
!pip install squarify

Collecting squarify
  Downloading squarify-0.4.3-py3-none-any.whl (4.3 kB)
Installing collected packages: squarify
Successfully installed squarify-0.4.3

WARNING: You are using pip version 21.1.2; however, version 22.3.1 is available.
You should consider upgrading via the 'c:\users\eshan jairath\appdata\local\programs\python\python39\python.exe -m pip install --upgrade pip' command.


from collections import Counter
import pandas as pd
import numpy as np
import squarify
import seaborn as sns
from seaborn import axes_style
import matplotlib.pyplot as plt
from matplotlib import gridspec
from matplotlib.pyplot import xticks

%matplotlib inline

print('Libraries Imported !!')

Libraries Imported !!


netflix_df = pd.read_csv('./DataSets/netflix_titles_2021.csv')
print('Dataset imported ! ')

Dataset imported !


netflix_df.head()


netflix_df.shape

(8807, 12)


# Show overview of columns that may have nulls
netflix_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8807 entries, 0 to 8806
Data columns (total 12 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   show_id       8807 non-null   object
 1   type          8807 non-null   object
 2   title         8807 non-null   object
 3   director      6173 non-null   object
 4   cast          7982 non-null   object
 5   country       7976 non-null   object
 6   date_added    8797 non-null   object
 7   release_year  8807 non-null   int64 
 8   rating        8803 non-null   object
 9   duration      8804 non-null   object
 10  listed_in     8807 non-null   object
 11  description   8807 non-null   object
dtypes: int64(1), object(11)
memory usage: 825.8+ KB


# A look at the basic stats for the release year
netflix_df.describe()


# Show the distinct types of media in the dataset
netflix_df.type.unique()

array(['Movie', 'TV Show'], dtype=object)


netflix_df[netflix_df.date_added.isna() == True].head()


netflix_df_copy = netflix_df


# Change date_added to datetime and assign to variable
netflix_df_copy['date_added'] = pd.to_datetime(netflix_df['date_added'])

#Extract year from date_added column
netflix_df_copy['year_added'] = pd.DatetimeIndex(netflix_df_copy.date_added).year
#Extract month from date_added column
netflix_df_copy['month_added'] = pd.DatetimeIndex(netflix_df_copy.date_added).month
#Extract day from date_added column
netflix_df_copy['day_added'] = pd.DatetimeIndex(netflix_df_copy.date_added).day

# Cast year's day_added to Int64 to catch NaN values
netflix_df_copy['year_added'] = netflix_df_copy['year_added'].astype('Int64')
# Cast month's day_added to Int64 to catch NaN values
netflix_df_copy['month_added'] = netflix_df_copy['month_added'].astype('Int64')
# Cast day's day_added to Int64 to catch NaN values
netflix_df_copy['day_added'] = netflix_df_copy['day_added'].astype('Int64')


netflix_df_copy.head(3)


#Show rating rows with NaN
netflix_df_copy.loc[netflix_df_copy['rating'].isna()]


#Replace Nan in rating column with Unknown
netflix_df_copy['rating'] = netflix_df_copy['rating'].fillna('Unknown')


#Locate minute string values in rating
netflix_df_copy.loc[netflix_df_copy['rating'].str.contains('min')]


# Create copy of data frame in case dataframe is inaccessible
netflix_df_copy2 = netflix_df_copy

# Change duration to string data type to set up for Nan removal
netflix_df_copy2['duration'] = netflix_df_copy2['duration'].astype('string')

# Remove Nans in duration
netflix_df_copy2.loc[[5541, 5794, 5813]] = netflix_df_copy2.loc[[5541, 5794, 5813]].replace(np.nan, '')


# Examine to see if duration column has no NaN
netflix_df_copy2.loc[netflix_df_copy2['rating'].str.contains('min')]


# Update data entry error rows to the correct duration values
netflix_df_copy2.at[5541, 'duration'] = '74 min'
netflix_df_copy2.at[5794, 'duration'] = '84 min'
netflix_df_copy2.at[5813, 'duration'] = '66 min'


# Update data entry errors (e.g., 'min') in rating column to 'Unknown'
for key, value in netflix_df_copy['rating'].items():
    if 'min' in value:
        netflix_df_copy2['rating'].replace([value], 'Unknown', regex= True, inplace= True)


# Now to check the dataframe for the changes made
netflix_df_copy2.loc[netflix_df_copy2['rating'].str.contains('Unknown')].head(3)


#Create a column for when streaming started
stream_start = 2007
netflix_df_copy['adjusted_release_year'] = stream_start


