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

Hello Guys, How are you all? Hope You all Are Fine. Today I get the following error ValueError: all the input arrays must have same number of dimensions in python. So Here I am Explain to you all the possible solutions here.

Without wasting your time, Let’s start This Article to Solve This Error.

How ValueError: all the input arrays must have same number of dimensions Error Occurs?

Today I get the following error ValueError: all the input arrays must have same number of dimensions in python.

How To Solve ValueError: all the input arrays must have same number of dimensions Error ?

  1. How To Solve ValueError: all the input arrays must have same number of dimensions Error ?

    To Solve ValueError: all the input arrays must have same number of dimensions Error The reason why you get your error is because a “1 by n” matrix is different from an array of length n.

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

    To Solve ValueError: all the input arrays must have same number of dimensions Error The reason why you get your error is because a “1 by n” matrix is different from an array of length n.

Solution 1

If I start with a 3×4 array, and concatenate a 3×1 array, with axis 1, I get a 3×5 array:

In [911]: x = np.arange(12).reshape(3,4)
In [912]: np.concatenate([x,x[:,-1:]], axis=1)
Out[912]: 
array([[ 0,  1,  2,  3,  3],
       [ 4,  5,  6,  7,  7],
       [ 8,  9, 10, 11, 11]])
In [913]: x.shape,x[:,-1:].shape
Out[913]: ((3, 4), (3, 1))

Note that both inputs to concatenate have 2 dimensions.

Omit the :, and x[:,-1] is (3,) shape – it is 1d, and hence the error:

In [914]: np.concatenate([x,x[:,-1]], axis=1)
...
ValueError: all the input arrays must have same number of dimensions

The code for np.append is (in this case where axis is specified)

return concatenate((arr, values), axis=axis)

So with a slight change of syntax append works. Instead of a list it takes 2 arguments. It imitates the list append is syntax, but should not be confused with that list method.

In [916]: np.append(x, x[:,-1:], axis=1)
Out[916]: 
array([[ 0,  1,  2,  3,  3],
       [ 4,  5,  6,  7,  7],
       [ 8,  9, 10, 11, 11]])

np.hstack first makes sure all inputs are atleast_1d, and then does concatenate:

return np.concatenate([np.atleast_1d(a) for a in arrs], 1)

So it requires the same x[:,-1:] input. Essentially the same action.

np.column_stack also does a concatenate on axis 1. But first it passes 1d inputs through

array(arr, copy=False, subok=True, ndmin=2).T

This is a general way of turning that (3,) array into a (3,1) array.

In [922]: np.array(x[:,-1], copy=False, subok=True, ndmin=2).T
Out[922]: 
array([[ 3],
       [ 7],
       [11]])
In [923]: np.column_stack([x,x[:,-1]])
Out[923]: 
array([[ 0,  1,  2,  3,  3],
       [ 4,  5,  6,  7,  7],
       [ 8,  9, 10, 11, 11]])

All these ‘stacks’ can be convenient, but in the long run, it’s important to understand dimensions and the base np.concatenate. Also know how to look up the code for functions like this. I use the ipython ?? magic a lot.

And in time tests, the np.concatenate is noticeably faster – with a small array like this the extra layers of function calls makes a big time difference.

Solution 2

The reason why you get your error is because a “1 by n” matrix is different from an array of length n.

I recommend using hstack() and vstack() instead. Like this:

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 repeated “7, 15, 23, 31” column. Also, notice that I used a[:,-1:] instead of a[:,-1]. My version generates a column:

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

Instead of a row array([7,15,23,31])


Edit: append() is much slower.

Summery

It’s all About this issue. Hope all solution helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which solution worked for you? Thank You.

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