# Top 10 Python best practices

## Some examples of Python Best Practices for Clean and Efficient Code

**Follow PEP 8 style guide:**PEP 8 is the official style guide for Python code. Following PEP 8 can make your code more readable and easier to maintain.`# Good def calculate_area(radius): """ Calculates the area of a circle with given radius. """ return 3.14 * radius ** 2 # Bad def calculateArea(r): return 3.14 * r ** 2`

**Use descriptive names:**Use descriptive and meaningful names for variables, functions, classes, and modules. This makes your code more understandable to others.`# Good def calculate_salary(employee): """ Calculates the salary for the given employee. """ hourly_rate = employee.hourly_rate hours_worked = employee.hours_worked salary = hourly_rate * hours_worked return salary # Bad def calc(employee): hr = employee.hr hw = employee.hw sal = hr * hw return sal`

**Write docstrings:**Write docstrings for all functions, classes, and modules. Docstrings describe what the code does and how to use it.`# Good def find_largest_number(numbers): """ Returns the largest number from a list of numbers. Args: numbers: list of integers Returns: largest number in the list """ return max(numbers) # Bad def largest(numbers): return max(numbers)`

**Avoid global variables:**Avoid using global variables as they can cause unexpected behaviour and make your code harder to understand and test.`# Good def calculate_tax(amount): tax_rate = 0.10 tax = amount * tax_rate return tax # Bad tax_rate = 0.10 def calculate_tax(amount): tax = amount * tax_rate return tax`

**Use list comprehensions**: Use list comprehensions instead of loops to create lists. List comprehensions are more concise and readable.`# Good numbers = [1, 2, 3, 4, 5] squares = [num ** 2 for num in numbers] # Bad numbers = [1, 2, 3, 4, 5] squares = [] for num in numbers: squares.append(num ** 2)`

**Use context managers:**Use context managers to properly manage resources such as files, sockets, and database connections.`# Good with open('file.txt', 'r') as f: data = f.read() # do something with data # Bad f = open('file.txt', 'r') data = f.read() # do something with data f.close()`

**Avoid unnecessary complexity:**Keep your code simple and easy to understand. Avoid unnecessary complexity and don't over-engineer solutions.`# Good def calculate_average(numbers): return sum(numbers) / len(numbers) # Bad def calculate_average(numbers): total = 0 for num in numbers: total += num average = total / len(numbers) return average`

**Write unit tests:**Write unit tests for your code to ensure that it works as expected and to catch any bugs or errors early.`# Good def test_calculate_area(): assert calculate_area(5) == 78.5 assert calculate_area(0) == 0 assert calculate_area(-5) == 78.5 # Bad def calculate_area(radius): return 3.14 * radius ** 2`

**Use virtual environments:**Use virtual environments to isolate your Python environment and avoid dependency conflicts.`# Good $ python3 -m venv venv $ source venv/bin/activate (myenv) $ pip install requests # Bad $ pip install requests`

**Optimize code:**Optimize your code for speed and efficiency. This includes using built-in functions and libraries, avoiding unnecessary computation, and using appropriate data structures.`# Good def sum_squares(numbers): return sum(num**2 for num in numbers) # Bad def sum_squares(numbers): squares = [num**2 for num in numbers] return sum(squares)`

*In the above example, the good code uses a generator expression to compute the squares of the numbers and sum them up in a single step, while the bad code first creates a list of squares and then sums them up. The good code is more efficient as it avoids creating an unnecessary list in memory.*