Downloaded 23 times
![Python×pandas
#
import pandas as pd
import numpy as np
# ( )
df = pd.read_html('http://baseball.yahoo.co.jp/npb/standings/')
#
df_cl = df[0].drop([0]) #](https://image.slidesharecdn.com/pycon-mini-hirosima-2016-baseball-161112004650/75/Py-Baseball-Data-pyconhiro-24-2048.jpg)
![Python×pandas
#
# ( )
df_cl.columns = ['rank', 'name', 'games', 'win', 'lose', 'draw',
'pct', 'gb', 're_games', 'r', 'er', 'hr', 'sb', 'ba', 'era']
#
df_cl['win'] = df_cl['win'].fillna(0).astype(np.int64) #
df_cl['lose'] = df_cl['lose'].fillna(0).astype(np.int64) #
df_cl['pct'] = df_cl['pct'].fillna(0).astype(np.float64) #
df_cl['r'] = df_cl['r'].fillna(0).astype(np.int64) #
df_cl['er'] = df_cl['er'].fillna(0).astype(np.int64) #](https://image.slidesharecdn.com/pycon-mini-hirosima-2016-baseball-161112004650/75/Py-Baseball-Data-pyconhiro-25-2048.jpg)
![Python×pandas
#
df_cl['difference'] = df_cl['r'] - df_cl['er']
#
df_cl['pythagorean_win_per'] = (df_cl['r'] ** 2) / (df_cl['r'] ** 2
+ df_cl['er'] ** 2)
#
df_cl['pythagorean_win'] = (df_cl['pythagorean_win_per'] *
143).fillna(0).astype(np.int64)
df_cl['pythagorean_lose'] = 143 - df_cl['pythagorean_win']
#
df_cl.sort_values(by='pythagorean_win_per', ascending=False)](https://image.slidesharecdn.com/pycon-mini-hirosima-2016-baseball-161112004650/75/Py-Baseball-Data-pyconhiro-26-2048.jpg)
![[ ]
• ,
FIP, WHIP, K/BB, etc…
• ,
RC27 3 ( 6 )
•](https://image.slidesharecdn.com/pycon-mini-hirosima-2016-baseball-161112004650/75/Py-Baseball-Data-pyconhiro-50-2048.jpg)
Uploaded byShinichi Nakagawa
Py "Baseball" Data入門 - 広島東洋カープ編 #pyconhiro
The document discusses the application of Python and various libraries like pandas in analyzing baseball data. It features techniques such as calculating Pythagorean win percentages and using metrics like FIP and RC27 to assess player performance. The presentation appears to be targeted towards a technical audience interested in data analysis within the context of baseball analytics.
More Related Content
Similar to Py "Baseball" Data入門 - 広島東洋カープ編 #pyconhiro
![[DSC Europe 25] Bassam Maharmeh - Artificial Intelligence: Opportunities and ...](https://cdn.slidesharecdn.com/ss_thumbnails/thhfmr2fqpawzj7hsjpg-5-251211083048-2c23204f-thumbnail.jpg?width=640&height=640&fit=bounds)
![[DSC Europe 25] Branko Dzakula - From Defense to Attack: How AI Redefines Cyb...](https://cdn.slidesharecdn.com/ss_thumbnails/80bdzdxpr3ky2g0qvyk9-8-251211083048-ce5fc1ee-thumbnail.jpg?width=640&height=640&fit=bounds)
![[DSC Europe 25] Dusan Nesic - Securing Tomorrow’s Infrastructure: Why Cyber-P...](https://cdn.slidesharecdn.com/ss_thumbnails/qikbszfftyowjm2q6duw-1-251211083848-8f2ead6b-thumbnail.jpg?width=640&height=640&fit=bounds)
![[DSC Europe 25] Imai Jen-La Plante - The New Generation: AI and the Future of...](https://cdn.slidesharecdn.com/ss_thumbnails/kxi8t2l5rggivgcenyba-1-jenlaplante-dsc-251208152532-d1e076c2-thumbnail.jpg?width=640&height=640&fit=bounds)
![[DSC Europe 25] Dusan Jovicic - AI Story: From on-prem to cloud and back agai...](https://cdn.slidesharecdn.com/ss_thumbnails/8kp49m6uq22ifnbwhfnk-2-251205085715-964d11a6-thumbnail.jpg?width=640&height=640&fit=bounds)
![[DSC Europe 25] Dusan Pavlov - There Is No Spoon: Inferring Vision from Neura...](https://cdn.slidesharecdn.com/ss_thumbnails/wg0v1umoqjm4nnbd3p0v-there-is-no-spoon-251205085715-6d81d6c5-thumbnail.jpg?width=640&height=640&fit=bounds)
![[DSC Europe 25] Dragan Vucic - Building the Learning Organization - How AI Tr...](https://cdn.slidesharecdn.com/ss_thumbnails/8brigo2sbu6qur6gxrra-7-251205085715-6ae07d24-thumbnail.jpg?width=640&height=640&fit=bounds)
