Python Type Hints Guide Explained with Real Examples

## Common Mistakes to Avoid When working with type hints, there are several common mistakes to avoid: * Not using type hints consistently throughout your codebase * Using type hints that are too vague (e.g. `Any`) or too specific (e.g. `int` when `float` would also be acceptable) * Not using type hints for function return values * Not using type hints for class attributes ## Quick Summary In this tutorial, we’ve covered the basics of Python type hints, including their benefits, core concepts, and practical examples. We’ve seen how type hints can be used to provide additional information about the expected types of variables, function parameters, and return values. We’ve also covered some common mistakes to avoid when working with type hints. ## Code Example 2: Real-world Example Let’s consider a more realistic example. Suppose we’re building a simple banking system, and we have a class that represents a bank account. “`python class BankAccount: def __init__(self, account_number: int, balance: float): self.account_number = account_number self.balance = balance def deposit(self, amount: float) -> None: self.balance += amount def withdraw(self, amount: float) -> None: if amount > self.balance: raise ValueError(“Insufficient funds”) self.balance -= amount def get_balance(self) -> float: return self.balance “` In this example, we’ve added type hints for the `account_number` and `balance` attributes, as well as the `deposit`, `withdraw`, and `get_balance` methods. These hints provide a clear understanding of how to work with the `BankAccount` class. ## Code Example 3: Advanced Pattern Let’s consider a more advanced example. Suppose we’re building a system that works with different types of vehicles, and we have a base class that represents a vehicle. “`python from typing import Protocol class Vehicle(Protocol): def start_engine(self) -> None: … def accelerate(self) -> None: … def brake(self) -> None: … class Car: def start_engine(self) -> None: print(“Car engine started”) def accelerate(self) -> None: print(“Car accelerating”) def brake(self) -> None: print(“Car braking”) class Truck: def start_engine(self) -> None: print(“Truck engine started”) def accelerate(self) -> None: print(“Truck accelerating”) def brake(self) -> None: print(“Truck braking”) def drive_vehicle(vehicle: Vehicle) -> None: vehicle.start_engine() vehicle.accelerate() vehicle.brake() drive_vehicle(Car()) drive_vehicle(Truck()) “` In this example, we’ve defined a `Vehicle` protocol that specifies the methods that a vehicle should have. We’ve then created `Car` and `Truck` classes that implement this protocol. The `drive_vehicle` function takes a `Vehicle` as an argument, and calls the methods specified in the protocol. This pattern allows us to work with different types of vehicles in a polymorphic way. ## Common Mistakes to Avoid When working with type hints, there are several common mistakes to avoid: * Not using type hints consistently throughout your codebase * Using type hints that are too vague (e.g. `Any`) or too specific (e.g. `int` when `float` would also be acceptable) * Not using type hints for function return values * Not using type hints for class attributes ## Quick Summary In this tutorial, we’ve covered the basics of Python type hints, including their benefits, core concepts, and practical examples. We’ve seen how type hints can be used to provide additional information about the expected types of variables, function parameters, and return values. We’ve also covered some common mistakes to avoid when working with type hints. ## Code Example 1: Basic Usage Let’s start with a simple example. Suppose we have a function that takes a string and an integer as arguments and returns a boolean value. “`python def greet(name: str, age: int) -> bool: print(f”Hello, {name}! You are {age} years old.”) return True “` In this example, we’ve added type hints for the `name` and `age` parameters, as well as the return value. These hints tell other developers (and tools) that the `greet` function expects a string and an integer, and returns a boolean value. ## Code Example 2: Real-world Example Let’s consider a more realistic example. Suppose we’re building a simple banking system, and we have a class that represents a bank account. “`python class BankAccount: def __init__(self, account_number: int, balance: float): self.account_number = account_number self.balance = balance def deposit(self, amount: float) -> None: self.balance += amount def withdraw(self, amount: float) -> None: if amount > self.balance: raise ValueError(“Insufficient funds”) self.balance -= amount def get_balance(self) -> float: return self.balance “` In this example, we’ve added type hints for the `account_number` and `balance` attributes, as well as the `deposit`, `withdraw`, and `get_balance` methods. These hints provide a clear understanding of