def top_words(s: TextSeries, normalize=False) -> pd.Series:
    r"""
    Return a pandas series with index the top words and as value the count.

    Tokenization: split by space and remove all punctuations that are not
    between characters.

    Parameters
    ----------
    normalize : bool, optional, default=False.
        When set to true, return normalized values.

    Examples
    --------
    >>> import pandas as pd
    >>> import texthero as hero
    >>> s = pd.Series("one two two three three three")
    >>> hero.top_words(s)
    three    3
    two      2
    one      1
    dtype: int64

    """

    # Replace all punctuation that are NOT in-between chacarters
    # This means, they have either a non word-bounding \B, are at the start ^, or at the end $
    # As re.sub replace all and not just the matching group, add matching parenthesis to the character
    # to keep during replacement.

    # TODO replace it with tokenizer.

    pattern = (
        rf"((\w)[{string.punctuation}](?:\B|$)|(?:^|\B)[{string.punctuation}](\w))"
    )

    return (
        s.str.replace(
            pattern, r"\2 \3"
        )  # \2 and \3 permits to keep the character around the punctuation.
        .str.split()  # now split by space
        .explode()  # one word for each line
        .value_counts(normalize=normalize)
    )