feat(api): translate GIAS codes to names at the query boundary

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

backend/data_loader.py

@@ -21,6 +21,38 @@ from .models import (
     FactDeprivation, FactFinance, FactPupilCharacteristics,
 )
 from .schemas import SCHOOL_TYPE_MAP
+from .gias_codes import (
+    ADMISSIONS_POLICY,
+    ESTABLISHMENT_STATUS,
+    PHASE_OF_EDUCATION,
+    RELIGIOUS_CHARACTER,
+    SCHOOL_TYPE,
+    translate,
+)
+
+# mart code column -> (API name column, dictionary)
+_GIAS_CODE_COLUMNS = {
+    "phase_code": ("phase", PHASE_OF_EDUCATION),
+    "school_type_code": ("school_type", SCHOOL_TYPE),
+    "status_code": ("status", ESTABLISHMENT_STATUS),
+    "religious_character_code": ("religious_denomination", RELIGIOUS_CHARACTER),
+    "admissions_policy_code": ("admissions_policy", ADMISSIONS_POLICY),
+}
+
+
+def translate_gias_code_columns(df: pd.DataFrame) -> pd.DataFrame:
+    """Map GIAS code columns to today's name columns (API contract).
+
+    Runs immediately after pd.read_sql so every downstream consumer —
+    filters, PHASE_GROUPS, payloads, /api/filters — keeps seeing names.
+    DataFrames without the code columns (old schema, test fixtures) pass
+    through unchanged.
+    """
+    for code_col, (name_col, mapping) in _GIAS_CODE_COLUMNS.items():
+        if code_col in df.columns:
+            df[name_col] = df[code_col].map(lambda c: translate(c, mapping))
+    return df
+
 
 _postcode_cache: Dict[str, Tuple[float, float]] = {}
 _typesense_client = None
@@ -121,16 +153,16 @@ _MAIN_QUERY = text("""
     SELECT
         s.urn,
         s.school_name,
-        s.phase,
+        s.phase_code,
-        s.school_type,
+        s.school_type_code,
         s.academy_trust_name   AS trust_name,
         s.academy_trust_uid    AS trust_uid,
-        s.religious_character  AS religious_denomination,
+        s.religious_character_code,
         s.gender,
         s.age_range,
         s.has_sixth_form,
-        s.status,
+        s.status_code,
-        s.admissions_policy,
+        s.admissions_policy_code,
         s.capacity,
         s.total_pupils         AS gias_total_pupils,
         s.headteacher_name,
@@ -256,6 +288,8 @@ def load_school_data_as_dataframe() -> pd.DataFrame:
     if df.empty:
         return df
 
+    df = translate_gias_code_columns(df)
+
     # Build address string
     df["address"] = df.apply(
         lambda r: ", ".join(

backend/models.py

@@ -17,11 +17,11 @@ class DimSchool(Base):
 
     urn = Column(Integer, primary_key=True)
     school_name = Column(String(255), nullable=False)
-    phase = Column(String(100))
+    phase_code = Column(Integer)
-    school_type = Column(String(100))
+    school_type_code = Column(Integer)
     academy_trust_name = Column(String(255))
     academy_trust_uid = Column(String(20))
-    religious_character = Column(String(100))
+    religious_character_code = Column(Integer)
     gender = Column(String(20))
     age_range = Column(String(20))
     has_sixth_form = Column(Boolean)
@@ -30,9 +30,9 @@ class DimSchool(Base):
     headteacher_name = Column(String(200))
     website = Column(String(255))
     telephone = Column(String(30))
-    status = Column(String(50))
+    status_code = Column(Integer)
     nursery_provision = Column(Boolean)
-    admissions_policy = Column(String(50))
+    admissions_policy_code = Column(Integer)
     # Denormalised Ofsted summary (updated by monthly pipeline)
     ofsted_grade = Column(Integer)
     ofsted_date = Column(Date)

backend/tests/test_gias_translation.py

@@ -0,0 +1,44 @@
+"""API-boundary translation: marts now carry GIAS codes; the DataFrame the
+rest of the backend sees must carry today's name strings."""
+
+import numpy as np
+import pandas as pd
+
+from backend.data_loader import translate_gias_code_columns
+from backend.gias_codes import ESTABLISHMENT_STATUS, PHASE_OF_EDUCATION
+
+
+def _code_for(mapping, name):
+    return next(c for c, n in mapping.items() if n == name)
+
+
+def test_codes_become_todays_names():
+    df = pd.DataFrame([{
+        "urn": 1,
+        "phase_code": float(_code_for(PHASE_OF_EDUCATION, "Primary")),
+        "school_type_code": np.nan,
+        "status_code": float(_code_for(ESTABLISHMENT_STATUS, "Open, but proposed to close")),
+        "religious_character_code": np.nan,
+        "admissions_policy_code": np.nan,
+    }])
+    out = translate_gias_code_columns(df)
+    row = out.iloc[0]
+    assert row["phase"] == "Primary"
+    assert row["status"] == "Open, but proposed to close"
+    assert row["school_type"] is None
+    assert row["religious_denomination"] is None
+    assert row["admissions_policy"] is None
+
+
+def test_unknown_code_degrades_not_blanks():
+    df = pd.DataFrame([{"urn": 1, "phase_code": 9999.0}])
+    out = translate_gias_code_columns(df)
+    assert out.iloc[0]["phase"] == "Unknown (9999)"
+
+
+def test_missing_code_columns_are_a_noop():
+    """Old-schema DataFrames (tests, pre-pipeline DBs) pass through untouched."""
+    df = pd.DataFrame([{"urn": 1, "phase": "Primary", "status": "Open"}])
+    out = translate_gias_code_columns(df)
+    assert out.iloc[0]["phase"] == "Primary"
+    assert out.iloc[0]["status"] == "Open"