manujeevanprakash/Web scraping exercise

## Web scraping exercise
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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline \n",
      "\n",
      "from fnmatch import fnmatch\n",
      "\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "import requests\n",
      "from pattern import web\n",
      "\n",
      "from matplotlib import rcParams\n",
      "\n",
      "#these colors come from colorbrewer2.org. Each is an RGB triplet\n",
      "dark2_colors = [(0.10588235294117647, 0.6196078431372549, 0.4666666666666667),\n",
      "                (0.8509803921568627, 0.37254901960784315, 0.00784313725490196),\n",
      "                (0.4588235294117647, 0.4392156862745098, 0.7019607843137254),\n",
      "                (0.9058823529411765, 0.1607843137254902, 0.5411764705882353),\n",
      "                (0.4, 0.6509803921568628, 0.11764705882352941),\n",
      "                (0.9019607843137255, 0.6705882352941176, 0.00784313725490196),\n",
      "                (0.6509803921568628, 0.4627450980392157, 0.11372549019607843),\n",
      "                (0.4, 0.4, 0.4)]\n",
      "\n",
      "rcParams['figure.figsize'] = (10, 6)\n",
      "rcParams['figure.dpi'] = 150\n",
      "rcParams['axes.color_cycle'] = dark2_colors\n",
      "rcParams['lines.linewidth'] = 2\n",
      "rcParams['axes.grid'] = True\n",
      "rcParams['axes.facecolor'] = '#eeeeee'\n",
      "rcParams['font.size'] = 14\n",
      "rcParams['patch.edgecolor'] = 'none'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "C:\\Users\\Manu\\AppData\\Local\\Enthought\\Canopy\\User\\lib\\site-packages\\pandas\\io\\excel.py:626: UserWarning: Installed openpyxl is not supported at this time. Use >=1.6.1 and <2.0.0.\n",
        "  .format(openpyxl_compat.start_ver, openpyxl_compat.stop_ver))\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Exercise 1: \n",
      "\n",
      "The [Real Clear Politics](http://www.realclearpolitics.com) website archives many political polls. In addition, they combine related polls to form an \"RCP average\" estimate of public opinion over time. For example, the chart on [this page](http://www.realclearpolitics.com/epolls/2012/president/us/general_election_romney_vs_obama-1171.html) shows historical polling data for the Obama-Romney presidential race. The chart is an average of the polling data table below the chart.\n",
      "\n",
      "The data used to generate plots like this are stored as XML pages, with URLs like:\n",
      "\n",
      "http://charts.realclearpolitics.com/charts/[id].xml\n",
      "\n",
      "Here, [id] is a unique integer, found at the end of the URL of the page that displays the graph. The id for the Obama-Romney race is 1171:\n",
      "\n",
      "http://charts.realclearpolitics.com/charts/1171.xml\n",
      "\n",
      "Opening this page in Google Chrome or Firefox will show you the XML content in an easy-to-read format. Notice that XML tags are nested inside each other, hierarchically (the jargony term for this is the \"Document Object Model\", or \"DOM\"). The first step of webscraping is almost always exploring the HTML/XML source in a browser, and getting a sense of this hierarchy.\n",
      "\n",
      "---\n",
      "\n",
      "#### Analyse the page\n",
      "\n",
      "The above XML page includes 5 distinct tags. The xml page is in the following format:\n",
