+ NV-j^RIHt^RIHt^RIt^RIHt^RIH t H t ^RI H t ]'d^RI Ht^RIHt^R IHtHtRR R lltR R ltRRltR#)) annotations) TYPE_CHECKINGN)remove_na_arraylike) MultiIndexconcat)unpack_single_str_list)Hashable) IndexLabel) DataFrameSeriesc$V^8dQhRRRRRR/#)datar kindstrreturnz"dict[Hashable, DataFrame | Series])formats"t/Volumes/fast/ai/experiments/ui-tars-smoke/.venv/lib/python3.14/site-packages/pandas/plotting/_matplotlib/groupby.py __annotate__rs$99 99'9c  VR8Xd^pM^p\VP\4'gQhVPPV,Uu/uF4pW0PRVPP V4V8H3,bK6 up#uupi)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} hist:NNN) isinstancecolumnsrlevelslocget_level_values)rrlevelcols&& rcreate_iter_data_given_byr!s\ v~ dllJ / // /<<&&u- -C XXa66u=DD EE-  s:B c(V^8dQhRRRRRRRR/#)rrr byr colsrr)rs"rrrWs,++ +#++5++rc\V4pVPV4p.pVF=wrg\P!V.V.4pWr,p WnVP V 4K? \ V^R7pV#)a 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 )axis)rgroupbyr from_productrappendr) rr#r$ by_modifiedgrouped data_listkeygroupr sub_groups &&& rreconstruct_data_with_byr0Wss<),Kll;'GI ))C5$-8K ##  )! $D Krc$V^8dQhRRRRRR/#)ryz np.ndarrayr#zIndexLabel | Nonerr)rs"rrrs""" "0A"j"rcVe^\VP4^8dD\P!VPUu.uFp\ V4NK up4P#\ V4#uupi)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. )lenshapenparrayTr)r2r#r s&& rreformat_hist_y_given_byr9sW  ~#agg,*xxQSSASc,S1SABDDD q !!BsA-)r) __future__rtypingrnumpyr6pandas.core.dtypes.missingrpandasrr pandas.plotting._matplotlib.miscrcollections.abcr pandas._typingr r r r!r0r9rrrrBs;" : D() 9x+\"r