pandas-dev/pandas · error · ValueError

Length of values ({len(data)}) does not match length of inde

Error message

Length of values ({len(data)}) does not match length of index ({len(index)})

What it means

Raised by require_length_match (pandas/core/common.py:611), invoked when assigning an array-like of values to a Series/DataFrame whose index has a different length. pandas requires the values to align 1:1 with the index unless an explicit index is provided, so a length mismatch is a hard error rather than silent broadcasting.

Source

Thrown at pandas/core/common.py:611

    ------
    object : obj with modified attribute.
    """
    if condition:
        old_value = getattr(obj, attr)
        setattr(obj, attr, value)
    try:
        yield obj
    finally:
        if condition:
            setattr(obj, attr, old_value)


def require_length_match(data: Any, index: Index) -> None:
    """
    Check the length of data matches the length of the index.
    """
    if len(data) != len(index):
        raise ValueError(
            "Length of values "
            f"({len(data)}) "
            "does not match length of index "
            f"({len(index)})"
        )


_cython_table = {
    builtins.sum: "sum",
    builtins.max: "max",
    builtins.min: "min",
    np.all: "all",
    np.any: "any",
    np.sum: "sum",
    np.nansum: "sum",
    np.mean: "mean",
    np.nanmean: "mean",
    np.prod: "prod",

View on GitHub (pinned to 71959b8cb9)

Solutions

Make the values length match the index: slice or pad to `len(df)` / `len(index)`.
If assigning per-group results, map them back with map/merge rather than positional assignment: `df['g'] = df['key'].map(group_result)`.
Provide an explicit index that matches: `pd.Series(values, index=matching_index)` before assignment.
Use transform to broadcast group results to the original length: `df.groupby('k')['v'].transform(func)`.

Example fix

# before
df['agg'] = df.groupby('k')['v'].mean()   # length = #groups != len(df)

# after
df['agg'] = df['k'].map(df.groupby('k')['v'].mean())

Defensive patterns

Strategy: validation

Validate before calling

def assert_length_match(values, index):
    if len(values) != len(index):
        raise ValueError(f'len(values)={len(values)} != len(index)={len(index)}')

df['new'] = values  # only after assert_length_match(values, df.index)

Type guard

def lengths_match(values, index) -> bool:
    return len(values) == len(index)

Try / catch

try:
    df['new'] = values
except ValueError as e:
    if 'Length of values' in str(e):
        if len(values) < len(df):
            values = df['key'].map(dict(zip(df['key'].unique(), values)))
        df['new'] = values
    else:
        raise

Prevention

Use df['key'].map(...) or merge to assign per-group results to the full index.
Use groupby.transform to broadcast reductions to the original length.
Check len(values) == len(df) before positional assignment.

When it happens

Trigger: `df['new'] = [1,2,3]` on a 4-row frame; `pd.Series([1,2,3], index=[0,1,2,3])`; `df.assign(col=np.zeros(5))` on a 3-row frame; setting a column from a groupby/aggregate whose length differs from the original index.

Common situations: Assigning a list/ndarray computed from a subset or aggregation back to the full frame without reindexing; off-by-one in generated lists; applying a per-group result to the parent index.

Related errors

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/162784ad0679f674. Report an issue: GitHub.