The Project attempts to create maps of three types of restaurants in the New York City based on their price settings. And analyze is based on what reflect on the maps. All the data sources are requested through the Yelp API.
The project has two parts: a Python script that makes an API call and performs data manipulation before converting to three csv file, and three Tableau maps built on the csv file. The python file calls a Yelp API for all three requested types restaurants in New York city. Although under certain regulations, Yelp can only provide restaurants information of each type with maximum 1000. So I wrote a loop inorder to request the restauratns information 20 times from yelp. Since I want to avoid hardcoding, I defined a function with three variables so that instead of writing the whole code three times, I only need to change the variables to get my results. Then I converts the data I have requested from yelp into a Panda Dataframe. Drops unwanted columns, and then converts to csv file. After converting all data to the csv file, then upload them to Tableau inorder to generate the map of restaurants with price details. All restaurants are colored differently by the price range.
From the three maps, we can visibly see that people are more willing to have meal in Manhattan from the world trade center to the upper east/west side. Also when you interact with the map, you can realize Japanese has more expensive restaurants than the other two, which means people are more willing to pay higher bill on Japanese restaurants.