Covid-19 is one of the topics you can work on in many unique data science projects. To work on any type of Covid-19 related project, you must work by using the latest dataset. So, if you want to learn how to get the latest covid-19 data using the Python programming language, this article is for you. In this article, I will take you through a web scraping tutorial on how to get live covid-19 data using Python.
Get Live Covid-19 Data using Python
To get live covid-19 data by using the Python programming language, it is very important for you to first learn about some of the popular methods of scraping data from the web. You can learn about web scraping using Python from here. Now coming back to get the latest covid-19 dataset, if you want to work on any project related to covid-19, it is very important to work on it by using the latest dataset as the cases are rising rapidly all around the world.
To get live covid-19 data using Python, I will be using the BeautifulSoup library in Python. If you have never used this library before then you can easily install it on your system by using the pip command:
- pip install beautifulsoup4
Below is how you can get live covid-19 data by using the beautifulsoup library in Python:
After executing the above code, a new CSV file will be saved in the same directory where your Python file is located as “Dataset.csv”. This dataset requires some preparation before you start using it in any type of data science project. For example, below is how you can change the name of its columns:
So this is how you can use web scraping to get the latest covid-19 data. Now all you have to do is clean and prepare this dataset according to the task you are working on.
Summary
So this is how you can get the latest dataset about covid-19 cases and store it in a CSV file. If you want to work on any project related to covid-19, it is very important to work on it by using the latest dataset as the cases are rising rapidly all around the world. I hope you liked this article on how to get the latest Covid-19 data using Python. Feel free to ask your valuable questions in the comments section below.
