`VgkddlmZddlmZmZmZmZmZddlZ ddl m Z m Z m Z mZddlmZmZmZddlmZddlmZddlmZmZmZmZdd lmZmZddlm cm!Z"dd l#m$Z$dd l%m&Z&dd l'm(Z(m)Z)m*Z*dd l+m,Z,ddl-m.Z.ddl/m0Z0erddl1m2Z2edee$dd d$ d%dZ3 d&dZ4 d' d(dZ5d)d*dZ6 d) d+dZ7 d) d,dZ8dZ9edee$ddeddg d- d.dZ: d/ d0d Z; d) d1d!Zy)5) annotations) TYPE_CHECKINGCallableHashableSequencecastN) AggFuncTypeAggFuncTypeBaseAggFuncTypeDict IndexLabel)Appender Substitutiondeprecate_nonkeyword_arguments)rewrite_warning)maybe_downcast_to_dtype)is_integer_dtype is_list_likeis_nested_list_like is_scalar) ABCDataFrame ABCSeries) _shared_docs)Grouper)Index MultiIndexget_objs_combined_axis)concat)cartesian_product)Series DataFramez data : DataFrame pivot_table)indentsdatac |t|}t|}t|trog} g} |D]E} t|||||| |||| |  }| j || j t | d| Gt | | d}|j|dSt|||||||||| | }|j|dS)N) valuesindexcolumns fill_valueaggfuncmarginsdropna margins_nameobservedsort__name__r#)keysaxisr")method) _convert_by isinstancelist__internal_pivot_tableappendgetattrr __finalize__)r%r'r(r)r+r*r,r-r.r/r0piecesr2func_tabletables Z/var/www/html/hubwallet-dev/venv/lib/python3.12/site-packages/pandas/core/reshape/pivot.pyr"r"8s  E'"G'4 "$ 9D+%)! F MM& ! KKj$7 8 9"vDq1!!$}!== "    E   d=  99c T ||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|}|j| | | }d}tdt|5|j|}ddd|rtt rt|jr|j#d }|D]}||vst%||s||vst%||r+t||t r?t||j&t(j&sgt+||||j&||<}|j,j.d kDr|r|j,j0dt|}g}t3t|t| D]D}|j,j0|}|||vr|j |4|j |F|j5|}|st|j,t6r[t7j8t;|j,j<|j,j0 }|j?|d }t|jt6r[t7j8t;|jj<|jj0 }|j?|d }| dur"t|t r|jAd }||jC|d}|r9|r#||jEjGd }tI||||||||| }| r. s,|jj.d kDr|jKd d }t|d k(rt|d kDr |jL}t|t r|r|j#dd }|S#t$rY7wxYw#tttf$rYwxYw#1swYxYw)zL Helper of :func:`pandas.pivot_table` for any non-list ``aggfunc``. NTF)r/r0zpivot_table dropped a column because it failed to aggregate. This behavior is deprecated and will raise in a future version of pandas. Select only the columns that can be aggregated.z!The default value of numeric_only)target_messagetarget_category new_messageall)howr#)namesrr3infer)downcast)rowscolsr+r/r.r*)rGr3)'rr7KeyErrorr6rkeyr9 TypeErrorlenr)drop ValueErrorgroupbyr FutureWarningaggrr-rdtypenprr(nlevelsrHrangeunstackr from_arraysrlevelsreindex sort_indexfillnanotnarF _add_margins droplevelT)r%r'r(r)r+r*r,r-r.r/r0r2 values_passed values_multii to_filterxrOgroupedmsgaggedvr? index_names to_unstacknamems r@r8r8qsf 7?D$&M  L&\F LXF "A}qk! "  A!W%EE 9$$Q'   y>C - - ?D C S)  fll4(l>G * :% %  G$ %*UL1c%--6H  ' PAT $T!W-J(q2!%(L9jGMM288? 7uQxaOE!H P" E {{Q5kk'' #e*5  s5z3t9- (A;;$$Q'D|t{2!!!$!!$'  (  j)  ekk: .&&!%++"4"45U[[=N=NAMM!!M,E emmZ 0&&!%--"6"67u}}?R?RAMM!!M,E t| 5,7  a ( Z' :  ((a(01D   %!  \emm.C.Ca.G* 5zQ3wQ?RRR'c t|ts tdd|d} |jjD]*} ||jj | vs!t| t ||||} |jdk(rF|jjddD]*} ||jj | vs!t| t|dkDr|fdt|dz zz} n|} |s/t|tr|jt| | |iS|r+t||||||||} t| ts| S| \}}}n;t|tsJt!