我有以下删节dataframe：
{'end': {0: 1995, 1: 1997, 2: 1999, 3: 2001, 4: 2003, 5: 2005, 6: 2007, 7: 2013, 8: 2014, 9: 1995, 10: 2007, 11: 2013, 12: 2014, 13: 1989,
14: 1991, 15: 1993, 16: 1995, 17: 1997, 18: 1999, 19: 2001, 20: 2003,
21: 2005, 22: 2007, 23: 2013, 24: 2014, 25: 1985, 26: 1987, 27: 1989, 28: 1991, 29: 1993}, 'idthomas': {0: 136, 1: 136, 2: 136, 3: 136, 4:136,
5: 136, 6: 136, 7: 136, 8: 136, 9: 172, 10: 172, 11: 172, 12: 172, 13: 174, 14: 174, 15: 174, 16: 174, 17: 174, 18: 174, 19: 174, 20: 174, 21: 174, 22: 174, 23: 174, 24: 174, 25: 179, 26: 179, 27: 179, 28: 179,
29: 179}, 'start': {0: 1993, 1: 1995, 2: 1997, 3: 1999, 4: 2001, 5: 2003, 6: 2005, 7: 2007, 8: 2013, 9: 1993, 10: 2001, 11: 2007, 12: 2013, 13: 1987, 14: 1989, 15: 1991, 16: 1993, 17: 1995, 18: 1997, 19: 1999, 20: 2001, 21: 2003, 22: 2005, 23: 2007, 24: 2013, 25: 1983, 26: 1985, 27: 1987, 28: 1989, 29: 1991}}
df_oddyears.head()
end start idthomas
0 1995 1993 136
1 1997 1995 136
2 1999 1997 136
3 2001 1999 136
4 2003 2001 136
5 2005 2003 136
6 2007 2005 136
7 2013 2007 136
8 2014 2013 136
9 1995 1993 172
10 2007 2001 172
11 2013 2007 172
12 2014 2013 172
13 1989 1987 174
14 1991 1989 174
它代表了美国立法者的国会条款。存在一些不方便的违规行为：start和end日期表示术语的开始和结束，并且根据立法者是否在众议院或参议院任职，将有2年或6年的差异。所有立法者都有一个独特的idthomas，如果他们愿意，可以从一个议院改为参议院。有时立法者不会再次当选，这会导致他们的服务出现差距。查看idthomas == 172，您可以看到end == 1995和start == 2001之间的差距。
我需要计算从立法者服务开始到立法者服务结束的每年积极累积公共服务的年数，甚至包括多年。在下一步中，我会将这个df与另一个df合并多年，因此我需要偶数和奇数年的有效服务。
这是我在深入研究问题之前所开发的：
df_oddyears['end']=df_oddyears['end'].map(lambda x: str(x)[:-6])
df_oddyears['start']=df_oddyears['start'].map(lambda x: str(x[:-6]))
df_oddyears['end'] = df_oddyears['end'].astype('int')
df_oddyears['start'] = df_oddyears['start'].astype('int')
df_oddyears['end'] = df_oddyears['end'].clip_upper(2014)
df_oddyears['term'] = df_oddyears.end - df_oddyears.start
df_oddyears['years_exp']=df_oddyears.groupby(['id.thomas']).term.cumsum()
df_oddyears.rename(columns={'id.thomas':'idthomas'},inplace=True)
df_oddyears.head()
end start idthomas term years_exp
0 1995 1993 136 2 2
1 1997 1995 136 2 4
2 1999 1997 136 2 6
3 2001 1999 136 2 8
4 2003 2001 136 2 10
5 2005 2003 136 2 12
6 2007 2005 136 2 14
7 2013 2007 136 6 20
8 2014 2013 136 1 21
9 1995 1993 172 2 2
10 2007 2001 172 6 8
11 2013 2007 172 6 14
12 2014 2013 172 1 15
{'end': {0: 1995, 1: 1997, 2: 1999, 3: 2001, 4: 2003, 5: 2005, 6: 2007,
