def METHOD_NAME(
    symbol: str,
) -> List[Any]:
    """Scrapes coin withdrawal fees per exchange
    [Source: https://withdrawalfees.com/]
    Parameters
    ----------
    symbol: str
        Coin to check withdrawal fees. By default bitcoin
    Returns
    -------
    List
        - str: Overall statistics (exchanges, lowest, average and median)
        - pd.DataFrame: Exchange, Withdrawal Fee, Minimum Withdrawal Amount
    """
    crypto_withdrawal_fees = BeautifulSoup(
        request(
            f"https://withdrawalfees.com/coins/{symbol}",
            headers={"User-Agent": get_user_agent()},
        ).text,
        "lxml",
    )
    if crypto_withdrawal_fees is None:
        return ["", pd.DataFrame()]
    table = crypto_withdrawal_fees.find_all("table")
    html_stats = crypto_withdrawal_fees.find("div", {"class": "details"})
    if len(table) == 0 or html_stats is None:
        return ["", pd.DataFrame()]
    df = pd.read_html(str(table))[0]
    df["Withdrawal Fee"] = df["Withdrawal Fee"].apply(
        lambda x: f'{x[:x.index(".")+3]} ({x[x.index(".")+3:]})'
        if "." in x and isinstance(x, str)
        else x
    )
    df["Minimum Withdrawal Amount"] = df["Minimum Withdrawal Amount"].apply(
        lambda x: f'{x[:x.index(".")+3]} ({x[x.index(".")+3:]})'
        if isinstance(x, str) and "." in x
        else x
    )
    df = df.fillna("")
    stats = html_stats.find_all("div", recursive=False)
    exchanges = stats[0].find("div", {"class": "value"}).text
    lowest = stats[1].find("div", {"class": "value"}).text
    average = stats[2].find("div", {"class": "value"}).text
    median = stats[3].find("div", {"class": "value"}).text
    stats_string = (
        f"{symbol} is available on {exchanges} exchanges with alowest fee of "
    )
    stats_string += f"{lowest}, average of {average} and median of {median}"
    return [stats_string, df]