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Using Python, the IMDb API, and web-scraping rotten tomatoes to find the best Star Trek movie

120 lines of code (python)

Star Trek is a very rare positive vision of the future. Which movie captures the audience the most with this hopeful message? I learned python to sample all the movie ratings I could find in order to answer this question. If you follow this guide, so can you.

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Which one is the best Star Trek movie?

The short answer is Star Trek of the 2009 reboot. The top five Star Trek movies according to an average of user ratings on IMDb and rotten tomatoes as well as critics’ ratings aggregated by metacritic and rotten tomatoes are:

Movie average rating
Star Trek (2009) 4.32 stars
Star Trek: First Contact 4.08 stars
Star Trek Into Darkness 4.04 stars
Star Trek II: The Wrath of Khan 4.04 stars
Star Trek IV: The Voyage Home 3.79 stars

To many trekkies the entries for First Contact and the Wrath of Khan won’t come as a surprise. These movies are generally regarded as high points in the franchise. However, what astonished me was just how successful the 2009 reboot was. Star Trek never before managed to churn out three decent movies in a row. There is hope that the next film will be a similarly good.

How you can get at all this information is shown in this blog post.

Data, the final frontier. These are the adventures of a data scientists. His continuing mission to explore strange new patterns, to seek out new insights and new visualisations, to boldly find out what no one has found out before…

Data acquisition: using IMDb API and web scraping

Start off by loading all the necessary modules. Because of IMDb-py I use python 2.7.

import imdb as imdb  # to access imdb API
import pandas as pd  # for data array handling
from BeautifulSoup import BeautifulSoup  # for website parsing and scraping (rotten tomatoes)
import requests  # for http access
import re  # for regular expressions
from ggplot import *  # for plotting
import urllib2  # for accessing url object (movie covers)
import matplotlib.pyplot as plt  # for plotting
from matplotlib.offsetbox import (OffsetImage, AnnotationBbox)

Next, we sample all Star Trek movies. We shall use the IMDb search function for that.

imdb_http = imdb.IMDb()  # create imdb API object
StarTrek = imdb_http.search_movie('Star Trek')  # general search for Star Trek among movie and series titles

Because we are not interested in series or video games, we extract only the movies with a simple list comprehension.

STF = [i for i in StarTrek if i.data['kind'] == 'movie']

Unfortunately, IMDb’s search function is not flawless and misses 5 movies. We search for them individually and add them to the list STF which holds the movies.

StarTrekIII = imdb_http.search_movie('Star Trek III the Search for Spock')
StarTrekIV = imdb_http.search_movie('Star Trek IV the voyage home')
StarTrekV = imdb_http.search_movie('Star Trek V the Final Frontier')
StarTrekVI = imdb_http.search_movie('Star Trek VI the Undiscovered Country')
StarTrekFC = imdb_http.search_movie('Star Trek First Contact')
STF.extend([StarTrekIII[0], StarTrekIV[0], StarTrekV[0], StarTrekVI[0], StarTrekFC[0]])

Now, we can loop through each movie and add all the information we want to a pandas data frame df. The IMDb API gives access to IMDb user ratings and metacritic ratings. However, in order to get rotten tomatoes ratings we turn to web scraping using BeautifulSoup.

Note that different ratings use different scales. I decided to turn all of them into an intuitive 6-point scale (zero to five stars).

df = pd.DataFrame(columns=['date', 'IMDb_rating', 'Metacritic_rating', 'title', 'image_url'])  # initialise data frame

for i in range(len(STF)):  # for each Star Trek movie

    imdb_http.update(STF[i])  # IMDb: augment movie info
    x = imdb_http.get_movie_critic_reviews(STF[i].movieID)  # Meta critic

    # rotten tomato: prepare website parsing
    tomato_base_url = 'https://www.rottentomatoes.com/m/'
    tomato_url = tomato_base_url + re.sub(':', '', re.sub(' ', '_', str(STF[i]['title'])))
    if 'Star Trek' not in STF[i]['title']:  # fix first contact problem
        tomato_url = tomato_base_url + re.sub(':', '', re.sub(' ', '_', 'Star Trek ' + str(STF[i]['title'])))
    elif 'Khan' in STF[i]['title']:  # fix wrath of khan problem
        tomato_url = tomato_base_url + re.sub(':', '_II', re.sub(' ', '_', str(STF[i]['title'])))
    soup = BeautifulSoup(requests.get(tomato_url).text)  # rotten tomatoes: website parse tree

    # add data to pandas data frame
    if 'year' in STF[i].data.keys() and bool(x['data']):  # filter out movies in production and those without MC data
        df = df.append(pd.DataFrame(data={
            'date': STF[i].data['year'],
            'IMDb_rating': [((STF[i].data['rating'] - 1) / 9.0) * 5],  # normalised to 5 star system
            'Metacritic_rating': [int(x['data']['metascore']) / 20.0],  # normalised to 5 star system
            'Tomatometer': [
                int(min(soup.find('span', {'class': 'meter-value superPageFontColor'}).contents[0])) / 20.0],
        # rotten tomatoe score (normalised to 5 star system)
            'Tomato_user': [
                int(filter(str.isdigit, str(soup.find('span', {'class': 'superPageFontColor'}).contents[0]))) / 20.0],
        # tomato audience score (normalised to 5 star system)
            'title': STF[i].data['title'],
            'image_url': STF[i]['cover url']}))

At this point one might want to save the data, for example by calling df.to_csv('Star_Trek_movie_data.csv', sep=';'). The result can be downloaded here.