      "    * chart\n",
      "      * series\n",
      "        * value\n",
      "      * graphs\n",
      "        * graph\n",
      "          * value\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_poll_data(poll_id):\n",
      "    url = \"http://charts.realclearpolitics.com/charts/%i.xml\" % int(poll_id)\n",
      "    return requests.get(url).text"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Our get_poll_data function pulls data from web into python in the form of text. I am going to use web module from pattern to parse the text and extract data into a pandas dataframe."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import re\n",
      "\n",
      "def strip(s):\n",
      "    \"\"\"This function removes non-letter characters from a word\n",
      "    \n",
      "    for example _strip('Hi there!') == 'Hi there'\n",
      "    \"\"\"\n",
      "    return re.sub(r'[\\W_]+', '', s)\n",
      "\n",
      "def color_function(xml):\n",
      "    dom = web.Element(xml)\n",
      "    colors_dict = {}\n",
      "    for i in dom.by_tag('graph'):\n",
      "        name = strip(i.attributes['title'])\n",
      "        colors_dict[name] = i.attributes['color']\n",
      "    return colors_dict\n",
      "\n",
      "#url = 'http://charts.realclearpolitics.com/charts/1171.xml'\n",
      "#some_variabel = requests.get(url).text\n",
      "#colors = color_function(some_variabel)\n",
      "#print len(colors)\n",
      "#print pd.DataFrame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def take_page(xml): \n",
      "    dom = web.Element(xml)\n",
      "    final = {}\n",
      "    \n",
      "    charts_page = dom.by_tag('series')[0]    \n",
      "    y = {i.attributes['xid']: str(i.content) for i in charts_page.by_tag('value')}\n",
      "    \n",
      "    key_of_y = y.keys()\n",
      "    \n",
      "    final['date'] = pd.to_datetime([y[k] for k in key_of_y])\n",
      "    \n",
      "    for each_value in dom.by_tag('graph'):\n",
      "        title_name = each_value.attributes['title']\n",
      "        new_dict = {n.attributes['xid']: float(n.content) \n",
      "                if n.content else np.nan for n in each_value.by_tag('value')}\n",
      "        final[title_name] = [new_dict[k] for k in key_of_y]\n",
      "        \n",
      "    finals = pd.DataFrame(final)    \n",
      "    finals = finals.sort(columns=['date'])\n",
      "        \n",
      "    return finals\n",
      "#It is always good to check how your function works\n",
      "#url = \"http://charts.realclearpolitics.com/charts/1044.xml\"\n",
      "#storage_variable = requests.get(url).text\n",
      "#x = take_page(storage_variable)\n",
      "#x.index = x.date\n",
      "#y = x.resample('D')\n",
      "#print y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def new_func(poll_id):\n",
      "    new_poll_id= get_poll_data(poll_id)\n",
      "    new_take_page = take_page(new_poll_id)\n",
      "    new_colors = color_function(new_poll_id)\n",
      "\n",
      "    #remove characters like apostrophes\n",
      "    new_take_page=  new_take_page.rename(columns = {c: strip(c) for c in  new_take_page.columns})\n",
      "\n",
      "    #normalize poll numbers so they add to 100%    \n",
      "    norm =  new_take_page[new_colors.keys()].sum(axis=1) / 100    \n",
      "    for c in new_colors.keys():\n",
