scrivener/Data Breaches.ipynb

## Data Breaches.ipynb
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     "input": "# Python's CSV module.\nimport csv\n\n# Matplotlib for plotting.\nimport matplotlib\nimport matplotlib.pyplot as plt\n%matplotlib inline\n%config InlineBackend.figure_format = 'svg'  # Figures as vector graphics\n\nimport numpy\nimport pprint\nimport collections\nimport locale\n",
     "prompt_number": 73,
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "data = csv.reader(open('databreaches.csv', 'r'))\nheaders = data.next()\nrow1 = data.next()\nrow2 = data.next()\npprint.pprint(zip(headers, row1))\npprint.pprint(zip(headers, row2))",
     "prompt_number": 15,
     "outputs": [
      {
       "text": "[('Date Made Public', 'January 10, 2005'),\n ('Name', 'George Mason University'),\n ('Entity', 'EDU'),\n ('Type', 'HACK'),\n ('Street Address', ''),\n ('Location', 'Fairfax'),\n (', ', 'Virginia'),\n ('Postal Code', ''),\n ('Total Records', '32,000'),\n ('Records Breached', '32,000'),\n ('', ''),\n ('',\n  \"Names, photos, and Social Security numbers of 32,000 students and staff were compromised because of a hacker attack on the University's main ID server.\"),\n ('', ''),\n ('', 'Dataloss DB'),\n ('Attached files', ''),\n ('', '32000'),\n ('All terms', '2005')]\n[('Date Made Public', 'January 18, 2005'),\n ('Name', 'University of California, San Diego'),\n ('Entity', 'EDU'),\n ('Type', 'HACK'),\n ('Street Address', ''),\n ('Location', 'San Diego'),\n (', ', 'California'),\n ('Postal Code', ''),\n ('Total Records', '3,500'),\n ('Records Breached', '3,500'),\n ('', ''),\n ('',\n  'A hacker breached the security of two University computers that stored the Social Security numbers and names of students and alumni of UCSD Extension.'),\n ('', ''),\n ('', 'Dataloss DB'),\n ('Attached files', ''),\n ('', '3500'),\n ('All terms', '2005')]\n",
       "output_type": "stream",
       "stream": "stdout"
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     "language": "python",
     "trusted": true,
     "collapsed": false
    },
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     "cell_type": "code",
     "input": "def barGraphOfTargetType():\n    # Read in the data\n    data = csv.reader(open('databreaches.csv', 'r'))\n    # Remove the header\n    data.next()\n    \n    # Aggregate to types\n    types = [d[2] for d in data]\n    typeCounts = collections.defaultdict(int)\n    for t in types:\n        typeCounts[t] += 1\n    print(typeCounts)\n    types = typeCounts.keys()\n    \n    # Plot\n    fig = plt.figure()\n    plt.plot([typeCounts[t] for t in types], '.', markersize=30)\n    axes = fig.gca()\n    axes.set_xticklabels(types)\n    \nbarGraphOfTargetType()",
     "prompt_number": 34,
     "outputs": [
      {
       "text": "defaultdict(<type 'int'>, {'MED': 1218, 'NGO': 104, 'BSR': 545, 'BSO': 606, 'EDU': 747, 'BSF': 596, 'GOV': 704})\n",
       "output_type": "stream",
       "stream": "stdout"
      },
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     "input": "import re\ndef breachSizes():\n    # Read in the data\n    data = csv.reader(open('databreaches.csv', 'r'))\n    # Remove the header\n    data.next()\n    \n    # Aggregate to types\n    sizes = []\n    exceptions = 0\n    total = 0\n    unknowns = 0\n    millions = 0\n    for d in data:\n        total += 1\n        try:\n\n            col = d[9]\n            if 'Unknown' in col or 'Uknown' in col or 'unknown' in col:\n                unknowns += 1\n                continue\n            col = col.replace('\\n', '')\n            col = col.replace(',', '')\n            col = re.sub(r' .*$', '', col)\n            col = float(col)\n            if 'Million' in d[9] or 'million' in d[9]:\n                col *= 10**6\n                millions += 1\n            sizes.append(col)\n        except Exception as e:\n            exceptions += 1\n            print(d[9])\n                         \n        \n    sizes.sort()\n    \n    print('Exceptions: %d/%d, Unknowns: %d, Millions: %d' % (exceptions,total,unknowns, millions))\n    plt.plot(sizes)\n    plt.yscale('log')\n    \n\n    \nbreachSizes()",
     "prompt_number": 61,
     "outputs": [
      {
       "text": "tens of \n            thousands (at least 20,000)\nNot disclosed\n[2,000,000] \n            Not included in total below.\nHundreds (at least 200)\nPotentially 2,000,000\n[1,300,000]              Not included in number below.\nthousands of domain \n            name registrations\nPotentially millions \n            of registered voters\nHundreds (at least 200)\nFewer than 1,000\n            [1,000 used in total]\nVery few of \n            41,000 members affected \n\n            [not included in total]\nthousands of city \n            workers and contractors (at least 2,000)\nOver 100,000 records\n            (The number containing SSNs is not known yet and not included in total \n            below.)\nAmong the 2 million postings \n            are \"thousands\" containings SSNs\nbetween 5,600 and \n            23,000 patients were affected \n\n            (23,000 added to total below)\n112,270 \nUp to 16,000 affected\nAt least 6,000 patients\nthousands of files\nthousands of customers\nMore than 100 patients\n\"Hundreds\"\nIndividuals in 17,000 households\nApprox. 200 victims or \n            suspects\nUp to 1,100 patients\nup to 20,000 employees\nUp to 1.4 million\nUp to 50 Compulinx employees\nAbout 1,000\nAt least 440\nApproximately 1,000\nConsiderably more than \n            22,500\nAbout 39,000 North Carolina residents\nAt least 101,000 students \n            and employees\nhundreds of employees (at least 200)\nThousands of school \n            employees (at least 2,000)\nhundreds of patients\nMore than 1,000 employees\nOver 1,000\nOver 100\nHundreds\nOriginally 4.5 million customer              records, raised to              12.5 million\n800-900\nOver 130 million\nOver 130 million\n(Not added to total. It does not \n            appear that SSNs or financial account numbers were exposed.)\nAt least 140\n208,000Additional 860,000 added June 3rd; (11/16/10) Estimate reaches 1.22 million\nAt least 9,386\nAt least 245\nOver 200 (0 reports of SSNs or financial information)\nAt least 1,085\nAt least 1,285\nAt least 89\nAbout 5,000\n2,416; 103 Social Security numbers\n100 \nHundreds (at least 200)\nHundreds (at least 200)\n50-250 million (No SSNs or financial information involved)\n7,093 \n172 \n87,667 \nAt least 15,000 people were affected.\n87 \nUnkown\nUnknowm\n17,300 \n1,200 \n6,700 \n2,400 \n9,900 \n1,700 \nClient files which included social security numbers and other asset information.\nSatellite systems that forecast weather\nExceptions: 75/4520, Unknowns: 1575, Millions: 43\n",
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     "language": "python",
     "trusted": true,
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    {
     "metadata": {},
     "cell_type": "code",
     "input": "re.sub(r' .*$', '', '25000 (This is bad)')\n",
     "prompt_number": 49,
     "outputs": [
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       "metadata": {},
       "text": "'25000'",
       "output_type": "pyout",
       "prompt_number": 49
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     ],
     "language": "python",
     "trusted": true,
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     "prompt_number": 64,
     "outputs": [
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