如何通过Python Seaborn实现相关性矩阵热力图实例展示？

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pythonimport pandas as pdimport numpy as npimport 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线矩阵型mask=np.zeros_like(corr)

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和

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

相关系数

EXCEL也能做

CORREL函数

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