how to work with the `BankAccount` class. ## Code Example 3: Advanced Pattern Let’s consider a more advanced example. Suppose we’re building a system that works with different types of vehicles, and we have a base class that represents a vehicle. “`python from typing import Protocol class Vehicle(Protocol): def start_engine(self) -> None: … def accelerate(self) -> None: … def brake(self) -> None: … class Car: def start_engine(self) -> None: print(“Car engine started”) def accelerate(self) -> None: print(“Car accelerating”) def brake(self) -> None: print(“Car braking”) class Truck: def start_engine(self) -> None: print(“Truck engine started”) def accelerate(self) -> None: print(“Truck accelerating”) def brake(self) -> None: print(“Truck braking”) def drive_vehicle(vehicle: Vehicle) -> None: vehicle.start_engine() vehicle.accelerate() vehicle.brake() drive_vehicle(Car()) drive_vehicle(Truck()) “` In this example, we’ve defined a `Vehicle` protocol that specifies the methods that a vehicle should have. We’ve then created `Car` and `Truck` classes that implement this protocol. The `drive_vehicle` function takes a `Vehicle` as an argument, and calls the methods specified in the protocol. This pattern allows us to work with different types of vehicles in a polymorphic way. ## Common Mistakes to Avoid When working with type hints, there are several common mistakes to avoid: * Not using type hints consistently throughout your codebase * Using type hints that are too vague (e.g. `Any`) or too specific (e.g. `int` when `float` would also be acceptable) * Not using type hints for function return values * Not using type hints for class attributes ## Quick Summary In this tutorial, we’ve covered the basics of Python type hints, including their benefits, core concepts, and practical examples. We’ve seen how type hints can be used to provide additional information about the expected types of variables, function parameters, and return values. We’ve also covered some common mistakes to avoid when working with type hints. ## Core Concept with Analogy Type hints can be thought of as a recipe for your code. Just as a recipe provides instructions for cooking a dish, type hints provide instructions for working with your code. They tell other developers (and tools) what ingredients (types) to expect, how to prepare them (function signatures), and what the final dish (return value) should look like. This analogy is not exact, but it gives you an idea of how type hints can help guide the development process. ## Code Example 1: Basic Usage Let’s start with a simple example. Suppose we have a function that takes a string and an integer as arguments and returns a boolean value. “`python def greet(name: str, age: int) -> bool: print(f”Hello, {name}! You are {age} years old.”) return True “` In this example, we’ve added type hints for the `name` and `age` parameters, as well as the return value. These hints tell other developers (and tools) that the `greet` function expects a string and an integer, and returns a boolean value. ## Code Example 2: Real-world Example Let’s consider a more realistic example. Suppose we’re building a simple banking system, and we have a class that represents a bank account. “`python class BankAccount: def __init__(self, account_number: int, balance: float): self.account_number = account_number self.balance = balance def deposit(self, amount: float) -> None: self.balance += amount def withdraw(self, amount: float) -> None: if amount > self.balance: raise ValueError(“Insufficient funds”) self.balance -= amount def get_balance(self) -> float: return self.balance “` In this example, we’ve added type hints for the `account_number` and `balance` attributes, as well as the `deposit`, `withdraw`, and `get_balance` methods. These hints provide a clear understanding of how to work with the `BankAccount` class. ## Code Example 3: Advanced Pattern Let’s consider a more advanced example. Suppose we’re building a system that works with different types of vehicles, and we have a base class that represents a vehicle. “`python from typing import Protocol class Vehicle(Protocol): def start_engine(self) -> None: … def accelerate(self) -> None: … def brake(self) -> None: … class Car: def start_engine(self) -> None: print(“Car engine started”) def accelerate(self) -> None: print(“Car accelerating”) def brake(self) -> None: print(“Car braking”) class Truck: def start_engine(self) -> None: print(“Truck engine started”) def accelerate(self) -> None: print(“Truck accelerating”) def brake(self) -> None: print(“Truck braking”) def drive_vehicle(vehicle: Vehicle) -> None: vehicle.start_engine() vehicle.accelerate() vehicle.brake() drive_vehicle(Car()) drive_vehicle(Truck()) “` In this example, we’ve defined a `Vehicle` protocol that specifies the methods that a vehicle should have. We’ve then created `Car` and `Truck` classes that implement this protocol. The `drive_vehicle` function takes a `Vehicle` as an argument, and calls the methods specified in the protocol. This pattern allows us to work with different types of vehicles in a polymorphic way. ## Common Mistakes to Avoid When working with type hints, there are several common mistakes to avoid: * Not using type hints consistently throughout your codebase * Using type hints that are too vague (e.g. `Any`) or too specific (e.g. `int` when `float` would also be acceptable) * Not using type hints for function return values * Not using type hints for class attributes ## Quick Summary In this tutorial, we’ve covered the basics of Python type hints, including their benefits, core concepts, and practical examples. We’ve seen how type hints can be used to provide additional information about the expected types of variables, function parameters, and return values. We’ve also covered some common mistakes to avoid when working with type hints. ## Prerequisites Before diving into type hints, you should have a basic understanding of Python programming, including variables, data types, functions, and modules. You should also be familiar with the concept of static typing and dynamic typing. ## Core Concept with Analogy Type hints can be thought of as a recipe for your code. Just as a recipe provides instructions for cooking a dish, type hints provide instructions for working with your code. They tell other developers (and tools) what ingredients (types) to expect, how to prepare them (function signatures), and what the final dish (return value) should look like. This analogy is not exact, but it gives you an idea of how type hints can help guide the development process. ## Code Example 1: Basic Usage Let’s start with a simple example. Suppose we have a function that takes a string and an integer as arguments and returns a boolean value. “`python def greet(name: str, age: int) -> bool: print(f”Hello, {name}! You are {age} years old.”) return True “` In this example, we’ve added type hints for the `name` and `age` parameters, as well as the return value. These hints tell other developers (and tools) that the `greet` function expects a string and an integer, and returns a boolean value. ## Code Example 2: Real-world Example Let’s consider a more realistic example. Suppose we’re building a simple banking system, and we have a class that represents a bank account. “`python class BankAccount: def __init__(self, account_number: int, balance: float): self.account_number = account_number self.balance = balance def deposit(self, amount: float) -> None: self.balance += amount def withdraw(self, amount: float) -> None: if amount > self.balance: raise ValueError(“Insufficient funds”) self.balance -= amount def get_balance(self) -> float: return self.balance “` In this example, we’ve added type hints for the `account_number` and `balance` attributes, as well as the `deposit`, `withdraw`, and `get_balance` methods. These hints provide a clear understanding of how to work with the `BankAccount` class. ## Code Example 3: Advanced Pattern Let’s consider a more advanced example. Suppose we’re building a system that works with different types of vehicles, and we have a base class that represents a vehicle. “`python from typing import Protocol class Vehicle(Protocol): def start_engine(self) -> None: … def accelerate(self) -> None: … def brake(self) -> None: … class Car: def start_engine(self) -> None: print(“Car engine started”) def accelerate(self) -> None: print(“Car accelerating”) def brake(self) -> None: print(“Car braking”) class Truck: def start_engine(self) -> None: print(“Truck engine started”) def accelerate(self) -> None: print(“Truck accelerating”) def brake(self) -> None: print(“Truck braking”) def drive_vehicle(vehicle: Vehicle) -> None: vehicle.start_engine() vehicle.accelerate() vehicle.brake() drive_vehicle(Car()) drive_vehicle(Truck()) “` In this example, we’ve defined a `Vehicle` protocol that specifies the methods that a vehicle should have. We’ve then created `Car` and `Truck` classes that implement this protocol. The `drive_vehicle` function takes a `Vehicle` as an argument, and calls the methods specified in the protocol. This pattern allows us to work with different types of vehicles in a polymorphic way. ## Common Mistakes to Avoid When working with type hints, there are several common mistakes to avoid: * Not using type hints consistently throughout your codebase * Using type hints that are too vague (e.g. `Any`) or too specific (e.g. `int` when `float` would also be acceptable) * Not using type hints for function return values * Not using type hints for class attributes ## Quick Summary In this tutorial, we’ve covered the basics of Python type hints, including their benefits, core concepts, and practical examples. We’ve seen how type hints can be used to provide additional information about the expected types of variables, function