(an imaginary situation for the project as if I am working in a company).
At our company, we only build apps that are free to download and install, and our main source of revenue consists of in-app ads. This means that our revenue for any given app is mostly influenced by the number of users that use our app.
I am working as data analyst for a company that builds Android and iOS mobile apps, and my goal in this project is to make a sufficient data analysis to help companies understand which applications will be most profitable by attracting more people.
As of September 2018, there were approximately 2 million iOS apps available on the App Store, and 2.1 million Android apps on Google Play.
Collecting data for over 4 million apps requires a significant amount of time and money, so I will try to analyze a sample of the data instead. Fortunately, there are two data sets that seem suitable for my goals of this project:
-
A dataset containing data about approximately 10,000 Android apps from Google Play; the data was collected in August 2018. You can download the data set directly from this link
-
A dataset containing data about approximately 7,000 iOS apps from the App Store; the data was collected in July 2017. You can download the data set directly from this link
First, these data sets should be opened and explored.
from csv import reader
### The Google Play data set ###
opened_file = open('googleplaystore.csv')
read_file = reader(opened_file)
android = list(read_file)
android_header = android[0]
android = android[1:]
### The App Store data set ###
opened_file = open('AppleStore.csv')
read_file = reader(opened_file)
ios = list(read_file)
ios_header = ios[0]
ios = ios[1:]In order to make the data sets easier to explore, I created a function called explore_data() that can be used to print rows in a readable way. First let's look on first three rows of the android dataset.
def explore_data(dataset, start, end, rows_and_columns = False):
dataset_slice = dataset[start:end]
for row in dataset_slice:
print(row)
print('\n') # adds a new (empty) line after each row
if rows_and_columns:
print('Number of rows:', len(dataset))
print('Number of columns:', len(dataset[0]))
print(android_header)
print('\n')
print(explore_data(android, 0, 3, True))['App', 'Category', 'Rating', 'Reviews', 'Size', 'Installs', 'Type', 'Price', 'Content Rating', 'Genres', 'Last Updated', 'Current Ver', 'Android Ver']
['Photo Editor & Candy Camera & Grid & ScrapBook', 'ART_AND_DESIGN', '4.1', '159', '19M', '10,000+', 'Free', '0', 'Everyone', 'Art & Design', 'January 7, 2018', '1.0.0', '4.0.3 and up']
['Coloring book moana', 'ART_AND_DESIGN', '3.9', '967', '14M', '500,000+', 'Free', '0', 'Everyone', 'Art & Design;Pretend Play', 'January 15, 2018', '2.0.0', '4.0.3 and up']
['U Launcher Lite – FREE Live Cool Themes, Hide Apps', 'ART_AND_DESIGN', '4.7', '87510', '8.7M', '5,000,000+', 'Free', '0', 'Everyone', 'Art & Design', 'August 1, 2018', '1.2.4', '4.0.3 and up']
Number of rows: 10841
Number of columns: 13
None
We can see that this data set has 10841 applications and 13 columns. We can also see important columns that can be used for our data analysis: 'App', 'Category', 'Rating', 'Reviews', 'Price', 'Genres'. Documentation for the columns in data set can be found on this link
Now let's look on Apple data set.
print(ios_header)
print('\n')
print(explore_data(ios, 0, 3, True))['id', 'track_name', 'size_bytes', 'currency', 'price', 'rating_count_tot', 'rating_count_ver', 'user_rating', 'user_rating_ver', 'ver', 'cont_rating', 'prime_genre', 'sup_devices.num', 'ipadSc_urls.num', 'lang.num', 'vpp_lic']
['284882215', 'Facebook', '389879808', 'USD', '0.0', '2974676', '212', '3.5', '3.5', '95.0', '4+', 'Social Networking', '37', '1', '29', '1']
['389801252', 'Instagram', '113954816', 'USD', '0.0', '2161558', '1289', '4.5', '4.0', '10.23', '12+', 'Photo & Video', '37', '0', '29', '1']
['529479190', 'Clash of Clans', '116476928', 'USD', '0.0', '2130805', '579', '4.5', '4.5', '9.24.12', '9+', 'Games', '38', '5', '18', '1']
Number of rows: 7197
Number of columns: 16
None
iOS data set has 7197 applications and 16 columns. Same as in previous data set, we can see useful columns in this data set for our data analysis: 'id', 'price', 'rating_count_tot', 'user_rating', 'prime_genre'. Not all columns in the data set are clear, documentation for this data set can be accessed through this link as well.
The Google Play data set has a dedicated discussion section, and we can see that one of the discussions describes an error for row 10472
print(android_header)
print('\n')
print(android[10472])
print('\n')
print(android[0])['App', 'Category', 'Rating', 'Reviews', 'Size', 'Installs', 'Type', 'Price', 'Content Rating', 'Genres', 'Last Updated', 'Current Ver', 'Android Ver']
['Life Made WI-Fi Touchscreen Photo Frame', '1.9', '19', '3.0M', '1,000+', 'Free', '0', 'Everyone', '', 'February 11, 2018', '1.0.19', '4.0 and up']
['Photo Editor & Candy Camera & Grid & ScrapBook', 'ART_AND_DESIGN', '4.1', '159', '19M', '10,000+', 'Free', '0', 'Everyone', 'Art & Design', 'January 7, 2018', '1.0.0', '4.0.3 and up']
The row with the mistake correspond to the Life made WI-Fi Touchscreen Photo Frame application. We can see that the rating is 19, while the maximum value is 5 ( as mentioned in the discussions section, this problem is caused by a missing value in the 'Category' column). We will delete this row.
print(len(android))
del android[10472]
print(len(android))10841
10840
I didn't find any errors in the discussion of iOS data set, however we can check the error by coding.
for row in ios:
if len(row) != len(ios_header):
print(row) # It will print the row with error
print(ios.index(row)) # It will print the index number of the row with mistakeAs you see, there are no row mistakes in the Apple data set.
