July 2018
Intermediate to advanced
596 pages
13h 35m
Japanese
Content preview from Pythonによるデータ分析入門 第2版 ―NumPy、pandasを使ったデータ処理
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14.4
アメリカ合衆国農務省の食糧データベース
475
units 375176 non-null object
value 375176 non-null float64
id 375176 non-null int64
food 375176 non-null object
fgroup 375176 non-null object
manufacturer 293054 non-null object
dtypes: float64(1), int64(1), object(6)
memory usage: 25.8+ MB
In [178]: ndata.iloc[30000]
Out[178]:
nutrient Gly
cine
nutgroup Amino Acids
units g
value 0.04
id 6158
food Soup, tomato bisque, canned, condensed
fgroup Soups, Sauces, and Gravies
manufacturer
Name: 30000, dtype: object
ある栄養素に対して、食品グループごとに含まれる量の中央値を描画してみましょう。今回は亜鉛
（
Zinc, Zn
）を例に取ります。図14-11を参照してください。
In [180]: result = ndata.groupby(['nutrient', 'fgroup'])['value'].quantile(0.5)
In [181]: result['Zinc, ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.