netflix-subscriptions-data-analysis

Netflix Subscriptions Data Analysis

The data was downloaded from Kaggle:https://www.kaggle.com/prasertk/netflix-subscription-price-in-different-countries

The dataset contains details on Netflix subsription fees and content offerings across several countries where Netflix services are available.

Note: The dataset does not include ALL countries where Netflix operates.

Goal

Determining where Netflix customers potentially experience the most satisfaction from their subscription.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

#Read the file
netflix_df = pd.read_csv('netflix dec 2021.csv')

netflix_df.head()
Country_code Country Total Library Size No. of TV Shows No. of Movies Cost Per Month - Basic ($) Cost Per Month - Standard ($) Cost Per Month - Premium ($)
0 ar Argentina 4760 3154 1606 3.74 6.30 9.26
1 au Australia 6114 4050 2064 7.84 12.12 16.39
2 at Austria 5640 3779 1861 9.03 14.67 20.32
3 be Belgium 4990 3374 1616 10.16 15.24 20.32
4 bo Bolivia 4991 3155 1836 7.99 10.99 13.99 
netflix_df.columns

Index(['Country_code', 'Country', 'Total Library Size', 'No. of TV Shows',
       'No. of Movies', 'Cost Per Month - Basic ($)',
       'Cost Per Month - Standard ($)', 'Cost Per Month - Premium ($)'],
      dtype='object')

Checking for Missing Values

#Checking the entire data frame for missing values
netflix_df.isna().any().any()

False

Creating new features

In this stage we are adding new features that will provide more insight into the data.

Region

We will identify the region each country falls under based on the country code.

import pycountry_convert as pc

#Creating a function to identify the continent from the country code
def country_to_continent(country_name):
    country_alpha2 = pc.country_name_to_country_alpha2(country_name)
    country_continent_code = pc.country_alpha2_to_continent_code(country_alpha2)
    country_continent_name = pc.convert_continent_code_to_continent_name(country_continent_code)
    return country_continent_name

#Creating a column to indicate the continent for each country
netflix_df['Region'] = netflix_df['Country'].apply(lambda country_name: country_to_continent(country_name))

netflix_df.head()
Country_code Country Total Library Size No. of TV Shows No. of Movies Cost Per Month - Basic ($) Cost Per Month - Standard ($) Cost Per Month - Premium ($) Region
0 ar Argentina 4760 3154 1606 3.74 6.30 9.26 South America
1 au Australia 6114 4050 2064 7.84 12.12 16.39 Oceania
2 at Austria 5640 3779 1861 9.03 14.67 20.32 Europe
3 be Belgium 4990 3374 1616 10.16 15.24 20.32 Europe
4 bo Bolivia 4991 3155 1836 7.99 10.99 13.99 South America

To make the Dataframe more readable we can move the “Region” column right after the “Country” column

#Creating a function to reposition columns
def movecol(df, cols_to_move=[], ref_col='', place='After'):
    
    #cols_to_move can be one or more columns we wish to move
    #ref_col is the column after or before which we want to reposition the column(s)
    
    cols = df.columns.tolist()
    if place == 'After':
        seg1 = cols[:list(cols).index(ref_col) + 1]
        seg2 = cols_to_move
    if place == 'Before':
        seg1 = cols[:list(cols).index(ref_col)]
        seg2 = cols_to_move + [ref_col]
    
    seg1 = [i for i in seg1 if i not in seg2]
    seg3 = [i for i in cols if i not in seg1 + seg2]
    
    return(df[seg1 + seg2 + seg3])

netflix_df = movecol(netflix_df, cols_to_move=['Region'], ref_col='Country', place='After')

Value for Money

We will examine the relationship between the amount of content available and the basic subscription cost to determine which customers receive the most value for money.

A higher score indicates that the customers can access more content per dollar spent on their Basic Netflix Subsciption.

netflix_df['Value for Money'] = (netflix_df['Total Library Size']/netflix_df['Cost Per Month - Basic ($)']).round()

Exploratory Data Analysis

In this stage, we will examine the data to identify any patterns, trends and relationships between the variables. It will help us analyze the data and extract insights that can be used to make decisions.

Data Visualization will give us a clear idea of what the data means by giving it visual context.