|||||||} t| ts| S| \}}}|j#|j|}|D]&}t|tr | |||<| |d||<(dd lm}||t)| g j*}|jj}t-|j.D]<}|j1|gj}||j3t4|f ||<>|j|}||j_|S) Nz&margins_name argument must be a stringzConflicting name "z " in marginsr#)r*rr )r))args)r6strrSr(rHget_level_values_compute_grand_marginndimr)rQr_appendr_generate_marginal_resultstupler)_generate_marginal_results_without_valuesr^pandasr!rrdsetdtypes select_dtypesapplyr)r?r%r'rLrMr+r/r.r*rklevel grand_marginrOmarginal_result_setresult margin_keys row_marginkr! margin_dummy row_namesrWs r@rbrb s lC (ABB |nL 9C""" 5;;77> >S/ !")vw ML zzQ]]((, &Eu}}==eDD o% &  4y1}oTQ 77 j 2}}VS,|*D$EFGG 8 4tWh  -u5& &*=' Z%...G 4tWh  -u5& &*=' Z##FNNz#JJ / a (OJqM(1.JqM / !Zu>@@L ""IV]]# ##UG,44)$/55 #5(6 T ^^L )F"FLL MrAc|ri}||jD]\}} t|trt||||<nUt|tr:t||trt|||||<n|||||<n ||||<|S|||j iS#t $rYwxYwN)itemsr6rwr:dictrPr()r%r'r+r.rrrms r@ryry\s  L&&( DAq gs+&9ga&9&;LO.!'!*c2*@'!WQZ*@*B Q*4'!*Q- Q&-ajLO gdjj122   sA:B11 B=<B=ctdkDrvg}g} fd} t|dkDr|||zj||j|} d} |jd| |D]G\} }| | }|j}| | ||<|j || j |Inddlm}d} |jd| |D]\} }tdkDr | | }n}|j |||j|j}t|g|jj|_ |j || j |t|| }t|dk(r|S|}|j} tdkDr||zj|j|}|j}tgtt!tz}|jj#||_ n%t%t&j(|j }|| |fS) Nrc0|fdtdz zzS)Nrtr#rQ)rOrMr.s r@_all_keyz,_generate_marginal_results.._all_keyzs &#d)a-)@@ @rAr/r#rr3r/r rprIr()rQrTrVcopyr9rr!rrdrr(rprr)stackr7rZreorder_levelsrrXnan)r?r%r'rLrMr+r/r. table_piecesrrmargincat_axisrOpieceall_keyr!transformed_piecerr new_orders ` ` r@r|r|rs. 4y1}   A t9q=$-(000IMMgVFH#mm!(XmV , U"3- !'g##E*""7+ , )H#mm!(XmV , Ut9q=&smG*G##E*%.ekk'.B$C$E$E!*/  @P@P*Q!'##$56""7+ , 84 t9>Mmm  4y1}$-(000IMMgV %%' YK$uSY'7"88 %++::9E BFF&..9 ; **rAc2tdkDrg}fd}t|dkDrE||j||j|} |} | || <|} |j| nR|jdd|j|} |} | || <|} |j| | S|} |j}tr&|j|j|} n%t t j| j} | || fS)NrcNtdk(rSfdtdz zzS)Nr#rtr)rMr.sr@rz;_generate_marginal_results_without_values.._all_keys.4yA~## ?Uc$i!m%<< cS > >,'++J7   65(A(8,f5F''V $$J(G..tF|/C/C:.VG ??+ ,,/ P>s"G  G Gc | | 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|||||d|}| 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) intersectr0row)prefixrrr r __dummy__)r+r*r+)r(r)r,r.r-) normalizer,r.)r(r3r#)r)r3)r)rSrr6rrr _get_names_build_names_mapperrr!rziprQr" _normalize rename_axis)r(r)r'rownamescolnamesr+r,r.r-r common_idxri pass_objsrownames_mapperunique_rownamescolnames_mapperunique_colnamesr!r%dfkwargsr?s r@crosstabrsT~'-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 LcYs E  E ct|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#rIrsumris r@z_normalize..s#QA!2!2!2!::rAc(||jz Srrrs r@rz_normalize..sQUUWrAcH|j|jddSr)divrrs r@rz_normalize..squuQUUU]u;rA)rFr)r(rFTrz not in pivoted DataFrame)rr,r#rIzNot a valid margins argument)r6boolrwrNrSr`r(r)ilocrprrrr{loc) r?rr,r. axis_subserr normalizersf table_index table_columnslast_ind_or_col column_margin index_margins r@rrs i$ -I.  