我尝试使用pandas对成员进行分组，计算成员购买的订阅类型的数量，并获得每个成员的总花费。加载后，数据类似于：
df =
Member Nbr Member Name-First Member Name-Last Date-Joined Member Type Amount Addr-Formatted Date-Birth Gender Status
1 Aboud Tordon 2010-03-31 00:00:00 1 Year Membership 331.00 ADDRESS_1 1972-08-01 00:00:00 Male Active
1 Aboud Tordon 2011-04-16 00:00:00 1 Year Membership 334.70 ADDRESS_1 1972-08-01 00:00:00 Male Active
1 Aboud Tordon 2012-08-06 00:00:00 1 Year Membership 344.34 ADDRESS_1 1972-08-01 00:00:00 Male Active
1 Aboud Tordon 2013-08-21 00:00:00 1 Year Membership 362.53 ADDRESS_1 1972-08-01 00:00:00 Male Active
1 Aboud Tordon 2015-08-31 00:00:00 1 Year Membership 289.47 ADDRESS_1 1972-08-01 00:00:00 Male Active
2 Jean Manuel 2012-12-10 00:00:00 4 Month Membership 148.79 ADDRESS_2 1984-08-01 00:00:00 Male In-Active
2 Jean Manuel 2013-03-13 00:00:00 1 Year Membership 348.46 ADDRESS_2 1984-08-01 00:00:00 Male In-Active
2 Jean Manuel 2014-03-15 00:00:00 1 Year Membership 316.86 ADDRESS_2 1984-08-01 00:00:00 Male In-Active
3 Val Adams 2010-02-09 00:00:00 1 Year Membership 333.25 ADDRESS_3 1934-10-26 00:00:00 Female Active
3 Val Adams 2011-03-09 00:00:00 1 Year Membership 333.88 ADDRESS_3 1934-10-26 00:00:00 Female Active
3 Val Adams 2012-04-03 00:00:00 1 Year Membership 318.34 ADDRESS_3 1934-10-26 00:00:00 Female Active
3 Val Adams 2013-04-15 00:00:00 1 Year Membership 350.73 ADDRESS_3 1934-10-26 00:00:00 Female Active
3 Val Adams 2014-04-19 00:00:00 1 Year Membership 291.63 ADDRESS_3 1934-10-26 00:00:00 Female Active
3 Val Adams 2015-04-19 00:00:00 1 Year Membership 247.35 ADDRESS_3 1934-10-26 00:00:00 Female Active
5 Michele Younes 2010-02-14 00:00:00 1 Year Membership 333.25 ADDRESS_4 1933-06-23 00:00:00 Female In-Active
5 Michele Younes 2011-05-23 00:00:00 1 Year Membership 317.77 ADDRESS_4 1933-06-23 00:00:00 Female In-Active
5 Michele Younes 2012-05-28 00:00:00 1 Year Membership 328.16 ADDRESS_4 1933-06-23 00:00:00 Female In-Active
5 Michele Younes 2013-05-31 00:00:00 1 Year Membership 360.02 ADDRESS_4 1933-06-23 00:00:00 Female In-Active
7 Adam Herzburg 2010-07-11 00:00:00 1 Year Membership 335 48 ADDRESS_5 1987-08-30 00:00:00 Male In-Active
...
由于最受欢迎的Member Type是1 Month，3 Month，4 Month，6 Month和1 Year，我想列一栏计算某个成员购买的Member Type的数量。
2 Month，5 Month，7 Month，8 Month和Pool-Only Member Type也很少出现，如果会员有这种合同我想把它算作“杂项”。
我也试图获得一个＆＃39; Total＆＃39;总结了给定成员花费的总金额的列。
基本上我想将之前的数据帧转换为类似：
df1=
Member Nbr Member Name-First Member Name-Last 1_Month 3_Month 4_Month 6_Month 1_Year Misc Total Addr-Formatted Date-Birth Gender Status
1 Aboud Tordon 0 0 0 0 5 0 1662.04 ADDRESS_1 1972-08-01 00:00:00 Male Active
2 Jean Manuel 0 0 1 0 2 0 813.86 ADDRESS_2 1984-08-01 00:00:00 Male In-Active
3 Val Adams 0 0 0 0 6 0 1875.18 ADDRESS_3 1934-10-26 00:00:00 Female Active
5 Michele Younes 0 0 0 0 4 0 1339.20 ADDRESS_4 1933-06-23 00:00:00 Female In-Active
7 Adam Herzburg 0 0 0 0 1 0 335.48 ADDRESS_5 1933-06-23 00:00:00 Male In-Active
...
