pandas-dev/pandas · error · NotImplementedError

No accumulation for implemented on BaseMaskedArray

Error message

No accumulation for {func} implemented on BaseMaskedArray

What it means

NotImplementedError raised in masked_accumulations when the func is not one of np.cumprod, np.maximum.accumulate, np.cumsum, np.minimum.accumulate. The fill-value lookup dict has no entry for the func, so it is rejected.

Solutions

Use a supported accumulation method (cumsum, cumprod, cummin, cummax).
Implement the accumulation in the calling code instead of relying on the masked dispatch.

Defensive patterns

Strategy: validation

Validate before calling

import numpy as np
SUPPORTED_FUNCS = {np.cumprod, np.maximum.accumulate, np.cumsum, np.minimum.accumulate}
def safe_accum(func, values):
    if func not in SUPPORTED_FUNCS:
        raise ValueError(f'{func.__name__} is not a supported masked accumulation')
    return func(values)

Try / catch

try:
    func(values)
except NotImplementedError as e:
    if 'No accumulation for' in str(e):
        np.cumsum(values)  # fall back to a supported accumulator
    else:
        raise

Prevention

Only route the four supported numpy accumulators through masked arrays.
For custom reductions, operate on the underlying numpy buffer directly.

When it happens

Trigger: Internal dispatch: routing an unsupported accumulation (e.g. np.cumprod on a path expecting something else, or a custom ufunc) through masked accumulations.

Common situations: Custom array code or third-party extension arrays that hand an unsupported accumulator to the masked path.

Related errors

AI-assisted analysis of pandas-dev/pandas@3b7651241d (2026-08-11). Data as JSON: /api/errors/32083ada175af0b4. Report an issue: GitHub.

Appendix: source

Thrown at pandas/core/array_algos/masked_accumulations.py:63

        dtype_info = np.iinfo(values.dtype.type)
    elif values.dtype.kind == "b":
        # Max value of bool is 1, but since we are setting into a boolean
        # array, 255 is fine as well. Min value has to be 0 when setting
        # into the boolean array.
        dtype_info = np.iinfo(np.uint8)
    else:
        raise NotImplementedError(
            f"No masked accumulation defined for dtype {values.dtype.type}"
        )
    try:
        fill_value = {
            np.cumprod: 1,
            np.maximum.accumulate: dtype_info.min,
            np.cumsum: 0,
            np.minimum.accumulate: dtype_info.max,
        }[func]
    except KeyError as err:
        raise NotImplementedError(
            f"No accumulation for {func} implemented on BaseMaskedArray"
        ) from err

    values[mask] = fill_value

    if not skipna:
        mask = np.maximum.accumulate(mask)

    values = func(values)
    return values, mask


def cumsum(
    values: np.ndarray, mask: npt.NDArray[np.bool_], *, skipna: bool = True
) -> tuple[np.ndarray, npt.NDArray[np.bool_]]:
    return _cum_func(np.cumsum, values, mask, skipna=skipna)

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