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这是我的另一个问题的后续问题：

我有以下数据框，从我的原始数据框中子集，包含ob、event、unixtime和day列，我想添加另一列arbday，这是自第一个事件以来的第 n 天（第一次访问是第 1 天) 按ob分组： 

import numpy as np  
import datetime as dt  

>>> newdf = pd.DataFrame({'ob': ['a','a','b','b','c', 'd', 'e', 'e', 'e', 'f', 'f', 'f'],'event': [1, 2, 1, 2, 1, 1, 1, 2, 3, 1, 2, 3], 'unixtime': [1346682124716, 1346682188598, 1346745432765, 1347080641650, 1346676710509, 1346702995184, 1346530405978, 1346530421609, 1346530570952, 1346617885925, 1346961625305,1347214217566]},index=[343340, 343341, 343342, 343343, 343344, 343345, 343349, 343350, 343351, 343352,343353,343354])
>>> newdf['day'] = newdf['unixtime'].apply(lambda x: dt.datetime.utcfromtimestamp(x/1000).date())

        ob  event        unixtime          day  arbday
343340   a      1   1346682124716   2012-09-03       1
343341   a      2   1346682188598   2012-09-03       1
343342   b      1   1346745432765   2012-09-04       1
343343   b      2   1347080641650   2012-09-08       5
343344   c      1   1346676710509   2012-09-03       1
343345   d      1   1346702995184   2012-09-03       1
343349   e      1   1346530405978   2012-09-01       1
343350   e      2   1346530421609   2012-09-01       1
343351   e      3   1346530570952   2012-09-01       1
343352   f      1   1346617885925   2012-09-02       1
343353   f      2   1346961625305   2012-09-06       5
343354   f      3   1347214217566   2012-09-09       8

在一个ob中，这将起作用： 

newdf['arbday'] = newdf['day'].map(lambda x: (x-testdf.get_value(newdf[newdf.event == 1].first_valid_index(), 'day')).days+1)

或者

newdf['arbday'] = newdf['day'].map(lambda x: (x-newdf.get_value(int(newdf[newdf.event == 1].index), 'day')).days+1)

我尝试了以下代码并且它有效： 

>>> newdf['arbday'] = newdf.groupby('ob')['day'].transform(lambda x: (x-x.min()).apply(lambda y: y.days)+1)

        event ob       unixtime         day arbday
343340      1  a  1346682124716  2012-09-03      1
343341      2  a  1346682188598  2012-09-03      1
343342      1  b  1346745432765  2012-09-04      1
343343      2  b  1347080641650  2012-09-08      5
343344      1  c  1346676710509  2012-09-03      1
343345      1  d  1346702995184  2012-09-03      1
343349      1  e  1346530405978  2012-09-01      1
343350      2  e  1346530421609  2012-09-01      1
343351      3  e  1346530570952  2012-09-01      1
343352      1  f  1346617885925  2012-09-02      1
343353      2  f  1346961625305  2012-09-06      5
343354      3  f  1347214217566  2012-09-09      8

但这显然不是最优雅的方式。另外，为什么event和ob的顺序发生了变化？

任何指针将不胜感激。谢谢！

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1 回答 1

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In [46]: firstdays = df.groupby('ob').day.first()

In [47]: firstdays
Out[47]: 
ob
a     2012-09-03
b     2012-09-04
c     2012-09-03
d     2012-09-03
e     2012-09-01
f     2012-09-02
Name: day

In [48]: df.apply(lambda row: (row['day'] - firstdays[row['ob']]).days + 1, axis=1)
Out[48]: 
343340    1
343341    1
343342    1
343343    5
343344    1
343345    1
343349    1
343350    1
343351    1
343352    1
343353    5
343354    8
于 2013-01-05T05:45:19.577 回答