Comprehensive Exploratory Data Analysis and Fixes

2 min read

Comprehensive Exploratory Data Analysis and Fixes

  • Day: 2025-09-12
  • Time: 09:20 to 11:40
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: EDA, Python, Npmi, Pandas, Data Analysis

Description

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.

Evidence

  • source_file=2025-09-12.sessions.jsonl, line_number=3, event_count=0, session_id=5dfdf335598597c842eee834a1705b6f9d4d9d06fb0b6fd9c49de27712d4e02e
  • event_ids: []

Graph View

Backlinks