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.
Store the resulting series in the variable total_global_sales_by_platform.
Store the resulting dataframe in the variable avg_sales_per_publisher.
Your result should look similar to this dataframe:
Store the resulting series in the variable platform_year_eu_sales.
Store the resulting dataframe in the variable no_publisher_per_platform.
Your result should look similar to this dataframe:
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:
Store the resulting dataframe in the variable top_publisher_year.
Your result should look similar to this dataframe:
Store the resulting dataframe in the variable max_sales_year_per_genre.
Your result should look similar to this dataframe:
Store the resulting dataframe in the variable descriptive_genres.
Your result should look similar to this dataframe:
Store the resulting dataframe in the variable jp_other_sales_paltform.
Your result should look similar to this dataframe:
Store the resulting series in the variable max_min_diff_NAsales_per_publisher.
Store the resulting dataframe in the variable sales_percentage_by_platform.
Your result should look similar to this dataframe:
Store the resulting series in the variable popular_platform_per_year.
Store the resulting dataframe in the variable genre_popularity_over_time.
Your result should look similar to this dataframe:
Store the result in a new column Global_Sales_Normalized in the original dataframe games_sales_df.
