python - generalisation between vector and matrix, matrix and tensor with numpy ndarray -

i found interesting thing when comparing matlab , numpy.

matlab: x = [1, 2] n = size(x, 2)  % n = 1  python: x = np.array([1, 2]) n = x.shape[1] # error  

the question is: how handle input may both ndarray shape (n,) , ndarray shape (n, m).

e.g.

def my_summation(x):     """     x : ndarray         each column of x observation.     """     # solution ndarray shape (n,)     # if x.ndim == 1:     #     x = x.reshape((-1, 1))      num_of_sample = x.shape[1]     sum = np.zeros(x.shape[0])     in range(num_of_sample):         sum = sum + x[:, i]      return sum   = np.array([[1, 2], [3, 4]]) b = np.array([1, 2])  print my_summation(a) print my_summation(b) 

my solution forcing ndarray shape (n,) shape (n, 1).

the summation used example. want find elegant way handle possibility of matrix 1 observation(vector) , matrix more 1 observation using ndarray.

does have better solutions?

i learned numpy.atleast_2d python control toolbox. don't need for-loop summation, rather use numpy.sum.

import numpy np   def my_summation(x):     """     x : ndarray         each column of x observation.     """     # solution ndarray shape (n,)     # if x.ndim == 1:     #     x = x.reshape((-1, 1))     x = np.atleast_2d(x)     return np.sum(x, axis=1)   = np.array([[1, 2], [3, 4]]) b = np.array([1, 2])  print my_summation(a) print my_summation(b) 

gives

[3 7] [3]