Tableau Conference 2023 — Hands-On Lab Supercharge your Tableau dashboards with Python-powered NBA analytics using TabPy.
This repository contains the companion code and datasets for the Tableau Conference 2023 (TC23) hands-on lab session. It demonstrates how to integrate Python scripts into Tableau via TabPy (Tableau Python Server) to build advanced NBA visualizations — from shot charts and trellis plots to Twitter sentiment analysis.
| File | Description |
|---|---|
Leaguegamelog_Trellis.py |
Fetches NBA league game logs for 7 seasons (2016–17 → 2022–23) via the nba_api and returns the data for a trellis chart in Tableau. |
Player_Shotchart_Hexbin.py |
Pulls detailed shot chart data for selected superstars (Stephen Curry, Kevin Durant, LeBron James) to create hexbin shot maps. |
Team_Shotchart_Pareto.py |
Retrieves team-level shot chart data (Bulls, Knicks, Lakers, Mavericks) for building pareto-style shot visualizations. |
Sentiment.py |
Performs sentiment analysis on scraped tweets — cleans text, counts word frequencies, and classifies sentiment (positive / negative / neutral) using TextBlob. |
Airmovie_scraped_tweets.csv |
Pre-scraped tweet dataset for sentiment analysis. |
Data23scraped_tweets.csv |
Additional scraped tweet dataset. |
league_data_trellis.csv |
Pre-fetched league game log data (for offline use). |
player_data_hexbin.csv |
Pre-fetched player shot chart data (for offline use). |
team_data_pareto.csv |
Pre-fetched team shot chart data (for offline use). |
basketball-court-lines.png |
Court overlay image for shot chart visualizations. |
hex_solid.png |
Custom hex shape for hexbin chart rendering. |
Lab Instructions.pdf |
Step-by-step lab guide. |
tabpy install instruction.rtf |
TabPy installation instructions. |