7: 2013, 8: 2014, 9: 1995, 10: 2007, 11: 2013, 12: 2014, 13: 1989,
14: 1991, 15: 1993, 16: 1995, 17: 1997, 18: 1999, 19: 2001, 20: 2003,
21: 2005, 22: 2007, 23: 2013, 24: 2014, 25: 1985, 26: 1987, 27: 1989,
28: 1991, 29: 1993}, 'idthomas': {0: 136, 1: 136, 2: 136, 3: 136,
4: 136, 5: 136, 6: 136, 7: 136, 8: 136, 9: 172, 10: 172, 11: 172, 12: 172, 13: 174, 14: 174, 15: 174, 16: 174, 17: 174, 18: 174, 19: 174,
20: 174, 21: 174, 22: 174, 23: 174, 24: 174, 25: 179, 26: 179, 27: 179, 28: 179, 29: 179},'start': {0: 1993, 1: 1995, 2: 1997, 3: 1999, 4: 2001, 5: 2003, 6: 2005, 7: 2007, 8: 2013, 9: 1993, 10: 2001, 11: 2007, 12: 2013, 13: 1987, 14: 1989, 15: 1991, 16: 1993, 17: 1995, 18: 1997, 19: 1999, 20: 2001, 21: 2003, 22: 2005, 23: 2007, 24: 2013, 25: 1983, 26: 1985, 27: 1987, 28: 1989, 29: 1991},'term': {0: 2, 1: 2, 2: 2, 3: 2, 4: 2, 5: 2, 6: 2, 7: 6, 8: 1, 9: 2, 10: 6, 11: 6, 12: 1, 13: 2, 14: 2, 15: 2, 16: 2, 17: 2, 18: 2, 19: 2, 20: 2, 21: 2, 22: 2, 23: 6,
24: 1, 25: 2, 26: 2, 27: 2, 28: 2, 29: 2},'years_exp': {0: 2, 1: 4,
2: 6, 3: 8, 4: 10, 5: 12, 6: 14, 7: 20, 8: 21, 9: 2, 10: 8, 11: 14,
12: 15, 13: 2, 14: 4, 15: 6, 16: 8, 17: 10, 18: 12, 19: 14, 20: 16,
21: 18, 22: 20, 23: 26, 24: 27, 25: 2, 26: 4, 27: 6, 28: 8, 29: 10}}
然后我df=df_oddyears.drop(['start', 'term'], axis=1, inplace=False)并从here
final_year = 2014
df= pd.DataFrame([(year, id_, n)
for id_, end, years_exp in df.groupby('idthomas').first().itertuples()
for n, year in enumerate(range(end, final_year + 1), years_exp)],
columns=['end', 'idthomas', 'years_exp'])
df.head()
end idthomas years_exp
673 1995 172 2
674 1996 172 3
675 1997 172 4
676 1998 172 5
677 1999 172 6
678 2000 172 7
679 2001 172 8
680 2002 172 9
681 2003 172 10
这非常接近我想要的，它使我能够在end上连接到另一个df，同时保持总years_exp。不幸的是，我在发布原始问题时未能认识到间歇性服务的问题;因此，years_exp没有考虑到公共服务方面的差距。这是今天（项目列表中的第一个）项目。如果有人有任何问题或建议或批评，欢迎他们。
我希望的最终结果如下：
end idthomas years_exp
0 1994 136 1
1 1995 136 2
2 1996 136 3
3 1997 136 4
4 1998 136 5
5 1999 136 6
6 2000 136 7
7 2001 136 8
8 2002 136 9
9 2003 136 10
10 2004 136 11
11 2005 136 12
12 2006 136 13
13 2007 136 14
14 2008 136 15
15 2009 136 16
16 2010 136 17
17 2011 136 18
18 2012 136 19
19 2013 136 20
20 2014 136 21
21 1994 172 1
22 1995 172 2
23 2001 172 2
24 2002 172 3
25 2003 172 4
26 2004 172 5
27 2005 172 6
28 2006 172 7
29 2007 172 8
30 2008 172 9
31 2009 172 10
32 2010 172 11
33 2011 172 12
34 2012 172 13
35 2013 172 14
36 2014 172 15