103 lines
2.8 KiB
Markdown
103 lines
2.8 KiB
Markdown
\[ [Index](index.md) | [Exercise 2.6](ex2_6.md) | [Exercise 3.2](ex3_2.md) \]
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# Exercise 3.1
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*Objectives:*
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- Define a simple class
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*Files Modified:* `stock.py`
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In [Exercise 1.5](ex1_5.md), you defined a simple class
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`Stock` for representing a holding of stock. In this exercise,
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we're simply going to add a few features to that class as well as
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write some utility functions.
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## (a) Adding a new method
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Add a new method `sell(nshares)` to Stock that sells a certain number
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of shares by decrementing the share count. Have it work like this:
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```python
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>>> s = Stock('GOOG',100,490.10)
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>>> s.shares
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100
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>>> s.sell(25)
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>>> s.shares
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75
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>>>
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```
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## (b) Reading a portfolio
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Add a function `read_portfolio()` to your `stock.py` program that
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reads a file of portfolio data into a list of `Stock` objects. Here's how it should work:
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```python
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>>> portfolio = read_portfolio('Data/portfolio.csv')
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>>> for s in portfolio:
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print(s)
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<__main__.Stock object at 0x3902f0>
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<__main__.Stock object at 0x390270>
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<__main__.Stock object at 0x390330>
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<__main__.Stock object at 0x390370>
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<__main__.Stock object at 0x3903b0>
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<__main__.Stock object at 0x3903f0>
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<__main__.Stock object at 0x390430>
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>>>
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```
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You already wrote a similar function as part of
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[Exercise 2.3](ex2_3.md). Design discussion: Should
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`read_portfolio()` be a separate function or part of the class
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definition?
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## (c) Printing a Table
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Table the data read in part (b) and use it to make a nicely formatted
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table. For example:
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```python
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>>> portfolio = read_portfolio('Data/portfolio.csv')
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>>> for s in portfolio:
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print('%10s %10d %10.2f' % (s.name, s.shares, s.price))
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AA 100 32.20
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IBM 50 91.10
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CAT 150 83.44
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MSFT 200 51.23
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GE 95 40.37
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MSFT 50 65.10
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IBM 100 70.44
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>>>
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```
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Take this code and put it in a function `print_portfolio()` that
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produces the same output, but additionally adds some table headers.
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For example:
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```python
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>>> portfolio = read_portfolio('Data/portfolio.csv')
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>>> print_portfolio(portfolio)
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name shares price
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---------- ---------- ----------
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AA 100 32.20
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IBM 50 91.10
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CAT 150 83.44
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MSFT 200 51.23
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GE 95 40.37
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MSFT 50 65.10
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IBM 100 70.44
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>>>
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```
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\[ [Solution](soln3_1.md) | [Index](index.md) | [Exercise 2.6](ex2_6.md) | [Exercise 3.2](ex3_2.md) \]
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----
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`>>>` Advanced Python Mastery
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`...` A course by [dabeaz](https://www.dabeaz.com)
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`...` Copyright 2007-2023
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. This work is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/)
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