import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('E:\\myPractice\\src\\main\\resources\\dataset.csv', header=0)
cols = ['WindNumber', 'Time', 'WindSpeed', 'Power', 'RotorSpeed']
cm = np.corrcoef(df[cols].values.T)
sns.set(font_scale=1.2)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f ',
    annot_kws={'size': 15}, yticklabels=cols, xticklabels=cols)
plt.show()
plt.savefig('sale_corr.png')

f57c79eeae73a941ca75968af48597fb87e025b4
<mask token>

<mask token>
sns.set(font_scale=1.2)
<mask token>
plt.show()
plt.savefig('sale_corr.png')

<mask token>
df = pd.read_csv('E:\\myPractice\\src\\main\\resources\\dataset.csv', header=0)
cols = ['WindNumber', 'Time', 'WindSpeed', 'Power', 'RotorSpeed']
cm = np.corrcoef(df[cols].values.T)
sns.set(font_scale=1.2)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f ',
    annot_kws={'size': 15}, yticklabels=cols, xticklabels=cols)
plt.show()
plt.savefig('sale_corr.png')

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('E:\\myPractice\\src\\main\\resources\\dataset.csv', header=0)
cols = ['WindNumber', 'Time', 'WindSpeed', 'Power', 'RotorSpeed']
cm = np.corrcoef(df[cols].values.T)
sns.set(font_scale=1.2)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f ',
    annot_kws={'size': 15}, yticklabels=cols, xticklabels=cols)
plt.show()
plt.savefig('sale_corr.png')
