analysis.ipynb

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     "text": [
      "Pretty printing has been turned OFF\n"
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   "source": [
    "%pprint"
   ]
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   "source": [
    "### 2016 Election Project\n",
    "### Part 3 of Processing Pipeline\n",
    "\n",
    "This notebook is intended to document the analysis of my data throughout this project. The data I am starting out with are transcripts of the presidential debates from the 2016 US Election- the 10 Democratic primary debates, the 12 Republican primary debates, and the 3 debates for the general election between Hillary Clinton and Donald Trump. The transcripts were taken from UCSB's American Presidency Project. The citations for these transcripts can be found in the README. Using these transcripts, I'll analyze the referring expressions the candidates use for each other. \n",
    "\n",
    "\n",
    "We have two factors at play: gender and experience. Hillary Clinton and Carly Fiorina are the only two female candidates. Clinton has several titles due to her experience in politics, and Fiorina has no professional titles other than Mrs. because she has no political experience. We can compare how the two are referred to by looking at whether they are called a professional title (Senator or Secretary for Clinton; Mrs. or Businesswoman for Fiorina) or by their first name only or first and last name. \n",
    "\n",
    "There is a similar situation among the male candidates. Donald Trump is the only male candidate without a professional title other than Mr., while Ben Carson is another male candidate with no political experience, but has the professional title of Doctor. Therefore, we can try to see if there are differences in the ways the moderators and candidates refer to those with or without government experience and if there are differences in the ways the male and female candidates are referred to. Does the frequency of being called by a first name or without a title have to do with gender? Or does it have to do with experience?"
   ]
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   "source": [
    "### Table of Contents\n",
    "- [Creating a Function to List Entities in an Utterance](#entity-listing-function)\n",
    "- [Basic Analysis](#basic-analysis)\n",
    "- [Building a DataFrame for Each Speaker](#building-speaker-dataframes)\n",
    "- [Defining Helpful Functions](#important-functions)\n",
    "- [Categorizing the Referring Expressions Used for Each Candidate](#creating-referring-expression-categories)\n",
    "- [Overall Distribution of Types of Referring Expressions Used for Candidates](#overall-distribution-of-referring-expressions)\n",
    "- [How Donald Trump Refers to Other Candidates](#donald-trump)\n",
    "- [How Hillary Clinton Refers to Other Candidates](#hillary-clinton)\n",
    "- [How Carly Fiorina Refers to Other Candidates](#carly-fiorina)\n",
    "- [How Ted Cruz Refers to Other Candidates](#ted-cruz)\n",
    "- [How Marco Rubio Refers to Other Candidates](#marco-rubio)\n",
    "- [How Ben Carson Refers to Other Candidates](#ben-carson)\n",
    "- [How Bernie Sanders Refers to Other Candidates](#bernie-sanders)\n",
    "- [How Moderators Refer to the Candidates](#moderators)\n",
    "    - [Dana Bash](#dana-bash)\n",
    "    - [David Muir](#david-muir)\n",
    "- [Differences Across Debates](#differences-across-debates)\n",
    "    - [Debate 1](#debate-1)\n",
    "    - [Debate 2](#debate-2)\n",
    "    - [Debate 3](#debate-3)\n",
    "- [Final Analysis](#final-analysis)"
   ]
  },
  {
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   "source": [
    "import nltk\n",
    "from nltk.corpus import PlaintextCorpusReader\n",
    "import pandas as pd\n",
    "import glob\n",
    "import os\n",
    "from collections import defaultdict\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [],
   "source": [
    "f = open('/Users/Paige/Documents/Data_Science/link_dict.pkl', 'rb')\n",
    "link_dict = pickle.load(f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Debate Type</th>\n",
       "      <th>Speaker</th>\n",
       "      <th>Sents</th>\n",
       "      <th>Tree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1-14-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>CAVUTO</td>\n",
       "      <td>It is 9:00 p.m. here at the North Charleston ...</td>\n",
       "      <td>[(It, PRP), (is, VBZ), (9:00, CD), (p.m., NN),...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1-14-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>CAVUTO</td>\n",
       "      <td>Welcome to the sixth Republican presidential o...</td>\n",
       "      <td>[(Welcome, VB), (to, TO), (the, DT), (sixth, J...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1-14-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>CAVUTO</td>\n",
       "      <td>I'm Neil Cavuto, alongside my friend and co-mo...</td>\n",
       "      <td>[(I, PRP), ('m, VBP), [(Neil, JJ), (Cavuto, NN...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1-14-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>BARTIROMO</td>\n",
       "      <td>Tonight we are working with Facebook to ask t...</td>\n",
       "      <td>[(Tonight, NN), (we, PRP), (are, VBP), (workin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1-14-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>BARTIROMO</td>\n",
       "      <td>And according to Facebook, the U.S. election h...</td>\n",
       "      <td>[(And, CC), (according, VBG), (to, TO), [(Face...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Date  Debate Type    Speaker  \\\n",
       "0  1-14-16  primary_rep     CAVUTO   \n",
       "1  1-14-16  primary_rep     CAVUTO   \n",
       "2  1-14-16  primary_rep     CAVUTO   \n",
       "3  1-14-16  primary_rep  BARTIROMO   \n",
       "4  1-14-16  primary_rep  BARTIROMO   \n",
       "\n",
       "                                               Sents  \\\n",
       "0   It is 9:00 p.m. here at the North Charleston ...   \n",
       "1  Welcome to the sixth Republican presidential o...   \n",
       "2  I'm Neil Cavuto, alongside my friend and co-mo...   \n",
       "3   Tonight we are working with Facebook to ask t...   \n",
       "4  And according to Facebook, the U.S. election h...   \n",
       "\n",
       "                                                Tree  \n",
       "0  [(It, PRP), (is, VBZ), (9:00, CD), (p.m., NN),...  \n",
       "1  [(Welcome, VB), (to, TO), (the, DT), (sixth, J...  \n",
       "2  [(I, PRP), ('m, VBP), [(Neil, JJ), (Cavuto, NN...  \n",
       "3  [(Tonight, NN), (we, PRP), (are, VBP), (workin...  \n",
       "4  [(And, CC), (according, VBG), (to, TO), [(Face...  "
      ]
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   "source": [
    "f = open('/Users/Paige/Documents/Data_Science/master_df.pkl', 'rb')\n",
    "master_df = pickle.load(f)\n",
    "f.close\n",
    "master_df = master_df.reset_index(drop=True)\n",
    "master_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Entity-Listing Function "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**First, I'm going to create a mapping function that will make it easier to see if a person is being talked to or about in a particular utterance. This function is just going to create a list of all of the entity labels that appear in an utterance's tree. Then, to see if a person is being referred to in a particular utterance, we can just see if they are in that list instead of traversing the tree each time.**"
   ]
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   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "source": [
    "def entity_tracking(tree):\n",
    "    name = ''\n",
    "    referred_to = []\n",
    "    entities = set(link_dict.values())\n",
    "    for chunk in tree:\n",
    "        if hasattr(chunk, 'label'):\n",
    "            if chunk.label() in entities:\n",
    "                referred_to.append(chunk.label())\n",
    "                    \n",
    "    return list(set(referred_to))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Debate Type</th>\n",
       "      <th>Speaker</th>\n",
       "      <th>Sents</th>\n",
       "      <th>Tree</th>\n",
       "      <th>Entities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1-14-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>CAVUTO</td>\n",
       "      <td>It is 9:00 p.m. here at the North Charleston ...</td>\n",
       "      <td>[(It, PRP), (is, VBZ), (9:00, CD), (p.m., NN),...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1-14-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>CAVUTO</td>\n",
       "      <td>Welcome to the sixth Republican presidential o...</td>\n",
       "      <td>[(Welcome, VB), (to, TO), (the, DT), (sixth, J...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1-14-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>CAVUTO</td>\n",
       "      <td>I'm Neil Cavuto, alongside my friend and co-mo...</td>\n",
       "      <td>[(I, PRP), ('m, VBP), [(Neil, JJ), (Cavuto, NN...</td>\n",
       "      <td>[Neil Cavuto, Maria Bartiromo]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1-14-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>BARTIROMO</td>\n",
       "      <td>Tonight we are working with Facebook to ask t...</td>\n",
       "      <td>[(Tonight, NN), (we, PRP), (are, VBP), (workin...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1-14-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>BARTIROMO</td>\n",
       "      <td>And according to Facebook, the U.S. election h...</td>\n",
       "      <td>[(And, CC), (according, VBG), (to, TO), [(Face...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Date  Debate Type    Speaker  \\\n",
       "0  1-14-16  primary_rep     CAVUTO   \n",
       "1  1-14-16  primary_rep     CAVUTO   \n",
       "2  1-14-16  primary_rep     CAVUTO   \n",
       "3  1-14-16  primary_rep  BARTIROMO   \n",
       "4  1-14-16  primary_rep  BARTIROMO   \n",
       "\n",
       "                                               Sents  \\\n",
       "0   It is 9:00 p.m. here at the North Charleston ...   \n",
       "1  Welcome to the sixth Republican presidential o...   \n",
       "2  I'm Neil Cavuto, alongside my friend and co-mo...   \n",
       "3   Tonight we are working with Facebook to ask t...   \n",
       "4  And according to Facebook, the U.S. election h...   \n",
       "\n",
       "                                                Tree  \\\n",
       "0  [(It, PRP), (is, VBZ), (9:00, CD), (p.m., NN),...   \n",
       "1  [(Welcome, VB), (to, TO), (the, DT), (sixth, J...   \n",
       "2  [(I, PRP), ('m, VBP), [(Neil, JJ), (Cavuto, NN...   \n",
       "3  [(Tonight, NN), (we, PRP), (are, VBP), (workin...   \n",
       "4  [(And, CC), (according, VBG), (to, TO), [(Face...   \n",
       "\n",
       "                         Entities  \n",
       "0                              []  \n",
       "1                              []  \n",
       "2  [Neil Cavuto, Maria Bartiromo]  \n",
       "3                              []  \n",
       "4                              []  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df['Entities'] = master_df.Tree.map(entity_tracking)\n",
    "master_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's see how many times each relevant entity was referred to throughout all of the debates:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hillary Clinton 1208\n",
      "Donald Trump 953\n",
      "TITLE 674\n",
      "Barack Obama 540\n",
      "Bernie Sanders 535\n",
      "Marco Rubio 354\n",
      "Ted Cruz 334\n",
      "John Kasich 246\n",
      "Jeb Bush 245\n",
      "Chris Christie 209\n",
      "Ben Carson 202\n",
      "Bashar al-Assad 137\n",
      "Ronald Reagan 135\n",
      "Rand Paul 114\n",
      "Martin O'Malley 110\n",
      "Vladimir Putin 105\n",
      "Carly Fiorina 88\n",
      "Jake Tapper 74\n",
      "Dana Bash 68\n",
      "Anderson Cooper 65\n",
      "Wolf Blitzer 60\n",
      "Maria Celeste Arraras 46\n",
      "John Kerry 46\n",
      "Muammar Gaddafi 46\n",
      "Martha Raddatz 45\n",
      "Bill Clinton 44\n",
      "Megyn Kelly 41\n",
      "David Muir 40\n",
      "Mike Huckabee 38\n",
      "George W. Bush 37\n",
      "Jim Webb 34\n",
      "Lester Holt 32\n",
      "Scott Walker 30\n",
      "Bret Baier 29\n",
      "Saddam Hussein 28\n",
      "Neil Cavuto 27\n",
      "Mitt Romney 26\n",
      "Andrea Mitchell 23\n",
      "Osama bin Laden 18\n",
      "Don Lemon 17\n",
      "Jeff Sessions 17\n",
      "Chuck Schumer 16\n",
      "Edward Snowden 16\n",
      "Hugh Hewitt 15\n",
      "Nancy Pelosi 14\n",
      "Antonin Scalia 14\n",
      "John McCain 13\n",
      "Carl Quintanilla 13\n",
      "Joe Biden 12\n",
      "Sean Hannity 12\n",
      "Rachel Maddow 12\n",
      "NICKNAME 11\n",
      "Nikki Haley 10\n",
      "Lindsey Graham 10\n",
      "Benjamin Netanyahu 10\n",
      "Kim Jong Un 10\n",
      "James Carter 9\n",
      "George Bush 8\n",
      "Theodore Roosevelt 8\n",
      "Abraham Lincoln 8\n",
      "Senator Clinton 7\n",
      "Fidel Castro 7\n",
      "Harry Truman 6\n",
      "James Comey 6\n",
      "Elizabeth Warren 6\n",
      "Chuck Todd 6\n",
      "Chris Cuomo 5\n",
      "Lincoln Chafee 5\n",
      "John Adams 5\n",
      "Deborah Wasserman Schultz 5\n",
      "Hosni Mubarak 5\n",
      "John Kennedy 5\n",
      "Jorge Ramos 5\n",
      "Al Gore 4\n",
      "Ivanka Trump 4\n",
      "Franklin D. Roosevelt 4\n",
      "Chris Wallace 4\n",
      "Alicia Machado 4\n",
      "Rick Santorum 4\n",
      "Michelle Obama 4\n",
      "Dwight Eisenhower 4\n",
      "Winston Churchill 4\n",
      "Joe Arpaio 4\n",
      "Nancy Reagan 4\n",
      "Chelsea Clinton 4\n",
      "Eric Trump 3\n",
      "Freddie Gray 3\n",
      "Tamir Rice 3\n",
      "Barbara Bush 3\n",
      "Kim Davis 3\n",
      "John Boehner 3\n",
      "Paul Ryan 3\n",
      "Humayun Khan 3\n",
      "Major Garrett 3\n",
      "Mitch McConnell 3\n",
      "David Duke 3\n",
      "Rosie O'Donnell 3\n",
      "Rosa Parks 3\n",
      "Maria Bartiromo 2\n",
      "Bobby Jindal 2\n",
      "Sonia Sotomayor 2\n",
      "Michael Flynn 2\n",
      "Woodrow Wilson 2\n",
      "Sandra Bland 2\n",
      "Pope Francis 2\n",
      "John Podesta 2\n",
      "Trayvon Martin 2\n",
      "Katie O'Malley 2\n",
      "George H. W. Bush 2\n",
      "Michael Bloomberg 2\n",
      "Tip O'Neill 2\n",
      "Richard Nixon 2\n",
      "Adolf Hitler 2\n",
      "Abigail Adams 2\n",
      "Dylann Roof 1\n",
      "Frederick Douglas 1\n",
      "Benjamin Franklin 1\n",
      "Rush Limbaugh 1\n",
      "John Quincy Adams 1\n",
      "Mark Zuckerburg 1\n",
      "George Washington 1\n",
      "Omran Daqneesh 1\n",
      "Ken Bone 1\n",
      "Andrew Cuomo 1\n",
      "Calvin Coolidge 1\n",
      "Eric Garner 1\n",
      "Michael Brown 1\n",
      "Joseph Mattis 1\n",
      "Kim Jong-Un 1\n",
      "Abdullah 1\n",
      "Nelson Mandela 1\n",
      "Kimberley Strassel 1\n",
      "Joseph Stalin 1\n",
      "Ashraf Ghani 1\n",
      "Angela Merkel 1\n",
      "Maria Elena Salinas 1\n",
      "Yasser Arafat 1\n",
      "Merrick Garland 1\n",
      "Abdel Fattah el-Sisi 1\n",
      "Lyndon Johnson 1\n",
      "Thomas Jefferson 1\n",
      "Alexander Hamilton 1\n",
      "Rudy Giuliani 1\n"
     ]
    }
   ],
   "source": [
    "ner_categories = defaultdict(int)\n",
    "\n",
    "# Create the nested for loop\n",
    "for tree in master_df['Tree']:\n",
    "    for chunk in tree:\n",
    "        if hasattr(chunk, 'label') and chunk.label() not in ['GPE', 'ORGANIZATION', 'PERSON', 'LOCATION', 'GSP', 'FACILITY' ]:\n",
    "            ner_categories[chunk.label()] += 1\n",
    "for category in sorted(ner_categories, key=ner_categories.get, reverse=True):\n",
    "    print(category, ner_categories[category])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here is a distribution across the candidates that remained in the race until the last primary debate.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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y9/tPnHd7neJJPbrMA3EA1Q6JnjXLNuCJ+Z71cZUMjy/9fsDotX8d4LZFlmzr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure object at 0x112cc7e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#This section is with help from a datacamp tutorial\n",
    "#https://campus.datacamp.com/courses/natural-language-processing-fundamentals-in-python/named-entity-recognition?ex=3\n",
    "\n",
    "ner_categories = defaultdict(int)\n",
    "\n",
    "# Create the nested for loop\n",
    "for tree in master_df['Tree']:\n",
    "    for chunk in tree:\n",
    "        if hasattr(chunk, 'label'):\n",
    "            if chunk.label() in ['Hillary Clinton', 'Donald Trump', 'Bernie Sanders', 'Ted Cruz', 'Marco Rubio', 'John Kasich']:\n",
    "                ner_categories[chunk.label()] += 1\n",
    "            \n",
    "# Create a list from the dictionary keys for the chart labels: labels\n",
    "labels = list(ner_categories.keys())\n",
    "\n",
    "# Create a list of the values: values\n",
    "values = [ner_categories.get(l) for l in labels]\n",
    "\n",
    "# Create the pie chart\n",
    "plt.pie(values, labels=labels, autopct='%1.1f%%', startangle=140)\n",
    "\n",
    "# Display the chart\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We see that Hillary Clinton is talked about the most, followed by Trump, Sanders, Rubio, Cruz, and Kasich. It makes sense that Hillary Clinton is addressed/referred to the most often because she made it through the Democratic primary debates and then into the general election debates. The Democratic party only ever had at most 5 candidates running, while the Republican party had at most 17 candidates running. That means that each candidate who was a Democrat had more opportunities in each debate to speak than the Republican candidates simply because there were less of them.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building Speaker DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's build a data frame for each speaker with only their utterances in it.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#There are 89 different speakers\n",
    "speaker_counts = master_df['Speaker'].value_counts()\n",
    "speaker_counts.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TRUMP        6013\n",
       "CLINTON      4890\n",
       "SANDERS      3545\n",
       "RUBIO        2617\n",
       "CRUZ         2117\n",
       "KASICH       1977\n",
       "BUSH         1461\n",
       "OMALLEY      1073\n",
       "CHRISTIE     1018\n",
       "CARSON        974\n",
       "COOPER        792\n",
       "BLITZER       756\n",
       "PAUL          752\n",