If you explore the Google Play data set long enough or look at the discussions section, you'll notice some apps have duplicate entries. For instance, Instagram has four entries:
for app in android:
name = app[0]
if name == 'Instagram':
print(app)['Instagram', 'SOCIAL', '4.5', '66577313', 'Varies with device', '1,000,000,000+', 'Free', '0', 'Teen', 'Social', 'July 31, 2018', 'Varies with device', 'Varies with device']
['Instagram', 'SOCIAL', '4.5', '66577446', 'Varies with device', '1,000,000,000+', 'Free', '0', 'Teen', 'Social', 'July 31, 2018', 'Varies with device', 'Varies with device']
['Instagram', 'SOCIAL', '4.5', '66577313', 'Varies with device', '1,000,000,000+', 'Free', '0', 'Teen', 'Social', 'July 31, 2018', 'Varies with device', 'Varies with device']
['Instagram', 'SOCIAL', '4.5', '66509917', 'Varies with device', '1,000,000,000+', 'Free', '0', 'Teen', 'Social', 'July 31, 2018', 'Varies with device', 'Varies with device']
In total, there are 1,181 cases where an app occurs more than once.
duplicate_apps = []
unique_apps = []
for app in android:
name = app[0]
if name in unique_apps:
duplicate_apps.append(name)
else:
unique_apps.append(name)
print('Number of duplicate apps:', len(duplicate_apps))
print('\n')
print('Examples of duplicate apps:', duplicate_apps[:15])Number of duplicate apps: 1181
Examples of duplicate apps: ['Quick PDF Scanner + OCR FREE', 'Box', 'Google My Business', 'ZOOM Cloud Meetings', 'join.me - Simple Meetings', 'Box', 'Zenefits', 'Google Ads', 'Google My Business', 'Slack', 'FreshBooks Classic', 'Insightly CRM', 'QuickBooks Accounting: Invoicing & Expenses', 'HipChat - Chat Built for Teams', 'Xero Accounting Software']
We don't want to count certain apps more than once when we analyze data, so we need to remove the duplicate entries and keep only one entry per app. One thing we could do is remove the duplicate rows randomly, but we could probably find a better way.
If you examine the rows we printed for the Instagram app, the main difference happens on the fourth position of each row, which corresponds to the number of reviews. The different numbers show the data was collected at different times. We can use this to build a criterion for removing or keeping rows. We are not going to remove rows randomly, but we'll only keep the row with the highest number of reviews and remove the other entries for any given app.
To remove the duplicates, we will do the following:
- Create a dictionary, where each dictionary key is a unique app name and the corresponsding dictionary value is the highest number of reviews of that app.
- Use the information stored in dictionary and create a new dataset, which will have only one entry per app.
Let's start by creating dictionary
reviews_max = {}
for app in android:
name = app[0]
n_reviews = float(app[3])
if name in reviews_max and reviews_max[name] < n_reviews:
reviews_max[name] = n_reviews
elif name not in reviews_max:
reviews_max[name] = n_reviews
In a previous code cell, we found that there are 1,181 cases where an app occurs more than once, so the length of our dictionary (of unique apps) should be equal to the difference between the length of our data set and 1,181.
print('Expected length:', len(android) - 1181)
print('Actual length:', len(reviews_max))Expected length: 9659
Actual length: 9659
Now we should use the dictionary 'reviews_max' we created to remove the duplicate rows.
android_clean = []
already_added = []
for app in android:
name = app[0]
n_reviews = float(app[3])
if (reviews_max[name] == n_reviews) and (name not in already_added):
android_clean.append(app)
already_added.append(name)
Now let's check the 'android_clean' data set to check if everything went as we wanted. The data set should havve 9659 rows.
explore_data(android_clean, 0, 3, True)['Photo Editor & Candy Camera & Grid & ScrapBook', 'ART_AND_DESIGN', '4.1', '159', '19M', '10,000+', 'Free', '0', 'Everyone', 'Art & Design', 'January 7, 2018', '1.0.0', '4.0.3 and up']
['U Launcher Lite – FREE Live Cool Themes, Hide Apps', 'ART_AND_DESIGN', '4.7', '87510', '8.7M', '5,000,000+', 'Free', '0', 'Everyone', 'Art & Design', 'August 1, 2018', '1.2.4', '4.0.3 and up']
['Sketch - Draw & Paint', 'ART_AND_DESIGN', '4.5', '215644', '25M', '50,000,000+', 'Free', '0', 'Teen', 'Art & Design', 'June 8, 2018', 'Varies with device', '4.2 and up']
Number of rows: 9659
Number of columns: 13
Everything went good as we expected.
If we explore the data carefully, we'll find that some names of applications in data sets are not directed towards English audience.
print(ios[813][1])
print(ios[6731][1])
print('\n')
print(android_clean[4412][0])
print(android_clean[7940][0])爱奇艺PPS -《欢乐颂2》电视剧热播
【脱出ゲーム】絶対に最後までプレイしないで 〜謎解き＆ブロックパズル〜
中国語 AQリスニング
لعبة تقدر تربح DZ
We're not interested in keeping these apps, so we'll remove them. One option to remove non-english applications is to remove each app with a name containing a symbol not commonly used in English text - English text usually includes letters from the English alphabet, digit numbers from 0 to 9, punctuation marks (., !, ?, ;), and other symbols (+, *, /)
All characters we use in an English text are encoded using ASCII standard. Each ASCII standard has a specific number from 0 to 127 associated with it. We could use this range of numbers to build a function that checks app name and tells us whether it contains non-ASCII characters.
def is_english(string):
for character in string:
if ord(character) > 127:
return False
return True
# Examples of the function
print(is_english('Instagram'))
print(is_english('爱奇艺PPS -《欢乐颂2》电视剧热播'))True
False
Our function seems to work fine, however some English applications might contain use of emojis or other symbols which will cause a huge data loss of useful apps. In order to prevent data loss, we will make a slight modifications to the program.
print(is_english('Docs To Go™ Free Office Suite'))
print(ord('™'), '\n')
print(is_english('Instachat 😜'))
print(ord('😜'))False
8482
False
128540
To minimize the impact of data loss, we'll only remove an app if its name has more than three characters with corresponding numbers falling outside the ASCII range. This means all English apps with up to three emoji or other special characters will still be labeled as English.
def is_english(string):
non_ascii = 0
for character in string:
if ord(character) > 127:
non_ascii += 1
if non_ascii > 3:
return False
else:
return True
print(is_english('Docs To Go™ Free Office Suite'))
print(is_english('Instachat 😜'))True
True
Our function is still not perfect, but it should be effective for filtering out non-English apps.
