CODEWITHSUNDEEP
pythonarraysrandom
user46242
user46242

Reputation: 453

Simple way of creating a 2D array with random numbers (Python)

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?

Upvotes: 26

Views: 137452

Answers (7)

Rasha
Rasha

Reputation: 11

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]]

Upvotes: 1

Abhishek Sengupta
Abhishek Sengupta

Reputation: 3291

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]

Upvotes: 20

Ra Kuz
Ra Kuz

Reputation: 61

This is how you create a 2d array:

k = np.random.random ([3,4]) * 10
k.astype(int)

Upvotes: 6

seakyourpeak
seakyourpeak

Reputation: 551

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))

Upvotes: 3

Sunjay Varma
Sunjay Varma

Reputation: 5115

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.

Upvotes: 3

DSM
DSM

Reputation: 353059

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.)

Upvotes: 51

timgeb
timgeb

Reputation: 78690

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]]

Upvotes: 4

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