Owgp ,ddlmZddlmZmZddlmZmZmZm Z ddl Z ddl Z ddl mZddlmZmZddlmZddlmZdd lmZmZmZdd lmZdd lmZmZddlm cm!Z"dd l#m$Z$dd l%m&Z&ddl'm(Z(m)Z)m*Z*ddl+m,Z,ddl-m.Z.ddl/m0Z0erddl1m2Z2m3Z3m4Z4m5Z5ddl6m7Z7edee$ddddddddddejpdf d*dZ9 d+dZ: d, d-dZ; d. d/dZ< d. d0d Z= d. d1d!Z>d"Z?edee$d#dejpejpd$ d2d%Z@ d3 d4d&ZA d. d5d'ZBd6d7d(ZC d8d)ZDy)9) annotations)HashableSequence) TYPE_CHECKINGCallableLiteralcastN)lib)Appender Substitution)find_stack_level)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)indentsmeanFTAllc |t|}t|}t|trog} g} |D]E} t|||||| |||| |  }| j || j t | d| Gt | | d}|j|dSt|||||||||| | }|j|dS)N) valuesindexcolumns fill_valueaggfuncmarginsdropna margins_nameobservedsort__name__r$)keysaxisr#)method) _convert_by isinstancelist__internal_pivot_tableappendgetattrr __finalize__)datar)r*r+r-r,r.r/r0r1r2piecesr4func_tabletables P/var/www/horilla/myenv/lib/python3.12/site-packages/pandas/core/reshape/pivot.pyr#r#=s  E'"G'4 "$ 9D+%)! F MM& ! KKj$7 8 9"vDq1!!$}!== "    E   d=  99c l ||z} |du} | rt|rd} t|}nd} |g}|D]}||vst|g}| |zD]4}t|tr |j } ||vr|j |6t|t|jkr6||}n0|j}| D]} |j|}t|}| tjurdn| }|j| || |}| tjurJtd|j j"Dr$t%j&dt(t+|j-|}|r7t|t.r't|jr|j1d }|}|j2j4d kDr|r|j2j6dt|}g}t9t|t| D]D}|j2j6|}|||vr|j |4|j |F|j;|| }|st|j2t<r\t=j>tA|j2jB|j2j6 }|jE|d |}t|jt<r\t=j>tA|jjB|jj6 }|jE|d |}| dur"t|t.r|jGd }|O|jI|}|tur6| s4tjJ|r|jMtNjP}|r9|r#||jSjUd }tW||||||||| }| r; s9|jj4d kDr |jjYd |_ t|d k(rt|d kDr |jZ}t|t.r|r|j1dd }|S#t$rYPwxYw#tttf$rYwxYw)zL Helper of :func:`pandas.pivot_table` for any non-list ``aggfunc``. NTF)r1r2r/c34K|]}|jywN)_passed_categorical).0pings rC z)__internal_pivot_table..s*%)  *szThe default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior)category stacklevelall)howr$r,namesr)r5r,r5)rowscolsr-r1r0r,)rOr5).rr9KeyErrorr8rkeyr; TypeErrorlenr+drop ValueErrorr no_defaultgroupbyany_grouper groupingswarningswarn FutureWarningr aggrr/r*nlevelsrRrangeunstackr from_arraysrlevelsreindex sort_indexfillna is_integerastypenpint64notnarN _add_margins droplevelT)r>r)r*r+r-r,r.r/r0r1r2r4 values_passed values_multii to_filterxrW observed_boolgroupedaggedrB index_names to_unstacknamems rCr:r:vsB 7?D$&M  L&\F LXF "A}qk! "  A!W%EE 9$$Q'   y>C - - ?D C S)  f%7EXMll4-d6lRG3>>!c*-4-=-=-G-G*'   U#')   KK E *UL1c%--6H  ' E {{Q5kk'' #e*5  s5z3t9- (A;;$$Q'D|t{2!!!$!!$'  (  jZ @  ekk: .&&!%++"4"45U[[=N=NAMM!! MCE emmZ 0&&!%--"6"67u}}?R?RAMM!! MCE t| 5,7  a ( Z( c>(s~~j/ILL*E  ((a(01D   %!  \emm.C.Ca.G //2  5zQ3wr)rTrUr-r1r0r,msglevel grand_marginrWmarginal_result_setresult margin_keys row_marginkr" margin_dummy row_namesdtypes rCrrrrs lC (ABB |nL 9C""" 5;;77> >S/ !")vw ML zzQ]]((, &Eu}}==eDD o% &  4y1}oTQ 77 j 2}}U//l<6P0QRSS 8 4tWh  -u5& &*=' Z%...G 4tWh  -u5& &*=' Z##FNNz#JJ / a (OJqM(1.JqM / !Zu>@@L ""IV]]#  e^ , ##UG,44)$/55 #5(6 T  ^^L )F"FLL MrDc|ri}||jD]\}} t|trt||||<nUt|tr:t||trt|||||<n|||||<n ||||<|S|||j iS#t $rYwxYwrG)itemsr8rr<dictrXr*)r>r)r-r0rrvs rCrrUs L&&( DAq gs+&9ga&9&;LO.!'!*c2*@'!WQZ*@*B Q*4'!