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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