def winsorise_uint32(df, invalid_data_behavior, column, *columns):
    """Drops any record where a value would not fit into a uint32.

    Parameters
    ----------
    df : pd.DataFrame
        The dataframe to winsorise.
    invalid_data_behavior : {'warn', 'raise', 'ignore'}
        What to do when data is outside the bounds of a uint32.
    *columns : iterable[str]
        The names of the columns to check.

    Returns
    -------
    truncated : pd.DataFrame
        ``df`` with values that do not fit into a uint32 zeroed out.
    """
    columns = list((column,) + columns)
    mask = df[columns] > UINT32_MAX

    if invalid_data_behavior != 'ignore':
        mask |= df[columns].isnull()
    else:
        # we are not going to generate a warning or error for this so just use
        # nan_to_num
        df[columns] = np.nan_to_num(df[columns])

    mv = mask.values
    if mv.any():
        if invalid_data_behavior == 'raise':
            raise ValueError(
                '%d values out of bounds for uint32: %r' % (
                    mv.sum(), df[mask.any(axis=1)],
                ),
            )
        if invalid_data_behavior == 'warn':
            warnings.warn(
                'Ignoring %d values because they are out of bounds for'
                ' uint32: %r' % (
                    mv.sum(), df[mask.any(axis=1)],
                ),
                stacklevel=3,  # one extra frame for `expect_element`
            )

    df[mask] = 0
    return df
Drops any record where a value would not fit into a uint32.

    Parameters
    ----------
    df : pd.DataFrame
        The dataframe to winsorise.
    invalid_data_behavior : {'warn', 'raise', 'ignore'}
        What to do when data is outside the bounds of a uint32.
    *columns : iterable[str]
        The names of the columns to check.

    Returns
    -------
    truncated : pd.DataFrame
        ``df`` with values that do not fit into a uint32 zeroed out.