Python 一组列上的条件操作

使用这两个简化的数据帧

df1=pd.DataFrame({'COUNTRY':['A','A','A','B','B','C','C','C'],'YEAR':[1,2,3,1,2,1,2,3],'VALUE':[100,100,100,100,100,100,100,100]})
df2=pd.DataFrame({'COUNTRY':['A','A','B','B','C'],'YEAR':[1,3,1,2,3],'PROPORTION':[0.5,0.1,0.5,0.2,0.1]})

df1

df2

如何将

df1.VALUE

乘以

df2.proporty

匹配的

df1.COUNTRY=df2.COUNTRY

和

df1.YEAR=df2.YEAR

得到

VALUE=[50,100,10,50,20,100,100,10]  

---

您可以使用

merge

进行检查，然后使用

mul

df1['New Value']=df1.merge(df2,how='left').PROPORTION.mul(df1.VALUE)

试试这个：

df1=pd.DataFrame({'COUNTRY':['A','A','A','B','B','C','C','C'],'YEAR':[1,2,3,1,2,1,2,3],'VALUE':[100,100,100,100,100,100,100,100]})
df2=pd.DataFrame({'COUNTRY':['A','A','B','B','C'],'YEAR':[1,3,1,2,3],'PROPORTION':[0.5,0.1,0.5,0.2,0.1]})
df = df1.merge(df2, on=['COUNTRY', 'YEAR'], how='left').fillna(1)
df['res'] = df['VALUE']*df['PROPORTION']
df

输出：

    COUNTRY YEAR    VALUE   PROPORTION  res
0   A   1   100 0.5 50.0
1   A   2   100 1.0 100.0
2   A   3   100 0.1 10.0
3   B   1   100 0.5 50.0
4   B   2   100 0.2 20.0
5   C   1   100 1.0 100.0
6   C   2   100 1.0 100.0
7   C   3   100 0.1 10.0

---

另一种方法是使用索引的内部数据对齐。 使用

set_index

和

mul

和

fill_value=1

df1i = df1.set_index(['COUNTRY','YEAR'])
df2i = df2.set_index(['COUNTRY','YEAR'])

df2i['PROPORTION'].mul(df1i['VALUE'], fill_value=1).rename('PROPORTION').reset_index()

输出：

  COUNTRY  YEAR  PROPORTION
0       A     1        50.0
1       A     2       100.0
2       A     3        10.0
3       B     1        50.0
4       B     2        20.0
5       C     1       100.0
6       C     2       100.0
7       C     3        10.0

df1['VALUE']=df1.merge(df2,how='left').fillna(1)['PROPORTION'].mul(df1['VALUE'])

df1i = df1.set_index(['COUNTRY','YEAR'])
df2i = df2.set_index(['COUNTRY','YEAR'])

df2i['PROPORTION'].mul(df1i['VALUE'], fill_value=1).rename('PROPORTION').reset_index()

  COUNTRY  YEAR  PROPORTION
0       A     1        50.0
1       A     2       100.0
2       A     3        10.0
3       B     1        50.0
4       B     2        20.0
5       C     1       100.0
6       C     2       100.0
7       C     3        10.0