How to fill 2d-array with increasing numbers?

Question

I want to create an array with numbers going from 0 to 10 in its 1st sub-array, from 11 to 20 in its 2nd and so on...

I can create the sub arrays with

for i in range(10):
    print np.arange(10*i, 10*(i+1))

which gives me

[0 1 2 3 4 5 6 7 8 9]
[10 11 12 13 14 15 16 17 18 19]
[20 21 22 23 24 25 26 27 28 29]
[30 31 32 33 34 35 36 37 38 39]
[40 41 42 43 44 45 46 47 48 49]
[50 51 52 53 54 55 56 57 58 59]
[60 61 62 63 64 65 66 67 68 69]
[70 71 72 73 74 75 76 77 78 79]
[80 81 82 83 84 85 86 87 88 89]
[90 91 92 93 94 95 96 97 98 99]

but I can't fit it inside an array... Tried -

a = np.array((10,10))
for i in range(10):
    a[i] = np.arange(10*i, 10*(i+1))

Which gave ValueError: setting an array element with a sequence. How can I fix this?

Edit:

All the answers here provide a working way to achieve what I want, which is the main thing I wanted, but I also want to understand why the error appears, since the np.arange(), from what I understand, returns an ndarray

Solution 1

3 solutions:

This works:

res = []
for i in range(10):
    res.append(np.arange(10*i, 10*(i+1)))

res = np.array(res)

>>> res
array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
       [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],
       [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
       [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],
       [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],
       [70, 71, 72, 73, 74, 75, 76, 77, 78, 79],
       [80, 81, 82, 83, 84, 85, 86, 87, 88, 89],
       [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])

Or, via direct assignment (most similar to what you were trying to do)

res = np.zeros((10,10))
for i in range(10):
    res[i] = np.arange(10*i, 10*(i+1))

(When attempting the above, your issue was that you were trying to assign onto an array that was just array([10, 10]), which is the wrong shape)

Or, res = np.array(np.arange(0,100)).reshape(10,10) gives you the same thing

Solution 2

I guess the easiest way to do this is to reshape an arange from 0 to 100:

>>> np.arange(100).reshape(10, -1)
array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
       [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],
       [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
       [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],
       [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],
       [70, 71, 72, 73, 74, 75, 76, 77, 78, 79],
       [80, 81, 82, 83, 84, 85, 86, 87, 88, 89],
       [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])

Here the .reshape(..) call will thus transform the matrix such that it is a 2D-array, with 10 "rows" and a number of columns such that the total amount of cells is 100.

In case you do not want to construct a 2D-array, but a Python list of 1D arrays, we can use list comprehension:

[np.arange(i, i+10) for i in range(0, 100, 10)]

Solution 3

A more pythonic way of doing your task would be one liner

import numpy as np

print(np.reshape(np.arange(0,100),(10,10)))

Solution 4

Here is how you can do it without using numpy library.

h = 10
w = 10
req_array = [[(j+1)+w*i for j in range(w)] for i in range(h)]
print(req_array)

View more solutions

Comments

[CIsForCookies]

Ok, but why append works and direct assignment fails? Also, how res is an array when you declared it as a list?

[sacuL]

See my update for the direct assignment problem you had. In my first example, res is an array because I explicitly tell it to be an array using res = np.array(res)