πŸ“… 2025-04-20 β€” Session: YAML and DataFrame Processing Enhancements

πŸ•’ 13:35–15:00
🏷️ Labels: YAML, Dataframe, Python, Promptflow, Automation
πŸ“‚ Project: Dev
⭐ Priority: MEDIUM

Session Goal

The primary aim of this session was to enhance YAML file processing and DataFrame manipulation capabilities, focusing on automation and data science tasks.

Key Activities

  • Developed a YAML file parsing script to generate structured header and node files.
  • Explored semi-structured data mining using Python, integrating jmespath and tinydb for querying.
  • Debugged issues related to empty DataFrames, identifying potential parsing problems with YAML files.
  • Implemented and corrected masking logic in Pandas DataFrames to handle NaN values effectively.
  • Analyzed PromptFlow node behaviors and modular design patterns, providing insights into AI and data pipeline optimizations.

Achievements

  • Successfully created a functional YAML parsing script, although input files are still required.
  • Improved understanding and handling of DataFrame operations, particularly in masking and NaN management.
  • Gained insights into PromptFlow’s modular design and node behavior, enhancing strategic AI design capabilities.

Pending Tasks

  • Further testing and validation of the YAML parsing script with actual data files.
  • Continued exploration of PromptFlow node behaviors for deeper AI workflow optimization.