![[DSC Europe 25] Dobrica Cosic - Savings by the Second: How Dynamic Pricing an...](https://cdn.slidesharecdn.com/ss_thumbnails/znp09f3smtqz3w2sq6wn-1-dobrica-cosic-savings-by-the-second-how-dynamic-pricing-and-smart-data-are-bu-251208151905-26e6f41e-thumbnail.jpg?width=640&height=640&fit=bounds)
![[DSC Europe 25] Aleksandra Dragicevic - AI-Boosted Research in Healthcare: Fr...](https://cdn.slidesharecdn.com/ss_thumbnails/iqwngszurf2r7pi1lnnj-4-aleksandra-dragicevic-ad-dsc-europe-conference-20-251208151905-37c3238a-thumbnail.jpg?width=640&height=640&fit=bounds)
![[DSC Europe 25] Vid Stimac - Policy Parsimony: Between Oversimplifying and Ov...](https://cdn.slidesharecdn.com/ss_thumbnails/eqlepagzqp2rhg3gbluh-dsc-stimac-251120-251205090438-059e7f54-thumbnail.jpg?width=640&height=640&fit=bounds)
![[DSC Europe 25] Milan Sekuloski - Data, Defence, and Development: Cybersecuri...](https://cdn.slidesharecdn.com/ss_thumbnails/dfrkwwx4qly6atqpbl4z-4-251209104645-c3d4b0ca-thumbnail.jpg?width=640&height=640&fit=bounds)
Py "Baseball" Data入門 - 広島東洋カープ編 #pyconhiro
- 1. Py “Baseball” Data PyConmini Hirosima 2016 Python Shinichi Nakagawa(Baseball Analyst&Pythonista)
- 2. Starting Member • Whoam I?( ) • PyData • PyData / # • Python • PyData + (FIP/RC27) •
- 3. Who am I? •Shinichi Nakagawa(@shinyorke) • Python , Hack ※ Python • HR . • Python/Agile/PyData/SABRmetrics( ) • • ( ) .
- 4. • ( )HR . • 1 2 . • (Django) Python . • https://service.visasq.com • https://tech.visasq.com
- 5. • • • • & • etc… •Web Python
- 6. • • IPython +pandas (Hello World ) •
- 7. • . . • DeepLearning , . • (Pandas ) & .
- 8.
- 9.
- 10.
- 11. PyData • , ,Python &( ) . • , or . • Excel Python, Deep Learning, etc… PyData PyData ( )
- 12.
- 13.
- 14. ( ) • , • 1970 , & • ( , )
- 15. • • ( ,,FA) • ( ) • • ( , etc…) • ( , J )
- 16. × ( ) ※× +
- 17. × ( ) ※× +
- 18.
- 19.
- 20. • ( -) = 5 ( ) • • • •
- 21. • . • ( -) 5 5 (ry . • = ( 2 )÷( 2 + 2 ) •
- 22.
- 23. Python×pandas # Python 3(3.4 ) ( ) $ pip install ipython pandas beautifulsoup4 numpy lxml html5lib # ipython ( Jupyter ) $ ipython
- 24. Python×pandas # import pandas aspd import numpy as np # ( ) df = pd.read_html('http://baseball.yahoo.co.jp/npb/standings/') # df_cl = df[0].drop([0]) #
- 25. Python×pandas # # ( ) df_cl.columns= ['rank', 'name', 'games', 'win', 'lose', 'draw', 'pct', 'gb', 're_games', 'r', 'er', 'hr', 'sb', 'ba', 'era'] # df_cl['win'] = df_cl['win'].fillna(0).astype(np.int64) # df_cl['lose'] = df_cl['lose'].fillna(0).astype(np.int64) # df_cl['pct'] = df_cl['pct'].fillna(0).astype(np.float64) # df_cl['r'] = df_cl['r'].fillna(0).astype(np.int64) # df_cl['er'] = df_cl['er'].fillna(0).astype(np.int64) #
- 26. Python×pandas # df_cl['difference'] = df_cl['r']- df_cl['er'] # df_cl['pythagorean_win_per'] = (df_cl['r'] ** 2) / (df_cl['r'] ** 2 + df_cl['er'] ** 2) # df_cl['pythagorean_win'] = (df_cl['pythagorean_win_per'] * 143).fillna(0).astype(np.int64) df_cl['pythagorean_lose'] = 143 - df_cl['pythagorean_win'] # df_cl.sort_values(by='pythagorean_win_per', ascending=False)
- 27.
- 28.
- 29.
- 30. • • (+187) • 5 •/ • DeNA , • • ( )
- 32.
- 33. ( ) • &(& ) • , , , •
- 34. ×PyData • FIP • (RC27) •scrapy CSV • CSV pandas, seaborn, jupyter & ( )
- 35. FIP(Fielding Independent Pitching) •, ( ) • , (+ ), • ( ) • xFIP FIP .
- 36.
- 37.
- 38.
- 39.
- 40.
- 41.
- 42. RC27 • 9 1 ? •VS , ? • RC(Run Created, ) 1 •
- 43.
- 44.
- 45.
- 46.
- 47.
- 48. RC27 • 1-6 • RC27Top30 6 • • • • ( )
- 49. • 6 Top30 • , • , , FIP ( ) •
- 50. [ ] • , FIP, WHIP, K/BB, etc… • , RC27 3 ( 6 ) •
- 51. Py "Baseball" Data- Python ※pandas, Re:dash (& ) MonotaRO TechTalk #4 http://www.kokuchpro.com/event/monotarotech4/
- 52.