python - 在 Pandas 中移动 Groupby

Question

我正在尝试按帐户计算累计收入。以下是一些示例数据:

import pandas as pd
data = {
    'account_id': ['111','111','111','222','222','333','333','333','666','666'],
    'company': ['initech','initech','initech','jackson steinem & co','jackson steinem & co','ingen','ingen','ingen','enron','enron'],
    'cohort_period': [0,1,2,0,1,0,1,2,0,1],
    'revenue':[3.67,9.95,9.95,193.29,299.95,83.03,499.95,99.95,1.52,19.95]
}
df = pd.DataFrame(data)

哪些输出:

In [17]: df
Out[17]:
  account_id  cohort_period               company  revenue
0        111              0               initech     3.67
1        111              1               initech     9.95
2        111              2               initech     9.95
3        222              0  jackson steinem & co   193.29
4        222              1  jackson steinem & co   299.95
5        333              0                 ingen    83.03
6        333              1                 ingen   499.95
7        333              2                 ingen    99.95
8        666              0                 enron     1.52
9        666              1                 enron    19.95

有大量关于如何执行此操作的示例，基本上是:

df['cumulative_revenue'] = df.groupby('account_id')['revenue'].cumsum()

但是，有一个问题:在此数据中，同类群组第 0 期期间的收入是按比例分配的，出于分析目的，我并不关心这一点。我需要的是从同类周期 1 开始累积总和。例如，Initech 的累积收入应如下所示:

0    nan
1    9.95
2    19.90

Answer 1

这是一种方法:

# check valid cohort_period
valid_cohort = df.cohort_period.ne(0)

# cumulative sum revenue where cohort_period is not equal to zero and mask otherwise as nan
df['cum_revenue'] = valid_cohort.mul(df.revenue).groupby(df.account_id).cumsum().where(valid_cohort)

print(df)
#  account_id  cohort_period               company  revenue  cum_revenue
#0        111              0               initech     3.67          NaN
#1        111              1               initech     9.95         9.95
#2        111              2               initech     9.95        19.90
#3        222              0  jackson steinem & co   193.29          NaN
#4        222              1  jackson steinem & co   299.95       299.95
#5        333              0                 ingen    83.03          NaN
#6        333              1                 ingen   499.95       499.95
#7        333              2                 ingen    99.95       599.90
#8        666              0                 enron     1.52          NaN
#9        666              1                 enron    19.95        19.95