H!),I%;,;3 (. D HI&A% Qf Lc Dkk  **RU+00  /LO4S T ~-FGH H 3B37+ zz"crc'*  3B38$5IuE  !)M,=,=,??ME=1:ELLOE)EM2 L/' !',*:*:*< >788K H=>C G H  H=>C G Hs.H H) H& H!!H&) I2 H>>IcN|eg}t|D]S\}}t|tr(|j|j |j>|j |d|U|St |t |k7r t dt|ts t|}|S)N_z*arrays and names must have the same length) enumerater6rrpr9rQAssertionErrorr7)arrsrHrrgarrs r@rrs }o .FAs#y)chh.B SXX& xq_-  . L u:T " !MN N%&KE LrAcd}t|jt|}||||z|z}t|Dcic]\}}||vs d||}}}t|Dcgc]\}}||vrd|n|}}}t|Dcic]\}}||vs d||} }}t|Dcgc]\}}||vrd|n|} }}||| | fScc}}wcc}}wcc}}wcc}}w)a Given the names of a DataFrame's rows and columns, returns a set of unique row and column names and mappers that convert to original names. A row or column name is replaced if it is duplicate among the rows of the inputs, among the columns of the inputs or between the rows and the columns. Parameters ---------- rownames: list[str] colnames: list[str] Returns ------- Tuple(Dict[str, str], List[str], Dict[str, str], List[str]) rownames_mapper: dict[str, str] a dictionary with new row names as keys and original rownames as values unique_rownames: list[str] a list of rownames with duplicate names replaced by dummy names colnames_mapper: dict[str, str] a dictionary with new column names as keys and original column names as values unique_colnames: list[str] a list of column names with duplicate names replaced by dummy names cJt}|Dchc] }||vs| c}Scc}wr)r)rHseenrps r@get_duplicatesz+_build_names_mapper..get_duplicatesJs$E!&;$d*:;;;s  row_col_)r intersectionr) rrr shared_names dup_namesrgrprrrrs r@rr,s?<<x=--c(m(+CClRI)2((;$Qty?P$qc DOBK8AT6=adi'$qc T1O )2((;$Qty?P$qc DOBK8AT6=adi'$qc T1O O_o MMs$ CC/C C%"C%;C+) NNNmeanNFTAllFT)r%r!r+r r,rr-rr.rwr/rr0rreturnr!)r%r!r+z!AggFuncTypeBase | AggFuncTypeDictr,rr-rr.rwr/rr0rrr!)NrN)r?zDataFrame | Seriesr%r!r.rw)r)r%r!r.rw)r.rw)r?r!r.rw)NNN) r%r!r(IndexLabel | Noner)rr'rrr!)NNNNFrTF)r,rr.rwr-rrr!)r?r!r,rrr!)r)rrw)r list[str]rrrz;tuple[dict[str, str], list[str], dict[str, str], list[str]])? __future__rtypingrrrrrnumpyrXpandas._typingr r r r pandas.util._decoratorsr rrpandas.util._exceptionsrpandas.core.dtypes.castrpandas.core.dtypes.commonrrrrpandas.core.dtypes.genericrrpandas.core.commoncorecommonrpandas.core.framerpandas.core.groupbyrpandas.core.indexes.apirrrpandas.core.reshape.concatrpandas.core.reshape.utilrpandas.core.seriesrrr!r"r8rbryr|r~r5rrrrrrAr@rs"  4;  ! *' .6% "# ,} %q1   !4: 4:  4:4: 4:4:4: 4:4:2$4:nX X / XX XXX XXDM M MM`3.MR=+FI=+BPU#+ #+IL#+L "# ,w +F8D $!% $ 3- 3- 3-3-  3-  3-E,$3-r    ggg ggV>CO O*.OOd"3N3N#,3N@3NrA