📅 2023-10-26 — Session: Enhanced Data Visualization with Python for Income Analysis
🕒 21:40–22:30
🏷️ Labels: Python, Data Visualization, Matplotlib, Income Analysis
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal: The goal of this session was to enhance data visualization techniques in Python for analyzing household and individual income data, focusing on plotting, visualization features, and statistical analysis.
Key Activities:
- Developed Python scripts using Matplotlib and Pandas for plotting household and individual datasets on the same figure with distinct markers.
- Implemented shading based on AGLOSI values and added a secondary y-axis for better data interpretation.
- Plotted observables ‘P47T_hogar’ and ‘P47T_persona’ with customization for markers and moving averages.
- Calculated and visualized the 25th and 75th percentiles directly from datasets, enhancing scatter plots with these statistical measures.
- Filtered datasets based on quantiles and visualized the results, filling areas between the 25th and 75th percentiles.
- Applied rolling averages to percentiles and visualized these alongside median income data.
- Created side-by-side time series plots for ‘Hogares’ and ‘Hogares Indigentes’, incorporating color coding, moving averages, and percentile shading.
- Added grid and y-axis limits to Matplotlib subplots for improved clarity in visualizing poverty metrics.
Achievements:
- Successfully implemented multiple data visualization features, enhancing the clarity and depth of income data analysis.
- Improved the interpretability of plots through advanced customization techniques.
Pending Tasks:
- Further exploration of interactive visualization features to enhance user engagement.
- Optimization of data processing steps for larger datasets.