pandas-dev/pandas · error · ValueError

Passing in 'timedelta' dtype with no precision is not allowe

Error message

Passing in 'timedelta' dtype with no precision is not allowed. Please pass in 'timedelta64[ns]' instead.

What it means

Raised by _validate_td64_dtype when dtype equals numpy 'm8' (timedelta64 with no resolution). Pandas refuses bare 'timedelta' because nanosecond resolution is the only representation it stores; you must specify timedelta64[ns]. GH#24806.

Source

Thrown at pandas/core/arrays/timedeltas.py:1413

    errors to be ignored; they are caught and subsequently ignored at a
    higher level.
    """
    # coerce Index to np.ndarray, converting string-dtype if necessary
    values = np.asarray(data, dtype=np.object_)

    result = array_to_timedelta64(values, unit=unit, errors=errors)
    return result


def _validate_td64_dtype(dtype) -> DtypeObj:
    dtype = pandas_dtype(dtype)
    if dtype == np.dtype("m8"):
        # no precision disallowed GH#24806
        msg = (
            "Passing in 'timedelta' dtype with no precision is not allowed. "
            "Please pass in 'timedelta64[ns]' instead."
        )
        raise ValueError(msg)

    if not lib.is_np_dtype(dtype, "m"):
        raise ValueError(f"dtype '{dtype}' is invalid, should be np.timedelta64 dtype")
    elif not is_supported_dtype(dtype):
        raise ValueError("Supported timedelta64 resolutions are 's', 'ms', 'us', 'ns'")

    return dtype

View on GitHub (pinned to 71959b8cb9)

Solutions

Specify the resolution explicitly: 'timedelta64[ns]'.
If accepting user dtype strings, validate/normalize before passing to pandas.
Use pd.to_timedelta() to infer dtype rather than passing a bare dtype.

Example fix

// before
s = df['x'].astype('timedelta')

// after
s = df['x'].astype('timedelta64[ns]')

Defensive patterns

Strategy: validation

Validate before calling

if str(dtype) == 'timedelta':
    dtype = 'timedelta64[ns]'

Type guard

def has_td_resolution(dtype_str) -> bool:
    return dtype_str not in ('timedelta', 'm8', 'timedelta64')

Try / catch

try:
    s = df['x'].astype(dtype)
except ValueError as e:
    if 'no precision' in str(e):
        s = df['x'].astype('timedelta64[ns]')
    else:
        raise

Prevention

Always specify [ns] for timedelta dtypes.
Normalize user dtype strings.
Use to_timedelta() to avoid manual dtype strings.

When it happens

Trigger: `pd.TimedeltaIndex(..., dtype='timedelta')`, `.astype('timedelta')`, or `pd.Series(..., dtype='timedelta')`. Anywhere a unit-less timedelta dtype string is supplied.

Common situations: Copy-pasted dtype strings; tutorials using older numpy syntax; user input not validated against supported resolutions.

Related errors

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/645da3b018f50624. Report an issue: GitHub.