ROLLOUT 4 · narwhals-dev/narwhals
narwhals-dev__narwhals-2864
Solved
293 stotal time
16tool calls
18model calls
4,699tokens generated
162,280prompt tokens read
No error recorded
⚠ 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 35 s610 tokens out4,792 prompt tokens
The user is reporting a bug in `ArrowSeries.scatter` where it raises an "out of bound" error when `indices` is not 0 and not the full length of the series.
Looking at the 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)
```
Output:
`Index 1 out of bounds`
`Index 2 out of bounds`
Wait, the series `a` has 3 elements: `[1, 2, 3]`.
The indices are 0, 1, 2.
So `ser.scatter(1, 999)` should be fine (index 1 is within [0, 2]).
`ser.scatter([0, 2], [999, 888])` should be fine (indices 0 and 2 are within [0, 2]).
So why does it say "out of bounds"?
Let's investigate where `scatter` is implemented for Arrow.
The workspace layout shows `narwhals/_arrow/series.py`.
I'll 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\nprint(\"Testing scatter(1, 999):\")\ntry:\n result = ser.scatter(1, 999)\n print(f\"Result: {result}\")\nexcept Exception as exc:\n print(f\"Caught exception: {exc}\")\n\nprint(\"\\nTesting scatter([0, 2], [999, 888]):\")\ntry:\n result = ser.scatter([0, 2], [999, 888])\n print(f\"Result: {result}\")\nexcept Exception as exc:\n print(f\"Caught exception: {exc}\")\nPY\n"
}Result of run_command 208 characters
{
"status": "ok",
"stdout": "Testing scatter(1, 999):\nCaught exception: Index 1 out of bounds\n\nTesting scatter([0, 2], [999, 888]):\nCaught exception: Index 2 out of bounds\n",
"stderr": "",
"exit_code": 0
}Turn 2at 41 s91 tokens out5,496 prompt tokens
The bug is reproduced. Now let's find the implementation of `scatter` for Arrow. I'll check `narwhals/series.py` first to see the public API and then `narwhals/_arrow/series.py`.
TOOL CALL · run_command
{
"command": "grep -rn \"def scatter\" narwhals/series.py narwhals/_arrow/series.py"
}Result of run_command 263 characters
{
"status": "ok",
"stdout": "narwhals/series.py:238: 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:\n",
"stderr": "",
"exit_code": 0
}Turn 3at 48 s84 tokens out5,696 prompt tokens
Okay, it's in `narwhals/_arrow/series.py` at line 440. Let's examine the implementation in `narwhals/_arrow/series.py`.
TOOL CALL · read_file
{
"end_line": 470,
"filepath": "narwhals/_arrow/series.py",
"start_line": 430
}Result of read_file 2,039 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)\n\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(",
"start_line": 430,
"end_line": 470,
"total_lines": 1170,
"is_truncated": false
}Turn 4at 53 s74 tokens out6,388 prompt tokens
Wait, the `read_file` output is truncated! I can't see the end of the `scatter` method. Let me read more lines.
TOOL CALL · read_file
{
"end_line": 480,
"filepath": "narwhals/_arrow/series.py",
"start_line": 460
}Result of read_file 1,038 characters
{
"status": "ok",
"filepath": "narwhals/_arrow/series.py",
"content": " values_native = values_native.take(sorting_indices)\n\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": 460,
"end_line": 480,
"total_lines": 1170,
"is_truncated": false
}Turn 5at 128 s1,089 tokens out6,814 prompt tokens
Here is the problematic part:
```python
mask: _1DArray = np.zeros(self.len(), dtype=bool)
mask[indices_native] = True
# ...
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))
)
```
The issue is likely `values_native.take(indices_native)`.
`indices_native` are the indices into the original series `self`.
But `values_native` is the array of *values to be scattered*, which has length equal to the number of indices.
If `indices` is `[0, 2]`, then `indices_native` is `[0, 2]`.
`values_native` will have length 2 (e.g., `[999, 888]`).
`values_native.take([0, 2])` will fail because index 2 is out of bounds for an array of length 2.
The `values_native` should already contain the values at the sorted positions if the sorting of indices was performed.
Let's trace:
1. `indices` = `[0, 2]`
2. `values` = `[999, 888]`
3. `indices_native` = `pa.array([0, 2])`
4. `values_native` = `pa.array([999, 888])`
5. `sorting_indices` = `pc.sort_indices([0, 2])` -> `[0, 1]` (since 0 < 2)
6. `indices_native` becomes `[0, 2].take([0, 1])` -> `[0, 2]`
7. `values_native` becomes `[999, 888].take([0, 1])` -> `[999, 888]`
8. `mask` = `[True, False, True]`
9. `values_native.take(indices_native)` -> `[999, 888].take([0, 2])` -> BOOM! Index 2 out of bounds.
The `pc.replace_with_mask` expected the third argument to be the replacements for the positions where the mask is True.
Wait, let's check `pyarrow.compute.replace_with_mask` documentation (mentally or by trying if I could).
Actually, `pc.replace_with_mask(array, mask, replacements)`:
If `mask` is a boolean array, `replacements` should be the values to use where the mask is true.
The length of `replacements` must be the number of true values in the mask.
In our case, `values_native` already has the correct length (number of indices) and it was already sorted to match the sorted `indices_native`.
So `values_native` is already the `replacements` array we need.
We just need to pass `values_native` itself, not `values_native.take(indices_native)`.
Let's double check the case where `indices` is a single `int`.
1. `indices` = 1
2. `values` = 999
3. `indices_native` = `pa.array([1])`
4. `values_native` = `pa.array([999])`
5. `sorting_indices` = `pc.sort_indices([1])` -> `[0]`
6. `indices_native` = `[1].take([0])` -> `[1]`
7. `values_native` = `[999].take([0])` -> `[999]`
8. `mask` = `[False, True, False]`
9. `values_native.take([1])` -> BOOM! Index 1 out of bounds for array of length 1.
Correct. It should just be `values_native`.
Let's apply the fix.
TOOL CALL · edit_file
{
"filepath": "narwhals/_arrow/series.py",
"new_string": " pc_replace_with_mask: Incomplete = pc.replace_with_mask\n return self._with_native(\n pc_replace_with_mask(self.native, mask, values_native)\n )",
"old_string": " 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 )"
}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 137 s258 tokens out8,113 prompt tokens
Now let's verify the fix with the reproduction script.
