Top Python Best Practices To Boost Code Efficiency

Top Python Best
Practices to Boost
Your Code
Efficiency
Writing clean and efficient Python code
improves performance, reduces errors, and
enhances scalability. These slides will cover
the top 10 best practices to help you write
better, high-performing code.
Devace Technologies

Python Best Practices to Follow for Efficient Coding
Adopting the right coding practices enhances code readability, maintainability, and
performance. Key best practices include following:
1. Follow the PEP 8 Style Guide
2. Document Your Code Properly
3. Optimize Loops and Conditional Statements
4. Write Unit Tests for Reliability
5. Use Virtual Environments for Dependency Management
6. Adhere to the DRY Principle (Don't Repeat Yourself)
7. Implement Effective Error Handling
8. Utilize Python’s Built-in Functions Efficiently
9. Focus on Readable and Maintainable Code
10. Use List Comprehensions for Cleaner Syntax

1. PEP 8 Style
PEP 8 (Python Enhancement Proposal 8) is the official style guide that
ensures clean, readable, and maintainable Python code. Created by
Guido van Rossum, Barry Warsaw, and Nick Coghlan, it covers naming
conventions, indentation, line length, and overall code structure. While
widely accepted, companies may adapt it to fit their needs.
Key PEP 8 Guidelines:
• Use 4 spaces per indentation level.
• Limit lines to 79 characters for readability.
• Follow snake_case for variables/functions and CamelCase for
classes.
• Organize imports: standard libraries, third-party libraries, local
modules.

1. PEP 8 Style
Here is the example:
# Good example following PEP 8
def calculate_area(length, width):
return length * width
# Bad example
def CalculateArea(LENGTH, WIDTH):return LENGTH*WIDTH

2. Document Your Code
Clear and concise documentation helps developers understand the
purpose and functionality of your code. It provides context, making it
easier to maintain and collaborate. Tools like Sphinx can automate
documentation from docstrings, ensuring consistency.
Best Practices for Code Documentation:
• Add docstrings for all public modules, functions, methods, and
classes.
• Follow PEP 257 for docstring conventions.
• Keep documentation up to date with code changes.
• Use type hints (Python 3.5+) for clarity.
• Leverage automated documentation tools to save time.

2. Document Your Code
def calculate_discount(price, discount_rate):
"""
Calculate the discounted price.
Parameters:
price (float): Original price of the item.
discount_rate (float): Discount rate as a decimal.
Returns:
float: Discounted price.
"""
return price * (1 - discount_rate)

• Optimize loops and conditional statements to enhance
performance.
• Avoid deeply nested loops and conditions to prevent
bottlenecks.
• Use break and continue effectively for better control flow.
• Leverage vectorized operations with libraries like NumPy
for efficient handling of large datasets.
• Ensure your code runs smoothly by refining loop structures
and conditions.

# Bad: Nested loops causing inefficiency
for i in range(100):
for j in range(100):
print(i * j)
# Good: Using list comprehension
results = [i * j for i in range(100) for j in
range(100)]

When developing a Python website, ensuring code reliability and early
bug detection is crucial. The sooner issues are caught, the better.
Writing unit tests helps verify code accuracy and identify bugs early.
The standard approach involves using Python’s built-in unittest
module, which provides a structured way to create and execute tests.
A best practice for writing unit tests is following the Arrange-Act-
Assert (AAA) pattern:
• Arrange: Set up necessary data and test conditions.
• Act: Call the function being tested.
• Assert: Verify the outcome using assertion methods like
assertEqual(), assertTrue(), or assertRaises() for exceptions.

import unittest
def add(a, b):
return a + b
class TestMathFunctions(unittest.TestCase):
def test_add(self):
self.assertEqual(add(2, 3), 5)
if __name__ == "__main__":
unittest.main()

A key Python best practice is utilizing virtual environments during
development. This ensures isolation from dependencies across
different projects, reducing conflicts between packages and
versions. Virtual environments allow you to manage project-
specific libraries without affecting the global Python setup.
Tools like venv and pipenv enable you to create dedicated
environments for each project, making collaboration smoother
and ensuring consistent performance across different machines.
Virtual environments also simplify dependency management,
making it easier to replicate setups and improving the scalability
and maintainability of your projects.

