Next Steps

Introduction to frameworks:

A framework in Python is a collection of pre-written code and tools that provides a structured foundation for building applications, allowing developers to focus more on functionality and less on repetitive setup. Frameworks speed up development, enforce best practices, and reduce errors.

Why Learn Frameworks?

• Faster development with built-in tools
• Promotes code organization and reusability
• Encourages best practices and security
• Used heavily in web development, data science, automation, and app development

Types of Frameworks

1. Web Development

Framework	Best For	Example Use Case
Flask	Lightweight web apps & APIs	Blog, REST API
Django	Full-featured web applications	E-commerce, CMS
FastAPI	High-performance APIs	Real-time dashboards, microservices

2. Data Science & AI

Framework	Purpose
TensorFlow	Deep learning models
PyTorch	Neural networks & training
Scikit-learn	Machine learning algorithms
Pandas	Data manipulation
Matplotlib	Data visualization

3. GUI and Desktop Apps

Framework	Purpose
Tkinter	Built-in UI toolkit
PyQt	Advanced GUIs
Kivy	Mobile/Multitouch

Key Concepts When Using Frameworks

• Routing – Mapping URLs to functions (Flask, Django)
• ORMs – Database interactions using Python classes (Django ORM, SQLAlchemy)
• Templates – Creating dynamic HTML (Jinja2 in Flask)
• Middleware – Functions that run before/after requests
• Modular Structure – Encouraging separation of concerns

Getting Started Example (Flask)

pip install flask

from flask import Flask

app = Flask(__name__)

@app.route(“/”)
def home():
return “Welcome to your first Flask app!”

if __name__ == “__main__”:
app.run(debug=True)

Run with:

python app.py

Visit http://127.0.0.1:5000 in your browser.

Tips for Learning Frameworks

• Start small: build a simple app or API
• Read official documentation
• Explore the directory structure and default files
• Use project-based learning to understand features in context
• Compare similar frameworks to understand trade-offs

Learning Outcomes

• Understand what frameworks do and why they matter
• Learn how to build faster and more maintainable applications
• Gain exposure to real-world tools used in the industry
• Prepare for larger projects with scalable structure

Web (Flask/Django)

Flask and Django are two of the most popular Python web frameworks used to build powerful and scalable web applications, REST APIs, and dynamic websites. They simplify common tasks like routing, templating, form handling, and working with databases.

Flask vs. Django Overview

Feature	Flask	Django
Type	Micro-framework	Full-stack framework
Philosophy	Lightweight & flexible	Batteries-included, convention-based
Setup Time	Quick	Slightly longer
Use Case	Small to medium apps, APIs	Large, complex web applications
Learning Curve	Beginner-friendly	Steeper but more powerful

Flask – Lightweight and Flexible

Flask is a micro-framework that gives you full control over the components you use.

Features:

• Simple routing and view functions
• Jinja2 templating engine
• Easy to extend with plugins
• Great for building RESTful APIs

Example:

from flask import Flask, render_template

app = Flask(__name__)

@app.route(“/”)
def home():
return “<h1>Hello from Flask!</h1>”

if __name__ == “__main__”:
app.run(debug=True)

Popular Flask Extensions:

• Flask-SQLAlchemy – ORM for database handling
• Flask-Login – Authentication and sessions
• Flask-RESTful – API development

Django – Feature-Rich and Scalable

Django is a high-level web framework that comes with everything you need to build a web app out of the box.

Features:

• Built-in admin interface
• ORM for databases
• User authentication
• Form handling, sessions, and middleware
• Templating system with security features

Quick Start:

pip install django
django-admin startproject mysite
cd mysite
python manage.py runserver

Visit http://127.0.0.1:8000 to see the starter project.

Django Structure:

mysite/
├── manage.py
├── mysite/
│ ├── settings.py
│ ├── urls.py
│ └── wsgi.py

When to Use Which?

• Flask: Ideal for microservices, simple APIs, and apps where flexibility is a priority.
• Django: Perfect for fully featured websites, admin dashboards, or apps with user accounts, forms, and databases.

Learning Outcomes

• Build and structure web apps from scratch
• Handle routing, templates, and forms
• Connect your app to a database
• Develop secure, scalable Python-based websites
• Understand MVC/MVT architecture and REST APIs

Data (NumPy/Pandas/Matplotlib)

Python is a powerhouse for data manipulation, analysis, and visualization thanks to libraries like NumPy, Pandas, and Matplotlib. These tools are essential in data science, machine learning, automation, and business intelligence workflows.

NumPy – Numerical Python

NumPy provides fast, efficient handling of large arrays and matrices, along with a wide variety of mathematical functions.

Key Features:

• Multi-dimensional arrays (ndarray)
• Vectorized operations (faster than native Python loops)
• Linear algebra, statistics, and random number generation
• Basis for most scientific computing libraries in Python

Example:

import numpy as np

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

print(a + b) # Output: [5 7 9]

Pandas – Data Analysis Made Easy

Pandas builds on NumPy and adds powerful DataFrame and Series structures that make data manipulation easy and intuitive.

Key Features:

• DataFrames for tabular data (like Excel sheets)
• Read/write CSV, Excel, JSON, SQL, etc.
• Data cleaning, filtering, grouping, merging
• Time series support

Example:

import pandas as pd

data = {‘Name’: [‘Alice’, ‘Bob’], ‘Age’: [25, 30]}
df = pd.DataFrame(data)

print(df[df[‘Age’] > 26])

Matplotlib – Data Visualization

Matplotlib is a flexible library for creating static, animated, and interactive plots in Python.

