#Fire up GraphLab Create



In [5]:

    
import graphlab as gl
from pandas import Series, DataFrame
import pandas as pd



In [13]:

    
## Here pandas is working
df = pd.read_csv('people-example.csv')

## GraphLab 
sf = gl.SFrame('people-example.csv')
sf.show()









    



PROGRESS: Finished parsing file /home/zax/ml-foundation/people-example.csv
PROGRESS: Parsing completed. Parsed 7 lines in 0.038828 secs.
------------------------------------------------------
Inferred types from first line of file as 
column_type_hints=[str,str,str,int]
If parsing fails due to incorrect types, you can correct
the inferred type list above and pass it to read_csv in
the column_type_hints argument
------------------------------------------------------
PROGRESS: Finished parsing file /home/zax/ml-foundation/people-example.csv
PROGRESS: Parsing completed. Parsed 7 lines in 0.017301 secs.

Pandas DataFrame

people_data = DataFrame(df) people_data



In [18]:

    
myFeatures = ['First Name', 'Last Name', 'age']



In [22]:

    
df[myFeatures]









    Out[22]:






  
    
      
      First Name
      Last Name
      age
    
  
  
    
      0
      Bob
      Smith
      24
    
    
      1
      Alice
      Williams
      23
    
    
      2
      Malcolm
      Jone
      22
    
    
      3
      Felix
      Brown
      23
    
    
      4
      Alex
      Cooper
      23
    
    
      5
      Tod
      Campbell
      22
    
    
      6
      Derek
      Ward
      25

Using canvas



In [12]:

    
gl.canvas.set_target('ipynb')
sf.show(view="Scatter Plot", x="Country", y="age")



In [ ]:

	First Name	Last Name	age
0	Bob	Smith	24
1	Alice	Williams	23
2	Malcolm	Jone	22
3	Felix	Brown	23
4	Alex	Cooper	23
5	Tod	Campbell	22
6	Derek	Ward	25