197 lines
6 KiB
Python
197 lines
6 KiB
Python
"""
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Read SAS sas7bdat or xport files.
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"""
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from __future__ import annotations
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from abc import (
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ABC,
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abstractmethod,
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)
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from collections.abc import Iterator
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from typing import (
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TYPE_CHECKING,
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Self,
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overload,
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)
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from pandas.util._decorators import set_module
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from pandas.io.common import stringify_path
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if TYPE_CHECKING:
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from collections.abc import Hashable
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from types import TracebackType
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from pandas._typing import (
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CompressionOptions,
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FilePath,
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ReadBuffer,
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)
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from pandas import DataFrame
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@set_module("pandas.api.typing")
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class SASReader(Iterator["DataFrame"], ABC):
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"""
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Abstract class for XportReader and SAS7BDATReader.
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"""
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@abstractmethod
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def read(self, nrows: int | None = None) -> DataFrame: ...
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@abstractmethod
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def close(self) -> None: ...
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def __enter__(self) -> Self:
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return self
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def __exit__(
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self,
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exc_type: type[BaseException] | None,
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exc_value: BaseException | None,
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traceback: TracebackType | None,
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) -> None:
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self.close()
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@overload
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def read_sas(
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filepath_or_buffer: FilePath | ReadBuffer[bytes],
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*,
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format: str | None = ...,
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index: Hashable | None = ...,
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encoding: str | None = ...,
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chunksize: int = ...,
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iterator: bool = ...,
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compression: CompressionOptions = ...,
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) -> SASReader: ...
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@overload
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def read_sas(
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filepath_or_buffer: FilePath | ReadBuffer[bytes],
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*,
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format: str | None = ...,
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index: Hashable | None = ...,
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encoding: str | None = ...,
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chunksize: None = ...,
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iterator: bool = ...,
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compression: CompressionOptions = ...,
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) -> DataFrame | SASReader: ...
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@set_module("pandas")
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def read_sas(
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filepath_or_buffer: FilePath | ReadBuffer[bytes],
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*,
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format: str | None = None,
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index: Hashable | None = None,
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encoding: str | None = None,
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chunksize: int | None = None,
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iterator: bool = False,
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compression: CompressionOptions = "infer",
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) -> DataFrame | SASReader:
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"""
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Read SAS files stored as either XPORT or SAS7BDAT format files.
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Parameters
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----------
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filepath_or_buffer : str, path object, or file-like object
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String, path object (implementing ``os.PathLike[str]``), or file-like
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object implementing a binary ``read()`` function. The string could be
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a URL. Valid URL schemes include http, ftp, s3, and file. For file
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URLs, a host is expected. A local file could be:
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``file://localhost/path/to/table.sas7bdat``.
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format : str {'xport', 'sas7bdat'} or None
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If None, file format is inferred from file extension. If 'xport' or
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'sas7bdat', uses the corresponding format.
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index : identifier of index column, defaults to None
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Identifier of column that should be used as index of the DataFrame.
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encoding : str, default is None
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Encoding for text data. If None, text data are stored as raw bytes.
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chunksize : int
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Read file `chunksize` lines at a time, returns iterator.
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iterator : bool, defaults to False
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If True, returns an iterator for reading the file incrementally.
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compression : str or dict, default 'infer'
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For on-the-fly decompression of on-disk data. If 'infer' and
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'filepath_or_buffer' is path-like, then detect compression from the
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following extensions: '.gz', '.bz2', '.zip', '.xz', '.zst', '.tar',
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'.tar.gz', '.tar.xz' or '.tar.bz2' (otherwise no compression).
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Set to ``None`` for no decompression.
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Can also be a dict with key ``'method'`` set to one of {``'zip'``,
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``'gzip'``, ``'bz2'``, ``'zstd'``, ``'xz'``, ``'tar'``} and other
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key-value pairs are forwarded to ``zipfile.ZipFile``,
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``gzip.GzipFile``, ``bz2.BZ2File``, ``zstandard.ZstdCompressor``,
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``lzma.LZMAFile`` or ``tarfile.TarFile``, respectively.
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As an example, the following could be passed for faster compression
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and to create a reproducible gzip archive:
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``compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}``.
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Returns
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-------
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DataFrame, SAS7BDATReader, or XportReader
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DataFrame if iterator=False and chunksize=None, else SAS7BDATReader
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or XportReader, file format is inferred from file extension.
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See Also
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--------
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read_csv : Read a comma-separated values (csv) file into a DataFrame.
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read_excel : Read an Excel file into a pandas DataFrame.
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read_spss : Read an SPSS file into a pandas DataFrame.
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read_orc : Load an ORC object into a pandas DataFrame.
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read_feather : Load a feather-format object into a pandas DataFrame.
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Examples
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--------
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>>> df = pd.read_sas("sas_data.sas7bdat") # doctest: +SKIP
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"""
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if format is None:
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buffer_error_msg = (
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"If this is a buffer object rather "
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"than a string name, you must specify a format string"
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)
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filepath_or_buffer = stringify_path(filepath_or_buffer)
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if not isinstance(filepath_or_buffer, str):
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raise ValueError(buffer_error_msg)
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fname = filepath_or_buffer.lower()
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if ".xpt" in fname:
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format = "xport"
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elif ".sas7bdat" in fname:
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format = "sas7bdat"
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else:
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raise ValueError(
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f"unable to infer format of SAS file from filename: {fname!r}"
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)
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reader: SASReader
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if format.lower() == "xport":
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from pandas.io.sas.sas_xport import XportReader
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reader = XportReader(
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filepath_or_buffer,
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index=index,
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encoding=encoding,
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chunksize=chunksize,
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compression=compression,
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)
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elif format.lower() == "sas7bdat":
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from pandas.io.sas.sas7bdat import SAS7BDATReader
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reader = SAS7BDATReader(
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filepath_or_buffer,
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index=index,
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encoding=encoding,
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chunksize=chunksize,
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compression=compression,
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)
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else:
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raise ValueError("unknown SAS format")
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if iterator or chunksize:
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return reader
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with reader:
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return reader.read()
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