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Partly fix #1493: error message changed #1515

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Partly fix #1493: error message changed #1515

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72e18cb
Partly fix #1493: error message changed
thevro Dec 5, 2024
a572614
Add test to reveal scalar index error
Dec 6, 2024
91b31fa
Fix #1493 by checking for numpy scalars
thevro Dec 8, 2024
7a5aa74
Correct docstring for is_numpy_scalar
Dec 10, 2024
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3 changes: 2 additions & 1 deletion narwhals/_pandas_like/series.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,7 @@
from narwhals._pandas_like.utils import select_columns_by_name
from narwhals._pandas_like.utils import set_axis
from narwhals._pandas_like.utils import to_datetime
from narwhals.dependencies import is_numpy_scalar
from narwhals.utils import Implementation
from narwhals.utils import import_dtypes_module

Expand Down Expand Up @@ -112,7 +113,7 @@ def __getitem__(self, idx: int) -> Any: ...
def __getitem__(self, idx: slice | Sequence[int]) -> Self: ...

def __getitem__(self, idx: int | slice | Sequence[int]) -> Any | Self:
if isinstance(idx, int):
if isinstance(idx, int) or is_numpy_scalar(idx):
return self._native_series.iloc[idx]
return self._from_native_series(self._native_series.iloc[idx])

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5 changes: 5 additions & 0 deletions narwhals/dependencies.py
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Expand Up @@ -220,6 +220,11 @@ def is_numpy_array(arr: Any) -> TypeGuard[np.ndarray]:
return (np := get_numpy()) is not None and isinstance(arr, np.ndarray)


def is_numpy_scalar(scalar: Any) -> TypeGuard[np.generic]:
"""Check whether `scalar` is a NumPy Scalar without importing NumPy."""
return (np := get_numpy()) is not None and np.isscalar(scalar)


def is_pandas_like_dataframe(df: Any) -> bool:
"""Check whether `df` is a pandas-like DataFrame without doing any imports.

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3 changes: 2 additions & 1 deletion narwhals/series.py
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Expand Up @@ -11,6 +11,7 @@
from typing import TypeVar
from typing import overload

from narwhals.dependencies import is_numpy_scalar
from narwhals.dtypes import _validate_dtype
from narwhals.typing import IntoSeriesT
from narwhals.utils import _validate_rolling_arguments
Expand Down Expand Up @@ -67,7 +68,7 @@ def __getitem__(self: Self, idx: int) -> Any: ...
def __getitem__(self: Self, idx: slice | Sequence[int]) -> Self: ...

def __getitem__(self: Self, idx: int | slice | Sequence[int]) -> Any | Self:
if isinstance(idx, int):
if isinstance(idx, int) or is_numpy_scalar(idx):
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can we check (is_numpy_scalar(idx) and idx.dtype.kind in ('i', 'u'))?

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As I understand it, we would ideally capture any sort of numpy scalar that can be used to index a pandas series. Can't strings, floats and datetime objects also be used?

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no, we only allow positional indexing here

return self._compliant_series[idx]
return self._from_compliant_series(self._compliant_series[idx])

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12 changes: 12 additions & 0 deletions tests/series_only/scalar_index_test.py
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@@ -0,0 +1,12 @@
from __future__ import annotations

import narwhals.stable.v1 as nw

np = nw.dependencies.get_numpy()
pd = nw.dependencies.get_pandas()
Comment on lines +5 to +6
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you can just import these directly here (though if you use constructor_eager they're probably not necessary)

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Thank you. I can confirm that constructor_eager works without them.



def test_index() -> None:
s = pd.Series([0, 1, 2])
snw = nw.from_native(s, series_only=True)
assert snw[snw[0]] == np.int64(0)
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can we use constructor_eager here? like

s = nw.from_native(constructor_eager({'a': [0,1,2]}), eager_only=True)['a']
assert s[s[0]] == 0
```

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I didn't understand constructor_eager before, but having tried it out, it seems like it runs the test for different libraries. Correct?
The problem here is that, while numpy.int64(0) == 0, for some reason <pyarrow.Int64Scalar: 0> != 0. Not sure about other libraries...

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thanks @thevro ! yeah Series.__getitem__ should return a Python scalar for PyArrow. i'll just make a PR, then if you fetch and merge then the comparison should work

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done #1554

if you fetch upstream and then merge upstream/main then the == 0 comparison should work

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And doing

import pyarrow as pa
from pyarrow import compute as pc
pazero = pc.cast(0, pa.int64())
...
idx = snw[0]  # test indexing using builtin int
assert idx == 0 or idx == pazero # idx should have type numpy.int64 or pa.int64 
assert snw[idx] == 0 or idx == pazero # test indexing using third-party int

produces a test error:

>       assert snw[idx] == 0 or idx == pazero # test indexing using numpy.int64

tests/series_only/scalar_index_test.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
narwhals/series.py:73: in __getitem__
    return self._from_compliant_series(self._compliant_series[idx])
narwhals/_arrow/series.py:378: in __getitem__
    return self._from_native_series(self._native_series[idx])
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   TypeError: 'pyarrow.lib.Int64Scalar' object cannot be interpreted as an integer

pyarrow/table.pxi:312: TypeError

I suppose this comes of handling only numpy scalars.

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yup, fixed now, if you fetch and merge upstream/main then == 0 should work

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