📅 2025-09-12 — Session: Comprehensive Exploratory Data Analysis and Fixes
🕒 09:20–11:40
🏷️ Labels: EDA, Python, Npmi, Pandas, Data Analysis
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The primary goal of this session was to conduct a comprehensive exploratory data analysis (EDA) on mock session data and address various technical challenges related to data processing and analysis.
Key Activities
- Exploratory Data Analysis: Initiated EDA on mock sessions, focusing on parsing JSON records, extracting tags, and building document-tag matrices.
- nPMI Calculation Fix: Implemented a fix for nPMI calculations to prevent division by zero errors.
- EDA Kit Development: Developed a portable EDA kit for LEV and SESS JSONL files, including Python scripts and setup instructions.
- Normalization and Tagging: Outlined strategies for schema normalization and document processing enhancements.
- Data Ingestion Block: Created a defensive data ingestion block for normalizing legacy files and integrating session data.
- Pandas Timestamp Fixes: Addressed issues with mixed ISO8601 parsing and milliseconds epoch timestamps in Pandas.
- Tag Enrichment Analysis: Conducted analysis on tag enrichment and association strength.
- Graph Analysis Insights: Provided insights on graph metrics for corpus structuring.
Achievements
- Successfully developed and packaged an EDA kit for JSONL files.
- Fixed critical issues in nPMI calculations and timestamp parsing in Pandas.
- Enhanced strategies for document processing and tag analysis.
Pending Tasks
- Further validation of EDA outputs and integration with existing data pipelines.
- Exploration of additional graph analysis techniques for improved corpus structuring.