Data Science/notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load in the transactions data\n",
    "We do this just to get an idea of what we are working with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                   Name Notification Date  \\\n",
      "0           Collins, Susan M. (Senator)        12/23/2021   \n",
      "1               Hagerty, Bill (Senator)        12/22/2021   \n",
      "2               Hagerty, Bill (Senator)        12/22/2021   \n",
      "3  McConnell, A. Mitchell Jr. (Senator)        12/16/2021   \n",
      "4          Hickenlooper, John (Senator)        12/10/2021   \n",
      "5          Hickenlooper, John (Senator)        12/10/2021   \n",
      "6          Hickenlooper, John (Senator)        12/10/2021   \n",
      "7          Hickenlooper, John (Senator)        12/10/2021   \n",
      "8          Hickenlooper, John (Senator)        12/10/2021   \n",
      "9          Hickenlooper, John (Senator)        12/10/2021   \n",
      "\n",
      "                                                Link Transaction Date   Owner  \\\n",
      "0  https://efdsearch.senate.gov/search/view/ptr/1...       11/10/2021  Spouse   \n",
      "1  https://efdsearch.senate.gov/search/view/ptr/a...       12/16/2021    Self   \n",
      "2  https://efdsearch.senate.gov/search/view/ptr/a...       12/01/2021    Self   \n",
      "3  https://efdsearch.senate.gov/search/view/ptr/0...       12/03/2021  Spouse   \n",
      "4  https://efdsearch.senate.gov/search/view/ptr/3...       11/01/2021    Self   \n",
      "5  https://efdsearch.senate.gov/search/view/ptr/3...       11/01/2021    Self   \n",
      "6  https://efdsearch.senate.gov/search/view/ptr/3...       11/01/2021    Self   \n",
      "7  https://efdsearch.senate.gov/search/view/ptr/3...       10/27/2021    Self   \n",
      "8  https://efdsearch.senate.gov/search/view/ptr/3...       10/29/2021    Self   \n",
      "9  https://efdsearch.senate.gov/search/view/ptr/3...       10/29/2021    Self   \n",
      "\n",
      "                                       Ticker  \\\n",
      "0                                          --   \n",
      "1    [VRM, https://finance.yahoo.com/q?s=VRM]   \n",
      "2  [CTAA, https://finance.yahoo.com/q?s=CTAA]   \n",
      "3    [WFC, https://finance.yahoo.com/q?s=WFC]   \n",
      "4  [PYPL, https://finance.yahoo.com/q?s=PYPL]   \n",
      "5  [NVDA, https://finance.yahoo.com/q?s=NVDA]   \n",
      "6  [MSFT, https://finance.yahoo.com/q?s=MSFT]   \n",
      "7      [FB, https://finance.yahoo.com/q?s=FB]   \n",
      "8  [AAPL, https://finance.yahoo.com/q?s=AAPL]   \n",
      "9  [AMZN, https://finance.yahoo.com/q?s=AMZN]   \n",
      "\n",
      "                                          Asset Name          Asset Type  \\\n",
      "0  [ME St Housing Auth (Social Bds) Ser D 56052FW...  Municipal Security   \n",
      "1                         Vroom, Inc. - Common Stock               Stock   \n",
      "2  [Qwest Corporation 7% Notes due 2056, [Rate/Co...      Corporate Bond   \n",
      "3                 Wells Fargo & Company Common Stock               Stock   \n",
      "4               PayPal Holdings, Inc. - Common Stock               Stock   \n",
      "5                  NVIDIA Corporation - Common Stock               Stock   \n",
      "6               Microsoft Corporation - Common Stock               Stock   \n",
      "7              Facebook, Inc. - Class A Common Stock               Stock   \n",
      "8                          Apple Inc. - Common Stock               Stock   \n",
      "9                    Amazon.com, Inc. - Common Stock               Stock   \n",
      "\n",
      "          Type                 Amount  \\\n",
      "0     Purchase     $50,001 - $100,000   \n",
      "1  Sale (Full)       $1,001 - $15,000   \n",
      "2  Sale (Full)     $50,001 - $100,000   \n",
      "3     Purchase       $1,001 - $15,000   \n",
      "4  Sale (Full)    $250,001 - $500,000   \n",
      "5  Sale (Full)  $500,001 - $1,000,000   \n",
      "6  Sale (Full)    $250,001 - $500,000   \n",
      "7  Sale (Full)    $250,001 - $500,000   \n",
      "8  Sale (Full)    $250,001 - $500,000   \n",
      "9  Sale (Full)    $250,001 - $500,000   \n",
      "\n",
      "                                    Comment  \n",
      "0                                        --  \n",
      "1   Asset owned by Roundstone Ventures, LLC  \n",
      "2  Asset owned by Roundstone Ventures, LLC.  \n",
      "3                     Dividend Reinvestment  \n",
      "4                                        --  \n",
      "5                                        --  \n",
      "6                                        --  \n",
      "7                                        --  \n",
      "8                                        --  \n",
      "9                                        --  \n"
     ]
    }
   ],
   "source": [
    "# Import Libraries\n",
    "import pandas as pd\n",
    "import math\n",
    "# Load in json data\n",
    "df = pd.read_json('transactions.json')\n",
    "print(df.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing the number of transactions that are Partail Sales or Full Sales \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Initialize Variables\n",
    "countSales = []\n",
    "\n",
    "# array to keep count of years\n",
    "years = ['2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021']\n",
    "\n",
    "# Partial Sales\n",
    "dfSalespartial = df[df['Type'] == 'Sale (Partial)']\n",
    "\n",
    "# Full sales\n",
    "dfSalesfull = df[df['Type'] == 'Sale (Full)']\n",
    "\n",
    "# Combine both dataframers to include all sales\n",
    "dfSales = pd.concat([dfSalespartial, dfSalesfull])\n",
    "\n",
    "# iterate through the number of years\n",
    "for year in years:\n",
    "    # filter dataframe to only include that year  and count it\n",
    "    countOfYear = dfSales[dfSales['Transaction Date'].str[-4:] == year].count()\n",
    "    \n",
    "    # append the count to array\n",
    "    countSales.append(countOfYear['Transaction Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Length of years array\n",
    "y_pos = np.arange(len(years))\n",
    "\n",
    "# initilize our graph\n",
    "plt.bar(y_pos, countSales, align='center', alpha=0.5)\n",
    "\n",
    "# set the x-axis values\n",
    "plt.xticks(y_pos, years)\n",
    "\n",
    "# set the x-title\n",
    "plt.xlabel('Years')\n",
    "\n",
    "# set the y-title\n",
    "plt.ylabel('Number of Sales')\n",
    "\n",
    "# set the title\n",
    "plt.title('Sales In The Senate Every Year')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing the number of transactions that are Purchases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Intialize array for number of purchases per month\n",
    "countBuys = []\n",
    "\n",
    "# Filter out the dataframe for all purchases \n",
    "dfPurchases = df[df['Type'] == 'Purchase']\n",
    "\n",
    "# iterate through the number of years\n",
    "for year in years:\n",
    "    # filter dataframe to only include that year  and count it\n",
    "    countOfYear = dfPurchases[dfPurchases['Transaction Date'].str[-4:] == year].count()\n",
    "    \n",
    "    # append the count to array\n",
    "    countBuys.append(countOfYear['Transaction Date'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph Purcahses for Senators\n",
    "\n",
    "# Length of years array\n",
    "y_pos = np.arange(len(years))\n",
    "\n",
    "# initilize our graph\n",
    "plt.bar(y_pos, countBuys, align='center', alpha=0.5)\n",
    "\n",
    "# set the x-axis values\n",
    "plt.xticks(y_pos, years)\n",
    "\n",
    "# set the x-title\n",
    "plt.xlabel('Years')\n",
    "\n",
    "# set the y-title\n",
    "plt.ylabel('Number of Purchases')\n",
    "\n",
    "# set the title\n",
    "plt.title('Number of Purchases In The Senate Every Year')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing the number of purchases and sales in the senate on a yearly basis\n",
    "We do this to get an idea what years had the most purchases or sales, and possibly notice any big relationships with the years and the transaction type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar graph with both sales and buys\n",
    "# initilize our graph\n",
    "width = 0.3       \n",
    "plt.bar(y_pos, countSales, width, color = 'red', label='Number of Sales')\n",
    "plt.bar(y_pos+width, countBuys, width, color = 'green', label='Number of purchases')\n",
    "plt.xticks(y_pos, years)\n",
    "plt.title('Sales and Purchases in the Senate every year')\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('Number of Sales and Purchases')\n",
    "plt.legend(['Sales', 'Purchases'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pos = np.arange(len(years))\n",
    "\n",
    "countTransactions = []\n",
    "for i in range(len(countSales)):\n",
    "    countTransactions.append(countSales[i] + countBuys[i])\n",
    "\n",
    "# initilize our graph\n",
    "plt.bar(y_pos, countTransactions, align='center', alpha=0.5)\n",
    "\n",
    "# set the x-axis values\n",
    "plt.xticks(y_pos, years)\n",
    "\n",
    "# set the x-title\n",
    "plt.xlabel('Years')\n",
    "\n",
    "# set the y-title\n",
    "plt.ylabel('Number of Transactions')\n",
    "\n",
    "# set the title\n",
    "plt.title('Number of Transactions In The Senate Every Year')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Total Trade Volume of a Specific Senator\n",
    "We accomplish this through iterating over all the transactions of a specific Senator and converting the string range into an integer and returning that value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~$54,178,112.5\n"
     ]
    }
   ],
   "source": [
    "def totalTradeVolumeSS(name):\n",
    "    dfTransactions = df[df['Name'] == name]\n",
    "\n",
    "    amounts = dfTransactions['Amount'].value_counts()\n",
    "    listOfAmounts = [\n",
    "        [\"$1,001 - $15,000\", (1001, 15000)],\n",
    "        [\"$15,001 - $50,000\", (15001, 50000)],\n",
    "        [\"$50,001 - $100,000\", (50001, 100000)],\n",
    "        [\"$100,001 - $250,000\", (100001, 250000)],\n",
    "        [\"$250,001 - $500,000\", (250001, 500000)],\n",
    "        [\"$500,001 - $1,000,000\", (500001, 1000000)],\n",
