ExploratoryAnalysis_FEC_2012_PresidentialElections.ipynb

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   "source": [
    "# Programming Assignment I: Exploratory Analysis over 2012 FEC Presidential Election Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assignment Details\n",
    "-------------------\n",
    "\n",
    "In this assignment, you will be learn more about three key steps in a data analytics process. \n",
    "\n",
    "### 1. Data Collection\n",
    "\n",
    "In the first set of tasks, you will put to use the various techniques we learnt in web scraping and use it to scrape three popular websites. \n",
    "\n",
    "### 2. Exploratory Analysis\n",
    "In the second and third set of tasks, you will conduct a guided exploration over 2012 FEC Presidential Election Dataset. You will learn and use some of the most common exploration/aggregation/descriptive operations. This should also help you learn most of the key functionalities in Pandas.\n",
    "\n",
    "### 3. Visualization\n",
    "In the third set of tasks (that is done in conjunction with #2), you will also learn how to use visualization libraries to identify patterns in data that will help in your further data analysis. You will also explore most popular chart types and how to use different libraries and styles to make your visualizations more attractive.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Python Packages\n",
    "\n",
    "Here are the set of packages that you will use extensively in this assignment. Do NOT import any new packages without confirming with the instructor. The objective is to make you identify and use the various functions in the packages imported below. Besides, to my knowledge, the libraries below do offer a concise way to achieve the homework tasks anyway."
   ]
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  {
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   "source": [
    "# special IPython command to prepare the notebook for matplotlib\n",
    "%matplotlib inline \n",
    "\n",
    "#Array processing\n",
    "import numpy as np\n",
    "#Data analysis, wrangling and common exploratory operations\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame\n",
    "\n",
    "#A sane way to get web data\n",
    "import requests\n",
    "\n",
    "#Packages for web scraping. No need to use both. Feel free to use one of them.\n",
    "from pattern import web\n",
    "from BeautifulSoup import BeautifulSoup\n",
    "\n",
    "#For visualization. Matplotlib for basic viz and seaborn for more stylish figures + statistical figures not in MPL.\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#For some of the date operations\n",
    "import datetime\n",
    "\n",
    "\n",
    "pd.set_option('display.notebook_repr_html',True)\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Part 1: Web Scraping\n",
    "----------------\n",
    "\n",
    "In the first series of tasks, we will seek to scrape information from diverse websites. To make your job easier, you can assume that you do not need to do any complex webdriver stuff. For each task, you will implement a function that accepts an url and produces output in the format that the question asks.\n",
    "\n",
    "NOTE: Make sure that you follow the input and output requirements. The assignment will be evaluated and graded automatically by a script. Any error will result in you getting a score of 0 for that task.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Website 1: Wikipedia\n",
    "-----------------------\n",
    "\n",
    "In the first task, we will seek to parse contents from a table in a Wikipedia page. You will implement a function that accepts two parameters. The first is the url and the second is the name of the table. For example, if the url was http://en.wikipedia.org/wiki/List_of_Test_cricket_records and the table name was \"Team wins, losses and draws\", then you have to parse the first table. Your code must produce as an output a Pandas data frame where the columns have same name as the table ('Team',\t'First Test match',\t'Matches',\t'Won',\t'Lost',\t'Tied',\t'Drawn' and '% Won') in this example. \n",
    "\n",
    "Do NOT worry about the data format of the Pandas data frame. Simply treat each of them as a string.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
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       "<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>Team</th>\n",
       "      <th>First Test match</th>\n",
       "      <th>Matches</th>\n",
       "      <th>Won</th>\n",
       "      <th>Lost</th>\n",
       "      <th>Tied</th>\n",
       "      <th>Drawn</th>\n",
       "      <th>% Won</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0 </th>\n",
       "      <td>    Australia</td>\n",
       "      <td>    01877-03-15-000015 March 1877</td>\n",
       "      <td> 773</td>\n",
       "      <td> 362</td>\n",
       "      <td> 205</td>\n",
       "      <td> 2</td>\n",
       "      <td> 204</td>\n",
       "      <td> 46.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 </th>\n",
       "      <td>   Bangladesh</td>\n",
       "      <td> 02000-11-10-000010 November 2000</td>\n",
       "      <td>  88</td>\n",
       "      <td>   7</td>\n",
       "      <td>  70</td>\n",
       "      <td> 0</td>\n",
       "      <td>  11</td>\n",
       "      <td>  7.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 </th>\n",
       "      <td>      England</td>\n",
       "      <td>    01877-03-15-000015 March 1877</td>\n",
       "      <td> 952</td>\n",
       "      <td> 339</td>\n",
       "      <td> 275</td>\n",
       "      <td> 0</td>\n",
       "      <td> 338</td>\n",
       "      <td> 35.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 </th>\n",
       "      <td>        India</td>\n",
       "      <td>     01932-06-25-000025 June 1932</td>\n",
       "      <td> 487</td>\n",
       "      <td> 122</td>\n",
       "      <td> 156</td>\n",
       "      <td> 1</td>\n",
       "      <td> 208</td>\n",
       "      <td> 25.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4 </th>\n",
       "      <td>  New Zealand</td>\n",
       "      <td>  01930-01-10-000010 January 1930</td>\n",
       "      <td> 399</td>\n",
       "      <td>  80</td>\n",
       "      <td> 160</td>\n",
       "      <td> 0</td>\n",
       "      <td> 159</td>\n",
       "      <td> 20.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 </th>\n",
       "      <td>     Pakistan</td>\n",
       "      <td>  01952-10-16-000016 October 1952</td>\n",
       "      <td> 387</td>\n",
       "      <td> 121</td>\n",
       "      <td> 110</td>\n",
       "      <td> 0</td>\n",
       "      <td> 156</td>\n",
       "      <td> 31.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6 </th>\n",
       "      <td> South Africa</td>\n",
       "      <td>    01889-03-12-000012 March 1889</td>\n",
       "      <td> 390</td>\n",
       "      <td> 144</td>\n",
       "      <td> 129</td>\n",
       "      <td> 0</td>\n",
       "      <td> 117</td>\n",
       "      <td> 36.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7 </th>\n",
       "      <td>    Sri Lanka</td>\n",
       "      <td> 01982-02-17-000017 February 1982</td>\n",
       "      <td> 235</td>\n",
       "      <td>  71</td>\n",
       "      <td>  84</td>\n",
       "      <td> 0</td>\n",
       "      <td>  80</td>\n",
       "      <td> 30.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8 </th>\n",
       "      <td>  West Indies</td>\n",
       "      <td>     01928-06-23-000023 June 1928</td>\n",
       "      <td> 503</td>\n",
       "      <td> 163</td>\n",
       "      <td> 170</td>\n",
       "      <td> 1</td>\n",
       "      <td> 169</td>\n",
       "      <td> 32.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9 </th>\n",
       "      <td>     Zimbabwe</td>\n",
       "      <td>  01992-10-18-000018 October 1992</td>\n",
       "      <td>  97</td>\n",
       "      <td>  11</td>\n",
       "      <td>  60</td>\n",
       "      <td> 0</td>\n",
       "      <td>  26</td>\n",
       "      <td> 11.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td> ICC World XI</td>\n",
       "      <td>  02005-10-14-000014 October 2005</td>\n",
       "      <td>   1</td>\n",
       "      <td>   0</td>\n",
       "      <td>   1</td>\n",
       "      <td> 0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Team                  First Test match Matches  Won Lost Tied  \\\n",
       "0      Australia     01877-03-15-000015 March 1877     773  362  205    2   \n",
       "1     Bangladesh  02000-11-10-000010 November 2000      88    7   70    0   \n",
       "2        England     01877-03-15-000015 March 1877     952  339  275    0   \n",
       "3          India      01932-06-25-000025 June 1932     487  122  156    1   \n",
       "4    New Zealand   01930-01-10-000010 January 1930     399   80  160    0   \n",
       "5       Pakistan   01952-10-16-000016 October 1952     387  121  110    0   \n",
       "6   South Africa     01889-03-12-000012 March 1889     390  144  129    0   \n",
       "7      Sri Lanka  01982-02-17-000017 February 1982     235   71   84    0   \n",
       "8    West Indies      01928-06-23-000023 June 1928     503  163  170    1   \n",
       "9       Zimbabwe   01992-10-18-000018 October 1992      97   11   60    0   \n",
       "10  ICC World XI   02005-10-14-000014 October 2005       1    0    1    0   \n",
       "\n",
       "   Drawn  % Won  \n",
       "0    204  46.83  \n",
       "1     11   7.95  \n",
       "2    338  35.60  \n",
       "3    208  25.05  \n",
       "4    159  20.05  \n",
       "5    156  31.26  \n",
       "6    117  36.92  \n",
       "7     80  30.21  \n",
       "8    169  32.40  \n",
       "9     26  11.34  \n",
       "10     0   0.00  "
      ]
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      "None\n"
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   "source": [
    "#Input:\n",
    "#    url: URL of a wikipedia page\n",
    "#    table_id: Index of the table to scrape\n",
    "#Output:\n",
    "#    df is a Pandas data frame that contains a tabular representation of the table\n",
    "#    The columns are named the same as the table columns\n",
    "#    Each row of df corresponds to a row in the table\n",
    "def scraping_wikipedia_table(url, table_id):\n",
    "    website_html = requests.get(url).text\n",
    "    dom = web.Element(website_html)\n",
    "    tbls = [t for t in dom.by_tag('table')]\n",
    "    tbl = tbls[table_id-1]  #correct the off by one error\n",
    "    headerRow = tbl.by_tag(\"tr\")[0] #simple trick - first row always gives the header\n",
    "    #the title could be inside a th tag or td tag\n",
    "    # use the in operator to determine which is the case and use the right filtering\n",
    "    if len(headerRow.by_tag(\"th\")) > 0:\n",
    "        heading = [web.plaintext(th.content) for th in headerRow.by_tag('th')]\n",
    "    else:\n",
    "        heading = [web.plaintext(td.content) for td in headerRow.by_tag('td')]\n",
    "\n",
    "    #DataFrame has many constructors. On a whim I am choosing the list of dicts\n",
    "    arrayOfDict = [] \n",
    "    for row in tbl.by_tag(\"tr\")[1:-1] : #ignore the last row about updated date \n",
    "        thContents = [web.plaintext(td.html) for td in row.by_tag(\"th\")]\n",
    "        tdContents = [web.plaintext(td.html) for td in row.by_tag(\"td\")]\n",
    "        contents = thContents + tdContents #big time hack - assumes th, if present is always the first column\n",
    "        #Convert contents to a dictionary with appropriate headings\n",
    "        rowAsDict = {heading[i] : contents[i] for i in range(len(contents))}\n",
    "        arrayOfDict.append(rowAsDict)\n",
    "    df = DataFrame(arrayOfDict,columns=heading)\n",
    "    return df\n",
    "\n",
    "print display(scraping_wikipedia_table(\"http://en.wikipedia.org/wiki/List_of_Test_cricket_records\", 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Website 2: Walmart\n",
    "\n",
    "In the second task, we will seek to scrape the results of Walmart. Specifically, we will focus on Movies department. Your job is to implement a function that accepts a valid search result url  (such as http://www.walmart.com/search/?query=marvel&cat_id=4096_530598 ), scrapes the 10-15 product listings in that url only and outputs a Pandas data frame with the following fields (with the EXACT names and data types as below): \n",
    "\n",
    "+ Product Title: String. Title of the product (such as Samsung Red WB1100F Smart Camera with 16.2 Megapixels and 35x Optical Zoom)\n",
    "+ Sale Price: Float. Price of the product (such as 249.99). This is the price that is highlighted in blue typically. Ignore the dollar sign and return the value only\n",
    "+ Number of Ratings: Integer. The number of ratings that users have provided for the product. This is the number associated with the star.\n",
    "+ Free Shipping: Boolean. Is True if the product has free shipping above $50 and false otherwise. \n",
    "+ Free Store Pickup: Boolean. Is True if the product has free store pickup and false otherwise. \n",
    "+ Starring: String. Contains the name of starring actors. Convert it to a simple string such as \"Chris Pratt Zoe Saldana Vin Diesel\" is okay. No need to parse it further.\n",
    "+ Running: Integer. The running time of the movie. Only the integer value is required (ie. NNNN minutes then return only NNNN).\n",
    "+ Format: String. Values such as Widescreen. Beware, some entries might not have this value.\n",
    "+ Rating: String. MPAA rating.\n",
    "\n",
    "Make sure that you observe the following:\n",
    "\n",
    "* If a data type is specified, then your data should be in that format. For eg, if a field is int, then ensure that it has an integer value.\n",
    "* Your code should not crash if some product did not have a valid value such as price. Instead you must fill it with NA (see Pandas tutorial for what NA is). "
   ]
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      "text/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>Product Title</th>\n",
       "      <th>Sale Price</th>\n",
       "      <th>Number of Ratings</th>\n",
       "      <th>Free Shipping</th>\n",
       "      <th>Free Store Pickup</th>\n",
       "      <th>Starring</th>\n",
       "      <th>Running</th>\n",
       "      <th>Format</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0 </th>\n",
       "      <td>                         Marvel Heroes: Collection</td>\n",
       "      <td> $33.98</td>\n",
       "      <td>  (1)\\nratings</td>\n",
       "      <td>  True</td>\n",
       "      <td>  True</td>\n",
       "      <td> * Rebecca Romijn\\n* Hugh Jackman\\n* Chris Evan...</td>\n",
       "      <td> 856 minutes</td>\n",
       "      <td>        NaN</td>\n",
       "      <td>     PG-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 </th>\n",
       "      <td>                Marvel's The Avengers (Widescreen)</td>\n",
       "      <td> $19.96</td>\n",
       "      <td> (68)\\nratings</td>\n",
       "      <td>  True</td>\n",
       "      <td>  True</td>\n",
       "      <td> * Robert Downey Jr.\\n* Chris Evans\\n* Mark Ruf...</td>\n",
       "      <td> 145 minutes</td>\n",
       "      <td> Widescreen</td>\n",
       "      <td>     PG-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 </th>\n",
       "      <td>      Marvel: Guardians Of The Galaxy (Widescreen)</td>\n",
       "      <td> $19.96</td>\n",
       "      <td> (44)\\nratings</td>\n",
       "      <td>  True</td>\n",
       "      <td>  True</td>\n",
       "      <td> * Chris Pratt\\n* Zoe Saldana\\n* Vin Diesel\\n* ...</td>\n",
       "      <td> 121 minutes</td>\n",
       "      <td> Widescreen</td>\n",
       "      <td>     PG-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 </th>\n",
       "      <td> Marvel Knights: Wolverine Versus Sabretooth - ...</td>\n",
       "      <td> $13.47</td>\n",
       "      <td>           NaN</td>\n",
       "      <td> False</td>\n",
       "      <td> False</td>\n",
       "      <td>                     * Jeph Loeb\\n* Simone Bianchi</td>\n",
       "      <td>  44 minutes</td>\n",
       "      <td> Widescreen</td>\n",
       "      <td> Not Rated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4 </th>\n",
       "      <td> Marvel's The Avengers (DVD + Blu-ray) (Widescr...</td>\n",
       "      <td> $29.96</td>\n",
       "      <td>  (5)\\nratings</td>\n",
       "      <td>  True</td>\n",
       "      <td>  True</td>\n",
       "      <td> * Robert Downey Jr.\\n* Chris Evans\\n* Mark Ruf...</td>\n",
       "      <td> 145 minutes</td>\n",
       "      <td> Widescreen</td>\n",
       "      <td>     PG-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 </th>\n",
       "      <td>              Marvel Complete Giftset (Widescreen)</td>\n",
       "      <td> $26.96</td>\n",
       "      <td>           NaN</td>\n",
       "      <td>  True</td>\n",
       "      <td>  True</td>\n",
       "      <td> * Justin Gross\\n* Grey Delisle\\n* Michael Mass...</td>\n",
       "      <td> 640 minutes</td>\n",
       "      <td> Widescreen</td>\n",
       "      <td>     PG-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6 </th>\n",
       "      <td> Elektra / Fantastic Four / Daredevil (Director...</td>\n",
       "      <td> $13.99</td>\n",
       "      <td>           NaN</td>\n",
       "      <td>  True</td>\n",
       "      <td>  True</td>\n",
       "      <td> * Jessica Alba\\n* Chris Evans\\n* Jennifer Garn...</td>\n",
       "      <td>         NaN</td>\n",
       "      <td>        NaN</td>\n",
       "      <td> Not Rated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7 </th>\n",
       "      <td>          The Punisher (Extended Cut) (Widescreen)</td>\n",
       "      <td>  $7.00</td>\n",
       "      <td>  (4)\\nratings</td>\n",
       "      <td>  True</td>\n",
       "      <td>  True</td>\n",
       "      <td> * John Travolta\\n* Thomas Jane\\n* Ben Foster\\n...</td>\n",
       "      <td> 140 minutes</td>\n",
       "      <td> Widescreen</td>\n",
       "      <td>         R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8 </th>\n",
       "      <td>                         Spider-Man 2 (Widescreen)</td>\n",
       "      <td> $12.96</td>\n",
       "      <td>  (3)\\nratings</td>\n",
       "      <td>  True</td>\n",
       "      <td>  True</td>\n",
       "      <td> * Tobey Maguire\\n* Kirsten Dunst\\n* James Fran...</td>\n",
       "      <td> 128 minutes</td>\n",
       "      <td> Widescreen</td>\n",
       "      <td>     PG-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9 </th>\n",
       "      <td> Superheroes Collection: The Incredible Hulk Re...</td>\n",
       "      <td> $13.47</td>\n",
       "      <td>           NaN</td>\n",
       "      <td>  True</td>\n",
       "      <td>  True</td>\n",
       "      <td> * Bill Bixby\\n* Jack Colvin\\n* Lou Ferrigno\\n*...</td>\n",
       "      <td> 247 minutes</td>\n",
       "      <td> Widescreen</td>\n",
       "      <td> Not Rated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td> Marvel: Iron Man &amp; Hulk - Heroes United (Wides...</td>\n",
       "      <td> $19.96</td>\n",
       "      <td>  (1)\\nratings</td>\n",
       "      <td>  True</td>\n",
       "      <td>  True</td>\n",
       "      <td>  * Adrian Pasdar\\n* Fred Tatasciore\\n* David Kaye</td>\n",
       "      <td>  72 minutes</td>\n",
       "      <td> Widescreen</td>\n",
       "      <td>        PG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>  Captain America: The Winter Soldier (Widescreen)</td>\n",
       "      <td> $19.96</td>\n",
       "      <td> (19)\\nratings</td>\n",
       "      <td>  True</td>\n",
       "      <td>  True</td>\n",
       "      <td> * Chris Evans\\n* Scarlett Johansson\\n* Anthony...</td>\n",
       "      <td> 136 minutes</td>\n",
       "      <td> Widescreen</td>\n",
       "      <td>     PG-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>      Iron Man 3 (DVD + Digital Copy) (Widescreen)</td>\n",
       "      <td> $19.96</td>\n",
       "      <td> (26)\\nratings</td>\n",
       "      <td>  True</td>\n",
       "      <td>  True</td>\n",
       "      <td> * Robert Downey Jr.\\n* Gwyneth Paltrow\\n* Don ...</td>\n",
       "      <td> 135 minutes</td>\n",
       "      <td> Widescreen</td>\n",
       "      <td>     PG-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>                 Thor: The Dark World (Widescreen)</td>\n",
       "      <td> $19.96</td>\n",
       "      <td> (22)\\nratings</td>\n",
       "      <td>  True</td>\n",
       "      <td>  True</td>\n",
       "      <td> * Chris Hemsworth\\n* Natalie Portman\\n* Tom Hi...</td>\n",
       "      <td> 112 minutes</td>\n",
       "      <td> Widescreen</td>\n",
       "      <td>     PG-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td> Spider-Man (2-Disc) (Special Edition) (Widescr...</td>\n",
       "      <td> $12.96</td>\n",
       "      <td> (11)\\nratings</td>\n",
       "      <td>  True</td>\n",
       "      <td>  True</td>\n",
       "      <td> * Tobey Maguire\\n* Willem Dafoe\\n* Kirsten Dun...</td>\n",
       "      <td> 121 minutes</td>\n",
       "      <td> Widescreen</td>\n",
       "      <td>     PG-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td> Spider-Man / Spider-Man 2 / Spider-Man 3 (Wide...</td>\n",
       "      <td> $25.46</td>\n",
       "      <td>           NaN</td>\n",
       "      <td> False</td>\n",
       "      <td>  True</td>\n",
       "      <td> * Tobey Maguire\\n* Kirsten Dunst\\n* James Fran...</td>\n",
       "      <td> 523 minutes</td>\n",
       "      <td> Widescreen</td>\n",
       "      <td>     PG-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td> DC Showcase: Superman / Shazam!: The Return Of...</td>\n",
       "      <td>  $9.07</td>\n",
       "      <td> (10)\\nratings</td>\n",
       "      <td>  True</td>\n",
       "      <td>  True</td>\n",
       "      <td>                                               NaN</td>\n",
       "      <td>  63 minutes</td>\n",
       "      <td>        NaN</td>\n",
       "      <td>     PG-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td> The Next Avengers: Heroes Of Tomorrow (Widescr...</td>\n",
       "      <td>  $5.00</td>\n",
       "      <td>  (9)\\nratings</td>\n",
       "      <td>  True</td>\n",
       "      <td>  True</td>\n",
       "      <td> * Noah Crawford\\n* Nicole Oliver\\n* Shawn MacD...</td>\n",
       "      <td>  78 minutes</td>\n",
       "      <td> Widescreen</td>\n",
       "      <td>        PG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>   Ultimate Avengers Movie Collection (Widescreen)</td>\n",
       "      <td> $11.17</td>\n",
       "      <td>  (5)\\nratings</td>\n",
       "      <td>  True</td>\n",
       "      <td>  True</td>\n",
       "      <td>                                               NaN</td>\n",
       "      <td> 222 minutes</td>\n",
       "      <td> Widescreen</td>\n",
       "      <td>     PG-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>         Ultimate Avengers: The Movie (Widescreen)</td>\n",
       "      <td>  $5.39</td>\n",
       "      <td>  (7)\\nratings</td>\n",
       "      <td>  True</td>\n",
       "      <td>  True</td>\n",
       "      <td> * Justin Gross\\n* Grey DeLisle\\n* Michael Mass...</td>\n",
       "      <td>  71 minutes</td>\n",
