This is a fun lil one. For an upcoming article, I need to know a list of (hopefully good) movies I haven’t yet seen, with similar runtimes. Now, I could have just scrolled down the list of IMDb.com’s top 250 movies, ctrl + clicking on the ones I haven’t seen, and then compared them by eye, because, to be honest, I think I’ve seen many (/most?) of them (we’ll see shortly).
But of course, that would be an efficient use of my time, and I’m learning pandas these days anyway, so why not use it!
To do what I want, I basically need to take that top 250 list (let’s say I don’t really care about the ratings within that list, I just want to select movies from it), get a column with the runtimes, and then manually make a column with 0/1 entries for if I haven’t/have seen it, and then select for the ones I have. Then, I could group, or at least visualize them.
The first (tiny) hurdle comes from the data source. IMDb.com very helpfully provides several zipped files of ALL their movies (beware, the “basics” one is 420MB unzipped!), buuut… they have separate files for the table with the runtimes (title.basics.tsv.gz) and the ratings (title.ratings.tsv.gz). That wouldn’t be too bad, you might think: you could just sort the ratings file by the rating column, take those entries, and select from the basics file, to get the runtimes.
Buuuut… a quick glance (or if you’ve ever just perused the dark back alleys of IMDb itself) reveals that there are many entries with very high ratings, higher than the top 250 scores (which range from 8.0 to 9.2). This is probably because there are lots of smaller productions where you get a selection bias, such that you pretty much only get people who really like the movie voting, so they’re all voting 10. Of course, IMDb links actually links to an explanation of this effect:
As indicated at the Top Rated Movies page, only votes from regular IMDb voters are considered when creating the top 250 out of the full voting database. This explains any difference between the vote averages reported in the top 250 lists and those on the individual title pages. This also explains why movies or shows you might think from their averages ought to appear on the list yet do not actually appear there.
To maintain the effectiveness of the top 250 lists, we deliberately do not disclose the criteria used for a person to be counted as a regular voter.
and says on the top 250 page itself:
The list is ranked by a formula which includes the number of ratings each movie received from users, and value of ratings received from regular users
To be included on the list, a movie must receive ratings from at least 25000 users
I assumed this before starting this thing but wasn’t sure how they did it. I always assumed that the simplest way would be to just have some threshold of a minimum number of votes (which they do), to even qualify. The “small, religiously devoted fanbase theory” of those artificially high ratings would probably break if you set it correctly — I mean, once you set the threshold of minimum votes high enough, if the rating is still high, it’s not really “artificial” anymore, is it? There are potential problems with that, like only selecting for really large productions (depending on the threshold). It appears they actually do this, but also add a secret “special sauce” where they weigh certain votes more, but they don’t say how.
Anyway, that’s a bit of a long winded way to say that it’d be hard to do what I said above, to get the runtimes of the top 250. At this point, I saw a few options: I probably could try their method manually, using the ratings file (which has average ratings and number of votes for each one), just taking the subset of movies that have a rating at and above the minimum of the top 250 list, and then thresholding those for the minimum number of votes. Maybe I’ll try this anyway, but I assumed (because they say they do something else in addition) that I might end up with another list if I did that.
Another thing I briefly considered (that, at this point, may have been much easier) would be some sort of web scraping. It’d be reaaaaaal easy (in theory) to have a script go to the link for each entry on the top 250 page (which would lead directly to the actual movie, which as we’ll see shortly, is actually a bit of a pain), and then each page has a well defined “runtime” field right below the title. I briefly debated this (and maybe I’ll try it later), but I don’t actually know how to do web scraping in python yet, so it would probably be a really hacky job on my part.
So, speaking of hack-y, here’s what I ended up doing. Everything is presented in bits because I did it in a Jupyter notebook.
To get around the “which are actually the top 250 movies” problem, I literally copied and pasted the top 250 list from the page, and pasted it into a text file, which I imported and dropped two things that ended up being garbage columns. Then I had to do a tiny bit of parsing, because using “\t” as a delimiter worked to separate the title and year, but not the rank and title. So, I had to delimit that column with the “.” after the rank #, but setting n=1, because some titles have a period in their name as well (Monsters, Inc. for example), so you wouldn’t want to chop those up into separate fields. Ahhh details!
movieRatings_df = pd.read_csv("copypasteRatings.csv","\t") display(movieRatings_df.head()) movieRatings_df = movieRatings_df.drop(["Your Rating","Unnamed: 3"],axis=1) display(movieRatings_df.head()) dotsplit = movieRatings_df["Rank & Title"].str.split(".",n=1) titleYears = pd.Series([entry for entry in dotsplit]) rank = pd.Series([entry for entry in dotsplit]).rename("Rank") years = pd.Series([int(title[-5:-1]) for title in titleYears]).rename("Year") titles = pd.Series([title[1:-7] for title in titleYears]).rename("Title") ratings_df = pd.concat([rank,titles,years],axis=1) ratings_df.head()