pandas-dev/pandas · error · TypeError

Cannot convert to ; subtypes are incompatible

Error message

Cannot convert {self.dtype} to {dtype}; subtypes are incompatible

What it means

Raised in IntervalArray.astype when the target dtype is an IntervalDtype, the current subtype is floating, and the target subtype requires i8 (datetime-like) conversion. Reinterpreting float bounds as timestamps is allowed on IntervalIndex.astype but explicitly disallowed at the array level to avoid ambiguous casts.

Solutions

Route through IntervalIndex if datetime semantics are genuinely required: pd.IntervalIndex(arr).astype(target).
Cast endpoint Indexes to datetime explicitly, then rebuild the IntervalArray.
Keep a numeric target subtype (float/integer) compatible with the float source.

Example fix

// before
arr.astype('interval[datetime64[ns]]')
// after
import pandas as pd
pd.IntervalIndex(arr).astype('interval[datetime64[ns]]')

Defensive patterns

Strategy: validation

Validate before calling

from pandas.api.types import is_float_dtype, pandas_dtype
from pandas.core.dtypes.common import needs_i8_conversion

def interval_astype_safe(arr, target):
    t = pandas_dtype(target)
    from pandas import IntervalDtype
    if isinstance(t, IntervalDtype) and is_float_dtype(arr.dtype.subtype) and needs_i8_conversion(t.subtype):
        raise ValueError('Use IntervalIndex.astype for float->datetime-like interval casts')
    return arr.astype(t)

Try / catch

try:
    arr.astype(target)
except TypeError as e:
    if 'subtypes are incompatible' in str(e):
        # route through IntervalIndex or pick a numeric subtype
        pass
    else:
        raise

Prevention

Inspect arr.dtype.subtype before astype to an IntervalDtype target.
Remember IntervalIndex.astype permits casts that IntervalArray.astype rejects.
Avoid reinterpreting float bounds as datetime-like at the array level.

When it happens

Trigger: float_backed_arr.astype('interval[datetime64[ns]]'); arr.astype(IntervalDtype('datetime64[ns]')) on a float-backed IntervalArray; arr.astype('interval[timedelta64[ns]]') from float subtype.

Common situations: Reinterpreting numeric interval bounds as timestamps after dtype inference produced floats; mixing time-based and numeric interval pipelines.

Related errors

AI-assisted analysis of pandas-dev/pandas@3b7651241d (2026-08-11). Data as JSON: /api/errors/0b1e16469b67165f. Report an issue: GitHub.

Appendix: source

Thrown at pandas/core/arrays/interval.py:947

            ExtensionArray or NumPy ndarray with 'dtype' for its dtype.
        """
        from pandas import Index

        if dtype is not None:
            dtype = pandas_dtype(dtype)

        if isinstance(dtype, IntervalDtype):
            if dtype == self.dtype:
                return self.copy() if copy else self

            if is_float_dtype(self.dtype.subtype) and needs_i8_conversion(
                dtype.subtype
            ):
                # This is allowed on the Index.astype but we disallow it here
                msg = (
                    f"Cannot convert {self.dtype} to {dtype}; subtypes are incompatible"
                )
                raise TypeError(msg)

            # need to cast to different subtype
            try:
                # We need to use Index rules for astype to prevent casting
                #  np.nan entries to int subtypes
                new_left = Index(self._left, copy=False).astype(dtype.subtype)
                new_right = Index(self._right, copy=False).astype(dtype.subtype)
            except IntCastingNaNError:
                # e.g test_subtype_integer
                raise
            except (TypeError, ValueError) as err:
                # e.g. test_subtype_integer_errors f8->u8 can be lossy
                #  and raises ValueError
                msg = (
                    f"Cannot convert {self.dtype} to {dtype}; subtypes are incompatible"
                )
                raise TypeError(msg) from err
            return self._shallow_copy(new_left, new_right)

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