Normal distribution with normal() in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the numpy.random.normal() function do?

It generates random numbers that follow a normal (bell-shaped) distribution, centered around a mean value with a given spread (standard deviation).

beginner

What are the main parameters of numpy.random.normal()?

The main parameters are loc (mean), scale (standard deviation), and size (number of values to generate).

beginner

Why is the standard deviation important in a normal distribution?

Standard deviation controls how spread out the numbers are around the mean. A small value means numbers are close to the mean; a large value means they are more spread out.

beginner

How can you generate 1000 random numbers with mean 10 and standard deviation 2 using numpy?

Use numpy.random.normal(loc=10, scale=2, size=1000) to get 1000 numbers centered at 10 with spread 2.

intermediate

What shape of data does numpy.random.normal() return when size is a tuple?

It returns a NumPy array with the shape given by the tuple, filled with random numbers from the normal distribution.

What does the loc parameter in numpy.random.normal() specify?

A The number of samples to generate

B The mean of the distribution

C The standard deviation of the distribution

D The minimum value of the distribution

loc sets the center (mean) of the normal distribution.

If you want to generate 500 random numbers from a normal distribution, which parameter controls this?

A loc

B scale

C size

D mean

size controls how many random numbers are generated.

What happens if you set scale=0 in numpy.random.normal()?

A You get random numbers spread out widely

B You get negative numbers only

C You get an error

D You get all numbers equal to the mean

With zero standard deviation, all values equal the mean.

Which shape will the output have if you use size=(3,4)?

A A 3 by 4 NumPy array

B A list of 12 numbers

C A single number

D A 4 by 3 NumPy array

The output shape matches the size tuple, so 3 rows and 4 columns.

Which of these is true about the normal distribution?

A It is symmetric around the mean

B It has no mean

C It only produces positive numbers

D It is always skewed to the right

The normal distribution is symmetric around its mean.

Explain how to use numpy.random.normal() to simulate real-world data like heights or test scores.

Think about how average and spread describe real data.

Describe the effect of changing the scale parameter on the shape of the data generated by numpy.random.normal().

Consider what happens when spread increases or decreases.