in Matlab I do this: >> E = []; >> A = [1 2 3 4 5; 10 20 30 40 50]; >> E = [E ; A] E = 1 2 3 4 5 10 20 30 40 50 Now I want the same thing in Numpy but I have problems, look at this: >>> E = array([],dtype=int) >>> E array([], dtype=int64) >>> A = array([[1,2,3,4,5],[10,20,30,40,50]]) >>> E = vstack((E,A)) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/numpy/core/shape_base.py", line 226, in vstack return _nx.concatenate(map(atleast_2d,tup),0) ValueError: array dimensions must agree except for d_0 I have a similar situation when I do this with: >>> E = concatenate((E,A),axis=0) Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: arrays must have same number of dimensions Or: >>> E = append([E],[A],axis=0) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/numpy/lib/function_base.py", line 3577, in append return concatenate((arr, values), axis=axis) ValueError: arrays must have same number of dimensions Continue reading...