def METHOD_NAME(limit: int = 100) -> pd.DataFrame:
    """Scrapes top coins withdrawal fees
    [Source: https://withdrawalfees.com/]
    Parameters
    ----------
    limit: int
        Number of coins to search, by default n=100, one page has 100 coins, so 1 page is scraped.
    Returns
    -------
    pd.DataFrame
        Coin, Lowest, Average, Median, Highest, Exchanges Compared
    """
    COINS_PER_PAGE = 100
    withdrawal_fees_homepage = BeautifulSoup(
        request(
            "https://withdrawalfees.com/",
            headers={"User-Agent": get_user_agent()},
        ).text,
        "lxml",
    )
    table = withdrawal_fees_homepage.find_all("table")
    tickers_html = withdrawal_fees_homepage.find_all("div", {"class": "name"})
    if table is None or tickers_html is None:
        return pd.DataFrame()
    df = pd.read_html(str(table))[0]
    df["Coin"] = [ticker.text for ticker in tickers_html]
    df["Lowest"] = df["Lowest"].apply(
        lambda x: f'{x[:x.index(".")+3]} ({x[x.index(".")+3:]})'
        if "." in x and isinstance(x, str)
        else x
    )
    num_pages = int(math.ceil(limit / COINS_PER_PAGE))
    if num_pages > 1:
        for idx in range(2, num_pages + 1):
            withdrawal_fees_homepage = BeautifulSoup(
                request(
                    f"https://withdrawalfees.com/coins/page/{idx}",
                    headers={"User-Agent": get_user_agent()},
                ).text,
                "lxml",
            )
            table = withdrawal_fees_homepage.find_all("table")
            tickers_html = withdrawal_fees_homepage.find_all("div", {"class": "name"})
            if table is not None and tickers_html is not None:
                new_df = pd.read_html(str(table))[0]
                new_df["Highest"] = new_df["Highest"].apply(
                    lambda x: f'{x[:x.index(".")+3]} ({x[x.index(".")+3:]})'
                    if "." in x
                    else x
                )
                new_df["Coin"] = [ticker.text for ticker in tickers_html]
                df = df.append(new_df)
    df = df.fillna("")
    return df