pandas-dev/pandas · error · ValueError

Values resolution does not match dtype.

Error message

Values resolution does not match dtype.

What it means

TimedeltaArray._validate_dtype raises ValueError when the requested dtype's resolution (e.g. timedelta64[s]) does not match the resolution of the supplied values array. The array's data and the dtype must agree on time unit to prevent silent rescaling.

Solutions

1. Ensure the dtype passed matches values.dtype: pass dtype=values.dtype.
2. Convert the values to the desired unit with astype_overflowsafe before constructing.
3. Use the public pd.TimedeltaIndex / pd.to_timedelta constructors, which handle resolution matching.

Example fix

// before
arr = np.array([1,2,3], dtype='timedelta64[s]')
TimedeltaArray._simple_new(arr, dtype=np.dtype('timedelta64[ns]'))  # ValueError
// after
TimedeltaArray._simple_new(arr, dtype=arr.dtype)

def build_td_array(values, dtype=None):
    if dtype is None:
        dtype = values.dtype
    if dtype != values.dtype:
        raise ValueError(f"dtype {dtype} != values.dtype {values.dtype}")
    return dtype

def td_dtype_matches_values(values, dtype) -> bool:
    return dtype == values.dtype

try:
    TimedeltaArray._simple_new(values, dtype=dtype)
except ValueError as e:
    if 'resolution does not match' in str(e):
        TimedeltaArray._simple_new(values, dtype=values.dtype)
    else:
        raise

When it happens

Trigger: Internally constructing a TimedeltaArray with values of dtype timedelta64[ms] but passing dtype=timedelta64('ns'); calling _simple_new or _validate_dtype with mismatched unit parameters.

Common situations: Custom subclasses or internal callers passing a stale dtype; building a TimedeltaArray from raw int64 views with one unit while declaring another; misalignment after astype_overflowsafe failures.

Related errors

• Values resolution does not match dtype.
• Cannot convert from to . Supported resolutions are 's'…
• cumprod not supported for Timedelta.
• does not have a resolution.
• Supported timedelta64 resolutions are 's', 'ms', 'us', 'ns'

AI-assisted analysis of pandas-dev/pandas@3b7651241d (2026-08-11). Data as JSON: /api/errors/43e71ff706d0b281. Report an issue: GitHub.

Appendix: source

Thrown at pandas/core/arrays/timedeltas.py:247

        -------
        numpy.dtype
        """
        return self._ndarray.dtype

    @property  # NB: override with cache_readonly in immutable subclasses
    def _resolution_obj(self) -> Resolution:
        return get_resolution(self.asi8, tz=None, reso=self._creso)

    # ----------------------------------------------------------------
    # Constructors

    @classmethod
    def _validate_dtype(cls, values, dtype):
        # used in TimeLikeOps.__init__
        dtype = _validate_td64_dtype(dtype)
        _validate_td64_dtype(values.dtype)
        if dtype != values.dtype:
            raise ValueError("Values resolution does not match dtype.")
        return dtype

    # error: Signature of "_simple_new" incompatible with supertype "NDArrayBacked"
    @classmethod
    def _simple_new(  # type: ignore[override]
        cls,
        values: npt.NDArray[np.timedelta64],
        dtype: np.dtype[np.timedelta64] = TD64NS_DTYPE,
    ) -> Self:
        # Require td64 dtype, not unit-less, matching values.dtype
        assert lib.is_np_dtype(dtype, "m")
        assert not tslibs.is_unitless(dtype)
        assert isinstance(values, np.ndarray), type(values)
        assert dtype == values.dtype

        return super()._simple_new(values=values, dtype=dtype)

    @classmethod

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