{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Pandas and the Data Ecosystem"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "with Some Things I Learned from PyData"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Today I'm assuming you're familiar with Python and Pandas.  I want to talk about some of the high level things in the SciPy ecosystem."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import YouTubeVideo"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "YouTubeVideo(\"RrPZza_vZ3w\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <iframe\n",
        "            width=\"400\"\n",
        "            height=300\"\n",
        "            src=\"http://www.youtube.com/embed/RrPZza_vZ3w\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
        "        ></iframe>\n",
        "        "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "<IPython.lib.display.YouTubeVideo at 0x1048b9650>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "YouTubeVideo(\"p8hle-ni-DM\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <iframe\n",
        "            width=\"400\"\n",
        "            height=300\"\n",
        "            src=\"http://www.youtube.com/embed/p8hle-ni-DM\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
        "        ></iframe>\n",
        "        "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<IPython.lib.display.YouTubeVideo at 0x1048b96d0>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Anaconda - you should be using it!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import HTML\n",
      "HTML('<iframe src=http://continuum.io/downloads width=700 height=350></iframe>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=http://continuum.io/downloads width=700 height=350></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<IPython.core.display.HTML at 0x1048b9810>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Some conda commands**\n",
      "\n",
      "conda update conda\n",
      "\n",
      "conda create -n demo python pip\n",
      "\n",
      "source activate demo\n",
      "\n",
      "conda info\n",
      "\n",
      "conda info -e\n",
      "\n",
      "Conda is also used for preparing packages. It is a full replacement for virtualenv and fills in the gaps where pip fails, like with the scipy stack."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "NumPy"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Numpy is the basis for the scipy stack. Let's take a look at some of the features."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import Image \n",
      "Image(filename='stack.png') "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
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PAT/PcJ/XtOs+b3jdZr/MNiVLHcQ5muE8iHqWGQJ7RjPalfD3cOcuS7hF1X2/\nLapSeEM5uv/++wPZeNDEI8wHz+j4n3322YnFD8dayeL+VP9kTZs9T1XDiRPeIGDiWEa1l3bFEYU4\nfjHKJAlvsF9UfkaF3a3vwjzLiCenTYVyEQaa/7GwE2o//GnBOL+xmQDTq9gGnrZODusGML2IHZa6\n/iHsLxa5Yn0FdDOlrVEJu83zjDVlWAOhBlMqco6qPma6Qk8c1I0kGSgGxS4saHAPG18tAaaIsTYQ\nGxV8/UhhN3wmgboRKLNNyVIHUUexbMFfQO8zxDQojs3DsgbohZROqEFblWSyhNsLdR/LN6BeCbo8\nkww2AGBKGhyTDPiOHTs2dscvtAFgvR12CQ8aNCjJK/sNdi6++GK7jjppDRuW7mC5DDbWxf32pAXW\nC/mZlsZOfB8AiTApYPw4+ibFum+1b++xUBUqLCDY4E+3eKOi4g8NHM69S9P6HQUQi4OxTg5CH3Ye\nQZjCugisu5Mp1SgnXfsO69WwXR1lCkz8LfJ+pKXXaeSwZoOt9/hDQwW7+MNOWBzTQ9N9BNptO9p1\n331EWo9RWW1KljqIRfVYt4XOFHSIQTUPdi1utNFGDaqSkDrsYLz77rvt4na0dTgBRRfw+6nPEi7s\n90Ldh743/B7gD78F2L07bNgw+4cOZ7ic+5zC92grISxDSJPRPbsLVJaLWOG5ld8T+I/TcyBoYuep\nTJlaNVFQ4YE44nfKV+wbjk/e517IzyxpDudpGXIThbcsOUE7JEACuQm024C16z53hOmABEiABAog\nUEXbxWnTAjKKXpAACZAACZAACZBAVQQovFVFmuGQAAmQAAmQAAmQQAEEKLwVAJFekAAJkAAJkAAJ\nkEBVBCi8VUWa4ZAACZAACZAACZBAAQQovBUAkV6QAAmQAAmQAAmQQFUEcgtv4V0UVUWU4ZAACdSH\nANuJ+uQVY0oCJFA/ArmFt/olkTEmARIgARIgARIggd4hQOGtd/KSKSGBnibA0byezl4mjgR6gkBV\n7VRLwltVkeuJnGQiSKDPCLB96LMMZ3JJgAQqJ9CS8FZ5LBkgCZAACZAACZAACZCAJZAqvMWdycXe\nNUsQCZBAmEBcuxDXjoTd85kESIAESCCdQKrwluRFXEOd5IbfSIAEepNAFe1BFWH0Zu4wVSRAAmUT\nqLJ9Sj2YXhObFin2rJUUryTQXwTKbBvi/GZ7019ljKklgW4nUHVblXnkLa2xRMTjIt/t0Bk/EiCB\n/ASy1Pm0diN/qN+6YFvTKjm6IwESKJpAJ9qjzCNvmthWIllWA65x4pUESKBcAp2s90lhs20pN9/p\nOwmQQDKBTrVPA5Oj1fwVjWVSZJtdmNz2o/zgOxIggfoQqEqoQltUVVj1oc+YkgAJVEEgryxUZJxy\nj7xp4J2MtMaBVxIgge4jUIYwlaW9KSPc7qPLGJEACXSaQDe0Ry0LbwovSyLULq8kQAK9S6Bs4amV\ntqbsOPVubjJlJEACINCt7U7bwptmbysJVLe8kgAJ1JdAlQIS25n6lhPGnAT6gUBV7WFhwls/ZArT\nSAIk0HkCFOA6nweMAQmQQDOBqgQ3hJx7w0JzdPmGBEiABKojoA0khbjqmDMkEiCBeALaJsXbKP4L\nhbfimdJHEiCBCgj4DSYFuQqAMwgSIAFHwG9/3MsKbyi8VQibQZEACZRDoNMNaTmpoq8kQAIkEE0g\n8wkL0c75lgRIgARIgARIgARIoEoCFN6qpM2wSIAESIAESIAESKBNAhTe2gRI5yRAAiRAAiRAAiRQ\nJQEKb1XSZlgkQAIkQAIkQAIk0CYBCm9tAqRzEiABEiABEiABEqiSAIW3KmkzLBIgARIgARIgARJo\nkwCFtzYB0jkJkAAJkAAJkAAJVEmAwluVtBkWCZAACZAACZAACbRJgMJbmwDpnARIgARIgARIgASq\nJEDhrUraDIsESIAESIAESIAE2iRA4a1NgHROAiRAAiRAAiRAAlUSoPBWJW2GRQIkQAIkQAIkQAJt\nEqDw1iZAOicBEiABEiABEiCBKglQeKuSNsMiARIgARIgARIggTYJUHhrEyCdkwAJkAAJkAAJkECV\nBAaWGdiAAQPK9J5+9yCBIAh6MFVMEgmQQDcT4G9VN+dOd8at079VAyQChf5ashJ0Z0GrY6wKLpq1\nQsB6VKvs6orI9nN9aSUDWMdaoUY3UQQ6UfcKFd5YGaKyle/aIdCJStFOfNtxy/rTDj269Qn0U73x\n0531nnUtKynay0qg6jpXiPDGipA1e2mvVQJVV4xW49mqO9ahVsnRXRyBXq8zcelOes96lkSH34og\nUFW9a1t4a6UyVJW4IjKCfpRDgOXmW66tcCgnR+hrrxJge9t6XSO7Xq0V2dPVShtdRblpS3jLkqgq\nEpE9G2izmwn0W3nKkt5wfrE+hYn03zPLTf48z8KMdSs/13510Q3lqWXhLS3yrAj9WqzbT3c/lK20\nNIIi61D7ZalffGB5is/pNDasZ/Hs+CWZQCfLVil63lgZkjOcX5MJ9Hr5yVLhe51Bcgng17wEUF7S\nykxaucsbZi/YT2PWC2lkGsoj0Mny05LwltQIdDIx5WURfa6aQFI5Sip/Vcez6PCS0l10WPSv9wiw\n/DTmaVJbQVaNrPjUGoGkcpRU/loL7TtXLQlv3zlvvEtKRKNNPpFAOoFeLE9JlbkX05uey7RRNIGk\ncpRU/oqORzf7l8Som+PNuHUngU6Up9zCW1zl70TkuzMbGasiCcSVq7hyWGTYVfoVl84q48CweocA\ny5MxcW0E2fROOe+mlMSVq7hy2G7ccwtv7QZI9yTQrwTiKnFcpe9XTkx3MQTiylVcOSwmVPpCAiRQ\nBYFChLe4RqKKBDCM3ifA8tX7ecwUkkAVBNiWVEG5f8OosnwVIrz1b1Yx5STQHoEqK3t7MaXrOhJg\n+apjrjHOJJBOgMJbOiPaIAESIAESIAESIIGuIUDhrWuyghEhARIgARIgARIggXQCbQtvHJZPh0wb\n7RNgOWufIX0ggX4mwDakn3O/urRXVc7aFt6qQ8KQSKC3CFRVyXuLGlOTlwDLWV5itE8C3U+Awlv3\n5xFjSAIkQAIkQAIkQAKOAIU3h4I3JEACJEACJEACJND9BCi8dX8eMYYkQAIkQAIkQAIk4AhQeHMo\neEMCJEACJEACJEAC3U+Awlv35xFjSAIkQAIkQAIkQAKOwEB3V+Obr776yrz88svmueeeMy+88IKZ\ncsopzXzzzWfmn39+M+ecc5pJJ520xqlj1EmABEiABEiABEjgOwK1Ft5efPFF87//+7/myiuvNBDg\noszUU09tdt99d3PAAQdYQS7KTtHv9t9/f3Prrbdab3fZZReD57BZbbXVzPvvvx9+Hfk8ySSTmLnn\nntssuuiiZvHFFzdbbbWV+f73vx9ply9JgGUrexm48MILzS9+8QvrYMMNNzR/+tOfsjumTRIgARLo\nFAHRAZTLSDwD/y+X44Isf/nll8GvfvWr4Hvf+15DXPx4he8nm2yyYK+99gpEyCsoFvHebLrppi5e\nBx10UKTFGWaYwdkJxzXtWQS54JJLLon0t5dfhrnULa1VxZ9lK3vJ+Otf/+rq4ZZbbpndYc1sVlX2\nuglLVJqvv/76YMiQIQ1/0hluOdr77bdfg1/w+6KLLmrZv253OGLECJfeE044oWuimxav4cOHu3if\ncsopTfGWgZFglllmsX/PP/980/e8L6LKXl4/0uzXcuTt6KOPNiNHjhQ+35oBAwaYJZZYwswzzzxm\nrrnmMm+//bZ5+umn7TTq559/bi2JwGdGjRplBg4caE477TR1WsvruHHjzBZbbGHA4aijjqplGhjp\n7iTAstWd+cJYFUMAsx2PPfZYg2ePP/64+e1vf2tnNxo+pDx88skn5s9//rOZOHFig81333234bmM\nB/lhN++8847zerrppqtkedCrr77q+L322msu/LJusqYzLV5YVqX5/sYbbzRF96233jKab19//XXT\n9258UTvh7c477zS//vWvHcs111zTnHzyyWappZZy7/Tmm2++MWeffbY58MADXQU7/fTT7dTjz372\nM7VW+BVTmxAgYeadd157Tfq3zTbbmEMOOSTSCtKABuell16y08OYIlZz3HHHmZ/85CdmmWWW0Ve8\nkkADAZatBhx8IIEmAhAQZLTM/PKXv2z6lvTi2muvdb8rSfbK+AbBcaaZZnJeY7BikUUWcc+9ctMv\n6Wwlv2onvO27775GJePlllvO3HjjjUamRCPTjrViMlVqIOCtu+66dlMDLEJQwnuM2JVhIFTlMTPO\nOKMZNmxYqhOsn0MPUaZirV2s89t7773Nfffdl+qWFvqTAMtWf+Y7U52PANY+5hXeIPDR1INA3gGV\nOqSqVqpCPv30Uzf0CbinnnpqrODmw0eP5Pjjj3evPvzwQ/Pf//7XPdfpBhs0MJqiZuzYseaLL77Q\nR15JoGUCLFsto6PDGhKYdtppzVRTTWVj/uCDD+b6Tfj444/NddddZ91iKQ46STTdSwADKv/+97/t\nX5mzblUSqJXwhjlrHXXDaNuyyy6bmRXWiE0xxRTO/sMPP+zuk24wpP7ss8+aSy+91PzrX/8yr7zy\nisFUZifNqquu6oLH6NszzzzjnnlDAu0QaKVsoQxi5zfqx0MPPWSwpqSoOoIfSdRVTJ+0Y+B+/Pjx\nrv1ox68i04v2DB3Jm2++2aAjhg4qTTUE8HuAZSdqMPqW1Vx99dUur374wx+aQYMGZXVKeyRQCIFa\nCW/+aBkaUIygZTWoqD/96U8N1CjgL+3HAD8YP/rRj2ylxMgdhL811ljDLmqVXSl2+jLOD2wkgFv8\nlaF6YMkll2xINnTbwSAs2e1k/4YOHWomTJjQYC/qAQwxZavuzj///ChrfNcnBOLKVjj56NTccMMN\nZr311jOy69vqVET9QIdqttlms6pstt12W3P77bcb2I0zUKOjZQ8CDAzKM5Y1oFziR3HppZc2P/jB\nD6w9qP3RhcVxfup7hA1/kCaMsmAzE9oBxNNfO6r2k65FpVfDePLJJ81GG21kdVIusMACZp111rFr\nV6HaaKGFFrLx/uijj9Q6ryUQgOC89dZbO5/zCG/+lCn80EEF51nKTZEdgJSgYj9jkf69995rZ7Py\nxj/W05gPr7/+uh2phAotXQ8eY7XrXqMDiY4pBo+Qb11jpFHKZSTibms97qs0Aq8h7D/+8Y+lBA9/\npZFvCCucbjyLAuBACmVTHPKqCtlnn32a/Eh6cfHFFzfE7YEHHrDWZdFqw3sR5pK8sd/OO+8850bW\nCAayayfVTacshPOgU/FoNdyq4u+rCimqbPlploY+2GSTTVy5Cacr/CwClO+84d6vK1B/g/IoOgwT\n/RZhLnjvvfca/Ak/nHXWWYEo5070R6ZSgiyqQopML+IpG65S4waGCy+8cPDoo4+Gk9bSczhPWvKk\nZo6i0vzPf/7TlQnpEAQy0hngqnbRhqaZDz74wKmpmnzyyW1ZFE0Hzg/RahDphYxGB1BVIuuvI/Mf\nvzmyJCa47bbbAtiNMscee2wgmhUCWcPlwkPcReC37/FNBjmcU9QvGRm0fzJrZN9fcMEFgWzwa3Av\n08eBjLoHt9xyi3MbdYO6rKxk/XmUFfdOZqwC6aAEs846q3OjbvFujz32iK3HedOZFi/RyuA4yCZG\nF0e9mX766V0cZSbL8j/xxBMDtDV+O4I8kg2CweWXX65OI6+aTr1GWmrzZW7pSyOj1zbDz+Ucjeg0\n00zjIAOqqP0IZKt2Ln+SLMtIgPMfaUQY/+///T+rVw6FTXrxDd+lF9+kO87/Qcqi5y3vDyx03Cl/\n2XQRSM/AJUlGPtw3NBJpBpVL/ZKRwjTrHf2u8dRrRyPTQuAab7224EUmJ+0Ib0llSwNHo61pwBVl\nDvqu/vCHPwTQ/YR64scBdtAYRxm/rkDPGjoQsC/riIKVV145kNG7QDYcNQl0K620UpR39t0RRxzR\nED/UEQhC0OcFd37HTEb3nN04PW9FpldGKwPER/khjfhhkV3wto1BHP34QZB96aWXYtOa9YOGp9es\n7upsT9OqV6TFF97kJB6bPJmRcfkRV059Dn6Hd4MNNrCf0JHXcKKEt6I6AHvuuacLR8MLX5966ikX\n3bBQcvDBBye6R/07/PDDm37T1MM0IUntyQxOw291OI76LKP0kYJQ3nSmxctvZ6J+k31OMutm2wqN\nY9wVYcbJHmE3yqXIa62ENyQcyvbCYGRKJNhhhx2C0aNHt9XQYWRPfzwQxlprrdXUM0CPSFSTNMRB\ndP005ElaQYFl/8ctj/D2yCOPNPyQoWfgG9nE4eIGxcQyxeR/briXKdOGH4q//e1vDd+77SGc790W\nv7T4VBX/ssoW0ifr2Vz5QnowihQ1SvDZZ58FP/7xj51dKMGMMn5dUT6LLbZYoKPJ6kamEIPll1/e\n+Qe7aGTD5u67726wg5EIjAD4BiMnUSOHUcJb0emVI/tc/MAuyqDnj3grj5133jnKWq536pdeczmu\nqWVNq16RDF94QwcB5pprrnGsBw8ebN8l/ZOTOJz9c88911r1R5eihLeiOgAnnXSSHSGD0K/pwhUd\nKIyc4U90mrno+0KJrO9zbpBOdLLw24O66fuFewgmUSZNSIKbcOcJym9Rz1HeZUdvsOKKKwb4bfLD\nRL74Jm860+LltzNpwpuo93Jxm3322YONN97Ydk7BFgK/H+/NNtvMj7a79+3gvgyT29cqIpWUUPRg\n0MiG4+E/A/52220XnHHGGbaBj/pxiQrD/7GR9TGBrAmIsmbf+cPOsuahwV5aQYHlPD+wiD8qJHqF\nsqupIe0YYvcNfmz8YV4ItHHmH//4h/MLhRLCXDcbP49xXzdTVfzLKlvgLQu1XZnBaFaSwYiRphmj\nTVEdCb+uwK6soQsg+EWZN998s6FzhWmNsMFIiIaJHzQIfVEGdQqjemoX1yjhrcj0oj3R8DC6JgrE\no6Jm32FqS+1idKJdo37ptV3/6uBe06pXxNkX3tBRh0E+iJJbx1uU9tr3Uf8wXY+pUviJPERHAEZ0\nrjn3YeGt6A4AwkO51nThGjfd6wtvsIffBvwmhH8TUVdXWGEF5yfsRU3ZpwlJiAeEYo3b+uuvb6em\nEWffiILfQNZ6OnsQ8KLah6zpTIuX386kCW+IO9Iga9cDnObkG7RB/m8/7IruWd+Kvdf067XJQgEv\ncv8CamT0WkAccnuBygbpPlwwNU7hKxo/WegcyOLM2LBkG7ErSHB/0003xdrFB/S4NBz0unyTVlBg\n1/+BxZqL+eabL/IPR2FpY6Hh6TXqxwZ+4wdQ7UAgjTP+6ENYAI1z08n3mia9djIurYSt8dZrK35k\ncVNm2YLABP/xJ+p3UqPjH2HnT+eoQ7+u4AdDFvLrp8grBEblhyUOvvnPf/7jvsEOpiiTjGyMaPih\niapPRaYXHS2NO4RZWbgdGz0IsD//+c8DUWtg/2ShdKzdLB80XL1mcVN3O5pWvSI9vvCGPFCz6667\nurzBb0ucweyE+ocRGTV+pzosvBXZAdDwsgo14d9IzBrFmRdffDGQDUIufWuvvXaT1TxCEmaukjoo\nWJuHkS3lid/osMmazjzxyiK8JQ16iNJ8u8ZQ4x21hEO/6TWcriKeaym8acKx1gs9VFFeG8wxxxyu\nECiwqKvsGg1kl6h64a5Yq6P28cOEEb4kI7rVgmOOOcauV8GaFRQyNf4PUlRBgT3/B1bDzXpFjxFr\nFxCHKPP3v//dpQWCHwpb2GCUzf9hRQPT7SbMp9vjG45fVfEvs2yF05T0HN5glCa8JXU0NBx/1B1r\nlXyDuqaM0TvOYrDGTN1ECW9Z/FA7aenFZiANC1ekt6rRbj9c3PeDiUpznPAmyt5d3iSNKPuzM5i5\nUJMkvBXZAdDwsgo1vvCGdXlJwhT89n87