ex3_1.md

[ Index | Exercise 2.6 | Exercise 3.2 ]

Exercise 3.1

Objectives:

• Define a simple class

Files Modified: stock.py

In Exercise 1.5, you defined a simple class Stock for representing a holding of stock. In this exercise, we're simply going to add a few features to that class as well as write some utility functions.

(a) Adding a new method

Add a new method sell(nshares) to Stock that sells a certain number of shares by decrementing the share count. Have it work like this:

>>> s = Stock('GOOG',100,490.10)
>>> s.shares
100
>>> s.sell(25)
>>> s.shares
75
>>>

(b) Reading a portfolio

Add a function read_portfolio() to your stock.py program that reads a file of portfolio data into a list of Stock objects. Here's how it should work:

>>> portfolio = read_portfolio('Data/portfolio.csv')
>>> for s in portfolio:
        print(s)

<__main__.Stock object at 0x3902f0>
<__main__.Stock object at 0x390270>
<__main__.Stock object at 0x390330>
<__main__.Stock object at 0x390370>
<__main__.Stock object at 0x3903b0>
<__main__.Stock object at 0x3903f0>
<__main__.Stock object at 0x390430>
>>>

You already wrote a similar function as part of Exercise 2.3. Design discussion: Should read_portfolio() be a separate function or part of the class definition?

(c) Printing a Table

Table the data read in part (b) and use it to make a nicely formatted table. For example:

>>> portfolio = read_portfolio('Data/portfolio.csv')
>>> for s in portfolio:
           print('%10s %10d %10.2f' % (s.name, s.shares, s.price))

        AA        100      32.20
       IBM         50      91.10
       CAT        150      83.44
      MSFT        200      51.23
        GE         95      40.37
      MSFT         50      65.10
       IBM        100      70.44
>>>

Take this code and put it in a function print_portfolio() that produces the same output, but additionally adds some table headers. For example:

>>> portfolio = read_portfolio('Data/portfolio.csv')
>>> print_portfolio(portfolio)
      name     shares      price
---------- ---------- ---------- 
        AA        100      32.20
       IBM         50      91.10
       CAT        150      83.44
      MSFT        200      51.23
        GE         95      40.37
      MSFT         50      65.10
       IBM        100      70.44
>>>

[ Solution | Index | Exercise 2.6 | Exercise 3.2 ]

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