Introduction¶

Recently the Streaming market has seen a sudden, stark increase in competition. New companies seem to be entering the market every day, challenging the current champion, Netflix. Each company has its own advantages and disadvantages, and for the purpose of this project, I am going to focus on two companies in particular: Netflix and Disney. Disney recnetly launched their own streaming platfomr Disney+. I am curious to see how a entertainment conglomerate such as Disney's content compares with that of the newer Netflix.

Specifically, I want to use the IMDB api available at http://www.omdbapi.com/, to compare these two companies average ratings for their projects. I want to seee how well movies these companies have produced has been recieved over time. Other questions include: does netflix thrive in producing well liked content in any specic Genre? How can netflix compete with the giant that is disney - are there any niches in which it can try to thrive? Does netflix utilize its ability to produce adult content to develop a competative advantage against disney's family friendly content?

Ultimatly, I hope to guide the reader of my tutorial through each step of my analysis. I would also like to reveal any insights within the data that may suggest one streaming service will be more liked by viewers other in the long term, although there are obvioulsy several exterior factors to this.

My first step for this project is to create a histogram for the mean IMDB scores for either companies streaming service. Disney+ specific shows have been announced at (https://www.theverge.com/2019/10/14/20913417/disney-plus-launch-lineup-marvel-star-wars-pixar-tv-shows-movies-simpsons-national-geographic), and netflix has a wikipedia page about its orignal movie and TV productions. Movies are here ((https://en.wikipedia.org/wiki/List_of_original_films_distributed_by_Netflix)) and Tv shows are https://en.wikipedia.org/wiki/List_of_original_programs_distributed_by_Netflix

One last note here is that there are several exterior legal and technical distinctions between what one company actually "owns" in comparison to another. Licensing & distribution deals make this even more complicated and nuanced. For the purpose of this tutorial, I am going to try to analyze just the original content that is present on either platform because I believe that original content will be a key factor in deciding which companies will win the streaming wars - as original content is the one prodcut that cannot be taken away, and serves to create a competative advantage between companies. But for now, let's get started!

Make sure before attempting this that you have install html5lib and lxml. you should also go to (http://www.omdbapi.com/apikey.aspx) and generate an API key. Each API key only gets 1000 (free) requests to the database per day, so you may need to replace the API key paramater with you own API key in various URLs throughout this project. (make sure you remember to activate your key, as the instrutions on the omdb website indicate)

you also need to make sure you have decent internet connection - poor connection may result in a pull failing wiht a timeout.

Getting Started with Data¶

Part 1: Data Collection From http://www.omdbapi.com/¶

Data Collection 1: Netflix¶

To collect ratings from Netflix, we are going to grab title names from the Wikipedia page of Netflix Origninal films (https://en.wikipedia.org/wiki/List_of_original_films_distributed_by_Netflix), Convert them into the appropratie URLs for OMDB, and then grab the necessary infomration out of the JSON unit that OMDB will return to us. We will then do the same for TV shows.

The First step, grabbing titles from wikipedia, is seen below

# Step 0: necessary imports
import urllib.request
import bs4 as bs
import pandas as pd
import numpy as np
import requests
import xml.etree.ElementTree as ET
from io import StringIO
import html5lib

#Step 1: Pull page of original Netlix movies from wikipedia

netflix_list = "https://en.wikipedia.org/wiki/List_of_original_films_distributed_by_Netflix"
request_netflix = requests.get(netflix_list)
source_netflix = urllib.request.urlopen(netflix_list).read()
soup = bs.BeautifulSoup(source_netflix, 'html.parser')

#Step 2: isolate the tables from this page
table = soup.find_all('table')

#step 3: remove additional excess information
table = table[:-22]

#step 4: create df to store netflix original movies
netflix_films_df = pd.DataFrame()

#step 5: iterate over every table, parse its html, and add each table as a DF to the netflix films df
for table in table:
    pd_table = pd.read_html(table.prettify(), header = 0)
    temp_df = pd_table[0]
    netflix_films_df = netflix_films_df.append(temp_df, sort=False)
    
#we want to see all titles, so
pd.set_option('display.max_rows', 1000) #we want to see a lot of entries 


#step 6: drop any duplicate wikipedia entries
netflix_films_df = netflix_films_df.drop_duplicates(subset="Title", keep='first')

#step7: drop any that have yet to be released (TBA only. we don't remove NaNs, because some of the wikipedia entries simply don't have premiere info)
netflix_films_df = netflix_films_df[netflix_films_df['Premiere'] != "TBA"]

#step 8: display df
netflix_films_df

Now we are going to load netflix tv shows, from https://en.wikipedia.org/wiki/List_of_original_programs_distributed_by_Netflix (You may notice a blank table - scroll down! The webscraping accidentally incorporated additional stuff that we don't need that clogs some of the columns.)

# Step 0: necessary imports
import urllib.request
import bs4 as bs
import pandas as pd
import numpy as np
import requests
import xml.etree.ElementTree as ET
from io import StringIO
import html5lib

#Step 1: Pull page of original Netlix movies from wikipedia

tv_netflix_list = "https://en.wikipedia.org/wiki/List_of_original_programs_distributed_by_Netflix"
request_netflix = requests.get(tv_netflix_list)
source_netflix = urllib.request.urlopen(tv_netflix_list).read()
soup = bs.BeautifulSoup(source_netflix, 'html.parser')

#Step 2: isolate the tables from this page
table = soup.find_all('table')

#step 3: remove additional excess information
table = table[:-22]

#step 4: create df to store netflix original movies
netflix_tv_df = pd.DataFrame()

#step 5: iterate over every table, parse its html, and add each table as a DF to the netflix films df
for table in table:
    pd_table = pd.read_html(table.prettify(), header = 0)
    temp_df = pd_table[0]
    netflix_tv_df = netflix_tv_df.append(temp_df, sort=False)
    
#we want to see all titles, so
pd.set_option('display.max_rows', 1000) #we want to see a lot of entries 


#step 6: drop any duplicate wikipedia entries
netflix_tv_df = netflix_tv_df.drop_duplicates(subset="Title", keep='first')

#step7: drop any that have yet to be released (TBA only. we don't remove NaNs, because some of the wikipedia entries simply don't have premiere info)
netflix_tv_df = netflix_tv_df[netflix_tv_df['Premiere'] != "TBA"]

#step 8: display df
netflix_tv_df

The next part is to push these titles to the OMDB API, and find the correlating JSON data item for each movie. We will do movies first, then tv shows, then combine them. We must do this because the API only allows 1000 queries at a time, and there are more than 1000 entries if we combine movies and tv

#step 1: create df to hold new ratings df info
film_netflix_ratings_df = pd.DataFrame()

errors = 0
#step 2: iterate through each row in netflix_df grabbing title. from there
for row in netflix_films_df['Title']:
    try:
        row_search = urllib.parse.quote(row)
        #convert each row to a ombdapi URL. notcie the %s, which is a placeholder for the approrpriate movie
        #it may be worth it to add in a placeholder for the API key as well. 
        url = ("http://www.omdbapi.com/?t=%s&r=json&apikey=67ca8c9d" % row_search)
        request = requests.get(url)
        source = urllib.request.urlopen(url).read()
        source = source.decode()
        d = {'Title' : row, 'JSON_DATA' : source}
        df = pd.DataFrame(data=d, index=[0])
        film_netflix_ratings_df = film_netflix_ratings_df.append(df)
    #test for any errors in retrieving data
    #This will likely only fire if you have an expired API key. 
    #if this happens, you need to generate a new api key(see intro) and replace the api key (currently 67ca8c9d) (try aa11aa8, 742f26a8, or 8564eff2, ed54ed5a) in the URL.
    except:
        print("Error with %s" % row)
        print(url)
        errors += 1

Now the tv shows. You may notice an error here - the api returns this error for a specific tv show, because there are several with that name. We deal with this error later.

#step 1: create df to hold new ratings df info
tv_netflix_ratings_df = pd.DataFrame()

errors = 0
#step 2: iterate through each row in netflix_df grabbing title. from there
for row in netflix_tv_df['Title']:
    try:
        row_search = urllib.parse.quote(row)
        #convert each row to a ombdapi URL. notcie the %s, which is a placeholder for the approrpriate movie
        #it may be worth it to add in a placeholder for the API key as well. 
        url = ("http://www.omdbapi.com/?t=%s&r=json&apikey=64b4a0cd" % row_search)
        request = requests.get(url)
        source = urllib.request.urlopen(url).read()
        source = source.decode()
        d = {'Title' : row, 'JSON_DATA' : source}
        df = pd.DataFrame(data=d, index=[0])
        tv_netflix_ratings_df = tv_netflix_ratings_df.append(df)
    #test for any errors in retrieving data
    #This will likely only fire if you have an expired API key. 
    #if this happens, you need to generate a new api key(see intro) and replace the api key (currently 76c90977) (try aa11aa8, 742f26a8, or 8564eff2, ed54ed5a) in the URL.
    except:
        print("Error with %s" % row)
        print(url)
        errors += 1

Error with nan
http://www.omdbapi.com/?t=You%20Me%20Her&r=json&apikey=64b4a0cd

print("%s error in getting json data" % errors)
pd.set_option('display.max_rows', 50) #we don't need to see a lot of entries  here
netflix_ratings_df = pd.concat([tv_netflix_ratings_df, film_netflix_ratings_df], sort=False)
netflix_ratings_df

1 error in getting json data

Notice how the index has become all 0. This is because of how we constructed the dataframe - we made each returned title + JSON value a seperate series, then added this series to temporary dataframe. Within this temporary dataframe, every item had an index of 0. Thus, when we then append this dataframe to the final dataframe, every value has an index of 0. This has a simple fix.

netflix_ratings_df.reset_index(inplace = True, drop=True)
netflix_ratings_df

You may notice how some of these say "response:false, error: movie not found!" lets isolate these movies and see if we can find a pattern.

error_netflix_movies = netflix_ratings_df[netflix_ratings_df['JSON_DATA'] == "{\"Response\":\"False\",\"Error\":\"Movie not found!\"}"]

#we will also remove these from the current dataframe, so we can add in the fixes later
netflix_ratings_df = netflix_ratings_df[netflix_ratings_df['JSON_DATA'] != "{\"Response\":\"False\",\"Error\":\"Movie not found!\"}"]

error_netflix_movies

Some of these appear to be still in development. Some however,appear to be creating an error because of wikipedia citaitons! lets edit those out

#because we are working with a slice of a dataframe here, we need to suppress the settingwithcopywarning
pd.options.mode.chained_assignment = None

#now we will edit out the citations
error_netflix_movies['Title'] = error_netflix_movies['Title'].str.split('\[').str[0]
error_netflix_movies

Now lets run these back through our previous steps to push it to OMDB, and reject any that are in development.

fixed_netflix_errors_df = pd.DataFrame()
for row in error_netflix_movies['Title']:
    try:
        row_search = urllib.parse.quote(row)
        url = ("http://www.omdbapi.com/?t=%s&r=json&apikey=accc7130" % row_search)
        request = requests.get(url)
        source = urllib.request.urlopen(url).read()
        source = source.decode()
        d = {'Title' : row, 'JSON_DATA' : source}
        df = pd.DataFrame(data=d, index=[0])
        fixed_netflix_errors_df = fixed_netflix_errors_df.append(df)
    #test for any errors in retrieving data
    #This will likelye only trigger if you have an expired api key (try a7e31369 or aa11aa8, instead!). Replace this key if errors are triggered
    except:
        print("Error with %s" % row)
        print(url)
        errors += 1

#now we can assume that any more not found movies are likely in development, and we should drop them.
fixed_netflix_errors_df = fixed_netflix_errors_df[fixed_netflix_errors_df['JSON_DATA'] != "{\"Response\":\"False\",\"Error\":\"Movie not found!\"}"]
fixed_netflix_errors_df

Now we just add these fixed values back to the original dataframe, and drop any remainint errors - these errors comopose a negligible amount of the dataframe, and are likely due to extreme circumstances that are not reflective of broader trends (e.x. error returned bc two movies with same name)

netflix_ratings_df = netflix_ratings_df.append(fixed_netflix_errors_df, sort=False)

#drop any additional values that returned multiple IMDB pages. Negligent amount and thus easy to dispose of
netflix_ratings_df = netflix_ratings_df[netflix_ratings_df['JSON_DATA'] != "{\"Response\":\"False\",\"Error\":\"Subquery returned more than 1 value. This is not permitted when the subquery follows =, !=, <, <= , >, >= or when the subquery is used as an expression.\"}"]
netflix_ratings_df =  netflix_ratings_df[netflix_ratings_df['JSON_DATA'] != "{\"Response\":\"False\",\"Error\":\"Error getting data.\"}"]
netflix_ratings_df

