{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook was prepared by [Donne Martin](https://github.com/donnemartin). Source and license info is on [GitHub](https://github.com/donnemartin/interactive-coding-challenges)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Solution Notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Problem: Sort an array of strings so all anagrams are next to each other.\n", "\n", "* [Constraints](#Constraints)\n", "* [Test Cases](#Test-Cases)\n", "* [Algorithm](#Algorithm)\n", "* [Code](#Code)\n", "* [Unit Test](#Unit-Test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Constraints\n", "\n", "* Are there any other sorting requirements other than the grouping of anagrams?\n", " * No\n", "* Can we assume the inputs are valid?\n", " * No\n", "* Can we assume this fits memory?\n", " * Yes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test Cases\n", "\n", "* None -> Exception\n", "* [] -> []\n", "* General case\n", " * Input: ['ram', 'act', 'arm', 'bat', 'cat', 'tab']\n", " * Result: ['arm', 'ram', 'act', 'cat', 'bat', 'tab']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Algorithm\n", "\n", "
\n", "Input: ['ram', 'act', 'arm', 'bat', 'cat', 'tab']\n", "\n", "Sort the chars for each item:\n", "\n", "'ram' -> 'amr'\n", "'act' -> 'act'\n", "'arm' -> 'amr'\n", "'abt' -> 'bat'\n", "'cat' -> 'act'\n", "'abt' -> 'tab'\n", "\n", "Use a map of sorted chars to each item to group anagrams:\n", "\n", "{\n", " 'amr': ['ram', 'arm'], \n", " 'act': ['act', 'cat'], \n", " 'abt': ['bat', 'tab']\n", "}\n", "\n", "Result: ['arm', 'ram', 'act', 'cat', 'bat', 'tab']\n", "\n", "\n", "Complexity:\n", "* Time: O(k * n), due to the modified bucket sort\n", "* Space: O(n)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Code" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from collections import OrderedDict\n", "\n", "\n", "class Anagram(object):\n", "\n", " def group_anagrams(self, items):\n", " if items is None:\n", " raise TypeError('items cannot be None')\n", " if not items:\n", " return items\n", " anagram_map = OrderedDict()\n", " for item in items:\n", " # Use a tuple, which is hashable and\n", " # serves as the key in anagram_map\n", " sorted_chars = tuple(sorted(item))\n", " if sorted_chars in anagram_map:\n", " anagram_map[sorted_chars].append(item)\n", " else:\n", " anagram_map[sorted_chars] = [item]\n", " result = []\n", " for value in anagram_map.values():\n", " result.extend(value)\n", " return result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Unit Test" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting test_anagrams.py\n" ] } ], "source": [ "%%writefile test_anagrams.py\n", "from nose.tools import assert_equal, assert_raises\n", "\n", "\n", "class TestAnagrams(object):\n", "\n", " def test_group_anagrams(self):\n", " anagram = Anagram()\n", " assert_raises(TypeError, anagram.group_anagrams, None)\n", " data = ['ram', 'act', 'arm', 'bat', 'cat', 'tab']\n", " expected = ['ram', 'arm', 'act', 'cat', 'bat', 'tab']\n", " assert_equal(anagram.group_anagrams(data), expected)\n", "\n", " print('Success: test_group_anagrams')\n", "\n", "\n", "def main():\n", " test = TestAnagrams()\n", " test.test_group_anagrams()\n", "\n", "\n", "if __name__ == '__main__':\n", " main()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Success: test_group_anagrams\n" ] } ], "source": [ "%run -i test_anagrams.py" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.0" } }, "nbformat": 4, "nbformat_minor": 0 }