diff --git a/backend/app.py b/backend/app.py
index 7914cf6..cddd625 100644
--- a/backend/app.py
+++ b/backend/app.py
@@ -5,20 +5,25 @@ Uses real data from UK Government Compare School Performance downloads.
"""
from contextlib import asynccontextmanager
-import pandas as pd
-from fastapi import FastAPI, HTTPException, Query
-from fastapi.staticfiles import StaticFiles
-from fastapi.responses import FileResponse
-from fastapi.middleware.cors import CORSMiddleware
from typing import Optional
+import pandas as pd
+from fastapi import FastAPI, HTTPException, Query
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import FileResponse
+from fastapi.staticfiles import StaticFiles
+
from .config import settings
-from .schemas import METRIC_DEFINITIONS, RANKING_COLUMNS, SCHOOL_COLUMNS
from .data_loader import (
- load_school_data, clear_cache, geocode_single_postcode,
- geocode_postcodes_bulk, haversine_distance, get_data_info as get_db_info
+ clear_cache,
+ geocode_postcodes_bulk,
+ geocode_single_postcode,
+ haversine_distance,
+ load_school_data,
)
+from .data_loader import get_data_info as get_db_info
from .database import init_db
+from .schemas import METRIC_DEFINITIONS, RANKING_COLUMNS, SCHOOL_COLUMNS
from .utils import clean_for_json
@@ -28,16 +33,16 @@ async def lifespan(app: FastAPI):
# Startup: initialize database and pre-load data
print("Starting up: Initializing database...")
init_db() # Ensure tables exist
-
+
print("Loading school data from database...")
df = load_school_data()
if df.empty:
print("Warning: No data in database. Run the migration script to import data.")
else:
print("Data loaded successfully.")
-
+
yield # Application runs here
-
+
# Shutdown: cleanup if needed
print("Shutting down...")
@@ -80,7 +85,9 @@ async def serve_rankings():
@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"),
+ local_authority: Optional[str] = Query(
+ None, description="Filter by local authority"
+ ),
school_type: Optional[str] = Query(None, description="Filter by school type"),
postcode: Optional[str] = Query(None, description="Search near postcode"),
radius: float = Query(5.0, ge=0.1, le=50, description="Search radius in miles"),
@@ -89,28 +96,40 @@ async def get_schools(
):
"""
Get list of unique primary schools with pagination.
-
+
Returns paginated results with total count for efficient loading.
Supports location-based search using postcode.
"""
df = load_school_data()
-
+
if df.empty:
return {"schools": [], "total": 0, "page": page, "page_size": 0}
-
+
# Use configured default if not specified
if page_size is None:
page_size = settings.default_page_size
-
+
# 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'])
-
- # Include lat/long in columns for location search
- location_cols = ['latitude', 'longitude']
- available_cols = [c for c in SCHOOL_COLUMNS + location_cols if c in df_latest.columns]
- schools_df = df_latest[available_cols].drop_duplicates(subset=['urn'])
-
+ latest_year = df.groupby("urn")["year"].max().reset_index()
+ df_latest = df.merge(latest_year, on=["urn", "year"])
+
+ # Include key result metrics for display on cards
+ location_cols = ["latitude", "longitude"]
+ result_cols = [
+ "year",
+ "rwm_expected_pct",
+ "reading_expected_pct",
+ "writing_expected_pct",
+ "maths_expected_pct",
+ "total_pupils",
+ ]
+ available_cols = [
+ c
+ for c in SCHOOL_COLUMNS + location_cols + result_cols
+ if c in df_latest.columns
+ ]
+ schools_df = df_latest[available_cols].drop_duplicates(subset=["urn"])
+
# Location-based search
search_coords = None
if postcode:
@@ -118,65 +137,81 @@ async def get_schools(
if coords:
search_coords = coords
schools_df = schools_df.copy()
-
+
# Geocode school postcodes on-demand if not already cached
- if 'postcode' in schools_df.columns:
- unique_postcodes = schools_df['postcode'].dropna().unique().tolist()
+ if "postcode" in schools_df.columns:
+ unique_postcodes = schools_df["postcode"].dropna().unique().tolist()
geocoded = geocode_postcodes_bulk(unique_postcodes)
-
+
# Add lat/long from geocoded data
- schools_df['latitude'] = schools_df['postcode'].apply(
