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Python Exercise: Visualize the state/province wise combine number of confirmed, deaths, recovered, active Novel Coronavirus cases in USA

Python Project: COVID-19 Exercise-13 with Solution

Write a Python program to visualize the state/province wise combine number of confirmed, deaths, recovered, active Novel Coronavirus (COVID-19) cases in USA.

Sample Solution:

Python Code:

import pandas as pd
import plotly.express as px
covid_data= pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-19-2020.csv')
covid_data['Active'] = covid_data['Confirmed'] - covid_data['Deaths'] - covid_data['Recovered']
combine_us_data = covid_data[covid_data['Country/Region']=='US'].drop(['Country/Region','Latitude', 'Longitude'], axis=1)
combine_us_data = combine_us_data[combine_us_data.sum(axis = 1) > 0]
combine_us_data = combine_us_data.groupby(['Province/State'])['Confirmed', 'Deaths', 'Recovered', 'Active'].sum().reset_index()
combine_us_data = pd.melt(combine_us_data, id_vars='Province/State', value_vars=['Confirmed', 'Deaths', 'Recovered', 'Active'], value_name='Count', var_name='Case')
fig = px.bar(combine_us_data, x='Province/State', y='Count', text='Count', barmode='group', color='Case', title='USA State wise combine number of confirmed, deaths, recovered, active COVID-19 cases')
fig.show()

Jupyter Notebook:


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Previous: Write a Python program to visualize the state/province wise Active cases of Novel Coronavirus (COVID-19) in USA.
Next: Write a Python program to visualize Worldwide Confirmed Novel Coronavirus (COVID-19) cases over time.

Download the above Notebook from here.

What is the difficulty level of this exercise?

Python: Tips of the Day

Creates a dictionary with the same keys as the provided dictionary and values generated by running the provided function for each value:

Example:

def tips_map_values(obj, fn):
  ret = {}
  for key in obj.keys():
    ret[key] = fn(obj[key])
  return ret
users = {
  'Owen': { 'user': 'Owen', 'age': 29 },
  'Eddie': { 'user': 'Eddie', 'age': 15 }
}

print(tips_map_values(users, lambda u : u['age'])) # {'Owen': 29, 'Eddie': 15}

Output:

{'Owen': 29, 'Eddie': 15}