# Create a column for the difference between year added and release year
netflix_df_copy2['year(s)_to_acquisition'] = netflix_df_copy2['year_added'] - netflix_df_copy2['release_year']


# Assign any value in release year less than 2007 to a variable
stream_year = netflix_df_copy2['release_year'] < 2007

# For any stream year, the adjusted release year value will be subtracted from the year added value
netflix_df_copy2.loc[stream_year, 'year(s)_to_acquisition'] = netflix_df_copy2['year_added'] - netflix_df_copy2['adjusted_release_year']


# Create absolute values for year(s) to acquisition to remove negative values
netflix_df_copy2['year(s)_to_acquisition'] = netflix_df_copy2['year(s)_to_acquisition'].abs()


# Create Netflix acqusition of media dataframe
acq_df = netflix_df_copy2[['title', 'type', 'listed_in', 'duration', 'year_added', 'release_year', 'adjusted_release_year', 'year(s)_to_acquisition']]


#Calculate the average years to acqusition
acq_df['year(s)_to_acquisition'].mean().round(2)

3.28


#Minimum years until acquisition
acq_df['year(s)_to_acquisition'].min()

0


#Assign variable to minimum year
min_year = acq_df['year(s)_to_acquisition'].min()


#Maximum years until acquisition
acq_df['year(s)_to_acquisition'].max()

14


#Assign variable to maximum year
max_year = acq_df['year(s)_to_acquisition'].max()


#Show Netflix titles that are TV related with zero year to acquisition
tv_acq_df = acq_df[acq_df['type'] == 'TV Show']

min_tv_df = tv_acq_df.loc[tv_acq_df['year(s)_to_acquisition'] == min_year]


#Show Netflix titles that are Movie related with zero year to acquisition
movie_acq_df = acq_df[acq_df['type'] == 'Movie']

min_movie_df = movie_acq_df.loc[movie_acq_df['year(s)_to_acquisition'] == min_year]


#Count how many Netflix titles are listed TV with zero years to acquisition
tv_acq_count = len(min_tv_df)

print(f'There are {tv_acq_count} content listed TV in Netflix titles aquired in less than a year.')

There are 1379 content listed TV in Netflix titles aquired in less than a year.


#Count how many Netflix titles are listed Movie with zero years to acquisition
movie_acq_count = len(min_movie_df)

print(f'There are {movie_acq_count} content listed movie in Netflix titles aquired in less than a year.')

There are 1862 content listed movie in Netflix titles aquired in less than a year.


#Separate TV Shows and Movies into dataframes
tv_media = netflix_df_copy2[netflix_df_copy2['type'] == 'TV Show']
movie_media = netflix_df_copy2[netflix_df_copy2['type'] == 'Movie']


tv_media.head(5)


movie_media.head(5)


#View the count of tv shows per rating
tv_media['rating'].value_counts()

TV-MA       1145
TV-14        733
TV-PG        323
TV-Y7        195
TV-Y         176
TV-G          94
NR             5
R              2
Unknown        2
TV-Y7-FV       1
Name: rating, dtype: int64


#View the count of movie per rating
movie_media['rating'].value_counts()

TV-MA       2062
TV-14       1427
R            797
TV-PG        540
PG-13        490
PG           287
TV-Y7        139
TV-Y         131
TV-G         126
NR            75
G             41
TV-Y7-FV       5
Unknown        5
UR             3
NC-17          3
Name: rating, dtype: int64


# Confirm total TV Show in tv media content
tv_media_count = len(tv_media['year(s)_to_acquisition'])


tv_media_count

2676


# Confirm total TV Shows added in less than a year
tv_acq_count

1379


# Divide the TV content added in less than a year by the total TV media content
result = tv_acq_count/tv_media_count

print('{:.0%} of the TV shows acquired were in less than a year.'.format(result))

52% of the TV shows acquired were in less than a year.


# Removing NaN values for plotting
tv_media_nonull = tv_media['year(s)_to_acquisition'][~np.isnan(tv_media['year(s)_to_acquisition'])]


# Plotting the TV media content to show the portion of TV media added through a yearly time frame
fig1, ax1 = plt.subplots(figsize=(14, 11))

thin_width = 0.03
n_lst = tv_media_nonull.value_counts()

m_labels = [ 'Less than yr.',  '1 yr.',  '3 yrs.',  '7 yrs.',  '6 yrs.',  '8 yrs.',  '2 yrs.',  '4 yrs.',  '5 yrs.',  '9 yrs.', '12 yrs.', 
            '10 yrs.', '13 yrs.', '11 yrs.', '14 yrs.']

