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fixing data load
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Tudor Sitaru
2026-01-06 22:06:59 +00:00
parent e601c499b6
commit 1a8ec670b9
3 changed files with 465 additions and 261 deletions
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130
backend/app.py
View File

@@ -5,20 +5,25 @@ Uses real data from UK Government Compare School Performance downloads.
""" """
from contextlib import asynccontextmanager 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 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 .config import settings
from .schemas import METRIC_DEFINITIONS, RANKING_COLUMNS, SCHOOL_COLUMNS
from .data_loader import ( from .data_loader import (
load_school_data, clear_cache, geocode_single_postcode, clear_cache,
geocode_postcodes_bulk, haversine_distance, get_data_info as get_db_info 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 .database import init_db
from .schemas import METRIC_DEFINITIONS, RANKING_COLUMNS, SCHOOL_COLUMNS
from .utils import clean_for_json from .utils import clean_for_json
@@ -80,7 +85,9 @@ async def serve_rankings():
@app.get("/api/schools") @app.get("/api/schools")
async def get_schools( async def get_schools(
search: Optional[str] = Query(None, description="Search by school name"), 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"), school_type: Optional[str] = Query(None, description="Filter by school type"),
postcode: Optional[str] = Query(None, description="Search near postcode"), postcode: Optional[str] = Query(None, description="Search near postcode"),
radius: float = Query(5.0, ge=0.1, le=50, description="Search radius in miles"), radius: float = Query(5.0, ge=0.1, le=50, description="Search radius in miles"),
@@ -103,13 +110,25 @@ async def get_schools(
page_size = settings.default_page_size page_size = settings.default_page_size
# Get unique schools (latest year data for each) # Get unique schools (latest year data for each)
latest_year = df.groupby('urn')['year'].max().reset_index() latest_year = df.groupby("urn")["year"].max().reset_index()
df_latest = df.merge(latest_year, on=['urn', 'year']) df_latest = df.merge(latest_year, on=["urn", "year"])
# Include lat/long in columns for location search # Include key result metrics for display on cards
location_cols = ['latitude', 'longitude'] location_cols = ["latitude", "longitude"]
available_cols = [c for c in SCHOOL_COLUMNS + location_cols if c in df_latest.columns] result_cols = [
schools_df = df_latest[available_cols].drop_duplicates(subset=['urn']) "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 # Location-based search
search_coords = None search_coords = None
@@ -120,44 +139,58 @@ async def get_schools(
schools_df = schools_df.copy() schools_df = schools_df.copy()
# Geocode school postcodes on-demand if not already cached # Geocode school postcodes on-demand if not already cached
if 'postcode' in schools_df.columns: if "postcode" in schools_df.columns:
unique_postcodes = schools_df['postcode'].dropna().unique().tolist() unique_postcodes = schools_df["postcode"].dropna().unique().tolist()
geocoded = geocode_postcodes_bulk(unique_postcodes) geocoded = geocode_postcodes_bulk(unique_postcodes)
# Add lat/long from geocoded data # Add lat/long from geocoded data
schools_df['latitude'] = schools_df['postcode'].apply( schools_df["latitude"] = schools_df["postcode"].apply(
lambda pc: geocoded.get(str(pc).strip().upper(), (None, None))[0] if pd.notna(pc) else None lambda pc: geocoded.get(str(pc).strip().upper(), (None, None))[0]
if pd.notna(pc)
else None
) )
schools_df['longitude'] = schools_df['postcode'].apply( schools_df["longitude"] = schools_df["postcode"].apply(
lambda pc: geocoded.get(str(pc).strip().upper(), (None, None))[1] if pd.notna(pc) else None lambda pc: geocoded.get(str(pc).strip().upper(), (None, None))[1]
if pd.notna(pc)
else None
) )
# Filter by distance # Filter by distance
def calc_distance(row): def calc_distance(row):
if pd.isna(row.get('latitude')) or pd.isna(row.get('longitude')): if pd.isna(row.get("latitude")) or pd.isna(row.get("longitude")):
return float('inf') return float("inf")
return haversine_distance( return haversine_distance(
