initial commit

.dockerignore

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+# Virtual environment
+venv/
+.venv/
+env/
+
+# Python cache
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+.cursor/
+
+# Git
+.git/
+.gitignore
+
+# Docker
+Dockerfile
+docker-compose.yml
+.dockerignore
+
+# Scripts (not needed in container)
+scripts/
+
+# Documentation
+README.md
+*.md
+
+# OS files
+.DS_Store
+Thumbs.db
+
+# Testing
+.pytest_cache/
+.coverage
+htmlcov/
+

.gitignore

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+venv

Dockerfile

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+# syntax=docker/dockerfile:1
+
+FROM python:3.11-slim
+
+# Set environment variables
+ENV PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_DISABLE_PIP_VERSION_CHECK=1
+
+# Install curl for healthcheck
+RUN apt-get update && apt-get install -y --no-install-recommends curl \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+# Install dependencies first for better caching
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY backend/ ./backend/
+COPY frontend/ ./frontend/
+COPY data/ ./data/
+
+# Expose the application port
+EXPOSE 80
+
+# Run the application
+CMD ["uvicorn", "backend.app:app", "--host", "0.0.0.0", "--port", "80"]
+

README.md

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+# Primary School Compass 🧒📚
+
+A modern web application for comparing **primary school (KS2)** performance data in **Wandsworth and Merton** over the last 5 years. Built with FastAPI and vanilla JavaScript with Chart.js visualizations.
+
+![Python](https://img.shields.io/badge/Python-3.9+-blue)
+![FastAPI](https://img.shields.io/badge/FastAPI-0.109-green)
+![License](https://img.shields.io/badge/License-MIT-yellow)
+
+## Features
+
+- 📊 **Interactive Charts** - Visualize KS2 performance trends over time
+- 🔍 **Smart Search** - Find primary schools by name in Wandsworth & Merton
+- ⚖️ **Side-by-Side Comparison** - Compare up to 5 schools simultaneously
+- 🏆 **Rankings** - View top-performing primary schools by various KS2 metrics
+- 📱 **Responsive Design** - Works beautifully on desktop and mobile
+
+## Key Metrics (KS2)
+
+The application tracks these Key Stage 2 performance indicators:
+
+| Metric | Description |
+|--------|-------------|
+| **Reading Progress** | Progress in reading from KS1 to KS2 |
+| **Writing Progress** | Progress in writing from KS1 to KS2 |
+| **Maths Progress** | Progress in maths from KS1 to KS2 |
+| **Reading Expected %** | Percentage meeting expected standard in reading |
+| **Writing Expected %** | Percentage meeting expected standard in writing |
+| **Maths Expected %** | Percentage meeting expected standard in maths |
+| **RWM Combined %** | Percentage meeting expected standard in all three subjects |
+
+## Quick Start
+
+### 1. Clone and Setup
+
+```bash
+cd school_results
+
+# Create virtual environment
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+### 2. Run the Application
+
+```bash
+# Start the server
+python -m uvicorn backend.app:app --reload --port 8000
+```
+
+Then open http://localhost:8000 in your browser.
+
+The app will run with **sample data** by default, showing **110 primary schools** (66 in Wandsworth, 44 in Merton) with 5 years of KS2 performance data.
+
+### 3. (Optional) Use Real Data
+
+To use real UK school performance data:
+
+1. Visit [Compare School Performance - Download Data](https://www.compare-school-performance.service.gov.uk/download-data)
+
+2. Download **Key Stage 2** data for the years you want (2019-2024)
+   - Select "Key Stage 2" as the data type
+
+3. Place the CSV files in the `data/` folder
+
+4. Restart the server - it will automatically load and filter to Wandsworth & Merton schools
+
+**Note:** The app only displays schools in Wandsworth and Merton. Data from other areas will be filtered out.
+
+See the helper script for more details:
+```bash
+python scripts/download_data.py
+```
+
+## Project Structure
+
+```
+school_results/
+├── backend/
+│   └── app.py           # FastAPI application with all API endpoints
+├── frontend/
+│   ├── index.html       # Main HTML page
+│   ├── styles.css       # Styling (warm, editorial design)
+│   └── app.js           # Frontend JavaScript
+├── data/
+│   └── .gitkeep         # Place CSV data files here
+├── scripts/
+│   └── download_data.py # Helper for downloading/processing data
+├── requirements.txt     # Python dependencies
+└── README.md
+```
+
+## API Endpoints
+
+| Endpoint | Description |
+|----------|-------------|
+| `GET /api/schools` | List schools with optional search/filter |
+| `GET /api/schools/{urn}` | Get detailed data for a specific school |
+| `GET /api/compare?urns=...` | Compare multiple schools |
+| `GET /api/rankings` | Get school rankings by metric |
+| `GET /api/filters` | Get available filter options |
+| `GET /api/metrics` | Get available performance metrics |
+
+### Example API Usage
+
+```bash
+# Search for schools
+curl "http://localhost:8000/api/schools?search=academy"
+
+# Get school details
+curl "http://localhost:8000/api/schools/100001"
+
+# Compare schools
+curl "http://localhost:8000/api/compare?urns=100001,100002,100003"
+
+# Get rankings
+curl "http://localhost:8000/api/rankings?metric=rwm_expected_pct&year=2024"
+```
+
+## Data Format
+
+If using your own CSV data, ensure it includes these columns (or similar):
+
+| Column | Type | Description |
+|--------|------|-------------|
+| URN | Integer | Unique Reference Number |
+| SCHNAME | String | School name |
+| LA | String | Local Authority (must be Wandsworth or Merton) |
+| READPROG | Float | Reading progress score |
+| WRITPROG | Float | Writing progress score |
+| MATPROG | Float | Maths progress score |
+| PTRWM_EXP | Float | % meeting expected standard in RWM |
+| PTREAD_EXP | Float | % meeting expected standard in reading |
+| PTWRIT_EXP | Float | % meeting expected standard in writing |
+| PTMAT_EXP | Float | % meeting expected standard in maths |
+
+The application normalizes column names automatically and filters to only show Wandsworth and Merton schools.
+
+## Technology Stack
+
+- **Backend**: FastAPI (Python) - High-performance async API framework
+- **Frontend**: Vanilla JavaScript with Chart.js
+- **Styling**: Custom CSS with CSS variables for theming
+- **Data**: Pandas for CSV processing
+
+## Design Philosophy
+
+The UI features a warm, editorial design inspired by quality publications:
+- **Typography**: DM Sans for body text, Playfair Display for headings
+- **Color Palette**: Warm cream background with coral and teal accents
+- **Interactions**: Smooth animations and hover effects
+- **Charts**: Clean, readable data visualizations
+
+## Development
+
+```bash
+# Run with auto-reload
+python -m uvicorn backend.app:app --reload --port 8000
+
+# Or run directly
+python backend/app.py
+```
+
+## Coverage
+
+This application is specifically designed for:
+
+- **School Phase**: Primary schools only (Key Stage 2)
+- **Geographic Area**: Wandsworth and Merton (London boroughs)
+- **Time Period**: Last 5 years of data (2020-2024)
+
+Note: 2021 data shows as unavailable because SATs were cancelled due to COVID-19.
+
+## Data Source
+
+Data is sourced from the UK Government's [Compare School Performance](https://www.compare-school-performance.service.gov.uk/) service, which provides official school performance data for England.
+
+**Important**: When using real data, please comply with the [terms of use](https://www.compare-school-performance.service.gov.uk/download-data) and data protection regulations.
+
+## License
+
+MIT License - feel free to use this project for educational purposes.
+
+---
+
+Built with ❤️ for Wandsworth & Merton families
+

