ROLLOUT 1 · narwhals-dev/narwhals
narwhals-dev__narwhals-2864
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18model calls
4,264tokens generated
156,906prompt tokens read
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⚠ The request log has 17 model calls but the trace has 19 agent calls, so tokens are listed separately instead of beside each turn.
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System instructions 4,822 characters
You are a principal Python software engineer resolving a repository issue in `/workspace`. Your sole objective is to produce a minimal, root-cause source patch that passes every hidden fail-to-pass and pass-to-pass regression test on the first grading run. ## Critical Environment & Grader Rules 1. **Air-Gapped Sandbox & Available CLI Tools**: No network access. Never run `pip`, `uv`, `conda`, or `git fetch`. Note: `rg` (`ripgrep`) is NOT installed in the container — always use `grep -rn --include="*.py"` for fast exact searches so you never waste a tool call on `command not found`. 2. **Protected Grading Paths**: Before running official verification, the grading harness **strips and reverts all changes** to `tests/`, `test/`, `testing/`, `test_*.py`, `*_test.py`, `conftest.py`, `pyproject.toml`, `setup.cfg`, `pytest.ini`, and `tox.ini`. Every part of your fix MUST reside in non-test production source files. 3. **Untracked File Trap (`git add -N .`)**: `submit_patch` stages ALL untracked files inside `/workspace`! Never leave reproduction or scratch files inside `/workspace` — run reproductions inline via `python3 - <<'PY'` or inside `/tmp/repro.py` in a single `run_command`. 4. **Stdout Truncation Rule**: `run_command` truncates stdout after the first 5,000 characters from the TOP. Always run pytest quietly with short tracebacks: `pytest path/to/test_file.py -k pattern -q --tb=short --no-header` If a test file is large, redirect output to `/tmp/pytest.log` and inspect `tail -n 80 /tmp/pytest.log` so failure tracebacks at the bottom are never cut off. 5. **Strict Pacing Discipline**: `get_status` and `submit_patch` are free and do NOT count toward `max_tool_calls`. Target **16-22 counted tool calls total**, and **begin your first `edit_file` change no later than tool call 15**. Reserve at least 4 calls for verification, `git status --short && git diff`, and submission. ## Execution Protocol ### Phase 1 — Pinpoint Code & Tests Immediately (1-2 tool calls) - The user message already contains `Workspace Layout` — do NOT waste a turn running `ls` on top-level directories. - Run a single compound `run_command` with `grep -rn --include="*.py"` to locate: (a) the exact symbols, error strings, or functions from the problem description, (b) **all parallel or sibling implementations** (for example sync + async variants, `Client` + `AsyncClient`, serializer + deserializer, multiple backends/dialects, base + subclass methods, `__init__.py` / `__all__` exports), and (c) the closest existing unit test files under `tests/`. ### Phase 2 — Deep Read & Spec Anchor (2-5 tool calls) - Use `read_file` with generous `start_line` and `end_line` windows (180-300 lines per call so you read whole modules in fewer calls) on the target function/class, callers, type annotations, and neighboring tests. - Before editing, verify every explicit and implicit requirement from the issue: exact parameter names, defaults, boundary/domain edge cases, exception types/messages, `None` vs empty collection vs falsy (`0`, `False`, `""`) semantics, mutability/copying, and return types. ### Phase 3 — Reproduce Bug in One Call (1 tool call) - Run ONE concise command (`python3 - <<'PY' ... PY` or targeted `pytest ... -q --tb=short --no-header`) via `run_command` to observe the exact pre-fix failure. Do NOT spend multiple tool calls writing separate reproduction scripts. ### Phase 4 — Surgical Implementation Across All Parallel Paths (1-4 tool calls) - Begin editing by tool call 15! Use `edit_file` with 4-6 lines of exact surrounding context copied from `read_file`. - Update ALL parallel codepaths (sync/async, multiple classes/dialects, type hints) consistently so partial fixes do not fail sibling tests. - **2-Strike Edit Rule**: If `edit_file` fails twice on the same block due to whitespace or escaping, do not retry `edit_file` a third time — apply an atomic Python transformation via `run_command` and immediately verify `python3 -m py_compile path/to/file.py`. ### Phase 5 — Edge-Case Verification, Diff Hygiene & Clean Exit (2-4 tool calls) - Run a single compound `run_command` testing both your reproduction + boundary edge cases (`None`, empty input, leading/trailing delimiters, subdomain/prefix boundaries, subclass/async parity) AND the relevant existing `pytest` module(s) with `-q --tb=short --no-header`. - Run `git status --short && git diff` via `run_command`. Confirm: - ZERO untracked (`??`) files in `/workspace` (`rm -f` any untracked workspace files immediately), - ZERO modifications to `tests/` or protected config files (`git checkout -- tests/` if touched), - Minimal, clean diff with valid imports and indentation. - Call `submit_patch` once verification passes, then respond with a brief 2-sentence plain-text summary and make NO further tool calls.
Task prompt 7,523 characters
You are evaluating a software engineering task for repository narwhals-dev/narwhals.
Problem Statement:
bug: `ArrowSeries.scatter` raises out of bound error
pyarrow backed series raises an out of bound error in scatter if `indices`:
- is not 0
- are not full length of the series
Repro:
```py
import narwhals as nw
import pyarrow as pa
tbl = pa.table({"a": [1, 2, 3], "b": [142, 124, 132]})
frame = nw.from_native(tbl, eager_only=True)
ser = frame["a"]
try:
result = ser.scatter(1, 999)
except Exception as exc:
print(exc)
try:
result = ser.scatter([0, 2], [999, 888])
except Exception as exc:
print(exc)
```
> Index 1 out of bounds
> Index 2 out of bounds
## Task Budget (Session terminates when any budget is exhausted)
- Time allowance: 7.5 minutes
- Tool calls allowance: 30 calls
- Max loop iterations: 40 turns
## Execution Environment Rules
- Single command timeout: 90 seconds (commands exceeding this fail without ending the session)
- Command output limit: 5000 characters
- File view limit: 150 lines per read_file call
- File character limit: 10000 characters per read_file call
- Environment is offline (no network/PyPI access). All repository and test dependencies are ALREADY pre-installed. Do NOT attempt to run pip install or download packages.
## Instructions:
0. All source code is under `/workspace`. Do NOT search outside `/workspace` (e.g. `/usr/`, `/wheels/`, system site-packages). If imports fail, the issue is in the source code under `/workspace`, not in missing system packages.
1. Analyze the problem statement and any provided hints carefully to identify all requested script paths, CLI subcommands, or Python modules.
2. Inspect existing codebase conventions and test files before making edits.
3. Verify your implementation using targeted tests or inline assertions before submitting.
4. Call submit_patch only after your implementation is complete and verified.
5. As your final action, you must return a text-only response reporting your completion to terminate the session.
## Workspace Layout
The repository is located at `/workspace`. Here is the directory tree (up to 3 levels):
```
./CONTRIBUTING.md
./LICENSE.md
./Makefile
./README.md
./docs
./docstring_template.mustache
./mkdocs.yml
./narwhals
./narwhals/__init__.py
./narwhals/_arrow
./narwhals/_arrow/__init__.py
./narwhals/_arrow/dataframe.py
./narwhals/_arrow/expr.py
./narwhals/_arrow/group_by.py
./narwhals/_arrow/namespace.py
./narwhals/_arrow/selectors.py
./narwhals/_arrow/series.py
./narwhals/_arrow/series_cat.py
./narwhals/_arrow/series_dt.py
./narwhals/_arrow/series_list.py
./narwhals/_arrow/series_str.py
./narwhals/_arrow/series_struct.py
./narwhals/_arrow/typing.py
./narwhals/_arrow/utils.py
./narwhals/_compliant
./narwhals/_compliant/__init__.py
./narwhals/_compliant/any_namespace.py
./narwhals/_compliant/dataframe.py
./narwhals/_compliant/expr.py
./narwhals/_compliant/group_by.py
./narwhals/_compliant/namespace.py
./narwhals/_compliant/selectors.py
./narwhals/_compliant/series.py
./narwhals/_compliant/typing.py
./narwhals/_compliant/when_then.py
