📅 2023-12-23 — Session: Enhanced Data Visualization for Power Law Analysis
🕒 00:00–00:10
🏷️ Labels: Data Visualization, Power Law, Python, Code Improvement
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The goal of this session was to enhance data visualization techniques to better highlight power law properties in economic datasets, focusing on improving the clarity and performance of plots.
Key Activities
- Adjusted plotting functions to emphasize power law properties, handling special cases like zeros and applying logarithmic transformations.
- Modified CDF plots to display complementary CDFs with negative power law characteristics.
- Developed a function for plotting accumulation quantiles of VART to provide insights into distribution.
- Computed degree distributions with VART aggregation, including logarithmic transformations for clarity.
- Revised the
plot_degree_distributionfunction for dynamic column selection and improved performance. - Conducted OLS regression analysis on a log-log scale to interpret power-law distribution characteristics.
- Analyzed network degree distribution, focusing on tail behaviors and suggesting further modeling steps.
- Outlined a methodology for lower tail analysis, from data preprocessing to model validation.
- Provided code improvement suggestions for clipped lognormal distribution and enhanced code for size distributions and quantiles.
- Improved code for experimentation with Gaussian and Laplace deviations.
Achievements
- Successfully enhanced visualization functions to better represent power law properties in data.
- Improved clarity and performance of various plotting and analysis functions.
- Developed systematic approaches for analyzing degree distributions and lower tail behaviors.
Pending Tasks
- Further model fitting and statistical testing for network degree distribution analysis.
- Continued refinement of code for efficiency and clarity in statistical distribution functions.