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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158
5
章
pandas
入門
方法 説明
df.iloc[where]
データフレームの
1
つ以上の行を、整数のインデックス位置を指定して選択
する。
df.iloc[:, where]
1
つ以上の列を、整数のインデックス位置を指定して選択する。
df.iloc[where_i, where_j]
行、列を、整数のインデックス位置を指定して選択する。
df.at[label_i, label_j]
行と列のラベルを指定して、
1
つの値を取得する。
df.iat[i, j]
行と列のインデックス位置（整数）を指定して、
1
つの値を取得する。
reindex
メソッド 行や列を、ラベルを指定して選択する。
get_value, set_value
メソッド 行と列のラベルを指定して、
1
つの値を選択する。
5.2.4
整数のインデックス
整数でインデックス付けされた
pandas
オブジェクトの扱いは、よく新しいユーザをつまずかせると
ころです。なぜなら、
Python
組み込み
のデータ構造であるリストやタプルとはインデックス付けの方
法が異なるからです。例えば、次のコードでエラーが出るとは予想しないでしょう。
ser = pd.Series(np.arange(3.))
ser
ser[-1]
この場合、
pandas
で整数でのインデックス参照のエラーをフォールバックして値を戻すように設計
することもできたのですが、これをすると大抵の場合、潜在的なバグを生み出してしまいます。この
ケースの場合は、インデックスとして
0
、
1
、
2
を持ちますが、
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.