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https://github.com/danpoynor/data-analysis-of-video-game-sales-2000-2015
This analysis reviews sales for the top 100 video games from the years 2000-2015 to gather insights. Within the notebook I use Python’s Pandas, Matplotlib, and Seaborn libraries to interact with the data and create graphs.
https://github.com/danpoynor/data-analysis-of-video-game-sales-2000-2015
data-analysis jupyter-notebook matplotlib pandas-dataframe python3 seaborn-plots video-game-sales
Last synced: 1 day ago
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This analysis reviews sales for the top 100 video games from the years 2000-2015 to gather insights. Within the notebook I use Python’s Pandas, Matplotlib, and Seaborn libraries to interact with the data and create graphs.
- Host: GitHub
- URL: https://github.com/danpoynor/data-analysis-of-video-game-sales-2000-2015
- Owner: danpoynor
- Created: 2022-10-15T20:18:17.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2022-10-17T19:38:15.000Z (about 2 years ago)
- Last Synced: 2023-03-06T09:01:43.726Z (over 1 year ago)
- Topics: data-analysis, jupyter-notebook, matplotlib, pandas-dataframe, python3, seaborn-plots, video-game-sales
- Language: Jupyter Notebook
- Homepage:
- Size: 388 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Data Analysis of Video Game Sales 2000-2015
This analysis reviews sales for the top 100 video games from the years 2000-2015 to gather insights. Within the notebook I use Python’s Pandas, Matplotlib, and Seaborn libraries to interact with the data and create graphs.