Enhanced Data Visualization and Processing Techniques

📅 2023-10-14 — Session: Enhanced Data Visualization and Processing Techniques

🕒 18:20–20:20
🏷️ Labels: Python, Data Visualization, Code Refactoring, Matplotlib, Data Processing
📂 Project: Dev
⭐ Priority: MEDIUM

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.