Project: Identify genres on mobile app stores which have more users.

The objective as a Data Analyst for this project is to analyze data to help my organisation understand type of apps likely to attract more users. This has the potential to help significantly improve the mobile app user base and subsequently increase mobile revenue stream.

Our mobile apps are free to download on the Google play store and Apple app store and main source of revenue is generated from in-app ads. This means the organisations revenue is mostly influenced by the number of users who use the apps — the more users that engage with the ads, the better.

Based on published material available on the web. The below datasets will be used during the data analysis.

  • Please click here to access documentation on google play store dataset.

  • Please click here to access documentation on app store dataset

The csv datasets downloaded from the web will be stored in separate lists for Google apps and Apple apps. The header row has additionally been excluded from the datasets.

In [28]:
# Read the data
import csv
file_google = open('googleplaystore.csv') 
read_google = csv.reader(file_google)
data_google = list(read_google)
data_google_header = data_google[0]
data_google = data_google[1:]

file_apple = open('AppleStore.csv')
read_apple = csv.reader(file_apple)
data_apple = list(read_apple)
data_apple_header = data_apple[0]
data_apple = data_apple[1:]

The defined function data_explore will help with sampling data from the google and apple datasets

In [29]:
# This function prints rows from the dataset
def data_explore(data, start, end, rows_and_columns=False):
    data_slice = data[start:end]
    for row in data_slice:
        print(row)
        print('\n')
        
    if rows_and_columns:
        print('Number of rows:', len(data))
        print('Number of columns:', len(data[0]))

There are 10841 rows in the playstore dataset. Based on the google sample data and headers displayed, the following data points will be very useful for getting insights on which category / genre tend to be popular on the playstore market, furthermore, this will be analysed by considering price: App, Category, Rating, Reviews, Installs, Price.

In [30]:
# Preview google playstore data
print(data_google_header)
data_explore(data_google, 1, 6, True)
['App', 'Category', 'Rating', 'Reviews', 'Size', 'Installs', 'Type', 'Price', 'Content Rating', 'Genres', 'Last Updated', 'Current Ver', 'Android Ver']
['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']


['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']


['Pixel Draw - Number Art Coloring Book', 'ART_AND_DESIGN', '4.3', '967', '2.8M', '100,000+', 'Free', '0', 'Everyone', 'Art & Design;Creativity', 'June 20, 2018', '1.1', '4.4 and up']


['Paper flowers instructions', 'ART_AND_DESIGN', '4.4', '167', '5.6M', '50,000+', 'Free', '0', 'Everyone', 'Art & Design', 'March 26, 2017', '1.0', '2.3 and up']


Number of rows: 10841
Number of columns: 13

There are 7197 rows in the app store dataset, the following data points will be very useful for getting insights on which genre tend to be popular on the apple app store market, furthermore, this will be analysed by considering price: track_name, price, rating_count_tot, user_rating, prime_genre.

In [31]:
# Preview apple app store data
print(data_apple_header)
data_explore(data_apple, 1, 6, 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']
['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']


['420009108', 'Temple Run', '65921024', 'USD', '0.0', '1724546', '3842', '4.5', '4.0', '1.6.2', '9+', 'Games', '40', '5', '1', '1']


['284035177', 'Pandora - Music & Radio', '130242560', 'USD', '0.0', '1126879', '3594', '4.0', '4.5', '8.4.1', '12+', 'Music', '37', '4', '1', '1']


['429047995', 'Pinterest', '74778624', 'USD', '0.0', '1061624', '1814', '4.5', '4.0', '6.26', '12+', 'Social Networking', '37', '5', '27', '1']


Number of rows: 7197
Number of columns: 16

Data cleaning

Before progressing further, the dataset is checked to detect and remove:

  • duplicate data
  • missing / inaccurate data
  • non-english apps like 爱奇艺PPS -《欢乐颂2》电视剧热播
  • paid apps

For the time being, my organisation is only interested in developing free mobile apps for an english speaking audience hence for this purpose, the data has to be streamlined.

