U Mf@sZdZddlmZddlmZddlZddlmZmZddl Z ddl m Z ddl Z ddl mZmZmZmZmZmZmZmZmZmZmZmZddlZddlZddlmZdd lm Z m!Z!ddl"m#m$Z%dd l&m'Z'm(Z(m)Z)m*Z*m+Z+m,Z,m-Z-m.Z.dd l/m0Z1dd l2m3Z3dd l4m5Z5m6Z6m7Z7m8Z8ddl9m:Z:ddl;mZ>m?Z?m@Z@mAZAmBZBmCZCmDZDddlEmFZFmGZGddlHmIZIddlJmKZKddlLmMmNZNddlOmPZPmQZQmRZRmSZSddlTmUZUmVZVmWZWddlXmMmYZZddl[m\Z\ddl]m^Z^ddl_m`Z`maZambZbddlcmdZdmeZeddlfmgZgmhZhmiZiddljmkZkddllmMmmZmddlnmoZoddlpmqZqddlrmsZsmtZtdZudd d!d"Zvd#Zwd$Zxd%Zyd&ZzeGd'd(d(eVZ{eeeeeegefeeegefeeeffZ|Gd)d*d*eVeWe)edZ}ed+e^d,Z~Gd-d.d.e}e)Ze8edDd1d2d3d4d5d5d5d5d5d5d5d.d6 d7d8Zd9d:d;d<d=d>Zd?d@dAdBdCZdS)Ea 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)contextmanagerN)partialwraps)dedent) CallableHashableIterableIteratorListLiteralMappingSequenceTypeVarUnioncastfinal)option_context) Timestamplib) ArrayLike IndexLabelNDFrameTPositionalIndexer RandomStateScalarTnpt)functionAbstractMethodError)Appender Substitutioncache_readonlydoc)find_stack_level) is_bool_dtypeis_datetime64_dtypeis_float_dtype is_integeris_integer_dtypeis_numeric_dtypeis_object_dtype is_scalaris_timedelta64_dtype)isnanotna)nanops)executor)BaseMaskedArray BooleanArray CategoricalExtensionArray) DataError PandasObjectSelectionMixin) DataFrame)NDFrame)basenumba_ops)GroupByIndexingMixinGroupByNthSelector)CategoricalIndexIndex MultiIndex)ensure_block_shape)Series)get_group_index_sorter)NUMBA_FUNC_CACHEmaybe_use_numbaz See Also -------- Series.%(name)s : Apply a function %(name)s to a Series. DataFrame.%(name)s : Apply a function %(name)s to each row or column of a DataFrame. a] Apply function ``func`` group-wise and combine the results together. The function passed to ``apply`` must take a {input} 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 {input} as its first argument, and returns a dataframe, a series or a scalar. In addition the callable may take positional and keyword arguments. args, kwargs : tuple and dict Optional positional and keyword arguments to pass to ``func``. Returns ------- applied : Series or DataFrame 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 ----- .. versionchanged:: 1.3.0 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 -------- {examples} aY >>> df = pd.DataFrame({'A': 'a a b'.split(), ... 'B': [1,2,3], ... 'C': [4,6,5]}) >>> g = df.groupby('A') Notice that ``g`` has two groups, ``a`` and ``b``. 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: >>> g[['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 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. .. versionchanged:: 1.3.0 The resulting dtype will reflect the return value of the passed ``func``. >>> g[['B', 'C']].apply(lambda x: x.astype(float).max() - x.min()) B C A a 1.0 2.0 b 0.0 0.0 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: >>> g.apply(lambda x: x.C.max() - x.B.min()) A a 5 b 2 dtype: int64a >>> s = pd.Series([0, 1, 2], index='a a b'.split()) >>> g = s.groupby(s.index) From ``s`` above we can see that ``g`` has two groups, ``a`` and ``b``. Calling `apply` in various ways, we can get different grouping results: Example 1: The function passed to `apply` takes a Series as its argument and returns a Series. `apply` combines the result for each group together into a new Series. .. versionchanged:: 1.3.0 The resulting dtype will reflect the return value of the passed ``func``. >>> g.apply(lambda x: x*2 if x.name == 'a' else x/2) a 0.0 a 2.0 b 1.0 dtype: float64 Example 2: The function passed to `apply` takes a Series as its argument and returns a scalar. `apply` combines the result for each group together into a Series, including setting the index as appropriate: >>> g.apply(lambda x: x.max() - x.min()) a 1 b 0 dtype: int64)templatedataframe_examplesZseries_examplesa Compute {fname} of group values. Parameters ---------- numeric_only : bool, default {no} Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. 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. Returns ------- Series or DataFrame Computed {fname} of values within each group. aa Apply a function `func` with arguments to this %(klass)s object and return the function's 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(g(f(df.groupby('group')), arg1=a), arg2=b, arg3=c) # doctest: +SKIP You can write >>> (df.groupby('group') ... .pipe(f) ... .pipe(g, arg1=a) ... .pipe(h, arg2=b, arg3=c)) # 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 ------- object : 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 %(klass)s object. Notes ----- See more `here `_ Examples -------- %(examples)s a Call function producing a like-indexed %(klass)s on each group and return a %(klass)s having the same indexes as the original object filled with the transformed values. Parameters ---------- f : function Function to apply to each group. Can also accept a Numba JIT function with ``engine='numba'`` specified. 