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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11.3
日付範囲、頻度、シフト
357
値、または、データの集合が戻されます。
In [67]: dup_ts['1/3/2000'] #
重複していない
Out[67]: 4
In [68]: dup_ts['1/2/2000'] #
重複している
Out[68]:
2000-01-02 1
2000-01-02 2
2000-01-02 3
dtype: int64
一意でないタイムスタンプを持つデータを集約したい場合を考えてみましょう。その場合、
groupby
を使い、
level=0
を指定します。
In [69]: grouped = dup_ts.groupby(level=0)
In [70]: grouped.mean()
Out[70]:
2000-01-01 0
2000-01-02 2
2000-01-03 4
dtype: int64
In [71]: grouped.c
ount()
Out[71]:
2000-01-01 1
2000-01-02 3
2000-01-03 1
dtype: int64
11.3
日付範囲、頻度、シフト
pandas
の時系列では基本的に不規則なデータを想定しています。つまり、一定頻度であることを想
定していません。多くのアプリケーションはこれで十分です。しかし、たとえ時系列に欠損値を追加す
ることになったとしても、日次、月次、
15
分おきなどの一定頻度のデータを扱える方が望ましい場合が
あります。幸い
pandas
は、標準的な時系列の頻度を扱う機能や、再サンプリング、頻度の推論、一定 ...
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