{"id":10061,"date":"2026-08-10T10:14:30","date_gmt":"2026-08-10T10:14:30","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/oreilly-fast-python-video-edition-2023-11\/"},"modified":"2026-08-10T10:14:30","modified_gmt":"2026-08-10T10:14:30","slug":"oreilly-fast-python-video-edition-2023-11","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/oreilly-fast-python-video-edition-2023-11\/","title":{"rendered":"Oreilly \u2013 Fast Python, Video Edition 2023-11"},"content":{"rendered":"<div class=\"w-post-elm post_content\">\n<h2 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Description<\/span><\/h2>\n<p dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Fast Python, Video Edition. This course provides a set of advanced techniques for improving the speed and efficiency of Python code, especially in the areas of big data and machine learning. In this course, you will learn how to write optimized Python code, effectively use the NumPy and pandas libraries, rewrite critical parts of your code with Cython, design persistent data structures, and tune your code for different architectures. You will also learn how to implement GPU-based computing in Python. This course is designed for experienced professionals and includes practical examples such as rewriting games with Cython and building a MapReduce framework from scratch. By learning these concepts, you will be able to fix inefficient code, take advantage of multithreading capabilities, and optimize datasets without sacrificing accuracy. Finally, this course shows you how using modern hardware can transform old inefficient patterns and provide more efficient solutions. These skills are essential for entering the world of machine learning and large-scale data analytics, helping you perform better with fewer resources. About the course: Fast Python is a collection of techniques for speeding up Python, with an emphasis on Big Data applications. Following clear examples and detailed instructions, you\u2019ll learn how to use common libraries like NumPy and pandas in more efficient ways and transform data for efficient storage and I\/O. More importantly, Fast Python takes a holistic approach to performance, so you\u2019ll see how to optimize the entire system, from code to architecture.<\/span><\/p>\n<h3 dir=\"ltr\" style=\"text-align: left\" data-sourcepos=\"30:1-30:25\"><span style=\"vertical-align: inherit\">What you will learn:<\/span><\/h3>\n<ul dir=\"ltr\" style=\"text-align: left\" data-sourcepos=\"32:1-38:0\">\n<li data-sourcepos=\"32:1-32:29\"><span style=\"vertical-align: inherit\">Writing efficient pure Python code<\/span><\/li>\n<li data-sourcepos=\"33:1-33:57\"><span style=\"vertical-align: inherit\">Optimize NumPy and pandas libraries for better performance<\/span><\/li>\n<li data-sourcepos=\"34:1-34:58\"><span style=\"vertical-align: inherit\">Rewrite critical code using Cython to increase speed<\/span><\/li>\n<li data-sourcepos=\"35:1-35:56\"><span style=\"vertical-align: inherit\">Designing stable data structures for efficient data management<\/span><\/li>\n<li data-sourcepos=\"36:1-36:50\"><span style=\"vertical-align: inherit\">Adapting Python code for different hardware architectures<\/span><\/li>\n<li data-sourcepos=\"37:1-38:0\"><span style=\"vertical-align: inherit\">Implementing GPU Computing in Python to Accelerate Machine Learning Applications<\/span><\/li>\n<\/ul>\n<h3 dir=\"ltr\" style=\"text-align: left\" data-sourcepos=\"39:1-39:32\"><span style=\"vertical-align: inherit\">Who is this course suitable for?<\/span><\/h3>\n<ul dir=\"ltr\" style=\"text-align: left\" data-sourcepos=\"41:1-45:0\">\n<li data-sourcepos=\"41:1-41:71\"><span style=\"vertical-align: inherit\">Intermediate Python programmers who are familiar with the basics of concurrency.<\/span><\/li>\n<li data-sourcepos=\"42:1-42:76\"><span style=\"vertical-align: inherit\">People looking to improve the performance of their Python code in Big Data projects.<\/span><\/li>\n<li data-sourcepos=\"43:1-43:82\"><span style=\"vertical-align: inherit\">Machine Learning professionals who want to optimize the execution of their complex applications.<\/span><\/li>\n<li data-sourcepos=\"44:1-45:0\"><span style=\"vertical-align: inherit\">Developers interested in leveraging the power of GPUs for Python computing.<\/span><\/li>\n<\/ul>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Fast Python, Video Edition Course Specifications<\/span><\/h3>\n<ul dir=\"ltr\" style=\"text-align: left\">\n<li><span style=\"vertical-align: inherit\">Publisher: <\/span><a href=\"https:\/\/href.li\/?https:\/\/www.oreilly.com\/library\/view\/fast-python-video\/9781617297939VE\/\" target=\"_blank\" rel=\"noopener\"><span style=\"vertical-align: inherit\">Oreilly<\/span><\/a><\/li>\n<li><span style=\"vertical-align: inherit\">Instructor: <\/span><a class=\"author-name\" href=\"https:\/\/downloadlynet.ir\/tag\/tiago-antao\/\"><span style=\"vertical-align: inherit\">Tiago Antao<\/span><\/a><\/li>\n<li><span style=\"vertical-align: inherit\">Training level: Beginner to advanced<\/span><\/li>\n<li><span style=\"vertical-align: inherit\">Training duration: 8 hours and 52 minutes<\/span><\/li>\n<\/ul>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Course headings<\/span><\/h3>\n<ul dir=\"ltr\" style=\"text-align: left\">\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 1. Foundational Approaches <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 1. An urgent need for efficiency in data processing <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 1. Modern computing architectures and high-performance computing <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 1. Working with Python\u2019s limitations <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 1. A summary of the solutions <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 1. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Extracting maximum performance from built-in features <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 2. Profiling code to detect performance bottlenecks <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 2. Optimizing basic data structures for speed: Lists, sets, and dictionaries <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 2. Finding excessive memory allocation <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 2. Using laziness and generators for big-data pipelining <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 2. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Concurrency, parallelism, and asynchronous processing <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 3. Implementing a basic MapReduce engine <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 3. Implementing a concurrent version of a MapReduce engine <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 3. Using multiprocessing to implement MapReduce <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 3. Tying it all together: An asynchronous multithreaded and multiprocessing MapReduce server <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 3. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. High-performance NumPy <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 4. Using array programming <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 4. Tuning NumPy\u2019s internal architecture for performance <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 4. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 2. Hardware <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 5. Re-implementing critical code with Cython <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 5. A whirlwind tour of Cython <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 5. Profiling Cython code <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 5. Optimizing array access with Cython memory views <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 5. Writing NumPy generalized universal functions in Cython <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 5. Advanced array access in Cython <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 5. Parallelism with Cython <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 5. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Memory hierarchy, storage, and networking <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 6. Efficient data storage with Blosc <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 6. Accelerating NumPy with NumExpr <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 6. The performance implications of using the local network <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 6. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 3. Applications and Libraries for Modern Data Processing <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 7. High-performance pandas and Apache Arrow <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 7. Techniques to increase data analysis speed <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 7. pandas on top of NumPy, Cython, and NumExpr <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 7. Reading data into pandas with Arrow <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 7. Using Arrow interop to delegate work to more efficient languages \u200b\u200band systems <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 7. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Storing big data <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 8. Parquet: An efficient format to store columnar data <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 8. 8. Dealing with larger-than-memory datasets the old-fashioned way <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 8. Zarr for large-array persistence <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 8. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 4. Advanced Topics <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 9. Data analysis using GPU computing <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 9. Using Numba to generate GPU code <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 9. Performance analysis of GPU code: The case of a CuPy application <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 9. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Analyzing big data with Dask <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 10. The computational cost of Dask operations <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 10. Using Dask\u2019s distributed scheduler <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 10. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix A. Setting up the environment<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix A. Installing your own Python distribution<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix A. Using Docker<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix A. Hardware considerations<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix B. Using Numba to generate efficient low-level code<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix B. Writing explicitly parallel functions in Numba<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix B. Writing NumPy-aware code in Numba<\/span><\/li>\n<\/ul>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Course images<\/span><\/h3>\n<p dir=\"ltr\" style=\"text-align: left\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-981099 size-full\" src=\"https:\/\/downloadly.ir\/wp-content\/uploads\/2025\/05\/Fast-Python-Video-Edition.png\" alt=\"Fast Python, Video Edition\" width=\"1242\" height=\"434\"><\/p>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Sample course video<\/span><\/h3>\n<div style=\"width: 640px;\" class=\"wp-video\"><span class=\"mejs-offscreen\">Video Player<\/span><\/p>\n<div id=\"mep_0\" class=\"mejs-container mejs-container-keyboard-inactive wp-video-shortcode mejs-video\" tabindex=\"0\" role=\"application\" aria-label=\"Video Player\" style=\"width: 640px; height: 360px; min-width: 217px;\">\n<div class=\"mejs-inner\">\n<div class=\"mejs-mediaelement\"><mediaelementwrapper id=\"video-163729-1\"><video class=\"wp-video-shortcode\" id=\"video-163729-1_html5\" width=\"640\" height=\"360\" preload=\"metadata\" src=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Fast_Python_Video_Edition_Downloadly.ir.mp4?