o ҷhQm@sddlmZddlmZmZddlmZmZmZddl Z ddl m Z ddl mZmZddlmZddlmZmZmZdd lmZdd lmZmZddlmmZdd lm Z dd l!m"Z"dd l#m$Z$m%Z%m&Z&ddl'm(Z(ddl)m*Z*ddl+m,Z,erddl-m.Z.m/Z/m0Z0m1Z1ddl2m3Z3edee ddd          dOdPd'dZ4dQd)d*Z5  dRdSd-d.Z6 dTdUd/d0Z7 dTdVd1d2Z8 dTdWd3d4Z9d5d6Z:edee d7dde j;e j;d8dXd>d7Z<        dYdZd@dAZ= dTd[dBdCZ>d\d]dGdHZ?d^dMdNZ@dS)_) annotations)HashableSequence) TYPE_CHECKINGCallablecastN)lib)Appender Substitution)maybe_downcast_to_dtype) is_list_likeis_nested_list_like is_scalar)ExtensionDtype) ABCDataFrame ABCSeries) _shared_docs)Grouper)Index MultiIndexget_objs_combined_axis)concat)cartesian_product)Series) AggFuncTypeAggFuncTypeBaseAggFuncTypeDict IndexLabel DataFramez data : DataFrame pivot_table)indentsmeanFTAlldataraggfuncrmarginsbooldropna margins_namerobservedsortreturnc Cst|}t|}t|trAg} g} |D]} t|||||| |||| | d }| || t| d| qt| | dd}|j|ddSt|||||||||| | }|j|ddS)N) valuesindexcolumns fill_valuer&r'r)r*r+r,__name__r!)keysaxisr )method) _convert_by isinstancelist__internal_pivot_tableappendgetattrr __finalize__)r%r.r/r0r&r1r'r)r*r+r,piecesr3func_tabletablerAL/var/www/html/venv/lib/python3.10/site-packages/pandas/core/reshape/pivot.pyr :sJ   !AggFuncTypeBase | AggFuncTypeDictc  Cs0||} |du} | rZt|rd} t|}nd} |g}|D] }||vr&t|qg}| |D]}t|tr7|j}z ||vrA||Wq-tyKYq-wt|t|j krY||}n|j }| D]}z| |}Wq_tt tfytYq_wt|}|j | | | |d}| |}|rt|trt|j r|jdd}|}|jjdkr|r|jjdt|}g}tt|t| D]}|jj|}|dus||vr||q||q|j||d}|st|jtrtjt|jj|jjd }|j|d |d }t|j trtjt|j j|j jd }|j|d|d }| dur&t|tr&|jdd }|durD||}|turD| sDt|rD|tj }|ra|rT||!j"dd }t#|||||||||d }| ru| su|j jdkru|j $d |_ t|d krt|d kr|j%}t|tr|r|jddd}|S)zL Helper of :func:`pandas.pivot_table` for any non-list ``aggfunc``. NTF)r+r,r)all)howr!r1namesr)r4r1r4)rowscolsr&r+r*r1)rEr4)&r r8KeyErrorr7rkeyr: TypeErrorlenr0drop ValueErrorgroupbyaggrr)r/nlevelsrHrangeunstackr from_arraysrlevelsreindex sort_indexfillnar is_integerastypenpint64notnarD _add_margins droplevelT)r%r.r/r0r&r1r'r)r*r+r,r3 values_passed values_multii to_filterxrMgroupedaggedr@ index_names to_unstacknamemrArArBr9ss                r9r@DataFrame | Seriesc  Cst|ts tdd|d} |jjD]} ||j| vr!t| qt||||} |jdkrE|jjddD]} ||j| vrDt| q6t |dkrW|fdt |d} n|} |slt|t rl| | | | |iS|rt ||||||||} t| ts| S| \}}}nt|tsJt|||||||} t| ts| S| \}}}|j|j|d}|D]}t|tr| |||<q| |d||<qdd lm}||t| gd j}|jj}t|jD]}t|trq||gj}||jt|fd ||<q| |}||j_|S) Nz&margins_name argument must be a stringzConflicting name "z " in marginsr!rFrr)r0)args)r7strrQr/rHget_level_values_compute_grand_marginndimr0rOr_append _constructor_generate_marginal_resultstupler)_generate_marginal_results_without_valuesrYpandasrrrcsetdtypesr select_dtypesapplyr )r@r%r.rJrKr&r+r*r1msglevel grand_marginrMmarginal_result_setresult margin_keys row_marginkr margin_dummy row_namesdtyperArArBrasd             rac Cs|rPi}||D]C\}}z6t|trt||||<n&t|tr=t||tr4t|||||<n|||||<n||||<Wq tyMYq w|S|||jiSN)itemsr7rtr;dictrNr/)r%r.r&r*rrvrArArBrvFs"    rvcstdkrg}g} fdd} t|dkrP|||j||d|} d} |jjd|dD]\} }|j}| | }|}| | ||<||| |q/nWddlm}d} |jd|dD]G\} }tdkrn| | }n}|||||j}t |j t rt j |g|j j dgd|_ n t|g|j jd |_ ||| |q_|s|St|| d }t|dkr|Sn|}|j} tdkr||j|d|}|jd d }tgttt}|j ||_ n |jtj|jd }|| |fS)Nrcs|fdtdS)Nrqr!rO)rMrKr*rArB_all_keymsz,_generate_marginal_results.._all_keyr+r!)rr+rrGrmrIT) future_stackr/)rOrRrSrccopyr:r}rrr7r/r from_tuplesrHrrmrr0stackr8rUreorder_levels_constructor_slicedr^nan)r@r%r.rJrKr&r+r* table_piecesrrmargincat_axisrMpieceall_keyrtransformed_piecerr new_orderrArrBrz]sZ                  rzc stdkrKg}fdd}t|dkr0||j||d|} |} | || <|} || n |jdd|d|} |} | || <|} || | S|} |j}tra|j|d|} nttj| jd} | || fS)Nrcs&tdkrSfdtdS)Nr!rqrrArrArBrs