In [32]:
# Based on published information on the discussion 
# section of the google dataset, the row index [10472]
# (excluding header) is missing its category datapoint.
# This row is deleted using the `del` python command
print (data_google[10472])
del data_google[10472]
['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']

The defined function duplicate will help with finding duplicate data from the google and apple datasets

In [33]:
#This function checks for duplicates 
def duplicate(data,index,dataset):
    duplicate_list = []
    unique_list = []
    for app in data:
        name=app[index]
        if name in unique_list:
            duplicate_list.append(name)
        else:
            unique_list.append(name)
    print ('There are' , len(duplicate_list) , 'duplicate apps in the ' + dataset)
    print ('\n')
    print ('These are duplicate instances in the ' + dataset , ':', duplicate_list[:16])
    print ('\n')
In [34]:
duplicate(data_google,0,'playstore dataset')
duplicate(data_apple,1,'appstore dataset')
There are 1181 duplicate apps in the playstore dataset


These are duplicate instances in the playstore dataset : ['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', 'MailChimp - Email, Marketing Automation']


There are 2 duplicate apps in the appstore dataset


These are duplicate instances in the appstore dataset : ['Mannequin Challenge', 'VR Roller Coaster']


The duplicate data has been detected, the next part is to delete these duplicates.

For the google playstore dataset, the differences between the duplicate apps mainly bothers on the review count. To address this, it is assumed that the higher the number of reviews, the more recent the data should be, hence, only the row with the highest number of reviews will be kept while other duplicate entries will be removed.

For the apple app store dataset, there seems to be consensus agreement on the discussion forum that the duplicate app entries detected by the duplicate function are actually unique apps, hence, these entries will be kept.

The data_google_clean list will not have duplicate enteries because the duplicates have been removed by keeping only data rows with the highest review.

In [35]:
reviews_max={}
for app in data_google:
    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

data_google_clean=[]
already_added=[]
for app in data_google:
    name=app[0]
    n_reviews=float(app[3])
    if n_reviews == reviews_max[name] and name not in already_added:
        data_google_clean.append(app)
        already_added.append(name)
data_explore(data_google_clean, 1, 6, True)
['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']


['Pixel Draw - Number Art Coloring Book', 'ART_AND_DESIGN', '4.3', '967', '2.8M', '100,000+', 'Free', '0', 'Everyone', 'Art & Design;Creativity', 'June 20, 2018', '1.1', '4.4 and up']


['Paper flowers instructions', 'ART_AND_DESIGN', '4.4', '167', '5.6M', '50,000+', 'Free', '0', 'Everyone', 'Art & Design', 'March 26, 2017', '1.0', '2.3 and up']


['Smoke Effect Photo Maker - Smoke Editor', 'ART_AND_DESIGN', '3.8', '178', '19M', '50,000+', 'Free', '0', 'Everyone', 'Art & Design', 'April 26, 2018', '1.1', '4.0.3 and up']


Number of rows: 9659
Number of columns: 13

Remember, my organisation is more focused on developing English based apps. The ord built in python function will be used to reduce the data to only the apps which have the character range between 0-127 in ASCII [American Standard Code for Information Interchange].

In [36]:
# Preview data on non-english apps
print(data_apple[813])
print(data_apple[6731])
['445375097', '爱奇艺PPS -《欢乐颂2》电视剧热播', '224617472', 'USD', '0.0', '14844', '0', '4.0', '0.0', '6.3.3', '17+', 'Entertainment', '38', '5', '3', '1']
['1120021683', '【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〜', '77551616', 'USD', '0.0', '0', '0', '0.0', '0.0', '1.3', '12+', 'Games', '38', '0', '1', '1']

The data_english function will help to remove data on non-English related mobile apps.

In [37]:
# This function checks for non-english characters
def data_english(string):
    non_ascii=0
    for char in string:
        if ord(char) > 127:
            non_ascii+=1
    if non_ascii > 3:
        return False
    else:
        return True
print(data_english('Instagram'))
print(data_english('爱奇艺PPS -《欢乐颂2》电视剧热播'))
print(data_english('Docs To Go™ Free Office Suite'))
print(data_english('Instachat 😜'))
True
False
True
True

The data_english function is working perfectly, we will progress ahead to delete non-english related apps using this function. The resulting data will be stored in data_eng_google and data_eng_apple list.

