pandas-dev/pandas · error · TypeError

'value' should be a compatible interval type, got {type(valu

Error message

'value' should be a compatible interval type, got {type(value)} instead.

What it means

Raised after the value is successfully parsed as an IntervalArray but its endpoint dtype is rejected by self.left._validate_fill_value (raises LossySetitemError or TypeError). This means the value is interval-shaped but its subtype is incompatible with the target's subtype, e.g. assigning datetime intervals into a numeric-backed IntervalArray. The error distinguishes 'shape ok, dtype wrong' from error 321 ('shape wrong').

Source

Thrown at pandas/core/arrays/interval.py:1169

    def _validate_listlike(self, value):
        # list-like of intervals
        try:
            array = IntervalArray(value)
            self._check_closed_matches(array, name="value")
            value_left, value_right = array.left, array.right
        except TypeError as err:
            # wrong type: not interval or NA
            msg = f"'value' should be an interval type, got {type(value)} instead."
            raise TypeError(msg) from err

        try:
            self.left._validate_fill_value(value_left)
        except (LossySetitemError, TypeError) as err:
            msg = (
                "'value' should be a compatible interval type, "
                f"got {type(value)} instead."
            )
            raise TypeError(msg) from err

        return value_left, value_right

    def _validate_scalar(self, value):
        if isinstance(value, Interval):
            self._check_closed_matches(value, name="value")
            left, right = value.left, value.right
            self.left._validate_fill_value(left)
            self.left._validate_fill_value(right)
        elif is_valid_na_for_dtype(value, self.left.dtype):
            # GH#18295
            left = right = self.left._na_value
        else:
            raise TypeError(
                "can only insert Interval objects and NA into an IntervalArray"
            )
        return left, right

View on GitHub (pinned to 71959b8cb9)

Solutions

Cast the target array to a wider subtype first: arr = arr.astype('interval[float64]') before assignment.
Rebuild the value with endpoints matching arr.dtype.subtype, e.g. pd.Interval(int(left), int(right)).
Confirm closed matches too via arr._check_closed_matches(value) before assigning.

Example fix

# before
arr = pd.arrays.IntervalArray.from_tuples([(0, 1)], dtype='interval[int64]')
arr[0] = pd.Interval(0.5, 1.5)

# after
arr = arr.astype('interval[float64]')
arr[0] = pd.Interval(0.5, 1.5)

Defensive patterns

Strategy: validation

Validate before calling

def compatible_value(arr, value):
    iv = value if isinstance(value, pd.Interval) else pd.arrays.IntervalArray(value)
    sub = iv.left.dtype if isinstance(iv, pd.arrays.IntervalArray) else type(iv.left)
    if not np.can_cast(sub, arr.dtype.subtype):
        raise TypeError(f'value subtype {sub} incompatible with {arr.dtype.subtype}')
    return iv

Type guard

def endpoints_compatible(arr, value) -> bool:
    try:
        arr.left._validate_fill_value(getattr(value, 'left', value))
        return True
    except (TypeError, Exception):
        return False

Try / catch

try:
    arr[i] = value
except TypeError as e:
    if 'compatible interval type' in str(e):
        arr = arr.astype('interval[float64]')
        arr[i] = value

Prevention

Standardize interval subtype across the pipeline (e.g. always float64 for analytic columns).
Cast the target array wider before assigning heterogeneous values.
Verify arr.dtype.subtype against the value's endpoint dtype before assignment.

When it happens

Trigger: Assigning intervals whose endpoints are floats into an int64-backed IntervalArray with values that would truncate, or assigning Timestamp-backed intervals into a numeric interval array, via arr[i] = other_array.

Common situations: Mixing interval arrays created with different subtypes (int vs float vs datetime), merging interval columns from heterogeneous sources, or attempting to widen/narrow precision during assignment.

Related errors

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/99cbf9a95bdec9e4. Report an issue: GitHub.