Sklearn's MinMaxScaler only returns zeros

Question

I am trying to scale a some number to a range of 0 - 1 using preprocessing from sklearn. Thats what i did:

data = [44.645, 44.055,          


        

Answer 1

You're putting your data into a list for some reason, but you shouldn't:

data_scaled = min_max_scaler.fit_transform(data)

Answer 2

I had the same problem when I tried scaling with MinMaxScaler from sklearn.preprocessing. Scaler returned me zeros when I used a shape a numpy array as list, i.e. [1, n] which looks like the following:

data = [[44.645, 44.055, 44.54, 44.04, 43.975, 43.49, 42.04, 42.6, 42.46, 41.405]]

I changed the shape of array to [n, 1]. In your case it would like the following

data = [[44.645], 
        [44.055], 
        [44.540], 
        [44.040], 
        [43.975], 
        [43.490], 
        [42.040], 
        [42.600], 
        [42.460], 
        [41.405]]

Then MinMaxScaler worked in proper way.

Answer 3

This is because data is a int32 or int64 and the MinMaxScaler needs a float. Try this:

import numpy as np
data = [44.645, 44.055, 44.54, 44.04, 43.975, 43.49, 42.04, 42.6, 42.46, 41.405]
min_max_scaler = preprocessing.MinMaxScaler(feature_range=(0, 1))
data_scaled = min_max_scaler.fit_transform([np.float32(data)])
print data_scaled

Answer 4

data = []
data = np.array(data)
data.append([44.645, 44.055, 44.54, 44.04, 43.975, 43.49, 42.04, 42.6, 42.46, 41.405])
min_max_scaler = preprocessing.MinMaxScaler(feature_range=(0, 1))
data_scaled = min_max_scaler.fit_transform(data.reshape(10,-1))
data = data_scaled.reshape( -1, 10)
print data

The reason behind this is when you're trying to apply fit_transform method of StandardScaler object to array of size (1, n) you obviously get all zeros, because for each number of array you subtract from it mean of this number, which equal to number and divide to std of this number. If you want to get correct scaling of your array, you should convert it to array with size (n, 1).

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