wC98zmeSkHTPPfc4hnArOzQ1urFXbCzSfMLvWnjUPWs6\nk+KFwNN+k31OGLEPj0yGE4BNjRpvXMMjn/433JdhanfCgoBwBgoWsU0bfzDPPfeckQJkZN2LVcaH\n7fhhIzvaQNLIjs2GExZU3QbsQ2+PFKSw04Zn6Jk78sgjG96V+SAbNYysBbJHe+GkBag7iTMyomaV\nT0KVCRT4XnXVVVZNim9f1nkYqSj2FRRM4gQKmv4kkKdsJRFCeZPpF/t3//33G1m3kmS96VuW432g\n7kONll99lh8avTU4fSWLgeb1Vk3e9Moog1VbIiOENkgoeYValc0339yI4GhWWWUV6gtrNTPadCej\nRPa4KaixgF5P5FFYbQ5U1Kg6G1lmYlW9ZAkWyq/xl8XgnNVwuc7iLosdWatnpDOfaBUqa3yDOgVV\nNlnMHXfc4azhtyrpN0otypoxe0qQDJZY3ZAIT9bj6eeOXGVdfYNsEBWJHXfc0eYp1IjAQH7I0n5F\n+dXqu1oLb+FEQz8S/gAWBgp10UDifFMowFQDhbvQuCzD4/rKwtcHmarU20quu+22m5GRv9iwIEjK\naErs9/AHCLXS0zDSi7KfIKhCx51vIMSqkdGHTCdVqH1e60Og6LKlKZcevP0hk6lA21mCDsZ29TdB\niEwzMtUVa8XvgInqhlh7/oesdb2o9KIjJTvUnTZ/CICyBMP+oZ7LiKHVnSdrhYws7K7ksHGfR7/e\nyxonK0TLlKdFAJ1vYeHtiiuuMLIGyn5H/kAnX7smbwegnfBkwX2qc5yXij+tyxCmsnbs/c6TTLmm\nhgULGDiQWTMzbtw4ax8K5zstvC244IKpcZdd4FbWgD5YGJlyTnVTtIXk4aWiQ6vYP/TSZcux1fou\nw+UNDaGsXWiIjd/w+wf+Nlgq6QG9OK00Udc8gptGUXbf6q2R9XtGhp/dM3oLom/IPft23Uve9ASB\nMsoWFG1CCTR68r/73e/s2bra2Cs0hLvTTjvpY6YrRrPTDEbN44yvxNsfoYuzj/cY+UozRaYX8brr\nrrvsaAMY+UamamxbhaP8MAqHjuiZZ55pZwp8e7wvh4DO4MB3Xwmvhua/8+3q97QrOgCY8TjggAOs\nYD7zzDNbAVB2FtuTHmRDWqkH3Wft0PjCk/+7mJY+X3jLWv/gJzio6YbTgmT9uUYn8ep3/Py2J9FR\ngR9rM/L2zjvvWG3tmnZMDaK3lNXgPNAnnnjCjrjBDWDLziEju4ysF/75oGlTplnD7KQ9nO6AkyBk\nd4xBoyFr2ozswLVRuvbaa93RLhgSlwWXnYwqw64RAZyqIOt+GgQK9JxlnYiRtUIGDR8EO4xaQDg5\n55xzKkudX29F/1KmcDEVlmTKSK9scDKyE96e0oJpOMwO3C6nQWDZh2/Qm5dNC3YGIWlk3nfD+9YJ\nYJoPwjxOA8Dvg6ircdPv+P2BEA+DURf/WK0sIcLt3nvvbadk4+yjvmAWpKw6k/UIL9RhnQLFb2RW\n06rwBp5qZPeu3nbkipH9rIM3GDVUI2tX9baya3bpp7IoRQeE4WWsC1GD9WyYV89jMJ+P6VI1GPLE\nWgcY/PDIlm57j+nWuhvZuWdwPJHofbNJwTSpCm+YRlWz/fbb6y2vJJBIAA0r1lvq6Bd+6DCivfrq\nq0e6w0hSlQYjVThgHEanYdLCT6rrZacXP1qyY9H+IZ6yocGOkqOjhREanaIT/Vh2KjWOc1oa+T0b\nAQj/OAZRFL9bB5g61bWTl112mTsaCYKbL3Ck+V5GByAtzHa++6PoOriR1z+sYctqICyryTrqpfaL\nvqJtw8xUliUcGBhRI+rJ9Laya22mTTFEifMN1cjOLr3NfJUdJQ12MTKlBsKbGiy6TjMYzYIwpBsm\nsvb00/wt8rs/HYoGBIUSQrA/ZUrhrUjive0XNgNpBwcplaN0YgU3fM8z5QL77RoIb2r8UQB9F3VN\nWqtSdXoxggnhGIICFsz77ZPokoqKPt8VTMCfDkUnVzsqrU6ZRnUAMKo1fvx4gzV0shPViDoYI6f4\n2E1mBSenwTvMPGUxfocm/JuZ5N6vf6KXNMlqwzd0WtSIEmu97dgVo6xZTKfjXRvhDTD9uXg51SAL\n3wY76M2q0R12+uzvFMGPkqjX0E+RV6wjw6gDKjVGAfP0xCI9LOGlnMFmRHu29VnO8DOYLsWfCppY\nEO0LrSVEgV72EAG/w4Tp9rSdmqhHVRr/xwOjV2lTPqgH/sadcFyLTi92lWK0En9yNmI4uIZnsPXX\nDEbtnG9wwIdCCMjJBUbXa2H0FgI8DnDHtDYMNilgs0JWU3UHIClefnmOs4fRcl/w8n8X49zoe7/+\nZV0DBoFSf4/gTzcIbzp6r+mKumKZlZ/GTsS7VsIbVHiowVoRLObNajBSBmFLDQQbf40Mdqjq7iEU\nJjmwWq02XdEb0+lIfMxTmZs8K/mFP/qGHyr/x8r/cSg5GvS+BwiIoliXCkzLJxmoO5DzDZOsFP4N\nZV03PaCzktbB+8tf/mKSetlFpxdTKxi5xB+m5NKMrzIiyw64NP/4PZ0A1jxh3Zka5BNGQnUaUM6C\ntms59Xva1ReYOt3h8eMSF2+k1R+4yDNV7wtv8McXyuLC83+TsXEh66aKOP+KeO/PTMX5h/Tp+jwM\nBKV1ZOP8aed9rYQ3OTDXDBkyxKUXi3mhCgGCWZLB7i4soJZzQZ019IJ9g4Ij5665VwhLFDe6Z/8G\na1CgIgEGlT3sl2+30/eY2lX1ClgYjZE3GDnaxenH63QcGX49CPiqE7C4fsyYMZERx5oZrCUNT/Xp\nFFSkowJe4scDi8LVQBUQ1AJFGYwKoo4nmaLTi1EdNaiHorRYH5uumJLBD4QaXXulz7yWR8CfOkVn\nVxTBu8B8wc69TLgpugOQEFTqJ7T/GAlMMv7GGGxC8neCJrnDN6wXxO8KDDYCQQVOksESHlF07Kzg\n9zfPJkTnsOAbaKJQfX5RXqMdw4YjNWhzsqyRU/uFXSUiuYwEjL367i+X4wIs47w1nCnnx0F6tIFU\nKnv2pzR4gQxxB9DcjONzZJNCw3mIcHfggQdGxgTanGVKw/mN0wlwcK+MIAT33XdfAI3avoZt+HXo\noYc2+ZWmzRkOfC34eQ6mbwoswwtZT+HSpNzAq25G465Xxj+aQFllS3qaDcfZ4Fg3UcUTiCAU4DB1\nGckKZDQ3wDmFyKNpp522wf4hhxwSSO/fnQeJ2Pt1ZcSIEdEJ8t6KmgVXlqNORJCRtIZzKmV0PZDG\n1R6LBC3o0KSPkxj0YGyZFor1r+j04tQW//xI2V0YQPu+TNPYkxZw2oL8wNrTU2TqzsULx+/weCyv\nEGS81XZCr3AmIz2Oq3RqY32SaTBnT92jvMtoaKSbuBMWkJ/qHuHFnTqAc2+Rz2pXrzJdHhkeyoPM\nFDn7qHtRxj85AH7iOCrZINBkFaf14HgqDRdXWZPXZC/tJAMcPK9+iEBj61uTJ/JCBlzsb7PaBT9Z\nk91kNWs60+LltzNRpx6FOcmO00DW/jXFB3XYP0pNlktFnoGu6dJrk0cFvMCCzFxGI6PXXI4LsiyK\nLgPZwOAKicYlyxVnBSYZnO8WPng2zl+c/RbVqKYVFIRf1g9sVNrCR3kgPfgRq5sJ5wPjH02gzLIl\nG18y1TvpsduzhP0jqzT/ZMG2i7hfV4oQ3uAxzjDOclyerPe0P6YaryhhsOj0yuh/AKFNw/SvEDT9\nZ9xDAJa1NY5Xqzdhf1v1p07uotKcVXhDRyPsPnwcm88iTngrugPgh7nBBhu4OGLQAXFGZ2DChAnO\nmi+UQHhEmnBMFjpdsiEjeOihhwL8PsiosPMLdkTVlPPDv0kTkmSdacNvGw54RxuA4xdlk4YNT3bz\nBjId2xCe6Iv0g2m4z5LOtHj57Uya8KbtJ4R1nP8to2y2g4ojNEX/YkO8DzvssIa46kO47Oj7Iq+1\nFN4AQNa0BKL2o6H3EQamz5COZXrV9nCzwJO5enteqvbO1R+9yry8PZgeUniUSSsocKMFBH6WPfKG\n0Qj/cHvEPy7uUenplnfKX6/dEq+s8dB46zWru7z2yi5bMuUXoAxpOvwrBBP04FHmYNAZwsi4b6ds\n4Q3hyjb+ph8IjQOEJJxxjDhCmNL3UcIb/CoyvfAPwpiom3Dhavj+FT+0sg43kOlTOGnb+H7jvh9M\nVJqzCm+y27cpf2TDWyy2OOENDoruAGgkZN11UxyRZv8MYV94g4DkP4f56PPSSy8de+ZumpCEuMnR\neEHUyKX6718xiyYnIGmSIq9Z0pkWr7TfZOWCeoeRcHSa/HhG3eNs8zgTth9nr533tdHzJjAajByq\nbkTqtdussa0ea3DwpyoCoFoEf/LDYXBUh2RGg/ukByhLHD16tN0QgXVy2H2CnWtYNIx1NVh3l3RG\nnL9WJS4c6R3FfSr8vRRMq68Ia/9gsE7Q36xReID0sKMEyi5bUNKLXWLYcY0rVAtArQV2LmNBt6/S\nB/UP9RPnDWOhPuokdjmryVJX1C6uJ598sv3z30XdY62OLJ+w68qwzgdnreJsUSiklqlL1x6gbkgD\nGuWFe1dkeuEpdFlhN6yMEBqcZYlda/hDncSuNfwtv/zyBpr3aTpDYNiwYfasStX4D31n4XM/s8YM\nx0thjSPWRvk7OdU9fm+w2QZrqVEeUZegxSBNZRWOPMTi+ri12eq/XrFJD2pKsEY7rBAadnCsooxK\n2TM7EadWDdZnQocqTjfy1wuG/UNbAHUsKOtJJm86k/zK8g2cwHXnnXeOVKoMWQKbJVVvahY/y7Bj\nJ/3zeCySaYP1tIavwTIfOkIAW92x/R3bm7EbD1vgoeW9bqbuZa/u8a9beWF8vyPQj2Wv29KMBfpZ\nOjzINexk9Ds8OC5NNwN8l6vf3mFzy7PPPms7IegsQfhXI6PwdvMAnrHBCOebQh0IBD4MSsAtVNdg\ntyS+Ff27AGEVnTf8YWAFO24hGOMPnZhwHmm8o65J6Yyy3+47cIJ+VOzSxW8oOlPo+OFwgLR4h7+X\nISdReGs3h2vgHj062VhhY4qdVEm9oW5OThUVosz01z3+ZbKh3+US6Mey149pDpeiKOEtbIfPxROo\nouzVSlVI8Yh730ccsePrwxs+fHjvJ5opJAESIAESIIEeJkDhrQczF0PimBrFwco4/kqPO5EdPg3r\njXow6UwSCZAACZAACfQ8gdpuWOj5nGkjgVhICSWJYTNy5MjwKz6TAAmQAAmQAAnUjABH3mqWYa1E\nFztjR40aZXwN7634QzckQAIkQAIkQAKdJ8CRt87nQeEx2GOPPQwOsh40aJAZPHiwWW+99czQoUML\nD4cekgAJkAAJkAAJVE+Au02rZ84QWyRQxQ6eFqOWyVnd458pkbTUlQT6sez1Y5rDhe83v/mNOyAe\n+j2hW42mfAJVlD0Kb+XnI0MoiEAVFaKgqEZ6U/f4RyaKL2tBoB/LXj+muRaFsQ8iWUXZa3vNWziS\nfZAvTGIHCLCcdQA6gySBHiLANqSHMrOLk1JVOWtbeOtihowaCXQ1gaoqeVdDYORKJ8ByVjpiBkAC\nlROg8FY5cgZIAiRAAiRAAiRAAq0ToPDWOju6JAESIAESIAESIIHKCRQivHFYvvJ866sAWb76KruZ\nWBIojQDbktLQ0mMhUGX5KkR4Y66RAAm0RqDKyt5aDOmqzgRYvuqce4w7CcQTyC28BUEQ6RsbiTAl\nZXsAABN9SURBVEgsfNkmgbhyFVcO2wyOzkmABHqEQFwbEdem9EiymYwOEYgrV3HlsN1o5hbekgKM\ni3ySG34jgTgCvVae4ipxr6UzLj/5vloCceUqrhxWG7vOhhbHprOxYuh1JdCJ8tSS8JZU+TuRiLpm\nOOMdTyCpHCWVv3gfu/tLUnq7O+aMXTcSYHn6NleS2goy6saSW784JZWjpPLXbkpbEt7SAk1KTJpb\nfieBXi4/SZW5l9PNUl0dgaRylFT+qoth94SUxKp7YsmYdCuBTpaf3Mdj+RCzRJyNhU+M90kE+qU8\npaWTdSaplPBbEgGWrWg6aVzgivUumh3fNhPohvLUlvCGJGVJRDjprCRhIv333O/lJkv6WU/6r160\nmmKWp3RyWRiFfWEdDBPpv+duLTdtC2/IylYS139FgCluh0AvNqKt1Jte5NBOuehHtyw3red6K+xa\nD40u+5FAVW10IcKbZhArhpLgtSgCVVWEouKb1x/WmbzEaD8vgV6vQ3l5wD7rXSvU6CaJQNX1rFDh\nDQljpUjKXn7LQ6DqypAnbkXbZb0pmij966f600pus861Qo1uogh0oq4VLrz5CWPl8GnwPguBTlSC\nLPGqwg7rSxWU+yOMfq5HreQw614r1PrbTafrWKnCW39nLVNPAq0T4I9J6+z61WWnf0z6lTvTTQKd\nIDCwE4EyTBIggWQC/CFO5sOvJEACJNDPBEpR0tvPQJl2EiABEiABEiABEiiTAIW3MunSbxIgARIg\nARIgARIomACFt4KB0jsSIAESIAESIAESKJMAhbcy6dJvEiABEiABEiABEiiYAIW3goHSOxIgARIg\nARIgARIokwCFtzLp0m8SIAESIAESIAESKJhAoapCqJuq4NyhdyRAAiRAAiRAAj1BoEgVUIUIbxTa\neqJcMREkQAIkQAIkQAIlEVBZqQghri3hTSNSUjrpLQmQAAmQAAmQAAn0FAGVndoR4lpe86aB9xRR\nJoYESIAESIAESIAEKiDQjhyVe+StlcDakS4r4McgSIAESIAESIAESKAQAnnkJLWbV07KLbxlSVne\nSGTxk3ZIgARIgARIgARIoNsJhGUgFdCKjPcACSTI6mFaBHJ4lTVI2iMBEiABEiABEiCB2hMoUobK\nLLwlBUqhrfZligkgARIgARIgARKogEAR8lTLGxY0fRTclASvJEACJEACJEACJJBMoAi5KZPwFicl\nFhGB5CTyKwmQAAmQAAmQAAn0FoE4+SlO3gqnPpPwFnbEZxIgARIgARIgARIggc4QSBXe4qTAOKmx\nM8lgqCRAAiRAAiRAAiRQHwJxclSc3OWnLFV48y3rfVyA+p1XEiABEiABEiABEiCBZAKtylMtCW/J\nUeFXEiABEiABEiABEiCBsghQeCuLLP0lARIgARIgARIggRIIUHgrASq9JAESIAESIAESIIGyCFB4\nK4ss/SUBEiABEiABEiCBEgjkFt5aXVxXQtzpJQmQAAmQAAmQAAnUmkArclVu4a3WhBh5EiABEiAB\nEiABEqg5AQpvNc9ARp8ESIAESIAESKC/CFB466/8ZmpJgARIgARIgARqToDCW80zkNEnARIgARIg\nARLoLwIU3vorv5laEiABEiABEiCBmhOg8FbzDGT0SYAESIAESIAE+osAhbf+ym+mlgRIgARIgARI\noOYEKLzVPAMZfRIgARIgARIggf4iQOGtv/KbqSUBEiABEiABEqg5AQpvNc9ARp8ESIAESIAESKC/\nCFB466/8ZmpJgARIgARIgARqToDCW80zkNEnARIgARIgARLoLwIU3vorv5naPiFw//33m6FDh7q/\npZZayowbNy5X6t977z3nHn6NHTs2l3taJgESIAESKIfAwHK8pa8kQAKdJPDxxx+bxx57rCEK++yz\nj7nqqqsa3iU9fPXVVw1+TJw4Mck6v5EACZAACVREgCNvFYFmMCTQaQJXX321ueyyyzodDYZPAiRA\nAiTQJgEKb20CpHMSqBOB/fbbz3z00Ud1ijLjSgIkQAIkECJA4S0EhI8k0MsEXn31VXP44Yf3chKZ\nNhIgARLoeQIU3no+i5lAEjBmzTXXdBhOP/108+CDD7rnrDdBEGS1SnskQAIkQAIlEqDwViJcek0C\n3ULg97//vRk0aJCNzjfffGP23HNP8/XXX3dL9DLF47PPPjNPPPGEeeSRRww2ZCQZpA0bNt56660k\na5m+wY97773X+lc3ZpkSSEskQAK1I0DhrXZZxgiTQH4Cs846qznxxBOdQ6j9OO2009xzKze77rqr\nGTJkiP07++yzU73YaaednP2LL7440v7+++/v7JxzzjnWDgS2rbfe2kw11VRmiSWWMEsuuaSZZppp\nzFprrWWgEsU3o0aNMj/60Y+soAr1JrPMMouZf/75zbbbbpuoKmWzzTaz7uB2/Pjx1ssLL7zQLL30\n0taPlVZayapNmXbaac1qq61mbr31Vj9Yd/+nP/3JxR/hT5gwwX2Lu/nwww/NsGHDnLvzzz8/zirf\nkwAJkMC3BGQqJNGILcyVuL9Ey/xIAiTQFQREuHB1FvVXRo8CGXELVl11Vfd+6qmnDl555ZXY+MKN\nX/fHjBnTYHf11Vd330eMGNHwLephlVVWcfZFyIqyEmy66abOzkknnRQgTMTTj4d/P+WUUwaPPvpo\nICNiwb777htrD24WXHDB4LXXXosMd/rpp3dun3nmmeDggw92z354ej/JJJMEsnYwEHUqDf49/fTT\nDe5EmGv4HvVw3nnnOTfwV9YlRlnjOxIggR4moG2LXtOSypE3IUVDAv1AYMCAAQYjZJNPPrlNLqYe\nsfu0W82dd95p1l9/fTtFipE2rNvbcsstzQILLOCi/Omnn5otttjCbLPNNm4kcbrppjNrrLGGEeHS\nDBz4nSrL559/3qyzzjoG08ZJ5sADDzS/+c1vrJXBgwebPfbYw0BH3qKLLuqcwY/jjjvOvncv5WaR\nRRYxyy67rHt1ySWXuPu4G38UEqOJs88+e5xVvicBEiABS4DCGwsCCfQRAQggv/rVr1yKL7/8cnPl\nlVe65266ueKKK8wnn3xidtllFyMjhOa2224zF110kYEQdtBBB7moPvvsswYC0KSTTmp30r7++uvm\n9ttvN3fccYd58803G+w+/vjj5p577nFuo26uueYa69fo0aPtOrezzjrLCoZPPfWUeemll8wKK6zg\nnGGaNKwMeYcddnDfEWecVBFnoLblxhtvdJ99t+4lb0iABEggRIDCWwgIH0mg1wkceuihZrHFFnPJ\nlOnG1A0AznLFN1tttZWBEIW1Zr454YQTzMwzz+y/MsOHDzcyfWummGIK916mQw3szjfffO4dhLo0\ng/WBEBoxWumbeeaZx1xwwQVu8wc2MGCkzjcYBYQgCfPll18aCKFxBoqTP//8c/tZpoAN1t7RkAAJ\nkEAaAQpvaYT4nQR6jACmTTF9qoIJRrWOPPLIrkslhDCdvgxHTtaG2Y0L+h7C3dFHH62PDVfYxfSn\nmrQdqHPOOWfTdKi6xXXeeec1ULei5uabbzYvvPCCPtoNDmuvvbZ7Tpo69adMN9poI7sRwznkDQmQ\nAAnEEKDwFgOGr0mglwnI5gGz++67uyT+4Q9/MA8//LB77oYb7CqFoBRnsLZNDaaDZWODPjZdfbvv\nvPNO03f/xYYbbujWBfrv/XusnfMNpnJ9409/3nLLLeaDDz7wP9t7TJnecMMN7r3vxr3kDQmQAAlE\nEKDwFgGFr0igHwhgVGu22WazScX0H3S/pS3mr5LL3HPPnRjcZJNN5r5DHUhWk5ZG2ZGb6tVMM81k\n8KcmLLxtsskmVrUJvn/xxRfmqquuUqvuirV10F0HM+OMM5p1113XfeMNCZAACSQRoPCWRIffSKCH\nCUBpL0bc1DzwwAPmzDPP1MeOX7G+LKuZYYYZslpNtZc1XH8q1p82RQDQSSdqT1xY/vSovvSnU7G2\nzxdG1Q6vJEACJBBFgMJbFBW+I4E+IQA1G5gmVHPYYYcZ0YWmj4Ve00a8woGFNymEv4sepPCr2Oc8\ndvUkiljP/u+DvwkiakepPw160003GUyTqoGaluuvv14fjW/XveQNCZAACcQQoPAWA4avSaBfCJxx\nxhluvRi0/Zel++3999/vKaRvv/22S4+/pk5f4rQGnPAAgx2l2Fmq5tprrzXQUQcDvXU4wYGGBEiA\nBLISoPCWlRTtkUCPEphrrrnM8ccf71J36aWXGggXRZuo0amiwyjCPxzHlcVgl64aqCQJG6gLwbFc\navxpUn8adfvtt1crvJIACZBAJgIU3jJhoiUS6G0COEFg+eWXd4nEMxTkZjVpU6JQz/HGG29k9a6j\n9sJKd6Mig/S+/PLL7hNOVogy/nQodpZiuhRc/SlTCm9R5PiOBEggiQCFtyQ6/EYCfUIAutCg+02P\nk8JJAnF60xQJjqxSI+eB6m3kFbrQ8qw7i/SkopdZhLfLLrvM+NPAOIoryiyzzDLuWC1Mk2JEE38T\nJ0601ldccUWz8MILRznlOxIgARKIJUDhLRYNP5BAfxEYNmxYw2kB55xzTiIAXc8FS0k64sJHVCV6\n2gUfr7vuutQjtHBqgxqcZRo+7UG/4eqPvmHq1J8+3WmnnXyrvCcBEiCBTAQovGXCREsk0B8Ejjrq\nqIaD35NSDSW6anDuJ875DJuvvvrKHhpf1g7WcHhFPEMvG3bhRk3z4rgrHFQ/duxYF9Thhx/u7qNu\ntttuO3eaBQRDXU+IEyS23nrrKCd8RwIkQAKJBAYmfuVHEiCBviKA8zVHjRpl/OOd4gBANxnO9YSw\nA7PXXnuZMWPGmJVXXtlOv+JkARzMPmHCBHtiAaYQ0w6Fjwuryvc4NgzC5nLLLWc22GADg12jUAJ8\n//33m/POO8/cfffdLjr4tvHGG7vnqBuoFAGTu+66y02Xwh7cRe1SjfKD70iABEjAJ0DhzafBexIg\nASus7Ljjjubcc89NpIGpQhy6jsPUcVIAFvH//e9/t39hh1hPh9GqOghvp5xyij3gfvz48eass86y\nf+H04HnppZc2WPuWxWDqFMKbb3bbbTf/kfckQAIkkJkAp00zo6JFEugfAieffLI9siktxeuvv77B\nVKAesxW2j6lVjMBhbReO4KqDwQjhHXfcYRZaaKHI6OL0hGOOOcbceeedmQ+Sxyjl5JNP7vzDKQ4Y\ntaMhARIggVYIDJAdYIlqyjGF4JsU675V3pMACfQRgVdffdU89NBDBuvf5phjDrvLEqNT2Mna7QbH\na7377rs2mpj6xfmmGEm88cYbzYMPPmiQNgioiy++uP0266yz5k7SKqus4kbfjj32WHPEEUfk9oMO\nSIAEepNAXlmLwltvlgOmigRIIAeBKOEth/NUq9BzB2XIWB+IM0zHjRtnWhEAUwOiBRIggVoSyCu8\ndX+XuJbZwEiTAAmQwHcERo8e7TZ2YI0gBbfv2PCOBEggPwEKb/mZ0QUJkAAJZCYA9SJnnnmmsz98\n+HB3zxsSIAESaIUAhbdWqNENCZAACSQQwFo5TI0+8MADBsdf6TmoOIkBpyrQkAAJkEA7BKgqpB16\ndEsCJEACEQSgmFc3QPifR44c6T/yngRIgARaIsCRt5aw0REJkAAJZCcANSFQfgxlvTQkQAIk0C4B\njry1S5DuSYAEak/goIMOcqcfQAdbuwZHaD355JNm0KBBZvDgwWa99dYzQ4cObddbuicBEiABS4Cq\nQlgQSIAESIAESIAESKCDBKgqpIPwGTQJkAAJkAAJkAAJlE2Aa97KJkz/SYAESIAESIAESKBAAhTe\nCoRJr0iABEiABEiABEigbAK5hbfwvGzZEaT/JEACJEACJEACJNCrBFqRq3ILb70Kj+kiARIgARIg\nARIggToQoPBWh1xiHEmABEiABEiABEjg/whQeGNRIAESIAESIAESIIEaEaDwVqPMYlRJgARIgARI\ngARIoCXhrZXFdURNAiRAAiRAAiRAAiTwHYFW5alU4S0Igu9C8e5aDdDzgrckQAIkQAIkQAIk0JcE\n4uSoOLnLh5QqvPmWeU8CJEACJEACJEACJNBZApmEtzgpME5q7GySGDoJkAAJkAAJkAAJdC+BOPkp\nTt4KpyST8BZ25D/HRcC3w3sSIAESIAESIAESIAFjipCbBoiUF72oLYJwWoA5vIrwna9IgARIgARI\ngARIoDcJFClD5RLegDMtcNihEAcKNCRAAiRAAiRAAv1OoAy5aWAZUMMRpTBXBmX6SQIkQAIkQAIk\n0G0EwjJQGfHLPfKmkagichoWryRAAiRAAiRAAiTQawRaHdxqecNCqwH2GnimhwRIgARIgARIgATy\nEmhHjmpr2lQD5ihc3iyjfRIgARIgARIggX4koLJTO2lvS3jTgDUiFOKUCK8kQAIkQAIkQAIk8B0B\nlZW+e9P6XSHCmwZfZMTUT15JgARIgARIgARIgAS+I9DymrfvvOAdCZAACZAACZAACZBAVQQovFVF\nmuGQAAmQAAmQAAmQQAEEKLwVAJFekAAJkAAJkAAJkEBVBCi8VUWa4ZAACZAACZAACZBAAQQovBUA\nkV6QAAmQAAmQAAmQQFUEKLxVRZrhkAAJkAAJkAAJkEABBCi8FQCRXpAACZAACZAACZBAVQQovFVF\nmuGQAAmQAAmQAAmQQAEEKLwVAJFekAAJkAAJkAAJkEBVBP4/GIcA0SwWtJAAAAAASUVORK5CYII=\n",
       "prompt_number": 5,
       "text": [
        "<IPython.core.display.Image at 0x1048b9a50>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "The basic datastructure in numpy is the ndarray, an N dimensional array. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.array([[1, 2, 3], \n",
      "              [4, 5, 6],\n",
      "              [7, 8, 9]])\n",
      "\n",
      "I = np.eye(3)\n",
      "z = np.zeros((3,3))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"x = \\n\", x, '\\n\\nI = \\n', I, '\\n\\n z = \\n', z"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "x = \n",
        "[[1 2 3]\n",
        " [4 5 6]\n",
        " [7 8 9]] \n",
        "\n",
        "I = \n",
        "[[ 1.  0.  0.]\n",
        " [ 0.  1.  0.]\n",
        " [ 0.  0.  1.]] \n",
        "\n",
        " z = \n",
        "[[ 0.  0.  0.]\n",
        " [ 0.  0.  0.]\n",
        " [ 0.  0.  0.]]\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "In a lot of ways, the ndarray can be treated like a list, but there are some interesting differences. "
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "You can make an ndarray from a list, but an ndarray can only have one type of data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "integer_list = [1, 2, 3]\n",
      "integer_array = np.array(integer_list)\n",
      "print integer_array\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[1 2 3]\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "my_list = [1, 1.0, None]\n",
      "print my_list\n",
      "my_list[-1] = \"one\"\n",
      "print my_list"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[1, 1.0, None]\n",
        "[1, 1.0, 'one']\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "my_array = np.array([1, 2, 3])\n",
      "print my_array\n",
      "my_array[-1] = \"three\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ValueError",
       "evalue": "invalid literal for long() with base 10: 'three'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-11-a1dd1105d9ae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmy_array\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mmy_array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmy_array\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"three\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;31mValueError\u001b[0m: invalid literal for long() with base 10: 'three'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[1 2 3]\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "The other interesting difference is that the ndarray supports \"fancy indexing\"!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"x = \\n\", x\n",
      "y = x[x > 3]\n",
      "print \"y = \\n\", y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "x = \n",
        "[[1 2 3]\n",
        " [4 5 6]\n",
        " [7 8 9]]\n",
        "y = \n",
        "[4 5 6 7 8 9]\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x[1,:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "array([4, 5, 6])"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Many of the functions that operate on ndarrays are written in C or Fortran, which makes them very fast. One common type of function is the ufunc - Universal Function.  This class of functions operate elementwise on the array."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.add(x,x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "array([[ 2,  4,  6],\n",
        "       [ 8, 10, 12],\n",
        "       [14, 16, 18]])"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.add(x,1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "array([[ 2,  3,  4],\n",
        "       [ 5,  6,  7],\n",
        "       [ 8,  9, 10]])"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.log(x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "array([[ 0.        ,  0.69314718,  1.09861229],\n",
        "       [ 1.38629436,  1.60943791,  1.79175947],\n",
        "       [ 1.94591015,  2.07944154,  2.19722458]])"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.greater(x, 3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "array([[False, False, False],\n",
        "       [ True,  True,  True],\n",
        "       [ True,  True,  True]], dtype=bool)"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Numpy also provides a Matrix data structure.  It's mostly there to make Matlab users feel more at home. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.matrix([[1, 2, 3],\n",
      "              [4, 5, 6],\n",
      "              [7, 8, 9]])\n",
      "x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "matrix([[1, 2, 3],\n",
        "        [4, 5, 6],\n",
        "        [7, 8, 9]])"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x * x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "matrix([[ 30,  36,  42],\n",
        "        [ 66,  81,  96],\n",
        "        [102, 126, 150]])"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y = np.array([[1, 2, 3],\n",
      "              [4, 5, 6],\n",
      "              [7, 8, 9]])\n",
      "y * y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "array([[ 1,  4,  9],\n",
        "       [16, 25, 36],\n",
        "       [49, 64, 81]])"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "SciPy"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "SciPy is the core library for scientific computing.  It has modules for:\n",
      "    \n",
      "    Integration (scipy.integrate)\n",
      "\n",
      "    Optimization (scipy.optimize)\n",
      "\n",
      "    Fourier Transforms (scipy.fftpack)\n",
      "\n",
      "    Linear Algebra (scipy.linalg)\n",
      "\n",
      "    Spatial data structures and algorithms (scipy.spatial)\n",
      "\n",
      "    Statistics (scipy.stats)\n",
      "\n",
      "And more!\n",
      "\n",
      "\n",
      "** Linear Algebra **\n",
      "\n",
      "Scipy and Numpy both have the same set of linear algebra functions.  They are mostly identical in name, function and performance.  Numpy does contain some additional functions not exposed by SciPy."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "from scipy import linalg as LA\n",
      "\n",
      "#Solve Ax = b\n",
      "A = np.random.rand(5,5)\n",
      "b = np.random.rand(5)\n",
      "\n",
      "x = LA.solve(A,b)\n",
      "print A\n",
      "print x\n",
      "print b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 0.92922464  0.49781215  0.01937578  0.62212312  0.35897636]\n",
        " [ 0.96401938  0.80590361  0.91703878  0.79958259  0.36575606]\n",
        " [ 0.18171917  0.97812798  0.61268685  0.07005332  0.24989442]\n",
        " [ 0.75688208  0.31959706  0.49975434  0.84763035  0.78576249]\n",
        " [ 0.54993672  0.0328287   0.48166429  0.73207162  0.57560584]]\n",
        "[-20.4381976   10.82454188  -9.06526981  29.23856419 -10.32956975]\n",
        "[ 0.70318052  0.30805896  0.78656003  0.1266843   0.20818588]\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Find eigenvalues of A\n",
      "e = LA.eig(A)\n",
      "print e"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(array([ 2.80077761+0.j, -0.40934814+0.j, -0.01012884+0.j,  0.85934335+0.j,\n",
        "        0.53040732+0.j]), array([[ 0.39087844,  0.32950893,  0.51420274, -0.52548141, -0.77733774],\n",
        "       [ 0.60909269, -0.57222447, -0.25281505,  0.32953648, -0.00487729],\n",
        "       [ 0.35910487,  0.60007381,  0.21969001,  0.7588856 ,  0.43043274],\n",
        "       [ 0.48035397, -0.00928752, -0.73993232, -0.16311976,  0.27844853],\n",
        "       [ 0.34135596, -0.4514512 ,  0.27552959, -0.11295835,  0.36457691]]))\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# QR Decomposition\n",
      "Q, R = LA.qr(A)\n",
      "print Q\n",
      "print R"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 0.56539143 -0.08272076 -0.78802579  0.02830417 -0.22738521]\n",
        " [ 0.58656246  0.21663769  0.33182691 -0.69157816  0.14360758]\n",
        " [ 0.11056795  0.90417212  0.05219594  0.3626531  -0.18975197]\n",
        " [ 0.46052872 -0.15712373  0.18134556  0.56206705  0.64375449]\n",
        " [ 0.33461177 -0.32250627  0.48300685  0.2710754  -0.69083079]]\n",
        "[[ 1.64350677  1.02048963  1.00792072  1.46381176  0.99960193]\n",
        " [-0.          0.957002    0.51717374 -0.18418304 -0.03360911]\n",
        " [-0.         -0.          0.64428468  0.28604006  0.27204465]\n",
        " [-0.          0.          0.          0.16491158  0.4455204 ]\n",
        " [-0.          0.         -0.         -0.          0.03167336]]\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# LU Decomposition\n",
      "p, l, u = LA.lu(A)\n",
      "print p  # Permutation Matrix\n",
      "print l  # Lower triangular matrix \n",
      "print u  # Upper triangluar matrix"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 0.  0.  1.  0.  0.]\n",
        " [ 1.  0.  0.  0.  0.]\n",
        " [ 0.  1.  0.  0.  0.]\n",
        " [ 0.  0.  0.  1.  0.]\n",
        " [ 0.  0.  0.  0.  1.]]\n",