       "TAPPER        677\n",
       "WALLACE       652\n",
       "FIORINA       614\n",
       "KELLY         493\n",
       "MUIR          488\n",
       "DICKERSON     481\n",
       "HOLT          416\n",
       "Name: Speaker, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#These are the top 20 speakers who had the most utterances. A larger count doesn't necissarily \n",
    "#mean a speaker spoke for more time than another, just that they had more utterances.\n",
    "speaker_counts[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['TRUMP', 'CLINTON', 'SANDERS', 'RUBIO', 'CRUZ', 'KASICH', 'BUSH', 'OMALLEY', 'CHRISTIE', 'CARSON', 'COOPER', 'BLITZER', 'PAUL', 'TAPPER', 'WALLACE', 'FIORINA', 'KELLY', 'MUIR', 'DICKERSON', 'HOLT', 'RADDATZ', 'BAIER', 'CUOMO', 'BASH', 'HUCKABEE', 'BARTIROMO', 'CAVUTO', 'WALKER', 'HEWITT', 'QUESTION', 'TODD', 'WEBB', 'QUINTANILLA', 'CHAFEE', 'QUICK', 'RAMOS', 'HARWOOD', 'MADDOW', 'SALINAS', 'BAKER', 'WOODRUFF', 'IFILL', 'MITCHELL', 'LEMON', 'TUMULTY', 'GARRETT', 'LOUIS', 'CORDES', 'MCELVEEN', 'DINAN', 'HAM', 'STRASSEL', 'UNKNOWN', 'UNIDENTIFIED', 'COONEY', 'LOPEZ', 'EPPERSON', 'OBRADOVICH', 'SANTELLI', 'CRAMER', 'ANNOUNCER', 'LEVESQUE', 'GOODSON', 'BROWNLEE', 'FRANCHESCA', 'FRANTA', 'JENNA', 'BISHOP', 'MODERATOR', 'PANELISTS', 'AUDIENCE', 'SMITH', 'PERRY', 'ARNOLD', 'CRAWFORD', 'LASSEN', 'ALEXIS', 'DICK', 'ROSENGREN', 'SEAN', 'WILKINS', 'PLUMMER', 'BRETT', 'HANNITY', 'MALE', 'COLLISON', 'JOY', 'UNIDENTIFIABLE', 'DEBORAH']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "speakers = list(speaker_counts.index)\n",
    "speakers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "speaker_dfs = []\n",
    "for speaker in speakers:\n",
    "    df = master_df.loc[master_df['Speaker']==speaker, :]\n",
    "    speaker_dfs.append(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**First, let's focus on the candidates. The ultimate goal of this project is to focus on the referring expressions Hillary Clinton and Donald Trump use for each other, but analyzing how they refer to other candidates, and how other candidates refer to them can be helpful in making comparisons.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trump = speaker_dfs[0]\n",
    "clinton = speaker_dfs[1]\n",
    "sanders = speaker_dfs[2]\n",
    "rubio = speaker_dfs[3]\n",
    "cruz = speaker_dfs[4]\n",
    "kasich = speaker_dfs[5]\n",
    "bush = speaker_dfs[6]\n",
    "omalley = speaker_dfs[7]\n",
    "christie = speaker_dfs[8]\n",
    "carson = speaker_dfs[9]\n",
    "paul = speaker_dfs[12]\n",
    "fiorina = speaker_dfs[15]\n",
    "huckabee = speaker_dfs[24]\n",
    "walker = speaker_dfs[27]\n",
    "webb = speaker_dfs[31]\n",
    "chafee = speaker_dfs[33]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Debate Type</th>\n",
       "      <th>Speaker</th>\n",
       "      <th>Sents</th>\n",
       "      <th>Tree</th>\n",
       "      <th>Entities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>1-14-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>TRUMP</td>\n",
       "      <td>It's not fear and terror, it's reality.</td>\n",
       "      <td>[(It, PRP), ('s, VBZ), (not, RB), (fear, JJ), ...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>1-14-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>TRUMP</td>\n",
       "      <td>You just have to look today at Indonesia, bomb...</td>\n",
       "      <td>[(You, PRP), (just, RB), (have, VBP), (to, TO)...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>1-14-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>TRUMP</td>\n",
       "      <td>[applause]\\n\\nYou look at California, you look...</td>\n",
       "      <td>[([, NN), (applause, NN), (], NNP), (You, PRP)...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>1-14-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>TRUMP</td>\n",
       "      <td>They're very, very badly wounded.</td>\n",
       "      <td>[(They, PRP), ('re, VBP), (very, RB), (,, ,), ...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>1-14-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>TRUMP</td>\n",
       "      <td>They will — some will follow.</td>\n",
       "      <td>[(They, PRP), (will, MD), (—, VB), (some, DT),...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Debate Type Speaker  \\\n",
       "170  1-14-16  primary_rep   TRUMP   \n",
       "171  1-14-16  primary_rep   TRUMP   \n",
       "172  1-14-16  primary_rep   TRUMP   \n",
       "173  1-14-16  primary_rep   TRUMP   \n",
       "174  1-14-16  primary_rep   TRUMP   \n",
       "\n",
       "                                                 Sents  \\\n",
       "170            It's not fear and terror, it's reality.   \n",
       "171  You just have to look today at Indonesia, bomb...   \n",
       "172  [applause]\\n\\nYou look at California, you look...   \n",
       "173                  They're very, very badly wounded.   \n",
       "174                      They will — some will follow.   \n",
       "\n",
       "                                                  Tree Entities  \n",
       "170  [(It, PRP), ('s, VBZ), (not, RB), (fear, JJ), ...       []  \n",
       "171  [(You, PRP), (just, RB), (have, VBP), (to, TO)...       []  \n",
       "172  [([, NN), (applause, NN), (], NNP), (You, PRP)...       []  \n",
       "173  [(They, PRP), ('re, VBP), (very, RB), (,, ,), ...       []  \n",
       "174  [(They, PRP), (will, MD), (—, VB), (some, DT),...       []  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Debate Type</th>\n",
       "      <th>Speaker</th>\n",
       "      <th>Sents</th>\n",
       "      <th>Tree</th>\n",
       "      <th>Entities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1617</th>\n",
       "      <td>1-17-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>Well, good evening.</td>\n",
       "      <td>[(Well, RB), (,, ,), (good, JJ), (evening, NN)...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1618</th>\n",
       "      <td>1-17-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>And I want to thank the Congressional Black Ca...</td>\n",
       "      <td>[(And, CC), (I, PRP), (want, VBP), (to, TO), (...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1619</th>\n",
       "      <td>1-17-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>You know, I remember well when my youth minist...</td>\n",
       "      <td>[(You, PRP), (know, VBP), (,, ,), (I, PRP), (r...</td>\n",
       "      <td>[TITLE]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1620</th>\n",
       "      <td>1-17-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>I was a teenager.</td>\n",
       "      <td>[(I, PRP), (was, VBD), (a, DT), (teenager, NN)...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1621</th>\n",
       "      <td>1-17-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>And his moral clarity, the message that he con...</td>\n",
       "      <td>[(And, CC), (his, PRP$), (moral, JJ), (clarity...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Debate Type  Speaker  \\\n",
       "1617  1-17-16  primary_dem  CLINTON   \n",
       "1618  1-17-16  primary_dem  CLINTON   \n",
       "1619  1-17-16  primary_dem  CLINTON   \n",
       "1620  1-17-16  primary_dem  CLINTON   \n",
       "1621  1-17-16  primary_dem  CLINTON   \n",
       "\n",
       "                                                  Sents  \\\n",
       "1617                                Well, good evening.   \n",
       "1618  And I want to thank the Congressional Black Ca...   \n",
       "1619  You know, I remember well when my youth minist...   \n",
       "1620                                  I was a teenager.   \n",
       "1621  And his moral clarity, the message that he con...   \n",
       "\n",
       "                                                   Tree Entities  \n",
       "1617  [(Well, RB), (,, ,), (good, JJ), (evening, NN)...       []  \n",
       "1618  [(And, CC), (I, PRP), (want, VBP), (to, TO), (...       []  \n",
       "1619  [(You, PRP), (know, VBP), (,, ,), (I, PRP), (r...  [TITLE]  \n",
       "1620  [(I, PRP), (was, VBD), (a, DT), (teenager, NN)...       []  \n",
       "1621  [(And, CC), (his, PRP$), (moral, JJ), (clarity...       []  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinton.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**First, we're going to get some stats.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Debate Type</th>\n",
       "      <th>Speaker</th>\n",
       "      <th>Sents</th>\n",
       "      <th>Tree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6013</td>\n",
       "      <td>6013</td>\n",
       "      <td>6013</td>\n",
       "      <td>6013</td>\n",
       "      <td>6013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5606</td>\n",
       "      <td>5541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>9-26-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>TRUMP</td>\n",
       "      <td>[applause]</td>\n",
       "      <td>[([, NN), (applause, NN), (], NN)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>751</td>\n",
       "      <td>3979</td>\n",
       "      <td>6013</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  Debate Type Speaker       Sents  \\\n",
       "count      6013         6013    6013        6013   \n",
       "unique       14            2       1        5606   \n",
       "top     9-26-16  primary_rep   TRUMP  [applause]   \n",
       "freq        751         3979    6013          89   \n",
       "\n",
       "                                      Tree  \n",
       "count                                 6013  \n",
       "unique                                5541  \n",
       "top     [([, NN), (applause, NN), (], NN)]  \n",
       "freq                                    89  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump[['Date', 'Debate Type', 'Speaker', 'Sents', 'Tree']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Debate Type</th>\n",
       "      <th>Speaker</th>\n",
       "      <th>Sents</th>\n",
       "      <th>Tree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4890</td>\n",
       "      <td>4890</td>\n",
       "      <td>4890</td>\n",
       "      <td>4890</td>\n",
       "      <td>4890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4686</td>\n",
       "      <td>4672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>4-14-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>[applause]</td>\n",
       "      <td>[([, NN), (applause, NN), (], NN)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>495</td>\n",
       "      <td>3660</td>\n",
       "      <td>4890</td>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  Debate Type  Speaker       Sents  \\\n",
       "count      4890         4890     4890        4890   \n",
       "unique       13            2        1        4686   \n",
       "top     4-14-16  primary_dem  CLINTON  [applause]   \n",
       "freq        495         3660     4890         118   \n",
       "\n",
       "                                      Tree  \n",
       "count                                 4890  \n",
       "unique                                4672  \n",
       "top     [([, NN), (applause, NN), (], NN)]  \n",
       "freq                                   118  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinton[['Date', 'Debate Type', 'Speaker', 'Sents', 'Tree']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**These descriptions show that out of all of the debates, Trump had his most utterances in the 9-26-16 debate, and Clinton had her most utterances in the 4-14-16 debate. The most frequent \"utterance\" by both of these candidates was the transcription for applause. Based on these transcriptions, Clinton was applauded 118 times while Trump was applauded 89 times. Out of Trump's 6013 utterances, 5606 of those were unique. Somehow, there are less unique trees (5541) than there are unique utterances. I'm not quite sure how that is. Out of Clinton's 4890 utterances, 4686 of those were unique, and there are only 4672 unique trees.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Important Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Defining some functions:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#This function returns a set of all of the relevant entities a given speaker refers to.\n",
    "def get_entities(entities):\n",
    "    all_ents = []\n",
    "    for entity_list in entities:\n",
    "        for ent in entity_list:\n",
    "            all_ents.append(ent)\n",
    "    return set(all_ents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#This function returns a list of REs a speaker used to describe a given entity.\n",
    "def get_RE_for_label(trees, label):\n",
    "    name = ''\n",
    "    REs = []\n",
    "    RE_trees = []\n",
    "    ents=set(link_dict.values())\n",
    "    for tree in trees:\n",
    "        for chunk in tree:\n",
    "            if hasattr(chunk, 'label'):\n",
    "                if label == 'all':\n",
    "                    if chunk.label() in ents:\n",
    "                        for leaf in chunk.leaves():\n",
    "                            name+=' '+str(leaf[0])\n",
    "                        name = name.strip()\n",
    "                        REs.append(name)\n",
    "                        name = ''\n",
    "                    else:\n",
    "                        name = ''\n",
    "                else:\n",
    "                    if chunk.label()==label:\n",
    "                        for leaf in chunk.leaves():\n",
    "                            name+=' '+str(leaf[0])\n",
    "                        name = name.strip()\n",
    "                        REs.append(name)\n",
    "                        name = ''\n",
    "                    else:\n",
    "                        name = ''\n",
    "    return REs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#This function maps a referring expression to the type of referring expression it is.\n",
    "def get_re_type_freq(re_map, freq_dict):\n",
    "    re_freq_dict = {}\n",
    "    for key in re_map:\n",
    "        if key in freq_dict.keys():\n",
    "            if re_map[key] in re_freq_dict.keys():\n",
    "                re_freq_dict[re_map[key]] += freq_dict[key]\n",
    "            else:\n",
    "                re_freq_dict[re_map[key]] = freq_dict[key]\n",
    "    return re_freq_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#This function takes a data frame, an entity label, and a specific RE and returns all rows with that specific RE in it\n",
    "#If no RE is passed in, any rows about the entity specified will be returned\n",
    "def get_trees_for(df, label, RE=''):\n",
    "    name = ''\n",
    "    REs = []\n",
    "    RE_trees = []\n",
    "    df2 = pd.DataFrame(columns=['Date', 'Debate Type', 'Speaker', 'Sents', 'Tree'])\n",
    "    row=0\n",
    "    if RE == '':\n",
    "        for ent_list in df['Entities']:\n",
    "            if label in ent_list:\n",
    "                df2.loc[row]=(df.iloc[row][:])\n",
    "            row+=1\n",
    "    else:\n",
    "        for tree in df['Tree']:\n",
    "            for chunk in tree:\n",
    "                if hasattr(chunk, 'label'):\n",
    "                    if chunk.label()==label:\n",
    "                        for leaf in chunk.leaves():\n",
    "                            name+=' '+str(leaf[0])\n",
    "                        name = name.strip()\n",
    "                        if name == RE:\n",
    "                            df2.loc[row]=(df.iloc[row][:])\n",
    "                        name = ''\n",
    "                    else:\n",
    "                        name = ''\n",
    "            row +=1\n",
    "    return df2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Referring Expression Categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's categorize the ways a person can be referred to into different types. I will define the types as follows:**\n",
    "\n",
    "**PROFESSIONAL- Senator, Secretary, Businessman, Dr., Governor, etc.**\n",
    "\n",
    "**FIRST NAME- Hillary, Donald, etc.**\n",
    "\n",
    "**FULL NAME- Hillary Clinton, Donald Trump, etc.**\n",
    "\n",
    "**LAST NAME- Clinton, Trump, etc.**\n",
    "\n",
    "**GENDERED- Mrs., Ms., Miss, Mr. (Only if they posess an alternate professional title)**\n",
    "\n",
    "**NAMECALLING- any form of namecalling**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**I'm going to find all of the ways Hillary Clinton and Donald Trump are referred to by anyone who participated in the debates.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Clinton', 'Madam Secretary', 'Hillary Clinton', 'Secretary', 'Hillary', 'Hillary Rodham Clinton', 'Mrs. Clinton', 'Secretary Secretary Clinton', 'Hilary Clinton', 'Hill', 'Secretary Clinton', 'Secretary Hillary Clinton'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_REclinton = get_RE_for_label(master_df['Tree'], 'Hillary Clinton')\n",
    "set(master_REclinton)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Secretary Clinton': 587, 'Hillary Clinton': 310, 'Hillary': 67, 'Hillary Rodham Clinton': 3, 'Secretary': 154, 'Clinton': 50, 'Secretary Hillary Clinton': 4, 'Secretary Secretary Clinton': 3, 'Madam Secretary': 16, 'Mrs. Clinton': 12, 'Hill': 1, 'Hilary Clinton': 1}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_REclinton_freq = {re: master_REclinton.count(re) for re in master_REclinton}\n",
    "master_REclinton_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(master_REclinton_freq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The length of this dictionary is 12, but since President Clinton is a tagging error that's difficult to fix (actually referring to President Bill Clinton).**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**In addition to Hilary Clinton being a mispelling, there are only 10 ways of referring to Hillary Clinton. That being said, I can categorize these RE's into the categories described above very easily by hand.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Madam Secretary could be considered professional because it has information about her previous job, but the use of \"Madam\" instead of just saying \"Secretary\" when that would suffice just as well makes this RE gendered. The use of Clinton's full name, including her maiden name (Hillary Rodham Clinton), could be an interesting case. I'm going to look at these three instances closer.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Debate Type</th>\n",
       "      <th>Speaker</th>\n",
       "      <th>Sents</th>\n",
       "      <th>Tree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>1-14-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>CHRISTIE</td>\n",
