pandas-dev/pandas · error · TypeError

Accumulation {name} not supported for {type(self)}

Error message

Accumulation {name} not supported for {type(self)}

What it means

Raised by Categorical._accumulate when the accumulation name is neither 'cummin' nor 'cummax'. Categoricals only support order-based accumulations; sum-style accumulations (cumsum, cumprod) have no meaning on category labels. The method dispatches on name and raises TypeError for anything unrecognized before checking orderedness.

Source

Thrown at pandas/core/arrays/categorical.py:2671

        Returns
        -------
        bool
        """
        if not isinstance(other, Categorical):
            return False
        elif self._categories_match_up_to_permutation(other):
            other = self._encode_with_my_categories(other)
            return lib.array_equivalent_bytes(self._codes, other._codes)
        return False

    def _accumulate(self, name: str, skipna: bool = True, **kwargs) -> Self:
        func: Callable
        if name == "cummin":
            func = np.minimum.accumulate
        elif name == "cummax":
            func = np.maximum.accumulate
        else:
            raise TypeError(f"Accumulation {name} not supported for {type(self)}")
        self.check_for_ordered(name)

        codes = self.codes.copy()
        mask = self.isna()
        if func == np.minimum.accumulate:
            codes[mask] = np.iinfo(codes.dtype.type).max
        # no need to change codes for maximum because codes[mask] is already -1
        if not skipna:
            mask = np.maximum.accumulate(mask)

        codes = func(codes)
        codes[mask] = -1
        return self._simple_new(codes, dtype=self._dtype)

    @classmethod
    def _concat_same_type(cls, to_concat: Sequence[Self], axis: AxisInt = 0) -> Self:
        from pandas.core.dtypes.concat import union_categoricals

View on GitHub (pinned to 71959b8cb9)

Solutions

1. Cast to a numeric dtype first (.astype('int64') etc.) if the categories are numeric and you want arithmetic accumulation.
2. Use .cummin() / .cummax() which are the only accumulations defined for categoricals (requires ordered=True).
3. Re-evaluate whether the column should be categorical at all for arithmetic operations.

Example fix

// before
s = pd.Series(pd.Categorical([1,2,3], ordered=True))
s.cumsum()  # TypeError: Accumulation cumsum not supported

// after
s.astype('int64').cumsum()

SUPPORTED_ACCUM = {'cummin', 'cummax'}
def safe_accum(s, name):
    import pandas as pd
    if isinstance(s.dtype, pd.CategoricalDtype) and name not in SUPPORTED_ACCUM:
        raise TypeError(f'{name} unsupported on Categorical; cast to numeric first')
    return getattr(s, name)()

import pandas as pd
from typing import Any

def supports_accumulation(obj: Any, name: str) -> bool:
    if isinstance(getattr(obj, 'dtype', None), pd.CategoricalDtype):
        return name in ('cummin', 'cummax')
    return True

try:
    s.cumsum()
except TypeError as e:
    if 'Accumulation' in str(e) and 'not supported' in str(e):
        s.astype('int64').cumsum()
    else:
        raise

When it happens

Trigger: Calling .cumsum() or .cumprod() on a categorical Series/Index; calling .cummin()/.cummax() routes here only if the name string is somehow altered; passing a custom accumulation name through internal APIs.

Common situations: Applying .cumsum() to a column mistakenly left as category dtype after pd.get_dummies was forgotten, or generic code that calls every accumulation method on each column type blindly.

Related errors

• Accumulation {name} not supported for {type(self)}
• overflow in timedelta operation
• No accumulation for {func} implemented on BaseMaskedArray
• No masked accumulation defined for dtype {values.dtype.type}
• No accumulation for {func} implemented on BaseMaskedArray

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/590bdea6829af19f. Report an issue: GitHub.