On this brief piece, I exploit public Wikipedia information, Python programming, and community evaluation to extract and draw up a community of Oscar-winning actors and actresses.

All photographs have been created by the creator.

Wikipedia, as the biggest free, crowdsourced on-line encyclopedia, serves as a tremendously wealthy information supply on numerous public domains. Many of those domains, from movie to politics, contain numerous layers of networks beneath, expressing different types of social phenomena resembling collaboration. As a result of approaching Academy Awards Ceremony, right here I present the instance of Oscar-winning actors and actresses on how we are able to use easy Pythonic strategies to show Wiki websites into networks.

First, let’s check out how, as an example, the Wiki checklist of all Oscar-winning actors is structured:

This subpage properly exhibits all of the individuals who have ever acquired an Oscar and have been granted a Wiki profile (more than likely, no actors and actresses have been missed by the followers). On this article, I give attention to performing, which might be discovered within the following 4 subpages — together with predominant and supporting actors and actresses:

urls = { 'actor'         :'https://en.wikipedia.org/wiki/Class:Best_Actor_Academy_Award_winners',
'actress'       : 'https://en.wikipedia.org/wiki/Class:Best_Actress_Academy_Award_winners',
'supporting_actor'   : 'https://en.wikipedia.org/wiki/Class:Best_Supporting_Actor_Academy_Award_winners',
'supporting_actress' : 'https://en.wikipedia.org/wiki/Class:Best_Supporting_Actress_Academy_Award_winners'}

Now let’s write a easy block of code that checks every of those 4 listings, and utilizing the packages urllib and beautifulsoup, extracts the identify of all artists:

from urllib.request import urlopen
import bs4 as bs
import re
# Iterate throughout the 4 classes
people_data = []
for class, url in urls.objects():
# Question the identify itemizing web page and…