This notebook was prepared by Donne Martin. Source and license info is on GitHub.

# Solution Notebook¶

## Constraints¶

• Are all prices positive ints?
• Yes
• Is the output an int?
• Yes
• If profit is negative, do we return the smallest negative loss?
• Yes
• If there are less than two prices, what do we return?
• Exception
• Can we assume the inputs are valid?
• No
• Can we assume this fits memory?
• Yes

## Test Cases¶

• None -> TypeError
• Zero or one price -> ValueError
• No profit
• [8, 5, 3, 2, 1] -> -1
• General case
• [5, 3, 7, 4, 2, 6, 9] -> 7

## Algorithm¶

We'll use a greedy approach and iterate through the prices once.

• Loop through the prices
• Update current profit (price = min_price)
• Update the min price
• Update the max profit
• Return max profit

Complexity:

• Time: O(n)
• Space: O(1)

## Code¶

In [1]:
import sys

class Solution(object):

def find_max_profit(self, prices):
if prices is None:
raise TypeError('prices cannot be None')
if len(prices) < 2:
raise ValueError('prices must have at least two values')
min_price = prices.pop(0)
max_profit = prices[0] - min_price
for price in prices:
profit = price - min_price
min_price = min(price, min_price)
max_profit = max(profit, max_profit)
return max_profit


## Unit Test¶

In [2]:
%%writefile test_max_profit.py
import unittest

class TestMaxProfit(unittest.TestCase):

def test_max_profit(self):
solution = Solution()
self.assertRaises(TypeError, solution.find_max_profit, None)
self.assertRaises(ValueError, solution.find_max_profit, [])
self.assertEqual(solution.find_max_profit([8, 5, 3, 2, 1]), -1)
self.assertEqual(solution.find_max_profit([5, 3, 7, 4, 2, 6, 9]), 7)
print('Success: test_max_profit')

def main():
test = TestMaxProfit()
test.test_max_profit()

if __name__ == '__main__':
main()

Overwriting test_max_profit.py

In [3]:
%run -i test_max_profit.py

Success: test_max_profit