If we have a two-dimensional array, `np.mean`

can calculate the means of the larger array as well as the interior values.

Let’s imagine a game of ring toss at a carnival. In this game, you have three different chances to get all three rings onto a stick. In our `ring_toss`

array, each interior array (the arrays within the larger array) is one try, and each number is one ring toss. **1** represents a successful toss, **0** represents a fail.

First, we can use `np.mean`

to find the mean across all the arrays:

>>> ring_toss = np.array([[1, 0, 0], [0, 0, 1], [1, 0, 1]]) >>> np.mean(ring_toss) 0.44444444444444442

To find the means of each interior array, we specify axis 1 (the “rows”):

>>> np.mean(ring_toss, axis=1) array([ 0.33333333, 0.33333333, 0.66666667])

To find the means of each index position (i.e, mean of all 1st tosses, mean of all 2nd tosses, …), we specify axis 0 (the “columns”):

>>> np.mean(ring_toss, axis=0) array([ 0.66666667, 0. , 0.66666667])

### Instructions

**1.**

In **script.py**, we’ve provided data about a trial for a new allergy medication, *AllerGeeThatSucks!* Five participants were asked to rate how drowsy the medication made them once a day for three days on a scale of one (least drowsy) to ten (most drowsy).

Use `np.mean`

to find the average level of drowsiness across all the trials and save the result to the variable `total_mean`

.

**2.**

Use `np.mean`

to find the average level of drowsiness across each day of the experiment and save to the variable `trial_mean`

.

**3.**

Use `np.mean`

to find the average level of drowsiness across for each individual patient to see if some were more sensitive to the drug than others and save it to the variable `patient_mean`

.

**4.**

Print the variables for `total_mean`

, `trial_mean`

, and `patient_mean`

on three separate lines.