search_coords[0], search_coords[1], search_coords[0],
row['latitude'], row['longitude'] search_coords[1],
row["latitude"],
row["longitude"],
) )
schools_df['distance'] = schools_df.apply(calc_distance, axis=1) schools_df["distance"] = schools_df.apply(calc_distance, axis=1)
schools_df = schools_df[schools_df['distance'] <= radius] schools_df = schools_df[schools_df["distance"] <= radius]
schools_df = schools_df.sort_values('distance') schools_df = schools_df.sort_values("distance")
# Apply filters # Apply filters
if search: if search:
search_lower = search.lower() 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: 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] schools_df = schools_df[mask]
if local_authority: 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: 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 # Pagination
total = len(schools_df) total = len(schools_df)
@@ -166,9 +199,9 @@ async def get_schools(
schools_df = schools_df.iloc[start_idx:end_idx] schools_df = schools_df.iloc[start_idx:end_idx]
# Remove internal columns before sending # Remove internal columns before sending
output_cols = [c for c in schools_df.columns if c not in ['latitude', 'longitude']] output_cols = [c for c in schools_df.columns if c not in ["latitude", "longitude"]]
if 'distance' in schools_df.columns: if "distance" in schools_df.columns:
output_cols.append('distance') output_cols.append("distance")
return { return {
"schools": clean_for_json(schools_df[output_cols]), "schools": clean_for_json(schools_df[output_cols]),
@@ -176,7 +209,9 @@ async def get_schools(
"page": page, "page": page,
"page_size": page_size, "page_size": page_size,
"total_pages": (total + page_size - 1) // page_size if page_size > 0 else 0, "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,
} }
@@ -208,7 +243,7 @@ async def get_school_details(urn: int):
"address": latest.get("address", ""), "address": latest.get("address", ""),
"phase": "Primary", "phase": "Primary",
}, },
"yearly_data": clean_for_json(school_data) "yearly_data": clean_for_json(school_data),
} }
@@ -242,7 +277,7 @@ async def compare_schools(urns: str = Query(..., description="Comma-separated UR
"local_authority": latest.get("local_authority", ""), "local_authority": latest.get("local_authority", ""),
"address": latest.get("address", ""), "address": latest.get("address", ""),
}, },
"yearly_data": clean_for_json(school_data) "yearly_data": clean_for_json(school_data),
} }
return {"comparison": result} return {"comparison": result}
@@ -288,9 +323,13 @@ async def get_available_metrics():
@app.get("/api/rankings") @app.get("/api/rankings")
async def get_rankings( async def get_rankings(
metric: str = Query("rwm_expected_pct", description="KS2 metric to rank by"), 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"), 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.""" """Get primary school rankings by a specific KS2 metric."""
df = load_school_data() df = load_school_data()
@@ -356,8 +395,14 @@ async def get_data_info():
} }
years = [int(y) for y in sorted(df["year"].unique())] 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()} schools_per_year = {
la_counts = {str(k): int(v) for k, v in df["local_authority"].value_counts().to_dict().items()} 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 { return {
"status": "loaded", "status": "loaded",
@@ -385,4 +430,5 @@ if settings.frontend_dir.exists():
if __name__ == "__main__": if __name__ == "__main__":
import uvicorn import uvicorn
uvicorn.run(app, host=settings.host, port=settings.port) uvicorn.run(app, host=settings.host, port=settings.port)

482
backend/schemas.py
View File

@@ -5,94 +5,117 @@ Single source of truth for all data transformations.
# Column name mappings from DfE CSV to API field names # Column name mappings from DfE CSV to API field names
COLUMN_MAPPINGS = { COLUMN_MAPPINGS = {
'URN': 'urn', "URN": "urn",
'SCHNAME': 'school_name', "SCHNAME": "school_name",
'ADDRESS1': 'address1', "ADDRESS1": "address1",
'ADDRESS2': 'address2', "ADDRESS2": "address2",
'TOWN': 'town', "TOWN": "town",
'PCODE': 'postcode', "PCODE": "postcode",
'NFTYPE': 'school_type_code', "NFTYPE": "school_type_code",
