In [23]:

    

import pandas as pd


    

In [24]:

    

# TODO: Load up the dataset
# Ensuring you set the appropriate header column names
df = pd.read_csv('Datasets/servo.data')
columns = ['motor', 'screw', 'pgain', 'vgain', 'class']
df.columns = columns
df.head(5)


    

    
Out[24]:






  

    

      

      
motor
      
screw
      
pgain
      
vgain
      
class
    
  
  

    

      
0
      
B
      
D
      
6
      
5
      
0.506252
    
    

      
1
      
D
      
D
      
4
      
3
      
0.356251
    
    

      
2
      
B
      
A
      
3
      
2
      
5.500033
    
    

      
3
      
D
      
B
      
6
      
5
      
0.356251
    
    

      
4
      
E
      
C
      
4
      
3
      
0.806255
    
  

In [25]:

    

# TODO: Create a slice that contains all entries
# having a vgain equal to 5. Then print the 
# length of (# of samples in) that slice:
eq5 = df[df.vgain ==5]
len(eq5)


    

    
Out[25]:





22

In [30]:

    

# TODO: Create a slice that contains all entries
# having a motor equal to E and screw equal
# to E. Then print the length of (# of
# samples in) that slice:
eqE = df[(df.motor == 'E') & (df.screw == 'E')]
eqE


    

    
Out[30]:






  

    

      

      
motor
      
screw
      
pgain
      
vgain
      
class
    
  
  

    

      
34
      
E
      
E
      
3
      
2
      
1.100000
    
    

      
85
      
E
      
E
      
4
      
3
      
0.843755
    
    

      
131
      
E
      
E
      
6
      
5
      
0.281251
    
    

      
139
      
E
      
E
      
4
      
2
      
0.506252
    
    

      
150
      
E
      
E
      
3
      
1
      
0.700001
    
  

In [32]:

    

df_servo = df

df_servo[(df_servo.motor == 'E') & (df_servo.screw == 'E')]


    

    
Out[32]:






  

    

      

      
motor
      
screw
      
pgain
      
vgain
      
class
    
  
  

    

      
34
      
E
      
E
      
3
      
2
      
1.100000
    
    

      
85
      
E
      
E
      
4
      
3
      
0.843755
    
    

      
131
      
E
      
E
      
6
      
5
      
0.281251
    
    

      
139
      
E
      
E
      
4
      
2
      
0.506252
    
    

      
150
      
E
      
E
      
3
      
1
      
0.700001
    
  

In [19]:

    

# TODO: Create a slice that contains all entries
# having a pgain equal to 4. Use one of the
# various methods of finding the mean vgain
# value for the samples in that slice. Once
# you've found it, print it:
eq4 = df[df.pgain == 4]
eq4.vgain.mean()


    

    
Out[19]:





2.0606060606060606

In [13]:

    

# TODO: (Bonus) See what happens when you run
# the .dtypes method on your dataframe!
df.dtypes


    

    
Out[13]:





motor     object
screw     object
pgain      int64
vgain      int64
class    float64
dtype: object