explode1 = [0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

color1 = sns.color_palette('Reds_r', n_colors= 42)

wedges, texts, autotexts = ax1.pie(n_lst, labels = m_labels, shadow = True, colors = color1, explode= explode1, 
                                   autopct= lambda pct:"{:.1f}".format(pct), textprops= {'fontsize': 14}, wedgeprops= {'alpha': 0.7})

ax1.set_title('Portion of TV Shows Added within Years', fontweight = 'bold')

# Drop the label of portion added if the value is 2 or less
dropoff = 2
for mlabel, perc_label in zip(texts, autotexts):
    perc_val = perc_label.get_text().rstrip('%')
    if float(perc_val) < dropoff:
        mlabel.set_text('')
        perc_label.set_text('')


# Confirm total movies in movie media content
movie_media_count = len(movie_media['year(s)_to_acquisition'])


movie_media_count

6131


# Divide the movies added in less than a year by the total movie media content
result = movie_acq_count/movie_media_count

print('{:.0%} of movies acquired were in less than a year.'.format(result))

30% of movies acquired were in less than a year.


# Removing NaN values for plotting
movie_media_nonull = movie_media['year(s)_to_acquisition'][~np.isnan(movie_media['year(s)_to_acquisition'])]


fig1, ax1 = plt.subplots(figsize=(14, 11))

thin_width = 0.03
n_lst = movie_media_nonull.value_counts()

m_labels = [ 'Less than yr.',  '1 yr.',  '3 yrs.',  '7 yrs.',  '6 yrs.',  '8 yrs.',  '2 yrs.',  '4 yrs.',  '5 yrs.',  '9 yrs.', '12 yrs.', 
            '10 yrs.', '13 yrs.', '11 yrs.', '14 yrs.']

explode1 = [0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

color1 = sns.color_palette('RdGy_r', n_colors= 42)

wedges, texts, autotexts = ax1.pie(n_lst, labels = m_labels, shadow = True, colors = color1, explode= explode1, 
                                   autopct= lambda pct:"{:.1f}".format(pct), textprops= {'fontsize': 14}, wedgeprops= {'alpha': 0.7})

ax1.set_title('Portion of Movies Added within Years', fontweight = 'bold');


#Assign tv ratings and number of ratings to variables
rating = tv_media['rating']
tv_shows = tv_media['rating'].value_counts()


#Create a barplot for tv ratings
plt.figure(figsize= (15, 8))
sns.countplot(x= 'rating', data= tv_media, 
              color= 'darkred', 
              order= tv_media['rating']. value_counts().index).set(title = 'TV Shows by Count per Rating')

ax = sns.countplot(x= 'rating', data= tv_media,
                   color= 'darkred',
                   order= tv_media['rating']. value_counts().index)

xticks(rotation = 40);


#Assign movie ratings and number of ratings to variables
rating = movie_media['rating']
movies = movie_media['rating'].value_counts()


#Create a barplot for movie ratings
plt.figure(figsize= (15, 8))
sns.countplot(x= 'rating', data= movie_media, 
              color= 'darkred', 
              order= movie_media['rating']. value_counts().index).set(title = 'Movies by Count per Rating')

ax = sns.countplot(x= 'rating', data= movie_media,
                   color= 'darkred',
                   order= movie_media['rating']. value_counts().index)

xticks(rotation = 40);


tv_media['country']

1                                        South Africa
2                                                 NaN
3                                                 NaN
4                                               India
5                                                 NaN
                            ...                      
8795                                    Japan, Canada
8796                                           Turkey
8797    United States, France, South Korea, Indonesia
8800                                         Pakistan
8803                                              NaN
Name: country, Length: 2676, dtype: object


tv_media['country'].isna().sum()

391


# Cleaning country data of NaNs
country_tv_media = tv_media['country'].fillna('Null')

# Separate out the countries by joining the countries into a string and using the comma as a separator, 
# replacing the comma and space with just a comma, and then splitting on the comma so items are contained in the Counter
country_tv_media_count = pd.Series(Counter(','.join(country_tv_media).replace(', ', ',').split(',')))
# Drop the Nulls from the Series
country_tv_media_clean = country_tv_media_count.drop(['Null'], axis= 0)
# Sort the countries descendig by the Counter
country_tv_media_sort = country_tv_media_clean.sort_values(ascending= False)


# Aggregate the overall total without Nulls
total_tv = sum(country_tv_media_sort)
# Aggregate totals for the top 10
top_ten = sum(country_tv_media_sort.head(10))
# et the top 10 countries
top_ten_countries = country_tv_media_sort.head(10)


top_ten_countries

United States     938
United Kingdom    272
Japan             199
South Korea       170
Canada            126
France             90
India              84
Taiwan             70
Australia          66
Spain              61
dtype: int64


print(f'The total TV shows is {total_tv}')
print(f'The total TV shows of the top ten is {top_ten}')