        "[[ 1.          0.          0.          0.          0.        ]\n",
        " [ 0.18850158  1.          0.          0.          0.        ]\n",
        " [ 0.9639066  -0.33768939  1.          0.          0.        ]\n",
        " [ 0.7851316  -0.37901001  0.07477825  1.          0.        ]\n",
        " [ 0.57046231 -0.51670511 -0.25946494  0.93185554  1.        ]]\n",
        "[[ 0.96401938  0.80590361  0.91703878  0.79958259  0.36575606]\n",
        " [ 0.          0.82621388  0.4398236  -0.08066926  0.18094882]\n",
        " [ 0.          0.         -0.71604019 -0.17584096  0.06752617]\n",
        " [ 0.          0.          0.          0.20242742  0.56212777]\n",
        " [ 0.          0.          0.          0.         -0.04584822]]\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Cholesky Factorization\n",
      "B = np.tril(A)  # Make sure B is positive definite!\n",
      "L = LA.cholesky(B)\n",
      "print B\n",
      "print L"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 0.92922464  0.          0.          0.          0.        ]\n",
        " [ 0.96401938  0.80590361  0.          0.          0.        ]\n",
        " [ 0.18171917  0.97812798  0.61268685  0.          0.        ]\n",
        " [ 0.75688208  0.31959706  0.49975434  0.84763035  0.        ]\n",
        " [ 0.54993672  0.0328287   0.48166429  0.73207162  0.57560584]]\n",
        "[[ 0.96396299  0.          0.          0.          0.        ]\n",
        " [ 0.          0.89772135  0.          0.          0.        ]\n",
        " [ 0.          0.          0.78274316  0.          0.        ]\n",
        " [ 0.          0.          0.          0.92066842  0.        ]\n",
        " [ 0.          0.          0.          0.          0.75868692]]\n"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "U, s, Vh = LA.svd(A)\n",
      "print U\n",
      "print s\n",
      "print Vh"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[-0.39813995  0.20643095 -0.84627344 -0.17386545  0.22904785]\n",
        " [-0.60336264 -0.25342941  0.01908682  0.73012951 -0.19563655]\n",
        " [-0.29637964 -0.79807961  0.10000792 -0.46554494  0.22021438]\n",
        " [-0.50019004  0.32392179  0.24745742 -0.46897601 -0.6030845 ]\n",
        " [-0.37337265  0.38897739  0.46067364  0.00333472  0.7050235 ]]\n",
        "[ 2.89926195  1.020993    0.58722569  0.35114502  0.00867173]\n",
        "[[-0.54820462 -0.39543335 -0.4043862  -0.49950798 -0.36064904]\n",
        " [ 0.25618887 -0.75005975 -0.36057043  0.4203798   0.25504396]\n",
        " [-0.52648798 -0.3642084   0.69468631  0.0728504   0.31979116]\n",
        " [ 0.29781095 -0.29410851  0.42201493  0.13653362 -0.79250918]\n",
        " [-0.5176466   0.24878758 -0.21395113  0.74150117 -0.27303419]]\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Calculate the condition number\n",
      "min(LA.svd(A, compute_uv=0))*min(LA.svd(LA.pinv(A), compute_uv=0))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "0.0029910123570078698"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Or an easier way\n",
      "np.linalg.cond(A, -2)  # -2 finds the smallest singular value"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "0.0029910123570078616"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "** Statistics **\n",
      "\n",
      "Unlike the linear algebra functions, there is not a lot of overlap with scipy and numpy with statistical functions.\n",
      "\n",
      "Numpy has mostly summary and order statistics that are applied to an ndarray.\n",
      "\n",
      "SciPy's stats package is much richer."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy import stats\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "numargs = stats.lognorm.numargs\n",
      "[ s ] = [0.9,] * numargs\n",
      "rv = stats.lognorm(s)\n",
      "\n",
      "#Display frozen pdf\n",
      "x = np.linspace(0, np.minimum(rv.dist.b, 3))\n",
      "h = plt.plot(x, rv.pdf(x))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/johndowns/Applications/anaconda/lib/python2.7/site-packages/scipy/stats/distributions.py:4658: RuntimeWarning: divide by zero encountered in log\n",
        "  return -log(x)**2 / (2*s**2) + np.where(x == 0, 0, -log(s*x*sqrt(2*pi)))\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VJNx17Qo/+QkUFsJ778GGDWaTkYoKswHJoEFw3XXw/POwe7fV1Ya+VlvuHo+H\ngQMHUlFRQVxcHFlZWZSUlJCamtpwzJdffsmWLVtYsmQJffr0Yfbs2U0/qJV/fZKSzBrTGjIlIkfz\neMxaNytWmLB/6y0T9mPGmNeZZ0K3blZXGRgBa7lXVlaSlJREYmIi0dHR5OfnU1ZW1uiYfv36kZmZ\nSXR0dLsLANPXvm2bWXJURORoXbpAVhbcfLMJ9y+/hLvuMmvc3HwzHH88jB5tnt29+ip8953VFVuv\n1XB3u90kHJG68fHxuN1uvxaxc6eZwNSjh19PKyIO1b276cK56y7Tiv/8c7jpJjMZ8tZboV8/GDXK\nrFX/0kvw7bdWVxx8rT6LjvDjbIOCgoKG73NycsjJyQE0DFJEOqdXLzPKZuxY837vXnjzTTOR6q67\n4N13Tdfv2WebgRtnnRW6meNyuXC5XJ0+T6vhHhcXR21tbcP72tpa4jv42PrIcD+SHqaKiD/17Glm\nvJ93nnlfX2/67FetgqeeMg9mu3eHkSPNGPwRI8zM2e7dra0bGjd8AebOnduh87Qa7pmZmVRXV1NT\nU8NJJ51EaWkpJSUlzR7b0VGVmp0qIoHUteuPIT57tumr37QJ1qwxryeegI8+Mg9pR4ww+8oOH27W\nx7HrUgltGue+bNkyrr/+ejweD9OnT+eWW26huLgYgBkzZrBjxw6ysrL49ttviYyM5Nhjj6Wqqoqe\nPXv++EE+nvjOmmUmL1x3nZ+uSkSknfbvN903hwP/nXdgzx444wzzMDcz03yNjw9u4Nt6ElNeHkyb\nBuPHB6MSEZG22bnThPw778Dbb5sXmDXshw0zXTnDhsEppwQu8G0d7unp8Nhj5n8oEZFQ5fWaYdvr\n1pk+/HXrzOvbb01+DR1q8iw9HVJT/dOHb+tw79PH9H8df3wwKhER8a9du34M+g8+gPffN5k2YAAM\nGWLCfsgQ06ff3m4d24b7t9+ajXf37LHvgwsRkaP98IPZkvD9981r/XqzpML+/Sbkj3ylpZkVMpvL\nwI6Gu+Vrrm3dasabKthFxEm6dTPdNEOHNv7zXbvgww9N0G/YYHauqqoyGZiaaoL+8Ne0tI5/vuXh\nrmGQIhJO+vaFc881r8O8XvjiC9PSr6oyX1980XztKIW7iIjFIiKgf3/zOmL+UsPPOsLynZgOd8uI\niIj/WB7uarmLiPifpeHu8cDKlU0fOIiISOdYGu4ulxn+c/rpVlYhIuI8lob7woVwxRVWViAi4kyW\nTWLatw++FxZoAAAF20lEQVTi4sxKbCeeGIwKRETsJ6AbZAfCc8+ZfQ8V7CIi/mdZuD/+OPzyl1Z9\nuoiIs1nSLeN2w+DB5qv2TRURaZmtumUWLYIJExTsIiKBEvRw93rNKBl1yYiIBE7Qw/39980yv6NG\nBfuTRUTCR9DD/fHHzdj2SMsXPhARca6gPlA9cMBLQoKZmTpwYDA+VUTE3mzxQLWiwqwAqWAXEQms\noIa7HqSKiARHq+FeXl5OSkoKycnJFBYWNnvMtddeS3JyMunp6axbt67Fcy1dClOmdLxYERFpG5/h\n7vF4mDVrFuXl5VRVVVFSUsLGo/Z9Wrp0KZs2baK6upq///3vzJw5s8Xz5eSYLaacyOVyWV1CQDn5\n+px8baDrC1c+w72yspKkpCQSExOJjo4mPz+fsrKyRsc8//zzTJ06FYDs7Gzq6urYuXNns+dz8gqQ\nTv8PzMnX5+RrA11fuPIZ7m63m4SEhIb38fHxuN3uVo/Ztm1bs+e78MLOlCoiIm3lM9wj2rgz69HD\ndFr6e926tbEqERHpHK8Pq1ev9ubm5ja8nzdvnnf+/PmNjpkxY4a3pKSk4f3AgQO9O3bsaHKuAQMG\neAG99NJLL73a8RowYICvmG5RFD5kZmZSXV1NTU0NJ510EqWlpZSUlDQ6Ji8vj6KiIvLz81mzZg29\ne/emf//+Tc61adMmXx8lIiJ+5DPco6KiKCoqIjc3F4/Hw/Tp00lNTaW4uBiAGTNmMG7cOJYuXUpS\nUhLHHHMMjz76aFAKFxGRlgVt+QEREQkev89Q9eekp1DT2rW5XC5iY2PJyMggIyODO++804IqO2ba\ntGn079+fwYMHt3iMXe8btH59dr53ALW1tYwePZrTTz+dQYMGsWDBgmaPs+s9bMv12fUefv/992Rn\nZzN06FDS0tK45ZZbmj2u3feuQz31LTh48KB3wIAB3s2bN3vr6+u96enp3qqqqkbH/Otf//KOHTvW\n6/V6vWvWrPFmZ2f7s4SAacu1vfrqq96LLrrIogo75/XXX/euXbvWO2jQoGZ/btf7dlhr12fne+f1\ner3bt2/3rlu3zuv1er179uzxnnbaaY75/57X27brs/M93Ldvn9fr9XoPHDjgzc7O9q5cubLRzzty\n7/zacvf3pKdQ0pZrAzq0elsoGDVqFH369Gnx53a9b4e1dn1g33sHcOKJJzJ06FAAevbsSWpqKp9/\n/nmjY+x8D9tyfWDfexgTEwNAfX09Ho+H4447rtHPO3Lv/Bru/p70FEracm0RERG8+eabpKenM27c\nOKqqqoJdZsDY9b61lZPuXU1NDevWrSM7O7vRnzvlHrZ0fXa+h4cOHWLo0KH079+f0aNHk5aW1ujn\nHbl3PkfLtJe/Jz2FkrbUOGzYMGpra4mJiWHZsmWMHz+eTz75JAjVBYcd71tbOeXe7d27l8mTJ/Pn\nP/+Znj17Nvm53e+hr+uz8z2MjIzkvffeY/fu3eTm5uJyucjJyWl0THvvnV9b7nFxcdTW1ja8r62t\nJT4+3ucx27ZtIy4uzp9lBERbru3YY49t+PVq7NixHDhwgK+//jqodQaKXe9bWznh3h04cIBJkyZx\n+eWXM378+CY/t/s9bO36nHAPY2NjueCCC3jnnXca/XlH7p1fw/3ISU/19fWUlpaSl5fX6Ji8vDwW\nLlwI4HPSU6hpy7Xt3Lmz4V/XyspKvF5vk74zu7LrfWsru987r9fL9OnTSUtL4/rrr2/2GDvfw7Zc\nn13v4a5du6irqwPgu+++4+WXXyYjI6PRMR25d37tlnHypKe2XNvTTz/NQw89RFRUFDExMSxevNji\nqtvu0ksv5bXXXmPXrl0kJCQwd+5cDhw4ANj7vh3W2vXZ+d4BrFq1iieeeIIhQ4Y0BMO8efPYunUr\nYP972Jbrs+s93L59O1OnTuXQoUMcOnSIK664gjFjxnQ6NzWJSUTEgYK6zZ6IiASHwl1ExIEU7iIi\nDqRwFxFxIIW7iIgDKdxFRBxI4S4i4kAKdxERB/o/cUsJcf0AQSEAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1062bcb90>"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats.describe?\n",
      "\n",
      "stats.describe(A)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "(5,\n",
        " (array([ 0.18171917,  0.0328287 ,  0.01937578,  0.07005332,  0.24989442]),\n",
        "  array([ 0.96401938,  0.97812798,  0.91703878,  0.84763035,  0.78576249])),\n",
        " array([ 0.6763564 ,  0.5268539 ,  0.50610401,  0.6142922 ,  0.46719903]),\n",
        " array([ 0.10345616,  0.14234419,  0.10444232,  0.09972712,  0.04561471]),\n",
        " array([-0.69335701, -0.08259186, -0.37031295, -1.24696622,  0.6033557 ]),\n",
        " array([-0.92424084, -1.31969207, -0.57151457, -0.1071196 , -1.06108566]))"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z = stats.zscore(A)  # Calculates the z score of each value in the sample, relative to the sample mean and standard deviation.\n",
      "print z"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 0.87896406 -0.0860613  -1.68384956  0.02772435 -0.56652728]\n",
        " [ 0.99990975  0.82692613  1.42164003  0.6559961  -0.53103669]\n",
        " [-1.71934735  1.33728977  0.36872625 -1.92680571 -1.13755266]\n",
        " [ 0.27990536 -0.61417765 -0.02196684  0.82610283  1.66762543]\n",
        " [-0.43943183 -1.46397695 -0.08454988  0.41698244  0.5674912 ]]\n"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.random.randn(10)\n",
      "y = np.random.randn(10)\n",
      "coef, p = stats.pearsonr(x, y)\n",
      "print coef\n",
      "print p"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.585566174753\n",
        "0.0753042348576\n"
       ]
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Scikits**\n",
      "\n",
      "Scikits are extensions to SciPy that are not appropriate for the core library for various reasons.  Two of the most interesting modules are scikit-learn and scikit-image.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Image\n",
      "Image(url=\"http://scikit-learn.org/stable/_static/ml_map.png\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<img src=\"http://scikit-learn.org/stable/_static/ml_map.png\"/>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "<IPython.core.display.Image at 0x104cfd690>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's take a look at how scikit-learn supports supervised and unsupervised learning.  \n",
      "\n",
      "Scikit-learn estimators have a uniform interface\n",
      "\n",
      "- Available in **all Estimators**\n",
      "  + `model.fit()` : fit training data. For supervised learning applications,\n",
      "    this accepts two arguments: the data `X` and the labels `y` (e.g. `model.fit(X, y)`).\n",
      "    For unsupervised learning applications, this accepts only a single argument,\n",
      "    the data `X` (e.g. `model.fit(X)`).\n",
      "- Available in **supervised estimators**\n",
      "  + `model.predict()` : given a trained model, predict the label of a new set of data.\n",
      "    This method accepts one argument, the new data `X_new` (e.g. `model.predict(X_new)`),\n",
      "    and returns the learned label for each object in the array.\n",
