backend/places.py

@@ -81,9 +81,12 @@ def _phase_urns(group, publishable: set[int]) -> dict[str, tuple[int, ...]]:
 # schools, and at least two. GIAS carries occasional postcode errors — EN6
 # lists two Shropshire schools among fourteen in Hertfordshire — and a bare
 # "any authority present" rule would print those as though they were real.
+# There is deliberately no cap on how many are named. An earlier cut stopped
+# at three, which silently dropped the fourth in exactly the case where the
+# information matters most — a genuinely fragmented place. The share rule is
+# the only limit, and it already bounds the list at ten.
 _AUTHORITY_MIN_SHARE = 0.10
 _AUTHORITY_MIN_SCHOOLS = 2
-_AUTHORITY_MAX_SHOWN = 3
 
 
 def _authorities(group) -> tuple[tuple[str, int], ...]:
@@ -110,43 +113,64 @@ def _authorities(group) -> tuple[tuple[str, int], ...]:
             if str(name) not in EXCLUDED_FILTER_VALUES:
                 return ((str(name), int(n)),)
         return ()
-    return tuple(kept[:_AUTHORITY_MAX_SHOWN])
+    return tuple(kept)
 
 
-def _parent_authority(group) -> str | None:
-    """The most common authority in a group — the useful 301 target.
+def _parent_authority(authorities: tuple[tuple[str, int], ...]) -> str | None:
+    """The 301 target: the largest authority a place sits in.
 
-    A town spanning several authorities has no single parent, so the mode is
-    the honest answer rather than an arbitrary first row.
+    Derived from `authorities` rather than computed separately. The first cut
+    used `mode()` here while `authorities` used `value_counts()`, and on an
+    exact tie pandas does not guarantee the two pick the same name — so the
+    redirect could have pointed somewhere other than the authority the page
+    named first. One computation, one answer.
+
+    Deriving it also inherits the sentinel filter, so a place can no longer
+    redirect to /schools/authority/does-not-apply.
     """
-    if "local_authority" not in group.columns:
-        return None
-    top = group["local_authority"].dropna()
-    return str(top.mode().iloc[0]) if not top.empty else None
+    return authorities[0][0] if authorities else None
 
 
 def _group(df, column: str, kind: str, publishable: set[int]) -> dict[str, Place]:
-    """One Place per distinct value of `column` that clears the threshold."""
+    """One Place per distinct SLUG in `column` that clears the threshold.
+
+    Grouped by slug, not by raw value, because GIAS spells the same place
+    several ways and they all resolve to one URL. Five town slugs come from
+    more than one spelling: "London" (1,819 schools) and "LONDON" (12) both
+    slugify to `london`; Weston-super-Mare is split 14/19 across two
+    spellings; Newcastle-under-Lyme across three.
+
+    Grouping by raw value meant the later group simply overwrote the earlier
+    one in this dict — so /schools/london could have shown twelve schools
+    instead of 1,819, silently and depending on row order.
+
+    The display name is the most common spelling, which is the one a reader
+    expects to see.
+    """
     from backend.app import _slugify
 
