Advertising with An Up and Coming Financial Publisher - Analysis Using Moat

Advertising Analysis Using Moat

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      "#Advertising with An Up and Coming Financial Publisher \n",
      "##Analysis Using Moat \n",
      "\n",
      "###Background:\n",
      "All the data has been collected from Moat. Make recommendations, based on data, to a budding new financial publisher (with a smaller-sized audience) interested in pursuing its first deals with premium ad networks and its first direct deals with advertisers. Which partners should this publisher go after? What sorts of KPI\u2019s might its prospective advertisers and networks have?\n",
      "\n",
      "\n",
      "###Goal: \n",
      "Determine which advertising companies are well suited for small sized financial publisher to work with. \n",
      "\n",
      "###The report consists of:\n",
      "-\tPulling csv files for all financial publishers.\n",
      "-\tPulling csv files for all advertisers. Determining which advertisers do business with financial publishers most frequently.\n",
      "-\tRunning logistic regression to see if there are any features that are predictive of whether an advertiser\u2019s trend activity will increase or decrease. \n",
      "-\tWorking with Na\u00efve Bayes to see how good the classifier is at correctly predicting the outcomes of trend activity for advertisers.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "from matplotlib import pyplot as plt\n",
      "\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "barrons = pd.read_csv(\"/Users/olehdubno/Desktop/Moat/moat_financial_publishers/Barron's.csv\")\n",
      "bloomberg = pd.read_csv(\"/Users/olehdubno/Desktop/Moat/moat_financial_publishers/Bloomberg.csv\")\n",
      "business_review = pd.read_csv(\"/Users/olehdubno/Desktop/Moat/moat_financial_publishers/Business Review.csv\")\n",
      "cnbc = pd.read_csv(\"/Users/olehdubno/Desktop/Moat/moat_financial_publishers/CNBC.csv\")\n",
      "daily_finance = pd.read_csv(\"/Users/olehdubno/Desktop/Moat/moat_financial_publishers/DailyFinance.csv\")\n",
      "ft = pd.read_csv(\"/Users/olehdubno/Desktop/Moat/moat_financial_publishers/Financial Times.csv\")\n",
      "forbes = pd.read_csv(\"/Users/olehdubno/Desktop/Moat/moat_financial_publishers/Forbes.csv\")\n",
      "fortune = pd.read_csv(\"/Users/olehdubno/Desktop/Moat/moat_financial_publishers/Fortune.csv\")\n",
      "inc_com = pd.read_csv(\"/Users/olehdubno/Desktop/Moat/moat_financial_publishers/Inc.com.csv\")\n",
      "investors_com = pd.read_csv(\"/Users/olehdubno/Desktop/Moat/moat_financial_publishers/Investors.com.csv\")\n",
      "market_watch = pd.read_csv(\"/Users/olehdubno/Desktop/Moat/moat_financial_publishers/MarketWatch.csv\")\n",
      "mornings_star = pd.read_csv(\"/Users/olehdubno/Desktop/Moat/moat_financial_publishers/Morningstar.csv\")\n",
      "reuters = pd.read_csv(\"/Users/olehdubno/Desktop/Moat/moat_financial_publishers/Reuters.csv\")\n",
      "the_street = pd.read_csv(\"/Users/olehdubno/Desktop/Moat/moat_financial_publishers/TheStreet.csv\")\n",
      "yahoo_finance = pd.read_csv(\"/Users/olehdubno/Desktop/Moat/moat_financial_publishers/Yahoo! Finance.csv\")\n",
      "zero_hedge = pd.read_csv(\"/Users/olehdubno/Desktop/Moat/moat_financial_publishers/ZeroHedge.csv\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Combining all of the excel files into one dataframe."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_publisher = pd.concat([barrons,bloomberg,business_review,\n",
      "                cnbc,daily_finance,ft,forbes,fortune,inc_com,\n",
      "                investors_com,market_watch,mornings_star,\n",
      "                reuters,the_street,yahoo_finance,zero_hedge],ignore_index=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Creating an additional column of ones for counting"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_publisher['Count'] = 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_publisher.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>Ad Type</th>\n",
        "      <th>Advertiser</th>\n",
        "      <th>Category</th>\n",
        "      <th>Date</th>\n",
        "      <th>Flag</th>\n",
        "      <th>Publisher</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>     Overlay</td>\n",
        "      <td>       Barron's</td>\n",
        "      <td> Financial Publishing</td>\n",
        "      <td> 12/1/14</td>\n",
        "      <td> house ad</td>\n",
        "      <td> Barron's</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>     Overlay</td>\n",
        "      <td>       Barron's</td>\n",
        "      <td> Financial Publishing</td>\n",
        "      <td> 12/1/14</td>\n",
        "      <td> house ad</td>\n",
        "      <td> Barron's</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> Standard Ad</td>\n",
        "      <td>        Allianz</td>\n",
        "      <td>  Insurance Companies</td>\n",
        "      <td> 12/1/14</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> Barron's</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> Standard Ad</td>\n",
        "      <td> Northern Trust</td>\n",
        "      <td>   Personal Investing</td>\n",
        "      <td> 12/2/14</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> Barron's</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>     Overlay</td>\n",
        "      <td>       Barron's</td>\n",
        "      <td> Financial Publishing</td>\n",
        "      <td> 12/3/14</td>\n",
        "      <td> house ad</td>\n",
        "      <td> Barron's</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
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       "prompt_number": 5,
       "text": [
        "       Ad Type      Advertiser              Category     Date      Flag  \\\n",
        "0      Overlay        Barron's  Financial Publishing  12/1/14  house ad   \n",
        "1      Overlay        Barron's  Financial Publishing  12/1/14  house ad   \n",
        "2  Standard Ad         Allianz   Insurance Companies  12/1/14       NaN   \n",
        "3  Standard Ad  Northern Trust    Personal Investing  12/2/14       NaN   \n",
        "4      Overlay        Barron's  Financial Publishing  12/3/14  house ad   \n",
        "\n",
        "  Publisher  Count  \n",
        "0  Barron's      1  \n",
        "1  Barron's      1  \n",
        "2  Barron's      1  \n",
        "3  Barron's      1  \n",
        "4  Barron's      1  "
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_publisher_groupby = df_publisher.groupby(['Publisher','Category','Advertiser']).sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_publisher_groupby"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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></th>\n",
        "      <th></th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Publisher</th>\n",
        "      <th>Category</th>\n",
        "      <th>Advertiser</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"10\" valign=\"top\">Barron's</th>\n",
        "      <th>Financial Publishing</th>\n",
        "      <th>Barron's</th>\n",
        "      <td> 12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Financial Services</th>\n",
        "      <th>Jp Morgan Asset Management</th>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TIAA CREF Financial Services</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Information Management</th>\n",
        "      <th>Factiva</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Insurance Companies</th>\n",
        "      <th>Allianz</th>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Investment Firms</th>\n",
        "      <th>Barron's</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Newspapers</th>\n",
        "      <th>The Wall Street Journal</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Personal Investing</th>\n",
        "      <th>Northern Trust</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Security &amp; Commodity Exchange</th>\n",
        "      <th>Cboe</th>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Software</th>\n",
        "      <th>IBM</th>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"12\" valign=\"top\">Bloomberg</th>\n",
        "      <th>Airlines</th>\n",
        "      <th>Delta Airlines</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Education</th>\n",
        "      <th>Ie Business School</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Financial Publishing</th>\n",
        "      <th>Bloomberg</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">Financial Services</th>\n",
        "      <th>Capital One 360</th>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Jp Morgan Asset Management</th>\n",
        "      <td>  8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Merrill Lynch</th>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Fuel</th>\n",
        "      <th>Chevron</th>\n",
        "      <td>  6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Jewelry/Accessories</th>\n",
        "      <th>Tiffany &amp; Co.</th>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Software</th>\n",
        "      <th>IBM</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">Wireless Providers</th>\n",
        "      <th>AT&amp;T</th>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Jp Morgan Asset Management</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sprint</th>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"8\" valign=\"top\">Business Review</th>\n",
        "      <th rowspan=\"3\" valign=\"top\">Business Services</th>\n",
        "      <th>Accenture</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>HourlyNerd</th>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SAS Analytics</th>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Information Technology Solutions</th>\n",
        "      <th>Cognizant</th>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Splunk</th>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Personal Investing</th>\n",
        "      <th>TD Ameritrade</th>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Software</th>\n",
        "      <th>Ca Technologies</th>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Cisco</th>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <th>...</th>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"8\" valign=\"top\">Yahoo! Finance</th>\n",
        "      <th>Lending Services</th>\n",
        "      <th>Lendingtree</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Online Legal Services</th>\n",
        "      <th>Instant Checkmate</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Online News</th>\n",
        "      <th>The Motley Fool</th>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Personal Investing</th>\n",
        "      <th>TD Ameritrade</th>\n",
        "      <td> 21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Retail</th>\n",
        "      <th>Wallgreens</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Self Learning</th>\n",
        "      <th>The Pimsleur Approach</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Software</th>\n",
        "      <th>Teradata</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Wireless Providers</th>\n",
        "      <th>AT&amp;T</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"22\" valign=\"top\">ZeroHedge</th>\n",
        "      <th>Airlines</th>\n",
        "      <th>Jetblue</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Banks/Credit Cards</th>\n",
        "      <th>Ally Bank</th>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Capital One</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Car Manufacturers</th>\n",
        "      <th>Dodge Charger</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>chevrolet Malibu</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Clothing</th>\n",
        "      <th>Suitsupply</th>\n",
        "      <td>  8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Electricity</th>\n",
        "      <th>Transcanada</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">Financial Services</th>\n",
        "      <th>Charles Schwab</th>\n",
        "      <td>  6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DH Corporation</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>OptionsXpress</th>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ShareBuilder by Capital One</th>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Food</th>\n",
        "      <th>Hormel</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Fuel</th>\n",
        "      <th>Gilbarco Veeder-root</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Insurance Companies</th>\n",
        "      <th>Allstate</th>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Magazines</th>\n",
        "      <th>People Magazine</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mutual Fund</th>\n",
        "      <th>Vanguard</th>\n",
        "      <td>  6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">Personal Investing</th>\n",
        "      <th>Aberdeen</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Prodigy Network</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Trade Monster</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Tradestation</th>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Software</th>\n",
        "      <th>Atlassian Hipchat</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Microsoft Office 365</th>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>240 rows \u00d7 1 columns</p>\n",
        "</div>"
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       "text": [
        "                                                                               Count\n",
        "Publisher       Category                         Advertiser                         \n",
        "Barron's        Financial Publishing             Barron's                         12\n",
        "                Financial Services               Jp Morgan Asset Management        5\n",
        "                                                 TIAA CREF Financial Services      1\n",
        "                Information Management           Factiva                           1\n",
        "                Insurance Companies              Allianz                           9\n",
        "                Investment Firms                 Barron's                          1\n",
        "                Newspapers                       The Wall Street Journal           1\n",
        "                Personal Investing               Northern Trust                    1\n",
        "                Security & Commodity Exchange    Cboe                              2\n",
        "                Software                         IBM                               2\n",
        "Bloomberg       Airlines                         Delta Airlines                    1\n",
        "                Education                        Ie Business School                1\n",
        "                Financial Publishing             Bloomberg                         1\n",
        "                Financial Services               Capital One 360                   4\n",
        "                                                 Jp Morgan Asset Management        8\n",
        "                                                 Merrill Lynch                     4\n",
        "                Fuel                             Chevron                           6\n",
        "                Jewelry/Accessories              Tiffany & Co.                     2\n",
        "                Software                         IBM                               1\n",
        "                Wireless Providers               AT&T                              2\n",
        "                                                 Jp Morgan Asset Management        1\n",
        "                                                 Sprint                            2\n",
        "Business Review Business Services                Accenture                         1\n",
        "                                                 HourlyNerd                       11\n",
        "                                                 SAS Analytics                     5\n",
        "                Information Technology Solutions Cognizant                         3\n",
