Editing Community Data Science Course (Spring 2023)/Week 5 coding challenges (section)

== #1 Wikipedia Page View API ==

# Identify a famous person who has been famous for at least a few years and that you have some personal interest in. Use the Wikimedia API to collect page view data from the English Wikipedia article on that person. Now use that data to generate a time-series visualization and include a link to it in your notebook.
# Identify 2 other languages editions of Wikipedia that have articles on that person. Collect page view data on the article in other languages and create a single visualization that shows how the dynamics and similar and/or different. (Note: My approach involved creating a TSV file with multiple columns.)
# Collect page view data on the articles about [https://en.wikipedia.org/wiki/Marvel_Comics Marvel Comics] and [https://en.wikipedia.org/wiki/DC_Comics DC Comics] in English Wikipedia. (If you'd rather replace these examples with some other comparison of popular rivals, that's just as good!)
## Which has more total page views in 2022?
## Can you draw a visualization in a spreadsheet that shows this? (Again, provide a link.)
## Were there any years when 2022's more popular page was instead the less popular of the two? How many and which ones?
## Were there any months was this reversal of relative popularity occurred? How many and which ones?
## How about any days? How many?
# I've made [https://github.com/kayleachampion/spr23_CDSW/blob/main/curriculum/week5/list_of_washington_alternative_rocks_bands_wikipedia-2023-04-25.jsonl this file available] which includes list of more than 100 Wikipedia articles about alternative rock bands from Washington state that I built from [https://en.wikipedia.org/wiki/Category:Alternative_rock_groups_from_Washington_(state) this category in Wikipedia].[*] It's a <code>.jsonl</code> file. Download the file (click "raw" and then save the file onto your drive). Now read it in, and request monthly page view data from all of them. If you need some help with loading it in, I've included some sample code at the bottom of this page.
## Once you've done this, sum up all of the page views from all of the pages and print out a TSV file with these total numbers.
## You know the routine by now! Now, make a time series graph of these numbers and include a link in your notebook.