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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.