Pi(UdZddlmZddlmZmZmZmZmZm Z ddl Z ddl m Z m Z ddlmZmZmZmZmZmZmZmZmZmZddlZddlZddlmZmZddlm Z ddl!m"cm#Z$dd l%m&Z&dd l'm(Z(m)Z)m*Z*m+Z+m,Z,m-Z-m.Z.m/Z/m0Z0dd l1m2Z3dd l4m5Z5m6Z6m7Z7dd l8m9Z9ddl:m;Z;ddlZ>ddl?m@Z@mAZAmBZBmCZCmDZDmEZEmFZFmGZGmHZHmIZImJZJmKZKmLZLddlMmNZNmOZOmPZPddlQmRZRmSZSddlTmUZUddlVmWZWmXZXmYZYmZZZm[Z[m\Z\ddl]m^Z^ddl_m`Z`ddlambZbmcZcddldmecmfZgddlhmiZiddljmkZkddllmmZmmnZnmoZoddlpmqZqddlrmsZsmtZtddlumvZvmwZwmxZxddlymzZzddl{m|Z|dd l}m~Z~dd!lmZmZmZer,dd"lmZdd#lmZdd$l'mZmZmZdd%lmZdd&lmZdd'lmZmZmZd(Zd)Zd*Zd+ZeGd,d-ebZeeezeegefzeeegefzeeefzZd.ed/<Gd0d1ebece-esZed2ek3ZGd4d5ee-Z dGdHd@ZdIdFZdS)Ja 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)sc(eZdZdZd dZdZd d Zd S) GroupByPlotzE Class implementing the .plot attribute for groupby objects. groupbyGroupByreturnNonec||_dSN)_groupby)selfres o/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/pandas/core/groupby/groupby.py__init__zGroupByPlot.__init__s  cjfd}d|_|j||jjS)Nc|jiSrj)plot)rlargskwargss rmfzGroupByPlot.__call__..fs49d-f-- -rorr)__name__rk_python_apply_general _selected_obj)rlrsrtrus `` rm__call__zGroupByPlot.__call__sH . . . . . . }221dm6QRRRronamestrcfd}|S)Nc`fd}j|jjS)Nc:t|jiSrj)getattrrr)rlrsrtrzs rmruz0GroupByPlot.__getattr__..attr..fs$/wty$//@@@@ro)rkrwrx)rsrtrurzrls`` rmattrz%GroupByPlot.__getattr__..attrsN A A A A A A A=66q$-:UVV Vro)rlrzrs`` rm __getattr__zGroupByPlot.__getattr__s/ W W W W W W  roN)rerfrgrhrzr{)rv __module__ __qualname____doc__rnryrrrormrdrdsX    SSSrordr _KeysArgTypeceZdZUejhdzZded<dZded<dZded<d ed <ed*d Z ed+dZ ee d,dZ ee d*dZ ee d-dZedZeedZed.dZed/d Zed0d#Zd1d%Zed2d'Zed3d)ZdS)4 BaseGroupBy> objkeyssortleveldropnagrouperas_indexobserved exclusions group_keysops.BaseGrouper_grouperN_KeysArgType | NonerIndexLabel | Nonerboolrrgintc|jjSrjrngroupsrls rm__len__zBaseGroupBy.__len__s }$$ror{c6t|Srj)object__repr__rs rmrzBaseGroupBy.__repr__st$$$rodict[Hashable, Index]ct|jtr]t|jdkrEt jd|jdd|jddt t|jj S)aQ 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. (rz,) , 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 rmrzBaseGroupBy.groupssB di & & 3ty>>Q+>+> MS6:ilSS-1Yq\SSS +--    }##roc|jjSrjrrs rmrzBaseGroupBy.ngroups(s}$$ro$dict[Hashable, npt.NDArray[np.intp]]c|jjS)ak Dict {group name -> group indices}. The dictionary keys represent the group labels (e.g., timestamps for a time-based resampling operation), and the values are arrays of integer positions indicating where the elements of each group are located in the original data. This property is particularly useful when working with resampled data, as it provides insight into how the original time-series data has been grouped. See Also -------- core.groupby.DataFrameGroupBy.indices : Provides a mapping of group rows to positions of the elements. core.groupby.SeriesGroupBy.indices : Provides a mapping of group rows to positions of the elements. core.resample.Resampler.indices : Provides a mapping of group rows to positions of the elements. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b"] >>> ser = pd.Series([1, 2, 3], index=lst) >>> ser a 1 a 2 b 3 dtype: int64 >>> ser.groupby(level=0).indices {'a': array([0, 1]), 'b': array([2])} For DataFrameGroupBy: >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]] >>> df = pd.DataFrame( ... data, columns=["a", "b", "c"], index=["owl", "toucan", "eagle"] ... ) >>> df a b c owl 1 2 3 toucan 1 5 6 eagle 7 8 9 >>> df.groupby(by=["a"]).indices {np.int64(1): array([0, 1]), np.int64(7): array([2])} For Resampler: >>> ser = pd.Series( ... [1, 2, 3, 4], ... index=pd.DatetimeIndex( ... ["2023-01-01", "2023-01-15", "2023-02-01", "2023-02-15"] ... ), ... ) >>> ser 2023-01-01 1 2023-01-15 2 2023-02-01 3 2023-02-15 4 dtype: int64 >>> ser.resample("MS").indices defaultdict(, {Timestamp('2023-01-01 00:00:00'): [0, 1], Timestamp('2023-02-01 00:00:00'): [2, 3]}) )rindicesrs rmrzBaseGroupBy.indices-sJ}$$roc2dt|r%|jtjgSt |t rt d|D}t|jdkr"tt|j}nd}t |t rt |t sd}t|t|t|ks2 |j|S#t$r}d}t||d}~wwxYwfd|D}t dt||d D}n|}||}|j|gS) zd Safe get multiple indices, translate keys for datelike to underlying repr. c|t|tjrdSt|tjrdSdS)Nc t|Srj)rkeys rmz?BaseGroupBy._get_index..get_converter..s 9S>>roc*t|jSrj)rasm8rs rmrz?BaseGroupBy._get_index..get_converter..s9S>>#6roc|Srjrrs rmrz?BaseGroupBy._get_index..get_converter..s3ro)rdatetimenp datetime64)ss rm get_converterz-BaseGroupBy._get_index..get_converter{sG!X.// '111Ar}-- '666&&roc3PK|]!}t|r tjn|V"dSrj)r8rnan).0comps rm z)BaseGroupBy._get_index..s3IID4::74IIIIIIrorNz.s-AAq--**AAAAAAroc32K|]\}}||VdSrjr)rruns rmrz)BaseGroupBy._get_index..s.MM$!Q1MMMMMMroTstrict) r8rgetrrrtuplernextiter ValueErrorKeyErrorzip)rlrz index_samplemsgerr converters converterrs @rm _get_indexzBaseGroupBy._get_indexts ' ' ' :: 0<##BFB// / dE " " JIIDIIIIID t|  q T\ 2 233LLL lE * * #dE** &T oo%t99L 1 111 3<--3336%S//s2 3BAAALAAAJMM#j$t*L*L*LMMMMMDD% l33I9T??D|b)))s? D D-D((D-ct|jtr|jS|j-t |jr|j|jS|jS|jSrj)rrrT _selectionr._obj_with_exclusionsrs rmrxzBaseGroupBy._selected_objs\ dh ' ' 8O ? &4?++ 1x00 , ,xroset[str]c4|jSrj)r_dir_additionsrs rmrzBaseGroupBy._dir_additionssx&&(((rofunc!Callable[Concatenate[Self, P], T]rsP.argsrtP.kwargsr]cdSrjrrlrrsrts rmpipezBaseGroupBy.pipe Crotuple[Callable[..., T], str]r[cdSrjrrs rmrzBaseGroupBy.piperro@Callable[Concatenate[Self, P], T] | tuple[Callable[..., T], str]c.tj||g|Ri|S)a Apply a ``func`` with arguments to this GroupBy 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 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 rmrzBaseGroupBy.pipes'Vxd4T444V444roDataFrame | Seriesc|j}|j}t|rt|dks"t|rSt|dkr@t |t rt|dkr |d}nt |||}t|st ||jj |S)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 rr) rrr1rrrrrrxiloc)rlrzrrindss rm get_groupzBaseGroupBy.get_groups\y     %CJJ!OO   %4#&t99>>$&& %3t99>>Awtnn$t$$4yy !4.. !&t,,ro#Iterator[tuple[Hashable, NDFrameT]]c |j}|j}|j|j}t |rt |dks(t|trt |dkr d|D}|S)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 rc3&K|] \}}|f|fV dSrjr)rrgroups rmrz'BaseGroupBy.__iter__..s,??*#uvuo??????ro) rrr get_iteratorrxr1rrr)rlrrresults rm__iter__zBaseGroupBy.__iter__|sty ++D,>??    @CJJ!OO tT " "%4'*4yyA~~@????F ro)rgr)rgr{)rgr)rgr)rgr)rrrsrrtrrgr])rrrsr[rtr[rgr])rrrsr[rtr[rgr]rgr)rgr)rvrrrF _hidden_attrs__annotations__rrrrrpropertyrrrrr'rxrrrrrrrormrrs9 . 2 2 2 M $D$$$$#E#### %%% U% %%% U%  I$I$I$X UI$V  %%%X U%  C%C%C%X UC%J 0*0* U0*d ^ U& ))) U)XXK5K5K5K5Z \-\-\- U\-| bbb UbbbrorOutputFrameOrSeries)boundceZdZUdZded<ded<e dddZddZeddZe ddd"Z edd%Z e ddd*Z e ddd+Z ddd.Z edd0Zdd7Zedd8d9Zedd8d:Zdd;dd=Ze dddBZe dddDddKZddPZe dddQZdddRZedddSdTZedddSdUZeddVZedWZedddZZeedd[Zeddd]Zeddd^Z edd_Z!e dddbZ"edddcZ#e dddfZ$e dddgZ%e dddkZ&e dddlZ'eddmZ(e dddoZ)e dddpZ*e dddqZ+e dddrZ,e dddsZ-e dddtZ.edduZ/ dddvZ0edd;ddxZ1e dddZ2e dddZ3e dddZ4edӐddZ5edӐddZ6edӐddZ7eeddZ8 dӐddZ9e dddZ:edd dZ;edd dZedd dZ?e dd dZ@e dd dZAedddeBjCdfd dZDe dddZEe dddZFedddZGedddZHeddZIe dddƄZJ dddɄZKdd˄ZLdS(rft 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 rrrrNTFrrrrrrrops.BaseGrouper | Nonerfrozenset[Hashable] | None selectionrrrrrgrhc r||_t|tsJt|||_||_||_||_| |_| |_ |t||||| |j \}}}| |_ ||_ ||_ |rt|n t|_dS)N)rrrr)rrrItyperrrrrrrMrrr frozensetr) rlrrrrrrrrrrrs rmrnzGroupBy.__init__,s$#w''22c22'     $ ?'2!{ ((( $GZ!  3=N)J///9;;rorr{c||jvrt||S||jvr||St dt |jd|d)N'z' object has no attribute ')_internal_names_setr__getattribute__rAttributeErrorrrv)rlrs rmrzGroupBy.__getattr__Usn 4+ + +**466 6 48  :  GT # G G G G G   rorzc^tt|j|fd}||_|tjvr|||jS|tjv}|||j|| }|j j r|r| |}|S)z.curriedds#1Q(((((( (ro) is_transformnot_indexed_same) rrrrvrJplotting_methodsrwrxtransformation_kernelsrhas_dropped_na_set_result_index_ordered)rlrzrsrtr r rrus `` @rm _op_via_applyzGroupBy._op_via_apply_s D233T : : ) ) ) ) ) ) )   4( ( (--gt7IJJ Jt:: ++   %%!-- ,   = ' )>r)B)B 4#,RZ88#\@@HH W15533VF+++F 8=A  8=DD  ) ) ?DDD ff % % $*:FK""48I">>>rorrc|jj}|jjr%|jjs||d}|St |jjd}||d}|d}|jjr1| tt|d}||d}|S)NrrFcopy) rrr is_monotonicrset_axisrP result_ilocs sort_indexr'rRr)rlrroriginal_positionss rmrz!GroupBy._set_result_index_ordereds = % dm.J __U_33FM#4=#=EJJJ!3!