       "      <td> Widescreen</td>\n",
       "      <td>     PG-13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Product Title Sale Price  \\\n",
       "0                           Marvel Heroes: Collection     $33.98   \n",
       "1                  Marvel's The Avengers (Widescreen)     $19.96   \n",
       "2        Marvel: Guardians Of The Galaxy (Widescreen)     $19.96   \n",
       "3   Marvel Knights: Wolverine Versus Sabretooth - ...     $13.47   \n",
       "4   Marvel's The Avengers (DVD + Blu-ray) (Widescr...     $29.96   \n",
       "5                Marvel Complete Giftset (Widescreen)     $26.96   \n",
       "6   Elektra / Fantastic Four / Daredevil (Director...     $13.99   \n",
       "7            The Punisher (Extended Cut) (Widescreen)      $7.00   \n",
       "8                           Spider-Man 2 (Widescreen)     $12.96   \n",
       "9   Superheroes Collection: The Incredible Hulk Re...     $13.47   \n",
       "10  Marvel: Iron Man & Hulk - Heroes United (Wides...     $19.96   \n",
       "11   Captain America: The Winter Soldier (Widescreen)     $19.96   \n",
       "12       Iron Man 3 (DVD + Digital Copy) (Widescreen)     $19.96   \n",
       "13                  Thor: The Dark World (Widescreen)     $19.96   \n",
       "14  Spider-Man (2-Disc) (Special Edition) (Widescr...     $12.96   \n",
       "15  Spider-Man / Spider-Man 2 / Spider-Man 3 (Wide...     $25.46   \n",
       "16  DC Showcase: Superman / Shazam!: The Return Of...      $9.07   \n",
       "17  The Next Avengers: Heroes Of Tomorrow (Widescr...      $5.00   \n",
       "18    Ultimate Avengers Movie Collection (Widescreen)     $11.17   \n",
       "19          Ultimate Avengers: The Movie (Widescreen)      $5.39   \n",
       "\n",
       "   Number of Ratings Free Shipping Free Store Pickup  \\\n",
       "0       (1)\\nratings          True              True   \n",
       "1      (68)\\nratings          True              True   \n",
       "2      (44)\\nratings          True              True   \n",
       "3                NaN         False             False   \n",
       "4       (5)\\nratings          True              True   \n",
       "5                NaN          True              True   \n",
       "6                NaN          True              True   \n",
       "7       (4)\\nratings          True              True   \n",
       "8       (3)\\nratings          True              True   \n",
       "9                NaN          True              True   \n",
       "10      (1)\\nratings          True              True   \n",
       "11     (19)\\nratings          True              True   \n",
       "12     (26)\\nratings          True              True   \n",
       "13     (22)\\nratings          True              True   \n",
       "14     (11)\\nratings          True              True   \n",
       "15               NaN         False              True   \n",
       "16     (10)\\nratings          True              True   \n",
       "17      (9)\\nratings          True              True   \n",
       "18      (5)\\nratings          True              True   \n",
       "19      (7)\\nratings          True              True   \n",
       "\n",
       "                                             Starring      Running  \\\n",
       "0   * Rebecca Romijn\\n* Hugh Jackman\\n* Chris Evan...  856 minutes   \n",
       "1   * Robert Downey Jr.\\n* Chris Evans\\n* Mark Ruf...  145 minutes   \n",
       "2   * Chris Pratt\\n* Zoe Saldana\\n* Vin Diesel\\n* ...  121 minutes   \n",
       "3                       * Jeph Loeb\\n* Simone Bianchi   44 minutes   \n",
       "4   * Robert Downey Jr.\\n* Chris Evans\\n* Mark Ruf...  145 minutes   \n",
       "5   * Justin Gross\\n* Grey Delisle\\n* Michael Mass...  640 minutes   \n",
       "6   * Jessica Alba\\n* Chris Evans\\n* Jennifer Garn...          NaN   \n",
       "7   * John Travolta\\n* Thomas Jane\\n* Ben Foster\\n...  140 minutes   \n",
       "8   * Tobey Maguire\\n* Kirsten Dunst\\n* James Fran...  128 minutes   \n",
       "9   * Bill Bixby\\n* Jack Colvin\\n* Lou Ferrigno\\n*...  247 minutes   \n",
       "10   * Adrian Pasdar\\n* Fred Tatasciore\\n* David Kaye   72 minutes   \n",
       "11  * Chris Evans\\n* Scarlett Johansson\\n* Anthony...  136 minutes   \n",
       "12  * Robert Downey Jr.\\n* Gwyneth Paltrow\\n* Don ...  135 minutes   \n",
       "13  * Chris Hemsworth\\n* Natalie Portman\\n* Tom Hi...  112 minutes   \n",
       "14  * Tobey Maguire\\n* Willem Dafoe\\n* Kirsten Dun...  121 minutes   \n",
       "15  * Tobey Maguire\\n* Kirsten Dunst\\n* James Fran...  523 minutes   \n",
       "16                                                NaN   63 minutes   \n",
       "17  * Noah Crawford\\n* Nicole Oliver\\n* Shawn MacD...   78 minutes   \n",
       "18                                                NaN  222 minutes   \n",
       "19  * Justin Gross\\n* Grey DeLisle\\n* Michael Mass...   71 minutes   \n",
       "\n",
       "        Format     Rating  \n",
       "0          NaN      PG-13  \n",
       "1   Widescreen      PG-13  \n",
       "2   Widescreen      PG-13  \n",
       "3   Widescreen  Not Rated  \n",
       "4   Widescreen      PG-13  \n",
       "5   Widescreen      PG-13  \n",
       "6          NaN  Not Rated  \n",
       "7   Widescreen          R  \n",
       "8   Widescreen      PG-13  \n",
       "9   Widescreen  Not Rated  \n",
       "10  Widescreen         PG  \n",
       "11  Widescreen      PG-13  \n",
       "12  Widescreen      PG-13  \n",
       "13  Widescreen      PG-13  \n",
       "14  Widescreen      PG-13  \n",
       "15  Widescreen      PG-13  \n",
       "16         NaN      PG-13  \n",
       "17  Widescreen         PG  \n",
       "18  Widescreen      PG-13  \n",
       "19  Widescreen      PG-13  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "#Input:\n",
    "#    url: URL of a Walmart results page for a search query in Movies department\n",
    "#Output:\n",
    "#    df is a Pandas data frame that contains a tabular representation of the results\n",
    "#    The df must have 9 columns that must have the same name and data type as described above\n",
    "#    Each row of df corresponds to a movie in the results table\n",
    "\n",
    "def getValuesSafely(domObj, domSearchString):\n",
    "    results = domObj(domSearchString)\n",
    "    if len(results) > 0:\n",
    "        return web.plaintext(results[0].content)\n",
    "    else:\n",
    "        return np.nan\n",
    "    \n",
    "def scraping_walmart_movies(url):\n",
    "    website_html = requests.get(url).text\n",
    "    dom = web.Element(website_html)\n",
    "    heading = ['Product Title', 'Sale Price', 'Number of Ratings', 'Free Shipping', 'Free Store Pickup', \n",
    "                'Starring', 'Running', 'Format', 'Rating']\n",
    "\n",
    "    arrayOfDict = []\n",
    "\n",
    "    movies = dom(\"div.js-tile.tile-landscape\")\n",
    "    for movie in movies:\n",
    "        productTitle = getValuesSafely(movie, 'h4 > a')\n",
    "        salesPrice = getValuesSafely(movie, 'div.item-price-container > span.price.price-display')\n",
    "        numRatings = getValuesSafely(movie,'span.stars-reviews')\n",
    "        freeShipping = \"shipping on orders over $50\" in movie #Horrible hack\n",
    "        freeStorePickup = \"store pickup\" in movie #Horrible Hack\n",
    "        dl = movie('dl.media-details.dl-horizontal')\n",
    "        dt = dl[0].by_tag(\"dt\")\n",
    "        dd = dl[0].by_tag(\"dd\")\n",
    "\n",
    "        tempOrder = [\"Director:\", \"Starring:\", \"Running:\", \"Format:\", \"Release:\", \"Rating:\"]\n",
    "        tempDict = {key : np.nan for key in tempOrder} \n",
    "        index = 0\n",
    "        for key in tempOrder:\n",
    "            if key in movie:\n",
    "                tempDict[key] = web.plaintext(dd[index].content)\n",
    "                index = index + 1\n",
    "                \n",
    "        movieAsDict = {'Product Title': productTitle, 'Sale Price': salesPrice, 'Number of Ratings': numRatings, \n",
    "                       'Free Shipping':freeShipping,  'Free Store Pickup':freeStorePickup, \n",
    "                       'Starring': tempDict[\"Starring:\"], 'Running': tempDict[\"Running:\"], \n",
    "                       'Format': tempDict[\"Format:\"], 'Rating': tempDict[\"Rating:\"]}\n",
    "        arrayOfDict.append(movieAsDict)\n",
    "        \n",
    "\n",
    "    df = DataFrame(arrayOfDict,columns=heading)\n",
    "    return df \n",
    "\n",
    "print display(scraping_walmart_movies('http://www.walmart.com/search/?query=marvel&cat_id=4096_530598'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Website 3: Facebook\n",
    "\n",
    "In the third task, we will scrape some specific sub-pages of Facebook profiles. In order to avoid using sophisticated tools like Selenium, for this task you can assume that the input to the function is the DOM of the relevant page. You can then use it to parse relevant contents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Books in Zuck's FB:\n",
      "[u'Gang Leader for a Day: A Rogue Sociologist Takes to the Streets', u'The Better Angels of Our Nature', u'American Lion: Andrew Jackson in the White House', u\"The House of Rothschild: Volume 1: Money's Prophets: 1798-1848\", u'Open: An Autobiography', u'The Information: A History, a Theory, a Flood', u\"Surely You're Joking, Mr. Feynman!\", u'The Idea Factory: Bell Labs and the Great Age of American Innovation', u'Decoded', u'Einstein: His Life and Universe', u'Physics of the Impossible', u\"Ender's Shadow\", u'The Giving Tree', u\"Ender's Game\"]\n",
      "\n",
      "\n",
      "Groups in Zuck's FB:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/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>Group Name</th>\n",
       "      <th>Number of Members</th>\n",
       "      <th>Group Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> Pulis</td>\n",
       "      <td> 30 members</td>\n",
       "      <td> This is a group for our favorite Hungarian bre...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Group Name Number of Members  \\\n",
       "0      Pulis        30 members   \n",
       "\n",
       "                                   Group Description  \n",
       "0  This is a group for our favorite Hungarian bre...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Music in Zuck's FB:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/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>Name</th>\n",
       "      <th>Type</th>\n",
       "      <th>Verified</th>\n",
       "      <th>Profile Url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0 </th>\n",
       "      <td>          Imagine Dragons</td>\n",
       "      <td> Musician/Band</td>\n",
       "      <td> True</td>\n",
       "      <td> https://www.facebook.com/ImagineDragons?ref=pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 </th>\n",
       "      <td>                  Beyoncé</td>\n",
       "      <td> Musician/Band</td>\n",
       "      <td> True</td>\n",
       "      <td>      https://www.facebook.com/beyonce?ref=profile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 </th>\n",
       "      <td>                  Rihanna</td>\n",
       "      <td>        Artist</td>\n",
       "      <td> True</td>\n",
       "      <td>      https://www.facebook.com/rihanna?ref=profile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 </th>\n",
       "      <td> The All-American Rejects</td>\n",
       "      <td> Musician/Band</td>\n",
       "      <td> True</td>\n",
       "      <td> https://www.facebook.com/AllAmericanRejects?re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4 </th>\n",
       "      <td>          LCD Soundsystem</td>\n",
       "      <td> Musician/Band</td>\n",
       "      <td> True</td>\n",
       "      <td> https://www.facebook.com/lcdsoundsystem?ref=pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 </th>\n",
       "      <td>                Daft Punk</td>\n",
       "      <td> Musician/Band</td>\n",
       "      <td> True</td>\n",
       "      <td>     https://www.facebook.com/daftpunk?ref=profile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6 </th>\n",
       "      <td>              Linkin Park</td>\n",
       "      <td> Musician/Band</td>\n",
       "      <td> True</td>\n",
       "      <td>   https://www.facebook.com/linkinPark?ref=profile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7 </th>\n",
       "      <td>        Ingrid Michaelson</td>\n",
       "      <td> Musician/Band</td>\n",
       "      <td> True</td>\n",
       "      <td> https://www.facebook.com/ingridmichaelson?ref=...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8 </th>\n",
       "      <td>                Radiohead</td>\n",
       "      <td> Musician/Band</td>\n",
       "      <td> True</td>\n",
       "      <td>    https://www.facebook.com/radiohead?ref=profile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9 </th>\n",
       "      <td>                  Shakira</td>\n",
       "      <td> Musician/Band</td>\n",
       "      <td> True</td>\n",
       "      <td>      https://www.facebook.com/shakira?ref=profile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>                  Nirvana</td>\n",
       "      <td> Musician/Band</td>\n",
       "      <td> True</td>\n",
       "      <td>      https://www.facebook.com/Nirvana?ref=profile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>                    JAY Z</td>\n",
       "      <td> Musician/Band</td>\n",
       "      <td> True</td>\n",
       "      <td>         https://www.facebook.com/JayZ?ref=profile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>              The Killers</td>\n",
       "      <td> Musician/Band</td>\n",
       "      <td> True</td>\n",
       "      <td>   https://www.facebook.com/Thekillers?ref=profile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>                Green Day</td>\n",
       "      <td> Musician/Band</td>\n",
       "      <td> True</td>\n",
       "      <td>     https://www.facebook.com/GreenDay?ref=profile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>             Taylor Swift</td>\n",
       "      <td> Musician/Band</td>\n",
       "      <td> True</td>\n",
       "      <td>  https://www.facebook.com/TaylorSwift?ref=profile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>                Lady Gaga</td>\n",
       "      <td> Musician/Band</td>\n",
       "      <td> True</td>\n",
       "      <td>     https://www.facebook.com/ladygaga?ref=profile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>               Katy Perry</td>\n",
       "      <td> Musician/Band</td>\n",
       "      <td> True</td>\n",
       "      <td>    https://www.facebook.com/katyperry?ref=profile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>                       U2</td>\n",
       "      <td> Musician/Band</td>\n",
       "      <td> True</td>\n",
       "      <td>           https://www.facebook.com/u2?ref=profile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>               John Mayer</td>\n",
       "      <td> Musician/Band</td>\n",
       "      <td> True</td>\n",
       "      <td>    https://www.facebook.com/johnmayer?ref=profile</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Name           Type Verified  \\\n",
       "0            Imagine Dragons  Musician/Band     True   \n",
       "1                    Beyoncé  Musician/Band     True   \n",
       "2                    Rihanna         Artist     True   \n",
       "3   The All-American Rejects  Musician/Band     True   \n",
       "4            LCD Soundsystem  Musician/Band     True   \n",
       "5                  Daft Punk  Musician/Band     True   \n",
       "6                Linkin Park  Musician/Band     True   \n",
       "7          Ingrid Michaelson  Musician/Band     True   \n",
       "8                  Radiohead  Musician/Band     True   \n",
       "9                    Shakira  Musician/Band     True   \n",
       "10                   Nirvana  Musician/Band     True   \n",
       "11                     JAY Z  Musician/Band     True   \n",
       "12               The Killers  Musician/Band     True   \n",
       "13                 Green Day  Musician/Band     True   \n",
       "14              Taylor Swift  Musician/Band     True   \n",
       "15                 Lady Gaga  Musician/Band     True   \n",
       "16                Katy Perry  Musician/Band     True   \n",
       "17                        U2  Musician/Band     True   \n",
       "18                John Mayer  Musician/Band     True   \n",
       "\n",
       "                                          Profile Url  \n",
       "0   https://www.facebook.com/ImagineDragons?ref=pr...  \n",
       "1        https://www.facebook.com/beyonce?ref=profile  \n",
       "2        https://www.facebook.com/rihanna?ref=profile  \n",
       "3   https://www.facebook.com/AllAmericanRejects?re...  \n",
       "4   https://www.facebook.com/lcdsoundsystem?ref=pr...  \n",
       "5       https://www.facebook.com/daftpunk?ref=profile  \n",
       "6     https://www.facebook.com/linkinPark?ref=profile  \n",
       "7   https://www.facebook.com/ingridmichaelson?ref=...  \n",
       "8      https://www.facebook.com/radiohead?ref=profile  \n",
       "9        https://www.facebook.com/shakira?ref=profile  \n",
       "10       https://www.facebook.com/Nirvana?ref=profile  \n",
       "11          https://www.facebook.com/JayZ?ref=profile  \n",
       "12    https://www.facebook.com/Thekillers?ref=profile  \n",
       "13      https://www.facebook.com/GreenDay?ref=profile  \n",
       "14   https://www.facebook.com/TaylorSwift?ref=profile  \n",
       "15      https://www.facebook.com/ladygaga?ref=profile  \n",
       "16     https://www.facebook.com/katyperry?ref=profile  \n",
       "17            https://www.facebook.com/u2?ref=profile  \n",
       "18     https://www.facebook.com/johnmayer?ref=profile  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Movies in Zuck's FB:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/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>Name</th>\n",
       "      <th>Type</th>\n",
       "      <th>Verified</th>\n",
       "      <th>Profile Url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0 </th>\n",
       "      <td>                    Hero</td>\n",
       "      <td>   Movie</td>\n",
       "      <td>  True</td>\n",
       "      <td>    https://www.facebook.com/HeroMovie?ref=profile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 </th>\n",
       "      <td>        Django Unchained</td>\n",
       "      <td>   Movie</td>\n",
       "      <td>  True</td>\n",
       "      <td> https://www.facebook.com/unchainedmovie?ref=pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 </th>\n",
       "      <td>               MoneyBall</td>\n",
       "      <td>   Movie</td>\n",
       "      <td>  True</td>\n",
       "      <td> https://www.facebook.com/MoneyballMovie?ref=pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 </th>\n",
       "      <td>               Gladiator</td>\n",
       "      <td>   Movie</td>\n",
       "      <td>  True</td>\n",
       "      <td> https://www.facebook.com/gladiatormovie?ref=pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4 </th>\n",
       "      <td>              The Matrix</td>\n",
       "      <td>   Movie</td>\n",
       "      <td>  True</td>\n",
       "      <td> https://www.facebook.com/TheMatrixMovie?ref=pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 </th>\n",
       "      <td>               Star Wars</td>\n",
       "      <td>   Movie</td>\n",
       "      <td>  True</td>\n",
       "      <td>     https://www.facebook.com/StarWars?ref=profile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6 </th>\n",
       "      <td> Batman: The Dark Knight</td>\n",
       "      <td>   Movie</td>\n",
       "      <td>  True</td>\n",
       "      <td>   https://www.facebook.com/darkknight?ref=profile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7 </th>\n",
       "      <td>  Waiting for \"Superman\"</td>\n",
       "      <td>   Movie</td>\n",
       "      <td>  True</td>\n",
       "      <td> https://www.facebook.com/waitingforsuperman?re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8 </th>\n",
       "      <td>                Iron Man</td>\n",
       "      <td>   Movie</td>\n",
       "      <td>  True</td>\n",
       "      <td>      https://www.facebook.com/ironman?ref=profile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9 </th>\n",
       "      <td>                Fearless</td>\n",
       "      <td>   Movie</td>\n",
       "      <td> False</td>\n",
       "      <td> https://www.facebook.com/pages/Fearless/112430...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>           The Godfather</td>\n",
       "      <td>   Movie</td>\n",
       "      <td>  True</td>\n",
       "      <td> https://www.facebook.com/thegodfather?ref=profile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>            Disney Pixar</td>\n",
       "      <td> Company</td>\n",
       "      <td>  True</td>\n",
       "      <td>  https://www.facebook.com/DisneyPixar?ref=profile</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Name     Type Verified  \\\n",
       "0                      Hero    Movie     True   \n",
       "1          Django Unchained    Movie     True   \n",
       "2                 MoneyBall    Movie     True   \n",
       "3                 Gladiator    Movie     True   \n",
       "4                The Matrix    Movie     True   \n",
       "5                 Star Wars    Movie     True   \n",
       "6   Batman: The Dark Knight    Movie     True   \n",
       "7    Waiting for \"Superman\"    Movie     True   \n",
       "8                  Iron Man    Movie     True   \n",
       "9                  Fearless    Movie    False   \n",
       "10            The Godfather    Movie     True   \n",
       "11             Disney Pixar  Company     True   \n",
       "\n",
       "                                          Profile Url  \n",
       "0      https://www.facebook.com/HeroMovie?ref=profile  \n",
       "1   https://www.facebook.com/unchainedmovie?ref=pr...  \n",
       "2   https://www.facebook.com/MoneyballMovie?ref=pr...  \n",
       "3   https://www.facebook.com/gladiatormovie?ref=pr...  \n",
       "4   https://www.facebook.com/TheMatrixMovie?ref=pr...  \n",
       "5       https://www.facebook.com/StarWars?ref=profile  \n",
       "6     https://www.facebook.com/darkknight?ref=profile  \n",
       "7   https://www.facebook.com/waitingforsuperman?re...  \n",
       "8        https://www.facebook.com/ironman?ref=profile  \n",
       "9   https://www.facebook.com/pages/Fearless/112430...  \n",
       "10  https://www.facebook.com/thegodfather?ref=profile  \n",
       "11   https://www.facebook.com/DisneyPixar?ref=profile  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Input: dom - DOM of the books page corresponding to an FB account's profile. Eg, DOM of https://www.facebook.com/zuck/books\n",
    "#Output: An array (Python list) of books listed in the profile page. \n",
    "#    Note that this function requires a list as an output not a Pandas data frame\n",
    "def scraping_facebook_books(dom):\n",
    "      return [li(\"a._gx7\")[0].title for li in dom(\"ul.uiList > li\")]\n",
    "    \n",
