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Data Structure ?
* 以上分析是由系统提取分析形成的结果,具体实际数据为准。
README.md
# Context
This data set contains a list of video games with sales greater than 100,000 copies along with critic and user ratings. It is a combined web scrape from [VGChartz][1] and [Metacritic][2] along with manually entered year of release values for most games with a missing year of release. The original coding was created by [Rush Kirubi][3] and can be found [here][4], but it limited the data to only include a subset of video game platforms. Not all of the listed video games have information on Metacritic, so there data set does have missing values.
# Content
The fields include:
- Name - The game's name
- Platform - Platform of the games release
- Year_of_Release - Year of the game's release
- Genre - Genre of the game
- Publisher - Publisher of the game
- NA_Sales - Sales in North America (in millions)
- EU_Sales - Sales in Europe (in millions)
- JP_Sales - Sales in Japan (in millions)
- Other_Sales - Sales in the rest of the world (in millions)
- Global_Sales - Total worldwide sales (in millions)
- Critic_score - Aggregate score compiled by Metacritic staff
- Critic_count - The number of critics used in coming up with the critic score
- User_score - Score by Metacritic's subscribers
- User_count - Number of users who gave the user score
- Rating - The ESRB ratings
# Acknowledgements
Again the main credit behind this data set goes to [Rush Kirubi][5]. I just commented out two lines of his code.
Also the original inspiration for this data set came from [Gregory Smith][6] who originally scraped the data from VGChartz, it can be found [here][7].
[1]: http://www.vgchartz.com/gamedb/
[2]: http://www.metacritic.com/game
[3]: https://www.kaggle.com/rush4ratio
[4]: https://www.kaggle.com/rush4ratio/video-game-sales-with-ratings
[5]: https://www.kaggle.com/rush4ratio
[6]: https://www.kaggle.com/gregorut
[7]: https://www.kaggle.com/gregorut/videogamesales
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