pandas-dev/pandas · error · ValueError

na_action must either be 'ignore' or None

Error message

na_action must either be 'ignore' or None, {na_action} was passed

What it means

The map_array() function (which powers Series.map and similar) accepts an na_action parameter that controls how NaN values are handled: either None (pass NaN to the mapping function) or 'ignore' (propagate NaN without calling the function). Any other value for na_action is rejected with a ValueError. This is a strict API contract — there is no third option.

Solutions

1. Use na_action='ignore' to skip NaN, or na_action=None to pass NaN to the function.
2. Validate the na_action value before calling map: assert na_action in (None, 'ignore').
3. If building a configurable wrapper, map user-friendly names to the valid values.

Example fix

# before
s.map(my_func, na_action='skip')

# after
s.map(my_func, na_action='ignore')

VALID_NA_ACTIONS = (None, 'ignore')

def safe_map(series, mapper, na_action=None):
    if na_action not in VALID_NA_ACTIONS:
        raise ValueError(f"na_action must be one of {VALID_NA_ACTIONS}, got {na_action!r}")
    return series.map(mapper, na_action=na_action)

def is_valid_na_action(na_action) -> bool:
    return na_action in (None, 'ignore')

try:
    result = s.map(func, na_action=na_action)
except ValueError as e:
    if "na_action" in str(e):
        result = s.map(func, na_action=None)  # default
    else:
        raise

When it happens

Trigger: Calling s.map(func, na_action='skip') or s.map(func, na_action=True) — misspelling the valid value or passing a boolean. Passing na_action from a variable or config that was not validated. Using 'drop' or 'omit' instead of the correct 'ignore'.

Common situations: Confusing pandas' na_action='ignore' with other libraries' NaN-handling parameters (e.g., skipna=True). Copying code from a different API that uses different sentinel values. Passing a dynamically-sourced parameter without validating it.

Related errors

• invalid value for result_type, must be one of
• by_row= not allowed
• cannot broadcast result
• cannot combine transform and aggregation operations
• cannot diff on axis=

AI-assisted analysis of pandas-dev/pandas@3b7651241d (2026-08-11). Data as JSON: /api/errors/b6b4291b4dd48b76. Report an issue: GitHub.

Appendix: source

Thrown at pandas/core/algorithms.py:1895

    ----------
    mapper : function, dict, or Series
        Mapping correspondence.
    na_action : {None, 'ignore'}, default None
        If 'ignore', propagate NA values, without passing them to the
        mapping correspondence.

    Returns
    -------
    Union[ndarray, Index, ExtensionArray]
        The output of the mapping function applied to the array.
        If the function returns a tuple with more than one element
        a MultiIndex will be returned.
    """
    from pandas import Index

    if na_action not in (None, "ignore"):
        msg = f"na_action must either be 'ignore' or None, {na_action} was passed"
        raise ValueError(msg)

    # we can fastpath dict/Series to an efficient map
    # as we know that we are not going to have to yield
    # python types
    if is_dict_like(mapper):
        if isinstance(mapper, dict) and hasattr(mapper, "__missing__"):
            # If a dictionary subclass defines a default value method,
            # convert mapper to a lookup function (GH #15999).
            dict_with_default = mapper
            mapper = lambda x: dict_with_default[
                np.nan if isinstance(x, float) and np.isnan(x) else x
            ]
        else:
            # Dictionary does not have a default. Thus it's safe to
            # convert to a Series for efficiency.
            # we specify the keys here to handle the
            # possibility that they are tuples

View on GitHub (pinned to 3b7651241d)