        "                                                 Splunk                            2\n",
        "                Personal Investing               TD Ameritrade                     3\n",
        "                Software                         Ca Technologies                   2\n",
        "                                                 Cisco                             2\n",
        "...                                                                              ...\n",
        "Yahoo! Finance  Lending Services                 Lendingtree                       1\n",
        "                Online Legal Services            Instant Checkmate                 1\n",
        "                Online News                      The Motley Fool                   2\n",
        "                Personal Investing               TD Ameritrade                    21\n",
        "                Retail                           Wallgreens                        1\n",
        "                Self Learning                    The Pimsleur Approach             1\n",
        "                Software                         Teradata                          1\n",
        "                Wireless Providers               AT&T                              1\n",
        "ZeroHedge       Airlines                         Jetblue                           1\n",
        "                Banks/Credit Cards               Ally Bank                         2\n",
        "                                                 Capital One                       1\n",
        "                Car Manufacturers                Dodge Charger                     1\n",
        "                                                 chevrolet Malibu                  1\n",
        "                Clothing                         Suitsupply                        8\n",
        "                Electricity                      Transcanada                       1\n",
        "                Financial Services               Charles Schwab                    6\n",
        "                                                 DH Corporation                    1\n",
        "                                                 OptionsXpress                     5\n",
        "                                                 ShareBuilder by Capital One       7\n",
        "                Food                             Hormel                            1\n",
        "                Fuel                             Gilbarco Veeder-root              1\n",
        "                Insurance Companies              Allstate                          5\n",
        "                Magazines                        People Magazine                   1\n",
        "                Mutual Fund                      Vanguard                          6\n",
        "                Personal Investing               Aberdeen                          1\n",
        "                                                 Prodigy Network                   1\n",
        "                                                 Trade Monster                     1\n",
        "                                                 Tradestation                      2\n",
        "                Software                         Atlassian Hipchat                 1\n",
        "                                                 Microsoft Office 365             10\n",
        "\n",
        "[240 rows x 1 columns]"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_publisher.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>Ad Type</th>\n",
        "      <th>Advertiser</th>\n",
        "      <th>Category</th>\n",
        "      <th>Date</th>\n",
        "      <th>Flag</th>\n",
        "      <th>Publisher</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>     Overlay</td>\n",
        "      <td>       Barron's</td>\n",
        "      <td> Financial Publishing</td>\n",
        "      <td> 12/1/14</td>\n",
        "      <td> house ad</td>\n",
        "      <td> Barron's</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>     Overlay</td>\n",
        "      <td>       Barron's</td>\n",
        "      <td> Financial Publishing</td>\n",
        "      <td> 12/1/14</td>\n",
        "      <td> house ad</td>\n",
        "      <td> Barron's</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> Standard Ad</td>\n",
        "      <td>        Allianz</td>\n",
        "      <td>  Insurance Companies</td>\n",
        "      <td> 12/1/14</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> Barron's</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> Standard Ad</td>\n",
        "      <td> Northern Trust</td>\n",
        "      <td>   Personal Investing</td>\n",
        "      <td> 12/2/14</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> Barron's</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>     Overlay</td>\n",
        "      <td>       Barron's</td>\n",
        "      <td> Financial Publishing</td>\n",
        "      <td> 12/3/14</td>\n",
        "      <td> house ad</td>\n",
        "      <td> Barron's</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "       Ad Type      Advertiser              Category     Date      Flag  \\\n",
        "0      Overlay        Barron's  Financial Publishing  12/1/14  house ad   \n",
        "1      Overlay        Barron's  Financial Publishing  12/1/14  house ad   \n",
        "2  Standard Ad         Allianz   Insurance Companies  12/1/14       NaN   \n",
        "3  Standard Ad  Northern Trust    Personal Investing  12/2/14       NaN   \n",
        "4      Overlay        Barron's  Financial Publishing  12/3/14  house ad   \n",
        "\n",
        "  Publisher  Count  \n",
        "0  Barron's      1  \n",
        "1  Barron's      1  \n",
        "2  Barron's      1  \n",
        "3  Barron's      1  \n",
        "4  Barron's      1  "
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_publisher.info()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 804 entries, 0 to 803\n",
        "Data columns (total 7 columns):\n",
        "Ad Type       804 non-null object\n",
        "Advertiser    804 non-null object\n",
        "Category      804 non-null object\n",
        "Date          804 non-null object\n",
        "Flag          30 non-null object\n",
        "Publisher     804 non-null object\n",
        "Count         804 non-null int64\n",
        "dtypes: int64(1), object(6)\n",
        "memory usage: 50.2+ KB\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_publisher[\"Category\"].value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "Financial Services                  209\n",
        "Personal Investing                  134\n",
        "Software                             67\n",
        "Insurance Companies                  51\n",
        "Health Industry                      35\n",
        "Information Technology Solutions     34\n",
        "Financial Publishing                 28\n",
        "Banks/Credit Cards                   27\n",
        "Airlines                             25\n",
        "Car Manufacturers                    22\n",
        "Fuel                                 20\n",
        "Business Services                    17\n",
        "Investment Firms                     16\n",
        "Jewelry/Accessories                  16\n",
        "Watch Manufacturing                  14\n",
        "Mutual Fund                          12\n",
        "Lending Services                     11\n",
        "Wireless Providers                   10\n",
        "Clothing                              9\n",
        "Education                             8\n",
        "Newspapers                            6\n",
        "Entertainment                         2\n",
        "Security & Commodity Exchange         2\n",
        "Beverage Industry                     2\n",
        "Electronics                           2\n",
        "Online News                           2\n",
        "Car Manufacturer                      1\n",
        "Electricity                           1\n",
        "Fast Food Industry                    1\n",
        "Special Interest Groups               1\n",
        "Celebrity News                        1\n",
        "Pet Protection                        1\n",
        "Stock Exchange                        1\n",
        "Online Legal Services                 1\n",
        "Whiskey Industry                      1\n",
        "Self Learning                         1\n",
        "Workspace Products                    1\n",
        "Steel Manufacturing                   1\n",
        "Information Management                1\n",
        "Chemicals                             1\n",
        "Aerospace                             1\n",
        "Sweetner                              1\n",
        "Superstore                            1\n",
        "Automotive Research                   1\n",
        "Options Exchange                      1\n",
        "Shipping                              1\n",
        "Retail                                1\n",
        "Magazines                             1\n",
        "Food                                  1\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_publisher.info()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 804 entries, 0 to 803\n",
        "Data columns (total 7 columns):\n",
        "Ad Type       804 non-null object\n",
        "Advertiser    804 non-null object\n",
        "Category      804 non-null object\n",
        "Date          804 non-null object\n",
        "Flag          30 non-null object\n",
        "Publisher     804 non-null object\n",
        "Count         804 non-null int64\n",
        "dtypes: int64(1), object(6)\n",
        "memory usage: 50.2+ KB\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Removing the Categories that occure the least \n",
      "df_publisher = df_publisher[df_publisher.Category!='Newspapers']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Entertainment']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Security & Commodity Exchange']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Beverage Industry']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Electronics']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Online News']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Car Manufacturer']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Electricity']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Fast Food Industry']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Special Interest Groups']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Celebrity News']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Pet Protectio']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Stock Exchange']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Online Legal Services']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Whiskey Industry']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Self Learning']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Workspace Products']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Steel Manufacturing']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Information Management']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Chemicals']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Aerospace']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Sweetner']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Superstore']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Automotive Research']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Options Exchange']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Shipping']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Retail']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Magazines']\n",
      "df_publisher = df_publisher[df_publisher.Category!='Food']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_publisher.info()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 767 entries, 0 to 803\n",
        "Data columns (total 7 columns):\n",
        "Ad Type       767 non-null object\n",
        "Advertiser    767 non-null object\n",
        "Category      767 non-null object\n",
        "Date          767 non-null object\n",
        "Flag          29 non-null object\n",
        "Publisher     767 non-null object\n",
        "Count         767 non-null int64\n",
        "dtypes: int64(1), object(6)\n",
        "memory usage: 47.9+ KB\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_publisher.Publisher.value_counts().plot(kind='bar',alpha=.30)\n",
      "plt.xlabel('Financial Publisher')\n",
      "plt.ylabel('Number of Occurances')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<matplotlib.text.Text at 0x107d02dd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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4FPghsEzSKwtq3gC8GfitpN9I+qKk15S085227wF2AWYCbwU+W1LzHGAT4HfA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\nTAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107c8ecd0>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Financial Services, Personal Investing, Software and Insurance Companies categories appear to be at the very top of the list for financial publishers to advertise. Let's look deeper and see which are the top advertisers within each of the 4 top categories."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_publisher.Category.value_counts().plot(kind='bar',alpha=.30)\n",
      "plt.xlabel('Categories')\n",
      "plt.ylabel('Number of Occurances')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "<matplotlib.text.Text at 0x107d8b590>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7hh2j83mmL+iLimPJ0Ft5sq9zvoVjrEzJyXYEn3XnGjkK2K/f599Hts4Ect2A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\nwMysYO4EzMwK9v929Q++W5zB9QAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107f188d0>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_publisher['Advertiser'].value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "TD Ameritrade                 75\n",
        "Charles Schwab                41\n",
        "IBM                           33\n",
        "Capital One Spark Business    23\n",
        "Cognizant                     21\n",
        "Jp Morgan Asset Management    21\n",
        "Air China                     20\n",
        "Chevron                       19\n",
        "Scottrade                     19\n",
        "Barron's                      18\n",
        "Fidelity                      18\n",
        "ETrade                        18\n",
        "Investor's Business Daily     16\n",
        "Tiffany & Co.                 16\n",
        "Rolex                         12\n",
        "...\n",
        "Wisdom Tree                     1\n",
        "Prodigy Network                 1\n",
        "Accenture                       1\n",
        "Aberdeen                        1\n",
        "T. Rowe Price                   1\n",
        "TIAA CREF Financial Services    1\n",
        "Forbes                          1\n",
        "Select Sector SPDRs             1\n",
        "MyCase                          1\n",
        "Jetblue                         1\n",
        "Healthcare Education Project    1\n",
        "Chevrolet                       1\n",
        "Gilbarco Veeder-root            1\n",
        "Toyota Corolla                  1\n",
        "Delta Airlines                  1\n",
        "Length: 143, dtype: int64"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_publisher['Ad Type'].value_counts().plot(kind='bar',alpha=.30)\n",
      "plt.xlabel(\"Advertising Type\")\n",
