1stAssignmentAML-p3170086.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> Kωνσταντίνος Κωνσταντόπουλος - 1η Εργασία Μηχανικής Μάθησης</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> AM: p3170086</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Exploration and data extraction from FEC data </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Initialization </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import matplotlib\n",
    "\n",
    "from collections import defaultdict\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "matplotlib.style.use('ggplot')\n",
    "\n",
    "pd.set_option(\"display.max_columns\", None)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> 1)Identify the top 5 Political Action Committees (PACs), or rather, super-PACs, that supported each of the two\n",
    "presidential candidates, giving the amount of money raised and spent by each one of them. </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Read Headers for the CSVs we are going to be using, which are:  </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The committees master file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "commitees_header=pd.read_csv(\"https://www.fec.gov/files/bulk-downloads/data_dictionaries/cm_header_file.csv\").columns.tolist()\n",
    "\n",
    "commitees= pd.read_csv('https://www.fec.gov/files/bulk-downloads/2016/cm16.zip', sep=\"|\", index_col=False, names=commitees_header)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The PAC summary file. Column titles are not given by FEC so I had to write them myself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pac_summary_header = ['CMTE_ID','CMTE_NM','CMTE_TP','CMTE_DSGN','CMTE_FILING_FREQ','TTL_RECEIPTS','TRANS_FROM_AFF','INDV_CONTRIB','OTHER_POL_CMTE_CONTRIB','CAND_CONTRIB','CAND_LOANS','TTL_LOANS_RECEIVED','TTL_DISB','TRANF_TO_AFF',\n",
    "        'INDV_REFUNDS','OTHER_POL_CMTE_REFUNDS','CAND_LOAN_REPAY','LOAN_REPAY','COH_BOP','COH_COP','DEBTS_OWED_BY','NONFED_TRANS_RECEIVED','CONTRIB_TO_OTHER_CMTE','IND_EXP','PTY_COORD_EXP','NONFED_SHARE_EXP','CVG_END_DT']\n",
    "\n",
    "data_types = { header: np.str for header in pac_summary_header }\n",
    "data_types['TTL_RECEIPTS'] = np.float\n",
    "\n",
    "pac_summary = pd.read_csv('https://www.fec.gov/files/bulk-downloads/2016/webk16.zip', \n",
    "                  sep=\"|\", \n",
    "                  index_col=False, \n",
    "                  names=pac_summary_header, \n",
    "                  dtype=data_types)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The Contributions from committees to candidates file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "contrib_h= pd.read_csv(\"https://www.fec.gov/files/bulk-downloads/data_dictionaries/pas2_header_file.csv\").columns.tolist()\n",
    "\n",
    "data_typ = { header: np.str for header in contrib_h }\n",
    "data_typ['TRANSACTION_AMT'] = np.float\n",
    "\n",
    "contrib = pd.read_csv('https://www.fec.gov/files/bulk-downloads/2016/pas216.zip', sep=\"|\", index_col=False,dtype=data_typ, names=contrib_h)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We know that **Hillary Clinton's** candidate ID is **P00003392** and that **Donald Trump's** candidate ID is **P80001571**, so we filter out all contributions not concerning those 2 CAND_IDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "contrib=contrib[contrib['CAND_ID'].isin(['P00003392','P80001571'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Merging the contributions file with committees master to access the name and type of each contribution's committee "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "contrib=pd.merge(contrib, \n",
    "               commitees[['CMTE_ID','CMTE_TP' ,'CMTE_NM']], \n",
    "               on='CMTE_ID', \n",
    "               how='inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We now filter out all transactions with a negative value an keep all committees of types N,O,Q. Types N and Q are PACs, whilst type O are Super-PACs. Conclusion drawn from https://www.fec.gov/campaign-finance-data/committee-type-code-descriptions/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "contrib=contrib[contrib['TRANSACTION_AMT']>0]\n",
    "\n",
    "contrib=contrib[contrib['CMTE_TP'].isin(['N','Q','O'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* By studying the documentation on the [transaction types](https://www.fec.gov/campaign-finance-data/transaction-type-code-descriptions/) , we can assume that all transaction types of 24E is money given for the benefit of a candidate, whereas type 24A is money given against a candidate. Thus we conclude, that transactions of type 24E for a named candidate and type 24A for the opposing candidate, both work for the benefit of named candidate. We split up in to 4 dataframes for each category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11850, 24)\n"
     ]
    }
   ],
   "source": [
    "contrib_copy = contrib.copy()\n",
    "contrib_copy.groupby(['CMTE_ID'])\n",
    "print(contrib_copy.shape)\n",
    "\n",
    "trump_for=contrib_copy.loc[(contrib_copy['CAND_ID']==\"P80001571\") & (contrib_copy['TRANSACTION_TP'].isin([\"24E\"]))].groupby(['CMTE_ID'])['TRANSACTION_AMT'].sum().reset_index()\n",
    "trump_for = trump_for.rename(columns={'TRANSACTION_AMT': 'FOR_TRUMP'})\n",
    "\n",
    "trump_against=contrib_copy.loc[(contrib_copy['CAND_ID']==\"P80001571\") & (contrib_copy['TRANSACTION_TP']==\"24A\")].groupby(['CMTE_ID'])['TRANSACTION_AMT'].sum().reset_index()\n",
    "trump_against = trump_against.rename(columns={'TRANSACTION_AMT': 'AGAINST_TRUMP'})\n",
    "\n",
    "hillary_for = contrib_copy.loc[(contrib_copy['CAND_ID']==\"P00003392\") & (contrib_copy['TRANSACTION_TP'].isin([\"24E\"]))].groupby(['CMTE_ID'])['TRANSACTION_AMT'].sum().reset_index()\n",
    "hillary_for = hillary_for.rename(columns={'TRANSACTION_AMT': 'FOR_HILLARY'})\n",
    "\n",
    "hillary_against = contrib_copy.loc[(contrib_copy['CAND_ID']==\"P00003392\") & (contrib_copy['TRANSACTION_TP']==\"24A\")].groupby(['CMTE_ID'])['TRANSACTION_AMT'].sum().reset_index()\n",
    "hillary_against = hillary_against.rename(columns={'TRANSACTION_AMT': 'AGAINST_HILLARY'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* By merging our 4 dataframes, the result will be a dataframe which each row contains a unique committee id and 4 columns, each containing information based on how money was spent for each candidate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "total=pd.merge(trump_for, \n",
    "               trump_against, \n",
    "               on='CMTE_ID', \n",
    "               how='outer')\n",
    "total=pd.merge(total, \n",
    "               hillary_for, \n",
    "               on='CMTE_ID', \n",
    "               how='outer')\n",
    "total=pd.merge(total, \n",
    "               hillary_against, \n",
    "               on='CMTE_ID', \n",
    "               how='outer')\n",
    "total.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* By carefully examining the committees we can see that some may only give money against each opposing candidate. We chose to excluse these committees since they only work against a candidate but do not support anyone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CMTE_ID</th>\n",
       "      <th>FOR_TRUMP</th>\n",
       "      <th>AGAINST_TRUMP</th>\n",
       "      <th>FOR_HILLARY</th>\n",
       "      <th>AGAINST_HILLARY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>C00487470</td>\n",
       "      <td>29.0</td>\n",
       "      <td>7054203.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>512.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>C00569517</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14409.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>C00575373</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93090.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4376616.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>C00587022</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59007.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       CMTE_ID  FOR_TRUMP  AGAINST_TRUMP  FOR_HILLARY  AGAINST_HILLARY\n",
       "17   C00487470       29.0      7054203.0          0.0            512.0\n",
       "94   C00569517        0.0        12000.0          0.0          14409.0\n",
       "96   C00575373        0.0        93090.0          0.0        4376616.0\n",
       "100  C00587022        0.0        59007.0          0.0            100.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noones = total[(total['FOR_HILLARY']<total['AGAINST_HILLARY'])&(total['FOR_TRUMP']<total['AGAINST_TRUMP'])]\n",
    "noones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We now devide our dataframe into 2 dataframes that each contain committees that are on each of the candidate's side. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "hillary = total[(total['FOR_HILLARY']+total['AGAINST_TRUMP'])>(total['FOR_TRUMP']+total['AGAINST_HILLARY'])]\n",
    "\n",
    "trump = total[(total['FOR_HILLARY']+total['AGAINST_TRUMP'])<=(total['FOR_TRUMP']+total['AGAINST_HILLARY'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* With the help of lambdas we create 2 new columns on each row, that contain information about how muh money is spent for each presidential candidate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn = lambda row: row.FOR_HILLARY + row.AGAINST_TRUMP\n",
    "col = hillary.apply(fn, axis=1) \n",
    "hillary = hillary.assign(TOTAL_HILLARY=col.values) \n",
    "\n",
    "fn = lambda row:  row.FOR_TRUMP + row.AGAINST_HILLARY\n",
    "col = trump.apply(fn, axis=1)\n",
    "trump = trump.assign(TOTAL_TRUMP=col.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We merge with the PAC summary to access information on how much money each PAC has gathered **We do not have enough information to decide who each individual contribution supports, so we assume that each contribution goes to the candidate that we believe each PAC supports.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "trump=pd.merge(trump, \n",
    "               pac_summary[['CMTE_ID','CMTE_NM','TTL_RECEIPTS','INDV_CONTRIB']], \n",
    "               on='CMTE_ID', \n",
    "               how='inner')\n",
    "\n",
    "hillary=pd.merge(hillary, \n",
    "               pac_summary[['CMTE_ID','CMTE_NM','TTL_RECEIPTS','INDV_CONTRIB']], \n",
    "               on='CMTE_ID', \n",
    "               how='inner')\n",
    "\n",
    "trump['TOTAL_TRUMP'] = pd.to_numeric(trump['TOTAL_TRUMP'],errors='coerce')\n",
    "trump['INDV_CONTRIB'] = pd.to_numeric(trump['INDV_CONTRIB'],errors='coerce')\n",
    "trump['TTL_RECEIPTS'] = pd.to_numeric(trump['TTL_RECEIPTS'],errors='coerce')\n",
    "hillary['TTL_RECEIPTS'] = pd.to_numeric(hillary['TTL_RECEIPTS'],errors='coerce')\n",
    "hillary['TOTAL_HILLARY'] = pd.to_numeric(hillary['TOTAL_HILLARY'],errors='coerce')\n",
    "hillary['INDV_CONTRIB'] = pd.to_numeric(hillary['INDV_CONTRIB'],errors='coerce')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Another piece of information that may prove to be useful in our \"investigation\", is the percentage of total contributions that was actually distributed for use towards our assigned candidate. This information is stored in new column PERCENTAGE_INDIV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "trump['PERCENTAGE_INDIV']=trump['TOTAL_TRUMP']/trump['TTL_RECEIPTS']\n",
    "\n",
    "hillary['PERCENTAGE_INDIV']=hillary['TOTAL_HILLARY']/hillary['TTL_RECEIPTS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "trump['PERCENTAGE_INDIV'] = pd.to_numeric(trump['PERCENTAGE_INDIV'],errors='coerce')\n",
    "\n",
    "hillary['PERCENTAGE_INDIV'] = pd.to_numeric(hillary['PERCENTAGE_INDIV'],errors='coerce')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Here we sort our PACs based on how much money they spent and raised."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "trump_top5_spenders=trump.sort_values(by='TOTAL_TRUMP', ascending=False)[:5]\n",
    "trump_top5_gainers = trump.sort_values(by='TTL_RECEIPTS', ascending=False)[:5]\n",
    "\n",
    "hillary_top5_spenders=hillary.sort_values(by='TOTAL_HILLARY', ascending=False)[:5]\n",
    "hillary_top5_gainers = hillary.sort_values(by='TTL_RECEIPTS', ascending=False)[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* It is interesting to see that only a couple of Donald Trump's PAC have spent the largest amount of money towards him or against Hillary. We haven't analyzed the rest of the data but we can make the assumption that the rest of the money was spent on other candidates of the Republican party or against the Democrats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CMTE_ID</th>\n",
       "      <th>FOR_TRUMP</th>\n",
       "      <th>AGAINST_TRUMP</th>\n",
       "      <th>FOR_HILLARY</th>\n",
       "      <th>AGAINST_HILLARY</th>\n",
       "      <th>TOTAL_TRUMP</th>\n",
       "      <th>CMTE_NM</th>\n",
       "      <th>TTL_RECEIPTS</th>\n",
       "      <th>INDV_CONTRIB</th>\n",
       "      <th>PERCENTAGE_INDIV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C00574533</td>\n",
       "      <td>23431.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24195670.0</td>\n",
       "      <td>24219101.0</td>\n",
       "      <td>FUTURE45</td>\n",
       "      <td>24996520.04</td>\n",
       "      <td>24955520.04</td>\n",
       "      <td>0.968899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>C00618876</td>\n",
       "      <td>4095640.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81000.0</td>\n",
       "      <td>17103458.0</td>\n",
       "      <td>21199098.0</td>\n",
       "      <td>REBUILDING AMERICA NOW</td>\n",
       "      <td>23616516.94</td>\n",
       "      <td>23472474.94</td>\n",
       "      <td>0.897639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C00053553</td>\n",
       "      <td>1867401.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>7448175.0</td>\n",
       "      <td>9315576.0</td>\n",
       "      <td>NATIONAL RIFLE ASSOCIATION OF AMERICA POLITICA...</td>\n",
       "      <td>21591111.40</td>\n",
       "      <td>21252709.45</td>\n",
       "      <td>0.431454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>C00490730</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5728857.0</td>\n",
       "      <td>5728857.0</td>\n",
       "      <td>RGA RIGHT DIRECTION PAC</td>\n",
       "      <td>22781720.74</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.251467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>C00575373</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93090.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4376616.0</td>\n",
       "      <td>4376616.0</td>\n",
       "      <td>MAKE AMERICA NUMBER 1</td>\n",
       "      <td>20737584.81</td>\n",
       "      <td>6389820.31</td>\n",
       "      <td>0.211048</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CMTE_ID  FOR_TRUMP  AGAINST_TRUMP  FOR_HILLARY  AGAINST_HILLARY  \\\n",
       "23  C00574533    23431.0            0.0          0.0       24195670.0   \n",
       "36  C00618876  4095640.0            0.0      81000.0       17103458.0   \n",
       "0   C00053553  1867401.0            0.0        265.0        7448175.0   \n",
       "65  C00490730        0.0            0.0          0.0        5728857.0   \n",
       "53  C00575373        0.0        93090.0          0.0        4376616.0   \n",
       "\n",
       "    TOTAL_TRUMP                                            CMTE_NM  \\\n",
       "23   24219101.0                                           FUTURE45   \n",
       "36   21199098.0                             REBUILDING AMERICA NOW   \n",
       "0     9315576.0  NATIONAL RIFLE ASSOCIATION OF AMERICA POLITICA...   \n",
       "65    5728857.0                            RGA RIGHT DIRECTION PAC   \n",
       "53    4376616.0                              MAKE AMERICA NUMBER 1   \n",
