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Current File : C:/Python312/Lib/site-packages/pandas/plotting/_matplotlib/__pycache__/groupby.cpython-312.pyc
�

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�Z								dd�Zdd�Zy)�)�annotations)�
TYPE_CHECKINGN)�remove_na_arraylike)�
MultiIndex�concat)�unpack_single_str_list)�Hashable)�
IndexLabel)�	DataFrame�Seriesc	��|dk(rd}nd}t|jt�sJ�|jj|D�cic]2}||jdd�|jj|�|k(f��4c}Scc}w)a~
    Create data for iteration given `by` is assigned or not, and it is only
    used in both hist and boxplot.

    If `by` is assigned, return a dictionary of DataFrames in which the key of
    dictionary is the values in groups.
    If `by` is not assigned, return input as is, and this preserves current
    status of iter_data.

    Parameters
    ----------
    data : reformatted grouped data from `_compute_plot_data` method.
    kind : str, plot kind. This function is only used for `hist` and `box` plots.

    Returns
    -------
    iter_data : DataFrame or Dictionary of DataFrames

    Examples
    --------
    If `by` is assigned:

    >>> import numpy as np
    >>> tuples = [('h1', 'a'), ('h1', 'b'), ('h2', 'a'), ('h2', 'b')]
    >>> mi = pd.MultiIndex.from_tuples(tuples)
    >>> value = [[1, 3, np.nan, np.nan],
    ...          [3, 4, np.nan, np.nan], [np.nan, np.nan, 5, 6]]
    >>> data = pd.DataFrame(value, columns=mi)
    >>> create_iter_data_given_by(data)
    {'h1':     h1
         a    b
    0  1.0  3.0
    1  3.0  4.0
    2  NaN  NaN, 'h2':     h2
         a    b
    0  NaN  NaN
    1  NaN  NaN
    2  5.0  6.0}
    �histr�N)�
isinstance�columnsr�levels�loc�get_level_values)�data�kind�level�cols    �EC:\Python312\Lib\site-packages\pandas/plotting/_matplotlib/groupby.py�create_iter_data_given_byrs���^�v�~������d�l�l�J�/�/�/��<�<�&�&�u�-���	�T�X�X�a����6�6�u�=��D�D�
E�E����s�7A<c���t|�}|j|�}g}|D]:\}}tj|g|g�}||}	||	_|j|	��<t
|d��}|S)al
    Internal function to group data, and reassign multiindex column names onto the
    result in order to let grouped data be used in _compute_plot_data method.

    Parameters
    ----------
    data : Original DataFrame to plot
    by : grouped `by` parameter selected by users
    cols : columns of data set (excluding columns used in `by`)

    Returns
    -------
    Output is the reconstructed DataFrame with MultiIndex columns. The first level
    of MI is unique values of groups, and second level of MI is the columns
    selected by users.

    Examples
    --------
    >>> d = {'h': ['h1', 'h1', 'h2'], 'a': [1, 3, 5], 'b': [3, 4, 6]}
    >>> df = pd.DataFrame(d)
    >>> reconstruct_data_with_by(df, by='h', cols=['a', 'b'])
       h1      h2
       a     b     a     b
    0  1.0   3.0   NaN   NaN
    1  3.0   4.0   NaN   NaN
    2  NaN   NaN   5.0   6.0
    r)�axis)r�groupbyr�from_productr�appendr)
r�by�cols�by_modified�grouped�	data_list�key�groupr�	sub_groups
          r�reconstruct_data_with_byr(Xs~��<)��,�K��l�l�;�'�G��I��$�
��U��)�)�C�5�$�-�8���$�K�	�#�	������#�
$��)�!�$�D��K�c���|�Xt|j�dkDr@tj|jD�cgc]
}t|���c}�jSt|�Scc}w)z�Internal function to reformat y given `by` is applied or not for hist plot.

    If by is None, input y is 1-d with NaN removed; and if by is not None, groupby
    will take place and input y is multi-dimensional array.
    r)�len�shape�np�array�Tr)�yr rs   r�reformat_hist_y_given_byr1�sS��
�~�#�a�g�g�,��*��x�x�Q�S�S�A�c�,�S�1�A�B�D�D�D��q�!�!��Bs�A&)r)rrr�str�returnz"dict[Hashable, DataFrame | Series])rrr r
r!r
r3r)r0�
np.ndarrayr zIndexLabel | Noner3r4)�
__future__r�typingr�numpyr-�pandas.core.dtypes.missingr�pandasrr� pandas.plotting._matplotlib.miscr�collections.abcr	�pandas._typingr
rrrr(r1�r)r�<module>r>sx��"� ��:��
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�:��:�'�:�z+�
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