# FAQ: Statistical Distributions with NumPy - Binomial Distributions, Part II

This community-built FAQ covers the "Binomial Distributions, Part II " exercise from the lesson “Statistical Distributions with NumPy”.

Paths and Courses
This exercise can be found in the following Codecademy content:

## Join the Discussion. Help a fellow learner on their journey.

Agree with a comment or answer? Like () to up-vote the contribution!

Found a bug? Report it!

Have a question about your account or billing? Reach out to our customer support team!

None of the above? Find out where to ask other questions here!

Hello,
I was confused at this point of the path, when getting the histogram of the binomial. In the histogram axis y depicts the probability of the successful outcomes on axis x, right?
but here on axis y, as shown in the screenshot, i got values from 0 to 4000, while i was expecting values from 0 to 1 (probabilities). What could have gone wrong, or am I missing something?
(FYI: the exercise is about an experiment of sending 500 adevertising emails, with probability 5% for somebody to open them and respond. )

I am not a subscriber, so I can’t view the exercise. But, basically

``````np.random.binomial(500, 0.05, size=10000)
``````

says that you are going to be conducting `10000` experiments.

In a single experiment, you are going to send `500` emails. And there is `5%` possibility (probability of `0.05`) of an individual email getting a response.

You will end up with an array of results (having `10000` elements), something like `[18, 39, 25, ...]`. For example in the first experiment, `500` emails were sent and `18` were responded. In the second experiment `500` emails were sent and `39` were responded and so on.

In the histogram, the number of successes are plotted on the x-axis and the frequency on the y-axis. So, for example, there were more than `3000` experiments in which the successes were in the `24-27` range. There were about `1000` experiments in which the successes were in the `16-20` range.
If you sum up the frequencies on the y-axis (i.e. sum up the heights of the bars), the total will be `10000`.

2 Likes

You’re also missing a bit of code in the `plt.hist()` part—the range, number of bins and set `normed = True` to normalize it.

``````plt.hist(emails, range=(0, 100), bins=100, normed=True)
``````
2 Likes

thank you! that makes it clearer indeed!
I am trying to figure out why it is different in the example given in the course. For e.g. it has an experiment that a basketball plaayer makes 10 shots and has a probability 30% to score. Then, give a binomial np.random.binomial(10, 0.3, 10000), in the histogram in the y-axis, it shows the probabilities (values 0-1) and not the frequencies of the different outcomes of the experiments as in the example above. I hope I am explaining it with clarity
@lisalisaj maybe setting the norm attribute to True, has to do with that?

1 Like

`normed=True` will normalize it. It does appear though that `normed` is deprecated and instead the `density` parameter is suggested. I am not familiar with the versions. @lisalisaj is more familiar with `numpy` and `matplotlib`.

1 Like

You’re right; it’s been deprecated. (My old code for that lesson was still in place. Whoops!)

1 Like