Python: Differences between lists and numpy array of objects

Question

What are the advantages and disadvantages of storing Python objects in a numpy.array with dtype='o' versus using list (or list of list, etc., in higher dimensions)?

Are numpy arrays more efficient in this case? (It seems that they cannot avoid the indirection, but might be more efficient in the multidimensional case.)

Answer 1

Slicing works differently with NumPy arrays. The NumPy docs devote a lengthy page on the topic. To highlight some points:

• NumPy slices can slice through multiple dimensions
• All arrays generated by NumPy basic slicing are always views of the original array, while slices of lists are shallow copies.
• You can assign a scalar into a NumPy slice.
• You can insert and delete items in a list by assigning a sequence of different length to a slice, whereas NumPy would raise an error.

Demo:

>>> a = np.arange(4, dtype=object).reshape((2,2))
>>> a
array([[0, 1],
       [2, 3]], dtype=object)
>>> a[:,0]             #multidimensional slicing
array([0, 2], dtype=object)
>>> b = a[:,0]
>>> b[:] = True        #can assign scalar
>>> a                  #contents of a changed because b is a view to a
array([[True, 1],
       [True, 3]], dtype=object)    

Also, NumPy arrays provide convenient mathematical operations with arrays of objects that support them (e.g. fraction.Fraction).

Answer 2

Numpy is less memory use than a Python list. Also Numpy is faster and more convenient than a list.

For example: if you want to add two lists in Python you have to do looping over all element in the list. On the other hand in Numpy you just add them.

# adding two lists in python
sum = []
l1 = [1, 2, 3]
l2 = [2, 3, 4]
for i in range(len(l1)):
   print sum.append(l1[i]+l2[i])
# adding in numpy
a1 = np.arange(3)
a2 = np.arange(3)
sum2 = a1+a2
print sum2