{"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":"validation","errorClass":"TypeError","httpStatus":null,"severity":"error","filePath":"pandas/core/arrays/arrow/array.py","lineNumber":3586,"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":3568,"sourceCodeEnd":3604,"githubUrl":"https://github.com/pandas-dev/pandas/blob/71959b8cb9b2459c16e14b34f28b178ccfe14735/pandas/core/arrays/arrow/array.py#L3568-L3604","documentation":"Raised in _groupby_quantile for a StringDtype column — quantile requires ordered numeric values, and strings have no quantile semantics in this path, so pandas raises TypeError naming 'quantile' explicitly.","triggerScenarios":"Calling `df.groupby('g').quantile(...)` on a DataFrame whose value column is `string[pyarrow]` (or StringDtype-backed-by-arrow).","commonSituations":"Running `.quantile()` across all groupby columns without filtering dtypes, or treating categorical/string codes as numeric.","solutions":["Exclude string columns before quantile: `df.groupby('g')[numeric_cols].quantile(0.5)`.","Cast the string column to numeric if its contents are numeric strings.","Use `.value_counts()` or mode for non-numeric 'typical value' queries."],"exampleFix":"// before\ndf = pd.DataFrame({\"g\": [\"a\", \"a\"], \"x\": [\"1\", \"2\"]}, dtype=\"string[pyarrow]\")\ndf.groupby(\"g\").quantile()\n\n// after\ndf[\"x\"] = df[\"x\"].astype(\"float64[pyarrow]\")\ndf.groupby(\"g\").quantile()","handlingStrategy":"type-guard","validationCode":"import pandas as pd\n\ndef can_quantile(series) -> bool:\n    return not isinstance(getattr(series, \"dtype\", None), pd.StringDtype)","typeGuard":"def supports_quantile(series) -> bool:\n    import pandas as pd\n    return not isinstance(getattr(series, \"dtype\", None), pd.StringDtype)","tryCatchPattern":"try:\n    df.groupby(\"g\").quantile()\nexcept TypeError as e:\n    if \"does not support operation 'quantile'\" in str(e):\n        df.groupby(\"g\")[df.select_dtypes(\"number\").columns].quantile()\n    else:\n        raise","preventionTips":["Exclude string columns before groupby quantile.","Use df.select_dtypes(include='number') in quantile helpers.","For categorical 'typical value' use mode/value_counts instead."],"tags":["arrow","groupby","quantile","string-dtype"],"analyzedSha":"71959b8cb9b2459c16e14b34f28b178ccfe14735","analyzedAt":"2026-08-07T01:30:20.476Z","schemaVersion":2},"datasetVersion":"2026-08-07T03:17:09.362Z"}