Developed Data Visualization Functions with Seaborn and Matplotlib

1 min read

📅 2023-03-06 — Session: Developed Data Visualization Functions with Seaborn and Matplotlib

🕒 14:35–15:15
🏷️ Labels: Python, Data Visualization, Seaborn, Matplotlib, Percentiles, Error Bars
📂 Project: Dev
⭐ Priority: MEDIUM

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.

Graph View

Backlinks