<<"""** = ' G^^M#e**$=$=A^FFFQ// roSeries | DataFrameqsnpt.NDArray[np.float64] | NonerHc t|tr|}t|jj}|J|dd|tj|t|t|zttt|jj |j dD]\}\}}||dkr|dn d||z dz }||jvr`||d||B|d|ttj|t|d|S) Nrlevel_TrrrFr5)rrTto_framerr groupingsinsertrtile enumeraterreversedrget_group_levelscolumnsrPrepeat)rlrr= n_groupingsrrzlevs rm_insert_inaxis_grouperzGroupBy._insert_inaxis_groupers| ff % % '__&&F$-122 > MM)K))272s6{{c"gg7M+N+N    #, ,-- ..00   # #  W W E;D#|#a''BJG;+"5"9;;6>)):MM!T3////MM!T53B1H1Hu+U+U+UVVV roc|jsM|||}|}tt ||_n%|jj}|t||}||_|S)z Wraps the output of GroupBy aggregations into the expected result. Parameters ---------- result : Series, DataFrame Returns ------- Series or DataFrame r=) rrL _consolidaterRrrrr_insert_quantile_level)rlrr=rs rm_wrap_aggregated_outputzGroupBy._wrap_aggregated_outputs~(} !00B0??F((**F(V55FLLM.E~/ub99 FL ror*rc t|rjr$)rldatar*r r s rm_wrap_applied_outputzGroupBy._wrap_applied_output*s"$'''rorTc|jj}|jj}|jj}||d}|j}t|trXt|jj dkrtd|jj dj }| |}||}tj||\} } | | ||fS)Nrrrz_Grouping with more than 1 grouping labels and a MultiIndex is not supported with engine='numba')rrr9 _sorted_idsr&to_numpyrrrQrrBNotImplementedErrorrzget_level_valuesrgenerate_slices) rlrTr sorted_index sorted_ids sorted_data index_data group_keysorted_index_datastartsendss rm _numba_prepzGroupBy._numba_prep6s-'}1 ]. ii 1i55>>@@ Z j* - - @4=*++a//)H /27I#44Y??J&OOL99BBDD*:w??        rorr dtype_mappingdict[np.dtype, Any] engine_kwargsdict[str, bool] | Nonec |jstd|j}|jdkr|n|}t j||dfit|}|jj }|jj } |j j |f|| d|} |jj | jd<|| | j} |jdkr"| d} |j| _n |j| _| S)zp Perform groupby with a standard numerical aggregation function (e.g. mean) with Numba. z>^^I..F)FKK!\FN ro)rgc R|j}|jj}|jdkr|n|}||\}} } } t j|t|||d\}}t j |fit|} | | | || t|j g|R} | tj|d} |j}|jdkrd|ji}| } n d|j i}|j| fd|i|S) 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. rjnum_required_argsrrrrzrHr)rrr9r(rArdrK validate_udfrXgenerate_numba_transform_funcrVrrHr&rargsortrrzravel _constructor)rlrrgrsrtrT index_sortingrqrbrcr\r^numba_transform_funcrr result_kwargss rm_transform_with_numbazGroupBy._transform_with_numba|s`( 2 Y!^^TT262B2B22F2F/lKD!!!1 $!   f &C   %m44  &%      OO      RZ 66Q??  9>>#TY/M\\^^FF& 5M t FFuF FFFroc p|j}|jdkr|n|}||\}}} } t j|t |||d\}}t j|fit|} | | | ||t|j g|R} |j j } |jdkrd|j i}| } n d|j i}|j| fd| i|}|js6||}t%t||_|S)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. rjrvrrzrHr)rr(rArdrKrxrXgenerate_numba_agg_funcrVrrHrrrzr{r|rrLrRr)rlrrgrsrtrTrqrbrcr\r^numba_agg_funcrrrress rm_aggregate_with_numbazGroupBy._aggregate_with_numbasn(Y!^^TT262B2B22F2F/lKD!!!1 $!   f 7   %m44         OO       * 9>>#TY/M\\^^FF& 5MdEEeE}EE} 0--c22C%c#hh//CI ro)include_groupsrc|rtdttrbt|r?t |}t |r|iSsrtd|St ddsr;t rtfd}ntd}|||j S)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 'rc|gRiSrjr)grsrrts rmruzGroupBy.apply..fgs#43D333F333roz6func must be a callable if args or kwargs are supplied) rrr{hasattrrcallable TypeErrorr rwr)rlrrrsrtrrus ` `` rmrmz GroupBy.applysMN  JHII I dC  tT"" PdD))C==R3////RVR$%P$%P%PQQQ  Nt N N NOOO  V ~~ t44444444!LA))!T-FGGGrorur bool | Noneis_aggcv|j||\}}||}|||||S)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_groupwiserU)rlrurTr r rr*mutateds rmrwzGroupBy._python_apply_generaltsPF-774@@  #& ((        ror)npfunc numeric_only min_countraliasrCallable | Nonec ^|jd||||d|}||jdS)N)howaltrrrerr_cython_agg_generalr)r)rlrrrrrtrs rm _agg_generalzGroupBy._agg_generalsU*) %      ""48I">>>rorrr(rcf|J|jdkrt|d}n?t|j|j}|jddksJ|jdddf} |j||d}n9#t$r,}d |d |jd } t|| |d}~wwxYw|j} | tkr| td}n:t| r+| } | || }t!|| S) zn Fallback to pure-python aggregation if _cython_operation raises NotImplementedError. NrFr5dtyperT)preserve_dtypezagg function failed [how->z,dtype->])r()r(rTrHr]rshaperr agg_series Exceptionrrastyper5construct_array_type_from_sequencerS) rlrr*r(rserrq res_valuesrrrstring_array_clss rm_agg_py_fallbackzGroupBy._agg_py_fallbacksb ;!  e,,,CC686<888B8A;!####'!!!Q$-C  *11#s41PPJJ * * *HsHHCIHHHC$s))C..c ) *   F??#**6*>>JJ U # # R$99;; )8858QQJ "*48888s"B B6 'B11B6c T t|std| d  fd } |}|}dvr|d }|} | S) Nz(numeric_only accepts only Boolean valuesrrzr*rrgc jjd|fjdz d}|S#t$r$dvrt |t rndvrYnwxYwJ|j}|S)N aggregaterrranyall)rrstdsem)r(r)r_cython_operationr(rYrrCr)r*rrrTrrtrrls rm array_funcz/GroupBy._cython_agg_general..array_funcs 88Q'   & '    .((Z -L-L([C+G$G$G ???**3TYC*PPFMs %+AAidxminidxmaxskipna)rrr*rrgr)r+r_get_data_to_aggregategrouped_reduce_wrap_agged_manager_wrap_idxmax_idxminrQ) rlrrrrrtrnew_mgrroutrTs ``` `` @rmrzGroupBy._cython_agg_generals|$$ IGHH H** 3*OO           6%%j11&&w// & & &**3Cx@P*QQC**3// roc  t|rjrS)rlrrrts rm_cython_transformzGroupBy._cython_transforms!