In some special cases, array can be concatenated without explicitly calling concatenate function. For example, given a 2D array A, the following code will yield an identical array B:
B = np.array([A[ii,:] for ii in range(A.shape[0])])
I know this method works, but do not quite understand the underlying mechanism. Can anyone demystify the code above a little bit?
A[ii,:] is ii-th row of array A.
The list comprehension [A[ii,:] for ii in range(A.shape[0])] basically makes a list of rows in A (A.shape[0] is number of rows in A).
Finally, B is an array, that its content is a list of A's rows, which is essentially the same as A itself.
By now you should be familiar with making an array from a list of lists:
In [178]: np.array([[1,2],[3,4]])
Out[178]:
array([[1, 2],
[3, 4]])
but that works just as well if it's a list of arrays:
In [179]: np.array([np.array([1,2]),np.array([3,4])])
Out[179]:
array([[1, 2],
[3, 4]])
stack also does this, by adding a dimension to the arrays and calling concatenate (read its code):
In [180]: np.stack([np.array([1,2]),np.array([3,4])])
Out[180]:
array([[1, 2],
[3, 4]])
concatenate joins the arrays - on an existing axis:
In [181]: np.concatenate([np.array([1,2]),np.array([3,4])])
Out[181]: array([1, 2, 3, 4])
stack adds a dimension first, as in:
In [182]: np.concatenate([np.array([[1,2]]),np.array([[3,4]])])
Out[182]:
array([[1, 2],
[3, 4]])
np.array and concatenate aren't identical, but there's a lot of overlap in their functionality.