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. .. versionchanged:: 1.1.0 *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`` .. versionadded:: 1.1.0 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 .. versionadded:: 1.1.0 **kwargs Keyword arguments to be passed into func. Returns ------- %(klass)s 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 over the specified axis. %(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. .. versionchanged:: 1.3.0 The resulting dtype will reflect the return value of the passed ``func``, see the examples below. Examples -------- >>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar', ... 'foo', 'bar'], ... 'B' : ['one', 'one', 'two', 'three', ... 'two', 'two'], ... 'C' : [1, 5, 5, 2, 5, 5], ... 'D' : [2.0, 5., 8., 1., 2., 9.]}) >>> grouped = df.groupby('A') >>> grouped.transform(lambda x: (x - x.mean()) / x.std()) C D 0 -1.154701 -0.577350 1 0.577350 0.000000 2 0.577350 1.154701 3 -1.154701 -1.000000 4 0.577350 -0.577350 5 0.577350 1.000000 Broadcast result of the transformation >>> grouped.transform(lambda x: x.max() - x.min()) C D 0 4 6.0 1 3 8.0 2 4 6.0 3 3 8.0 4 4 6.0 5 3 8.0 .. versionchanged:: 1.3.0 The resulting dtype will reflect the return value of the passed ``func``, for example: >>> grouped[['C', 'D']].transform(lambda x: x.astype(int).max()) C D 0 5 8 1 5 9 2 5 8 3 5 9 4 5 8 5 5 9 a Aggregate using one or more operations over the specified axis. Parameters ---------- func : function, str, list or dict Function to use for aggregating the data. If a function, must either work when passed a {klass} or when passed to {klass}.apply. Accepted combinations are: - function - string function name - list of functions and/or function names, e.g. ``[np.sum, 'mean']`` - dict of axis labels -> functions, function names or list of such. Can also accept a 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. .. versionchanged:: 1.1.0 *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 globally setting ``compute.use_numba`` .. versionadded:: 1.1.0 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 .. versionadded:: 1.1.0 **kwargs Keyword arguments to be passed into func. Returns ------- {klass} See Also -------- {klass}.groupby.apply : Apply function func group-wise and combine the results together. {klass}.groupby.transform : Aggregate using one or more operations over the specified axis. {klass}.aggregate : Transforms the Series on each group based on the given function. Notes ----- 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. Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See :ref:`gotchas.udf-mutation` for more details. .. versionchanged:: 1.3.0 The resulting dtype will reflect the return value of the passed ``func``, see the examples below. {examples}c@s4eZdZdZddddZddZdd d d Zd S) GroupByPlotzE Class implementing the .plot attribute for groupby objects. 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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 axis : int, default 0 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. 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N)rrrUrQrQrRrs z GroupBy._group_selection_contextzIterable[Series]cCs t|dSrNrrUrQrQrR_iterate_slicesszGroupBy._iterate_slicesnot_indexed_samecsnddlm}fdd}|s||jd}jj}jrZjjd}|dk}||}|jr|j j |s|j |j \} } t| } |j| jd}n|j|jdd}n~jr||}jrjj} jj} jj} ||j| | | dd }n ttt|}||j|d }n||}||jd}jjd krHjjnj}t|trj|dk rj||_|S) Nr)concatcs(tj|D]}|j}|q |SrN)rZnot_none _get_axisrlZ_reset_identity)valuesvrrUrQrRreset_identitys  z/GroupBy._concat_objects..reset_identityrF)rlcopy)rlrqlevelsrrv)rlrqr) Zpandas.core.reshape.concatrrlrrrmro group_infoZhas_duplicatesaxesequalsindexZget_indexer_non_unique_values algorithmsZunique1dtakereindexrprk result_indexrrlistranger}rtrr_rrrE)rPrrrrresultrlabelsmaskindexer_rpZ group_levelsZ group_namesrqr_rQrUrR_concat_objectssH     zGroupBy._concat_objectsr)rr|cCs|jjr(|j|j|j|jdd|Stt| |jj }|j||jdd|j |jd}t |j t |jj k}|r|j }|jj ||_ n|j|j|j|jdd|S)NT)rlZinplacer)roZ is_monotonicZset_axisrtrrlrBr concatenaterr sort_indexr}rr)rPrZoriginal_positionsZ dropped_rowsZsorted_indexerrQrQrR_set_result_index_ordered;sz!GroupBy._set_result_index_orderedz"Mapping[base.OutputKey, ArrayLike]zSeries | DataFramecCs t|dSrNrrPrrQrQrR_indexed_output_to_ndframeZsz"GroupBy._indexed_output_to_ndframez7Series | DataFrame | Mapping[base.OutputKey, ArrayLike]znpt.NDArray[np.float64] | None)outputqscCst|ttfr|}n ||}|jsH|||}tt|j j }n|j j }|dk rbt ||}||_ |jdkr|j}|j |jj r|jj |_ |j||dS)a Wraps the output of GroupBy aggregations into the expected result. Parameters ---------- output : Series, DataFrame, or Mapping[base.OutputKey, ArrayLike] Data to wrap. Returns ------- Series or DataFrame Nrr)rrEr:rrkZ_insert_inaxis_grouper_inplaceZ _consolidaterBrrorr_insert_quantile_levelrrlrrrtr_reindex_output)rPrrrrrQrQrR_wrap_aggregated_output_s     zGroupBy._wrap_aggregated_output)rr|cCsFt|ttfr|}n ||}|jdkr8|j}|jj|_|jj|_|S)aN Wraps the output of GroupBy transformations into the expected result. Parameters ---------- output : Mapping[base.OutputKey, ArrayLike] Data to wrap. Returns ------- Series or DataFrame Series for SeriesGroupBy, DataFrame for DataFrameGroupBy r) rrEr:rrlrrtcolumnsr)rPrrrQrQrR_wrap_transformed_outputs    z GroupBy._wrap_transformed_outputr)rrcCs t|dSrNr)rPdatarrrQrQrR_wrap_applied_outputszGroupBy._wrap_applied_outputzbool | lib.NoDefault) numeric_onlyr|cCs`|tjkr\|jjdkrXd}|jr*|jj}n|j}|}t|j r\t|j s\|j s\d}nd}|S)at Determine subclass-specific default value for 'numeric_only'. 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Parameters ---------- numeric_only : bool or lib.no_default Returns ------- bool TF) r no_defaultrtrrlrrZ_get_numeric_datar}rempty)rPr rtcheckrQrQrR_resolve_numeric_onlys   zGroupBy._resolve_numeric_onlyc Csxt|std|jj\}}}t||}tj||dd}|j||jd }|j | } t ||\} } | | | |fS)Nz5Numba engine can only be used with a single function.F)Z allow_fillr) callableNotImplementedErrorrorrFrtake_ndrrlto_numpyrrZgenerate_slices) rPrr idsrr sorted_indexZ sorted_ids sorted_dataZsorted_index_datastartsendsrQrQrR _numba_preps zGroupBy._numba_prepzdict[str, bool] | None)r engine_kwargsnumba_cache_key_strc Gs|jstd|jdkr td| |j}W5QRX|jdkrH|n|}|||\}}} } t |||} | | ||df|} ||f} | t kr| t | <|j j }|jdkrd|j i}| } n d|ji}|j| fd|i|S) zp Perform groupby with a standard numerical aggregation function (e.g. mean) with Numba. z.fnanz6func must be a callable if args or kwargs are suppliedz$apply func should be callable, not 'rzmode.chained_assignment)ris_builtin_funcrrhasattrr1r`rrr]rrrrr)rPrrWrXrYresrrQr*rRr[Zs2      @z GroupBy.applyrz bool | None)rYr rr|cCs8|j|||j\}}|dkr(|p&|j}|j|||dS)aB 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. 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Returns ------- Series or DataFrame data after applying f Nr)ror[rlrsr )rPrYr rrrsrQrQrRrs zGroupBy._python_apply_generalc stfdd}i}|jdkr6|||jSt|D]^\}}|j}z|j ||} Wn&t k rt t |dYqBYnXt j||d} | || <qB|s|||jS||S)Ncs|fSrNrQrr*rQrRrrz-GroupBy._python_agg_general..rZagg)labelposition)rr,rrr enumeraterr_ro agg_seriesr)warn_dropping_nuisance_columns_deprecatedrr<Z OutputKeyr) rPrrWrXrYridxrtr_rrrQr*rR_python_agg_generals"    zGroupBy._python_agg_generalrr  min_countaliasnpfuncc CsB|0|j||||d}|j|jddW5QRSQRXdS)Nhowaltr r7rMmethod)r_cython_agg_general __finalize__rt)rPr r7r8r9rrQrQrR _agg_generals zGroupBy._agg_generalr)rrr<r|cCs~|jdkrt|}n.t|j}|jddks0t|jdddf}|jj||dd}t |t rrt |j ||j d}t||dS)zn Fallback to pure-python aggregation if _cython_operation raises NotImplementedError. rNrT)Zpreserve_dtypedtype)r)rrEr:rshaperilocror2rr5rZ_from_sequencerCrD)rPrrr<Zserr! res_valuesrQrQrR_agg_py_fallbacks    zGroupBy._agg_py_fallbackr:c sjdk}|rh|rXtjjsXd}dkr6d}ttjdd|dn|shjddd d d fd d }j |d d}|st |t krt t |} |rֈj j| _| S| SdS)Nrr anyr)Z bool_only.z does not implement Frrrr|csLz jjd|jdd}Wn&tk rFj|jd}YnX|S)N aggregaterrlr7)rr<)ro_cython_operationrrrG)rrr<r r;r7rPrQrR array_func(s z/GroupBy._cython_agg_general..array_funcTZignore_failures)_get_data_to_aggregaterr+rrCrrrZget_numeric_datagrouped_reducer}r3_wrap_agged_managerrorrr) rPr;r<r r7is_serZkwd_namerQnew_mgrr.rQrPrRr?s*     zGroupBy._cython_agg_general)r;r rlcKs t|dSrNr)rPr;r rlrXrQrQrR_cython_transformDszGroupBy._cython_transform)enginerc Osdt|r| |j}W5QRX|jdkr0|n|}|j||f|d|i|}|jjdkr|tt|jj ||j |j dStt |jj | |j |jdSt|p|}t|ts|j|f||S|tjkrd|d} t| nz|tjks|tjkrt||||St|ddt||||}W5QRX||rN||S|j|f||SdS) Nr rrrrr_rz2' is not a valid function name for transform(name)ruT)rHrrrrr%rtrr:r rrrErr_rZget_cython_funcrr]Z_transform_generalr<Ztransform_kernel_allowlistrZcythonized_kernelsZtransformation_kernelsr` temp_setattrZ_can_use_transform_fast_wrap_transform_fast_result) rPrrZrrWrXr r!rrrQrQrR _transformIsL           zGroupBy._transformcCsp|j}|jj\}}}|j|jjdd}|jjdkrVt|j |}|j ||j |j d}n|j |dd}|j |_ |S)z7 Fast transform path for aggregations. FrKrr\rr)rrorrrrtrrrrr rr_r)rPrrtrroutrrQrQrRr^{s z#GroupBy._wrap_transform_fast_resultcCst|dkrtjgdd}ntt|}|rD|jj||jd}n^tjt|jj t d}| dd|| t <t|t|jjdddgj}|j|}|S)Nrint64rBrFTr)r}rarrayrvrrrrlrrrzfillastyperxtilerrDrwhere)rPrrmfilteredrrQrQrR _apply_filters  $ zGroupBy._apply_filter