    "#Input: dom - DOM of the groups page corresponding to an FB account's profile. Eg, DOM of https://www.facebook.com/zuck/groups\n",
    "#Output: A Pandas data frame with one row per group. \n",
    "#    It must have three columns - 'Group Name', 'Number of Members', 'Group Description'\n",
    "#    Note that all information of a group is in the same page (eg. https://www.facebook.com/zuck/groups)\n",
    "#    You can collect all data from same page even if they are incomplete (such as group description)\n",
    "#    Ensure that the column names as given above\n",
    "def scraping_facebook_groups(dom):\n",
    "    arrayOfDict = []\n",
    "    for li in dom(\"ul.uiList > li\"):\n",
    "        groupAsDict = {}\n",
    "        groupAsDict[\"Group Name\"] = web.plaintext(li(\"div.mbs.fwb\")[0].content)\n",
    "        groupAsDict[\"Number of Members\"] = web.plaintext(li(\"div.mbs.fcg\")[0].content)\n",
    "        groupAsDict[\"Group Description\"] = web.plaintext(li(\"div.mtm > span\")[0].content)\n",
    "        arrayOfDict.append(groupAsDict)\n",
    "    df = DataFrame(arrayOfDict,columns=[\"Group Name\", \"Number of Members\", \"Group Description\"])\n",
    "    return df\n",
    "\n",
    "#Input: dom - DOM of the music page corresponding to an FB account's profile. Eg, DOM of https://www.facebook.com/zuck/music\n",
    "#Output: A Pandas data frame with one row per group. \n",
    "#    It must have four columns \n",
    "#    'Name', 'Type' (eg. Musician/Band or Bands&Musicians) and 'Verified' (boolean: True/False), 'Profile Url'\n",
    "#    Note that all information of a group is in the same page (eg. https://www.facebook.com/zuck/music)\n",
    "#    Ensure that the column names as given above\n",
    "def scraping_facebook_music(dom):\n",
    "    arrayOfDict = []\n",
    "    for li in dom(\"ul.uiList > li\"):\n",
    "        musicAsDict = {}\n",
    "        musicAsDict[\"Name\"] = li(\"a._gx7\")[0].title\n",
    "        musicAsDict[\"Type\"] = web.plaintext(li(\"div.fsm.fwn.fcg\")[0].content)\n",
    "        musicAsDict[\"Verified\"] = \"Verified Page\" in li\n",
    "        musicAsDict[\"Profile Url\"] =  li(\"a._gx7\")[0].href\n",
    "        arrayOfDict.append(musicAsDict)\n",
    "    df = DataFrame(arrayOfDict,columns=[\"Name\", \"Type\", \"Verified\", \"Profile Url\"])\n",
    "    return df\n",
    "\n",
    "#Input: dom - DOM of the music page corresponding to an FB account's profile. Eg, DOM of https://www.facebook.com/zuck/movies\n",
    "#Output: A Pandas data frame with one row per group. \n",
    "#    It must have following columns - \n",
    "#        'Name', 'Type' (eg. Movie), 'Verified', 'Profile Url' - as before\n",
    "#    Ensure that the column names as given above\n",
    "def scraping_facebook_movies(dom):\n",
    "    arrayOfDict = []\n",
    "    for li in dom(\"ul.uiList > li\"):\n",
    "        movieAsDict = {}\n",
    "        movieAsDict[\"Name\"] = li(\"a._gx7\")[0].title\n",
    "        movieAsDict[\"Type\"] = web.plaintext(li(\"div.fsm.fwn.fcg\")[0].content)\n",
    "        movieAsDict[\"Verified\"] = \"Verified Page\" in li\n",
    "        movieAsDict[\"Profile Url\"] =  li(\"a._gx7\")[0].href\n",
    "        arrayOfDict.append(movieAsDict)\n",
    "    df = DataFrame(arrayOfDict,columns=[\"Name\", \"Type\", \"Verified\", \"Profile Url\"])\n",
    "    return df\n",
    "\n",
    "books_html = open(\"zuckBooks.html\").read()\n",
    "#Uncomment one of these lines based on whether you used Pattern or BeautifulSoup\n",
    "dom = web.Element(books_html)\n",
    "#dom = BeautifulSoup(books_html)\n",
    "print \"Books in Zuck's FB:\\n\", scraping_facebook_books(dom)\n",
    "\n",
    "group_html = open(\"zuckGroups.html\").read()\n",
    "dom = web.Element(group_html)\n",
    "#dom = BeautifulSoup(group_html)\n",
    "print \"\\n\\nGroups in Zuck's FB:\\n\"\n",
    "display(scraping_facebook_groups(dom))\n",
    "\n",
    "\n",
    "music_html = open(\"zuckMusic.html\").read()\n",
    "dom = web.Element(music_html)\n",
    "#dom = BeautifulSoup(music_html)\n",
    "print \"\\n\\nMusic in Zuck's FB:\\n\"\n",
    "display(scraping_facebook_music(dom))\n",
    "\n",
    "\n",
    "movies_html = open(\"zuckMovies.html\").read()\n",
    "dom = web.Element(movies_html)\n",
    "#dom = BeautifulSoup(movies_html)\n",
    "print \"\\n\\nMovies in Zuck's FB:\\n\"\n",
    "display(scraping_facebook_movies(dom))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Part 2 and 3: Exploratory Analysis and Visualization\n",
    "======================\n",
    "\n",
    "Data Set Description\n",
    "---------------------\n",
    "\n",
    "In this assignment, you will use the FEC dataset. The US Federal Election Commission publishes data on contributions to political campaigns. This includes contributor names, occupation and employer, address, and contribution amount. Specifically, we will be using the FEC data from 2012 election between Barack Obama and Mitt Romney. This is widely considered as a landmark election as both sides spent an unprecedented amount of 1 Billion dollars each (i.e. more than 6000 Crores in INR each). If you are interested, you can download the entire list of contributor details at the [FEC site](http://www.fec.gov/disclosurep/PDownload.do). It is relatively large (150 MB compressed, 1 GB uncompressed). For our experiments, we will use a smaller subset of the data collected (and cleaned) by Wes McKinney (the creator of Pandas). For the download link, see the [Assignments page](http://saravanan-thirumuruganathan.github.io/cse5334Spring2015/assignments.html). It is small by most standards (around 1.4 million records, 20 MB compressed, 160 MB uncompressed) but large enough to give a taste of why data mining is compute intensive. Hopefully, this will also give you an appreciation as to the awesomeness of Pandas/Numpy - you can do really cool stuff with 2-3 lines of code that runs in seconds. \n",
    "\n",
    "**Dataset Description:** You can find the details (such as meaning of the column names) of the dataset in the [FEC website](ftp://ftp.fec.gov/FEC/Presidential_Map/2012/DATA_DICTIONARIES/CONTRIBUTOR_FORMAT.txt).\n",
    "\n",
    "While a knowledge of US Elections or FEC Campaign finance rules is not necessary for solving this assignment, you can check out the following links if you are curious. \n",
    "\n",
    "[How to Become the US President: A Step-by-Step Guide](http://2012election.procon.org/view.resource.php?resourceID=004333)\n",
    "\n",
    "[US Election guide: how does the election work?](http://www.telegraph.co.uk/news/worldnews/us-election/9480396/US-Election-guide-how-does-the-election-work.html)\n",
    "\n",
    "If you are fascinated with Campaign finance reform (as I am), here are some good links:\n",
    "\n",
    "[Super-PACs and Dark Money: ProPublica’s Guide to the New World of Campaign Finance](http://www.propublica.org/blog/item/super-pacs-propublicas-guide-to-the-new-world-of-campaign-finance)\n",
    "\n",
    "[40 charts that explain money in politics](http://www.vox.com/2014/7/30/5949581/money-in-politics-charts-explain)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualization\n",
    "-------------\n",
    "\n",
    "Visualization is a key component of exploration. You will perform a number of 1-D and 2-D charts to get some intuition about the data. You can choose to use either Matplotlib or Seaborn for plotting. The default figures generated from Matplotlib might look a bit ugly. So you might want to try Seaborn to get better figures. The defaults in Seaborn are much saner and sometimes makes your life lot easier. Seaborn has number of styles - so feel free to experiment with them and choose the one you like. We have earmarked 10 points for the aesthetics of your visualizations.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas\n",
    "----------\n",
    "Almost all the tasks below could be purely solved using Pandas. Most analytic tasks should not require more than 2-3 lines of code (visualization on the other hand is a different matter - you might need 5-10 lines for each chart unless you use Seaborn). Here is a NON COMPREHENSIVE list of functions that you might want to know to solve this assignment : agg, apply, argmax, argmin, count, crosstab, cumsum, cut, describe, groupby, head, idxmax, idxmin, info, isin, map, max, min, pivot_table, size, sum, transform, unique, value_counts .\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exploration Tasks\n",
    "==================\n",
    "\n",
    "You can find a set of exploratory analytics tasks below. Ensure that you clearly follow the instructions. The assignment will be graded automatically - so failure to follow might cause some issues. Also do NOT rename the functions or variables that the instructor has set. Of course, feel free to create new variables/functions that will hep your code. \n",
    "\n",
    "Reading and Filtering Dataset\n",
    "------------------------------\n",
    "The FEC data contains information about all presidential candidates. As a sitting president, Barack Obama was the only candidate from the Democratic party. In the Republican party, there was a process called Primary (see links above) where number of candidates competed to be the nominee. Mitt Romney won the Republican primary and competed with Barack Obama in the elections, which Obama won. \n",
    "\n",
    "The Python code below reads the FEC dataset into a Pandas data frame with the name fec_all. If your machine has less than 2 GB of RAM, then change the function argument low_memory to True. Once the frame is loaded, we remove all negative contributions (where the campaign refunded amount to a contributor for some reason). Finally, we create a new data frame called fec that contains the contributions to Barack Obama and Mitt Romney alone. \n",
    "\n",
    "For this code to work, the file 'fec_2012_contribution_subset.csv' must be in the same folder as the notebook. \n",
    "\n",
    "To reduce my typing, I might refer to Obama as BO and Romney as MR in the text below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#read the csv file into a Pandas data frame\n",
    "fec_all = pd.read_csv('fec_2012_contribution_subset.csv', low_memory=False)\n",
    "\n",
    "#We will be doing party wise analysis later. So, we want to associate each candidate with their party\n",
    "parties = {'Bachmann, Michelle': 'Republican',\n",
    "           'Cain, Herman': 'Republican',\n",
    "           'Gingrich, Newt': 'Republican',\n",
    "           'Huntsman, Jon': 'Republican',\n",
    "           'Johnson, Gary Earl': 'Republican',\n",
    "           'McCotter, Thaddeus G': 'Republican',\n",
    "           'Obama, Barack': 'Democrat',\n",
    "           'Paul, Ron': 'Republican',\n",
    "           'Pawlenty, Timothy': 'Republican',\n",
    "           'Perry, Rick': 'Republican',\n",
    "           \"Roemer, Charles E. 'Buddy' III\": 'Republican',\n",
    "           'Romney, Mitt': 'Republican',\n",
    "           'Santorum, Rick': 'Republican'}\n",
    "\n",
    "#create a new column called party that sets the value to the party of the candidate\n",
    "# The way this line works is as follows:\n",
    "#  1. fec_all.cand_nm gives a vector (or Series in Pandas terminology)\n",
    "#  2. For each row, the code looks up the candidate name to the dictionary parties\n",
    "#  3. If the name of the candidate (cand_nm) is in parties, it returns the value (i.e. Republican or Democrat)\n",
    "#  4. This whole thing is done for each row and you get another vector as output\n",
    "#  5. Finally, you create a new column in fec_all called 'party' and assign the vector you got to it.\n",
    "#  6. All in a single line :)\n",
    "fec_all['party'] = fec_all.cand_nm.map(parties)\n",
    "\n",
    "#ignore the refunds\n",
    "# Get the subset of dataset where contribution amount is positive\n",
    "fec_all = fec_all[fec_all.contb_receipt_amt > 0]\n",
    "\n",
    "#fec_all contains details about all presidential candidates. \n",
    "#fec contains the details about contributions to Barack Obama and Mitt Romney only\n",
    "#for the rest of the tasks, unless explicitly specified, work on the fec data frame.\n",
    "fec = fec_all[fec_all.cand_nm.isin(['Obama, Barack', 'Romney, Mitt'])]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Task 1: Descriptive Statistics\n",
    "--------------------------------\n",
    "\n",
    "Let us start with some easy stuff. As of now, you do not know anything about the dataset. So the first task will be to get some basic sense. \n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Task 1a: Details of FEC data frame are: \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 694282 entries, 411 to 701385\n",
      "Data columns (total 17 columns):\n",
      "cmte_id              694282 non-null object\n",
      "cand_id              694282 non-null object\n",
      "cand_nm              694282 non-null object\n",
      "contbr_nm            694282 non-null object\n",
      "contbr_city          694275 non-null object\n",
      "contbr_st            694278 non-null object\n",
      "contbr_zip           694234 non-null object\n",
      "contbr_employer      693607 non-null object\n",
      "contbr_occupation    693524 non-null object\n",
      "contb_receipt_amt    694282 non-null float64\n",
      "contb_receipt_dt     694282 non-null object\n",
      "receipt_desc         2345 non-null object\n",
      "memo_cd              87387 non-null object\n",
      "memo_text            90672 non-null object\n",
      "form_tp              694282 non-null object\n",
      "file_num             694282 non-null int64\n",
      "party                694282 non-null object\n",
      "dtypes: float64(1), int64(1), object(15)\n",
      "memory usage: 95.3+ MB\n",
      "None\n",
      "\n",
      "\n",
      "Task 1b: #Rows=694282, #Columns=17\n",
      "\n",
      "\n",
      "Task 1c: Descriptive details of contb_receipt_amt is \n",
      "count     694282.000000\n",
      "mean         322.942745\n",
      "std         4482.130250\n",
      "min            0.010000\n",
      "25%           35.000000\n",
      "50%          100.000000\n",
      "75%          250.000000\n",
      "max      2014490.510000\n",
      "Name: contb_receipt_amt, dtype: float64\n",
      "\n",
      "\n",
      "Task 1d: #Uniq cand_id =  2\n",
      "Task 1d: #Uniq cand_num =  2\n",
      "Task 1d: #Uniq contbr_city =  11856\n",
      "Task 1d: #Uniq contbr_st =  67\n"
     ]
    }
   ],
   "source": [
    "#Task 1a: print the details of the data frame. \n",
    "# Basically, this should contain information such as number of rows,columns, name of columns, #non-null values for each column etc\n",
    "# Hint: there is a Pandas function to do this\n",
    "#replace None with your code\n",
    "print \"Task 1a: Details of FEC data frame are: \\n\", fec.info() \n",
    "\n",
    "#Task 1b: finding the number of rows and columns in the data frame.\n",
    "#Hint: find the property of the data frame that gives the number of rows and columns\n",
    "#replace None with your code\n",
    "t1b_num_rows = fec.shape[0]\n",
    "t1b_num_cols = fec.shape[1]\n",
    "print \"\\n\\nTask 1b: #Rows=%s, #Columns=%s\" % (t1b_num_rows, t1b_num_cols) \n",
    "\n",
    "#Task 1c: The only numeric data is 'contb_receipt_amt' which is the amount of contribution. \n",
    "# Print the descriptive details (min, max, quantiles etc) for 'contb_receipt_amt'\n",
    "#Hint: as above there is a direct pandas command for it.\n",
    "#replace None with your code\n",
    "print \"\\n\\nTask 1c: Descriptive details of contb_receipt_amt is \\n\", fec.contb_receipt_amt.describe()\n",
    "\n",
    "#Task 1d: Let us now print the number of unique values for few columns\n",
    "#Hint: Look for a Pandas function to do this.\n",
    "t1d_num_uniq_cand_id = fec.cand_id.nunique()\n",
    "t1d_num_uniq_cand_nm = fec.cand_nm.nunique()\n",
    "t1d_num_uniq_contbr_city = fec.contbr_city.nunique()\n",
    "t1d_num_uniq_contbr_st = fec.contbr_st.nunique()\n",
    "print \"\\n\\nTask 1d: #Uniq cand_id = \", t1d_num_uniq_cand_id\n",
    "print \"Task 1d: #Uniq cand_num = \", t1d_num_uniq_cand_nm\n",
    "print \"Task 1d: #Uniq contbr_city = \", t1d_num_uniq_contbr_city\n",
    "print \"Task 1d: #Uniq contbr_st = \", t1d_num_uniq_contbr_st"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Task 2: Basic Filtering\n",
    "-----------------------\n",
    "In this task, we will perform some very high level filtering. Pandas has a convenient and powerful syntax for filtering (for eg, see above for how I filtered negative contributions and non-Obama, Romney candidates). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Task 2a: Total Contribution for Obama is 135877427.24 and for Romney is 88335907.53\n",
      "\n",
      "\n",
      "Task 2b: Total contributions from California is 35062620.84, New York is 24836131.14 and Texas is 12792822.13\n",
      "\n",
      "\n",
      "Task 2c: From TX, BO got 6570832.45 and MR got 6221989.68 dollars\n",
      "Task 2c: From UTA, BO got 750.0 and MR got 0 dollars\n",
      "\n",
      "\n",
      "Task 2d: From Google Engineers, BO got 87212.4 and MR got 2850.0 dollars\n"
     ]
    }
   ],
   "source": [
    "#Task 2a: Let us find out how much contributions did Obama and Romney made in this dataset\n",
    "# Remember, that this is not the complete amount as it excludes other sources like PACs, Super PACs and \n",
    "#  spending by party committes.\n",
    "#Hint: use cand_nm field\n",
    "t2a_tot_amt_obama = fec.contb_receipt_amt[fec.cand_nm == 'Obama, Barack'].sum()\n",
    "t2a_tot_amt_romney = fec.contb_receipt_amt[fec.cand_nm == 'Romney, Mitt'].sum()\n",
    "print \"Task 2a: Total Contribution for Obama is %s and for Romney is %s\" % (t2a_tot_amt_obama, t2a_tot_amt_romney)\n",
    "\n",
    "#Task 2b: How much contribution did folks from California, New York and Texas make totally (i.e. to both Obama and Romney).\n",
    "#use contbr_st field\n",
    "t2b_tot_amt_CA = fec.contb_receipt_amt[fec.contbr_st == 'CA'].sum()\n",
    "t2b_tot_amt_NY = fec.contb_receipt_amt[fec.contbr_st == 'NY'].sum()\n",
    "t2b_tot_amt_TX = fec.contb_receipt_amt[fec.contbr_st == 'TX'].sum()\n",
    "print \"\\n\\nTask 2b: Total contributions from California is %s, New York is %s and Texas is %s\" % (t2b_tot_amt_CA, t2b_tot_amt_NY, t2b_tot_amt_TX)\n",
    "\n",
    "#Task 2c: Let us now use multiple filtering criteria\n",
    "# How much money did folks from Texas made to BO and MR?\n",
    "# How much money did folks from UT Arlington made to BO and MR?\n",
    "t2c_tot_contr_tx_bo = fec.contb_receipt_amt[(fec.cand_nm == 'Obama, Barack') & (fec.contbr_st == 'TX')].sum()\n",
    "t2c_tot_contr_tx_mr = fec.contb_receipt_amt[(fec.cand_nm == 'Romney, Mitt') & (fec.contbr_st == 'TX')].sum()\n",
    "t2c_tot_contr_uta_bo = fec.contb_receipt_amt[(fec.cand_nm == 'Obama, Barack') & (fec.contbr_employer == 'UT ARLINGTON') \n",
    "                                             & (fec.contbr_st == 'TX')].sum()\n",
    "t2c_tot_contr_uta_mr = fec.contb_receipt_amt[(fec.cand_nm == 'Romney, Mitt') & (fec.contbr_employer == 'UT ARLINGTON') \n",
    "                                             & (fec.contbr_st == 'TX')].sum()\n",
    "\n",
    "print \"\\n\\nTask 2c: From TX, BO got %s and MR got %s dollars\" % (t2c_tot_contr_tx_bo, t2c_tot_contr_tx_mr)\n",
    "print \"Task 2c: From UTA, BO got %s and MR got %s dollars\" % (t2c_tot_contr_uta_bo, t2c_tot_contr_uta_mr)\n",
    "\n",
    "#Task 2d: How much did Engineers from Google gave to BO and MR.\n",
    "# This task is a bit tricky as there are many variations: eg, SOFTWARE ENGINEER vs ENGINEER and GOOGLE INC. vs GOOGLE\n",
    "t2d_tot_engr_goog_bo = fec.contb_receipt_amt[(fec.cand_nm == 'Obama, Barack') & (fec.contbr_employer.str.contains(\"GOOGLE\") == True) \n",
    "                                             & (fec.contbr_occupation.str.contains(\"ENGINEER\") == True)].sum()\n",
    "t2d_tot_engr_goog_mr = fec.contb_receipt_amt[(fec.cand_nm == 'Romney, Mitt') & (fec.contbr_employer.str.contains(\"GOOGLE\") == True) \n",
    "                                             & (fec.contbr_occupation.str.contains(\"ENGINEER\") == True)].sum()\n",
    "\n",
    "print \"\\n\\nTask 2d: From Google Engineers, BO got %s and MR got %s dollars\" % (t2d_tot_engr_goog_bo, t2d_tot_engr_goog_mr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Task 3: Basic Aggregation \n",
    "--------------------------\n",
    "In this task, we will perform some very high level aggregation. Pandas has some convenient functions for aggregation (do NOT write a for loop - Pandas has some very efficient, vectorized code)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Task 3a: Total contribution made to both candidates by each state are:\n",
      "contbr_st\n",
      "AA              56540.00\n",
      "AB               2048.00\n",
      "AE              48653.75\n",
      "AK             368044.39\n",
      "AL            1070426.99\n",
      "AP              38785.50\n",
      "AR             464803.28\n",
      "AS               2955.00\n",
      "AZ            3394913.21\n",
      "CA           35062620.84\n",
      "CO            3639143.61\n",
      "CT            5567766.71\n",
      "DC            5398676.30\n",
      "DE             419381.14\n",
      "FF              99030.00\n",
      "...\n",
      "SC            1033475.80\n",
      "SD             253088.76\n",
      "TN            2636233.06\n",
      "TX           12792822.13\n",
      "UK               2500.00\n",
      "UT            4237151.85\n",
      "VA            7725743.04\n",
      "VI              84212.00\n",
      "VT            1041740.03\n",
      "WA            5592454.72\n",
      "WI            1400471.78\n",
      "WV             295879.59\n",
      "WY             446642.58\n",
      "XX             400250.00\n",
      "ZZ               5963.00\n",
      "Name: contb_receipt_amt, Length: 67, dtype: float64\n",
      "\n",
      "\n",
      "Task 3b: Total contribution made to both candidates by each city in TX are:\n",
      "contbr_city\n",
      "ABERNATHY          210.00\n",
      "ABILENE          45748.00\n",
      "ADDISON          12244.25\n",
      "ADKINS             600.00\n",
      "ALAMO              152.00\n",
      "ALAMO HEIGHTS     2500.00\n",
      "ALBA                69.00\n",
      "ALBANY            6500.00\n",
      "ALEDO             5880.00\n",
      "ALICE             2760.00\n",
      "ALLEN            21066.00\n",
      "ALLISON            474.00\n",
      "ALPINE            5166.00\n",
      "ALTO               120.00\n",
      "ALTON              125.00\n",
      "...\n",
      "WIMBERLEY      19060\n",
      "WINDCREST        525\n",
      "WINDTHORST       400\n",
      "WINFIELD        1000\n",
      "WINNIE           230\n",
      "WINNSBORO        375\n",
      "WOODLANDS       5112\n",
      "WOODVILLE        175\n",
      "WOODWAY         2965\n",
      "WORTHAM          250\n",
      "WYLIE           8405\n",
      "YANTIS           340\n",
      "YOAKUM           100\n",
      "YORKTOWN         100\n",
      "ZAPATA           300\n",
      "Name: contb_receipt_amt, Length: 729, dtype: float64\n",
      "\n",
      "\n",
      "Task 3c: Total contribution made to both candidates by each zipcode in Arlington are:\n",
      "contbr_zip\n",
      "75237            85\n",
      "76001          1130\n",
      "760015368      5175\n",
      "760015696       150\n",
      "760017554       300\n",