#Number of countries we have data for
len(netflix_df['Country'])

65

netflix_df['Region'].value_counts().to_frame()
Region
Europe 34
Asia 12
South America 10
North America 6
Oceania 2
Africa 1 
regions = netflix_df['Region'].value_counts()

regions.plot.pie(figsize=(8, 8), autopct='%1.0f%%',fontsize=15,labels=regions.index, pctdistance=0.85, startangle=55)
plt.title("Netflix Worldwide Coverage by Region", fontsize=16)

# autopct="%1.0f%%" shows the percentage to 0 decimal place 
# pctdistance controls how far the values appear from the center of the circle
# startangle controls the angle of the slices, allowing the pie chart to be rotated to improve readability

Text(0.5, 1.0, 'Netflix Worldwide Coverage by Region')

png

We have data on 65 countries where Netflix operates across 6 regions.

Content

content_stats = netflix_df['Total Library Size'].describe().round().to_frame()
tv = netflix_df['No. of TV Shows'].describe().round()
movies = netflix_df['No. of Movies'].describe().round()
content_stats = pd.concat([content_stats, tv, movies], axis=1)
content_stats
Total Library Size No. of TV Shows No. of Movies
count 65.0 65.0 65.0
mean 5314.0 3519.0 1795.0
std 980.0 723.0 327.0
min 2274.0 1675.0 373.0
25% 4948.0 3154.0 1628.0
50% 5195.0 3512.0 1841.0
75% 5952.0 3832.0 1980.0
max 7325.0 5234.0 2387.0 
fig_dims = (10,10)
fig, ax = plt.subplots(figsize=fig_dims)

sns.histplot(data=netflix_df, x="Total Library Size",kde=True,binwidth=200)
plt.title("Total Netflix Content Library Size", fontsize=15)

Text(0.5, 1.0, 'Total Netflix Content Library Size')

png

netflix_content_stats_df = pd.DataFrame(columns = ['Description', 'Country', 'Total Library Size', 'No. of TV Shows', 'No. of Movies','Cost Per Month - Basic ($)', 'Cost Per Month - Standard ($)','Cost Per Month - Premium ($)'])

#Largest Library
max_lib = netflix_df[netflix_df['Total Library Size'] == netflix_df['Total Library Size'].max()]
max_lib.insert(0, "Description", "Largest Content Library", True)

#Smallest Library
min_lib = netflix_df[netflix_df['Total Library Size'] == netflix_df['Total Library Size'].min()]
min_lib.insert(0, "Description", "Smallest Content Library", True)

#Largest TV Library
max_tv = netflix_df[netflix_df['No. of TV Shows'] == netflix_df['No. of TV Shows'].max()]
max_tv.insert(0, "Description", "Largest Library of TV Shows", True)

#Smallest TV Library
min_tv = netflix_df[netflix_df['No. of TV Shows'] == netflix_df['No. of TV Shows'].min()]
min_tv.insert(0, "Description", "Smallest Library of TV Shows", True)

#Largest Movie Library
max_mov = netflix_df[netflix_df['No. of Movies'] == netflix_df['No. of Movies'].max()]
max_mov.insert(0, "Description", "Largest Library of Movies", True)

#Smallest Movie Library
min_mov = netflix_df[netflix_df['No. of Movies'] == netflix_df['No. of Movies'].min()]
min_mov.insert(0, "Description", "Smallest Library of Movies", True)

#Adding columns to empty dataframe
netflix_content_stats_df = pd.concat([netflix_content_stats_df, max_lib, min_lib, max_tv, min_tv, max_mov, min_mov], axis=0)

#Removing extra columns
netflix_content_stats_df = netflix_content_stats_df[['Description', 'Country', 'Total Library Size', 'No. of TV Shows', 'No. of Movies']]

netflix_content_stats_df
Description Country Total Library Size No. of TV Shows No. of Movies
12 Largest Content Library Czechia 7325 5234 2091
11 Smallest Content Library Croatia 2274 1675 599
12 Largest Library of TV Shows Czechia 7325 5234 2091
11 Smallest Library of TV Shows Croatia 2274 1675 599
35 Largest Library of Movies Malaysia 5952 3565 2387
49 Smallest Library of Movies San Marino 2310 1937 373

As seen above,

sns.set_theme(style="whitegrid")

fig_dims = (10, 6)
fig, ax = plt.subplots(figsize=fig_dims)

