First, we will read the close price data. Second, we will split the prices according to the day of the week. Third, for each weekday, we will calculate the average price. Finally, we will find out which day of the week has the highest average and which has the lowest average. A word of warning before we commence: you might be tempted to use the result to buy stock on one day and sell on the other. However, we don't have enough data to make this kind of decisions.

Coders hate dates because they are so complicated! NumPy is very much oriented toward floating point operations. For this reason, we need to take extra effort to process dates. Try it out yourself; put the following code in a script or use the one that comes with this book:

dates, close=np.loadtxt('data.csv', delimiter=',',
  usecols=(1,6), unpack=True)

Execute the script and the following error will appear:

ValueError: invalid literal for float(): 28-01-2011

Now, perform the following steps to...