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()

You can use python providers as well.

class MyModel(factory.django.DjangoModelFactory)
  number_field = factory.Faker('pyint', min_value=0, max_value=1000)
  class Meta:
    model = SomeModel

Documentation

pyint generates a random number effectively sampled from range(min_value, max_value+1, step). That's where the step comes in.

The documentation for pyint is severely lacking, but if we go to the source code:

def pyint(self, min_value=0, max_value=9999, step=1):
    return self.generator.random_int(min_value, max_value, step=step)

we see that it delegates to this method:

def random_int(self, min=0, max=9999, step=1):
    """Generate a random integer between two integers ``min`` and ``max`` inclusive
    while observing the provided ``step`` value.
    This method is functionally equivalent to randomly sampling an integer
    from the sequence ``range(min, max + 1, step)``.
    :sample:
    :sample size=10: min=0, max=15
    :sample size=10: min=0, max=15, step=3
    """
    return self.generator.random.randrange(min, max + 1, step)

which has a useful docstring.