fixing data load
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Tudor Sitaru
130
backend/app.py
130
backend/app.py
@@ -5,20 +5,25 @@ Uses real data from UK Government Compare School Performance downloads.
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"""
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from contextlib import asynccontextmanager
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import pandas as pd
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from fastapi import FastAPI, HTTPException, Query
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from fastapi.staticfiles import StaticFiles
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from fastapi.responses import FileResponse
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from fastapi.middleware.cors import CORSMiddleware
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from typing import Optional
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import pandas as pd
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from fastapi import FastAPI, HTTPException, Query
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import FileResponse
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from fastapi.staticfiles import StaticFiles
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from .config import settings
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from .schemas import METRIC_DEFINITIONS, RANKING_COLUMNS, SCHOOL_COLUMNS
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from .data_loader import (
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load_school_data, clear_cache, geocode_single_postcode,
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geocode_postcodes_bulk, haversine_distance, get_data_info as get_db_info
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clear_cache,
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geocode_postcodes_bulk,
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geocode_single_postcode,
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haversine_distance,
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load_school_data,
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)
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from .data_loader import get_data_info as get_db_info
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from .database import init_db
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from .schemas import METRIC_DEFINITIONS, RANKING_COLUMNS, SCHOOL_COLUMNS
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from .utils import clean_for_json
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@@ -80,7 +85,9 @@ async def serve_rankings():
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@app.get("/api/schools")
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async def get_schools(
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search: Optional[str] = Query(None, description="Search by school name"),
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local_authority: Optional[str] = Query(None, description="Filter by local authority"),
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local_authority: Optional[str] = Query(
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None, description="Filter by local authority"
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),
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school_type: Optional[str] = Query(None, description="Filter by school type"),
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postcode: Optional[str] = Query(None, description="Search near postcode"),
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radius: float = Query(5.0, ge=0.1, le=50, description="Search radius in miles"),
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@@ -103,13 +110,25 @@ async def get_schools(
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page_size = settings.default_page_size
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# Get unique schools (latest year data for each)
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latest_year = df.groupby('urn')['year'].max().reset_index()
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df_latest = df.merge(latest_year, on=['urn', 'year'])
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latest_year = df.groupby("urn")["year"].max().reset_index()
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df_latest = df.merge(latest_year, on=["urn", "year"])
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# Include lat/long in columns for location search
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location_cols = ['latitude', 'longitude']
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available_cols = [c for c in SCHOOL_COLUMNS + location_cols if c in df_latest.columns]
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schools_df = df_latest[available_cols].drop_duplicates(subset=['urn'])
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# Include key result metrics for display on cards
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location_cols = ["latitude", "longitude"]
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result_cols = [
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"year",
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"rwm_expected_pct",
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"reading_expected_pct",
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"writing_expected_pct",
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"maths_expected_pct",
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"total_pupils",
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]
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available_cols = [
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c
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for c in SCHOOL_COLUMNS + location_cols + result_cols
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if c in df_latest.columns
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]
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schools_df = df_latest[available_cols].drop_duplicates(subset=["urn"])
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# Location-based search
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search_coords = None
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@@ -120,44 +139,58 @@ async def get_schools(
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schools_df = schools_df.copy()
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# Geocode school postcodes on-demand if not already cached
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if 'postcode' in schools_df.columns:
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unique_postcodes = schools_df['postcode'].dropna().unique().tolist()
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if "postcode" in schools_df.columns:
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unique_postcodes = schools_df["postcode"].dropna().unique().tolist()
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geocoded = geocode_postcodes_bulk(unique_postcodes)
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# Add lat/long from geocoded data
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schools_df['latitude'] = schools_df['postcode'].apply(
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lambda pc: geocoded.get(str(pc).strip().upper(), (None, None))[0] if pd.notna(pc) else None
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schools_df["latitude"] = schools_df["postcode"].apply(
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lambda pc: geocoded.get(str(pc).strip().upper(), (None, None))[0]
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if pd.notna(pc)
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else None
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)
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schools_df['longitude'] = schools_df['postcode'].apply(
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lambda pc: geocoded.get(str(pc).strip().upper(), (None, None))[1] if pd.notna(pc) else None
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schools_df["longitude"] = schools_df["postcode"].apply(
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lambda pc: geocoded.get(str(pc).strip().upper(), (None, None))[1]
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if pd.notna(pc)
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else None
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)
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# Filter by distance
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def calc_distance(row):
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if pd.isna(row.get('latitude')) or pd.isna(row.get('longitude')):
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return float('inf')
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if pd.isna(row.get("latitude")) or pd.isna(row.get("longitude")):
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return float("inf")
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return haversine_distance(
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search_coords[0], search_coords[1],
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row['latitude'], row['longitude']
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search_coords[0],
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search_coords[1],
