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from __future__ import annotations
from typing import TYPE_CHECKING
from pandas._libs import lib
from pandas.compat._optional import import_optional_dependency
from pandas.util._validators import check_dtype_backend
from pandas.core.dtypes.inference import is_list_like
from pandas.io.common import stringify_path
if TYPE_CHECKING:
from collections.abc import Sequence
from pathlib import Path
from pandas._typing import DtypeBackend
from pandas import DataFrame
def read_spss(
path: str | Path,
usecols: Sequence[str] | None = None,
convert_categoricals: bool = True,
dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
) -> DataFrame:
"""
Load an SPSS file from the file path, returning a DataFrame.
Parameters
----------
path : str or Path
File path.
usecols : list-like, optional
Return a subset of the columns. If None, return all columns.
convert_categoricals : bool, default is True
Convert categorical columns into pd.Categorical.
dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
Back-end data type applied to the resultant :class:`DataFrame`
(still experimental). Behaviour is as follows:
* ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
(default).
* ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
DataFrame.
.. versionadded:: 2.0
Returns
-------
DataFrame
Examples
--------
>>> df = pd.read_spss("spss_data.sav") # doctest: +SKIP
"""
pyreadstat = import_optional_dependency("pyreadstat")
check_dtype_backend(dtype_backend)
if usecols is not None:
if not is_list_like(usecols):
raise TypeError("usecols must be list-like.")
usecols = list(usecols) # pyreadstat requires a list
df, metadata = pyreadstat.read_sav(
stringify_path(path), usecols=usecols, apply_value_formats=convert_categoricals
)
df.attrs = metadata.__dict__
if dtype_backend is not lib.no_default:
df = df.convert_dtypes(dtype_backend=dtype_backend)
return df
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