问相关矩阵图，系数在一边，散点图在另一边，分布在对角线上

EN

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)

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), ycoords=ax.transAxes)

 
def cor_matrix(df, save=False):
    # ======= NEW ITERATION FUNCTION ====
    label_iter = iter(df).__next__
    # ====================================
    def annotate_colname(x, **kws):
        ax = plt.gca()
        # ===== GHANGE below x.name by label_iter() ======
        ax.annotate(label_iter(), xy=(0.05, 0.9), xycoords=ax.transAxes, fontweight='bold')


    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}, line_kws={"color": "red"})
    g.map_diag(sns.histplot, kde=True)  # fix deprecated message
    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('')
    if save:
        plt.savefig('corr_mat.png')
    return g