ENH: Add future.python_scalars #63016
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Plan to run a full set of ASVs next week, some microbenchmarks from pandas.core.dtypes.cast import maybe_unbox_numpy_scalar
with pd.option_context("python_scalars", True):
%timeit maybe_unbox_numpy_scalar(np.int64(2))
# 828 ns ± 9.91 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
%timeit maybe_unbox_numpy_scalar(2)
# 161 ns ± 0.414 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)
ser = pd.Series([1, 2, 3] * 10_000)
with pd.option_context("python_scalars", True):
%timeit ser.sum()
# 9.42 μs ± 423 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
with pd.option_context("python_scalars", False):
%timeit ser.sum()
# 8.28 μs ± 137 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each) |
1 task
Dr-Irv
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Nov 10, 2025
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Full ASVs are below, only showing where there was a 10% of more regression. In the full list, only the following two actually hit the function Full listI was curious why only min/max showed up as being regressions in series_methods.NanOps |
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doc/source/whatsnew/vX.X.X.rstfile if fixing a bug or adding a new feature.Adds an experimental option to return Python scalars instead of NumPy scalars across the API. This is not yet fully implemented everywhere, e.g.
Series.__getitem__, but I'm hoping reductions are a substantial chunk.This is complicated by #62988 where it was found that many of our doctests are not running. We run those doctests using NumPy>=2, and if we were to get those doctests to pass as-is, we would need to change the NumPy reprs from e.g.
2tonp.int64(2). If we then change reductions et al to returning Python scalars, we'd then change all the reprs back from e.g.np.int64(2)to2. So instead I think we can:future.python_scalarstoTruein 4.0, deprecate the future option.