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\nprint(\"Testing scatter(1, 999):\")\ntry:\n result = ser.scatter(1, 999)\n print(f\"Result: {result.to_list()}\")\nexcept Exception as exc:\n print(f\"Caught exception: {exc}\")\n\nprint(\"\\nTesting scatter([0, 2], [999, 888]):\")\ntry:\n result = ser.scatter([0, 2], [999, 888])\n print(f\"Result: {result.to_list()}\")\nexcept Exception as exc:\n print(f\"Caught exception: {exc}\")\nPY\n"
}Result of run_command 170 characters
{
"status": "ok",
"stdout": "Testing scatter(1, 999):\nResult: [1, 999, 3]\n\nTesting scatter([0, 2], [999, 888]):\nResult: [999, 2, 888]\n",
"stderr": "",
"exit_code": 0
}Turn 7at 146 s80 tokens out8,472 prompt tokens
The repro tests now pass. I should also check if this happens in other backends. The issue specifically mentioned `ArrowSeries`, but Narwhals often has similar implementations across backends. Let's search for `def scatter` in other files.
TOOL CALL · run_command
{
"command": "grep -rn \"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 8at 153 s75 tokens out8,773 prompt tokens
I missed `_polars/series.py`. Let's see how it's implemented. Also `_pandas_like/series.py`.
TOOL CALL · read_file
{
"end_line": 450,
"filepath": "narwhals/_polars/series.py",
"start_line": 430
}Result of read_file 1,050 characters
{
"status": "ok",
"filepath": "narwhals/_polars/series.py",
"content": " 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 **{\n (self.native.name): pl.col(self.native.name),\n value_name_: pl.col(\"count\") / pl.sum(\"count\")\n if normalize\n else pl.col(\"count\"),\n }",
"start_line": 430,
"end_line": 450,
"total_lines": 741,
"is_truncated": false
}Turn 9at 159 s75 tokens out9,174 prompt tokens
Polars' implementation is very simple and uses Polars' own `scatter`. Now let's check `_pandas_like/series.py`.
TOOL CALL · read_file
{
"end_line": 290,
"filepath": "narwhals/_pandas_like/series.py",
"start_line": 270
}Result of read_file 969 characters
{
"status": "ok",
"filepath": "narwhals/_pandas_like/series.py",
"content": " 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:\n implementation = self._implementation\n backend_version = self._backend_version\n # Scatter, modifying original Series. Use with care!\n values_native = set_index(\n values.native,",
"start_line": 270,
"end_line": 290,
"total_lines": 1106,
"is_truncated": false
}Turn 10at 167 s78 tokens out9,540 prompt tokens
Pandas-like's implementation uses `.iloc[indices] = values`, which should work correctly. So it seems only the Arrow implementation was flawed. Now I should check if there are any existing tests for `scatter` that I should run.
TOOL CALL · run_command
{
"command": "grep -rn \"test_scatter\" tests/"
}Result of run_command 573 characters
{
"status": "ok",
"stdout": "tests/series_only/scatter_test.py:9:def test_scatter(constructor_eager: ConstructorEager) -> None:\ntests/series_only/scatter_test.py:20:def test_scatter_indices() -> None:\ntests/series_only/scatter_test.py:27:def test_scatter_unchanged(constructor_eager: ConstructorEager) -> None:\ntests/series_only/scatter_test.py:48:def test_scatter_integer(constructor_eager: ConstructorEager) -> None:\ntests/series_only/scatter_test.py:58:def test_scatter_unordered_indices(constructor_eager: ConstructorEager) -> None:\n",
"stderr": "",
"exit_code": 0
}Turn 11at 169 s45 tokens out9,822 prompt tokens
Let's run these tests.
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 12at 196 s473 tokens out9,928 prompt tokens
All existing tests passed. Wait, the issue said: - is not 0 - are not full length of the series In my repro: - `ser.scatter(1, 999)`: index is 1 (not 0), and length is 1 (not 3). Failed. - `ser.scatter([0, 2], [999, 888])`: indices are 0 and 2, length is 2 (not 3). Failed. If indices were `[0, 1, 2]`, it would be full length. Let's verify if full length works (even before my fix, but just to be sure). Actually, if `indices` = `[0, 1, 2]`, then `indices_native` = `[0, 1, 2]` and `values_native` = `[999, 888, 777]`. `values_native.take([0, 1, 2])` would work! That's why it only failed when not full length. Let's confirm this behavior with a small script.
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\n# Full length\ntry:\n result = ser.scatter([0, 1, 2], [999, 888, 777])\n print(f\"Full length result: {result.to_list()}\")\nexcept Exception as exc:\n print(f\"Full length failed: {exc}\")\n\n# Index 0 only\ntry:\n result = ser.scatter(0, 999)\n print(f\"Index 0 result: {result.to_list()}\")\nexcept Exception as exc:\n print(f\"Index 0 failed: {exc}\")\nPY\n"
}Result of run_command 126 characters
{
"status": "ok",
"stdout": "Full length result: [999, 888, 777]\nIndex 0 result: [999, 2, 3]\n",
"stderr": "",
"exit_code": 0
}Turn 13at 223 s496 tokens out10,476 prompt tokens
Wait, `ser.scatter(0, 999)` worked? Let's trace `ser.scatter(0, 999)`: 1. `indices` = 0 2. `values` = 999 3. `indices_native` = `pa.array([0])` 4. `values_native` = `pa.array([999])` 5. `sorting_indices` = `[0]` 6. `indices_native` = `[0]` 7. `values_native` = `[999]` 8. `mask` = `[True, False, False]` 9. `values_native.take([0])` -> `[999].take([0])` -> `[999]`. WORKS! So the issue is specifically when `indices` (after sorting) contains any value $\ge$ length of `values_native`. Since `len(values_native) == len(indices)`, this happens whenever `max(indices)` $\ge$ `len(indices)`. If `indices` = `[0, 2]`, `len(indices)` = 2, and `max(indices)` = 2. $2 \ge 2$ is True. Failure. If `indices` = `[0, 1]`, `len(indices)` = 2, and `max(indices)` = 1. $1 \ge 2$ is False. Success? Wait, let's check `[0, 1]`.