      "  + `model.predict_proba()` : For classification problems, some estimators also provide\n",
      "    this method, which returns the probability that a new observation has each categorical label.\n",
      "    In this case, the label with the highest probability is returned by `model.predict()`.\n",
      "  + `model.score()` : for classification or regression problems, most (all?) estimators implement\n",
      "    a score method.  Scores are between 0 and 1, with a larger score indicating a better fit.\n",
      "- Available in **unsupervised estimators**\n",
      "  + `model.transform()` : given an unsupervised model, transform new data into the new basis.\n",
      "    This also accepts one argument `X_new`, and returns the new representation of the data based\n",
      "    on the unsupervised model.\n",
      "  + `model.fit_transform()` : some estimators implement this method,\n",
      "    which more efficiently performs a fit and a transform on the same input data.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# The famous Iris dataset\n",
      "from sklearn import neighbors, datasets\n",
      "\n",
      "iris = datasets.load_iris()\n",
      "X, y = iris.data, iris.target\n",
      "\n",
      "print X[:10], \"...\"\n",
      "print y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 5.1  3.5  1.4  0.2]\n",
        " [ 4.9  3.   1.4  0.2]\n",
        " [ 4.7  3.2  1.3  0.2]\n",
        " [ 4.6  3.1  1.5  0.2]\n",
        " [ 5.   3.6  1.4  0.2]\n",
        " [ 5.4  3.9  1.7  0.4]\n",
        " [ 4.6  3.4  1.4  0.3]\n",
        " [ 5.   3.4  1.5  0.2]\n",
        " [ 4.4  2.9  1.4  0.2]\n",
        " [ 4.9  3.1  1.5  0.1]] ...\n",
        "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
        " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
        " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
        " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
        " 2 2]\n"
       ]
      }
     ],
     "prompt_number": 220
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Visualize the data\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "x_index = 2\n",
      "y_index = 3\n",
      "\n",
      "# this formatter will label the colorbar with the correct target names\n",
      "formatter = plt.FuncFormatter(lambda i, *args: iris.target_names[int(i)])\n",
      "\n",
      "plt.scatter(iris.data[:, x_index], iris.data[:, y_index],\n",
      "            c=iris.target)\n",
      "plt.colorbar(ticks=[0, 1, 2], format=formatter)\n",
      "plt.xlabel(iris.feature_names[x_index])\n",
      "plt.ylabel(iris.feature_names[y_index]);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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kE2MmpsrqBxs1apT0999/l29n20My4zYEweLefPNNSengILmAVNnDQwoLCpLc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\nQghhM0kmQgghbPb/zNXbjRRwMvEAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1136ba790>"
       ]
      }
     ],
     "prompt_number": 234
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Make a prediction\n",
      "# create the model\n",
      "knn = neighbors.KNeighborsClassifier(n_neighbors=3)\n",
      "\n",
      "# fit the model\n",
      "knn.fit(X, y)\n",
      "\n",
      "# What kind of iris has 3cm x 5cm sepal and 4cm x 2cm petal?\n",
      "# call the \"predict\" method:\n",
      "result = knn.predict([[3, 5, 4, 2],])\n",
      "\n",
      "print iris.target_names[result]\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['versicolor']\n"
       ]
      }
     ],
     "prompt_number": 226
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Exercise: try this with a SVC classifier\n",
      "from sklearn.svm import SVC"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 221
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Dimensionality Reduction - Principal Component Analysis\n",
      "X, y = iris.data, iris.target\n",
      "from sklearn.decomposition import PCA\n",
      "pca = PCA(n_components=2)\n",
      "pca.fit(X)\n",
      "X_reduced = pca.transform(X)\n",
      "print \"Reduced dataset shape:\", X_reduced.shape\n",
      "\n",
      "import pylab as pl\n",
      "pl.scatter(X_reduced[:, 0], X_reduced[:, 1], c=y)\n",
      "\n",
      "print \"Meaning of the 2 components:\"\n",
      "for component in pca.components_:\n",
      "    print \" + \".join(\"%.3f x %s\" % (value, name)\n",
      "                     for value, name in zip(component,\n",
      "                                            iris.feature_names))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Reduced dataset shape: (150, 2)\n",
        "Meaning of the 2 components:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.362 x sepal length (cm) + -0.082 x sepal width (cm) + 0.857 x petal length (cm) + 0.359 x petal width (cm)\n",
        "-0.657 x sepal length (cm) + -0.730 x sepal width (cm) + 0.176 x petal length (cm) + 0.075 x petal width (cm)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Penr6sxs/QXx8PBM//RSXzEy8heDgwYOcPHkyV8empKTQr1cvumm19E5IoH9q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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10941aa90>"
       ]
      }
     ],
     "prompt_number": 235
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Clustering\n",
      "from sklearn.cluster import KMeans\n",
      "k_means = KMeans(n_clusters=3, random_state=0)\n",
      "k_means.fit(X)\n",
      "y_pred = k_means.predict(X)\n",
      "\n",
      "pl.scatter(X_reduced[:, 0], X_reduced[:, 1], c=y_pred);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+bNmytcAxXr9+ncqVfShVqizW1rZ89933z9zn0KFDVKlSA2dnV3r06MP33//A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\ncFToCSHEwP0/gAKs6RJ/rAIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11556be10>"
       ]
      }
     ],
     "prompt_number": 236
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Validation\n",
      "# Generate an un-balanced 2D dataset\n",
      "np.random.seed(0)\n",
      "X = np.vstack([np.random.normal(0, 1, (950, 2)),\n",
      "               np.random.normal(-1.8, 0.8, (50, 2))])\n",
      "y = np.hstack([np.zeros(950), np.ones(50)])\n",
      "\n",
      "plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='none',\n",
      "            cmap=plt.cm.Accent)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 237,
       "text": [
        "<matplotlib.collections.PathCollection at 0x11556b210>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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i+wtOfIZ8QzgxdCKhjSt3yI23m96G1WeFRqrB0yVPI1116/tlVBmrUJJUgiAd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+lpQb1//x6vX79GbGwsNm3a5DFSt62tDdeuXXPf12g0qKur89u+3/e1D3///BsZKzM8ti8j+h2w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\niFSKBZyISKVYwImIVIoFnIhIpf4H3k4u7riWMx4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x115564a10>"
       ]
      }
     ],
     "prompt_number": 237
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import metrics\n",
      "from sklearn.svm import SVC\n",
      "from sklearn.cross_validation import train_test_split\n",
      "\n",
      "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
      "clf = SVC().fit(X_train, y_train)\n",
      "y_pred = clf.predict(X_test)\n",
      "\n",
      "print \"accuracy:\", metrics.accuracy_score(y_test, y_pred)\n",
      "print \"precision:\", metrics.precision_score(y_test, y_pred)\n",
      "print \"recall:\", metrics.recall_score(y_test, y_pred)\n",
      "print \"f1 score:\", metrics.f1_score(y_test, y_pred)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "accuracy: 0.968\n",
        "precision: 0.833333333333\n",
        "recall: 0.625\n",
        "f1 score: 0.714285714286\n"
       ]
      }
     ],
     "prompt_number": 239
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### What do these mean?\n",
      "\n",
      "These are ways of taking into account not just the classification results, but the results **relative to the true category**.\n",
      "\n",
      "- $$ {\\rm accuracy} \\equiv \\frac{\\rm correct~labels}{\\rm total~samples} $$\n",
      "\n",
      "- $$ {\\rm precision} \\equiv \\frac{\\rm true~positives}{\\rm true~positives + false~positives} $$\n",
      "\n",
      "- $$ {\\rm recall} \\equiv \\frac{\\rm true~positives}{\\rm true~positives + false~negatives} $$\n",
      "\n",
      "- $$ F_1 \\equiv \\frac{\\rm precision \\cdot recall}{\\rm precision + recall} $$\n",
      "\n",
      "The **accuracy**, **precision**, **recall**, and **f1-score** all range from 0 to 1, with 1 being optimal.\n",
      "Here we've used the following definitions:\n",
      "\n",
      "- *True Positives* are those which are labeled ``1`` which are actually ``1``\n",
      "- *False Positives* are those which are labeled ``1`` which are actually ``0``\n",
      "- *True Negatives* are those which are labeled ``0`` which are actually ``0``\n",
      "- *False Negatives* are those which are labeled ``0`` which are actually ``1``"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X1, X2, y1, y2 = train_test_split(X, y, test_size=0.5)\n",
      "print X1.shape\n",
      "print X2.shape\n",
      "\n",
      "y2_pred = SVC().fit(X1, y1).predict(X2)\n",
      "y1_pred = SVC().fit(X2, y2).predict(X1)\n",
      "\n",
      "print np.mean([metrics.precision_score(y1, y1_pred),\n",
      "               metrics.precision_score(y2, y2_pred)])\n",
      "\n",
      "from sklearn.cross_validation import cross_val_score\n",
      "\n",
      "# Let's do a 2-fold cross-validation of the SVC estimator\n",
      "print cross_val_score(SVC(), X, y, cv=2, scoring='precision')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(500, 2)\n",
        "(500, 2)\n",
        "0.794117647059\n",
        "[ 0.90909091  0.76190476]\n"
       ]
      }
     ],
     "prompt_number": 240
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Matplotlib**\n",
      "\n",
      "Matplotlib is the plotting library for the SciPy ecosystem.  \n",
      "\n",
      "More interesting: there is a new library, prettyplotlib, that wraps matplotlib with some sensible defaults to make the plots a more readable.\n",
      "\n",
      "https://github.com/olgabot/prettyplotlib\n",
      "\n",
      "** A boxplot example **"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image(\"https://raw.github.com/olgabot/prettyplotlib/master/examples/boxplot_matplotlib_default.png\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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       "prompt_number": 66,
       "text": [
        "<IPython.core.display.Image at 0x1062ca890>"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image(\"https://raw.github.com/olgabot/prettyplotlib/master/examples/boxplot_prettyplotlib_default.png\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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gAAAjETAAgJEIGADASAQMAGAkAgYAMJJjAnbkyBEVFhYqOztbH3300YTr2tvbVVBQIL/f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\n",
