• Introduction

• K-Means Clustering

• NumPy

File Description: This Jupyter/Colab notebook serves as a brief introduction to the NumPy library, which is essential for numerical and scientific computing in Python. It is intended for students and beginners in data science and related fields.

✅ Topics Covered:

1. Introduction to NumPy and Arrays

   • Creating NumPy arrays from Python lists.

   • Understanding the difference between Python lists and NumPy arrays.
     
     

2. Array Creation Techniques

   • Using np.array(), np.arange(), and np.linspace() to create arrays.

   • Demonstrations of arrays with specified start, stop, and step values.
     
     

3. Array Attributes and Properties

   • Examining ndim, shape, and size of arrays.

   • Understanding array dimensionality and reshaping arrays.
     
     

4. Array Operations and Broadcasting

   • Performing element-wise arithmetic operations.

   • Demonstration of broadcasting rules.

   • Use of universal functions like np.sqrt(), np.exp(), etc.
     
     

5. Boolean Array Indexing

   • Filtering arrays based on conditions.

   • Creating boolean masks to extract specific elements.
     
     

6. Data Types and Type Conversion

   • Introduction to NumPy data types (dtype).

   • Typecasting arrays from one type to another.
     
     

7. Special Array Functions

   • Creating arrays using zeros(), ones(), and empty().

   • Using reshape() and flatten() for dimensional transformations.
     
     

8. Matrix-Specific Operations

   • Creating 2D matrices.

   • Accessing and slicing elements in multi-dimensional arrays.