def generate_accounts(amount):
known_accounts = []
for person in range(0, amount):
accounts = []
first_name, last_name = gen_data.create_name()
zip, city, state = gen_data.create_city_state_zip()
address_id = str(random.randint(0, sys.maxint))
UID = str(random.randint(0, sys.maxint))
birth_day = gen_data.create_birthday()
street_address = gen_data.create_street()
email_address = gen_data.create_email(name=(first_name, last_name))
print_instance(get_random_uuid_uri(), "nco:PersonContact")
print_property("nco:fullname", str.join(" ", [first_name, last_name]))
print_property("nco:nameGiven", first_name)
print_property("nco:nameFamily", last_name)
for j in range(0, random.randint(0, 4)):
account_data = get_random_in_list(ACCOUNTS)
user_account = str.join("", [account_data[2], str(j), email_address])
print_property("nco:hasIMAccount", user_account, t="uri")
accounts.append((user_account, account_data))
known_accounts.insert(0, user_account)
print_property("nco:birthDate", str(birth_day), final=True)
return known_accounts
def generate_accounts(amount):
known_accounts = []
for person in range(0, amount):
accounts = []
first_name, last_name = gen_data.create_name()
zip, city, state = gen_data.create_city_state_zip()
address_id = str(random.randint(0, sys.maxint))
UID = str(random.randint(0, sys.maxint))
birth_day = gen_data.create_birthday()
street_address = gen_data.create_street()
email_address = gen_data.create_email(name=(first_name, last_name))
print_instance(get_random_uuid_uri(), "nco:PersonContact")
print_property("nco:fullname", str.join(' ', [first_name, last_name]))
print_property("nco:nameGiven", first_name)
print_property("nco:nameFamily", last_name)
for j in range(0, random.randint(0, 4)):
account_data = get_random_in_list(ACCOUNTS)
user_account = str.join(
'', [account_data[2], str(j), email_address])
print_property("nco:hasIMAccount", user_account, t="uri")
accounts.append((user_account, account_data))
known_accounts.insert(0, user_account)
print_property("nco:birthDate", str(birth_day), final=True)
return known_accounts
def open_account(person):
"""
In this function create a person object with dummy data
:param person: Proto buffer data structure object
:return: None
"""
person.name = gen_data.create_name()[0]
person.account_no = gen_data.create_cc_number(length=10)[1][0]
person.pin_no = gen_data.create_pw(length=2)
person.amount = 5000
print "Thank You", person.name, "to open your account..."
def new_character(user):
game = get_game(user)
owner = user
name = gen_data.create_name()
name = name[0] + ' ' + name[1]
newchar = Character(owner=owner,
name=name,
display_name=name,
game=game,
experience=0)
newchar.save()
return newchar
def generate_vCard():
gender_initial = gender_vcard_list[random.randint(0, 4)]
gender = None
if gender_initial == 'M':
gender = 'Male'
elif gender_initial == 'F':
gender = 'Female'
(first_name, last_name) = gen_data.create_name(gender=gender)
adr = gen_data.create_street()
zip, city, state = gen_data.create_city_state_zip()
properties = []
properties.append('FN:{} {}\r\n'.format(first_name, last_name))
if random.randint(0, 1):
properties.append('N:{};{};;;\r\n'.format(last_name, first_name))
if random.randint(0, 1):
properties.append('TEL:tel:{}\r\n'.format(gen_data.create_phone()))
if random.randint(0, 1):
properties.append('GENDER:{}\r\n'.format(gender_initial))
if random.randint(0, 1):
properties.append('EMAIL:{}\r\n'.format(
gen_data.create_email(name=(first_name, last_name)).lower()))
if random.randint(0, 1):
properties.append('IMPP:sip:{}@{}\r\n'.format(first_name.lower(),
'sip.linphone.org'))
if random.randint(0, 1):
properties.append('ADR:;;{};{};{};{};\r\n'.format(
adr, city, state, zip))
if random.randint(0, 1):
properties.append('NOTE:{}\r\n'.format(gen_data.create_sentence()))
if random.randint(0, 1):
properties.append('ORG:{}\r\n'.format(gen_data.create_company_name()))
if random.randint(0, 1):
properties.append('BDAY:{0:%Y%m%d}\r\n'.format(
gen_data.create_birthday()))
shuffle(properties)
vCard = 'BEGIN:VCARD\r\n'
vCard += 'VERSION:4.0\r\n'
for property in properties:
vCard += property
vCard += 'END:VCARD\r\n'
return vCard
def create_players_via_draft_batch(start, end):
list_of_players = []
players_overalls = create_player_attributes(create_players_via_draft_batch_overalls(72, NUM_OF_PLAYERS))
number_of_draft_classes = range(start, end)
# gen names for each player:
list_of_names = get_player_names(number_of_draft_classes * NUM_OF_PLAYERS)
for year in number_of_draft_classes:
for i in range(NUM_OF_PLAYERS):
potential_overall = int(players_overalls[i][0])
name = gen_data.create_name()
list_of_players.append(Player(name=name,
potential_overall=potential_overall,
draft_year=year,
drafted_by=None))
i += 1
print("Year: ", year)
print("Num of players: ", i)
year += 1
return list_of_players
def generate_vCard():
gender_initial = gender_vcard_list[random.randint(0, 4)]
gender = None
if gender_initial == 'M':
gender = 'Male'
elif gender_initial == 'F':
gender = 'Female'
(first_name, last_name) = gen_data.create_name(gender=gender)
adr = gen_data.create_street()
zip, city, state = gen_data.create_city_state_zip()
properties = []
properties.append('FN:{} {}\r\n'.format(first_name, last_name))
if random.randint(0, 1):
properties.append('N:{};{};;;\r\n'.format(last_name, first_name))
if random.randint(0, 1):
properties.append('TEL:tel:{}\r\n'.format(gen_data.create_phone()))
if random.randint(0, 1):
properties.append('GENDER:{}\r\n'.format(gender_initial))
if random.randint(0, 1):
properties.append('EMAIL:{}\r\n'.format(gen_data.create_email(name=(first_name, last_name)).lower()))
if random.randint(0, 1):
properties.append('IMPP:sip:{}@{}\r\n'.format(first_name.lower(), 'sip.linphone.org'))
if random.randint(0, 1):
properties.append('ADR:;;{};{};{};{};\r\n'.format(adr, city, state, zip))
if random.randint(0, 1):
properties.append('NOTE:{}\r\n'.format(gen_data.create_sentence()))
if random.randint(0, 1):
properties.append('ORG:{}\r\n'.format(gen_data.create_company_name()))
if random.randint(0, 1):
properties.append('BDAY:{0:%Y%m%d}\r\n'.format(gen_data.create_birthday()))
shuffle(properties)
vCard = 'BEGIN:VCARD\r\n'
vCard += 'VERSION:4.0\r\n'
for property in properties:
vCard += property
vCard += 'END:VCARD\r\n'
return vCard
def create_players_via_draft_batch(start, end):
list_of_players = []
players_overalls = create_player_attributes(
create_players_via_draft_batch_overalls(72, NUM_OF_PLAYERS))
number_of_draft_classes = range(start, end)
# gen names for each player:
list_of_names = get_player_names(number_of_draft_classes * NUM_OF_PLAYERS)
for year in number_of_draft_classes:
for i in range(NUM_OF_PLAYERS):
potential_overall = int(players_overalls[i][0])
name = gen_data.create_name()
list_of_players.append(
Player(name=name,
potential_overall=potential_overall,
draft_year=year,
drafted_by=None))
i += 1
print("Year: ", year)
print("Num of players: ", i)
year += 1
return list_of_players
def get_rows():
i=501
#line = input("Enter a row (python dict) into the table: ")
while i < 1000:
fake = Faker()
#Pick an account number and store it in acct
#if the account hasn't been already generated then generate a record with all fields
i=i+1
line = "{'rownum':"+str(i)+",'dunno':"+str(10)+",'CC':"+str(gen_data.cc_number())+",'Employer':"+str(gen_data.create_company_name())+\
",'Custemail':"+str(gen_data.create_email())+",'name':"+\
str(gen_data.create_name())+",'occupation':"+str(gen_data.create_job_title())+",'address_street':"+\
str(gen_data.create_city_state_zip())+",'DOB':"+str(gen_data.create_birthday(min_age=2, max_age=85))+\
",'previous_address_city_state_zip':"+str(gen_data.create_city_state_zip())+",'altcustomer_name':"+str(fake.name())+\
",'altcustomer_occupation':"+str(gen_data.create_job_title())+",'altcustomer_dob':"+str(gen_data.create_birthday(min_age=2, max_age=85))+\
",'ssn':"+str((randrange(101,1000,1),randrange(10,100,1),randrange(1000,10000,1)))+",'phone':"+\
str((randrange(101,1000,1),randrange(101,999,1),randrange(1000,10000,1)))+ \
",'AccountID':"+str(randrange(100000,100000000,1))+",'PepFlag':"+str(max((randrange(0,101,1)-99,0)))+",'altcustomerssn':"+\
str((randrange(101,1000,1),randrange(10,100,1),randrange(1000,10000,1)))+",'demarketed_customer_flag':"+\
str(max((randrange(0,101,1)-99),0))+\
",'SAR_flag':"+str(max((randrange(0,101,1)-99),0))+",'nolonger_a_customer':"+str(max((randrange(0,101,1)-99),0))+\
",'closed_account'"+str(max((randrange(0,101,1)-90),0))+",'High_risk_flag':"+str(max((randrange(0,101,1)-99),0))+\
",'Risk_rating':"+str(max((randrange(0,101,1)-99),0))+"}"
yield ast.literal_eval(line)
sys.stdout.write('\tnco:hasEmailAddress <mailto:[email protected]>;\n')
sys.stdout.write('\tnco:hasPhoneNumber <tel:+11111111111>.\n')
sys.stdout.write('\n')
sys.stdout.write('<tel:+11111111111> a nco:PhoneNumber; \n')
sys.stdout.write('\tnco:phoneNumber "(111) 111-1111".\n')
sys.stdout.write('\n')
#TODO need to create some email folders
myOwnPhoneNumberURI = "tel:+11111111111"
previousContacts = []
previousEmailAddresses = []
previousIMAccounts = []
allchars = string.maketrans('','')
for dummy in range (0, count):
firstName, lastName = gen_data.create_name()
zip, city, state = gen_data.create_city_state_zip()
postalAddressID=str(random.randint(0, sys.maxint))
UID = str(random.randint(0, sys.maxint))
phoneNumber = gen_data.create_phone()
phoneUri = 'tel:+1' + phoneNumber.translate(allchars,' -()')
birthDay = gen_data.create_birthday()
streetAddress = gen_data.create_street()
emailAddress = gen_data.create_email(name=(firstName, lastName))
xmppAddress = str(firstName+"." + lastName + "@gmail.com").lower()
hasIMAccount = False
hasPhoneNumber = False
jobTitle = gen_data.create_job_title()
generatePostalAddress()
import random
from barnum import gen_data
import addressbook_pb2
# Barnum generates US data but that's ok for the example
names = [gen_data.create_name() for _ in range(0, 15)]
phones = [gen_data.create_phone() for _ in range(0, 30)]
postcodes = [gen_data.create_city_state_zip() for _ in range(0, 15)]
streets = [gen_data.create_street() for _ in range(0, 30)]
contacts = []
for name in names:
address = {}
# Simulate the fact that postcode are optionals
if random.choice([True, False]):
address['postcode'] = random.choice(postcodes)[0]
address['address_lines'] = random.sample(streets, random.randint(0, 2))
phone_numbers = []
for _ in range(0, random.randint(0, 2)):
phone_numbers.append({
'type': random.choice(['MOBILE', 'LANDLINE']),
'number': random.choice(phones)
})
contacts.append({
'first_name': name[0],
'last_name': name[1],
'address': address,
from barnum import gen_data
import csv
with open('demographic.csv','w') as csvfile:
csvwriter =csv.writer(csvfile, delimiter=' ')
for i in range (0,100):
name=gen_data.create_name()
job_title=gen_data.create_job_title()
phone=gen_data.create_phone()
address=gen_data.create_city_state_zip()
csvwriter.writerow([name,job_title,phone,address])
csvfile.close()
sys.stdout.write('\tnco:hasEmailAddress <mailto:[email protected]>;\n')
sys.stdout.write('\tnco:hasPhoneNumber <tel:+11111111111>.\n')
sys.stdout.write('\n')
sys.stdout.write('<tel:+11111111111> a nco:PhoneNumber; \n')
sys.stdout.write('\tnco:phoneNumber "(111) 111-1111".\n')
sys.stdout.write('\n')
#TODO need to create some email folders
myOwnPhoneNumberURI = "tel:+11111111111"
previousContacts = []
previousEmailAddresses = []
previousIMAccounts = []
allchars = string.maketrans('', '')
for dummy in range(0, count):
firstName, lastName = gen_data.create_name()
zip, city, state = gen_data.create_city_state_zip()
postalAddressID = str(random.randint(0, sys.maxint))
UID = str(random.randint(0, sys.maxint))
phoneNumber = gen_data.create_phone()
phoneUri = 'tel:+1' + phoneNumber.translate(allchars, ' -()')
birthDay = gen_data.create_birthday()
streetAddress = gen_data.create_street()
emailAddress = gen_data.create_email(name=(firstName, lastName))
xmppAddress = str(firstName + "." + lastName + "@gmail.com").lower()
hasIMAccount = False
hasPhoneNumber = False
jobTitle = gen_data.create_job_title()
generatePostalAddress()
from barnum import gen_data
import random
import pandas as pd
import datetime
"""
creates a fake set of popular products
being sold by the Blooth store
to company customers.
"""
humans = []
for i in range(100):
humans.append(
[
gen_data.create_name(full_name=False),
str(gen_data.create_birthday(min_age=18, max_age=65)),
gen_data.create_company_name(biz_type='Generic'),
]
)
humans.append(
[
gen_data.create_name(full_name=False),
str(gen_data.create_birthday(min_age=30, max_age=50)),
gen_data.create_company_name(biz_type='Generic'),
]
)
humans.append(
[
+ ["closed_account"]
+ ["High_risk_flag"]
+ ["Risk_rating"]
)
while i < 50000000:
# Pick an account number and store it in acct
acct = randrange(100000, 100000000, 1)
# if the account hasn't been already generated then generate a record with all fields
if d.has_key(str(acct)) == False:
row = (
[i]
+ [10]
+ [gen_data.cc_number()]
+ [gen_data.create_company_name()]
+ [gen_data.create_email()]
+ [gen_data.create_name()]
+ [gen_data.create_job_title()]
+ [gen_data.create_city_state_zip()]
+ [gen_data.create_birthday(min_age=2, max_age=85)]
+ [gen_data.create_city_state_zip()]
+ [fake.name()]
+ [gen_data.create_job_title()]
+ [gen_data.create_birthday(min_age=2, max_age=85)]
+ [(randrange(101, 1000, 1), randrange(10, 100, 1), randrange(1000, 10000, 1))]
+ [(randrange(101, 1000, 1), randrange(101, 999, 1), randrange(1000, 10000, 1))]
+ [acct]
+ [max((randrange(0, 101, 1) - 99), 0)]
+ [(randrange(101, 1000, 1), randrange(10, 100, 1), randrange(1000, 10000, 1))]
+ [max((randrange(0, 101, 1) - 99), 0)]
+ [max((randrange(0, 101, 1) - 99), 0)]
+ [max((randrange(0, 101, 1) - 99), 0)]
def gen_tran(MCC_credits,MCC_debits,Tran_Country_Credits,Tran_Country_Debits,Tran_Type_C,Tran_Type_D,Upper_Limit,Delta,count,j,usecase):
liTrans = []
#Initiate start date for transactions
startDate=date(2015,01,01)
#Pick out account based on counter
acct=ACCTs[j]
#Set customer credit limit - skew to clients with $1000-$25000 and 10% with $25K - $50K
limit = max(max((randrange(1,101,1)-99),0)* randrange(25000,50000,1000),randrange(1000,25000,1000))
#local Amt variable to calculate customer total usage
usedAmt = 0
tmpAmt = 0
Balance = limit
maxDate= startDate
#Random number generator for transactions per customer
NoTrans = randrange(100,150,1)
desc=''
flag=0
maxCheckin=''
maxBook=''
#loop to generate NoTrans transactions per customer
for k in range(NoTrans):
dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
cr_dbt='D'
tranType = ''
country=[]
cat_desc=''
flag=0
#If Balance is within the credit limit, generate credits/debits
if(Balance>0 and Balance<=limit*1.2):
#Probability of credits (tmpAmt>0) and debits (tmpAmt==0) is driven by parameters Upper_Limit and Delta
tmpAmt = max((randrange(1,Upper_Limit,1)+Delta),0)*randrange(1,Balance+1,1)
flag=1
#Define time delta for next transaction
tdelta = timedelta(days=randrange(1,4,1))
row = [str(count)+'_'+dt] + [acct]
#If we have credit or debit within balance
if tmpAmt == 0 and flag==1:
tmpAmt=random.randrange(1,Balance+1,1)
tranType = random.choice(Tran_Type_D)
cat = random.choice(MCC_debits)
cat_desc=python_merchant_cat.All_Merchant_Cat[cat]
Balance = Balance - tmpAmt
merch=gen_data.create_company_name()
row.append(merch)
row.append(cat)
row.append(cat_desc)
country=random.choice(Tran_Country_Debits)
else:
if tmpAmt > 0 and flag==1:
cr_dbt='C'
tranType=random.choice(Tran_Type_C)
Balance = Balance + tmpAmt
merch=''
cat = random.choice(MCC_credits)
cat_desc=python_merchant_cat.All_Merchant_Cat[cat]
if(tranType=='Merchant Credit'):
merch=gen_data.create_company_name()
cat=random.choice(Merchant_Category.Green)
cat_desc=python_merchant_cat.All_Merchant_Cat[cat]
if(tranType=='Refund'):
cat='0000'
cat_desc=python_merchant_cat.All_Merchant_Cat[cat]
row.append(merch)
row.append(cat)
row.append(cat_desc)
country=random.choice(Tran_Country_Credits)
#If we need to make a payment or get credit then assign codes
if Balance > limit and flag==0:
tmpAmt=random.randrange(1,Balance-limit+1,1)
tranType = random.choice(Tran_Type_D)
cat = random.choice(MCC_debits)
cat_desc=python_merchant_cat.All_Merchant_Cat[cat]
Balance = Balance - tmpAmt
merch=gen_data.create_company_name()
row.append(merch)
row.append(cat)
row.append(cat_desc)
country=random.choice(Tran_Country_Debits)
else:
if ((Balance < 0 or Balance==0)and flag==0):
cr_dbt='C'
tranType='Payment'
tmpAmt = random.randrange(1,limit/2,1)
Balance = Balance + tmpAmt
merch = ''
cat = '1111'
cat_desc=python_merchant_cat.All_Merchant_Cat[cat]
row.append(merch)
row.append(cat)
row.append(cat_desc)
country=random.choice(Tran_Country_Credits)
#date posted
date1 = maxDate+tdelta
maxDate = date1
#date of transaction a day later
date2 = date1-timedelta(days=1)
row.extend([country,date1,date2,tranType,cr_dbt,limit,tmpAmt,Balance,CCs[j],
CCTypes[j],usecase,Holders[j],CCsCount[j],Cities[j],States[j],ZIPs[j],Countries[j]])
count = count + 1
checkin=''
checkout=''
transDetail=''
#Add details or Hotel Transactions
if((cat_desc=='Hotels/Motels/Inns/Resorts' or cat_desc=='Hotels, Motels, and Resorts') and (UseCase[j]=='28' or UseCase[j]=='29')):
if (maxCheckin == ''):
checkin=maxDate+timedelta(days=randrange(365,389,1))
checkout=checkin+timedelta(days=randrange(4,11,1))
maxCheckin=checkin
tmp2=gen_data.create_name()
addr=gen_data.create_city_state_zip()
hotel=tmp2[1]+' Hotels; '+'; Address: '+addr[1]+' '+addr[2]+', '+addr[0]
transDetail='Checkin: '+str(checkin)+'; Checkout: '+str(checkout)+'; Hotel: '+hotel
else:
checkin=maxCheckin + timedelta(days=randrange(2,5,1))
checkout=checkin+timedelta(days=randrange(4,11,1))
maxCheckin=checkin
tmp2=gen_data.create_name()
addr=gen_data.create_city_state_zip()
hotel=tmp2[1]+' Hotels; '+'; Address: '+addr[1]+' '+addr[2]+', '+addr[0]
transDetail='Checkin: '+str(checkin)+'; Checkout: '+str(checkout)+'; Hotel: '+hotel
if((cat_desc=='Hotels/Motels/Inns/Resorts' or cat_desc=='Hotels, Motels, and Resorts') and UseCase[j]=='30'):
checkin=maxDate+timedelta(days=randrange(30,200,1))
checkout=checkin+timedelta(days=randrange(4,11,1))
tmp2=gen_data.create_name()
addr=gen_data.create_city_state_zip()
hotel=tmp2[1]+' Hotels; '+'; Address: '+addr[1]+' '+addr[2]+', '+addr[0]
transDetail='Checkin: '+str(checkin)+'; Checkout: '+str(checkout)+'; Hotel: '+hotel
#Add details or Airline Transactions
if(cat_desc=='Airlines' and (UseCase[j]=='31' or UseCase[j]=='32')):
if (maxBook == ''):
booking=maxDate+timedelta(days=randrange(1,15,1))
maxBook=booking
tmp2=gen_data.create_name()
addr=gen_data.create_city_state_zip()
transDetail='Date Booked: '+str(booking)+'; Name Booked: '+tmp2[0]+tmp2[1]+'; Address: '+addr[1]+' '+addr[2]+', '+addr[0]+'; Source :'+random.choice(Airport_Code)+'; Destination:'+random.choice(Airport_Code)
else:
booking=maxBook + timedelta(days=randrange(1,15,1))
maxBook=booking
tmp2=gen_data.create_name()
addr=gen_data.create_city_state_zip()
transDetail='Date Booked: '+str(booking)+'; Name Booked: '+ tmp2[0] + tmp2[1] + '; Address: '+ addr[1] + ' ' + addr[2]+', '+addr[0]+'; Source :'+random.choice(Airport_Code)+'; Destination:'+random.choice(Airport_Code)
if(cat_desc=='Airlines' and UseCase[j]=='33'):
booking=maxDate + timedelta(days=randrange(1,15,1))
tmp2=gen_data.create_name()
addr=gen_data.create_city_state_zip()
transDetail='Date Booked: '+str(booking)+'; Name Booked: '+ tmp2[0] + tmp2[1] + '; Address: '+addr[1]+' '+addr[2]+', '+addr[0]+'; Source :'+random.choice(Airport_Code)+'; Destination:'+random.choice(Airport_Code)
row.append(transDetail)
writer.writerow(row)
#post generating all transactions, check account balance - if overpaid - refund $ and add a refund transaction
if Balance > limit:
row = [str(count)+'_'+dt]+ [acct]+['Uber Bank']+['0000']+['Refund to Customer from Bank']+[random.choice(Tran_Country_Debits)]
date1=maxDate+timedelta(days=90)
date2=date1-timedelta(days=1)
row.extend([date1, date2, 'Credit Balance Refund','D',limit,Balance-limit,limit,CCs[j],CCTypes[j],
usecase,Holders[j],CCsCount[j],Cities[j],States[j],ZIPs[j],Countries[j],''])
count = count + 1
usedAmt = 0
maxDate= datetime(0001,01,01)
else:
date1 = maxDate+tdelta
maxDate = date1
#date of transaction a day later
date2 = date1-timedelta(days=1)
row = [str(count)+'_'+dt]+[acct]+['Customer Payment']+['1111']+['Customer Payment']+[random.choice(Tran_Country_Credits)]
row.extend([date1, date2, 'Payment','C',limit,limit-Balance,limit,CCs[j],CCTypes[j],usecase,
Holders[j],CCsCount[j],Cities[j],States[j],ZIPs[j],Countries[j],''])
count = count + 1
usedAmt = 0
writer.writerow(row)
def generate_customers():
with get_file('uber_cust.csv', 'w') as f1:
# Writer for CSV...Pipe delimited...Return for a new line
writer = csv.writer(
f1,
delimiter='|',
lineterminator='\n',
)
# Header Row
writer.writerow(
['ROWNUM'] + ['accountNumber'] + ['accountCategory'] + ['accountType'] + ['NUM_CCS'] + ['NAME'] + [
'M_NAME'] + [
'SSN'] + [
'AUTHORIZED_NAME2'] + ['M_NAME2'] + ['SSN2'] + \
['AUTHORIZED_NAME3'] + ['M_NAME3'] + ['SSN3'] + ['AUTHORIZED_NAME4'] + ['M_NAME4'] + ['SSN4'] + [
'CREDITCARDNUMBER'] + ['CREDITCARDTYPE'] + ['EMPLOYER'] + ['CUSTEMAIL'] + \
['OCCUPATION'] + ['CITY'] + ['STATE'] + ['ZIP'] + ['COUNTRY'] + ['PREVIOUS_CITY'] + [
'PREVIOUS_STATE'] + \
['PREVIOUS_ZIP'] + ['PREVIOUS_COUNTRY'] + ['DOB'] + ['politically_exposed_person'] + [
'suspicious_activity_report'] + ['CLOSEDACCOUNT'] + [
'RELATED_ACCT'] + ['RELATED_TYPE'] + ['PARTY_TYPE'] + ['PARTY_RELATION'] + [
'PARTY_STARTDATE'] + ['PARTY_ENDDATE'] + \
['LARGE_CASH_EXEMPT'] + ['DEMARKET_FLAG'] + ['DEMARKET_DATE'] + ['PROB_DEFAULT_RISKR'] + [
'OFFICIAL_LANG_PREF'] + ['CONSENT_SHARING'] + \
['PREFERRED_CHANNEL'] + ['PRIMARY_BRANCH_NO'] + ['DEPENDANTS_COUNT'] + ['SEG_MODEL_ID'] + [
'SEG_MODEL_TYPE'] + \
['SEG_MODEL_NAME'] + ['SEG_MODEL_GROUP'] + ['SEG_M_GRP_DESC'] + ['SEG_MODEL_SCORE'] + [
'ARMS_MANUFACTURER'] + ['AUCTION'] + \
['CASHINTENSIVE_BUSINESS'] + ['CASINO_GAMBLING'] + ['CHANNEL_ONBOARDING'] + [
'CHANNEL_ONGOING_TRANSACTIONS'] + ['CLIENT_NET_WORTH'] + \
['COMPLEX_HI_VEHICLE'] + ['DEALER_PRECIOUS_METAL'] + ['DIGITAL_PM_OPERATOR'] + [
'EMBASSY_CONSULATE'] + ['EXCHANGE_CURRENCY'] + \
['FOREIGN_FINANCIAL_INSTITUTION'] + ['FOREIGN_GOVERNMENT'] + [
'FOREIGN_NONBANK_FINANCIAL_INSTITUTION'] + ['INTERNET_GAMBLING'] + \
['MEDICAL_MARIJUANA_DISPENSARY'] + ['MONEY_SERVICE_BUSINESS'] + ['NAICS_CODE'] + [
'NONREGULATED_FINANCIAL_INSTITUTION'] + \
['NOT_PROFIT'] + ['PRIVATELY_ATM_OPERATOR'] + ['PRODUCTS'] + ['SALES_USED_VEHICLES'] + [
'SERVICES'] + \
['SIC_CODE'] + ['STOCK_MARKET_LISTING'] + ['THIRD_PARTY_PAYMENT_PROCESSOR'] + [
'TRANSACTING_PROVIDER'] + ['HIGH_NET_WORTH'] + ['HIGH_RISK'] + ['RISK_RATING'] + [
'USE_CASE_SCENARIO'])
# Loop for number of accounts to generate
start = 10
acct_list = []
li_ssn_master = list(
set([
''.join(str(random.randint(0, 9)) for _ in xrange(9))
for i in xrange(30)
]))
if len(li_ssn_master) < 30:
li_ssn_master = list(
set([
''.join(str(random.randint(0, 9)) for _ in xrange(9))
for i in xrange(30)
]))
for i in xrange(30):
# Initiate High Risk Flags
politically_exposed_person = 'No'
suspicious_activity_report = 'No'
closed_cust_acct = 'No'
# High risk customer flag
high_risk = 'No'
# High Risk Rating
hr_rating = ''
# Customer that was demarketed by the bank
demarket = 'No'
dem_date = ''
# generate closed acct flag
if max((randrange(0, 98, 1) - 96), 0) == 1:
closed_cust_acct = 'Yes'
# Random number generator for account number
# acct = randrange(100000,100000000,1)
# Random choice for number of credit cards per account number
no_ccs = weighted_options('number_cc')
# while acct_list.count(acct) > 0:
# acct = randrange(100000,100000000,1)
# dt = str(datetime.now())
# acct=str(i)++re.sub('\W','',dt)
acct = start + 1 + randrange(1, 10, 1)
start = acct
name = fake.name()
tmp = gen_data.create_name()
# Adds account number to account dictionary
acct_list.extend([acct])
# Creates a new row and adds data elements
## JS - Main Account Holder SSN as current index in master SSN list
## row = [i]+[acct]+[random.choice(acct_type)]+[No_CCs]+[name]+[tmp[0]]+[(str(randrange(101,1000,1))+str(randrange(10,100,1))+str(randrange(1000,10000,1)))]
row = [i] + [acct] + [weighted_options('acct_type')] + [no_ccs] + [
name
] + [tmp[0]] + [li_ssn_master[i]]
# Dictionary for names list set to blank
names = []
# Dictionary for Social Security Number list set to blank
ssn = []
# Generates Name and SSN for Credit Users
# Middle Name to reduce name dups
mdl = []
for j in range(no_ccs - 1):
names.insert(j, fake.name())
tmp2 = gen_data.create_name()
mdl.insert(j, tmp2[0])
## JS - Pull from SSN Master list
# ssn.insert(j,(str(randrange(101,1000,1))+str(randrange(10,100,1))+str(randrange(1000,10000,1))))
randInt = randrange(1, len(li_ssn_master), 1)
if randInt != i:
ssn.insert(j, li_ssn_master[randInt])
else:
ssn.insert(j, li_ssn_master[randInt - 1])
# Name and SSN is set to blank if less than 4 customers on an account
for k in range(4 - no_ccs):