Below, we used our created function to filter out the non-English apps from both data sets:
android_english = []
ios_english = []
for app in android_clean:
name = app[0]
if is_english(name):
android_english.append(app)
for app in ios:
name = app[1]
if is_english(name):
ios_english.append(app)
explore_data(android_english, 0, 3, True)
print('\n')
explore_data(ios_english, 0, 3, True)['Photo Editor & Candy Camera & Grid & ScrapBook', 'ART_AND_DESIGN', '4.1', '159', '19M', '10,000+', 'Free', '0', 'Everyone', 'Art & Design', 'January 7, 2018', '1.0.0', '4.0.3 and up']
['U Launcher Lite – FREE Live Cool Themes, Hide Apps', 'ART_AND_DESIGN', '4.7', '87510', '8.7M', '5,000,000+', 'Free', '0', 'Everyone', 'Art & Design', 'August 1, 2018', '1.2.4', '4.0.3 and up']
['Sketch - Draw & Paint', 'ART_AND_DESIGN', '4.5', '215644', '25M', '50,000,000+', 'Free', '0', 'Teen', 'Art & Design', 'June 8, 2018', 'Varies with device', '4.2 and up']
Number of rows: 9614
Number of columns: 13
['284882215', 'Facebook', '389879808', 'USD', '0.0', '2974676', '212', '3.5', '3.5', '95.0', '4+', 'Social Networking', '37', '1', '29', '1']
['389801252', 'Instagram', '113954816', 'USD', '0.0', '2161558', '1289', '4.5', '4.0', '10.23', '12+', 'Photo & Video', '37', '0', '29', '1']
['529479190', 'Clash of Clans', '116476928', 'USD', '0.0', '2130805', '579', '4.5', '4.5', '9.24.12', '9+', 'Games', '38', '5', '18', '1']
Number of rows: 6183
Number of columns: 16
Now we can see that we are left with 6183 iOS applications and 9614 Android applications.
As we mentioned before, we only make apps that are free to download and install and our main source or revenue consists of in-app ads. Our datasets contain both free and non-free apps. We'll need to isolate only the free apps for our analysis.
print(android_header)
explore_data(android_english, 0, 1)
print('\n')
print(ios_header)
explore_data(ios_english, 0, 1)
# We need to see where is the price column located in each data set.
android_final = []
ios_final = []
for app in android_english:
price = app[7]
if price == '0':
android_final.append(app)
for app in ios_english:
price = app[4]
if price == '0.0':
ios_final.append(app)
print(len(android_final))
print(len(ios_final))
['App', 'Category', 'Rating', 'Reviews', 'Size', 'Installs', 'Type', 'Price', 'Content Rating', 'Genres', 'Last Updated', 'Current Ver', 'Android Ver']
['Photo Editor & Candy Camera & Grid & ScrapBook', 'ART_AND_DESIGN', '4.1', '159', '19M', '10,000+', 'Free', '0', 'Everyone', 'Art & Design', 'January 7, 2018', '1.0.0', '4.0.3 and up']
['id', 'track_name', 'size_bytes', 'currency', 'price', 'rating_count_tot', 'rating_count_ver', 'user_rating', 'user_rating_ver', 'ver', 'cont_rating', 'prime_genre', 'sup_devices.num', 'ipadSc_urls.num', 'lang.num', 'vpp_lic']
['284882215', 'Facebook', '389879808', 'USD', '0.0', '2974676', '212', '3.5', '3.5', '95.0', '4+', 'Social Networking', '37', '1', '29', '1']
8864
3222
After filtering out some of the apps, we are left with 8864 Android apps and 3222 iOS apps.
As it was mentioned in the introduction, goal of this data analysis is to identify apps that are likely to attract more users because the number of people using our apps affect our revenues.
Our final goal after the data analysis is to create an app that will be profitable both in Google Play and App Sotre, that is why we need to find apps that are already successful in both markets.
To minimize risks and overhead, our validation strategy for an app idea has three steps:
- Build a minimal Android version of the app, and add it to Google Play.
- If the app has a good response from users, we develop it further. 3.If the app is profitable after six months, we build an iOS version of the app and add it to the App Store.
Let's begin data analysis by determining the most common genres for each market. For this I will build frequency tables for the 'prime_genre' column of the iOS data set and 'Genres' and 'Category' columns of the Android data set.
We'll build two functions that can be used to analyze the frequency tables.
- One function to create frequency tables that show percentages
- Another function we can use to display the percentages in a descending order
def freq_table(dataset, index):
table = {}
total = 0
for row in dataset:
total += 1
value = row[index]
if value in table:
table[value] += 1
else:
table[value] = 1
table_percentages = {}
for key in table:
percentage = (table[key] / total) * 100
table_percentages[key] = percentage
return table_percentages
def display_table(dataset, index):
table = freq_table(dataset, index)
table_display = []
for key in table:
key_val_as_tuple = (table[key], key)
table_display.append(key_val_as_tuple)
table_sorted = sorted(table_display, reverse = True)
for entry in table_sorted:
print(entry[1], ':', entry[0])We will start by looking on frequency table for the 'prime_genre' column of iOS data set.
display_table(ios_final, -5)Games : 58.16263190564867
Entertainment : 7.883302296710118
Photo & Video : 4.9658597144630665
Education : 3.662321539416512
Social Networking : 3.2898820608317814
Shopping : 2.60707635009311
Utilities : 2.5139664804469275
Sports : 2.1415270018621975
Music : 2.0484171322160147
Health & Fitness : 2.0173805090006205
Productivity : 1.7380509000620732
Lifestyle : 1.5828677839851024
News : 1.3345747982619491
Travel : 1.2414649286157666
Finance : 1.1173184357541899
Weather : 0.8690254500310366
Food & Drink : 0.8069522036002483
Reference : 0.5586592178770949
Business : 0.5276225946617008
Book : 0.4345127250155183
Navigation : 0.186219739292365
Medical : 0.186219739292365
Catalogs : 0.12414649286157665
According to the data, greater than half(58.16%) of the applications in the Apple Store are games. Entertainment(8%) is the second most common genre after Games followed by Photo & Video applications that are close to 5%.
