The below code uses Faker library which has many functions to get random values for address, people, telephone number and various other types of data. I used the Faker library and randomized form for BIC data add to the test data.

script_31_faker_data_gen.py

from faker import Faker
from random import randint
import pycountry
import pandas as pd

fake = Faker()
df = pd.read_csv("total_bic.csv")
swift_bics=list(df["swift"])
val = {}

# Generate a random number with N digits
def random_with_N_digits(n):
    range_start = 10**(n-1)
    range_end = (10**n)-1
    return randint(range_start, range_end)

 # Function to generate the test data for the POC that has to be masked
def gen_rand_test_data():
    val['ACCOUNT_ID'] = fake.country_code()+"%010d" %(random_with_N_digits(5))
    val['TEL_NUM'] = fake.phone_number()
    val['STREET_ADDRESS'] = fake.street_address()
    val['SECONDARY_ADDRESS']= fake.secondary_address()
    val['CITY']= fake.city()
    val['COUNTRY_CODE']=fake.country_code()
    val['COUNTRY']= pycountry.countries.get(alpha_2=val['COUNTRY_CODE']).name
    if val['COUNTRY_CODE']=="US":
        val['ZIP_CODE']= fake.zipcode()
    else:    
        val['POSTAL_CODE']=fake.postalcode()
    val['SWIFT_ADDR']=unicode(swift_bics[randint(0,len(swift_bics)-1)].strip())
    val['EMAIL_ADDR']=fake.company_email()
    val['CNTCT_PRSN']=fake.name()
    val['CMPNY_NAME']=fake.company()
    val['FAX_NUM']=fake.phone_number()
    return val
 
# Save the data to CSV
import csv
import codecs
with codecs.open('test_bank_dat.csv', 'w','utf-8','ignore') as csvfile:
    writer = csv.DictWriter(csvfile, fieldnames=header)
    writer.writeheader()
    for i in range(10000000):
        tval = gen_rand_test_data()
        for key in tval:
            try:
                tval[key]=tval[key].encode('ascii','ignore')
            except:
                print tval      
        writer.writerow(tval)
print "Test bank Data Created"