熊猫是否会替代性能？

Question

下面是使用迭代的功能代码，该代码运行时间太长。我不断听说使用其他方法可以获得更好的性能。有人可以提供示例吗？

def payback_period(cohorts, sales):
for index, row in cohorts.iterrows():
    subset = sales[sales['cohort'] == index]
    cumsum = {}
    for k, proceeds in subset.groupby(['purchase_time', 'duration'])['proceeds'].agg('sum').to_dict().items():
        month = k[0]
        duration = k[1]
        for m in range(int(duration)):
            r_month = month+pd.DateOffset(months=m)
            if r_month not in cumsum:
                cumsum[r_month] = 0
            cumsum[r_month]+= proceeds/duration
    payback_period = 999
    for month in sorted(cumsum.keys()):
        revenue = cumsum[month]
        cum_revenue = sum(dict((k, cumsum[k]) for k in cumsum.keys() if k <=month).values())
        if cum_revenue > cohorts.loc[index, 'spend']:
            m_delta = round((month - index)/np.timedelta64(1, 'M'))

            if payback_period > m_delta:
                payback_period = m_delta
    cohorts.loc[index, 'payback_period'] = payback_period
return cohorts
#     print(index, payback_period, sum(cumsum.values()), cohorts.loc[index, 'spend'])