      "76002           904\n",
      "760023037       550\n",
      "760024202       215\n",
      "760025010      2474\n",
      "760025461        35\n",
      "760041185      1000\n",
      "76006         10050\n",
      "760062026       400\n",
      "760062725      1000\n",
      "760062778       365\n",
      "...\n",
      "760172730      125\n",
      "760172754     1000\n",
      "760172768       80\n",
      "760173004      135\n",
      "760173541      636\n",
      "760174554      200\n",
      "760176032      210\n",
      "760176250      145\n",
      "760177925       25\n",
      "760177973      650\n",
      "76018          105\n",
      "760181913      170\n",
      "760182501      100\n",
      "760184920      100\n",
      "760185108       35\n",
      "Name: contb_receipt_amt, Length: 121, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Task 3a: For each state, print the total contribution they made to both candidates. \n",
    "t3a_state_contr_both = fec.contb_receipt_amt.groupby(fec.contbr_st).sum()\n",
    "print \"Task 3a: Total contribution made to both candidates by each state are:\\n\", t3a_state_contr_both\n",
    "\n",
    "#Task 3b: Now let us limit ourselves to TX. For each city in TX, print the total contribution made to both candidates\n",
    "t3b_tx_city_contr_both = fec.contb_receipt_amt[fec.contbr_st == 'TX'].groupby(fec.contbr_city).sum()\n",
    "print \"\\n\\nTask 3b: Total contribution made to both candidates by each city in TX are:\\n\", t3b_tx_city_contr_both\n",
    "\n",
    "#Task 3c: Now let us zoom into  Arlington, TX. For each zipcode in Arlington, print the total contribution made to both candidates\n",
    "t3c_arlington_contr_both = fec.contb_receipt_amt[(fec.contbr_st == 'TX') & (fec.contbr_city == 'ARLINGTON')].groupby(fec.contbr_zip).sum()\n",
    "print \"\\n\\nTask 3c: Total contribution made to both candidates by each zipcode in Arlington are:\\n\", t3c_arlington_contr_both"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Task 4: Aggregation+Filtering+Ranking\n",
    "-----------------------------------------\n",
    "In this task, you will try to combine aggregation with filtering and then rank the results based on the results. Pandas is often quite clever and might sort the data for you already. \n",
    "\n",
    "**Hint:** Pandas has ready made functions for all the following."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of contributions to Obama in each state is \n",
      "contbr_st\n",
      "AA               74\n",
      "AB                4\n",
      "AE              395\n",
      "AK             2036\n",
      "AL             3854\n",
      "AP              158\n",
      "AR             1747\n",
      "AS               31\n",
      "AZ            10509\n",
      "CA           100182\n",
      "CO            12289\n",
      "CT             9977\n",
      "DC            11491\n",
      "DE             1782\n",
      "FL            29797\n",
      "...\n",
      "QU               1\n",
      "RI            2038\n",
      "SC            4228\n",
      "SD             713\n",
      "TN            6534\n",
      "TX           32292\n",
      "UT            2790\n",
      "VA           21451\n",
      "VI             415\n",
      "VT            3563\n",
      "WA           20783\n",
      "WI            8050\n",
      "WV            1330\n",
      "WY            1055\n",
      "ZZ              15\n",
      "Name: contbr_nm, Length: 64, dtype: int64\n",
      "\n",
      "\n",
      "Top-10 states with most contributors to Obama are :\n",
      "CA    100182\n",
      "NY     50383\n",
      "IL     33240\n",
      "TX     32292\n",
      "FL     29797\n",
      "MA     24864\n",
      "MD     22552\n",
      "VA     21451\n",
      "WA     20783\n",
      "PA     19280\n",
      "dtype: int64\n",
      "\n",
      "\n",
      "Top-20 Occupations with most contributors are :\n",
      "RETIRED                                   177473\n",
      "ATTORNEY                                   30133\n",
      "INFORMATION REQUESTED                      24747\n",
      "HOMEMAKER                                  19626\n",
      "PHYSICIAN                                  17206\n",
      "INFORMATION REQUESTED PER BEST EFFORTS     12545\n",
      "PROFESSOR                                  11804\n",
      "TEACHER                                    11512\n",
      "CONSULTANT                                 10061\n",
      "NOT EMPLOYED                                9696\n",
      "LAWYER                                      7438\n",
      "ENGINEER                                    6107\n",
      "PRESIDENT                                   4864\n",
      "MANAGER                                     4745\n",
      "WRITER                                      4439\n",
      "SELF-EMPLOYED                               3763\n",
      "SALES                                       3697\n",
      "EXECUTIVE                                   3609\n",
      "OWNER                                       3408\n",
      "EDUCATOR                                    3360\n",
      "dtype: int64\n",
      "\n",
      "\n",
      "Top-10 Employers with most contributors are :\n",
      "RETIRED                                   173287\n",
      "SELF-EMPLOYED                              79638\n",
      "NOT EMPLOYED                               45727\n",
      "INFORMATION REQUESTED                      25524\n",
      "INFORMATION REQUESTED PER BEST EFFORTS     13104\n",
      "HOMEMAKER                                  12081\n",
      "OBAMA FOR AMERICA                           1741\n",
      "STUDENT                                     1417\n",
      "SELF                                        1228\n",
      "DISABLED                                    1096\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Task 4a: Print the number of contributors to Obama in each state.\n",
    "t4a_num_contr_obama_per_state = fec.contbr_nm[fec.cand_nm == 'Obama, Barack'].groupby(fec.contbr_st).count()\n",
    "print \"Number of contributions to Obama in each state is \\n\", t4a_num_contr_obama_per_state\n",
    "\n",
    "#Task 4b: Print the top-10 states (based on number of contributors) that contributed to Obama.\n",
    "# print both state name and number of contributors\n",
    "t4b_top10_obama_contr_states = fec[fec.cand_nm == 'Obama, Barack'].contbr_st.value_counts()[:10]\n",
    "print \"\\n\\nTop-10 states with most contributors to Obama are :\\n\", t4b_top10_obama_contr_states\n",
    "\n",
    "#Task 4c: Print the top-20 occupations that contributed overall (to both BO and MR)\n",
    "t4c_top20_contr_occupation = fec.contbr_occupation.value_counts()[:20]\n",
    "print \"\\n\\nTop-20 Occupations with most contributors are :\\n\", t4c_top20_contr_occupation\n",
    "\n",
    "#Task 4d: Print the top-10 Employers that contributed overall (to both BO and MR)\n",
    "t4d_top10_contr_employer_all = fec.contbr_employer.value_counts()[:10]\n",
    "print \"\\n\\nTop-10 Employers with most contributors are :\\n\", t4d_top10_contr_employer_all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Task 5: Basic Visualization\n",
    "-----------------------------\n",
    "\n",
    "Let us take a break from analytics to do some Visualization. Notice that the visualization tasks are NOT ordered based on their complexity. So some of them might be more challenging than others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/pandas/core/indexing.py:245: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[key] = np.nan\n",
      "/usr/local/lib/python2.7/dist-packages/pandas/core/indexing.py:415: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa6502e7bd0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JiHUj4nfA3Zl5KkVrc3OKluO7ImJ8REwGbqCYjftnwIERMbFsue6FXZ3C4JNG\nU/OX7tuA90bEbymuR9q/PM51PXBwRBwDfIqiu+dGim7MayhagM875hYR61F0631lwPOdSXF25ziG\n/sJvUBwfewb438AXyjPnji7vQ9FyOjoi5lGc+NJofuwgr+9mYE5EDDyrdLBjhV+mOKnnOIoTa06N\niJsoZtT+HjCtqca/UJwQdGtE3EYxV98FFMcjHwDuAG6hmBLo+sz8HkX43Q38iOK4keS0RJKkerHF\nJ0mqFYNPklQrBp8kqVYMPklSrRh8kqRaMfgkSbVi8EmSasXgkyTVyv8HcYpuQ83E3mcAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa65ef42110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa65ef2ed10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PvXwV26n+YJhaff1Da2AurJ63RZkEHcr3YFLbc7Q/3s0jrJjSAu7IzBur698c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\n/X/iEmiE4qwirAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa65f337b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa65f4b8790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa65e4faed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa65e4fab90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Dc+IOv9/dJ5pZCevXtcsH3gUucfcVZjYKGAwsDI/pADzo7leH6+HdDfSNS/Lz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\ndfdrzOylhLaKAr8GJia5zqH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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa61942d890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa61942d550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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q0GGIiDQp+SRmmVtjpMOvqchrEREREakndSZm7j7KzMqB7YG/A53cfXqDRyYi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\nJFyYmZmZmZWECzMzMzOzkvgv9fwjRoaDJD4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa65f424f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jux8xvv2kqrpbkucnOSXJY5O8vKq2J3luko+Ml31TkheP7/Y1Sb6/ux+e5MFV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\n/mmmt9GP38w+VDrHxvis1oY0u0zU1W9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sjMcfX2YNm3dLeijVrZctzew/G+wmUxNrav1v\nCTDqyDMKVlInrCVdBHypQtAeYGaVcV3pf3IJfg/vwZOibA3sYP3HKo050nMEFatWTc9Rrl1G/XqF\n1YxMQdZqJ3f7K+J7y3QZ3HYm95pK9r3i+Xwzq4yRzrmm3PuQWdbfGBaZC80m2U0ujqPjMzuo5zyV\nVRan+3QUtIO8fxPxtnYWnnBqAr6d0w5Wk213UDbJ7mLghArxfKSZvaPOrl9G0vdK2gDffmwaPh5Z\nHV882LVpPJJRx3WAuWl1bQ1gPTP7wWjbSTrJmrP0j4pNsqsVtHV1rPhfPQncbWaPN5QzC///XGwd\nY3tzySlLHkNcxPa+BU9c1Rbbm1u/ubignU73ZFpZ9ZN0eyrrCDN7Jo0hzweuM7NDRnotbYQIDl6w\n9CtMk02WeM6o22Sew2J2kOQKWknL4NksJ+CJEi6x9j2Jg+cBAxZkk4tj0s958b2jLOxzrylH2E8u\nzp1+tl5T7n0YhbKec8J0kGQK2snFMQzg/kkahyermYhn8W5tawdokyWeB02a7NiN4e2fzrb+tqoa\nk+QI2rGAhmN7NzWzNg/Cfs89YsHdtX6StjGzqyvKP8nMPpF5CZ0JERwEPeSI52BkhKANygxSkA2K\nEVzTQFaqg0XDC1XcD5Ic8RwEzydUHdt7NXDNwvZ+6yJoF2X9RkKI4CAIgiB4jvJcFvZBEATBwkfS\nNbiovJqOsb0jKKtvQTvI+o0mIYKDIAiCIAiCIAjGOM9XQZtDiOAgCIIgCIIgCIJgzNDvXmdBEARB\nEARBEARB8LwlRHAQBEEQBEEQBEEwZggRHARBEARBEARBEIwZXrSoKxAEQRAEYxFJ7wE+hffFi+N7\nh56UvjsWuNrMrm+w3wFY08y+0rG8ycBVwBvM7Lelz581s5gUD4IgCMYM0ekFQRAEwYCR9CrgJGAb\nM1sf2ATYRdL/pEPeAlTuyVhiA2C5PoseAs6SFP1/EARBMGaJleAgCIIgGDwrAksCLwH+amZ/l7Q7\n8M/0c0NgmqSdgFcAnwOWAZYHDgV+A3wUGJI0B7gYOB1YFxfPXzSz7/WUOQT8EvgncBhwfPlLScsB\nZwCvAl4JXGdmu6cV5CPSYWsAFwFzgXfi+xZvZ2YPSdoWODZd133AVDN7bGS3KQiCIAhGn5gJDoIg\nCIIBY2a3A5cA90q6SdIJwIvM7B4zOxuYDexpZncCHwc+YmYbAHsCR5vZXcDXga+b2XeAI4HZZrYh\nsDlwhKSJPcUuln5OBQ6UtE7P99sBt5jZpsDawCaSJqXvNgY+hIvsjwEPmdlGwK/xFeyVcFH9VjOb\nBPwE+OJI71MQBEEQLAxCBAdBEATBIsDM9gFWw8XsasCNkt5VOqQQrbsC60k6EjgIXz0uvi+O2Rr4\nqKRbgWvxVeNekVuU+wDwaXrcotPK8UxJBwCn4SvQRVl3mtmfzOwp4BFgZvr8fnx1emNgVWBWqsO+\nwJr93ZEgCIIgGAzhDh0EQRAEA0bS9sAyZnYhcBYuSPcEPgL8IB02lH5ej4vOWennd0unKo5ZHJhi\nZrel8/8X8Ghd+WY2XdLOeGKuok77Ae8Gvglcja/6FiL76Z5TPNPz9xLA9Wb2jnSuFwPL1pUfBEEQ\nBIuSWAkOgiAIgsHzd+B4SasCSFoMF523pO+fAZaUtAKwFnCMmV0JvI3hhFn/wuNvAX4K7JPOtTJw\nKzC+pQ57AgcyLKS3Br5pZjPS3+vTPlleiOSbcPfptdLfRwInttgGQRAEwSIhRHAQBEEQDBgzmwV8\nFrhM0l3AXbig/Gw65ErgG4CA6cBvJN0APAEsLWkccB0wRdK+eEKqcZLuwFeLDzWz+3qKHWJY8BZu\n0YeXvj8ZOEbSjcDRwKXAhF67qnOa2V+APYALJP0aeAPuuh0EQRAEzzkWGxqq69eCIAiCIAiCIAiC\n4IVFrAQHQRAEQRAEQRAEY4YQwUEQBEEQBEEQBMGYIURwEARBEARBEARBMGYIERwEQRAEQRAEQRCM\nGUIEB0EQBEEQBEEQBGOGEMFBEARBEARBEATBmCFEcBAEQRAEQRAEQTBm+H+14YHC1/G0ngAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa65f424c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9p19u/vkg1MbMbHjxkjlmZlYoBx4zMyuUA4+ZmRXKgcfMzArlyQUN1tuK1uBV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\nmRXKgcfMzAr1/wGi1wbR2nvIegAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa65e3c71d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4+8/5YOuPXRGxALgZuJdB+rMREReQHeEuy4uqGKR9QXYkd72k08lOod7bbnmv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\nM0vGIWNmZsk4ZMzMLBmHjJmZJfP/ATa0vWPfF1KDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa65e3c7310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5c81s22binnucasLBQuq++1zUq+diwwYz331nL7aTd/paQmkN3IS+jnzzHA9F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\nSZs8jfhHHwq7OHMTjtczNypO/8otzuh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IiMRCCUVERGKhhCIiIrFQQhERkVgooYiISCyU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6503c4750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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12PXgI/h790XrzqS/uBKKiEwxxrTE3kPeDXwmIt8nMzCl0sWKFU569oRffnFx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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa62fe05e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1ZEckku3xx/18952D6dPN8aju3dN3PKq8Lr4uAEqpkVrrOxMXkhAiGsuW2Rk4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\nVEIIIQ5XRWpBXQ64tdYdgKHAS0mORwg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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa65084e110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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t+24RuRfYHvCr6sq66isuLqnPbqRVUVGB1ZlEjbnOigo4//w83nsvyNFHVzJq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa650807d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Task 5a: Draw a \"horizontal\" bar chart with one bar each for Obama and Romney with the value corresponding to total amount they raised.\n",
    "# Remember to make the bar chart into a horizontal bar chart\n",
    "#########################begin code for Task 5a\n",
    "plt.figure()\n",
    "figObj5a = fec.contb_receipt_amt.groupby(fec.cand_nm).sum().plot(kind='barh', title='Task 5a',color=['blue','red'])\n",
    "figObj5a.set_xlabel('Total Amount Raised')\n",
    "figObj5a.set_ylabel('Candidate Names')\n",
    "#########################end code for Task 5a\n",
    "\n",
    "\n",
    "#Task 5b: Draw the \"horizontal\" bar chart of total NUMBER of contributions made per Candidate.\n",
    "# ie Candidate name vs number of contributions for that candidate\n",
    "#########################begin code for Task 5b\n",
    "plt.figure()\n",
    "figObj5b = fec.contbr_nm.groupby(fec.cand_nm).agg('count').plot(kind='barh', title='Task 5b',color=['blue','red'])\n",
    "figObj5b.set_xlabel('#Contributors')\n",
    "figObj5b.set_ylabel('Candidate Names')\n",
    "#########################end code for Task 5b\n",
    "\n",
    "#Task 5c: Draw the \"horizontal\" bar chart of average contributions made per Candidate.\n",
    "# ie Candidate Name vs avg contribution\n",
    "#########################begin code for Task 5c\n",
    "plt.figure()\n",
    "figObj5c = fec.contb_receipt_amt.groupby(fec.cand_nm).mean().plot(kind='barh',title='Task 5c',color=['blue','red'])\n",
    "figObj5c.set_xlabel('Average Value of Contributions')\n",
    "figObj5c.set_ylabel('Candidate Names') \n",
    "#########################end code for Task 5c\n",
    "\n",
    "\n",
    "#Task 5d: Draw a \"horizontal\" bar chart that lists the top-10 states based on the TOTAL contribution they made to both candidates\n",
    "# For each state, draw two lines - one in blue for Obama and one in red for Romney\n",
    "# Display the proportion of the total contribution that came from the state.\n",
    "# For eg, if Obama made 1 billion and TX gave 100 million of it, the proportion is 10% \n",
    "# Remember to make the bar chart into a horizontal bar chart\n",
    "#########################begin code for Task 5d\n",
    "plt.figure()\n",
    "t5dTotalContrib = fec.groupby(['cand_nm', 'contbr_st']).contb_receipt_amt.sum().unstack(0).fillna(0)\n",
    "t5dTotalContrib['Total'] = t5dTotalContrib['Obama, Barack'] + t5dTotalContrib['Romney, Mitt']\n",
    "t5dPercentContrib = t5dTotalContrib / t5dTotalContrib.sum(axis=0)\n",
    "t5dTotalContrib['Obama Pct'],  t5dTotalContrib['Romney Pct'] = t5dPercentContrib['Obama, Barack'], t5dPercentContrib['Romney, Mitt']\n",
    "t5dTop10States = t5dTotalContrib.sort('Total', ascending=False)[:10]\n",
    "figObj5d = t5dTop10States[['Obama Pct', 'Romney Pct']].plot(kind='barh', title='Task 5d', color=['blue','red'])\n",
    "figObj5d.set_xlabel('Percent Contributions')\n",
    "figObj5d.set_ylabel('States') \n",
    "#########################end code for Task 5d\n",
    "\n",
    "\n",
    "#Task 5e: Now repeat the same process based on Occupation (again top-10)\n",
    "#########################begin code for Task 5e\n",
    "plt.figure()\n",
    "t5eTotalContrib = fec.groupby(['cand_nm', 'contbr_occupation']).contb_receipt_amt.sum().unstack(0).fillna(0)\n",
    "t5eTotalContrib['Total'] = t5eTotalContrib['Obama, Barack'] + t5eTotalContrib['Romney, Mitt']\n",
    "t5ePercentContrib = t5eTotalContrib / t5eTotalContrib.sum(axis=0)\n",
    "t5eTotalContrib['Obama Pct'],  t5eTotalContrib['Romney Pct'] = t5ePercentContrib['Obama, Barack'], t5ePercentContrib['Romney, Mitt']\n",
    "t5eTop10Occuation = t5eTotalContrib.sort('Total', ascending=False)[:10]\n",
    "figObj5e = t5eTop10Occuation[['Obama Pct', 'Romney Pct']].plot(kind='barh', title='Task 5e', color=['blue','red'])\n",
    "figObj5e.set_xlabel('Percent Contributions')\n",
    "figObj5e.set_ylabel('Occupations') \n",
    "#########################end code for Task 5e\n",
    "\n",
    "\n",
    "#Task 5f: Now repeat the same process based on Employers (again top-10)\n",
    "#########################begin code for Task 5f\n",
    "plt.figure()\n",
    "t5fTotalContrib = fec.groupby(['cand_nm', 'contbr_employer']).contb_receipt_amt.sum().unstack(0).fillna(0)\n",
    "t5fTotalContrib['Total'] = t5fTotalContrib['Obama, Barack'] + t5fTotalContrib['Romney, Mitt']\n",
    "t5fPercentContrib = t5fTotalContrib / t5fTotalContrib.sum(axis=0)\n",
    "t5fTotalContrib['Obama Pct'],  t5fTotalContrib['Romney Pct'] = t5fPercentContrib['Obama, Barack'], t5fPercentContrib['Romney, Mitt']\n",
    "t5fTop10Employers = t5fTotalContrib.sort('Total', ascending=False)[:10]\n",
    "figObj5f = t5fTop10Employers[['Obama Pct', 'Romney Pct']].plot(kind='barh', title='Task 5f', color=['blue','red'])\n",
    "figObj5f.set_xlabel('Percent Contributions')\n",
    "figObj5f.set_ylabel('Employers')\n",
    "#########################end code for Task 5f\n",
    "\n",
    "\n",
    "#Task 5g: Draw the histogram of total NUMBER of contributions made per each state.\n",
    "# X-axis : state, Y-axis : number of contribution from that state\n",
    "#########################begin code for Task 5g\n",
    "plt.figure(figsize=(16, 8)) \n",
    "figObj5g = fec.contbr_nm.groupby(fec.contbr_st).count().plot(kind = 'bar',title='Task 5g')\n",
    "figObj5g.set_xlabel('State Name')\n",
    "figObj5g.set_ylabel('Number of Contributors')\n",
    "#########################end code for Task 5g\n",
    "\n",
    "\n",
    "#Task 5h: Draw a histogram of actual contributions made for Obama. Set bin size as 50\n",
    "#X-axis: contribution amount bin, Y-axis: frequency\n",
    "#########################begin code for Task 5h\n",
    "\n",
    "#####Note: Hist by default, tries to create bins that are neatly arranged from min to max. \n",
    "# However, the contribution amounts are very skewed. \n",
    "# If you run the command fec.contb_receipt_amt.describe() you can see that 75% of them are below 250 but max value is 2014490.51\n",
    "# Hence default hist will not work here as most people gave small amount\n",
    "# For eg, only 10 contributions to Obama was above $5000 (you can verify by the line below)\n",
    "# len(fec.contb_receipt_amt[(fec.cand_nm == 'Obama, Barack') & (fec.contb_receipt_amt > 5000)])\n",
    "#There are many ways to fix it - including cut and qcut of pandas.\n",
    "# I am going for a simple solution - which is to ignore the top-10 contributions (above 5000)\n",
    "# and plt only the rest\n",
    "plt.figure()\n",
    "figObj5h = fec.contb_receipt_amt[(fec.cand_nm == 'Obama, Barack') & (fec.contb_receipt_amt <= 5000)].hist(bins = 50)\n",
    "figObj5h.set_xlabel('Contribution Bins')\n",
    "figObj5h.set_ylabel('Frequency')\n",
    "figObj5h.set_title('Task 5h')\n",
    "#########################end code for Task 5h\n",
    "\n",
    "\n",
    "#Task 5i: Draw a histogram of actual contributions made for Romney. Set bin size as 50\n",
    "#X-axis: contribution amount bin, Y-axis: frequency\n",
    "#########################begin code for Task 5i\n",
    "plt.figure()\n",
    "figObj5i = fec.contb_receipt_amt[(fec.cand_nm == 'Romney, Mitt') & (fec.contb_receipt_amt <= 5000)].hist(bins = 50)\n",
    "figObj5i.set_xlabel('Contribution Bins')\n",
    "figObj5i.set_ylabel('Frequency')\n",
    "figObj5i.set_title('Task 5i')\n",
    "\n",
    "#########################end code for Task 5i\n",
    "\n",
    "\n",
    "#Harder\n",
    "#Task 5j: Draw a line chart showing how the campaign contribution of Obama and Romney varied every quarter\n",
    "#Use blue for Obama and red for Romney\n",
    "#This is a bit tricky because, you have to convert contribution date to quarter.\n",
    "#You can either do it on your own or see if Pandas has some function\n",
    "#########################begin code for Task 5j\n",
    "\n",
    "#Ignore the warning \n",
    "plt.figure()\n",
    "t5jContbDateTime = pd.to_datetime(fec.contb_receipt_dt)\n",
    "fec.loc[:,'contb_receipt_dt_quarter'] = map(lambda dateTime: dateTime.quarter, t5jContbDateTime)\n",
    "fec.loc[:,'contb_receipt_dt_year'] = map(lambda dateTime: dateTime.year, t5jContbDateTime)\n",
    "figObj5j = fec['contb_receipt_amt'][fec.cand_nm=='Obama, Barack'].groupby(\n",
    "                [fec.contb_receipt_dt_year,fec.contb_receipt_dt_quarter]).sum().plot(color='Blue')\n",
    "fec['contb_receipt_amt'][fec.cand_nm=='Romney, Mitt'].groupby(\n",
    "                [fec.contb_receipt_dt_year,fec.contb_receipt_dt_quarter]).sum().plot(color='Red')\n",
    "figObj5j.set_title('Task 5j')\n",
    "figObj5j.set_xlabel('Quarters/Years')\n",
    "figObj5j.set_ylabel('Per Quarter Contributions')\n",
    "figObj5j.legend (['Obama', 'Romney'])\n",
    "#########################end code for Task 5j\n",
    "\n",
    "\n",
    "#Harder\n",
    "#Task 5k: Draw a line chart showing the CUMULATIVE campaign contribution of Obama and Romney varied every quarter\n",
    "# In other words, if Obama made X, Y, Z in first, second and third quarters\n",
    "#  then plot X for first quarter, X+Y for second quarter and X+Y+Z for third quarter.\n",
    "#Use blue for Obama and red for Romney\n",
    "#This is a bit tricky because, you have to convert contribution date to quarter.\n",
    "#You can either do it on your own or see if Pandas has some function\n",
    "#########################begin code for Task 5k\n",
    "plt.figure()\n",
    "figObj5k = fec['contb_receipt_amt'][fec.cand_nm=='Obama, Barack'].groupby(\n",
    "                [fec.contb_receipt_dt_year,fec.contb_receipt_dt_quarter]).sum().cumsum().plot(color='Blue')\n",
    "fec['contb_receipt_amt'][fec.cand_nm=='Romney, Mitt'].groupby(\n",
    "                [fec.contb_receipt_dt_year,fec.contb_receipt_dt_quarter]).sum().cumsum().plot(color='Red')\n",
    "figObj5k.set_title('Task 5j')\n",
    "figObj5k.set_xlabel('Quarters/Years')\n",
    "figObj5k.set_ylabel('Per Quarter Contributions')\n",
    "figObj5k.legend (['Obama', 'Romney'])\n",
