How does np.random.binomial work in numpy python

I have the following code but I'm unsure how it works even after reading documentation..

print(np.random.binomial(n = 1, [0.1,0.9]))

I want to produce either 0 or 1 values with probability 10% being 1 and 90% being 0. Is the code above how you would do it?

What if I changed the code to the following? What does that mean for the distributions when I have 3 values in the p argument? Does n being 2 mean the possible values could be 0,1,2?

print(np.random.binomial(n = 2, p = [0.1,0.5,0.4]))

I took a look at the function implementation.

The parameter n in the binomial distribution represents the number of Bernoulli experiments to perform. In the binomial distribution, p signifies the equal probability of success in every Bernoulli experiment.

So, what does a list of p values mean for a binomial distribution? Nothing. When you pass a list of probabilities, list_of_p, to np.random.binomial(n, list_of_p), the function will produce as many samples as there are elements in list_of_p. The function will check every element in list_of_p falls within the range of 0 and 1.

In the particular case where all elements in list_of_p are the same, you can siplify your function call by using the third parameter: size. In other words, this both lines are equivalent:

np.random.binomial(n = 1, [0.1, 0.1])
np.random.binomial(n = 1, 0.1, 2)

So, if you specify the number of samples using two different arguments, we can provide the function contradictory information, don't you? Yes, but the function will raise an error if you do so.

# Works fine
np.random.binomial(n = 1, [0.1, 0.3], 2)
# Raises an exception
np.random.binomial(n = 1, [0.1, 0.3], 1)

# Works fine also if the list has a length of 1
np.random.binomial(n = 1, [0.1], 20)

To answer your question. A binomial distribution with n = 2 generates values ranging from 0 to 2. Take a look at the Binomial distribution definition. I will not elaborate as this would be math, not coding.

The function call
np.random.binomial(n = 2, p = [0.1, 0.5, 0.4])
will produce three samples of binomial distribution.
Specifically, each of them B(2, 0.1), B(2, 0.5) and B(2, 0.4).

And the answer to your main question I think would be clear now. If you want to use the binomial function to simulate a Bernoulli experiment with probability 0.1 of yielding 1, you should write:

np.random.binomial(n = 1, p = 0.1)