pola-rs/polars · error · ValueError

incorrect NumPy datetime resolution 'D' (datetime only)…

Error message

incorrect NumPy datetime resolution

'D' (datetime only), 'ms', 'us', and 'ns' resolutions are supported when converting from numpy.{datetime64,timedelta64}. Please cast to the closest supported unit before converting.

What it means

When converting a numpy datetime64/timedelta64 array to a Series, _normalise_numpy_dtype only supports 'D', 'ms', 'us', and 'ns' resolutions. Any other unit ('Y', 'M', 'W', 'h', 'm', 's', or sub-ns like 'fs', 'as') raises this ValueError because polars' internal time representation cannot hold them losslessly.

Solutions

1. Cast the numpy array first: arr.astype('datetime64[us]') (or 'ns'/'ms'/'D').
2. For timedelta: arr.astype('timedelta64[us]').
3. Convert via Python objects: pl.Series(arr.astype('datetime64[us]')) or pass a list of datetimes.

Example fix

// before
pl.Series(np.array(['2024-01-01'], dtype='datetime64[s]'))
// after
arr = np.array(['2024-01-01'], dtype='datetime64[s]').astype('datetime64[us]')
pl.Series(arr)

SUPPORTED = {'D', 'ms', 'us', 'ns'}
def ensure_supported_dt64(arr: np.ndarray) -> np.ndarray:
    if arr.dtype.kind in 'mM':
        unit = arr.dtype.name.split('[')[1].rstrip(']') if '[' in arr.dtype.str else 'us'
        if unit not in SUPPORTED:
            arr = arr.astype('datetime64[us]' if arr.dtype.kind == 'M' else 'timedelta64[us]')
    return arr

try:
    s = pl.Series(arr)
except ValueError as e:
    if "incorrect NumPy datetime resolution" in str(e):
        s = pl.Series(arr.astype('datetime64[us]'))
    else:
        raise

When it happens

Trigger: pl.Series(np.array(['2024-01-01'], dtype='datetime64[s]')) or dtype='datetime64[h]', timedelta64 with 's'/'m'/'h' units, or numpy 2.x attosecond/femtosecond dtypes passed to pl.DataFrame/from_numpy/Series.

Common situations: Reading coarse-grained dates stored as datetime64[s] or [h] from other pipelines; pandas-to-polars conversions of second-resolution timestamps; numpy arrays produced with default units like 's'.

Related errors

• cannot convert DataFrame to
• cannot convert List column
• cannot parse numpy data type
• arr.dot query vector must be one-dimensional
• cannot compare datetime.datetime to Series of type

AI-assisted analysis of pola-rs/polars@fe841f959e (2026-09-18). Data as JSON: /api/errors/f441b7d0ad6d994c. Report an issue: GitHub.

Appendix: source

Thrown at py-polars/src/polars/datatypes/constructor.py:120

@functools.lru_cache(maxsize=32)
def _normalise_numpy_dtype(dtype: Any) -> tuple[Any, Any]:
    normalised_dtype = (
        np.dtype(dtype.base.name) if dtype.kind in ("i", "u", "f") else dtype
    ).type
    if normalised_dtype in (np.datetime64, np.timedelta64):
        time_unit = np.datetime_data(dtype)[0]
        if time_unit in dt.DTYPE_TEMPORAL_UNITS or (
            time_unit == "D" and normalised_dtype == np.datetime64
        ):
            return normalised_dtype, np.int64
        else:
            msg = (
                "incorrect NumPy datetime resolution"
                "\n\n'D' (datetime only), 'ms', 'us', and 'ns' resolutions are supported when converting from numpy.{datetime64,timedelta64}."
                " Please cast to the closest supported unit before converting."
            )
            raise ValueError(msg)
    return normalised_dtype, None


def numpy_values_and_dtype(
    values: np.ndarray[Any, Any],
) -> tuple[np.ndarray[Any, Any], type]:
    """Return numpy values and their associated dtype, adjusting if required."""
    # Create new dtype object from dtype base name so architecture specific
    # dtypes (np.longlong np.ulonglong np.intc np.uintc np.longdouble, ...)
    # get converted to their normalized dtype (np.int*, np.uint*, np.float*).
    dtype, cast_as = _normalise_numpy_dtype(values.dtype)
    if cast_as:
        values = values.astype(cast_as)
    return values, dtype


def numpy_type_to_constructor(
    values: np.ndarray[Any, Any], dtype: type[np.dtype[Any]]

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