    "#########################end code for Task 5k\n",
    "\n",
    "#Tasks 5l and 5m\n",
    "#Repeat 5j and 5k but do it for NUMBER of contributors\n",
    "#In other words, 5l plots the number of contributors for Obama and Romney, quarter over quarter\n",
    "#5m plots cumulative number of contributors quarter over quarter.\n",
    "\n",
    "#########################begin code for Task 5l\n",
    "plt.figure()\n",
    "figObj5l = fec['contb_receipt_amt'][fec.cand_nm=='Obama, Barack'].groupby(\n",
    "                        [fec.contb_receipt_dt_year,fec.contb_receipt_dt_quarter]).count().plot(color='Blue')\n",
    "fec['contb_receipt_amt'][fec.cand_nm=='Romney, Mitt'].groupby(\n",
    "                        [fec.contb_receipt_dt_year,fec.contb_receipt_dt_quarter]).count().plot(color='Red')\n",
    "figObj5l.set_title('Tak 5l')\n",
    "figObj5l.set_xlabel('Quarters/Years')\n",
    "figObj5l.set_ylabel('#Contributions')\n",
    "figObj5l.legend (('Obama', 'Romney'))\n",
    "#########################end code for Task 5l\n",
    "\n",
    "\n",
    "#########################begin code for Task 5m\n",
    "plt.figure()\n",
    "figObj5m = fec['contb_receipt_amt'][fec.cand_nm=='Obama, Barack'].groupby(\n",
    "                        [fec.contb_receipt_dt_year,fec.contb_receipt_dt_quarter]).count().cumsum().plot(color='Blue')\n",
    "fec['contb_receipt_amt'][fec.cand_nm=='Romney, Mitt'].groupby(\n",
    "                        [fec.contb_receipt_dt_year,fec.contb_receipt_dt_quarter]).count().cumsum().plot(color='Red')\n",
    "figObj5m.set_title('Tak 5m')\n",
    "figObj5m.set_xlabel('Quarters/Years')\n",
    "figObj5m.set_ylabel('#Contributions')\n",
    "figObj5m.legend (('Obama', 'Romney'))\n",
    "#########################end code for Task 5m\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Task 6: Discretization\n",
    "-----------------------\n",
    "\n",
    "Recall the IMDB dataset that we discussed in the class where we were able to draw lot more interesting plots. The key challenge here is that most of the attributes have too many values (ie. the domain cardinality is very large). There are few ways to work around this issue. We already did something above (ie focussing only top-k). The other option is **discretization** where we create buckets and put contributions based on the buckets. Discretization in Pandas is acheived  by cut function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Task 6a:\n",
      "cand_nm              Obama, Barack  Romney, Mitt\n",
      "contb_receipt_amt                               \n",
      "(0, 1]                         493            77\n",
      "(1, 10]                      40070          3681\n",
      "(10, 100]                   372280         31853\n",
      "(100, 1000]                 153991         43357\n",
      "(1000, 10000]                22284         26186\n",
      "(10000, 100000]                  2             1\n",
      "(100000, 1000000]                3           NaN\n",
      "(1000000, 10000000]              4           NaN\n",
      "\n",
      "\n",
      "Task 6b:\n",
      "cand_nm              Obama, Barack  Romney, Mitt\n",
      "contb_receipt_amt                               \n",
      "(0, 1]                      318.24         77.00\n",
      "(1, 10]                  337267.62      29819.66\n",
      "(10, 100]              20288981.41    1987783.76\n",
      "(100, 1000]            54798531.46   22363381.69\n",
      "(1000, 10000]          51753705.67   63942145.42\n",
      "(10000, 100000]           59100.00      12700.00\n",
      "(100000, 1000000]       1490683.08           NaN\n",
      "(1000000, 10000000]     7148839.76           NaN\n",
      "\n",
      "\n",
      "Task 6c:\n",
      "cand_nm              Obama, Barack  Romney, Mitt\n",
      "contb_receipt_amt                               \n",
      "(0, 1]                    0.805182      0.194818\n",
      "(1, 10]                   0.918767      0.081233\n",
      "(10, 100]                 0.910769      0.089231\n",
      "(100, 1000]               0.710176      0.289824\n",
      "(1000, 10000]             0.447326      0.552674\n",
      "(10000, 100000]           0.823120      0.176880\n",
      "(100000, 1000000]         1.000000           NaN\n",
      "(1000000, 10000000]       1.000000           NaN\n",
      "\n",
      "\n",
      "Task 6d:\n",
      "cand_nm              Obama, Barack  Romney, Mitt\n",
      "contb_receipt_amt                               \n",
      "(0, 1]                    0.000002      0.000001\n",
      "(1, 10]                   0.002482      0.000338\n",
      "(10, 100]                 0.149318      0.022503\n",
      "(100, 1000]               0.403294      0.253163\n",
      "(1000, 10000]             0.380885      0.723852\n",
      "(10000, 100000]           0.000435      0.000144\n",
      "(100000, 1000000]         0.010971           NaN\n",
      "(1000000, 10000000]       0.052612           NaN\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa65eb48090>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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47cbo5dKDcvc/uPvug5yjJvq6DujfYh9AbXSfiIhkiVxIhIOAqjbK7ARK4rbz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa62f5ca610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gIiKNL50E+wfgKeA/CU4pnw08DpwQYVwNqTOwKkWdEqAwYTvP3beZ2faEsvbA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\nRCTnKcGKiIhEQAlWREQkAkq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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa65eb4ef50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#The following set of tasks are a bit tricky: \n",
    "#    you need to use multiple commands to achieve. Specifically look at cut, groupby and unstack\n",
    "\n",
    "#Task 6a: Discretize the contributions of Obama and Romney based on the bins given below.\n",
    "# For example, if Obama got contributions such as 2, 6, 16, 18, 120, then he has \n",
    "#  0 contribution in (0,1], 2 contributions in (1,10], 2 contributions in (10, 100] and 1 contribution in (100, 1000]\n",
    "bins = np.array([0, 1, 10, 100, 1000, 10000, 100000, 1000000, 10000000])\n",
    "labels = pd.cut(fec.contb_receipt_amt, bins) # set the variable labels to the output of pd.cut\n",
    "grouped = fec.groupby(['cand_nm', labels]) #Group the data based on labels and candidate names\n",
    "#Replace None below in the print statement with grouped.size().unstack(0) . \n",
    "#    If your code for labels and grouped is correct, this should print number of people in each bin per candidate\n",
    "print \"Task 6a:\\n\", grouped.size().unstack(0)\n",
    "\n",
    "#Task 6b: In Task 6a, we calculated the COUNT (i.e. the number of contributors in each bin)\n",
    "# This by itself is not sufficient.\n",
    "# In this task, let us compute the TOTAL AMOUNT in each bucket.\n",
    "# Specifically, compute for each candidate the total amount of contributions that each got in each bucket.\n",
    "# Continuing the example above, Obama's total contribution in each bucket is\n",
    "#  0 in (0,1], 8 in (1,10], 34 in (10, 100] and 120 in (100, 1000]\n",
    "#This could be done in 1 line from the variable grouped above\n",
    "t6b_bucket_sums = grouped.contb_receipt_amt.sum().unstack(0)\n",
    "print \"\\n\\nTask 6b:\\n\", t6b_bucket_sums\n",
    "\n",
    "\n",
    "#Task 6c: Even this does not fully specify the disparity in the funding scenario.\n",
    "# To see this let us now compute the PROPORTION of total contribution in each bucket\n",
    "# This is called normalization and is a common operation. \n",
    "# Typically normalization is done over the same candidate.\n",
    "# But for the point I am trying to make, let us normalize within bucket.\n",
    "# For example, if obama made X in a bucket and Y in another bucket, \n",
    "#    then normalization will give X/(X+Y) for Obama and Y/(X+Y) for Romney\n",
    "# This is also quite easy in Pandas and can be done in 1 line\n",
    "# The key idea is to realize that sum function takes an argument called axis\n",
    "#  that does different things for axis=0 and axis=1 (figure out which does what)\n",
    "t6c_normed_bucket_sums = t6b_bucket_sums.div(t6b_bucket_sums.sum(axis=1), axis=0)\n",
    "print \"\\n\\nTask 6c:\\n\", t6c_normed_bucket_sums\n",
    "\n",
    "#Once you have done this , uncomment the following line to a horizontal stacked bar chart\n",
    "t6c_normed_bucket_sums[:-2].plot(kind='barh', stacked=True, color=['blue','red'])\n",
    "\n",
    "\n",
    "#Task 6d: Let us go back and try to do the other analysis\n",
    "# Let us now try to see what PROPORTION of each candidate's amount came from each bucket.\n",
    "#  This is the classical case of normalization.\n",
    "#  Continuing the example above, Obama has made 0+8+34+120=162\n",
    "#  We can normalize it by diving each bucket by the total\n",
    "#   For example, 0/162, 8/162, 34/162 and 120/162.\n",
    "#  If you finished t6c, then this just a matter of playing with axis values.\n",
    "t6d_normed_bucket_sums = t6b_bucket_sums.div(t6b_bucket_sums.sum(axis=0),axis=1)\n",
    "print \"\\n\\nTask 6d:\\n\", t6d_normed_bucket_sums\n",
    "\n",
    "\n",
    "#Once you have done this , uncomment the following line to a horizontal stacked bar chart\n",
    "t6d_normed_bucket_sums.plot(kind='barh', stacked=True,color=['blue','red'])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Task 7: Big Money in Politics\n",
    "------------------------------\n",
    "\n",
    "Using the tool of discretization, we were able to analyze how much people contributed in each bucket. While it showed some sense of imbalance, it did not show it clearly. Recently, the concept of income inequality is becoming a rallying cry among liberals. Let us see if there is any inequality in the contributions (spoiler alert: yes!). We can see if we find anything interesting, not by analyzing, total amount but by the amount contributed by top-X%.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N=1, Obama proportion=0.150775678905 and Romney proportion = 0.0463774533432\n",
      "N=2, Obama proportion=0.233644483744 and Romney proportion = 0.078658416654\n",
      "N=5, Obama proportion=0.424594528848 and Romney proportion = 0.138822888029\n",
      "N=10, Obama proportion=0.5666857722 and Romney proportion = 0.2390652498\n",
      "N=20, Obama proportion=0.709150146108 and Romney proportion = 0.439549973343\n"
     ]
    },
    {
     "data": {
      "text/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></th>\n",
       "      <th>cand_nm</th>\n",
       "      <th>Obama, Barack</th>\n",
       "      <th>Romney, Mitt</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contbr_nm</th>\n",
       "      <th>contbr_occupation</th>\n",
       "      <th>contbr_employer</th>\n",
       "      <th>contbr_city</th>\n",
       "      <th>contbr_st</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">OBAMA VICTORY FUND 2012 - UNITEMIZED</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">ZOOLOGY EDUCATION</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">]</th>\n",
       "      <th>CHICAGO</th>\n",
       "      <th>IL</th>\n",
       "      <td> 8187796.84</td>\n",
       "      <td>     0</td>\n",
       "      <td> 8187796.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WASHINGTON</th>\n",
       "      <th>DC</th>\n",
       "      <td>  451726.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>  451726.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MURPHY, CYNTHIA C.</th>\n",
       "      <th>PUBLIC RELATIONS</th>\n",
       "      <th>MURPHY GROUP</th>\n",
       "      <th>LITTLE ROCK</th>\n",
       "      <th>AR</th>\n",
       "      <td>   35800.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>   35800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAVIS, STEPHEN JAMES</th>\n",
       "      <th>ATTORNEY</th>\n",
       "      <th>BANNEKER PARTNERS</th>\n",
       "      <th>SAN FRANCISCO</th>\n",
       "      <th>CA</th>\n",
       "      <td>   30800.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>   30800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOULIND, JEANNETTE</th>\n",
       "      <th>NOT EMPLOYED</th>\n",
       "      <th>NOT EMPLOYED</th>\n",
       "      <th>PHILADELPHIA</th>\n",
       "      <th>PA</th>\n",
       "      <td>   20000.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>   20000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HIEMSTRA, SHERRON</th>\n",
       "      <th>RETIRED</th>\n",
       "      <th>RETIRED</th>\n",
       "      <th>WASHINGTON</th>\n",
       "      <th>DC</th>\n",
       "      <td>   20000.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>   20000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEROT, FRANCIS KINCAID</th>\n",
       "      <th>CONSULTANT</th>\n",
       "      <th>SELF-EMPLOYED</th>\n",
       "      <th>WARREN</th>\n",
       "      <th>VT</th>\n",
       "      <td>   20000.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>   20000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KLEIN, PATRICIA</th>\n",
       "      <th>RETIRED</th>\n",
       "      <th>RETIRED</th>\n",
       "      <th>ZIONSVILLE</th>\n",
       "      <th>PA</th>\n",
       "      <td>   17750.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>   17750.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEDORE, MARK</th>\n",
       "      <th>INVESTOR</th>\n",
       "      <th>SELF-EMPLOYED</th>\n",
       "      <th>OAKLAND PARK</th>\n",
       "      <th>FL</th>\n",
       "      <td>   15195.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>   15195.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABRAMS, EDWIN</th>\n",
       "      <th>RETIRED</th>\n",
       "      <th>RETIRED</th>\n",
       "      <th>VERO BEACH</th>\n",
       "      <th>FL</th>\n",
       "      <td>   15000.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>   15000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANDERSON, SALLY</th>\n",
       "      <th>ADMINISTRATOR</th>\n",
       "      <th>ROSWELL ARTIST IN RESIDENCE FOUNDATION</th>\n",
       "      <th>ROSWELL</th>\n",
       "      <th>NM</th>\n",
       "      <td>   15000.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>   15000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRAUB, JANET E.</th>\n",
       "      <th>NOT EMPLOYED</th>\n",
       "      <th>NOT EMPLOYED</th>\n",
       "      <th>SAN FRANCISCO</th>\n",
       "      <th>CA</th>\n",
       "      <td>   15000.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>   15000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROLFE, RONALD</th>\n",
       "      <th>LAWYER</th>\n",
       "      <th>SELF-EMPLOYED</th>\n",
       "      <th>NEW YORK</th>\n",
       "      <th>NY</th>\n",
       "      <td>   15000.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>   15000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BISHOP, ROBERT C.</th>\n",
       "      <th>RETIRED</th>\n",
       "      <th>RETIRED</th>\n",
       "      <th>REDWOOD CITY</th>\n",
       "      <th>CA</th>\n",
       "      <td>   15000.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>   15000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STEINBRING, YVONNE</th>\n",
       "      <th>RETIRED</th>\n",
       "      <th>RETIRED</th>\n",
       "      <th>GREEN VALLEY</th>\n",
       "      <th>AZ</th>\n",
       "      <td>   15000.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>   15000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COHN, NANCY</th>\n",
       "      <th>VOLUNTEER</th>\n",
       "      <th>VOLUNTEER</th>\n",
       "      <th>TALLAHASSEE</th>\n",
       "      <th>FL</th>\n",
       "      <td>   15000.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>   15000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANSTROM, DECKER</th>\n",
       "      <th>RETIRED</th>\n",
       "      <th>RETIRED</th>\n",
       "      <th>WASHINGTON</th>\n",
       "      <th>DC</th>\n",
       "      <td>   15000.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>   15000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DENNE, CONSTANCE AYERS</th>\n",
       "      <th>RETIRED</th>\n",
       "      <th>RETIRED</th>\n",
       "      <th>GREENPORT</th>\n",
       "      <th>NY</th>\n",
       "      <td>   15000.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>   15000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LANDIS, HOWARD KEL</th>\n",
       "      <th>FINANCIAL SERVICES</th>\n",
       "      <th>PLEXUS CAPITAL</th>\n",
       "      <th>RALEIGH</th>\n",
       "      <th>NC</th>\n",
       "      <td>   15000.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>   15000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEST, RUTH ANN</th>\n",
       "      <th>INVESTOR</th>\n",
       "      <th>SELF-EMPLOYED</th>\n",
       "      <th>DURANGO</th>\n",
       "      <th>CO</th>\n",
       "      <td>   15000.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>   15000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRANKLIN, VIRGINIA</th>\n",
       "      <th>RETIRED</th>\n",
       "      <th>RETIRED</th>\n",
       "      <th>TULSA</th>\n",
       "      <th>OK</th>\n",
       "      <td>   14000.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>   14000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CARY, LAURA</th>\n",
       "      <th>RETIRED</th>\n",
       "      <th>RETIRED</th>\n",
       "      <th>DENVER</th>\n",
       "      <th>CO</th>\n",
       "      <td>   13000.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>   13000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEISSLER, STUART C.</th>\n",
       "      <th>RETIRED</th>\n",
       "      <th>RETIRED</th>\n",
       "      <th>COCONUT CREEK</th>\n",
       "      <th>FL</th>\n",
       "      <td>   13000.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>   13000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NORPAC</th>\n",
       "      <th>ZOOLOGY EDUCATION</th>\n",
       "      <th>]</th>\n",
       "      <th>ENGLEWOOD CLIFFS</th>\n",
       "      <th>NJ</th>\n",
       "      <td>       0.00</td>\n",
       "      <td> 12700</td>\n",
       "      <td>   12700.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRANDT, RICK MR.</th>\n",
       "      <th>EXECUTIVE</th>\n",
       "      <th>BRANDT CONSOLUDATED INC</th>\n",
       "      <th>TAMPA</th>\n",
       "      <th>FL</th>\n",
       "      <td>       0.00</td>\n",
       "      <td> 12500</td>\n",
       "      <td>   12500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BROCKMAN, DOROTHY K. MRS.</th>\n",
       "      <th>INVESTMENTS</th>\n",
       "      <th>SELF-EMPLOYED</th>\n",
       "      <th>HOUSTON</th>\n",
       "      <th>TX</th>\n",
       "      <td>       0.00</td>\n",
       "      <td> 12500</td>\n",
       "      <td>   12500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OKUNO, DALE MR.</th>\n",
       "      <th>ANGEL INVESTOR</th>\n",
       "      <th>OKUNO ASSOCIATES INC.</th>\n",
       "      <th>PASADENA</th>\n",
       "      <th>CA</th>\n",
       "      <td>       0.00</td>\n",
       "      <td> 12500</td>\n",
       "      <td>   12500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRYE, MEGAN MS.</th>\n",
       "      <th>COMMODITY BROKER</th>\n",
       "      <th>WATER STREET ADVISORY</th>\n",
       "      <th>PEORIA</th>\n",
       "      <th>IL</th>\n",
       "      <td>       0.00</td>\n",
       "      <td> 12500</td>\n",
       "      <td>   12500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GREEN, JEFFREY MR.</th>\n",
       "      <th>OWNER</th>\n",
       "      <th>GREEN CHEVROLET</th>\n",
       "      <th>PEORIA</th>\n",
       "      <th>IL</th>\n",
       "      <td>       0.00</td>\n",
       "      <td> 12500</td>\n",
       "      <td>   12500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRINCAT, JEFF MR.</th>\n",
       "      <th>PRESIDENT</th>\n",
       "      <th>CONSUMER FINANCIAL SERVICES</th>\n",
       "      <th>LAKE FOREST</th>\n",
       "      <th>IL</th>\n",
       "      <td>       0.00</td>\n",
       "      <td> 12500</td>\n",
       "      <td>   12500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GREENBAUM MD, SCOTT DR.</th>\n",
       "      <th>PHYSICIAN</th>\n",
       "      <th>SELF-EMPLOYED</th>\n",
       "      <th>KINGS POINT</th>\n",
       "      <th>NY</th>\n",
       "      <td>       0.00</td>\n",
       "      <td>     3</td>\n",
       "      <td>       3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BERKOWITZ, PAUL</th>\n",
       "      <th>NOT EMPLOYED</th>\n",
       "      <th>NOT EMPLOYED</th>\n",
       "      <th>FOREST GROVE</th>\n",
       "      <th>OR</th>\n",
       "      <td>       3.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>       3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SNYDER, KAREN A. MRS.</th>\n",
       "      <th>HOMEMAKER</th>\n",
       "      <th>HOMEMAKER</th>\n",
       "      <th>MCLEAN</th>\n",
       "      <th>VA</th>\n",
       "      <td>       0.00</td>\n",
       "      <td>     3</td>\n",
       "      <td>       3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GRAVES, CRYSTAL</th>\n",
       "      <th>ADMINISTRATION/INSTRUCTOR</th>\n",
       "      <th>INTER-CITY SERVICES</th>\n",
       "      <th>BERKELEY</th>\n",
       "      <th>CA</th>\n",
       "      <td>       3.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>       3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENGLANDER, EVELYN</th>\n",
       "      <th>RETIRED</th>\n",
       "      <th>RETIRED</th>\n",
       "      <th>ESCONDIDO</th>\n",
       "      <th>CA</th>\n",
       "      <td>       3.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>       3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARD, COURTNEY</th>\n",
       "      <th>SPRINKLER FITTER FIRE PROTECTION SPECI</th>\n",
       "      <th>LOCAL 281 SPRINKLER FITTERS UN EMPL.3Y</th>\n",
       "      <th>CHICAGO</th>\n",
       "      <th>IL</th>\n",
       "      <td>       3.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>       3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MANTONE, MARC L. MR.</th>\n",
       "      <th>SALES</th>\n",
       "      <th>MERRILL LYNCH</th>\n",
       "      <th>TOMS RIVER</th>\n",
       "      <th>NJ</th>\n",
       "      <td>       0.00</td>\n",
       "      <td>     3</td>\n",
       "      <td>       3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LARKIN, KATHLEEN</th>\n",
       "      <th>UNDERGRADUATE STUDENT</th>\n",
       "      <th>PENN STATE UNIVERSITY</th>\n",
       "      <th>WEST ISLIP</th>\n",
       "      <th>NY</th>\n",
       "      <td>       3.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>       3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRESSLEY, NORM MR.</th>\n",
       "      <th>FARMER</th>\n",
       "      <th>SELF-EMPLOYED</th>\n",
       "      <th>SAN DIEGO</th>\n",
       "      <th>CA</th>\n",
       "      <td>       0.00</td>\n",
       "      <td>     3</td>\n",
       "      <td>       3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KUNESH, JASON</th>\n",
       "      <th>LEAD UI/UX DEVELOPER</th>\n",
       "      <th>OBAMA FOR AMERICA</th>\n",
       "      <th>CHICAGO</th>\n",
       "      <th>IL</th>\n",
