python - Numpy: Fix array with rows of different lengths by filling the empty elements with zeros -

the functionality looking looks this:

data = np.array([[1, 2, 3, 4],                  [2, 3, 1],                  [5, 5, 5, 5],                  [1, 1]])  result = fix(data) print result  [[ 1.  2.  3.  4.]  [ 2.  3.  1.  0.]  [ 5.  5.  5.  5.]  [ 1.  1.  0.  0.]] 

these data arrays i'm working large appreciate efficient solution.

edit: data read in disk python list of lists.

this 1 approach -

def numpy_fillna(data):     # lengths of each row of data     lens = np.array([len(i) in data])      # mask of valid places in each row     mask = np.arange(lens.max()) < lens[:,none]      # setup output array , put elements data masked positions     out = np.zeros(mask.shape, dtype=data.dtype)     out[mask] = np.concatenate(data)     return out 

sample input, output -

in [222]: # input object dtype array      ...: data = np.array([[1, 2, 3, 4],      ...:                  [2, 3, 1],      ...:                  [5, 5, 5, 5, 8 ,9 ,5],      ...:                  [1, 1]])  in [223]: numpy_fillna(data) out[223]:  array([[1, 2, 3, 4, 0, 0, 0],        [2, 3, 1, 0, 0, 0, 0],        [5, 5, 5, 5, 8, 9, 5],        [1, 1, 0, 0, 0, 0, 0]], dtype=object)