python - Simpler code to combine multiple lists into one array? I actually wish "lists" are still "lists" in that array -

i have question similar this , that, solutions lists "united" lack of "differentiation".

my python code following:

y = np.empty([1,len(test)])  count = -1  feature in test :     n = engine.neighbours(np.asarray(feature))         if len(n) != 4096:             print "error"             continue                 count = count + 1     y [count] = engine.neighbours(np.asarray(feature)) 

i wondering if there simplified code job?

the linked questions have flattening list of lists. in code processing list of lists (i'm guessing), filtering out some, , collecting rest in 2d array.

oops! y initialized 1 row, yet try place values in y[count], count increase size of test.

y = np.empty([1,len(test)])  count = -1     # prefer start 0, thats style issue feature in test :     n = engine.neighbours(np.asarray(feature))     # engine.neighbors requires array; , returns array?         if len(n) != 4096:             print "error"             continue                 count = count + 1     y [count] = engine.neighbours(np.asarray(feature))     # why call neighbors again?  why not reuse n? 

a common way of creating array incrementally is:

alist = [] feature in tests:      n = engine.neighbors(np.array(feature)      # test n length      alist.append(n) y = np.array(alist) 

since length test, n have same length, resulting array 2d, shape (n,4096), n number of tests correct length.

initializing y np.empty((length(tests),4096)], , inserting y[count,:] = n may faster. may end unfilled rows if fail length test, can remove those.

initializing y np.empty((1,4096)], , inserting y=np.append(y,n) should work. note append different list append. , slower. prefer people use concatenate directly:

y = np.concatenate([y, n[none,:]], axis=0) 

the concatenation explicit, , required dimension manipulation clear.

to make 1d array of arrays, have this:

y=np.empty((4,),dtype=object) in range(len(y)):     y[i]=np.arange(i,2*i) 

producing:

array([array([], dtype=int32), array([1]), array([2, 3]), array([3, 4, 5])], dtype=object) 

which arguably little more y list produced by

y=[] in range(4):     y.append(np.arange(i,2*i)) 

in i'm assuming engine.neighbors() takes 1d array, , returns 1d array. if if took/returned multiple feature 'vectorize' things. long can give 1 feature @ time stuck form of iteration.