pandas-dev/pandas · error · ValueError

Unable to avoid copy while creating an array as requested.

Error message

Unable to avoid copy while creating an array as requested.

What it means

Raised by SparseArray.__array__ when copy=False is requested but the array has gaps (sp_index.ngaps > 0). Returning sp_values without copying would lose the fill-value positions, so a no-copy materialization is impossible.

Solutions

1. Allow the copy: np.asarray(sparse_arr) or np.asarray(sparse_arr, copy=True).
2. Operate on .sp_values directly when you only need the non-fill entries.
3. If you must avoid the copy, work with a 0-gap array (no fill values present).

Example fix

# before
np.asarray(sparse_arr, copy=False)  # has gaps
# after
np.asarray(sparse_arr)  # materializes dense, copying fill_value into place

def can_avoid_copy(sparse_arr) -> bool:
    return sparse_arr.sp_index.ngaps == 0

def zero_gap_sparse(sparse_arr) -> bool:
    return getattr(sparse_arr.sp_index, 'ngaps', 1) == 0

try:
    arr = np.asarray(sparse_arr, copy=False)
except ValueError as e:
    if 'Unable to avoid copy' in str(e):
        arr = np.asarray(sparse_arr)  # accept the copy
    else:
        raise

When it happens

Trigger: np.asarray(sparse_arr, copy=False) on a SparseArray with at least one fill_value gap; passing the array into a NumPy function that requests copy=False via the NEP 50 __array__ protocol.

Common situations: Performance-minded code trying to avoid copies; library internals that probe copy=False; upgrading numpy which now propagates copy= into __array__.

Related errors

• Can only use the '.sparse' accessor with Sparse data.
• Cannot construct from scalar data. Pass a sequence instead.
• Cannot modify read-only array
• Cannot return a copy of the target
• Column length mismatch

AI-assisted analysis of pandas-dev/pandas@3b7651241d (2026-08-11). Data as JSON: /api/errors/06b7cf4999aca2cf. Report an issue: GitHub.

Appendix: source

Thrown at pandas/core/arrays/sparse/array.py:588

        return cls._simple_new(arr, index, dtype)

    def __array__(
        self, dtype: NpDtype | None = None, copy: bool | None = None
    ) -> np.ndarray:
        if self.sp_index.ngaps == 0:
            # Compat for na dtype and int values.
            if copy is True:
                return np.array(self.sp_values)
            else:
                result = self.sp_values
                if self._readonly:
                    result = result.view()
                    result.flags.writeable = False
                return result

        if copy is False:
            raise ValueError(
                "Unable to avoid copy while creating an array as requested."
            )

        fill_value = self.fill_value

        if dtype is None:
            # Can NumPy represent this type?
            # If not, `np.result_type` will raise. We catch that
            # and return object.
            if self.sp_values.dtype.kind == "M":
                # However, we *do* special-case the common case of
                # a datetime64 with pandas NaT.
                if fill_value is NaT:
                    # Can't put pd.NaT in a datetime64[ns]
                    unit = np.datetime_data(self.sp_values.dtype)[0]
                    fill_value = np.datetime64("NaT", unit)  # type: ignore[call-overload]
            try:
                dtype = np.result_type(self.sp_values.dtype, type(fill_value))

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