Python Best Practices: Type Hints, Docstrings, and Doctests Explained with Examples

 In modern Python development, writing clean, maintainable, and well-documented code is not just a luxury—it's essential. Three important features that help with this are type hints, docstrings, and doctests.

In this guide, you’ll learn what they are, why they matter, and how to use them together effectively—with practical examples.

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🧠 Why These Practices Matter

When you're writing Python code—especially for real-world projects or sharing your work—clarity is everything. Here’s how each of these practices contributes:

• Type hints make your code easier to understand and debug.
  
  

• Docstrings tell others (and your future self) what the code does.
  
  

• Doctests provide a simple way to validate code examples directly from the documentation.
  
  

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✅ Type Hints (Type Annotations)

Type hints allow you to explicitly declare the expected types for variables, function parameters, and return values. Python is dynamically typed, so type hints don’t enforce rules at runtime—but they do help with code clarity, IDE autocomplete, and static analysis tools like mypy or Pyright.

📌 Syntax:

def add(a: int, b: int) -> int:     return a + b

🎯 Benefits:

• Easier debugging
  
  

• Better editor support
  
  

• Fewer runtime surprises
  
  

🛠 Example:

def is_even(n: int) -> bool:     return n % 2 == 0

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✅ Docstrings

A docstring is a string literal placed right after the definition of a function, class, or module. It's used to describe what the code does, including its inputs and outputs.

📌 Format (Google style or reStructuredText is common):

def greet(name: str) -> str:     """     Returns a personalized greeting.     Args:         name (str): The name of the person.     Returns:         str: A greeting message.     """     return f"Hello, {name}!"

✨ Best Practices:

• Always write a docstring for public functions and classes.
  
  

• Be concise but descriptive.
  
  

• Include parameter types and return types if not using type hints.
  
  

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✅ Doctests

Doctests are examples embedded in docstrings that act as test cases. You can run them using Python’s built-in doctest module to verify that your code behaves as expected.

📌 How to write a doctest:

def square(x: int) -> int:     """     Returns the square of a number.     Args:         x (int): A number to square.     Returns:         int: The square of the number.     Examples:         >>> square(3)         9         >>> square(-4)         16     """     return x * x

▶️ Running Doctests

Add this block to the bottom of your Python script:

if __name__ == "__main__":     import doctest     doctest.testmod()

Then run the script as usual. If all tests pass, it’ll be silent. If not, you’ll see which examples failed.

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🧪 Complete Example: All Three in Action

def factorial(n: int) -> int:     """     Returns the factorial of a non-negative integer.     Args:         n (int): A non-negative integer.     Returns:         int: Factorial of the input number.     Raises:         ValueError: If n is negative.     Examples:         >>> factorial(0)         1         >>> factorial(5)         120     """     if n < 0:         raise ValueError("Input must be non-negative")     result = 1     for i in range(2, n + 1):         result *= i     return result if __name__ == "__main__":     import doctest     doctest.testmod()

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🧰 Tools That Work Well with These Practices

Tool	Purpose
mypy	Static type checker for Python
pylint	Code quality and linting
doctest	Runs doctests in docstrings
Sphinx	Builds documentation from docstrings

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📘 Final Thoughts

Combining type hints, docstrings, and doctests makes your code:

• Easier to read
  
  

• Easier to test
  
  

• Easier to collaborate on
  
  

• More professional and production-ready
  
  

Even if you're writing code just for yourself today, adopting these best practices early builds the discipline needed for writing robust, clean, and shareable Python code.

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