       "      <td>He's a good guy, a smart guy, and he would be ...</td>\n",
       "      <td>[(He, PRP), ('s, VBZ), (a, DT), (good, JJ), (g...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1277</th>\n",
       "      <td>1-14-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>CHRISTIE</td>\n",
       "      <td>And I'm the only one up on this stage who back...</td>\n",
       "      <td>[(And, CC), (I, PRP), ('m, VBP), (the, DT), (o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4340</th>\n",
       "      <td>1-28-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>CHRISTIE</td>\n",
       "      <td>Hillary Rodham Clinton did that to our country.</td>\n",
       "      <td>[[(Hillary, NNP), (Rodham, NNP), (Clinton, NNP...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Debate Type   Speaker  \\\n",
       "435   1-14-16  primary_rep  CHRISTIE   \n",
       "1277  1-14-16  primary_rep  CHRISTIE   \n",
       "4340  1-28-16  primary_rep  CHRISTIE   \n",
       "\n",
       "                                                  Sents  \\\n",
       "435   He's a good guy, a smart guy, and he would be ...   \n",
       "1277  And I'm the only one up on this stage who back...   \n",
       "4340    Hillary Rodham Clinton did that to our country.   \n",
       "\n",
       "                                                   Tree  \n",
       "435   [(He, PRP), ('s, VBZ), (a, DT), (good, JJ), (g...  \n",
       "1277  [(And, CC), (I, PRP), ('m, VBP), (the, DT), (o...  \n",
       "4340  [[(Hillary, NNP), (Rodham, NNP), (Clinton, NNP...  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_rodham = get_trees_for(master_df, 'Hillary Clinton', 'Hillary Rodham Clinton')\n",
    "master_rodham"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interesting. Chris Christie is the only one to use Hillary Clinton's maiden name.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Secretary Clinton': 'PROFESSIONAL', 'Hillary Clinton': 'FULL NAME', 'Hillary': 'FIRST NAME', 'Hillary Rodham Clinton': 'FULL NAME', 'Clinton': 'LAST NAME', 'Secretary Hillary Clinton': 'PROFESSIONAL', 'Mrs. Clinton': 'GENDERED', 'Madam Secretary': 'GENDERED', 'Hill': 'FIRST NAME', 'Senator Clinton': 'PROFESSIONAL', 'Hilary Clinton': 'FULL NAME'}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinton_re_maps = {'Secretary Clinton': 'PROFESSIONAL', 'Hillary Clinton': \"FULL NAME\", 'Hillary': \"FIRST NAME\", 'Hillary Rodham Clinton': \"FULL NAME\", 'Clinton': \"LAST NAME\", 'Secretary Hillary Clinton': \"PROFESSIONAL\", 'Mrs. Clinton': \"GENDERED\", 'Madam Secretary': \"GENDERED\", 'Hill': \"FIRST NAME\", 'Senator Clinton': \"PROFESSIONAL\", 'Hilary Clinton': \"FULL NAME\"}\n",
    "clinton_re_maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 591, 'FULL NAME': 314, 'FIRST NAME': 68, 'LAST NAME': 50, 'GENDERED': 28}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinton_freq = get_re_type_freq(clinton_re_maps, master_REclinton_freq)\n",
    "clinton_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's do the same for Donald Trump and map his REs.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'President Trump', 'Donald', 'Mr. Donald Trump', 'Trump', 'Senator Trump', 'Mr. Trump', 'Donald Trump', 'Mr. Trmup', 'Donald J. Trump', 'Businessman Donald Trump'}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_REtrump = get_RE_for_label(master_df['Tree'], 'Donald Trump')\n",
    "set(master_REtrump)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Businessman Donald Trump': 2, 'Mr. Trump': 458, 'Donald Trump': 176, 'Donald': 255, 'Donald J. Trump': 6, 'Trump': 47, 'President Trump': 4, 'Mr. Trmup': 1, 'Mr. Donald Trump': 3, 'Senator Trump': 1}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_REtrump_freq = {re: master_REtrump.count(re) for re in master_REtrump}\n",
    "master_REtrump_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**That Senator Trump is weird because Donald Trump was never a senator. Let's see what's up with that.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Debate Type</th>\n",
       "      <th>Speaker</th>\n",
       "      <th>Sents</th>\n",
       "      <th>Tree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25524</th>\n",
       "      <td>3-10-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>HEWITT</td>\n",
       "      <td>Senator Trump...[crosstalk]</td>\n",
       "      <td>[[(Senator, NNP), (Trump, NNP)], (..., :), ([,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Debate Type Speaker                         Sents  \\\n",
       "25524  3-10-16  primary_rep  HEWITT   Senator Trump...[crosstalk]   \n",
       "\n",
       "                                                    Tree  \n",
       "25524  [[(Senator, NNP), (Trump, NNP)], (..., :), ([,...  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_sentrump = get_trees_for(master_df, 'Donald Trump', 'Senator Trump')\n",
    "master_sentrump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Debate Type</th>\n",
       "      <th>Speaker</th>\n",
       "      <th>Sents</th>\n",
       "      <th>Tree</th>\n",
       "      <th>Entities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25520</th>\n",
       "      <td>3-10-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>TRUMP</td>\n",
       "      <td>But I would like to at least have the other si...</td>\n",
       "      <td>[(But, CC), (I, PRP), (would, MD), (like, VB),...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25521</th>\n",
       "      <td>3-10-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>TRUMP</td>\n",
       "      <td>Maybe we can get a deal.</td>\n",
       "      <td>[(Maybe, RB), (we, PRP), (can, MD), (get, VB),...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25522</th>\n",
       "      <td>3-10-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>TRUMP</td>\n",
       "      <td>I think it's probably the toughest negotiation...</td>\n",
       "      <td>[(I, PRP), (think, VBP), (it, PRP), ('s, VBZ),...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25523</th>\n",
       "      <td>3-10-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>TRUMP</td>\n",
       "      <td>But maybe we can get a deal done.</td>\n",
       "      <td>[(But, CC), (maybe, RB), (we, PRP), (can, MD),...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25524</th>\n",
       "      <td>3-10-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>HEWITT</td>\n",
       "      <td>Senator Trump...[crosstalk]</td>\n",
       "      <td>[[(Senator, NNP), (Trump, NNP)], (..., :), ([,...</td>\n",
       "      <td>[Donald Trump]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25525</th>\n",
       "      <td>3-10-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>TRUMP</td>\n",
       "      <td>And, by the way, just so you understand, as f...</td>\n",
       "      <td>[(And, CC), (,, ,), (by, IN), (the, DT), (way,...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25526</th>\n",
       "      <td>3-10-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>TRUMP</td>\n",
       "      <td>I think it's maybe the world deal I've ever seen.</td>\n",
       "      <td>[(I, PRP), (think, VBP), (it, PRP), ('s, VBZ),...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25527</th>\n",
       "      <td>3-10-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>TRUMP</td>\n",
       "      <td>I think it's the worst deal I've ever seen neg...</td>\n",
       "      <td>[(I, PRP), (think, VBP), (it, PRP), ('s, VBZ),...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25528</th>\n",
       "      <td>3-10-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>TRUMP</td>\n",
       "      <td>I will be so tough on them and ultimately that...</td>\n",
       "      <td>[(I, PRP), (will, MD), (be, VB), (so, RB), (to...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25529</th>\n",
       "      <td>3-10-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>TRUMP</td>\n",
       "      <td>That deal will be broken.</td>\n",
       "      <td>[(That, DT), (deal, NN), (will, MD), (be, VB),...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25530</th>\n",
       "      <td>3-10-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>HEWITT</td>\n",
       "      <td>Thank you Mr. Trump.</td>\n",
       "      <td>[(Thank, NNP), (you, PRP), [(Mr., NNP), (Trump...</td>\n",
       "      <td>[Donald Trump]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Debate Type Speaker  \\\n",
       "25520  3-10-16  primary_rep   TRUMP   \n",
       "25521  3-10-16  primary_rep   TRUMP   \n",
       "25522  3-10-16  primary_rep   TRUMP   \n",
       "25523  3-10-16  primary_rep   TRUMP   \n",
       "25524  3-10-16  primary_rep  HEWITT   \n",
       "25525  3-10-16  primary_rep   TRUMP   \n",
       "25526  3-10-16  primary_rep   TRUMP   \n",
       "25527  3-10-16  primary_rep   TRUMP   \n",
       "25528  3-10-16  primary_rep   TRUMP   \n",
       "25529  3-10-16  primary_rep   TRUMP   \n",
       "25530  3-10-16  primary_rep  HEWITT   \n",
       "\n",
       "                                                   Sents  \\\n",
       "25520  But I would like to at least have the other si...   \n",
       "25521                           Maybe we can get a deal.   \n",
       "25522  I think it's probably the toughest negotiation...   \n",
       "25523                  But maybe we can get a deal done.   \n",
       "25524                        Senator Trump...[crosstalk]   \n",
       "25525   And, by the way, just so you understand, as f...   \n",
       "25526  I think it's maybe the world deal I've ever seen.   \n",
       "25527  I think it's the worst deal I've ever seen neg...   \n",
       "25528  I will be so tough on them and ultimately that...   \n",
       "25529                          That deal will be broken.   \n",
       "25530                               Thank you Mr. Trump.   \n",
       "\n",
       "                                                    Tree        Entities  \n",
       "25520  [(But, CC), (I, PRP), (would, MD), (like, VB),...              []  \n",
       "25521  [(Maybe, RB), (we, PRP), (can, MD), (get, VB),...              []  \n",
       "25522  [(I, PRP), (think, VBP), (it, PRP), ('s, VBZ),...              []  \n",
       "25523  [(But, CC), (maybe, RB), (we, PRP), (can, MD),...              []  \n",
       "25524  [[(Senator, NNP), (Trump, NNP)], (..., :), ([,...  [Donald Trump]  \n",
       "25525  [(And, CC), (,, ,), (by, IN), (the, DT), (way,...              []  \n",
       "25526  [(I, PRP), (think, VBP), (it, PRP), ('s, VBZ),...              []  \n",
       "25527  [(I, PRP), (think, VBP), (it, PRP), ('s, VBZ),...              []  \n",
       "25528  [(I, PRP), (will, MD), (be, VB), (so, RB), (to...              []  \n",
       "25529  [(That, DT), (deal, NN), (will, MD), (be, VB),...              []  \n",
       "25530  [(Thank, NNP), (you, PRP), [(Mr., NNP), (Trump...  [Donald Trump]  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df.loc[25520:25530][:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Looks like it wasn't a tagging mistake. It was a slip of the tongue.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(master_REtrump_freq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The length of this dictionary is 10, but since one of these keys 'Mr. Trmup', is just a typo, we will say Donald Trump is only referred to in 9 different ways throughout this entire corpus. There is only one more RE for Clinton than there are for Trump. I expected there to be more variation in the ways speakers refer to Clinton because she has had many different titles over the course of her career (Senator, Secretary, First Lady), she has a maiden name, and there are two common titles to refer to married women (Ms. and Mrs.) as opposed to one title for men (Mr.). **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The line between professional title and gendered title gets a little fuzzy with Donald Trump because of his lack of political experience. His professional title is simply Mr., which is inherently gendered. I have decided to define Mr. as professional in this case because of a lack of any other term.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Businessman Donald Trump': 'PROFESSIONAL', 'Mr. Trump': 'PROFESSIONAL', 'Donald Trump': 'FULL NAME', 'Donald': 'FIRST NAME', 'Donald J. Trump': 'FULL NAME', 'Trump': 'LAST NAME', 'President Trump': 'PROFESSIONAL', 'Mr. Trmup': 'PROFESSIONAL', 'Mr. Donald Trump': 'PROFESSIONAL', 'Senator Trump': 'PROFESSIONAL'}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_re_maps = {'Businessman Donald Trump': 'PROFESSIONAL', 'Mr. Trump': 'PROFESSIONAL', 'Donald Trump': 'FULL NAME', 'Donald': 'FIRST NAME', 'Donald J. Trump': 'FULL NAME', 'Trump': 'LAST NAME', 'President Trump': 'PROFESSIONAL', 'Mr. Trmup': 'PROFESSIONAL', 'Mr. Donald Trump': 'PROFESSIONAL', 'Senator Trump': 'PROFESSIONAL'}\n",
    "trump_re_maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 469, 'FULL NAME': 182, 'FIRST NAME': 255, 'LAST NAME': 47}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_freq = get_re_type_freq(trump_re_maps, master_REtrump_freq)\n",
    "trump_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's do the same for Carly Fiorina since she is the only other female candidate and she also does not have a political title, and then for Ben Carson, another candidate lacking political experience, and for all of the candidates who made it to the last primary debate.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Carly Fiorina"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Mrs. Carly Fiorina', 'Fiorina', 'Businesswoman Carly Fiorina', 'President Fiorina', 'Carly', 'Mrs. Fiorina', 'Carly Fiorina'}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_REfiorina = get_RE_for_label(master_df['Tree'], 'Carly Fiorina')\n",
    "set(master_REfiorina)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Carly Fiorina': 16, 'Mrs. Carly Fiorina': 1, 'Mrs. Fiorina': 17, 'Fiorina': 38, 'Businesswoman Carly Fiorina': 1, 'Carly': 13, 'President Fiorina': 2}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_REfiorina_freq = {re: master_REfiorina.count(re) for re in master_REfiorina}\n",
    "master_REfiorina_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Carly Fiorina': 'FULL NAME', 'Mrs. Fiorina': 'PROFESSIONAL', 'Fiorina': 'LAST NAME', 'Businesswoman Carly Fiorina': 'PROFESSIONAL', 'Carly': 'FIRST NAME', 'President Fiorina': 'PROFESSIONAL'}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fiorina_re_maps = {'Carly Fiorina': \"FULL NAME\", 'Mrs. Fiorina': \"PROFESSIONAL\", 'Fiorina': \"LAST NAME\", 'Businesswoman Carly Fiorina': \"PROFESSIONAL\", 'Carly': \"FIRST NAME\", 'President Fiorina': \"PROFESSIONAL\"}\n",
    "fiorina_re_maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FULL NAME': 16, 'PROFESSIONAL': 20, 'LAST NAME': 38, 'FIRST NAME': 13}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fiorina_freq = get_re_type_freq(fiorina_re_maps, master_REfiorina_freq)\n",
    "fiorina_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Bernie Sanders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Vermont Bernie Sanders', 'Bernie', 'Governor Governor Sanders', 'Senator Sander', 'Senator Sanders', 'Sanders', 'Senator Bernie Sanders', 'Bernie Sanders', 'Secretary Sanders', 'Senator Senator Sanders', 'President Sanders'}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_REsanders = get_RE_for_label(master_df['Tree'], 'Bernie Sanders')\n",
    "set(master_REsanders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Senator Bernie Sanders': 8, 'Bernie': 13, 'Bernie Sanders': 61, 'Senator Sanders': 415, 'Governor Governor Sanders': 1, 'Sanders': 21, 'President Sanders': 8, 'Secretary Sanders': 3, 'Senator Senator Sanders': 3, 'Senator Sander': 1, 'Vermont Bernie Sanders': 1}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_REsanders_freq = {re: master_REsanders.count(re) for re in master_REsanders}\n",
    "master_REsanders_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Senator Bernie Sanders': 'PROFESSIONAL', 'Bernie': 'FIRST NAME', 'Bernie Sanders': 'FULL NAME', 'Senator Sanders': 'PROFESSIONAL', 'Governor Sanders': 'PROFESSIONAL', 'Sanders': 'LAST NAME', 'President Sanders': 'PROFESSIONAL', 'Secretary Sanders': 'PROFESSIONAL', 'Senator Sander': 'PROFESSIONAL', 'Vermont Bernie Sanders': 'PROFESSIONAL'}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#There are some REs in here that are typos, and some where people just used incorrect titles (Secretary Sanders)\n",
    "#We are still going to map those to professional\n",
    "sanders_re_maps = {'Senator Bernie Sanders': \"PROFESSIONAL\", 'Bernie': \"FIRST NAME\", 'Bernie Sanders': \"FULL NAME\", 'Senator Sanders': \"PROFESSIONAL\", 'Governor Sanders': \"PROFESSIONAL\", 'Sanders': \"LAST NAME\", 'President Sanders': \"PROFESSIONAL\", 'Secretary Sanders': \"PROFESSIONAL\", 'Senator Sander': \"PROFESSIONAL\", 'Vermont Bernie Sanders': \"PROFESSIONAL\"}\n",
    "sanders_re_maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 436, 'FIRST NAME': 13, 'FULL NAME': 61, 'LAST NAME': 21}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sanders_freq = get_re_type_freq(sanders_re_maps, master_REsanders_freq)\n",
    "sanders_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Marco Rubio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Senator Marco Rubio', 'Senator Rubio', 'Mr. Rubio', 'President Rubio', 'Florida Senator Marco Rubio', 'Senator Senator Rubio', 'Florida Senator', 'Rubio', 'Marco Rubio', 'Marco'}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_RErubio = get_RE_for_label(master_df['Tree'], 'Marco Rubio')\n",
    "set(master_RErubio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Marco Rubio': 17, 'Senator Rubio': 223, 'Marco': 88, 'Senator Marco Rubio': 14, 'Florida Senator Marco Rubio': 2, 'Rubio': 5, 'Senator Senator Rubio': 1, 'Mr. Rubio': 2, 'Florida Senator': 1, 'President Rubio': 1}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_RErubio_freq = {re: master_RErubio.count(re) for re in master_RErubio}\n",
    "master_RErubio_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Marco Rubio': 'FULL NAME', 'Senator Rubio': 'PROFESSIONAL', 'Marco': 'FIRST NAME', 'Senator Marco Rubio': 'PROFESSIONAL', 'Florida Senator Marco Rubio': 'PROFESSIONAL', 'Rubio': 'LAST NAME', 'Mr. Rubio': 'GENDERED', 'Florida Senator': 'PROFESSIONAL', 'President Rubio': 'PROFESSIONAL'}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rubio_re_maps = {'Marco Rubio': \"FULL NAME\", 'Senator Rubio': \"PROFESSIONAL\", 'Marco': \"FIRST NAME\", 'Senator Marco Rubio': \"PROFESSIONAL\", 'Florida Senator Marco Rubio': \"PROFESSIONAL\", 'Rubio': \"LAST NAME\", 'Mr. Rubio': \"GENDERED\", 'Florida Senator': \"PROFESSIONAL\", 'President Rubio': \"PROFESSIONAL\"}\n",
