbnnW56G ))..wQ.?H 88C 2 2 8 89FD IIe (,D$ %774!C4#5#5#;#;B#?!C!CDFFD))//5Hroc V^8dQhRRRR/#)re ascendingrrh np.ndarrayrj)rks"rlrmr ~s$$$$roc  VPPpVPPp\W#4pW$,\ V4rRV^8Xd'\ P !^\ PR7#\ PRVRRVR,8g3,p\ P!\ P\ P!V4^,V3,4pV(P4pV'd&V\ P!W,V4,pMC\ P!V\ PVR,R3,,V4V, pVPP'dJ\ P!VR8H\ PVP!\ P"RR74pM!VP!\ PRR7p\ P !V\ P$R7p \ P&!V\ P$R7W&W,#)z Parameters ---------- ascending : bool, default True If False, number in reverse, from length of group - 1 to 0. Notes ----- this is currently implementing sort=False (though the default is sort=True) for groupby in general rTNr%FrarE)rrIrrTrrr)r$r_diffnonzerocumsumrwr6r+rrfloat64intparange) rtr0rIrsortercountrunreprrevs && rl_cumcount_arrayGroupBy._cumcount_array}stmm--'''5[#c(U A:88ARXX. .eeD#cr(c"g--.ggbeeBJJsOA.567tmmo  299SXs+ +C))Cc"gtm 45s;cAC == ' ' '((3"9bffcjj%j.PQC**RXXE*2ChhuBGG,iiRWW5 xrocV^8dQhRR/#)rerhrrj)rks"rlrmr s%%X%roc \VP\4'dVPP#\VP\4'gQhVPP #rq)rrrG_constructor_slicedrSrrs&rl_obj_1d_constructorGroupBy._obj_1d_constructorsL dhh * *88// /$((F++++xx$$$roc V^8dQhRRRR/#rerrrhrrj)rks"rlrmr s9 9 $9 (9 roc 4aVPRV3RlSR7#)a Return True if any value in the group is truthful, else False. Parameters ---------- skipna : bool, default True Flag to ignore nan values during truth testing. Returns ------- Series or DataFrame DataFrame or Series of boolean values, where a value is True if any element is True within its respective group, False otherwise. See Also -------- Series.any : Apply function any to a Series. DataFrame.any : Apply function any to each row or column of a DataFrame. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b"] >>> ser = pd.Series([1, 2, 0], index=lst) >>> ser a 1 a 2 b 0 dtype: int64 >>> ser.groupby(level=0).any() a True b False dtype: bool For DataFrameGroupBy: >>> data = [[1, 0, 3], [1, 0, 6], [7, 1, 9]] >>> df = pd.DataFrame( ... data, columns=["a", "b", "c"], index=["ostrich", "penguin", "parrot"] ... ) >>> df a b c ostrich 1 0 3 penguin 1 0 6 parrot 7 1 9 >>> df.groupby(by=["a"]).any() b c a 1 False True 7 True True rc><\VRR7PSR7#Fra)r)rSrr/rs&rlrGroupBy.any..&/3363Brorrrrtrs&frlr GroupBy.anys'l'' B(  roc V^8dQhRRRR/#rGrj)rks"rlrmr s: : $: (: roc 4aVPRV3RlSR7#)a Return True if all values in the group are truthful, else False. Parameters ---------- skipna : bool, default True Flag to ignore nan values during truth testing. Returns ------- Series or DataFrame DataFrame or Series of boolean values, where a value is True if all elements are True within its respective group, False otherwise. See Also -------- Series.all : Apply function all to a Series. DataFrame.all : Apply function all to each row or column of a DataFrame. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b"] >>> ser = pd.Series([1, 2, 0], index=lst) >>> ser a 1 a 2 b 0 dtype: int64 >>> ser.groupby(level=0).all() a True b False dtype: bool For DataFrameGroupBy: >>> data = [[1, 0, 3], [1, 5, 6], [7, 8, 9]] >>> df = pd.DataFrame( ... data, columns=["a", "b", "c"], index=["ostrich", "penguin", "parrot"] ... ) >>> df a b c ostrich 1 0 3 penguin 1 5 6 parrot 7 8 9 >>> df.groupby(by=["a"]).all() b c a 1 False True 7 True True rc><\VRR7PSR7#rJ)rSrrKs&rlrGroupBy.all..$rMrorNrOrPs&frlr GroupBy.alls'n'' B(  rocV^8dQhRR/#rerhrrj)rks"rlrmr )seexeroc ,aaaa VP4pVPPoVPPo SR8goVP^8HoRVVVV 3RllpVP V4pVP V4pVPV4pV#)a Compute count of group, excluding missing values. Returns ------- Series or DataFrame Count of values within each group. See Also -------- Series.count : Apply function count to a Series. DataFrame.count : Apply function count to each row or column of a DataFrame. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b"] >>> ser = pd.Series([1, 2, np.nan], index=lst) >>> ser a 1.0 a 2.0 b NaN dtype: float64 >>> ser.groupby(level=0).count() a 2 b 0 dtype: int64 For DataFrameGroupBy: >>> data = [[1, np.nan, 3], [1, np.nan, 6], [7, 8, 9]] >>> df = pd.DataFrame( ... data, columns=["a", "b", "c"], index=["cow", "horse", "bull"] ... ) >>> df a b c cow 1 NaN 3 horse 1 NaN 6 bull 7 8.0 9 >>> df.groupby("a").count() b c a 1 0 2 7 1 1 For Resampler: >>> ser = pd.Series( ... [1, 2, 3, 4], ... index=pd.DatetimeIndex( ... ["2023-01-01", "2023-01-15", "2023-02-01", "2023-02-15"] ... ), ... ) >>> ser 2023-01-01 1 2023-01-15 2 2023-02-01 3 2023-02-15 4 dtype: int64 >>> ser.resample("MS").count() 2023-01-01 2 2023-02-01 2 Freq: MS, dtype: int64 c V^8dQhRRRR/#)rebvaluesrrhrj)rks"rlrm#GroupBy.count..__annotate__rs  9  roc<VP^8Xd%S\V4P^R4(,pMS\V4(,p\P!VSSR7p\ V\ 4'dJ\V^,\P!VP^,\PR7R7#\ V\4'dO\ VP\4'g/\R4p\!V4P#V^,VR7#S'd7VP^8XgQhVP^,^8XgQhV^,#V#)r&)rXmax_binr)rYzint64[pyarrow]rE)rRr7reshapercount_level_2drr>rArzerosrbool_r=rrCr6r"r)r[maskedcountedrrI is_seriesrYrs& rlhfuncGroupBy.count..hfuncrs||q g!6!6q"!= ==g.((WMG'?33#AJRXXgmmA.>bhh%OG%899* {CC%%56G}33GAJe3LL||q(((}}Q'1,,,qz!NrorE)rrrIrrRrrr) rtrrfrnew_objr rIrerYrs & @@@@rlr; GroupBy.count(sF**,mm--''byIIN   0%%e,**73--g6 roc(V^8dQhRRRRRRRR/#)rerrrr !Literal['cython', 'numba'] | Nonerrrj)rks"rlrmr s>dCdCdCdC2 dC . dCroc aa\V4'd+^RIHpVPV\P V^SR7#VP RVV3RlSSR7pVPVPRR7#)a Compute mean of groups, excluding missing values. Parameters ---------- numeric_only : bool, default False Include only float, int, boolean columns. .. versionchanged:: 2.0.0 numeric_only no longer accepts ``None`` and defaults to ``False``. skipna : bool, default True Exclude NA/null values. If an entire group is NA, the result will be NA. engine : str, default None * ``'cython'`` : Runs the operation through C-extensions from cython. * ``'numba'`` : Runs the operation through JIT compiled code from numba. * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba`` engine_kwargs : dict, default None * For ``'cython'`` engine, there are no accepted ``engine_kwargs`` * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil`` and ``parallel`` dictionary keys. The values must either be ``True`` or ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is ``{{'nopython': True, 'nogil': False, 'parallel': False}}`` Returns ------- pandas.Series or pandas.DataFrame Mean of values within each group. Same object type as the caller. See Also -------- Series.mean : Apply function mean to a Series. DataFrame.mean : Apply function mean to each row or column of a DataFrame. Examples -------- >>> df = pd.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() B C A 1 3.0 1.333333 2 4.0 1.500000 Groupby two columns and return the mean of the remaining column. >>> df.groupby(["A", "B"]).mean() C A B 1 2.0 2.0 4.0 1.0 2 3.0 1.0 5.0 2.0 Groupby one column and return the mean of only particular column in the group. >>> df.groupby("A")["B"].mean() A 1 3.0 2 4.0 Name: B, dtype: float64 ) grouped_mean min_periodsrmeanc@<\VRR7PSSR7#Frarr)rSrpr/rrs&rlrGroupBy.mean..s!fQU388!-f9rorrrrfrC) rVpandas.core._numba.kernelsrmrr<float_dtype_mappingrrSr)rtrrr rrmr s&ff&& rlrp GroupBy.meansd 6 " " ?**,, + --* .F&&txx &B Broc$V^8dQhRRRRRR/#)rerrrrhrrj)rks"rlrmr s&a?a?4a?a?a?roc taaVPRVV3RlSSR7pVPVPRR7#)a Compute median of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex Parameters ---------- numeric_only : bool, default False Include only float, int, boolean columns. .. versionchanged:: 2.0.0 numeric_only no longer accepts ``None`` and defaults to False. skipna : bool, default True Exclude NA/null values. If an entire group is NA, the result will be NA. .. versionadded:: 3.0.0 Returns ------- Series or DataFrame Median of values within each group. See Also -------- Series.median : Apply function median to a Series. DataFrame.median : Apply function median to each row or column of a DataFrame. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "a", "b", "b", "b"] >>> ser = pd.Series([7, 2, 8, 4, 3, 3], index=lst) >>> ser a 7 a 2 a 8 b 4 b 3 b 3 dtype: int64 >>> ser.groupby(level=0).median() a 7.0 b 3.0 dtype: float64 For DataFrameGroupBy: >>> data = {"a": [1, 3, 5, 7, 7, 8, 3], "b": [1, 4, 8, 4, 4, 2, 1]} >>> df = pd.DataFrame( ... data, index=["dog", "dog", "dog", "mouse", "mouse", "mouse", "mouse"] ... ) >>> df a b dog 1 1 dog 3 4 dog 5 8 mouse 7 4 mouse 7 4 mouse 8 2 mouse 3 1 >>> df.groupby(level=0).median() a b dog 3.0 4.0 mouse 7.0 3.0 For Resampler: >>> ser = pd.Series( ... [1, 2, 3, 3, 4, 5], ... index=pd.DatetimeIndex( ... [ ... "2023-01-01", ... "2023-01-10", ... "2023-01-15", ... "2023-02-01", ... "2023-02-10", ... "2023-02-15", ... ] ... ), ... ) >>> ser.resample("MS").median() 2023-01-01 2.0 2023-02-01 4.0 Freq: MS, dtype: float64 medianc@<\VRR7PSSR7#rr)rSr|rts&rlr GroupBy.median..S s!&/66)&7rorvrfrCr)rtrrr s&ff rlr|GroupBy.mediansGt)) & * ""488I">>roc ,V^8dQhRRRRRRRRR R/# reddofrr rkrrrrrrj)rks"rlrmr \ sCqqq2q. q  q  qroc  aa\V4'd@^RIHp\P!VP V\ PV^SSR74#VPRVV3RlVSSR7#)a Compute standard deviation of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters ---------- ddof : int, default 1 Delta Degrees of Freedom. The divisor used in calculations is ``N - ddof``, where ``N`` represents the number of elements. engine : str, default None * ``'cython'`` : Runs the operation through C-extensions from cython. * ``'numba'`` : Runs the operation through JIT compiled code from numba. * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba`` engine_kwargs : dict, default None * For ``'cython'`` engine, there are no accepted ``engine_kwargs`` * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil`` and ``parallel`` dictionary keys. The values must either be ``True`` or ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is ``{{'nopython': True, 'nogil': False, 'parallel': False}}`` numeric_only : bool, default False Include only `float`, `int` or `boolean` data. .. versionchanged:: 2.0.0 numeric_only now defaults to ``False``. skipna : bool, default True Exclude NA/null values. If an entire group is NA, the result will be NA. .. versionadded:: 3.0.0 Returns ------- Series or DataFrame Standard deviation of values within each group. See Also -------- Series.std : Apply function std to a Series. DataFrame.std : Apply function std to each row or column of a DataFrame. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "a", "b", "b", "b"] >>> ser = pd.Series([7, 2, 8, 4, 3, 3], index=lst) >>> ser a 7 a 2 a 8 b 4 b 3 b 3 dtype: int64 >>> ser.groupby(level=0).std() a 3.21455 b 0.57735 dtype: float64 For DataFrameGroupBy: >>> data = {"a": [1, 3, 5, 7, 7, 8, 3], "b": [1, 4, 8, 4, 4, 2, 1]} >>> df = pd.DataFrame( ... data, index=["dog", "dog", "dog", "mouse", "mouse", "mouse", "mouse"] ... ) >>> df a b dog 1 1 dog 3 4 dog 5 8 mouse 7 4 mouse 7 4 mouse 8 2 mouse 3 1 >>> df.groupby(level=0).std() a b dog 2.000000 3.511885 mouse 2.217356 1.500000  grouped_varrorrrc@<\VRR7PSSR7#Fra)rr)rSrr/rrs&rlrGroupBy.std.. fQU377T&7Qrorrrr) rVrwrrsqrtrr<rxrrtrr rrrrs&f&&&f rlr GroupBy.std[ sy| 6 " " >77''00! !! (  ++Q) , roc ,V^8dQhRRRRRRRRR R/#rrj)rks"rlrmr  sCnnn2n. n  n  nroc  aa\V4'd,^RIHpVPV\P V^SSR7#VP RVV3RlVSSR7#)a$ Compute variance of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters ---------- ddof : int, default 1 Degrees of freedom. engine : str, default None * ``'cython'`` : Runs the operation through C-extensions from cython. * ``'numba'`` : Runs the operation through JIT compiled code from numba. * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba`` engine_kwargs : dict, default None * For ``'cython'`` engine, there are no accepted ``engine_kwargs`` * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil`` and ``parallel`` dictionary keys. The values must either be ``True`` or ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is ``{{'nopython': True, 'nogil': False, 'parallel': False}}`` numeric_only : bool, default False Include only `float`, `int` or `boolean` data. .. versionchanged:: 2.0.0 numeric_only now defaults to ``False``. skipna : bool, default True Exclude NA/null values. If an entire group is NA, the result will be NA. .. versionadded:: 3.0.0 Returns ------- Series or DataFrame Variance of values within each group. See Also -------- Series.var : Apply function var to a Series. DataFrame.var : Apply function var to each row or column of a DataFrame. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "a", "b", "b", "b"] >>> ser = pd.Series([7, 2, 8, 4, 3, 3], index=lst) >>> ser a 7 a 2 a 8 b 4 b 3 b 3 dtype: int64 >>> ser.groupby(level=0).var() a 10.333333 b 0.333333 dtype: float64 For DataFrameGroupBy: >>> data = {"a": [1, 3, 5, 7, 7, 8, 3], "b": [1, 4, 8, 4, 4, 2, 1]} >>> df = pd.DataFrame( ... data, index=["dog", "dog", "dog", "mouse", "mouse", "mouse", "mouse"] ... ) >>> df a b dog 1 1 dog 3 4 dog 5 8 mouse 7 4 mouse 7 4 mouse 8 2 mouse 3 1 >>> df.groupby(level=0).var() a b dog 4.000000 12.333333 mouse 4.916667 2.250000 rrvarc@<\VRR7PSSR7#r)rSrrs&rlrGroupBy.var..: rror)rVrwrrr<rxrrs&f&&&f rlr GroupBy.var smz 6 " " >**,, + ++Q) , roc 0V^8dQhRRRRRRRRRRRR /#) resubsetzSequence[Hashable] | None normalizerrr0rrhrrj)rks"rlrmr A sVzDzD)zDzD zD  zD  zD zDroc  aaa V'dRMRpVPpVPoVPPUu0uF"qP'gKVP kK$ upo\ S\4'dSP p V S9d.MS.p M\SP4p VeV\V4o S \S4,p V 'd\RV R24hS V , p V 'd\RV R24hMV o VVV 3Rl\SP44p \VPP4pV F3p\VVRRVR7wppV\VP4, pK5 VPVRVPVP R 7p\#\VP%44pVVnV'dVP'VR R 7pVP('dVP*P,p\/\1V44VP*n\/\1VPP44pVP3VRR 7pVVP*nV'd\\/\1VPP4VP*P444pVPVP*P7V4VP(VP RR 7P9R4pVV,pVP;R4pVP<'dTpMVP*p\>P@!VP,4pVV9d\RV R24hVVnVPC\/\1V444VnVPE4pVPP^,PPPFp\IVVR7PK\1V4V4pVVn TpVPMVPRR7#uupi)z Shared implementation of value_counts for SeriesGroupBy and DataFrameGroupBy. SeriesGroupBy additionally supports a bins argument. See the docstring of DataFrameGroupBy.value_counts for a description of arguments. proportionr;zKeys z0 in subset cannot be in the groupby column keys.z) in subset do not exist in the DataFrame.c3v<"TF.wrVS9gKVS9gKSPRV3,xK0 R#5i)rN)r)ridx_name in_axis_namesr subsetteds& rlr(GroupBy._value_counts..l sC#9JC -!2792D!C  "8s 999F)rrrr)rrrstable)r0kind)rsort_remaining)rrrsumgzColumn label 'z' is duplicate of result columnr value_countsrC)'rrrrpin_axisrrrSsetrvrrsrrLrfrrrsize sort_valuesrrHrArangerrfnlevels droplevel transformfillnarrfill_missing_names set_names reset_indexrrOrqrS)!rtrrrr0rrrgroupingrr unique_colsclashing doesnt_existrprrr\gb result_seriesrA index_levelr@indexed_group_sizer rHrv result_frame orig_dtypecolsrrrs!&&&&&& @@@rl _value_countsGroupBy._value_counts@ s )|g XX''+/--*A*A *AhEUEUMHMM*A  c6 " "HHE+0M+ARuDckk*K!K $s='99$z*33 );6 $ ~-VW ( #,CKK"8D001 C' MGQ g//0 0IZZ ]];;   VRWWY/ !  )55#(6M 999!''--E(-c%j(9M   %DMM$;$; <=K)44!%5M).M   % c$--112M4G4G4O4OPF"/!6!6##--f5YY{{ "7" i  / /M*005M ==="F"''E,,U[[9Gw >$7V!WXX!%M "'//%G 2E"FM (446L00377??EEJ 3::3w<ND#'L !F""488N"CCO s QQc(V^8dQhRRRRRRRR/#)rerrrrrrhrrj)rks"rlrmr  s2g g g +/g AEg g roc @aaV'd{VPP^8Xd`\VPP4'g;\ \ V4P RV RVPP 24hVPRVV3RlVSSR7#)a* Compute standard error of the mean of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters ---------- ddof : int, default 1 Degrees of freedom. numeric_only : bool, default False Include only `float`, `int` or `boolean` data. .. versionchanged:: 2.0.0 numeric_only now defaults to ``False``. skipna : bool, default True Exclude NA/null values. If an entire group is NA, the result will be NA. .. versionadded:: 3.0.0 Returns ------- Series or DataFrame Standard error of the mean of values within each group. See Also -------- DataFrame.sem : Return unbiased standard error of the mean over requested axis. Series.sem : Return unbiased standard error of the mean over requested axis. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b", "b"] >>> ser = pd.Series([5, 10, 8, 14], index=lst) >>> ser a 5 a 10 b 8 b 14 dtype: int64 >>> ser.groupby(level=0).sem() a 2.5 b 3.0 dtype: float64 For DataFrameGroupBy: >>> data = [[1, 12, 11], [1, 15, 2], [2, 5, 8], [2, 6, 12]] >>> df = pd.DataFrame( ... data, ... columns=["a", "b", "c"], ... index=["tuna", "salmon", "catfish", "goldfish"], ... ) >>> df a b c tuna 1 12 11 salmon 1 15 2 catfish 2 5 8 goldfish 2 6 12 >>> df.groupby("a").sem() b c a 1 1.5 4.5 2 0.5 2.0 For Resampler: >>> ser = pd.Series( ... [1, 3, 2, 4, 3, 8], ... index=pd.DatetimeIndex( ... [ ... "2023-01-01", ... "2023-01-10", ... "2023-01-15", ... "2023-02-01", ... "2023-02-10", ... "2023-02-15", ... ] ... ), ... ) >>> ser.resample("MS").sem() 2023-01-01 0.577350 2023-02-01 1.527525 Freq: MS, dtype: float64 z.sem called with numeric_only=z and dtype rc@<\VRR7PSSR7#r)rSrrs&rlrGroupBy.sem..! s&/33f3Mror)rrRr1rrr"r~r)rtrrrs&f&frlr GroupBy.sem sz DHHMMQ.7G7W7W:&&'( ,~[8HJ '' M% (  rocV^8dQhRR/#rrj)rks"rlrmr ( s__(_roc NVPP4pRp\VP\4'd\VPP \ 4'dp\VPP \4'dCVPP PP\PJdRpM2RpM/RpM,\VPP \4'dRp\VP\4'd(VPWPPR7pMVPV4pVeVPRRRRVR7pVP 'g VP#R4P%4pV#)a= Compute group sizes. Returns ------- DataFrame or Series Number of rows in each group as a Series if as_index is True or a DataFrame if as_index is False. See Also -------- Series.size : Apply function size to a Series. DataFrame.size : Apply function size to each row or column of a DataFrame. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b"] >>> ser = pd.Series([1, 2, 3], index=lst) >>> ser a 1 a 2 b 3 dtype: int64 >>> ser.groupby(level=0).size() a 2 b 1 dtype: int64 >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]] >>> df = pd.DataFrame( ... data, columns=["a", "b", "c"], index=["owl", "toucan", "eagle"] ... ) >>> df a b c owl 1 2 3 toucan 1 5 6 eagle 7 8 9 >>> df.groupby("a").size() a 1 2 7 1 dtype: int64 For Resampler: >>> ser = pd.Series( ... [1, 2, 3], ... index=pd.DatetimeIndex(["2023-01-01", "2023-01-15", "2023-02-01"]), ... ) >>> ser 2023-01-01 1 2023-01-15 2 2023-02-01 3 dtype: int64 >>> ser.resample("MS").size() 2023-01-01 2 2023-02-01 1 Freq: MS, dtype: int64 Nnumpy_nullablepyarrow)rF) infer_objectsconvert_stringconvert_booleanconvert_floating dtype_backendr)rrrrrSr'r=rDrna_valuerrr>rDrconvert_dtypesrrenamer)rtr rs& rlr GroupBy.size' s1@##%EI dhh ' '$((..*=>>dhhnn.>??xx~~++44>(, (8 $-MDHHNNO<< 0  dhh ' '--f88==-IF--f5F  $**#$ %!&+ +F}}}]]6*668F roc ,V^8dQhRRRRRRRRRR /# rerrrrrr rkrrrj)rks"rlrmr  sCwwww w 2 w . wroc  >\V4'd+^RIHpVPV\P VVVR7#\ P!VRR4;_uu_4VPVVR\PVR7pRRR4V# +'giX#;i)a~ Compute sum of group values. Parameters ---------- numeric_only : bool, default False Include only float, int, boolean columns. .. versionchanged:: 2.0.0 numeric_only no longer accepts ``None``. 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. skipna : bool, default True Exclude NA/null values. If the entire group is NA and ``skipna`` is ``True``, the result will be NA. .. versionchanged:: 3.0.0 engine : str, default None None * ``'cython'`` : Runs rolling apply through C-extensions from cython. * ``'numba'`` : Runs rolling apply through JIT compiled code from numba. Only available when ``raw`` is set to ``True``. * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba`` engine_kwargs : dict, default None None * For ``'cython'`` engine, there are no accepted ``engine_kwargs`` * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil`` and ``parallel`` dictionary keys. The values must either be ``True`` or ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is ``{'nopython': True, 'nogil': False, 'parallel': False}`` and will be applied to both the ``func`` and the ``apply`` groupby aggregation. Returns ------- Series or DataFrame Computed sum of values within each group. See Also -------- SeriesGroupBy.min : Return the min of the group values. DataFrameGroupBy.min : Return the min of the group values. SeriesGroupBy.max : Return the max of the group values. DataFrameGroupBy.max : Return the max of the group values. SeriesGroupBy.sum : Return the sum of the group values. DataFrameGroupBy.sum : Return the sum of the group values. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b", "b"] >>> ser = pd.Series([1, 2, 3, 4], index=lst) >>> ser a 1 a 2 b 3 b 4 dtype: int64 >>> ser.groupby(level=0).sum() a 3 b 7 dtype: int64 For DataFrameGroupBy: >>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]] >>> df = pd.DataFrame( ... data, ... columns=["a", "b", "c"], ... index=["tiger", "leopard", "cheetah", "lion"], ... ) >>> df a b c tiger 1 8 2 leopard 1 2 5 cheetah 2 5 8 lion 2 6 9 >>> df.groupby("a").sum() b c a 1 10 7 2 11 17 ) grouped_sumrnrTrrrrrrN) rVrwrrr<default_dtype_mappingrrrrr)rtrrrr rrr s&&&&&& rlr GroupBy.sum sB 6 " " >**..% + !!$ D99**!-'66! +:M:9Ms %B  B c(V^8dQhRRRRRRRR/#rerrrrrrhrrj)rks"rlrmr  s2M M  M 58M FJM M roc JVPVVVR\PR7#)a\ Compute prod of group values. Parameters ---------- numeric_only : bool, default False Include only float, int, boolean columns. .. versionchanged:: 2.0.0 numeric_only no longer accepts ``None``. 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. skipna : bool, default True Exclude NA/null values. If an entire group is NA, the result will be NA. .. versionadded:: 3.0.0 Returns ------- Series or DataFrame Computed prod of values within each group. See Also -------- Series.prod : Return the product of the values over the requested axis. DataFrame.prod : Return the product of the values over the requested axis. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b", "b"] >>> ser = pd.Series([1, 2, 3, 4], index=lst) >>> ser a 1 a 2 b 3 b 4 dtype: int64 >>> ser.groupby(level=0).prod() a 2 b 12 dtype: int64 For DataFrameGroupBy: >>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]] >>> df = pd.DataFrame( ... data, ... columns=["a", "b", "c"], ... index=["tiger", "leopard", "cheetah", "lion"], ... ) >>> df a b c tiger 1 8 2 leopard 1 2 5 cheetah 2 5 8 lion 2 6 9 >>> df.groupby("a").prod() b c a 1 16 10 2 30 72 prodrrrrr)rrr)rtrrrs&&&&rlr GroupBy.prod s0P  %77 !  roc ,V^8dQhRRRRRRRRRR /#rrj)rks"rlrmr T Crrrr r 2 r . rroc  \V4'd,^RIHpVPV\P VVRVR7#VP VVVR\PR7#)ay Compute min of group values. Parameters ---------- numeric_only : bool, default False Include only float, int, boolean columns. .. versionchanged:: 2.0.0 numeric_only no longer accepts ``None``. 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. skipna : bool, default True Exclude NA/null values. If the entire group is NA and ``skipna`` is ``True``, the result will be NA. .. versionchanged:: 3.0.0 engine : str, default None None * ``'cython'`` : Runs rolling apply through C-extensions from cython. * ``'numba'`` : Runs rolling apply through JIT compiled code from numba. Only available when ``raw`` is set to ``True``. * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba`` engine_kwargs : dict, default None None * For ``'cython'`` engine, there are no accepted ``engine_kwargs`` * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil`` and ``parallel`` dictionary keys. The values must either be ``True`` or ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is ``{'nopython': True, 'nogil': False, 'parallel': False}`` and will be applied to both the ``func`` and the ``apply`` groupby aggregation. Returns ------- Series or DataFrame Computed min of values within each group. See Also -------- SeriesGroupBy.min : Return the min of the group values. DataFrameGroupBy.min : Return the min of the group values. SeriesGroupBy.max : Return the max of the group values. DataFrameGroupBy.max : Return the max of the group values. SeriesGroupBy.sum : Return the sum of the group values. DataFrameGroupBy.sum : Return the sum of the group values. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b", "b"] >>> ser = pd.Series([1, 2, 3, 4], index=lst) >>> ser a 1 a 2 b 3 b 4 dtype: int64 >>> ser.groupby(level=0).min() a 1 b 3 dtype: int64 For DataFrameGroupBy: >>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]] >>> df = pd.DataFrame( ... data, ... columns=["a", "b", "c"], ... index=["tiger", "leopard", "cheetah", "lion"], ... ) >>> df a b c tiger 1 8 2 leopard 1 2 5 cheetah 2 5 8 lion 2 6 9 >>> df.groupby("a").min() b c a 1 2 2 2 5 8 grouped_min_maxFrois_maxrminr) rVrwrrr<identity_dtype_mappingrrrrtrrrr rrs&&&&&& rlr GroupBy.minS spB 6 " " B**//% + $$)#vv % roc ,V^8dQhRRRRRRRRRR /#rrj)rks"rlrmr  rroc  \V4'd,^RIHpVPV\P VVRVR7#VP VVVR\PR7#)ay Compute max of group values. Parameters ---------- numeric_only : bool, default False Include only float, int, boolean columns. .. versionchanged:: 2.0.0 numeric_only no longer accepts ``None``. 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. skipna : bool, default True Exclude NA/null values. If the entire group is NA and ``skipna`` is ``True``, the result will be NA. .. versionchanged:: 3.0.0 engine : str, default None None * ``'cython'`` : Runs rolling apply through C-extensions from cython. * ``'numba'`` : Runs rolling apply through JIT compiled code from numba. Only available when ``raw`` is set to ``True``. * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba`` engine_kwargs : dict, default None None * For ``'cython'`` engine, there are no accepted ``engine_kwargs`` * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil`` and ``parallel`` dictionary keys. The values must either be ``True`` or ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is ``{'nopython': True, 'nogil': False, 'parallel': False}`` and will be applied to both the ``func`` and the ``apply`` groupby aggregation. Returns ------- Series or DataFrame Computed max of values within each group. See Also -------- SeriesGroupBy.min : Return the min of the group values. DataFrameGroupBy.min : Return the min of the group values. SeriesGroupBy.max : Return the max of the group values. DataFrameGroupBy.max : Return the max of the group values. SeriesGroupBy.sum : Return the sum of the group values. DataFrameGroupBy.sum : Return the sum of the group values. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b", "b"] >>> ser = pd.Series([1, 2, 3, 4], index=lst) >>> ser a 1 a 2 b 3 b 4 dtype: int64 >>> ser.groupby(level=0).max() a 2 b 4 dtype: int64 For DataFrameGroupBy: >>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]] >>> df = pd.DataFrame( ... data, ... columns=["a", "b", "c"], ... index=["tiger", "leopard", "cheetah", "lion"], ... ) >>> df a b c tiger 1 8 2 leopard 1 2 5 cheetah 2 5 8 lion 2 6 9 >>> df.groupby("a").max() b c a 1 8 5 2 6 9 rTrmaxr) rVrwrrr<rrrrrs&&&&&& rlr GroupBy.max spB 6 " " B**//% + $$)#vv % roc(V^8dQhRRRRRRRR/#rrj)rks"rlrmr > s2R R  R 58R GKR R roc :RRlpVPVVRVVR7#)aT Compute the first entry of each column within each group. Defaults to skipping NA elements. Parameters ---------- numeric_only : bool, default False Include only float, int, boolean columns. min_count : int, default -1 The required number of valid values to perform the operation. If fewer than ``min_count`` valid values are present the result will be NA. skipna : bool, default True Exclude NA/null values. If an entire group is NA, the result will be NA. .. versionadded:: 2.2.1 Returns ------- Series or DataFrame First values within each group. See Also -------- DataFrame.groupby : Apply a function groupby to each row or column of a DataFrame. core.groupby.DataFrameGroupBy.last : Compute the last non-null entry of each column. core.groupby.DataFrameGroupBy.nth : Take the nth row from each group. Examples -------- >>> df = pd.DataFrame( ... dict( ... A=[1, 1, 3], ... B=[None, 5, 6], ... C=[1, 2, 3], ... D=["3/11/2000", "3/12/2000", "3/13/2000"], ... ) ... ) >>> df["D"] = pd.to_datetime(df["D"]) >>> df.groupby("A").first() B C D A 1 5.0 1 2000-03-11 3 6.0 3 2000-03-13 >>> df.groupby("A").first(min_count=2) B C D A 1 NaN 1.0 2000-03-11 3 NaN NaN NaT >>> df.groupby("A").first(numeric_only=True) B C A 1 5.0 1 3 6.0 3 cV^8dQhRR/#rerrrj)rks"rlrm#GroupBy.first..__annotate__{ s + +h +rocRRlp\V\4'dVPV4#\V\4'd V!V4#\ \ V44h)cV^8dQhRR/#rer/rSrj)rks"rlrm9GroupBy.first..first_compat..__annotate__| s   rocVP\VP4,p\V4'g!VPPP#V^,#)z-Helper function for first item that isn't NA.r'r9rrrr/arrs& rlfirst2GroupBy.first..first_compat..first| s>ggeAGGn-3xx77==1111v rorrGrrSrr")rrs& rl first_compat#GroupBy.first..first_compat{ sK #y))yy''C((Sz!S **rorrr)rtrrrrs&&&& rlr GroupBy.first= s1| +  % !  roc(V^8dQhRRRRRRRR/#rrj)rks"rlrmr  s2A A  A 58A GKA A roc :RRlpVPVVRVVR7#)a, Compute the last entry of each column within each group. Defaults to skipping NA elements. Parameters ---------- numeric_only : bool, default False Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. min_count : int, default -1 The required number of valid values to perform the operation. If fewer than ``min_count`` valid values are present the result will be NA. skipna : bool, default True Exclude NA/null values. If an entire group is NA, the result will be NA. .. versionadded:: 2.2.1 Returns ------- Series or DataFrame Last of values within each group. See Also -------- DataFrame.groupby : Apply a function groupby to each row or column of a DataFrame. core.groupby.DataFrameGroupBy.first : Compute the first non-null entry of each column. core.groupby.DataFrameGroupBy.nth : Take the nth row from each group. Examples -------- >>> df = pd.DataFrame(dict(A=[1, 1, 3], B=[5, None, 6], C=[1, 2, 3])) >>> df.groupby("A").last() B C A 1 5.0 2 3 6.0 3 cV^8dQhRR/#rrj)rks"rlrm"GroupBy.last..