$'''roenginergc\t|ts|j|||g|Ri|S|tjvrd|d}t ||tjvs|tjvr"| ||d<||d<t|||i|S|j r|j |g|R||d|Stj |dd5tj |d|j j5|j |g|R||d|cdddcdddS#1swxYwYddddS#1swxYwYdS) Nrz2' is not a valid function name for transform(name)rrgrrTr)rr{_transform_generalrJtransform_kernel_allowlistrcythonized_kernelsrrr_reduction_kernel_transformr temp_setattrrobserved_grouper)rlrrrgrsrtrs rm _transformzGroupBy._transformsS$$$ *4*4XXXXQWXX X 8 8 8NdNNNCS// ! T, , ,8S0S0S!#)x *7'&74&&777 7} 7t7(.mOU  z488   z4=3QRR  8t7(.mOU                                          s6;!D!D 0 D! D D!D D!!D%(D%c0tj|dd5|dvr.ttd|}|j|dg|Ri|}n"| ||d<||d<t |||i|}dddn #1swxYwY||S)NrTrrrg)rrrr_idxmax_idxminr_wrap_transform_fast_result)rlrrrgrsrtrs rmrz#GroupBy._reduction_kernel_transform:s dJ 5 5 > >+++G$67>>,,T4I$III&II%'-F8$.;F?+,t,,d=f== > > > > > > > > > > > > > > >//777sAA88A<?A<c|j}|jj}||jjd}|jjdkr=tj|j |}| ||j |j }nQ|j |}|d||fid}||j d}|S)z7 Fast transform path for aggregations. rrrrrzT) allow_dups)rrrr'rrr(r;take_ndr$r|rrzr&_reindex_with_indexersr8)rlrrrroutputnew_axs rmrz#GroupBy._wrap_transform_fast_resultPs 'm :CC 8=A  $V^S99C%%c%JJFF \&&s++F22A}3ERV2WWF__SYQ_77F roc@t|dkrtjgd}n&tjtj|}|r|j|d}ntjt|jjt}| dd|| t<tj |g|jjdddj}|j|}|S)Nrint64rrFTr)rrarrayr concatenaterxr&emptyrrfillrrrDrr]where)rlrrfilteredr/s rm _apply_filterzGroupBy._apply_filterls w<<1  hr111GGgbnW5566G  6)..wQ.??HH8C 2 899FFFD IIe   (,D$$ %74!C4#5#;ABB#?!C!CDDFD)//55Hro ascending np.ndarrayc|jj}|jj}t||}||t |}}|dkr t jdt jSt jd|dd|ddkf}t j t jt j |d|f}| }|r|t j |||z}n8t j |t j|dddf||z }|jj rDt j|dkt j|t jd}n!|t jd}t j|t j} t j|t j| |<|| S) a. 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 rrTNrrFr5)rrrrUrrrrr_diffnonzerocumsumrIrrrrfloat64intparange) rlrrrsortercountrunreprrevs rm_cumcount_arrayzGroupBy._cumcount_array}sm-''W55[#c((U A::8ARX... .eD#crc(c!""g--.gbeBJsOOA.5677tmmoo  B 29SXs++ +CC)Cc!""gtm 45s;;cAC = ' 3(3"9bfcjj%j.P.PQQCC**RXE*22ChuBG,,,iRW555F 3xroct|jtr |jjSt|jtsJ|jjSrj)rrrH_constructor_slicedrTr|rs rm_obj_1d_constructorzGroupBy._obj_1d_constructorsF dh * * 08/ /$(F+++++x$$rorc:|dfdS)aa 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 rcNt|dSNFr5)r)rTrr rs rmrzGroupBy.any..%&///3363BBrorrrrlrs `rmrz GroupBy.anys5l'' BBBB(   roc:|dfdS)af 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 rcNt|dSr)rTrrs rmrzGroupBy.all..$rrorrrs `rmrz GroupBy.alls5n'' BBBB(   roc |}|jj|jj dk|jdkd fd }||}||}||}|S)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 rrbvaluesrrgc|jdkr(t|ddz}nt|z}tj|}t |t r@t|dtj |j dtj St |trSt |j ts9td}t!||d|Sr(|jdksJ|j ddksJ|dS|S) Nrr)r.max_binrr)r/zint64[pyarrow]rj)r(r8reshapercount_level_2drr?rBrzerosrbool_r>rrDr7rr)rmaskedcountedrr is_seriesr/rs rmhfunczGroupBy.count..hfuncrsO|q  g!6!6q"!=!= ==g.(WMMMG'?33 M#AJRXgmA.>bh%O%O%OG%899 M* {CC M%%566G}}33GAJe3LLL "|q((((}Q'1,,,,qz!Nro)rrrgr)rrrrr(rrrQ) rlrTr rnew_objrrr r/rs @@@@rmrz GroupBy.count(sF**,,m-'byIN          0%%e,,**733--g66 ror!Literal['cython', 'numba'] | Nonect|r*ddlm}||tj|dS|dfd}||jdS) 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 r) grouped_mean min_periodsrmeancPt|dSNFr5rr)rTrr rrs rmrzGroupBy.mean..s.fQU33388!-f9rorrrrer) rWpandas.core._numba.kernelsrrtr=float_dtype_mappingrr)r)rlrrrrgrrs `` rmrz GroupBy.meansd 6 " " C ? ? ? ? ? ?**, + --* .F&&tx &BB Brocx|dfd}||jdS)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 mediancPt|dSr)rTrrs rmrz GroupBy.median..S s.&///66)&7rorrerr)rlrrrs `` rmrzGroupBy.mediansbt)) & *  ""48I">>>rorddofc t|r=ddlm}tj||t j|dS|dfd|S)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 r grouped_varrrrrcPt|dSNFr5)rr)rTrr rrs rmrzGroupBy.std.. 'fQU33377T&7QQrorrrr) rWrr rsqrtrtr=rrrlrrrgrrr s ` ` rmrz GroupBy.std[ s| 6 " "  > > > > > >7''0! !! (   ++QQQQQ) , roct|r+ddlm}||tj|dS|dfd|S)aD 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 rrr!varcPt|dSr#)rTr*r$s rmrzGroupBy.var..: r%ror&)rWrr rtr=rrr(s ` ` rmr*z GroupBy.var sz 6 " "  > > > > > >**, + ++QQQQQ) , rosubsetSequence[Hashable] | None normalizec|rdnd}|j}|jd|jjDt t rj}|vrgng} ntj} |Qt|tz} | rtd| d| z } | rtd| dn| fdtjD} t|jj} | D]1}t||d d | \}}}| t|jz } 2| | d |j|j }t!t |}||_|r||d }|jr{|jj}t-t/||j_t-t/|jj}||d }||j_|rtt-t/|jj|jj}| |j||j|jd d}||z}|d}|jr|}n|j}t=j|j}||vrtd|d||_| t-t/||_|!