Web Scraping Project N 2
Scraping All the Restaurants on the Micheline guide website
The Michelin Guides are a series of guide books that have been published by the French tyre company Michelin since 1904. The Guide awards up to three Michelin stars for excellence to a select few establishments. The acquisition or loss of a star can have dramatic effects on the success of a restaurant.
On their website you can find all the best restaurants in the world, a description of their cuisine and many other information relevant for food lovers.
You can explore the website and search different type of information.
- Who are the restaurants that were awarded with the important recognitions, 3,2 and 1 Micheline Stars,
- Indication on the type of cuisine and a small description about it
- All the relevant information such as address, website, telephone number and other
In this project I have retrieved information from Micheline guides's web page using _web_scarping techniques: the process of extracting information from a website in an automated fashion using code. I have used python libraries [Requests] (https://docs.python-requests.org/en/latest/) and BeautifulSoup4 to scrape data from this page.
This is an outline of the steps that we were followed:
- Download the web page using "requests"
- Parse the HTML code using "BeautifulSoup"
- Extract restaurant's subpage, restaurant's info, web url, telephone number where applicable and some other info
- Compile extracted information into python dictionaries
- Extract data from multiple pages
- Save the extracted info into a csv file
By the end of the project I have created a csv file in the following format:
Restaurant Name, Restaurant Region, Michelin Website reference, Cuisine, Price,Phone Number
!pip install jovian --upgrade --quiet
# Execute this to save new versions of the notebook jovian.commit(project="web-scraping-project-n-2")
[jovian] Updating notebook "francesco-palmieri05/web-scraping-project-n-2" on https://jovian.ai [jovian] Committed successfully! https://jovian.ai/francesco-palmieri05/web-scraping-project-n-2