Py “Baseball” Data PyCon mini Hirosima 2016

Python

Shinichi Nakagawa(Baseball Analyst&Pythonista)

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• Shinichi Nakagawa(@shinyorke)

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# Python 3 (3.4 ) ( )$ pip install ipython pandas beautifulsoup4 numpy lxml html5lib# ipython ( Jupyter )$ ipython

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# import pandas as pdimport numpy as np

# ( )df = pd.read_html('http://baseball.yahoo.co.jp/npb/standings/')

# df_cl = df[0].drop([0]) #

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) #

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://gist.github.com/Shinichi-Nakagawa/8ff55af83390fcd2e2dd34bcb914868c

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Py "Baseball" Data - Python ※pandas, Re:dash (& )

MonotaRO TechTalk #4

http://www.kokuchpro.com/event/monotarotech4/

&

Shinichi Nakagawa(Twitter/Facebook/visasQ:@shinyorke)