o
    5ck                     @  s  d dl mZ d dlmZmZmZmZmZ d dlZ	d dl
mZmZmZmZ d dlmZmZmZ d dlmZ d dlmZ d dlmZmZmZmZ d d	lmZmZ d dlm   m!Z" d d
l#m$Z$ d dl%m&Z& d dl'm(Z(m)Z)m*Z* d dl+m,Z, d dl-m.Z. d dl/m0Z0 erd dl1m2Z2 edee$d dd										dJdKd%dZ3dLd'd(Z4			dMdNd+d,Z5dOdPd-d.Z6	dOdQd/d0Z7	dOdRd1d2Z8d3d4 Z9edee$d5 ddeddgd6			dSdTd;d5Z:								dUdVd<d=Z;	dOdWd>d?Z<dXdYdBdCZ=dZdHdIZ>dS )[    )annotations)TYPE_CHECKINGCallableHashableSequencecastN)AggFuncTypeAggFuncTypeBaseAggFuncTypeDict
IndexLabel)AppenderSubstitutiondeprecate_nonkeyword_arguments)rewrite_warning)maybe_downcast_to_dtype)is_integer_dtypeis_list_likeis_nested_list_like	is_scalar)ABCDataFrame	ABCSeries)_shared_docs)Grouper)Index
MultiIndexget_objs_combined_axis)concat)cartesian_product)Series	DataFramez
data : DataFramepivot_table   )indentsmeanFTAlldatar    aggfuncr   marginsbooldropnamargins_namestrobservedsortreturnc                 C  s   t |}t |}t|trAg }g }|D ]}t| |||||||||	|
d}|| |t|d| qt||dd}|j| ddS t| |||||||||	|
}|j| ddS )N)
valuesindexcolumns
fill_valuer'   r(   r*   r+   r-   r.   __name__r"   )keysaxisr!   )method)_convert_by
isinstancelist__internal_pivot_tableappendgetattrr   __finalize__)r&   r0   r1   r2   r'   r3   r(   r*   r+   r-   r.   piecesr5   func_tabletable rC   O/var/www/html/gps/gps/lib/python3.10/site-packages/pandas/core/reshape/pivot.pyr!   8   sJ   

!AggFuncTypeBase | AggFuncTypeDictc                 C  s  || }|du}|rZt |rd}t|}nd}|g}|D ]
}|| vr&t|qg }|| D ]}t|tr7|j}z|| v rA|| W q- tyK   Y q-w t|t| j	k rY| | } n| j	}|D ]}z|
|}W q_ tttfyt   Y q_w t|}| j||	|
d}d}tdt|d ||}W d   n1 sw   Y  |rt|trt|j	r|jdd	}|D ]2}|| v rt| | r||v rt|| st|| tst| | jtjrt|| | | j||< q|}|jjd
kr,|r,|jjdt| }g }tt|t|D ]}|jj| }|du s||v r || q|| q||}|set|jtrJtjt|jj|jjd}|j|dd}t|j	tretjt|j	j|j	jd}|j|d
d}|
du rvt|trv|j d
d}|dur|j!|dd}|r|r| | " j#d
d } t$|| |||||||d	}|r|s|j	jd
kr|j%dd
d}t|dkrt|dkr|j&}t|tr|r|jdd
d}|S )zL
    Helper of :func:`pandas.pivot_table` for any non-list ``aggfunc``.
    NTF)r-   r.   zpivot_table dropped a column because it failed to aggregate. This behavior is deprecated and will raise in a future version of pandas. Select only the columns that can be aggregated.z!The default value of numeric_only)target_messagetarget_categorynew_messageall)howr"   namesr   r6   infer)downcast)rowscolsr'   r-   r+   r3   )rJ   r6   )'r   r:   KeyErrorr9   r   keyr<   	TypeErrorlenr2   drop
ValueErrorgroupbyr   FutureWarningaggr   r*   r   dtypenpr   r1   nlevelsrL   rangeunstackr   from_arraysr   levelsreindex
sort_indexfillnanotnarI   _add_margins	droplevelT)r&   r0   r1   r2   r'   r3   r(   r*   r+   r-   r.   r5   values_passedvalues_multii	to_filterxrS   groupedmsgaggedvrB   index_names
to_unstacknamemrC   rC   rD   r;   q   s   






