How to use the quantstats.stats.worst function in QuantStats

df[df.index >= _dt(today.year-5, today.month, today.day)
           ], 0., compounded) * pct
    metrics['10Y (ann.) %'] = _stats.cagr(
        df[df.index >= _dt(today.year-10, today.month, today.day)
           ], 0., compounded) * pct
    metrics['All-time (ann.) %'] = _stats.cagr(df, 0., compounded) * pct

    # best/worst
    if mode.lower() == 'full':
        metrics['~~~'] = blank
        metrics['Best Day %'] = _stats.best(df) * pct
        metrics['Worst Day %'] = _stats.worst(df) * pct
        metrics['Best Month %'] = _stats.best(df, aggregate='M') * pct
        metrics['Worst Month %'] = _stats.worst(df, aggregate='M') * pct
        metrics['Best Year %'] = _stats.best(df, aggregate='A') * pct
        metrics['Worst Year %'] = _stats.worst(df, aggregate='A') * pct

    # dd
    metrics['~~~~'] = blank
    for ix, row in dd.iterrows():
        metrics[ix] = row
    metrics['Recovery Factor'] = _stats.recovery_factor(df)
    metrics['Ulcer Index'] = _stats.ulcer_index(df, rf)

    # win rate
    if mode.lower() == 'full':
        metrics['~~~~~'] = blank
        metrics['Avg. Up Month %'] = _stats.avg_win(df, aggregate='M') * pct
        metrics['Avg. Down Month %'] = _stats.avg_loss(df, aggregate='M') * pct
        metrics['Win Days %%'] = _stats.win_rate(df) * pct
        metrics['Win Month %%'] = _stats.win_rate(df, aggregate='M') * pct
        metrics['Win Quarter %%'] = _stats.win_rate(df, aggregate='Q') * pct

def extend_pandas():
    """
    extends pandas by exposing methods to be used like:
    df.sharpe(), df.best('day'), ...
    """
    from pandas.core.base import PandasObject as _po

    _po.compsum = stats.compsum
    _po.comp = stats.comp
    _po.expected_return = stats.expected_return
    _po.geometric_mean = stats.geometric_mean
    _po.ghpr = stats.ghpr
    _po.outliers = stats.outliers
    _po.remove_outliers = stats.remove_outliers
    _po.best = stats.best
    _po.worst = stats.worst
    _po.consecutive_wins = stats.consecutive_wins
    _po.consecutive_losses = stats.consecutive_losses
    _po.exposure = stats.exposure
    _po.win_rate = stats.win_rate
    _po.avg_return = stats.avg_return
    _po.avg_win = stats.avg_win
    _po.avg_loss = stats.avg_loss
    _po.volatility = stats.volatility
    _po.implied_volatility = stats.implied_volatility
    _po.sharpe = stats.sharpe
    _po.sortino = stats.sortino
    _po.cagr = stats.cagr
    _po.rar = stats.rar
    _po.skew = stats.skew
    _po.kurtosis = stats.kurtosis
    _po.calmar = stats.calmar

metrics['3Y (ann.) %'] = _stats.cagr(
        df[df.index >= _dt(today.year-3, today.month, today.day)
           ], 0., compounded) * pct
    metrics['5Y (ann.) %'] = _stats.cagr(
        df[df.index >= _dt(today.year-5, today.month, today.day)
           ], 0., compounded) * pct
    metrics['10Y (ann.) %'] = _stats.cagr(
        df[df.index >= _dt(today.year-10, today.month, today.day)
           ], 0., compounded) * pct
    metrics['All-time (ann.) %'] = _stats.cagr(df, 0., compounded) * pct

    # best/worst
    if mode.lower() == 'full':
        metrics['~~~'] = blank
        metrics['Best Day %'] = _stats.best(df) * pct
        metrics['Worst Day %'] = _stats.worst(df) * pct
        metrics['Best Month %'] = _stats.best(df, aggregate='M') * pct
        metrics['Worst Month %'] = _stats.worst(df, aggregate='M') * pct
        metrics['Best Year %'] = _stats.best(df, aggregate='A') * pct
        metrics['Worst Year %'] = _stats.worst(df, aggregate='A') * pct

    # dd
    metrics['~~~~'] = blank
    for ix, row in dd.iterrows():
        metrics[ix] = row
    metrics['Recovery Factor'] = _stats.recovery_factor(df)
    metrics['Ulcer Index'] = _stats.ulcer_index(df, rf)

    # win rate
    if mode.lower() == 'full':
        metrics['~~~~~'] = blank
        metrics['Avg. Up Month %'] = _stats.avg_win(df, aggregate='M') * pct

], 0., compounded) * pct
    metrics['5Y (ann.) %'] = _stats.cagr(
        df[df.index >= _dt(today.year-5, today.month, today.day)
           ], 0., compounded) * pct
    metrics['10Y (ann.) %'] = _stats.cagr(
        df[df.index >= _dt(today.year-10, today.month, today.day)
           ], 0., compounded) * pct
    metrics['All-time (ann.) %'] = _stats.cagr(df, 0., compounded) * pct

    # best/worst
    if mode.lower() == 'full':
        metrics['~~~'] = blank
        metrics['Best Day %'] = _stats.best(df) * pct
        metrics['Worst Day %'] = _stats.worst(df) * pct
        metrics['Best Month %'] = _stats.best(df, aggregate='M') * pct
        metrics['Worst Month %'] = _stats.worst(df, aggregate='M') * pct
        metrics['Best Year %'] = _stats.best(df, aggregate='A') * pct
        metrics['Worst Year %'] = _stats.worst(df, aggregate='A') * pct

    # dd
    metrics['~~~~'] = blank
    for ix, row in dd.iterrows():
        metrics[ix] = row
    metrics['Recovery Factor'] = _stats.recovery_factor(df)
    metrics['Ulcer Index'] = _stats.ulcer_index(df, rf)

    # win rate
    if mode.lower() == 'full':
        metrics['~~~~~'] = blank
        metrics['Avg. Up Month %'] = _stats.avg_win(df, aggregate='M') * pct
        metrics['Avg. Down Month %'] = _stats.avg_loss(df, aggregate='M') * pct
        metrics['Win Days %%'] = _stats.win_rate(df) * pct