We must once again reset the index, for the same reason previously discussed

netflix_ratings_df.reset_index(inplace = True, drop=True)
netflix_ratings_df

Now that we have a dataframe with each row representing one movie and its associated JSON data, we can parse this JSON data for interesting data. Specifically, we are looking for: Rating data, Rating count, Release Year, and Genre

netflix_ratings_df['Year'] = netflix_ratings_df['JSON_DATA'].apply(lambda x: x.split("Year")[1][3:7])
netflix_ratings_df['Rating'] = netflix_ratings_df['JSON_DATA'].apply(lambda x: x.split("imdbRating")[1][3:6])
netflix_ratings_df['Genre'] = netflix_ratings_df['JSON_DATA'].apply(lambda x: x.split("Genre")[1][3:6])

netflix_ratings_df['Genre'].unique()

#N/A will automatically be converted to np.nan
netflix_ratings_df["Genre"] = netflix_ratings_df["Genre"].map({
       "Dra": "Drama",
       "Cri": "Crime",
       "Act": "Action",
       "Com": "Comedy",
       "Thr": "Thriller",
       "Hor": "Horror",
       "Bio": "Biography",
       "Ani": "Animation",
       "Mys": "Myster",
       "Sci": "Sci-Fi",
       "Sho": "Short",
       "Adv": "Adventure",
       "Rom": "Romance",
       "Doc": "Documentary",
       "Mus": "Music",
       "Rea": "Reality",
       "Fam": "Family",
       "Fan": "Fantasy",
       "Adu": "Adult"
})
#for now, we will drop the JSON data column. If in the future, we decide we want more data, we can always load it in again
netflix_ratings_df['Ratings_Count'] = netflix_ratings_df['JSON_DATA'].apply(lambda x: x.split("imdbVotes")[1].split(":")[1].replace("\"", '').replace("imdbID", '').replace(",", ''))
netflix_ratings_df['Ratings_Count'] = netflix_ratings_df['Ratings_Count'].replace(['N/A'], np.nan)
netflix_ratings_df['Ratings_Count'] = netflix_ratings_df['Ratings_Count'].astype(float)
netflix_ratings_df = netflix_ratings_df.drop(['JSON_DATA'], axis=1)
netflix_ratings_df

Our data is almost perfect, but there are still some flaws. Specifically, let's take a look at what happens when we run netflix_ratings_df.Year.unique()

netflix_ratings_df.Year.unique()

array(['2013', '2014', '2015', '2016', ' the', '2017', '2018', '1998',
       '1961', '1999', '2019', '2003', '2011', 'ater', '1986', '2006',
       '1996', '2005', '1966', '1989', '2010', '1995', '1993', '1979',
       '2007', '2012', '2004', '1978', '1908', '1982', '2008', '1994',
       '1967', '1974', '', '1990', '1924', '1992', '2002', 'f My', '2000',
       '2020', 'ght"', '1983', '1956', '1954', 'd Ma', '1991', '2001',
       '2009', '1898', '1985', '1932', '1939', '1970', '1955', '1997',
       '1940'], dtype=object)

Netflix started releasing original content in 2013. So what's going on here? Well let's take another look at this data.

pd.set_option('display.max_rows', 50)
netflix_ratings_df

Notice "into the deep" is listed as 1994. It looks like our API is conflating a netflix movie (release date TBD) with an already existing movie (see https://www.imdb.com/title/tt0110150/)

How can we solve this? Well, most of the movies this appears to be occuring with (including other examples in this dataframe, such as war party (https://www.imdb.com/title/tt0098619/), night on earth, pinnochio, etc, are conflating newly planned netflix movies with older films with the same name. Many of these movies have not yet been released, so they don't have IMDB pages. If they did, the API defaults to the newest verison. This can be show with the newest cinderella movie. If we give the api just the term "cinderella", it returns the 2015 version. see: (http://www.omdbapi.com/?t=cinderella&r=json&apikey=ed54ed5a). Thus, we can conclude that any film that returns and older version is a planned netflix movie that has not yet been released.

Thus, we can simply drop these.

#identify valid years
valid_years = ['2013', '2014', '2015', '2016', '2017', '2018', '2019']

#remove those that are in valid years
netflix_ratings_df = netflix_ratings_df[netflix_ratings_df['Year'].isin(valid_years)]

#confirm
netflix_ratings_df.Year.unique()

array(['2013', '2014', '2015', '2016', '2017', '2018', '2019'],
      dtype=object)

Great! the only years present in this dataframe are those in which netflix actually produced original content

Now we are going to find a Distibution of IMDB ratings for netflix movies!

#step 1: convert necessary dtypes, and replace any missing values in ratings column

netflix_ratings_df['Rating'] = netflix_ratings_df['Rating'].replace(['N/A'], np.nan)
netflix_ratings_df['Rating'] = netflix_ratings_df['Rating'].astype(float)

#step 2: plot it!
netflix_s = netflix_ratings_df['Rating'].dropna()
#netflix_ax = netflix_s.plot.hist(bins=10) #,  cumulative=True, weights=netflix_w)
netflix_ratings_hist = netflix_ratings_df['Rating'].plot.hist(bins = 15, by="Rating") #, weights=netflix_ratings_df["Ratings_Count"])

#stpe 3: edit labels
netflix_ratings_hist.text(6.5, -20, 'IMBD Rating', ha='center')
netflix_ratings_hist.text(5.5, 180, 'Netflix Original Content')

Text(5.5, 180, 'Netflix Original Content')

Data Collection 2: Disney+¶

To aggregate information for disney, we are going to grab the list of every title that is currently available on disney+ from this verge article (https://www.theverge.com/2019/10/14/20913417/disney-plus-launch-lineup-marvel-star-wars-pixar-tv-shows-movies-simpsons-national-geographic). We will then, much like we did for Netflix, convert these movies to the approriate URL for OMDB, and then grab the information we want out of the returned JSON unit.

#step 0: imports
import urllib.request
import bs4 as bs
import pandas as pd
import numpy as np
import requests
import xml.etree.ElementTree as ET
from io import StringIO

#step 1: Necessary Setting adjustments
pd.set_option('display.max_rows', 100) #we want to see a lot of rows
apikey = 'b9b7add3' #apikey to access omdb. 

#step 2: pull page from theverge
disney_list = "https://www.theverge.com/2019/10/14/20913417/disney-plus-launch-lineup-marvel-star-wars-pixar-tv-shows-movies-simpsons-national-geographic"
request_disney = requests.get(disney_list)
source_disney = urllib.request.urlopen(disney_list).read()
soup = bs.BeautifulSoup(source_disney, 'html.parser')

#step 3: isolate title lines in HTML 
list2 = soup.find_all('em')
list_true = list2[20:] #first 20 characters are explanatory, here we skip over htese

#step 4: create series out of title list
disney_films = pd.Series(list_true)

#step 5: turn it into a dataframe
disney_films_df = pd.DataFrame(disney_films, columns=['Title'])

#step 6: convert all titles to strings only
disney_films_df['Title'] =  disney_films_df['Title'].apply(lambda x: str(x))

#step 7: replace <em> tags with. '' so we isolatetitles
disney_films_df['Title'] =  disney_films_df['Title'].apply(lambda x: x.replace('<em>','').replace('</em>',''))

#Step 8: filter out extra data
disney_films_df = disney_films_df[:-5] #last 5 rows are explanatory/irrelevnt <em> tags. remove mannually

#step 9: display
disney_films_df

Now that we have a list of titles, we can push them to the OMBD API to get a JSON object containing relevant infomration.

#current api key: ed54ed5a
#optional api keys: aa11aa8, a7e31369 or your own!
#if you get the "failed at url" message, create a new api key,  activate it, and paste it into the line ("http://www.omdbapi.com/?t=%s&r=json&apikey=PASTEHERE)"


from urllib.parse import urlencode
problematic_titles = []
returned_null = []
errors = 0
apikey="ed54ed5a"
disney_films_df_best = pd.DataFrame()
for row in disney_films_df['Title']:
    try:
        row_search = urllib.parse.quote(row)
        url = ("http://www.omdbapi.com/?t=%s&r=json&apikey=4148dbf1" % row_search)
        request = requests.get(url)
        source = urllib.request.urlopen(url).read()
        source = source.decode()
        d = {'Title' : row, 'JSON_DATA' : source}
        df = pd.DataFrame(data=d, index=[0])
        disney_films_df_best = disney_films_df_best.append(df)
    except:
        print("failed at %s" % url)
disney_films_df_best

#drop duplicates and measure and display table
disney_films_df_best = disney_films_df_best.drop_duplicates(subset="Title", keep='first')
disney_films_df_best.count()
disney_films_df_best

Now, Let's check for errors, Like we did with netflix

error_checking = disney_films_df_best.copy()
disney_film_errors = error_checking[error_checking['JSON_DATA'] == "{\"Response\":\"False\",\"Error\":\"Movie not found!\"}"]

disney_film_errors

The errors here are a bit more complicated than they were for netflix.

"teen beach movie 2", for insantce, fails because IMDB lists it as "Teen beach 2." That being said, there are still some patterns we can try to fix.

Some movies, for instance, have the year they were released in parenthesis next to their name. in the case of blackbeard's ghost, for instance, this makes the API fail to find it. The first thing well do is thus detect and fix those parenthesis.

disney_film_errors['Title'] = disney_film_errors['Title'].str.split('(').str[0]
disney_film_errors

Another common issue is that some movies have a II, whereas the API expects a 2. or the movie is listed as III, wheras the API expects 3. This is also a simple fix

disney_film_errors['Title'] = disney_film_errors['Title'].str.replace('III', '3')
disney_film_errors['Title'] = disney_film_errors['Title'].str.replace('II', '2')
disney_film_errors

Now we can again try to find matching imdb pages, with this edited df

fixes = pd.DataFrame()
for row in disney_film_errors['Title']:
    try:
        row_search = urllib.parse.quote(row)
        url = ("http://www.omdbapi.com/?t=%s&r=json&apikey=d28b037d" % row_search)
        request = requests.get(url)
        source = urllib.request.urlopen(url).read()
        source = source.decode()
        d = {'Title' : row, 'JSON_DATA' : source}
        df = pd.DataFrame(data=d, index=[0])
        fixes = fixes.append(df)
    except:
        print("failed at %s" % url)

Now we can remove the erros from the initial df, and add back the components we were able to fix

#drop errored entries
disney_films_df_best = disney_films_df_best[disney_films_df_best['JSON_DATA'] != "{\"Response\":\"False\",\"Error\":\"Movie not found!\"}"]
#add back entries we were able to fix
disney_films_df_best.append(fixes)

#drop entries that yielded multiple pages
disney_films_df_best = disney_films_df_best[disney_films_df_best['JSON_DATA'] != "{\"Response\":\"False\",\"Error\":\"Subquery returned more than 1 value. This is not permitted when the subquery follows =, !=, <, <= , >, >= or when the subquery is used as an expression.\"}"]

disney_films_df_best

For the purpose of our analysis we want Movie title (which we arleady have), Rating data, Release Year, and Genre

disney_films_df_best['Year'] = disney_films_df_best['JSON_DATA'].apply(lambda x: x.split("Year")[1][3:7])
disney_films_df_best['Rating'] = disney_films_df_best['JSON_DATA'].apply(lambda x: x.split("imdbRating")[1][3:6])
disney_films_df_best['Genre'] = disney_films_df_best['JSON_DATA'].apply(lambda x: x.split("Genre")[1][3:6])
disney_films_df_best['Genre'].unique()

#N/A will automatically be converted to np.nan
disney_films_df_best["Genre"] = disney_films_df_best["Genre"].map({
       "Dra": "Drama",
       "Cri": "Crime",
       "Act": "Action",
       "Com": "Comedy",
       "Thr": "Thriller",
       "Hor": "Horror",
       "Bio": "Biography",
       "Ani": "Animation",
       "Mys": "Myster",
       "Sci": "Sci-Fi",
       "Sho": "Short",
       "Adv": "Adventure",
       "Rom": "Romance",
       "Doc": "Documentary",
       "Mus": "Music",
       "Rea": "Reality",
       "Fam": "Family",
       "Fan": "Fantasy",
       "Adu": "Adult"
})
disney_films_df_best['Ratings_Count'] = disney_films_df_best['JSON_DATA'].apply(lambda x: x.split("imdbVotes")[1].split(":")[1].replace("\"", '').replace("imdbID", '').replace(",", ''))
disney_films_df_best['Ratings_Count'] = disney_films_df_best['Ratings_Count'].replace(['N/A'], np.nan)
disney_films_df_best['Ratings_Count'] = disney_films_df_best['Ratings_Count'].astype(float)