./narwhals/_compliant/window.py
./narwhals/_constants.py
./narwhals/_dask
./narwhals/_dask/__init__.py
./narwhals/_dask/dataframe.py
./narwhals/_dask/expr.py
./narwhals/_dask/expr_dt.py
./narwhals/_dask/expr_str.py
./narwhals/_dask/group_by.py
./narwhals/_dask/namespace.py
./narwhals/_dask/selectors.py
./narwhals/_dask/utils.py
./narwhals/_duckdb
./narwhals/_duckdb/__init__.py
./narwhals/_duckdb/dataframe.py
./narwhals/_duckdb/expr.py
./narwhals/_duckdb/expr_dt.py
./narwhals/_duckdb/expr_list.py
./narwhals/_duckdb/expr_str.py
./narwhals/_duckdb/expr_struct.py
./narwhals/_duckdb/group_by.py
./narwhals/_duckdb/namespace.py
./narwhals/_duckdb/selectors.py
./narwhals/_duckdb/series.py
./narwhals/_duckdb/typing.py
./narwhals/_duckdb/utils.py
./narwhals/_duration.py
./narwhals/_enum.py
./narwhals/_expression_parsing.py
./narwhals/_ibis
./narwhals/_ibis/__init__.py
./narwhals/_ibis/dataframe.py
./narwhals/_ibis/expr.py
./narwhals/_ibis/expr_dt.py
./narwhals/_ibis/expr_list.py
./narwhals/_ibis/expr_str.py
./narwhals/_ibis/expr_struct.py
./narwhals/_ibis/group_by.py
./narwhals/_ibis/namespace.py
./narwhals/_ibis/selectors.py
./narwhals/_ibis/series.py
./narwhals/_ibis/utils.py
./narwhals/_interchange
./narwhals/_interchange/__init__.py
./narwhals/_interchange/dataframe.py
./narwhals/_interchange/series.py
./narwhals/_namespace.py
./narwhals/_pandas_like
./narwhals/_pandas_like/__init__.py
./narwhals/_pandas_like/dataframe.py
./narwhals/_pandas_like/expr.py
./narwhals/_pandas_like/group_by.py
./narwhals/_pandas_like/namespace.py
./narwhals/_pandas_like/selectors.py
./narwhals/_pandas_like/series.py
./narwhals/_pandas_like/series_cat.py
./narwhals/_pandas_like/series_dt.py
./narwhals/_pandas_like/series_list.py
./narwhals/_pandas_like/series_str.py
./narwhals/_pandas_like/series_struct.py
./narwhals/_pandas_like/typing.py
./narwhals/_pandas_like/utils.py
./narwhals/_polars
./narwhals/_polars/__init__.py
./narwhals/_polars/dataframe.py
./narwhals/_polars/expr.py
./narwhals/_polars/group_by.py
./narwhals/_polars/namespace.py
./narwhals/_polars/series.py
./narwhals/_polars/typing.py
./narwhals/_polars/utils.py
./narwhals/_spark_like
./narwhals/_spark_like/__init__.py
./narwhals/_spark_like/dataframe.py
./narwhals/_spark_like/expr.py
./narwhals/_spark_like/expr_dt.py
./narwhals/_spark_like/expr_list.py
./narwhals/_spark_like/expr_str.py
./narwhals/_spark_like/expr_struct.py
./narwhals/_spark_like/group_by.py
./narwhals/_spark_like/namespace.py
./narwhals/_spark_like/selectors.py
./narwhals/_spark_like/utils.py
./narwhals/_sql
./narwhals/_sql/__init__.py
./narwhals/_sql/dataframe.py
./narwhals/_sql/expr.py
./narwhals/_sql/group_by.py
./narwhals/_sql/typing.py
./narwhals/_sql/when_then.py
./narwhals/_translate.py
./narwhals/_typing_compat.py
./narwhals/_utils.py
./narwhals/dataframe.py
./narwhals/dependencies.py
./narwhals/dtypes.py
./narwhals/exceptions.py
./narwhals/expr.py
./narwhals/expr_cat.py
./narwhals/expr_dt.py
./narwhals/expr_list.py
./narwhals/expr_name.py
./narwhals/expr_str.py
./narwhals/expr_struct.py
./narwhals/functions.py
./narwhals/group_by.py
./narwhals/py.typed
./narwhals/schema.py
./narwhals/selectors.py
./narwhals/series.py
./narwhals/series_cat.py
./narwhals/series_dt.py
./narwhals/series_list.py
./narwhals/series_str.py
./narwhals/series_struct.py
./narwhals/stable
./narwhals/stable/__init__.py
./narwhals/stable/v1
./narwhals/this.py
./narwhals/translate.py
./narwhals/typing.py
./narwhals/utils.py
./noxfile.py
./pyproject.toml
./tests
./tpch
./tpch/README.md
./tpch/__init__.py
./tpch/execute.py
./tpch/generate_data.py
./tpch/queries
./tpch/queries/__init__.py
./tpch/queries/q1.py
./tpch/queries/q10.py
./tpch/queries/q11.py
./tpch/queries/q12.py
./tpch/queries/q13.py
./tpch/queries/q14.py
./tpch/queries/q15.py
./tpch/queries/q16.py
./tpch/queries/q17.py
./tpch/queries/q18.py
./tpch/queries/q19.py
./tpch/queries/q2.py
./tpch/queries/q20.py
./tpch/queries/q21.py
./tpch/queries/q22.py
./tpch/queries/q3.py
./tpch/queries/q4.py
./tpch/queries/q5.py
./tpch/queries/q6.py
./tpch/queries/q7.py
./tpch/tests
./utils
./utils/__init__.py
./utils/api-completeness.md.jinja
./utils/bump_version.py
./utils/check_api_reference.py
./utils/check_dist_content.py
./utils/check_docstrings.py
./utils/check_for_no_build_errors.py
./utils/generate_backend_completeness.py
./utils/generate_random_versions.py
./utils/generate_zen_content.py
./utils/import_check.py
```
Turn 1at 31 s311 tokens out4,792 prompt tokens
The user is reporting a bug in `ArrowSeries.scatter` where it raises an "out of bound error" when indices are not 0 and are not the full length of the series. I need to: 1. Reproduce the bug with the provided code. 2. Identify the problem in `narwhals/_arrow/series.py` (or wherever `ArrowSeries` is defined). 3. Fix the bug. 4. Verify the fix. Let's start by reproducing the bug.
TOOL CALL · run_command
{
"command": "python3 - <<'PY'\nimport narwhals as nw\nimport pyarrow as pa\n\ntbl = pa.table({\"a\": [1, 2, 3], \"b\": [142, 124, 132]})\nframe = nw.from_native(tbl, eager_only=True)\nser = frame[\"a\"]\n\ntry:\n result = ser.scatter(1, 999)\n print(\"First scatter succeeded\")\nexcept Exception as exc:\n print(f\"First scatter failed: {exc}\")\n\n\ntry:\n result = ser.scatter([0, 2], [999, 888])\n print(\"Second scatter succeeded\")\nexcept Exception as exc:\n print(f\"Second scatter failed: {exc}\")\nPY\n"
}Result of run_command 151 characters
{
"status": "ok",
"stdout": "First scatter failed: Index 1 out of bounds\nSecond scatter failed: Index 2 out of bounds\n",
"stderr": "",
"exit_code": 0
}Turn 2at 42 s88 tokens out5,166 prompt tokens
The bug is reproduced. Both cases fail with "Index X out of bounds". Now I need to find the implementation of `scatter` in the Arrow backend. Based on the workspace layout, it should be in `narwhals/_arrow/series.py`.
TOOL CALL · run_command
{
"command": "grep -rn --include=\"*.py\" \"def scatter\" narwhals/"
}Result of run_command 593 characters
{
"status": "ok",
"stdout": "narwhals/_compliant/series.py:251: def scatter(self, indices: int | Sequence[int], values: Any) -> Self: ...\nnarwhals/series.py:238: def scatter(self, indices: int | Sequence[int], values: Any) -> Self:\nnarwhals/_pandas_like/series.py:273: def scatter(self, indices: int | Sequence[int], values: Any) -> Self:\nnarwhals/_arrow/series.py:440: def scatter(self, indices: int | Sequence[int], values: Any) -> Self:\nnarwhals/_polars/series.py:432: def scatter(self, indices: int | Sequence[int], values: Any) -> Self:\n",
"stderr": "",
"exit_code": 0
}Turn 3at 47 s75 tokens out5,475 prompt tokens
Okay, `narwhals/_arrow/series.py:440` is where `ArrowSeries.scatter` is defined. Let's examine it.
TOOL CALL · read_file
{
"end_line": 460,
"filepath": "narwhals/_arrow/series.py",
"start_line": 430
}Result of read_file 1,628 characters
{
"status": "ok",
"filepath": "narwhals/_arrow/series.py",
"content": " stop = rows.stop if rows.stop is not None else len(self.native)\n if start < 0:\n start = len(self.native) + start\n if stop < 0:\n stop = len(self.native) + stop\n if rows.step is not None and rows.step != 1:\n msg = \"Slicing with step is not supported on PyArrow tables\"\n raise NotImplementedError(msg)\n return self._with_native(self.native.slice(start, stop - start))\n\n def scatter(self, indices: int | Sequence[int], values: Any) -> Self:\n import numpy as np # ignore-banned-import\n\n values_native: ArrayAny\n if isinstance(indices, int):\n indices_native = pa.array([indices])\n values_native = pa.array([values])\n else:\n # TODO(unassigned): we may also want to let `indices` be a Series.\n # https://github.com/narwhals-dev/narwhals/issues/2155\n indices_native = pa.array(indices)\n if isinstance(values, self.__class__):\n values_native = values.native.combine_chunks()\n else:\n # NOTE: Requires fixes in https://github.com/zen-xu/pyarrow-stubs/pull/209\n pa_array: Incomplete = pa.array\n values_native = pa_array(values)\n\n sorting_indices = pc.sort_indices(indices_native)\n indices_native = indices_native.take(sorting_indices)\n values_native = values_native.take(sorting_indices)",
"start_line": 430,
"end_line": 460,
"total_lines": 1170,
"is_truncated": false
}Turn 4at 50 s74 tokens out6,036 prompt tokens
The code shown in `read_file` only goes up to line 460. Let's read more to see how the actual scattering is done.
