1. Google Play Store apps and reviews
Mobile apps are everywhere. They are easy to create and can be lucrative. Because of these two factors, more and more apps are being developed. In this notebook, we will do a comprehensive analysis of the Android app market by comparing over ten thousand apps in Google Play across different categories. We'll look for insights in the data to devise strategies to drive growth and retention.
Let's take a look at the data, which consists of two files:
apps.csv: contains all the details of the applications on Google Play. There are 13 features that describe a given app.user_reviews.csv: contains 100 reviews for each app, most helpful first. The text in each review has been pre-processed and attributed with three new features: Sentiment (Positive, Negative or Neutral), Sentiment Polarity and Sentiment Subjectivity.
# Read in dataset
import pandas as pd
apps_with_duplicates = pd.read_csv('datasets/apps.csv')
# Drop duplicates from apps_with_duplicates
apps = apps_with_duplicates.drop_duplicates()
# Print the total number of apps
print('Total number of apps in the dataset = ', apps.shape[0])
# Have a look at a random sample of 5 rows
print(apps.sample(5))2. Data cleaning
Data cleaning is one of the most essential subtask any data science project. Although it can be a very tedious process, it's worth should never be undermined.
By looking at a random sample of the dataset rows (from the above task), we observe that some entries in the columns like Installs and Price have a few special characters (+ , $) due to the way the numbers have been represented. This prevents the columns from being purely numeric, making it difficult to use them in subsequent future mathematical calculations. Ideally, as their names suggest, we would want these columns to contain only digits from [0-9].
Hence, we now proceed to clean our data. Specifically, the special characters , and + present in Installs column and $ present in Price column need to be removed.
It is also always a good practice to print a summary of your dataframe after completing data cleaning. We will use the info() method to acheive this.
# List of characters to remove
chars_to_remove = ['+',',','$']
# List of column names to clean
cols_to_clean = ['Installs','Price']
# Loop for each column in cols_to_clean
for col in cols_to_clean:
# Loop for each char in chars_to_remove
for char in chars_to_remove:
# Replace the character with an empty string
apps[col] = apps[col].apply(lambda x: x.replace(char, ''))
# Print a summary of the apps dataframe
print(apps.info())3. Correcting data types
From the previous task we noticed that Installs and Price were categorized as object data type (and not int or float) as we would like. This is because these two columns originally had mixed input types: digits and special characters. To know more about Pandas data types, read this.
The four features that we will be working with most frequently henceforth are Installs, Size, Rating and Price. While Size and Rating are both float (i.e. purely numerical data types), we still need to work on Installs and Price to make them numeric.
import numpy as np
# Convert Installs to float data type
apps['Installs'] = apps['Installs'].astype('float')
# Convert Price to float data type
apps['Price'] = apps['Price'].astype('float')
# Checking dtypes of the apps dataframe
print(apps.dtypes)4. Exploring app categories
With more than 1 billion active users in 190 countries around the world, Google Play continues to be an important distribution platform to build a global audience. For businesses to get their apps in front of users, it's important to make them more quickly and easily discoverable on Google Play. To improve the overall search experience, Google has introduced the concept of grouping apps into categories.
This brings us to the following questions:
- Which category has the highest share of (active) apps in the market?
- Is any specific category dominating the market?
- Which categories have the fewest number of apps?
We will see that there are 33 unique app categories present in our dataset. Family and Game apps have the highest market prevalence. Interestingly, Tools, Business and Medical apps are also at the top.
import plotly
plotly.offline.init_notebook_mode(connected=True)
import plotly.graph_objs as go
from plotly.offline import iplot
import matplotlib.pyplot as plt
import seaborn as sns
# Print the total number of unique categories
num_categories = len(apps['Category'].unique())
print('Number of categories = ', num_categories)
# Count the number of apps in each 'Category'.
num_apps_in_category = apps['Category'].value_counts()
# Sort num_apps_in_category in descending order based on the count of apps in each category
sorted_num_apps_in_category = num_apps_in_category.sort_values(ascending = False)
data = [go.Bar(
x = num_apps_in_category.index, # index = category name
y = num_apps_in_category.values, # value = count
)]
fig = go.Figure(data=data)
iplot(fig)
# plotly.offline.iplot(data)5. Distribution of app ratings
After having witnessed the market share for each category of apps, let's see how all these apps perform on an average. App ratings (on a scale of 1 to 5) impact the discoverability, conversion of apps as well as the company's overall brand image. Ratings are a key performance indicator of an app.
