In [1]:

    

import numpy as np
import pandas as pd

%pylab inline
import seaborn as sns
sns.set_style('darkgrid')


    

    




Populating the interactive namespace from numpy and matplotlib






    




/Users/davekensinger/anaconda/lib/python3.5/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.
  warnings.warn(self.msg_depr % (key, alt_key))

In [2]:

    

names = [
       'mpg'
    ,  'cylinders'
    ,  'displacement'
    ,  'horsepower'
    ,  'weight'
    ,  'acceleration'
    ,  'model_year'
    ,  'origin'
    ,  'car_name'
]
df = pd.read_csv("http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data", sep='\s+', names=names)
df['maker'] = df.car_name.map(lambda x: x.split()[0])
df.origin = df.origin.map({1: 'America', 2: 'Europe', 3: 'Asia'})
df=df.applymap(lambda x: np.nan if x == '?' else x).dropna()
df['horsepower'] = df.horsepower.astype(float)
df.head()


    

    
Out[2]:






  

    

      

      
mpg
      
cylinders
      
displacement
      
horsepower
      
weight
      
acceleration
      
model_year
      
origin
      
car_name
      
maker
    
  
  

    

      
0
      
18
      
8
      
307
      
130
      
3504
      
12.0
      
70
      
America
      
chevrolet chevelle malibu
      
chevrolet
    
    

      
1
      
15
      
8
      
350
      
165
      
3693
      
11.5
      
70
      
America
      
buick skylark 320
      
buick
    
    

      
2
      
18
      
8
      
318
      
150
      
3436
      
11.0
      
70
      
America
      
plymouth satellite
      
plymouth
    
    

      
3
      
16
      
8
      
304
      
150
      
3433
      
12.0
      
70
      
America
      
amc rebel sst
      
amc
    
    

      
4
      
17
      
8
      
302
      
140
      
3449
      
10.5
      
70
      
America
      
ford torino
      
ford
    
  

In [5]:

    

g = sns.JointGrid(x="horsepower", y="mpg", data=df)
g.plot_joint(sns.regplot, order=2)
g.plot_marginals(sns.distplot)


    

    
Out[5]:





<seaborn.axisgrid.JointGrid at 0x10a8cc908>






    

In [6]:

    

sns.jointplot("mpg", "horsepower", data=df, kind='kde')


    

    




/Users/davekensinger/anaconda/lib/python3.5/site-packages/matplotlib/__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.
  warnings.warn(self.msg_depr % (key, alt_key))






    
Out[6]:





<seaborn.axisgrid.JointGrid at 0x10e028400>






    

In [7]:

    

g = sns.PairGrid(df[["mpg", "horsepower", "weight", "origin"]], hue="origin")
g.map_upper(sns.regplot)
g.map_lower(sns.residplot)
g.map_diag(plt.hist)
for ax in g.axes.flat:
    plt.setp(ax.get_xticklabels(), rotation=45)
g.add_legend()
g.set(alpha=0.5)


    

    




/Users/davekensinger/anaconda/lib/python3.5/site-packages/matplotlib/__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.
  warnings.warn(self.msg_depr % (key, alt_key))






    
Out[7]:





<seaborn.axisgrid.PairGrid at 0x10e23e9b0>






    

In [ ]: