在python的公共列上加入两个数据框 [英] JOIN two dataframes on common column in python

问题描述

我有一个数据框df:

id   name   count
1    a       10
2    b       20
3    c       30
4    d       40
5    e       50

这里我还有另一个数据框df2:

Here I have another dataframe df2:

id1  price   rating
 1     100     1.0
 2     200     2.0
 3     300     3.0
 5     500     5.0

我想在ID和ID1列上连接这两个数据框(两者均引用相同).这是df3的示例:

I want to join these two dataframes on column id and id1(both refer same). Here is an example of df3:

id   name   count   price   rating
1    a       10      100      1.0
2    b       20      200      2.0
3    c       30      300      3.0
4    d       40      Nan      Nan
5    e       50      500      5.0

我应该使用df.merge还是pd.concat?

Should I use df.merge or pd.concat?

推荐答案

使用另一种解决方案是简单的重命名列:

Another solution is simple rename column:

print (pd.merge(df1, df2.rename(columns={'id1':'id'}), on='id',  how='left'))
   id name  count  price  rating
0   1    a     10  100.0     1.0
1   2    b     20  200.0     2.0
2   3    c     30  300.0     3.0
3   4    d     40    NaN     NaN
4   5    e     50  500.0     5.0

如果仅需要列price，则最简单的是 map :

If need only column price the simpliest is map:

df1['price'] = df1.id.map(df2.set_index('id1')['price'])
print (df1)
   id name  count  price
0   1    a     10  100.0
1   2    b     20  200.0
2   3    c     30  300.0
3   4    d     40    NaN
4   5    e     50  500.0

另外2种解决方案:

print (pd.merge(df1, df2, left_on='id', right_on='id1', how='left')
         .drop(['id1', 'rating'], axis=1))
   id name  count  price
0   1    a     10  100.0
1   2    b     20  200.0
2   3    c     30  300.0
3   4    d     40    NaN
4   5    e     50  500.0

---

print (pd.merge(df1, df2[['id1','price']], left_on='id', right_on='id1', how='left')
         .drop('id1', axis=1))
   id name  count  price
0   1    a     10  100.0
1   2    b     20  200.0
2   3    c     30  300.0
3   4    d     40    NaN
4   5    e     50  500.0

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