     if column not in df.columns:
         return {}
 
+    working = df.assign(_slug=df[column].map(
+        lambda v: _slugify(str(v).strip()) if isinstance(v, str) and v.strip() else None))
+    working = working[working["_slug"].notna() & (working["_slug"] != "")]
+
     out: dict[str, Place] = {}
-    for name, group in df.groupby(column, dropna=True):
-        name = str(name).strip()
-        if not name:
-            continue
+    for slug, group in working.groupby("_slug"):
+        slug = str(slug)
         urns = tuple(sorted({int(u) for u in group["urn"]} & publishable))
         if len(urns) < MIN_SCHOOLS:
             continue
-        slug = _slugify(name)
-        if not slug:
+        spellings = group[column].dropna().value_counts()
+        if spellings.empty:
             continue
+        name = str(spellings.index[0]).strip()
+        authorities = () if kind == "authority" else _authorities(group)
         place = Place(
             kind=kind, slug=slug, name=name, urns=urns,
-            parent_authority=_parent_authority(group) if kind == "town" else None,
-            authorities=() if kind == "authority" else _authorities(group),
+            parent_authority=_parent_authority(authorities),
+            authorities=authorities,
             phase_urns=_phase_urns(group, publishable),
         )
         out[place.key] = place
@@ -179,9 +203,10 @@ def _outcode_places(df, publishable: set[int]) -> dict[str, Place]:
         urns = tuple(sorted({int(u) for u in group["urn"]} & publishable))
         if len(urns) < MIN_SCHOOLS:
             continue
+        authorities = _authorities(group)
         place = Place(kind="outcode", slug=str(oc).lower(), name=str(oc),
-                      urns=urns, parent_authority=_parent_authority(group),
-                      authorities=_authorities(group),
+                      urns=urns, parent_authority=_parent_authority(authorities),
+                      authorities=authorities,
                       phase_urns=_phase_urns(group, publishable))
         out[place.key] = place
     return out
@@ -223,9 +248,10 @@ def _locality_places(df, publishable: set[int],
                 "threshold of %d - not published",
                 slug, ", ".join(outcodes), len(urns), MIN_SCHOOLS)
             continue
+        authorities = _authorities(group)
         place = Place(kind="locality", slug=slug, name=name, urns=urns,
-                      parent_authority=_parent_authority(group),
-                      authorities=_authorities(group),
+                      parent_authority=_parent_authority(authorities),
+                      authorities=authorities,
                       phase_urns=_phase_urns(group, publishable))
         out[place.key] = place
     return out

backend/tests/test_places.py

@@ -290,3 +290,56 @@ def test_a_place_always_names_at_least_one_authority():
     place = reg.get("town:fragmented")
     assert place is not None
     assert len(place.authorities) == 1
+
+
+def test_every_qualifying_authority_is_named_with_no_cap():
+    """An earlier cut stopped at three, dropping the fourth silently.
+
+    That truncation bit exactly where the information matters most — a
+    genuinely fragmented place — and nothing recorded it.
+    """
+    rows = []
+    for i, la in enumerate(["Hackney", "Lambeth", "Westminster", "Lewisham"]):
+        rows += _town(3, "Fourway", la, start=300000 + i * 100)
+    reg = build_place_registry(_df(rows))
+    assert len(reg["town:fourway"].authorities) == 4
+
+
+def test_the_redirect_target_is_the_authority_named_first():
+    """They were computed separately — mode() against value_counts() — and on
+    an exact tie pandas does not guarantee the two agree."""
+    rows = (_town(26, "London", "Merton", start=300000)
+            + _town(7, "London", "Wandsworth", start=400000))
+    for r in rows:
+        r["postcode"] = "SW19 1AA"
+    place = build_place_registry(_df(rows))["outcode:sw19"]
+    assert place.parent_authority == place.authorities[0][0]
+
+
+def test_a_place_never_redirects_to_a_sentinel_authority():
+    # Deriving the parent from `authorities` inherits its sentinel filter.
+    rows = (_town(6, "Someplace", "Does not apply", start=300000)
+            + _town(5, "Someplace", "Essex", start=400000))
+    reg = build_place_registry(_df(rows))
+    assert reg["town:someplace"].parent_authority == "Essex"
+
+
+def test_spellings_of_one_place_are_merged_not_overwritten():
+    """GIAS spells the same place several ways, and they share a URL.
+
+    "London" (1,819 schools) and "LONDON" (12) both slugify to `london`.
+    Grouping by raw value let the later group overwrite the earlier one, so
+    the page could have shown twelve schools instead of 1,819 — silently, and
+    depending on row order.
+    """
+    rows = (_town(6, "Weston-super-Mare", "North Somerset", start=300000)
+            + _town(5, "Weston-Super-Mare", "North Somerset", start=400000))
+    reg = build_place_registry(_df(rows))
+    assert len(reg["town:weston-super-mare"].urns) == 11
+
+
+def test_the_merged_place_takes_its_most_common_spelling():
+    rows = (_town(9, "Newcastle-under-Lyme", "Staffordshire", start=300000)
+            + _town(5, "NEWCASTLE-UNDER-LYME", "Staffordshire", start=400000))
+    reg = build_place_registry(_df(rows))
+    assert reg["town:newcastle-under-lyme"].name == "Newcastle-under-Lyme"