In [38]:
data_eng_google=[]
data_eng_apple=[]
for app in data_google_clean:
    name=app[0]
    if data_english(name):
        data_eng_google.append(app)

for app in data_apple:
    name=app[1]
    if data_english(name):
        data_eng_apple.append(app)

The final part of the data cleaning process will be to detect and delte the paid mobile apps, we are more interested in data relating to free apps in both markets.

In [39]:
data_final_google=[]
data_final_apple=[]

for app in data_eng_google:
    price=app[7]
    if price == '0':
        data_final_google.append(app)

for app in data_eng_apple:
    price=app[4]
    if price == '0.0':
        data_final_apple.append(app)

print(len(data_final_google) , 'mobile apps to be analysed in the google playstore dataset')
print(len(data_final_apple) , 'mobile apps to be analysed in the apple app store dataset')
8864 mobile apps to be analysed in the google playstore dataset
3222 mobile apps to be analysed in the apple app store dataset

Prepare the data for final analysis by building computations on percentages

The data is ready to be analysed. The objective as a Data Analyst for this project was to analyze the sample data on mobile apps in the Google playstore and Apple apps store to help my organisation understand genre / category of apps likely to attract more users. This has the potential to help the organisation significantly improve its mobile app user base and subsequently increase mobile revenue.

In [40]:
# This function builds a frequency table for the 
# prime_genre data column of the app store dataset 
# and the genres and catrgory column of the playstore 
# dataset which will be displayed in percentages
def data_freq(data, index):
    datafreq={}
    total=0
    for app in data:
        total +=1
        datac = app[index]
        if datac in datafreq:
            datafreq[datac]+=1
        else:
            datafreq[datac]=1
    
    data_perc={}
    for app in datafreq:
        perc = (datafreq[app]/total)*100
        data_perc[app] = perc
    return data_perc

# This function will display the percentages 
# developed in data_freq by descending order
def display_table(data, index):
    dataf = data_freq(data, index)
    data_display = []
    for key in dataf:
        key_val_as_tuple = (dataf[key], key)
        data_display.append(key_val_as_tuple)
        
    data_sorted = sorted(data_display, reverse = True)
    for entry in data_sorted:
        print(entry[1], ':', entry[0])

Percentage computations for Data on Apple app store

Based on the data below generated from the Apple app store dataset, the Games genre seems to be miles ahead in representation with 58% while Entertainment genre takes distant second place with 7.8%. The genral impression is that mobile apps designed for entertainment such as games, social networking seem to be more available in the app store market than mobile apps which are designed for more practical purposes for instance (productivity, news, weather, finace).

In [41]:
display_table(data_final_apple, -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

Percentage computations for Data on Google play store

Based on the data below generated from the Google playstore dataset, the Family and Game genre have higher availability with combined percentage of 28.64%. The genral impression here as was similarly observed in the app store dataset is that mobile apps designed for entertainment seem to be highly represented than mobile apps designed for practical purposes.

But, based on further observation, unlike the app store data which had a combined percentage of 66% for the top two popular genres, the google play store on the other hand only has 28.64%. This implies that productivity apps tend to be better represented in the google playstore market. Looking more closely, mobile apps within the genres for tools, business, productivity, finance and medical have combined percentage of 24.11%.

This data tell us that there could be a more balanced landscape of both practical and fun apps in the Google playstore market when compared with the Apple app store.

In [42]:
display_table(data_final_google, 1)
FAMILY : 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

Discovering the genres with more users on Apple app store

The below will help to discover the most popular genres in the app store dataset, based on the total number of users, remember, the objective is to find the genre of apps which have the most users for both datasets. For the playstore data, the installs column will be useful as-is, but for the app store data, the total number of user ratings rating_count_tot column will be used as a proxy, since we dont have a column that directly shows the total number of installs by users.

