NumPy append vs concatenate

Question

What is the difference between NumPy append and concatenate?

My observation is that concatenate is a bit faster and append flattens the array if axis is not specified.

In [52]: print a [[1 2]  [3 4]  [5 6]  [5 6]  [1 2]  [3 4]  [5 6]  [5 6]  [1 2]  [3 4]  [5 6]  [5 6]  [5 6]]  In [53]: print b [[1 2]  [3 4]  [5 6]  [5 6]  [1 2]  [3 4]  [5 6]  [5 6]  [5 6]]  In [54]: timeit -n 10000 -r 5 np.concatenate((a, b)) 10000 loops, best of 5: 2.05 µs per loop  In [55]: timeit -n 10000 -r 5 np.append(a, b, axis = 0) 10000 loops, best of 5: 2.41 µs per loop  In [58]: np.concatenate((a, b)) Out[58]:  array([[1, 2],        [3, 4],        [5, 6],        [5, 6],        [1, 2],        [3, 4],        [5, 6],        [5, 6],        [1, 2],        [3, 4],        [5, 6],        [5, 6],        [5, 6],        [1, 2],        [3, 4],        [5, 6],        [5, 6],        [1, 2],        [3, 4],        [5, 6],        [5, 6],        [5, 6]])  In [59]: np.append(a, b, axis = 0) Out[59]:  array([[1, 2],        [3, 4],        [5, 6],        [5, 6],        [1, 2],        [3, 4],        [5, 6],        [5, 6],        [1, 2],        [3, 4],        [5, 6],        [5, 6],        [5, 6],        [1, 2],        [3, 4],        [5, 6],        [5, 6],        [1, 2],        [3, 4],        [5, 6],        [5, 6],        [5, 6]])  In [60]: np.append(a, b) Out[60]:  array([1, 2, 3, 4, 5, 6, 5, 6, 1, 2, 3, 4, 5, 6, 5, 6, 1, 2, 3, 4, 5, 6, 5,        6, 5, 6, 1, 2, 3, 4, 5, 6, 5, 6, 1, 2, 3, 4, 5, 6, 5, 6, 5, 6]) 

Answer 1

np.append uses np.concatenate:

def append(arr, values, axis=None):     arr = asanyarray(arr)     if axis is None:         if arr.ndim != 1:             arr = arr.ravel()         values = ravel(values)         axis = arr.ndim-1     return concatenate((arr, values), axis=axis)