parameters, and return values. We’ve also covered some common mistakes to avoid when working with type hints. ## Introduction Type hints are not a replacement for Python’s dynamic typing, but rather a way to provide additional information about the expected types of variables, function parameters, and return values. This information can be used by IDEs, type checkers, and other tools to provide better code completion, error detection, and documentation. In this tutorial, we will explore the world of Python type hints, including their benefits, core concepts, and practical examples. ## Prerequisites Before diving into type hints, you should have a basic understanding of Python programming, including variables, data types, functions, and modules. You should also be familiar with the concept of static typing and dynamic typing. ## Core Concept with Analogy Type hints can be thought of as a recipe for your code. Just as a recipe provides instructions for cooking a dish, type hints provide instructions for working with your code. They tell other developers (and tools) what ingredients (types) to expect, how to prepare them (function signatures), and what the final dish (return value) should look like. This analogy is not exact, but it gives you an idea of how type hints can help guide the development process. ## Code Example 1: Basic Usage Let’s start with a simple example. Suppose we have a function that takes a string and an integer as arguments and returns a boolean value. “`python def greet(name: str, age: int) -> bool: print(f”Hello, {name}! You are {age} years old.”) return True “` In this example, we’ve added type hints for the `name` and `age` parameters, as well as the return value. These hints tell other developers (and tools) that the `greet` function expects a string and an integer, and returns a boolean value. ## Code Example 2: Real-world Example Let’s consider a more realistic example. Suppose we’re building a simple banking system, and we have a class that represents a bank account. “`python class BankAccount: def __init__(self, account_number: int, balance: float): self.account_number = account_number self.balance = balance def deposit(self, amount: float) -> None: self.balance += amount def withdraw(self, amount: float) -> None: if amount > self.balance: raise ValueError(“Insufficient funds”) self.balance -= amount def get_balance(self) -> float: return self.balance “` In this example, we’ve added type hints for the `account_number` and `balance` attributes, as well as the `deposit`, `withdraw`, and `get_balance` methods. These hints provide a clear understanding of how to work with the `BankAccount` class. ## Code Example 3: Advanced Pattern Let’s consider a more advanced example. Suppose we’re building a system that works with different types of vehicles, and we have a base class that represents a vehicle. “`python from typing import Protocol class Vehicle(Protocol): def start_engine(self) -> None: … def accelerate(self) -> None: … def brake(self) -> None: … class Car: def start_engine(self) -> None: print(“Car engine started”) def accelerate(self) -> None: print(“Car accelerating”) def brake(self) -> None: print(“Car braking”) class Truck: def start_engine(self) -> None: print(“Truck engine started”) def accelerate(self) -> None: print(“Truck accelerating”) def brake(self) -> None: print(“Truck braking”) def drive_vehicle(vehicle: Vehicle) -> None: vehicle.start_engine() vehicle.accelerate() vehicle.brake() drive_vehicle(Car()) drive_vehicle(Truck()) “` In this example, we’ve defined a `Vehicle` protocol that specifies the methods that a vehicle should have. We’ve then created `Car` and `Truck` classes that implement this protocol. The `drive_vehicle` function takes a `Vehicle` as an argument, and calls the methods specified in the protocol. This pattern allows us to work with different types of vehicles in a polymorphic way. ## Common Mistakes to Avoid When working with type hints, there are several common mistakes to avoid: * Not using type hints consistently throughout your codebase * Using type hints that are too vague (e.g. `Any`) or too specific (e.g. `int` when `float` would also be acceptable) * Not using type hints for function return values * Not using type hints for class attributes ## Quick Summary In this tutorial, we’ve covered the basics of Python type hints, including their benefits, core concepts, and practical examples. We’ve seen how type hints can be used to provide additional information about the expected types of variables, function parameters, and return values. We’ve also covered some common mistakes to avoid when working with type hints. Python is a dynamically-typed language, which means that the data type of a variable is determined at runtime, not at compile time. While this flexibility can be beneficial, it can also lead to type-related errors that are difficult to catch. This is where