Enhanced Data Visualization and Food Data Processing

• Day: 2023-03-06
• Time: 01:10 to 03:00
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Python, Data Visualization, Pandas, CSV, Nutrition

Description

Session Goal

The primary goal of this session was to enhance data visualization techniques using Python libraries and to process food nutritional data for meal planning.

Key Activities

• Date Formatting in Pandas: Implemented strftime to format dates as year-week labels.
• Code Optimization: Improved Python code organization for data visualization using seaborn and matplotlib.
• Data Visualization Enhancements: Added horizontal lines in Matplotlib plots and rotated x-axis labels in Seaborn boxplots.
• Polynomial Fitting: Utilized NumPy to fit a second-order polynomial to data.
• Food Data Export: Exported nutritional data to a CSV file using pandas.
• CSV Processing: Developed functions to process CSV files for calculating macros and meal planning.

Achievements

• Successfully formatted dates for better week separation in datasets.
• Improved readability and maintainability of data visualization code.
• Enhanced data visualization with additional plotting techniques.
• Efficiently exported and processed food data for nutritional analysis.

Pending Tasks

• Further refine the meal planning algorithm to incorporate more dietary preferences and constraints.

Evidence

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