The general impression is that Apple Store is controlled by apps created designed for fun (games, photo and video, social networking, sports, music). While application designed for practical purposes (education, shopping, utilities, productivity, lifestyle) are more rare. However, we can't say that the most common applications in Apple Store have the highest number of users as Supply can be not same as Demand.
Now we should look on 'Category' and 'Genres' columns of the Android data set.
display_table(android_final, 1) # CategoryFAMILY : 18.907942238267147
GAME : 9.724729241877256
TOOLS : 8.461191335740072
BUSINESS : 4.591606498194946
LIFESTYLE : 3.9034296028880866
PRODUCTIVITY : 3.892148014440433
FINANCE : 3.7003610108303246
MEDICAL : 3.531137184115524
SPORTS : 3.395758122743682
PERSONALIZATION : 3.3167870036101084
COMMUNICATION : 3.2378158844765346
HEALTH_AND_FITNESS : 3.0798736462093865
PHOTOGRAPHY : 2.944494584837545
NEWS_AND_MAGAZINES : 2.7978339350180503
SOCIAL : 2.6624548736462095
TRAVEL_AND_LOCAL : 2.33528880866426
SHOPPING : 2.2450361010830324
BOOKS_AND_REFERENCE : 2.1435018050541514
DATING : 1.861462093862816
VIDEO_PLAYERS : 1.7937725631768955
MAPS_AND_NAVIGATION : 1.3989169675090252
FOOD_AND_DRINK : 1.2409747292418771
EDUCATION : 1.1620036101083033
ENTERTAINMENT : 0.9589350180505415
LIBRARIES_AND_DEMO : 0.9363718411552346
AUTO_AND_VEHICLES : 0.9250902527075812
HOUSE_AND_HOME : 0.8235559566787004
WEATHER : 0.8009927797833934
EVENTS : 0.7107400722021661
PARENTING : 0.6543321299638989
ART_AND_DESIGN : 0.6430505415162455
COMICS : 0.6204873646209386
BEAUTY : 0.5979241877256317
The patterns are not really different in Google Play market. The most common category is Family (18.9 ~ 19% of all apps), which is mostly games designed for kids. Family category is followed by Games with 9.72% and tools with 8.46%.
However, Google Play market has a better representation of practical applications compared to App Store.
display_table(android_final, -4)Tools : 8.449909747292418
Entertainment : 6.069494584837545
Education : 5.347472924187725
Business : 4.591606498194946
Productivity : 3.892148014440433
Lifestyle : 3.892148014440433
Finance : 3.7003610108303246
Medical : 3.531137184115524
Sports : 3.463447653429603
Personalization : 3.3167870036101084
Communication : 3.2378158844765346
Action : 3.1024368231046933
Health & Fitness : 3.0798736462093865
Photography : 2.944494584837545
News & Magazines : 2.7978339350180503
Social : 2.6624548736462095
Travel & Local : 2.3240072202166067
Shopping : 2.2450361010830324
Books & Reference : 2.1435018050541514
Simulation : 2.0419675090252705
Dating : 1.861462093862816
Arcade : 1.8501805054151623
Video Players & Editors : 1.7712093862815883
Casual : 1.7599277978339352
Maps & Navigation : 1.3989169675090252
Food & Drink : 1.2409747292418771
Puzzle : 1.128158844765343
Racing : 0.9927797833935018
Role Playing : 0.9363718411552346
Libraries & Demo : 0.9363718411552346
Auto & Vehicles : 0.9250902527075812
Strategy : 0.9138086642599278
House & Home : 0.8235559566787004
Weather : 0.8009927797833934
Events : 0.7107400722021661
Adventure : 0.6768953068592057
Comics : 0.6092057761732852
Beauty : 0.5979241877256317
Art & Design : 0.5979241877256317
Parenting : 0.4963898916967509
Card : 0.45126353790613716
Casino : 0.42870036101083037
Trivia : 0.41741877256317694
Educational;Education : 0.39485559566787
Board : 0.3835740072202166
Educational : 0.3722924187725632
Education;Education : 0.33844765342960287
Word : 0.2594765342960289
Casual;Pretend Play : 0.236913357400722
Music : 0.2030685920577617
Racing;Action & Adventure : 0.16922382671480143
Puzzle;Brain Games : 0.16922382671480143
Entertainment;Music & Video : 0.16922382671480143
Casual;Brain Games : 0.13537906137184114
Casual;Action & Adventure : 0.13537906137184114
Arcade;Action & Adventure : 0.12409747292418773
Action;Action & Adventure : 0.10153429602888085
Educational;Pretend Play : 0.09025270758122744
Simulation;Action & Adventure : 0.078971119133574
Parenting;Education : 0.078971119133574
Entertainment;Brain Games : 0.078971119133574
Board;Brain Games : 0.078971119133574
Parenting;Music & Video : 0.06768953068592057
Educational;Brain Games : 0.06768953068592057
Casual;Creativity : 0.06768953068592057
Art & Design;Creativity : 0.06768953068592057
Education;Pretend Play : 0.056407942238267145
Role Playing;Pretend Play : 0.04512635379061372
Education;Creativity : 0.04512635379061372
Role Playing;Action & Adventure : 0.033844765342960284
Puzzle;Action & Adventure : 0.033844765342960284
Entertainment;Creativity : 0.033844765342960284
Entertainment;Action & Adventure : 0.033844765342960284
Educational;Creativity : 0.033844765342960284
Educational;Action & Adventure : 0.033844765342960284
Education;Music & Video : 0.033844765342960284
Education;Brain Games : 0.033844765342960284
Education;Action & Adventure : 0.033844765342960284
Adventure;Action & Adventure : 0.033844765342960284
Video Players & Editors;Music & Video : 0.02256317689530686
Sports;Action & Adventure : 0.02256317689530686
Simulation;Pretend Play : 0.02256317689530686
Puzzle;Creativity : 0.02256317689530686
Music;Music & Video : 0.02256317689530686
Entertainment;Pretend Play : 0.02256317689530686
Casual;Education : 0.02256317689530686
Board;Action & Adventure : 0.02256317689530686
Video Players & Editors;Creativity : 0.01128158844765343
Trivia;Education : 0.01128158844765343
