feat(ees): keep DfE's KS4 LA averages from the summary data set (H2)

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

pipeline/tests/test_ees_ks4_summary.py

@@ -0,0 +1,72 @@
+"""DfE's KS4 "summary, all state-funded" data set holds England, regional and
+LA rows. The England stream kept only the England row; its LA rows match
+DfE's published LA averages exactly, where the API's own mean was 7 points
+low (audit H2). Values below are DfE's for Kensington and Chelsea (207) and
+Wandsworth (212).
+"""
+import importlib.util
+import io
+from pathlib import Path
+
+import pandas as pd
+import pytest
+
+MODULE = (Path(__file__).resolve().parents[1] / 'plugins' / 'extractors' / 'tap-uk-ees'
+          / 'tap_uk_ees' / 'ks4_summary.py')
+
+CSV = """time_period,geographic_level,old_la_code,new_la_code,la_name,establishment_type_group,breakdown_topic,breakdown,attainment8_average,progress8_average,engmath_94_percent,engmath_95_percent,ebacc_entering_percent,ebacc_94_percent,ebacc_95_percent,ebacc_aps_average
+202425,National,,,,All state-funded,Total,Total,46.1,z,64.5,45.7,40.5,26.9,17.7,4.1
+202425,National,,,,All state-funded,Sex,Boys,44.1,z,62.0,43.0,38.0,24.0,16.0,3.9
+202425,Regional,,,,All state-funded,Total,Total,47.2,z,66.0,47.0,41.0,28.0,18.0,4.2
+202425,Local authority,207,E09000020,Kensington and Chelsea,All state-funded,Total,Total,54.5,z,77,61.4,45.6,32,26.6,4.89
+202425,Local authority,207,E09000020,Kensington and Chelsea,All state-funded,Sex,Girls,57.0,z,80,64.0,48.0,35,28.0,5.1
+202324,Local authority,207,E09000020,Kensington and Chelsea,All state-funded,Total,Total,54.5,0.29,76,60.0,44.0,31,25.0,4.8
+202425,Local authority,212,E09000032,Wandsworth,All state-funded,Total,Total,51.8,z,72,55.0,50.0,33,24.0,4.6
+202425,Local authority,212,E09000032,Wandsworth,Academies and free schools,Total,Total,52.0,z,73,56.0,51.0,34,25.0,4.7
+"""
+
+LA_KEYS = ('time_period', 'old_la_code', 'new_la_code', 'la_name')
+
+
+def _df():
+    return pd.read_csv(io.StringIO(CSV), dtype=str, keep_default_na=False)
+
+
+@pytest.fixture
+def summary():
+    spec = importlib.util.spec_from_file_location('ks4_summary', MODULE)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+def test_la_rows_are_one_per_la_and_year(summary):
+    rows = summary.headline_rows(_df(), 'Local authority')
+    assert sorted(zip(rows['time_period'], rows['old_la_code'])) == [
+        ('202324', '207'), ('202425', '207'), ('202425', '212')]
+
+
+def test_an_la_record_carries_codes_name_and_the_headline_measures(summary):
+    rows = summary.headline_rows(_df(), 'Local authority')
+    row = rows[(rows['time_period'] == '202425') & (rows['old_la_code'] == '207')].iloc[0]
+    assert summary.headline_record(row, LA_KEYS) == {
+        'time_period': '202425', 'old_la_code': '207', 'new_la_code': 'E09000020',
+        'la_name': 'Kensington and Chelsea',
+        'attainment_8_score': '54.5', 'progress_8_score': 'z',
+        'english_maths_standard_pass_pct': '77', 'english_maths_strong_pass_pct': '61.4',
+        'ebacc_entry_pct': '45.6', 'ebacc_standard_pass_pct': '32',
+        'ebacc_strong_pass_pct': '26.6', 'ebacc_avg_score': '4.89',
+    }
+
+
+def test_the_england_rows_are_one_per_year(summary):
+    rows = summary.headline_rows(_df(), 'National')
+    assert list(rows['time_period']) == ['202425']
+    assert summary.headline_record(rows.iloc[0], ('time_period',))['attainment_8_score'] == '46.1'
+
+
+def test_column_names_and_labels_match_whatever_their_case(summary):
+    df = _df()
+    df.columns = [c.upper() for c in df.columns]
+    df['GEOGRAPHIC_LEVEL'] = df['GEOGRAPHIC_LEVEL'].str.upper()
+    assert len(summary.headline_rows(df, 'Local authority')) == 3