Developed Data Analysis and Visualization Functions

📅 2023-02-20 — Session: Developed Data Analysis and Visualization Functions

🕒 08:35–10:35
🏷️ Labels: Python, Data Analysis, Visualization, Seaborn, Matplotlib
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal:

The session aimed to enhance data analysis and visualization capabilities using Python, focusing on improving code efficiency and visual aesthetics.

Key Activities:

• Implemented Python code for concatenating dataframes and visualizing data using Seaborn and Matplotlib.
• Customized box plot aesthetics and x-axis tick labels in Seaborn catplots.
• Addressed floating-point precision issues in Python for accurate data representation.
• Developed functions for running experiments with parameter sweeps, matching, and regression analysis, saving results to CSV files.
• Modified regression analysis functions to use pd.concat instead of the deprecated append method.
• Enhanced data visualization with scatter plots including error bars and diagonal lines using Matplotlib.

Achievements:

• Successfully created and tested functions for data concatenation, visualization, and experimental analysis.
• Improved code efficiency and visual presentation of data plots.

Pending Tasks:

• Further optimization of visualization functions to handle larger datasets efficiently.
• Exploration of additional customization options in Seaborn and Matplotlib for more complex visualizations.