       "\n",
       "    TTL_RECEIPTS  INDV_CONTRIB  PERCENTAGE_INDIV  \n",
       "23   24996520.04   24955520.04          0.968899  \n",
       "36   23616516.94   23472474.94          0.897639  \n",
       "0    21591111.40   21252709.45          0.431454  \n",
       "65   22781720.74          0.00          0.251467  \n",
       "53   20737584.81    6389820.31          0.211048  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_top5_spenders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The FREEDOM PARTNERS ACTION FUND, INC may have only spent 36.000 \\\\$ against H. Clinton but according to [open secrets](https://www.opensecrets.org/outsidespending/recips.php?cmte=C00564765&cycle=2016) , it spent  \\\\$ 30 million against Democrats in 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CMTE_ID</th>\n",
       "      <th>FOR_TRUMP</th>\n",
       "      <th>AGAINST_TRUMP</th>\n",
       "      <th>FOR_HILLARY</th>\n",
       "      <th>AGAINST_HILLARY</th>\n",
       "      <th>TOTAL_TRUMP</th>\n",
       "      <th>CMTE_NM</th>\n",
       "      <th>TTL_RECEIPTS</th>\n",
       "      <th>INDV_CONTRIB</th>\n",
       "      <th>PERCENTAGE_INDIV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>C00564765</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36329.0</td>\n",
       "      <td>36329.0</td>\n",
       "      <td>FREEDOM PARTNERS ACTION FUND, INC.</td>\n",
       "      <td>28201407.05</td>\n",
       "      <td>26563016.84</td>\n",
       "      <td>0.001288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C00574533</td>\n",
       "      <td>23431.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24195670.0</td>\n",
       "      <td>24219101.0</td>\n",
       "      <td>FUTURE45</td>\n",
       "      <td>24996520.04</td>\n",
       "      <td>24955520.04</td>\n",
       "      <td>0.968899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>C00618876</td>\n",
       "      <td>4095640.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81000.0</td>\n",
       "      <td>17103458.0</td>\n",
       "      <td>21199098.0</td>\n",
       "      <td>REBUILDING AMERICA NOW</td>\n",
       "      <td>23616516.94</td>\n",
       "      <td>23472474.94</td>\n",
       "      <td>0.897639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>C00490730</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5728857.0</td>\n",
       "      <td>5728857.0</td>\n",
       "      <td>RGA RIGHT DIRECTION PAC</td>\n",
       "      <td>22781720.74</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.251467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C00053553</td>\n",
       "      <td>1867401.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>7448175.0</td>\n",
       "      <td>9315576.0</td>\n",
       "      <td>NATIONAL RIFLE ASSOCIATION OF AMERICA POLITICA...</td>\n",
       "      <td>21591111.40</td>\n",
       "      <td>21252709.45</td>\n",
       "      <td>0.431454</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CMTE_ID  FOR_TRUMP  AGAINST_TRUMP  FOR_HILLARY  AGAINST_HILLARY  \\\n",
       "77  C00564765        0.0            0.0          0.0          36329.0   \n",
       "23  C00574533    23431.0            0.0          0.0       24195670.0   \n",
       "36  C00618876  4095640.0            0.0      81000.0       17103458.0   \n",
       "65  C00490730        0.0            0.0          0.0        5728857.0   \n",
       "0   C00053553  1867401.0            0.0        265.0        7448175.0   \n",
       "\n",
       "    TOTAL_TRUMP                                            CMTE_NM  \\\n",
       "77      36329.0                 FREEDOM PARTNERS ACTION FUND, INC.   \n",
       "23   24219101.0                                           FUTURE45   \n",
       "36   21199098.0                             REBUILDING AMERICA NOW   \n",
       "65    5728857.0                            RGA RIGHT DIRECTION PAC   \n",
       "0     9315576.0  NATIONAL RIFLE ASSOCIATION OF AMERICA POLITICA...   \n",
       "\n",
       "    TTL_RECEIPTS  INDV_CONTRIB  PERCENTAGE_INDIV  \n",
       "77   28201407.05   26563016.84          0.001288  \n",
       "23   24996520.04   24955520.04          0.968899  \n",
       "36   23616516.94   23472474.94          0.897639  \n",
       "65   22781720.74          0.00          0.251467  \n",
       "0    21591111.40   21252709.45          0.431454  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_top5_gainers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We can make the same assumption about Hillary Clinton's PACs, but we can notice that the amount of contributions that PACs have gathered and/or spent are much larger of them at D. Trumps side. With the **PRIORITIES USA ACTION PAC** being the star of the show having raised whopping **\\\\$192 million** and spent **\\\\$132 million** for Hillary Clinton, of which **\\\\$126 million** is spent against D. Trump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CMTE_ID</th>\n",
       "      <th>FOR_TRUMP</th>\n",
       "      <th>AGAINST_TRUMP</th>\n",
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       "      <th>AGAINST_HILLARY</th>\n",
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       "      <th>PERCENTAGE_INDIV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>C00495861</td>\n",
       "      <td>0.0</td>\n",
       "      <td>126062193.0</td>\n",
       "      <td>6473727.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132535920.0</td>\n",
       "      <td>PRIORITIES USA ACTION</td>\n",
       "      <td>1.920658e+08</td>\n",
       "      <td>1.567163e+08</td>\n",
       "      <td>0.690055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>C00603621</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16353117.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16353117.0</td>\n",
       "      <td>OUR PRINCIPLES PAC</td>\n",
       "      <td>1.902490e+07</td>\n",
       "      <td>1.866296e+07</td>\n",
       "      <td>0.859564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>C00523621</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8674114.0</td>\n",
       "      <td>4825838.0</td>\n",
       "      <td>4392.0</td>\n",
       "      <td>13499952.0</td>\n",
       "      <td>UNITED WE CAN</td>\n",
       "      <td>2.420699e+07</td>\n",
       "      <td>1.599422e+07</td>\n",
       "      <td>0.557688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>C00615815</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13240861.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13240861.0</td>\n",
       "      <td>NEXTGEN CALIFORNIA ACTION COMMITTEE</td>\n",
       "      <td>1.323263e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>C00473918</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5975968.0</td>\n",
       "      <td>5651780.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11627748.0</td>\n",
       "      <td>WOMEN VOTE!</td>\n",
       "      <td>3.668587e+07</td>\n",
       "      <td>1.481483e+07</td>\n",
       "      <td>0.316954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CMTE_ID  FOR_TRUMP  AGAINST_TRUMP  FOR_HILLARY  AGAINST_HILLARY  \\\n",
       "25  C00495861        0.0    126062193.0    6473727.0              0.0   \n",
       "50  C00603621        0.0     16353117.0          0.0              0.0   \n",
       "30  C00523621        0.0      8674114.0    4825838.0           4392.0   \n",
       "56  C00615815        0.0     13240861.0          0.0              0.0   \n",
       "17  C00473918        0.0      5975968.0    5651780.0              0.0   \n",
       "\n",
       "    TOTAL_HILLARY                              CMTE_NM  TTL_RECEIPTS  \\\n",
       "25    132535920.0                PRIORITIES USA ACTION  1.920658e+08   \n",
       "50     16353117.0                   OUR PRINCIPLES PAC  1.902490e+07   \n",
       "30     13499952.0                        UNITED WE CAN  2.420699e+07   \n",
       "56     13240861.0  NEXTGEN CALIFORNIA ACTION COMMITTEE  1.323263e+07   \n",
       "17     11627748.0                          WOMEN VOTE!  3.668587e+07   \n",
       "\n",
       "    INDV_CONTRIB  PERCENTAGE_INDIV  \n",
       "25  1.567163e+08          0.690055  \n",
       "50  1.866296e+07          0.859564  \n",
       "30  1.599422e+07          0.557688  \n",
       "56  0.000000e+00          1.000622  \n",
       "17  1.481483e+07          0.316954  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hillary_top5_spenders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CMTE_ID</th>\n",
       "      <th>FOR_TRUMP</th>\n",
       "      <th>AGAINST_TRUMP</th>\n",
       "      <th>FOR_HILLARY</th>\n",
       "      <th>AGAINST_HILLARY</th>\n",
       "      <th>TOTAL_HILLARY</th>\n",
       "      <th>CMTE_NM</th>\n",
       "      <th>TTL_RECEIPTS</th>\n",
       "      <th>INDV_CONTRIB</th>\n",
       "      <th>PERCENTAGE_INDIV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>C00495861</td>\n",
       "      <td>0.0</td>\n",
       "      <td>126062193.0</td>\n",
       "      <td>6473727.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132535920.0</td>\n",
       "      <td>PRIORITIES USA ACTION</td>\n",
       "      <td>1.920658e+08</td>\n",
       "      <td>1.567163e+08</td>\n",
       "      <td>0.690055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>C00571372</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25162.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25162.0</td>\n",
       "      <td>RIGHT TO RISE USA</td>\n",
       "      <td>1.216952e+08</td>\n",
       "      <td>1.183599e+08</td>\n",
       "      <td>0.000207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>C00547349</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4675455.0</td>\n",
       "      <td>2420617.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7096072.0</td>\n",
       "      <td>NEXTGEN CLIMATE ACTION COMMITTEE</td>\n",
       "      <td>9.301024e+07</td>\n",
       "      <td>9.110163e+07</td>\n",
       "      <td>0.076293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>C00484642</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1823372.0</td>\n",
       "      <td>228496.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2051868.0</td>\n",
       "      <td>SENATE MAJORITY PAC</td>\n",
       "      <td>9.282108e+07</td>\n",
       "      <td>6.771575e+07</td>\n",
       "      <td>0.022106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>C00541292</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6080765.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6080765.0</td>\n",
       "      <td>CONSERVATIVE SOLUTIONS PAC</td>\n",
       "      <td>6.056422e+07</td>\n",
       "      <td>6.023422e+07</td>\n",
       "      <td>0.100402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CMTE_ID  FOR_TRUMP  AGAINST_TRUMP  FOR_HILLARY  AGAINST_HILLARY  \\\n",
       "25  C00495861        0.0    126062193.0    6473727.0              0.0   \n",
       "42  C00571372        0.0        25162.0          0.0              0.0   \n",
       "36  C00547349        0.0      4675455.0    2420617.0              0.0   \n",
       "19  C00484642        0.0      1823372.0     228496.0              0.0   \n",
       "35  C00541292        0.0      6080765.0          0.0              0.0   \n",
       "\n",
       "    TOTAL_HILLARY                           CMTE_NM  TTL_RECEIPTS  \\\n",
       "25    132535920.0             PRIORITIES USA ACTION  1.920658e+08   \n",
       "42        25162.0                 RIGHT TO RISE USA  1.216952e+08   \n",
       "36      7096072.0  NEXTGEN CLIMATE ACTION COMMITTEE  9.301024e+07   \n",
       "19      2051868.0               SENATE MAJORITY PAC  9.282108e+07   \n",
       "35      6080765.0        CONSERVATIVE SOLUTIONS PAC  6.056422e+07   \n",
       "\n",
       "    INDV_CONTRIB  PERCENTAGE_INDIV  \n",
       "25  1.567163e+08          0.690055  \n",
       "42  1.183599e+08          0.000207  \n",
       "36  9.110163e+07          0.076293  \n",
       "19  6.771575e+07          0.022106  \n",
       "35  6.023422e+07          0.100402  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hillary_top5_gainers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> 2) Identify the top 10 individual donors (i.e., persons) for each of the two presidential candidates and the\n",
    "amount they spent. In order to do that, you should know that donations are not always to a fundraising\n",
    "committee that can be directly linked to a candidate, but they can be due to other entities such as PACs.\n",
    " </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We read the header and CSV of the the Contributions by Individuals file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "individuals_header=pd.read_csv(\"contributions_by_individuals/indiv_header_file.csv\").columns.tolist()\n",
    "\n",
    "data_types_com = { headers: np.str for headers in individuals_header }\n",
    "data_types_com['TRANSACTION_AMT'] = np.float\n",
    "\n",
    "contributions = pd.read_csv('contributions_by_individuals/itcont.txt', sep=\"|\", index_col=False, names=individuals_header,dtype=data_types_com,parse_dates=['TRANSACTION_DT'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* A copy is made everytime we need to re-run the cells instead of reading the CSV from the beginning (takes much longer)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "contribution_by_indiv=contributions.copy()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* After our initial sort of individual contributors we can notice some names popping up with slight differences, so we map them out. Another this we can notice is that a PAC shows up (maybe false data?) in put contributors list, so the necessary changes are made."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "contribution_by_indiv=contribution_by_indiv.loc[contribution_by_indiv['ENTITY_TP'].isin(['IND'])]\n",
    "contribution_by_indiv.loc[contribution_by_indiv['NAME'] == 'ASSOCIATION, NATIONAL EDUCATION', ['ENTITY_TP']] = 'PAC'\n",
    "\n",
    "emp_mapping = {\n",
    "    'SIMONS, JAMES H.' : 'SIMONS, JAMES',\n",
    "    'SUSSMAN, S. DONALD' : 'SUSSMAN, DONALD',\n",
    "    'HENDRICKS, DIANE MS.' : 'HENDRICKS, DIANE'\n",
    "}\n",
    "\n",
    "f = lambda x: emp_mapping.get(x, x) \n",
    "contribution_by_indiv.loc[:, 'NAME'] = contribution_by_indiv.loc[:, 'NAME'].map(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We use the dataframes from the previous question to access the ids of PACs spread out to each candidate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "trump_ids=trump['CMTE_ID'].tolist()\n",
    "hillary_ids=hillary['CMTE_ID'].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Distribution of PACs in to the correct Dataframes and then grouping by name and sorting by the amount of money given (TRANSACTION_AMT)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "contrib_hillary=contribution_by_indiv[contribution_by_indiv['CMTE_ID'].isin(hillary_ids)]\n",
    "contrib_trump=contribution_by_indiv[contribution_by_indiv['CMTE_ID'].isin(trump_ids)]\n",
    "\n",
    "contrib_hillary=contrib_hillary.groupby(['NAME']).sum().sort_values(by='TRANSACTION_AMT', ascending=False)\n",
    "contrib_trump=contrib_trump.groupby(['NAME']).sum().sort_values(by='TRANSACTION_AMT', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* If we were to compare the amount of money donated by individuals it is made clear that Hillary Clinton had the support of some very wealthy people (The sum of D. Trumps top 10 contributor's money is less than the amount that Mr. Thomas Steyer donated to Hillary Clinton)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>TRANSACTION_AMT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAME</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>STEYER, THOMAS F.</th>\n",
