<jupyter_start><jupyter_text>Foramattimg<jupyter_code>#py_seaborn_regression.ipynb
import numpy as np    
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

s1=np.fromiter( [np.sin(x/10) for x in range(0, 300)], np.float)
s2=np.fromiter( [np.sin(x/5 + 50) for x in range(0, 300)], np.float)

sns.regplot(x=s1, y=s2,  color="m")
s1=pd.Series([1,2,4,5,6,7,8,7])
s2=pd.Series([3,4,6,5,8,9,9,9])
sns.regplot(x=s1, y=s2,  color="b",ci=95, marker='x')
sns.jointplot(x=s1, y=s2, kind='kde', color='k')
sns.distplot(s1)
from scipy import stats
s1=pd.Series([np.random.rand() for _ in range(2000)])
mu, sigma = stats.norm.fit(s1)  #mean, standard deviation
ax=sns.distplot(s1, kde=False, fit=stats.norm, bins=100)
plt.title="Check against normal distribution"
ax.legend(["normal ($\mu=${0:.2g}, $\sigma=${1:.2f})".format(mu, sigma)])
plt.ylabel("frequency")
plt.xlabel("observation")
s1.plot.box()<jupyter_output><empty_output>
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