Python has become a staple in the toolkit of developers and data scientists due to its readability, simplicity, and extensive libraries. However, to maximize efficiency and maintainability, it’s crucial to follow best practices. This article outlines essential guidelines for writing clean, efficient, and robust Python code, whether you’re developing applications or working with data.

1. Writing Clean Code

a. Follow PEP 8

PEP 8 is the style guide for Python code. Adhering to it ensures consistency and readability across your codebase.

• Indentation: Use 4 spaces per indentation level.
• Line Length: Limit lines to 79 characters.
• Blank Lines: Use blank lines to separate functions and classes, and larger blocks of code inside functions.
• Imports: Import one module per line and group standard library imports, third-party imports, and local imports separately.

import os
import sys

import numpy as np
import pandas as pd

from my_module import my_function

b. Use Meaningful Variable Names

Choose descriptive names for variables, functions, and classes to make the code self-documenting.

Bad

a = 10

Good

number_of_apples = 10

c. Write Docstrings

Docstrings are essential for documenting modules, classes, functions, and methods. They help others understand the purpose and usage of your code.

def calculate_area(radius):
"""
Calculate the area of a circle given its radius.

Parameters:
radius (float): The radius of the circle.

Returns:
float: The area of the circle.
"""
return 3.14159 * radius ** 2

2. Efficient Data Handling

a. Use Vectorized Operations with NumPy and Pandas

Avoid using loops for operations on large datasets. Instead, use vectorized operations provided by libraries like NumPy and Pandas.

import numpy as np

Bad

numbers = [1, 2, 3, 4, 5]
squared_numbers = []
for number in numbers:
squared_numbers.append(number ** 2)

Good

numbers = np.array([1, 2, 3, 4, 5])
squared_numbers = numbers ** 2

b. Leverage Pandas for Data Manipulation

Pandas is a powerful library for data manipulation and analysis. Familiarize yourself with its capabilities to handle data more efficiently.

import pandas as pd

Load data

df = pd.read_csv('data.csv')

Select columns

df = df[['column1', 'column2']]

Filter rows

df = df[df['column1'] > 10]

Group and aggregate

grouped_df = df.groupby('column1').mean()

c. Optimize Memory Usage

When working with large datasets, optimizing memory usage is crucial. Use appropriate data types and consider using libraries like Dask for parallel computing.

Use appropriate data types

df['column'] = df['column'].astype('float32')

Use Dask for parallel computing

import dask.dataframe as dd

ddf = dd.read_csv('large_data.csv')
result = ddf.groupby('column').mean().compute()

3. Enhancing Code Performance

a. Profile Your Code

Use profiling tools to identify performance bottlenecks. The cProfile module in

Python provides detailed profiling information.

import cProfile

def my_function():
# Your code here

cProfile.run('my_function()')

b. Use Built-in Functions and Libraries

Built-in functions and libraries are often optimized for performance. Use them instead of writing custom implementations.

Bad

squared_numbers = [number ** 2 for number in range(1000)]

Good

squared_numbers = list(map(lambda x: x ** 2, range(1000)))

c. Implement Caching

Caching can significantly improve performance by storing the results of expensive function calls and reusing them when the same inputs occur again.

from functools import lru_cache

@lru_cache(maxsize=100)
def expensive_function(x):
# Expensive computation
return x ** 2

4. Ensuring Code Quality

a. Write Unit Tests

Unit tests are essential for verifying the correctness of your code. Use frameworks like unittest or pytest to write and run tests.

import unittest

def add(a, b):
return a + b

class TestAddFunction(unittest.TestCase):
def test_add(self):
self.assertEqual(add(2, 3), 5)
self.assertEqual(add(-1, 1), 0)

if name == 'main':
unittest.main()

b. Continuous Integration

Set up continuous integration (CI) to automatically run tests and checks on your codebase. Tools like GitHub Actions, Travis CI, or Jenkins can help automate this process.
c. Code Reviews

Regular code reviews help maintain code quality and share knowledge among team members. Use platforms like GitHub or GitLab to facilitate code reviews.

5. Effective Use of Python Libraries

a. Utilize Python’s Extensive Standard Library

Python’s standard library is rich with modules and functions that can save you time and effort. Familiarize yourself with libraries like os, sys, json, and datetime.

import json
import os

Load JSON data

with open('data.json', 'r') as file:
data = json.load(file)

Get environment variables

home_dir = os.getenv('HOME')

b. Explore Popular Third-Party Libraries

In addition to the standard library,Python has a vast ecosystem of third-party libraries that can extend its functionality.

Requests: For making HTTP requests.
BeautifulSoup: For web scraping.
Pandas: For data manipulation and analysis.
NumPy: For numerical computing.
Matplotlib/Seaborn: For data visualization.

import requests
from bs4 import BeautifulSoup

Fetch web page

response = requests.get('https://example.com')
soup = BeautifulSoup(response.text, 'html.parser')

Extract and print title

title = soup.title.string
print(title)

Read the complete article on: https://futuristicgeeks.com/master-python-like-a-pro-essential-best-practices-for-developers/