Django plot user growth

gistfile1.txt

from django.contrib.auth.models import User
from datetime import datetime, timedelta
import pytz
import time
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)

# Set a start date
start_date = datetime(2020, 7, 13, tzinfo=pytz.UTC)
today = pytz.utc.localize(datetime.today())

# Setup for weekly metrics
week_start = start_date
weeks = []
while week_start <= today:
    weeks.append(week_start)
    week_start += timedelta(days=7)
    week_strings = [week.strftime("%d/%m") for week in weeks]

# Count number of cumulative sign ups
tot_users = [] # total users
pc_growth = [] # percentage growth week on week
for (index, week) in enumerate(weeks):
    weekly_signups = User.objects.filter(is_active=True).filter(date_joined__gte=start_date).filter(date_joined__lt=weeks[index])
    tot_users.append(len(weekly_signups))
    if index > 1:
        pc_growth.append(np.around((100* (tot_users[index] - tot_users[index-1]))/(tot_users[index-1]), 2))
    else:
        pc_growth.append(0)
users_per_week_df = pd.DataFrame({"tot_users":tot_users, "pc_growth":pc_growth}, index=week_strings)

print(np.mean(np.around(pc_growth, 2)))
print(users_per_week_df)

fig, axs = plt.subplots(figsize=(10,6))
axs.plot(users_per_week_df["tot_users"])
axs.set_xticks(axs.get_xticks()[::4])
axs.set_xlabel("Date")
axs.set_ylabel("Total Users")
axs.set_title("Total User Signups",{'fontsize': 18, 'fontweight': 600})
axs.grid()
plt.show()