}|jjdjjj"}tG||$t/||}||_|}|%|jdS)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. proportionrc*h|]}|j |jSr)in_axisrz)rgroupings rm z(GroupBy._value_counts..T s2   &XEU M   roNzKeys z0 in subset cannot be in the groupby column keys.z) in subset do not exist in the DataFrame.c3PK|] \}}|v |v jdd|fV!dSrj)r)ridx_name in_axis_namesr subsetteds rmrz(GroupBy._value_counts..l sYC --%92D2DC 2D2D2D2D roF)rrrr)rrrstable)rkind)rsort_remaining)rrrsumgzColumn label 'z' is duplicate of result columnrr value_countsr)&rrrrBrrTrzsetrHrrErrMrerrrsize sort_valuesrrrrangerr:nlevels droplevel transformfillnarrfill_missing_names set_names reset_indexrrPrCr)) rlr,r.rrrrzrqr7r unique_colsclashing doesnt_existrBrrr2gb result_seriesr index_levelrindexed_group_sizerrrH result_frame orig_dtypecolsr8rr9s @@@rm _value_countszGroupBy._value_counts@ sE )5||g X'  *.-*A    c6 " " HE+0M+A+ARRuDDck**K!KK $s='9'99$3333 );6 $W WWW ( #,CK"8"8D011  1 1C' MGQ g/00 0IIZZ ];    VRWWYY// !   )55#(6M 9 .!'-E(-c%jj(9(9M  %DM$; < <==K)44!%5M).M  %  6c$-122M4G4OPPF"/!6!6#--f55Y{ "7"" i  / /M*0055M = ""FF"'E,U[99Gw !W$!W!W!WXXX!%M "'//%G 2E2E"F"FM (4466L037?EJ 333::3w<<NND#'L !F""48N"CCCroc|r\|jjdkrLt|jjs3t t |jd|d|jj|dfd|S)ar 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 rz.sem called with numeric_only=z and dtype rcPt|dSr#)rTrr$s rmrzGroupBy.sem..! s'&///33f3MMror&)rr(r2rrrrvr)rlrrrs ` `rmrz GroupBy.sem sz  DHMQ..7G7W7W.::&JJ ,JJ9=JJ '' MMMMM% (   roc|j}d}t|jtrt|jjt rJt|jjtr(|jjjj tj urd}n'd}n$d}n!t|jjtrd}t|jtr"| ||jj}n| |}||dddd|}|js'|d}|S)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)rzF) infer_objectsconvert_stringconvert_booleanconvert_floating dtype_backendr@)rr@rrrTrr>rErna_valuerrr?rrzconvert_dtypesrrenamerI)rlrr^s rmr@z GroupBy.size' sJ@##%%EI dh ' ' 1$(.*=>> 1dhn.>??.x~+4>>(, (8 $-MMDHNO<< 1 0  dh ' ' 6--f48=-IIFF--f55F  $**#$ %!&+ +F} 9]]6**6688F rorct|r*ddlm}||tj|||St j|dd5|||dtj |}dddn #1swxYwY|S) 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 r) grouped_sumrrTr=rrrrrN) rWrrcrtr=default_dtype_mappingrrrrr=)rlrrrrrgrcrs rmr=z GroupBy.sum sB 6 " "  > > > > > >**.% + !$ D99  **!-'6! +               Ms%BBBcJ||||dtjS)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)rrrg)rlrrrs rmrgz GroupBy.prod s3P  %7 !   roct|r+ddlm}||tj||d|S||||dtjS)a 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 rgrouped_min_maxFris_maxrminrh) rWrrkrtr=identity_dtype_mappingrrrnrlrrrrrgrks rmrnz GroupBy.minS sB 6 " "  B B B B B B**/% + $$)#v % roct|r+ddlm}||tj||d|S||||dtjS)a 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 rrjTrlmaxrh) rWrrkrtr=rorrrrrps rmrrz GroupBy.max sB 6 " "  B B B B B B**/% + $$)#v % roc>dd}|||d||S)a 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 rrcdd}t|tr||St|tr ||St t |)Nr rTc|jt|j}t|s|jjjS|dS)z-Helper function for first item that isn't NA.rrr:rrr_r arrs rmfirstz2GroupBy.first..first_compat..first| s:geAGnn-3xx27=111v ror rTrrHrmrTrr)rrys rm first_compatz#GroupBy.first..first_compat{ sp    #y)) +yy'''C(( +uSzz!S ***roryrdrrr)rlrrrr|s rmryz GroupBy.first= sE| + + + +  % !   roc>dd}|||d||S)a4 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 rrcdd}t|tr||St|tr ||St t |)Nr rTc|jt|j}t|s|jjjS|dS)z,Helper function for last item that isn't NA.rrvrws rmlastz/GroupBy.last..last_compat..last s:geAGnn-3xx27=112wrorzr{)rrs rm last_compatz!GroupBy.last..last_compat sn    #y)) +yy&C(( +tCyy S ***rorrdr}r~)rlrrrrs rmrz GroupBy.last sEZ + + + +  % !   rocL|jjdkr}|j}t|j}|st d|jd|jddd}gd}|j ||jj | }|S| d }|S) aX 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 rzNo numeric types to aggregaterohlcrrr)openhighlowclose)rrHc*|Srj)r)sgbs rmrzGroupBy.ohlc..EsCHHJJro) rr(rxr2rr%rrr$_constructor_expanddimr_apply_to_column_groupbys)rlr is_numericr agg_namesrs rmrz GroupBy.ohlc s| 8=A  $C)#)44J A ?@@@88S[&qB9J988IX44$-">> 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 r percentilesincludeexcluderNrTc4|S)Nr)describe)r rrrs rmrz"GroupBy.describe..Ks!!** +Wg%ro)r )rrrr(unstackrAr]rrrrwrrLrRr)rlrrrr describedrs ``` rmrzGroupBy.describeHstj' s88q== ''%Ix1}}""**,,??$$&+BQB/ /  dJ 5 5  //!% 0F               !!