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row["latitude"],
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row["longitude"],
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)
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schools_df['distance'] = schools_df.apply(calc_distance, axis=1)
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schools_df = schools_df[schools_df['distance'] <= radius]
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schools_df = schools_df.sort_values('distance')
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schools_df["distance"] = schools_df.apply(calc_distance, axis=1)
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schools_df = schools_df[schools_df["distance"] <= radius]
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schools_df = schools_df.sort_values("distance")
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# Apply filters
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if search:
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search_lower = search.lower()
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mask = schools_df["school_name"].str.lower().str.contains(search_lower, na=False)
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mask = (
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schools_df["school_name"].str.lower().str.contains(search_lower, na=False)
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)
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if "address" in schools_df.columns:
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mask = mask | schools_df["address"].str.lower().str.contains(search_lower, na=False)
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mask = mask | schools_df["address"].str.lower().str.contains(
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search_lower, na=False
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)
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schools_df = schools_df[mask]
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if local_authority:
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schools_df = schools_df[schools_df["local_authority"].str.lower() == local_authority.lower()]
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schools_df = schools_df[
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schools_df["local_authority"].str.lower() == local_authority.lower()
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]
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if school_type:
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schools_df = schools_df[schools_df["school_type"].str.lower() == school_type.lower()]
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schools_df = schools_df[
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schools_df["school_type"].str.lower() == school_type.lower()
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]
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# Pagination
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total = len(schools_df)
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@@ -166,9 +199,9 @@ async def get_schools(
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schools_df = schools_df.iloc[start_idx:end_idx]
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# Remove internal columns before sending
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output_cols = [c for c in schools_df.columns if c not in ['latitude', 'longitude']]
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if 'distance' in schools_df.columns:
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output_cols.append('distance')
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output_cols = [c for c in schools_df.columns if c not in ["latitude", "longitude"]]
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if "distance" in schools_df.columns:
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output_cols.append("distance")
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return {
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"schools": clean_for_json(schools_df[output_cols]),
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@@ -176,7 +209,9 @@ async def get_schools(
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"page": page,
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"page_size": page_size,
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"total_pages": (total + page_size - 1) // page_size if page_size > 0 else 0,
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"search_location": {"postcode": postcode, "radius": radius} if search_coords else None,
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"search_location": {"postcode": postcode, "radius": radius}
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if search_coords
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else None,
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}
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@@ -208,7 +243,7 @@ async def get_school_details(urn: int):
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"address": latest.get("address", ""),
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"phase": "Primary",
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},
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"yearly_data": clean_for_json(school_data)
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"yearly_data": clean_for_json(school_data),
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}
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@@ -242,7 +277,7 @@ async def compare_schools(urns: str = Query(..., description="Comma-separated UR
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"local_authority": latest.get("local_authority", ""),
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"address": latest.get("address", ""),
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},
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"yearly_data": clean_for_json(school_data)
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"yearly_data": clean_for_json(school_data),
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}
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return {"comparison": result}
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@@ -288,9 +323,13 @@ async def get_available_metrics():
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@app.get("/api/rankings")
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async def get_rankings(
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metric: str = Query("rwm_expected_pct", description="KS2 metric to rank by"),
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year: Optional[int] = Query(None, description="Specific year (defaults to most recent)"),
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year: Optional[int] = Query(
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None, description="Specific year (defaults to most recent)"
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),
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limit: int = Query(20, ge=1, le=100, description="Number of schools to return"),
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local_authority: Optional[str] = Query(None, description="Filter by local authority"),
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local_authority: Optional[str] = Query(
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None, description="Filter by local authority"
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),
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):
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"""Get primary school rankings by a specific KS2 metric."""
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df = load_school_data()
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@@ -356,8 +395,14 @@ async def get_data_info():
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}
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years = [int(y) for y in sorted(df["year"].unique())]
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schools_per_year = {str(int(k)): int(v) for k, v in df.groupby("year")["urn"].nunique().to_dict().items()}
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la_counts = {str(k): int(v) for k, v in df["local_authority"].value_counts().to_dict().items()}
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schools_per_year = {
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str(int(k)): int(v)
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for k, v in df.groupby("year")["urn"].nunique().to_dict().items()
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}
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la_counts = {
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str(k): int(v)
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for k, v in df["local_authority"].value_counts().to_dict().items()
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}
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return {
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"status": "loaded",
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@@ -385,4 +430,5 @@ if settings.frontend_dir.exists():
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host=settings.host, port=settings.port)
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@@ -5,94 +5,117 @@ Single source of truth for all data transformations.
