pandas-dev/pandas · error · TypeError

dtype ' ' does not support operation 'quantile

Error message

dtype '{self.dtype}' does not support operation 'quantile'

What it means

Raised by ExtensionArray._groupby_quantile when the grouped array has a StringDtype or object dtype. Quantiles are only defined for ordered numeric data, so pandas refuses to compute them over string or opaque-object columns rather than silently producing meaningless results. The error names the offending dtype so you can identify the column.

Solutions

Exclude non-numeric columns before quantile: df.select_dtypes(include='number').groupby(g).quantile(...).
Convert the column to numeric first: df['col'] = pd.to_numeric(df['col'], errors='coerce').
Drop or filter the string/object column from the grouping selection: groupby on a numeric-only subset.
If you genuinely need positional statistics for strings, use a different aggregation (e.g. first/last/min/max) instead of quantile.

Example fix

// before
df.groupby('key')['name'].quantile(0.5)
// after
df.groupby('key')['value'].quantile(0.5)
// or
df['value'] = pd.to_numeric(df['value'], errors='coerce')

Defensive patterns

Strategy: validation

Validate before calling

numeric_df = df.select_dtypes(include=['number', 'datetime'])
if df[col].dtype in ('object', 'string'):
    raise TypeError(f'{col} is not numeric; quantile unsupported')

Type guard

def is_quantile_supported(s) -> bool:
    from pandas.api.types import is_string_dtype, is_object_dtype, is_bool_dtype
    return not (is_string_dtype(s) or is_object_dtype(s) or is_bool_dtype(s))

Try / catch

try:
    df.groupby('key')[col].quantile(q)
except TypeError as e:
    if 'does not support operation' in str(e):
        # skip non-numeric columns
        ...

Prevention

Always subset to numeric columns before groupby quantile.
Centralize quantile logic behind a helper that filters dtypes.

When it happens

Trigger: Calling df.groupby(...).quantile() (or Series.groupby quantile) on a column whose dtype is 'string', StringDtype, or object. Also reached via .agg('quantile') or named aggregation targeting a string/object column. The check fires before any Cython dispatch.

Common situations: Selecting all numeric columns with a string column accidentally included (e.g. an ID column stored as string); mixed-type DataFrames where groupby picks up object columns; reading CSVs where numeric-looking codes stay as strings; migrating from object dtype to the new 'string' dtype and re-running an existing quantile pipeline.

Related errors

AI-assisted analysis of pandas-dev/pandas@3b7651241d (2026-08-11). Data as JSON: /api/errors/fa5ecc5569bdc4d9. Report an issue: GitHub.

Appendix: source

Thrown at pandas/core/arrays/base.py:3151

        Parameters
        ----------
        qs : np.ndarray[float64]
            Quantile(s) to compute.
        interpolation : {'linear', 'lower', 'higher', 'nearest', 'midpoint'}
        ids : np.ndarray[np.intp]
            Group labels.
        ngroups : int
        starts : np.ndarray[int64]
        ends : np.ndarray[int64]

        Returns
        -------
        np.ndarray or ExtensionArray
        """
        from pandas.core.arrays.string_ import StringDtype

        if isinstance(self.dtype, StringDtype) or is_object_dtype(self.dtype):
            raise TypeError(
                f"dtype '{self.dtype}' does not support operation 'quantile'"
            )
        if is_bool_dtype(self.dtype):
            raise TypeError("Cannot use quantile with bool dtype")

        mask = np.asarray(isna(self))
        nqs = len(qs)

        if is_integer_dtype(self.dtype) or is_float_dtype(self.dtype):
            vals = self.to_numpy(dtype=float, na_value=np.nan)
        else:
            vals = np.asarray(self)

        inference: np.dtype | None = (
            np.dtype(np.int64) if is_integer_dtype(self.dtype) else None
        )

        out = np.empty((ngroups, nqs), dtype=np.float64)

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