anushkadixit1708 / Visualizing-COVID

Visualizing the impact if COVID-19 on the countries of EU, Asia and the World using data visualization techniques

Visualizing-COVID

Visualizing the impact if COVID-19 on the countries of EU, Asia and the World using data visualization techniques

Libraries Used

Pandas
Matplotlib
Numpy
Seaborn

Data Source

Name: Our World in Data (University of Oxford) URL: https://ourworldindata.org/coronavirus

This dataset contains data on a daily basis for all the countries. The data has been collected and verified by a variety of sources including United Nations, World Bank, Global Burden of Disease, Blavatnik School of Government, etc.

Technologies Used

Python
Jupyter Notebook
Visualization library: Ploty (https://plotly.com/)

Plots

1. Parallel Co-ordinates plot: To see how hospital systems (i.e. beds) in a EU countries affect death rate and what is the pattern between median age people, population, and death rate.
2. Pie Chart plot: Created for the continent of Europe, which will include the percentage of tests carried out by that country compared to the whole of Europe. The more the tests performed in a country, the more reliable the numbers (i.e. total cases) are.
3. Choropleth map plot: To see the death rate of the covid-19. The EU countries map is colored on the basis of the death rate.