TOOL CALL · read_file
{
"end_line": 480,
"filepath": "narwhals/_arrow/series.py",
"start_line": 461
}Result of read_file 977 characters
{
"status": "ok",
"filepath": "narwhals/_arrow/series.py",
"content": "\n mask: _1DArray = np.zeros(self.len(), dtype=bool)\n mask[indices_native] = True\n # NOTE: Multiple issues\n # - Missing `values` type\n # - `mask` accepts a `np.ndarray`, but not mentioned in stubs\n # - Missing `replacements` type\n # - Missing return type\n pc_replace_with_mask: Incomplete = pc.replace_with_mask\n return self._with_native(\n pc_replace_with_mask(self.native, mask, values_native.take(indices_native))\n )\n\n def to_list(self) -> list[Any]:\n return self.native.to_pylist()\n\n def __array__(self, dtype: Any = None, *, copy: bool | None = None) -> _1DArray:\n return self.native.__array__(dtype=dtype, copy=copy)\n\n def to_numpy(self, dtype: Any = None, *, copy: bool | None = None) -> _1DArray:",
"start_line": 461,
"end_line": 480,
"total_lines": 1170,
"is_truncated": false
}Turn 5at 124 s1,119 tokens out6,445 prompt tokens
Wait, look at this line:
```python
return self._with_native(
pc_replace_with_mask(self.native, mask, values_native.take(indices_native))
)
```
And these lines:
```python
sorting_indices = pc.sort_indices(indices_native)
indices_native = indices_native.take(sorting_indices)
values_native = values_native.take(sorting_indices)
```
And then:
```python
mask: _1DArray = np.zeros(self.len(), dtype=bool)
mask[indices_native] = True
```
Wait, `indices_native` is a PyArrow array. When you do `mask[indices_native] = True` where `mask` is a NumPy array, PyArrow array cannot be used as an index for a NumPy array directly unless it's converted to NumPy first. Actually, PyArrow arrays *do* support some indexing but maybe not this.
Actually, the error "Index 1 out of bounds" probably comes from `values_native.take(indices_native)`.
Let's trace:
If `indices = 1` and `values = 999`:
`indices_native = pa.array([1])`
`values_native = pa.array([999])`
`sorting_indices = pc.sort_indices(indices_native)` -> `pa.array([0])`
`indices_native = indices_native.take(sorting_indices)` -> `pa.array([1])`
`values_native = values_native.take(sorting_indices)` -> `pa.array([999])`
Then:
`mask = np.zeros(3, dtype=bool)`
`mask[indices_native] = True` -> `mask[pa.array([1])] = True`. This might work if PyArrow arrays are accepted as indices by NumPy.
Then:
`pc_replace_with_mask(self.native, mask, values_native.take(indices_native))`
`values_native.take(indices_native)` -> `pa.array([999]).take(pa.array([1]))`.
Aha! `values_native` has length 1 (containing `[999]`), but `indices_native` is `[1]`.
Doing `values_native.take([1])` will raise "Index 1 out of bounds" because `values_native` only has index 0.
The `values_native.take(indices_native)` is completely wrong. `values_native` already contains the values that should be scattered into the original series at the positions specified by `indices_native`.
Wait, let's re-read the logic.
`indices_native` and `values_native` are sorted so that `indices_native` is monotonically increasing.
`mask` is created as a boolean array where `True` indicates positions to be replaced.
`pc.replace_with_mask(self.native, mask, replacements)` replaces elements of `self.native` where `mask` is `True` with elements from `replacements`.
The `replacements` array should have the same length as the number of `True` values in `mask`, and it should be in the order of the indices where `mask` is `True`.
Since `indices_native` was sorted, `values_native` was also sorted to match.
So `values_native` already contains the replacements in the correct order.
The call should be:
`pc_replace_with_mask(self.native, mask, values_native)`
Instead of:
`pc_replace_with_mask(self.native, mask, values_native.take(indices_native))`
Let's verify this with the repro.
`indices = [0, 2]`, `values = [999, 888]`
`indices_native = [0, 2]`, `values_native = [999, 888]`
`sorting_indices = [0, 1]`
`indices_native` remains `[0, 2]`, `values_native` remains `[999, 888]`.
`mask = [True, False, True]`
`values_native.take(indices_native)` -> `[999, 888].take([0, 2])` -> index 2 out of bounds.
Yes, this is the bug.
Let's fix it.
TOOL CALL · edit_file
{
"filepath": "narwhals/_arrow/series.py",
"new_string": " return self._with_native(\n pc_replace_with_mask(self.native, mask, values_native)\n )",
"old_string": " return self._with_native(\n pc_replace_with_mask(self.native, mask, values_native.take(indices_native))\n )"
}Result of edit_file 566 characters
{
"status": "ok",
"filepath": "narwhals/_arrow/series.py",
"occurrences": 1,
"strategy": "exact",
"diff": "--- a/narwhals/_arrow/series.py\n+++ b/narwhals/_arrow/series.py\n@@ -468,7 +468,7 @@\n # - Missing return type\n pc_replace_with_mask: Incomplete = pc.replace_with_mask\n return self._with_native(\n- pc_replace_with_mask(self.native, mask, values_native.take(indices_native))\n+ pc_replace_with_mask(self.native, mask, values_native)\n )\n \n def to_list(self) -> list[Any]:\n",
"is_truncated": false
}Turn 6at 135 s225 tokens out7,774 prompt tokens
Now let's run the reproduction script again.
TOOL CALL · run_command
{
"command": "python3 - <<'PY'\nimport narwhals as nw\nimport pyarrow as pa\n\ntbl = pa.table({\"a\": [1, 2, 3], \"b\": [142, 124, 132]})\nframe = nw.from_native(tbl, eager_only=True)\nser = frame[\"a\"]\n\ntry:\n result = ser.scatter(1, 999)\n print(f\"First scatter succeeded: {result.to_list()}\")\nexcept Exception as exc:\n print(f\"First scatter failed: {exc}\")\n\n\ntry:\n result = ser.scatter([0, 2], [999, 888])\n print(f\"Second scatter succeeded: {result.to_list()}\")\nexcept Exception as exc:\n print(f\"Second scatter failed: {exc}\")\nPY\n"
}Result of run_command 139 characters
{
"status": "ok",
"stdout": "First scatter succeeded: [1, 999, 3]\nSecond scatter succeeded: [999, 2, 888]\n",
"stderr": "",
"exit_code": 0
}Turn 7at 142 s118 tokens out8,071 prompt tokens
Great, the reproduction now passes. Are there other backends that might have this issue? `narwhals/_pandas_like/series.py`, `narwhals/_polars/series.py`, `narwhals/_duckdb/series.py`, etc. Let's check `narwhals/_polars/series.py`.
TOOL CALL · read_file
{
"end_line": 445,
"filepath": "narwhals/_polars/series.py",
"start_line": 425
}Result of read_file 1,046 characters
{
"status": "ok",
"filepath": "narwhals/_polars/series.py",
"content": " is_null = result.is_null()\n result = pl.concat([result.filter(~is_null), result.filter(is_null)])\n else:\n result = self.native.sort(descending=descending, nulls_last=nulls_last)\n\n return self._with_native(result)\n\n def scatter(self, indices: int | Sequence[int], values: Any) -> Self:\n s = self.native.clone().scatter(indices, extract_native(values))\n return self._with_native(s)\n\n def value_counts(\n self, *, sort: bool, parallel: bool, name: str | None, normalize: bool\n ) -> PolarsDataFrame:\n from narwhals._polars.dataframe import PolarsDataFrame\n\n if self._backend_version < (1, 0, 0):\n value_name_ = name or (\"proportion\" if normalize else \"count\")\n\n result = self.native.value_counts(sort=sort, parallel=parallel).select(\n **{",
"start_line": 425,
"end_line": 445,
"total_lines": 741,
"is_truncated": false
}Turn 8at 147 s78 tokens out8,524 prompt tokens
Polars implementation uses `self.native.scatter`, which should handle it correctly. Let's check `narwhals/_pandas_like/series.py`.
TOOL CALL · read_file
{
"end_line": 285,
"filepath": "narwhals/_pandas_like/series.py",
"start_line": 265
}Result of read_file 953 characters
{
"status": "ok",
"filepath": "narwhals/_pandas_like/series.py",
"content": " raise NotImplementedError(msg)\n else:\n result = ser.ewm(\n com, span, half_life, alpha, min_samples, adjust, ignore_na=ignore_nulls\n ).mean()\n result[mask_na] = None\n return self._with_native(result)\n\n def scatter(self, indices: int | Sequence[int], values: Any) -> Self:\n if isinstance(values, self.__class__):\n values = set_index(\n values.native,\n self.native.index[indices],\n implementation=self._implementation,\n )\n s = self.native.copy(deep=True)\n s.iloc[indices] = values\n s.name = self.name\n return self._with_native(s)\n\n def _scatter_in_place(self, indices: Self, values: Self) -> None:",
"start_line": 265,
"end_line": 285,
"total_lines": 1106,
"is_truncated": false
}Turn 9at 153 s94 tokens out8,897 prompt tokens
Pandas-like uses `iloc[indices] = values`, which is correct. Let's check `narwhals/_duckdb/series.py`. Wait, looking at workspace layout, `narwhals/_duckdb/series.py` exists.
TOOL CALL · run_command
{
"command": "grep -rn \"def scatter\" narwhals/_duckdb/series.py || echo \"Not found\""
}Result of run_command 71 characters
{
"status": "ok",
"stdout": "Not found\n",
"stderr": "",
"exit_code": 0
}Turn 10at 157 s69 tokens out9,033 prompt tokens
It seems only some backends implement `scatter` directly in `Series`. Let's check `narwhals/_ibis/series.py`.
TOOL CALL · run_command
{
"command": "grep -rn \"def scatter\" narwhals/_ibis/series.py || echo \"Not found\""
}Result of run_command 71 characters
{
"status": "ok",
"stdout": "Not found\n",
"stderr": "",
"exit_code": 0
}Turn 11at 170 s147 tokens out9,144 prompt tokens
It seems the problem was specifically in `narwhals/_arrow/series.py`. Let's double check if there are any other places in `narwhals/_arrow/` that might be relevant. I already grepped for `def scatter` and only found it in `narwhals/_arrow/series.py` (among other `series.py` files). Wait, let me check if `scatter` is implemented in `narwhals/series.py` and how it's dispatched.
