3_select_sort.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# sorting: select sort"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "there are several well-known sorting algorithms, and they are generally taught least-efficient first because the least efficient is the easiest to understand and implement. select sort suffers from the same worst-case performance as bubblesort..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## select sort"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>\n",
    "<p>'The algorithm divides the input list into two parts: the sublist of items already sorted, which is built up from left to right at the front (left) of the list, and the sublist of items remaining to be sorted that occupy the rest of the list. Initially, the sorted sublist is empty and the unsorted sublist is the entire input list. The algorithm proceeds by finding the smallest (or largest, depending on sorting order) element in the unsorted sublist, exchanging it with the leftmost unsorted element (putting it in sorted order), and moving the sublist boundaries one element to the right.'</p>\n",
    "<p>-- en.wikipedia.org/wiki/Selection_sort</p>\n",
    "</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src=http://en.wikipedia.org/wiki/Select_sort width=950 height=750></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "HTML('<iframe src=http://en.wikipedia.org/wiki/Select_sort width=950 height=750></iframe>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b><p>'Our intellectual powers are rather geared to master static relations and that our powers to visualize processes evolving in time are relatively poorly developed. For that reason we should do (as wise programmers aware of our limitations) our utmost to shorten the conceptual gap between the static program and the dynamic process, to make the correspondence between the program (spread out in text space) and the process (spread out in time) as trivial as possible'</p>\n",
    "<p> -- edsger dijkstra, <i>a case against the GOTO statement</i>, 1968</p></b>\n",
    "<p></p>\n",
