crime_data.ipynb

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    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Outdated** -- use fbi_crime_data instead\n",
      "\n",
      "This notebook scrapes US crime data from http://www.disastercenter.com/crime/, and merges it into a single csv file"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import requests\n",
      "from fnmatch import fnmatch\n",
      "import re\n",
      "\n",
      "from bs4 import BeautifulSoup, SoupStrainer\n",
      "import pandas as pd\n",
      "import numpy as np\n",
      "\n",
      "pd.set_option('display.width', 500)\n",
      "pd.set_option('display.max_columns', 50)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "columns = 'Year Population Index Violent Property Murder Rape Robbery Assault Burglary Larceny Vehicle'.split()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Gather the URLs for each state's page"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dom = BeautifulSoup(requests.get('http://www.disastercenter.com/crime/vacrime.htm').text, \"html5lib\")\n",
      "urls = [a['href'] for a in dom.findAll('a') if fnmatch(a['href'], 'http://www.disastercenter.com/crime/*.htm')]\n",
      "urls = {u.split('/')[-1].split('crim')[0]: u for u in urls}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def number(element):\n",
      "    try:\n",
      "        node = 'font' if element.find('font') else 'small'\n",
      "        d = element.find(node).contents[0]\n",
      "        d = d.replace(',', '').replace(u'\\xa0', '').replace('*', '').replace(\"'\",'')\n",
      "        return float(d)\n",
      "    except:\n",
      "        return np.nan\n",
      "\n",
      "def crime_stats(url):\n",
      "    dom = BeautifulSoup(requests.get(url).text, \"html5lib\")    \n",
      "    table = dom.find('center', text=re.compile('100,000')).find_next('table')\n",
      "    rows = filter(lambda x: len(x.findAll('td')) == 12, table.find_all('tr'))\n",
      "    data = [map(number, r.findAll('td')) for r in rows[2:]]\n",
      "    return pd.DataFrame(data, columns=columns)\n",
      "\n",
      "states = {}\n",
      "for state, url in urls.items():\n",
      "    print state,\n",
      "    if state in ['oh']:  # Ohio is a malformed table. Ignore it.\n",
      "        continue        \n",
      "    states[state] = crime_stats(url)\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
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     "input": [
      "for s in states:\n",
      "    states[s]['state'] = s\n",
      "\n",
      "data = pd.concat(states.values(), ignore_index=True)\n",
      "data.to_csv('crime.csv', index=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    }
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}