      "        new_take_page[c] /= norm\n",
      "    \n",
      "    for label, color in new_colors.items():\n",
      "        plt.plot(new_take_page.date, new_take_page[label], color=color, label=label)        \n",
      "        \n",
      "    plt.xticks(rotation=70)\n",
      "    plt.legend(loc='best')\n",
      "    plt.xlabel(\"Date\")\n",
      "    plt.ylabel(\"Normalized Poll Percentage\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "new_func(1044)\n",
      "plt.title(\"Polling\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.text.Text at 0xaaa4080>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2goNecyKk3/oi/dYX6bfIDWcDt2lAOeABQJtBvwno4upG6c3999/PtGnTrPal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\nry8TJkzggQceAKxrum3bto0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a7Uv44QcSvvmGxC++wJTJKiJOMRi4PWIECVu28D3g9/vv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KUFKS2rxgZIv61F+m0t0m9rsWq/fUUGbgFgS08HIPu223ApU6VCCCGEECUmMW4BEDV7NrFPPUXW\nsGFkvvxysJsjhBBCiACTGLdQkZdHRHIyAK64uOC2RQghhBAhTQZufhbz/PNErF4N+GbgZtXYAOm3\ntUi/rUX6bS1W7bevyMDNz+wHDuiXnfXrB7ElQgghhAh1EuPmZ5UGDSJy5UqyHnqIzOeeg7CwYDdJ\nCCGEEAEmMW4hwqZWTMjt1k0GbUIIIYQoExm4+VturvJvGWuUaqwaGyD9thbpt7VIv63Fqv32FRm4\n+ZktJwcAV0REkFsihBBCiFAnMW5+Ft+1K+E//0zqd9/haNMm2M0RQgghRBBIjFuI0GfcIiOD3BIh\nhBBChDoZuPmbOnDDRwM3q8YGSL+tRfptLdJva7Fqv30lPNgNqIhsR44Qvnkzjssu8/nATQghhBDW\nJTFufhDx+efEPfCA27mUXbtw1awZpBYJIYQQIpgkxq0cc9ap43bsiovDlZgYpNYIIYQQoqKQgZsf\nODp0cDtO/f57iInxyWNbNTZA+m0t0m9rkX5bi1X77SsycPOznOuuw3nRRcFuhhBCCCEqAIlx85PE\nFi2wHztG5qhRZD3zTLCbI4QQQogg8lWMm+wq9ZO0Dz8kYuVKsocODXZThBBCCFFByFKpnzjatiVr\n3DhcygjbZ6waGyD9thbpt7VIv63Fqv32FRm4CSGEEEKECIlxE0IIIYTwM8njJoQQQghhMTJwCzFW\njQ2QfluL9NtapN/WYtV++4oM3IQQQgghQoTEuAkhhBBC+JnEuAkhhBBCWIwM3EKMVWMDpN/WIv22\nFum3tVi1374iAzchhBBCiBAhMW5CCCGEEH4mMW5CCCGEEBYT6IHbeGALcA44CSwBWni43STgKJAB\nfA80D1D7yj2rxgZIv61F+m0t0m9rsWq/fSXQA7drgNeBK4FuQB6wGqhiuM1Y4HFgBNAeZYC3CogL\naEvLqZ07dwa7CUEh/bYW6be1SL+txar99pXwAP++60zHg1Fm3zoCy1DWfkcBLwNfqLe5C2Xwdjsw\nNzDNLL/OnTsX7CYEhfTbWqTf1iL9thar9ttXgh3jlqC24ax63BCoAXxruE0WsA5lcCeEEEIIYVnB\nHrjNBLYDG9Xjmuq/J0y3O2m4ztIOHz4c7CYEhfTbWqTf1iL9thar9ttXgpkOZBpwK9AJOKie6wis\nB+oBRwy3fQeoBfQ2nNsLXOT3VgohhBBClN0+oHGwG1Fa01F2jTYxnW8EOIF2pvPLgHcD0C4hhBBC\nCGEwE/gbaOrhOpt63XjDuWiUDQwP+L9pQgghhBBC8wbKIKwrSsya9lPJcJsngBSgP3Ap8AnKsmkl\nhBBCCCFEwDgBh/qv8ecZ0+0mosy8ZSIJeIUQQgghhBBCCCGE8L9ooHqwGxEE4QQ/hYsQgRTMne+B\nJu9rQlhDmd7XwnzVigCpDjwMvAJ0ASKAXzzcrqK+2TsBF0r/wtXLFd29QC7wT7AbEmDxwJ3qv4eC\n3JZAuxCoC5w2nbdTMV/z8r4m72tWIe9r1nlf072PUkXhXeBTlD/AIPU6u+nfiqQ/8DNKHVdzCpWW\nQFTAWxQYtwNbyO9zHEruv85A7WA1KgCuQqnPeww4D0xSz1elYr6+NQ2BqcBvKLkd/wE+QtnMVJHJ\n+5q8r8n7WsVl1fc1QHlyzwNJhnMvAGuByoZzTwO3BbBdgfARkAqsAf4ElgM3Aw8BGwy3C7UZ1OJs\nACarl3sD3wHHgTzgDMpzHUnFm4lYA7yNkpD6QeBrlN3Wf6KUh5uMsqxW0SxDKXf3PMrrezTKgMYB\n7AB6qberSM+3vK/J+5q8r8n7GlS85xtQnuiNKCPzSPVcfZRMxP9SjyNR3gQrWl3TNigJi8cAI4H/\nojzh6Sj9703Fe3OLA3YD7dXj7cAM4HLgAuARlNJotweldf5TFUhDqR6iOYLyZv8AyodaCjAu8E3z\nqyoou8ibGc7ZUF4HnYHPgB+peNVS5H1N3tfkfU3e1yra+5ruSZSlhKrqsTa1+gzKdDvATRRcPw51\n2ih8JPAf9XJNYCDKiH0D8DvKFGydQDfOjyqjfFMZhtLffbjPQNiA+cAiIDbgrfOf7sBP5NfmbYPy\nH7++4TYzgBVAQmCb5lcdgD9Q+utJE5TXwLNUrGUVeV+T9zV5X1PI+1oFYwOuQam6YPb/7Z17jF1V\nFcZ/d9oOLX2ARcRKabGPQGiBlorBArU82hLEGBAkDUhLUFKBiDwEURsxpoQGopZGaiG0g0psjElL\nEaFIBIuapkAggJgQ2qooIggFWu1zOv7xneM598y9Q2c6596Zvb9fcjNzz33M+mafs87ae6+99mGo\n5tu5aKj9rgba1SgqKPdhPXBNcuyraM58EnAJugGExkJ085oPPI56pZCd4HNRrkhIjAY2