    "<p>with that in mind, let's look at dynamic demonstrations of select sort</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>here's a demonstration of select sort done by cards: </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/TW3_7cD9L1A\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7fde0f488ef0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('TW3_7cD9L1A')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><b>more vivid, more entertaining, but just as accurate, is this demonstration of select sort done through hungarian (\"csángó\") folk dance:</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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A6n9XLx69ZUO/4a0zf80f1UkeTREVBW/CRtxPh/8AN/QqoVrwubcSYef3wSR24yN+Kq/+\nMf8AaFSK44uN+KMQ/mfoFTpAIiIPYOP/AAvb/qv/ACXj165x/wCGTR/m/wBV5FSAREVG0fzjfSF6\nz+0Z2bFqT/Tj8yvJM89vpXqf7QTfFaT/AEzfzKg8qiIqC9hwj/ytxH/I/wDFy8evW8KOtwzxEP3A\n/JykjySIioIN0RB7D+0U/wB7w4f5b9V49et/tBdmrMP/ANK0rySkAiIqPX8Hn/DnEf8Ap/8AxcvI\nL1fCRtw/xCP8t+jl5RQERFRccI/80Yf/ADf0KcW68UYh/N/QLHCenE+H/wA0fks8Va8T4h/NKn6K\nhYWywqMIiICIiD0FbYVM3hRN/RUchu86AehXdYbyVR7qWMf9qo3jtFRI+NVmyxZbAaqqNC3YOySs\nNC2YOwVFayCzPWFrDpPGf2gt5/Nb6T+i5s0e0+KqL7jJ/Oxwuvf5JoVG0aKwxqUzV5fv2AFBaLhQ\nANStZRbKurRqVzn0LfQgvcImycJYtH9ctXnbKyo5smD1kd/OIVeAgxZLLYLFlRP4ffy8don90oKz\nxC7m47Wv75CVHoHcuvgf3PBW2Iv5ldM/vcVBEsllslkGtl6GaW/A0EN9qkn81QKwfLfAo4u6UlBW\n2SyysqjWyn4C7l45Rv7pQVCXegfy66B/c8FQSuJXcziCuf3ykqrspmJv5mIzv+s66iIMIsoqPROm\n/wABMh/zN15xWZm/w+Iv3t1WqDCLKWVBvnBeg4zm52IUx7qZgXnxurHG5edUxHuiAUFaiIqC9Hw7\nNy8Bxxn14QPwK84rLDZuXQV7L+ewD81JFaiIqCIiD0PGM/PqaI/VpmheeVjjM3OlgPdEAq5IBZCw\nstQei4bm5eD42369Pb8151WWFy8ujrm38+O35quI0UGEWbaIqLDhx/Kx+if9WULOPP5uP1r/AK0p\nUXDn8rEIH9zwVtXP5mJTO+s8qDjZaLrbRc7aKjVOi2ssdEGqIiC7kcXR1rt/k4R+Sp3ecfSrWI56\nKsd/JCqza5UGoWw3WGrYbqjLFuzYrVg0K6Rt19QRWlV9D1/muDd13q/PYB9X9VxAsiO80nNkzeC1\njFwUAWrXluw0UEhje0VxqvOZ/CpMYDnGxBFgo9X84z+AIstWSZYns+stAFgbro0INbLIC3yrICGG\nrDke13cbrMhzuLu9Zy3WbIuHIBLaroBosEIjSy2MnyIj7jdCFrZBiyWWy3jiMjwxoJcdgBclDDms\nxnJI13cV0lhfDIY5WOY9u7XCxC0ypkw1ldnlLu83Wtl0LViyDnZFvbuWLIjbmf3fleN1yst7JZBq\nllmyzZBquk8nNcD3Cy1slkGlkWxCWQarpHJkjkb9YWWlkQaos2RUYRZIsiDeaTmFp7hZc1lYQFsz\nqtVvGL39CDrE/Ix7frCy5kLYBHDRQakaLVdCNAtSiMMdkka7uKzmzz5u8rU7rMXzjfSqrqdlp0XY\njRceiDCwRot7a+panYINOqwtuqwUFpR64TUnvfGPzVb3qyo9MGnP75n5OVb0KgywLYDtetGjRZaN\nfWqrZg0cuzB2h/CucQ7Ll22LT+yiotV8/buaPyXMC66VJvVSeBstWhRl1awvdlDS4kbAXuvYYcWU\nWAsnq6OIsAGzb9ppIBdbqqegb5kVK3mPcLkt3PuV7HBHTUT6N1XUDmNjlnLY2lkWc6b66rjaZv1D\n00rFe5eUZZ0pNg24vYdNVEqx8u0fshegxGmENXPTvcHSQEtDw22YDvXn6vWpt3NC6VtnpyvXDkBq\nurAtANV2jC0zBZAF0sstaiuYbqtsq3y6pZQdcMw6oxOsbS0rQZHakuNg0d5W+M4TPg1U2Gocxwe3\nM17divV8GUPJwo1hJa6aawsLZmjQD0XuVLxSpr6OtZLT4WapgbZ7gL6d3fZeflnk2uldPMZfObLU\ngXXtH4DT8QUTqnDo2UWIRkielOgv6OnpXlqigqqeqfTy07xKw2LQ267bmNs/IRSLNK9zwxhMLGG7\nTnYO3I3RxcRffoBfoqDCcJk8sikroskANznNrnoCO5ewwdxloamdmUEOOrtrBoH6Lle2eoerRpas\nTaYeM4mLnYxJnjc3IAzM76fW/wCIVVZepPy2FESS8yolzOcx3mi/m2VC+hqmi5p3gA2K3W0RH1xv\np6s3nNUQhWGF4RPV1dMZoJG0skjQXkZQR4FXWG4TSYXRNxXFyyQkAw041uel+8rrS1mJYsOdLMyK\nBr+axrmgkBpHXoLpa3i00ptOFfxbh9HRup30ULYmuzNIB3svOWXsMfghxAR1TZgYczmMc25N+u68\ntLCYZXRu3abKadusT9b19CaxF4+S45V0pIopayGOd7o4nvDXuAuQLoQvV8LcNU9bSsr6oyPJcckY\nFm6bEnqulpxDzYRuJuH6DC6Jk1LJIH58mV2ubx8F5kNX1qsw7yun5VVTRysLm5gbes+C8RxRgMeF\nuiqKNsgpZbtIcb5HenuK5advyW7xH487lWMq65VsyCR9sjHEFwbcDS56LrljD0fCGHUVRRVEtTBH\nLK6TlsMgu1otfZQ6vhSvhhfNyYyLvcWxv8wD09FfYFQQ4TzfKpBCJQ0sbNJYutucqs4Kq8VTFiLG\nhrLts03D22F/H6Q9q8+627MO3HXHf18tyrUr0XEWE0lHaallc0ydvkPGzSdLKgLV6cuExhoQlhfV\nbWV5BgQioqaqllikkqQOXDe1rmwPj6EtaIhaUm9tsIUOD1Io3VshbHCG5mE6l/cLKtO69vNgs8WH\nSgPhD4bvkDXHsi1wAOi8/VxNqmEhjQ9jdMosXW71yjU77euf481maznCnXY0lQKXyowSCAmwkLTl\nJ9K5levxDB8QnwTCaajbJIx8eeQX7IO4J9q6TbDxxGVLNhUVDBA+s5jjUR52CPTKe436jRRaijdS\nTAXLo5GB8biLZmkr382ETVmECkrnsE0bA5srAXZTb23XlcRo5oKLk1TCJaaQcuS1hJE7r7R+K501\nN04dZpGOlMB4LVw0XUN7K0cNF2cWp2WpXQjRcyNPWqmGjki+db6UOyzD88z+IIJTh2VwI0ClyN0U\ncjUBFakbrR3RdTuVo8eaiOR3Ryz1WH7oLODTAJXfv2/9rlXdFati/wAMSScxgtUN7JOp7PvVX0Ui\nVZbst27n0rDOi2aN/SqOsQ7Dl1c35v2LnEOyfSpQYDlsdQbo0qnnNNIe8lbMtmGlxfULm3UuK6xj\nUIyvI66OCVrsOjLKm1m66M7/AEq9bT1lRFJK2nfUeXUtOA5pFmvae0DrovKQ1kdILOhzudrmBsVl\n2MODSIogy/iVyxNZ6d5mJjuV9iVZhZxR1QZJhJnPPjy3ub7X/BeUrcprZCxpYwm7Wk7DopcNVNUE\nOLGBrHDM4b+Cj4gCKu53LAVqtcdsXtEuLRqpMTdFxYNVKjatMwZUA12XSyW1RWuVDG5wytF3O7IA\n6krpZbRlzHtex2V7XBzT3EahFfRxak5FHThvMfEQ3N5rS1ot6AtaSvk8proKgsPIbG5jgMubNp7L\n7FRuHpX4tTCuqrPkGaJwy5Wjobd+iluwaGmoqhlNGZnyRCJolkPmt81oPSy80dRMS1M+PNcUeVYX\ni9PilLMGSSN5TwOpGuveFVy46aipklqI3AyOuTG62lu5QqiaqqS3yyaSR8V2APPmnY/kuDmrpFOs\nS6aevbTnNVvQVTKuqbASWtc5gAc3U62P5r12KvqaLGMJpaeaOOlqZCx0LYhqAL6leIwSKSXFYGxN\ncXDXQbAar6DiWHPrsUwurZM1raN7nOaQbuuBt7EiIrLf9GvfWrEy8txU9seNVDI2saxrWA6WAuFX\n4jzaWKB8VRFIJw46Dp/T+i78ZmaLG3GVh5Urg5jujrNAt+Cp7ZrHeymyJnLtP9lq6daV/IaTSTT5\nebI6RzRlYDsO4AL1WEyGmfUsg1dBSkR9nMbjw66rzUYeJ4jEBzeYMgPV19F7jC8IZTVUk73h/wBF\np/P8VjVtFcJoalZpffPcqzGaZ1HgGGRzOaZm5nSHbtO1K87XQB8QmB7TLB3i07L6RU0lNUR5XxNe\nLEWIXk63AKmLDqilDRIIXc6nkG7m9WHxAXOt4tbOXSmvWNDhmMvLwQc6phhJtzHhmbuud17SCobH\nj1JhFNP/AHOFkjXtjdsMptc9/VeMFjqDtqpOGYozBqo1XIE7yLBrjb1r0zGXzfj0b+LamKmdVRwM\nnheSWRtd2omAgXd6VW4lxU/F8Omp30oZzWtsc18pDr3SXjZ4iEdLhlJE1wAcMuno8VVTVbsQnfVP\nYxjpDq2MWASKx4ZmXC2iuMGxWWlh8ibTiZjnF9jrfbS3foqzKp+AhpxL6Xm5bt0y36qX6hqv17qG\nrp5qSKvewtIaQ1xb2h3gd+2y5Nw/CmNkqJ5HdplnPkkIO97+m654c6WKly5nEFxOcuud/T6F5vHq\nKeurp5JZXMp45csYOuZxaDYDx1Xl0dWL32+McsWnGEDEYZarFHSROE0D3iCNwfcv6AhUz2Fji0ix\nBsQvbMwd8lJHC3LHUUsgax4Go6gfiF5CogkgnkimbaVjiHi99V7c5JhEICvJaylqcaw80oLWtdGw\ni1gLEKmc1ai7XBzSWuBuCOikxlql9ncPcVDsXo8RrMlG0F8DppHF1w6zjY+ywt4Kv+GKB8AkrMNZ\nG9zspMY19Kp5uIsWmidHNWyva4ZSO8KDLPJKwB7y7W9j3qTTLtp6sUie+2shaZnOYOxmJaHd3ivY\n4BxPUV1bDQyxMEkhID2nKAMugHdtZeMst6fmipiMF+aHjLY2N1q1YmHmz3l9Up3TQ0b5Kt0rXSOM\noa4i0eUDs36AlUnFrDJg0MwdbLMGuB3bcXLfHUArSPEcV5wfLNE2IMAkjJDie8jbVa8WVcr6OKPI\nDDK8vDi2xblGgPW+t15q93dN1cdS8m1vZB8Fze3ZSw2zR6B+S5ObsvW5OJbouWXT1qS5q55dD6VU\nRnDRIB8tH/EFvKLBawfPx/xD81EWEzbMUXL2wp9SLMKiEWe7wCquYFyVzl0I9C7gaFcpB2vUg49V\n0MOaB8n1SB7brQ+cpsTb4RWO+q6P9UZR5gORGOpXMbLec/NjuatRso02Zstm7lasWw0uqsO8Q0Pp\nUqIfqo0O+veu8Z7Dj+yT+CKqGbLqwahcWb2UlnRGYa1W7PQo53XeqcC5oHQKOhKXTVTYYnMc0m5u\nCO9da9wkmZIB58YP4lV6kNe6QNDtcosPag6xbqUxRGHKdlKhcD6VG4dLLNlkBEUC3asBbsQfQOG4\n2RYBREDQszH/AORurOzQL3NrbKvwQu+AsPc1t2+TtuPQpzHOFrMvpf03Xjt9b/Hz/iejNJj1QA2z\nJwJWHvvv+P5qpe3Zey43p70NLWW1ikyE/su94C8bUPEcZduu9JzDL0nCUZp2z1ZDflGkN77NOv43\n9ivhiLjySNpHObp0/wB2Ki8PwujwCmZPA7mlurL2Ns5IP4qYKYR1ETI4Q6O5fqbWOa9x37rhee3e\nk1iO4VOOXxigfAGXlb24rdXNcQR/vvXkommzr7g2I7ivoPJc2OW8LYzG8vjcDmJ1/rdeNxxjKbEZ\nXxANjcSSL38QfWCD7VvTt+M3iJ7hjCRbGKI5M45wFv19W6+hZQNANtF894XbJV8Q0s7gRAxzi3xI\nadPxX0Mm4u21h1C4/wBPcpXLAvqq7H3vjwaokYS2SMZmubuDdWPQ69Nu5ec4uxFkNGaRjg6ae2Yf\nVboT7Vw04mbxDUzh41wyx6dAq15c4jNqp0tS1mlruUBztSvqw89mhNirCkH92ae8lQXODsvgLKbS\nyXYxh7tElmPqSr7hkNNPUDltdI2Zrv2iLe5eellEY7yrnheZroa64ADTG4nr12XDWjOnJqTir1cb\n87g0xljgd+lu9eUrOMZRNJHDB2Y3OEby/bpew0UzibGDTYeIKeUiSoFrD6LB/VeGJ1Xn/j0cZvLj