       "      <td>       3.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>       3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RICHO, JOHN H</th>\n",
       "      <th>ZOOLOGY EDUCATION</th>\n",
       "      <th>RETIRED</th>\n",
       "      <th>LOS ANGELES</th>\n",
       "      <th>CA</th>\n",
       "      <td>       3.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>       3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SCHNEIDER, THOMAS</th>\n",
       "      <th>DIRECTOR</th>\n",
       "      <th>COPS TRUST</th>\n",
       "      <th>ROCHESTER HILLS</th>\n",
       "      <th>MI</th>\n",
       "      <td>       3.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>       3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SCOTT, MATTHEW</th>\n",
       "      <th>STUDENT</th>\n",
       "      <th>GWU</th>\n",
       "      <th>WEST ORANGE</th>\n",
       "      <th>NJ</th>\n",
       "      <td>       3.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>       3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GREYF, JANE MS.</th>\n",
       "      <th>ATTORNEY</th>\n",
       "      <th>GOODWIN PROCTER</th>\n",
       "      <th>NEW YORK</th>\n",
       "      <th>NY</th>\n",
       "      <td>       0.00</td>\n",
       "      <td>     3</td>\n",
       "      <td>       3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ODLE, TERESA</th>\n",
       "      <th>EDITOR</th>\n",
       "      <th>SELF-EMPLOYED</th>\n",
       "      <th>ALBUQUERQUE</th>\n",
       "      <th>NM</th>\n",
       "      <td>       3.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>       3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCCARTHY, JAMES</th>\n",
       "      <th>PROGRAMMER</th>\n",
       "      <th>SELF</th>\n",
       "      <th>BUTTE</th>\n",
       "      <th>MT</th>\n",
       "      <td>       3.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>       3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GALLAGHER, PATRICK</th>\n",
       "      <th>3RD GENERATION FAMILY BUSINESS OWNER</th>\n",
       "      <th>GALLAGHER ASPHALT CORPORATION</th>\n",
       "      <th>CHICAGO</th>\n",
       "      <th>IL</th>\n",
       "      <td>       0.00</td>\n",
       "      <td>     3</td>\n",
       "      <td>       3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KERBER, JOHN BERNARD</th>\n",
       "      <th>NOT EMPLOYED</th>\n",
       "      <th>NOT EMPLOYED</th>\n",
       "      <th>EDEN PRAIRIE</th>\n",
       "      <th>MN</th>\n",
       "      <td>       3.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>       3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GREEN, PETER</th>\n",
       "      <th>FILM AND TELEVISION PRODUCER</th>\n",
       "      <th>NOT EMPLOYED</th>\n",
       "      <th>SHERMAN OAKS</th>\n",
       "      <th>CA</th>\n",
       "      <td>       3.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>       3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLEAS, OCTAVIA</th>\n",
       "      <th>RETIRED MSW</th>\n",
       "      <th>RETIRED</th>\n",
       "      <th>OVERLAND PARK</th>\n",
       "      <th>KS</th>\n",
       "      <td>       3.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>       3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WHEELER, LINDA L</th>\n",
       "      <th>PHYSICIAN</th>\n",
       "      <th>CASS REGIONAL MED. CENTER</th>\n",
       "      <th>HARRISONVILLE</th>\n",
       "      <th>MO</th>\n",
       "      <td>       2.50</td>\n",
       "      <td>     0</td>\n",
       "      <td>       2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BROWN, SHERESE MONIQUE MONIQUE</th>\n",
       "      <th>DATA MANAGER</th>\n",
       "      <th>SEIU</th>\n",
       "      <th>HYATTSVILLE</th>\n",
       "      <th>MD</th>\n",
       "      <td>       2.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>       2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOULT, RODERICK</th>\n",
       "      <th>CONSULTANT</th>\n",
       "      <th>THE VICTORIA GROUP, INC.</th>\n",
       "      <th>SALEM</th>\n",
       "      <th>NH</th>\n",
       "      <td>       2.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>       2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOYCE, LOUIS</th>\n",
       "      <th>FIELD ORGANIZER</th>\n",
       "      <th>ORGANIZING FOR AMERICA - VIRGINIA</th>\n",
       "      <th>LEESBURG</th>\n",
       "      <th>VA</th>\n",
       "      <td>       2.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>       2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WINTERSTEINER, PETER P.</th>\n",
       "      <th>SCIENTIST</th>\n",
       "      <th>ARCON CORP</th>\n",
       "      <th>WABAN</th>\n",
       "      <th>MA</th>\n",
       "      <td>       2.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>       2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FOSS, MARNA</th>\n",
       "      <th>NOT EMPLOYED</th>\n",
       "      <th>NOT EMPLOYED</th>\n",
       "      <th>APEX</th>\n",
       "      <th>NC</th>\n",
       "      <td>       2.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>       2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRAN, KHIEM</th>\n",
       "      <th>INFORMATION REQUESTED PER BEST EFFORTS</th>\n",
       "      <th>INFORMATION REQUESTED PER BEST EFFORTS</th>\n",
       "      <th>QUINCY</th>\n",
       "      <th>MA</th>\n",
       "      <td>       0.00</td>\n",
       "      <td>     2</td>\n",
       "      <td>       2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MICHAL, JUNE B</th>\n",
       "      <th>LEGAL SECRETARY</th>\n",
       "      <th>RETIRED</th>\n",
       "      <th>PUYALLUP</th>\n",
       "      <th>WA</th>\n",
       "      <td>       1.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>       1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHUBIN, ALAN</th>\n",
       "      <th>CONSULTANT</th>\n",
       "      <th>TOWERS WATSON</th>\n",
       "      <th>CHARLOTTE</th>\n",
       "      <th>NC</th>\n",
       "      <td>       1.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>       1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WILSON, LAURA</th>\n",
       "      <th>WRITER</th>\n",
       "      <th>OBAMA FOR AMERICA</th>\n",
       "      <th>CHICAGO</th>\n",
       "      <th>IL</th>\n",
       "      <td>       0.08</td>\n",
       "      <td>     0</td>\n",
       "      <td>       0.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>289862 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "cand_nm                                                                                                                                        Obama, Barack  \\\n",
       "contbr_nm                            contbr_occupation                      contbr_employer                        contbr_city      contbr_st                  \n",
       "OBAMA VICTORY FUND 2012 - UNITEMIZED ZOOLOGY EDUCATION                      ]                                      CHICAGO          IL            8187796.84   \n",
       "                                                                                                                   WASHINGTON       DC             451726.00   \n",
       "MURPHY, CYNTHIA C.                   PUBLIC RELATIONS                       MURPHY GROUP                           LITTLE ROCK      AR              35800.00   \n",
       "DAVIS, STEPHEN JAMES                 ATTORNEY                               BANNEKER PARTNERS                      SAN FRANCISCO    CA              30800.00   \n",
       "BOULIND, JEANNETTE                   NOT EMPLOYED                           NOT EMPLOYED                           PHILADELPHIA     PA              20000.00   \n",
       "HIEMSTRA, SHERRON                    RETIRED                                RETIRED                                WASHINGTON       DC              20000.00   \n",
       "PEROT, FRANCIS KINCAID               CONSULTANT                             SELF-EMPLOYED                          WARREN           VT              20000.00   \n",
       "KLEIN, PATRICIA                      RETIRED                                RETIRED                                ZIONSVILLE       PA              17750.00   \n",
       "MEDORE, MARK                         INVESTOR                               SELF-EMPLOYED                          OAKLAND PARK     FL              15195.00   \n",
       "ABRAMS, EDWIN                        RETIRED                                RETIRED                                VERO BEACH       FL              15000.00   \n",
       "ANDERSON, SALLY                      ADMINISTRATOR                          ROSWELL ARTIST IN RESIDENCE FOUNDATION ROSWELL          NM              15000.00   \n",
       "TRAUB, JANET E.                      NOT EMPLOYED                           NOT EMPLOYED                           SAN FRANCISCO    CA              15000.00   \n",
       "ROLFE, RONALD                        LAWYER                                 SELF-EMPLOYED                          NEW YORK         NY              15000.00   \n",
       "BISHOP, ROBERT C.                    RETIRED                                RETIRED                                REDWOOD CITY     CA              15000.00   \n",
       "STEINBRING, YVONNE                   RETIRED                                RETIRED                                GREEN VALLEY     AZ              15000.00   \n",
       "COHN, NANCY                          VOLUNTEER                              VOLUNTEER                              TALLAHASSEE      FL              15000.00   \n",
       "ANSTROM, DECKER                      RETIRED                                RETIRED                                WASHINGTON       DC              15000.00   \n",
       "DENNE, CONSTANCE AYERS               RETIRED                                RETIRED                                GREENPORT        NY              15000.00   \n",
       "LANDIS, HOWARD KEL                   FINANCIAL SERVICES                     PLEXUS CAPITAL                         RALEIGH          NC              15000.00   \n",
       "WEST, RUTH ANN                       INVESTOR                               SELF-EMPLOYED                          DURANGO          CO              15000.00   \n",
       "FRANKLIN, VIRGINIA                   RETIRED                                RETIRED                                TULSA            OK              14000.00   \n",
       "CARY, LAURA                          RETIRED                                RETIRED                                DENVER           CO              13000.00   \n",
       "WEISSLER, STUART C.                  RETIRED                                RETIRED                                COCONUT CREEK    FL              13000.00   \n",
       "NORPAC                               ZOOLOGY EDUCATION                      ]                                      ENGLEWOOD CLIFFS NJ                  0.00   \n",
       "BRANDT, RICK MR.                     EXECUTIVE                              BRANDT CONSOLUDATED INC                TAMPA            FL                  0.00   \n",
       "BROCKMAN, DOROTHY K. MRS.            INVESTMENTS                            SELF-EMPLOYED                          HOUSTON          TX                  0.00   \n",
       "OKUNO, DALE MR.                      ANGEL INVESTOR                         OKUNO ASSOCIATES INC.                  PASADENA         CA                  0.00   \n",
       "FRYE, MEGAN MS.                      COMMODITY BROKER                       WATER STREET ADVISORY                  PEORIA           IL                  0.00   \n",
       "GREEN, JEFFREY MR.                   OWNER                                  GREEN CHEVROLET                        PEORIA           IL                  0.00   \n",
       "BRINCAT, JEFF MR.                    PRESIDENT                              CONSUMER FINANCIAL SERVICES            LAKE FOREST      IL                  0.00   \n",
       "...                                                                                                                                                      ...   \n",
       "GREENBAUM MD, SCOTT DR.              PHYSICIAN                              SELF-EMPLOYED                          KINGS POINT      NY                  0.00   \n",
       "BERKOWITZ, PAUL                      NOT EMPLOYED                           NOT EMPLOYED                           FOREST GROVE     OR                  3.00   \n",
       "SNYDER, KAREN A. MRS.                HOMEMAKER                              HOMEMAKER                              MCLEAN           VA                  0.00   \n",
       "GRAVES, CRYSTAL                      ADMINISTRATION/INSTRUCTOR              INTER-CITY SERVICES                    BERKELEY         CA                  3.00   \n",
       "ENGLANDER, EVELYN                    RETIRED                                RETIRED                                ESCONDIDO        CA                  3.00   \n",
       "ARD, COURTNEY                        SPRINKLER FITTER FIRE PROTECTION SPECI LOCAL 281 SPRINKLER FITTERS UN EMPL.3Y CHICAGO          IL                  3.00   \n",
       "MANTONE, MARC L. MR.                 SALES                                  MERRILL LYNCH                          TOMS RIVER       NJ                  0.00   \n",
       "LARKIN, KATHLEEN                     UNDERGRADUATE STUDENT                  PENN STATE UNIVERSITY                  WEST ISLIP       NY                  3.00   \n",
       "PRESSLEY, NORM MR.                   FARMER                                 SELF-EMPLOYED                          SAN DIEGO        CA                  0.00   \n",
       "KUNESH, JASON                        LEAD UI/UX DEVELOPER                   OBAMA FOR AMERICA                      CHICAGO          IL                  3.00   \n",
       "RICHO, JOHN H                        ZOOLOGY EDUCATION                      RETIRED                                LOS ANGELES      CA                  3.00   \n",
       "SCHNEIDER, THOMAS                    DIRECTOR                               COPS TRUST                             ROCHESTER HILLS  MI                  3.00   \n",
       "SCOTT, MATTHEW                       STUDENT                                GWU                                    WEST ORANGE      NJ                  3.00   \n",
       "GREYF, JANE MS.                      ATTORNEY                               GOODWIN PROCTER                        NEW YORK         NY                  0.00   \n",
       "ODLE, TERESA                         EDITOR                                 SELF-EMPLOYED                          ALBUQUERQUE      NM                  3.00   \n",
       "MCCARTHY, JAMES                      PROGRAMMER                             SELF                                   BUTTE            MT                  3.00   \n",
       "GALLAGHER, PATRICK                   3RD GENERATION FAMILY BUSINESS OWNER   GALLAGHER ASPHALT CORPORATION          CHICAGO          IL                  0.00   \n",
       "KERBER, JOHN BERNARD                 NOT EMPLOYED                           NOT EMPLOYED                           EDEN PRAIRIE     MN                  3.00   \n",
       "GREEN, PETER                         FILM AND TELEVISION PRODUCER           NOT EMPLOYED                           SHERMAN OAKS     CA                  3.00   \n",
       "PLEAS, OCTAVIA                       RETIRED MSW                            RETIRED                                OVERLAND PARK    KS                  3.00   \n",
       "WHEELER, LINDA L                     PHYSICIAN                              CASS REGIONAL MED. CENTER              HARRISONVILLE    MO                  2.50   \n",
       "BROWN, SHERESE MONIQUE MONIQUE       DATA MANAGER                           SEIU                                   HYATTSVILLE      MD                  2.00   \n",
       "GOULT, RODERICK                      CONSULTANT                             THE VICTORIA GROUP, INC.               SALEM            NH                  2.00   \n",
       "JOYCE, LOUIS                         FIELD ORGANIZER                        ORGANIZING FOR AMERICA - VIRGINIA      LEESBURG         VA                  2.00   \n",
       "WINTERSTEINER, PETER P.              SCIENTIST                              ARCON CORP                             WABAN            MA                  2.00   \n",
       "FOSS, MARNA                          NOT EMPLOYED                           NOT EMPLOYED                           APEX             NC                  2.00   \n",
       "TRAN, KHIEM                          INFORMATION REQUESTED PER BEST EFFORTS INFORMATION REQUESTED PER BEST EFFORTS QUINCY           MA                  0.00   \n",
       "MICHAL, JUNE B                       LEGAL SECRETARY                        RETIRED                                PUYALLUP         WA                  1.00   \n",
       "SHUBIN, ALAN                         CONSULTANT                             TOWERS WATSON                          CHARLOTTE        NC                  1.00   \n",
       "WILSON, LAURA                        WRITER                                 OBAMA FOR AMERICA                      CHICAGO          IL                  0.08   \n",
       "\n",
       "cand_nm                                                                                                                                        Romney, Mitt  \\\n",
       "contbr_nm                            contbr_occupation                      contbr_employer                        contbr_city      contbr_st                 \n",
       "OBAMA VICTORY FUND 2012 - UNITEMIZED ZOOLOGY EDUCATION                      ]                                      CHICAGO          IL                    0   \n",
       "                                                                                                                   WASHINGTON       DC                    0   \n",
       "MURPHY, CYNTHIA C.                   PUBLIC RELATIONS                       MURPHY GROUP                           LITTLE ROCK      AR                    0   \n",
       "DAVIS, STEPHEN JAMES                 ATTORNEY                               BANNEKER PARTNERS                      SAN FRANCISCO    CA                    0   \n",
       "BOULIND, JEANNETTE                   NOT EMPLOYED                           NOT EMPLOYED                           PHILADELPHIA     PA                    0   \n",
       "HIEMSTRA, SHERRON                    RETIRED                                RETIRED                                WASHINGTON       DC                    0   \n",
       "PEROT, FRANCIS KINCAID               CONSULTANT                             SELF-EMPLOYED                          WARREN           VT                    0   \n",
       "KLEIN, PATRICIA                      RETIRED                                RETIRED                                ZIONSVILLE       PA                    0   \n",
       "MEDORE, MARK                         INVESTOR                               SELF-EMPLOYED                          OAKLAND PARK     FL                    0   \n",
       "ABRAMS, EDWIN                        RETIRED                                RETIRED                                VERO BEACH       FL                    0   \n",
       "ANDERSON, SALLY                      ADMINISTRATOR                          ROSWELL ARTIST IN RESIDENCE FOUNDATION ROSWELL          NM                    0   \n",
       "TRAUB, JANET E.                      NOT EMPLOYED                           NOT EMPLOYED                           SAN FRANCISCO    CA                    0   \n",
       "ROLFE, RONALD                        LAWYER                                 SELF-EMPLOYED                          NEW YORK         NY                    0   \n",
       "BISHOP, ROBERT C.                    RETIRED                                RETIRED                                REDWOOD CITY     CA                    0   \n",
       "STEINBRING, YVONNE                   RETIRED                                RETIRED                                GREEN VALLEY     AZ                    0   \n",
       "COHN, NANCY                          VOLUNTEER                              VOLUNTEER                              TALLAHASSEE      FL                    0   \n",
       "ANSTROM, DECKER                      RETIRED                                RETIRED                                WASHINGTON       DC                    0   \n",
       "DENNE, CONSTANCE AYERS               RETIRED                                RETIRED                                GREENPORT        NY                    0   \n",
       "LANDIS, HOWARD KEL                   FINANCIAL SERVICES                     PLEXUS CAPITAL                         RALEIGH          NC                    0   \n",
       "WEST, RUTH ANN                       INVESTOR                               SELF-EMPLOYED                          DURANGO          CO                    0   \n",
       "FRANKLIN, VIRGINIA                   RETIRED                                RETIRED                                TULSA            OK                    0   \n",
       "CARY, LAURA                          RETIRED                                RETIRED                                DENVER           CO                    0   \n",
       "WEISSLER, STUART C.                  RETIRED                                RETIRED                                COCONUT CREEK    FL                    0   \n",
       "NORPAC                               ZOOLOGY EDUCATION                      ]                                      ENGLEWOOD CLIFFS NJ                12700   \n",
       "BRANDT, RICK MR.                     EXECUTIVE                              BRANDT CONSOLUDATED INC                TAMPA            FL                12500   \n",
       "BROCKMAN, DOROTHY K. MRS.            INVESTMENTS                            SELF-EMPLOYED                          HOUSTON          TX                12500   \n",
       "OKUNO, DALE MR.                      ANGEL INVESTOR                         OKUNO ASSOCIATES INC.                  PASADENA         CA                12500   \n",
       "FRYE, MEGAN MS.                      COMMODITY BROKER                       WATER STREET ADVISORY                  PEORIA           IL                12500   \n",
       "GREEN, JEFFREY MR.                   OWNER                                  GREEN CHEVROLET                        PEORIA           IL                12500   \n",
       "BRINCAT, JEFF MR.                    PRESIDENT                              CONSUMER FINANCIAL SERVICES            LAKE FOREST      IL                12500   \n",
       "...                                                                                                                                                     ...   \n",
       "GREENBAUM MD, SCOTT DR.              PHYSICIAN                              SELF-EMPLOYED                          KINGS POINT      NY                    3   \n",
       "BERKOWITZ, PAUL                      NOT EMPLOYED                           NOT EMPLOYED                           FOREST GROVE     OR                    0   \n",
       "SNYDER, KAREN A. MRS.                HOMEMAKER                              HOMEMAKER                              MCLEAN           VA                    3   \n",
       "GRAVES, CRYSTAL                      ADMINISTRATION/INSTRUCTOR              INTER-CITY SERVICES                    BERKELEY         CA                    0   \n",
