NumPy Tutorial Overview

Introduction

• Host: Dathan
• Purpose: Complete hands-on tutorial on NumPy in Python
• Importance: Key library for machine learning, data science, etc.

Content Schedule

• 3 videos per week
  • Machine Learning Course: Monday & Wednesday evenings
  • Project video: Friday evening
• Join Telegram group for updates

What is NumPy?

• Full form: Numerical Python
• Used for numerical operations and processing large datasets (millions of data points)

Advantages of NumPy Arrays over Lists and Tuples

• Mathematical Operations: NumPy allows several mathematical operations compared to lists.
• Performance: Operations on NumPy arrays are faster than on lists.

Getting Started with NumPy

1. Importing NumPy: import numpy as np
2. Using Google Colab: For running Python codes.
3. Libraries: Pre-made functions/classes stored in Python files.

Performance Comparison: List vs NumPy Array

• Example task: Adding 5 to all values in a list and a NumPy array.
• Performance measured using time library.
• Result: NumPy array operations are significantly faster when dealing with larger data.

Creating NumPy Arrays

1D Arrays

• Syntax: np.array([1, 2, 3])
• Check shape: array.shape

2D Arrays

• Syntax: np.array([[1, 2], [3, 4]])
• Check shape: array.shape

Data Types

• Default: Integer values; specify using dtype for floating point, etc.

Creating Arrays with Placeholders

• Zeros: np.zeros(shape)
• Ones: np.ones(shape)
• Full: np.full(shape, value)
• Identity Matrix: np.eye(size)

Creating Random Arrays

• Random values: np.random.random(size)
• Random integers: np.random.randint(low, high, size)
• Evenly spaced values: np.linspace(start, stop, num)

Converting Data Types

• Convert lists to NumPy arrays: np.array(list)
• Convert tuples to NumPy arrays: np.array(tuple)

Analyzing Arrays

• Check shape: array.shape
• Check dimensions: array.ndim
• Check number of elements: array.size
• Check data type: array.dtype

Mathematical Operations on Arrays

• Element-wise addition, subtraction, multiplication, and division.
• Use of functions like np.add(), np.subtract(), np.multiply(), np.divide().

Array Manipulation

Transposing Arrays

• Use np.transpose(array) or array.T.

Reshaping Arrays

• Reshape using: array.reshape(new_shape).

Conclusion

• NumPy is essential for numerical operations in Python.
• Faster operations and a variety of features for handling arrays.
• Recommended to explore further for machine learning and data science applications.