'RELDENOM': 'religious_denomination', "RELDENOM": "religious_denomination",
'AGERANGE': 'age_range', "AGERANGE": "age_range",
'TOTPUPS': 'total_pupils', "TOTPUPS": "total_pupils",
'TELIG': 'eligible_pupils', "TELIG": "eligible_pupils",
# Core KS2 metrics # Core KS2 metrics
'PTRWM_EXP': 'rwm_expected_pct', "PTRWM_EXP": "rwm_expected_pct",
'PTRWM_HIGH': 'rwm_high_pct', "PTRWM_HIGH": "rwm_high_pct",
'READPROG': 'reading_progress', "READPROG": "reading_progress",
'WRITPROG': 'writing_progress', "WRITPROG": "writing_progress",
'MATPROG': 'maths_progress', "MATPROG": "maths_progress",
'PTREAD_EXP': 'reading_expected_pct', "PTREAD_EXP": "reading_expected_pct",
'PTWRITTA_EXP': 'writing_expected_pct', "PTWRITTA_EXP": "writing_expected_pct",
'PTMAT_EXP': 'maths_expected_pct', "PTMAT_EXP": "maths_expected_pct",
'READ_AVERAGE': 'reading_avg_score', "READ_AVERAGE": "reading_avg_score",
'MAT_AVERAGE': 'maths_avg_score', "MAT_AVERAGE": "maths_avg_score",
'PTREAD_HIGH': 'reading_high_pct', "PTREAD_HIGH": "reading_high_pct",
'PTWRITTA_HIGH': 'writing_high_pct', "PTWRITTA_HIGH": "writing_high_pct",
'PTMAT_HIGH': 'maths_high_pct', "PTMAT_HIGH": "maths_high_pct",
# GPS (Grammar, Punctuation & Spelling) # GPS (Grammar, Punctuation & Spelling)
'PTGPS_EXP': 'gps_expected_pct', "PTGPS_EXP": "gps_expected_pct",
'PTGPS_HIGH': 'gps_high_pct', "PTGPS_HIGH": "gps_high_pct",
'GPS_AVERAGE': 'gps_avg_score', "GPS_AVERAGE": "gps_avg_score",
# Science # Science
'PTSCITA_EXP': 'science_expected_pct', "PTSCITA_EXP": "science_expected_pct",
# School context # School context
'PTFSM6CLA1A': 'disadvantaged_pct', "PTFSM6CLA1A": "disadvantaged_pct",
'PTEALGRP2': 'eal_pct', "PTEALGRP2": "eal_pct",
'PSENELK': 'sen_support_pct', "PSENELK": "sen_support_pct",
'PSENELE': 'sen_ehcp_pct', "PSENELE": "sen_ehcp_pct",
'PTMOBN': 'stability_pct', "PTMOBN": "stability_pct",
# Gender breakdown # Gender breakdown
'PTRWM_EXP_B': 'rwm_expected_boys_pct', "PTRWM_EXP_B": "rwm_expected_boys_pct",
'PTRWM_EXP_G': 'rwm_expected_girls_pct', "PTRWM_EXP_G": "rwm_expected_girls_pct",
'PTRWM_HIGH_B': 'rwm_high_boys_pct', "PTRWM_HIGH_B": "rwm_high_boys_pct",
'PTRWM_HIGH_G': 'rwm_high_girls_pct', "PTRWM_HIGH_G": "rwm_high_girls_pct",
# Disadvantaged performance # Disadvantaged performance
'PTRWM_EXP_FSM6CLA1A': 'rwm_expected_disadvantaged_pct', "PTRWM_EXP_FSM6CLA1A": "rwm_expected_disadvantaged_pct",
'PTRWM_EXP_NotFSM6CLA1A': 'rwm_expected_non_disadvantaged_pct', "PTRWM_EXP_NotFSM6CLA1A": "rwm_expected_non_disadvantaged_pct",
'DIFFN_RWM_EXP': 'disadvantaged_gap', "DIFFN_RWM_EXP": "disadvantaged_gap",
# 3-year averages # 3-year averages
'PTRWM_EXP_3YR': 'rwm_expected_3yr_pct', "PTRWM_EXP_3YR": "rwm_expected_3yr_pct",
'READ_AVERAGE_3YR': 'reading_avg_3yr', "READ_AVERAGE_3YR": "reading_avg_3yr",
'MAT_AVERAGE_3YR': 'maths_avg_3yr', "MAT_AVERAGE_3YR": "maths_avg_3yr",
} }
# Numeric columns that need parsing # Numeric columns that need parsing
NUMERIC_COLUMNS = [ NUMERIC_COLUMNS = [
# Core metrics # Core metrics
'rwm_expected_pct', 'rwm_high_pct', 'reading_progress', "rwm_expected_pct",
'writing_progress', 'maths_progress', 'reading_expected_pct', "rwm_high_pct",
'writing_expected_pct', 'maths_expected_pct', 'reading_avg_score', "reading_progress",
'maths_avg_score', 'reading_high_pct', 'writing_high_pct', 'maths_high_pct', "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 & 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 # School context
'total_pupils', 'eligible_pupils', 'disadvantaged_pct', 'eal_pct', "total_pupils",
'sen_support_pct', 'sen_ehcp_pct', 'stability_pct', "eligible_pupils",
"disadvantaged_pct",
"eal_pct",
"sen_support_pct",
"sen_ehcp_pct",
"stability_pct",
# Gender breakdown # Gender breakdown
'rwm_expected_boys_pct', 'rwm_expected_girls_pct', "rwm_expected_boys_pct",
'rwm_high_boys_pct', 'rwm_high_girls_pct', "rwm_expected_girls_pct",
"rwm_high_boys_pct",
"rwm_high_girls_pct",
# Disadvantaged performance # 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 # 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 code to name mapping
SCHOOL_TYPE_MAP = { SCHOOL_TYPE_MAP = {
'AC': 'Academy', "AC": "Academy",
'ACC': 'Academy Converter', "ACC": "Academy Converter",