Key Features:

• Line charts, bar charts, scatter plots, histograms, etc.
• Fully customizable plots
• Can be combined with Pandas and NumPy

Example:

import matplotlib.pyplot as plt

x = [1, 2, 3]
y = [10, 20, 30]

plt.plot(x, y)
plt.title(“Simple Line Plot”)
plt.xlabel(“X Axis”)
plt.ylabel(“Y Axis”)
plt.show()

Combined Example: Pandas + Matplotlib

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv(“sales.csv”)
df[‘Revenue’].plot(kind=’bar’)
plt.title(“Monthly Revenue”)
plt.show()

Learning Outcomes

• Work with large datasets using NumPy arrays and vectorized operations
• Analyze and clean real-world datasets with Pandas
• Visualize trends and patterns with Matplotlib
• Perform exploratory data analysis (EDA)
• Build the foundation for machine learning, automation, or dashboard projects

Automation (Selenium)

Selenium is a powerful Python tool for automating web browsers. It simulates user actions like clicking buttons, filling forms, or scraping data from dynamic websites—making it perfect for testing, data collection, and workflow automation.

What is Selenium?
Selenium is a browser automation framework that allows Python scripts to control web browsers (Chrome, Firefox, etc.) just like a human user would.

Key Features:

• Open web pages and interact with elements (buttons, forms, links)
• Fill out and submit forms automatically
• Scrape content from dynamic websites (JavaScript-heavy)
• Automate repetitive web-based tasks (e.g., logins, downloads)
• Capture screenshots or generate reports
• Supports headless operation (run browser in background)

Setup:

1. Install Selenium:

pip install selenium

2. Download a WebDriver (e.g., ChromeDriver):

• Match the version with your browser.
• Place the driver in your system PATH or reference it in code.

Basic Example:

from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys

# Set up the driver
driver = webdriver.Chrome()

# Open a website
driver.get(“https://www.google.com”)

# Find the search box and search
search_box = driver.find_element(By.NAME, “q”)
search_box.send_keys(“Python Selenium”)
search_box.send_keys(Keys.RETURN)

# Wait and close
driver.implicitly_wait(5)
driver.quit()

Useful Selenium Functions

Action	Method
Navigate to URL	driver.get(url)
Locate elements	find_element(By.ID / NAME / CLASS_NAME)
Click a button	element.click()
Type into a field	element.send_keys("text")
Get text or attributes	element.text, element.get_attribute()
Take a screenshot	driver.save_screenshot("screenshot.png")

Use Cases

• Web scraping where JavaScript rendering is required
• Automated testing of web applications
• Auto-login scripts for accounts
• Automating form submissions and downloads
• Monitoring price changes or content updates

Important Notes

• Websites can detect bots—add wait times and mimic human behavior.
• Always comply with website terms of service.
• Selenium is not as fast as APIs or direct HTML parsing, but it works where other tools fail.

Learning Outcomes

• Automate browser interactions using Python
• Handle dynamic content not accessible via static scraping
• Understand how to locate and manipulate HTML elements
• Build robust bots or scripts for data collection and testing

Best practices and community resources

Writing clean, efficient, and maintainable code is just as important as writing code that works. Best practices help you become a more professional Python developer, while community resources connect you with tools, mentors, and continued learning.

Best Practices

1. Follow PEP 8

• Python’s official style guide.
• Covers naming conventions, indentation, imports, line length, etc.
• Use tools like flake8, black, or pylint to auto-format and lint your code.

2. Write Readable Code

• Use meaningful variable/function names.
• Keep functions short and focused on one task.
• Use comments and docstrings to explain non-obvious parts.

3. Avoid Hardcoding

• Use configuration files or environment variables.
• Abstract repeated logic into reusable functions or classes.

4. Use Virtual Environments

• Prevent dependency conflicts across projects.

python -m venv env
source env/bin/activate # or .\env\Scripts\activate on Windows

5. Handle Exceptions Gracefully

• Avoid crashing your program by using try-except blocks properly.
• Don’t use bare except:—be specific.

6. Test Your Code

• Use unittest, pytest, or doctest for automated testing.
• Write tests for critical features or edge cases.

7. Use Version Control (Git)

• Track changes, collaborate, and back up your work.
• Commit often with meaningful messages.

8. Keep Learning

• Stay up to date with the language, libraries, and tooling.
• Refactor old code when you learn something new.

Community Resources

Documentation

• Python Docs
• Real Python
• W3Schools Python

Tutorials & Courses

• FreeCodeCamp, Codecademy, Coursera, Udemy
• YouTube channels: Tech With Tim, Corey Schafer, Programming with Mosh

Books

• Automate the Boring Stuff with Python by Al Sweigart
• Python Crash Course by Eric Matthes
• Fluent Python by Luciano Ramalho

Communities

• Stack Overflow
• Reddit: r/learnpython
• Python Discord
• GitHub Python Projects

Tools to Know

• Jupyter Notebooks – for data science and experimentation
• VS Code – powerful editor with Python support
• GitHub – version control and collaboration
• Pip & PyPI – package installation and discovery

Learning Outcomes

• Adopt a clean coding style for better collaboration and maintenance
• Understand how to structure real-world Python projects
• Discover resources to expand your skills and get help when stuck
• Stay connected with the global Python community