pandas-dev/pandas · error · NotImplementedError

The 'numba' engine doesn't support list-like/dict likes of c

Error message

The 'numba' engine doesn't support list-like/dict likes of callables yet.

What it means

Raised in `apply_list_or_dict_like` when `engine='numba'` is requested together with a list-like or dict-like `func`. The numba engine in `apply` only supports a single callable operating on raw numpy values; iterating over multiple callables or column-specific mappings is not implemented.

Source

Thrown at pandas/core/apply.py:755

                raise ValueError(f"Operation {func} does not support axis=1")
            if "axis" in arg_names and not isinstance(
                obj, (SeriesGroupBy, DataFrameGroupBy)
            ):
                self.kwargs["axis"] = self.axis
        return self._apply_str(obj, func, *self.args, **self.kwargs)

    def apply_list_or_dict_like(self) -> DataFrame | Series:
        """
        Compute apply in case of a list-like or dict-like.

        Returns
        -------
        result: Series, DataFrame, or None
            Result when self.func is a list-like or dict-like, None otherwise.
        """

        if self.engine == "numba":
            raise NotImplementedError(
                "The 'numba' engine doesn't support list-like/"
                "dict likes of callables yet."
            )

        if self.axis == 1 and isinstance(self.obj, ABCDataFrame):
            return self.obj.T.apply(self.func, 0, args=self.args, **self.kwargs).T

        func = self.func
        kwargs = self.kwargs

        if is_dict_like(func):
            result = self.agg_or_apply_dict_like(op_name="apply")
        else:
            result = self.agg_or_apply_list_like(op_name="apply")

        result = reconstruct_and_relabel_result(result, func, **kwargs)

        return result

View on GitHub (pinned to 71959b8cb9)

Solutions

Drop `engine='numba'` (use the default 'python' engine) for list/dict func.
Call each function separately with the numba engine if each is a single compatible callable: `[df.apply(f, engine='numba') for f in funcs]`.
Reimplement the multi-function logic as one combined callable suitable for numba.

Example fix

# before
df.apply(['sum', 'mean'], engine='numba')
# after
df.agg(['sum', 'mean'])  # python engine
# or per-function numba
df.apply(my_single_func, engine='numba', raw=True)

Defensive patterns

Strategy: validation

Validate before calling

def safe_apply(df, func, engine='python', **kw):
    import collections.abc
    if engine == 'numba' and isinstance(func, (list, tuple, dict)):
        raise ValueError('numba engine requires a single callable, not list/dict')
    return df.apply(func, engine=engine, **kw)

Type guard

def is_single_callable_for_numba(func, engine) -> bool:
    import collections.abc, typing
    return engine != 'numba' or (callable(func) and not isinstance(func, (list, tuple, dict)))

Try / catch

try:
    df.apply(func, engine='numba')
except NotImplementedError as e:
    if 'numba' in str(e).lower():
        df.apply(func)  # fall back to python engine
    else:
        raise

Prevention

Reserve `engine='numba'` for single-callable apply calls only.
Document the numba engine's limitations for teammates.

When it happens

Trigger: `df.apply(['sum', 'mean'], engine='numba')` or `df.apply({'A': 'sum'}, engine='numba')`. The check at apply.py:754 fires before any numba work begins.

Common situations: Trying to speed up multi-function aggregations with numba; copy-pasting engine='numba' from a working single-callable call into a list-based call; assuming numba supports the full agg surface.

Related errors

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/191ceb5e0d2b7bd6. Report an issue: GitHub.