'ACS': 'Academy Sponsor Led', "ACS": "Academy Sponsor Led",
'CY': 'Community School', "CY": "Community School",
'VA': 'Voluntary Aided', "VA": "Voluntary Aided",
'VC': 'Voluntary Controlled', "VC": "Voluntary Controlled",
'FD': 'Foundation', "FD": "Foundation",
'F': 'Foundation', "F": "Foundation",
'FS': 'Free School', "FS": "Free School",
} }
# Special values to treat as null # 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 # KS2 Metric definitions - single source of truth
# Used by both backend API and frontend # Used by both backend API and frontend
@@ -103,42 +126,42 @@ METRIC_DEFINITIONS = {
"short_name": "RWM %", "short_name": "RWM %",
"description": "% meeting expected standard in reading, writing and maths", "description": "% meeting expected standard in reading, writing and maths",
"type": "percentage", "type": "percentage",
"category": "expected" "category": "expected",
}, },
"reading_expected_pct": { "reading_expected_pct": {
"name": "Reading Expected %", "name": "Reading Expected %",
"short_name": "Reading %", "short_name": "Reading %",
"description": "% meeting expected standard in reading", "description": "% meeting expected standard in reading",
"type": "percentage", "type": "percentage",
"category": "expected" "category": "expected",
}, },
"writing_expected_pct": { "writing_expected_pct": {
"name": "Writing Expected %", "name": "Writing Expected %",
"short_name": "Writing %", "short_name": "Writing %",
"description": "% meeting expected standard in writing", "description": "% meeting expected standard in writing",
"type": "percentage", "type": "percentage",
"category": "expected" "category": "expected",
}, },
"maths_expected_pct": { "maths_expected_pct": {
"name": "Maths Expected %", "name": "Maths Expected %",
"short_name": "Maths %", "short_name": "Maths %",
"description": "% meeting expected standard in maths", "description": "% meeting expected standard in maths",
"type": "percentage", "type": "percentage",
"category": "expected" "category": "expected",
}, },
"gps_expected_pct": { "gps_expected_pct": {
"name": "GPS Expected %", "name": "GPS Expected %",
"short_name": "GPS %", "short_name": "GPS %",
"description": "% meeting expected standard in grammar, punctuation & spelling", "description": "% meeting expected standard in grammar, punctuation & spelling",
"type": "percentage", "type": "percentage",
"category": "expected" "category": "expected",
}, },
"science_expected_pct": { "science_expected_pct": {
"name": "Science Expected %", "name": "Science Expected %",
"short_name": "Science %", "short_name": "Science %",
"description": "% meeting expected standard in science", "description": "% meeting expected standard in science",
"type": "percentage", "type": "percentage",
"category": "expected" "category": "expected",
}, },
# Higher Standard # Higher Standard
"rwm_high_pct": { "rwm_high_pct": {
@@ -146,35 +169,35 @@ METRIC_DEFINITIONS = {
"short_name": "RWM Higher %", "short_name": "RWM Higher %",
"description": "% achieving higher standard in RWM combined", "description": "% achieving higher standard in RWM combined",
"type": "percentage", "type": "percentage",
"category": "higher" "category": "higher",
}, },
"reading_high_pct": { "reading_high_pct": {
"name": "Reading Higher %", "name": "Reading Higher %",
"short_name": "Reading Higher %", "short_name": "Reading Higher %",
"description": "% achieving higher standard in reading", "description": "% achieving higher standard in reading",
"type": "percentage", "type": "percentage",
"category": "higher" "category": "higher",
}, },
"writing_high_pct": { "writing_high_pct": {
"name": "Writing Higher %", "name": "Writing Higher %",
"short_name": "Writing Higher %", "short_name": "Writing Higher %",
"description": "% achieving greater depth in writing", "description": "% achieving greater depth in writing",
"type": "percentage", "type": "percentage",
"category": "higher" "category": "higher",
}, },
"maths_high_pct": { "maths_high_pct": {
"name": "Maths Higher %", "name": "Maths Higher %",
"short_name": "Maths Higher %", "short_name": "Maths Higher %",
"description": "% achieving higher standard in maths", "description": "% achieving higher standard in maths",