       "      <td>89500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUSSMAN, DONALD</th>\n",
       "      <td>37000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EYCHANER, FRED</th>\n",
       "      <td>35250000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SIMONS, JAMES</th>\n",
       "      <td>22000680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLOOMBERG, MICHAEL R.</th>\n",
       "      <td>21665124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOROS, GEORGE</th>\n",
       "      <td>19699693.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOSKOVITZ, DUSTIN</th>\n",
       "      <td>13625000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRITZKER, JAY ROBERT</th>\n",
       "      <td>9650848.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABRAHAM, DANIEL</th>\n",
       "      <td>9250000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRITZKER, MARY KATHRYN</th>\n",
       "      <td>7550000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        TRANSACTION_AMT\n",
       "NAME                                   \n",
       "STEYER, THOMAS F.            89500000.0\n",
       "SUSSMAN, DONALD              37000000.0\n",
       "EYCHANER, FRED               35250000.0\n",
       "SIMONS, JAMES                22000680.0\n",
       "BLOOMBERG, MICHAEL R.        21665124.0\n",
       "SOROS, GEORGE                19699693.0\n",
       "MOSKOVITZ, DUSTIN            13625000.0\n",
       "PRITZKER, JAY ROBERT          9650848.0\n",
       "ABRAHAM, DANIEL               9250000.0\n",
       "PRITZKER, MARY KATHRYN        7550000.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contrib_hillary.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The data shown in this classification is not completely true since the names in our dataset are not consistent. Only with the correct mapping of all names would we be able to draw conclusions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>TRANSACTION_AMT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAME</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MERCER, ROBERT</th>\n",
       "      <td>15500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HENDRICKS, DIANE</th>\n",
       "      <td>12152900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADELSON, MIRIAM</th>\n",
       "      <td>10000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADELSON, SHELDON</th>\n",
       "      <td>10000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCMAHON, LINDA E.</th>\n",
       "      <td>7000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARCUS, BERNARD</th>\n",
       "      <td>7000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UIHLEIN, RICHARD E.</th>\n",
       "      <td>5240000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PALMER, GEOFFREY H.</th>\n",
       "      <td>4000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BEAL, DANIEL A.</th>\n",
       "      <td>3615764.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KOCH, CHARLES MR.</th>\n",
       "      <td>3000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     TRANSACTION_AMT\n",
       "NAME                                \n",
       "MERCER, ROBERT            15500000.0\n",
       "HENDRICKS, DIANE          12152900.0\n",
       "ADELSON, MIRIAM           10000000.0\n",
       "ADELSON, SHELDON          10000000.0\n",
       "MCMAHON, LINDA E.          7000000.0\n",
       "MARCUS, BERNARD            7000000.0\n",
       "UIHLEIN, RICHARD E.        5240000.0\n",
       "PALMER, GEOFFREY H.        4000000.0\n",
       "BEAL, DANIEL A.            3615764.0\n",
       "KOCH, CHARLES MR.          3000000.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contrib_trump.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> 3) Investigate the chronological evolution of the contributions made to and the expenditures made by the\n",
    "campaigns\n",
    " </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We read the header and CSV of the Operating Expenditures file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "expenses_header=pd.read_csv(\"https://www.fec.gov/files/bulk-downloads/data_dictionaries/oppexp_header_file.csv\").columns.tolist()\n",
    "data_types_com = { headers: np.str for headers in expenses_header }\n",
    "data_types_com['TRANSACTION_AMT'] = np.float\n",
    "\n",
    "expenses = pd.read_csv('https://www.fec.gov/files/bulk-downloads/2016/oppexp16.zip', sep=\"|\", index_col=False, names=expenses_header,dtype=data_types_com,parse_dates=['TRANSACTION_DT'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We merge with the committees data to gain access on information concerning the name, type and candidate that the committee supports. According to the [documentation](https://www.fec.gov/campaign-finance-data/committee-master-file-description/), the CAND_ID column is not null only when CMTE_TP is equal to 'P', meaning that the committee is clearly a supporter of the presidential candidate.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "\n",
    "expenses=pd.merge(expenses, \n",
    "               commitees[['CMTE_ID','CMTE_NM','CMTE_TP','CAND_ID']], \n",
    "               on='CMTE_ID', \n",
    "               how='inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Here we filter out all dates that are not in the 2015-2016 dates and cast the TRANSACTION_DT column from an object to a date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "expenses['TRANSACTION_DT'] = pd.to_datetime(expenses['TRANSACTION_DT'], errors='coerce').dt.date\n",
    "expenses = expenses[(expenses['TRANSACTION_DT']>datetime.date(2014,12,31))&(expenses['TRANSACTION_DT']<datetime.date(2017,1,1))]\n",
    "expenses = expenses.set_index(pd.DatetimeIndex(expenses['TRANSACTION_DT']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The committees we take into concideration are the ones we know for sure that support a candidate. There are only 2 possibilities, either the committe is presidential (explained above), or it is one of the PACs which we carefully assigned to each candidate earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "trump_expenses=expenses[((expenses['CMTE_TP']=='P')&(expenses['CAND_ID']==\"P80001571\"))|(expenses['CMTE_ID'].isin(trump_ids))]\n",
    "hillary_expenses=expenses[((expenses['CMTE_TP']=='P')&(expenses['CAND_ID']==\"P00003392\"))|(expenses['CMTE_ID'].isin(hillary_ids))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "845932277.1999999"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hillary_expenses['TRANSACTION_AMT'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We group our collected data for each month in the 2015-2016 period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "trump_expenses=trump_expenses.groupby(pd.Grouper(freq='M')).sum().sort_values(by='TRANSACTION_DT', ascending=True).reset_index()\n",
    "hillary_expenses= hillary_expenses.groupby(pd.Grouper(freq='M')).sum().sort_values(by='TRANSACTION_DT', ascending=True).reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* It is interesting to see that Hillary Clinton actually spent **twice** the amount of money that Donald Trump spent on his campaign! (possible data missing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "845932277.2000014"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hillary_total_expenses = hillary_expenses['TRANSACTION_AMT'].sum()\n",
    "hillary_total_expenses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "418996007.1899994"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_total_expenses=trump_expenses['TRANSACTION_AMT'].sum()\n",
    "trump_total_expenses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Below we see how the data is destributed by month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>TRANSACTION_DT</th>\n",
       "      <th>TRANSACTION_AMT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>1.226227e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-02-28</td>\n",
       "      <td>1.552177e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-03-31</td>\n",
       "      <td>1.961462e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-04-30</td>\n",
       "      <td>2.290474e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-05-31</td>\n",
       "      <td>2.222634e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>2.846405e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>3.230750e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>3.754978e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>3.896815e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015-10-31</td>\n",
       "      <td>3.691653e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>4.857007e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>5.076333e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2016-01-31</td>\n",
       "      <td>1.405044e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2016-02-29</td>\n",
       "      <td>1.330325e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016-03-31</td>\n",
       "      <td>1.634657e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2016-04-30</td>\n",
       "      <td>1.224233e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016-05-31</td>\n",
       "      <td>9.768688e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2016-06-30</td>\n",
       "      <td>1.156664e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2016-07-31</td>\n",
       "      <td>2.267973e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2016-08-31</td>\n",
       "      <td>3.630032e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2016-09-30</td>\n",
       "      <td>7.712371e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2016-10-31</td>\n",
       "      <td>1.163524e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2016-11-30</td>\n",
       "      <td>4.083760e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2016-12-31</td>\n",
       "      <td>1.181744e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TRANSACTION_DT  TRANSACTION_AMT\n",
       "0      2015-01-31     1.226227e+06\n",
       "1      2015-02-28     1.552177e+06\n",
       "2      2015-03-31     1.961462e+06\n",
       "3      2015-04-30     2.290474e+06\n",
       "4      2015-05-31     2.222634e+06\n",
       "5      2015-06-30     2.846405e+06\n",
       "6      2015-07-31     3.230750e+06\n",
       "7      2015-08-31     3.754978e+06\n",
       "8      2015-09-30     3.896815e+06\n",
       "9      2015-10-31     3.691653e+06\n",
       "10     2015-11-30     4.857007e+06\n",
       "11     2015-12-31     5.076333e+06\n",
       "12     2016-01-31     1.405044e+07\n",
       "13     2016-02-29     1.330325e+07\n",
       "14     2016-03-31     1.634657e+07\n",
       "15     2016-04-30     1.224233e+07\n",
       "16     2016-05-31     9.768688e+06\n",
       "17     2016-06-30     1.156664e+07\n",
       "18     2016-07-31     2.267973e+07\n",
       "19     2016-08-31     3.630032e+07\n",
       "20     2016-09-30     7.712371e+07\n",
       "21     2016-10-31     1.163524e+08\n",
       "22     2016-11-30     4.083760e+07\n",
       "23     2016-12-31     1.181744e+07"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_expenses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>TRANSACTION_DT</th>\n",
       "      <th>TRANSACTION_AMT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>2.467923e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-02-28</td>\n",
       "      <td>2.658381e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-03-31</td>\n",
       "      <td>4.210487e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-04-30</td>\n",
       "      <td>7.232264e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-05-31</td>\n",
       "      <td>1.234020e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>1.394988e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>2.055562e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>1.455028e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>1.591543e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015-10-31</td>\n",
       "      <td>2.155387e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>1.939148e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>2.273743e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2016-01-31</td>\n",
       "      <td>2.638437e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2016-02-29</td>\n",
       "      <td>4.763552e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016-03-31</td>\n",
       "      <td>4.220962e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2016-04-30</td>\n",
       "      <td>3.646777e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016-05-31</td>\n",
       "      <td>2.330323e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2016-06-30</td>\n",
       "      <td>5.636266e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2016-07-31</td>\n",
       "      <td>4.474994e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2016-08-31</td>\n",
       "      <td>6.406468e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2016-09-30</td>\n",
       "      <td>1.182407e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2016-10-31</td>\n",
       "      <td>1.317963e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2016-11-30</td>\n",
       "      <td>8.520782e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2016-12-31</td>\n",
       "      <td>1.194639e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TRANSACTION_DT  TRANSACTION_AMT\n",
       "0      2015-01-31     2.467923e+06\n",
       "1      2015-02-28     2.658381e+06\n",
       "2      2015-03-31     4.210487e+06\n",
       "3      2015-04-30     7.232264e+06\n",
       "4      2015-05-31     1.234020e+07\n",
       "5      2015-06-30     1.394988e+07\n",
       "6      2015-07-31     2.055562e+07\n",
       "7      2015-08-31     1.455028e+07\n",
       "8      2015-09-30     1.591543e+07\n",
       "9      2015-10-31     2.155387e+07\n",
       "10     2015-11-30     1.939148e+07\n",
       "11     2015-12-31     2.273743e+07\n",
       "12     2016-01-31     2.638437e+07\n",
       "13     2016-02-29     4.763552e+07\n",
       "14     2016-03-31     4.220962e+07\n",
       "15     2016-04-30     3.646777e+07\n",
       "16     2016-05-31     2.330323e+07\n",
       "17     2016-06-30     5.636266e+07\n",
       "18     2016-07-31     4.474994e+07\n",
       "19     2016-08-31     6.406468e+07\n",
       "20     2016-09-30     1.182407e+08\n",
       "21     2016-10-31     1.317963e+08\n",
       "22     2016-11-30     8.520782e+07\n",
       "23     2016-12-31     1.194639e+07"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hillary_expenses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We merge with the committees data to gain access on information concerning the name, type and candidate that the committee supports. According to the [documentation](https://www.fec.gov/campaign-finance-data/committee-master-file-description/), the CAND_ID column is not null only when CMTE_TP is equal to 'P', meaning that the committee is clearly a supporter of the presidential candidate.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20468532, 21)\n"
     ]
    }
   ],
   "source": [
    "print(contributions.shape)\n",
    "\n",
    "contributions=pd.merge(contributions, \n",
    "               commitees[['CMTE_ID','CMTE_NM','CMTE_TP','CAND_ID']], \n",
    "               on='CMTE_ID', \n",
    "               how='inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Necessary actions are taken to convert TRANSACTION_DT into readable dates and filter out data that doesn't belong to the 2015-2016 period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CMTE_ID                    object\n",
      "AMNDT_IND                  object\n",
      "RPT_TP                     object\n",
      "TRANSACTION_PGI            object\n",
      "IMAGE_NUM                  object\n",
