MhSSKJr SSKJr SSKJr SSKJr SSKJ r SSK J r SSK J r \(aSSKJr SS KJr SS KJr SS KJr S S \R,4SSjjrg )) annotations) TYPE_CHECKING)lib)import_optional_dependency)check_dtype_backend) is_list_like)stringify_path)Sequence)Path) DtypeBackend) DataFrameNTc [S5n[U5 Ub&[U5(d [S5e[ U5nUR [ U5XS9upVURUlU[RLaURUS9nU$)a1 Load an SPSS file from the file path, returning a DataFrame. Parameters ---------- path : str or Path File path. usecols : list-like, optional Return a subset of the columns. If None, return all columns. convert_categoricals : bool, default is True Convert categorical columns into pd.Categorical. dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable' Back-end data type applied to the resultant :class:`DataFrame` (still experimental). Behaviour is as follows: * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame` (default). * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype` DataFrame. .. versionadded:: 2.0 Returns ------- DataFrame Examples -------- >>> df = pd.read_spss("spss_data.sav") # doctest: +SKIP pyreadstatzusecols must be list-like.)usecolsapply_value_formats) dtype_backend) rrr TypeErrorlistread_savr __dict__attrsr no_defaultconvert_dtypes)pathrconvert_categoricalsrrdfmetadatas @/var/www/html/env/lib/python3.13/site-packages/pandas/io/spss.py read_spssrsH,L9J &G$$89 9w-&&tg'LB  BHCNN*   ]  ; I) rz str | PathrzSequence[str] | NonerboolrzDtypeBackend | lib.NoDefaultreturnr ) __future__rtypingr pandas._libsrpandas.compat._optionalrpandas.util._validatorsrpandas.core.dtypes.inferencerpandas.io.commonr collections.abcr pathlibr pandas._typingr pandasr rrr rr/sk" >75+(+ %)!%25.. 2 2 !220 2  2r