This project will focus on analyzing and cleaning data from the Apple Store and Googe Plays store with an emphasis on using concepts such as basic python, lists and loops, conditional statements, dictionaries and frequency tables, functions in order to assist a theortical company design apps that will bring in revenue. The theortical company in question only builds apps that are free, and their revenue comes from in-app adds. Therefore we will be analyzing data from the most popular mobile devices: android and apple products. As such we will be using data from the App Store and Google Play.
Link for the Apple Store dataset (~7,200 rows and 16 columns).
Link for the Google Store dataset (~10,841 rows and 13 columns).
# Imports the reader function from the csv module
from csv import reader
# Create a list of lists for both datasets
open_apple = open(r'C:\Users\david\OneDrive\Desktop\AppleStore.csv', encoding="utf8")
read_apple = reader(open_apple)
apple_data = list(read_apple)
apple_header = apple_data[0]
apple_data = apple_data[1:]
open_google = open(r'C:\Users\david\OneDrive\Desktop\googleplaystoremaster.csv', encoding="utf8")
read_google = reader(open_google)
google_data = list(read_google)
google_header = google_data[0]
google_data = google_data[1:]
# Create a function that prints a "slice" or section of data, as well total number of rows
def explore_data(dataset, start, end, rows_and_columns=False):
dataset_slice = dataset[start:end]
for row in dataset_slice:
print(row)
print('\n')
if rows_and_columns:
print('Number of rows:', len(dataset))
print('Number of columns:', len(dataset[0]))
# Using the function about to print rows 0 through 3 with the total number of rows and columns
explore_data(google_data, 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'] ['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
There was a row in the Google Play Stores data that had a missing data point so we removed it with del google_data[10473]
. There are now 10,840 rows instead of 10,841 rows. Furthermore, in the cell below I created a for loop to count the number of duplicate app titles there are in the Google Play dataset. There are 1,181 duplicate app names as shown below.
duplicate_apps = []
unique_apps = []
del google_data[10472] # Deleted row with bad data
for app in google_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('\n')
print('Examples of duplicate apps:', duplicate_apps[:10])
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']
In order to clean our data I have created a dictionary where the duplicate datas name column and number of reviews column will be the key:value pair and out of the key:value pairs that have the highest number of reviews that pair will be placed into reviews_max. This operation will be done with every duplicate value using a for loop, as shown below.
reviews_max = {} # Dictionary where app names and number of reviews will go
for app in google_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
print('Expected length:', len(google_data) - 1181)
print('Actual length:', len(reviews_max))
Expected length: 9659 Actual length: 9659
As shown above, I filtered the duplicate data using a dictionary. Our calculated number of duplicate rows was 1,181. Therefore, we would expect a clean list of data to have a length of 9,659 (original length of the google_data list after the removal of row[10472] is 10,840. So 10,840 - 1,181 = 9,659).
Below I created two lists, google_clean
where the cleaned google_data
will be stored and google_app_names
for app names. The google_app_names list is a secondary condition where we filter out the results that would have gone into the google_clean
list if the results where exactly the same. This is because our for loop above will store multiple duplicate columns if the reviews are exactly the same.
google_clean = []
google_app_names = []
for app in google_data:
name = app[0]
n_reviews = float(app[3])
if (reviews_max[name] == n_reviews) and (name not in google_app_names):
google_clean.append(app)
google_app_names.append(name)
Now that the duplicate data has been removed we need to attended do that data that is non-english. In order to accomplish this I've created a function below that counts the ASCII value of each letter in a string when inputed into said function. For further information, all english letters, as well as numbers and special characters have a ASCII number correlating to them that is under 127.