type hints come in – a feature introduced in Python 3.5 that allows developers to add type annotations to their code, making it easier to catch type-related errors and improve code readability. ## Introduction Type hints are not a replacement for Python’s dynamic typing, but rather a way to provide additional information about the expected types of variables, function parameters, and return values. This information can be used by IDEs, type checkers, and other tools to provide better code completion, error detection, and documentation. In this tutorial, we will explore the world of Python type hints, including their benefits, core concepts, and practical examples. ## Prerequisites Before diving into type hints, you should have a basic understanding of Python programming, including variables, data types, functions, and modules. You should also be familiar with the concept of static typing and dynamic typing. ## Core Concept with Analogy Type hints can be thought of as a recipe for your code. Just as a recipe provides instructions for cooking a dish, type hints provide instructions for working with your code. They tell other developers (and tools) what ingredients (types) to expect, how to prepare them (function signatures), and what the final dish (return value) should look like. This analogy is not exact, but it gives you an idea of how type hints can help guide the development process. ## Code Example 1: Basic Usage Let’s start with a simple example. Suppose we have a function that takes a string and an integer as arguments and returns a boolean value. “`python def greet(name: str, age: int) -> bool: print(f”Hello, {name}! You are {age} years old.”) return True “` In this example, we’ve added type hints for the `name` and `age` parameters, as well as the return value. These hints tell other developers (and tools) that the `greet` function expects a string and an integer, and returns a boolean value. ## Code Example 2: Real-world Example Let’s consider a more realistic example. Suppose we’re building a simple banking system, and we have a class that represents a bank account. “`python class BankAccount: def __init__(self, account_number: int, balance: float): self.account_number = account_number self.balance = balance def deposit(self, amount: float) -> None: self.balance += amount def withdraw(self, amount: float) -> None: if amount > self.balance: raise ValueError(“Insufficient funds”) self.balance -= amount def get_balance(self) -> float: return self.balance “` In this example, we’ve added type hints for the `account_number` and `balance` attributes, as well as the `deposit`, `withdraw`, and `get_balance` methods. These hints provide a clear understanding of how to work with the `BankAccount` class. ## Code Example 3: Advanced Pattern Let’s consider a more advanced example. Suppose we’re building a system that works with different types of vehicles, and we have a base class that represents a vehicle. “`python from typing import Protocol class Vehicle(Protocol): def start_engine(self) -> None: … def accelerate(self) -> None: … def brake(self) -> None: … class Car: def start_engine(self) -> None: print(“Car engine started”) def accelerate(self) -> None: print(“Car accelerating”) def brake(self) -> None: print(“Car braking”) class Truck: def start_engine(self) -> None: print(“Truck engine started”) def accelerate(self) -> None: print(“Truck accelerating”) def brake(self) -> None: print(“Truck braking”) def drive_vehicle(vehicle: Vehicle) -> None: vehicle.start_engine() vehicle.accelerate() vehicle.brake() drive_vehicle(Car()) drive_vehicle(Truck()) “` In this example, we’ve defined a `Vehicle` protocol that specifies the methods that a vehicle should have. We’ve then created `Car` and `Truck` classes that implement this protocol. The `drive_vehicle` function takes a `Vehicle` as an argument, and calls the methods specified in the protocol. This pattern allows us to work with different types of vehicles in a polymorphic way. ## Common Mistakes to Avoid When working with type hints, there are several common mistakes to avoid: * Not using type hints consistently throughout your codebase * Using type hints that are too vague (e.g. `Any`) or too specific (e.g. `int` when `float` would also be acceptable) * Not using type hints for function return values * Not using type hints for class attributes ## Quick Summary In this tutorial, we’ve covered the basics of Python type hints, including their benefits, core concepts, and practical examples. We’ve seen how type hints can be used to provide additional information about the expected types of variables, function parameters, and return values. We’ve also covered some common mistakes to avoid when working with type hints. Python is a dynamically-typed language, which means that the data type of a variable is determined at runtime, not at compile time. While this flexibility can be beneficial, it