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

# 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
        metrics['Win Year %%'] = _stats.win_rate(df, aggregate='A') * pct

        if "benchmark" in df:
            metrics['~~~~~~~'] = blank
            greeks = _stats.greeks(df['returns'], df['benchmark'])
            metrics['Beta'] = [str(round(greeks['beta'], 2)), '-']
            metrics['Alpha'] = [str(round(greeks['alpha'], 2)), '-']

    # prepare for display
    for col in metrics.columns:
        try:
            metrics[col] = metrics[col].astype(float).round(2)
            if display or "internal" in kwargs:
                metrics[col] = metrics[col].astype(str)
        except Exception:
            pass
        if (display or "internal" in kwargs) and "%" in col:

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
        metrics['Win Year %%'] = _stats.win_rate(df, aggregate='A') * pct

        if "benchmark" in df:
            metrics['~~~~~~~'] = blank
            greeks = _stats.greeks(df['returns'], df['benchmark'])
            metrics['Beta'] = [str(round(greeks['beta'], 2)), '-']
            metrics['Alpha'] = [str(round(greeks['alpha'], 2)), '-']

    # prepare for display
    for col in metrics.columns:
        try:
            metrics[col] = metrics[col].astype(float).round(2)
            if display or "internal" in kwargs:
                metrics[col] = metrics[col].astype(str)

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
        metrics['Win Year %%'] = _stats.win_rate(df, aggregate='A') * pct

        if "benchmark" in df:
            metrics['~~~~~~~'] = blank
            greeks = _stats.greeks(df['returns'], df['benchmark'])
            metrics['Beta'] = [str(round(greeks['beta'], 2)), '-']
            metrics['Alpha'] = [str(round(greeks['alpha'], 2)), '-']

    # prepare for display
    for col in metrics.columns:
        try:
            metrics[col] = metrics[col].astype(float).round(2)
            if display or "internal" in kwargs:
                metrics[col] = metrics[col].astype(str)
        except Exception:

# 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
        metrics['Win Year %%'] = _stats.win_rate(df, aggregate='A') * pct

        if "benchmark" in df:
            metrics['~~~~~~~'] = blank
            greeks = _stats.greeks(df['returns'], df['benchmark'])
            metrics['Beta'] = [str(round(greeks['beta'], 2)), '-']
            metrics['Alpha'] = [str(round(greeks['alpha'], 2)), '-']

    # prepare for display
    for col in metrics.columns:
        try:
            metrics[col] = metrics[col].astype(float).round(2)
            if display or "internal" in kwargs:
                metrics[col] = metrics[col].astype(str)
        except Exception:
            pass

"""
    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
    _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