      "plt.ylabel(\"Number of Occurances\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "<matplotlib.text.Text at 0x1080fb110>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MOhccxyIylTORmSRhe3B3PJCpRiBpdUmTi9trAHsAfcDFwOHFww4HftLOHFXs\n+45M5VQxE9S7n3ksIlM5ncqUq2tofeDHkpoZzrF9uaSbgQskHUkxfDRTvhCWEHWLsCLL0hDYvhvo\nHmL/I8DuncoR1xGUE5nKXW8Bcc1FWZGpnE5liiuLQ1hOlT1L6e/vY/78kR8YZyn1VuuGoGqfciEy\nlVXFTNDZXGXPUso8ptNnKVX75A31zpT7grIQQgiZ1bohqOLc/5GpnCpmgmrmqmKm1jH7VVHnTLVu\nCEIIIdS8IahiP3NkKqeKmaCauaqYqc798WMRNYIQQggdUeuGoIp9p5GpnCpmgmrmqmKmOvfHj0XU\nCEIIIXRErRuCKvadRqZyqpgJqpmripnq3B8/FlEjCCGE0BG1bgiq2HcamcqpYiaoZq4qZqpzf/xY\nRI0ghBBCR9S6Iahi32lkKqeKmaCauaqYqc798WMRNYIQQggdUbmGQNKekuZLukPSJ9t5rCr2nUam\ncqqYCaqZq4qZ6twfPxadylSpaaglrQx8k7Q4zT3AbyRdbPv37TjeXXfNrdxpc2Qqp4qZoJq5Op2p\nzDoJV13108qtkTB37tzKdQ91KlOlGgJgZ+BO2wsAJJ0H7A20pSF46qlH2/Gy4xKZyqliJqhmrk5n\nKrNOwqRJt4z6mIlcI6FM43TllTey6qojH7PTjdOjj3bmd1e1hmBj4C8t2wuBV2XKEkJYQZRpnCZP\n7mzjVCVVawjcyYM9+OCCTh6ulMhUThUzQTVzRaZyOp2pzFnKZZddywYbjN74jPdMRXZH33tHJOnV\nwAzbexbbxwGLbH+x5THVCRxCCMsR2xpqf9UagknAH4A3AvcCNwEHt6tYHEIIoWJdQ7aflfRh4DJg\nZeB70QiEEEJ7VeqMIIQQQudV7oKyEELoJEmr586QW6W6htpFUt8Id9v2th0LU5DUOhTAQGsRx7b3\n6nAkJO3ICCO3bN/awTgASNqXxT+fpbLZ/lGnM7WStLLt53JmGEzSXsDPbC/KnaVJ0uuAE4AuFr/v\n2PZLM2baBTgDmAxsIqkb+H+2j8qYaX/bF462b8KPW4euIUldxc3mL/hs0hvLIQC22zqVxTCZeoqb\n7wQ2AH5QZDoYeMD2sRkyNRi5Iditc2kSSTNJmV4E7AL8srhrN+B622/vdKZWku4CLgLOtD0vZ5Ym\nSecArwH+D/i+7fmZIyHpD8CxwK3AQMNp+6GMmW4C9gN+anv7Yt/vbG+TMdOcZpaR9k34cevQEDRJ\nmmu7e9Dumd/sAAAaB0lEQVS+tv+QR8l0i+0dR9tXd5KuAA6zfV+xvSEwy/YemXOtBRwE9JIGOHwf\nONf245lzrU36UNFLakjPLHKNMnK9bXlutF2pi0Ml3WR759b3AEm32d4uQ5a3AG8FDgTOY3EPwWRg\nqu2d23n8utUIVJyiNjdey5JdMjmsLullzQ1JLwWy91lKmibpAEmHNb8yR9oEuL9l+wHgJZmyDLD9\nuO3v2N4F+CTwX8D9kmZJ2jxjrsdIZwTnAxuRzjznSDomU6SrJX1Z0msk7dD8ypSl6c/FewCSVpH0\ncdo0nU0J9wK3AE8X/za/Lgbe3O6D1+2MYEfSJ6O1i12PAkfk6PtuybQn8B3g7mJXF6mf8rKMmWYA\nuwLbAD8H3gJcZ3u/jJm+CWwJ/JDUeB8I3GH76FyZilyTgLcBR5B+d2eRMr4OOMn2lhky7U06E9ii\nyDPT9oNFUXSe7a4MmRoMXePpeHdjk6T1gP8hTXIp4HLgGNsPZ8z0PNvPdPy4dWoImiRNIRWqHpO0\nk+3fZM6zKrAV6T/KfGCK7Qcy5rkd2A641fZ2ktYHzrG9e65MRa53Af9WbF5j+8c588BAjaABnGH7\n+kH3fSNHQyVpFukanGuGuG9321d2OlMYmaQLbe8/zMCWtg9oqWtDsA2p//Qg4LEq9MdLWgfYl5Rr\na9sbZczyG9s7SboFeAPwODDf9ssz5ZkE3G57qxzHH4mkybn63Zcnkv4I3ABcC1xr+3cZs3xjhLtt\nu+PdZ5I2sn1vy8CWJTRnZG6XWgwfBZC0GemN/2DgX6TT+Fe2+wc8SqbVSdNsHwx0A2sB+5D+s+T0\nm6Jh+i5wM/AUcP3IT2mf4orzP0ja1PafcuVoJWk1UvfUI5J+BnwCeD1wJ/C5zKNhXgN8HZgKrEIq\nYj9pe61cmUjdjK8idZl9RdKWQJ/tfTJkuYXF3VSDa4RZPhnbvrf4d0GO49fijEDSr0n/IS4ELrB9\nl6S7bW+WMdO5pP8YlwMXAL8ircWQLdNQigZ0Ldu3Zc5xLbA9af6pp4rdWa63KPJcSPpAsQawDnA7\n8DPSG912OYe1FmdyB5H+rl4JHAa83Pb0jJkmkdYbeT2pe+8FwG22358rU1MxwmpRFc7scjXidTkj\neAB4BbA+aTz6XXnjALA18CBplMLvbT8n5R3AJGlr278fajSHpB1yFtWBz2Q89lC2tv2K4g1uoe1d\ni/2XSMraaALYvqPlYrczJc0FsjUEpO7FPuAUUj0l2xlTk6SdSMN91yq2HwWOtH1zxljfZIhGvN0H\nrUVDYHufokD8LuDEYljfOpJeZfvGTJm6JW1N6ha6WtJfgcmSNrB9/yhPb5f/AN5H+s861KlithEe\nthu5jj2MZ2Cg2+q+QfflvqL3KUnPB26T9CXSsNvcw6QPJp0JHAW8T9L1pIJ/zsL194GjbF8LA1c/\nfx/o+EwDrXI04rXoGhqsGAVzAOmPcxPbm2SOhKRXFnn2J33C3CVjllVtPz3avg5nap4ybw08n8z9\n3kXDfS6Lh7K2XgR0oO0X5chVZOsinQWvAnyU9In3W7bvzJWpSdJWpAunjgVeZHvVjFmGuor3VtvZ\nrm+QdA3wJtLUF/eRGvHD232RWy0bglaSunIWjAeTtBLwb7Z/lTHDUv8ZKvAfpFL93pJ6WbLguMRt\n27Ny5KoqSReRBkT8EbiGNCDiJtv/yJClOUrwUGA1UoMOqUF/2vZHO52pSdKmpC7jjjbitW8IwmLF\ntA0bAecA72bxG9xawOk5h282p92Q9NvmmGoNMWVInQ0zBr2p7WPRR1L0x9/qCkzQN+jitqEa8Sxd\noEW9aZbtQzp97FrUCEJpbwYOBzYGvtqy/wng+CyJFqtiv3fVNBetHTybbRXcBnxY0uuL7Qbpw0XH\nr6K13dPpY5ZR1Js2lfR82//s5LHjjCAsRdK+ti/KnaNVlfu9q6T4VHlFzqkbhiLpe6QPnrNIjdSh\nwLO2/z1zrreThmoO1Cpsn5gxz9mkWQYuBv6+OJJPaedxa3FGMOhKwqHm/s81EVdzeol9WXqe9o7/\nMUo61PbZQJek/2i9iw78MY7E9oLijGAT0rTPf7D9r1x5qqr4VLlI0hTbj+bO02KnQV1TV0n6bbY0\ngKRvk2oEbyBdPLk/kGUUYYs/Fl8rAWt26qC1aAhIVxJCms9+KmlGRpF+8dkudS/8lDT5XXPmwZya\ns55OJtMVlsNRWr9hFtC8svglkg7PVVSv8ocL0gV3fUpTd7defJcz07OSNm+ewSnNuPtsxjwAu9ie\nVtSdPivpq8ClmTPNs31B6w5JB7T7oLVoCGzPBJD0QeB1zX5JSacB12WMBrCx7bZPM1uG7W8XN6+0\nvcTPRS3Td2dyCrCH7T8UebYkDdnMNZKpyh8uflR8DVUQzeUTwC8ltc6ye0S+OAA0Ryz9XdLGwMOk\nRaJyOo40Mm60fROqFg1BiymkvuXmNLOTi305XS9pW9tZT5MH+QZpOodWXyffmy7ApGYjAGC7v+gP\nz6LKHy5szyzmsXqJK7A6GYDtq4rG++WkRukPnS6IDuFnxZxaX2Zxw/7dHEG0eGGajSV9nSUXpml7\nQb1uDcHJwK2Srib9oHcFZmRNlK62PKL4pNT8j5FlqF9x0dYuwHpFjaD1j3HlTucZ5BZJZ7B4Sc9D\nSBPi5Va5DxdKaxZ/mXThXZek7YHP5piXScOvOb25pKxrTrfU4S6S9HNg1Yx1lebCNHsV/zZ/Xk+Q\nBke0VW0aguJCrX7g1aTJ3gxMd7H0YUZvKf4dbjbETlqFxW/6k1v2P05a2zWnDwIfApr93NcC38oX\nZ0Dzw0Wj2K7Ch4sZpL/xqwFsz1Fa+S6HdzDCmtOkLqyOammchrovS+Nk+zZJvyN1f3b8YsRaDR+t\n6gVIkrpJZwYmzdWee6bPTV1M9yxpZWBNp6UPsypGDQ0s4FOVUUPFhXjNNWVvzDhXVDPPjbZfpSXX\n4v1tjrPMlkyVWXNa0kxGaJycd+bY64A3drrbrG5rFl8paT/lnuazhaSPkLo71iPNjvoD5VtXtukL\nktaStAZpxsh5kv4zZyBJbyPN9f91Ug3jj5LemjMTDJxp7k6aevqnwCqS2rrQeAm/k3QIMEnSFsUI\np2zrSRQqs+a07V7bR5DOgKfa3tf2vqQ1E1bJkanF3cB1kj4j6WPF13+M+qxxqtsZwZOkIZLPsXio\npnNNXFZk6gNebfupYnsN4Abb0zJmus1picpDSAXi6aTpAXJm+gPwtkHDD3/hTKumteQ6nTTb6G62\nt5a0LnC57VdmzLQG8Cmg+Wn7MtJiOTknDazcmtOS5pOmE3exvRJp+GbOqVRmFDcHT3vx2XYetzY1\nAgDbHbtAY4wWDXM7l0mSnkdaLe1/bT8jKfcnhscHXUV8F6l2kdurbG8vaQ6A7UeKn11OL7d9PPmn\nBWl1NPBO0sI0Br7t/GtOXwlcJqm1cboiZyDbMwAkTS62O7JYTq0aAqC5NvAWLHlJ+VKLfHfQmcCN\nkn5E+mPchzQnek7fBhYAvwWuKaZ3yFIjKAp7ADdL+gWLx1PvTzVGDf2rqKMAIGk98jfmp0jagLQi\n3/m2b8+ch+JTd/P6hqqoXOM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WSPo3UrfLuwEkvQLYtuV5PyatSXswqVEAuArYT9J6xXPW\nlfSSYY77BGm++iVI2pDUaJwDfAUYy9TFNxTfD6QunrE4CziHxd0/TQcWuV4HPFqcoVwGHNOSuXbT\nK4dyoiEIy4uDSG/qrS4q9p8GrClpHvBZ4ObmA2w/CswDXmL75mLf74FPA5dLug24HNig+ZRBx/gO\nacGSqwbtnwbcWHS7/Bfw+SEye9Dt5vaxwH8UBemXMfo6ua2v80PSBGmDu5SelnQr8C3gyGLf54Dn\nFUXp20k/mxCWEsNHQ+gwSavZ/kdx+yDgQNvvHOVpzefuB7zD9uEt+yZkuGyor6gRhNB5O0r6Jqmu\n8TfgvWWeVAx9fTPw1jZmCzUUZwQhhFBzUSMIIYSai4YghBBqLhqCEEKouWgIQgih5qIhCCGEmouG\nIIQQau7/A+OnAz1VhSLfAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107f4c050>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Advertiser by Industry\n",
      "\n",
      "financial_services = pd.read_csv('/Users/olehdubno/Desktop/Moat/moat_advertisers/Advertisers By Industry Financial Services.csv')\n",
      "personal_investing = pd.read_csv('/Users/olehdubno/Desktop/Moat/moat_advertisers/Advertisers By Industry Personal Investing.csv')\n",
      "insurance_companies = pd.read_csv('/Users/olehdubno/Desktop/Moat/moat_advertisers/Advertisers By Industry - Insurance Companies.csv')\n",
      "software = pd.read_csv('/Users/olehdubno/Desktop/Moat/moat_advertisers/Advertisers By Industry Software.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Percentage In-Play\n",
      "Advertisers which are likely to be working closely with a specific publisher are identified as In-Play for that publisher.\n",
      "\n",
      "These advertisers are typically buying ad impressions directly from the publisher, instead of through a network or exchange.\n",
      "\n",
      "Advertisers considered In-Play are reported on advertiser and publisher pages.\n",
      "\n",
      "####Activity\n",
      "Activity is an indicator of total impression volume observed from an advertiser. The more impressions an advertiser is observed running on a publisher, the higher the advertiser's Activity will be for that publisher.\n",
      "\n",
      "####Tags\n",
      "Any third-party which plays a technical role in serving ads or measuring their effectiveness, including Ad Networks, Ad Servers, and Ad Verification providers.\n",
      "\n",
      "####Creatives\n",
      "Total ads in circulation\n",
      "\n",
      "####Trend\n",
      "Change in rank since last period\n",
      "\n",
      "####Rank\n",
      "Indiates greatest Activity. (Activity Rank)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "financial_services.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>Rank</th>\n",
        "      <th>Trend</th>\n",
        "      <th>Advertiser</th>\n",
        "      <th>Industry</th>\n",
        "      <th>Activity</th>\n",
        "      <th>Percentage In-Play</th>\n",
        "      <th>Creatives</th>\n",
        "      <th>Publishers</th>\n",
        "      <th>Tags</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Merrill Lynch</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td> 100.00</td>\n",
        "      <td> 28.67</td>\n",
        "      <td> 92</td>\n",
        "      <td> 1298</td>\n",
        "      <td> 29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td> 0</td>\n",
        "      <td>  Merrill Edge</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td>  79.15</td>\n",
        "      <td> 23.51</td>\n",
        "      <td> 86</td>\n",
        "      <td> 1253</td>\n",
        "      <td> 28</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3</td>\n",
        "      <td> 5</td>\n",
        "      <td>       Equifax</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td>  18.78</td>\n",
        "      <td> 35.86</td>\n",
        "      <td> 39</td>\n",
        "      <td>  869</td>\n",
        "      <td> 26</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4</td>\n",
        "      <td> 5</td>\n",
        "      <td>     Moneygram</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td>  14.73</td>\n",
        "      <td>  0.67</td>\n",
        "      <td> 49</td>\n",
        "      <td> 1408</td>\n",
        "      <td> 31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 5</td>\n",
        "      <td>-2</td>\n",
        "      <td>          Usaa</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td>  11.38</td>\n",
        "      <td> 72.79</td>\n",
        "      <td> 45</td>\n",
        "      <td>  144</td>\n",
        "      <td> 13</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "   Rank  Trend     Advertiser            Industry  Activity  \\\n",
        "0     1      0  Merrill Lynch  Financial Services    100.00   \n",
        "1     2      0   Merrill Edge  Financial Services     79.15   \n",
        "2     3      5        Equifax  Financial Services     18.78   \n",
        "3     4      5      Moneygram  Financial Services     14.73   \n",
        "4     5     -2           Usaa  Financial Services     11.38   \n",
        "\n",
        "   Percentage In-Play  Creatives  Publishers  Tags  \n",
        "0               28.67         92        1298    29  \n",
        "1               23.51         86        1253    28  \n",
        "2               35.86         39         869    26  \n",
        "3                0.67         49        1408    31  \n",
        "4               72.79         45         144    13  "
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_advertiser = pd.concat([financial_services,personal_investing,insurance_companies,software],ignore_index=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_advertiser['Count'] = 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_advertiser.Industry.value_counts().plot(kind='bar',alpha =.30)\n",
      "plt.xlabel(\"Category\")\n",
      "plt.ylabel('Number of Occurances')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "<matplotlib.text.Text at 0x1080bc9d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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LT7Ss5LOs5LOcLvTs1zf08ijg3cA2wIem+PqeQ4koIiKKW2ext/0F4AuS3m376DmMqTXS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\nStJ/9rOvTXJmPzO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5jjFiLqTYR2epWnniK8C3bW9n+6nA24Gt1vGWBwFvnoO4sthIzLkU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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10842f6d0>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_advertiser.groupby(['Industry','Advertiser']).sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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></th>\n",
        "      <th>Rank</th>\n",
        "      <th>Trend</th>\n",
        "      <th>Activity</th>\n",
        "      <th>Percentage In-Play</th>\n",
        "      <th>Creatives</th>\n",
        "      <th>Publishers</th>\n",
        "      <th>Tags</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Industry</th>\n",
        "      <th>Advertiser</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"18\" valign=\"top\">Financial Services</th>\n",
        "      <th>Advance America</th>\n",
        "      <td>  9</td>\n",
        "      <td> -3</td>\n",
        "      <td>   8.81</td>\n",
        "      <td>  2.93</td>\n",
        "      <td>  49</td>\n",
        "      <td>  688</td>\n",
        "      <td> 37</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ameriprise Financial</th>\n",
        "      <td> 17</td>\n",
        "      <td>  0</td>\n",
        "      <td>   0.26</td>\n",
        "      <td> 84.84</td>\n",
        "      <td>  14</td>\n",
        "      <td>   25</td>\n",
        "      <td> 14</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Barclays</th>\n",
        "      <td> 11</td>\n",
        "      <td> -4</td>\n",
        "      <td>   2.22</td>\n",
        "      <td> 45.51</td>\n",
        "      <td>  18</td>\n",
        "      <td>   17</td>\n",
        "      <td>  6</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Bloomberg</th>\n",
        "      <td> 16</td>\n",
        "      <td> -1</td>\n",
        "      <td>   0.62</td>\n",
        "      <td> 41.23</td>\n",
        "      <td>  57</td>\n",
        "      <td>   36</td>\n",
        "      <td>  8</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CFP</th>\n",
        "      <td> 18</td>\n",
        "      <td> -2</td>\n",
        "      <td>   0.05</td>\n",
        "      <td>  0.00</td>\n",
        "      <td>   8</td>\n",
        "      <td>   32</td>\n",
        "      <td> 39</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Credit Suisse</th>\n",
        "      <td> 13</td>\n",
        "      <td> -1</td>\n",
        "      <td>   2.06</td>\n",
        "      <td> 45.59</td>\n",
        "      <td>  19</td>\n",
        "      <td>   51</td>\n",
        "      <td> 18</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equifax</th>\n",
        "      <td>  3</td>\n",
        "      <td>  5</td>\n",
        "      <td>  18.78</td>\n",
        "      <td> 35.86</td>\n",
        "      <td>  39</td>\n",
        "      <td>  869</td>\n",
        "      <td> 26</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>GE Capital</th>\n",
        "      <td> 12</td>\n",
        "      <td> -1</td>\n",
        "      <td>   2.16</td>\n",
        "      <td> 92.88</td>\n",
        "      <td>  14</td>\n",
        "      <td>   24</td>\n",
        "      <td>  7</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mass Mutual Financial Group</th>\n",
        "      <td> 15</td>\n",
        "      <td> -1</td>\n",
        "      <td>   0.83</td>\n",
        "      <td> 62.11</td>\n",
        "      <td>  23</td>\n",
        "      <td>   10</td>\n",
        "      <td>  4</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Merrill Edge</th>\n",
        "      <td>  2</td>\n",
        "      <td>  0</td>\n",
        "      <td>  79.15</td>\n",
        "      <td> 23.51</td>\n",
        "      <td>  86</td>\n",
        "      <td> 1253</td>\n",
        "      <td> 28</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Merrill Lynch</th>\n",
        "      <td>  1</td>\n",
        "      <td>  0</td>\n",
        "      <td> 100.00</td>\n",
        "      <td> 28.67</td>\n",
        "      <td>  92</td>\n",
        "      <td> 1298</td>\n",
        "      <td> 29</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Moneygram</th>\n",
        "      <td>  4</td>\n",
        "      <td>  5</td>\n",
        "      <td>  14.73</td>\n",
        "      <td>  0.67</td>\n",
        "      <td>  49</td>\n",
        "      <td> 1408</td>\n",
        "      <td> 31</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Pimco</th>\n",
        "      <td>  6</td>\n",
        "      <td> -1</td>\n",
        "      <td>  10.37</td>\n",
        "      <td> 39.02</td>\n",
        "      <td>  27</td>\n",
        "      <td>  465</td>\n",
        "      <td> 18</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ul</th>\n",
        "      <td> 10</td>\n",
        "      <td>  3</td>\n",
        "      <td>   2.55</td>\n",
        "      <td> 84.09</td>\n",
        "      <td>  38</td>\n",
        "      <td>   81</td>\n",
        "      <td>  9</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Usaa</th>\n",
        "      <td>  5</td>\n",
        "      <td> -2</td>\n",
        "      <td>  11.38</td>\n",
        "      <td> 72.79</td>\n",
        "      <td>  45</td>\n",
        "      <td>  144</td>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Veterans United Home Loans</th>\n",
        "      <td> 14</td>\n",
        "      <td>  0</td>\n",
        "      <td>   1.98</td>\n",
        "      <td>  8.72</td>\n",
        "      <td>   6</td>\n",
        "      <td>  235</td>\n",
        "      <td> 22</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Vulcan Inc.</th>\n",
        "      <td>  7</td>\n",
        "      <td>  3</td>\n",
        "      <td>   9.90</td>\n",
        "      <td> 71.67</td>\n",
        "      <td>  66</td>\n",
        "      <td>  269</td>\n",
