95 lines
2.9 KiB
Python
95 lines
2.9 KiB
Python
from __future__ import annotations
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from typing import (
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TYPE_CHECKING,
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Any,
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)
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from pandas._libs import lib
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from pandas.compat._optional import import_optional_dependency
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from pandas.util._decorators import set_module
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from pandas.util._validators import check_dtype_backend
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from pandas.core.dtypes.inference import is_list_like
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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 Sequence
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from pathlib import Path
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from pandas._typing import DtypeBackend
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from pandas import DataFrame
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@set_module("pandas")
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def read_spss(
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path: str | Path,
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usecols: Sequence[str] | None = None,
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convert_categoricals: bool = True,
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dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
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**kwargs: Any,
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) -> DataFrame:
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"""
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Load an SPSS file from the file path, returning a DataFrame.
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Parameters
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----------
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path : str or Path
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File path.
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usecols : list-like, optional
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Return a subset of the columns. If None, return all columns.
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convert_categoricals : bool, default is True
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Convert categorical columns into pd.Categorical.
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dtype_backend : {'numpy_nullable', 'pyarrow'}
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Back-end data type applied to the resultant :class:`DataFrame`
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(still experimental). If not specified, the default behavior
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is to not use nullable data types. If specified, the behavior
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is as follows:
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* ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
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* ``"pyarrow"``: returns pyarrow-backed
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nullable :class:`ArrowDtype` :class:`DataFrame`
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.. versionadded:: 2.0
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**kwargs
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Additional keyword arguments that can be passed to :func:`pyreadstat.read_sav`.
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.. versionadded:: 3.0
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Returns
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-------
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DataFrame
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DataFrame based on the SPSS file.
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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 pandas DataFrame.
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read_excel : Read an Excel file into a pandas DataFrame.
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read_sas : Read an SAS 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_spss("spss_data.sav") # doctest: +SKIP
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"""
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pyreadstat = import_optional_dependency("pyreadstat")
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check_dtype_backend(dtype_backend)
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if usecols is not None:
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if not is_list_like(usecols):
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raise TypeError("usecols must be list-like.")
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usecols = list(usecols) # pyreadstat requires a list
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df, metadata = pyreadstat.read_sav(
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stringify_path(path),
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usecols=usecols,
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apply_value_formats=convert_categoricals,
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**kwargs,
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)
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df.attrs = metadata.__dict__
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if dtype_backend is not lib.no_default:
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df = df.convert_dtypes(dtype_backend=dtype_backend)
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return df
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