[lucaluo925]

Description

Floor division by zero returns null on Polars, PyArrow and DuckDB, but not on the pandas-like backends, where the result depends on the dtype backend:

backend	2 // 0 before	after
Polars / PyArrow / DuckDB	null	null
pandas (numpy)	inf	null
pandas (nullable)	0	null
pandas (pyarrow)	raises ArrowInvalid: divide by zero	null

The nullable case is the easiest to miss, since 2 // 0 silently evaluates to 0. True division is already consistent across backends, so only the floor-division path is changed.

floordiv_by_zero_is_null replaces zero divisors with 1 before dividing and masks those positions afterwards. When no divisor is zero it falls through to operator.floordiv unchanged, so the common path keeps its dtype and does no extra work.

On numpy-backed pandas a result containing nulls widens to float64/NaN, which is what narwhals already treats as null there — is_null() returns True, as asserted by test_expr_floordiv_by_zero.

The expected values were already written in tests/expr_and_series/division_by_zero_test.py (expected_floordiv = [None, None, None]) with the pandas-like cases marked xfail; those markers are now removed. cudf stays xfail since I cannot run it.

What type of PR is this? (check all applicable)

• 💾 Refactor
• ✨ Feature
• 🐛 Bug Fix
• 🔧 Optimization
• 📝 Documentation
• ✅ Test
• 🐳 Other

Related issues

• Related issue [Enh]: Align floordiv behavior across different backends #2748

AI assistance

• No AI tools were used for this PR.
• AI tools were used.

The implementation and the test changes were written with the help of an AI assistant. I reproduced the divergence across all four backends locally, ran the tests, and checked that the 20 previously xfailed cases now pass (they were reported as XPASS(strict) before the markers were removed).

Checklist

• Code follows style guide (ruff)

• Tests added

• Documented the changes

• If this is your first PR to narwhals, attach a screenshot of pytest passing locally (not CI):

  PYTEST_ADDOPTS="--numprocesses=logical" \
  make run-ci DEPS="--extra pandas --extra dask --group core-tests --group sklearn --group plugins" \
  CMD="pytest tests --cov=src --cov=tests --runslow --constructors=pandas,pandas[nullable],pandas[pyarrow],pyarrow,polars[eager],polars[lazy],dask,duckdb,sqlframe"

pytest tests -q -k "not sqlframe and not pyspark and not ibis and not modin" on macOS, Python 3.14, pandas 3.0.6 / polars 1.44.2 / pyarrow 25.0.1 / duckdb 1.5.6 — this covers every backend the change touches (pandas numpy, pandas nullable, pandas pyarrow, polars, pyarrow, duckdb):

Summary by CodeRabbit

• Bug Fixes
  • Floor division now returns null for zero divisors, including mixed column-and-scalar operations.
  • Division results better preserve the input data type in cases involving zero divisors.

[FBruzzesi]

Hey @lucaluo925 thanks for your contribution, last time I tried to implement it there were some issue to keep the pyarrow backed series output on the same backend. The implementation I did was 3552f91 , followed by typing fixes fd759b3 and rfloordiv 542b9b3 - The feedback was:

the pandas implementation looks a bit more complex than i was hoping for

so if you can figure out a way to simplify those ideas it would be great.

Additionally, could you add tests to check that pandas dtype backends are properly maintained after the operation?

[lucaluo925]

Thanks for the pointers to your implementation. I've reviewed it, and the issue you ran into, where pyarrow-backed results did not stay on the original backend, does not reproduce with the current implementation.

I added the tests you asked for in the new commit c96ec19, modifying only division_by_zero_test.py:

• Covers the three pandas dtype backends (numpy, nullable, pyarrow), with both int and float data.
• Tests s // series (where the series contains zeros), s // 0, the reverse case 7 // s, and the expression forms col // col, col // lit(0) and 7 // col.
• Uses a non-default index and checks the results together with their dtypes.

Expected behavior:

• nullable and pyarrow keep their original dtypes.
• numpy-backed integers cannot hold nulls, so they fall back to float64 with NaN, matching your earlier implementation.

Validation: all of these tests fail against the implementation on main and pass on this PR. I ran them locally under pandas 2.1, 2.2 and 3.0.

I marked the pyarrow cases as requiring pandas ≥ 2.1, because pandas 2.0 computes the pyarrow integer 3 // -2 as -1, which is unrelated to division by zero. This follows the same pattern as sqrt_test in the repo.

On simplifying: I avoided backend-specific branching with a small helper that replaces zero divisors with ones before dividing, then sets those positions to null. There's no direct pyarrow array manipulation or manual dtype specification, so pandas keeps the original dtype on its own, and the new tests check this for all three backends. The source change is about 22 lines.

Happy to simplify further or reorganize the tests (e.g. merge them into the existing parametrized tests) if you prefer.