        "      <td> 14</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Zurich</th>\n",
        "      <td>  8</td>\n",
        "      <td> -4</td>\n",
        "      <td>   9.15</td>\n",
        "      <td> 99.11</td>\n",
        "      <td>  34</td>\n",
        "      <td>   27</td>\n",
        "      <td>  5</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"12\" valign=\"top\">Insurance Company</th>\n",
        "      <th>AAA insurance</th>\n",
        "      <td> 22</td>\n",
        "      <td> -1</td>\n",
        "      <td>   2.08</td>\n",
        "      <td> 65.66</td>\n",
        "      <td>  60</td>\n",
        "      <td>  543</td>\n",
        "      <td> 27</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Aetna</th>\n",
        "      <td>  7</td>\n",
        "      <td>  0</td>\n",
        "      <td>  27.11</td>\n",
        "      <td> 43.49</td>\n",
        "      <td>  83</td>\n",
        "      <td> 1509</td>\n",
        "      <td> 24</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Aflac</th>\n",
        "      <td> 21</td>\n",
        "      <td>  2</td>\n",
        "      <td>   2.16</td>\n",
        "      <td> 41.72</td>\n",
        "      <td>  35</td>\n",
        "      <td>  308</td>\n",
        "      <td> 12</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Allianz</th>\n",
        "      <td> 20</td>\n",
        "      <td>  2</td>\n",
        "      <td>   2.64</td>\n",
        "      <td> 61.97</td>\n",
        "      <td>  57</td>\n",
        "      <td>  223</td>\n",
        "      <td> 17</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Allstate</th>\n",
        "      <td>  1</td>\n",
        "      <td>  1</td>\n",
        "      <td> 100.00</td>\n",
        "      <td> 22.69</td>\n",
        "      <td> 281</td>\n",
        "      <td> 4058</td>\n",
        "      <td> 33</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>American Family Insurance</th>\n",
        "      <td>  6</td>\n",
        "      <td>  6</td>\n",
        "      <td>  33.02</td>\n",
        "      <td> 19.23</td>\n",
        "      <td>  89</td>\n",
        "      <td> 2016</td>\n",
        "      <td> 37</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Bluecross Blueshield</th>\n",
        "      <td> 14</td>\n",
        "      <td> -3</td>\n",
        "      <td>  11.81</td>\n",
        "      <td> 37.62</td>\n",
        "      <td> 227</td>\n",
        "      <td> 1800</td>\n",
        "      <td> 33</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Cigna</th>\n",
        "      <td> 16</td>\n",
        "      <td>  8</td>\n",
        "      <td>   4.70</td>\n",
        "      <td>  5.57</td>\n",
        "      <td>  40</td>\n",
        "      <td>  891</td>\n",
        "      <td> 23</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Cuidadodesalud.gov</th>\n",
        "      <td> 25</td>\n",
        "      <td>  2</td>\n",
        "      <td>   0.71</td>\n",
        "      <td>  2.25</td>\n",
        "      <td>   7</td>\n",
        "      <td>  221</td>\n",
        "      <td> 30</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Esurance</th>\n",
        "      <td> 24</td>\n",
        "      <td>  1</td>\n",
        "      <td>   0.73</td>\n",
        "      <td> 34.92</td>\n",
        "      <td>  23</td>\n",
        "      <td>  318</td>\n",
        "      <td> 20</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Farmers Insurance</th>\n",
        "      <td> 10</td>\n",
        "      <td> -1</td>\n",
        "      <td>  15.89</td>\n",
        "      <td> 12.27</td>\n",
        "      <td>  58</td>\n",
        "      <td> 2134</td>\n",
        "      <td> 31</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Fidelity</th>\n",
        "      <td>  9</td>\n",
        "      <td> -1</td>\n",
        "      <td>  20.78</td>\n",
        "      <td> 57.13</td>\n",
        "      <td> 243</td>\n",
        "      <td> 1770</td>\n",
        "      <td> 24</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"30\" valign=\"top\">Software</th>\n",
        "      <th>Apple</th>\n",
        "      <td> 25</td>\n",
        "      <td> -5</td>\n",
        "      <td>   0.25</td>\n",
        "      <td> 73.76</td>\n",
        "      <td>  15</td>\n",
        "      <td>  112</td>\n",
        "      <td> 17</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ca Technologies</th>\n",
        "      <td> 23</td>\n",
        "      <td> -4</td>\n",
        "      <td>   0.30</td>\n",
        "      <td> 92.89</td>\n",
        "      <td>  58</td>\n",
        "      <td>  147</td>\n",
        "      <td>  9</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Carbonite</th>\n",
        "      <td> 19</td>\n",
        "      <td>  3</td>\n",
        "      <td>   2.09</td>\n",
        "      <td> 82.34</td>\n",
        "      <td>  72</td>\n",
        "      <td>  708</td>\n",
        "      <td> 19</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Cisco</th>\n",
        "      <td>  6</td>\n",
        "      <td>  3</td>\n",
        "      <td>  13.42</td>\n",
        "      <td> 51.48</td>\n",
        "      <td>  97</td>\n",
        "      <td> 1147</td>\n",
        "      <td> 19</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Cisco Webex</th>\n",
        "      <td> 16</td>\n",
        "      <td>  2</td>\n",
        "      <td>   2.38</td>\n",
        "      <td>  0.00</td>\n",
        "      <td>  10</td>\n",
        "      <td>  666</td>\n",
        "      <td> 27</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Citrix</th>\n",
        "      <td> 11</td>\n",
        "      <td>  3</td>\n",
        "      <td>   5.54</td>\n",
        "      <td> 47.06</td>\n",
        "      <td> 199</td>\n",
        "      <td> 2256</td>\n",
        "      <td> 26</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>E-verify</th>\n",
        "      <td> 12</td>\n",
        "      <td> -6</td>\n",
        "      <td>   5.09</td>\n",
        "      <td> 30.76</td>\n",
        "      <td>  63</td>\n",
        "      <td> 1224</td>\n",
        "      <td> 28</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Emc2</th>\n",
        "      <td> 14</td>\n",
        "      <td>  2</td>\n",
        "      <td>   3.07</td>\n",
        "      <td> 73.16</td>\n",
        "      <td> 205</td>\n",
        "      <td>  962</td>\n",
        "      <td> 15</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Film Fanatic</th>\n",
        "      <td> 31</td>\n",
        "      <td> -4</td>\n",
        "      <td>   0.02</td>\n",
        "      <td>  8.43</td>\n",
        "      <td>  11</td>\n",
        "      <td>   24</td>\n",
        "      <td> 12</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Google</th>\n",
        "      <td>  2</td>\n",
        "      <td> -1</td>\n",
        "      <td>  97.37</td>\n",
        "      <td> 13.92</td>\n",
        "      <td> 341</td>\n",
        "      <td> 5666</td>\n",
        "      <td> 18</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Google Play</th>\n",
        "      <td> 10</td>\n",
        "      <td>  2</td>\n",
        "      <td>   7.20</td>\n",
        "      <td> 81.15</td>\n",
        "      <td>  76</td>\n",
        "      <td>  428</td>\n",
        "      <td> 10</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Google Software</th>\n",
        "      <td>  8</td>\n",
        "      <td> -3</td>\n",
        "      <td>  10.25</td>\n",
        "      <td> 56.41</td>\n",
        "      <td> 159</td>\n",
        "      <td> 1351</td>\n",
        "      <td> 12</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>IBM</th>\n",
        "      <td>  3</td>\n",
        "      <td>  0</td>\n",
        "      <td>  43.27</td>\n",
        "      <td> 47.57</td>\n",
        "      <td> 379</td>\n",
        "      <td> 2004</td>\n",
        "      <td> 24</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Join.me</th>\n",
        "      <td> 22</td>\n",
        "      <td>  1</td>\n",
        "      <td>   0.38</td>\n",
        "      <td>  3.59</td>\n",
        "      <td>   8</td>\n",
        "      <td>  316</td>\n",
        "      <td> 21</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LifeLock</th>\n",
        "      <td>  9</td>\n",
        "      <td>  1</td>\n",
        "      <td>   7.26</td>\n",
        "      <td> 10.26</td>\n",
        "      <td>  61</td>\n",
        "      <td> 2052</td>\n",
        "      <td> 34</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Log Me In Rescue</th>\n",
        "      <td> 28</td>\n",
        "      <td>  0</td>\n",
        "      <td>   0.18</td>\n",
        "      <td> 75.00</td>\n",
        "      <td>   5</td>\n",
        "      <td>  162</td>\n",
        "      <td> 11</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Manage Engine</th>\n",
        "      <td> 21</td>\n",
        "      <td> -4</td>\n",
        "      <td>   0.86</td>\n",
        "      <td> 31.78</td>\n",
        "      <td>  64</td>\n",
        "      <td>  122</td>\n",
        "      <td>  9</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Microsoft</th>\n",
        "      <td>  1</td>\n",
        "      <td>  1</td>\n",
        "      <td> 100.00</td>\n",
        "      <td> 46.31</td>\n",
        "      <td> 757</td>\n",
        "      <td> 6314</td>\n",
        "      <td> 43</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Microsoft Software</th>\n",
        "      <td>  4</td>\n",
        "      <td>  4</td>\n",
        "      <td>  21.03</td>\n",
        "      <td>  7.65</td>\n",
        "      <td> 127</td>\n",
        "      <td> 3860</td>\n",
        "      <td> 39</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Norton</th>\n",
        "      <td>  5</td>\n",
        "      <td>  2</td>\n",
        "      <td>  15.96</td>\n",
        "      <td> 24.02</td>\n",
        "      <td>  51</td>\n",
        "      <td> 2771</td>\n",
        "      <td> 32</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Redhat</th>\n",
        "      <td> 24</td>\n",
        "      <td> -3</td>\n",
        "      <td>   0.27</td>\n",
        "      <td> 99.44</td>\n",
        "      <td>  30</td>\n",
        "      <td>   30</td>\n",
        "      <td>  2</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Rescue</th>\n",
        "      <td> 27</td>\n",
        "      <td>  0</td>\n",
        "      <td>   0.19</td>\n",
        "      <td> 72.86</td>\n",
        "      <td>   1</td>\n",
        "      <td>  165</td>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Safecount.net</th>\n",
        "      <td> 26</td>\n",
        "      <td>  0</td>\n",
        "      <td>   0.21</td>\n",
        "      <td>  0.00</td>\n",
        "      <td>  11</td>\n",
        "      <td>  225</td>\n",
        "      <td> 37</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sage</th>\n",
        "      <td> 30</td>\n",
        "      <td> -5</td>\n",
        "      <td>   0.04</td>\n",
        "      <td> 63.49</td>\n",
        "      <td>  42</td>\n",
        "      <td>   44</td>\n",
        "      <td> 10</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Skype</th>\n",
        "      <td> 20</td>\n",
        "      <td>  0</td>\n",
        "      <td>   1.66</td>\n",
        "      <td> 55.32</td>\n",
        "      <td>  28</td>\n",
        "      <td>   30</td>\n",
        "      <td> 11</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Visual Studio</th>\n",
        "      <td> 29</td>\n",
        "      <td>  0</td>\n",
        "      <td>   0.06</td>\n",
        "      <td> 99.77</td>\n",
        "      <td>   4</td>\n",
        "      <td>   14</td>\n",
        "      <td>  1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Windows</th>\n",
        "      <td> 17</td>\n",
        "      <td> 11</td>\n",
        "      <td>   2.13</td>\n",
        "      <td> 78.59</td>\n",
        "      <td>  21</td>\n",
        "      <td>  223</td>\n",
        "      <td> 14</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Xfinity</th>\n",
        "      <td>  7</td>\n",
        "      <td> -3</td>\n",
        "      <td>  11.24</td>\n",
        "      <td> 41.17</td>\n",
        "      <td> 247</td>\n",
        "      <td> 1895</td>\n",
        "      <td> 34</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Zendesk</th>\n",
        "      <td> 18</td>\n",
        "      <td> -5</td>\n",
        "      <td>   2.13</td>\n",
        "      <td> 19.69</td>\n",
        "      <td>  70</td>\n",
        "      <td> 1591</td>\n",
        "      <td> 21</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>iLivid</th>\n",
        "      <td> 15</td>\n",
        "      <td>  0</td>\n",
        "      <td>   2.99</td>\n",
        "      <td> 50.13</td>\n",
        "      <td> 117</td>\n",
        "      <td>  174</td>\n",
        "      <td> 21</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>82 rows \u00d7 8 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "                                                Rank  Trend  Activity  \\\n",
        "Industry           Advertiser                                           \n",
        "Financial Services Advance America                 9     -3      8.81   \n",
        "                   Ameriprise Financial           17      0      0.26   \n",
        "                   Barclays                       11     -4      2.22   \n",
        "                   Bloomberg                      16     -1      0.62   \n",
        "                   CFP                            18     -2      0.05   \n",
        "                   Credit Suisse                  13     -1      2.06   \n",
        "                   Equifax                         3      5     18.78   \n",
        "                   GE Capital                     12     -1      2.16   \n",
        "                   Mass Mutual Financial Group    15     -1      0.83   \n",
        "                   Merrill Edge                    2      0     79.15   \n",
        "                   Merrill Lynch                   1      0    100.00   \n",
        "                   Moneygram                       4      5     14.73   \n",
        "                   Pimco                           6     -1     10.37   \n",
        "                   Ul                             10      3      2.55   \n",
        "                   Usaa                            5     -2     11.38   \n",
        "                   Veterans United Home Loans     14      0      1.98   \n",
        "                   Vulcan Inc.                     7      3      9.90   \n",
        "                   Zurich                          8     -4      9.15   \n",
        "Insurance Company  AAA insurance                  22     -1      2.08   \n",
        "                   Aetna                           7      0     27.11   \n",
        "                   Aflac                          21      2      2.16   \n",
        "                   Allianz                        20      2      2.64   \n",
        "                   Allstate                        1      1    100.00   \n",
        "                   American Family Insurance       6      6     33.02   \n",
        "                   Bluecross Blueshield           14     -3     11.81   \n",
        "                   Cigna                          16      8      4.70   \n",
        "                   Cuidadodesalud.gov             25      2      0.71   \n",
        "                   Esurance                       24      1      0.73   \n",
        "                   Farmers Insurance              10     -1     15.89   \n",
        "                   Fidelity                        9     -1     20.78   \n",
        "...                                              ...    ...       ...   \n",
        "Software           Apple                          25     -5      0.25   \n",
        "                   Ca Technologies                23     -4      0.30   \n",
        "                   Carbonite                      19      3      2.09   \n",
        "                   Cisco                           6      3     13.42   \n",
        "                   Cisco Webex                    16      2      2.38   \n",
        "                   Citrix                         11      3      5.54   \n",
        "                   E-verify                       12     -6      5.09   \n",