我遇到的问题是，每当我使用groupby时，我只能总结金额，或者单独计算一种特定类型的合同，但是我会这样做。我无法使其类似于df1。
答案 0 :(得分：2)
您可以先按Member Type列d列map，然后按值Misc fillna添加size：
d = {'1 Year Membership':'1_Year','1 Month Membership':'1_Month', '3 Month Membership':'3_Month', '4 Month Membership':'4_Month', '6 Month Membership':'6_Month'}
df['Type'] = df['Member Type'].map(d).fillna('Misc')
#print (df)
然后groupby并汇总sum：
df0 = df.groupby(['Member Nbr','Member Name-First','Member Name-Last','Addr-Formatted','Date-Birth','Gender','Status'])['Amount'].sum()
#print (df0)
将列Type添加到分组列列表并聚合unstack，然后按concat重新整形：
df1 = df.groupby(['Member Nbr','Member Name-First','Member Name-Last','Addr-Formatted','Date-Birth','Gender','Status', 'Type']).size().unstack(fill_value=0)
#print (df1)
最后reindex DataFrames：
print (pd.concat([df0, df1], axis=1).reset_index())
Member Nbr Member Name-First Member Name-Last Addr-Formatted \
0 1 Aboud Tordon ADDRESS_1
1 2 Jean Manuel ADDRESS_2
2 3 Val Adams ADDRESS_3
3 5 Michele Younes ADDRESS_4
4 7 Adam Herzburg ADDRESS_5
Date-Birth Gender Status Amount 1_Year 4_Month
0 1972-08-01 00:00:00 Male Active 1662.04 5 0
1 1984-08-01 00:00:00 Male In-Active 814.11 2 1
2 1934-10-26 00:00:00 Female Active 1875.18 6 0
3 1933-06-23 00:00:00 Female In-Active 1339.20 4 0
4 1987-08-30 00:00:00 Male In-Active 335.48 1 0
编辑：
如果列Member Type中缺少某些值，则必须添加pivot_table：
df1 = df.groupby(['Member Nbr','Member Name-First','Member Name-Last','Addr-Formatted','Date-Birth','Gender','Status', 'Type']).size().unstack(fill_value=0).reindex(columns=d.values(), fill_value=0)
#print (df1)
print (pd.concat([df0, df1], axis=1).reset_index())
Member Nbr Member Name-First Member Name-Last Addr-Formatted \
0 1 Aboud Tordon ADDRESS_1
1 2 Jean Manuel ADDRESS_2
2 3 Val Adams ADDRESS_3
3 5 Michele Younes ADDRESS_4
4 7 Adam Herzburg ADDRESS_5
Date-Birth Gender Status Amount 6_Month 3_Month 4_Month \
0 1972-08-01 00:00:00 Male Active 1662.04 0 0 0
1 1984-08-01 00:00:00 Male In-Active 814.11 0 0 1
2 1934-10-26 00:00:00 Female Active 1875.18 0 0 0
3 1933-06-23 00:00:00 Female In-Active 1339.20 0 0 0
4 1987-08-30 00:00:00 Male In-Active 335.48 0 0 0
1_Year 1_Month
0 5 0
1 2 0
2 6 0
3 4 0
4 1 0
相反，第二个groupby（最快的）可以使用MongoDB Atomicity and Transactions：
df2 = df.pivot_table(index=['Member Nbr','Member Name-First','Member Name-Last','Addr-Formatted','Date-Birth','Gender','Status'], columns='Type', values='Amount', aggfunc=len, fill_value=0).reindex(columns=d.values(), fill_value=0)
print (pd.concat([df0, df2], axis=1).reset_index())
Member Nbr Member Name-First Member Name-Last Addr-Formatted \
0 1 Aboud Tordon ADDRESS_1
1 2 Jean Manuel ADDRESS_2
2 3 Val Adams ADDRESS_3
3 5 Michele Younes ADDRESS_4
4 7 Adam Herzburg ADDRESS_5
Date-Birth Gender Status Amount 6_Month 3_Month 4_Month \
0 1972-08-01 00:00:00 Male Active 1662.04 0 0 0
1 1984-08-01 00:00:00 Male In-Active 814.11 0 0 1
2 1934-10-26 00:00:00 Female Active 1875.18 0 0 0
3 1933-06-23 00:00:00 Female In-Active 1339.20 0 0 0
4 1987-08-30 00:00:00 Male In-Active 335.48 0 0 0
1_Year 1_Month
0 5 0
1 2 0
2 6 0
3 4 0
4 1 0