In this project, we will assume a role of data analysis for a company that develops free to download mobile applications. Our goal is to find what kind of app attracts more user for Google Play and the App Store.
We will be using these following data sets:
Let's begin by first, opening the two data sets.
from csv import reader
# App Store data
app_store_file = open('AppleStore.csv', encoding='utf8')
read_app_store_file = reader(app_store_file)
apps_store_data = list(read_app_store_file)
apps_store_header = apps_store_data[0]
apps_store_data = apps_store_data[1:]
# Google Play Store data
google_play_store_file = open('googleplaystore.csv', encoding='utf8')
read_google_play_file = reader(google_play_store_file)
google_play_data = list(read_google_play_file)
google_play_header = google_play_data[0]
google_play_data = google_play_data[1:]
Then, we create a function named explore_data() to make our more readable. Also, we can either show or hide the number of rows and columns of the data set.
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]))
Now, we will start using our defined function. Let's start with the google play data set:
print(google_play_header, "\n")
explore_data(google_play_data, 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
We see that Google Play data set has 10,841 apps and 13 columns. If we look at the headers, we see columns that might help with our analysis are App
, Category
, Reviews
, Installs
, Type
, Price
, and Genres
.
print(apps_store_header, "\n")
explore_data(apps_store_data, 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
We have 7,197 apps for the App Store data set and 16 columns. The headers we find useful are track_name
, price
, rating_count_tot
, user_rating
and prime_genre
Before to start our analysis we first need clean thoroughly our data.
If we check the discussions from our Google Play set source, we can see one of the discussion is saying that row index 10472 has a missing Category
header. Let's print the row to confirm.
print(google_play_data[10472]) # incorrect row
print('\n')
print(google_play_header) # complete header
print('\n')
print(google_play_data[0]) # correct row
['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'] ['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']
We can see that the app Life Made WI-Fi Touchscreen Photo Frame has a rating of 19 but Google Play only accepts rating up to 5. This confirms that row index 10472 is indeed invalid. Thus, we will remove it from our data set.
print(len(google_play_data))
del google_play_data[10472]
print(len(google_play_data))
10841 10840
Now, we need make to sure every entry is unique. As we explore our data, there are some apps with more than one entry, like the app Instagram:
for app in google_play_data:
name = app[0]
if name == 'Instagram':
print(app, "\n")
['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 apps occured more than once.
duplicate_apps = []
unique_apps = []
for app in google_play_data:
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("Example of duplicate apps:", duplicate_apps[:15])
Number of duplicate apps: 1181 Example 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 duplicate apps to our analysis, so we need to remove them. If we look at the example of duplicate instagram, the main difference is the 4th column which is the Review
column. We can assume that the higher reviews the more up-to-date data. Therefore, we will only keep those data that are up-to-date.
To do that we will:
reviews_max = {}
for app in google_play_data:
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
Since we know that there are 1,181 duplicate apps. Our new dictionary named reviews_max
should be equal to the diffence between our data set and 1181.
print("Expected length:", len(google_play_data) - 1181)
print("Actual length:", len(reviews_max))
Expected length: 9659 Actual length: 9659
Now, we will start to create a new data set named android_clean
of Google Play that contains only unique apps with ther highest review.
android_clean = []
already_added = []
for app in google_play_data:
name = app[0]
n_reviews = float(app[3])
if (n_reviews == reviews_max[name]) and (name not in already_added):
android_clean.append(app)
already_added.append(name)
Let's quickly check our new data set and confirm it has 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
Our new data set android_clean
has 9,659 rows, as expected.
Since our company only develops mobile apps directly only to English speaking audience. Let's confirm if our data contains any non-English apps. In ASCII (American Standard Code for Information Interchange) system the letter we commonly use in English are all in range of 0 - 127, we can check each app name of our data set if it has a character with greater 127 number encoding.
We created the function below using the built-in ord() function to see the encoding number.
def is_english(string):
for character in string:
if ord(character) > 127:
return False
return True
print(is_english('Instagram'))
print(is_english('爱奇艺PPS -《欢乐颂2》电视剧热播'))
print(is_english('Docs To Go™ Free Office Suite'))
print(is_english('Instachat 😜'))
True False False False
We see that the last two is most likely targeted for English audience but our function failed to recognize it is an English app. This is because emojis and ™ fall outside of the ASCII range.