can also lead to type-related errors that are difficult to catch. This is where type hints come in – a feature introduced in Python 3.5 that allows developers to add type annotations to their code, making it easier to catch type-related errors and improve code readability. ## Introduction Type hints are not a replacement for Python’s dynamic typing, but rather a way to provide additional information about the expected types of variables, function parameters, and return values. This information can be used by IDEs, type checkers, and other tools to provide better code completion, error detection, and documentation. In this tutorial, we will explore the world of Python type hints, including their benefits, core concepts, and practical examples. ## Prerequisites Before diving into type hints, you should have a basic understanding of Python programming, including variables, data types, functions, and modules. You should also be familiar with the concept of static typing and dynamic typing. ## Core Concept with Analogy Type hints can be thought of as a recipe for your code. Just as a recipe provides instructions for cooking a dish, type hints provide instructions for working with your code. They tell other developers (and tools) what ingredients (types) to expect, how to prepare them (function signatures), and what the final dish (return value) should look like. This analogy is not exact, but it gives you an idea of how type hints can help guide the development process. ## Code Example 1: Basic Usage Let’s start with a simple example. Suppose we have a function that takes a string and an integer as arguments and returns a boolean value. “`python def greet(name: str, age: int) -> bool: print(f”Hello, {name}! You are {age} years old.”) return True “` In this example, we’ve added type hints for the `name` and `age` parameters, as well as the return value. These hints tell other developers (and tools) that the `greet` function expects a string and an integer, and returns a boolean value. ## Code Example 2: Real-world Example Let’s consider a more realistic example. Suppose we’re building a simple banking system, and we have a class that represents a bank account. “`python class BankAccount: def __init__(self, account_number: int, balance: float): self.account_number = account_number self.balance = balance def deposit(self, amount: float) -> None: self.balance += amount def withdraw(self, amount: float) -> None: if amount > self.balance: raise ValueError(“Insufficient funds”) self.balance -= amount def get_balance(self) -> float: return self.balance “` In this example, we’ve added type hints for the `account_number` and `balance` attributes, as well as the `deposit`, `withdraw`, and `get_balance` methods. These hints provide a clear understanding of how to work with the `BankAccount` class. ## Code Example 3: Advanced Pattern Let’s consider a more advanced example. Suppose we’re building a system that works with different types of vehicles, and we have a base class that represents a vehicle. “`python from typing import Protocol class Vehicle(Protocol): def start_engine(self) -> None: … def accelerate(self) -> None: … def brake(self) -> None: … class Car: def start_engine(self) -> None: print(“Car engine started”) def accelerate(self) -> None: print(“Car accelerating”) def brake(self) -> None: print(“Car braking”) class Truck: def start_engine(self) -> None: print(“Truck engine started”) def accelerate(self) -> None: print(“Truck accelerating”) def brake(self) -> None: print(“Truck braking”) def drive_vehicle(vehicle: Vehicle) -> None: vehicle.start_engine() vehicle.accelerate() vehicle.brake() drive_vehicle(Car()) drive_vehicle(Truck()) “` In this example, we’ve defined a `Vehicle` protocol that specifies the methods that a vehicle should have. We’ve then created `Car` and `Truck` classes that implement this protocol. The `drive_vehicle` function takes a `Vehicle` as an argument, and calls the methods specified in the protocol. This pattern allows us to work with different types of vehicles in a polymorphic way. ## Common Mistakes to Avoid When working with type hints, there are several common mistakes to avoid: * Not using type hints consistently throughout your codebase * Using type hints that are too vague (e.g. `Any`) or too specific (e.g. `int` when `float` would also be acceptable) * Not using type hints for function return values * Not using type hints for class attributes ## Quick Summary In this tutorial, we’ve covered the basics of Python type hints, including their benefits, core concepts, and practical examples. We’ve seen how type hints can be used to provide additional information about the expected types of variables, function parameters, and return values. We’ve also covered some common mistakes to avoid when working with type hints. Python is a dynamically-typed language, which means that the data type of a variable