{"id":22854782,"url":"https://github.com/samarth4023/numpy-linear-algebra","last_synced_at":"2026-02-22T12:01:58.879Z","repository":{"id":244567259,"uuid":"815622879","full_name":"Samarth4023/Numpy-Linear-Algebra","owner":"Samarth4023","description":"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. 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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.\n\n## Table of Contents\n\n- [Introduction](#introduction)\n- [Installation](#installation)\n- [Basic Operations](#basic-operations)\n  - [Array Creation](#array-creation)\n  - [Array Manipulation](#array-manipulation)\n  - [Mathematical Operations](#mathematical-operations)\n  - [Statistical Operations](#statistical-operations)\n- [Examples](#examples)\n- [Contributing](#contributing)\n\n## Introduction\n\nNumPy 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.\n\n## Installation\n\nTo install NumPy, you can use pip:\n\n```bash\npip install numpy\n```\n\nAlternatively, if you are using Anaconda, you can install NumPy with:\n\n```bash\nconda install numpy\n```\n\n## Basic Operations\n\n### Array Creation\n\nLearn how to create arrays from lists, use functions like `arange`, `linspace`, and create multidimensional arrays.\n\n### Array Manipulation\n\nExplore reshaping arrays, indexing, slicing, and joining arrays.\n\n### Mathematical Operations\n\nPerform element-wise operations, use universal functions (ufuncs), and apply mathematical functions like `sin`, `cos`, and `exp`.\n\n### Statistical Operations\n\nCalculate mean, median, standard deviation, and other statistical measures on arrays.\n\n## Examples\n\nHere are some examples of basic operations using NumPy:\n\n```python\nimport numpy as np\n\n# Creating an array\narr = np.array([1, 2, 3, 4, 5])\nprint(\"Array:\", arr)\n\n# Array manipulation\nreshaped_arr = arr.reshape((5, 1))\nprint(\"Reshaped Array:\\n\", reshaped_arr)\n\n# Mathematical operations\nsquared_arr = np.square(arr)\nprint(\"Squared Array:\", squared_arr)\n\n# Statistical operations\nmean_val = np.mean(arr)\nprint(\"Mean Value:\", mean_val)\n```\n\n## Contributing\n\nFeel free to fork this repository, create issues, or submit pull requests. Any contributions to enhance the examples or add more functionalities are welcome.\n\n---\nBy 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. \n\n---\n### Happy coding!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsamarth4023%2Fnumpy-linear-algebra","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsamarth4023%2Fnumpy-linear-algebra","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsamarth4023%2Fnumpy-linear-algebra/lists"}