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, 1], [999, 888])\n print(f\"Indices [0, 1] result: {result.to_list()}\")\nexcept Exception as exc:\n print(f\"Indices [0, 1] failed: {exc}\")\nPY\n"
}Result of run_command 98 characters
{
"status": "ok",
"stdout": "Indices [0, 1] result: [999, 888, 3]\n",
"stderr": "",
"exit_code": 0
}Turn 14at 240 s225 tokens out11,034 prompt tokens
Yes, that makes sense. Anyway, my fix of removing `.take(indices_native)` is correct because `values_native` is already aligned with the sorted `indices_native` and its length matches the number of items being replaced. I've verified the fix with the repro and existing tests. I'll double check if I missed any other `_arrow` implementation that might have the same bug. I already checked `_arrow/series.py`. Are there other series-like things in `_arrow`? `narwhals/_arrow/series_cat.py`, `_arrow/series_dt.py`, etc. Usually, these inherit from `ArrowSeries` (defined in `narwhals/_arrow/series.py`). If they don't override `scatter`, they use the one in `ArrowSeries`. Let's check if they override it.
TOOL CALL · run_command
{
"command": "grep -rn \"def scatter\" narwhals/_arrow/series_*.py"
}Result of run_command 127 characters
{
"status": "error",
"error_type": "CommandError",
"error_message": "",
"details": {
"stdout": "",
"stderr": "",
"exit_code": 1
}
}Turn 15at 243 s60 tokens out11,314 prompt tokens
The `grep` failed because of how I used `*`. I should use `grep -rn "def scatter" narwhals/_arrow/`.
TOOL CALL · run_command
{
"command": "grep -rn \"def scatter\" narwhals/_arrow/"
}Result of run_command 165 characters
{
"status": "ok",
"stdout": "narwhals/_arrow/series.py:440: def scatter(self, indices: int | Sequence[int], values: Any) -> Self:\n",
"stderr": "",
"exit_code": 0
}Turn 16at 278 s759 tokens out11,448 prompt tokens
Only `narwhals/_arrow/series.py` implements `scatter`.
I'm confident in the fix.
Final check of the changes:
`narwhals/_arrow/series.py`
Old: `pc_replace_with_mask(self.native, mask, values_native.take(indices_native))`
New: `pc_replace_with_mask(self.native, mask, values_native)`
Wait, let me re-read the `scatter` method to be absolutely sure about the sorting.
```python
sorting_indices = pc.sort_indices(indices_native)
indices_native = indices_native.take(sorting_indices)
values_native = values_native.take(sorting_indices)
mask: _1DArray = np.zeros(self.len(), dtype=bool)
mask[indices_native] = True
# ...
return self._with_native(
pc_replace_with_mask(self.native, mask, values_native)
)
```
If `indices` = `[2, 0]` and `values` = `[888, 999]`.
1. `indices_native` = `[2, 0]`
2. `values_native` = `[888, 999]`
3. `sorting_indices` = `pc.sort_indices([2, 0])` -> `[1, 0]`
4. `indices_native` = `[2, 0].take([1, 0])` -> `[0, 2]`
5. `values_native` = `[888, 999].take([1, 0])` -> `[999, 888]`
6. `mask` = `[True, False, True]`
7. `pc_replace_with_mask(self.native, mask, [999, 888])`
- Index 0 is True -> replaced by 999.
- Index 2 is True -> replaced by 888.
Result: `[999, original[1], 888]`. Correct.
Yes, the sorting ensures that `values_native` is ordered according to the indices in `indices_native` (which is sorted ascendingly). `pc.replace_with_mask` expects replacements to be in the order that the mask is True.
One last check: what if `indices` is an `int`?
1. `indices` = 2
2. `values` = 888
3. `indices_native` = `[2]`
4. `values_native` = `[888]`
5. `sorting_indices` = `[0]`
6. `indices_native` = `[2]`
7. `values_native` = `[888]`
8. `mask` = `[False, False, True]`
9. `pc_replace_with_mask(self.native, mask, [888])` -> `[original[0], original[1], 888]`. Correct.
The fix is correct.
```bash
git status --short
git diff
```
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 17at 279 s15 tokens out12,473 prompt tokens
Everything looks clean.
TOOL CALL · submit_patch
Result of submit_patch 55 characters
{
"status": "ok",
"patch_size": 572,
"files_changed": 1
}Turn 18at 288 s112 tokens out12,527 prompt tokens
DONE.
Turn 19at 288 s
The bug was caused by calling `.take(indices_native)` on the `values_native` array in `ArrowSeries.scatter`. Since `values_native` has a length equal to the number of indices being scattered, and `indices_native` contains the actual indices into the original series, any index value greater than or equal to the number of scattered elements would cause an "out of bounds" error. Removing this call fixes the issue as `values_native` is already sorted to match the mask's True values.
Model calls (not matched to turns)
Generated tokens 610 tokens · stopped: STOP
The user is reporting a bug in `ArrowSeries.scatter` where it raises an "out of bound" error when `indices` is not 0 and not the full length of the series.
Looking at the 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)
```
Output:
`Index 1 out of bounds`
`Index 2 out of bounds`
Wait, the series `a` has 3 elements: `[1, 2, 3]`.
The indices are 0, 1, 2.
So `ser.scatter(1, 999)` should be fine (index 1 is within [0, 2]).
`ser.scatter([0, 2], [999, 888])` should be fine (indices 0 and 2 are within [0, 2]).
So why does it say "out of bounds"?
Let's investigate where `scatter` is implemented for Arrow.
The workspace layout shows `narwhals/_arrow/series.py`.
I'll 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` is not 0 and not the full length of the series.
Looking at the 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)
```
Output:
`Index 1 out of bounds`
`Index 2 out of bounds`
Wait, the series `a` has 3 elements: `[1, 2, 3]`.
The indices are 0, 1, 2.
So `ser.scatter(1, 999)` should be fine (index 1 is within [0, 2]).
`ser.scatter([0, 2], [999, 888])` should be fine (indices 0 and 2 are within [0, 2]).
So why does it say "out of bounds"?
