pandas-dev/pandas · error · TypeError

can only insert Interval objects and NA into an…

Error message

can only insert Interval objects and NA into an IntervalArray

What it means

Raised in IntervalArray._validate_scalar when the scalar value is neither a pandas.Interval nor a valid NA for the dtype. Scalars inserted into an IntervalArray must be Interval objects or recognized NA.

Solutions

Wrap the scalar as pd.Interval(left, right, closed=arr.closed).
Use pd.NA (or np.nan where the subtype allows) for missing.
Ensure the Interval's closed matches the array's closed.

Example fix

// before
arr.insert(0, 5)
// after
arr.insert(0, pd.Interval(0, 5, closed=arr.closed))

Defensive patterns

Strategy: type-guard

Validate before calling

import pandas as pd

def as_interval_scalar(v, closed):
    if v is pd.NA:
        return pd.NA
    if isinstance(v, pd.Interval):
        return v
    raise TypeError('expected pandas.Interval or pd.NA')

Type guard

import pandas as pd

def is_interval_or_na(v):
    return v is pd.NA or isinstance(v, pd.Interval)

Prevention

Always pass pd.Interval(...) or pd.NA to insert and scalar setitem.
Match the Interval's closed to the target array's closed.
Avoid passing raw scalars or strings into interval arrays.

When it happens

Trigger: arr.insert(0, 5); arr.insert(0, 'x'); _validate_scalar(3.14) on an IntervalArray; assigning an unrecognized sentinel.

Common situations: Inserting raw scalars; using non-standard NA values not recognized for the dtype; building rows programmatically.

Related errors

AI-assisted analysis of pandas-dev/pandas@3b7651241d (2026-08-11). Data as JSON: /api/errors/93de93a453f1d067. Report an issue: GitHub.

Appendix: source

Thrown at pandas/core/arrays/interval.py:1183

            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

    def _validate_setitem_value(self, value):
        if is_list_like(value):
            return self._validate_listlike(value)

        left, right = self._validate_scalar(value)

        if is_valid_na_for_dtype(value, self.left.dtype):
            if is_integer_dtype(self.dtype.subtype):
                # can't set NaN on a numpy integer array
                # GH#45484 TypeError, not ValueError, matches what we get with
                #  non-NA un-holdable value.
                raise TypeError("Cannot set float NaN to integer-backed IntervalArray")

        return left, right

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