Generate random integers between 0 and 9

Question

How can I generate random integers between 0 and 9 (inclusive) in Python?

For example, 0, 1, 2, 3, 4

Answer 1

While many posts demonstrate how to get one random integer, the original question asks how to generate random integers (plural):

How can I generate random integers between 0 and 9 (inclusive) in Python?

For clarity, here we demonstrate how to get multiple random integers.

Given

>>> import random


lo = 0
hi = 10
size = 5

Code

Multiple, Random Integers

# A
>>> [lo + int(random.random() * (hi - lo)) for _ in range(size)]
[5, 6, 1, 3, 0]

# B
>>> [random.randint(lo, hi) for _ in range(size)]
[9, 7, 0, 7, 3]

# C
>>> [random.randrange(lo, hi) for _ in range(size)]
[8, 3, 6, 8, 7]

# D
>>> lst = list(range(lo, hi))
>>> random.shuffle(lst)
>>> [lst[i] for i in range(size)]
[6, 8, 2, 5, 1]

# E
>>> [random.choice(range(lo, hi)) for _ in range(size)]
[2, 1, 6, 9, 5]

Sample of Random Integers

# F
>>> random.choices(range(lo, hi), k=size)
[3, 2, 0, 8, 2]

# G
>>> random.sample(range(lo, hi), k=size)
[4, 5, 1, 2, 3]

---

Details

Some posts demonstrate how to natively generate multiple random integers.¹ Here are some options that address the implied question:

• A: random.random returns a random float in the range [0.0, 1.0)
• B: random.randint returns a random integer N such that a <= N <= b
• C: random.randrange alias to randint(a, b+1)
• D: random.shuffle shuffles a sequence in place
• E: random.choice returns a random element from the non-empty sequence
• F: random.choices returns k selections from a population (with replacement, Python 3.6+)
• G: random.sample returns k unique selections from a population (without replacement):²

See also R. Hettinger's talk on Chunking and Aliasing using examples from the random module.

Here is a comparison of some random functions in the Standard Library and Numpy:

| | random                | numpy.random                     |
|-|-----------------------|----------------------------------|
|A| random()              | random()                         |
|B| randint(low, high)    | randint(low, high)               |
|C| randrange(low, high)  | randint(low, high)               |
|D| shuffle(seq)          | shuffle(seq)                     |
|E| choice(seq)           | choice(seq)                      |
|F| choices(seq, k)       | choice(seq, size)                |
|G| sample(seq, k)        | choice(seq, size, replace=False) |

You can also quickly convert one of many distributions in Numpy to a sample of random integers.³

Examples

>>> np.random.normal(loc=5, scale=10, size=size).astype(int)
array([17, 10,  3,  1, 16])

>>> np.random.poisson(lam=1, size=size).astype(int)
array([1, 3, 0, 2, 0])

>>> np.random.lognormal(mean=0.0, sigma=1.0, size=size).astype(int)
array([1, 3, 1, 5, 1])

_{¹Namely @John Lawrence Aspden, @S T Mohammed, @SiddTheKid, @user14372, @zangw, et al. ²@prashanth mentions this module showing one integer. ³Demonstrated by @Siddharth Satpathy}