    "        [\"$1,000,001 - $5,000,000\", (1000001, 5000000)],\n",
    "        [\"$5,000,001 - $25,000,000\", (5000001, 25000000)],\n",
    "        [\"$25,000,001 - $50,000,000\", (25000001, 50000000)],\n",
    "        [\"Over $50,000,000\", (50000000, 50000000)],\n",
    "    ]\n",
    "    \n",
    "\n",
    "    sumMin = 0\n",
    "    sumMax = 0\n",
    "    sumMid = 0 \n",
    "\n",
    "    #iterate through the amount of each transaction \n",
    "    for i in range(len(amounts.index)):\n",
    "        # iterate through the \n",
    "        for j in range(len(listOfAmounts)):\n",
    "            if amounts.index[i] == listOfAmounts[j][0]:\n",
    "                sumMin += listOfAmounts[j][1][0] * amounts.values[i]\n",
    "                sumMax += listOfAmounts[j][1][1] * amounts.values[i]\n",
    "    \n",
    "    sumMid += (sumMax + sumMin) / 2\n",
    "\n",
    "    return sumMin, sumMax, sumMid\n",
    "\n",
    "\n",
    "minTradeVolume, maxTradeVolume, aveargeTradeVolume = totalTradeVolumeSS('Hoeven, John (Senator)')\n",
    "# averageTradeVolume:, adds a thousands seperate to make the number more readable\n",
    "print(f\"~${aveargeTradeVolume:,}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Total Trade Volume of the entire senate on a yearly basis\n",
    "\n",
    "We accomplish this by using the logic above but use it with all the transactons rather than all the transactions for a specific senator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~$92,119,284.5\n"
     ]
    }
   ],
   "source": [
    "def totalTradeVolumeSenate(year):\n",
    "    dfTransactions = df[df['Transaction Date'].str[-4:] == year]\n",
    "\n",
    "    amounts = dfTransactions['Amount'].value_counts()\n",
    "    listOfAmounts = [\n",
    "        [\"$1,001 - $15,000\", (1001, 15000)],\n",
    "        [\"$15,001 - $50,000\", (15001, 50000)],\n",
    "        [\"$50,001 - $100,000\", (50001, 100000)],\n",
    "        [\"$100,001 - $250,000\", (100001, 250000)],\n",
    "        [\"$250,001 - $500,000\", (250001, 500000)],\n",
    "        [\"$500,001 - $1,000,000\", (500001, 1000000)],\n",
    "        [\"$1,000,001 - $5,000,000\", (1000001, 5000000)],\n",
    "        [\"$5,000,001 - $25,000,000\", (5000001, 25000000)],\n",
    "        [\"$25,000,001 - $50,000,000\", (25000001, 50000000)],\n",
    "        [\"Over $50,000,000\", (50000000, 50000000)],\n",
    "    ]\n",
    "    \n",
    "\n",
    "    sumMin = 0\n",
    "    sumMax = 0\n",
    "    sumMid = 0 \n",
    "\n",
    "    #iterate through the amount of each transaction \n",
    "    for i in range(len(amounts.index)):\n",
    "        # iterate through the \n",
    "        for j in range(len(listOfAmounts)):\n",
    "            if amounts.index[i] == listOfAmounts[j][0]:\n",
    "                sumMin += listOfAmounts[j][1][0] * amounts.values[i]\n",
    "                sumMax += listOfAmounts[j][1][1] * amounts.values[i]\n",
    "    \n",
    "    sumMid += (sumMax + sumMin) / 2\n",
    "\n",
    "    return sumMin, sumMax, sumMid\n",
    "\n",
    "# Testing function\n",
    "minTradeVolume, maxTradeVolume, aveargeTradeVolume = totalTradeVolumeSenate('2021')\n",
    "# averageTradeVolume:, adds a thousands seperate to make the number more readable\n",
    "print(f\"~${aveargeTradeVolume:,}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pos = np.arange(len(years))\n",
    "\n",
    "tradeVolume = []\n",
    "for i in range(len(years)):\n",
    "    tradeVolume.append(totalTradeVolumeSenate(years[i]))\n",
    "\n",
    "# initilize our graph\n",
    "plt.bar(y_pos, countTransactions, align='center', alpha=0.5)\n",
    "\n",
    "# set the x-axis values\n",
    "plt.xticks(y_pos, years)\n",
    "\n",
    "# set the x-title\n",
    "plt.xlabel('Years')\n",
    "\n",
    "# set the y-title\n",
    "plt.ylabel('$ in Volume')\n",
    "\n",
    "# set the title\n",
    "plt.title('Total Volume of Transactions In The Senate Every Year')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing the volume of tickers\n",
    "We do this to hopefully notice outlier tickers that people all over the senate are completing transactions with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_b714b_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Estimated Volume</th>        <th class=\"col_heading level0 col1\" >Number of occurances</th>        <th class=\"col_heading level0 col2\" >Percentage of Stock Volume</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_b714b_level0_row0\" class=\"row_heading level0 row0\" >Other Assets</th>\n",
       "                        <td id=\"T_b714b_row0_col0\" class=\"data row0 col0\" >33522515.500000</td>\n",
       "                        <td id=\"T_b714b_row0_col1\" class=\"data row0 col1\" >31</td>\n",
       "                        <td id=\"T_b714b_row0_col2\" class=\"data row0 col2\" >61.874646</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row1\" class=\"row_heading level0 row1\" >WLTW</th>\n",
       "                        <td id=\"T_b714b_row1_col0\" class=\"data row1 col0\" >1069507.000000</td>\n",
       "                        <td id=\"T_b714b_row1_col1\" class=\"data row1 col1\" >14</td>\n",
       "                        <td id=\"T_b714b_row1_col2\" class=\"data row1 col2\" >1.974057</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row2\" class=\"row_heading level0 row2\" >ABBV</th>\n",
       "                        <td id=\"T_b714b_row2_col0\" class=\"data row2 col0\" >675002.500000</td>\n",
       "                        <td id=\"T_b714b_row2_col1\" class=\"data row2 col1\" >5</td>\n",
       "                        <td id=\"T_b714b_row2_col2\" class=\"data row2 col2\" >1.245895</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row3\" class=\"row_heading level0 row3\" >F</th>\n",
       "                        <td id=\"T_b714b_row3_col0\" class=\"data row3 col0\" >600002.000000</td>\n",
       "                        <td id=\"T_b714b_row3_col1\" class=\"data row3 col1\" >4</td>\n",
       "                        <td id=\"T_b714b_row3_col2\" class=\"data row3 col2\" >1.107462</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row4\" class=\"row_heading level0 row4\" >KHC</th>\n",
       "                        <td id=\"T_b714b_row4_col0\" class=\"data row4 col0\" >550001.000000</td>\n",
       "                        <td id=\"T_b714b_row4_col1\" class=\"data row4 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row4_col2\" class=\"data row4 col2\" >1.015172</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row5\" class=\"row_heading level0 row5\" >MDU</th>\n",
       "                        <td id=\"T_b714b_row5_col0\" class=\"data row5 col0\" >525001.500000</td>\n",
       "                        <td id=\"T_b714b_row5_col1\" class=\"data row5 col1\" >3</td>\n",
       "                        <td id=\"T_b714b_row5_col2\" class=\"data row5 col2\" >0.969029</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row6\" class=\"row_heading level0 row6\" >BX</th>\n",
       "                        <td id=\"T_b714b_row6_col0\" class=\"data row6 col0\" >525001.500000</td>\n",
       "                        <td id=\"T_b714b_row6_col1\" class=\"data row6 col1\" >3</td>\n",
       "                        <td id=\"T_b714b_row6_col2\" class=\"data row6 col2\" >0.969029</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row7\" class=\"row_heading level0 row7\" >SPG</th>\n",
       "                        <td id=\"T_b714b_row7_col0\" class=\"data row7 col0\" >525001.500000</td>\n",
       "                        <td id=\"T_b714b_row7_col1\" class=\"data row7 col1\" >3</td>\n",
       "                        <td id=\"T_b714b_row7_col2\" class=\"data row7 col2\" >0.969029</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row8\" class=\"row_heading level0 row8\" >IBM</th>\n",
       "                        <td id=\"T_b714b_row8_col0\" class=\"data row8 col0\" >507503.000000</td>\n",
       "                        <td id=\"T_b714b_row8_col1\" class=\"data row8 col1\" >6</td>\n",
       "                        <td id=\"T_b714b_row8_col2\" class=\"data row8 col2\" >0.936731</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row9\" class=\"row_heading level0 row9\" >CAT</th>\n",
       "                        <td id=\"T_b714b_row9_col0\" class=\"data row9 col0\" >500002.000000</td>\n",
       "                        <td id=\"T_b714b_row9_col1\" class=\"data row9 col1\" >4</td>\n",
       "                        <td id=\"T_b714b_row9_col2\" class=\"data row9 col2\" >0.922886</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row10\" class=\"row_heading level0 row10\" >KRFT</th>\n",
       "                        <td id=\"T_b714b_row10_col0\" class=\"data row10 col0\" >375000.500000</td>\n",
       "                        <td id=\"T_b714b_row10_col1\" class=\"data row10 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row10_col2\" class=\"data row10 col2\" >0.692162</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row11\" class=\"row_heading level0 row11\" >NGG</th>\n",
       "                        <td id=\"T_b714b_row11_col0\" class=\"data row11 col0\" >350001.000000</td>\n",
       "                        <td id=\"T_b714b_row11_col1\" class=\"data row11 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row11_col2\" class=\"data row11 col2\" >0.646019</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row12\" class=\"row_heading level0 row12\" >TAP</th>\n",
       "                        <td id=\"T_b714b_row12_col0\" class=\"data row12 col0\" >350001.000000</td>\n",
       "                        <td id=\"T_b714b_row12_col1\" class=\"data row12 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row12_col2\" class=\"data row12 col2\" >0.646019</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row13\" class=\"row_heading level0 row13\" >CSCO</th>\n",
       "                        <td id=\"T_b714b_row13_col0\" class=\"data row13 col0\" >350001.000000</td>\n",
       "                        <td id=\"T_b714b_row13_col1\" class=\"data row13 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row13_col2\" class=\"data row13 col2\" >0.646019</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row14\" class=\"row_heading level0 row14\" >JCI</th>\n",
       "                        <td id=\"T_b714b_row14_col0\" class=\"data row14 col0\" >350001.000000</td>\n",