    "rubio_re_maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FULL NAME': 17, 'PROFESSIONAL': 241, 'FIRST NAME': 88, 'LAST NAME': 5, 'GENDERED': 2}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rubio_freq = get_re_type_freq(rubio_re_maps, master_RErubio_freq)\n",
    "rubio_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Ted Cruz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Texas Senator', 'Cruz', 'Ted', 'President Cruz', 'Texas Senator Ted Cruz', 'Senator Cruz', 'Mr. Cruz', 'Senator Ted Cruz', 'Lyin', 'Ted Cruz'}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_REcruz = get_RE_for_label(master_df['Tree'], 'Ted Cruz')\n",
    "set(master_REcruz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Ted Cruz': 23, 'Senator Cruz': 209, 'Ted': 73, 'Cruz': 9, 'Senator Ted Cruz': 12, 'Texas Senator Ted Cruz': 2, 'Mr. Cruz': 2, 'Texas Senator': 1, 'Lyin': 2, 'President Cruz': 1}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_REcruz_freq = {re: master_REcruz.count(re) for re in master_REcruz}\n",
    "master_REcruz_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Ted Cruz': 'FULL NAME', 'Senator Cruz': 'PROFESSIONAL', 'Ted': 'FIRST NAME', 'Cruz': 'LAST NAME', 'Texas Senator Ted Cruz': 'PROFESSIONAL', 'Mr. Cruz': 'GENDERED', 'Texas Senator': 'PROFESSIONAL', 'Lyin': 'NAMECALLING', 'President Cruz': 'PROFESSIONAL', 'Senator Ted Cruz': 'PROFESSIONAL'}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cruz_re_maps = {'Ted Cruz': 'FULL NAME', 'Senator Cruz': \"PROFESSIONAL\", 'Ted': \"FIRST NAME\", 'Cruz': \"LAST NAME\", 'Texas Senator Ted Cruz': \"PROFESSIONAL\", 'Mr. Cruz': \"GENDERED\", 'Texas Senator': \"PROFESSIONAL\", 'Lyin': \"NAMECALLING\", 'President Cruz': \"PROFESSIONAL\", 'Senator Ted Cruz': \"PROFESSIONAL\"}\n",
    "cruz_re_maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FULL NAME': 23, 'PROFESSIONAL': 225, 'FIRST NAME': 73, 'LAST NAME': 9, 'GENDERED': 2, 'NAMECALLING': 2}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cruz_freq = get_re_type_freq(cruz_re_maps, master_REcruz_freq)\n",
    "cruz_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Ben Carson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Neurosurgeon', 'Mr. Carson', 'Dr. Ben Carson', 'Dr. Carson', 'Ben Carson', 'President Carson', 'Dr. Dr. Carson', 'Carson', 'Ben'}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_REcarson = get_RE_for_label(master_df['Tree'], 'Ben Carson')\n",
    "set(master_REcarson)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Ben Carson': 13, 'Dr. Carson': 141, 'Dr. Ben Carson': 14, 'Neurosurgeon': 4, 'Ben': 23, 'President Carson': 1, 'Mr. Carson': 1, 'Carson': 4, 'Dr. Dr. Carson': 1}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_REcarson_freq = {re: master_REcarson.count(re) for re in master_REcarson}\n",
    "master_REcarson_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Ben Carson': 'FULL NAME', 'Dr. Carson': 'PROFESSIONAL', 'Neurosurgeon': 'PROFESSIONAL', 'Ben': 'FIRST NAME', 'President Carson': 'PROFESSIONAL', 'Mr. Carson': 'GENDERED', 'Dr. Ben Carson': 'PROFESSIONAL', 'Carson': 'LAST NAME'}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carson_re_maps = {'Ben Carson': 'FULL NAME', 'Dr. Carson': 'PROFESSIONAL', 'Neurosurgeon': 'PROFESSIONAL', 'Ben': 'FIRST NAME', 'President Carson': 'PROFESSIONAL', 'Mr. Carson': 'GENDERED', 'Dr. Ben Carson': 'PROFESSIONAL', 'Carson': 'LAST NAME'}\n",
    "carson_re_maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FULL NAME': 13, 'PROFESSIONAL': 160, 'FIRST NAME': 23, 'GENDERED': 1, 'LAST NAME': 4}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carson_freq = get_re_type_freq(carson_re_maps, master_REcarson_freq)\n",
    "carson_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We are going to exclude John Kasich from this analysis because there were issues discerning whether \"John\" referred to John Kasich or one of the many other Johns, including moderator John Harwood, while tagging.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overall Distribution of Referring Expressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIRST NAME</th>\n",
       "      <th>FULL NAME</th>\n",
       "      <th>GENDERED</th>\n",
       "      <th>LAST NAME</th>\n",
       "      <th>NAMECALLING</th>\n",
       "      <th>PROFESSIONAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CLINTON</th>\n",
       "      <td>68</td>\n",
       "      <td>314</td>\n",
       "      <td>28.0</td>\n",
       "      <td>50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRUMP</th>\n",
       "      <td>255</td>\n",
       "      <td>182</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47</td>\n",
       "      <td>0.0</td>\n",
       "      <td>469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SANDERS</th>\n",
       "      <td>13</td>\n",
       "      <td>61</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FIORINA</th>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRUZ</th>\n",
       "      <td>73</td>\n",
       "      <td>23</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RUBIO</th>\n",
       "      <td>88</td>\n",
       "      <td>17</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CARSON</th>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         FIRST NAME  FULL NAME  GENDERED  LAST NAME  NAMECALLING  PROFESSIONAL\n",
       "CLINTON          68        314      28.0         50          0.0           591\n",
       "TRUMP           255        182       0.0         47          0.0           469\n",
       "SANDERS          13         61       0.0         21          0.0           436\n",
       "FIORINA          13         16       0.0         38          0.0            20\n",
       "CRUZ             73         23       2.0          9          2.0           225\n",
       "RUBIO            88         17       2.0          5          0.0           241\n",
       "CARSON           23         13       1.0          4          0.0           160"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distribution = pd.DataFrame([clinton_freq, trump_freq, sanders_freq, fiorina_freq, cruz_freq, rubio_freq, carson_freq], index = ['CLINTON', 'TRUMP', 'SANDERS', 'FIORINA', 'CRUZ', 'RUBIO', 'CARSON'])\n",
    "distribution.fillna(0, inplace=True)\n",
    "distribution.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIRST NAME</th>\n",
       "      <th>FULL NAME</th>\n",
       "      <th>GENDERED</th>\n",
       "      <th>LAST NAME</th>\n",
       "      <th>NAMECALLING</th>\n",
       "      <th>PROFESSIONAL</th>\n",
       "      <th>TOTAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CLINTON</th>\n",
       "      <td>68</td>\n",
       "      <td>314</td>\n",
       "      <td>28.0</td>\n",
       "      <td>50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>591</td>\n",
       "      <td>1051.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRUMP</th>\n",
       "      <td>255</td>\n",
       "      <td>182</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47</td>\n",
       "      <td>0.0</td>\n",
       "      <td>469</td>\n",
       "      <td>953.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SANDERS</th>\n",
       "      <td>13</td>\n",
       "      <td>61</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>436</td>\n",
       "      <td>531.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FIORINA</th>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRUZ</th>\n",
       "      <td>73</td>\n",
       "      <td>23</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>225</td>\n",
       "      <td>334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RUBIO</th>\n",
       "      <td>88</td>\n",
       "      <td>17</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>241</td>\n",
       "      <td>353.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CARSON</th>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160</td>\n",
       "      <td>201.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         FIRST NAME  FULL NAME  GENDERED  LAST NAME  NAMECALLING  \\\n",
       "CLINTON          68        314      28.0         50          0.0   \n",
       "TRUMP           255        182       0.0         47          0.0   \n",
       "SANDERS          13         61       0.0         21          0.0   \n",
       "FIORINA          13         16       0.0         38          0.0   \n",
       "CRUZ             73         23       2.0          9          2.0   \n",
       "RUBIO            88         17       2.0          5          0.0   \n",
       "CARSON           23         13       1.0          4          0.0   \n",
       "\n",
       "         PROFESSIONAL   TOTAL  \n",
       "CLINTON           591  1051.0  \n",
       "TRUMP             469   953.0  \n",
       "SANDERS           436   531.0  \n",
       "FIORINA            20    87.0  \n",
       "CRUZ              225   334.0  \n",
       "RUBIO             241   353.0  \n",
       "CARSON            160   201.0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "distribution['TOTAL']=distribution.sum(axis=1)\n",
    "distribution.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIRST NAME</th>\n",
       "      <th>FULL NAME</th>\n",
       "      <th>GENDERED</th>\n",
       "      <th>LAST NAME</th>\n",
       "      <th>NAMECALLING</th>\n",
       "      <th>PROFESSIONAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CLINTON</th>\n",
       "      <td>6.470029</td>\n",
       "      <td>29.876308</td>\n",
       "      <td>2.664129</td>\n",
       "      <td>4.757374</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>56.232160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRUMP</th>\n",
       "      <td>26.757608</td>\n",
       "      <td>19.097587</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.931794</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>49.213012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SANDERS</th>\n",
       "      <td>2.448211</td>\n",
       "      <td>11.487759</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.954802</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>82.109228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FIORINA</th>\n",
       "      <td>14.942529</td>\n",
       "      <td>18.390805</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.678161</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.988506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRUZ</th>\n",
       "      <td>21.856287</td>\n",
       "      <td>6.886228</td>\n",
       "      <td>0.598802</td>\n",
       "      <td>2.694611</td>\n",
       "      <td>0.598802</td>\n",
       "      <td>67.365269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RUBIO</th>\n",
       "      <td>24.929178</td>\n",
       "      <td>4.815864</td>\n",
       "      <td>0.566572</td>\n",
       "      <td>1.416431</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>68.271955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CARSON</th>\n",
       "      <td>11.442786</td>\n",
       "      <td>6.467662</td>\n",
       "      <td>0.497512</td>\n",
       "      <td>1.990050</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>79.601990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         FIRST NAME  FULL NAME  GENDERED  LAST NAME  NAMECALLING  PROFESSIONAL\n",
       "CLINTON    6.470029  29.876308  2.664129   4.757374     0.000000     56.232160\n",
       "TRUMP     26.757608  19.097587  0.000000   4.931794     0.000000     49.213012\n",
       "SANDERS    2.448211  11.487759  0.000000   3.954802     0.000000     82.109228\n",
       "FIORINA   14.942529  18.390805  0.000000  43.678161     0.000000     22.988506\n",
       "CRUZ      21.856287   6.886228  0.598802   2.694611     0.598802     67.365269\n",
       "RUBIO     24.929178   4.815864  0.566572   1.416431     0.000000     68.271955\n",
       "CARSON    11.442786   6.467662  0.497512   1.990050     0.000000     79.601990"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get the percentage of each type of RE for each candidate.\n",
    "distribution['FIRST NAME'] = distribution['FIRST NAME']/distribution['TOTAL']*100\n",
    "distribution['FULL NAME'] = distribution['FULL NAME']/distribution['TOTAL']*100\n",
    "distribution['GENDERED'] = distribution['GENDERED']/distribution['TOTAL']*100\n",
    "distribution['LAST NAME'] = distribution['LAST NAME']/distribution['TOTAL']*100\n",
    "distribution['NAMECALLING'] = distribution['NAMECALLING']/distribution['TOTAL']*100\n",
    "distribution['PROFESSIONAL'] = distribution['PROFESSIONAL']/distribution['TOTAL']*100\n",
    "del distribution['TOTAL']\n",
    "distribution.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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p04VJ0BRNuhYfHw9PT08A74tfZYVgfjGybNkyxMfHo3bt2ujcuXPpJf8BCwsLDBkyBM+e\nPcOGDRuE9vwJ3vr06YMNGzYofG3fvh0aGhoIDw8vsTO5pUEikWDatGkIDw+Hvr4+Fi1aVKTlVq5c\nqfCRW8D7R2XdvHkTGhoaaNmyZQlmK2vWrFmoUKECfvrppwLPYhfV1q1bkZ2dDQMDA5lJ6nr27Ald\nXV0cO3YM0dHRcstFR0fj2LFj0NXVLdLM/p+iX79+cHBwQEpKCgYPHiw3GZ9UKsWWLVuwYcMG6Ojo\nYOXKlUUaV1NTE4sXL0Zubi6WLFlSrJyqVKkiFLpjxoxBXFycXMzVq1cxcOBA5OTkYNasWTA1NS3W\nOkrCuHHjULVqVezatUvmCggiIio6TvRGRFQOzZo1C1lZWVi/fj0cHBzg6OiIhg0bIi8vDxcuXEBC\nQgI0NDSwevVq9O/fX+k4+RO+JSYmAgA8PDwKvW+3oEeiAcCUKVMgFouLtB0zZsxAaGgo/P39MXbs\nWFStWlW4nL6gM/Y1atSAk5MTjhw5gv3798tMBge8n708/2yyovzq1asnvA8KClJ677Szs3ORC8H8\nfZKXl4fMzEwkJycjMTERb968gbW1NbZs2QILC4sijfXzzz9j5cqVsLW1RdOmTVGtWjVkZmbi0qVL\nOHfuHABgyZIlqFWrVpHG+xSmpqYYNmwYAgICsH79eixevLhIy338SLQXL17g0qVLSEhIgJqaGtau\nXQtNTU2hX1dXF5s3b8aIESPQr18/dOzYEY0bNwbw/liLjY1FhQoVsGXLFoWTApYEdXV17Ny5EyNH\njkRMTAyaNm2Kb7/9FmZmZnjx4gVOnz6NGzduoFq1aggKCpI5fgrj7OyMdu3afdL9+YMGDcKLFy8w\nb948uLi44JtvvkGzZs2grq6Oq1evIjY2Fjk5OZg6darKJpPT0dHB5MmTsWDBAvj4+Ai3nnyooN8X\nXbp0QYsWLUozRSKiMo9FORFROSQSibBo0SL07t0bAQEBSEhIwJkzZyASiWBsbIwxY8bg+++/R926\ndQscRywWC7Opq6urw8PDo9B1F/RINADw8vIqclFuZGSEkSNH4ueff8aaNWvg5OSEu3fvwsbGBs2b\nNy9w2WHDhuHIkSPYuXOnXFF+8OBBpcsNGjRIpqhKSkpSeum0qalpkYvy/EvUNTU1UblyZRgZGcHV\n1RXOzs7o1q2bTCFamN27dyM6OhoJCQmIjo7GkydPIBKJUKtWLQwYMACjRo0q1bPk+aZPn46QkBBs\n3boV33//fZG+BPh4NndNTU3UqFED3333HcaPHw97e3u5Zbp3747ff/8dmzZtQlxcnFDA1qpVC4MH\nD8aECRNgbW1dchumgJ6eHvbt24dDhw4hLCwMCQkJOHjwICpWrAhLS0vMmzcPY8aM+aRnxS9btgwd\nO3b8pNsNxo4diy5dumDz5s04deoUfvnlF+Tl5cHQ0BADBgzAmDFjPmuegpIwevRo/Pzzz4iMjMTk\nyZNhZ2cn01/QI9F0dXVZlBPRf55IIpGUzg1pRERERERERFQg3lNOREREREREpCIsyomIiIiIiIhU\nhEU5ERERERERkYqwKCciIiIiIiJSERblRERERERERCrCopyIiIiIiIhIRViUExEREREREakIi/Kv\nVHJysqpToDKIxwUpwuOCFOFxQYrwuCBleGyQIjwuioZFOREREREREZGKsCgnIiIiIiIiUhEW5URE\nREREREQqwqKciIiIiIiISEU0VJ0AERFRaXv16hVycnJUnUa5IhaLkZGRoeo0yp1KlSpBQ4N/XhER\nUdHxfw0iIvqqvX37FgCgq6ur4kzKFy0tLYjFYlWnUa5IpVJIJBLo6OiwMCcioiLj5etERPRVy8rK\ngra2tqrToP8AkUgEPT09vHr1StWpEBFROcKinIiIvnoikUjVKdB/BI81IiIqLhblRERERERERCrC\nopyIiIiIiIhIRViUExEREREREakIpwYlIqL/HL0dD7/o+iQjjIsV7+XlhdDQULn206dPw9bWFl5e\nXnj27Bl2794NAFixYgV8fX0BvL+n2dDQEO3bt4e3tzdMTEyE5VNSUrB8+XLEx8cjPT0dBgYGsLW1\nxbx585CZmQkXF5cC89q0aRMGDx4s1+7s7IyEhARs3rwZ7u7uQntISAhmzpyJhw/l93fbtm1x/fp1\nnD17FpaWlgq3f8iQIdi4caNMn7e3N/z8/ODk5KRw+z9Uo0YN3Lx5s8BtIiIiUjUW5URERGWQo6Mj\ntmzZItNWtWpVpfFWVlY4fPgw8vLycPfuXUyfPh0jRozAiRMnAADZ2dlwc3NDnTp1sGPHDtSqVQuP\nHz9GTEwMJBIJvvnmG9y4cUMYz9vbG3fu3EFQUJDQVqVKFaXrF4vF8PHxgZubG7S0tArctvPnzyM9\nPR3u7u4ICgrC4sWL5WJMTEwQGRkJX19fVKpUCQCQk5ODsLAwmS8aPt7+D6mrqxeYBxERUVnAopyI\niKgM0tLSgqGhYZHjNTQ0hPhatWph2LBhmDVrFjIzM1GlShVcu3YNd+/exb59+2BhYQEAMDU1RcuW\nLYUxPlxfxYoVoampWeQc3NzccPLkSQQEBGDChAkFxgYFBaFv375wdnbGiBEjsGDBArnnejds2BCP\nHj3C/v37MWTIEADAb7/9Bi0tLbRp0wbPnz9Xuv1ERETlCYtyIiIqFX5ma4oVPyl1eill8t+TlpaG\nQ4cOQV1dXThbXK1aNaipqeHgwYOYMGGCXBH8uSpXroxZs2bBx8cHQ4YMgZ6ensK4V69eISIiAocO\nHYKtrS3EYjGOHTuGnj17ysV6eHggJCREKMqDg4MxePBgpKSklGjuREREqsSJ3oiIiMqgkydPwtjY\nWHj17du3wPgbN27A2NgYtWrVgrW1NeLj4zF69Gjh0m8jIyP4+vpi1apVMDMzQ48ePbBs2TJcu3at\nxHIePnw4DAwMsH79eqUxkZGRMDY2hp2dHUQiEfr37y9zifyH+vXrhwsXLuD27dtIS0tDdHQ0Bg0a\npDA2f/s/fI0aNapEtouIiKg08Uw5ERFRGdSmTRv4+fkJ78VicYHxderUwZ49e/D27VtERUXh4MGD\nWLhwoUzMmDFj4O7ujri4OJw/fx5RUVFYv349Nm7cKDNB26fS0NDAggUL4OXlBU9PT4UxQUFBGDBg\ngPDe3d0d69atw6NHj1CrVi2ZWD09PfTs2RPBwcHQ1dVFu3btULt2bYXj5m//h/K/kCAiIirLeKac\niIioDNLW1oaFhYXwMjIyKjC+QoUKsLCwQIMGDTBt2jQ0bNgQ06fL3xKgo6ODHj16YMGCBUhISED7\n9u3h4+NTYnn37t0bNjY2WL58uVzfzZs3kZSUhCVLlqBq1aqoWrUqWrZsidzcXISEhCgcb8iQIQgL\nC0NwcLBwGbsi+dv/4Yv3mBMRUXmgsqI8NzcXy5Ytg62tLQwNDWFra4tly5YhJydHiJFKpVixYgXq\n16+PmjVrwtnZuUQvsyMiIvpazZgxA+Hh4bh48aLSGJFIBCsrK7x69apE17148WKEhobi+vXrMu1B\nQUFo3rw54uPjERcXJ7xmz56N4OBgSKVSubEcHBygqamJp0+fwtnZuUTzJCIiKgtUdvn6+vXrsW3b\nNvj7+8PGxgb//PMPxo0bhwoVKmDmzJkAAD8/P2zatAmbNm2ClZUVVq1aBTc3N/zxxx/Q0dFRVepE\nRERlXp06ddCjRw/4+Phgz549uHz5MlasWAF3d3dYW1ujQoUKiI+PR0hICL777rsSXXe7du3QpUsX\nBAQECBPNZWdnIywsDNOmTYONjY1MvIGBAVatWoXTp0/DwcFBpk8kEiEhIQFSqbTAR63l5OQgLS1N\nrp1ny4mIqKxTWVF+7tw5dOvWDd27dwcAmJmZoVu3bjh//jyA92fJ/f39MXnyZLi6ugIA/P39YWVl\nhb1792LEiBGqSp2IiKhcmDBhApycnHD27FlYWlrC3Nwcvr6+uH//PvLy8mBiYoIJEyZgypQpJb5u\nb29vtGvXDhUrVgQAHD16FOnp6ejVq5dcbM2aNdGqVSsEBQXJFeUAivRFfHJyMqytreXa09PTS3ym\neSIiopIkkkgk8teKfQE//vgjtm/fjoiICNSrVw/Xr1/Hd999hylTpmD06NFISUlBkyZNEBMTA3t7\ne2G5/v37w8DAAJs3b1ZF2uVGcnIyrKysVJ0GlTE8LkiR0jouysoj0TIyMqCrq1sqY3/NsrKyCp1c\njhT7mo85/j9CyvDYIEV4XBSNyr46njx5Ml6+fIlWrVpBXV0dOTk5mD59OkaPHg0AwiVo1atXl1mu\nevXqePTokdJxk5OTSy/pcob7ghThcUGKlIXjorRyEIvFBV72TMplZWWpOoVyKTMzE//++6+q0yg1\nZeH3BZVNPDZIER4XKPSLCZUV5REREQgLC8O2bdtQv359/P3335g9ezZMTU0xdOjQTx6X38S8x2+l\nSBEeF6RIWTkuSiuHjIwMnvH9BDxT/umqVKmi9NFt5V1Z+X1BZQ+PDVKEx0XRqKwoX7hwISZMmCBM\nLtOwYUPcv38fP/74I4YOHSpMzPLkyROZ/9iePHmCGjVqqCRnIiIiIiIiUq3i3CJXWrfHlSSVPRLt\n9evXwoys+dTV1ZGXlwfg/cRvhoaGiI2NFfqzsrKQmJiIVq1afdFciYiIiIiIiEqDys6Ud+vWDevX\nr4eZmRnq16+Py5cvY9OmTXB3dwfw/hEoXl5eWLduHaysrGBpaYk1a9agUqVK6Nu3r6rSJiIiIiIi\nIioxKivKV61aBR8fH0ybNg3p6ekwNDTEsGHDhGeUA8CkSZPw5s0bzJgxAxKJBM2aNUNERASfUU5E\nRERERERfBZUV5To6Oli5ciVWrlypNEYkEmHOnDmYM2fOF8yMiIiIiIiI6MtQ2T3lRERERERERP91\nLMqJiIiIiIiIVIRFOREREREREZGKqOyeciIiIlWpPMzxi67v5c7fixXv5eWF0NBQufbTp0/D1tYW\nzs7OsLGxwerVq2X6Q0JCMHPmTDx8+BAAsGLFChw8eBCJiYkK16NsHGVWrFgBX19fDBkyBBs3bhTa\nU1NTYWdnh9jYWDRt2lRmmZkzZ2Lbtm348ccfMWzYMLl8x48fDwsLC/z1118yfSdOnEC/fv1QqVIl\nYXvi4uLg4uKiMLdz586hXr16RdoOIiKisoRnyomIiMogR0dH3LhxQ+ZlY2Oj6rQgFosRGhqK69ev\nFxr79u1b7NmzB1OmTEFgYKDS8TIyMhAfHy/THhQUBBMTE4XLJCUlye2bunXrFn9jiIiIygAW5URE\nRGWQlpYWDA0NZV4aGqq/wK1OnTro3LkzFi1aVGjsoUOHYGpqimnTpuHGjRu4evWqXIy6ujoGDBiA\n4OBgoe3p06f47bffMHDgQIXjVq9eXW7fqKurf/I2ERERqRKLciIiIioWb29vHD9+HGfOnCkwLjAw\nEP3794e2tjZcXFyUni338PDAoUOH8OLFCwBAWFgYWrZsCXNz85JOnYiIqMxhUU5ERFQGnTx5EsbG\nxsKrb9++qk5J0LBhQ7i7u8Pb21tpTEpKChITE4W83d3dER4ejrdv38rFNmjQAPXr10dERAQAIDg4\nGEOGDFE6tq2trcy+KQuX9RMREX0q1V8HR0RERHLatGkDPz8/4b1YLFZhNvLmzp2L5s2b4+DBg7Cz\ns5PrDw4OhoODAwwNDQEA7du3h7a2No4cOYI+ffrIxXt4eCA4OBgNGzbEw4cP0atXL6FI