_=1\" style=\"width: 640px; height: 360px;\"><source type=\"video\/mp4\" src=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Fast_Python_Video_Edition_Downloadly.ir.mp4?_=1\"><a href=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Fast_Python_Video_Edition_Downloadly.ir.mp4?nocache=1786137022728\">https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Fast_Python_Video_Edition_Downloadly.ir.mp4<\/a><\/video><\/mediaelementwrapper><\/div>\n<div class=\"mejs-layers\">\n<div class=\"mejs-poster mejs-layer\" style=\"display: none; width: 100%; height: 100%;\"><\/div>\n<div class=\"mejs-overlay mejs-layer\" style=\"display: none; width: 100%; height: 100%;\">\n<div class=\"mejs-overlay-loading\"><span class=\"mejs-overlay-loading-bg-img\"><\/span><\/div>\n<\/div>\n<div class=\"mejs-overlay mejs-layer\" style=\"display: none; width: 100%; height: 100%;\">\n<div class=\"mejs-overlay-error\"><\/div>\n<\/div>\n<div class=\"mejs-overlay mejs-layer mejs-overlay-play\" style=\"width: 100%; height: 100%;\">\n<div class=\"mejs-overlay-button\" role=\"button\" tabindex=\"0\" aria-label=\"Play\" aria-pressed=\"false\"><\/div>\n<\/div>\n<\/div>\n<div class=\"mejs-controls\">\n<div class=\"mejs-button mejs-playpause-button mejs-play\"><button type=\"button\" aria-controls=\"mep_0\" title=\"Play\" aria-label=\"Play\" tabindex=\"0\"><\/button><\/div>\n<div class=\"mejs-time mejs-currenttime-container\" role=\"timer\" aria-live=\"off\"><span class=\"mejs-currenttime\">00:00<\/span><\/div>\n<div class=\"mejs-time-rail\"><span class=\"mejs-time-total mejs-time-slider\" role=\"slider\" tabindex=\"0\" aria-label=\"Time Slider\" aria-valuemin=\"0\" aria-valuemax=\"0\" aria-valuenow=\"0\" aria-valuetext=\"00:00\"><span class=\"mejs-time-buffering\" style=\"display: none;\"><\/span><span class=\"mejs-time-loaded\"><\/span><span class=\"mejs-time-current\"><\/span><span class=\"mejs-time-hovered no-hover\"><\/span><span class=\"mejs-time-handle\"><span class=\"mejs-time-handle-content\"><\/span><\/span><span class=\"mejs-time-float\"><span class=\"mejs-time-float-current\">00:00<\/span><span class=\"mejs-time-float-corner\"><\/span><\/span><\/span><\/div>\n<div class=\"mejs-time mejs-duration-container\"><span class=\"mejs-duration\">00:00<\/span><\/div>\n<div class=\"mejs-button mejs-volume-button mejs-mute\"><button type=\"button\" aria-controls=\"mep_0\" title=\"Mute\" aria-label=\"Mute\" tabindex=\"0\"><\/button><a href=\"javascript:void(0);\" class=\"mejs-volume-slider\" aria-label=\"Volume Slider\" aria-valuemin=\"0\" aria-valuemax=\"100\" role=\"slider\" aria-orientation=\"vertical\"><span class=\"mejs-offscreen\">Use Up\/Down Arrow keys to increase or decrease volume.<\/span><\/p>\n<div class=\"mejs-volume-total\">\n<div class=\"mejs-volume-current\" style=\"bottom: 0px; height: 100%;\"><\/div>\n<div class=\"mejs-volume-handle\" style=\"bottom: 100%; margin-bottom: -3px;\"><\/div>\n<\/div>\n<p><\/a><\/div>\n<div class=\"mejs-button mejs-fullscreen-button\"><button type=\"button\" aria-controls=\"mep_0\" title=\"Fullscreen\" aria-label=\"Fullscreen\" tabindex=\"0\"><\/button><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div dir=\"ltr\" style=\"text-align: left\">\n<h3><span style=\"vertical-align: inherit\">Installation Guide<\/span><\/h3>\n<p><span style=\"vertical-align: inherit\">After Extract, view with your favorite player.<\/span><\/p>\n<p><span style=\"vertical-align: inherit\">Subtitles: None<\/span><\/p>\n<p><span style=\"vertical-align: inherit\">Quality: 720p<\/span><\/p>\n<\/div>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Download link<\/span><\/h3>\n<p dir=\"ltr\" style=\"text-align: left\"><a href=\"https:\/\/dl3.downloadly.ir\/Files\/Elearning\/Oreilly_Fast_Python_Video_Edition_2023-11_Downloadly.ir.rar?nocache=1786137021\"><span style=\"vertical-align: inherit\">Download file \u2013 829 MB<\/span><\/a><\/p>\n<p dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">File(s) password: www.downloadly.ir<\/span><\/p>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">File size<\/span><\/h3>\n<p dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">829 MB<\/span><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Description Fast Python, Video Edition. This course provides a set of advanced techniques for improving the speed and efficiency of Python code, especially in t<\/p>\n","protected":false},"author":1,"template":"","dgi_category":[10458],"dgi_tag":[85745,85746,85747,85748,85749,85750],"class_list":["post-10061","digital_item","type-digital_item","status-publish","has-post-thumbnail","hentry","dgi_category-video-tutorials","dgi_tag-course-fast-python-video-edition","dgi_tag-download-course-fast-python-video-edition","dgi_tag-download-fast-python-video-edition","dgi_tag-free-download-fast-python-video-edition","dgi_tag-free-fast-python-video-edition","dgi_tag-tiago-antao"],"_links":{"self":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/digital_item\/10061","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/digital_item"}],"about":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/types\/digital_item"}],"author":[{"embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"version-history":[{"count":0,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/digital_item\/10061\/revisions"}],"wp:attachment":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/media?parent=10061"}],"wp:term":[{"taxonomy":"dgi_category","embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/dgi_category?post=10061"},{"taxonomy":"dgi_tag","embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/dgi_tag?post=10061"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}