names.insert(no_ccs + k, '')
ssn.insert(no_ccs + k, '')
mdl.insert(no_ccs, '')
# Sets CC_NO to a random credit card number
CC_NO = gen_data.create_cc_number()
# Extract CC_Number from the tuple returned by CC_Number...Tuple contains CC Number and Type
# while credit_cards.count(CC_NO[1][0]) > 0:
CC_TRANS = CC_NO[1][0]
dt = str(datetime.now())
clean = re.sub('\W', '', dt)
printCC = str(CC_TRANS[-4:]) + str(clean[-12:-3]) + str(
randrange(1111, 9999, randrange(1, 10, 1)))
# str(CC_TRANS[-4:])+str(clean[-12:-2])+str(randrange(1111,9999,randrange(1,10,1)))
# Add CC_Number to control list to prevent duplicates
# Add data elements to current csv row
row.extend([
names[0], mdl[0], ssn[0], names[1], mdl[1], ssn[1], names[2],
mdl[2], ssn[2], printCC, CC_NO[0],
gen_data.create_company_name() + ' ' + tmp[1],
gen_data.create_email(),
gen_data.create_job_title()
])
# Creates Current Address
zip = random.choice(zips.zip)
addr = geo_data.create_city_state_zip[zip]
# Creates Previous address
zip2 = random.choice(zips.zip)
addr2 = geo_data.create_city_state_zip[zip2]
# Add additional data elements to current csv row
lrg_cash_ex = weighted_options('yes_no')
# Condition for SARs and Demarketed Clients
if closed_cust_acct == 'Yes':
# 1% of closed accounts are demarketed but never had a suspicious_activity_report filed
if risk_range() and suspicious_activity_report == 'No':
demarket = 'Yes'
dem_date = gen_data.create_date(past=True)
if risk_range() and demarket == 'No':
# 10% of closed accounts have SARs
suspicious_activity_report = 'Yes'
# 90% of closed accounts with SARs are demarketed
if max((randrange(0, 11, 1) - 9), 0) == 0:
demarket = 'Yes'
dem_date = gen_data.create_date(past=True)
if risk_range():
politically_exposed_person = 'Yes'
row.extend([
addr[0], addr[1], zip, 'US', addr2[0], addr2[1], zip2, 'US',
gen_data.create_birthday(min_age=2, max_age=85),
politically_exposed_person, suspicious_activity_report,
closed_cust_acct
])
# Start Generating related accounts from account list once 10,000 accounts are generated
if i > 10000:
rel = int(random.choice(acct_list)) * max(
(randrange(0, 10001, 1) - 9999), 0)
if rel <> 0:
row.append(rel)
row.append(weighted_options('related_type'))
else:
row.append('')
row.append('')
else:
row.append('')
row.append('')
# Randomly generates account start date
party_start = gen_data.create_date(past=True)
# Randomly selects consent option for sharing info
consent_share = weighted_options('yes_no')
# Add additional data elements to current csv row
row.extend([
weighted_options('party_type'),
weighted_options('party_relation'), party_start,
gen_data.create_date(past=True), lrg_cash_ex, demarket,
dem_date,
randrange(0, 100, 1),
weighted_options('official_lang')
])
# Add data element preferred methond of contact for yes to share info...if not then blank to current row
if consent_share == 'Yes':
row.extend(['Yes', weighted_options('preferred_channel')])
else:
row.extend(['No', ''])
# DO NOT USE CUST STATUS BELOW - NOT INTEGRATED WITH CLOSED STATUS! Add additional data elements to current csv row
row.extend([zip, randrange(0, 5, 1)])
# Generates Segment ID then adds additional Segment data based on the selection to the current csv row
Segment_ID = randrange(0, 5, 1) % 5
if Segment_ID == 0:
row.extend([
MODEL_ID[0], SEG_MODEL_TYPE[0], SEG_MODEL_NAME[0],
SEG_MODEL_GROUP[0], SEG_MODEL_DESCRIPTION[0],
SEG_MODEL_SCORE[0]
])
if Segment_ID == 1:
row.extend([
MODEL_ID[1], SEG_MODEL_TYPE[1], SEG_MODEL_NAME[1],
SEG_MODEL_GROUP[1], SEG_MODEL_DESCRIPTION[1],
SEG_MODEL_SCORE[1]
])
if Segment_ID == 2:
row.extend([
MODEL_ID[2], SEG_MODEL_TYPE[2], SEG_MODEL_NAME[2],
SEG_MODEL_GROUP[2], SEG_MODEL_DESCRIPTION[2],
SEG_MODEL_SCORE[2]
])
if Segment_ID == 3:
row.extend([
MODEL_ID[3], SEG_MODEL_TYPE[3], SEG_MODEL_NAME[3],
SEG_MODEL_GROUP[3], SEG_MODEL_DESCRIPTION[3],
SEG_MODEL_SCORE[3]
])
if Segment_ID == 4:
row.extend([
MODEL_ID[4], SEG_MODEL_TYPE[4], SEG_MODEL_NAME[4],
SEG_MODEL_GROUP[4], SEG_MODEL_DESCRIPTION[4],
SEG_MODEL_SCORE[4]
])
# Add additional data elements to current csv row
arms_manufacturer = weighted_options('arms_manufacturers')
auction = weighted_options('auction')
cash_intensive_business = weighted_options(
'cash_intensive_business')
casino_gambling = weighted_options('casino_gambling')
chan_ob = weighted_options('channel_onboarding')
chan_txn = weighted_options('channel_ongoing_txn')
row.extend([
arms_manufacturer, auction, cash_intensive_business,
casino_gambling, chan_ob, chan_txn
])
# Randomly select whether customer has a High Net Worth
high_net_worth_flag = weighted_options('high_net_worth')
# Randomly Generates customer net worth based on the above flag
if high_net_worth_flag == 'Yes':
row.append(
max(
max((randrange(0, 101, 1) - 99), 0) *
randrange(1000000, 25000000, 1),
randrange(1000000, 5000000, 1)))
else:
flag = weighted_options('low_net')
if flag == 0:
row.append(randrange(-250000, 600000, 1))
else:
if flag == 1:
row.append(randrange(149000, 151000, 1))
else:
row.append(randrange(40000, 50000, 1))
# Add data elements to current csv row
hr1 = weighted_options('complex_hi_vehicle')
hr2 = weighted_options('dealer_precious_metal')
hr3 = weighted_options('digital_pm_operator')
hr4 = weighted_options(EMBASSY_CONSULATE)
hr5 = weighted_options(EXCHANGE_CURRENCY)
hr6 = weighted_options(FOREIGN_FINANCIAL_INSTITUTION)
hr7 = weighted_options(FOREIGN_GOVT)
hr8 = weighted_options(FOREIGN_NONBANK_FINANCIAL_INSTITUTION)
hr9 = weighted_options(INTERNET_GAMBLING)
hr10 = weighted_options(MEDICAL_MARIJUANA_DISPENSARY)
hr11 = weighted_options(MONEY_SERVICE_BUSINESS)
hr12 = random.choice(NAICS.NAICS_Code)
hr13 = weighted_options(NONREGULATED_FINANCIAL_INSTITUTION)
hr14 = weighted_options(NOT_PROFIT)
# hr15=random.choice(occupation)
hr16 = weighted_options(PRIVATE_ATM_OPERATOR)
hr17 = weighted_options('products')
hr18 = weighted_options(SALES_USED_VEHICLES)
hr19 = weighted_options('services')
hr20 = weighted_options('sic_code')
hr21 = weighted_options('stock_market_listing')
hr22 = weighted_options(THIRD_PARTY_PAYMENT_PROCESSOR)
hr23 = weighted_options(TRANSACTING_PROVIDER)
if 'Yes' in (politically_exposed_person,
suspicious_activity_report, lrg_cash_ex, demarket,
arms_manufacturer, auction, cash_intensive_business,
casino_gambling, hr1, hr2, hr3, hr4, hr5, hr6, hr7,
hr8, hr9, hr10, hr11, hr13, hr14, hr16, hr17, hr18,
hr22, hr23, high_net_worth_flag):
high_risk = 'Yes'
hr_rating = weighted_options('refrating')
if suspicious_activity_report == 'No' and high_risk == 'No':
if risk_range():
high_risk = 'Yes'
hr_rating = weighted_options('refrating')
if politically_exposed_person == 'No' and high_risk == 'No':
if risk_range():
high_risk = 'Yes'
hr_rating = weighted_options('refrating')
if high_risk == 'No':
if risk_range():
high_risk = 'Yes'
hr_rating = weighted_options('refrating')
row.extend([
hr1, hr2, hr3, hr4, hr5, hr6, hr7, hr8, hr9, hr10, hr11, hr12,
hr13, hr14, hr16, hr17, hr18, hr19, hr20, hr21, hr22, hr23,
high_net_worth_flag, high_risk, hr_rating,
random.choice(USE_CASE)
])
# End the current row
writer.writerow(row)
def createCusts(N):
#List for client whose net worth is over $500K
HighNetWorth = ['Yes'] + ['No'] * 30
#List for type of account
Related_Type = ['Primary', 'Secondary', 'Joint']
#List for how the account was opened
Party_Type = ['Person', 'Non-Person']
#List for a BMO customer
Party_Relation = ['Customer', 'Non-Customer']
#List for random Yes/No Flag
Yes_No = ['Yes'] + ['No'] * 12
#List for random Yes/No Consent
Yes_No_Consent = ['Yes'] + ['No'] * 4
#List for equal Yes/No Flag
Yes_No_50 = ['Yes', 'No']
#List for official language
Official_Lang = ['English'] * 3 + ['French']
#List for method of communication
Preffered_Channel = ['Direct Mail', 'Telemarketing', 'Email', 'SMS']
#List for status of customer
#Customer_Status = ['Prospect','Inactive Customer','Past Customer'] + ['Active Customer'] * 56
#List for LOB Segment Type
Seg_Model_Type = [
'LOB Specific', 'Profitability', 'Geographical', 'Behavioral',
'Risk Tolerance'
]
#List for Model ID
Model_ID = ['01', '02', '03', '04', '05']
#List for Model Name
Seg_Model_Name = [
'IRRI', 'CRS Risk Score', 'Geo Risk', 'Financial Behavior Risk',
'CM Risk'
]
#List for Model Score
Seg_Model_Score = ['200', '300', '400', '100', '500']
#List for Model Group
Seg_Model_Group = ['Group 1'] * 2 + ['Group 2', 'Group 3', 'Group 4']
#List for Model Description
Seg_Model_Description = [
'High Risk Tier', 'Mid Risk Tier', 'Low Risk Tier', 'Vertical Risk',
'Geographical Risk'
]
#List for random Arms Dealer flag
Arms_Manufacturer = ['Yes'] + ['No'] * 2 + [''] * 392
#List for random auction flag
Auction = ['Yes'] + ['No'] * 2 + [''] * 392
#List for random Cash Intensive flag
CashIntensive_Business = ['Yes'] + ['No'] * 2 + [''] * 392
#List for random Casino?Gaming flag
Casino_Gambling = ['Yes'] + ['No'] * 2 + [''] * 392
#List for random Client Onboarding flag
Channel_Onboarding = [
'E-mail', 'In Person', 'In person - In Branch/Bank Office',
'In person - Offsite/Client Location', 'Mail', 'Online', 'Phone',
'Request for Proposal (RFP)'
] + ['Not Applicable'] * 10
#List for random Transaction flag
Channel_Ongoing_Transactions = [
'ATM', 'E-mail', 'Fax', 'Mail', 'Not Applicable',
'OTC Communication System', 'Phone'
] + ['Online'] * 4 + ['In Person'] * 31
#List for random HI_Vehicle flag
Complex_HI_Vehicle = ['Yes'] + ['No'] * 2 + [''] * 392
#List for random Metals flag
Dealer_Precious_Metal = ['Yes'] + ['No'] * 2 + [''] * 392
#List for random Arms Dealer flag
Digital_PM_Operator = ['Yes'] + ['No'] * 2 + [''] * 392
#List for random Embassy flag
Embassy_Consulate = ['Yes'] + ['No'] * 2 + [''] * 392
#Sets variable to Embassy flag
Exchange_Currency = Embassy_Consulate
#Sets variable to Embassy flag
Foreign_Financial_Institution = Embassy_Consulate
#Sets variable to Embassy flag
Foreign_Government = Embassy_Consulate
#Sets variable to Embassy flag
Foreign_NonBank_Financial_Institution = Embassy_Consulate
#Sets variable to Embassy flag
Internet_Gambling = Embassy_Consulate
#Sets variable to Embassy flag
Medical_Marijuana_Dispensary = Embassy_Consulate
#Sets variable to Embassy flag
Money_Service_Business = Embassy_Consulate
#Sets variable to Embassy flag
NonRegulated_Financial_Institution = Embassy_Consulate
#Sets variable to Embassy flag
Not_Profit = Embassy_Consulate
#List for random occupation
Occupation=['11-1011 Chief Executives',\
'11-3011 Administrative Services Managers',\
'11-3031 Financial Managers',\
'11-3061 Purchasing Managers',\
'13-1011 Agents and Business Managers of Artists Performers and Athletes',\
'13-1031 Claims Adjusters Examiners, and Investigators',\
'13-1199 Business Operations Specialists, All Other',\
'13-2099 Financial Specialists All Other',\
'17-1011 Architects Except Landscape and Naval',\
'23-1011 Lawyers',\
'23-1023 Judges, Magistrate Judges and Magistrates',\
'25-2012 Kindergarten Teachers Except Special Education',\
'25-2021 Elementary School Teachers Except Special Education',\
'29-1041 Optometrists',\
'29-2054 Respiratory Therapy Technicians',\
'33-2011 Firefighters',\
'37-1012 First-Line Supervisors of Landscaping Lawn Service and Groundskeeping Workers',\
'39-1011 Gaming Supervisors',\
'39-2011 Animal Trainers',\
'41-1011 First-Line Supervisors of Retail Sales Workers',\
'41-1012 First-Line Supervisors of Non-Retail Sales Workers',\
'41-2011 Cashiers',\
'41-2031 Retail Salespersons',\
'43-3021 Billing and Posting Clerks',\
'45-1011 First-Line Supervisors of Farming, Fishing, and Forestry Workers',\
'49-2011 Computer Automated Teller and Office Machine Repairers',\
'53-3021 Bus Drivers Transit and Intercity',\
'53-4031 Railroad Conductors and Yardmasters',\
'55-1011 Air Crew Officers',\
'55-1012 Aircraft Launch and Recovery Officers',\
'55-1013 Armored Assault Vehicle Officers',\
]
#Sets variable to Embassy flag
Privately_ATM_Operator = Embassy_Consulate
#List for random products
Products=['Certificate of Deposit',\
'Checking Account',\
'Credit Card',\
'Custodial and Investment Agency - Institutional',\
'Custodial and Investment Agency - Personal',\
'Custodial/Trust Outsourcing Services (BTOS)',\
'Custody Accounts (PTIM)',\
'Custody Accounts (RSTC)',\
'DTF (BHFA)',\
'Investment Agency - Personal',\
'Investment Management Account (PTIM)',\
'Lease',\
'Loan / Letter of Credit',\
'Money Market',\
'Mortgage / Bond / Debentures',\
'None',\
'Savings Account',\
'Trust Administration - Irrevocable and Revocable (PTIM)',\
'Trust Administration - Irrevocable and Revocable Trusts (BDTC)',\
] + ['Nondeposit Investment Products'] * 14 + ['Investment Agency - Institutional'] * 5
#Sets variable to Embassy flag
Sales_Used_Vehicles = Embassy_Consulate
#Dictionary for random Services
