trev.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get started by installing and importing dependencies, and reading in the CSV data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install prophet\n",
    "%pip install numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from prophet import Prophet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ticker</th>\n",
       "      <th>TickerType</th>\n",
       "      <th>Company</th>\n",
       "      <th>Traded</th>\n",
       "      <th>Transaction</th>\n",
       "      <th>Trade_Size_USD</th>\n",
       "      <th>Status</th>\n",
       "      <th>Subholding</th>\n",
       "      <th>Description</th>\n",
       "      <th>Name</th>\n",
       "      <th>BioGuideID</th>\n",
       "      <th>Filed</th>\n",
       "      <th>Party</th>\n",
       "      <th>District</th>\n",
       "      <th>Chamber</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Quiver_Upload_Time</th>\n",
       "      <th>excess_return</th>\n",
       "      <th>State</th>\n",
       "      <th>last_modified</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GLW</td>\n",
       "      <td>ST</td>\n",
       "      <td>CORNING INCORPORATED COMMON STOCK</td>\n",
       "      <td>2024-07-23</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>NEW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marjorie Taylor Mrs Greene</td>\n",
       "      <td>G000596</td>\n",
       "      <td>2024-07-24</td>\n",
       "      <td>R</td>\n",
       "      <td>GA14</td>\n",
       "      <td>House</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-07-25</td>\n",
       "      <td>-1.054504</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>2024-07-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABCB</td>\n",
       "      <td>ST</td>\n",
       "      <td>AMERIS BANCORP - COMMON STOCK</td>\n",
       "      <td>2024-07-23</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$100,001 - $250,000</td>\n",
       "      <td>NEW</td>\n",
       "      <td>STIFEL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Earl Leroy Carter</td>\n",
       "      <td>C001103</td>\n",
       "      <td>2024-07-24</td>\n",
       "      <td>R</td>\n",
       "      <td>GA01</td>\n",
       "      <td>House</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-07-25</td>\n",
       "      <td>2.772833</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>2024-07-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UPS</td>\n",
       "      <td>ST</td>\n",
       "      <td>UNITED PARCEL SERVICE, INC. COMMON STOCK</td>\n",
       "      <td>2024-07-23</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>NEW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marjorie Taylor Mrs Greene</td>\n",
       "      <td>G000596</td>\n",
       "      <td>2024-07-24</td>\n",
       "      <td>R</td>\n",
       "      <td>GA14</td>\n",
       "      <td>House</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-07-25</td>\n",
       "      <td>2.592441</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>2024-07-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DUK</td>\n",
       "      <td>ST</td>\n",
       "      <td>DUKE ENERGY CORPORATION</td>\n",
       "      <td>2024-07-23</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>NEW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marjorie Taylor Mrs Greene</td>\n",
       "      <td>G000596</td>\n",
       "      <td>2024-07-24</td>\n",
       "      <td>R</td>\n",
       "      <td>GA14</td>\n",
       "      <td>House</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-07-25</td>\n",
       "      <td>2.541266</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>2024-07-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JPM</td>\n",
       "      <td>ST</td>\n",
       "      <td>JP MORGAN CHASE &amp; CO. COMMON STOCK</td>\n",
       "      <td>2024-07-23</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>NEW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marjorie Taylor Mrs Greene</td>\n",
       "      <td>G000596</td>\n",
       "      <td>2024-07-24</td>\n",
       "      <td>R</td>\n",
       "      <td>GA14</td>\n",
       "      <td>House</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-07-25</td>\n",
       "      <td>2.599848</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>2024-07-25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Ticker TickerType                                   Company      Traded  \\\n",
       "0    GLW         ST         CORNING INCORPORATED COMMON STOCK  2024-07-23   \n",
       "1   ABCB         ST             AMERIS BANCORP - COMMON STOCK  2024-07-23   \n",
       "2    UPS         ST  UNITED PARCEL SERVICE, INC. COMMON STOCK  2024-07-23   \n",
       "3    DUK         ST                   DUKE ENERGY CORPORATION  2024-07-23   \n",
       "4    JPM         ST        JP MORGAN CHASE & CO. COMMON STOCK  2024-07-23   \n",
       "\n",
       "  Transaction       Trade_Size_USD Status Subholding Description  \\\n",
       "0    Purchase     $1,001 - $15,000    NEW        NaN         NaN   \n",
       "1        Sale  $100,001 - $250,000    NEW     STIFEL         NaN   \n",
       "2    Purchase     $1,001 - $15,000    NEW        NaN         NaN   \n",
       "3    Purchase     $1,001 - $15,000    NEW        NaN         NaN   \n",
       "4    Purchase     $1,001 - $15,000    NEW        NaN         NaN   \n",
       "\n",
       "                         Name BioGuideID       Filed Party District Chamber  \\\n",
       "0  Marjorie Taylor Mrs Greene    G000596  2024-07-24     R     GA14   House   \n",
       "1           Earl Leroy Carter    C001103  2024-07-24     R     GA01   House   \n",
       "2  Marjorie Taylor Mrs Greene    G000596  2024-07-24     R     GA14   House   \n",
       "3  Marjorie Taylor Mrs Greene    G000596  2024-07-24     R     GA14   House   \n",
       "4  Marjorie Taylor Mrs Greene    G000596  2024-07-24     R     GA14   House   \n",
       "\n",
       "  Comments Quiver_Upload_Time  excess_return    State last_modified  \n",
       "0      NaN         2024-07-25      -1.054504  Georgia    2024-07-25  \n",
       "1      NaN         2024-07-25       2.772833  Georgia    2024-07-25  \n",
       "2      NaN         2024-07-25       2.592441  Georgia    2024-07-25  \n",
       "3      NaN         2024-07-25       2.541266  Georgia    2024-07-25  \n",
       "4      NaN         2024-07-25       2.599848  Georgia    2024-07-25  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_x = pd.read_csv(\"files/congress-trading-all.csv\")\n",
    "x_df = pd.DataFrame(data_x)\n",
    "display(x_df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean and inspect the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/k4/ds2_p0md3jn6b2dh7nt_yzth0000gn/T/ipykernel_92448/2575007471.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  x_df_clean['excess_return'] = x_df_clean['excess_return'].fillna(0).round(2)\n",
      "/var/folders/k4/ds2_p0md3jn6b2dh7nt_yzth0000gn/T/ipykernel_92448/2575007471.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  x_df_clean['excess_return'] = x_df_clean['excess_return'].round(2)\n",
      "/var/folders/k4/ds2_p0md3jn6b2dh7nt_yzth0000gn/T/ipykernel_92448/2575007471.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  x_df_clean['Traded'] = pd.to_datetime(x_df_clean['Traded'])\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Party</th>\n",
       "      <th>Company</th>\n",
       "      <th>Traded</th>\n",
       "      <th>Transaction</th>\n",
       "      <th>Trade_Size_USD</th>\n",
       "      <th>excess_return</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Marjorie Taylor Mrs Greene</td>\n",
       "      <td>R</td>\n",
       "      <td>CORNING INCORPORATED COMMON STOCK</td>\n",
       "      <td>2024-07-23</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Earl Leroy Carter</td>\n",
       "      <td>R</td>\n",
       "      <td>AMERIS BANCORP - COMMON STOCK</td>\n",
       "      <td>2024-07-23</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$100,001 - $250,000</td>\n",
       "      <td>2.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Marjorie Taylor Mrs Greene</td>\n",
       "      <td>R</td>\n",
       "      <td>UNITED PARCEL SERVICE, INC. COMMON STOCK</td>\n",
       "      <td>2024-07-23</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>2.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Marjorie Taylor Mrs Greene</td>\n",
       "      <td>R</td>\n",
       "      <td>DUKE ENERGY CORPORATION</td>\n",
       "      <td>2024-07-23</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>2.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Marjorie Taylor Mrs Greene</td>\n",
       "      <td>R</td>\n",
       "      <td>JP MORGAN CHASE &amp; CO. COMMON STOCK</td>\n",
       "      <td>2024-07-23</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>2.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Name Party                                   Company  \\\n",
       "0  Marjorie Taylor Mrs Greene     R         CORNING INCORPORATED COMMON STOCK   \n",
       "1           Earl Leroy Carter     R             AMERIS BANCORP - COMMON STOCK   \n",
       "2  Marjorie Taylor Mrs Greene     R  UNITED PARCEL SERVICE, INC. COMMON STOCK   \n",
       "3  Marjorie Taylor Mrs Greene     R                   DUKE ENERGY CORPORATION   \n",
       "4  Marjorie Taylor Mrs Greene     R        JP MORGAN CHASE & CO. COMMON STOCK   \n",
       "\n",
       "      Traded Transaction       Trade_Size_USD  excess_return  \n",
       "0 2024-07-23    Purchase     $1,001 - $15,000          -1.05  \n",
       "1 2024-07-23        Sale  $100,001 - $250,000           2.77  \n",
       "2 2024-07-23    Purchase     $1,001 - $15,000           2.59  \n",
       "3 2024-07-23    Purchase     $1,001 - $15,000           2.54  \n",
       "4 2024-07-23    Purchase     $1,001 - $15,000           2.60  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Party</th>\n",
       "      <th>Company</th>\n",
       "      <th>Traded</th>\n",
       "      <th>Transaction</th>\n",
       "      <th>Trade_Size_USD</th>\n",
       "      <th>excess_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47266</th>\n",
       "      <td>Ms. Tammy Duckworth</td>\n",
       "      <td>D</td>\n",
       "      <td>CATERPILLAR, INC.</td>\n",
       "      <td>2012-07-26</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>19.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47267</th>\n",
       "      <td>Ms. Tammy Duckworth</td>\n",
       "      <td>D</td>\n",
       "      <td>PROCTER &amp; GAMBLE COMPANY</td>\n",
       "      <td>2012-07-24</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-143.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47268</th>\n",
       "      <td>Mr. Alan S. Lowenthal</td>\n",
       "      <td>D</td>\n",
       "      <td>KANSAS CITY SOUTHERN</td>\n",
       "      <td>2012-06-06</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>89.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47269</th>\n",
       "      <td>Mr. Alan S. Lowenthal</td>\n",
       "      <td>D</td>\n",
       "      <td>EL PASO CORPORATION PREFERRED STOCK</td>\n",
       "      <td>2012-03-20</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47270</th>\n",
       "      <td>Mr. Alan S. Lowenthal</td>\n",
       "      <td>D</td>\n",
       "      <td>EL PASO CORPORATION PREFERRED STOCK</td>\n",
       "      <td>2012-02-27</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Name Party                              Company  \\\n",
       "47266    Ms. Tammy Duckworth     D                    CATERPILLAR, INC.   \n",
       "47267    Ms. Tammy Duckworth     D             PROCTER & GAMBLE COMPANY   \n",
       "47268  Mr. Alan S. Lowenthal     D                 KANSAS CITY SOUTHERN   \n",
       "47269  Mr. Alan S. Lowenthal     D  EL PASO CORPORATION PREFERRED STOCK   \n",
       "47270  Mr. Alan S. Lowenthal     D  EL PASO CORPORATION PREFERRED STOCK   \n",
       "\n",
       "          Traded Transaction    Trade_Size_USD  excess_return  \n",
       "47266 2012-07-26    Purchase  $1,001 - $15,000          19.00  \n",
       "47267 2012-07-24        Sale  $1,001 - $15,000        -143.88  \n",
       "47268 2012-06-06    Purchase  $1,001 - $15,000          89.24  \n",
       "47269 2012-03-20        Sale  $1,001 - $15,000           0.00  \n",
       "47270 2012-02-27        Sale  $1,001 - $15,000           0.00  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Clean the data by keeping the needed columns\n",
    "x_df_clean = x_df[['Name','Party', 'Company','Traded', 'Transaction', 'Trade_Size_USD', 'excess_return']]\n",
    "x_df_clean['excess_return'] = x_df_clean['excess_return'].fillna(0).round(2)\n",
    "x_df_clean['excess_return'] = x_df_clean['excess_return'].round(2)\n",
    "x_df_clean['Traded'] = pd.to_datetime(x_df_clean['Traded'])\n",
    "x_df_clean = x_df_clean[x_df_clean['Party'].isin(['D', 'R'])]\n",
    "display(x_df_clean.head())\n",
    "display(x_df_clean.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Marjorie Taylor Mrs Greene', 'Earl Leroy Carter',\n",
       "       'Robert E. Latta', 'Kathy Castor', 'Rudy Yakym Iii',\n",
       "       'Boozman, John', 'Rick Larsen', 'Jamie Raskin',\n",
       "       'Gerald E. Connolly', 'Nancy Pelosi', 'Mark Dr Green',\n",
       "       'Josh Gottheimer', 'Victoria Spartz', 'Kevin Hern',\n",
       "       'Morgan Mcgarvey', 'Earl Blumenauer', 'Whitehouse, Sheldon',\n",
       "       'Capito, Shelley Moore', 'Max Miller', 'Mullin, Markwayne',\n",
       "       'Lloyd Doggett', 'Debbie Dingell', 'Richard W. Allen',\n",
       "       'Kathy Manning', 'Thomas H. Kean Jr.', 'Pete Sessions',\n",
       "       'Abigail Spanberger', 'McConnell, A. Mitchell Jr.',\n",
       "       'Hickenlooper, John', 'Carper, Thomas R.', 'Stephen F. Lynch',\n",
       "       'Virginia Foxx', 'Chip Roy', 'William R. Keating',\n",
       "       'Debbie Wasserman Schultz', 'Peters, Gary', 'Mikie Sherrill',\n",
       "       'Tuberville, Tommy', 'Susie Lee', 'Carol Devine Miller',\n",
       "       'C. Scott Franklin', 'Maria Elvira Salazar', 'Jonathan Jackson',\n",
       "       'Deborah Ross', 'Cruz, Ted', 'Mike Kelly', 'Dan Newhouse',\n",
       "       'Michael C. Burgess', 'Eleanor Holmes Norton', 'Smith, Tina',\n",
       "       'Shri Thanedar', 'Greg Steube', 'Wyden, Ron', 'Brian Mast',\n",
       "       'Roger Williams', 'Thomas R. Suozzi', 'Katherine M. Clark',\n",
       "       'Suzan K. Delbene', 'Daniel Meuser', 'Michael Patrick Guest',\n",
       "       'Steve Cohen', 'James Calhoun', 'Graham, Lindsey', 'Sullivan, Dan',\n",
       "       'Blake Moore', 'Garret Graves', 'Judy Chu', 'Laurel Mrs Lee',\n",
       "       'James French Hill', 'Jared Moskowitz', 'Hagerty, Bill',\n",
       "       'Scott, Rick', 'Dwight Evans', 'Moran, Jerry', 'John Curtis',\n",
       "       'David Trone', 'HON. GREG STANTON', 'Vance, J.D.',\n",
       "       'Robert J. Wittman', 'Ricketts, Pete', 'Lois Frankel',\n",
       "       'Jeff Duncan', 'Seth Moulton', 'Warner, Mark', 'Daniel Goldman',\n",
       "       'Michael Garcia', 'James E Hon Banks', 'Zoe Lofgren',\n",
       "       'Mary Gay Scanlon', 'Jennifer Mcclellan', 'Scott Mr Franklin',\n",
       "       'Duckworth, Tammy', 'Daniel Crenshaw', 'Thomas H. Kean',\n",
       "       'Michael K. Simpson', 'Austin Scott', 'Rosen, Jacky',\n",
       "       'Cantwell, Maria', 'Cindy Axne', 'Bob Gibbs', 'Collins, Susan M.',\n",
       "       'James R. Langevin', 'Alan S. Lowenthal', 'Christopher L. Jacobs',\n",
       "       'Stephanie Bice', 'Robert B. Aderholt', 'John W. Rose',\n",
       "       'Thomas Suozzi', 'Nicholas V. Taylor', 'Brad Sherman',\n",
       "       'Marie Newman', 'Ed Perlmutter', 'Patrick Fallon', 'Marcy Kaptur',\n",
       "       'Ron Estes', 'David B. Mckinley', 'Bradley S. Schneider',\n",
       "       'Scott Franklin', 'Tillis, Thom', 'Brian Babin', 'Mo Brooks',\n",
       "       'Anthony E. Gonzalez', 'Peter Meijer', 'Fischer, Deb',\n",
       "       'Marjorie Taylor Greene', 'Blunt, Roy', 'David P. Joyce',\n",
       "       'Thune, John', 'August Lee Pfluger Ii', 'David Kustoff',\n",
       "       'Joe Courtney', 'John Rutherford', 'Kim Dr Schrier',\n",
       "       'David E. Price', 'Russ Fulcher', 'Donald Sternoff Beyer Jr.',\n",
       "       'Deborah K. Ross', 'John Garamendi', 'Andrew Garbarino',\n",
       "       'David Cheston Rouzer', 'Robert C. \"Bobby\" Scott', 'Lori Trahan',\n",
       "       'Bill Pascrell Jr.', 'Raúl M. Grijalva', 'Aston Donald Mceachin',\n",
       "       'Adam B. Schiff', 'Donald Sternoff  Beyer Jr', 'Peter Welch',\n",
       "       'Larry Bucshon', 'Felix Barry Moore', 'John A. Yarmuth',\n",
       "       'Toomey, Pat', 'David Madison Cawthorn', 'Kenneth R. Buck',\n",
       "       'Lummis, Cynthia', 'Frank Pallone Jr.', 'Dean Phillips', 'Ed Case',\n",
       "       'Sara Jacobs', 'Tom Malinowski', 'Ashley Hinson Arenholz',\n",
       "       'Elaine Luria', 'Michael John Gallagher', 'Mr. Peter Meijer',\n",
       "       'Mr. Scott Franklin', 'Mrs. Marjorie Taylor Greene', 'Greg Murphy',\n",
       "       'Teresa Leger Fernandez', 'Inhofe, James M.', 'Gilbert Cisneros',\n",
       "       'Donald Sternoff Beyer Jr', 'Toomey, Patrick', 'Roberts, Pat',\n",
       "       'Greg Gianforte', 'John B. Larson', 'K. Michael Conaway',\n",
       "       'James Comer', 'Mark Green', 'Hakeem S. Jeffries',\n",
       "       'James E. Banks', 'Joseph D. Morelle', 'Patrick T. Mchenry',\n",
       "       'W. Greg Steube', 'Cassidy, Bill', 'Susan A. Davis',\n",
       "       'Peter J. Visclosky', 'Lloyd K. Smucker', 'Loeffler, Kelly',\n",
       "       'Warren Davidson', 'Roger W. Marshall', 'Brian Higgins',\n",
       "       'Scott H. Peters', 'Adam Kinzinger', 'Thomas Massie',\n",
       "       'William R. Timmons Iv', 'Sean Patrick Maloney', 'Donna Shalala',\n",
       "       'Wicker, Roger', 'Perdue, David', 'Susan W. Brooks',\n",
       "       'Cheri Bustos', \"Tom O'Halleran\", 'Tom Rice',\n",
       "       'Nicholas Van Taylor', 'Kim Schrier', 'Harley E. Rouda Jr.',\n",
       "       'Tim Burchett', 'Hoeven, John', 'Thomas Macarthur', 'Bill Flores',\n",
       "       'Matt Gaetz', 'Brenda Lulenar Lawrence', 'Bryan George Steil',\n",
       "       'Elizabeth Fletcher', 'Grace Meng', 'Theodore E. Deutch',\n",
       "       'Coons, Chris', 'Kaine, Tim', 'Reed, John F.', 'Rob Woodall',\n",
       "       'Jacky Rosen', 'Lamar Smith', 'Kyl, Jon', 'Thomas J. Rooney',\n",
       "       'Barbara J  Comstock', 'Heitkamp, Heidi', 'Keith J. Rothfus',\n",
       "       'David A. Trott', 'Udall, Tom', 'Rodney Leland Blum',\n",
       "       'Frank A. Lobiondo', 'Heller, Dean', 'McCaskill, Claire',\n",
       "       'Jackie Speier', 'Kennedy, John', 'Gary Palmer', 'Mark R Meadows',\n",
       "       'Carlos Curbelo', 'Martha Roby', 'Steve Knight',\n",
       "       'Jr, David A Perdue ', 'Kevin Yoder', 'Cochran, Thad',\n",
       "       'Louise Mcintosh Slaughter', 'Stephanie Murphy', 'Flake, Jeffry',\n",
       "       'Bennet, Michael', 'Richard L. Hanna', 'Casey, Robert P. Jr.',\n",
       "       'James A. Himes', 'Linda T. Sánchez', 'Raul Ruiz', 'Neal P. Dunn',\n",
       "       'Murray, Patty', 'Luis V. Gutierrez', 'Mario Diaz-Balart',\n",