__annotate__ s + +X +rocRRlp\V\4'dVPV4#\V\4'd V!V4#\ \ V44h)cV^8dQhRR/#rrj)rks"rlrm7GroupBy.last..last_compat..__annotate__ s   rocVP\VP4,p\V4'g!VPPP#VR,#)z,Helper function for last item that isn't NA.rErrs& rllast/GroupBy.last..last_compat..last s>ggeAGGn-3xx77==1112wror)rr s& rl last_compat!GroupBy.last..last_compat sJ #y))yy&C((Cy S **ror rr)rtrrrr s&&&& rlr  GroupBy.last s1Z +  % !  rocV^8dQhRR/#)rerhrGrj)rks"rlrmr  sooioroc VPP^8XdVPp\VP4pV'g \ R4hVP PRVPR^RR7p.ROpVPPW0P PVR7pV#VPR4pV#) a Compute open, high, low and close values of a group, excluding missing values. For multiple groupings, the result index will be a MultiIndex Returns ------- DataFrame Open, high, low and close values within each group. See Also -------- DataFrame.agg : Aggregate using one or more operations over the specified axis. DataFrame.resample : Resample time-series data. DataFrame.groupby : Group DataFrame using a mapper or by a Series of columns. Examples -------- For SeriesGroupBy: >>> lst = [ ... "SPX", ... "CAC", ... "SPX", ... "CAC", ... "SPX", ... "CAC", ... "SPX", ... "CAC", ... ] >>> ser = pd.Series([3.4, 9.0, 7.2, 5.2, 8.8, 9.4, 0.1, 0.5], index=lst) >>> ser SPX 3.4 CAC 9.0 SPX 7.2 CAC 5.2 SPX 8.8 CAC 9.4 SPX 0.1 CAC 0.5 dtype: float64 >>> ser.groupby(level=0).ohlc() open high low close CAC 9.0 9.4 0.5 0.5 SPX 3.4 8.8 0.1 0.1 For DataFrameGroupBy: >>> data = { ... 2022: [1.2, 2.3, 8.9, 4.5, 4.4, 3, 2, 1], ... 2023: [3.4, 9.0, 7.2, 5.2, 8.8, 9.4, 8.2, 1.0], ... } >>> df = pd.DataFrame( ... data, index=["SPX", "CAC", "SPX", "CAC", "SPX", "CAC", "SPX", "CAC"] ... ) >>> df 2022 2023 SPX 1.2 3.4 CAC 2.3 9.0 SPX 8.9 7.2 CAC 4.5 5.2 SPX 4.4 8.8 CAC 3.0 9.4 SPX 2.0 8.2 CAC 1.0 1.0 >>> df.groupby(level=0).ohlc() 2022 2023 open high low close open high low close CAC 2.3 4.5 1.0 1.0 9.0 9.4 1.0 1.0 SPX 1.2 8.9 1.2 2.0 3.4 8.8 3.4 8.2 For Resampler: >>> ser = pd.Series( ... [1, 3, 2, 4, 3, 5], ... index=pd.DatetimeIndex( ... [ ... "2023-01-01", ... "2023-01-10", ... "2023-01-15", ... "2023-02-01", ... "2023-02-10", ... "2023-02-15", ... ] ... ), ... ) >>> ser.resample("MS").ohlc() open high low close 2023-01-01 1 3 1 2 2023-02-01 4 5 3 5 zNo numeric types to aggregaterohlc)r?r)rHrvc"VP4#rq)r)sgbs&rlrGroupBy.ohlc..Es CHHJrorE)openhighlowclose) rrRrr1rr$rrrN_constructor_expanddimrG_apply_to_column_groupbys)rtr is_numericr agg_namesr s& rlr GroupBy.ohlc s| 88==A $$C)#))4J ?@@88S[[&qB9J9IXX44--"<">> s = pd.Series([1, 2, 3]) >>> s.describe() count 3.0 mean 2.0 std 1.0 min 1.0 25% 1.5 50% 2.0 75% 2.5 max 3.0 dtype: float64 Describing a categorical ``Series``. >>> s = pd.Series(["a", "a", "b", "c"]) >>> s.describe() count 4 unique 3 top a freq 2 dtype: object Describing a timestamp ``Series``. >>> s = pd.Series( ... [ ... np.datetime64("2000-01-01"), ... np.datetime64("2010-01-01"), ... np.datetime64("2010-01-01"), ... ] ... ) >>> s.describe() count 3 mean 2006-09-01 08:00:00 min 2000-01-01 00:00:00 25% 2004-12-31 12:00:00 50% 2010-01-01 00:00:00 75% 2010-01-01 00:00:00 max 2010-01-01 00:00:00 dtype: object Describing a ``DataFrame``. By default only numeric fields are returned. >>> df = pd.DataFrame( ... { ... "categorical": pd.Categorical(["d", "e", "f"]), ... "numeric": [1, 2, 3], ... "object": ["a", "b", "c"], ... } ... ) >>> df.describe() numeric count 3.0 mean 2.0 std 1.0 min 1.0 25% 1.5 50% 2.0 75% 2.5 max 3.0 Describing all columns of a ``DataFrame`` regardless of data type. >>> df.describe(include="all") # doctest: +SKIP categorical numeric object count 3 3.0 3 unique 3 NaN 3 top f NaN a freq 1 NaN 1 mean NaN 2.0 NaN std NaN 1.0 NaN min NaN 1.0 NaN 25% NaN 1.5 NaN 50% NaN 2.0 NaN 75% NaN 2.5 NaN max NaN 3.0 NaN Describing a column from a ``DataFrame`` by accessing it as an attribute. >>> df.numeric.describe() count 3.0 mean 2.0 std 1.0 min 1.0 25% 1.5 50% 2.0 75% 2.5 max 3.0 Name: numeric, dtype: float64 Including only numeric columns in a ``DataFrame`` description. >>> df.describe(include=[np.number]) numeric count 3.0 mean 2.0 std 1.0 min 1.0 25% 1.5 50% 2.0 75% 2.5 max 3.0 Including only string columns in a ``DataFrame`` description. >>> df.describe(include=[object]) # doctest: +SKIP object count 3 unique 3 top a freq 1 Including only categorical columns from a ``DataFrame`` description. >>> df.describe(include=["category"]) categorical count 3 unique 3 top d freq 1 Excluding numeric columns from a ``DataFrame`` description. >>> df.describe(exclude=[np.number]) # doctest: +SKIP categorical object count 3 3 unique 3 3 top f a freq 1 1 Excluding object columns from a ``DataFrame`` description. >>> df.describe(exclude=[object]) # doctest: +SKIP categorical numeric count 3 3.0 unique 3 NaN top f NaN freq 1 NaN mean NaN 2.0 std NaN 1.0 min NaN 1.0 25% NaN 1.5 50% NaN 2.0 75% NaN 2.5 max NaN 3.0  percentilesincludeexclude:Nr<NrTc,<VPSSSR7#)r)describe)r/r"r!r s&rlr"GroupBy.describe..Ks!** +Wg%ro)r3N)rrr$rRunstackror\rrrrrrzrQrH)rtr r!r"r describedr s&fff rlr$GroupBy.describeHsj'' s8q= ''%Ixx1}""**,??$&&++B/ /   dJ 5 5//!% 0F6!}}}008F(V5FL 6 5s #D D$ c V^8dQhRRRR/#)rerrrhr^rj)rks"rlrmr [s$uGuG+/uG uGroc L^RIHpV'd \R4hV!W.VO5/VB#)a Provide resampling when using a TimeGrouper. Given a grouper, the function resamples it according to a string "string" -> "frequency". See the :ref:`frequency aliases ` documentation for more details. Parameters ---------- rule : str or DateOffset The offset string or object representing target grouper conversion. *args Possible arguments are `how`, `fill_method`, `limit`, `kind` and `on`, and other arguments of `TimeGrouper`. include_groups : bool, default True When True, will attempt to include the groupings in the operation in the case that they are columns of the DataFrame. If this raises a TypeError, the result will be computed with the groupings excluded. When False, the groupings will be excluded when applying ``func``. .. versionadded:: 2.2.0 .. versionchanged:: 3.0 The default was changed to False, and True is no longer allowed. **kwargs Possible arguments are `how`, `fill_method`, `limit`, `kind` and `on`, and other arguments of `TimeGrouper`. Returns ------- DatetimeIndexResampler, PeriodIndexResampler or TimdeltaResampler Resampler object for the type of the index. See Also -------- Grouper : Specify a frequency to resample with when grouping by a key. DatetimeIndex.resample : Frequency conversion and resampling of time series. Examples -------- >>> idx = pd.date_range("1/1/2000", periods=4, freq="min") >>> df = pd.DataFrame(data=4 * [range(2)], index=idx, columns=["a", "b"]) >>> df.iloc[2, 0] = 5 >>> df a b 2000-01-01 00:00:00 0 1 2000-01-01 00:01:00 0 1 2000-01-01 00:02:00 5 1 2000-01-01 00:03:00 0 1 Downsample the DataFrame into 3 minute bins and sum the values of the timestamps falling into a bin. >>> df.groupby("a").resample("3min").sum() b a 0 2000-01-01 00:00:00 2 2000-01-01 00:03:00 1 5 2000-01-01 00:00:00 1 Upsample the series into 30 second bins. >>> df.groupby("a").resample("30s").sum() b a 0 2000-01-01 00:00:00 1 2000-01-01 00:00:30 0 2000-01-01 00:01:00 1 2000-01-01 00:01:30 0 2000-01-01 00:02:00 0 2000-01-01 00:02:30 0 2000-01-01 00:03:00 1 5 2000-01-01 00:02:00 1 Resample by month. Values are assigned to the month of the period. >>> df.groupby("a").resample("ME").sum() b a 0 2000-01-31 3 5 2000-01-31 1 Downsample the series into 3 minute bins as above, but close the right side of the bin interval. >>> (df.groupby("a").resample("3min", closed="right").sum()) b a 0 1999-12-31 23:57:00 1 2000-01-01 00:00:00 2 5 2000-01-01 00:00:00 1 Downsample the series into 3 minute bins and close the right side of the bin interval, but label each bin using the right edge instead of the left. >>> (df.groupby("a").resample("3min", closed="right", label="right").sum()) b a 0 2000-01-01 00:00:00 1 2000-01-01 00:03:00 2 5 2000-01-01 00:03:00 1 )get_resampler_for_groupingr)pandas.core.resampler+r)rtrulerrzr{r+s&&$*, rlresampleGroupBy.resampleZs-b D HI I)$FtFvFFroc8V^8dQhRRRRRRRRR RR R R R RR/#)rewindowz9int | datetime.timedelta | str | BaseOffset | BaseIndexerro int | Nonecenterrwin_type str | NoneonclosedzIntervalClosedType | NonerDrrhrarj)rks"rlrmr sd\ \ I\  \  \  \  \ *\ \  \ roc  n^RIHpV!VPVVVVVVVVPVPR7 #)a Return a rolling grouper, providing rolling functionality per group. Parameters ---------- window : int, timedelta, str, offset, or BaseIndexer subclass Interval of the moving window. If an integer, the delta between the start and end of each window. The number of points in the window depends on the ``closed`` argument. If a timedelta, str, or offset, the time period of each window. Each window will be a variable sized based on the observations included in the time-period. This is only valid for datetimelike indexes. To learn more about the offsets & frequency strings, please see :ref:`this link`. If a BaseIndexer subclass, the window boundaries based on the defined ``get_window_bounds`` method. Additional rolling keyword arguments, namely ``min_periods``, ``center``, ``closed`` and ``step`` will be passed to ``get_window_bounds``. min_periods : int, default None Minimum number of observations in window required to have a value; otherwise, result is ``np.nan``. For a window that is specified by an offset, ``min_periods`` will default to 1. For a window that is specified by an integer, ``min_periods`` will default to the size of the window. center : bool, default False If False, set the window labels as the right edge of the window index. If True, set the window labels as the center of the window index. win_type : str, default None If ``None``, all points are evenly weighted. If a string, it must be a valid `scipy.signal window function `__. Certain Scipy window types require additional parameters to be passed in the aggregation function. The additional parameters must match the keywords specified in the Scipy window type method signature. on : str, optional For a DataFrame, a column label or Index level on which to calculate the rolling window, rather than the DataFrame's index. Provided integer column is ignored and excluded from result since an integer index is not used to calculate the rolling window. closed : str, default None Determines the inclusivity of points in the window If ``'right'``, uses the window (first, last] meaning the last point is included in the calculations. If ``'left'``, uses the window [first, last) meaning the first point is included in the calculations. If ``'both'``, uses the window [first, last] meaning all points in the window are included in the calculations. If ``'neither'``, uses the window (first, last) meaning the first and last points in the window are excluded from calculations. () and [] are referencing open and closed set notation respetively. Default ``None`` (``'right'``). method : str {'single', 'table'}, default 'single' Execute the rolling operation per single column or row (``'single'``) or over the entire object (``'table'``). This argument is only implemented when specifying ``engine='numba'`` in the method call. Returns ------- pandas.api.typing.RollingGroupby Return a new grouper with our rolling appended. See Also -------- Series.rolling : Calling object with Series data. DataFrame.rolling : Calling object with DataFrames. Series.groupby : Apply a function groupby to a Series. DataFrame.groupby : Apply a function groupby. Examples -------- >>> df = pd.DataFrame( ... { ... "A": [1, 1, 2, 2], ... "B": [1, 2, 3, 4], ... "C": [0.362, 0.227, 1.267, -0.562], ... } ... ) >>> df A B C 0 1 1 0.362 1 1 2 0.227 2 2 3 1.267 3 2 4 -0.562 >>> df.groupby("A").rolling(2).sum() B C A 1 0 NaN NaN 1 3.0 0.589 2 2 NaN NaN 3 7.0 0.705 >>> df.groupby("A").rolling(2, min_periods=1).sum() B C A 1 0 1.0 0.362 1 3.0 0.589 2 2 3.0 1.267 3 7.0 0.705 >>> df.groupby("A").rolling(2, on="B").sum() B C A 1 0 1 NaN 1 2 0.589 2 2 3 NaN 3 4 0.705 )ra) r1ror3r4r6r7rDr _as_index)pandas.core.windowrarrr) rtr1ror3r4r6r7rDras &&&&&&&& rlrollingGroupBy.rollingsA` 6   #]]mm  roc$V^8dQhRRRRRR/#)rerorrDrrhr_rj)rks"rlrmr rs-C C C C   C roc N^RIHpV!VPVVVPR7#)a Return an expanding grouper, providing expanding functionality per group. Parameters ---------- min_periods : int, default 1 Minimum number of observations in window required to have a value; otherwise, result is ``np.nan``. method : str {'single', 'table'}, default 'single' Execute the expanding operation per single column or row (``'single'``) or over the entire object (``'table'``). This argument is only implemented when specifying ``engine='numba'`` in the method call. Returns ------- pandas.api.typing.ExpandingGroupby An object that supports expanding transformations over each group. See Also -------- Series.expanding : Expanding transformations for Series. DataFrame.expanding : Expanding transformations for DataFrames. Series.groupby : Apply a function groupby to a Series. DataFrame.groupby : Apply a function groupby. Examples -------- >>> df = pd.DataFrame( ... { ... "Class": ["A", "A", "A", "B", "B", "B"], ... "Value": [10, 20, 30, 40, 50, 60], ... } ... ) >>> df Class Value 0 A 10 1 A 20 2 A 30 3 B 40 4 B 50 5 B 60 >>> df.groupby("Class").expanding().mean() Value Class A 0 10.0 1 15.0 2 20.0 B 3 40.0 4 45.0 5 50.0 )r_)rorDr)r:r_rr)rtrorDr_s&&& rl expandingGroupBy.expandingqs+z 8   #]]   roc@V^8dQhRRRRRRRRRRR R R R R R RRRR/ #)rer float | Nonespanhalflifezfloat | str | Timedelta | Nonealpharor2adjustr ignore_natimesznp.ndarray | Series | NonerDrrhr`rj)rks"rlrmr szg g g g 1 g  g  g g g *g g  (g roc  \^RIHp V !VPVVVVVVVVV VPR7 #)a Return an ewm grouper, providing ewm functionality per group. Parameters ---------- com : float, optional Specify decay in terms of center of mass. Alternative to ``span``, ``halflife``, and ``alpha``. span : float, optional Specify decay in terms of span. halflife : float, str, or Timedelta, optional Specify decay in terms of half-life. alpha : float, optional Specify smoothing factor directly. min_periods : int, default 0 Minimum number of observations in the window required to have a value; otherwise, result is ``np.nan``. adjust : bool, default True Divide by decaying adjustment factor to account for imbalance in relative weights. ignore_na : bool, default False Ignore missing values when calculating weights. times : str or array-like of datetime64, optional Times corresponding to the observations. method : {'single', 'table'}, default 'single' Execute the operation per group independently (``'single'``) or over the entire object before regrouping (``'table'``). Only applicable to ``mean()``, and only when using ``engine='numba'``. Returns ------- pandas.api.typing.ExponentialMovingWindowGroupby An object that supports exponentially weighted moving transformations over each group. See Also -------- Series.ewm : EWM transformations for Series. DataFrame.ewm : EWM transformations for DataFrames. Series.groupby : Apply a function groupby to a Series. DataFrame.groupby : Apply a function groupby. Examples -------- >>> df = pd.DataFrame( ... { ... "Class": ["A", "A", "A", "B", "B", "B"], ... "Value": [10, 20, 30, 40, 50, 60], ... } ... ) >>> df Class Value 0 A 10 1 A 20 2 A 30 3 B 40 4 B 50 5 B 60 >>> df.groupby("Class").ewm(com=0.5).mean() Value Class A 0 10.000000 1 17.500000 2 26.153846 B 3 40.000000 4 47.500000 5 56.153846 )r`) rrCrDrErorFrGrHrDr)r:r`rr) rtrrCrDrErorFrGrHrDr`s &&&&&&&&&& rlewm GroupBy.ewmsAt F-   #]]  roc V^8dQhRRRR/#)re directionzLiteral['ffill', 'bfill']limitr2rj)rks"rlrmr "sII8IIroc ^aa VfRpSPPpSPPp\\P VVVR8HVR7o RV V3RllpSP 4pVPV4pSPV4pSPPVn V#)a} Shared function for `pad` and `backfill` to call Cython method. Parameters ---------- direction : {'ffill', 'bfill'} Direction passed to underlying Cython function. `bfill` will cause values to be filled backwards. `ffill` and any other values will default to a forward fill limit : int, default None Maximum number of consecutive values to fill. If `None`, this method will convert to -1 prior to passing to Cython Returns ------- `Series` or `DataFrame` with filled values See Also -------- pad : Returns Series with minimum number of char in object. backfill : Backward fill the missing values in the dataset. ffill)rXrN compute_ffillrc V^8dQhRRRR/#rrj)rks"rlrm#GroupBy._fill..__annotate__Hs  Y 9 roc(<\V4pVP^8XdP\P!VP\P R7pS!W!R7\ P!W4#\V\P4'daVPpSPP'd\VP4p\P!VPVR7pM0\V4PVPVPR7p\!V4FfwrV\P!VP^,\P R7pS!W!V,R7\ P!Wb4WER3&Kh V#)r&r)rrYr)r7rRrr)rr8r:rrndarrayrrr6r)r"_emptyrs) rTrYr[rri value_elementcol_funcrts & rlblk_funcGroupBy._fill..blk_funcHs>> key = [0, 0, 1, 1] >>> ser = pd.Series([np.nan, 2, 3, np.nan], index=key) >>> ser 0 NaN 0 2.0 1 3.0 1 NaN dtype: float64 >>> ser.groupby(level=0).ffill() 0 NaN 0 2.0 1 3.0 1 3.0 dtype: float64 For DataFrameGroupBy: >>> df = pd.DataFrame( ... { ... "key": [0, 0, 1, 1, 1], ... "A": [np.nan, 2, np.nan, 3, np.nan], ... "B": [2, 3, np.nan, np.nan, np.nan], ... "C": [np.nan, np.nan, 2, np.nan, np.nan], ... } ... ) >>> df key A B C 0 0 NaN 2.0 NaN 1 0 2.0 3.0 NaN 2 1 NaN NaN 2.0 3 1 3.0 NaN NaN 4 1 NaN NaN NaN Propagate non-null values forward or backward within each group along columns. >>> df.groupby("key").ffill() A B C 0 NaN 2.0 NaN 1 2.0 3.0 NaN 2 NaN NaN 2.0 3 3.0 NaN 2.0 4 3.0 NaN 2.0 Propagate non-null values forward or backward within each group along rows. >>> df.T.groupby(np.array([0, 0, 1, 1])).ffill().T key A B C 0 0.0 0.0 2.0 2.0 1 0.0 2.0 3.0 3.0 2 1.0 1.0 NaN 2.0 3 1.0 3.0 NaN NaN 4 1.0 1.0 NaN NaN Only replace the first NaN element within a group along columns. >>> df.groupby("key").ffill(limit=1) A B C 0 NaN 2.0 NaN 1 2.0 3.0 NaN 2 NaN NaN 2.0 3 3.0 NaN 2.0 4 3.0 NaN NaN rPrNr_rtrNs&&rlrP GroupBy.ffillmstzz'z//rocV^8dQhRR/#rbrj)rks"rlrmr sN0N0:N0roc (VPRVR7#)a Backward fill the values. Parameters ---------- limit : int, optional Limit of how many values to fill. Returns ------- Series or DataFrame Object with missing values filled. See Also -------- Series.bfill : Backward fill the missing values in the dataset. DataFrame.bfill: Backward fill the missing values in the dataset. Series.fillna: Fill NaN values of a Series. DataFrame.fillna: Fill NaN values of a DataFrame. Examples -------- With Series: >>> index = ["Falcon", "Falcon", "Parrot", "Parrot", "Parrot"] >>> s = pd.Series([None, 1, None, None, 3], index=index) >>> s Falcon NaN Falcon 1.0 Parrot NaN Parrot NaN Parrot 3.0 dtype: float64 >>> s.groupby(level=0).bfill() Falcon 1.0 Falcon 1.0 Parrot 3.0 Parrot 3.0 Parrot 3.0 dtype: float64 >>> s.groupby(level=0).bfill(limit=1) Falcon 1.0 Falcon 1.0 Parrot NaN Parrot 3.0 Parrot 3.0 dtype: float64 With DataFrame: >>> df = pd.DataFrame( ... {"A": [1, None, None, None, 4], "B": [None, None, 5, None, 7]}, ... index=index, ... ) >>> df A B Falcon 1.0 NaN Falcon NaN NaN Parrot NaN 5.0 Parrot NaN NaN Parrot 4.0 7.0 >>> df.groupby(level=0).bfill() A B Falcon 1.0 NaN Falcon NaN NaN Parrot 4.0 5.0 Parrot 4.0 7.0 Parrot 4.0 7.0 >>> df.groupby(level=0).bfill(limit=1) A B Falcon 1.0 NaN Falcon NaN NaN Parrot NaN 5.0 Parrot 4.0 7.0 Parrot 4.0 7.0 bfillrdrerfs&&rlrj GroupBy.bfills^zz'z//rocV^8dQhRR/#)rerhrNrj)rks"rlrmr sQ(Q('Q(roc \V4#)ar Take the nth row from each group if n is an int, otherwise a subset of rows. Can be either a call or an index. dropna is not available with index notation. Index notation accepts a comma separated list of integers and slices. If dropna, will take the nth non-null row, dropna is either 'all' or 'any'; this is equivalent to calling dropna(how=dropna) before the groupby. Returns ------- Series or DataFrame N-th value within each group. See Also -------- Series.nth : Apply function nth to a Series. DataFrame.nth : Apply function nth to each row or column of a DataFrame. Examples -------- >>> df = pd.