将两个数据框中的两列与另一列上的条件进行比较

Question

I have a multilevel dataframe and I want to compare the value in column secret with a condition on column group . If group = A, we allow the value in another dataframe to be empty or na. Otherwise, output INVALID for the mismatching ones.

multilevel dataframe:

        name               secret              group
        df1     df2     df1     df2        df1     df2
id                       
1       Tim     Tim     random  na          A        A
2       Tom     Tom     tree                A        A
3       Alex    Alex    apple   apple       B        B
4       May     May     file    cheese      C        C

expected output for secret

id     name    secret    group
1      Tim       na        A
2      Tom                 A
3      Alex     apple      B
4      May     INVALID     C

so far I have:

result_df['result'] = multilevel_df.groupby(level=0, axis=0).apply(lambda x: secret_check(x)) 

#take care of the rest by compare column by column
result_df = multilevel_df.groupby(level=0, axis=1).apply(lambda x: validate(x))

def validate(x):
  if x[0] == x[1]:
        return x[1]
    else:
        return 'INVALID'

def secret_check(x): 
   if (x['group'] == 'A' and pd.isnull(['secret']):  #this line is off
      return x[1] 
   elif x[0] == x[1]:
      return x[1]
   else:
      return 'INVALID'

Answer 1

If I understand you right, you want to mark "secret" in df2 as invalid if the secrets in df1 and df2 differ and the group is not A. There you go:

condition = (df[('secret', 'df1')] != df[('secret', 'df2')]) &\
             df[('group',  'df1')] != 'A')
df.loc[condition, ('secret', 'df2')] = 'INVALID'

Answer 2

Assuming we have the following dataframe:

df = pd.DataFrame({0: {0: 1, 1: 2, 2: 3, 3: 4},
 1: {0: 'Tim', 1: 'Tom', 2: 'Alex', 3: 'May'},
 2: {0: 'Tim', 1: 'Tom', 2: 'Alex', 3: 'May'},
 3: {0: 'random', 1: 'tree', 2: 'apple', 3: 'file'},
 4: {0: 'na', 1: '', 2: 'apple', 3: 'cheese'},
 5: {0: 'A', 1: 'A', 2: 'B', 3: 'C'},
 6: {0: 'A', 1: 'A', 2: 'B', 3: 'C'}})
df
df.columns = pd.MultiIndex.from_tuples([('id',''), ('name', 'df1'), ('name', 'df2'),
                            ('secret', 'df1'), ('secret', 'df2'), ('group', 'df1'), ('group', 'df2')])
df
In[1]: 
  id  name        secret         group    
       df1   df2     df1     df2   df1 df2
0  1   Tim   Tim  random      na     A   A
1  2   Tom   Tom    tree             A   A
2  3  Alex  Alex   apple   apple     B   B
3  4   May   May    file  cheese     C   C

---

1. You can use np.select() to return results based on conditions.
2. .droplevel() to get out of a multiindex dataframe
3. and df.loc[:,~df.columns.duplicated()] to drop duplicate columns. Since we are setting the answer to df1 columns, df2 columns are not needed.

---

df[('secret', 'df1')] = np.select([(df[('group', 'df2')] != 'A') & 
                                   (df[('secret', 'df1')] != df[('secret', 'df2')])], #condition 1
                                  [df[('secret', 'df1')] + ' > ' + df[('secret', 'df2')]], #result 1
                                   df[('secret', 'df2')]) #alterantive if conditions not met
df.columns = df.columns.droplevel(1)
df = df.loc[:,~df.columns.duplicated()]
df
Out[1]: 
   id  name         secret group
0   1   Tim             na     A
1   2   Tom                    A
2   3  Alex          apple     B
3   4   May  file > cheese     C