pandas : for analyzing and manipulating data in a tabular format.
geopandas : for interfacing map data with pandas.
matplotlib : for plotting bar graphs.
geoplot : for plotting choropleth maps
Frequency but not quality, and only for the top level comments and with no parsing of the relationships between the number of upvotes and replies, or the qualities of lower level comments. It’s an interesting start, but there is a lot more data to consider
If I have to guess/assume what those percentage numbers mean exactly or go read an article first, is it beautiful data?
Perhaps I’m being too harsh.
It’s obviously not true almost every post and comment across all these subs are jokes.
I think there has to be something wrong with your model.
4 comments
Check out the full article here [https://www.scrapingbee.com/blog/funniest-us-states-on-reddit/](https://www.scrapingbee.com/blog/funniest-us-states-on-reddit/)
Technology used:
Mistal 7B: for the AI categorization of comments
PRAW : Python wrapper for the Reddit API.
pandas : for analyzing and manipulating data in a tabular format.
geopandas : for interfacing map data with pandas.
matplotlib : for plotting bar graphs.
geoplot : for plotting choropleth maps
Frequency but not quality, and only for the top level comments and with no parsing of the relationships between the number of upvotes and replies, or the qualities of lower level comments. It’s an interesting start, but there is a lot more data to consider
If I have to guess/assume what those percentage numbers mean exactly or go read an article first, is it beautiful data?
Perhaps I’m being too harsh.
It’s obviously not true almost every post and comment across all these subs are jokes.
I think there has to be something wrong with your model.