#for now, we will drop the JSON data column. If in the future, we decide we want more data, we can always load it in again

disney_films_df_best = disney_films_df_best.drop(['JSON_DATA'], axis=1)
disney_films_df_best

Now lets verify the years, like for netflix.

disney_films_df_best.Year.unique()

array(['1960', '1947', '1950', '1952', '1953', '1954', '1955', '1956',
       '1957', '1958', '2006', '2008', '1907', '1998', '1961', '1990',
       '1962', '1963', '1964', '2001', '1965', '1966', '1967', '1968',
       '1969', '1971', '1972', '1974', '1975', '1976', '2003', '1977',
       '2016', '1978', '1979', '1980', '2015', '1981', '1982', '1985',
       '1986', '1987', '1994', '1988', '1989', '1991', '1992', '1993', '',
       '2011', '1995', '1996', '1997', '1999', '2000', '1921', '2002',
       '2004', '2005', '2007', '2009', '2010', '2012', '2013', '2014',
       '2017', '2018', '1941', '2019', '1937', '1940', '1942', '1944',
       '1948', '1949', '1959', '1970', '1984', '1983', 'ater'],
      dtype=object)

Disney has been around for a while, so these make more sense. Excpet for the last one "Ater", and a few ''. These must have been picked up from the webscraping.

disney_films_df_best = disney_films_df_best[disney_films_df_best['Year'] != "ater"]
disney_films_df_best = disney_films_df_best[disney_films_df_best['Year'] != ""]
disney_films_df_best.Year.unique()

array(['1960', '1947', '1950', '1952', '1953', '1954', '1955', '1956',
       '1957', '1958', '2006', '2008', '1907', '1998', '1961', '1990',
       '1962', '1963', '1964', '2001', '1965', '1966', '1967', '1968',
       '1969', '1971', '1972', '1974', '1975', '1976', '2003', '1977',
       '2016', '1978', '1979', '1980', '2015', '1981', '1982', '1985',
       '1986', '1987', '1994', '1988', '1989', '1991', '1992', '1993',
       '2011', '1995', '1996', '1997', '1999', '2000', '1921', '2002',
       '2004', '2005', '2007', '2009', '2010', '2012', '2013', '2014',
       '2017', '2018', '1941', '2019', '1937', '1940', '1942', '1944',
       '1948', '1949', '1959', '1970', '1984', '1983'], dtype=object)

Now lets reset the index, just like we did for netflix

disney_films_df_best.reset_index(drop=True, inplace=True)

disney_films_df_best

Now lets see a quick distribution of disney plus' IMDB ratings, just like we did with Netflix

#Replace n/a with np.nan
disney_films_df_best['Rating'] = disney_films_df_best['Rating'].replace(['N/A'], np.nan)

#convert ratings to float
disney_films_df_best['Rating'] = disney_films_df_best['Rating'].astype(float)


#drop na in ratings, keep track of rating as a seperate series, in case we need it later
disney_s = disney_films_df_best['Rating'].dropna()


ax = disney_films_df_best.Rating.plot.hist(bins=25)
ax.text(5.5, -10, 'IMBD_Rating', ha='center')
ax.text(5.5, 80, 'Dinsey+ IMBD Ratings', ha='center')

Text(5.5, 80, 'Dinsey+ IMBD Ratings')

PART 2: Analysis & Visualization¶

Ratings Distrubitons¶

Now that we have collected, processed, tidies, and done some bried exploratory analysis on this data, We can begin to do some real analysis to try to generate any insight into which platform has an advantage in the coming streaming wars in terms of content quantity and quality.

Let's start out by simply blotting a distribution of the ratings for the content on either platform.

ax.legend(["Disney", "Netflix"]);

import matplotlib.pyplot as plt
plt.hist([disney_s, netflix_s], label=['disney', 'netflix'])
plt.suptitle('Distribution of IMDB Ratings', x=0.5, y=1.05, ha='center', fontsize='xx-large')
plt.text(6, -50, 'IMDB Rating', ha='center')
plt.text(0.04, 150, 'Number of movie ratings', va='center', rotation='vertical')
plt.legend(loc='upper right')

<matplotlib.legend.Legend at 0x7f6b6602beb8>

The above histogram shows a distribution of Disney+ media content ratings vs Netflix orginal content ratings. This is a trend over hundreds of media pieces, and each rating represents many peoples votes.

The initial perspective of this graph is that netflix is actually beating disney by wide margins in terms of quantity of content! This definitly suprised me and subverted my expectations.

How can we more accuratley detect if there is a signficant distinction in terms of quality or populatrity?? Well the first step is to criticize the above figure.

Notice how the above figure treats every movie with equal merit. The rating for a move that very few people have seen holds as much merit as a movie that many people have seen. Think about it this way: in the above chart, Swiss Family Robinson holds as much weight in terms of representing disney as Star wars does.

The question is thus: how do we display popularity?

Thankfully, we can use the number of ratings a movie has as a proxy for this. Essentially, we will weight each movies rating by how many reviews that movie has on IMDB.

import matplotlib.pyplot as plt


#keep track of ratings_count as a seperate series, so that we can use it to weigth the ratings later. 
disney_w = disney_films_df_best['Ratings_Count'].dropna()
netflix_w = netflix_ratings_df['Ratings_Count'].dropna()

#disney_s and netflix_s are series of the disney and netflix ratings. they were generated in a seperate step.
plt.hist([disney_s, netflix_s], label=['disney', 'netflix'], weights=(disney_w, netflix_w))
plt.legend(loc='upper right')
plt.suptitle('Number of IMDB Ratings', x=0.5, y=1.05, ha='center', fontsize='xx-large')
plt.text(6, -3000000, 'IMDB Rating', ha='center')
plt.text(0.04, 10500000, 'Number of individual ratings  ', va='center', rotation='vertical')

Text(0.04, 10500000, 'Number of individual ratings  ')

Interesting! There are several insights that we can draw from this graph. Notice how disney absolutely drawfs netflix in terms of content popularity. One important thing to note is that Netflix is actually taking the lead in terms of quantity of content. Specifically, Disney has 562 rows in this dataframe, while Netflix has 1261! In spite of this, Netflix has significantly lower imdb ratings. this means that even though Disney+ has less content, its content is both more well liked and more popular. Disney's content gets millions more ratings.

Distribution by Genre¶

The question then arises: which type of content is more popular on which platforms? We can try to determine this by dividing the content into Generes, and seeing the rankings for each assocaited content on each platform.

First, we will need to make two sepereate dataframes, which will each be the result of performing a groupby function on both the disney and netflix dataframes, based on their genre column. From there, we will calculate the mean rating for each genre on either dataframe. we will then merge these two dataframes, so that we can run analysis on the resulting df. See below.

#group by netflix
netflix_by_genre = netflix_ratings_df.groupby('Genre', sort=False)
netflix_genre_ratings = netflix_by_genre['Rating'].mean()


#group by Disney
disney_by_genre = disney_films_df_best.groupby('Genre', sort=False)
disney_genre_ratings = disney_by_genre['Rating'].mean()


#make new dataframes
netflix_genre_ratings_df = pd.DataFrame(data=netflix_genre_ratings)
disney_genre_ratings_df = pd.DataFrame(data=disney_genre_ratings)

#merge
ratings_by_genre_df = disney_genre_ratings_df.merge(netflix_genre_ratings_df, how="outer", on = "Genre")

#specify column names so we don't get confused
ratings_by_genre_df = ratings_by_genre_df.rename(columns={"Rating_x": "Disney_Ratings", "Rating_y": "Netflix_Ratings"})

ratings_by_genre_df

When looking at this, the first thing to notice is incredibly obvious: Why are there no Disney Romances? no disney Sci-Fi or Fantasy? The answer is that our API takes the first genre given, and disney will always list the first genre of their movies as that which has the broadest appeal. Take, for instance, mary poppins. The move on IMDB (see here https://www.imdb.com/title/tt0058331/?ref_=fn_al_tt_1) is listed as "comedy, family, fantasy." our API looks at and only returns "comedy". This is an unfortunate shortcoming of our data, but I believe its necessary. The alternative is to tag every movie with every single one of its genres, and this means every movie would be double or triple counted - each would have to appear several times in each of the genre groups. This would make any conclusions we draw extremely muddled and ripe for innacuracies. If we want to make conclusions about which content either company is better at, then it simply makes the most sense to assocaite each movie with their "primary" genre - that is, the genre that is first listed on IMDB. While its not perfect, the alternatives are far worse.

Now that we have a df wiith each genre and either companies assoicated mean ratings for that genre, lets plot them!

ax = ratings_by_genre_df.plot.bar(figsize=(8,6),title="Average Ratings for Netflix and Disney by Genre")#(x='Genre', y='Ratin', rot=0)
ax.legend(["Disney", "Netflix"]);
ax.set_xlabel("Genre")

# Set the label for the y-axis
ax.set_ylabel("Average IMDB Rating")

Text(0, 0.5, 'Average IMDB Rating')

The above graph is really interesting! It highlights how, generally speaking, The two companies produce pretty comparable content. Netflix just barely beats out disney in adventure, comedy, and familty, while disney absolutely drwarfs netflix in Crime, Music, and Reality. The two giants are virtually tied for Action. Netflix doesn't seem to be losing by a massive degree in terms of quality, as per our predictions via the histogram thaat was weighted by populatrity. One reason this might be the case is that Netflix beats Disney in every Genre that disney is not competing in. In Genres such as Mystery, horror, or thriller, Netflix beats Disney because Disney refused to compete, perhaps due to the potential that an r rate movie would hurt their family friendly image.

You may think that this graphic falls to the same issue that the graphic at the start of part 2 fell to - each movie is weighed equally. However, I would argue that this graph is more insightful - we are trying to draw conclusiosn about which type of genee either company is good at producing, in terms of quality. If we weighed for ratings counts by genre, we would just see disney drawfing netflix again, due to the popularity of their content. This would hinder our ability to draw interesting and insightful conclusions.

Distribution by year¶

The last thing I wanted to cover in tutorial is trends over time. Specifically, I wanted to see any trends over time - has either company gotten better at producing content over time? Is there any Genre that disney has gotten better - or worse- at producing in the past 50 years?

I also want to address concerns about time bias in this data. Specifically, as this one guy (http://www.georgesaines.com/blog/2015/2/25/imdb-score-bias-its-temporal) and this one redditor (https://www.reddit.com/r/movies/comments/bxu247/analysis_of_recency_bias_in_imdb_movie_ratings/) argues, IMDB ratings are inflated when a movie premieres. This creates a bias of newer movies having generally higher ratings. I would like to investigate to what extent this bias exists here, and how it affects either platform.

First, lets try to see some trends over time!

Let's start out with some basics: just ratings over time for netflix and disney. Since netflix has existed for such a shorter time, it doesn't make a lot of sense to plot the two together, as you would barely even be able to see netflix.

disney_over_time = disney_films_df_best.pivot_table(index=["Year"], values="Rating", aggfunc="mean")

ax = disney_over_time.plot(title='Disney ratings Over Time')
ax.set_xlabel("Year")
ax.set_ylabel("IMDB rating")

Text(0, 0.5, 'IMDB rating')

netflix_over_time = netflix_ratings_df.pivot_table(index=["Year"], values="Rating", aggfunc="mean")
ax = netflix_over_time.plot(title='Disney ratings Over Time')
ax.set_xlabel("Year")
ax.set_ylabel("IMDB rating")

Text(0, 0.5, 'IMDB rating')

These are interesting! Notice How disney has seen massive volatility over time. Another interesting point is that netflix appears to have peaked in 2014, and been steadily decreasing since. To be fair, however, netflix has a much smaller time frame, so it would be innacurate to draw conclusions from just this graph.

One thing I want to discusss here is the issue of recency bias. Recency bias is the idea that movies that were recently released see a bonus in ratings. These charts do not depict this trend. There are serveral reasons this could be the case. The first is that the recency bias is not really a thing - or that it is something that only affects a specific subset of movies, and thus largely does not affect our data in the long run. Another possibility is that this bias does exist, and we will in future years see today's ratings of recent movies go down.

