• NumPy: A powerful library for numerical computations. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on them.

Example:

  import numpy as np

arr = np.array([1, 2, 3, 4])
print(arr.mean())  # Calculate the mean of the array
  • Pandas: A library for data manipulation and analysis. It provides data structures like Series and DataFrame, making it easier to handle structured data.

Example:

  import pandas as pd

data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35]}
df = pd.DataFrame(data)
print(df)
  • Matplotlib: A plotting library used to create static, interactive, and animated visualizations in Python.

Example:

  import matplotlib.pyplot as plt

plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Simple Plot')
plt.show()

Introduction to Web Frameworks

  • Flask: A lightweight web framework that is easy to get started with. Flask is known for its simplicity and flexibility, making it a good choice for small to medium-sized web applications.

Example:

  from flask import Flask, render_template

app = Flask(__name__)

@app.route('/')
def home():
    return render_template('index.html')

if __name__ == '__main__':
    app.run(debug=True)
  • Django: A high-level web framework that encourages rapid development and clean, pragmatic design. Django includes many built-in features, such as an admin panel, authentication, and ORM (Object-Relational Mapping).

Example:

  # Django example (project setup)
django-admin startproject mysite
cd mysite
python manage.py startapp myapp
python manage.py runserver

Working with APIs

APIs (Application Programming Interfaces): Allow applications to interact with each other. In Python, you can use libraries like requests to send HTTP requests to interact with web APIs.

Example:

  import requests

response = requests.get('https://api.example.com/data')
if response.status_code == 200:
    data = response.json()
    print(data)
else:
    print("Failed to retrieve data")

Common API Operations:

  • GET: Retrieve data from an API.
  • POST: Send data to an API to create or update resources.
  • PUT: Update existing resources on the server.
  • DELETE: Remove resources from the server.

requests — HTTP Client 

  import requests

response = requests.post(
    'https://api.example.com/users',
    json={'name': 'Alice', 'email': '[email protected]'},
    headers={'Authorization': f'Bearer {token}'},
    timeout=10
)
response.raise_for_status()
user = response.json()

Always set timeout to prevent hanging indefinitely.

FastAPI vs Flask vs Django

Framework Best for
Flask Microservices, APIs, learning
Django Full-stack apps with admin, ORM, auth
FastAPI High-performance async APIs with auto docs

Virtual Environments per Project 

  python -m venv .venv
source .venv/bin/activate
pip install flask pandas requests

Isolate dependencies so Project A’s Django 4 does not conflict with Project B’s Django 5.

Library Purpose
httpx Async HTTP client
pydantic Data validation
click CLI applications
pytest Testing framework
black Code formatting

API Error Handling 

  try:
    response = requests.get(url, timeout=5)
    response.raise_for_status()
except requests.HTTPError as e:
    print(f"HTTP error: {e.response.status_code}")
except requests.ConnectionError:
    print("Network unreachable")