bi]ddlmZddlZddlmcmZddlmZm Z m Z ddl m Z ddl mZddlmZddddddefd ZGd d e Zy) ) annotationsN) DataFrameSeriesnew_collection) Blockwisehas_known_categories)get_meta_library_Fc ttjtjfrtjf|||||||d|Sd} d} ttr6t j s t| tst| ttr|xjdk(jr t| jdk(jr t| jjdgj}ntfd|Ds t| tfd |Ds t| tt!|||||||S) a Convert categorical variable into dummy/indicator variables. Data must have category dtype to infer result's ``columns``. Parameters ---------- data : Series, or DataFrame For Series, the dtype must be categorical. For DataFrame, at least one column must be categorical. prefix : string, list of strings, or dict of strings, default None String to append DataFrame column names. Pass a list with length equal to the number of columns when calling get_dummies on a DataFrame. Alternatively, `prefix` can be a dictionary mapping column names to prefixes. prefix_sep : string, default '_' If appending prefix, separator/delimiter to use. Or pass a list or dictionary as with `prefix.` dummy_na : bool, default False Add a column to indicate NaNs, if False NaNs are ignored. columns : list-like, default None Column names in the DataFrame to be encoded. If `columns` is None then all the columns with `category` dtype will be converted. sparse : bool, default False Whether the dummy columns should be sparse or not. Returns SparseDataFrame if `data` is a Series or if all columns are included. Otherwise returns a DataFrame with some SparseBlocks. .. versionadded:: 0.18.2 drop_first : bool, default False Whether to get k-1 dummies out of k categorical levels by removing the first level. dtype : dtype, default bool Data type for new columns. Only a single dtype is allowed. .. versionadded:: 0.18.2 Returns ------- dummies : DataFrame Examples -------- Dask's version only works with Categorical data, as this is the only way to know the output shape without computing all the data. >>> import pandas as pd >>> import dask.dataframe as dd >>> s = dd.from_pandas(pd.Series(list('abca')), npartitions=2) >>> dd.get_dummies(s) Traceback (most recent call last): ... NotImplementedError: `get_dummies` with non-categorical dtypes is not supported... With categorical data: >>> s = dd.from_pandas(pd.Series(list('abca'), dtype='category'), npartitions=2) >>> dd.get_dummies(s) # doctest: +NORMALIZE_WHITESPACE Dask DataFrame Structure: a b c npartitions=2 0 bool bool bool 2 ... ... ... 3 ... ... ... Dask Name: operation, 2 expressions Expr=GetDummies(frame=df) >>> dd.get_dummies(s).compute() # doctest: +ELLIPSIS a b c 0 True False False 1 False True False 2 False False True 3 True False False See Also -------- pandas.get_dummies prefix prefix_sepdummy_nacolumnssparse drop_firstdtypez`get_dummies` with non-categorical dtypes is not supported. Please use `df.categorize()` beforehand to convert to categorical dtype.z`get_dummies` with unknown categories is not supported. Please use `column.cat.as_known()` or `df.categorize()` beforehand to ensure known categoriesobjectstringcategory)includec3NK|]}tj|ywN)methodsis_categorical_dtype.0cdatas \/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/dask/dataframe/dask_expr/_dummies.py zget_dummies..s Nw33DG<Ns"%c3:K|]}t|ywrrrs r!r"zget_dummies..sBQ'Q0Bs) isinstancepdrr get_dummiesrrNotImplementedErrorr dtypesany_meta select_dtypesrallr GetDummies) r rrrrrrrkwargs not_cat_msgunknown_cat_msgs ` r!r&r& sgv$BLL12~~   !!     ( $++D1%k2 2#D)%o6 6 D) $ ? x',,.)+66 x',,.)+66jj.. |.DLLGNgNN)+66B'BB%o6 6  &*hU  c@eZdZgdZddddddedZgdZedZy)r-)framerrrrrrrNr Fr )rrrc@t|j|g|i|Sr)r r&)dfargsr.s r! operationzGetDummies.operations%/#//DTDVDDr1) __name__ __module__ __qualname__ _parametersbool _defaults _keyword_only staticmethodr7r1r!r-r-sB KI6MEEr1r-) __future__rpandasr%dask.dataframe.methods dataframer$dask.dataframe.dask_expr._collectionrrrdask.dataframe.dask_expr._exprrdask.dataframe.utilsr dask.utilsr r<r&r-r@r1r!rIsL"((RR45'     L^EEr1