[FIXED] Select a row from secondary dataframe to place adjacent to primary dataframe fulfilling a condition

Issue

I have two dataframes which are ranked in the order of priority (i.e higher priority at the top and lower priority at bottom) and they need to be appended (meaning combine all the columns to build a new table) with row 1 of dataframe 1 placed adjacent of row 1 of dataframe 2 but it should skip to the next available row if current row of second dataframe does not fullfill the condition described below.

The first dataframe (check) has an ID column ('rmkt_account_managers_id') which needs to be assigned a merchant from second dataframe (df) with a condition that the 'assigned_from' which is another column in second dataframe (df) does not match with the representative ID(('rmkt_account_managers_id') to which the merchant will be assigned.

Here is what I did but it doesn't work:

import random
import requests
import pandas as pd
import numpy as np

df=pd.read_excel(r'C:\Users\Downloads\LRPool_Leads_1st_April_19.xlsx')
df['merchant_id'] = pd.to_numeric(df['merchant_id'])
df['assigned_from'] = pd.to_numeric(df['assigned_from'])
df['assignment_done']=0

df1=pd.read_excel(r'C:\Users\Downloads\assignments_to_be_done.xlsx')

check=df1.reindex(df1.index.repeat(df1.Assignments))
check['C'] = check.groupby('rmkt_account_managers_id')['rmkt_account_managers_id'].cumcount()
check.sort_values(by=['C', 'rmkt_account_managers_id'], inplace=True)

check['action']="ADD"
check['Remarketing_Account_Type']="ACCOUNT"
check['merchant_id']=""
check['rmkt_account_managers_id'] = pd.to_numeric(check['rmkt_account_managers_id'])

for i, row in check.iterrows(): 
    for j, row2 in df.iterrows(): 
        if ((row['rmkt_account_managers_id'] != row2['assigned_from']) & (row2['assignment_done'] !=1) & (row['merchant_id'] !=0)): 
            row.loc['merchant_id'] = row2.loc['merchant_id'] 
            row2.loc['assignment_done'] = 1 
        else:
            row2.loc['assignment_done'] = 0
        
check.to_excel(r'C:\Downloads\result.xlsx', sheet_name='assignments', index = False)

Assignment Done is column I wanted to use as helper

Example: df name: check

{'rmkt_account_managers_id': [792, 649741, 1506572, 2159133, 2435783, 2710213, 3469133, 4085263, 5048463, 5112823, 5279163, 6192544, 6204534, 6235820, 6255350, 6272151, 6272180, 6286834, 6305702, 6329553, 6341883, 6341890, 6341910, 6341913, 6342486, 6342635, 6351103, 6360980, 6364038, 6389945, 6421707, 792, 649741, 1506572, 2159133, 2435783, 2710213, 3469133, 4085263, 5048463, 5112823, 5279163, 6192544, 6204534, 6235820, 6255350, 6272151, 6272180, 6286834, 6305702, 6329553, 6341883, 6341890, 6341910, 6341913, 6342486, 6342635, 6351103, 6360980, 6364038, 6389945, 6421707, 792, 649741, 1506572, 2159133, 2435783, 2710213, 3469133, 4085263, 5048463, 5112823, 5279163, 6192544, 6204534, 6235820, 6255350, 6272151, 6272180, 6286834, 6305702, 6329553, 6341883, 6341890, 6341910, 6341913, 6342486, 6342635, 6351103, 6360980, 6364038, 6389945, 6421707, 792, 649741, 1506572, 2159133, 2435783, 2710213, 3469133, 4085263, 5048463, 5112823, 5279163, 6192544, 6204534, 6235820, 6255350, 6272151, 6272180, 6286834, 6305702, 6329553, 6341883, 6341890, 6341910, 6341913, 6342486, 6342635, 6351103, 6360980, 6364038, 6389945, 6421707, 792, 649741, 1506572, 2159133, 2435783, 2710213, 3469133, 4085263, 5048463, 5112823, 5279163, 6192544, 6204534, 6235820, 6255350, 6272151, 6272180, 6286834, 6305702, 6329553, 6341883, 6341890, 6341910, 6341913, 6342486, 6342635, 6351103, 6360980, 6364038, 6389945, 6421707, 792, 649741, 1506572, 2159133, 2435783, 2710213, 3469133, 4085263, 5048463, 5112823, 5279163, 6192544, 6204534, 6235820, 6255350, 6272151, 6272180, 6286834, 6305702, 6329553, 6341883, 6341890, 6341910, 6341913, 6342486, 6342635, 6351103, 6360980, 6364038, 6389945, 6421707, 792, 649741, 1506572, 2159133, 2435783, 2710213, 3469133, 4085263, 5048463, 5112823, 5279163, 6192544, 6204534, 6235820, 6255350, 6272151, 6272180, 6286834, 6305702, 6329553, 6341883, 6341890, 6341910, 6341913, 6342486, 6342635, 6351103, 6360980, 6364038, 6389945, 6421707, 792, 649741, 1506572, 2159133, 2435783, 2710213, 3469133, 4085263, 5048463, 5112823, 5279163, 6192544, 6204534, 6235820, 6255350, 6272151, 6272180, 6286834, 6305702, 6329553, 6341883, 6341890, 6341910, 6341913, 6342486, 6342635, 6351103, 6360980, 6364038, 6389945, 6421707, 792, 649741, 1506572, 2159133, 2435783, 2710213, 3469133, 4085263, 5048463, 5112823, 5279163, 6192544, 6204534, 6235820, 6255350, 6272151, 6272180, 6286834, 6305702, 6329553, 6341883, 6341890, 6341910, 6341913, 6342486, 6342635, 6351103, 6360980, 6364038, 6389945, 6421707, 792, 649741, 1506572, 2159133, 2435783, 2710213, 3469133, 4085263, 5048463, 5112823, 5279163, 6192544, 6204534, 6235820, 6255350, 6272151, 6272180, 6286834, 6305702, 6329553, 6341883, 6341890, 6341910, 6341913, 6342486, 6342635, 6351103, 6360980, 6364038, 6389945, 6421707, 792, 649741, 1506572, 2159133, 2435783, 2710213, 3469133, 4085263, 5048463, 5112823, 5279163, 6192544, 6204534, 6235820, 6255350, 6272151, 6272180, 6286834, 6305702, 6329553, 6341883, 6341890, 6341910, 6341913, 6342486, 6342635, 6351103, 6360980, 6364038, 6389945, 6421707, 792, 649741, 1506572, 2159133, 2435783, 2710213, 3469133, 4085263, 5048463, 5112823, 5279163, 6192544, 6204534, 6235820, 6255350, 6272151, 6272180, 6286834, 6305702, 6329553, 6341883, 6341890, 6341910, 6341913, 6342486, 6342635, 6351103, 6360980, 6364038, 6389945, 6421707, 792, 649741, 1506572, 2159133, 2435783, 2710213, 3469133, 4085263, 5048463, 5112823, 5279163, 6192544, 6204534, 6235820, 6255350, 6272151, 6272180, 6286834, 6305702, 6329553, 6341883, 6341890, 6341910, 6341913, 6342486, 6342635, 6351103, 6360980], 'action': ['ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD', 'ADD'], 'Remarketing_Account_Type': ['ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT', 'ACCOUNT']}

