Here is a not up to date list: link Within the Bay Area, there are also a lot of sub-regional locations, which we’ll define here, there loop through them all. We’ll loop through all of our locations, and pull a number of entries for each one.We’ll use a pandas dataframe to store everything, because this will be useful for future analysis.
There are even how-to guides (for placing the ad and responding, not for what to do when you meet up).
If both parties "like" their match, chatting capabilities open up.
This is how it works: The app matches up two people using data from Facebook.
Apparently, many people want some or all of those things (and get them, and go back for more of them).
The section, which has a loyal "community" of followers as well as newbies, started in 2000 on the free classified website and now accounts for two per cent of all Craigslist postings, which run in 570 cities and 50 countries and get more than 50 million visitors a month.
This is really useful, but how can we possibly parse it all?