       "                        <td id=\"T_b714b_row14_col1\" class=\"data row14 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row14_col2\" class=\"data row14 col2\" >0.646019</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row15\" class=\"row_heading level0 row15\" >PEP</th>\n",
       "                        <td id=\"T_b714b_row15_col0\" class=\"data row15 col0\" >350001.000000</td>\n",
       "                        <td id=\"T_b714b_row15_col1\" class=\"data row15 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row15_col2\" class=\"data row15 col2\" >0.646019</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row16\" class=\"row_heading level0 row16\" >BA</th>\n",
       "                        <td id=\"T_b714b_row16_col0\" class=\"data row16 col0\" >350001.000000</td>\n",
       "                        <td id=\"T_b714b_row16_col1\" class=\"data row16 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row16_col2\" class=\"data row16 col2\" >0.646019</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row17\" class=\"row_heading level0 row17\" >QCOM</th>\n",
       "                        <td id=\"T_b714b_row17_col0\" class=\"data row17 col0\" >350001.000000</td>\n",
       "                        <td id=\"T_b714b_row17_col1\" class=\"data row17 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row17_col2\" class=\"data row17 col2\" >0.646019</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row18\" class=\"row_heading level0 row18\" >NKE</th>\n",
       "                        <td id=\"T_b714b_row18_col0\" class=\"data row18 col0\" >350001.000000</td>\n",
       "                        <td id=\"T_b714b_row18_col1\" class=\"data row18 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row18_col2\" class=\"data row18 col2\" >0.646019</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row19\" class=\"row_heading level0 row19\" >VLO</th>\n",
       "                        <td id=\"T_b714b_row19_col0\" class=\"data row19 col0\" >350001.000000</td>\n",
       "                        <td id=\"T_b714b_row19_col1\" class=\"data row19 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row19_col2\" class=\"data row19 col2\" >0.646019</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row20\" class=\"row_heading level0 row20\" >BSTZ</th>\n",
       "                        <td id=\"T_b714b_row20_col0\" class=\"data row20 col0\" >350001.000000</td>\n",
       "                        <td id=\"T_b714b_row20_col1\" class=\"data row20 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row20_col2\" class=\"data row20 col2\" >0.646019</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row21\" class=\"row_heading level0 row21\" >IVZ</th>\n",
       "                        <td id=\"T_b714b_row21_col0\" class=\"data row21 col0\" >332502.500000</td>\n",
       "                        <td id=\"T_b714b_row21_col1\" class=\"data row21 col1\" >5</td>\n",
       "                        <td id=\"T_b714b_row21_col2\" class=\"data row21 col2\" >0.613721</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row22\" class=\"row_heading level0 row22\" >UPS</th>\n",
       "                        <td id=\"T_b714b_row22_col0\" class=\"data row22 col0\" >325001.500000</td>\n",
       "                        <td id=\"T_b714b_row22_col1\" class=\"data row22 col1\" >3</td>\n",
       "                        <td id=\"T_b714b_row22_col2\" class=\"data row22 col2\" >0.599876</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row23\" class=\"row_heading level0 row23\" >PRU</th>\n",
       "                        <td id=\"T_b714b_row23_col0\" class=\"data row23 col0\" >325001.500000</td>\n",
       "                        <td id=\"T_b714b_row23_col1\" class=\"data row23 col1\" >3</td>\n",
       "                        <td id=\"T_b714b_row23_col2\" class=\"data row23 col2\" >0.599876</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row24\" class=\"row_heading level0 row24\" >MCD</th>\n",
       "                        <td id=\"T_b714b_row24_col0\" class=\"data row24 col0\" >300002.000000</td>\n",
       "                        <td id=\"T_b714b_row24_col1\" class=\"data row24 col1\" >4</td>\n",
       "                        <td id=\"T_b714b_row24_col2\" class=\"data row24 col2\" >0.553733</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row25\" class=\"row_heading level0 row25\" >T</th>\n",
       "                        <td id=\"T_b714b_row25_col0\" class=\"data row25 col0\" >257502.000000</td>\n",
       "                        <td id=\"T_b714b_row25_col1\" class=\"data row25 col1\" >4</td>\n",
       "                        <td id=\"T_b714b_row25_col2\" class=\"data row25 col2\" >0.475288</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row26\" class=\"row_heading level0 row26\" >IRET</th>\n",
       "                        <td id=\"T_b714b_row26_col0\" class=\"data row26 col0\" >250001.000000</td>\n",
       "                        <td id=\"T_b714b_row26_col1\" class=\"data row26 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row26_col2\" class=\"data row26 col2\" >0.461443</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row27\" class=\"row_heading level0 row27\" >WMT</th>\n",
       "                        <td id=\"T_b714b_row27_col0\" class=\"data row27 col0\" >250001.000000</td>\n",
       "                        <td id=\"T_b714b_row27_col1\" class=\"data row27 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row27_col2\" class=\"data row27 col2\" >0.461443</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row28\" class=\"row_heading level0 row28\" >GIS</th>\n",
       "                        <td id=\"T_b714b_row28_col0\" class=\"data row28 col0\" >250001.000000</td>\n",
       "                        <td id=\"T_b714b_row28_col1\" class=\"data row28 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row28_col2\" class=\"data row28 col2\" >0.461443</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row29\" class=\"row_heading level0 row29\" >SBUX</th>\n",
       "                        <td id=\"T_b714b_row29_col0\" class=\"data row29 col0\" >250001.000000</td>\n",
       "                        <td id=\"T_b714b_row29_col1\" class=\"data row29 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row29_col2\" class=\"data row29 col2\" >0.461443</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row30\" class=\"row_heading level0 row30\" >RL</th>\n",
       "                        <td id=\"T_b714b_row30_col0\" class=\"data row30 col0\" >250001.000000</td>\n",
       "                        <td id=\"T_b714b_row30_col1\" class=\"data row30 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row30_col2\" class=\"data row30 col2\" >0.461443</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row31\" class=\"row_heading level0 row31\" >DWDP</th>\n",
       "                        <td id=\"T_b714b_row31_col0\" class=\"data row31 col0\" >225001.500000</td>\n",
       "                        <td id=\"T_b714b_row31_col1\" class=\"data row31 col1\" >3</td>\n",
       "                        <td id=\"T_b714b_row31_col2\" class=\"data row31 col2\" >0.415300</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row32\" class=\"row_heading level0 row32\" >BUD</th>\n",
       "                        <td id=\"T_b714b_row32_col0\" class=\"data row32 col0\" >225001.500000</td>\n",
       "                        <td id=\"T_b714b_row32_col1\" class=\"data row32 col1\" >3</td>\n",
       "                        <td id=\"T_b714b_row32_col2\" class=\"data row32 col2\" >0.415300</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row33\" class=\"row_heading level0 row33\" >PG</th>\n",
       "                        <td id=\"T_b714b_row33_col0\" class=\"data row33 col0\" >175000.500000</td>\n",
       "                        <td id=\"T_b714b_row33_col1\" class=\"data row33 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row33_col2\" class=\"data row33 col2\" >0.323010</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row34\" class=\"row_heading level0 row34\" >CG</th>\n",
       "                        <td id=\"T_b714b_row34_col0\" class=\"data row34 col0\" >175000.500000</td>\n",
       "                        <td id=\"T_b714b_row34_col1\" class=\"data row34 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row34_col2\" class=\"data row34 col2\" >0.323010</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row35\" class=\"row_heading level0 row35\" >BMEZ</th>\n",
       "                        <td id=\"T_b714b_row35_col0\" class=\"data row35 col0\" >175000.500000</td>\n",
       "                        <td id=\"T_b714b_row35_col1\" class=\"data row35 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row35_col2\" class=\"data row35 col2\" >0.323010</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row36\" class=\"row_heading level0 row36\" >JNJ</th>\n",
       "                        <td id=\"T_b714b_row36_col0\" class=\"data row36 col0\" >175000.500000</td>\n",
       "                        <td id=\"T_b714b_row36_col1\" class=\"data row36 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row36_col2\" class=\"data row36 col2\" >0.323010</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row37\" class=\"row_heading level0 row37\" >DPS</th>\n",
       "                        <td id=\"T_b714b_row37_col0\" class=\"data row37 col0\" >175000.500000</td>\n",
       "                        <td id=\"T_b714b_row37_col1\" class=\"data row37 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row37_col2\" class=\"data row37 col2\" >0.323010</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row38\" class=\"row_heading level0 row38\" >WFC</th>\n",
       "                        <td id=\"T_b714b_row38_col0\" class=\"data row38 col0\" >175000.500000</td>\n",
       "                        <td id=\"T_b714b_row38_col1\" class=\"data row38 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row38_col2\" class=\"data row38 col2\" >0.323010</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row39\" class=\"row_heading level0 row39\" >EL</th>\n",
       "                        <td id=\"T_b714b_row39_col0\" class=\"data row39 col0\" >175000.500000</td>\n",
