Practice GroupBy operations with Video Games Sales data
Practice GroupBy operations with Video Games Sales data Data Science Project
Data Wrangling with Pandas

Practice GroupBy operations with Video Games Sales data

Unleash your data mastery with the thrilling Video Game Sales lab! Dive deep into the captivating world of data analysis as you conquer the dynamic Video Game Sales dataset. Master the art of aggregation and grouping techniques, unlocking hidden patterns, trends, and mind-blowing insights within the ever-evolving gaming industry. Unleash your analytical prowess to dissect sales data, navigate the labyrinth of platform preferences, and unveil the secret realms of the most popular genres. Join us and level up your data analysis skills today!
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Practice GroupBy operations with Video Games Sales dataPractice GroupBy operations with Video Games Sales data
Project Created by

Mohamed Rawash

Project Activities

All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.

All our activities include solutions with explanations on how they work and why we chose them.

codevalidated

Calculate the total global sales for each `Platform`.

Store the resulting series in the variable total_global_sales_by_platform.

input

Find the top-selling genre based on global sales.

codevalidated

Calculate the average global sales for each `Publisher`.

Store the resulting dataframe in the variable avg_sales_per_publisher.

Your result should look similar to this dataframe:

activity3-answer

codevalidated

Calculate the total `EU_Sales` for each platform-year combination.

Store the resulting series in the variable platform_year_eu_sales.

codevalidated

Find the number of publishers in each `Platform`.

Store the resulting dataframe in the variable no_publisher_per_platform.

Your result should look similar to this dataframe:

activity5-answer

codevalidated

Find the publisher with the most occurences for each `Platform`.

Store the resulting dataframe in the variable most_frequent_publisher_platform.

Note: return the publisher with the smallest lexicographical name in case there is a tie in the number of occurences.

Your result should look similar to this dataframe:

activity6-answer

codevalidated

Find the publisher with the highest total global sales for each `Year`.

Store the resulting dataframe in the variable top_publisher_year.

Your result should look similar to this dataframe:

activity7-answer

codevalidated

Find the maximum sales year for each `Genre`.

Store the resulting dataframe in the variable max_sales_year_per_genre.

Your result should look similar to this dataframe:

activity8-answer

codevalidated

Compute descriptive statistics for each `Genre` using its `Global_Sales`.

Store the resulting dataframe in the variable descriptive_genres.

Your result should look similar to this dataframe:

activity9-answer

codevalidated

Calculate the total `JP_Sales` and average `Other_Sales` for each `Platform`.

Store the resulting dataframe in the variable jp_other_sales_paltform.

Your result should look similar to this dataframe:

activity10-answer

codevalidated

Calculate the range (max-min) of `NA_Sales` for each `Publisher`.

Store the resulting series in the variable max_min_diff_NAsales_per_publisher.

codevalidated

Calculate the percentage of `Global_Sales` contribution by each `Platform`.

Store the resulting dataframe in the variable sales_percentage_by_platform.

Your result should look similar to this dataframe:

activity12-answer

codevalidated

Which platforms for playing video games have been the most popular each year?

Store the resulting series in the variable popular_platform_per_year.

codevalidated

Which genres have been more or less popular with the passing of time?

Store the resulting dataframe in the variable genre_popularity_over_time.

Your result should look similar to this dataframe:

activity14-answer

codevalidated

Calculate the z-score normalization for `Global_Sales` for each `Genre`.

Store the result in a new column Global_Sales_Normalized in the original dataframe games_sales_df.

Practice GroupBy operations with Video Games Sales dataPractice GroupBy operations with Video Games Sales data
Project Created by

Mohamed Rawash

This project is part of

Data Wrangling with Pandas

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