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 IntervalArray.__array__ when called with copy=False. The numpy-compatible conversion must build a fresh object array of Interval/NA values, which is inherently a copy; the protocol therefore refuses the no-copy contract rather than silently violating it. Triggered by np.asarray(arr, copy=False) or any code path that requests a zero-copy numpy view.

Source

Thrown at pandas/core/arrays/interval.py:1568

        # non-strict inequality when closed != 'both'; at least one side is
        # not included in the intervals, so equality does not imply overlapping
        return bool(
            (self._right[:-1] <= self._left[1:]).all()
            or (self._left[:-1] >= self._right[1:]).all()
        )

    # ---------------------------------------------------------------------
    # Conversion

    def __array__(
        self, dtype: NpDtype | None = None, copy: bool | None = None
    ) -> np.ndarray:
        """
        Return the IntervalArray's data as a numpy array of Interval
        objects (with dtype='object')
        """
        if copy is False:
            raise ValueError(
                "Unable to avoid copy while creating an array as requested."
            )

        left = self._left
        right = self._right
        mask = self.isna()
        closed = self.closed

        result = np.empty(len(left), dtype=object)
        for i, left_value in enumerate(left):
            if mask[i]:
                result[i] = np.nan
            else:
                result[i] = Interval(left_value, right[i], closed)
        return result

    def __arrow_array__(self, type=None):
        """

View on GitHub (pinned to 71959b8cb9)

Solutions

1. Let pandas convert: call arr.to_numpy() (defaults to copy=True) or np.asarray(arr) without copy=False.
2. If a downstream library passes copy=False, upgrade it or call np.asarray(arr, dtype=object) explicitly.
3. Convert to object dtype ahead of time with arr.astype(object).

Example fix

# before
np.asarray(interval_arr, copy=False)

# after
np.asarray(interval_arr, dtype=object)

def to_numpy_object(arr):
    return np.asarray(arr, dtype=object)

try:
    return np.asarray(arr, copy=False)
except ValueError as e:
    if 'avoid copy' in str(e):
        return np.asarray(arr, dtype=object)

When it happens

Trigger: Calling np.asarray(interval_array) under NumPy versions that pass copy=False, or library code (e.g. some sklearn/dask paths) explicitly requesting copy=False via __array__.

Common situations: NumPy 2.0 changed __array__ signature to add copy=None|True|False; downstream libs that pass copy=False now hit this. Upgrading NumPy without pinning compatible libs.

Related errors

• {left_base!r} is {right_base!r}
• Unable to avoid copy while creating an array as requested.
• Unable to avoid copy while creating an array as requested.
• Unable to avoid copy while creating an array as requested.
• Unable to import required dependency {_dependency}. Please s

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/49b1a19a13cdc593. Report an issue: GitHub.