Steps to install tableau public 9.2 code#
Steps to install tableau public 9.2 download#

Instructions for installing and basic usage for the nba_api package are at the following URL. In Python, install the 'nba_api' package library via pip install nba-api.

Steps to install tableau public 9.2 code#

Additionally, there should be an IDE to work in the Python files and R Studio for the R code work. To install and setup the code, ensure that the above listed tools, Python, R, Gephi, and Tableau, are installed on the working computer. The package's site is hosted on GitHub at. We used this package to do the web scraping.

There is a Python package, 'nba_api', that abstracts the API details and allows developers to use Python to scrape that data and manipulate using Python's libraries (pandas, numpy, etc.). This endpoint provides the player scoring information for the analysis. Includes breakouts for each team and season totals, when a player was on multiple teams for one season. LeagueDashPlayerStats - Extensive individual player statistics by season. This endpoint provides the team scoring information for the analysis. LeagueDashTeamStats - Extensive team summary statistics by team and season. This endpoint provides the detailed information for our metrics and analysis. LeagueSeasonMatchups - Detailed offensive-defensive matchup information by season. We used the following endpoints to gather our data:Д¬ommonAllPlayers - Overall career information on each NBA player, both past and current.Д¬ommonPlayerInfo - Detailed biographical information on current players, by season. The API is fully documented on GitHub at the following URL.

Steps to install tableau public 9.2 download#

The NBA data is scraped from the site, which provides documented API endpoints for developers to download data from. Our network visualization was created using Gephi per the instructions at the link below, and our dashboard visualizations are created in and served by Tableau. We used the R language, in R Studio, for some additional EDA and data file outputs. We ultimately used *.py files for all of our Python code. However, these IDEs are not required for replication. Our IDE's were Visual Studio Code, Sublime Text, Pењњharm, and Jupyter notebook. We used Python to scrape the NBA data, perform exploratory data analysis (EDA), conduct data manipulation & cleansing, and prepare the final data file output. The technologies required for this project are Python 3.7+, R 4.1+, Tableau 2021.3+, and Gephi 0.9.2+. The package contains Python and R code files and Tableau workbook files. Project to analyze NBA defensive matchup data DESCRIPTION