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We can also use `np.mean` to calculate the percent of array elements that have a certain property.

As we know, a logical operator will evaluate each item in an array to see if it matches the specified condition. If the item matches the given condition, the item will evaluate as `True` and equal 1. If it does not match, it will be `False` and equal 0.

When `np.mean` calculates a logical statement, the resulting mean value will be equivalent to the total number of `True` items divided by the total array length.

In our produce survey example, we can use this calculation to find out the percentage of people who bought more than 8 pounds of produce each week:

``````>>> np.mean(survey_array > 8)
0.2``````

The logical statement `survey_array > 8` evaluates which survey answers were greater than 8, and assigns them a value of 1. `np.mean` adds all of the 1s up and divides them by the length of `survey_array`. The resulting output tells us that 20% of responders purchased more than 8 pounds of produce.

### Instructions

1.

You’re running an alumni reunion at your local college. You have a list of the names of each person in attendance and the year that they graduated.

We’ve saved this list as a NumPy array to the variable `class_year`. Calculate the percent of attending alumni who graduated on and after 2005 and save your result to the variable `millennials`.

2.

Print the value of `millennials` to the terminal.