pandas-dev/pandas · error · TypeError

Cannot perform reduction '{name}' with string dtype

Error message

Cannot perform reduction '{name}' with string dtype

What it means

StringArray._reduce supports only a fixed set of reductions: any, all, count, min, max, argmin, argmax, sum. Any other reduction name (mean, median, std, var, prod, sem, skew) raises TypeError because those operations are not defined for string data.

Source

Thrown at pandas/core/arrays/string_.py:978

        skipna: bool = True,
        keepdims: bool = False,
        axis: AxisInt | None = 0,
        **kwargs,
    ):
        if self.dtype.na_value is np.nan and name in ["any", "all"]:
            if name == "any":
                return nanops.nanany(self._ndarray, skipna=skipna)
            else:
                return nanops.nanall(self._ndarray, skipna=skipna)
        elif name == "count":
            return super().count()
        elif name in ["min", "max", "argmin", "argmax", "sum"]:
            result = getattr(self, name)(skipna=skipna, axis=axis, **kwargs)
            if keepdims:
                return self._from_sequence([result], dtype=self.dtype)
            return result

        raise TypeError(f"Cannot perform reduction '{name}' with string dtype")

    def _accumulate(self, name: str, *, skipna: bool = True, **kwargs) -> StringArray:
        """
        Return an ExtensionArray performing an accumulation operation.

        The underlying data type might change.

        Parameters
        ----------
        name : str
            Name of the function, supported values are:
            - cummin
            - cummax
            - cumsum
            - cumprod
        skipna : bool, default True
            If True, skip NA values.
        **kwargs

View on GitHub (pinned to 71959b8cb9)

Solutions

1. Select only numeric columns before applying numeric reductions (e.g., df.select_dtypes('number')).
2. If the strings encode numbers, convert first: s.astype(float).mean().
3. Use the supported reductions (count, min, max, sum, any, all) for string data.

Example fix

// before
string_series.mean()

// after
string_series.astype(float).mean()

SUPPORTED = {'any', 'all', 'count', 'min', 'max', 'argmin', 'argmax', 'sum'}
if name not in SUPPORTED:
    raise TypeError(f'Reduction {name} not supported for string dtype')

SUPPORTED = {'any', 'all', 'count', 'min', 'max', 'argmin', 'argmax', 'sum'}

def is_supported_string_reduction(name: str) -> bool:
    return name in SUPPORTED

When it happens

Trigger: Calling string_series.mean(), string_series.median(), string_series.std(), string_series.prod(), string_series.sem(), or any numeric-only reduction on a string-typed Series/array.

Common situations: Generic describe()/agg() pipelines that apply numeric reductions to every column; statistical code run over a DataFrame that includes string columns.

Related errors

• timedelta64 type does not support {how} operations
• 'std' and 'sem' are not valid for PeriodDtype
• Cannot change data-type for string array.
• Invalid value '{value}' for dtype '{self.dtype}'. Value shou
• Invalid value for dtype 'str'. Value should be a string or m

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/81e47015614b963f. Report an issue: GitHub.