      "TRANSACTION_TP             object\n",
      "ENTITY_TP                  object\n",
      "NAME                       object\n",
      "CITY                       object\n",
      "STATE                      object\n",
      "ZIP_CODE                   object\n",
      "EMPLOYER                   object\n",
      "OCCUPATION                 object\n",
      "TRANSACTION_DT     datetime64[ns]\n",
      "TRANSACTION_AMT           float64\n",
      "OTHER_ID                   object\n",
      "TRAN_ID                    object\n",
      "FILE_NUM                   object\n",
      "MEMO_CD                    object\n",
      "MEMO_TEXT                  object\n",
      "SUB_ID                     object\n",
      "CMTE_NM                    object\n",
      "CMTE_TP                    object\n",
      "CAND_ID                    object\n",
      "dtype: object\n",
      "(6585682, 24)\n"
     ]
    }
   ],
   "source": [
    "contributions['TRANSACTION_DT'] = pd.to_datetime(contributions['TRANSACTION_DT'], format='%d%m%Y', errors='coerce')\n",
    "print(contributions.dtypes)\n",
    "\n",
    "contributions = contributions[(contributions['TRANSACTION_DT'].dt.date >= datetime.date(year=2015,month=1,day=1))& (contributions['TRANSACTION_DT'].dt.date < datetime.date(year=2017,month=1,day=1))]\n",
    "contributions = contributions.set_index(pd.DatetimeIndex(contributions['TRANSACTION_DT']))\n",
    "print(contributions.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "contrib_hillary=contributions[((contributions['CMTE_TP']=='P')&(contributions['CAND_ID']==\"P00003392\"))|(contributions['CMTE_ID'].isin(hillary_ids))]\n",
    "contrib_hillary= contrib_hillary.groupby(pd.Grouper(freq='M')).sum().sort_values(by='TRANSACTION_DT', ascending=True).reset_index()\n",
    "\n",
    "contrib_trump=contributions[((contributions['CMTE_TP']=='P')&(contributions['CAND_ID']==\"P80001571\"))|(contributions['CMTE_ID'].isin(trump_ids))]\n",
    "contrib_trump= contrib_trump.groupby(pd.Grouper(freq='M')).sum().sort_values(by='TRANSACTION_DT', ascending=True).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19a63e39310>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "contrib_trump.plot(x ='TRANSACTION_DT', y='TRANSACTION_AMT', kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19a4c4f4d00>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "contrib_hillary.plot(x ='TRANSACTION_DT', y='TRANSACTION_AMT', kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* By carefully examining the plots below, we can see that expenses for both candidates sky rockets during the election period (September, October, November) ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19a8fc195e0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hillary_expenses.plot(x ='TRANSACTION_DT', y='TRANSACTION_AMT', kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19a4c5ab970>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trump_expenses.plot(x ='TRANSACTION_DT', y='TRANSACTION_AMT', kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> 4) Identify the biggest recipients of campaign expenditures. </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* All we have to do is filter out the correct committees, examine all expenses made from them and then group by name of the company that received the money."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "trump_expenses=expenses[((expenses['CMTE_TP']=='P')&(expenses['CAND_ID']==\"P80001571\"))|(expenses['CMTE_ID'].isin(trump_ids))]\n",
    "hillary_expenses=expenses[((expenses['CMTE_TP']=='P')&(expenses['CAND_ID']==\"P00003392\"))|(expenses['CMTE_ID'].isin(hillary_ids))]\n",
    "\n",
    "trump_comps = trump_expenses.groupby(['NAME'])['TRANSACTION_AMT'].sum().sort_values(ascending=False).reset_index()\n",
    "hillary_comps = hillary_expenses.groupby(['NAME'])['TRANSACTION_AMT'].sum().sort_values(ascending=False).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>TRANSACTION_AMT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GILES-PARSCALE</td>\n",
       "      <td>87838378.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AMERICAN MEDIA &amp; ADVOCACY GROUP</td>\n",
       "      <td>74176379.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RICK REED MEDIA, INC.</td>\n",
       "      <td>24460099.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AMERICAN EXPRESS</td>\n",
       "      <td>19427054.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACE SPECIALTIES, LLC</td>\n",
       "      <td>15885323.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PRIVATE JET SERVICES, LLC</td>\n",
       "      <td>9953216.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>JAMESTOWN ASSOCIATES</td>\n",
       "      <td>8836595.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TAG AIR, INC.</td>\n",
       "      <td>8741464.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AIR CHARTER TEAM</td>\n",
       "      <td>8321403.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAMBRIDGE ANALYTICA, LLC</td>\n",
       "      <td>5912500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>STRIPE</td>\n",
       "      <td>3424613.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>JONES DAY</td>\n",
       "      <td>3389452.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>RFP SERVICES, LLC</td>\n",
       "      <td>3101452.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CALI-FAME</td>\n",
       "      <td>2887446.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HOTELS.COM</td>\n",
       "      <td>2877804.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>POLITICAL ISSUE ADVOCACY LLC</td>\n",
       "      <td>2753303.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>POLITICAL ADVERTISING</td>\n",
       "      <td>2424697.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>WIZBANG SOLUTIONS, INC.</td>\n",
       "      <td>2108589.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>TRUMP TOWER COMMERCIAL LLC</td>\n",
       "      <td>2011743.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CONSOLIDATED MAILING SERVICES</td>\n",
       "      <td>1850252.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               NAME  TRANSACTION_AMT\n",
       "0                    GILES-PARSCALE      87838378.10\n",
       "1   AMERICAN MEDIA & ADVOCACY GROUP      74176379.30\n",
       "2             RICK REED MEDIA, INC.      24460099.45\n",
       "3                  AMERICAN EXPRESS      19427054.67\n",
       "4              ACE SPECIALTIES, LLC      15885323.63\n",
       "5         PRIVATE JET SERVICES, LLC       9953216.06\n",
       "6              JAMESTOWN ASSOCIATES       8836595.01\n",
       "7                     TAG AIR, INC.       8741464.25\n",
       "8                  AIR CHARTER TEAM       8321403.26\n",
       "9          CAMBRIDGE ANALYTICA, LLC       5912500.00\n",
       "10                           STRIPE       3424613.52\n",
       "11                        JONES DAY       3389452.77\n",
       "12                RFP SERVICES, LLC       3101452.43\n",
       "13                        CALI-FAME       2887446.22\n",
       "14                       HOTELS.COM       2877804.99\n",
       "15     POLITICAL ISSUE ADVOCACY LLC       2753303.03\n",
       "16            POLITICAL ADVERTISING       2424697.87\n",
       "17          WIZBANG SOLUTIONS, INC.       2108589.94\n",
       "18       TRUMP TOWER COMMERCIAL LLC       2011743.74\n",
       "19    CONSOLIDATED MAILING SERVICES       1850252.47"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_comps.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>TRANSACTION_AMT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GMMB</td>\n",
       "      <td>3.084634e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BULLY PULPIT INTERACTIVE LLC</td>\n",
       "      <td>4.013508e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ADP</td>\n",
       "      <td>2.909583e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AMERICAN EXPRESS</td>\n",
       "      <td>2.720724e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MARKHAM PRODUCTIONS</td>\n",
       "      <td>1.602585e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>EXECUTIVE FLITEWAYS</td>\n",
       "      <td>1.586629e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PAYROLL DATA PROCESSING</td>\n",
       "      <td>1.124838e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AIR PARTNERS, INC.</td>\n",
       "      <td>1.115633e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>STRIPE</td>\n",
       "      <td>1.033458e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SEIU GENERAL FUND</td>\n",
       "      <td>7.886324e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NEXTGEN CLIMATE ACTION</td>\n",
       "      <td>6.457817e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>AETNA</td>\n",
       "      <td>5.883909e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>MISSION CONTROL, INC.</td>\n",
       "      <td>5.837600e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PRECISION NETWORK LLC</td>\n",
       "      <td>5.783531e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>PERKINS COIE</td>\n",
       "      <td>5.695987e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CHAPMAN CUBINE ADAMS + HUSSEY</td>\n",
       "      <td>5.690366e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ADP, INC.</td>\n",
       "      <td>4.758216e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>BENENSON STRATEGY GROUP</td>\n",
       "      <td>4.441177e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>AFL-CIO COPE TREASURY</td>\n",
       "      <td>4.299823e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>FINANCIAL INNOVATIONS</td>\n",
       "      <td>3.506385e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             NAME  TRANSACTION_AMT\n",
       "0                            GMMB     3.084634e+08\n",
       "1    BULLY PULPIT INTERACTIVE LLC     4.013508e+07\n",
       "2                             ADP     2.909583e+07\n",
       "3                AMERICAN EXPRESS     2.720724e+07\n",
       "4             MARKHAM PRODUCTIONS     1.602585e+07\n",
       "5             EXECUTIVE FLITEWAYS     1.586629e+07\n",
       "6         PAYROLL DATA PROCESSING     1.124838e+07\n",
       "7              AIR PARTNERS, INC.     1.115633e+07\n",
       "8                          STRIPE     1.033458e+07\n",
       "9               SEIU GENERAL FUND     7.886324e+06\n",
       "10         NEXTGEN CLIMATE ACTION     6.457817e+06\n",
       "11                          AETNA     5.883909e+06\n",
       "12          MISSION CONTROL, INC.     5.837600e+06\n",
       "13          PRECISION NETWORK LLC     5.783531e+06\n",
       "14                   PERKINS COIE     5.695987e+06\n",
       "15  CHAPMAN CUBINE ADAMS + HUSSEY     5.690366e+06\n",
       "16                      ADP, INC.     4.758216e+06\n",
       "17        BENENSON STRATEGY GROUP     4.441177e+06\n",
       "18          AFL-CIO COPE TREASURY     4.299823e+06\n",
       "19          FINANCIAL INNOVATIONS     3.506385e+06"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hillary_comps.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> 5) Examine the geographical distribution, at the state level, of campaign expenditures. For each state,\n",
    "calculate the expenditures per voter. This will require that you find a source with the number of registered\n",
    "voters per state. Examine the situation for swing states.\n",
    " </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Government data that contains eligibility of voters per state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "registered = pd.read_csv('https://www.eac.gov/sites/default/files/Research/EAVS_2016_Final_Data_for_Public_Release_v2.csv.zip', usecols=[\"State\", \"A1a\"],low_memory=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Searched for datasets on the internet that contain the party that won per state to analyze their expenses and to determine if the amount of money they spend actually gives them a state win."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "full = pd.read_csv('https://query.data.world/s/5li6jmqclpzydielgcwynkkvup5zqo') #this data is mising for some reason the state of Louisiana\n",
    "states = pd.read_csv('https://query.data.world/s/lx674mcokakfgsyof5z6tk62tmrio6')\n",
    "full=pd.merge(full, \n",
    "               states, \n",
    "               on='State',\n",
    "               how=\"inner\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Clear up our data, Puerto Rico is not a US state. We group by states since al cities are in the data and sum their population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "registered['A1a'] =pd.to_numeric(registered['A1a'], errors='coerce')\n",
    "registered = registered.rename(columns={'State': 'STATE'})\n",
    "registered= registered[~registered['STATE'].isin(['PR'])]\n",
    "registered = registered.groupby(['STATE']).sum().sort_values(by='A1a')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Merge all our data so we now have all needed information gathered in one dataframe. A correction is made in the data since North Dakota's population is all registered to vote."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "registered=pd.merge(registered, full, how='inner', left_on='STATE', right_on='Abbreviation')\n",
    "registered.loc[registered['Abbreviation'] == 'ND', ['A1a']] = 754.434 #In North Dakota everyone is registered to vote, in the data it's A1a is 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Filtering out all correct expenses based on assigned committees and grouping them by state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "trump_expenses=expenses[((expenses['CMTE_TP']=='P')&(expenses['CAND_ID']==\"P80001571\"))|(expenses['CMTE_ID'].isin(trump_ids))]\n",
    "hillary_expenses=expenses[((expenses['CMTE_TP']=='P')&(expenses['CAND_ID']==\"P00003392\"))|(expenses['CMTE_ID'].isin(hillary_ids))]\n",
    "\n",
    "\n",
    "trump_expenses = trump_expenses.groupby(['STATE'])['TRANSACTION_AMT'].sum().reset_index()\n",
    "hillary_expenses = hillary_expenses.groupby(['STATE'])['TRANSACTION_AMT'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Finally, we merge all our data based on the Abbreviation of each state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "trump_expenses=pd.merge(trump_expenses, registered, how='inner', left_on='STATE', right_on='Abbreviation')\n",
    "\n",
    "hillary_expenses=pd.merge(hillary_expenses, registered, how='inner', left_on='STATE', right_on='Abbreviation')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* For each of the 2 candidates we now divide all expenses by the population in each state to get the amount of \\\\$/person ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We can notice a huge spike in USA's capital's state, DC, where most are the PACs are stationed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>STATE</th>\n",
       "      <th>TRANSACTION_AMT</th>\n",
       "      <th>A1a</th>\n",
       "      <th>State</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Winning Party</th>\n",
       "      <th>Abbreviation</th>\n",
       "      <th>TOTAL_TRUMP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AK</td>\n",
       "      <td>2.024487e+04</td>\n",
       "      <td>5.873030e+05</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>3</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>AK</td>\n",
       "      <td>0.034471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>8.937236e+05</td>\n",
       "      <td>3.333946e+06</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>9</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>AL</td>\n",
       "      <td>0.268068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AR</td>\n",
       "      <td>2.623814e+05</td>\n",