       "ENGLANDER, EVELYN                    RETIRED                                RETIRED                                ESCONDIDO        CA                    0   \n",
       "ARD, COURTNEY                        SPRINKLER FITTER FIRE PROTECTION SPECI LOCAL 281 SPRINKLER FITTERS UN EMPL.3Y CHICAGO          IL                    0   \n",
       "MANTONE, MARC L. MR.                 SALES                                  MERRILL LYNCH                          TOMS RIVER       NJ                    3   \n",
       "LARKIN, KATHLEEN                     UNDERGRADUATE STUDENT                  PENN STATE UNIVERSITY                  WEST ISLIP       NY                    0   \n",
       "PRESSLEY, NORM MR.                   FARMER                                 SELF-EMPLOYED                          SAN DIEGO        CA                    3   \n",
       "KUNESH, JASON                        LEAD UI/UX DEVELOPER                   OBAMA FOR AMERICA                      CHICAGO          IL                    0   \n",
       "RICHO, JOHN H                        ZOOLOGY EDUCATION                      RETIRED                                LOS ANGELES      CA                    0   \n",
       "SCHNEIDER, THOMAS                    DIRECTOR                               COPS TRUST                             ROCHESTER HILLS  MI                    0   \n",
       "SCOTT, MATTHEW                       STUDENT                                GWU                                    WEST ORANGE      NJ                    0   \n",
       "GREYF, JANE MS.                      ATTORNEY                               GOODWIN PROCTER                        NEW YORK         NY                    3   \n",
       "ODLE, TERESA                         EDITOR                                 SELF-EMPLOYED                          ALBUQUERQUE      NM                    0   \n",
       "MCCARTHY, JAMES                      PROGRAMMER                             SELF                                   BUTTE            MT                    0   \n",
       "GALLAGHER, PATRICK                   3RD GENERATION FAMILY BUSINESS OWNER   GALLAGHER ASPHALT CORPORATION          CHICAGO          IL                    3   \n",
       "KERBER, JOHN BERNARD                 NOT EMPLOYED                           NOT EMPLOYED                           EDEN PRAIRIE     MN                    0   \n",
       "GREEN, PETER                         FILM AND TELEVISION PRODUCER           NOT EMPLOYED                           SHERMAN OAKS     CA                    0   \n",
       "PLEAS, OCTAVIA                       RETIRED MSW                            RETIRED                                OVERLAND PARK    KS                    0   \n",
       "WHEELER, LINDA L                     PHYSICIAN                              CASS REGIONAL MED. CENTER              HARRISONVILLE    MO                    0   \n",
       "BROWN, SHERESE MONIQUE MONIQUE       DATA MANAGER                           SEIU                                   HYATTSVILLE      MD                    0   \n",
       "GOULT, RODERICK                      CONSULTANT                             THE VICTORIA GROUP, INC.               SALEM            NH                    0   \n",
       "JOYCE, LOUIS                         FIELD ORGANIZER                        ORGANIZING FOR AMERICA - VIRGINIA      LEESBURG         VA                    0   \n",
       "WINTERSTEINER, PETER P.              SCIENTIST                              ARCON CORP                             WABAN            MA                    0   \n",
       "FOSS, MARNA                          NOT EMPLOYED                           NOT EMPLOYED                           APEX             NC                    0   \n",
       "TRAN, KHIEM                          INFORMATION REQUESTED PER BEST EFFORTS INFORMATION REQUESTED PER BEST EFFORTS QUINCY           MA                    2   \n",
       "MICHAL, JUNE B                       LEGAL SECRETARY                        RETIRED                                PUYALLUP         WA                    0   \n",
       "SHUBIN, ALAN                         CONSULTANT                             TOWERS WATSON                          CHARLOTTE        NC                    0   \n",
       "WILSON, LAURA                        WRITER                                 OBAMA FOR AMERICA                      CHICAGO          IL                    0   \n",
       "\n",
       "cand_nm                                                                                                                                             Total  \n",
       "contbr_nm                            contbr_occupation                      contbr_employer                        contbr_city      contbr_st              \n",
       "OBAMA VICTORY FUND 2012 - UNITEMIZED ZOOLOGY EDUCATION                      ]                                      CHICAGO          IL         8187796.84  \n",
       "                                                                                                                   WASHINGTON       DC          451726.00  \n",
       "MURPHY, CYNTHIA C.                   PUBLIC RELATIONS                       MURPHY GROUP                           LITTLE ROCK      AR           35800.00  \n",
       "DAVIS, STEPHEN JAMES                 ATTORNEY                               BANNEKER PARTNERS                      SAN FRANCISCO    CA           30800.00  \n",
       "BOULIND, JEANNETTE                   NOT EMPLOYED                           NOT EMPLOYED                           PHILADELPHIA     PA           20000.00  \n",
       "HIEMSTRA, SHERRON                    RETIRED                                RETIRED                                WASHINGTON       DC           20000.00  \n",
       "PEROT, FRANCIS KINCAID               CONSULTANT                             SELF-EMPLOYED                          WARREN           VT           20000.00  \n",
       "KLEIN, PATRICIA                      RETIRED                                RETIRED                                ZIONSVILLE       PA           17750.00  \n",
       "MEDORE, MARK                         INVESTOR                               SELF-EMPLOYED                          OAKLAND PARK     FL           15195.00  \n",
       "ABRAMS, EDWIN                        RETIRED                                RETIRED                                VERO BEACH       FL           15000.00  \n",
       "ANDERSON, SALLY                      ADMINISTRATOR                          ROSWELL ARTIST IN RESIDENCE FOUNDATION ROSWELL          NM           15000.00  \n",
       "TRAUB, JANET E.                      NOT EMPLOYED                           NOT EMPLOYED                           SAN FRANCISCO    CA           15000.00  \n",
       "ROLFE, RONALD                        LAWYER                                 SELF-EMPLOYED                          NEW YORK         NY           15000.00  \n",
       "BISHOP, ROBERT C.                    RETIRED                                RETIRED                                REDWOOD CITY     CA           15000.00  \n",
       "STEINBRING, YVONNE                   RETIRED                                RETIRED                                GREEN VALLEY     AZ           15000.00  \n",
       "COHN, NANCY                          VOLUNTEER                              VOLUNTEER                              TALLAHASSEE      FL           15000.00  \n",
       "ANSTROM, DECKER                      RETIRED                                RETIRED                                WASHINGTON       DC           15000.00  \n",
       "DENNE, CONSTANCE AYERS               RETIRED                                RETIRED                                GREENPORT        NY           15000.00  \n",
       "LANDIS, HOWARD KEL                   FINANCIAL SERVICES                     PLEXUS CAPITAL                         RALEIGH          NC           15000.00  \n",
       "WEST, RUTH ANN                       INVESTOR                               SELF-EMPLOYED                          DURANGO          CO           15000.00  \n",
       "FRANKLIN, VIRGINIA                   RETIRED                                RETIRED                                TULSA            OK           14000.00  \n",
       "CARY, LAURA                          RETIRED                                RETIRED                                DENVER           CO           13000.00  \n",
       "WEISSLER, STUART C.                  RETIRED                                RETIRED                                COCONUT CREEK    FL           13000.00  \n",
       "NORPAC                               ZOOLOGY EDUCATION                      ]                                      ENGLEWOOD CLIFFS NJ           12700.00  \n",
       "BRANDT, RICK MR.                     EXECUTIVE                              BRANDT CONSOLUDATED INC                TAMPA            FL           12500.00  \n",
       "BROCKMAN, DOROTHY K. MRS.            INVESTMENTS                            SELF-EMPLOYED                          HOUSTON          TX           12500.00  \n",
       "OKUNO, DALE MR.                      ANGEL INVESTOR                         OKUNO ASSOCIATES INC.                  PASADENA         CA           12500.00  \n",
       "FRYE, MEGAN MS.                      COMMODITY BROKER                       WATER STREET ADVISORY                  PEORIA           IL           12500.00  \n",
       "GREEN, JEFFREY MR.                   OWNER                                  GREEN CHEVROLET                        PEORIA           IL           12500.00  \n",
       "BRINCAT, JEFF MR.                    PRESIDENT                              CONSUMER FINANCIAL SERVICES            LAKE FOREST      IL           12500.00  \n",
       "...                                                                                                                                                   ...  \n",
       "GREENBAUM MD, SCOTT DR.              PHYSICIAN                              SELF-EMPLOYED                          KINGS POINT      NY               3.00  \n",
       "BERKOWITZ, PAUL                      NOT EMPLOYED                           NOT EMPLOYED                           FOREST GROVE     OR               3.00  \n",
       "SNYDER, KAREN A. MRS.                HOMEMAKER                              HOMEMAKER                              MCLEAN           VA               3.00  \n",
       "GRAVES, CRYSTAL                      ADMINISTRATION/INSTRUCTOR              INTER-CITY SERVICES                    BERKELEY         CA               3.00  \n",
       "ENGLANDER, EVELYN                    RETIRED                                RETIRED                                ESCONDIDO        CA               3.00  \n",
       "ARD, COURTNEY                        SPRINKLER FITTER FIRE PROTECTION SPECI LOCAL 281 SPRINKLER FITTERS UN EMPL.3Y CHICAGO          IL               3.00  \n",
       "MANTONE, MARC L. MR.                 SALES                                  MERRILL LYNCH                          TOMS RIVER       NJ               3.00  \n",
       "LARKIN, KATHLEEN                     UNDERGRADUATE STUDENT                  PENN STATE UNIVERSITY                  WEST ISLIP       NY               3.00  \n",
       "PRESSLEY, NORM MR.                   FARMER                                 SELF-EMPLOYED                          SAN DIEGO        CA               3.00  \n",
       "KUNESH, JASON                        LEAD UI/UX DEVELOPER                   OBAMA FOR AMERICA                      CHICAGO          IL               3.00  \n",
       "RICHO, JOHN H                        ZOOLOGY EDUCATION                      RETIRED                                LOS ANGELES      CA               3.00  \n",
       "SCHNEIDER, THOMAS                    DIRECTOR                               COPS TRUST                             ROCHESTER HILLS  MI               3.00  \n",
       "SCOTT, MATTHEW                       STUDENT                                GWU                                    WEST ORANGE      NJ               3.00  \n",
       "GREYF, JANE MS.                      ATTORNEY                               GOODWIN PROCTER                        NEW YORK         NY               3.00  \n",
       "ODLE, TERESA                         EDITOR                                 SELF-EMPLOYED                          ALBUQUERQUE      NM               3.00  \n",
       "MCCARTHY, JAMES                      PROGRAMMER                             SELF                                   BUTTE            MT               3.00  \n",
       "GALLAGHER, PATRICK                   3RD GENERATION FAMILY BUSINESS OWNER   GALLAGHER ASPHALT CORPORATION          CHICAGO          IL               3.00  \n",
       "KERBER, JOHN BERNARD                 NOT EMPLOYED                           NOT EMPLOYED                           EDEN PRAIRIE     MN               3.00  \n",
       "GREEN, PETER                         FILM AND TELEVISION PRODUCER           NOT EMPLOYED                           SHERMAN OAKS     CA               3.00  \n",
       "PLEAS, OCTAVIA                       RETIRED MSW                            RETIRED                                OVERLAND PARK    KS               3.00  \n",
       "WHEELER, LINDA L                     PHYSICIAN                              CASS REGIONAL MED. CENTER              HARRISONVILLE    MO               2.50  \n",
       "BROWN, SHERESE MONIQUE MONIQUE       DATA MANAGER                           SEIU                                   HYATTSVILLE      MD               2.00  \n",
       "GOULT, RODERICK                      CONSULTANT                             THE VICTORIA GROUP, INC.               SALEM            NH               2.00  \n",
       "JOYCE, LOUIS                         FIELD ORGANIZER                        ORGANIZING FOR AMERICA - VIRGINIA      LEESBURG         VA               2.00  \n",
       "WINTERSTEINER, PETER P.              SCIENTIST                              ARCON CORP                             WABAN            MA               2.00  \n",
       "FOSS, MARNA                          NOT EMPLOYED                           NOT EMPLOYED                           APEX             NC               2.00  \n",
       "TRAN, KHIEM                          INFORMATION REQUESTED PER BEST EFFORTS INFORMATION REQUESTED PER BEST EFFORTS QUINCY           MA               2.00  \n",
       "MICHAL, JUNE B                       LEGAL SECRETARY                        RETIRED                                PUYALLUP         WA               1.00  \n",
       "SHUBIN, ALAN                         CONSULTANT                             TOWERS WATSON                          CHARLOTTE        NC               1.00  \n",
       "WILSON, LAURA                        WRITER                                 OBAMA FOR AMERICA                      CHICAGO          IL               0.08  \n",
       "\n",
       "[289862 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Task 7a: Write two functions: one for Obama and one for Romney that does the following:\n",
    "# Given a value of N (N can be 1, 2, 5, 10 etc), sort the contributions made to the candidate in decreasing order\n",
    "#  Then find how much contribution the top-N% made\n",
    "#  Then compute the fraction to the overall campaign collection\n",
    "# For example, if Obama collected 1 billion dollars and the top-1% gave 100 million then their contribution is 10%\n",
    "\n",
    "def t7a_helper_contributions_by_top_N_pct(candidate_name, N):\n",
    "    pivotTblUniqContr = pd.tools.pivot.pivot_table(fec[fec.cand_nm == candidate_name], \n",
    "                        values='contb_receipt_amt', \n",
    "                        index=['contbr_nm', 'contbr_occupation', 'contbr_employer', 'contbr_zip', 'contbr_city','contbr_st'], \n",
    "                        aggfunc=np.sum).order(ascending=False)\n",
    "    totalContrib = pivotTblUniqContr.sum()\n",
    "    numContrib = pivotTblUniqContr.count()\n",
    "    percent = int(N/100.0 *  numContrib)\n",
    "    topNContrib = pivotTblUniqContr.iloc[:percent].sum()\n",
    "    \n",
    "    return topNContrib/totalContrib\n",
    "\n",
    "\n",
    "def t7a_contributions_by_top_N_pct_obama(N):\n",
    "    return t7a_helper_contributions_by_top_N_pct('Obama, Barack', N)\n",
    "    \n",
    "def t7a_contributions_by_top_N_pct_romney(N):\n",
    "    return t7a_helper_contributions_by_top_N_pct('Romney, Mitt', N)\n",
    "\n",
    "\n",
    "for N in [1, 2, 5, 10, 20]:\n",
    "    print \"N=%s, Obama proportion=%s and Romney proportion = %s\" % (N, \n",
    "                t7a_contributions_by_top_N_pct_obama(N), t7a_contributions_by_top_N_pct_romney(N))\n",
    "\n",
    "    \n",
    "\n",
    "#Task 7b: Now let us see who these people in 1% are\n",
    "# Compute the top-1% based on total contribution (ie. money they gave to Obama + Romney)\n",
    "# Now let us see some information about them.\n",
    "# For each of the folks in 1%, print the following:\n",
    "#    name, state, occupation, employer, total amount they gave to Obama, total amount they gave to Romney\n",
    "contributors_and_contrib = fec.pivot_table(values= 'contb_receipt_amt', \n",
    "                        index= ['contbr_nm', 'contbr_occupation', 'contbr_employer', 'contbr_city', 'contbr_st'],\n",
    "                        columns='cand_nm', aggfunc=np.sum, fill_value=0)\n",
    "num1Percenters = int(0.01 * len(contributors_and_contrib))\n",
    "contributors_and_contrib[\"Total\"] = contributors_and_contrib[\"Obama, Barack\"] + contributors_and_contrib[\"Romney, Mitt\"]\n",
    "contributors_ordered_by_contrib = contributors_and_contrib.sort([\"Total\"], ascending = False)\n",
    "t7b_1pcters = contributors_ordered_by_contrib.iloc[:num1Percenters]\n",
    "\n",
    "display(contributors_ordered_by_contrib)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Task 8: Political Polarization in USA\n",
    "------------------------------------------\n",
    "\n",
    "It has been argued that political polarization has dramatically increased in USA in the last 10-15 years. While there are many ways to analyze the dataset for polarization, let us use some simple measures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "T8a: Top 10 states for Obama:\n",
      "contbr_st\n",
      "CA           23824984.24\n",
      "IL           16443895.84\n",
      "NY           14651918.51\n",
      "FL            7318178.58\n",
      "MA            6649015.25\n",
      "TX            6570832.45\n",
      "MD            4832663.93\n",
      "DC            4373538.80\n",
      "VA            4259977.19\n",
      "WA            4250933.16\n",
      "Name: contb_receipt_amt, dtype: float64\n",
      "\n",
      "\n",
      "T8a: Top 10 states for Romney:\n",
      "contbr_st\n",
      "CA           11237636.60\n",
      "NY           10184212.63\n",
      "FL            8338458.81\n",
      "TX            6221989.68\n",
      "MA            4710542.30\n",
      "UT            3717300.48\n",
      "IL            3628571.53\n",
      "CT            3499475.45\n",
      "VA            3465765.85\n",
      "NJ            3333320.20\n",
      "Name: contb_receipt_amt, dtype: float64\n",
      "\n",
      "\n",
      "T8b: Top 10 Occupation for Obama:\n",
      "contbr_occupation\n",
      "RETIRED                  25305116.38\n",
      "ATTORNEY                 11141982.97\n",
      "INFORMATION REQUESTED     4866973.96\n",
      "HOMEMAKER                 4248875.80\n",
      "PHYSICIAN                 3735124.94\n",
      "LAWYER                    3160478.87\n",
      "CONSULTANT                2459912.71\n",
      "PROFESSOR                 2165071.08\n",
      "CEO                       2073284.79\n",
      "PRESIDENT                 1878509.95\n",
      "Name: contb_receipt_amt, dtype: float64\n",
      "\n",
      "\n",
      "T8b: Top 10 Occupation for Romney:\n",
      "contbr_occupation\n",
      "RETIRED                                   11508473.59\n",
      "INFORMATION REQUESTED PER BEST EFFORTS    11396894.84\n",
      "HOMEMAKER                                  8147446.22\n",
      "ATTORNEY                                   5364718.82\n",
      "PRESIDENT                                  2491244.89\n",
      "EXECUTIVE                                  2300947.03\n",
      "C.E.O.                                     1968386.11\n",
      "INVESTOR                                   1537595.12\n",
      "CONSULTANT                                 1424894.01\n",
      "PHYSICIAN                                  1368023.96\n",
      "Name: contb_receipt_amt, dtype: float64\n",
      "\n",
      "\n",
      "T8b: Top 10 Employers for Obama:\n",
      "contbr_employer\n",
      "RETIRED                  22694358.85\n",
      "SELF-EMPLOYED            17080985.96\n",
      "NOT EMPLOYED              8586308.70\n",
      "INFORMATION REQUESTED     5053480.37\n",
      "HOMEMAKER                 2605408.54\n",
      "SELF                      1076531.20\n",
      "SELF EMPLOYED              469290.00\n",
      "STUDENT                    318831.45\n",
      "VOLUNTEER                  257104.00\n",
      "MICROSOFT                  215585.36\n",
      "Name: contb_receipt_amt, dtype: float64\n",
      "\n",
      "\n",
      "T8b: Top 10 Employers for Romney:\n",
      "contbr_employer\n",
      "INFORMATION REQUESTED PER BEST EFFORTS    12059527.24\n",
      "RETIRED                                   11506225.71\n",
      "HOMEMAKER                                  8147196.22\n",
      "SELF-EMPLOYED                              7409860.98\n",
      "STUDENT                                     496490.94\n",
      "CREDIT SUISSE                               281150.00\n",
      "MORGAN STANLEY                              267266.00\n",
      "GOLDMAN SACH & CO.                          238250.00\n",
      "BARCLAYS CAPITAL                            162750.00\n",
      "H.I.G. CAPITAL                              139500.00\n",
      "Name: contb_receipt_amt, dtype: float64\n",
      "\n",
      "\n",
      "T8c:Both, Obama only, Romney only =  0 747 253\n",
      "\n",
      "\n",
      "t8e:States ordered by polarity \n",
      "cand_nm    Obama, Barack  Romney, Mitt       polarity\n",
      "contbr_st                                            \n",
      "XX                  0.00        400250  400250.000000\n",
      "FF                  0.00         99030   99030.000000\n",
      "ZZ               5963.00             0    5963.000000\n",
      "AS               2955.00             0    2955.000000\n",
      "UK                  0.00          2500    2500.000000\n",
      "AB               2048.00             0    2048.000000\n",
      "ON               1955.00             0    1955.000000\n",
      "FM                600.00             0     600.000000\n",
      "QU                500.00             0     500.000000\n",
      "AA              56405.00           135     414.742647\n",
      "VI              80712.00          3500      23.053985\n",
      "AP              37130.50          1655      22.421800\n",
      "VT             986510.59         55229      17.861861\n",
      "ME            1167760.12        117152       9.967821\n",
      "AE              42973.75          5680       7.564469\n",
      "PR             216019.00         29125       7.416707\n",
      "UT             519851.37       3717300       7.150684\n",
