2.9. Array Random¶
Since
numpy v1.17
: BitGenerator for the PCG-64 (Parallel Congruent Generator 64 bit) pseudo-random number generatorBefore
numpy v1.17
: Mersenne Twister algorithm for pseudorandom number generation
2.9.1. Seed¶
Seed the generator
from datetime import datetime
def seed():
timestamp = datetime.now().timestamp()
return int(timestamp)
seed() % 10 # 3
seed() % 10 # 4
seed() % 10 # 5
seed() % 10 # 6
from datetime import datetime
def seed():
timestamp = datetime.now().timestamp()
cpu_temperature = 52.4
return int(timestamp + cpu_temperature)
seed() % 10 # 7
seed() % 10 # 2
seed() % 10 # 5
seed() % 10 # 1
from datetime import datetime
def seed():
timestamp = datetime.now().timestamp()
cpu_temperature = 52.4
ram_voltage = 68.8
network_card_crc = 9876
return int(timestamp + cpu_temperature + ram_voltage + network_card_crc)
seed() % 10 # 3
seed() % 10 # 0
seed() % 10 # 2
seed() % 10 # 8
import numpy as np
np.random.seed(0)
2.9.2. Generate¶
Random
int
from low (inclusive) to high (exclusive)Random
float
in the half-open interval[0.0, 1.0)
Generate pseudorandom int
:
import numpy as np
np.random.randint(0, 10)
# 5
np.random.randint(0, 10, size=5)
# array([4, 3, 0, 4, 3])
np.random.randint(0, 10, size=(2,3))
# array([[8, 8, 3],
# [8, 2, 8]])
Generate pseudorandom float
:
import numpy as np
np.random.random()
# 0.8472517387841254
np.random.random(size=5)
# array([0.88173536, 0.69253159, 0.72525428, 0.50132438, 0.95608363])
np.random.random(size=(2,3))
# array([[0.69947928, 0.29743695, 0.81379782],
# [0.39650574, 0.8811032 , 0.58127287]])
2.9.3. Distributions¶
2.9.4. Uniform Distribution¶
Results are from the "continuous uniform" distribution over the stated interval
Random
float
in the half-open interval[0.0, 1.0)

Figure 2.11. Continuous Uniform Distribution [NumpyWik20]¶
import numpy as np
np.random.rand(5)
# array([0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ])
np.random.rand(2,3)
# array([[0.5488135 , 0.71518937, 0.60276338],
# [0.54488318, 0.4236548 , 0.64589411]])
np.random.rand(3,2)
# array([[0.5488135 , 0.71518937],
# [0.60276338, 0.54488318],
# [0.4236548 , 0.64589411]])
2.9.5. Normal (Gaussian) Distribution¶
Draw pseudorandom samples from a normal (Gaussian) distribution
Default:
μ -
loc=0.0
σ -
scale=1.0
import numpy as np
np.random.normal()
# 0.9500884175255894
np.random.normal(0.0, 1.0)
# 0.4001572083672233
np.random.normal(loc=0.0, scale=1.0)
# -0.977277879876411
import numpy as np
np.random.normal(size=5)
# array([-1.67215088, 0.65813053, -0.70150614, 0.91452499, 0.71440557])
np.random.normal(loc=0.0, scale=1.0, size=(2,3))
# array([[-0.99090328, 1.01788005, 0.3415874 ],
# [-1.25088622, 0.92525075, -0.90478616]])

Figure 2.12. Normal (Gaussian) distribution [NumpyWik19a]¶
2.9.6. Poisson Distribution¶
Draw samples from a Poisson distribution
import numpy as np
np.random.poisson(6.0)
# 5
np.random.poisson(lam=6.0)
# 5
import numpy as np
np.random.poisson(lam=6.0, size=5)
# array([5, 7, 3, 5, 6])
np.random.poisson(lam=6.0, size=(2,3))
# array([[4, 9, 7],
# [8, 5, 5]])