} 60088F(V55FL sB>>CCr_cJddlm}|rtd|||g|Ri|S)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 r)get_resampler_for_groupingr)pandas.core.resamplerr)rlrulerrsrtrs rmresamplezGroupBy.resampleZsSb DCCCCC  JHII I))$FtFFFvFFFrosinglewindow9int | datetime.timedelta | str | BaseOffset | BaseIndexerr int | Nonecenterwin_type str | NoneonclosedIntervalClosedType | Nonerrbc Vddlm}||j||||||||j|j S)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 r)rb) rrrrrrrr _as_index)pandas.core.windowrbrxrr) rlrrrrrrrrbs rmrollingzGroupBy.rollingsU` 655555~  #]m    ror`c@ddlm}||j|||jS)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)r`)rrr)rr`rxr)rlrrr`s rm expandingzGroupBy.expandingqsBz 877777  #]     ror float | Nonespanhalflifefloat | str | Timedelta | Nonealphaadjust ignore_natimesnp.ndarray | Series | Nonerac Nddlm} | |j||||||||| |j S)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)ra) rrrrrrrrrr)rrarxr) rlrrrrrrrrrras rmewmz GroupBy.ewmsXt FEEEEE--  #]    ro directionLiteral['ffill', 'bfill']limitc2 |d}jj}jj}ttj|||dk| d fd }}||}|}j j |_ |S) 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. Nrffill)r.r compute_ffillrr*rrgct|}|jdkrGtj|jtj}||t j||St|tj rC|j }j j rt|j }tj|j|}n.t||j|j }t!|D]_\}}tj|jdtj}|||t j||||ddf<`|S)Nrr)rr/)r8r(rrrrr;rrndarrayrrrr*r_emptyrE) r*r/r1rri value_elementcol_funcrls rmblk_funczGroupBy._fill..blk_funcHsJ<>> 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 rrrrlrs rmrz GroupBy.ffillmstzz'z///roc0|d|S)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 bfillrrrs rmrz GroupBy.bfills^zz'z///rorOc t|S)a 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: [] )rOrs rmnthz GroupBy.nthsf"$'''rorPositionalIndexer | tupleLiteral['any', 'all'] | Nonec|sA||}|jj}||dkz}||}|St |st d|dvrt d|dt t|}|j |d}t|t|jkr|j}nu|jj }|jj | |j}|jjr3|dk} t!j| t$|} t'| dd }|||j|j } | |S) Nrz4dropna option only supported for an integer argumentrz_For a DataFrame or Series groupby.nth, dropna must be either None, 'any' or 'all', (was passed z).r)rrInt64F)rr6)rr)"_make_mask_from_positional_indexerrr_mask_selected_objr/rrrrxrrr codes_infoisinrrrrrrPrerrr) rlrrr/rrdroppedrrnullsr*grbs rm_nthz GroupBy._nthos  ::1==D-#C3"9%D))$//CJ!}} USTT T  ' '*%***  aLL$++Q+?? w<<3t122 2 2mGG =%Dm.tyy/G/GHG}+ C2 %W55gEBBBoog DIoNNwwqzzro?linearqfloat | AnyArrayLike interpolation;Literal['linear', 'lower', 'higher', 'nearest', 'midpoint']c||d}||}|j|}|j}t j|j|j\}} dddfd t|r$tj |gtj } d} n"tj |tj } | } |jj} |jj|jr | | dk} t!| t#t$j| | || dfd } |j| }||}||| S)a Return group values at the given quantile, a la numpy.percentile. Parameters ---------- q : float or array-like, default 0.5 (50% quantile) Value(s) between 0 and 1 providing the quantile(s) to compute. interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} Method to use when the desired quantile falls between two points. numeric_only : bool, default False Include only `float`, `int` or `boolean` data. .. versionchanged:: 2.0.0 numeric_only now defaults to ``False``. Returns ------- Series or DataFrame Return type determined by caller of GroupBy object. See Also -------- Series.quantile : Similar method for Series. DataFrame.quantile : Similar method for DataFrame. numpy.percentile : NumPy method to compute qth percentile. Examples -------- >>> df = pd.DataFrame( ... [["a", 1], ["a", 2], ["a", 3], ["b", 1], ["b", 3], ["b", 5]], ... columns=["key", "val"], ... ) >>> df.groupby("key").quantile() val key a 2.0 b 3.0 quantilervalsrrg"tuple[np.ndarray, DtypeObj | None]cVt|jtst|jrt d|jdd}t|t rCt |jr/|ttj }|j}nt|jr^t|tr'|ttj }n|}tjtj }nt|jr.pre_processors$*k22 odj6Q6Q QdjQQQ*.I$00 '5Edj5Q5Q 'mm%"&mAA J !$*-- 'dN33--ebf-EECCCHRX.. tz** 'z$/O/O 'mm%"&mAAtz** ' EFFF$TZ00 ' J Y&D.11 'nTZ6P6P 'HRZ00 mm%"&mAAj&& > !rorrDtypeObj | None result_masknp.ndarray | None orig_valsc|rNt|tr|Jdvrt|st||St j5t jdtt|| |j |cdddS#1swxYwYnt|rdvst|rG| d |jj}||St|t"jsJ| |S|S)N>rmidpointignore)categoryi8)rr?r-rArcatch_warningsfilterwarningsRuntimeWarningrr numpy_dtyper0r6view_ndarrayr_from_backing_datar)rrrrrs rmpost_processorz(GroupBy.quantile..post_processors ( 2i99&2&222$(>>>~!HH> -T;??? &466$3H~VVVV#24 ?? $ $-$9!"!"!, $$%Y//2%)???*955  ${{40055%.4   );;   &i:::::;;y111KsA B--B14B1rNr)r.r=rrbrcr*c|}t|tr*|j}tj ftj}nt |}d}t|j}|\}}d}|j dkr |j d}tj | ftj }|r| d}|j dkr |d||||n4t|D]$}  || || || d|%|j dkr-|d}||d}n|| z} ||||S)Nrrrjrr)r*r/ris_datetimelikeK)rr?_maskrrrr8r6rr(rrrrrBr{r)r*rr/rrrrncolsrrrrnqsrrs rmrz"GroupBy.quantile..blk_funcLsI&/22 #| h~RXFFF F||" 1&,??O+mF33OD)EyA~~ 1 (E7C0 CCCC 'yyyA~~F +$3 uADA#Aw!!W$((7 yA~~iinn*"-"3"3C"8"8Kkk%377!