\np9dvRP4vxN7LAxMN73bGNPEeKr6iZ80j5pHGR8hzOcepVe0ZntHebKXK/tWaLL3vRFplyaSTqsSa\ndm262bYHULSRwc8kIkuf0l1ygBq5ixeuuwF0YgspmDtb8JsLi0ENJGba/RRmkEdAApuCuAxKPLrc\nEaarN/8AmVn49CMjg7mGVxLg06Bwd4g+pdeIZW1nD2bllr43sffMDe9230WsbbAHlsuAXaxnUH0L\nqylE1DWxgkh0WhyWFxqPxsvn0ti0PPWe3mXN7JXF4spDiHMYR11XKVtl9SPj0uLRc3Wpbpt1UhrN\nCmTsj0oIFQ3RcYRaeP8AiH5qZVt39SiRD5dn8Q/NElc1LbkDxUPKC93ddWFULPFu9Q4231KqueXR\nRnj5Qqc5vZUNze08+JURwO6sKRubBsT/AGeUf/soNrFWuDR87DsXZ+6afY5GLdQp3uzEHwC2Gy52\n0W7DpYo03YthtdatIWfBGoSIb5l2a60cn8sn81yi2WXODWEHqxzUVWs3XeI9sDoo4F13jcW30CmW\nYhpUAiU/guS6zuLnAkAadFyVJbxtzyNaDa5tdS6iPkMaxo81xF+//dlGp5BFLmLc3SykVFRzm2DP\naVCGjXg7kj0BdoZY2nUO9ijj0Bdmm3/SB9ZUmWkrymPud7E8oZf6XsXNshGhgB9a3bIOtK37yzmW\num4qI/2vYhqWN2DvYheCLNpmj/5e5OZrrA23g5TMr0+hcKTmThyleDZseZpHXQn9FbjmbZhb8l5X\ngzEKX4NkpZJRFOx7nmM9QeoV3UYlh9O3NNiMTWnqXjVcLxOfjcQlVFNFVUr4ZwJIpLtc0r5rjuFS\n4diLKOUkxPPyUp2c0nr4hesqOMcIp2uMLpamRpOUNaQD6yvLPxWoxvFqU1eobJaNg1Db+HXotUi0\nJiJnD1jqy+R0LiG5Io7jvDy39CtKmufGJ2Z9WvsLC5AOtvbZZw9rIY5I5XtMebQOYQScxOl9baqZ\nAMjpHkRcov0sO0SDbW65T9d4xXrCJBXSS4hA17nkTQnK0NsL9VDrYYqyJsVTTuPOiac2wYWE3uT4\nEBW0cgp4WmZkILHFsZHd0v8AqoGIjnQTwNfeQSjzhoAbHfa2hSPvSWnM9QYXJQyzMkEcdM+JpGUE\nBvcSPYrWKpZNRiVj7tcMzcpGngvFcSU78Pq4ZzPLlqGfKGPQl46+vQqtoMZ+Dpi6B8zoyCCx+odf\nvC1fR3w1bUpM48fSad7g14vnbYm/jc/pZfPeI6gjHas5Tlu3Lp0yiynQ8Y1Uea1Gx0nnbEWC8/X4\nhNiNc+pms0vt2WbAKaGjak5lx1Jj8RHPLpCStDupDul811xf5y9kS88tFIZLYDKDcBcX5bDLfxup\nTZYQ3s8wH1JJVq+XPqdCrrhCoiGISU8xDGztFnk2ykG49SpnPY/cvPiQkFSKaUSMbnI+i8aEeKxa\nN0YLRmMJGPyvkxedr3NeI3ZGlu1gpjOHwOH5MQlmYJCzOyPNqBf8bi6qq6qFZUumELIS7drL2U2n\nxKniw8RytldMAWgjzbFJiYrEVc8YOGaKHEsZZTVGYNc11i3obaKNNZsz2F4JY4tvbeyhxvdG8Pa5\nzSOrTYqZngc0DK/1WWp+ulYcJJBaw1XMO71Isxp2eR+K5yv2tm9aRKy5HRwXZzxoCud+0MykNDHf\nRe4d4KsyzDg54LSBdWeBPZ8JxADQA308FCfGL6MdbxU7A8Ubg1c6pcwm7C0DKDuR/RZt3BaOnsWM\nl5ThYkaNLRITcXUunpahjMwpb5iHZc2ttt1TfH2IRgBs7n95a0Kui4pxESmUhzonuNs7iB+C8fBL\nhxyVtE+kr5aZ1iI7ZbbWP+7epQ3NMk5aNm7+lcp8QrKmo5kxuO5pssis5YtHBYD9pe6vzt6ISeVb\notMjs400C5HEehhcPEOBWvl7GAkNkcfEBaHKsLQJDmF9gFBj1lb/ABD81INSwNcA1xLgdT3rhSxv\nmmaxjC43ubdFEle1g+VIHQEri1lvwUqUCSWUjuA/VcXOa1zm6hw203WoVzdHdirT5ni4qyqKhkUb\nhftvFmgd6r3ZeYANmi3rUJcX9kEq54efS0tLWOqKqEGeEtDDe4PsVNU9mw71YRsvhotbzLqSxZUD\nZbtje7Zvr2WWyODQGgD0DVYzHc7qN4btid1fG30uRwyjsvznwGi05gHS6c420ATtcQ6sdIDoDbuW\n75DazxYKMZHnqsG6dmYbjICbXI6aLINzvb0rjZb3ubuNymEy1eNRY3WLDL4rc27lkBpVyjeJkQIz\nEqfHTxuZmDm27lXhu2q6BmyzLUQs5KB0RGZgF2hws5pBHqK58s7ZQosbbbHX2KRGJL6E+1Yy1ENh\nG/uC3bAXDUtHous3fsSfat2PcDqSFNy4HUZEZfn0HXKbe1augaHFsU/MHQmPL+CktlY62a59S7xC\nHNoN1JtgwgswionBdHleCC25HetWcNVI88gL2OFhnk9h37KaY2dy4zrTDWx4X4tzAXLtFJwqk+Cs\nXgnmYXcsk5fN1t4r1k0bBH5pVBWMaH2YLXVrqTKxWIntcNraXF2zGSQRPaGjM118oOo1UmPD/l5n\nvqSQW2aCR2brzeH1suHTSOiiEjJGgPadNr2N/WVK+NRdPNGMMbnY0kuMlwbdBotbcuk3x8WkdDTN\npKiKarc8Zrkg9pq3bHTsjjjjzudLFygSNdBpe+x39qpKXiiero3OioYWvvkLnPLund71ykxKulp3\nQHlxNd1hYWn81MYN25WcSSy1le6OJ4kgpzkZI0efoNT393qVMKSV52JK9NT0egbl09Cmw0H0iAEn\nWw5Tp5+vJsw+rOt3bW36IcOkZ1K9mKTv/JRKinGfbZSNeZWdN5mWiebOY2UNtoXde/X0qJLTPadW\nkL05pwNCTbuWvkrD0W41WJ03luS4dy6NhcV6Q0bPqgekLPkrbCzLq8ycbz7aVzhpck9yy+gkBIyP\nBGhuF6LkXFg0FdI4CRfIpOrK7HlHUcgWkFPLUS8qCN0kh+i0XXrzTs6xhQJY2fCJhje2HmZWk5so\nAvrqrTWyzNHm5YnwyFkjS1w3BVlBTNIDrG2UEkC9lri/LkqS9j2l+YsLG62A2N+t16GBkYp4vkxc\nMbfr0XS9uspWMypvJm/RefYtTSsGge77oV+Mh82JpPoWbgN8wN7rhct7ex5xlFA17DmcbOB1G6s2\nsDjI4xPNyXDK3Q67eCsRcm5bp6Fhz+yRchXemxAyy6DyA2IzAl41HqXGWlfJa9CGj03VpcEWDn3W\nzHWOgdr43V3m15+HCppKh3OhABGlhpdTmYZNPI0VIhZExtmsY7bxVoXfsn2rlc30uD6U35T44uwq\nmEbnANIbv8oAfYor6KlbfsO9RXeS5PUHxXF7j3qxKYRZKaAea13rUWSFjfcpT7k+cuBjffcFaiTC\nK6NgOt/Yp9NXxU1LyaanyvOrnudqSorwQf6LmC7NdrlrKJnlkhy52ANv2iw6k+tdZKyBt3Mie53c\nf6qNJI9zi97sznalx3K5Oue5Mq1lldJMXtY1rjtfWy6xRMlgvK+niGoGZxzE9+gUV4O/Vah7m9QR\n3HVE6SIIoo5mzSVMb3Nfox0ZcHenbRSKqoc2C0UAGa9y2MtaR6LqNFiNRCzltI5fVhALT6iuIlaG\nncaEaEhDpzDH6WITkuO5CxzQBYBamQnwV7XpvyiNC5o9K5kWNr39CXJS2qrPTIKzdaoiMhZWqzdB\nlZCwgKiugOq3DlyB0WQbKYahIa9dWyadCoocF1a87LMtQlskJOwXZrha5IURrxYaC67Ne33LEw0l\nsy6EqTEWh2qgsfpsAF3ieLg5rLEtPS4dIBEAC0etThKPrBU1HK3IO0AVM59h54HpXntHbo7VNQGx\nk5rrzWI1U7QXwlo79LqfW1kfm89ntUATwOcWuewg76rdImGbKt1dW20lFvBoURk1VFI97JCHOBB9\nasHRMa8hrw4dLLtFTRnVxB9C9MWw5TEqqjkqoLsgLxm6N7+9WUMeJPIMtUWfsg3JU1gYwZWiwVhS\nmnjZ8m4Fx3J3WLXWtZZooZY7c2Vx8Cbn2qwZZRBJd1t10zkLy27l3SDe2gUGpJubLo+TsqJNMwD9\nVKx2jS57iPUmfQ9kj0BRzK3dY5zDpr7V1wiQXGwdY+xC++97+Ki85g209K0FR0JCuEzCcLHfcrYF\no7tFXc8X0dYehY5rb9p1/Wm3KTPSc+ry7QAetVzq2RlS98ccbL9CwOut3SRW6KFM8A3a9apXEucy\njVkhmkvIADfcMVhRTcuNojdcAKte5rjqSV1gs0gg2XaY6SPq6FU0+C3bUW2tsqsyjodlrzze91z2\nt5W/P11b+CCpb3+0Kr8