np.ndarray) ascendingr|c Cs|jj\}}}t||}||t|}}|dkrBtjdtjdStjd|dd|ddkf}ttjt |d|f}| } |r| t | ||8} n&t | tj|dddf|| } tj|tj d} tj |tj d| |<| | jtjddS) 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 rrBTNrrFrK)rorrFr}rrraZr_ZdiffZnonzerocumsumrepeatintpZarangerd) rPrjrrrZsortercountrunrepr`revrQrQrR_cumcount_arrays  " &zGroupBy._cumcount_arraycCs,t|jtr|jjSt|jts$t|jjSrN)rrtr:Z_constructor_slicedrErr rUrQrQrR_obj_1d_constructors zGroupBy._obj_1d_constructorzLiteral[('any', 'all')])val_testskipnac sPdddfdd }dddd dd d d }|jtjdttjd d |||d S)zO Shared func to call any / all Cython GroupBy implementations. rztuple[np.ndarray, type]valsr|cst|jrHr,tjddtgd}||}n|jtdd}ttj|}n*t|t rd|j jtdd}n|jtdd}| tj tfS)NcSst|st|SdS)NT)r/rzrrQrQrRrrz9GroupBy._bool_agg..objs_to_bool..)ZotypesFrK) r,rCrZ vectorizerzrdrndarrayrr3_dataviewint8)rwrrurQrR objs_to_bools   z'GroupBy._bool_agg..objs_to_boolFrirrz)r inferencenullabler|cSs.|rt|jtdd|dkS|j|ddSdS)NFrKr)r4rdrz)rr~rrQrQrRresult_to_boolsz)GroupBy._bool_agg..result_to_boolT)r  cython_dtype needs_maskneeds_nullablepre_processingpost_processingrtru)F)_get_cythonized_result libgroupbyZ group_any_allrrCr{)rPrtrur}rrQr|rR _bool_aggs  zGroupBy._bool_aggrMr^r|cCs |d|S)a Return True if any value in the group is truthful, else False. Parameters ---------- skipna : bool, default True Flag to ignore nan values during truth testing. Returns ------- Series or DataFrame DataFrame or Series of boolean values, where a value is True if any element is True within its respective group, False otherwise. rIrrPrurQrQrRrIsz GroupBy.anycCs |d|S)a Return True if all values in the group are truthful, else False. Parameters ---------- skipna : bool, default True Flag to ignore nan values during truth testing. Returns ------- Series or DataFrame DataFrame or Series of boolean values, where a value is True if all elements are True within its respective group, False otherwise. r)rrrQrQrRr)sz GroupBy.allc s|}|jj\}dk|jdkdddfdd }||}t|dd||}W5QRX|jdkr|jj|_ |j |d d S) z Compute count of group, excluding missing values. Returns ------- Series or DataFrame Count of values within each group. rrr)bvaluesr|csr|jdkr"t|dd@}nt|@}tj|dd}rn|jdksTt|jddksft|dS|S)Nrr)rZmax_binrlr r)rr/reshaperZcount_level_2drrD)rZmaskedZcountedrZ is_seriesrrrQrRhfunc8s zGroupBy.count..hfuncruTr fill_value) rSrorrrUrr]rVrrr)rPr rrrXrrQrrRrn&s     z GroupBy.count)Zsee_alsoZcython)r rZrcsX||t|r,ddlm}|||dS|jdfddd}|j|jdd Sd S) a, Compute mean of groups, excluding missing values. Parameters ---------- numeric_only : bool, default True Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. 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`` .. versionadded:: 1.4.0 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}}`` .. versionadded:: 1.4.0 Returns ------- pandas.Series or pandas.DataFrame %(see_also)s 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) sliding_meanZ groupby_meanmeancst|jdSNr )rErrZnumeric_only_boolrQrRrrzGroupBy.mean..r<r rMr=N)rrHpandas.core._numba.kernelsrr#r?r@rt)rPr rZrrrrQrrRrTsI   z GroupBy.meanrcs2|||jdfddd}|j|jddS)a Compute median of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex Parameters ---------- numeric_only : bool, default True Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Returns ------- Series or DataFrame Median of values within each group. mediancst|jdSr)rErrrrQrRrrz GroupBy.median..rrMr=)rr?r@rt)rPr rrQrrRrs  zGroupBy.medianrz str | None)ddofrZrcCsPt|r*ddlm}t|||d|S|jtjdt tj dd|dSdS) a Compute standard deviation 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`` .. versionadded:: 1.4.0 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}}`` .. versionadded:: 1.4.0 Returns ------- Series or DataFrame Standard deviation of values within each group. r sliding_varZ groupby_stdTcSs t|SrN)rsqrtrwr~rQrQrRrrzGroupBy.std..) needs_countsrrrN) rHrrrrr#rrZ group_varrCfloat64)rPrrZrrrQrQrRstds)  z GroupBy.stdc st|r$ddlm}|||dSdkrP|tj}|jdfdd|dSfd d}|| |W5QRSQRXd S) a Compute variance of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters ---------- ddof : int, default 1 Degrees of freedom. engine : str, default None * ``'cython'`` : Runs the operation through C-extensions from cython. * ``'numba'`` : Runs the operation through JIT compiled code from numba. * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba`` .. versionadded:: 1.4.0 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}}`` .. versionadded:: 1.4.0 Returns ------- Series or DataFrame Variance of values within each group. rrZ groupby_varrvarcst|jdSNr)rErrrrQrRr4rzGroupBy.var..rcs |jdSr)rrrrQrRr8rN) rHrrr#rrrr?rr5)rPrrZrrr rrQrrRrs$)     z GroupBy.varrcCs|j|d}|jdkr*|t|}n`|j|j}|}|j |}|j |}|j dd|ft|j dd|f<|S)a Compute standard error of the mean of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters ---------- ddof : int, default 1 Degrees of freedom. Returns ------- Series or DataFrame Standard error of the mean of values within each group. rrN) rrrrrnrrrnuniqueZget_indexer_forrE)rPrrcolscountsZ result_ilocsZ count_ilocsrQrQrRsem<s    .z GroupBy.semcCsV|j}t|jtr*|j||jjd}n ||}|jsH|d }|j |ddS)z Compute group sizes. Returns ------- DataFrame or Series Number of rows in each group as a Series if as_index is True or a DataFrame if as_index is False. r^sizerr) rorrrtrErsr_rkrename reset_indexrrrQrQrRrZs   z GroupBy.sizesum)fnamenoZmc)r r7rZrc Cslt|r"ddlm}|||dS||}t|dd|j||dtj d}W5QRX|j |ddSdS) Nr) sliding_sumZ groupby_sumruTaddr6r) rHrrr#rrr]rArrr)rPr r7rZrrrrQrQrRrus   z GroupBy.sumprod)r r7cCs||}|j||dtjdS)Nrr6)rrArrrPr r7rQrQrRrs z GroupBy.prodmincCs|j||dtjdS)Nrr6)rArrrrQrQrRrs z GroupBy.minmaxcCs|j||dtjdS)Nrr6)rArrrrQrQrRrs z GroupBy.maxfirstcCs$d ddddd}|j||d|dS) NrrrxrtrlcSsHdddd}t|tr&|j||dSt|tr8||Stt|dS)NrErcSs&|jt|j}t|stjS|dS)z-Helper function for first item that isn't NA.rrbr0r}rr+rZarrrQrQrRrsz2GroupBy.first..first_compat..firstrrr:r[rErr)rtrlrrQrQrR first_compats   z#GroupBy.first..first_compatrr6)rrA)rPr r7rrQrQrRrsz GroupBy.firstlastcCs$d ddddd}|j||d|dS) NrrrxrcSsHdddd}t|tr&|j||dSt|tr8||Stt|dS)NrErcSs&|jt|j}t|stjS|dS)z,Helper function for last item that isn't NA.rrrrQrQrRrsz/GroupBy.last..last_compat..lastrr)rtrlrrQrQrR last_compats   z!GroupBy.last..last_compatrr6)rr)rPr r7rrQrQrRrsz GroupBy.lastr:cCs~|jjdkrl|j}t|j}|s(td|jjd|jdddd}dd d d g}|jj ||jj |d }| |S| d d|j S)a Compute open, high, low and close values of a group, excluding missing values. For multiple groupings, the result index will be a MultiIndex Returns ------- DataFrame Open, high, low and close values within each group. rzNo numeric types to aggregaterMohlcrrrNopenhighlowcloser[cSs|SrN)rrrQrQrRr rzGroupBy.ohlc..)rtrrr+rCr7rorOrZ_constructor_expanddimrrZ_apply_to_column_groupbysr)rPrtZ is_numericrFZ agg_namesrrQrQrRrs.    z GroupBy.ohlcc sV|D|fdd}|jdkr8|jW5QRS|W5QRSQRXdS)Ncs |jfSrN)describerrXrQrRr rz"GroupBy.describe..r)rr[rlrZunstack)rPrXrrQrrRr s   zGroupBy.describecOsddlm}|||f||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, **kwargs Possible arguments are `how`, `fill_method`, `limit`, `kind` and `on`, and other arguments of `TimeGrouper`. Returns ------- Grouper Return a new grouper with our resampler appended. 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='T') >>> 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('3T').sum() a b a 0 2000-01-01 00:00:00 0 2 2000-01-01 00:03:00 0 1 5 2000-01-01 00:00:00 5 1 Upsample the series into 30 second bins. >>> df.groupby('a').resample('30S').sum() a b a 0 2000-01-01 00:00:00 0 1 2000-01-01 00:00:30 0 0 2000-01-01 00:01:00 0 1 2000-01-01 00:01:30 0 0 2000-01-01 00:02:00 0 0 2000-01-01 00:02:30 0 0 2000-01-01 00:03:00 0 1 5 2000-01-01 00:02:00 5 1 Resample by month. Values are assigned to the month of the period. >>> df.groupby('a').resample('M').sum() a b a 0 2000-01-31 0 3 5 2000-01-31 5 1 Downsample the series into 3 minute bins as above, but close the right side of the bin interval. >>> df.groupby('a').resample('3T', closed='right').sum() a b a 0 1999-12-31 23:57:00 0 1 2000-01-01 00:00:00 0 2 5 2000-01-01 00:00:00 5 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('3T', closed='right', label='right').sum() a b a 0 2000-01-01 00:00:00 0 1 2000-01-01 00:03:00 0 2 5 2000-01-01 00:03:00 5 1 r)get_resampler_for_grouping)Zpandas.core.resampler)rPZrulerWrXrrQrQrRresample sb zGroupBy.resamplecOs,ddlm}||jf||j|jd|S)zV Return a rolling grouper, providing rolling functionality per group. r)RollingGroupby)_grouperZ _as_index)pandas.core.windowrrrork)rPrWrXrrQrQrRrollings s zGroupBy.rollingcOs(ddlm}||jf|d|ji|S)zc Return an expanding grouper, providing expanding functionality per group. r)ExpandingGroupbyr)rrrro)rPrWrXrrQrQrR expanding s zGroupBy.expandingcOs(ddlm}||jf|d|ji|S)zO Return an ewm grouper, providing ewm functionality per group. r)ExponentialMovingWindowGroupbyr)rrrro)rPrWrXrrQrQrRewm s z GroupBy.ewmzLiteral[('ffill', 'bfill')]) directionc s|dkr d}|jj\}}}tj|ddjtjdd}|dkrJ|ddd}ttj|||||j dd d d fd d }|j }|j d kr|j }|j }||} || } t| tr|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. NrZ mergesort)kindFrKbfill)r sorted_labelsrlimitrmrrLcst|}|jdkr.blk_funcr)rorrr$rdrmrrZgroup_fillna_indexerrmrrlrZ_mgrr[r rrEr_r ) rPrrrrrrrtmgrres_mgrZnew_objrQrrR_fill s0     z GroupBy._fillcCs|jd|dS)a: Forward 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.ffill: Returns Series with minimum number of