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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9.2
pandas
と
seaborn
のプロット関数
307
図9-23 混合正規分布の正規化されたヒストグラムと密度推定
9.2.4
散布図
散布図（点でのプロット）は、
2
つの
1
次元データ（シリーズ）の間の相関を調べるのに有効な場合が
あります。ここでは例として
statsmodels
プロジェクトの
macrodata
データセットを読み込み、いくつか
の変数を選択してそれらの対数の差分を計算します。
In [100]: macro = pd.read_csv('examples/macrodata.csv')
In [101]: data = macro[['cpi', 'm1', 'tbilrate', 'unemp']]
In [102]: trans_data = np.log(data).diff().dropna()
In [103]: trans_data[-5:]
Out[103]:
cpi m1 tbilrate
unemp
198 -0.007904 0.045361 -0.396881 0.105361
199 -0.021979 0.066753 -2.277267 0.139762
200 0.002340 0.010286 0.606136 0.160343
201 0.008419 0.037461 -0.200671 0.127339
202 0.008894 0.012202 -0.405465 0.042560
読み込んだら、
seaborn
の
regplot
メソッドを使いましょ
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