Enhanced Data Visualization and Processing Techniques

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Enhanced Data Visualization and Processing Techniques

  • Day: 2023-10-14
  • Time: 18:20 to 20:20
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Python, Data Visualization, Code Refactoring, Matplotlib, Data Processing

Description

Session Goal

The session aimed to enhance data visualization and processing techniques using Python, focusing on improving code modularity, clarity, and effectiveness.

Key Activities

  • Developed essential documentation for resource-limited projects to maintain clarity and usability.
  • Adjusted data processing code to include unique combinations involving base_str for more comprehensive data handling.
  • Modified DataFrame operations to manage multiple groupers and filter data using compound boolean masks.
  • Refactored code to modularize data retrieval processes, enhancing readability and maintainability.
  • Created a function for plotting time series data with Matplotlib, iterating over multiple files.
  • Implemented subplots for visualizing CBA and CBT data, including economic indicators like CB_EQUIV, Poverty, and Indigencia.
  • Updated plotting code to improve subplot arrangements, figure height, grid addition, and legend positioning.
  • Ensured consistent color mapping across plots and corrected color mapping in grouped DataFrame.
  • Plotted raw data and moving averages, adding statistical analysis for poverty and indigence data.

Achievements

  • Successfully modularized data retrieval and visualization processes, improving code structure and performance.
  • Enhanced visualization techniques with consistent color mapping and improved subplot management.

Pending Tasks

  • Further refine documentation to ensure all project stakeholders can easily understand and utilize the code.
  • Explore additional statistical methods for data analysis and visualization to provide deeper insights.

Evidence

  • source_file=2023-10-14.sessions.jsonl, line_number=1, event_count=0, session_id=8a21347653520631ba912a594f311cbe013bacb1d734a6d7d7e069588ff8cec3
  • event_ids: []

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