Reshape dictionary values from 1D to 2D array

I want to calculate cosine similarity for words in two dictionaries. The words are keys, arrays are values. In order to do this, I need to convert them to 2D array first, I did some research and found x = x.reshape(1,-1)
I successfully converted it with a single value in dictionary, however, I don't know how to convert the whole dictionary's values by using for loops.

x = x.reshape(1,-1)

The data

D
'A': array([ 4.80625004e-01, -1.40245005e-01, -9.99999046e-03]),
'B': array([-0.46553 , -0.1519755 , 0.41836]),
'C': array([0.0090175 , 0.05817001, -0.09712502])

D2 (same format as D)
'D': ([8.11059952e-01, 6.84859991e-01, 1.01619996e-01]),
'E':([-0.82868 , 0.49513 , 0.67581]),
'F':([-0.17093 , 0.88746 , 0.0931135])


I tried

for i in D2.items():
D2[i] = D2[i].reshape(1, -1) #Error on this line


but received error: TypeError: unhashable type: 'numpy.ndarray'

Some advice please? thank you in advanced!

D2_RESHAPE

2 Answers
2

Dict.items() returns tuples of the key, value pairs. Therefore in your case i is a tuple of the key and the value. Try:

i

for i, value in D2.items():
D2[i] = value.reshape(1, -1)


try this

from numpy import array

d = dict('A': array([ 4.80625004e-01, -1.40245005e-01, -9.99999046e-03]),
'B': array([-0.46553 , -0.1519755 , 0.41836]),
'C': array([0.0090175 , 0.05817001, -0.09712502]))

reshaped_arrays = dict()
for x, j in d.items():
dd[x]=j.reshape(1,-1)


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