is determined at runtime, not at compile time. While this flexibility can be beneficial, it can also lead to type-related errors that are difficult to catch. This is where type hints come in – a feature introduced in Python 3.5 that allows developers to add type annotations to their code, making it easier to catch type-related errors and improve code readability. ## Introduction Type hints are not a replacement for Python’s dynamic typing, but rather a way to provide additional information about the expected types of variables, function parameters, and return values. This information can be used by IDEs, type checkers, and other tools to provide better code completion, error detection, and documentation. In this tutorial, we will explore the world of Python type hints, including their benefits, core concepts, and practical examples. ## Prerequisites Before diving into type hints, you should have a basic understanding of Python programming, including variables, data types, functions, and modules. You should also be familiar with the concept of static typing and dynamic typing. ## Core Concept with Analogy Type hints can be thought of as a recipe for your code. Just as a recipe provides instructions for cooking a dish, type hints provide instructions for working with your code. They tell other developers (and tools) what ingredients (types) to expect, how to prepare them (function signatures), and what the final dish (return value) should look like. This analogy is not exact, but it gives you an idea of how type hints can help guide the development process. ## Code Example 1: Basic Usage Let’s start with a simple example. Suppose we have a function that takes a string and an integer as arguments and returns a boolean value. “`python def greet(name: str, age: int) -> bool: print(f”Hello, {name}! You are {age} years old.”) return True “` In this example, we’ve added type hints for the `name` and `age` parameters, as well as the return value. These hints tell other developers (and tools) that the `greet` function expects a string and an integer, and returns a boolean value. ## Code Example 2: Real-world Example Let’s consider a more realistic example. Suppose we’re building a simple banking system, and we have a class that represents a bank account. “`python class BankAccount: def __init__(self, account_number: int, balance: float): self.account_number = account_number self.balance = balance def deposit(self, amount: float) -> None: self.balance += amount def withdraw(self, amount: float) -> None: if amount > self.balance: raise ValueError(“Insufficient funds”) self.balance -= amount def get_balance(self) -> float: return self.balance “` In this example, we’ve added type hints for the `account_number` and `balance` attributes, as well as the `deposit`, `withdraw`, and `get_balance` methods. These hints provide a clear understanding of how to work with the `BankAccount` class. ## Code Example 3: Advanced Pattern Let’s consider a more advanced example. Suppose we’re building a system that works with different types of vehicles, and we have a base class that represents a vehicle. “`python from typing import Protocol class Vehicle(Protocol): def start_engine(self) -> None: … def accelerate(self) -> None: … def brake(self) -> None: … class Car: def start_engine(self) -> None: print(“Car engine started”) def accelerate(self) -> None: print(“Car accelerating”) def brake(self) -> None: print(“Car braking”) class Truck: def start_engine(self) -> None: print(“Truck engine started”) def accelerate(self) -> None: print(“Truck accelerating”) def brake(self) -> None: print(“Truck braking”) def drive_vehicle(vehicle: Vehicle) -> None: vehicle.start_engine() vehicle.accelerate() vehicle.brake() drive_vehicle(Car()) drive_vehicle(Truck()) “` In this example, we’ve defined a `Vehicle` protocol that specifies the methods that a vehicle should have. We’ve then created `Car` and `Truck` classes that implement this protocol. The `drive_vehicle` function takes a `Vehicle` as an argument, and calls the methods specified in the protocol. This pattern allows us to work with different types of vehicles in a polymorphic way. ## Common Mistakes to Avoid When working with type hints, there are several common mistakes to avoid: * Not using type hints consistently throughout your codebase * Using type hints that are too vague (e.g. `Any`) or too specific (e.g. `int` when `float` would also be acceptable) * Not using type hints for function return values * Not using type hints for class attributes ## Quick Summary In this tutorial, we’ve covered the basics of Python type hints, including their benefits, core concepts, and practical examples. We’ve seen how type hints can be used to provide additional information about the expected types of variables, function parameters, and return values. We’ve also covered some common mistakes to avoid when working with type hints.