backend/app.py

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+"""
+Primary School Performance Comparison API
+Serves primary school (KS2) performance data for Wandsworth and Merton.
+Uses real data from UK Government Compare School Performance downloads.
+"""
+
+from fastapi import FastAPI, HTTPException, Query
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import FileResponse
+from fastapi.middleware.cors import CORSMiddleware
+import pandas as pd
+import numpy as np
+from pathlib import Path
+from typing import Optional
+import os
+import re
+
+# Local Authority codes for Wandsworth and Merton
+LA_CODES = {
+    212: "Wandsworth",
+    315: "Merton"
+}
+ALLOWED_LA_CODES = list(LA_CODES.keys())
+
+app = FastAPI(
+    title="Primary School Performance API - Wandsworth & Merton",
+    description="API for comparing primary school (KS2) performance data in Wandsworth and Merton",
+    version="1.0.0"
+)
+
+# CORS middleware for development
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Data directory
+DATA_DIR = Path(__file__).parent.parent / "data"
+FRONTEND_DIR = Path(__file__).parent.parent / "frontend"
+
+# Cache for loaded data - cleared on reload (updated for 2016-2017 data)
+_data_cache: Optional[pd.DataFrame] = None
+
+
+def convert_to_native(value):
+    """Convert numpy types to native Python types for JSON serialization."""
+    if pd.isna(value):
+        return None
+    if isinstance(value, (np.integer,)):
+        return int(value)
+    if isinstance(value, (np.floating,)):
+        if np.isnan(value) or np.isinf(value):
+            return None
+        return float(value)
+    if isinstance(value, np.ndarray):
+        return value.tolist()
+    if value == "SUPP" or value == "NE" or value == "NA" or value == "NP":
+        return None
+    return value
+
+
+def clean_for_json(df: pd.DataFrame) -> list:
+    """Convert DataFrame to list of dicts, replacing NaN/inf with None for JSON serialization."""
+    records = df.to_dict(orient="records")
+    cleaned = []
+    for record in records:
+        clean_record = {}
+        for key, value in record.items():
+            clean_record[key] = convert_to_native(value)
+        cleaned.append(clean_record)
+    return cleaned
+
+
+def parse_numeric(value):
+    """Parse a value to numeric, handling SUPP, NE, NA, %, etc."""
+    if pd.isna(value):
+        return None
+    if isinstance(value, (int, float)):
+        if np.isnan(value) or np.isinf(value):
+            return None
+        return value
+    if isinstance(value, str):
+        value = value.strip()
+        if value in ["SUPP", "NE", "NA", "NP", "NEW", "LOW", ""]:
+            return None
+        # Remove % sign if present
+        if value.endswith('%'):
+            value = value[:-1]
+        try:
+            return float(value)
+        except ValueError:
+            return None
+    return None
+
+
+def extract_year_from_folder(folder_name: str) -> Optional[int]:
+    """Extract the end year from folder name like '2023-2024' -> 2024."""
+    match = re.search(r'(\d{4})-(\d{4})', folder_name)
+    if match:
+        return int(match.group(2))
+    return None
+
+
+def load_school_data() -> pd.DataFrame:
+    """Load and combine all school data from CSV files in year folders."""
+    global _data_cache
+    
+    if _data_cache is not None:
+        return _data_cache
+    
+    all_data = []
+    
+    # Look for year folders in data directory
+    if DATA_DIR.exists():
+        for year_folder in DATA_DIR.iterdir():
+            if year_folder.is_dir() and re.match(r'\d{4}-\d{4}', year_folder.name):
+                year = extract_year_from_folder(year_folder.name)
+                if year is None:
+                    continue
+                
+                # Look for KS2 data file
+                ks2_file = year_folder / "england_ks2final.csv"
+                if ks2_file.exists():
+                    try:
+                        print(f"Loading data from {ks2_file}")
+                        df = pd.read_csv(ks2_file, low_memory=False)
+                        
+                        # Filter to Wandsworth (212) and Merton (315)
+                        # Handle both string and integer columns
+                        if df['LEA'].dtype == 'object':
+                            df['LEA'] = pd.to_numeric(df['LEA'], errors='coerce')
+                        if df['URN'].dtype == 'object':
+                            df['URN'] = pd.to_numeric(df['URN'], errors='coerce')
+                        df = df[df['LEA'].isin(ALLOWED_LA_CODES)]
+                        
+                        # Filter to schools only (RECTYPE == 1 means school level data)
+                        if 'RECTYPE' in df.columns:
+                            df = df[df['RECTYPE'] == 1]
+                        
+                        # Add year and local authority name
+                        df['year'] = year
+                        df['local_authority'] = df['LEA'].map(LA_CODES)
+                        
+                        # Standardize column names for our API
+                        df = df.rename(columns={
+                            'URN': 'urn',
+                            'SCHNAME': 'school_name',
+                            'ADDRESS1': 'address1',
+                            'ADDRESS2': 'address2',
+                            'TOWN': 'town',
+                            'PCODE': 'postcode',
+                            'NFTYPE': 'school_type_code',
+                            'RELDENOM': 'religious_denomination',
+                            'AGERANGE': 'age_range',
+                            'TOTPUPS': 'total_pupils',
+                            'TELIG': 'eligible_pupils',
+                            # Core KS2 metrics
+                            'PTRWM_EXP': 'rwm_expected_pct',
+                            'PTRWM_HIGH': 'rwm_high_pct',
+                            'READPROG': 'reading_progress',
+                            'WRITPROG': 'writing_progress',
+                            'MATPROG': 'maths_progress',
+                            'PTREAD_EXP': 'reading_expected_pct',
+                            'PTWRITTA_EXP': 'writing_expected_pct',
+                            'PTMAT_EXP': 'maths_expected_pct',
+                            'READ_AVERAGE': 'reading_avg_score',
+                            'MAT_AVERAGE': 'maths_avg_score',
+                            'PTREAD_HIGH': 'reading_high_pct',
+                            'PTWRITTA_HIGH': 'writing_high_pct',
+                            'PTMAT_HIGH': 'maths_high_pct',
+                            # GPS (Grammar, Punctuation & Spelling)
+                            'PTGPS_EXP': 'gps_expected_pct',
+                            'PTGPS_HIGH': 'gps_high_pct',
+                            'GPS_AVERAGE': 'gps_avg_score',
+                            # Science
+                            'PTSCITA_EXP': 'science_expected_pct',
+                            # School context
+                            'PTFSM6CLA1A': 'disadvantaged_pct',
+                            'PTEALGRP2': 'eal_pct',
+                            'PSENELK': 'sen_support_pct',
+                            'PSENELE': 'sen_ehcp_pct',
+                            'PTMOBN': 'stability_pct',
+                            # Gender breakdown
+                            'PTRWM_EXP_B': 'rwm_expected_boys_pct',
+                            'PTRWM_EXP_G': 'rwm_expected_girls_pct',
+                            'PTRWM_HIGH_B': 'rwm_high_boys_pct',
+                            'PTRWM_HIGH_G': 'rwm_high_girls_pct',
+                            # Disadvantaged performance
+                            'PTRWM_EXP_FSM6CLA1A': 'rwm_expected_disadvantaged_pct',
+                            'PTRWM_EXP_NotFSM6CLA1A': 'rwm_expected_non_disadvantaged_pct',
+                            'DIFFN_RWM_EXP': 'disadvantaged_gap',
+                            # 3-year averages
+                            'PTRWM_EXP_3YR': 'rwm_expected_3yr_pct',
+                            'READ_AVERAGE_3YR': 'reading_avg_3yr',
+                            'MAT_AVERAGE_3YR': 'maths_avg_3yr',
+                        })
+                        
+                        # Create address field
+                        def make_address(row):
+                            parts = []
+                            if pd.notna(row.get('address1')) and row.get('address1'):
+                                parts.append(str(row['address1']))
+                            if pd.notna(row.get('town')) and row.get('town'):
+                                parts.append(str(row['town']))
+                            if pd.notna(row.get('postcode')) and row.get('postcode'):
+                                parts.append(str(row['postcode']))
+                            return ', '.join(parts) if parts else ''
+                        
+                        df['address'] = df.apply(make_address, axis=1)
+                        
+                        # Map school type codes to names
+                        school_type_map = {
+                            'AC': 'Academy', 'ACC': 'Academy Converter', 'ACS': 'Academy Sponsor Led',
+                            'CY': 'Community School', 'VA': 'Voluntary Aided', 'VC': 'Voluntary Controlled',
+                            'FD': 'Foundation', 'F': 'Foundation', 'FS': 'Free School',
+                        }
+                        df['school_type'] = df['school_type_code'].map(school_type_map).fillna('Other')
+                        
+                        # Parse numeric columns
+                        numeric_cols = [
+                            # Core metrics
+                            'rwm_expected_pct', 'rwm_high_pct', 'reading_progress', 
+                            'writing_progress', 'maths_progress', 'reading_expected_pct',
+                            'writing_expected_pct', 'maths_expected_pct', 'reading_avg_score',
+                            'maths_avg_score', 'reading_high_pct', 'writing_high_pct', 'maths_high_pct',
+                            # GPS & Science
+                            'gps_expected_pct', 'gps_high_pct', 'gps_avg_score', 'science_expected_pct',
+                            # School context
+                            'total_pupils', 'eligible_pupils', 'disadvantaged_pct', 'eal_pct',
+                            'sen_support_pct', 'sen_ehcp_pct', 'stability_pct',
+                            # Gender breakdown
+                            'rwm_expected_boys_pct', 'rwm_expected_girls_pct',
+                            'rwm_high_boys_pct', 'rwm_high_girls_pct',
+                            # Disadvantaged performance
+                            'rwm_expected_disadvantaged_pct', 'rwm_expected_non_disadvantaged_pct', 'disadvantaged_gap',
+                            # 3-year averages
+                            'rwm_expected_3yr_pct', 'reading_avg_3yr', 'maths_avg_3yr',
+                        ]
+                        
+                        for col in numeric_cols:
+                            if col in df.columns:
+                                df[col] = df[col].apply(parse_numeric)
+                        
+                        all_data.append(df)
+                        print(f"  Loaded {len(df)} schools for year {year}")
+                        
+                    except Exception as e:
+                        print(f"Error loading {ks2_file}: {e}")
+    
+    if all_data:
+        _data_cache = pd.concat(all_data, ignore_index=True)
+        print(f"\nTotal records loaded: {len(_data_cache)}")
+        print(f"Unique schools: {_data_cache['urn'].nunique()}")
+        print(f"Years: {sorted(_data_cache['year'].unique())}")
+    else:
+        print("No data files found. Creating empty DataFrame.")
+        _data_cache = pd.DataFrame()
+    
+    return _data_cache
+
+
+@app.get("/")
+async def root():
+    """Serve the frontend."""
+    return FileResponse(FRONTEND_DIR / "index.html")
+
+
+@app.get("/api/schools")
+async def get_schools(
+    search: Optional[str] = Query(None, description="Search by school name"),
+    local_authority: Optional[str] = Query(None, description="Filter by local authority (Wandsworth or Merton)"),
+    school_type: Optional[str] = Query(None, description="Filter by school type"),
+):
+    """Get list of unique primary schools in Wandsworth and Merton."""
+    df = load_school_data()
+    
+    if df.empty:
+        return {"schools": []}
+    
+    # Get unique schools (latest year data for each)
+    latest_year = df.groupby('urn')['year'].max().reset_index()
+    df_latest = df.merge(latest_year, on=['urn', 'year'])
+    
+    school_cols = ["urn", "school_name", "local_authority", "school_type", "address", "town", "postcode"]
+    available_cols = [c for c in school_cols if c in df_latest.columns]
+    schools_df = df_latest[available_cols].drop_duplicates(subset=['urn'])
+    
+    # Apply filters
+    if search:
+        search_lower = search.lower()
+        mask = schools_df["school_name"].str.lower().str.contains(search_lower, na=False)
+        if "address" in schools_df.columns:
+            mask = mask | schools_df["address"].str.lower().str.contains(search_lower, na=False)
+        schools_df = schools_df[mask]
+    
+    if local_authority:
+        schools_df = schools_df[schools_df["local_authority"].str.lower() == local_authority.lower()]
+    
+    if school_type:
+        schools_df = schools_df[schools_df["school_type"].str.lower() == school_type.lower()]
+    
+    return {"schools": clean_for_json(schools_df)}
+
+
+@app.get("/api/schools/{urn}")
+async def get_school_details(urn: int):
+    """Get detailed KS2 data for a specific primary school across all years."""
+    df = load_school_data()
+    
+    if df.empty:
+        raise HTTPException(status_code=404, detail="No data available")
+    
+    school_data = df[df["urn"] == urn]
+    
+    if school_data.empty:
+        raise HTTPException(status_code=404, detail="School not found")
+    
+    # Sort by year
+    school_data = school_data.sort_values("year")
+    
+    # Get latest info for the school
+    latest = school_data.iloc[-1]
+    
+    return {
+        "school_info": {
+            "urn": urn,
+            "school_name": latest.get("school_name", ""),
+            "local_authority": latest.get("local_authority", ""),
+            "school_type": latest.get("school_type", ""),
+            "address": latest.get("address", ""),
+            "phase": "Primary",
+        },
+        "yearly_data": clean_for_json(school_data)
+    }
+
+
+@app.get("/api/compare")
+async def compare_schools(urns: str = Query(..., description="Comma-separated URNs")):
+    """Compare multiple primary schools side by side."""
+    df = load_school_data()
+    
+    if df.empty:
+        raise HTTPException(status_code=404, detail="No data available")
+    
+    try:
+        urn_list = [int(u.strip()) for u in urns.split(",")]
+    except ValueError:
+        raise HTTPException(status_code=400, detail="Invalid URN format")
+    
+    comparison_data = df[df["urn"].isin(urn_list)]
+    
+    if comparison_data.empty:
+        raise HTTPException(status_code=404, detail="No schools found")
+    
+    result = {}
+    for urn in urn_list:
+        school_data = comparison_data[comparison_data["urn"] == urn].sort_values("year")
+        if not school_data.empty:
+            latest = school_data.iloc[-1]
+            result[str(urn)] = {
+                "school_info": {
+                    "urn": urn,
+                    "school_name": latest.get("school_name", ""),
+                    "local_authority": latest.get("local_authority", ""),
+                    "address": latest.get("address", ""),
+                },
+                "yearly_data": clean_for_json(school_data)
+            }
+    
+    return {"comparison": result}
+
+
+@app.get("/api/filters")
+async def get_filter_options():
+    """Get available filter options (local authorities, school types, years)."""
+    df = load_school_data()
+    
+    if df.empty:
+        return {
+            "local_authorities": ["Wandsworth", "Merton"],
+            "school_types": [],
+            "years": [],
+        }
+    
+    return {
+        "local_authorities": sorted(df["local_authority"].dropna().unique().tolist()),
+        "school_types": sorted(df["school_type"].dropna().unique().tolist()),
+        "years": sorted(df["year"].dropna().unique().tolist()),
+    }
+
+
+@app.get("/api/metrics")
+async def get_available_metrics():
+    """Get list of available KS2 performance metrics for primary schools."""
+    df = load_school_data()
+    
+    # Define KS2 metric metadata organized by category
+    metric_info = {
+        # Expected Standard
+        "rwm_expected_pct": {"name": "RWM Combined %", "description": "% meeting expected standard in reading, writing and maths", "type": "percentage", "category": "expected"},
+        "reading_expected_pct": {"name": "Reading Expected %", "description": "% meeting expected standard in reading", "type": "percentage", "category": "expected"},
+        "writing_expected_pct": {"name": "Writing Expected %", "description": "% meeting expected standard in writing", "type": "percentage", "category": "expected"},
+        "maths_expected_pct": {"name": "Maths Expected %", "description": "% meeting expected standard in maths", "type": "percentage", "category": "expected"},
+        "gps_expected_pct": {"name": "GPS Expected %", "description": "% meeting expected standard in grammar, punctuation & spelling", "type": "percentage", "category": "expected"},
+        "science_expected_pct": {"name": "Science Expected %", "description": "% meeting expected standard in science", "type": "percentage", "category": "expected"},
+        # Higher Standard
+        "rwm_high_pct": {"name": "RWM Combined Higher %", "description": "% achieving higher standard in RWM combined", "type": "percentage", "category": "higher"},
+        "reading_high_pct": {"name": "Reading Higher %", "description": "% achieving higher standard in reading", "type": "percentage", "category": "higher"},
+        "writing_high_pct": {"name": "Writing Higher %", "description": "% achieving greater depth in writing", "type": "percentage", "category": "higher"},
+        "maths_high_pct": {"name": "Maths Higher %", "description": "% achieving higher standard in maths", "type": "percentage", "category": "higher"},
+        "gps_high_pct": {"name": "GPS Higher %", "description": "% achieving higher standard in GPS", "type": "percentage", "category": "higher"},
+        # Progress Scores
+        "reading_progress": {"name": "Reading Progress", "description": "Progress in reading from KS1 to KS2", "type": "score", "category": "progress"},
+        "writing_progress": {"name": "Writing Progress", "description": "Progress in writing from KS1 to KS2", "type": "score", "category": "progress"},
+        "maths_progress": {"name": "Maths Progress", "description": "Progress in maths from KS1 to KS2", "type": "score", "category": "progress"},
+        # Average Scores
+        "reading_avg_score": {"name": "Reading Avg Score", "description": "Average scaled score in reading", "type": "score", "category": "average"},
+        "maths_avg_score": {"name": "Maths Avg Score", "description": "Average scaled score in maths", "type": "score", "category": "average"},
+        "gps_avg_score": {"name": "GPS Avg Score", "description": "Average scaled score in GPS", "type": "score", "category": "average"},
+        # Gender Performance
+        "rwm_expected_boys_pct": {"name": "RWM Expected % (Boys)", "description": "% of boys meeting expected standard", "type": "percentage", "category": "gender"},
+        "rwm_expected_girls_pct": {"name": "RWM Expected % (Girls)", "description": "% of girls meeting expected standard", "type": "percentage", "category": "gender"},
+        "rwm_high_boys_pct": {"name": "RWM Higher % (Boys)", "description": "% of boys at higher standard", "type": "percentage", "category": "gender"},
+        "rwm_high_girls_pct": {"name": "RWM Higher % (Girls)", "description": "% of girls at higher standard", "type": "percentage", "category": "gender"},
+        # Disadvantaged Performance
+        "rwm_expected_disadvantaged_pct": {"name": "RWM Expected % (Disadvantaged)", "description": "% of disadvantaged pupils meeting expected", "type": "percentage", "category": "equity"},
+        "rwm_expected_non_disadvantaged_pct": {"name": "RWM Expected % (Non-Disadvantaged)", "description": "% of non-disadvantaged pupils meeting expected", "type": "percentage", "category": "equity"},
+        "disadvantaged_gap": {"name": "Disadvantaged Gap", "description": "Gap between disadvantaged and national non-disadvantaged", "type": "score", "category": "equity"},
+        # School Context
+        "disadvantaged_pct": {"name": "% Disadvantaged Pupils", "description": "% of pupils eligible for free school meals or looked after", "type": "percentage", "category": "context"},
+        "eal_pct": {"name": "% EAL Pupils", "description": "% of pupils with English as additional language", "type": "percentage", "category": "context"},
+        "sen_support_pct": {"name": "% SEN Support", "description": "% of pupils with SEN support", "type": "percentage", "category": "context"},
+        "stability_pct": {"name": "% Pupil Stability", "description": "% of non-mobile pupils (stayed at school)", "type": "percentage", "category": "context"},
+        # 3-Year Averages
+        "rwm_expected_3yr_pct": {"name": "RWM Expected % (3-Year Avg)", "description": "3-year average % meeting expected", "type": "percentage", "category": "trends"},
+        "reading_avg_3yr": {"name": "Reading Score (3-Year Avg)", "description": "3-year average reading score", "type": "score", "category": "trends"},
+        "maths_avg_3yr": {"name": "Maths Score (3-Year Avg)", "description": "3-year average maths score", "type": "score", "category": "trends"},
+    }
+    
+    available = []
+    for col, info in metric_info.items():
+        if df.empty or col in df.columns:
+            available.append({"key": col, **info})
+    
+    return {"metrics": available}
+
+
+@app.get("/api/rankings")
+async def get_rankings(
+    metric: str = Query("rwm_expected_pct", description="KS2 metric to rank by"),
+    year: Optional[int] = Query(None, description="Specific year (defaults to most recent)"),
+    limit: int = Query(20, description="Number of schools to return"),
+):
+    """Get primary school rankings by a specific KS2 metric."""
+    df = load_school_data()
+    
+    if df.empty:
+        return {"metric": metric, "year": None, "rankings": []}
+    
+    if metric not in df.columns:
+        raise HTTPException(status_code=400, detail=f"Metric '{metric}' not available")
+    
+    # Filter by year
+    if year:
+        df = df[df["year"] == year]
+    else:
+        # Use most recent year
+        max_year = df["year"].max()
+        df = df[df["year"] == max_year]
+    
+    # Sort and rank (exclude rows with no data for this metric)
+    df = df.dropna(subset=[metric])
+    
+    # For progress scores, higher is better. For percentages, higher is also better.
+    df = df.sort_values(metric, ascending=False).head(limit)
+    
+    # Return only relevant fields for rankings
+    ranking_cols = [
+        "urn", "school_name", "local_authority", "school_type", "address", "year", "total_pupils",
+        # Core expected
+        "rwm_expected_pct", "reading_expected_pct", "writing_expected_pct", "maths_expected_pct",
+        "gps_expected_pct", "science_expected_pct",
+        # Core higher
+        "rwm_high_pct", "reading_high_pct", "writing_high_pct", "maths_high_pct", "gps_high_pct",
+        # Progress & averages
+        "reading_progress", "writing_progress", "maths_progress",
+        "reading_avg_score", "maths_avg_score", "gps_avg_score",
+        # Gender
+        "rwm_expected_boys_pct", "rwm_expected_girls_pct", "rwm_high_boys_pct", "rwm_high_girls_pct",
+        # Equity
+        "rwm_expected_disadvantaged_pct", "rwm_expected_non_disadvantaged_pct", "disadvantaged_gap",
+        # Context
+        "disadvantaged_pct", "eal_pct", "sen_support_pct", "stability_pct",
+        # 3-year
+        "rwm_expected_3yr_pct", "reading_avg_3yr", "maths_avg_3yr",
+    ]
+    available_cols = [c for c in ranking_cols if c in df.columns]
+    df = df[available_cols]
+    
+    return {
+        "metric": metric,
+        "year": int(df["year"].iloc[0]) if not df.empty else None,
+        "rankings": clean_for_json(df)
+    }
+
+
+@app.get("/api/data-info")
+async def get_data_info():
+    """Get information about loaded data."""
+    df = load_school_data()
+    
+    if df.empty:
+        return {
+            "status": "no_data",
+            "message": "No data files found in data folder. Please download KS2 data from the government website.",
+            "data_folder": str(DATA_DIR),
+        }
+    
+    years = [int(y) for y in sorted(df["year"].unique())]
+    schools_per_year = {str(int(k)): int(v) for k, v in df.groupby("year")["urn"].nunique().to_dict().items()}
+    la_counts = {str(k): int(v) for k, v in df["local_authority"].value_counts().to_dict().items()}
+    
+    return {
+        "status": "loaded",
+        "total_records": int(len(df)),
+        "unique_schools": int(df["urn"].nunique()),
+        "years_available": years,
+        "schools_per_year": schools_per_year,
+        "local_authorities": la_counts,
+    }
+
+
+# Mount static files
+@app.on_event("startup")
+async def startup():
+    """Setup static file serving and load data on startup."""
+    if FRONTEND_DIR.exists():
+        app.mount("/static", StaticFiles(directory=FRONTEND_DIR), name="static")
+    
+    # Pre-load data
+    load_school_data()
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

data/.gitkeep

@@ -0,0 +1,3 @@
+# Place your CSV data files here
+# Download from: https://www.compare-school-performance.service.gov.uk/download-data
+

data/meta/census_meta.csv

@@ -0,0 +1,24 @@
+Field Number,Field Reference,Field Name,Values,Data Format,LA level field?,National level field?
+1,URN,School Unique Reference Number,999999,I6,No,No
+2,LA,LA number,999,I3,Yes,No
+3,ESTAB,ESTAB number,9999,I4,No,No
+4,SCHOOLTYPE,Type of school,String,,No,No
+5,NOR,Total number of pupils on roll,9999 or NA,,Yes,Yes
+6,NORG,Number of girls on roll,9999 or NA,,Yes,Yes
+7,NORB,Number of boys on roll,9999 or NA,,Yes,Yes
+8,PNORG,Percentage of girls on roll,99.9 or NA,,Yes,Yes
+9,PNORB,Percentage of boys on roll,99.9 or NA,,Yes,Yes
+10,TSENELSE,Number of eligible pupils with an EHC plan,9999 or NA,A4,Yes,Yes
+11,PSENELSE,Percentage of eligible pupils with an EHC plan,99.9 or NA,A4,Yes,Yes
+12,TSENELK,Number of eligible pupils with SEN support,9999 or NA,A4,Yes,Yes
+13,PSENELK,Percentage of eligible pupils with SEN support,99.9 or NA,A4,Yes,Yes
+14,NUMEAL,No.  pupils where English not first language,9999 or NA,A4,Yes,Yes
+15,NUMENGFL,No. pupils with English first language,9999 or NA,A4,Yes,Yes
+16,NUMUNCFL,No. pupils where first language is unclassified,9999 or NA,A4,Yes,Yes
+17,PNUMEAL,%  pupils where English not first language,99.9 or NA,A4,Yes,Yes
+18,PNUMENGFL,% pupils with English first language,99.9 or NA,A4,Yes,Yes
+19,PNUMUNCFL,% pupils where first language is unclassified,99.9 or NA,A4,Yes,Yes
+20,NUMFSM,No. pupils eligible for free school meals,9999 or NA,A4,Yes,Yes
+21,NUMFSMEVER,Number of pupils eligible for FSM at any time during the past 6 years,9999 or NA,A6,Yes,Yes
+22,NORFSMEVER,Total pupils for FSMEver,9999 or NA,,Yes,Yes
+23,PNUMFSMEVER,Percentage of pupils eligible for FSM at any time during the past 6 years,99.9 or NA,A4,Yes,Yes

