Mini-Project - DE with Python and Pandas - Google Play Store App Performance Analysis

[akash-coded]

This is a continued project with 10 business-oriented questions that can be answered using pandas and core Python techniques. We'll emphasize data ingestion, transformation, and business logic application. Continued from #45

Project: Google Play Store App Performance Analysis

Dataset: https://github.com/schlende/practical-pandas-projects/blob/master/datasets/google-play-store-11-2018.csv

1. What are the top 5 app categories by total installs, and what percentage of total installs do they represent?

Hint: Use groupby, sum, and sort_values functions.

import pandas as pd

df = pd.read_csv('google-play-store-11-2018.csv')

category_installs = df.groupby('genre')['min_installs'].____().____().head()
total_installs = df['min_installs'].____()
category_percentages = (category_installs / total_installs) * 100

print(category_percentages)

2. For each app category, what is the average rating of free apps versus paid apps?

Hint: Use groupby with multiple columns and unstack.

df['is_paid'] = df['price'] > 0
avg_ratings = df.groupby(['genre', 'is_paid'])['score'].____().____()
print(avg_ratings)

3. What is the correlation between app size and number of installs? Does this vary by category?

Hint: Use corr() function and a custom function with apply.

def category_size_install_corr(group):
    return group['size'].corr(group['min_installs'])

correlations = df.groupby('genre').____(____)
print(correlations.sort_values(ascending=False))

4. Which apps have the highest ratio of ratings to installs, potentially indicating high user engagement?

Hint: Create a new column for the ratio and use nlargest.

df['rating_install_ratio'] = df['ratings'] / df['min_installs']
top_engaging_apps = df.nlargest(____, 'rating_install_ratio')[['title', 'genre', 'rating_install_ratio']]
print(top_engaging_apps)

5. For apps that offer in-app purchases, what is the average rating compared to those that don't?

Hint: Use boolean indexing and mean().

iap_rating = df[df['offers_iap'] == True]['score'].____()
non_iap_rating = df[df['offers_iap'] == False]['score'].____()
print(f"IAP apps average rating: {iap_rating:.2f}")
print(f"Non-IAP apps average rating: {non_iap_rating:.2f}")

6. What is the distribution of app prices for paid apps in each category?

Hint: Use describe() function with groupby.

paid_apps = df[df['price'] > 0]
price_distribution = paid_apps.groupby('genre')['price'].____()
print(price_distribution)

7. Which day of the week sees the most app releases? Does this vary by category?

Hint: Extract day of week from 'released' column and use value_counts().

df['release_day'] = pd.to_datetime(df['released']).dt.____
overall_popular_day = df['release_day'].____().index[0]
print(f"Most popular release day overall: {overall_popular_day}")

category_popular_days = df.groupby('genre')['release_day'].____().____
print(category_popular_days)

8. For each category, what is the average time between app updates (assuming 'released' is the last update)?

Hint: Use groupby and apply with a custom function.

from datetime import datetime

def avg_update_time(group):
    latest_date = datetime.now()
    release_dates = pd.to_datetime(group['released'])
    return (latest_date - release_dates).mean().days

avg_update_times = df.groupby('genre').____(____)
print(avg_update_times)

9. What percentage of apps in each category have a perfect 5.0 rating? Consider only apps with a minimum number of ratings to ensure significance.

Hint: Use boolean indexing, groupby, and agg functions.

min_ratings = 100  # Set a minimum number of ratings for significance

df_significant = df[df['ratings'] >= min_ratings]
perfect_ratings = df_significant[df_significant['score'] == 5.0].groupby('genre').size()
total_apps = df_significant.groupby('genre').size()

percentage_perfect = (perfect_ratings / total_apps * 100).____()
print(percentage_perfect.sort_values(ascending=False))

10. Create a 'revenue_estimate' for each app based on its price and install count. What are the top 10 apps by estimated revenue?

Hint: Use a custom function to estimate revenue and sort_values.

def estimate_revenue(row):
    if row['price'] > 0:
        return row['price'] * row['min_installs']
    else:
        return 0.1 * row['min_installs']  # Assume $0.1 per install for free apps

df['revenue_estimate'] = df.____(____)
top_revenue_apps = df.nlargest(____, 'revenue_estimate')[['title', 'genre', 'revenue_estimate']]
print(top_revenue_apps)

These questions cover various aspects of data analysis using pandas and core Python. Here are some additional tips for working on this project:

1. Data Ingestion:

   • Always start by examining your data using df.head(), df.info(), and df.describe() to understand its structure and content.
   • Handle missing values appropriately (df.dropna() or df.fillna() depending on the context).

2. Data Transformation:

   • Use df.astype() to convert data types when necessary.
   • Utilize pandas datetime functions (pd.to_datetime()) for working with dates.
   • Create new columns using df['new_column'] = calculation or df.apply() for more complex operations.

3. Grouping and Aggregation:

   • df.groupby() is a powerful function for segmenting your data.
   • Use agg() with groupby to perform multiple aggregations at once.

4. Filtering and Sorting:

   • Boolean indexing (df[df['column'] > value]) is useful for filtering data.
   • Use sort_values() to order your results.

5. Function Application:

   • apply() and applymap() are useful for applying custom functions to your data.

6. Performance Considerations:

   • For large datasets, consider using more efficient methods like vectorization instead of apply() where possible.
   • Use inplace=True in functions like fillna() to avoid creating unnecessary copies of your data.

7. Error Handling:

   • Implement try-except blocks to handle potential errors, especially when applying custom functions.

Remember to document your code and explain your reasoning for each analysis step. This will make your work more understandable and maintainable.

By working through these questions, you'll gain practical experience in data manipulation, transformation, and analysis using pandas and Python, which are essential skills in data engineering.