*Q- Q&-ajLO gdjj122   sA:B11 B=<B=ctdkDrg}g} fd} t|dkDr|||zj||j|} d} |jjd|D]S\} }|j}| | }|j }| | ||<|j || j |Unddlm}d} |jd|D]\} }tdkDr | | }n}|j |||j|j}t|jtr6tj|g|jjdgz|_ n't|g|jj|_ |j || j ||s|St!|| }t|dk(r|S|}|j"} tdkDr||zj|j|}|j%d }tgt't)tz}|Dcgc]}|jj|}}|jj+||_ n+|j-t.j0|j" }|| |fScc}w) Nrc0|fdtdz zzS)Nrr$rY)rWrUr0s rC_all_keyz,_generate_marginal_results.._all_key|s &#d)a-)@@ @rDr1r$)rr1r!rQrrST) future_stackr*)rYr]rdrtcopyr;rr"rr8r*r from_tuplesrRrrrr+stackr9rfreorder_levels_constructor_slicedronan)rBr>r)rTrUr-r1r0 table_piecesrrmargincat_axisrWpieceall_keyr"transformed_piecerrnew_order_indicesrwnew_order_namess ` ` rCrrls 4y1}   A t9q=$-(000IMMgVFH#ggooAoI , U"3- !'g##E*""7+ , )H#mm!hmG , Ut9q=&smG*G##E*%.ekk'.B$C$E$E!ekk:6.8.D.D ):):dV)C/%+/4WIEKKDTDT.U%+##$56""7+' ,*LLx8F t9>Mmm  4y1}$-(000IMMgV %%4%8 !YK$uSY/?*@@>OP:++11!4PP%++::?K --bffFNN-K ; ** Qs5 K(c2tdkDrg}fd}t|dkDrE|j|||j|} |} | || <|} |j| nR|jdd|j|} |} | || <|} |j| | S|} |j}tr&|j|j|} n%t t j| j} | || fS)NrcNtdk(rSfdtdz zzS)Nr$rr)rUr0srCrz;_generate_marginal_results_without_values.._all_keys.4yA~## ?Uc$i!m%<< rTrUr-r1r0rrrrrrs ` ` rCrrs  4y1}  = t9q=\\$\:4@FFwOFjG#E'NF   w '\\H\EKKGTFjG#E'NF   w 'Mmm  4y\\$\:4@FFwO BFF&..9 ; **rDc|g}|St|s5t|tjtt t fs t|r|g}|St|}|SrG) rr8rondarrayrrrcallabler9)bys rCr7r7sX z  I " b2::uiA B B<T I"X IrDpivot)r*r)ctj|}|jd}|jj|_|jjDcgc]}||nt j c}|j_|t j urT|t j urtj|}ng}|t j u}|j||z|}nv|t j urt|jtrFt|jjD cgc]} |jj| } } nX|j|j|jjg} n%tj|D cgc]} ||  } } |D cgc]} ||  } } | j| tj | }t#|rIt|t$s9t't(t*|}|j-||j.||}n |j||j.|}|j1|}|jjDcgc]}|t j ur|ndc}|j_|Scc}wcc} wcc} wcc} wcc}w)NF)deep)r;r)r*r+r)comconvert_to_list_likerr*rRr r\ set_indexr8rrfrerrrextendrhrrr rrr_valuesrg)r>r+r*r)columns_listlikerrUr;indexedrw index_listidxcol data_columns multiindexrs rCrrsr//8 99%9 D"DJAEAQAQ9= cnn4DJJ   &++E2DD#..(.. # #F!  CNN "$**j1=B$**BTBTcS > >,'++J7   65(A(8,f5F''V $$J(G..tF|/C/C:.VG__- .FAGASAS9=CNN*4FLL Mi,P> sK?"K. K  KKc | | td| | tdt|s|g}t|s|g}d} ||zD cgc]} t| ttfs| } } | rt | dd} t ||d}t ||d }t||\} }}}d d lm }itt||tt||}||| }|d |d <td d}n ||d <d|i}|j d|||||dd|}| durt|| ||}|j| d }|j|d}|Scc} w)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.TF) intersectr2row)prefixrrr!r __dummy__)r-r,r-)r*r+r.r0r/r1) normalizer.r0)r*r5r$)r+r5)r)r[rr8rrr _get_names_build_names_mapperrr"rziprYr# _normalize rename_axis)r*r+r)rownamescolnamesr-r.r0r/r common_idxry pass_objsrownames_mapperunique_rownamescolnames_mapperunique_colnamesr"r>dfkwargsrBs rCcrosstabrBsT~'-ABB goDEE u % w ')J!GOXqz!i=V/WXIX+IER %%8H'8E:H Hh/ !  s?E* +  s?G, - D 4z *B ~; 2 ;W% BNN !   E Yl    O!  E LiYs E Ect|ttfs ddd} ||}|dur2dddd }|d |d < ||}||}|j d }|S|d ur|j }|j} |jd ddfj} || v|| k7zrt |d|jdd d f} |jd dd f} |jdd dd f}t||d}|dk(r<| | jz } t|| gd}|j d }| |_|S|dk(r>| | jz } |j| }|j d }||_|S|d k(s|d urv| | jz } | | jz } d| j|<t|| gd}|j| }|j d }||_| |_|St dt d#t$r}t d|d}~wwxYw#t$r}t d|d}~wwxYw)Nr*r+)rr$zNot a valid normalize argumentFcL||jdjdz SNr$rSrsumrys rCz_normalize..s#QA!2!2!2!::rDc(||jz SrGrrs rCrz_normalize..sQUUWrDcH|j|jddSr)divrrs rCrz_normalize..squuQUUU]u;rD)rNr+r*rNTrz not in pivoted DataFrame)rr.r$rSzNot a valid margins argument)r8boolrrVr[rlr*r+ilocrrrrrloc) rBrr.r0 axis_subserr normalizersf table_index table_columnslast_ind_or_col column_margin index_margins rCrrs i$ -I.  