Скачать с ютуб Read Giant Datasets Fast - 3 Tips For Better Data Science Skills в хорошем качестве

Read Giant Datasets Fast - 3 Tips For Better Data Science Skills

We've learned how to work with data. But how about massive amounts of data? as in - files with millions of rows, tens of gigabytes in size, and ages of staring at your computer waiting for everything to load? Luckily, in this tutorial, I will show you how to work with a gigantic dataset of Amazon Best Seller Products that has over 2 million rows, and takes up 11GB in size 😱😱😱 A huge shoutout to Bright Data for supplying it and helping this video come to life! ⭐ you can get a free sample of this dataset here: https://get.brightdata.com/pythonsimp... Additionally, I will demonstrate that slight improvements to your code make a huge impact on the processing speed - regardless of how strong and powerful your computer is!! For this, we will compare the performance across 2 different systems: 🖥️ my custom build new-gen PC 💻 my poor old laptop (yes, the one that is held by scotch tape and is barely operational 😅) You will see that well-written code can even make my old laptop run like a supercomputer! 💪💪💪 #python #datasets #brightdata #data #ecommerce #datascience #pandas #pythonprogramming 📽️ RELATED TUTORIALS 📽️ ---------------------------------------------- ⭐ Anaconda Guide For Beginners (Install Jupyter Notebook):    • Anaconda Beginners Guide for Linux an...   ⭐ Pandas Guide For Beginners:    • Basic Guide to Pandas! Tricks, Shortc...   ⭐ For Loop For Beginners:    • Python For Loops - Programming for Be...   ⏰ TIME STAMPS ⏰ ---------------------------------------------- 00:00 - intro 01:05 - intro to working with professional data platforms 03:38 - complexity of loading very large datasets 06:43 - focus on relevant data ⭐ 09:09 - load data in small chunks ⭐ 10:25 - access and change data chunks values 12:19 - save modified data into a new csv file ⭐ 14:49 - Thanks for watching! 😀 🤝 Connect with me 🤝 ---------------------------------------------- 🔗 Github: https://github.com/mariyasha 🔗 Discord:   / discord   🔗 LinkedIn:   / mariyasha888   🔗 Twitter:   / mariyasha888   🔗 Blog: https://www.pythonsimplified.org 💳 Credits 💳 ---------------------------------------------- ⭐ Beautiful titles, transitions, sound FX, and music: mixkit.co ⭐ Beautiful icons: flaticon.com ⭐ Beautiful graphics: freepik.com