data/meta/ks2_meta.csv

@@ -0,0 +1,312 @@
+Column,Field Name,Label/Description
+1,RECTYPE,Record type
+2,AlphaIND,Alphabetic index
+3,LEA,Local authority number
+4,ESTAB,Establishment number
+5,URN,School unique reference number
+6,SCHNAME,School/Local authority name
+7,ADDRESS1,School address (1)
+8,ADDRESS2,School address (2)
+9,ADDRESS3,School address (3)
+10,TOWN,School town
+11,PCODE,School postcode
+12,TELNUM,School telephone number 
+13,PCON_CODE,School parliamentary constituency code
+14,PCON_NAME,School parliamentary constituency name
+15,URN_AC,Converter academy: URN 
+16,SCHNAME_AC,Converter academy: name
+17,OPEN_AC,Converter academy: open date
+18,NFTYPE,School type
+19,ICLOSE,Closed Flag
+20,RELDENOM,Religious denomination
+21,AGERANGE,Age range
+22,TAB15,School published in secondary school (key stage 4) performance tables
+23,TAB1618,School published in school and college (key stage 5) performance tables
+24,TOTPUPS,Total number of pupils (including part-time pupils)
+25,TPUPYEAR,Number of pupils aged 11 
+26,TELIG,Published eligible pupil number
+27,BELIG,Eligible boys on school roll at time of tests
+28,GELIG,Eligible girls on school roll at time of tests
+29,PBELIG,Percentage of eligible boys on school roll at time of tests
+30,PGELIG,Percentage of eligible girls on school roll at time of tests
+31,TKS1AVERAGE,Cohort level key stage 1 average points score [not populated in 2025]
+32,TKS1GROUP_L,Number of pupils in cohort with low KS1 attainment [not populated in 2025]
+33,PTKS1GROUP_L,Percentage of pupils in cohort with low KS1 attainment [not populated in 2025]
+34,TKS1GROUP_M,Number of pupils in cohort with medium KS1 attainment [not populated in 2025]
+35,PTKS1GROUP_M,Percentage of pupils in cohort with medium KS1 attainment [not populated in 2025]
+36,TKS1GROUP_H,Number of pupils in cohort high KS1 attainment [not populated in 2025]
+37,PTKS1GROUP_H,Percentage of pupils in cohort with high KS1 attainment [not populated in 2025]
+38,TKS1GROUP_NA,No. of pupils in KS1 group not calculable [not populated in 2025]
+39,PTKS1GROUP_NA,Percentage of pupils in KS1group not calculable [not populated in 2025]
+40,TFSM6CLA1A,Number of key stage 2 disadvantaged pupils (those who were eligible for free school meals in last 6 years or are looked after by the LA for a day or more or who have been adopted from care)
+41,PTFSM6CLA1A,Percentage of key stage 2 disadvantaged pupils
+42,TNotFSM6CLA1A,Number of key stage 2 pupils who are not disadvantaged
+43,PTNotFSM6CLA1A,Percentage of key stage 2 pupils who are not disadvantaged
+44,TEALGRP2,Number of eligible pupils with English as additional language (EAL)
+45,PTEALGRP2,Percentage of eligible pupils with English as additional language (EAL)
+46,TMOBN,Number of eligible pupils classified as non-mobile
+47,PTMOBN,Percentage of eligible pupils classified as non-mobile
+48,PTRWM_EXP,"Percentage of pupils reaching the expected standard in reading, writing and maths"
+49,PTRWM_HIGH,Percentage of pupils achieving a high score in reading and maths and working at greater depth in writing
+50,READPROG,Reading progress measure [not populated in 2025]
+51,READPROG_LOWER,Reading progress measure - lower confidence limit [not populated in 2025]
+52,READPROG_UPPER,Reading progress measure - upper confidence limit [not populated in 2025]
+53,READCOV,Reading progress measure - coverage [not populated in 2025]
+54,WRITPROG,Writing progress measure [not populated in 2025]
+55,WRITPROG_LOWER,Writing progress measure - lower confidence limit [not populated in 2025]
+56,WRITPROG_UPPER,Writing progress measure - upper confidence limit [not populated in 2025]
+57,WRITCOV,Writing progress measure - coverage [not populated in 2025]
+58,MATPROG,Maths progress measure [not populated in 2025]
+59,MATPROG_LOWER,Maths progress measure - lower confidence limit [not populated in 2025]
+60,MATPROG_UPPER,Maths progress measure - upper confidence limit [not populated in 2025]
+61,MATCOV,Maths progress measure - coverage [not populated in 2025]
+62,PTREAD_EXP,Percentage of pupils reaching the expected standard in reading
+63,PTREAD_HIGH,Percentage of pupils achieving a high score in reading
+64,PTREAD_AT,Percentage of pupils absent from or not able to access the test in reading
+65,READ_AVERAGE,Average scaled score in reading
+66,PTGPS_EXP,"Percentage of pupils reaching the expected standard in grammar, punctuation and spelling"
+67,PTGPS_HIGH,"Percentage of pupils achieving a high score in grammar, punctuation and spelling"
+68,PTGPS_AT,"Percentage of pupils absent from or not able to access the test in grammar, punctuation and spelling"
+69,GPS_AVERAGE,"Average scaled score in grammar, punctuation and spelling"
+70,PTMAT_EXP,Percentage of pupils reaching the expected standard in maths
+71,PTMAT_HIGH,Percentage of pupils achieving a high score in maths
+72,PTMAT_AT,Percentage of pupils absent from or not able to access the test in maths
+73,MAT_AVERAGE,Average scaled score in maths
+74,PTWRITTA_EXP,Percentage of pupils reaching the expected standard in writing
+75,PTWRITTA_HIGH,Percentage of pupils working at greater depth within the expected standard in writing
+76,PTWRITTA_WTS,Percentage of pupils working towards the expected standard in writing
+77,PTWRITTA_AD,Percentage of pupils absent or disapplied in writing TA
+78,PTSCITA_EXP,Percentage of pupils reaching the expected standard in science TA
+79,PTSCITA_AD,Percentage of pupils absent or disapplied in science TA
+80,PTRWM_EXP_B,"Percentage of boys reaching the expected standard in reading, writing and maths"
+81,PTRWM_EXP_G,"Percentage of girls reaching the expected standard in reading, writing and maths"
+82,PTRWM_EXP_L,"Percentage of pupils with low prior attainment reaching the expected standard in reading, writing and maths [not populated in 2025]"
+83,PTRWM_EXP_M,"Percentage of pupils with medium prior attainment reaching the expected standard in reading, writing and maths [not populated in 2025]"
+84,PTRWM_EXP_H,"Percentage of pupils with high prior attainment reaching the expected standard in reading, writing and maths [not populated in 2025]"
+85,PTRWM_EXP_FSM6CLA1A,"Percentage of disadvantaged pupils reaching the expected standard in reading, writing and maths"
+86,PTRWM_EXP_NotFSM6CLA1A,"Percentage of non-disadvantaged pupils reaching the expected standard in reading, writing and maths"
+87,DIFFN_RWM_EXP,"Difference between school percentage of disavantaged pupils and national percentage of other pupils reaching the expected standard in reading, writing and maths "
+88,PTRWM_EXP_EAL,"Percentage of EAL pupils reaching the expected standard in reading, writing and maths"
+89,PTRWM_EXP_MOBN,"Percentage of non-mobile pupils reaching the expected standard in reading, writing and maths"
+90,PTRWM_HIGH_B,Percentage of boys achieving a high score in reading and maths and working at greater depth in writing
+91,PTRWM_HIGH_G,"Percentage of girls reaching the HIGHected standard in reading, writing and maths"
+92,PTRWM_HIGH_L,Percentage of pupils with low prior attainment achieving a high score in reading and maths and working at greater depth in writing [not populated in 2025]
+93,PTRWM_HIGH_M,Percentage of pupils with medium prior attainment achieving a high score in reading and maths and working at greater depth in writing [not populated in 2025]
+94,PTRWM_HIGH_H,Percentage of pupils with high prior attainment achieving a high score in reading and maths and working at greater depth in writing [not populated in 2025]
+95,PTRWM_HIGH_FSM6CLA1A,Percentage of disadvantaged pupils achieving a high score in reading and maths and working at greater depth in writing
+96,PTRWM_HIGH_NotFSM6CLA1A,Percentage of non-disadvantaged pupils achieving a high score in reading and maths and working at greater depth in writing
+97,DIFFN_RWM_HIGH,"Difference between school percentage of disavantaged pupils and national percentage of other pupils achieving a high score in reading, writing and maths "
+98,PTRWM_HIGH_EAL,Percentage of EAL pupils achieving a high score in reading and maths and working at greater depth in writing
+99,PTRWM_HIGH_MOBN,Percentage of non-mobile pupils achieving a high score in reading and maths and working at greater depth in writing
+100,READPROG_B,Reading progress measure for boys [not populated in 2025]
+101,READPROG_B_LOWER,Reading progress measure for boys - lower confidence limit [not populated in 2025]
+102,READPROG_B_UPPER,Reading progress measure for boys - upper confidence limit [not populated in 2025]
+103,READPROG_G,Reading progress measure for girls [not populated in 2025]
+104,READPROG_G_LOWER,Reading progress measure for girls - lower confidence limit [not populated in 2025]
+105,READPROG_G_UPPER,Reading progress measure for girls - upper confidence limit [not populated in 2025]
+106,READPROG_L,Reading progress measure for pupils with low prior attainment [not populated in 2025]
+107,READPROG_L_LOWER,Reading progress measure for pupils with low prior attainment - lower confidence limit [not populated in 2025]
+108,READPROG_L_UPPER,Reading progress measure for pupils with low prior attainment - upper confidence limit [not populated in 2025]
+109,READPROG_M,Reading progress measure for pupils with medium prior attainment [not populated in 2025]
+110,READPROG_M_LOWER,Reading progress measure for pupils with medium prior attainment - lower confidence limit [not populated in 2025]
+111,READPROG_M_UPPER,Reading progress measure for pupils with medium prior attainment - upper confidence limit [not populated in 2025]
+112,READPROG_H,Reading progress measure for pupils with high prior attainment [not populated in 2025]
+113,READPROG_H_LOWER,Reading progress measure for pupils with high prior attainment - lower confidence limit [not populated in 2025]
+114,READPROG_H_UPPER,Reading progress measure for pupils with high prior attainment - upper confidence limit [not populated in 2025]
+115,READPROG_FSM6CLA1A,Reading progress measure for disadvantaged pupils [not populated in 2025]
+116,READPROG_FSM6CLA1A_LOWER,Reading progress measure for disadvantaged pupils - lower confidence limit [not populated in 2025]
+117,READPROG_FSM6CLA1A_UPPER,Reading progress measure for disadvantaged pupils - upper confidence limit [not populated in 2025]
+118,READPROG_NotFSM6CLA1A,Reading progress measure for non-disadvantaged pupils [not populated in 2025]
+119,READPROG_NotFSM6CLA1A_LOWER,Reading progress measure for non-disadvantaged pupils - lower confidence limit [not populated in 2025]
+120,READPROG_NotFSM6CLA1A_UPPER,Reading progress measure for non-disadvantaged pupils - upper confidence limit [not populated in 2025]
+121,DIFFN_READPROG,Difference between reading progress measure for disadvantaged pupils in school and other pupils nationally [not populated in 2025]
+122,READPROG_EAL,Reading progress measure for EAL pupils [not populated in 2025]
+123,READPROG_EAL_LOWER,Reading progress measure for EAL pupils - lower confidence limit [not populated in 2025]
+124,READPROG_EAL_UPPER,Reading progress measure for EAL pupils - upper confidence limit [not populated in 2025]
+125,READPROG_MOBN,Reading progress measure for non-mobile pupils [not populated in 2025]
+126,READPROG_MOBN_LOWER,Reading progress measure for non-mobile pupils - lower confidence limit [not populated in 2025]
+127,READPROG_MOBN_UPPER,Reading progress measure for non-mobile pupils - upper confidence limit [not populated in 2025]
+128,WRITPROG_B,Writing progress measure for boys [not populated in 2025]
+129,WRITPROG_B_LOWER,Writing progress measure for boys - lower confidence limit [not populated in 2025]
+130,WRITPROG_B_UPPER,Writing progress measure for boys - upper confidence limit [not populated in 2025]
+131,WRITPROG_G,Writing progress measure for girls [not populated in 2025]
+132,WRITPROG_G_LOWER,Writing progress measure for girls - lower confidence limit [not populated in 2025]
+133,WRITPROG_G_UPPER,Writing progress measure for girls - upper confidence limit [not populated in 2025]
+134,WRITPROG_L,Writing progress measure for pupils with low prior attainment [not populated in 2025]
+135,WRITPROG_L_LOWER,Writing progress measure for pupils with low prior attainment - lower confidence limit [not populated in 2025]
+136,WRITPROG_L_UPPER,Writing progress measure for pupils with low prior attainment - upper confidence limit [not populated in 2025]
+137,WRITPROG_M,Writing progress measure for pupils with medium prior attainment [not populated in 2025]
+138,WRITPROG_M_LOWER,Writing progress measure for pupils with medium prior attainment - lower confidence limit [not populated in 2025]
+139,WRITPROG_M_UPPER,Writing progress measure for pupils with medium prior attainment - upper confidence limit [not populated in 2025]
+140,WRITPROG_H,Writing progress measure for pupils with high prior attainment [not populated in 2025]
+141,WRITPROG_H_LOWER,Writing progress measure for pupils with high prior attainment - lower confidence limit [not populated in 2025]
+142,WRITPROG_H_UPPER,Writing progress measure for pupils with high prior attainment - upper confidence limit [not populated in 2025]
+143,WRITPROG_FSM6CLA1A,Writing progress measure for disadvantaged pupils [not populated in 2025]
+144,WRITPROG_FSM6CLA1A_LOWER,Writing progress measure for disadvantaged pupils - lower confidence limit [not populated in 2025]
+145,WRITPROG_FSM6CLA1A_UPPER,Writing progress measure for disadvantaged pupils - upper confidence limit [not populated in 2025]
+146,WRITPROG_NotFSM6CLA1A,Writing progress measure for non-disadvantaged pupils [not populated in 2025]
+147,WRITPROG_NotFSM6CLA1A_LOWER,Writing progress measure for non-disadvantaged pupils - lower confidence limit [not populated in 2025]
+148,WRITPROG_NotFSM6CLA1A_UPPER,Writing progress measure for non-disadvantaged pupils - upper confidence limit [not populated in 2025]
+149,DIFFN_WRITPROG,Difference between writing progress measure for disadvantaged pupils in school and other pupils nationally [not populated in 2025]
+150,WRITPROG_EAL,Writing progress measure for EAL pupils [not populated in 2025]
+151,WRITPROG_EAL_LOWER,Writing progress measure for EAL pupils - lower confidence limit [not populated in 2025]
+152,WRITPROG_EAL_UPPER,Writing progress measure for EAL pupils - upper confidence limit [not populated in 2025]
+153,WRITPROG_MOBN,Writing progress measure for non-mobile pupils [not populated in 2025]
+154,WRITPROG_MOBN_LOWER,Writing progress measure for non-mobile pupils - lower confidence limit [not populated in 2025]
+155,WRITPROG_MOBN_UPPER,Writing progress measure for non-mobile pupils - upper confidence limit [not populated in 2025]
+156,MATPROG_B,Maths progress measure for boys [not populated in 2025]
+157,MATPROG_B_LOWER,Maths progress measure for boys - lower confidence limit [not populated in 2025]
+158,MATPROG_B_UPPER,Maths progress measure for boys - upper confidence limit [not populated in 2025]
+159,MATPROG_G,Maths progress measure for girls [not populated in 2025]
+160,MATPROG_G_LOWER,Maths progress measure for girls - lower confidence limit [not populated in 2025]
+161,MATPROG_G_UPPER,Maths progress measure for girls - upper confidence limit [not populated in 2025]
+162,MATPROG_L,Maths progress measure for pupils with low prior attainment [not populated in 2025]
+163,MATPROG_L_LOWER,Maths progress measure for pupils with low prior attainment - lower confidence limit [not populated in 2025]
+164,MATPROG_L_UPPER,Maths progress measure for pupils with low prior attainment - upper confidence limit [not populated in 2025]
+165,MATPROG_M,Maths progress measure for pupils with medium prior attainment [not populated in 2025]
+166,MATPROG_M_LOWER,Maths progress measure for pupils with medium prior attainment - lower confidence limit [not populated in 2025]
+167,MATPROG_M_UPPER,Maths progress measure for pupils with medium prior attainment - upper confidence limit [not populated in 2025]
+168,MATPROG_H,Maths progress measure for pupils with high prior attainment [not populated in 2025]
+169,MATPROG_H_LOWER,Maths progress measure for pupils with high prior attainment - lower confidence limit [not populated in 2025]
+170,MATPROG_H_UPPER,Maths progress measure for pupils with high prior attainment - upper confidence limit [not populated in 2025]
+171,MATPROG_FSM6CLA1A,Maths progress measure for disadvantaged pupils [not populated in 2025]
+172,MATPROG_FSM6CLA1A_LOWER,Maths progress measure for disadvantaged pupils - lower confidence limit [not populated in 2025]
+173,MATPROG_FSM6CLA1A_UPPER,Maths progress measure for disadvantaged pupils - upper confidence limit [not populated in 2025]
+174,MATPROG_NotFSM6CLA1A,Maths progress measure for non-disadvantaged pupils [not populated in 2025]
+175,MATPROG_NotFSM6CLA1A_LOWER,Maths progress measure for non-disadvantaged pupils - lower confidence limit [not populated in 2025]
+176,MATPROG_NotFSM6CLA1A_UPPER,Maths progress measure for non-disadvantaged pupils - upper confidence limit [not populated in 2025]
+177,DIFFN_MATPROG,Difference between maths progress measure for disadvantaged pupils in school and other pupils nationally [not populated in 2025]
+178,MATPROG_EAL,Maths progress measure for EAL pupils [not populated in 2025]
+179,MATPROG_EAL_LOWER,Maths progress measure for EAL pupils - lower confidence limit [not populated in 2025]
+180,MATPROG_EAL_UPPER,Maths progress measure for EAL pupils - upper confidence limit [not populated in 2025]
+181,MATPROG_MOBN,Maths progress measure for non-mobile pupils [not populated in 2025]
+182,MATPROG_MOBN_LOWER,Maths progress measure for non-mobile pupils - lower confidence limit [not populated in 2025]
+183,MATPROG_MOBN_UPPER,Maths progress measure for non-mobile pupils - upper confidence limit [not populated in 2025]
+184,READ_AVERAGE_B,Average scaled score in reading for boys
+185,READ_AVERAGE_G,Average scaled score in reading for girls
+186,READ_AVERAGE_L,Average scaled score in reading for pupils with low prior attainment [not populated in 2025]
+187,READ_AVERAGE_M,Average scaled score in reading for pupils with medium prior attainment [not populated in 2025]
+188,READ_AVERAGE_H,Average scaled score in reading for pupils with high prior attainment [not populated in 2025]
+189,READ_AVERAGE_FSM6CLA1A,Average scaled score in reading for disadvantaged pupils
+190,READ_AVERAGE_NotFSM6CLA1A,Average scaled score in reading for non-disadvantaged pupils
+191,READ_AVERAGE_EAL,Average scaled score in reading for EAL pupils
+192,READ_AVERAGE_MOBN,Average scaled score in reading for MOBN pupils
+193,MAT_AVERAGE_B,Average scaled score in maths for boys
+194,MAT_AVERAGE_G,Average scaled score in maths for girls
+195,MAT_AVERAGE_L,Average scaled score in maths for pupils with low prior attainment [not populated in 2025]
+196,MAT_AVERAGE_M,Average scaled score in maths for pupils with medium prior attainment [not populated in 2025]
+197,MAT_AVERAGE_H,Average scaled score in maths for pupils with high prior attainment [not populated in 2025]
+198,MAT_AVERAGE_FSM6CLA1A,Average scaled score in maths for disadvantaged pupils
+199,MAT_AVERAGE_NotFSM6CLA1A,Average scaled score in maths for non-disadvantaged pupils
+200,MAT_AVERAGE_EAL,Average scaled score in maths for EAL pupils
+201,MAT_AVERAGE_MOBN,Average scaled score in maths for MOBN pupils
+202,GPS_AVERAGE_B,Average scaled score in GPS for boys
+203,GPS_AVERAGE_G,Average scaled score in GPS for girls
+204,GPS_AVERAGE_L,Average scaled score in GPS for pupils with low prior attainment [not populated in 2025]
+205,GPS_AVERAGE_M,Average scaled score in GPS for pupils with medium prior attainment [not populated in 2025]
+206,GPS_AVERAGE_H,Average scaled score in GPS for pupils with high prior attainment [not populated in 2025]
+207,GPS_AVERAGE_FSM6CLA1A,Average scaled score in GPS for disadvantaged pupils
+208,GPS_AVERAGE_NotFSM6CLA1A,Average scaled score in GPS for non-disadvantaged pupils
+209,GPS_AVERAGE_EAL,Average scaled score in GPS for EAL pupils
+210,GPS_AVERAGE_MOBN,Average scaled score in GPS for MOBN pupils
+211,PTREAD_EXP_L,Percentage of pupils with low prior attainment reaching the expected standard in reading [not populated in 2025]
+212,PTREAD_EXP_M,Percentage of pupils with medium prior attainment reaching the expected standard in reading [not populated in 2025]
+213,PTREAD_EXP_H,Percentage of pupils with high prior attainment reaching the expected standard in reading [not populated in 2025]
+214,PTREAD_EXP_FSM6CLA1A,Percentage of disadvantaged pupils reaching the expected standard in reading
+215,PTREAD_EXP_NotFSM6CLA1A,Percentage of non-disadvantaged pupils reaching the expected standard in reading
+216,PTGPS_EXP_L,"Percentage of pupils with low prior attainment reaching the expected standard in grammar, punctuation and spelling [not populated in 2025]"
+217,PTGPS_EXP_M,"Percentage of pupils with medium prior attainment reaching the expected standard in grammar, punctuation and spelling [not populated in 2025]"
+218,PTGPS_EXP_H,"Percentage of pupils with high prior attainment reaching the expected standard in grammar, punctuation and spelling [not populated in 2025]"
+219,PTGPS_EXP_FSM6CLA1A,"Percentage of disadvantaged pupils reaching the expected standard in grammar, punctuation and spelling"
+220,PTGPS_EXP_NotFSM6CLA1A,"Percentage of non-disadvantaged pupils reaching the expected standard in grammar, punctuation and spelling"
+221,PTMAT_EXP_L,Percentage of pupils with low prior attainment reaching the expected standard in maths [not populated in 2025]
+222,PTMAT_EXP_M,Percentage of pupils with medium prior attainment reaching the expected standard in maths [not populated in 2025]
+223,PTMAT_EXP_H,Percentage of pupils with high prior attainment reaching the expected standard in maths [not populated in 2025]
+224,PTMAT_EXP_FSM6CLA1A,Percentage of disadvantaged pupils reaching the expected standard in maths
+225,PTMAT_EXP_NotFSM6CLA1A,Percentage of non-disadvantaged pupils reaching the expected standard in maths
+226,PTWRITTA_EXP_L,Percentage of pupils with low prior attainment reaching the expected standard in writing [not populated in 2025]
+227,PTWRITTA_EXP_M,Percentage of pupils with medium prior attainment reaching the expected standard in writing [not populated in 2025]
+228,PTWRITTA_EXP_H,Percentage of pupils with high prior attainment reaching the expected standard in writing [not populated in 2025]
+229,PTWRITTA_EXP_FSM6CLA1A,Percentage of disadvantaged pupils reaching the expected standard in writing
+230,PTWRITTA_EXP_NotFSM6CLA1A,Percentage of non-disadvantaged pupils reaching the expected standard in writing
+231,PTREAD_HIGH_L,Percentage of pupils with low prior attainment achieving a high score in reading [not populated in 2025]
+232,PTREAD_HIGH_M,Percentage of pupils with medium prior attainment achieving a high score in reading [not populated in 2025]
+233,PTREAD_HIGH_H,Percentage of pupils with high prior attainment achieving a high score in reading [not populated in 2025]
+234,PTREAD_HIGH_FSM6CLA1A,Percentage of disadvantaged pupils achieving a high score in reading
+235,PTREAD_HIGH_NotFSM6CLA1A,Percentage of non-disadvantaged pupils achieving a high score in reading
+236,PTGPS_HIGH_L,"Percentage of pupils with low prior attainment achieving a high score in grammar, punctuation and spelling [not populated in 2025]"
+237,PTGPS_HIGH_M,"Percentage of pupils with medium prior attainment achieving a high score in grammar, punctuation and spelling [not populated in 2025]"
+238,PTGPS_HIGH_H,"Percentage of pupils with high prior attainment achieving a high score in grammar, punctuation and spelling [not populated in 2025]"
+239,PTGPS_HIGH_FSM6CLA1A,"Percentage of disadvantaged pupils achieving a high score in grammar, punctuation and spelling"
+240,PTGPS_HIGH_NotFSM6CLA1A,"Percentage of non-disadvantaged pupils achieving a high score in grammar, punctuation and spelling"
+241,PTMAT_HIGH_L,Percentage of pupils with low prior attainment achieving a high score in maths [not populated in 2025]
+242,PTMAT_HIGH_M,Percentage of pupils with medium prior attainment achieving a high score in maths [not populated in 2025]
+243,PTMAT_HIGH_H,Percentage of pupils with high prior attainment achieving a high score in maths [not populated in 2025]
+244,PTMAT_HIGH_FSM6CLA1A,Percentage of disadvantaged pupils achieving a high score in maths
+245,PTMAT_HIGH_NotFSM6CLA1A,Percentage of non-disadvantaged pupils achieving a high score in maths
+246,PTWRITTA_HIGH_L,Percentage of pupils with low prior attainment working at greater depth in writing [not populated in 2025]
+247,PTWRITTA_HIGH_M,Percentage of pupils with medium prior attainment working at greater depth in writing [not populated in 2025]
+248,PTWRITTA_HIGH_H,Percentage of pupils with high prior attainment working at greater depth in writing [not populated in 2025]
+249,PTWRITTA_HIGH_FSM6CLA1A,Percentage of disadvantaged pupils working at greater depth in writing
+250,PTWRITTA_HIGH_NotFSM6CLA1A,Percentage of non-disadvantaged pupils working at greater depth in writing
+251,TEALGRP1,Number of eligible pupils with English as first language
+252,PTEALGRP1,Percentage of eligible pupils with English as first language
+253,TEALGRP3,Number of eligible pupils with unclassified language
+254,PTEALGRP3,Percentage of eligible pupils with unclassified language
+255,TSENELE,Number of eligible pupils with EHC plan
+256,PSENELE,Percentage of eligible pupils with EHC plan
+257,TSENELK,Number of eligible pupils with SEN support
+258,PSENELK,Percentage of eligible pupils with SEN support
+259,TSENELEK,Number of eligible pupils with SEN (EHC plan or SEN support)
+260,PSENELEK,Percentage of eligible pupils with SEN  (EHC plan or SEN support)
+261,TELIG_24,Number of eligible pupils 2024
+262,PTFSM6CLA1A_24,Percentage of key stage 2 disadvantaged pupils one year prior
+263,PTNOTFSM6CLA1A_24,Percentage of key stage 2 pupils who are not disadvantaged one year prior
+264,PTRWM_EXP_24,"Percentage of pupils reaching the expected standard in reading, writing and maths one year prior"
+265,PTRWM_HIGH_24,Percentage of pupils achieving a high score in reading and maths and working at greater depth in writing  one year prior
+266,PTRWM_EXP_FSM6CLA1A_24,"Percentage of disadvantaged pupils reaching the expected standard in reading, writing and maths one year prior"
+267,PTRWM_HIGH_FSM6CLA1A_24,Percentage of disadvantaged pupils achieving a high score in reading and maths and working at greater depth in writing one year prior
+268,PTRWM_EXP_NotFSM6CLA1A_24,"Percentage of non-disadvantaged pupils reaching the expected standard in reading, writing and maths one year prior"
+269,PTRWM_HIGH_NotFSM6CLA1A_24,Percentage of non-disadvantaged pupils achieving a high score in reading and maths and working at greater depth in writing one year prior
+270,READPROG_24,Reading progress measure - one year prior [not populated in 2025]
+271,READPROG_LOWER_24,Reading progress measure - lower confidence limit - one year prior [not populated in 2025]
+272,READPROG_UPPER_24,Reading progress measure - upper confidence limit - one year prior [not populated in 2025]
+273,WRITPROG_24,Writing progress measure - one year prior [not populated in 2025]
+274,WRITPROG_LOWER_24,Writing progress measure - lower confidence limit - one year prior [not populated in 2025]
+275,WRITPROG_UPPER_24,Writing progress measure - upper confidence limit - one year prior [not populated in 2025]
+276,MATPROG_24,Maths progress measure - one year prior [not populated in 2025]
+277,MATPROG_LOWER_24,Maths progress measure - lower confidence limit - one year prior [not populated in 2025]
+278,MATPROG_UPPER_24,Maths progress measure - upper confidence limit - one year prior [not populated in 2025]
+279,READ_AVERAGE_24,Average scaled score in reading - one year prior
+280,MAT_AVERAGE_24,Average scaled score in maths - one year prior
+281,TELIG_23,Number of eligible pupils 2023
+282,PTFSM6CLA1A_23,Percentage of key stage 2 disadvantaged pupils - two years prior
+283,PTNOTFSM6CLA1A_23,Percentage of key stage 2 pupils who are not disadvantaged - two years prior
+284,PTRWM_EXP_23,"Percentage of pupils reaching the expected standard in reading, writing and maths - two years prior"
+285,PTRWM_HIGH_23,Percentage of pupils achieving a high score in reading and maths and working at greater depth in writing - two years prior
+286,PTRWM_EXP_FSM6CLA1A_23,"Percentage of disadvantaged pupils reaching the expected standard in reading, writing and maths - two years prior"
+287,PTRWM_HIGH_FSM6CLA1A_23,Percentage of disadvantaged pupils achieving a high score in reading and maths and working at greater depth in writing - two years prior
+288,PTRWM_EXP_NotFSM6CLA1A_23,"Percentage of non-disadvantaged pupils reaching the expected standard in reading, writing and maths - two years prior"
+289,PTRWM_HIGH_NotFSM6CLA1A_23,Percentage of non-disadvantaged pupils achieving a high score in reading and maths and working at greater depth in writing - two years prior
+290,READPROG_23,Reading progress measure - two years prior
+291,READPROG_LOWER_23,Reading progress measure - lower confidence limit - two years prior
+292,READPROG_UPPER_23,Reading progress measure - upper confidence limit - two years prior
+293,WRITPROG_23,Writing progress measure - two years prior
+294,WRITPROG_LOWER_23,Writing progress measure - lower confidence limit - two years prior
+295,WRITPROG_UPPER_23,Writing progress measure - upper confidence limit -  two years prior
+296,MATPROG_23,Maths progress measure - two years prior
+297,MATPROG_LOWER_23,Maths progress measure - lower confidence limit - two years prior
+298,MATPROG_UPPER_23,Maths progress measure - upper confidence limit  - two years prior
+299,READ_AVERAGE_23,Average scaled score in reading - two years prior
+300,MAT_AVERAGE_23,Average scaled score in maths - two years prior
+301,TELIG_3YR,Total number of pupils at the end of Key Stage 2 over the past three years
+302,PTRWM_EXP_3YR,"Percentage of pupils reaching the expected standard in reading, writing and maths  - 3 year total"
+303,PTRWM_HIGH_3YR,Percentage of pupils achieving a high score in reading and maths and working at greater depth in writing  - 3 year total
+304,READ_AVERAGE_3YR,Average scaled score in reading - 3 year average
+305,MAT_AVERAGE_3YR,Average scaled score in maths - 3 year average
+306,READPROG_UNADJUSTED,Unadjusted reading progress measure [not populated in 2025]
+307,WRITPROG_UNADJUSTED,Unadjusted writing progress measure [not populated in 2025]
+308,MATPROG_UNADJUSTED,Unadjusted maths progress measure [not populated in 2025]
+309,READPROG_DESCR,Reading progress measure 'description' [not populated in 2025]
+310,WRITPROG_DESCR,Writing progress measure 'description' [not populated in 2025]
+311,MATPROG_DESCR,Maths progress measure 'description' [not populated in 2025]