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# Column name mappings from DfE CSV to API field names
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COLUMN_MAPPINGS = {
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'URN': 'urn',
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'SCHNAME': 'school_name',
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'ADDRESS1': 'address1',
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'ADDRESS2': 'address2',
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'TOWN': 'town',
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'PCODE': 'postcode',
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'NFTYPE': 'school_type_code',
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'RELDENOM': 'religious_denomination',
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'AGERANGE': 'age_range',
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'TOTPUPS': 'total_pupils',
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'TELIG': 'eligible_pupils',
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"URN": "urn",
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"SCHNAME": "school_name",
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"ADDRESS1": "address1",
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"ADDRESS2": "address2",
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"TOWN": "town",
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"PCODE": "postcode",
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"NFTYPE": "school_type_code",
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"RELDENOM": "religious_denomination",
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"AGERANGE": "age_range",
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"TOTPUPS": "total_pupils",
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"TELIG": "eligible_pupils",
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# Core KS2 metrics
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'PTRWM_EXP': 'rwm_expected_pct',
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'PTRWM_HIGH': 'rwm_high_pct',
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'READPROG': 'reading_progress',
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'WRITPROG': 'writing_progress',
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'MATPROG': 'maths_progress',
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'PTREAD_EXP': 'reading_expected_pct',
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'PTWRITTA_EXP': 'writing_expected_pct',
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'PTMAT_EXP': 'maths_expected_pct',
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'READ_AVERAGE': 'reading_avg_score',
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'MAT_AVERAGE': 'maths_avg_score',
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'PTREAD_HIGH': 'reading_high_pct',
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'PTWRITTA_HIGH': 'writing_high_pct',
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'PTMAT_HIGH': 'maths_high_pct',
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"PTRWM_EXP": "rwm_expected_pct",
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"PTRWM_HIGH": "rwm_high_pct",
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"READPROG": "reading_progress",
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"WRITPROG": "writing_progress",
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"MATPROG": "maths_progress",
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"PTREAD_EXP": "reading_expected_pct",
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"PTWRITTA_EXP": "writing_expected_pct",
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"PTMAT_EXP": "maths_expected_pct",
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"READ_AVERAGE": "reading_avg_score",
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"MAT_AVERAGE": "maths_avg_score",
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"PTREAD_HIGH": "reading_high_pct",
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"PTWRITTA_HIGH": "writing_high_pct",
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"PTMAT_HIGH": "maths_high_pct",
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# GPS (Grammar, Punctuation & Spelling)
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'PTGPS_EXP': 'gps_expected_pct',
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'PTGPS_HIGH': 'gps_high_pct',
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'GPS_AVERAGE': 'gps_avg_score',
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"PTGPS_EXP": "gps_expected_pct",
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"PTGPS_HIGH": "gps_high_pct",
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"GPS_AVERAGE": "gps_avg_score",
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# Science
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'PTSCITA_EXP': 'science_expected_pct',
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"PTSCITA_EXP": "science_expected_pct",
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# School context
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'PTFSM6CLA1A': 'disadvantaged_pct',
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'PTEALGRP2': 'eal_pct',
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'PSENELK': 'sen_support_pct',
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'PSENELE': 'sen_ehcp_pct',
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'PTMOBN': 'stability_pct',