From our research, we found that the average volume of ratings across all app categories is 4.17. The histogram plot is skewed to the left indicating that the majority of the apps are highly rated with only a few exceptions in the low-rated apps.
# Average rating of apps
avg_app_rating = apps.Rating.mean()
print('Average app rating = ', avg_app_rating)
# Distribution of apps according to their ratings
data = [go.Histogram(
x = apps['Rating']
)]
# Vertical dashed line to indicate the average app rating
layout = {'shapes': [{
'type' :'line',
'x0': avg_app_rating,
'y0': 0,
'x1': avg_app_rating,
'y1': 1000,
'line': { 'dash': 'dashdot'}
}]
}
fig = go.Figure(data=data, layout=layout)
iplot(fig)6. Size and price of an app
Let's now examine app size and app price. For size, if the mobile app is too large, it may be difficult and/or expensive for users to download. Lengthy download times could turn users off before they even experience your mobile app. Plus, each user's device has a finite amount of disk space. For price, some users expect their apps to be free or inexpensive. These problems compound if the developing world is part of your target market; especially due to internet speeds, earning power and exchange rates.
How can we effectively come up with strategies to size and price our app?
- Does the size of an app affect its rating?
- Do users really care about system-heavy apps or do they prefer light-weighted apps?
- Does the price of an app affect its rating?
- Do users always prefer free apps over paid apps?
We find that the majority of top rated apps (rating over 4) range from 2 MB to 20 MB. We also find that the vast majority of apps price themselves under \$10.
%matplotlib inline
import seaborn as sns
sns.set_style("darkgrid")
import warnings
warnings.filterwarnings("ignore")
# Select rows where both 'Rating' and 'Size' values are present (ie. the two values are not null)
apps_with_size_and_rating_present = apps.dropna(subset=['Rating','Size'])
# Subset for categories with at least 250 apps
large_categories = apps_with_size_and_rating_present.groupby('Category').filter(lambda x: len(x) >= 250)
# Plot size vs. rating
plt1 = sns.jointplot(x = large_categories['Size'], y = large_categories['Rating'])
# Select apps whose 'Type' is 'Paid'
paid_apps = apps_with_size_and_rating_present[apps_with_size_and_rating_present.Type == 'Paid']
# Plot price vs. rating
plt2 = sns.jointplot(x = paid_apps['Price'], y = paid_apps['Rating'])7. Relation between app category and app price
So now comes the hard part. How are companies and developers supposed to make ends meet? What monetization strategies can companies use to maximize profit? The costs of apps are largely based on features, complexity, and platform.
There are many factors to consider when selecting the right pricing strategy for your mobile app. It is important to consider the willingness of your customer to pay for your app. A wrong price could break the deal before the download even happens. Potential customers could be turned off by what they perceive to be a shocking cost, or they might delete an app they’ve downloaded after receiving too many ads or simply not getting their money's worth.
Different categories demand different price ranges. Some apps that are simple and used daily, like the calculator app, should probably be kept free. However, it would make sense to charge for a highly-specialized medical app that diagnoses diabetic patients. Below, we see that Medical and Family apps are the most expensive. Some medical apps extend even up to \$80! All game apps are reasonably priced below \$20.
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
fig.set_size_inches(15, 8)
# Select a few popular app categories
popular_app_cats = apps[apps.Category.isin(['GAME', 'FAMILY', 'PHOTOGRAPHY',
'MEDICAL', 'TOOLS', 'FINANCE',
'LIFESTYLE','BUSINESS'])]
# Examine the price trend by plotting Price vs Category
ax = sns.stripplot(x = popular_app_cats['Price'], y = popular_app_cats['Category'], jitter=True, linewidth=1)
ax.set_title('App pricing trend across categories')
# Apps whose Price is greater than 200
apps_above_200 = popular_app_cats[popular_app_cats.Price > 200]
apps_above_200[['Category', 'App', 'Price']]8. Filter out "junk" apps
It looks like a bunch of the really expensive apps are "junk" apps. That is, apps that don't really have a purpose. Some app developer may create an app called I Am Rich Premium or most expensive app (H) just for a joke or to test their app development skills. Some developers even do this with malicious intent and try to make money by hoping people accidentally click purchase on their app in the store.
Let's filter out these junk apps and re-do our visualization.