Services=['Benefit Payment Services',\
'Domestic Wires and Direct Deposit / ACH',\
'Family Office Services (FOS)',\
'Fiduciary Services',\
'International Wires and IAT',\
'Investment Advisory Services (IAS)',\
'Investment Services',\
'None',\
'Online / Mobile Banking',\
'Payroll',\
'Short Term Cash Management',\
'Trust Services',\
'Trustee Services',\
'Vault Cash Services',\
] + ['Financial Planning'] * 6 + ['Retirement Plans'] * 19
#Dictionary for random SIC_Code
SIC_Code=['6021 National Commercial Banks',\
'6211 Security Brokers Dealers and Flotation Companies',\
'6282 Investment Advice',\
'6311 Life Insurance',\
'6733 Trusts Except Educational Religious and Charitable',\
'8999 Services NEC',\
] + ['6722 Management Investment Offices Open-End'] * 12
#Dictionary for random Market Listing
Stock_Market_Listing=['Australian Stock Exchange',\
'Brussels Stock Exchange',\
'Montreal Stock Exchange',\
'Tiers 1 and 2 of the TSX Venture Exchange (also known as Tiers 1 and 2 of the Canadian Venture Exchange)',\
'Toronto Stock Exchange',\
] + ['Not Found'] * 30
#Sets variable to Embassy flag
Third_Party_Payment_Processor = Embassy_Consulate
#Sets variable to Embassy flag
Transacting_Provider = Embassy_Consulate
#Dictionary for random Low Net Worth
LowNet = [1, 2] + [0] * 5
#Dictionary for Consumer vs Business
Acct_Type = ['B'] + ['C'] * 5
#Dictionary for random number of credits cards per account
Number_CC = [1] * 7 + [2] * 11 + [3] * 3 + [4]
#Dictionary for Account list set to blank
acct_list = []
#Dictionary for CreditCard list set to blank
CC_list = []
#Dictionary for random Wolfsberg scenario
Use_Case = [1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 39] * 4 + [
2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 32, 35, 38
] * 7 + [3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36
] * 65 + [37] * 73 + [40, 41] * 2
refrating = [
'1', '1', '1', '2', '3', '4', '2', '4', '5', '5', '5', '5', '5', '5',
'5', '5', '5', '5', '5', '5'
]
fake = Faker()
global liSSNMaster
start = 10786147
acct_list = []
liCSV = []
for i in xrange(N):
#Initiate High Risk Flags
#Politically Exposed Person
PEP = 'No'
#Customer with a Suspicous Activity Report
SAR = 'No'
#Customer with a closed account
Clsd = 'No'
#High risk customer flag
high_risk = 'No'
#High Risk Rating
hr_rating = ''
#Customer that was demarketed by the bank
demarket = 'No'
dem_date = ''
#generate closed acct flag
if (max((randrange(0, 98, 1) - 96), 0) == 1):
Clsd = 'Yes'
#Random choice for number of credit card users per account number
No_CCs = random.choice(Number_CC)
#Generate account number
acct = start + 1 + randrange(1, 10, 1)
start = acct
#Randomly generate customer name + middle name in tmp
name = fake.name()
tmp = gen_data.create_name()
#Adds account number to account dictionary
acct_list.extend([acct])
#Creates a new row and adds data elements
row = [i] + [acct] + [random.choice(Acct_Type)] + [No_CCs] + [name] + [
tmp[0]
] + [liSSNMaster[i]]
#Dictionary for names list set to blank
names = []
#Dictionary for Social Security Number list set to blank
ssn = []
#Middle Name to reduce name dups
mdl = []
for j in range(No_CCs - 1):
names.insert(j, fake.name())
tmp2 = gen_data.create_name()
mdl.insert(j, tmp2[0])
##Pull from SSN Master list
randInt = randrange(1, len(liSSNMaster), 1)
if randInt != i:
ssn.insert(j, liSSNMaster[randInt])
else:
ssn.insert(j, liSSNMaster[randInt - 1])
#Name and SSN is set to blank if less than 4 customers on an account
for k in range(4 - No_CCs):
names.insert(No_CCs + k, '')
ssn.insert(No_CCs + k, '')
mdl.insert(No_CCs, '')
#Sets CC_NO to a random credit card number
CC_NO = gen_data.cc_number()
#Extract CC_Number from the tuple returned by CC_Number then scramble to ensure uniqueness...Tuple contains CC Number and Type
CC_TRANS = CC_NO[1][0]
dt = str(datetime.now())
clean = re.sub('\W', '', dt)
printCC = str(CC_TRANS[-4:]) + str(clean[-12:-3]) + str(
randrange(1111, 9999, randrange(1, 10, 1)))
#Add data elements to current csv row
row.extend([names[0],mdl[0],ssn[0],names[1],mdl[1],ssn[1],names[2],mdl[2],ssn[2],printCC,CC_NO[0],gen_data.create_company_name()+' '+tmp[1],\
gen_data.create_email(),gen_data.create_job_title()])
#Create Current Address
zip = random.choice(zips.zip)
addr = geo_data.create_city_state_zip[zip]
#Create Previous address
zip2 = random.choice(zips.zip)
addr2 = geo_data.create_city_state_zip[zip2]
#Add additional data elements to current csv row
lrg_cash_ex = random.choice(Yes_No)
#Condition for SARs and Demarketed Clients
if (Clsd == 'Yes'):
#1% of closed accounts are demarketed but never had a SAR filed
if (max((randrange(0, 101, 1) - 99), 0) == 1 and SAR == 'No'):
demarket = 'Yes'
dem_date = gen_data.create_date(past=True)
if (max((randrange(0, 11, 1) - 9), 0) == 1 and demarket == 'No'):
#10% of closed accounts have SARs
SAR = 'Yes'
#90% of closed accounts with SARs are demarketed
if (max((randrange(0, 11, 1) - 9), 0) == 0):
demarket = 'Yes'
dem_date = gen_data.create_date(past=True)
if (max((randrange(0, 101, 1) - 99), 0) == 1):
PEP = 'Yes'
row.extend([
addr[0], addr[1], zip, 'US', addr2[0], addr2[1], zip2, 'US',
gen_data.create_birthday(min_age=2, max_age=85), PEP, SAR, Clsd
])
#Start Generating related accounts from account list once 10,000 accounts are generated - to avoid duplicating accounts in the beginning
if i > 10000:
rel = int(random.choice(acct_list)) * max(
(randrange(0, 10001, 1) - 9999), 0)
if rel <> 0:
row.append(rel)
row.append(random.choice(Related_Type))
else:
row.append('')
row.append('')
else:
row.append('')
row.append('')
#Randomly generates account start date
party_start = gen_data.create_date(past=True)
#Randomly selects consent option for sharing info
Consent_Share = random.choice(Yes_No_Consent)
#Add additional data elements to current csv row
row.extend([random.choice(Party_Type),random.choice(Party_Relation),party_start,gen_data.create_date(past=True),\
lrg_cash_ex,demarket,dem_date,randrange(0,100,1),random.choice(Official_Lang)])
#Add data element preferred methond of contact for yes to share info...if not then blank to current row
if Consent_Share == 'Yes':
row.extend(['Yes', random.choice(Preffered_Channel)])
else:
row.extend(['No', ''])
row.extend([zip, randrange(0, 5, 1)])
#Generate Segment ID then add additional Segment data based on the selection to the current csv row
Segment_ID = randrange(0, 5, 1) % 5
if Segment_ID == 0:
row.extend([
Model_ID[0], Seg_Model_Type[0], Seg_Model_Name[0],
Seg_Model_Group[0], Seg_Model_Description[0],
Seg_Model_Score[0]
])
if Segment_ID == 1:
row.extend([
Model_ID[1], Seg_Model_Type[1], Seg_Model_Name[1],
Seg_Model_Group[1], Seg_Model_Description[1],
Seg_Model_Score[1]
])
if Segment_ID == 2:
row.extend([
Model_ID[2], Seg_Model_Type[2], Seg_Model_Name[2],
Seg_Model_Group[2], Seg_Model_Description[2],
Seg_Model_Score[2]
])
if Segment_ID == 3:
row.extend([
Model_ID[3], Seg_Model_Type[3], Seg_Model_Name[3],
Seg_Model_Group[3], Seg_Model_Description[3],
Seg_Model_Score[3]
])
if Segment_ID == 4:
row.extend([
Model_ID[4], Seg_Model_Type[4], Seg_Model_Name[4],
Seg_Model_Group[4], Seg_Model_Description[4],
Seg_Model_Score[4]
])
#Add additional data elements to current csv row
hr0 = random.choice(Arms_Manufacturer)
hr01 = random.choice(Auction)
hr02 = random.choice(CashIntensive_Business)
hr03 = random.choice(Casino_Gambling)
hr04 = random.choice(Channel_Onboarding)
hr05 = random.choice(Channel_Ongoing_Transactions)
row.extend([hr0, hr01, hr02, hr03, hr04, hr05])
#Randomly select whether customer has a High Net Worth
HighNetWorthFlag = random.choice(HighNetWorth)
#Randomly Generate customer net worth based on the above flag
if HighNetWorthFlag == 'Yes':
row.append(
max(
max((randrange(0, 101, 1) - 99), 0) *
randrange(1000000, 25000000, 1),
randrange(1000000, 5000000, 1)))
else:
flag = random.choice(LowNet)
if flag == 0:
row.append(randrange(-250000, 600000, 1))
else:
if flag == 1:
row.append(randrange(149000, 151000, 1))
else:
row.append(randrange(40000, 50000, 1))
#Add data elements to current csv row
hr1 = random.choice(Complex_HI_Vehicle)
hr2 = random.choice(Dealer_Precious_Metal)
hr3 = random.choice(Digital_PM_Operator)
hr4 = random.choice(Embassy_Consulate)
hr5 = random.choice(Exchange_Currency)
hr6 = random.choice(Foreign_Financial_Institution)
hr7 = random.choice(Foreign_Government)
hr8 = random.choice(Foreign_NonBank_Financial_Institution)
hr9 = random.choice(Internet_Gambling)
hr10 = random.choice(Medical_Marijuana_Dispensary)
hr11 = random.choice(Money_Service_Business)
hr12 = random.choice(NAICS.NAICS_Code)
hr13 = random.choice(NonRegulated_Financial_Institution)
hr14 = random.choice(Not_Profit)
#hr15=random.choice(Occupation) - added before through gen_data
hr16 = random.choice(Privately_ATM_Operator)
hr17 = random.choice(Products)
hr18 = random.choice(Sales_Used_Vehicles)
hr19 = random.choice(Services)
hr20 = random.choice(SIC_Code)
hr21 = random.choice(Stock_Market_Listing)
hr22 = random.choice(Third_Party_Payment_Processor)
hr23 = random.choice(Transacting_Provider)
if (PEP == 'Yes' or SAR == 'Yes' or lrg_cash_ex == 'Yes'
or demarket == 'Yes' or hr0 == 'Yes' or hr01 == 'Yes'
or hr02 == 'Yes' or hr03 == 'Yes' or hr1 == 'Yes'
or hr2 == 'Yes' or hr3 == 'Yes' or hr4 == 'Yes' or hr5 == 'Yes'
or hr6 == 'Yes' or hr7 == 'Yes' or hr8 == 'Yes' or hr9 == 'Yes'
or hr10 == 'Yes' or hr11 == 'Yes' or hr13 == 'Yes'
or hr14 == 'Yes' or hr16 == 'Yes' or hr17 == 'Yes'
or hr18 == 'Yes' or hr22 == 'Yes' or hr23 == 'Yes'
or HighNetWorthFlag == 'Yes'):
high_risk = 'Yes'
hr_rating = random.choice(refrating)
if (SAR == 'No' and high_risk == 'No'):
if (max((randrange(0, 101, 1) - 99), 0) == 1):
high_risk = 'Yes'
hr_rating = random.choice(refrating)
if (PEP == 'No' and high_risk == 'No'):
if (max((randrange(0, 101, 1) - 99), 0) == 1):
high_risk = 'Yes'
hr_rating = random.choice(refrating)
if (high_risk == 'No'):
if (max((randrange(0, 101, 1) - 99), 0) == 1):
high_risk = 'Yes'
hr_rating = random.choice(refrating)
row.extend([
hr1, hr2, hr3, hr4, hr5, hr6, hr7, hr8, hr9, hr10, hr11, hr12,
hr13, hr14, hr16, hr17, hr18, hr19, hr20, hr21, hr22, hr23,
HighNetWorthFlag, high_risk, hr_rating,
random.choice(Use_Case)
])
liCSV.append(row)
return liCSV
from random import random
from random import shuffle
from faker import Faker
from barnum import gen_data
import csv
fake = Faker()
with open('large.csv','w') as f1:
writer=csv.writer(f1, delimiter=',',lineterminator='\n',)
writer.writerow(['rownum'] +['dunno'] + ['CC'] + ['Employer'] + ['Custemail'] + ['name'] \
+ ['occupation'] + ['address_street'] + ['DOB']+['previous address_city_state_zip']+ ['altcustomer_name'] \
+ ['altcustomer_occupation'] + ['altcustomer_dob'] + ['ssn'] + ['phone'] + \
['AccountID'] + ['PepFlag'] + ['altcustomerssn'] + ['demarketed_customer_flag'] + \
['SAR_flag'] + ['nolonger_a_customer'] + ['closed_account'] +['High_risk_flag'] +['Risk_rating'])
for i in range(50000000):
row = [i] + [10] + [gen_data.cc_number()]+[gen_data.create_company_name()] + \
[gen_data.create_email()]+[gen_data.create_name()] +[gen_data.create_job_title()] + \
[gen_data.create_city_state_zip()] + [gen_data.create_birthday(min_age=2, max_age=85)] + \
[gen_data.create_city_state_zip()] + [fake.name()] + [gen_data.create_job_title()] + \
[gen_data.create_birthday(min_age=2, max_age=85)] +\
[(randrange(101,1000,1),randrange(10,100,1),randrange(1000,10000,1))] + \
[(randrange(101,1000,1),randrange(101,999,1),randrange(1000,10000,1))] + \
[randrange(100000,100000000,1)] + \
[max((randrange(0,101,1)-99),0)] + \
[(randrange(101,1000,1),randrange(10,100,1),randrange(1000,10000,1))] + \
[max((randrange(0,101,1)-99),0)] + [max((randrange(0,101,1)-99),0)] + \
[max((randrange(0,101,1)-99),0)] + [max((randrange(0,101,1)-90),0)] + \
[max((randrange(0,101,1)-99),0)] + [max((randrange(0,101,1)-99),0)]
writer.writerow(row)
def gen_cust(liSSNMaster, acct_list, i):
fake = Faker()
#Initiate High Risk Flags
#Politically Exposed Person
PEP = 'No'
#Customer with a Suspicous Activity Report
SAR = 'No'
#Customer with a closed account
#generate closed acct flag
Clsd = choice(Clsd_flag)
#High risk customer flag
high_risk = 'No'
#High Risk Rating
hr_rating = ''
#Customer that was demarketed by the bank
demarket = 'No'
dem_date = ''
#Random choice for number of credit cards per account number
No_CCs = choice(Number_CC)
acct = start + 1 + randrange(1, 10, 1)
start = acct
#Randomly generates customer name
name = fake.name()
tmp = gen_data.create_name()
#Adds account number to account dictionary
acct_list.extend([acct])
#Creates a new row and adds data elements
## JS - Main Account Holder SSN as current index in master SSN list
row = [i] + [acct] + [choice(Acct_Type)
