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    _~#g                     @   s   d dl Zd dlZd dlmZmZmZmZ d dlm	Z	m
Z
 ejddgddd Zejddgdd	d
 Zejddgddd Zejddgddd Zejdd Zejdd Zejdd Zejdd Ze dd Ze dd Zejee	ddd Zejee
ddd Zejee	ee
 ddd  Zejddgdd!d" Zejdgdd#d$ Zejdgdd%d& Zejd'i fd(d)d*ifd(d)d ifd+d)d*ifd+d)d ifd,i fd-i fd.i fd,d/d0ifd-d/d0ifd.d/d0ifgg d1d2d3d4 ZdS )5    N)	DataFrameIndexSeries
date_range)reduction_kernelstransformation_kernelsTF)paramsc                 C      | j S Nparamrequest r   /var/www/static.ux5.de/https/Moving-Object-Detection-with-OpenCV/env/lib/python3.10/site-packages/pandas/tests/groupby/conftest.pysort      r   c                 C   r	   r
   r   r   r   r   r   as_index   r   r   c                 C   r	   r
   r   r   r   r   r   dropna   r   r   c                 C   r	   r
   r   r   r   r   r   observed   r   r   c                   C   s6   t g dg dtjddtjdddS )N)foobarr   r   r   r   r   r   )oner   twothreer   r   r   r         )ABCDr   nprandomdefault_rngstandard_normalr   r   r   r   df$   s   r&   c                   C   s$   t tjddtdddddS )Nr      
2000-01-01r   periodsfreq)index)r   r"   r#   r$   r%   r   r   r   r   r   ts0   s   r-   c                   C   s2   t tjddttdtdtddddd	S )
Nr   )r'      ABCD)dtyper(   r'   r   r)   )columnsr,   )	r   r"   r#   r$   r%   r   listobjectr   r   r   r   r   tsframe8   s
   r4   c                	   C   sL   t g dg dg dtjddtjddtjdddS )N)r   r   r   r   r   r   r   r   r   r   r   )r   r   r   r   r   r   r   r   r   r   r   )dullr5   shinyr5   r5   r6   r6   r5   r6   r6   r6   r      )r   r   r   r    EFr!   r   r   r   r   three_groupA   s   r:   c               	   C   sN   g dg dg dg dg dg dg dg dg} t | g d	d
}|dS )N)r   aa0_at_0)   bb0_at_1)r   r;   a1_at_2)   r>   b1_at_3)r.   cc0_at_4)   r;   a2_at_5)   r;   a3_at_6)   r;   a4_at_7)r   GroupValue)r1   r   )r   	set_index)datar&   r   r   r   slice_test_dfs   s   

rO   c                 C   s   | j dddS )NrK   F)r   )groupby)rO   r   r   r   slice_test_grouped   s   rQ   c                 C   r	   )zT
    yields the string names of all groupby reduction functions, one at a time.
    r   r   r   r   r   reduction_func   s   rR   c                 C   r	   )z@yields the string names of all groupby transformation functions.r   r   r   r   r   transformation_func      rS   c                 C   r	   )z5yields both aggregation and transformation functions.r   r   r   r   r   groupby_func   rT   rU   c                 C   r	   )z'parallel keyword argument for numba.jitr   r   r   r   r   parallel   rT   rV   c                 C   r	   )z$nogil keyword argument for numba.jitr   r   r   r   r   nogil   rT   rW   c                 C   r	   )z'nopython keyword argument for numba.jitr   r   r   r   r   nopython   rT   rX   meanvarddofr=   stdsumminmax	min_countr   )rY   var_1var_0std_1std_0r]   r^   r_   zsum-min_countzmin-min_countzmax-min_count)r   idsc                 C   r	   )z(reductions supported with engine='numba'r   r   r   r   r   numba_supported_reductions   s   rf   )numpyr"   pytestpandasr   r   r   r   pandas.core.groupby.baser   r   fixturer   r   r   r   r&   r-   r4   r:   rO   rQ   sortedrR   rS   rU   rV   rW   rX   rf   r   r   r   r   <module>   sh    

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



1





	



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

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