How to create synthetic customer data in python

I have some customer data with me -

Name |  Age |  Gender |  Phone Number | Email Id |

abc. |  25  |  M.     | 234 567 890   | example.com|

There are 60k rows of data like this and multiple tables. How can I make synthetic data for this dataset using python ?

I have no knowledge about this. Any suggestions would be helpful. Thanks!

Solution

Pyhton faker is your friend here. It can generate locaclized fake data for names, addresses, phone and credit card numbers and many more.

from faker import Faker
fake = Faker()
n = 1000
df = pd.DataFrame([[fake.name(),
        np.random.randint(19,91),
        np.random.choice(['M.', 'F.']),
        fake.phone_number(),
        fake.email()] for _ in range(n)],
        columns=['Name', 'Age', 'Gender', 'Phone number', 'Email ID'])

Output of df.head():

                 Name  Age Gender        Phone number                      Email ID
0      Miranda Hinton   21     F.        018.482.1404            [email protected]
1      Donald Donovan   51     F.    572.846.4120x995        [email protected]
2      Shannon Grimes   72     F.          0289879995           [email protected]
3       Heather Perez   87     F.        012-033-2318  [email protected]
4  Jacqueline Pearson   22     M.  178-913-4566x89793        [email protected]