Online Python classes in Regensburg

1. Week 1: set up and speak. The mentor helps install Python and a free editor while everyone is on the call, then the learner writes and runs a first program within the first lesson. The second lesson is about variables, and about saying out loud what a line will do before running it.
2. Week 2: decisions. if, elif and else, with a small quiz program the learner builds and then breaks on purpose. Error messages are read together, top to bottom, until they stop being frightening.
3. Week 3: repetition. for and while loops. Each learner predicts how many times a loop will run, then checks. The mentor watches screens in turn and stops the class when two people hit the same mistake.
4. Week 4: a first real file. Reading a CSV file line by line and counting something in it. For many learners this is the moment Python stops being an exercise and becomes a tool.

The mentors live in India and teach live over video; the time table further down shows how that maps onto a Regensburg week. A batch keeps the same mentor for the whole course, so the person who reads your code in week 4 knows how you worked in week 1.

import csv

def load(name):
    with open(name, encoding="utf-8") as f:
        return [(r["place"], float(r["x"]), float(r["y"])) for r in csv.DictReader(f)]

glass = load("glass.csv")
clothes = load("clothes.csv")
print("Glass sites:", len(glass), " Clothes sites:", len(clothes))

names = {place for place, _, _ in clothes}
same_text = [g for g in glass if g[0] in names]
print("Same address text:", len(same_text))

Fifty-nine looks precise, but read the matches and the method falls apart. One of the 59 is a site whose description is empty in both lists; Python happily treats two empty texts as equal, and those two containers are 846 metres apart. Meanwhile real neighbours are missed because people typed the same place differently: "Klenzestraße 4" in one list and "Klenzstraße 4" in the other, 16 metres apart; "Schopperplatz 4" and "Schopperplatz 4/ PP".

The positions are more honest than the words. Version 2 measures, for each glass site, the distance to the nearest clothes container, and calls it a match if that is 50 metres or less. Then, because any limit is a choice, it lists the sites that only just missed.

import math

def nearest(site, others):
    return min(math.dist(site[1:], o[1:]) for o in others)

gaps = [(nearest(g, clothes), g[0]) for g in glass]
within = [d for d, _ in gaps if d <= 50]
print("Clothes container within 50 m:", len(within))

close_misses = sorted((round(d), p) for d, p in gaps if 50 < d <= 100)
print("Between 50 and 100 m:", len(close_misses))
print("Closest miss:", close_misses[0])

Seventy-seven glass sites have a clothes container within 50 metres, far more than the 59 the text found, and the near misses are worth a look. The closest one is 53 metres away at a site the glass list only calls "Altglascontainer", which no text match could ever have found. Another, "Wöhrdstraße - Busparkplatz", has identical text in both lists but the containers stand 72 metres apart on what the name suggests is a coach car park. Whether that counts as "next to" is a decision for the person asking, not for the program.

Data: Stadt Regensburg, Altglas im Stadtgebiet Regensburg and Kleidercontainer im Stadtgebiet Regensburg, licence CC BY 4.0, downloaded 3 November 2026. Distances, matches and counts are our own; positions are the coordinates in the city's files, in metres. Of the 58 non-empty text matches, 57 are also within 50 metres.

1. Ages 10 to 13. On squared paper, mark five glass sites and five clothes sites with made-up coordinates, then count the pairs closer than two squares. Which pair would a text comparison get wrong?
2. Ages 14 to 17. Run both versions. Add one line to Version 1 so that empty descriptions are never matched, and predict the new count before running it.
3. Abitur practice. On paper, trace Version 2 for three glass sites and two clothes sites you invent. Write down every value of d. This is exactly the kind of tracing the Bavarian paper Abitur rewards.
4. Adults. Try limits of 25, 50 and 100 metres and plot how the match count changes. Explain why the curve flattens.

Legal time in Germany comes from the Einheitenzeitgesetz: MEZ is UTC+1 and MESZ is UTC+2. Summer time ended on 25 October 2026 and returns on 28 March 2027. India keeps UTC+5:30 throughout, so it leads Regensburg by four and a half hours in winter and three and a half in summer. Slots shown are samples, not set batch times.

Your booking shows times in your own clock. Gymnasium pupils with afternoon lessons usually choose a later weekday slot or Sunday; adults often prefer a weekday evening.