       "       'James P. Mcgovern', 'Manchin, Joe', 'Carlos Mr Curbelo',\n",
       "       'Derek Kilmer', 'Michael R. Turner', 'Lou Barletta',\n",
       "       'Charlie Joseph Crist', 'Van Hollen, Chris', 'John J. Faso',\n",
       "       'Paul D. Ryan', 'Cornyn, John', 'Mick Mulvaney', 'Sam Farr',\n",
       "       'Barbara J. Comstock', 'Rubén Hinojosa', 'E. Scott Rigell',\n",
       "       'Tammy Duckworth', 'Jim Cooper', 'Brad Ashford',\n",
       "       'Randy Neugebauer', 'Marc A. Veasey', 'Ed Whitfield',\n",
       "       'Jr, Robert P Corker', 'Tom Graves Jr.', 'Bill Johnson',\n",
       "       'Enzi, Michael B.', 'Jim Mcdermott', 'Jim Costa',\n",
       "       'John A. Boehner', 'Vern Buchanan', 'Steve Chabot',\n",
       "       'Michael G. Fitzpatrick', 'George Holding', 'Nick J. Rahall Ii',\n",
       "       'William M. Cassidy', 'John D. Dingell', 'Gary C. Peters',\n",
       "       'Mr. Gary C. Peters', 'Cardin, Benjamin L.', 'Mr. Earl Blumenauer',\n",
       "       'Ms. Lois Frankel', 'Mr. William L. Owens', 'Mr. Mo Brooks',\n",
       "       'Mr. Bob Gibbs', 'Mr. Alan S. Lowenthal', 'Mr. Raúl M. Grijalva',\n",
       "       'Mr. Ed Whitfield', 'Mr. Nick J. Rahall Ii',\n",
       "       'Mr. K. Michael Conaway', 'Mr. Paul Ryan', 'Mr. Lamar Smith',\n",
       "       'Ms. Nancy Pelosi', 'Mr. Richard L. Hanna', 'Mr. Lou Barletta',\n",
       "       'Ms. Suzan K. Delbene', 'Mr. Bill Flores', \"Mr. Beto O'Rourke\",\n",
       "       'Mr. Michael G. Fitzpatrick', 'Mr. Michael K. Simpson',\n",
       "       'Ms. Virginia Foxx', 'Mr. Randy Neugebauer', 'Ms. Kathy Castor',\n",
       "       'Mr. Gerald E. Connolly', 'Mr. John D. Dingell',\n",
       "       'Mr. Lloyd Doggett', 'Mr. Kevin Yoder', 'Mr. Brad R. Wenstrup',\n",
       "       'Ms. Tammy Duckworth', 'Mr. David B. Mckinley',\n",
       "       'Mr. Scott H. Peters', 'Ms. Louise Mcintosh Slaughter',\n",
       "       'Booker, Cory', 'Mr. Mick Mulvaney', 'Mrs. Susan A. Davis',\n",
       "       'Mr. Rubén Hinojosa', 'Mr. James R. Langevin', 'Mr. Jim Mcdermott',\n",
       "       'Steve Womack', 'Mr. George Miller', 'Mr. Luis V. Gutierrez',\n",
       "       'Mr. Michael C. Burgess', 'Ms. Grace Meng', 'Mr. Eric Cantor',\n",
       "       'Mr. Robert E. Andrews', 'Mr. Sam Farr', 'Mr. Austin Scott',\n",
       "       'Mr. John A. Yarmuth'], dtype=object)"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_df_clean['Name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name_Party</th>\n",
       "      <th>Party</th>\n",
       "      <th>Company</th>\n",
       "      <th>Traded</th>\n",
       "      <th>Transaction</th>\n",
       "      <th>Trade_Size_USD</th>\n",
       "      <th>excess_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Marjorie Taylor Mrs Greene (R)</td>\n",
       "      <td>R</td>\n",
       "      <td>CORNING INCORPORATED COMMON STOCK</td>\n",
       "      <td>2024-07-23</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Earl Leroy Carter (R)</td>\n",
       "      <td>R</td>\n",
       "      <td>AMERIS BANCORP - COMMON STOCK</td>\n",
       "      <td>2024-07-23</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$100,001 - $250,000</td>\n",
       "      <td>2.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Marjorie Taylor Mrs Greene (R)</td>\n",
       "      <td>R</td>\n",
       "      <td>UNITED PARCEL SERVICE, INC. COMMON STOCK</td>\n",
       "      <td>2024-07-23</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>2.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Marjorie Taylor Mrs Greene (R)</td>\n",
       "      <td>R</td>\n",
       "      <td>DUKE ENERGY CORPORATION</td>\n",
       "      <td>2024-07-23</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>2.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Marjorie Taylor Mrs Greene (R)</td>\n",
       "      <td>R</td>\n",
       "      <td>JP MORGAN CHASE &amp; CO. COMMON STOCK</td>\n",
       "      <td>2024-07-23</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>2.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Name_Party Party  \\\n",
       "0  Marjorie Taylor Mrs Greene (R)     R   \n",
       "1           Earl Leroy Carter (R)     R   \n",
       "2  Marjorie Taylor Mrs Greene (R)     R   \n",
       "3  Marjorie Taylor Mrs Greene (R)     R   \n",
       "4  Marjorie Taylor Mrs Greene (R)     R   \n",
       "\n",
       "                                    Company     Traded Transaction  \\\n",
       "0         CORNING INCORPORATED COMMON STOCK 2024-07-23    Purchase   \n",
       "1             AMERIS BANCORP - COMMON STOCK 2024-07-23        Sale   \n",
       "2  UNITED PARCEL SERVICE, INC. COMMON STOCK 2024-07-23    Purchase   \n",
       "3                   DUKE ENERGY CORPORATION 2024-07-23    Purchase   \n",
       "4        JP MORGAN CHASE & CO. COMMON STOCK 2024-07-23    Purchase   \n",
       "\n",
       "        Trade_Size_USD  excess_return  \n",
       "0     $1,001 - $15,000          -1.05  \n",
       "1  $100,001 - $250,000           2.77  \n",
       "2     $1,001 - $15,000           2.59  \n",
       "3     $1,001 - $15,000           2.54  \n",
       "4     $1,001 - $15,000           2.60  "
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new column for 'Name_Party'\n",
    "x_df_clean['Name_Party'] = x_df_clean['Name'] + ' (' + x_df_clean['Party'] + ')'\n",
    "x_df_clean2 = x_df_clean[['Name_Party', 'Party', 'Company', 'Traded', 'Transaction', 'Trade_Size_USD', 'excess_return']]\n",
    "x_df_clean2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find the politicians who had the most trades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name_Party\n",
      "Josh Gottheimer (D)         2786\n",
      "Thomas Macarthur (R)        2758\n",
      "Perdue, David (R)           2242\n",
      "Lois Frankel (D)            1358\n",
      "Greg Gianforte (R)          1331\n",
      "                            ... \n",
      "Thune, John (R)                1\n",
      "Mr. Paul Ryan (R)              1\n",
      "Russ Fulcher (R)               1\n",
      "Patrick T. Mchenry (R)         1\n",
      "Casey, Robert P. Jr. (D)       1\n",
      "Name: count, Length: 346, dtype: int64\n",
      "Name_Party\n",
      "Josh Gottheimer (D)         2786\n",
      "Thomas Macarthur (R)        2758\n",
      "Perdue, David (R)           2242\n",
      "Lois Frankel (D)            1358\n",
      "Greg Gianforte (R)          1331\n",
      "                            ... \n",
      "Thune, John (R)                1\n",
      "Mr. Paul Ryan (R)              1\n",
      "Russ Fulcher (R)               1\n",
      "Patrick T. Mchenry (R)         1\n",
      "Casey, Robert P. Jr. (D)       1\n",
      "Name: count, Length: 346, dtype: int64\n",
      "Name_Party\n",
      "Josh Gottheimer (D)         2786\n",
      "Thomas Macarthur (R)        2758\n",
      "Perdue, David (R)           2242\n",
      "Lois Frankel (D)            1358\n",
      "Greg Gianforte (R)          1331\n",
      "                            ... \n",
      "Thune, John (R)                1\n",
      "Mr. Paul Ryan (R)              1\n",
      "Russ Fulcher (R)               1\n",
      "Patrick T. Mchenry (R)         1\n",
      "Casey, Robert P. Jr. (D)       1\n",
      "Name: count, Length: 346, dtype: int64\n",
      "Name_Party\n",
      "Josh Gottheimer (D)         2786\n",
      "Thomas Macarthur (R)        2758\n",
      "Perdue, David (R)           2242\n",
      "Lois Frankel (D)            1358\n",
      "Greg Gianforte (R)          1331\n",
      "                            ... \n",
      "Thune, John (R)                1\n",
      "Mr. Paul Ryan (R)              1\n",
      "Russ Fulcher (R)               1\n",
      "Patrick T. Mchenry (R)         1\n",
      "Casey, Robert P. Jr. (D)       1\n",
      "Name: count, Length: 346, dtype: int64\n",
      "Name_Party\n",
      "Josh Gottheimer (D)         2786\n",
      "Thomas Macarthur (R)        2758\n",
      "Perdue, David (R)           2242\n",
      "Lois Frankel (D)            1358\n",
      "Greg Gianforte (R)          1331\n",
      "                            ... \n",
      "Thune, John (R)                1\n",
      "Mr. Paul Ryan (R)              1\n",
      "Russ Fulcher (R)               1\n",
      "Patrick T. Mchenry (R)         1\n",
      "Casey, Robert P. Jr. (D)       1\n",
      "Name: count, Length: 346, dtype: int64\n",
      "Name_Party\n",
      "Josh Gottheimer (D)         2786\n",
      "Thomas Macarthur (R)        2758\n",
      "Perdue, David (R)           2242\n",
      "Lois Frankel (D)            1358\n",
      "Greg Gianforte (R)          1331\n",
      "                            ... \n",
      "Thune, John (R)                1\n",
      "Mr. Paul Ryan (R)              1\n",
      "Russ Fulcher (R)               1\n",
      "Patrick T. Mchenry (R)         1\n",
      "Casey, Robert P. Jr. (D)       1\n",
      "Name: count, Length: 346, dtype: int64\n",
      "Name_Party\n",
      "Josh Gottheimer (D)         2786\n",
      "Thomas Macarthur (R)        2758\n",
      "Perdue, David (R)           2242\n",
      "Lois Frankel (D)            1358\n",
      "Greg Gianforte (R)          1331\n",
      "                            ... \n",
      "Thune, John (R)                1\n",
      "Mr. Paul Ryan (R)              1\n",
      "Russ Fulcher (R)               1\n",
      "Patrick T. Mchenry (R)         1\n",
      "Casey, Robert P. Jr. (D)       1\n",
      "Name: count, Length: 346, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for column in x_df_clean2.columns:\n",
    "    print(x_df_clean2['Name_Party'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name_Party\n",
       "Josh Gottheimer (D)     2786\n",
       "Thomas Macarthur (R)    2758\n",
       "Perdue, David (R)       2242\n",
       "Lois Frankel (D)        1358\n",
       "Greg Gianforte (R)      1331\n",
       "Susie Lee (D)           1262\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_subjects = x_df_clean2['Name_Party'].value_counts().head(6)\n",
    "top_subjects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a new dataframe with the top 6 politicians with the most trades across all years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Name_Party'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/anaconda3/envs/dev/lib/python3.12/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Name_Party'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[219], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df_josh \u001b[38;5;241m=\u001b[39m x_df_clean[x_df_clean[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mName_Party\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mJosh Gottheimer (D)\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m      2\u001b[0m df_thomas \u001b[38;5;241m=\u001b[39m x_df_clean[x_df_clean[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mName_Party\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThomas Macarthur (R)\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m      3\u001b[0m df_perdue \u001b[38;5;241m=\u001b[39m x_df_clean[x_df_clean[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mName_Party\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPerdue, David (R)\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
      "File \u001b[0;32m~/anaconda3/envs/dev/lib/python3.12/site-packages/pandas/core/frame.py:4090\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4088\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   4089\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4090\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[1;32m   4091\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   4092\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m~/anaconda3/envs/dev/lib/python3.12/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3807\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m   3808\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m   3809\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m   3810\u001b[0m     ):\n\u001b[1;32m   3811\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3817\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Name_Party'"
     ]
    }
   ],
   "source": [
    "df_josh = x_df_clean[x_df_clean['Name_Party'] == 'Josh Gottheimer (D)']\n",
    "df_thomas = x_df_clean[x_df_clean['Name_Party'] == 'Thomas Macarthur (R)']\n",
    "df_perdue = x_df_clean[x_df_clean['Name_Party'] == 'Perdue, David (R)']\n",
    "df_lois = x_df_clean[x_df_clean['Name_Party'] == 'Lois Frankel (D)']\n",
    "df_greg = x_df_clean[x_df_clean['Name_Party'] == 'Greg Gianforte (R)']\n",
    "df_susie = x_df_clean[x_df_clean['Name_Party'] == 'Susie Lee (D)']\n",
    "\n",
    "top6_traded_df = pd.concat([\n",
    "    df_josh,\n",
    "    df_thomas,\n",
    "    df_perdue,\n",
    "    df_lois,\n",
    "    df_greg,\n",
    "    df_susie\n",
    "], ignore_index=True)\n",
    "top6_traded_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a new dataframe with the top 6 politicians with the most trades from 2019-2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Party</th>\n",
       "      <th>Company</th>\n",
       "      <th>Traded</th>\n",
       "      <th>Transaction</th>\n",
       "      <th>Trade_Size_USD</th>\n",
       "      <th>excess_return</th>\n",
       "      <th>Name_Party</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1265</th>\n",
       "      <td>Josh Gottheimer</td>\n",
       "      <td>D</td>\n",
       "      <td>META PLATFORMS, INC. - CLASS A</td>\n",
       "      <td>2021-12-29</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-28.76</td>\n",
       "      <td>Josh Gottheimer (D)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1266</th>\n",
       "      <td>Josh Gottheimer</td>\n",
       "      <td>D</td>\n",
       "      <td>PING AN INSURANCE</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-53.92</td>\n",
       "      <td>Josh Gottheimer (D)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1267</th>\n",
       "      <td>Josh Gottheimer</td>\n",
       "      <td>D</td>\n",
       "      <td>AFFIRM HOLDINGS, INC. - CLASS A</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-87.48</td>\n",
       "      <td>Josh Gottheimer (D)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>Josh Gottheimer</td>\n",
       "      <td>D</td>\n",
       "      <td>TENCENT HOLDINGS LIMITED UNSPONSORED ADR REPRE...</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-35.96</td>\n",
       "      <td>Josh Gottheimer (D)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1269</th>\n",
       "      <td>Josh Gottheimer</td>\n",
       "      <td>D</td>\n",
       "      <td>XP INC. - CLASS A</td>\n",
       "      <td>2021-12-22</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-56.30</td>\n",
       "      <td>Josh Gottheimer (D)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11732</th>\n",
       "      <td>Susie Lee</td>\n",
       "      <td>D</td>\n",
       "      <td>DELL TECHNOLOGIES INC. CLASS C</td>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>53.84</td>\n",
       "      <td>Susie Lee (D)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11733</th>\n",
       "      <td>Susie Lee</td>\n",
       "      <td>D</td>\n",
       "      <td>BP P.L.C.</td>\n",
       "      <td>2019-01-11</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$15,001 - $50,000</td>\n",
       "      <td>-119.94</td>\n",
       "      <td>Susie Lee (D)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11734</th>\n",
       "      <td>Susie Lee</td>\n",
       "      <td>D</td>\n",
       "      <td>GREAT CANADIAN GAMING CORP ORDINARY SHARES</td>\n",
       "      <td>2019-01-11</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Susie Lee (D)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11735</th>\n",
       "      <td>Susie Lee</td>\n",
       "      <td>D</td>\n",
       "      <td>GREAT CANADIAN GAMING CORP ORDINARY SHARES</td>\n",
       "      <td>2019-01-11</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Susie Lee (D)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11736</th>\n",
       "      <td>Susie Lee</td>\n",
       "      <td>D</td>\n",
       "      <td>GREAT CANADIAN GAMING CORP ORDINARY SHARES</td>\n",
       "      <td>2019-01-11</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Susie Lee (D)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4395 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name Party  \\\n",
       "1265   Josh Gottheimer     D   \n",
       "1266   Josh Gottheimer     D   \n",
       "1267   Josh Gottheimer     D   \n",
       "1268   Josh Gottheimer     D   \n",
       "1269   Josh Gottheimer     D   \n",
       "...                ...   ...   \n",
       "11732        Susie Lee     D   \n",
       "11733        Susie Lee     D   \n",
       "11734        Susie Lee     D   \n",
       "11735        Susie Lee     D   \n",
       "11736        Susie Lee     D   \n",
       "\n",
       "                                                 Company     Traded  \\\n",
       "1265                      META PLATFORMS, INC. - CLASS A 2021-12-29   \n",
       "1266                                   PING AN INSURANCE 2021-12-28   \n",
       "1267                     AFFIRM HOLDINGS, INC. - CLASS A 2021-12-28   \n",
       "1268   TENCENT HOLDINGS LIMITED UNSPONSORED ADR REPRE... 2021-12-28   \n",
       "1269                                   XP INC. - CLASS A 2021-12-22   \n",
       "...                                                  ...        ...   \n",
       "11732                     DELL TECHNOLOGIES INC. CLASS C 2019-01-14   \n",
       "11733                                          BP P.L.C. 2019-01-11   \n",
       "11734         GREAT CANADIAN GAMING CORP ORDINARY SHARES 2019-01-11   \n",
       "11735         GREAT CANADIAN GAMING CORP ORDINARY SHARES 2019-01-11   \n",
       "11736         GREAT CANADIAN GAMING CORP ORDINARY SHARES 2019-01-11   \n",
       "\n",
       "      Transaction     Trade_Size_USD  excess_return           Name_Party  \n",
       "1265         Sale   $1,001 - $15,000         -28.76  Josh Gottheimer (D)  \n",
       "1266         Sale   $1,001 - $15,000         -53.92  Josh Gottheimer (D)  \n",
       "1267         Sale   $1,001 - $15,000         -87.48  Josh Gottheimer (D)  \n",
       "1268         Sale   $1,001 - $15,000         -35.96  Josh Gottheimer (D)  \n",
       "1269     Purchase   $1,001 - $15,000         -56.30  Josh Gottheimer (D)  \n",
       "...           ...                ...            ...                  ...  \n",
       "11732    Purchase   $1,001 - $15,000          53.84        Susie Lee (D)  \n",
       "11733        Sale  $15,001 - $50,000        -119.94        Susie Lee (D)  \n",
       "11734        Sale   $1,001 - $15,000           0.00        Susie Lee (D)  \n",
       "11735        Sale   $1,001 - $15,000           0.00        Susie Lee (D)  \n",
       "11736        Sale   $1,001 - $15,000           0.00        Susie Lee (D)  \n",
       "\n",
       "[4395 rows x 8 columns]"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top6_traded_19_21 = top6_traded_df[(top6_traded_df['Traded'] >= '2019-01-01') & (top6_traded_df['Traded'] <= '2021-12-31')]\n",
    "top6_traded_19_21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "josh_fil = top6_traded_19_21[top6_traded_19_21['Name_Party'] == 'Josh Gottheimer (D)']\n",
