Enhanced Data Generation and Visualization Functions

📅 2023-02-23 — Session: Enhanced Data Generation and Visualization Functions

🕒 06:45–08:20
🏷️ Labels: Python, Data Generation, Visualization, Refactoring, Function Enhancement
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal: The objective of this session was to enhance and refactor Python functions related to data generation and visualization, focusing on flexibility and maintainability.

Key Activities:

• Updated the generate_data() function to include a boolean argument use_formula_2 for selecting treatment distribution formulas.
• Implemented the use of np.random.choice to generate a treatment variable with equal probability of 0s and 1s.
• Modified the generate_data function to include a use_prob_dist argument, allowing selection between binomial and choice distributions.
• Defined a function to generate synthetic data for regression problems with options for treatment group balancing.
• Refactored code to reduce duplication by creating functions for similar operations.
• Updated a Python plotting function to pass figure and axis objects as arguments for streamlined plotting.
• Enhanced the scatterplot_results function with a plot_kwargs parameter for additional plotting arguments.
• Demonstrated the use of plot_kwargs in the scatterplot_sts() function to customize plot appearance.

Achievements:

• Successfully updated and refactored functions to improve code flexibility and maintainability.
• Enhanced data visualization functions to allow for more customizable plots.

Pending Tasks:

• Further testing of the updated functions to ensure robustness in various scenarios.
• Documentation updates to reflect changes in function parameters and usage examples.