Python Development Environment

Python Development Environment Setup in Detail

1. Install Python

$ python --version
$ python3 --version

Output (example):
Python 3.10.5

2. Choosing an IDE or Code Editor

Install VS Code and Python Extension

3. Setting Up a Virtual Environment

$ python3 -m venv project_env

$ source project_env/bin/activate
      

$ project_env\Scripts\activate
      

$ deactivate

Activating and deactivating virtual environments helps manage dependencies across projects.

4. Package Management with pip

$ pip install package_name

$ pip list

$ pip freeze > requirements.txt

Use pip to install and manage libraries. The `requirements.txt` file can later be used to install all packages in another environment.

5. Configuring the IDE

Python Interpreter Configuration

Linting and Formatting

6. Version Control with Git

$ sudo apt install git  # For Linux
$ brew install git      # For macOS

$ git init
    

$ git add .
    

$ git commit -m "Initial commit"
    

Using Git for version control ensures you can track, revert, and collaborate on code effectively.

7. Running and Debugging Code

$ python script.py

8. Testing with unittest

import unittest
from example import function_to_test

class TestExample(unittest.TestCase):
    def test_function(self):
        result = function_to_test(5)
        self.assertEqual(result, 25)

if __name__ == '__main__':
    unittest.main()

$ python -m unittest test_example.py

Output (example):
.
----------------------------------------------------------------------
Ran 1 test in 0.001s

OK

This setup verifies the functionality of specific components, helping prevent bugs.

9. Using Jupyter Notebooks

$ pip install jupyter

$ jupyter notebook

Output: The Jupyter Notebook opens in your default web browser, allowing you to work interactively with Python code and view outputs immediately below each cell.

10. Popular Libraries and Tools

import requests
  response = requests.get('https://api.example.com/data')
  print(response.json())
  

import numpy as np
import pandas as pd

data = pd.DataFrame({
  'A': np.random.rand(10),
  'B': np.random.rand(10)
})
print(data.describe())

import matplotlib.pyplot as plt
import seaborn as sns

sns.histplot(data['A'])
plt.show()

Installing and using these libraries provides powerful functionality for various Python-based applications.

11. Deploying Python Applications

Summary