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I have a very basic question regarding to arrays in numpy, but I cannot find a fast way to do it. I have three 2D arrays A,B,C with the same dimensions. I want to convert these in one 3D array (D) where each element is an array
D[column][row] = [A[column][row] B[column][row] c[column][row]]
What is the best way to do it?
483
One way is to use np. dstack which concatenates the arrays along the third axis (d is for depth). You could also use np. concatenate((a, a), axis=2) .
In Python you can use : '+' to combine two lists into a new list. you can use the extend() method to extend a list in place (ie add one list to the end of another).
Use numpy. concatenate() to merge the content of two or multiple arrays into a single array. This function takes several arguments along with the NumPy arrays to concatenate and returns a Numpy array ndarray. Note that this method also takes axis as another argument, when not specified it defaults to 0.
Use numpy. array() to create a 3D NumPy array with specific values. Call numpy. array(object) with object as a list containing x nested lists, y nested lists inside each of the x nested lists, and z values inside each of the y nested lists to create a x -by- y -by- z 3D NumPy array.
You can use numpy.dstack:
>>> import numpy as np
>>> a = np.random.random((11, 13))
>>> b = np.random.random((11, 13))
>>> c = np.random.random((11, 13))
>>>
>>> d = np.dstack([a,b,c])
>>>
>>> d.shape
(11, 13, 3)
>>>
>>> a[1,5], b[1,5], c[1,5]
(0.92522736614222956, 0.64294050918477097, 0.28230222357027068)
>>> d[1,5]
array([ 0.92522737, 0.64294051, 0.28230222])
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