Generate graphs of relative trends of daily reported COVID19 cases and deaths

top-30-covid.py

import dateparser
import pandas as pd
import urllib.request

# Load data from Our World in Data (OWID)

url = "https://covid.ourworldindata.org/data/owid-covid-data.csv"
urllib.request.urlretrieve(url, "owid-data.csv")
df = pd.read_csv('owid-data.csv')

m = 20

countries = df['iso_code'] != 'OWID_WRL'
cases = 'new_cases'
rcases = 'new_cases_per_million'

absolute = df[countries][['iso_code', cases]].groupby(['iso_code']).sum().sort_values(cases, ascending=False).reset_index()
relative = df[countries][['iso_code', rcases]].groupby(['iso_code']).sum().sort_values(rcases, ascending=False).reset_index()
countries = sorted(set(list(absolute['iso_code'][0:m]) + list(relative['iso_code'][0:m])))

version = max([dateparser.parse(d).date() for d in set(df['date'])])
print(version)

# Generate graphs of relative trends of daily reported COVID19 cases and deaths

fig, _ = plt.subplots(8, 4, figsize=(30, 30), facecolor='white', sharex=True, sharey=True)

n = 7 # 7-days moving average
for ax, country in zip(fig.axes, countries):
    data = df[df['iso_code'] == country][['date', 'new_cases', 'new_deaths']]
    xs = [dateparser.parse(d) for d in data['date']]
    for kind in ['new_cases', 'new_deaths']:
        m = data[kind].rolling(n).mean().max()
        ys = data[kind].rolling(n).mean() / m
        ax.plot(xs, ys, label=f"{ kind }", alpha=0.5, linewidth=2)
    
    ax.set_title(country)
    ax.set_facecolor("whitesmoke")
    ax.set_ylim(0.0, 1.0)
    ax.xaxis.set_tick_params(rotation=90)
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['bottom'].set_visible(False)
    ax.spines['left'].set_visible(False)
    ax.legend(frameon=False, facecolor="white")
    ax.yaxis.grid(True, alpha=0.75)
    
plt.savefig("TOP30-countriy-trends.png")
plt.show()