Odev3-Merge

Food Data Analysis

This project performs an analysis of food data, including visualizations and statistical insights. The data is processed and analyzed to provide insights on nutritional values, categories, and distributions. It utilizes pandas for data manipulation and matplotlib for visualization.

Features

1. Loading and Inspecting Data

   • The food data is loaded from a CSV file (food_data.csv).
   • Basic inspections of the data are performed, including checking the first few rows and the overall data structure.

2. Groupwise Analysis

   • The data is grouped by description_category, and the average nutritional values (amount) are computed for each category.
   • A bar chart visualizes the average nutritional values for each food category.

3. Finding Extremes

   • The highest and lowest nutritional values are identified and printed.

4. Category Distribution

   • The distribution of food categories is displayed using a horizontal bar chart showing the number of entries in each category.

5. Time Series Analysis (If applicable)

   • If a date column is available, the data is resampled monthly to calculate the average nutritional values over time.
   • A time series plot is generated to show the trend in nutritional values.

6. Sum of Nutritional Values by Category

   • A bar chart visualizes the total nutritional value (amount) for each food category.

7. Boxplot of Nutritional Values

   • A boxplot displays the distribution of nutritional values within each category.

8. Histogram of Nutritional Values

   • A histogram is generated to visualize the distribution of nutritional values across all food items.

Requirements

To run this project, ensure you have the following Python libraries installed:

• pandas
• matplotlib

You can install these libraries using pip:

pip install pandas matplotlib

git clone https://github.com/deliprofesor/Odev3-Merge.git

cd Odev3-Merge

python aap.py