       "                        <td id=\"T_b714b_row39_col1\" class=\"data row39 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row39_col2\" class=\"data row39 col2\" >0.323010</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row40\" class=\"row_heading level0 row40\" >AVGO</th>\n",
       "                        <td id=\"T_b714b_row40_col0\" class=\"data row40 col0\" >175000.500000</td>\n",
       "                        <td id=\"T_b714b_row40_col1\" class=\"data row40 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row40_col2\" class=\"data row40 col2\" >0.323010</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row41\" class=\"row_heading level0 row41\" >CMI</th>\n",
       "                        <td id=\"T_b714b_row41_col0\" class=\"data row41 col0\" >175000.500000</td>\n",
       "                        <td id=\"T_b714b_row41_col1\" class=\"data row41 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row41_col2\" class=\"data row41 col2\" >0.323010</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row42\" class=\"row_heading level0 row42\" >DEO</th>\n",
       "                        <td id=\"T_b714b_row42_col0\" class=\"data row42 col0\" >175000.500000</td>\n",
       "                        <td id=\"T_b714b_row42_col1\" class=\"data row42 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row42_col2\" class=\"data row42 col2\" >0.323010</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row43\" class=\"row_heading level0 row43\" >KO</th>\n",
       "                        <td id=\"T_b714b_row43_col0\" class=\"data row43 col0\" >175000.500000</td>\n",
       "                        <td id=\"T_b714b_row43_col1\" class=\"data row43 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row43_col2\" class=\"data row43 col2\" >0.323010</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row44\" class=\"row_heading level0 row44\" >CTL</th>\n",
       "                        <td id=\"T_b714b_row44_col0\" class=\"data row44 col0\" >175000.500000</td>\n",
       "                        <td id=\"T_b714b_row44_col1\" class=\"data row44 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row44_col2\" class=\"data row44 col2\" >0.323010</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row45\" class=\"row_heading level0 row45\" >AJG</th>\n",
       "                        <td id=\"T_b714b_row45_col0\" class=\"data row45 col0\" >175000.500000</td>\n",
       "                        <td id=\"T_b714b_row45_col1\" class=\"data row45 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row45_col2\" class=\"data row45 col2\" >0.323010</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row46\" class=\"row_heading level0 row46\" >TGBAX</th>\n",
       "                        <td id=\"T_b714b_row46_col0\" class=\"data row46 col0\" >175000.500000</td>\n",
       "                        <td id=\"T_b714b_row46_col1\" class=\"data row46 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row46_col2\" class=\"data row46 col2\" >0.323010</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row47\" class=\"row_heading level0 row47\" >BIV</th>\n",
       "                        <td id=\"T_b714b_row47_col0\" class=\"data row47 col0\" >175000.500000</td>\n",
       "                        <td id=\"T_b714b_row47_col1\" class=\"data row47 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row47_col2\" class=\"data row47 col2\" >0.323010</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row48\" class=\"row_heading level0 row48\" >HON</th>\n",
       "                        <td id=\"T_b714b_row48_col0\" class=\"data row48 col0\" >175000.500000</td>\n",
       "                        <td id=\"T_b714b_row48_col1\" class=\"data row48 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row48_col2\" class=\"data row48 col2\" >0.323010</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row49\" class=\"row_heading level0 row49\" >UNP</th>\n",
       "                        <td id=\"T_b714b_row49_col0\" class=\"data row49 col0\" >175000.500000</td>\n",
       "                        <td id=\"T_b714b_row49_col1\" class=\"data row49 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row49_col2\" class=\"data row49 col2\" >0.323010</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row50\" class=\"row_heading level0 row50\" >PFE</th>\n",
       "                        <td id=\"T_b714b_row50_col0\" class=\"data row50 col0\" >150001.000000</td>\n",
       "                        <td id=\"T_b714b_row50_col1\" class=\"data row50 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row50_col2\" class=\"data row50 col2\" >0.276866</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row51\" class=\"row_heading level0 row51\" >BMY</th>\n",
       "                        <td id=\"T_b714b_row51_col0\" class=\"data row51 col0\" >150001.000000</td>\n",
       "                        <td id=\"T_b714b_row51_col1\" class=\"data row51 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row51_col2\" class=\"data row51 col2\" >0.276866</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row52\" class=\"row_heading level0 row52\" >VFC</th>\n",
       "                        <td id=\"T_b714b_row52_col0\" class=\"data row52 col0\" >150001.000000</td>\n",
       "                        <td id=\"T_b714b_row52_col1\" class=\"data row52 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row52_col2\" class=\"data row52 col2\" >0.276866</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row53\" class=\"row_heading level0 row53\" >AMGN</th>\n",
       "                        <td id=\"T_b714b_row53_col0\" class=\"data row53 col0\" >150001.000000</td>\n",
       "                        <td id=\"T_b714b_row53_col1\" class=\"data row53 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row53_col2\" class=\"data row53 col2\" >0.276866</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row54\" class=\"row_heading level0 row54\" >KSS</th>\n",
       "                        <td id=\"T_b714b_row54_col0\" class=\"data row54 col0\" >150001.000000</td>\n",
       "                        <td id=\"T_b714b_row54_col1\" class=\"data row54 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row54_col2\" class=\"data row54 col2\" >0.276866</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row55\" class=\"row_heading level0 row55\" >MRK</th>\n",
       "                        <td id=\"T_b714b_row55_col0\" class=\"data row55 col0\" >150001.000000</td>\n",
       "                        <td id=\"T_b714b_row55_col1\" class=\"data row55 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row55_col2\" class=\"data row55 col2\" >0.276866</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row56\" class=\"row_heading level0 row56\" >AMZN</th>\n",
       "                        <td id=\"T_b714b_row56_col0\" class=\"data row56 col0\" >150001.000000</td>\n",
       "                        <td id=\"T_b714b_row56_col1\" class=\"data row56 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row56_col2\" class=\"data row56 col2\" >0.276866</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row57\" class=\"row_heading level0 row57\" >UA</th>\n",
       "                        <td id=\"T_b714b_row57_col0\" class=\"data row57 col0\" >150001.000000</td>\n",
       "                        <td id=\"T_b714b_row57_col1\" class=\"data row57 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row57_col2\" class=\"data row57 col2\" >0.276866</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row58\" class=\"row_heading level0 row58\" >XOM</th>\n",
       "                        <td id=\"T_b714b_row58_col0\" class=\"data row58 col0\" >150001.000000</td>\n",
       "                        <td id=\"T_b714b_row58_col1\" class=\"data row58 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row58_col2\" class=\"data row58 col2\" >0.276866</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row59\" class=\"row_heading level0 row59\" >FB</th>\n",
       "                        <td id=\"T_b714b_row59_col0\" class=\"data row59 col0\" >150001.000000</td>\n",
       "                        <td id=\"T_b714b_row59_col1\" class=\"data row59 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row59_col2\" class=\"data row59 col2\" >0.276866</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row60\" class=\"row_heading level0 row60\" >AAPL</th>\n",
       "                        <td id=\"T_b714b_row60_col0\" class=\"data row60 col0\" >150001.000000</td>\n",
       "                        <td id=\"T_b714b_row60_col1\" class=\"data row60 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row60_col2\" class=\"data row60 col2\" >0.276866</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row61\" class=\"row_heading level0 row61\" >LYB</th>\n",
       "                        <td id=\"T_b714b_row61_col0\" class=\"data row61 col0\" >150001.000000</td>\n",
       "                        <td id=\"T_b714b_row61_col1\" class=\"data row61 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row61_col2\" class=\"data row61 col2\" >0.276866</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row62\" class=\"row_heading level0 row62\" >CCI</th>\n",
       "                        <td id=\"T_b714b_row62_col0\" class=\"data row62 col0\" >150001.000000</td>\n",
       "                        <td id=\"T_b714b_row62_col1\" class=\"data row62 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row62_col2\" class=\"data row62 col2\" >0.276866</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row63\" class=\"row_heading level0 row63\" >MET</th>\n",
       "                        <td id=\"T_b714b_row63_col0\" class=\"data row63 col0\" >150001.000000</td>\n",
       "                        <td id=\"T_b714b_row63_col1\" class=\"data row63 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row63_col2\" class=\"data row63 col2\" >0.276866</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row64\" class=\"row_heading level0 row64\" >CSX</th>\n",
       "                        <td id=\"T_b714b_row64_col0\" class=\"data row64 col0\" >150001.000000</td>\n",
       "                        <td id=\"T_b714b_row64_col1\" class=\"data row64 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row64_col2\" class=\"data row64 col2\" >0.276866</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row65\" class=\"row_heading level0 row65\" >DOW</th>\n",
