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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%! 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