"type": "percentage", "type": "percentage",
"category": "higher" "category": "higher",
}, },
"gps_high_pct": { "gps_high_pct": {
"name": "GPS Higher %", "name": "GPS Higher %",
"short_name": "GPS Higher %", "short_name": "GPS Higher %",
"description": "% achieving higher standard in GPS", "description": "% achieving higher standard in GPS",
"type": "percentage", "type": "percentage",
"category": "higher" "category": "higher",
}, },
# Progress Scores # Progress Scores
"reading_progress": { "reading_progress": {
@@ -182,21 +205,21 @@ METRIC_DEFINITIONS = {
"short_name": "Reading Progress", "short_name": "Reading Progress",
"description": "Progress in reading from KS1 to KS2", "description": "Progress in reading from KS1 to KS2",
"type": "score", "type": "score",
"category": "progress" "category": "progress",
}, },
"writing_progress": { "writing_progress": {
"name": "Writing Progress", "name": "Writing Progress",
"short_name": "Writing Progress", "short_name": "Writing Progress",
"description": "Progress in writing from KS1 to KS2", "description": "Progress in writing from KS1 to KS2",
"type": "score", "type": "score",
"category": "progress" "category": "progress",
}, },
"maths_progress": { "maths_progress": {
"name": "Maths Progress", "name": "Maths Progress",
"short_name": "Maths Progress", "short_name": "Maths Progress",
"description": "Progress in maths from KS1 to KS2", "description": "Progress in maths from KS1 to KS2",
"type": "score", "type": "score",
"category": "progress" "category": "progress",
}, },
# Average Scores # Average Scores
"reading_avg_score": { "reading_avg_score": {
@@ -204,21 +227,21 @@ METRIC_DEFINITIONS = {
"short_name": "Reading Avg", "short_name": "Reading Avg",
"description": "Average scaled score in reading", "description": "Average scaled score in reading",
"type": "score", "type": "score",
"category": "average" "category": "average",
}, },
"maths_avg_score": { "maths_avg_score": {
"name": "Maths Average Score", "name": "Maths Average Score",
"short_name": "Maths Avg", "short_name": "Maths Avg",
"description": "Average scaled score in maths", "description": "Average scaled score in maths",
"type": "score", "type": "score",
"category": "average" "category": "average",
}, },
"gps_avg_score": { "gps_avg_score": {
"name": "GPS Average Score", "name": "GPS Average Score",
"short_name": "GPS Avg", "short_name": "GPS Avg",
"description": "Average scaled score in GPS", "description": "Average scaled score in GPS",
"type": "score", "type": "score",
"category": "average" "category": "average",
}, },
# Gender Performance # Gender Performance
"rwm_expected_boys_pct": { "rwm_expected_boys_pct": {
@@ -226,28 +249,28 @@ METRIC_DEFINITIONS = {
"short_name": "Boys RWM %", "short_name": "Boys RWM %",
"description": "% of boys meeting expected standard", "description": "% of boys meeting expected standard",
"type": "percentage", "type": "percentage",
"category": "gender" "category": "gender",
}, },
"rwm_expected_girls_pct": { "rwm_expected_girls_pct": {
"name": "RWM Expected % (Girls)", "name": "RWM Expected % (Girls)",
"short_name": "Girls RWM %", "short_name": "Girls RWM %",
"description": "% of girls meeting expected standard", "description": "% of girls meeting expected standard",
"type": "percentage", "type": "percentage",
"category": "gender" "category": "gender",
}, },
"rwm_high_boys_pct": { "rwm_high_boys_pct": {
"name": "RWM Higher % (Boys)", "name": "RWM Higher % (Boys)",
"short_name": "Boys Higher %", "short_name": "Boys Higher %",
"description": "% of boys at higher standard", "description": "% of boys at higher standard",
"type": "percentage", "type": "percentage",
"category": "gender" "category": "gender",
}, },
"rwm_high_girls_pct": { "rwm_high_girls_pct": {
"name": "RWM Higher % (Girls)", "name": "RWM Higher % (Girls)",
"short_name": "Girls Higher %", "short_name": "Girls Higher %",
"description": "% of girls at higher standard", "description": "% of girls at higher standard",
"type": "percentage", "type": "percentage",
"category": "gender" "category": "gender",
}, },
# Disadvantaged Performance # Disadvantaged Performance
"rwm_expected_disadvantaged_pct": { "rwm_expected_disadvantaged_pct": {
@@ -255,21 +278,21 @@ METRIC_DEFINITIONS = {