        "                   Emc2                           14      2      3.07   \n",
        "                   Film Fanatic                   31     -4      0.02   \n",
        "                   Google                          2     -1     97.37   \n",
        "                   Google Play                    10      2      7.20   \n",
        "                   Google Software                 8     -3     10.25   \n",
        "                   IBM                             3      0     43.27   \n",
        "                   Join.me                        22      1      0.38   \n",
        "                   LifeLock                        9      1      7.26   \n",
        "                   Log Me In Rescue               28      0      0.18   \n",
        "                   Manage Engine                  21     -4      0.86   \n",
        "                   Microsoft                       1      1    100.00   \n",
        "                   Microsoft Software              4      4     21.03   \n",
        "                   Norton                          5      2     15.96   \n",
        "                   Redhat                         24     -3      0.27   \n",
        "                   Rescue                         27      0      0.19   \n",
        "                   Safecount.net                  26      0      0.21   \n",
        "                   Sage                           30     -5      0.04   \n",
        "                   Skype                          20      0      1.66   \n",
        "                   Visual Studio                  29      0      0.06   \n",
        "                   Windows                        17     11      2.13   \n",
        "                   Xfinity                         7     -3     11.24   \n",
        "                   Zendesk                        18     -5      2.13   \n",
        "                   iLivid                         15      0      2.99   \n",
        "\n",
        "                                                Percentage In-Play  Creatives  \\\n",
        "Industry           Advertiser                                                   \n",
        "Financial Services Advance America                            2.93         49   \n",
        "                   Ameriprise Financial                      84.84         14   \n",
        "                   Barclays                                  45.51         18   \n",
        "                   Bloomberg                                 41.23         57   \n",
        "                   CFP                                        0.00          8   \n",
        "                   Credit Suisse                             45.59         19   \n",
        "                   Equifax                                   35.86         39   \n",
        "                   GE Capital                                92.88         14   \n",
        "                   Mass Mutual Financial Group               62.11         23   \n",
        "                   Merrill Edge                              23.51         86   \n",
        "                   Merrill Lynch                             28.67         92   \n",
        "                   Moneygram                                  0.67         49   \n",
        "                   Pimco                                     39.02         27   \n",
        "                   Ul                                        84.09         38   \n",
        "                   Usaa                                      72.79         45   \n",
        "                   Veterans United Home Loans                 8.72          6   \n",
        "                   Vulcan Inc.                               71.67         66   \n",
        "                   Zurich                                    99.11         34   \n",
        "Insurance Company  AAA insurance                             65.66         60   \n",
        "                   Aetna                                     43.49         83   \n",
        "                   Aflac                                     41.72         35   \n",
        "                   Allianz                                   61.97         57   \n",
        "                   Allstate                                  22.69        281   \n",
        "                   American Family Insurance                 19.23         89   \n",
        "                   Bluecross Blueshield                      37.62        227   \n",
        "                   Cigna                                      5.57         40   \n",
        "                   Cuidadodesalud.gov                         2.25          7   \n",
        "                   Esurance                                  34.92         23   \n",
        "                   Farmers Insurance                         12.27         58   \n",
        "                   Fidelity                                  57.13        243   \n",
        "...                                                            ...        ...   \n",
        "Software           Apple                                     73.76         15   \n",
        "                   Ca Technologies                           92.89         58   \n",
        "                   Carbonite                                 82.34         72   \n",
        "                   Cisco                                     51.48         97   \n",
        "                   Cisco Webex                                0.00         10   \n",
        "                   Citrix                                    47.06        199   \n",
        "                   E-verify                                  30.76         63   \n",
        "                   Emc2                                      73.16        205   \n",
        "                   Film Fanatic                               8.43         11   \n",
        "                   Google                                    13.92        341   \n",
        "                   Google Play                               81.15         76   \n",
        "                   Google Software                           56.41        159   \n",
        "                   IBM                                       47.57        379   \n",
        "                   Join.me                                    3.59          8   \n",
        "                   LifeLock                                  10.26         61   \n",
        "                   Log Me In Rescue                          75.00          5   \n",
        "                   Manage Engine                             31.78         64   \n",
        "                   Microsoft                                 46.31        757   \n",
        "                   Microsoft Software                         7.65        127   \n",
        "                   Norton                                    24.02         51   \n",
        "                   Redhat                                    99.44         30   \n",
        "                   Rescue                                    72.86          1   \n",
        "                   Safecount.net                              0.00         11   \n",
        "                   Sage                                      63.49         42   \n",
        "                   Skype                                     55.32         28   \n",
        "                   Visual Studio                             99.77          4   \n",
        "                   Windows                                   78.59         21   \n",
        "                   Xfinity                                   41.17        247   \n",
        "                   Zendesk                                   19.69         70   \n",
        "                   iLivid                                    50.13        117   \n",
        "\n",
        "                                                Publishers  Tags  Count  \n",
        "Industry           Advertiser                                            \n",
        "Financial Services Advance America                     688    37      1  \n",
        "                   Ameriprise Financial                 25    14      1  \n",
        "                   Barclays                             17     6      1  \n",
        "                   Bloomberg                            36     8      1  \n",
        "                   CFP                                  32    39      1  \n",
        "                   Credit Suisse                        51    18      1  \n",
        "                   Equifax                             869    26      1  \n",
        "                   GE Capital                           24     7      1  \n",
        "                   Mass Mutual Financial Group          10     4      1  \n",
        "                   Merrill Edge                       1253    28      1  \n",
        "                   Merrill Lynch                      1298    29      1  \n",
        "                   Moneygram                          1408    31      1  \n",
        "                   Pimco                               465    18      1  \n",
        "                   Ul                                   81     9      1  \n",
        "                   Usaa                                144    13      1  \n",
        "                   Veterans United Home Loans          235    22      1  \n",
        "                   Vulcan Inc.                         269    14      1  \n",
        "                   Zurich                               27     5      1  \n",
        "Insurance Company  AAA insurance                       543    27      1  \n",
        "                   Aetna                              1509    24      1  \n",
        "                   Aflac                               308    12      1  \n",
        "                   Allianz                             223    17      1  \n",
        "                   Allstate                           4058    33      1  \n",
        "                   American Family Insurance          2016    37      1  \n",
        "                   Bluecross Blueshield               1800    33      1  \n",
        "                   Cigna                               891    23      1  \n",
        "                   Cuidadodesalud.gov                  221    30      1  \n",
        "                   Esurance                            318    20      1  \n",
        "                   Farmers Insurance                  2134    31      1  \n",
        "                   Fidelity                           1770    24      1  \n",
        "...                                                    ...   ...    ...  \n",
        "Software           Apple                               112    17      1  \n",
        "                   Ca Technologies                     147     9      1  \n",
        "                   Carbonite                           708    19      1  \n",
        "                   Cisco                              1147    19      1  \n",
        "                   Cisco Webex                         666    27      1  \n",
        "                   Citrix                             2256    26      1  \n",
        "                   E-verify                           1224    28      1  \n",
        "                   Emc2                                962    15      1  \n",
        "                   Film Fanatic                         24    12      1  \n",
        "                   Google                             5666    18      1  \n",
        "                   Google Play                         428    10      1  \n",
        "                   Google Software                    1351    12      1  \n",
        "                   IBM                                2004    24      1  \n",
        "                   Join.me                             316    21      1  \n",
        "                   LifeLock                           2052    34      1  \n",
        "                   Log Me In Rescue                    162    11      1  \n",
        "                   Manage Engine                       122     9      1  \n",
        "                   Microsoft                          6314    43      1  \n",
        "                   Microsoft Software                 3860    39      1  \n",
        "                   Norton                             2771    32      1  \n",
        "                   Redhat                               30     2      1  \n",
        "                   Rescue                              165    13      1  \n",
        "                   Safecount.net                       225    37      1  \n",
        "                   Sage                                 44    10      1  \n",
        "                   Skype                                30    11      1  \n",
        "                   Visual Studio                        14     1      1  \n",
        "                   Windows                             223    14      1  \n",
        "                   Xfinity                            1895    34      1  \n",
        "                   Zendesk                            1591    21      1  \n",
        "                   iLivid                              174    21      1  \n",
        "\n",
        "[82 rows x 8 columns]"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_advertiser.info()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 82 entries, 0 to 81\n",
        "Data columns (total 10 columns):\n",
        "Rank                  82 non-null int64\n",
        "Trend                 82 non-null int64\n",
        "Advertiser            82 non-null object\n",
        "Industry              82 non-null object\n",
        "Activity              82 non-null float64\n",
        "Percentage In-Play    82 non-null float64\n",
        "Creatives             82 non-null int64\n",
        "Publishers            82 non-null int64\n",
        "Tags                  82 non-null int64\n",
        "Count                 82 non-null int64\n",
        "dtypes: float64(2), int64(6), object(2)\n",
        "memory usage: 7.0+ KB\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Converting the Trend column to binary. 1 for Yes as in the trend went up and 0 for No as in no the trend either remained the same or went down. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_advertiser['trend_clean'] = df_advertiser['Trend'].apply(lambda Trend: 1 if Trend > 0 else 0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A lot more "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_advertiser.trend_clean.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "0    55\n",
        "1    27\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_advertiser.trend_clean.value_counts().plot(kind='bar',alpha=.30)\n",
      "plt.xlabel(\"Not Trending                                Trending\")\n",
      "plt.ylabel(\"Number of Occurances\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "<matplotlib.text.Text at 0x108444dd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10859a710>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import statsmodels.api as sm\n",
      "from sklearn import linear_model, datasets\n",
      "from sklearn.cross_validation import train_test_split"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_advertiser.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>Rank</th>\n",
        "      <th>Trend</th>\n",
        "      <th>Advertiser</th>\n",
        "      <th>Industry</th>\n",
        "      <th>Activity</th>\n",
        "      <th>Percentage In-Play</th>\n",
        "      <th>Creatives</th>\n",
        "      <th>Publishers</th>\n",
        "      <th>Tags</th>\n",
        "      <th>Count</th>\n",
        "      <th>trend_clean</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Merrill Lynch</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td> 100.00</td>\n",
        "      <td> 28.67</td>\n",
        "      <td> 92</td>\n",
        "      <td> 1298</td>\n",
        "      <td> 29</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td> 0</td>\n",
        "      <td>  Merrill Edge</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td>  79.15</td>\n",
        "      <td> 23.51</td>\n",
