First, you should not be using NumPy arrays as lists of lists.
Second, let’s forget about NumPy; your listcomp doesn’t make any sense in the first place, even for lists of lists.
In the inner comprehension, for i in X is going to iterate over the rows in X. Those rows aren’t numbers, they’re lists (or, in NumPy, 1D arrays), so X[i] makes no sense whatsoever. You may have wanted i[j] instead.
In the outer comprehension, for j in X[i] has the same problem, but is has an even bigger problem: there is no i value. You have a comprehension looping over each i inside this comprehension.
If you’re confused by a comprehension, write it out as an explicit for statement, as explained in the tutorial section on List Comprehensions:
tmp = []
for j in X[i]:
tmp.append([X[i,j] for i in X])
… which expands to:
tmp = []
for j in X[i]:
tmp2 = []
for i in X:
tmp2.append(X[i,j])
tmp.append(tmp2)
… which should make it obvious what’s wrong here.
I think what you wanted was:
[[cell for cell in row] for row in X]
Again, turn it back into explicit for statements:
tmp = []
for row in X;
tmp2 = []
for cell in row:
tmp2.append(cell)
tmp.append(tmp2)
That’s obviously right.
Or, if you really want to use indexing (but you don’t):
[[X[i][j] for j in range(len(X[i]))] for i in range(len(X))]
So, back to NumPy. In NumPy terms, that last version is:
[[X[i,j] for j in range(X.shape[1])] for i in range(X.shape[0])]
… and if you want to go in column-major order instead of row-major, you can (unlike with a list of lists):
[[X[i,j] for i in range(X.shape[0])] for j in range(X.shape[1])]
… but that will of course transpose the array, which isn’t what you wanted to do.
The one thing you can’t do is mix up column-major and row-major order in the same expression, because you end up with nonsense.
Of course the right way to make a copy of an array is to use the copy method:
X.copy()
Just as the right way to transpose an array is:
X.T