Covid-19 Data Analysis using Python

By Madhulika Damor

In this tutorial, we are going to analyze Covid-19 data using Python. We mainly use the Plotly and Matplotlib libraries for this work.

It works on the information related to the total confirmed cases, active cases, recovered cases, serious/critical cases, and death cases. In particular, we analyze data of top 20 countries' cases and plot information using treemap, pie chart, bar graph, and line graph.

The ratio between population and the total number of tests done is analyzed. In addition to this, the information relates to a particular country can also be obtained along with the date.

 

Title of the project:

Covid-19 Data Analysis using Python.

Description:

Hello everyone!

In this tutorial, we are going to analyze Covid-19 data using Python. We mainly use the Plotly and Matplotlib libraries for this work.

It works on the information related to the total confirmed cases, active cases, recovered cases, serious/critical cases, and death cases. In particular, we analyze data of top 20 countries' cases and plot information using treemap, pie chart, bar graph, and line graph.

The ratio between population and the total number of tests done is analyzed. In addition to this, the information relates to a particular country can also be obtained along with the date.

 

Prerequisites: 

    1)  Dataset files of covid cases with a  .csv extension.

    2)  Install Jupyter Notebook or any similar working environment with the latest version of Python installed. 

    3)  Python language.

    4)  Knowledge of Python libraries like numpy,  pandas, matplotlib.

 

Datasets:

It contains the datasets of-

i. worldometer data, (209, 16)

ii. country wise data, (187, 15) 

iii. day wise data, (188, 12)

iv. combined data, (35156, 10)

 

Implementation:

1. Import the required Python libraries.
2. Reading the datasets.  It contains datasets of worldometer data, Country wise data,  day wise data and  combined data.  All these datasets are present in .csv extension files.
3. First, we analyze country-wise information. We obtain information of the countries in terms of total cases, active-cases, recovered cases and death cases . We plot this information using treemap and pie charts.
4. After that, we analyze day-wise information. It includes information of confirmed cases, active cases, recovered cases and death cases . We plot this information using a line plot.
5. Next, we calculate the ratio between population and test done.
6. Now, we check for the top 20 countries in terms of max total confirmed cases, max total active cases, max  total recovered cases,  max total deaths and serious critical condition cases.
7. At last, we analyze information for a particular country. We can choose any country in the world. It provides information about confirmed, active, recovered and death cases along with dates. We plot this information using a line graph.