https://github.com/lmizner/codecademy_nba_trends
Investigate whether there is an association between two variables
https://github.com/lmizner/codecademy_nba_trends
chi2-contingency jupyter-notebook matplotlib numpy pandas pearson-correlation pyplot python seaborn
Last synced: about 2 months ago
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Investigate whether there is an association between two variables
- Host: GitHub
- URL: https://github.com/lmizner/codecademy_nba_trends
- Owner: lmizner
- Created: 2022-10-08T03:19:44.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-10-08T03:21:45.000Z (over 3 years ago)
- Last Synced: 2025-06-11T07:11:35.793Z (12 months ago)
- Topics: chi2-contingency, jupyter-notebook, matplotlib, numpy, pandas, pearson-correlation, pyplot, python, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 539 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# codecademy_nba_trends
### NBA Trends
In this project, you’ll analyze data from the NBA (National Basketball Association) and explore possible associations.
This data was originally sourced from 538’s Analysis of the Complete History Of The NBA and contains the original, unmodified data from Basketball Reference as well as several additional variables 538 added to perform their own analysis.
You can read more about the data and how it’s being used by 538 here. For this project we’ve limited the data to just 5 teams and 10 columns (plus one constructed column, point_diff, the difference between pts and opp_pts).
You will create several charts and tables in this project, so you’ll need to use plt.clf() between plots in your code so that the plots don’t layer on top of one another.