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

df[df.index >= _dt(today.year, today.month, 1)]) * pct

    d = today - _td(3*365/12)
    metrics['3M %'] = comp_func(
        df[df.index >= _dt(d.year, d.month, d.day)]) * pct

    d = today - _td(6*365/12)
    metrics['6M %'] = comp_func(
        df[df.index >= _dt(d.year, d.month, d.day)]) * pct

    metrics['YTD %'] = comp_func(df[df.index >= _dt(today.year, 1, 1)]) * pct

    d = today - _td(12*365/12)
    metrics['1Y %'] = comp_func(
        df[df.index >= _dt(d.year, d.month, d.day)]) * pct
    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

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:
            metrics['Volatility (ann.) %'] = [ret_vol]

        metrics['Calmar'] = _stats.calmar(df)
        metrics['Skew'] = _stats.skew(df)

_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
    _po.ulcer_index = stats.ulcer_index
    _po.ulcer_performance_index = stats.ulcer_performance_index
    _po.upi = stats.upi
    _po.risk_of_ruin = stats.risk_of_ruin
    _po.ror = stats.ror
    _po.value_at_risk = stats.value_at_risk
    _po.var = stats.var
    _po.conditional_value_at_risk = stats.conditional_value_at_risk
    _po.cvar = stats.cvar
    _po.expected_shortfall = stats.expected_shortfall
    _po.tail_ratio = stats.tail_ratio
    _po.payoff_ratio = stats.payoff_ratio

metrics['YTD %'] = comp_func(df[df.index >= _dt(today.year, 1, 1)]) * pct

    d = today - _td(12*365/12)
    metrics['1Y %'] = comp_func(
        df[df.index >= _dt(d.year, d.month, d.day)]) * pct
    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)