In [1]:

    

# Import required modules
import pandas as pd
from sklearn import preprocessing

# Set charts to view inline
%matplotlib inline


    

In [2]:

    

# Create an example dataframe with a column of unnormalized data
data = {'score': [234,24,14,27,-74,46,73,-18,59,160]}
df = pd.DataFrame(data)
df


    

    
Out[2]:






  

    

      

      
score
    
  
  

    

      
0
      
234
    
    

      
1
      
24
    
    

      
2
      
14
    
    

      
3
      
27
    
    

      
4
      
-74
    
    

      
5
      
46
    
    

      
6
      
73
    
    

      
7
      
-18
    
    

      
8
      
59
    
    

      
9
      
160
    
  

In [3]:

    

# View the unnormalized data
df['score'].plot(kind='bar')


    

    
Out[3]:





<matplotlib.axes._subplots.AxesSubplot at 0x113a84eb8>






    

In [4]:

    

# Create x, where x the 'scores' column's values as floats
x = df['score'].values.astype(float)

# Create a minimum and maximum processor object
min_max_scaler = preprocessing.MinMaxScaler()

# Create an object to transform the data to fit minmax processor
x_scaled = min_max_scaler.fit_transform(x)

# Run the normalizer on the dataframe
df_normalized = pd.DataFrame(x_scaled)


    

    




/Users/chrisralbon/anaconda/lib/python3.5/site-packages/sklearn/preprocessing/data.py:324: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
/Users/chrisralbon/anaconda/lib/python3.5/site-packages/sklearn/preprocessing/data.py:359: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)

In [5]:

    

# View the dataframe
df_normalized


    

    
Out[5]:






  

    

      

      
0
    
  
  

    

      
0
      
1.000000
    
    

      
1
      
0.318182
    
    

      
2
      
0.285714
    
    

      
3
      
0.327922
    
    

      
4
      
0.000000
    
    

      
5
      
0.389610
    
    

      
6
      
0.477273
    
    

      
7
      
0.181818
    
    

      
8
      
0.431818
    
    

      
9
      
0.759740
    
  

In [6]:

    

# Plot the dataframe
df_normalized.plot(kind='bar')


    

    
Out[6]:





<matplotlib.axes._subplots.AxesSubplot at 0x115f11278>