"short_name": "Disadvantaged %", "short_name": "Disadvantaged %",
"description": "% of disadvantaged pupils meeting expected", "description": "% of disadvantaged pupils meeting expected",
"type": "percentage", "type": "percentage",
"category": "equity" "category": "equity",
}, },
"rwm_expected_non_disadvantaged_pct": { "rwm_expected_non_disadvantaged_pct": {
"name": "RWM Expected % (Non-Disadvantaged)", "name": "RWM Expected % (Non-Disadvantaged)",
"short_name": "Non-Disadv %", "short_name": "Non-Disadv %",
"description": "% of non-disadvantaged pupils meeting expected", "description": "% of non-disadvantaged pupils meeting expected",
"type": "percentage", "type": "percentage",
"category": "equity" "category": "equity",
}, },
"disadvantaged_gap": { "disadvantaged_gap": {
"name": "Disadvantaged Gap", "name": "Disadvantaged Gap",
"short_name": "Disadv Gap", "short_name": "Disadv Gap",
"description": "Gap between disadvantaged and national non-disadvantaged", "description": "Gap between disadvantaged and national non-disadvantaged",
"type": "score", "type": "score",
"category": "equity" "category": "equity",
}, },
# School Context # School Context
"disadvantaged_pct": { "disadvantaged_pct": {
@@ -277,28 +300,28 @@ METRIC_DEFINITIONS = {
"short_name": "% Disadvantaged", "short_name": "% Disadvantaged",
"description": "% of pupils eligible for free school meals or looked after", "description": "% of pupils eligible for free school meals or looked after",
"type": "percentage", "type": "percentage",
"category": "context" "category": "context",
}, },
"eal_pct": { "eal_pct": {
"name": "% EAL Pupils", "name": "% EAL Pupils",
"short_name": "% EAL", "short_name": "% EAL",
"description": "% of pupils with English as additional language", "description": "% of pupils with English as additional language",
"type": "percentage", "type": "percentage",
"category": "context" "category": "context",
}, },
"sen_support_pct": { "sen_support_pct": {
"name": "% SEN Support", "name": "% SEN Support",
"short_name": "% SEN", "short_name": "% SEN",
"description": "% of pupils with SEN support", "description": "% of pupils with SEN support",
"type": "percentage", "type": "percentage",
"category": "context" "category": "context",
}, },
"stability_pct": { "stability_pct": {
"name": "% Pupil Stability", "name": "% Pupil Stability",
"short_name": "% Stable", "short_name": "% Stable",
"description": "% of non-mobile pupils (stayed at school)", "description": "% of non-mobile pupils (stayed at school)",
"type": "percentage", "type": "percentage",
"category": "context" "category": "context",
}, },
# 3-Year Averages # 3-Year Averages
"rwm_expected_3yr_pct": { "rwm_expected_3yr_pct": {
@@ -306,122 +329,257 @@ METRIC_DEFINITIONS = {
"short_name": "RWM 3yr %", "short_name": "RWM 3yr %",
"description": "3-year average % meeting expected", "description": "3-year average % meeting expected",
"type": "percentage", "type": "percentage",
"category": "trends" "category": "trends",
}, },
"reading_avg_3yr": { "reading_avg_3yr": {
"name": "Reading Score (3-Year Avg)", "name": "Reading Score (3-Year Avg)",
"short_name": "Reading 3yr", "short_name": "Reading 3yr",
"description": "3-year average reading score", "description": "3-year average reading score",
"type": "score", "type": "score",
"category": "trends" "category": "trends",
}, },
"maths_avg_3yr": { "maths_avg_3yr": {
"name": "Maths Score (3-Year Avg)", "name": "Maths Score (3-Year Avg)",
"short_name": "Maths 3yr", "short_name": "Maths 3yr",
"description": "3-year average maths score", "description": "3-year average maths score",
"type": "score", "type": "score",
"category": "trends" "category": "trends",
}, },
} }
# Ranking columns to include in rankings response # Ranking columns to include in rankings response
RANKING_COLUMNS = [ 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 # Core expected
"rwm_expected_pct", "reading_expected_pct", "writing_expected_pct", "maths_expected_pct", "rwm_expected_pct",
"gps_expected_pct", "science_expected_pct", "reading_expected_pct",
"writing_expected_pct",
"maths_expected_pct",
"gps_expected_pct",
"science_expected_pct",
