{"id":25019643,"url":"https://github.com/djeada/numpy-tutorials","last_synced_at":"2026-03-09T06:32:35.970Z","repository":{"id":41114704,"uuid":"328769034","full_name":"djeada/Numpy-Tutorials","owner":"djeada","description":"Welcome to the NumPy Tutorials repository, your one-stop collection of learning materials for mastering NumPy, a fundamental library for scientific computing in Python.","archived":false,"fork":false,"pushed_at":"2025-05-24T19:10:47.000Z","size":422,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-05-24T20:19:04.162Z","etag":null,"topics":["linear-equation-solver","matrix-manipulations","numpy","numpy-arrays","system-of-equations","vectors"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/djeada.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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,"zenodo":null}},"created_at":"2021-01-11T19:24:25.000Z","updated_at":"2025-05-24T19:10:44.000Z","dependencies_parsed_at":"2024-04-02T02:28:06.742Z","dependency_job_id":"f43da7b4-f90e-4d83-940c-c25424686d5b","html_url":"https://github.com/djeada/Numpy-Tutorials","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/djeada/Numpy-Tutorials","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/djeada%2FNumpy-Tutorials","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/djeada%2FNumpy-Tutorials/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/djeada%2FNumpy-Tutorials/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/djeada%2FNumpy-Tutorials/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/djeada","download_url":"https://codeload.github.com/djeada/Numpy-Tutorials/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/djeada%2FNumpy-Tutorials/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30284776,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-09T02:57:19.223Z","status":"ssl_error","status_checked_at":"2026-03-09T02:56:26.373Z","response_time":61,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["linear-equation-solver","matrix-manipulations","numpy","numpy-arrays","system-of-equations","vectors"],"created_at":"2025-02-05T11:51:18.887Z","updated_at":"2026-03-09T06:32:35.944Z","avatar_url":"https://github.com/djeada.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# NumPy Tutorials\n\nWelcome to the NumPy Tutorials repository, your one-stop collection of learning materials for mastering NumPy, a fundamental library for scientific computing in Python.\n\n## About NumPy\nNumPy, or Numerical Python, is the cornerstone of scientific computation in Python. It offers powerful tools and features for:\n\n* Handling high-performance array operations.\n* Working with a wide range of mathematical tasks including linear algebra, Fourier transform, and matrices.\n* A platform that's open-source and free, fostering an inclusive scientific computing environment.\n\n## Tutorial Index\n\nOur tutorials are categorized for ease of access. Each tutorial comes in three formats: Notes (Markdown), Python scripts, and Jupyter notebooks.\n\nNumber | Notes | Python | Jupyter\n------ | ----- | -------------- | --------\n| 01 | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/notes/1_creating_arrays.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/src/1_creating_arrays.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/src/1_creating_arrays.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| 02 | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/notes/2_accessing_modifying_elements.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/src/2_accessing_modifying_elements.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/src/2_accessing_modifying_elements.py\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| 03 | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/notes/3_vector_operations.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/src/3_vector_operations.py\" /\u003e \u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/src/3_vector_operations.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| 04 | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/notes/4_matrix_operations.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/src/4_matrix_operations.py\" /\u003e \u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/src/4_matrix_operations.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| 05 | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/notes/5_reshaping_arrays.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/src/5_reshaping_arrays.py\" /\u003e \u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/src/5_reshaping_arrays.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| 06 | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/notes/6_searching_filtering_and_sorting.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/src/6_searching_filtering_and_sorting.py\" /\u003e \u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/src/6_searching_filtering_and_sorting.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| 07 | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/notes/7_combining_arrays.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/src/7_combining_arrays.py\" /\u003e \u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/src/7_combining_arrays.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| 08 | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/notes/8_linear_equations.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/src/8_linear_equations.py\" /\u003e \u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/src/8_linear_equations.py\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| 09 | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/notes/9_statistics_and_random_numbers.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/src/9_statistics_and_random_numbers.py\" /\u003e \u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/NumPy-Tutorials/blob/main/src/9_statistics_and_random_numbers.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n\n## Additional References\n\nFor a broader understanding of NumPy, we recommend these resources:\n\n- [NumPy: The Illustrated Guide](https://betterprogramming.pub/numpy-illustrated-the-visual-guide-to-numpy-3b1d4976de1d) - A visually engaging guide to NumPy's core concepts.\n- [NumPy Official Documentation](https://numpy.org/doc/stable/) - The definitive guide to NumPy functions and features.\n- [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/) - Offers a deeper dive into NumPy in the context of data science.\n- [SciPy Lecture Notes](https://scipy-lectures.org/) - A comprehensive resource covering scientific computing with Python, including NumPy.\n- [Real Python NumPy Tutorials](https://realpython.com/tutorials/numpy/) - A collection of practical tutorials on using NumPy for various applications.\n\n## How to Contribute\n\nWe encourage contributions that enhance the repository's value. To contribute:\n\n1. Fork the repository.\n2. Create your feature branch (`git checkout -b feature/AmazingFeature`).\n3. Commit your changes (`git commit -m 'Add some AmazingFeature'`).\n4. Push to the branch (`git push origin feature/AmazingFeature`).\n5. Open a Pull Request.\n   \n## License\n\nThis project is licensed under the [MIT License](LICENSE) - see the LICENSE file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdjeada%2Fnumpy-tutorials","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdjeada%2Fnumpy-tutorials","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdjeada%2Fnumpy-tutorials/lists"}