There is nothing like a challenge to bring out the best in man. – Sean Connery
Hi Pythonistas, a new week, a new ‘bite’ of Python coding! After last week’s tictactoe game, we’d like to sharpen your data analysis skills this week by parsing a movie data set in search for highest rated directors. Enjoy and we review solutions end of this week.
Details
There is this great ML article Predict Movie Rating. In this week’s code challenge we use its data set to get the 20 highest rated directors based on their average movie IMDB ratings.
Steps:
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As mentioned in the article the dataset is here, but we provided a copy in the repo’s 13/ subfolder.
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Parse the movie_metadata.csv, using csv.DictReader you get a bunch of OrderedDicts from which you only need the following k,v pairs:
OrderedDict([... ('director_name', 'Lawrence Kasdan'), ... ('movie_title', 'Mumfordxa0'), ... ('title_year', '1999'), ... ('imdb_score', '6.9'), ... -
Only consider directors with a minimum of 4 movies, otherwise you get misrepresentative data. However going to min 5 movies we miss Sergio Leone 🙁
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Take movies of year >= 1960.
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Print the top 20 highest rated directors with their movies ordered desc on rating.
It should look something like this (indeed some awesome movies here!):
$ python directors.py
01. Sergio Leone 8.5
------------------------------------------------------------
1966] The Good, the Bad and the Ugly 8.9
1968] Once Upon a Time in the West 8.6
1984] Once Upon a Time in America 8.4
1964] A Fistful of Dollars 8.0
02. Christopher Nolan 8.4
------------------------------------------------------------
2008] The Dark Knight 9.0
2010] Inception 8.8
2014] Interstellar 8.6
2012] The Dark Knight Rises 8.5
2006] The Prestige 8.5
2000] Memento 8.5
2005] Batman Begins 8.3
2002] Insomnia 7.2
03. Hayao Miyazaki 8.2
------------------------------------------------------------
2001] Spirited Away 8.6
1997] Princess Mononoke 8.4
2004] Howl's Moving Castle 8.2
2008] Ponyo 7.7
04. Quentin Tarantino 8.2
------------------------------------------------------------
1994] Pulp Fiction 8.9
2012] Django Unchained 8.5
1992] Reservoir Dogs 8.4
2009] Inglourious Basterds 8.3
2003] Kill Bill: Vol. 1 8.1
2004] Kill Bill: Vol. 2 8.0
2015] The Hateful Eight 7.9
1997] Jackie Brown 7.5
...
16 more
...
We included a template but maybe you want to code this up from scratch and/or use your favorite power tools (Pandas, SQL, etc.)
We also included some tests.
Getting ready
See our INSTALL doc how to fork our challenges repo to get cracking. If you want to share your solution do a PR and we will add it to our community branch and link to it in our end-of-the-week review.
Archive
You can find all our code challenges so far here. If you have ideas for a future challenge or find any issues, please contact us or open a GH Issue.
Last but not least: there is no best solution, only learning more and better Python. Good luck!
Keep Calm and Code in Python!
— Bob and Julian