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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342
10
章 データの集約とグループ演算
10.3.6
例：グループ指向の線形回帰
関数の戻り値が
pandas
オブジェクトやスカラー値である限りは、
1
つ前の例と同様に、
groupby
はよ
り複雑なグループ指向の統計分析に使うことができます。例えば、次のような
regress
関数を定義する
ことができます（計量経済学向けのライブラリ
statsmodels
を使用しています）。この
regress
関数は、
最小二乗法（
OLS
：
Ordinary Least Squares
）によるデータの線形回帰の結果を求めています。
import statsmodels.api as sm
def regress(data, yvar, xvars):
Y = data[yvar]
X = data[xvars]
X['intercept'] = 1.
result = sm.OLS(Y, X).fit()
return result.pa
rams
それでは、
AAPL
と
SPX
の利益のデータにおける年ごとの線形回帰の結果を求めてみましょう。
In [129]: by_year.apply(regress, 'AAPL', ['SPX'])
Out[129]:
SPX intercept
2003 1.195406 0.000710
2004 1.363463 0.004201
2005 1.766415 0.003246
2006 1.645496 0.000080
2007 1.198761 0.003438
2008 0.968016 ...
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