Datasets:

danielrosehill
/

ifvi_valuefactors_deriv

+{
+  "data_sections": [
+    {
+      "category": "Data Files Root",
+      "name": "Main Data Directory",
+      "url": "https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv/tree/main/data",
+      "emoji": "📁"
+    },
+    {
+      "category": "Aggregated Value Factors",
+      "name": "Aggregated Data (CSV, JSON, Parquet)",
+      "url": "https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv/tree/main/data/aggregated",
+      "emoji": "📊"
+    },
+    {
+      "category": "Value Factors by Type/Methodology",
+      "name": "Impact Type Data (JSON)",
+      "url": "https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv/tree/main/data/by-impact-type",
+      "emoji": "🔄"
+    },
+    {
+      "category": "Value Factors by Geography",
+      "name": "Continental Data Root",
+      "url": "https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv/tree/main/data/by-region/continental",
+      "emoji": "🌍"
+    },
+    {
+      "category": "CSV Format Data",
+      "name": "CSV Data Files",
+      "url": "https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv/tree/main/data/csv",
+      "emoji": "📄"
+    },
+    {
+      "category": "Data By Impact Type",
+      "name": "Air Pollution Data",
+      "url": "https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv/tree/main/data/by-impact-type/air-pollution",
+      "emoji": "🌫️"
+    },
+    {
+      "category": "Data By Impact Type",
+      "name": "GHG Impacts Data (1 Impact Only)",
+      "url": "https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv/tree/main/data/by-impact-type/ghg",
+      "emoji": "🏭"
+    },
+    {
+      "category": "Data By Impact Type",
+      "name": "Land Conversion Data",
+      "url": "https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv/tree/main/data/by-impact-type/land-conversion",
+      "emoji": "🌱"
+    },
+    {
+      "category": "Data By Impact Type",
+      "name": "Land Use Data",
+      "url": "https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv/tree/main/data/by-impact-type/land-use",
+      "emoji": "🌿"
+    },
+    {
+      "category": "Data By Impact Type",
+      "name": "Waste Data",
+      "url": "https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv/tree/main/data/by-impact-type/waste",
+      "emoji": "🗑️"
+    },
+    {
+      "category": "Data By Impact Type",
+      "name": "Water Consumption Data",
+      "url": "https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv/tree/main/data/by-impact-type/water-consumption",
+      "emoji": "💧"
+    },
+    {
+      "category": "Data By Impact Type",
+      "name": "Water Pollution Data (JSON Chunked Due To Size)",
+      "url": "https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv/tree/main/data/by-impact-type/water-pollution",
+      "emoji": "💦"
+    },
+    {
+      "category": "Data By Region",
+      "name": "Africa",
+      "url": "https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv/tree/main/data/by-region/continental/Africa",
+      "emoji": "🌍"
+    },
+    {
+      "category": "Data By Region",
+      "name": "Asia",
+      "url": "https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv/tree/main/data/by-region/continental/Asia",
+      "emoji": "🌏"
+    },
+    {
+      "category": "Data By Region",
+      "name": "Europe",
+      "url": "https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv/tree/main/data/by-region/continental/Europe",
+      "emoji": "🌍"
+    },
+    {
+      "category": "Data By Region",
+      "name": "North America",
+      "url": "https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv/tree/main/data/by-region/continental/North%20America",
+      "emoji": "🌎"
+    },
+    {
+      "category": "Data By Region",
+      "name": "Oceania",
+      "url": "https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv/tree/main/data/by-region/continental/Oceania",
+      "emoji": "🌏"
+    },
+    {
+      "category": "Data By Region",
+      "name": "South America",
+      "url": "https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv/tree/main/data/by-region/continental/South%20America",
+      "emoji": "🌎"
+    }
+  ]
+}

