🧩 Introduction: Python Beyond the Standard Library
Up to this point, the notebooks have focused on core Python — built-in types, functions, and logic.
This notebook marks an important milestone: learning how Python is extended through modules, specifically with an introduction to NumPy, one of the most widely used libraries in data science, engineering, and scientific computing.
This is where Python starts to scale.
🎯 Purpose: Understanding Python’s Ecosystem
The goal of this notebook is to help beginners understand:
- What modules are and why they matter
- The difference between built-in modules and third-party libraries
- How to import and use NumPy
- Why NumPy is preferred for numerical operations
Python’s real power comes from its ecosystem — this notebook opens that door.
🧠 How It Works: From Import to Usage
At a high level, the notebook demonstrates:
- Importing modules using
importstatements - Using NumPy to create and manipulate numerical data
- Performing operations more efficiently than with plain Python lists
- Observing how NumPy simplifies mathematical work
This helps demystify what happens when you write import numpy as np.
🧩 The Technical Part: Modules and NumPy Arrays
A simplified example of the concepts covered looks like this:
import numpy as np
numbers = np.array([1, 2, 3, 4, 5])
result = numbers * 2
print(result)
🔍 What’s Happening Here?
- 🧱 NumPy introduces arrays, not regular lists
- ⚡ Operations apply to the entire array at once
- 📐 Code becomes shorter, clearer, and faster
- 🧠 The intent of the computation is more obvious
This is known as vectorised computation — a core NumPy concept.
💡 Key Takeaways: Why NumPy Matters
This notebook highlights several important ideas:
- 📦 Modules extend Python’s capabilities
- 🔢 NumPy is designed for numerical work
- ⚡ Vectorised operations are efficient and expressive
- 🧠 Libraries help you focus on what to compute, not how
These concepts are foundational for data analysis, machine learning, and scientific workflows.
🏁 Conclusion: A Gateway to Data Work
The Modules and NumPy notebook represents a turning point:
You’re no longer limited to core Python — you’re learning how to stand on the shoulders of powerful libraries.
With NumPy introduced, you’re now well positioned to explore:
- Pandas for data analysis
- Matplotlib for visualisation
- SciPy for scientific computing
- Machine learning frameworks
This is the beginning of Python as a data and computation tool, not just a scripting language.
🔗 Link to Notebook
Notebook link: Coming Soon