Normalization

from sklearn import preprocessing
# Create x, where x the 'Fare' column's values as floats
x = df[['Fare']].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['Fare'] = pd.DataFrame(x_scaled)
df.Fare.head()