Let's investigate where `scatter` is implemented for Arrow.
The workspace layout shows `narwhals/_arrow/series.py`.
I'll 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"]
print("Testing scatter(1, 999):")
try:
result = ser.scatter(1, 999)
print(f"Result: {result}")
except Exception as exc:
print(f"Caught exception: {exc}")
print("\nTesting scatter([0, 2], [999, 888]):")
try:
result = ser.scatter([0, 2], [999, 888])
print(f"Result: {result}")
except Exception as exc:
print(f"Caught exception: {exc}")
PY
<|"|>}<tool_call|><|tool_response>
Generated tokens 91 tokens · stopped: STOP
The bug is reproduced. Now let's find the implementation of `scatter` for Arrow. I'll check `narwhals/series.py` first to see the public API and then `narwhals/_arrow/series.py`.
The bug is reproduced.
Now let's find the implementation of `scatter` for Arrow.
I'll check `narwhals/series.py` first to see the public API and then `narwhals/_arrow/series.py`.<channel|><|tool_call>call:run_command{command:<|"|>grep -rn "def scatter" narwhals/series.py narwhals/_arrow/series.py<|"|>}<tool_call|><|tool_response>
Generated tokens 84 tokens · stopped: STOP
Okay, it's in `narwhals/_arrow/series.py` at line 440. Let's examine the implementation in `narwhals/_arrow/series.py`.
Okay, it's in `narwhals/_arrow/series.py` at line 440.
Let's examine the implementation in `narwhals/_arrow/series.py`.
<channel|><|tool_call>call:read_file{filepath:<|"|>narwhals/_arrow/series.py<|"|>,start_line:430,end_line:470}<tool_call|><|tool_response>
Generated tokens 74 tokens · stopped: STOP
Wait, the `read_file` output is truncated! I can't see the end of the `scatter` method. Let me read more lines.
Wait, the `read_file` output is truncated! I can't see the end of the `scatter` method.
Let me read more lines.
<channel|><|tool_call>call:read_file{end_line:480,filepath:<|"|>narwhals/_arrow/series.py<|"|>,start_line:460}<tool_call|><|tool_response>
Generated tokens 1,089 tokens · stopped: STOP
Here is the problematic part:
```python
mask: _1DArray = np.zeros(self.len(), dtype=bool)
mask[indices_native] = True
# ...
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))
)
```
The issue is likely `values_native.take(indices_native)`.
`indices_native` are the indices into the original series `self`.
But `values_native` is the array of *values to be scattered*, which has length equal to the number of indices.
If `indices` is `[0, 2]`, then `indices_native` is `[0, 2]`.
`values_native` will have length 2 (e.g., `[999, 888]`).
`values_native.take([0, 2])` will fail because index 2 is out of bounds for an array of length 2.
The `values_native` should already contain the values at the sorted positions if the sorting of indices was performed.
Let's trace:
1. `indices` = `[0, 2]`
2. `values` = `[999, 888]`
3. `indices_native` = `pa.array([0, 2])`
4. `values_native` = `pa.array([999, 888])`
5. `sorting_indices` = `pc.sort_indices([0, 2])` -> `[0, 1]` (since 0 < 2)
6. `indices_native` becomes `[0, 2].take([0, 1])` -> `[0, 2]`
7. `values_native` becomes `[999, 888].take([0, 1])` -> `[999, 888]`
8. `mask` = `[True, False, True]`
9. `values_native.take(indices_native)` -> `[999, 888].take([0, 2])` -> BOOM! Index 2 out of bounds.
The `pc.replace_with_mask` expected the third argument to be the replacements for the positions where the mask is True.
Wait, let's check `pyarrow.compute.replace_with_mask` documentation (mentally or by trying if I could).
Actually, `pc.replace_with_mask(array, mask, replacements)`:
If `mask` is a boolean array, `replacements` should be the values to use where the mask is true.
The length of `replacements` must be the number of true values in the mask.
In our case, `values_native` already has the correct length (number of indices) and it was already sorted to match the sorted `indices_native`.
So `values_native` is already the `replacements` array we need.
We just need to pass `values_native` itself, not `values_native.take(indices_native)`.
Let's double check the case where `indices` is a single `int`.
1. `indices` = 1
2. `values` = 999
3. `indices_native` = `pa.array([1])`
4. `values_native` = `pa.array([999])`
5. `sorting_indices` = `pc.sort_indices([1])` -> `[0]`
6. `indices_native` = `[1].take([0])` -> `[1]`
7. `values_native` = `[999].take([0])` -> `[999]`
8. `mask` = `[False, True, False]`
9. `values_native.take([1])` -> BOOM! Index 1 out of bounds for array of length 1.
Correct. It should just be `values_native`.
Let's apply the fix.
Here is the problematic part:
```python
mask: _1DArray = np.zeros(self.len(), dtype=bool)
mask[indices_native] = True
# ...
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))
)
```
The issue is likely `values_native.take(indices_native)`.
`indices_native` are the indices into the original series `self`.
But `values_native` is the array of *values to be scattered*, which has length equal to the number of indices.
If `indices` is `[0, 2]`, then `indices_native` is `[0, 2]`.
`values_native` will have length 2 (e.g., `[999, 888]`).
`values_native.take([0, 2])` will fail because index 2 is out of bounds for an array of length 2.
The `values_native` should already contain the values at the sorted positions if the sorting of indices was performed.
Let's trace:
1. `indices` = `[0, 2]`
2. `values` = `[999, 888]`
3. `indices_native` = `pa.array([0, 2])`
4. `values_native` = `pa.array([999, 888])`
5. `sorting_indices` = `pc.sort_indices([0, 2])` -> `[0, 1]` (since 0 < 2)
6. `indices_native` becomes `[0, 2].take([0, 1])` -> `[0, 2]`
7. `values_native` becomes `[999, 888].take([0, 1])` -> `[999, 888]`
8. `mask` = `[True, False, True]`
9. `values_native.take(indices_native)` -> `[999, 888].take([0, 2])` -> BOOM! Index 2 out of bounds.
The `pc.replace_with_mask` expected the third argument to be the replacements for the positions where the mask is True.
Wait, let's check `pyarrow.compute.replace_with_mask` documentation (mentally or by trying if I could).
