+ NV-j. Rt^RIHt^RIHtHt^RIHt^RIH t H t H t ^RI H t ^RIHt] 'd^RIHt^RIHt^R IHtHtHt^R IHt] !R 4!R R ]R,]44t] RRRRRRRRRRRR/RRll4t] RRRRRRRRRRRR/RRll4t] !R4RRRRRRRRRRRR/RRll4tR#) z# Read SAS sas7bdat or xport files. ) annotations)ABCabstractmethod)Iterator) TYPE_CHECKINGSelfoverload) set_module)stringify_path)Hashable) TracebackType)CompressionOptionsFilePath ReadBuffer) DataFramezpandas.api.typingcb]tRt^#tRt]R RRll4t]RRl4tRRltR R lt R t R#) SASReaderz4 Abstract class for XportReader and SAS7BDATReader. Nc V^8dQhRRRR/#)nrows int | Nonereturnr)formats"h/Volumes/fast/ai/experiments/ui-tars-smoke/.venv/lib/python3.14/site-packages/pandas/io/sas/sasreader.py __annotate__SASReader.__annotate__*s>>*> >c R#Nr)selfrs&&rreadSASReader.read)s;>rcV^8dQhRR/#)rrNoner)rs"rrr-s   t rc R#rrr s&rcloseSASReader.close,s rcV^8dQhRR/#)rrrr)rs"rrr/s4rc V#rrr&s&r __enter__SASReader.__enter__/s rc(V^8dQhRRRRRRRR/#) rexc_typeztype[BaseException] | None exc_valuezBaseException | None tracebackzTracebackType | Nonerr$r)rs"rrr2s2,((   rc &VP4R#r)r')r r.r/r0s&&&&r__exit__SASReader.__exit__2s rrr) __name__ __module__ __qualname____firstlineno____doc__rr!r'r+r2__static_attributes__rrrrr#s4>>  rrrr.indexencoding chunksizeiterator compressionc8V^8dQhRRRRRRRRRR R R R R RR/#)rfilepath_or_bufferFilePath | ReadBuffer[bytes]r str | Noner:Hashable | Noner;r<intr=boolr>r rrr)rs"rrr<sZ  4          $  rcR#rrr@rr:r;r<r=r>s&$$$$$$rread_sasrH;src8V^8dQhRRRRRRRRRR R R R R RR/#)rr@rArrBr:rCr;r<r$r=rEr>r rDataFrame | SASReaderr)rs"rrrIsZ   4              $    rcR#rrrGs&$$$$$$rrHrHHs rpandasNFinferc8V^8dQhRRRRRRRRRR R R R R RR/#)rr@rArrBr:rCr;r<rr=rEr>r rrJr)rs"rrrVsdoo4o o  o  o  oo$oorcVfbRp\V4p\V\4'g \V4hVP 4pRV9dRpMRV9dRpM\RV: 24hVP 4R8Xd^RIHp V !VVVVVR 7p M4VP 4R8Xd^R IHp V !VVVVVR 7p M \R 4hV'g V'dV #V ;_uu_4V P4uuRRR4# +'giR#;i) a Read SAS files stored as either XPORT or SAS7BDAT format files. 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``. 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, default is None Encoding for text data. If None, text data are stored as raw bytes. 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: ) XportReader)r:r;r<r>)SAS7BDATReaderzunknown SAS format) r isinstancestr ValueErrorlowerpandas.io.sas.sas_xportrRpandas.io.sas.sas7bdatrSr!) r@rr:r;r<r=r>buffer_error_msgfnamerRreaderrSs &$$$$$$ rrHrHUsD~ C ,,>?,c22-. ."((* U?F E !FDUIN  ||~ 7 #   : %9 #  -..9 {{} s C:: D )r8 __future__rabcrrcollections.abcrtypingrrrpandas.util._decoratorsr pandas.io.commonr r typesr pandas._typingr rrrLrrrHrrrresh#% /+(# !  %s!.   !      '*        !        '*      Hoo" o  o ! oo'.oor