data/meta/la_and_region_codes_meta.csv

@@ -0,0 +1,154 @@
+LEA,LA Name,REGION,REGION NAME
+841,Darlington,1,North East A
+840,County Durham,1,North East A
+805,Hartlepool,1,North East A
+806,Middlesbrough,1,North East A
+807,Redcar and Cleveland,1,North East A
+808,Stockton-on-Tees,1,North East A
+390,Gateshead,3,North East B
+391,Newcastle upon Tyne,3,North East B
+392,North Tyneside,3,North East B
+929,Northumberland,3,North East B
+393,South Tyneside,3,North East B
+394,Sunderland,3,North East B
+889,Blackburn with Darwen,6,North West A
+890,Blackpool,6,North West A
+942,Cumberland,6,North West A
+943,Westmorland and Furness ,6,North West A
+888,Lancashire,6,North West A
+350,Bolton,7,North West B
+351,Bury,7,North West B
+352,Manchester,7,North West B
+353,Oldham,7,North West B
+354,Rochdale,7,North West B
+355,Salford,7,North West B
+356,Stockport,7,North West B
+357,Tameside,7,North West B
+358,Trafford,7,North West B
+359,Wigan,7,North West B
+895,Cheshire East,9,North West C
+896,Cheshire West and Chester,9,North West C
+876,Halton,9,North West C
+340,Knowsley,9,North West C
+341,Liverpool,9,North West C
+343,Sefton,9,North West C
+342,St. Helens,9,North West C
+877,Warrington,9,North West C
+344,Wirral,9,North West C
+811,East Riding of Yorkshire,10,North Yorkshire and The Humber
+810,"Kingston Upon Hull, City of",10,North Yorkshire and The Humber
+812,North East Lincolnshire,10,North Yorkshire and The Humber
+813,North Lincolnshire,10,North Yorkshire and The Humber
+815,North Yorkshire,10,North Yorkshire and The Humber
+816,York,10,North Yorkshire and The Humber
+370,Barnsley,12,South and West Yorkshire
+380,Bradford,12,South and West Yorkshire
+381,Calderdale,12,South and West Yorkshire
+371,Doncaster,12,South and West Yorkshire
+382,Kirklees,12,South and West Yorkshire
+383,Leeds,12,South and West Yorkshire
+372,Rotherham,12,South and West Yorkshire
+373,Sheffield,12,South and West Yorkshire
+384,Wakefield,12,South and West Yorkshire
+831,Derby,14,East Midlands A
+830,Derbyshire,14,East Midlands A
+892,Nottingham,14,East Midlands A
+891,Nottinghamshire,14,East Midlands A
+856,Leicester,16,East Midlands B
+855,Leicestershire,16,East Midlands B
+925,Lincolnshire,16,East Midlands B
+940,North Northamptonshire,16,East Midlands B
+941,West Northamptonshire,16,East Midlands B
+857,Rutland,16,East Midlands B
+893,Shropshire,20,West Midlands A
+860,Staffordshire,20,West Midlands A
+861,Stoke-on-Trent,20,West Midlands A
+894,Telford and Wrekin,20,West Midlands A
+884,"Herefordshire, County of",22,West Midlands B
+885,Worcestershire,22,West Midlands B
+330,Birmingham,24,West Midlands C
+331,Coventry,24,West Midlands C
+332,Dudley,24,West Midlands C
+333,Sandwell,24,West Midlands C
+334,Solihull,24,West Midlands C
+335,Walsall,24,West Midlands C
+937,Warwickshire,24,West Midlands C
+336,Wolverhampton,24,West Midlands C
+822,Bedford,25,East of England A
+873,Cambridgeshire,25,East of England A
+823,Central Bedfordshire,25,East of England A
+919,Hertfordshire,25,East of England A
+821,Luton,25,East of England A
+874,Peterborough,25,East of England A
+881,Essex,27,East of England B
+926,Norfolk,27,East of England B
+882,Southend-on-Sea,27,East of England B
+935,Suffolk,27,East of England B
+883,Thurrock,27,East of England B
+202,Camden,31,London Central
+206,Islington,31,London Central
+207,Kensington and Chelsea,31,London Central
+208,Lambeth,31,London Central
+210,Southwark,31,London Central
+212,Wandsworth,31,London Central
+213,Westminster,31,London Central
+301,Barking and Dagenham,32,London East
+303,Bexley,32,London East
+201,City of London,32,London East
+203,Greenwich,32,London East
+204,Hackney,32,London East
+311,Havering,32,London East
+209,Lewisham,32,London East
+316,Newham,32,London East
+317,Redbridge,32,London East
+211,Tower Hamlets,32,London East
+302,Barnet,33,London North
+308,Enfield,33,London North
+309,Haringey,33,London North
+320,Waltham Forest,33,London North
+305,Bromley,34,London South
+306,Croydon,34,London South
+314,Kingston upon Thames,34,London South
+315,Merton,34,London South
+318,Richmond upon Thames,34,London South
+319,Sutton,34,London South
+304,Brent,35,London West
+307,Ealing,35,London West
+205,Hammersmith and Fulham,35,London West
+310,Harrow,35,London West
+312,Hillingdon,35,London West
+313,Hounslow,35,London West
+867,Bracknell Forest,36,South East A
+825,Buckinghamshire,36,South East A
+826,Milton Keynes,36,South East A
+931,Oxfordshire,36,South East A
+870,Reading,36,South East A
+871,Slough,36,South East A
+869,West Berkshire,36,South East A
+868,Windsor and Maidenhead,36,South East A
+872,Wokingham,36,South East A
+850,Hampshire,37,South East B
+921,Isle of Wight,37,South East B
+851,Portsmouth,37,South East B
+852,Southampton,37,South East B
+936,Surrey,38,South East C
+938,West Sussex,38,South East C
+846,Brighton and Hove,39,South East D
+845,East Sussex,39,South East D
+886,Kent,39,South East D
+887,Medway,39,South East D
+839,"Bournemouth, Christchurch and Poole",43,South West A
+908,Cornwall,43,South West A
+878,Devon,43,South West A
+838,Dorset,43,South West A
+420,Isles of Scilly,43,South West A
+879,Plymouth,43,South West A
+933,Somerset,43,South West A
+880,Torbay,43,South West A
+800,Bath and North East Somerset,45,South West B
+801,"Bristol, City of",45,South West B
+916,Gloucestershire,45,South West B
+802,North Somerset,45,South West B
+803,South Gloucestershire,45,South West B
+866,Swindon,45,South West B
+865,Wiltshire,45,South West B

docker-compose.yml

@@ -0,0 +1,16 @@
+services:
+  app:
+    build: .
+    ports:
+      - "80:80"
+    volumes:
+      # Mount data directory for easy updates without rebuilding
+      - ./data:/app/data:ro
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:80/api/data-info"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 10s
+