] + [No_CCs] + [name] + [tmp[0]] + [liSSNMaster[i]]
#Dictionary for names list set to blank
names = []
#Dictionary for Social Security Number list set to blank
ssn = []
#Generates Name and SSN for Credit Users
#Middle Name to reduce name dups
mdl = []
for j in range(No_CCs - 1):
names.insert(j, fake.name())
tmp2 = gen_data.create_name()
mdl.insert(j, tmp2[0])
## JS - Pull from SSN Master list
randInt = randrange(1, len(liSSNMaster), 1)
if randInt != i:
ssn.insert(j, liSSNMaster[randInt])
else:
ssn.insert(j, liSSNMaster[randInt - 1])
#Name and SSN is set to blank if less than 4 customers on an account
for k in range(4 - No_CCs):
names.insert(No_CCs + k, '')
ssn.insert(No_CCs + k, '')
mdl.insert(No_CCs, '')
#Sets CC_NO to a random credit card number
CC_NO = gen_data.create_cc_number()
CC_TRANS = CC_NO[1][0]
dt = str(datetime.now())
clean = re.sub('\W', '', dt)
printCC = str(CC_TRANS[-4:]) + str(clean[-12:-3]) + str(
randrange(1111, 9999, randrange(1, 10, 1)))
#Add data elements to current csv row
row.extend([names[0],mdl[0],ssn[0],names[1],mdl[1],ssn[1],names[2],mdl[2],ssn[2],printCC,CC_NO[0],gen_data.create_company_name()+' '+tmp[1],\
gen_data.create_email(),gen_data.create_job_title()])
#Creates Current Address
zip = choice(zips.zip)
addr = geo_data.create_city_state_zip[zip]
#Creates Previous address
zip2 = choice(zips.zip)
addr2 = geo_data.create_city_state_zip[zip2]
#Add additional data elements to current csv row
lrg_cash_ex = choice(Yes_No)
#Condition for SARs and Demarketed Clients
if (Clsd == 'Yes'):
#1% of closed accounts are demarketed but never had a SAR filed
if (max((randrange(0, 101, 1) - 99), 0) == 1 and SAR == 'No'):
demarket = 'Yes'
dem_date = gen_data.create_date(past=True)
if (max((randrange(0, 11, 1) - 9), 0) == 1 and demarket == 'No'):
#10% of closed accounts have SARs
SAR = 'Yes'
#90% of closed accounts with SARs are demarketed
if (max((randrange(0, 11, 1) - 9), 0) == 0):
demarket = 'Yes'
dem_date = gen_data.create_date(past=True)
#1% of accounts are PEP
if (max((randrange(0, 101, 1) - 99), 0) == 1):
PEP = 'Yes'
row.extend([
addr[0], addr[1], zip, 'US', addr2[0], addr2[1], zip2, 'US',
gen_data.create_birthday(min_age=2, max_age=85), PEP, SAR, Clsd
])
#Start Generating related accounts from account list once 10,000 accounts are generated
if i > 10000:
rel = int(choice(acct_list)) * max((randrange(0, 10001, 1) - 9999), 0)
if rel <> 0:
row.append(rel)
row.append(choice(Related_Type))
else:
row.append('')
row.append('')
else:
row.append('')
row.append('')
#Randomly generates account start date
party_start = gen_data.create_date(past=True)
#Randomly selects consent option for sharing info
Consent_Share = choice(Yes_No_Consent)
#Add additional data elements to current csv row
row.extend([choice(Party_Type),choice(Party_Relation),party_start,gen_data.create_date(past=True),\
lrg_cash_ex,demarket,dem_date,randrange(0,100,1),choice(Official_Lang)])
#Add data element preferred methond of contact for yes to share info...if not then blank to current row
if Consent_Share == 'Yes':
row.extend(['Yes', choice(Preffered_Channel)])
else:
row.extend(['No', ''])
#DO NOT USE CUST STATUS BELOW - NOT INTEGRATED WITH CLOSED STATUS! Add additional data elements to current csv row
row.extend([zip, randrange(0, 5, 1)])
#Generates Segment ID then adds additional Segment data based on the selection to the current csv row
Segment_ID = randrange(0, 5, 1)
if Segment_ID == 0:
row.extend(
['01', 'LOB Specific', 'IRRI', 'Group 1', 'High Risk Tier', '200'])
if Segment_ID == 1:
row.extend([
'02', 'Profitability', 'CRS Risk Score', 'Group 1',
'Mid Risk Tier', '300'
])
if Segment_ID == 2:
row.extend([
'03', 'Geographical', 'Geo Risk', 'Group 2', 'Low Risk Tier', '400'
])
if Segment_ID == 3:
row.extend([
'04', 'Behavioral', 'Financial Behavior Risk', 'Group 3',
'Vertical Risk', '100'
])
if Segment_ID == 4:
row.extend([
'05', 'Risk Tolerance', 'CM Risk', 'Group 4', 'Geographical Risk',
'500'
])
#Arms Manufacturer random choice
hr0 = choice(Yes_No_Cust_Flag)
#Auction random choice
hr01 = choice(Yes_No_Cust_Flag)
#Cash Intensive Business random choice
hr02 = choice(Yes_No_Cust_Flag)
#Casino Gambling random choice
hr03 = choice(Yes_No_Cust_Flag)
#Channel Onboarding random choice
hr04 = choice(Channel_Onboarding)
#Channel Ongoing Transactions random choice
hr05 = choice(Channel_Ongoing_Transactions)
#Add additional data elements to current csv row
row.extend([hr0, hr01, hr02, hr03, hr04, hr05])
#Randomly select whther customer has a High Net Worth
HighNetWorthFlag = choice(HighNetWorth)
#Randomly Generates customer net worth based on the above flag
if HighNetWorthFlag == 'Yes':
row.append(
max(
max((randrange(0, 101, 1) - 99), 0) *
randrange(5000000, 25000000, 1),
randrange(1000000, 5000000, 1)))
else:
flag = choice(LowNet)
if flag == 0:
row.append(randrange(-250000, 600000, 1))
else:
if flag == 1:
row.append(randrange(149000, 151000, 1))
else:
row.append(randrange(40000, 50000, 1))
#Add data elements to current csv row
#Complex_HI_Vehicle random choice
hr1 = choice(Yes_No_Cust_Flag)
#Dealer_Precious_Metal random choice
hr2 = choice(Yes_No_Cust_Flag)
#Digital_PM_Operator random choice
hr3 = choice(Yes_No_Cust_Flag)
#Embassy_Consulate random choice
hr4 = choice(Yes_No_Cust_Flag)
#Exchange_Currency random choice
hr5 = choice(Yes_No_Cust_Flag)
#Foreign_Financial_Institution random choice
hr6 = choice(Yes_No_Cust_Flag)
#Foreign_Government random choice
hr7 = choice(Yes_No_Cust_Flag)
#Foreign_NonBank_Financial_Institution random choice
hr8 = choice(Yes_No_Cust_Flag)
#Internet_Gambling random choice
hr9 = choice(Yes_No_Cust_Flag)
#Medical_Marijuana_Dispensary random choice
hr10 = choice(Yes_No_Cust_Flag)
#Money_Service_Business random choice
hr11 = choice(Yes_No_Cust_Flag)
hr12 = choice(NAICS.NAICS_Code)
#NonRegulated_Financial_Institution random choice
hr13 = choice(Yes_No_Cust_Flag)
#Not_Profit random choice
hr14 = choice(Yes_No_Cust_Flag)
#Occupation random choice
#hr15=choice(Occupation)
#Privately_ATM_Operator random choice
hr16 = choice(Yes_No_Cust_Flag)
#Products random choice
hr17 = choice(Products)
#Sales_Used_Vehicles random choice
hr18 = choice(Yes_No_Cust_Flag)
#Services random choice
hr19 = choice(Services)
#SIC_Code random choice
hr20 = choice(SIC_Code)
#Stock_Market_Listing random choice
hr21 = choice(Stock_Market_Listing)
#Third_Party_Payment_Processor random choice
hr22 = choice(Yes_No_Cust_Flag)
#Transacting_Provider random choice
hr23 = choice(Yes_No_Cust_Flag)
refrating = ['1'] * 3 + ['2', '4'] * 2 + ['3'] + ['5'] * 12
if (PEP == 'Yes' or SAR == 'Yes' or lrg_cash_ex == 'Yes'
or demarket == 'Yes' or hr0 == 'Yes' or hr01 == 'Yes'
or hr02 == 'Yes' or hr03 == 'Yes' or hr1 == 'Yes' or hr2 == 'Yes'
or hr3 == 'Yes' or hr4 == 'Yes' or hr5 == 'Yes' or hr6 == 'Yes'
or hr7 == 'Yes' or hr8 == 'Yes' or hr9 == 'Yes' or hr10 == 'Yes'
or hr11 == 'Yes' or hr13 == 'Yes' or hr14 == 'Yes' or hr16 == 'Yes'
or hr17 == 'Yes' or hr18 == 'Yes' or hr22 == 'Yes' or hr23 == 'Yes'
or HighNetWorthFlag == 'Yes'):
high_risk = 'Yes'
hr_rating = choice(refrating)
if (high_risk == 'No'):
if (max((randrange(0, 101, 1) - 99), 0) == 1):
high_risk = 'Yes'
hr_rating = choice(refrating)
row.extend([
hr1, hr2, hr3, hr4, hr5, hr6, hr7, hr8, hr9, hr10, hr11, hr12, hr13,
hr14, hr16, hr17, hr18, hr19, hr20, hr21, hr22, hr23, HighNetWorthFlag,
high_risk, hr_rating,
choice(Use_Case)
])
#End the current row
return row
if (max((randrange(0,98,1)-96),0)==1):
Clsd='Yes'
#Random number generator for account number
#acct = randrange(100000,100000000,1)
#Random choice for number of credit cards per account number
No_CCs = random.choice(Number_CC)
#while acct_list.count(acct) > 0:
# acct = randrange(100000,100000000,1)
#dt = str(datetime.now())
#acct=str(i)++re.sub('\W','',dt)
acct=start+1+randrange(1,10,1)
start=acct
#Randomly generates customer name
name = fake.name()
tmp=gen_data.create_name()
#Adds account number to account dictionary
acct_list.extend([acct])
#Creates a new row and adds data elements
row = [i]+[acct]+[random.choice(Acct_Type)]+[No_CCs]+[name]+[tmp[0]]+[(str(randrange(101,1000,1))+str(randrange(10,100,1))+str(randrange(1000,10000,1)))]
#Dictionary for names list set to blank
names=[]
#Dictionary for Social Security Number list set to blank
ssn=[]
#Generates Name and SSN for Credit Users
#Middle Name to reduce name dups
mdl=[]
for j in range(No_CCs-1):
names.insert(j,fake.name())
tmp2=gen_data.create_name()
mdl.insert(j,tmp2[0])
def __init__(self, i, acct, liSSNMaster, acct_list):
self.ROWNUM = i
self.ACCOUNTID = acct
self.SSN = liSSNMaster[i]
self.ACCT_TYPE = choice(Acct_Type)
self.NUM_CCS = choice(Number_CC)
self.NAME = fake.name()
self.CUSTEMAIL = gen_data.create_email()
self.OCCUPATION = gen_data.create_job_title()
self.COUNTRY = 'US'
self.PREVIOUS_COUNTRY = 'US'
self.DOB = gen_data.create_birthday(min_age=2, max_age=85)
self.PARTY_ENDDATE = gen_data.create_date(past=True)
self.CONSENT_SHARING = choice(Yes_No_Consent)
self.LARGE_CASH_EXEMPT = choice(Yes_No)
self.PARTY_TYPE = choice(Party_Type)
self.PARTY_RELATION = choice(Party_Relation)
self.PROB_DEFAULT_RISKR = randrange(0, 100, 1)
self.OFFICIAL_LANG_PREF = choice(Official_Lang)
self.DEPENDANTS_COUNT = randrange(0, 5, 1)
self.USE_CASE_SCENARIO = choice(Use_Case)
self.CLOSEDACCOUNT = choice(Clsd_flag)
self.HIGH_NET_WORTH = choice(HighNetWorth)
self.PARTY_STARTDATE = gen_data.create_date(past=True)
self.ARMS_MANUFACTURER = choice(Yes_No_Cust_Flag)
self.AUCTION = choice(Yes_No_Cust_Flag)
self.CASHINTENSIVE_BUSINESS = choice(Yes_No_Cust_Flag)
self.CASINO_GAMBLING = choice(Yes_No_Cust_Flag)
self.CHANNEL_ONBOARDING = choice(Channel_Onboarding)
self.CHANNEL_ONGOING_TRANSACTIONS = choice(
Channel_Ongoing_Transactions)
self.COMPLEX_HI_VEHICLE = choice(Yes_No_Cust_Flag)
self.DEALER_PRECIOUS_METAL = choice(Yes_No_Cust_Flag)
self.DIGITAL_PM_OPERATOR = choice(Yes_No_Cust_Flag)
self.EMBASSY_CONSULATE = choice(Yes_No_Cust_Flag)
self.EXCHANGE_CURRENCY = choice(Yes_No_Cust_Flag)
self.FOREIGN_FINANCIAL_INSTITUTION = choice(Yes_No_Cust_Flag)
self.FOREIGN_GOVERNMENT = choice(Yes_No_Cust_Flag)
self.FOREIGN_NONBANK_FINANCIAL_INSTITUTION = choice(Yes_No_Cust_Flag)
self.INTERNET_GAMBLING = choice(Yes_No_Cust_Flag)
self.MEDICAL_MARIJUANA_DISPENSARY = choice(Yes_No_Cust_Flag)
self.MONEY_SERVICE_BUSINESS = choice(Yes_No_Cust_Flag)
self.NAICS_CODE = choice(NAICS.NAICS_Code)
self.NONREGULATED_FINANCIAL_INSTITUTION = choice(Yes_No_Cust_Flag)
self.NOT_PROFIT = choice(Yes_No_Cust_Flag)
self.PRIVATELY_ATM_OPERATOR = choice(Yes_No_Cust_Flag)
self.PRODUCTS = choice(Products)
self.SALES_USED_VEHICLES = choice(Yes_No_Cust_Flag)
self.SERVICES = choice(Services)
self.SIC_CODE = choice(SIC_Code)
self.STOCK_MARKET_LISTING = choice(Stock_Market_Listing)
self.THIRD_PARTY_PAYMENT_PROCESSOR = choice(Yes_No_Cust_Flag)
self.TRANSACTING_PROVIDER = choice(Yes_No_Cust_Flag)
self.ZIP = choice(zips.zip)
self.PREVIOUS_ZIP = choice(zips.zip)
addr = geo_data.create_city_state_zip[self.ZIP]
addr2 = geo_data.create_city_state_zip[self.PREVIOUS_ZIP]
self.CITY = addr[0]
self.STATE = addr[1]
self.PREVIOUS_CITY = addr2[0]
self.PREVIOUS_STATE = addr2[1]
self.PRIMARY_BRANCH_NO = self.ZIP
tmp = gen_data.create_name()
self.M_NAME = tmp[0]
self.EMPLOYER = gen_data.create_company_name() + ' ' + tmp[1]
No_CCs = choice(Number_CC)
#Dictionary for names list set to blank
names = []
#Dictionary for Social Security Number list set to blank
ssn = []
#Middle Name to reduce name dups
mdl = []
#Generates Name and SSN for Credit Users
for j in range(4):
if No_CCs > j:
names.insert(j, fake.name())
tmp2 = gen_data.create_name()
mdl.insert(j, tmp2[0])
randInt = randrange(1, len(liSSNMaster), 1)
if randInt != i:
ssn.insert(j, liSSNMaster[randInt])
else:
ssn.insert(j, liSSNMaster[randInt - 1])
#Name and SSN is set to blank if less than 4 customers on an account
else:
names.insert(No_CCs + j, '')
ssn.insert(No_CCs + j, '')
mdl.insert(No_CCs + j, '')
self.AUTHORIZED_NAME2 = names[0]
self.M_NAME2 = mdl[0]
self.SSN2 = ssn[0]
self.AUTHORIZED_NAME3 = names[1]
self.M_NAME3 = mdl[1]
self.SSN3 = ssn[1]
self.AUTHORIZED_NAME4 = names[2]
self.M_NAME4 = mdl[2]
self.SSN4 = ssn[2]
#Sets CC_NO to a random credit card number
CC_NO = gen_data.create_cc_number()
CC_TRANS = CC_NO[1][0]
dt = str(datetime.now())
clean = re.sub('\W', '', dt)
self.CREDITCARDNUMBER = str(CC_TRANS[-4:]) + str(clean[-12:-3]) + str(
randrange(1111, 9999, randrange(1, 10, 1)))
self.CREDITCARDTYPE = CC_NO[0]
self.RELATED_ACCT = ''
self.RELATED_TYPE = ''
if i > 10000:
rel = int(choice(acct_list)) * max(
(randrange(0, 10001, 1) - 9999), 0)
if rel <> 0:
self.RELATED_ACCT = rel
self.RELATED_TYPE = choice(Related_Type)
self.PREFERRED_CHANNEL = ''
if self.CONSENT_SHARING == 'Yes':
