Time for action – dealing with dates

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 health 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 decision. Please consult a professional statistician first!

Coders hate dates because they are so complicated! NumPy is very much oriented towards floating point operations. For that 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 the 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()...