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https://github.com/samarth4023/numpy-linear-algebra
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.
https://github.com/samarth4023/numpy-linear-algebra
anaconda anaconda-environment array arrays conda jupyter-notebook numpy pip python
Last synced: 16 days ago
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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.
- Host: GitHub
- URL: https://github.com/samarth4023/numpy-linear-algebra
- Owner: Samarth4023
- Created: 2024-06-15T16:31:36.000Z (7 months ago)
- Default Branch: master
- Last Pushed: 2024-11-16T13:53:43.000Z (about 1 month ago)
- Last Synced: 2024-11-16T14:32:28.105Z (about 1 month ago)
- Topics: anaconda, anaconda-environment, array, arrays, conda, jupyter-notebook, numpy, pip, python
- Language: Jupyter Notebook
- Homepage:
- Size: 2.14 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 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](#introduction)
- [Installation](#installation)
- [Basic Operations](#basic-operations)
- [Array Creation](#array-creation)
- [Array Manipulation](#array-manipulation)
- [Mathematical Operations](#mathematical-operations)
- [Statistical Operations](#statistical-operations)
- [Examples](#examples)
- [Contributing](#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:
```bash
pip install numpy
```
Alternatively, if you are using Anaconda, you can install NumPy with:
```bash
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:
```python
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.
---
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!