Enhanced Data Visualization and Food Data Processing

1 min read

📅 2023-03-06 — Session: Enhanced Data Visualization and Food Data Processing

🕒 01:10–03:00
🏷️ Labels: Python, Data Visualization, Pandas, Nutrition, Code Optimization
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to enhance data visualization techniques and process nutritional data efficiently using Python libraries such as Pandas, Matplotlib, and Seaborn.

Key Activities

  • Utilized strftime in Pandas to format dates for clear weekly labels.
  • Organized and optimized Python code for data visualization, including moving function definitions outside loops and defining constants.
  • Implemented horizontal line plotting in Matplotlib for better plot maintenance.
  • Improved data visualization code readability and organization using Seaborn.
  • Fixed date formatting for Matplotlib x-axis limits using pd.to_datetime().
  • Rotated x-axis labels in Seaborn boxplots and generated comprehensive plots.
  • Fitted a second-order polynomial to data using NumPy for predictive analysis.
  • Exported food nutritional data to CSV using Pandas.
  • Developed functions to process CSV food data for macro calculations and meal planning.

Achievements

  • Successfully formatted dates and improved code organization for data visualization.
  • Enhanced readability and maintainability of Python scripts for plotting.
  • Achieved efficient processing of nutritional data for macro calculations and meal planning.

Pending Tasks

  • Further refine the meal planning function for more personalized diet recommendations.

Graph View

Backlinks