π 2025-04-20 β Session: YAML and DataFrame Processing Enhancements
π 13:35β15:00
π·οΈ Labels: YAML, Dataframe, Python, Promptflow, Automation
π Project: Dev
β 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 PromptFlowβs 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.