Unfortunatly, the API we use in this tutorial cannot access previous ratings. As such, it is impossible to perform analysis as to whether or not ratings will lower over time. If recency bias were substantial, we would expect to see recnet years have a significant peak in terms of average ratings. This is not the case for either Netflix or Disney. Both companies average ratings are lower than they were at their peak, and Netflix's specificlaly is almost as low as it has ever been. Thus, we can argue that this bias is generally insignificant and will not largly impact our dataa.

Let's see if we can break down any of these values by genre, to notice any trends! We'll start with Netflix

netflix_years = netflix_ratings_df.pivot_table(index=["Year"], values="Rating",  columns=["Genre"], aggfunc="mean")
ax = netflix_years.plot(figsize=(10,10), title ="Netflix Ratings over time by genre", colormap="nipy_spectral")

#step3: remove scientific notation for consistency
plt.ticklabel_format(style='plain', axis='y')

#step 4: set labels
ax.set_ylabel("Mean Rating")
ax.set_xlabel("Year")
ax

<matplotlib.axes._subplots.AxesSubplot at 0x7f6b5b1f5240>

First off, I would like to say that it is very difficult to find a colormap that adequatly represents every genre. I chose nipy_spectral because I believe it has the greatest contrast, but if you find another I encourage you to use it.

We noted before that netflix can compete with disney by doing the things that disney won't: i.e. horror films, scifi, and thrillers. Among these, netflix appears to be largely unscucessful. Scifi is the red bar that has decreased dramatically over the past few years, and Thriller has also largely stagnated. The genres that appear to be successfull for netflix over time are animation, Documentary, and biographies. This is a poor stratagy for netflix, who appears to be trying to beat Disney on Disney's home field: Animation. A smarter tactic would be to invest into horror, scifi, etc to try to compete in markets that Disney won't enter.

disney_years = disney_films_df_best.pivot_table(index=["Year"], values="Rating",  columns=["Genre"], aggfunc="mean")
ax = disney_years.plot(figsize=(10,10), title = "Disney ratings over time by Genre")

#step3: remove scientific notation for consistency
plt.ticklabel_format(style='plain', axis='y')

#step 4: set labels
ax.set_ylabel("Mean Rating")
ax.set_xlabel("Year")
ax

<matplotlib.axes._subplots.AxesSubplot at 0x7f1a501c86d8>

The above graph is interesting, but it's too volatile and cluttered to really gain any insights from. How can we make this better? We can discretize these results into five year periods!

#first we have to convert disney's year columns into ints.
disney_films_df_best['Year'] = disney_films_df_best['Year'].astype(int)

#next, we have to identify the potential year sections
year_sections = np.arange(1921, 2019, 5)

#now we will make a copy of the df
discrete_disney = disney_films_df_best.copy()

#now we will split it into discrete sections
discrete_disney['Year_range'] = pd.cut(discrete_disney['Year'], year_sections, right=True)
discrete_disney.head()

Now that we have broken this up by time range, we can run some analysis!

discrete_disney_by_genre = discrete_disney.pivot_table(index=["Year_range"], values="Rating",  columns=["Genre"], aggfunc="mean")

ax = discrete_disney_by_genre.plot(figsize=(10,10), title = "Disney ratings over time by Genre")

This graph is much clearer, and allows us to notice trends. One thing I want to point out is disney's peak in animation an comedy during 1986-1991. Michael Eisner became CEO of disney in 1984, and among his many successes, some view him as revitalizing disney's movie business. He took a ton of actions to rebuild the company culture from the ground up, and many say the company would not be what it was today without him. There is a whole case study about mostly his work, you can find it here (https://www.hbs.edu/faculty/Pages/item.aspx?num=27931). I just think its really interesting that we can see his impact through these rating scores.

Another interesting trend to note is how much the Action, Comedy, and Animation ratings have changed since the 80s. There was a massive downfall in ratings from the 80s into the 90s, but it looks like Disney has, in recent years, begun to fight back by reinvesting into these categories. This makes a lot of sense, and it definitly serves to leverage Disney's Core competencies.

Part 3 Conclusion¶

Ultimatley, there will be many factors that influence the future of the streaming wars. What we have done together, however, shows that Disney's content is dramatically more popular. We've also shown how Netflix has failed to capatalize on their capacity to create content that Disney cannot. This shows that the competition for the streaming industry is likely just getting started. Netflix has chosen fight over flight, and intends to capture the same consumer love for Animation, comedy, and action that disney has had a grasp on for the past century.

It's a very complicated industry wiht lots of exterior facets, but I think that in the long run, Disney will beat out netflix. The data in this tutorial tells a consistent story of Disney producing higher quality content that gains more popularity, while netflix consistently chooses quantity over quality. Since netflix is choosing to compete with Dinsey by throwing massive amounts of poorly rated content at its subscribers, I can't help but think that the streaming giant just doesn't know how to compete in entertainment.

A note on Bias¶

Lastly, I want to end this tutorial with a few grains of salt. Specifically, I want everyone to consider the significance that the lack of diversity of ratings on IMDB has on our analyis. This Wired article points out how the majority of viewers who rate on IMDB are straight white males, a sample that is likely not representative of the population as a whole. Though there is nothing we can do to control for this in the context of the tutorial, I think it is worth mentioning. https://www.wired.co.uk/article/which-film-ranking-site-should-i-trust-rotten-tomatoes-imdb-metacritic

	Title	JSON_DATA
0	House of Cards	{"Title":"House of Cards","Year":"2013–2018","...
0	Hemlock Grove	{"Title":"Hemlock Grove","Year":"2013–2015","R...
0	Orange Is the New Black	{"Title":"Orange Is the New Black","Year":"201...
0	Marco Polo	{"Title":"Marco Polo","Year":"2014–2016","Rate...
0	Bloodline	{"Title":"Bloodline","Year":"2015–2017","Rated...
0	Sense8	{"Title":"Sense8","Year":"2015–2018","Rated":"...
0	Narcos	{"Title":"Narcos","Year":"2015–2017","Rated":"...
0	Stranger Things	{"Title":"Stranger Things","Year":"2016–","Rat...
0	The Get Down	{"Title":"The Get Down","Year":"2016–2017","Ra...
0	The Crown	{"Title":"The Crown","Year":"2016–","Rated":"T...
0	Gilmore Girls: A Year in the Life	{"Title":"Gilmore Girls: A Year in the Life","...
0	The OA	{"Title":"The OA","Year":"2016–2019","Rated":"...
0	A Series of Unfortunate Events	{"Title":"A Series of Unfortunate Events","Yea...
0	13 Reasons Why	{"Title":"13 Reasons Why","Year":"2017–","Rate...
0	Gypsy	{"Title":"Gypsy","Year":"2017","Rated":"TV-MA"...
0	Ozark	{"Title":"Ozark","Year":"2017–","Rated":"TV-MA...
0	Mindhunter	{"Title":"Mindhunter","Year":"2017–","Rated":"...
0	Godless	{"Title":"Godless","Year":"2017","Rated":"TV-M...
0	Altered Carbon	{"Title":"Altered Carbon","Year":"2018–","Rate...
0	Seven Seconds	{"Title":"Seven Seconds","Year":"2018","Rated"...
0	Lost in Space	{"Title":"Lost in Space","Year":"1998","Rated"...
0	The Innocents	{"Title":"The Innocents","Year":"1961","Rated"...
0	The Haunting	{"Title":"The Haunting","Year":"1999","Rated":...
0	Chilling Adventures of Sabrina	{"Title":"Chilling Adventures of Sabrina","Yea...
0	Narcos: Mexico	{"Title":"Narcos: Mexico","Year":"2018–","Rate...
...	...	...
0	The Wandering Earth	{"Title":"The Wandering Earth","Year":"2019","...
0	Us and Them	{"Title":"Us and Them","Year":"2018","Rated":"...
0	A Shaun the Sheep Movie: Farmageddon [294]	{"Response":"False","Error":"Movie not found!"}
0	Angela's Christmas	{"Title":"Angela's Christmas","Year":"2017","R...
0	Arctic Dogs	{"Title":"Arctic Dogs","Year":"2019","Rated":"...
0	Big Fish & Begonia	{"Title":"Big Fish & Begonia","Year":"2016","R...
0	Duck Duck Goose	{"Title":"Duck Duck Goose","Year":"2018","Rate...
0	Flavors of Youth: International Version	{"Response":"False","Error":"Movie not found!"}
0	Godzilla: City on the Edge of Battle	{"Title":"Godzilla: City on the Edge of Battle...
0	Godzilla: Planet of the Monsters	{"Title":"Godzilla: Planet of the Monsters","Y...
0	Godzilla: The Planet Eater	{"Title":"Godzilla: The Planet Eater","Year":"...
0	Gnome Alone	{"Title":"Gnome Alone","Year":"2017","Rated":"...
0	I Lost My Body	{"Title":"I Lost My Body","Year":"2019","Rated...
0	Kung Fu Panda 3	{"Title":"Kung Fu Panda 3","Year":"2016","Rate...
0	Modest Heroes: Ponoc Short Films Theater	{"Response":"False","Error":"Movie not found!"}
0	Pachamama	{"Title":"Pachamama","Year":"2018","Rated":"PG...
0	Sahara	{"Title":"Sahara","Year":"2005","Rated":"PG-13...
0	The Little Prince	{"Title":"The Little Prince","Year":"2015","Ra...
0	White Fang	{"Title":"White Fang","Year":"1991","Rated":"P...
0	This 15 Me	{"Title":"Sarah Geronimo: This 15 Me","Year":"...
0	An Afternoon with SCTV	{"Response":"False","Error":"Movie not found!"}
0	Derren Brown: Miracle	{"Title":"Derren Brown: Miracle","Year":"2016"...
0	Derren Brown: The Push	{"Title":"Derren Brown: Pushed to the Edge","Y...
0	Dying to Tell	{"Title":"Dying to Tell","Year":"2018","Rated"...
0	Kevin Hart: What Now?	{"Title":"Kevin Hart: What Now?","Year":"2016"...

	Title	JSON_DATA
0	House of Cards	{"Title":"House of Cards","Year":"2013–2018","...
1	Hemlock Grove	{"Title":"Hemlock Grove","Year":"2013–2015","R...
2	Orange Is the New Black	{"Title":"Orange Is the New Black","Year":"201...
3	Marco Polo	{"Title":"Marco Polo","Year":"2014–2016","Rate...
4	Bloodline	{"Title":"Bloodline","Year":"2015–2017","Rated...
5	Sense8	{"Title":"Sense8","Year":"2015–2018","Rated":"...
6	Narcos	{"Title":"Narcos","Year":"2015–2017","Rated":"...
7	Stranger Things	{"Title":"Stranger Things","Year":"2016–","Rat...
8	The Get Down	{"Title":"The Get Down","Year":"2016–2017","Ra...
9	The Crown	{"Title":"The Crown","Year":"2016–","Rated":"T...
10	Gilmore Girls: A Year in the Life	{"Title":"Gilmore Girls: A Year in the Life","...
11	The OA	{"Title":"The OA","Year":"2016–2019","Rated":"...
12	A Series of Unfortunate Events	{"Title":"A Series of Unfortunate Events","Yea...
13	13 Reasons Why	{"Title":"13 Reasons Why","Year":"2017–","Rate...
14	Gypsy	{"Title":"Gypsy","Year":"2017","Rated":"TV-MA"...
15	Ozark	{"Title":"Ozark","Year":"2017–","Rated":"TV-MA...
16	Mindhunter	{"Title":"Mindhunter","Year":"2017–","Rated":"...
17	Godless	{"Title":"Godless","Year":"2017","Rated":"TV-M...
18	Altered Carbon	{"Title":"Altered Carbon","Year":"2018–","Rate...
19	Seven Seconds	{"Title":"Seven Seconds","Year":"2018","Rated"...
20	Lost in Space	{"Title":"Lost in Space","Year":"1998","Rated"...
21	The Innocents	{"Title":"The Innocents","Year":"1961","Rated"...
22	The Haunting	{"Title":"The Haunting","Year":"1999","Rated":...
23	Chilling Adventures of Sabrina	{"Title":"Chilling Adventures of Sabrina","Yea...
24	Narcos: Mexico	{"Title":"Narcos: Mexico","Year":"2018–","Rate...
...	...	...
1302	The Wandering Earth	{"Title":"The Wandering Earth","Year":"2019","...
1303	Us and Them	{"Title":"Us and Them","Year":"2018","Rated":"...
1304	A Shaun the Sheep Movie: Farmageddon [294]	{"Response":"False","Error":"Movie not found!"}
1305	Angela's Christmas	{"Title":"Angela's Christmas","Year":"2017","R...
1306	Arctic Dogs	{"Title":"Arctic Dogs","Year":"2019","Rated":"...
1307	Big Fish & Begonia	{"Title":"Big Fish & Begonia","Year":"2016","R...
1308	Duck Duck Goose	{"Title":"Duck Duck Goose","Year":"2018","Rate...
1309	Flavors of Youth: International Version	{"Response":"False","Error":"Movie not found!"}
1310	Godzilla: City on the Edge of Battle	{"Title":"Godzilla: City on the Edge of Battle...
1311	Godzilla: Planet of the Monsters	{"Title":"Godzilla: Planet of the Monsters","Y...
1312	Godzilla: The Planet Eater	{"Title":"Godzilla: The Planet Eater","Year":"...
1313	Gnome Alone	{"Title":"Gnome Alone","Year":"2017","Rated":"...
1314	I Lost My Body	{"Title":"I Lost My Body","Year":"2019","Rated...
1315	Kung Fu Panda 3	{"Title":"Kung Fu Panda 3","Year":"2016","Rate...
1316	Modest Heroes: Ponoc Short Films Theater	{"Response":"False","Error":"Movie not found!"}
1317	Pachamama	{"Title":"Pachamama","Year":"2018","Rated":"PG...
1318	Sahara	{"Title":"Sahara","Year":"2005","Rated":"PG-13...
1319	The Little Prince	{"Title":"The Little Prince","Year":"2015","Ra...
1320	White Fang	{"Title":"White Fang","Year":"1991","Rated":"P...
1321	This 15 Me	{"Title":"Sarah Geronimo: This 15 Me","Year":"...
1322	An Afternoon with SCTV	{"Response":"False","Error":"Movie not found!"}
1323	Derren Brown: Miracle	{"Title":"Derren Brown: Miracle","Year":"2016"...
1324	Derren Brown: The Push	{"Title":"Derren Brown: Pushed to the Edge","Y...
1325	Dying to Tell	{"Title":"Dying to Tell","Year":"2018","Rated"...
1326	Kevin Hart: What Now?	{"Title":"Kevin Hart: What Now?","Year":"2016"...