Actually, `pc.replace_with_mask(array, mask, replacements)`:
If `mask` is a boolean array, `replacements` should be the values to use where the mask is true.
The length of `replacements` must be the number of true values in the mask.
In our case, `values_native` already has the correct length (number of indices) and it was already sorted to match the sorted `indices_native`.
So `values_native` is already the `replacements` array we need.
We just need to pass `values_native` itself, not `values_native.take(indices_native)`.
Let's double check the case where `indices` is a single `int`.
1. `indices` = 1
2. `values` = 999
3. `indices_native` = `pa.array([1])`
4. `values_native` = `pa.array([999])`
5. `sorting_indices` = `pc.sort_indices([1])` -> `[0]`
6. `indices_native` = `[1].take([0])` -> `[1]`
7. `values_native` = `[999].take([0])` -> `[999]`
8. `mask` = `[False, True, False]`
9. `values_native.take([1])` -> BOOM! Index 1 out of bounds for array of length 1.
Correct. It should just be `values_native`.
Let's apply the fix.
<channel|><|tool_call>call:edit_file{filepath:<|"|>narwhals/_arrow/series.py<|"|>,new_string:<|"|> pc_replace_with_mask: Incomplete = pc.replace_with_mask
return self._with_native(
pc_replace_with_mask(self.native, mask, values_native)
)<|"|>,old_string:<|"|> 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))
)<|"|>}<tool_call|><|tool_response>
Generated tokens 258 tokens · stopped: STOP
Now let's verify the fix with the reproduction script.
Now let's verify the fix with the reproduction script.
<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"]
print("Testing scatter(1, 999):")
try:
result = ser.scatter(1, 999)
print(f"Result: {result.to_list()}")
except Exception as exc:
print(f"Caught exception: {exc}")
print("\nTesting scatter([0, 2], [999, 888]):")
try:
result = ser.scatter([0, 2], [999, 888])
print(f"Result: {result.to_list()}")
except Exception as exc:
print(f"Caught exception: {exc}")
PY
<|"|>}<tool_call|><|tool_response>
Generated tokens 80 tokens · stopped: STOP
The repro tests now pass. I should also check if this happens in other backends. The issue specifically mentioned `ArrowSeries`, but Narwhals often has similar implementations across backends. Let's search for `def scatter` in other files.
The repro tests now pass.
I should also check if this happens in other backends. The issue specifically mentioned `ArrowSeries`, but Narwhals often has similar implementations across backends.
Let's search for `def scatter` in other files.
<channel|><|tool_call>call:run_command{command:<|"|>grep -rn "def scatter" narwhals/<|"|>}<tool_call|><|tool_response>
Generated tokens 75 tokens · stopped: STOP
I missed `_polars/series.py`. Let's see how it's implemented. Also `_pandas_like/series.py`.
I missed `_polars/series.py`. Let's see how it's implemented.
Also `_pandas_like/series.py`.
<channel|><|tool_call>call:read_file{end_line:450,filepath:<|"|>narwhals/_polars/series.py<|"|>,start_line:430}<tool_call|><|tool_response>
Generated tokens 75 tokens · stopped: STOP
Polars' implementation is very simple and uses Polars' own `scatter`. Now let's check `_pandas_like/series.py`.
Polars' implementation is very simple and uses Polars' own `scatter`.
Now let's check `_pandas_like/series.py`.
<channel|><|tool_call>call:read_file{end_line:290,filepath:<|"|>narwhals/_pandas_like/series.py<|"|>,start_line:270}<tool_call|><|tool_response>
Generated tokens 78 tokens · stopped: STOP
Pandas-like's implementation uses `.iloc[indices] = values`, which should work correctly. So it seems only the Arrow implementation was flawed. Now I should check if there are any existing tests for `scatter` that I should run.
Pandas-like's implementation uses `.iloc[indices] = values`, which should work correctly.
So it seems only the Arrow implementation was flawed.
Now I should check if there are any existing tests for `scatter` that I should run.
<channel|><|tool_call>call:run_command{command:<|"|>grep -rn "test_scatter" tests/<|"|>}<tool_call|><|tool_response>
Generated tokens 45 tokens · stopped: STOP
Let's run these tests.
Let's run these tests.
<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 473 tokens · stopped: STOP
All existing tests passed. Wait, the issue said: - is not 0 - are not full length of the series In my repro: - `ser.scatter(1, 999)`: index is 1 (not 0), and length is 1 (not 3). Failed. - `ser.scatter([0, 2], [999, 888])`: indices are 0 and 2, length is 2 (not 3). Failed. If indices were `[0, 1, 2]`, it would be full length. Let's verify if full length works (even before my fix, but just to be sure). Actually, if `indices` = `[0, 1, 2]`, then `indices_native` = `[0, 1, 2]` and `values_native` = `[999, 888, 777]`. `values_native.take([0, 1, 2])` would work! That's why it only failed when not full length. Let's confirm this behavior with a small script.
All existing tests passed.
Wait, the issue said:
- is not 0
- are not full length of the series
In my repro:
- `ser.scatter(1, 999)`: index is 1 (not 0), and length is 1 (not 3). Failed.
- `ser.scatter([0, 2], [999, 888])`: indices are 0 and 2, length is 2 (not 3). Failed.
If indices were `[0, 1, 2]`, it would be full length.
Let's verify if full length works (even before my fix, but just to be sure).
Actually, if `indices` = `[0, 1, 2]`, then `indices_native` = `[0, 1, 2]` and `values_native` = `[999, 888, 777]`.
`values_native.take([0, 1, 2])` would work! That's why it only failed when not full length.
Let's confirm this behavior with a small script.
