Home/ Questions/Valueerror: all the input arrays must have same number of dimensions - simple solution when encountering the error
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Tiago Morel
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Asked: 2022-05-17T13:13:33+00:00 In:

Valueerror: all the input arrays must have same number of dimensions – simple solution when encountering the error

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I’m building a new program, but when I run it, an error pops up. The error displayed is as follows:

ValueError: all the input arrays must have same number of dimensions

I have tried several workarounds, but they still do not get the desired results. If you have come across this situation and have a solution for the “valueerror: all the input arrays must have same number of dimensions” problem, pls let me know. Here is what I do:

n_last = []
 n_last = n_list_converted[:, -1]
 n_lists = np.append(n_list_converted, n_last, axis=1)
print(n_last.shape, type(n_last), n_list_converted.shape, type(n_list_converted))

Thanks!

all the input arrays must have same number of dimensions
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2 Answers

  1. Best Answer
    Expert

    The cause:

    The reason of your error is that you concatenating two arrays that have different dimensions. In particular, in your example “1 by n” and an array of length n are different.

    Solution: 

    I suggest you using hstack() and vstack() to replace. Like as below: 

    import numpy as np
a = np.arange(32).reshape(4,8) # 4 rows 8 columns matrix.
b = a[:,-1:] # last column of that matrix.

result = np.hstack((a,b)) # stack them horizontally like this:
#array([[ 0, 1, 2, 3, 4, 5, 6, 7, 7],
# [ 8, 9, 10, 11, 12, 13, 14, 15, 15],
# [16, 17, 18, 19, 20, 21, 22, 23, 23],
# [24, 25, 26, 27, 28, 29, 30, 31, 31]])

    Notice the columns “7, 15, 23, 31” are repeated and notice that I used a[:,-1:] to replace a[:,-1].  A column is created with this my version:

    array([[7],
[15],
[23],
[31]])

    To replace for a row array([7,15,23,31])

    • 22

  2. The shapes (n1,1) and (n1,1) are different. Use [:, None] to convert the vector into an array. 

    n_lists = np.append(n_list_converted, n_last[:, None], axis=1)

    Alternativ, you can also use n_last when you extract it 

    n_last = n_list_converted[:, -1:]

    To obtain a (20, 1) array.

    • 20

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