pandas-dev/pandas · error · ValueError

Lengths must match to compare

Error message

Lengths must match to compare

What it means

Raised by BaseMaskedArray._comparison_method when 'other' is a 1-D list-like whose length differs from len(self). Element-wise comparison between masked arrays and 1-D structures requires equal length; pandas does not rebroadcast or pad.

Solutions

1. Reindex or align both sides to a shared index before comparing.
2. Trim or extend the operand so lengths match (e.g. other[:len(arr)]).
3. Compare against a scalar or use isin() for membership tests instead of ==.

Example fix

// before
arr == other[len(arr) - 1:]   # wrong length
// after
aligned = other[:len(arr)]
arr == aligned

import numpy as np
other_arr = np.asarray(other)
if other_arr.ndim == 1 and len(other_arr) != len(arr):
    raise ValueError(f'Length mismatch: {len(arr)} vs {len(other_arr)}')
result = arr == other_arr

def lengths_match(arr, other) -> bool:
    import numpy as np
    if np.isscalar(other):
        return True
    return len(other) == len(arr)

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

When it happens

Trigger: Comparing two masked arrays/Series of different lengths: arr1 == arr2 where len(arr1) != len(arr2); passing a list whose length differs from the array.

Common situations: Comparing columns from misaligned DataFrames without reindexing; comparing against a list literal of the wrong length; off-by-one after slicing or filtering.

Related errors

• can only perform ops with 1-d structures
• cannot broadcast result
• Cannot cast NaN value to Integer dtype.
• cannot convert float NaN to bool
• cannot convert NA to integer

AI-assisted analysis of pandas-dev/pandas@3b7651241d (2026-08-11). Data as JSON: /api/errors/5ac60f395a2d2cfe. Report an issue: GitHub.

Appendix: source

Thrown at pandas/core/arrays/masked.py:1095

            other, mask = other._data, other._mask

        elif is_list_like(other):
            if not isinstance(
                other, (list, np.ndarray, ExtensionArray)
            ) and not ops.has_castable_attr(other):
                warnings.warn(
                    f"Operation with {type(other).__name__} is deprecated. "
                    "In a future version these will be treated as scalar-like. "
                    "To retain the old behavior, explicitly wrap in a Series "
                    "instead.",
                    Pandas4Warning,
                    stacklevel=find_stack_level(),
                )
            other = np.asarray(other)
            if other.ndim > 1:
                raise NotImplementedError("can only perform ops with 1-d structures")
            if len(self) != len(other):
                raise ValueError("Lengths must match to compare")

        if other is libmissing.NA:
            # numpy does not handle pd.NA well as "other" scalar (it returns
            # a scalar False instead of an array)
            # This may be fixed by NA.__array_ufunc__. Revisit this check
            # once that's implemented.
            result = np.zeros(self._data.shape, dtype="bool")
            mask = np.ones(self._data.shape, dtype="bool")
        else:
            with warnings.catch_warnings():
                # numpy may show a FutureWarning or DeprecationWarning:
                #     elementwise comparison failed; returning scalar instead,
                #     but in the future will perform elementwise comparison
                # before returning NotImplemented. We fall back to the correct
                # behavior today, so that should be fine to ignore.
                warnings.filterwarnings("ignore", "elementwise", FutureWarning)
                warnings.filterwarnings("ignore", "elementwise", DeprecationWarning)
                method = getattr(self._data, f"__{op.__name__}__")

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