pandas-dev/pandas · error · ValueError

Lengths must match

Error message

Lengths must match

What it means

Raised by DatetimeLikeArrayMixin._validate_comparison_value when comparing the array against a list-like whose length differs from the array's. Element-wise comparison requires equal lengths; broadcasting rules for datetime-like arrays do not auto-broadcast a mismatched-length list. This guard runs before the comparison so a clean ValueError is surfaced.

Source

Thrown at pandas/core/arrays/datetimelike.py:510

                # failed to parse as Timestamp/Timedelta/Period
                raise InvalidComparison(other) from err

        if isinstance(other, self._recognized_scalars) or other is NaT:
            # error: Argument 1 to "Timestamp" has incompatible type "object";
            # expected "integer[Any] | float | str | date | datetime |
            # datetime64[date | int | None]"  [arg-type]
            other = self._scalar_type(other)  # type: ignore[arg-type]
            try:
                self._check_compatible_with(other)
            except TypeError as err:
                # e.g. tzawareness mismatch
                raise InvalidComparison(other) from err

        elif not is_list_like(other):
            raise InvalidComparison(other)

        elif len(other) != len(self):
            raise ValueError("Lengths must match")

        else:
            try:
                other = self._validate_listlike(other, allow_object=True)
                self._check_compatible_with(other)
            except TypeError as err:
                if is_object_dtype(getattr(other, "dtype", None)):
                    # We will have to operate element-wise
                    pass
                else:
                    raise InvalidComparison(other) from err

        return other

    def _validate_scalar(
        self,
        value,
        *,

View on GitHub (pinned to 71959b8cb9)

Solutions

1. Ensure both sides have the same length, or use a scalar for broadcasting.
2. Align via index: reindex or construct a Series with a matching index and let pandas align.
3. Validate len(other) == len(array) before the comparison.

Example fix

// before
ts = pd.date_range('2020', periods=3)
ts == [pd.Timestamp('2020-01-01'), pd.Timestamp('2020-01-02')]  # ValueError

// after
ts == [pd.Timestamp('2020-01-01')]*3  # broadcast scalar list of correct length

def compare_safe(arr, other):
    import pandas as pd
    if pd.api.types.is_list_like(other) and len(other) != len(arr):
        raise ValueError(f'length {len(other)} != {len(arr)}')
    return arr == other

import pandas as pd
from typing import Any

def lengths_match(a: Any, b: Any) -> bool:
    if pd.api.types.is_list_like(b):
        return len(a) == len(b)
    return True

try:
    arr == other
except ValueError as e:
    if 'Lengths must match' in str(e):
        arr == [other[0]] * len(arr)
    else:
        raise

When it happens

Trigger: datetime_array == [1,2,3] where the right side has a different length; Series of length N compared with a list of length M; comparison ops (<, >, ==, !=) between a datetime Series and a list/array/Index of mismatched length that is not a scalar.

Common situations: Passing a list derived from another column or a filtered subset without realigning the index; building a boolean mask from external data of the wrong length.

Related errors

• Cannot compare tz-naive and tz-aware datetime-like objects.
• Cannot compare tz-naive and tz-aware datetime-like objects
• [datetimelike_compat=True] {left._values} is not equal to {r
• overflow in timedelta operation
• Cannot compare types {!r} and {!r}

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/eeb9c657b3290ee0. Report an issue: GitHub.