并排比较相同列但不同行的两个数据框

Question

I have two data frames with same column labels like below:

 df1 = {'key_1': {0: 'F', 1: 'H', 2: 'E'},
         'key_2': {0: 'F', 1: 'G', 2: 'E'},
         'min': {0: -158, 1: -881, 2: -674},
         'count': {0: 58, 1: 24, 2: 13}}
    
    df2 = {'key_1': {0: 'C', 1: 'L', 2: 'F', 3: 'K'},
     'key_2': {0: 'C', 1: 'D', 2: 'F', 3: 'K'},
     'min': {0: -452, 1: -153, 2: -181, 3: -120},
     'count': {0: 7470, 1: 1262, 2: 171, 3: 86}}

pandas.DataFrame.compare is useful for side by side comparison of each column, but it is not working for comparing data frames with different rows

df1.compare(df2, keep_shape=True, keep_equal=True)

ValueError: Can only compare identically-labeled DataFrame objects

can we achieve the same functionality using pandas.merge?

I tried below but it is NOT giving side by side comparison for each corresponding column

pd.merge(df1,df2, on=['key_1','key_2'], suffixes=['_df1','_df2'], how='outer')

    key_1   key_2   min_df1  count_df1  min_df2 count_df2
0   F         F     -158.0   58.0     -181.0    171.0
1   H         G     -881.0   24.0      NaN      NaN
2   E         E     -674.0   13.0      NaN      NaN
3   C         C      NaN     NaN      -452.0    7470.0
4   L         D      NaN     NaN      -153.0    1262.0
5   K         K      NaN     NaN      -120.0    86.0

Answer 1

Use concat with convert ['key_1','key_2'] to MultiIndex :

df = (pd.concat([df1.set_index(['key_1','key_2']), 
                 df2.set_index(['key_1','key_2'])], keys=['df1','df2'], axis=1)
        .sort_index(level=1, axis=1))
print (df)

              df1     df2    df1    df2
            count   count    min    min
key_1 key_2                            
C     C       NaN  7470.0    NaN -452.0
E     E      13.0     NaN -674.0    NaN
F     F      58.0   171.0 -158.0 -181.0
H     G      24.0     NaN -881.0    NaN
K     K       NaN    86.0    NaN -120.0
L     D       NaN  1262.0    NaN -153.0

Answer 2

After the merge, ou can re-order the columns alphabetically in order to have them side by side:

first_columns = ['key_1','key_2']
merged_df = pd.merge(df1,df2, on=['key_1','key_2'], suffixes=['_df1','_df2'], how='outer')
merged_df = merged_df[first_columns + sorted([col for col in merged_df.columns if col not in first_columns ])]

Answer 3

One way:

merged_df = pd.merge(df1, df2, on=['key_1', 'key_2'], suffixes=[
                     '_df1', '_df2'], how='outer').set_index(['key_1', 'key_2'])
merged_df.columns = merged_df.columns.str.split('_', expand=True)
merged_df = merged_df.sort_index(level=0, axis=1)