python - Numpy array creation -

first off apologize arbitraryness of question rewriting of scripts use numpy arrays instead of nested python lists (for performance , memory) i'm still struggling declaration.

i trying create structure using numpy arrays, starting off 1000 (arbitrary value) elements in array each element should contain float (as [x][0]) , nested array containing coordinates (so 10.0000 x 2 floats per top level element) (as [x][1], each element in nested array accessible [x][1][y][z] y element in nested array , z specified of 2 coordinates). following question nested structured numpy array creates nigh identical structure (as reference question , desired structure).

schematic raw data example:

time 0   m/z 10 int 10   m/z 20 int 20   m/z 30 int 1000   ... time 1   <repeat> 

i have read haveto use dtype part define nested array not quite sure on declaration part of dimensions empty array, give me hand? here came far.

data=np.zeroes((1000,2 /* add nested array */), dtype=[('time', 'f'), [('m/z','f'), ('intensity','f')]])

ps: matrix might better option this?

>>> = np.zeros(1000, dtype='float32, (10000,2)float32') >>> a[200][0] 0.0 >>> a[200][1][2000] array([ 0.,  0.], dtype=float32) 

note creates 1000 arrays of dimension (10000,2). that's fine if ever operations @ 1 of arrays. using separate (1000,10000,2) array instead take better advantage of vectorized operations in numpy. example increment second coordinates in 1 operation this:

>>> b = np.zeros((1000,10000,2)) >>> b[:,:,1] += 1 

trying same a[:][1][:,1] error.