#Creating a DataFrame with the Top 10 countires by library content
top_lib = netflix_df.sort_values(by=['Total Library Size'], ascending=False).head(10)

sns.set_color_codes("pastel")
sns.barplot(x=top_lib['Total Library Size'], y=top_lib['Country'], data=top_lib, label="All Content", color="b")
plt.title("Top Countries by Netflix Library Size", fontsize=15)

sns.set_color_codes("muted")
sns.barplot(x=top_lib['No. of TV Shows'], y=top_lib['Country'], data=top_lib, label="TV Shows", color="b")

sns.set_color_codes("pastel")
sns.barplot(x=top_lib['No. of Movies'], y=top_lib['Country'], data=top_lib, label="Movies", color="darkblue")

# Add a legend and informative axis label
ax.set(xlim=(0, 8000), ylabel="", xlabel="Library Size")
sns.despine(left=True, bottom=True)

# Put the legend out of the figure
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)

<matplotlib.legend.Legend at 0x1e78187c8e0>

png

In the top 10 countries by library size we see that the Movie content remains around 2000 and the main differentiator is the number of TV Shows available in each country.

sns.set_theme(style="whitegrid")

fig_dims = (10, 6)
fig, ax = plt.subplots(figsize=fig_dims)

#Creating a DataFrame with the Bottom 10 countires by library content
bot_lib = netflix_df.sort_values(by=['Total Library Size'], ascending=True).head(10)

sns.set_color_codes("pastel")
sns.barplot(x=bot_lib['Total Library Size'], y=bot_lib['Country'], data=bot_lib, label="All Content", color="b")
plt.title("Top Countries by Smallest Netflix Library Size", fontsize=15)

sns.set_color_codes("muted")
sns.barplot(x=bot_lib['No. of TV Shows'], y=bot_lib['Country'], data=bot_lib, label="TV Shows", color="b")

sns.set_color_codes("pastel")
sns.barplot(x=bot_lib['No. of Movies'], y=bot_lib['Country'], data=bot_lib, label="Movies", color="darkblue")

# Add a legend and informative axis label
ax.set(xlim=(0, 5000), ylabel="", xlabel="Library Size")
sns.despine(left=True, bottom=True)

# Put the legend out of the figure
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)

<matplotlib.legend.Legend at 0x1e781bdac40>

png

In the 10 countries by smallest library size we see that the Movie content remains around 1500 (except for Croatia and San Marino) and once again the main differentiator is the number of TV Shows available in each country.

Subscription

subcription_stats = netflix_df['Cost Per Month - Basic ($)'].describe().round(2).to_frame()
standard = netflix_df['Cost Per Month - Standard ($)'].describe().round(2)
premium = netflix_df['Cost Per Month - Premium ($)'].describe().round(2)
subcription_stats = pd.concat([subcription_stats, standard, premium], axis=1)
subcription_stats
Cost Per Month - Basic ($) Cost Per Month - Standard ($) Cost Per Month - Premium ($)
count 65.00 65.00 65.00
mean 8.37 11.99 15.61
std 1.94 2.86 4.04
min 1.97 3.00 4.02
25% 7.99 10.71 13.54
50% 8.99 11.49 14.45
75% 9.03 13.54 18.06
max 12.88 20.46 26.96

The cost of a customer’s Netflix subscription tier increases by as much as $22.94 if they relocate to a different country.

fig_dims = (8,8)
fig, ax = plt.subplots(figsize=fig_dims)

sns.histplot(data=netflix_df, x="Cost Per Month - Basic ($)",kde=True,binwidth=0.5)
plt.title("Cost Per Month - Basic ($)", fontsize=15)

Text(0.5, 1.0, 'Cost Per Month - Basic ($)')

png

fig_dims = (8,8)
fig, ax = plt.subplots(figsize=fig_dims)

sns.histplot(data=netflix_df, x="Cost Per Month - Standard ($)",kde=True,binwidth=1)
plt.title("Cost Per Month - Standard ($)", fontsize=15)

Text(0.5, 1.0, 'Cost Per Month - Standard ($)')

png

fig_dims = (8,8)
fig, ax = plt.subplots(figsize=fig_dims)

sns.histplot(data=netflix_df, x="Cost Per Month - Premium ($)",kde=True,binwidth=1)
plt.title("Cost Per Month - Premium ($)", fontsize=15)