H!),I%;,;3 (. D HI&A% Qf Lc Dkk  **RU+00  /LO4S T ~-FGH H 3B37+ zz"crc'*  3B38$5IuE  !)M,=,=,??ME=1:ELLOE)EM2 L/' !',*:*:*< >788I H=>C G H H=>C G Hs.H H) H& H!!H&) I2 H>>IcN|eg}t|D]S\}}t|tr(|j|j |j>|j |d|U|St |t |k7r t dt|ts t|}|S)N_z*arrays and names must have the same length) enumerater8rrr;rYAssertionErrorr9)arrsrRrrwarrs rCrr?s }o .FAs#y)chh.B SXX& xq_-  . L u:T " !MN N%&KE LrDcd}t|jt|}||||z|z}t|Dcic]\}}||vs d||}}}t|Dcgc]\}}||vrd|n|}}}t|Dcic]\}}||vs d||} }}t|Dcgc]\}}||vrd|n|} }}||| | fScc}}wcc}}wcc}}wcc}}w)a Given the names of a DataFrame's rows and columns, returns a set of unique row and column names and mappers that convert to original names. A row or column name is replaced if it is duplicate among the rows of the inputs, among the columns of the inputs or between the rows and the columns. Parameters ---------- rownames: list[str] colnames: list[str] Returns ------- Tuple(Dict[str, str], List[str], Dict[str, str], List[str]) rownames_mapper: dict[str, str] a dictionary with new row names as keys and original rownames as values unique_rownames: list[str] a list of rownames with duplicate names replaced by dummy names colnames_mapper: dict[str, str] a dictionary with new column names as keys and original column names as values unique_colnames: list[str] a list of column names with duplicate names replaced by dummy names cJt}|Dchc] }||vs| c}Scc}wrG)r)rRseenrs rCget_duplicatesz+_build_names_mapper..get_duplicatesns$E!&;$d*:;;;s  row_col_)r intersectionr) rrr shared_names dup_namesrwrrrrrs rCrrPs?<<x=--c(m(+CClRI)2((;$Qty?P$qc DOBK8AT6=adi'$qc T1O )2((;$Qty?P$qc DOBK8AT6=adi'$qc T1O O_o MMs$ CC/C C%"C%;C+)r>r"r-rr.rr/rr0rr1bool | lib.NoDefaultr2rreturnr")r>r"r-z!AggFuncTypeBase | AggFuncTypeDictr.rr/rr0rr1r r2rr r")r'N)rBzDataFrame | Seriesr>r"r1rr0r)r')r>r"r0r)r>r"r1rr0r)rBr"r>r"r1rr0r) r>r"r+r r*IndexLabel | lib.NoDefaultr)r r r")NNNNFr'TF) r.rr0rr/rrz/bool | Literal[0, 1, 'all', 'index', 'columns']r r")rBr"r.rr0rr r")r)rr)r list[str]rr r z;tuple[dict[str, str], list[str], dict[str, str], list[str]])E __future__rcollections.abcrrtypingrrrr ranumpyro pandas._libsr pandas.util._decoratorsr r pandas.util._exceptionsr pandas.core.dtypes.castrpandas.core.dtypes.commonrrrpandas.core.dtypes.dtypesrpandas.core.dtypes.genericrrpandas.core.commoncorecommonrpandas.core.framerpandas.core.groupbyrpandas.core.indexes.apirrrpandas.core.reshape.concatrpandas.core.reshape.utilrpandas.core.seriesrpandas._typingrrrr rr"r\r#r:rrrrrr7rrrrrrDrCr$sN" 5; 5 ! *' .6%! "# ,} %q1   !"%(^^4: 4:  4:4: 4:4:#4: 4:4:2$4:nG G / GG GG#G GGd#R R RRRl@E3 34<3>#Q+ Q+Q+Q+v#*+ *+ *+  *+*+Z "# ,w + ),), B BB & B ' B  B,$BP    "AFjjj j?jj\JOM M*.M>FMM`"3N3N#,3N@3NrD