My SECOND assignment that made me hungry but happy

What to eat is often a very distressing problem when we arrive in an unfamiliar place (e.g. Sham Shui Po). Even if we open openrice, it is not an easy work to choose what we want to eat from the large number of recommended restaurants, especially when we don’t even have a good idea of what dishes to eat.
To solve this common problem, I followed the steps Bernard described in the design thinking lecture, and firstly empathised, putting myself in the context of an openrice user in Sham Shui Po.
Generally, it is always easy to follow others’ choices, so users usually pay attention to the number of bookmarks and good reviews of restaurants. According to my analysis, most of the restaurants at the top are Hong Kong style, which is not hard to understand since we are in Hong Kong right now. As I expanded my filter to the top 30, different styles of restaurants started to appear, with Japanese making up a significant portion of the list.
Combining the problem set earlier and the result of the existing analysis, we can see that Hong Kong style and Japanese restaurants are more popular. However, as I said, “we are in Hong Kong”, so we cannot rule out that this is an effect of the fact that Hong Kong style restaurants have a large base in Sham Shui Po. Therefore, I have counted the restaurants in different styles. As we can see, this type do occupy the top place.
In order to reduce the impact of this reason, I should also refer to some other factors, such as price, when making my choice. That is why I intentionally took the price data when using the Parsehub to scrape data and split it in the lowest and highest prices when using the Openrefine to clean data.
The next question is how can I get the average of different dishes’ highest and lowest prices from the Openrice dataset of these Sham Shui Po restaurants?
For this part of data analysis, I not only combined Python and SQL, but also called on the panda third-party library in Python as support to get a more visual comparison of prices.
In this way, we can more accurately find our choice among the restaurants that openrice helped us to filter out, combining their popularity as well as their prices.
You can access the site here.