| 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) |
|
|
| 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 |
|
|