<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"]
# Full length
try:
result = ser.scatter([0, 1, 2], [999, 888, 777])
print(f"Full length result: {result.to_list()}")
except Exception as exc:
print(f"Full length failed: {exc}")
# Index 0 only
try:
result = ser.scatter(0, 999)
print(f"Index 0 result: {result.to_list()}")
except Exception as exc:
print(f"Index 0 failed: {exc}")
PY
<|"|>}<tool_call|><|tool_response>
Generated tokens 496 tokens · stopped: STOP
Wait, `ser.scatter(0, 999)` worked? Let's trace `ser.scatter(0, 999)`: 1. `indices` = 0 2. `values` = 999 3. `indices_native` = `pa.array([0])` 4. `values_native` = `pa.array([999])` 5. `sorting_indices` = `[0]` 6. `indices_native` = `[0]` 7. `values_native` = `[999]` 8. `mask` = `[True, False, False]` 9. `values_native.take([0])` -> `[999].take([0])` -> `[999]`. WORKS! So the issue is specifically when `indices` (after sorting) contains any value $\ge$ length of `values_native`. Since `len(values_native) == len(indices)`, this happens whenever `max(indices)` $\ge$ `len(indices)`. If `indices` = `[0, 2]`, `len(indices)` = 2, and `max(indices)` = 2. $2 \ge 2$ is True. Failure. If `indices` = `[0, 1]`, `len(indices)` = 2, and `max(indices)` = 1. $1 \ge 2$ is False. Success? Wait, let's check `[0, 1]`.
Wait, `ser.scatter(0, 999)` worked?
Let's trace `ser.scatter(0, 999)`:
1. `indices` = 0
2. `values` = 999
3. `indices_native` = `pa.array([0])`
4. `values_native` = `pa.array([999])`
5. `sorting_indices` = `[0]`
6. `indices_native` = `[0]`
7. `values_native` = `[999]`
8. `mask` = `[True, False, False]`
9. `values_native.take([0])` -> `[999].take([0])` -> `[999]`. WORKS!
So the issue is specifically when `indices` (after sorting) contains any value $\ge$ length of `values_native`.
Since `len(values_native) == len(indices)`, this happens whenever `max(indices)` $\ge$ `len(indices)`.
If `indices` = `[0, 2]`, `len(indices)` = 2, and `max(indices)` = 2. $2 \ge 2$ is True. Failure.
If `indices` = `[0, 1]`, `len(indices)` = 2, and `max(indices)` = 1. $1 \ge 2$ is False. Success?
Wait, let's check `[0, 1]`.<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, 1], [999, 888])
print(f"Indices [0, 1] result: {result.to_list()}")
except Exception as exc:
print(f"Indices [0, 1] failed: {exc}")
PY
<|"|>}<tool_call|><|tool_response>
Generated tokens 225 tokens · stopped: STOP
Yes, that makes sense. Anyway, my fix of removing `.take(indices_native)` is correct because `values_native` is already aligned with the sorted `indices_native` and its length matches the number of items being replaced. I've verified the fix with the repro and existing tests. I'll double check if I missed any other `_arrow` implementation that might have the same bug. I already checked `_arrow/series.py`. Are there other series-like things in `_arrow`? `narwhals/_arrow/series_cat.py`, `_arrow/series_dt.py`, etc. Usually, these inherit from `ArrowSeries` (defined in `narwhals/_arrow/series.py`). If they don't override `scatter`, they use the one in `ArrowSeries`. Let's check if they override it.
Yes, that makes sense.
Anyway, my fix of removing `.take(indices_native)` is correct because `values_native` is already aligned with the sorted `indices_native` and its length matches the number of items being replaced.
I've verified the fix with the repro and existing tests.
I'll double check if I missed any other `_arrow` implementation that might have the same bug.
I already checked `_arrow/series.py`.
Are there other series-like things in `_arrow`?
`narwhals/_arrow/series_cat.py`, `_arrow/series_dt.py`, etc.
Usually, these inherit from `ArrowSeries` (defined in `narwhals/_arrow/series.py`).
If they don't override `scatter`, they use the one in `ArrowSeries`.
Let's check if they override it.
<channel|><|tool_call>call:run_command{command:<|"|>grep -rn "def scatter" narwhals/_arrow/series_*.py<|"|>}<tool_call|><|tool_response>
Generated tokens 60 tokens · stopped: STOP
The `grep` failed because of how I used `*`. I should use `grep -rn "def scatter" narwhals/_arrow/`.
The `grep` failed because of how I used `*`. I should use `grep -rn "def scatter" narwhals/_arrow/`.
<channel|><|tool_call>call:run_command{command:<|"|>grep -rn "def scatter" narwhals/_arrow/<|"|>}<tool_call|><|tool_response>
Generated tokens 759 tokens · stopped: STOP
Only `narwhals/_arrow/series.py` implements `scatter`.
I'm confident in the fix.
Final check of the changes:
`narwhals/_arrow/series.py`
Old: `pc_replace_with_mask(self.native, mask, values_native.take(indices_native))`
New: `pc_replace_with_mask(self.native, mask, values_native)`
Wait, let me re-read the `scatter` method to be absolutely sure about the sorting.
```python
sorting_indices = pc.sort_indices(indices_native)
indices_native = indices_native.take(sorting_indices)
values_native = values_native.take(sorting_indices)
mask: _1DArray = np.zeros(self.len(), dtype=bool)
mask[indices_native] = True
# ...
return self._with_native(
pc_replace_with_mask(self.native, mask, values_native)
)
```
If `indices` = `[2, 0]` and `values` = `[888, 999]`.
1. `indices_native` = `[2, 0]`
2. `values_native` = `[888, 999]`
3. `sorting_indices` = `pc.sort_indices([2, 0])` -> `[1, 0]`
4. `indices_native` = `[2, 0].take([1, 0])` -> `[0, 2]`
5. `values_native` = `[888, 999].take([1, 0])` -> `[999, 888]`
6. `mask` = `[True, False, True]`
7. `pc_replace_with_mask(self.native, mask, [999, 888])`
- Index 0 is True -> replaced by 999.
- Index 2 is True -> replaced by 888.
Result: `[999, original[1], 888]`. Correct.
Yes, the sorting ensures that `values_native` is ordered according to the indices in `indices_native` (which is sorted ascendingly). `pc.replace_with_mask` expects replacements to be in the order that the mask is True.
One last check: what if `indices` is an `int`?
1. `indices` = 2
2. `values` = 888
3. `indices_native` = `[2]`
4. `values_native` = `[888]`
5. `sorting_indices` = `[0]`
6. `indices_native` = `[2]`
7. `values_native` = `[888]`
8. `mask` = `[False, False, True]`
9. `pc_replace_with_mask(self.native, mask, [888])` -> `[original[0], original[1], 888]`. Correct.