    "thomas_fil = top6_traded_19_21[top6_traded_19_21['Name_Party'] == 'Thomas Macarthur (R)']\n",
    "perdue_fil = top6_traded_19_21[top6_traded_19_21['Name_Party'] == 'Perdue, David (R)']\n",
    "lois_fil = top6_traded_19_21[top6_traded_19_21['Name_Party'] == 'Lois Frankel (D)']\n",
    "greg_fil = top6_traded_19_21[top6_traded_19_21['Name_Party'] == 'Greg Gianforte (R)']\n",
    "susie_fil = top6_traded_19_21[top6_traded_19_21['Name_Party'] == 'Susie Lee (D)']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the trades of each politician from 2019-2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.plot(perdue_fil['Traded'], perdue_fil['excess_return'], color='r')\n",
    "plt.grid(True)\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('Excess Returns')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.plot(josh_fil['Traded'], josh_fil['excess_return'], color= 'b')\n",
    "plt.grid(True)\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('Excess Returns')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.plot(greg_fil['Traded'], greg_fil['excess_return'], color= 'r')\n",
    "plt.grid(True)\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('Excess Returns')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DXC6XxzIFBQXo06cP1q1bhzFjxhhus66uDnV1de73lZWVAACXywWXy2V3Fd3IdYdzG7FKMtcdYP1Zf9Zf+2qWuroUAKle64k3kvn4J3PdAdbf7vqHup64Esmvv/46Nm7ciPXr13t9V1paCgDIy8vz+DwvLw/79+93L5ORkeHhQMtl5O+NmDdvHmbPnu31eVFREbKysizXwyrFxcVh30asksx1B1h/1p/1t8K2becA6Ot+X1hYaHOJIksyH/9krjvA+ttV/5qampB+Hzci+eDBg/jVr36FoqIiNGvWzOdyDofD472iKF6f6Qm0zCOPPILp06e731dWVqJz584YPXo0WrRoYbIG1nG5XCguLsaoUaOQnp4etu3EIslcd4D1Z/1Z/2Dq/+23nhGEY8eOtbtoESGZj38y1x1g/e2uv3zyHyxxI5I3bNiAsrIyDBw40P1ZY2Mj1qxZg6VLl2Lnzp0AhFvcoUMH9zJlZWVudzk/Px/19fUoLy/3cJPLysowZMgQn9t2Op1wOp1en6enp0ekEUdqO7FIMtcdYP1Zf9Y/lPrH+75L5uOfzHUHWH+76h/qOuJm4N6IESOwdetWbN682f03aNAg3HLLLdi8eTPOOecc5Ofne1j09fX1WL16tVsADxw4EOnp6R7LlJSUYNu2bX5FMiGEkNiHeZIJIXYSN05yTk4O+vTp4/FZdnY22rRp4/582rRpmDt3Lrp3747u3btj7ty5yMrKwqRJkwAALVu2xJ133okZM2agTZs2yM3NxcyZM9G3b1+MHDky4nUihBBiH8xuQQixk7gRyWZ48MEHUVtbiylTpqC8vByDBw9GUVERcnJy3Ms8/fTTSEtLw8SJE1FbW4sRI0Zg2bJlSE1N9bNmQgghsQ5FMiHETuJaJH/yySce7x0OB2bNmoVZs2b5/E2zZs2wZMkSLFmyJLyFI4QQElEokgkhdhI3McmEEEKIPxiTTAixE4pkQgghCQGdZEKInVAkE0IISQgokgkhdkKRTAghJCFguAUhxE4okgkhhCQEdJIJIXZCkUwIISQhoEgmhNgJRTIhhJCEgCKZEGInFMmEEEISAsYkE0LshCKZEEJIQkAnmRBiJxTJhBBCEgKKZEKInVAkE0IISQgokgkhdkKRTAghJCFgTDIhxE4okgkhhCQEdJIJIXZCkUwIISQhoEgmhNgJRTIhhJCEgCKZEGInFMmEEEISAsYkE0LshCKZEEJIQkAnmRBiJxTJhBBCEgKKZEKInVAkE0IISQgokgkhdkKRTAghJCFgTDIhxE4okgkhhCQEdJIJIXZCkUwIISQhoEgmhNgJRTIhhJCEgCKZEGInFMmEEEISAsYkE0LshCKZEEJIQkAnmRBiJxTJhBBCEgKKZEKInVAkE0IISQgokgkhdkKRTAghJCFgTDIhxE4okgkhhCQEdJIJIXZCkUwIISQhoEgmhNgJRTIhhJCEgCKZEGInFMmEEEISAsYkE0LshCKZEEJIQkAnmRBiJxTJhBBCEgKKZEKInVAkE0IISQgokgkhdkKRTAghJCFgTDIhxE4okgkhhCQEdJIJIXZCkUwIISTuURSgqSnapSCEJBIUyYQQQuIeCmRCiN1QJBNCCIl7GI9MCLEbimRCCCFxD+ORCSF2Q5FMCCEk7qFIJoTYDUUyIYSQuIcimRBiNxTJhBBC4h7GJBNC7CZuRPKzzz6Lfv36oUWLFmjRogUuvfRSfPDBB+7vFUXBrFmzUFBQgMzMTAwbNgzbt2/3WEddXR2mTp2Ktm3bIjs7G+PGjcOhQ4ciXRVCCCE2QyeZEGI3cSOSO3XqhD/+8Y/46quv8NVXX+HKK6/E9ddf7xbCCxYswKJFi7B06VKsX78e+fn5GDVqFKqqqtzrmDZtGpYvX47XX38da9euRXV1Na699lo0snclhJC4ht04IcRu4kYkX3fddRg7dix69OiBHj16YM6cOWjevDk+//xzKIqCxYsX49FHH8X48ePRp08fvPzyy6ipqcFrr70GADh16hRefPFFLFy4ECNHjsSAAQPw6quvYuvWrVi5cmWUa0cIISQUGG5BCLGbuBHJWhobG/H666/j9OnTuPTSS7F3716UlpZi9OjR7mWcTieGDh2KdevWAQA2bNgAl8vlsUxBQQH69OnjXoYQQkh8QieZEGI3adEugBW2bt2KSy+9FGfOnEHz5s2xfPly9OrVyy1y8/LyPJbPy8vD/v37AQClpaXIyMhA69atvZYpLS31u926ujrU1dW531dWVgIAXC4XXC5XyPXyhVx3OLcRqyRz3QHWn/Vn/bWvZjhzBgDSDdcTbyTz8U/mugOsv931D3U9cSWSzzvvPGzevBkVFRV4++23MXnyZKxevdr9vcPh8FheURSvz/SYWWbevHmYPXu21+dFRUXIysqyUIPgKC4uDvs2YpVkrjvA+rP+rL9ZDh9uDmCEx2eFhYU2lyiyJPPxT+a6A6y/XfWvqakJ6fdxJZIzMjJw7rnnAgAGDRqE9evX409/+hMeeughAMIt7tChg3v5srIyt7ucn5+P+vp6lJeXe7jJZWVlGDJkiN/tPvLII5g+fbr7fWVlJTp37ozRo0ejRYsWttVPj8vlQnFxMUaNGoX09PTAP0ggkrnuAOvP+rP+VuuvS2YEABg7dqzNJYsMyXz8k7nuAOtvd/3lk/9giSuRrEdRFNTV1aFr167Iz89HcXExBgwYAACor6/H6tWrMX/+fADAwIEDkZ6ejuLiYkycOBEAUFJSgm3btmHBggV+t+N0OuF0Or0+T09Pj0gjjtR2YpFkrjvA+rP+rL/Z+qcYjLCJ932XzMc/mesOsP521T/UdcSNSP7Nb36Dq6++Gp07d0ZVVRVef/11fPLJJ/jwww/hcDgwbdo0zJ07F927d0f37t0xd+5cZGVlYdKkSQCAli1b4s4778SMGTPQpk0b5ObmYubMmejbty9GjhwZ5doRQggJBQ7cI4TYTdyI5KNHj+K2225DSUkJWrZsiX79+uHDDz/EqFGjAAAPPvggamtrMWXKFJSXl2Pw4MEoKipCTk6Oex1PP/000tLSMHHiRNTW1mLEiBFYtmwZUlNTo1UtQgghNkCRTAixm7gRyS+++KLf7x0OB2bNmoVZs2b5XKZZs2ZYsmQJlixZYnPpCCGERBPmSSaE2E1c5kkmhBBCtNBJJoTYDUUyIYSQuIcimRBiNxTJhBBC4h6KZEKI3VAkE0IIiXsYk0wIsRuKZEIIIXEPnWRCiN1QJBNCCIl7KJIJIXZji0iuqKiwYzWEEEJIUFAkE0LsxrJInj9/Pt544w33+4kTJ6JNmzbo2LEjvv76a1sLRwghhJiBMcmEELuxLJKfe+45dO7cGQBQXFyM4uJifPDBB7j66qvxwAMP2F5AQgghJBB0kgkhdmN5xr2SkhK3SH7vvfcwceJEjB49GmeffTYGDx5sewEJIYSQQFAkE0LsxrKT3Lp1axw8eBAA8OGHH2LkyJEAAEVR0MheihBCSBTg5YcQYjeWneTx48dj0qRJ6N69O06cOIGrr74aALB582ace+65theQEEIICQRjkgkhdmNZJD/99NM4++yzcfDgQSxYsADNmzcHIMIwpkyZYnsBCSGEkEDQSSaE2I1lkZyeno6ZM2d6fT5t2jQ7ykMIIYRYhiKZEGI3lkUyAOzatQuffPIJysrK0NTU5PHd73//e1sKRgghhJiFIpkQYjeWRfILL7yAX/ziF2jbti3y8/PhcDjc3zkcDopkQgghEYcxyYQQu7Eskp944gnMmTMHDz30UDjKQwghCcX27UCfPsC0acDTT0e7NIkLnWRCiN1YTgFXXl6OCRMmhKMshBCScMyYIV4XL45qMRIeimRCiN1YFskTJkxAUVFROMpCCCEJR319tEuQHFAkE0LsxnK4xbnnnovf/e53+Pzzz9G3b1+kp6d7fH///ffbVjhCCCHEDIxJJoTYjWWR/Pzzz6N58+ZYvXo1Vq9e7fGdw+GgSCaEEBJx6CQTQuzGkkhWFAWrVq1C+/btkZWVFa4yEUIIIZagSCaE2I2lmGRFUdCjRw8cPnw4XOUhhBBCLEORTAixG0siOSUlBd27d8eJEyfCVR5CSBCcOAE8/zxw6lS0S0JIdGBMMiHEbixnt1iwYAEeeOABbNu2LRzlIYQEwbhxwD33ALfdFu2SEBId6CQTQuzG8sC9W2+9FTU1Nejfvz8yMjKQmZnp8f3JkydtKxwhxBzr1onX//43uuUgJFpQJBNC7MaySF7MjPiEEEJiDIpkQojdWBbJkydPDkc5CCE2cfIkkJsb7VIQElkYk0wIsRvLIvnAgQN+v+/SpUvQhSGEhA7FAklG6CQTQuzGskg+++yz4XA4fH7fyJ6KEEJIhOGlhxBiN5ZF8qZNmzzeu1wubNq0CYsWLcKcOXNsKxghhBBiFopkQojdWBbJ/fv39/ps0KBBKCgowJNPPonx48fbUjBCCIk2W7YAL78MPPoo47xjHYYZEULsxnKeZF/06NED69evt2t1hBASdZ56Cli0CHjjjWiXhASCTjIhxG4sO8mVlZUe7xVFQUlJCWbNmoXu3bvbVjBCCIk2dXXilZOMxj4UyYQQu7Esklu1auU1cE9RFHTu3Bmvv/66bQUjhJBYoaoq2iUggbBDJG/fDigK0KdP6OsihMQ/lkXyqlWrPN6npKSgXbt2OPfcc5GWZnl1hBAS8+geoJEYJNSY5Lo6VRzX1AC6yWQJIUmIZVXrcDgwZMgQL0Hc0NCANWvW4Ec/+pFthSOEkFiATnLsE6qTfPq0+n9FBUUyISSIgXvDhw/HyZMnvT4/deoUhg8fbkuhCCEklqCTHPuEKpK1UYSKEtq6CCGJgWWRrCiK4WQiJ06cQHZ2ti2FIoSQWIJOcuwTarhFiuZqSJFMCAEshFvI/McOhwN33HEHnE6n+7vGxkZs2bIFQ4YMsb+EhBASZegkxz52OslNTaGtixCSGJgWyS1btgQgnOScnBxkagK2MjIycMkll+Duu++2v4SEEBJl6CTHPgy3IITYjWmR/NJLLwEAzj77bMycOZOhFYSQpIFOcuxDkUwIsRvLMcmPPfYYnE4nVq5cieeeew5VP1gsR44cQXV1te0FJISQaEMnOfZhTDIhxG4sp4Dbv38/rrrqKhw4cAB1dXUYNWoUcnJysGDBApw5cwZ/+ctfwlFOQgiJGjU1QoQxFXzswphkQojdWHaSf/WrX2HQoEEoLy/3iEu+4YYb8NFHH9laOEIIiRXoJsc2dk5LTSeZEAIEIZLXrl2L3/72t8jIyPD4/KyzzsLhw4dtK5ieefPm4aKLLkJOTg7at2+PH//4x9i5c6fHMoqiYNasWSgoKEBmZiaGDRuG7du3eyxTV1eHqVOnom3btsjOzsa4ceNw6NChsJWbEJIYUCTHNhTJhBC7sSySm5qa0GjQGx06dAg5OTm2FMqI1atX47777sPnn3+O4uJiNDQ0YPTo0TitmSZpwYIFWLRoEZYuXYr169cjPz8fo0aNcsdNA8C0adOwfPlyvP7661i7di2qq6tx7bXXGtaJEEIkHLwX24Qak6yFIpkQAgQhkkeNGoXFixe73zscDlRXV+Oxxx7D2LFj7SybBx9++CHuuOMO9O7dG/3798dLL72EAwcOYMOGDQCEi7x48WI8+uijGD9+PPr06YOXX34ZNTU1eO211wCIWQFffPFFLFy4ECNHjsSAAQPw6quvYuvWrVi5cmXYyk4IiX/oJMc2dvocjEkmhABBDNx7+umnMXz4cPTq1QtnzpzBpEmT8N1336Ft27b45z//GY4yGnLq1CkAQG5uLgBg7969KC0txejRo93LOJ1ODB06FOvWrcM999yDDRs2wOVyeSxTUFCAPn36YN26dRgzZozhturq6lBXV+d+X/mDpeRyueByuWyvm0SuO5zbiFWSue5AMPVP9/htvO+2WDn+TU2pkF7CyZMNcLmsW4yKoq7DbH1ipf7RIpj6NzSkAfCcDdbK78Wi4jyqr4/uOZTMxz+Z6w6w/nbXP9T1WBbJBQUF2Lx5M/75z39i48aNaGpqwp133olbbrnFYyBfOFEUBdOnT8fll1+OPn36AABKS0sBAHl5eR7L5uXlYf/+/e5lMjIy0Lp1a69l5O+NmDdvHmbPnu31eVFREbKyskKqixmKi4vDvo1YJZnrDlip//Xu/1auXIlWrerDU6AIE+3jX1IyCEBHAMCaNRvR0FBieR0nTgwB0A4AUFhYaOm30a5/tLFS/6qqEQCae3xmZX/X1aUAuA4AsHr1GuzZE/2Upsl8/JO57gDrb1f9a2pqQvp9UAmNMjMz8bOf/Qw/+9nP3J+VlJTggQcewNKlS0MqkBl++ctfYsuWLVi7dq3Xdw6Hp5OgKIrXZ3oCLfPII49g+vTp7veVlZXo3LkzRo8ejRYtWlgsvXlcLheKi4sxatQopKenB/5BApHMdQdCq//IkSPRvn2YChYhYuX4v/pqqvv/c8+9EGPHWneSFy9W12E2JC1W6h8tgqm/0+l9ObMSAlhbq/5/xRU/Qq9epn9qO8l8/JO57gDrb3f9K0McTGJJJO/YsQOrVq1Ceno6Jk6ciFatWuH48eOYM2cO/vKXv6Br164hFcYMU6dOxbvvvos1a9agU6dO7s/z8/MBCLe4Q4cO7s/Lysrc7nJ+fj7q6+tRXl7u4SaXlZVhyJAhPrfpdDrhdDq9Pk9PT49II47UdmKRZK47EFz9xW/CVKAIE+3jr51goqYmze9+VRSgpAQoKPD8XHv/HdyxTJCDGQRW6m8Uk2xl32kH/qWlxcY5lMzHP5nrDrD+dtU/1HWYHrj33nvvYcCAAZg6dSruvfdeDBo0CKtWrcL555+PzZs346233sKOHTtCKow/FEXBL3/5S/z73//Gxx9/7CXIu3btivz8fA+Lvr6+HqtXr3YL4IEDByI9Pd1jmZKSEmzbts2vSCaEkECGxF13AR07AhEcmkE0cOAeIcRuTIvkOXPm4N5770VlZSWeeuop7NmzB/feey/efvttrFq1Ctdee204y4n77rsPr776Kl577TXk5OSgtLQUpaWlqP3hGZnD4cC0adMwd+5cLF++HNu2bcMdd9yBrKwsTJo0CQDQsmVL3HnnnZgxYwY++ugjbNq0Cbfeeiv69u2LkSNHhrX8hJD4JlB2i7/9Tbz+/vfhL4sR5eXCtQ4QXZawME8yIcRuTIdbfPPNN3j55ZfRvHlz3H///XjwwQexePFi/OhHPwpn+dw8++yzAIBhw4Z5fP7SSy/hjjvuAAA8+OCDqK2txZQpU1BeXo7BgwejqKjII3/z008/jbS0NEycOBG1tbUYMWIEli1bhtTUVBBCiBZNUhvTeZKj5UK+/350thsrME8yIcRuTIvkyspKtGrVSvwoLQ2ZmZno0aNHuMrlhWKi13I4HJg1axZmzZrlc5lmzZphyZIlWLJkiY2lI4QkGuXlwH/+o743myeZj+qjA51kQojdWB64J1OlKYqCnTt3esx4BwD9+vWzr3SEEBIltAIZiH0nOdlhTDIhxG4sieQRI0Z4OLoyDtnhcLjTqHF6Z0JIIqB3E82KZLqQ0YFOMiHEbkyL5L1794azHIQQElPohRLDLWIbxiQTQuzGtEg+66yzwlkOQgiJOqdPAw8/DNx4Y/BOMkVydKCTTAixG9Mp4AghJNH5+GNg6VLgscfoJMcbjEkmhNgNRTIhhPxAfb14PXqUTnI80dRkr/tLJ5kQAlAkE0KIF8ePewulM2cAlyvwbymSI4/d48UpkgkhAEUyIYR4UV5uLLzMhFxQJEceimRCSDiwLJJra2tRU1Pjfr9//34sXrwYRUVFthaMEEKiRVOTEMp6KJJjE7tFMo8hIQQIQiRff/31eOWVVwAAFRUVGDx4MBYuXIjrr7/ePXU0IYTEO8ePe39mJi6ZAivy0EkmhIQDyyJ548aNuOKKKwAA//rXv5CXl4f9+/fjlVdewTPPPGN7AQkhJBoYiWQ6ybGJnTmSAYpkQojAskiuqalBTk4OAKCoqAjjx49HSkoKLrnkEuzfv9/2AhJCSDSgkxw/0EkmhIQDyyL53HPPxTvvvIODBw9ixYoVGD16NACgrKwMLVq0sL2AhBASDY4d8/6MIjk2kSLZ4bBnfTyGhBAgCJH8+9//HjNnzsTZZ5+NwYMH49JLLwUgXOUBAwbYXkBCCIkGwYZb0IWMPFIkp6basz4eQ0IIYGFaaslPfvITXH755SgpKUH//v3dn48YMQI33HCDrYUjhJBowXCL+EHGJKem2hOfTJFMCAGCEMkAkJ+fj/z8fABAZWUlPv74Y5x33nno2bOnrYUjhJBoUV3t/RkH7sUm0klOSwPq6kJfH0UyIQQIItxi4sSJWLp0KQCRM3nQoEGYOHEi+vXrh7ffftv2AhJCSLRp1ky80kmOTRhuQQgJB5ZF8po1a9wp4JYvXw5FUVBRUYFnnnkGTzzxhO0FJISQaPNDQh9TTjKJPHaLZN7oEEKAIETyqVOnkJubCwD48MMPceONNyIrKwvXXHMNvvvuO9sLSAgh0UYm7jHjJJPIo41JtgM6yYQQIAiR3LlzZ3z22Wc4ffo0PvzwQ3cKuPLycjSTzyQJISSBoJMc22hjku2AIpkQAgQxcG/atGm45ZZb0Lx5c3Tp0gXDhg0DIMIw+vbta3f5CCEk6tBJjm0Yk0wICQeWRfKUKVNw8cUX4+DBgxg1ahRSUoQZfc455zAmmZAoUFsb7RIkPnSSYxvGJBNCwkFQD6cGDRqEfv36Ye/evejWrRvS0tJwzTXX2F02QogJysqiXYLEh05ybMOYZEJIOLAck1xTU4M777wTWVlZ6N27Nw4cOAAAuP/++/HHP/7R9gISQvxDJzn8SCeZIjk2YUwyISQcWBbJjzzyCL7++mt88sknHgP1Ro4ciTfeeMPWwhFCAlNTE+0SJD7SSWa4RWzCmGRCSDiwfN/9zjvv4I033sAll1wCh8Ph/rxXr174/vvvbS0cISQwdJLDj3SS6+qA+nogIyO65SGe2B1uwZhkQggQhJN87NgxtG/f3uvz06dPe4hmEj7oZhEtdJLDj9Op/u9yRa8cxBg6yYSQcGBZJF900UV4//333e+lMH7hhRdw6aWX2lcyYsgDD4hHvx98EO2SkFiBTjJJdhiTTAgJB5a7lHnz5uGqq67Cjh070NDQgD/96U/Yvn07PvvsM6xevTocZSQannpKvM6cCVx9dXTLQmIDimSS7NBJJoSEA8tO8pAhQ/C///0PNTU16NatG4qKipCXl4fPPvsMAwcODEcZiQGMbCEShluQZIcxyYSQcBDUw6m+ffvi5ZdftrssxAIUyURCJ5kkO3SSCSHhwLKTXFhYiBUrVnh9vmLFCnzAQNmIQZFMJHSSSbITSzHJNTXAhg0U2oQkApZF8sMPP4xG2SNpUBQFDz/8sC2FIoGhSCYSOsn2w/MrvoglJ/n664FBg4DcXHvKQgiJHpZF8nfffYdevXp5fd6zZ0/s3r3blkKRwPAiTiQUyfZjl9gikSGWpqVeuVK8VlTYUhRCSBSxLJJbtmyJPXv2eH2+e/duZGdn21IoEhiKZCJhuIX9UCTHF3Y7yRy4RwgBghDJ48aNw7Rp0zxm19u9ezdmzJiBcePG2Vo44huKZCKhk2w/FMnxRSzFJBNCEgfLIvnJJ59EdnY2evbsia5du6Jr1644//zz0aZNGzwlk/iSsEORTCR0ku3HLrFFIkMsxSQTQhIHy5eCli1bYt26dSguLsbXX3+NzMxM9OvXDz/60Y/CUT7iA4pkIqGTbD90kuOLWIlJrquzZ/uEkNjAskg+evQo8vLyMHr0aIwePdrjuy1btqBfv362FY74hiKZSOgk2w9FcnwRKzHJp07Zs31CSGxgOdyib9++ePfdd70+f+qppzB48GBbCkUCQ5FMJHSS7YciOb6IlZhkimRCEgvLIvmhhx7CTTfdhHvvvRe1tbU4fPgwrrzySjz55JN44403wlFGYgBFMpFQJNtPMCI5xXJvSuwiVmKSKZIJSSwsd+szZszA559/jv/973/o168f+vXrh8zMTGzZsoXZLSIIRXLs8sgjwHnnRW57DLewn0QTyY2NQHl5tEsRPmIlJpkimZDEIqhu/ZxzzkHv3r2xb98+VFZWYuLEicjLy7O7bMQPFMmxyx//COzaBURqlnY6yfaTaCI5LU3MAHfgQLRLEh4iFZMsxbgvKJIJSSwsd+vSQd69eze2bNmCZ599FlOnTsXEiRNRnshWRYxBkRz7HDsWme3QSbafRBPJko8/jnYJVL7+Gvjd74DTp0NfVyRikv/4R6BVK2DzZt+/o0gmJLGw3K1feeWVuOmmm/DZZ5/h/PPPx1133YVNmzbh0KFD6Nu3bzjK6GbNmjW47rrrUFBQAIfDgXfeecfje0VRMGvWLBQUFCAzMxPDhg3D9u3bPZapq6vD1KlT0bZtW2RnZ2PcuHE4dOhQWMsdDiiSiYROsv0kqkiOJWbPBp54AvjXv0JfVyRikj/+WAj6L7/0/TuKZEISC8vdelFREf74xz8iPT3d/Vm3bt2wdu1a3HPPPbYWTs/p06fRv39/LF261PD7BQsWYNGiRVi6dCnWr1+P/Px8jBo1ClVVVe5lpk2bhuXLl+P111/H2rVrUV1djWuvvRaNspeNEyiSiYQi2X4oksOPFJQHD4a+rkjEJFdXi9czZ3z/rrIy8HoI8ceZM8CyZb2wdi0v8rGA5W596NChxitKScHvfve7kAvkj6uvvhpPPPEExo8f7/WdoihYvHgxHn30UYwfPx59+vTByy+/jJqaGrz22msAgFOnTuHFF1/EwoULMXLkSAwYMACvvvoqtm7dipUrV4a17HZDkUwAETvp76JNgoMiOfzIdltaGvq6IhGTLMNC/N2U6p1ku72X998Hxo4FSkrsXS+JHSZMSMU773THlVdy2s9YwHS3PnbsWJzS9ABz5sxBRUWF+/2JEyfQq1cvWwtnhb1796K0tNRjghOn04mhQ4di3bp1AIANGzbA5XJ5LFNQUIA+ffq4l4kXKJJji9WrxV+koUAOD8GILZ6T1pCz09kpksMZk2zGSQ63SH7+eTEg+D//sXe9VqipAdavp0seLlas4N12LGG6S1mxYgXqNHNuzp8/Hz/96U/RqlUrAEBDQwN27txpewHNUvpDT6vPspGXl4f9+/e7l8nIyEDr1q29lin101PX1dV51L3yh2dqLpcLLpfLlvIbIdftuQ0Z5tIElyu+QkSsYFz32KSuDrj66jQ4HMDRow2Qx6ihoQEuV3BXErP1FxfldI/PRLsMarMxQ7SOf0ODA0AaUlIUAKrqFeFYqe4yGRUrJSXN/RttuRUlFdKPMFufYOovy278O9FGGhuDb5N2U1sr9ldJiXdfZrX+LlcKxPFRj5OV34tlAfXcbYTL5Wknnz4tyltd7f2dpLxcPdYAcOaMK6gnDL7q39Ag1l9S4rsM4Wb48FR8+WUKXnihAZMn29+W4qnvtxtx46H25/X1rqS7+bb7+Ie6HtMiWdHdNurfxwoOXYtSFMXrMz2Blpk3bx5mz57t9XlRURGysrKCK6gFiouLNe+uBwCcPHkShYX/C/u2o41n3WOTqqp01NaOBQC8/fZHAK4CIKZpLywMLeAyUP2PHWsGYIzHZytXrkSrVvUhbTdWiPTx37ixA4CLUV1dAUC9md6581sAvQEIw6BZM+8b1IaGqwA4AQCFhYXuz0+cGAKgndfnZrBS/6+/7gRgoI/tXP/DMqG3SbsoLx8BoDn27q1BYeFHhsuYrf/u3X0AdMO+fd8D6OH+3Mr+rqtLAXAdAGD79h0oLNzj8f2pU9cASMO33+5DYeE2w3V8//2lANq733/wQREyMwPkjfODvv5lZYMB5OOrr/ajsHBr0OsNhS+/FG3pqacq0a7dp2HbTjz0/XZTUeGEvH4AwDvvrIDTmbhmmD/sOv41IaZ/Spigl/z8fADCLe7QoYP787KyMre7nJ+fj/r6epSXl3u4yWVlZRgyZIjPdT/yyCOYPn26+31lZSU6d+6M0aNHo0WLFnZXxY3L5UJxcTFGjRrlMVASANq2zcXYsWPDtu1o46/usYb2IcRFF41w/9+vXz+MHRtcxhez9Td6eDNy5Ei0b+/9eTwRreN/5oy4Wc7Nbenx+Xnn9XT/P2bMGGRne/+2WbM0yDHC2nNz8WLV2TR7zgZT//Jy9Ubf13b69w++TdpNaqq4/FRVZXuV12r95SPqHj26eXxupY/Uxhr37NkLY8eqx7ypCairE8cxP/9sjB3bxXAdc+Z4xumMGDEaPzxstYSv+j//vFh/dvbZGDu2s/UVB0lJCZCX5xl337Jlq7Bcg+Kp77ebdes8zborrhiDtm2jVJgoYffxr9SPprWIaZHscDi83NZADm0k6dq1K/Lz81FcXIwBAwYAAOrr67F69WrMnz8fADBw4ECkp6ejuLgYEydOBACUlJRg27ZtWLBggc91O51OOJ1Or8/T09MjchIbbSclJQXp6YkfuxSpfRwK2kE+Z86oZU1LS0OoRQ9Uf6PJDcRvQtturBDp4y9jWtPSPM+tVE2Qsq/9qxUQ2jJru0mrdbFSf208rq/fpKaG3ibtQkawVVc7UFeXjubNvZcxW3/5YNPp9BSpVva39lxKSUlFerq6rtOn1W3U13t+p0V/PU5JCe1c1NdftrETJyLX/69YAVx1FXDDDcC//61+riiiDEuXij7w/vvt3W489P12s2+f5/v6+sTpy61i1/EPdR2Wwi3uuOMOt1g8c+YM7r33XmT/YKloY3bDRXV1NXbv3u1+v3fvXmzevBm5ubno0qULpk2bhrlz56J79+7o3r075s6di6ysLEyaNAkA0LJlS9x5552YMWMG2rRpg9zcXMycORN9+/bFyJEjw15+O4mh+5Okp14T2RDiTatlmP4tPAQTR8rsFtbQXjKOHoWhSDZLuPMkayc8iWZ2C0mkJioCgKeeEq/Ll3t+3tgo9svUqeL9LbcAbdpErlyJiEbeALBnop1oc/Ik8NOfArffLtpIvGFaJE+ePNnj/a233uq1zO233x56ifzw1VdfYfjw4e73MgRi8uTJWLZsGR588EHU1tZiypQpKC8vx+DBg1FUVIScnBz3b55++mmkpaVh4sSJqK2txYgRI7Bs2TIPpyge+Phj4OGHxSxQJLpEUyRztr3YgSLZGtosEaWlQLduvpcNRLjzJGvFipU8yYkgkn2166Ymz/1UVRU9kbxihdjX8R6B+P33nu8ToX+fPRsoKhJ/CS2SX3rppXCWwxTDhg3zO2DQ4XBg1qxZmDVrls9lmjVrhiVLlmDJkiVhKGFkmT8f+MMfkLSPY2IFrSOmmbcmItBJjh0oks2jKJ43l6GmgQu3kyzTvwG+zzmXy1vUhEskHz8uyhiJJ4q+9mlTk2ebNwr9igR1dSIcBAC+/RY477zolMMOEtFJPn482iUIDXbrcY4mVTWJEnSSCUCRbAV9dJ5dItmuPMn6yUTMOMlG5364RHJDQ+SmwPYlkhsbPUV6tDK2aY0JOf5+9+7olScUElEkx2giNNOwW49zysujXQKiveAzJjl5oUg2T7hEcjSdZCPRGi6RDEQu5MJfuIWWaIlS7bE5eRL49FOge3f7BxKGm5Mnva/nFMnRh916nEORHH20TjLDLZIXimTz6N3YUEVyLMQkJ6pI9hduoSVaIlkvJL/5Rrzu3Rv5soSCPh4ZSIwnhUZTvMcT7NbjnJMno10CwnALAlAkW4FOcuhEw0nW7hd93WLBSQbit0/Uh1oAdJJjAXbrcQ6d5OjDcAsCMC2jFex2kmMhJjlRRbL2xqOoSP1fv4+iNXBPLyQpkmMLimQSVSiSo080wy3i9YKQiNBJNk+knORgRSqdZBVtu96xQ/0/VpxkvZCMV2FpFG4Rr3XRwnALElUYbhF96CQTgCLZCtKNlc7v0aOhXUx9xSQHmwGCMckq2n2qbeOxEpOcyOEW8VoXLXSSSVShkxx9GJNMAIpkK8gby06dxKvLFVpf5stJDnadgWbcM7rwG5374XTRouEkHzmi/h8rIpnhFrENRTKJGB995P0ZRXL0YXYLAlAkW0G6sTk5QG6u+D+UkAtfMcnB9o96Aah3K7XnvCQZnOQFC9T/YyXcQn9s4lFYVlWJpyl64rEuehhuQSLGyJHenzHcIvow3IIAFMlWkOeM0wnk54v/7RDJkXCSAePzLhlEshY6yfaxZ494bdvW8/N4rIseOskkqtBJjj4cuEcAimQrSCe5WTN7RLKvmGS7RLLerTSKS05UkWx2MpFoZbdIBCdZhlqce67n51brcvQo8NhjwIED9pTLDiiSSVShSI4+WieZ4RbJC0WyeSLlJAf7pC1enORICBB/01JroZMcPHaJ5L/9DXj8ceDpp4FDh4ChQ4FbbwXefjt6Nw/xHm5hU1ZJEi0okoHf/17kqJ09Ozrb1zrJkb5rjscLQqJCkWweu53kSMckx4KTfOaMED7Nm4dvG0DsT0udSCK5WzfPz60KW9lOS0uBTz4B1qwR7//xD3GuXXUVcMMNwHXXAa1bh1Rk09BJJlEl2WOSKyqAP/xB3D37Ss0UbowG8UQKOsmxA0Wyeex2ksMdbhGLTjIQmZCLWI9JToRwC5kjWe8kWxX8sr2Vl6ttOC8P6NpVXB/feQeYPBlo3x4YPRp49lmgpCSkogeEIplEldpa78T8ycTx4+r/0XqsE839T5EcO1AkmydcTnIyxSQDkRHJvto1wy3sw65wC3kN1JpnF1wgRPjmzeKpa58+4qayuBiYMgXo2BG47DLgqaeMJzQJlXgPt2C3ngAkc8hFLDjp0XSS4/GCkKhQJJsnEbNbGGW2SQSRbNZJjpWBe/HWJ545I+KHgeBFsmyvRiIZEOGI/fuLkMStW4Fdu4D584HBg8Vv160DHnhAbP+CC9TltOdBsPuVTjKJOrEgFKPFiRPRLgGdZCJwOKJdgvghUjHJdg/ca9FCvOqd5MZG40G7iSCS481Jjrdwi717RXtr0QJo08bzOzPC9PLLxTF69ln1mARq9927Aw8+CHz+uRDoS5cCV14pboi+/hqYNQvo1w/o0UMs98ADQHa22IZV6CSTqEMnObrQSSZA8jrJTU3Av/5lLe2UkZN8/HjwQsvumGRfA/dkHlv9zamvrDaxLpLN3ODHekyyXhRHa2xKsGhDLfQ32jU1gUXm//4nXp98Ul22osK8OO3YEbjvPjFZ2dGjwEsviYF9Tqco25NPilAMQIRnWIVOMok68SKSP/wQKCy0d52x4CRHSyQrCp3kWCJZRfLq1cCECcAvf2n+N1onuU0bVYiVlQVXhnCGW9TXqyJcimS9EJPxyE6ncbnspmVL8RqKSP70U+Fezpzpf7lYF8n6cIt4w1c8ssRfH79rl+d72d4UxThGPhBt2gB33AG8+664aX3zTeCmm6yvRwtFMok6seCmBuLkSWDcOGD8eHtFZSzUPVrhFuFwTJ58EjjrrNhKRh8vaEVyvF8YrCDPwcOHzf9G6ySnpIgR+EDwIRfhFMlaESYfh+uFixQkUrzqy2U37dqJ12BF8qpVwI9+JPrihQv9L+vr5k/fxmPFSY43fKV/k/ir344dnu+1Ny6hXhubNxc3v8uWWf9tfb163jDcgkSdeHCS168XnWhdnb0DPGLJSU5Pj+x2w+Ei//vfQiB/8YX96050tGIi3i8MwWBFrGidZCD0uGRfMcmVlcEJVa0AlPXKyPAdkxxvInnePPPL+nKS9dBJDg5f6d8k/kLqsrPV/xXFXpEcLIoCDBwo4p7r6uLfMKBITgDiQSR/+WV41htLTrJ8FBsppEjWC4NQkB1yvHds4aa+HnjxRWDfPvWzZBfJVsSK1kkGQhfJvmKSARGfaRXt8ZP1ys5WRX28O8lWbujNhhFFI7tFQ0N0x4TYQaBwC383n1qRfOaMZ3uLli74/ntg2zZxLp84Ef/XEorkBCAWhGIg1q8Pz3pjyUm2SyT/859A795p2L8/x+9yUtBmZtqzXUDtkJNR5Fnh/feBu+4SI78lyS6SAznJLhfwyivAwYPhc5KNRHIw/aORk9y8uXquxbuT7O/G+p//BP7zH/V9LDvJ8R5q4XKpN9qhiuTTp2PDSdY/hYz3vpDTUicAse4kK0piO8l2i+R//xv47jsHNmzI87ucdLOysnyPrreKFN7x3rGFG5kT9+BB9TOtSF66VIxU//WvI1uuaBLISX7vPTHb1803+3aSjx4Nbtv+RHIw/aNRTLI/J1m2h3gRyb6c5CNHgFtuEd+Xl4u+xayTHA2RHO+hFvv3izaSmQl06GC8jD+RrG3vp097trdwXRsbGvzfZOlFMp1kEjVk5xXrIvngweAvfoGIBSfZ7nALdWrRZn6Xo5McfbTnnlZMzJwJzJgBvPFG5MsULQI9+j5yRLyeOBG5mGTAf//46afiiYBeUMSLk1xdHdwAXl8iecsWUff6enVQGJ3k8CHjkbt1851n3Wyaz6amyDjJgcKXPv/c8z1FMokacqR1LLip/giXiwzERt1DcZIPHRJTgr71lvqZvLBWVDiNf/QDWidZT0kJMGaM52NTMzAm2Rq+RLLk/vtj40YuUvhz9uR3jY2RjUn2J5J/9CMRW/7b33p+rhUbUoiZiUmWA/sk4UwBJ4VuMG6yL5G8fbv6/9at4jWWRXK8O8mB4pEBazcC2nYbLvPM33rPnBHTX2uJd8OFIjmOkaIs1p3kcInkxsbgBuXYTShO8jvviClBX35Z/Ux1ks2JZCMn+f33gaIi4K9/NV8WbT7YeO/YIkV5uXpDYSSSjx0T7lyyYFYkRzIm2Uz/KKcFlvgKt4gVJ9nhUPubYESyr8flWpEs220sD9yLdyfZbpEciXALf+fT5s3eN0vxbrhQJMcx7duL11gXyeEatBcr9Q7FSZb5iLUdieokBx9uIWMkrYhd7WM9O0TyH/8oZm4KZbrhWMflUveb0eNSM1NV79snpqZNBPxd0MPpJIc6cE/vAPsKt4iV7BZAaHHJeif5yBExYG/TJvWzeHCSE0Uk+8qRDFibVVXbb4er7fm77hrFI1Mkk6ghO8mTJ2O3ITY2Al99FZ51x0KoBWCPSNZi1Uk2CrcI5jGk9oITqkhWFOCRR8RgrZ//PLR1xTqyHRo5boFmoXO5RE7RQYOiNymNnYTqJFdVWRc+ihJ8TLJEL25j3UkG7BXJ06cDkyZ5PiqXIpkD98JHoBzJgP/zQd++IvEEsLxc9OtG5dKL5M2b4/+pJEVyHCM7Sa2bFWt8+63oyOzM5SuJlVjPUMIt/Ink06cz/A7K8eckB5Ptwk4nWTslajDTo8YTUoQZiYm5c/3/9uhRIbJPnvTMlBGv+Lugy++MnOTmzdWbPauDfLVtNdhwixYtfN8kJqKTrO+PtWEW6eniCUhZmfijkxweGhtDF8lPPOG9znDz4IPiCWHz5t7f6Qft1dbGroFnForkOKZVK7Wzi5XQAz0yHrl/f/vXnQhO8v793p9pOzp/giGcTnKoHZtW/DfzHzUS98hzTx9a4XCIC8nkyb5/qxU4iTAVuBknuaHB20l2OIIPudCeL1ZEslYIt2wJLFigvrfLSQ6ni2ank6ydFOf889XH/1u30kkOF4cPi2tHejrQubPv5fyJ5H/9y/N9JFxbXzfzx455h425XBTJJIqkpQGtW4v/Y1Uky3jkwYPtX3csOMmKEryTXF8vslDo8RTJvoNaY9lJ1gq+SLgb0aS8XOwvfbo3KS6GDxevV1/t/dtEE8nBxiQDkRfJ2nOkZUvPsLBki0nWis3evYG+fcX/W7fSSQ4X0kXu2tX/Pjb7lPjXv45uXytDLXr2VD9raGC4BYkiWpEcK66qnsOHxau/x0nBEgt11o7ozskBMjLM//bwYeO7bG1H508w+MtuEe2YZK3gi/dOMhAyRk+PGQeurEz9PxIiOdyuTrAxyYA9ItkorMtXP6ENA3I6RWiYxNe01L6cZDlQNlIp4AB7wy209OnjKZKZ3SI8mMlsAZiv41lnRbevlSL5kkvUz+gkk6iSlgbk5or/Y9VJlnfB2ukz7SIWnGTt5AkZGd4XSX/4EkVmnWR/4RZ0kiPHyZPqRBlazIiLSDvJDz0U3vWbcZIbGux1krXizIqTLIUtIESv9qmOth6B8iQrSuRn3APsdZK19O4N9Osn/t+yJbad5HgOt7BbJAOxIZK1T40pkklUiVS4hb6Rv/02cMUV5tJWaS8wdhMLTrI2I4HTab9I9icY/IVbRNtJ1sZah7vjPnUK+OQTdTvjxomBJZGivNx41jOrItkoPt1ugp3