frontend/app.js

@@ -0,0 +1,831 @@
+/**
+ * School Performance Compass - Frontend Application
+ * Interactive UK School Data Visualization
+ */
+
+const API_BASE = '';
+
+// State
+let allSchools = [];
+let selectedSchools = [];
+let comparisonChart = null;
+let schoolDetailChart = null;
+let currentSchoolData = null;
+
+// Chart colors
+const CHART_COLORS = [
+    '#e07256',  // coral
+    '#2d7d7d',  // teal
+    '#c9a227',  // gold
+    '#7b68a6',  // purple
+    '#3498db',  // blue
+    '#27ae60',  // green
+    '#e74c3c',  // red
+    '#9b59b6',  // violet
+];
+
+// DOM Elements
+const elements = {
+    schoolSearch: document.getElementById('school-search'),
+    localAuthorityFilter: document.getElementById('local-authority-filter'),
+    typeFilter: document.getElementById('type-filter'),
+    schoolsGrid: document.getElementById('schools-grid'),
+    compareSearch: document.getElementById('compare-search'),
+    compareResults: document.getElementById('compare-results'),
+    selectedSchools: document.getElementById('selected-schools'),
+    chartsSection: document.getElementById('charts-section'),
+    metricSelect: document.getElementById('metric-select'),
+    comparisonChart: document.getElementById('comparison-chart'),
+    comparisonTable: document.getElementById('comparison-table'),
+    tableHeader: document.getElementById('table-header'),
+    tableBody: document.getElementById('table-body'),
+    rankingMetric: document.getElementById('ranking-metric'),
+    rankingYear: document.getElementById('ranking-year'),
+    rankingsList: document.getElementById('rankings-list'),
+    modal: document.getElementById('school-modal'),
+    modalClose: document.getElementById('modal-close'),
+    modalSchoolName: document.getElementById('modal-school-name'),
+    modalMeta: document.getElementById('modal-meta'),
+    modalStats: document.getElementById('modal-stats'),
+    schoolDetailChart: document.getElementById('school-detail-chart'),
+    addToCompare: document.getElementById('add-to-compare'),
+};
+
+// Initialize
+document.addEventListener('DOMContentLoaded', init);
+
+async function init() {
+    await loadFilters();
+    await loadSchools();
+    await loadRankingYears();
+    await loadRankings();
+    setupEventListeners();
+}
+
+// API Functions
+async function fetchAPI(endpoint) {
+    try {
+        const response = await fetch(`${API_BASE}${endpoint}`);
+        if (!response.ok) throw new Error(`HTTP ${response.status}`);
+        return await response.json();
+    } catch (error) {
+        console.error(`API Error (${endpoint}):`, error);
+        return null;
+    }
+}
+
+async function loadFilters() {
+    const data = await fetchAPI('/api/filters');
+    if (!data) return;
+
+    // Populate school type filter
+    data.school_types.forEach(type => {
+        const option = document.createElement('option');
+        option.value = type;
+        option.textContent = type;
+        elements.typeFilter.appendChild(option);
+    });
+}
+
+async function loadSchools() {
+    const params = new URLSearchParams();
+    
+    const search = elements.schoolSearch.value.trim();
+    if (search) params.append('search', search);
+    
+    const localAuthority = elements.localAuthorityFilter.value;
+    if (localAuthority) params.append('local_authority', localAuthority);
+    
+    const type = elements.typeFilter.value;
+    if (type) params.append('school_type', type);
+    
+    const queryString = params.toString();
+    const endpoint = `/api/schools${queryString ? '?' + queryString : ''}`;
+    
+    const data = await fetchAPI(endpoint);
+    if (!data) {
+        showEmptyState(elements.schoolsGrid, 'Unable to load schools');
+        return;
+    }
+    
+    allSchools = data.schools;
+    renderSchools(allSchools);
+}
+
+async function loadSchoolDetails(urn) {
+    const data = await fetchAPI(`/api/schools/${urn}`);
+    return data;
+}
+
+async function loadComparison() {
+    if (selectedSchools.length === 0) return null;
+    
+    const urns = selectedSchools.map(s => s.urn).join(',');
+    const data = await fetchAPI(`/api/compare?urns=${urns}`);
+    return data;
+}
+
+async function loadRankingYears() {
+    const data = await fetchAPI('/api/filters');
+    if (!data) return;
+    
+    elements.rankingYear.innerHTML = '';
+    data.years.sort((a, b) => b - a).forEach(year => {
+        const option = document.createElement('option');
+        option.value = year;
+        option.textContent = `${year}`;
+        elements.rankingYear.appendChild(option);
+    });
+}
+
+async function loadRankings() {
+    const metric = elements.rankingMetric.value;
+    const year = elements.rankingYear.value;
+    
+    let endpoint = `/api/rankings?metric=${metric}&limit=20`;
+    if (year) endpoint += `&year=${year}`;
+    
+    const data = await fetchAPI(endpoint);
+    if (!data) {
+        showEmptyState(elements.rankingsList, 'Unable to load rankings');
+        return;
+    }
+    
+    renderRankings(data.rankings, metric);
+}
+
+// Render Functions
+function renderSchools(schools) {
+    if (schools.length === 0) {
+        showEmptyState(elements.schoolsGrid, 'No primary schools found matching your criteria');
+        return;
+    }
+    
+    elements.schoolsGrid.innerHTML = schools.map(school => `
+        <div class="school-card" data-urn="${school.urn}">
+            <h3 class="school-name">${escapeHtml(school.school_name)}</h3>
+            <div class="school-meta">
+                <span class="school-tag">${escapeHtml(school.local_authority || '')}</span>
+                <span class="school-tag type">${escapeHtml(school.school_type || '')}</span>
+            </div>
+            <div class="school-address">${escapeHtml(school.address || '')}</div>
+            <div class="school-stats">
+                <div class="stat">
+                    <div class="stat-value">Primary</div>
+                    <div class="stat-label">Phase</div>
+                </div>
+                <div class="stat">
+                    <div class="stat-value">KS2</div>
+                    <div class="stat-label">Data</div>
+                </div>
+            </div>
+        </div>
+    `).join('');
+    
+    // Add click handlers
+    elements.schoolsGrid.querySelectorAll('.school-card').forEach(card => {
+        card.addEventListener('click', () => {
+            const urn = parseInt(card.dataset.urn);
+            openSchoolModal(urn);
+        });
+    });
+}
+
+function renderRankings(rankings, metric) {
+    if (rankings.length === 0) {
+        showEmptyState(elements.rankingsList, 'No ranking data available for this year/metric');
+        return;
+    }
+    
+    const metricLabels = {
+        // Expected
+        rwm_expected_pct: 'RWM %',
+        reading_expected_pct: 'Reading %',
+        writing_expected_pct: 'Writing %',
+        maths_expected_pct: 'Maths %',
+        gps_expected_pct: 'GPS %',
+        science_expected_pct: 'Science %',
+        // Higher
+        rwm_high_pct: 'RWM Higher %',
+        reading_high_pct: 'Reading Higher %',
+        writing_high_pct: 'Writing Higher %',
+        maths_high_pct: 'Maths Higher %',
+        gps_high_pct: 'GPS Higher %',
+        // Progress
+        reading_progress: 'Reading Progress',
+        writing_progress: 'Writing Progress',
+        maths_progress: 'Maths Progress',
+        // Averages
+        reading_avg_score: 'Reading Avg',
+        maths_avg_score: 'Maths Avg',
+        gps_avg_score: 'GPS Avg',
+        // Gender
+        rwm_expected_boys_pct: 'Boys RWM %',
+        rwm_expected_girls_pct: 'Girls RWM %',
+        rwm_high_boys_pct: 'Boys Higher %',
+        rwm_high_girls_pct: 'Girls Higher %',
+        // Equity
+        rwm_expected_disadvantaged_pct: 'Disadvantaged %',
+        rwm_expected_non_disadvantaged_pct: 'Non-Disadv %',
+        disadvantaged_gap: 'Disadv Gap',
+        // Context
+        disadvantaged_pct: '% Disadvantaged',
+        eal_pct: '% EAL',
+        sen_support_pct: '% SEN',
+        stability_pct: '% Stable',
+        // 3-Year
+        rwm_expected_3yr_pct: 'RWM 3yr %',
+        reading_avg_3yr: 'Reading 3yr',
+        maths_avg_3yr: 'Maths 3yr',
+    };
+    
+    elements.rankingsList.innerHTML = rankings.map((school, index) => {
+        const value = school[metric];
+        if (value === null || value === undefined) return '';
+        
+        const isProgress = metric.includes('progress');
+        const isScore = metric.includes('_avg_');
+        let displayValue;
+        if (isProgress) {
+            displayValue = (value >= 0 ? '+' : '') + value.toFixed(1);
+        } else if (isScore) {
+            displayValue = value.toFixed(0);
+        } else {
+            displayValue = value.toFixed(0) + '%';
+        }
+        
+        return `
+            <div class="ranking-item" data-urn="${school.urn}">
+                <div class="ranking-position ${index < 3 ? 'top-3' : ''}">${index + 1}</div>
+                <div class="ranking-info">
+                    <div class="ranking-name">${escapeHtml(school.school_name)}</div>
+                    <div class="ranking-location">${escapeHtml(school.local_authority || '')}</div>
+                </div>
+                <div class="ranking-score">
+                    <div class="ranking-score-value">${displayValue}</div>
+                    <div class="ranking-score-label">${metricLabels[metric] || metric}</div>
+                </div>
+            </div>
+        `;
+    }).filter(Boolean).join('');
+    
+    // Add click handlers
+    elements.rankingsList.querySelectorAll('.ranking-item').forEach(item => {
+        item.addEventListener('click', () => {
+            const urn = parseInt(item.dataset.urn);
+            openSchoolModal(urn);
+        });
+    });
+}
+
+function renderSelectedSchools() {
+    if (selectedSchools.length === 0) {
+        elements.selectedSchools.innerHTML = `
+            <div class="empty-selection">
+                <div class="empty-icon">
+                    <svg viewBox="0 0 48 48" fill="none" stroke="currentColor" stroke-width="1.5">
+                        <rect x="6" y="10" width="36" height="28" rx="2"/>
+                        <path d="M6 18h36"/>
+                        <circle cx="14" cy="14" r="2" fill="currentColor"/>
+                        <circle cx="22" cy="14" r="2" fill="currentColor"/>
+                    </svg>
+                </div>
+                <p>Search and add schools to compare</p>
+            </div>
+        `;
+        elements.chartsSection.style.display = 'none';
+        return;
+    }
+    
+    elements.selectedSchools.innerHTML = selectedSchools.map((school, index) => `
+        <div class="selected-school-tag" style="border-left: 3px solid ${CHART_COLORS[index % CHART_COLORS.length]}">
+            <span>${escapeHtml(school.school_name)}</span>
+            <button class="remove" data-urn="${school.urn}" title="Remove">
+                <svg viewBox="0 0 16 16" width="12" height="12" fill="none" stroke="currentColor" stroke-width="2">
+                    <path d="M12 4L4 12M4 4l8 8"/>
+                </svg>
+            </button>
+        </div>
+    `).join('');
+    
+    // Add remove handlers
+    elements.selectedSchools.querySelectorAll('.remove').forEach(btn => {
+        btn.addEventListener('click', (e) => {
+            e.stopPropagation();
+            const urn = parseInt(btn.dataset.urn);
+            removeFromComparison(urn);
+        });
+    });
+    
+    elements.chartsSection.style.display = 'block';
+    updateComparisonChart();
+}
+
+async function updateComparisonChart() {
+    if (selectedSchools.length === 0) return;
+    
+    const data = await loadComparison();
+    if (!data) return;
+    
+    const metric = elements.metricSelect.value;
+    const metricLabels = {
+        // Expected Standard
+        rwm_expected_pct: 'Reading, Writing & Maths Combined (%)',
+        reading_expected_pct: 'Reading Expected Standard (%)',
+        writing_expected_pct: 'Writing Expected Standard (%)',
+        maths_expected_pct: 'Maths Expected Standard (%)',
+        gps_expected_pct: 'GPS Expected Standard (%)',
+        science_expected_pct: 'Science Expected Standard (%)',
+        // Higher Standard
+        rwm_high_pct: 'RWM Combined Higher Standard (%)',
+        reading_high_pct: 'Reading Higher Standard (%)',
+        writing_high_pct: 'Writing Greater Depth (%)',
+        maths_high_pct: 'Maths Higher Standard (%)',
+        gps_high_pct: 'GPS Higher Standard (%)',
+        // Progress
+        reading_progress: 'Reading Progress Score',
+        writing_progress: 'Writing Progress Score',
+        maths_progress: 'Maths Progress Score',
+        // Averages
+        reading_avg_score: 'Reading Average Scaled Score',
+        maths_avg_score: 'Maths Average Scaled Score',
+        gps_avg_score: 'GPS Average Scaled Score',
+        // Gender
+        rwm_expected_boys_pct: 'RWM Expected % (Boys)',
+        rwm_expected_girls_pct: 'RWM Expected % (Girls)',
+        rwm_high_boys_pct: 'RWM Higher % (Boys)',
+        rwm_high_girls_pct: 'RWM Higher % (Girls)',
+        // Equity
+        rwm_expected_disadvantaged_pct: 'RWM Expected % (Disadvantaged)',
+        rwm_expected_non_disadvantaged_pct: 'RWM Expected % (Non-Disadvantaged)',
+        disadvantaged_gap: 'Disadvantaged Gap vs National',
+        // Context
+        disadvantaged_pct: '% Disadvantaged Pupils',
+        eal_pct: '% EAL Pupils',
+        sen_support_pct: '% SEN Support',
+        stability_pct: '% Pupil Stability',
+        // 3-Year
+        rwm_expected_3yr_pct: 'RWM Expected % (3-Year Avg)',
+        reading_avg_3yr: 'Reading Score (3-Year Avg)',
+        maths_avg_3yr: 'Maths Score (3-Year Avg)',
+    };
+    
+    // Prepare chart data - iterate in same order as selectedSchools for color consistency
+    const datasets = [];
+    const allYears = new Set();
+    
+    selectedSchools.forEach((school, index) => {
+        const schoolData = data.comparison[school.urn];
+        if (!schoolData) return;
+        
+        const yearlyData = schoolData.yearly_data;
+        yearlyData.forEach(d => allYears.add(d.year));
+        
+        const sortedData = yearlyData.sort((a, b) => a.year - b.year);
+        
+        datasets.push({
+            label: schoolData.school_info.school_name,
+            data: sortedData.map(d => ({ x: d.year, y: d[metric] })),
+            borderColor: CHART_COLORS[index % CHART_COLORS.length],
+            backgroundColor: CHART_COLORS[index % CHART_COLORS.length] + '20',
+            borderWidth: 3,
+            pointRadius: 5,
+            pointHoverRadius: 7,
+            tension: 0.3,
+            fill: false,
+        });
+    });
+    
+    const years = Array.from(allYears).sort();
+    
+    // Destroy existing chart
+    if (comparisonChart) {
+        comparisonChart.destroy();
+    }
+    
+    // Create new chart
+    const ctx = elements.comparisonChart.getContext('2d');
+    comparisonChart = new Chart(ctx, {
+        type: 'line',
+        data: {
+            labels: years,
+            datasets: datasets,
+        },
+        options: {
+            responsive: true,
+            maintainAspectRatio: true,
+            aspectRatio: 2,
+            plugins: {
+                legend: {
+                    position: 'bottom',
+                    labels: {
+                        font: { family: "'DM Sans', sans-serif", size: 12 },
+                        padding: 20,
+                        usePointStyle: true,
+                    },
+                },
+                title: {
+                    display: true,
+                    text: metricLabels[metric],
+                    font: { family: "'Playfair Display', serif", size: 18, weight: 600 },
+                    padding: { bottom: 20 },
+                },
+                tooltip: {
+                    backgroundColor: '#1a1612',
+                    titleFont: { family: "'DM Sans', sans-serif" },
+                    bodyFont: { family: "'DM Sans', sans-serif" },
+                    padding: 12,
+                    cornerRadius: 8,
+                },
+            },
+            scales: {
+                x: {
+                    title: {
+                        display: true,
+                        text: 'Academic Year',
+                        font: { family: "'DM Sans', sans-serif", weight: 500 },
+                    },
+                    grid: { display: false },
+                },
+                y: {
+                    title: {
+                        display: true,
+                        text: metricLabels[metric],
+                        font: { family: "'DM Sans', sans-serif", weight: 500 },
+                    },
+                    grid: { color: '#e5dfd5' },
+                },
+            },
+            interaction: {
+                intersect: false,
+                mode: 'index',
+            },
+        },
+    });
+    
+    // Update comparison table
+    updateComparisonTable(data.comparison, metric, years);
+}
+
+function updateComparisonTable(comparison, metric, years) {
+    // Build header
+    let headerHtml = '<th>School</th>';
+    years.forEach(year => {
+        headerHtml += `<th>${year}</th>`;
+    });
+    headerHtml += '<th>Change</th>';
+    elements.tableHeader.innerHTML = headerHtml;
+    
+    // Build body - iterate in same order as selectedSchools for color consistency
+    let bodyHtml = '';
+    selectedSchools.forEach((school, index) => {
+        const schoolData = comparison[school.urn];
+        if (!schoolData) return;
+        
+        const yearlyMap = {};
+        schoolData.yearly_data.forEach(d => {
+            yearlyMap[d.year] = d[metric];
+        });
+        
+        const firstValue = yearlyMap[years[0]];
+        const lastValue = yearlyMap[years[years.length - 1]];
+        const change = firstValue && lastValue ? (lastValue - firstValue).toFixed(2) : 'N/A';
+        const changeClass = parseFloat(change) >= 0 ? 'positive' : 'negative';
+        const color = CHART_COLORS[index % CHART_COLORS.length];
+        
+        bodyHtml += `<tr>`;
+        bodyHtml += `<td><strong style="border-left: 3px solid ${color}; padding-left: 8px;">${escapeHtml(schoolData.school_info.school_name)}</strong></td>`;
+        years.forEach(year => {
+            const value = yearlyMap[year];
+            bodyHtml += `<td>${value !== undefined ? formatMetricValue(value, metric) : '-'}</td>`;
+        });
+        bodyHtml += `<td class="${changeClass}">${change !== 'N/A' ? (parseFloat(change) >= 0 ? '+' : '') + change : change}</td>`;
+        bodyHtml += `</tr>`;
+    });
+    elements.tableBody.innerHTML = bodyHtml;
+}
+
+function formatMetricValue(value, metric) {
+    if (value === null || value === undefined) return '-';
+    if (metric.includes('progress')) {
+        return (value >= 0 ? '+' : '') + value.toFixed(1);
+    }
+    if (metric.includes('pct')) {
+        return value.toFixed(0) + '%';
+    }
+    return value.toFixed(1);
+}
+
+async function openSchoolModal(urn) {
+    const data = await loadSchoolDetails(urn);
+    if (!data) return;
+    
+    currentSchoolData = data;
+    
+    elements.modalSchoolName.textContent = data.school_info.school_name;
+    elements.modalMeta.innerHTML = `
+        <span class="school-tag">${escapeHtml(data.school_info.local_authority || '')}</span>
+        <span class="school-tag type">${escapeHtml(data.school_info.school_type || '')}</span>
+        <span class="school-tag">Primary</span>
+    `;
+    
+    // Get latest year data with actual results (skip 2021 - no SATs)
+    const sortedData = data.yearly_data.sort((a, b) => b.year - a.year);
+    const latest = sortedData.find(d => d.rwm_expected_pct !== null) || sortedData[0];
+    
+    elements.modalStats.innerHTML = `
+        <div class="modal-stats-section">
+            <h4>KS2 Results (${latest.year})</h4>
+            <div class="modal-stats-grid">
+                <div class="modal-stat">
+                    <div class="modal-stat-value">${formatMetricValue(latest.rwm_expected_pct, 'rwm_expected_pct')}</div>
+                    <div class="modal-stat-label">RWM Expected</div>
+                </div>
+                <div class="modal-stat">
+                    <div class="modal-stat-value">${formatMetricValue(latest.rwm_high_pct, 'rwm_high_pct')}</div>
+                    <div class="modal-stat-label">RWM Higher</div>
+                </div>
+                <div class="modal-stat">
+                    <div class="modal-stat-value">${formatMetricValue(latest.gps_expected_pct, 'gps_expected_pct')}</div>
+                    <div class="modal-stat-label">GPS Expected</div>
+                </div>
+                <div class="modal-stat">
+                    <div class="modal-stat-value">${formatMetricValue(latest.science_expected_pct, 'science_expected_pct')}</div>
+                    <div class="modal-stat-label">Science Expected</div>
+                </div>
+            </div>
+        </div>
+        <div class="modal-stats-section">
+            <h4>Progress Scores</h4>
+            <div class="modal-stats-grid">
+                <div class="modal-stat">
+                    <div class="modal-stat-value ${getProgressClass(latest.reading_progress)}">${formatMetricValue(latest.reading_progress, 'reading_progress')}</div>
+                    <div class="modal-stat-label">Reading</div>
+                </div>
+                <div class="modal-stat">
+                    <div class="modal-stat-value ${getProgressClass(latest.writing_progress)}">${formatMetricValue(latest.writing_progress, 'writing_progress')}</div>
+                    <div class="modal-stat-label">Writing</div>
+                </div>
+                <div class="modal-stat">
+                    <div class="modal-stat-value ${getProgressClass(latest.maths_progress)}">${formatMetricValue(latest.maths_progress, 'maths_progress')}</div>
+                    <div class="modal-stat-label">Maths</div>
+                </div>
+            </div>
+        </div>
+        <div class="modal-stats-section">
+            <h4>School Context</h4>
+            <div class="modal-stats-grid">
+                <div class="modal-stat">
+                    <div class="modal-stat-value">${latest.total_pupils || '-'}</div>
+                    <div class="modal-stat-label">Total Pupils</div>
+                </div>
+                <div class="modal-stat">
+                    <div class="modal-stat-value">${formatMetricValue(latest.disadvantaged_pct, 'disadvantaged_pct')}</div>
+                    <div class="modal-stat-label">% Disadvantaged</div>
+                </div>
+                <div class="modal-stat">
+                    <div class="modal-stat-value">${formatMetricValue(latest.eal_pct, 'eal_pct')}</div>
+                    <div class="modal-stat-label">% EAL</div>
+                </div>
+                <div class="modal-stat">
+                    <div class="modal-stat-value">${formatMetricValue(latest.sen_support_pct, 'sen_support_pct')}</div>
+                    <div class="modal-stat-label">% SEN Support</div>
+                </div>
+            </div>
+        </div>
+    `;
+    
+    function getProgressClass(value) {
+        if (value === null || value === undefined) return '';
+        return value >= 0 ? 'positive' : 'negative';
+    }
+    
+    // Create chart - filter out years with no data (2021)
+    if (schoolDetailChart) {
+        schoolDetailChart.destroy();
+    }
+    
+    const validData = sortedData.filter(d => d.rwm_expected_pct !== null).reverse();
+    const years = validData.map(d => d.year);
+    const ctx = elements.schoolDetailChart.getContext('2d');
+    
+    schoolDetailChart = new Chart(ctx, {
+        type: 'bar',
+        data: {
+            labels: years,
+            datasets: [
+                {
+                    label: 'Reading %',
+                    data: validData.map(d => d.reading_expected_pct),
+                    backgroundColor: '#2d7d7d',
+                    borderRadius: 4,
+                },
+                {
+                    label: 'Writing %',
+                    data: validData.map(d => d.writing_expected_pct),
+                    backgroundColor: '#c9a227',
+                    borderRadius: 4,
+                },
+                {
+                    label: 'Maths %',
+                    data: validData.map(d => d.maths_expected_pct),
+                    backgroundColor: '#e07256',
+                    borderRadius: 4,
+                },
+            ],
+        },
+        options: {
+            responsive: true,
+            maintainAspectRatio: true,
+            aspectRatio: 2,
+            plugins: {
+                legend: {
+                    position: 'bottom',
+                    labels: {
+                        font: { family: "'DM Sans', sans-serif" },
+                        usePointStyle: true,
+                    },
+                },
+                title: {
+                    display: true,
+                    text: 'KS2 Attainment Over Time (% meeting expected standard)',
+                    font: { family: "'Playfair Display', serif", size: 16, weight: 600 },
+                },
+            },
+            scales: {
+                y: {
+                    beginAtZero: true,
+                    max: 100,
+                    grid: { color: '#e5dfd5' },
+                },
+                x: {
+                    grid: { display: false },
+                },
+            },
+        },
+    });
+    
+    // Update add to compare button
+    const isSelected = selectedSchools.some(s => s.urn === data.school_info.urn);
+    elements.addToCompare.textContent = isSelected ? 'Remove from Compare' : 'Add to Compare';
+    elements.addToCompare.dataset.urn = data.school_info.urn;
+    
+    elements.modal.classList.add('active');
+    document.body.style.overflow = 'hidden';
+}
+
+function closeModal() {
+    elements.modal.classList.remove('active');
+    document.body.style.overflow = '';
+    currentSchoolData = null;
+}
+
+function addToComparison(school) {
+    if (selectedSchools.some(s => s.urn === school.urn)) return;
+    if (selectedSchools.length >= 5) {
+        alert('Maximum 5 schools can be compared at once');
+        return;
+    }
+    
+    selectedSchools.push(school);
+    renderSelectedSchools();
+}
+
+function removeFromComparison(urn) {
+    selectedSchools = selectedSchools.filter(s => s.urn !== urn);
+    renderSelectedSchools();
+}
+
+function showEmptyState(container, message) {
+    container.innerHTML = `
+        <div class="empty-state">
+            <svg viewBox="0 0 48 48" fill="none" stroke="currentColor" stroke-width="1.5">
+                <circle cx="24" cy="24" r="20"/>
+                <path d="M16 20h16M16 28h10"/>
+            </svg>
+            <p>${escapeHtml(message)}</p>
+        </div>
+    `;
+}
+
+function escapeHtml(text) {
+    if (!text) return '';
+    const div = document.createElement('div');
+    div.textContent = text;
+    return div.innerHTML;
+}
+
+// Event Listeners
+function setupEventListeners() {
+    // Navigation
+    document.querySelectorAll('.nav-link').forEach(link => {
+        link.addEventListener('click', (e) => {
+            e.preventDefault();
+            const view = link.dataset.view;
+            
+            document.querySelectorAll('.nav-link').forEach(l => l.classList.remove('active'));
+            link.classList.add('active');
+            
+            document.querySelectorAll('.view').forEach(v => v.classList.remove('active'));
+            document.getElementById(`${view}-view`).classList.add('active');
+        });
+    });
+    
+    // Search and filters
+    let searchTimeout;
+    elements.schoolSearch.addEventListener('input', () => {
+        clearTimeout(searchTimeout);
+        searchTimeout = setTimeout(loadSchools, 300);
+    });
+    
+    elements.localAuthorityFilter.addEventListener('change', loadSchools);
+    elements.typeFilter.addEventListener('change', loadSchools);
+    
+    // Compare search
+    let compareSearchTimeout;
+    elements.compareSearch.addEventListener('input', async () => {
+        clearTimeout(compareSearchTimeout);
+        const query = elements.compareSearch.value.trim();
+        
+        if (query.length < 2) {
+            elements.compareResults.classList.remove('active');
+            return;
+        }
+        
+        compareSearchTimeout = setTimeout(async () => {
+            const data = await fetchAPI(`/api/schools?search=${encodeURIComponent(query)}`);
+            if (!data) return;
+            
+            const results = data.schools.filter(s => !selectedSchools.some(sel => sel.urn === s.urn));
+            
+            if (results.length === 0) {
+                elements.compareResults.innerHTML = '<div class="compare-result-item"><span class="name">No schools found</span></div>';
+            } else {
+                elements.compareResults.innerHTML = results.slice(0, 10).map(school => `
+                    <div class="compare-result-item" data-urn="${school.urn}" data-name="${escapeHtml(school.school_name)}">
+                        <div class="name">${escapeHtml(school.school_name)}</div>
+                        <div class="location">${escapeHtml(school.local_authority || '')}${school.postcode ? ' • ' + escapeHtml(school.postcode) : ''}</div>
+                    </div>
+                `).join('');
+                
+                elements.compareResults.querySelectorAll('.compare-result-item').forEach(item => {
+                    item.addEventListener('click', () => {
+                        const urn = parseInt(item.dataset.urn);
+                        const school = data.schools.find(s => s.urn === urn);
+                        if (school) {
+                            addToComparison(school);
+                            elements.compareSearch.value = '';
+                            elements.compareResults.classList.remove('active');
+                        }
+                    });
+                });
+            }
+            
+            elements.compareResults.classList.add('active');
+        }, 300);
+    });
+    
+    elements.compareSearch.addEventListener('blur', () => {
+        setTimeout(() => elements.compareResults.classList.remove('active'), 200);
+    });
+    
+    elements.compareSearch.addEventListener('focus', () => {
+        if (elements.compareSearch.value.trim().length >= 2) {
+            elements.compareResults.classList.add('active');
+        }
+    });
+    
+    // Metric selector
+    elements.metricSelect.addEventListener('change', updateComparisonChart);
+    
+    // Rankings
+    elements.rankingMetric.addEventListener('change', loadRankings);
+    elements.rankingYear.addEventListener('change', loadRankings);
+    
+    // Modal
+    elements.modalClose.addEventListener('click', closeModal);
+    elements.modal.querySelector('.modal-backdrop').addEventListener('click', closeModal);
+    
+    elements.addToCompare.addEventListener('click', () => {
+        if (!currentSchoolData) return;
+        
+        const urn = currentSchoolData.school_info.urn;
+        const isSelected = selectedSchools.some(s => s.urn === urn);
+        
+        if (isSelected) {
+            removeFromComparison(urn);
+            elements.addToCompare.textContent = 'Add to Compare';
+        } else {
+            addToComparison(currentSchoolData.school_info);
+            elements.addToCompare.textContent = 'Remove from Compare';
+        }
+    });
+    
+    // Keyboard
+    document.addEventListener('keydown', (e) => {
+        if (e.key === 'Escape') {
+            closeModal();
+        }
+    });
+}
+