scripts/convert_csv_to_json.py DELETED Viewed

@@ -1,214 +0,0 @@
-#!/usr/bin/env python3
-"""
-Script to convert CSV files to JSON for the IFVI Value Factors dataset.
-This script fetches CSV data from GitHub and creates JSON files for the missing impact types.
-"""
-import os
-import json
-import pandas as pd
-import requests
-from pathlib import Path
-from io import StringIO
-# Get the repository root directory
-REPO_ROOT = Path(__file__).parent.parent.absolute()
-# GitHub repository URL for the CSV files
-GITHUB_CSV_BASE_URL = "https://raw.githubusercontent.com/danielrosehill/Global-Value-Factors-Explorer/main/Data/GVFD-Deriv/data/csv/by-methodology"
-def create_directory_if_not_exists(directory_path):
-    """
-    Create a directory if it doesn't exist.
-    Args:
-        directory_path (Path): Path to the directory to create
-    """
-    if not directory_path.exists():
-        os.makedirs(directory_path)
-        print(f"Created directory: {directory_path}")
-def fetch_csv_from_github(impact_type):
-    """
-    Fetch a CSV file from GitHub.
-    Args:
-        impact_type (str): Name of the impact type (e.g., 'land-conversion')
-    Returns:
-        pandas.DataFrame or None: DataFrame containing the CSV data, or None if the fetch failed
-    """
-    csv_url = f"{GITHUB_CSV_BASE_URL}/{impact_type}.csv"
-    print(f"Fetching CSV from: {csv_url}")
-    try:
-        response = requests.get(csv_url)
-        response.raise_for_status()  # Raise an exception for HTTP errors
-        # Parse the CSV content
-        csv_content = StringIO(response.text)
-        df = pd.read_csv(csv_content)
-        print(f"Successfully fetched CSV for {impact_type}")
-        return df
-    except Exception as e:
-        print(f"Error fetching CSV for {impact_type}: {str(e)}")
-        return None
-def convert_csv_to_json(impact_type, output_filename=None, split_file=False, num_parts=4):
-    """
-    Convert a CSV file to JSON for a specific impact type.
-    Args:
-        impact_type (str): Name of the impact type (e.g., 'land-conversion')
-        output_filename (str, optional): Name of the output JSON file.
-                                        Defaults to impact_type + '.json'
-        split_file (bool, optional): Whether to split the file into multiple parts.
-                                    Defaults to False.
-        num_parts (int, optional): Number of parts to split the file into.
-                                  Defaults to 4.
-    Returns:
-        bool: True if conversion was successful, False otherwise
-    """
-    # Fetch the CSV data from GitHub
-    df = fetch_csv_from_github(impact_type)
-    if df is None:
-        print(f"Failed to fetch CSV data for {impact_type}")
-        return False
-    # Create the output directory
-    output_dir = REPO_ROOT / "data" / "by-impact-type" / impact_type
-    create_directory_if_not_exists(output_dir)
-    # Set the output filename if not provided
-    if output_filename is None:
-        output_filename = f"{impact_type}_by_impact.json"
-    try:
-        # Convert the DataFrame to a nested dictionary structure
-        # Get the column names
-        columns = df.columns.tolist()
-        # Create a hierarchical structure
-        metadata = {
-            "impact_type": impact_type,
-            "description": f"Value factors for {impact_type}",
-            "source": f"Derived from {impact_type}.csv",
-            "columns": columns
-        }
-        if not split_file:
-            # Regular processing for non-split files
-            data = {
-                "metadata": metadata,
-                "data": {}
-            }
-            # Convert DataFrame to dictionary
-            # If the DataFrame has 'Country' or 'Region' columns, organize by those
-            if 'Country' in df.columns:
-                for country in df['Country'].unique():
-                    country_data = df[df['Country'] == country].to_dict(orient='records')
-                    data['data'][country] = country_data
-            elif 'Region' in df.columns:
-                for region in df['Region'].unique():
-                    region_data = df[df['Region'] == region].to_dict(orient='records')
-                    data['data'][region] = region_data
-            else:
-                # If no country or region columns, just convert all rows
-                data['data']['all'] = df.to_dict(orient='records')
-            # Write the JSON file
-            output_path = output_dir / output_filename
-            with open(output_path, 'w') as f:
-                json.dump(data, f, indent=2)
-            print(f"Created JSON file: {output_path}")
-        else:
-            # Split processing for large files
-            # Calculate the size of each part
-            part_size = len(df) // num_parts
-            output_paths = []
-            # Process each part
-            for i in range(num_parts):
-                start_idx = i * part_size
-                end_idx = (i + 1) * part_size if i < num_parts - 1 else len(df)
-                df_part = df.iloc[start_idx:end_idx]
-                # Create data structure for this part
-                data_part = {
-                    "metadata": {**metadata, "part": i + 1, "total_parts": num_parts},
-                    "data": {}
-                }
-                # Convert DataFrame to dictionary for this part
-                if 'Country' in df_part.columns:
-                    for country in df_part['Country'].unique():
-                        country_data = df_part[df_part['Country'] == country].to_dict(orient='records')
-                        data_part['data'][country] = country_data
-                elif 'Region' in df_part.columns:
-                    for region in df_part['Region'].unique():
-                        region_data = df_part[df_part['Region'] == region].to_dict(orient='records')
-                        data_part['data'][region] = region_data
-                else:
-                    data_part['data']['all'] = df_part.to_dict(orient='records')
-                # Write the JSON file for this part
-                base_name = output_filename.replace('.json', '')
-                output_path = output_dir / f"{base_name}_part{i+1}.json"
-                output_paths.append(output_path)
-                with open(output_path, 'w') as f:
-                    json.dump(data_part, f, indent=2)
-            print(f"Created {num_parts} JSON files: {', '.join(str(p) for p in output_paths)}")
-        return True
-    except Exception as e:
-        print(f"Error converting {impact_type} to JSON: {str(e)}")
-        return False
-def main():
-    """
-    Main function to convert all missing impact types to JSON.
-    """
-    # List of impact types to convert
-    impact_types = [
-        "land-conversion",
-        "land-use",
-        "water-pollution"
-    ]
-    # Output filenames for each impact type
-    output_filenames = {
-        "land-conversion": "land_conversion_by_impact.json",
-        "land-use": "land_use_by_impact.json",
-        "water-pollution": "water_pollution_by_impact.json"
-    }
-    # Convert each impact type
-    for impact_type in impact_types:
-        output_filename = output_filenames.get(impact_type)
-        # Split the water-pollution file into four parts
-        split_file = (impact_type == "water-pollution")
-        num_parts = 4 if split_file else 2
-        success = convert_csv_to_json(impact_type, output_filename, split_file, num_parts)
-        if success:
-            print(f"Successfully converted {impact_type} to JSON")
-        else:
-            print(f"Failed to convert {impact_type} to JSON")
-    print("\nDone! You can now add and commit the files.")
-if __name__ == "__main__":
-    main()