char in object. DataFrame.ffill: Object with missing values filled or None if inplace=True. Series.fillna: Fill NaN values of a Series. DataFrame.fillna: Fill NaN values of a DataFrame. ffillrrrPrrQrQrRr sz GroupBy.ffillcCstjdttd|j|dS)NzMpad is deprecated and will be removed in a future version. Use ffill instead. stacklevelr)warningswarn FutureWarningr%rrrQrQrRpad s z GroupBy.padcCs|jd|dS)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. rrrrrQrQrRr sz GroupBy.bfillcCstjdttd|j|dS)NzRbackfill is deprecated and will be removed in a future version. Use bfill instead.rr)rrrr%rrrQrQrRbackfill3 s zGroupBy.backfillzPositionalIndexer | tuplezLiteral[('any', 'all', None)])rrmr|c Cs*|s|||}|jj\}}}||dk@}||}|jsR|W5QRS|jj}|jdkr||||_|j st |t r| |}| |}n||||_|jr|j|jdn|W5QRSQRXt|std|dkrtd|dtt|}|dkr|nd|}|jj||jd} |jd kr\|jd kr\|jj} | | | j} n0dd lm} | | |j|j|j|j|jd \} }}| j| |j|j|jd } | | |}}||kj }t!|r|"rt#j$|j%|<t!|jt!| ks t!|t!|jjkr|jj|_n| |jj}|S) ae 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. Parameters ---------- n : int, slice or list of ints and slices A single nth value for the row or a list of nth values or slices. .. versionchanged:: 1.4.0 Added slice and lists containing slices. Added index notation. dropna : {'any', 'all', None}, default None Apply the specified dropna operation before counting which row is the nth row. Only supported if n is an int. Returns ------- Series or DataFrame N-th value within each group. %(see_also)s 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) B A 1 NaN 2 3.0 >>> g.nth(1) B A 1 2.0 2 5.0 >>> g.nth(-1) B A 1 4.0 2 5.0 >>> g.nth([0, 1]) B A 1 NaN 1 2.0 2 3.0 2 5.0 >>> g.nth(slice(None, -1)) B A 1 NaN 1 2.0 2 3.0 Index notation may also be used >>> g.nth[0, 1] B A 1 NaN 1 2.0 2 3.0 2 5.0 >>> g.nth[:-1] B A 1 NaN 1 2.0 2 3.0 Specifying `dropna` allows count ignoring ``NaN`` >>> g.nth(0, dropna='any') B A 1 2.0 2 3.0 NaNs denote group exhausted when using dropna >>> g.nth(3, dropna='any') B A 1 NaN 2 NaN Specifying `as_index=False` in `groupby` keeps the original index. >>> df.groupby('A', as_index=False).nth(1) A B 1 1 2.0 4 2 5.0 rrrz4dropna option only supported for an integer argumentrHz_For a DataFrame or Series groupby.nth, dropna must be either None, 'any' or 'all', (was passed z).)r;rlNr)rrlrrrvrs)rkrvrl)&r"_make_mask_from_positional_indexerror_mask_selected_objrkrrlrrurrArrrrvrr)rrrxrtrmrqrrisinrrrsrMrrrr}rIrr+loc)rPrrmrrrr`rmax_lenZdroppedrlrorZgrbZsizesrrQrQrRr> sjn       *       z GroupBy.nth?linear interpolationcsldddddddddfd d t|}|r8|g}tj|tjd }|jj\}}t|ttj ||d t|d kr| dnd }t |dk||dddfdd }|j } | j dk} |} | j|dd} | s2t| jt| jkr2tt|dt| jd kr2| j|ddtd| rD|| } n | | } |r^|| S|j| |dS)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. 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 rz"tuple[np.ndarray, np.dtype | None]rvcSst|rtdd}t|jrLt|tr:|jttj d}n|}ttj }nt |jrrt|trr|jttj d}nt |jrtd}t |t}n`t|jrtd}t |t}n:t|trt|rttj}|jttj d}n t |}||fS)Nz7'quantile' cannot be performed against 'object' dtypes!)rCZna_valuezdatetime64[ns]ztimedelta64[ns])r,rr*rCrr6rfloatrr+rar&r'Zasarrayrdr.r(r)rwr~r`rQrQrR pre_processor' s.        z'GroupBy.quantile..pre_processorriznp.dtype | None)rwr~r|cs"|rt|rdks||}|S)N>rmidpoint)r*rdrrrQrRpost_processorD s z(GroupBy.quantile..post_processorrB)rrrrrrrLc st|}|\}}d}|jdkrB|jd}t|tf}n}tj|ftjd}||f}t|j tj dd}|jdkr|d|||dn.t |D]$} || || || || dq|jdkr| d}n| |}||S) Nrr rrBFrK)rrZ sort_indexerK)r/rrDrZ broadcast_tor}rrZlexsortrdrmrrr) rrrwr~ncolsZ shaped_labelsr`orderZsort_arrr)rlabels_for_lexsortrnqsrrrQrRra s*      "  z"GroupBy.quantile..blk_funcTrRquantileF*All columns were dropped in grouped_reducer)r-rrbrrorr}rrZgroup_quantilerrfrrrSrUitemsr3rrrVr r)rPqrZ orig_scalarrrrZna_label_for_sortingrrtrWrrr.rQ)rrrrrrrrRr sD$     zGroupBy.quantilerjc CsX|F|jj}|j|jjd|tjd}|s>|jd|}|W5QRSQRXdS)a) Number each group from 0 to the number of groups - 1. This is the enumerative complement of cumcount. Note that the numbers given to the groups match the order in which the groups would be seen when iterating over the groupby object, not the order they are first observed. Parameters ---------- ascending : bool, default True If False, number in reverse, from number of group - 1 to 0. Returns ------- Series Unique numbers for each group. See Also -------- .cumcount : Number the rows in each group. Examples -------- >>> df = pd.DataFrame({"A": list("aaabba")}) >>> df A 0 a 1 a 2 a 3 b 4 b 5 a >>> df.groupby('A').ngroup() 0 0 1 0 2 0 3 1 4 1 5 0 dtype: int64 >>> df.groupby('A').ngroup(ascending=False) 0 1 1 1 2 1 3 0 4 0 5 1 dtype: int64 >>> df.groupby(["A", [1,1,2,3,2,1]]).ngroup() 0 0 1 0 2 1 3 3 4 2 5 0 dtype: int64 rrBrN) rrrrsrorrrar)rPrjrrrQrQrRngroup s=  zGroupBy.ngroupc CsF|4|j|j}|j|d}|||W5QRSQRXdS)a Number each item in each group from 0 to the length of that group - 1. Essentially this is equivalent to .. code-block:: python self.apply(lambda x: pd.Series(np.arange(len(x)), x.index)) Parameters ---------- ascending : bool, default True If False, number in reverse, from length of group - 1 to 0. Returns ------- Series Sequence number of each element within each group. See Also -------- .ngroup : Number the groups themselves. Examples -------- >>> df = pd.DataFrame([['a'], ['a'], ['a'], ['b'], ['b'], ['a']], ... columns=['A']) >>> df A 0 a 1 a 2 a 3 b 4 b 5 a >>> df.groupby('A').cumcount() 0 0 1 1 2 2 3 0 4 1 5 3 dtype: int64 >>> df.groupby('A').cumcount(ascending=False) 0 3 1 2 2 1 3 1 4 0 5 0 dtype: int64 rN)rrrrlrrrs)rPrjrZ cumcountsrQrQrRcumcount s7  zGroupBy.cumcountaveragekeep)r>rj na_optionpctrlcsb|dkrd}t|||||ddkrLdd<|fddS|jd d d 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. axis : int, default 0 The axis of the object over which to compute the rank. Returns ------- DataFrame with ranking of values within each group %(see_also)s 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 >rtopbottomz3na_option must be one of 'keep', 'top', or 'bottom') ties_methodrjrrrrr>cs|jfddS)NF)rlr )rankrrlrXrQrRrq rzGroupBy.rank..rF)r rl)r)rpopr[rY)rPr>rjrrrlrrQrrRr s&Hz GroupBy.rankcs<td|ddgdkr0|fddS|jdS)zq Cumulative product for each group. Returns ------- Series or DataFrame cumprodr rurcs|jfdiSNrl)rrrrQrRr rz!GroupBy.cumprod..)rnvZvalidate_groupby_funcr[rYrPrlrWrXrQrrRrz s zGroupBy.cumprodcs<td|ddgdkr0|fddS|jdS)zm Cumulative sum for each group. Returns ------- Series or DataFrame rkr rurcs|jfdiSr )rkrrrQrRr rz GroupBy.cumsum..)rkr r rQrrRrk s zGroupBy.cumsumc s6|dd}dkr&|fddS|jdd|dS) zm Cumulative min for each group. Returns ------- Series or DataFrame ruTrcstj|SrN)rZminimum accumulaterrrQrRr rz GroupBy.cummin..cumminFr rurr[rYrPrlrXrurQrrRr s zGroupBy.cumminc s6|dd}dkr&|fddS|jdd|dS) zm Cumulative max for each group. Returns ------- Series or DataFrame ruTrcstj|SrN)rmaximumr rrrQrRr rz GroupBy.cummax..cummaxFrrrrQrrRr s zGroupBy.cummaxznp.dtype) base_funcrr rrrc  s0 |}rtstdr2ts2td j} | j\} } j} t| dddd f dd } j}|jdk} }|r| }|j |d d }|s t |j t |j kr | d d }tt |t |j d kr |j |dd td|r |}n ||} |S)ap Get result for Cythonized functions. Parameters ---------- base_func : callable, Cythonized function to be called cython_dtype : np.dtype Type of the array that will be modified by the Cython call. numeric_only : bool, default True Whether only numeric datatypes should be computed needs_counts : bool, default False Whether the counts should be a part of the Cython call needs_mask : bool, default False Whether boolean mask needs to be part of the Cython call signature needs_nullable : bool, default False Whether a bool specifying if the input is nullable is part of the Cython call signature pre_processing : function, default None Function to be applied to `values` prior to passing to Cython. Function should return a tuple where the first element is the values to be passed to Cython and the second element is an optional type which the values should be converted to after being returned by the Cython operation. This function is also responsible for raising a TypeError if the values have an invalid type. Raises if `needs_values` is False. post_processing : function, default None Function to be applied to result of Cython function. Should accept an array of values as the first argument and type inferences as its second argument, i.e. the signature should be (ndarray, Type). If `needs_nullable=True`, a third argument should be `nullable`, to allow for processing specific to nullable values. **kwargs : dict Extra arguments to be passed back to Cython funcs Returns ------- `Series` or `DataFrame` with filled values z%'post_processing' must be a callable!z$'pre_processing' must be a callable!)rrrLc sv|j}|jdkrdn|jd}tj|d}||f}t|d}d}rptj jtjd}t||d}|}r|\}}|j dd}|jdkr|d}t||d}rt | tj }|jdkr|d d}t||d }rt |t}t||d }|f|jdkrD|jddks4t|j|ddd f}rpi} rbt |t| d <||f| }|jS)NrrB)r`)rFrK)rr)rr)r)rrr)rrrDrzerosrrrrardr/rzZuint8rr3r) rrrrZ inferencesrrwrZ is_nullableZ pp_kwargs rrrXrrrrrrrPrQrRr sD             z0GroupBy._get_cythonized_result..blk_funcrTrRZgroup_rFr)rrrrorrZrrrrSrTrUr}rreplacer3rrrVr r)rPrrr rrrrrrXrorrr;rrtrWrrZhowstrr`rQrrRr s84     &2    zGroupBy._get_cythonized_resultc sdk sdkr(|fddS|jj\}}}tjt|tjd}t||||j } | j |j | j |j |fidd} | S)u Shift each group by periods observations. If freq is passed, the index will be increased using the periods and the freq. Parameters ---------- periods : int, default 1 Number of periods to shift. freq : str, optional Frequency string. axis : axis to shift, default 0 Shift direction. fill_value : optional The scalar value to use for newly introduced missing values. Returns ------- Series or DataFrame Object shifted within each group. See Also -------- Index.shift : Shift values of Index. tshift : Shift the time index, using the index’s frequency if available. Nrcs|SrN)shiftrrlrfreqperiodsrQrRro rzGroupBy.shift..rBT)rZ allow_dups) r[rorrrr}rarZgroup_shift_indexerrZ_reindex_with_indexersrlr) rPrrrlrrrrZ res_indexerrtr.rQrrRrP sz GroupBy.shiftrc s|dk sdkr*|fddSdkr:ddt|d}|j|jj|jd}|j|jd}||d S) z Calculate pct_change of each value to previous entry in group. Returns ------- Series or DataFrame Percentage changes within each group. Nrcs|jdS)N)r fill_methodrrrl) pct_changerrlrrrrrQrRr s z$GroupBy.pct_change..rrr)rrrlr)r[r`rMrocodesrlr) rPrrrrrlZfilledZfill_grpZshiftedrQrrRr s zGroupBy.pct_changecCs"||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)s 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 NrrslicerrPrrrQrQrRhead s#z GroupBy.headcCs4||r |t| d}n |g}||S)a 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)s 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 Nr"r$rQrQrRtail s $ z GroupBy.tail)rr|cCsD|jjd}||dk@}|jdkr,|j|S|jjdd|fSdS)a Return _selected_obj with mask applied to the correct axis. Parameters ---------- mask : np.ndarray Boolean mask to apply. Returns ------- Series or DataFrame Filtered _selected_obj. rrN)rorrlrrE)rPrrrQrQrRr s     zGroupBy._mask_selected_objr)rrrr|c Cs|jj}t|dkr|S|jr"|Stdd|Ds8|Sdd|D}|jj}|dk rj|||dg}tj||d \}}|j r|j |j |dd d |i} |jf| Sd dt|D} t| \} } |jt| dd }||jjj|d |d }|j| d}|jddS)aI If we have categorical groupers, then we might want to make sure that we have a fully re-indexed output to the levels. This means expanding the output space to accommodate all values in the cartesian product of our groups, regardless of whether they were observed in the data or not. This will expand the output space if there are missing groups. The method returns early without modifying the input if the number of groupings is less than 2, self.observed == True or none of the groupers are categorical. Parameters ---------- output : Series or DataFrame Object resulting from grouping and applying an operation. fill_value : scalar, default np.NaN Value to use for unobserved categories if self.observed is False. qs : np.ndarray[float64] or None, default None quantile values, only relevant for quantile. Returns ------- Series or DataFrame Object (potentially) re-indexed to include all possible groups. rcss|]}t|jttfVqdSrN)rZgrouping_vectorr5rArpingrQrQrRr3sz*GroupBy._reindex_output..cSsg|] }|jqSrQ)Z group_indexr'rQrQrRr9sz+GroupBy._reindex_output..N)rrFrcss"|]\}}|jr||jfVqdSrN)rr_)rrr(rQrQrRrUs)rrl)rr)rrT)drop)rorr}rurIrappendrC from_productZ sortlevelrkrtZ_get_axis_namerlrr1rr)rZ set_indexrr) rPrrrrZ levels_listrrrdZ in_axis_grpsZg_numsZg_namesrQrQrRr sH        zGroupBy._reindex_outputz int | Nonez float | NonezSequence | Series | NonezRandomState | None)rfracrweights random_statecCst|||}|dk r*tj|j||jd}t|}|j|j|j}g} |D]r\} } |j | } t | } |dk rv|}n|dk st t || }tj| |||dkrdn|| |d}| | |qNt| } |jj| |jdS)a Return a random sample of items from each group. You can use `random_state` for reproducibility. .. versionadded:: 1.1.0 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. .. versionchanged:: 1.4.0 np.random.Generator objects now accepted 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. 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 Nr)rrr.r/)sampleZprocess_sampling_sizeZpreprocess_weightsrrlrr/rorrr}rroundr*rrr)rPrr-rr.r/rZ weights_arrZgroup_iteratorZsampled_indicesrrtZ grp_indicesZ group_sizeZ sample_sizeZ grp_samplerQrQrRr0gs6`      zGroupBy.sample) NrNNNNTTTFFFT)F)N)F)N)Tr)r)Tr)T)T)T)rNN)rNN)r)Fr)Fr)Fr)Fr)N)N)N)N)N)N)rr)T)T)rTrFr)r)r)r)r)rNrN)rrNNr)r!)r!)NNFNN)`rZrdrerfrrrSrcrrrrrrrrrrrr r rrr#r%r&r! _apply_docsformatr[rr5rArGr?rYr_r^rhrrrrsrr"_common_see_alsorIr)rnrrrrrrrrr$_groupby_agg_method_templaterrrrrrrr:rrrrrrrrrrrrrrrrrkrrrrrr%r&rrNaNrr0 __classcell__rQrQrrRrLs  C,:  (  C6* ' 6" (21 ".+T59 !  e P  CD:[-$)]rLTFr;rrxrrz) rtbyrlrorkrvrprwrursrmr|cCsjt|trddlm}|}n*t|tr8ddlm}|}ntd|||||||||||| | | | | dS)Nr) SeriesGroupBy)DataFrameGroupByzinvalid type: r)rrEZpandas.core.groupby.genericr9r:r:r)rtr8rlrrrornrrkrvrprwrursrmr9rr:rQrQrR get_groupbys.    r;rBznpt.NDArray[np.float64]rC)r4rr|cst||jrvtt|}t|\}}t|j|g}fdd|jDt |t|g}t|||j dgd}nt ||g}|S)a Insert the sequence 'qs' of quantiles as the inner-most level of a MultiIndex. 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