frontend/index.html

@@ -0,0 +1,261 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Primary School Compass | Wandsworth & Merton</title>
+    <link rel="preconnect" href="https://fonts.googleapis.com">
+    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+    <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,400;0,9..40,500;0,9..40,600;0,9..40,700&family=Playfair+Display:wght@600;700&display=swap" rel="stylesheet">
+    <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
+    <link rel="stylesheet" href="/static/styles.css">
+</head>
+<body>
+    <div class="noise-overlay"></div>
+    
+    <header class="header">
+        <div class="header-content">
+            <div class="logo">
+                <div class="logo-icon">
+                    <svg viewBox="0 0 40 40" fill="none" xmlns="http://www.w3.org/2000/svg">
+                        <circle cx="20" cy="20" r="18" stroke="currentColor" stroke-width="2"/>
+                        <path d="M20 8L20 32M12 14L28 14M10 20L30 20M12 26L28 26" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"/>
+                        <circle cx="20" cy="20" r="4" fill="currentColor"/>
+                    </svg>
+                </div>
+                <div class="logo-text">
+                    <span class="logo-title">Primary School Compass</span>
+                    <span class="logo-subtitle">Wandsworth & Merton</span>
+                </div>
+            </div>
+            <nav class="nav">
+                <a href="#" class="nav-link active" data-view="dashboard">Dashboard</a>
+                <a href="#" class="nav-link" data-view="compare">Compare</a>
+                <a href="#" class="nav-link" data-view="rankings">Rankings</a>
+            </nav>
+        </div>
+    </header>
+
+    <main class="main">
+        <!-- Dashboard View -->
+        <section id="dashboard-view" class="view active">
+            <div class="hero">
+                <h1 class="hero-title">Primary Schools in Wandsworth & Merton</h1>
+                <p class="hero-subtitle">Compare KS2 performance data from the last 5 years across local primary schools</p>
+            </div>
+
+            <div class="search-section">
+                <div class="search-container">
+                    <input type="text" id="school-search" class="search-input" placeholder="Search primary schools by name...">
+                    <div class="search-icon">
+                        <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
+                            <circle cx="11" cy="11" r="8"/>
+                            <path d="M21 21l-4.35-4.35"/>
+                        </svg>
+                    </div>
+                </div>
+                <div class="filter-row">
+                    <select id="local-authority-filter" class="filter-select">
+                        <option value="">All Areas</option>
+                        <option value="Wandsworth">Wandsworth</option>
+                        <option value="Merton">Merton</option>
+                    </select>
+                    <select id="type-filter" class="filter-select">
+                        <option value="">All School Types</option>
+                    </select>
+                </div>
+            </div>
+
+            <div class="schools-grid" id="schools-grid">
+                <!-- School cards populated by JS -->
+            </div>
+        </section>
+
+        <!-- Compare View -->
+        <section id="compare-view" class="view">
+            <div class="compare-header">
+                <h2 class="section-title">Compare Primary Schools</h2>
+                <p class="section-subtitle">Select schools to compare their KS2 performance over time</p>
+            </div>
+
+            <div class="selected-schools" id="selected-schools">
+                <div class="empty-selection">
+                    <div class="empty-icon">
+                        <svg viewBox="0 0 48 48" fill="none" stroke="currentColor" stroke-width="1.5">
+                            <rect x="6" y="10" width="36" height="28" rx="2"/>
+                            <path d="M6 18h36"/>
+                            <circle cx="14" cy="14" r="2" fill="currentColor"/>
+                            <circle cx="22" cy="14" r="2" fill="currentColor"/>
+                        </svg>
+                    </div>
+                    <p>Search and add schools to compare</p>
+                </div>
+            </div>
+
+            <div class="compare-search-section">
+                <input type="text" id="compare-search" class="search-input" placeholder="Add a school to compare...">
+                <div id="compare-results" class="compare-results"></div>
+            </div>
+
+            <div class="charts-section" id="charts-section" style="display: none;">
+                <div class="metric-selector">
+                    <label>Select KS2 Metric:</label>
+                    <select id="metric-select" class="filter-select">
+                        <optgroup label="Expected Standard">
+                            <option value="rwm_expected_pct">Reading, Writing & Maths Combined %</option>
+                            <option value="reading_expected_pct">Reading Expected %</option>
+                            <option value="writing_expected_pct">Writing Expected %</option>
+                            <option value="maths_expected_pct">Maths Expected %</option>
+                            <option value="gps_expected_pct">GPS Expected %</option>
+                            <option value="science_expected_pct">Science Expected %</option>
+                        </optgroup>
+                        <optgroup label="Higher Standard">
+                            <option value="rwm_high_pct">RWM Combined Higher %</option>
+                            <option value="reading_high_pct">Reading Higher %</option>
+                            <option value="writing_high_pct">Writing Higher %</option>
+                            <option value="maths_high_pct">Maths Higher %</option>
+                            <option value="gps_high_pct">GPS Higher %</option>
+                        </optgroup>
+                        <optgroup label="Progress Scores">
+                            <option value="reading_progress">Reading Progress</option>
+                            <option value="writing_progress">Writing Progress</option>
+                            <option value="maths_progress">Maths Progress</option>
+                        </optgroup>
+                        <optgroup label="Average Scores">
+                            <option value="reading_avg_score">Reading Avg Score</option>
+                            <option value="maths_avg_score">Maths Avg Score</option>
+                            <option value="gps_avg_score">GPS Avg Score</option>
+                        </optgroup>
+                        <optgroup label="Gender Performance">
+                            <option value="rwm_expected_boys_pct">RWM Expected % (Boys)</option>
+                            <option value="rwm_expected_girls_pct">RWM Expected % (Girls)</option>
+                            <option value="rwm_high_boys_pct">RWM Higher % (Boys)</option>
+                            <option value="rwm_high_girls_pct">RWM Higher % (Girls)</option>
+                        </optgroup>
+                        <optgroup label="Equity (Disadvantaged)">
+                            <option value="rwm_expected_disadvantaged_pct">RWM Expected % (Disadvantaged)</option>
+                            <option value="rwm_expected_non_disadvantaged_pct">RWM Expected % (Non-Disadvantaged)</option>
+                            <option value="disadvantaged_gap">Disadvantaged Gap vs National</option>
+                        </optgroup>
+                        <optgroup label="School Context">
+                            <option value="disadvantaged_pct">% Disadvantaged Pupils</option>
+                            <option value="eal_pct">% EAL Pupils</option>
+                            <option value="sen_support_pct">% SEN Support</option>
+                            <option value="stability_pct">% Pupil Stability</option>
+                        </optgroup>
+                        <optgroup label="3-Year Trends">
+                            <option value="rwm_expected_3yr_pct">RWM Expected % (3-Year Avg)</option>
+                            <option value="reading_avg_3yr">Reading Score (3-Year Avg)</option>
+                            <option value="maths_avg_3yr">Maths Score (3-Year Avg)</option>
+                        </optgroup>
+                    </select>
+                </div>
+
+                <div class="chart-container">
+                    <canvas id="comparison-chart"></canvas>
+                </div>
+
+                <div class="data-table-container">
+                    <table class="data-table" id="comparison-table">
+                        <thead>
+                            <tr id="table-header"></tr>
+                        </thead>
+                        <tbody id="table-body"></tbody>
+                    </table>
+                </div>
+            </div>
+        </section>
+
+        <!-- Rankings View -->
+        <section id="rankings-view" class="view">
+            <div class="rankings-header">
+                <h2 class="section-title">Primary School Rankings</h2>
+                <p class="section-subtitle">Top performing schools in Wandsworth & Merton by KS2 metric</p>
+            </div>
+
+            <div class="rankings-controls">
+                <select id="ranking-metric" class="filter-select">
+                    <optgroup label="Expected Standard">
+                        <option value="rwm_expected_pct">Reading, Writing & Maths Combined %</option>
+                        <option value="reading_expected_pct">Reading Expected %</option>
+                        <option value="writing_expected_pct">Writing Expected %</option>
+                        <option value="maths_expected_pct">Maths Expected %</option>
+                        <option value="gps_expected_pct">GPS Expected %</option>
+                        <option value="science_expected_pct">Science Expected %</option>
+                    </optgroup>
+                    <optgroup label="Higher Standard">
+                        <option value="rwm_high_pct">RWM Combined Higher %</option>
+                        <option value="reading_high_pct">Reading Higher %</option>
+                        <option value="writing_high_pct">Writing Higher %</option>
+                        <option value="maths_high_pct">Maths Higher %</option>
+                        <option value="gps_high_pct">GPS Higher %</option>
+                    </optgroup>
+                    <optgroup label="Progress Scores">
+                        <option value="reading_progress">Reading Progress</option>
+                        <option value="writing_progress">Writing Progress</option>
+                        <option value="maths_progress">Maths Progress</option>
+                    </optgroup>
+                    <optgroup label="Average Scores">
+                        <option value="reading_avg_score">Reading Avg Score</option>
+                        <option value="maths_avg_score">Maths Avg Score</option>
+                        <option value="gps_avg_score">GPS Avg Score</option>
+                    </optgroup>
+                    <optgroup label="Gender Performance">
+                        <option value="rwm_expected_boys_pct">RWM Expected % (Boys)</option>
+                        <option value="rwm_expected_girls_pct">RWM Expected % (Girls)</option>
+                        <option value="rwm_high_boys_pct">RWM Higher % (Boys)</option>
+                        <option value="rwm_high_girls_pct">RWM Higher % (Girls)</option>
+                    </optgroup>
+                    <optgroup label="Equity (Disadvantaged)">
+                        <option value="rwm_expected_disadvantaged_pct">RWM Expected % (Disadvantaged)</option>
+                        <option value="rwm_expected_non_disadvantaged_pct">RWM Expected % (Non-Disadvantaged)</option>
+                    </optgroup>
+                    <optgroup label="3-Year Trends">
+                        <option value="rwm_expected_3yr_pct">RWM Expected % (3-Year Avg)</option>
+                    </optgroup>
+                </select>
+                <select id="ranking-year" class="filter-select">
+                    <!-- Populated by JS -->
+                </select>
+            </div>
+
+            <div class="rankings-list" id="rankings-list">
+                <!-- Rankings populated by JS -->
+            </div>
+        </section>
+    </main>
+
+    <!-- School Detail Modal -->
+    <div class="modal" id="school-modal">
+        <div class="modal-backdrop"></div>
+        <div class="modal-content">
+            <button class="modal-close" id="modal-close">
+                <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
+                    <path d="M18 6L6 18M6 6l12 12"/>
+                </svg>
+            </button>
+            <div class="modal-header">
+                <h2 id="modal-school-name"></h2>
+                <div class="modal-meta" id="modal-meta"></div>
+            </div>
+            <div class="modal-body">
+                <div class="modal-chart-container">
+                    <canvas id="school-detail-chart"></canvas>
+                </div>
+                <div class="modal-stats" id="modal-stats"></div>
+            </div>
+            <div class="modal-footer">
+                <button class="btn btn-primary" id="add-to-compare">Add to Compare</button>
+            </div>
+        </div>
+    </div>
+
+    <footer class="footer">
+        <p>Data source: <a href="https://www.compare-school-performance.service.gov.uk/download-data" target="_blank">UK Government - Compare School Performance</a></p>
+        <p class="footer-note">Primary school (KS2) data for Wandsworth and Merton. Data from 2019-2020, 2020-2021, 2021-2022 unavailable due to COVID-19 disruption.</p>
+    </footer>
+
+    <script src="/static/app.js"></script>
+</body>
+</html>
+