       "      <td>1.765513e+06</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>6</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>AR</td>\n",
       "      <td>0.148615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AZ</td>\n",
       "      <td>6.319766e+06</td>\n",
       "      <td>4.080680e+06</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>11</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>AZ</td>\n",
       "      <td>1.548704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>1.142289e+07</td>\n",
       "      <td>2.448664e+07</td>\n",
       "      <td>California</td>\n",
       "      <td>55</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>CA</td>\n",
       "      <td>0.466495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CO</td>\n",
       "      <td>3.057243e+06</td>\n",
       "      <td>3.840303e+06</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>9</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>CO</td>\n",
       "      <td>0.796094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CT</td>\n",
       "      <td>1.666276e+06</td>\n",
       "      <td>2.331684e+06</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>7</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>CT</td>\n",
       "      <td>0.714623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DC</td>\n",
       "      <td>1.571156e+07</td>\n",
       "      <td>4.932870e+05</td>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>3</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>DC</td>\n",
       "      <td>31.850756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DE</td>\n",
       "      <td>3.393263e+06</td>\n",
       "      <td>6.756630e+05</td>\n",
       "      <td>Delaware</td>\n",
       "      <td>3</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>DE</td>\n",
       "      <td>5.022124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FL</td>\n",
       "      <td>1.639515e+07</td>\n",
       "      <td>1.350557e+07</td>\n",
       "      <td>Florida</td>\n",
       "      <td>29</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>FL</td>\n",
       "      <td>1.213955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GA</td>\n",
       "      <td>4.258014e+06</td>\n",
       "      <td>6.657621e+06</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>16</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>GA</td>\n",
       "      <td>0.639570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>HI</td>\n",
       "      <td>1.640275e+04</td>\n",
       "      <td>7.514830e+05</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>4</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>HI</td>\n",
       "      <td>0.021827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>IA</td>\n",
       "      <td>3.246835e+06</td>\n",
       "      <td>2.222380e+06</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>6</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>IA</td>\n",
       "      <td>1.460972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ID</td>\n",
       "      <td>8.354981e+04</td>\n",
       "      <td>9.365290e+05</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>4</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>ID</td>\n",
       "      <td>0.089212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>IL</td>\n",
       "      <td>2.020994e+06</td>\n",
       "      <td>8.843038e+06</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>20</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>IL</td>\n",
       "      <td>0.228541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>IN</td>\n",
       "      <td>1.370971e+06</td>\n",
       "      <td>4.839038e+06</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>11</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>IN</td>\n",
       "      <td>0.283315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>KS</td>\n",
       "      <td>9.073726e+05</td>\n",
       "      <td>1.785834e+06</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>6</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>KS</td>\n",
       "      <td>0.508095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>KY</td>\n",
       "      <td>1.394536e+06</td>\n",
       "      <td>3.306120e+06</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>8</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>KY</td>\n",
       "      <td>0.421804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>MA</td>\n",
       "      <td>3.077924e+06</td>\n",
       "      <td>4.534974e+06</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>11</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>MA</td>\n",
       "      <td>0.678708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MD</td>\n",
       "      <td>3.807301e+06</td>\n",
       "      <td>3.900090e+06</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>10</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>MD</td>\n",
       "      <td>0.976209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ME</td>\n",
       "      <td>1.173822e+05</td>\n",
       "      <td>1.065100e+06</td>\n",
       "      <td>Maine</td>\n",
       "      <td>3</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>ME</td>\n",
       "      <td>0.110208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ME</td>\n",
       "      <td>1.173822e+05</td>\n",
       "      <td>1.065100e+06</td>\n",
       "      <td>Maine</td>\n",
       "      <td>1</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>ME</td>\n",
       "      <td>0.110208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>MI</td>\n",
       "      <td>9.298226e+05</td>\n",
       "      <td>7.514055e+06</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>16</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>MI</td>\n",
       "      <td>0.123744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MN</td>\n",
       "      <td>4.828978e+05</td>\n",
       "      <td>3.473972e+06</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>10</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>MN</td>\n",
       "      <td>0.139005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MO</td>\n",
       "      <td>9.622272e+06</td>\n",
       "      <td>4.215860e+06</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>10</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>MO</td>\n",
       "      <td>2.282398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>MS</td>\n",
       "      <td>4.022545e+05</td>\n",
       "      <td>2.072395e+06</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>6</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>MS</td>\n",
       "      <td>0.194101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>MT</td>\n",
       "      <td>5.817308e+04</td>\n",
       "      <td>6.943700e+05</td>\n",
       "      <td>Montana</td>\n",
       "      <td>3</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>MT</td>\n",
       "      <td>0.083778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NC</td>\n",
       "      <td>2.483727e+06</td>\n",
       "      <td>6.924469e+06</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>15</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>NC</td>\n",
       "      <td>0.358688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ND</td>\n",
       "      <td>1.535000e+04</td>\n",
       "      <td>7.544340e+02</td>\n",
       "      <td>North Dakota</td>\n",
       "      <td>3</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>ND</td>\n",
       "      <td>20.346379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NE</td>\n",
       "      <td>1.797129e+05</td>\n",
       "      <td>1.211101e+06</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>5</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>NE</td>\n",
       "      <td>0.148388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>NH</td>\n",
       "      <td>1.174327e+07</td>\n",
       "      <td>9.883980e+05</td>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>4</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>NH</td>\n",
       "      <td>11.881112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>NJ</td>\n",
       "      <td>1.954377e+07</td>\n",
       "      <td>5.751090e+06</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>14</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>NJ</td>\n",
       "      <td>3.398273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>NM</td>\n",
       "      <td>1.834263e+05</td>\n",
       "      <td>1.289420e+06</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>5</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>NM</td>\n",
       "      <td>0.142255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>NV</td>\n",
       "      <td>1.404659e+06</td>\n",
       "      <td>1.678883e+06</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>6</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>NV</td>\n",
       "      <td>0.836663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>NY</td>\n",
       "      <td>2.806493e+07</td>\n",
       "      <td>1.620089e+07</td>\n",
       "      <td>New York</td>\n",
       "      <td>29</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>NY</td>\n",
       "      <td>1.732308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>OH</td>\n",
       "      <td>6.998113e+06</td>\n",
       "      <td>7.861025e+06</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>18</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>OH</td>\n",
       "      <td>0.890229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>OK</td>\n",
       "      <td>5.309361e+05</td>\n",
       "      <td>2.157450e+06</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>7</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>OK</td>\n",
       "      <td>0.246094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>OR</td>\n",
       "      <td>6.477216e+05</td>\n",
       "      <td>2.553810e+06</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>7</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>OR</td>\n",
       "      <td>0.253630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>PA</td>\n",
       "      <td>2.338879e+06</td>\n",
       "      <td>8.722975e+06</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>20</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>PA</td>\n",
       "      <td>0.268129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>RI</td>\n",
       "      <td>4.512901e+05</td>\n",
       "      <td>7.540650e+05</td>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>4</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>RI</td>\n",
       "      <td>0.598476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>SC</td>\n",
       "      <td>1.651122e+06</td>\n",
       "      <td>3.157027e+06</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>9</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>SC</td>\n",
       "      <td>0.522999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>SD</td>\n",
       "      <td>2.852902e+06</td>\n",
       "      <td>5.953220e+05</td>\n",
       "      <td>South Dakota</td>\n",
       "      <td>3</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>SD</td>\n",
       "      <td>4.792199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>TN</td>\n",
       "      <td>1.205921e+06</td>\n",
       "      <td>4.110318e+06</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>11</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>TN</td>\n",
       "      <td>0.293389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>TX</td>\n",
       "      <td>9.786662e+07</td>\n",
       "      <td>1.438239e+07</td>\n",
       "      <td>Texas</td>\n",
       "      <td>38</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>TX</td>\n",
       "      <td>6.804616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>UT</td>\n",
       "      <td>4.490452e+05</td>\n",
       "      <td>1.577069e+06</td>\n",
       "      <td>Utah</td>\n",
       "      <td>6</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>UT</td>\n",
       "      <td>0.284734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>VA</td>\n",
       "      <td>1.288244e+08</td>\n",
       "      <td>5.604106e+06</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>13</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>VA</td>\n",
       "      <td>22.987511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>VT</td>\n",
       "      <td>8.146759e+04</td>\n",
       "      <td>4.722890e+05</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>3</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>VT</td>\n",
       "      <td>0.172495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>WA</td>\n",
       "      <td>1.253546e+06</td>\n",
       "      <td>4.872385e+06</td>\n",
       "      <td>Washington</td>\n",
       "      <td>12</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>WA</td>\n",
       "      <td>0.257276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>WI</td>\n",
       "      <td>1.039735e+06</td>\n",
       "      <td>3.768373e+06</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>10</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>WI</td>\n",
       "      <td>0.275911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>WV</td>\n",
       "      <td>7.885706e+05</td>\n",
       "      <td>1.254768e+06</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>5</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>WV</td>\n",
       "      <td>0.628459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>WY</td>\n",
       "      <td>1.513496e+05</td>\n",
       "      <td>2.842030e+05</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>3</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>WY</td>\n",
       "      <td>0.532540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   STATE  TRANSACTION_AMT           A1a                 State  Votes  \\\n",
       "0     AK     2.024487e+04  5.873030e+05                Alaska      3   \n",
       "1     AL     8.937236e+05  3.333946e+06               Alabama      9   \n",
       "2     AR     2.623814e+05  1.765513e+06              Arkansas      6   \n",
       "3     AZ     6.319766e+06  4.080680e+06               Arizona     11   \n",
       "4     CA     1.142289e+07  2.448664e+07            California     55   \n",
       "5     CO     3.057243e+06  3.840303e+06              Colorado      9   \n",
       "6     CT     1.666276e+06  2.331684e+06           Connecticut      7   \n",
       "7     DC     1.571156e+07  4.932870e+05  District of Columbia      3   \n",
       "8     DE     3.393263e+06  6.756630e+05              Delaware      3   \n",
       "9     FL     1.639515e+07  1.350557e+07               Florida     29   \n",
       "10    GA     4.258014e+06  6.657621e+06               Georgia     16   \n",
       "11    HI     1.640275e+04  7.514830e+05                Hawaii      4   \n",
       "12    IA     3.246835e+06  2.222380e+06                  Iowa      6   \n",
       "13    ID     8.354981e+04  9.365290e+05                 Idaho      4   \n",
       "14    IL     2.020994e+06  8.843038e+06              Illinois     20   \n",
       "15    IN     1.370971e+06  4.839038e+06               Indiana     11   \n",
       "16    KS     9.073726e+05  1.785834e+06                Kansas      6   \n",
       "17    KY     1.394536e+06  3.306120e+06              Kentucky      8   \n",
       "18    MA     3.077924e+06  4.534974e+06         Massachusetts     11   \n",
       "19    MD     3.807301e+06  3.900090e+06              Maryland     10   \n",
       "20    ME     1.173822e+05  1.065100e+06                 Maine      3   \n",
       "21    ME     1.173822e+05  1.065100e+06                 Maine      1   \n",
       "22    MI     9.298226e+05  7.514055e+06              Michigan     16   \n",