If we use our currently function is_english()
to remove non-English apps it may cause huge data loss. To minimize, we will only remove if the app name has more than 3 non-ASCII characters.
def is_english(string):
is_ascii = 0
for character in string:
if ord(character) > 127:
is_ascii += 1
if is_ascii > 3:
return False
return True
print(is_english('Instagram'))
print(is_english('Docs To Go™ Free Office Suite'))
print(is_english('Instachat 😜'))
True True True
Our function works fine but we know it is not yet really perfect because it may remove some few English apps, but this can be good enough for our analysis.
android_english_apps = []
app_store_english_apps = []
for app in android_clean:
name = app[0]
if is_english(name):
android_english_apps.append(app)
for app in apps_store_data:
name = app[1]
if is_english(name):
app_store_english_apps.append(app)
explore_data(android_english_apps, 0, 3, True)
print('\n')
explore_data(app_store_english_apps, 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
After removing non-English apps from our data set, Google Play has now 9,614 and the App Store has 6,183.
We mention earlier that we develop only free apps to download. Both of our data set contains free and non-free apps. So, we'll need to remove non-free apps to our data set.
android_free_apps = []
ios_free_apps = []
for app in android_english_apps:
price = app[7]
if price == "0":
android_free_apps.append(app)
for app in app_store_english_apps:
price = app[4]
if price == "0.0":
ios_free_apps.append(app)
print(len(android_free_apps))
print(len(ios_free_apps))
8864 3222
So far we cleaned the data with the following steps:
In our final data cleaning we have 8,864 apps for Google Play and 3,222 apps for the App Store.
It's time to find what kind of app profile are more likely to succeed on both Google Play and the App Store
Our strategy for an app ideas is:
We will build these two functions to analyze frequency tables:
def freq_table(dataset, index):
frequency_table = {}
for row in dataset:
value = row[index]
if value in frequency_table:
frequency_table[value] += 1
else:
frequency_table[value] = 1
for value in frequency_table:
frequency_table[value] /= len(dataset)
frequency_table[value] *= 100
return frequency_table
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 can now use our function named display_table()
to analyze our data set. Let's start with the App Store data set for the prime_genre
column.
display_table(ios_free_apps, -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
We can see from the result that more than half of the App Store is Games(58.16%). While Entertainment with almost 8% followed by Photo & Video with almost 5%.
The general impression is that the App Store(at least the part container free English Apps) dominated by apps for fun, such as games, entertainment, photo and video, etc. While practical purposes apps like utilities, shopping, sports, health & fitness, etc are more rare. However more apps in particular part doesn't mean is also has numerous users - demand might not be the same as the offer.
Let's continue by exploring Google Play data set Category
and Genres
column.
display_table(android_free_apps, 1) # category
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
We see Google Play has different result, being family in the top with almost 19% followed by game with 9.7%. Seems like Google Play has much more with practical apps like tools, business, lifestyle, productivity, etc. Howerver, if check in Google Play family category is consist of games for kids.
We can have a better represention of Google Play if we check the Genres
column.
display_table(android_free_apps, 9) # genre
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 Genres
and Category
is unclear but we can notice is that the Genres
are much more granular. For this analysis we will only work with Category
column and move on.
This first analysis we can conclude that App Store is dominated by apps designed for fun, while Google Play has much more balance on for-fun and practical apps.
To find out what genres has more user in the app store is to calculate the average of installs for each genre. In Google Play we can use the Installs
column but this column is missing for the App Store. As a workaround we can use the number of ratings which is the rating_count_tot.
Below, we calculate the average users of each genre on the App Store.
genres_ios = freq_table(ios_free_apps, -5)
frequency_rating_genre = {}
for genre in genres_ios:
total = 0
len_genre = 0
for row in ios_free_apps:
genre_app = row[-5]
if genre_app == genre:
n_ratings = float(row[5])
total += n_ratings
len_genre += 1
avg_n_ratings = total / len_genre
frequency_rating_genre[genre] = avg_n_ratings
# To sort out the values in frequency_rating_genre by descending order
frequency_rating_genre = {k: v for k, v in sorted(frequency_rating_genre.items(), key=lambda item: item[1], reverse = True)}
for rating in frequency_rating_genre:
print(rating,":",frequency_rating_genre[rating])
Navigation : 86090.33333333333 Reference : 74942.11111111111 Social Networking : 71548.34905660378 Music : 57326.530303030304 Weather : 52279.892857142855 Book : 39758.5 Food & Drink : 33333.92307692308 Finance : 31467.944444444445 Photo & Video : 28441.54375 Travel : 28243.8 Shopping : 26919.690476190477 Health & Fitness : 23298.015384615384 Sports : 23008.898550724636 Games : 22788.6696905016 News : 21248.023255813954 Productivity : 21028.410714285714 Utilities : 18684.456790123455 Lifestyle : 16485.764705882353 Entertainment : 14029.830708661417 Business : 7491.117647058823 Education : 7003.983050847458 Catalogs : 4004.0 Medical : 612.0
We see that navigation has the highest number of users on average. Let's check if this result is fairly distributed.