The fix is correct.
```bash
git status --short
git diff
```
Only `narwhals/_arrow/series.py` implements `scatter`.
I'm confident in the fix.
Final check of the changes:
`narwhals/_arrow/series.py`
Old: `pc_replace_with_mask(self.native, mask, values_native.take(indices_native))`
New: `pc_replace_with_mask(self.native, mask, values_native)`
Wait, let me re-read the `scatter` method to be absolutely sure about the sorting.
```python
sorting_indices = pc.sort_indices(indices_native)
indices_native = indices_native.take(sorting_indices)
values_native = values_native.take(sorting_indices)
mask: _1DArray = np.zeros(self.len(), dtype=bool)
mask[indices_native] = True
# ...
return self._with_native(
pc_replace_with_mask(self.native, mask, values_native)
)
```
If `indices` = `[2, 0]` and `values` = `[888, 999]`.
1. `indices_native` = `[2, 0]`
2. `values_native` = `[888, 999]`
3. `sorting_indices` = `pc.sort_indices([2, 0])` -> `[1, 0]`
4. `indices_native` = `[2, 0].take([1, 0])` -> `[0, 2]`
5. `values_native` = `[888, 999].take([1, 0])` -> `[999, 888]`
6. `mask` = `[True, False, True]`
7. `pc_replace_with_mask(self.native, mask, [999, 888])`
- Index 0 is True -> replaced by 999.
- Index 2 is True -> replaced by 888.
Result: `[999, original[1], 888]`. Correct.
Yes, the sorting ensures that `values_native` is ordered according to the indices in `indices_native` (which is sorted ascendingly). `pc.replace_with_mask` expects replacements to be in the order that the mask is True.
One last check: what if `indices` is an `int`?
1. `indices` = 2
2. `values` = 888
3. `indices_native` = `[2]`
4. `values_native` = `[888]`
5. `sorting_indices` = `[0]`
6. `indices_native` = `[2]`
7. `values_native` = `[888]`
8. `mask` = `[False, False, True]`
9. `pc_replace_with_mask(self.native, mask, [888])` -> `[original[0], original[1], 888]`. Correct.
The fix is correct.
```bash
git status --short
git diff
```
<channel|><|tool_call>call:run_command{command:<|"|>git status --short && git diff<|"|>}<tool_call|><|tool_response>
Generated tokens 15 tokens · stopped: STOP
Everything looks clean.
Everything looks clean.
<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-da9bb2609f1b48c596ca56f52cb17a09.xml -- =========================== short test summary info ============================ PASSED tests/series_only/scatter_test.py::test_single_series[polars[eager]] PASSED tests/series_only/scatter_test.py::test_scatter_integer[pandas] PASSED tests/series_only/scatter_test.py::test_scatter_unchanged[pyarrow] PASSED tests/series_only/scatter_test.py::test_scatter_indices PASSED tests/series_only/scatter_test.py::test_scatter_2862[pandas[pyarrow]] PASSED tests/series_only/scatter_test.py::test_scatter[pyarrow] PASSED tests/series_only/scatter_test.py::test_single_series[pandas] PASSED tests/series_only/scatter_test.py::test_scatter_unchanged[polars[eager]] PASSED tests/series_only/scatter_test.py::test_scatter_2862[pyarrow] PASSED tests/series_only/scatter_test.py::test_scatter_integer[pandas[pyarrow]] PASSED tests/series_only/scatter_test.py::test_single_series[pyarrow] PASSED tests/series_only/scatter_test.py::test_scatter_unordered_indices[polars[eager]] PASSED tests/series_only/scatter_test.py::test_scatter_unchanged[pandas[pyarrow]] PASSED tests/series_only/scatter_test.py::test_scatter_integer[polars[eager]] PASSED tests/series_only/scatter_test.py::test_scatter[pandas[pyarrow]] PASSED tests/series_only/scatter_test.py::test_scatter_unordered_indices[pyarrow] PASSED tests/series_only/scatter_test.py::test_scatter[polars[eager]] PASSED tests/series_only/scatter_test.py::test_scatter_2862[polars[eager]] PASSED tests/series_only/scatter_test.py::test_scatter_unchanged[pandas] PASSED tests/series_only/scatter_test.py::test_single_series[pandas[pyarrow]] PASSED tests/series_only/scatter_test.py::test_scatter[pandas] PASSED tests/series_only/scatter_test.py::test_scatter_unordered_indices[pandas[pyarrow]] PASSED tests/series_only/scatter_test.py::test_scatter_unordered_indices[pandas] PASSED tests/series_only/scatter_test.py::test_scatter_integer[pyarrow] PASSED tests/series_only/scatter_test.py::test_scatter_2862[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="0.094" timestamp="2026-11-14T04:05:33.345098+00:00" hostname="5e1af039c1a2"><testcase classname="tests.series_only.scatter_test" name="test_single_series[polars[eager]]" time="0.011" /><testcase classname="tests.series_only.scatter_test" name="test_scatter_integer[pandas]" time="0.020" /><testcase classname="tests.series_only.scatter_test" name="test_scatter_unchanged[pyarrow]" time="0.021" /><testcase classname="tests.series_only.scatter_test" name="test_scatter_indices" time="0.001" /><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[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_unchanged[polars[eager]]" time="0.002" /><testcase classname="tests.series_only.scatter_test" name="test_scatter_2862[pyarrow]" 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_single_series[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_unchanged[pandas[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[pandas[pyarrow]]" 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_scatter[polars[eager]]" time="0.001" /><testcase classname="tests.series_only.scatter_test" name="test_scatter_2862[polars[eager]]" time="0.000" /><testcase classname="tests.series_only.scatter_test" name="test_scatter_unchanged[pandas]" 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[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_scatter_unordered_indices[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_2862[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.
[ 33.2s] 💭 (996 chars) "The user is reporting a bug in `ArrowSeries.scatter` where it raises an "out of bound" error
when `indices` is not 0 and..."
[ 33.2s] 🔧 run_command (22 lines) python3 - <<'PY'
[ 34.3s] → Testing scatter(1, 999):
[ 39.2s] 💭 (179 chars) "The bug is reproduced.
Now let's find the implementation of `scatter` for Arrow.