>#y+yII IrorN)rrrgr) rrrrrrrrrgrr)rrr _get_splitter _sorted_datarr[_slabelsrr4rrrrrrrr rgroup_quantilerlrrQ)rlrrrrrsplittersdatarbrcr=pass_qsrrrsrrrrrrs ` @@@@@rmrzGroupBy.quantiles`))|*)UU&&s++=..s33%*8+NOO $ "$ "$ "$ "L0 0 0 0 0 0 d Q<< 1#RZ000B)-GGARZ000BGm-' ; cQh-C"gg  %'    0 J0 J0 J0 J0 J0 J0 J0 J0 J0 Jd*++H55&&w//++CG+<<>> 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 rc3$K|] }|jV dSrj_passed_categoricalrpings rmrz!GroupBy.ngroup..s%LLDt'LLLLLLrodense) ties_methodrr)rrrrrrrrrrrrBrrr)rlrrrcomp_idsrrs rmngroupzGroupBy.ngroups~' =$ = ' xBAAHJEEHE LLDM4KLLL L L BxW===AH))(E)GG /\A%.F rocr|jj}||}|||S)ae 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 )r)rrrr)rlrr cumcountss rmcumcountzGroupBy.cumcounts<j)/((9(== '' 5999roaveragekeep na_optionpctcZ|dvrd}t|||||d}|j dddi|S)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 >toprbottomz3na_option must be one of 'keep', 'top', or 'bottom')rrrrrankrF)r)rr)rlrrrrrrts rmrz GroupBy.ranksoR 5 5 5GCS// !"""   &t%       rocPtjd||dg|jd|fi|S)aN 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_funcrrlrrsrts rmr!zGroupBy.cumprodis;z  D&8*EEE%t%iHHHHHrocPtjd||dg|jd|fi|S)aT 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 rrr"r%s rmrzGroupBy.cumsums;z  4(DDD%t%h GGGGGroc ^|dd}|d||S)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 rTcumminrrrrlrrtrs rmr(zGroupBy.cummins<JHd++%% <&   roc ^|dd}|d||S)ao 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 rTcummaxrr)r*s rmr,zGroupBy.cummax3s<RHd++%% <&   roperiodsint | Sequence[int]suffixc t|r@tt|}t|dkrt dddlm}d}n[t|s#td|dt|d|rt dtt|g}d }g}|D]ts#tddtdtt%fd }| ||j d } ntjurd |jj} |jj} t%jt| t$j } t+j| | | |j} | d| j| fid} |r]t5| t6r'tt8| } | |r|dnd} |tt@t6tBf| t|dkr|dn ||dd S)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 rz0If `periods` is an iterable, it cannot be empty.rTzPeriods must be integer, but z is .z/Cannot specify `suffix` if `periods` is an int.FNc4|dS)Nr)shift)r  fill_valuefreqperiods rmrzGroupBy.shift..s!agg ror r)r4rr2r)rr)"r1rr rrrrr/rrrrwrxr no_defaultrrrrrrrgroup_shift_indexerrrrrrTrrA add_suffixappendrrH)rlr-r5r4r/rr:shifted_dataframesrushiftedrr res_indexerrr6s `` @rmr3z GroupBy.shifts`   8W--G7||q   !STTT 9 9 9 9 9 9JJg&& QGQQgQQQ T !RSSSC))*GJ' O' OFf%% OFOOV OOO#v&&F 44t)5//!%Jm'-/ hs3xxrx@@@ .{C&QQQ/44K01)#5  gv..A"8W-=-=-?-?@@G!,,,2Dv((((( F   % %d51B+CW&M&M N N N N%&&!++ q ! !*??? rocN|j}||}ddg|jdkr|jvr|d}nUfd|jD}|r.|t|d}||z S)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 )r-int8int16rfloat32c"g|] \}}|v | Srr)rcr dtypes_to_f32s rm z GroupBy.diff..fs'XXXxq%-AWAWAWAWAWro) rr3r(rrdtypesitemsdictfromkeys)rlr-rr= to_coercerEs @rmrz GroupBy.diffsT'**W*-- ) 8q==yM))!..33XXXX3:+;+;+=+=XXXI N!..y))L)LMMW}ro fill_methodcF|td|d#fd}|||jdS|d}n|}t||d }||jj|j }| }||z d z S) a 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 Nz*fill_method must be None; got fill_method=r1c4|dS)Nr)r-r5r) pct_change)r r5r-s rmrz$GroupBy.pct_change..s#!,,'roTr7rrr)r)r-r5r) rrwrxrrercodesrr3) rlr-rLr5ruopfilledfill_grpr=s ` ` rmrOzGroupBy.pct_changels\  "MkMMMNN N  A --a1CRV-WW W  BBB"r""+++>>$-"5$/>RR..t.<< A%%rocr|td|}||S)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 Nrslicerrlrr/s rmheadz GroupBy.heads4J66uT1~~FF&&t,,,roc|r%|t| d}n|g}||S)aI 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 NrVrXs rmtailz GroupBy.tailsSP  ?::5!T??KKDD::2>>D&&t,,,ror/npt.NDArray[np.bool_]cF|jj}||dkz}|j|S)a Return _selected_obj with mask applied. Parameters ---------- mask : np.ndarray[bool] Boolean mask to apply. Returns ------- Series or DataFrame Filtered _selected_obj. r)rrrx)rlr/rs rmrzGroupBy._mask_selected_obj's*msby!!