Section 1.1: Meaningful Variable Naming

# Poor

x = 10

y = 20

# Improved

total_score = 10

maximum_score = 20

Using clear and descriptive variable names greatly enhances the readability of your code, making it easier for both you and your collaborators to comprehend.

Section 1.2: PEP 8 Compliance

# Poor

def calculate_average(listOfNumbers):

return sum(listOfNumbers) / len(listOfNumbers)

# Improved

def calculate_average(numbers):

return sum(numbers) / len(numbers)

Following the PEP 8 style guidelines promotes uniformity and clarity across Python projects.

Section 1.3: Utilizing Virtual Environments

# Poor

pip install requests

# Improved

python -m venv myenv

source myenv/bin/activate

pip install requests

Virtual environments are vital for isolating project dependencies and avoiding conflicts.

Section 1.4: Embracing List Comprehensions

# Poor

squared_numbers = []

for num in range(1, 6):

squared_numbers.append(num**2)

# Improved

squared_numbers = [num**2 for num in range(1, 6)]

List comprehensions streamline code and enhance efficiency when generating lists.

Section 1.5: Exception Handling Techniques

# Poor

try:

result = divide(a, b)

except ZeroDivisionError:

result = None

# Improved

try:

result = divide(a, b)

except ZeroDivisionError as e:

result = str(e)

Explicitly managing exceptions and providing clear error messages is crucial for effective debugging.

Section 1.6: The Importance of Docstrings

def add(a, b):

return a + b

# Improved

def add(a, b):

"""

Adds two numbers.

Args:

a (int): The first number.

b (int): The second number.

Returns:

int: The sum of the two numbers.

"""

return a + b

Docstrings serve as documentation for your code, making it easier for others to utilize your functions.

Section 1.7: Modularizing Your Code

# Poor

# A single monolithic script

# Improved

# Divide code into modules and import as needed

Breaking your code into modules enhances organization and maintainability.

Section 1.8: Leveraging Built-in Functions

# Poor

result = []

for item in my_list:

if item > 5:

result.append(item)

# Improved

result = filter(lambda x: x > 5, my_list)

Utilizing Python's built-in functions simplifies your code and boosts performance.

Section 1.9: Version Control Best Practices

# Poor

backup_final.py

backup_final_final.py

backup_final_final_final.py

# Improved

# Use version control (e.g., Git) for tracking code history and collaboration.

Avoid manually naming different versions; instead, rely on version control systems to manage changes.

Section 1.10: Writing Effective Tests

def divide(a, b):

if b == 0:

return None

return a / b

# Improved

import unittest

class TestDivideFunction(unittest.TestCase):

def test_divide_by_zero(self):

self.assertIsNone(divide(10, 0))

def test_divide_positive_numbers(self):

self.assertEqual(divide(10, 2), 5.0)

Implementing tests ensures your code behaves as expected and remains functional amid updates.