Online Python classes in Mülheim an der Ruhr

import csv
from datetime import datetime, timezone

july = []
with open("wetterdaten_2025.csv", encoding="utf-8") as raw:
    for r in csv.DictReader(raw, delimiter=";"):
        moment = datetime.fromtimestamp(int(r["time"]), timezone.utc)
        if moment.month == 7:
            july.append(r)

print("Readings in July:", len(july), "of", 31 * 24 * 6)

def average(column):
    return sum(int(r[column]) for r in july) / len(july)

print("Average of every column, the lazy way:")
for column in ["T0", "M0", "S0", "V0_max"]:
    print(" ", column, round(average(column), 2))

It prints Readings in July: 4462 of 4464, then the lazy averages T0 213.56, M0 0.17, S0 3.32 and V0_max 8.24.

rules = {
    "T0": ("mean temperature, C", lambda v: sum(v) / len(v) / 10),
    "M0": ("total rain, mm", lambda v: sum(v) / 10),
    "S0": ("total sunshine, hours", lambda v: sum(v) / 60),
    "V0_max": ("strongest gust, km/h", lambda v: max(v) * 1.852),
}

print("July 2025, one rule per column:")
for column, (label, rule) in rules.items():
    values = [int(r[column]) for r in july]
    print(" ", label, round(rule(values), 1))

Run after Step 1. It prints mean temperature, C 21.4, total rain, mm 75.9, total sunshine, hours 247.1 and strongest gust, km/h 66.7.

import calendar

months = {}
with open("wetterdaten_2025.csv", encoding="utf-8") as raw:
    for r in csv.DictReader(raw, delimiter=";"):
        m = datetime.fromtimestamp(int(r["time"]), timezone.utc).month
        months.setdefault(m, []).append(int(r["M0"]))

for m, rain in sorted(months.items()):
    slots = calendar.monthrange(2025, m)[1] * 24 * 6
    share = 100 * len(rain) / slots
    note = "" if share >= 95 else "  <- incomplete"
    print(calendar.month_abbr[m], round(sum(rain) / 10, 1), "mm", f"{share:.0f} % of readings{note}")

Run after Step 1. It prints twelve lines, from Jan 104.2 mm 94 % of readings <- incomplete to Dec 54.4 mm 84 % of readings <- incomplete.