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COVID-19

Visualizing live data from around the world

In this notebook we will have an opportunity to visualize the latest number of confirmed cases of COVID-19 around the world.

Use this notebook to explore each affected country and continent.

Press the Run button to run the next cell.

Note: You only need to install dependencies once. If you receive Success! after running the cell below, comment out the dependencies as follows

# !pip install pycountry_convert
# !pip install requests
# !pip install pandas
# !pip install plotly

#!pip install pycountry_convert 
#!pip install requests 
#!pip install pandas 
#!pip install plotly 
import requests as r
import pandas as pd
# Need to remove this if we upgrade to pandas 1.0.1 
from pandas.io.json import json_normalize
import pycountry_convert as pc
import plotly.offline as offline 
import plotly.graph_objs as go
print("Success!")

Success!

We are now going to download the data.

Run the cell below to download the number of confirmed cases, the number of deaths and the latest updates.

# Get the latest data
# Confirmed
try:
    API_response_confirmed = r.get("https://covid19api.herokuapp.com/confirmed")
    data = API_response_confirmed.json() 
    # If using pandas 1.0.1 use json_normalize(data,record_path=["locations"])
    confirmed_df = json_normalize(data,record_path=["locations"])
except:
    print("Error: check GitHub is functioning appropriately, check https://covid19api.herokuapp.com/ is not down, check fields were not renamed")
# Deaths
try:
    API_response_death = r.get("https://covid19api.herokuapp.com/deaths")
    data = API_response_death.json() 
    # If using pandas 1.0.1 use json_normalize(data,record_path=["locations"])
    death_df = json_normalize(data,record_path=["locations"])
except:
    print("Error: check GitHub is functioning appropriately, check https://covid19api.herokuapp.com/ is not down, check fields were not renamed")
# Latest
try:
    API_summary = r.get("https://covid19api.herokuapp.com/latest")
    data2 = API_summary.json()
    # If using pandas 1.0.1 use json_normalize(data2)
    summary  = json_normalize(data2)
    print("Data downloaded")
except:
    print("Error: check GitHub is functioning appropriately, check https://covid19api.herokuapp.com/ is not down, check fields were not renamed")

Data downloaded

COVID-19 confirmed cases (first five entries)

confirmed_df.head(5)
country country_code latest province coordinates.latitude coordinates.longitude history.1/22/20 history.1/23/20 history.1/24/20 history.1/25/20 ... history.9/19/20 history.9/2/20 history.9/20/20 history.9/3/20 history.9/4/20 history.9/5/20 history.9/6/20 history.9/7/20 history.9/8/20 history.9/9/20
0 Afghanistan AF 39044 nan 33.93911 67.709953 0 0 0 0 ... 38919 38243 39044 38288 38304 38324 38398 38494 38520 38544
1 Albania AL 12385 nan 41.15330 20.168300 0 0 0 0 ... 12226 9728 12385 9844 9967 10102 10255 10406 10553 10704
2 Algeria DZ 49826 nan 28.03390 1.659600 0 0 0 0 ... 49623 45158 49826 45469 45773 46071 46364 46653 46938 47216
3 Andorra AD 1564 nan 42.50630 1.521800 0 0 0 0 ... 1564 1199 1564 1199 1215 1215 1215 1261 1261 1301
4 Angola AO 3991 nan -11.20270 17.873900 0 0 0 0 ... 3901 2777 3991 2805 2876 2935 2965 2981 3033 3092

5 rows × 249 columns

Confirmed deaths as a result of COVID-19 (first five entries)

death_df.head(5)
country country_code latest province coordinates.latitude coordinates.longitude history.1/22/20 history.1/23/20 history.1/24/20 history.1/25/20 ... history.9/19/20 history.9/2/20 history.9/20/20 history.9/3/20 history.9/4/20 history.9/5/20 history.9/6/20 history.9/7/20 history.9/8/20 history.9/9/20
0 Afghanistan AF 1441 nan 33.93911 67.709953 0 0 0 0 ... 1437 1409 1441 1409 1409 1409 1412 1415 1418 1420
1 Albania AL 362 nan 41.15330 20.168300 0 0 0 0 ... 358 296 362 301 306 312 316 319 321 322
2 Algeria DZ 1672 nan 28.03390 1.659600 0 0 0 0 ... 1665 1523 1672 1529 1539 1549 1556 1562 1571 1581
3 Andorra AD 53 nan 42.50630 1.521800 0 0 0 0 ... 53 53 53 53 53 53 53 53 53 53
4 Angola AO 152 nan -11.20270 17.873900 0 0 0 0 ... 147 112 152 113 115 117 117 120 124 126

5 rows × 249 columns

Let’s assign a continent code to our tables. Run the cell below to do so.

# Assign continent codes for sunburst plot
cont_codes = []
for item in confirmed_df["country_code"]:
    try:
        cont_code = pc.country_alpha2_to_continent_code(item)
        cont_codes.append(cont_code)
    except:
        cont_codes.append(item)
        
confirmed_df["continent code"] = cont_codes
death_df["continent code"] = cont_codes
print("Continent codes appended.")

