{"id":51294420,"url":"https://github.com/xylambda/numpy_tutorial","last_synced_at":"2026-06-30T13:02:24.717Z","repository":{"id":268016078,"uuid":"903028579","full_name":"Xylambda/numpy_tutorial","owner":"Xylambda","description":"A short introduction to NumPy","archived":false,"fork":false,"pushed_at":"2024-12-13T19:34:08.000Z","size":0,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-12-13T20:27:54.874Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Xylambda.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":"2024-12-13T19:21:05.000Z","updated_at":"2024-12-13T19:34:12.000Z","dependencies_parsed_at":"2024-12-13T20:27:56.879Z","dependency_job_id":"9d64be88-709d-4fd3-b7f3-d8c56a3d28d4","html_url":"https://github.com/Xylambda/numpy_tutorial","commit_stats":null,"previous_names":["xylambda/numpy_tutorial"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Xylambda/numpy_tutorial","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Xylambda%2Fnumpy_tutorial","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Xylambda%2Fnumpy_tutorial/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Xylambda%2Fnumpy_tutorial/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Xylambda%2Fnumpy_tutorial/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Xylambda","download_url":"https://codeload.github.com/Xylambda/numpy_tutorial/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Xylambda%2Fnumpy_tutorial/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34967638,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-30T02:00:05.919Z","response_time":92,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":[],"created_at":"2026-06-30T13:02:23.602Z","updated_at":"2026-06-30T13:02:24.711Z","avatar_url":"https://github.com/Xylambda.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# NumPy Tutorial\n\nThis repository provides a simple introduction to NumPy.\n\n## What and why NumPy\nNumPy is the fundamental package in Python for numerical computing, offering significant advantages in performance and memory efficiency. Its arrays are implemented in C, enabling faster execution compared to native Python lists.\n\nNumPy uses contiguous memory allocation and supports vectorized operations, reducing the need for explicit Python loops and minimizing overhead.\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"assets/performance.png\" width=\"700\"\u003e\n\u003c/p\u003e\n\nAdditionally, its optimized memory management and ability to handle large datasets efficiently make it an essential tool for scientific computing, data analysis, and machine learning.\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"assets/memory.png\" width=\"700\"\u003e\n\u003c/p\u003e\n\n\n## Contents\n\nEach lesson is contained in a single jupyter notebook.\n\n1. [Array creation](notebooks/0_array_creation.ipynb)\n1. [Data access](notebooks/1_array_data_access.ipynb)\n1. [Shape manipulation](notebooks/2_array_shape_manip.ipynb)\n1. [Operations on arrays](notebooks/3_array_ops.ipynb)\n1. [Advanced indexing and masking](notebooks/4_advanced_indexing_and_masking.ipynb)\n1. [Views and copies](notebooks/5_views_and_copies.ipynb)\n1. [Random numbers](notebooks/6_random_numbers.ipynb)\n\n\n## How do I use this repo?\n\nYou need to install `numpy` and `jupyter notebook`. Using `pip` as package manager:\n\n```bash\npip install numpy jupyter notebook\n```\n\nThen, clone the repository and open its contents with your favourite code editor.\n\n```bash\ngit clone https://github.com/Xylambda/numpy_tutorial.git\n```\n\nRead notebooks in order and execute cells one at a time, studying the output as well as the code.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fxylambda%2Fnumpy_tutorial","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fxylambda%2Fnumpy_tutorial","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fxylambda%2Fnumpy_tutorial/lists"}