        "      <td> 86</td>\n",
        "      <td> 1253</td>\n",
        "      <td> 28</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3</td>\n",
        "      <td> 5</td>\n",
        "      <td>       Equifax</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td>  18.78</td>\n",
        "      <td> 35.86</td>\n",
        "      <td> 39</td>\n",
        "      <td>  869</td>\n",
        "      <td> 26</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4</td>\n",
        "      <td> 5</td>\n",
        "      <td>     Moneygram</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td>  14.73</td>\n",
        "      <td>  0.67</td>\n",
        "      <td> 49</td>\n",
        "      <td> 1408</td>\n",
        "      <td> 31</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 5</td>\n",
        "      <td>-2</td>\n",
        "      <td>          Usaa</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td>  11.38</td>\n",
        "      <td> 72.79</td>\n",
        "      <td> 45</td>\n",
        "      <td>  144</td>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "   Rank  Trend     Advertiser            Industry  Activity  \\\n",
        "0     1      0  Merrill Lynch  Financial Services    100.00   \n",
        "1     2      0   Merrill Edge  Financial Services     79.15   \n",
        "2     3      5        Equifax  Financial Services     18.78   \n",
        "3     4      5      Moneygram  Financial Services     14.73   \n",
        "4     5     -2           Usaa  Financial Services     11.38   \n",
        "\n",
        "   Percentage In-Play  Creatives  Publishers  Tags  Count  trend_clean  \n",
        "0               28.67         92        1298    29      1            0  \n",
        "1               23.51         86        1253    28      1            0  \n",
        "2               35.86         39         869    26      1            1  \n",
        "3                0.67         49        1408    31      1            1  \n",
        "4               72.79         45         144    13      1            0  "
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_advertiser.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>Rank</th>\n",
        "      <th>Trend</th>\n",
        "      <th>Activity</th>\n",
        "      <th>Percentage In-Play</th>\n",
        "      <th>Creatives</th>\n",
        "      <th>Publishers</th>\n",
        "      <th>Tags</th>\n",
        "      <th>Count</th>\n",
        "      <th>trend_clean</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 82.000000</td>\n",
        "      <td> 82.000000</td>\n",
        "      <td>  82.000000</td>\n",
        "      <td>  82.000000</td>\n",
        "      <td>  82.000000</td>\n",
        "      <td>   82.000000</td>\n",
        "      <td> 82.000000</td>\n",
        "      <td> 82</td>\n",
        "      <td> 82.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td> 12.756098</td>\n",
        "      <td> -0.036585</td>\n",
        "      <td>  16.204512</td>\n",
        "      <td>  44.011951</td>\n",
        "      <td>  87.804878</td>\n",
        "      <td> 1044.878049</td>\n",
        "      <td> 21.195122</td>\n",
        "      <td>  1</td>\n",
        "      <td>  0.329268</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>  8.257523</td>\n",
        "      <td>  3.084990</td>\n",
        "      <td>  26.345483</td>\n",
        "      <td>  29.323490</td>\n",
        "      <td> 113.337629</td>\n",
        "      <td> 1279.607100</td>\n",
        "      <td> 10.383319</td>\n",
        "      <td>  0</td>\n",
        "      <td>  0.472840</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>  1.000000</td>\n",
        "      <td> -6.000000</td>\n",
        "      <td>   0.020000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   10.000000</td>\n",
        "      <td>  1.000000</td>\n",
        "      <td>  1</td>\n",
        "      <td>  0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>  6.000000</td>\n",
        "      <td> -2.000000</td>\n",
        "      <td>   1.987500</td>\n",
        "      <td>  19.345000</td>\n",
        "      <td>  24.000000</td>\n",
        "      <td>  144.750000</td>\n",
        "      <td> 12.250000</td>\n",
        "      <td>  1</td>\n",
        "      <td>  0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td> 12.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>   4.895000</td>\n",
        "      <td>  41.475000</td>\n",
        "      <td>  57.000000</td>\n",
        "      <td>  604.000000</td>\n",
        "      <td> 21.000000</td>\n",
        "      <td>  1</td>\n",
        "      <td>  0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td> 18.750000</td>\n",
        "      <td>  1.000000</td>\n",
        "      <td>  15.600000</td>\n",
        "      <td>  71.685000</td>\n",
        "      <td>  94.250000</td>\n",
        "      <td> 1570.500000</td>\n",
        "      <td> 28.750000</td>\n",
        "      <td>  1</td>\n",
        "      <td>  1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 31.000000</td>\n",
        "      <td> 11.000000</td>\n",
        "      <td> 100.000000</td>\n",
        "      <td> 100.000000</td>\n",
        "      <td> 757.000000</td>\n",
        "      <td> 6314.000000</td>\n",
        "      <td> 43.000000</td>\n",
        "      <td>  1</td>\n",
        "      <td>  1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "            Rank      Trend    Activity  Percentage In-Play   Creatives  \\\n",
        "count  82.000000  82.000000   82.000000           82.000000   82.000000   \n",
        "mean   12.756098  -0.036585   16.204512           44.011951   87.804878   \n",
        "std     8.257523   3.084990   26.345483           29.323490  113.337629   \n",
        "min     1.000000  -6.000000    0.020000            0.000000    1.000000   \n",
        "25%     6.000000  -2.000000    1.987500           19.345000   24.000000   \n",
        "50%    12.000000   0.000000    4.895000           41.475000   57.000000   \n",
        "75%    18.750000   1.000000   15.600000           71.685000   94.250000   \n",
        "max    31.000000  11.000000  100.000000          100.000000  757.000000   \n",
        "\n",
        "        Publishers       Tags  Count  trend_clean  \n",
        "count    82.000000  82.000000     82    82.000000  \n",
        "mean   1044.878049  21.195122      1     0.329268  \n",
        "std    1279.607100  10.383319      0     0.472840  \n",
        "min      10.000000   1.000000      1     0.000000  \n",
        "25%     144.750000  12.250000      1     0.000000  \n",
        "50%     604.000000  21.000000      1     0.000000  \n",
        "75%    1570.500000  28.750000      1     1.000000  \n",
        "max    6314.000000  43.000000      1     1.000000  "
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_Variables = ['Percentage In-Play','Creatives','Publishers','Tags']\n",
      "X = df_advertiser[X_Variables]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = X.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 353
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y = df_advertiser['trend_clean'].values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 354
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf = linear_model.LogisticRegression()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 355
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = clf.fit(X,y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 356
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.score(X,y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 357,
       "text": [
        "0.68292682926829273"
       ]
      }
     ],
     "prompt_number": 357
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.DataFrame(zip(X_Variables,model.coef_.T))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>0</th>\n",
        "      <th>1</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> Percentage In-Play</td>\n",
        "      <td> [-0.00410534253935]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>          Creatives</td>\n",
        "      <td> [-0.00272258724099]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>         Publishers</td>\n",
        "      <td> [0.000488549466095]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>               Tags</td>\n",
        "      <td>  [-0.0143146064198]</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 358,
       "text": [
        "                    0                    1\n",
        "0  Percentage In-Play  [-0.00410534253935]\n",
        "1           Creatives  [-0.00272258724099]\n",
        "2          Publishers  [0.000488549466095]\n",
        "3                Tags   [-0.0143146064198]"
       ]
      }
     ],
     "prompt_number": 358
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "With on percentage increase in activity the chance of the trend "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.naive_bayes import GaussianNB\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 361
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_Variables = ['Percentage In-Play','Creatives','Publishers','Tags']\n",
      "X = df_advertiser[X_Variables]\n",
      "\n",
      "X = X.values\n",
      "y = df_advertiser['trend_clean'].values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.cross_validation import train_test_split\n",
      "\n",
      "X_train, X_test, Y_train, Y_test = train_test_split(X,y,test_size=0.25)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf = GaussianNB()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 367
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.fit(X_train,Y_train)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 368,
       "text": [
        "GaussianNB()"
       ]
      }
     ],
     "prompt_number": 368
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import metrics\n",
      "def measure_performance(X,y,clf, show_accuracy=True, show_classification_report=True, show_confusion_matrix=True):\n",
      "    y_pred=clf.predict(X)   \n",
      "    if show_accuracy:\n",
      "        print \"Accuracy:{0:.3f}\".format(metrics.accuracy_score(y,y_pred)),\"\\n\"\n",
      "\n",
      "    if show_classification_report:\n",
      "        print \"Classification report\"\n",
      "        print metrics.classification_report(y,y_pred),\"\\n\"\n",
      "        \n",
      "    if show_confusion_matrix:\n",
      "        print \"Confusion matrix\"\n",
      "        print metrics.confusion_matrix(y,y_pred),\"\\n\"\n",
      "        \n",
      "measure_performance(X_train,Y_train,clf, show_classification_report=True, show_confusion_matrix=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Accuracy:0.672 \n",
        "\n",
        "Classification report\n",
        "             precision    recall  f1-score   support\n",
        "\n",
        "          0       0.70      0.90      0.79        41\n",
        "          1       0.50      0.20      0.29        20\n",
        "\n",
        "avg / total       0.63      0.67      0.62        61\n",
        "\n",
        "\n",
        "Confusion matrix\n",
        "[[37  4]\n",
        " [16  4]] \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 371
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Image\n",
      "Image(filename='/Users/olehdubno/Desktop/Moat/trendingvsnontrending.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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9YrlU2Dv4vV6Pj5IFR3JFIL8Hp0m4GPQvzSzunZ8XxrB+v9/r94ulAfoSCRor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\n8khXplPJ0g839d3BdiBlO/4KpdAnU+E8h0Ty3pi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       "prompt_number": 419,
       "text": [
        "<IPython.core.display.Image at 0x11a46fc10>"
       ]
      }
     ],
     "prompt_number": 419
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Confusion Matrix allows for more detailed analysis than mere proportion of correct guesses.\n",
      "\n",
      "For instance 16 advertisers from Yes trending were incorrecly predicted as No trending. \n",
      "\n",
      "Based on the entries in the confusion matrix, the total number of correct predictions made by the model is (37 + 4) and the total number of incorrect predictions is (16 + 4).\n",
      "\n",
      "The confusion matrix provides the information needed to determine how well a classification model performs. The perforamnce metric, accuracy, summarizes this information with a single number .672\n",
      "\n",
      "Accuracy takes the total number of correct predictions and divides it by the total number of all predictions made.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_advertiser"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>Rank</th>\n",
        "      <th>Trend</th>\n",
        "      <th>Advertiser</th>\n",
        "      <th>Industry</th>\n",
        "      <th>Activity</th>\n",
        "      <th>Percentage In-Play</th>\n",
        "      <th>Creatives</th>\n",
        "      <th>Publishers</th>\n",
        "      <th>Tags</th>\n",
        "      <th>Count</th>\n",
        "      <th>trend_clean</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td>  1</td>\n",
        "      <td>  0</td>\n",
        "      <td>               Merrill Lynch</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td> 100.00</td>\n",
        "      <td>  28.67</td>\n",
        "      <td>  92</td>\n",
        "      <td> 1298</td>\n",
        "      <td> 29</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>  2</td>\n",
        "      <td>  0</td>\n",
        "      <td>                Merrill Edge</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td>  79.15</td>\n",
        "      <td>  23.51</td>\n",
        "      <td>  86</td>\n",
        "      <td> 1253</td>\n",
        "      <td> 28</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>  3</td>\n",
        "      <td>  5</td>\n",
        "      <td>                     Equifax</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td>  18.78</td>\n",
        "      <td>  35.86</td>\n",
        "      <td>  39</td>\n",
        "      <td>  869</td>\n",
        "      <td> 26</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>  4</td>\n",
        "      <td>  5</td>\n",
        "      <td>                   Moneygram</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td>  14.73</td>\n",
        "      <td>   0.67</td>\n",
        "      <td>  49</td>\n",
        "      <td> 1408</td>\n",
        "      <td> 31</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>  5</td>\n",
        "      <td> -2</td>\n",
        "      <td>                        Usaa</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td>  11.38</td>\n",
        "      <td>  72.79</td>\n",
        "      <td>  45</td>\n",
        "      <td>  144</td>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>  6</td>\n",
        "      <td> -1</td>\n",
        "      <td>                       Pimco</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td>  10.37</td>\n",
        "      <td>  39.02</td>\n",
        "      <td>  27</td>\n",
        "      <td>  465</td>\n",
        "      <td> 18</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>  7</td>\n",
        "      <td>  3</td>\n",
        "      <td>                 Vulcan Inc.</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td>   9.90</td>\n",
        "      <td>  71.67</td>\n",
        "      <td>  66</td>\n",
        "      <td>  269</td>\n",
        "      <td> 14</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>  8</td>\n",
        "      <td> -4</td>\n",
        "      <td>                      Zurich</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td>   9.15</td>\n",
        "      <td>  99.11</td>\n",
        "      <td>  34</td>\n",
        "      <td>   27</td>\n",
        "      <td>  5</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>  9</td>\n",
        "      <td> -3</td>\n",
        "      <td>             Advance America</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td>   8.81</td>\n",
        "      <td>   2.93</td>\n",
        "      <td>  49</td>\n",
        "      <td>  688</td>\n",
        "      <td> 37</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> 10</td>\n",
        "      <td>  3</td>\n",