Figure 2.13. Poisson distribution [NumpyWik19c]¶
2.9.7. Drawing and Sampling¶
Choice:
import numpy as np
np.random.choice([1, 2, 3])
# 2
np.random.choice([1, 2, 3], size=2)
# array([3, 1])
np.random.choice([1, 2, 3], size=2)
# array([3, 3])
np.random.choice([1, 2, 3], 2, replace=False)
# array([1, 3])
Sample:
import numpy as np
np.random.sample(size=5)
# array([0.44792617, 0.09956909, 0.35231166, 0.46924917, 0.84114013])
np.random.sample(size=(2,3))
# array([[0.90464774, 0.03755938, 0.50831545],
# [0.16684751, 0.77905102, 0.8649333 ]])
np.random.sample(size=(3,2))
# array([[0.41139672, 0.13997259],
# [0.03322239, 0.98257496],
# [0.37329075, 0.42007537]])
2.9.8. Shuffle¶
Modify sequence in-place (!!)
Multi-dimensional arrays are only shuffled along the first axis
1-dimensional Array:
import numpy as np
a = np.array([1, 2, 3])
np.random.shuffle(a)
# array([3, 1, 2])
2-dimensional Array:
import numpy as np
a = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
np.random.shuffle(a)
# array([[7, 8, 9],
# [1, 2, 3],
# [4, 5, 6]])
2.9.9. Assignments¶
"""
* Assignment: Numpy Random Float
* Complexity: medium
* Lines of code: 1 lines
* Time: 3 min
English:
1. Set random seed to zero
2. Define `result: np.ndarray` of 10 random floats
Polish:
1. Ustaw ziarno losowości na zero
2. Zdefiniuj `result: np.ndarray` z 10 losowymi liczbami zmiennoprzecinkowymi
Tests:
>>> type(result) is np.ndarray
True
>>> result
array([0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ,
0.64589411, 0.43758721, 0.891773 , 0.96366276, 0.38344152])
"""
# Given
import numpy as np
np.random.seed(0)
result = ...
"""
* Assignment: Numpy Random Int
* Complexity: easy
* Lines of code: 1 lines
* Time: 3 min
English:
1. Set random seed to zero
2. Define `result: np.ndarray` of size 16x16 with random integers `[0;9]` (inclusive)
Polish:
1. Ustaw ziarno losowości na zero
2. Zdefiniuj `result: np.ndarray` o rozmiarze 16x16 z losowymi liczbami całkowitymi `<0,9>` (włącznie)
Tests:
>>> type(result) is np.ndarray
True
>>> result
array([[5, 0, 3, 3, 7, 9, 3, 5, 2, 4, 7, 6, 8, 8, 1, 6],
[7, 7, 8, 1, 5, 9, 8, 9, 4, 3, 0, 3, 5, 0, 2, 3],
[8, 1, 3, 3, 3, 7, 0, 1, 9, 9, 0, 4, 7, 3, 2, 7],
[2, 0, 0, 4, 5, 5, 6, 8, 4, 1, 4, 9, 8, 1, 1, 7],
[9, 9, 3, 6, 7, 2, 0, 3, 5, 9, 4, 4, 6, 4, 4, 3],
[4, 4, 8, 4, 3, 7, 5, 5, 0, 1, 5, 9, 3, 0, 5, 0],
[1, 2, 4, 2, 0, 3, 2, 0, 7, 5, 9, 0, 2, 7, 2, 9],
[2, 3, 3, 2, 3, 4, 1, 2, 9, 1, 4, 6, 8, 2, 3, 0],
[0, 6, 0, 6, 3, 3, 8, 8, 8, 2, 3, 2, 0, 8, 8, 3],
[8, 2, 8, 4, 3, 0, 4, 3, 6, 9, 8, 0, 8, 5, 9, 0],
[9, 6, 5, 3, 1, 8, 0, 4, 9, 6, 5, 7, 8, 8, 9, 2],
[8, 6, 6, 9, 1, 6, 8, 8, 3, 2, 3, 6, 3, 6, 5, 7],
[0, 8, 4, 6, 5, 8, 2, 3, 9, 7, 5, 3, 4, 5, 3, 3],
[7, 9, 9, 9, 7, 3, 2, 3, 9, 7, 7, 5, 1, 2, 2, 8],
[1, 5, 8, 4, 0, 2, 5, 5, 0, 8, 1, 1, 0, 3, 8, 8],
[4, 4, 0, 9, 3, 7, 3, 2, 1, 1, 2, 1, 4, 2, 5, 5]])
"""
# Given
import numpy as np
np.random.seed(0)
result = ...
"""
* Assignment: Numpy Random Sample
* Complexity: medium
* Lines of code: 1 lines
* Time: 3 min
English:
1. Set random seed to zero
2. Print 6 random integers without repetition in range from 1 to 49
Polish:
1. Ustaw ziarno losowości na zero
2. Wyświetl 6 losowych i nie powtarzających się liczb całkowitych z zakresu od 1 do 49.
Tests:
>>> type(result) is np.ndarray
True
>>> result
array([30, 5, 27, 31, 33, 38])
"""
# Given
import numpy as np
np.random.seed(0)
result = ...