/9ihQ4eg\np6cnvFdT4zkGIiIqv1iUExERlUHa2tqwsLBQ2Kejo4OMjAy59oyMDFSpUqW0UwMAmJiYwNPTE0uW\nLEF4eLhMX25uLnbt2oVHjx6hatWqQnteXh4CAwMVFuV9+vTB3LlzsWjRIvTt2xcVK1ZUum4zMzOZ\ncYmIiMozFuVERETljJWVFU6cOAGpVAqRSCS0X7p0CZaWll8sj6lTpyIwMFDuXvGTJ0/i2bNniI2N\nRYUKFYT2Bw8eYMCAAUhNTYWZmZnMMlWqVEGvXr0QFhaGpUuXfpH8iYiIygIW5UREROXMqFGjEBAQ\ngJkzZ2Lo0KEQi8U4fvw49u3bJ/d886ysLFy+fFmmTVtbWyjenz59KtdfvXp16OvrF5qHnp4epk2b\nBh8fH5nYe9L6AAAgAElEQVT2wMBAdOnSBU2aNJFpt7GxgZWVFYKDgzFv3jy58davX4/ly5fDwMCg\nwPU+efIEOTk5Mm36+voyXwAQERGVFyzKiYiIyhlzc3NERUVh2bJl6NOnD96+fQsrKyv8+uuv+Pbb\nb2Vi7969iw4dOsi0NWnSBL///jsAICIiQu7e7QkTJmD+/PlFysXT0xNbtmzBgwcPAAD//vsvfvvt\nN/j7+yuMd3V1xa5duzBnzhy5PrFYXKR751u3bi3XFhkZCUdHxyLlTEREVJaIJBKJVNVJUMlLTk6G\nlZWVqtOgMobHBSlSWseFn9maYsVPSp1e4jkA7++z1tXVLZWxv2ZZWVllbnK58uJrPub4/wgpw2OD\nFCkLf2OU1t8XJYnTlRIRERERERGpCItyIiIiIiIiIhVhUU5ERERERESkIizKiYiIiIiIiFSERTkR\nERERERGRirAoJyIiIiIiIlIRFuVEREREREREKsKinIiIiIiIiEhFWJQTERERERERqQiLciIiIiIi\nIiIV0VB1AkRERF/aq5huX3R9lTod+6Tl/v33X/z444/47bff8PDhQ+jo6MDCwgLfffcdBg8ejMqV\nK6Nx48a4f/++3LKTJ0/GokWLkJqaCjs7O+jr6+PixYvQ1dUVYpydnWFjY4PVq1cDALy8vBAaGgoA\n0NDQgJ6eHurXrw9XV1cMHz4cmpqaMssmJCTIrbdPnz745ZdfAAB6enr/vw8qVYK5uTnGjRuHwYMH\nC+1xcXFwcXFRuP3nzp1DvXr1sGLFCvj6+gIA1NXVoaOjg3r16qFbt27w9PRE5cqVi7xPiYiIyhoW\n5URERGVQamoqunXrBh0dHcybNw8NGzaEWCzG9evXERgYCAMDA/Tr1w8AMHPmTIwaNUpm+UqVKsm8\nf/PmDdavXw9vb+8C1+vo6IgtW7bg9evXePHiBU6fPo0VK1Zg9+7dOHDggMy4gwcPxsKFC2WWF4vF\nMu9/+uknODk54fXr14iIiMD48eNRs2ZNdO7cWSYuKSkJ+vr6Mm3VqlUT/m1lZYXDhw9DKpXi+fPn\nSEpKwrp16xAcHIyjR4/C0NCwwO0iIiIqq1iUExERlUHTpk2DmpoaYmNjZQphc3NzdOvWDVKpVGjT\n0dEptCgdO3YsNm/ejDFjxsDIyEhpnJaWFgwNDZGVlYU6derA1tYWnTp1goODA/z8/DB37lwhVltb\nu9D16urqCjHTpk3Dxo0bERMTI1eUV69eHVWrVlU6joaGhjBOzZo10aBBA3Tv3h3ffPMNvL29sXnz\n5gLzICIiKqt4TzkREVEZ8+zZM0RHR2P06NFyZ7zziUSiYo3Zu3dv2NjYYPny5cXOx8bGBp07d8ah\nQ4eKvWy+3Nxc7N+/H8+fP5e5DP5z1KxZE/369UNUVBTy8vJKZEwiIqIvjUU5ERFRGXPnzh1IpVJY\nWlrKtNvY2MDY2BjGxsaYMmWK0L506VKhPf917Jj8feyLFy9GaGgorl27Vuyc6tevj5SUFJm2X3/9\nVW6927Ztk4kZO3YsjI2NUaNGDYwYMQIGBgYYOnSo3Pi2trYy49jY2BQ5r8zMTDx9+rTY20RERFQW\n8PJ1IiKiciL/jPCkSZOQlZUltI8fPx4eHh4ysYouK2/Xrh06d+6MxYsXIywsrFjrlkqlcmfn3dzc\nMHv2bJm2jy9BX7JkCbp06YIHDx5g3rx5+OGHH2BhYSE3/qFDh2QmhlNTK9p5g/zL+It75QAREVFZ\nobKiXNlssV27dkV4eDikUilWrlyJnTt3QiKRoFmzZlizZg0aNGiggmyJiIi+HAsLC4hEIiQnJ8u0\nm5ubA3h/L/eHDAwMFBa6inh7e6N9+/Y4c+ZMsXK6fv26sP58urq6ha7X0NAQFhYWsLCwwM6dO+Hg\n4AA7OzvUq1dPJs7MzKzAe8oLyqtKlSowMDAo9rJERERlgcouX4+NjcWNGzeE16lTpyASidC7d28A\ngJ+fHzZt2gRfX1/ExMSgevXqcHNzw4sXL1SVMhER0RdhYGCATp06ISAgAC9fvizRsRs2bAh3d/dC\nZ2H/0NWrVxEdHY1evXp91rotLCzQs2dPuRnbP9Xjx4+xd+9e9OzZs8hn1omIiMoalZ0p//AxJwAQ\nFBQEHR0duLm5QSqVwt/fH5MnT4arqysAwN/fH1ZWVti7dy9GjBihipSJiIi+mLVr18LJyQmOjo6Y\nPXs2GjVqBA0NDVy8eBFXrlxBx44dhdgXL14gLS1NZnmxWCzzTPIPzZ07F82bNwcAuXu33759i7S0\nNLx58wYvXrzAqVOnsG7dOjRp0gQTJ06UiX39+rXceitUqCD3aLMPjR8/Hu3bt8f58+fRrFkzof3J\nkyfIycmRidXX10eFChUAADk5OUhLSxMeiXb27FmsXbsW+vr6xfqCgYiIqKwpE/eUS6VSBAUFYcCA\nAahYsSJSUlKQlpaGTp06CTEVK1ZEmzZtcPbsWRblRET01TM3N8fp06exbt06+Pj44OHDh9DU1ES9\nevUwatQojBkzRohdtWoVVq1aJbN8//79sXXrVoVjm5iYYOzYsfDz85Pr+/3332FtbQ11dXXo6uqi\nQYMGmD17NoYPHy4UyPlCQkIQEhIi09a6dWuFk8zla9SoERwdHbFs2TLs379fZrmPRUZGwtHREQCQ\nnJwMa2trqKmpQUdHB/Xq1cPw4cPh6ekJHR0dpesjIiIq60QSiURaeFjpiomJQZ8+fRAXF4fGjRvj\n7NmzcHJywt9//43atWsLcePHj8ejR48QERGhdKyP778jIiLViOpyoFjxPU66lkoeYrEY1atXL5Wx\niRR58uSJzER8RERUsorzN0Zp/X1RHFZWVgX2l4kz5Tt37oS9vT0aN2782WMVtsH/FcnJydwXJIfH\nBSlSVo6L0sohIyMDYrG4VMb+mmVlZXG/faIqVarInFT4mpSV3xdU9vDYIEXKwnGh6vUXhcpnRXny\n5AmioqIwbNgwoS3/MS5PnjyRi61Ro8YXzY+IiIiIiIiotKi8KN+1axe0tLTw3XffCW1mZmYwNDRE\nbGys0JaVlYXExES0atVKFWkSERERERERlTiVXr4ulUoRGBiIPn36oHLlykK7SCSCl5cX1q1bBysr\nK1haWmLNmjWoVKkS+vbtq8KMiYiIiIiIiEqOSovyuLg43L59W+HssJMmTcKbN28wY8YMSCQSNGvW\nDBEREZxhlYiIiIiIiL4aKi3KO3ToAIlEorBPJBJhzpw5mDNnzhfOioiIiIiIiOjLKBOzrxMRERER\nEX3Mz2xNkWMnpU4vxUyISo/KJ3ojIiIiIiIi+q9iUU5ERERERESkIizKiYiIiIiIiFSE95QTEdF/\nzoJfh33R9S0dvrNY8V5eXnj27Bl2795dYNzGjRuxcOFCTJkyBQsWLJDrDwwMREBAAO7cuQN1dXWY\nmJigR48emD9/PpydnZGQkKB0bBMTE1y5ckWuPS4uDi4uLrCyskJiYiI0NP7/T4nGjRvD09MTEydO\nlFnm4MGDGD58OL777jsEBATI9KWmpsLOzg5qamq4dOkSateuLfRJJBLUr18fWVlZiI2NRdOmTQEA\nenp6CnNet24dRo4cqXSbqGzjvcNE9F/FM+VERETlVHBwMKZMmYJdu3YhNzdXpi8oKAizZs3CyJEj\nERcXhxMnTmDmzJl4/fq1sOyNGzdw48YNJCUlAXhfxOe3HTt2rMB1379/H0FBQUXKMygoCJMmTcKR\nI0eUPnXFyMgIISEhMm3h4eGoVq2awviffvpJyDX/NXDgwCLlQ0REVJawKCciIiqHzp07h6dPn2L2\n7NkQi8U4ceKETP/Ro0fh4uKCESNGwMLCAtbW1ujduzeWL18OANDX14ehoSEMDQ1RvXp1uTZlxXC+\nsWPHYuXKlXj16lWBcQ8fPkRcXBwmTpyI5s2bIzw8XGHcwIEDsWvXLkilUqEtKCgIgwYNUhivq6sr\n5Jr/qlixYoG5EBERlUUsyomIiMqhwMBA9OnTB5qamujfvz8CAwNl+g0NDXH+/HmkpKSUyvo9PT2h\nqamJTZs2FRgXEhKCjh07wsDAAAMGDFB6dr1r1654+/YtTp8+DQC4dOkSUlJS4ObmVuK5ExERlSUs\nyomIiMqZly9fIjIyEu7u7gAAd3d3nDhxAmlpaULMrFmzYGBggCZNmsDe3h6enp4IDQ1FdnZ2ieQg\nFosxd+5cbNiwAenp6QpjpFIpQkJChDxdXV1x+/ZtXLhwQS5WQ0MD7u7uCA4OBvD+8vrevXtDW1tb\n4dhjx46FsbGxzOuff/4pkW0jIiL6kliUExERlTMREREwMjISJj6rU6cO7O3tERoaKsTUrFkTJ06c\nwJkzZ+Dl5QWpVIopU6agU6dOwn3ln8vd3R21a9fGqlWrFPafOnUKEokE3bp1AwBUrlwZzs7OSs+W\nDxkyBIcPH0ZaWhr27NkDDw8PpetesmQJ4uLiZF5WVlafv1FERERfGGdfJyIiKmcCAwORnJyMqlWr\nCm15eXlIT0/H5MmTZWJtbGxgY2ODMWPGIDExEd27d8f+/fsxePDgz85DTU0NixYtwuDBg+Hl5aUw\nz4yMDBgZGQltUqkUlStXxrJly+TOgltZWcHOzg6jRo2CoaEhWrZsidTUVIXrNjQ0hIWFxWdvAxER\nkaqxKCciIipHrl27hj///BORkZGoUaOG0J6VlQUnJyckJCSgbdu2CpetX78+ABQ6OVtxdO3aFa1a\ntcLSpUtl2p8/f44jR47A398fdnZ2Mn2urq44cOCAwtnShwwZggkTJsiNR0RE9LViUU5ERFQGZWZm\n4vLlyzJturq6CAwMhK2tLRwdHeWWcXBwQGBgINq2bYupU6eiZs2a6NChA4yMjJCWloY1a9ZAW1sb\nnTp1KtFclyxZgi5dukBTU1NoCwsLg46ODvr37w91dXWZeBcXFwQGBiosygcOHIju3btDV1e3wHVm\nZGTI3EMPAJUqVULlypU/Y0uIiIi+PBblREREZVBiYiI6dOgg09azZ08kJiZi3LhxCpdxdXXFzJkz\nsWrVKjg6OiIkJAQ7duzA06dPoa+vjyZNmmD//v2wtLQs0Vzt7e3h6uqK/fv3C21BQUFwdnaWK8jz\n8/zll19w69YtmUIeANTV1WUuy1fmhx9+kGubPn065s+f/wlbQEREpDosyomI6D9n6fCdqk6hQP7+\n/vD39y/2ch4eHsLkaL169UKvXr2KtFzVqlUhkUiKFNu+fXuFsTt27MCOHTuE92fOnFE6hoODg8wY\nBa3bzMxMrr+ouRIREZUHnH2diIiIiIiISEVYlBMRERERERGpCItyIiIiIiIiIhVhUU5ERERERESk\nIizKiYiIiIiIiFSERTkRERERERGRirAoJyIiIiIiIlIRFuVEREREREREKsKinIiIiIiIiEhFWJQT\nERERERERqYiGqhMgIiL60gICAr7o+saMGVPsZby8vBAaGoq5c+di5syZQntcXBxcXFxw+/ZtVK1a\nVWh3d3fH8ePHsW/fPnTs2FFmrBUrVsDX1xcdOnTAwYMHZfq2b9+OadOmoUGDBkhMTAQAhISEYPz4\n8Qrzevz4McRiMQDg33//xdq1a/Hbb7/hf//7H6pWrYqGDRvC09MTXbt2lVlu48aNWLhwIaZMmYIF\nCxbI9Cnbpg/zP3jwoJDfx5ydnWFjY4PVq1d/0r47fPgwAgICcOnSJWRlZcHY2BgtWrSAp6cn7O3t\nFa6TiIiopPBMORERURklFouxYcMGpKenFxj3+PFjnD59GuPGjUNgYKDCGENDQyQlJSE1NVWmPSgo\nCCYmJnLxFStWxI0bN+Re+QV5amoqHBwcEBMTA29vbyQkJCAyMhJdu3bF1KlT5cYLDg7GlClTsGvX\nLuTm5hZ1F3yyou67pUuXYtiwYbCxsUFISAjOnTuHX375BfXr15f78oCIiKg0sCgnIiIqo9q3b4/a\ntWtj1apVBcbt2rULnTt3hqenJ44ePYpnz57JxRgYGKBr164ICQkR2q5cuYLk5GS4urrKxYtEIhga\nGsq98k2fPh0AEBsbCzc3N1hZWcHa2hqenp6Ij4+XGevcuXN4+vQpZs+eDbFYjBMnThRrP3yKouy7\nP//8E2vXroWPjw9WrFiBtm3bwtTUFHZ2dpg8eTIOHz5c6nkSERGxKCciIiqj1NTUsGjRIuzYsQN3\n795VGCOVShEcHIz+/fvD1NQUzZo1Q1hYmMJYDw8PhIaGIi8vD8D7s+S9e/dG5cqVi5XX8+fPcfLk\nSYwePVrhsnp6ejLvAwMD0adPH2hqaqJ///5Kz+aXpKLsuz179qBy5coYPXq0wn6RSFSaKRIREQFQ\ncVH++PFjfP/996hbty4MDQ3RqlUrmW/XpVIpVqxYgfr166NmzZpwdnbGtWvXVJgxERHRl9W1a1e0\natUKS5cuVdgfHx+P58+fw8nJCcD7e8uDg4MVxnbp0gU5OTk4deoU3r59i/DwcAwZMkRh7OvXr2Fs\nbCzzyr9P/M6dO5BKpahXr16h+b98+RKRkZFwd3cX8jtx4gTS0tIKXfZzFbbvbt++DTMzM2ho/P8U\nO9u2bZPZ5vv375d6nkRE9N+msqJcIpHAyckJUqkU4eHhOHv2LFatWoXq1asLMX5+fti0aRN8fX0R\nExOD6tWrw83NDS9evFBV2kRERF/c4sWLERkZiYsXL8r1BQUFwc3NDRUqVAAAuLq64u7du/jzzz/l\nYtXV1TFw4EAEBwfjyJEjqFatGr755huF66xYsSLi4uJkXr/88guA91+aF1VERASMjIzQtGlTAECd\nOnVgb2+P0NDQIo/xOQrad4r069cPcXFx2LJlC169eiVcVUBERFRaVDb7+k8//YSaNWtiy5YtQpu5\nubnwb6lUCn9/f0yePFm4183f3x9WVlbYu3cvRowY8aVTJiIiUolmzZqhV69eWLhwIWbMmCG0SyQS\nHDx4EO/evcPOnTuF9tzcXAQGBqJ58+ZyYw0ePBht27bFvXv3MHjwYKXrFIlEsLCwUNhXt25diEQi\n3Lx5s9DcAwMDkZycLDPbeV5eHtLT0zF58uRCl/9cyvYd8H47EhMTkZ2dDU1NTQCArq4udHV1kZGR\nUeq5ERERASo8U37kyBE0a9YMI0aMgKWlJdq1a4etW7cK376npqYiLS0NnTp1EpapWLEi2rRpg7Nn\nz6oqbSIiIpVYuHAhEhMTER0dLbTt2bMH1apVQ3x8vMwZbT8/P+zfvx+vXr2SG6du3bqwt7fHhQsX\nMHDgwE/KRV9fH507d0ZAQABevnwp1y+RSAAA165dw59//on9+/fL5BcdHY179+4hISHhk9ZfXIr2\nHQD07dsXr169kjlBQERE9KWp7Ex5SkoKtm/fjnHjxmHy5Mn4+++/MWvWLACAp6encK/Zh5ez579/\n9OjRF8+XiIhIlSwsLDB8+HBs3rxZaAsKCkKvXr1gY2MjE2tpaYkFCxYgIiICHh4ecmPt3bsX7969\nk5uQ7UNSqVThfd/VqlWDuro61qxZAycnJ3Ts2BHz5s1Dw4YNIZVKERcXhx9//BFXrlxBYGAgbG1t\n4ejoKDeOg4MDAgMD0bZtW6Ht6tWr0NXVlYlr1KgRACArKwuXL1+W6dPW1oalpaXSbcinaN8BQIsW\nLTBp0iQsXLgQ9+/fh6urK0xMTPDkyRP8+uuvAN5f8k9ERFSaVFaU5+XloWnTpvD29gYA2NnZ4c6d\nO9i2bRs8PT0/edzk5OSSSrHc474gRXhckCJl4bgorRzEYjG0tLRKZeyiysrKKvYyubm5yM3NlVn2\nhx9+wK5duwAAN27cwOXLl+Hj46Nw/K5du2Lnzp3o168fcnJykJeXJ8SpqalBLBYL7z/uz87Oxps3\nb2BtbS03bmJiIurUqYOaNWvi+PHj8PPzw8KFC/H48WPo6+ujfv36WLRoETIzM7F7926MHTtWYX49\nevTAvHnzsHTpUrx79w4A4OLiIhd3+/Zt5OTk4O7du+jQoYNMn62tLY4fP468vDzk5OQI6yls32Vl\nZQl9c+bMQePGjbFjxw6EhYXh1atXqF69Olq2bInIyEhUq1at2J9fZmYm/v3332ItU558zb8v6POo\n+nNR9fpJMVV/LqpePwBYWVkV2C+SSCRFn62lBDVq1AgdO3bEhg0bhLawsDBMnToV//vf/5CSkoIm\nTZogJiYG9vb2Qkz//v1hYGAg9203yUpOTi70w6f/Hh4XpEhpHRd+ZmuKFT8pdXqJ5wAAGRkZcmdf\nqXBZWVkQi8WqTqNc+pqPudL8f6Q4vzNK6/cFfbqy8H8Jj4uyh8dF0ajsnvLWrVvj1q1bMm23bt1C\n7dq1AQBmZmYwNDREbGys0J+VlYXExES0atXqi+ZKREREREREVBpUVpSPGzcOf/zxB9asWYM7d+4g\nMjISW7duxejRowG8n/XVy8sLfn5+OHjwIK5evYpx48ahUqVK6Nu3r6rSJiIiIiIiIioxKrun3N7e\nHiEhIViyZAlWr14NExMTzJ07VyjKAWDSpEl48+YNZsyYAYlEgmbNmiEiIgI6OjqqSpuIiIiIiIio\nxKisKAcAJycnODk5Ke0XiUSYM2cO5syZ8wWzIiIiIiIiIvoyVHb5OhEREREREdF/HYtyIiIiIiIi\nIhVhUU5ERERERESkIizKiYiIiIiIiFSERTkRERERERGRirAoJyIiIiIiIlIRlT4SjYiISBX8zNZ8\n0fVNSp1e7GW8vLwQGhoKANDQ0ICxsTFcXFwwZ84cpKenw87OToitUqUK6tWrh2nTpqF79+4y42Rl\nZeGnn37C3r17kZqaiooVK6JVq1aYMWMGmjdvLsSFhIRg/PjxcnksX74c48aNU9oPAI8fP4ZYLEZ6\nejpWrFiB48ePIy0tDbq6umjQoAGmTJmCjh07AgBSUlKwfPlyxMfHIz09HQYGBrC1tcW8efOEbWrc\nuDE8PT0xceJEYR03b96Er68vTp8+jYyMDNSsWRMuLi6YMWMG9PT0hDhnZ2ckJCRg8+bNcHd3l9m+\nmTNn4uHDh3L5t23bFtevX8fZs2dhaWkp9zk8e/YMu3fvVrjtREREn4tFORERURnl6OiILVu2IDs7\nG4mJifjhhx/w5s0b/PDDDwCAffv2oVGjRsjIyMC2bdswdOhQnDp1CjY2NgCAd+/ewc3NDSkpKVi8\neDFat26N58+fY+vWrejevTsCAwNlinhtbW1cuHABAPD27VtoaWlBR0dHYf+HxGIxAMDDwwNv3rzB\nxo0bUadOHaSnpyMhIQHPnj0DAGRnZ8PNzQ116tTBjh07UKtWLTx+/BgxMTGQSCRK98P58+fh6uqK\ntm3bIiQkBLVq1cI///wDb29vnDhxAsePH5cpzMViMXx8fODm5gYtLa0C9/H58+eRnp4Od3d3BAUF\nYfHixQXGExERlTQW5URERGWUlpYWDA0NAQD9+vVDfHw8jhw5IhTlBgYGMDQ0hKGhIRYsWICtW7ci\nLi5OKMr9/f2RlJSE2NhYNGnSBABgamqKTZs24fnz55g4cSIuX74MbW1tAIBIJBLWl5WVJRTb+T7s\n/5hEIkFiYiIiIyPh4OAgrMve3l6IuXbtGu7evYt9+/bBwsJCiGnZsqXSfSCVSjFhwgTUrVsXoaGh\nUFN7f+dd7dq1YWdnB3t7eyxbtgxr1vz/1Q9ubm44efIkAgICMGHChAL3cVBQEPr27QtnZ2eMGDEC\nCxYsgIYG/zwiIqIvh//rULEU55LPT7lck4iIlNPS0sLbt2/l2rOzs7Fz504AgKamptAeHh4OR0dH\noSD/0A8//IBu3bohNjYWzs7On51b5cqVUblyZURFRaF169ZyBT0AVKtWDWpqajh48CAmTJhQpOL3\n8uXLuHbtGgICAoSCPF+tWrXQr18/7N27F6tXr4ZIJBJymTVrFnx8fDBkyBCZs+gfevXqFSIiInDo\n0CHY2tpCLBbj2LFj6Nmz5yfsASIiok/Did6IiIjKgfPnz2PPnj1wdHQU2nr06AFjY2MYGhpi/vz5\nMDMzg5ubm9B/+/Zt1KtXT+F49evXBwDcunVLaHv16hWMjY1hbGwMCwsLGBsbyyzzYX/+q2vXrgDe\n3/e+adMmhIeHw8zMDN9++y3mz5+PP//8U1jeyMgIvr6+WLVqFczMzNCjRw8sW7YM165dU7rdt2/f\nBgCl22FtbQ2JRIL09HSZ9uHDh8PAwADr169XOnZkZCSMjY1hZ2cHkUiE/v37IygoSGk8ERFRaeCZ\nciIiojLq5MmTMDY2Rk5ODrKzs9GjRw+sWrUKr1+/BgAEBASgQYMGuHXrFubOnYv169dDX1//k9en\nra2NuLg4AP9/T7my/nwVKlQQ/u3q6gonJyckJibi3LlziI6OxsaNG7FgwQJMmzYNADBmzBi4u7sj\nLi4O58+fR1RUFNavX4+NGzfKTMz2uTQ0NLBgwQJ4eXnB09NTYUxQUBAGDBggvHd3d8e6devw6NEj\n1KpVq8RyISIiKgjPlBMREZVRbdq0QVxcHP744w+kpaUhODgY1atXF/qNjY1Rt25dODk5wc/PDyNG\njMDTp0+F/rp16+LGjRsKx75+/boQk08kEsHCwgIWFhaoU6eOcN+3ov78l4mJiUyMWCxGx44dMWvW\nLBw/fhweHh5YuXIl3r17J8To6OigR48eWLBgARISEtC+fXv4+PgozDM/P2XbcePGDejp6aFatWpy\nfb1794aNjQ2WL18u13fz5k0kJSVhyZIlqFq1KqpWrYqWLVsiNzcXISEhCtdFRERUGliUExERlVHa\n2tqwsLCAqampzL3iirRr1w7W1tbw9fUV2vr164dTp07h4sWLcvF+fn6oWrUqOnXqVOJ5f8ja2ho5\nOTnIyspS2C8SiWBlZYVXr14p7Le1tYW1tTU2btyIvLw8mb5Hjx5hz5496Nu3r3A/+ccWL16M0NBQ\n4UuIfEFBQWjevDni4+MRFxcnvGbPno3g4GBIpdJP2FoiIqLiY1FORET0lZgwYQJ27tyJBw8eAHj/\njLH51pEAACAASURBVO2WLVti0KBB2LNnD+7du4fLly9j/PjxOHnyJDZs2CDMvF4UUqkUaWlpcq/c\n3Fw8e/YMLi4u2L17N65cuYKUlBRERkbip59+goODA6pUqYLLly9j4MCBOHDgAK5fv447d+4gMDAQ\nISEhSidXE4lE2LhxI27fvo2BAwfi3LlzePDgAX777Tf07t0btWvXxvz585Xm3K5dO3Tp0gUBAQFC\nW3Z2NsLCwtC3b1/Y2NjIvIYPH4579+7h9OnTQnxmZiYuX74s80pNTS3yfiMiIioI7yknIiL6SnTr\n1g2mpqZYvXo1/Pz8oKWlhcjISPj5+cHX1xf37t2DWCxG69atERUVhRYtWhRr/NevX8Pa2lqu/a+/\n/oKxsTFatGiBzZs3486dO3j37h1q1aqFvn37YsaMGQDeX25vbm4OX19f3P8/9u48Kqq68eP4Z0BT\nRBRMBNd8RAwxl1zLTAXTelJxK7VNrVxySVwz6nErC1EzNZXS3EnNFLfcKlxKXLKe1B5zwUgzF8QF\nFAXLmN8fneYXsYgG852B9+scz2nu/TL3w8w9Q5+5937vqVNKT09XpUqVNGjQIA0dOjTb7TZq1Egx\nMTGaNGmSnn76aSUnJ8vX11ft27fXK6+8ku3s6n8aO3asmjVrJjc3N0nSpk2bdOHCBYWEhGQa6+vr\nqyZNmmjJkiW2W7vt3r1bzZs3zzAuJCREixcvzvkFAwAgFyxJSUmcn1UAxcXFyd/fP8+fl1uiObf8\n2i/g3Bzh80LKv8+M5ORklS5dOl+euyDL6j7lyJ2CvM/l598R/h/DuTnC3xL2C8fDfpE7nL4OAAAA\nAIAhlHIAAAAAAAyhlAMAAAAAYAilHAAAAAAAQyjlAAAAAAAYQikHAAAAAMAQSjkAoEBzcXHRr7/+\najoGCgGr1apr166pSJEipqMAAJwIfzUAAAVayZIllZKSotTUVNNRnMqVK1dUqlQp0zGcTvHixVWs\nWDHTMQAAToRSDgAo0CwWizw8PEzHcDrnz59X5cqVTccAAKDA4/R1AAAAAAAMoZQDAAAAAGAIpRwA\nAAAAAEMo5QAAAAAAGGKslIeHh8vT0zPDvxo1atjWW61WhYeHKyAgQL6+vmrbtq0OHz5sKi4AAAAA\nAHkux1K+cOFC/fTTT7bH6enpOn78uNLS0jKNPXDggMaMGXNbG/f399fRo0dt/3bt2mVbN336dM2a\nNUsRERHaunWrvL291alTJ129evW2tgEAAAAAgKPKsZQPGzZM33zzje1xUlKSGjdurL1792Yae/To\nUc2cOfO2Nl6kSBH5+PjY/pUtW1bSH0fJIyMjNWTIEHXo0EGBgYGKjIxUSkqKVq5ceVvbAAAAAADA\nUeVYyq1Wa66W3akTJ04oICBAderU0QsvvKATJ05Ikk6ePKmEhAQFBwfbxrq5ualp06ZZfiEAAAAA\nAIAzKmJqww0bNtTs2bPl7++vCxcuaPLkyWrTpo327NmjhIQESZK3t3eGn/H29tbZs2dzfN64uLh8\ny+xsTL8WprePrPG+ICuOsF84QgZkxHuCrDjCfuEIGZCZ6ffF9PaRNdPvi+ntS39ctp0TY6W8devW\nGR43atRIdevW1dKlS9WoUaM7ft5b/cKFRVxcnPHXwvT2kZkj7BdwPI6yXzhCBvw/R9kv4FgcZb9w\nhAzIyBH2DdPbR2bsF7njMLdEc3d3V0BAgOLj4+Xj4yNJSkxMzDAmMTFR5cqVMxEPAAAAAIA8d8sj\n5d98842KFPlj2NWrV2WxWBQbG6tLly5lGLdv375/FCQtLU1xcXF6+OGHdc8998jHx0fbtm1T/fr1\nbet3796tN9544x9tB0Dem37PlFyPDT05Ih+TAAAAAM7llqV8zpw5mjNnToZlkydPznKsxWLJ9Yb/\n85//6LHHHlOlSpVs15Rfv35dTz31lCwWi/r376+pU6fK399f1atX15QpU+Tu7q4nnngi19sAAAAA\nAMCR5VjK169fn28bPnPmjHr37q2LFy+qbNmyatiwoT7//HNVqVJFkhQaGqrU1FSNHDlSSUlJatCg\ngaKjo+Xh4ZFvmQAAAAAAsKccS3mzZs3ybcPz58/Pcb3FYlFYWJjCwsLyLQMAAAAAACbl2URvN2/e\nVFRUVF49HQAAAAAABd4/LuWpqamKjIxUvXr1NHjw4LzIBAAAAABAoXDLUr548WI9+OCD8vX1VUBA\ngEaNGqUbN25Ikt5//33Vrl1br732mjw9PTV79ux8DwwAAAAAQEGR4zXly5cvV2hoqEqWLKnAwECd\nOXNGc+fOVVpampKTk7V27Vq1aNFCoaGhCgoKsldmAAAAAAAKhBxL+dy5c+Xv769Nmzbp7rvv1u+/\n/64BAwZoyZIl8vT01KpVqxQcHGyvrAAAAAAAFCg5nr5+5MgRPffcc7r77rslSa6urhoyZIisVqtG\njBhBIQcAAAAA4B/IsZRfv35dvr6+GZb5+PhIkmrWrJl/qQAAAAAAKARuOdGbxWLJcrmrq2uehwEA\nAAAAoDDJ8ZpySZo+fbo+/vhj2+PffvtNkjR+/HiVKVMmw1iLxaIVK1bkcUQAAAAAAAqmHEt5pUqV\nlJycrOTk5AzLK1eurMTERCUmJmZYnt1RdQAAAAAAkFmOpfz777+3Vw4AAAAAAAqdW15TDgAAAAAA\n8gelHAAAAAAAQyjlAAAAAAAYQikHAAAAAMAQSjkAAAAAAIZkW8rff/99HT9+3J5ZAAAAAAAoVLIt\n5a+99pq+++472+MyZcrok08+sUsoAAAAAAAKg2xLuZeXl86dO2d7bLVa7RIIAAAAAIDCokh2K5o3\nb66JEyfq66+/VqlSpSRJCxcu1Pbt27N9MovFopkzZ+Z5SAAAYF+LYydIsbkb+2avRfkbBgCAAizb\nUv7OO+9o7Nixio2N1YULF2SxWPTtt9/qwIED2T4ZpRwAAAAAgNzLtpSXKVNG7733nu2xl5eX3nvv\nPT355JN2CQYAAAAAQEGX61uizZo1S40bN87PLAAAAAAAFCrZHin/u6efflqS9Pvvv+vAgQP6+eef\nJUlVqlRRvXr15OLCLc8BAAAAALgduS7lkhQdHa3XX39dCQkJttnYLRaLfHx89Pbbb6tTp075EhIA\nAAAAgIIo16V8w4YN6t27t2rUqKFhw4apRo0akqRjx45p/vz56t27t4oVK6bHH38838ICAAAAAFCQ\n5LqUv/POO6pXr542btyo4sWL25a3aNFCPXr00GOPPaYpU6ZQygEAAAAAyKVcXwh++PBhde3aNUMh\n/1OxYsXUrVs3HT58OE/DAQAAAABQkOW6lLu5uenixYvZrr9w4YLc3NzyJBQAAAAAAIVBrkt5ixYt\n9MEHH2jXrl2Z1u3Zs0dz5sxRy5Yt7zjI1KlT5enpqZEjR9qWWa1WhYeHKyAgQL6+vmrbti1H4wEA\nAAAABUaurykfP368du/erXbt2qlu3bry9/eXJMXFxenAgQPy8fHRuHHj7ijEvn37tHDhQtWqVSvD\n8unTp2vWrFmaNWuW/P39NWnSJHXq1En79u2Th4fHHW0LAAAAAABHkesj5VWqVNHOnTv10ksvKSUl\nRevWrdO6deuUkpKiAQMG6KuvvlKVKlVuO0BycrL69OmjmTNnytPT07bcarUqMjJSQ4YMUYcOHRQY\nGKjIyEilpKRo5cqVt70dAAAAAAAcTa5LuSSVLVtWb7/9tvbt26dz587p3Llz2rdvnyZMmKCyZcve\nUYA/S3fz5s0zLD958qQSEhIUHBxsW+bm5qamTZtq7969d7QtAAAAAAAcSa5PX88PixYtUnx8vObM\nmZNpXUJCgiTJ29s7w3Jvb2+dPXvWLvkAAAAAAMhPxkp5XFyc3njjDW3evFlFixbN0+fFH0y/Fqa3\nj6yZfl9Mbx9Zc4T3xREy4M7w3hUujvB+O0IGZGb6fTG9fWTN9PtievuSbPOxZcdYKf/666918eJF\nPfDAA7Zlv//+u3bt2qX58+drz549kqTExERVrlzZNiYxMVHlypXL9nlv9QsXFnFxccZfC9PbR2bs\nF8iKI+wXEvuGw4nN/VDeu8KDzwtkxxH2DdPbR2bsF7ljrJS3bdtW999/f4ZlAwcOlJ+fn4YNG6bq\n1avLx8dH27ZtU/369SVJaWlp2r17t9544w0TkQEAAAAAyFPGSrmnp2eG2dYlqUSJEvLy8lJgYKAk\nqX///po6dar8/f1VvXp1TZkyRe7u7nriiSdMRAYAAAAAIE8ZnejtVkJDQ5WamqqRI0cqKSlJDRo0\nUHR0NPcoBwAAAAAUCHdcypcuXapPPvlEZ8+eVbly5dS5c2f17NlTFovljsNs2LAhw2OLxaKwsDCF\nhYXd8XMCAAAAAOCo7qiUT5o0SfPmzVOvXr1Uvnx5HTlyRGFhYYqPj+d6bwAAAAAAcinHUv7zzz+r\nSpUqmZYvXbpU8+bNU7NmzWzLfHx8NGvWLEo5AAAAAAC55JLTygcffFATJ05UWlpahuUlS5bUqVOn\nMiz75ZdfVLJkybxPCAAAAABAAZVjKV+/fr2++OILNW7cWGvXrrUtf+WVVxQaGqr27durb9++atmy\npRYsWKBXX3013wMDAAAAAFBQ5FjK69evry+++EIjR47UyJEjFRISoiNHjigkJESxsbFq3ry5PDw8\n9Nhjj2nnzp3q3r27vXIDAAAAAOD0cjXR23PPPaeQkBCFh4erZcuW6tWrl1577TWNHDkyv/MBAAAA\nAFBg5Xik/K9Kly6tiRMnatu2bTp8+LAaNGigxYsX52c2AAAAAAAKtFuW8lOnTmnRokWKjIzUvn37\nVLNmTa1du1aTJ0/W5MmT1bJlS+3bt88eWQEAAAAAKFByLOWbN29W48aN9e6772r58uV67LHH9Prr\nr0uSOnbsqK+//lqPPPKIQkJC1K9fPyUkJNglNAAAAAAABUGOpXzs2LEKCQnR/v37tWPHDkVGRioy\nMlJnzpyRJLm5uek///mPdu/erStXrqhhw4Z2CQ0AAAAAQEGQYyk/e/asHnjgAdvjJk2ayGq16ty5\ncxnGVa1aVcuWLdPChQvzJSQAAAAAAAVRjrOvP/jgg/rggw9Us2ZNlS5dWu+88468vLwUEBCQ5fhW\nrVrlS0gAAAAAAAqiHI+UT5s2TT4+Pnr88cfVtGlTfffdd1q4cKFKlChhr3wAAAAAABRYOR4pL1++\nvNauXavU1FTduHFDnp6e9soFAAAAAECBl2Mp/5Obm5vc3NzyOwsAAAAAAIXKLe9TDgAAAAAA8gel\nHAAAAAAAQyjlAAAAAAAYQikHAAAAAMAQSjkAAAAAAIZQygEAAAAAMIRSDgAAAACAIZRyAAAAAAAM\noZQDAAAAAGAIpRwAAAAAAEMo5QAAAAAAGEIpBwAAAADAEEo5AAAAAACGUMoBAAAAADCEUg4AAAAA\ngCHGSvncuXPVtGlTVa5cWZUrV1br1q21ZcsW23qr1arw8HAFBATI19dXbdu21eHDh03FBQAAAAAg\nzxkr5RUqVND48eO1Y8cObdu2Tc2bN9czzzyj//3vf5Kk6dOna9asWYqIiNDWrVvl7e2tTp066erV\nq6YiAwAAAACQp4yV8rZt26p169aqVq2aqlevrtGjR6tkyZLat2+frFarIiMjNWTIEHXo0EGBgYGK\njIxUSkqKVq5caSoyAAAAAAB5yiGuKf/999+1atUqXbt2TY0bN9bJkyeVkJCg4OBg2xg3Nzc1bdpU\ne/fuNZgUAAAAAIC8U8Tkxg8dOqQ2bdooLS1N7u7uioqKUq1atWzF29vbO8N4b29vnT17NsfnjIuL\ny7e8zsb0a2F6+8ia6ffF9PaRNUd4XxwhA+4M713h4gjvtyNkQGam3xfT20fWTL8vprcvSf7+/jmu\nN1rK/f399dVXX+nKlStau3at+vfvr08//fQfP6ezKNmzZa7HpizaflvPHRcXZ/y1ML19ZMZ+gaw4\nwn4hsW84nNjcD+W9Kzz4vEB2HGHfML19ZMZ+kTtGT1+/6667VK1aNdWrV09jx45V7dq1NXv2bPn4\n+EiSEhMTM4xPTExUuXLlTEQFAAAAACDPOcQ15X9KT0/Xr7/+qnvuuUc+Pj7atm2bbV1aWpp2796t\nJk2aGEwIAAAAAEDeMXb6+rhx49SmTRtVrFjRNqv6zp07tWLFClksFvXv319Tp06Vv7+/qlevrilT\npsjd3V1PPPGEqcgAAAAAAOQpY6U8ISFBffv21fnz51WqVCnVqlVLK1euVKtWrSRJoaGhSk1N1ciR\nI5WUlKQGDRooOjpaHh4epiIDAAAAAJCnjJXyyMjIHNdbLBaFhYUpLCzMTokAAAAAALAvh7qmHAAA\nAACAwoRSDgAAAACAIZRyAAAAAAAMoZQDAAAAAGAIpRwAAAAAAEMo5QAAAAAAGGLslmhAYeK54HSu\nxyY9XzEfkwAAAABwJBwpBwAAAADAEEo5AAAAAACGUMoBAAAAADCEUg4AAAAAgCGUcgAAAAAADKGU\nAwAAAABgCKUcAAAAAABDuE85AAAA4KQ8F5zO9dik5yvmYxIAd4oj5QAAAAAAGEIpBwAAAADAEEo5\nAAAAAACGUMoBAAAAADCEUg4AAAAAgCGUcgAAAAAADKGUAwAAAABgCPcpBwAAdjX9nim5Hht6ckQ+\nJgEAwDyOlAMAAAAAYAilHAAAAAAAQyjlAAAAAAAYwjXlAAAATsBzwelcj016vmI+JgEA5CWOlAMA\nAAAAYAilHAAAAAAAQ4yV8qlTpyooKEiVK1eWn5+funXrph9++CHDGKvVqvDwcAUEBMjX11dt27bV\n4cOHDSUGAAAAACBvGSvlO3fu1IsvvqgtW7Zo3bp1KlKkiDp27KjLly/bxkyfPl2zZs1SRESEtm7d\nKm9vb3Xq1ElXr141FRsAAAAAgDxjbKK36OjoDI8/+OADValSRXv27NG///1vWa1WRUZGasiQIerQ\noYMkKTIyUv7+/lq5cqWef/55E7EBAAAAAMgzDnNNeUpKitLT0+Xp6SlJOnnypBISEhQcHGwb4+bm\npqZNm2rv3r2mYgIAAAAAkGccppS/+uqrql27tho3bixJSkhIkCR5e3tnGOft7a3z58/bPR