groupby col=col.groupby(['id','money'],as_index=False).sum()按照id 和 money 这两个特征进行转换新表并自动排序 并对其他列进行sum求和 count等
id money a b
1 1 x y
2 1 xx yy
3 2 x yyy
4 7 xxx y
重新索引
pivot(index='id',columns='money',values='a').reset_index() fillna(0)
money 1 1 2 7
id
1 x 0 0 0
2
3
4
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as_index=False 和 as_index=True
df = pd.DataFrame(data={'books':['bk1','bk1','bk1','bk2','bk2','bk3'], 'price': [12,12,12,15,15,17]})
print df
print
print df.groupby('books', as_index=True).sum()
print
print df.groupby('books', as_index=False).sum()
books price
0 bk1 12
1 bk1 12
2 bk1 12
3 bk2 15
4 bk2 15
5 bk3 17
price
books
bk1 36
bk2 30
bk3 17
books price
0 bk1 36
1 bk2 30
2 bk3 17
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