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 >>> g.nth([0, 1]) A B 0 1 NaN 1 1 2.0 2 2 3.0 4 2 5.0 >>> g.nth(slice(None, -1)) A B 0 1 NaN 1 1 2.0 2 2 3.0 Index notation may also be used >>> g.nth[0, 1] A B 0 1 NaN 1 1 2.0 2 2 3.0 4 2 5.0 >>> g.nth[:-1] A B 0 1 NaN 1 1 2.0 2 2 3.0 Specifying `dropna` allows ignoring ``NaN`` values >>> g.nth(0, dropna="any") A B 1 1 2.0 2 2 3.0 When the specified ``n`` is larger than any of the groups, an empty DataFrame is returned >>> g.nth(3, dropna="any") Empty DataFrame Columns: [A, B] Index: [] )rNrs&rlnth GroupBy.nthsf"$''roc$V^8dQhRRRRRR/#)rerzPositionalIndexer | tuplerzLiteral['any', 'all'] | Nonerhrrj)rks"rlrmr os(55 $5-5  5roc XV'gFVPV4pVPPpW4R 8g,pVPV4pV#\ V4'g \ R4hVR 9d\ RV R24h\ \V4pVPPV^R7p\V4\VP48XdVPpMVPPpVPPVPVP4,pVPP'd0VR 8Hp \ P"!V \$V4p \'V RRR7pVP)WpP*VP,R7p V P/V4#) r&z4dropna option only supported for an integer argumentz_For a DataFrame or Series groupby.nth, dropna must be either None, 'any' or 'all', (was passed z).)rr?Int64F)rrb)rrrEr)"_make_mask_from_positional_indexerrrI_mask_selected_objr.rrrrrrr? codes_infoisinrHr6rr+rrOrfrrrn) rtrrrYrIrdroppedrr?nullsrTgrbs &&& rl_nth GroupBy._nthose ::1=D--##C"9%D))$/CJ!}}ST T  '%hb*  aL$$++Q+? w<3t112 2mmG ==%%Dmm..tyy/GHG}}+++2 %W5gEBoog DIIoNwwqzroc$V^8dQhRRRRRR/#)reqzfloat | AnyArrayLike interpolationz;Literal['linear', 'lower', 'higher', 'nearest', 'midpoint']rrrj)rks"rlrmr s-Z=Z= Z= Z=  Z=roc  ^aaaaaaVPVRR7pVPV4pVPPV4pVPp\ P !VPVP4wrRRloRV3Rllo\V4'd+\P!V.\PR7p Rp M(\P!V\PR7p T p VPPp VPPoVP'd W^8,p \!V 4o\#\$P&V V SVV R 7oR VVVVV3R llp VP(P+V 4pVPV4pVP-WR 7#) a Return group values at the given quantile, a la numpy.percentile. Parameters ---------- q : float or array-like, default 0.5 (50% quantile) Value(s) between 0 and 1 providing the quantile(s) to compute. interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} Method to use when the desired quantile falls between two points. numeric_only : bool, default False Include only `float`, `int` or `boolean` data. .. versionchanged:: 2.0.0 numeric_only now defaults to ``False``. Returns ------- Series or DataFrame Return type determined by caller of GroupBy object. See Also -------- Series.quantile : Similar method for Series. DataFrame.quantile : Similar method for DataFrame. numpy.percentile : NumPy method to compute qth percentile. Examples -------- >>> df = pd.DataFrame( ... [["a", 1], ["a", 2], ["a", 3], ["b", 1], ["b", 3], ["b", 5]], ... columns=["key", "val"], ... ) >>> df.groupby("key").quantile() val key a 2.0 b 3.0 quantilerc V^8dQhRRRR/#)revalsrrhz"tuple[np.ndarray, DtypeObj | None]rj)rks"rlrm&GroupBy.quantile..__annotate__s$ "$ " $ ".P$ "roc\VP\4'g\VP4'd\ RVP R24hRp\V\ 4'dP\ VP4'd5VP\\PR7pVPpW!3#\VP4'df\V\4'd'VP\\PR7pMTp\P!\P4pW!3#\VP4'd?\V\4'd)VP\\PR7pW!3#\VP4'd \ R4h\VP4'dVPpW3#\V\4'dh\!VP4'dM\P!\P"4pVP\\PR7pW!3#\P$!V4pW!3#)zdtype 'z'' does not support operation 'quantile'N)rrz#Cannot use quantile with bool dtype)rrrCr2rr>r1rfloatrrr/r?r$r+r5r,r7asarray)r inferencers& rl pre_processor'GroupBy.quantile..pre_processors$**k22odjj6Q6Qdjj\)PQ*.I$005Edjj5Q5Qmm%"&&mA JJ 6> !5"$**--dN33--ebff-ECCHHRXX. *> !)tzz**z$/O/Omm%"&&mA&> !%tzz** EFF$TZZ00 JJ &D.11nTZZ6P6PHHRZZ0 mm%"&&mA> !jj&> !roc ,V^8dQhRRRRRRRRR R/#) rerr1rzDtypeObj | None result_maskznp.ndarray | None orig_valsrrhrj)rks"rlrmrs:0 0 0 &0 +0 ! 0   0 roc<V'EdP\V\4'dVfQhSR9d\V4'g \W4#\P !4;_uu_4\P !R\R7\V4!VPVP4V4uuRRR4#\V4'dSR9g\V4'dFVPR4PVPP4pVP!V4#\V\"P4'gQhVPV4#V# +'giT#;i)Nignore)categoryi8>linearmidpoint)rr>r,r@rcatch_warningsfilterwarningsRuntimeWarningr"r numpy_dtyper/r5view_ndarrayr_from_backing_datar)rrrrr~s&&&&rlpost_processor(GroupBy.quantile..post_processors9 yi99&222$(>>~!HH -T?? &4466$33H~V#' ? $ $-$9$9!"!, $76%Y//%)??*955 ${{4055%..44  );;  &i::::;;y11K;76:Ks 'AE E- rN)rXrkr~rrc V^8dQhRRRR/#rrj)rks"rlrmrLs0 J0 JY0 J90 Jroc (<Tp\V\4'd6VPp\P!S S 3\P R7pM \ V4pRp\VP4pS!V4wrV^pVP^8XdVP^,p\P!VS S 3\PR7pV'dVPR4pVP^8XdS !V^,VVVVR7M1\V4F"p S !W,WY,W),RVR7K$ VP^8Xd(VPR4pVeVPR4pMVP!VS S ,4pS !WW14#)rNr)rTrYris_datetimelikeK)rr>_maskrrarbr7r5rrRrr)r7rrrr_)rTrrYrrrrncolsrrWrrnqsrrs& rlrZ"GroupBy.quantile..blk_funcLsKI&/22|| hh~RXXF F|" 1&,,?O+F3ODEyyA~ 1 ((E7C0 CCyyyyA~F +$3 uA#w!W$((7 &yyA~iin*"-"3"3C"8Kkk%37!#+I Iror~)rrr _get_splitter _sorted_datarr_slabelsrr3rr'r7rrIrrr r\group_quantilerrr)rtr}r~rr^rsplittersdatarrrkpass_qsrIrZrrrrrrrs&&f& @@@@@rlrGroupBy.quantiles^`))|*)U&&s+==..s3%%**8+<+N>NO $ "L0 0 d Q<<1#RZZ0B)-GARZZ0BGmm--'' ;;;Qh-C"g  % %'  0 J0 Jd**++H5&&w/++C+<>> df = pd.DataFrame({"color": ["red", None, "red", "blue", "blue", "red"]}) >>> df color 0 red 1 NaN 2 red 3 blue 4 blue 5 red >>> df.groupby("color").ngroup() 0 1.0 1 NaN 2 1.0 3 0.0 4 0.0 5 1.0 dtype: float64 >>> df.groupby("color", dropna=False).ngroup() 0 1 1 2 2 1 3 0 4 0 5 1 dtype: int64 >>> df.groupby("color", dropna=False).ngroup(ascending=False) 0 1 1 0 2 1 3 2 4 2 5 1 dtype: int64 c38"TFqPxK R#5irq_passed_categoricalrpings& rlr!GroupBy.ngroup..sL4KD''4KTFdense) ties_methodrrE)rrHrrIr6rr+rr7r$rrprrDr)rtr0rrHcomp_idsrr s&& rlngroupGroupBy.ngroups~'' ==$$ == ' ' 'xxBAHJJEHHE 3LDMM4K4KL333LDMM4K4KL L LxW=AH))()G\\A%.F rocV^8dQhRR/#rrj)rks"rlrmr s6:6:$6:roc tVPPpVPVR7pVPW24#)a  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. See Also -------- .ngroup : Number the groups themselves. Examples -------- >>> df = pd.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() 0 0 1 1 2 2 3 0 4 1 5 3 dtype: int64 >>> df.groupby("A").cumcount(ascending=False) 0 3 1 2 2 1 3 1 4 0 5 0 dtype: int64 )r0)rrHr?rD)rtr0rH cumcountss&& rlcumcountGroupBy.cumcounts:j))//((9(= '' 99roc ,V^8dQhRRRRRRRRRR/#) rerDrr0r na_optionpctrhrrj)rks"rlrmr sCW W W W  W  W  W roc fVR9dRp\V4hRVRVRVRV/pVP!R RR/VB#) a 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 : bool, default True False for ranks by high (1) to low (N). na_option : {'keep', 'top', 'bottom'}, default 'keep' * keep: leave NA values where they are. * top: smallest rank if ascending. * bottom: smallest rank if descending. pct : bool, default False Compute percentage rank of data within each group. Returns ------- DataFrame The ranking of values within each group. See Also -------- Series.rank : Apply function rank to a Series. DataFrame.rank : Apply function rank to each row or column of a DataFrame. Examples -------- >>> df = pd.DataFrame( ... { ... "group": ["a", "a", "a", "a", "a", "b", "b", "b", "b", "b"], ... "value": [2, 4, 2, 3, 5, 1, 2, 4, 1, 5], ... } ... ) >>> df group value 0 a 2 1 a 4 2 a 2 3 a 3 4 a 5 5 b 1 6 b 2 7 b 4 8 b 1 9 b 5 >>> for method in ["average", "min", "max", "dense", "first"]: ... df[f"{method}_rank"] = df.groupby("group")["value"].rank(method) >>> df group value average_rank min_rank max_rank dense_rank first_rank 0 a 2 1.5 1.0 2.0 1.0 1.0 1 a 4 4.0 4.0 4.0 3.0 4.0 2 a 2 1.5 1.0 2.0 1.0 2.0 3 a 3 3.0 3.0 3.0 2.0 3.0 4 a 5 5.0 5.0 5.0 4.0 5.0 5 b 1 1.5 1.0 2.0 1.0 1.0 6 b 2 3.0 3.0 3.0 2.0 3.0 7 b 4 4.0 4.0 4.0 3.0 4.0 8 b 1 1.5 1.0 2.0 1.0 2.0 9 b 5 5.0 5.0 5.0 4.0 5.0 z3na_option must be one of 'keep', 'top', or 'bottom'rr0rrrF>topkeepbottom)rank)rr )rtrDr0rrrr{s&&&&& rlr GroupBy.ranksaR 5 5GCS/ ! 6   3  %%    roc V^8dQhRRRR/#rerrrhrrj)rks"rlrmr js=I=ID=Ih=Iroc \\P!RW#R.4VP!RV3/VB#)a Cumulative product for each group. Parameters ---------- numeric_only : bool, default False Include only float, int, boolean columns. *args : tuple Positional arguments to be passed to `func`. **kwargs : dict Additional/specific keyword arguments to be passed to the function, such as `numeric_only` and `skipna`. Returns ------- Series or DataFrame Cumulative product for each group. Same object type as the caller. See Also -------- Series.cumprod : Apply function cumprod to a Series. DataFrame.cumprod : Apply function cumprod to each row or column of a DataFrame. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b"] >>> ser = pd.Series([6, 2, 0], index=lst) >>> ser a 6 a 2 b 0 dtype: int64 >>> ser.groupby(level=0).cumprod() a 6 a 12 b 0 dtype: int64 For DataFrameGroupBy: >>> data = [[1, 8, 2], [1, 2, 5], [2, 6, 9]] >>> df = pd.DataFrame( ... data, columns=["a", "b", "c"], index=["cow", "horse", "bull"] ... ) >>> df a b c cow 1 8 2 horse 1 2 5 bull 2 6 9 >>> df.groupby("a").groups {1: ['cow', 'horse'], 2: ['bull']} >>> df.groupby("a").cumprod() b c cow 8 2 horse 16 10 bull 6 9 cumprodrnvvalidate_groupby_funcr rtrrzr{s&&*,rlrGroupBy.cumprodis1z   D8*E%%iHHHroc V^8dQhRRRR/#rrj)rks"rlrmr s=H=H4=HX=Hroc \\P!RW#R.4VP!RV3/VB#)a Cumulative sum for each group. Parameters ---------- numeric_only : bool, default False Include only float, int, boolean columns. *args : tuple Positional arguments to be passed to `func`. **kwargs : dict Additional/specific keyword arguments to be passed to the function, such as `numeric_only` and `skipna`. Returns ------- Series or DataFrame Cumulative sum for each group. Same object type as the caller. See Also -------- Series.cumsum : Apply function cumsum to a Series. DataFrame.cumsum : Apply function cumsum to each row or column of a DataFrame. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b"] >>> ser = pd.Series([6, 2, 0], index=lst) >>> ser a 6 a 2 b 0 dtype: int64 >>> ser.groupby(level=0).cumsum() a 6 a 8 b 0 dtype: int64 For DataFrameGroupBy: >>> data = [[1, 8, 2], [1, 2, 5], [2, 6, 9]] >>> df = pd.DataFrame( ... data, columns=["a", "b", "c"], index=["fox", "gorilla", "lion"] ... ) >>> df a b c fox 1 8 2 gorilla 1 2 5 lion 2 6 9 >>> df.groupby("a").groups {1: ['fox', 'gorilla'], 2: ['lion']} >>> df.groupby("a").cumsum() b c fox 8 2 gorilla 10 7 lion 6 9 r6rrrs&&*,rlr6GroupBy.cumsums1z   4(D%%h GGGroc V^8dQhRRRR/#rrj)rks"rlrmr s"G G G   G roc LVPRR4pVPRWR7#)a Cumulative min for each group. Parameters ---------- numeric_only : bool, default False Include only `float`, `int` or `boolean` data. **kwargs : dict, optional Additional keyword arguments to be passed to the function, such as `skipna`, to control whether NA/null values are ignored. Returns ------- Series or DataFrame Cumulative min for each group. Same object type as the caller. See Also -------- Series.cummin : Apply function cummin to a Series. DataFrame.cummin : Apply function cummin to each row or column of a DataFrame. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "a", "b", "b", "b"] >>> ser = pd.Series([1, 6, 2, 3, 0, 4], index=lst) >>> ser a 1 a 6 a 2 b 3 b 0 b 4 dtype: int64 >>> ser.groupby(level=0).cummin() a 1 a 1 a 1 b 3 b 0 b 0 dtype: int64 For DataFrameGroupBy: >>> data = [[1, 0, 2], [1, 1, 5], [6, 6, 9]] >>> df = pd.DataFrame( ... data, columns=["a", "b", "c"], index=["snake", "rabbit", "turtle"] ... ) >>> df a b c snake 1 0 2 rabbit 1 1 5 turtle 6 6 9 >>> df.groupby("a").groups {1: ['snake', 'rabbit'], 6: ['turtle']} >>> df.groupby("a").cummin() b c snake 0 2 rabbit 0 2 turtle 6 9 rTcumminrsrr rtrr{rs&&, rlrGroupBy.cummins2JHd+%% <&  roc V^8dQhRRRR/#rrj)rks"rlrmr 4s"K K K   K roc LVPRR4pVPRWR7#)a Cumulative max for each group. Returns the cumulative maximum of values within each group. The result has the same size as the input, with each element representing the maximum of all preceding elements (including itself) within its group. Parameters ---------- numeric_only : bool, default False Include only `float`, `int` or `boolean` data. **kwargs : dict, optional Additional keyword arguments to be passed to the function, such as `skipna`, to control whether NA/null values are ignored. Returns ------- Series or DataFrame Cumulative max for each group. Same object type as the caller. See Also -------- Series.cummax : Apply function cummax to a Series. DataFrame.cummax : Apply function cummax to each row or column of a DataFrame. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "a", "b", "b", "b"] >>> ser = pd.Series([1, 6, 2, 3, 1, 4], index=lst) >>> ser a 1 a 6 a 2 b 3 b 1 b 4 dtype: int64 >>> ser.groupby(level=0).cummax() a 1 a 6 a 6 b 3 b 3 b 4 dtype: int64 For DataFrameGroupBy: >>> data = [[1, 8, 2], [1, 1, 0], [2, 6, 9]] >>> df = pd.DataFrame( ... data, columns=["a", "b", "c"], index=["cow", "horse", "bull"] ... ) >>> df a b c cow 1 8 2 horse 1 1 0 bull 2 6 9 >>> df.groupby("a").groups {1: ['cow', 'horse'], 2: ['bull']} >>> df.groupby("a").cummax() b c cow 8 2 horse 8 2 bull 6 9 rTcummaxrsrrs&&, rlrGroupBy.cummax3s2RHd+%% <&  roc V^8dQhRRRR/#)reperiodszint | Sequence[int]suffixr5rj)rks"rlrmr s"N N $N  N roc  aaa\V4'd5\\V4p\V4^8Xd \ R4h^RIHpRpMR\V4'g\RV R\V4 R24hV'd \ R4h\\V4.pRp.pVEFo\S4'g\RS R\S4 R24h\\S4oSe'VVV3R lpVPWPRR 7p MS\PJdR oVPP p VPP"p \$P&!\V 4\$P(R 7p \*P,!WV S4VP.p V P1^V P2V 3/SRR 7p V'dX\5V \64'd\\8V P;44p T P=V'dV RS 2MRS 24p VP?\\@\6\B3,V 44EK \V4^8Xd V^,#X!V^RR7#)a Shift each group by periods observations. If freq is passed, the index will be increased using the periods and the freq. Parameters ---------- periods : int | Sequence[int], default 1 Number of periods to shift. If a list of values, shift each group by each period. freq : str, optional Frequency string. fill_value : optional The scalar value to use for newly introduced missing values. .. versionchanged:: 2.1.0 Will raise a ``ValueError`` if ``freq`` is provided too. suffix : str, optional A string to add to each shifted column if there are multiple periods. Ignored otherwise. Returns ------- Series or DataFrame Object shifted within each group. See Also -------- Index.shift : Shift values of Index. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b", "b"] >>> ser = pd.Series([1, 2, 3, 4], index=lst) >>> ser a 1 a 2 b 3 b 4 dtype: int64 >>> ser.groupby(level=0).shift(1) a NaN a 1.0 b NaN b 3.0 dtype: float64 For DataFrameGroupBy: >>> data = [[1, 2, 3], [1, 5, 6], [2, 5, 8], [2, 6, 9]] >>> df = pd.DataFrame( ... data, ... columns=["a", "b", "c"], ... index=["tuna", "salmon", "catfish", "goldfish"], ... ) >>> df a b c tuna 1 2 3 salmon 1 5 6 catfish 2 5 8 goldfish 2 6 9 >>> df.groupby("a").shift(1) b c tuna NaN NaN salmon 2.0 3.0 catfish NaN NaN goldfish 5.0 8.0 z0If `periods` is an iterable, it cannot be empty.r=TzPeriods must be integer, but z is .z/Cannot specify `suffix` if `periods` is an int.FNc,<VPSS^S4#)r<)shift)r/ fill_valuefreqperiods&rlrGroupBy.shift..sagg ror2r)rrr\)r?r)"r0rrrrrFr>r.rr"rrrr no_defaultrrIrrrar$r\group_shift_indexerrrrHrrSrro add_suffixappendrrG)rtrrrrr>rshifted_dataframesr|shiftedrIr res_indexerrrs&&ff& @rlr GroupBy.shiftsB`  8W-G7|q  !STT 9Jg&&3G9DgqQ !RSSC)*GJFf%%3F84V ~QO#v&F 44))5/!%Jmm''--// hhs3xrxx@ ..{&Q//44K01)#5 gv.."8W-=-=-?@G!,,,2vhax(!F8   % %d51B+CW&M NOV%&!+ q ! *? roc V^8dQhRRRR/#)rerrrhrrj)rks"rlrmr s"WWW Wroc VPpVPVR7pRR.pVP^8Xd+VPV9dVP R4pW#, #VP P 4UUu.uFwrVWd9gK VNK pppV'd&VP \PVR44pW#, #uuppi)a First discrete difference of element. Calculates the difference of each element compared with another element in the group (default is element in previous row). Parameters ---------- periods : int, default 1 Periods to shift for calculating difference, accepts negative values. Returns ------- Series or DataFrame First differences. See Also -------- Series.diff : Apply function diff to a Series. DataFrame.diff : Apply function diff to each row or column of a DataFrame. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "a", "b", "b", "b"] >>> ser = pd.Series([7, 2, 8, 4, 3, 3], index=lst) >>> ser a 7 a 2 a 8 b 4 b 3 b 3 dtype: int64 >>> ser.groupby(level=0).diff() a NaN a -5.0 a 6.0 b NaN b -1.0 b 0.0 dtype: float64 For DataFrameGroupBy: >>> data = {"a": [1, 3, 5, 7, 7, 8, 3], "b": [1, 4, 8, 4, 4, 2, 1]} >>> df = pd.DataFrame( ... data, index=["dog", "dog", "dog", "mouse", "mouse", "mouse", "mouse"] ... ) >>> df a b dog 1 1 dog 3 4 dog 5 8 mouse 7 4 mouse 7 4 mouse 8 2 mouse 3 1 >>> df.groupby(level=0).diff() a b dog NaN NaN dog 2.0 3.0 dog 2.0 4.0 mouse NaN NaN mouse 0.0 0.0 mouse 1.0 -2.0 mouse -5.0 -1.0 )rint8int16float32) rrrRrrdtypesitemsdictfromkeys)rtrrr dtypes_to_f32cr to_coerces&& rlr4 GroupBy.diffsT''**W*- ) 88q=yyM)!..3 } ,/::+;+;+=X+=xqAW+=IX!..y))LM} Ys ; C Cc V^8dQhRRRR/#)rerr fill_methodrirj)rks"rlrmr ms"a&a&a&a&roc ZaaVe\RV: R24hSe%VV3RlpVPW@PRR7#VfRpMTp\W4!^R7pVP VP P VPR7pVPSSR 7pWh, ^, #) aA Calculate pct_change of each value to previous entry in group. Parameters ---------- periods : int, default 1 Periods to shift for calculating percentage change. Comparing with a period of 1 means adjacent elements are compared, whereas a period of 2 compares every other element. fill_method : None Must be None. This argument will be removed in a future version of pandas. freq : str, pandas offset object, or None, default None The frequency increment for time series data (e.g., 'M' for month-end). If None, the frequency is inferred from the index. Relevant for time series data only. Returns ------- Series or DataFrame Percentage changes within each group. See Also -------- Series.pct_change : Apply function pct_change to a Series. DataFrame.pct_change : Apply function pct_change to each row or column of a DataFrame. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b", "b"] >>> ser = pd.Series([1, 2, 3, 4], index=lst) >>> ser a 1 a 2 b 3 b 4 dtype: int64 >>> ser.groupby(level=0).pct_change() a NaN a 1.000000 b NaN b 0.333333 dtype: float64 For DataFrameGroupBy: >>> data = [[1, 2, 3], [1, 5, 6], [2, 5, 8], [2, 6, 9]] >>> df = pd.DataFrame( ... data, ... columns=["a", "b", "c"], ... index=["tuna", "salmon", "catfish", "goldfish"], ... ) >>> df a b c tuna 1 2 3 salmon 1 5 6 catfish 2 5 8 goldfish 2 6 9 >>> df.groupby("a").pct_change() b c tuna NaN NaN salmon 1.5 1.000 catfish NaN NaN goldfish 0.2 0.125 z*fill_method must be None; got fill_method=rc,<VPSS^R7#)r<)rrr?) pct_change)r/rrs&rlr$GroupBy.pct_change..s!,,'roTrrPrd)r)rr) rrrrrfrcodesrr) rtrrrr|opfilledfill_grprs &f&f rlrGroupBy.pct_changels\  "Jk^1MN N  A --a1C1CRV-W W  BB"+>>$--"5"5$//>R..t.