8AAAAA\nAHnNIe5T/tprr2nPnj3avHmzXF1d/9FzxcXF5VGq/Hf/bYy9k9/L9GthevuOpUSuR+b362b6fTG9\nfWTNEd4XR8iAO5Of7x37xV85xt8SR3hPHCGD43CM/cIez+/o20fWTL8vprcvSf7+/jmuN17Kw8LC\nFB0drfXr16tq1aq25T4+PpKkxMREVa5c2bY8MTFR5cqVy/b5bvULO6vb/b3i4uKMvxamt+9Qdp7O\n9dD8fN3YL5AVR9gvJPYNhxOb+6H5+d6xX/yFA/wt4fPCATnAfiE5xr5hevvIjP0id4yevj5q1Cit\nWrVK69atU40aNTKsu+eee+Tj46Nt27bZlqWlpWn37t1q0qSJvaMCAAAAAJDnjB0pHzFihD7++GNF\nRUXJ09PTdg25u7u7SpYsKYvFov79+2vq1Kny9/dX9erVNWXKFLm7u+uJJ54wFRsAAAAAgDxjrJR/\n+OGHkmS73dmfRo0apbCwMElSaGioUlNTNXLkSCUlJalBgwaKjo6Wh4eH3fMC9lKyZ8vbGp+yaHu+\n5AAAAACQ/4yV8qSkpFuOsVgsCgsLs5V0AAAAAAAKEuMTvQEAgDt3O2fXcGYNAACOx2HuUw4AAAAA\nQGFDKQcAAAAAwBBKOQAAAAAAhlDKAQAAAAAwhFIOAAAAAIAhlHIAAAAAAAyhlAMAAAAAYAilHAAA\nAAAAQyjlAAAAAAAYQikHAAAAAMAQSjkAAAAAAIZQygEAAAAAMIRSDgAAAACAIUVMBwAAABl5Ljid\n67E38zEHAADIfxwpBwAAAADAEEo5AAAAAACGUMoBAAAAADCEUg4AAAAAgCGUcgAAAAAADKGUAwAA\nAABgCKUcAAAAAABDKOUAAAAAABhCKQcAAAAAwBBKOQAAAAAAhlDKAQAAAAAwhFIOAAAAAIAhlHIA\nAAAAAAwpYjoAACB3SvZsmeuxKYu251sOAAAA5B1KOQAAAAAUIJ4LTt/W+KTnK+ZTEuSG0dPXY2Nj\n1b17d9WsWVOenp766KOPMqy3Wq0KDw9XQECAfH191bZtWx0+fNhQWgAAAAAA8pbRUn7t2jUFBgZq\n4sSJcnNzy7R++vTpmjVrliIiIrR161Z5e3urU6dOunr1qoG0AAAAAADkLaOlvE2bNhozZow6dOgg\nF5eMUaxWqyIjIzVkyBB16NBBgYGBioyMVEpKilauXGkoMQAAAAAAecdhZ18/efKkEhISFBwcbFvm\n5uampk2bau/evQaTAQAAAACQNxx2oreEhARJkre3d4bl3t7eOnv2bLY/FxcXl6+58tL9tzH2Tn4v\n06+F6e07lhL59sy3+zqbfl9Mb9+Z5ednhiO8L46QwXHkz2dGfr3G+fnesV/8Ve73i4L+njhCBsfh\nGPuFPZ7f0bfvWG7v70hB/swwvX1J8vf3z3G9w5byO3WrX9hZ3e7vFRcXZ/y1ML19h7Lz9mbAvB23\n8zqzXxQezrZfSOwbGeTTZ8Ztvcax+fS8t4n94i9uY7/Ir9eNzwsH5AD7heQY+4bp7TuU2/w7UpA/\nM0xvPzcc9vR1Hx8fSVJiYmKG5YmJiSpXrpyJSAAAAAAA5CmHLeX33HOPfHx8tG3bNtuytLQ07d69\nW02aNDGYDAAAAACAvGH09PWUlBTFx8dLktLT0/XLL7/o4MGD8vLyUuXKldW/f39NnTpV/v7+ql69\nuqZMmSJ3d3c98cQTJmMDAAAAAJAnjJby7777Tu3bt7c9Dg8PV3h4uJ566ilFRkYqNDRUqampGjly\npJKSktSgQQNFR0fLw8PDYGoAAAAAAPKG0VL+8MMPKykpKdv1FotFYWFhCgsLs2MqAAAAAADsw2Gv\nKQcAAAAAoKCjlAMAAAAAYAilHAAAAAAAQyjlAAAAAAAYQikHAAAAAMAQSjkAAAAAAIYYvSUaAAAA\n8l7Jni1zPTZl0fZ8ywEAuDWOlAMAAAAAYAhHyvOY54LTuR57Mx9zAAAAAEBucHaNWRwpBwAAAADA\nEEo5AAAAAACGUMoBAAAAADCEUg4AAAAAgCGUcgAAAAAADGH2daAQWRw7QYrN3dg3ey3K3zAAAAAA\nKOUFFeULAAAAf3U7t72SuPUVYC+cvg4AAAAAgCGUcgAAAAAADOH0dQAo5LjcBQAAwByOlAMAAAAA\nYAilHAAAAAAAQyjlAAAAAAAYQikHAAAAAMAQJnpzEte2PnabP+GTLzkAAEDhdTsTQ0pMDgkAuUEp\nBwBDPBecvq3xN/MpBwAAAMzh9HUAAAAAAAzhSDkAAAAAIM/dziUvhflyF46UAwAAAABgCEfKAQAA\nAPwjHBEF7hxHygEAAAAAMMQpSvmHH36oOnXqyMfHRy1atNCuXbtMRwIAAAAA4B9z+FIeHR2tV199\nVcOHD9eXX36pxo0b68knn9SpU6dMRwMAAAAA4B9x+GvKZ82apaefflo9e/aUJE2ePFkxMTGaP3++\nxo4dazgdAACAc7u29bHbGO1zW889d+7c2wsDh5Kf+wacF/tF3rMkJSVZTYfIzq+//qry5ctr3rx5\n6tixo235iBEj9MMPP2jjxo0G0wEAAAAA8M849OnrFy9e1O+//y5vb+8My729vXX+/HlDqQAAAAAA\nyBsOXcoBAAAAACjIHLqU33333XJ1dVViYmKG5YmJiSpXrpyhVAAAAAAA5A2HLuV33XWX6tWrp23b\ntmVYvm3bNjVp0sRQKgAAAAAA8obDz74+cOBA9evXTw0aNFCTJk00f/58nTt3Ts8//7zpaAAAAAAA\n/CMOX8o7d+6sS5cuafLkyUpISFDNmjW1YsUKValSxXQ0wGF9++232rRpk27cuKGgoCAFBwebjgQA\nAAAgCw59SzQAt2/9+vXq1auXihcvLldXV6WkpGjChAkaMGCA6WhwQGlpaVq9erWuXbum4OBgVatW\nzXQkAA5g+fLlevTRR+Xl5ZXl+itXrigsLEyzZs2yczIAjujy5cu5GpfdZ0phRyl3Yk2bNr3lGIvF\notjYWDukgaMICgrSfffdp2nTpsnV1VWTJk3S+++/r/j4eNPRYNhbb72l1NRUTZgwQZJ08+ZNtWrV\nSgcPHpQkubu7a/Xq1WrUqJHJmHAwqampio6O1pIlS7R582bTcWAnXl5eqlq1qj766CMFBgZmWn/+\n/HkFBATo0qVLBtLBEaxdu1YrV67U8ePHJUl+fn7q2rWrQkJCDCeDCV5eXrJYLDmOsVgsunjxop0S\nOReHP30d2cvpQ+/ixYuKiorSjRs37JgIjuD48eOaO3euXF1dJUmhoaGKiIjQxYsXdffddxtOB5M2\nbtyoV155xfZ45cqVOnbsmDZv3qwaNWropZde0jvvvKPly5cbTAlH8d///leLFy9WdHS0LBaL/v3v\nf5uOBDurWrWqHn30Uc2aNYuiBRur1aq+fftq5cqV8vPzU40aNSRJhw8fVq9evdSlSxfNnTvXcErY\n2/r167NdFxMTo/fff19FilA9s8Mr48ReffXVTMtSU1M1c+ZMffzxx6pWrZrGjx9vIBlMSklJUenS\npW2PixUrJjc3N129epVSXsidOnUqwxGvrVu3qn379ra7WYwYMUI9evQwFQ8OICkpScuXL9eSJUv0\n008/KS0tTdOmTdNTTz2lokWLmo4HO7JYLJozZ44WLVqk559/XsOHD9drr71mOhYcwAcffKDPPvtM\nUVFRatu2bYZ169ev16BBgzRnzhz17dvXUEKY0KxZs0zLDhw4oDFjxmj37t3q1atXhgMDyIhSXkCk\np6dr8eLFioiIUJEiRRQREaHu3bvf8jQSFExbtmxRqVKlbI/T09MVExMjb29v2zKOehQ+6enpGYrV\nt99+m2GuAV9f31xfE4aCZceOHVq0aJE2bdqkBg0aqH///goJCVG1atXUuHFjCnkhZLX+cXXjQ/il\nVwAAIABJREFUiBEjdN9996lv3746dOiQ5syZI3d3d8PpYFJUVJTeeOONTIVcktq3b6+LFy/qww8/\npJQXYidOnNCECRO0Zs0atW/fXnv37tW//vUv07EcGqW8APj00081fvx4XbhwQcOGDVPfvn1VrFgx\n07Fg0Msvv5xp2YgRI2z/bbFYuA6wEKpWrZq+/PJLVa1aVSdOnNBPP/2khx56yLb+zJkznE1RSHXu\n3FkDBw7U119/rcqVK5uOAwfz2GOP6YsvvtAzzzyjRx55RMuWLVOJEiVMx4IhP/74o4KCgrJdHxQU\npLCwMDsmgqO4dOmSIiIitGDBAjVp0kSfffaZ6tevbzqWU6CUO7E9e/Zo3LhxOnjwoPr27ashQ4bI\n09PTdCwYxpFOZKd379569dVXFRsbq//+979q1KiRAgICbOu//PJL1a5d22BCmNK6dWvNmzdPJ0+e\nVLdu3fToo4/a5qVA4fT3M+1q1KihmJgY9e7dW0FBQQoPDzeUDKYVLVpU169fz3Z9amoqZ9cUQlOm\nTNGMGTNUpUoVLV26VI888ojpSE6F2dedmJeXl9zc3NSzZ09VrFgx23GDBg2yYyoAjuzPGbR9fHw0\natQo+fj42NYNHz5cLVu2VPv27Q0mhCkJCQlaunSpoqKilJSUpE6dOmnhwoWKjY3Vvffeazoe7MzL\ny0vHjh3LcNmT9Mdp7W+++aamTZsmSZx1VQh16NBB9evX19ixY7NcP3bsWH333Xdat26dnZPBpD97\nycMPP5zj5bNMJps1SrkTq127dq5uPXDgwAE7JYIj2L9/f67G1atXL5+TAHBWX331lZYsWaL169er\nbNmy6tChgzp27KiGDRuajgY7GTBggCIiIuTh4ZHl+tWrV2v+/Pk5zriMgikmJkZdu3bVSy+9pMGD\nB9u+3D137pxmzJihDz74QB9//DFHSguZ/v3752ouq9mzZ9shjfOhlAMFzF/vE/nnRD1/xzXlyMp3\n332nCRMmaNWqVaajwEEkJydrxYoVioqK0vfff8/nBgBJ0ocffqjXX39dv/32mzw8PGSxWHTlyhUV\nKVJEEyZMYJI34DZRyoECxtfXV97e3nrmmWfUqVMnubm5ZTmuSpUqdk4GR7Bt2zZt3bpVRYsWVY8e\nPVS1alUdP35co0eP1pYtWxQUFEQpR5YOHDigunXrmo4BO5k5c2aWy0uVKiV/f389+OCDdk4ER3Pm\nzBmtXr1a8fHxkiQ/Pz916NAhx0sqUTgkJyfb9ot//etfzHmVC5RyJ3fz5k1FRkZq5cqVOn78uKQ/\nPhT/PK2oSBHm8itskpKS9Mknn2jJkiU6ceKEOnXqpB49eqhBgwamo8GwpUuXauDAgfLy8tLly5dV\ntmxZTZw4UUOHDlW7du00cOBA1apVy3RMGJDbCSK9vLzyOQkcRZ06dbJcnpycrCtXrqhJkyZatmwZ\n+wQAm1OnTmnEiBH64osvbGdrWiwWtW7dWpMmTeKAUA4o5U7sxo0b6tSpk/bs2aMWLVrYJuI5evSo\nduzYoaZNmyo6Olp33XWX4aQw5cCBA4qKitLKlSvl6+urZ599Vv3795eLi4vpaDCgWbNm6tKli4YO\nHarVq1frhRdeUJ06dbRw4ULuH1rI/fWyl+xYLBZdvHjRTongyE6cOKG+ffvqvvvu09SpU03HgZ0x\ndw2ycubMGQUHB8vFxUUvvviirZccOXJE8+bNkyRt3bpV5cuXNxnTYVHKnVhERIQWLVqkZcuWZTql\ncP/+/XrmmWfUs2dPvfLKK4YSwlGcP39eL774omJjY/Xjjz9yZKOQqlSpknbu3KmqVasqPT1d5cqV\n05o1a9SsWTPT0WDYzp07s10XExOj999/X0WKFNGpU6fsmAqOLDY2VgMHDsx1QUPB8eeXeNnNWyMx\nd01hFBoaqiNHjmjNmjWZLp28fv26OnfurICAANudG5AR5zY7sVWrVmnChAlZXuNXr149vfHGG4qI\niKCUF2JffvmloqKitGHDBtWpU0fvvfce1/UUYteuXZO7u7skycXFRcWLF1elSpUMp4IjyOqLmQMH\nDmjMmDHavXu3evXqxd8SZFCpUiUlJiaajgEDuKsPsvL5559rzpw5Wc5lVKJECb3++uvq16+fgWTO\ngVLuxE6ePKnGjRtnu75Jkyb6+eef7ZgIjuD06dP66KOPtHTpUt24cUPdunXTjh07VL16ddPR4AC2\nbNmiUqVKSZLS09MVExOT6T7EISEhJqLBQZw4cUITJkzQmjVr1L59e+3du5fLG5DJoUOHVLlyZdMx\nYADXBSMrFy5cyPFvRbVq1XThwgU7JnIulHInVqJECV26dCnbI12XLl3KduZtFFx169ZV+fLl9fTT\nT6tNmzZydXVVSkpKplMMudarcHr55ZczPB4xYkSGx5xyWHhdunRJERERWrBggZo0aaLPPvtM9evX\nNx0LhmQ3+V9ycrIOHDig0aNH69lnn7VzKjiq69evKzo6WqmpqQoODpafn5/pSLAzb29vxcfHZzv7\n/o8//pjpIAD+H9eUO7GnnnpKZcuW1XvvvZfl+pdfflmJiYlavny5nZPBpL9eL57d/copXgD+asqU\nKZoxY4aqVKmicePG6ZFHHjEdCYblNPmfxWJRjx49FBERwWSyhVBCQoJefvllHThwQA0bNtTUqVMV\nEhKiY8eOSZLc3Nz0ySef6KGHHjKcFPYUGhqqo0ePau3atSpWrFiGdWlpaerYsSPXlOeAUu7Evvnm\nGz3++ON69NFHFRoaKn9/f0l/zL4+Y8YMffbZZ9q4caMaNmxoOCnsKbeXLHD6GYA/eXl5yc3NTQ8/\n/HCOs7DzJW/hkd3kfx4eHvLz81PJkiXtnAiOok+fPjpy5Ih69OihDRs2KDExUZUqVdLMmTNlsVg0\nbNgwXb58WevXrzcdFXZ09uxZBQUFydXVVX369MnQS+bNm6ebN29q27ZtqlChguGkjolS7uQ2btyo\nwYMHZzrq6eXlpenTp6tdu3aGkgFwNL1799a7774rDw8PSdL8+fP15JNP2h4nJSXpqaee0qZNm0zG\nhAH9+/e/5S3RJGn27Nl2SAPAkd17772KiopSo0aNdOHCBfn7+2vLli22eY6+//57dejQQfHx8YaT\nwt5OnjypESNGKCYmJsN9ylu1aqXJkyeratWqZgM6MEp5AZCamqovvvjC9uHn5+en4OBglShRwnAy\nmLBu3bosl5cuXVrVq1fP9lofFHxlypTR0aNHbdd0Va5cWV999ZXtj+T58+cVEBDApQ0Abmnt2rWa\nOHGidu/ebToK7KxMmTI6fPiwfHx8JEkVK1ZUbGwsf0tgk5SUpB9//FHSHxO8cSveW2OiNyfWvn17\nLVmyRJ6enmrfvr3pOHAQPXv2zHadxWJR586dNWPGDL60KYT+PrdATveYBYBFixZp69atKlq0qF56\n6SU1bNhQO3fuVFhYmI4fP65u3bqZjggDrFarXF1dbY9zc5YNChdPT081aNBAknTq1CklJCTo3nvv\nZV/JAaXcie3cuVO//fab6RhwMDnNmLt//36NHDlSU6ZM0ZgxY+ycDICjyu09yCdNmpTPSeAo3nvv\nPb3xxhuqVauWjh07po0bN2rUqFGaMWOG+vTpo969e6ts2bKmY8KQ8ePH2+7w8+uvv2rixIm2222m\npqaajAZDoqOjdfnyZb344ou2ZcOGDdPChQsl/XHZw6pVq7imPBuUcqCQKF26tFq0aKHw8HC9+uqr\nlPJCim+pkZUffvjhlmPYdwqXJUuWaOrUqXruuef01VdfKSQkRFu3btW3334rT09P0/FgUNOmTfXT\nTz/ZHjdu3Fi//PJLpjEoXObMmZPh7Jnt27drwYIFev3111WjRg1NmDBBkydP1rvvvmswpeOilDu5\n06dPKy0tLccxlStXtlMaOIPq1avrzJkzpmPAEI5uICuffvqp6QhwMKdOnVLLli0lSQ8//LCKFi2q\n0aNHU8ihDRs2mI4ABxQXF5fhjk8bNmxQcHCwRowYIUkqXry4Ro4caSqew6OUO7ng4OBs11mtVu5H\njUxOnDghX19f0zFgAEc38E/s27dPjRo1Mh0DdpKWlqbixYvbHt91112crg4gW9euXcvwpd3XX3+t\nzp072x4HBATo/PnzJqI5BUq5k1u5cqXKlCljOgacwO+//66DBw/q9ddfV5s2bUzHgQEc3cCtpKSk\nyNXV1XY2hSQdOHBAb731lr744gu+5C1k5s+fL3d3d0nSzZs3tWTJkkyzKA8aNMhENBjUvXv3LJeX\nKlVK/v7+6tWrl+0uHyg8KlSooCNHjqhy5cq6cuWKfvjhB0VERNjWX7p0yfZ5gswo5U6udu3afPAh\ng0qVKmV57WdqaqrS09MVFBSksLAwA8kAOKrTp0/rhRde0L59++Tq6qo+ffpo9OjRGj58uFasWKHH\nH39cmzdvNh0TdlSpUiV99NFHtsflypXTJ598kmGMxWKhlBdC2R0MSk5OVlRUlGbOnKlNmzYpMDDQ\nzslgUseOHTVq1CidO3dOMTEx8vHxyXB21f79++Xv728woWPjPuVOzMvLS8eOHaOUI4OlS5dmudzD\nw0P+/v4KCAiwcyI4ktTUVE2fPl3r1q3TyZMnZbFYdM8996hjx44aNGhQhiOkKDz69u2rQ4cOqWfP\nnlq3bp127dqlunXrKjAwUCNHjrTdfxgAcpKenq7BgwcrMTFRH3/8sek4sKPU1FQNGTJEmzdvlo+P\nj6ZNm5bhkrh27drpkUce0ZAhQwymdFyUcidWp04dbd++ndPXAeTKzZs39e9//1sHDhxQq1atFBAQ\nIKvVqiNHjigmJkb169fXhg0bVKQIJ1EVNjVr1tSCBQv0wAMPKCEhQQEBARozZoyGDh1qOhoM+fzz\nzzVs2DDFxsbaJoP8U3Jyspo1a6bw8HC1a9fOUEI4qu+//15PPvmkjhw5YjoK4DRcTAfAnTt48CCF\nHLftu+++U5cuXUzHgAELFy5UfHy8duzYoWXLlmns2LEaN26cli9frh07duj48eNatGiR6ZgwICEh\nQf/6178kST4+PnJzc9Pjjz9uOBVMmjNnjgYPHpypkEt/3GJzyJAhWrBggYFkcHQlSpTQ9evXTceA\ng0lNTeV2aDngcIgTy26ijb+yWCxatmyZHdLAkWzbtk1bt25V0aJF1aNHD1WtWlXHjx/X6NGjtWXL\nFgUFBZmOCAPWrl2r4cOHq2bNmpnWBQYGaujQoVqzZo1efPFFA+lgmqurq+2/XVxcVKxYMYNpYNoP\nP/ygt99+O9v1zZs31zvvvGPHRHAW27ZtU/Xq1U3HgAEXL17UN998o6JFi6pFixZydXXVb7/9prlz\n5+rdd9/V77//zhlY2aCUO7GcjpL/+uuv2rBhg27cuGHHRHAES5cu1cCBA+Xl5aXLly9ryZIlmjhx\nooYOHap27drpq6++Uq1atUzHhAFHjhxReHh4tutbtmypadOm2TERHIXValXbtm1txTw1NVVPPvmk\nihYtmmHcrl27TMSDARcuXJCLS/YnVHLL1cJr3bp1WS6/cuWKDhw4oKioKM2aNcvOqWDa3r171bVr\nV125ckUWi0X169fX7Nmz9eyzz+q3337TyJEj9eyzz5qO6bAo5U5s9uzZWS7/+OOP9dZbb6lUqVIa\nNWqUnVPBtNmzZ9uuBV29erVeeOEFzZgxQzt27LCdnorCKSkpKcf7DJctW1bJycl2TARHwd8K/F2F\nChX0v//9T35+flmuP3TokMqXL2/nVHAEPXv2zHK5h4eHqlevrpkzZ2a4PzUKh7feekutWrXSiBEj\nFBUVpcjISHXr1k2vvPKKunfvnuWdgfD/mOitANm6davGjRunEydOaODAgRo0aBD3AyyEKlWqpJ07\nd6pq1apKT09XuXLltGbNGjVr1sx0NBhWpkwZHTt2LNtifv78eQUEBHD0C4BGjRql7du3a/v27Znu\nynD9+nUFBQWpZcuWGe5DDPzpl19+UaVKlUzHgB1Vq1ZNGzZsUM2aNXX9+nVVqlRJ8+fPV8eOHU1H\ncwocKS8ADhw4oDFjxmj37t3q2bOnVq1axW3SCrFr167ZvoxxcXFR8eLF+cMISX+coty3b1/ddddd\nWa7/9ddf7ZwIjujQoUM6fvy4JMnPz0/33Xef4UQwYcSIEVq3bp0aNmyoPn362O4vfOzYMc2dO1dW\nq1XDhw83nBKOJiEhQZMnT1ZUVJTOnTtnOg7s6PLly7Yv/UuUKKESJUqoTp06hlM5D0q5Ezt58qQm\nTJig1atXKyQkRHv37uX0ZEiStmzZYpsxNz09XTExMZm+qAkJCTERDQY99dRTtxyTmwkkUTDt379f\nAwYM0JEjR2S1/nESncViUc2aNTVr1izVq1fPcELYk7e3t7Zs2aLhw4frjTfeyLBPtGrVSpMnT1a5\ncuUMp4QJSUlJGjlypG1C2SFDhqhfv36KiIjQ9OnTde+992rmzJmmY8KApKQkFSlSRFarVRaLRVeu\nXNHly5czjPHy8jKUzrFx+roTK1eunIoUKaK+ffuqfv362Y6jfBUuufmwY4IeAH8VFxen4OBgVatW\nTYMGDdK9994r6Y/JAWfOnKkTJ04oJibGdrQUhUtSUpLi4+NltVrl5+cnT09P05Fg0PDhw7VlyxZ1\n7NhRMTExOnr0qFq3bq3r169r1KhRXC5XSHl5eWW4bvzPYv73x/z/Z9Yo5U6M8gUAyAsvvviirl27\npmXLlmWajMdqteqpp56Su7u75s2bZyghAEdx3333aebMmWrZsqVOnDih+++/X/369dPEiRNNR4NB\nO3fuzNU4vrTJGqUcKGAGDhyoiRMnysPDw3QUOJhXXnnllmMsFgsTNxVC/v7+Wr58uRo0aJDl+n37\n9unpp59WXFycnZMBcDRly5bV999/b5t9v3z58tq6datq1qxpOBkc3dWrV/n/02xwTTlQwCxbtkzj\nxo3jQw+Z/PDDDzmu//bbb3Xjxg1KeSGUnJwsX1/fbNdXqFBBV65csWMiAI4qPT1dRYsWtT12dXXN\nNEM/8Fe7d+/WokWLtH79ep0+fdp0HIdEKXdi+/fvz9U4JucpXP6cjAf4u08//TTL5bt379bYsWMl\nSYMHD7ZnJDiIChUq6NChQ6pYsWKW6/96VAxA4fb3O3mkpaUpNDQ0UzFfvny5iXhwEImJiVq2bJmW\nLFmin3/+Wc2bN+dL/xxQyp1YUFCQLBZLjiWMa8oLp79fEwpk5ejRoxo/frw+++wzde/eXQsWLMi2\nlKFga9euncaMGaN69eplmlE7ISFB48ePV7t27QylA+BI/n4nj65duxpKAkdjtVr1+eefa9GiRfri\niy90//33Kz4+XjExMRwkvAWuKXdiP//8c67GValSJZ+TwJH8ffbL7PBlTeF17tw5vfXWW1q2bJla\ntWqlsWPHKjAw0HQsGJScnKzWrVvrzJkz6tq1q22W9aNHj2rlypUqX768Pv/8c2bdBgBkacKECVq2\nbJmKFSumrl27qnv37qpatarKli2rnTt3KiAgwHREh8aRcid2q7J96dIlRUdHq3fv3nZKBEcxbdo0\nlS5d2nQMOJirV69q2rRpev/99xUYGKi1a9fqoYceMh0LDqB06dL67LPP9Oabbyo6OlpJSUm25d26\nddPo0aMp5ACAbL377rsaOnSowsLC5OrqajqO0+FIeQH2/fffq0WLFhwRLWS8vLx07NgxeXt7m44C\nB+Pn56fU1FT169dPHTp0yHYcp5gVblarVRcuXJD0xyzLXA4DALiVmTNn6qOPPtKlS5fUuXNnde/e\nXXXr1uVIeS5RygswSnnhVKZMGR09epRSjky8vLxs/53dfBTMQwEAAO7U119/rSVLlmjNmjWqWLGi\n4uLitHr1ajVv3tx0NIdGKS/AKOWFE0fKkR3moUB2unfvnqtxzKYMAMiNa9euadWqVVqyZIm++eYb\n1a1bVx07dtSQIUNMR3NIXFMOFDCXL182HQEOKjdlOz4+3g5J4GjKlCljOgIAoABxd3dXjx491KNH\nDx05ckSLFy/WrFmzKOXZoJQ7sZkzZ+a4/ty5c3ZKAsCZpaWlae3atVq0aJH27NnD2TWF0IABA1Sz\nZk0m5wEA3JHPP/9cw4YNU2xsrEqVKpVhXfny5bVu3TpNnjzZUDrHx+nrTqxOnTq5Gnfw4MF8TgLA\nGe3fv19RUVFauXKlihUrpnbt2qlDhw5c91UI/X0uiq5du2rGjBny9fU1nAwA4Ay6du2q1q1bq0+f\nPlmunzdvnjZu3KhVq1bZOZlz4Ei5E6NsA7hdycnJ+uSTT7R48WLFx8fr8ccfV0pKijZv3szMqIXY\n3yf927Vrl9LS0gylAQA4m0OHDumtt97Kdn3z5s31zjvv2DGRc3ExHQB37vPPP1ft2rV15cqVTOuS\nk5NVu3ZtffrppwaSAXBEffv2Va1atbR+/Xq99NJLOnr0qObMmWM6FgAAcHIXLlyQi0v21ZK7u+SM\nUu7E5syZo8GDB2e6bkOSSpcurSFDhmjBggUGkgFwRKtWrVK/fv20aNEiPf3003J3dzcdCQ7CYrFw\nP3IAwB2rUKGC/ve//2W7/tChQypfvrwdEzkXTl93Yj/88IPefvvtbNdzmgiAv1qwYIGioqIUGBio\noKAgde/eXY899pjpWHAAVqtVffv21V133SXpj8n/QkND5ebmlmEct0QDAGSlTZs2evvtt9WmTZtM\nfzuuX79uW4esMdGbE/Px8dGuXbvk5+eX5frjx4+rWbNmzMIOIIPTp0/ro48+0tKlS5WcnKzk5GS9\n//77evLJJzlaWkgNGDAgV+Nmz56dz0kAAM4oMTFRzZs3l4uLi/r06SN/f39J0rFjxzR37lxZrVbt\n2LFD5cqVM5zUMVHKndj999+vcePGqUOHDlmuX7t2rcaNG6fvvvvOzskAOIvt27dr8eLF2rhxo0qV\nKqV27dpp6tSppmMBAAAn8/PPP2v48OGKiYmxTSBqsVjUqlUrTZ48WVWrVjUb0IFRyp3YqFGjtH37\ndm3fvj3L00SCgoLUsmVLRUREGEoIwBncvHlT586d06effqolS5YoNjbWdCQAAOCkkpKSFB8fL6vV\nKj8/P3l6epqO5PAo5U6M00QA3I4dO3bo0qVL6tSpk23Zu+++q4kTJ+rmzZtq2bKl5s2bxx9PAAAA\nO6KUOzlOEwGQWx07dtQjjzyiQYMGSZK+/fZbPfLII3ruuedUo0YNvffee+ratavefPNNw0kBAAAK\nD0p5AcFpIgBupUaNGlqxYoXq1asnSRo9erT27dunzZs3S5LWrFmjCRMm6JtvvjEZEwAAoFDhlmgF\nhKenp+rXr286BgAHlpycrLJly9oe7927V61bt7Y9vv/++3X27FkT0QAAAAotF9MBAAD24ePjo59+\n+kmSdOPGDR08eFCNGze2rU9JSbHdpxoAAAD2QSkHgEKidevWGjt2rHbs2KGxY8eqRIkSevDBB23r\nDx06pGrVqhlMCAAAUPhQygGgkHjttddUrFgxdezYUR999JGmT5+e4ch4VFSUgoKCDCYEAAAofJjo\nDQAKmeTkZJUsWVKurq4Zll++fFnu7u6cwg4AAGBHlHIAAAAAAAzh9HUAAAAAAAyhlAMAAAAAYAil\nHAAAAAAAQyjlAADkowsXLmjcuHF64IEHVKFCBZUvX15NmzbVuHHjdPbs2Sx/pl+/fvL09NSQIUOy\nXH/y5El5enrK09NTq1atyrT+vffek6enp06ePGlb1r9/f9vPeHp6qmLFiqpbt6569OihtWvXKj09\nPdPztG3bVo0aNcqwrHbt2rbn8PLyUpUqVdS0aVOFhobqm2++yfG1SElJUYUKFeTp6amvv/46w7r4\n+PgM+XL6t3v37luOnzBhQo5ZAABwFEVMBwAAoKDav3+/nnzySV25ckVdunRRnz595OLiokOHDmnx\n4sVav369vv322ww/c/36dW3YsEFVqlTRmjVrNGnSpBxnxJ88ebI6deokF5dbf89etGhRzZw5U5KU\nlpamU6dOadOmTerZs6eaNWumpUuXqlSpUrd8nlq1amnw4MGS/ijaR48e1dq1a7Vo0SINGDBAb7/9\ndpY/t379ev3222+qUKGCVqxYocaNG9vWlStXTh988EGm3y01NVVjxozJsNzf319XrlyRJD3xxBNq\n3bp1pm0FBgbe8vcAAMARUMoBAMgHycnJevbZZyVJ27dvV82aNTOsHz16tKZPn57p5zZs2KDr16/r\n448/Vtu2bbVlyxa1b98+y23Url1b33//vVavXq0uXbrcMpOLi4u6deuWYdl//vMfvfvuuxo/frxC\nQ0O1YMGCWz6Pr69vpucZP368evfurdmzZ8vPz08vvvhipp9bsWKFgoKCVLt2bS1cuFDh4eEqWrSo\nJKlkyZKZnnPBggVKTk7OtFySrZTXrVs3y/UAADgLTl8HACAfLFy4UL/88osmTJiQqZBLUunSpTMd\nAZb+KK7NmjXTQw89pHr16umTTz7JdhsdO3ZUQECAJk2alOXp57k1dOhQBQcHa82aNTp+/PgdPYeb\nm5s++OADeXl56Z133pHVmvGOq+fOndOXX36pzp07q0uXLrp48aJiYmLuODMAAAUFpRwAgHywadMm\nFS9eXJ06dcr1zyQmJmrbtm164oknJEldunTRli1blJycnOV4FxcXvfLKKzp69Kiio6P/Ud5u3brJ\narVq27Ztd/wcJUuWVLt27XTmzBkdOXIkw7qVK1eqaNGiatu2rQIDAxUYGKgVK1b8o8zSH6f7X7x4\nMdO/33777R8/NwAA9kApBwAgHxw5ckTVq1fP8Xrwv1u1apUsFotCQkIkSZ07d9avv/6qtWvXZvsz\nHTt2VM2aNTV58uR/dLT8z6P5P/300x0/R07Ps2LFCrVp00YeHh6S/vjCYdOmTbp69eo/2l54eLj8\n/Pwy/YuNjf1HzwsAgL1QygEAyAdXr161FdDcWrFihYKDg+Xp6SlJqlixoh588MEcjyhPxA+yAAAE\nHUlEQVS7uLho5MiROnr0aJYzsedWyZIlJf0xcds/kdXzHD16VAcPHsxw3XuXLl2Umpqq9evX/6Pt\n9erVS2vWrMn0r27duv/oeQEAsBdKOQAA+cDDw+O2jgIfP35c//3vf/Xggw8qPj7e9u+hhx5SbGys\n/q+9ewmF94vjOP55ZiPqV5SFkEIiKdcNctsNJeW2c9sYSilkNkxTs7MxCzasLeYWSmFjSZbkvhKp\nsZApxFAz/4Vm+tWMfi5/v0m/96uezZxzvud5lp8553nO1dXVm2P/j9XySIiOhOrPilfH5XIpKSlJ\nBQUF0ecKhUIqKir68hb2/Px8NTY2xlxpaWlfqgsAwN/C19cBAPgGhYWF2tvb0/Pz87u2sLtcLkmS\n3W6X3W6Pafd4PG+eWx55t3xgYODTq+XHx8eSpLy8vE+Nf6tOOByWx+NRMBhUTU1NTP+zszP5/X5l\nZGR8aV4AAH4qQjkAAN+gublZu7u7WllZUXd39x/7ezwe1dbWanBwMKZtfn5ebrf7zVAuva6Wz8zM\naGZmJnoU20e4XC4ZhqGmpqYPj424v7/X2tqasrOzVVhYKEna3t7W5eWlrFZrzNnhT09PGhoaktfr\n1cjIyKfnBQDgJyOUAwDwDfr7+7WwsKCpqSmVlpZGQ2rE3d2dnE6npqentbu7q/Pzc01MTKitrS2m\n1s3NjcbGxnRwcKCSkpK48xmGIavVqv7+/g+vls/Ozmpra0sdHR3Kz8//0NiIx8dHWSwW3d7eymaz\nyTAMSa/vyScnJ2t0dFQpKSkx4xYXF+V2uwnlAIB/FqEcAIBvkJqaqqWlJXV1damhoUGdnZ2qqKiQ\nyWTS0dGRfD6f0tLSND09LbfbLZPJJLPZHLeW2WzW+Pi43G73m6Fcktra2lRcXKz9/f247aFQKLpN\nPhgM6uLiQuvr6zo8PFRdXZ2cTue7ns3v90frPDw86OTkRKurq7q+vtbIyIgGBgaic6yurqq+vj5u\nIJdedxQ4HA6dnp7G/HHxHnt7e9F7+V1OTo6qq6s/XA8AgL+NUA4AwDcpLy/Xzs6O5ubmtLGxIZ/P\np3A4rNzcXPX09Gh4eFgvLy9aXl5WVVWV0tPT49bJzMxUWVmZfD5f3PfNIyKr5X19fXHbX15eZLFY\nJEkpKSlKT09XWVmZJicn1draKpPpfd9/PTw8lMVikWEY+vXrl7KysmQ2m9Xb26vKyspov83NTQUC\nATU3N79Zq6WlRQ6HQy6XSzab7V3z/87r9crr9cb83t7eTigHAPwIRiAQCCf6JgAAAAAA+BdxJBoA\nAAAAAAlCKAcAAAAAIEEI5QAAAAAAJAihHAAAAACABCGUAwAAAACQIIRyAAAAAAAShFAOAAAAAECC\nEMoBAAAAAEgQQjkAAAAAAAlCKAcAAAAAIEH+AwqSk3E2cHbgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure object at 0x1177cbdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "distribution.plot(kind='bar', figsize=(15,6))\n",
    "plt.xlabel('CANDIDATE')\n",
    "plt.ylabel('% of RE')\n",
    "plt.title('OVERALL DISTRIBUTION OF RE')\n",
    "plt.show()\n",
    "#Why are there two?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some Conclusions\n",
    "**The person who is referred to by their full name the most often is Hillary Clinton, most likely implying that Clinton was discussed very often throughout the campaign. The person who is referred to by their first name the most often is Donald Trump. The person who is referred to by their professional title the most often is Bernie Sanders. Every candidate is referred to by a professional title more often than any other type of RE except for Carly Fiorina, who is mostly referred to by her last name. Out of all of the times she is referred to, Hillary Clinton is referred to by a professional title a higher percentage of the time than Donald Trump is out of all of the times he is referred to, but less often than Bernie Sanders, Ted Cruz, and Marco Rubio, and Ben Carson.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some Questions\n",
    "**What does it mean to call someone by their professional title vs their first name? What does the choice of using a first name/full name instead of a title mean? Is the distribution in type of RE for each candidate different depending on who is referring to/addressing them? Is this distribution different when a candidate is being referred to as opposed to addressed?**\n",
    "\n",