Text(0.5, 1.0, 'Cost Per Month - Premium ($)')

png

netflix_subscription_stats_df = pd.DataFrame(columns = ['Description', 'Country', 'Total Library Size', 'No. of TV Shows', 'No. of Movies','Cost Per Month - Basic ($)', 'Cost Per Month - Standard ($)','Cost Per Month - Premium ($)'])

#Highest basic cost per month
max_basic = netflix_df[netflix_df['Cost Per Month - Basic ($)'] == netflix_df['Cost Per Month - Basic ($)'].max()]
max_basic.insert(0, "Description", "Highest Subsription Costs", True)

#Smallest basic cost per month
min_basic = netflix_df[netflix_df['Cost Per Month - Basic ($)'] == netflix_df['Cost Per Month - Basic ($)'].min()]
min_basic.insert(0, "Description", "Lowest Subsription Costs", True)

#Highest standard cost per month
max_standard = netflix_df[netflix_df['Cost Per Month - Standard ($)'] == netflix_df['Cost Per Month - Standard ($)'].max()]
max_standard.insert(0, "Description", "Highest Standard Subsription", True)

#Smallest standard cost per month
min_standard = netflix_df[netflix_df['Cost Per Month - Standard ($)'] == netflix_df['Cost Per Month - Standard ($)'].min()]
min_standard.insert(0, "Description", "Lowest Standard Subsription", True)

#Highest premium cost per month
max_premium = netflix_df[netflix_df['Cost Per Month - Premium ($)'] == netflix_df['Cost Per Month - Premium ($)'].max()]
max_premium.insert(0, "Description", "Highest Premium Subsription", True)

#Smallest premium cost per month
min_premium = netflix_df[netflix_df['Cost Per Month - Premium ($)'] == netflix_df['Cost Per Month - Premium ($)'].min()]
min_premium.insert(0, "Description", "Lowest Premium Subsription", True)

#Adding columns to empty dataframe
netflix_subscription_stats_df = pd.concat([netflix_subscription_stats_df, max_basic, min_basic, max_standard, min_standard, max_premium, min_premium], axis=0)

#Removing extra columns
netflix_subscription_stats_df = netflix_subscription_stats_df[['Description', 'Country', 'Cost Per Month - Basic ($)', 'Cost Per Month - Standard ($)','Cost Per Month - Premium ($)']]

netflix_subscription_stats_df
Description Country Cost Per Month - Basic ($) Cost Per Month - Standard ($) Cost Per Month - Premium ($)
33 Highest Subsription Costs Liechtenstein 12.88 20.46 26.96
56 Highest Subsription Costs Switzerland 12.88 20.46 26.96
59 Lowest Subsription Costs Turkey 1.97 3.00 4.02
33 Highest Standard Subsription Liechtenstein 12.88 20.46 26.96
56 Highest Standard Subsription Switzerland 12.88 20.46 26.96
59 Lowest Standard Subsription Turkey 1.97 3.00 4.02
33 Highest Premium Subsription Liechtenstein 12.88 20.46 26.96
56 Highest Premium Subsription Switzerland 12.88 20.46 26.96
59 Lowest Premium Subsription Turkey 1.97 3.00 4.02

As seen above,

sns.set_theme(style="whitegrid")

fig_dims = (10, 6)
fig, ax = plt.subplots(figsize=fig_dims)

#Creating a DataFrame with the Top 10 countires by Subscription Cost
top_cost = netflix_df.sort_values(by=['Cost Per Month - Premium ($)'], ascending=False).head(10)

sns.set_color_codes("pastel")
sns.barplot(x=top_cost['Cost Per Month - Premium ($)'], y=top_cost['Country'], data=top_lib, label="Premium", color="b")
plt.title("Top Countries by Netflix Subscription Cost", fontsize=15)

sns.set_color_codes("muted")
sns.barplot(x=top_cost['Cost Per Month - Standard ($)'], y=top_cost['Country'], data=top_lib, label="Standard", color="b")

sns.set_color_codes("pastel")
sns.barplot(x=top_cost['Cost Per Month - Basic ($)'], y=top_cost['Country'], data=top_lib, label="Basic", color="darkblue")

# Add a legend and informative axis label
ax.set(xlim=(0, 30), ylabel="", xlabel="Subscription Cost ($)")
sns.despine(left=True, bottom=True)

