Coding classes in Thuringia

Taken from the GI's Informatik-Monitor 2026/27 (Thüringen page, consulted 2 November 2026). Informatik can stand in for a second science in a pupil's course choices, and as an exam subject it is treated as equal to the sciences. We did not obtain the 2027 Abitur dates from the Thuringian ministry, so this page prints none.

Two numbers from the monitor show how the Oberstufe works in practice. In 2024/25, 33 % of Thuringian upper secondary pupils took Informatik, from KMK figures, more than twice the national average of about 15 % that the GI quotes; yet the Land's education ministry counts only 1 % of Abitur candidates sitting an exam in it. Many pupils keep the subject without examining it.

import csv

with open("einheiten_thueringen.csv", encoding="utf-8") as f:
    units = list(csv.DictReader(f, delimiter=";"))
gemeinden = [u for u in units if u["ebene"] == "Kommunales Gebiet"]

by_name = {g["name"]: g["code"] for g in gemeinden}
print("Municipality rows:", len(gemeinden))
print("Entries keyed by name:", len(by_name))

Three municipalities have vanished, with no error and no warning. A Python dictionary keeps one value per key: when the same name comes again, the new row silently replaces the old one. Version 2 keys the data by the official code instead and lists the names that appear twice.

by_code = {g["code"]: g["name"] for g in gemeinden}
seen, twice = set(), []
for g in gemeinden:
    if g["name"] in seen:
        twice.append(g["name"])
    seen.add(g["name"])
print("Entries keyed by code:", len(by_code))
print("Names used twice:", sorted(twice))

from collections import Counter

kreis_name = {u["code"][:7]: u["name"] for u in units if u["ebene"] == "Landkreis/Stadtkreis"}
per_kreis = Counter(code[:7] for code in by_code)
top = per_kreis.most_common(1)[0]
print("Districts:", len(kreis_name))
print("Most municipalities:", kreis_name[top[0]], top[1])
for name in ["Eichenberg", "Lehesten", "Elxleben"]:
    print(name, "in", sorted(kreis_name[c[:7]] for c, n in by_code.items() if n == name))

Data: Thüringer Ministerium für Digitales und Infrastruktur, GDI-Th, INSPIRE WFS Verwaltungseinheiten, licence CC BY 4.0, credit © GDI-Th, retrieved 3 November 2026. The counts are of the units the service returned on that day; the district of each municipality is read from the first five digits of its official code. All counts and the table are our own analysis.

Each pair is two separate places in different districts, and a lookup by name would have sent anyone looking for one of them to the other. The fix is simple once you see it: use the identifier as the key, and keep the name as a label. The habit carries far beyond maps: pupils with the same first name in a class list, two products with one title in a shop, two files called final.docx.

1. Ages 9 to 11, no screen. Write a class list where two pupils share a first name, then try to find "the" one. What would make each entry unique?
2. Ages 11 to 13, Scratch. Store names in one list and codes in another, look up a name, and see which code Scratch returns when a name appears twice.
3. Teenagers, in Python. Run Version 1, explain the missing three in one sentence, then write Version 2 before reading ours.
4. Going further. Count municipalities per district from the code and rank the districts. Then check your total against the number of municipality rows.