I create two matrices
import numpy as np
arrA = np.zeros((9000,3))
arrB = np.zerros((9000,6))
I want to concatenate pieces of those matrices. But when I try to do:
arrC = np.hstack((arrA, arrB[:,1]))
I get an error:
ValueError: all the input arrays must have same number of dimensions
I guess it's because np.shape(arrB[:,1]) is equal (9000,) instead of (9000,1), but I cannot figure out how to resolve it.
Could you please comment on this issue?
You could preserve dimensions by passing a list of indices, not an index:
>>> arrB[:,1].shape
(9000,)
>>> arrB[:,[1]].shape
(9000, 1)
>>> out = np.hstack([arrA, arrB[:,[1]]])
>>> out.shape
(9000, 4)
This is easier to see visually.
Assume:
>>> arrA=np.arange(9000*3).reshape(9000,3)
>>> arrA
array([[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8],
...,
[26991, 26992, 26993],
[26994, 26995, 26996],
[26997, 26998, 26999]])
>>> arrB=np.arange(9000*6).reshape(9000,6)
>>> arrB
array([[ 0, 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10, 11],
[ 12, 13, 14, 15, 16, 17],
...,
[53982, 53983, 53984, 53985, 53986, 53987],
[53988, 53989, 53990, 53991, 53992, 53993],
[53994, 53995, 53996, 53997, 53998, 53999]])
If you take a slice of arrB, you are producing a series that looks more like a row:
>>> arrB[:,1]
array([ 1, 7, 13, ..., 53983, 53989, 53995])
What you need is a column the same shape as a column to add to arrA:
>>> arrB[:,[1]]
array([[ 1],
[ 7],
[ 13],
...,
[53983],
[53989],
[53995]])
Then hstack works as expected:
>>> arrC=np.hstack((arrA, arrB[:,[1]]))
>>> arrC
array([[ 0, 1, 2, 1],
[ 3, 4, 5, 7],
[ 6, 7, 8, 13],
...,
[26991, 26992, 26993, 53983],
[26994, 26995, 26996, 53989],
[26997, 26998, 26999, 53995]])
An alternate form is to specify -1 in one dimension and the number of rows or cols desired as the other in .reshape():
>>> arrB[:,1].reshape(-1,1) # one col
array([[ 1],
[ 7],
[ 13],
...,
[53983],
[53989],
[53995]])
>>> arrB[:,1].reshape(-1,6) # 6 cols
array([[ 1, 7, 13, 19, 25, 31],
[ 37, 43, 49, 55, 61, 67],
[ 73, 79, 85, 91, 97, 103],
...,
[53893, 53899, 53905, 53911, 53917, 53923],
[53929, 53935, 53941, 53947, 53953, 53959],
[53965, 53971, 53977, 53983, 53989, 53995]])
>>> arrB[:,1].reshape(2,-1) # 2 rows
array([[ 1, 7, 13, ..., 26983, 26989, 26995],
[27001, 27007, 27013, ..., 53983, 53989, 53995]])
There is more on array shaping and stacking here
I would try something like this:
np.vstack((arrA.transpose(), arrB[:,1])).transpose()
There several ways of making your selection from arrB a (9000,1) array:
np.hstack((arrA,arrB[:,[1]]))
np.hstack((arrA,arrB[:,1][:,None]))
np.hstack((arrA,arrB[:,1].reshape(9000,1)))
np.hstack((arrA,arrB[:,1].reshape(-1,1)))
One uses the concept of indexing with an array or list, the next adds a new axis (e.g. np.newaxis), the third uses reshape. These are all basic numpy array manipulation tasks.
