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 in SparseArray.__array__ (the numpy array protocol) when copy is False but a copy is unavoidable. When the array has gaps (sp_index.ngaps > 0), the dense representation must materialize a new buffer filled with the fill value, so promising numpy 'no copy' is impossible. This surfaces via np.asarray(arr, copy=False) or the __array__(copy=False) protocol.

Source

Thrown at pandas/core/arrays/sparse/array.py:583

        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))

View on GitHub (pinned to 71959b8cb9)

Solutions

1. Allow a copy: np.asarray(arr) or np.array(arr) without copy=False.
2. Densify once and reuse: dense = arr.to_dense() then operate on the ndarray.
3. If you must avoid copies, operate on sp_values + sp_index directly instead of the dense view.

Example fix

// before
out = np.asarray(sparse_arr, copy=False)
// after
out = np.asarray(sparse_arr)

import numpy as np

def densify_sparse(arr, allow_copy=True):
    if arr.sp_index.ngaps > 0 and not allow_copy:
        raise ValueError('a copy is required for a gapped sparse array')
    return np.asarray(arr)

def sparse_array_needs_copy(arr) -> bool:
    return arr.sp_index.ngaps > 0

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

When it happens

Trigger: np.asarray(sparse_arr_with_gaps, copy=False); np.array(sparse_arr, copy=False); any code path (numpy 2.0 __array__ protocol) requesting a zero-copy view of a gapped sparse array.

Common situations: Libraries that default to copy=False for memory efficiency; numpy 2.x adoption where __array__(copy=False) is honored; passing a SparseArray to a function that asserts no-copy.

Related errors

• Cannot construct {type(self).__name__} from scalar data. Pas
• 'data' must have a single column, not '{ncol}'
• Cannot modify read-only array
• SparseArray does not support item assignment via setitem
• SparseArray does not support in-place sort

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/06b7cf4999aca2cf. Report an issue: GitHub.