       "prompt_number": 67,
       "text": [
        "<IPython.core.display.Image at 0x105e8ca50>"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Pandas**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import VimeoVideo\n",
      "VimeoVideo(\"79562736\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <iframe\n",
        "            width=\"400\"\n",
        "            height=300\"\n",
        "            src=\"http://player.vimeo.com/video/79562736\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
        "        ></iframe>\n",
        "        "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "<IPython.lib.display.VimeoVideo at 0x104d65a50>"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Let's try something!**\n",
      "\n",
      "I want to try out scikit learn.  Let's get a dataset and experiment with some of the algorithms.\n",
      "\n",
      "I'm getting the movielens data set, which is a collection of movie ratings by a number of users.  We're going to try to classify each user and predict whether they will like a particular movie.  We will use a few classifiers and see which ones perform better.\n",
      "\n",
      "Some of this tutorial comes from http://www.gregreda.com/2013/10/26/working-with-pandas-dataframes/"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!wget http://files.grouplens.org/datasets/movielens/ml-100k.zip\n",
      "!unzip ml-100k.zip\n",
      "!cd ml-100k"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "--2013-11-16 22:20:45--  http://files.grouplens.org/datasets/movielens/ml-100k.zip\r\n",
        "Resolving files.grouplens.org... 128.101.34.146\r\n",
        "Connecting to files.grouplens.org|128.101.34.146|:80... connected.\r\n",
        "HTTP request sent, awaiting response... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "200 OK\r\n",
        "Length: 4945826 (4.7M) [application/zip]\r\n",
        "Saving to: `ml-100k.zip'\r\n",
        "\r\n",
        "\r",
        " 0% [                                       ] 0           --.-K/s              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 1% [                                       ] 76,440       360K/s              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 3% [>                                      ] 192,280      445K/s              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 7% [=>                                     ] 348,664      547K/s              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "11% [===>                                   ] 573,104      678K/s              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "16% [=====>                                 ] 816,368      772K/s              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "21% [=======>                               ] 1,075,560    846K/s              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "26% [=========>                             ] 1,315,928    882K/s              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "31% [===========>                           ] 1,579,464    926K/s              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "37% [=============>                         ] 1,840,104    956K/s              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "42% [===============>                       ] 2,090,608    969K/s              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "47% [=================>                     ] 2,348,352    994K/s              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "52% [===================>                   ] 2,588,720   1008K/s              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "57% [=====================>                 ] 2,843,568   1020K/s              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "62% [=======================>               ] 3,108,552   1.01M/s              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "67% [=========================>             ] 3,348,920   1.02M/s  eta 1s      "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "72% [===========================>           ] 3,584,944   1.06M/s  eta 1s      "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "77% [=============================>         ] 3,820,968   1.09M/s  eta 1s      "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "81% [==============================>        ] 4,048,304   1.11M/s  eta 1s      "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "86% [================================>      ] 4,279,984   1.13M/s  eta 1s      "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "91% [==================================>    ] 4,532,712   1.14M/s  eta 0s      "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "96% [====================================>  ] 4,784,664   1.14M/s  eta 0s      "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "100%[======================================>] 4,945,826   1.13M/s   in 4.5s    \r\n",
        "\r\n",
        "2013-11-16 22:20:50 (1.05 MB/s) - `ml-100k.zip' saved [4945826/4945826]\r\n",
        "\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Archive:  ml-100k.zip\r\n",
        "   creating: ml-100k/\r\n",
        "  inflating: ml-100k/allbut.pl       \r\n",
        "  inflating: ml-100k/mku.sh          \r\n",
        "  inflating: ml-100k/README          \r\n",
        "  inflating: ml-100k/u.data          \r\n",
        "  inflating: ml-100k/u.genre         \r\n",
        "  inflating: ml-100k/u.info          \r\n",
        "  inflating: ml-100k/u.item          \r\n",
        "  inflating: ml-100k/u.occupation    \r\n",
        "  inflating: ml-100k/u.user          "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "  inflating: ml-100k/u1.base         \r\n",
        "  inflating: ml-100k/u1.test         \r\n",
        "  inflating: ml-100k/u2.base         "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "  inflating: ml-100k/u2.test         \r\n",
        "  inflating: ml-100k/u3.base         \r\n",
        "  inflating: ml-100k/u3.test         "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "  inflating: ml-100k/u4.base         \r\n",
        "  inflating: ml-100k/u4.test         \r\n",
        "  inflating: ml-100k/u5.base         "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "  inflating: ml-100k/u5.test         \r\n",
        "  inflating: ml-100k/ua.base         "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "  inflating: ml-100k/ua.test         \r\n",
        "  inflating: ml-100k/ub.base         "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "  inflating: ml-100k/ub.test         \r\n"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "txt = open('ml-100k/README').read()\n",
      "print txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "SUMMARY & USAGE LICENSE\n",
        "=============================================\n",
        "\n",
        "MovieLens data sets were collected by the GroupLens Research Project\n",
        "at the University of Minnesota.\n",
        " \n",
        "This data set consists of:\n",
        "\t* 100,000 ratings (1-5) from 943 users on 1682 movies. \n",
        "\t* Each user has rated at least 20 movies. \n",
        "        * Simple demographic info for the users (age, gender, occupation, zip)\n",
        "\n",
        "The data was collected through the MovieLens web site\n",
        "(movielens.umn.edu) during the seven-month period from September 19th, \n",
        "1997 through April 22nd, 1998. This data has been cleaned up - users\n",
        "who had less than 20 ratings or did not have complete demographic\n",
        "information were removed from this data set. Detailed descriptions of\n",
        "the data file can be found at the end of this file.\n",
        "\n",
        "Neither the University of Minnesota nor any of the researchers\n",
        "involved can guarantee the correctness of the data, its suitability\n",
        "for any particular purpose, or the validity of results based on the\n",
        "use of the data set.  The data set may be used for any research\n",
        "purposes under the following conditions:\n",
        "\n",
        "     * The user may not state or imply any endorsement from the\n",
        "       University of Minnesota or the GroupLens Research Group.\n",
        "\n",
        "     * The user must acknowledge the use of the data set in\n",
        "       publications resulting from the use of the data set, and must\n",
        "       send us an electronic or paper copy of those publications.\n",
        "\n",
        "     * The user may not redistribute the data without separate\n",
        "       permission.\n",
        "\n",
        "     * The user may not use this information for any commercial or\n",
        "       revenue-bearing purposes without first obtaining permission\n",
        "       from a faculty member of the GroupLens Research Project at the\n",
        "       University of Minnesota.\n",
        "\n",
        "If you have any further questions or comments, please contact Jon Herlocker\n",
        "<herlocke@cs.umn.edu>. \n",
        "\n",
        "ACKNOWLEDGEMENTS\n",
        "==============================================\n",
        "\n",
        "Thanks to Al Borchers for cleaning up this data and writing the\n",
        "accompanying scripts.\n",
        "\n",
        "PUBLISHED WORK THAT HAS USED THIS DATASET\n",
        "==============================================\n",
        "\n",
        "Herlocker, J., Konstan, J., Borchers, A., Riedl, J.. An Algorithmic\n",
        "Framework for Performing Collaborative Filtering. Proceedings of the\n",
        "1999 Conference on Research and Development in Information\n",
        "Retrieval. Aug. 1999.\n",
        "\n",
        "FURTHER INFORMATION ABOUT THE GROUPLENS RESEARCH PROJECT\n",
        "==============================================\n",
        "\n",
        "The GroupLens Research Project is a research group in the Department\n",
        "of Computer Science and Engineering at the University of Minnesota.\n",
        "Members of the GroupLens Research Project are involved in many\n",
        "research projects related to the fields of information filtering,\n",
        "collaborative filtering, and recommender systems. The project is lead\n",
        "by professors John Riedl and Joseph Konstan. The project began to\n",
        "explore automated collaborative filtering in 1992, but is most well\n",
        "known for its world wide trial of an automated collaborative filtering\n",
        "system for Usenet news in 1996.  The technology developed in the\n",