# Create a virtual environment
python -m venv my_env
# Activate the environment (Windows)
my_envScriptsactivate
# Activate the environment (macOS/Linux)
source my_env/bin/activate

6. Adhere to the DRY Principle
A fundamental Python best practice for maintaining high-quality
code is adhering to the DRY principle, which emphasizes avoiding
code repetition by using reusable functions and classes.
Duplicating code increases the likelihood of bugs and complicates
maintenance. It's important to steer clear of the WET principle
(Write Everything Twice), as redundancy can lead to confusion and
make the code harder to understand, both for you and others.
To follow the DRY principle, you can use tools like Common Table
Expressions (CTEs), dbt macros, decorators, modules, and linters
like pylint and flake8 (which help identify code duplication).
Additionally, Git hooks and CI/CD pipelines can automate checks
for duplicate code before deployment.

6. Adhere to the DRY Principle
# Bad: Repetitive code
def calculate_rectangle_area(length, width):
return length * width
def calculate_square_area(side):
return side * side
# Good: Reusable function
def calculate_area(length, width=None):
return length * (width if width else length)

To write reliable and maintainable Python code, it's essential
to implement error handling. This ensures your program
can gracefully manage unexpected situations like invalid
inputs or file errors without crashing.
The try…except block is a fundamental tool for catching and
handling exceptions. For example:
• When performing division, a ZeroDivisionError might
occur.
• Using try…except, you can display a fallback message
instead of halting the entire program.

try:
number = int(input("Enter a number: "))
result = 100 / number
print(f"Result: {result}")
except ZeroDivisionError:
print("Error: Division by zero is not
allowed.")
except ValueError:
print("Error: Please enter a valid
number.")

Python is renowned for its robust security features. Developers
should take advantage of built-in functions to improve both
performance and readability. Functions such as sum(), min(),
max(), enumerate(), and zip() can replace manual loops,
optimizing the code and minimizing the risk of inefficiencies or
errors. Here's an example:
# Using built-in functions
numbers = [4, 7, 2, 9, 1]
total = sum(numbers)
maximum = max(numbers)

Here's a comparison of the two approaches: Instead of using a
loop to sum a list of numbers, you can use a built-in function:
# Without built-in function
numbers = [1, 2, 3, 4, 5]
total = 0
for num in numbers:
total += num
print("Sum:", total)

A key best practice in Python coding is using proper commenting
techniques. Comments help simplify code, making it cleaner, more
understandable, and easier to maintain. They clarify the purpose of
your code, allowing both you and others to better understand the
logic when revisiting it.
Python offers various commenting methods: single-line comments,
multi-line comments, and multi-line string comments (docstrings).
Single-line Comments:
Use the # symbol for brief comments. Example:
x = 5 # Assigning value to x
print(x) # Printing the value of x

Multi-line Comments (Using #):
Use multiple # symbols for comments spanning multiple lines. Example:
# explaining the next block of code
y = 10
print(y)
Multi-line String as a Comment (Docstrings):
Use triple quotes (''' or """) for documentation purposes.
"""
This function adds two numbers
and returns the result.
"""
def add(a, b):
return a + b

One of the final best practices in Python is utilizing list
comprehensions. These allow you to generate lists in a
single line, making the code more concise and efficient
compared to traditional loops.
List comprehensions are ideal for transforming data and
filtering elements in a readable manner. However, for
complex operations, it's best to avoid them, as they can
reduce readability. Here's an example:

# Good: List comprehension
even_numbers = [x for x in range(20) if x % 2
== 0]
# Bad: Loop-based approach
even_numbers = []
for x in range(20):
if x % 2 == 0:
even_numbers.append(x)

Thank You For Your Time
Any Question? Contact Us
Original Blog: Python Best Practices Guide
Website: https://www.devacetech.com/
Email: info@devacetech.com
Phone: (848) 208-659

Top Python Best Practices To Boost Code Efficiency

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