Please generate a singular random integer within a certain range.
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Singular values:
generate a random integer
import random
min_int = 0
max_int = 10
rand_int = random.randint(min_int, max_int) # returns an integer equals to or in between min_int and max_int.
generate a random float
{python} .uniform(...)stands for the uniform distribution, meaning all values in that range have the same chance to be choosen.
import random
min_fp = 0
max_fp = 10
rand_floating_point = random.uniform(min_fp, max_fp) # returns a floating point between min_fp and max_fp
For non normal distributions I would immediately use numpy functions.
generate a random float
Please generate a singular random float within a certain range
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Singular values:
generate a random integer
import random
min_int = 0
max_int = 10
rand_int = random.randint(min_int, max_int) # returns an integer equals to or in between min_int and max_int.
generate a random float
{python} .uniform(...)stands for the uniform distribution, meaning all values in that range have the same chance to be choosen.
import random
min_fp = 0
max_fp = 10
rand_floating_point = random.uniform(min_fp, max_fp) # returns a floating point between min_fp and max_fp
For non normal distributions I would immediately use numpy functions.
More than one value:
generate random integers with a certain shape
Please generate a random numpy integer array of shape (2,3) within a certain range
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generate random integers with a certain shape
import numpy as np
min_value = 0
max_value = 10
rand_ints = np.random.randint(low=min_value, high=max_value, size=(2,3))
# output will be a np 2x3 array with random integers between or including low and high.
generate random floating points with a certain shape
Please generate a random floating point numpy array of shape (2,3) within a certain range.
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generate random floating points with a certain shape
import numpy as np
min_value = 0
max_value = 10
rand_fp = np.random.uniform(low=min_value, high=max_value, size=(2,3))
# output will be a np 2x3 array with random integers between or including low and high.
generate n points with a normal distribution
Please generate a floating point numpy array of shape (10000,) that follows a normal distribution. Explicitly state the center and standard deviation please.
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More than one value:
Once you start introducing shapes immediately move onto the np.random module.
generate random integers with a certain shape
import numpy as np
min_value = 0
max_value = 10
rand_ints = np.random.randint(low=min_value, high=max_value, size=(2,3))
# output will be a np 2x3 array with random integers between or including low and high.
generate random floating points with a certain shape
import numpy as np
min_value = 0
max_value = 10
rand_fp = np.random.uniform(low=min_value, high=max_value, size=(2,3))
# output will be a np 2x3 array with random integers between or including low and high.
generate n points with a normal distribution
import numpy as np
import matplotlib.pyplot as plt
n = 10000
center = 1 # center of normal distribution
std_dev = 1 # standard deviation
values = np.random.normal(loc=center, scale=std_dev, size=(n)))
Make the results reproducible
In both the numpy.random and random modules, make sure that two people working on different machines get the same result even if they use random number generation.
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Make the results reproducible
Often, we want to be able to compare our results with others. Therefore they need to generate the same "random" numbers as we do.
The seed value is actually inconsequential. It just needs to the same for everyone.
import random
# if two people execute the same code, with the same seed, then the results will be the same.
random.seed(42)