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"PTFSM6CLA1A": "disadvantaged_pct",
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"PTEALGRP2": "eal_pct",
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"PSENELK": "sen_support_pct",
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"PSENELE": "sen_ehcp_pct",
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"PTMOBN": "stability_pct",
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# Gender breakdown
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'PTRWM_EXP_B': 'rwm_expected_boys_pct',
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'PTRWM_EXP_G': 'rwm_expected_girls_pct',
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'PTRWM_HIGH_B': 'rwm_high_boys_pct',
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'PTRWM_HIGH_G': 'rwm_high_girls_pct',
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"PTRWM_EXP_B": "rwm_expected_boys_pct",
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"PTRWM_EXP_G": "rwm_expected_girls_pct",
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"PTRWM_HIGH_B": "rwm_high_boys_pct",
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"PTRWM_HIGH_G": "rwm_high_girls_pct",
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# Disadvantaged performance
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'PTRWM_EXP_FSM6CLA1A': 'rwm_expected_disadvantaged_pct',
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'PTRWM_EXP_NotFSM6CLA1A': 'rwm_expected_non_disadvantaged_pct',
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'DIFFN_RWM_EXP': 'disadvantaged_gap',
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"PTRWM_EXP_FSM6CLA1A": "rwm_expected_disadvantaged_pct",
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"PTRWM_EXP_NotFSM6CLA1A": "rwm_expected_non_disadvantaged_pct",
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"DIFFN_RWM_EXP": "disadvantaged_gap",
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# 3-year averages
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'PTRWM_EXP_3YR': 'rwm_expected_3yr_pct',
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'READ_AVERAGE_3YR': 'reading_avg_3yr',
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'MAT_AVERAGE_3YR': 'maths_avg_3yr',
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"PTRWM_EXP_3YR": "rwm_expected_3yr_pct",
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"READ_AVERAGE_3YR": "reading_avg_3yr",
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"MAT_AVERAGE_3YR": "maths_avg_3yr",
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}
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# Numeric columns that need parsing
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NUMERIC_COLUMNS = [
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# Core metrics
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'rwm_expected_pct', 'rwm_high_pct', 'reading_progress',
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'writing_progress', 'maths_progress', 'reading_expected_pct',
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'writing_expected_pct', 'maths_expected_pct', 'reading_avg_score',
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'maths_avg_score', 'reading_high_pct', 'writing_high_pct', 'maths_high_pct',
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"rwm_expected_pct",
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"rwm_high_pct",
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"reading_progress",
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"writing_progress",
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"maths_progress",
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"reading_expected_pct",
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"writing_expected_pct",
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"maths_expected_pct",
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"reading_avg_score",
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"maths_avg_score",
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"reading_high_pct",
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"writing_high_pct",
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"maths_high_pct",
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# GPS & Science
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'gps_expected_pct', 'gps_high_pct', 'gps_avg_score', 'science_expected_pct',
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"gps_expected_pct",
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"gps_high_pct",
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"gps_avg_score",
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"science_expected_pct",
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# School context
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'total_pupils', 'eligible_pupils', 'disadvantaged_pct', 'eal_pct',
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'sen_support_pct', 'sen_ehcp_pct', 'stability_pct',
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"total_pupils",
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"eligible_pupils",
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"disadvantaged_pct",
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"eal_pct",
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"sen_support_pct",
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"sen_ehcp_pct",
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"stability_pct",
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# Gender breakdown
