{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook was prepared by [Donne Martin](http://donnemartin.com). Source and license info is on [GitHub](https://github.com/donnemartin/interactive-coding-challenges)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Challenge Notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Problem: Implement a stack with push, pop, and min methods running O(1) time.\n", "\n", "* [Constraints](#Constraints)\n", "* [Test Cases](#Test-Cases)\n", "* [Algorithm](#Algorithm)\n", "* [Code](#Code)\n", "* [Unit Test](#Unit-Test)\n", "* [Solution Notebook](#Solution-Notebook)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Constraints\n", "\n", "* Can we assume this is a stack of ints?\n", " * Yes\n", "* If we call this function on an empty stack, can we return maxsize?\n", " * Yes\n", "* Can we assume we already have a stack class that can be used for this problem?\n", " * Yes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test Cases\n", "\n", "* Push/pop on empty stack\n", "* Push/pop on non-empty stack" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Algorithm\n", "\n", "Refer to the [Solution Notebook](http://nbviewer.ipython.org/github/donnemartin/interactive-coding-challenges/blob/master/stacks_queues/stack_min/stack_min_solution.ipynb). If you are stuck and need a hint, the solution notebook's algorithm discussion might be a good place to start." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Code" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%run ../stack/stack.py\n", "%load ../stack/stack.py" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sys\n", "\n", "\n", "class MyStack(Stack):\n", "\n", " def __init__(self, top=None):\n", " # TODO: Implement me\n", " pass\n", "\n", " def min(self):\n", " # TODO: Implement me\n", " pass\n", "\n", " def push(self, data):\n", " # TODO: Implement me\n", " pass\n", "\n", " def pop(self):\n", " # TODO: Implement me\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Unit Test\n", "\n", "\n", "\n", "**The following unit test is expected to fail until you solve the challenge.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# %load test_stack_min.py\n", "from nose.tools import assert_equal\n", "\n", "\n", "class TestStackMin(object):\n", "\n", " def test_stack_min(self):\n", " print('Test: Push on empty stack, non-empty stack')\n", " stack = MyStack()\n", " stack.push(5)\n", " assert_equal(stack.peek(), 5)\n", " assert_equal(stack.min(), 5)\n", " stack.push(1)\n", " assert_equal(stack.peek(), 1)\n", " assert_equal(stack.min(), 1)\n", " stack.push(3)\n", " assert_equal(stack.peek(), 3)\n", " assert_equal(stack.min(), 1)\n", " stack.push(0)\n", " assert_equal(stack.peek(), 0)\n", " assert_equal(stack.min(), 0)\n", "\n", " print('Test: Pop on non-empty stack')\n", " assert_equal(stack.pop(), 0)\n", " assert_equal(stack.min(), 1)\n", " assert_equal(stack.pop(), 3)\n", " assert_equal(stack.min(), 1)\n", " assert_equal(stack.pop(), 1)\n", " assert_equal(stack.min(), 5)\n", " assert_equal(stack.pop(), 5)\n", " assert_equal(stack.min(), sys.maxsize)\n", "\n", " print('Test: Pop empty stack')\n", " assert_equal(stack.pop(), None)\n", "\n", " print('Success: test_stack_min')\n", "\n", "\n", "def main():\n", " test = TestStackMin()\n", " test.test_stack_min()\n", "\n", "\n", "if __name__ == '__main__':\n", " main()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solution Notebook\n", "\n", "Review the [Solution Notebook](http://nbviewer.ipython.org/github/donnemartin/interactive-coding-challenges/blob/master/stacks_queues/stack_min/stack_min_solution.ipynb) for a discussion on algorithms and code solutions." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }