如何将具有相同列值的两个熊猫数据框合并以形成显示值差异的第三个数据框

Question

dataframe:df1

     name  age  id  salary
0   Smith   30   2    2000
1     Ron   24   3   30000
2    Mike   35   4   40000
3    Jack   21   5    5000
4  Roshan   20   6   60000
5   Steve   45   8    8000
6   Peter   32   1    1000

dataframe:df2

    name  age  salary  id
0  Peter   28   10000   1
1  Smith   30    1500   2
2    Ron   24    7000   3
3   Mike   35   20000   4
4   Jack   21    5000   5
5  Cathy   20    9000   6
6  Steve   45   56000   8

df1 and df2 To be merged on ID's.Please not that ID's are same in both df1 and df2 but id order is different.df3 needs to be created like below-

     name       age    id    salary
0   Smith        30     2    2000|1500
1     Ron        24     3    30000|7000
2    Mike        35     4    40000 |20000
3    Jack        21     5    5000
4  Roshan|Cathy  20     6    60000|9000
5   Steve        45     8    8000|56000
6   Peter        32|28  1    1000|10000

I am planning to put the above output to excel sheet using to_excel functionality. Before that i want to add one more extra column to this data frame which says 'match and 'mismatch' . Logic would be if any one of the row showing minimum of one difference value result should be mismatch else match.I am mocking the output below something like this-

id age name salary Result 0 2 30 Smith 2000|1500 Mismatch 1 3 24 Ron 30000|7000 Mismatch 3 5 21 Jack 5000 Match 4 6 20 Roshan|Cathy 60000|9000 MisMatch 5 8 45 Steve 8000|56000 MisMatch 6 1 32|28 Peter 1000|10000 MisMatch

What can i use for achieving such result

Answer 1

Use merge first and then join columns by condition with numpy.where , last filter only columns by df1.columns :

cols = df1.columns.difference(['id'])
df = df1.merge(df2, on='id', suffixes=('','_'))

s = df[cols].astype(str) + '|' + df[cols + '_'].astype(str).values
mask = df[cols].values != df[cols + '_'].values

arr =  np.where(mask, s, df[cols].astype(str))

df = df1[['id']].join(pd.DataFrame(arr, columns=cols))
print (df)
   id    age          name       salary
0   2     30         Smith    2000|1500
1   3     24           Ron   30000|7000
2   4     35          Mike  40000|20000
3   5     21          Jack         5000
4   6     20  Roshan|Cathy   60000|9000
5   8     45         Steve   8000|56000
6   1  32|28         Peter   1000|10000