Travel & Local;Action & Adventure : 0.01128158844765343
Tools;Education : 0.01128158844765343
Strategy;Education : 0.01128158844765343
Strategy;Creativity : 0.01128158844765343
Strategy;Action & Adventure : 0.01128158844765343
Simulation;Education : 0.01128158844765343
Role Playing;Brain Games : 0.01128158844765343
Racing;Pretend Play : 0.01128158844765343
Puzzle;Education : 0.01128158844765343
Parenting;Brain Games : 0.01128158844765343
Music & Audio;Music & Video : 0.01128158844765343
Lifestyle;Pretend Play : 0.01128158844765343
Lifestyle;Education : 0.01128158844765343
Health & Fitness;Education : 0.01128158844765343
Health & Fitness;Action & Adventure : 0.01128158844765343
Entertainment;Education : 0.01128158844765343
Communication;Creativity : 0.01128158844765343
Comics;Creativity : 0.01128158844765343
Casual;Music & Video : 0.01128158844765343
Card;Action & Adventure : 0.01128158844765343
Books & Reference;Education : 0.01128158844765343
Art & Design;Pretend Play : 0.01128158844765343
Art & Design;Action & Adventure : 0.01128158844765343
Arcade;Pretend Play : 0.01128158844765343
Adventure;Education : 0.01128158844765343
The difference between category and genre is not really clear, however we can see that Tools is most common genre with 8.45%, followed by Entertainment with 6% and Education reaching only 5.35%. The one potential difference between category and genre columns is that genre has more variations, so it is better to continue focusing only on category column for now.
To summarize, it was found that App Store is dominated by applications designed for fun, while Google Play market has a more balanced distribution between for-fun and practical apps.
Frequency tables that we created showed us that apps designed for fun dominate the App Store, while Google Play has a mar balanced market of applications. Now we need to determine applications with the most number of users.
We could calculate the mean value of installs per each app genre in order to accomplish this task. We can easily do it in Google Play data set by using 'Installs'column, however we don't have such data in App Store data set. We could use 'Rating_countr_tot' column instead in iOS data set.
First, we need to isolate the apps of each genre and calculate average number of user ratings per app genre.
ios_genre = freq_table(ios_final, -5)
for genre in ios_genre:
total = 0
len_genre = 0
for app in ios_final:
genre_app = app[-5]
if genre_app == genre:
n_ratings = float(app[5])
total += n_ratings
len_genre += 1
average_ratings = total / len_genre
print(genre, ':', average_ratings)Social Networking : 71548.34905660378
Photo & Video : 28441.54375
Games : 22788.6696905016
Music : 57326.530303030304
Reference : 74942.11111111111
Health & Fitness : 23298.015384615384
Weather : 52279.892857142855
Utilities : 18684.456790123455
Travel : 28243.8
Shopping : 26919.690476190477
News : 21248.023255813954
Navigation : 86090.33333333333
Lifestyle : 16485.764705882353
Entertainment : 14029.830708661417
Food & Drink : 33333.92307692308
Sports : 23008.898550724636
Book : 39758.5
Finance : 31467.944444444445
Education : 7003.983050847458
Productivity : 21028.410714285714
Business : 7491.117647058823
Catalogs : 4004.0
Medical : 612.0
Navigation applications have the highest average number of user reviews. We could go even further and check which apps in this genre are the most popular in App Store.
for app in ios_final:
if app[-5] == 'Navigation':
print(app[1], ':', app[5])Waze - GPS Navigation, Maps & Real-time Traffic : 345046
Google Maps - Navigation & Transit : 154911
Geocaching® : 12811
CoPilot GPS – Car Navigation & Offline Maps : 3582
ImmobilienScout24: Real Estate Search in Germany : 187
Railway Route Search : 5
It is clear that average number of revie in Navigation genre is dominated by applications from giant firms such as Waze - GPS Navigation and Google Maps, while other applications in this genre can't even reach more than 20 thousand reviews.
The second most popular genre is Reference. We could check this genre too.
for app in ios_final:
if app[-5] == 'Reference':
print(app[1], ':', app[5])Bible : 985920
Dictionary.com Dictionary & Thesaurus : 200047
Dictionary.com Dictionary & Thesaurus for iPad : 54175
Google Translate : 26786
Muslim Pro: Ramadan 2017 Prayer Times, Azan, Quran : 18418
New Furniture Mods - Pocket Wiki & Game Tools for Minecraft PC Edition : 17588
Merriam-Webster Dictionary : 16849
Night Sky : 12122
City Maps for Minecraft PE - The Best Maps for Minecraft Pocket Edition (MCPE) : 8535
LUCKY BLOCK MOD ™ for Minecraft PC Edition - The Best Pocket Wiki & Mods Installer Tools : 4693
GUNS MODS for Minecraft PC Edition - Mods Tools : 1497
Guides for Pokémon GO - Pokemon GO News and Cheats : 826
WWDC : 762
Horror Maps for Minecraft PE - Download The Scariest Maps for Minecraft Pocket Edition (MCPE) Free : 718
VPN Express : 14
Real Bike Traffic Rider Virtual Reality Glasses : 8
教えて!goo : 0
Jishokun-Japanese English Dictionary & Translator : 0
This genre is not dominated by famous companies and there is some potential for creating our application. We could focus on this side of the market in App Store. For example, we could create an application which will allow users to install any books freely, including Dictionary, Bible or other popular books. This will allow our users to read any books they want without closing our application and installing others.
