Pandas 合并不等于

Question

df1：

 Con1   Con2    Con3
 p      s       100
 q      t       200
 p      t       300
 q      s       400

df2:

Ton1    Ton2    Ton3
p       s       150
p       t       110
p       s       108
q       t       409
q       s       410
q       t       406

output 应该是：

Con1    Con2    Con3    Ton1    Ton2    Ton3
p       s       100     p       s       108
q       t       200     Nan     Nan     Nan
p       t       300     Nan     Nan     Nan
q       s       400     Nan     Nan     Nan

合并应基于逻辑 Ton3 应比 Con3 高 6-10%

逻辑：(Ton3 => (6%*Con3) +Con3) & (Ton3 =< (10%*Con3) +Con3)

pd.merge(df1,df2,left_on=['Con1','Con2']+logic, right_on=['Ton1','Ton2']+logic, how='left')

Answer 1

DataFrame代

df1 = pd.DataFrame({'Con1':['p','q','p','q'], 'Con2':['s','t','t','s'], 'Con3':[100,200,300,400]})
df2 = pd.DataFrame({'Ton1':['p','p','p','q','q','q'], 'Ton2':['s','t','s','t','s','t'], 'Ton3':[150,110,108,409,410,406]})

合并数据框并应用给定条件：

df = pd.merge(df1,df2,how='left',left_on=['Con1','Con2'], right_on=['Ton1','Ton2'])
df = df[(df['Ton3']>=((0.06*df['Con3']) +df['Con3'])) & (df['Ton3']<=((0.1*df['Con3']) +df['Con3']))]
print(df)

我想这应该是output的最终格式。 另请注意，对于df1中的特定Con1和Con2对，df2 中可能有多对Ton1和Ton2 。

如果您也必须使用 NaN 获取行，请在运行上部块后运行此代码块

df = pd.merge(df1,df,how='left',left_on=['Con1','Con2','Con3'], right_on=['Con1','Con2','Con3'])
print(df)