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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358
11
章 時系列データ
2011-01-12 1.393406
dtype: float64
In [73]: resampler = ts.resample('D')
ここで指定している
'D'
は、日次の頻度という意味です。
一定頻度を持つデータ間での変換や再サンプリングは大きなテーマなので、後ほど独立した節（「11.6
再サンプリングと頻度変換」）で説明をします。ここでは、基準頻度とその倍数の扱い方を説明します。
11.3.1
日付範囲の生成
先 ほど 説 明 なし で 使 いまし た が、
pandas.date_range
は 一 定 の 頻 度 に 従 う指 定 し た 長 さ の
DatetimeIndex
を生成する機能があります。
In [74]: index = pd.date_range('2012-04-01', '2012-06-01')
In [75]: index
Out[75]:
DatetimeIndex(['2012-04-01', '2012-0
4-02', '2012-04-03', '2012-04-04',
'2012-04-05', '2012-04-06', '2012-04-07', '2012-04-08',
'2012-04-09', '2012-04-10', '2012-04-11', '2012-04-12',
'2012-04-13', '2012-04-14', '2012-04-15', '2012-04-16',
'2012-04-17', ...
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