Difference between list(numpy_array) and numpy_array.tolist()

Question

What is the difference between applying list() on a numpy array vs. calling tolist()?

I was checking the types of both outputs and they both show that what I'm getting as a result is a list, however, the outputs don't look exactly the same. Is it because that list() is not a numpy-specific method (i.e. could be applied on any sequence) and tolist() is numpy-specific, and just in this case they are returning the same thing?

Input:

points = numpy.random.random((5,2))
print "Points type: " + str(type(points))

Output:

Points type: <type 'numpy.ndarray'>

Input:

points_list = list(points)
print points_list
print "Points_list type: " + str(type(points_list))

Output:

[array([ 0.15920058, 0.60861985]), array([ 0.77414769, 0.15181626]), array([ 0.99826806, 0.96183059]), array([ 0.61830768, 0.20023207]), array([ 0.28422605, 0.94669097])]
Points_list type: 'type 'list''

Input:

points_list_alt = points.tolist()
print points_list_alt
print "Points_list_alt type: " + str(type(points_list_alt))

Output:

[[0.15920057939342847, 0.6086198537462152], [0.7741476852713319, 0.15181626186774055], [0.9982680580550761, 0.9618305944859845], [0.6183076760274226, 0.20023206937408744], [0.28422604852159594, 0.9466909685812506]]
Points_list_alt type: 'type 'list''

• Alex Martelli: One is a list of lists, the other a list of numpy arrays -- because .tolist "walks down the tree" and correctly makes every level into lists, while list(...) just "walks" the top level. So, what is the question...?
• Mohammed: There is no need for this statement at all: print "Points type: " + str(type(points)), you can check the type of the variable by using type(varName_here) i.e., type(points)

Answer 1

Your example already shows the difference; consider the following 2D array:

>>> import numpy as np
>>> a = np.arange(4).reshape(2, 2)
>>> a
array([[0, 1],
 [2, 3]])
>>> a.tolist()
[[0, 1], [2, 3]] # nested vanilla lists
>>> list(a)
[array([0, 1]), array([2, 3])] # list of arrays

tolist handles the full conversion to nested vanilla lists (i.e. list of list of int), whereas list just iterates over the first dimension of the array, creating a list of arrays (list of np.array of np.int64). Although both are lists:

>>> type(list(a))
<type 'list'>
>>> type(a.tolist())
<type 'list'>

the elements of each list have a different type:

>>> type(list(a)[0])
<type 'numpy.ndarray'>
>>> type(a.tolist()[0])
<type 'list'>

The other difference, as you note, is that list will work on any iterable, whereas tolist can only be called on objects that specifically implement that method.

Answer 2

.tolist() appears to convert all of the values recursively to python primitives (list), whereas list creates a python list from an iterable. Since the numpy array is an array of arrays, list(...) creates a list of arrays

You can think of list as a function that looks like this:

# Not the actually implementation, just for demo purposes
def list(iterable):
 newlist = []
 for obj in iter(iterable):
 newlist.append(obj)
 return newlist

Answer 3

The major difference is that tolist recursively converts all data to python standard library types.

For instance:

>>> arr = numpy.arange(2)
>>> [type(item) for item in list(arr)]
[numpy.int64, numpy.int64]
>>> [type(item) for item in arr.tolist()]
[builtins.int, builtins.int]

Aside from the functional differences tolist will generally be quicker as it knows it has a numpy array and access to the backing array. Whereas, list will fall back to using an iterator to add all the elements.

In [2]: arr = numpy.arange(1000)
In [3]: %timeit arr.tolist()
10000 loops, best of 3: 33 μs per loop
In [4]: %timeit list(arr)
10000 loops, best of 3: 80.7 μs per loop

I would expect the tolist to be

Answer 4

Additional differences:

1. If you have a 1D numpy array and convert it to a list by using tolist() that will change numpy scalar to the nearest compatible built-in python type. On the contrary, list() does not, it keeps the type of numpy scalar as it is.

# list(...)
a = np.uint32([1, 2])
a_list = list(a) # [1, 2]
type(a_list[0]) # <class 'numpy.uint32'> 
# .tolist()
a_tolist = a.tolist() # [1, 2]
 
type(a_tolist[0]) # <class 'int'>

2. If the numpy array with a scalar value meaning 0D array, using list() will throw an error but tolist() will only convert it as python scalar without any errors.

a = np.array(5)
list(a) # TypeError: iteration over a 0-d array
a.tolist() # 5