相关矩阵图，一侧为系数，另一侧为散点图，对角线分布

问题描述

我喜欢PerformanceAnalytics R包的 chart.Correlation函数:

I love this correlation matrix from the PerformanceAnalytics R package's chart.Correlation function:

如何在Python中创建它?我见过的相关矩阵图主要是热图，例如此seaborn示例

How can I create this in Python? The correlation matrix plots I've seen are primarily heatmaps, such as this seaborn example.

推荐答案

下面的cor_matrix函数可以执行此操作，并添加一个双变量内核密度图.感谢@ karl-anka的评论让我入门.

The cor_matrix function below does this, plus adds a bivariate kernel density plot. Thanks to @karl-anka's comment for getting me started.

import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats

sns.set(style='white')
iris = sns.load_dataset('iris')

def corrfunc(x, y, **kws):
  r, p = stats.pearsonr(x, y)
  p_stars = ''
  if p <= 0.05:
    p_stars = '*'
  if p <= 0.01:
    p_stars = '**'
  if p <= 0.001:
    p_stars = '***'
  ax = plt.gca()
  ax.annotate('r = {:.2f} '.format(r) + p_stars,
              xy=(0.05, 0.9), xycoords=ax.transAxes)

def annotate_colname(x, **kws):
  ax = plt.gca()
  ax.annotate(x.name, xy=(0.05, 0.9), xycoords=ax.transAxes,
              fontweight='bold')

def cor_matrix(df):
  g = sns.PairGrid(df, palette=['red'])
  # Use normal regplot as `lowess=True` doesn't provide CIs.
  g.map_upper(sns.regplot, scatter_kws={'s':10})
  g.map_diag(sns.distplot)
  g.map_diag(annotate_colname)
  g.map_lower(sns.kdeplot, cmap='Blues_d')
  g.map_lower(corrfunc)
  # Remove axis labels, as they're in the diagonals.
  for ax in g.axes.flatten():
    ax.set_ylabel('')
    ax.set_xlabel('')
  return g

cor_matrix(iris)

这篇关于相关矩阵图，一侧为系数，另一侧为散点图，对角线分布的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！