$''rofracreplaceweightsSequence | Series | None random_stateRandomState | NonecT|jjr|jStj|||}|tj|j|d}t j|}|j|j}g} |D]z\} } |j | } t| } ||}n|Jt|| z}tj| |||dn|| |}| | |{tj| } |j| dS)a 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 Nrr)r@r_r`rb)rxrr<process_sampling_sizepreprocess_weightsrrbrrrrroundr;rrr&)rlrr^r_r`rbr@ weights_arrgroup_iteratorsampled_indicesr.r grp_indices group_size sample_size grp_samples rmr<zGroupBy.sample:sPx   # &% %+AtW==   3D4FVWXXXK' 55 33D4FGG) < .s6% % )-D $% % % % % % rorz Can't get zZ of an empty group due to unobserved categories. Specify observed=True in groupby instead.Nrz+ with skipna=False encountered an NA value.r)rrrr)rrrrBrrr@rrrrH_get_numeric_datarHrr8r) rlrrprr expected_len group_sizes result_lenhas_unobserved raise_errrTrs rmrzGroupBy._idxmax_idxmins4} R% % 151H% % % " "  Rt}9::L-,,..K$[1_5;A>J----',6N->)>)Q>I,D 2Zi88 241133D --1   @@@@   RD5::<<@@d@KK RPPPQQ Q""% #    rorc"|jj}|jdkr||j}n[|r;|ddrt|dt|tr| }|j }t|tj sJt|jd}t|tr>||j|d||j|j}nii}t)|jD]%\} } |j| d||| <&|j||j }|j|_|S) Nrrz7 with skipna=True encountered all NA values in a group.F)compatT) allow_fillr4r)r)rrr@rrltrrrrQ to_flat_indexr$rrr9rTr|rr&rzrEr]rH) rlrrrrrr*r_rTk column_valuess rmrzGroupBy._wrap_idxmax_idxmins 8q==ZZ ,,FF  -q 4 00 -OOO %,, .++--[Ffbj11 1 11)%+eDDDH#v&& -))K$$V$RR)* (1&((;(;$A}#k..%$8/DGG..t39.EE!$ ro) NNNNNTTTFT)rrrrrrrrrrrrrrrrrrrrrrrgrh)rr{r)FF)r rr r)rrrgrrj)rr<r=r>rgrH)rr<r=r>)r*rr rr r)rTrH)rrrerfrgrh)rrrgr)NFF) rurrTrr rr rrrrgr)Fr)rrrrrr{rr) rr{r*rr(rrrrgr)NFr)rr{rrrrrr)F)rr{rr)rrrgr)T)rrrgr)rgr)rrrgr)rgr)FTNN)rrrrrr rgrh)FT)rrrrrgr)rNNFT) rrrr rgrhrrrr)NFTFT) r,r-r.rrrrrrrrgr)rFT)rrrrrrrgrr)FrTNN) rrrrrrrr rgrh)FrT)rrrrrrrgr)FrTNN)FrT)rgrH)NNN)rrrgr_)NFNNNr)rrrrrrrrrrrrrr{rgrb)rr)rrrr{rgr`) NNNNrTFNr)rrrrrrrrrrrrrrrrrr{rgra)rrrr)rr)rgrO)rrrrrgr)rrF)rrrrrr)rr)rTrF) rr{rrrr{rrrgr)rrrgr)r-r.r/r)r)r-rrgr)rNN)r-rrLrh)rT)rrrgr)r/r\rgr)NNFNN) rrr^rr_rr`rarbrc)FTF) rrorprrrrrrgr)rrrrorrrgr)Mrvrrrrrrnrrr3rrLrQrUrdrtrrrmrwrrrrrrrrrrrrrrrrrr*rTrr@r=rgrnrrryrrrrrrrrrrrrrrrrr!rr(r,rr8r3rrOrYr[rr<rrrrormrfrfs @@DNNN %)#'*.15'+&O&O&O&O U&OP      UB "'" >?>?>?>? U>?@  U2 OS&&&& U&P .2"""" U"P"'" (((((     U 4))))V ?C%G%G%G%G U%GN ?C%%%% U%T9>cHcHcHcHcHcHJ )-" + + + +  U+ Z #? #' ????? U?$-9-9-9-9^  $" 1111 U1f((((( -1 U< "&d8888 U8*  U6  U  $$$$ U$P  %%%X U% 9 9 9 9  U9 v : : : :  U: x eee UeN #4804 dCdCdCdC UdCL a?a?a?a? Ua?F 4804" qqqq Uqf 4804" nnnn Un` -1 zDzDzDzD UzDx HLg g g g  Ug R ___ U_B #4804 wwww Uwr MQM M M M  UM ^ #4804 rrrr Urh #4804 rrrr Urh NRR R R R  UR h NRA A A A  UA F ooo Uof PPPPPd 27uGuGuGuGuG UuGn #'#,0\ \ \ \  U\ | C C C C  UC J !!37""#,0g g g g  Ug R IIII UIV Y0Y0Y0Y0 UY0v N0N0N0N0 UN0`  Q(Q(Q(X UQ(l0455555n #& " Z=Z=Z=Z= UZ=x PPPP UPd 6:6:6:6: U6:p   W W W W  UW r =I=I=I=I U=I~ =H=H=H=H U=H~ #G G G G  UG R #K K K K  UK Z () >! N N N N  UN ` WWWW UWr   a&a&a&a& Ua&F %-%-%-%- U%-N ,-,-,-,- U,-\ ((( U($ !,0+/ z@z@z@z@ Uz@~#(" <<<<<|rorfTrrIbyrrrrrrgct|trddlm}|||||St|trddlm}|||||St d|)rr) SeriesGroupBy)rrrr)DataFrameGroupByzinvalid type: )rrTpandas.core.groupby.genericrrHrr)rrrrrrs rm get_groupbyrsN#v0======}!     C # # 0@@@@@@!     ...///ror6rPr=npt.NDArray[np.float64]rQcv t| t|d\}}t||}|jrst t |}g|j|} fd|jDtj |t|gz}t ||g|j d}nxt|}ttj ||}||g}tj | tj ||g}t |||jdg}|S)a 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 Fr5c:g|]}tj|Sr)rrI)rr rs rmrFz*_insert_quantile_level..s%666q1c""666roN)rrPr)rrP factorizer) _is_multirrQrrPrrDrrrIrz) r6r= lev_codesrKrrPminidx idx_codesrs @rmrPrPts2 b''C2E***4466NIs$Y44I } L:s###3:#s#6666CI666"')SQTXX:V:V9WW vU:LCI:Lt:L M M M3xx(4#>> s9c**BGIt,D,DE vU38T:J K K K Iro)NNT) rrIrrrrrrrgrf)r6rPr=rrgrQ)r __future__rcollections.abcrrrrrr r functoolsr r typingr r rrrrrrrrrnumpyr pandas._libsrrpandas._libs.algosrpandas._libs.groupby_libsrerpandas._libs.missingrpandas._typingrrrrrrr r!r"pandas.compat.numpyr#r# pandas.errorsr$r%r&pandas.util._decoratorsr'pandas.util._exceptionsr(pandas.core.dtypes.castr)r*pandas.core.dtypes.commonr+r,r-r.r/r0r1r2r3r4r5r6r7pandas.core.dtypes.missingr8r9r: pandas.corer;r<pandas.core._numbar=pandas.core.arraysr>r?r@rArBrCpandas.core.arrays.string_rDpandas.core.arrays.string_arrowrEpandas.core.baserFrGpandas.core.commoncorecommonrpandas.core.framerHpandas.core.genericrIpandas.core.groupbyrJrKrLpandas.core.groupby.grouperrMpandas.core.groupby.indexingrNrOpandas.core.indexes.apirPrQrRpandas.core.internals.blocksrSpandas.core.seriesrTpandas.core.sortingrUpandas.core.util.numba_rVrWrXpandas._libs.tslibsrYpandas._libs.tslibs.timedeltasrZr[r\r]pandas.core.indexers.objectsr^rr_rr`rarb#_groupby_agg_method_engine_template*_groupby_agg_method_skipna_engine_template_pipe_template_transform_templaterdrrrrrrfrrPrrormrs#"""""                        '&&&&&)))))))))######                      /..... 322222444444 ('''''322222<<<<<<! 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