def __post_init__()

in datasets/thelook_ecommerce/pipelines/_images/run_thelook_kub/fake.py [0:0]

• 40 lines of code
• 7 McCabe index (conditional complexity)

    def __post_init__(self):
        self.gender = self.random_item(population=["M", "F"])  # uniform distribution
        if self.gender == "M":
            self.first_name = fake.first_name_male()
            self.traffic_source = self.random_item(
                population=["Organic", "Facebook", "Search", "Email", "Display"],
                distribution=[0.15, 0.06, 0.7, 0.05, 0.04],
            )
        if self.gender == "F":
            self.first_name = fake.first_name_female()
            self.traffic_source = self.random_item(
                population=["Organic", "Facebook", "Search", "Email", "Display"],
                distribution=[0.15, 0.06, 0.7, 0.05, 0.04],
            )
        self.last_name = fake.last_name_nonbinary()
        address = Address(get_address())
        self.state = address.state
        self.street_address = address.street
        self.postal_code = address.postal_code
        self.city = address.city
        self.country = address.country
        self.latitude = address.latitude
        self.longitude = address.longitude
        self.email = f"{self.first_name.lower()}{self.last_name.lower()}@{fake.safe_domain_name()}"
        self.age = random.randrange(MIN_AGE, MAX_AGE)
        # weight newer users/orders
        choice = random.choices([0, 1], weights=[0.975, 0.025])[0]
        if choice == 0:
            self.created_at = created_at(datetime.datetime(2019, 1, 1))
        if choice == 1:
            self.created_at = created_at(
                datetime.datetime.now() - datetime.timedelta(days=7)
            )
        num_of_orders = random.choices(
            population=[0, 1, 2, 3, 4], weights=[0.2, 0.5, 0.2, 0.05, 0.05]
        )[0]
        if num_of_orders == 0:
            pass
        else:
            for _ in range(num_of_orders):
                orders.append(dataclasses.asdict(Order(user=self)))