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'rwm_expected_boys_pct', 'rwm_expected_girls_pct',
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'rwm_high_boys_pct', 'rwm_high_girls_pct',
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"rwm_expected_boys_pct",
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"rwm_expected_girls_pct",
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"rwm_high_boys_pct",
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"rwm_high_girls_pct",
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# Disadvantaged performance
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'rwm_expected_disadvantaged_pct', 'rwm_expected_non_disadvantaged_pct', 'disadvantaged_gap',
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"rwm_expected_disadvantaged_pct",
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"rwm_expected_non_disadvantaged_pct",
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"disadvantaged_gap",
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# 3-year averages
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'rwm_expected_3yr_pct', 'reading_avg_3yr', 'maths_avg_3yr',
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"rwm_expected_3yr_pct",
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"reading_avg_3yr",
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"maths_avg_3yr",
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]
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# School type code to name mapping
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SCHOOL_TYPE_MAP = {
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'AC': 'Academy',
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'ACC': 'Academy Converter',
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'ACS': 'Academy Sponsor Led',
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||||
'CY': 'Community School',
|
||||
'VA': 'Voluntary Aided',
|
||||
'VC': 'Voluntary Controlled',
|
||||
'FD': 'Foundation',
|
||||
'F': 'Foundation',
|
||||
'FS': 'Free School',
|
||||
"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",
|
||||
}
|
||||
|
||||
# Special values to treat as null
|
||||
NULL_VALUES = ['SUPP', 'NE', 'NA', 'NP', 'NEW', 'LOW', '']
|
||||
NULL_VALUES = ["SUPP", "NE", "NA", "NP", "NEW", "LOW", ""]
|
||||
|
||||
# KS2 Metric definitions - single source of truth
|
||||
# Used by both backend API and frontend
|
||||
@@ -103,42 +126,42 @@ METRIC_DEFINITIONS = {
|
||||
"short_name": "RWM %",
|
||||
"description": "% meeting expected standard in reading, writing and maths",
|
||||
"type": "percentage",
|
||||
"category": "expected"
|
||||
"category": "expected",
|
||||
},
|
||||
"reading_expected_pct": {
|
||||
"name": "Reading Expected %",
|
||||
"short_name": "Reading %",
|
||||
"description": "% meeting expected standard in reading",
|
||||
"type": "percentage",
|
||||
"category": "expected"
|
||||
"category": "expected",
|
||||
},
|
||||
"writing_expected_pct": {
|
||||
"name": "Writing Expected %",
|
||||
"short_name": "Writing %",
|
||||
"description": "% meeting expected standard in writing",
|
||||
"type": "percentage",
|
||||
"category": "expected"
|
||||
"category": "expected",
|
||||
},
|
||||
"maths_expected_pct": {
|
||||
"name": "Maths Expected %",
|
||||
"short_name": "Maths %",
|
||||
"description": "% meeting expected standard in maths",
|
||||
"type": "percentage",
|
||||
"category": "expected"
|
||||
"category": "expected",
|
||||
},
|
||||
"gps_expected_pct": {
|
||||
"name": "GPS Expected %",
|
||||
"short_name": "GPS %",
|
||||
"description": "% meeting expected standard in grammar, punctuation & spelling",
|
||||
"type": "percentage",
|
||||
"category": "expected"
|
||||
"category": "expected",
|
||||
},
|
||||
"science_expected_pct": {
|
||||
"name": "Science Expected %",
|
||||
"short_name": "Science %",
|
||||
"description": "% meeting expected standard in science",
|
||||
"type": "percentage",
|
||||
"category": "expected"
|
||||
"category": "expected",
|
||||
},
|
||||
# Higher Standard
|
||||
"rwm_high_pct": {
|
||||
@@ -146,35 +169,35 @@ METRIC_DEFINITIONS = {
|
||||
"short_name": "RWM Higher %",
|
||||
"description": "% achieving higher standard in RWM combined",
|
||||
"type": "percentage",
|
||||
"category": "higher"
|
||||
"category": "higher",
|
||||
},
|
||||
"reading_high_pct": {
|
||||
"name": "Reading Higher %",
|
||||
"short_name": "Reading Higher %",
|
||||
"description": "% achieving higher standard in reading",
|
||||
"type": "percentage",
|
||||
"category": "higher"
|
||||
"category": "higher",
|
||||
},
|
||||
"writing_high_pct": {
|
||||
"name": "Writing Higher %",
|
||||
"short_name": "Writing Higher %",
|
||||
"description": "% achieving greater depth in writing",
|
||||
"type": "percentage",
|
||||
"category": "higher"
|
||||
"category": "higher",
|
||||
},
|
||||
"maths_high_pct": {
|
||||
"name": "Maths Higher %",
|
||||
"short_name": "Maths Higher %",
|
||||
"description": "% achieving higher standard in maths",
|
||||
"type": "percentage",
|
||||
"category": "higher"
|
||||
"category": "higher",
|
||||
},
|
||||
"gps_high_pct": {
|
||||
"name": "GPS Higher %",
|
||||
"short_name": "GPS Higher %",
|
||||
"description": "% achieving higher standard in GPS",
|
||||
"type": "percentage",
|
||||
"category": "higher"
|
||||
"category": "higher",
|
||||
},
|
||||
# Progress Scores
|
||||
"reading_progress": {
|
||||
@@ -182,21 +205,21 @@ METRIC_DEFINITIONS = {
|
||||
"short_name": "Reading Progress",
|
||||
