Blogs & Webinars

Session 23: Python Notebooks 101

A foundation session on Python notebooks for beginners.

Hey folks, we've been doing a lot of theory sessions for a while now. We though we'll do a hands on session for a change.

The session is conducted by Bala Panneerselvam. Bala is a founding member of Applied AI club, founder of ZORP with 17+ years in technology and product.

If you've missed the session or if you'd like to go through it again, here's the session video - https://youtu.be/2-8CmjksxFc

Here's the notes from the meeting:

Meeting Purpose Introduce non-technical professionals to Python notebooks for AI and data analysis.

Key Takeaways

  • Python notebooks provide an accessible way to work with large datasets and perform AI/ML tasks without extensive coding knowledge
  • Google Colab offers free cloud-based Python notebooks with pre-installed libraries and GPU/TPU access
  • Pandas is a key library for data manipulation; matplotlib for visualization
  • OpenAI API integration allows leveraging LLMs for data analysis within notebooks

Topics Introduction to Python Notebooks

  • Python is a programming language commonly used for data analysis
  • Notebooks allow writing and executing code in discrete cells
  • Google Colab provides free cloud-based Python notebooks
  • Advantages: No local setup required, pre-installed libraries, GPU/TPU access

Working with Data using Pandas

  • Imported Pandas library as 'pd'
  • Loaded Excel file using pd.read_excel()
  • Created new columns with calculations (e.g. revenue = quantity * unit_price)
  • Demonstrated grouping, sorting, and pivoting data
  • Visualized data using matplotlib library

Integrating OpenAI API

  • Installed OpenAI library and set up API key
  • Sampled 2000 rows from dataset to stay within token limits
  • Demonstrated prompting GPT model for open-ended data analysis
  • Discussed handling conversation context and token limitations

AI/ML Concepts Discussion

  • Explained how ChatGPT maintains conversation context
  • Discussed methods for handling context window limitations (truncation, summarization)
  • Covered concepts of persistent storage and efficient retrieval of past conversations

Next Steps

  • Participants to practice using Python notebooks independently
  • Explore more advanced data analysis and ML techniques in notebooks
  • Consider joining the AI club for future sessions on related topics

Here's the entire recording of the session.