August 2015
Intermediate to advanced
272 pages
5h 46m
Japanese
Content preview from Think Stats 第2版 ―プログラマのための統計入門
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9
9
1.6 変換
1.6
変換
このようなデータをインポートするとき、エラーがないか調べ、特別な値を処理し、
データを異なったフォーマットに変換し、計算する必要がよくあります。これらの操
作は、データクリーニング（
data cleaning
）と呼ばれます。
nsfg.py
には、これから使う変数をデータクリーニングする関数
CleanFemPreg
が
あります。
def CleanFemPreg(df):
df.agepreg /= 100.0
na_vals = [97, 98, 99]
df.birthwgt_lb.replace(na_vals, np.nan, inplace=True)
df.birthwgt_oz.replace(na_vals, np.nan, inplace=True)
df['totalwgt_lb'] = df.birthwgt_lb + df.birthwgt_oz / 16.0
agepreg
は、妊娠終了時の母親の年齢です。データファイルでは、
agepreg
は、セ
ンチ年を単位とする整数で符号化されています。したがって、
1
行目で
agepreg
の要
素を
100
で割って、年を単位とする浮動小数点数にします。
birthwgt_lb
と
birthwgt_oz
は生児出生した妊娠について新生児の体重をポンドと
オンスで含みます。さらに、特別な符号がいくつかあります。
97 NOT ASCERTAINED
（未確認）
98 REFUSED
（拒絶）
99 DON'T KNOW
（不明） ...
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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.