a oEbr@sddlmZddlZddlmZmZddlmZddl m Z m Z m Z m Z dddd d d d Zdd d ddddZddddddZdS)) annotationsN)Dict IndexLabelremove_na_arraylike) DataFrame MultiIndexSeriesconcathistrstrzDict[str, DataFrame | Series])datakindreturncs>|dkrdndtjts"JfddjjDS)ax 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 = MultiIndex.from_tuples(tuples) >>> value = [[1, 3, np.nan, np.nan], ... [3, 4, np.nan, np.nan], [np.nan, np.nan, 5, 6]] >>> data = 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} r rcs,i|]$}|jddj|kfqS)N)loccolumnsZget_level_values.0colr levelc/home/ja/django-apps/lartica_env/lib/python3.9/site-packages/pandas/plotting/_matplotlib/groupby.py Ksz-create_iter_data_given_by..) isinstancerrZlevels)r rrrrcreate_iter_data_given_bys/  rr)r bycolsrc CsT||}g}|D]0\}}t|g|g}||}||_||qt|dd}|S)ai Internal function to group data, and reassign multiindex column names onto the result in order to let grouped data be used in _compute_plot_data method. Parameters ---------- data : Original DataFrame to plot by : grouped `by` parameter selected by users cols : columns of data set (excluding columns used in `by`) Returns ------- Output is the reconstructed DataFrame with MultiIndex columns. The first level of MI is unique values of groups, and second level of MI is the columns selected by users. Examples -------- >>> d = {'h': ['h1', 'h1', 'h2'], 'a': [1, 3, 5], 'b': [3, 4, 6]} >>> df = DataFrame(d) >>> reconstruct_data_with_by(df, by='h', cols=['a', 'b']) h1 h2 a b a b 0 1.0 3.0 NaN NaN 1 3.0 4.0 NaN NaN 2 NaN NaN 5.0 6.0 r)Zaxis)groupbyrZ from_productrappendr ) r rrgroupedZ data_listkeygrouprZ sub_grouprrrreconstruct_data_with_byQs    r$zSeries | np.ndarrayzIndexLabel | None)yrrcCs6|dur.t|jdkr.tdd|jDjSt|S)zInternal 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. NrcSsg|] }t|qSrrrrrr z,reformat_hist_y_given_by..)lenshapenparrayTr)r%rrrrreformat_hist_y_given_by|sr-)r ) __future__rnumpyr*Zpandas._typingrrZpandas.core.dtypes.missingrZpandasrrr r rr$r-rrrrs   =+