{"record":{"id":"cc1d8790f122443a","repo":"pandas-dev/pandas","slug":"operation-name-not-supported-for-dtype-self-cc1d87","errorCode":null,"errorMessage":"operation '{name}' not supported for dtype '{self.dtype}'","messagePattern":"operation '(.+?)' not supported for dtype '(.+?)'","errorType":"exception","errorClass":"TypeError","httpStatus":null,"severity":"error","filePath":"pandas/core/arrays/string_.py","lineNumber":1010,"sourceCode":"            - cumsum\n            - cumprod\n        skipna : bool, default True\n            If True, skip NA values.\n        **kwargs\n            Additional keyword arguments passed to the accumulation function.\n            Currently, there is no supported kwarg.\n\n        Returns\n        -------\n        array\n\n        Raises\n        ------\n        NotImplementedError : subclass does not define accumulations\n        \"\"\"\n        if name == \"cumprod\":\n            msg = f\"operation '{name}' not supported for dtype '{self.dtype}'\"\n            raise TypeError(msg)\n\n        # We may need to strip out trailing NA values\n        tail: np.ndarray | None = None\n        na_mask: np.ndarray | None = None\n        ndarray = self._ndarray\n        np_func = {\n            \"cumsum\": np.cumsum,\n            \"cummin\": np.minimum.accumulate,\n            \"cummax\": np.maximum.accumulate,\n        }[name]\n\n        if self._hasna:\n            na_mask = cast(\"npt.NDArray[np.bool_]\", isna(ndarray))\n            if np.all(na_mask):\n                return type(self)(ndarray, dtype=self.dtype)\n            if skipna:\n                if name == \"cumsum\":\n                    ndarray = np.where(na_mask, \"\", ndarray)","sourceCodeStart":992,"sourceCodeEnd":1028,"githubUrl":"https://github.com/pandas-dev/pandas/blob/3b7651241d4da534b3559b60ef128e1c34f54116/pandas/core/arrays/string_.py#L992-L1028","documentation":"StringArray._accumulate raises TypeError specifically for cumprod, since cumulative product is undefined for text. cumsum, cummin, and cummax are supported (string concatenation and lexicographic accumulation), but multiplying strings is not.","triggerScenarios":"Calling df['col'].cumprod() on a string-dtyped column; invoking Series.cumprod() on dtype 'string' or 'string[pyarrow]'.","commonSituations":"Applying a generic accumulation suite (cumsum, cumprod, cummin, cummax) across all columns without dtype checks; refactoring a numeric column to string and forgetting cumprod breaks.","solutions":["Remove cumprod from operations applied to string columns.","Convert to numeric first if the strings represent numbers: pd.to_numeric(s, errors='coerce').cumprod().","Use cumsum for string concatenation accumulation if that was the intent."],"exampleFix":"// before\ns = pd.Series(['1','2','3'], dtype='string')\ns.cumprod()  # TypeError\n// after\npd.to_numeric(s, errors='coerce').cumprod()","handlingStrategy":"type-guard","validationCode":"def safe_cumprod(s):\n    if pd.api.types.is_string_dtype(s):\n        s = pd.to_numeric(s, errors='coerce')\n    return s.cumprod()","typeGuard":"def cumprod_safe_dtype(series) -> bool:\n    return pd.api.types.is_numeric_dtype(series)","tryCatchPattern":"try:\n    s.cumprod()\nexcept TypeError as e:\n    if 'not supported for dtype' in str(e):\n        pd.to_numeric(s, errors='coerce').cumprod()\n    else:\n        raise","preventionTips":["Exclude string columns from cumprod.","Cast string-encoded numbers to numeric before accumulation.","Use cumsum for string concatenation, not cumprod."],"tags":["pandas","string-array","accumulation","cumprod","dtype"],"backgroundTag":null,"analyzedSha":"3b7651241d4da534b3559b60ef128e1c34f54116","analyzedAt":"2026-08-11T22:10:44.015Z","contentChangedAt":null,"schemaVersion":2},"datasetVersion":"2026-09-23T08:17:48.524Z"}