{"id":24864749,"url":"https://github.com/emmanuel10701/numpy","last_synced_at":"2025-10-15T09:32:02.733Z","repository":{"id":272298989,"uuid":"915710957","full_name":"Emmanuel10701/Numpy","owner":"Emmanuel10701","description":"Numpy","archived":false,"fork":false,"pushed_at":"2025-01-28T10:52:54.000Z","size":6,"stargazers_count":4,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-28T11:35:32.846Z","etag":null,"topics":["jupyter-notebook","machine-learning","numpy","pandas","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Emmanuel10701.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-01-12T15:53:06.000Z","updated_at":"2025-01-28T10:52:58.000Z","dependencies_parsed_at":"2025-01-13T15:28:59.283Z","dependency_job_id":"77d01e27-369e-4146-896d-6f17554976cf","html_url":"https://github.com/Emmanuel10701/Numpy","commit_stats":null,"previous_names":["emmanuel10701/numpy"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Emmanuel10701%2FNumpy","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Emmanuel10701%2FNumpy/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Emmanuel10701%2FNumpy/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Emmanuel10701%2FNumpy/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Emmanuel10701","download_url":"https://codeload.github.com/Emmanuel10701/Numpy/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":236600376,"owners_count":19175173,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["jupyter-notebook","machine-learning","numpy","pandas","python"],"created_at":"2025-01-31T23:55:34.374Z","updated_at":"2025-10-15T09:32:02.728Z","avatar_url":"https://github.com/Emmanuel10701.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# NumPy CRUD Operations Walkthrough\n\nThis README focuses on demonstrating **CRUD (Create, Read, Update, Delete)** operations in **NumPy** arrays through a Jupyter Notebook. The notebook showcases essential operations for working with numerical data efficiently and converting them into NumPy arrays.\n\n---\n\n## Table of Contents\n\n1. [Introduction](#introduction)  \n2. [Setup and Dependencies](#setup-and-dependencies)  \n3. [CRUD Operations in NumPy](#crud-operations-in-numpy)  \n   - [Create Arrays](#create-arrays)  \n   - [Read Arrays](#read-arrays)  \n   - [Update Arrays](#update-arrays)  \n   - [Delete Elements from Arrays](#delete-elements-from-arrays)  \n4. [Conclusion](#conclusion)\n\n\n## Introduction\n\n**NumPy** is a powerful library for numerical computing in Python. This walkthrough covers the **CRUD operations** that form the basis of array manipulation and data analysis. CRUD operations help you manipulate data programmatically and are fundamental when working with arrays in data science and scientific computing.\n\n---\n\n## Setup and Dependencies\n\nMake sure you have the following tools and libraries installed on your machine:\n\n- Python 3.x  \n- Jupyter Notebook or JupyterLab  \n- NumPy  \n\n### Install NumPy\n\nIf NumPy is not installed, you can install it using pip:\n\n```bash\npip install numpy\n````\n\nYou can launch Jupyter Notebook using:\n\n```bash\njupyter notebook\n```\n\n---\n\n## CRUD Operations in NumPy\n\nThis section demonstrates how to perform **Create**, **Read**, **Update**, and **Delete** operations on NumPy arrays.\n\n### Create Arrays\n\nUse various NumPy functions to create arrays:\n\n```python\nimport numpy as np\n\n# Create array from list\narr1 = np.array([1, 2, 3])\nprint(\"arr1:\", arr1)\n\n# Create array of zeros\narr2 = np.zeros((2, 3))\nprint(\"arr2:\\n\", arr2)\n\n# Create array of ones\narr3 = np.ones((3, 2))\nprint(\"arr3:\\n\", arr3)\n\n# Create array with range of numbers\narr4 = np.arange(0, 10, 2)\nprint(\"arr4:\", arr4)\n```\n\n---\n\n### Read Arrays\n\nAccess specific elements, rows, or columns from NumPy arrays:\n\n```python\narr = np.array([[10, 20, 30], [40, 50, 60]])\n\n# Read single element\nprint(arr[0][1])  # Output: 20\n\n# Read entire row\nprint(arr[1])     # Output: [40 50 60]\n\n# Read specific column\nprint(arr[:, 2])  # Output: [30 60]\n\n# Slice array\nprint(arr[0:2, 1:3])  # Output: [[20 30] [50 60]]\n```\n\n---\n\n### Update Arrays\n\nModify the values of arrays using direct indexing:\n\n```python\narr = np.array([5, 10, 15, 20, 25])\n\n# Update single value\narr[2] = 100\nprint(arr)  # Output: [  5  10 100  20  25]\n\n# Update multiple values\narr[1:4] = [200, 300, 400]\nprint(arr)  # Output: [  5 200 300 400  25]\n```\n\n---\n\n### Delete Elements from Arrays\n\nUse `np.delete()` to remove elements:\n\n```python\narr = np.array([1, 2, 3, 4, 5])\n\n# Delete element at index 2\nnew_arr = np.delete(arr, 2)\nprint(new_arr)  # Output: [1 2 4 5]\n\n# 2D array example\narr2d = np.array([[1, 2], [3, 4], [5, 6]])\n\n# Delete row 1 (second row)\nnew_arr2d = np.delete(arr2d, 1, axis=0)\nprint(new_arr2d)  # Output: [[1 2] [5 6]]\n\n# Delete column 0 (first column)\nnew_arr2d_col = np.delete(arr2d, 0, axis=1)\nprint(new_arr2d_col)  # Output: [[2] [4] [6]]\n```\n\n---\n\n## Conclusion\n\nThis walkthrough has demonstrated how to perform basic **CRUD operations** with **NumPy**, a core Python library for numerical computation. Mastering these operations is essential for effective data manipulation, analysis, and preprocessing tasks in data science and machine learning workflows.\n\n---\n\nHappy Coding! 🎉\n\n```\n\n### ✅ How to Use:\n1. Save this as `README.md` in your project folder.\n2. Run the code snippets in a Jupyter Notebook or any Python environment.\n3. Add screenshots, visuals, or outputs if desired for a more interactive guide.\n\nLet me know if you'd like the notebook (`.ipynb`) version or want to include visuals or links to GitHub/Colab.\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Femmanuel10701%2Fnumpy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Femmanuel10701%2Fnumpy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Femmanuel10701%2Fnumpy/lists"}