Google Play Store Apps Data

This dataset (source) consists of two tables: One consists of actual data about applications on the Google Play Store such as category, number of installs, genre, etc. The other table consists of data about reviews on the apps such as the review itself, sentiment, etc. You can either join the two tables on the key, App, or keep them separately to perform your data analysis.

# Load packages
import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt

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Load your data

The dataset consists of two seperate tables. We will load them separately and join them on the App-key. During your analysis, you can choose which table of the three to use.

# Load data from the googleplaystore.csv file
apps_data = pd.read_csv('apps_data.csv', index_col=0)
apps_data.head()

# Load data from the googleplaystore_user_reviews.csv file
review_data = pd.read_csv('review_data.csv', index_col=0)
review_data.head()

# Merge the two dataframes
data = pd.merge(apps_data, review_data, on="App")
data.head()

Understand your variables

# Understand your variables
variables = pd.DataFrame(columns=['Variable','Number of unique values','Values'])

for i, var in enumerate(data.columns):
    variables.loc[i] = [var, data[var].nunique(), data[var].unique().tolist()]
    
variables

Answer interesting questions:

Now you get to explore this exciting dataset! Can't think of where to start? Try your hand at these questions:

• Which genre tends to get a higher rating?
• Is there a substantial difference between the amount of downloads of free apps and paid apps?
• Are people more likely to review bad applications or good applications?
• Do the number of words on average in a review correlate with the overall rating of an application?

# Start coding