       "23    MN     4.828978e+05  3.473972e+06             Minnesota     10   \n",
       "24    MO     9.622272e+06  4.215860e+06              Missouri     10   \n",
       "25    MS     4.022545e+05  2.072395e+06           Mississippi      6   \n",
       "26    MT     5.817308e+04  6.943700e+05               Montana      3   \n",
       "27    NC     2.483727e+06  6.924469e+06        North Carolina     15   \n",
       "28    ND     1.535000e+04  7.544340e+02          North Dakota      3   \n",
       "29    NE     1.797129e+05  1.211101e+06              Nebraska      5   \n",
       "30    NH     1.174327e+07  9.883980e+05         New Hampshire      4   \n",
       "31    NJ     1.954377e+07  5.751090e+06            New Jersey     14   \n",
       "32    NM     1.834263e+05  1.289420e+06            New Mexico      5   \n",
       "33    NV     1.404659e+06  1.678883e+06                Nevada      6   \n",
       "34    NY     2.806493e+07  1.620089e+07              New York     29   \n",
       "35    OH     6.998113e+06  7.861025e+06                  Ohio     18   \n",
       "36    OK     5.309361e+05  2.157450e+06              Oklahoma      7   \n",
       "37    OR     6.477216e+05  2.553810e+06                Oregon      7   \n",
       "38    PA     2.338879e+06  8.722975e+06          Pennsylvania     20   \n",
       "39    RI     4.512901e+05  7.540650e+05          Rhode Island      4   \n",
       "40    SC     1.651122e+06  3.157027e+06        South Carolina      9   \n",
       "41    SD     2.852902e+06  5.953220e+05          South Dakota      3   \n",
       "42    TN     1.205921e+06  4.110318e+06             Tennessee     11   \n",
       "43    TX     9.786662e+07  1.438239e+07                 Texas     38   \n",
       "44    UT     4.490452e+05  1.577069e+06                  Utah      6   \n",
       "45    VA     1.288244e+08  5.604106e+06              Virginia     13   \n",
       "46    VT     8.146759e+04  4.722890e+05               Vermont      3   \n",
       "47    WA     1.253546e+06  4.872385e+06            Washington     12   \n",
       "48    WI     1.039735e+06  3.768373e+06             Wisconsin     10   \n",
       "49    WV     7.885706e+05  1.254768e+06         West Virginia      5   \n",
       "50    WY     1.513496e+05  2.842030e+05               Wyoming      3   \n",
       "\n",
       "   Winning Party Abbreviation  TOTAL_TRUMP  \n",
       "0    Republicans           AK     0.034471  \n",
       "1    Republicans           AL     0.268068  \n",
       "2    Republicans           AR     0.148615  \n",
       "3    Republicans           AZ     1.548704  \n",
       "4      Democrats           CA     0.466495  \n",
       "5      Democrats           CO     0.796094  \n",
       "6      Democrats           CT     0.714623  \n",
       "7      Democrats           DC    31.850756  \n",
       "8      Democrats           DE     5.022124  \n",
       "9    Republicans           FL     1.213955  \n",
       "10   Republicans           GA     0.639570  \n",
       "11     Democrats           HI     0.021827  \n",
       "12   Republicans           IA     1.460972  \n",
       "13   Republicans           ID     0.089212  \n",
       "14     Democrats           IL     0.228541  \n",
       "15   Republicans           IN     0.283315  \n",
       "16   Republicans           KS     0.508095  \n",
       "17   Republicans           KY     0.421804  \n",
       "18     Democrats           MA     0.678708  \n",
       "19     Democrats           MD     0.976209  \n",
       "20     Democrats           ME     0.110208  \n",
       "21   Republicans           ME     0.110208  \n",
       "22   Republicans           MI     0.123744  \n",
       "23     Democrats           MN     0.139005  \n",
       "24   Republicans           MO     2.282398  \n",
       "25   Republicans           MS     0.194101  \n",
       "26   Republicans           MT     0.083778  \n",
       "27   Republicans           NC     0.358688  \n",
       "28   Republicans           ND    20.346379  \n",
       "29   Republicans           NE     0.148388  \n",
       "30     Democrats           NH    11.881112  \n",
       "31     Democrats           NJ     3.398273  \n",
       "32     Democrats           NM     0.142255  \n",
       "33     Democrats           NV     0.836663  \n",
       "34     Democrats           NY     1.732308  \n",
       "35   Republicans           OH     0.890229  \n",
       "36   Republicans           OK     0.246094  \n",
       "37     Democrats           OR     0.253630  \n",
       "38   Republicans           PA     0.268129  \n",
       "39     Democrats           RI     0.598476  \n",
       "40   Republicans           SC     0.522999  \n",
       "41   Republicans           SD     4.792199  \n",
       "42   Republicans           TN     0.293389  \n",
       "43   Republicans           TX     6.804616  \n",
       "44   Republicans           UT     0.284734  \n",
       "45     Democrats           VA    22.987511  \n",
       "46     Democrats           VT     0.172495  \n",
       "47     Democrats           WA     0.257276  \n",
       "48   Republicans           WI     0.275911  \n",
       "49   Republicans           WV     0.628459  \n",
       "50   Republicans           WY     0.532540  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_expenses['TOTAL_TRUMP']=trump_expenses['TRANSACTION_AMT']/trump_expenses['A1a']\n",
    "\n",
    "trump_expenses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATE</th>\n",
       "      <th>TRANSACTION_AMT</th>\n",
       "      <th>A1a</th>\n",
       "      <th>State</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Winning Party</th>\n",
       "      <th>Abbreviation</th>\n",
       "      <th>TOTAL_HILLARY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AK</td>\n",
       "      <td>3.555430e+04</td>\n",
       "      <td>5.873030e+05</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>3</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>AK</td>\n",
       "      <td>0.060538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>3.159969e+06</td>\n",
       "      <td>3.333946e+06</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>9</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>AL</td>\n",
       "      <td>0.947817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AR</td>\n",
       "      <td>1.682843e+07</td>\n",
       "      <td>1.765513e+06</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>6</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>AR</td>\n",
       "      <td>9.531748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AZ</td>\n",
       "      <td>6.610760e+05</td>\n",
       "      <td>4.080680e+06</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>11</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>AZ</td>\n",
       "      <td>0.162001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>3.704482e+07</td>\n",
       "      <td>2.448664e+07</td>\n",
       "      <td>California</td>\n",
       "      <td>55</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>CA</td>\n",
       "      <td>1.512858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CO</td>\n",
       "      <td>1.195911e+07</td>\n",
       "      <td>3.840303e+06</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>9</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>CO</td>\n",
       "      <td>3.114107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CT</td>\n",
       "      <td>1.201941e+07</td>\n",
       "      <td>2.331684e+06</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>7</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>CT</td>\n",
       "      <td>5.154819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DC</td>\n",
       "      <td>4.449680e+08</td>\n",
       "      <td>4.932870e+05</td>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>3</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>DC</td>\n",
       "      <td>902.046974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DE</td>\n",
       "      <td>1.744907e+06</td>\n",
       "      <td>6.756630e+05</td>\n",
       "      <td>Delaware</td>\n",
       "      <td>3</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>DE</td>\n",
       "      <td>2.582510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FL</td>\n",
       "      <td>2.807361e+07</td>\n",
       "      <td>1.350557e+07</td>\n",
       "      <td>Florida</td>\n",
       "      <td>29</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>FL</td>\n",
       "      <td>2.078669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GA</td>\n",
       "      <td>3.313178e+06</td>\n",
       "      <td>6.657621e+06</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>16</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>GA</td>\n",
       "      <td>0.497652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>HI</td>\n",
       "      <td>6.069555e+04</td>\n",
       "      <td>7.514830e+05</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>4</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>HI</td>\n",
       "      <td>0.080768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>IA</td>\n",
       "      <td>7.879875e+06</td>\n",
       "      <td>2.222380e+06</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>6</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>IA</td>\n",
       "      <td>3.545692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ID</td>\n",
       "      <td>2.958218e+04</td>\n",
       "      <td>9.365290e+05</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>4</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>ID</td>\n",
       "      <td>0.031587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>IL</td>\n",
       "      <td>1.371644e+07</td>\n",
       "      <td>8.843038e+06</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>20</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>IL</td>\n",
       "      <td>1.551100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>IN</td>\n",
       "      <td>2.428832e+05</td>\n",
       "      <td>4.839038e+06</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>11</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>IN</td>\n",
       "      <td>0.050192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>KS</td>\n",
       "      <td>7.174947e+05</td>\n",
       "      <td>1.785834e+06</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>6</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>KS</td>\n",
       "      <td>0.401770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>KY</td>\n",
       "      <td>4.597146e+05</td>\n",
       "      <td>3.306120e+06</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>8</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>KY</td>\n",
       "      <td>0.139050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>MA</td>\n",
       "      <td>4.595593e+06</td>\n",
       "      <td>4.534974e+06</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>11</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>MA</td>\n",
       "      <td>1.013367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MD</td>\n",
       "      <td>8.006173e+06</td>\n",
       "      <td>3.900090e+06</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>10</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>MD</td>\n",
       "      <td>2.052817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ME</td>\n",
       "      <td>1.310730e+05</td>\n",
       "      <td>1.065100e+06</td>\n",
       "      <td>Maine</td>\n",
       "      <td>3</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>ME</td>\n",
       "      <td>0.123062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ME</td>\n",
       "      <td>1.310730e+05</td>\n",
       "      <td>1.065100e+06</td>\n",
       "      <td>Maine</td>\n",
       "      <td>1</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>ME</td>\n",
       "      <td>0.123062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>MI</td>\n",
       "      <td>1.262812e+06</td>\n",
       "      <td>7.514055e+06</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>16</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>MI</td>\n",
       "      <td>0.168060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MN</td>\n",
       "      <td>1.482726e+06</td>\n",
       "      <td>3.473972e+06</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>10</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>MN</td>\n",
       "      <td>0.426810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MO</td>\n",
       "      <td>2.055877e+06</td>\n",
       "      <td>4.215860e+06</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>10</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>MO</td>\n",
       "      <td>0.487653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>MS</td>\n",
       "      <td>4.870150e+06</td>\n",
       "      <td>2.072395e+06</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>6</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>MS</td>\n",
       "      <td>2.350010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>MT</td>\n",
       "      <td>5.786374e+04</td>\n",
       "      <td>6.943700e+05</td>\n",
       "      <td>Montana</td>\n",
       "      <td>3</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>MT</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NC</td>\n",
       "      <td>1.338697e+06</td>\n",
       "      <td>6.924469e+06</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>15</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>NC</td>\n",
       "      <td>0.193328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ND</td>\n",
       "      <td>2.998134e+04</td>\n",
       "      <td>7.544340e+02</td>\n",
       "      <td>North Dakota</td>\n",
       "      <td>3</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>ND</td>\n",
       "      <td>39.740176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NE</td>\n",
       "      <td>5.802214e+05</td>\n",
       "      <td>1.211101e+06</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>5</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>NE</td>\n",
       "      <td>0.479086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>NH</td>\n",
       "      <td>3.966222e+06</td>\n",
       "      <td>9.883980e+05</td>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>4</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>NH</td>\n",
       "      <td>4.012779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>NJ</td>\n",
       "      <td>5.632078e+07</td>\n",
       "      <td>5.751090e+06</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>14</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>NJ</td>\n",
       "      <td>9.793062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>NM</td>\n",
       "      <td>1.571557e+05</td>\n",
       "      <td>1.289420e+06</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>5</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>NM</td>\n",
       "      <td>0.121881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>NV</td>\n",
       "      <td>2.895261e+06</td>\n",
       "      <td>1.678883e+06</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>6</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>NV</td>\n",
       "      <td>1.724516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>NY</td>\n",
       "      <td>1.025113e+08</td>\n",
       "      <td>1.620089e+07</td>\n",
       "      <td>New York</td>\n",
       "      <td>29</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>NY</td>\n",
       "      <td>6.327512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>OH</td>\n",
       "      <td>1.038232e+07</td>\n",
