from __future__ import annotations
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from pathlib import Path
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from typing import (
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TYPE_CHECKING,
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Sequence,
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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._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 pandas._typing import DtypeBackend
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from pandas import DataFrame
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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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) -> 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"}, defaults to NumPy backed DataFrames
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Which dtype_backend to use, e.g. whether a DataFrame should have NumPy
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arrays, nullable dtypes are used for all dtypes that have a nullable
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implementation when "numpy_nullable" is set, pyarrow is used for all
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dtypes if "pyarrow" is set.
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The dtype_backends are still experimential.
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.. versionadded:: 2.0
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Returns
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-------
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DataFrame
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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, _ = pyreadstat.read_sav(
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stringify_path(path), usecols=usecols, apply_value_formats=convert_categoricals
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)
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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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