Python 大熊猫的群居迁移
我正在尝试按帐户计算累计收入。以下是一些示例数据:Python 大熊猫的群居迁移,python,pandas,Python,Pandas,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
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)
哪些产出:
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
关于如何做到这一点,有很多例子,基本上是:
df['cumulative_revenue'] = df.groupby('account_id')['revenue'].cumsum()
然而,这里有一个陷阱:在这个数据中,队列期间0的收入是按比例分配的,为了我的分析目的,我不关心这一点。我需要的是在队列周期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
这里有一个方法:
# 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
我将创建一个新变量“new”
df['New']=df.revenue
df.loc[df['cohort_period']==0,'New']=np.nan
df['cumulative_revenue']=df.groupby('account_id')['New'].cumsum()
df
Out[63]:
account_id cohort_period company revenue New \
0 111 0 initech 3.67 NaN
1 111 1 initech 9.95 9.95
2 111 2 initech 9.95 9.95
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 99.95
8 666 0 enron 1.52 NaN
9 666 1 enron 19.95 19.95
cumulative_revenue
0 NaN
1 9.95
2 19.90
3 NaN
4 299.95
5 NaN
6 499.95
7 599.90
8 NaN
9 19.95
或mask
df.groupby('account_id').apply(lambda x :x['revenue'].mask(x['cohort_period'].eq(0)).cumsum())