Coding and AI classes in Braunschweig

import csv
from datetime import date

counts = {}
with open("braunschweig_footfall.csv", encoding="latin-1") as f:
    rows = csv.reader(f, delimiter=";")
    next(rows)
    for row in rows:
        if row[0].count(".") == 2 and row[13] not in ("", "0"):
            d, m, y = row[0].split(".")
            counts[date(int(y), int(m), int(d))] = int(row[13])   # column 13: Hutfiltern

# learn: the average footfall for each weekday, from 2017 to 2019
sums, days = [0] * 7, [0] * 7
for day, n in counts.items():
    if 2017 <= day.year <= 2019:
        sums[day.weekday()] += n
        days[day.weekday()] += 1
typical = [round(sums[i] / days[i]) for i in range(7)]
print("Typical Monday to Sunday:", typical)

test = {day: n for day, n in counts.items() if day.year == 2022}
errors = {day: n - typical[day.weekday()] for day, n in test.items()}
average_miss = sum(abs(e) for e in errors.values()) / len(errors)
print("Days tested in 2022:", len(test), " average miss:", round(average_miss))

It prints: Typical Monday to Sunday: [17867, 17826, 18412, 18578, 21861, 32978, 7947] and Days tested in 2022: 349 average miss: 5696.

# a crowd walks past more than one counter: check the neighbour, Schuhstraße (column 25)
schuh = {}
with open("braunschweig_footfall.csv", encoding="latin-1") as f:
    rows = csv.reader(f, delimiter=";")
    next(rows)
    for row in rows:
        if row[0].count(".") == 2 and row[25] not in ("", "0"):
            d, m, y = row[0].split(".")
            schuh[date(int(y), int(m), int(d))] = int(row[25])

normal = [counts[d] / schuh[d] for d in counts if d in schuh and 2017 <= d.year <= 2019]
print("Usual ratio Hutfiltern / Schuhstraße:", round(sum(normal) / len(normal), 2))
worst = sorted(errors, key=lambda day: abs(errors[day]), reverse=True)[:4]
for day in worst:
    print(day, "Hutfiltern", test[day], "Schuhstraße", schuh[day], "ratio", round(test[day] / schuh[day], 2))

It prints: Usual ratio Hutfiltern / Schuhstraße: 1.12, then 2022-11-10 Hutfiltern 79097 Schuhstraße 18864 ratio 4.19, 2022-07-19 Hutfiltern 62926 Schuhstraße 15236 ratio 4.13, 2022-08-25 Hutfiltern 63272 Schuhstraße 19432 ratio 3.26 and 2022-07-18 Hutfiltern 56940 Schuhstraße 22103 ratio 2.58.