G2CxmnGTtvtI6yXk/zMIeqXALrZP83Xee32n3U6AZ96qr1baXKCJZOsnbt5s/fxluYY1wiORoGRJNTer4I61IZrgFiSqRCLfYtk10xk8/Ld6fOgX85CfA2rXAn/4U+PfyBDdyO0Mllpxkh0NcoHNyAv/mww+B4mKzTrLv9YTTSbZz4F64O+4rrhBxvy+8IOr93/+K8AetUyj55htg/nzvmNJQ8CWSzThwyRiTrF0mEjHJ1dXGAk4rkrWhFoDncdGGWxg5yXI9aWneN6zxKJK7dhUD9zIzRT337DG3TjrJ1tBOSe2PYPMkR5KdO8V5kJmp3mABdJJJlElNDb+T/MtfCjE6fbp4r3WPd+4M/PtwhlvEkpPsdAqhrHWSjWL0XnhBDOC6/nrVSdCj7ehKS4MbuBdtJzmSIlnmc335ZU9n30i49uoFPPww8Ic/2Lf98nLjHMfBhFvE+wXFX7uT7Ut70bdTJKek+J68xWhmTu1NlF4ka2PFAznJ2kF7+u1HQiRXVFgXqP5iklNTxV+vXuK9PL8CEQ6RfPKk/5sAu53kDRuAGTPCn7ZSUex3kvfsAT76KLRyBYsMtRg40LNt0UkmUSUSMcn+3LCNGwNf1JPFSZYD9rQi+c47PZd9+23g3nvF//4cGqsD92ItJtnl8ozR9bWuKVPEn11YmYzDzlkgQwm30IqxM2eA48ftK1c08CWS6+vVc0X2CRkZnqIy1Jhko75Khj8Y3VBrhZA+1MWMkyz7Pl+ZLYDwiuTcXLWNWW03/pxkiXbwnhmCEcn+rh/V1SJ1aK9evtuV3SJ50CBg0SLg97+3d716jh4VZU9JAc4+2/+yZuv4zDMhF8sQMzfuRvHIAJ1kEmUiEW7hz3EoKwMOHfL/+0R3kvUi2Ve4xdq1YkYrrWC0a+Ce3klWlOg6yUeOBJ4etaICePZZ8WeXMPQ38YoeMyLBLCdP2hOTDMR/yIWvC7rR5/r82VqRbOXC6m9Kan9P2rQiWb89bWpHIydZUVRRGC2RnJICtGkj/rcacmFFJJsNtzCT3aKuTr2B//ZbccwXLTJe9h//SMGhQ6J/2LHDeJlwhVvs2hWe9Uqki3zWWYEzIgUzUZidppSZ42qU2QKgSCZRJhID9wLNlBdoyulwDtyLBSdZP0rf18C9pUuFoB41Sv3M1w2G3kn21cn4CreoqwsuHZNdTrJe6AValzZDSChYcZLtnAHSjoF7UmDFu0j2JVqMPteGWgDqwD2Xy1p/FqxINopZ1yKPjZGTDKg3qZEUyfK8lg58sHHJZtq/NrbUDIGc5KYmkTWjf3+xTx94QBgtM2YYL68N7fMVmhaugXtWBmAHg9l4ZCC4OsqbJzto3hy49lrgyiuNv6+pEVlQAG8nmeEWJKpEItwiFJHscqmdut3hFi5XcCEFduMv3EKLFPQ33qh+5kvIai+sdXUOn/FxvsItgnVX7JpxTy/0jISCVtDYlV81Wk5yebmx2yNFsnzVi4j6elVgDRwoXiMhkkO9aDkc4u/DD72/83VBN2qTeifZ6VRFrZWQC9m+jPoqs06yEceOeeZ0bt7cM0REnn++ciRry+ZyWbuJ84UMz5HiOFiRbGZQqd0iub5eOLR79wKvvhr4HNT27/rsIxIzfV1jo/Xro5kB2KFgNh4ZCE4kBzP7qy8cDjEY+qOPjK/jGzaIfdyhA9Cpk+d3dJJJVIl2uAXgXyRrT267neRYCLUAvJ1kX52rP7dJj15U+hIMvpzkYG8e7HKSZXznWWf5XpfWZbXLbbMiQuwWyUaupKyjDCPQ51KWYSapqeq07ZEQycE8vpVoB8A9+aT396E4yYC6r/yFGenxF5McipNcVua5r7KzhWCQ4l6K50BOckODyBjRr1/oNygyl3OHDuI1lAwXgcjLsya2rMQk/9//GQ841lJdrbaBUJzk3/9e1GPtWvPliwWRnJkp1OWZM9b7SNku7EaeT1q08cj6gasNDRTJJIpEIruFGZHs6ySQHVhqqr2iBFBFst3rtYpZJzkcIjmcTrId4RZdu4pXow5e25na5SRHK9yisdH/ZCKdO4tXfXiNFDZt2qiDdyIhkkOJ49QeK5kXVUsoMclAcIP3/IVbyCdtgQbuGXHsmLqvtOJYijuz4RZ79ojZNXftCm3fNzaqKSEjIZIdDnVSETNYEcnbtgkH0h9mnGQzInnVKtGfvf66+fLZIZILC33fkEqR7C/cQlsG2dbq6kTo3ief+N+2nU6ylkAiWY/LxXALEkX04RbhaIy+Hsu1aSME6smTwL59xstoB+35Ss0ULDJ8wc7Yq2AIp0jOyhJXnXhzkqXQk8Iv0LoCuSSKAvz0p8D55wPLlvleLlrhFoDxRCBSJMtHkJWVnu6l9tF5ly7i/0iIZLviOKurvdtaNJxkOwbuGZXn2DHPQXuyD7PqJGvbhtkpno04dkycSykpQPv24rOCAvFqdnCdVayEXFi92Q2UQlTbTo2cZEUx15al+756tf/ltCI/1JjkefOAa67x/QRVxiT7c5K1v5X1fP99YOpUkRf+m298/zaWRDKdZBI1tOEWTU3hidH15bg5narL4CvkIpzp36QzJG8SooXVcAt95+tvsE+bNsI+COQk60VyPDjJWgJ9v2GDcIG+/Rb4f/9P/Bk5NFacLCmSFywQf6FiNOmLFG3NmwOtWon/Dx5Uv5fuX7yKZAD4+mtz6zYTkwyE5iRbjUnWh1v07u35XuskawWLVSfZrmMqxV779mpdL75YvK5bF/j3t94qZqK0cp2wIpLtzpOsbTMnTngfQ20aPl8oirrftm3zn0lHu1+M+vGmJiG0jXJu61m1yvd3J0+qdTnnHN/Lpaaq1055XmnPL39PD8IlkmU/JjlyRPRpKSkifZ4ehlvEMX/+85/RtWtXNGvWDAMHDsSnn34a7SJZRs7wJAVaOEIu/D2WlieFViT/7W+iM3a5wpv+LZ6c5Pp61XXSX0ilONIiL/q5ueJHRoJBUXyHW9jhJAfbsSmK6pyZdZIDOVDvvKP+n5Ii3OTBg81Ni+6LtDRR34ceEn/hGCWvdQ1lyIWRSG7fXm0HpaX2DPDyRzA3Ub6O4aZN5tYdqpOsKMATT3i2BUmwMcl6J7lPH8/3eidZEoqTHAoypEeGWgAi5VZKijgXpBg0orYW+Mc/xEyUN99s3vW1IpKt3FibCeOoqvJsA3o32Uw7rqjwPJ/WrPG9rPamyei698orwLBh6o1JsMh6dOzo30BKSVGvnVbHEUTKSZYucu/exjcWDLeIU9544w1MmzYNjz76KDZt2oQrrrgCV199NQ7EWf4leSKHM8OFVZG8YIHojD//PLzp36STHCsi2V8KOG3nq//en0hu08a3SK6vVzufWHKST51Sty9FcqhOshRG//gHsHKlGFC0bRvw2GPBlREQTrL2wiPrLsVDKI/FJWZFcrt2oh3L4xgo93ioWL0hmDFDlG/7du/v9CK5vt7YUbTqJOud+U8+AX73O+CGG7x/Y1e4Rbw4yVqR3KKFKmT/9z9z6yks9J5x0tdAr9697Q+VA7wnETLqb/RPIvVxyfJ7f+XT3zj4C7kIZC7861/G5bCK2fRvKSneTrJZIi2SjUItADrJccuiRYtw55134q677sL555+PxYsXo3Pnznj22WejXTRLyItCODJcyIZtRiRv2KAuLy9YFRWqCAnnbHuxEm7hbzIReRHNzvben/5Fsu9wC3mBBsLjJAcrkqUgaNtWdd8CiWB/rtZ33wlxlpYGjB0rYvFkeITVmdm0+GrX8lGqUeydVYxEslYAa0Wyw6FmAwn3vbqVC+5f/yome6ioMB6opxfJvtZv1UnWT8euHRipv+gGO3BPe/PaoYP3MS8riy0n2UgkA8CQIeLVTMiFRD+ORLY9PdnZ/kMC9JgVRDfe6NlvGY0nkP3YgAHiVe8kGx0bPXqR7G/AW6B+0+imLhjMpn/TOsmnT1sTm7EikhMhJtnGMd7xQX19PTZs2ICHH37Y4/PRo0djnY9epq6uDnWaZzaVP/SuLpcLrnBMWP8Dct3qNvSjjRrgcilo1SoVQAqOHRPvQ6GxEbjyylSkpAAff9yIlJQUAKnucvzlL+L9kSNAly4uAOk4dQqorHT90OmlAXDgxImGH0RCGrKymuByNbrLL/ab1bp7cvy4KEerVo3u8tXXuwIOyHrzTQdSUoCf/MSeM7e2VpQjLU3UUbhMaiFcLtcPgj4dLVsqcLkaPL7v2FGUX1HkPgIaG8U+lE5ySYn8nYq4MKcjJUUB4LnOykp1n2jXG4jTp8V2Rbkb4XJZV8rff+8AkIYuXZrQ1CSO+ZEjQFmZy6ODFYdVlLmuzrvdyuP+zjvi82HDmpCd3QiXC0hJEdtwuTzblfo7f+1MfJeSIuvnuWxZmWxXTdB6CI2N6j51uVxoaBBl8IfDoR63ggKx3v371eNRWirO29xcUZbOnVPx7bcp2LOnAYMH+2//RmjL5P07dR+dOmWun/jqKwfuuy8Vsk00Nsrfqevavl1BTU0jtPuivNzl9fSoslLtRyQZGd5tUzwZSnffALlcLlRUALfeqm6zvNzlcTNaVyfqnZrqfX7l5Ij2UF7ueQ6dOQPU16vL9ejRBKDJox7HjimoqBB1U/swoFkzcdyqqsT+qKwU501Wlvf+cbmacOCAA+p5Fbjvk8tpXwHg8GGxD/PyPM/NwYMdePbZNKxd6/tc155vs2Y1YtYsz2PhdKrtXd92evdOxfffG/tp2vMNAM6ccfm8AdWWISXFhfvuS8GTT4pynDql9t0ul8tj1tD+/Rvxv/+lYudOz/pVVIjjnp2teIVmAKLvO3hQHNPevRVs3+7A1q0Kjh5tMDRXysvV80dt6yoZGeK4q/X2jaL4XnbXLvFd166++lixIxwO5Yc0cCkYMQIYMEDBffep51pDQwN89UEpKQ3IyUlFVZXD0jUgEC1aeJ7HX32lAHDgwgv17Vr27U1oavJs/4EIdO23SqjrSTqRfPz4cTQ2NiJPTu/0A3l5eSj1YU3NmzcPs2fP9vq8qKgIWeGwSXUUFxfj5MlmAMZ4fL5+/WeoqTmJ+vrBAPLx6afbkJkZmm1x8GAOPvtMTK3zzjuFOHiwJwBxy1tYWIjnnrvevWxR0QoA1wIAVqxYAaezEadPjwDQHJ99tgNpaU0ALkBV1VGsWLHBa1kzFBcXG36+ffsgAB1x8uQ3AEQw4QcfFCEry7ctWVubhttvvxoA0NT0AZo3Dz332ObN3QD0wYkTh1FYuBHV1WkArnF/X1hYiK1b2wK4DCkp1VixYjXkfkhLa0Rp6RYAA1BWVobCQnFb7nJdCyAVubnCLj5woA6FhSs8tltSkgVgFDIyGvHBB4UA1OOyY8dBAGcDgMd6A1FdLbYLALt370FhoY+5YP3w4YddAfRDRkYp1q37FoBoS9Onf4cJE9TnlHV1qZD7Ye3az3DSx2OQv/+9CkAbnHPOVhQW7gMAbN3aAcDFOHq0HIWFa6Gt+8qVKwFc7f6/VSv9dH5i2f37d2Plyj1ey27Y0BvAuaio+B5Ad/evdu78FoB4Hr9ixQps2VIA4EK/+6KmphqFhR8DAE6c6AzgQmzefByFhZ8BAL777nIAbXDw4EYUFh4B0B/A2fjoo+/Qtq2YF9dX+zfi6687ARCzkhQWFhrWGwC++GIbWrXy309UVmZg+vShqK9X+7ctW7bC6SyF3GcA4HI58N//fg/gPPdnH3ywBh07elrH27f3BeBpSR47dgiFhZ5WdEVFBoCrceKEEP3FxcX4/HNxvCVvv70K7durj1K+/rodgCGoqalCYeEqj7pu3vwJgFE4frzRY5/I7UgyM/dj27ZyaI/pkSMN+PLLHQD6o6qqFIWF6wEAp05dDKADvvpqK1q3PoBjx64C4MTXX69BeXmVx/aPHDmKffvyIEXCihUrkJlpXrA8/fRGHDyYg6uv3ofNm8V2T5zY5j4XAKCuLhPAaGzcCCxfbty31tWlALgOAHDeeR/i2mt74r331Of9FRUnAQj7Ud92mjXrCe3x1fLhhx+61wsA7733ITIyjG+u6+vVMhQVFeHCCwHZV77//iq0a6ce04YGxw83fYDDIfrIDRsqUFiojh36+mvRrwKnAXjbyceOHcMnnxwH0Btt2x5Cp06tcOhQDhYv3ohLLvG+1v/vfwUALgIg2nph4QHd+i4AICx37/NLv+1LAbQ3XParr8R5X1kpz3s9ov2cPl0Fh6MegIiF2bTJgXff3Qd5Pf78888BXG64/S++WIdmzQahqioLx44dQ2Hh537La5b9+9U+BhC5rJs1a8C+fYUeoWSyDocPH8WZM60BCBs+0H7TYqXv80dNKInhAUBJMg4fPqwAUNatW+fx+RNPPKGcd955hr85c+aMcurUKfffwYMHFQDK8ePHlfr6+rD9nT59WnnnnXeU06dPK9OmNSjiwYX6t2aNS6mvr1duvbVRARRlzpyGkLf54osu9/orKuqV6dPV7dbX13ts/+TJeo9l6+vrlXPPbVIARXn88QZl4ULx24kTG5Xycu9lzdbd6Pvhw0WdX3pJLe+RI/7XuXmzWoaPP3bZcoyeeELUcfLkRqW+vl6pqfHcR/X19cpbb4kyXnyx537o1q1J+etfxXdXX93oXmdamtiHTz21SgEUJSWlSamt9dzuxo1iPe3aNXkdl4kTG93/a9fr76+21nMd06cH15ZmzBD7Y+rUBo/9/fvfe66vokL9rrjY+1icPn1aeemlDxSHQ+yLPXvU7/71L7HPBg8WdUtJafJoA/L/Q4e8yye/+93vGgyXnTxZ7Dt5XOXfnDnq+/Lyevdx8/fXq1eTe7srVojle/RQP+veXZR75UpR/8ceE9v4f/+vMWD7N/rTngu+6g0oyoIF/o9tbW29MmKE2A/nntukXHaZ+P+FF1we+0z+jR3b6PH+88+913n77Y1ev/v5z73LUVtbr6Smiv3yt799qJw+fVqZNcvzWHzxhedv3ntP1LtfP+9z4ehR9X11tfqb7ds9l1u4sMFj/8m/xx8X277lFvU8+slPRF2efrpBqatTz1fZRrW/HzLEs94nT5o7lvL4y98VFbmUiy4S63rrLc/zpa6uXiko8GxL+r9Tp9RynThRr5w5o76/9dZGZehQtZz63370kdpHOZ1NHvXRnsdy3b7qpC9DfX290rq1WN/WrZ51f/PNd93Lrloltt+mTZPH+mQ/0K+fZ5nk3+jRjcqvftXg7s9+/nPx//33G7f/559Xj/9zz3nvx1/8osHnPtL/jRzpe3/m5TUZtmP9udq/f6Ny9dWe7Ud7PZbHxejvf/9zKRdc0OTeD1b6cH9/y5d7b3PYMO/1y++uvrpRad++yfR+07Z9K32fv7/jx48rAJRTp04FpRmTzklu27YtUlNTvVzjsrIyL3dZ4nQ64TQIoEtPT0d6BGazqKhIx/PPewfcNWuWhvR0dfBaZWUq0tNNzDfqB21Kp/T0dHesrXyvRfte7Av1u+rqVHeMYE5OCtLTU3wu6w9f+1gOxMnPT0NqqggTURT/65V5aQHgm2/SMHy4uTL4Q8ZDZmaKOuq3n56e7o71bdXKcz906eJAaqo4BR0O9Tu5zlat6pCSoqCpyYGKinR3vCagDo7KzHR47Z/Tp9VtaNfrD31GBYcjuLZ0+LB4Pfvs1B8eSwsUxXN92iI7HGmGx239+nwoigMXXQR07aouIAdONTSIujmdaoy2vzappVkzfXnEsrJdtWvnWfemJs9lzUxGkpqqHhuZDu/QIQfS0tLhcKjpqDp0EPWXcYTV1Snu31npY7Rl8vebM2f8H9vHHlOnoF2+3IGHH3b8UB/j47R5s2f7qqvz3u9GZk5mpnc50tNFto+SEqC83In09HR8+63nMlVV6br2I17T0rzPhZwc9b22f9CWZ+xY4JZbUvHxx95lPHgw9Yf1qOeRfHjocqWioSHVHVPftq13vfft89w3Vvo+z/WkuUNQOnf2Pg6XXQa89RbwxRdpGDHC+/fauP/09HSPePDzz09xn7fyey1XXiniqjt2TPEKo9m3T18Z3/WT8fhOJ9CqVbp7YFp5OeBy+f7deeeJhn3ihAPV1enusC3ZZzVvbjxyz+FIcQ8A7dQpFQUFwPPPA2vWGLd/bZswauvah8aBzkntYELtslVV6qDU887z3xZSU4HmzT3bj9OpljvNTyeUkZHm1gVmrwFmMBrgecklvtff2JjiMb7Fil6yS1+Fuo6kG7iXkZGBgQMHeln5xcXFGCJHQMQYzzyTYniRkeeInbPu6WdBCnZmssrKyAzca9NGHTRXr3+yrkN7Idi61Z5y6AfuGeFrYI/RoD15fw4AaWlN7kkD9JFAvtK/AcFlt9APtgp14F6XLp4D1/yFhfkauPfFF+Ku4Mc/9vxctkn5O6MBYEZoB5D46jd95d8OJkWctv5yQpGaGjEIzuXSCnLxGo4sAkb4ax/vvCMmQgDEoD19WjQtst/Rzzb4k5+Y26avgVDyZrC8XBzYbds8vy8vB9avVwcS+suT7As5aK93bzFBQ16e58A/eV7JNIPawWHa7Bby3E5JMR5AZjQTYzC4XGofoB+4B6iD98xmuNATKJtLly7GAyNvvtnz/eWXAxMnArNmAW++KY6d7CPlvjzrLHV7cl/6eyLevLlaZ+3gPdmmzAzc69ABGDpU/P/114HzZhtlmDHbz/hDZrZo1y7wxFKpqd6ZofT9Vo8evn9rx+BjPfo8yYDvQXtAYgzcSzqRDADTp0/HX//6V/ztb3/DN998g1//+tc4cOAA7r333mgXzYvq6nT8+c/Gh0l2WnalgGts9B6tHqxIPnUqMingcnNVgRooPj8cIlmfJ9kIKyJZmwkiJUWBfLhRUiIcCIdD5Ov0NdseEFx2C/1Fyg6RrL2o+stgYZT9oqpKxpoGFslmR51ryyDzJEvk7F++UguGKpIzM1Wn+OBB9SbP4Yh8hhZfddm1C7j9dvH/r34lZjn0x8CBxp8bTXJgNrsFoIrkU6eaweXynpmtrAwYMUI4nLW1/vMk+8LonNT+Xt64yCwQ2j5MtreNGz0nCQrnTc6xY2r/pn2iJLnsMvH62WfBnbvBlv3bbz3ff/ONcLRnzwZuukmkp8vLE/2tFMnyqQqg3owEChuVmSC0ItnX9UVbF61Izs8HzjtPiDajaRG0/easWd5ZZuzIbmE2swXgmQJOor8eG/X/8rfh6FeMhDdFcgJy0003YfHixXj88cdxwQUXYM2aNSgsLMRZvvLgRJEPPuiKqiqHYVJ3vZMcagq4nTu9Oys7RLLdTnJdnbruYJ3kbdvsOXn1M+4Z4Wu2PSORrL3ApaQoyM8XhSwtBX7xC/H55MnRc5IbGkS+UKOJC1wu1TnTukXyd74wEskrVjjQ0JCKc89VcP75nt+po+DFq1mHR3sTlZ7uWYcf/Ui8+kot6EtY+pphEfAWbdpcyTL0p00ba+LODnylaBs/XgiFyy8Hnnwy8Hp69TK/74N1knfv9r75PXpUlPP0aXFe+EsB5wujc1Lb18knODJ9m9atlP3Gf/6jthcz082HghwU1bat8Q35BReooQt64WqGYPKCT5gAvPACMGaMmAVz3z6Rg/mpp4Cf/UxMdNKsmdjXn3+uimRtSjmzIrn7D2NotTmKfYlk7Xv9BCzSTTbKl6w3F8aP90y1qW2vwV47zORI7t5drHziRMWrbvrrsTaEUEukRHLnzsZPNiQNDZ7XknicWCQpRTIATJkyBfv27UNdXR02bNiAH8mrZIwxduwe/OEPjXjiCe/v7A63MJpeWntSWukYTp0K34x78mYgJUVc5MyKZO0jtFOnoBuNGxhFEZNZaDumcDrJqalwO8mlpZ43Qb6mpAbscZJ9Hev33xcXx1/+0vu7w4dFJ+h0CidOK1qshlu8+67omsaNa/JyuYJ1kvUiWT9hhaL4DrfwdeOhXU6fT1YvPGTIxcGDnjmSI41eJCsKcNddIh91fr54TG4mjM/pND8jm5EwD+QkV1Q4sWOHt8WpPQ+OHg1OJMtH64GcZNlmtH2Y9hzdvNl7PeFA9lW+BEl6ujoTXDAhF8E4ya1bi3bz4YdiptWzzgKuvlpMPvPii8LVvlYksEF9vX8nWStGjTBykn2FW8h1Vlery8j9NmyYeDXKl6yfpnzDBjHpiewLtf1MsFnFzDjJa9c24LHH1mHKlCava6e2T2lq8j01dWpqeESy/npzySX+l9c7yXPmiHb6n//YX7ZwkbQiOV7Izm7AQw81Ydw47+9CmXGvtNT7rk4fj6zdBmDtLtDOcIvSUtHZvv++eK91+1JS1Au6FScZsB5ysXo1MGoUcMst6mf6GfeM8CWSjR5ceIdbqE6yFn/hFuF0kmUbM0opLh9Pdu4sjkuwTnJ9PfDBB+Kqff313mpd7yQHOu5GZUhL896n1dXqOs2GW2idlREj8ENaK4FeJGudZO2U1JFG3z7+9CfgjTfEPnnrLW8hJm8m9McwJUWd6MHqNgEzTnIzQ5Gs7eeOHlXLZeWpl5GTbCSSJUYxyYA6mYLRTJt2Im/w/bl2MuTCSCRrRaiRIA7GSTZzPZDmQV0dsGeP+D+YcAsrTrJcp3xSlJ2tPvGRTvLmzd4zLurNhZQUYNkyQM4xpm2vRpOfmMGMSG7dGhgw4JhhTLKW8nLf/arWSQ5nGJC/UAtAlE+7n199VYwn0O/7WIYiOY4JNtzigw9EZztpkufngUSyP6Gjx86Be9OmCYEsXQm922c13OK8H1J+WhXJUix89JHqJpsZuCcFQk6OEHhZWeJPiiYtepGszkDmuZy/cItIxCSXlnqHXGjjkQHzMcn671avBk6dcqBVqzO4+GJvkax3krXOvr9ya92f1FTvfSrbldPpffNhRiS3aOEpNnyJ5EOHwuMkr1ljbjltXdasAWbOFP8vXChCLfTIJ0zyJlWSkgL0729um8HEJJtxksvKwuskS4xikgFVJEfbSQb8D94rKhKvPXoYD3QLRkiZEcnyGPtyks0M3AOsOcnyWBnNUFhQIAR3UxOwdq3n77T95qWXAvPni/9/9SuxrLa96kWyoogwtBtuELHqvrASkwz4v3b6CrUAxLk5dqy4KfjZz8xtKxj69fP/vd5x37VLHHOj6eVjFYrkOEYvkk+d8j8F8AcfAK+/Dvzxj+L9G2+o3xkN2gPMCx09djrJ+s5Am9kCMCeSxWxq4v+rrhKvVkWyvCgoCvDuu57b9CeS5eOmlBSxXFERUFxs3AHa4SQHQzDZLfTtRS+SzWa30LfZd94RrxddVGoofGS7LykR5dSWXZ/KTotRbKsW7c2XXjT4EsnNmqmiyaxIDle4xQsvmFtO1uXIEZGJoLFR3DBPner/d/oR/ykp5pxw7expWoJ1kvXhFtrzyyxmB+5JtH2Y9pzbtct7Pf4INpZVCriCAt/LXHqpeN2927tt//vf4nX8eOPfhttJrqhQyxTKwL3jx9Wp4wM5ydJI0N9YSDdZH3KhFcmpqSJs5KabxHXvJz/xzFSiFclffy0GkU6YIPquRYuM61Bbq55D/mKStfi7duqPsZbUVFHvTz4R5QoX/q57gHG/f8MN/sdyxBoUyXGM7NS1bpa/xxgTJogR60aO07ffio5K/8gyFCdZXhhDdZL1QsmXk+xPiJWUiAtUerro0ADrIlkr5pYvF69mBu7puewy1fXxt41gneRgCCa7hd4x8eck+7uB0da5qUmNVxs82GB0IDzjZV991fM7fyJZ3371+1R/86XFX3YL2Q5zcsyLZHnDFo2Y5OpqcTwmTBAX2r59Rf7YQG6ifh+kpJi7Aa6tNRaHgZzkEyeaeTxel+hFsh7ZZz38sO8yhRJuYSTu/YlkbTsIdbCwPye5dWtVgGr325kzYkAd4NvBC7eTLDOUtGzpeb0yK5KbN1fbhXRjA4lkif7GQsYl6wfvaWOS588X++TFF8X5cfSoyHghOXNG3Oj+4hcixEoruI2eyAKqk96ypXEfY4S/8yuQkxwLGGmGW2+NfDlCIUZ2JQkGeTHIyFBPJn8hF/4u9PLE1g/ECVYkK4p6EofqJOtFcjBOsgy1KChQHxF9+621ARjai8LKlaJTNeMkW0EKRodDgcOBqDnJZi7mvpxkGWut7ajNitcNG8Sxat5cQb9+xw2X17bJN9/0/M5frKD+WPsKtzAa8OLPMZUXfSvhFtEUyadPCxGwbp24YP/73+bOUb0b7Cs3cKDfSQI5yXV1aWhocHjF++pjkvXs2gX85S+eokZPoHAL/fH25SRL/Inks89W/w91dL8/kQyo/ZD2/C0uFse8Uydg0CDj3wUjqsz0EbI8MuNG166egtzswD1AjUuWIjlQuIXEl5O8YYOnMJZO8qZNqomRnS0MkVatPPuwJUtE6Mpf/iKO6cSJat7unTuNQ960oRZmb0qCFcnhzJijbStWwy3atRPjeuIJiuQ4RisWQs1wIUWyPvep2cFXRsgLWKgiWe9uBxOTLEVyx45CxOXkwDAHqz+0jmd9vXBnzAzcs4I+vlIKBm2MN+A/u0Uw2OEky3RZRk6yP5Gs3a8y1GLMGAUZGcaF0DrJMqWSme1YCbfQIwfvGCEvwu3a+RfJ0tE6c0YVDdESyTKW9rHHzMdHGjnJZkSyr1SQvs6ZnBygWTNVgfXq5fl9ICe5a1fgnnusD6YNl5McSZFshHzq9eMf+xbD4XKSZd8sz1NtqAVg3kkG1HYqXXKzTrJ+n3XqJMIdmpo847elsNXflHXrBvzzn577aOlSEfZxwQXCkX7jDeCii8S6FcU4dNFM+jc9/p4U+gu3CKeTLHOpZ2cHnrBErxl++tPg08pGC4rkOCYSIlnbMfiLdzZCdqJ2h1vonWQz2S20ItnhUGcSsxJyoa//8uXmBu5ZQS+SW7RQL8raTtHucItgYpL37VMFi6L4j0k26yRLkTxunO8CaNu9diCPle0A5sMtLrvMOF2fZP584IknRL5Y7fmiv1A5nWpKP3lzFo3sFtpjbTSDlpnfAdadZP2yvpxkh8NzwozevT2/17ZNf26aPwLlSTYbkywxK5LDGW5hREODOn7CVzwyEL6YZHmjIm9QQxHJZp3kQCIZ8I5LbmxU27dRvOxVV4n0ZZK2bYHnnhODWrXZY+X106gfsjpoDzDnJBvNuhdOkSyfiBkN8tWjNyZuu83+8oQbiuQ4RtupS/fLV7hFoBRc8s5XK5Ifesj8OvwRC06yHDDRsaN4lWElVkSyvCjIdRQWqo/rwiWSHQ61k9eKOrvDLYKdcU/mia2oUC9ashO16iTv2gXs2CGO99VX+1YT/kKAzIZbKIq36PPlJAdy2S68EHj0USEItBcno0eect/I/RsNJ7m+Prg8r/rzK1SR7M/plWFGgLdI1uLPTfNHoHAL/eDNeHWSP/1U3Py1aQNccYXv5cI9cE+izyVuNrsFYN5JDhRuAXhPKqINCfI1qOyRR4SDPGuWKMPPf+59jvsKZwHsF8my7RvNfBnOcAsr013r+xlfs3TGMhTJcYz2RPDnJP/jH/5Hk8pBe82be96VLl3qmeYr2ATqdjvJehfVargFEJxIlmLuoovEeqqrxcx9QPjCLQDVVdPWL1xOsuyUzV7MZciFdJHbtVMvfGadZFlnOWBv+HD/Dqe/iS7MhlvoJw4AfItk+dTBDP7CLQDvtH/REMlAcNNs6wmUx1Vi1UkGVMcd8C+Sy8vN58nWEijcIjXV84mC9jwzujH1lyf5rLNUwR2KSG7VyvpNscxqMW6c/8fc4Q63kPhykoOJSZZtOBQn+auvRJiFDLVIS/Pfl993nwhR8tU/+ROBwYhkf/27vNZrc7NLwukky3PTzCRO2v44Pz+8OZvDBUVyHGMkko2c5H//27/DJkMtBgzwHdoABJ9A3e6BexJ5wlkRyXLWs1Cc5LQ0Ed+nJVxOMuD56FkSLidZ3kz94x/mLlzyCYQ+1AIw7yRLN1iGWuj3rR5/F3t/bVTrOutDLQDvKam//BL49a/VlIlmsCqSzY5ytxs7RLLZ7BbBOMlyOnbAv0gGrIdcNDYax5/q+xl5A5OZ6fmdVSdZO0V7KCLZqovc1KTGIwfKSxuugXv6YxxKuIWM5T12TNzkyHZlNSYZEMfk7LNFW1i3Tr1pbtEiNCHnSyS7XOqYDSsxyWbOL7MT+tjFuHHAAw+Ip2eB0Pb7kS6nXVAkxzHak/nvfxevv/2t93I7dvhfj694ZMDTmTYjmvSkpITusgYK9DeTAs6Xk7x/v7GraIRWwOrj+yLhJGsJl5OsFTJz5/peXu5zvZOsnUXQipNcWiqmsQVgOLukFn9OslYI67MqaNuHkUiWN5hSuF50kch5amWiiEAiWd6kAUKMB2rbK1Y4cMMNIrRHL0pcLuCVV7zzF5shmFkZ9aSkGJdf/0g9FCe5ZUvFb25gwLpI1tbdl5MMqPHieqFiNSa5S5foiOSvvhL9XnZ24IwCkXKStaEngOrIyv7DHzk5al+4Y4fxlOH6906n7/AA7RTV8qYp1Py97dsbTxL15Zein8vMtHYczYhkbfuSWB0/ZIXmzYEFC0T/aAW7rpGRhiI5jnj6ad/f+YoXrquDYa5RLf5EstaZDsZJzsoy1wEvXSpOdqNsE4HiqwI5yYriLZJzc9VsAzJkAvA/W528KKSkiMEa2sfykXaSw5XdQnuR+Pxz38vLm4ydO4XoMHKStR23P5e/oQH473/FcZIjxP1hJMzkcdWizweuFclGsaz+sluYxYqTbCbUYsGCFLzzDnDNNeIRsXY0/n/+A0yerM6YZwW7nGQzn2sdP+3EBv4umh06iDuCXr2UgP2H1bhkGWqRkeFZBl9Osl6oWHGSHQ7RnqMhkqWLPHZs4Efj4R64B4h+TN9fXXaZOJ937vTOVGOEDFX4+mv1M3/hFh06+L7+aOOS7RLJgPd1tK5OZFsBgBtvtHZDYiakoX177z7TzO8iDUUyiUm++87/XaWvQXsSrUgOxkk2G2rx9ttikoWPP/b+LpDbFii7RXm5KvC1rpQ+5CI9XTxuW7bMeD1aAZuW5jkQxi6RLIVcpMMtjJxkf45tXp64ACkKsGWLd/o3PYGcZBlqYWa6Ur2YSUtTt6u9kSsu9lzOarhFMFgRyWYyW3zzjcO9rk8/FSPKZQomGeOonx4cCPz4Opwi2de2mjf3PL7+LuTjxikYPLgEDz0klNiOHcC8ecZPGayKZKNBe4BvkawXYdr+aNIkMTmRkXsIiPR16en2iORAjroWRQk8y56WSDjJ+icMgHCSZXaI//438PpkXLIcMJyW5t336kWyL6STvH692h/YIZL1g/ceewzYvl2c7/6MLiMCHZeMDHHN0orkl1+2lrUmUoRzMGE4oUhOEHzFB23f7v93vgbtSexwks0gpxrVxkBLQnWSpYvcpo3nhVkvkqWI+n//z3g9WidZu13Avrtk2VlrBVQw4RZWnQQpqrSCINDNiRwwsnGjsZOsxZ9IrqgQk7MAgeORAe8LR8eO6n7Qbqe42DNEwWq4RTDY6SRXVmbg+HFR2W++Ae6+W3z+97+L80TWwUjwytAVX0RSJGvDLbTHx98507498MgjX2LsWHEAzz9fzKBn9JQhWCfZrEj2d6P/j38AH33kuS/++lfxROS//1VnurNj4J4VJ/mbb0S2mIwM4SQHIhLZLfTxyJLrrhOvZkSydJKlSDbKrqK9ufe3z84+W/RXDQ3AihXiM38DMM2iN5uefFK8Pv+8SB1nJ3l56tMKSaymWYvHQXsARXJc4a+RycEA11zj+bnZeGSjQXtA5JxkKZKPG0yyZpdI1j+SlzcF+/Z5/8ZoXUahEPoy6LGaF1XGl3bqpP4wGCfZqiMSyEk2ErlSJG/aFJpIfu89sb979AB69jRfZknnzqrg0m7n2DHPR7hakWwUxypvksLpJBcUqJ8HEskHD4qDcdZZYt88/7wq4I8cUR1ko/hi7TS5RtgRsxiMSNbeaAdzY2l0bKzGJBvlSAY8z+umJt9OciDuvFPEoF57rXfO8FDyJFsRydJFHjnSnPALRsBYHbgXSCSvWaNeB3whneQtW8RroOtLoH0m3WR5MxOOcIumJiFcr78+9HXrkWZKoBC1WCBWpsq2SpwWm5glkJOsj0fWd5ahOslWRbKRk2x24J4vkazPkaxfr1FnL2dE06J3krW/83XB18bOmeHgQfEaSCQHcpKtXtiNYpKlSF6+XIjxgQPVsAhAHa38xRdCtAG+RbK/mORdu8Trj38c3MW6c2fVOde3UW3IhTbcwpdIdDpDC2EJJJLT0jxn6PPHoUPiYJx/vvqZ/G1JiX8nedUqkwW2iLbNh+okB3PRNBqEZdRn+MNXuIW2n2lsFGEAOTlCaIZKpGOSZdiamfAlILpOcrduIiyloQH48EP/65NOsuz/QhXJMi5ZXufsEMn687qgAPjTn0JfrxFSJPsK94kl6CSTsBNMIwvkJH/1lXiVIlnfWWovasE4yWbCLZqaVHcnlHALX9kt9OnfzGAkbvVOslZ0+XKSpeg1iyqS1c+0OWMlkXSSv/pKiKONG9VcxoDqJG/fLr53On3H2ZrJZWsm1MIIX04y4CmSzeT5btMmtM48kEgG1GMbqkiWTrKRSP7yS3tCKvRoj6PZGENfTnIwhOLyS8yEWzQ2Av36ifEMjzwS+jZlW/jNb4JfhxWR3NAgthkoU4wkXDHJZpxkwHzIhT7HcCAzwKyTLLEj3ELPiy9am4DDCvLaEA9OMkUyiTnq6wNntpCxXb5EspZwhVtUV6sOVTicZF/hFv4wEsl6J1m7PV8i2dcMiL4wCrcwSmMkBbsvkRysk+wvJnngQLWd9OolXGOtaOnSJfiOMC8PGDw4uN/6c5I//li9mTEzY2SoIizQjHsAMHq0aC9Dhvhf16FD4mBoRbIcvBXISXa5AsclB4N2W1ad5Oxs/2E3ZrBTJOsFkTa8SPZHdg02kvvqrbfUJydWsZrd4vLLzU97bvZYao+fHQP3JFIkFxb6v5nNyfE0DYKZbU9L166eAtMOJxnwjAu+6ip71mkEwy3CT5wWOzmxKkC++y6wMNAP2vN3UQjXwD1tHFowTnKg7BZ2iWS9k2xmEJLVR8FGTjJgHHIB+N6/Vjp77RTN/rJbXHaZcJWPHgXmzxftUTvbk69QCzNcf33wnagvJzkjQwgi+bTEjJMcqgjTnqO+6vP446Jc/qawBYydZCkQ9u5V01b5mmY6HCEXwYhkbduKBSfZV7hFbi5wyy3AzTfbPxOitl0EYzYA1gWcmawWknCHW6Sl+Rdyl1wiBrVVVADr1vm/0Mm4ZEC0KW0IkN4cCCSSHQ5PN9kukfzb34rrzZIl9qzPF1IkDxokwt+uvDK82wsFOskk5pDxyIHSk2kH7UXDSdaKZKOBe9Fwkjdv9o5V9uck+xLyVpxkRTGOSQaMRbLD4VucW3GS6+rUuhrFJOtp317dB9pZlKyIZL37GWyoBeBbJMsLhgy5MBtuEQpmwi2AwNlHqquBY8fEHZBWJMtjop9AxMhNDjR4Lxi0gwRDjUkOBjseW/tykgHg1VeBf/4z9G34I1KpsKycU+EauHfOOeKYDRvmv96pqeqg8/fe818YbchFdranWNeLXDNp82RcstHvg6VHD3GO/vKX9qzPF/KmOS1NjC9auTJ2xSidZBJzSJHcv7//5bSjcf2dYJEQyadOebvf4cpu4Y/jx73zz+qdZO32fO03KyK5vFzdx/qyGonkzEzf27XS2WsFllZcm8mAEKyTPH+++r/DEZoD4ivcQs4yJkWyXeEWH39sfDMHmBfJgZCP5Nu1UwyFu75tRiouOZRwi1h3kiNFJMTCwIGes18GIlxOcqtW4sY/0IA8QA25eP/9FL8CXOsk60Wy9nqTlmYu5ZrWSQ5HTHI40YbTOByxK5CB2C6bPyiS4wirjUwO2vM1n7wknCLZargF4C0sQxHJdXWqoLEikgHvkAu9k2zGFbMSbiFd5HbtvJ1GXyLZF1acZBmPnJHh+dThueeEy/Lcc75/q82tbUUkawfo/PjH1tOBad2ktm2NnWQpkj/7TIQm6J1kI8HlS4Slpaku7rhxvme5s0sky0lEevY0Vgv6PM/6NHAy/+u6dcGXwYhQRbLdTnIwM4v5GrgXToLJChIKZrNaSMI1cA8QwtWMey5j9XfvduDwYd8dmPbc9xdukZdnbl9366Y6zvEskmMdimQSc0gn2YpI9ke4UsDpRbJeWIYSbiFTkzmd1l0ovUj25yT7wqyTvHq1uj2jdD5GsXX+RHIwTrLRFOJr1qjHw0jgaC9+wYZbzJtn/neS2bPFa7duosxGTvI554i/hgaxf/Ui2eimyVe4RUaGeBQ/YYK4MZD7SZ/X2S6R/O23/kWyPjew3jEePly82h2XbCSS/c3MCHiKZLMpIX3RrJnnjbdR5pdA+Au3CBdaIRfOcIsBA8Q+njTJ2u+Caat2i56cHLXdrl/vYxAGvJ1kbTm0M82ZHejocIg+6PrrPUMvYhH9jUkw7T9aMNyChB0rnZI2s4U/Eexrpj0jIjFwD/B+lK29qBg9hpMXaaOYU22ohdVOXWb+kMgOymjgni/MOsmnTwOTJ4v/jQa4GDnJ/vatFZEsneTsbM+ObPVqdbYoAFi71vu32uNh9vFuRYV6XCoqgPPOM19WidxHctZEXyngtCEX+nALo33q70Zq4kTgzTeBnTuF8Pv2W8/9A4RDJBt/rw+F0Ytk+QjZ7rjkUERydrYY0PSjH/me+t0MWjc5GJEQL+EWjY3WVeirr4obKH/p1owIRvA++6z13wRChlz4E8l6J1nb1/Xpo96sX3CB+e3efrvIAR+LTvJHH6n/y0HIErtn8AsndJJJTCEzW+TkiHRdvvA1054RkYhJBoSwnDYtBX/9ax8A3kn+9Wid5OPHgZ/9TIiXEye0KdWsl92Xk2w0cM8XVlPAAcZOcjjDLbROsvYC3rWrCCuQ2zESkNpjF2gfS0H9zTfitWPH4IXKFVeIi8fzz4v3vkSyjHX+7DPvmygjgWX2aUNWlhD3+nMnUk6yHn24hRTJ69cbz8gXLEYi2d/A4LIycXPtcIgLert24uZL3hAGg/YYBfO4OdrhFmb72++/b2V5O6mp5owJPVbb6vjxngNK7eLaa8Xr7t2+R2hq08DJ68vJk8AzzwDTp4u2v317+DNLRIorrwSmThX/a/O+5+YGvkGNJSiSSUwh45F79RICQjpuesyGWgCRE8mHDgF//nMq3nuvG06c8LyoGA2+0orkDz4AXnoJePBBIdp++lPxndV4ZEAMntLWOZzhFlrMimR/F8NA+333buGcrFvnOcWr9mIpO+DNm8V+/POfvdejFeqBZqrbvVu8yjCg3r39L+8POdhPpurylSdZDizcutW7/Rrt00hlt/CHy6VOp21WJOud5LPPFn8NDcD//hdcOQJtR54H/i7UmzaJ1+7drefu9oVWJEcj3CLU9HBm28WWLZGzCa0KmHAJnrPOAvr1C9zmpZss+7nWrYWQlDdNvXoFF68eq8jMHzLtIxBfoRYAwy1IBLDSMUkhIl1k7SMqbVJ3KyLZSriFP/dRj14kHzum/n/kiPdMWHq0IlnrJGrLu3dv4HJoaddOhFds26Z+FszAvaoqc2Jai9lwC3+i1F+H1NAg8pLecIPIfSzTFOnDS6SD36MH8Npr4lGmnosvBhYvBoqKjLc1Y4b6v4yP1d7A2YUvJ/mcc4TzdOaM9xTtVsMtzGBmMpFA7N4NNDQ40KxZQ0B3Xm7DKItFOEIujFLAmRHJ2lSBoRLtcIt27UQMq3yKYYZgBu5t2WJzsmY/xJKAueaawCMC771XtCkZTpXoDB3qbYrE06A9gE4yiTGkEJFuna8plM8+2/w6zTjJ0i384x+BJ54QI5YDIUWyFH3amOSSEodHuEUgJ1kKoSlTPEf2W42nlmnztCEXwTjJgEjtZgUjJ9nI4Qx2IpF9+9RYaX0eUe1NiJlHeQ4H8Ktf+b5YPfUUMGuW+P/jj8WrHU6yHl9OckqKeizXr/f8LpRwC1/Y4STLcJROnaoCXljk+WsUUhEOkWw13EKKZG2qwFAJxUk+c0Y9b0MJt3j4YeDuu4P7rZl2ceYM8O23NuS7M0ksCZhrrw3sJN96K7Bxo/9Z/BKJZs2AkSM9P6NIjgwUyXFEKE6yVlxqHS6zaXwAa+EWlZXAo48GnsgEUEVyt27iVTvYraTEmpP8+efi/0svFX+PPy7EnhXXB1AHfWjd1WCcZMB6yIWRSDa6sAY7JfXrr4vXK68UA+i++UaIjWee8WwndsW7ybjgVauEo6Zvm3bgy0kG1GOpH0QZq+EWso136hQ4mFg+IfLnJOtvDkLB6sC9cDjJoYhkGWrhcNgX/mEVM/345587UF8foVlHYL2thlPwDByooHXrEBNqJyAy5ELCcIvIEKfFJv6or1cnIzBykgOJTl9YEcnSyTaDP5F85IgjYHnlRbq6Wr0oX3KJeP3d78TnF11kvjyAOSfZ7L6zOjW1mVmiAN8i2Z+TrCjAK6+I/2+7Tbz27CluRqZOte4km+Hii0VZy8rEADqZ2SLSIlmP/iLTrFnguOpA2DVwDxBOcqBtSSfZSCSfdZYYfGnlHA+EFZFcWalm2AmXSLbqpslQi5ycyF60zcxOp2XVqsjabmb3hexHH344vGUZNKg08IJJxtixnu/pJEcGiuQERMQ0CqdEupJ2iGQrIQtW8qEGcpK1Hbi/cIvSUiGS2rZV16X93gpSJG/Zol7g9E6yWar8ax0P8vLMT6zhK9zCn0P25ZdCuGRmAjfeqH4uOzDtk4VA+anN4nSK2GcA+L//E68FBZ45TUPFV7gFYCzQHA7vAVh2zOamvRCEWyS3b6+GDPiaWU87m5gdWAm3kDeYnTrZm6oqlJjkaORIBmJfJJsVMF9+KepiZSxLMFx00dHwbiAO6dTJc/ZciuTIQJEcR5htZNrH2fI3WjEcLpGsvRCEIpK1MclHjnhW2l+4heSSS0I/IXv2FOutrBQxvNpth3MyAKNQC18E4yT//e/i9YYbjJfT7l87OzUZcvHWW+LVznhkwL+T3Lu3t+Bv1877s1BDLQB7neT8fP9zSufnq+eZrzRvkRDJvpzkcIRaAPY4ydHKkWyGqipg/Xpx8nXoYFFdB0msPQrv3/+Ye0r2UJ/uJBLakItYDreQ/bsMfwRir42ZJU6LTfyhH7QHRCbcQitQzIrkpibV3ZHxlXonWYs/J1kiQy1CIT1dDQeQccnBOsn+0O9TK/mcrTrJ9fVqPLIMtdBj56N5LXImLZmr2M5QC0AVyUY3ck6n9/bCMWgPsFckt2nj/660Qwf1PLPiJFudAlxLMCLZzkF7gOdxatbMmuCVqfUiLZKtOMlr1oiJRPLzq9G1a2REcqy5fE5nI776qgFbttj3RCsRuOsu9f9w5Km2i23bRJsfPFj9LNbamFkokuOIYJxkid0iefp07++1F1CzIrm6WhWf0kmWM8ABIruFFrNOsh3IWFb52DgcTrJ+dsFwOskffihuQPLyvEdKS4xuQuxg0CDPctntJMtwC1+DKfVxyeFI/wbYJ5IzMxU0b24whaQGrZPsSyR36eKdASCUqaG1jrU8D3yFW0TCSQbMu8mDBwM//7n4P9Rcx+FEZoHp2/d4WJ9aaYlFl69DB/PjM5KFrl1FitSvvgpuptJoQpFMYoZATrK2Q7QikqULeNddwMKF3t9rL6Bm7/5lqIXTaTzhR0mJZ5xsY6NaZnnSaS/SDof1QXq+0A/eC4eTbEUk65PjW81uIUMtJk3yfXzC5SSnpYlZ8iThcpJ9iWS9UAtHZgvAPpFsZhr1Dh3UY+1vVj29mxyKSDbrJNfVqTfrdotkbUwyYP6x844doq/4yU9EnuNIYsVJltMQ9+9/DP37J6eTTHzTtm34Y8LDQSzeiJmBDzISDJdLzWzhy0nWdojBiCJfj1d9uVn+kCK5VSshArOyPJ3kxkaHx+QiDQ1qnlMpjLTl6d3bvkE5epEcCSfZX7iFfrtW8iRXVAD//a/4//bbfW/DSkpAq1x5JVBYKP63WyQHml1L7yRHItwilHZSUBBYHJkJtwBEqMvf/qa+DyX1mVmRvG2bOFdzc609HTGD9mYmJcW/SM7MFDl1DxwQs0bedJO3yI4ljh9X+5s+fY5j3LgmZGWlYsKE8G43XgUMiR/i9UaMIjmOMNPIdu8WQrl5c/GoVeLrMXqsiGQZI9i2rbigaTlyRP2/sVF1C6VI1jrJl15qvQy+kCJ5714RN+3LSQ7lAqN3mPwJCr3o8uUkGzmFb70l9lufPp4jpPWEy0kGgKuuEtOFn3uu/UIlUJxtvIVbmJlG3Uy4BSBm69Jit5NsFG6hDbWw++LYogUwe7Zoq1lZgZ1k+QQlHpCzUvbpo6BVq3o0bw7Mnx/+7cargCHxQ7y2MYrkBENOv3z++Z6N0pdIDhSDmp6uhlloPzMiVCcZEC6RP5Hc0OBfJNsVjwwI0dSpE3DokEgF58tJDibFnGTOHOCf/1TfWxHJVgbuaXMj++uswimSe/cWs7+FY1R2IJHcqpXIKSwzlfTt671MLIVbmHWS5bH0F26hb1OhiGRtX+DPSQ5XPLLk979X/4/lUf4Ss+EWMh55+PAwPtIxgE4yCTfx2sbitNjJiVbc+HqUKztj/cCoYJ1kI3fNVzyrXSJZTyAnOT1dPQG1o2ntQDt4z5eTHEq2gK5dgaVL1ff+Bqrot2s2JnnvXmDtWtF+Jk3yXx4r05QHwxVXAD162L9eM8fgllvEjcWbbxpPox1LTrKZLCdmnWQ9oYhkLbJ+ctrxQYPU78KV2cIIKZJj+SJsViTLeOThwyMTiyyJV5ePxA/x2sZiuFsh/gg0ME4f8xmsSDZ6LB5uJ1lPIJGckiKcpV//2v5YV21ccjicZEA41RJ/s9wF6yS/+qp4vfLKwOJr/HjgiSeAlSv9LxdrpKUFjgF+4gmRg3bCBOMOO5ZEshknOViRbNd0zLJ+vXqJc/JPfxLvGxvVuNpwOclaxo0TNz133x3+bYWTgwfFRD8pKcAVV0ROJF94YWzfYJDEIF7bGMMt4pRAIjmcTrIvIefvka8vrIpko3ALAHjsMevbNoNWJMsbBr0YC8VJBnyLXT1mY5KbN1dn+VMUNSbTV25kLSkpwKOPmitPrNGsWWCx6K+jjqVwi44dvQd1asnJEQJZCt5oOsmA543igQNqvHD37vZsyx8dOgBFReHfTiiYcZJlqMVFF0Umj/OOHeIJ01VXqc4/IeEiXp1kiuQ4QtvIrDrJvsSwnSLZDifZaPpafUy0zG4RqoNrBimSt25V45314sdfOVJSAmeMuP9+YOdOMfreH8GI5I0bgaNHhWAZP97/+uMdpzO4NiixI3duKCJZey527Kj4FckdOohXKXitZCUJh0jWIuvRv394Z6dMNKRIlrNThpvzz1cnpIhXl4/ED5F4qhQOeGrEKf4uPtnZnpktgOjHJGsFvvaCLmfb8+ck6zFyksNFt25CYJ45A3z7rfhM7nuZf/a++4x/+8IL5rI4tGwpQiK0U44aYTbcQiuCjh4Vr76moU4kQm0PRhkvrKJt51aFR1mZ+n+gwWhSJJt9CqHFrnCLQAI4Xi+K4SCQk6woajzyiBHhL48eimQSLr7+Gli2DLjuumiXJDjoJMcRZp3k88/37vSiHZOcmSkc1/p64ORJ9UJtJtxCTyRFcmoq0K+fmIO+tFR8Jvft+++Lx5S+0s5lZIibDO0026EQ7MA9wFyoRbwTKFdyJAjFSdbGpgd6UiQFfWqqaAeBpozXEm4nWRKJQXuJwnffAYcPiz5tyJDIbz9eH4WT2KdfP/EXr8TN/eOcOXMwZMgQZGVloZVUVDoOHDiA6667DtnZ2Wjbti3uv/9+1Mtn8z+wdetWDB06FJmZmejYsSMef/xxKFamQ4oR/F1Ejab8jURMciAnWU4fq3XMYl0kA955haWDlpUFXHZZ+ONc9duVmB24l58fHXcq0lhtD/4GSQZLKCL58OHAy8j2pJ2S1qrojZRIppNsHukiDxni++Y3nNBJJsSYuHGS6+vrMWHCBFx66aV48cUXvb5vbGzENddcg3bt2mHt2rU4ceIEJk+eDEVRsGTJEgBAZWUlRo0aheHDh2P9+vXYtWsX7rjjDmRnZ2PGjBmRrlJIhCKSBw0CPv1U/B8ukWwkWNq3F26ZP5FsFJOspaFBDdeIlki2ckGxI2OCxGxMsn7Qj79pqBMJq06ydj/9+c/2lCGUGffkkwp/3HOPCKUaPVr9LDvb/yA/PZEQyWlpxv1QshLIh4l0PLIeOsmEGBM3l87Zs2cDAJYtW2b4fVFREXbs2IGDBw+i4IdkswsXLsQdd9yBOXPmoEWLFvjHP/6BM2fOYNmyZXA6nejTpw927dqFRYsWYfr06XDEeE9hNtzCKA2aViQ/9RTw9NPifxlX6wujyTnMxCT//Ofe3wfjJOfmKjh5Uq24dJGB6DvJZginSPYlCvXNOBlCLYDg2oOiiHYbDuEYysA9X2RlATfe6PmZ1Rhju1PAGdG7d+TOz3jAn0hualJn2ovWEx86yYQYEzciORCfffYZ+vTp4xbIADBmzBjU1dVhw4YNGD58OD777DMMHToUTk3vPWbMGDzyyCPYt28funbtarjuuro61GnUWWVlJQDA5XLBpU+9YCNy3fK1sdEBechSUxW4XKry1X7Xo4fLKyNEY2O65n8XDh8Wrm7fvt7ZIwB12f79Xfjznx2YMkVtKuvXN+K225p++F26u4yVlakAUvB//9eItLQmr/W2bSu+LylphMsl7OCKijQADmRnizK3aOG5/fx8T5F8+nSDu54Oh3c9rSD3maI0weVq9Niu9rj27OlZpqamBrhc/qwhsWxDQwNat3YASP1he4F+p6I/9gDgcIh9BQDNmilobGzQCCvPsqelibr17q2gV6+GkPZTNDCqfyAyMkT70q7DzM8zMozOgeBQlBTI460o5o+3WF49vlbqn5XlWW/j36ntw+lUzyFzbdL40VFjo+f+bWhQ+6D+/eU5FRzBHP/YxvPYaqu1eTNw4kQ6mjdX0L+/OFcjXf+mJrXdRnufJ96xtwbrb2/9Q11Pwojk0tJS5OmGhLdu3RoZGRko/eE5ZmlpKc7WTSkmf1NaWupTJM+bN8/tZGspKipCVjDDyy1SXFwMANi6tQsAEejXvfteFBZudS+zZYv4zulswLZthdixQ7+W693/FRYWuv8vKTHaoueyu3d3BKBOp7VhQxkKC79EbW0qgGsBACtWrMCBAxcDaI/vvtuMwsJD0FNd3QtAd3zxxV4UFm6HogAVFeN+WOdH2Lu3DooCpKZeh8ZGcdHPyDgOoL17HZ9/vhHAxXA4FBQVFYb0mFDus7KyMhQWfuFVby0dOoxASYmw4DZuXA+gDL65/of1b8Hx45kARJ6lr7/egsLCg5bKKI89AJw+PQyAiBFIS3OhsPADr22K36xEs2aNGDeuJ4YNO4QPPjhlaZuxhLb+gaiquhTatrJixQo0axbGebYN2LnzHABizutt27aisPCA/x9oqK6+EoBIQSLrbab+Z84MAeCZv07ffrXtY/v2LwGI0WFbtpgp4/WGn27aVITvvlNv1L/9tjWAHwEAMjK2obBwb8CyB8LK8Y9lmpqugxTJH330EXJzVdPlnXe6AeiDnj2Porj4C4/fRar+33zTFYAYXeXddqJDohz7YGH97al/TU1NSL+PqkieNWuWofjUsn79egzSznfqB6NwCUVRPD7XLyMH7fkLtXjkkUcwffp09/vKykp07twZo0ePRgthfYYFl8uF4uJijBo1Cunp6ejeHfghvBr/+EdnZGd3di977Jgof58+Kbj22rF+1zt2rP/v9ctqnVwAuOqq9hg7dqzH5CGjR4/B008LJ2LIkP4YO9Z7OOs336TgnXeA5s3PwdixZ6GqCmhqEuu+8cYR7hjbdu0c7vjMPn1ysXmzuo7evcWQeacTuOYa8/UwQu6z9u3be+0T/ftLLknF8uXi/8GDL8KoUYEdwn79+qG62oF//lO879+/H8aO7WuqbPpjDwCzZ6dh3z7xfcuW6T6P47XXjkROjkj7BpxlanuxhlH9A/HCC6kebWXMmDG2hVGY5fvvVUf3ggv6YuzYPqZ/27y52h2PGjXKdP1feCEVW7d6fubvHL/yyovdk+/062etjJ7bGO0RuiGemghuuaUXLrvs/KDWCwR3/GMZ7fVlxIgR7hR+APDcc6LfnDixnfu4Rbr++/er7dbK9SEcJNqxtwrrb2/95ZP/YImqSP7lL3+Jm2++2e8yeufXF/n5+fjiC8+78PLycrhcLrdbnJ+f73aVJWU/BMjqXWgtTqfTI0RDkp6eHpFGLLfTqxfwzTdi0oNWrTy3e9llYsrhyZNTkJ7uHWDWsaOaYshKmdPT09FXo+sefBD4zW9SkZ6e6jFwzOFIh7xha9kyzXBwn7wwHD8uyihjmDMygJycdLcr3KaNOoipY0dPgd7YKJqs0+kIed/LGF+Hw3uf6dc9YADcItnpNK6fnrS0NI8JKlJTzf1OXw5ZFm0seGamZ/1XrgRGjgR+9SsgNzdxOlYr55j+oY74bRgK5Qft9tLTrR1v7X26rLOZ+hvlv/b3m5Yt1YYUTJvUbkP7W9k+HQ5g4MDg1+u9jfhvz9qYZO1+c7nUQdSjR4t+VUvkrjGe24wFEuXYBwvrb0/9Q11HVEVy27Zt0TZQOgOTXHrppZgzZw5KSkrQ4Qc1VlRUBKfTiYEDB7qX+c1vfoP6+npk/DBNWlFREQoKCkyL8Wgj4mO9Of984KCfJ/lFRWK64WCmbx40SExhes45noNxtG3P5VIH7vly7/QD97SD9rQCQTt4TxNiDiDy6d8k2sF7sZDdQp/ZYsQIc1PfJjKxMFDMrmmprWDGLR86FFi92vzywSDber9+iT9xjV2sXw9UV4usPn3NPWQKCzE+Zp2QqBE3Y1oPHDiAzZs348CBA2hsbMTmzZuxefNmVP/wzH/06NHo1asXbrvtNmzatAkfffQRZs6cibvvvtsdEjFp0iQ4nU7ccccd2LZtG5YvX465c+fGRWaLUOnVSzihF1wQ3O/PP99bhKSmqkIgVJGsRSuSO3TwVH6xIJKtZLewM0+yVnRFIBQ+7qBI9o22LOESyT17Ah9+CLz9dnjWn4jI/MjDh0c3wwSzWxBiTNwM3Pv973+Pl19+2f1+wA+Z6letWoVhw4YhNTUV77//PqZMmYLLLrsMmZmZmDRpEp566in3b1q2bIni4mLcd999GDRoEFq3bo3p06d7xBsTa6SnC+FaX29NJItBe+K9XiRrHy7EipPcpYsoZ0VFbKSAi8aEA7FOLMy4F8q01MESrRRwRowZE751xzO+nvJEOz+yhCKZEGPiRiQvW7bMZ45kSZcuXfDee+/5XaZv375Ys2aNjSVLbqRIdrngHsjn6yIs43NdLuDUqdCc5B+iZSKGwyHCVVassDbdLp3kyBFrTrLVyUSCxaozzBusyGMkkmtqgHXrxP/RnhEzwR+kEhI0vH8kISHjkquq1AuBr4t2s2YyD7Jwk82I5NxcT/EjZxmPhiCaORMoLrYmMlq0CI9YotDxJhac5FgNt9CSksKbrFhg3TrRn3XqBJx7bnTLQieZEGN4apCQkI6uFLyA/4u2dJPNimSHwzPkIlrhFsHicACtW9u/Xoocb2KhTURDJAcTPhHOkAvijZGTLOORr7wy+k5utLdPSKxCkUxCQjrJ5eXi1en075xq45LNxCQD8S2SAXtDLiR0kr2JhTYRD05ysL8h9iLjkaMdagHQSSbEFzw1SEhIkSwFb6CLrxmRrBeV8S6S7Ry8J6FI9obhFuH5zfjx4nX+fOvbIcZUVABffSX+j/agPYBOMiG+oEgmIUGRHJjzzhOv+fn2rZPhFt7EQpuIl3ALKyL57bdFuMDo0da3Q4xZswZoagJ69BAxydGGea0JMSZusluQ2ETGJMtwCztEsjbcwldMcqSzW4TC4sXAz34mZkW0CzrJ3tBJNk+4hTXxjzYeORa47jrgJz8BLr442iUhJLagSCYhoXeSA118zTrJd93ViIMHDyAzs5PHRB7RzG4RLC1bAldcYe866SR7EwttIl5E8sUXA598IiYZCud2iDGxFI8MiHEkb70V7VIQEnsw3IKEhH7gnh1OMgD8+c9N+MUvtgDwfMwbj+EW4YBOsjex0CbiJdxizhzgxAlg8ODwbod4c/QosG2b+H/YsKgWhRASAIpkEhLhiEnW43CoeUQpkgUUyd7owy2iMRgpXpxkh0M84Qj3dog3q1aJ1wsu8M7kQwiJLSiSSUjo8ySbFclHj5oXyVookgUMt/BG2yZyc6Ozj+Jhxr1gSU3leWcHsRaPTAjxDUUyCYlgneSTJ8XobiBxRfJ114Vv3XSSvdG2iZ49o1MGrXsdKSe5WbPYdq2JJ2vWiNdYiUcmhPiGIpmEhNWY5DZtPIVERoa1rATxIJIVRfyFY6Y9CZ1kb7TtSKbdizTRCLdwODzPu3BmKKBIDp2qKiAtzf7BvIQQ+6FIJiFhVSSnpnrG4bVsaS12NB5TwNmFVgTK6b2JSiw4ydEQyYDneRfOMA+KZHu4+GLmJiYkHqBIJiEhxWptrXg1MwJehlwA1kItgPhMAWcXzzwjXs86C+jXL7pliUWS1UkGPM+7cG6XGS7sgfHIhMQHFMkkJKSTLDHjNAUjkr//3vM1GUVyjx4ijGPfPk4ja4T26UIyO8nhDPOhk2wPjEcmJD6gSCYhESmRrCie75NRJBP/aB9fn3NOdMoQCyL52Wcjsx0SHM2aAZdcEu1SEELMwBn3SEhESiTroUgmenJzgRUrREiAvl1GimiHW2RkAJ06hW87FMmhc/nlsTGFOiEkMHSSSUjoB9CFSyS//DKQl6e+P3PG3O9IcjF6NDBkSPS2H20nOdzbpEgOHcYjExI/UCSTkIiUk3z77cCRI+r7aA3MIsQf0ZhMBFDPu3BvkyI5OLp1U/9nPDIh8QNFMgmJSIZbpKQAhw8Db74JDBtm/neERIpoO8nhFsnMbhEchYXi9YILgIEDo1oUQogFKJKJF0VFIrThv/8NvKxeJIc7BVxBATBhgj3ZHVq2FK9Hj4a+LkKA6Mck00mOTWRmmk2bIvuEgRASGhy4R7wYNQooKTEnRCMVkxwO+vQRrzt2AI2NYjBgXR2wcGH0ykTim2g7yYxJJoQQ+6CTTAwx69RGK7uFHXTrJkaZ19YCe/aoA2pyc6NXJhLfaM+bWAq3sGuwGEUyISSZoEgmIRGMSM7JUVO4RVMkp6YCvXqJ/7dujV45SOIQq+EWv/0t8MEHQE1NaNuhSCaEJBMUySQkggm3cDjEAJaMDODcc8NSLNP07SteKZKJHcRyuMVVVwGZmfZshxBCkgHGJJOQ0DrJTqf5QSnFxUBFhWfoRTSQInnbtuiWgyQG0RbJzG5BCCH2QSeZhIRWJFtxmXJygM6d7S+PVegkEzuJ1XALu6CTTAhJJiiSSUhoRXI8ukwyw8V334kBfISEQrREsoztDzWcIhAUyYSQZILhFiQktDHJ8XgB7dBBZLM4eRL45ptol4bEO9Gace+ii4AHHgAuvzy824nHc5wQQoKFIpmERLDhFrGCwyFCLlav5qQiJHSi5SSnpgILFoR/O/F4jhNCSLAw3IKERLyLZECNSyYkVKKVJzlS5OSo/9sx6yUhhMQydJJJSCSCSJZxyYTYSSKK5ObNgV//Gmhqit/znRBCzEKRTEIi3mOSATrJJDwkokgGgEWLol0CQgiJDAnajZNIQSeZEGMSVSQTQkiywG6chES8p4ADgBYtgLPOinYpSCKgKOr/FMmEEBLfsBsnIZEITjLAkAtiPxTJhBAS37AbJyGRCDHJAEMuiD3QSSaEkMSB3TgJCTrJhBgTyclECCGE2A9FMgkJimRCjKGTTAgh8Q27cRISiSKSzzsPSGNCRGIjFMmEEBLfsBsnIZEoMckZGUIoExIKzZqp/3NGOkIIiW/onZGQSIQUcJIbbwT27AEuuCDaJSHxSvfuwC9+AbRpE+2SEEIICRU6ySQkEiXcAgBmzwYqKiiSSWj8+c/AH/5g/Xe//rV4HTjQ3vIQQggJDopkEhKJJJIBz/ARQiLJ3XcDjY3AV19FuySEEEKAOBHJ+/btw5133omuXbsiMzMT3bp1w2OPPYb6+nqP5Q4cOIDrrrsO2dnZaNu2Le6//36vZbZu3YqhQ4ciMzMTHTt2xOOPPw5Fm9yUWCJRYpIJiQU42I8QQmKHuIhJ/vbbb9HU1ITnnnsO5557LrZt24a7774bp0+fxlNPPQUAaGxsxDXXXIN27dph7dq1OHHiBCZPngxFUbBkyRIAQGVlJUaNGoXhw4dj/fr12LVrF+644w5kZ2djxowZ0axi3JKeLi7sTU1iemdCCCGEkEQgLkTyVVddhauuusr9/pxzzsHOnTvx7LPPukVyUVERduzYgYMHD6KgoAAAsHDhQtxxxx2YM2cOWrRogX/84x84c+YMli1bBqfTiT59+mDXrl1YtGgRpk+fDgeHo1vG4QDmzgXKy4H27aNdGkIIIYQQe4jbh3unTp1Cbm6u+/1nn32GPn36uAUyAIwZMwZ1dXXYsGGDe5mhQ4fC6XR6LHPkyBHs27cvYmVPNB56CPjjH6NdCkIIIYQQ+4gLJ1nP999/jyVLlmDhwoXuz0pLS5GXl+exXOvWrZGRkYHS0lL3MmeffbbHMvI3paWl6Nq1q+H26urqUFdX535fWVkJAHC5XHC5XCHXxxdy3eHcRqySzHUHWH/Wn/XXviYbyVz/ZK47wPrbXf9Q1xNVkTxr1izMnj3b7zLr16/HoEGD3O+PHDmCq666ChMmTMBdd93lsaxRuISiKB6f65eRg/b8hVrMmzfPsJxFRUXIysryW347KC4uDvs2YpVkrjvA+rP+rH8yk8z1T+a6A6y/XfWvqakJ6fdRFcm//OUvcfPNN/tdRuv8HjlyBMOHD8ell16K559/3mO5/Px8fPHFFx6flZeXw+Vyud3i/Px8t6ssKSsrAwAvF1rLI488gunTp7vfV1ZWonPnzhg9ejRahHG0msvlQnFxMUaNGoV0ba61JCCZ6w6w/qw/68/6J2f9k7nuAOtvd/3lk/9giapIbtu2Ldq2bWtq2cOHD2P48OEYOHAgXnrpJaTociVdeumlmDNnDkpKStChQwcAwul1Op0Y+EN2/ksvvRS/+c1vUF9fj4wfcpcVFRWhoKDAKwxDi9Pp9IhjlqSnp0ekEUdqO7FIMtcdYP1Zf9af9U/O+idz3QHW3676h7qOuBi4d+TIEQwbNgydO3fGU089hWPHjqG0tNTDFR49ejR69eqF2267DZs2bcJHH32EmTNn4u6773a7vZMmTYLT6cQdd9yBbdu2Yfny5Zg7dy4zWxBCCCGEEA/iYuBeUVERdu/ejd27d6NTp04e38mY4tTUVLz//vuYMmUKLrvsMmRmZmLSpEnuFHEA0LJlSxQXF+O+++7DoEGD0Lp1a0yfPt0jlIIQQgghhJC4EMl33HEH7rjjjoDLdenSBe+9957fZfr27Ys1a9bYVDJCCCGEEJKIxEW4BSGEEEIIIZGEIpkQQgghhBAdFMmEEEIIIYTooEgmhBBCCCFEB0UyIYQQQgghOiiSCSGEEEII0UGRTAghhBBCiA6KZEIIIYQQQnRQJBNCCCGEEKKDIpkQQgghhBAdFMmEEEIIIYToSIt2AeIRRVEAAJWVlWHdjsvlQk1NDSorK5Genh7WbcUayVx3gPVn/Vl/1j8565/MdQdYf7vrL3Wa1G1WoUgOgqqqKgBA586do1wSQgghhBDij6qqKrRs2dLy7xxKsPI6iWlqasKRI0eQk5MDh8MRtu1UVlaic+fOOHjwIFq0aBG27cQiyVx3gPVn/Vl/1j8565/MdQdYf7vrrygKqqqqUFBQgJQU6xHGdJKDICUlBZ06dYrY9lq0aJGUJwuQ3HUHWH/Wn/Vn/ZOz/slcd4D1t7P+wTjIEg7cI4QQQgghRAdFMiGEEEIIITookmMYp9OJxx57DE6nM9pFiTjJXHeA9Wf9WX/WPznrn8x1B1j/WKs/B+4RQgghhBCig04yIYQQQgghOiiSCSGEEEII0UGRTAghhBBCiA6KZEIIIYQQQnRQJBOS4HBsLklW2PZJMsP2HzoUyQlMU1NTtIsQcTZt2oT/+7//i3YxosqZM2dQXV2NhoYGAIDD4UjKtpCMdU729s+2L0jGOid72wfY/iV21pkiOcHYt28fXnnlFTQ2NiIlJSWpTpAtW7Zg4MCB2L9/f7SLEjW2bduGCRMm4IorrsCECRPw29/+FgCCmrM+HmH7T972z7bPtp+sbR9g+w9X+0+zZS0kJti1axcuueQS5Obmora2FnfddRdSU1PR1NSU8CfK119/jSFDhuCBBx7A/Pnzo12cqLBz504MHToUkydPxsSJE/Htt9/iL3/5C7Zt24aXX34ZLVu2hKIocDgc0S5qWGD7T972z7bPtp+sbR9g+w9n++dkIglCeXk5brnlFmRmZiIlJQVHjhzBbbfdhrvvvjvhO8sDBw7g7LPPxkMPPYR58+bB5XLh6aefxrZt29C8eXMMGjQIP/vZz6JdzLDS2NiIBx98ENXV1XjuuecAALW1tZg0aRL+85//YPjw4fjoo48AICE7S7b/5G3/bPts+8na9gG2/3C3fzrJCUJDQwO6deuGa665Bpdccgnuu+8+/P3vfwcAd2NJxBMEAA4dOoRWrVrh8OHDAICrrroKp0+fRufOnXHo0CF8/PHH2LhxI5YuXRrlkoaP1NRU7N69Gzk5OQBETFZmZiaGDh2KvLw8vP/++/h//+//4aWXXkrINsD2n7ztn22fbT9Z2z7A9h/29q+QuKepqUlRFEU5evSo+/8TJ04okyZNUoYMGaL8+c9/VhobGxVFUZT6+vqolTNcNDQ0KGvWrFHy8/MVh8Oh3Hjjjcrhw4cVRVGU6upqZeHChcp5552nfPrpp1EuaXhoaGhQXC6XMnPmTOW6665TNm7cqCiKouzdu1fJzc1Vnn/+eWXJkiXKBRdcoJSWlka5tPbD9p+87Z9tn20/Wdu+orD9R6L9UyTHMfLgy8bR0NCgKIraGE6ePKn89Kc/VYYMGaI8++yzSk1NjfKrX/1KmTlzZnQKbCP6utfX1yurVq1Sbr75ZmXVqlUe3x08eFBxOp3KSy+9FI2ihg39Pli3bp3Sp08fpX///sqIESOUzMxM5Z577lEURVH27NmjpKenK5999lnUyms3bP/J2/7Z9tn2k7XtKwrbfyTbP2OS45SdO3fir3/9K8rLy9GlSxfcc889yMvLc3/f2NiI1NRUVFRU4L777sOBAwfgcrmwZcsWrF27FhdeeGEUSx8a+rr//Oc/R35+PhoaGnDo0CF06NABTqfTnSPy8OHDuPHGGzF//nwMGzYsuoW3Ce0+6Ny5M37+85+jQ4cO2Lp1K4qLi3HixAn07NkTt912GxRFwVdffYW7774b7777Lrp06RLt4ocM23/ytn+2fbb9ZG37ANt/xNt/iIKeRIHt27crLVu2VG666SZlxIgRysUXX6y0bdtW+eCDD9x3Voqi3m2VlpYqBQUFSuvWrZWvv/46WsW2BaO6t2nTRvnggw98/ua3v/2tcv7557sfw8U7RvsgNzdXef/9933+5oEHHlAGDBigHD9+PIIlDQ9s/8nb/tn22faTte0rCtt/NNo/RXKc0dDQoNx8883KT3/6U0VRxOOG0tJS5Wc/+5mSlZWl/Otf/3J/riiKcubMGeXuu+9WmjdvrmzdujVq5bYDf3XPzMx0113yxRdfKPfdd5/SqlUrZfPmzdEosu2Y3Qeyk9i4caMyefJkpVWrVsqmTZuiVWzbYPtP3vbPts+2n6xtX1HY/qPV/pndIs5wOBw4duwYLr/8cvdneXl5ePHFF9GsWTPccccdOOecczBgwAA0NTXB6XTi8OHDKC4uRp8+faJY8tCxUvfS0lK888472LlzJ1avXo1+/fpFseT2YWUf1NXVIS0tDU6nE2vWrEHfvn2jWHJ7YPtP3vbPts+2n6xtH2D7j1r7D13fk0gzadIkZeDAgV5B642NjcqPf/xj5cILL1RqamqiWcSwYabup0+fVhRFUcrKypQTJ05Erazhwso+UJTEG9XO9p+87Z9tn20/Wdu+orD9R6P9J2aG8QRF+WEwwi233IKmpiY88cQTcLlcSE1NRUNDA1JSUnD33Xfj5MmTOHDgQJRLay/B1L1du3bIzc2NZrFtxco+OHjwoPt36enp0SqyrbD9J2/7Z9tn20/Wtg+w/Uez/VMkxxEyGfaVV16Jyy+/HP/973/xzDPP4MyZM0hLE5EzZ511FgCgrq4uauUMB1bqXl9fH7VyhpNkPv5Actc/2dt/Mh97ILnrn+xtH0ju4w9Et/4UyXFGfX09mjVrhnnz5mHgwIF48803cf/99+PUqVM4cuQIXnvtNWRkZKBDhw7RLqrtJHPdJcm+D5K5/slcd4D1T+b6J3PdJcm+D6JWf1uDN4itaFOaKIoaf7Nv3z7lrbfeUurq6pR58+YpF1xwgZKamqr07dtX6dChg7Jhw4ZoFNdWkrnukmTfB8lc/2Suu6Kw/nqSuf7JXHdJsu2DWDr/KZJjEBl4f+rUKfdnstHs27dP6dixo3vmmIaGBqWqqkpZvny58umnnyoHDhyIfIFtJJnrLkn2fVBdXa00NDQkZf2Tue6KwvpLjHK+Jkv9k7nukmTdB3KgoRx819jY6CGQo1F/iuQYY+vWrcpVV12lDB8+XBkwYIDywgsvKGVlZYqiiBG77dq1U+69916vO61EIJnrLkn2fbB161blyiuvVC666CKld+/eynPPPaeUlpYqiqIox44dS+j6J3PdFYX137lzp/Luu++632vrWVpaquTl5SVs/ZO57pJk3wfffPONcueddyojR45UJkyYoHzxxRfu70pKSqJWf4rkGGLnzp1K27ZtlRkzZih/+9vflNmzZysOh0O5/fbblQ0bNigVFRXKokWL3HeWiUQy112S7Pvg+++/V1q3bq3cf//9ypIlS5RHH31UcTqdyuTJk5WvvvpKOXXqlLJo0SK3s5BIJHPdFYX137Vrl9KiRQvF4XAor7zyivtzKQiOHj2qPPnkkwkpkJK57pJk3wdbt25VcnNzlXvvvVe57777lPHjxys9e/ZU9uzZoyhKdOtPkRxD/OpXv1ImTZrk8dktt9yiZGRkKJMmTUqIxym+SOa6S5J9HyxcuFC57LLLPD5bsWKF0qNHD2XixInuDjMRSea6K0py1//EiRPK+PHjlXHjxilTp05VcnJylJdeesn9faLlutWSzHWXJPs+KCkpUS666CLlgQcecH+2YcMGpW/fvsp7770XxZIJmN0iRlAUBbt370abNm0AADU1NQCA8847D1dffTXef/99vPDCC+5lE4lkrruE+wA4ffo06uvr0dTUhMbGRjQ2NmL06NFYunQp1q5di6VLlwJIzPonc92B5K7/qVOn0KpVK9x777146KGHMGXKFNx///1YtmwZAJHrNhHrDSR33SXJvg++/fZbNG/eHJMmTXLX88ILL0TLli2xefNmAFE+76OhzIkxDz30kNK1a1flyJEjiqIoyqFDh5QWLVooa9asUV588UUlOztb2b9/f5RLGR6Sue6SZN8Hb731lpKamqqsX79eURRFcblc7sdrb775ppKSkqJ89tln0Sxi2EjmuisK6691yg8cOKA8+OCDXo6iy+VSamtro1C68JLMdZck8z7Ys2eP8uabb7rfu1wuRVEUZfTo0cpjjz3mtXykww0pkqOMNsZm/fr1ypgxY5TmzZsr48aNU7KyspR77rlHURRF2b17d0KleFGU5K67hPtAxeVyKRMnTlR69OihfPPNN4qiKEpdXZ2iKOKRY69evZSlS5dGs4hhI5nrriisv/7Cf+jQIS+hNHXqVOWZZ55JuDEJyVx3SbLvA3kd1NZt4sSJym9+8xv3+1mzZimff/55xMuWFj0PO7k5evQo8vLy4HA40NTUhJSUFAwaNAj/93//h//85z+oqqrChAkTcOuttwIAKisr0apVK2RlZUW55KGTzHWXJPs+2LdvH/7zn/+gvLwc5557Lm699VakpaVhypQpmDt3Lm699Va8+uqr6NmzJwAx41JmZiYyMzOjXPLQSea6A6y/Uf1TUlKgKIp7ZrGOHTvi/vvvBwBMnz4dL730Ej799FNs2LABKSnxGyWZzHWXJPs+0Na/W7duuO222zyug1oaGxsBAL/73e8wZ84cXHfddZEvcMRlOVF27NihOBwO5brrrnN/Jh8x+GLGjBnKhRdeqJw4cSLcxQsryVx3SbLvgy1btigdO3ZURo4cqVx00UWK0+lUZs+e7f5+xYoVytVXX620bt1aefHFF5W33npLefjhh5Xc3Fzl+++/j2LJQyeZ664orL9R/R9//HGfy+/Zs0fp2bOnkpubq3z99dcRLKn9JHPdJcm+D4zq/4c//MFjGekmjx07VpkzZ47yzDPPKE6nM2pPUSmSI0xJSYly2WWXKUOHDlXy8/OVH//4x+7vjB6jrFy5UvnFL36htGjRQtm0aVMES2o/yVx3SbLvg3379indunVTHnzwQaWpqUmprKxUnnvuOaVXr17Kd999515u9+7dyoMPPqgUFBQovXr1Ui666CJl48aNUSx56CRz3RWF9fdXf6PsHY2NjcrMmTOVtLQ0ZcuWLVEosX0kc90lyb4PrNZ/0qRJSmpqqpKTk6N8+eWXUSixgOEWEeaLL75A586dMWXKFDQ0NODmm2/GDTfcgOXLlyMlJcXrkUNOTg6qq6uxbt069O7dO4olD51krrskmfdBU1MT3njjDXTv3h2PPvooHA4HcnJyMHDgQBw7dgwul8u9bLdu3TB//nxMnToVzZs3BwC0atUqSiUPnWSuO8D6B6r/mTNnvH5z5MgRHD58GOvXr0ffvn2jUGp7SOa6S5J9HwRT/3bt2iErKwvr1q1Dnz59olBqAUVyhBk6dCicTieuuOIKAMDrr7+Om2++Gf+/vTuLjenv4zj+mU77R6arSisYVftEW1HDDUoQFVH7khIitpCKteJGiBASS6IRghEioUEQN0RbaV1YUktbqpZY2tpqa2vJREyn8/tf9Dnn0fE8UdrOdM7380p60THG7/1NIz/jN+dMmTIFFy5c0M8mAQ1n8YYOHQqHw4F27dr5c9ktQnK7RvIMtHPXHo8H4eHhABou7ZOUlISwsDDU1tb+8nu6dOkS8GfwANntAPv/pr9bt244evQo2rdv7+vltijJ7RrpM/ib/gULFiAzMxPdunXz9XIb89db2NTA4/GogoICFRMToyZPnqw/fvDgQXX9+nX9OUYkuV0jbQY/Xxj/565evXqpK1eu6N/n5eUZ7lPcktuVYv+f9P/uMwqBRnK7RvoMmtqfm5vr03X9jjH+md6GvXz5EhcvXsSRI0dQVVWl3yTC4/EAaHi3MCUlBadPn8bNmzcxbdo0rFixAsuXL0fnzp315wQiye0a6TPQ+h0OB6qqquByuQA0fGrZZDLB7XbD6XTC7XbrVy/YuHEjxo0bh3fv3vlz6c0muR1gf3P6P3z44M+lN5vkdo30Gfxtf2pqKt68eePPpTfm7126kd27d0/FxsaqQYMGqcjISGW1WlVmZqZ+SN373ZK8vDxlMplUx44d1Z07d/yx5BYjuV0jfQa/6/d4PKqurk45nU4VFxeniouL1fbt21VoaKh+U4lAJbldKfZL7pfcrpE+AyP1c5PcSmpra9XgwYPV+vXrVU1NjVJKqS1btqgRI0aoSZMm6Z/m/vki2kuWLFEWi0WVlZX5bd0tQXK7RvoMmtqvSU5OVkOGDFH//PNPm/tL8k9JbleK/ZL7JbdrpM/AaP3cJLeSyspKFRcXp3Jycho9fvz4cZWSkqLmzJmj335YKaWuXr2qkpKS2uQPyZ+S3K6RPoOm9FdVVSmllKqpqVERERGGudSR5Hal2C+5X3K7RvoMjNbPM8mtxGw2o0OHDnj79i0AwO12AwDmz5+PuXPn4sGDB8jLy9OfP3jwYFy5cgV2u90v621Jkts10mfQlP7c3FwAQFRUFPbv34/S0tKAv9QRILsdYL/kfsntGukzMFq/San/XG+KWtykSZPw6tUrFBQUIDIyEm63G8HBDVfdmzlzJt68eYMbN240uh2lUUhu10ifQVP7AfzPW5IGMsntAPsl90tu10ifgZH62+7KAozT6cS3b9/w9etX/bGjR4/iy5cvmDVrFlwul/5DAgCpqalQSsHlcgX8Bklyu0b6DP62/8ePHwDQpv+S/B3J7QD7JfdLbtdIn4HR+9v26gLEw4cPMW3aNIwcORI2mw0nT56Ex+NBp06dkJ2djcePH2PcuHF48uSJfmeZW7duISwsDIH+Rr7kdo30GTSnP9BJbgfYL7lfcrtG+gxE9Pv6ELTRlJWVqejoaLVmzRqVnZ2t1q5dq0JCQlRRUZH+nNLSUpWYmKh69eql7Ha7SktLU2FhYaqkpMSPK28+ye0a6TOQ3C+5XSn2S+6X3K6RPgMp/TyT3Aw1NTVIT09H//79kZWVpT8+evRoJCYmIisrq9F50/379+P169fo0KEDZs+ejX79+vlr6c0muV0jfQaS+yW3A+yX3C+5XSN9BpL6g3//FPp/6urq8PnzZ8yYMQPAfw+g9+zZE9XV1QAa7pZWX18Ps9mMjIwMfy63RUlu10ifgeR+ye0A+yX3S27XSJ+BpH6eSW6G2NhYnDhxAiNGjADQcLtFAOjatWujw+hmsxnfvn3TvzfCm/eS2zXSZyC5X3I7wH7J/ZLbNdJnIKmfm+Rm6tOnD4CGf0mFhIQAaPiBef/+vf6cHTt2wOFw6NcLNMLVDADZ7RrpM5DcL7kdYL/kfsntGukzkNLP4xYtJCgoSD+DYzKZYDabAQCbNm3Ctm3bUFxc3OgyKEYiuV0jfQaS+yW3A+yX3C+5XSN9Bkbv5zvJLUj7rwSz2Qyr1Yrdu3dj586duHPnDgYOHOjn1bUuye0a6TOQ3C+5HWC/5H7J7RrpMzByf+Bu79sg7SxOSEgIHA4HwsPDce3aNSQnJ/t5Za1PcrtG+gwk90tuB9gvuV9yu0b6DIzcz3eSW0FqaioA4MaNG7Db7X5ejW9JbtdIn4HkfsntAPsl90tu10ifgRH7eZ3kVuJ0OmGxWPy9DL+Q3K6RPgPJ/ZLbAfZL7pfcrpE+A6P1c5NMREREROSFxy2IiIiIiLxwk0xERERE5IWbZCIiIiIiL9wkExERERF54SaZiIiIiMgLN8lERERERF64SSYiIiIi8sJNMhFRAFNKYezYsfrdrn524MABRERE4OXLl35YGRFRYOMmmYgogJlMJhw7dgyFhYU4dOiQ/nh5eTk2bNiArKwsdO/evUX/zLq6uhZ9PSKitoibZCKiAGe1WpGVlYXMzEyUl5dDKYVFixZhzJgxGDp0KCZMmIDQ0FDExsZi3rx5+PTpk/57L1++jOHDhyMyMhLR0dGYOHEinj9/rv96RUUFTCYTzpw5g1GjRqF9+/Y4ceIEKisrkZaWhqioKFgsFgwYMACXLl3yRz4RUavgbamJiAxiypQp+Pz5M6ZPn46tW7fi9u3bsNvtWLJkCebPn4/v379jw4YNcLvdyM/PBwCcO3cOJpMJiYmJcDqd2LRpEyoqKlBSUoKgoCBUVFQgPj4ePXr0wJ49ezBo0CC0a9cOS5cuhcvlwp49e2CxWPDw4UOEh4cjJSXFz1MgImoZ3CQTERnEhw8fkJCQgOrqapw9exbFxcUoLCxETk6O/pzXr1/DarXiyZMn6Nu37y+v8fHjR8TExKC0tBQJCQn6Jnnv3r1YtWqV/rykpCRMnz4dmzdv9kkbEZGv8bgFEZFBxMTEYOnSpbDZbJg6dSru3r2LgoIChIaG6l/9+/cHAP1IxfPnzzFnzhz07NkT4eHhiI+PB4BfPuxnt9sbfb9y5Ups27YNw4YNw+bNm3H//n0fFBIR+Q43yUREBhIcHIzg4GAAgMfjQVpaGkpKShp9PX36VD8WkZaWhurqajgcDhQWFqKwsBAA4HK5Gr2uxWJp9P3ixYvx4sULzJs3D6WlpbDb7di3b58PComIfIObZCIig0pOTkZZWRl69OiB3r17N/qyWCyorq7Go0ePsHHjRowZMwY2mw21tbVNfn2r1Yply5bh/PnzWLduHRwORyvWEBH5FjfJREQGlZGRgZqaGqSnp+PWrVt48eIFcnNzsXDhQtTX1yMqKgrR0dE4fPgwnj17hvz8fKxdu7ZJr7169Wrk5OSgvLwcRUVFyM/Ph81ma+UiIiLf4SaZiMigunTpguvXr6O+vh6pqalISEjAqlWrEBERgaCgIAQFBeHUqVO4e/cuEhISsGbNGuzatatJr11fX4+MjAzYbDaMHz8e/fr1w4EDB1q5iIjId3h1CyIiIiIiL3wnmYiIiIjICzfJREREREReuEkmIiIiIvLCTTIRERERkRdukomIiIiIvHCTTERERETkhZtkIiIiIiIv3CQTEREREXnhJpmIiIiIyAs3yUREREREXrhJJiIiIiLywk0yEREREZGXfwHmfN6p5ojR+wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.plot(lois_fil['Traded'], lois_fil['excess_return'], color= 'b')\n",
