Unfortunately, Faker doesn't have a built-in function to do this.

You have two options.

Write functions yourself, for example:

def fake_phone_number(fake: Faker) -> str:
    return f'+91 {fake.msisdn()[3:]}'

from faker import Faker

def main():
    fake = Faker()
    print(fake_phone_number(fake))

Or create an appropriate provider and suggest adding it: https://faker.readthedocs.io/en/master/communityproviders.html

Python src MSISDN. What is MSISDN.

Or

from faker import Faker
from faker.providers.phone_number import Provider


class IndiaPhoneNumberProvider(Provider):
    """
    A Provider for phone number.
    """

    def india_phone_number(self):
        return f'+91 {self.msisdn()[3:]}'


def main():
    fake = Faker()
    fake.add_provider(IndiaPhoneNumberProvider)
    print(fake.india_phone_number())

To answer the question:

now how do I make sure the generated id is unique in the dataset?

You have to use: unique.random_int

So, your code will be like this as you see below:

from faker import Faker
import random
import pandas as pd

Faker.seed(0)
random.seed(0)
fake = Faker("en_US") 
fixed_digits = 6
concatid = 'ID'
idcode,name, city, country, job, age = [[] for k in range(0,6)] 

for row in range(0,100):
    idcode.append(concatid + str(fake.unique.random_int(min=111111, max=999999)))
    name.append(fake.name())
    city.append(fake.city())
    country.append(fake.country())
    job.append(fake.job())
    age.append(random.randint(20,100))

d = {"ID Code":idcode, "Name":name, "Age":age, "City":city, "Country":country, "Job":job}
df = pd.DataFrame(d)
df.head()