infections_by_locations.py

import datetime as dt

import pandas as pd
import plotly.express as px


def location_timeline():
    df = pd.read_csv("covid_out_infections_0.csv")

    start_date = dt.datetime.strptime("2020 September 1", "%Y %B %d").date()
    df["date"] = [start_date + dt.timedelta(days=x) for x in df["#time"]]

    df = df[["date", "location_type"]]
    df["number"] = 0

    tab = df.groupby(["date", "location_type"])["number"].count()
    tab = pd.DataFrame(tab.to_frame()).reset_index()

    dd = pd.DataFrame()
    end_date = max(df["date"])
    num_days = (end_date - start_date).days
    dd["date"] = [start_date + dt.timedelta(days=x) for x in range(num_days + 1)]
    tt = list(set(tab["location_type"]))
    tt.sort()
    for t in tt:
        dd[t] = 0

    for i in range(len(dd)):
        curr_date = dd.iloc[i]["date"]

        temp = tab[tab["date"] == curr_date]

        for t in tt:
            data = list(temp[temp["location_type"] == t]["number"])
            if len(data) > 0:
                dd[t].iloc[i] = data[0]

    dd = dd.set_index("date")
    fig = px.line(dd)
    fig.update_layout(
        title="No. of infections per location type",
        xaxis_title="Date",
        yaxis_title="No. of infections",
        legend_title="Types of locations",
    )

    fig.show()


def map_plotter(date="2020 September 30"):
    start_date = dt.datetime.strptime("2020 September 1", "%Y %B %d").date()

    rd = dt.datetime.strptime(date, "%Y %B %d").date()

    df = pd.read_csv("covid_out_infections_0.csv")
    df["date"] = [start_date + dt.timedelta(days=x) for x in df["#time"]]
    mp = df[df["date"] == rd]

    fig = px.scatter_mapbox(df, lat="y", lon="x", color="location_type", zoom=10.3)
    fig.update_layout(mapbox_style="open-street-map")
    fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
    fig.update_layout(legend_title="Types of locations")

    fig.show()


if __name__ == "__main__":
    location_timeline()
    map_plotter()