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venv/Lib/site-packages/pandas/io/sas/sasreader.py

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+"""
+Read SAS sas7bdat or xport files.
+"""
+
+from __future__ import annotations
+
+from abc import (
+    ABC,
+    abstractmethod,
+)
+from collections.abc import Iterator
+from typing import (
+    TYPE_CHECKING,
+    Self,
+    overload,
+)
+
+from pandas.util._decorators import set_module
+
+from pandas.io.common import stringify_path
+
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+    from types import TracebackType
+
+    from pandas._typing import (
+        CompressionOptions,
+        FilePath,
+        ReadBuffer,
+    )
+
+    from pandas import DataFrame
+
+
+@set_module("pandas.api.typing")
+class SASReader(Iterator["DataFrame"], ABC):
+    """
+    Abstract class for XportReader and SAS7BDATReader.
+    """
+
+    @abstractmethod
+    def read(self, nrows: int | None = None) -> DataFrame: ...
+
+    @abstractmethod
+    def close(self) -> None: ...
+
+    def __enter__(self) -> Self:
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        self.close()
+
+
+@overload
+def read_sas(
+    filepath_or_buffer: FilePath | ReadBuffer[bytes],
+    *,
+    format: str | None = ...,
+    index: Hashable | None = ...,
+    encoding: str | None = ...,
+    chunksize: int = ...,
+    iterator: bool = ...,
+    compression: CompressionOptions = ...,
+) -> SASReader: ...
+
+
+@overload
+def read_sas(
+    filepath_or_buffer: FilePath | ReadBuffer[bytes],
+    *,
+    format: str | None = ...,
+    index: Hashable | None = ...,
+    encoding: str | None = ...,
+    chunksize: None = ...,
+    iterator: bool = ...,
+    compression: CompressionOptions = ...,
+) -> DataFrame | SASReader: ...
+
+
+@set_module("pandas")
+def read_sas(
+    filepath_or_buffer: FilePath | ReadBuffer[bytes],
+    *,
+    format: str | None = None,
+    index: Hashable | None = None,
+    encoding: str | None = None,
+    chunksize: int | None = None,
+    iterator: bool = False,
+    compression: CompressionOptions = "infer",
+) -> DataFrame | SASReader:
+    """
+    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
+    """
+    if format is None:
+        buffer_error_msg = (
+            "If this is a buffer object rather "
+            "than a string name, you must specify a format string"
+        )
+        filepath_or_buffer = stringify_path(filepath_or_buffer)
+        if not isinstance(filepath_or_buffer, str):
+            raise ValueError(buffer_error_msg)
+        fname = filepath_or_buffer.lower()
+        if ".xpt" in fname:
+            format = "xport"
+        elif ".sas7bdat" in fname:
+            format = "sas7bdat"
+        else:
+            raise ValueError(
+                f"unable to infer format of SAS file from filename: {fname!r}"
+            )
+
+    reader: SASReader
+    if format.lower() == "xport":
+        from pandas.io.sas.sas_xport import XportReader
+
+        reader = XportReader(
+            filepath_or_buffer,
+            index=index,
+            encoding=encoding,
+            chunksize=chunksize,
+            compression=compression,
+        )
+    elif format.lower() == "sas7bdat":
+        from pandas.io.sas.sas7bdat import SAS7BDATReader
+
+        reader = SAS7BDATReader(
+            filepath_or_buffer,
+            index=index,
+            encoding=encoding,
+            chunksize=chunksize,
+            compression=compression,
+        )
+    else:
+        raise ValueError("unknown SAS format")
+
+    if iterator or chunksize:
+        return reader
+
+    with reader:
+        return reader.read()