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
if you know the number of columns before hand:
>>> xs = np.array([[1,2,3,4,5],[10,20,30,40,50]])
>>> ys = np.array([], dtype=np.int64).reshape(0,5)
>>> ys
array([], shape=(0, 5), dtype=int64)
>>> np.vstack([ys, xs])
array([[ 1., 2., 3., 4., 5.],
[ 10., 20., 30., 40., 50.]])
if not:
>>> ys = np.array([])
>>> ys = np.vstack([ys, xs]) if ys.size else xs
array([[ 1, 2, 3, 4, 5],
[10, 20, 30, 40, 50]])