       "      <td>7.861025e+06</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>18</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>OH</td>\n",
       "      <td>1.320733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>OK</td>\n",
       "      <td>2.997143e+05</td>\n",
       "      <td>2.157450e+06</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>7</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>OK</td>\n",
       "      <td>0.138921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>OR</td>\n",
       "      <td>2.460465e+05</td>\n",
       "      <td>2.553810e+06</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>7</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>OR</td>\n",
       "      <td>0.096345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>PA</td>\n",
       "      <td>1.273979e+07</td>\n",
       "      <td>8.722975e+06</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>20</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>PA</td>\n",
       "      <td>1.460487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>RI</td>\n",
       "      <td>3.597175e+06</td>\n",
       "      <td>7.540650e+05</td>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>4</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>RI</td>\n",
       "      <td>4.770377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>SC</td>\n",
       "      <td>1.532404e+06</td>\n",
       "      <td>3.157027e+06</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>9</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>SC</td>\n",
       "      <td>0.485395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>SD</td>\n",
       "      <td>3.929920e+04</td>\n",
       "      <td>5.953220e+05</td>\n",
       "      <td>South Dakota</td>\n",
       "      <td>3</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>SD</td>\n",
       "      <td>0.066013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>TN</td>\n",
       "      <td>6.384270e+05</td>\n",
       "      <td>4.110318e+06</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>11</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>TN</td>\n",
       "      <td>0.155323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>TX</td>\n",
       "      <td>1.075595e+07</td>\n",
       "      <td>1.438239e+07</td>\n",
       "      <td>Texas</td>\n",
       "      <td>38</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>TX</td>\n",
       "      <td>0.747856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>UT</td>\n",
       "      <td>1.036965e+06</td>\n",
       "      <td>1.577069e+06</td>\n",
       "      <td>Utah</td>\n",
       "      <td>6</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>UT</td>\n",
       "      <td>0.657527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>VA</td>\n",
       "      <td>1.833960e+07</td>\n",
       "      <td>5.604106e+06</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>13</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>VA</td>\n",
       "      <td>3.272529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>VT</td>\n",
       "      <td>7.938407e+04</td>\n",
       "      <td>4.722890e+05</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>3</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>VT</td>\n",
       "      <td>0.168084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>WA</td>\n",
       "      <td>1.135761e+07</td>\n",
       "      <td>4.872385e+06</td>\n",
       "      <td>Washington</td>\n",
       "      <td>12</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>WA</td>\n",
       "      <td>2.331017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>WI</td>\n",
       "      <td>9.524350e+05</td>\n",
       "      <td>3.768373e+06</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>10</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>WI</td>\n",
       "      <td>0.252744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>WV</td>\n",
       "      <td>9.821953e+04</td>\n",
       "      <td>1.254768e+06</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>5</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>WV</td>\n",
       "      <td>0.078277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>WY</td>\n",
       "      <td>5.401322e+04</td>\n",
       "      <td>2.842030e+05</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>3</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>WY</td>\n",
       "      <td>0.190052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   STATE  TRANSACTION_AMT           A1a                 State  Votes  \\\n",
       "0     AK     3.555430e+04  5.873030e+05                Alaska      3   \n",
       "1     AL     3.159969e+06  3.333946e+06               Alabama      9   \n",
       "2     AR     1.682843e+07  1.765513e+06              Arkansas      6   \n",
       "3     AZ     6.610760e+05  4.080680e+06               Arizona     11   \n",
       "4     CA     3.704482e+07  2.448664e+07            California     55   \n",
       "5     CO     1.195911e+07  3.840303e+06              Colorado      9   \n",
       "6     CT     1.201941e+07  2.331684e+06           Connecticut      7   \n",
       "7     DC     4.449680e+08  4.932870e+05  District of Columbia      3   \n",
       "8     DE     1.744907e+06  6.756630e+05              Delaware      3   \n",
       "9     FL     2.807361e+07  1.350557e+07               Florida     29   \n",
       "10    GA     3.313178e+06  6.657621e+06               Georgia     16   \n",
       "11    HI     6.069555e+04  7.514830e+05                Hawaii      4   \n",
       "12    IA     7.879875e+06  2.222380e+06                  Iowa      6   \n",
       "13    ID     2.958218e+04  9.365290e+05                 Idaho      4   \n",
       "14    IL     1.371644e+07  8.843038e+06              Illinois     20   \n",
       "15    IN     2.428832e+05  4.839038e+06               Indiana     11   \n",
       "16    KS     7.174947e+05  1.785834e+06                Kansas      6   \n",
       "17    KY     4.597146e+05  3.306120e+06              Kentucky      8   \n",
       "18    MA     4.595593e+06  4.534974e+06         Massachusetts     11   \n",
       "19    MD     8.006173e+06  3.900090e+06              Maryland     10   \n",
       "20    ME     1.310730e+05  1.065100e+06                 Maine      3   \n",
       "21    ME     1.310730e+05  1.065100e+06                 Maine      1   \n",
       "22    MI     1.262812e+06  7.514055e+06              Michigan     16   \n",
       "23    MN     1.482726e+06  3.473972e+06             Minnesota     10   \n",
       "24    MO     2.055877e+06  4.215860e+06              Missouri     10   \n",
       "25    MS     4.870150e+06  2.072395e+06           Mississippi      6   \n",
       "26    MT     5.786374e+04  6.943700e+05               Montana      3   \n",
       "27    NC     1.338697e+06  6.924469e+06        North Carolina     15   \n",
       "28    ND     2.998134e+04  7.544340e+02          North Dakota      3   \n",
       "29    NE     5.802214e+05  1.211101e+06              Nebraska      5   \n",
       "30    NH     3.966222e+06  9.883980e+05         New Hampshire      4   \n",
       "31    NJ     5.632078e+07  5.751090e+06            New Jersey     14   \n",
       "32    NM     1.571557e+05  1.289420e+06            New Mexico      5   \n",
       "33    NV     2.895261e+06  1.678883e+06                Nevada      6   \n",
       "34    NY     1.025113e+08  1.620089e+07              New York     29   \n",
       "35    OH     1.038232e+07  7.861025e+06                  Ohio     18   \n",
       "36    OK     2.997143e+05  2.157450e+06              Oklahoma      7   \n",
       "37    OR     2.460465e+05  2.553810e+06                Oregon      7   \n",
       "38    PA     1.273979e+07  8.722975e+06          Pennsylvania     20   \n",
       "39    RI     3.597175e+06  7.540650e+05          Rhode Island      4   \n",
       "40    SC     1.532404e+06  3.157027e+06        South Carolina      9   \n",
       "41    SD     3.929920e+04  5.953220e+05          South Dakota      3   \n",
       "42    TN     6.384270e+05  4.110318e+06             Tennessee     11   \n",
       "43    TX     1.075595e+07  1.438239e+07                 Texas     38   \n",
       "44    UT     1.036965e+06  1.577069e+06                  Utah      6   \n",
       "45    VA     1.833960e+07  5.604106e+06              Virginia     13   \n",
       "46    VT     7.938407e+04  4.722890e+05               Vermont      3   \n",
       "47    WA     1.135761e+07  4.872385e+06            Washington     12   \n",
       "48    WI     9.524350e+05  3.768373e+06             Wisconsin     10   \n",
       "49    WV     9.821953e+04  1.254768e+06         West Virginia      5   \n",
       "50    WY     5.401322e+04  2.842030e+05               Wyoming      3   \n",
       "\n",
       "   Winning Party Abbreviation  TOTAL_HILLARY  \n",
       "0    Republicans           AK       0.060538  \n",
       "1    Republicans           AL       0.947817  \n",
       "2    Republicans           AR       9.531748  \n",
       "3    Republicans           AZ       0.162001  \n",
       "4      Democrats           CA       1.512858  \n",
       "5      Democrats           CO       3.114107  \n",
       "6      Democrats           CT       5.154819  \n",
       "7      Democrats           DC     902.046974  \n",
       "8      Democrats           DE       2.582510  \n",
       "9    Republicans           FL       2.078669  \n",
       "10   Republicans           GA       0.497652  \n",
       "11     Democrats           HI       0.080768  \n",
       "12   Republicans           IA       3.545692  \n",
       "13   Republicans           ID       0.031587  \n",
       "14     Democrats           IL       1.551100  \n",
       "15   Republicans           IN       0.050192  \n",
       "16   Republicans           KS       0.401770  \n",
       "17   Republicans           KY       0.139050  \n",
       "18     Democrats           MA       1.013367  \n",
       "19     Democrats           MD       2.052817  \n",
       "20     Democrats           ME       0.123062  \n",
       "21   Republicans           ME       0.123062  \n",
       "22   Republicans           MI       0.168060  \n",
       "23     Democrats           MN       0.426810  \n",
       "24   Republicans           MO       0.487653  \n",
       "25   Republicans           MS       2.350010  \n",
       "26   Republicans           MT       0.083333  \n",
       "27   Republicans           NC       0.193328  \n",
       "28   Republicans           ND      39.740176  \n",
       "29   Republicans           NE       0.479086  \n",
       "30     Democrats           NH       4.012779  \n",
       "31     Democrats           NJ       9.793062  \n",
       "32     Democrats           NM       0.121881  \n",
       "33     Democrats           NV       1.724516  \n",
       "34     Democrats           NY       6.327512  \n",
       "35   Republicans           OH       1.320733  \n",
       "36   Republicans           OK       0.138921  \n",
       "37     Democrats           OR       0.096345  \n",
       "38   Republicans           PA       1.460487  \n",
       "39     Democrats           RI       4.770377  \n",
       "40   Republicans           SC       0.485395  \n",
       "41   Republicans           SD       0.066013  \n",
       "42   Republicans           TN       0.155323  \n",
       "43   Republicans           TX       0.747856  \n",
       "44   Republicans           UT       0.657527  \n",
       "45     Democrats           VA       3.272529  \n",
       "46     Democrats           VT       0.168084  \n",
       "47     Democrats           WA       2.331017  \n",
       "48   Republicans           WI       0.252744  \n",
       "49   Republicans           WV       0.078277  \n",
       "50   Republicans           WY       0.190052  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hillary_expenses['TOTAL_HILLARY']=hillary_expenses['TRANSACTION_AMT']/hillary_expenses['A1a']\n",
    "\n",
    "hillary_expenses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* To narrow down our investigation we look closely at [2016's swing states](https://en.wikipedia.org/wiki/Swing_state#Determining_swing_states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATE</th>\n",
       "      <th>TRANSACTION_AMT</th>\n",
       "      <th>A1a</th>\n",
       "      <th>State</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Winning Party</th>\n",
       "      <th>Abbreviation</th>\n",
       "      <th>TOTAL_HILLARY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ME</td>\n",
       "      <td>131073.03</td>\n",
       "      <td>1065100.0</td>\n",
       "      <td>Maine</td>\n",
       "      <td>3</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>ME</td>\n",
       "      <td>0.123062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MN</td>\n",
       "      <td>1482725.63</td>\n",
       "      <td>3473972.0</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>10</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>MN</td>\n",
       "      <td>0.426810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>NH</td>\n",
       "      <td>3966222.50</td>\n",
       "      <td>988398.0</td>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>4</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>NH</td>\n",
       "      <td>4.012779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>NV</td>\n",
       "      <td>2895260.98</td>\n",
       "      <td>1678883.0</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>6</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>NV</td>\n",
       "      <td>1.724516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AZ</td>\n",
       "      <td>661076.04</td>\n",
       "      <td>4080680.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>11</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>AZ</td>\n",
       "      <td>0.162001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FL</td>\n",
       "      <td>28073614.09</td>\n",
       "      <td>13505571.0</td>\n",
       "      <td>Florida</td>\n",
       "      <td>29</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>FL</td>\n",
       "      <td>2.078669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GA</td>\n",
       "      <td>3313178.46</td>\n",
       "      <td>6657621.0</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>16</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>GA</td>\n",
       "      <td>0.497652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ME</td>\n",
       "      <td>131073.03</td>\n",
       "      <td>1065100.0</td>\n",
       "      <td>Maine</td>\n",
       "      <td>1</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>ME</td>\n",
       "      <td>0.123062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>MI</td>\n",
       "      <td>1262811.90</td>\n",
       "      <td>7514055.0</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>16</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>MI</td>\n",
       "      <td>0.168060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NC</td>\n",
       "      <td>1338696.70</td>\n",
       "      <td>6924469.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>15</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>NC</td>\n",
       "      <td>0.193328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>PA</td>\n",
       "      <td>12739792.54</td>\n",
       "      <td>8722975.0</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>20</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>PA</td>\n",
       "      <td>1.460487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>WI</td>\n",
       "      <td>952435.04</td>\n",
       "      <td>3768373.0</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>10</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>WI</td>\n",