    "plt.grid(True)\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('Excess Returns')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.plot(susie_fil['Traded'], susie_fil['excess_return'], color= 'b')\n",
    "plt.grid(True)\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('Excess Returns')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.plot(thomas_fil['Traded'], thomas_fil['excess_return'], color= 'r')\n",
    "plt.grid(True)\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('Excess Returns')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display the dataframe for each top republican traders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Party</th>\n",
       "      <th>Company</th>\n",
       "      <th>Traded</th>\n",
       "      <th>Transaction</th>\n",
       "      <th>Trade_Size_USD</th>\n",
       "      <th>excess_return</th>\n",
       "      <th>Name_Party</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5544</th>\n",
       "      <td>Perdue, David</td>\n",
       "      <td>R</td>\n",
       "      <td>The Southern Company JR SUB NT 2015A</td>\n",
       "      <td>2020-04-16</td>\n",
       "      <td>Sale (Full)</td>\n",
       "      <td>$15,001 - $50,000</td>\n",
       "      <td>-25.49</td>\n",
       "      <td>Perdue, David (R)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5545</th>\n",
       "      <td>Perdue, David</td>\n",
       "      <td>R</td>\n",
       "      <td>Philip Morris International Inc.</td>\n",
       "      <td>2020-04-16</td>\n",
       "      <td>Sale (Full)</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-44.67</td>\n",
       "      <td>Perdue, David (R)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5546</th>\n",
       "      <td>Perdue, David</td>\n",
       "      <td>R</td>\n",
       "      <td>The Walt Disney Company</td>\n",
       "      <td>2020-04-16</td>\n",
       "      <td>Sale (Full)</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-106.89</td>\n",
       "      <td>Perdue, David (R)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5547</th>\n",
       "      <td>Perdue, David</td>\n",
       "      <td>R</td>\n",
       "      <td>General Mills, Inc.</td>\n",
       "      <td>2020-04-16</td>\n",
       "      <td>Sale (Full)</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-85.76</td>\n",
       "      <td>Perdue, David (R)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5548</th>\n",
       "      <td>Perdue, David</td>\n",
       "      <td>R</td>\n",
       "      <td>ConocoPhillips</td>\n",
       "      <td>2020-04-16</td>\n",
       "      <td>Sale (Full)</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>161.77</td>\n",
       "      <td>Perdue, David (R)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6367</th>\n",
       "      <td>Perdue, David</td>\n",
       "      <td>R</td>\n",
       "      <td>Devon Energy Corporation</td>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$15,001 - $50,000</td>\n",
       "      <td>-27.14</td>\n",
       "      <td>Perdue, David (R)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6368</th>\n",
       "      <td>Perdue, David</td>\n",
       "      <td>R</td>\n",
       "      <td>Knowles Corporation</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>Sale (Partial)</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-72.94</td>\n",
       "      <td>Perdue, David (R)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6369</th>\n",
       "      <td>Perdue, David</td>\n",
       "      <td>R</td>\n",
       "      <td>BWX Technologies, Inc.</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>37.35</td>\n",
       "      <td>Perdue, David (R)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6370</th>\n",
       "      <td>Perdue, David</td>\n",
       "      <td>R</td>\n",
       "      <td>WPX Energy, Inc.</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-71.22</td>\n",
       "      <td>Perdue, David (R)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6371</th>\n",
       "      <td>Perdue, David</td>\n",
       "      <td>R</td>\n",
       "      <td>Apple Inc.</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>390.29</td>\n",
       "      <td>Perdue, David (R)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>828 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name Party                               Company     Traded  \\\n",
       "5544  Perdue, David     R  The Southern Company JR SUB NT 2015A 2020-04-16   \n",
       "5545  Perdue, David     R      Philip Morris International Inc. 2020-04-16   \n",
       "5546  Perdue, David     R               The Walt Disney Company 2020-04-16   \n",
       "5547  Perdue, David     R                   General Mills, Inc. 2020-04-16   \n",
       "5548  Perdue, David     R                        ConocoPhillips 2020-04-16   \n",
       "...             ...   ...                                   ...        ...   \n",
       "6367  Perdue, David     R              Devon Energy Corporation 2019-01-04   \n",
       "6368  Perdue, David     R                   Knowles Corporation 2019-01-03   \n",
       "6369  Perdue, David     R                BWX Technologies, Inc. 2019-01-03   \n",
       "6370  Perdue, David     R                      WPX Energy, Inc. 2019-01-03   \n",
       "6371  Perdue, David     R                            Apple Inc. 2019-01-03   \n",
       "\n",
       "         Transaction     Trade_Size_USD  excess_return         Name_Party  \n",
       "5544     Sale (Full)  $15,001 - $50,000         -25.49  Perdue, David (R)  \n",
       "5545     Sale (Full)   $1,001 - $15,000         -44.67  Perdue, David (R)  \n",
       "5546     Sale (Full)   $1,001 - $15,000        -106.89  Perdue, David (R)  \n",
       "5547     Sale (Full)   $1,001 - $15,000         -85.76  Perdue, David (R)  \n",
       "5548     Sale (Full)   $1,001 - $15,000         161.77  Perdue, David (R)  \n",
       "...              ...                ...            ...                ...  \n",
       "6367        Purchase  $15,001 - $50,000         -27.14  Perdue, David (R)  \n",
       "6368  Sale (Partial)   $1,001 - $15,000         -72.94  Perdue, David (R)  \n",
       "6369        Purchase   $1,001 - $15,000          37.35  Perdue, David (R)  \n",
       "6370        Purchase   $1,001 - $15,000         -71.22  Perdue, David (R)  \n",
       "6371        Purchase   $1,001 - $15,000         390.29  Perdue, David (R)  \n",
       "\n",
       "[828 rows x 8 columns]"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
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       "      <th>Name</th>\n",
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       "      <th>9144</th>\n",
       "      <td>Greg Gianforte</td>\n",
       "      <td>R</td>\n",
       "      <td>CHINA MOBILE LIMITED</td>\n",
       "      <td>2020-12-21</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$100,001 - $250,000</td>\n",
       "      <td>-5.53</td>\n",
       "      <td>Greg Gianforte (R)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9145</th>\n",
       "      <td>Greg Gianforte</td>\n",
       "      <td>R</td>\n",
       "      <td>VIASAT, INC.</td>\n",
       "      <td>2020-12-18</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$100,001 - $250,000</td>\n",
       "      <td>-82.09</td>\n",
       "      <td>Greg Gianforte (R)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9146</th>\n",
       "      <td>Greg Gianforte</td>\n",
       "      <td>R</td>\n",
       "      <td>ISHARES RUSSELL 2000 VALUE ETF</td>\n",
       "      <td>2020-12-18</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$250,001 - $500,000</td>\n",
       "      <td>-15.49</td>\n",
       "      <td>Greg Gianforte (R)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9147</th>\n",
       "      <td>Greg Gianforte</td>\n",
       "      <td>R</td>\n",
       "      <td>CARDTRONICS PLC</td>\n",
       "      <td>2020-12-17</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-3.60</td>\n",
       "      <td>Greg Gianforte (R)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9148</th>\n",
       "      <td>Greg Gianforte</td>\n",
       "      <td>R</td>\n",
       "      <td>CARDTRONICS PLC</td>\n",
       "      <td>2020-12-17</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-3.60</td>\n",
       "      <td>Greg Gianforte (R)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>9910</th>\n",
       "      <td>Greg Gianforte</td>\n",
       "      <td>R</td>\n",
       "      <td>ATLAS COPCO AB SPONSORED ADR REPSTG CLASS A</td>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$100,001 - $250,000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Greg Gianforte (R)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9911</th>\n",
       "      <td>Greg Gianforte</td>\n",
       "      <td>R</td>\n",
       "      <td>INFINEON TECHNOLOGIES AG</td>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$250,001 - $500,000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Greg Gianforte (R)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9912</th>\n",
       "      <td>Greg Gianforte</td>\n",
       "      <td>R</td>\n",
       "      <td>MULTI-COLOR CORPORATION</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$100,001 - $250,000</td>\n",
       "      <td>27.97</td>\n",
       "      <td>Greg Gianforte (R)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9913</th>\n",
       "      <td>Greg Gianforte</td>\n",
       "      <td>R</td>\n",
       "      <td>THERMON GROUP HOLDINGS, INC.</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$50,001 - $100,000</td>\n",
       "      <td>-60.96</td>\n",
       "      <td>Greg Gianforte (R)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9914</th>\n",
       "      <td>Greg Gianforte</td>\n",
       "      <td>R</td>\n",
       "      <td>FRESENIUS MEDICAL CARE AG</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$100,001 - $250,000</td>\n",
       "      <td>-155.27</td>\n",
       "      <td>Greg Gianforte (R)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>771 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name Party                                      Company  \\\n",
       "9144  Greg Gianforte     R                         CHINA MOBILE LIMITED   \n",
       "9145  Greg Gianforte     R                                 VIASAT, INC.   \n",
       "9146  Greg Gianforte     R               ISHARES RUSSELL 2000 VALUE ETF   \n",
       "9147  Greg Gianforte     R                              CARDTRONICS PLC   \n",
       "9148  Greg Gianforte     R                              CARDTRONICS PLC   \n",
       "...              ...   ...                                          ...   \n",
       "9910  Greg Gianforte     R  ATLAS COPCO AB SPONSORED ADR REPSTG CLASS A   \n",
       "9911  Greg Gianforte     R                     INFINEON TECHNOLOGIES AG   \n",
       "9912  Greg Gianforte     R                      MULTI-COLOR CORPORATION   \n",
       "9913  Greg Gianforte     R                 THERMON GROUP HOLDINGS, INC.   \n",
       "9914  Greg Gianforte     R                    FRESENIUS MEDICAL CARE AG   \n",
       "\n",
       "         Traded Transaction       Trade_Size_USD  excess_return  \\\n",
       "9144 2020-12-21        Sale  $100,001 - $250,000          -5.53   \n",
       "9145 2020-12-18    Purchase  $100,001 - $250,000         -82.09   \n",
       "9146 2020-12-18        Sale  $250,001 - $500,000         -15.49   \n",
       "9147 2020-12-17        Sale     $1,001 - $15,000          -3.60   \n",
       "9148 2020-12-17        Sale     $1,001 - $15,000          -3.60   \n",
       "...         ...         ...                  ...            ...   \n",
       "9910 2019-01-04    Purchase  $100,001 - $250,000           0.00   \n",
       "9911 2019-01-04    Purchase  $250,001 - $500,000           0.00   \n",
       "9912 2019-01-03    Purchase  $100,001 - $250,000          27.97   \n",
       "9913 2019-01-03    Purchase   $50,001 - $100,000         -60.96   \n",
       "9914 2019-01-02        Sale  $100,001 - $250,000        -155.27   \n",
       "\n",
       "              Name_Party  \n",
       "9144  Greg Gianforte (R)  \n",
       "9145  Greg Gianforte (R)  \n",
       "9146  Greg Gianforte (R)  \n",
       "9147  Greg Gianforte (R)  \n",
       "9148  Greg Gianforte (R)  \n",
       "...                  ...  \n",
       "9910  Greg Gianforte (R)  \n",
       "9911  Greg Gianforte (R)  \n",
       "9912  Greg Gianforte (R)  \n",
       "9913  Greg Gianforte (R)  \n",
       "9914  Greg Gianforte (R)  \n",
       "\n",
       "[771 rows x 8 columns]"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Party</th>\n",
       "      <th>Company</th>\n",
       "      <th>Traded</th>\n",
       "      <th>Transaction</th>\n",
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       "      <th>Name_Party</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2786</th>\n",
       "      <td>Thomas Macarthur</td>\n",
       "      <td>R</td>\n",
       "      <td>INTERACTIVE BROKERS GROUP, INC.</td>\n",
       "      <td>2019-12-21</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>83.47</td>\n",
       "      <td>Thomas Macarthur (R)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name Party                          Company     Traded  \\\n",
       "2786  Thomas Macarthur     R  INTERACTIVE BROKERS GROUP, INC. 2019-12-21   \n",
       "\n",
       "     Transaction    Trade_Size_USD  excess_return            Name_Party  \n",
       "2786    Purchase  $1,001 - $15,000          83.47  Thomas Macarthur (R)  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(perdue_fil)\n",
    "display(greg_fil)\n",
    "display(thomas_fil)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display the dataframe for each top democrat traders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Party</th>\n",
       "      <th>Company</th>\n",
       "      <th>Traded</th>\n",
       "      <th>Transaction</th>\n",
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       "      <th>excess_return</th>\n",
       "      <th>Name_Party</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1265</th>\n",
       "      <td>Josh Gottheimer</td>\n",
       "      <td>D</td>\n",
       "      <td>META PLATFORMS, INC. - CLASS A</td>\n",
       "      <td>2021-12-29</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-28.76</td>\n",
       "      <td>Josh Gottheimer (D)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1266</th>\n",
       "      <td>Josh Gottheimer</td>\n",
       "      <td>D</td>\n",
       "      <td>PING AN INSURANCE</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-53.92</td>\n",
       "      <td>Josh Gottheimer (D)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1267</th>\n",
       "      <td>Josh Gottheimer</td>\n",
       "      <td>D</td>\n",
       "      <td>AFFIRM HOLDINGS, INC. - CLASS A</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-87.48</td>\n",
       "      <td>Josh Gottheimer (D)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>Josh Gottheimer</td>\n",
       "      <td>D</td>\n",
       "      <td>TENCENT HOLDINGS LIMITED UNSPONSORED ADR REPRE...</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-35.96</td>\n",
       "      <td>Josh Gottheimer (D)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1269</th>\n",
       "      <td>Josh Gottheimer</td>\n",
       "      <td>D</td>\n",
       "      <td>XP INC. - CLASS A</td>\n",
       "      <td>2021-12-22</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-56.30</td>\n",
       "      <td>Josh Gottheimer (D)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2439</th>\n",
       "      <td>Josh Gottheimer</td>\n",
       "      <td>D</td>\n",
       "      <td>ALTRIA GROUP, INC.</td>\n",
       "      <td>2019-01-11</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-107.19</td>\n",
       "      <td>Josh Gottheimer (D)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2440</th>\n",
       "      <td>Josh Gottheimer</td>\n",
       "      <td>D</td>\n",
       "      <td>UNITED PARCEL SERVICE, INC.</td>\n",
       "      <td>2019-01-11</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-78.74</td>\n",
       "      <td>Josh Gottheimer (D)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2441</th>\n",
       "      <td>Josh Gottheimer</td>\n",
       "      <td>D</td>\n",
       "      <td>MICROSOFT CORPORATION</td>\n",
       "      <td>2019-01-10</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>200.27</td>\n",
       "      <td>Josh Gottheimer (D)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2442</th>\n",
       "      <td>Josh Gottheimer</td>\n",
       "      <td>D</td>\n",
       "      <td>NEWMARKET CORP</td>\n",
       "      <td>2019-01-10</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-71.84</td>\n",
       "      <td>Josh Gottheimer (D)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2443</th>\n",
       "      <td>Josh Gottheimer</td>\n",
       "      <td>D</td>\n",
       "      <td>APPLE INC.</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>390.29</td>\n",
       "      <td>Josh Gottheimer (D)</td>\n",
       "    </tr>\n",
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       "<p>1179 rows × 8 columns</p>\n",
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       "                 Name Party  \\\n",
       "1265  Josh Gottheimer     D   \n",
       "1266  Josh Gottheimer     D   \n",
       "1267  Josh Gottheimer     D   \n",
       "1268  Josh Gottheimer     D   \n",
       "1269  Josh Gottheimer     D   \n",
       "...               ...   ...   \n",
       "2439  Josh Gottheimer     D   \n",
       "2440  Josh Gottheimer     D   \n",
       "2441  Josh Gottheimer     D   \n",
       "2442  Josh Gottheimer     D   \n",
       "2443  Josh Gottheimer     D   \n",
