我有一个DataFrame(Main),如下所示。列具有组Dict中描述的组分类。存在具有组值的第二个DataFrame。我需要从Main中的每一列中减去Group Value DataFrame中相应组的值。结果表也如下所示。 (Exp:Main [" AAA"] - Group_Value [" Group1"]等) 有这样的矩阵形式还是我需要使用for循环?
代码:
import random
df = pd.DataFrame(index=pd.date_range("1/1/2018","1/10/2018"), columns=
["AAA","BBB","CCC","DDD"])
df["AAA"]=range(100,110)
df["BBB"]=range(200,210)
df["CCC"]=range(300,310)
df["DDD"]=range(400,410)
Group_Dict = dict({"AAA":"Group1", "BBB":"Group2", "CCC":"Group1", "DDD":"Group2"})
group_value = pd.DataFrame(index=pd.date_range("1/1/2018","1/10/2018"), columns=["Group1","Group2"])
group_value["Group1"]=range(10,29)[::2]
group_value["Group2"]=range(100,600)[::50]
## I need to do the following AAA-Group1, BBB-Group2 , CCC-Group1, DDD-Group2
谢谢。
答案 0 :(得分:1)
df = pd.DataFrame(index=pd.date_range("1/1/2018","1/10/2018"), columns=
["AAA","BBB","CCC","DDD"])
df["AAA"]=range(100,110)
df["BBB"]=range(200,210)
df["CCC"]=range(300,310)
df["DDD"]=range(400,410)
Group_Dict = dict({"AAA":"Group1", "BBB":"Group2", "CCC":"Group1", "DDD":"Group2"})
group_value = pd.DataFrame(index=pd.date_range("1/1/2018","1/10/2018"), columns=["Group1","Group2"])
group_value["Group1"]=range(10,29)[::2]
group_value["Group2"]=range(100,600)[::50]
sub_group = group_value.reindex(Group_Dict.values(), axis=1)\
.set_axis(Group_Dict.keys(), axis=1, inplace=False)
df_out = (df - sub_group).reset_index()
print(df_out)
输出:
index AAA BBB CCC DDD
0 2018-01-01 90 100 290 300
1 2018-01-02 89 51 289 251
2 2018-01-03 88 2 288 202
3 2018-01-04 87 -47 287 153
4 2018-01-05 86 -96 286 104
5 2018-01-06 85 -145 285 55
6 2018-01-07 84 -194 284 6
7 2018-01-08 83 -243 283 -43
8 2018-01-09 82 -292 282 -92
9 2018-01-10 81 -341 281 -141
让我们试试这个:
main = pd.DataFrame({'Date':pd.date_range('01-01-2018',periods=10,freq='D'),
'AAA':np.arange(100,110),'BBB':np.arange(200,210),
'CCC':np.arange(300,310),'DDD':np.arange(400,410)})
groupdict=pd.DataFrame({'Key':['AAA','BBB','CCC','DDD'],
'Group':['Group1','Group1','Group2','Group2']})
groupvalue=pd.DataFrame({'Date':pd.date_range('01-01-2018',periods=10,freq='D'),
'Group1':np.arange(10,29,2),'Group2':np.arange(100,575,50)})
groupvalue=groupvalue.set_index('Date')
main = main.set_index('Date')
#Use reindex and set_axis to expand and match your main dataframe columns
sub_group = groupvalue.reindex(groupdict.Group,axis=1)\
.set_axis(groupdict.Key, axis=1, inplace=False)
#Subtract letting pandas handle data alighnment with indexes.
df_out = (main - sub_group).reset_index()
print(df_out)
输出:
Date AAA BBB CCC DDD
0 2018-01-01 90 190 200 300
1 2018-01-02 89 189 151 251
2 2018-01-03 88 188 102 202
3 2018-01-04 87 187 53 153
4 2018-01-05 86 186 4 104
5 2018-01-06 85 185 -45 55
6 2018-01-07 84 184 -94 6
7 2018-01-08 83 183 -143 -43
8 2018-01-09 82 182 -192 -92
9 2018-01-10 81 181 -241 -141
答案 1 :(得分:0)
如果我已正确理解您的问题。您可以使用merge()
根据日期加入MAIN和group_value数据框。它将导致由AAA
和GROUP1
组成的数据框作为列。那么简单的df['AAA']-df['Group1']
应该给出预期的输出。我错过了什么吗?