# High School Reunion Solution¶

(dailywts[5::7] + dailywts[6::7])/2
# array([183.9, 182.5, 181.1, 179.7, 178.3])


### Explanation¶

We can use slicing to get the weights for every Saturday. (Keep in mind, index 0 represents a Monday, so the first Saturday occurs at index 5.)

dailywts[5::7]  # (1)!
# array([184. , 182.6, 181.2, 179.8, 178.4])

1. This is like telling NumPy "give me every value from index 5 to the end of the array, stepping by 7"

Similarly, we can use dailywts[6::7] to select the weights for every Sunday.

dailywts[6::7]
# array([183.8, 182.4, 181. , 179.6, 178.2])


We can calculate the total weight per weekend by adding these same-sized arrays.

dailywts[5::7] + dailywts[6::7]  # (1)!
# array([367.8, 365. , 362.2, 359.4, 356.6])


1. Here, NumPy uses element-wise addition.

Lastly, we can get the average weight per weekend by dividing by the previous array by 2.

(dailywts[5::7] + dailywts[6::7])/2  # (1)!
# array([183.9, 182.5, 181.1, 179.7, 178.3])

1. NumPy divides each element of the array by 2, thanks to broadcasting.