ujdZddlmZddlmZmZddlmZddlm Z m Z m Z ddl m Z ddlmZddlmZe rdd lmZdd lmZdd lmZmZmZdd lmZed GddedeZe ddddddd ddZe ddddddd ddZeddde j:dddd ddZy)z# Read SAS sas7bdat or xport files. ) annotations)ABCabstractmethod)Iterator) TYPE_CHECKINGSelfoverload)lib) set_module)stringify_path)Hashable) TracebackType)CompressionOptionsFilePath ReadBuffer) DataFramezpandas.api.typingcVeZdZdZedddZed dZd dZ d dZy) SASReaderz< Abstract class for XportReader and SAS7BDATReader. NcyN)selfnrowss b/var/www/html/python-recuperauto-other/env/lib/python3.12/site-packages/pandas/io/sas/sasreader.pyreadzSASReader.read*s;>cyrrrs rclosezSASReader.close-s rc|Srrrs r __enter__zSASReader.__enter__0s rc$|jyr)r)rexc_type exc_value tracebacks r__exit__zSASReader.__exit__3s rr)r int | Nonereturnr)r(None)r(r)r#ztype[BaseException] | Noner$zBaseException | Noner%zTracebackType | Noner(r)) __name__ __module__ __qualname____doc__rrrr!r&rrrrr$sU>>  ,((   rrr.)formatindexencoding chunksizeiterator compressioncyrrfilepath_or_bufferr.r/r0r1r2r3s rread_sasr7<srcyrrr5s rr7r7Is rpandasNFinferc|Td}t|}t|ts t||j }d|vrd}nd|vrd}ntd||j dk(rdd lm} | ||||| } n2|j dk(rdd lm} | ||||| } n td |s|r| S| 5| jcdddS#1swYyxYw) a Read SAS files stored as either XPORT or SAS7BDAT format files. The file format is inferred from the file extension if not explicitly specified. Supports both full reads and chunked iteration. Parameters ---------- filepath_or_buffer : str, path object, or file-like object String, path object (implementing ``os.PathLike[str]``), or file-like object implementing a binary ``read()`` function. The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. A local file could be: ``file://localhost/path/to/table.sas7bdat``. Certain URL schemes may require additional packages. For example, S3 URLs require the ``s3fs`` library. See :ref:`install.optional_dependencies` for a full list. format : str {'xport', 'sas7bdat'} or None If None, file format is inferred from file extension. If 'xport' or 'sas7bdat', uses the corresponding format. index : identifier of index column, defaults to None Identifier of column that should be used as index of the DataFrame. encoding : str, 'infer' or None, default is None Encoding for text data. If None, text data are stored as raw bytes. ``'infer'`` decodes SAS7BDAT files using the encoding recorded in the file header, falling back to latin-1 if the header does not name one pandas recognizes; XPORT files record no encoding, so ``'infer'`` means ISO-8859-1 there. .. deprecated:: 3.1.0 The default will change from ``None`` to ``'infer'``, so text data will be decoded rather than returned as bytes. Pass ``None`` explicitly to keep the current behavior. chunksize : int Read file `chunksize` lines at a time, returns iterator. iterator : bool, defaults to False If True, returns an iterator for reading the file incrementally. compression : str or dict, default 'infer' For on-the-fly decompression of on-disk data. If 'infer' and 'filepath_or_buffer' is path-like, then detect compression from the following extensions: '.gz', '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2' (otherwise no compression). Set to ``None`` for no decompression. Can also be a dict with key ``'method'`` set to one of {``'zip'``, ``'gzip'``, ``'bz2'``, ``'zstd'``, ``'xz'``, ``'tar'``} and other key-value pairs are forwarded to ``zipfile.ZipFile``, ``gzip.GzipFile``, ``bz2.BZ2File``, ``zstandard.ZstdCompressor``, ``lzma.LZMAFile`` or ``tarfile.TarFile``, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive: ``compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}``. Returns ------- DataFrame, SAS7BDATReader, or XportReader DataFrame if iterator=False and chunksize=None, else SAS7BDATReader or XportReader, file format is inferred from file extension. See Also -------- read_csv : Read a comma-separated values (csv) file into a DataFrame. read_excel : Read an Excel file into a pandas DataFrame. read_spss : Read an SPSS file into a pandas DataFrame. read_orc : Load an ORC object into a pandas DataFrame. read_feather : Load a feather-format object into a pandas DataFrame. Examples -------- >>> df = pd.read_sas("sas_data.sas7bdat") # doctest: +SKIP NzVIf this is a buffer object rather than a string name, you must specify a format stringz.xptxportz .sas7bdatsas7bdatz2unable to infer format of SAS file from filename: r) XportReader)r/r0r1r3)SAS7BDATReaderzunknown SAS format) r isinstancestr ValueErrorlowerpandas.io.sas.sas_xportr>pandas.io.sas.sas7bdatr?r) r6r.r/r0r1r2r3buffer_error_msgfnamer>readerr?s rr7r7Vs d~ C ,,>?,c2-. ."((* U?F E !FDUIN  ||~ 7 #   : %9 #  -..9 {{}s 8CC)r6FilePath | ReadBuffer[bytes]r. str | Noner/Hashable | Noner0str | lib.NoDefault | Noner1intr2boolr3rr(r)r6rIr.rJr/rKr0rLr1r)r2rNr3rr(DataFrame | SASReader)r6rIr.rJr/rKr0rLr1r'r2rNr3rr(rO)r- __future__rabcrrcollections.abcrtypingrrr pandas._libsr pandas.util._decoratorsr pandas.io.commonr r typesrpandas._typingrrrr9rrr7 no_defaultrrrrZs#% .+(# !  %s!.  +.&) 4      )    $      +.&)  4        )      $        H!+.>> &-4    )   $r