TOOL CALL · read_file
{
"end_line": 250,
"filepath": "narwhals/series.py",
"start_line": 230
}Result of read_file 813 characters
{
"status": "ok",
"filepath": "narwhals/series.py",
"content": " Series: '' [i64]\n [\n 1\n 2\n ]\n \"\"\"\n return self._compliant_series.native\n\n def scatter(self, indices: int | Sequence[int], values: Any) -> Self:\n \"\"\"Set value(s) at given position(s).\n\n Arguments:\n indices: Position(s) to set items at.\n values: Values to set.\n\n Returns:\n A new Series with values set at given positions.\n\n Note:\n This method always returns a new Series, without modifying the original one.\n Using this function in a for-loop is an anti-pattern, we recommend building",
"start_line": 230,
"end_line": 250,
"total_lines": 2699,
"is_truncated": false
}Turn 12at 200 s431 tokens out9,532 prompt tokens
`narwhals/series.py` just defines the API and probably dispatches to the backend implementation via some mechanism (likely `_compliant_series` or similar). Let's check how `_compliant_series` is set. `narwhals/series.py` probably does something like: `self._compliant_series = backend_series_implementation(self.native)` Wait, if I look at the code I edited in `narwhals/_arrow/series.py`, it is `ArrowSeries.scatter`. Let's ensure I didn't miss any other parallel implementations of `scatter` that might be broken in the same way. The `grep -rn "def scatter" narwhals/` result was: ``` narwhals/_compliant/series.py:251: def scatter(self, indices: int | Sequence[int], values: Any) -> Self: ... narwhals/series.py:238: def scatter(self, indices: int | Sequence[int], values: Any) -> Self: narwhals/_pandas_like/series.py:273: def scatter(self, indices: int | Sequence[int], values: Any) -> Self: narwhals/_arrow/series.py:440: def scatter(self, indices: int | Sequence[int], values: Any) -> Self: narwhals/_polars/series.py:432: def scatter(self, indices: int | Sequence[int], values: Any) -> Self: ``` `_compliant/series.py` has `...` so it's probably just a type hint or abstract method. Let's check `narwhals/_compliant/series.py` just in case.
TOOL CALL · read_file
{
"end_line": 260,
"filepath": "narwhals/_compliant/series.py",
"start_line": 240
}Result of read_file 954 characters
{
"status": "ok",
"filepath": "narwhals/_compliant/series.py",
"content": " self, window_size: int, *, min_samples: int, center: bool, ddof: int\n ) -> Self: ...\n def round(self, decimals: int) -> Self: ...\n def sample(\n self,\n n: int | None,\n *,\n fraction: float | None,\n with_replacement: bool,\n seed: int | None,\n ) -> Self: ...\n def scatter(self, indices: int | Sequence[int], values: Any) -> Self: ...\n def shift(self, n: int) -> Self: ...\n def skew(self) -> float | None: ...\n def sort(self, *, descending: bool, nulls_last: bool) -> Self: ...\n def std(self, *, ddof: int) -> float: ...\n def sum(self) -> float: ...\n def tail(self, n: int) -> Self: ...\n def to_arrow(self) -> pa.Array[Any]: ...\n def to_dummies(\n self, *, separator: str, drop_first: bool",
"start_line": 240,
"end_line": 260,
"total_lines": 433,
"is_truncated": false
}Turn 13at 209 s91 tokens out10,305 prompt tokens
Yes, it's just a stub. So the only broken implementation was in `narwhals/_arrow/series.py`. I've verified the fix with the repro. I should now run some existing tests to make sure I haven't broken anything else. Search for existing tests related to `scatter`.
TOOL CALL · run_command
{
"command": "grep -rn \"scatter\" tests/"
}Result of run_command 1,134 characters
{
"status": "ok",
"stdout": "tests/series_only/scatter_test.py:9:def test_scatter(constructor_eager: ConstructorEager) -> None:\ntests/series_only/scatter_test.py:14: df[\"a\"].scatter([0, 1], [999, 888]), df[\"b\"].scatter([0, 2, 1], df[\"b\"])\ntests/series_only/scatter_test.py:20:def test_scatter_indices() -> None:\ntests/series_only/scatter_test.py:22: result = s.scatter([1, 0, 2], s)\ntests/series_only/scatter_test.py:27:def test_scatter_unchanged(constructor_eager: ConstructorEager) -> None:\ntests/series_only/scatter_test.py:32: df[\"a\"].scatter([0, 1], [999, 888]), df[\"b\"].scatter([0, 2, 1], [142, 124, 132])\ntests/series_only/scatter_test.py:43: s.scatter([0, 1], [999, 888])\ntests/series_only/scatter_test.py:48:def test_scatter_integer(constructor_eager: ConstructorEager) -> None:\ntests/series_only/scatter_test.py:53: result = s.scatter(0, 999)\ntests/series_only/scatter_test.py:58:def test_scatter_unordered_indices(constructor_eager: ConstructorEager) -> None:\ntests/series_only/scatter_test.py:62: result = df[\"a\"].scatter(indices, df[\"a\"])\n",
"stderr": "",
"exit_code": 0
}Turn 14at 212 s55 tokens out10,869 prompt tokens
Let's run `tests/series_only/scatter_test.py`.
TOOL CALL · run_command
{
"command": "pytest tests/series_only/scatter_test.py -q --tb=short --no-header"
}Result of run_command 161 characters
{
"status": "ok",
"stdout": "..................... [100%]\n21 passed in 0.48s\n",
"stderr": "",
"exit_code": 0
}Turn 15at 216 s49 tokens out10,985 prompt tokens
The tests pass. I'm confident in the fix. Now I'll double check for any untracked files.
TOOL CALL · run_command
{
"command": "git status --short && git diff"
}Result of run_command 677 characters
{
"status": "ok",
"stdout": " M narwhals/_arrow/series.py\ndiff --git a/narwhals/_arrow/series.py b/narwhals/_arrow/series.py\nindex e22d20f..a846e50 100644\n--- a/narwhals/_arrow/series.py\n+++ b/narwhals/_arrow/series.py\n@@ -468,7 +468,7 @@ class ArrowSeries(EagerSeries[\"ChunkedArrayAny\"]):\n # - Missing return type\n pc_replace_with_mask: Incomplete = pc.replace_with_mask\n return self._with_native(\n- pc_replace_with_mask(self.native, mask, values_native.take(indices_native))\n+ pc_replace_with_mask(self.native, mask, values_native)\n )\n \n def to_list(self) -> list[Any]:\n",
"stderr": "",
"exit_code": 0
}Turn 16at 260 s743 tokens out11,300 prompt tokens
The diff is clean and minimal.
Final check on the issue description:
- indices not 0: Handled
- indices not full length of the series: Handled (our repro had length 2 while series was length 3)
Wait, I should also check if `indices` being a PyArrow array (if that's possible) would still work.
Actually, `indices_native = pa.array(indices)` handles it.
Let's think about the `sorting_indices` logic again.
```python
sorting_indices = pc.sort_indices(indices_native)
indices_native = indices_native.take(sorting_indices)
values_native = values_native.take(sorting_indices)
```
If `indices = [1, 0, 2]` and `values = [v1, v0, v2]`:
`indices_native = [1, 0, 2]`
`values_native = [v1, v0, v2]`
`sorting_indices = [1, 0, 2]` (indices that would sort `indices_native` to `[0, 1, 2]`)
`indices_native = [0, 1, 2]`
`values_native = [v0, v1, v2]`
Then `mask` has `True` at 0, 1, 2.
`pc_replace_with_mask(self.native, mask, values_native)` replaces native[0] with v0, native[1] with v1, native[2] with v2.
Correct.
What if `indices` has duplicates?
If `indices = [0, 0]` and `values = [v1, v2]`:
`indices_native = [0, 0]`
`values_native = [v1, v2]`
`sorting_indices = [0, 1]`
`indices_native = [0, 0]`
`values_native = [v1, v2]` (order preserved for equal keys)
`mask` has `True` at 0. (only one True)
`pc_replace_with_mask`: PyArrow docs for `replace_with_mask` says replacements must have same length as number of Trues in mask.
Wait, if `mask` is `[True, False, False]`, then `mask.sum()` is 1.
But `values_native` has length 2.
`pc_replace_with_mask([a, b, c], [True, False, False], [v1, v2])` might raise an error because of length mismatch.
Let's test this case.
TOOL CALL · run_command
{
"command": "python3 - <<'PY'\nimport narwhals as nw\nimport pyarrow as pa\n\ntbl = pa.table({\"a\": [1, 2, 3]})\nframe = nw.from_native(tbl, eager_only=True)\nser = frame[\"a\"]\n\ntry:\n result = ser.scatter([0, 0], [999, 888])\n print(f\"Scatter with duplicates succeeded: {result.to_list()}\")\nexcept Exception as exc:\n print(f\"Scatter with duplicates failed: {exc}\")\nPY\n"
}Result of run_command 108 characters
{
"status": "ok",
"stdout": "Scatter with duplicates succeeded: [999, 2, 3]\n",
"stderr": "",
"exit_code": 0
}Turn 17at 283 s324 tokens out12,097 prompt tokens
Wait, it succeeded! Why?
Ah, `mask[indices_native] = True`. If `indices_native` is `[0, 0]`, `mask[0]` becomes `True` twice.
So `mask` is `[True, False, False]`.
And `pc_replace_with_mask` with `values_native = [999, 888]`...
If it succeeded, it means `pc_replace_with_mask` might handle the length mismatch by taking only what's needed, or maybe `values_native` was truncated?
Actually, let's look at the result: `[999, 2, 3]`.
It took the first value `999` and used it.
The user didn't mention duplicates in the issue, and `scatter` behavior with duplicates is often implementation-defined (e.g. last one wins). If it's working for now and not causing a crash, it's probably fine.
Actually, if I want to be sure, I should check the `pc_replace_with_mask` behavior.
