Fix pandas Memory Errors with Large CSV and Parquet Files | 3 Easy Solutions

Video Description

Running out of memory while loading large CSV or Parquet files into pandas? This video walks through 3 practical solutions to handle large datasets in Python—without crashing your system. Learn memory-efficient techniques that work with pandas DataFrames and keep your workflows running smoothly. 💡Hint: The third solution is the best if you are on Google Colab, Kaggle, or simply using a GPU. 00:00 - Intro 00:30 - Solution 1 - Swap Space 02:04 - Solution 2 - Sampling 06:06 - Solution 3 - Unified Virtual Sampling 7:43 - nv_dashboard 8:19 - UVM - Getting Started 10:19 - Pros / Cons of Solutions 📝 Learn more about cuDF UVM: https://developer.nvidia.com/blog/unified-virtual-memory-supercharges-pandas-with-rapids-cudf/ ➡️ Join the NVIDIA Developer Program: https://nvda.ws/3OhiXfl ➡️ Read and subscribe to the NVIDIA Technical Blog: https://nvda.ws/3XHae9F #pandas #datascience #gpu #python #nvidia #cudf