Accelerated Data Science with Python Polars

Video Description

Today we will explore Polars - the fastest data science library in Python!! 🐻‍❄️🐻‍❄️🐻‍❄️ The best part is, as of earlier this month, it even got faster with a brand new release of a GPU engine! 🤩 We will learn about Queries, Lazy Frames, Engines, and use them in real life settings, analyzing and visualizing a free dataset with over 260 million rows (and 22GB in size!!! way bigger than what programs like Excel or Sheets can process). So not only will we learn how to load, compress and process so much data all at once, but we will also plot it with millions of data nodes on the same graph!! 😱 If you think it might be challenging for Polars - prepare to be surprised!!! because that's exactly where it shines, especially when the new GPU engine is involved! ⭐ More about Polars GPU on GitHub: https://nvda.ws/gpu-polars-xr ⭐ Official GPU Polars Colab Notebook: https://nvda.ws/gpu-polars-xt 💻Tutorial GitHub Repository 💻 ---------------------------------------------------------------- https://github.com/MariyaSha/GPU_Polars.git 🎥 Video Commands and Links 🎥 ---------------------------------------------------------------- ⭐ Install Polars GPU: !pip install polars[gpu] --extra-index-url=https://pypi.nvidia.com ⭐ Mount Google Drive from google.colab import drive drive.mount('/content/drive') ⭐ Download Compressed Parquet Dataset (4GB): For Google Colab: !wget https://storage.googleapis.com/rapidsai/polars-demo/transactions-t4-20.parquet -O transactions.parquet For PC: !wget https://storage.googleapis.com/rapidsai/polars-demo/transactions.parquet -O transactions.parquet 📺 Related Videos 📺 ---------------------------------------------------------------- ⭐ Anaconda for beginners: https://youtu.be/MUZtVEDKXsk ⭐ Basic Guide to Pandas: https://youtu.be/zN2Hua6oII0 ⏰ TIMESTAMPS ⏰ ------------------------------------------------------- 00:00 - intro ------------------------------------------------------- ⭐ QUICKSTART 00:48 - Polars in Google Colab 01:01 - Lazy Frame 02:36 - Querying 03:29 - GPU Engine ------------------------------------------------------- ⭐ WORKFLOW 04:51 - Simulated Transactions Dataset 05:25 - Install Polars and GPU Engine locally 06:33 - Read CSV File with Polars 07:07 - Compress CSV to Parquet 07:54 - Read Parquet File with Polars ------------------------------------------------------- ⭐ QUERYING 08:38 - Select Statement 09:09 - Filter Statement 10:05 - Column Data Types 10:37 - Multiple Filters 11:15 - Group By Statement 12:32 - GPU Versus CPU 13:06 - Multiple Aggregations ------------------------------------------------------- ⭐ DATA VISUALIZATION 15:40 - Bar Chart 16:15 - Scatter Plot 16:58 - Chart Width 17:17 - Chart Z Axis with Colors 17:38 - Mark Styling 18:09 - Chart Title 18:29 - Tooltip Customization 19:10 - Solve Max Rows Error ------------------------------------------------------- 20:33 - Thanks for Watching 🤝 Connect with me 🤝 ---------------------------------------------------------------- 🔗 Github: https://github.com/mariyasha 🔗 X: https://x.com/MariyaSha888 🔗 LinkedIn: https://ca.linkedin.com/in/mariyasha888 🔗 Blog: https://www.pythonsimplified.org 🔗 Discord: https://discord.com/invite/wgTTmsWmXA 💳 Credits 💳 ---------------------------------------------------------------- ⭐ Beautiful titles, transitions, sound FX: mixkit.co ⭐ Thumbnail: flaticon.com freepik.com #python #pythonprogramming #polars #pandas #datascience #querying #database #cuda #gpu #pythonprojects #pythonforbeginners #graphs #plotting #dataanalytics #dataanalysis #dsa #coding #learnpython #bigdata #beginners #tutorial #codingtutorial #technology #tech