Data visualisation: using ggplot and matplotlib

I start off by using the ggplot module as I am very familiar with the syntax from R.

p = ggplot(aes(x='date', y='IMDb_rating'), data=df) + geom_point() + \
    geom_line(size=5, color='orange') + theme_bw()  # basic plot
p = p + geom_line(aes(x='date', y='Metacritic_rating'), data=df, size=5, color='purple')
p = p + geom_line(aes(x='date', y='Tomatometer'), data=df, size=5, color='grey')
p = p + geom_line(aes(x='date', y='Tomato_user'), data=df, size=5, color='blue')
p = p + ylim(0, 5) + xlim(1975, 2016) + xlab(' ') + ylab(' ') + ggtitle('Star Trek movie ratings')  # make axes pretty

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The plot p now holds essentially all the information we need. But it is not pretty yet, as you can see by calling print p which is what I did to produce the figure above. For visual gimicks we shall leave ggplot and turn to matplotlib.

The module matplotlib works very much like matlab figure production. So, the figure should not be saved in a variable like p above, but instead be open.

p.make()  # exporting the figure to use it in matplotlib

The first thing to improve is to tell the reader what the different lines represent. I personally believe that it is best practice to avoid separate legends and, instead, use intuitive explanations in the figure itself.

plt.text(2017.5, 0, '@rikunert', color='black')  # keep figure open for this to work
plt.text(2017.5, 4.25, 'Rotten\nTomatoes', color='grey')  # keep figure open for this to work
plt.text(2017.5, 3.75, 'Rotten\nTomatoes\nusers', color='blue')  # keep figure open for this to work
plt.text(2017.5, 3.4, 'IMDb users', color='orange')  # keep figure open for this to work
plt.text(2017.5, 3.2, 'Metacritic', color='purple')  # keep figure open for this to work

The result:

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How to tell the viewer which movie is where? The film posters are the easiest way to achieve this.

Including an image in a plot is not straight forward. I will use the annotation box approach and hide the box itself behind the image.

First off though, we need the drawing area called axes or ax.

ax = plt.gca()

Next we define a new function add_image() to place an image from url at the coordinates xy of drawing area ax_ with the image zoom imzoom.

def add_image(ax_, url, xy, imzoom):
    if 'http' in url:  # image on internet
        f = urllib2.urlopen(url)
    else:  # image in working directory
        f = url
    arr_img = plt.imread(f, format='jpg')
    imagebox = OffsetImage(arr_img, zoom=imzoom)
    imagebox.image.axes = ax_
    ab = AnnotationBbox(imagebox, xy, xybox=(0., 0.), boxcoords="offset points", pad=-0.5)  # hide box behind image
    ax_.add_artist(ab)
    return ax_

Adding each film poster is now easy. For the ordinate (y-axis) position, by the way, I chose the average rating.

for i in range(len(df)):  # for each Star Trek movie
    add_image(ax, df['image_url'][i], [df['date'][i], sum(df.iloc[i, 1:5]) / 4.0], 0.3)

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I think the x-axis could be simplified.

ax.xaxis.set_ticks(range(1980, 2020, 10))  # minimal x-axis style

I replace the y-axis by star symbols. All in the interest of avoiding text.

ax.yaxis.set_visible(False)  # no numerical y-axis at all
add_image(ax, 'grey_star.jpg', [1975, 0], 0.05)  # zero stars on sort of y-axis
for i in range(1, 6):  # for each star rating from 1 onwards
    for j in range(i):  # for each individual star
        add_image(ax, 'gold_star.jpg', [1975 + j * 0.7, i], 0.05)

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What stands out the most from the figure is just how awful Star Trek V was. Let’s highlight this.

ax.annotate('The absolute worst movie:\n' + df[df['IMDb_rating'] == min(df['IMDb_rating'])]['title'].iloc[0],
            xy=(1988, 1.5), xytext=(1978, 1), arrowprops=dict(facecolor='black', shrink=0.05))

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Finally, the figure dimensions are not optimised for social media like twitter. And the margins are simply too big. The last statements deal with these problems

fig = plt.gcf()  # get current figure to show it
fig.set_size_inches(1024 / 70, 512 / 70)  # reset the figure size to twitter standard
fig.savefig('Star Trek movie ratings_dates.png', dpi=96, bbox_inches='tight')  # save figure

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