o2mE+WiI\n/RmkOb3ZXYqcYEgMAZ5F5/i4Ou+5CdV1DAX7Nfs1+zX7tdD8WifS3ROKkemtaLhxH0oEDJXTUS7E\nBNQbzQ8rD2qKReVyMPBTdNJvRbtoTExem4Ta/M7mmFYq56O8l7+jaYSXyJx7K3L2dzTHtFI5CdgI\nPIpu4KeQlcc4HPg9Ybb3sOSn/Zr9mv1aeMTq14DauQ5ppD4ZJXaub5w5DaGS+zkw+X0BsAHpnZi8\nFmJSY6ppHCqT8CK6gb2Bem6vA2vJht5DIL1xD0K6B6Me2yOoh3oX8CTKAxrdBPvKItU9AOU1rQFe\nQz3y1UjzJpQLFtK0GdSeDkuDldD9Wgtx+bW8nvHE49fSe3cr8msHEY9fS7XPQr6sV/xaf7gwBgGf\nQEtnhyHBTyPb09onLcDX0Im/pgk2lkleJ+iCXoZ6alciRxciRd1jgNPQhb8XeBP4GbC78aaVSgty\n5HmmAbegEed3gaVUr7oLgcHAztzz44HPo/Z+DyVs35v8DIFDUN7PXLSycjHwSuE9rcBVKM8rNL+W\nb+8K8mvLUamEkP1a8Twfi6bLxxC2XyvqBo223Yw6L6H6tSKTgc8Cx6AFRz3ya/0hcLsOuALdtP6K\nVqYsQMOtxZt7SNQLWFOGoOTOWjf6/kxe93DgQdQTD5287hFohd1Gqs/xw1G5gPZmGFgS+QBmG5oy\neLnwnoGEdyNfBHwO+bGR6Pq+B5iHRl9WEdZ1nfJBAetBqDBtaNqLuu9E08KhU7y+F6NVtXlGoXM+\npHv5ZBSQP4CC0uh4B9UvGg5MRfPiKwvvmUP9xL/+ynVI6xY0pPorsmHk4Faf5OhK98A6nwmBrnSH\nmOeTsgjpfhQFqregxPyvA5dRnS4QEu8Bs9Go2nkoz+UFNHW2E/1PJjfNuvKo1943osUIofq2rnR/\nkXDP83q6b6Bad2jt/hDqfGxF9evSgtofB36MRiAHEV65G0AB28tUn8xTkdP7ZO7YW6iwXUjsT8B6\nNnB0Y80qHeuur3sW4enenwDmuKZZVw5zka70ZnUEcvJXoy1xTkC5MFc2xbpyibG9wbq70j2padaV\nxzyk9Sto9mQrilPeQItPguYONN9fXHV1HxqCBA1H7miwXWUTa8Bq3Rkx6I41gLk3eaRbOn0L5fak\n/4cK8n0rGm9aqcTa3tYtYtENSndZh67tCvAplM+3C/gv8CpwN1mljG7Rl4cnK2j1xVaU85BnBVpS\nexQaXn+4saaVzjRUzyZf/uQ5VGX52uTYp1FA+8uGW1ce1i1i0X0mWlGVTgV/CeUzLkMO/UWU63VK\nU6wrhwrS9BBZ3t4/0DZX+9DUSQfK+wktQT3G9gbrjk03KI/xG6ijfRb6P7yB4plL0PU/Be0S0m36\ncs5QB4pYt6AotUKWsLoBJfVej7YGmVPrC/opacB6GrUD1p8QZsBq3fHpXoWC1XoBTDsKYLY3w8CS\n6EDFNgeTLTJpy73ejkYmzkFTS6EQa3tbd1y6UwagzvdSVLvtceBy4OdoIdJjZDOJUTEHnQB/abId\nZXAo2RRZfvpsANp8+gfoZD+1wXaVjXXHpbuFrrf1OQKVhwixR55Sobp6/Ei03dXzTbOoPGJtb+uu\nTai683wILTS7DY2undz128OgqxU2Q1D16asbZEtfIeSAtSusO2xiCmC6YirKhQlpFqEWsba3dYtY\ndIOCtT1oK7chybEDWj3cl6dKoeu6LjtQCYUezRH3cbqqT7ceLcwIsbaZdXcmZN15Oqj+H4xFKRI3\nN8ecpvEcqmtWLFYaGrG2t3WLWHRXUP3Vw1B5p3QhZUg16z6QtObJaLK9vkKre7M/fJgsco8J646L\noQRa56hAqvEoMr8WI7G0dxHrDpuixqA119qrLn3+fTR9dEZDLWoesQas1h2n7pADmP3xazMbaVAT\niaG9a2HdceoejTrhQTKCzmVK8o6uBbgIFSdtbZRRDSTWgNW6q4+Bdc9spEElY78WV3unWHf1MbDu\nmQf6R/rikN1StCfjXpTnsavwegfKAVlNWHs2jkAJjPm576JjH46K991NONqtO8O6q3UvIxzd9msZ\nMbS3dWdYd7i6AdVk24cqxm8GlgMXABPIcnyGoeJ1oe3jdw9wBXA8Oglq0dcXk/QE67buIqHptl+L\nq73Buq27MyHqBrQNzHJgPNp89yUUoT6P6qCcifb+KhYq7e/E6tit27pj0G2/Fld7W7d1x6AbUDT6\nTeD2wvFJwBK0U8LbaIjxvsaaVjqxOnbrtu7QdduvxdXeYN3WHYfu/3MocEzyeyudk/wuRpHtlEYa\nVTKxOnbrrsa6w9QN9mt5Qm9v667GusPU/YEMIJsbvhJFsqERo2MH6wbrzhOq7lrYr4Xb3tZt3Xl6\nXXd/SJbLr74YDtzaJDvK5N3kAbA7+TkAnQB7gUPQSrTQtgexbuuOQXct7NfCbW/rtu5SdfeHwC3P\nEuLZKiIGx14L67bu2LBfCx/rtm4TGQPpmzX3ysa64yJW3bESa3tbd1zEqtsYY4wxxhhjjDHGGGOM\nMcYYY4wxxhhjjDHGGGOMCZIZwFpUGHcfMK8H37EEeBqV+NhS5z3nAhuA94G3gDXAxB78rZq09NYX\nGWOMMcb0YYYCLwDXAjvoWRmeCtAG3F/n8xNQoPYkKrp7NjAY+HUP/pYxxkRDG+pR70NFMf8F/Ba4\niu7Vr5yZfMfI3jXPGNNktgGXFY61AouB14D/ABuB2XU+fyO1R9wuRIV38zsonEEv+hGPuBljQqQD\n+A3wUWAsMAt4CPgu8BRwcDe/r7iNjTEmPFYCpwNz0X6j9yO/cUI3vuMPwHbgy6h+23BgPgoC3+lF\nW40xJijakMMtMgnYRVbJ/FKUr/I+GpX7BfCx5LWjyUbt0seK5LUKcBPwKtpn9AXgkl5VYIwpk+KI\n23i048FRhfetAX5U4/P1RtwApgNvAnuS73wGOPxAjDXGmNBpo3bgBvAg8GLy++XAOShIOxlNp/4u\nea0FOB8FbMcCH0G9Z4BFwJ/RNMpY1EPfjpKSjTF9n2LgdhG61rcVHruBR2p8vl7gNg4FbYuBE9EI\n3hPJwyP3xhhThzbqB263o/yVWhyLnHc66jaTzrkpQ9Eo26mFz/4QeLj7phpjmkAxcLsYjY4dg4Kv\n/GNUjc/XC9wWA88Wjh2J/Mj0AzNZ9LdN5o0x5kCpICcKcBLwHdQzHknWIx4DvF7n88ehVWLrqF5V\nNoj6UyfGmL7Nc+j6H4VWhPaUvH9JSZ/3yroCB27GmNg4DtiMFiisAx5DuW5vojyUp9Dqsnqkzvc8\n4G+F1/b0qqXGmN5kKFk9tRaU5jAFeBt4BXgAjdbfgAK5kWjUfROwOvncBGAYGpVvRZ2+CvAndP2v\nTT6/EFiF0ituQ76iOBJnjDEmoQ050CKTUc7KQmAa6gmPzb1+QXJsRvJ8evI8n1g8HNWAmt+bBhtj\nSmcm2UKjdjovOhqIRuA3oUVM/0SLE6bmvuOJGt/RjkbpUy5ECxK2oUVPa1AahjHGmDq0oZG0I1DP\n+ETgelTF/I/AEBSM7QDuRHksn0G95nzgdiRyypcn7x+aHP8e8O/k+ATUa1+ASgAYY4wxxphusJKs\nV7wHBWy1CvB+AZX02IG2qJmNArUZufd8G+W7tZP1zAGuQYHeTjTNug44q/elGCFnC6YAAAA/SURB\nVGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhgT\nOP8DeCv2runicxIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xaa8b2b0>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Exercise number 2\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "\n",
      "This url has links to many governer races. \n",
      "\n",
      "http://www.realclearpolitics.com/epolls/2010/governor/2010_elections_governor_map.html\n",
      "\n",
      "Notice that each link to a governor race has the following URL pattern:\n",
      "\n",
      "http://www.realclearpolitics.com/epolls/[YEAR]/governor/[STATE]/[TITLE]-[ID].html\n",
      "\n",
      "\n",
      "I will write a function that scans html for links to URLs like this\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def gen_func(n):\n",
      "    \"\"\"return True if a URL refers to a Governor race\"\"\"    \n",
      "    pattern = 'http://www.realclearpolitics.com/epolls/????/governor/??/*-*.html'\n",
      "    return fnmatch(n, pattern)\n",
      "        \n",
      "def scraping(html):\n",
      "    dom = web.Element(html)\n",
      "    link = [x.attributes.get('href', '') for x in dom.by_tag('a')] \n",
      "    links = [n for n in link if gen_func(n)]\n",
      "    #eliminate duplicates!\n",
      "    the = list(set(links))\n",
      "    return the"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Exercise 3:\n",
      "\n",
      "Write a function that looks up and returns the election result on a page like [this one](http://www.realclearpolitics.com/epolls/2010/governor/ca/california_governor_whitman_vs_brown-1113.html). \n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def jaya_vs_karuna(url):\n",
      "    dom = web.Element(requests.get(url).text)\n",
      "    \n",
      "    the_tag = dom.by_tag('div#polling-data-rcp')[0]\n",
      "    inside_tag = the_tag.by_tag('tr.final')[0]\n",
      "    td_tag = inside_tag.by_tag('td')\n",
      "    jaya = [float(i.content) for i in td_tag[3:5]]\n",
      "    tot = sum(jaya)/100\n",
      "    \n",
      "    new_table = the_tag.by_tag('th')\n",
      "    new_list = [str(i.content).split('(')[0].strip() for i in new_table[3:-1]]\n",
      "    \n",
      "    dictionary_of_politcs = {na:nu / tot for na,nu in zip(new_list, jaya)}\n",
      "    return dictionary_of_politcs\n",
      "  \n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def remove_from_url(url):\n",
      "    \"\"\"Given a URL, look up the RCP identifier number\"\"\"\n",
      "    return url.split('-')[-1].split('.html')[0]\n",
      "\n",
      "def plot_elections(url):\n",
      "    \"\"\"Make a plot summarizing a senate race\n",
      "    \n",
      "    Overplots the actual race results as dashed horizontal lines\n",
      "    \"\"\"\n",
      "    xid = remove_from_url(url)\n",
      "    xml = get_poll_data(xid)\n",
      "    colors = color_function(xml)\n",
      "    \n",
      "    \n",
      "    if len(colors) == 0:\n",
      "        return\n",
      "    \n",
