title: "Random Sampling" date: 2026-05-24T13:50:59Z

Random Sampling

1import numpy as np

1np.__version__

'1.11.2'

1__author__ = 'kyubyong. longinglove@nate.com'

Simple random data

Q1. Create an array of shape (3, 2) and populate it with random samples from a uniform distribution over [0, 1).

array([[ 0.13879034,  0.71300174],
       [ 0.08121322,  0.00393554],
       [ 0.02349471,  0.56677474]])

Q2. Create an array of shape (1000, 1000) and populate it with random samples from a standard normal distribution. And verify that the mean and standard deviation is close enough to 0 and 1 repectively.

-0.00110028519551
0.999683483393

Q3. Create an array of shape (3, 2) and populate it with random integers ranging from 0 to 3 (inclusive) from a discrete uniform distribution.

array([[1, 3],
       [3, 0],
       [0, 0]])

Q4. Extract 1 elements from x randomly such that each of them would be associated with probabilities .3, .5, .2. Then print the result 10 times.

1x = [b'3 out of 10', b'5 out of 10', b'2 out of 10']

5 out of 10
2 out of 10
3 out of 10
5 out of 10
2 out of 10
5 out of 10
2 out of 10
2 out of 10
2 out of 10
5 out of 10

Q5. Extract 3 different integers from 0 to 9 randomly with the same probabilities.

array([5, 4, 0])

Permutations

Q6. Shuffle numbers between 0 and 9 (inclusive).

[2 3 8 4 5 1 0 6 9 7]

1# Or

[5 2 7 4 1 0 6 8 9 3]

Random generator

Q7. Assign number 10 to the seed of the random generator so that you can get the same value next time.