	Title	JSON_DATA
48	Messiah [34]	{"Response":"False","Error":"Movie not found!"}
49	Locke & Key [36]	{"Response":"False","Error":"Movie not found!"}
60	W/ Bob & David	{"Response":"False","Error":"Movie not found!"}
64	Netflix Presents: The Characters	{"Response":"False","Error":"Movie not found!"}
69	Trailer Park Boys Out of the Park: Europe	{"Response":"False","Error":"Movie not found!"}
82	Trailer Park Boys Out of the Park: USA	{"Response":"False","Error":"Movie not found!"}
107	Astronomy Club: The Sketch Show	{"Response":"False","Error":"Movie not found!"}
129	Rilakkuma and Kaoru	{"Response":"False","Error":"Movie not found!"}
131	Knights of the Zodiac: Saint Seiya	{"Response":"False","Error":"Movie not found!"}
136	The Disastrous Life of Saiki K.: Reawakened [88]	{"Response":"False","Error":"Movie not found!"}
152	Justin Time GO!	{"Response":"False","Error":"Movie not found!"}
161	Edgar Rice Burroughs' Tarzan and Jane	{"Response":"False","Error":"Movie not found!"}
173	Stretch Armstrong and the Flex Fighters	{"Response":"False","Error":"Movie not found!"}
180	True: Magical Friends	{"Response":"False","Error":"Movie not found!"}
181	True: Wonderful Wishes	{"Response":"False","Error":"Movie not found!"}
207	Dino Girl Gauko	{"Response":"False","Error":"Movie not found!"}
208	Fast & Furious: Spy Racers	{"Response":"False","Error":"Movie not found!"}
209	Go! Go! Cory Carson [108]	{"Response":"False","Error":"Movie not found!"}
211	Super Monsters Monster Party	{"Response":"False","Error":"Movie not found!"}
214	Project Mc 2	{"Response":"False","Error":"Movie not found!"}
220	The Ponysitters Club	{"Response":"False","Error":"Movie not found!"}
221	Best.Worst.Weekend.Ever.	{"Response":"False","Error":"Movie not found!"}
225	Malibu Rescue: The Series	{"Response":"False","Error":"Movie not found!"}
232	Cable Girls	{"Response":"False","Error":"Movie not found!"}
240	Club de Cuervos Presents: The Ballad of Hugo S...	{"Response":"False","Error":"Movie not found!"}
...	...	...
1107	The Main Event [39]	{"Response":"False","Error":"Movie not found!"}
1108	The Princess Switch: Switched Again [40]	{"Response":"False","Error":"Movie not found!"}
1109	Under the Sun of Riccione [22]	{"Response":"False","Error":"Movie not found!"}
1110	Welcome to Sudden Death [41]	{"Response":"False","Error":"Movie not found!"}
1111	My Father's Dragon [14]	{"Response":"False","Error":"Movie not found!"}
1112	Pinocchio [42]	{"Response":"False","Error":"Movie not found!"}
1113	The Power of the Dog [44]	{"Response":"False","Error":"Movie not found!"}
1114	Untitled The Loud House film [45]	{"Response":"False","Error":"Movie not found!"}
1115	Wendell and Wild [47]	{"Response":"False","Error":"Movie not found!"}
1116	Escape from Hat [48]	{"Response":"False","Error":"Movie not found!"}
1117	Jacob and the Sea Beast [49]	{"Response":"False","Error":"Movie not found!"}
1118	Taylor Swift: Miss Americana [216]	{"Response":"False","Error":"Movie not found!"}
1176	The Last Letter from Your Lover	{"Response":"False","Error":"Movie not found!"}
1179	The Lighthouse of the Orcas	{"Response":"False","Error":"Movie not found!"}
1197	The Photographer of Mauthausen	{"Response":"False","Error":"Movie not found!"}
1205	All the Freckles in the World	{"Response":"False","Error":"Movie not found!"}
1209	The Endless Trench	{"Response":"False","Error":"Movie not found!"}
1242	Tune in for Love	{"Response":"False","Error":"Movie not found!"}
1285	Jaoon Kahan Bata Ae Dil	{"Response":"False","Error":"Movie not found!"}
1296	Shadows In My Eyes	{"Response":"False","Error":"Movie not found!"}
1299	The Awakening of Motti Wolkenbruch	{"Response":"False","Error":"Movie not found!"}
1304	A Shaun the Sheep Movie: Farmageddon [294]	{"Response":"False","Error":"Movie not found!"}
1309	Flavors of Youth: International Version	{"Response":"False","Error":"Movie not found!"}
1316	Modest Heroes: Ponoc Short Films Theater	{"Response":"False","Error":"Movie not found!"}
1322	An Afternoon with SCTV	{"Response":"False","Error":"Movie not found!"}

	Title	JSON_DATA
48	Messiah	{"Response":"False","Error":"Movie not found!"}
49	Locke & Key	{"Response":"False","Error":"Movie not found!"}
60	W/ Bob & David	{"Response":"False","Error":"Movie not found!"}
64	Netflix Presents: The Characters	{"Response":"False","Error":"Movie not found!"}
69	Trailer Park Boys Out of the Park: Europe	{"Response":"False","Error":"Movie not found!"}
82	Trailer Park Boys Out of the Park: USA	{"Response":"False","Error":"Movie not found!"}
107	Astronomy Club: The Sketch Show	{"Response":"False","Error":"Movie not found!"}
129	Rilakkuma and Kaoru	{"Response":"False","Error":"Movie not found!"}
131	Knights of the Zodiac: Saint Seiya	{"Response":"False","Error":"Movie not found!"}
136	The Disastrous Life of Saiki K.: Reawakened	{"Response":"False","Error":"Movie not found!"}
152	Justin Time GO!	{"Response":"False","Error":"Movie not found!"}
161	Edgar Rice Burroughs' Tarzan and Jane	{"Response":"False","Error":"Movie not found!"}
173	Stretch Armstrong and the Flex Fighters	{"Response":"False","Error":"Movie not found!"}
180	True: Magical Friends	{"Response":"False","Error":"Movie not found!"}
181	True: Wonderful Wishes	{"Response":"False","Error":"Movie not found!"}
207	Dino Girl Gauko	{"Response":"False","Error":"Movie not found!"}
208	Fast & Furious: Spy Racers	{"Response":"False","Error":"Movie not found!"}
209	Go! Go! Cory Carson	{"Response":"False","Error":"Movie not found!"}
211	Super Monsters Monster Party	{"Response":"False","Error":"Movie not found!"}
214	Project Mc 2	{"Response":"False","Error":"Movie not found!"}
220	The Ponysitters Club	{"Response":"False","Error":"Movie not found!"}
221	Best.Worst.Weekend.Ever.	{"Response":"False","Error":"Movie not found!"}
225	Malibu Rescue: The Series	{"Response":"False","Error":"Movie not found!"}
232	Cable Girls	{"Response":"False","Error":"Movie not found!"}
240	Club de Cuervos Presents: The Ballad of Hugo S...	{"Response":"False","Error":"Movie not found!"}
...	...	...
1107	The Main Event	{"Response":"False","Error":"Movie not found!"}
1108	The Princess Switch: Switched Again	{"Response":"False","Error":"Movie not found!"}
1109	Under the Sun of Riccione	{"Response":"False","Error":"Movie not found!"}
1110	Welcome to Sudden Death	{"Response":"False","Error":"Movie not found!"}
1111	My Father's Dragon	{"Response":"False","Error":"Movie not found!"}
1112	Pinocchio	{"Response":"False","Error":"Movie not found!"}
1113	The Power of the Dog	{"Response":"False","Error":"Movie not found!"}
1114	Untitled The Loud House film	{"Response":"False","Error":"Movie not found!"}
1115	Wendell and Wild	{"Response":"False","Error":"Movie not found!"}
1116	Escape from Hat	{"Response":"False","Error":"Movie not found!"}
1117	Jacob and the Sea Beast	{"Response":"False","Error":"Movie not found!"}
1118	Taylor Swift: Miss Americana	{"Response":"False","Error":"Movie not found!"}
1176	The Last Letter from Your Lover	{"Response":"False","Error":"Movie not found!"}
1179	The Lighthouse of the Orcas	{"Response":"False","Error":"Movie not found!"}
1197	The Photographer of Mauthausen	{"Response":"False","Error":"Movie not found!"}
1205	All the Freckles in the World	{"Response":"False","Error":"Movie not found!"}
1209	The Endless Trench	{"Response":"False","Error":"Movie not found!"}
1242	Tune in for Love	{"Response":"False","Error":"Movie not found!"}
1285	Jaoon Kahan Bata Ae Dil	{"Response":"False","Error":"Movie not found!"}
1296	Shadows In My Eyes	{"Response":"False","Error":"Movie not found!"}
1299	The Awakening of Motti Wolkenbruch	{"Response":"False","Error":"Movie not found!"}
1304	A Shaun the Sheep Movie: Farmageddon	{"Response":"False","Error":"Movie not found!"}
1309	Flavors of Youth: International Version	{"Response":"False","Error":"Movie not found!"}
1316	Modest Heroes: Ponoc Short Films Theater	{"Response":"False","Error":"Movie not found!"}
1322	An Afternoon with SCTV	{"Response":"False","Error":"Movie not found!"}

