pandas.read_sas#

pandas.read_sas(filepath_or_buffer, *, format=None, index=None, encoding=None, chunksize=None, iterator=False, compression='infer')[source]#

Read SAS files stored as either XPORT or SAS7BDAT format files.

Parameters:

filepath_or_bufferstr, 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.
formatstr {‘xport’, ‘sas7bdat’} or None: If None, file format is inferred from file extension. If ‘xport’ or ‘sas7bdat’, uses the corresponding format.
indexidentifier of index column, defaults to None: Identifier of column that should be used as index of the DataFrame.
encodingstr, default is None: Encoding for text data. If None, text data are stored as raw bytes.
chunksizeint: Read file chunksize lines at a time, returns iterator.
iteratorbool, defaults to False: If True, returns an iterator for reading the file incrementally.
compressionstr 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). If using ‘zip’ or ‘tar’, the ZIP file must contain only one data file to be read in. 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.ZstdDecompressor, lzma.LZMAFile or tarfile.TarFile, respectively. As an example, the following could be passed for Zstandard decompression using a custom compression dictionary: compression={'method': 'zstd', 'dict_data': my_compression_dict}.

Added in version 1.5.0: Added support for .tar files.

Returns:

DataFrame if iterator=False and chunksize=None, else SAS7BDATReader
or XportReader

Examples

>>> df = pd.read_sas("sas_data.sas7bdat")  