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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410
12
章
pandas
：応用編
6 0.387298
7 0.387298
8 0.387298
9 1.161895
10 1.161895
11 1.161895
Name: value, dtype: float64
この例に
g.transform
が
2
回登場したことからもわかるように、グループ演算の分解をすると、グルー
プごとの集約が複数回発生する場合があります。しかし大抵の場合、集約の回数が増えるデメリット
よりも、ベクトル演算の全体的なメリットの方が上回ります。
12.2.2
時系列データの再サンプリングを伴うグループ化
時系列データについては、機能の意味を考えれば、
resample
メソッドが時間区分に基づくグループ
演算を行うメソッドと言えます。例えば、まず次のような小さなテーブルを考えてみましょう。
In [89]: N = 15
In [90]: times = pd.date_range
('2017-05-20 00:00', freq='1min', periods=N)
In [91]: df = pd.DataFrame({'time': times,
....: 'value': np.arange(N)})
In [92]: df
Out[92]:
time value
0 2017-05-20 00:00:00 0
1 2017-05-20 00:01:00 1
2 2017-05-20 00:02:00 2
3 2017-05-20 00:03:00 3
4 2017-05-20 00:04 ...
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