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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|qSr@)r7rr)rr]r@r@rArszcrosstab..TF)Z intersectr,row)prefixrrrr __dummy__)r&r1r&)r/r0r'r*r))rr'r*)r/r4r!)r0r4)r) rOr r _get_names_build_names_mapperrorr{ziprNr _normalizeZ rename_axis)r/r0r.rownamescolnamesr&r'r*r)rZ common_idxZ pass_objsrownames_mapperunique_rownamescolnames_mapperunique_colnamesrr%Zdfkwargsr?r@r@rAcrosstab1sdj      rc Cs,t|ttfs$ddd}z||}Wnty#}ztd|d}~ww|dur]ddddd dd }|d |d <z||}WntyQ}ztd|d}~ww||}|d }|S|d ur|j}|j} |jdddfj } || v|| k@rt|d|jdddf} |jdddf} |jddddf}t ||dd}|dkr| | } t || gdd}|d }| |_|S|dkr| | } | | }|d }||_|S|d ks|d ur| | } | | } d| j|<t || gdd}| | }|d }||_| |_|Stdtd)Nr/r0)rr!zNot a valid normalize argumentFcSs||jddjddSNr!rHrsumr]r@r@rAz_normalize..cSs ||Sryrrr@r@rArs cSs|j|jddddSr)divrrr@r@rArs)rCr0r/rCTrz not in pivoted DataFrame)rr'r!rHzNot a valid margins argument)r7r(rfrKrOrWr/r0Zilocr_rrrrjloc) r?rr'r*Z axis_subserrZ normalizersfZ table_index table_columnsZlast_ind_or_colZ column_marginZ index_marginr@r@rArsr       3           rrrrfcCs|dur,g}t|D]\}}t|tr|jdur||jq ||d|q |St|t|kr8tdt|tsAt|}|S)N_z*arrays and names must have the same length) enumerater7rr_r:rNAssertionErrorr8)ZarrsrGrr\Zarrr@r@rAr-s rr list[str]r;tuple[dict[str, str], list[str], dict[str, str], list[str]]csdd}t|t|}||||B|Bfddt|D}fddt|D}fddt|D}fddt|D}||||fS) a Given the names of a DataFrame's rows and columns, returns a set of unique row and column names and mappers that convert to original names. 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Parameters ---------- rownames: list[str] colnames: list[str] Returns ------- Tuple(Dict[str, str], List[str], Dict[str, str], List[str]) rownames_mapper: dict[str, str] a dictionary with new row names as keys and original rownames as values unique_rownames: list[str] a list of rownames with duplicate names replaced by dummy names colnames_mapper: dict[str, str] a dictionary with new column names as keys and original column names as values unique_colnames: list[str] a list of column names with duplicate names replaced by dummy names cstfdd|DS)Ncsh|]}|vr|qSr@r@rseenr@rA ^rz>_build_names_mapper..get_duplicates..)rprFr@rrAget_duplicates\sz+_build_names_mapper..get_duplicatesc$i|]\}}|vrd||qSZrow_r@rr\r_Z dup_namesr@rA c   z'_build_names_mapper..c&g|]\}}|vrd|n|qSrr@rrr@rArfz'_build_names_mapper..crZcol_r@rrr@rArjrcrrr@rrr@rArmr)rp intersectionr)rrrZ shared_namesrrrrr@rrAr>s      r) NNNr#NFTr$FT)r%rr&rr'r(r)r(r*rr+r(r,r(r-r)r%rr&rBr'r(r)r(r*rr+r(r,r(r-r)r$N)r?rar%rr+r(r*r)r$)r%rr*r)r%rr+r(r*r)r?rr%rr+r(r*r) r%rr0rr/rr.rr-r)NNNNFr$TF) r'r(r*rr)r(rr(r-r)r?rr'r(r*rr-r)r)rrf)rrrrr-r)A __future__rcollections.abcrrtypingrrrnumpyrYZ pandas._libsrZpandas.util._decoratorsr r Zpandas.core.dtypes.castr Zpandas.core.dtypes.commonr r rZpandas.core.dtypes.dtypesrZpandas.core.dtypes.genericrrZpandas.core.commoncorecommonrZpandas.core.framerZpandas.core.groupbyrZpandas.core.indexes.apirrrZpandas.core.reshape.concatrZpandas.core.reshape.utilrZpandas.core.seriesrZpandas._typingrrrrrorr r9rZrhrlrnr6rrrrrrr@r@r@rAs           7 U  Z -G . 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