       "\n",
       "                                                Company     Traded  \\\n",
       "1265                     META PLATFORMS, INC. - CLASS A 2021-12-29   \n",
       "1266                                  PING AN INSURANCE 2021-12-28   \n",
       "1267                    AFFIRM HOLDINGS, INC. - CLASS A 2021-12-28   \n",
       "1268  TENCENT HOLDINGS LIMITED UNSPONSORED ADR REPRE... 2021-12-28   \n",
       "1269                                  XP INC. - CLASS A 2021-12-22   \n",
       "...                                                 ...        ...   \n",
       "2439                                 ALTRIA GROUP, INC. 2019-01-11   \n",
       "2440                        UNITED PARCEL SERVICE, INC. 2019-01-11   \n",
       "2441                              MICROSOFT CORPORATION 2019-01-10   \n",
       "2442                                     NEWMARKET CORP 2019-01-10   \n",
       "2443                                         APPLE INC. 2019-01-03   \n",
       "\n",
       "     Transaction    Trade_Size_USD  excess_return           Name_Party  \n",
       "1265        Sale  $1,001 - $15,000         -28.76  Josh Gottheimer (D)  \n",
       "1266        Sale  $1,001 - $15,000         -53.92  Josh Gottheimer (D)  \n",
       "1267        Sale  $1,001 - $15,000         -87.48  Josh Gottheimer (D)  \n",
       "1268        Sale  $1,001 - $15,000         -35.96  Josh Gottheimer (D)  \n",
       "1269    Purchase  $1,001 - $15,000         -56.30  Josh Gottheimer (D)  \n",
       "...          ...               ...            ...                  ...  \n",
       "2439    Purchase  $1,001 - $15,000        -107.19  Josh Gottheimer (D)  \n",
       "2440    Purchase  $1,001 - $15,000         -78.74  Josh Gottheimer (D)  \n",
       "2441        Sale  $1,001 - $15,000         200.27  Josh Gottheimer (D)  \n",
       "2442        Sale  $1,001 - $15,000         -71.84  Josh Gottheimer (D)  \n",
       "2443        Sale  $1,001 - $15,000         390.29  Josh Gottheimer (D)  \n",
       "\n",
       "[1179 rows x 8 columns]"
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       "      <td>Lois Frankel</td>\n",
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       "      <th>8155</th>\n",
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       "      <th>8156</th>\n",
       "      <td>Lois Frankel</td>\n",
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       "      <td>Lois Frankel (D)</td>\n",
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       "      <th>8157</th>\n",
       "      <td>Lois Frankel</td>\n",
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       "      <td>$1,001 - $15,000</td>\n",
       "      <td>91.69</td>\n",
       "      <td>Lois Frankel (D)</td>\n",
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       "      <th>8158</th>\n",
       "      <td>Lois Frankel</td>\n",
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       "      <td>-86.54</td>\n",
       "      <td>Lois Frankel (D)</td>\n",
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       "      <th>8585</th>\n",
       "      <td>Lois Frankel</td>\n",
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       "      <td>HUNTSMAN CORPORATION</td>\n",
       "      <td>2019-01-10</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-101.49</td>\n",
       "      <td>Lois Frankel (D)</td>\n",
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       "      <th>8586</th>\n",
       "      <td>Lois Frankel</td>\n",
       "      <td>D</td>\n",
       "      <td>GOLDMAN SACHS GROUP, INC.</td>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>71.68</td>\n",
       "      <td>Lois Frankel (D)</td>\n",
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       "    <tr>\n",
       "      <th>8587</th>\n",
       "      <td>Lois Frankel</td>\n",
       "      <td>D</td>\n",
       "      <td>EXPEDITORS INTERNATIONAL OF WASHINGTON, INC.</td>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-30.26</td>\n",
       "      <td>Lois Frankel (D)</td>\n",
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       "      <th>8588</th>\n",
       "      <td>Lois Frankel</td>\n",
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       "      <td>ROCHE HOLDINGS AG BASEL AMERICAN DEPOSITARY SH...</td>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Lois Frankel (D)</td>\n",
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       "      <th>8589</th>\n",
       "      <td>Lois Frankel</td>\n",
       "      <td>D</td>\n",
       "      <td>ESTERLINE TECHNOLOGIES CORPORATION</td>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-9.75</td>\n",
       "      <td>Lois Frankel (D)</td>\n",
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       "              Name Party                                            Company  \\\n",
       "8154  Lois Frankel     D                         KIMBERLY-CLARK CORPORATION   \n",
       "8155  Lois Frankel     D                       ZIMMER BIOMET HOLDINGS, INC.   \n",
       "8156  Lois Frankel     D                                    INSPERITY, INC.   \n",
       "8157  Lois Frankel     D                             BWX TECHNOLOGIES, INC.   \n",
       "8158  Lois Frankel     D                                 EPAM SYSTEMS, INC.   \n",
       "...            ...   ...                                                ...   \n",
       "8585  Lois Frankel     D                               HUNTSMAN CORPORATION   \n",
       "8586  Lois Frankel     D                          GOLDMAN SACHS GROUP, INC.   \n",
       "8587  Lois Frankel     D       EXPEDITORS INTERNATIONAL OF WASHINGTON, INC.   \n",
       "8588  Lois Frankel     D  ROCHE HOLDINGS AG BASEL AMERICAN DEPOSITARY SH...   \n",
       "8589  Lois Frankel     D                 ESTERLINE TECHNOLOGIES CORPORATION   \n",
       "\n",
       "         Traded Transaction    Trade_Size_USD  excess_return        Name_Party  \n",
       "8154 2021-12-15        Sale  $1,001 - $15,000         -13.08  Lois Frankel (D)  \n",
       "8155 2021-12-10    Purchase  $1,001 - $15,000         -26.19  Lois Frankel (D)  \n",
       "8156 2021-12-10    Purchase  $1,001 - $15,000         -24.84  Lois Frankel (D)  \n",
       "8157 2021-12-10    Purchase  $1,001 - $15,000          91.69  Lois Frankel (D)  \n",
       "8158 2021-12-09        Sale  $1,001 - $15,000         -86.54  Lois Frankel (D)  \n",
       "...         ...         ...               ...            ...               ...  \n",
       "8585 2019-01-10    Purchase  $1,001 - $15,000        -101.49  Lois Frankel (D)  \n",
       "8586 2019-01-09        Sale  $1,001 - $15,000          71.68  Lois Frankel (D)  \n",
       "8587 2019-01-09    Purchase  $1,001 - $15,000         -30.26  Lois Frankel (D)  \n",
       "8588 2019-01-09    Purchase  $1,001 - $15,000           0.00  Lois Frankel (D)  \n",
       "8589 2019-01-04        Sale  $1,001 - $15,000          -9.75  Lois Frankel (D)  \n",
       "\n",
       "[436 rows x 8 columns]"
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       "      <th>10557</th>\n",
       "      <td>Susie Lee</td>\n",
       "      <td>D</td>\n",
       "      <td>RACKSPACE TECHNOLOGY, INC.</td>\n",
       "      <td>2021-10-14</td>\n",
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       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-107.67</td>\n",
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       "      <th>10558</th>\n",
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       "      <td>-13.26</td>\n",
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       "      <th>10559</th>\n",
       "      <td>Susie Lee</td>\n",
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       "      <td>-108.93</td>\n",
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       "      <th>10560</th>\n",
       "      <td>Susie Lee</td>\n",
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       "      <td>2021-09-30</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$15,001 - $50,000</td>\n",
       "      <td>-50.82</td>\n",
       "      <td>Susie Lee (D)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10561</th>\n",
       "      <td>Susie Lee</td>\n",
       "      <td>D</td>\n",
       "      <td>SYNCHRONY FINANCIAL</td>\n",
       "      <td>2021-09-30</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-22.97</td>\n",
       "      <td>Susie Lee (D)</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>11732</th>\n",
       "      <td>Susie Lee</td>\n",
       "      <td>D</td>\n",
       "      <td>DELL TECHNOLOGIES INC. CLASS C</td>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>53.84</td>\n",
       "      <td>Susie Lee (D)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11733</th>\n",
       "      <td>Susie Lee</td>\n",
       "      <td>D</td>\n",
       "      <td>BP P.L.C.</td>\n",
       "      <td>2019-01-11</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$15,001 - $50,000</td>\n",
       "      <td>-119.94</td>\n",
       "      <td>Susie Lee (D)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11734</th>\n",
       "      <td>Susie Lee</td>\n",
       "      <td>D</td>\n",
       "      <td>GREAT CANADIAN GAMING CORP ORDINARY SHARES</td>\n",
       "      <td>2019-01-11</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Susie Lee (D)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11735</th>\n",
       "      <td>Susie Lee</td>\n",
       "      <td>D</td>\n",
       "      <td>GREAT CANADIAN GAMING CORP ORDINARY SHARES</td>\n",
       "      <td>2019-01-11</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Susie Lee (D)</td>\n",
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       "    <tr>\n",
       "      <th>11736</th>\n",
       "      <td>Susie Lee</td>\n",
       "      <td>D</td>\n",
       "      <td>GREAT CANADIAN GAMING CORP ORDINARY SHARES</td>\n",
       "      <td>2019-01-11</td>\n",
       "      <td>Sale</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Susie Lee (D)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1180 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name Party                                     Company     Traded  \\\n",
       "10557  Susie Lee     D                  RACKSPACE TECHNOLOGY, INC. 2021-10-14   \n",
       "10558  Susie Lee     D                   HOWARD HUGHES CORPORATION 2021-10-13   \n",
       "10559  Susie Lee     D             COMMSCOPE HOLDING COMPANY, INC. 2021-10-06   \n",
       "10560  Susie Lee     D                ZIMMER BIOMET HOLDINGS, INC. 2021-09-30   \n",
       "10561  Susie Lee     D                         SYNCHRONY FINANCIAL 2021-09-30   \n",
       "...          ...   ...                                         ...        ...   \n",
       "11732  Susie Lee     D              DELL TECHNOLOGIES INC. CLASS C 2019-01-14   \n",
       "11733  Susie Lee     D                                   BP P.L.C. 2019-01-11   \n",
       "11734  Susie Lee     D  GREAT CANADIAN GAMING CORP ORDINARY SHARES 2019-01-11   \n",
       "11735  Susie Lee     D  GREAT CANADIAN GAMING CORP ORDINARY SHARES 2019-01-11   \n",
       "11736  Susie Lee     D  GREAT CANADIAN GAMING CORP ORDINARY SHARES 2019-01-11   \n",
       "\n",
       "      Transaction     Trade_Size_USD  excess_return     Name_Party  \n",
       "10557        Sale   $1,001 - $15,000        -107.67  Susie Lee (D)  \n",
       "10558        Sale   $1,001 - $15,000         -13.26  Susie Lee (D)  \n",
       "10559        Sale   $1,001 - $15,000        -108.93  Susie Lee (D)  \n",
       "10560        Sale  $15,001 - $50,000         -50.82  Susie Lee (D)  \n",
       "10561        Sale   $1,001 - $15,000         -22.97  Susie Lee (D)  \n",
       "...           ...                ...            ...            ...  \n",
       "11732    Purchase   $1,001 - $15,000          53.84  Susie Lee (D)  \n",
       "11733        Sale  $15,001 - $50,000        -119.94  Susie Lee (D)  \n",
       "11734        Sale   $1,001 - $15,000           0.00  Susie Lee (D)  \n",
       "11735        Sale   $1,001 - $15,000           0.00  Susie Lee (D)  \n",
       "11736        Sale   $1,001 - $15,000           0.00  Susie Lee (D)  \n",
       "\n",
       "[1180 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(josh_fil)\n",
    "display(lois_fil)\n",
    "display(susie_fil)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display the top traded companies for each Republican"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perdue, David (R) had 33 trades for Caesars Entertainment Corporation\n",
      "Greg Gianforte (R) had 15 trades for AMN HEALTHCARE SERVICES INC AMN HEALTHCARE SERVICES INC\n",
      "Thomas Macarthur (R) had 1 trades for INTERACTIVE BROKERS GROUP, INC.\n"
     ]
    }
   ],
   "source": [
    "perdue_name = perdue_fil['Name_Party'].iloc[0]\n",
    "counts = perdue_fil['Company'].value_counts().head(1)\n",
    "company_name = counts.index[0]\n",
    "count = counts.values[0]\n",
    "print(f\"{perdue_name} had {count} trades for {company_name}\")\n",
    "\n",
    "greg_name = greg_fil['Name_Party'].iloc[0]\n",
    "counts = greg_fil['Company'].value_counts().head(1)\n",
    "company_name = counts.index[0]\n",
    "count = counts.values[0]\n",
    "print(f\"{greg_name} had {count} trades for {company_name}\")\n",
    "\n",
    "thomas_name = thomas_fil['Name_Party'].iloc[0]\n",
    "counts = thomas_fil['Company'].value_counts().head(1)\n",
    "company_name = counts.index[0]\n",
    "count = counts.values[0]\n",
    "print(f\"{thomas_name} had {count} trades for {company_name}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Most Common Company of Stocks Purchased from 2019-2021-- Republicans**\n",
    "\n",
    "*Perdue- Caesar's Entertainment Corporation- 33 Total*\n",
    "\n",
    "*Greg- AMN Healthcare Services Inc.- 15 Total*\n",
    "\n",
    "*Thomas- Interactive Brokers Group, Inc.- 1 Total*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display the top traded companies for each Democrat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Josh Gottheimer (D) had 156 trades for MICROSOFT CORPORATION\n",
      "Lois Frankel (D) had 10 trades for DUPONT DE NEMOURS, INC.\n",
      "Susie Lee (D) had 21 trades for BALL CORPORATION\n"
     ]
    }
   ],
   "source": [
    "josh_name = josh_fil['Name_Party'].iloc[0]\n",
    "counts = josh_fil['Company'].value_counts().head(1)\n",
    "company_name = counts.index[0]\n",
    "count = counts.values[0]\n",
    "print(f\"{josh_name} had {count} trades for {company_name}\")\n",
    "\n",
    "lois_name = lois_fil['Name_Party'].iloc[0]\n",
    "counts = lois_fil['Company'].value_counts().head(1)\n",
    "company_name = counts.index[0]\n",
    "count = counts.values[0]\n",
    "print(f\"{lois_name} had {count} trades for {company_name}\")\n",
    "\n",
    "susie_name = susie_fil['Name_Party'].iloc[0]\n",
    "counts = susie_fil['Company'].value_counts().head(1)\n",
    "company_name = counts.index[0]\n",
    "count = counts.values[0]\n",
    "print(f\"{susie_name} had {count} trades for {company_name}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Most Common Company of Stocks Purchased from 2019-2021-- Democrats**\n",
    "\n",
    "*Josh- Microsoft Corporation- 156 Total*\n",
    "\n",
    "*Lois- Dupont De Nemours, Inc. - 10 Total*\n",
    "\n",
    "*Susie- Ball Corporation - 21 Total*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average Excess Return from Priorly Determined Companies from 2019-2021--Republicans**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average excess return for Perdue from Caesars Entertainment Corp is 109.63 from 2019-2021.\n"
     ]
    }
   ],
   "source": [
    "perdue__comp = perdue_fil[perdue_fil['Company'] == 'Caesars Entertainment Corporation']\n",
    "cec_avg =perdue__comp['excess_return'].mean().round(2)\n",
    "print(f\"The average excess return for Perdue from Caesars Entertainment Corp is {cec_avg} from 2019-2021.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average excess return for Greg from AMN Healthcare Services is -48.22 from 2019-2021.\n"
     ]
    }
   ],
   "source": [
    "greg__comp = greg_fil[greg_fil['Company'] == 'AMN HEALTHCARE SERVICES INC AMN HEALTHCARE SERVICES INC']\n",
    "amn_avg =greg__comp['excess_return'].mean().round(2)\n",
    "amn_avg\n",
    "print(f\"The average excess return for Greg from AMN Healthcare Services is {amn_avg} from 2019-2021.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average excess return for Thomas from Interactive Brokers Group is 83.47 from 2019-2021.\n"
     ]
    }
   ],
   "source": [
    "thomas__comp = thomas_fil[thomas_fil['Company'] == 'INTERACTIVE BROKERS GROUP, INC.']\n",
    "ibg_avg =thomas__comp['excess_return'].mean().round(2)\n",
    "ibg_avg\n",
    "print(f\"The average excess return for Thomas from Interactive Brokers Group is {ibg_avg} from 2019-2021.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Republican</th>\n",
       "      <th>Average Excess Return 2019-2021</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Perdue</td>\n",
       "      <td>109.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Greg</td>\n",
       "      <td>-48.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>83.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Republican  Average Excess Return 2019-2021\n",
       "0     Perdue                           109.63\n",
       "1       Greg                           -48.22\n",
       "2     Thomas                            83.47"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "repub_19_21 = {\n",
    "    \"Republican\": ['Perdue', 'Greg', 'Thomas'],\n",
    "    \"Average Excess Return 2019-2021\": [cec_avg, amn_avg, ibg_avg],\n",
    "}\n",
    "\n",
    "repub_19_21_df = pd.DataFrame(repub_19_21)\n",
    "repub_19_21_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2]),\n",
       " [Text(0, 0, 'Perdue'), Text(1, 0, 'Greg'), Text(2, 0, 'Thomas')])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "repub_19_21_df.plot.bar(x='Republican', y=\"Average Excess Return 2019-2021\", color=\"r\")\n",
    "plt.axhline(0, color='black', linestyle='-')\n",
    "plt.ylabel('Average Excess Returns')\n",
    "plt.title('2019-2021 Average Excess Return--Most Traded Companies')\n",
    "plt.xticks(rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Average Excess Return for Prior Mentioned Companies from 2019-2021--Democrats**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average excess return for Josh from Microsoft Corporation is 84.84 from 2019-2021.\n"
     ]
    }
   ],
   "source": [
    "josh__comp = josh_fil[josh_fil['Company'] == 'MICROSOFT CORPORATION']\n",
    "mc_avg =josh__comp['excess_return'].mean().round(2)\n",
    "print(f\"The average excess return for Josh from Microsoft Corporation is {mc_avg} from 2019-2021.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average excess return for Lois from Dupont De Nemours, Inc. is -27.15 from 2019-2021.\n"
     ]
    }
   ],
   "source": [
    "lois__comp = lois_fil[lois_fil['Company'] == 'DUPONT DE NEMOURS, INC.']\n",
    "ddn_avg =lois__comp['excess_return'].mean().round(2)\n",
    "print(f\"The average excess return for Lois from Dupont De Nemours, Inc. is {ddn_avg} from 2019-2021.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average excess return for Susie from Ball Corporation is -23.38 from 2019-2021.\n"
     ]
    }
   ],