Other most popular genres are:
- Social Networking
- Music
- Weather
- Finance
for app in ios_final:
if app[-5] == 'Social Networking':
print(app[1], ':', app[5])Facebook : 2974676
Pinterest : 1061624
Skype for iPhone : 373519
Messenger : 351466
Tumblr : 334293
WhatsApp Messenger : 287589
Kik : 260965
ooVoo – Free Video Call, Text and Voice : 177501
TextNow - Unlimited Text + Calls : 164963
Viber Messenger – Text & Call : 164249
Followers - Social Analytics For Instagram : 112778
MeetMe - Chat and Meet New People : 97072
We Heart It - Fashion, wallpapers, quotes, tattoos : 90414
InsTrack for Instagram - Analytics Plus More : 85535
Tango - Free Video Call, Voice and Chat : 75412
LinkedIn : 71856
Match™ - #1 Dating App. : 60659
Skype for iPad : 60163
POF - Best Dating App for Conversations : 52642
Timehop : 49510
Find My Family, Friends & iPhone - Life360 Locator : 43877
Whisper - Share, Express, Meet : 39819
Hangouts : 36404
LINE PLAY - Your Avatar World : 34677
WeChat : 34584
Badoo - Meet New People, Chat, Socialize. : 34428
Followers + for Instagram - Follower Analytics : 28633
GroupMe : 28260
Marco Polo Video Walkie Talkie : 27662
Miitomo : 23965
SimSimi : 23530
Grindr - Gay and same sex guys chat, meet and date : 23201
Wishbone - Compare Anything : 20649
imo video calls and chat : 18841
After School - Funny Anonymous School News : 18482
Quick Reposter - Repost, Regram and Reshare Photos : 17694
Weibo HD : 16772
Repost for Instagram : 15185
Live.me – Live Video Chat & Make Friends Nearby : 14724
Nextdoor : 14402
Followers Analytics for Instagram - InstaReport : 13914
YouNow: Live Stream Video Chat : 12079
FollowMeter for Instagram - Followers Tracking : 11976
LINE : 11437
eHarmony™ Dating App - Meet Singles : 11124
Discord - Chat for Gamers : 9152
QQ : 9109
Telegram Messenger : 7573
Weibo : 7265
Periscope - Live Video Streaming Around the World : 6062
Chat for Whatsapp - iPad Version : 5060
QQ HD : 5058
Followers Analysis Tool For Instagram App Free : 4253
live.ly - live video streaming : 4145
Houseparty - Group Video Chat : 3991
SOMA Messenger : 3232
Monkey : 3060
Down To Lunch : 2535
Flinch - Video Chat Staring Contest : 2134
Highrise - Your Avatar Community : 2011
LOVOO - Dating Chat : 1985
PlayStation®Messages : 1918
BOO! - Video chat camera with filters & stickers : 1805
Qzone : 1649
Chatous - Chat with new people : 1609
Kiwi - Q&A : 1538
GhostCodes - a discovery app for Snapchat : 1313
Jodel : 1193
FireChat : 1037
Google Duo - simple video calling : 1033
Fiesta by Tango - Chat & Meet New People : 885
Google Allo — smart messaging : 862
Peach — share vividly : 727
Hey! VINA - Where Women Meet New Friends : 719
Battlefield™ Companion : 689
All Devices for WhatsApp - Messenger for iPad : 682
Chat for Pokemon Go - GoChat : 500
IAmNaughty – Dating App to Meet New People Online : 463
Qzone HD : 458
Zenly - Locate your friends in realtime : 427
League of Legends Friends : 420
豆瓣 : 407
Candid - Speak Your Mind Freely : 398
知乎 : 397
Selfeo : 366
Fake-A-Location Free ™ : 354
Popcorn Buzz - Free Group Calls : 281
Fam — Group video calling for iMessage : 279
QQ International : 274
Ameba : 269
SoundCloud Pulse: for creators : 240
Tantan : 235
Cougar Dating & Life Style App for Mature Women : 213
Rawr Messenger - Dab your chat : 180
WhenToPost: Best Time to Post Photos for Instagram : 158
Inke—Broadcast an amazing life : 147
Mustknow - anonymous video Q&A : 53
CTFxCmoji : 39
Lobi : 36
Chain: Collaborate On MyVideo Story/Group Video : 35
botman - Real time video chat : 7
BestieBox : 0
MATCH ON LINE chat : 0
niconico ch : 0
LINE BLOG : 0
bit-tube - Live Stream Video Chat : 0
for app in ios_final:
if app[-5] == 'Music':
print(app[1], ':', app[5])Pandora - Music & Radio : 1126879
Spotify Music : 878563
Shazam - Discover music, artists, videos & lyrics : 402925
iHeartRadio – Free Music & Radio Stations : 293228
SoundCloud - Music & Audio : 135744
Magic Piano by Smule : 131695
Smule Sing! : 119316
TuneIn Radio - MLB NBA Audiobooks Podcasts Music : 110420
Amazon Music : 106235
SoundHound Song Search & Music Player : 82602
Sonos Controller : 48905
Bandsintown Concerts : 30845
Karaoke - Sing Karaoke, Unlimited Songs! : 28606
My Mixtapez Music : 26286
Sing Karaoke Songs Unlimited with StarMaker : 26227
Ringtones for iPhone & Ringtone Maker : 25403
Musi - Unlimited Music For YouTube : 25193
AutoRap by Smule : 18202
Spinrilla - Mixtapes For Free : 15053
Napster - Top Music & Radio : 14268
edjing Mix:DJ turntable to remix and scratch music : 13580
Free Music - MP3 Streamer & Playlist Manager Pro : 13443
Free Piano app by Yokee : 13016
Google Play Music : 10118
Certified Mixtapes - Hip Hop Albums & Mixtapes : 9975
TIDAL : 7398
YouTube Music : 7109
Nicki Minaj: The Empire : 5196
Sounds app - Music And Friends : 5126
SongFlip - Free Music Streamer : 5004
Simple Radio - Live AM & FM Radio Stations : 4787
Deezer - Listen to your Favorite Music & Playlists : 4677
Ringtones for iPhone with Ringtone Maker : 4013
Bose SoundTouch : 3687
Amazon Alexa : 3018
DatPiff : 2815
Trebel Music - Unlimited Music Downloader : 2570
Free Music Play - Mp3 Streamer & Player : 2496
Acapella from PicPlayPost : 2487
Coach Guitar - Lessons & Easy Tabs For Beginners : 2416
Musicloud - MP3 and FLAC Music Player for Cloud Platforms. : 2211
Piano - Play Keyboard Music Games with Magic Tiles : 1636
Boom: Best Equalizer & Magical Surround Sound : 1375
Music Freedom - Unlimited Free MP3 Music Streaming : 1246
AmpMe - A Portable Social Party Music Speaker : 1047
Medly - Music Maker : 933
Bose Connect : 915
Music Memos : 909
UE BOOM : 612
LiveMixtapes : 555
NOISE : 355
MP3 Music Player & Streamer for Clouds : 329
Musical Video Maker - Create Music clips lip sync : 320
Cloud Music Player - Downloader & Playlist Manager : 319
Remixlive - Remix loops with pads : 288
QQ音乐HD : 224
Blocs Wave - Make & Record Music : 158
PlayGround • Music At Your Fingertips : 150
Music and Chill : 135
The Singing Machine Mobile Karaoke App : 130
radio.de - Der Radioplayer : 64
Free Music - Player & Streamer for Dropbox, OneDrive & Google Drive : 46
NRJ Radio : 38
Smart Music: Streaming Videos and Radio : 17
BOSS Tuner : 13
PetitLyrics : 0
- Social Networking - genre is also dominated by giant firms and there is no potential for us to develop our application which could give us profit.
