pandas-dev/pandas · error · NotImplementedError

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

Error message

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

What it means

Raised at the top of `apply_list_or_dict_like` when `engine='numba'` is combined with a list-like or dict-like `func`. The numba engine in DataFrame.apply only supports a single callable operating on each column's values; vectorizing a sequence/dict of callables is not implemented, so pandas fails fast with NotImplementedError rather than silently falling back to the python engine.

Solutions

Drop `engine='numba'` and use the default python engine for list/dict funcs.
Apply each callable separately with `engine='numba'` and assemble the results manually: `pd.concat([df.apply(f, engine='numba') for f in [f1, f2]], axis=1)`.
Verify numba is installed (the python engine fallback path is not taken implicitly here).

Example fix

// before
df.apply([f1, f2], engine='numba')
// after
df.apply([f1, f2])  # python engine

Defensive patterns

Strategy: validation

Validate before calling

def apply_with_engine(df, func, engine='python'):
    import collections.abc as cabc
    is_multi = isinstance(func, (list, tuple, dict)) or cabc.Mapping
    if engine == 'numba' and is_multi:
        # fall back to python engine or apply each callable separately
        return df.apply(func)  # python engine
    return df.apply(func, engine=engine)

Type guard

def numba_supports_func(func) -> bool:
    import collections.abc as cabc
    return callable(func) and not isinstance(func, (list, tuple, dict)) and not isinstance(func, cabc.Mapping)

Try / catch

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

Prevention

Only enable engine='numba' for single-callable apply.
Gate numba behind a helper that detects list/dict funcs.
Verify numba is installed before passing engine='numba'.

When it happens

Trigger: `df.apply([f1, f2], engine='numba')`, `df.apply({'A': f1}, engine='numba')`, `df.agg([...], engine='numba')`. Any path that reaches apply_list_or_dict_like with self.engine == 'numba'.

Common situations: Users enable numba for speed on a pipeline that uses list/dict agg specs; copy-pasting `engine='numba'` from a working single-callable call into a multi-function call.

Related errors

AI-assisted analysis of pandas-dev/pandas@3b7651241d (2026-08-11). Data as JSON: /api/errors/191ceb5e0d2b7bd6. Report an issue: GitHub.

Appendix: 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

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