In [1]:

    

from sklearn import preprocessing


    

In [2]:

    

l = [0, 1, 2, 3, 4]
print(l)


    

    




[0, 1, 2, 3, 4]

In [3]:

    

l_2d = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
print(l_2d)


    

    




[[0, 1, 2], [3, 4, 5], [6, 7, 8]]

In [4]:

    

mm = preprocessing.MinMaxScaler()


    

In [5]:

    

# mm.fit_transform(l)
# ValueError: Expected 2D array, got 1D array instead:


    

In [6]:

    

l_2d_min_max = mm.fit_transform(l_2d)


    

In [7]:

    

print(l_2d_min_max)


    

    




[[0.  0.  0. ]
 [0.5 0.5 0.5]
 [1.  1.  1. ]]

In [8]:

    

print(type(l_2d_min_max))


    

    




<class 'numpy.ndarray'>

In [9]:

    

print(preprocessing.minmax_scale(l))


    

    




[0.   0.25 0.5  0.75 1.  ]

In [10]:

    

print(preprocessing.minmax_scale(l_2d))


    

    




[[0.  0.  0. ]
 [0.5 0.5 0.5]
 [1.  1.  1. ]]

In [11]:

    

print(preprocessing.minmax_scale(l_2d, axis=1))


    

    




[[0.  0.5 1. ]
 [0.  0.5 1. ]
 [0.  0.5 1. ]]

In [12]:

    

ss = preprocessing.StandardScaler()


    

In [13]:

    

# print(ss.fit_transform(l))
# ValueError: Expected 2D array, got 1D array instead:


    

In [14]:

    

l_2d_standardization = ss.fit_transform(l_2d)


    

In [15]:

    

print(l_2d_standardization)


    

    




[[-1.22474487 -1.22474487 -1.22474487]
 [ 0.          0.          0.        ]
 [ 1.22474487  1.22474487  1.22474487]]

In [16]:

    

print(type(l_2d_standardization))


    

    




<class 'numpy.ndarray'>

In [17]:

    

print(preprocessing.scale(l))


    

    




[-1.41421356 -0.70710678  0.          0.70710678  1.41421356]

In [18]:

    

print(preprocessing.scale(l_2d))


    

    




[[-1.22474487 -1.22474487 -1.22474487]
 [ 0.          0.          0.        ]
 [ 1.22474487  1.22474487  1.22474487]]

In [19]:

    

print(preprocessing.scale(l_2d, axis=1))


    

    




[[-1.22474487  0.          1.22474487]
 [-1.22474487  0.          1.22474487]
 [-1.22474487  0.          1.22474487]]