Machine Learning with H2O - Tutorial 2: Basic Data Manipulation

In [1]:

    

# Start and connect to a local H2O cluster
import h2o
h2o.init(nthreads = -1)


    

    




Checking whether there is an H2O instance running at http://localhost:54321..... not found.
Attempting to start a local H2O server...
  Java Version: openjdk version "1.8.0_131"; OpenJDK Runtime Environment (build 1.8.0_131-8u131-b11-0ubuntu1.16.04.2-b11); OpenJDK 64-Bit Server VM (build 25.131-b11, mixed mode)
  Starting server from /home/joe/anaconda3/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar
  Ice root: /tmp/tmp0cekp48d
  JVM stdout: /tmp/tmp0cekp48d/h2o_joe_started_from_python.out
  JVM stderr: /tmp/tmp0cekp48d/h2o_joe_started_from_python.err
  Server is running at http://127.0.0.1:54321
Connecting to H2O server at http://127.0.0.1:54321... successful.






    





H2O cluster uptime:
01 secs
H2O cluster version:
3.10.5.2
H2O cluster version age:
10 days 
H2O cluster name:
H2O_from_python_joe_74ajjq
H2O cluster total nodes:
1
H2O cluster free memory:
5.210 Gb
H2O cluster total cores:
8
H2O cluster allowed cores:
8
H2O cluster status:
accepting new members, healthy
H2O connection url:
http://127.0.0.1:54321
H2O connection proxy:
None
H2O internal security:
False
Python version:
3.6.1 final

In [2]:

    

# Import Titanic data (local CSV)
titanic = h2o.import_file("kaggle_titanic.csv")


    

    




Parse progress: |█████████████████████████████████████████████████████████| 100%

In [3]:

    

# Explore the dataset using various functions
titanic.head(10)


    

    







  PassengerId
  Survived
  Pclass
Name                                               
Sex   
  Age
  SibSp
  Parch
  Ticket
   Fare
Cabin  
Embarked  


            1
         0
       3
Braund, Mr. Owen Harris                            
male  
   22
      1
      0
     nan
 7.25  
       
S         
            2
         1
       1
Cumings, Mrs. John Bradley (Florence Briggs Thayer)
female
   38
      1
      0
     nan
71.2833
C85    
C         
            3
         1
       3
Heikkinen, Miss. Laina                             
female
   26
      0
      0
     nan
 7.925 
       
S         
            4
         1
       1
Futrelle, Mrs. Jacques Heath (Lily May Peel)       
female
   35
      1
      0
  113803
53.1   
C123   
S         
            5
         0
       3
Allen, Mr. William Henry                           
male  
   35
      0
      0
  373450
 8.05  
       
S         
            6
         0
       3
Moran, Mr. James                                   
male  
  nan
      0
      0
  330877
 8.4583
       
Q         
            7
         0
       1
McCarthy, Mr. Timothy J                            
male  
   54
      0
      0
   17463
51.8625
E46    
S         
            8
         0
       3
Palsson, Master. Gosta Leonard                     
male  
    2
      3
      1
  349909
21.075 
       
S         
            9
         1
       3
Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  
female
   27
      0
      2
  347742
11.1333
       
S         
           10
         1
       2
Nasser, Mrs. Nicholas (Adele Achem)                
female
   14
      1
      0
  237736
30.0708
       
C         








    
Out[3]:

In [4]:

    

# Explore the column 'Survived'
titanic['Survived'].summary()


    

    







       
Survived          


type   
int               
mins   
0.0               
mean   
0.3838383838383838
maxs   
1.0               
sigma  
0.4865924542648575
zeros  
549               
missing
0                 
0      
0.0               
1      
1.0               
2      
1.0               
3      
1.0               
4      
0.0               
5      
0.0               
6      
0.0               
7      
0.0               
8      
1.0               
9      
1.0               

In [5]:

    

# Use hist() to create a histogram
titanic['Survived'].hist()


    

    




/home/joe/anaconda3/lib/python3.6/site-packages/matplotlib/font_manager.py:280: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
  'Matplotlib is building the font cache using fc-list. '






    

In [6]:

    

# Use table() to summarize 0s and 1s
titanic['Survived'].table()


    

    







  Survived
  Count


         0
    549
         1
    342








    
Out[6]:

In [7]:

    

# Convert 'Survived' to categorical variable
titanic['Survived'] = titanic['Survived'].asfactor()


    

In [8]:

    

# Look at the summary of 'Survived' again
# The feature is now an 'enum' (enum is the name of categorical variable in Java)
titanic['Survived'].summary()


    

    







       
Survived  


type   
enum      
mins   
          
mean   
          
maxs   
          
sigma  
          
zeros  
          
missing
0         
0      
0         
1      
1         
2      
1         
3      
1         
4      
0         
5      
0         
6      
0         
7      
0         
8      
1         
9      
1         

In [9]:

    

# Explore the column 'Pclass'
titanic['Pclass'].summary()


    

    







       
Pclass            


type   
int               
mins   
1.0               
mean   
2.3086419753086447
maxs   
3.0               
sigma  
0.8360712409770491
zeros  
0                 
missing
0                 
0      
3.0               
1      
1.0               
2      
3.0               
3      
1.0               
4      
3.0               
5      
3.0               
6      
1.0               
7      
3.0               
8      
3.0               
9      
2.0               

In [10]:

    

# Use hist() to create a histogram
titanic['Pclass'].hist()


    

In [11]:

    

# Use table() to summarize 1s, 2s and 3s
titanic['Pclass'].table()


    

    







  Pclass
  Count


       1
    216
       2
    184
       3
    491








    
Out[11]:

In [12]:

    

# Convert 'Pclass' to categorical variable
titanic['Pclass'] = titanic['Pclass'].asfactor()


    

In [13]:

    

# Explore the column 'Pclass' again
titanic['Pclass'].summary()


    

    







       
Pclass  


type   
enum    
mins   
        
mean   
        
maxs   
        
sigma  
        
zeros  
        
missing
0       
0      
3       
1      
1       
2      
3       
3      
1       
4      
3       
5      
3       
6      
1       
7      
3       
8      
3       
9      
2