Data Explorer¶

Interactive exploration of scorecard data (coming soon).

Under Development

The data explorer is currently under development. This page will feature:

- Filter by country, region, or indicator
- Sort and search capabilities
- Export filtered results
- Compare multiple countries side-by-side

Current Options¶

While the interactive explorer is being built, you can explore the data through:

1. CSV Exports¶

Export scorecard data to CSV:

python pipeline_runner.py --mode scorecard --scorecard-action export

Then analyze with your preferred tool:

Excel/Google Sheets - Pivot tables and charts
Python pandas - Programmatic analysis
R - Statistical analysis
Tableau/Power BI - Advanced visualizations

2. Direct File Access¶

Scorecard data is stored in:

data/scorecard/scorecard_main_presentation.xlsx

Open directly in Excel to view raw data with all 10 indicators across 194 countries. Use the "UN_194" sheet for the main data.

3. Python Analysis¶

Quick analysis with pandas:

import pandas as pd

# Load scorecard data
df = pd.read_excel('data/scorecard/scorecard_main_presentation.xlsx', sheet_name='UN_194')

# Filter by region
africa = df[df['Region'] == 'Africa']

# Count by indicator value
ai_policy_counts = df['AI_Policy_Status'].value_counts()
print(ai_policy_counts)

# Countries with comprehensive data protection
data_protection = df[df['Data_Protection_Law'] == 'Comprehensive Law']
print(data_protection[['Country', 'Region']])

4. Metadata JSON¶

Enriched document metadata includes scorecard data:

import json

with open('data/metadata/metadata.json', 'r') as f:
    metadata = json.load(f)

# Find documents with scorecard enrichment
for doc in metadata['documents']:
    if 'scorecard' in doc:
        print(f"{doc['id']}: {doc['scorecard']['matched_country']}")

Planned Features¶

The data explorer will include:

Filters¶

Country - Select single or multiple countries
Region - Africa, Americas, Asia, Europe, Oceania
Indicator - Filter by specific indicators
Value - Filter by indicator values

Visualizations¶

Table View - Sortable, searchable data table
Chart View - Bar charts, pie charts, treemaps
Map View - Choropleth maps showing global distribution
Comparison View - Side-by-side country comparison

Export Options¶

CSV - Filtered results as CSV
JSON - Structured data export
PDF - Printable report
Image - Export charts as PNG

Development Timeline¶

See Roadmap for detailed timeline.

Phase 3 (Current): CSV exports and basic visualization
Phase 4 (2026): Interactive dashboard with filters
Phase 5 (2027): Advanced analytics and comparisons

Contribute¶

Want to help build the data explorer?

Check open issues
Review contribution guidelines
Submit pull requests with visualization improvements

Technologies we're considering:

Plotly.js - Interactive charts
Dash - Python dashboard framework
D3.js - Custom visualizations
React - Frontend framework

Temporary Workaround¶

Until the explorer is ready, use this Python script for quick exploration:

import pandas as pd

def explore_scorecard():
    """Interactive scorecard exploration."""
    df = pd.read_excel('data/scorecard/scorecard_main_presentation.xlsx', sheet_name='UN_194')

    print("Available columns:")
    print(df.columns.tolist())

    while True:
        print("\n=== Scorecard Explorer ===")
        print("1. View country")
        print("2. View indicator")
        print("3. View region")
        print("4. Export filtered data")
        print("5. Exit")

        choice = input("Select option: ")

        if choice == '1':
            country = input("Enter country name: ")
            result = df[df['Country'] == country]
            print(result.T)  # Transpose for readability

        elif choice == '2':
            indicator = input("Enter indicator name: ")
            if indicator in df.columns:
                print(df[['Country', indicator]])
            else:
                print("Indicator not found")

        elif choice == '3':
            region = input("Enter region: ")
            result = df[df['Region'] == region]
            print(result[['Country', 'AI_Policy_Status', 'Data_Protection_Law']])

        elif choice == '4':
            filename = input("Export filename (e.g., filtered.csv): ")
            # Apply your filters here
            df.to_csv(filename, index=False)
            print(f"Exported to {filename}")

        elif choice == '5':
            break

if __name__ == '__main__':
    explore_scorecard()

Save as explore_scorecard.py and run:

python explore_scorecard.py

Questions?¶

Check back soon for the interactive data explorer! Watch this repo for updates.