Python 3.x minor version usage stats, by proxy of PyPI downloads

pypi_stats.py

'''
Python 3.x minor version usage stats, by proxy of PyPI downloads.

Placed into public domain by Mia yun Ruse.
(I wouldn't recommend using it though. It's a tad hacky.)
'''

from datetime import date as Date, timedelta
from requests import get
from json import loads

URL = "https://pypistats.org/api/packages/{package}/python_minor"


VersionData = dict[str, int]


def get_version_stats(package):
    data: dict[Date, VersionData] = dict()
    entries: list[dict[str, str]] = loads(
        get(URL.format(package=package)).text)['data']

    for e in entries:
        date = Date.fromisoformat(e['date'])
        data.setdefault(date, dict())
        N = e['downloads']
        v = e['category']
        data[date][v] = N

    return data


def average_version_states(data: dict[Date, VersionData], start: Date = None, end: Date = None):
    version_data: VersionData = dict()
    for date, entries in data.items():
        if not (start is None or start <= date) and (end is None or date <= end):
            continue
        for v, N in entries.items():
            version_data.setdefault(v, 0)
            version_data[v] += N

    return version_data


package = 'urllib3'
end_date = Date.today()
N_days = 30

data = get_version_stats(package)
data = average_version_states(
    data, end_date - timedelta(days=N_days), end_date)

for k in list(data):
    if not k.startswith('3.'):
        del data[k]

labels = sorted(data.keys(), reverse=True,
                key=lambda x: tuple(map(int, x.split('.'))))
usage = [data[i] for i in labels]
usage_cumulative = [sum(usage[:i+1]) for i in range(len(usage))]
N = sum(usage)

print('Version   Downloads   Cumulative (%)')
for i, u, U in zip(labels, usage, usage_cumulative):
    print(f'{i:<5}    {u:10} {U:>12}  {U/N*100:>6.2f}%')

from matplotlib import pyplot  # noqa
from matplotlib.ticker import PercentFormatter  # noqa

pyplot.title('Python version usage (avg of last {} days, as of {})'.format(
    N_days, end_date))

pyplot.xlabel('Python version')
pyplot.xlim(0, len(labels)-1)

usage_display = [u/N for u in usage]
pyplot.ylabel('Downloads for PyPI package {!r}'.format(package))
pyplot.locator_params(axis='y', nbins=11)
pyplot.gca().yaxis.set_major_formatter(PercentFormatter(1))

pyplot.grid(linewidth=0.2)
pyplot.hist(labels, histtype='step', weights=usage_display, cumulative=True)
pyplot.savefig('usage.png')

Which version of Python should you support to ensure you are supporting the most users?

Version   Downloads    Cumulative (%)
3.12          17766        17766    0.01%
3.11        7112527      7130293    2.66%
3.10       18557639     25687932    9.57%
3.9        70130541     95818473   35.69%
3.8        53781913    149600386   55.73%
3.7        82474033    232074419   86.45%
3.6        34914607    266989026   99.45%
3.5         1116711    268105737   99.87%
3.4          347573    268453310  100.00%
3.3             303    268453613  100.00%
3.2              12    268453625  100.00%