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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@)r8rr)rr\r@r@rArszcrosstab..TF)Z intersectr-rowprefixrrrr __dummy__)r(r2r()r0r1r)r+r*)rr)r+)r0r5r")r1r5)r) rMrr _get_names_build_names_mapperrmr ryziprLr! _normalizeZ rename_axis)r0r1r/rownamescolnamesr(r)r+r*rZ common_idxZ pass_objsrownames_mapperunique_rownamescolnames_mapperunique_colnamesr r'Zdfkwargsr?r@r@rAcrosstab5sfj      r)r?r)r+r.c CsHt|ttfsRddd}z ||}Wn,tk rP}ztd|W5d}~XYnX|dkrddddd dd }|d |d <z ||}Wn,tk r}ztd|W5d}~XYnX||}|d }nv|d kr<|j}|j} |jdddfj } || k|| k@rt|d|jdddf} |jdddf} |jddddf}t ||dd}|dkr| | } t || gdd}|d }| |_n|dkr| | } | | }|d }||_np|d ks|d kr2| | } | | } d| j|<t || gdd}| | }|d }||_| |_ntdntd|S)Nr0r1)rr"zNot a valid normalize argumentFcSs||jddjddSNr"rFrsumr\r@r@rAz_normalize..cSs ||Srwrrr@r@rArrcSs|j|jddddSr)divrrr@r@rArr)rBr1r0rBTrz not in pivoted DataFrame)rr)r"rFzNot a valid margins argument)r8r&rdrIrMrXr0r1Zilocr^rrrrhloc) r?rr)r+Z axis_subserrZ normalizersfZ table_indexZ table_columnsZlast_ind_or_colZ column_marginZ index_marginr@r@rArsd                  rrrdrcCs|dkrVg}t|D]>\}}t|tr>|jdk r>||jq||d|qn*t|t|krntdt|tst|}|S)N_z*arrays and names must have the same length) enumerater8rr^r;rLrkr9)ZarrsrErr[Zarrr@r@rAr1s rz list[str]z;tuple[dict[str, str], list[str], dict[str, str], list[str]])rrr.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|]}|kr|qSr@r@rseenr@rA bsz>_build_names_mapper..get_duplicates..)rnrDr@rrAget_duplicates`sz+_build_names_mapper..get_duplicatescs$i|]\}}|krd||qSZrow_r@rr[r^Z dup_namesr@rA gsz'_build_names_mapper..cs&g|]\}}|krd|n|qSrr@rrr@rArjsz'_build_names_mapper..cs$i|]\}}|krd||qSZcol_r@rrr@rArnscs&g|]\}}|krd|n|qSrr@rrr@rArqs)rn intersectionr)rrrZ shared_namesrrrrr@rrArBs     r) NNNr$NFTr%FT)Nr%N)r%)r%)r%)NNNNFr%TF)r%)r)@ __future__rtypingrrrrrZnumpyrQZ pandas._libsrZpandas._typingr r r r Zpandas.util._decoratorsr rZpandas.core.dtypes.castrZpandas.core.dtypes.commonrrrrrZpandas.core.dtypes.genericrrZpandas.core.commoncorecommonrZpandas.core.framerZpandas.core.groupbyrZpandas.core.indexes.apirrrZpandas.core.reshape.concatrZpandas.core.reshape.utilrZpandas.core.seriesrrmr r!r:rYrfrirlr7rrrrrrr@r@r@rAs|         "7UQ,E.P