In [43]:
data_genres_apple = data_freq(data_final_apple, -5)

for genre in data_genres_apple:
    total = 0
    len_genre = 0
    for app in data_final_apple:
        data_genre = app[-5]
        if data_genre == genre:            
            ratings = float(app[5])
            total += ratings
            len_genre += 1
    ratings_avg = total / len_genre
    print(genre, ':', ratings_avg)
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

The data shows that the highest number of user reviews belong to Navigation genre, but this figure seems to be highly influenced by the popularity of google maps and waze, which have close to half a million user reviews combined. This pattern seems to be the same with the Social Networking genre where the average number of user reviews is highly influenced by popular organisations like Facebook and Microsoft.

In [44]:
for app in data_final_apple:
    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

Besides the Navigation and Social Networking genre which have data that might be skewing the results due to popularity of certain popular apps, the Reference genre seems to be doing quite well on the Apple app store with 74,942 user based reviews on the average. By diving further into this genre, below, it is observered that the Bible and Dictionary.com apps skew up the average rating for the Reference genre. Despite this, publishing a free mobile app in the Reference genre could be promising for the organisation. This genre seems to be doing quite well in keeping up with popularity of genres like social networking.

Publishing mobile versions of highly popular books could prove to be a success for bringing in more mobile users. Possibly, this could be a book which could be geared towards family / fun by integrating within the app elements such as puzzles, mini games, and quizzes.

In [45]:
for app in data_final_apple:
    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

Discovering the genres with more users on Google play store

The total count on installs for the playstore data is not precise enough, for instance, we are not particularly certain whether an app with 100,000+ installs has 100,000 installs, 200,000, or 350,000.

In [46]:
# Preview data based on installs 
display_table(data_final_google, 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

The below helps to compute the average number of installs for each genre (category) in the playstore data which would produce better data analysis.

In [47]:
google_categories = data_freq(data_final_google, 1)

for category in google_categories:
    total = 0
    len_category = 0
    for app in data_final_google:
        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
    avg_n_installs = total / len_category
    print(category, ':', avg_n_installs)
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

The google playstore market seems to be dominated by popular apps from high profile organisations. The data shows that communication apps have the most installs: 38,456,119 but like previously observed in the app store data, this number is skewed by popular social networking apps which tend to have over one billion installs for example whatsapp, facebook messenger, youtube, and instagram.

In [48]:
for app in data_final_google:
    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+

Besides Communication genre, further analysis shows that genres for Game and Books and Reference seem to do quite well with the average number of installs being 15,588,015 and 8,767,811 respectively. This data is very insightful because the previous recommendation for the Apple app store could potentially be used for the Google playstore market. This would mean my organisation could practically publish the same mobile app with versions compatible on both markets.

