Developed Data Analysis and Visualization Functions

• Day: 2023-02-20
• Time: 08:35 to 10:35
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Python, Data Analysis, Visualization, Seaborn, Matplotlib

Description

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.

Evidence

• source_file=2023-02-20.sessions.jsonl, line_number=0, event_count=0, session_id=8b9eef8a956b554fb9d036704b79b20bfbd5ed23175616cb8887f7099b41194f
• event_ids: []