Developed Data Visualization Functions with Seaborn and Matplotlib

• Day: 2023-03-06
• Time: 14:35 to 15:15
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Python, Data Visualization, Seaborn, Matplotlib, Percentiles, Error Bars

Description

Session Goal: The goal of this session was to enhance [[data visualization]] techniques for analyzing treatment and control groups using Python libraries, specifically Seaborn and Matplotlib.

Key Activities:

• Implemented Python code to compute percentiles for treatment and control groups using Pandas, ensuring accurate statistical representation.
• Resolved DataFrame column mismatch issues during percentile calculations by transposing results.
• Developed a series of functions to visualize balance information between treatment groups, incorporating error bars and adjusting plot aesthetics.
• Modified Seaborn’s catplot() to adjust bar widths and ensure error bars align with data points.
• Enhanced the plot_balance function to include legends and improved styling with sns.set_style("whitegrid").

Achievements:

• Successfully created and refined multiple Python functions for visualizing data with error bars, improving the clarity and interpretability of treatment group analyses.
• Established a robust method for plotting balance information with legends and customized plot aesthetics.

Pending Tasks:

• Further testing and validation of visualization functions with additional datasets to ensure adaptability and accuracy across various data scenarios.

Evidence

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• event_ids: []