In [49]:
for app in data_final_google:
    if app[1] == 'BOOKS_AND_REFERENCE':
        print(app[0], ':', app[5])
E-Book Read - Read Book for free : 50,000+
Download free book with green book : 100,000+
Wikipedia : 10,000,000+
Cool Reader : 10,000,000+
Free Panda Radio Music : 100,000+
Book store : 1,000,000+
FBReader: Favorite Book Reader : 10,000,000+
English Grammar Complete Handbook : 500,000+
Free Books - Spirit Fanfiction and Stories : 1,000,000+
Google Play Books : 1,000,000,000+
AlReader -any text book reader : 5,000,000+
Offline English Dictionary : 100,000+
Offline: English to Tagalog Dictionary : 500,000+
FamilySearch Tree : 1,000,000+
Cloud of Books : 1,000,000+
Recipes of Prophetic Medicine for free : 500,000+
ReadEra – free ebook reader : 1,000,000+
Anonymous caller detection : 10,000+
Ebook Reader : 5,000,000+
Litnet - E-books : 100,000+
Read books online : 5,000,000+
English to Urdu Dictionary : 500,000+
eBoox: book reader fb2 epub zip : 1,000,000+
English Persian Dictionary : 500,000+
Flybook : 500,000+
All Maths Formulas : 1,000,000+
Ancestry : 5,000,000+
HTC Help : 10,000,000+
English translation from Bengali : 100,000+
Pdf Book Download - Read Pdf Book : 100,000+
Free Book Reader : 100,000+
eBoox new: Reader for fb2 epub zip books : 50,000+
Only 30 days in English, the guideline is guaranteed : 500,000+
Moon+ Reader : 10,000,000+
SH-02J Owner's Manual (Android 8.0) : 50,000+
English-Myanmar Dictionary : 1,000,000+
Golden Dictionary (EN-AR) : 1,000,000+
All Language Translator Free : 1,000,000+
Azpen eReader : 500,000+
URBANO V 02 instruction manual : 100,000+
Bible : 100,000,000+
C Programs and Reference : 50,000+
C Offline Tutorial : 1,000+
C Programs Handbook : 50,000+
Amazon Kindle : 100,000,000+
Aab e Hayat Full Novel : 100,000+
Aldiko Book Reader : 10,000,000+
Google I/O 2018 : 500,000+
R Language Reference Guide : 10,000+
Learn R Programming Full : 5,000+
R Programing Offline Tutorial : 1,000+
Guide for R Programming : 5+
Learn R Programming : 10+
R Quick Reference Big Data : 1,000+
V Made : 100,000+
Wattpad 📖 Free Books : 100,000,000+
Dictionary - WordWeb : 5,000,000+
Guide (for X-MEN) : 100,000+
AC Air condition Troubleshoot,Repair,Maintenance : 5,000+
AE Bulletins : 1,000+
Ae Allah na Dai (Rasa) : 10,000+
50000 Free eBooks & Free AudioBooks : 5,000,000+
Ag PhD Field Guide : 10,000+
Ag PhD Deficiencies : 10,000+
Ag PhD Planting Population Calculator : 1,000+
Ag PhD Soybean Diseases : 1,000+
Fertilizer Removal By Crop : 50,000+
A-J Media Vault : 50+
Al-Quran (Free) : 10,000,000+
Al Quran (Tafsir & by Word) : 500,000+
Al Quran Indonesia : 10,000,000+
Al'Quran Bahasa Indonesia : 10,000,000+
Al Quran Al karim : 1,000,000+
Al-Muhaffiz : 50,000+
Al Quran : EAlim - Translations & MP3 Offline : 5,000,000+
Al-Quran 30 Juz free copies : 500,000+
Koran Read &MP3 30 Juz Offline : 1,000,000+
Hafizi Quran 15 lines per page : 1,000,000+
Quran for Android : 10,000,000+
Surah Al-Waqiah : 100,000+
Hisnul Al Muslim - Hisn Invocations & Adhkaar : 100,000+
Satellite AR : 1,000,000+
Audiobooks from Audible : 100,000,000+
Kinot & Eichah for Tisha B'Av : 10,000+
AW Tozer Devotionals - Daily : 5,000+
Tozer Devotional -Series 1 : 1,000+
The Pursuit of God : 1,000+
AY Sing : 5,000+
Ay Hasnain k Nana Milad Naat : 10,000+
Ay Mohabbat Teri Khatir Novel : 10,000+
Arizona Statutes, ARS (AZ Law) : 1,000+
Oxford A-Z of English Usage : 1,000,000+
BD Fishpedia : 1,000+
BD All Sim Offer : 10,000+
Youboox - Livres, BD et magazines : 500,000+
B&H Kids AR : 10,000+
B y H Niños ES : 5,000+