    "**Let's look at some individual speakers now.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Donald Trump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Trump': 15, 'Cruz': 1, 'Ted': 37, 'Neil': 4, 'Nikki': 4, 'Jeb': 36, 'Barack Obama': 12, 'Donald': 2, 'President Obama': 26, 'Carl': 4, 'Marco': 19, 'Maria': 3, 'Ivanka': 3, 'Eric': 2, 'Don': 2, 'Hillary': 43, 'Hillary Clinton': 33, 'Chris': 18, 'Obama': 15, 'Putin': 26, 'Podesta': 1, 'Bernie Sanders': 10, 'John Podesta': 1, 'Assad': 21, 'Ronald Reagan': 11, 'John': 3, 'Ben': 8, 'Governor': 7, 'Bill Clinton': 2, 'President Clinton': 1, 'Michelle Obama': 2, 'Deborah Wasserman Schultz': 2, 'Anderson': 3, 'President': 1, 'Captain Khan': 1, 'Abraham Lincoln': 3, 'Honest Abe': 2, 'Warren': 1, 'Donald Trump': 9, 'Clinton': 2, 'Gadhafi': 4, 'Kerry': 2, 'Scalia': 1, 'Dwight Eisenhower': 3, 'Mr. Trump': 6, 'Carly': 7, 'Wolf': 2, 'Lindsey Graham': 2, 'Jeb Bush': 4, 'Governor Bush': 1, 'Mister': 1, 'Santorum': 1, 'Governor Huckabee': 1, 'Mitch McConnell': 1, 'Bush': 3, 'George Bush': 4, 'Chris Christie': 3, 'Ben Carson': 6, 'Ted Cruz': 3, 'Sheriff Joe Arpaio': 3, 'Mitt Romney': 5, 'Robin Hood': 2, 'Anderson Cooper': 2, 'Bill': 2, 'Saddam Hussein': 1, 'John Kerry': 2, 'Dr. Ben Carson': 2, 'Senator Rubio': 1, 'Tip': 2, 'Jake': 4, 'Sheriff Joe': 1, 'Jeff Sessions': 1, 'Senator': 1, 'Senator Jeff Sessions': 1, 'Merkel': 1, 'David Duke': 1, 'Megyn': 3, 'Bret': 1, 'Lyin': 2, 'Snowden': 1, 'Sean': 2, 'Senator Marco Rubio': 1, 'Vladimir Putin': 1, \"Rosie O'Donnell\": 2, 'Rand Paul': 1, 'Dana': 2, 'Hugh Hewitt': 1, 'Rand': 2, 'Rosa Parks': 1, 'Lester': 9, 'Secretary': 3, 'Secretary Clinton': 21, 'Kennedy': 1, 'Mayor Giuliani': 1, 'Mayor Bloomberg': 1, 'Wolf Blitzer': 1, 'Debbie Wasserman Schultz': 1, 'Neil Cavuto': 1, 'Sean Hannity': 7, 'Secretary Kerry': 1, 'Bibi Netanyahu': 1}"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_re = get_RE_for_label(trump['Tree'], 'all')\n",
    "trump_re_freq = {re: trump_re.count(re) for re in trump_re}\n",
    "trump_re_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interesting. It looks like Trump refers to himself as Mr. Trump 6 times. Let's see those instances in context.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trump_mrtrump_trees = get_trees_for(trump, 'Donald Trump', 'Mr. Trump')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Debate Type</th>\n",
       "      <th>Speaker</th>\n",
       "      <th>Sents</th>\n",
       "      <th>Tree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1917</th>\n",
       "      <td>11-10-15</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>TRUMP</td>\n",
       "      <td>He said, \"You know, Mr. Trump?</td>\n",
       "      <td>[(He, PRP), (said, VBD), (,, ,), (``, ``), (Yo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2150</th>\n",
       "      <td>12-15-15</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>TRUMP</td>\n",
       "      <td>I think it's very sad that CNN leads Jeb Bush...</td>\n",
       "      <td>[(I, PRP), (think, VBP), (it, PRP), ('s, VBZ),...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2152</th>\n",
       "      <td>12-15-15</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>TRUMP</td>\n",
       "      <td>And, frankly, I watched the first debate, and ...</td>\n",
       "      <td>[(And, CC), (,, ,), (frankly, RB), (,, ,), (I,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2153</th>\n",
       "      <td>12-15-15</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>TRUMP</td>\n",
       "      <td>Mr. Trump\" — these poor guys — although, I mus...</td>\n",
       "      <td>[[(Mr., NNP), (Trump, NNP)], ('', ''), (—, IN)...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3247</th>\n",
       "      <td>2-25-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>TRUMP</td>\n",
       "      <td>Mr. Trump, you're doing a great job.</td>\n",
       "      <td>[[(Mr., NNP), (Trump, NNP)], (,, ,), (you, PRP...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Debate Type Speaker  \\\n",
       "1917  11-10-15  primary_rep   TRUMP   \n",
       "2150  12-15-15  primary_rep   TRUMP   \n",
       "2152  12-15-15  primary_rep   TRUMP   \n",
       "2153  12-15-15  primary_rep   TRUMP   \n",
       "3247   2-25-16  primary_rep   TRUMP   \n",
       "\n",
       "                                                  Sents  \\\n",
       "1917                     He said, \"You know, Mr. Trump?   \n",
       "2150   I think it's very sad that CNN leads Jeb Bush...   \n",
       "2152  And, frankly, I watched the first debate, and ...   \n",
       "2153  Mr. Trump\" — these poor guys — although, I mus...   \n",
       "3247               Mr. Trump, you're doing a great job.   \n",
       "\n",
       "                                                   Tree  \n",
       "1917  [(He, PRP), (said, VBD), (,, ,), (``, ``), (Yo...  \n",
       "2150  [(I, PRP), (think, VBP), (it, PRP), ('s, VBZ),...  \n",
       "2152  [(And, CC), (,, ,), (frankly, RB), (,, ,), (I,...  \n",
       "2153  [[(Mr., NNP), (Trump, NNP)], ('', ''), (—, IN)...  \n",
       "3247  [[(Mr., NNP), (Trump, NNP)], (,, ,), (you, PRP...  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_mrtrump_trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'And, frankly, I watched the first debate, and the first long number of questions were, \"Mr. Trump said this, Mr. Trump said that.'"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_mrtrump_trees.loc[2152]['Sents']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mr. Trump\" — these poor guys — although, I must tell you, Santorum, good guy.'"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_mrtrump_trees.loc[2153]['Sents']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**It looks like every time Donald Trump refers to himself as Mr. Trump, he is quoting someone else. This could be his way of showing the audience or making them believe that other people respect him.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Debate Type</th>\n",
       "      <th>Speaker</th>\n",
       "      <th>Sents</th>\n",
       "      <th>Tree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>10-19-16</td>\n",
       "      <td>general</td>\n",
       "      <td>TRUMP</td>\n",
       "      <td>But Hillary was extremely upset, extremely angry.</td>\n",
       "      <td>[(But, CC), [(Hillary, NNP)], (was, VBD), (ext...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>10-19-16</td>\n",
       "      <td>general</td>\n",
       "      <td>TRUMP</td>\n",
       "      <td>And I am—I don't know if Hillary was saying it...</td>\n",
       "      <td>[(And, CC), (I, PRP), (am—I, VBP), (do, VBP), ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>10-19-16</td>\n",
       "      <td>general</td>\n",
       "      <td>TRUMP</td>\n",
       "      <td>If you go with what Hillary is saying, in the ...</td>\n",
       "      <td>[(If, IN), (you, PRP), (go, VBP), (with, IN), ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>10-19-16</td>\n",
       "      <td>general</td>\n",
       "      <td>TRUMP</td>\n",
       "      <td>Now, you can say that that's OK and Hillary ca...</td>\n",
       "      <td>[(Now, RB), (,, ,), (you, PRP), (can, MD), (sa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>10-19-16</td>\n",
       "      <td>general</td>\n",
       "      <td>TRUMP</td>\n",
       "      <td>Hillary wants to give amnesty.</td>\n",
       "      <td>[[(Hillary, JJ)], (wants, VBZ), (to, TO), (giv...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Debate Type Speaker  \\\n",
       "331  10-19-16     general   TRUMP   \n",
       "337  10-19-16     general   TRUMP   \n",
       "349  10-19-16     general   TRUMP   \n",
       "350  10-19-16     general   TRUMP   \n",
       "362  10-19-16     general   TRUMP   \n",
       "\n",
       "                                                 Sents  \\\n",
       "331  But Hillary was extremely upset, extremely angry.   \n",
       "337  And I am—I don't know if Hillary was saying it...   \n",
       "349  If you go with what Hillary is saying, in the ...   \n",
       "350  Now, you can say that that's OK and Hillary ca...   \n",
       "362                     Hillary wants to give amnesty.   \n",
       "\n",
       "                                                  Tree  \n",
       "331  [(But, CC), [(Hillary, NNP)], (was, VBD), (ext...  \n",
       "337  [(And, CC), (I, PRP), (am—I, VBP), (do, VBP), ...  \n",
       "349  [(If, IN), (you, PRP), (go, VBP), (with, IN), ...  \n",
       "350  [(Now, RB), (,, ,), (you, PRP), (can, MD), (sa...  \n",
       "362  [[(Hillary, JJ)], (wants, VBZ), (to, TO), (giv...  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_clinton_tree = get_trees_for(trump, 'Hillary Clinton')\n",
    "trump_clinton_tree.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Hillary', 'Hillary', 'Hillary', 'Hillary', 'Hillary', 'Hillary Clinton', 'Hillary Clinton', 'Hillary Clinton', 'Hillary Clinton', 'Hillary', 'Hillary Clinton', 'Hillary', 'Hillary', 'Hillary', 'Hillary Clinton', 'Hillary', 'Hillary', 'Hillary', 'Hillary', 'Hillary', 'Hillary', 'Hillary Clinton', 'Hillary', 'Hillary', 'Hillary', 'Hillary', 'Hillary Clinton', 'Hillary', 'Hillary Clinton', 'Hillary Clinton', 'Hillary Clinton', 'Hillary Clinton', 'Hillary Clinton', 'Hillary', 'Hillary Clinton', 'Hillary', 'Hillary', 'Clinton', 'Hillary', 'Hillary', 'Hillary', 'Hillary Clinton', 'Hillary', 'Hillary', 'Hillary Clinton', 'Hillary', 'Hillary Clinton', 'Hillary', 'Hillary Clinton', 'Hillary Clinton', 'Hillary', 'Hillary Clinton', 'Hillary Clinton', 'Hillary', 'Hillary', 'Hillary', 'Hillary Clinton', 'Hillary Clinton', 'Hillary Clinton', 'Hillary Clinton', 'Hillary Clinton', 'Hillary Clinton', 'Hillary Clinton', 'Hillary Clinton', 'Hillary', 'Hillary Clinton', 'Hillary Clinton', 'Hillary Clinton', 'Hillary Clinton', 'Clinton', 'Hillary', 'Secretary', 'Secretary Clinton', 'Secretary Clinton', 'Hillary', 'Secretary Clinton', 'Secretary', 'Secretary', 'Secretary Clinton', 'Secretary Clinton', 'Secretary Clinton', 'Secretary Clinton', 'Secretary Clinton', 'Secretary Clinton', 'Secretary Clinton', 'Secretary Clinton', 'Secretary Clinton', 'Secretary Clinton', 'Secretary Clinton', 'Secretary Clinton', 'Secretary Clinton', 'Secretary Clinton', 'Secretary Clinton', 'Secretary Clinton', 'Secretary Clinton', 'Secretary Clinton', 'Hillary', 'Hillary', 'Hillary', 'Hillary', 'Hillary', 'Hillary']"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_REclinton = get_RE_for_label(trump['Tree'], 'Hillary Clinton')\n",
    "trump_REclinton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Hillary': 43, 'Hillary Clinton': 33, 'Clinton': 2, 'Secretary': 3, 'Secretary Clinton': 21}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_clinton_freq = {re: trump_REclinton.count(re) for re in trump_REclinton}\n",
    "trump_clinton_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 21, 'FULL NAME': 33, 'FIRST NAME': 43, 'LAST NAME': 2}"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_clinton_REtype_freq = get_re_type_freq(clinton_re_maps, trump_clinton_freq)\n",
    "trump_clinton_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Carly Fiorina"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Carly': 7}"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_REfiorina = get_RE_for_label(trump['Tree'], 'Carly Fiorina')\n",
    "trump_fiorina_freq = {re: trump_REfiorina.count(re) for re in trump_REfiorina}\n",
    "trump_fiorina_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FIRST NAME': 7}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_fiorina_REtype_freq = get_re_type_freq(fiorina_re_maps, trump_fiorina_freq)\n",
    "trump_fiorina_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Ted Cruz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Cruz': 1, 'Ted': 37, 'Ted Cruz': 3, 'Lyin': 2}"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_REcruz = get_RE_for_label(trump['Tree'], 'Ted Cruz')\n",
    "trump_cruz_freq = {re: trump_REcruz.count(re) for re in trump_REcruz}\n",
    "trump_cruz_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FULL NAME': 3, 'FIRST NAME': 37, 'LAST NAME': 1, 'NAMECALLING': 2}"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_cruz_REtype_freq = get_re_type_freq(cruz_re_maps, trump_cruz_freq)\n",
    "trump_cruz_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Marco Rubio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Marco': 19, 'Senator Rubio': 1, 'Senator Marco Rubio': 1}"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_RErubio = get_RE_for_label(trump['Tree'], 'Marco Rubio')\n",
    "trump_rubio_freq = {re: trump_RErubio.count(re) for re in trump_RErubio}\n",
    "trump_rubio_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 2, 'FIRST NAME': 19}"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_rubio_REtype_freq = get_re_type_freq(rubio_re_maps, trump_rubio_freq)\n",
    "trump_rubio_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Bernie Sanders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Bernie Sanders': 10}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_REsanders = get_RE_for_label(trump['Tree'], 'Bernie Sanders')\n",
    "trump_sanders_freq = {re: trump_REsanders.count(re) for re in trump_REsanders}\n",
    "trump_sanders_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FULL NAME': 10}"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_sanders_REtype_freq = get_re_type_freq(sanders_re_maps, trump_sanders_freq)\n",
    "trump_sanders_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Ben Carson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Ben': 8, 'Ben Carson': 6, 'Dr. Ben Carson': 2}"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_REcarson = get_RE_for_label(trump['Tree'], 'Ben Carson')\n",
    "trump_carson_freq = {re: trump_REcarson.count(re) for re in trump_REcarson}\n",
    "trump_carson_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FULL NAME': 6, 'FIRST NAME': 8, 'PROFESSIONAL': 2}"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_carson_REtype_freq = get_re_type_freq(carson_re_maps, trump_carson_freq)\n",
    "trump_carson_REtype_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIRST NAME</th>\n",
       "      <th>FULL NAME</th>\n",
       "      <th>LAST NAME</th>\n",
       "      <th>NAMECALLING</th>\n",
       "      <th>PROFESSIONAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CLINTON</th>\n",
       "      <td>43.434343</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>2.020202</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.212121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SANDERS</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FIORINA</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRUZ</th>\n",
       "      <td>86.046512</td>\n",
       "      <td>6.976744</td>\n",
       "      <td>2.325581</td>\n",
       "      <td>4.651163</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RUBIO</th>\n",
       "      <td>90.476190</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.523810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CARSON</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         FIRST NAME   FULL NAME  LAST NAME  NAMECALLING  PROFESSIONAL\n",
       "CLINTON   43.434343   33.333333   2.020202     0.000000     21.212121\n",
       "SANDERS    0.000000  100.000000   0.000000     0.000000      0.000000\n",
       "FIORINA  100.000000    0.000000   0.000000     0.000000      0.000000\n",
       "CRUZ      86.046512    6.976744   2.325581     4.651163      0.000000\n",
       "RUBIO     90.476190    0.000000   0.000000     0.000000      9.523810\n",
       "CARSON    50.000000   37.500000   0.000000     0.000000     12.500000"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distribution_trump = pd.DataFrame([trump_clinton_REtype_freq, trump_sanders_REtype_freq, trump_fiorina_REtype_freq, trump_cruz_REtype_freq, trump_rubio_REtype_freq, trump_carson_REtype_freq], index = ['CLINTON', 'SANDERS', 'FIORINA', 'CRUZ', 'RUBIO', 'CARSON'])\n",
    "distribution_trump.fillna(0, inplace=True)\n",
    "\n",
    "distribution_trump['TOTAL']=distribution_trump.sum(axis=1)\n",
    "\n",
    "#Get the percentage of each type of RE for each candidate.\n",
    "distribution_trump['FIRST NAME'] = distribution_trump['FIRST NAME']/distribution_trump['TOTAL']*100\n",
    "distribution_trump['FULL NAME'] = distribution_trump['FULL NAME']/distribution_trump['TOTAL']*100\n",
    "distribution_trump['LAST NAME'] = distribution_trump['LAST NAME']/distribution_trump['TOTAL']*100\n",
    "distribution_trump['NAMECALLING'] = distribution_trump['NAMECALLING']/distribution_trump['TOTAL']*100\n",
    "distribution_trump['PROFESSIONAL'] = distribution_trump['PROFESSIONAL']/distribution_trump['TOTAL']*100\n",
    "del distribution_trump['TOTAL']\n",
    "distribution_trump.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4fv06fv/9dzx9+hRNmzbFunXrSr0TfGxsLObNm4fWrVujc+fOMDMzk2/yduLECchkMvj4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\nb40aNdKkSZNUokQJg1PCShITEzVo0CDFxMTI3d1d/fr1U48ePTRhwgR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      "text/plain": [
       "<matplotlib.figure.Figure object at 0x11685a160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "distribution_trump.plot(kind='bar', figsize=(15,6))\n",
    "plt.xlabel('CANDIDATE')\n",
    "plt.ylabel('% of RE')\n",
    "plt.title('TRUMP DISTRIBUTION OF RE')\n",
    "plt.show()\n",
    "#Why are there two?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some Conclusions\n",
    "**We can see Trump's most frequent type of RE for all of the candidates we're looking at (besides Bernie Sanders) is their first name. Trump refers to Hillary Clinton by her first name or her full name more often than by a professional title, but calls her by a professional title more often than he calls any of the other candidates by one. In fact, the only other people he uses a professional title for at all are Marco Rubio and Ben Carson. Trump refers to the only other female candidate, Carly Fiorina, exclusively by her first name. This could be a strategy to make her appear less powerful than him, or a way of making them seem like they are on the same level and good friends. In politics, the former is more likely. Every move should be strategic. He also calls Ted Cruz names more often than he refers to him as Cruz.**\n",
    "\n",
