Python Pandas检查值是否从一个DataFrame到另一个DataFrame

Question

I have an Excel file in which I use each columns as DataFrame. Here are the 5 DataFrames that I am using - ( I will be adding a row number column for easier clarification in my question. It is not included in the original file ):

row_no   svc_no   i_status   caller_id   f_status   remarks
1        11111    WO         22222       WO
2        22222    WO         11111       WO

3        33333    WO         n/a         FA
4        NULL     FA         33333       WO

5        444444   WO         55555       WO
6        55555    WO         new_num     WO

I need to put a value in remarks column after I satisfy the conditions. In this case there are 3 scenarios in which each scenario have a different conditions.

Note: The rows of each data may vary but for my example, I put them consecutively

Scenario 1 conditions (rows 1 and 2):

• svc_no is not equal to caller_id
• svc_no is not NULL
• caller_id is not n/a
• svc_no is in caller_id and vice versa
• i_status and f_status is WO

Scenario 2 conditions (rows 3 and 4):

• svc_no is not equal to caller_id
• svc_no is in caller_id and vice versa
• The value in svc_no is matched with n/a while the value its pair in caller_id is matched with NULL
• If i_status and f_status is FA if value is NULL or n/a

Scenario 3 conditions (rows 5 and 6):

• svc_no is not equal to caller_id
• svc_no is 6 numerical characters
• caller_id is new_num
• i_status and f_status is WO
• svc_no is in caller_id and vice versa

Now let's say I satisfy the conditions for each scenario, I will have to put a designated value in remarks. So my desired output would be:

row_no   svc_no   i_status   caller_id   f_status   remarks
1        11111    WO         22222       WO         S1 Transpose
2        22222    WO         11111       WO         S1 Transpose

3        33333    WO         n/a         FA         S2 Transpose
4        NULL     FA         33333       WO         S2 Transpose

5        444444   WO         55555       WO         S3 Transpose
6        55555    WO         new_num     WO         S3 Transpose

My problem is that even though my code is working and followed the conditions, the output is not accurate. Here is my code:

# Scenario 1

df.loc[(df['svc_no'] != df['caller_id']) &
       (df['svc_no'].isin(df['caller_id'])) &
       (df['caller_id'].isin(df['svc_no'])) &
       (df['svc_no'] != 'NULL') &
       (df['caller_id'] != 'n/a') &
       (df['i_status'] == 'WO') &
       (df['f_status'] == 'WO'), ['remarks']] = 'S1 Transpose'

# Scenario 2
# NULL svc_no
df.loc[(df['svc_no'] == 'NULL') &
       (df['caller_id'] !='n/a') &
       (df['svc_no'].isin(df['caller_id'])) &
       (df['caller_id'].isin(df['svc_no'])) &
       (df['i_status'] == 'FA') &
       (df['f_status'] == 'WO')['remarks']] = 'S2 Transpose'

# n/a in caller_id
df.loc[(df['svc_no'] != 'NULL') &
       (df['caller_id'] =='n/a') &
       (df['svc_no'].isin(df['caller_id'])) &
       (df['caller_id'].isin(df['svc_no'])) &
       (df['i_status'] == 'WO') &
       (df['f_status'] == 'FA')['remarks']] = 'S2 Transpose'

# Scenario 3
df.loc[(c_merge['svc_no'] != 'NULL') &
       (df['svc_no'].isin(c_merge['caller_id'])) &
       (df['caller_id'].isin(c_merge['svc_no'])) &
       (df['i_status'] == 'WO') &
       (df['caller_id'] != c_merge['svc_no']) &
       (df['f_status'] == 'WO') &
       (df['caller_id'] == 'new_num', ['remarks']] = s3_wo_wo

The output that I am having is:

row_no   svc_no   i_status   caller_id   f_status   remarks
1        11111    WO         22222       WO         S1 Transpose
2        22222    WO         11111       WO         S1 Transpose

3        33333    WO         n/a         FA         S1 Transpose
4        NULL     FA         33333       WO         S1 Transpose

5        444444   WO         55555       WO         
6        55555    WO         new_num     WO         S3 Transpose

S1 Transpose also input those in S2 Transpose and S3 Transpose only put input in one rows.

Is there a way in which I can group 2 rows that apply the conditions? Or is there a way around for my code in which they will be applied to its specific rows affected?

Answer 1

I answered this by doing: I used df.loc for each row following.

I was able to achieve my output without grouping the two rows that contains the transposed value.

In line (df['svc_no']isint(df['caller_id'])) , I locate if the value exists in svc_no in caller_id and created another df.loc for the other other row

Scenario 1 :

df.loc[(c_merge['svc_no'] != 'NULL') & /
      (df['i_status'] == 'WO') & /
      (df['caller_id'] != 'n/a') & /
      (df['f_status'] == 'WO') & /
      (df['svc_no'] != df['caller_id']) & /
      (df['svc_no'].isin(df['caller_id'])), ['remarks']] = 'S1 Transpose'

df.loc[(c_merge['svc_no'] != 'NULL') & /
      (df['i_status'] == 'WO') & /
      (df['caller_id'] != 'n/a') & /
      (df['f_status'] == 'WO') & /
      (df['svc_no'] != df['caller_id']) & /
      (df['caller_id'].isin(df['svc_no'])), ['remarks']] = 'S1 Transpose'

I will apply this to the other scenarios as I think this is the way to it.