r;   rB   DataFrame | Seriesc	              	   C  s  t |ts	tdd| d}	| jjD ]}
|| j|
v r!t|	qt||||}| jdkrE| jjdd  D ]}
|| j|
v rDt|	q6t	|dkrW|fdt	|d   }n|}|skt | t
rk| t||| iS |rt| |||||||}t |ts|S |\}}}nt | tsJ t| ||||||}t |ts|S |\}}}|j|j|d}|D ]}t |tr|| ||< q||d  ||< qdd	lm} ||t|gd
j}|jj}t|jD ]}||gj}|| jt|fd||< q||}||j_|S )Nz&margins_name argument must be a stringzConflicting name "z" in margins   r"    )r3   r   r   )r2   )args)r9   r,   rW   r1   rL   get_level_values_compute_grand_marginndimr2   rU   r   _appendr   _generate_marginal_resultstupler   )_generate_marginal_results_without_valuesrb   pandasr    r   rh   setdtypesselect_dtypesapplyr   )rB   r&   r0   rP   rQ   r'   r-   r+   r3   ro   levelgrand_marginrS   marginal_result_setresultmargin_keys
row_marginkr    margin_dummy	row_namesr[   rC   rC   rD   rf     s`   






rf   c              	   C  s   |rPi }| |   D ]C\}}z6t|trt|| ||< n&t|tr=t|| tr4t|||  ||< n|| |||< n||||< W q
 tyM   Y q
w |S ||| jiS N)itemsr9   r,   r=   dictrT   r1   )r&   r0   r'   r+   r   r   rq   rC   rC   rD   r|   \  s"   

r|   c                   s  t  dkrg }g }	 fdd}
t |dkrM|||  j||d|}d}| jd||dD ]\}}|
|}| }|| ||< || |	| q/nCddlm} d}| jd||dD ]2\}}t  dkrl|
|}n}|| |||j}t	|g|j
jd|_
|| |	| q]t||d	}t |dkr|S n| }| j}	t  dkr| |  j |d|}| }t  gttt   }|j
||_
nttj|jd
}||	|fS )Nr   c                   s   | fdt  d   S )Nrx   r"   rU   )rS   rQ   r+   rC   rD   _all_keyz  s   z,_generate_marginal_results.<locals>._all_keyr-   r"   r   r6   r-   r   rt   rM   r1   )rU   rX   rZ   copyr<   r   r    r   rh   r   r1   rt   r   r2   stackr:   r^   reorder_levelsr   r\   nan)rB   r&   r0   rP   rQ   r'   r-   r+   table_piecesr   r   margincat_axisrS   pieceall_keyr    transformed_piecer   r   	new_orderrC   r   rD   r   r  sL   





r   c                   s   t  dkrKg } fdd}t |dkr0|| j||d|}	| }
|	| |
< | }||
 n |jdd|d|}	| }
|	| |
< | }||
 |S | }| j}t  ra|  j |d|}nttj|jd}|||fS )Nr   c                     s&   t  dkrS fdt  d   S )Nr"   rx   r   rC   r   rC   rD   r     s   z;_generate_marginal_results_without_values.<locals>._all_keyr   r   r   )rU   rX   r   r<   r2   r   r\   r   )rB   r&   rP   rQ   r'   r-   r+   r   r   r   r   r   r   rC   r   rD   r     s*   

r   c                 C  sJ   | d u rg } | S t | st| tjtttfst| r| g} | S t| } | S r   )	r   r9   r\   ndarrayr   r   r   callabler:   )byrC   rC   rD   r8     s   	r8   pivot)versionallowed_argsr1   IndexLabel | Noner2   r0   c                   s0  |d u rt dt|}|d u r+|d urt|}ng }|d u } j|| |d}nh|d u rNt jtrC fddt jjD }nt	 j jj
dg}n fddt|D } fdd|D }	||	 t|}
t|rt|tsttt |} j | j|
|d}n
 j | j|
d	}||S )
Nz.pivot() missing 1 required argument: 'columns')r<   c                   s   g | ]} j |qS rC   )r1   r{   ).0rk   r&   rC   rD   
<listcomp>  s    zpivot.<locals>.<listcomp>r   c                      g | ]} | qS rC   rC   )r   idxr   rC   rD   r         c                   r   rC   rC   )r   colr   rC   rD   r     r   )r1   r2   r   )rT   comconvert_to_list_like	set_indexr9   r1   r   r^   r]   r   rt   extendr`   r   r   r   r   r   _constructor_values_constructor_slicedr_   )r&   r1   r2   r0   columns_listlikerQ   r<   indexed
index_listdata_columns
multiindexrC   r   rD   r     s8   	