	Title	JSON_DATA
0	Messiah	{"Title":"Messiah","Year":"2001","Rated":"N/A"...
0	Locke & Key	{"Title":"Locke & Key","Year":"2011","Rated":"...
0	The Gift	{"Title":"The Gift","Year":"2015","Rated":"R",...
0	The Neighbor	{"Title":"The Neighbor","Year":"2018","Rated":...
0	Ares	{"Title":"Ares","Year":"2016","Rated":"N/A","R...
0	Followers	{"Title":"Followers","Year":"2017","Rated":"No...
0	Ghost in the Shell: SAC_2045	{"Title":"Ghost in the Shell SAC_2045","Year":...
0	EDEN	{"Title":"Eden","Year":"2012","Rated":"R","Rel...
0	Alma	{"Title":"Alma","Year":"2009","Rated":"Not Rat...
0	Behind Her Eyes	{"Title":"Behind Her Eyes","Year":"2018","Rate...
0	Blood and Water	{"Title":"Blood and Water","Year":"2015–","Rat...
0	Boca a Boca	{"Title":"Boca a Boca","Year":"2005–","Rated":...
0	Bridgerton	{"Title":"The Untitled Bridgerton Series","Yea...
0	Cursed	{"Title":"Cursed","Year":"2005","Rated":"PG-13...
0	Grand Army	{"Title":"Grand Army Veterans of New York","Ye...
0	Hollywood	{"Title":"Once Upon a Time ... in Hollywood","...
0	I Am Not Okay with This	{"Title":"I Am Not Okay with This","Year":"202...
0	Into the Night	{"Title":"Into the Night","Year":"1985","Rated...
0	Ju-On	{"Title":"Ju-on","Year":"2000","Rated":"Not Ra...
0	Kipo and the Age of Wonderbeasts	{"Title":"Kipo and the Age of Wonderbeasts","Y...
0	Never Have I Ever	{"Title":"Never Have I Ever","Year":"2009","Ra...
0	October Faction	{"Title":"October Faction","Year":"2019–","Rat...
0	Outer Banks	{"Title":"Outer Banks","Year":"2012","Rated":"...
0	Pacific Rim	{"Title":"Pacific Rim","Year":"2013","Rated":"...
0	Ratched	{"Title":"Ratched","Year":"2018–","Rated":"N/A...
...	...	...
0	The Magic Finger	{"Title":"The Magic Finger","Year":"1990","Rat...
0	Warrior Nun	{"Title":"Warrior Nun","Year":"2015","Rated":"...
0	The App	{"Title":"The App","Year":"2016","Rated":"N/A"...
0	A Fall from Grace	{"Title":"A Fall from Grace","Year":"2015","Ra...
0	The Paramedic	{"Title":"The Paramedic","Year":"1982","Rated"...
0	The Willoughbys	{"Title":"The Willoughbys","Year":"2020","Rate...
0	The Prom	{"Title":"The Prom","Year":"1982","Rated":"N/A...
0	Robin Robin	{"Title":"Robin & Robin","Year":"2012","Rated"...
0	All the Bright Places	{"Title":"All the Bright Places","Year":"2020"...
0	Beyond the Result	{"Title":"Al di la' del risultato (Beyond the ...
0	First Ladies	{"Title":"First Ladies","Year":"2012","Rated":...
0	Freaks	{"Title":"Freaks","Year":"1932","Rated":"Not R...
0	Hogar	{"Title":"Hogar","Year":"2011","Rated":"N/A","...
0	Jingle Jangle	{"Title":"Jingle Jangle","Year":"2017","Rated"...
0	Over the Moon	{"Title":"Over the Moon","Year":"1939","Rated"...
0	Sergio	{"Title":"Sergio","Year":"2009","Rated":"Not R...
0	The Boys in the Band	{"Title":"The Boys in the Band","Year":"1970",...
0	The Dig	{"Title":"The Dig","Year":"2018","Rated":"Not ...
0	The Last Paradise	{"Title":"The Last Paradise","Year":"1955","Ra...
0	The Last Thing He Wanted	{"Title":"The Last Thing He Wanted","Year":"20...
0	The Main Event	{"Title":"The Main Event","Year":"1979","Rated...
0	My Father's Dragon	{"Title":"My Father's Dragon","Year":"1997","R...
0	Pinocchio	{"Title":"Pinocchio","Year":"1940","Rated":"G"...
0	The Power of the Dog	{"Title":"The Power of the Dog","Year":"2014",...
0	A Shaun the Sheep Movie: Farmageddon	{"Title":"A Shaun the Sheep Movie: Farmageddon...

	Title	JSON_DATA	Year
0	House of Cards	{"Title":"House of Cards","Year":"2013–2018","...	2013
1	Hemlock Grove	{"Title":"Hemlock Grove","Year":"2013–2015","R...	2013
2	Orange Is the New Black	{"Title":"Orange Is the New Black","Year":"201...	2013
3	Marco Polo	{"Title":"Marco Polo","Year":"2014–2016","Rate...	2014
4	Bloodline	{"Title":"Bloodline","Year":"2015–2017","Rated...	2015
5	Sense8	{"Title":"Sense8","Year":"2015–2018","Rated":"...	2015
6	Narcos	{"Title":"Narcos","Year":"2015–2017","Rated":"...	2015
7	Stranger Things	{"Title":"Stranger Things","Year":"2016–","Rat...	2016
8	The Get Down	{"Title":"The Get Down","Year":"2016–2017","Ra...	2016
9	The Crown	{"Title":"The Crown","Year":"2016–","Rated":"T...	2016
10	Gilmore Girls: A Year in the Life	{"Title":"Gilmore Girls: A Year in the Life","...	the
11	The OA	{"Title":"The OA","Year":"2016–2019","Rated":"...	2016
12	A Series of Unfortunate Events	{"Title":"A Series of Unfortunate Events","Yea...	2017
13	13 Reasons Why	{"Title":"13 Reasons Why","Year":"2017–","Rate...	2017
14	Gypsy	{"Title":"Gypsy","Year":"2017","Rated":"TV-MA"...	2017
15	Ozark	{"Title":"Ozark","Year":"2017–","Rated":"TV-MA...	2017
16	Mindhunter	{"Title":"Mindhunter","Year":"2017–","Rated":"...	2017
17	Godless	{"Title":"Godless","Year":"2017","Rated":"TV-M...	2017
18	Altered Carbon	{"Title":"Altered Carbon","Year":"2018–","Rate...	2018
19	Seven Seconds	{"Title":"Seven Seconds","Year":"2018","Rated"...	2018
20	Lost in Space	{"Title":"Lost in Space","Year":"1998","Rated"...	1998
21	The Innocents	{"Title":"The Innocents","Year":"1961","Rated"...	1961
22	The Haunting	{"Title":"The Haunting","Year":"1999","Rated":...	1999
23	Chilling Adventures of Sabrina	{"Title":"Chilling Adventures of Sabrina","Yea...	2018
24	Narcos: Mexico	{"Title":"Narcos: Mexico","Year":"2018–","Rate...	2018
...	...	...	...
0	The Magic Finger	{"Title":"The Magic Finger","Year":"1990","Rat...	NaN
0	Warrior Nun	{"Title":"Warrior Nun","Year":"2015","Rated":"...	NaN
0	The App	{"Title":"The App","Year":"2016","Rated":"N/A"...	NaN
0	A Fall from Grace	{"Title":"A Fall from Grace","Year":"2015","Ra...	NaN
0	The Paramedic	{"Title":"The Paramedic","Year":"1982","Rated"...	NaN
0	The Willoughbys	{"Title":"The Willoughbys","Year":"2020","Rate...	NaN
0	The Prom	{"Title":"The Prom","Year":"1982","Rated":"N/A...	NaN
0	Robin Robin	{"Title":"Robin & Robin","Year":"2012","Rated"...	NaN
0	All the Bright Places	{"Title":"All the Bright Places","Year":"2020"...	NaN
0	Beyond the Result	{"Title":"Al di la' del risultato (Beyond the ...	NaN
0	First Ladies	{"Title":"First Ladies","Year":"2012","Rated":...	NaN
0	Freaks	{"Title":"Freaks","Year":"1932","Rated":"Not R...	NaN
0	Hogar	{"Title":"Hogar","Year":"2011","Rated":"N/A","...	NaN
0	Jingle Jangle	{"Title":"Jingle Jangle","Year":"2017","Rated"...	NaN
0	Over the Moon	{"Title":"Over the Moon","Year":"1939","Rated"...	NaN
0	Sergio	{"Title":"Sergio","Year":"2009","Rated":"Not R...	NaN
0	The Boys in the Band	{"Title":"The Boys in the Band","Year":"1970",...	NaN
0	The Dig	{"Title":"The Dig","Year":"2018","Rated":"Not ...	NaN
0	The Last Paradise	{"Title":"The Last Paradise","Year":"1955","Ra...	NaN
0	The Last Thing He Wanted	{"Title":"The Last Thing He Wanted","Year":"20...	NaN
0	The Main Event	{"Title":"The Main Event","Year":"1979","Rated...	NaN
0	My Father's Dragon	{"Title":"My Father's Dragon","Year":"1997","R...	NaN
0	Pinocchio	{"Title":"Pinocchio","Year":"1940","Rated":"G"...	NaN
0	The Power of the Dog	{"Title":"The Power of the Dog","Year":"2014",...	NaN
0	A Shaun the Sheep Movie: Farmageddon	{"Title":"A Shaun the Sheep Movie: Farmageddon...	NaN

	Title	Year	Rating	Genre	Ratings_Count
0	House of Cards	2013	8.8	Drama	440131.0
1	Hemlock Grove	2013	7.2	Horror	34914.0
2	Orange Is the New Black	2013	8.1	Comedy	266392.0
3	Marco Polo	2014	8.0	Adventure	58101.0
4	Bloodline	2015	8.0	Drama	39969.0
5	Sense8	2015	8.3	Drama	127594.0
6	Narcos	2015	8.8	Biography	311316.0
7	Stranger Things	2016	8.8	Drama	674756.0
8	The Get Down	2016	8.3	Drama	19773.0
9	The Crown	2016	8.7	Drama	94336.0
10	Gilmore Girls: A Year in the Life	the	7.8	Comedy	25478.0
11	The OA	2016	7.9	Drama	80249.0
12	A Series of Unfortunate Events	2017	7.8	Adventure	48499.0
13	13 Reasons Why	2017	7.8	Drama	228605.0
14	Gypsy	2017	6.9	Drama	12198.0
15	Ozark	2017	8.3	Crime	116734.0
16	Mindhunter	2017	8.6	Crime	166449.0
17	Godless	2017	8.3	Drama	40933.0
18	Altered Carbon	2018	8.1	Action	110771.0
19	Seven Seconds	2018	7.7	Crime	10917.0
20	Lost in Space	1998	5.2	Action	67090.0
21	The Innocents	1961	7.8	Horror	24219.0
22	The Haunting	1999	5.0	Fantasy	68013.0
23	Chilling Adventures of Sabrina	2018	7.7	Drama	54931.0
24	Narcos: Mexico	2018	8.4	Crime	39194.0
...	...	...	...	...	...
1236	The Magic Finger	1990	N/A	Animation	NaN
1237	Warrior Nun	2015	N/A	Action	NaN
1238	The App	2016	6.9	Short	17.0
1239	A Fall from Grace	2015	N/A	Drama	NaN
1240	The Paramedic	1982	5.9	Comedy	69.0
1241	The Willoughbys	2020	N/A	Animation	NaN
1242	The Prom	1982	6.7	NaN	92.0
1243	Robin Robin	2012	N/A	Short	NaN
1244	All the Bright Places	2020	N/A	Drama	NaN
1245	Beyond the Result	2016	N/A	Biography	NaN
1246	First Ladies	2012	N/A	Short	NaN
1247	Freaks	1932	7.9	Drama	39211.0
1248	Hogar	2011	N/A	Short	NaN
1249	Jingle Jangle	2017	N/A	Music	NaN
1250	Over the Moon	1939	5.8	Comedy	173.0
1251	Sergio	2009	7.8	Documentary	228.0
1252	The Boys in the Band	1970	7.6	Drama	3740.0
1253	The Dig	2018	6.1	Thriller	510.0
1254	The Last Paradise	1955	7.0	Documentary	9.0
1255	The Last Thing He Wanted	2020	N/A	Crime	NaN
1256	The Main Event	1979	5.9	Comedy	2364.0
1257	My Father's Dragon	1997	6.5	Animation	16.0
1258	Pinocchio	1940	7.4	Animation	120078.0
1259	The Power of the Dog	2014	N/A	Crime	NaN
1260	A Shaun the Sheep Movie: Farmageddon	2019	7.1	Animation	1618.0

