Enhanced Python Data Analysis and Visualization

• Day: 2023-05-21
• Time: 17:20 to 21:15
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Python, Pandas, JSON, Data Visualization, Color Palette, Mapbox

Description

Session Goal

The session aimed to enhance data analysis and visualization capabilities using Python, focusing on Pandas for data manipulation and JSON for [[data visualization]].

Key Activities

• Data Analysis with Pandas: Implemented techniques to calculate percentages within grouped data using groupby and transform. Excluded specific entries labeled ‘NO POSITIVO’ to refine vote calculations.
• Aggregation Techniques: Utilized Pandas agg function to aggregate votes and identify unique forces.
• JSON Manipulation: Extracted and modified JSON objects to update visualization properties, specifically targeting fill colors in Mapbox styles.
• Color Palette Generation: Developed Python functions to generate color palettes using colorsys and [[matplotlib]], including reverse order generation and interpolation between colors.
• Mapbox Integration: Created and updated Mapbox style JSONs, modifying fill colors and uploading changes via API.

Achievements

• Successfully calculated and aggregated data using Pandas, enhancing data analysis workflows.
• Developed robust color palette generation scripts for improved [[data visualization]].
• Implemented JSON manipulation techniques for dynamic visualization updates in Mapbox.

Pending Tasks

• Further refine JSON manipulation scripts to handle additional visualization parameters.
• Explore additional [[data visualization]] libraries for enhanced graphical outputs.

Evidence

• source_file=2023-05-21.sessions.jsonl, line_number=1, event_count=0, session_id=abf419628015abbdbf3eac66e1d2299fc773734234773a6ecbf8fa48779f002c
• event_ids: []