# Put the legend out of the figure
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)

<matplotlib.legend.Legend at 0x1e782008880>

png

In the Top 10 countries by Subscription Cost we see that the subscription cost remains almost the same across tiers (except for Switzerland and Liechtenstein)

Region

netflix_region_df = netflix_df.groupby('Region')['Total Library Size'].mean().round()

#Converting the series netflix_region_df to a dataframe
netflix_region_df = netflix_region_df.to_frame()

#Identifying average number of TV shows offered in each region
netflix_region_df['Average No. of TV Shows'] = netflix_df.groupby('Region')['No. of TV Shows'].mean().round()

#Identifying average number of Movies offered in each region
netflix_region_df['Average No. of Movies'] = netflix_df.groupby('Region')['No. of Movies'].mean().round()

#Identifying average basic subscription cost in each region
netflix_region_df['Average Basic Subscription Cost ($)'] = netflix_df.groupby('Region')['Cost Per Month - Basic ($)'].mean().round(2)

#Identifying average standard subscription cost in each region
netflix_region_df['Average Standard Subscription Cost ($)'] = netflix_df.groupby('Region')['Cost Per Month - Standard ($)'].mean().round(2)

#Identifying average premium subscription cost in each region
netflix_region_df['Average Premium Subscription Cost ($)'] = netflix_df.groupby('Region')['Cost Per Month - Premium ($)'].mean().round(2)

#Identifying average value for money in each region
netflix_region_df['Average Value for Money'] = netflix_df.groupby('Region')['Value for Money'].mean().round()

#Renaming columns
netflix_region_df.rename(columns={'Total Library Size':'Average Overall Library Size'}, inplace=True)

#The index for this dataframe contains the station names so we want to put that information in a seaprate column
#Resetting the index (also creates a new column called 'index')
netflix_region_df .reset_index(level=0, inplace=True)

netflix_region_df
Region Average Overall Library Size Average No. of TV Shows Average No. of Movies Average Basic Subscription Cost ($) Average Standard Subscription Cost ($) Average Premium Subscription Cost ($) Average Value for Money
0 Africa 5736.0 3686.0 2050.0 6.26 10.05 12.58 916.0
1 Asia 5347.0 3377.0 1970.0 7.64 10.40 12.97 900.0
2 Europe 5361.0 3652.0 1709.0 9.23 13.30 17.55 599.0
3 North America 5299.0 3459.0 1840.0 8.08 11.88 15.20 661.0
4 Oceania 6099.0 4026.0 2072.0 8.32 12.32 16.66 736.0
5 South America 4927.0 3156.0 1771.0 6.71 9.62 12.56 802.0

Content

sns.set_theme(style="whitegrid")

fig_dims = (10, 6)
fig, ax = plt.subplots(figsize=fig_dims)

#Creating a DataFrame with the Top 10 countires by library content
region_lib = netflix_region_df.sort_values(by=['Average Overall Library Size'], ascending=False).head(10)

sns.set_color_codes("pastel")
sns.barplot(x=region_lib['Average Overall Library Size'], y=region_lib['Region'], label="All Content", color="b")
plt.title("Regions by Netflix Library Size", fontsize=15)

sns.set_color_codes("muted")
sns.barplot(x=region_lib['Average No. of TV Shows'], y=region_lib['Region'], label="TV Shows", color="b")

sns.set_color_codes("pastel")
sns.barplot(x=region_lib['Average No. of Movies'], y=region_lib['Region'], label="Movies", color="darkblue")

# Add a legend and informative axis label
ax.set(xlim=(0, 8000), ylabel="", xlabel="Library Size")
sns.despine(left=True, bottom=True)

# Put the legend out of the figure
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)

<matplotlib.legend.Legend at 0x1e78225f7c0>

png

Subscription Cost

sns.set_theme(style="whitegrid")

fig_dims = (10, 6)
fig, ax = plt.subplots(figsize=fig_dims)

#Creating a DataFrame with the countires by Subscription Cost
region_cost = netflix_region_df.sort_values(by=['Average Premium Subscription Cost ($)'], ascending=False)

sns.set_color_codes("pastel")
sns.barplot(x=region_cost['Average Premium Subscription Cost ($)'], y=region_cost['Region'], label="Premium", color="b")
plt.title("Regions by Netflix Subscription Cost", fontsize=15)

sns.set_color_codes("muted")
sns.barplot(x=region_cost['Average Standard Subscription Cost ($)'], y=region_cost['Region'], label="Standard", color="b")

sns.set_color_codes("pastel")
sns.barplot(x=region_cost['Average Basic Subscription Cost ($)'], y=region_cost['Region'], label="Basic", color="darkblue")