      "    result = jaya_vs_karuna(url)\n",
      "    \n",
      "    new_func(xid)\n",
      "    plt.xlabel(\"Date\")\n",
      "    plt.ylabel(\"Polling Percentage\")\n",
      "    \n",
      "    for r in result:\n",
      "        plt.axhline(result[r], color=colors[strip(r)], alpha=0.6, ls='--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "page = requests.get('http://www.realclearpolitics.com/epolls/2010/governor/2010_elections_governor_map.html').text.encode('ascii', 'ignore')\n",
      "#ignore is used to replace nonprintable characters with nothing (delete them)\n",
      "for race in scraping(page):\n",
      "    plot_elections(race)\n",
      "    plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MiIiIiBTO0aLtfeA9jMXhb8h6PQ78H8ZtU4B2wFaXRiduo74Ec5Q/5yl35ih/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\nDVVjdyva6/fm2JWC4heM4heM4uefVkSQyKgYPIi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       "text": [
        "<matplotlib.figure.Figure at 0xa3afdd8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Mk+Nr1qxh/vz5dO/ePXLskksuoa6ujuLi4vC12Zg2bRqXXHIJM2fO5LzzzqNLly6MHTuW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\nuiQtYutfLcrbLjbmnR3qgfP6IoZID5y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       "text": [
        "<matplotlib.figure.Figure at 0xa71a940>"
       ]
      },
      {
       "ename": "ConnectionError",
       "evalue": "HTTPConnectionPool(host='charts.realclearpolitics.com', port=80): Max retries exceeded with url: /charts/1055.xml (Caused by <class 'socket.gaierror'>: [Errno 11002] getaddrinfo failed)",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mConnectionError\u001b[0m                           Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-14-fa68227caf8e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m#ignore is used to replace nonprintable characters with nothing (delete them)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mrace\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mscraping\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0mplot_elections\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m<ipython-input-12-55fb38d39d88>\u001b[0m in \u001b[0;36mplot_elections\u001b[1;34m(url)\u001b[0m\n\u001b[0;32m     18\u001b[0m     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjaya_vs_karuna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m     \u001b[0mnew_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     21\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Date\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Polling Percentage\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m<ipython-input-5-5a62f9882b51>\u001b[0m in \u001b[0;36mnew_func\u001b[1;34m(poll_id)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mnew_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpoll_id\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mnew_poll_id\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mget_poll_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpoll_id\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m     \u001b[0mnew_take_page\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtake_page\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_poll_id\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mnew_colors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcolor_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_poll_id\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m<ipython-input-2-5f19a15fd06a>\u001b[0m