基于this answer，我使用以下代码绘制相关性矩阵，该矩阵仅绘制p〈0.05的数据：

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

# Simulate 3  correlated variables
num_samples = 100
mu = np.array([5.0, 0.0, 10.0])
# The desired covariance matrix.
r = np.array([
        [  3.40, -2.75, -2.00],
        [ -2.75,  5.50,  1.50],
        [ -2.00,  1.50,  1.25]
    ])
y = np.random.multivariate_normal(mu, r, size=num_samples)
df = pd.DataFrame(y)
df.columns = ["Correlated1","Correlated2","Correlated3"]

# Create two random variables 
for i in range(2):
    df.loc[:,f"Uncorrelated{i}"] = np.random.randint(-2000,2000,len(df))

def corr_sig(df=None):
    p_matrix = np.zeros(shape=(df.shape[1],df.shape[1]))
    for col in df.columns:
        for col2 in df.drop(col,axis=1).columns:
            _ , p = stats.pearsonr(df[col],df[col2])
            p_matrix[df.columns.to_list().index(col),df.columns.to_list().index(col2)] = p
    return p_matrix

p_values = corr_sig(df)
mask = np.invert(np.tril(p_values<0.05))

def plot_cor_matrix(corr, mask=None):
    f, ax = plt.subplots(figsize=(11, 9))
    sns.heatmap(corr, ax=ax,
                mask=mask,
                # cosmetics
                annot=True, 
                cmap='coolwarm')

# Plotting with significance filter
corr = df.corr()                            # get correlation
p_values = corr_sig(df)                     # get p-Value
mask = np.invert(np.tril(p_values<0.05))    # mask - only get significant corr
plot_cor_matrix(corr,mask)

如何还能过滤掉对角线上的相关性，在对角线上，特征与自身进行比较（即相关性为1）？