Optimizing python loops for millions of rows

I'm trying to simulate test datasets using Python-Faker. The goal is to have a few million records for my use-case. Following is the code I use to populate 5 data elements for 1 Million records.

for i in range(500000):
    df = df.append(
        {'COL1': fake.first_name_female(),
         'COL2': fake.last_name_female(),
         'COL3': 'F',
         'COL4': fake.street_address(),
         'COL5': fake.zipcode_in_state()
         }, ignore_index=True)
    df = df.append(
        {'COL1': fake.first_name_male(),
         'COL2': fake.last_name_male(),
         'COL3': 'M',
         'COL4': fake.street_address(),
         'COL5': fake.zipcode_in_state()
         }, ignore_index=True)

It took nearly 8 hours to run this. How could I optimize this loop to run faster?

Conventional way of implementing this,

import pandas as pd
from time import time
from faker import Faker
fake = Faker()

def fake_row(i):
    if i % 2 == 0:
        row = [fake.first_name_female(), fake.last_name_female(), 'F', fake.street_address(), fake.zipcode_in_state()]
    else:
        row = [fake.first_name_male(), fake.last_name_male(), 'M', fake.street_address(), fake.zipcode_in_state()]
    return row

start = time()
fake_data = [fake_row(i) for i in range(500000)]
df = pd.DataFrame(fake_data, columns=['COL1', 'COL2', 'COL3', 'COL4', 'COL5'])
print('[TIME]', time() - start)
[TIME] 171.82 secs

To improve the efficiency of the code, you can use parallelization. The deco library provides a convenient and easy way of making your code run concurrently.

import pandas as pd
from time import time
from faker import Faker
from deco import concurrent, synchronized
fake = Faker()

@concurrent
def fake_row(i):
    if i % 2 == 0:
        row = [fake.first_name_female(), fake.last_name_female(), 'F', fake.street_address(), fake.zipcode_in_state()]
        return row
    else:
        row = [fake.first_name_male(), fake.last_name_male(), 'M', fake.street_address(), fake.zipcode_in_state()]
        return row

@synchronized
def run(size):
    res = []
    for i in range(size):
        res.append(fake_row(i))
    return pd.DataFrame(res, columns=['COL1', 'COL2', 'COL3', 'COL4', 'COL5'])
    
start = time()
df = run(500000)
print('[TIME]', time() - start)
[TIME] 88.11 secs