Profitable App Profiles for the App Store and Google Play Markets

Our aim in this project is to find mobile app profiles that are profitable for the App Store and Google Play markets. We're working as data analysts for a company that builds Android and iOS mobile apps, and our job is to enable our team of developers to make data-driven decisions with respect to the kind of apps they build.

At our company, we only build apps that are free to download and install, and our main source of revenue consists of in-app ads. This means that our revenue for any given app is mostly influenced by the number of users that use our app. Our goal for this project is to analyze data to help our developers understand what kinds of apps are likely to attract more users.

Opening and Exploring the Data

As of September 2018, there were approximately 2 million iOS apps available on the App Store, and 2.1 million Android apps on Google Play.

from csv import reader ### The Google Play data set ### opened_file = open('/datasets/appstoregoogleplay/googleplaystore.csv') read_file = reader(opened_file) android = list(read_file) android_header = android[0] android = android[1:] ### The App Store data set ### opened_file = open('/datasets/appstoregoogleplay/AppleStore.csv') read_file = reader(opened_file) ios = list(read_file) ios_header = ios[0] ios = ios[1:]

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 between rows if rows_and_columns: print('Number of rows:', len(dataset)) print('Number of columns:', len(dataset[0])) print(android_header) print('\n') explore_data(android, 0, 3, True)

print(ios_header) print('\n') explore_data(ios, 0, 3, True)

Deleting Wrong Data

The Google Play data set has a [dedicated discussion](https://www.kaggle.com/lava18/google-play-store-apps/discussion) section, and we can see that [one of the discussions](https://www.kaggle.com/lava18/google-play-store-apps/discussion/66015) outlines an error for row 10472. Let's print this row and compare it against the header and another row that is correct.

print(android[10472]) # incorrect row print("\n") print(android_header) # header print("\n") print(android[0])

print(len(android)) del android[10472] # don't run this more than once print(len(android))

Removing Duplicate Entries

Part One

If we explore the Google Play data set long enough, we'll find that some apps have more than one entry. For instance, the application Instagram has four entries:

for app in android: name = app[0] if name == 'Instagram': print(app)

In total, there are 1,181 cases where an app occurs more than once:

duplicate_apps = [] unique_apps = [] for app in android: name = app[0] if name in unique_apps: duplicate_apps.append(name) else: unique_apps.append(name) print('Number of duplicate apps:', len(duplicate_apps)) print('\n') print('Examples of duplicate apps:', duplicate_apps[:15])

Part Two

Let's Start by building the dictionary.

reviews_max = {} for app in android: name = app[0] n_reviews = float(app[3]) if name in reviews_max and reviews_max[name] < n_reviews: reviews_max[name] = n_reviews elif name not in reviews_max: reviews_max[name] = n_reviews

print('Expected length:', len(android) - 1181) print('Actual length:', len(reviews_max))

android_clean = [] already_added = [] for app in android: name = app[0] n_reviews = float(app[3]) if (reviews_max[name] == n_reviews) and (name not in already_added): android_clean.append(app) already_added.append(name) # make sure this is inside the if block

explore_data(android_clean, 0, 3, True)

Removing Non-English Apps

Part One

print(ios[813][1]) print(ios[6731][1]) print(android_clean[4412][0]) print(android_clean[7940][0])

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 😜')) print(ord('™')) print(ord('😜'))

Part Two

To minimize the impact of data loss, we'll only remove an app if its name has more than three non-ASCII characters:

def is_english(string): non_ascii = 0 for character in string: if ord(character) > 127: non_ascii += 1 if non_ascii > 3: return False else: return True print(is_english('Docs To Go™ Free Office Suite')) print(is_english('Instachat 😜'))

android_english = [] ios_english = [] for app in android_clean: name = app[0] if is_english(name): android_english.append(app) for app in ios: name = app[1] if is_english(name): ios_english.append(app) explore_data(android_english, 0, 3, True) print('\n') explore_data(ios_english, 0, 3, True)

Isolating the Free Apps

android_final = [] ios_final = [] for app in android_english: price = app[7] if price == '0': android_final.append(app) for app in ios_english: price = app[4] if price == '0.0': ios_final.append(app) print(len(android_final)) print(len(ios_final))

We're left with 8864 Android apps and 3222 iOS apps, which should be enough for our analysis.

Most Common Apps by Genre

Part One

Part Two

def freq_table(dataset, index): table = {} total = 0 for row in dataset: total += 1 value = row[index] if value in table: table[value] += 1 else: table[value] = 1 table_percentages = {} for key in table: percentage = (table[key] / total) * 100 table_percentages[key] = percentage return table_percentages def display_table(dataset, index): table = freq_table(dataset, index) table_display = [] for key in table: key_val_as_tuple = (table[key], key) table_display.append(key_val_as_tuple) table_sorted = sorted(table_display, reverse = True) for entry in table_sorted: print(entry[1], ':', entry[0])

Part Three

display_table(ios_final, -5)

display_table(android_final, 1) # Category

display_table(android_final, -4)

Most Popular Apps by Genre on the App Store

genres_ios = freq_table(ios_final, -5) for genre in genres_ios: total = 0 len_genre = 0 for app in ios_final: genre_app = app[-5] if genre_app == genre: n_ratings = float(app[5]) total += n_ratings len_genre += 1 avg_n_ratings = total / len_genre print(genre, ':', avg_n_ratings)

for app in ios_final: if app[-5] == 'Navigation': print(app[1], ':', app[5]) # print name and number of ratings

for app in ios_final: if app[-5] == 'Reference': print(app[1], ':', app[5])

Most Popular Apps by Genre on Google Play

display_table(android_final, 5) # the Installs columns

categories_android = freq_table(android_final, 1) for category in categories_android: total = 0 len_category = 0 for app in android_final: category_app = app[1] if category_app == category: n_installs = app[5] n_installs = n_installs.replace(',', '') n_installs = n_installs.replace('+', '') total += float(n_installs) len_category += 1 avg_n_installs = total / len_category print(category, ':', avg_n_installs)

for app in android_final: if app[1] == 'COMMUNICATION' and (app[5] == '1,000,000,000+' or app[5] == '500,000,000+' or app[5] == '100,000,000+'): print(app[0], ':', app[5])

under_100_m = [] for app in android_final: 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)

for app in android_final: if app[1] == 'BOOKS_AND_REFERENCE': print(app[0], ':', app[5])

for app in android_final: if app[1] == 'BOOKS_AND_REFERENCE' and (app[5] == '1,000,000,000+' or app[5] == '500,000,000+' or app[5] == '100,000,000+'): print(app[0], ':', app[5])

for app in android_final: if app[1] == 'BOOKS_AND_REFERENCE' and (app[5] == '1,000,000+' or app[5] == '5,000,000+' or app[5] == '10,000,000+' or app[5] == '50,000,000+'): print(app[0], ':', app[5])

Profitable App Profiles for the App Store and Google Play Markets

Opening and Exploring the Data

Deleting Wrong Data

Removing Duplicate Entries

Part One

Part Two

Removing Non-English Apps

Part One

Part Two

Isolating the Free Apps

Most Common Apps by Genre

Part One

Part Two

Part Three

Most Popular Apps by Genre on the App Store

Most Popular Apps by Genre on Google Play

Conclusions