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Before the Lab

Read

Getting Started

Make a directory ~/cs2613/labs/L16
Download Work.zip and unzip in your newly created directory.

Sequences

Time: 25 minutes
Activity: Convert REPL session to tests

Exercise 2.13,2.14 Convert the given REPL examples to pytest tests. Use the same trick of converting prints to list appends we discussed in L15
Exercise 2.17. Build a script embedding tests as directed below.
- Use list() to fix the following test

def test_items():
    assert prices.items() == \
        [('GOOG', 490.1), ('AA', 23.45), ('IBM', 91.1), ('MSFT', 34.23)]

- Question for your journal: what is `list()` being used for in this section?
- Embed the following tests and make sure they pass

def test_zip():
    assert pricelist == \
        [(490.1, 'GOOG'), (23.45, 'AA'), (91.1, 'IBM'), (34.23, 'MSFT')]

def test_min_max():
    assert min(pricelist) == (23.45, 'AA')
    assert max(pricelist) == (490.1, 'GOOG')

List Comprehensions

Time: 25 minutes
Activity: Write tests based on REPL session

Exercise 2.19
Exercise 2.20. Start with the following skeleton, and convert the given examples into pytest tests.

import pytest
from report2 import read_portfolio
prices = {
        'GOOG' : 490.1,
        'AA' : 23.45,
        'CAT': 35.46,
        'IBM' : 91.1,
        'MSFT' : 34.23,
        'GE': 13.48,
    }

Note that the value test will need to be adjusted for a value of approximately 31167.10, since our price list is different from the one used in the book.

Higher order functions

Time: 20 minutes
Activity: Assemble given pieces

Complete Exercise 3.5. Note that you are given an initial version of fileparse.py containing a function parse_csv in Exercise 3.4 just above. Make sure that your modified function passes the following test.

def test_portfolio():
    portfolio = parse_csv('Data/portfolio.csv', types=[str, int, float])
    assert portfolio == [{'price': 32.2, 'name': 'AA', 'shares': 100},
                         {'price': 91.1, 'name': 'IBM', 'shares': 50},
                         {'price': 83.44, 'name': 'CAT', 'shares': 150},
                         {'price': 51.23, 'name': 'MSFT', 'shares': 200},
                         {'price': 40.37, 'name': 'GE', 'shares': 95},
                         {'price': 65.1, 'name': 'MSFT', 'shares': 50},
                         {'price': 70.44, 'name': 'IBM', 'shares': 100}]

def test_shares():
    shares_held = parse_csv('Data/portfolio.csv', select=['name', 'shares'], types=[str, int])
    assert shares_held == [{'name': 'AA', 'shares': 100}, {'name': 'IBM', 'shares': 50},
                           {'name': 'CAT', 'shares': 150}, {'name': 'MSFT', 'shares': 200},
                           {'name': 'GE', 'shares': 95}, {'name': 'MSFT', 'shares': 50},
                           {'name': 'IBM', 'shares': 100}]

Question for your journal: what is the type of the argument types?

Refactoring a function

Time: 30 minutes
Activity: Refactor function to add feature

Complete Exercise 3.6. You only need to write a few lines of code, but you will need to make several blocks of code conditional on has_headers. Be careful when re-indenting code, remember Python is picky about indentation.
Make sure the following test (and your previous tests) pass

def test_tuple():
    prices = parse_csv('Data/prices.csv', types=[str,float], has_headers=False)
    assert prices == [('AA', 9.22), ('AXP', 24.85), ('BA', 44.85), ('BAC', 11.27),
                      ('C', 3.72), ('CAT', 35.46), ('CVX', 66.67), ('DD', 28.47),
                      ('DIS', 24.22), ('GE', 13.48), ('GM', 0.75), ('HD', 23.16),
                      ('HPQ', 34.35), ('IBM', 106.28), ('INTC', 15.72), ('JNJ', 55.16),
                      ('JPM', 36.9), ('KFT', 26.11), ('KO', 49.16), ('MCD', 58.99),
                      ('MMM', 57.1), ('MRK', 27.58), ('MSFT', 20.89), ('PFE', 15.19),
                      ('PG', 51.94), ('T', 24.79), ('UTX', 52.61), ('VZ', 29.26),
                      ('WMT', 49.74), ('XOM', 69.35)]