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Project: When Was the Golden Era of Video Games?
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  • Video games are big business: the global gaming market is projected to be worth more than $300 billion by 2027 according to Mordor Intelligence. With so much money at stake, the major game publishers are hugely incentivized to create the next big hit. But are games getting better, or has the golden age of video games already passed?

    In this project, you'll analyze video game critic and user scores as well as sales data for the top 400 video games released since 1977. You'll search for a golden age of video games by identifying release years that users and critics liked best, and you'll explore the business side of gaming by looking at game sales data.

    Your search will involve joining datasets and comparing results with set theory. You'll also filter, group, and order data. Make sure you brush up on these skills before trying this project! The database contains two tables. Each table has been limited to 400 rows for this project, but you can find the complete dataset with over 13,000 games on Kaggle.

    game_sales table

    ColumnDefinitionData Type
    nameName of the video gamevarchar
    platformGaming platformvarchar
    publisherGame publishervarchar
    developerGame developervarchar
    games_soldNumber of copies sold (millions)float
    yearRelease yearint

    reviews table

    ColumnDefinitionData Type
    nameName of the video gamevarchar
    critic_scoreCritic score according to Metacriticfloat
    user_scoreUser score according to Metacriticfloat

    users_avg_year_rating table

    ColumnDefinitionData Type
    yearRelease year of the games reviewedint
    num_gamesNumber of games released that yearint
    avg_user_scoreAverage score of all the games ratings for the yearfloat

    critics_avg_year_rating table

    ColumnDefinitionData Type
    yearRelease year of the games reviewedint
    num_gamesNumber of games released that yearint
    avg_critic_scoreAverage score of all the games ratings for the yearfloat

    Top Ten Best Selling Games

    1. Find the ten best-selling games. The output should contain all the columns in the game_sales table and be sorted by the games_sold column in descending order.
    Unknown integration
    DataFrameavailable as
    best_selling_games
    variable
    -- best_selling_games
    SELECT *
    FROM game_sales
    ORDER BY games_sold DESC
    LIMIT 10;
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.

    Identifying Top Ten Years: Average Critic Scores and Game Releases

    1. Find the ten years with the highest average critic score, where at least four games were released (to ensure a good sample size). Return an output with the columns year, num_games released, and avg_critic_score. The avg_critic_score should be rounded to 2 decimal places. The table should be ordered by avg_critic_score in descending order. Save the output as critics_top_ten_years.
    Unknown integration
    DataFrameavailable as
    critics_top_ten_years
    variable
    -- critics_top_ten_years
    SELECT
    	g.year, COUNT(g.name) AS num_games, ROUND(AVG(r.critic_score),2) AS avg_critic_score
    FROM public.game_sales g
    INNER JOIN public.reviews r
    ON g.name = r.name
    GROUP BY g.year
    HAVING COUNT(g.name) > 4
    ORDER BY avg_critic_score DESC
    LIMIT 10;
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.

    Identifying Golden Years: Agreement in High Game Ratings by Critics and Users

    1. Find the years where critics and users broadly agreed that games released were highly rated. Specifically, return the years where the average critic score was over 9 OR the average user score was over 9. The pre-computed average critic and user scores per year are stored in users_avg_year_rating and critics_avg_year_rating tables respectively. The query should return the following columns: year, num_games, avg_critic_score, avg_user_score, and diff. The diff column should be the difference between the avg_critic_score and avg_user_score, it should be a positive float. The table should be ordered by the diff column in ascending order, save this as a dataframe named golden_years.
    Unknown integration
    DataFrameavailable as
    golden_years
    variable
    -- golden_years
    
    SELECT
    	u.year, u.num_games, c.avg_critic_score, u.avg_user_score,
    	CAST(ABS(c.avg_critic_score - u.avg_user_score) AS FLOAT) AS diff
    FROM public.users_avg_year_rating u
    INNER JOIN public.critics_avg_year_rating c
    USING(year)
    WHERE u.avg_user_score > 9 OR c.avg_critic_score > 9
    ORDER BY diff ASC;
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.