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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. 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.c S s g | ]}t |ttf�r|�qS r= )r4 r r )ry rW r= r= r> r{ y s z crosstab.<locals>.<listcomp>TF)Z intersectr. �row)�prefixr| r )r )r' � __dummy__)r$ r) r$ )r' r( r* r, r+ )� normalizer* r, )r' r1 r )r( r1 )r� )rJ r} Zmaybe_make_listr � _get_names�_build_names_mapperrd r ro �ziprI r � _normalizeZ rename_axis)r' r( r& �rownames�colnamesr$ r* r, r+ r� Z common_idxZ pass_objs�rownames_mapper�unique_rownames�colnames_mapper�unique_colnamesr r# Zdf�kwargsr< r= r= r> �crosstab sH g r� )r* c C sH t |ttf�sRddd�}y || }W n, tk rP } ztd�|�W d d }~X Y nX |dkr�dd� dd� d d� d �}|d |d < y || }W n, tk r� } ztd�|�W d d }~X Y nX || �} | �d �} �nv|d k�r<| j}| j} | jdd d �f j } || k|| k@ �rt|� d���| jd d�df } | jdd d�f } | jd d�d d�f } t | |dd�} |dk�r�| | � � } t | | gdd�} | �d �} | | _n�|dk�r�| | � � } | � | �} | �d �} || _np|d k�s�|d k�r2| | � � } | | � � } d| j|< t | | gdd�} | � | �} | �d �} || _| | _ntd��ntd��| S )Nr' r( )r r z Not a valid normalize argumentFc S s | | j dd�j dd� S )Nr )r1 r )�sum)rW r= r= r> �<lambda>� � z _normalize.<locals>.<lambda>c S s | | � � S )N)r� )rW r= r= r> r� � r� c S s | j | jdd�dd�S )Nr )r1 r )�divr� )rW r= r= r> r� � r� )rA r( r' rA Tr ���z not in pivoted DataFrame)r� r* r )r1 z Not a valid margins argument)r4 r? r@ rF rJ rR r' r( ZilocrZ r� r� r r7 �loc) r< r� r* r, Z axis_subs�errZ normalizers�fZ table_index� table_columnsZlast_ind_or_colZ column_marginZ index_marginr= r= r> r� � sb r� r )r� c C s� |d krZg }xvt | �D ]>\}}t|t�r@|jd k r@|�|j� q|�|� d|� �� qW n*t|�t| �krrtd��t|t�s�t|�}|S )N�_z*arrays and names must have the same length)� enumerater4 r rZ r7 rI rS r5 )ZarrsrC r� rV Zarrr= r= r> r� � s r� z list[str]z;tuple[dict[str, str], list[str], dict[str, str], list[str]])r� r� r% c s� dd� }t | ��t |��}|| �||�B |B � � fdd�t| �D �}� fdd�t| �D �}� fdd�t|�D �}� fdd�t|�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 c s t � � � fdd�| D �S )Nc s h | ]}|� kr|�qS r= r= )ry rZ )�seenr= r> � <setcomp>. s z>_build_names_mapper.<locals>.get_duplicates.<locals>.<setcomp>)re )rC r= )r� r> �get_duplicates, s z+_build_names_mapper.<locals>.get_duplicatesc s$ i | ] \}}|� kr|d |� ��qS )�row_r= )ry rV rZ )� dup_namesr= r> � <dictcomp>3 s z'_build_names_mapper.<locals>.<dictcomp>c s& g | ] \}}|� kr d |� �n|�qS )r� r= )ry rV rZ )r� r= r> r{ 7 s z'_build_names_mapper.<locals>.<listcomp>c s$ i | ] \}}|� kr|d |� ��qS )�col_r= )ry rV rZ )r� r= r> r� : s c s& g | ] \}}|� kr d |� �n|�qS )r� r= )ry rV rZ )r� r= r> r{ > s )re � intersectionr� )r� r� r� Z shared_namesr� r� r� r� r= )r� r> r� s r� ) NNNr! NFTr" FT)Nr" N)r" )r" )r" )NNN)NNNNFr" TF)r" )r )<� __future__r �typingr r r r r �numpyrs Zpandas._typingr r r r r Zpandas.util._decoratorsr r Zpandas.core.dtypes.castr Zpandas.core.dtypes.commonr r r Zpandas.core.dtypes.genericr r Zpandas.core.common�core�commonr} Zpandas.core.framer Zpandas.core.groupbyr Zpandas.core.indexes.apir r r Zpandas.core.reshape.concatr Zpandas.core.reshape.utilr Zpandas.core.seriesr rd r r r6 rT r_ ra rc r3 rx r� r� r� r� r= r= r= r> �<module> sl , G@% + P
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