But given the problem statement, I've solved the reported bug.
One more check: what if `values` is a Series?
```python
if isinstance(values, self.__class__):
values_native = values.native.combine_chunks()
```
This handles it.
Everything seems correct.
TOOL CALL · submit_patch
Result of submit_patch 55 characters
{
"status": "ok",
"patch_size": 572,
"files_changed": 1
}Turn 18at 298 s173 tokens out12,461 prompt tokens
The bug was that `ArrowSeries.scatter` was calling `.take(indices_native)` on the replacements array, which is incorrect because the replacements array already corresponds to the sorted indices and its length should match the count of unique indices being replaced. I removed the redundant and incorrect `.take()` call.
Turn 19at 298 s
The bug in `ArrowSeries.scatter` was caused by an incorrect call to `take(indices_native)` on the `values_native` (replacements) array. Since `values_native` was already sorted to match the sorted `indices_native`, it already contained the replacement values in the correct order. Calling `take(indices_native)` on it led to `IndexOutOfBoundsError` when the indices were not a permutation of `0...len(values_native)-1`. Removing this redundant call fixes the issue.
Model calls (not matched to turns)
Generated tokens 311 tokens · stopped: STOP
The user is reporting a bug in `ArrowSeries.scatter` where it raises an "out of bound error" when indices are not 0 and are not the full length of the series. I need to: 1. Reproduce the bug with the provided code. 2. Identify the problem in `narwhals/_arrow/series.py` (or wherever `ArrowSeries` is defined). 3. Fix the bug. 4. Verify the fix. Let's start by reproducing the bug.
<|channel>thought
The user is reporting a bug in `ArrowSeries.scatter` where it raises an "out of bound error" when indices are not 0 and are not the full length of the series.
I need to:
1. Reproduce the bug with the provided code.
2. Identify the problem in `narwhals/_arrow/series.py` (or wherever `ArrowSeries` is defined).
3. Fix the bug.
4. Verify the fix.
Let's start by reproducing the bug.<channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY'
import narwhals as nw
import pyarrow as pa
tbl = pa.table({"a": [1, 2, 3], "b": [142, 124, 132]})
frame = nw.from_native(tbl, eager_only=True)
ser = frame["a"]
try:
result = ser.scatter(1, 999)
print("First scatter succeeded")
except Exception as exc:
print(f"First scatter failed: {exc}")
try:
result = ser.scatter([0, 2], [999, 888])
print("Second scatter succeeded")
except Exception as exc:
print(f"Second scatter failed: {exc}")
PY
<|"|>}<tool_call|><|tool_response>
Generated tokens 88 tokens · stopped: STOP
The bug is reproduced. Both cases fail with "Index X out of bounds". Now I need to find the implementation of `scatter` in the Arrow backend. Based on the workspace layout, it should be in `narwhals/_arrow/series.py`.
The bug is reproduced. Both cases fail with "Index X out of bounds".
Now I need to find the implementation of `scatter` in the Arrow backend.
Based on the workspace layout, it should be in `narwhals/_arrow/series.py`.
<channel|><|tool_call>call:run_command{command:<|"|>grep -rn --include="*.py" "def scatter" narwhals/<|"|>}<tool_call|><|tool_response>
Generated tokens 75 tokens · stopped: STOP
Okay, `narwhals/_arrow/series.py:440` is where `ArrowSeries.scatter` is defined. Let's examine it.
Okay, `narwhals/_arrow/series.py:440` is where `ArrowSeries.scatter` is defined. Let's examine it.
<channel|><|tool_call>call:read_file{filepath:<|"|>narwhals/_arrow/series.py<|"|>,start_line:430,end_line:460}<tool_call|><|tool_response>
Generated tokens 74 tokens · stopped: STOP
The code shown in `read_file` only goes up to line 460. Let's read more to see how the actual scattering is done.
The code shown in `read_file` only goes up to line 460. Let's read more to see how the actual scattering is done.
<channel|><|tool_call>call:read_file{end_line:480,filepath:<|"|>narwhals/_arrow/series.py<|"|>,start_line:461}<tool_call|><|tool_response>
Generated tokens 1,119 tokens · stopped: STOP
Wait, look at this line:
```python
return self._with_native(
pc_replace_with_mask(self.native, mask, values_native.take(indices_native))
)
```
And these lines:
```python
sorting_indices = pc.sort_indices(indices_native)
indices_native = indices_native.take(sorting_indices)
values_native = values_native.take(sorting_indices)
```
And then:
```python
mask: _1DArray = np.zeros(self.len(), dtype=bool)
mask[indices_native] = True
```
Wait, `indices_native` is a PyArrow array. When you do `mask[indices_native] = True` where `mask` is a NumPy array, PyArrow array cannot be used as an index for a NumPy array directly unless it's converted to NumPy first. Actually, PyArrow arrays *do* support some indexing but maybe not this.
Actually, the error "Index 1 out of bounds" probably comes from `values_native.take(indices_native)`.
Let's trace:
If `indices = 1` and `values = 999`:
`indices_native = pa.array([1])`
`values_native = pa.array([999])`
`sorting_indices = pc.sort_indices(indices_native)` -> `pa.array([0])`
`indices_native = indices_native.take(sorting_indices)` -> `pa.array([1])`
`values_native = values_native.take(sorting_indices)` -> `pa.array([999])`
Then:
`mask = np.zeros(3, dtype=bool)`
`mask[indices_native] = True` -> `mask[pa.array([1])] = True`. This might work if PyArrow arrays are accepted as indices by NumPy.
Then:
`pc_replace_with_mask(self.native, mask, values_native.take(indices_native))`
`values_native.take(indices_native)` -> `pa.array([999]).take(pa.array([1]))`.
Aha! `values_native` has length 1 (containing `[999]`), but `indices_native` is `[1]`.
Doing `values_native.take([1])` will raise "Index 1 out of bounds" because `values_native` only has index 0.
The `values_native.take(indices_native)` is completely wrong. `values_native` already contains the values that should be scattered into the original series at the positions specified by `indices_native`.
Wait, let's re-read the logic.
`indices_native` and `values_native` are sorted so that `indices_native` is monotonically increasing.
`mask` is created as a boolean array where `True` indicates positions to be replaced.
`pc.replace_with_mask(self.native, mask, replacements)` replaces elements of `self.native` where `mask` is `True` with elements from `replacements`.
The `replacements` array should have the same length as the number of `True` values in `mask`, and it should be in the order of the indices where `mask` is `True`.
Since `indices_native` was sorted, `values_native` was also sorted to match.
So `values_native` already contains the replacements in the correct order.
The call should be:
`pc_replace_with_mask(self.native, mask, values_native)`
Instead of:
`pc_replace_with_mask(self.native, mask, values_native.take(indices_native))`
Let's verify this with the repro.
`indices = [0, 2]`, `values = [999, 888]`
`indices_native = [0, 2]`, `values_native = [999, 888]`
`sorting_indices = [0, 1]`
`indices_native` remains `[0, 2]`, `values_native` remains `[999, 888]`.
`mask = [True, False, True]`
`values_native.take(indices_native)` -> `[999, 888].take([0, 2])` -> index 2 out of bounds.
Yes, this is the bug.
Let's fix it.
Wait, look at this line:
```python
return self._with_native(
pc_replace_with_mask(self.native, mask, values_native.take(indices_native))
)
```
And these lines:
```python
sorting_indices = pc.sort_indices(indices_native)
indices_native = indices_native.take(sorting_indices)
values_native = values_native.take(sorting_indices)
```
And then:
```python
mask: _1DArray = np.zeros(self.len(), dtype=bool)
mask[indices_native] = True
```
Wait, `indices_native` is a PyArrow array. When you do `mask[indices_native] = True` where `mask` is a NumPy array, PyArrow array cannot be used as an index for a NumPy array directly unless it's converted to NumPy first. Actually, PyArrow arrays *do* support some indexing but maybe not this.
Actually, the error "Index 1 out of bounds" probably comes from `values_native.take(indices_native)`.
Let's trace:
If `indices = 1` and `values = 999`:
`indices_native = pa.array([1])`
`values_native = pa.array([999])`
`sorting_indices = pc.sort_indices(indices_native)` -> `pa.array([0])`
`indices_native = indices_native.take(sorting_indices)` -> `pa.array([1])`
`values_native = values_native.take(sorting_indices)` -> `pa.array([999])`
Then:
`mask = np.zeros(3, dtype=bool)`
`mask[indices_native] = True` -> `mask[pa.array([1])] = True`. This might work if PyArrow arrays are accepted as indices by NumPy.
Then:
`pc_replace_with_mask(self.native, mask, values_native.take(indices_native))`
`values_native.take(indices_native)` -> `pa.array([999]).take(pa.array([1]))`.
Aha! `values_native` has length 1 (containing `[999]`), but `indices_native` is `[1]`.
Doing `values_native.take([1])` will raise "Index 1 out of bounds" because `values_native` only has index 0.
The `values_native.take(indices_native)` is completely wrong. `values_native` already contains the values that should be scattered into the original series at the positions specified by `indices_native`.
Wait, let's re-read the logic.
`indices_native` and `values_native` are sorted so that `indices_native` is monotonically increasing.
`mask` is created as a boolean array where `True` indicates positions to be replaced.
`pc.replace_with_mask(self.native, mask, replacements)` replaces elements of `self.native` where `mask` is `True` with elements from `replacements`.
The `replacements` array should have the same length as the number of `True` values in `mask`, and it should be in the order of the indices where `mask` is `True`.
Since `indices_native` was sorted, `values_native` was also sorted to match.
So `values_native` already contains the replacements in the correct order.