"description": "Progress in reading from KS1 to KS2",
|
||||
"type": "score",
|
||||
"category": "progress"
|
||||
"category": "progress",
|
||||
},
|
||||
"writing_progress": {
|
||||
"name": "Writing Progress",
|
||||
"short_name": "Writing Progress",
|
||||
"description": "Progress in writing from KS1 to KS2",
|
||||
"type": "score",
|
||||
"category": "progress"
|
||||
"category": "progress",
|
||||
},
|
||||
"maths_progress": {
|
||||
"name": "Maths Progress",
|
||||
"short_name": "Maths Progress",
|
||||
"description": "Progress in maths from KS1 to KS2",
|
||||
"type": "score",
|
||||
"category": "progress"
|
||||
"category": "progress",
|
||||
},
|
||||
# Average Scores
|
||||
"reading_avg_score": {
|
||||
@@ -204,21 +227,21 @@ METRIC_DEFINITIONS = {
|
||||
"short_name": "Reading Avg",
|
||||
"description": "Average scaled score in reading",
|
||||
"type": "score",
|
||||
"category": "average"
|
||||
"category": "average",
|
||||
},
|
||||
"maths_avg_score": {
|
||||
"name": "Maths Average Score",
|
||||
"short_name": "Maths Avg",
|
||||
"description": "Average scaled score in maths",
|
||||
"type": "score",
|
||||
"category": "average"
|
||||
"category": "average",
|
||||
},
|
||||
"gps_avg_score": {
|
||||
"name": "GPS Average Score",
|
||||
"short_name": "GPS Avg",
|
||||
"description": "Average scaled score in GPS",
|
||||
"type": "score",
|
||||
"category": "average"
|
||||
"category": "average",
|
||||
},
|
||||
# Gender Performance
|
||||
"rwm_expected_boys_pct": {
|
||||
@@ -226,28 +249,28 @@ METRIC_DEFINITIONS = {
|
||||
"short_name": "Boys RWM %",
|
||||
"description": "% of boys meeting expected standard",
|
||||
"type": "percentage",
|
||||
"category": "gender"
|
||||
"category": "gender",
|
||||
},
|
||||
"rwm_expected_girls_pct": {
|
||||
"name": "RWM Expected % (Girls)",
|
||||
"short_name": "Girls RWM %",
|
||||
"description": "% of girls meeting expected standard",
|
||||
"type": "percentage",
|
||||
"category": "gender"
|
||||
"category": "gender",
|
||||
},
|
||||
"rwm_high_boys_pct": {
|
||||
"name": "RWM Higher % (Boys)",
|
||||
"short_name": "Boys Higher %",
|
||||
"description": "% of boys at higher standard",
|
||||
"type": "percentage",
|
||||
"category": "gender"
|
||||
"category": "gender",
|
||||
},
|
||||
"rwm_high_girls_pct": {
|
||||
"name": "RWM Higher % (Girls)",
|
||||
"short_name": "Girls Higher %",
|
||||
"description": "% of girls at higher standard",
|
||||
"type": "percentage",
|
||||
"category": "gender"
|
||||
"category": "gender",
|
||||
},
|
||||
# Disadvantaged Performance
|
||||
"rwm_expected_disadvantaged_pct": {
|
||||
@@ -255,21 +278,21 @@ METRIC_DEFINITIONS = {
|
||||
"short_name": "Disadvantaged %",
|
||||
"description": "% of disadvantaged pupils meeting expected",
|
||||
"type": "percentage",
|
||||
"category": "equity"
|
||||
"category": "equity",
|
||||
},
|
||||
"rwm_expected_non_disadvantaged_pct": {
|
||||
"name": "RWM Expected % (Non-Disadvantaged)",
|
||||
"short_name": "Non-Disadv %",
|
||||
"description": "% of non-disadvantaged pupils meeting expected",
|
||||
"type": "percentage",
|
||||
"category": "equity"
|
||||
"category": "equity",
|
||||
},
|
||||
"disadvantaged_gap": {
|
||||
"name": "Disadvantaged Gap",
|
||||
"short_name": "Disadv Gap",
|
||||
"description": "Gap between disadvantaged and national non-disadvantaged",
|
||||
"type": "score",
|
||||
"category": "equity"
|
||||
"category": "equity",
|
||||
},
|
||||
# School Context
|
||||
"disadvantaged_pct": {
|
||||
@@ -277,28 +300,28 @@ METRIC_DEFINITIONS = {
|
||||
"short_name": "% Disadvantaged",
|
||||
"description": "% of pupils eligible for free school meals or looked after",
|
||||
"type": "percentage",
|
||||
"category": "context"
|
||||
"category": "context",
|
||||
},
|
||||
"eal_pct": {
|
||||
"name": "% EAL Pupils",
|
||||
"short_name": "% EAL",
|
||||
"description": "% of pupils with English as additional language",
|
||||
"type": "percentage",
|
||||
"category": "context"
|
||||
"category": "context",
|
||||
},
|
||||
"sen_support_pct": {
|
||||
"name": "% SEN Support",
|
||||
"short_name": "% SEN",
|
||||
"description": "% of pupils with SEN support",
|
||||
"type": "percentage",
|
||||
"category": "context"
|
||||
"category": "context",
|
||||
},
|
||||
"stability_pct": {
|
||||
"name": "% Pupil Stability",
|
||||
"short_name": "% Stable",
|
||||
"description": "% of non-mobile pupils (stayed at school)",
|
||||
"type": "percentage",
|
||||
"category": "context"
|
||||
"category": "context",
|
||||
},
|
||||
# 3-Year Averages
|
||||
"rwm_expected_3yr_pct": {
|
||||
@@ -306,122 +329,257 @@ METRIC_DEFINITIONS = {
|
||||
"short_name": "RWM 3yr %",
|
||||
"description": "3-year average % meeting expected",
|
||||
"type": "percentage",
|
||||
"category": "trends"
|
||||
"category": "trends",
|
||||
},
|
||||
"reading_avg_3yr": {
|
||||
"name": "Reading Score (3-Year Avg)",
|
||||
"short_name": "Reading 3yr",
|
||||
"description": "3-year average reading score",
|
||||
"type": "score",
|
||||
"category": "trends"
|
||||
"category": "trends",
|
||||
},
|
||||
"maths_avg_3yr": {
|
||||
"name": "Maths Score (3-Year Avg)",
|
||||
"short_name": "Maths 3yr",
|
||||
"description": "3-year average maths score",
|
||||
"type": "score",
|
||||
"category": "trends"
|
||||
"category": "trends",
|
||||
},
|
||||
}
|
||||
|
||||
# Ranking columns to include in rankings response
|
||||
RANKING_COLUMNS = [
|
||||
"urn", "school_name", "local_authority", "school_type", "address", "year", "total_pupils",
|
||||
"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",