       "      <td>0.252744</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   STATE  TRANSACTION_AMT         A1a           State  Votes Winning Party  \\\n",
       "20    ME        131073.03   1065100.0           Maine      3     Democrats   \n",
       "23    MN       1482725.63   3473972.0       Minnesota     10     Democrats   \n",
       "30    NH       3966222.50    988398.0   New Hampshire      4     Democrats   \n",
       "33    NV       2895260.98   1678883.0          Nevada      6     Democrats   \n",
       "3     AZ        661076.04   4080680.0         Arizona     11   Republicans   \n",
       "9     FL      28073614.09  13505571.0         Florida     29   Republicans   \n",
       "10    GA       3313178.46   6657621.0         Georgia     16   Republicans   \n",
       "21    ME        131073.03   1065100.0           Maine      1   Republicans   \n",
       "22    MI       1262811.90   7514055.0        Michigan     16   Republicans   \n",
       "27    NC       1338696.70   6924469.0  North Carolina     15   Republicans   \n",
       "38    PA      12739792.54   8722975.0    Pennsylvania     20   Republicans   \n",
       "48    WI        952435.04   3768373.0       Wisconsin     10   Republicans   \n",
       "\n",
       "   Abbreviation  TOTAL_HILLARY  \n",
       "20           ME       0.123062  \n",
       "23           MN       0.426810  \n",
       "30           NH       4.012779  \n",
       "33           NV       1.724516  \n",
       "3            AZ       0.162001  \n",
       "9            FL       2.078669  \n",
       "10           GA       0.497652  \n",
       "21           ME       0.123062  \n",
       "22           MI       0.168060  \n",
       "27           NC       0.193328  \n",
       "38           PA       1.460487  \n",
       "48           WI       0.252744  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "swing_states=['ME', 'NC','MN','NH','MI','GA','WI','FL','AZ','NV','PA']\n",
    "hillary_expenses.loc[hillary_expenses['STATE'].isin(swing_states)].sort_values(by='Winning Party')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATE</th>\n",
       "      <th>TRANSACTION_AMT</th>\n",
       "      <th>A1a</th>\n",
       "      <th>State</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Winning Party</th>\n",
       "      <th>Abbreviation</th>\n",
       "      <th>TOTAL_TRUMP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AZ</td>\n",
       "      <td>6319765.74</td>\n",
       "      <td>4080680.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>11</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>AZ</td>\n",
       "      <td>1.548704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FL</td>\n",
       "      <td>16395149.35</td>\n",
       "      <td>13505571.0</td>\n",
       "      <td>Florida</td>\n",
       "      <td>29</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>FL</td>\n",
       "      <td>1.213955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GA</td>\n",
       "      <td>4258013.71</td>\n",
       "      <td>6657621.0</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>16</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>GA</td>\n",
       "      <td>0.639570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ME</td>\n",
       "      <td>117382.21</td>\n",
       "      <td>1065100.0</td>\n",
       "      <td>Maine</td>\n",
       "      <td>1</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>ME</td>\n",
       "      <td>0.110208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>MI</td>\n",
       "      <td>929822.64</td>\n",
       "      <td>7514055.0</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>16</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>MI</td>\n",
       "      <td>0.123744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NC</td>\n",
       "      <td>2483726.78</td>\n",
       "      <td>6924469.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>15</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>NC</td>\n",
       "      <td>0.358688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>PA</td>\n",
       "      <td>2338879.22</td>\n",
       "      <td>8722975.0</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>20</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>PA</td>\n",
       "      <td>0.268129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>WI</td>\n",
       "      <td>1039734.58</td>\n",
       "      <td>3768373.0</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>10</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>WI</td>\n",
       "      <td>0.275911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ME</td>\n",
       "      <td>117382.21</td>\n",
       "      <td>1065100.0</td>\n",
       "      <td>Maine</td>\n",
       "      <td>3</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>ME</td>\n",
       "      <td>0.110208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MN</td>\n",
       "      <td>482897.77</td>\n",
       "      <td>3473972.0</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>10</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>MN</td>\n",
       "      <td>0.139005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>NH</td>\n",
       "      <td>11743267.33</td>\n",
       "      <td>988398.0</td>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>4</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>NH</td>\n",
       "      <td>11.881112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>NV</td>\n",
       "      <td>1404658.85</td>\n",
       "      <td>1678883.0</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>6</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>NV</td>\n",
       "      <td>0.836663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   STATE  TRANSACTION_AMT         A1a           State  Votes Winning Party  \\\n",
       "3     AZ       6319765.74   4080680.0         Arizona     11   Republicans   \n",
       "9     FL      16395149.35  13505571.0         Florida     29   Republicans   \n",
       "10    GA       4258013.71   6657621.0         Georgia     16   Republicans   \n",
       "21    ME        117382.21   1065100.0           Maine      1   Republicans   \n",
       "22    MI        929822.64   7514055.0        Michigan     16   Republicans   \n",
       "27    NC       2483726.78   6924469.0  North Carolina     15   Republicans   \n",
       "38    PA       2338879.22   8722975.0    Pennsylvania     20   Republicans   \n",
       "48    WI       1039734.58   3768373.0       Wisconsin     10   Republicans   \n",
       "20    ME        117382.21   1065100.0           Maine      3     Democrats   \n",
       "23    MN        482897.77   3473972.0       Minnesota     10     Democrats   \n",
       "30    NH      11743267.33    988398.0   New Hampshire      4     Democrats   \n",
       "33    NV       1404658.85   1678883.0          Nevada      6     Democrats   \n",
       "\n",
       "   Abbreviation  TOTAL_TRUMP  \n",
       "3            AZ     1.548704  \n",
       "9            FL     1.213955  \n",
       "10           GA     0.639570  \n",
       "21           ME     0.110208  \n",
       "22           MI     0.123744  \n",
       "27           NC     0.358688  \n",
       "38           PA     0.268129  \n",
       "48           WI     0.275911  \n",
       "20           ME     0.110208  \n",
       "23           MN     0.139005  \n",
       "30           NH    11.881112  \n",
       "33           NV     0.836663  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_expenses.loc[trump_expenses['STATE'].isin(swing_states)].sort_values(by='Winning Party',ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We create a dataframe that contains both candidate's \\\\$/person to see if the amount of money spent actually gives you a win."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>STATE</th>\n",
       "      <th>TRANSACTION_AMT</th>\n",
       "      <th>A1a</th>\n",
       "      <th>State</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Winning Party</th>\n",
       "      <th>Abbreviation</th>\n",
       "      <th>TOTAL_TRUMP</th>\n",
       "      <th>TOTAL_HILLARY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ME</td>\n",
       "      <td>117382.21</td>\n",
       "      <td>1065100.0</td>\n",
       "      <td>Maine</td>\n",
       "      <td>3</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>ME</td>\n",
       "      <td>0.110208</td>\n",
       "      <td>0.123062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ME</td>\n",
       "      <td>117382.21</td>\n",
       "      <td>1065100.0</td>\n",
       "      <td>Maine</td>\n",
       "      <td>3</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>ME</td>\n",
       "      <td>0.110208</td>\n",
       "      <td>0.123062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>MN</td>\n",
       "      <td>482897.77</td>\n",
       "      <td>3473972.0</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>10</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>MN</td>\n",
       "      <td>0.139005</td>\n",
       "      <td>0.426810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>NH</td>\n",
       "      <td>11743267.33</td>\n",
       "      <td>988398.0</td>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>4</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>NH</td>\n",
       "      <td>11.881112</td>\n",
       "      <td>4.012779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>NV</td>\n",
       "      <td>1404658.85</td>\n",
       "      <td>1678883.0</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>6</td>\n",
       "      <td>Democrats</td>\n",
       "      <td>NV</td>\n",
       "      <td>0.836663</td>\n",
       "      <td>1.724516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AZ</td>\n",
       "      <td>6319765.74</td>\n",
       "      <td>4080680.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>11</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>AZ</td>\n",
       "      <td>1.548704</td>\n",
       "      <td>0.162001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FL</td>\n",
       "      <td>16395149.35</td>\n",
       "      <td>13505571.0</td>\n",
       "      <td>Florida</td>\n",
       "      <td>29</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>FL</td>\n",
       "      <td>1.213955</td>\n",
       "      <td>2.078669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GA</td>\n",
       "      <td>4258013.71</td>\n",
       "      <td>6657621.0</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>16</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>GA</td>\n",
       "      <td>0.639570</td>\n",
       "      <td>0.497652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ME</td>\n",
       "      <td>117382.21</td>\n",
       "      <td>1065100.0</td>\n",
       "      <td>Maine</td>\n",
       "      <td>1</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>ME</td>\n",
       "      <td>0.110208</td>\n",
       "      <td>0.123062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ME</td>\n",
       "      <td>117382.21</td>\n",
       "      <td>1065100.0</td>\n",
       "      <td>Maine</td>\n",
       "      <td>1</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>ME</td>\n",
       "      <td>0.110208</td>\n",
       "      <td>0.123062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MI</td>\n",
       "      <td>929822.64</td>\n",
       "      <td>7514055.0</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>16</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>MI</td>\n",
       "      <td>0.123744</td>\n",
       "      <td>0.168060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NC</td>\n",
       "      <td>2483726.78</td>\n",
       "      <td>6924469.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>15</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>NC</td>\n",
       "      <td>0.358688</td>\n",
       "      <td>0.193328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>PA</td>\n",
       "      <td>2338879.22</td>\n",
       "      <td>8722975.0</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>20</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>PA</td>\n",
       "      <td>0.268129</td>\n",
       "      <td>1.460487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>WI</td>\n",
       "      <td>1039734.58</td>\n",
       "      <td>3768373.0</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>10</td>\n",
       "      <td>Republicans</td>\n",
       "      <td>WI</td>\n",
       "      <td>0.275911</td>\n",
       "      <td>0.252744</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   STATE  TRANSACTION_AMT         A1a           State  Votes Winning Party  \\\n",
       "20    ME        117382.21   1065100.0           Maine      3     Democrats   \n",
       "21    ME        117382.21   1065100.0           Maine      3     Democrats   \n",
       "25    MN        482897.77   3473972.0       Minnesota     10     Democrats   \n",
       "32    NH      11743267.33    988398.0   New Hampshire      4     Democrats   \n",
       "35    NV       1404658.85   1678883.0          Nevada      6     Democrats   \n",
       "3     AZ       6319765.74   4080680.0         Arizona     11   Republicans   \n",
       "9     FL      16395149.35  13505571.0         Florida     29   Republicans   \n",
       "10    GA       4258013.71   6657621.0         Georgia     16   Republicans   \n",
       "22    ME        117382.21   1065100.0           Maine      1   Republicans   \n",
       "23    ME        117382.21   1065100.0           Maine      1   Republicans   \n",
       "24    MI        929822.64   7514055.0        Michigan     16   Republicans   \n",
       "29    NC       2483726.78   6924469.0  North Carolina     15   Republicans   \n",
       "40    PA       2338879.22   8722975.0    Pennsylvania     20   Republicans   \n",
       "50    WI       1039734.58   3768373.0       Wisconsin     10   Republicans   \n",
       "\n",
       "   Abbreviation  TOTAL_TRUMP  TOTAL_HILLARY  \n",
       "20           ME     0.110208       0.123062  \n",
       "21           ME     0.110208       0.123062  \n",
       "25           MN     0.139005       0.426810  \n",
       "32           NH    11.881112       4.012779  \n",
       "35           NV     0.836663       1.724516  \n",
       "3            AZ     1.548704       0.162001  \n",
       "9            FL     1.213955       2.078669  \n",
       "10           GA     0.639570       0.497652  \n",
       "22           ME     0.110208       0.123062  \n",
       "23           ME     0.110208       0.123062  \n",
       "24           MI     0.123744       0.168060  \n",
       "29           NC     0.358688       0.193328  \n",
       "40           PA     0.268129       1.460487  \n",
       "50           WI     0.275911       0.252744  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_states = pd.merge(trump_expenses,hillary_expenses[['Abbreviation','TOTAL_HILLARY']],how='inner',on='Abbreviation')\n",
    "total_states=total_states.loc[total_states['Abbreviation'].isin(swing_states)].sort_values(by='Winning Party')\n",
    "total_states"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We can see that both parties spent a huge amount of money on New Hampshire. Even though Trump's campaign spent 3 times the amount they still managed to lose that state. By looking at 2020's election results we can see that Joe Biden won that state with a 7% difference, not so much of a swing state is it?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Based on our data one can not make the assumption that spending more money will actually give you the win. Specific examples of such are New Hampshire, Florida, Michigan, Pennsylvania, which happen to be the swing states that give out the most electoral votes."
   ]
  }
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