sI3N1g1Icm0yspJmZb39i5sqhzLi6r+cB0WzagX2VwkytubpqVqZPFQhP\n3kLBnuN1MMuz5PG/qXJzgdzf1KO+o11K5mbqHFdIhEgubbb8FzNhtquPNts5amYd4C1gbvv4+pcC\nwl1xf1rLpL63JWnMHiqjo4O9K5kHuQyNstS8WQaPC0IW5IWjiFUloQmXMDYj2oStSqy1B8Al/ALV\nFVy3zAdAsgtt5oXNZumDLpmZ9VZvHp2Vyul9kwu51BjP0fxWQYhuDr4riHW6BC7bwUwu6EjNDbzT\n37rPyNibHQX3UYuv3bWWcxsfFMG6PEkiEE3zaWWLw9zvao7nl17garGbW9gm1d8eJXyXjobbrYGK\n9gXb2UTOb303umY3v43U2rvjxOaYjYB5uei6xiJxb8oe0bBVjZC1wItobrZk7mFlgOzeyzsXkr4t\nWCns080nNsu0QgOX+8NaHXtcXVI2ZzS0gDsrPPdlYLN7F7etTi/1rkr49bTy07Y2k4nGAbkAQm5s\nuwno5gCK+Ak6jPE4EheNFS8Bgyt7AIHjdBUvGTst7IIHrWeFeWPHqpaenktaopH5gSLN3C5x0lIS\nMzqU3Fx6F5ttZI3Jo3sMLB6Df+qw2qe0tsG9lhYPQb/1Thn1OSvj1nkNI9nyU9M02uDmOy4eSNH/\nAKmC5F9HLzYq5G2sG6RmPrsf/wBWfLZezo3sxmMb7G/9U4p9OSnj1EFG0kh1RDtfNzLLr5HGNefA\ndL/OdF5FtZI3YN+bMfXYrPl0ttm/Ncrrt7U4Z9Xkp49Y2kYTdtRGBa+jyu7aaQDsVsXrevHNxGZr\nbBrLckw9fNve++6DEJR9FnzPJ67d++6zOhM/q8tPHsXMmaO3U0zvEPUaQ5tOdDp3vsvKmumNvN0i\n5XXZbDEJRm7LDeHk6g6Dv33SNCY/Tlq9E6JpazLUR59S+8gLbdLfiuZpyTbnR9+l1QeXy6dlmkXK\n67e3dbMxKZgIDWawmHUHzT133WuGfU5KL51I/wD9+O3pXM0bz/1o/aqT4QmtazfmuV129u61fWyP\ntdrPmxHt0HX0qcVvTk0/F2aVw3mi70bTE7VEJtr6FS/CEwvawvFyTv5vtWzsSmc6QlrPlIhEdDsL\neO+ivFb036a3fSk71EOy5Gi3vUR6C9gPeqry2W7tG9qMRnfb/YW3whLY9hmsXJO+2njvorx29Z3a\nfix8kZuahgIGoLVl1IY8okmDczcwBba479/BVkmITSF5Ib24xGbX2FvHwWPLpLuOVnai5Wx2/qrx\n29N1PFi6G20zTpfbotOSDf5UaC/qUA1khFtPm+X12Q1kh6N+b5fXZOOfTfTxPMLR/wBUaC506LUx\ni9uadr6Dp3qv8ofrtq3L6lnyl+YOs3RmT1Wsrsn0318TcrQLmU7Xvbot2tiDu1K8C19B071Xc92W\n2Vvm5fxTnu10bq3KmyU318WzWwDV9Q8WFzoNls5tMM5E0hDQCdBpdU5ncQdBq0N9iGocQ7RvaaGn\n1f8A4pxz6b6+LJzaUZg90pcNTsLfguZZCCe07QXOvRQXVD3FxIHaaGn8P6LHOdro3VuVa2Sb6+Jx\nbC2/acLWvqsOZG0OdqQ0gE32vsobp3ODhYdoAexbGqeY3ss2z8t9O5Nkm+viS4RC+h7Jse0sFsQv\ndp0OU6qIZnHPoO0QSsunc7NcN7RBKbU318SyIhoWnzsvndVgiLUWNw7Luorqhzr3DdXZlgzOLibC\n5dmTab6+JmWPTsfSy79VraM2+TGrsu/VRvKH3vZvnZvWnPd3N87Mm2V3VSMrCwOyt87La+oXJwbl\nvbfRc+c7uHnZlgyEi1hvdXbLM2q0REW3MREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQ\nEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBER\nAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQER\nEBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAR\nEQEREBERAREQEREBERAREQEREH//2Q==\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/Ns4TPTC8whw\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7fde0ec02400>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('Ns4TPTC8whw')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>pseudo-code</b> from <i>http://stackoverflow.com/questions/10852522/selection-sort-for-java</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Given: A - an array containing the list of numbers</p>\n",
    "<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numItems - the number of numbers in the list</p>\n",
    "<p></p>\n",
    "<p><b>for</b> i = 0 <b>to</b> numItems - 1</p>\n",
    "<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<b>for</b> j = i + 1 <b>to</b> numItems</p>\n",
    "<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<b>if</b> A[i] > A[j]</p>\n",
    "<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;// Swap the entries</p>\n",
    "<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Temp = A[i]</p>\n",
    "<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A[i] = A[j]</p>\n",
    "<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A[j] = Temp</p>\n",
    "<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<b>End</b> If</p>\n",
    "<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<b>Next</b> j</p>\n",
    "<p><b>Next</b> i</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>python</b> implementation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "A = [4,3,2,1]\n",
    "\n",
    "#for i = 0 to numItems - 1\n",
    "for i in range(len(A)):\n",
    "     #for j = i + 1 to numItems\n",
    "    for j in range(i+1, len(A)):\n",
    "          #if A[i] > A[j]\n",
    "        if A[i] > A[j]:\n",
    "               # swap the entries\n",
    "            A[i], A[j] = A[j], A[i]\n",
    "               \n",
    "\n",
    "          #End If\n",
    "\n",
    "     #Next j\n",
    "\n",
    "#Next i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4]\n"
     ]
    }
   ],
   "source": [
    "print(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pylab\n",
    "\n",
    "steps = []\n",
    "pylab.plot(steps)\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4]\n"
     ]
    }
   ],
   "source": [
    "print(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}