August 2017
Intermediate to advanced
500 pages
12h 3m
Japanese
Content preview from PythonとJavaScriptではじめるデータビジュアライゼーション
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206
9 章 pandas によるデータのクリーニング
np.nan == np.nan
Out: False
そのため、
pandas
はカウントされない（
null
）フィールドを調べるための専用の
isnull
フィー
ルドを提供しています。
df = df[df.born_in.isnull()] #
❶
df.count()
Out:
born_in 0 #
現在は全エントリが空である
category 910
...
dtype: int64
❶
isnull
は、
born_in
フィールドが空の行が
True
となるブールマスクを作成する。
born_in
列はもう使わないので削除しましょう
†
。
df = df.drop('born_in', axis=1) #
❶
❶
drop
は第
1
引数としての
1
つのラベルかインデックス（またはインデックスのリスト）と、行（
0
、
デフォルト）か列（
1
）インデックスを示す
axis
引数を取る。
9.4.4
重複の検出
インターネットをざっと検索すると、
2014
年までに
889
の個人および団体がノーベル賞を受賞
していることがわかります。残りの行が
910
なので、まだいくつかの重複か異常データがあるこ
とになります。
pandas
の
duplicated
メソッドは一致する行を探します。
duplicated
メソッドは、列名または
列名のリストで照合します。名前が重複する行の全リストを取得しましょう。
dupes_by_name = df[df.duplicated('name')] #
❶
dupes_by_name.count() ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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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.