Py "Baseball" Data入門〜サービス(と野球)を支えるデータ分析基盤 #monotarotech
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This document discusses using Python and PyData tools for baseball analytics. It introduces Shinichi Nakagawa, a baseball analyst and Python expert. It explains common PyData tools like Grafana, Redash and Jupyter Notebook, and how they can be used to visualize and analyze baseball metrics and stats. It also discusses using Python and scraping to analyze run creation (RC) and run creation per 27 outs (RC27) stats to evaluate player and team performance.
Py "Baseball" Data入門〜サービス(と野球)を支えるデータ分析基盤 #monotarotech
- 1. Py “Baseball” Data ( ) MonotaRO TechTalk #4 2016/11/25 Shinichi Nakagawa(Baseball Analyst&Pythonista)
- 2. Starting Member • Who am I?( ) • PyData • Python •
- 3. Who am I? • Shinichi Nakagawa(@shinyorke) • Python , Hack ※ Python • HR . • Python/PyData/Agile/Scrum Master • • ( ) .
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- 21. Re:dash • Web • RDBMS, KVS, Google Analytics,etc… • ( ) OSS AWS(EC2)/GCE/Docker • Flask(Python) + Angular JS Mail Slack •
- 23. Re:dash
- 24. Docker VISASQ TECH BLOG Re:dash Docker for Mac https://tech.visasq.com/redash-docker/
- 25. Jupyter • & • Python & • notebook • (kernel) Python • Google Cloud Datalab
- 26. Jupyter ( ) • Jupyter ipykernel jupyter-console notebook , • ipykernel , Jupyter IPython • jupyter-console Jupyter • notebook tornado(Web ),Jinja2( ) , Web . • notebook Jupyter ( )
- 27. Jupyter (Jupyter lab) PyCon JP 2016 Jupyter notebook Jupyter lab
- 28. PyData • Re:dash ( ) CVR • Jupyter( ) • Grafana
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- 46. RC 18%(99.8 / 544.8 *100) ※RC (544) (499) ( )
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