|
||||
"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",
|
||||
"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",
|
||||
"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",
|
||||
"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",
|
||||
"rwm_expected_disadvantaged_pct",
|
||||
"rwm_expected_non_disadvantaged_pct",
|
||||
"disadvantaged_gap",
|
||||
# Context
|
||||
"disadvantaged_pct", "eal_pct", "sen_support_pct", "stability_pct",
|
||||
"disadvantaged_pct",
|
||||
"eal_pct",
|
||||
"sen_support_pct",
|
||||
"stability_pct",
|
||||
# 3-year
|
||||
"rwm_expected_3yr_pct", "reading_avg_3yr", "maths_avg_3yr",
|
||||
"rwm_expected_3yr_pct",
|
||||
"reading_avg_3yr",
|
||||
"maths_avg_3yr",
|
||||
]
|
||||
|
||||
# School listing columns
|
||||
SCHOOL_COLUMNS = ["urn", "school_name", "local_authority", "school_type", "address", "town", "postcode"]
|
||||
SCHOOL_COLUMNS = [
|
||||
"urn",
|
||||
"school_name",
|
||||
"local_authority",
|
||||
"school_type",
|
||||
"address",
|
||||
"town",
|
||||
"postcode",
|
||||
]
|
||||
|
||||
# Local Authority code to name mapping (for fallback when LANAME column missing)
|
||||
# Source: https://www.gov.uk/government/publications/local-authority-codes
|
||||
LA_CODE_TO_NAME = {
|
||||
# Inner London
|
||||
201: "City of London", 202: "Camden", 203: "Greenwich", 204: "Hackney",
|
||||
205: "Hammersmith and Fulham", 206: "Islington", 207: "Kensington and Chelsea",
|
||||
208: "Lambeth", 209: "Lewisham", 210: "Southwark", 211: "Tower Hamlets",
|
||||
212: "Wandsworth", 213: "Westminster",
|
||||
201: "City of London",
|
||||
202: "Camden",
|
||||
203: "Greenwich",
|
||||
204: "Hackney",
|
||||
205: "Hammersmith and Fulham",
|
||||
206: "Islington",
|
||||
207: "Kensington and Chelsea",
|
||||
208: "Lambeth",
|
||||
209: "Lewisham",
|
||||
210: "Southwark",
|
||||
211: "Tower Hamlets",
|
||||
212: "Wandsworth",
|
||||
213: "Westminster",
|
||||
# Outer London
|
||||
301: "Barking and Dagenham", 302: "Barnet", 303: "Bexley", 304: "Brent",
|
||||
305: "Bromley", 306: "Croydon", 307: "Ealing", 308: "Enfield", 309: "Haringey",
|
||||
310: "Harrow", 311: "Havering", 312: "Hillingdon", 313: "Hounslow",
|
||||
314: "Kingston upon Thames", 315: "Merton", 316: "Newham", 317: "Redbridge",
|
||||
318: "Richmond upon Thames", 319: "Sutton", 320: "Waltham Forest",
|
||||
301: "Barking and Dagenham",
|
||||
302: "Barnet",
|
||||
303: "Bexley",
|
||||
304: "Brent",
|
||||
305: "Bromley",
|
||||
306: "Croydon",
|
||||
307: "Ealing",
|
||||
308: "Enfield",
|
||||
309: "Haringey",
|
||||
310: "Harrow",
|
||||
311: "Havering",
|
||||
312: "Hillingdon",
|
||||
313: "Hounslow",
|
||||
314: "Kingston upon Thames",
|
||||
315: "Merton",
|
||||
316: "Newham",
|
||||
317: "Redbridge",
|
||||
318: "Richmond upon Thames",
|
||||
319: "Sutton",
|
||||
320: "Waltham Forest",
|
||||
# West Midlands
|
||||
330: "Birmingham", 331: "Coventry", 332: "Dudley", 333: "Sandwell",
|
||||
334: "Solihull", 335: "Walsall", 336: "Wolverhampton",
|
||||
330: "Birmingham",
|
||||
331: "Coventry",
|
||||
332: "Dudley",
|
||||
333: "Sandwell",
|
||||
334: "Solihull",
|
||||
335: "Walsall",
|
||||
336: "Wolverhampton",
|
||||
# Merseyside
|
||||
340: "Knowsley", 341: "Liverpool", 342: "St. Helens", 343: "Sefton", 344: "Wirral",
|
||||
340: "Knowsley",
|
||||
341: "Liverpool",
|
||||
342: "St. Helens",
|
||||
343: "Sefton",
|
||||
344: "Wirral",
|
||||
# Greater Manchester
|
||||
350: "Bolton", 351: "Bury", 352: "Manchester", 353: "Oldham", 354: "Rochdale",
|
||||
355: "Salford", 356: "Stockport", 357: "Tameside", 358: "Trafford", 359: "Wigan",
|
||||
350: "Bolton",
|
||||
351: "Bury",
|
||||
352: "Manchester",
|
||||
353: "Oldham",
|
||||
354: "Rochdale",
|
||||
355: "Salford",
|
||||
356: "Stockport",
|
||||
357: "Tameside",
|
||||
358: "Trafford",
|
||||
359: "Wigan",
|
||||
# South Yorkshire
|
||||
370: "Barnsley", 371: "Doncaster", 372: "Rotherham", 373: "Sheffield",
|
||||
370: "Barnsley",
|
||||
371: "Doncaster",
|
||||
372: "Rotherham",
|
||||
373: "Sheffield",
|
||||
# West Yorkshire
|
||||
380: "Bradford", 381: "Calderdale", 382: "Kirklees", 383: "Leeds", 384: "Wakefield",
|
||||
380: "Bradford",
|
||||
381: "Calderdale",
|
||||
382: "Kirklees",
|
||||
383: "Leeds",
|
||||
384: "Wakefield",
|
||||
# Tyne and Wear
|
||||
390: "Gateshead", 391: "Newcastle upon Tyne", 392: "North Tyneside",
|
||||
393: "South Tyneside", 394: "Sunderland",
|
||||
390: "Gateshead",
|
||||
391: "Newcastle upon Tyne",
|
||||
392: "North Tyneside",
|
||||
393: "South Tyneside",
|
||||
394: "Sunderland",
|
||||
# Isles of Scilly
|
||||
420: "Isles of Scilly",
|
||||
# Unitary authorities (800+)
|
||||
800: "Bath and North East Somerset", 801: "Bristol, City of", 802: "North Somerset",
|
||||
803: "South Gloucestershire", 805: "Hartlepool", 806: "Middlesbrough",
|
||||
807: "Redcar and Cleveland", 808: "Stockton-on-Tees",
|
||||
810: "Kingston Upon Hull, City of", 811: "East Riding of Yorkshire",
|
||||
812: "North East Lincolnshire", 813: "North Lincolnshire",
|
||||
815: "North Yorkshire", 816: "York",
|
||||
820: "Bedford", 821: "Central Bedfordshire", 822: "Luton",
|
||||
823: "West Northamptonshire", 824: "North Northamptonshire",
|
||||
825: "Buckinghamshire", 826: "Milton Keynes",
|
||||
830: "Derbyshire", 831: "Derby",
|
||||
835: "Dorset", 836: "Bournemouth, Christchurch and Poole",
|
||||
837: "Poole", 838: "Bournemouth", # Historic codes (merged into 836)
|
||||
839: "Durham", 840: "Darlington",
|
||||
841: "East Sussex", 845: "Brighton and Hove",
|
||||
