June 2024
Intermediate to advanced
200 pages
4h 46m
Japanese
Content preview from Python2年生 データ分析のしくみ 第2版 体験してわかる！会話でまなべる！
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Chapter
2
68
欠損値の処理
まずは、わざと欠損値のあるデータフレームを作って、欠損値を確認してみましょう。
わざと欠損値を入力するときは「
None」と指定します（リスト2.27）。
import pandas as pd
data = {
"
国語" : [90,50,None,40],
"
数学" : [80,None,None,50]
}
idx = ["A
太","B介","C子","D郎"]
dfA = pd.DataFrame(data, index=idx)
dfA
※データ内に None が含まれる場合、pandas は自動的にNone を扱えるデータ型の浮動小数点数型に変換します。そのため、整
数の値も小数として表現されるようになります。
pandasは欠損値のところに「NaN」と返してきます。
これはデータが少ないので確認するのが簡単ですが、データが大量にあるときは大変で
す。そこで、欠損値があるのかどうか「欠損値の個数」を数えてみましょう。もし、欠損
値の個数が
0個だったら、「欠損値はない」とわかります（リスト2.28）。
データフレーム.isnull().sum()
【入力プログラム】リスト 2.27
出力結果
欠損値は、
NaN
なんだ！
書式：欠損値の個数
69
データのミスをチェックする
LESSON ...
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