I have a 2D array in Python either a normal one or a numpy array with dimensions (150, 5), I wish to split it into two arrays of dimensions (150, 3) and (150, 2) respectively. Somehow I haven't been able to do it.
Any suggestions?
for numpy arrays you can slice them like this:
a, b = the_array[...,:3], the_array[...,3:]
and with lists of lists (that's what I understand for "normal arrays")
a, b = [i[:3] for i in the_array], [i[3:] for i in the_array]
Related
I have a numpy array of shape (29, 10) and a list of 29 elements and I want to end up with an array of shape (29,11)
I am basically converting the list to a numpy array and trying to vstack, but it complain about dimensions not being the same.
Toy example
a = np.zeros((29,10))
a.shape
(29,10)
b = np.array(['A']*29)
b.shape
(29,)
np.vstack((a, b))
ValueError: all the input array dimensions except for the concatenation axis must match exactly
Dimensions do actually match, why am I getting this error and how can I solve it?
I think you are looking for np.hstack.
np.hstack((a, b.reshape(-1,1)))
Moreover b must be 2-dimensional, that's why I used a reshape.
The problem is that you want to append a 1D array to a 2D array.
Also, for the dimension you've given for b, you are probably looking for hstack.
Try this:
a = np.zeros((29,10))
a.shape
(29,10)
b = np.array(['A']*29)[:,None] #to ensure 2D structure
b.shape
(29,1)
np.hstack((a, b))
If you do want to vertically stack, you'd need this:
a = np.zeros((29,10))
a.shape
(29,10)
b = np.array(['A']*10)[None,:] #to ensure 2D structure
b.shape
(1,10)
np.vstack((a, b))
I want to multiply two numpy arrays. One numpy array is given by matrix of shape (10, 10) and the other is given by a matrix of matrices, i.e. shape (10, 10, 256, 256).
I now simply want to multiply each matrix in the second matrix of matrices with the corresponding component in the first matrix. For instance, the matrix at position (0, 0) in the second matrix shall be multiplied by the value at position (0, 0) in the first matrix.
Intuitively, this is not really complicated, but numpy does not seem to support that. Or at least I am not smart enough to make it work. The ValueError that is thrown says:
ValueError: operands could not be broadcast together with shapes (10,10) (10,10,256,256)
Can anybody of you help me please? How can I achieve what I want in a numpyy way.
You can use the NumPy einsum function, e.g., (using zeros arrays as dummies in this example):
import numpy as np
x = np.zeros((10, 10))
y = np.zeros((10, 10, 256, 256))
z = np.einsum("ij,ijkm->km", x, y)
print(z.shape)
(256, 256)
See here for a nice description of einsum's usage.
I want to create a 3D np.array named output of varying size. An array of size (5,a,b); with a and b varying (b decreasing):
(a,b) = (1000,20)
(a,b) = (1000,19)
(a,b) = (1000,18)
(a,b) = (1000,17)
(a,b) = (1000,16)
I could create an array of arrays in order to do so, but later on I want to get the first column of all the arrays (without a loop) then I cannot use:
output[:,:,0]
Concatenating them wont work also, it asks for the same size of the arrays...
Any alternatives to be able to have a varying single array instead of an array of arrays?
Thanks!
Like #Divakar said, create an empty array with type object and assign the different sized arrays to their respective indices.
import numpy as np
arrs = [np.ones((5, i, 10 - i)) for i in range(10)]
arrs[0].shape
(5, 0, 10)
arrs[1].shape
(5, 1, 9)
out = np.emtpy(len(arrs), dtype=object)
out[:] = arrs
out[0].shape
(5, 0, 10)
out[1].shape
(5, 1, 9)
Maybe you could make a list and add this 5 arrays.
I have a list of numpy arrays, and each one of them has a dtype=object in the first dimension (3 objects), where there are arrays of different shapes e.g. (200, 10), (100, 10), (50, 10).
Although this works well, when it happens that the three objects get the same first dimension (e.g. (200, 10), (200,10), (200,10)), the array automatically goes from dtype=object to dtype=float). So, i end up with one of these arrays, being (3,200,10), instead of (3,) object type.
That ends up with an error when i try to make the list a numpy array, since one of the arrays has different shape.
Is there any solution to that?
I have a function in Python that returns a numpy.mat of shape (100, 1). I am calling this function 4 times in a loop and would like to take the resulting 4 matricies and create a matrix of shape (100, 4). I have looked for sometime at numpy.append, numpy.concatenate, and numpy.insert but have not been able to get this working.
Here is a short SSCCE of my issue
zeros = np.zeros(shape=(100, 4))
for i in range(1, 5):
np.append(zeros, np.empty(shape=(100, 1)))
print(zeros)
Where zeros should results in a matrix of shape (100, 4) with "junk" values from each of the calls to numpy.empty and not all 0..
Do something along these lines -
zeros = np.zeros(shape=(100, 4))
for i in range(1, 5):
data = np.random.rand(100,1) # func that returns (100,1) shaped array
zeros[:,i-1] = data.ravel()
In place of ravel(), we could also use : data[:,0] or np.squeeze(data), basic idea is to feed a 1D array there, because the LHS zeros[:,i-1] expects a 1D array there.
As an alternative, inside the loop, we could also do -
zeros[:,[i-1]] = data
Thus, with that list of column index [i-1] instead of i-1, we are keeping the dimensions into which data is to be assigned (keeps as 2D) and that allows us to feed in data, which is also 2D without any change.