# Add a legend and informative axis label
ax.set(xlim=(0, 20), ylabel="", xlabel="Subscription Cost ($)")
sns.despine(left=True, bottom=True)

# Put the legend out of the figure
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)

<matplotlib.legend.Legend at 0x1e7823130a0>

png

Value for Money

netflix_df['Value for Money'].describe().round().to_frame()
Value for Money
count 65.0
mean 700.0
std 342.0
min 237.0
25% 555.0
50% 628.0
75% 753.0
max 2355.0 
netflix_df[(netflix_df['Value for Money']==237)|(netflix_df['Value for Money']==2355)]
Country_code Country Region Total Library Size No. of TV Shows No. of Movies Cost Per Month - Basic ($) Cost Per Month - Standard ($) Cost Per Month - Premium ($) Value for Money
33 li Liechtenstein Europe 3048 1712 1336 12.88 20.46 26.96 237.0
59 tr Turkey Asia 4639 2930 1709 1.97 3.00 4.02 2355.0

The average Netflix customer has access to 700 TV Shows and Movies for every dollar spent on their subscription.

Therefore, a cutomer in Turkey country is getting 10 times the value from their Netflix subscription compared to a customer in Liechtenstein

sns.set_theme(style="whitegrid")

fig_dims = (10, 6)
fig, ax = plt.subplots(figsize=fig_dims)

#Creating a DataFrame with the countires by Subscription Cost
value = netflix_df.sort_values(by=['Value for Money'], ascending=False).head(10)

sns.set_color_codes("pastel")
sns.barplot(x=value['Value for Money'], y=value['Country'], color="b")
plt.title("Top Countries by Customer Value for Money", fontsize=15)

Text(0.5, 1.0, 'Top Countries by Customer Value for Money')

png

Customers in Turkey and India have the greatest value for money for their Basic Netflix Subscription

sns.set_theme(style="whitegrid")

fig_dims = (10, 6)
fig, ax = plt.subplots(figsize=fig_dims)

#Creating a DataFrame with the countires by Subscription Cost
value = netflix_df.sort_values(by=['Value for Money'], ascending=True).head(10)

sns.set_color_codes("pastel")
sns.barplot(x=value['Value for Money'], y=value['Country'], color="b")
plt.title("Top Countries by Lowest Customer Value for Money", fontsize=15)

Text(0.5, 1.0, 'Top Countries by Lowest Customer Value for Money')

png

Customers in Liechtenstein, Croatia and San Marino have the least value for money for their Basic Netflix Subscription

Region

sns.set_theme(style="whitegrid")

fig_dims = (10, 6)
fig, ax = plt.subplots(figsize=fig_dims)

#Creating a DataFrame with the countires by Subscription Cost
value_region = netflix_region_df.sort_values(by=['Average Value for Money'], ascending=False)

sns.set_color_codes("pastel")
sns.barplot(x=value_region['Average Value for Money'], y=value_region['Region'], color="b")
plt.title("Customer Value for Money by Region", fontsize=15)

Text(0.5, 1.0, 'Customer Value for Money by Region')

png

Content vs. Subscription

Another way to determine the customer’s value for money is to look at countries where customers get access to above average library size and below average basic subsciption cost.

#Identifying countries with below average basic cost and above average library size
highlight = netflix_df[(netflix_df['Cost Per Month - Basic ($)']<netflix_df['Cost Per Month - Basic ($)'].mean())&(netflix_df['Total Library Size']>netflix_df['Total Library Size'].mean())]

len(highlight)

10

We have 10 countries where customers get access to above average library size and below average basic subsciption cost.

fig_dims = (15, 5)
fig, ax = plt.subplots(figsize=fig_dims)

sns.scatterplot(x='Total Library Size',y='Cost Per Month - Basic ($)', data=netflix_df, s=50, color="lightblue")
sns.scatterplot(x='Total Library Size',y='Cost Per Month - Basic ($)', data=highlight, s=50, color="green")

plt.title("Content vs. Subsciption Cost",fontsize=15)

Text(0.5, 1.0, 'Content vs. Subsciption Cost')

png

Points highlighted in green indicate countries with access to an above average library size at a below average basic subscription cost.

