Generating random samples in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What function in numpy is commonly used to generate random samples from a uniform distribution?

The function numpy.random.rand() generates random samples from a uniform distribution over [0, 1).

beginner

How do you generate 5 random integers between 10 and 20 using numpy?

Use numpy.random.randint(10, 21, size=5). It generates 5 integers from 10 (inclusive) to 21 (exclusive), so 10 to 20.

beginner

What does the size parameter control in numpy random sampling functions?

The size parameter controls how many random samples you want to generate. For example, size=3 returns 3 samples.

intermediate

How can you set a seed in numpy to get reproducible random samples?

Use numpy.random.seed(your_number) before generating samples. This makes sure you get the same random numbers every time you run the code.

intermediate

Which numpy function would you use to generate random samples from a normal (Gaussian) distribution?

Use numpy.random.normal(loc=0.0, scale=1.0, size=None). It generates samples from a normal distribution with mean loc and standard deviation scale.

Which numpy function generates random floats between 0 and 1?

A numpy.random.choice()

B numpy.random.randint()

C numpy.random.normal()

D numpy.random.rand()

numpy.random.rand() generates random floats uniformly between 0 and 1.

How do you generate 10 random integers from 5 to 15 inclusive using numpy?

A numpy.random.randint(5, 15, size=10)

B numpy.random.randint(5, 16, size=10)

C numpy.random.randint(6, 15, size=10)

D numpy.random.randint(5, 14, size=10)

The upper bound in numpy.random.randint() is exclusive, so use 16 to include 15.

What does setting a seed with numpy.random.seed() do?

A Changes the distribution type

B Generates more random numbers

C Makes random numbers predictable and reproducible

D Clears all previous random numbers

Setting a seed makes the random numbers the same every time you run the code.

Which function generates random samples from a normal distribution?

A numpy.random.normal()

B numpy.random.uniform()

C numpy.random.randint()

D numpy.random.choice()

numpy.random.normal() generates samples from a normal (Gaussian) distribution.

What parameter controls the number of samples generated in numpy random functions?

A size

B count

C length

D number

The size parameter sets how many random samples to generate.

Explain how to generate 5 random integers between 1 and 10 using numpy. Include how to make the results reproducible.

Think about the function for integers and how to fix randomness.

Describe the difference between numpy.random.rand() and numpy.random.normal(). When would you use each?

Consider the shape of the data each function produces.