My NumPy Journey 🚀

I am eager to share my knowledge with the world! This repository contains my implementations of basic operations using NumPy, a popular Python library for numerical computing. As part of my journey in the mathematics for machine learning course, I explored various functionalities of NumPy and documented my learning process here.

Table of Contents

• Introduction
• Installation
• Basic Operations
  • Array Creation
  • Array Manipulation
  • Mathematical Operations
  • Statistical Operations
• Examples
• Contributing

Introduction

NumPy is a fundamental package for scientific computing in Python. It provides support for arrays, matrices, and many mathematical functions to operate on these data structures efficiently.

Installation

To install NumPy, you can use pip:

pip install numpy

Alternatively, if you are using Anaconda, you can install NumPy with:

conda install numpy

Basic Operations

Array Creation

Learn how to create arrays from lists, use functions like arange, linspace, and create multidimensional arrays.

Array Manipulation

Explore reshaping arrays, indexing, slicing, and joining arrays.

Mathematical Operations

Perform element-wise operations, use universal functions (ufuncs), and apply mathematical functions like sin, cos, and exp.

Statistical Operations

Calculate mean, median, standard deviation, and other statistical measures on arrays.

Examples

Here are some examples of basic operations using NumPy:

import numpy as np

# Creating an array
arr = np.array([1, 2, 3, 4, 5])
print("Array:", arr)

# Array manipulation
reshaped_arr = arr.reshape((5, 1))
print("Reshaped Array:\n", reshaped_arr)

# Mathematical operations
squared_arr = np.square(arr)
print("Squared Array:", squared_arr)

# Statistical operations
mean_val = np.mean(arr)
print("Mean Value:", mean_val)

Contributing

Feel free to fork this repository, create issues, or submit pull requests. Any contributions to enhance the examples or add more functionalities are welcome.

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By sharing this repository, I hope to help others understand the basics of NumPy and encourage them to explore its powerful features in their own projects.

Happy coding!