Processed JSONL and DataFrame for Data Analysis

• Day: 2026-01-09
• Time: 22:45 to 23:00
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Python, JSON, Dataframe, EDA, Data Processing

Description

Session Goal

The session aimed to process JSON Lines files and manipulate data within DataFrames to extract meaningful insights and prepare for exploratory data analysis (EDA).

Key Activities

• Loaded and read JSON Lines files using Python libraries such as [[json]] and [[pandas]], demonstrating file handling techniques.
• Extracted unique stages from JSON records and sorted them into a set.
• Manipulated DataFrames by displaying specific columns and filtering/sorting based on stages like ‘D.materialize’, ‘F.views’, and ‘A.ingest’.
• Defined functions to extract and list stages within DataFrames, providing insights into data structure and content.
• Consolidated sessions to plan for immediate EDA on financial data, setting criteria for reports and visualizations.

Achievements

• Successfully demonstrated reading and processing JSON Lines files.
• Extracted and sorted unique stages from data records.
• Filtered and sorted DataFrames to focus on relevant data stages.
• Established a plan for EDA, integrating insights from previous sessions.

Pending Tasks

• Execute the planned exploratory data analysis (EDA) on financial data using the criteria and artifacts defined in this session.

Evidence

• source_file=2026-01-09.sessions.jsonl, line_number=4, event_count=0, session_id=8216c040b7cbd6b8a7b9c0d0290a597d1afdd8efa0f739e0fed70a19319f2736
• event_ids: []