NumPy Quick-Revision

This is a collection of my personal notes and examples while learning NumPy, the fundamental package for scientific computing in Python.

NumPy (Numerical Python) is a powerful library that adds support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. It's the foundation for data science and machine learning in Python!

These notes cover:

• Basic NumPy array creation and manipulation
• Array types, dimensions, and shapes
• Indexing and slicing operations
• Mathematical operations and statistics
• Broadcasting and vectorized operations
• Boolean arrays and conditional operations
• Linear algebra operations
• Utility functions (random number generation, reshaping, etc.)

These notes are meant to be a quick reference guide. Each section includes code examples that you can run to see NumPy in action. Feel free to clone this repository and experiment with the examples!

# Example: Creating arrays and performing operations
import numpy as np

# Create an array
arr = np.array([1, 2, 3, 4, 5])

# Perform operations
print(arr * 2)  # [2 4 6 8 10]
print(arr.mean())  # 3.0

To use NumPy, you need to install it first:

pip install numpy

or with conda:

conda install numpy

Found a mistake or want to add more notes? Feel free to submit a pull request or open an issue!

Here are some additional resources for learning NumPy:

• Official NumPy Documentation
• NumPy Quickstart Tutorial
• Python Data Science Handbook
• FreeCodeCamp's Video Tutorial

Feel free to use these notes for your personal learning