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

def run(data, runs=1000):
    weights = []
    sharpes = np.zeros(runs)
    returns = np.zeros(sharpes.shape)
    drawdowns = np.zeros(sharpes.shape)
    volatility = np.zeros(sharpes.shape)

    for i in range(runs):
        w = create_random_weights(len(data.columns))
        r = (data * w).sum(axis=1)

        weights.append(w)
        returns[i] = r.add(1).prod()
        sharpes[i] = stats.sharpe(r)
        drawdowns[i] = stats.max_drawdown(r)
        volatility[i] = stats.volatility(r)

    return Weights({
        'data': data,
        'weights': weights,
        'sharpes': sharpes,
        'returns': returns,
        'drawdowns': drawdowns,
        'volatility': volatility
    })

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)
        metrics['Kurtosis'] = _stats.kurtosis(df)

        metrics['~~~~~~~~~~'] = blank

        metrics['Expected Daily %%'] = _stats.expected_return(df) * pct
        metrics['Expected Monthly %%'] = _stats.expected_return(
            df, aggregate='M') * pct
        metrics['Expected Yearly %%'] = _stats.expected_return(
            df, aggregate='A') * pct

_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
    _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

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)
        metrics['Kurtosis'] = _stats.kurtosis(df)

        metrics['~~~~~~~~~~'] = blank

        metrics['Expected Daily %%'] = _stats.expected_return(df) * pct
        metrics['Expected Monthly %%'] = _stats.expected_return(
            df, aggregate='M') * pct