Converting list to array and reshaping?

Question

Attempting to make an array of 2d points.

import random
import numpy as np

world_x=500
world_y=500

num_points = 7
points_list = []

def fill_points():
    for i in range(num_points):
        points_list.append(random.randrange(-1*world_x,world_x+1))
        points_list.append(random.randrange(-1*world_y,world_y+1))
    points_array = np.array(points_list)
    points_array.reshape((num_points,2))
    print(points_array)
    print (points_array[0,])
    print (points_array[2,])
    print (points_array[4,])

fill_points()

Returns

[ -70 -491 -326  -35 -408  407   94 -330 -493  499  -61  -12   62 -357]
-70
-326
-408

I was expecting [-70,-491],[-408,-407], and [-493,499]. I've also tried to do this just using shape instead of reshape, and got similar results. Am I converting the list incorrectly, or using reshape incorrectly?

I see, if anyone else is wondering, you can just reassign the new object to the variable name points_array=points_array.reshape((num_points,2)) though @ktzr's solution below saves a line. — fakedane
– fakedane, Commented May 12, 2018 at 18:43
Well, you assign to variables, not objects. It doesn't make sense to say "reassign to itself*. You could assign it to a new variable or to the same variable, and use that variable to access the object, it is up to you. See my answer for an option that works in-place as well. — juanpa.arrivillaga
– juanpa.arrivillaga, Commented May 12, 2018 at 18:47

juanpa.arrivillaga · Accepted Answer · 2018-05-12 18:55:38Z

The .reshape method returns a new array object, that is, it doesn't work in-place. You can either reassign the results of reshape back to the same variable, or modify the .shape attribute directly, which does work in-place:

In [1]: import numpy as np

In [2]: arr = np.arange(10)

In [3]: arr
Out[3]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

In [4]: arr.reshape(2, 5)
Out[4]:
array([[0, 1, 2, 3, 4],
       [5, 6, 7, 8, 9]])

In [5]: arr
Out[5]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

On the other hand:

In [6]: arr.shape = 2, 5

In [7]: arr
Out[7]:
array([[0, 1, 2, 3, 4],
       [5, 6, 7, 8, 9]])

Or use the .resize method for in-place modifications:

In [8]: arr = np.arange(4)

In [9]: arr
Out[9]: array([0, 1, 2, 3])

In [10]: arr.resize(2, 2)

In [11]: arr
Out[11]:
array([[0, 1],
       [2, 3]])

Note: the different array objects can share the same underlying buffer, so be aware that this happens:

In [12]: arr = np.arange(10)

In [13]: arr
Out[13]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

In [14]: arr2 = arr.reshape(2, 5)

In [15]: arr
Out[15]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

In [16]: arr2
Out[16]:
array([[0, 1, 2, 3, 4],
       [5, 6, 7, 8, 9]])

In [17]: arr[0] = 99

In [18]: arr
Out[18]: array([99,  1,  2,  3,  4,  5,  6,  7,  8,  9])

In [19]: arr2
Out[19]:
array([[99,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9]])

So, this makes the re-assigning approach relatively cheap:

In [20]: arr = arr.reshape(2, 5)

In [21]: arr
Out[21]:
array([[99,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9]])

Note, I tend to avoid .resize, because you can accidentally do:

In [33]: arr = np.arange(4)

In [34]: arr.resize(4,4)

In [35]: arr
Out[35]:
array([[0, 1, 2, 3],
       [0, 0, 0, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 0]])

But it will at least warn you... if other arrays are referenced:

In [36]: arr = np.arange(4)

In [37]: arr2 = arr.reshape(2,2)

In [38]: arr
Out[38]: array([0, 1, 2, 3])

In [39]: arr2
Out[39]:
array([[0, 1],
       [2, 3]])

In [40]: arr.resize(4,4)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-40-c4464d98ed0e> in <module>()
----> 1 arr.resize(4,4)

ValueError: cannot resize an array that references or is referenced
by another array in this way.  Use the resize function

However, you can override that behavior at your own peril:

In [41]: arr.resize(4,4, refcheck=False)

In [42]: arr
Out[42]:
array([[0, 1, 2, 3],
       [0, 0, 0, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 0]])

In [43]: arr2
Out[43]:
array([[4611686018427387904, 4611686018427387904],
       [                  6,                   0]])

ktzr · Accepted Answer · 2018-05-12 18:41:58Z

It looks like you want to keep the x and y cords together, try adding them as a list. E.g.:

import random
import numpy as np

world_x=500
world_y=500

num_points = 7
points_list = []

def fill_points():
    for i in range(num_points):
        points_list.append([random.randrange(-1*world_x,world_x+1),
                            random.randrange(-1*world_y,world_y+1)])
    points_array = np.array(points_list)
    print(points_array)
    print (points_array[0,0])
    print (points_array[2,0])
    print (points_array[4,0])

fill_points()

Outputs:

[[ 354 -147]
 [ 193  288]
 [ 157 -319]
 [ 133  426]
 [-109  -54]
 [ -61  224]
 [-251 -411]]
354
157
-109

Or if you want to use reshape. Remember reshape returns a new list, it doesnt change the list you input:

def fill_points():
    for i in range(num_points):
        points_list.append(random.randrange(-1*world_x,world_x+1))
        points_list.append(random.randrange(-1*world_y,world_y+1))
    points_array = np.array(points_list).reshape((num_points,2))
    print(points_array)
    print (points_array[0,0])
    print (points_array[2,0])
    print (points_array[4,0])

fill_points()

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