for app in ios_free_apps:
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
for app in ios_free_apps:
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
Both Navigation and Reference are heavily influnced by few giants like Waze, Google Maps, Bible, etc. Also applies with music with Pandora, Spotify and Shazam heavily influcend the average number.
Now, let's check the most install apps for Google Play Store.
display_table(android_free_apps, 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
One problem with this data is that is not precise. For example we have 100,000+ installs, 500,000+ install. However, in this analysis we don't need very precise data as we are only looking for which apps has more installs for genre.
Therefore, we will leave the number of installs as it is and remove plus character of each end. Before we start we need to convert our each install to float, so we need to remove commas and plus characters. We will do this on a loop and compute the average of installs for each genre.
categories_android = freq_table(android_free_apps, 1)
frequency_category_install = {}
for category in categories_android:
total = 0
len_category = 0
for app in android_free_apps:
category_app = app[1]
if category_app == category:
n_install = app[5]
n_install = n_install.replace('+', '')
n_install = n_install.replace(',', '')
total += float(n_install)
len_category += 1
avg_n_installs = total / len_category
frequency_category_install[category] = avg_n_installs
# To sort out the values in frequency_category_install by descending order
frequency_category_install = {k: v for k, v in sorted(frequency_category_install.items(), key=lambda item: item[1], reverse = True)}
for install in frequency_category_install:
print(install, ":", frequency_category_install[install])
COMMUNICATION : 38456119.167247385 VIDEO_PLAYERS : 24727872.452830188 SOCIAL : 23253652.127118643 PHOTOGRAPHY : 17840110.40229885 PRODUCTIVITY : 16787331.344927534 GAME : 15588015.603248259 TRAVEL_AND_LOCAL : 13984077.710144928 ENTERTAINMENT : 11640705.88235294 TOOLS : 10801391.298666667 NEWS_AND_MAGAZINES : 9549178.467741935 BOOKS_AND_REFERENCE : 8767811.894736841 SHOPPING : 7036877.311557789 PERSONALIZATION : 5201482.6122448975 WEATHER : 5074486.197183099 HEALTH_AND_FITNESS : 4188821.9853479853 MAPS_AND_NAVIGATION : 4056941.7741935486 FAMILY : 3695641.8198090694 SPORTS : 3638640.1428571427 ART_AND_DESIGN : 1986335.0877192982 FOOD_AND_DRINK : 1924897.7363636363 EDUCATION : 1833495.145631068 BUSINESS : 1712290.1474201474 LIFESTYLE : 1437816.2687861272 FINANCE : 1387692.475609756 HOUSE_AND_HOME : 1331540.5616438356 DATING : 854028.8303030303 COMICS : 817657.2727272727 AUTO_AND_VEHICLES : 647317.8170731707 LIBRARIES_AND_DEMO : 638503.734939759 PARENTING : 542603.6206896552 BEAUTY : 513151.88679245283 EVENTS : 253542.22222222222 MEDICAL : 120550.61980830671
Let's check a number of apps heavily influenced our result.
for app in android_free_apps:
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+
for app in android_free_apps:
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+
We can say that that communication have a fine share with giants like Google Chrome, Messenger, WhatsApp and Skype while the rest being in 100,000+ installs. While Video players is heavily influenced by both YouTube and Google Play Movies & TV which has 1,000,000+ installs.
In this project, we analyzed the data about Google Play and the App Store with an aim of finding a profitable app for both markets.
It's very clear that the App Store and Google Play are dominated by Games. We conclude that taking a game is profitable. However, there are really many kind of games. After taking this data analyzing we may want to look for another data set this time is about games in both Google Play and the App store before deriving to a final conclusion what kind of game we will make.