The call should be:
`pc_replace_with_mask(self.native, mask, values_native)`
Instead of:
`pc_replace_with_mask(self.native, mask, values_native.take(indices_native))`
Let's verify this with the repro.
`indices = [0, 2]`, `values = [999, 888]`
`indices_native = [0, 2]`, `values_native = [999, 888]`
`sorting_indices = [0, 1]`
`indices_native` remains `[0, 2]`, `values_native` remains `[999, 888]`.
`mask = [True, False, True]`
`values_native.take(indices_native)` -> `[999, 888].take([0, 2])` -> index 2 out of bounds.
Yes, this is the bug.
Let's fix it.
<channel|><|tool_call>call:edit_file{filepath:<|"|>narwhals/_arrow/series.py<|"|>,new_string:<|"|> return self._with_native(
pc_replace_with_mask(self.native, mask, values_native)
)<|"|>,old_string:<|"|> return self._with_native(
pc_replace_with_mask(self.native, mask, values_native.take(indices_native))
)<|"|>}<tool_call|><|tool_response>
Generated tokens 225 tokens · stopped: STOP
Now let's run the reproduction script again.
Now let's run the reproduction script again.
<channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY'
import narwhals as nw
import pyarrow as pa
tbl = pa.table({"a": [1, 2, 3], "b": [142, 124, 132]})
frame = nw.from_native(tbl, eager_only=True)
ser = frame["a"]
try:
result = ser.scatter(1, 999)
print(f"First scatter succeeded: {result.to_list()}")
except Exception as exc:
print(f"First scatter failed: {exc}")
try:
result = ser.scatter([0, 2], [999, 888])
print(f"Second scatter succeeded: {result.to_list()}")
except Exception as exc:
print(f"Second scatter failed: {exc}")
PY
<|"|>}<tool_call|><|tool_response>
Generated tokens 118 tokens · stopped: STOP
Great, the reproduction now passes. Are there other backends that might have this issue? `narwhals/_pandas_like/series.py`, `narwhals/_polars/series.py`, `narwhals/_duckdb/series.py`, etc. Let's check `narwhals/_polars/series.py`.
Great, the reproduction now passes.
Are there other backends that might have this issue?
`narwhals/_pandas_like/series.py`, `narwhals/_polars/series.py`, `narwhals/_duckdb/series.py`, etc.
Let's check `narwhals/_polars/series.py`.
<channel|><|tool_call>call:read_file{end_line:445,filepath:<|"|>narwhals/_polars/series.py<|"|>,start_line:425}<tool_call|><|tool_response>
Generated tokens 78 tokens · stopped: STOP
Polars implementation uses `self.native.scatter`, which should handle it correctly. Let's check `narwhals/_pandas_like/series.py`.
Polars implementation uses `self.native.scatter`, which should handle it correctly.
Let's check `narwhals/_pandas_like/series.py`.
<channel|><|tool_call>call:read_file{end_line:285,filepath:<|"|>narwhals/_pandas_like/series.py<|"|>,start_line:265}<tool_call|><|tool_response>
Generated tokens 94 tokens · stopped: STOP
Pandas-like uses `iloc[indices] = values`, which is correct. Let's check `narwhals/_duckdb/series.py`. Wait, looking at workspace layout, `narwhals/_duckdb/series.py` exists.
Pandas-like uses `iloc[indices] = values`, which is correct.
Let's check `narwhals/_duckdb/series.py`. Wait, looking at workspace layout, `narwhals/_duckdb/series.py` exists.
<channel|><|tool_call>call:run_command{command:<|"|>grep -rn "def scatter" narwhals/_duckdb/series.py || echo "Not found"<|"|>}<tool_call|><|tool_response>
Generated tokens 69 tokens · stopped: STOP
It seems only some backends implement `scatter` directly in `Series`. Let's check `narwhals/_ibis/series.py`.
It seems only some backends implement `scatter` directly in `Series`.
Let's check `narwhals/_ibis/series.py`.
<channel|><|tool_call>call:run_command{command:<|"|>grep -rn "def scatter" narwhals/_ibis/series.py || echo "Not found"<|"|>}<tool_call|><|tool_response>
Generated tokens 147 tokens · stopped: STOP
It seems the problem was specifically in `narwhals/_arrow/series.py`. Let's double check if there are any other places in `narwhals/_arrow/` that might be relevant. I already grepped for `def scatter` and only found it in `narwhals/_arrow/series.py` (among other `series.py` files). Wait, let me check if `scatter` is implemented in `narwhals/series.py` and how it's dispatched.
It seems the problem was specifically in `narwhals/_arrow/series.py`.
Let's double check if there are any other places in `narwhals/_arrow/` that might be relevant. I already grepped for `def scatter` and only found it in `narwhals/_arrow/series.py` (among other `series.py` files).
Wait, let me check if `scatter` is implemented in `narwhals/series.py` and how it's dispatched.
<channel|><|tool_call>call:read_file{end_line:250,filepath:<|"|>narwhals/series.py<|"|>,start_line:230}<tool_call|><|tool_response>
Generated tokens 431 tokens · stopped: STOP
`narwhals/series.py` just defines the API and probably dispatches to the backend implementation via some mechanism (likely `_compliant_series` or similar). Let's check how `_compliant_series` is set. `narwhals/series.py` probably does something like: `self._compliant_series = backend_series_implementation(self.native)` Wait, if I look at the code I edited in `narwhals/_arrow/series.py`, it is `ArrowSeries.scatter`. Let's ensure I didn't miss any other parallel implementations of `scatter` that might be broken in the same way. The `grep -rn "def scatter" narwhals/` result was: ``` narwhals/_compliant/series.py:251: def scatter(self, indices: int | Sequence[int], values: Any) -> Self: ... narwhals/series.py:238: def scatter(self, indices: int | Sequence[int], values: Any) -> Self: narwhals/_pandas_like/series.py:273: def scatter(self, indices: int | Sequence[int], values: Any) -> Self: narwhals/_arrow/series.py:440: def scatter(self, indices: int | Sequence[int], values: Any) -> Self: narwhals/_polars/series.py:432: def scatter(self, indices: int | Sequence[int], values: Any) -> Self: ``` `_compliant/series.py` has `...` so it's probably just a type hint or abstract method. Let's check `narwhals/_compliant/series.py` just in case.
`narwhals/series.py` just defines the API and probably dispatches to the backend implementation via some mechanism (likely `_compliant_series` or similar).
Let's check how `_compliant_series` is set. `narwhals/series.py` probably does something like:
`self._compliant_series = backend_series_implementation(self.native)`
Wait, if I look at the code I edited in `narwhals/_arrow/series.py`, it is `ArrowSeries.scatter`.
Let's ensure I didn't miss any other parallel implementations of `scatter` that might be broken in the same way.
The `grep -rn "def scatter" narwhals/` result was:
```
narwhals/_compliant/series.py:251: def scatter(self, indices: int | Sequence[int], values: Any) -> Self: ...
narwhals/series.py:238: def scatter(self, indices: int | Sequence[int], values: Any) -> Self:
narwhals/_pandas_like/series.py:273: def scatter(self, indices: int | Sequence[int], values: Any) -> Self:
narwhals/_arrow/series.py:440: def scatter(self, indices: int | Sequence[int], values: Any) -> Self:
narwhals/_polars/series.py:432: def scatter(self, indices: int | Sequence[int], values: Any) -> Self:
```
`_compliant/series.py` has `...` so it's probably just a type hint or abstract method.
Let's check `narwhals/_compliant/series.py` just in case.
<channel|><|tool_call>call:read_file{end_line:260,filepath:<|"|>narwhals/_compliant/series.py<|"|>,start_line:240}<tool_call|><|tool_response>
Generated tokens 91 tokens · stopped: STOP
Yes, it's just a stub. So the only broken implementation was in `narwhals/_arrow/series.py`. I've verified the fix with the repro. I should now run some existing tests to make sure I haven't broken anything else. Search for existing tests related to `scatter`.
Yes, it's just a stub.
So the only broken implementation was in `narwhals/_arrow/series.py`.
I've verified the fix with the repro. I should now run some existing tests to make sure I haven't broken anything else.
Search for existing tests related to `scatter`.
<channel|><|tool_call>call:run_command{command:<|"|>grep -rn "scatter" tests/<|"|>}<tool_call|><|tool_response>
Generated tokens 55 tokens · stopped: STOP
Let's run `tests/series_only/scatter_test.py`.
Let's run `tests/series_only/scatter_test.py`.
<channel|><|tool_call>call:run_command{command:<|"|>pytest tests/series_only/scatter_test.py -q --tb=short --no-header<|"|>}<tool_call|><|tool_response>
Generated tokens 49 tokens · stopped: STOP
The tests pass. I'm confident in the fix. Now I'll double check for any untracked files.
The tests pass. I'm confident in the fix.
Now I'll double check for any untracked files.
<channel|><|tool_call>call:run_command{command:<|"|>git status --short && git diff<|"|>}<tool_call|><|tool_response>
Generated tokens 743 tokens · stopped: STOP
The diff is clean and minimal.
Final check on the issue description:
- indices not 0: Handled
- indices not full length of the series: Handled (our repro had length 2 while series was length 3)
Wait, I should also check if `indices` being a PyArrow array (if that's possible) would still work.
Actually, `indices_native = pa.array(indices)` handles it.
Let's think about the `sorting_indices` logic again.
```python
sorting_indices = pc.sort_indices(indices_native)
indices_native = indices_native.take(sorting_indices)
values_native = values_native.take(sorting_indices)
```
If `indices = [1, 0, 2]` and `values = [v1, v0, v2]`:
`indices_native = [1, 0, 2]`
`values_native = [v1, v0, v2]`
`sorting_indices = [1, 0, 2]` (indices that would sort `indices_native` to `[0, 1, 2]`)
`indices_native = [0, 1, 2]`
`values_native = [v0, v1, v2]`
Then `mask` has `True` at 0, 1, 2.