Continent codes appended.

COVID-19 confirmed cases (first five entries)

confirmed_df.head(5)
country country_code latest province coordinates.latitude coordinates.longitude history.1/22/20 history.1/23/20 history.1/24/20 history.1/25/20 ... history.9/2/20 history.9/20/20 history.9/3/20 history.9/4/20 history.9/5/20 history.9/6/20 history.9/7/20 history.9/8/20 history.9/9/20 continent code
0 Afghanistan AF 39044 nan 33.93911 67.709953 0 0 0 0 ... 38243 39044 38288 38304 38324 38398 38494 38520 38544 AS
1 Albania AL 12385 nan 41.15330 20.168300 0 0 0 0 ... 9728 12385 9844 9967 10102 10255 10406 10553 10704 EU
2 Algeria DZ 49826 nan 28.03390 1.659600 0 0 0 0 ... 45158 49826 45469 45773 46071 46364 46653 46938 47216 AF
3 Andorra AD 1564 nan 42.50630 1.521800 0 0 0 0 ... 1199 1564 1199 1215 1215 1215 1261 1261 1301 EU
4 Angola AO 3991 nan -11.20270 17.873900 0 0 0 0 ... 2777 3991 2805 2876 2935 2965 2981 3033 3092 AF

5 rows × 250 columns

Confirmed deaths as a result of COVID-19 (first five entries)

death_df.head(5)
country country_code latest province coordinates.latitude coordinates.longitude history.1/22/20 history.1/23/20 history.1/24/20 history.1/25/20 ... history.9/2/20 history.9/20/20 history.9/3/20 history.9/4/20 history.9/5/20 history.9/6/20 history.9/7/20 history.9/8/20 history.9/9/20 continent code
0 Afghanistan AF 1441 nan 33.93911 67.709953 0 0 0 0 ... 1409 1441 1409 1409 1409 1412 1415 1418 1420 AS
1 Albania AL 362 nan 41.15330 20.168300 0 0 0 0 ... 296 362 301 306 312 316 319 321 322 EU
2 Algeria DZ 1672 nan 28.03390 1.659600 0 0 0 0 ... 1523 1672 1529 1539 1549 1556 1562 1571 1581 AF
3 Andorra AD 53 nan 42.50630 1.521800 0 0 0 0 ... 53 53 53 53 53 53 53 53 53 EU
4 Angola AO 152 nan -11.20270 17.873900 0 0 0 0 ... 112 152 113 115 117 117 120 124 126 AF

5 rows × 250 columns

Time to visualize!

We will build a sunburst chart, where we display the latest number of confirmed cases in each country, as well as the latest number of deaths in each continent.

We will need to manipulate our data a bit to create a plot. It may look like overwhelming at first, but don’t worry - we separated each step.

# Build sub DataFrame for each
conf_df = confirmed_df[['country','continent code','latest']]
deat_df = death_df[['country','continent code','latest']]

# The sunburst plot requires weights (values), labels, and parent (region, or World)
# We build the corresponding table here
# Inspired and adapted from https://pypi.org/project/world-bank-data/ 
columns = ['labels','parents',  'values']

# Build the levels 
# Level 1 - Countries
level1 = conf_df.copy()
# Rename columns
level1.columns = columns
# Add a text column - format values column
level1['text'] = level1['values'].apply(lambda pop: 'Conf {:,.0f}'.format(pop))

# Create level 2 - Continents
# Group by continent code
death_by_cont = deat_df.groupby('continent code').latest.sum().reset_index()[['continent code', 'continent code', 'latest']]
# Group by continent code
level2 = conf_df.groupby('continent code').latest.sum().reset_index()[['continent code', 'continent code', 'latest']]
# Rename columns
level2.columns = columns
level2['parents'] = 'World'
# move value to text for this level
level2['text'] = level2['values'].apply(lambda pop: ' Confirmed {:,.0f}'.format(pop))
level2['values'] = death_by_cont["latest"]

# Create level 3 - world total as of latest date
level3 = pd.DataFrame({'parents': [''], 'labels': ['World'],
                       'values': [0.0], 'text': ['{:,.0f}'.format(summary["confirmed"].sum())]})
# Create master dataframe with all levels
all_levels = pd.concat([level1,level2,level3], axis=0,sort=True).reset_index(drop=True)
print("Data manipulated")

Data manipulated

Run the cell below to display the sunburst plot.

Click on each continent to look at each country’s COVID-19 confirmed cases. To go back, click on the continent code.

The codes are as follows:

EU: Europe
AS: Asia
NA: North America
SA: South America
AF: Africa
OC: Oceania
TL and XX contain various items not recognized by Python - the Diamond Princess Cruise ship can be found there. 

#Sunburst plot
last_date = pd.to_datetime('today').date()
fig = offline.iplot(dict(
    data=[dict(type='sunburst', hoverinfo='values', **all_levels)],
    layout=dict(title='COVID-19 Confirmed Cases as of ' + str(last_date),
                width=800,height=800,insidetextorientation='radial')),validate=False)

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