- Weather - Users usually do not spend long time looking on the weather. We are trying to make free app which will give us profit solely from advertisements, so there are very low chances that we could benefit from this genre.
- Music - This genre is also occupied by the biggest firms that we know. For example: Pandora, Spotify, Amazon, Shazam. This genre is also not a good to choice for us to make profit from advertisements.
It seems that Reference genre is the most suitable for our goal. Now, let's analyze Google Play data set.
It will be easier to analyze the Google Play market data as we have the actual number of installs in this data set. However, the only issue is that the values in 'installs' column are open_ended (100+, 500+, 1000+, 5000+, etc.)
display_table(android_final, 5)1,000,000+ : 15.726534296028879
100,000+ : 11.552346570397113
10,000,000+ : 10.548285198555957
10,000+ : 10.198555956678701
1,000+ : 8.393501805054152
100+ : 6.915613718411552
5,000,000+ : 6.825361010830325
500,000+ : 5.561823104693141
50,000+ : 4.7721119133574
5,000+ : 4.512635379061372
10+ : 3.5424187725631766
500+ : 3.2490974729241873
50,000,000+ : 2.3014440433213
100,000,000+ : 2.1322202166064983
50+ : 1.917870036101083
5+ : 0.78971119133574
1+ : 0.5076714801444043
500,000,000+ : 0.2707581227436823
1,000,000,000+ : 0.22563176895306858
0+ : 0.04512635379061372
0 : 0.01128158844765343
Luckily, we don't need very precise data as we only want to find out which app genres in Google Play marker attract the most number of users.
We will consider that an app with 100,000+ installs has 100,000 installs, and an app with 50,000,000+ installs has 50,000,00 installs. But we still need to convert this numbers into float for out data analysis. We will have to remove the commas and the plus characters.
Below, we are isolating the apps by genre and computing average value of apps installations per each genre.
android_categories = freq_table(android_final, 1)
for category in android_categories:
total = 0
len_category = 0
for app in android_final:
category_app = app[1]
if category_app == category:
n_installs = app[5]
n_installs = n_installs.replace(',', '')
n_installs = n_installs.replace('+', '' )
total += float(n_installs)
len_category += 1
average_number = total / len_category
print(category, ":", average_number)ART_AND_DESIGN : 1986335.0877192982
AUTO_AND_VEHICLES : 647317.8170731707
BEAUTY : 513151.88679245283
BOOKS_AND_REFERENCE : 8767811.894736841
BUSINESS : 1712290.1474201474
COMICS : 817657.2727272727
COMMUNICATION : 38456119.167247385
DATING : 854028.8303030303
EDUCATION : 1833495.145631068
ENTERTAINMENT : 11640705.88235294
EVENTS : 253542.22222222222
FINANCE : 1387692.475609756
FOOD_AND_DRINK : 1924897.7363636363
HEALTH_AND_FITNESS : 4188821.9853479853
HOUSE_AND_HOME : 1331540.5616438356
LIBRARIES_AND_DEMO : 638503.734939759
LIFESTYLE : 1437816.2687861272
GAME : 15588015.603248259
FAMILY : 3695641.8198090694
MEDICAL : 120550.61980830671
SOCIAL : 23253652.127118643
SHOPPING : 7036877.311557789
PHOTOGRAPHY : 17840110.40229885
SPORTS : 3638640.1428571427
TRAVEL_AND_LOCAL : 13984077.710144928
TOOLS : 10801391.298666667
PERSONALIZATION : 5201482.6122448975
PRODUCTIVITY : 16787331.344927534
PARENTING : 542603.6206896552
WEATHER : 5074486.197183099
VIDEO_PLAYERS : 24727872.452830188
NEWS_AND_MAGAZINES : 9549178.467741935
MAPS_AND_NAVIGATION : 4056941.7741935486
On average, communications apps have the most installs in the Google Play market : 38,456,119. However, number of installs is heavily influenced by giant firms.
