Normalization to bring in the range of [0,1]

Question

I have a huge data set from which I derive two sets of datapoints, which I then have to plot and compare. These two plots differ in their in their range, so I want them to be in the range of [0,1]. For the following code and a specific data set I get a constant line at 1 as the dataset plot, but this normalization works well for other sets:

plt.plot(range(len(rvalue)),np.array(rvalue)/(max(rvalue)))

and for this code :

oldrange = max(rvalue) - min(rvalue)  # NORMALIZING
newmin = 0
newrange = 1 + 0.9999999999 - newmin
normal = map(
    lambda x, r=float(rvalue[-1] - rvalue[0]): ((x - rvalue[0]) / r)*1 - 0, 
    rvalue)
plt.plot(range(len(rvalue)), normal)

I get the error:

ZeroDivisionError: float division by zero

for all the data sets. I am unable to figure out how to get both the plots in one range for comparison.

Answer 1

Use the following method to normalize your data in the range of 0 to 1 using min and max value from the data sequence:

import numpy as np

def NormalizeData(data):
    return (data - np.min(data)) / (np.max(data) - np.min(data))

Answer 2

Use scikit: http://scikit-learn.org/stable/modules/preprocessing.html#scaling-features-to-a-range

It has built in functions to scale features to a specified range. You'll find other functions to normalize and standardize here.

See this example:

>>> import numpy as np
>>> from sklearn import preprocessing
>>> X_train = np.array([[ 1., -1.,  2.],
...                     [ 2.,  0.,  0.],
...                     [ 0.,  1., -1.]])
...
>>> min_max_scaler = preprocessing.MinMaxScaler()
>>> X_train_minmax = min_max_scaler.fit_transform(X_train)
>>> X_train_minmax
array([[ 0.5       ,  0.        ,  1.        ],
       [ 1.        ,  0.5       ,  0.33333333],
       [ 0.        ,  1.        ,  0.        ]])