        "Usenet trial formed the base for the formation of Net Perceptions,\n",
        "Inc., which was founded by members of GroupLens Research. Since then\n",
        "the project has expanded its scope to research overall information\n",
        "filtering solutions, integrating in content-based methods as well as\n",
        "improving current collaborative filtering technology.\n",
        "\n",
        "Further information on the GroupLens Research project, including\n",
        "research publications, can be found at the following web site:\n",
        "        \n",
        "        http://www.grouplens.org/\n",
        "\n",
        "GroupLens Research currently operates a movie recommender based on\n",
        "collaborative filtering:\n",
        "\n",
        "        http://www.movielens.org/\n",
        "\n",
        "DETAILED DESCRIPTIONS OF DATA FILES\n",
        "==============================================\n",
        "\n",
        "Here are brief descriptions of the data.\n",
        "\n",
        "ml-data.tar.gz   -- Compressed tar file.  To rebuild the u data files do this:\n",
        "                gunzip ml-data.tar.gz\n",
        "                tar xvf ml-data.tar\n",
        "                mku.sh\n",
        "\n",
        "u.data     -- The full u data set, 100000 ratings by 943 users on 1682 items.\n",
        "              Each user has rated at least 20 movies.  Users and items are\n",
        "              numbered consecutively from 1.  The data is randomly\n",
        "              ordered. This is a tab separated list of \n",
        "\t         user id | item id | rating | timestamp. \n",
        "              The time stamps are unix seconds since 1/1/1970 UTC   \n",
        "\n",
        "u.info     -- The number of users, items, and ratings in the u data set.\n",
        "\n",
        "u.item     -- Information about the items (movies); this is a tab separated\n",
        "              list of\n",
        "              movie id | movie title | release date | video release date |\n",
        "              IMDb URL | unknown | Action | Adventure | Animation |\n",
        "              Children's | Comedy | Crime | Documentary | Drama | Fantasy |\n",
        "              Film-Noir | Horror | Musical | Mystery | Romance | Sci-Fi |\n",
        "              Thriller | War | Western |\n",
        "              The last 19 fields are the genres, a 1 indicates the movie\n",
        "              is of that genre, a 0 indicates it is not; movies can be in\n",
        "              several genres at once.\n",
        "              The movie ids are the ones used in the u.data data set.\n",
        "\n",
        "u.genre    -- A list of the genres.\n",
        "\n",
        "u.user     -- Demographic information about the users; this is a tab\n",
        "              separated list of\n",
        "              user id | age | gender | occupation | zip code\n",
        "              The user ids are the ones used in the u.data data set.\n",
        "\n",
        "u.occupation -- A list of the occupations.\n",
        "\n",
        "u1.base    -- The data sets u1.base and u1.test through u5.base and u5.test\n",
        "u1.test       are 80%/20% splits of the u data into training and test data.\n",
        "u2.base       Each of u1, ..., u5 have disjoint test sets; this if for\n",
        "u2.test       5 fold cross validation (where you repeat your experiment\n",
        "u3.base       with each training and test set and average the results).\n",
        "u3.test       These data sets can be generated from u.data by mku.sh.\n",
        "u4.base\n",
        "u4.test\n",
        "u5.base\n",
        "u5.test\n",
        "\n",
        "ua.base    -- The data sets ua.base, ua.test, ub.base, and ub.test\n",
        "ua.test       split the u data into a training set and a test set with\n",
        "ub.base       exactly 10 ratings per user in the test set.  The sets\n",
        "ub.test       ua.test and ub.test are disjoint.  These data sets can\n",
        "              be generated from u.data by mku.sh.\n",
        "\n",
        "allbut.pl  -- The script that generates training and test sets where\n",
        "              all but n of a users ratings are in the training data.\n",
        "\n",
        "mku.sh     -- A shell script to generate all the u data sets from u.data.\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "\n",
      "user_columns = ['user_id', 'age', 'sex', 'occupation', 'zip_code']\n",
      "users = pd.read_csv('ml-100k/u.user', names=user_columns, sep='|')\n",
      "\n",
      "rating_columns = ['user_id', 'movie_id', 'rating', 'unix_timestamp']\n",
      "ratings = pd.read_csv('ml-100k/u.data', names=rating_columns, delim_whitespace=True)\n",
      "\n",
      "movie_columns = ['movie_id', 'title', 'release_date', 'video_release_date', 'imdb_url']\n",
      "movies = pd.read_csv('ml-100k/u.item', names=movie_columns, sep='|', usecols=range(5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 158
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "users"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/johndowns/Applications/anaconda/lib/python2.7/site-packages/pandas/core/config.py:570: DeprecationWarning: height has been deprecated.\n",
        "\n",
        "  warnings.warn(d.msg, DeprecationWarning)\n"
       ]
      },
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 943 entries, 0 to 942\n",
        "Data columns (total 5 columns):\n",
        "user_id       943  non-null values\n",
        "age           943  non-null values\n",
        "sex           943  non-null values\n",
        "occupation    943  non-null values\n",
        "zip_code      943  non-null values\n",
        "dtypes: int64(2), object(3)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 159,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 943 entries, 0 to 942\n",
        "Data columns (total 5 columns):\n",
        "user_id       943  non-null values\n",
        "age           943  non-null values\n",
        "sex           943  non-null values\n",
        "occupation    943  non-null values\n",
        "zip_code      943  non-null values\n",
        "dtypes: int64(2), object(3)"
       ]
      }
     ],
     "prompt_number": 159
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ratings"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/johndowns/Applications/anaconda/lib/python2.7/site-packages/pandas/core/config.py:570: DeprecationWarning: height has been deprecated.\n",
        "\n",
        "  warnings.warn(d.msg, DeprecationWarning)\n"
       ]
      },
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 100000 entries, 0 to 99999\n",
        "Data columns (total 4 columns):\n",
        "user_id           100000  non-null values\n",
        "movie_id          100000  non-null values\n",
        "rating            100000  non-null values\n",
        "unix_timestamp    100000  non-null values\n",
        "dtypes: int64(4)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 160,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 100000 entries, 0 to 99999\n",
        "Data columns (total 4 columns):\n",
        "user_id           100000  non-null values\n",
        "movie_id          100000  non-null values\n",
        "rating            100000  non-null values\n",
        "unix_timestamp    100000  non-null values\n",
        "dtypes: int64(4)"
       ]
      }
     ],
     "prompt_number": 160
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "movies"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/johndowns/Applications/anaconda/lib/python2.7/site-packages/pandas/core/config.py:570: DeprecationWarning: height has been deprecated.\n",
        "\n",
        "  warnings.warn(d.msg, DeprecationWarning)\n"
       ]
      },
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 1682 entries, 0 to 1681\n",
        "Data columns (total 5 columns):\n",
        "movie_id              1682  non-null values\n",
        "title                 1682  non-null values\n",
        "release_date          1681  non-null values\n",
        "video_release_date    0  non-null values\n",
        "imdb_url              1679  non-null values\n",
        "dtypes: float64(1), int64(1), object(3)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 161,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 1682 entries, 0 to 1681\n",
        "Data columns (total 5 columns):\n",
        "movie_id              1682  non-null values\n",
        "title                 1682  non-null values\n",
        "release_date          1681  non-null values\n",
        "video_release_date    0  non-null values\n",
        "imdb_url              1679  non-null values\n",
        "dtypes: float64(1), int64(1), object(3)"
       ]
      }
     ],
     "prompt_number": 161
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "movie_ratings = pd.merge(movies, ratings)\n",
      "movie_ratings"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/johndowns/Applications/anaconda/lib/python2.7/site-packages/pandas/core/config.py:570: DeprecationWarning: height has been deprecated.\n",
        "\n",
        "  warnings.warn(d.msg, DeprecationWarning)\n"
       ]
      },
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 100000 entries, 0 to 99999\n",
        "Data columns (total 8 columns):\n",
        "movie_id              100000  non-null values\n",
        "title                 100000  non-null values\n",
        "release_date          99991  non-null values\n",
        "video_release_date    0  non-null values\n",
        "imdb_url              99987  non-null values\n",
        "user_id               100000  non-null values\n",
        "rating                100000  non-null values\n",
        "unix_timestamp        100000  non-null values\n",
        "dtypes: float64(1), int64(4), object(3)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 162,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 100000 entries, 0 to 99999\n",
        "Data columns (total 8 columns):\n",
        "movie_id              100000  non-null values\n",
        "title                 100000  non-null values\n",
        "release_date          99991  non-null values\n",
        "video_release_date    0  non-null values\n",
        "imdb_url              99987  non-null values\n",
        "user_id               100000  non-null values\n",
        "rating                100000  non-null values\n",
        "unix_timestamp        100000  non-null values\n",
        "dtypes: float64(1), int64(4), object(3)"
       ]
      }
     ],
     "prompt_number": 162
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = pd.merge(movie_ratings, users)\n",