      "HI             795212.64        111763       7.115105\n",
      "MN            1744387.14        293656       5.940220\n",
      "NM             906162.36        168011       5.393438\n",
      "IL           16443895.84       3628572       4.531780\n",
      "DC            4373538.80       1025137       4.266293\n",
      "WI            1130155.46        270316       4.180852\n",
      "DE             336669.14         82712       4.070329\n",
      "ID             197538.06        787158       3.984822\n",
      "AR             359247.28        105556       3.403349\n",
      "AK             281840.15         86204       3.269418\n",
      "WA            4250933.16       1341522       3.168737\n",
      "MP               3855.00          1250       3.081535\n",
      "GU              11581.50          3850       3.007401\n",
      "...                  ...           ...            ...\n",
      "MT             300225.24        161629       1.857485\n",
      "NC            2357067.63       1273604       1.850705\n",
      "LA             548013.54        991237       1.808779\n",
      "CT            2068291.26       3499475       1.691963\n",
      "MS             195197.17        330183       1.691527\n",
      "IN             883691.81        542086       1.630166\n",
      "SC             630732.94        402743       1.566089\n",
      "ND              58999.90         39392       1.497725\n",
      "NH             616994.85        424839       1.452299\n",
      "NY           14651918.51      10184213       1.438689\n",
      "CO            2132429.49       1506714       1.415284\n",
      "OK             594342.52        839484       1.412456\n",
      "MA            6649015.25       4710542       1.411518\n",
      "NE             251311.97        178600       1.407114\n",
      "GA            2786399.49       1995726       1.396183\n",
      "KS             448038.57        326634       1.371680\n",
      "TN            1119315.02       1516918       1.355219\n",
      "WV             169154.47        126725       1.334805\n",
      "WY             194046.74        252596       1.301721\n",
      "AZ            1506476.98       1888436       1.253544\n",
      "VA            4259977.19       3465766       1.229159\n",
      "FL            7318178.58       8338459       1.139417\n",
      "NV             710693.67        630049       1.127996\n",
      "KY             714954.32        666903       1.072050\n",
      "TX            6570832.45       6221990       1.056066\n",
      "MI            2570307.25       2448110       1.049915\n",
      "OH            1822728.83       1901561       1.043249\n",
      "NJ            3203257.93       3333320       1.040603\n",
      "MO            1320780.71       1371333       1.038274\n",
      "AL             543123.48        527304       1.029999\n",
      "\n",
      "[67 rows x 3 columns]\n",
      "\n",
      "\n",
      "t8f: Occupation ordered by polarity:\n",
      "cand_nm                                 Obama, Barack  Romney, Mitt  \\\n",
      "contbr_occupation                                                     \n",
      "INFORMATION REQUESTED PER BEST EFFORTS           0.00   11396894.84   \n",
      "INFORMATION REQUESTED                      4866973.96          0.00   \n",
      "NOT EMPLOYED                               1709188.20          0.00   \n",
      "PRESIDENT & C.E.O.                               0.00     574161.28   \n",
      "C.P.A.                                          50.00     412107.11   \n",
      "C.E.O.                                        1690.00    1968386.11   \n",
      "LAWYER                                     3160478.87       7705.20   \n",
      "SOFTWARE ENGINEER                           396985.65      19589.54   \n",
      "PSYCHOLOGIST                                427299.92      21341.00   \n",
      "PROFESSOR                                  2165071.08     161362.12   \n",
      "WRITER                                     1084188.88      96613.12   \n",
      "EDUCATOR                                    436600.89      40735.00   \n",
      "ARTIST                                      763125.00      84920.12   \n",
      "TEACHER                                    1250969.15     141338.39   \n",
      "PRIVATE EQUITY                               60374.00     418240.00   \n",
      "EXECUTIVE DIRECTOR                          348180.94      54498.00   \n",
      "ARCHITECT                                   483859.89      82980.00   \n",
      "CEO                                        2073284.79     355910.92   \n",
      "BANKER                                      224084.40    1009186.24   \n",
      "INVESTMENT BANKER                           164011.70     661035.00   \n",
      "INVESTMENTS                                 160480.00     634922.00   \n",
      "FINANCE                                     296031.40    1143592.25   \n",
      "ENGINEER                                    951525.55     326049.24   \n",
      "INVESTMENT MANAGER                          136840.00     382826.00   \n",
      "INVESTMENT MANAGEMENT                       138224.00     382332.12   \n",
      "PHYSICIAN                                  3735124.94    1368023.96   \n",
      "DIRECTOR                                    471741.73     186255.12   \n",
      "REAL ESTATE DEVELOPER                       139454.00     317940.00   \n",
      "RETIRED                                   25305116.38   11508473.59   \n",
      "ATTORNEY                                  11141982.97    5364718.82   \n",
      "FINANCIAL ADVISOR                           169075.50     344951.00   \n",
      "REAL ESTATE                                 528902.09    1054801.00   \n",
      "HOMEMAKER                                  4248875.80    8147446.22   \n",
      "BUSINESS EXECUTIVE                          155780.00     278301.48   \n",
      "INVESTOR                                    884133.00    1537595.12   \n",
      "CONSULTANT                                 2459912.71    1424894.01   \n",
      "EXECUTIVE                                  1355161.05    2300947.03   \n",
      "SELF-EMPLOYED                               672393.40    1092538.44   \n",
      "CHAIRMAN                                    496547.00     805131.75   \n",
      "VICE PRESIDENT                              325647.15     525540.89   \n",
      "MANAGING DIRECTOR                           329688.25     519665.12   \n",
      "PRINCIPAL                                   250005.44     162740.00   \n",
      "BUSINESS OWNER                              449979.30     617389.86   \n",
      "PRESIDENT                                  1878509.95    2491244.89   \n",
      "MANAGER                                     762883.22     577924.94   \n",
      "SALES                                       392886.91     501946.86   \n",
      "STUDENT                                     628099.75     496590.94   \n",
      "PARTNER                                     395759.50     488100.11   \n",
      "OWNER                                      1001567.36     875186.24   \n",
      "ACCOUNTANT                                  249660.24     262555.62   \n",
      "\n",
      "cand_nm                                       Total         polarity  \n",
      "contbr_occupation                                                     \n",
      "INFORMATION REQUESTED PER BEST EFFORTS  11396894.84  11396894.840000  \n",
      "INFORMATION REQUESTED                    4866973.96   4866973.960000  \n",
      "NOT EMPLOYED                             1709188.20   1709188.200000  \n",
      "PRESIDENT & C.E.O.                        574161.28    574161.280000  \n",
      "C.P.A.                                    412157.11      8080.531569  \n",
      "C.E.O.                                   1970076.11      1164.036730  \n",
      "LAWYER                                   3168184.07       410.121574  \n",
      "SOFTWARE ENGINEER                         416575.19        20.264150  \n",
      "PSYCHOLOGIST                              448640.92        20.021550  \n",
      "PROFESSOR                                2326433.20        13.417385  \n",
      "WRITER                                   1180802.00        11.221847  \n",
      "EDUCATOR                                  477335.89        10.717814  \n",
      "ARTIST                                    848045.12         8.986280  \n",
      "TEACHER                                  1392307.54         8.850818  \n",
      "PRIVATE EQUITY                            478614.00         6.927371  \n",
      "EXECUTIVE DIRECTOR                        402678.94         6.388758  \n",
      "ARCHITECT                                 566839.89         5.830972  \n",
      "CEO                                      2429195.71         5.825275  \n",
      "BANKER                                   1233270.64         4.503579  \n",
      "INVESTMENT BANKER                         825046.70         4.030389  \n",
      "INVESTMENTS                               795402.00         3.956369  \n",
      "FINANCE                                  1439623.65         3.863064  \n",
      "ENGINEER                                 1277574.79         2.918340  \n",
      "INVESTMENT MANAGER                        519666.00         2.797597  \n",
      "INVESTMENT MANAGEMENT                     520556.12         2.766013  \n",
      "PHYSICIAN                                5103148.90         2.730305  \n",
      "DIRECTOR                                  657996.85         2.532758  \n",
      "REAL ESTATE DEVELOPER                     457394.00         2.279875  \n",
      "RETIRED                                 36813589.97         2.198825  \n",
      "ATTORNEY                                16506701.79         2.076899  \n",
      "FINANCIAL ADVISOR                         514026.50         2.040207  \n",
      "REAL ESTATE                              1583703.09         1.994318  \n",
      "HOMEMAKER                               12396322.02         1.917553  \n",
      "BUSINESS EXECUTIVE                        434081.48         1.786492  \n",
      "INVESTOR                                 2421728.12         1.739097  \n",
      "CONSULTANT                               3884806.72         1.726382  \n",
      "EXECUTIVE                                3656108.08         1.697913  \n",
      "SELF-EMPLOYED                            1764931.84         1.624848  \n",
      "CHAIRMAN                                 1301678.75         1.621458  \n",
      "VICE PRESIDENT                            851188.04         1.613830  \n",
      "MANAGING DIRECTOR                         849353.37         1.576227  \n",
      "PRINCIPAL                                 412745.44         1.536217  \n",
      "BUSINESS OWNER                           1067369.16         1.372038  \n",
      "PRESIDENT                                4369754.84         1.326181  \n",
      "MANAGER                                  1340808.16         1.320036  \n",
      "SALES                                     894833.77         1.277583  \n",
      "STUDENT                                  1124690.69         1.264821  \n",
      "PARTNER                                   883859.61         1.233322  \n",
      "OWNER                                    1876753.60         1.144404  \n",
      "ACCOUNTANT                                512215.86         1.051648  \n",
      "\n",
      "\n",
      "t8f: Texas cities ordered by polarity:\n",
      "cand_nm           Obama, Barack  Romney, Mitt       Total      polarity\n",
      "contbr_city                                                            \n",
      "STAFFORD               29982.25          0.00    29982.25  29982.250000\n",
      "WHEELER                11622.00          0.00    11622.00  11622.000000\n",
      "DAINGERFIELD           10125.00          0.00    10125.00  10125.000000\n",
      "LUMBERTON              10000.00          0.00    10000.00  10000.000000\n",
      "FARMERS BRANCH          9225.00          0.00     9225.00   9225.000000\n",
      "DUNCANVILLE             9089.20          0.00     9089.20   9089.200000\n",
      "FLORESVILLE             8898.50          0.00     8898.50   8898.500000\n",
      "TROUP                   8686.00          0.00     8686.00   8686.000000\n",
      "BUDA                    7056.00          0.00     7056.00   7056.000000\n",
      "DEER PARK               6693.00          0.00     6693.00   6693.000000\n",
      "ALBANY                     0.00       6500.00     6500.00   6500.000000\n",
      "ROSHARON                5830.00          0.00     5830.00   5830.000000\n",
      "HOCKLEY                 5696.00          0.00     5696.00   5696.000000\n",
      "FREDERICKSBRG           5607.00          0.00     5607.00   5607.000000\n",
      "NAPLES                  5300.00          0.00     5300.00   5300.000000\n",
      "UVALDE                  5080.00          0.00     5080.00   5080.000000\n",
      "GILMER                     0.00       5055.00     5055.00   5055.000000\n",
      "STERLING CITY              0.00       5000.00     5000.00   5000.000000\n",
      "MOORE                   5000.00          0.00     5000.00   5000.000000\n",
      "DANGERFIELD             5000.00          0.00     5000.00   5000.000000\n",
      "GAIL                       0.00       5000.00     5000.00   5000.000000\n",
      "FULSHEAR                   0.00       5000.00     5000.00   5000.000000\n",
      "GRAPELAND                  0.00       5000.00     5000.00   5000.000000\n",
      "JONESBORO                  0.00       5000.00     5000.00   5000.000000\n",
      "HENRIETTA               3980.00          0.00     3980.00   3980.000000\n",
      "CARTHAGE                3910.00          0.00     3910.00   3910.000000\n",
      "FRESNO                  3905.00          0.00     3905.00   3905.000000\n",
      "ELGIN                   3780.00          0.00     3780.00   3780.000000\n",
      "LLANO                   3550.00          0.00     3550.00   3550.000000\n",
      "HIGHLAND VILLAGE        3231.00          0.00     3231.00   3231.000000\n",
      "...                         ...           ...         ...           ...\n",
      "BAYTOWN                10847.00       8600.00    19447.00      1.261132\n",
      "HUNTSVILLE              2750.00       2200.00     4950.00      1.249432\n",
      "SUNRISE BEACH            200.00        250.00      450.00      1.243781\n",
      "SHENANDOAH               160.00        200.00      360.00      1.242236\n",
      "COMFORT                  205.00        250.00      455.00      1.213592\n",
      "CORSICANA               2200.00       2650.00     4850.00      1.203998\n",
      "SPRING BRANCH           2960.00       3555.00     6515.00      1.200608\n",
      "KATY                   28411.30      33605.12    62016.42      1.182767\n",
      "BASTROP                 5923.75       5050.00    10973.75      1.172788\n",
      "BELTON                  2713.00       2340.12     5053.12      1.158847\n",
      "FAIRVIEW                3499.00       3030.00     6529.00      1.154404\n",
      "MANSFIELD               7785.00       8850.00    16635.00      1.136656\n",
      "PLANO                  92154.00      81357.12   173511.12      1.132696\n",
      "SAN ANTONIO           373400.76     422340.48   795741.24      1.131062\n",
      "GRAPEVINE              13723.00      12188.00    25911.00      1.125851\n",
      "DALLAS               1068911.89    1186395.96  2255307.85      1.109909\n",
      "WESTLAKE                7500.00       8255.00    15755.00      1.100520\n",
      "PROSPER                 2631.00       2895.36     5526.36      1.100061\n",
      "DENTON                 20477.00      21916.36    42393.36      1.070239\n",
      "GRANBURY                3114.00       2925.00     6039.00      1.064252\n",
      "HARLINGEN               7374.00       7750.00    15124.00      1.050847\n",
      "BOERNE                 15968.09      16657.00    32625.09      1.043078\n",
      "HEMPSTEAD               1395.00       1350.00     2745.00      1.032568\n",
      "KINGWOOD               18729.75      18176.00    36905.75      1.030409\n",
      "SUGAR LAND             57750.56      58951.65   116702.21      1.020780\n",
      "MELISSA                  750.00        735.00     1485.00      1.019022\n",
      "MCALLEN                10397.00      10436.00    20833.00      1.003655\n",
      "COLLEGE STA             1000.00       1000.00     2000.00      0.999001\n",
      "CHINA SPRING            1000.00       1000.00     2000.00      0.999001\n",
      "HORIZON CITY             250.00        250.00      500.00      0.996016\n",
      "\n",
      "[729 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "#Task 8a: One way to measure polarity is to see how different some distribution are. \n",
    "# For both Obama and Romney, print the top-10 states based on their total AMOUNT of contribution\n",
    "# Do you see lot of common states? \n",
    "t8a_top_10_states_obama = fec.contb_receipt_amt[fec.cand_nm == 'Obama, Barack'].groupby(fec.contbr_st).sum().order(ascending=False)[:10]\n",
    "t8a_top_10_states_romney = fec.contb_receipt_amt[fec.cand_nm == 'Romney, Mitt'].groupby(fec.contbr_st).sum().order(ascending=False)[:10]\n",
    "\n",
    "print \"\\n\\nT8a: Top 10 states for Obama:\\n\",t8a_top_10_states_obama\n",
    "print \"\\n\\nT8a: Top 10 states for Romney:\\n\",t8a_top_10_states_romney\n",
    "\n",
    "#Task 8b: For both Obama and Romney, print the top-10 occupation based on their total AMOUNT of contribution\n",
    "# Do you see lot of common occupation? \n",
    "t8b_top_10_occu_obama = fec['contb_receipt_amt'][fec.cand_nm == 'Obama, Barack'].groupby(fec.contbr_occupation).sum().order(ascending=False)[:10]\n",
    "t8b_top_10_occu_romney = fec['contb_receipt_amt'][fec.cand_nm == 'Romney, Mitt'].groupby(fec.contbr_occupation).sum().order(ascending=False)[:10]\n",
    "\n",
    "print \"\\n\\nT8b: Top 10 Occupation for Obama:\\n\", t8b_top_10_occu_obama\n",
    "print \"\\n\\nT8b: Top 10 Occupation for Romney:\\n\", t8b_top_10_occu_romney\n",
    "\n",
    "\n",
    "\n",
    "#Task 8c: For both Obama and Romney, print the top-10 employers based on their total AMOUNT of contribution\n",
    "# Do you see lot of common employers? \n",
    "t8c_top_10_emp_obama = fec['contb_receipt_amt'][fec.cand_nm == 'Obama, Barack'].groupby(fec.contbr_employer).sum().order(ascending=False)[:10]\n",
    "t8c_top_10_emp_romney = fec['contb_receipt_amt'][fec.cand_nm == 'Romney, Mitt'].groupby(fec.contbr_employer).sum().order(ascending=False)[:10]\n",
    "\n",
    "print \"\\n\\nT8b: Top 10 Employers for Obama:\\n\", t8c_top_10_emp_obama\n",
    "print \"\\n\\nT8b: Top 10 Employers for Romney:\\n\", t8c_top_10_emp_romney\n",
    "\n",
    "\n",
    "#Harder\n",
    "#Task 8d: Here is another way to compute polarization\n",
    "# Find the top-1000 contributors based on their TOTAL contribution (to both Obama and Romney)\n",
    "# For each of the top-1000 folks count the number of people who donated to both, to Obama only and to Romney only\n",
    "top1000Contributors = fec.pivot_table(values='contb_receipt_amt',\n",
    "                                      index=['contbr_nm','contbr_employer','contbr_st','cand_nm'],\n",
    "                                      aggfunc = np.sum).order(ascending = False).head(1000).unstack().fillna(0)\n",
    "t8d_top_1000_both = len(top1000Contributors[ (top1000Contributors[\"Obama, Barack\"] > 0) & (top1000Contributors[\"Romney, Mitt\"] > 0)])\n",
    "t8d_top_1000_BO_only = len(top1000Contributors[ (top1000Contributors[\"Obama, Barack\"] > 0) & (top1000Contributors[\"Romney, Mitt\"] == 0)])\n",
    "t8d_top_1000_MR_only = len(top1000Contributors[ (top1000Contributors[\"Obama, Barack\"] == 0) & (top1000Contributors[\"Romney, Mitt\"] > 0)])\n",
    "\n",
    "print \"\\n\\nT8c:Both, Obama only, Romney only = \", t8d_top_1000_both, t8d_top_1000_BO_only, t8d_top_1000_MR_only\n",
    "\n",
    "#Harder:\n",
    "#Task 8e: Here is yet another way\n",
    "# For each state, compute what fraction of amount went to Obama and what fraction went to Romney\n",
    "# If there is no polarization, then both will get more or less equal amount. \n",
    "# If there is polarization, then the amount will skewed.\n",
    "# Let us use a very crude measure to compute polarization\n",
    "#    If X is what Obama got from a state and Y is what Romney got, compute the value of max(X,Y)/ (min(X,Y) + 1)\n",
    "# For each state compute this value and sort the results in a descending order. \n",
    "# Do you see any pattern?\n",
    "t8e = fec.pivot_table(values = 'contb_receipt_amt', index = ['contbr_st'], columns = ['cand_nm'],  aggfunc = np.sum, fill_value=0)\n",
    "t8e[\"polarity\"] = t8e.apply(\n",
    "                        lambda row: max(row[\"Obama, Barack\"], row[\"Romney, Mitt\"])  / (min(row[\"Obama, Barack\"], row[\"Romney, Mitt\"])+1), \n",
    "                        axis=1)\n",
    "t8e_state_contr_ranked_by_polarity = t8e.sort([\"polarity\"], ascending=False)\n",
    "print \"\\n\\nt8e:States ordered by polarity \\n\", t8e_state_contr_ranked_by_polarity\n",
    "\n",
    "\n",
    "#Harder:\n",
    "#Task 8f: Repeat the above analysis for occupation\n",
    "# However, instead of taking all occupations, let us only take the top-50 occupations based on TOTAL contributions made\n",
    "# For each occupation compute this value and sort the results in a descending order and displ\n",
    "# Do you see any pattern?\n",
    "t8f = fec.pivot_table(values = 'contb_receipt_amt', index = ['contbr_occupation'], columns = ['cand_nm'],  aggfunc = np.sum, fill_value=0)\n",
    "t8f['Total'] = t8f['Obama, Barack'] + t8f['Romney, Mitt']\n",
    "t8f = t8f.sort(['Total'], ascending=False)[:50]\n",
    "t8f[\"polarity\"] = t8f.apply(\n",
    "                        lambda row: max(row[\"Obama, Barack\"], row[\"Romney, Mitt\"])  / (min(row[\"Obama, Barack\"], row[\"Romney, Mitt\"])+1), \n",
    "                        axis=1)\n",
    "t8f_occu_contr_ranked_by_polarity = t8f.sort([\"polarity\"], ascending=False)\n",
    "print \"\\n\\nt8f: Occupation ordered by polarity:\\n\", t8f_occu_contr_ranked_by_polarity\n",
    "\n",
    "\n",
    "#Harder:\n",
    "#Task 8g: A known variable of polarization is based on where a person lives.\n",
    "# At the risk of too much generalization, liberals dominate cities while conservations dominate rural areas\n",
    "# Let us see if this holds in Texas.\n",
    "# Texas is a known solid red (i.e. conservative) state.\n",
    "# For each city in Texas, compute the polarity and order them by polarity.\n",
    "# Do you see any pattern?\n",
    "t8g = fec[fec.contbr_st == 'TX'].pivot_table(values = 'contb_receipt_amt', \n",
    "                                            index = ['contbr_city'], columns=['cand_nm'], aggfunc=np.sum, fill_value=0)\n",
    "t8g['Total'] = t8g['Obama, Barack'] + t8g['Romney, Mitt']\t\n",
    "t8g[\"polarity\"] = t8g.apply(\n",
    "                        lambda row: max(row[\"Obama, Barack\"], row[\"Romney, Mitt\"])  / (min(row[\"Obama, Barack\"], row[\"Romney, Mitt\"])+1), \n",
    "                        axis=1)\n",
    "t8g_tx_city_contr_ranked_by_polarity = t8g.sort(['polarity'], ascending=False)\n",
    "print \"\\n\\nt8f: Texas cities ordered by polarity:\\n\", t8g_tx_city_contr_ranked_by_polarity\n"
   ]
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