pandas-dev/pandas · error · TypeError

only list-like objects are allowed to be passed to isin()…

Error message

only list-like objects are allowed to be passed to isin(), you passed a `{type(values).__name__}`

What it means

The isin() function's second argument (values — the set of values to test membership against) must be list-like. This is the companion check to the comps validation. While sets and frozensets are accepted (they get special fast-path handling later in the function), bare scalars, None, and other non-iterable types are rejected with a TypeError naming the actual type.

Solutions

Wrap the scalar in a list before passing: s.isin([scalar_value]).
If the variable may be scalar or list-like, normalize it: vals = [vals] if not hasattr(vals, '__iter__') or isinstance(vals, str) else vals.
Use pd.Series() or np.array() to ensure the values argument is always array-like.

Example fix

# before
s.isin(lookup_value)

# after
s.isin([lookup_value])

Defensive patterns

Strategy: validation

Validate before calling

from pandas.api.types import is_list_like

def safe_isin_values(series, lookup_values):
    if not is_list_like(lookup_values):
        lookup_values = [lookup_values]
    return series.isin(lookup_values)

Type guard

from pandas.api.types import is_list_like

def is_valid_isin_values(value) -> bool:
    return is_list_like(value) and not isinstance(value, (str, bytes))

Try / catch

try:
    mask = s.isin(lookup)
except TypeError as e:
    if "list-like" in str(e):
        mask = s.isin([lookup])
    else:
        raise

Prevention

Normalize lookup values to a list/array before passing to isin.
When extracting a value for lookup, use [df['col'].iloc[0]] not df['col'].iloc[0].
Write a unit test that checks isin with both single and multiple values.

When it happens

Trigger: Calling s.isin(some_variable) where some_variable is a scalar (int, float, str) rather than a collection. Passing None as the values argument. Using a variable that was expected to be a list/Series but is actually a single element due to upstream slicing or extraction logic.

Common situations: Extracting a single value from a column (df['col'].iloc[0]) and passing it to isin() instead of passing the column. Receiving a value from an API or config that is a scalar when a list was expected. Migration from SQL IN clauses where a single value is common.

Related errors

AI-assisted analysis of pandas-dev/pandas@3b7651241d (2026-08-11). Data as JSON: /api/errors/ecb7d85e0a8eb12f. Report an issue: GitHub.

Appendix: source

Thrown at pandas/core/algorithms.py:530

    Compute the isin boolean array.

    Parameters
    ----------
    comps : list-like
    values : list-like

    Returns
    -------
    ndarray[bool]
        Same length as `comps`.
    """
    if not is_list_like(comps):
        raise TypeError(
            "only list-like objects are allowed to be passed "
            f"to isin(), you passed a `{type(comps).__name__}`"
        )
    if not is_list_like(values):
        raise TypeError(
            "only list-like objects are allowed to be passed "
            f"to isin(), you passed a `{type(values).__name__}`"
        )

    if isinstance(values, (set, frozenset)) and len(values) > 0:
        # GH#25507: for a set of values, membership can be tested directly
        # via the set, avoiding an O(len(values)) materialization that
        # otherwise dominates when comps is much smaller than values.
        # Restrict to integer/bool comps (i.e. dtypes that cannot contain
        # NaN), since Python set membership would mis-handle the case where
        # both sides contain NaN values that are not identical.
        if isinstance(comps, (ABCSeries, ABCIndex)):
            comps_arr = comps._values
        else:
            comps_arr = comps
        if (
            isinstance(comps_arr, np.ndarray)
            and comps_arr.ndim == 1

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