for app in android_final:
if app[1] == 'COMMUNICATION' and (app[5] == '1,000,000,000+'
or app[5] == '500,000,000+'
or app[5] == '100,000,000+'):
print(app[0], ':', app[5])WhatsApp Messenger : 1,000,000,000+
imo beta free calls and text : 100,000,000+
Android Messages : 100,000,000+
Google Duo - High Quality Video Calls : 500,000,000+
Messenger – Text and Video Chat for Free : 1,000,000,000+
imo free video calls and chat : 500,000,000+
Skype - free IM & video calls : 1,000,000,000+
Who : 100,000,000+
GO SMS Pro - Messenger, Free Themes, Emoji : 100,000,000+
LINE: Free Calls & Messages : 500,000,000+
Google Chrome: Fast & Secure : 1,000,000,000+
Firefox Browser fast & private : 100,000,000+
UC Browser - Fast Download Private & Secure : 500,000,000+
Gmail : 1,000,000,000+
Hangouts : 1,000,000,000+
Messenger Lite: Free Calls & Messages : 100,000,000+
Kik : 100,000,000+
KakaoTalk: Free Calls & Text : 100,000,000+
Opera Mini - fast web browser : 100,000,000+
Opera Browser: Fast and Secure : 100,000,000+
Telegram : 100,000,000+
Truecaller: Caller ID, SMS spam blocking & Dialer : 100,000,000+
UC Browser Mini -Tiny Fast Private & Secure : 100,000,000+
Viber Messenger : 500,000,000+
WeChat : 100,000,000+
Yahoo Mail – Stay Organized : 100,000,000+
BBM - Free Calls & Messages : 100,000,000+
Another popular genre among Android users is Video Players. Same patter can be viewed for Video Players too, which means it is not a profitable area to create our application.
for app in android_final:
if app[1] == 'VIDEO_PLAYERS' and (app[5] == '1,000,000,000+'
or app[5] == '500,000,000+'
or app[5] == '100,000,000+'):
print(app[0], ':', app[5])YouTube : 1,000,000,000+
Motorola Gallery : 100,000,000+
VLC for Android : 100,000,000+
Google Play Movies & TV : 1,000,000,000+
MX Player : 500,000,000+
Dubsmash : 100,000,000+
VivaVideo - Video Editor & Photo Movie : 100,000,000+
VideoShow-Video Editor, Video Maker, Beauty Camera : 100,000,000+
Motorola FM Radio : 100,000,000+
Book and Reference genre is very popular as well. Let's see if the Google Play market has a potential for this genre, so we could make profit from advertisements there both in Google Play and App Store market.
for app in android_final:
if app[1] == 'BOOKS_AND_REFERENCE' and (app[5] == '1,000,000,000+'
or app[5] == '500,000,000+'
or app[5] == '100,000,000+'):
print(app[0], ':', app[5])Google Play Books : 1,000,000,000+
Bible : 100,000,000+
Amazon Kindle : 100,000,000+
Wattpad 📖 Free Books : 100,000,000+
Audiobooks from Audible : 100,000,000+
There are only few applications that have the most popularity in Books and Reference genre. This area might have a potential for us in Google Play market as well. However, it is also important to check number of applications somewhere in the middle of popularity (greater or equal to 1,000,000 installations).
for app in android_final:
if app[1] == 'BOOKS_AND_REFERENCE' and (app[5] == '1,000,000+'
or app[5] == '5,000,000+'
or app[5] == '10,000,000+'
or app[5] == '50,000,000+'):
print(app[0], ':', app[5])Wikipedia : 10,000,000+
Cool Reader : 10,000,000+
Book store : 1,000,000+
FBReader: Favorite Book Reader : 10,000,000+
Free Books - Spirit Fanfiction and Stories : 1,000,000+
AlReader -any text book reader : 5,000,000+
FamilySearch Tree : 1,000,000+
Cloud of Books : 1,000,000+
ReadEra – free ebook reader : 1,000,000+
Ebook Reader : 5,000,000+
Read books online : 5,000,000+
eBoox: book reader fb2 epub zip : 1,000,000+
All Maths Formulas : 1,000,000+
Ancestry : 5,000,000+
HTC Help : 10,000,000+
Moon+ Reader : 10,000,000+
English-Myanmar Dictionary : 1,000,000+
Golden Dictionary (EN-AR) : 1,000,000+
All Language Translator Free : 1,000,000+
Aldiko Book Reader : 10,000,000+
Dictionary - WordWeb : 5,000,000+
50000 Free eBooks & Free AudioBooks : 5,000,000+
Al-Quran (Free) : 10,000,000+
Al Quran Indonesia : 10,000,000+
Al'Quran Bahasa Indonesia : 10,000,000+
Al Quran Al karim : 1,000,000+
Al Quran : EAlim - Translations & MP3 Offline : 5,000,000+
Koran Read &MP3 30 Juz Offline : 1,000,000+
Hafizi Quran 15 lines per page : 1,000,000+
Quran for Android : 10,000,000+
Satellite AR : 1,000,000+
Oxford A-Z of English Usage : 1,000,000+
Dictionary.com: Find Definitions for English Words : 10,000,000+
English Dictionary - Offline : 10,000,000+
Bible KJV : 5,000,000+
NOOK: Read eBooks & Magazines : 10,000,000+
Brilliant Quotes: Life, Love, Family & Motivation : 1,000,000+
Stats Royale for Clash Royale : 1,000,000+
Dictionary : 10,000,000+
wikiHow: how to do anything : 1,000,000+
EGW Writings : 1,000,000+
My Little Pony AR Guide : 1,000,000+
Spanish English Translator : 10,000,000+
Dictionary - Merriam-Webster : 10,000,000+
JW Library : 10,000,000+
Oxford Dictionary of English : Free : 10,000,000+
English Hindi Dictionary : 10,000,000+
English to Hindi Dictionary : 5,000,000+
It looks like this area is dominated by reading ebooks and software processing applications, as well as lots of libraries and dictionaries. It is probably not a good idea to build similar apps like here as there will be a significant competition.
However, if we create an application with free access to all past and recent new books, it should work well. But we also should make some original features to our application to avoid making it similar to others. For example: audio version of the books, popular quotes in the books, discussion groups or quizzes.
In this project we analyzed data about App Store and Google Play mobile applications with the goal of creating an application that will be profitable for both markets.
It was concluded that creating a universal mobile app that will have all recent and past books will be profitable for our company. However for sakes of avoiding the competition, we should add additional features for our mobile application, such as audio version of the books, quizzes or discussion groups where people can interact and discuss books.