self.PREFERRED_CHANNEL = choice(Prefered_Channel)
## #Generates Segment ID then adds additional Segment data based on the selection to the current csv row
Segment_ID = randrange(0, 5, 1)
if Segment_ID == 0:
self.SEG_MODEL_ID = '01'
self.SEG_MODEL_TYPE = 'LOB Specific'
self.SEG_MODEL_NAME = 'IRRI'
self.SEG_MODEL_GROUP = 'Group 1'
self.SEG_M_GRP_DESC = 'High Risk Tier'
self.SEG_MODEL_SCORE = '200'
if Segment_ID == 1:
self.SEG_MODEL_ID = '02'
self.SEG_MODEL_TYPE = 'Profitability'
self.SEG_MODEL_NAME = 'CRS Risk Score'
self.SEG_MODEL_GROUP = 'Group 1'
self.SEG_M_GRP_DESC = 'Mid Risk Tier'
self.SEG_MODEL_SCORE = '300'
if Segment_ID == 2:
self.SEG_MODEL_ID = '03'
self.SEG_MODEL_TYPE = 'Geographical'
self.SEG_MODEL_NAME = 'Geo Risk'
self.SEG_MODEL_GROUP = 'Group 2'
self.SEG_M_GRP_DESC = 'Low Risk Tier'
self.SEG_MODEL_SCORE = '400'
if Segment_ID == 3:
self.SEG_MODEL_ID = '04'
self.SEG_MODEL_TYPE = 'Behavioral'
self.SEG_MODEL_NAME = 'Financial Behavior Risk'
self.SEG_MODEL_GROUP = 'Group 3'
self.SEG_M_GRP_DESC = 'Vertical Risk'
self.SEG_MODEL_SCORE = '100'
if Segment_ID == 4:
self.SEG_MODEL_ID = '05'
self.SEG_MODEL_TYPE = 'Risk Tolerance'
self.SEG_MODEL_NAME = 'CM Risk'
self.SEG_MODEL_GROUP = 'Group 4'
self.SEG_M_GRP_DESC = 'Geographical Risk'
self.SEG_MODEL_SCORE = '500'
self.CLIENT_NET_WORTH = ''
if self.HIGH_NET_WORTH == 'Yes':
self.CLIENT_NET_WORTH = max(
max((randrange(0, 101, 1) - 99), 0) *
randrange(5000000, 25000000, 1),
randrange(1000000, 5000000, 1))
else:
flag = choice(LowNet)
if flag == 0:
self.CLIENT_NET_WORTH = randrange(-250000, 600000, 1)
else:
if flag == 1:
self.CLIENT_NET_WORTH = randrange(149000, 151000, 1)
else:
self.CLIENT_NET_WORTH = randrange(40000, 50000, 1)
#Politically Exposed Person
self.PEP = 'No'
#1% of accounts are PEP
if (max((randrange(0, 101, 1) - 99), 0) == 1):
self.PEP = 'Yes'
#Customer that was demarketed by the bank
self.DEMARKET_FLAG = 'No'
self.DEMARKET_DATE = ''
#Customer with a Suspicous Activity Report
self.SAR = 'No'
#Customer with a closed account
#generate closed acct flag
#Condition for SARs and Demarketed Clients
if (self.CLOSEDACCOUNT == 'Yes'):
#1% of closed accounts are demarketed but never had a SAR filed
if (max((randrange(0, 101, 1) - 99), 0) == 1):
self.DEMARKET_FLAG = 'Yes'
self.DEMARKET_DATE = gen_data.create_date(past=True)
if (self.DEMARKET_FLAG == 'No' and max(
(randrange(0, 11, 1) - 9), 0) == 1):
#10% of closed accounts have SARs
self.SAR = 'Yes'
#90% of closed accounts with SARs are demarketed
if (max((randrange(0, 11, 1) - 9), 0) == 0):
self.DEMARKET_FLAG = 'Yes'
self.DEMARKET_DATE = gen_data.create_date(past=True)
self.HIGH_RISK = 'No'
self.RISK_RATING = ''
if (self.PEP == 'Yes' or self.SAR == 'Yes'
or self.LARGE_CASH_EXEMPT == 'Yes'
or self.DEMARKET_FLAG == 'Yes'
or self.ARMS_MANUFACTURER == 'Yes' or self.AUCTION == 'Yes'
or self.CASHINTENSIVE_BUSINESS == 'Yes'
or self.CASINO_GAMBLING == 'Yes'
or self.COMPLEX_HI_VEHICLE == 'Yes'
or self.DEALER_PRECIOUS_METAL == 'Yes'
or self.DIGITAL_PM_OPERATOR == 'Yes'
or self.EMBASSY_CONSULATE == 'Yes'
or self.EXCHANGE_CURRENCY == 'Yes'
or self.FOREIGN_FINANCIAL_INSTITUTION == 'Yes'
or self.FOREIGN_GOVERNMENT == 'Yes'
or self.FOREIGN_NONBANK_FINANCIAL_INSTITUTION == 'Yes'
or self.INTERNET_GAMBLING == 'Yes'
or self.MEDICAL_MARIJUANA_DISPENSARY == 'Yes'
or self.MONEY_SERVICE_BUSINESS == 'Yes'
or self.NONREGULATED_FINANCIAL_INSTITUTION == 'Yes'
or self.NOT_PROFIT == 'Yes'
or self.PRIVATELY_ATM_OPERATOR == 'Yes'
or self.SALES_USED_VEHICLES == 'Yes'
or self.THIRD_PARTY_PAYMENT_PROCESSOR == 'Yes'
or self.TRANSACTING_PROVIDER == 'Yes'
or self.HIGH_NET_WORTH == 'Yes'):
self.HIGH_RISK = 'Yes'
self.RISK_RATING = choice(refrating)
elif (max((randrange(0, 101, 1) - 99), 0) == 1):
self.HIGH_RISK = 'Yes'
self.RISK_RATING = choice(refrating)
import random
import article_collection_pb2
from barnum import gen_data
""" Random generated data for demo """
names = [gen_data.create_name() for _ in range(0, 15)]
emails = [gen_data.create_email() for _ in range(0, 15)]
titles = [gen_data.create_nouns() for _ in range(0, 15)]
contents = [gen_data.create_paragraphs(8) for _ in range(0, 15)]
articles = []
""" Construct articles data """
for title in titles:
content = random.choice(contents)
name = random.choice(names)
email = random.choice(emails)
articles.append({
"id": random.randint(10010, 20020),
"title": title,
"snippet": content[0:100],
"content": content,
"isFeatured": random.choice([False, True]),
"topics": random.sample([0, 1, 2, 3, 4], 3),
"author": {
"id": random.randint(10010, 20020),
"name": name[0] + " " + name[1],
"email": email
}
})
from barnum import gen_data
import csv
#gen_data = gen_data()
with open('large.csv','w') as f1:
writer=csv.writer(f1, delimiter=',',lineterminator='\n',)
writer.writerow([''] + range(10))
for i in range(50000000):
row = [i] + [10] + [gen_data.cc_number()]+[gen_data.create_company_name()] +[gen_data.create_email()]+[gen_data.create_name()] +[gen_data.create_job_title()] + [gen_data.create_city_state_zip()] + [gen_data.create_birthday(min_age=2, max_age=85)]
writer.writerow(row)
#row = [i] + [10] + [fake.name()] +[fake.address()]
def pop_transDetail(cat_desc, maxDate, j, maxBook, maxCheckin, randomrange,
randomchoice):
checkin = date(2000, 1, 1)
checkout = date(2000, 1, 1)
booking = date(2000, 1, 1)
transDetail = ''
tmp2 = gen_data.create_name()
addr = gen_data.create_city_state_zip()
#Add details or Hotel Transactions
if (cat_desc == 'Hotels/Motels/Inns/Resorts'
or cat_desc == 'Hotels, Motels, and Resorts'):
if (UseCase[j] == '28' or UseCase[j] == '29'):
if (maxCheckin == ''):
checkin = maxDate + timedelta(days=randomrange(365, 389, 1))
else:
checkin = maxCheckin + timedelta(days=randomrange(2, 5, 1))
maxCheckin = checkin
elif UseCase[j] == '30':
checkin = maxDate + timedelta(days=randomrange(30, 200, 1))
checkout = checkin + timedelta(days=randomrange(4, 11, 1))
hotel = tmp2[1] + ' Hotels; ' + '; Address: ' + addr[1] + ' ' + addr[
2] + ', ' + addr[0]
transDetail = 'Checkin: ' + str(checkin) + '; Checkout: ' + str(
checkout) + '; Hotel: ' + hotel
#Add details or Airline Transactions
elif cat_desc == 'Airlines':
if (UseCase[j] == '31' or UseCase[j] == '32'):
if (maxBook == ''):
booking = maxDate + timedelta(days=randomrange(1, 15, 1))
else:
booking = maxBook + timedelta(days=randomrange(1, 15, 1))
maxBook = booking
elif UseCase[j] == '33':
booking = maxDate + timedelta(days=randomrange(1, 15, 1))
Airport_Code = [
'0AK', '16A', '1G4', '2A3', '2A9', '3A5', '3T7', '3W2', '6R7',
'74S', 'A61', 'A85', 'ABE', 'ABI', 'ABQ', 'ABR', 'ABY', 'ACB',
'ACK', 'ACT', 'ACV', 'ACY', 'ADK', 'ADQ', 'AEX', 'AFM', 'AGC',
'AGN', 'AGS', 'AHN', 'AIA', 'AID', 'AIY', 'AIZ', 'AKN', 'AKP',
'AKW', 'ALB', 'ALM', 'ALN', 'ALO', 'ALS', 'ALW', 'AMA', 'ANB',
'ANC', 'AND', 'ANI', 'AOO', 'APF', 'APN', 'AQH', 'AQT', 'ART',
'ASE', 'ASN', 'AST', 'ATK', 'ATL', 'ATW', 'ATY', 'AUG', 'AUK',
'AUS', 'AVL', 'AVP', 'AWI', 'AXN', 'AZO', 'BAF', 'BAK', 'BCE',
'BDE', 'BDL', 'BDR', 'BED', 'BEH', 'BET', 'BFD', 'BFF', 'BFI',
'BFL', 'BGM', 'BGR', 'BHB', 'BHM', 'BID', 'BIG', 'BIL', 'BIS',
'BJI', 'BKL', 'BKW', 'BKX', 'BLI', 'BLM', 'BLV', 'BMG', 'BMI',
'BNA', 'BOI', 'BOS', 'BPK', 'BPT', 'BQK', 'BQN', 'BRD', 'BRL',
'BRO', 'BRW', 'BTI', 'BTL', 'BTM', 'BTR', 'BTV', 'BUF', 'BUR',
'BVK', 'BWG', 'BWI', 'BZN', 'CAE', 'CAK', 'CCR', 'CDB', 'CDC',
'CDV', 'CDW', 'CEC', 'CEF', 'CEZ', 'CFK', 'CGA', 'CGF', 'CGI',
'CGX', 'CHA', 'CHO', 'CHS', 'CIC', 'CID', 'CIU', 'CKB', 'CLE',
'CLL', 'CLM', 'CLT', 'CMH', 'CMI', 'CMX', 'CNM', 'CNY', 'COD',
'COE', 'COS', 'COU', 'CPR', 'CPX', 'CRP', 'CRQ', 'CRW', 'CSG',
'CVG', 'CVO', 'CVX', 'CWA', 'CWI', 'CYS', 'D76', 'DAB', 'DAL',
'DAN', 'DAY', 'DBQ', 'DCA', 'DDC', 'DDH', 'DEC', 'DEN', 'DET',
'DFW', 'DHN', 'DIK', 'DLG', 'DLH', 'DNV', 'DRO', 'DRT', 'DSM',
'DTW', 'DUJ', 'DUT', 'DUY', 'DVL', 'DVT', 'DXR', 'EAR', 'EAT',
'EAU', 'EEK', 'EEN', 'EFD', 'EFK', 'EGE', 'EKM', 'EKO', 'ELI',
'ELM', 'ELO', 'ELP', 'ELY', 'ENA', 'ENM', 'ENW', 'ERI', 'ESC',
'ESF', 'EUG', 'EVV', 'EWB', 'EWN', 'EWR', 'EWU', 'EYW', 'FAI',
'FAQ', 'FAR', 'FAT', 'FAY', 'FHR', 'FHU', 'FKL', 'FLG', 'FLL',
'FLO', 'FMN', 'FNL', 'FNT', 'FOD', 'FOE', 'FRG', 'FRM', 'FSD',
'FSM', 'FTW', 'FWA', 'FYU', 'FYV', 'GAL', 'GAM', 'GBD', 'GBH',
'GCC', 'GCK', 'GCN', 'GED', 'GEG', 'GFK', 'GFL', 'GGG', 'GGV',
'GGW', 'GJT', 'GKN', 'GLD', 'GLH', 'GLR', 'GLS', 'GNV', 'GON',
'GPI', 'GPT', 'GPZ', 'GRB', 'GRI', 'GRK', 'GRO', 'GRR', 'GSN',
'GSO', 'GSP', 'GST', 'GTF', 'GTR', 'GUC', 'GUM', 'GUP', 'GYH',
'GYR', 'GYY', 'HDN', 'HFD', 'HGR', 'HIB', 'HII', 'HKS', 'HKY',
'HLA', 'HLN', 'HND', 'HNH', 'HNL', 'HNM', 'HNS', 'HOB', 'HOM',
'HON', 'HOT', 'HOU', 'HPB', 'HPN', 'HRL', 'HRO', 'HSL', 'HSV',
'HTS', 'HUF', 'HUT', 'HVN', 'HXD', 'HYA', 'HYL', 'HYS', 'IAD',
'IAH', 'IAN', 'ICT', 'IDA', 'IFP', 'IGM', 'IIK', 'ILE', 'ILG',
'ILI', 'ILL', 'ILM', 'IMT', 'IND', 'INL', 'INT', 'IPL', 'IPT',
'IRK', 'ISN', 'ISO', 'ISP', 'ITH', 'ITO', 'IWA', 'IWD', 'IXD',
'IYK', 'JAC', 'JAN', 'JAX', 'JBR', 'JEF', 'JFK', 'JHW', 'JLN',
'JMS', 'JNU', 'JRB', 'JST', 'JVL', 'JXN', 'KAE', 'KAL', 'KDK',
'KEB', 'KKA', 'KLG', 'KOA', 'KSM', 'KTB', 'KTN', 'KVC', 'KVL',
'KWT', 'LAA', 'LAF', 'LAL', 'LAN', 'LAR', 'LAS', 'LAW', 'LAX',
'LBB', 'LBE', 'LBF', 'LBL', 'LBX', 'LCH', 'LCK', 'LEB', 'LEX',
'LFT', 'LGA', 'LGB', 'LHD', 'LIH', 'LIT', 'LMT', 'LNK', 'LNS',
'LNY', 'LPR', 'LRD', 'LRU', 'LSE', 'LUK', 'LWB', 'LWS', 'LYH',
'MAF', 'MAZ', 'MBA', 'MBL', 'MBS', 'MCC', 'MCE', 'MCG', 'MCI',
'MCK', 'MCN', 'MCO', 'MCW', 'MDH', 'MDM', 'MDT', 'MDW', 'MDY',
'MEI', 'MEM', 'MFD', 'MFE', 'MFR', 'MGM', 'MGW', 'MHE', 'MHK',
'MHT', 'MIA', 'MIE', 'MIV', 'MJX', 'MKC', 'MKE', 'MKG', 'MKK',
'MKL', 'MKT', 'MLB', 'MLI', 'MLL', 'MLU', 'MMH', 'MMU', 'MMV',
'MNM', 'MOB', 'MOD', 'MOT', 'MOU', 'MPV', 'MQI', 'MQJ', 'MQY',
'MRI', 'MRY', 'MSL', 'MSN', 'MSO', 'MSP', 'MSS', 'MSV', 'MSY',
'MTH', 'MTJ', 'MTM', 'MTO', 'MUE', 'MVL', 'MVN', 'MVY', 'MWA',
'MWH', 'MYR', 'MZJ', 'N93', 'NEW', 'NQA', 'NUL', 'OAJ', 'OAK',
'OCF', 'OFK', 'OGD', 'OGG', 'OGS', 'OKC', 'OLM', 'OMA', 'OME',
'ONP', 'ONT', 'OOK', 'OQU', 'ORD', 'ORF', 'ORH', 'ORI', 'ORS',
'ORV', 'OSH', 'OSU', 'OTG', 'OTH', 'OTM', 'OTZ', 'OWB', 'OXC',
'OXR', 'PAE', 'PAH', 'PBI', 'PCW', 'PDT', 'PDX', 'PFN', 'PGA',
'PGD', 'PGM', 'PGV', 'PHF', 'PHL', 'PHO', 'PHX', 'PIA', 'PIB',
'PIE', 'PIH', 'PIR', 'PIT', 'PKB', 'PLB', 'PLK', 'PLN', 'PMD',
'PNC', 'PNS', 'POU', 'PPC', 'PPG', 'PQI', 'PQL', 'PRB', 'PRC',
'PSC', 'PSE', 'PSG', 'PSM', 'PSP', 'PTH', 'PTK', 'PUB', 'PUW',
'PVC', 'PVD', 'PVU', 'PWM', 'PWT', 'RAP', 'RDD', 'RDG', 'RDM',
'RDU', 'RFD', 'RHI', 'RIC', 'RIW', 'RKD', 'RKS', 'RME', 'RMG',
'RNO', 'ROA', 'ROC', 'ROW', 'RSH', 'RST', 'RSW', 'RUT', 'RWI',
'SAF', 'SAN', 'SAT', 'SAV', 'SAW', 'SBA', 'SBD', 'SBN', 'SBP',
'SBY', 'SCC', 'SCK', 'SCM', 'SDF', 'SDP', 'SDY', 'SEA', 'SFB',
'SFO', 'SFZ', 'SGF', 'SGH', 'SGJ', 'SGU', 'SGY', 'SHD', 'SHG',
'SHH', 'SHR', 'SHV', 'SIG', 'SIT', 'SJC', 'SJT', 'SJU', 'SKX',
'SLC', 'SLE', 'SLK', 'SLN', 'SMF', 'SMX', 'SNA', 'SNP', 'SOP',
'SOV', 'SOW', 'SPI', 'SPS', 'SQI', 'SRQ', 'SRR', 'STC', 'STJ',
'STL', 'STP', 'STS', 'STT', 'STX', 'SUN', 'SUS', 'SUX', 'SVA',
'SVC', 'SWF', 'SWO', 'SYR', 'T44', 'TAL', 'TBN', 'TCL', 'TEB',
'TEX', 'TIX', 'TLH', 'TLT', 'TNI', 'TOG', 'TOL', 'TPA', 'TPL',
'TRI', 'TTN', 'TUL', 'TUP', 'TUS', 'TVC', 'TVF', 'TVL', 'TVR',
'TWF', 'TXK', 'TYR', 'TYS', 'UCA', 'UIN', 'UNK', 'UNV', 'UOX',
'UUU', 'VAK', 'VCT', 'VCV', 'VDZ', 'VGT', 'VIS', 'VLD', 'VPS',
'VPZ', 'VQQ', 'VQS', 'VRB', 'WBB', 'WDG', 'WLK', 'WNA', 'WRG',
'WRL', 'WST', 'WTK', 'WWD', 'WYS', 'X44', 'X95', 'XNA', 'YAK',
'YKM', 'YKN', 'YNG', 'YUM', 'Z08', 'Z09'
]
transDetail = 'Date Booked: ' + str(
booking
) + '; Name Booked: ' + tmp2[0] + tmp2[1] + '; Address: ' + addr[
1] + ' ' + addr[2] + ', ' + addr[0] + '; Source :' + randomchoice(
Airport_Code) + '; Destination:' + randomchoice(Airport_Code)
return transDetail
f1,
delimiter=',',
lineterminator='\n',
)
writer.writerow(['rownum'] +['dunno'] + ['CC'] + ['Employer'] + ['Custemail'] + ['name'] \
+ ['occupation'] + ['address_street'] + ['DOB']+['previous address_city_state_zip']+ ['altcustomer_name'] \
+ ['altcustomer_occupation'] + ['altcustomer_dob'] + ['ssn'] + ['phone'] + \
['AccountID'] + ['PepFlag'] + ['altcustomerssn'] + ['demarketed_customer_flag'] + \
['SAR_flag'] + ['nolonger_a_customer'] + ['closed_account'] +['High_risk_flag'] +['Risk_rating'])
while i < 50000000:
#Pick an account number and store it in acct
acct = randrange(100000, 100000000, 1)
#if the account hasn't been already generated then generate a record with all fields
if d.has_key(str(acct)) == False:
row = [i] + [10] + [gen_data.cc_number()]+[gen_data.create_company_name()] + \
[gen_data.create_email()]+[gen_data.create_name()] +[gen_data.create_job_title()] + \
