Pseudo-random number generation
Sampling in Python
James Chapman
Curriculum Manager, DataCamp
What does random mean?
{adjective} made, done, happening, or chosen without method or conscious decision.
1 Oxford Languages
True random numbers
- Generated from physical processes, like flipping coins
- Hotbits uses radioactive decay
- RANDOM.ORG uses atmospheric noise
- True randomness is expensive
1 https://www.fourmilab.ch/hotbits 2 https://www.random.org
Pseudo-random number generation
- Pseudo-random number generation is cheap and fast
- Next "random" number calculated from previous "random" number
- The first "random" number calculated from a seed
- The same seed value yields the same random numbers
Pseudo-random number generation example
seed = 1
calc_next_random(seed)
3
calc_next_random(3)
2
calc_next_random(2)
6
Random number generating functions
- Prepend with
numpy.random, such asnumpy.random.beta()
| function | distribution | function | distribution |
|---|---|---|---|
| .beta | Beta | .hypergeometric | Hypergeometric |
| .binomial | Binomial | .lognormal | Lognormal |
| .chisquare | Chi-squared | .negative_binomial | Negative binomial |
| .exponential | Exponential | .normal | Normal |
| .f | F | .poisson | Poisson |
| .gamma | Gamma | .standard_t | t |
| .geometric | Geometric | .uniform | Uniform |
Visualizing random numbers
randoms = np.random.beta(a=2, b=2, size=5000)
randoms
array([0.6208281 , 0.73216171, 0.44298403, ...,
0.13411873, 0.52198411, 0.72355098])
plt.hist(randoms, bins=np.arange(0, 1, 0.05))
plt.show()
Random numbers seeds
np.random.seed(20000229)
np.random.normal(loc=2, scale=1.5, size=2)
array([-0.59030264, 1.87821258])
np.random.normal(loc=2, scale=1.5, size=2)
array([2.52619561, 4.9684949 ])
np.random.seed(20000229)
np.random.normal(loc=2, scale=1.5, size=2)
array([-0.59030264, 1.87821258])
np.random.normal(loc=2, scale=1.5, size=2)
array([2.52619561, 4.9684949 ])
Using a different seed
np.random.seed(20000229)
np.random.normal(loc=2, scale=1.5, size=2)
array([-0.59030264, 1.87821258])
np.random.normal(loc=2, scale=1.5, size=2)
array([2.52619561, 4.9684949 ])
np.random.seed(20041004)
np.random.normal(loc=2, scale=1.5, size=2)
array([1.09364337, 4.55285159])
np.random.normal(loc=2, scale=1.5, size=2)
array([2.67038916, 2.36677492])
Let's practice!
Sampling in Python
