SO response

department.py

import pandas as pd
import re
import numpy as np

data= [['Empty','CMI-General Liability | 05-9632','Empty','Empty'],['Empty','Central Operations','Empty','Empty'],['Empty','Alarm Central 05-8642','Empty','Empty'],['Empty','Market 466','Empty','Empty'],['Empty','Talent, Experience','Empty','Empty'],['Empty','Food Division','Empty','Empty'],['Empty','Quality WMCC','Empty','Empty'],['Empty','Modular Execution Team | 01-9700','Empty','Empty'],['Empty','US Central Operations','Empty','Empty'],['Empty','CE - Engineering - US','Empty','Empty'],['Empty','Fresh, Freezer & Cooler - 18-8110','Empty','Empty'],['Empty','9701','Empty','Empty'],['Empty','Contact Center','Empty','Empty'],['Empty','Central Operations','Empty','Empty'],['Empty','US Central Operations','Empty','Empty'],['Empty','Private Brands GM - 01-8683','Empty','Empty']]
df2=pd.DataFrame(data,columns=['JobTitle','Department','TrueDepartment','Dept_Function'])

data5 = [[1,'TRUCKING, MARCY, NY','Empty','Empty'],[2,'TRUCKING-GREENVILLE,TN','Empty','Empty'],[3,'DC 40, HOPE MILLS, NC','Empty','Empty'],[4,'TRUCKING, SHARON SPRINGS','Empty','Empty'],[5,'DISP PAULS VALLEY OK FDC','Empty','Empty'],[6,'COLDWATER, MI','Empty','Empty'],[7,'AMERICOLD LOGISTICS','Empty','Empty'],[8,'DFW3N FORT WORTH FC WHS.COM','Empty','Empty'],[9,'PCCC CURRENTLY BEING REVIEWED','Empty','Empty'],[466,'Springfield, MO','Empty','Empty'],[8110,'Fresh Dept','Empty','Empty'],[8642,'Security','Security & Compliance','Empty'],[8683,'General Merchandise','Empty','Empty'],[9362,'General Liability','Empty','Empty'],[9700,'Execution Team','Empty','Empty'],[9701,'Produce TN','Empty','Empty']]
df5=pd.DataFrame(data5,columns=['Dept_Nbr','Dept_Desc_good','Dept_Desc_better','Dept_Abrv'])

numbers = df5['Dept_Nbr'].tolist()
df5['Dept_Nbr'] = [int(i) for i in df5['Dept_Nbr']]
df5 = df5.set_index('Dept_Nbr')  #<-- need to actually set df5 to the new index

for n in numbers:
    for i in range(len(df5.index)):  #<-- you want to iterate through the number of elements not the elements themselves
        if str(n) == df2.loc[i, 'Department'][-4:]: #<-- convert n to str and slice df2 string for the last 4 chars
            if df5.loc[n, 'Dept_Desc_better'] != "Empty":  #<-- you're actually checking against a string, not a NaN
                df2.loc[i, 'TrueDepartment'] = df5.loc[n, 'Dept_Desc_better']  #<-- use .loc not .at
            else:
                df2.loc[i, 'TrueDepartment'] = df5.loc[n, 'Dept_Desc_good']

df2 = df2.replace(to_replace="Empty", value="")

print(df2)