I know that an easy way to create a NxN array full of zeroes in Python is with:
[[0]*N for x in range(N)]
However, let's suppose I want to create the array by filling it with random numbers:
[[random.random()]*N for x in range(N)]
This doesn't work because each random number that is created is then replicated N times, so my array doesn't have NxN unique random numbers.
Is there a way of doing this in a single line, without using for loops?
You could use a nested list comprehension:
>>> N = 5
>>> import random
>>> [[random.random() for i in range(N)] for j in range(N)]
[[0.9520388778975947, 0.29456222450756675, 0.33025941906885714, 0.6154639550493386, 0.11409250305307261], [0.6149070141685593, 0.3579148659939374, 0.031188652624532298, 0.4607597656919963, 0.2523207155544883], [0.6372935479559158, 0.32063181293207754, 0.700897108426278, 0.822287873035571, 0.7721460935656276], [0.31035121801363097, 0.2691153671697625, 0.1185063432179293, 0.14822226436085928, 0.5490604341460457], [0.9650509333411779, 0.7795665950184245, 0.5778752066273084, 0.3868760955504583, 0.5364495147637446]]
Or use numpy (non-stdlib but very popular):
>>> import numpy as np
>>> np.random.random((N,N))
array([[ 0.26045197, 0.66184973, 0.79957904, 0.82613958, 0.39644677],
[ 0.09284838, 0.59098542, 0.13045167, 0.06170584, 0.01265676],
[ 0.16456109, 0.87820099, 0.79891448, 0.02966868, 0.27810629],
[ 0.03037986, 0.31481138, 0.06477025, 0.37205248, 0.59648463],
[ 0.08084797, 0.10305354, 0.72488268, 0.30258304, 0.230913 ]])
(P.S. It's a good idea to get in the habit of saying list when you mean list and reserving array for numpy ndarrays. There's actually a built-in array module with its own array type, so that confuses things even more, but it's relatively seldom used.)
(A + np.random.random((N,N)) * (B - A)).astype(int)Use this simple function from numpy:
Array of size (4,4) filled with numbers 1-4
np.random.randint(1, 5, size=(4, 4))
[1 2 1 2]
[2 2 2 4]
[4 1 1 2]
[4 2 2 4]
This is how you create a 2d array:
k = np.random.random ([3,4]) * 10
k.astype(int)
Just use [random.random() for i in range(N)] inside your list comprehension.
Demo:
>>> import random
>>> N = 3
>>> [random.random() for i in range(N)]
[0.24578599816668256, 0.34567935734766164, 0.6482845150243465]
>>> M = 3
>>> [[random.random() for i in range(N)] for j in range(M)]
[[0.9883394519621589, 0.6533595743059281, 0.866522328922242], [0.5906410405671291, 0.4429977939796209, 0.9472377762689498], [0.6883677407216132, 0.8215813727822125, 0.9770711299473647]]
You can use list comprehensions.
[[random.random() for x in xrange(N)] for y in xrange(N)]
https://docs.python.org/2/tutorial/datastructures.html#list-comprehensions
For large multi dimensional arrays, I suggest you use numpy though.
It can be done without a loop. Try this simple line of code for generating a 2 by 3 matrix of random numbers with mean 0 and standard deviation 1.
The syntax is :
import numpy
numpy.random.normal(mean, standard deviation, (rows,columns))
example :
numpy.random.normal(0,1,(2,3))
import numpy as np #np is shortcut of numpy
#Syntax : np.random.randint(the range for ex if you choose 100 then your array elements will be within the range 0 to 100, size = (row size, col size)
a = np.random.randint(100, size = (5,4)) #a is a variable(object)
print(a)
OUTPUT
[[49 81 57 96]
[64 95 54 53]
[63 77 68 74]
[96 38 29 41]
[13 39 99 43]]
[[0]*N]*N.np.zeros((N, N)). For any other value, one can usenp.full((N, N), fill_value).