narwhals-dev__narwhals-2864

{
  "status": "ok",
  "stdout": "Test 1: scatter(1, 999)\nException: Index 1 out of bounds\n\nTest 2: scatter([0, 2], [999, 888])\nException: Index 2 out of bounds\n",
  "stderr": "",
  "exit_code": 0
}

{
  "status": "ok",
  "stdout": "./narwhals/_compliant/series.py:251:    def scatter(self, indices: int | Sequence[int], values: Any) -> Self: ...\n./narwhals/series.py:238:    def scatter(self, indices: int | Sequence[int], values: Any) -> Self:\n./narwhals/_pandas_like/series.py:273:    def scatter(self, indices: int | Sequence[int], values: Any) -> Self:\n./narwhals/_arrow/series.py:440:    def scatter(self, indices: int | Sequence[int], values: Any) -> Self:\n./narwhals/_polars/series.py:432:    def scatter(self, indices: int | Sequence[int], values: Any) -> Self:\n",
  "stderr": "",
  "exit_code": 0
}

{
  "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
}

{
  "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:\n        return self.native.to_numpy()\n\n    def alias(self, name: str) -> Self:\n        result = self.__class__(self.native, name=name, version=self._version)\n        result._broadcast = self._broadcast\n        return result\n\n    @property\n    def dtype(self) -> DType:\n        return native_to_narwhals_dtype(self.native.type, self._version)\n\n    def abs(self) -> Self:\n        return self._with_native(pc.abs(self.native))\n\n    def cum_sum(self, *, reverse: bool) -> Self:\n        cum_sum = pc.cumulative_sum\n        result = (\n            cum_sum(self.native, skip_nulls=True)\n            if not reverse\n            else cum_sum(self.native[::-1], skip_nulls=True)[::-1]",
  "start_line": 461,
  "end_line": 500,
  "total_lines": 1170,
  "is_truncated": false
}

{
  "status": "ok",
  "stdout": "Attempting mask[indices_native] = True\nSuccess!\n",
  "stderr": "",
  "exit_code": 0
}

{
  "status": "ok",
  "stdout": "432:    def scatter(self, indices: int | Sequence[int], values: Any) -> Self:\n",
  "stderr": "",
  "exit_code": 0
}

{
  "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                }\n            )\n        else:\n            result = self.native.value_counts(\n                sort=sort, parallel=parallel, name=name, normalize=normalize\n            )\n        return PolarsDataFrame.from_native(result, context=self)\n\n    def cum_count(self, *, reverse: bool) -> Self:\n        return self._with_native(self.native.cum_count(reverse=reverse))\n",
  "start_line": 430,
  "end_line": 460,
  "total_lines": 741,
  "is_truncated": false
}

{
  "status": "error",
  "error_type": "CommandError",
  "error_message": "",
  "details": {
    "stdout": "",
    "stderr": "",
    "exit_code": 1
  }
}

{
  "status": "error",
  "error_type": "CommandError",
  "error_message": "",
  "details": {
    "stdout": "",
    "stderr": "",
    "exit_code": 1
  }
}

{
  "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,\n            self.native.index[indices.native],\n            implementation=implementation,\n        )\n        if implementation is Implementation.PANDAS and parse_version(np) < (2,):\n            values_native = values_native.copy()  # pragma: no cover\n        min_pd_version = (1, 2)\n        if implementation is Implementation.PANDAS and backend_version < min_pd_version:\n            self.native.iloc[indices.native.values] = values_native  # noqa: PD011\n        else:\n            self.native.iloc[indices.native] = values_native",
  "start_line": 270,
  "end_line": 300,
  "total_lines": 1106,
  "is_truncated": false
}

{
  "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(",
  "start_line": 460,
  "end_line": 470,
  "total_lines": 1170,
  "is_truncated": false
}

{
  "status": "ok",
  "filepath": "narwhals/_arrow/series.py",
  "content": "            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": 471,
  "end_line": 480,
  "total_lines": 1170,
  "is_truncated": false
}

{
  "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
}

{
  "status": "ok",
  "stdout": "Test 1: scatter(1, 999)\nResult: ┌───────────────────────────────────────────────────┐\n|                  Narwhals Series                  |\n|---------------------------------------------------|\n|<pyarrow.lib.ChunkedArray object at 0x760619a75900>|\n|[                                                  |\n|  [                                                |\n|    1,                                             |\n|    999,                                           |\n|    3                                              |\n|  ]                                                |\n|]                                                  |\n└───────────────────────────────────────────────────┘\n\nTest 2: scatter([0, 2], [999, 888])\nResult: ┌───────────────────────────────────────────────────┐\n|                  Narwhals Series                  |\n|---------------------------------------------------|\n|<pyarrow.lib.ChunkedArray object at 0x760619a75960>|\n|[                                                  |\n|  [                                                |\n|    999,                                           |\n|    2,                                             |\n|    888                                            |\n|  ]                                                |\n|]                                                  |\n└───────────────────────────────────────────────────┘\n\nTest 3: scatter([2, 0], [888, 999])\nResult: ┌───────────────────────────────────────────────────┐\n|                  Narwhals Series                  |\n|---------------------------------------------------|\n|<pyarrow.lib.ChunkedArray object at 0x760619a758a0>|\n|[                                                  |\n|  [                                                |\n|    888,                                           |\n|    2,                                             |\n|    999                                            |\n|  ]                                                |\n|]                                                  |\n└───────────────────────────────────────────────────┘\n",
  "stderr": "",
  "exit_code": 0
}

{
  "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
}

{
  "status": "ok",
  "stdout": ".....................                                                    [100%]\n21 passed in 0.47s\n",
  "stderr": "",
  "exit_code": 0
}

{
  "status": "ok",
  "stdout": "narwhals/_arrow/series.py:440:    def scatter(self, indices: int | Sequence[int], values: Any) -> Self:\nnarwhals/_arrow/expr.py:137:                # again calling `sort_indices`. `ArrowSeries.scatter` would also sort.\n",
  "stderr": "",
  "exit_code": 0
}

{
  "status": "ok",
  "stdout": "diff --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
}

{
  "status": "ok",
  "stdout": "All tests passed!\n",
  "stderr": "",
  "exit_code": 0
}

{
  "status": "ok",
  "patch_size": 572,
  "files_changed": 1
}