ValueError: all the input arrays must have same number of dimensions
Question:
I’m having a problem with np.append
.
I’m trying to duplicate the last column of 20×361 matrix n_list_converted
by using the code below:
n_last = []
n_last = n_list_converted[:, -1]
n_lists = np.append(n_list_converted, n_last, axis=1)
But I get error:
ValueError: all the input arrays must have same number of dimensions
However, I’ve checked the matrix dimensions by doing
print(n_last.shape, type(n_last), n_list_converted.shape, type(n_list_converted))
and I get
(20L,) (20L, 361L)
so the dimensions match? Where is the mistake?
Answers:
(n,) and (n,1) are not the same shape. Try casting the vector to an array by using the [:, None]
notation:
n_lists = np.append(n_list_converted, n_last[:, None], axis=1)
Alternatively, when extracting n_last
you can use
n_last = n_list_converted[:, -1:]
to get a (20, 1)
array.
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. Read this answer.
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.
You can also cast (n,) to (n,1) by enclosing within brackets [ ].
e.g. Instead of np.append(b,a,axis=0)
use np.append(b,[a],axis=0)
a=[1,2]
b=[[5,6],[7,8]]
np.append(b,[a],axis=0)
returns
array([[5, 6],
[7, 8],
[1, 2]])
I’m having a problem with np.append
.
I’m trying to duplicate the last column of 20×361 matrix n_list_converted
by using the code below:
n_last = []
n_last = n_list_converted[:, -1]
n_lists = np.append(n_list_converted, n_last, axis=1)
But I get error:
ValueError: all the input arrays must have same number of dimensions
However, I’ve checked the matrix dimensions by doing
print(n_last.shape, type(n_last), n_list_converted.shape, type(n_list_converted))
and I get
(20L,) (20L, 361L)
so the dimensions match? Where is the mistake?
(n,) and (n,1) are not the same shape. Try casting the vector to an array by using the [:, None]
notation:
n_lists = np.append(n_list_converted, n_last[:, None], axis=1)
Alternatively, when extracting n_last
you can use
n_last = n_list_converted[:, -1:]
to get a (20, 1)
array.
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. Read this answer.
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.
You can also cast (n,) to (n,1) by enclosing within brackets [ ].
e.g. Instead of np.append(b,a,axis=0)
use np.append(b,[a],axis=0)
a=[1,2]
b=[[5,6],[7,8]]
np.append(b,[a],axis=0)
returns
array([[5, 6],
[7, 8],
[1, 2]])