14-pandas左右归并

import numpy as np
import pandas as pd
1.
df1=pd.DataFrame({"name":["wangchenyang","guanchenhao","dongshuai"],
                  "age":[30,32,33]})
df2=pd.DataFrame({"name":["wangchenyang","guanchenhao","dongshuai"],
                  "tall":[310,312,313],
                  "age":[130,132,133]
})
print(pd.merge(df1,df2,on="name"))#通过name归并,指定归并的主键

#2.左右归并
a1=np.array([[120,130],[110,120],[230,250],[100,98],[98,97]])
data1=pd.DataFrame(a1,columns=["A","B"],
                   index=["语文","数学","英语","理综","计算机"])
a2=np.array([[120,30],[110,120],[230,250],[100,98],[98,97]])
data2=pd.DataFrame(a2,columns=["C","D"],
                   index=["语文","数学","英语","理综","计算机"])
print(pd.merge(data1,data2,left_on="A",right_on="D"))#左右合并两个没有共同点的数据

#3.
df1=pd.DataFrame({"name":["A","B","C"],
                  "age":[30,32,33]})
df2=pd.DataFrame({"name":["D","A","E"],
                  "tall":[310,312,313],
})
print(pd.merge(df1,df2))#内归并，缺失的丢掉
print(pd.merge(df1,df2,how="outer"))#内归并，缺失的填NaN
print(pd.merge(df1,df2,how="left"))#以左边为准
print(pd.merge(df1,df2,how="right"))#以右边为准

#.列冲突
df1=pd.DataFrame({"name":["A","B","C"],
                  "age":[30,32,33]})
df2=pd.DataFrame({"name":["A","B","C"],
                  "age":[310,312,313],
})
print("-------------------")
print(pd.merge(df1,df2,on="name",suffixes=["_x","_y"]))#解决列冲突