📅 2023-12-19 — Session: Developed AI-driven Jupyter Notebook Analysis Framework
🕒 19:15–21:35
🏷️ Labels: AI, Jupyter, Python, Openai, Automation, Data Analysis
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal:
The session aimed to develop a comprehensive framework for analyzing Jupyter notebooks using AI, focusing on enhancing data analysis projects through structured methodologies.
Key Activities:
- Notebook Processing Framework: Outlined a structured approach for analyzing and processing notebooks, emphasizing objectives, methodologies, findings, code quality, and reorganization.
- Prompt Structure and Strategies: Designed prompts for Jupyter notebook analysis to improve AI response clarity and comprehensiveness, and explored strategies for applying prompts efficiently.
- Workflow Development: Created a detailed workflow for automating the analysis of notebooks using the OpenAI API, including content extraction and response analysis.
- Python Scripting: Developed scripts using
nbformat
for content extraction and modifying scripts to access local variables within functions. - API Integration: Guided the integration and execution of Python scripts with the OpenAI API locally, addressing errors and upgrading to the new OpenAI SDK.
- Data Analysis Exercises: Formulated strategies for transforming notebook comments into educational exercises and leveraging markdown analyses for project enhancement.
Achievements:
- Established a robust framework and workflow for Jupyter notebook analysis using AI and automation tools.
- Developed Python scripts for content extraction and API interaction.
- Addressed API errors and upgraded to the latest OpenAI SDK.
Pending Tasks:
- Implement the developed framework in a live project to validate its effectiveness.
- Further refine educational strategies and exercise designs based on real-world application.