frontend/styles.css

@@ -0,0 +1,931 @@
+/* 
+ * School Performance Compass
+ * A warm, editorial design inspired by quality publications
+ */
+
+:root {
+    /* Warm, sophisticated palette */
+    --bg-primary: #faf7f2;
+    --bg-secondary: #f3ede4;
+    --bg-card: #ffffff;
+    --bg-accent: #1a1612;
+    
+    --text-primary: #1a1612;
+    --text-secondary: #5c564d;
+    --text-muted: #8a847a;
+    --text-inverse: #faf7f2;
+    
+    --accent-coral: #e07256;
+    --accent-coral-dark: #c45a3f;
+    --accent-teal: #2d7d7d;
+    --accent-teal-light: #3a9e9e;
+    --accent-gold: #c9a227;
+    --accent-navy: #2c3e50;
+    
+    /* Chart colors */
+    --chart-1: #e07256;
+    --chart-2: #2d7d7d;
+    --chart-3: #c9a227;
+    --chart-4: #7b68a6;
+    --chart-5: #3498db;
+    
+    --border-color: #e5dfd5;
+    --shadow-soft: 0 2px 8px rgba(26, 22, 18, 0.06);
+    --shadow-medium: 0 4px 20px rgba(26, 22, 18, 0.1);
+    --shadow-strong: 0 8px 40px rgba(26, 22, 18, 0.15);
+    
+    --radius-sm: 4px;
+    --radius-md: 8px;
+    --radius-lg: 16px;
+    --radius-xl: 24px;
+    
+    --transition: 0.2s ease;
+    --transition-slow: 0.4s ease;
+}
+
+* {
+    margin: 0;
+    padding: 0;
+    box-sizing: border-box;
+}
+
+html {
+    scroll-behavior: smooth;
+}
+
+body {
+    font-family: 'DM Sans', -apple-system, BlinkMacSystemFont, sans-serif;
+    background: var(--bg-primary);
+    color: var(--text-primary);
+    line-height: 1.6;
+    min-height: 100vh;
+}
+
+/* Subtle noise texture overlay */
+.noise-overlay {
+    position: fixed;
+    top: 0;
+    left: 0;
+    width: 100%;
+    height: 100%;
+    pointer-events: none;
+    opacity: 0.03;
+    z-index: 1000;
+    background-image: url("data:image/svg+xml,%3Csvg viewBox='0 0 256 256' xmlns='http://www.w3.org/2000/svg'%3E%3Cfilter id='noise'%3E%3CfeTurbulence type='fractalNoise' baseFrequency='0.9' numOctaves='4' stitchTiles='stitch'/%3E%3C/filter%3E%3Crect width='100%25' height='100%25' filter='url(%23noise)'/%3E%3C/svg%3E");
+}
+
+/* Header */
+.header {
+    background: var(--bg-card);
+    border-bottom: 1px solid var(--border-color);
+    position: sticky;
+    top: 0;
+    z-index: 100;
+}
+
+.header-content {
+    max-width: 1400px;
+    margin: 0 auto;
+    padding: 1rem 2rem;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+}
+
+.logo {
+    display: flex;
+    align-items: center;
+    gap: 0.75rem;
+}
+
+.logo-icon {
+    width: 40px;
+    height: 40px;
+    color: var(--accent-coral);
+}
+
+.logo-text {
+    display: flex;
+    flex-direction: column;
+}
+
+.logo-title {
+    font-family: 'Playfair Display', Georgia, serif;
+    font-size: 1.25rem;
+    font-weight: 700;
+    color: var(--text-primary);
+    line-height: 1.2;
+}
+
+.logo-subtitle {
+    font-size: 0.7rem;
+    color: var(--text-muted);
+    text-transform: uppercase;
+    letter-spacing: 0.1em;
+}
+
+.nav {
+    display: flex;
+    gap: 0.5rem;
+}
+
+.nav-link {
+    padding: 0.6rem 1.2rem;
+    text-decoration: none;
+    color: var(--text-secondary);
+    font-weight: 500;
+    font-size: 0.9rem;
+    border-radius: var(--radius-md);
+    transition: var(--transition);
+}
+
+.nav-link:hover {
+    color: var(--text-primary);
+    background: var(--bg-secondary);
+}
+
+.nav-link.active {
+    background: var(--bg-accent);
+    color: var(--text-inverse);
+}
+
+/* Main Content */
+.main {
+    max-width: 1400px;
+    margin: 0 auto;
+    padding: 2rem;
+}
+
+.view {
+    display: none;
+    animation: fadeIn 0.3s ease;
+}
+
+.view.active {
+    display: block;
+}
+
+@keyframes fadeIn {
+    from { opacity: 0; transform: translateY(10px); }
+    to { opacity: 1; transform: translateY(0); }
+}
+
+/* Hero Section */
+.hero {
+    text-align: center;
+    padding: 3rem 0 2rem;
+}
+
+.hero-title {
+    font-family: 'Playfair Display', Georgia, serif;
+    font-size: clamp(2rem, 5vw, 3.5rem);
+    font-weight: 700;
+    color: var(--text-primary);
+    margin-bottom: 0.75rem;
+    line-height: 1.2;
+}
+
+.hero-subtitle {
+    font-size: 1.1rem;
+    color: var(--text-secondary);
+    max-width: 600px;
+    margin: 0 auto;
+}
+
+/* Search Section */
+.search-section {
+    max-width: 700px;
+    margin: 2rem auto 3rem;
+}
+
+.search-container {
+    position: relative;
+    margin-bottom: 1rem;
+}
+
+.search-input {
+    width: 100%;
+    padding: 1rem 1.25rem 1rem 3.5rem;
+    font-size: 1rem;
+    font-family: inherit;
+    border: 2px solid var(--border-color);
+    border-radius: var(--radius-lg);
+    background: var(--bg-card);
+    color: var(--text-primary);
+    transition: var(--transition);
+}
+
+.search-input:focus {
+    outline: none;
+    border-color: var(--accent-coral);
+    box-shadow: 0 0 0 4px rgba(224, 114, 86, 0.1);
+}
+
+.search-input::placeholder {
+    color: var(--text-muted);
+}
+
+.search-icon {
+    position: absolute;
+    left: 1.25rem;
+    top: 50%;
+    transform: translateY(-50%);
+    width: 20px;
+    height: 20px;
+    color: var(--text-muted);
+}
+
+.filter-row {
+    display: flex;
+    gap: 1rem;
+    justify-content: center;
+}
+
+.filter-select {
+    padding: 0.6rem 2rem 0.6rem 1rem;
+    font-size: 0.9rem;
+    font-family: inherit;
+    border: 1px solid var(--border-color);
+    border-radius: var(--radius-md);
+    background: var(--bg-card);
+    color: var(--text-primary);
+    cursor: pointer;
+    appearance: none;
+    background-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='12' height='12' viewBox='0 0 12 12'%3E%3Cpath fill='%235c564d' d='M6 8L1 3h10z'/%3E%3C/svg%3E");
+    background-repeat: no-repeat;
+    background-position: right 0.75rem center;
+    transition: var(--transition);
+}
+
+.filter-select:focus {
+    outline: none;
+    border-color: var(--accent-teal);
+}
+
+/* Schools Grid */
+.schools-grid {
+    display: grid;
+    grid-template-columns: repeat(auto-fill, minmax(320px, 1fr));
+    gap: 1.5rem;
+}
+
+.school-card {
+    background: var(--bg-card);
+    border: 1px solid var(--border-color);
+    border-radius: var(--radius-lg);
+    padding: 1.5rem;
+    cursor: pointer;
+    transition: var(--transition);
+    position: relative;
+    overflow: hidden;
+}
+
+.school-card::before {
+    content: '';
+    position: absolute;
+    top: 0;
+    left: 0;
+    width: 4px;
+    height: 100%;
+    background: var(--accent-coral);
+    transform: scaleY(0);
+    transition: var(--transition);
+}
+
+.school-card:hover {
+    border-color: var(--accent-coral);
+    box-shadow: var(--shadow-medium);
+    transform: translateY(-2px);
+}
+
+.school-card:hover::before {
+    transform: scaleY(1);
+}
+
+.school-name {
+    font-family: 'Playfair Display', Georgia, serif;
+    font-size: 1.15rem;
+    font-weight: 600;
+    color: var(--text-primary);
+    margin-bottom: 0.5rem;
+}
+
+.school-meta {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 0.5rem;
+    margin-bottom: 1rem;
+}
+
+.school-tag {
+    font-size: 0.75rem;
+    padding: 0.25rem 0.6rem;
+    background: var(--bg-secondary);
+    color: var(--text-secondary);
+    border-radius: var(--radius-sm);
+}
+
+.school-tag.type {
+    background: rgba(45, 125, 125, 0.1);
+    color: var(--accent-teal);
+}
+
+.school-address {
+    font-size: 0.85rem;
+    color: var(--text-muted);
+    margin-bottom: 1rem;
+}
+
+.school-stats {
+    display: grid;
+    grid-template-columns: repeat(2, 1fr);
+    gap: 0.75rem;
+    padding-top: 1rem;
+    border-top: 1px solid var(--border-color);
+}
+
+.stat {
+    text-align: center;
+}
+
+.stat-value {
+    font-size: 1.25rem;
+    font-weight: 700;
+    color: var(--text-primary);
+}
+
+.stat-value.positive {
+    color: var(--accent-teal);
+}
+
+.stat-value.negative {
+    color: var(--accent-coral);
+}
+
+.stat-label {
+    font-size: 0.7rem;
+    color: var(--text-muted);
+    text-transform: uppercase;
+    letter-spacing: 0.05em;
+}
+
+/* Section Titles */
+.section-title {
+    font-family: 'Playfair Display', Georgia, serif;
+    font-size: 2rem;
+    font-weight: 700;
+    color: var(--text-primary);
+    margin-bottom: 0.5rem;
+}
+
+.section-subtitle {
+    font-size: 1rem;
+    color: var(--text-secondary);
+    margin-bottom: 2rem;
+}
+
+/* Compare View */
+.compare-header {
+    text-align: center;
+    padding: 2rem 0;
+}
+
+.selected-schools {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 1rem;
+    margin-bottom: 2rem;
+    min-height: 100px;
+    padding: 1.5rem;
+    background: var(--bg-secondary);
+    border-radius: var(--radius-lg);
+    justify-content: center;
+    align-items: center;
+}
+
+.empty-selection {
+    text-align: center;
+    color: var(--text-muted);
+}
+
+.empty-icon {
+    width: 48px;
+    height: 48px;
+    margin: 0 auto 0.5rem;
+    color: var(--text-muted);
+}
+
+.selected-school-tag {
+    display: flex;
+    align-items: center;
+    gap: 0.5rem;
+    padding: 0.6rem 1rem;
+    background: var(--bg-card);
+    border: 1px solid var(--border-color);
+    border-radius: var(--radius-md);
+    font-size: 0.9rem;
+    color: var(--text-primary);
+    animation: slideIn 0.2s ease;
+}
+
+@keyframes slideIn {
+    from { opacity: 0; transform: scale(0.9); }
+    to { opacity: 1; transform: scale(1); }
+}
+
+.selected-school-tag .remove {
+    width: 18px;
+    height: 18px;
+    border: none;
+    background: var(--bg-secondary);
+    border-radius: 50%;
+    cursor: pointer;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    transition: var(--transition);
+}
+
+.selected-school-tag .remove:hover {
+    background: var(--accent-coral);
+    color: white;
+}
+
+.compare-search-section {
+    max-width: 500px;
+    margin: 0 auto 2rem;
+    position: relative;
+}
+
+.compare-results {
+    position: absolute;
+    top: 100%;
+    left: 0;
+    right: 0;
+    background: var(--bg-card);
+    border: 1px solid var(--border-color);
+    border-radius: var(--radius-md);
+    box-shadow: var(--shadow-medium);
+    max-height: 300px;
+    overflow-y: auto;
+    z-index: 50;
+    display: none;
+}
+
+.compare-results.active {
+    display: block;
+}
+
+.compare-result-item {
+    padding: 0.75rem 1rem;
+    cursor: pointer;
+    border-bottom: 1px solid var(--border-color);
+    transition: var(--transition);
+}
+
+.compare-result-item:last-child {
+    border-bottom: none;
+}
+
+.compare-result-item:hover {
+    background: var(--bg-secondary);
+}
+
+.compare-result-item .name {
+    font-weight: 500;
+    color: var(--text-primary);
+}
+
+.compare-result-item .location {
+    font-size: 0.8rem;
+    color: var(--text-muted);
+}
+
+/* Charts Section */
+.charts-section {
+    margin-top: 2rem;
+}
+
+.metric-selector {
+    display: flex;
+    align-items: center;
+    gap: 1rem;
+    margin-bottom: 1.5rem;
+}
+
+.metric-selector label {
+    font-weight: 500;
+    color: var(--text-secondary);
+}
+
+.chart-container {
+    background: var(--bg-card);
+    border: 1px solid var(--border-color);
+    border-radius: var(--radius-lg);
+    padding: 2rem;
+    margin-bottom: 2rem;
+}
+
+/* Data Table */
+.data-table-container {
+    overflow-x: auto;
+    background: var(--bg-card);
+    border: 1px solid var(--border-color);
+    border-radius: var(--radius-lg);
+}
+
+.data-table {
+    width: 100%;
+    border-collapse: collapse;
+}
+
+.data-table th,
+.data-table td {
+    padding: 1rem;
+    text-align: left;
+    border-bottom: 1px solid var(--border-color);
+}
+
+.data-table th {
+    background: var(--bg-secondary);
+    font-weight: 600;
+    font-size: 0.85rem;
+    text-transform: uppercase;
+    letter-spacing: 0.05em;
+    color: var(--text-secondary);
+}
+
+.data-table tr:last-child td {
+    border-bottom: none;
+}
+
+.data-table tr:hover td {
+    background: rgba(224, 114, 86, 0.03);
+}
+
+/* Rankings View */
+.rankings-header {
+    text-align: center;
+    padding: 2rem 0;
+}
+
+.rankings-controls {
+    display: flex;
+    justify-content: center;
+    gap: 1rem;
+    margin-bottom: 2rem;
+}
+
+.rankings-list {
+    max-width: 800px;
+    margin: 0 auto;
+}
+
+.ranking-item {
+    display: flex;
+    align-items: center;
+    gap: 1.5rem;
+    padding: 1.25rem 1.5rem;
+    background: var(--bg-card);
+    border: 1px solid var(--border-color);
+    border-radius: var(--radius-md);
+    margin-bottom: 0.75rem;
+    transition: var(--transition);
+    cursor: pointer;
+}
+
+.ranking-item:hover {
+    border-color: var(--accent-coral);
+    box-shadow: var(--shadow-soft);
+}
+
+.ranking-position {
+    width: 40px;
+    height: 40px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    border-radius: 50%;
+    font-weight: 700;
+    font-size: 1rem;
+}
+
+.ranking-position.top-3 {
+    background: linear-gradient(135deg, var(--accent-gold), #d4af37);
+    color: white;
+}
+
+.ranking-position:not(.top-3) {
+    background: var(--bg-secondary);
+    color: var(--text-secondary);
+}
+
+.ranking-info {
+    flex: 1;
+}
+
+.ranking-name {
+    font-family: 'Playfair Display', Georgia, serif;
+    font-weight: 600;
+    color: var(--text-primary);
+    margin-bottom: 0.25rem;
+}
+
+.ranking-location {
+    font-size: 0.85rem;
+    color: var(--text-muted);
+}
+
+.ranking-score {
+    text-align: right;
+}
+
+.ranking-score-value {
+    font-size: 1.5rem;
+    font-weight: 700;
+    color: var(--accent-teal);
+}
+
+.ranking-score-label {
+    font-size: 0.7rem;
+    color: var(--text-muted);
+    text-transform: uppercase;
+}
+
+/* Modal */
+.modal {
+    position: fixed;
+    top: 0;
+    left: 0;
+    width: 100%;
+    height: 100%;
+    z-index: 200;
+    display: none;
+    align-items: center;
+    justify-content: center;
+    padding: 2rem;
+}
+
+.modal.active {
+    display: flex;
+}
+
+.modal-backdrop {
+    position: absolute;
+    top: 0;
+    left: 0;
+    width: 100%;
+    height: 100%;
+    background: rgba(26, 22, 18, 0.6);
+    backdrop-filter: blur(4px);
+}
+
+.modal-content {
+    position: relative;
+    background: var(--bg-card);
+    border-radius: var(--radius-xl);
+    max-width: 800px;
+    width: 100%;
+    max-height: 90vh;
+    overflow-y: auto;
+    box-shadow: var(--shadow-strong);
+    animation: modalIn 0.3s ease;
+}
+
+@keyframes modalIn {
+    from { opacity: 0; transform: scale(0.95) translateY(20px); }
+    to { opacity: 1; transform: scale(1) translateY(0); }
+}
+
+.modal-close {
+    position: absolute;
+    top: 1rem;
+    right: 1rem;
+    width: 40px;
+    height: 40px;
+    border: none;
+    background: var(--bg-secondary);
+    border-radius: 50%;
+    cursor: pointer;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    transition: var(--transition);
+    z-index: 10;
+}
+
+.modal-close:hover {
+    background: var(--accent-coral);
+    color: white;
+}
+
+.modal-close svg {
+    width: 20px;
+    height: 20px;
+}
+
+.modal-header {
+    padding: 2rem 2rem 1rem;
+    border-bottom: 1px solid var(--border-color);
+}
+
+.modal-header h2 {
+    font-family: 'Playfair Display', Georgia, serif;
+    font-size: 1.75rem;
+    font-weight: 700;
+    color: var(--text-primary);
+    margin-bottom: 0.5rem;
+    padding-right: 3rem;
+}
+
+.modal-meta {
+    display: flex;
+    gap: 0.75rem;
+    flex-wrap: wrap;
+}
+
+.modal-body {
+    padding: 2rem;
+}
+
+.modal-chart-container {
+    margin-bottom: 2rem;
+}
+
+.modal-stats {
+    display: flex;
+    flex-direction: column;
+    gap: 1.5rem;
+}
+
+.modal-stats-section {
+    padding: 1rem;
+    background: var(--bg-secondary);
+    border-radius: var(--radius-md);
+}
+
+.modal-stats-section h4 {
+    margin: 0 0 0.75rem 0;
+    font-size: 0.85rem;
+    font-weight: 600;
+    color: var(--text-muted);
+    text-transform: uppercase;
+    letter-spacing: 0.5px;
+}
+
+.modal-stats-grid {
+    display: grid;
+    grid-template-columns: repeat(auto-fit, minmax(80px, 1fr));
+    gap: 0.75rem;
+}
+
+.modal-stat {
+    text-align: center;
+    padding: 1rem;
+    background: var(--bg-secondary);
+    border-radius: var(--radius-md);
+}
+
+.modal-stat-value {
+    font-size: 1.5rem;
+    font-weight: 700;
+    color: var(--text-primary);
+}
+
+.modal-stat-label {
+    font-size: 0.75rem;
+    color: var(--text-muted);
+    text-transform: uppercase;
+    margin-top: 0.25rem;
+}
+
+.modal-footer {
+    padding: 1.5rem 2rem;
+    border-top: 1px solid var(--border-color);
+    display: flex;
+    justify-content: flex-end;
+}
+
+/* Buttons */
+.btn {
+    padding: 0.75rem 1.5rem;
+    font-size: 0.9rem;
+    font-family: inherit;
+    font-weight: 600;
+    border: none;
+    border-radius: var(--radius-md);
+    cursor: pointer;
+    transition: var(--transition);
+}
+
+.btn-primary {
+    background: var(--accent-coral);
+    color: white;
+}
+
+.btn-primary:hover {
+    background: var(--accent-coral-dark);
+    transform: translateY(-1px);
+}
+
+/* Footer */
+.footer {
+    text-align: center;
+    padding: 2rem;
+    margin-top: 3rem;
+    border-top: 1px solid var(--border-color);
+    color: var(--text-muted);
+    font-size: 0.85rem;
+}
+
+.footer a {
+    color: var(--accent-teal);
+    text-decoration: none;
+}
+
+.footer a:hover {
+    text-decoration: underline;
+}
+
+.footer-note {
+    margin-top: 0.5rem;
+    font-size: 0.75rem;
+}
+
+/* Loading State */
+.loading {
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    justify-content: center;
+    padding: 3rem;
+    color: var(--text-muted);
+}
+
+.loading-spinner {
+    width: 40px;
+    height: 40px;
+    border: 3px solid var(--border-color);
+    border-top-color: var(--accent-coral);
+    border-radius: 50%;
+    animation: spin 0.8s linear infinite;
+    margin-bottom: 1rem;
+}
+
+@keyframes spin {
+    to { transform: rotate(360deg); }
+}
+
+/* Empty State */
+.empty-state {
+    text-align: center;
+    padding: 4rem 2rem;
+    color: var(--text-muted);
+}
+
+.empty-state svg {
+    width: 64px;
+    height: 64px;
+    margin-bottom: 1rem;
+    opacity: 0.5;
+}
+
+/* Responsive */
+@media (max-width: 768px) {
+    .header-content {
+        flex-direction: column;
+        gap: 1rem;
+    }
+    
+    .nav {
+        width: 100%;
+        justify-content: center;
+    }
+    
+    .main {
+        padding: 1rem;
+    }
+    
+    .hero-title {
+        font-size: 1.75rem;
+    }
+    
+    .filter-row {
+        flex-direction: column;
+    }
+    
+    .schools-grid {
+        grid-template-columns: 1fr;
+    }
+    
+    .modal-content {
+        margin: 1rem;
+        max-height: calc(100vh - 2rem);
+    }
+    
+    .rankings-controls {
+        flex-direction: column;
+        align-items: stretch;
+    }
+}
+

