Customized Seaborn Boxplots for Voting Patterns

2 min read

📅 2023-11-02 — Session: Customized Seaborn Boxplots for Voting Patterns

🕒 02:00–02:20
🏷️ Labels: Seaborn, Boxplot, Data Visualization, Python, Error Handling
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The primary objective of this session was to enhance the visual representation of voting patterns using Seaborn boxplots. The focus was on customizing the appearance of these plots to improve clarity and aesthetics.

Key Activities

  • Customizing Boxplot Appearance: Utilized the boxprops argument to make box faces transparent and adjusted edge colors and line widths for better differentiation.
  • Addressing Compatibility Issues: Tackled an issue with the unrecognized fill parameter in Seaborn by manually setting facecolors and ensuring version compatibility.
  • Error Handling: Resolved an IndexError related to color assignment by dynamically calculating component numbers and verifying list lengths.
  • Data Cleaning: Discussed resolving SettingWithCopyWarning in DataFrames and prepared data for visualization by addressing NaN values.
  • Dynamic Calculations: Adapted code to dynamically calculate the number of components per group and handle insufficient color cases.

Achievements

  • Successfully customized Seaborn boxplots to reflect voting patterns across different political parties, enhancing visual clarity and presentation.
  • Implemented robust error handling and dynamic calculations to ensure accurate and efficient data visualization.

Pending Tasks

  • Further exploration of additional customization options for Seaborn visualizations.
  • Continued monitoring for any compatibility issues with future Seaborn updates.

Graph View

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