def test_cost():
    d = parse_row(row)
    cost = compute_cost (d)
    assert cost == pytest.approx(3220.000,abs=0.00000001)

def test_d():
    d = parse_row(row)
    update_dict(d)
    assert d == {'name': 'AA', 'shares': 75, 'price':32.2, 'date': (6, 11, 2007),
                 'account': 12345}

def test_read():
    portfolio = read_portfolio('Data/portfolio.csv')
    assert portfolio == \
        [('AA', 100, 32.2), ('IBM', 50, 91.1),
         ('CAT', 150, 83.44), ('MSFT', 200, 51.23),
         ('GE', 95, 40.37), ('MSFT', 50, 65.1), ('IBM', 100, 70.44)]

def test_total():
    portfolio = read_portfolio('Data/portfolio.csv')
    total = 0.0
    for name, shares, price in portfolio:
        total += shares*price
    assert total == pytest.approx(44671.15,abs=0.001)

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

def test_total():
    portfolio = read_portfolio('Data/portfolio.csv')

    total = 0.0
    for s in portfolio:
        total += s['shares']*s['price']
    assert total == pytest.approx(44671.15,abs=0.001)