- lambda pc: geocoded.get(str(pc).strip().upper(), (None, None))[0] if pd.notna(pc) else None
+ schools_df["latitude"] = schools_df["postcode"].apply(
+ lambda pc: geocoded.get(str(pc).strip().upper(), (None, None))[0]
+ if pd.notna(pc)
+ else None
)
- schools_df['longitude'] = schools_df['postcode'].apply(
- lambda pc: geocoded.get(str(pc).strip().upper(), (None, None))[1] if pd.notna(pc) else None
+ schools_df["longitude"] = schools_df["postcode"].apply(
+ lambda pc: geocoded.get(str(pc).strip().upper(), (None, None))[1]
+ if pd.notna(pc)
+ else None
)
-
+
# Filter by distance
def calc_distance(row):
- if pd.isna(row.get('latitude')) or pd.isna(row.get('longitude')):
- return float('inf')
+ if pd.isna(row.get("latitude")) or pd.isna(row.get("longitude")):
+ return float("inf")
return haversine_distance(
- search_coords[0], search_coords[1],
- row['latitude'], row['longitude']
+ search_coords[0],
+ search_coords[1],
+ row["latitude"],
+ row["longitude"],
)
-
- schools_df['distance'] = schools_df.apply(calc_distance, axis=1)
- schools_df = schools_df[schools_df['distance'] <= radius]
- schools_df = schools_df.sort_values('distance')
-
+
+ schools_df["distance"] = schools_df.apply(calc_distance, axis=1)
+ schools_df = schools_df[schools_df["distance"] <= radius]
+ schools_df = schools_df.sort_values("distance")
+
# Apply filters
if search:
search_lower = search.lower()
- mask = schools_df["school_name"].str.lower().str.contains(search_lower, na=False)
+ 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)
+ 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()]
-
+ 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()]
-
+ schools_df = schools_df[
+ schools_df["school_type"].str.lower() == school_type.lower()
+ ]
+
# Pagination
total = len(schools_df)
start_idx = (page - 1) * page_size
end_idx = start_idx + page_size
schools_df = schools_df.iloc[start_idx:end_idx]
-
+
# Remove internal columns before sending
- output_cols = [c for c in schools_df.columns if c not in ['latitude', 'longitude']]
- if 'distance' in schools_df.columns:
- output_cols.append('distance')
-
+ output_cols = [c for c in schools_df.columns if c not in ["latitude", "longitude"]]
+ if "distance" in schools_df.columns:
+ output_cols.append("distance")
+
return {
"schools": clean_for_json(schools_df[output_cols]),
"total": total,
"page": page,
"page_size": page_size,
"total_pages": (total + page_size - 1) // page_size if page_size > 0 else 0,
- "search_location": {"postcode": postcode, "radius": radius} if search_coords else None,
+ "search_location": {"postcode": postcode, "radius": radius}
+ if search_coords
+ else None,
}
@@ -184,21 +219,21 @@ async def get_schools(
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,
@@ -208,7 +243,7 @@ async def get_school_details(urn: int):
"address": latest.get("address", ""),
"phase": "Primary",
},
- "yearly_data": clean_for_json(school_data)
+ "yearly_data": clean_for_json(school_data),
}
@@ -216,20 +251,20 @@ async def get_school_details(urn: int):
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")
@@ -242,9 +277,9 @@ async def compare_schools(urns: str = Query(..., description="Comma-separated UR
"local_authority": latest.get("local_authority", ""),
"address": latest.get("address", ""),
},
- "yearly_data": clean_for_json(school_data)
+ "yearly_data": clean_for_json(school_data),
}
-
+
return {"comparison": result}
@@ -252,14 +287,14 @@ async def compare_schools(urns: str = Query(..., description="Comma-separated UR
async def get_filter_options():
"""Get available filter options (local authorities, school types, years)."""
df = load_school_data()
-
+
if df.empty:
return {
"local_authorities": [],
"school_types": [],
"years": [],
}
-
+
return {
"local_authorities": sorted(df["local_authority"].dropna().unique().tolist()),
"school_types": sorted(df["school_type"].dropna().unique().tolist()),
@@ -271,36 +306,40 @@ async def get_filter_options():
async def get_available_metrics():
"""
Get list of available KS2 performance metrics for primary schools.
-
+
This is the single source of truth for metric definitions.
Frontend should consume this to avoid duplication.
