pandas-dev/pandas · error · ValueError

Cannot multiply with unequal lengths

Error message

Cannot multiply with unequal lengths

What it means

Raised by TimedeltaArray.__mul__ when multiplying against an array operand whose length differs from self, and whose dtype is not timedelta64 (timedelta is excluded so it can surface a TypeError elsewhere). Pandas requires length-matched operands for vectorized scaling to avoid silent broadcasting mistakes.

Source

Thrown at pandas/core/arrays/timedeltas.py:556

        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():

View on GitHub (pinned to 71959b8cb9)

Solutions

1. Reindex or align both operands to the same length/index before multiplying.
2. Filter the longer operand to match, or broadcast a scalar instead of an array.
3. If lengths differ by design, decide the intended semantics (pairwise vs broadcast) and reindex explicitly.

Example fix

// before
out = td_series * mult_series  # different lengths

// after
mult = mult_series.reindex(td_series.index, fill_value=1)
out = td_series * mult

import numpy as np
a, b = np.asarray(td), np.asarray(other)
assert a.shape[0] == b.shape[0], f'length mismatch: {a.shape[0]} vs {b.shape[0]}'

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

try:
    out = td * other
except ValueError as e:
    if 'unequal lengths' in str(e):
        other = other.reindex(td.index) if hasattr(other, 'reindex') else other[:len(td)]
        out = td * other
    else:
        raise

When it happens

Trigger: `pd.to_timedelta(['1d','2d','3d']) * np.array([1,2])` or `td_series * int_series_of_different_len`. Hit in the array branch after list/tuple conversion to np.ndarray.

Common situations: Misaligned indices/Series from merges or filters; reusing a multiplier computed on a filtered subset; off-by-one in user-constructed arrays.

Related errors

• Cannot divide vectors with unequal lengths
• Function did not transform
• Cannot add or subtract timedelta64[ns] dtype from {self.dtyp
• Cannot add/subtract timedelta-like from PeriodArray that is
• Values resolution does not match dtype.

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/7cc3da00a1d96f78. Report an issue: GitHub.