requirements.txt

@@ -0,0 +1,6 @@
+fastapi==0.109.0
+uvicorn[standard]==0.27.0
+pandas==2.1.4
+python-multipart==0.0.6
+aiofiles==23.2.1
+

scripts/download_data.py

@@ -0,0 +1,181 @@
+#!/usr/bin/env python3
+"""
+Data Download Helper Script
+
+This script provides instructions and utilities for downloading
+UK school performance data from the official government source.
+
+Data Source: https://www.compare-school-performance.service.gov.uk/download-data
+
+Note: The actual CSV downloads require manual selection on the website
+as they use dynamic form submissions. This script helps prepare and
+organize the downloaded data.
+"""
+
+import os
+import sys
+from pathlib import Path
+import pandas as pd
+
+DATA_DIR = Path(__file__).parent.parent / "data"
+
+
+def print_instructions():
+    """Print instructions for downloading the data."""
+    print("""
+╔══════════════════════════════════════════════════════════════════════════════╗
+║           UK School Performance Data Download Instructions                    ║
+╠══════════════════════════════════════════════════════════════════════════════╣
+║                                                                              ║
+║  1. Visit: https://www.compare-school-performance.service.gov.uk/download-data║
+║                                                                              ║
+║  2. For each year (2019-2020 through 2023-2024), select:                    ║
+║     • Year: Select the academic year                                         ║
+║     • Data type: "Key Stage 4" (for secondary school GCSE data)             ║
+║     • File type: "All data" or specific metrics you need                    ║
+║                                                                              ║
+║  3. Key metrics available:                                                   ║
+║     • Progress 8 - measures pupil progress from KS2 to KS4                  ║
+║     • Attainment 8 - average attainment across 8 qualifications             ║
+║     • English & Maths Grade 5+ percentage                                   ║
+║     • EBacc entry and achievement percentages                               ║
+║                                                                              ║
+║  4. Download the CSV files and place them in the 'data' folder              ║
+║                                                                              ║
+║  5. Rename files with the year for clarity, e.g.:                           ║
+║     • ks4_2020.csv                                                          ║
+║     • ks4_2021.csv                                                          ║
+║     • ks4_2022.csv                                                          ║
+║     • ks4_2023.csv                                                          ║
+║     • ks4_2024.csv                                                          ║
+║                                                                              ║
+╚══════════════════════════════════════════════════════════════════════════════╝
+""")
+
+
+def check_data_files():
+    """Check what data files are present in the data directory."""
+    if not DATA_DIR.exists():
+        print(f"Data directory not found: {DATA_DIR}")
+        return []
+    
+    csv_files = list(DATA_DIR.glob("*.csv"))
+    
+    if not csv_files:
+        print("No CSV files found in the data directory.")
+        print(f"Please place your downloaded CSV files in: {DATA_DIR}")
+        return []
+    
+    print(f"\nFound {len(csv_files)} CSV file(s):")
+    for f in csv_files:
+        size_mb = f.stat().st_size / (1024 * 1024)
+        print(f"  • {f.name} ({size_mb:.2f} MB)")
+    
+    return csv_files
+
+
+def preview_data(file_path: Path, rows: int = 5):
+    """Preview a CSV file."""
+    try:
+        df = pd.read_csv(file_path, nrows=rows)
+        print(f"\n--- Preview of {file_path.name} ---")
+        print(f"Columns ({len(df.columns)}):")
+        for col in df.columns[:20]:
+            print(f"  • {col}")
+        if len(df.columns) > 20:
+            print(f"  ... and {len(df.columns) - 20} more columns")
+        print(f"\nFirst {rows} rows:")
+        print(df.to_string())
+    except Exception as e:
+        print(f"Error reading {file_path}: {e}")
+
+
+def standardize_columns(df: pd.DataFrame) -> pd.DataFrame:
+    """Standardize column names for consistency."""
+    # Common column mappings from the official data
+    column_mappings = {
+        'URN': 'urn',
+        'SCHNAME': 'school_name',
+        'TOWN': 'town',
+        'REGION': 'region',
+        'RELDENOM': 'school_type',
+        'P8MEA': 'progress_8',
+        'ATT8SCR': 'attainment_8',
+        'PTAC5EM': 'grade_5_eng_maths_pct',
+        'PTEBACCEG': 'ebacc_entry_pct',
+        'TPUP': 'pupils',
+    }
+    
+    # Normalize column names
+    df.columns = df.columns.str.strip().str.upper()
+    
+    # Apply mappings
+    df = df.rename(columns={k.upper(): v for k, v in column_mappings.items()})
+    
+    return df
+
+
+def process_and_combine_data():
+    """Process and combine all CSV files into a single dataset."""
+    csv_files = check_data_files()
+    if not csv_files:
+        return None
+    
+    all_data = []
+    
+    for csv_file in csv_files:
+        print(f"\nProcessing: {csv_file.name}")
+        try:
+            df = pd.read_csv(csv_file, low_memory=False)
+            df = standardize_columns(df)
+            
+            # Try to extract year from filename
+            import re
+            year_match = re.search(r'20\d{2}', csv_file.stem)
+            if year_match:
+                df['year'] = int(year_match.group())
+            
+            all_data.append(df)
+            print(f"  Loaded {len(df)} rows")
+        except Exception as e:
+            print(f"  Error: {e}")
+    
+    if all_data:
+        combined = pd.concat(all_data, ignore_index=True)
+        output_path = DATA_DIR / "combined_data.csv"
+        combined.to_csv(output_path, index=False)
+        print(f"\nCombined data saved to: {output_path}")
+        print(f"Total rows: {len(combined)}")
+        return combined
+    
+    return None
+
+
+def main():
+    """Main entry point."""
+    if len(sys.argv) > 1:
+        command = sys.argv[1].lower()
+        
+        if command == "check":
+            check_data_files()
+        elif command == "preview" and len(sys.argv) > 2:
+            file_path = DATA_DIR / sys.argv[2]
+            if file_path.exists():
+                preview_data(file_path)
+            else:
+                print(f"File not found: {file_path}")
+        elif command == "combine":
+            process_and_combine_data()
+        else:
+            print_instructions()
+    else:
+        print_instructions()
+        print("\nAvailable commands:")
+        print("  python download_data.py check    - Check for existing data files")
+        print("  python download_data.py preview <filename> - Preview a CSV file")
+        print("  python download_data.py combine  - Combine all CSV files")
+
+
+if __name__ == "__main__":
+    main()
+

scripts/fetch_real_data.py

@@ -0,0 +1,253 @@
+#!/usr/bin/env python3
+"""
+Fetch real school performance data from UK Government sources.
+
+This script downloads KS2 (Key Stage 2) primary school data from:
+- Compare School Performance service
+- Get Information about Schools (GIAS)
+
+Data is filtered to only include schools in Wandsworth and Merton.
+"""
+
+import os
+import sys
+import requests
+import pandas as pd
+from pathlib import Path
+from io import StringIO
+
+# Output directory
+DATA_DIR = Path(__file__).parent.parent / "data"
+
+# Local Authority codes for Wandsworth and Merton
+LA_CODES = {
+    "Wandsworth": "212",
+    "Merton": "315"
+}
+
+# Academic years to fetch (last 5 years available)
+YEARS = ["2023-2024", "2022-2023", "2021-2022", "2019-2020", "2018-2019"]
+# Note: 2020-2021 had no SATs due to COVID
+
+
+def fetch_gias_data():
+    """
+    Fetch school establishment data from Get Information About Schools.
+    This gives us the list of schools with URN, name, address, type, etc.
+    """
+    print("Fetching school establishment data from GIAS...")
+    
+    # GIAS provides downloadable extracts
+    # Main extract URL (this may need to be updated periodically)
+    gias_url = "https://ea-edubase-api-prod.azurewebsites.net/edubase/downloads/public/edubasealldata.csv"
+    
+    try:
+        response = requests.get(gias_url, timeout=60)
+        response.raise_for_status()
+        
+        # Parse CSV
+        df = pd.read_csv(StringIO(response.text), encoding='utf-8-sig', low_memory=False)
+        
+        # Filter to primary schools in Wandsworth and Merton
+        # Phase of education: Primary, Middle deemed primary
+        # LA codes: 212 (Wandsworth), 315 (Merton)
+        df = df[
+            (df['LA (code)'].astype(str).isin(LA_CODES.values())) &
+            (df['PhaseOfEducation (name)'].str.contains('Primary', na=False))
+        ]
+        
+        # Select relevant columns
+        columns_to_keep = [
+            'URN', 'EstablishmentName', 'LA (name)', 'TypeOfEstablishment (name)',
+            'Street', 'Locality', 'Town', 'Postcode',
+            'SchoolCapacity', 'NumberOfPupils', 'OfstedRating (name)'
+        ]
+        available_cols = [c for c in columns_to_keep if c in df.columns]
+        df = df[available_cols]
+        
+        # Rename columns
+        df = df.rename(columns={
+            'URN': 'urn',
+            'EstablishmentName': 'school_name',
+            'LA (name)': 'local_authority',
+            'TypeOfEstablishment (name)': 'school_type',
+            'Street': 'street',
+            'Town': 'town',
+            'Postcode': 'postcode',
+            'NumberOfPupils': 'pupils',
+            'OfstedRating (name)': 'ofsted_rating'
+        })
+        
+        # Create address field
+        df['address'] = df.apply(
+            lambda row: f"{row.get('street', '')}, {row.get('postcode', '')}".strip(', '),
+            axis=1
+        )
+        
+        print(f"Found {len(df)} primary schools in Wandsworth and Merton")
+        return df
+        
+    except Exception as e:
+        print(f"Error fetching GIAS data: {e}")
+        return None
+
+
+def fetch_ks2_performance_data():
+    """
+    Fetch KS2 performance data from Compare School Performance.
+    
+    Note: The official download page requires form submission.
+    We'll try to access the underlying data files directly.
+    """
+    print("\nFetching KS2 performance data...")
+    
+    # The performance data is available at gov.uk statistics pages
+    # KS2 data URLs follow a pattern
+    base_urls = {
+        "2023-2024": "https://content.explore-education-statistics.service.gov.uk/api/releases/",
+        "2022-2023": "https://content.explore-education-statistics.service.gov.uk/api/releases/",
+    }
+    
+    # Alternative: Direct download links from gov.uk (when available)
+    # These URLs may need to be updated when new data is released
+    data_urls = {
+        # 2024 KS2 results (provisional)
+        "2024": "https://content.explore-education-statistics.service.gov.uk/api/releases/b4cb82e3-6dca-4c98-a3b0-ba7d1d3ef555/files",
+    }
+    
+    print("Note: For the most accurate data, please download manually from:")
+    print("https://www.compare-school-performance.service.gov.uk/download-data")
+    print("\nSteps:")
+    print("1. Select 'Key Stage 2' for Data type")
+    print("2. Select 'All data' for File type")
+    print("3. Select desired academic year")
+    print("4. Download and place CSV files in the 'data' folder")
+    
+    return None
+
+
+def download_from_explore_education_statistics():
+    """
+    Try to fetch data from the Explore Education Statistics API.
+    API docs: https://dfe-analytical-services.github.io/explore-education-statistics-api-docs/
+    """
+    print("\nAttempting to fetch from Explore Education Statistics API...")
+    
+    api_base = "https://explore-education-statistics.service.gov.uk/api/v1"
+    
+    # First, list available publications
+    try:
+        # Get KS2 publication
+        publications_url = f"{api_base}/publications"
+        response = requests.get(publications_url, timeout=30)
+        
+        if response.status_code == 200:
+            publications = response.json()
+            
+            # Find KS2 related publication
+            ks2_pubs = [p for p in publications.get('results', []) 
+                       if 'key stage 2' in p.get('title', '').lower() 
+                       or 'ks2' in p.get('title', '').lower()]
+            
+            if ks2_pubs:
+                print(f"Found KS2 publications: {[p['title'] for p in ks2_pubs]}")
+                
+                # Get the latest release
+                for pub in ks2_pubs:
+                    pub_id = pub.get('id')
+                    if pub_id:
+                        release_url = f"{api_base}/publications/{pub_id}/releases/latest"
+                        release_response = requests.get(release_url, timeout=30)
+                        
+                        if release_response.status_code == 200:
+                            release = release_response.json()
+                            print(f"Latest release: {release.get('title')}")
+                            
+                            # Get data files
+                            data_sets = release.get('dataSets', [])
+                            for ds in data_sets:
+                                print(f"  - Dataset: {ds.get('name')}")
+            else:
+                print("No KS2 publications found via API")
+        else:
+            print(f"API returned status {response.status_code}")
+            
+    except Exception as e:
+        print(f"Error accessing API: {e}")
+    
+    return None
+
+
+def create_combined_dataset(schools_df, performance_data=None):
+    """
+    Combine school information with performance data.
+    If no performance data is available, returns school info only.
+    """
+    if schools_df is None:
+        return None
+    
+    # Add year column for compatibility
+    schools_df['year'] = 2024
+    
+    # Add placeholder performance columns if no real data
+    if performance_data is None:
+        print("\nNo performance data available - school list saved without metrics")
+        print("Download KS2 data manually and re-run to add performance metrics")
+    
+    return schools_df
+
+
+def main():
+    """Main entry point."""
+    print("=" * 60)
+    print("Fetching Real School Data for Wandsworth & Merton")
+    print("=" * 60)
+    
+    # Create data directory
+    DATA_DIR.mkdir(exist_ok=True)
+    
+    # Fetch school establishment data
+    schools_df = fetch_gias_data()
+    
+    # Try to fetch performance data
+    fetch_ks2_performance_data()
+    download_from_explore_education_statistics()
+    
+    # Save school data
+    if schools_df is not None:
+        output_file = DATA_DIR / "schools_wandsworth_merton.csv"
+        schools_df.to_csv(output_file, index=False)
+        print(f"\nSchool data saved to: {output_file}")
+        print(f"Total schools: {len(schools_df)}")
+        
+        # Show breakdown
+        print("\nBreakdown by Local Authority:")
+        print(schools_df['local_authority'].value_counts())
+    else:
+        print("\nFailed to fetch school data")
+    
+    print("\n" + "=" * 60)
+    print("NEXT STEPS:")
+    print("=" * 60)
+    print("""
+To get complete performance data:
+
+1. Go to: https://www.compare-school-performance.service.gov.uk/download-data
+
+2. Download KS2 data for each year (2019-2024):
+   - Select: Key Stage 2
+   - Select: All data (or specific metrics)
+   - Select: Academic year
+   - Click: Download data
+
+3. Place downloaded CSV files in the 'data' folder
+
+4. Restart the application - it will automatically load the real data
+
+The app will merge school info with performance metrics.
+""")
+
+
+if __name__ == "__main__":
+    main()
+