	Title
0	Swiss Family Robinson
1	Miracle on 34th Street
2	Treasure Island
3	The Story of Robin Hood and His Merrie Men
4	The Living Desert
5	The Vanishing Prairie
6	20,000 Leagues Under the Sea
7	Davy Crockett, King of the Wild Frontier
8	The African Lion
9	Davy Crockett and the River Pirates
10	Secrets of Life
11	Perri
12	Old Yeller
13	White Wilderness
14	The Sign of Zorro
15	The Shaggy Dog
16	Darby O’Gill and the Little People
17	Journey to the Center of the Earth
18	Jungle Cat
19	Pollyanna
20	The Absent-Minded Professor
21	The Parent Trap
22	Greyfriars Bobby
23	Babes in Toyland
24	The Prince and the Pauper
25	Almost Angels
26	Sammy, the Way-Out Seal
27	The Incredible Journey
28	Mary Poppins
29	Emil and the Detectives
30	Those Calloways
31	The Sound of Music
32	That Darn Cat!
33	The Ugly Dachshund
34	The Adventures of Bullwhip Griffin
35	Blackbeard’s Ghost (1968)
36	The Love Bug
37	The Computer Wore Tennis Shoes
38	The Barefoot Executive
39	The Million Dollar Duck
40	Bedknobs and Broomsticks
41	Justin Morgan Had a Horse
42	The Biscuit Eater
43	Snowball Express
44	Herbie Rides Again
45	The Bears and I
46	The Castaway Cowboy
47	The Strongest Man in the World
48	Escape to Witch Mountain
49	The Apple Dumpling Gang
...	...
601	Journey to Shark Eden
602	Great Migrations
603	Brain Games
604	The Incredible Dr. Pol
605	Secrets of the King Cobra
606	Winged Seduction: Birds of Paradise
607	Life Below Zero
608	Sharks of Lost Island
609	Miracle Landing on the Hudson
610	Dr. Oakley, Yukon Vet
611	Dr. K’s Exotic Animal ER
612	Wild Yellowstone
613	World’s Greatest Dogs
614	Expedition Mars: Spirit & Opportunity
615	Before the Flood
616	Sea of Hope: America’s Underwater Treasures
617	Atlantis Rising
618	Origins: The Journey of Humankind
619	Earth Live
620	Kingdom of the Apes: Battle Lines
621	Diana: In Her Own Words
622	Breaking2
623	Jane
624	Titanic: 20 Years Later With James Cameron
625	Secrets of Christ’s Tomb: Explorer Special
626	Man Among Cheetahs
627	Decorating Disney: Holiday Magic
628	Planet of the Birds
629	Giants of the Deep Blue
630	Rocky Mountain Animal Rescue
631	Into the Okavango
632	Drain the Oceans
633	Science Fair
634	Free Solo
635	Under The Sea: A Descendants Short Story
636	Mars: Inside SpaceX
637	Mission to the Sun
638	The Flood
639	Tree Climbing Lions
640	Paris to Pittsburgh
641	Into the Grand Canyon
642	Lost Treasures of the Maya
643	The Lost Tomb of Alexander the Great
644	Hostile Planet
645	Apollo: Missions to the Moon
646	Gordon Ramsay: Uncharted
647	Kingdom of the White Wolf
648	The Simpsons
649	Ice Age: The Great Egg-Scapade
650	Little Einsteins

	Title	JSON_DATA
0	Swiss Family Robinson	{"Title":"Swiss Family Robinson","Year":"1960"...
0	Miracle on 34th Street	{"Title":"Miracle on 34th Street","Year":"1947...
0	Treasure Island	{"Title":"Treasure Island","Year":"1950","Rate...
0	The Story of Robin Hood and His Merrie Men	{"Title":"The Story of Robin Hood and His Merr...
0	The Living Desert	{"Title":"The Living Desert","Year":"1953","Ra...
0	The Vanishing Prairie	{"Title":"The Vanishing Prairie","Year":"1954"...
0	20,000 Leagues Under the Sea	{"Title":"20,000 Leagues Under the Sea","Year"...
0	Davy Crockett, King of the Wild Frontier	{"Title":"Davy Crockett: King of the Wild Fron...
0	The African Lion	{"Title":"The African Lion","Year":"1955","Rat...
0	Davy Crockett and the River Pirates	{"Title":"Davy Crockett and the River Pirates"...
0	Secrets of Life	{"Title":"Secrets of Life","Year":"1956","Rate...
0	Perri	{"Title":"Perri","Year":"1957","Rated":"Approv...
0	Old Yeller	{"Title":"Old Yeller","Year":"1957","Rated":"A...
0	White Wilderness	{"Title":"White Wilderness","Year":"1958","Rat...
0	The Sign of Zorro	{"Title":"The Sign of Zorro","Year":"1958","Ra...
0	The Shaggy Dog	{"Title":"The Shaggy Dog","Year":"2006","Rated...
0	Darby O’Gill and the Little People	{"Response":"False","Error":"Movie not found!"}
0	Journey to the Center of the Earth	{"Title":"Journey to the Center of the Earth",...
0	Jungle Cat	{"Title":"Jungle Cat","Year":"1960","Rated":"A...
0	Pollyanna	{"Title":"Pollyanna","Year":"1960","Rated":"Ap...
0	The Absent-Minded Professor	{"Title":"The Absent-Minded Professor","Year":...
0	The Parent Trap	{"Title":"The Parent Trap","Year":"1998","Rate...
0	Greyfriars Bobby	{"Title":"Greyfriars Bobby: The True Story of ...
0	Babes in Toyland	{"Title":"Babes in Toyland","Year":"1960","Rat...
0	The Prince and the Pauper	{"Title":"The Prince and the Pauper","Year":"1...
0	Almost Angels	{"Title":"Almost Angels","Year":"1962","Rated"...
0	Sammy, the Way-Out Seal	{"Response":"False","Error":"Movie not found!"}
0	The Incredible Journey	{"Title":"The Incredible Journey","Year":"1963...
0	Mary Poppins	{"Title":"Mary Poppins","Year":"1964","Rated":...
0	Emil and the Detectives	{"Title":"Emil and the Detectives","Year":"200...
0	Those Calloways	{"Title":"Those Calloways","Year":"1965","Rate...
0	The Sound of Music	{"Title":"The Sound of Music","Year":"1965","R...
0	That Darn Cat!	{"Title":"That Darn Cat!","Year":"1965","Rated...
0	The Ugly Dachshund	{"Title":"The Ugly Dachshund","Year":"1966","R...
0	The Adventures of Bullwhip Griffin	{"Title":"The Adventures of Bullwhip Griffin",...
0	Blackbeard’s Ghost (1968)	{"Response":"False","Error":"Movie not found!"}
0	The Love Bug	{"Title":"The Love Bug","Year":"1968","Rated":...
0	The Computer Wore Tennis Shoes	{"Title":"The Computer Wore Tennis Shoes","Yea...
0	The Barefoot Executive	{"Title":"The Barefoot Executive","Year":"1971...
0	The Million Dollar Duck	{"Title":"The Million Dollar Duck","Year":"197...
0	Bedknobs and Broomsticks	{"Title":"Bedknobs and Broomsticks","Year":"19...
0	Justin Morgan Had a Horse	{"Title":"Justin Morgan Had a Horse","Year":"1...
0	The Biscuit Eater	{"Title":"The Biscuit Eater","Year":"1972","Ra...
0	Snowball Express	{"Title":"Snowball Express","Year":"1972","Rat...
0	Herbie Rides Again	{"Title":"Herbie Rides Again","Year":"1974","R...
0	The Bears and I	{"Title":"The Bears and I","Year":"1974","Rate...
0	The Castaway Cowboy	{"Title":"The Castaway Cowboy","Year":"1974","...
0	The Strongest Man in the World	{"Title":"The Strongest Man in the World","Yea...
0	Escape to Witch Mountain	{"Title":"Escape to Witch Mountain","Year":"19...
0	The Apple Dumpling Gang	{"Title":"The Apple Dumpling Gang","Year":"197...
...	...	...
0	Journey to Shark Eden	{"Response":"False","Error":"Movie not found!"}
0	Great Migrations	{"Title":"Great Migrations","Year":"2010–","Ra...
0	Brain Games	{"Title":"Brain Games","Year":"2011–","Rated":...
0	The Incredible Dr. Pol	{"Title":"The Incredible Dr. Pol","Year":"2011...
0	Secrets of the King Cobra	{"Response":"False","Error":"Movie not found!"}
0	Winged Seduction: Birds of Paradise	{"Title":"Winged Seduction: Birds of Paradise"...
0	Life Below Zero	{"Title":"Life Below Zero","Year":"2013–","Rat...
0	Sharks of Lost Island	{"Title":"Sharks of Lost Island","Year":"2013"...
0	Miracle Landing on the Hudson	{"Title":"Miracle Landing on the Hudson","Year...
0	Dr. Oakley, Yukon Vet	{"Title":"Dr. Oakley, Yukon Vet","Year":"2014–...
0	Dr. K’s Exotic Animal ER	{"Title":"Dr K's Exotic Animal ER","Year":"201...
0	Wild Yellowstone	{"Title":"Wild Yellowstone","Year":"2015","Rat...
0	World’s Greatest Dogs	{"Response":"False","Error":"Movie not found!"}
0	Expedition Mars: Spirit & Opportunity	{"Response":"False","Error":"Movie not found!"}
0	Before the Flood	{"Title":"Before the Flood","Year":"2016","Rat...
0	Sea of Hope: America’s Underwater Treasures	{"Title":"Sea of Hope: America's Underwater Tr...
0	Atlantis Rising	{"Title":"Atlantis Rising","Year":"2017","Rate...
0	Origins: The Journey of Humankind	{"Title":"Origins: The Journey of Humankind","...
0	Earth Live	{"Title":"Earth Live","Year":"2017","Rated":"N...
0	Kingdom of the Apes: Battle Lines	{"Response":"False","Error":"Movie not found!"}
0	Diana: In Her Own Words	{"Title":"Diana: In Her Own Words","Year":"201...
0	Breaking2	{"Title":"Breaking2","Year":"2017","Rated":"N/...
0	Jane	{"Title":"Jane","Year":"2017","Rated":"PG","Re...
0	Titanic: 20 Years Later With James Cameron	{"Title":"Titanic: 20 Years Later with James C...
0	Secrets of Christ’s Tomb: Explorer Special	{"Response":"False","Error":"Movie not found!"}
0	Man Among Cheetahs	{"Title":"Man Among Cheetahs","Year":"2017","R...
0	Decorating Disney: Holiday Magic	{"Title":"Decorating Disney: Holiday Magic","Y...
0	Planet of the Birds	{"Response":"False","Error":"Movie not found!"}
0	Giants of the Deep Blue	{"Response":"False","Error":"Movie not found!"}
0	Rocky Mountain Animal Rescue	{"Title":"Rocky Mountain Animal Rescue","Year"...
0	Into the Okavango	{"Title":"Into The Okavango","Year":"2017","Ra...
0	Drain the Oceans	{"Title":"Drain the Oceans","Year":"2018–","Ra...
0	Science Fair	{"Title":"Science Fair","Year":"2018","Rated":...
0	Free Solo	{"Title":"Free Solo","Year":"2018","Rated":"PG...
0	Under The Sea: A Descendants Short Story	{"Response":"False","Error":"Movie not found!"}
0	Mars: Inside SpaceX	{"Title":"MARS: Inside SpaceX","Year":"2018","...
0	Mission to the Sun	{"Title":"Mission to the Sun","Year":"2018","R...
0	The Flood	{"Title":"The Flood","Year":"2019","Rated":"N/...
0	Tree Climbing Lions	{"Response":"False","Error":"Movie not found!"}
0	Paris to Pittsburgh	{"Title":"Paris to Pittsburgh","Year":"2018","...
0	Into the Grand Canyon	{"Response":"False","Error":"Movie not found!"}
0	Lost Treasures of the Maya	{"Title":"Lost Treasures of the Maya Snake Kin...
0	The Lost Tomb of Alexander the Great	{"Title":"The Lost Tomb of Alexander the Great...
0	Hostile Planet	{"Title":"Hostile Planet","Year":"2019–","Rate...
0	Apollo: Missions to the Moon	{"Title":"Apollo: Missions to the Moon","Year"...
0	Gordon Ramsay: Uncharted	{"Title":"Gordon Ramsay: Uncharted","Year":"20...
0	Kingdom of the White Wolf	{"Title":"Kingdom of the White Wolf","Year":"2...
0	The Simpsons	{"Title":"The Simpsons","Year":"1989–","Rated"...
0	Ice Age: The Great Egg-Scapade	{"Title":"Ice Age: The Great Egg-Scapade","Yea...
0	Little Einsteins	{"Title":"Little Einsteins","Year":"2005–","Ra...

