python绘制相关性矩阵_python seaborn heatmap可视化相关性矩阵实例

方法

import pandas as pd

import numpy as np

import seaborn as sns

df = pd.DataFrame(np.random.randn(50).reshape(10,5))

corr = df.corr()

sns.heatmap(corr, cmap=’Blues’, annot=True)

将矩阵型简化为对角矩阵型：

mask = np.zeros_like(corr)

mask[np.tril_indices_from(mask)] = True

sns.heatmap(corr, cmap=’Blues’, annot=True, mask=mask.T)

补充知识：Python【相关矩阵】和【协方差矩阵】

相关系数矩阵

pandas.DataFrame(数据).corr()

import pandas as pd

df = pd.DataFrame({

‘a’: [11, 22, 33, 44, 55, 66, 77, 88, 99],

‘b’: [10, 24, 30, 48, 50, 72, 70, 96, 90],

‘c’: [91, 79, 72, 58, 53, 47, 34, 16, 10],

‘d’: [99, 10, 98, 10, 17, 10, 77, 89, 10]})

df_corr = df.corr()

# 可视化

import matplotlib.pyplot as mp, seaborn

seaborn.heatmap(df_corr, center=0, annot=True, cmap=’YlGnBu’)

mp.show()

协方差矩阵

numpy.cov(数据)

import numpy as np

matric = [

[11, 22, 33, 44, 55, 66, 77, 88, 99],

[10, 24, 30, 48, 50, 72, 70, 96, 90],

[91, 79, 72, 58, 53, 47, 34, 16, 10],

[55, 20, 98, 19, 17, 10, 77, 89, 14]]

covariance_matrix = np.cov(matric)

# 可视化

print(covariance_matrix)

import matplotlib.pyplot as mp, seaborn

seaborn.heatmap(covariance_matrix, center=0, annot=True, xticklabels=list(‘abcd’), yticklabels=list(‘ABCD’))

mp.show()

补充

协方差