[gen_data.create_city_state_zip()] + [gen_data.create_birthday(min_age=2, max_age=85)] + \
[gen_data.create_city_state_zip()] + [fake.name()] + [gen_data.create_job_title()] + \
[gen_data.create_birthday(min_age=2, max_age=85)] +\
[(randrange(101,1000,1),randrange(10,100,1),randrange(1000,10000,1))] + \
[(randrange(101,1000,1),randrange(101,999,1),randrange(1000,10000,1))] + \
[acct] + \
[max((randrange(0,101,1)-99),0)] + \
[(randrange(101,1000,1),randrange(10,100,1),randrange(1000,10000,1))] + \
[max((randrange(0,101,1)-99),0)] + [max((randrange(0,101,1)-99),0)] + \
[max((randrange(0,101,1)-99),0)] + [max((randrange(0,101,1)-90),0)] + \
[max((randrange(0,101,1)-99),0)] + [max((randrange(0,101,1)-99),0)]
d[str(acct)] = acct
i = i + 1
writer.writerow(row)
def gen_tran(MCC_credits, MCC_debits, Tran_Country_Credits,
Tran_Country_Debits, Tran_Type_C, Tran_Type_D, Upper_Limit, Delta,
count, j, usecase):
liTrans = []
#Initiate start date for transactions
startDate = date(2015, 01, 01)
#Pick out account based on counter
acct = ACCTs[j]
#Set customer credit limit - skew to clients with $1000-$25000 and 10% with $25K - $50K
limit = max(
max((randrange(1, 101, 1) - 99), 0) * randrange(25000, 50000, 1000),
randrange(1000, 25000, 1000))
#local Amt variable to calculate customer total usage
usedAmt = 0
tmpAmt = 0
Balance = limit
maxDate = startDate
#Random number generator for transactions per customer
NoTrans = randrange(100, 150, 1)
desc = ''
flag = 0
maxCheckin = ''
maxBook = ''
#loop to generate NoTrans transactions per customer
for k in range(NoTrans):
dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
cr_dbt = 'D'
tranType = ''
country = []
cat_desc = ''
flag = 0
#If Balance is within the credit limit, generate credits/debits
if (Balance > 0 and Balance <= limit * 1.2):
#Probability of credits (tmpAmt>0) and debits (tmpAmt==0) is driven by parameters Upper_Limit and Delta
tmpAmt = max(
(randrange(1, Upper_Limit, 1) + Delta), 0) * randrange(
1, Balance + 1, 1)
flag = 1
#Define time delta for next transaction
tdelta = timedelta(days=randrange(1, 4, 1))
row = [str(count) + '_' + dt] + [acct]
#If we have credit or debit within balance
if tmpAmt == 0 and flag == 1:
tmpAmt = random.randrange(1, Balance + 1, 1)
tranType = random.choice(Tran_Type_D)
cat = random.choice(MCC_debits)
cat_desc = python_merchant_cat.All_Merchant_Cat[cat]
Balance = Balance - tmpAmt
merch = gen_data.create_company_name()
row.append(merch)
row.append(cat)
row.append(cat_desc)
country = random.choice(Tran_Country_Debits)
else:
if tmpAmt > 0 and flag == 1:
cr_dbt = 'C'
tranType = random.choice(Tran_Type_C)
Balance = Balance + tmpAmt
merch = ''
cat = random.choice(MCC_credits)
cat_desc = python_merchant_cat.All_Merchant_Cat[cat]
if (tranType == 'Merchant Credit'):
merch = gen_data.create_company_name()
cat = random.choice(Merchant_Category.Green)
cat_desc = python_merchant_cat.All_Merchant_Cat[cat]
if (tranType == 'Refund'):
cat = '0000'
cat_desc = python_merchant_cat.All_Merchant_Cat[cat]
row.append(merch)
row.append(cat)
row.append(cat_desc)
country = random.choice(Tran_Country_Credits)
#If we need to make a payment or get credit then assign codes
if Balance > limit and flag == 0:
tmpAmt = random.randrange(1, Balance - limit + 1, 1)
tranType = random.choice(Tran_Type_D)
cat = random.choice(MCC_debits)
cat_desc = python_merchant_cat.All_Merchant_Cat[cat]
Balance = Balance - tmpAmt
merch = gen_data.create_company_name()
row.append(merch)
row.append(cat)
row.append(cat_desc)
country = random.choice(Tran_Country_Debits)
else:
if ((Balance < 0 or Balance == 0) and flag == 0):
cr_dbt = 'C'
tranType = 'Payment'
tmpAmt = random.randrange(1, limit / 2, 1)
Balance = Balance + tmpAmt
merch = ''
cat = '1111'
cat_desc = python_merchant_cat.All_Merchant_Cat[cat]
row.append(merch)
row.append(cat)
row.append(cat_desc)
country = random.choice(Tran_Country_Credits)
#date posted
date1 = maxDate + tdelta
maxDate = date1
#date of transaction a day later
date2 = date1 - timedelta(days=1)
row.extend([
country, date1, date2, tranType, cr_dbt, limit, tmpAmt, Balance,
CCs[j], CCTypes[j], usecase, Holders[j], CCsCount[j], Cities[j],
States[j], ZIPs[j], Countries[j]
])
count = count + 1
checkin = ''
checkout = ''
transDetail = ''
#Add details or Hotel Transactions
if ((cat_desc == 'Hotels/Motels/Inns/Resorts'
or cat_desc == 'Hotels, Motels, and Resorts')
and (UseCase[j] == '28' or UseCase[j] == '29')):
if (maxCheckin == ''):
checkin = maxDate + timedelta(days=randrange(365, 389, 1))
checkout = checkin + timedelta(days=randrange(4, 11, 1))
maxCheckin = checkin
tmp2 = gen_data.create_name()
addr = gen_data.create_city_state_zip()
hotel = tmp2[1] + ' Hotels; ' + '; Address: ' + addr[
1] + ' ' + addr[2] + ', ' + addr[0]
transDetail = 'Checkin: ' + str(
checkin) + '; Checkout: ' + str(
checkout) + '; Hotel: ' + hotel
else:
checkin = maxCheckin + timedelta(days=randrange(2, 5, 1))
checkout = checkin + timedelta(days=randrange(4, 11, 1))
maxCheckin = checkin
tmp2 = gen_data.create_name()
addr = gen_data.create_city_state_zip()
hotel = tmp2[1] + ' Hotels; ' + '; Address: ' + addr[
1] + ' ' + addr[2] + ', ' + addr[0]
transDetail = 'Checkin: ' + str(
checkin) + '; Checkout: ' + str(
checkout) + '; Hotel: ' + hotel
if ((cat_desc == 'Hotels/Motels/Inns/Resorts'
or cat_desc == 'Hotels, Motels, and Resorts')
and UseCase[j] == '30'):
checkin = maxDate + timedelta(days=randrange(30, 200, 1))
checkout = checkin + timedelta(days=randrange(4, 11, 1))
tmp2 = gen_data.create_name()
addr = gen_data.create_city_state_zip()
hotel = tmp2[1] + ' Hotels; ' + '; Address: ' + addr[
1] + ' ' + addr[2] + ', ' + addr[0]
transDetail = 'Checkin: ' + str(checkin) + '; Checkout: ' + str(
checkout) + '; Hotel: ' + hotel
#Add details or Airline Transactions
if (cat_desc == 'Airlines'
and (UseCase[j] == '31' or UseCase[j] == '32')):
if (maxBook == ''):
booking = maxDate + timedelta(days=randrange(1, 15, 1))
maxBook = booking
tmp2 = gen_data.create_name()
addr = gen_data.create_city_state_zip()
transDetail = 'Date Booked: ' + str(
booking) + '; Name Booked: ' + tmp2[0] + tmp2[
1] + '; Address: ' + addr[1] + ' ' + addr[
2] + ', ' + addr[0] + '; Source :' + random.choice(
Airport_Code
) + '; Destination:' + random.choice(Airport_Code)
else:
booking = maxBook + timedelta(days=randrange(1, 15, 1))
maxBook = booking
tmp2 = gen_data.create_name()
addr = gen_data.create_city_state_zip()
transDetail = 'Date Booked: ' + str(
booking) + '; Name Booked: ' + tmp2[0] + tmp2[
1] + '; Address: ' + addr[1] + ' ' + addr[
2] + ', ' + addr[0] + '; Source :' + random.choice(
Airport_Code
) + '; Destination:' + random.choice(Airport_Code)
if (cat_desc == 'Airlines' and UseCase[j] == '33'):
booking = maxDate + timedelta(days=randrange(1, 15, 1))
tmp2 = gen_data.create_name()
addr = gen_data.create_city_state_zip()
transDetail = 'Date Booked: ' + str(
booking) + '; Name Booked: ' + tmp2[0] + tmp2[
1] + '; Address: ' + addr[1] + ' ' + addr[2] + ', ' + addr[
0] + '; Source :' + random.choice(
Airport_Code) + '; Destination:' + random.choice(
Airport_Code)
row.append(transDetail)
writer.writerow(row)
#post generating all transactions, check account balance - if overpaid - refund $ and add a refund transaction
if Balance > limit:
row = [str(count) + '_' + dt] + [acct] + ['Uber Bank'] + ['0000'] + [
'Refund to Customer from Bank'
] + [random.choice(Tran_Country_Debits)]
date1 = maxDate + timedelta(days=90)
date2 = date1 - timedelta(days=1)
row.extend([
date1, date2, 'Credit Balance Refund', 'D', limit, Balance - limit,
limit, CCs[j], CCTypes[j], usecase, Holders[j], CCsCount[j],
Cities[j], States[j], ZIPs[j], Countries[j], ''
])
count = count + 1
usedAmt = 0
maxDate = datetime(0001, 01, 01)
else:
date1 = maxDate + tdelta
maxDate = date1
#date of transaction a day later
date2 = date1 - timedelta(days=1)
row = [str(count) + '_' + dt] + [acct] + ['Customer Payment'] + [
'1111'
] + ['Customer Payment'] + [random.choice(Tran_Country_Credits)]
row.extend([
date1, date2, 'Payment', 'C', limit, limit - Balance, limit,
CCs[j], CCTypes[j], usecase, Holders[j], CCsCount[j], Cities[j],
States[j], ZIPs[j], Countries[j], ''
])
count = count + 1
usedAmt = 0
writer.writerow(row)
def createCusts(N):
#List for client whose net worth is over $500K
HighNetWorth = ['Yes'] + ['No'] * 30
#List for type of account
Related_Type = ['Primary','Secondary','Joint']
#List for how the account was opened
Party_Type = ['Person','Non-Person']
#List for a BMO customer
Party_Relation = ['Customer','Non-Customer']
#List for random Yes/No Flag
Yes_No = ['Yes'] + ['No'] * 12
#List for random Yes/No Consent
Yes_No_Consent = ['Yes'] + ['No'] * 4
#List for equal Yes/No Flag
Yes_No_50 = ['Yes','No']
#List for official language
Official_Lang = ['English'] * 3 + ['French']
#List for method of communication
Preffered_Channel = ['Direct Mail','Telemarketing','Email','SMS']
#List for status of customer
#Customer_Status = ['Prospect','Inactive Customer','Past Customer'] + ['Active Customer'] * 56
#List for LOB Segment Type
Seg_Model_Type = ['LOB Specific','Profitability','Geographical','Behavioral','Risk Tolerance']
#List for Model ID
Model_ID = ['01','02','03','04','05']
#List for Model Name
Seg_Model_Name = ['IRRI', 'CRS Risk Score','Geo Risk','Financial Behavior Risk','CM Risk']
#List for Model Score
Seg_Model_Score = ['200','300','400','100','500']
#List for Model Group
Seg_Model_Group = ['Group 1'] * 2 + ['Group 2','Group 3','Group 4']
#List for Model Description
Seg_Model_Description = ['High Risk Tier','Mid Risk Tier','Low Risk Tier','Vertical Risk','Geographical Risk']
#List for random Arms Dealer flag
Arms_Manufacturer=['Yes'] + ['No'] * 2 + [''] * 392
#List for random auction flag
Auction=['Yes'] + ['No'] * 2 + [''] * 392
#List for random Cash Intensive flag
CashIntensive_Business=['Yes'] + ['No'] * 2 + [''] * 392
#List for random Casino?Gaming flag
Casino_Gambling=['Yes'] + ['No'] * 2 + [''] * 392
#List for random Client Onboarding flag
Channel_Onboarding=['E-mail','In Person','In person - In Branch/Bank Office','In person - Offsite/Client Location','Mail','Online','Phone','Request for Proposal (RFP)'] + ['Not Applicable'] * 10
#List for random Transaction flag
Channel_Ongoing_Transactions=['ATM','E-mail','Fax','Mail','Not Applicable','OTC Communication System','Phone'] + ['Online'] * 4 + ['In Person'] * 31
#List for random HI_Vehicle flag
Complex_HI_Vehicle=['Yes'] + ['No'] * 2 + [''] * 392
#List for random Metals flag
Dealer_Precious_Metal=['Yes'] + ['No'] * 2 + [''] * 392
#List for random Arms Dealer flag
Digital_PM_Operator=['Yes'] + ['No'] * 2 + [''] * 392
#List for random Embassy flag
Embassy_Consulate=['Yes'] + ['No'] * 2 + [''] * 392
#Sets variable to Embassy flag
Exchange_Currency=Embassy_Consulate
#Sets variable to Embassy flag
Foreign_Financial_Institution=Embassy_Consulate
#Sets variable to Embassy flag
Foreign_Government=Embassy_Consulate
#Sets variable to Embassy flag
Foreign_NonBank_Financial_Institution=Embassy_Consulate
#Sets variable to Embassy flag
Internet_Gambling=Embassy_Consulate
#Sets variable to Embassy flag
Medical_Marijuana_Dispensary=Embassy_Consulate
#Sets variable to Embassy flag
Money_Service_Business=Embassy_Consulate
#Sets variable to Embassy flag
NonRegulated_Financial_Institution=Embassy_Consulate
#Sets variable to Embassy flag
Not_Profit=Embassy_Consulate
#List for random occupation
Occupation=['11-1011 Chief Executives',\
'11-3011 Administrative Services Managers',\
'11-3031 Financial Managers',\
'11-3061 Purchasing Managers',\
'13-1011 Agents and Business Managers of Artists Performers and Athletes',\
'13-1031 Claims Adjusters Examiners, and Investigators',\
'13-1199 Business Operations Specialists, All Other',\
'13-2099 Financial Specialists All Other',\
'17-1011 Architects Except Landscape and Naval',\
'23-1011 Lawyers',\
'23-1023 Judges, Magistrate Judges and Magistrates',\
'25-2012 Kindergarten Teachers Except Special Education',\
'25-2021 Elementary School Teachers Except Special Education',\
'29-1041 Optometrists',\
'29-2054 Respiratory Therapy Technicians',\
'33-2011 Firefighters',\
'37-1012 First-Line Supervisors of Landscaping Lawn Service and Groundskeeping Workers',\
'39-1011 Gaming Supervisors',\
'39-2011 Animal Trainers',\
'41-1011 First-Line Supervisors of Retail Sales Workers',\
'41-1012 First-Line Supervisors of Non-Retail Sales Workers',\
'41-2011 Cashiers',\
'41-2031 Retail Salespersons',\
'43-3021 Billing and Posting Clerks',\
'45-1011 First-Line Supervisors of Farming, Fishing, and Forestry Workers',\
'49-2011 Computer Automated Teller and Office Machine Repairers',\
'53-3021 Bus Drivers Transit and Intercity',\
'53-4031 Railroad Conductors and Yardmasters',\
'55-1011 Air Crew Officers',\
