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

def group_returns(returns, groupby, compounded=False):
    """ summarize returns
    group_returns(df, df.index.year)
    group_returns(df, [df.index.year, df.index.month])
    """
    if compounded:
        return returns.groupby(groupby).apply(_stats.comp)
    return returns.groupby(groupby).sum()

if "benchmark" in df:
        s_start['benchmark'] = df['benchmark'].index.strftime('%Y-%m-%d')[0]
        s_end['benchmark'] = df['benchmark'].index.strftime('%Y-%m-%d')[-1]
        s_rf['benchmark'] = rf

    metrics['Start Period'] = _pd.Series(s_start)
    metrics['End Period'] = _pd.Series(s_end)
    metrics['Risk-Free Rate %'] = _pd.Series(s_rf)
    metrics['Time in Market %'] = _stats.exposure(df) * pct

    metrics['~'] = blank

    if compounded:
        metrics['Cumulative Return %'] = (
            _stats.comp(df) * pct).map('{:,.2f}'.format)
    else:
        metrics['Total Return %'] = (df.sum() * pct).map('{:,.2f}'.format)

    metrics['CAGR%%'] = _stats.cagr(df, rf, compounded) * pct
    metrics['Sharpe'] = _stats.sharpe(df, rf)
    metrics['Sortino'] = _stats.sortino(df, rf)
    metrics['Max Drawdown %'] = blank
    metrics['Longest DD Days'] = blank

    if mode.lower() == 'full':
        ret_vol = _stats.volatility(df['returns']) * pct
        if "benchmark" in df:
            bench_vol = _stats.volatility(df['benchmark']) * pct
            metrics['Volatility (ann.) %'] = [ret_vol, bench_vol]
            metrics['R^2'] = _stats.r_squared(df['returns'], df['benchmark'])
        else:

def plot_distribution(returns, figsize=(10, 6),
                      fontname='Arial', grayscale=False, ylabel=True,
                      subtitle=True, compounded=True,
                      savefig=None, show=True):

    colors = _FLATUI_COLORS
    if grayscale:
        colors = ['#f9f9f9', '#dddddd', '#bbbbbb', '#999999', '#808080']
    # colors, ls, alpha = _get_colors(grayscale)

    port = _pd.DataFrame(returns.fillna(0))
    port.columns = ['Daily']

    apply_fnc = _stats.comp if compounded else _np.sum

    port['Weekly'] = port['Daily'].resample(
        'W-MON').apply(apply_fnc).resample('W-MON').last()
    port['Weekly'].ffill(inplace=True)

    port['Monthly'] = port['Daily'].resample(
        'M').apply(apply_fnc).resample('M').last()
    port['Monthly'].ffill(inplace=True)

    port['Quarterly'] = port['Daily'].resample(
        'Q').apply(apply_fnc).resample('Q').last()
    port['Quarterly'].ffill(inplace=True)

    port['Yearly'] = port['Daily'].resample(
        'A').apply(apply_fnc).resample('A').last()
    port['Yearly'].ffill(inplace=True)

'benchmark.')
    if match_volatility and benchmark is not None:
        bmark_vol = benchmark.loc[returns.index].std()
        returns = (returns / returns.std()) * bmark_vol

    # ---------------
    colors, _, _ = _get_colors(grayscale)
    df = _pd.DataFrame(index=returns.index, data={returns_label: returns})
    if isinstance(benchmark, _pd.Series):
        df['Benchmark'] = benchmark[benchmark.index.isin(returns.index)]
        df = df[['Benchmark', returns_label]]

    df = df.dropna()
    if resample is not None:
        df = df.resample(resample).apply(
            _stats.comp).resample(resample).last()
    # ---------------

    fig, ax = _plt.subplots(figsize=figsize)
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['bottom'].set_visible(False)
    ax.spines['left'].set_visible(False)

    # use a more precise date string for the x axis locations in the toolbar
    fig.suptitle(title+"\n", y=.99, fontweight="bold", fontname=fontname,
                 fontsize=14, color="black")

    if subtitle:
        ax.set_title("\n%s - %s                   " % (
            df.index.date[:1][0].strftime('%Y'),
            df.index.date[-1:][0].strftime('%Y')

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