Mr. Pesticide: Understanding His Role in Global Trade and Agriculture

Project Description

This project explores the role of pesticides in agriculture and their impact on global trade. It provides an informative and interactive webpage that highlights:

• What pesticides are and how they work.
• Their importance in protecting crops from pests, diseases, and weeds.
• Their role in ensuring global food security and enhancing agricultural productivity.

Preprocessing

1. Load the dataset: Read the original CSV file (original_merged.csv) into a Pandas DataFrame.
2. Select relevant columns: Identify and keep only the necessary columns: Area, Year, Fungicides and Bactericides, Herbicides, Insecticides, Other Pesticides nes, Pesticides (total), Plant Growth Regulators, and Rodenticides.
3. Filter the dataset: Remove all columns not listed in the columns_to_keep list to retain only relevant data.
4. Save the transformed dataset: Write the filtered dataset to a new CSV file (cleaned_dataset.csv).

Top Global Pesticide Consumers by Continent

1. Load the dataset: Read the CSV file (modified_tableau_wide_format_countries_with_flags.csv) into a Pandas DataFrame.
2. Map countries to continents: Create a new Continent column by mapping country names in the Area column to their corresponding continent using the country_to_continent dictionary.
3. Drop missing mappings: Remove rows where the Continent mapping is missing (i.e., NaN in the Continent column).
4. Aggregate data by continent: Group the dataset by the Continent column and sum all numeric columns.
5. Save the transformed dataset: Write the aggregated dataset to a new CSV file at the specified output path.

Top Global Pesticide Consumers by Country

1. Load the Dataset: Read the CSV file (cleaned_dataset.csv) into a Pandas DataFrame.
2. Reshape with Melt: Use the melt function to reshape the DataFrame. Include Area and Year as identifier columns (id_vars) and transform selected pesticide-related columns (value_vars) into a long format with Pesticide Type and Usage columns.
3. Filter Rows: Exclude rows where the Pesticide Type column equals 'Pesticides (total)'.
4. Harmonize Country Names: Replace China, mainland and Mainland in the Area column with China.
5. Pivot Data: Transform the long-format DataFrame into a wide format with Area as rows and Year as columns. Aggregate Usage values using the sum function.
6. Generate Flag URLs: Create a dictionary mapping country names to their flag URLs using the ISO codes provided.
7. Map Flag URLs: Add a Flag URL column to the DataFrame by mapping the Area column to the flag URL dictionary.
8. Save Transformed Data: Write the transformed dataset with flag URLs to a new CSV file (modified_tableau_wide_format_countries_with_flags.csv).

Trends in Global Pesticide Usage by Type

1. Load the Dataset: Read the CSV file (cleaned_dataset.csv) into a Pandas DataFrame.
2. Reshape with Melt: Use the melt function to reshape the DataFrame. Keep Year as the identifier column (id_vars) and transform selected pesticide-related columns (value_vars) into a long format with Pesticide Type and Usage columns.
3. Filter Rows: Exclude rows where the Pesticide Type column equals 'Pesticides (total)'.
4. Pivot Data: Transform the long-format DataFrame back into a wide format with Year as rows and Pesticide Type as columns. Aggregate usage values using the sum function.
5. Save Transformed Data: Write the transformed dataset to a new CSV file (modified_tableau_long_format_exclude_total.csv).

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Project Structure

The project includes the following components:

1. HTML

• The main structure of the webpage is in index.html.
• Key sections:
  • Titles and subtitles introducing the theme.
  • Informative content with highlighted keywords.
  • Images and captions for better understanding.

2. CSS

• Custom styling to ensure a clean, responsive, and visually appealing design.
• Key features:
  • Background images and color themes.
  • Responsive layout optimized for desktop and mobile devices.
  • Hover effects for interactivity.

3. Assets

• Images and other static resources are stored in the assets/ folder.

4. Data Preprocessing Scripts

• Located in the preprocessing/ folder:
  • dataset_cleaner.py: Script for cleaning and preprocessing raw datasets.
  • extract_countries.py: Script for mapping all the countries

Data visualization course

SUPSI
Bachelor in Data Science and Artificial Intelligence Teacher: Giovanni Profeta https://dataviz-supsi.github.io/2024/