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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|qSrD)r:rr)rIryrDrDrErszcrosstab..TF) intersectr/row)prefixrrr r __dummy__)r(r4r()r2r3r)r,r+r-)rr)r,)r2r7r#)r3r7)r) r[rr _get_names_build_names_mapperrr!rziprYr" _normalize rename_axis)r2r3r1rownamescolnamesr(r)r,r+r common_idx pass_objsrownames_mapperunique_rownamescolnames_mapperunique_colnamesr!r'dfkwargsrCrDrDrEcrosstabBsfj      rc Cs,t|ttfs$ddd}z||}Wnty#}ztd|d}~ww|dur]ddddd dd }|d |d <z||}WntyQ}ztd|d}~ww||}|d }|S|d ur|j}|j} |jdddfj } || v|| k@rt|d|jdddf} |jdddf} |jddddf}t ||dd}|dkr| | } t || gdd}|d }| |_|S|dkr| | } | | }|d }||_|S|d ks|d ur| | } | | } d| j|<t || gdd}| | }|d }||_| |_|Stdtd)Nr2r3)rr#zNot a valid normalize argumentFcSs||jddjddSNr#rSrsumryrDrDrErz_normalize..cSs ||SrGrrrDrDrErs cSs|j|jddddSr)divrrrDrDrErs)rNr3r2rNTrz not in pivoted DataFrame)rr)r#rSzNot a valid margins argument)r:r*rrVr[rlr2r3ilocrrrrrloc) rCrr)r, axis_subserr normalizersf table_index table_columnslast_ind_or_col column_margin index_marginrDrDrErsr       3           rrrrcCs|dur,g}t|D]\}}t|tr|jdur||jq ||d|q |St|t|kr8tdt|tsAt|}|S)N_z*arrays and names must have the same length) enumerater:rrr=rYAssertionErrorr;)arrsrRrrwarrrDrDrEr?s rr list[str]r;tuple[dict[str, str], list[str], dict[str, str], list[str]]csdd}t|t|}||||B|Bfddt|D}fddt|D}fddt|D}fddt|D}||||fS) a Given the names of a DataFrame's rows and columns, returns a set of unique row and column names and mappers that convert to original names. A row or column name is replaced if it is duplicate among the rows of the inputs, among the columns of the inputs or between the rows and the columns. Parameters ---------- rownames: list[str] colnames: list[str] Returns ------- Tuple(Dict[str, str], List[str], Dict[str, str], List[str]) rownames_mapper: dict[str, str] a dictionary with new row names as keys and original rownames as values unique_rownames: list[str] a list of rownames with duplicate names replaced by dummy names colnames_mapper: dict[str, str] a dictionary with new column names as keys and original column names as values unique_colnames: list[str] a list of column names with duplicate names replaced by dummy names cstfdd|DS)Ncsh|]}|vr|qSrDrDrseenrDrE prz>_build_names_mapper..get_duplicates..)rrQrDrrEget_duplicatesnsz+_build_names_mapper..get_duplicatesc$i|]\}}|vrd||qSrow_rDrIrwr dup_namesrDrE uz'_build_names_mapper..c&g|]\}}|vrd|n|qSrrDrr rDrErxz'_build_names_mapper..crcol_rDrr rDrEr |r cr rrDrr rDrErr)r intersectionr)rrr shared_namesrrrrrDr rErPs      r)r'r!r(rr)r*r+r*r,rr-r.r/r*r0r!)r'r!r(rFr)r*r+r*r,rr-r.r/r*r0r!)r&N)rCrr'r!r-r*r,r)r&)r'r!r,r)r'r!r-r*r,r)rCr!r'r!r-r*r,r) r'r!r3rr2rr1rr0r!)NNNNFr&TF) r)r*r,rr+r*rrr0r!)rCr!r)r*r,rr0r!)r)rr)rrrrr0r)E __future__rcollections.abcrrtypingrrrrranumpyro pandas._libsr pandas.util._decoratorsr r pandas.util._exceptionsr pandas.core.dtypes.castr pandas.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._typingrrrrrr!r\r"r<rrrrrr9rrrrrrDrDrDrEs            7 V  [ -H / P