A Bayesian model for myself to debug - PyData Berlin

Bayesian_Model_2.py

df_avg = pd.DataFrame({'avg_att': atts.stats()['mean'],
                       'avg_def': defs.stats()['mean']}, 
                      index=teams.team.values)
df_avg = pd.merge(df_avg, df_observed, left_index=True, right_on='team', how='left')

fig, ax = plt.subplots(figsize=(8,6))
for outcome in ['winner', 'triple_crown', 'wooden_spooon', '']:
    ax.plot(df_avg.avg_att[df_avg.QR == outcome], 
            df_avg.avg_def[df_avg.QR == outcome], 'o', label=outcome)
    
for label, x, y in zip(df_avg.Team.values, df_avg.avg_att.values, df_avg.avg_def.values):
    ax.annotate(label, xy=(x,y), xytext = (-5,5), textcoords = 'offset points')
ax.set_title('Attack vs Defense avg effect: 13-14 Six Nations')
ax.set_xlabel('Avg attack effect')
ax.set_ylabel('Avg defense effect')
ax.legend()