pandas-dev/pandas · error · NotImplementedError

No accumulation for {func} implemented on BaseMaskedArray

Error message

No accumulation for {func} implemented on BaseMaskedArray

What it means

Raised by _cum_func in masked_accumulations.py:63 as a NotImplementedError when the accumulation func passed in is not one of np.cumsum, np.cumprod, np.maximum.accumulate, np.minimum.accumulate. The fill-value lookup dict only contains those four; any other numpy accumulator raises KeyError, which is converted to NotImplementedError. This is an internal dispatch guard - public cum methods only ever pass one of the four supported funcs.

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)

View on GitHub (pinned to 71959b8cb9)

Solutions

1. Use only the supported accumulators: cumsum, cumprod, cummin, cummax.
2. For custom accumulation needs, implement the loop directly on the array rather than routing through _cum_func.
3. If you are an EA author and need a new accumulator, add it to the fill_value dict and contribute upstream.

Example fix

// before
from pandas.core.array_algos.masked_accumulations import _cum_func
_cum_func(np.add.accumulate, vals, mask)  # unsupported
// after
_cum_func(np.cumsum, vals, mask)  # supported

import numpy as np
_ALLOWED = {np.cumsum, np.cumprod, np.maximum.accumulate, np.minimum.accumulate}
if func not in _ALLOWED:
    raise NotImplementedError(f'{func} not supported by masked accumulation; use one of cumsum/cumprod/cummin/cummax')

def supported_masked_cum(func) -> bool:
    import numpy as np
    return func in {np.cumsum, np.cumprod, np.maximum.accumulate, np.minimum.accumulate}

When it happens

Trigger: Internally dispatching an unsupported numpy accumulator (e.g. np.add.accumulate) on a masked numeric array. Hit at masked_accumulations.py:55-65 when func is not a key in the supported dict.

Common situations: Custom EA code or third-party integrations calling _cum_func directly with an unsupported func; pandas internal refactors that introduce new cum variants before extending the dispatch table; misuse of the internal API.

Related errors

• No accumulation for {func} implemented on BaseMaskedArray
• No masked accumulation defined for dtype {values.dtype.type}
• Could not infer freq from start/end
• keyword error in function call '{node.func.id}'
• Invalid binary operator {op!r}, valid operators are {keys}

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/32083ada175af0b4. Report an issue: GitHub.