Quickly read massive data sets – 3 tips for better data science skills

Quickly read massive data sets – 3 tips for better data science skills

HomePython SimplifiedQuickly read massive data sets – 3 tips for better data science skills
Quickly read massive data sets – 3 tips for better data science skills
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We learned how to work with data. But what about huge amounts of data? as in – files with millions of rows, tens of gigabytes in size and staring at your computer for ages, waiting for everything to load?
Luckily, in this tutorial I'll show you how to work with a massive dataset of Amazon Best Seller products that contains over 2 million rows and takes up 11 GB
A huge shoutout to Bright Data for providing it and helping bring this video to life!
you can get a free sample of this dataset here:
https://get.brightdata.com/pythonsimplified

Plus, I'll demonstrate that small improvements to your code have a huge impact on processing speed – no matter how strong and powerful your computer is!!
To do this, we compare the performance of two different systems:
️ my custom-made new generation PC
my poor old laptop (yes, the one held down with tape and barely operational)

You'll see that well-written code can even make my old laptop work like a supercomputer! #python #datasets #brightdata #data #ecommerce #datascience #pandas #pythonprogramming

️ RELATED TUTORIALS ️
——————————————
Anaconda Guide for Beginners (Install Jupyter Notebook):
https://youtu.be/MUZtVEDKXsk
Panda guide for beginners:
https://youtu.be/zN2Hua6oII0
For beginner loop:
https://youtu.be/dHANJ4l6fwA

TIME STAMPS
——————————————
00:00 – introduction
01:05 – introduction to working with professional data platforms
03:38 – complexity of loading very large data sets
06:43 – focus on relevant data
09:09 – loading data in small chunks
10:25 – access and change data fragment values
12:19 – save changed data to a new csv file
2:49 PM – Thanks for watching!

Connect with me
——————————————
Github:
https://github.com/mariyasha
Disagreement:
https://discord.com/invite/wgTTmsWmXA
LinkedIn:
https://ca.linkedin.com/in/mariyasha888
Twitter:
https://twitter.com/mariyasha888
Blog:
https://www.pythonsimplified.org

Credits
——————————————
Beautiful titles, transitions, sound effects and music:
mixkit.co
Beautiful icons:
flaticon.com
Beautiful graphics:
freepik.com

Please take the opportunity to connect and share this video with your friends and family if you find it helpful.