   "source": [
    "susie__comp = susie_fil[susie_fil['Company'] == 'BALL CORPORATION']\n",
    "bc_avg =susie__comp['excess_return'].mean().round(2)\n",
    "print(f\"The average excess return for Susie from Ball Corporation is {bc_avg} from 2019-2021.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Democrat</th>\n",
       "      <th>Average Excess Return 2019-2021</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>84.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lois</td>\n",
       "      <td>-27.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Susie</td>\n",
       "      <td>-23.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Democrat  Average Excess Return 2019-2021\n",
       "0     Josh                            84.84\n",
       "1     Lois                           -27.15\n",
       "2    Susie                           -23.38"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "democrat_19_21 = {\n",
    "    \"Democrat\": ['Josh', 'Lois', 'Susie'],\n",
    "    \"Average Excess Return 2019-2021\": [mc_avg, ddn_avg, bc_avg],\n",
    "}\n",
    "\n",
    "democrat_19_21_df = pd.DataFrame(democrat_19_21)\n",
    "democrat_19_21_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2]),\n",
       " [Text(0, 0, 'Josh'), Text(1, 0, 'Lois'), Text(2, 0, 'Susie')])"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "democrat_19_21_df.plot.bar(x='Democrat', y=\"Average Excess Return 2019-2021\", color=\"b\")\n",
    "plt.axhline(0, color='black', linestyle='-')\n",
    "plt.ylabel('Average Excess Returns')\n",
    "plt.title('2019-2021 Average Excess Return--Most Traded Companies')\n",
    "plt.xticks(rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Average Excess Returns in 2019-2021 (all companies accounted for)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Republican</th>\n",
       "      <th>Excess Return Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Perdue</td>\n",
       "      <td>-6.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Greg</td>\n",
       "      <td>-16.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>83.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Republican  Excess Return Average\n",
       "0     Perdue                  -6.54\n",
       "1       Greg                 -16.95\n",
       "2     Thomas                  83.47"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perdue_exc_avg = perdue_fil['excess_return'].mean().round(2)\n",
    "greg_exc_avg = greg_fil['excess_return'].mean().round(2)\n",
    "thomas_exc_avg = thomas_fil['excess_return'].mean().round(2)\n",
    "\n",
    "rep_exc_avg = {\n",
    "    'Republican': ['Perdue', 'Greg', 'Thomas'],\n",
    "    'Excess Return Average': [perdue_exc_avg, greg_exc_avg, thomas_exc_avg],\n",
    "}\n",
    "\n",
    "rep_excavg_df = pd.DataFrame(rep_exc_avg)\n",
    "rep_excavg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2]),\n",
       " [Text(0, 0, 'Perdue'), Text(1, 0, 'Greg'), Text(2, 0, 'Thomas')])"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rep_excavg_df.plot.bar(x='Republican', y=\"Excess Return Average\", color=\"r\")\n",
    "plt.axhline(0, color='black', linestyle='-')\n",
    "plt.ylabel('Average Excess Returns')\n",
    "plt.title('Republican Average Excess Returns--2019-2021')\n",
    "plt.xticks(rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Democrat</th>\n",
       "      <th>Excess Return Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>5.269873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lois</td>\n",
       "      <td>-19.122706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Susie</td>\n",
       "      <td>-16.986975</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Democrat  Excess Return Average\n",
       "0     Josh               5.269873\n",
       "1     Lois             -19.122706\n",
       "2    Susie             -16.986975"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "josh_exc_avg = josh_fil['excess_return'].mean()\n",
    "lois_exc_avg = lois_fil['excess_return'].mean()\n",
    "susie_exc_avg = susie_fil['excess_return'].mean()\n",
    "\n",
    "dem_exc_avg = {\n",
    "    'Democrat': ['Josh', 'Lois', 'Susie'],\n",
    "    'Excess Return Average': [josh_exc_avg, lois_exc_avg, susie_exc_avg],\n",
    "}\n",
    "\n",
    "dem_excavg_df = pd.DataFrame(dem_exc_avg)\n",
    "dem_excavg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2]),\n",
       " [Text(0, 0, 'Josh'), Text(1, 0, 'Lois'), Text(2, 0, 'Susie')])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dem_excavg_df.plot.bar(x='Democrat', y=\"Excess Return Average\", color=\"b\")\n",
    "plt.axhline(0, color='black', linestyle='-')\n",
    "plt.ylabel('Average Excess Returns')\n",
    "plt.title('Democrat Average Excess Returns--2019-2021')\n",
    "plt.xticks(rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Is there a difference in excess return based on the party of politician?\n",
    "\n",
    "This section aims to identify the excess return of each party, the highest performing politician,\n",
    "the excess return from 2019-2021, and the highest performing politician from 2019-2021.\n",
    "\n",
    "The goal is to identify if there is in party performance, or a trend in the performance of an individual\n",
    "that is worth making similar investments for someone outside of politics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a dataframe based on the top 6 politicians who had the highest excess return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/k4/ds2_p0md3jn6b2dh7nt_yzth0000gn/T/ipykernel_92448/323414636.py:2: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n",
      "  republicans = x_df_clean2[x_df_clean2['Name_Party'].str.contains('(R)')]\n",
      "/var/folders/k4/ds2_p0md3jn6b2dh7nt_yzth0000gn/T/ipykernel_92448/323414636.py:3: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n",
      "  democrats = x_df_clean2[x_df_clean2['Name_Party'].str.contains('(D)')]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name_Party</th>\n",
       "      <th>Party</th>\n",
       "      <th>Company</th>\n",
       "      <th>Traded</th>\n",
       "      <th>Transaction</th>\n",
       "      <th>Trade_Size_USD</th>\n",
       "      <th>excess_return</th>\n",
       "      <th>total_excess_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Whitehouse, Sheldon (D)</td>\n",
       "      <td>D</td>\n",
       "      <td>Goldman Sachs Group, Inc. (The) Common Stock</td>\n",
       "      <td>2024-06-27</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>12.60</td>\n",
       "      <td>63956.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Whitehouse, Sheldon (D)</td>\n",
       "      <td>D</td>\n",
       "      <td>Home Depot</td>\n",
       "      <td>2024-06-13</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>2.99</td>\n",
       "      <td>63956.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Whitehouse, Sheldon (D)</td>\n",
       "      <td>D</td>\n",
       "      <td>Microsoft Corp</td>\n",
       "      <td>2024-06-13</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-4.04</td>\n",
       "      <td>63956.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Debbie Wasserman Schultz (D)</td>\n",
       "      <td>D</td>\n",
       "      <td>PERION NETWORK LTD - ORDINARY SHARES</td>\n",
       "      <td>2024-05-16</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-32.91</td>\n",
       "      <td>64404.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Whitehouse, Sheldon (D)</td>\n",
       "      <td>D</td>\n",
       "      <td>Crown Castle Inc</td>\n",
       "      <td>2024-05-10</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>3.54</td>\n",
       "      <td>63956.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2269</th>\n",
       "      <td>Whitehouse, Sheldon (D)</td>\n",
       "      <td>D</td>\n",
       "      <td>Altria Group Inc. (NYSE)</td>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-163.43</td>\n",
       "      <td>63956.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2270</th>\n",
       "      <td>Whitehouse, Sheldon (D)</td>\n",
       "      <td>D</td>\n",
       "      <td>The Clorox Company (NYSE)</td>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>Sale (Full)</td>\n",
       "      <td>$15,001 - $50,000</td>\n",
       "      <td>-154.11</td>\n",
       "      <td>63956.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2271</th>\n",
       "      <td>Whitehouse, Sheldon (D)</td>\n",
       "      <td>D</td>\n",
       "      <td>LinkedIn Corporation (NYSE)</td>\n",
       "      <td>2014-01-29</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>-30.99</td>\n",
       "      <td>63956.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2272</th>\n",
       "      <td>Whitehouse, Sheldon (D)</td>\n",
       "      <td>D</td>\n",
       "      <td>Symantec Corporation (NASDAQ)</td>\n",
       "      <td>2014-01-29</td>\n",
       "      <td>Sale (Full)</td>\n",
       "      <td>$15,001 - $50,000</td>\n",
       "      <td>-75.52</td>\n",
       "      <td>63956.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2273</th>\n",
       "      <td>Whitehouse, Sheldon (D)</td>\n",
       "      <td>D</td>\n",
       "      <td>The Boeing Company (NYSE)</td>\n",
       "      <td>2014-01-29</td>\n",
       "      <td>Sale (Full)</td>\n",
       "      <td>$15,001 - $50,000</td>\n",
       "      <td>-164.12</td>\n",
       "      <td>63956.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2274 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Name_Party Party  \\\n",
       "0          Whitehouse, Sheldon (D)     D   \n",
       "1          Whitehouse, Sheldon (D)     D   \n",
       "2          Whitehouse, Sheldon (D)     D   \n",
       "3     Debbie Wasserman Schultz (D)     D   \n",
       "4          Whitehouse, Sheldon (D)     D   \n",
       "...                            ...   ...   \n",
       "2269       Whitehouse, Sheldon (D)     D   \n",
       "2270       Whitehouse, Sheldon (D)     D   \n",
       "2271       Whitehouse, Sheldon (D)     D   \n",
       "2272       Whitehouse, Sheldon (D)     D   \n",
       "2273       Whitehouse, Sheldon (D)     D   \n",
       "\n",
       "                                           Company     Traded  Transaction  \\\n",
       "0     Goldman Sachs Group, Inc. (The) Common Stock 2024-06-27     Purchase   \n",
       "1                                       Home Depot 2024-06-13     Purchase   \n",
       "2                                   Microsoft Corp 2024-06-13     Purchase   \n",
       "3             PERION NETWORK LTD - ORDINARY SHARES 2024-05-16     Purchase   \n",
       "4                                 Crown Castle Inc 2024-05-10     Purchase   \n",
       "...                                            ...        ...          ...   \n",
       "2269                      Altria Group Inc. (NYSE) 2014-01-31     Purchase   \n",
       "2270                     The Clorox Company (NYSE) 2014-01-31  Sale (Full)   \n",
       "2271                   LinkedIn Corporation (NYSE) 2014-01-29     Purchase   \n",
       "2272                 Symantec Corporation (NASDAQ) 2014-01-29  Sale (Full)   \n",
       "2273                     The Boeing Company (NYSE) 2014-01-29  Sale (Full)   \n",
       "\n",
       "         Trade_Size_USD  excess_return  total_excess_return  \n",
       "0      $1,001 - $15,000          12.60             63956.88  \n",
       "1      $1,001 - $15,000           2.99             63956.88  \n",
       "2      $1,001 - $15,000          -4.04             63956.88  \n",
       "3      $1,001 - $15,000         -32.91             64404.63  \n",
       "4      $1,001 - $15,000           3.54             63956.88  \n",
       "...                 ...            ...                  ...  \n",
       "2269   $1,001 - $15,000        -163.43             63956.88  \n",
       "2270  $15,001 - $50,000        -154.11             63956.88  \n",
       "2271   $1,001 - $15,000         -30.99             63956.88  \n",
       "2272  $15,001 - $50,000         -75.52             63956.88  \n",
       "2273  $15,001 - $50,000        -164.12             63956.88  \n",
       "\n",
       "[2274 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter Republicans and Democrats\n",
    "republicans = x_df_clean2[x_df_clean2['Name_Party'].str.contains('(R)')]\n",
    "democrats = x_df_clean2[x_df_clean2['Name_Party'].str.contains('(D)')]\n",
    "\n",
    "# Group by Name_Party and sum the excess_return\n",
    "republican_sums = republicans.groupby('Name_Party')['excess_return'].sum().reset_index()\n",
    "democrat_sums = democrats.groupby('Name_Party')['excess_return'].sum().reset_index()\n",
    "\n",
    "# Sort and get top 3\n",
    "top_republicans = republican_sums.sort_values(by='excess_return', ascending=False).head(3)\n",
    "top_democrats = democrat_sums.sort_values(by='excess_return', ascending=False).head(3)\n",
    "\n",
    "# Combine the top Republicans and Democrats\n",
    "top_politicians = pd.concat([top_republicans, top_democrats])\n",
    "top_politicians = top_politicians.rename(columns={'excess_return': 'total_excess_return'})\n",
    "\n",
    "# Merge with original DataFrame to get all columns\n",
    "top_politicians_df = pd.merge(x_df_clean2, top_politicians, on='Name_Party', how='inner')\n",
    "display(top_politicians_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print the sum of all excess returns for each party across all years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Republican excess return for all years -569,214.59\n",
      "Democrat excess return for all years -211,929.01\n"
     ]
    }
   ],
   "source": [
    "print(f\"Republican excess return for all years {republicans['excess_return'].sum():,.2f}\")\n",
    "print(f\"Democrat excess return for all years {democrats['excess_return'].sum():,.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print the excess return of the top performing politicians for each party for all years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Party: D, Excess Return: 151,888.77\n",
      "Party: R, Excess Return: 77,908.50\n"
     ]
    }
   ],
   "source": [
    "grouped_excess_return = top_politicians_df.groupby('Party')['excess_return'].sum()\n",
    "for party, excess_return in grouped_excess_return.items():\n",
    "    print(f\"Party: {party}, Excess Return: {excess_return:,.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name_Party\n",
       "Debbie Wasserman Schultz (D)    64404.63\n",
       "Mark Green (R)                  18811.46\n",
       "Roberts, Pat (R)                46277.25\n",
       "Roger W. Marshall (R)           12819.79\n",
       "Thomas Suozzi (D)               23527.26\n",
       "Whitehouse, Sheldon (D)         63956.88\n",
       "Name: excess_return, dtype: float64"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "excess_return_sum = top_politicians_df.groupby('Name_Party')['excess_return'].sum()\n",
    "excess_return_sum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Politician with the highest excess return across all years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Debbie Wasserman Schultz (D) had the highest excess return of 64,404.63 for all years\n"
     ]
    }
   ],
   "source": [
    "person = excess_return_sum.idxmax()\n",
    "highest_sum = excess_return_sum.max()\n",
    "print(f\"{person} had the highest excess return of {highest_sum:,.2f} for all years\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Excess return for each party from 2019-2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Party: D, Excess Return for 2019-2021: -83,601.10\n",
      "Party: R, Excess Return for 2019-2021: -45,513.04\n"
     ]
    }
   ],
   "source": [
    "excess_all_19_21 = x_df_clean2[(x_df_clean2['Traded'] >= '2019-01-01') & (x_df_clean2['Traded'] <= '2021-12-31')]\n",
    "excess_grouped_19_21 = excess_all_19_21.groupby('Party')['excess_return'].sum()\n",
    "for party, excess_return in excess_grouped_19_21.items():\n",
    "    print(f\"Party: {party}, Excess Return for 2019-2021: {excess_return:,.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Excess return of the best performing politicians from each party from 2019-2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Party: D, Excess Return for 2019-2021: 30,051.73\n",
      "Party: R, Excess Return for 2019-2021: 35,392.47\n"
     ]
    }
   ],
   "source": [
    "combined_19_21 = top_politicians_df[(top_politicians_df['Traded'].dt.year >= 2019) & (top_politicians_df['Traded'].dt.year <= 2021)]\n",
    "combined_19_21_group = combined_19_21.groupby('Party')['excess_return'].sum()\n",
    "for party, excess_return in combined_19_21_group.items():\n",
    "    print(f\"Party: {party}, Excess Return for 2019-2021: {excess_return:,.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Politician with the highest excess return from 2019-2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thomas Suozzi (D) had the highest excess return of 27,117.85 from 2019 - 2021\n"
     ]
    }
   ],
   "source": [
    "top_politicians_df_19_21 = top_politicians_df[(top_politicians_df['Traded'] >= '2019-01-01') & (top_politicians_df['Traded'] <= '2021-12-31')]\n",
    "grouped_19_21 = top_politicians_df_19_21.groupby('Name_Party')['excess_return'].sum()\n",
    "person_19_21 = grouped_19_21.idxmax()\n",
    "highest_sum_19_21 = grouped_19_21.max()\n",
    "\n",
    "print(f\"{person_19_21} had the highest excess return of {highest_sum_19_21:,.2f} from 2019 - 2021\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:** Which party had the highest excess return for all years?\n",
    "\n",
    "**Answer:** Both parties had a positive excess return. The Democrats had a higher return than the Republicans.\n",
    "The excess return for the Democrats was 151,888.77, while the excess return for the Republicans was 77,908.50.\n",
    "The Democrats excess return is 48.71% higher that of the Republicans.\n",
    "\n",
    "**Question:** Which party had the highest excess return from 2019-2021?\n",
    "\n",
    "**Answer:** When comparing the excess return by party from 2019-2021 the excess return was a lot closer.\n",
    "Both parties were positive. The excess return for the Republicans was 35,392.47. The Democrats excess return was 30,051.73.\n",
    "The Republicans' excess return is 17.80% higher that of the Democrats.\n",
    "\n",
    "**Question:** Which politician performed the best across all years?\n",
    "\n",
    "**Answer:** Debbie Wasserman Schultz (D) had the highest excess return of 64,404.63 for all years\n",
    "\n",
    "**Question:** Which politician performed the best from 2019-2021?\n",
    "\n",
    "**Answer:** Thomas Suozzi (D) had the highest excess return of 27,117.85 from 2019 - 2021\n",
    "\n",
    "**Question:** Is it worth making investments based on the performance of a political party?\n",
    "\n",
    "**Answer:** No, it is not worth making investments based on the performance of a party. Both parties as a whole had a very high negative return.\n",
    "\n",
    "**Question:** Is it worth making investments based on the performance of a a single politician?\n",
    "\n",
    "**Answer:** Yes, it is worth making investments based on the performance of a single politician based on how well they are currently performing."
   ]
  }
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