I'll check `narwhals/series.py` first ..."
[ 39.2s] 🔧 run_command grep -rn "def scatter" narwhals/series.py narwhals/_arrow/series.py
[ 39.3s] → narwhals/series.py:238: def scatter(self, indices: int | Sequence[int], values: Any) -> Self:
[ 45.6s] 💭 (119 chars) "Okay, it's in `narwhals/_arrow/series.py` at line 440.
Let's examine the implementation in `narwhals/_arrow/series.py`."
[ 45.6s] 📖 read_file (narwhals/_arrow/series.py)
[ 45.6s] ✓ {'status': 'ok', 'filepath': 'narwhals/_arrow/series.py', 'content': ' stop = rows.stop if
rows.stop is not None …
[ 51.2s] 💭 (111 chars) "Wait, the `read_file` output is truncated! I can't see the end of the `scatter` method.
Let me read more lines."
[ 51.2s] 📖 read_file (narwhals/_arrow/series.py)
[ 51.3s] ✓ {'status': 'ok', 'filepath': 'narwhals/_arrow/series.py', 'content': ' values_native =
values_native.take(sorting…
[ 125.7s] 💭 (2680 chars) "Here is the problematic part:
```python
mask: _1DArray = np.zeros(self.len(), dtype=bool)
mask[indices_..."
[ 125.7s] ✏️ edit_file (narwhals/_arrow/series.py)
[ 125.9s] ✓ {'status': 'ok', 'filepath': 'narwhals/_arrow/series.py', 'occurrences': 1, 'strategy': 'exact', 'diff':
'--- a/narwhals…
[ 135.3s] 💭 (54 chars) "Now let's verify the fix with the reproduction script."
[ 135.3s] 🔧 run_command (22 lines) python3 - <<'PY'
[ 135.7s] → Testing scatter(1, 999):
[ 144.0s] 💭 (240 chars) "The repro tests now pass.
I should also check if this happens in other backends. The issue specifically mentioned `Arro..."
[ 144.0s] 🔧 run_command grep -rn "def scatter" narwhals/
[ 144.0s] → narwhals/_compliant/series.py:251: def scatter(self, indices: int | Sequence[int], values: Any) ->
Self: ...
[ 150.6s] 💭 (92 chars) "I missed `_polars/series.py`. Let's see how it's implemented.
Also `_pandas_like/series.py`."
[ 150.6s] 📖 read_file (narwhals/_polars/series.py)
[ 150.6s] ✓ {'status': 'ok', 'filepath': 'narwhals/_polars/series.py', 'content': ' return
self._with_native(result)\n\n d…
[ 157.3s] 💭 (112 chars) "Polars' implementation is very simple and uses Polars' own `scatter`.
Now let's check `_pandas_like/series.py`."
[ 157.3s] 📖 read_file (narwhals/_pandas_like/series.py)
[ 157.4s] ✓ {'status': 'ok', 'filepath': 'narwhals/_pandas_like/series.py', 'content': ' result[mask_na] =
None\n retu…
[ 165.0s] 💭 (229 chars) "Pandas-like's implementation uses `.iloc[indices] = values`, which should work correctly.
So it seems only the Arrow im..."
[ 165.0s] 🔧 run_command grep -rn "test_scatter" tests/
[ 165.0s] → tests/series_only/scatter_test.py:9:def test_scatter(constructor_eager: ConstructorEager) -> None:
[ 167.3s] 💭 (22 chars) "Let's run these tests."
[ 167.3s] 🔧 run_command pytest tests/series_only/scatter_test.py -q --tb=short --no-header
[ 168.8s] → ..................... [100%]
[ 194.5s] 💭 (664 chars) "All existing tests passed.
Wait, the issue said:
- is not 0
- are not full length of the series
In my repro:
- `ser.sc..."
[ 194.5s] 🔧 run_command (22 lines) python3 - <<'PY'
[ 194.9s] → Full length result: [999, 888, 777]
[ 221.1s] 💭 (812 chars) "Wait, `ser.scatter(0, 999)` worked?
Let's trace `ser.scatter(0, 999)`:
1. `indices` = 0
2. `values` = 999
3. `indices_na..."
[ 221.1s] 🔧 run_command (14 lines) python3 - <<'PY'
[ 221.6s] → Indices [0, 1] result: [999, 888, 3]
[ 237.5s] 💭 (706 chars) "Yes, that makes sense.
Anyway, my fix of removing `.take(indices_native)` is correct because `values_native` is already..."
[ 237.5s] 🔧 run_command grep -rn "def scatter" narwhals/_arrow/series_*.py
[ 237.6s] → error (exit 0):
[ 240.8s] 💭 (100 chars) "The `grep` failed because of how I used `*`. I should use `grep -rn "def scatter"
narwhals/_arrow/`."
[ 240.8s] 🔧 run_command grep -rn "def scatter" narwhals/_arrow/
[ 240.9s] → narwhals/_arrow/series.py:440: def scatter(self, indices: int | Sequence[int], values: Any) -> Self:
[ 276.3s] 💭 (1943 chars) "Only `narwhals/_arrow/series.py` implements `scatter`.
I'm confident in the fix.
Final check of the changes:
`narwhals..."
[ 276.3s] 🔧 run_command git status --short && git diff
[ 276.3s] → M narwhals/_arrow/series.py
[ 277.3s] 💭 (23 chars) "Everything looks clean."
[ 277.3s] 🤖 submit_patch
[ 277.4s] → submit_patch: {"status": "ok", "patch_size": 572, "files_changed": 1}
[ 286.0s] 💭 (5 chars) "DONE."
[ 286.0s] ✅ Agent finished: The bug was caused by calling `.take(indices_native)` on the `values_native` array in
`ArrowSeries.scatter`. Since `values_native` has a length equal to the number of indices being scattered, and `ind...
Native result row
{
"agent_error": null,
"duration_seconds": 292.97,
"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": 290.26,
"repo": "narwhals-dev/narwhals",
"request_log_path": "/kaggle-root/working/r31/rollouts_X/requests/narwhals-dev__narwhals-2864__p0__r4.jsonl",
"resolved": true,
"rollout_idx": 4,
"run_key": "narwhals-dev__narwhals-2864__p0__r4",
"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__r4.json"
}