[code] pearson | kendall | spearman matrix

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
inputfile = 'data.csv' ## 输入的数据文件
data = pd.read_csv(inputfile) ## 读取数据
correlations = data.corr()
fig = plt.figure() 
ax = fig.add_subplot(figsize=(20,20)) #图片大小为20*20
ax = sns.heatmap(correlations,cmap=plt.cm.Greys, linewidths=0.05,vmax=1, vmin=0 ,annot=True,annot_kws={'size':6,'weight':'bold'})
plt.show()

CORR_PRSN = np.round(data.corr(method = 'pearson'), 2)

CORR_KNDL = np.round(data.corr(method = 'kendall'), 2)

CORR_SPMN = np.round(data.corr(method = 'spearman'), 2)

[Other Sample of Correlation View]

import numpy as np
import pandas as pd
df = pd.DataFrame({ 'A':np.random.randint(1, 100, 10),
                    'B':np.random.randint(1, 100, 10),
                    'C':np.random.randint(1, 100, 10)})
def test(df):
    dfData = df.corr()
    plt.subplots(figsize=(9, 9)) # 设置画面大小
    sns.heatmap(dfData, annot=True, vmax=1, square=True, cmap="Blues")
    #plt.savefig('./BluesStateRelation.png')
    plt.show()
test(df)
#ns.heapmap中annot=True，意思是显式热力图上的数值大小。
#sns.heapmap中square=True，意思是将图变成一个正方形，默认是一个矩形
#sns.heapmap中cmap="Blues"是一种模式，就是图颜色配置方案啦，我很喜欢这一款的。
#sns.heapmap中vmax是显示最大值
#利用热力图可以看数据表里多个特征两两的相似度。参考官方API参数及地址：

seaborn.heatmap(data, vmin=None, vmax=None, cmap=None, center=None, robust=False, annot=None, fmt=’.2g’, annot_kws=None, linewidths=0, linecolor=’white’, cbar=True, cbar_kws=None, cbar_ax=None, square=False, xticklabels=’auto’, yticklabels=’auto’, mask=None, ax=None, **kwargs)
http://seaborn.pydata.org/generated/seaborn.heatmap.html #seaborn.heatmap