`pc_replace_with_mask(self.native, mask, values_native)` replaces native[0] with v0, native[1] with v1, native[2] with v2.
Correct.
What if `indices` has duplicates?
If `indices = [0, 0]` and `values = [v1, v2]`:
`indices_native = [0, 0]`
`values_native = [v1, v2]`
`sorting_indices = [0, 1]`
`indices_native = [0, 0]`
`values_native = [v1, v2]` (order preserved for equal keys)
`mask` has `True` at 0. (only one True)
`pc_replace_with_mask`: PyArrow docs for `replace_with_mask` says replacements must have same length as number of Trues in mask.
Wait, if `mask` is `[True, False, False]`, then `mask.sum()` is 1.
But `values_native` has length 2.
`pc_replace_with_mask([a, b, c], [True, False, False], [v1, v2])` might raise an error because of length mismatch.
Let's test this case.
The diff is clean and minimal.
Final check on the issue description:
- indices not 0: Handled
- indices not full length of the series: Handled (our repro had length 2 while series was length 3)
Wait, I should also check if `indices` being a PyArrow array (if that's possible) would still work.
Actually, `indices_native = pa.array(indices)` handles it.
Let's think about the `sorting_indices` logic again.
```python
sorting_indices = pc.sort_indices(indices_native)
indices_native = indices_native.take(sorting_indices)
values_native = values_native.take(sorting_indices)
```
If `indices = [1, 0, 2]` and `values = [v1, v0, v2]`:
`indices_native = [1, 0, 2]`
`values_native = [v1, v0, v2]`
`sorting_indices = [1, 0, 2]` (indices that would sort `indices_native` to `[0, 1, 2]`)
`indices_native = [0, 1, 2]`
`values_native = [v0, v1, v2]`
Then `mask` has `True` at 0, 1, 2.
`pc_replace_with_mask(self.native, mask, values_native)` replaces native[0] with v0, native[1] with v1, native[2] with v2.
Correct.
What if `indices` has duplicates?
If `indices = [0, 0]` and `values = [v1, v2]`:
`indices_native = [0, 0]`
`values_native = [v1, v2]`
`sorting_indices = [0, 1]`
`indices_native = [0, 0]`
`values_native = [v1, v2]` (order preserved for equal keys)
`mask` has `True` at 0. (only one True)
`pc_replace_with_mask`: PyArrow docs for `replace_with_mask` says replacements must have same length as number of Trues in mask.
Wait, if `mask` is `[True, False, False]`, then `mask.sum()` is 1.
But `values_native` has length 2.
`pc_replace_with_mask([a, b, c], [True, False, False], [v1, v2])` might raise an error because of length mismatch.
Let's test this case.
<channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY'
import narwhals as nw
import pyarrow as pa
tbl = pa.table({"a": [1, 2, 3]})
frame = nw.from_native(tbl, eager_only=True)
ser = frame["a"]
try:
result = ser.scatter([0, 0], [999, 888])
print(f"Scatter with duplicates succeeded: {result.to_list()}")
except Exception as exc:
print(f"Scatter with duplicates failed: {exc}")
PY
<|"|>}<tool_call|><|tool_response>
Generated tokens 324 tokens · stopped: STOP
Wait, it succeeded! Why?
Ah, `mask[indices_native] = True`. If `indices_native` is `[0, 0]`, `mask[0]` becomes `True` twice.
So `mask` is `[True, False, False]`.
And `pc_replace_with_mask` with `values_native = [999, 888]`...
If it succeeded, it means `pc_replace_with_mask` might handle the length mismatch by taking only what's needed, or maybe `values_native` was truncated?
Actually, let's look at the result: `[999, 2, 3]`.
It took the first value `999` and used it.
The user didn't mention duplicates in the issue, and `scatter` behavior with duplicates is often implementation-defined (e.g. last one wins). If it's working for now and not causing a crash, it's probably fine.
Actually, if I want to be sure, I should check the `pc_replace_with_mask` behavior.
But given the problem statement, I've solved the reported bug.
One more check: what if `values` is a Series?
```python
if isinstance(values, self.__class__):
values_native = values.native.combine_chunks()
```
This handles it.
Everything seems correct.Wait, it succeeded! Why?
Ah, `mask[indices_native] = True`. If `indices_native` is `[0, 0]`, `mask[0]` becomes `True` twice.
So `mask` is `[True, False, False]`.
And `pc_replace_with_mask` with `values_native = [999, 888]`...
If it succeeded, it means `pc_replace_with_mask` might handle the length mismatch by taking only what's needed, or maybe `values_native` was truncated?
Actually, let's look at the result: `[999, 2, 3]`.
It took the first value `999` and used it.
The user didn't mention duplicates in the issue, and `scatter` behavior with duplicates is often implementation-defined (e.g. last one wins). If it's working for now and not causing a crash, it's probably fine.
Actually, if I want to be sure, I should check the `pc_replace_with_mask` behavior.
But given the problem statement, I've solved the reported bug.
One more check: what if `values` is a Series?
```python
if isinstance(values, self.__class__):
values_native = values.native.combine_chunks()
```
This handles it.
Everything seems correct.<channel|><|tool_call>call:submit_patch{}<tool_call|><|tool_response>
Patch, tests and logs
Final code change Included
diff --git a/narwhals/_arrow/series.py b/narwhals/_arrow/series.py
index e22d20f..a846e50 100644
--- a/narwhals/_arrow/series.py
+++ b/narwhals/_arrow/series.py
@@ -468,7 +468,7 @@ class ArrowSeries(EagerSeries["ChunkedArrayAny"]):
# - Missing return type
pc_replace_with_mask: Incomplete = pc.replace_with_mask
return self._with_native(
- pc_replace_with_mask(self.native, mask, values_native.take(indices_native))
+ pc_replace_with_mask(self.native, mask, values_native)
)
def to_list(self) -> list[Any]:
Test output Included
============================= test session starts ============================== collected 25 items tests/series_only/scatter_test.py ......................... [100%] ==================================== PASSES ==================================== - generated xml file: /tmp/rebench-grade-ff2be721468646679cc32cf0bd3603d9.xml -- =========================== short test summary info ============================ PASSED tests/series_only/scatter_test.py::test_scatter_indices PASSED tests/series_only/scatter_test.py::test_scatter_2862[polars[eager]] PASSED tests/series_only/scatter_test.py::test_scatter_unchanged[pandas[pyarrow]] PASSED tests/series_only/scatter_test.py::test_single_series[pyarrow] PASSED tests/series_only/scatter_test.py::test_scatter_2862[pandas[pyarrow]] PASSED tests/series_only/scatter_test.py::test_scatter_2862[pyarrow] PASSED tests/series_only/scatter_test.py::test_single_series[pandas[pyarrow]] PASSED tests/series_only/scatter_test.py::test_scatter_2862[pandas] PASSED tests/series_only/scatter_test.py::test_scatter_unordered_indices[pandas[pyarrow]] PASSED tests/series_only/scatter_test.py::test_single_series[pandas] PASSED tests/series_only/scatter_test.py::test_scatter[pandas[pyarrow]] PASSED tests/series_only/scatter_test.py::test_scatter_unordered_indices[polars[eager]] PASSED tests/series_only/scatter_test.py::test_scatter[polars[eager]] PASSED tests/series_only/scatter_test.py::test_scatter_unordered_indices[pandas] PASSED tests/series_only/scatter_test.py::test_scatter_integer[pandas] PASSED tests/series_only/scatter_test.py::test_scatter_integer[pandas[pyarrow]] PASSED tests/series_only/scatter_test.py::test_scatter_unchanged[pyarrow] PASSED tests/series_only/scatter_test.py::test_scatter_integer[polars[eager]] PASSED tests/series_only/scatter_test.py::test_scatter_unchanged[polars[eager]] PASSED tests/series_only/scatter_test.py::test_scatter_unordered_indices[pyarrow] PASSED tests/series_only/scatter_test.py::test_single_series[polars[eager]] PASSED tests/series_only/scatter_test.py::test_scatter[pandas] PASSED tests/series_only/scatter_test.py::test_scatter_integer[pyarrow] PASSED tests/series_only/scatter_test.py::test_scatter[pyarrow] PASSED tests/series_only/scatter_test.py::test_scatter_unchanged[pandas] ============================== 25 passed in 0.53s ==============================
Test errors Included
Test report (JUnit) Included
<?xml version="1.0" encoding="utf-8"?><testsuites name="pytest tests"><testsuite name="pytest" errors="0" failures="0" skipped="0" tests="25" time="0.093" timestamp="2026-11-14T04:05:13.738151+00:00" hostname="8b36235e7f4e"><testcase classname="tests.series_only.scatter_test" name="test_scatter_indices" time="0.021" /><testcase classname="tests.series_only.scatter_test" name="test_scatter_2862[polars[eager]]" time="0.010" /><testcase classname="tests.series_only.scatter_test" name="test_scatter_unchanged[pandas[pyarrow]]" time="0.007" /><testcase classname="tests.series_only.scatter_test" name="test_single_series[pyarrow]" time="0.015" /><testcase classname="tests.series_only.scatter_test" name="test_scatter_2862[pandas[pyarrow]]" time="0.001" /><testcase classname="tests.series_only.scatter_test" name="test_scatter_2862[pyarrow]" time="0.001" /><testcase classname="tests.series_only.scatter_test" name="test_single_series[pandas[pyarrow]]" time="0.001" /><testcase classname="tests.series_only.scatter_test" name="test_scatter_2862[pandas]" time="0.001" /><testcase classname="tests.series_only.scatter_test" name="test_scatter_unordered_indices[pandas[pyarrow]]" time="0.001" /><testcase classname="tests.series_only.scatter_test" name="test_single_series[pandas]" time="0.001" /><testcase classname="tests.series_only.scatter_test" name="test_scatter[pandas[pyarrow]]" time="0.001" /><testcase classname="tests.series_only.scatter_test" name="test_scatter_unordered_indices[polars[eager]]" time="0.000" /><testcase classname="tests.series_only.scatter_test" name="test_scatter[polars[eager]]" time="0.002" /><testcase classname="tests.series_only.scatter_test" name="test_scatter_unordered_indices[pandas]" time="0.001" /><testcase classname="tests.series_only.scatter_test" name="test_scatter_integer[pandas]" time="0.001" /><testcase classname="tests.series_only.scatter_test" name="test_scatter_integer[pandas[pyarrow]]" time="0.001" /><testcase classname="tests.series_only.scatter_test" name="test_scatter_unchanged[pyarrow]" time="0.001" /><testcase classname="tests.series_only.scatter_test" name="test_scatter_integer[polars[eager]]" time="0.000" /><testcase classname="tests.series_only.scatter_test" name="test_scatter_unchanged[polars[eager]]" time="0.001" /><testcase classname="tests.series_only.scatter_test" name="test_scatter_unordered_indices[pyarrow]" time="0.001" /><testcase classname="tests.series_only.scatter_test" name="test_single_series[polars[eager]]" time="0.000" /><testcase classname="tests.series_only.scatter_test" name="test_scatter[pandas]" time="0.001" /><testcase classname="tests.series_only.scatter_test" name="test_scatter_integer[pyarrow]" time="0.000" /><testcase classname="tests.series_only.scatter_test" name="test_scatter[pyarrow]" time="0.001" /><testcase classname="tests.series_only.scatter_test" name="test_scatter_unchanged[pandas]" time="0.001" /></testsuite></testsuites>
Agent log Included
[ 0.0s] 📋 System Prompt: You are a principal Python software engineer resolving a repository issue in `/workspace`.
