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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11.2
時系列の基本
353
datetime
オブジェクトを使えるところではどこでも、タイムスタンプで代用することができます。さ
らにタイムスタンプは頻度の情報も（もしあれば）保存でき、タイムゾーンの変換やその他の操作も可
能です。これらについては、後ほど説明します。
11.2.1
インデックス参照、データの選択、サブセットの抽出
ラベルに基づいたインデックス参照やデータの選択に関して、時系列は、
pandas.Series
と同様な振
る舞いをします。
In [48]: stamp = ts.index[2]
In [49]: ts[stamp]
Out[49]: -0.51943871505673811
利便性のため、日付として解釈可能な文字列を使って参照することができます。
In [50]: ts['1/10/2011']
Out[50]: 1.9657805725027142
In [51]: ts['201101
10']
Out[51]: 1.9657805725027142
長い時系列の場合、ある年やある年月を指定して、簡単にデータの一部分を選択することができま
す。
In [52]: longer_ts = pd.Series(np.random.randn(1000),
....: index=pd.date_range('1/1/2000', periods=1000))
In [53]: longer_ts
Out[53]:
2000-01-01 0.092908
2000-01-02 0.281746 ...
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