Get daily Covid19 active cases worldmap JHU

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Tags: #wsr #covid #active-cases #analytics #plotly #automation #naas
Author: Florent Ravenel​
Input
Import libraries
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import pandas as pd
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from datetime import datetime
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try:
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    from dataprep.clean import clean_country
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except:
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    !pip install dataprep --user
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    from dataprep.clean import clean_country
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import plotly.graph_objects as go
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import naas
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Setup chart title
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title = "COVID 19 - Active cases (in milions)"
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Variables
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# Input URLs of the raw csv dataset
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urls = [
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    'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv',
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    'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv',
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    'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv'
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]
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​
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# Output paths
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output_image = f"{title}.png"
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output_html = f"{title}.html"
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Schedule your automation
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# Schedule your job everyday at 8:00 AM (NB: you can choose the time of your scheduling bot)
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naas.scheduler.add(cron="0 8 * * *")
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​
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#-> Uncomment the line below (by removing the hashtag) to remove your scheduler
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# naas.scheduler.delete()
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Model
Get data from JHU
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def get_data_url(urls):
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    df = pd.DataFrame()
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    for url in urls:
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        tmp_df = pd.read_csv(url)
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        tmp_df["Indicator"] = url.split("/time_series_covid19_")[-1].split("_global.csv")[0].capitalize()
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        df = pd.concat([df, tmp_df])
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    return df
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​
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df_init = get_data_url(urls)
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df_init
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Get all data from JHU
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def get_all_data(df_init):
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    df = df_init.copy()
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    # Cleaning
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    df = df.drop("Province/State", axis=1)
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    # Melt data
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    df = pd.melt(df,
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                 id_vars=["Country/Region", "Lat", "Long", "Indicator"],
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                 var_name="Date",
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                 value_name="Value").fillna(0)
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    df["Date"] = pd.to_datetime(df["Date"])
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    # Calc active cases
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    df_active = df.copy()
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    df_active.loc[df_active["Indicator"].isin(["Deaths", "Recovered"]), "Value"] = df_active["Value"] * (-1)
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    df_active["Indicator"] = "Active cases"
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    # Concat data
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    df = pd.concat([df, df_active])
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    # Group by country/region
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    to_group = ["Country/Region", "Lat", "Long", "Indicator", "Date"]
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    df = df.groupby(to_group, as_index=False).agg({"Value": "sum"})
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    # Cleaning
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    df = df.rename(columns={"Country/Region": "COUNTRY"})
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    df.columns = df.columns.str.upper()
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    return df.reset_index(drop=True)
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​
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df_clean = get_all_data(df_init)
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df_clean
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Prep data for worldmap
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def prep_data(df_init):
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    df = df_init.copy()
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    # Filter
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    date_max = df["DATE"].max()
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    df = df[
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        (df["INDICATOR"] == "Active cases") & 
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        (df["DATE"] == date_max)
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    ].reset_index(drop=True)
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​
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​
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    # Clean country
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    df = clean_country(df, 'COUNTRY', output_format='alpha-3').dropna()
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    df = df.rename(columns={'COUNTRY_clean': 'COUNTRY_ISO'})
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    return df.reset_index(drop=True)
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​
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df_worldmap = prep_data(df_clean)
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df_worldmap
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Create worldmap
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def create_worldmap(df):
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    fig = go.Figure()
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​
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    fig = go.Figure(data=go.Choropleth(
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        locations=df['COUNTRY_ISO'],
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        z=df['VALUE'],
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        text=df["COUNTRY"] + ": " + df['VALUE'].map("{:,.0f}".format).str.replace(",", " ") + " active cases",
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        hoverinfo="text",
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        colorscale='Blues',
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        autocolorscale=False,
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        reversescale=False,
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        marker_line_color='darkgray',
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        marker_line_width=0.5,
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        colorbar_tickprefix='',
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        colorbar_title='Active cases',
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    ))
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​
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    fig.update_layout(
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        title=title,
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        plot_bgcolor="#ffffff",
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        legend_x=1,
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        geo=dict(
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            showframe=False,
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            showcoastlines=False,
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            #projection_type='equirectangular'
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        ),
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        dragmode= False,
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        width=1200,
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        height=800,
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​
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    )
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    config = {'displayModeBar': False}
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    fig.show(config=config)
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    return fig
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​
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fig = create_worldmap(df_worldmap)
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Output
Export in PNG and HTML
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fig.write_image(output_image, width=1200)
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fig.write_html(output_html)
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Generate shareable assets
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link_image = naas.asset.add(output_image)
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link_html = naas.asset.add(output_html, {"inline":True})
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​
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#-> Uncomment the line below to remove your assets
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# naas.asset.delete(output_image)
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# naas.asset.delete(output_html)
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Input

Model

Output