The total TV shows is 2640
The total TV shows of the top ten is 2076


fig = plt.figure(figsize= (23, 9))
gridsp = gridspec.GridSpec(nrows= 1, ncols= 2)

# Plotting a barplot
ax1 = plt.subplot(gridsp[0])
sns.barplot(x= top_ten_countries.values, y= top_ten_countries.index, ax = ax1, palette = 'gray')

ax1.set_title('The 10 Countries with most TV Shows')

# Plotting a treemap
ax2 = plt.subplot(gridsp[1])
y_axis = top_ten_countries
squarify.plot(sizes= y_axis.values, label= y_axis.index, color= sns.color_palette('Reds_r', n_colors= 10), linewidth = 3,
             text_kwargs={'fontsize': 17})
plt.axis('off');


yearly_content = netflix_df_copy2


yearly_content['year_added'].min()

2008


# Grouping the data to aggregate the titles added in that respective year
grouped_content = yearly_content.groupby('year_added', as_index=False).count()


grouped_content[['year_added', 'show_id']]


yearly_content = grouped_content[['year_added', 'show_id']]


max_content = yearly_content['show_id'].max()


# Identifying the year with the most content added
yearly_content.loc[yearly_content['show_id'] == max_content]


fig, ax = plt.subplots(figsize= (16,9))
ax.set_title('Total Content per Year', fontdict= { 'fontsize': 24})
sns.set_style('whitegrid')
sns.barplot(data= yearly_content, x= 'year_added', y= 'show_id', palette= 'gray_r', ax= ax, alpha= 0.7)
sns.lineplot(x= np.arange(0,len(yearly_content.year_added)), y= yearly_content.show_id, hue= None, color= 'Red', ax= ax);

	release_year
count	8807.000000
mean	2014.180198
std	8.819312
min	1925.000000
25%	2013.000000
50%	2017.000000
75%	2019.000000
max	2021.000000

	show_id	type	title	director	cast	country	date_added	release_year	rating	duration	listed_in	description
6066	s6067	TV Show	A Young Doctor's Notebook and Other Stories	NaN	Daniel Radcliffe, Jon Hamm, Adam Godley, Chris...	United Kingdom	NaN	2013	TV-MA	2 Seasons	British TV Shows, TV Comedies, TV Dramas	Set during the Russian Revolution, this comic ...
6174	s6175	TV Show	Anthony Bourdain: Parts Unknown	NaN	Anthony Bourdain	United States	NaN	2018	TV-PG	5 Seasons	Docuseries	This CNN original series has chef Anthony Bour...
6795	s6796	TV Show	Frasier	NaN	Kelsey Grammer, Jane Leeves, David Hyde Pierce...	United States	NaN	2003	TV-PG	11 Seasons	Classic & Cult TV, TV Comedies	Frasier Crane is a snooty but lovable Seattle ...
6806	s6807	TV Show	Friends	NaN	Jennifer Aniston, Courteney Cox, Lisa Kudrow, ...	United States	NaN	2003	TV-14	10 Seasons	Classic & Cult TV, TV Comedies	This hit sitcom follows the merry misadventure...
6901	s6902	TV Show	Gunslinger Girl	NaN	Yuuka Nanri, Kanako Mitsuhashi, Eri Sendai, Am...	Japan	NaN	2008	TV-14	2 Seasons	Anime Series, Crime TV Shows	On the surface, the Social Welfare Agency appe...

	show_id	type	title	director	cast	country	date_added	release_year	rating	duration	listed_in	description	year_added	month_added	day_added
5989	s5990	Movie	13TH: A Conversation with Oprah Winfrey & Ava ...	NaN	Oprah Winfrey, Ava DuVernay	NaN	2017-01-26	2017	NaN	37 min	Movies	Oprah Winfrey sits down with director Ava DuVe...	2017	1	26
6827	s6828	TV Show	Gargantia on the Verdurous Planet	NaN	Kaito Ishikawa, Hisako Kanemoto, Ai Kayano, Ka...	Japan	2016-12-01	2013	NaN	1 Season	Anime Series, International TV Shows	After falling through a wormhole, a space-dwel...	2016	12	1
7312	s7313	TV Show	Little Lunch	NaN	Flynn Curry, Olivia Deeble, Madison Lu, Oisín ...	Australia	2018-02-01	2015	NaN	1 Season	Kids' TV, TV Comedies	Adopting a child's perspective, this show take...	2018	2	1
7537	s7538	Movie	My Honor Was Loyalty	Alessandro Pepe	Leone Frisa, Paolo Vaccarino, Francesco Miglio...	Italy	2017-03-01	2015	NaN	115 min	Dramas	Amid the chaos and horror of World War II, a c...	2017	3	1

	year_added	show_id
0	2008	2
1	2009	2
2	2010	1
3	2011	13
4	2012	3
5	2013	11
6	2014	24
7	2015	82
8	2016	429
9	2017	1188
10	2018	1649
11	2019	2016
12	2020	1879
13	2021	1498