< A%%roc V^8dQhRRRR/#rerrrhrrj)rks"rlrmr s%-%-c%-(%-roc ZVP\RV44pVPV4#)a, Return first n rows of each group. Similar to ``.apply(lambda x: x.head(n))``, but it returns a subset of rows from the original DataFrame with original index and order preserved (``as_index`` flag is ignored). Parameters ---------- n : int If positive: number of entries to include from start of each group. If negative: number of entries to exclude from end of each group. Returns ------- Series or DataFrame Subset of original Series or DataFrame as determined by n. See Also -------- Series.head : Apply function head to a Series. DataFrame.head : Apply function head to each row or column of a DataFrame. Examples -------- >>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]], columns=["A", "B"]) >>> df.groupby("A").head(1) A B 0 1 2 2 5 6 >>> df.groupby("A").head(-1) A B 0 1 2 NrsslicertrtrrYs&& rlhead GroupBy.heads,J66uT1~F&&t,,roc V^8dQhRRRR/#r rj)rks"rlrmr s,-,-c,-(,-roc V'dVP\V)R44pMVP.4pVPV4#)aQ 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). Parameters ---------- n : int If positive: number of entries to include from end of each group. If negative: number of entries to exclude from start of each group. Returns ------- Series or DataFrame Subset of original Series or DataFrame as determined by n. See Also -------- Series.tail : Apply function tail to a Series. DataFrame.tail : Apply function tail to each row or column of a DataFrame. Examples -------- >>> df = pd.DataFrame( ... [["a", 1], ["a", 2], ["b", 1], ["b", 2]], columns=["A", "B"] ... ) >>> df.groupby("A").tail(1) A B 1 a 2 3 b 2 >>> df.groupby("A").tail(-1) A B 1 a 2 3 b 2 Nrrs&& rltail GroupBy.tailsAP ::5!T?KD::2>D&&t,,roc V^8dQhRRRR/#)rerYznpt.NDArray[np.bool_]rhrrj)rks"rlrmr (s(('<((roc jVPPpWR8g,pVPV,#)z Return _selected_obj with mask applied. Parameters ---------- mask : np.ndarray[bool] Boolean mask to apply. Returns ------- Series or DataFrame Filtered _selected_obj. rE)rrIr)rtrYrIs&& rlrtGroupBy._mask_selected_obj's0mmby!!!$''roc ,V^8dQhRRRRRRRRR R /#) rerr2fracrBreplacerweightszSequence | Series | None random_statezRandomState | Nonerj)rks"rlrmr ;sJz@z@ z@z@ z@ * z@ ) z@roc  VPP'd VP#\P!WV4pVe$\P!VPV^R7p\ P !V4pVPPVP4p.p VFwrVPV ,p \V 4p VeTpMVfQh\W-,4p\P!T TTVfRMXV ,VR7pV PW,4K \P!V 4p VPPV ^R7#)ad Return a random sample of items from each group. You can use `random_state` for reproducibility. Parameters ---------- n : int, optional Number of items to return for each group. Cannot be used with `frac` and must be no larger than the smallest group unless `replace` is True. Default is one if `frac` is None. frac : float, optional Fraction of items to return. Cannot be used with `n`. replace : bool, default False Allow or disallow sampling of the same row more than once. weights : list-like, optional Default None results in equal probability weighting. If passed a list-like then values must have the same length as the underlying DataFrame or Series object and will be used as sampling probabilities after normalization within each group. Values must be non-negative with at least one positive element within each group. random_state : int, array-like, BitGenerator, np.random.RandomState, np.random.Generator, optional If int, array-like, or BitGenerator, seed for random number generator. If np.random.RandomState or np.random.Generator, use as given. Default ``None`` results in sampling with the current state of np.random. Returns ------- Series or DataFrame A new object of same type as caller containing items randomly sampled within each group from the caller object. See Also -------- DataFrame.sample: Generate random samples from a DataFrame object. Series.sample: Generate random samples from a Series object. numpy.random.choice: Generate a random sample from a given 1-D numpy array. Examples -------- >>> df = pd.DataFrame( ... {"a": ["red"] * 2 + ["blue"] * 2 + ["black"] * 2, "b": range(6)} ... ) >>> df a b 0 red 0 1 red 1 2 blue 2 3 blue 3 4 black 4 5 black 5 Select one row at random for each distinct value in column a. The `random_state` argument can be used to guarantee reproducibility: >>> df.groupby("a").sample(n=1, random_state=1) a b 4 black 4 2 blue 2 1 red 1 Set `frac` to sample fixed proportions rather than counts: >>> df.groupby("a")["b"].sample(frac=0.5, random_state=2) 5 5 2 2 0 0 Name: b, dtype: int64 Control sample probabilities within groups by setting weights: >>> df.groupby("a").sample( ... n=1, ... weights=[1, 1, 1, 0, 0, 1], ... random_state=1, ... ) a b 5 black 5 2 blue 2 0 red 0 NrB)rrrr)rr)r;process_sampling_sizepreprocess_weightsrrrr rrroundrrr(rP)rtrrrrrr weights_arrgroup_iteratorsampled_indicesrXr grp_indices group_size sample_size grp_samples&&&&&& rlr;GroupBy.sample:s4x    # # #%% %++AW=   33D4F4FVWXK'' 5 33D4F4FG)KF,,v.K[)J" '''#D$56   '[5M) J  " ";#: ;!*$..9!!&&Q&??roc ,V^8dQhRRRRRRRRRR/#) rerLiteral['idxmax', 'idxmin']ignore_unobservedrrrrhrrj)rks"rlrmr s<<< (< < <  <  <roc nVP'EgG\;QJd0RVPP4F 'gK RM% RM!!RVPP44'd\ VPP 4pVPP 4pWf^8,P^,pWu8:gQhWu8pV'*;'dTp VPp V 'dG\V \4'd1V'dV P4p \ V P4^8p V 'd\RV R24hMFV'g?VPP4PRR7'd\V R24hVPV^VVR 7p V #) aFCompute idxmax/idxmin. Parameters ---------- how : {'idxmin', 'idxmax'} Whether to compute idxmin or idxmax. numeric_only : bool, default False Include only float, int, boolean columns. skipna : bool, default True Exclude NA/null values. If an entire group is NA, the result will be NA. ignore_unobserved : bool, default False When True and an unobserved group is encountered, do not raise. This used for transform where unobserved groups do not play an impact on the result. Returns ------- Series or DataFrame idxmax or idxmin for the groupby operation. c38"TFqPxK R#5irqrrs& rlr)GroupBy._idxmax_idxmin..s% 1H $ $1HrTFz Can't get zZ of an empty group due to unobserved categories. Specify observed=True in groupby instead.NrBz+ with skipna=False encountered an NA value.)rrrr)rrrrprrGrrrrrG_get_numeric_datarvrr7r) rtrr-rr expected_len group_sizes result_lenhas_unobserved raise_errrr s &&&&& rlrGroupBy._idxmax_idxmins|4}}}% 151H1H% % 151H1H% " " t}}99:L--,,.K$1_5;;A>J- --'6N->)>)Q)Q>I,,DZi88113D -1   &@@ D55::<@@d@KKu$OPQ Q""% #   roc(V^8dQhRRRRRRRR/#)rerrrr,rrrhrj)rks"rlrmr s,"=GK roc VPPpVP^8XdVPVP4pV#V'd6VP ^4P RR7'd\V R24h\V\4'dVP4pVPp\V\P4'gQh\VPRR7p\V\4'dGVP!VP"P%VRVR7VPVP&R7pV#/p\)VP*4F%wrVP"P%V RVR7W&K' VPP!WPR 7pVP,VnV#) r<NrBz7 with skipna=True encountered all NA values in a group.F)compatT) allow_fillrr)rH)rrHrrrltrrrrP to_flat_indexrNrrUr8rSrr'rPrrsr\rv) rtrrrrHr rTrrk column_valuess &&&& rlrGroupBy._wrap_idxmax_idxminss 88q=ZZ ,F4 3q 4 00%NO %,,++-[[Ffbjj11 11)%++eDH#v&&))KK$$V$R))* (1&(((;$A#kk..%$8/DG)<..t99.E!$ ro) rrrrrrrrrrr) NNNNNTTTFT)FFrq)NFF)FrE)NFrE)F)T)FTNN)FT)r&NNFT)NFTFT)r&FT)Fr<TNN)Fr<T)FrETNN)FrET)NNN)NFNNNsingle)r&rA) NNNNr<TFNrA)g?rF)averageTrF)r&)r&NN))NNFNN)FTF)Or~rrrrrrrurr8r]r7rzrrrrrrrrrrrr rrrr-r?rrDrrr;rpr|rrrrrrrrrrr rr$r.r;r?rJr_rPrjrnrzrrrrrr6rrrrrr4rrrrtr;rrrrjrorlrgrgs@DN &O &OP   B >? >?@  2 & &P " "H(    4)V %Gt%G %GN %t% %TcHcHJ +  + Z ? #' ? ?$-9^ 1 1f( T < 8"&86:8 8*  6    $ $P  % % 9  9 v :  : x e eN dC dCL a? a?F q qf n n` zD zDx g  g R _ _B w wr M  M ^ r rh r rh R  R h A  A F o obPd uG27uG uGn \  \ | C  C J g  g R I IV Y0 Y0v N0 N0`  Q( Q(f5n Z= Z=x P Pd 6: 6:p W  W r =I =I~ =H =H~ G  G R K  K Z () >>! N  N ` W Wr a& a&F %- %-N ,- ,-\ ( ($ z@ z@x<|rorgc ,V^8dQhRRRRRRRRR R /#) rerrHbyrrrrrrhrgrj)rks"rlrmrmsAZ0Z0 Z0Z0$Z0 Z0  Z0roc\V\4'd^RIHpV!VVVVR7#\V\4'd^RIHpV!VVVVR7#\ RV 24h)r) SeriesGroupBy)rrrr)DataFrameGroupByzinvalid type: )rrSpandas.core.groupby.genericrGrGrHr)rrErrrGrHs&&&& rl get_groupbyrJsmN#v=!   C # #@!   ..//roc$V^8dQhRRRRRR/#)rerrOrkznpt.NDArray[np.float64]rhrPrj)rks"rlrmrmts"+Bzroc\V4p\VRR7P4wr4\W44pVP'd\ \ V4p.VPOVNpVPUu.uFp\P!Wb4NK up\P!V\V44.,p\ WW.VPORNR7pV#\V4p \\P!V 4V4p W.p\P!W4\P!W94.p\ WWVPR.R7pV#uupi)z Insert the sequence 'qs' of quantiles as the inner-most level of a MultiIndex. The quantile level in the MultiIndex is a repeated copy of 'qs'. Parameters ---------- idx : Index qs : np.ndarray[float64] Returns ------- MultiIndex FraN)r@rrA)rrO factorizer( _is_multirrPr@rrrwrrrAr9r) rrkr lev_codesryr@r/rminidx idx_codess && rlrrts b'C2E*446NI$Y4I }}}:s##3::#s#,/II6Iq1"I6"'')SQTX:V9WW v:LCII:Lt:L M I 3x(4#> 9*BGGI,DE v388T:J K I7s4E)NNT)__conditional_annotations__r __future__rcollections.abcrrrrrrr functoolsr r typingr r r rrrrrrrrnumpyr pandas._libsrrpandas._libs.algosrpandas._libs.groupby_libsrfr\pandas._libs.missingrpandas._typingrrrrrrrr r!pandas.compat.numpyr"r pandas.errorsr#r$r%pandas.util._decoratorsr&pandas.util._exceptionsr'pandas.core.dtypes.castr(r)pandas.core.dtypes.commonr*r+r,r-r.r/r0r1r2r3r4r5r6pandas.core.dtypes.missingr7r8r9 pandas.corer:r;pandas.core._numbar<pandas.core.arraysr=r>r?r@rArBpandas.core.arrays.string_rCpandas.core.arrays.string_arrowrDpandas.core.baserErFpandas.core.commoncorecommonrpandas.core.framerGpandas.core.genericrHpandas.core.groupbyrIrJrKpandas.core.groupby.grouperrLpandas.core.groupby.indexingrMrNpandas.core.indexes.apirOrPrQpandas.core.internals.blocksrRpandas.core.seriesrSpandas.core.sortingrTpandas.core.util.numba_rUrVrWpandas._libs.tslibsrXpandas._libs.tslibs.timedeltasrYrZr[r\pandas.core.indexers.objectsr]r,r^r:r_r`ra#_groupby_agg_method_engine_template*_groupby_agg_method_skipna_engine_template_pipe_template_transform_templatercrrrrrrgrJr)rSs@rlrs #   '))#   / 34 (3<! 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