In [1]:

    

from __future__ import print_function, division

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd


    

In [2]:

    

x = np.linspace(0, 10, 1000)
plt.plot(x, np.sin(x), x, np.cos(x));


    

In [3]:

    

import seaborn as sns
sns.set()
plt.plot(x, np.sin(x), x, np.cos(x));


    

In [4]:

    

data = np.random.multivariate_normal([0, 0], [[5, 2], [2, 2]], size=2000)
data = pd.DataFrame(data, columns=['x', 'y'])

for col in 'xy':
    plt.hist(data[col], normed=True, alpha=0.5)


    

In [5]:

    

for col in 'xy':
    sns.kdeplot(data[col], shade=True)


    

In [6]:

    

sns.distplot(data['x']);


    

In [7]:

    

sns.kdeplot(data);


    

In [8]:

    

with sns.axes_style('white'):
    sns.jointplot("x", "y", data, kind='kde');


    

In [9]:

    

with sns.axes_style('white'):
    sns.jointplot("x", "y", data, kind='hex')


    

In [10]:

    

iris = sns.load_dataset("iris")
iris.head()


    

    
Out[10]:






  

    

      

      
sepal_length
      
sepal_width
      
petal_length
      
petal_width
      
species
    
  
  

    

      
0
      
5.1
      
3.5
      
1.4
      
0.2
      
setosa
    
    

      
1
      
4.9
      
3.0
      
1.4
      
0.2
      
setosa
    
    

      
2
      
4.7
      
3.2
      
1.3
      
0.2
      
setosa
    
    

      
3
      
4.6
      
3.1
      
1.5
      
0.2
      
setosa
    
    

      
4
      
5.0
      
3.6
      
1.4
      
0.2
      
setosa
    
  

In [11]:

    

tips = sns.load_dataset('tips')
tips.head()


    

    
Out[11]:






  

    

      

      
total_bill
      
tip
      
sex
      
smoker
      
day
      
time
      
size
    
  
  

    

      
0
      
16.99
      
1.01
      
Female
      
No
      
Sun
      
Dinner
      
2
    
    

      
1
      
10.34
      
1.66
      
Male
      
No
      
Sun
      
Dinner
      
3
    
    

      
2
      
21.01
      
3.50
      
Male
      
No
      
Sun
      
Dinner
      
3
    
    

      
3
      
23.68
      
3.31
      
Male
      
No
      
Sun
      
Dinner
      
2
    
    

      
4
      
24.59
      
3.61
      
Female
      
No
      
Sun
      
Dinner
      
4
    
  

In [12]:

    

tips['tip_pct'] = 100 * tips['tip'] / tips['total_bill']

grid = sns.FacetGrid(tips, row="sex", col="time", margin_titles=True)
grid.map(plt.hist, "tip_pct", bins=np.linspace(0, 40, 15));


    

In [13]:

    

with sns.axes_style(style='ticks'):
    g = sns.factorplot("day", "total_bill", "sex", data=tips, kind="box")
    g.set_axis_labels("Day", "Total Bill");


    

In [14]:

    

with sns.axes_style('white'):
    sns.jointplot("total_bill", "tip", data=tips, kind='hex')


    

In [15]:

    

sns.jointplot("total_bill", "tip", data=tips, kind='reg');


    

In [16]:

    

planets = sns.load_dataset('planets')
planets.head()


    

    
Out[16]:






  

    

      

      
method
      
number
      
orbital_period
      
mass
      
distance
      
year
    
  
  

    

      
0
      
Radial Velocity
      
1
      
269.300
      
7.10
      
77.40
      
2006
    
    

      
1
      
Radial Velocity
      
1
      
874.774
      
2.21
      
56.95
      
2008
    
    

      
2
      
Radial Velocity
      
1
      
763.000
      
2.60
      
19.84
      
2011
    
    

      
3
      
Radial Velocity
      
1
      
326.030
      
19.40
      
110.62
      
2007
    
    

      
4
      
Radial Velocity
      
1
      
516.220
      
10.50
      
119.47
      
2009
    
  

In [17]:

    

with sns.axes_style('white'):
    g = sns.factorplot("year", data=planets, aspect=1.5)
    g.set_xticklabels(step=5)


    

In [18]:

    

with sns.axes_style('white'):
    g = sns.factorplot("year", data=planets, aspect=4.0,
                       hue='method', order=range(2001, 2015), kind="count")
    g.set_ylabels('Number of Planets Discovered')


    

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