Expected Output:

'rmkt_account_managers_id' 'action' 'Remarketing_Account_Type' 'Merchant ID'

Solution

I can't think of any good way to solve this in pandas itself with sorting/grouping or anything else. Since you always want to fill the rows from top to bottom and always want to find the first merchand_id which can be assigned to a rmkt_account_managers_id I think collections.deque is useful here.

Basically we create a deque of the data of DataFrame df, where each element is a tuple like (39191, 6342486.0) in the order like the rows appear in df. deque's are preferred over lists where you have to use append and pop operations from both the ends of your data/stack. We loop through each row of DataFrame check, pop the first element of the deque (take_first) and while equal to the assigned_from we continue popping the first element till we find one that fits the condition. After that we use appendleft to add the non-used tuples back to the deque. At the end we have a list called result which we assign to a new column.

I deleted both old answers, it just makes things confusing. I can't follow the logic of your attempt, too many things that are not understandable for me what you actually want to achieve. I used exactly the naming you provided with the exact same data in your question and this is my code that is working properly on it.

Try to copy paste just this code and see if it works:

import random
import requests
import pandas as pd
import numpy as np

df=pd.read_excel(r'C:\Users\Downloads\LRPool_Leads_1st_April_19.xlsx')
df['merchant_id'] = pd.to_numeric(df['merchant_id'])
df['assigned_from'] = pd.to_numeric(df['assigned_from'])


df1=pd.read_excel(r'C:\Users\Downloads\assignments_to_be_done.xlsx')
check=df1.reindex(df1.index.repeat(df1.Assignments))
check['C'] = check.groupby('rmkt_account_managers_id')['rmkt_account_managers_id'].cumcount()
check['rmkt_account_managers_id'] = pd.to_numeric(check['rmkt_account_managers_id'])
check.sort_values(by=['C', 'rmkt_account_managers_id'], inplace=True)

check['action']="ADD"
check['Remarketing_Account_Type']="ACCOUNT"

pairs = deque(df.itertuples(name=None, index=None)) 

result = [] 
for row in check['rmkt_account_managers_id']: 
    if pairs: 
        take_first = pairs.popleft() 
        tmp=[] 
        while take_first[1] == row:
            tmp.append(take_first) 
            take_first = pairs.popleft() 
        if tmp: 
            pairs.extendleft(tmp[::-1])
        result.append(take_first[0]) 
check.loc[:,'new_Merchant_ID'] = result
print(check.head(10))
        
check.to_excel(r'C:\Downloads\result.xlsx', sheet_name='assignments', index = False)

Output:

   rmkt_account_managers_id action Remarketing_Account_Type  new_Merchant_ID
0                       792    ADD                  ACCOUNT            39191
1                    649741    ADD                  ACCOUNT            84173
2                   1506572    ADD                  ACCOUNT          6235186
3                   2159133    ADD                  ACCOUNT           353171
4                   2435783    ADD                  ACCOUNT           282081
5                   2710213    ADD                  ACCOUNT          6233030
6                   3469133    ADD                  ACCOUNT           911811
7                   4085263    ADD                  ACCOUNT             1186
8                   5048463    ADD                  ACCOUNT            24945
9                   5112823    ADD                  ACCOUNT            15156

Answered By - Rabinzel