pandas-dev/pandas · error · TypeError

value should be a ' ', 'NaT', or array of those. Got…

Error message

value should be a '{self._scalar_type.__name__}', 'NaT', or array of those. Got {msg_got} instead.

What it means

Same validation path as error 255 but raised from a call site that sets allow_listlike=True (e.g., __setitem__ accepting array-like values, line 558). The message additionally tells the user an array of the scalar type is acceptable. Otherwise the trigger is identical: a string that cannot be parsed as the scalar type for the array.

Solutions

1. Pre-coerce the whole batch: pd.to_datetime(values, errors='coerce') then assign.
2. Pass Timestamp/Timedelta objects or a DatetimeIndex instead of strings.
3. Filter out unparseable values before assignment.
4. Validate each element with pd.to_datetime(value, errors='coerce') in a loop for small batches.

Example fix

# before
dti[0:2] = ['2020-01-01', 'bad']  # TypeError

# after
vals = pd.to_datetime(['2020-01-01', 'bad'], errors='coerce')
dti[0:2] = vals

import pandas as pd
parsed = pd.to_datetime(list(values), errors='coerce')
dti[0:len(parsed)] = parsed

import pandas as pd

def all_parse_as_datetime(values) -> bool:
    parsed = pd.to_datetime(list(values), errors='coerce')
    return not (parsed.isna() ^ pd.isna(list(values))).any()

try:
    dti[0:n] = values
except TypeError as e:
    if 'value should be' in str(e):
        dti[0:n] = pd.to_datetime(list(values), errors='coerce')
    else:
        raise

When it happens

Trigger: Assigning a list/array containing malformed date strings to a DatetimeArray slice: dti[0:3] = ['2020','bad']. fillna on a datetime column with a list whose element fails parsing. _validate_scalar called with allow_listlike=True from setitem.

Common situations: Bulk-assigning string dates where one row has a bad value. Mixed-format date strings in a single batch assignment. Locale mismatches surfacing only for some rows.

Related errors

• value should be a ' ' or 'NaT'. Got instead.
• 'value' should be an interval type, got
• can only insert Interval objects and NA into an…
• cannot assign without a target object
• Cannot cast to dtype

AI-assisted analysis of pandas-dev/pandas@3b7651241d (2026-08-11). Data as JSON: /api/errors/527010d3ae489144. Report an issue: GitHub.

Appendix: source

Thrown at pandas/core/arrays/datetimelike.py:558

            listlike inputs are allowed.
        unbox : bool, default True
            Whether to unbox the result before returning.  Note: unbox=False
            skips the setitem compatibility check.

        Returns
        -------
        self._scalar_type or NaT
        """
        if isinstance(value, self._scalar_type):
            pass

        elif isinstance(value, str):
            # NB: Careful about tzawareness
            try:
                value = self._scalar_from_string(value)
            except ValueError as err:
                msg = self._validation_error_message(value, allow_listlike)
                raise TypeError(msg) from err

        elif is_valid_na_for_dtype(value, self.dtype):
            # GH#18295
            value = NaT

        elif isna(value):
            # if we are dt64tz and value is dt64("NaT"), dont cast to NaT,
            #  or else we'll fail to raise in _unbox_scalar
            msg = self._validation_error_message(value, allow_listlike)
            raise TypeError(msg)

        elif isinstance(value, self._recognized_scalars):
            # error: Argument 1 to "Timestamp" has incompatible type "object"; expected
            # "integer[Any] | float | str | date | datetime | datetime64"
            value = self._scalar_type(value)  # type: ignore[arg-type]

        else:
            msg = self._validation_error_message(value, allow_listlike)

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