'55-1012 Aircraft Launch and Recovery Officers',\
'55-1013 Armored Assault Vehicle Officers',\
]
#Sets variable to Embassy flag
Privately_ATM_Operator=Embassy_Consulate
#List for random products
Products=['Certificate of Deposit',\
'Checking Account',\
'Credit Card',\
'Custodial and Investment Agency - Institutional',\
'Custodial and Investment Agency - Personal',\
'Custodial/Trust Outsourcing Services (BTOS)',\
'Custody Accounts (PTIM)',\
'Custody Accounts (RSTC)',\
'DTF (BHFA)',\
'Investment Agency - Personal',\
'Investment Management Account (PTIM)',\
'Lease',\
'Loan / Letter of Credit',\
'Money Market',\
'Mortgage / Bond / Debentures',\
'None',\
'Savings Account',\
'Trust Administration - Irrevocable and Revocable (PTIM)',\
'Trust Administration - Irrevocable and Revocable Trusts (BDTC)',\
] + ['Nondeposit Investment Products'] * 14 + ['Investment Agency - Institutional'] * 5
#Sets variable to Embassy flag
Sales_Used_Vehicles=Embassy_Consulate
#Dictionary for random Services
Services=['Benefit Payment Services',\
'Domestic Wires and Direct Deposit / ACH',\
'Family Office Services (FOS)',\
'Fiduciary Services',\
'International Wires and IAT',\
'Investment Advisory Services (IAS)',\
'Investment Services',\
'None',\
'Online / Mobile Banking',\
'Payroll',\
'Short Term Cash Management',\
'Trust Services',\
'Trustee Services',\
'Vault Cash Services',\
] + ['Financial Planning'] * 6 + ['Retirement Plans'] * 19
#Dictionary for random SIC_Code
SIC_Code=['6021 National Commercial Banks',\
'6211 Security Brokers Dealers and Flotation Companies',\
'6282 Investment Advice',\
'6311 Life Insurance',\
'6733 Trusts Except Educational Religious and Charitable',\
'8999 Services NEC',\
] + ['6722 Management Investment Offices Open-End'] * 12
#Dictionary for random Market Listing
Stock_Market_Listing=['Australian Stock Exchange',\
'Brussels Stock Exchange',\
'Montreal Stock Exchange',\
'Tiers 1 and 2 of the TSX Venture Exchange (also known as Tiers 1 and 2 of the Canadian Venture Exchange)',\
'Toronto Stock Exchange',\
] + ['Not Found'] * 30
#Sets variable to Embassy flag
Third_Party_Payment_Processor=Embassy_Consulate
#Sets variable to Embassy flag
Transacting_Provider=Embassy_Consulate
#Dictionary for random Low Net Worth
LowNet=[1,2] + [0] * 5
#Dictionary for Consumer vs Business
Acct_Type = ['B'] + ['C'] * 5
#Dictionary for random number of credits cards per account
Number_CC = [1] * 7 + [2] * 11 + [3] * 3 + [4]
#Dictionary for Account list set to blank
acct_list=[]
#Dictionary for CreditCard list set to blank
CC_list = []
#Dictionary for random Wolfsberg scenario
Use_Case = [1,4,7,10,13,16,19,22,25,28,31,34,39] * 4 + [2,5,8,11,14,17,20,23,26,29,32,35,38] * 7 + [3,6,9,12,15,18,21,24,27,30,33,36] * 65 + [37] * 73 + [40,41] * 2
refrating = ['1','1','1','2','3','4','2','4','5','5','5','5','5','5','5','5','5','5','5','5']
fake = Faker()
global liSSNMaster
start=10786147
acct_list=[]
liCSV = []
for i in xrange(N):
#Initiate High Risk Flags
#Politically Exposed Person
PEP='No'
#Customer with a Suspicous Activity Report
SAR='No'
#Customer with a closed account
Clsd='No'
#High risk customer flag
high_risk='No'
#High Risk Rating
hr_rating=''
#Customer that was demarketed by the bank
demarket='No'
dem_date=''
#generate closed acct flag
if (max((randrange(0,98,1)-96),0)==1):
Clsd='Yes'
#Random choice for number of credit card users per account number
No_CCs = random.choice(Number_CC)
#Generate account number
acct=start+1+randrange(1,10,1)
start=acct
#Randomly generate customer name + middle name in tmp
name = fake.name()
tmp=gen_data.create_name()
#Adds account number to account dictionary
acct_list.extend([acct])
#Creates a new row and adds data elements
row = [i]+[acct]+[random.choice(Acct_Type)]+[No_CCs]+[name]+[tmp[0]]+[liSSNMaster[i]]
#Dictionary for names list set to blank
names=[]
#Dictionary for Social Security Number list set to blank
ssn=[]
#Middle Name to reduce name dups
mdl=[]
for j in range(No_CCs-1):
names.insert(j,fake.name())
tmp2=gen_data.create_name()
mdl.insert(j,tmp2[0])
##Pull from SSN Master list
randInt = randrange(1,len(liSSNMaster),1)
if randInt != i:
ssn.insert(j,liSSNMaster[randInt])
else:
ssn.insert(j,liSSNMaster[randInt - 1])
#Name and SSN is set to blank if less than 4 customers on an account
for k in range(4-No_CCs):
names.insert(No_CCs+k,'')
ssn.insert(No_CCs+k,'')
mdl.insert(No_CCs,'')
#Sets CC_NO to a random credit card number
CC_NO=gen_data.cc_number()
#Extract CC_Number from the tuple returned by CC_Number then scramble to ensure uniqueness...Tuple contains CC Number and Type
CC_TRANS=CC_NO[1][0]
dt = str(datetime.now())
clean=re.sub('\W','',dt)
printCC=str(CC_TRANS[-4:])+str(clean[-12:-3])+str(randrange(1111,9999,randrange(1,10,1)))
#Add data elements to current csv row
row.extend([names[0],mdl[0],ssn[0],names[1],mdl[1],ssn[1],names[2],mdl[2],ssn[2],printCC,CC_NO[0],gen_data.create_company_name()+' '+tmp[1],\
gen_data.create_email(),gen_data.create_job_title()])
#Create Current Address
zip=random.choice(zips.zip)
addr=geo_data.create_city_state_zip[zip]
#Create Previous address
zip2=random.choice(zips.zip)
addr2=geo_data.create_city_state_zip[zip2]
#Add additional data elements to current csv row
lrg_cash_ex=random.choice(Yes_No)
#Condition for SARs and Demarketed Clients
if(Clsd=='Yes'):
#1% of closed accounts are demarketed but never had a SAR filed
if (max((randrange(0,101,1)-99),0)==1 and SAR=='No'):
demarket='Yes'
dem_date=gen_data.create_date(past=True)
if (max((randrange(0,11,1)-9),0)==1 and demarket=='No'):
#10% of closed accounts have SARs
SAR='Yes'
#90% of closed accounts with SARs are demarketed
if(max((randrange(0,11,1)-9),0)==0):
demarket='Yes'
dem_date=gen_data.create_date(past=True)
if (max((randrange(0,101,1)-99),0)==1):
PEP='Yes'
row.extend([addr[0],addr[1],zip,'US',addr2[0],addr2[1],zip2,'US',gen_data.create_birthday(min_age=2, max_age=85),PEP,SAR,Clsd])
#Start Generating related accounts from account list once 10,000 accounts are generated - to avoid duplicating accounts in the beginning
if i > 10000:
rel = int(random.choice(acct_list))*max((randrange(0,10001,1)-9999),0)
if rel <> 0:
row.append(rel)
row.append(random.choice(Related_Type))
else:
row.append('')
row.append('')
else:
row.append('')
row.append('')
#Randomly generates account start date
party_start=gen_data.create_date(past=True)
#Randomly selects consent option for sharing info
Consent_Share = random.choice(Yes_No_Consent)
#Add additional data elements to current csv row
row.extend([random.choice(Party_Type),random.choice(Party_Relation),party_start,gen_data.create_date(past=True),\
lrg_cash_ex,demarket,dem_date,randrange(0,100,1),random.choice(Official_Lang)])
#Add data element preferred methond of contact for yes to share info...if not then blank to current row
if Consent_Share == 'Yes':
row.extend(['Yes',random.choice(Preffered_Channel)])
else:
row.extend(['No',''])
row.extend([zip,randrange(0,5,1)])
#Generate Segment ID then add additional Segment data based on the selection to the current csv row
Segment_ID = randrange(0,5,1)%5
if Segment_ID == 0:
row.extend([Model_ID[0],Seg_Model_Type[0],Seg_Model_Name[0],Seg_Model_Group[0],Seg_Model_Description[0],Seg_Model_Score[0]])
if Segment_ID == 1:
row.extend([Model_ID[1],Seg_Model_Type[1],Seg_Model_Name[1],Seg_Model_Group[1],Seg_Model_Description[1],Seg_Model_Score[1]])
if Segment_ID == 2:
row.extend([Model_ID[2],Seg_Model_Type[2],Seg_Model_Name[2],Seg_Model_Group[2],Seg_Model_Description[2],Seg_Model_Score[2]])
if Segment_ID == 3:
row.extend([Model_ID[3],Seg_Model_Type[3],Seg_Model_Name[3],Seg_Model_Group[3],Seg_Model_Description[3],Seg_Model_Score[3]])
if Segment_ID == 4:
row.extend([Model_ID[4],Seg_Model_Type[4],Seg_Model_Name[4],Seg_Model_Group[4],Seg_Model_Description[4],Seg_Model_Score[4]])
#Add additional data elements to current csv row
hr0=random.choice(Arms_Manufacturer)
hr01=random.choice(Auction)
hr02=random.choice(CashIntensive_Business)
hr03=random.choice(Casino_Gambling)
hr04=random.choice(Channel_Onboarding)
hr05=random.choice(Channel_Ongoing_Transactions)
row.extend([hr0,hr01,hr02,hr03,hr04,hr05])
#Randomly select whether customer has a High Net Worth
HighNetWorthFlag = random.choice(HighNetWorth)
#Randomly Generate customer net worth based on the above flag
if HighNetWorthFlag == 'Yes':
row.append(max(max((randrange(0,101,1)-99),0)*randrange(1000000,25000000,1),randrange(1000000,5000000,1)))
else:
flag=random.choice(LowNet)
if flag==0:
row.append(randrange(-250000,600000,1))
else:
if flag==1:
row.append(randrange(149000,151000,1))
else:
row.append(randrange(40000,50000,1))
#Add data elements to current csv row
hr1=random.choice(Complex_HI_Vehicle)
hr2=random.choice(Dealer_Precious_Metal)
hr3=random.choice(Digital_PM_Operator)
hr4=random.choice(Embassy_Consulate)
hr5=random.choice(Exchange_Currency)
hr6=random.choice(Foreign_Financial_Institution)
hr7=random.choice(Foreign_Government)
hr8=random.choice(Foreign_NonBank_Financial_Institution)
hr9=random.choice(Internet_Gambling)
hr10=random.choice(Medical_Marijuana_Dispensary)
hr11=random.choice(Money_Service_Business)
hr12=random.choice(NAICS.NAICS_Code)
hr13=random.choice(NonRegulated_Financial_Institution)
hr14=random.choice(Not_Profit)
#hr15=random.choice(Occupation) - added before through gen_data
hr16=random.choice(Privately_ATM_Operator)
hr17=random.choice(Products)
hr18=random.choice(Sales_Used_Vehicles)
hr19=random.choice(Services)
hr20=random.choice(SIC_Code)
hr21=random.choice(Stock_Market_Listing)
hr22=random.choice(Third_Party_Payment_Processor)
hr23=random.choice(Transacting_Provider)
if(PEP=='Yes' or SAR=='Yes' or lrg_cash_ex=='Yes' or demarket=='Yes' or hr0=='Yes'
or hr01=='Yes' or hr02=='Yes' or hr03=='Yes' or hr1=='Yes' or hr2=='Yes' or hr3=='Yes' or hr4=='Yes' or
hr5=='Yes' or hr6=='Yes' or hr7=='Yes' or hr8=='Yes' or hr9=='Yes' or hr10=='Yes' or hr11=='Yes' or hr13=='Yes' or hr14=='Yes' or
hr16=='Yes' or hr17=='Yes' or hr18=='Yes' or hr22=='Yes' or hr23=='Yes' or HighNetWorthFlag=='Yes'):
high_risk='Yes'
hr_rating=random.choice(refrating)
if(SAR=='No' and high_risk=='No'):
if(max((randrange(0,101,1)-99),0)==1):
high_risk='Yes'
hr_rating=random.choice(refrating)
if(PEP=='No' and high_risk=='No'):
if(max((randrange(0,101,1)-99),0)==1):
high_risk='Yes'
hr_rating=random.choice(refrating)
if(high_risk=='No'):
if(max((randrange(0,101,1)-99),0)==1):
high_risk='Yes'
hr_rating=random.choice(refrating)
row.extend([hr1,hr2,hr3,hr4,hr5,hr6,hr7,hr8,hr9,hr10,hr11,hr12,hr13,hr14,hr16,hr17,hr18,hr19,hr20,hr21,hr22,hr23,
HighNetWorthFlag,high_risk,hr_rating,random.choice(Use_Case)])
liCSV.append(row)
return liCSV
if (max((randrange(0, 98, 1) - 96), 0) == 1):
Clsd = 'Yes'
#Random number generator for account number
#acct = randrange(100000,100000000,1)
#Random choice for number of credit cards per account number
No_CCs = random.choice(Number_CC)
#while acct_list.count(acct) > 0:
# acct = randrange(100000,100000000,1)
#dt = str(datetime.now())
#acct=str(i)++re.sub('\W','',dt)
acct = start + 1 + randrange(1, 10, 1)
start = acct
#Randomly generates customer name
name = fake.name()
tmp = gen_data.create_name()
#Adds account number to account dictionary
acct_list.extend([acct])
#Creates a new row and adds data elements
## JS - Main Account Holder SSN as current index in master SSN list
## row = [i]+[acct]+[random.choice(Acct_Type)]+[No_CCs]+[name]+[tmp[0]]+[(str(randrange(101,1000,1))+str(randrange(10,100,1))+str(randrange(1000,10000,1)))]
row = [i] + [acct] + [random.choice(Acct_Type)] + [No_CCs] + [name] + [
tmp[0]
] + [liSSNMaster[i]]
#Dictionary for names list set to blank
names = []
#Dictionary for Social Security Number list set to blank
ssn = []
#Generates Name and SSN for Credit Users
#Middle Name to reduce name dups
mdl = []
# barnum Python library - https://pypi.org/project/barnum/
# import the pandas library
import pandas as pd
# impor the barnum library
from barnum import gen_data
# Create an empty list to store users
users = []
# Create 1000 records
for i in range(1000):
company = gen_data.create_company_name()
fname = gen_data.create_name(full_name=False)
lname = gen_data.create_name(full_name=False)
title = gen_data.create_job_title()
email = gen_data.create_email(name=(fname, lname))
pw = gen_data.create_pw()
street = gen_data.create_street()
city_state_zip = gen_data.create_city_state_zip()
cc = gen_data.create_cc_number()
# append a new user to the users list
users.append(
(company, fname, lname, title, email, pw, street, city_state_zip, cc))
# Create a set of labels for the first row of the excel spreadsheet
labels = [
'Company', 'First', 'Last', 'Title', 'Email', 'Password', 'Street',
'City/State/ZIP', 'Credit Card'
]
# Create a pandas dataframe
