Google Play Store Data Analysis with Python

google play store data analysis

The Google Play Store apps data analysis provides enough potential to drive apps making businesses to succeed. Actionable stats can be drawn for developers to work on and capture the Android market. The data set that I have taken in this article is a web scrapped data of 10 thousand Playstore applications to analyze the android competition.

In this Article I will do some Exploratory Data Analysis on the Google Play Store apps data with Python.

Lets start with importing the libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

Download the Data set

df = pd.read_csv('googleplaystore.csv')

Lets see at some insights of the data
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10841 entries, 0 to 10840
Data columns (total 13 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   App             10841 non-null  object 
 1   Category        10841 non-null  object 
 2   Rating          9367 non-null   float64
 3   Reviews         10841 non-null  object 
 4   Size            10841 non-null  object 
 5   Installs        10841 non-null  object 
 6   Type            10840 non-null  object 
 7   Price           10841 non-null  object 
 8   Content Rating  10840 non-null  object 
 9   Genres          10841 non-null  object 
 10  Last Updated    10841 non-null  object 
 11  Current Ver     10833 non-null  object 
 12  Android Ver     10838 non-null  object 
dtypes: float64(1), object(12)
memory usage: 1.1+ MB

Exploratory Data Analysis on Google Play Store

Let’s take a look on all the category

# Category
cat = df.Category.unique()
       '1.9'], dtype=object)

So we got 34 category on this dat aset, let’s see which one is the famous category

most_cat = df.Category.value_counts()
sns.barplot(x=most_cat, y=most_cat.index, data=df)
apps categories

So, there is around 2000 app with family category, followed by game category with 1200 app. And this ‘1.9’ Category, i don’t know what it is, but it only had 1 app so far, so its not visible on the graph.

Let’s look at the rating, and what kind of correlation share between category and rating.

# Rating
array([ 4.1,  3.9,  4.7,  4.5,  4.3,  4.4,  3.8,  4.2,  4.6,  3.2,  4. ,
        nan,  4.8,  4.9,  3.6,  3.7,  3.3,  3.4,  3.5,  3.1,  5. ,  2.6,
        3. ,  1.9,  2.5,  2.8,  2.7,  1. ,  2.9,  2.3,  2.2,  1.7,  2. ,
        1.8,  2.4,  1.6,  2.1,  1.4,  1.5,  1.2, 19. ])

There we had a null values, I am going to leave it as it is. And a 19 for rating is not possible, so i assume it’s a ‘1.9’. So let’s change it and see the distribution value on rating column.

df['Rating'].replace(to_replace=[19.0], value=[1.9],inplace=True)

Most of the rating is around 4. Let’s see how rating is distributed by category column.

g = sns.FacetGrid(df, col='Category', palette="Set1",  col_wrap=5, height=4)
g = (, "Rating", hist=False, rug=True, color="r"))

By the horizontal is the rating value, and vertically is quantity of the rating.

# Mean Rating
mean_rat = df.groupby(['Category'])['Rating'].mean().sort_values(ascending=False)
sns.barplot(x=mean_rat, y=mean_rat.index, data=df)
apps categories

And this is the average of rating by category, family and game has a lot of quantity causing the low on average rating, on the other side event has the highest average rating by category.

Next is reviews, review sometime can measure the app popularity. The more reviews, the better.

# Reviews

array([‘159’, ‘967’, ‘87510’, …, ‘603’, ‘1195’, ‘398307’], dtype=object)

# inside review there is a value with 3.0M with M stand for million, lets change it so it can be measure as float

Reviews = []

for x in df.Reviews:
    x = x.replace('M','00')

Reviews = list(map(float, Reviews))
df['reviews'] = Reviews

This graph is the distribution of total reviews on each app.

g = sns.FacetGrid(df, col='Category', palette="Set1",  col_wrap=5, height=4)
g = (, "Reviews", color="g"))

This graph is the correlation between category and reviews, Family and game category had a lot of reviews.

Some app also almost had no review at all, like event, beauty, medical, parenting and more. it is interesting Event app has a high rating but rare review on it.

# Total reviews
sum_rew = df.groupby(['Category'])['reviews'].sum().sort_values(ascending=False)
sns.barplot(x=sum_rew, y=sum_rew.index, data=df)
google play store data analysis

Showing the amount of total reviews.

# Mean reviews
mean_rew = df.groupby(['Category'])['reviews'].mean().sort_values(ascending=False)
sns.barplot(x=mean_rew, y=mean_rew.index, data=df)

This is the average of reviews on each category. Let’s move on to next column, installs.

# Installs
array(['10,000+', '500,000+', '5,000,000+', '50,000,000+', '100,000+',
       '50,000+', '1,000,000+', '10,000,000+', '5,000+', '100,000,000+',
       '1,000,000,000+', '1,000+', '500,000,000+', '50+', '100+', '500+',
       '10+', '1+', '5+', '0+', '0', 'Free'], dtype=object)

Now i’m going to transform this column into float as well like review. First we need to change the 0 and Free value to 0+.