scripts/extract_country_links.py DELETED Viewed

@@ -1,86 +0,0 @@
-#!/usr/bin/env python3
-"""
-Script to extract all country links from the README.md file and create a comprehensive
-country-data-links.json file.
-"""
-import re
-import json
-import os
-# Path to the README.md file
-readme_path = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "README.md")
-output_path = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "country-data-links.json")
-# Regular expression to match country entries in the README
-# Format: | 🇩🇿 Algeria | [JSON](https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv/blob/main/data/by-region/continental/Africa/Algeria.json) | ...
-country_pattern = r'\|\s+(?:(\S+)\s+)?([^|]+)\s+\|\s+\[JSON\]\(([^)]+)\)\s+\|'
-# Dictionary to store country data by continent
-country_data = {
-    "Africa": [],
-    "Asia": [],
-    "Europe": [],
-    "North America": [],
-    "South America": [],
-    "Oceania": []
-}
-current_continent = None
-# Read the README.md file
-with open(readme_path, 'r', encoding='utf-8') as f:
-    readme_content = f.read()
-# Split the content by lines
-lines = readme_content.split('\n')
-# Process each line
-for line in lines:
-    # Check if this is a continent header
-    if "#### 🌍 Africa" in line or "#### 🌏 Asia" in line or "#### 🌍 Europe" in line or \
-       "#### 🌎 North America" in line or "#### 🌏 Oceania" in line or "#### 🌎 South America" in line:
-        if "Africa" in line:
-            current_continent = "Africa"
-        elif "Asia" in line:
-            current_continent = "Asia"
-        elif "Europe" in line:
-            current_continent = "Europe"
-        elif "North America" in line:
-            current_continent = "North America"
-        elif "Oceania" in line:
-            current_continent = "Oceania"
-        elif "South America" in line:
-            current_continent = "South America"
-    # If we're in a continent section, look for country entries
-    if current_continent and "|" in line:
-        # Find all country entries in the line
-        matches = re.findall(country_pattern, line)
-        for match in matches:
-            emoji, country, url = match
-            # Clean up the country name
-            country = country.strip()
-            # Add to the appropriate continent list
-            if current_continent in country_data and country and url:
-                country_data[current_continent].append({
-                    "country": country,
-                    "emoji": emoji.strip() if emoji else "",
-                    "url": url
-                })
-# Create the final JSON structure
-final_json = {
-    "country_data": country_data
-}
-# Write to the output file
-with open(output_path, 'w', encoding='utf-8') as f:
-    json.dump(final_json, f, indent=2)
-print(f"Successfully extracted country links to {output_path}")
-print(f"Total countries extracted: {sum(len(countries) for countries in country_data.values())}")
-for continent, countries in country_data.items():
-    print(f"  {continent}: {len(countries)} countries")