[FBruzzesi]

Hi @lucaluo925, thanks a lot for picking this up, and welcome to Narwhals 🎉

I left a few inline comments. In short:

• One regression to fix: a null scalar divisor (e.g. nw.col("a") // nw.col("b").max() where b is all null) now raises on nullable and pyarrow-backed pandas, though it works on main.
• Boolean divisors regress too, because of the literal 1 written into the divisor.
• Typing and style: we'd like to avoid hasattr duck-typing and Any in the helper.
• Tests: we prefer generic tests that run against every backend through the constructor fixtures, rather than pandas-only ones.

I am suggesting a couple of diffs but please treat them as suggestions, not the only valid approach, double check and verify them for edge cases.

[lucaluo925]

Thanks for the review — both regressions are real. I reproduced them on this branch before changing anything, and checked each against main:

case	main	this PR, before	now
nw.col("a") // nw.col("b").max(), b all null, nullable	[<NA>, <NA>]	TypeError: boolean value of NA is ambiguous	[<NA>, <NA>]
the same, pyarrow-backed	[<NA>, <NA>]	TypeError	[<NA>, <NA>]
Int64 // boolean([True, False])	[7, 0]	TypeError: Invalid value '1' for dtype 'boolean'	[7, <NA>]
int64 // bool([True, False])	[7.0, inf], float64	[7, nan], object	[7.0, nan], float64

I've taken your diffs essentially as written. Two things I checked rather than assumed:

• divisor.dtype.type(1) across every dtype it can meet here — numpy int/float/bool, the nullable ones, and ArrowDtype, whose .type is the plain Python int / float / bool. All of them give a 1 that pandas accepts without upcasting.
• the scalar branch now recurses once, with the scalar broadcast to a series, so a zero scalar and a zero inside a series share one code path.

On the tests, I ran your two against three trees, to check that each one actually guards something:

test	main	this PR, before	now
test_floordiv_by_mixed_divisor	fails on pandas, pandas[nullable], pandas[pyarrow]	passes	passes
test_floordiv_by_null_scalar	passes	fails on pandas[nullable], pandas[pyarrow]	passes

Full suite with --constructors=pandas,pandas[nullable],pandas[pyarrow],polars[eager],polars[lazy],pyarrow,duckdb: 14057 passed, 347 skipped, 599 xfailed.

One thing I deliberately left out: a test for the boolean divisor. With xfail_strict = true, and a boolean divisor only accepted natively by pandas (numpy and nullable), Polars and Dask — pandas[pyarrow], PyArrow and DuckDB all reject it on main too — I would be guessing at the marker list for sqlframe, ibis, modin and cuDF, which I can't run locally, and a wrong guess goes red in either direction. The first table above is what I verified by hand instead. Happy to add the test with whatever markers you think are right, or to leave it for the separate xfail PR.

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📚 Code guidelines (1)

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📝 Walkthrough

Walkthrough

Pandas-like Series floor division now uses a helper that returns nulls for zero divisors. Tests cover mixed and scalar divisors, null propagation, and dtype preservation across supported backends.

Changes

Pandas-like floor division

Layer / File(s)	Summary
Null-on-zero implementation src/narwhals/_pandas_like/utils.py, src/narwhals/_pandas_like/series.py	A new helper handles zero divisors and masks their results. Forward and reverse floor-division operators use the helper.
Floor-division tests tests/expr_and_series/division_by_zero_test.py	Tests cover mixed and scalar divisors, null results, and dtype preservation. Backend-specific skips and expected failures are updated.

Priority: ⬇️ Low

Estimated code review effort: 2 (Simple) | ~15 minutes

Change: Bug fix

Suggested reviewers: fbruzzesi

Merge Risk: 🔵 Low · up to 255dd

Floor division by a multi-element NumPy array containing zero may return a backend-native result or raise instead of returning null at that position. This bounded case warrants a fix or explicit acceptance before merging.

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 13.33% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 15 functions across 3 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly and concisely identifies the fix: floor division by zero now returns null on pandas-like backends.
Description check	✅ Passed	The description explains the bug, the behavior change, the testing, and the related issue. The “Tests added” checklist item is unchecked even though the description and changes indicate that tests wer…
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

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Actionable comments posted: 1

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Inline comments:
Review comments at @src/narwhals/_pandas_like/utils.py:
- Around line 737-738: Update the divisor-zero handling around the `except
(TypeError, ValueError)` check so multi-element NumPy array divisors use an
elementwise nonzero mask, avoid dividing by zero, and mask zero results as null;
preserve the existing scalar-zero behavior for one-element arrays.

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📒 Files selected for processing (3)

• src/narwhals/_pandas_like/series.py
• src/narwhals/_pandas_like/utils.py
• tests/expr_and_series/division_by_zero_test.py

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[FBruzzesi]

Thanks @lucaluo925 really lovely solution, much simpler than the original I tried 🙏🏼