Prepping the Data¶

Cleaning the Data¶

Exploratory Analysis and Vizualization¶

Conclusions and Inference of the Data¶

	show_id	type	title	director	cast	country	date_added	release_year	rating	duration	listed_in	description
0	s1	Movie	Dick Johnson Is Dead	Kirsten Johnson	NaN	United States	September 25, 2021	2020	PG-13	90 min	Documentaries	As her father nears the end of his life, filmm...
1	s2	TV Show	Blood & Water	NaN	Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban...	South Africa	September 24, 2021	2021	TV-MA	2 Seasons	International TV Shows, TV Dramas, TV Mysteries	After crossing paths at a party, a Cape Town t...
2	s3	TV Show	Ganglands	Julien Leclercq	Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi...	NaN	September 24, 2021	2021	TV-MA	1 Season	Crime TV Shows, International TV Shows, TV Act...	To protect his family from a powerful drug lor...
3	s4	TV Show	Jailbirds New Orleans	NaN	NaN	NaN	September 24, 2021	2021	TV-MA	1 Season	Docuseries, Reality TV	Feuds, flirtations and toilet talk go down amo...
4	s5	TV Show	Kota Factory	NaN	Mayur More, Jitendra Kumar, Ranjan Raj, Alam K...	India	September 24, 2021	2021	TV-MA	2 Seasons	International TV Shows, Romantic TV Shows, TV ...	In a city of coaching centers known to train I...

	show_id	type	title	director	cast	country	date_added	release_year	rating	duration	listed_in	description	year_added	month_added	day_added
5541	s5542	Movie	Louis C.K. 2017	Louis C.K.	Louis C.K.	United States	2017-04-04	2017	74 min	NaN	Movies	Louis C.K. muses on religion, eternal love, gi...	2017	4	4
5794	s5795	Movie	Louis C.K.: Hilarious	Louis C.K.	Louis C.K.	United States	2016-09-16	2010	84 min	NaN	Movies	Emmy-winning comedy writer Louis C.K. brings h...	2016	9	16
5813	s5814	Movie	Louis C.K.: Live at the Comedy Store	Louis C.K.	Louis C.K.	United States	2016-08-15	2015	66 min	NaN	Movies	The comic puts his trademark hilarious/thought...	2016	8	15

	show_id	type	title	director	cast	country	date_added	release_year	rating	duration	listed_in	description	year_added	month_added	day_added	adjusted_release_year	year(s)_to_acquisition
0	s1	Movie	Dick Johnson Is Dead	Kirsten Johnson	NaN	United States	2021-09-25	2020	PG-13	90 min	Documentaries	As her father nears the end of his life, filmm...	2021	9	25	2007	1
6	s7	Movie	My Little Pony: A New Generation	Robert Cullen, José Luis Ucha	Vanessa Hudgens, Kimiko Glenn, James Marsden, ...	NaN	2021-09-24	2021	PG	91 min	Children & Family Movies	Equestria's divided. But a bright-eyed hero be...	2021	9	24	2007	0
7	s8	Movie	Sankofa	Haile Gerima	Kofi Ghanaba, Oyafunmike Ogunlano, Alexandra D...	United States, Ghana, Burkina Faso, United Kin...	2021-09-24	1993	TV-MA	125 min	Dramas, Independent Movies, International Movies	On a photo shoot in Ghana, an American model s...	2021	9	24	2007	14
9	s10	Movie	The Starling	Theodore Melfi	Melissa McCarthy, Chris O'Dowd, Kevin Kline, T...	United States	2021-09-24	2021	PG-13	104 min	Comedies, Dramas	A woman adjusting to life after a loss contend...	2021	9	24	2007	0
12	s13	Movie	Je Suis Karl	Christian Schwochow	Luna Wedler, Jannis Niewöhner, Milan Peschel, ...	Germany, Czech Republic	2021-09-23	2021	TV-MA	127 min	Dramas, International Movies	After most of her family is murdered in a terr...	2021	9	23	2007	0