Dictionary.com: Find Definitions for English Words : 10,000,000+
English Dictionary - Offline : 10,000,000+
Bible KJV : 5,000,000+
Borneo Bible, BM Bible : 10,000+
MOD Black for BM : 100+
BM Box : 1,000+
Anime Mod for BM : 100+
NOOK: Read eBooks & Magazines : 10,000,000+
NOOK Audiobooks : 500,000+
NOOK App for NOOK Devices : 500,000+
Browsery by Barnes & Noble : 5,000+
bp e-store : 1,000+
Brilliant Quotes: Life, Love, Family & Motivation : 1,000,000+
BR Ambedkar Biography & Quotes : 10,000+
BU Alsace : 100+
Catholic La Bu Zo Kam : 500+
Khrifa Hla Bu (Solfa) : 10+
Kristian Hla Bu : 10,000+
SA HLA BU : 1,000+
Learn SAP BW : 500+
Learn SAP BW on HANA : 500+
CA Laws 2018 (California Laws and Codes) : 5,000+
Bootable Methods(USB-CD-DVD) : 10,000+
cloudLibrary : 100,000+
SDA Collegiate Quarterly : 500+
Sabbath School : 100,000+
Cypress College Library : 100+
Stats Royale for Clash Royale : 1,000,000+
GATE 21 years CS Papers(2011-2018 Solved) : 50+
Learn CT Scan Of Head : 5,000+
Easy Cv maker 2018 : 10,000+
How to Write CV : 100,000+
CW Nuclear : 1,000+
CY Spray nozzle : 10+
BibleRead En Cy Zh Yue : 5+
CZ-Help : 5+
Modlitební knížka CZ : 500+
Guide for DB Xenoverse : 10,000+
Guide for DB Xenoverse 2 : 10,000+
Guide for IMS DB : 10+
DC HSEMA : 5,000+
DC Public Library : 1,000+
Painting Lulu DC Super Friends : 1,000+
Dictionary : 10,000,000+
Fix Error Google Playstore : 1,000+
D. H. Lawrence Poems FREE : 1,000+
Bilingual Dictionary Audio App : 5,000+
DM Screen : 10,000+
wikiHow: how to do anything : 1,000,000+
Dr. Doug's Tips : 1,000+
Bible du Semeur-BDS (French) : 50,000+
La citadelle du musulman : 50,000+
DV 2019 Entry Guide : 10,000+
DV 2019 - EDV Photo & Form : 50,000+
DV 2018 Winners Guide : 1,000+
EB Annual Meetings : 1,000+
EC - AP & Telangana : 5,000+
TN Patta Citta & EC : 10,000+
AP Stamps and Registration : 10,000+
CompactiMa EC pH Calibration : 100+
EGW Writings 2 : 100,000+
EGW Writings : 1,000,000+
Bible with EGW Comments : 100,000+
My Little Pony AR Guide : 1,000,000+
SDA Sabbath School Quarterly : 500,000+
Duaa Ek Ibaadat : 5,000+
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+
EP Research Service : 1,000+
Hymnes et Louanges : 100,000+
EU Charter : 1,000+
EU Data Protection : 1,000+
EU IP Codes : 100+
EW PDF : 5+
BakaReader EX : 100,000+
EZ Quran : 50,000+
FA Part 1 & 2 Past Papers Solved Free – Offline : 5,000+
La Fe de Jesus : 1,000+
La Fe de Jesús : 500+
Le Fe de Jesus : 500+
Florida - Pocket Brainbook : 1,000+
Florida Statutes (FL Code) : 1,000+
English To Shona Dictionary : 10,000+
Greek Bible FP (Audio) : 1,000+
Golden Dictionary (FR-AR) : 500,000+
Fanfic-FR : 5,000+
Bulgarian French Dictionary Fr : 10,000+
Chemin (fr) : 1,000+
The SCP Foundation DB fr nn5n : 1,000+

Recommendation

Publishing a free mobile app in the Books and Reference genre could be promising for the organisation, this genre seems to be doing quite well in keeping up with the popularity of the top genres on the app store market mostly geared towards fun and entertainement such as games, social networking, photography and video. This pattern is observed for both sample datasets from the google playstore and apple app store.

Publishing a compatible mobile version of a highly popular book on both app store markets could prove to be a success for bringing in more mobile users. Possibly, this could be a book which could be geared towards both family and fun category by trying to integrate puzzles, mini games, quizzes, thereby building parts of the highly popular genres on the market into this one mobile app.