First, read our file again and store the values for the high and low prices into arrays:

h,l=np.loadtxt('data.csv', delimiter=',', usecols=(4,5), unpack=True)

The only thing that changed is the usecols parameter, since the high and low prices are situated in different columns.

The following code gets the price range:

print("highest =", np.max(h))
print("lowest =", np.min(l))

These are the values returned:

highest = 364.9
lowest = 333.53

Now, it's easy to get a midpoint, so it is left as an exercise for you to attempt.

NumPy allows us to compute the spread of an array with a function called ptp(). The ptp() function returns the difference between the maximum and minimum values of an array. In other words, it is equal to max(array)—min(array). Call the ptp() function:

print("Spread high price", np.ptp(h))
print("Spread...