# Core higher # 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 # Progress & averages
"reading_progress", "writing_progress", "maths_progress", "reading_progress",
"reading_avg_score", "maths_avg_score", "gps_avg_score", "writing_progress",
"maths_progress",
"reading_avg_score",
"maths_avg_score",
"gps_avg_score",
# Gender # 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 # Equity
"rwm_expected_disadvantaged_pct", "rwm_expected_non_disadvantaged_pct", "disadvantaged_gap", "rwm_expected_disadvantaged_pct",
"rwm_expected_non_disadvantaged_pct",
"disadvantaged_gap",
# Context # Context
"disadvantaged_pct", "eal_pct", "sen_support_pct", "stability_pct", "disadvantaged_pct",
"eal_pct",
"sen_support_pct",
"stability_pct",
# 3-year # 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 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) # Local Authority code to name mapping (for fallback when LANAME column missing)
# Source: https://www.gov.uk/government/publications/local-authority-codes # Source: https://www.gov.uk/government/publications/local-authority-codes
LA_CODE_TO_NAME = { LA_CODE_TO_NAME = {
# Inner London # Inner London
201: "City of London", 202: "Camden", 203: "Greenwich", 204: "Hackney", 201: "City of London",
205: "Hammersmith and Fulham", 206: "Islington", 207: "Kensington and Chelsea", 202: "Camden",
208: "Lambeth", 209: "Lewisham", 210: "Southwark", 211: "Tower Hamlets", 203: "Greenwich",
212: "Wandsworth", 213: "Westminster", 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 # Outer London
301: "Barking and Dagenham", 302: "Barnet", 303: "Bexley", 304: "Brent", 301: "Barking and Dagenham",
305: "Bromley", 306: "Croydon", 307: "Ealing", 308: "Enfield", 309: "Haringey", 302: "Barnet",
310: "Harrow", 311: "Havering", 312: "Hillingdon", 313: "Hounslow", 303: "Bexley",
314: "Kingston upon Thames", 315: "Merton", 316: "Newham", 317: "Redbridge", 304: "Brent",
318: "Richmond upon Thames", 319: "Sutton", 320: "Waltham Forest", 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 # West Midlands
330: "Birmingham", 331: "Coventry", 332: "Dudley", 333: "Sandwell", 330: "Birmingham",
334: "Solihull", 335: "Walsall", 336: "Wolverhampton", 331: "Coventry",
332: "Dudley",
333: "Sandwell",
334: "Solihull",
335: "Walsall",
336: "Wolverhampton",
# Merseyside # 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 # Greater Manchester
350: "Bolton", 351: "Bury", 352: "Manchester", 353: "Oldham", 354: "Rochdale", 350: "Bolton",
355: "Salford", 356: "Stockport", 357: "Tameside", 358: "Trafford", 359: "Wigan", 351: "Bury",
352: "Manchester",
353: "Oldham",
354: "Rochdale",
355: "Salford",
356: "Stockport",
357: "Tameside",
358: "Trafford",
359: "Wigan",
# South Yorkshire # South Yorkshire
370: "Barnsley", 371: "Doncaster", 372: "Rotherham", 373: "Sheffield", 370: "Barnsley",
371: "Doncaster",
372: "Rotherham",
373: "Sheffield",
# West Yorkshire # 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 # Tyne and Wear
390: "Gateshead", 391: "Newcastle upon Tyne", 392: "North Tyneside", 390: "Gateshead",
393: "South Tyneside", 394: "Sunderland", 391: "Newcastle upon Tyne",
392: "North Tyneside",
393: "South Tyneside",
394: "Sunderland",
# Isles of Scilly # Isles of Scilly
420: "Isles of Scilly", 420: "Isles of Scilly",
# Unitary authorities (800+) # Unitary authorities (800+)
800: "Bath and North East Somerset", 801: "Bristol, City of", 802: "North Somerset", 800: "Bath and North East Somerset",
803: "South Gloucestershire", 805: "Hartlepool", 806: "Middlesbrough", 801: "Bristol, City of",
807: "Redcar and Cleveland", 808: "Stockton-on-Tees", 802: "North Somerset",
810: "Kingston Upon Hull, City of", 811: "East Riding of Yorkshire", 803: "South Gloucestershire",
812: "North East Lincolnshire", 813: "North Lincolnshire", 805: "Hartlepool",
815: "North Yorkshire", 816: "York", 806: "Middlesbrough",
820: "Bedford", 821: "Central Bedfordshire", 822: "Luton", 807: "Redcar and Cleveland",
823: "West Northamptonshire", 824: "North Northamptonshire", 808: "Stockton-on-Tees",
825: "Buckinghamshire", 826: "Milton Keynes", 810: "Kingston Upon Hull, City of",
830: "Derbyshire", 831: "Derby", 811: "East Riding of Yorkshire",