        "      <td>                          Ul</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td>   2.55</td>\n",
        "      <td>  84.09</td>\n",
        "      <td>  38</td>\n",
        "      <td>   81</td>\n",
        "      <td>  9</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> 11</td>\n",
        "      <td> -4</td>\n",
        "      <td>                    Barclays</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td>   2.22</td>\n",
        "      <td>  45.51</td>\n",
        "      <td>  18</td>\n",
        "      <td>   17</td>\n",
        "      <td>  6</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> 12</td>\n",
        "      <td> -1</td>\n",
        "      <td>                  GE Capital</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td>   2.16</td>\n",
        "      <td>  92.88</td>\n",
        "      <td>  14</td>\n",
        "      <td>   24</td>\n",
        "      <td>  7</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> 13</td>\n",
        "      <td> -1</td>\n",
        "      <td>               Credit Suisse</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td>   2.06</td>\n",
        "      <td>  45.59</td>\n",
        "      <td>  19</td>\n",
        "      <td>   51</td>\n",
        "      <td> 18</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> 14</td>\n",
        "      <td>  0</td>\n",
        "      <td>  Veterans United Home Loans</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td>   1.98</td>\n",
        "      <td>   8.72</td>\n",
        "      <td>   6</td>\n",
        "      <td>  235</td>\n",
        "      <td> 22</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td> 15</td>\n",
        "      <td> -1</td>\n",
        "      <td> Mass Mutual Financial Group</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td>   0.83</td>\n",
        "      <td>  62.11</td>\n",
        "      <td>  23</td>\n",
        "      <td>   10</td>\n",
        "      <td>  4</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td> 16</td>\n",
        "      <td> -1</td>\n",
        "      <td>                   Bloomberg</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td>   0.62</td>\n",
        "      <td>  41.23</td>\n",
        "      <td>  57</td>\n",
        "      <td>   36</td>\n",
        "      <td>  8</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> 17</td>\n",
        "      <td>  0</td>\n",
        "      <td>        Ameriprise Financial</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td>   0.26</td>\n",
        "      <td>  84.84</td>\n",
        "      <td>  14</td>\n",
        "      <td>   25</td>\n",
        "      <td> 14</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> 18</td>\n",
        "      <td> -2</td>\n",
        "      <td>                         CFP</td>\n",
        "      <td> Financial Services</td>\n",
        "      <td>   0.05</td>\n",
        "      <td>   0.00</td>\n",
        "      <td>   8</td>\n",
        "      <td>   32</td>\n",
        "      <td> 39</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>  1</td>\n",
        "      <td>  0</td>\n",
        "      <td>               TD Ameritrade</td>\n",
        "      <td> Personal Investing</td>\n",
        "      <td> 100.00</td>\n",
        "      <td>  18.71</td>\n",
        "      <td> 135</td>\n",
        "      <td> 1844</td>\n",
        "      <td> 24</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>  2</td>\n",
        "      <td>  0</td>\n",
        "      <td>               T. Rowe Price</td>\n",
        "      <td> Personal Investing</td>\n",
        "      <td>  31.69</td>\n",
        "      <td>  29.43</td>\n",
        "      <td>  51</td>\n",
        "      <td>  855</td>\n",
        "      <td> 26</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>  3</td>\n",
        "      <td>  0</td>\n",
        "      <td>          Fisher Investments</td>\n",
        "      <td> Personal Investing</td>\n",
        "      <td>  21.14</td>\n",
        "      <td>  24.75</td>\n",
        "      <td>  95</td>\n",
        "      <td>  734</td>\n",
        "      <td> 14</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>  4</td>\n",
        "      <td>  0</td>\n",
        "      <td>                        Spdr</td>\n",
        "      <td> Personal Investing</td>\n",
        "      <td>  10.26</td>\n",
        "      <td>  82.54</td>\n",
        "      <td>  75</td>\n",
        "      <td>   46</td>\n",
        "      <td> 15</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>  5</td>\n",
        "      <td>  0</td>\n",
        "      <td>                Virginia 529</td>\n",
        "      <td> Personal Investing</td>\n",
        "      <td>   5.51</td>\n",
        "      <td>   2.90</td>\n",
        "      <td>  14</td>\n",
        "      <td>  561</td>\n",
        "      <td> 20</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>  6</td>\n",
        "      <td>  1</td>\n",
        "      <td>                Tradestation</td>\n",
        "      <td> Personal Investing</td>\n",
        "      <td>   2.50</td>\n",
        "      <td>  72.37</td>\n",
        "      <td>  47</td>\n",
        "      <td>   78</td>\n",
        "      <td> 12</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>  7</td>\n",
        "      <td>  2</td>\n",
        "      <td>               Raymond James</td>\n",
        "      <td> Personal Investing</td>\n",
        "      <td>   0.17</td>\n",
        "      <td> 100.00</td>\n",
        "      <td>  15</td>\n",
        "      <td>   11</td>\n",
        "      <td>  3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>  1</td>\n",
        "      <td>  1</td>\n",
        "      <td>                    Allstate</td>\n",
        "      <td>  Insurance Company</td>\n",
        "      <td> 100.00</td>\n",
        "      <td>  22.69</td>\n",
        "      <td> 281</td>\n",
        "      <td> 4058</td>\n",
        "      <td> 33</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td>  2</td>\n",
        "      <td>  1</td>\n",
        "      <td>                       Geico</td>\n",
        "      <td>  Insurance Company</td>\n",
        "      <td>  77.07</td>\n",
        "      <td>  15.94</td>\n",
        "      <td> 137</td>\n",
        "      <td> 2201</td>\n",
        "      <td> 17</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td>  3</td>\n",
        "      <td> -2</td>\n",
        "      <td>                 Progressive</td>\n",
        "      <td>  Insurance Company</td>\n",
        "      <td>  50.81</td>\n",
        "      <td>  11.95</td>\n",
        "      <td>  83</td>\n",
        "      <td> 4042</td>\n",
        "      <td> 26</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td>  4</td>\n",
        "      <td>  0</td>\n",
        "      <td>                  State Farm</td>\n",
        "      <td>  Insurance Company</td>\n",
        "      <td>  45.71</td>\n",
        "      <td>  40.10</td>\n",
        "      <td> 421</td>\n",
        "      <td> 3916</td>\n",
        "      <td> 39</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td>  5</td>\n",
        "      <td>  9</td>\n",
        "      <td>                     Metlife</td>\n",
        "      <td>  Insurance Company</td>\n",
        "      <td>  39.26</td>\n",
        "      <td>   8.96</td>\n",
        "      <td>  79</td>\n",
        "      <td> 2383</td>\n",
        "      <td> 34</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>52</th>\n",
        "      <td>  2</td>\n",
        "      <td> -1</td>\n",
        "      <td>                      Google</td>\n",
        "      <td>           Software</td>\n",
        "      <td>  97.37</td>\n",
        "      <td>  13.92</td>\n",
        "      <td> 341</td>\n",
        "      <td> 5666</td>\n",
        "      <td> 18</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>53</th>\n",
        "      <td>  3</td>\n",
        "      <td>  0</td>\n",
        "      <td>                         IBM</td>\n",
        "      <td>           Software</td>\n",
        "      <td>  43.27</td>\n",
        "      <td>  47.57</td>\n",
        "      <td> 379</td>\n",
        "      <td> 2004</td>\n",
        "      <td> 24</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>54</th>\n",
        "      <td>  4</td>\n",
        "      <td>  4</td>\n",
        "      <td>          Microsoft Software</td>\n",
        "      <td>           Software</td>\n",
        "      <td>  21.03</td>\n",
        "      <td>   7.65</td>\n",
        "      <td> 127</td>\n",
        "      <td> 3860</td>\n",
        "      <td> 39</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>55</th>\n",
        "      <td>  5</td>\n",
        "      <td>  2</td>\n",
        "      <td>                      Norton</td>\n",
        "      <td>           Software</td>\n",
        "      <td>  15.96</td>\n",
        "      <td>  24.02</td>\n",
        "      <td>  51</td>\n",
        "      <td> 2771</td>\n",
        "      <td> 32</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>56</th>\n",
        "      <td>  6</td>\n",
        "      <td>  3</td>\n",
        "      <td>                       Cisco</td>\n",
        "      <td>           Software</td>\n",
        "      <td>  13.42</td>\n",
        "      <td>  51.48</td>\n",
        "      <td>  97</td>\n",
        "      <td> 1147</td>\n",
        "      <td> 19</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>57</th>\n",
        "      <td>  7</td>\n",
        "      <td> -3</td>\n",
        "      <td>                     Xfinity</td>\n",
        "      <td>           Software</td>\n",
        "      <td>  11.24</td>\n",
        "      <td>  41.17</td>\n",
        "      <td> 247</td>\n",
        "      <td> 1895</td>\n",
        "      <td> 34</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>58</th>\n",
        "      <td>  8</td>\n",
        "      <td> -3</td>\n",
        "      <td>             Google Software</td>\n",
        "      <td>           Software</td>\n",
        "      <td>  10.25</td>\n",
        "      <td>  56.41</td>\n",
        "      <td> 159</td>\n",
        "      <td> 1351</td>\n",
        "      <td> 12</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>59</th>\n",
        "      <td>  9</td>\n",
        "      <td>  1</td>\n",
        "      <td>                    LifeLock</td>\n",
        "      <td>           Software</td>\n",
        "      <td>   7.26</td>\n",
        "      <td>  10.26</td>\n",
        "      <td>  61</td>\n",
        "      <td> 2052</td>\n",
        "      <td> 34</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>60</th>\n",
        "      <td> 10</td>\n",
        "      <td>  2</td>\n",
        "      <td>                 Google Play</td>\n",
        "      <td>           Software</td>\n",
        "      <td>   7.20</td>\n",
        "      <td>  81.15</td>\n",
        "      <td>  76</td>\n",
        "      <td>  428</td>\n",
        "      <td> 10</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>61</th>\n",
        "      <td> 11</td>\n",
        "      <td>  3</td>\n",
        "      <td>                      Citrix</td>\n",
        "      <td>           Software</td>\n",
        "      <td>   5.54</td>\n",
        "      <td>  47.06</td>\n",
        "      <td> 199</td>\n",
        "      <td> 2256</td>\n",
        "      <td> 26</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>62</th>\n",
        "      <td> 12</td>\n",
        "      <td> -6</td>\n",
        "      <td>                    E-verify</td>\n",
        "      <td>           Software</td>\n",
        "      <td>   5.09</td>\n",
        "      <td>  30.76</td>\n",
        "      <td>  63</td>\n",
        "      <td> 1224</td>\n",
        "      <td> 28</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>63</th>\n",
        "      <td> 13</td>\n",
        "      <td> -2</td>\n",
        "      <td>                       Adobe</td>\n",
        "      <td>           Software</td>\n",
        "      <td>   4.29</td>\n",
        "      <td>  23.10</td>\n",
        "      <td>  96</td>\n",
        "      <td> 2004</td>\n",
        "      <td> 36</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>64</th>\n",
        "      <td> 14</td>\n",
        "      <td>  2</td>\n",
        "      <td>                        Emc2</td>\n",
        "      <td>           Software</td>\n",
        "      <td>   3.07</td>\n",
        "      <td>  73.16</td>\n",
        "      <td> 205</td>\n",
        "      <td>  962</td>\n",
        "      <td> 15</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>65</th>\n",
        "      <td> 15</td>\n",
        "      <td>  0</td>\n",
        "      <td>                      iLivid</td>\n",
        "      <td>           Software</td>\n",
        "      <td>   2.99</td>\n",
        "      <td>  50.13</td>\n",
        "      <td> 117</td>\n",
        "      <td>  174</td>\n",
        "      <td> 21</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>66</th>\n",
        "      <td> 16</td>\n",
        "      <td>  2</td>\n",
        "      <td>                 Cisco Webex</td>\n",
        "      <td>           Software</td>\n",
        "      <td>   2.38</td>\n",
        "      <td>   0.00</td>\n",
        "      <td>  10</td>\n",
        "      <td>  666</td>\n",
        "      <td> 27</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>67</th>\n",
        "      <td> 17</td>\n",
        "      <td> 11</td>\n",
        "      <td>                     Windows</td>\n",
        "      <td>           Software</td>\n",
        "      <td>   2.13</td>\n",
        "      <td>  78.59</td>\n",
        "      <td>  21</td>\n",
        "      <td>  223</td>\n",
        "      <td> 14</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>68</th>\n",
        "      <td> 18</td>\n",
        "      <td> -5</td>\n",
        "      <td>                     Zendesk</td>\n",
        "      <td>           Software</td>\n",
        "      <td>   2.13</td>\n",
        "      <td>  19.69</td>\n",
        "      <td>  70</td>\n",
        "      <td> 1591</td>\n",
        "      <td> 21</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>69</th>\n",
        "      <td> 19</td>\n",
        "      <td>  3</td>\n",
        "      <td>                   Carbonite</td>\n",
        "      <td>           Software</td>\n",
        "      <td>   2.09</td>\n",
        "      <td>  82.34</td>\n",
        "      <td>  72</td>\n",
        "      <td>  708</td>\n",
        "      <td> 19</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>70</th>\n",
        "      <td> 20</td>\n",
        "      <td>  0</td>\n",
        "      <td>                       Skype</td>\n",
        "      <td>           Software</td>\n",
        "      <td>   1.66</td>\n",
        "      <td>  55.32</td>\n",
        "      <td>  28</td>\n",
        "      <td>   30</td>\n",
        "      <td> 11</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>71</th>\n",
        "      <td> 21</td>\n",
        "      <td> -4</td>\n",
        "      <td>               Manage Engine</td>\n",
        "      <td>           Software</td>\n",
        "      <td>   0.86</td>\n",
        "      <td>  31.78</td>\n",
        "      <td>  64</td>\n",
        "      <td>  122</td>\n",
        "      <td>  9</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>72</th>\n",
        "      <td> 22</td>\n",
        "      <td>  1</td>\n",
        "      <td>                     Join.me</td>\n",
        "      <td>           Software</td>\n",
        "      <td>   0.38</td>\n",
        "      <td>   3.59</td>\n",
        "      <td>   8</td>\n",
        "      <td>  316</td>\n",
        "      <td> 21</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>73</th>\n",
        "      <td> 23</td>\n",
        "      <td> -4</td>\n",
        "      <td>             Ca Technologies</td>\n",
        "      <td>           Software</td>\n",
        "      <td>   0.30</td>\n",
        "      <td>  92.89</td>\n",
        "      <td>  58</td>\n",
        "      <td>  147</td>\n",
        "      <td>  9</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>74</th>\n",
        "      <td> 24</td>\n",
        "      <td> -3</td>\n",
        "      <td>                      Redhat</td>\n",
        "      <td>           Software</td>\n",
        "      <td>   0.27</td>\n",
        "      <td>  99.44</td>\n",