Next we need to replace the ‘,’ value and discard the + sign form the value.

df['Installs'].replace(to_replace=['0', 'Free'], value=['0+','0+'],inplace=True)
Installs = []

for x in df.Installs:
    x = x.replace(',', '')

Installs = list(map(float, Installs))
df['installs'] = Installs

Distributed value of Install on each category.

g = sns.FacetGrid(df, col='Category', palette="Set1",  col_wrap=5, height=4)
g = (, "installs", bins=5, color='c'))
# Total Installs
sum_inst = df.groupby(['Category'])['installs'].sum().sort_values(ascending=False)
sns.barplot(x=sum_inst, y=sum_inst.index, data=df)
google play store data analysis
# Mean Install
mean_ints = df.groupby(['Category'])['installs'].mean().sort_values(ascending=False)
sns.barplot(x=mean_ints, y=mean_ints.index, data=df)
google playstore categories

The Type column, let’s check if the app is free or paid.

# Type for category
array(['Free', 'Paid', nan, '0'], dtype=object)

There is 0 and null value, let’s change them to free.

df['Type'].replace(to_replace=['0'], value=['Free'],inplace=True)
df['Type'].fillna('Free', inplace=True)
Type_cat = df.groupby('Category')['Type'].value_counts().unstack().plot.barh(figsize=(10,20), width=0.7)
Category           Type
1.9                Free      1
ART_AND_DESIGN     Free     62
                   Paid      3
                   Paid      3
TRAVEL_AND_LOCAL   Paid     12
VIDEO_PLAYERS      Free    171
                   Paid      4
WEATHER            Free     74
                   Paid      8
Name: Type, Length: 64, dtype: int64
google palystore data analysis

So again, family category has the most free and paid app on the google play store. We can see social app is always free, like entertainment, event, education, comic, and more.

The medical has a high amount of paid app considering quantity of medical app is not much.

Last is the version of android you should have before accessing the app.

# Android Version
df['Android Ver'].unique()
array(['4.0.3 and up', '4.2 and up', '4.4 and up', '2.3 and up',
       '3.0 and up', '4.1 and up', '4.0 and up', '2.3.3 and up',
       'Varies with device', '2.2 and up', '5.0 and up', '6.0 and up',
       '1.6 and up', '1.5 and up', '2.1 and up', '7.0 and up',
       '5.1 and up', '4.3 and up', '4.0.3 - 7.1.1', '2.0 and up',
       '3.2 and up', '4.4W and up', '7.1 and up', '7.0 - 7.1.1',
       '8.0 and up', '5.0 - 8.0', '3.1 and up', '2.0.1 and up',
       '4.1 - 7.1.1', nan, '5.0 - 6.0', '1.0 and up', '2.2 - 7.1.1',
       '5.0 - 7.1.1'], dtype=object)

Now I am going to group it to 1 till 8 version of android. Change the null value to 1.0.

df['Android Ver'].replace(to_replace=['4.4W and up','Varies with device'], value=['4.4','1.0'],inplace=True)
df['Android Ver'].replace({k: '1.0' for k in ['1.0','1.0 and up','1.5 and up','1.6 and up']},inplace=True)
df['Android Ver'].replace({k: '2.0' for k in ['2.0 and up','2.0.1 and up','2.1 and up','2.2 and up','2.2 - 7.1.1','2.3 and up','2.3.3 and up']},inplace=True)
df['Android Ver'].replace({k: '3.0' for k in ['3.0 and up','3.1 and up','3.2 and up']},inplace=True)
df['Android Ver'].replace({k: '4.0' for k in ['4.0 and up','4.0.3 and up','4.0.3 - 7.1.1','4.1 and up','4.1 - 7.1.1','4.2 and up','4.3 and up','4.4','4.4 and up']},inplace=True)
df['Android Ver'].replace({k: '5.0' for k in ['5.0 - 6.0','5.0 - 7.1.1','5.0 - 8.0','5.0 and up','5.1 and up']},inplace=True)
df['Android Ver'].replace({k: '6.0' for k in ['6.0 and up']},inplace=True)
df['Android Ver'].replace({k: '7.0' for k in ['7.0 - 7.1.1','7.0 and up','7.1 and up']},inplace=True)
df['Android Ver'].replace({k: '8.0' for k in ['8.0 and up']},inplace=True)
df['Android Ver'].fillna('1.0', inplace=True)
print(df.groupby('Category')['Android Ver'].value_counts())
Type_cat = df.groupby('Category')['Android Ver'].value_counts().unstack().plot.barh(figsize=(10,18), width=1)
Category        Android Ver
1.9             1.0             1
ART_AND_DESIGN  4.0            51
                2.0             9
                1.0             2
                3.0             2
WEATHER         4.0            38
                1.0            26
                2.0            10
                5.0             7
                3.0             1
Name: Android Ver, Length: 200, dtype: int64
play store data analysis

Also, read – 10 Machine Learning Projects to Boost your Portfolio

This dataset contains a good set of possibilities, to work more on the business values and leaving with a positive impact. This work is not restricted to until the exploration of this article. You can explore more interesting facts and figures using this article.

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Aman Kharwal

I am a programmer from India, and I am here to guide you with Data Science, Machine Learning, Python, and C++ for free. I hope you will learn a lot in your journey towards Coding, Machine Learning and Artificial Intelligence with me.

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