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

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
        metrics['Kelly Criterion %'] = _stats.kelly_criterion(df) * pct
        metrics['Risk of Ruin %'] = _stats.risk_of_ruin(df)

        metrics['Daily Value-at-Risk %'] = -abs(_stats.var(df) * pct)
        metrics['Expected Shortfall (cVaR) %'] = -abs(_stats.cvar(df) * pct)

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

    metrics['Payoff Ratio'] = _stats.payoff_ratio(df)
    metrics['Profit Factor'] = _stats.profit_factor(df)
    metrics['Common Sense Ratio'] = _stats.common_sense_ratio(df)
    metrics['CPC Index'] = _stats.cpc_index(df)

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
        metrics['Kelly Criterion %'] = _stats.kelly_criterion(df) * pct
        metrics['Risk of Ruin %'] = _stats.risk_of_ruin(df)

        metrics['Daily Value-at-Risk %'] = -abs(_stats.var(df) * pct)
        metrics['Expected Shortfall (cVaR) %'] = -abs(_stats.cvar(df) * pct)

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

    metrics['Payoff Ratio'] = _stats.payoff_ratio(df)
    metrics['Profit Factor'] = _stats.profit_factor(df)
    metrics['Common Sense Ratio'] = _stats.common_sense_ratio(df)
    metrics['CPC Index'] = _stats.cpc_index(df)
    metrics['Tail Ratio'] = _stats.tail_ratio(df)

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

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
        metrics['Kelly Criterion %'] = _stats.kelly_criterion(df) * pct
        metrics['Risk of Ruin %'] = _stats.risk_of_ruin(df)

        metrics['Daily Value-at-Risk %'] = -abs(_stats.var(df) * pct)
        metrics['Expected Shortfall (cVaR) %'] = -abs(_stats.cvar(df) * pct)

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

    metrics['Payoff Ratio'] = _stats.payoff_ratio(df)
    metrics['Profit Factor'] = _stats.profit_factor(df)
    metrics['Common Sense Ratio'] = _stats.common_sense_ratio(df)
    metrics['CPC Index'] = _stats.cpc_index(df)
    metrics['Tail Ratio'] = _stats.tail_ratio(df)
    metrics['Outlier Win Ratio'] = _stats.outlier_win_ratio(df)
    metrics['Outlier Loss Ratio'] = _stats.outlier_loss_ratio(df)