       "                        <td id=\"T_b714b_row65_col0\" class=\"data row65 col0\" >150001.000000</td>\n",
       "                        <td id=\"T_b714b_row65_col1\" class=\"data row65 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row65_col2\" class=\"data row65 col2\" >0.276866</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row66\" class=\"row_heading level0 row66\" >CVS</th>\n",
       "                        <td id=\"T_b714b_row66_col0\" class=\"data row66 col0\" >150001.000000</td>\n",
       "                        <td id=\"T_b714b_row66_col1\" class=\"data row66 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row66_col2\" class=\"data row66 col2\" >0.276866</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row67\" class=\"row_heading level0 row67\" >XSLV</th>\n",
       "                        <td id=\"T_b714b_row67_col0\" class=\"data row67 col0\" >150001.000000</td>\n",
       "                        <td id=\"T_b714b_row67_col1\" class=\"data row67 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row67_col2\" class=\"data row67 col2\" >0.276866</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row68\" class=\"row_heading level0 row68\" >KDP</th>\n",
       "                        <td id=\"T_b714b_row68_col0\" class=\"data row68 col0\" >107501.000000</td>\n",
       "                        <td id=\"T_b714b_row68_col1\" class=\"data row68 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row68_col2\" class=\"data row68 col2\" >0.198421</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row69\" class=\"row_heading level0 row69\" >FDX</th>\n",
       "                        <td id=\"T_b714b_row69_col0\" class=\"data row69 col0\" >107501.000000</td>\n",
       "                        <td id=\"T_b714b_row69_col1\" class=\"data row69 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row69_col2\" class=\"data row69 col2\" >0.198421</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row70\" class=\"row_heading level0 row70\" >YUM</th>\n",
       "                        <td id=\"T_b714b_row70_col0\" class=\"data row70 col0\" >75000.500000</td>\n",
       "                        <td id=\"T_b714b_row70_col1\" class=\"data row70 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row70_col2\" class=\"data row70 col2\" >0.138433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row71\" class=\"row_heading level0 row71\" >HAS</th>\n",
       "                        <td id=\"T_b714b_row71_col0\" class=\"data row71 col0\" >75000.500000</td>\n",
       "                        <td id=\"T_b714b_row71_col1\" class=\"data row71 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row71_col2\" class=\"data row71 col2\" >0.138433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row72\" class=\"row_heading level0 row72\" >COH</th>\n",
       "                        <td id=\"T_b714b_row72_col0\" class=\"data row72 col0\" >75000.500000</td>\n",
       "                        <td id=\"T_b714b_row72_col1\" class=\"data row72 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row72_col2\" class=\"data row72 col2\" >0.138433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row73\" class=\"row_heading level0 row73\" >TJX</th>\n",
       "                        <td id=\"T_b714b_row73_col0\" class=\"data row73 col0\" >75000.500000</td>\n",
       "                        <td id=\"T_b714b_row73_col1\" class=\"data row73 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row73_col2\" class=\"data row73 col2\" >0.138433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row74\" class=\"row_heading level0 row74\" >ADP</th>\n",
       "                        <td id=\"T_b714b_row74_col0\" class=\"data row74 col0\" >75000.500000</td>\n",
       "                        <td id=\"T_b714b_row74_col1\" class=\"data row74 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row74_col2\" class=\"data row74 col2\" >0.138433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row75\" class=\"row_heading level0 row75\" >UA-C</th>\n",
       "                        <td id=\"T_b714b_row75_col0\" class=\"data row75 col0\" >75000.500000</td>\n",
       "                        <td id=\"T_b714b_row75_col1\" class=\"data row75 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row75_col2\" class=\"data row75 col2\" >0.138433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row76\" class=\"row_heading level0 row76\" >FCX</th>\n",
       "                        <td id=\"T_b714b_row76_col0\" class=\"data row76 col0\" >75000.500000</td>\n",
       "                        <td id=\"T_b714b_row76_col1\" class=\"data row76 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row76_col2\" class=\"data row76 col2\" >0.138433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row77\" class=\"row_heading level0 row77\" >CTSH</th>\n",
       "                        <td id=\"T_b714b_row77_col0\" class=\"data row77 col0\" >75000.500000</td>\n",
       "                        <td id=\"T_b714b_row77_col1\" class=\"data row77 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row77_col2\" class=\"data row77 col2\" >0.138433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row78\" class=\"row_heading level0 row78\" >TGT</th>\n",
       "                        <td id=\"T_b714b_row78_col0\" class=\"data row78 col0\" >75000.500000</td>\n",
       "                        <td id=\"T_b714b_row78_col1\" class=\"data row78 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row78_col2\" class=\"data row78 col2\" >0.138433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row79\" class=\"row_heading level0 row79\" >EXC</th>\n",
       "                        <td id=\"T_b714b_row79_col0\" class=\"data row79 col0\" >75000.500000</td>\n",
       "                        <td id=\"T_b714b_row79_col1\" class=\"data row79 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row79_col2\" class=\"data row79 col2\" >0.138433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row80\" class=\"row_heading level0 row80\" >NFLX</th>\n",
       "                        <td id=\"T_b714b_row80_col0\" class=\"data row80 col0\" >75000.500000</td>\n",
       "                        <td id=\"T_b714b_row80_col1\" class=\"data row80 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row80_col2\" class=\"data row80 col2\" >0.138433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row81\" class=\"row_heading level0 row81\" >PRNHX</th>\n",
       "                        <td id=\"T_b714b_row81_col0\" class=\"data row81 col0\" >75000.500000</td>\n",
       "                        <td id=\"T_b714b_row81_col1\" class=\"data row81 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row81_col2\" class=\"data row81 col2\" >0.138433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row82\" class=\"row_heading level0 row82\" >FLMVX</th>\n",
       "                        <td id=\"T_b714b_row82_col0\" class=\"data row82 col0\" >75000.500000</td>\n",
       "                        <td id=\"T_b714b_row82_col1\" class=\"data row82 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row82_col2\" class=\"data row82 col2\" >0.138433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row83\" class=\"row_heading level0 row83\" >FCNTX</th>\n",
       "                        <td id=\"T_b714b_row83_col0\" class=\"data row83 col0\" >75000.500000</td>\n",
       "                        <td id=\"T_b714b_row83_col1\" class=\"data row83 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row83_col2\" class=\"data row83 col2\" >0.138433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row84\" class=\"row_heading level0 row84\" >CVX</th>\n",
       "                        <td id=\"T_b714b_row84_col0\" class=\"data row84 col0\" >75000.500000</td>\n",
       "                        <td id=\"T_b714b_row84_col1\" class=\"data row84 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row84_col2\" class=\"data row84 col2\" >0.138433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row85\" class=\"row_heading level0 row85\" >MPC</th>\n",
       "                        <td id=\"T_b714b_row85_col0\" class=\"data row85 col0\" >75000.500000</td>\n",
       "                        <td id=\"T_b714b_row85_col1\" class=\"data row85 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row85_col2\" class=\"data row85 col2\" >0.138433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row86\" class=\"row_heading level0 row86\" >JETS</th>\n",
       "                        <td id=\"T_b714b_row86_col0\" class=\"data row86 col0\" >75000.500000</td>\n",
       "                        <td id=\"T_b714b_row86_col1\" class=\"data row86 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row86_col2\" class=\"data row86 col2\" >0.138433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row87\" class=\"row_heading level0 row87\" >SLB</th>\n",
       "                        <td id=\"T_b714b_row87_col0\" class=\"data row87 col0\" >75000.500000</td>\n",
       "                        <td id=\"T_b714b_row87_col1\" class=\"data row87 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row87_col2\" class=\"data row87 col2\" >0.138433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row88\" class=\"row_heading level0 row88\" >MDLZ</th>\n",
       "                        <td id=\"T_b714b_row88_col0\" class=\"data row88 col0\" >75000.500000</td>\n",
       "                        <td id=\"T_b714b_row88_col1\" class=\"data row88 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row88_col2\" class=\"data row88 col2\" >0.138433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row89\" class=\"row_heading level0 row89\" >VFIJX</th>\n",
       "                        <td id=\"T_b714b_row89_col0\" class=\"data row89 col0\" >75000.500000</td>\n",
       "                        <td id=\"T_b714b_row89_col1\" class=\"data row89 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row89_col2\" class=\"data row89 col2\" >0.138433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row90\" class=\"row_heading level0 row90\" >VZ</th>\n",
       "                        <td id=\"T_b714b_row90_col0\" class=\"data row90 col0\" >75000.500000</td>\n",
       "                        <td id=\"T_b714b_row90_col1\" class=\"data row90 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row90_col2\" class=\"data row90 col2\" >0.138433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row91\" class=\"row_heading level0 row91\" >INTC</th>\n",
