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@@ -2,7 +2,7 @@ import math
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import pathlib as pl
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import random
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import re
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-from typing import Callable, Dict, Iterator, List, Tuple
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+from typing import Callable, Dict, Iterator, List, Tuple, cast
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import nibabel as nib
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import pandas as pd
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@@ -47,9 +47,21 @@ def xls_pre(df: pd.DataFrame) -> pd.DataFrame:
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# Work on an explicit copy so column assignment writes back reliably instead
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# of raising SettingWithCopyWarning against a view of ``df``.
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- data = df[["Image Data ID", "Sex", "Age (current)"]].copy()
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- data["Sex"] = data["Sex"].str.strip()
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- data = data.replace({"M": 0, "F": 1})
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+ #
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+ # The ``cast``s are needed because pandas ships no type stubs: basedpyright
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+ # widens ``.copy()``/``.replace()``/``__getitem__`` to
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+ # ``DataFrame | Series | Unknown``, which then has no ``.str`` accessor and
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+ # isn't assignable to the declared return type. ``cast`` asserts the concrete
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+ # pandas type at each step without any runtime effect.
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+ data = cast(pd.DataFrame, df[["Image Data ID", "Sex", "Age (current)"]].copy())
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+
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+ # ``.str`` is pandas' vectorized string accessor: it applies a str method
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+ # element-wise over every value in the Series. Here it strips surrounding
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+ # whitespace from each Sex entry (e.g. " M " -> "M") before encoding.
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+ sex_col = cast(pd.Series, data["Sex"])
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+ data["Sex"] = sex_col.str.strip()
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+
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+ data = cast(pd.DataFrame, data.replace({"M": 0, "F": 1}))
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return data
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Nicholas Schense