The Android App Market on Google Play

# Read in dataset import pandas as pd apps_with_duplicates = pd.read_csv('/work/The-Android-App-Market-on-Google-Play/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 = ', len(apps)) # Have a look at a random sample of 5 rows print(apps.sample(5))

Total number of apps in the dataset =  9659
      Unnamed: 0                       App             Category  Rating  \
8470        9604    Texas Holdem Poker Pro                 GAME     4.5   
7449        8544                 DM Screen  BOOKS_AND_REFERENCE     4.4   
665          812                  HomeWork            EDUCATION     4.3   
6669        7728  HR Team CQ Region Ed Qld             BUSINESS     NaN   
1964        2487        Anthem BC Anywhere              MEDICAL     2.6   

      Reviews  Size    Installs  Type Price Content Rating             Genres  \
8470   114479  26.0  5,000,000+  Free     0           Teen               Card   
7449      283   5.4     10,000+  Free     0   Everyone 10+  Books & Reference   
665     16195   5.2  1,000,000+  Free     0       Everyone          Education   
6669        0   4.1        500+  Free     0       Everyone           Business   
1964      496  24.0    100,000+  Free     0       Everyone            Medical   

            Last Updated Current Ver   Android Ver  
8470   December 20, 2017       4.7.0    3.0 and up  
7449    January 12, 2018     1.1.10a    4.0 and up  
665   September 20, 2016       8.5.2    4.0 and up  
6669    January 12, 2018         3.8  4.0.3 and up  
1964       July 27, 2018     8.0.226    4.4 and up

# 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())

<class 'pandas.core.frame.DataFrame'>
Int64Index: 9659 entries, 0 to 9658
Data columns (total 14 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   Unnamed: 0      9659 non-null   int64  
 1   App             9659 non-null   object 
 2   Category        9659 non-null   object 
 3   Rating          8196 non-null   float64
 4   Reviews         9659 non-null   int64  
 5   Size            8432 non-null   float64
 6   Installs        9659 non-null   object 
 7   Type            9659 non-null   object 
 8   Price           9659 non-null   object 
 9   Content Rating  9659 non-null   object 
 10  Genres          9659 non-null   object 
 11  Last Updated    9659 non-null   object 
 12  Current Ver     9651 non-null   object 
 13  Android Ver     9657 non-null   object 
dtypes: float64(2), int64(2), object(10)
memory usage: 1.1+ MB
None

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)

Unnamed: 0          int64
App                object
Category           object
Rating            float64
Reviews             int64
Size              float64
Installs          float64
Type               object
Price             float64
Content Rating     object
Genres             object
Last Updated       object
Current Ver        object
Android Ver        object
dtype: object

import plotly.graph_objs as go # 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) # Create bar chart data data = [go.Bar( x = sorted_num_apps_in_category.index, # index = category name y = sorted_num_apps_in_category.values, # value = count )] fig = go.Figure(data=data) fig.show()

Number of categories =  33

# 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) fig.show()

Average app rating =  4.173243045387994

%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[(~apps['Rating'].isnull()) & (~apps['Size'].isnull())] # 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'])

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 = apps[apps['Price'] > 200] apps_above_200[['Category', 'App', 'Price']]

# Select apps priced below $100 apps_under_100 = popular_app_cats[popular_app_cats['Price'] < 100] fig, ax = plt.subplots() fig.set_size_inches(15, 8) # Examine price vs category with the authentic apps (apps_under_100) ax = sns.stripplot(x=apps_under_100['Price'], y=apps_under_100['Category'], data=apps_under_100, jitter=True, linewidth=1) ax.set_title('App pricing trend across categories after filtering for junk apps')