in \u001b[0;36mget_poll_data\u001b[1;34m(poll_id)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget_poll_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpoll_id\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"http://charts.realclearpolitics.com/charts/%i.xml\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpoll_id\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mrequests\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;32mC:\\Users\\Manu\\AppData\\Local\\Enthought\\Canopy\\User\\lib\\site-packages\\requests\\api.pyc\u001b[0m in \u001b[0;36mget\u001b[1;34m(url, **kwargs)\u001b[0m\n\u001b[0;32m     53\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'allow_redirects'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'get'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32mC:\\Users\\Manu\\AppData\\Local\\Enthought\\Canopy\\User\\lib\\site-packages\\requests\\api.pyc\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     42\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m     \u001b[0msession\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msessions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     45\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32mC:\\Users\\Manu\\AppData\\Local\\Enthought\\Canopy\\User\\lib\\site-packages\\requests\\sessions.pyc\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert)\u001b[0m\n\u001b[0;32m    454\u001b[0m             \u001b[1;34m'allow_redirects'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mallow_redirects\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    455\u001b[0m         }\n\u001b[1;32m--> 456\u001b[1;33m         \u001b[0mresp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    457\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    458\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32mC:\\Users\\Manu\\AppData\\Local\\Enthought\\Canopy\\User\\lib\\site-packages\\requests\\sessions.pyc\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m    557\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    558\u001b[0m         \u001b[1;31m# Send the request\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 559\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    560\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    561\u001b[0m         \u001b[1;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32mC:\\Users\\Manu\\AppData\\Local\\Enthought\\Canopy\\User\\lib\\site-packages\\requests\\adapters.pyc\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    373\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    374\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mMaxRetryError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 375\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    376\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    377\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0m_ProxyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mConnectionError\u001b[0m: HTTPConnectionPool(host='charts.realclearpolitics.com', port=80): Max retries exceeded with url: /charts/1055.xml (Caused by <class 'socket.gaierror'>: [Errno 11002] getaddrinfo failed)"
       ]
      }
     ],
     "prompt_number": 14
    }
   ],
   "metadata": {}
  }
 ]
}