Ultimate Guide to Polars Fastest Python Data Science Library

>> YOUR LINK HERE: ___ http://youtube.com/watch?v=8GoBlwgbirE

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_Pola... • 🎥 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/rapids... -O transactions.parquet • For PC: • !wget https://storage.googleapis.com/rapids... -O transactions.parquet • 📺 Related Videos 📺 • ---------------------------------------------------------------- • ⭐ Anaconda for beginners: • • Anaconda Beginners Guide for Linux an... • ⭐ Basic Guide to Pandas: • • Basic Guide to Pandas! Tricks, Shortc... • ⏰ 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: • / mariyasha888 • 🔗 Blog: • https://www.pythonsimplified.org • 🔗 Discord: • / discord • 💳 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

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