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CNN "$**j11=B$**BTBT-=cS -= >,'++J7    65(A(A(8,f5F'' $$J(G..t|/C/C:.VG__- .FAGASASASCNN*4ASFLL Mi,P> sK$K4KK  K%c UcUb [S5eUbUc [S5e[U5(dU/n[U5(dU/nSn X-V s/sH"n [U [[45(dM U PM$ n n U (a [ U SSS9n [ XSS9n[ XS S9n[X45un nnnS S KJ n 0[[X55E[[UU55EnU"UU S 9nUcS US '[S S.nO UUS 'SU0nUR"SUUUUUSS.UD6nU SLa [UXUS9nURU S S9nURUSS9nU$s sn f)av 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"rziprZr# _normalize rename_axis)r*r+r)rownamescolnamesr-r.r0r/r common_idxrz pass_objsrownames_mapperunique_rownamescolnames_mapperunique_colnamesr"r>dfkwargsrBs rCcrosstabrBsT~'-ABB goDEE u % % w ' ')J!OXOqz!i=V/WOIX+IER %%8H'E:H H/ !  s?* +  s?G, - D 4z *B ~; 2 ;W% NN !   E Yl    O!  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D H&A% Qf Lc Dkk  **RU+00  /L4S T ~-FGH H 3B37+ zz"crc'*  3B38$5uE  !),=,=,??ME1:ELLOE)M2 L/' !'*:*:*< >788I H=>C G H H=>C G Hs.HH# H  HH # H>- H99H>clUcm/n[U5HZup4[U[5(a*URbUR UR5 MDUR USU35 M\ U$[ U5[ U5:wa [ S5e[U[5(d [U5nU$)N_z*arrays and names must have the same length) enumerater8rrr;rZAssertionErrorr9)arrsrSrrxarrs rCrr?s }oFA#y))chh.B SXX& xq_- & L u:T " !MN N%&&KE LrDcSn[U5R[U55nU"U5U"U5-U-n[U5VVs0sHupVXd;dM SU3U_M nnn[U5VVs/sHupVXd;aSU3OUPM nnn[U5VVs0sHupVXd;dM SU3U_M n nn[U5VVs/sHupVXd;aSU3OUPM n nnXxX4$s snnfs snnfs snnfs snnf)aT 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 cT[5nUVs1sH o"U;dM UiM sn$s snfrG)r)rSseenrs rCget_duplicates+_build_names_mapper..get_duplicatesns&E!&;d*:;;;s %%row_col_)r intersectionr) rrr shared_names dup_namesrxrrrrrs rCrrPs=<<x=--c(m(+CClRI)2((;(;WQt?P$qc D(;BK8ATATgad'$qc T1AT )2((;(;WQt?P$qc D(;BK8ATATgad'$qc T1AT _ MMs$ C% C%3C+ C1+ C1C7)r>r"r-rr.rr/rr0rr1bool | lib.NoDefaultr2rreturnr")r>r"r-z!AggFuncTypeBase | AggFuncTypeDictr.rr/rr0rr1rr2rrr")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)rrr")NNNNFr'TF) r.rr0rr/rrz/bool | Literal[0, 1, 'all', 'index', 'columns']rr")rBr"r.rr0rrr")r)rr)r list[str]rrrz;tuple[dict[str, str], list[str], dict[str, str], list[str]])E __future__rcollections.abcrrtypingrrrr rbnumpyrp 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:rsrrrr7rrrrrrDrCr)sN" 5; 5 ! *' .6%! 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