z;_generate_marginal_results_without_values.._all_keyr)rr4r+r)rOrRrr:r0rr^r) r@r%rJrKr&r+r*rrrrrrrArrBr|s*     r|cCsJ|durg}|St|st|tjtttfst|r|g}|St|}|Sr) rr7r^ndarrayrrrcallabler8)byrArArBr6s r6pivot)r/r.r0rr/IndexLabel | lib.NoDefaultr.c srt|}jddj_ddjjDj_|tjur=|tjur,t|}ng}|tju}j|||d}nj|tjurbtjt rVfddt jj D}nj jjj dg}n fddt|D}fd d|D} || t |} t|rt|tsttt|}j|j| |d }n j |j| d }||} d d| jjD| j_| S) NF)deepcSsg|] }|dur |ntjqSrr no_default.0rmrArArB zpivot..)r:csg|]}j|qSrA)r/ru)rrfr%rArBrs rcg|]}|qSrArA)ridxrrArBrcrrArA)rcolrrArBrr)r/r0rcSsg|] }|tjur |ndqSrrrrArArBr*r)comconvert_to_list_likerr/rHrr set_indexr7rrUrTrrmextendrWr r{rrrry_valuesrV) r%r0r/r.columns_listlikerKr:indexed index_list data_columns multiindexrrArrBrsH                normalizec Cs\|dur |dur td|dur|durtdt|s|g}t|s&|g}d} dd||D} | r:t| ddd} t||d d }t||d d }t||\} } }}d d lm}itt| |tt||}||| d}|dur{d |d<t d d}n||d<d|i}|j d| ||||d|}| durt || ||d}|j | d d}|j |dd}|S)a Compute a simple cross tabulation of two (or more) factors. By default, computes a frequency table of the factors unless an array of values and an aggregation function are passed. Parameters ---------- index : array-like, Series, or list of arrays/Series Values to group by in the rows. columns : array-like, Series, or list of arrays/Series Values to group by in the columns. values : array-like, optional Array of values to aggregate according to the factors. Requires `aggfunc` be specified. rownames : sequence, default None If passed, must match number of row arrays passed. colnames : sequence, default None If passed, must match number of column arrays passed. aggfunc : function, optional If specified, requires `values` be specified as well. margins : bool, default False Add row/column margins (subtotals). margins_name : str, default 'All' Name of the row/column that will contain the totals when margins is True. dropna : bool, default True Do not include columns whose entries are all NaN. normalize : bool, {'all', 'index', 'columns'}, or {0,1}, default False Normalize by dividing all values by the sum of values. - If passed 'all' or `True`, will normalize over all values. - If passed 'index' will normalize over each row. - If passed 'columns' will normalize over each column. - If margins is `True`, will also normalize margin values. Returns ------- DataFrame Cross tabulation of the data. See Also -------- DataFrame.pivot : Reshape data based on column values. pivot_table : Create a pivot table as a DataFrame. Notes ----- Any Series passed will have their name attributes used unless row or column names for the cross-tabulation are specified. Any input passed containing Categorical data will have **all** of its categories included in the cross-tabulation, even if the actual data does not contain any instances of a particular category. In the event that there aren't overlapping indexes an empty DataFrame will be returned. Reference :ref:`the user guide ` for more examples. Examples -------- >>> a = np.array(["foo", "foo", "foo", "foo", "bar", "bar", ... "bar", "bar", "foo", "foo", "foo"], dtype=object) >>> b = np.array(["one", "one", "one", "two", "one", "one", ... "one", "two", "two", "two", "one"], dtype=object) >>> c = np.array(["dull", "dull", "shiny", "dull", "dull", "shiny", ... "shiny", "dull", "shiny", "shiny", "shiny"], ... dtype=object) >>> pd.crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c']) b one two c dull shiny dull shiny a bar 1 2 1 0 foo 2 2 1 2 Here 'c' and 'f' are not represented in the data and will not be shown in the output because