pandas-dev/pandas · error · ValueError

Cannot multiply with unequal lengths

Error message

Cannot multiply with unequal lengths

What it means

Raised in TimedeltaArray.__mul__ when the array operand's length differs from the timedelta array's length, except when the operand is itself timedelta64 (which is dispatched to a type-error path instead). This is the standard broadcast-length guard for elementwise multiplication.

Solutions

1. Ensure both operands have equal length.
2. Use pandas Series/Index objects so index alignment applies automatically, then drop or fill NaNs as needed.
3. Broadcast a scalar instead of a length-mismatched array if a uniform scale was intended.

Example fix

# before
pd.to_timedelta([1, 2, 3], unit='D') * np.array([1, 2])  # ValueError

# after
pd.to_timedelta([1, 2, 3], unit='D') * np.array([1, 2, 3])

def lengths_match(a, b) -> bool:
    return len(a) == len(b)

null

try:
    result = td * other
except ValueError as e:
    if 'unequal lengths' in str(e):
        # align via Series or trim to common length
        raise
    raise

When it happens

Trigger: `pd.to_timedelta([1, 2, 3], unit='D') * np.array([1, 2])`, or multiplying two Series of different lengths without alignment.

Common situations: Forgetting that raw numpy arrays/lists do not align by index; mismatched filter or weight arrays.

Related errors

• Cannot divide vectors with unequal lengths
• cannot broadcast result
• Cannot multiply ' ' by bool, explicitly cast to integers…
• Cannot multiply with
• dtype ' ' is invalid, should be np.timedelta64 dtype

AI-assisted analysis of pandas-dev/pandas@3b7651241d (2026-08-11). Data as JSON: /api/errors/7cc3da00a1d96f78. Report an issue: GitHub.

Appendix: source

Thrown at pandas/core/arrays/timedeltas.py:575

        if not hasattr(other, "dtype"):
            # list, tuple
            other = np.array(other)

        if other.dtype.kind == "b":
            # GH#58054
            raise TypeError(
                f"Cannot multiply '{self.dtype}' by bool, explicitly cast to "
                "integers instead"
            )
        if isinstance(other.dtype, (ArrowDtype, BaseMaskedDtype)):
            # GH#58054
            return NotImplemented

        if len(other) != len(self) and not lib.is_np_dtype(other.dtype, "m"):
            # Exclude timedelta64 here so we correctly raise TypeError
            #  for that instead of ValueError
            raise ValueError("Cannot multiply with unequal lengths")

        if is_object_dtype(other.dtype):
            # this multiplication will succeed only if all elements of other
            #  are int or float scalars, so we will end up with
            #  timedelta64[ns]-dtyped result
            arr = self._ndarray
            obj_result = np.array([arr[n] * other[n] for n in range(len(self))])
            return type(self)._simple_new(obj_result, dtype=obj_result.dtype)

        if other.dtype.kind in "iu":
            # GH#43178: detect int64 overflow rather than silently wrapping.
            #  Cast to int64 first: an unsigned multiplier above int64.max wraps
            #  to negative, which we detect by sign. We check the sign rather
            #  than ``other > i8max`` because comparing a broadcast unsigned
            #  array to a Python int segfaults on numpy < 2.2 (hit via the
            #  DataFrame blockwise path).
            i8_other = other.astype("i8", copy=False)
            if other.dtype.kind == "u" and (i8_other < 0).any():

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