846: "Hampshire", 850: "Portsmouth", 851: "Southampton", 852: "Isle of Wight",
|
||||
855: "Leicestershire", 856: "Leicester", 857: "Rutland",
|
||||
860: "Staffordshire", 861: "Stoke-on-Trent",
|
||||
865: "Wiltshire", 866: "Swindon",
|
||||
867: "Bracknell Forest", 868: "Windsor and Maidenhead", 869: "West Berkshire",
|
||||
870: "Reading", 871: "Slough", 872: "Wokingham",
|
||||
873: "Cambridgeshire", 874: "Peterborough",
|
||||
876: "Halton", 877: "Warrington",
|
||||
878: "Devon", 879: "Plymouth", 880: "Torbay",
|
||||
881: "Essex", 882: "Southend-on-Sea", 883: "Thurrock",
|
||||
884: "Herefordshire", 885: "Worcestershire",
|
||||
886: "Kent", 887: "Medway",
|
||||
888: "Lancashire", 889: "Blackburn with Darwen", 890: "Blackpool",
|
||||
891: "Nottinghamshire", 892: "Nottingham",
|
||||
893: "Shropshire", 894: "Telford and Wrekin",
|
||||
895: "Cheshire East", 896: "Cheshire West and Chester",
|
||||
800: "Bath and North East Somerset",
|
||||
801: "Bristol, City of",
|
||||
802: "North Somerset",
|
||||
803: "South Gloucestershire",
|
||||
805: "Hartlepool",
|
||||
806: "Middlesbrough",
|
||||
807: "Redcar and Cleveland",
|
||||
808: "Stockton-on-Tees",
|
||||
810: "Kingston Upon Hull, City of",
|
||||
811: "East Riding of Yorkshire",
|
||||
812: "North East Lincolnshire",
|
||||
813: "North Lincolnshire",
|
||||
815: "North Yorkshire",
|
||||
816: "York",
|
||||
820: "Bedford",
|
||||
821: "Central Bedfordshire",
|
||||
822: "Luton",
|
||||
823: "West Northamptonshire",
|
||||
824: "North Northamptonshire",
|
||||
825: "Buckinghamshire",
|
||||
826: "Milton Keynes",
|
||||
830: "Derbyshire",
|
||||
831: "Derby",
|
||||
835: "Dorset",
|
||||
836: "Bournemouth, Christchurch and Poole",
|
||||
837: "Poole",
|
||||
838: "Bournemouth", # Historic codes (merged into 836)
|
||||
839: "Durham",
|
||||
840: "Darlington",
|
||||
841: "East Sussex",
|
||||
845: "Brighton and Hove",
|
||||
846: "Hampshire",
|
||||
850: "Portsmouth",
|
||||
851: "Southampton",
|
||||
852: "Isle of Wight",
|
||||
855: "Leicestershire",
|
||||
856: "Leicester",
|
||||
857: "Rutland",
|
||||
860: "Staffordshire",
|
||||
861: "Stoke-on-Trent",
|
||||
865: "Wiltshire",
|
||||
866: "Swindon",
|
||||
867: "Bracknell Forest",
|
||||
868: "Windsor and Maidenhead",
|
||||
869: "West Berkshire",
|
||||
870: "Reading",
|
||||
871: "Slough",
|
||||
872: "Wokingham",
|
||||
873: "Cambridgeshire",
|
||||
874: "Peterborough",
|
||||
876: "Halton",
|
||||
877: "Warrington",
|
||||
878: "Devon",
|
||||
879: "Plymouth",
|
||||
880: "Torbay",
|
||||
881: "Essex",
|
||||
882: "Southend-on-Sea",
|
||||
883: "Thurrock",
|
||||
884: "Herefordshire",
|
||||
885: "Worcestershire",
|
||||
886: "Kent",
|
||||
887: "Medway",
|
||||
888: "Lancashire",
|
||||
889: "Blackburn with Darwen",
|
||||
890: "Blackpool",
|
||||
891: "Nottinghamshire",
|
||||
892: "Nottingham",
|
||||
893: "Shropshire",
|
||||
894: "Telford and Wrekin",
|
||||
895: "Cheshire East",
|
||||
896: "Cheshire West and Chester",
|
||||
# County councils (900+)
|
||||
908: "Cornwall", 909: "Cumbria",
|
||||
916: "Gloucestershire", 919: "Hertfordshire",
|
||||
921: "Norfolk", 925: "Lincolnshire",
|
||||
908: "Cornwall",
|
||||
909: "Cumbria",
|
||||
916: "Gloucestershire",
|
||||
919: "Hertfordshire",
|
||||
921: "Norfolk",
|
||||
925: "Lincolnshire",
|
||||
926: "Northamptonshire", # Historic (split into 823/824 in 2021)
|
||||
928: "Northumberland", 929: "Oxfordshire",
|
||||
931: "Somerset", 933: "Suffolk", 935: "Surrey",
|
||||
936: "Warwickshire", 937: "West Sussex",
|
||||
928: "Northumberland",
|
||||
929: "Oxfordshire",
|
||||
931: "Somerset",
|
||||
933: "Suffolk",
|
||||
935: "Surrey",
|
||||
936: "Warwickshire",
|
||||
937: "West Sussex",
|
||||
# New authorities (2023 reorganization)
|
||||
938: "Westmorland and Furness", 940: "Cumberland",
|
||||
938: "Westmorland and Furness",
|
||||
940: "Cumberland",
|
||||
941: "North Yorkshire", # New unitary
|
||||
942: "Somerset", # New unitary (replaced 931)
|
||||
943: "Buckinghamshire", # New unitary (2020, replacing 825 in some datasets)
|
||||
}
|
||||
|
||||
|
||||
@@ -465,12 +465,12 @@ function renderFeaturedSchools(schools) {
|
||||
<div class="school-address">${escapeHtml(school.address || "")}</div>
|
||||
<div class="school-stats">
|
||||
<div class="stat">
|
||||
<div class="stat-value">Primary</div>
|
||||
<div class="stat-label">Phase</div>
|
||||
<div class="stat-value">${formatMetricValue(school.rwm_expected_pct, "rwm_expected_pct")}</div>
|
||||
<div class="stat-label">RWM Expected</div>
|
||||
</div>
|
||||
<div class="stat">
|
||||
<div class="stat-value">KS2</div>
|
||||
<div class="stat-label">Data</div>
|
||||
<div class="stat-value">${school.total_pupils || "-"}</div>
|
||||
<div class="stat-label">Pupils</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -587,12 +587,12 @@ function renderSchools(schools) {
|
||||
<div class="school-address">${escapeHtml(school.address || "")}</div>
|
||||
<div class="school-stats">
|
||||
<div class="stat">
|
||||
<div class="stat-value">Primary</div>
|
||||
<div class="stat-label">Phase</div>
|
||||
<div class="stat-value">${formatMetricValue(school.rwm_expected_pct, "rwm_expected_pct")}</div>
|
||||
<div class="stat-label">RWM Expected</div>
|
||||
</div>
|
||||
<div class="stat">
|
||||
<div class="stat-value">KS2</div>
|
||||
<div class="stat-label">Data</div>
|
||||
<div class="stat-value">${school.total_pupils || "-"}</div>
|
||||
<div class="stat-label">Pupils</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
Reference in New Issue
Block a user