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 newgen PC
my poor old laptop (yes, the one that is held by scotch tape and is barely operational )

You will see that wellwritten code can even make my old laptop run like a supercomputer! #python #datasets #brightdata #data #ecommerce #datascience #pandas #pythonprogramming

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⏰ 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