import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
import math
mu = 0
variance = 1
sigma = math.sqrt(variance)
x = np.linspace(mu - 3 * sigma, mu + 3 * sigma, 100)
plt.plot(x, stats.norm.pdf(x, mu, sigma))
plt.show()sns.distplot(df['Rating'], hist=True, kde=True,
bins=int(15), color = 'darkblue',
hist_kws={'edgecolor':'black'},
kde_kws={'linewidth': 4})x = np.random.normal(size=100)
sns.distplot(x);mean =[0,0]
covariance = [[1,0],[0,100]]
ds = np.random.multivariate_normal(mean,covariance,500)
dframe = pd.DataFrame(ds, columns=['col1', 'col2'])
fig = sns.kdeplot(dframe).get_figure()
fig.savefig('kde1.png')