#Identifying the top 3 countries by Total Library Size
highlight.sort_values(by=['Total Library Size'], ascending=True).head(3)
Country_code Country Region Total Library Size No. of TV Shows No. of Movies Cost Per Month - Basic ($) Cost Per Month - Standard ($) Cost Per Month - Premium ($) Value for Money
60 ua Ukraine Europe 5336 3261 2075 5.64 8.46 11.29 946.0
48 ru Russia Europe 5711 3624 2087 8.13 10.84 13.56 702.0
52 za South Africa Africa 5736 3686 2050 6.26 10.05 12.58 916.0 
#Identifying the top 3 countries by Basic Subscription Cost
highlight.sort_values(by=['Cost Per Month - Basic ($)'], ascending=True).head(3)
Country_code Country Region Total Library Size No. of TV Shows No. of Movies Cost Per Month - Basic ($) Cost Per Month - Standard ($) Cost Per Month - Premium ($) Value for Money
26 in India Asia 5843 3718 2125 2.64 6.61 8.60 2213.0
60 ua Ukraine Europe 5336 3261 2075 5.64 8.46 11.29 946.0
52 za South Africa Africa 5736 3686 2050 6.26 10.05 12.58 916.0

In terms of Content, the top 3 countries for customers are Ukraine, Russia and South Africa

In terms of Basic Subscription Cost, the top 3 countries for customers are India, Ukraine and South Africa

Taking both Content and Subscription Cost into consideration Ukrainian and South African Customers receive the most value for their Netflix Subscription.

Summary of Netflix Data

We have data on 65 countries where Netflix operates across 6 regions.

In terms of number of countries served,

Content

Based on the data we can say that the majority of Netflix’s content in any given country consists of TV Shows.

Subscription Cost

The cost of a customer’s Netflix subscription tier increases by as much as $22.94 if they relocate to a different country.

Value for Money

The average Netflix customer has access to 700 TV Shows and Movies for every dollar spent on their subscription.

Thereofore, the disparity between the value a customer experiences from country to country may be almost as high as 10 times the value for money

Regional Breakdown

Europe

Content: European countries on average have access to the 3rd largest content library, only slightly bigger than Asian countries.

Subscription Cost and Value for Money: Customers experience the least value for money for their Basic Netflix Subscription despite the fact that it has the highest overall subscription cost

Country Details:

Asia

Content: Asian countries on average have access to the 4rd largest content library

Subscription Cost and Value for Money: Customers experience the 2nd highest value for money for their Basic Netflix Subscription and have the 4th highest overall subscription cost

Country Details:

Africa

Content: African countries on average have access to the 2nd largest content library

Subscription Cost and Value for Money: Customers experience the greatest value for money for their Basic Netflix Subscription and it has the lowest basic subscription cost

North America

Content: North American countries on average have access to the 2nd smallest overall content library

Subscription Cost and Value for Money:Customers experience the 2nd lowest value for money for their Basic Netflix Subscription and it has the 3rd highest overall subscription cost.

South America

Content: South American Countries on average have access to the smallest overall content library

Subscription Cost and Value for Money: Customers experience the 3rd highest value for money for their Basic Netflix Subscription and it has the lowest overall subscription cost.

Oceania

Content: Oceanian countries on average have access to the largest overall content library

Subscription Cost and Value for Money: Customers experience the 4th highest value for money for their Basic Netflix Subscription and it has the 2nd highest overall subscription cost.

Where do Netflix customers experience the highest satisfaction?

A Netflix customer’s primary concern is determining a balance between the value of entertainment and the cost of that entertainment. Ideally, the customer is seeking to pay the lowest amount possible for access to the most content. Based on the information above we can see that Cutomers in Africa would experience the most satisfaction for three key reasons:

Content: Access to the 2nd largest content library
Subscription Cost: It has the lowest basic subscription cost
Value for Money: Region with the greatest value for money

In particular, South Africa, which ranks 3rd in terms of above average access to content and below average basic subscription cost is the best location for Netflix Customers.

Additional Data necessary
The data only tells how much content is available to customers in each country and how much the different subscription tiers cost. For a holistic comparison, we need to consider factors such as

Together this data can help paint a detailed picture of what customers enjoy the most and where the most satisfied customers may be located.