835: "Dorset", 836: "Bournemouth, Christchurch and Poole", 812: "North East Lincolnshire",
837: "Poole", 838: "Bournemouth", # Historic codes (merged into 836) 813: "North Lincolnshire",
839: "Durham", 840: "Darlington", 815: "North Yorkshire",
841: "East Sussex", 845: "Brighton and Hove", 816: "York",
846: "Hampshire", 850: "Portsmouth", 851: "Southampton", 852: "Isle of Wight", 820: "Bedford",
855: "Leicestershire", 856: "Leicester", 857: "Rutland", 821: "Central Bedfordshire",
860: "Staffordshire", 861: "Stoke-on-Trent", 822: "Luton",
865: "Wiltshire", 866: "Swindon", 823: "West Northamptonshire",
867: "Bracknell Forest", 868: "Windsor and Maidenhead", 869: "West Berkshire", 824: "North Northamptonshire",
870: "Reading", 871: "Slough", 872: "Wokingham", 825: "Buckinghamshire",
873: "Cambridgeshire", 874: "Peterborough", 826: "Milton Keynes",
876: "Halton", 877: "Warrington", 830: "Derbyshire",
878: "Devon", 879: "Plymouth", 880: "Torbay", 831: "Derby",
881: "Essex", 882: "Southend-on-Sea", 883: "Thurrock", 835: "Dorset",
884: "Herefordshire", 885: "Worcestershire", 836: "Bournemouth, Christchurch and Poole",
886: "Kent", 887: "Medway", 837: "Poole",
888: "Lancashire", 889: "Blackburn with Darwen", 890: "Blackpool", 838: "Bournemouth", # Historic codes (merged into 836)
891: "Nottinghamshire", 892: "Nottingham", 839: "Durham",
893: "Shropshire", 894: "Telford and Wrekin", 840: "Darlington",
895: "Cheshire East", 896: "Cheshire West and Chester", 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+) # County councils (900+)
908: "Cornwall", 909: "Cumbria", 908: "Cornwall",
916: "Gloucestershire", 919: "Hertfordshire", 909: "Cumbria",
921: "Norfolk", 925: "Lincolnshire", 916: "Gloucestershire",
919: "Hertfordshire",
921: "Norfolk",
925: "Lincolnshire",
926: "Northamptonshire", # Historic (split into 823/824 in 2021) 926: "Northamptonshire", # Historic (split into 823/824 in 2021)
928: "Northumberland", 929: "Oxfordshire", 928: "Northumberland",
931: "Somerset", 933: "Suffolk", 935: "Surrey", 929: "Oxfordshire",
936: "Warwickshire", 937: "West Sussex", 931: "Somerset",
933: "Suffolk",
935: "Surrey",
936: "Warwickshire",
937: "West Sussex",
# New authorities (2023 reorganization) # New authorities (2023 reorganization)
938: "Westmorland and Furness", 940: "Cumberland", 938: "Westmorland and Furness",
940: "Cumberland",
941: "North Yorkshire", # New unitary 941: "North Yorkshire", # New unitary
942: "Somerset", # New unitary (replaced 931) 942: "Somerset", # New unitary (replaced 931)
943: "Buckinghamshire", # New unitary (2020, replacing 825 in some datasets) 943: "Buckinghamshire", # New unitary (2020, replacing 825 in some datasets)
} }

16
frontend/app.js
View File

@@ -465,12 +465,12 @@ function renderFeaturedSchools(schools) {
<div class="school-address">${escapeHtml(school.address || "")}</div> <div class="school-address">${escapeHtml(school.address || "")}</div>
<div class="school-stats"> <div class="school-stats">
<div class="stat"> <div class="stat">
<div class="stat-value">Primary</div> <div class="stat-value">${formatMetricValue(school.rwm_expected_pct, "rwm_expected_pct")}</div>
<div class="stat-label">Phase</div> <div class="stat-label">RWM Expected</div>
</div> </div>
<div class="stat"> <div class="stat">
<div class="stat-value">KS2</div> <div class="stat-value">${school.total_pupils || "-"}</div>
<div class="stat-label">Data</div> <div class="stat-label">Pupils</div>
</div> </div>
</div> </div>
</div> </div>
@@ -587,12 +587,12 @@ function renderSchools(schools) {
<div class="school-address">${escapeHtml(school.address || "")}</div> <div class="school-address">${escapeHtml(school.address || "")}</div>
<div class="school-stats"> <div class="school-stats">
<div class="stat"> <div class="stat">
<div class="stat-value">Primary</div> <div class="stat-value">${formatMetricValue(school.rwm_expected_pct, "rwm_expected_pct")}</div>
<div class="stat-label">Phase</div> <div class="stat-label">RWM Expected</div>
</div> </div>
<div class="stat"> <div class="stat">
<div class="stat-value">KS2</div> <div class="stat-value">${school.total_pupils || "-"}</div>
<div class="stat-label">Data</div> <div class="stat-label">Pupils</div>
</div> </div>
</div> </div>
</div> </div>