Primitive Obsession refers to a situation in Python where a program relies too heavily on primitive data types such as strings, integers, and booleans. This can lead to code duplication, reduced readability, and increased risk of errors. Fortunately, there are several techniques that can be used to solve Primitive Obsession in Python.

?One technique is to create custom data types or classes to represent the program's domain concepts. Let's say we have a Python program that deals with a user's contact information - name, phone number, and email. Instead of representing these values as separate string variables, we can create a UserContact class:

class UserContact:

? ? def __init__(self, name, phone_number, email):

? ? ? ? self.name = name

? ? ? ? self.phone_number = phone_number

? ? ? ? self.email = email

With this custom data type, we can now pass around instances of UserContact instead of multiple separate string variables. This not only simplifies the code, but also makes it easier to add or modify user contact fields in the future.

?Another technique to solve Primitive Obsession is to use functions or methods to encapsulate complex behavior. Let's say we have a Python program that deals with financial transactions, and that program has to perform complex calculations on currency amounts. Instead of representing these values as float variables, we can encapsulate the complex behavior in a Money class:

class Money:

? ? def __init__(self, amount, currency):

? ? ? ? self.amount = amount

? ? ? ? self.currency = currency

? ? ?def add(self, money):

? ? ? ? if self.currency != money.currency:

? ? ? ? ? ? raise ValueError('Currencies do not match')

? ? ? ? return Money(self.amount + money.amount, self.currency)

? ? ?def multiply(self, factor):

? ? ? ? return Money(self.amount * factor, self.currency)

With this custom data type, we can now encapsulate complex financial behavior within the Money class. For example, we can add and multiply Money objects with different currency types, and the Money class will handle the conversion automatically.

?Lastly, we can use data validation to solve Primitive Obsession. Data validation is the process of checking whether data meets certain criteria before it is used in a program. For example, let's say we have a Python program that accepts a user's age as an input. Instead of representing the user's age as a simple integer variable, we can validate the input to ensure that it is within a certain range:

def process_age(age):

? ? if not isinstance(age, int):

? ? ? ? raise ValueError('Age must be an integer')

? ? ?if age < 0 or age > 120:

? ? ? ? raise ValueError('Age must be between 0 and 120')

? ? ?# process age here

With data validation, we can ensure that the input data is valid before using it in our program. This helps to prevent errors and improve the reliability of our code.

?In conclusion, Primitive Obsession can be a serious issue in Python code, but there are several techniques that can be used to solve it. By creating custom data types, encapsulating complex behavior in functions or methods, and using data validation, we can simplify our code and reduce the risk of errors.