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seabornのpairplotをカスタムして相関係数を表示する(欠損値にも対応)

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seabornのpairplotは便利だが、相関係数も確認できればな・・・と思ったので作ってみた。
欠損値があってもwarningが出ないようにしている。

import numpy as np
import pandas as pd
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

def draw_hist(x, **kws):
    plt.hist(x[~np.isnan(x)])

def corr_func(x, y, **kws):
    mask = ~np.logical_or(np.isnan(x), np.isnan(y))
    x, y = x[mask], y[mask]
    r, _ = stats.pearsonr(x, y)
    ax = plt.gca()
    ax.annotate("r = {:.3f}".format(r),
               xy=(.2, .5), 
               xycoords=ax.transAxes,
               size=16)

def pairplot(df):
    g = sns.PairGrid(df, height=1.6, dropna=False)
    g.map_diag(draw_hist)
    g.map_upper(sns.regplot, scatter_kws={"s": 8}, line_kws={"color":  "r"})
    g.map_lower(corr_func)
from sklearn.datasets  import load_boston
boston = load_boston()
boston_df = pd.DataFrame(boston["data"],  columns=boston["feature_names"])

pairplot(boston_df.iloc[:,:6])

ちなみに、sns.heatmapでannot=Trueとすると値が表示されることに後から気づいた。まあ散布図と一緒に確認できるので。。

sns.heatmap(boston_df.iloc[:,:6].corr(), annot=True)

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