       "                        <td id=\"T_b714b_row91_col0\" class=\"data row91 col0\" >75000.500000</td>\n",
       "                        <td id=\"T_b714b_row91_col1\" class=\"data row91 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row91_col2\" class=\"data row91 col2\" >0.138433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row92\" class=\"row_heading level0 row92\" >TWO</th>\n",
       "                        <td id=\"T_b714b_row92_col0\" class=\"data row92 col0\" >75000.500000</td>\n",
       "                        <td id=\"T_b714b_row92_col1\" class=\"data row92 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row92_col2\" class=\"data row92 col2\" >0.138433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row93\" class=\"row_heading level0 row93\" >CE</th>\n",
       "                        <td id=\"T_b714b_row93_col0\" class=\"data row93 col0\" >75000.500000</td>\n",
       "                        <td id=\"T_b714b_row93_col1\" class=\"data row93 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row93_col2\" class=\"data row93 col2\" >0.138433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row94\" class=\"row_heading level0 row94\" >PRPFX</th>\n",
       "                        <td id=\"T_b714b_row94_col0\" class=\"data row94 col0\" >65001.000000</td>\n",
       "                        <td id=\"T_b714b_row94_col1\" class=\"data row94 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row94_col2\" class=\"data row94 col2\" >0.119976</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row95\" class=\"row_heading level0 row95\" >NOG</th>\n",
       "                        <td id=\"T_b714b_row95_col0\" class=\"data row95 col0\" >65001.000000</td>\n",
       "                        <td id=\"T_b714b_row95_col1\" class=\"data row95 col1\" >2</td>\n",
       "                        <td id=\"T_b714b_row95_col2\" class=\"data row95 col2\" >0.119976</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row96\" class=\"row_heading level0 row96\" >KTB</th>\n",
       "                        <td id=\"T_b714b_row96_col0\" class=\"data row96 col0\" >32500.500000</td>\n",
       "                        <td id=\"T_b714b_row96_col1\" class=\"data row96 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row96_col2\" class=\"data row96 col2\" >0.059988</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row97\" class=\"row_heading level0 row97\" >KMP</th>\n",
       "                        <td id=\"T_b714b_row97_col0\" class=\"data row97 col0\" >32500.500000</td>\n",
       "                        <td id=\"T_b714b_row97_col1\" class=\"data row97 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row97_col2\" class=\"data row97 col2\" >0.059988</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row98\" class=\"row_heading level0 row98\" >UAA</th>\n",
       "                        <td id=\"T_b714b_row98_col0\" class=\"data row98 col0\" >32500.500000</td>\n",
       "                        <td id=\"T_b714b_row98_col1\" class=\"data row98 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row98_col2\" class=\"data row98 col2\" >0.059988</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row99\" class=\"row_heading level0 row99\" >CDK</th>\n",
       "                        <td id=\"T_b714b_row99_col0\" class=\"data row99 col0\" >32500.500000</td>\n",
       "                        <td id=\"T_b714b_row99_col1\" class=\"data row99 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row99_col2\" class=\"data row99 col2\" >0.059988</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row100\" class=\"row_heading level0 row100\" >VSAT</th>\n",
       "                        <td id=\"T_b714b_row100_col0\" class=\"data row100 col0\" >32500.500000</td>\n",
       "                        <td id=\"T_b714b_row100_col1\" class=\"data row100 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row100_col2\" class=\"data row100 col2\" >0.059988</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row101\" class=\"row_heading level0 row101\" >GOOG</th>\n",
       "                        <td id=\"T_b714b_row101_col0\" class=\"data row101 col0\" >32500.500000</td>\n",
       "                        <td id=\"T_b714b_row101_col1\" class=\"data row101 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row101_col2\" class=\"data row101 col2\" >0.059988</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row102\" class=\"row_heading level0 row102\" >USB</th>\n",
       "                        <td id=\"T_b714b_row102_col0\" class=\"data row102 col0\" >32500.500000</td>\n",
       "                        <td id=\"T_b714b_row102_col1\" class=\"data row102 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row102_col2\" class=\"data row102 col2\" >0.059988</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row103\" class=\"row_heading level0 row103\" >GTX</th>\n",
       "                        <td id=\"T_b714b_row103_col0\" class=\"data row103 col0\" >8000.500000</td>\n",
       "                        <td id=\"T_b714b_row103_col1\" class=\"data row103 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row103_col2\" class=\"data row103 col2\" >0.014767</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b714b_level0_row104\" class=\"row_heading level0 row104\" >REZI</th>\n",
       "                        <td id=\"T_b714b_row104_col0\" class=\"data row104 col0\" >8000.500000</td>\n",
       "                        <td id=\"T_b714b_row104_col1\" class=\"data row104 col1\" >1</td>\n",
       "                        <td id=\"T_b714b_row104_col2\" class=\"data row104 col2\" >0.014767</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1d5cb81dfd0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def tickerTradedbyVolumeSS(name):\n",
    "    # creat e a dictionary to keep track of ticker name, volume, and occurences \n",
    "    tickerDict = {}\n",
    "    # filter out the main df by senator name\n",
    "    dfTransactions = df[df['Name'] == name]\n",
    "    # get the total trade volume, max min and estimate\n",
    "    volumeMin, volumeMax, volumeMid = totalTradeVolumeSS(name)\n",
    "    # iterate through the dataframe\n",
    "    for i in range(len(dfTransactions)):\n",
    "        #keep track of how many transactions we have with that ticker\n",
    "        occorrences = 0\n",
    "        # keep track of the volume of that ticker\n",
    "        volumePerTicker = 0\n",
    "        # get the ticker name\n",
    "        tickerName = dfTransactions.iloc[i]['Ticker'][0]\n",
    "        if tickerName == \"-\" or tickerName == \"--\":\n",
    "            tickerName = \"Other Assets\"\n",
    "\n",
    "        # create a dictionary to keep track of possible trade volumes\n",
    "        listOfAmounts = [\n",
    "        [\"$1,001 - $15,000\", (1001, 15000)],\n",
    "        [\"$15,001 - $50,000\", (15001, 50000)],\n",
    "        [\"$50,001 - $100,000\", (50001, 100000)],\n",
    "        [\"$100,001 - $250,000\", (100001, 250000)],\n",
    "        [\"$250,001 - $500,000\", (250001, 500000)],\n",
    "        [\"$500,001 - $1,000,000\", (500001, 1000000)],\n",
    "        [\"$1,000,001 - $5,000,000\", (1000001, 5000000)],\n",
    "        [\"$5,000,001 - $25,000,000\", (5000001, 25000000)],\n",
    "        [\"$25,000,001 - $50,000,000\", (25000001, 50000000)],\n",
    "        [\"Over $50,000,000\", (50000000, 50000000)],\n",
    "        ]\n",
    "        # get the volume of that ticker\n",
    "        volumePerTicker = dfTransactions.iloc[i]['Amount']\n",
    "        # iterate through the possible ticker amounts\n",
    "        for i in range(len(listOfAmounts)):\n",
    "            # if there is a match \n",
    "            if volumePerTicker == listOfAmounts[i][0]:\n",
    "                    # estimate the volume of that ticker\n",
    "                    esitimatedVolume = (listOfAmounts[i][1][1] + listOfAmounts[i][1][0]) / 2\n",
    "\n",
    "        # if the ticker does not already exist in the dictionary add it\n",
    "        if tickerName not in tickerDict:\n",
    "            tickerDict[tickerName] = [0, 0, 0]\n",
    "        # add the volume of the ticker to the total volume\n",
    "        tickerDict[tickerName][0] += esitimatedVolume\n",
    "        # increment the number of transactions with that ticker\n",
    "        tickerDict[tickerName][1] += 1\n",
    "        # % of ticker from total volume = (volume of ticker / total volume) * 100\n",
    "        percentageVolume = (tickerDict[tickerName][0] / volumeMid) * 100\n",
    "        tickerDict[tickerName][2] = percentageVolume\n",
    "\n",
    "    return tickerDict\n",
    "    \n",
    "\n",
    "dict = tickerTradedbyVolumeSS('Hoeven, John (Senator)')   \n",
    "# turn dictionary into dataframe \n",
    "dfSSTickerVolume = pd.DataFrame.from_dict(dict, orient='index')\n",
    "dfSSTickerVolume= dfSSTickerVolume.rename(columns = {2: 'Percentage of Stock Volume'})\n",
    "dfSSTickerVolume= dfSSTickerVolume.rename(columns = {1: 'Number of occurances'})\n",
    "dfSSTickerVolume= dfSSTickerVolume.rename(columns = {0: 'Estimated Volume'})\n",
    "# sort the dataframe by the percentage of stock volume\n",
    "dfSSTickerVolume = dfSSTickerVolume.sort_values(by=['Percentage of Stock Volume'], ascending=False)\n",
    "# pie chart the data frame\n",
    "# dfSSTickerVolume.plot.pie(y='Percentage of Stock Volume', figsize=(20,20), autopct=\"%.2f\",  shadow=True, startangle=90 ,title=\"Percentage of Stock Volume Traded by Ticker\")\n",
    "# plt.legend(loc='lower left')\n",
    "# plt.ylabel('')\n",
    "\n",
    "dfSSTickerVolume.style\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Type of assets bought\n",
    "We do this just to get an idea of what type of assets people in the senate invest most in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stock               181\n",
      "                     27\n",
      "Non-Public Stock     12\n",
      "Other Securities      5\n",
      "Name: Asset Type, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get all transactions of congress person\n",
    "def popularAssetsOfTransactions(name):\n",
    "    dfTransactions = df[df['Name'] == name]\n",
    "\n",
    "    executors = dfTransactions['Asset Type'].value_counts()\n",