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/johndowns/Applications/anaconda/lib/python2.7/site-packages/pandas/core/config.py:570: DeprecationWarning: height has been deprecated.\n",
        "\n",
        "  warnings.warn(d.msg, DeprecationWarning)\n"
       ]
      },
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 100000 entries, 0 to 99999\n",
        "Data columns (total 12 columns):\n",
        "movie_id              100000  non-null values\n",
        "title                 100000  non-null values\n",
        "release_date          99991  non-null values\n",
        "video_release_date    0  non-null values\n",
        "imdb_url              99987  non-null values\n",
        "user_id               100000  non-null values\n",
        "rating                100000  non-null values\n",
        "unix_timestamp        100000  non-null values\n",
        "age                   100000  non-null values\n",
        "sex                   100000  non-null values\n",
        "occupation            100000  non-null values\n",
        "zip_code              100000  non-null values\n",
        "dtypes: float64(1), int64(5), object(6)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 163,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 100000 entries, 0 to 99999\n",
        "Data columns (total 12 columns):\n",
        "movie_id              100000  non-null values\n",
        "title                 100000  non-null values\n",
        "release_date          99991  non-null values\n",
        "video_release_date    0  non-null values\n",
        "imdb_url              99987  non-null values\n",
        "user_id               100000  non-null values\n",
        "rating                100000  non-null values\n",
        "unix_timestamp        100000  non-null values\n",
        "age                   100000  non-null values\n",
        "sex                   100000  non-null values\n",
        "occupation            100000  non-null values\n",
        "zip_code              100000  non-null values\n",
        "dtypes: float64(1), int64(5), object(6)"
       ]
      }
     ],
     "prompt_number": 163
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/johndowns/Applications/anaconda/lib/python2.7/site-packages/pandas/core/config.py:570: DeprecationWarning: height has been deprecated.\n",
        "\n",
        "  warnings.warn(d.msg, DeprecationWarning)\n",
        "/Users/johndowns/Applications/anaconda/lib/python2.7/site-packages/pandas/core/config.py:570: DeprecationWarning: height has been deprecated.\n",
        "\n",
        "  warnings.warn(d.msg, DeprecationWarning)\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/johndowns/Applications/anaconda/lib/python2.7/site-packages/pandas/core/config.py:570: DeprecationWarning: height has been deprecated.\n",
        "\n",
        "  warnings.warn(d.msg, DeprecationWarning)\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>movie_id</th>\n",
        "      <th>title</th>\n",
        "      <th>release_date</th>\n",
        "      <th>video_release_date</th>\n",
        "      <th>imdb_url</th>\n",
        "      <th>user_id</th>\n",
        "      <th>rating</th>\n",
        "      <th>unix_timestamp</th>\n",
        "      <th>age</th>\n",
        "      <th>sex</th>\n",
        "      <th>occupation</th>\n",
        "      <th>zip_code</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td>      Toy Story (1995)</td>\n",
        "      <td> 01-Jan-1995</td>\n",
        "      <td>NaN</td>\n",
        "      <td> http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
        "      <td> 308</td>\n",
        "      <td> 4</td>\n",
        "      <td> 887736532</td>\n",
        "      <td> 60</td>\n",
        "      <td> M</td>\n",
        "      <td> retired</td>\n",
        "      <td> 95076</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 4</td>\n",
        "      <td>     Get Shorty (1995)</td>\n",
        "      <td> 01-Jan-1995</td>\n",
        "      <td>NaN</td>\n",
        "      <td> http://us.imdb.com/M/title-exact?Get%20Shorty%...</td>\n",
        "      <td> 308</td>\n",
        "      <td> 5</td>\n",
        "      <td> 887737890</td>\n",
        "      <td> 60</td>\n",
        "      <td> M</td>\n",
        "      <td> retired</td>\n",
        "      <td> 95076</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 5</td>\n",
        "      <td>        Copycat (1995)</td>\n",
        "      <td> 01-Jan-1995</td>\n",
        "      <td>NaN</td>\n",
        "      <td> http://us.imdb.com/M/title-exact?Copycat%20(1995)</td>\n",
        "      <td> 308</td>\n",
        "      <td> 4</td>\n",
        "      <td> 887739608</td>\n",
        "      <td> 60</td>\n",
        "      <td> M</td>\n",
        "      <td> retired</td>\n",
        "      <td> 95076</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 7</td>\n",
        "      <td> Twelve Monkeys (1995)</td>\n",
        "      <td> 01-Jan-1995</td>\n",
        "      <td>NaN</td>\n",
        "      <td> http://us.imdb.com/M/title-exact?Twelve%20Monk...</td>\n",
        "      <td> 308</td>\n",
        "      <td> 4</td>\n",
        "      <td> 887738847</td>\n",
        "      <td> 60</td>\n",
        "      <td> M</td>\n",
        "      <td> retired</td>\n",
        "      <td> 95076</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 8</td>\n",
        "      <td>           Babe (1995)</td>\n",
        "      <td> 01-Jan-1995</td>\n",
        "      <td>NaN</td>\n",
        "      <td>    http://us.imdb.com/M/title-exact?Babe%20(1995)</td>\n",
        "      <td> 308</td>\n",
        "      <td> 5</td>\n",
        "      <td> 887736696</td>\n",
        "      <td> 60</td>\n",
        "      <td> M</td>\n",
        "      <td> retired</td>\n",
        "      <td> 95076</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 164,
       "text": [
        "   movie_id                  title release_date  video_release_date  \\\n",
        "0         1       Toy Story (1995)  01-Jan-1995                 NaN   \n",
        "1         4      Get Shorty (1995)  01-Jan-1995                 NaN   \n",
        "2         5         Copycat (1995)  01-Jan-1995                 NaN   \n",
        "3         7  Twelve Monkeys (1995)  01-Jan-1995                 NaN   \n",
        "4         8            Babe (1995)  01-Jan-1995                 NaN   \n",
        "\n",
        "                                            imdb_url  user_id  rating  \\\n",
        "0  http://us.imdb.com/M/title-exact?Toy%20Story%2...      308       4   \n",
        "1  http://us.imdb.com/M/title-exact?Get%20Shorty%...      308       5   \n",
        "2  http://us.imdb.com/M/title-exact?Copycat%20(1995)      308       4   \n",
        "3  http://us.imdb.com/M/title-exact?Twelve%20Monk...      308       4   \n",
        "4     http://us.imdb.com/M/title-exact?Babe%20(1995)      308       5   \n",
        "\n",
        "   unix_timestamp  age sex occupation zip_code  \n",
        "0       887736532   60   M    retired    95076  \n",
        "1       887737890   60   M    retired    95076  \n",
        "2       887739608   60   M    retired    95076  \n",
        "3       887738847   60   M    retired    95076  \n",
        "4       887736696   60   M    retired    95076  "
       ]
      }
     ],
     "prompt_number": 164
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Some questions**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# How are ages distributed?\n",
      "data.age.hist(bins=30)\n",
      "plt.title(\"Distribution of users' ages\")\n",
      "plt.ylabel('count of users')\n",
      "plt.xlabel('age');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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G4MCBAwCA2tpaPProozh+/DjMZjPWr18PX19fAEBaWhpWrlwJDw8PZGRkYMKECQCAffv2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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109400f10>"
       ]
      }
     ],
     "prompt_number": 165
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# How are occupations distributed?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 166
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Which movie is the highest rated?\n",
      "most_rated = data.title.value_counts()[0:10]\n",
      "print most_rated"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Star Wars (1977)                 583\n",
        "Contact (1997)                   509\n",
        "Fargo (1996)                     508\n",
        "Return of the Jedi (1983)        507\n",
        "Liar Liar (1997)                 485\n",
        "English Patient, The (1996)      481\n",
        "Scream (1996)                    478\n",
        "Toy Story (1995)                 452\n",
        "Air Force One (1997)             431\n",
        "Independence Day (ID4) (1996)    429\n",
        "dtype: int64\n"
       ]
      }
     ],
     "prompt_number": 168
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# What movie is the most popular"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# How many movies have less than 100 ratings"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Remove movies with less than 100 ratings from the dataset"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Extra Credit: How would we go about making a recommender system based on this data set?\n",
      "#Hint:\n",
      "VimeoVideo('64445499')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <iframe\n",
        "            width=\"400\"\n",
        "            height=300\"\n",
        "            src=\"http://player.vimeo.com/video/64445499\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
        "        ></iframe>\n",
        "        "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 216,
       "text": [
        "<IPython.lib.display.VimeoVideo at 0x115906b90>"
       ]
      }
     ],
     "prompt_number": 216
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Generators**\n",
      "\n",
      "Watch this! It was my favorite talk from PyData."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "VimeoVideo(\"79535180\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <iframe\n",
        "            width=\"400\"\n",
        "            height=300\"\n",
        "            src=\"http://player.vimeo.com/video/79535180\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
        "        ></iframe>\n",
        "        "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 74,
       "text": [
        "<IPython.lib.display.VimeoVideo at 0x1062fddd0>"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Some material taken from:\n",
      "    \n",
      "    https://github.com/jakevdp/2013_fall_ASTR599/\n",
      "\n",
      "    http://www.gregreda.com/2013/10/26/working-with-pandas-dataframes/\n",
      "        \n",
      "    "
     ]
    }
   ],
   "metadata": {}
  }
 ]
}