        "      <td>  30</td>\n",
        "      <td>   30</td>\n",
        "      <td>  2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75</th>\n",
        "      <td> 25</td>\n",
        "      <td> -5</td>\n",
        "      <td>                       Apple</td>\n",
        "      <td>           Software</td>\n",
        "      <td>   0.25</td>\n",
        "      <td>  73.76</td>\n",
        "      <td>  15</td>\n",
        "      <td>  112</td>\n",
        "      <td> 17</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>76</th>\n",
        "      <td> 26</td>\n",
        "      <td>  0</td>\n",
        "      <td>               Safecount.net</td>\n",
        "      <td>           Software</td>\n",
        "      <td>   0.21</td>\n",
        "      <td>   0.00</td>\n",
        "      <td>  11</td>\n",
        "      <td>  225</td>\n",
        "      <td> 37</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>77</th>\n",
        "      <td> 27</td>\n",
        "      <td>  0</td>\n",
        "      <td>                      Rescue</td>\n",
        "      <td>           Software</td>\n",
        "      <td>   0.19</td>\n",
        "      <td>  72.86</td>\n",
        "      <td>   1</td>\n",
        "      <td>  165</td>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>78</th>\n",
        "      <td> 28</td>\n",
        "      <td>  0</td>\n",
        "      <td>            Log Me In Rescue</td>\n",
        "      <td>           Software</td>\n",
        "      <td>   0.18</td>\n",
        "      <td>  75.00</td>\n",
        "      <td>   5</td>\n",
        "      <td>  162</td>\n",
        "      <td> 11</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>79</th>\n",
        "      <td> 29</td>\n",
        "      <td>  0</td>\n",
        "      <td>               Visual Studio</td>\n",
        "      <td>           Software</td>\n",
        "      <td>   0.06</td>\n",
        "      <td>  99.77</td>\n",
        "      <td>   4</td>\n",
        "      <td>   14</td>\n",
        "      <td>  1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80</th>\n",
        "      <td> 30</td>\n",
        "      <td> -5</td>\n",
        "      <td>                        Sage</td>\n",
        "      <td>           Software</td>\n",
        "      <td>   0.04</td>\n",
        "      <td>  63.49</td>\n",
        "      <td>  42</td>\n",
        "      <td>   44</td>\n",
        "      <td> 10</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>81</th>\n",
        "      <td> 31</td>\n",
        "      <td> -4</td>\n",
        "      <td>                Film Fanatic</td>\n",
        "      <td>           Software</td>\n",
        "      <td>   0.02</td>\n",
        "      <td>   8.43</td>\n",
        "      <td>  11</td>\n",
        "      <td>   24</td>\n",
        "      <td> 12</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>82 rows \u00d7 11 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 448,
       "text": [
        "    Rank  Trend                   Advertiser            Industry  Activity  \\\n",
        "0      1      0                Merrill Lynch  Financial Services    100.00   \n",
        "1      2      0                 Merrill Edge  Financial Services     79.15   \n",
        "2      3      5                      Equifax  Financial Services     18.78   \n",
        "3      4      5                    Moneygram  Financial Services     14.73   \n",
        "4      5     -2                         Usaa  Financial Services     11.38   \n",
        "5      6     -1                        Pimco  Financial Services     10.37   \n",
        "6      7      3                  Vulcan Inc.  Financial Services      9.90   \n",
        "7      8     -4                       Zurich  Financial Services      9.15   \n",
        "8      9     -3              Advance America  Financial Services      8.81   \n",
        "9     10      3                           Ul  Financial Services      2.55   \n",
        "10    11     -4                     Barclays  Financial Services      2.22   \n",
        "11    12     -1                   GE Capital  Financial Services      2.16   \n",
        "12    13     -1                Credit Suisse  Financial Services      2.06   \n",
        "13    14      0   Veterans United Home Loans  Financial Services      1.98   \n",
        "14    15     -1  Mass Mutual Financial Group  Financial Services      0.83   \n",
        "15    16     -1                    Bloomberg  Financial Services      0.62   \n",
        "16    17      0         Ameriprise Financial  Financial Services      0.26   \n",
        "17    18     -2                          CFP  Financial Services      0.05   \n",
        "18     1      0                TD Ameritrade  Personal Investing    100.00   \n",
        "19     2      0                T. Rowe Price  Personal Investing     31.69   \n",
        "20     3      0           Fisher Investments  Personal Investing     21.14   \n",
        "21     4      0                         Spdr  Personal Investing     10.26   \n",
        "22     5      0                 Virginia 529  Personal Investing      5.51   \n",
        "23     6      1                 Tradestation  Personal Investing      2.50   \n",
        "24     7      2                Raymond James  Personal Investing      0.17   \n",
        "25     1      1                     Allstate   Insurance Company    100.00   \n",
        "26     2      1                        Geico   Insurance Company     77.07   \n",
        "27     3     -2                  Progressive   Insurance Company     50.81   \n",
        "28     4      0                   State Farm   Insurance Company     45.71   \n",
        "29     5      9                      Metlife   Insurance Company     39.26   \n",
        "..   ...    ...                          ...                 ...       ...   \n",
        "52     2     -1                       Google            Software     97.37   \n",
        "53     3      0                          IBM            Software     43.27   \n",
        "54     4      4           Microsoft Software            Software     21.03   \n",
        "55     5      2                       Norton            Software     15.96   \n",
        "56     6      3                        Cisco            Software     13.42   \n",
        "57     7     -3                      Xfinity            Software     11.24   \n",
        "58     8     -3              Google Software            Software     10.25   \n",
        "59     9      1                     LifeLock            Software      7.26   \n",
        "60    10      2                  Google Play            Software      7.20   \n",
        "61    11      3                       Citrix            Software      5.54   \n",
        "62    12     -6                     E-verify            Software      5.09   \n",
        "63    13     -2                        Adobe            Software      4.29   \n",
        "64    14      2                         Emc2            Software      3.07   \n",
        "65    15      0                       iLivid            Software      2.99   \n",
        "66    16      2                  Cisco Webex            Software      2.38   \n",
        "67    17     11                      Windows            Software      2.13   \n",
        "68    18     -5                      Zendesk            Software      2.13   \n",
        "69    19      3                    Carbonite            Software      2.09   \n",
        "70    20      0                        Skype            Software      1.66   \n",
        "71    21     -4                Manage Engine            Software      0.86   \n",
        "72    22      1                      Join.me            Software      0.38   \n",
        "73    23     -4              Ca Technologies            Software      0.30   \n",
        "74    24     -3                       Redhat            Software      0.27   \n",
        "75    25     -5                        Apple            Software      0.25   \n",
        "76    26      0                Safecount.net            Software      0.21   \n",
        "77    27      0                       Rescue            Software      0.19   \n",
        "78    28      0             Log Me In Rescue            Software      0.18   \n",
        "79    29      0                Visual Studio            Software      0.06   \n",
        "80    30     -5                         Sage            Software      0.04   \n",
        "81    31     -4                 Film Fanatic            Software      0.02   \n",
        "\n",
        "    Percentage In-Play  Creatives  Publishers  Tags  Count  trend_clean  \n",
        "0                28.67         92        1298    29      1            0  \n",
        "1                23.51         86        1253    28      1            0  \n",
        "2                35.86         39         869    26      1            1  \n",
        "3                 0.67         49        1408    31      1            1  \n",
        "4                72.79         45         144    13      1            0  \n",
        "5                39.02         27         465    18      1            0  \n",
        "6                71.67         66         269    14      1            1  \n",
        "7                99.11         34          27     5      1            0  \n",
        "8                 2.93         49         688    37      1            0  \n",
        "9                84.09         38          81     9      1            1  \n",
        "10               45.51         18          17     6      1            0  \n",
        "11               92.88         14          24     7      1            0  \n",
        "12               45.59         19          51    18      1            0  \n",
        "13                8.72          6         235    22      1            0  \n",
        "14               62.11         23          10     4      1            0  \n",
        "15               41.23         57          36     8      1            0  \n",
        "16               84.84         14          25    14      1            0  \n",
        "17                0.00          8          32    39      1            0  \n",
        "18               18.71        135        1844    24      1            0  \n",
        "19               29.43         51         855    26      1            0  \n",
        "20               24.75         95         734    14      1            0  \n",
        "21               82.54         75          46    15      1            0  \n",
        "22                2.90         14         561    20      1            0  \n",
        "23               72.37         47          78    12      1            1  \n",
        "24              100.00         15          11     3      1            1  \n",
        "25               22.69        281        4058    33      1            1  \n",
        "26               15.94        137        2201    17      1            1  \n",
        "27               11.95         83        4042    26      1            0  \n",
        "28               40.10        421        3916    39      1            0  \n",
        "29                8.96         79        2383    34      1            1  \n",
        "..                 ...        ...         ...   ...    ...          ...  \n",
        "52               13.92        341        5666    18      1            0  \n",
        "53               47.57        379        2004    24      1            0  \n",
        "54                7.65        127        3860    39      1            1  \n",
        "55               24.02         51        2771    32      1            1  \n",
        "56               51.48         97        1147    19      1            1  \n",
        "57               41.17        247        1895    34      1            0  \n",
        "58               56.41        159        1351    12      1            0  \n",
        "59               10.26         61        2052    34      1            1  \n",
        "60               81.15         76         428    10      1            1  \n",
        "61               47.06        199        2256    26      1            1  \n",
        "62               30.76         63        1224    28      1            0  \n",
        "63               23.10         96        2004    36      1            0  \n",
        "64               73.16        205         962    15      1            1  \n",
        "65               50.13        117         174    21      1            0  \n",
        "66                0.00         10         666    27      1            1  \n",
        "67               78.59         21         223    14      1            1  \n",
        "68               19.69         70        1591    21      1            0  \n",
        "69               82.34         72         708    19      1            1  \n",
        "70               55.32         28          30    11      1            0  \n",
        "71               31.78         64         122     9      1            0  \n",
        "72                3.59          8         316    21      1            1  \n",
        "73               92.89         58         147     9      1            0  \n",
        "74               99.44         30          30     2      1            0  \n",
        "75               73.76         15         112    17      1            0  \n",
        "76                0.00         11         225    37      1            0  \n",
        "77               72.86          1         165    13      1            0  \n",
        "78               75.00          5         162    11      1            0  \n",
        "79               99.77          4          14     1      1            0  \n",
        "80               63.49         42          44    10      1            0  \n",
        "81                8.43         11          24    12      1            0  \n",
        "\n",
        "[82 rows x 11 columns]"
       ]
      }
     ],
     "prompt_number": 448
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "software['Advertiser'].value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 464,
       "text": [
        "iLivid                1\n",
        "Norton                1\n",
        "IBM                   1\n",
        "Google Software       1\n",
        "Windows               1\n",
        "Visual Studio         1\n",
        "LifeLock              1\n",
        "Microsoft             1\n",
        "Adobe                 1\n",
        "Log Me In Rescue      1\n",
        "Manage Engine         1\n",
        "E-verify              1\n",
        "Zendesk               1\n",
        "Join.me               1\n",
        "Cisco Webex           1\n",
        "Rescue                1\n",
        "Xfinity               1\n",
        "Ca Technologies       1\n",
        "Google Play           1\n",
        "Google                1\n",
        "Cisco                 1\n",
        "Apple                 1\n",
        "Redhat                1\n",
        "Sage                  1\n",
        "Skype                 1\n",
        "Film Fanatic          1\n",
        "Carbonite             1\n",
        "Citrix                1\n",
        "Emc2                  1\n",
        "Safecount.net         1\n",
        "Microsoft Software    1\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 464
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_Variables = ['Percentage In-Play','Creatives','Publishers','Tags']\n",
      "X = df_advertiser[X_Variables]\n",
      "\n",
      "X = X.values\n",
      "y = df_advertiser['trend_clean'].values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.cross_validation import train_test_split\n",
      "\n",
      "X_train, X_test, Y_train, Y_test = train_test_split(X,y,test_size=0.25)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.linear_model import LinearRegression\n",
      "\n",
      "reg = LinearRegression()\n",
      "\n",
      "reg.fit(X_train,Y_train)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "LinearRegression(copy_X=True, fit_intercept=True, normalize=False)"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "reg.score(X_test,Y_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "-0.13369450300659103"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}