"""
df = load_school_data()
-
+
available = []
for key, info in METRIC_DEFINITIONS.items():
if df.empty or key in df.columns:
available.append({"key": key, **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)"),
+ year: Optional[int] = Query(
+ None, description="Specific year (defaults to most recent)"
+ ),
limit: int = Query(20, ge=1, le=100, description="Number of schools to return"),
- local_authority: Optional[str] = Query(None, description="Filter by local authority"),
+ local_authority: Optional[str] = Query(
+ None, description="Filter by local authority"
+ ),
):
"""Get primary school rankings by a specific KS2 metric."""
df = load_school_data()
-
+
if df.empty:
return {"metric": metric, "year": None, "rankings": [], "total": 0}
-
+
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]
@@ -308,22 +347,22 @@ async def get_rankings(
# Use most recent year
max_year = df["year"].max()
df = df[df["year"] == max_year]
-
+
# Filter by local authority if specified
if local_authority:
df = df[df["local_authority"].str.lower() == local_authority.lower()]
-
+
# Sort and rank (exclude rows with no data for this metric)
df = df.dropna(subset=[metric])
total = len(df)
-
+
# 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
available_cols = [c for c in RANKING_COLUMNS if c in df.columns]
df = df[available_cols]
-
+
return {
"metric": metric,
"year": int(df["year"].iloc[0]) if not df.empty else None,
@@ -337,28 +376,34 @@ async def get_data_info():
"""Get information about loaded data."""
# Get info directly from database
db_info = get_db_info()
-
+
if db_info["total_schools"] == 0:
return {
"status": "no_data",
"message": "No data in database. Run the migration script: python scripts/migrate_csv_to_db.py",
"data_source": "PostgreSQL",
}
-
+
# Also get DataFrame-based stats for backwards compatibility
df = load_school_data()
-
+
if df.empty:
return {
"status": "no_data",
"message": "No data available",
"data_source": "PostgreSQL",
}
-
+
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()}
-
+ 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",
"data_source": "PostgreSQL",
@@ -385,4 +430,5 @@ if settings.frontend_dir.exists():
if __name__ == "__main__":
import uvicorn
+
uvicorn.run(app, host=settings.host, port=settings.port)
diff --git a/backend/schemas.py b/backend/schemas.py
index 7c41ce8..1317449 100644
--- a/backend/schemas.py
+++ b/backend/schemas.py
@@ -5,94 +5,117 @@ Single source of truth for all data transformations.
# Column name mappings from DfE CSV to API field names
COLUMN_MAPPINGS = {
- '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',
+ "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',
+ "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',
+ "PTGPS_EXP": "gps_expected_pct",
+ "PTGPS_HIGH": "gps_high_pct",
+ "GPS_AVERAGE": "gps_avg_score",
# Science
- 'PTSCITA_EXP': 'science_expected_pct',
+ "PTSCITA_EXP": "science_expected_pct",
# School context
- 'PTFSM6CLA1A': 'disadvantaged_pct',
- 'PTEALGRP2': 'eal_pct',
- 'PSENELK': 'sen_support_pct',
- 'PSENELE': 'sen_ehcp_pct',
- 'PTMOBN': 'stability_pct',
+ "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',
+ "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',
+ "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',
+ "PTRWM_EXP_3YR": "rwm_expected_3yr_pct",
+ "READ_AVERAGE_3YR": "reading_avg_3yr",
+ "MAT_AVERAGE_3YR": "maths_avg_3yr",
}
# Numeric columns that need parsing
NUMERIC_COLUMNS = [
# 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',
+ "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',
+ "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',
+ "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',
+ "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',
+ "rwm_expected_disadvantaged_pct",
+ "rwm_expected_non_disadvantaged_pct",
+ "disadvantaged_gap",
# 3-year averages
- 'rwm_expected_3yr_pct', 'reading_avg_3yr', 'maths_avg_3yr',
+ "rwm_expected_3yr_pct",
+ "reading_avg_3yr",
+ "maths_avg_3yr",
]
# School type code to name mapping
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',
+ "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)
}
-
diff --git a/frontend/app.js b/frontend/app.js
index 2952b9e..dcf097c 100644
--- a/frontend/app.js
+++ b/frontend/app.js
@@ -465,12 +465,12 @@ function renderFeaturedSchools(schools) {
${escapeHtml(school.address || "")}
-
Primary
-
Phase
+
${formatMetricValue(school.rwm_expected_pct, "rwm_expected_pct")}
+
RWM Expected
-
KS2
-
Data
+
${school.total_pupils || "-"}
+
Pupils
@@ -587,12 +587,12 @@ function renderSchools(schools) {
${escapeHtml(school.address || "")}
-
Primary
-
Phase
+
${formatMetricValue(school.rwm_expected_pct, "rwm_expected_pct")}
+
RWM Expected
-
KS2
-
Data
+
${school.total_pupils || "-"}
+
Pupils