	Title	Year	Rating	Genre	Ratings_Count
0	Swiss Family Robinson	1960	7.2	Adventure	12491.0
0	Miracle on 34th Street	1947	7.9	Comedy	37479.0
0	Treasure Island	1950	6.9	Adventure	6167.0
0	The Story of Robin Hood and His Merrie Men	1952	6.7	Action	1147.0
0	The Living Desert	1953	7.6	Family	1083.0
0	The Vanishing Prairie	1954	7.7	Documentary	341.0
0	20,000 Leagues Under the Sea	1954	7.2	Adventure	26637.0
0	Davy Crockett, King of the Wild Frontier	1955	7.0	Adventure	3081.0
0	The African Lion	1955	7.5	Family	165.0
0	Davy Crockett and the River Pirates	1956	6.8	Adventure	1490.0
0	Secrets of Life	1956	7.8	Documentary	126.0
0	Perri	1957	7.2	Adventure	272.0
0	Old Yeller	1957	7.3	Adventure	10742.0
0	White Wilderness	1958	5.4	Documentary	322.0
0	The Sign of Zorro	1958	7.1	Adventure	378.0
0	The Shaggy Dog	2006	4.4	Comedy	17390.0
0	Journey to the Center of the Earth	2008	5.8	Action	106981.0
0	Jungle Cat	1960	7.6	Documentary	175.0
0	Pollyanna	1960	7.4	Comedy	8228.0
0	The Absent-Minded Professor	1907	N/A	Short	NaN
0	The Parent Trap	1998	6.5	Adventure	110689.0
0	Greyfriars Bobby	1961	7.4	Drama	1094.0
0	Babes in Toyland	1960	6.3	Comedy	3057.0
0	The Prince and the Pauper	1990	7.1	Animation	2629.0
0	Almost Angels	1962	7.1	Comedy	309.0
0	The Incredible Journey	1963	7.1	Adventure	3477.0
0	Mary Poppins	1964	7.8	Comedy	147008.0
0	Emil and the Detectives	2001	5.9	Family	938.0
0	Those Calloways	1965	6.8	Drama	423.0
0	The Sound of Music	1965	8.0	Biography	186246.0
0	That Darn Cat!	1965	6.7	Comedy	5171.0
0	The Ugly Dachshund	1966	6.7	Comedy	3043.0
0	The Adventures of Bullwhip Griffin	1967	6.6	Action	603.0
0	The Love Bug	1968	6.5	Comedy	13001.0
0	The Computer Wore Tennis Shoes	1969	6.0	Comedy	3924.0
0	The Barefoot Executive	1971	6.0	Family	1539.0
0	The Million Dollar Duck	1971	5.9	Comedy	1742.0
0	Bedknobs and Broomsticks	1971	7.1	Animation	31829.0
0	Justin Morgan Had a Horse	1972	7.3	Family	91.0
0	The Biscuit Eater	1972	5.6	Drama	255.0
0	Snowball Express	1972	6.5	Comedy	1225.0
0	Herbie Rides Again	1974	5.7	Comedy	6738.0
0	The Bears and I	1974	6.1	Drama	233.0
0	The Castaway Cowboy	1974	5.9	Comedy	382.0
0	The Strongest Man in the World	1975	6.1	Comedy	2390.0
0	Escape to Witch Mountain	1975	6.4	Adventure	8391.0
0	The Apple Dumpling Gang	1975	6.4	Comedy	4458.0
0	Gus	1976	5.9	Comedy	1443.0
0	Treasure of Matecumbe	1976	6.3	Adventure	397.0
0	The Shaggy D.A.	1976	5.9	Comedy	2332.0
...	...	...	...	...	...
0	Ant-Man	2015	7.3	Action	521484.0
0	Captain America: Civil War	2016	7.8	Action	610916.0
0	Doctor Strange	2016	7.5	Action	543758.0
0	Guardians of the Galaxy Vol. 2	2017	7.6	Action	513275.0
0	Marvel Rising: Secret Warriors	2018	5.0	Animation	754.0
0	Captain Marvel	2019	7.0	Action	368679.0
0	Avengers: Endgame	2019	8.5	Action	613329.0
0	How Dogs Got Their Shapes	2016	N/A	Documentary	NaN
0	Dog Whisperer With Cesar Millan	2004	8.0	Family	2509.0
0	Kingdom of the Blue Whale	2009	7.3	Documentary	65.0
0	Bizarre Dinosaurs	2009	6.6	Documentary	47.0
0	Great Migrations	2010	8.1	Documentary	675.0
0	Brain Games	2011	8.3	Documentary	2702.0
0	The Incredible Dr. Pol	2011	8.6	Documentary	857.0
0	Winged Seduction: Birds of Paradise	2012	6.5	Documentary	10.0
0	Life Below Zero	2013	8.1	Documentary	1265.0
0	Sharks of Lost Island	2013	N/A	NaN	NaN
0	Miracle Landing on the Hudson	2014	7.4	Drama	21.0
0	Dr. Oakley, Yukon Vet	2014	8.2	Reality	266.0
0	Dr. K’s Exotic Animal ER	2014	7.9	Reality	211.0
0	Wild Yellowstone	2015	7.3	Documentary	19.0
0	Before the Flood	2016	8.3	Documentary	24232.0
0	Sea of Hope: America’s Underwater Treasures	2017	N/A	Documentary	NaN
0	Atlantis Rising	2017	6.8	Documentary	178.0
0	Origins: The Journey of Humankind	2017	6.9	Documentary	321.0
0	Earth Live	2017	5.6	Documentary	24.0
0	Diana: In Her Own Words	2017	7.9	Documentary	1012.0
0	Breaking2	2017	7.5	Documentary	42.0
0	Jane	2017	7.8	Documentary	3242.0
0	Titanic: 20 Years Later With James Cameron	ater	7.3	Documentary	177.0
0	Man Among Cheetahs	2017	N/A	Documentary	NaN
0	Decorating Disney: Holiday Magic	2017	N/A	Reality	NaN
0	Rocky Mountain Animal Rescue	2018	7.8	Reality	5.0
0	Into the Okavango	2017	9.2	Documentary	11.0
0	Drain the Oceans	2018	7.5	Documentary	153.0
0	Science Fair	2018	7.7	Documentary	331.0
0	Free Solo	2018	8.2	Documentary	41204.0
0	Mars: Inside SpaceX	2018	7.2	Documentary	412.0
0	Mission to the Sun	2018	7.3	Documentary	23.0
0	The Flood	2019	4.8	Drama	432.0
0	Paris to Pittsburgh	2018	5.9	NaN	9.0
0	Lost Treasures of the Maya	2018	5.7	Documentary	10.0
0	The Lost Tomb of Alexander the Great	2019	N/A	Documentary	NaN
0	Hostile Planet	2019	8.2	Documentary	268.0
0	Apollo: Missions to the Moon	2019	7.7	NaN	90.0
0	Gordon Ramsay: Uncharted	2019	8.0	Adventure	238.0
0	Kingdom of the White Wolf	2019	7.0	Documentary	10.0
0	The Simpsons	1989	8.7	Animation	328282.0
0	Ice Age: The Great Egg-Scapade	2016	5.9	Animation	2741.0
0	Little Einsteins	2005	6.4	Animation	948.0

	Disney_Ratings	Netflix_Ratings
Genre
Adventure	6.285915	6.517391
Comedy	5.916547	6.531068
Action	6.629032	6.639326
Family	6.052632	6.644444
Documentary	7.169565	7.235359
Short	NaN	7.378947
Drama	6.454839	6.679747
Animation	6.954777	6.898246
Biography	7.092308	6.992000
Crime	7.800000	7.021875
Music	8.400000	7.050000
Reality	7.800000	6.808333
Horror	NaN	5.740000
Thriller	NaN	5.538462
Fantasy	NaN	6.666667
Sci-Fi	NaN	5.600000
Romance	NaN	5.985714
Myster	NaN	6.250000

	Title	Genre	Premiere	Length	Language	Netflix exclusive region	Runtime	Languages
0	Beasts of No Nation	War / drama	October 16, 2015	2 hours, 16 min.	English/Twi	NaN	NaN	NaN
1	Crouching Tiger, Hidden Dragon: Sword of Destiny	Action	February 26, 2016	1 hour, 43 min.	English	NaN	NaN	NaN
2	The Fundamentals of Caring	Comedy-drama	June 24, 2016	1 hour, 37 min.	English	NaN	NaN	NaN
3	Rebirth	Thriller	July 15, 2016	1 hour, 40 min.	English	NaN	NaN	NaN
4	Tallulah	Comedy-drama	July 29, 2016	1 hour, 51 min.	English	NaN	NaN	NaN
5	XOXO	Drama	August 26, 2016	1 hour, 32 min.	English	NaN	NaN	NaN
6	ARQ	Science fiction / Thriller	September 16, 2016	1 hour, 28 min.	English	NaN	NaN	NaN
7	The Siege of Jadotville	War film	October 7, 2016	1 hour, 48 min.	English	NaN	NaN	NaN
8	I Am the Pretty Thing That Lives in the House	Horror	October 28, 2016	1 hour, 29 min.	English	NaN	NaN	NaN
9	7 años	Drama	October 28, 2016	1 hour, 16 min.	Spanish	NaN	NaN	NaN
10	Mercy	Thriller	November 22, 2016	1 hour, 30 min.	English	NaN	NaN	NaN
11	Spectral	Science fiction / Action	December 9, 2016	1 hour, 48 min.	English	NaN	NaN	NaN
12	Barry	Biopic	December 16, 2016	1 hour, 44 min.	English	NaN	NaN	NaN
0	Coin Heist	Heist film	January 6, 2017	1 hour, 37 min.	English	NaN	NaN	NaN
1	Clinical	Thriller	January 13, 2017	1 hour, 44 min.	English	NaN	NaN	NaN
2	iBoy	Science fiction / Thriller	January 27, 2017	1 hour, 30 min.	English	NaN	NaN	NaN
3	Imperial Dreams	Drama	February 3, 2017	1 hour, 27 min.	English	NaN	NaN	NaN
4	I Don't Feel at Home in This World Anymore	Drama	February 24, 2017	1 hour, 36 min.	English	NaN	NaN	NaN
5	Burning Sands	Drama	March 10, 2017	1 hour, 42 min.	English	NaN	NaN	NaN
6	Deidra & Laney Rob a Train	Drama	March 17, 2017	1 hour, 34 min.	English	NaN	NaN	NaN
7	The Most Hated Woman in America	Biopic	March 24, 2017	1 hour, 32 min.	English	NaN	NaN	NaN
8	The Discovery	Science fiction / Drama	March 31, 2017	1 hour, 42 min.	English	NaN	NaN	NaN
9	Sand Castle	War film	April 21, 2017	1 hour, 53 min.	English	NaN	NaN	NaN
10	Tramps	Romance	April 21, 2017	1 hour, 23 min.	English	NaN	NaN	NaN
11	Blame!	Anime / Science fiction	May 20, 2017	1 hour, 46 min.	Japanese	NaN	NaN	NaN
12	Shimmer Lake	Crime thriller	June 9, 2017	1 hour, 26 min.	English	NaN	NaN	NaN
13	Okja	Science fiction / Adventure	June 28, 2017	2 hours, 1 min.	English/Korean	NaN	NaN	NaN
14	To the Bone	Drama	July 14, 2017	1 hour, 47 min.	English	NaN	NaN	NaN
15	Death Note	Horror thriller	August 25, 2017	1 hour, 40 min.	English	NaN	NaN	NaN
16	First They Killed My Father	Drama	September 15, 2017	2 hours, 16 min.	English/Khmer	NaN	NaN	NaN
17	Gerald's Game	Horror thriller	September 29, 2017	1 hour, 43 min.	English	NaN	NaN	NaN
18	Our Souls at Night	Romance	September 29, 2017	1 hour, 43 min.	English	NaN	NaN	NaN
19	The Meyerowitz Stories (New and Selected)	Comedy-drama	October 13, 2017	1 hour, 52 min.	English	NaN	NaN	NaN
20	1922	Horror / crime drama	October 20, 2017	1 hour, 42 min.	English	NaN	NaN	NaN
21	Wheelman	Action thriller	October 20, 2017	1 hour, 22 min.	English	NaN	NaN	NaN
22	The Killer	Western	November 10, 2017	1 hour, 39 min.	Portuguese	NaN	NaN	NaN
23	Christmas Inheritance	Romantic drama	December 15, 2017	1 hour, 44 min.	English	NaN	NaN	NaN
24	Bright	Urban fantasy	December 22, 2017	1 hour, 57 min.	English	NaN	NaN	NaN
0	The Open House	Horror thriller	January 19, 2018	1 hour, 34 min.	English	NaN	NaN	NaN
1	The Cloverfield Paradox	Science fiction	February 4, 2018	1 hour, 42 min.	English	NaN	NaN	NaN