Example: a = 65 and ! = 33
Furthermore, some app names contain symbols, dashes and emojis, yet they are usually still english based. In order to clean through app names which could pass as english-based yet have excessive amounts of emojis or special character we will limit the number of non-ASCII values by three per string as shown below.
def is_eng(string): # This function loops over ever letter or number and checks it's ASCII value
non_ascii = 0
for character in string:
if ord(character) > 127:
non_ascii += 1
if non_ascii > 3:
return False
else:
return True
google_english = [] # In this cell we input both datasets into the function to clean it of all non-english ASCII values
apple_english = []
for row in google_clean:
name = row[0]
if is_eng(name):
google_english.append(row)
for row in apple_data:
name = row[1]
if is_eng(name):
apple_english.append(row)
Our next step is to isolate the applications on both datasets that are free, as we are only analyzing free, english-based apps in this project.
google_free = []
apple_free = []
for row in google_english: # For loop iterates through price in google dataset, if price is free it assigns it to a new dataset
price = row[7]
if price == '0':
google_free.append(row)
for row in apple_english: # For loop iterates through price in apple dataset, if price is free it assigns it to a new dataset
price = row[4]
if price == '0.0':
apple_free.append(row)
print('Number of free apps on Google Play: ', len(google_free))
print('Number of free apps on Apple Store: ', len(apple_free))
Number of free apps on Google Play: 8864 Number of free apps on Apple Store: 3222
Now that we have our cleaned data for both datasets we can begin to analyze. To start I've created a function that makes frequency tables with percentages and another function to display those percentages in descending order.
def freq_table(dataset, index): # Frequency table for with percentages
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): # Sorts through frequency table and sorts by highest to lowest percentage
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])
print('Categories in the Apple Store by percentage: ')
print('\n')
display_table(apple_free, -5)
Categories in the Apple Store by percentage: 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
As shown above the category that is most present in our cleaned data for the App Store is the gaming category, with a staggering approximate percentage of 58.2%. The second most common category being entertainment, with an approximate percentage of 7.9%.
To extrapolate further on the above data; it seems as if the most prominent type of applications made are those design for fun or enjoyment, rather than practical purpose. For instance, the top 5 most common categories (games, entertainment, photo & video, education and social networking) account for approximately 78% of the number of apps in our dataset (Note: this analysis does not reflect all apps on the App Store, only english-based ones). While apps that have more utility or practical usage account for the remaining percentage of apps (around 22%). However, the fact that the most numerous kind of apps are enjoyment-based doesn't mean they have the most number of users.
print('Categorys in the Google Store by percentage: ')
print('\n')
display_table(google_free, 1)
Categorys in the Google Store by percentage: 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
Above is a visualization of catogries most present in Google Play, each category has a corresponding percentage for how many apps exist with these categories. As we can immediately see the Google Play store varies from the App Store in the fact that the percentages shown above are spread out amongst every genre -- even with the increased number of categories. However, we can still see some sort of trend in fun-focused apps, even with this increased range of catgories to apps. We can see this in the family and games categories (as the family categories in Google Play is essentially just games focused for children).
print('\n')
print('Genres in the Google Store by percentage: ')
print('\n')
display_table(google_free, -4)
Genres in the Google Store by percentage: 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
Above is a text visualization of Genres for Google Play. The correlation between categories and genres for this data set is not crystal clear. However, we can note that there is a much large quantity of rows in the genres column than the categories coloumns. As we are focusing on the bigger picture for this analysis we will only be working with the categories column from now on. To recap, we've seen that the App Store has a prominent number of fun-based apps when compare to Google Play, offers a more balanced landscape between utility apps and fun-based apps.
Our next step is to determine popularity of different categories. We will do this by counting the number of installs, unfotunately the App Store dataset lacks this column so we will be count the number of user ratings instead of installs as a next best proxy.
Below we calculate the average number of user ratings per app category in the Appe Store.
apple_categories = freq_table(apple_free, -5)
for category in apple_categories:
total = 0
len_category = 0
for app in apple_free:
category_app = app[-5]
if category_app == category:
n_ratings = float(app[5])
total += n_ratings
len_category += 1
avg_n_ratings = total / len_category # Divide the total number of apps by the number of categories
print(category, ':', avg_n_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
As show above with Navigation : 86090.33333333333
, navigation apps have the highest number of user reviews, but this figure is heavily influenced by Waze and Google Maps, which have close to half a million user reviews together:
for app in apple_free:
if app[-5] == 'Navigation':
print(app[1], ':', app[5]) # print name and number of ratings
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
The same also applies for social networking apps, where a few giants like Facebook, Pintrest, Skype and more. This can also be seen with music apps, where Pandorda, Spotify and Shazam contain the large number of reviews compared to all the other music apps. Additionally, we can see this with genre Reference
where the app bible and dictionary are reponsible for a large quantity of reviews as shown below.
As this analysis is supposed to help me theortical company it is important to note that the correlation found with some of these genres are because of tech giants that have so many reviews and install simply because they're a large company, and not neccesarily because the app they made alone is worth that many reviews and installs. However I will leave this data.
for app in apple_free:
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
The Google Store data has a column specifically for number of installs, unlike the App Store data. However, the data is only measured in values like 100,000+ or 10,000+. Meaning that an app labeled with 200,000+ installs could actually have 200,000, 300,000, 4000,000 or more installs. For the purpose of our analysis we do not need very precise data -- just a ballpark. So we will leave this as is.
Below I've created a for loop that will convert the installs
column into just numbers so that we can process it later
categories_google = freq_table(google_free, 1)
for category in categories_google:
total = 0
len_category = 0
for app in google_free:
category_app = app[1]
if category_app == category:
n_installs = app[5]
n_installs = n_installs.replace(',', '') # Replaces the "," in 100,000+ with a space. Therefore 100,000+ = 100000+
n_installs = n_installs.replace('+', '') # Replaces the "+" in 100,000+ with a space. Therefore 100,000+ = 100,000
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
As show by the above cell, communication apps have the highest number of installs at 38456119.167247385.
for app in google_free:
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+
print(google_header)
['App', 'Category', 'Rating', 'Reviews', 'Size', 'Installs', 'Type', 'Price', 'Content Rating', 'Genres', 'Last Updated', 'Current Ver', 'Android Ver']
As show above, a few apps account for an extremely large number of the installs like Facebook Messenger, Skype, and Gmail to name a few. If we removed the communication apps that have over 100 million installs the average would be reduced by ten times as shown below.
under_100_m = [] # This loops calculates the average for the the communication category if we removed the few giant apps
for app in google_free:
n_installs = app[5]
n_installs = n_installs.replace(',', '')
n_installs = n_installs.replace('+', '')
if (app[1] == 'COMMUNICATION') and (float(n_installs) < 100000000):
under_100_m.append(float(n_installs))
sum(under_100_m) / len(under_100_m)
3603485.3884615386
Since the game category is so satured we wont be attempting to analyze that. The books_and_reference
category has 8,767,811 however so we will inspect this next. Lets print out the apps and their number of installs under the books_and_reference
category below.
for app in google_free: # This loop prints every app and it's number of installs as a dictionary
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+
Immediately after creating a loop to look at the number of installs under the books_and_reference
category I am going to filter the last for apps that have an extremely large number of installs. Lets do that below
for app in google_free: # This loop prints apps that have a high number of installs in the books_and_reference category
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+
As shown above, it seems that there is only one giant competitor and just a few small ones in this specific category. There is potential for building and publishing an app here.
Lastly, lets look at the apps that are in the middle in terms of popularity to get an idea what my theortical company might profit off of the most when building an app. Let's do this in the cell below now.
for app in google_free: # This loop prints apps that have a moderate number of installs in the books_and_reference category
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+
Looking through the dictionary above I can see that there seems to be a focus on dictionaries, electronic books, and audiobooks. Moreover, throughout the data analyzed there seems to be a popularity among famous books. From the Bible to the Quran. Therefore it might be worth building an app around a certain popular book. Even then though, there would still be considerable competition so the design, implementation and features would have to top of the line.
In this project, we acquired, cleaned and analyzed the data for application stores on both major mobile phone builders (android and apple), with the goal of finding out what app would become popular to maximize profits. Unfortunately, it seems many of the categories in both markets have a small number of apps that already do very well what most other apps offer. However, I have determined that building an app around a decently popular and well-known book with top of the line features would be an optimal choice.
# Read in the data
opened_file = open('Super_Bowl.csv')
read_file = opened_file.read()
# Transform read_file into a list of lists
super_bowl_split = read_file.split('\n')
super_bowl = []
for row in super_bowl_split:
super_bowl.append(row.split(','))
--------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) ~\AppData\Local\Temp\ipykernel_6532\3177108956.py in <module> 1 # Read in the data ----> 2 opened_file = open('Super_Bowl.csv') 3 read_file = opened_file.read() 4 # Transform read_file into a list of lists 5 super_bowl_split = read_file.split('\n') FileNotFoundError: [Errno 2] No such file or directory: 'Super_Bowl.csv'