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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7.1
欠損値の取り扱い
211
表7-1 欠損値を扱うメソッド
メソッド 説明
dropna
指定した軸について、その軸のラベルの値に欠損値が含まれる場合はラベルを削除する。含
まれる欠損値の数がいくつまでなら削除しないか、閾値で指定することもできる。
fillna
欠損値を指定した値で穴埋めする。または、
'ffill'
や
'bfill'
などの指定した方法で穴埋め
する。
isnull
各値が欠損値（
NA
）であるかどうかを示す一連の真偽値を戻す。
notnull isnull
の反対の動作をする。
7.1.1
欠損値を削除する
欠損値を削除する方法はいくつかあります。
pandas.isnull
と真偽値の配列を用いて手動で削除す
るという方法も常に選択肢の
1
つではありますが、
dropna
を使う方法が便利です。シリーズに対して
dropna
を用いると、欠損値でないデータとそのインデックスのみを持ったシリーズが戻されます。
In [15]: from numpy import nan as NA
In
[16]: data = pd.Series([1, NA, 3.5, NA, 7])
In [17]: data.dropna()
Out[17]:
0 1.0
2 3.5
4 7.0
dtype: float64
これは次の方法と等価です。
In [18]: data[data.notnull()]
Out[18]:
0 1.0
2 3.5
4 7.0
dtype: float64
データフレームオブジェクトの場合は、少し複雑になります。この場 ...
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