dropna is True by default. Set dropna=False to preserve categories with no data. >>> foo = pd.Categorical(['a', 'b'], categories=['a', 'b', 'c']) >>> bar = pd.Categorical(['d', 'e'], categories=['d', 'e', 'f']) >>> pd.crosstab(foo, bar) col_0 d e row_0 a 1 0 b 0 1 >>> pd.crosstab(foo, bar, dropna=False) col_0 d e f row_0 a 1 0 0 b 0 1 0 c 0 0 0 Nz&aggfunc cannot be used without values.z)values cannot be used without an aggfunc.cSsg|] }t|ttfr|qSrA)r7rr)rrhrArArBrszcrosstab..TF) intersectr,row)prefixrrrr __dummy__)r&r1r&)r/r0r'r*r))rr'r*)r/r4r!)r0r4)r) rQr r _get_names_build_names_mapperr}rrziprOr _normalize rename_axis)r/r0r.rownamescolnamesr&r'r*r)r common_idx pass_objsrownames_mapperunique_rownamescolnames_mapperunique_colnamesrr%dfkwargsr@rArArBcrosstab1sdj      rc Cs,t|ttfs$ddd}z||}Wnty#}ztd|d}~ww|dur]ddddd dd }|d |d <z||}WntyQ}ztd|d}~ww||}|d }|S|d ur|j}|j} |jdddfj } || v|| k@rt|d|jdddf} |jdddf} |jddddf}t ||dd}|dkr| | } t || gdd}|d }| |_|S|dkr| | } | | }|d }||_|S|d ks|d ur| | } | | } d| j|<t || gdd}| | }|d }||_| |_|Stdtd)Nr/r0)rr!zNot a valid normalize argumentFcSs||jddjddSNr!rIrsumrhrArArBz_normalize..cSs ||SrrrrArArBrs cSs|j|jddddSr)divrrrArArBrs)rDr0r/rDTrz not in pivoted DataFrame)rr'r!rIzNot a valid margins argument)r7r(rtrLrQr[r/r0ilocrmrrrrxloc) r@rr'r* axis_subserr normalizersf table_index table_columnslast_ind_or_col column_margin index_marginrArArBrsr       3           rrrrtcCs|dur,g}t|D]\}}t|tr|jdur||jq ||d|q |St|t|kr8tdt|tsAt|}|S)N_z*arrays and names must have the same length) enumerater7rrmr:rOAssertionErrorr8)arrsrHrrfarrrArArBr-s rr list[str]r;tuple[dict[str, str], list[str], dict[str, str], list[str]]csdd}t|t|}||||B|Bfddt|D}fddt|D}fddt|D}fddt|D}||||fS) a Given the names of a DataFrame's rows and columns, returns a set of unique row and column names and mappers that convert to original names. A row or column name is replaced if it is duplicate among the rows of the inputs, among the columns of the inputs or between the rows and the columns. Parameters ---------- rownames: list[str] colnames: list[str] Returns ------- Tuple(Dict[str, str], List[str], Dict[str, str], List[str]) rownames_mapper: dict[str, str] a dictionary with new row names as keys and original rownames as values unique_rownames: list[str] a list of rownames with duplicate names replaced by dummy names colnames_mapper: dict[str, str] a dictionary with new column names as keys and original column names as values unique_colnames: list[str] a list of column names with duplicate names replaced by dummy names cstfdd|DS)Ncsh|]}|vr|qSrArArseenrArB ^rz>_build_names_mapper..get_duplicates..)r~rGrArrBget_duplicates\sz+_build_names_mapper..get_duplicatesc$i|]\}}|vrd||qSrow_rArrfrm dup_namesrArB cz'_build_names_mapper..c&g|]\}}|vrd|n|qSrrArrrArBrfz'_build_names_mapper..crcol_rArrrArBrjrcrrrArrrArBrmr)r~ intersectionr)rrr shared_namesrrrrrArrBr>s      r) NNNr#NFTr$FT)r%rr&rr'r(r)r(r*rr+r(r,r(r-r)r%rr&rCr'r(r)r(r*rr+r(r,r(r-r)r$N)r@ror%rr+r(r*r)r$)r%rr*r)r%rr+r(r*r)r@rr%rr+r(r*r) r%rr0rr/rr.rr-r)NNNNFr$TF) r'r(r*rr)r(rr(r-r)r@rr'r(r*rr-r)r)rrt)rrrrr-r)A __future__rcollections.abcrrtypingrrrnumpyr^ pandas._libsrpandas.util._decoratorsr r pandas.core.dtypes.castr pandas.core.dtypes.commonr r rpandas.core.dtypes.dtypesrpandas.core.dtypes.genericrrpandas.core.commoncorecommonrpandas.core.framerpandas.core.groupbyrpandas.core.indexes.apirrrpandas.core.reshape.concatrpandas.core.reshape.utilrpandas.core.seriesrpandas._typingrrrrr}rr r9rarvrzr|r6rrrrrrrArArArBs           7 U  Z -G . 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