c
                 C  s\  |du r|durt d|dur|du rt dt| s| g} t|s&|g}d}
dd | | D }|r:t|ddd}
t| |d	d
}t||dd
}t||\}}}}ddlm} i tt|| tt||}|||
d}|du r{d|d< t	dd}n||d< d|i}|j
	d|||||d|}|	durt||	||d}|j|dd}|j|dd}|S )a  
    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 <reshaping.crosstabulations>` 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.c                 S  s   g | ]}t |ttfr|qS rC   )r9   r   r   )r   rm   rC   rC   rD   r     s    zcrosstab.<locals>.<listcomp>TF)	intersectr.   row)prefixr   r   r   r   	__dummy__)r'   r3   r'   )r1   r2   r(   r+   r*   )	normalizer(   r+   )r1   r6   r"   )r2   r6   )r   )rW   r   r   
_get_names_build_names_mapperr   r    r   ziprU   r!   
_normalizerename_axis)r1   r2   r0   rownamescolnamesr'   r(   r+   r*   r   
common_idx	pass_objsrownames_mapperunique_rownamescolnames_mapperunique_colnamesr    r&   dfkwargsrB   rC   rC   rD   crosstab  sd   jr   c              
   C  s,  t |ttfs$ddd}z|| }W n ty# } ztd|d }~ww |du r]dd dd d	d d
}|d |d< z|| }W n tyQ } ztd|d }~ww || } | d} | S |du r| j}| j}	| jdd d f j	}
||
v||
k@ rt| d| jd ddf }| jdd df }| jd dd df } t
| |dd} |dkr||  }t| |gdd} | d} |	| _| S |dkr||  }| |} | d} || _| S |dks|du r||  }||  }d|j|< t| |gdd} | |} | d} || _|	| _| S tdtd)Nr1   r2   )r   r"   zNot a valid normalize argumentFc                 S  s   | | j ddj dd S Nr"   rM   r   sumrm   rC   rC   rD   <lambda>      z_normalize.<locals>.<lambda>c                 S  s   | |    S r   r   r   rC   rC   rD   r     s    c                 S  s   | j | jddddS r   )divr   r   rC   rC   rD   r     s    )rI   r2   r1   rI   Tr   z not in pivoted DataFrame)r   r(   r"   rM   zNot a valid margins argument)r9   r)   r,   rR   rW   rd   r1   r2   ilocrt   r   r   r   r~   loc)rB   r   r(   r+   	axis_subserrnormalizersftable_indextable_columnslast_ind_or_colcolumn_marginindex_marginrC   rC   rD   r     sr   



3






r   r   r   c                 C  s   |d u r,g }t | D ]\}}t|tr|jd ur||j q
|| d|  q
|S t|t| kr8tdt|tsAt|}|S )N_z*arrays and names must have the same length)	enumerater9   r   rt   r<   rU   AssertionErrorr:   )arrsrL   r   rk   arrrC   rC   rD   r     s   
r   r   	list[str]r   ;tuple[dict[str, str], list[str], dict[str, str], list[str]]c                   s   dd }t | t |}|| ||B |B   fddt| D } fddt| D } fddt|D } fddt|D }||||fS )	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    fdd| D S )Nc                   s   h | ]}| vr|qS rC   rC   )r   rt   seenrC   rD   	<setcomp>L  r   z>_build_names_mapper.<locals>.get_duplicates.<locals>.<setcomp>)r   rK   rC   r   rD   get_duplicatesJ  s   z+_build_names_mapper.<locals>.get_duplicatesc                   $   i | ]\}}| v rd | |qS row_rC   r   rk   rt   	dup_namesrC   rD   
<dictcomp>Q  
    
z'_build_names_mapper.<locals>.<dictcomp>c                   &   g | ]\}}| v rd | n|qS r   rC   r   r   rC   rD   r   T      z'_build_names_mapper.<locals>.<listcomp>c                   r   col_rC   r   r   rC   rD   r   X  r   c                   r   r   rC   r   r   rC   rD   r   [  r   )r   intersectionr   )r   r   r   shared_namesr   r   r   r   rC   r   rD   r   ,  s    



r   )
NNNr$   NFTr%   FT)r&   r    r'   r   r(   r)   r*   r)   r+   r,   r-   r)   r.   r)   r/   r    )r&   r    r'   rE   r(   r)   r*   r)   r+   r,   r-   r)   r.   r)   r/   r    )Nr%   N)rB   rv   r&   r    r+   r,   )r%   )r&   r    r+   r,   )r+   r,   )rB   r    r+   r,   )NNN)
r&   r    r1   r   r2   r   r0   r   r/   r    )NNNNFr%   TF)r(   r)   r+   r,   r*   r)   r/   r    )rB   r    r(   r)   r/   r    )r   )r   r,   )r   r   r   r   r/   r   )?
__future__r   typingr   r   r   r   r   numpyr\   pandas._typingr   r	   r
   r   pandas.util._decoratorsr   r   r   pandas.util._exceptionsr   pandas.core.dtypes.castr   pandas.core.dtypes.commonr   r   r   r   pandas.core.dtypes.genericr   r   pandas.core.commoncorecommonr   pandas.core.framer   pandas.core.groupbyr   pandas.core.indexes.apir   r   r   pandas.core.reshape.concatr   pandas.core.reshape.utilr   pandas.core.seriesr   r   r    r!   r;   rf   r|   r   r   r8   r   r   r   r   r   rC   rC   rC   rD   <module>   s    
7 #PA&9 ,R