Python NumPy basics

1. What is NumPy?

NumPy stands for Numerical Python.

In simple words:
👉 NumPy is used to work with numbers
👉 It is faster than normal Python lists
👉 It is very useful in Data Science, AI, and ML

2. Why Use NumPy?

We use NumPy because:
✔ Fast calculations
✔ Less code
✔ Works with large data
✔ Used in real projects

3. Install NumPy

If NumPy is not installed, run:

4. Import NumPy

Example

👉 np is a short name (standard practice).

5. NumPy Array (Very Important)

A NumPy array is like a list, but faster and smarter.

Example

6. Difference: List vs NumPy Array

Python List

Output:

NumPy Array

Output:

👉 NumPy does real math.

7. Create NumPy Arrays

From List

Using arange()

Using zeros()

Using ones()

8. Array Data Type

Example

9. Basic Array Operations

Addition

Multiplication

Subtraction

10. Array Indexing

Example

11. Array Slicing

Example

12. NumPy Math Functions

Example

13. 2D NumPy Array (Matrix)

Example

Access 2D Values

14. Shape of Array

Example

15. Real-World Example (Marks Analysis)

Example

16. Common Beginner Mistakes

❌ Forgetting to import NumPy
❌ Using list instead of array for math
❌ Mixing data types unnecessarily
❌ Not understanding array shape

17. Simple Practice Examples

Example 1: Square Numbers

Example 2: Create Range

Example 3: Sum of Array

18. Where NumPy is Used?

✔ Data Science
✔ Machine Learning
✔ Image processing
✔ Scientific computing
✔ Finance & analytics

19. Summary (Python NumPy Basics)

✔ NumPy is fast and powerful
✔ Uses arrays instead of lists
✔ Perfect for numerical work
✔ Easy to learn
✔ Foundation of Data Science

📘 Perfect for Beginner Data Science eBook

This chapter is ideal for:

• Python learners

• Data Science beginners

• Students & professionals

• Career-focused readers

If you want next, I can write:

• NumPy Array Indexing (Deep)

• NumPy Mathematical Functions

• NumPy vs List (Comparison)

• NumPy Mini Projects

Just tell me 😊