{"record":{"id":"4237f5bd3742e4d4","repo":"pandas-dev/pandas","slug":"dtype-self-dtype-does-not-support-operation-q","errorCode":null,"errorMessage":"dtype '{self.dtype}' does not support operation 'quantile'","messagePattern":"dtype '(.+?)' does not support operation 'quantile'","errorType":"exception","errorClass":"TypeError","httpStatus":null,"severity":"error","filePath":"pandas/core/arrays/arrow/array.py","lineNumber":3609,"sourceCode":"            ids=ids,\n            **kwargs,\n        )\n        return self._groupby_result_to_arrow(result)\n\n    def _groupby_quantile(\n        self,\n        *,\n        qs: npt.NDArray[np.float64],\n        interpolation: Literal[\"linear\", \"lower\", \"higher\", \"nearest\", \"midpoint\"],\n        ids: npt.NDArray[np.intp],\n        ngroups: int,\n        starts: npt.NDArray[np.int64],\n        ends: npt.NDArray[np.int64],\n    ) -> ArrayLike:\n        from pandas.core.arrays.string_ import StringDtype\n\n        if isinstance(self.dtype, StringDtype):\n            raise TypeError(\n                f\"dtype '{self.dtype}' does not support operation 'quantile'\"\n            )\n\n        values = self._to_groupby_compatible()\n        result = values._groupby_quantile(\n            qs=qs,\n            interpolation=interpolation,\n            ids=ids,\n            ngroups=ngroups,\n            starts=starts,\n            ends=ends,\n        )\n        return self._groupby_result_to_arrow(result)\n\n    def _apply_elementwise(self, func: Callable) -> list[list[Any]]:\n        \"\"\"Apply a callable to each element while maintaining the chunking structure.\"\"\"\n        return [\n            [","sourceCodeStart":3591,"sourceCodeEnd":3627,"githubUrl":"https://github.com/pandas-dev/pandas/blob/3b7651241d4da534b3559b60ef128e1c34f54116/pandas/core/arrays/arrow/array.py#L3591-L3627","documentation":"_groupby_quantile rejects quantile on StringDtype-backed-by-arrow arrays with TypeError. Quantiles require ordering and arithmetic (interpolation); strings can be ordered but interpolation methods like 'linear'/'midpoint' are undefined. Rather than partially support some interpolations, pandas refuses the whole operation for string arrow types.","triggerScenarios":"df.groupby('key')['string_col'].quantile(0.5) on a string[pyarrow] column.","commonSituations":"Generic .quantile() over all groupby columns; building summary statistics without dtype awareness.","solutions":["Filter numeric columns before quantile: df.groupby(key)[numeric_cols].quantile(q).","If only ordering matters, use .rank() or .min()/.max() on string columns instead.","Cast the column to a numeric/categorical type if it encodes ordered data."],"exampleFix":"// before\ndf = pd.DataFrame({\"k\": [\"a\"], \"v\": [\"x\"]}, dtype={\"v\": \"string[pyarrow]\"})\ndf.groupby(\"k\")[\"v\"].quantile(0.5)\n// after\ndf = pd.DataFrame({\"k\": [\"a\"], \"v\": [1]}, dtype={\"v\": \"int64[pyarrow]\"})\ndf.groupby(\"k\")[\"v\"].quantile(0.5)","handlingStrategy":"validation","validationCode":"from pandas.core.arrays.string_ import StringDtype\n\ndef quantile_supported(dtype) -> bool:\n    return not isinstance(dtype, StringDtype)","typeGuard":"def is_string_arrow_dtype(dtype) -> bool:\n    from pandas.core.arrays.string_ import StringDtype\n    return isinstance(dtype, StringDtype)","tryCatchPattern":"try:\n    df.groupby(\"k\")[col].quantile(0.5)\nexcept TypeError:\n    df.groupby(\"k\")[numeric_cols].quantile(0.5)","preventionTips":["Apply quantile only to numeric groupby columns.","Use rank/min/max for ordered string columns."],"tags":["pyarrow","groupby","quantile","string-dtype"],"backgroundTag":null,"analyzedSha":"3b7651241d4da534b3559b60ef128e1c34f54116","analyzedAt":"2026-08-11T22:10:44.015Z","contentChangedAt":null,"schemaVersion":2},"datasetVersion":"2026-09-23T08:17:48.524Z"}