    "\n",
    "    print(executors)\n",
    "\n",
    "    cols = []\n",
    "    for col in executors.index:\n",
    "        if col == \"\":\n",
    "            cols.append(\"Unknown\")\n",
    "        else:\n",
    "            cols.append(col)\n",
    "\n",
    "    rows = executors.values\n",
    "\n",
    "    yAxis = np.array(rows)\n",
    "    \n",
    "    plt.pie(yAxis, labels=cols)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "popularAssetsOfTransactions('Hoeven, John (Senator)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Executor of transactions\n",
    "Anaylze and determinen who is the main executor of transactions for each senator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get all transactions of congress person\n",
    "def executorOfTransactions(name):\n",
    "    dfTransactions = df[df['Name'] == name]\n",
    "\n",
    "    executors = dfTransactions['Owner'].value_counts()\n",
    "\n",
    "    cols = executors.index\n",
    "    rows = executors.values\n",
    "\n",
    "    yAxis = np.array(rows)\n",
    "    \n",
    "    plt.pie(yAxis, labels=cols)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "executorOfTransactions('Sullivan, Dan (Senator)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sector in which they are buying into\n",
    "We accomplish this by iterating through the stocks with the largest trade volume calculated above, and finding out the sectors of each of the stocks there. We then rank the sectors based off of total trade volume and visualize it in a table. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_90a2d_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Volume within sector</th>        <th class=\"col_heading level0 col1\" >Occurences</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_90a2d_level0_row0\" class=\"row_heading level0 row0\" >Technology</th>\n",
       "                        <td id=\"T_90a2d_row0_col0\" class=\"data row0 col0\" >1573003.000000</td>\n",
       "                        <td id=\"T_90a2d_row0_col1\" class=\"data row0 col1\" >6</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_90a2d_level0_row1\" class=\"row_heading level0 row1\" >Communication Services</th>\n",
       "                        <td id=\"T_90a2d_row1_col0\" class=\"data row1 col0\" >823002.500000</td>\n",
       "                        <td id=\"T_90a2d_row1_col1\" class=\"data row1 col1\" >5</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_90a2d_level0_row2\" class=\"row_heading level0 row2\" >Consumer Cyclical</th>\n",
       "                        <td id=\"T_90a2d_row2_col0\" class=\"data row2 col0\" >733002.000000</td>\n",
       "                        <td id=\"T_90a2d_row2_col1\" class=\"data row2 col1\" >4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_90a2d_level0_row3\" class=\"row_heading level0 row3\" >Financial Services</th>\n",
       "                        <td id=\"T_90a2d_row3_col0\" class=\"data row3 col0\" >450001.000000</td>\n",
       "                        <td id=\"T_90a2d_row3_col1\" class=\"data row3 col1\" >2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_90a2d_level0_row4\" class=\"row_heading level0 row4\" >Energy</th>\n",
       "                        <td id=\"T_90a2d_row4_col0\" class=\"data row4 col0\" >175000.500000</td>\n",
       "                        <td id=\"T_90a2d_row4_col1\" class=\"data row4 col1\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_90a2d_level0_row5\" class=\"row_heading level0 row5\" >Healthcare</th>\n",
       "                        <td id=\"T_90a2d_row5_col0\" class=\"data row5 col0\" >16001.000000</td>\n",
       "                        <td id=\"T_90a2d_row5_col1\" class=\"data row5 col1\" >2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_90a2d_level0_row6\" class=\"row_heading level0 row6\" >Unknown</th>\n",
       "                        <td id=\"T_90a2d_row6_col0\" class=\"data row6 col0\" >8000.500000</td>\n",
       "                        <td id=\"T_90a2d_row6_col1\" class=\"data row6 col1\" >1</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1d5b36c2c40>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import yfinance as yf\n",
    "def sectorTradesSS(name):\n",
    "    dfTransactions = df[df['Name'] == name]\n",
    "    sectorsDict = {}\n",
    "    volumeMin, volumeMax, volumeMid = totalTradeVolumeSS(name)\n",
    "    # iterate through the dataframe\n",
    "    for i in range(len(dfTransactions)):\n",
    "        # keep track of the volume of that sector\n",
    "        volumePerTicker = 0\n",
    "        # keep track of occurences of that sector\n",
    "        occorrences = 0\n",
    "        # get the ticker name\n",
    "        tickerName = dfTransactions.iloc[i]['Ticker'][0]\n",
    "        if tickerName == \"-\" or tickerName == \"--\":\n",
    "            tickerName = \"Other Assets\"\n",
    "\n",
    "        # create a dictionary to keep track of possible trade volumes\n",
    "        listOfAmounts = [\n",
    "        [\"$1,001 - $15,000\", (1001, 15000)],\n",
    "        [\"$15,001 - $50,000\", (15001, 50000)],\n",
    "        [\"$50,001 - $100,000\", (50001, 100000)],\n",
    "        [\"$100,001 - $250,000\", (100001, 250000)],\n",
    "        [\"$250,001 - $500,000\", (250001, 500000)],\n",
    "        [\"$500,001 - $1,000,000\", (500001, 1000000)],\n",
    "        [\"$1,000,001 - $5,000,000\", (1000001, 5000000)],\n",
    "        [\"$5,000,001 - $25,000,000\", (5000001, 25000000)],\n",
    "        [\"$25,000,001 - $50,000,000\", (25000001, 50000000)],\n",
    "        [\"Over $50,000,000\", (50000000, 50000000)],\n",
    "        ]\n",
    "        # get the volume of that ticker\n",
    "        volumePerTicker = dfTransactions.iloc[i]['Amount']\n",
    "        # iterate through the possible ticker amounts\n",
    "        for i in range(len(listOfAmounts)):\n",
    "            # if there is a match \n",
    "            if volumePerTicker == listOfAmounts[i][0]:\n",
    "                    # estimate the volume of that ticker\n",
    "                    esitimatedVolume = (listOfAmounts[i][1][1] + listOfAmounts[i][1][0]) / 2\n",
    "        data = yf.Ticker(tickerName)\n",
    "        try: \n",
    "            sector = data.info['sector']\n",
    "        except:\n",
    "            sector = \"Unknown\"\n",
    "\n",
    "        if sector not in sectorsDict:\n",
    "            sectorsDict[sector] = [0, 0]\n",
    "\n",
    "        sectorsDict[sector][0] += esitimatedVolume\n",
    "        sectorsDict[sector][1] += 1\n",
    "\n",
    "    return sectorsDict\n",
    "    \n",
    "dict = sectorTradesSS('Hickenlooper, John (Senator)')   \n",
    "# turn dictionary into dataframe \n",
    "dfSSTickerVolume = pd.DataFrame.from_dict(dict, orient='index')\n",
    "dfSSTickerVolume= dfSSTickerVolume.rename(columns = {1: 'Occurences'})\n",
    "dfSSTickerVolume= dfSSTickerVolume.rename(columns = {0: 'Volume within sector'})\n",
    "df.index.name = \"Sector\"\n",
    "\n",
    "# sort the dataframe by the volume\n",
    "dfSSTickerVolume = dfSSTickerVolume.sort_values(by=['Volume within sector'], ascending=False)\n",
    "# style the dataframe\n",
    "dfSSTickerVolume.stylep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 10 stocks purchased by the senate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- 1606\n",
      "AAPL 151\n",
      "DISCA 72\n",
      "BAC 69\n",
      "PCLN 66\n",
      "PFE 61\n",
      "CBS 59\n",
      "NFLX 58\n",
      "MSFT 57\n",
      "T 56\n",
      "DIS 55\n"
     ]
    }
   ],
   "source": [
    "# Df of purchaes\n",
    "dfPurchases = df[df['Type'] == 'Purchase']\n",
    "# value change of tickers\n",
    "freq = dfPurchases['Ticker'].value_counts()\n",
    "\n",
    "for i in range(11):\n",
    "    print(freq.index[i][0], freq.values[i])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_f5a97_\" ><thead>    <tr>        <th class=\"index_name level0\" >Stocks</th>        <th class=\"col_heading level0 col0\" >Number of Occurences</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_f5a97_level0_row0\" class=\"row_heading level0 row0\" >AAPL</th>\n",
       "                        <td id=\"T_f5a97_row0_col0\" class=\"data row0 col0\" >151</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f5a97_level0_row1\" class=\"row_heading level0 row1\" >DISCA</th>\n",
       "                        <td id=\"T_f5a97_row1_col0\" class=\"data row1 col0\" >72</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f5a97_level0_row2\" class=\"row_heading level0 row2\" >BAC</th>\n",
       "                        <td id=\"T_f5a97_row2_col0\" class=\"data row2 col0\" >69</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f5a97_level0_row3\" class=\"row_heading level0 row3\" >PCLN</th>\n",
       "                        <td id=\"T_f5a97_row3_col0\" class=\"data row3 col0\" >66</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f5a97_level0_row4\" class=\"row_heading level0 row4\" >PFE</th>\n",
       "                        <td id=\"T_f5a97_row4_col0\" class=\"data row4 col0\" >61</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f5a97_level0_row5\" class=\"row_heading level0 row5\" >CBS</th>\n",
       "                        <td id=\"T_f5a97_row5_col0\" class=\"data row5 col0\" >59</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f5a97_level0_row6\" class=\"row_heading level0 row6\" >NFLX</th>\n",
       "                        <td id=\"T_f5a97_row6_col0\" class=\"data row6 col0\" >58</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f5a97_level0_row7\" class=\"row_heading level0 row7\" >MSFT</th>\n",
       "                        <td id=\"T_f5a97_row7_col0\" class=\"data row7 col0\" >57</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f5a97_level0_row8\" class=\"row_heading level0 row8\" >T</th>\n",
       "                        <td id=\"T_f5a97_row8_col0\" class=\"data row8 col0\" >56</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f5a97_level0_row9\" class=\"row_heading level0 row9\" >DIS</th>\n",
       "                        <td id=\"T_f5a97_row9_col0\" class=\"data row9 col0\" >55</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1c5d5d14cd0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict = {}\n",
    "\n",
    "for i in range(11):\n",