[ 0.0s] 🎯 Task Prompt: You are evaluating a software engineering task for repository narwhals-dev/narwhals.
[ 25.9s] 💭 (386 chars) "The user is reporting a bug in `ArrowSeries.scatter` where it raises an "out of bound error"
when indices are not 0 and ..."
[ 25.9s] 🔧 run_command (21 lines) python3 - <<'PY'
[ 27.0s] → First scatter failed: Index 1 out of bounds
[ 36.5s] 💭 (218 chars) "The bug is reproduced. Both cases fail with "Index X out of bounds".
Now I need to find the implementation of `scatter`..."
[ 36.5s] 🔧 run_command grep -rn --include="*.py" "def scatter" narwhals/
[ 36.6s] → narwhals/_compliant/series.py:251: def scatter(self, indices: int | Sequence[int], values: Any) ->
Self: ...
[ 41.7s] 💭 (98 chars) "Okay, `narwhals/_arrow/series.py:440` is where `ArrowSeries.scatter` is defined. Let's
examine it."
[ 41.7s] 📖 read_file (narwhals/_arrow/series.py)
[ 41.7s] ✓ {'status': 'ok', 'filepath': 'narwhals/_arrow/series.py', 'content': ' stop = rows.stop if
rows.stop is not None …
[ 45.3s] 💭 (113 chars) "The code shown in `read_file` only goes up to line 460. Let's read more to see how the
actual scattering is done."
[ 45.3s] 📖 read_file (narwhals/_arrow/series.py)
[ 45.3s] ✓ {'status': 'ok', 'filepath': 'narwhals/_arrow/series.py', 'content': '\n mask: _1DArray =
np.zeros(self.len(), dt…
[ 119.0s] 💭 (3219 chars) "Wait, look at this line:
```python
return self._with_native(
pc_replace_with_mask(self.native, mask,..."
[ 119.0s] ✏️ edit_file (narwhals/_arrow/series.py)
[ 119.2s] ✓ {'status': 'ok', 'filepath': 'narwhals/_arrow/series.py', 'occurrences': 1, 'strategy': 'exact', 'diff':
'--- a/narwhals…
[ 129.3s] 💭 (44 chars) "Now let's run the reproduction script again."
[ 129.3s] 🔧 run_command (21 lines) python3 - <<'PY'
[ 129.7s] → First scatter succeeded: [1, 999, 3]
[ 136.9s] 💭 (232 chars) "Great, the reproduction now passes.
Are there other backends that might have this issue?
`narwhals/_pandas_like/series...."
[ 136.9s] 📖 read_file (narwhals/_polars/series.py)
[ 137.0s] ✓ {'status': 'ok', 'filepath': 'narwhals/_polars/series.py', 'content': ' is_null =
result.is_null()\n …
[ 141.5s] 💭 (131 chars) "Polars implementation uses `self.native.scatter`, which should handle it correctly.
Let's check `narwhals/_pandas_like/..."
[ 141.5s] 📖 read_file (narwhals/_pandas_like/series.py)
[ 141.6s] ✓ {'status': 'ok', 'filepath': 'narwhals/_pandas_like/series.py', 'content': ' raise
NotImplementedError(ms…
[ 148.1s] 💭 (175 chars) "Pandas-like uses `iloc[indices] = values`, which is correct.
Let's check `narwhals/_duckdb/series.py`. Wait, looking at..."
[ 148.1s] 🔧 run_command grep -rn "def scatter" narwhals/_duckdb/series.py || echo "Not found"
[ 148.1s] → Not found
[ 151.5s] 💭 (110 chars) "It seems only some backends implement `scatter` directly in `Series`.
Let's check `narwhals/_ibis/series.py`."
[ 151.5s] 🔧 run_command grep -rn "def scatter" narwhals/_ibis/series.py || echo "Not found"
[ 151.6s] → Not found
[ 165.2s] 💭 (380 chars) "It seems the problem was specifically in `narwhals/_arrow/series.py`.
Let's double check if there are any other places ..."
[ 165.2s] 📖 read_file (narwhals/series.py)
[ 165.2s] ✓ {'status': 'ok', 'filepath': 'narwhals/series.py', 'content': ' Series: \'\' [i64]\n
[\n …
[ 194.8s] 💭 (1267 chars) "`narwhals/series.py` just defines the API and probably dispatches to the backend
implementation via some mechanism (like..."
[ 194.8s] 📖 read_file (narwhals/_compliant/series.py)
[ 194.8s] ✓ {'status': 'ok', 'filepath': 'narwhals/_compliant/series.py', 'content': ' self, window_size:
int, *, min_samples…
[ 203.8s] 💭 (262 chars) "Yes, it's just a stub.
So the only broken implementation was in `narwhals/_arrow/series.py`.
I've verified the fix wit..."
[ 203.8s] 🔧 run_command grep -rn "scatter" tests/
[ 203.8s] → tests/series_only/scatter_test.py:9:def test_scatter(constructor_eager: ConstructorEager) -> None:
[ 206.6s] 💭 (46 chars) "Let's run `tests/series_only/scatter_test.py`."
[ 206.6s] 🔧 run_command pytest tests/series_only/scatter_test.py -q --tb=short --no-header
[ 208.1s] → ..................... [100%]
[ 211.0s] 💭 (89 chars) "The tests pass. I'm confident in the fix.
Now I'll double check for any untracked files."
[ 211.0s] 🔧 run_command git status --short && git diff
[ 211.0s] → M narwhals/_arrow/series.py
[ 254.7s] 💭 (1687 chars) "The diff is clean and minimal.
Final check on the issue description:
- indices not 0: Handled
- indices not full length..."
[ 254.7s] 🔧 run_command (14 lines) python3 - <<'PY'
[ 255.1s] → Scatter with duplicates succeeded: [999, 2, 3]
[ 278.1s] 💭 (1050 chars) "Wait, it succeeded! Why?
Ah, `mask[indices_native] = True`. If `indices_native` is `[0, 0]`, `mask[0]` becomes `True` tw..."
[ 278.1s] 🤖 submit_patch
[ 278.2s] → submit_patch: {"status": "ok", "patch_size": 572, "files_changed": 1}
[ 292.5s] 💭 (319 chars) "The bug was that `ArrowSeries.scatter` was calling `.take(indices_native)` on the
replacements array, which is incorrect..."
[ 292.5s] ✅ Agent finished: The bug in `ArrowSeries.scatter` was caused by an incorrect call to
`take(indices_native)` on the `values_native` (replacements) array. Since `values_native` was already sorted to match
the sorted `in...
Native result row
{
"agent_error": null,
"duration_seconds": 302.71,
"error": null,
"grade": {
"collected_tests": 25,
"competition_junit_ok": true,
"error": null,
"required_issues_count": 0,
"resolved": true,
"status_counts": {
"PASSED": 25
},
"strict_clean_patch": true,
"test_exit_code": 0
},
"instance_id": "narwhals-dev__narwhals-2864",
"pass_idx": 0,
"patch_chars": 572,
"phase1_seconds": 299.95,
"repo": "narwhals-dev/narwhals",
"request_log_path": "/kaggle-root/working/r31/rollouts_X/requests/narwhals-dev__narwhals-2864__p0__r1.jsonl",
"resolved": true,
"rollout_idx": 1,
"run_key": "narwhals-dev__narwhals-2864__p0__r1",
"strict_clean_patch": true,
"tool_calls": 16,
"total_llm_calls": 18,
"trace_path": "/kaggle-root/working/r31/rollouts_X/traces/trace_narwhals-dev__narwhals-2864__p0__r1.json"
}