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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412
12
章
pandas
：応用編
In [99]: resampled.reset_index()
Out[99]:
key time value
0 a 2017-05-20 00:00:00 30.0
1 a 2017-05-20 00:05:00 105.0
2 a 2017-05-20 00:10:00 180.0
3 b 2017-05-20 00:00:00 35.0
4 b 2017-05-20 00:05:00 110.0
5 b 2017-05-20 00:10:00 185.0
6 c 2017-05-20 00:00:00 40.0
7 c 2017-05-20 00:05:00 115.0
8 c 2017-05-20 00:10:00 190.0
TimeGrouper
を使う際には制約が
1
つあります。グループ化を行う時刻がシリーズやデータフレーム
の列ではなく、インデックスとなっていなければいけない、という制約です
＊
1
。注意してください。
12.3
メソッドチェーンを行うためのテクニック
データセットに対していくつかの変換を続けて行う際に、いつの間にか、その後の分析では一切使
わない一時変数をたくさん作成してしまっていることがあります。例えば次のような例を考えてみま
しょう。
df = load_data()
df2 = df[df['col2'] < 0]
df2['col1_demeaned'] = df2['col1'] - df2['col1'].mean()
result = df2.groupby('key').col1_demeaned.std() ...
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