    "    if freq.index[i] == '--':\n",
    "        continue\n",
    "\n",
    "    # add to dict dictionary\n",
    "    dict[freq.index[i][0]] = freq.values[i]\n",
    "    # keys.append(freq.values[i])\n",
    "    # values.append(freq.index[i][0])\n",
    "\n",
    "# turn dictionary into dataframe \n",
    "dfMostSold = pd.DataFrame.from_dict(dict, orient='index')\n",
    "\n",
    "# Name first column\n",
    "dfMostSold.columns.name = \"Stocks\"\n",
    "\n",
    "# Name the second column\n",
    "dfMostSold = dfMostSold.rename(columns = {0: 'Number of Occurences'})\n",
    "\n",
    "\n",
    "# Make the dataframe styled\n",
    "dfMostSold.style"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 10 stocks sold by the senate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Partial Sales\n",
    "dfSalespartial = df[df['Type'] == 'Sale (Partial)']\n",
    "\n",
    "# Full sales\n",
    "dfSalesfull = df[df['Type'] == 'Sale (Full)']\n",
    "\n",
    "# Combine both dataframers to include all sales\n",
    "dfSales = pd.concat([dfSalespartial, dfSalesfull])\n",
    "\n",
    "# value change of tickers\n",
    "freq = dfSales['Ticker'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_6fb2c_\" ><thead>    <tr>        <th class=\"index_name level0\" >Stocks</th>        <th class=\"col_heading level0 col0\" >Number of Occurences</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_6fb2c_level0_row0\" class=\"row_heading level0 row0\" >PCLN</th>\n",
       "                        <td id=\"T_6fb2c_row0_col0\" class=\"data row0 col0\" >146</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6fb2c_level0_row1\" class=\"row_heading level0 row1\" >AAPL</th>\n",
       "                        <td id=\"T_6fb2c_row1_col0\" class=\"data row1 col0\" >122</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6fb2c_level0_row2\" class=\"row_heading level0 row2\" >MSFT</th>\n",
       "                        <td id=\"T_6fb2c_row2_col0\" class=\"data row2 col0\" >79</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6fb2c_level0_row3\" class=\"row_heading level0 row3\" >PFE</th>\n",
       "                        <td id=\"T_6fb2c_row3_col0\" class=\"data row3 col0\" >78</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6fb2c_level0_row4\" class=\"row_heading level0 row4\" >GE</th>\n",
       "                        <td id=\"T_6fb2c_row4_col0\" class=\"data row4 col0\" >73</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6fb2c_level0_row5\" class=\"row_heading level0 row5\" >T</th>\n",
       "                        <td id=\"T_6fb2c_row5_col0\" class=\"data row5 col0\" >60</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6fb2c_level0_row6\" class=\"row_heading level0 row6\" >BAC</th>\n",
       "                        <td id=\"T_6fb2c_row6_col0\" class=\"data row6 col0\" >51</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6fb2c_level0_row7\" class=\"row_heading level0 row7\" >CZR</th>\n",
       "                        <td id=\"T_6fb2c_row7_col0\" class=\"data row7 col0\" >45</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6fb2c_level0_row8\" class=\"row_heading level0 row8\" >NFLX</th>\n",
       "                        <td id=\"T_6fb2c_row8_col0\" class=\"data row8 col0\" >44</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6fb2c_level0_row9\" class=\"row_heading level0 row9\" >ENTG</th>\n",
       "                        <td id=\"T_6fb2c_row9_col0\" class=\"data row9 col0\" >43</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1c5d5d14280>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict = {}\n",
    "\n",
    "for i in range(11):\n",
    "    if freq.index[i] == '--':\n",
    "        continue\n",
    "\n",
    "    # add to dict dictionary\n",
    "    dict[freq.index[i][0]] = freq.values[i]\n",
    "    # keys.append(freq.values[i])\n",
    "    # values.append(freq.index[i][0])\n",
    "\n",
    "# turn dictionary into dataframe \n",
    "dfMostSold = pd.DataFrame.from_dict(dict, orient='index')\n",
    "\n",
    "# Name first column\n",
    "dfMostSold.columns.name = \"Stocks\"\n",
    "\n",
    "# Name the second column\n",
    "dfMostSold = dfMostSold.rename(columns = {0: 'Number of Occurences'})\n",
    "\n",
    "\n",
    "# Make the dataframe styled\n",
    "dfMostSold.style\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sectors in which the senate are buying into\n",
    "Analyze the sectors majority of the senate is investing in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_86c29_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Volume within sector</th>        <th class=\"col_heading level0 col1\" >Occurences</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_86c29_level0_row0\" class=\"row_heading level0 row0\" >Technology</th>\n",
       "                        <td id=\"T_86c29_row0_col0\" class=\"data row0 col0\" >1573003.000000</td>\n",
       "                        <td id=\"T_86c29_row0_col1\" class=\"data row0 col1\" >6</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_86c29_level0_row1\" class=\"row_heading level0 row1\" >Communication Services</th>\n",
       "                        <td id=\"T_86c29_row1_col0\" class=\"data row1 col0\" >823002.500000</td>\n",
       "                        <td id=\"T_86c29_row1_col1\" class=\"data row1 col1\" >5</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_86c29_level0_row2\" class=\"row_heading level0 row2\" >Consumer Cyclical</th>\n",
       "                        <td id=\"T_86c29_row2_col0\" class=\"data row2 col0\" >733002.000000</td>\n",
       "                        <td id=\"T_86c29_row2_col1\" class=\"data row2 col1\" >4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_86c29_level0_row3\" class=\"row_heading level0 row3\" >Financial Services</th>\n",
       "                        <td id=\"T_86c29_row3_col0\" class=\"data row3 col0\" >450001.000000</td>\n",
       "                        <td id=\"T_86c29_row3_col1\" class=\"data row3 col1\" >2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_86c29_level0_row4\" class=\"row_heading level0 row4\" >Energy</th>\n",
       "                        <td id=\"T_86c29_row4_col0\" class=\"data row4 col0\" >175000.500000</td>\n",
       "                        <td id=\"T_86c29_row4_col1\" class=\"data row4 col1\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_86c29_level0_row5\" class=\"row_heading level0 row5\" >Healthcare</th>\n",
       "                        <td id=\"T_86c29_row5_col0\" class=\"data row5 col0\" >16001.000000</td>\n",
       "                        <td id=\"T_86c29_row5_col1\" class=\"data row5 col1\" >2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_86c29_level0_row6\" class=\"row_heading level0 row6\" >Unknown</th>\n",
       "                        <td id=\"T_86c29_row6_col0\" class=\"data row6 col0\" >8000.500000</td>\n",
       "                        <td id=\"T_86c29_row6_col1\" class=\"data row6 col1\" >1</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1d5cbd029a0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
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    }
   ],
   "source": [
    "def sectorTradesInSenate(year):\n",
    "    dfTransactions = df[df['Transaction Date'].str[-4:] == year]\n",
    "    sectorsDict = {}\n",
    "    volumeMin, volumeMax, volumeMid = totalTradeVolumeSenate(year)\n",
    "    # iterate through the dataframe\n",
    "    print(len(dfTransactions))\n",
    "    for i in range(len(dfTransactions)):\n",
    "        print(i)\n",
    "        # keep track of the volume of that sector\n",
    "        volumePerTicker = 0\n",
    "        # keep track of occurences of that sector\n",
    "        occorrences = 0\n",
    "        # get the ticker name\n",
    "        tickerName = dfTransactions.iloc[i]['Ticker'][0]\n",
    "        if tickerName == \"-\" or tickerName == \"--\":\n",
    "            tickerName = \"Other Assets\"\n",
    "\n",
    "        # create a dictionary to keep track of possible trade volumes\n",
    "        listOfAmounts = [\n",
    "        [\"$1,001 - $15,000\", (1001, 15000)],\n",
    "        [\"$15,001 - $50,000\", (15001, 50000)],\n",
    "        [\"$50,001 - $100,000\", (50001, 100000)],\n",
    "        [\"$100,001 - $250,000\", (100001, 250000)],\n",
    "        [\"$250,001 - $500,000\", (250001, 500000)],\n",
    "        [\"$500,001 - $1,000,000\", (500001, 1000000)],\n",
    "        [\"$1,000,001 - $5,000,000\", (1000001, 5000000)],\n",
    "        [\"$5,000,001 - $25,000,000\", (5000001, 25000000)],\n",
    "        [\"$25,000,001 - $50,000,000\", (25000001, 50000000)],\n",
    "        [\"Over $50,000,000\", (50000000, 50000000)],\n",
    "        ]\n",
    "        # get the volume of that ticker\n",
    "        volumePerTicker = dfTransactions.iloc[i]['Amount']\n",
    "        # iterate through the possible ticker amounts\n",
    "        for i in range(len(listOfAmounts)):\n",
    "            # if there is a match \n",
    "            if volumePerTicker == listOfAmounts[i][0]:\n",
    "                    # estimate the volume of that ticker\n",
    "                    esitimatedVolume = (listOfAmounts[i][1][1] + listOfAmounts[i][1][0]) / 2\n",
    "        data = yf.Ticker(tickerName)\n",
    "        try: \n",
    "            sector = data.info['sector']\n",
    "        except:\n",
    "            sector = \"Unknown\"\n",
    "\n",
    "        if sector not in sectorsDict:\n",
    "            sectorsDict[sector] = [0, 0]\n",
    "\n",
    "        sectorsDict[sector][0] += esitimatedVolume\n",
    "        sectorsDict[sector][1] += 1\n",
    "\n",
    "    return sectorsDict\n",
    "\n",
    "\n",
    "dictTemp = sectorTradesInSenate('2021')   \n",
    "# turn dictionary into dataframe \n",
    "dfSSTickerVolume = pd.DataFrame.from_dict(dict, orient='index')\n",
    "dfSSTickerVolume= dfSSTickerVolume.rename(columns = {1: 'Occurences'})\n",
    "dfSSTickerVolume= dfSSTickerVolume.rename(columns = {0: 'Volume within sector'})\n",
    "df.index.name = \"Sector\"\n",
    "\n",
    "# sort the dataframe by the volume\n",
    "dfSSTickerVolume = dfSSTickerVolume.sort_values(by=['Volume within sector'], ascending=False)\n",
    "# style the dataframe\n",
    "dfSSTickerVolume.style"
   ]
  }
 ],
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