    "**Let's check out how Hillary Clinton refers to these candidates.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hillary Clinton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Dr.': 3, 'Senator Sanders': 111, 'Lester': 8, 'Andrea': 1, 'Harry Truman': 2, 'President Obama': 91, \"Governor O'Malley\": 10, 'Lindsey Graham': 1, 'Secretary': 14, 'Governor': 5, 'Secretary Kerry': 5, 'Assad': 22, 'Putin': 15, 'Hillary Clinton': 2, 'Chris': 14, 'George W.': 1, 'President': 14, 'Bush': 12, 'Chris Cuomo': 1, 'Lady': 1, 'Chairman': 1, 'Ronald Reagan': 3, 'Bill': 5, 'Abraham Lincoln': 2, 'Abraham': 1, 'Lincoln': 2, 'Anderson': 6, 'Senator Obama': 6, 'Osama': 2, 'Laden': 8, 'Gadhafi': 10, 'Senator Webb': 1, 'Bernie': 2, 'Clinton': 2, 'Barack Obama': 7, 'Donald': 81, 'George W. Bush': 10, 'Donald Trump': 23, 'Vladimir Putin': 2, 'Bernie Sanders': 3, 'Alicia Machado': 2, 'Mr.': 2, 'Mrs.': 3, 'John McCain': 1, 'President Reagan': 3, 'Michelle Obama': 2, 'Captain Khan': 2, 'Martha': 7, 'George Washington': 1, 'President Lincoln': 2, 'Mr. Carter': 1, 'Trump': 4, 'John': 6, 'Gaddafi': 2, 'Senator': 21, 'Eric Garner': 1, 'Tamir Rice': 1, 'Michael Brown': 1, 'Trayvon Martin': 2, 'Bin Laden': 2, 'Hillary': 1, 'Martin': 2, 'Mr. Trump': 1, 'David': 3, 'Mubarak': 1, 'Mrs. Obama': 2, 'Scott Walker': 2, 'Obama': 8, 'Saddam Hussein': 1, 'Nelson Mandela': 1, 'President Biden': 1, 'Chuck': 3, 'Rachel': 3, 'Ashraf Ghani': 1, 'Secretary Carter': 3, 'President Bush': 1, 'Nikki': 1, 'Don': 2, 'Sandra Bland': 1, 'Jorge': 4, 'David Duke': 1, 'Senator Warren': 1, 'President Clinton': 1, 'Gore': 1, 'Dana': 3, 'Governor Cuomo': 2, 'Qadafi': 2, 'Wolf': 2, 'Netanyahu': 2, 'Yasser Arafat': 1, 'Lester Holt': 1, 'John Kerry': 1, 'Miss Piggy': 1, 'Miss Housekeeping': 1}"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinton_re = get_RE_for_label(clinton['Tree'], 'all')\n",
    "clinton_re_freq = {re: clinton_re.count(re) for re in clinton_re}\n",
    "clinton_re_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'George Washington', 'Wolf Blitzer', 'John McCain', 'Andrea Mitchell', 'Chuck Todd', 'Vladimir Putin', 'David Muir', 'Chris Christie', 'Elizabeth Warren', 'Bashar al-Assad', 'Tamir Rice', 'Rachel Maddow', 'James Carter', 'Muammar Gaddafi', 'Michael Brown', 'Dana Bash', 'Lester Holt', 'Barack Obama', 'Saddam Hussein', 'Don Lemon', 'George W. Bush', 'Martha Raddatz', 'Al Gore', 'Jorge Ramos', 'Lincoln Chafee', 'Anderson Cooper', 'Abraham Lincoln', 'Ronald Reagan', \"Martin O'Malley\", 'TITLE', 'Eric Garner', 'Jeb Bush', 'Harry Truman', 'Donald Trump', 'David Duke', 'Joe Biden', 'Alicia Machado', 'Lindsey Graham', 'Chris Cuomo', 'Sandra Bland', 'Humayun Khan', 'Bill Clinton', 'Nikki Haley', 'Scott Walker', 'Yasser Arafat', 'Osama bin Laden', 'Hillary Clinton', 'Ashraf Ghani', 'Jim Webb', 'John Kerry', 'Michelle Obama', 'Nelson Mandela', 'Bernie Sanders', 'Trayvon Martin', 'Hosni Mubarak', 'Benjamin Netanyahu', 'John Kasich'}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinton_ents = get_entities(clinton['Entities'])\n",
    "clinton_ents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Carly Fiorina' in clinton_ents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Ted Cruz' in clinton_ents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Marco Rubio' in clinton_ents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hillary Clinton doesn't refer to any of the Republican candidates listed above except for Donald Trump. Therefore, we're going to look at the way she refers to the other candidates she was in debates with, and we are going to examine the titles she uses.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Debate Type</th>\n",
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       "      <th>2</th>\n",
       "      <td>1-17-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>You know, I remember well when my youth minist...</td>\n",
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       "      <td>CLINTON</td>\n",
       "      <td>I did it as a First Lady when I worked, as I s...</td>\n",
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       "      <td>CLINTON</td>\n",
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       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>10-13-15</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>There's no doubt that when Putin came back in ...</td>\n",
       "      <td>[(There, EX), ('s, VBZ), (no, DT), (doubt, NN)...</td>\n",
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       "      <th>1178</th>\n",
       "      <td>10-19-16</td>\n",
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       "      <td>CLINTON</td>\n",
       "      <td>He went after Mr. and Mrs. Khan, the parents ...</td>\n",
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       "    <tr>\n",
       "      <th>1400</th>\n",
       "      <td>10-9-16</td>\n",
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       "      <td>CLINTON</td>\n",
       "      <td>He never apologized to Mr. and Mrs. Khan, the ...</td>\n",
       "      <td>[(He, PRP), (never, RB), (apologized, VBD), (t...</td>\n",
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       "    <tr>\n",
       "      <th>1839</th>\n",
       "      <td>11-14-15</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>Well, first of all, I know that the President...</td>\n",
       "      <td>[(Well, RB), (,, ,), (first, RB), (of, IN), (a...</td>\n",
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       "    <tr>\n",
       "      <th>1840</th>\n",
       "      <td>11-14-15</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>And my reading of the law and the Constitution...</td>\n",
       "      <td>[(And, CC), (my, PRP$), (reading, NN), (of, IN...</td>\n",
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       "      <th>1926</th>\n",
       "      <td>11-14-15</td>\n",
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       "      <td>CLINTON</td>\n",
       "      <td>But wait, I just want to say this Senator.</td>\n",
       "      <td>[(But, CC), (wait, NN), (,, ,), (I, PRP), (jus...</td>\n",
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       "    <tr>\n",
       "      <th>2173</th>\n",
       "      <td>12-19-15</td>\n",
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       "      <td>And, you know, with all due respect, Senator, ...</td>\n",
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       "      <td>12-19-15</td>\n",
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       "      <td>Senator, I have been -- I have been fighting ...</td>\n",
       "      <td>[[(Senator, NNP)], (,, ,), (I, PRP), (have, VB...</td>\n",
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       "    <tr>\n",
       "      <th>2332</th>\n",
       "      <td>12-19-15</td>\n",
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       "      <td>CLINTON</td>\n",
       "      <td>I would like the federal government to offer $...</td>\n",
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       "      <th>2409</th>\n",
       "      <td>2-11-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
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       "      <th>2508</th>\n",
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       "      <th>2569</th>\n",
       "      <td>2-11-16</td>\n",
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       "      <td>Well, Senator, look, I think we're in vigorou...</td>\n",
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       "      <th>2598</th>\n",
       "      <td>2-11-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>But, the real issue, I think, that the Senator...</td>\n",
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       "    <tr>\n",
       "      <th>2745</th>\n",
       "      <td>2-11-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>You know, Senator, what I am concerned about,...</td>\n",
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       "      <th>2805</th>\n",
       "      <td>2-4-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
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       "      <th>2806</th>\n",
       "      <td>2-4-16</td>\n",
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       "      <td>Even the late, great Senator Paul Wellstone wo...</td>\n",
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       "      <th>2824</th>\n",
       "      <td>2-4-16</td>\n",
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       "      <td>It is fair to say, Senator, that in your defin...</td>\n",
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       "      <th>2826</th>\n",
       "      <td>2-4-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>Let's talk about what we would do as President...</td>\n",
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       "      <th>2829</th>\n",
       "      <td>2-4-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>You know, the person who first put out the id...</td>\n",
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       "      <td>2-4-16</td>\n",
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       "      <td>CLINTON</td>\n",
       "      <td>And I just absolutely reject that, Senator.</td>\n",
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       "      <th>2870</th>\n",
       "      <td>2-4-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>Well, you know, Senator, I don't think—I don'...</td>\n",
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       "      <th>2937</th>\n",
       "      <td>2-4-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>I probably described more times than I can rem...</td>\n",
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       "      <th>2946</th>\n",
       "      <td>2-4-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>... Well, Senator, no one wants to see that h...</td>\n",
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       "      <th>3009</th>\n",
       "      <td>2-4-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>Well Senator, let me just correct the record ...</td>\n",
       "      <td>[[(Well, NNP)], [(Senator, NNP)], (,, ,), (let...</td>\n",
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       "    <tr>\n",
       "      <th>3021</th>\n",
       "      <td>2-4-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>That's exactly the right...[crosstalk]...and, ...</td>\n",
       "      <td>[(That, DT), ('s, VBZ), (exactly, RB), (the, D...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3173</th>\n",
       "      <td>3-6-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>I agree, the Governor should resign, or be rec...</td>\n",
       "      <td>[(I, PRP), (agree, VBP), (,, ,), (the, DT), [(...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3189</th>\n",
       "      <td>3-6-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>So I will make sure as President that I double...</td>\n",
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       "    <tr>\n",
       "      <th>3404</th>\n",
       "      <td>3-6-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>It was also important for me to be a baby-sitt...</td>\n",
       "      <td>[(It, PRP), (was, VBD), (also, RB), (important...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3406</th>\n",
       "      <td>3-6-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>When I was in law school, I had the opportunit...</td>\n",
       "      <td>[(When, WRB), (I, PRP), (was, VBD), (in, IN), ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3443</th>\n",
       "      <td>3-6-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>Mrs. Kim, here is what I would do as president.</td>\n",
       "      <td>[[(Mrs., NNP)], [(Kim, NNP)], (,, ,), (here, R...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3445</th>\n",
       "      <td>3-6-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>[applause]\\n\\nSecondly, I would use every lega...</td>\n",
       "      <td>[([, NN), (applause, NN), (], NNP), (Secondly,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3634</th>\n",
       "      <td>3-9-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>So I think when you were running for the Senat...</td>\n",
       "      <td>[(So, RB), (I, PRP), (think, VBP), (when, WRB)...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4006</th>\n",
       "      <td>4-14-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>I spoke out against Wall Street when I was a S...</td>\n",
       "      <td>[(I, PRP), (spoke, VBD), (out, RP), (against, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4194</th>\n",
       "      <td>4-14-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>... And, our President led the effort to prot...</td>\n",
       "      <td>[(..., :), (And, CC), (,, ,), (our, PRP$), [(P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4195</th>\n",
       "      <td>4-14-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>Well, I'm a little bewildered about how to re...</td>\n",
       "      <td>[(Well, RB), (,, ,), (I, PRP), ('m, VBP), (a, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4197</th>\n",
       "      <td>4-14-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>[applause]\\n\\nAnd, you know, I am getting a li...</td>\n",
       "      <td>[([, NN), (applause, NN), (], NN), (And, CC), ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4232</th>\n",
       "      <td>4-14-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>Senator sanders voted for that, and that's exa...</td>\n",
       "      <td>[[(Senator, NNP)], (sanders, NNS), (voted, VBD...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4242</th>\n",
       "      <td>4-14-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>There was also in that a reference to the Sec...</td>\n",
       "      <td>[(There, EX), (was, VBD), (also, RB), (in, IN)...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4281</th>\n",
       "      <td>4-14-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>I did it in concert with... [applause]\\n\\nPres...</td>\n",
       "      <td>[(I, PRP), (did, VBD), (it, PRP), (in, IN), (c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4363</th>\n",
       "      <td>4-14-16</td>\n",
       "      <td>primary_dem</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>You know, we are in vigorous agreement here, S...</td>\n",
       "      <td>[(You, PRP), (know, VBP), (,, ,), (we, PRP), (...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Debate Type  Speaker  \\\n",
       "2      1-17-16  primary_dem  CLINTON   \n",
       "132    1-17-16  primary_dem  CLINTON   \n",
       "375    1-25-16  primary_dem  CLINTON   \n",
       "453    1-25-16  primary_dem  CLINTON   \n",
       "491    1-25-16  primary_dem  CLINTON   \n",
       "669   10-13-15  primary_dem  CLINTON   \n",
       "1178  10-19-16      general  CLINTON   \n",
       "1400   10-9-16      general  CLINTON   \n",
       "1839  11-14-15  primary_dem  CLINTON   \n",
       "1840  11-14-15  primary_dem  CLINTON   \n",
       "1926  11-14-15  primary_dem  CLINTON   \n",
       "2173  12-19-15  primary_dem  CLINTON   \n",
       "2310  12-19-15  primary_dem  CLINTON   \n",
       "2332  12-19-15  primary_dem  CLINTON   \n",
       "2409   2-11-16  primary_dem  CLINTON   \n",
       "2508   2-11-16  primary_dem  CLINTON   \n",
       "2552   2-11-16  primary_dem  CLINTON   \n",
       "2559   2-11-16  primary_dem  CLINTON   \n",
       "2569   2-11-16  primary_dem  CLINTON   \n",
       "2598   2-11-16  primary_dem  CLINTON   \n",
       "2745   2-11-16  primary_dem  CLINTON   \n",
       "2805    2-4-16  primary_dem  CLINTON   \n",
       "2806    2-4-16  primary_dem  CLINTON   \n",
       "2824    2-4-16  primary_dem  CLINTON   \n",
       "2826    2-4-16  primary_dem  CLINTON   \n",
       "2829    2-4-16  primary_dem  CLINTON   \n",
       "2856    2-4-16  primary_dem  CLINTON   \n",
       "2870    2-4-16  primary_dem  CLINTON   \n",
       "2937    2-4-16  primary_dem  CLINTON   \n",
       "2946    2-4-16  primary_dem  CLINTON   \n",
       "3009    2-4-16  primary_dem  CLINTON   \n",
       "3021    2-4-16  primary_dem  CLINTON   \n",
       "3173    3-6-16  primary_dem  CLINTON   \n",
       "3189    3-6-16  primary_dem  CLINTON   \n",
       "3404    3-6-16  primary_dem  CLINTON   \n",
       "3406    3-6-16  primary_dem  CLINTON   \n",
       "3443    3-6-16  primary_dem  CLINTON   \n",
       "3445    3-6-16  primary_dem  CLINTON   \n",
       "3634    3-9-16  primary_dem  CLINTON   \n",
       "4006   4-14-16  primary_dem  CLINTON   \n",
       "4194   4-14-16  primary_dem  CLINTON   \n",
       "4195   4-14-16  primary_dem  CLINTON   \n",
       "4197   4-14-16  primary_dem  CLINTON   \n",
       "4232   4-14-16  primary_dem  CLINTON   \n",
       "4242   4-14-16  primary_dem  CLINTON   \n",
       "4281   4-14-16  primary_dem  CLINTON   \n",
       "4363   4-14-16  primary_dem  CLINTON   \n",
       "\n",
       "                                                  Sents  \\\n",
       "2     You know, I remember well when my youth minist...   \n",
       "132   And, you know, Governor, you have raised money...   \n",
       "375   I flew from Cambodia, where I was with the pre...   \n",
       "453   I did it as a First Lady when I worked, as I s...   \n",
       "491   And, at the end of it, the Chairman said, \"No,...   \n",
       "669   There's no doubt that when Putin came back in ...   \n",
       "1178   He went after Mr. and Mrs. Khan, the parents ...   \n",
       "1400  He never apologized to Mr. and Mrs. Khan, the ...   \n",
       "1839   Well, first of all, I know that the President...   \n",
       "1840  And my reading of the law and the Constitution...   \n",
       "1926         But wait, I just want to say this Senator.   \n",
       "2173  And, you know, with all due respect, Senator, ...   \n",
       "2310   Senator, I have been -- I have been fighting ...   \n",
       "2332  I would like the federal government to offer $...   \n",
       "2409  Last week in a CNN town hall, the Senator told...   \n",
       "2508  I think what President Obama did was to exempl...   \n",
       "2552   Well, that just wasn't — that just wasn't the...   \n",
       "2559  I've said I'm looking at a couple of different...   \n",
       "2569   Well, Senator, look, I think we're in vigorou...   \n",
       "2598  But, the real issue, I think, that the Senator...   \n",
       "2745   You know, Senator, what I am concerned about,...   \n",
       "2805  Under his definition, President Obama is not p...   \n",
       "2806  Even the late, great Senator Paul Wellstone wo...   \n",
       "2824  It is fair to say, Senator, that in your defin...   \n",
       "2826  Let's talk about what we would do as President...   \n",
       "2829   You know, the person who first put out the id...   \n",
       "2856        And I just absolutely reject that, Senator.   \n",
       "2870   Well, you know, Senator, I don't think—I don'...   \n",
       "2937  I probably described more times than I can rem...   \n",
       "2946   ... Well, Senator, no one wants to see that h...   \n",
       "3009   Well Senator, let me just correct the record ...   \n",
       "3021  That's exactly the right...[crosstalk]...and, ...   \n",
       "3173  I agree, the Governor should resign, or be rec...   \n",
       "3189  So I will make sure as President that I double...   \n",
       "3404  It was also important for me to be a baby-sitt...   \n",
       "3406  When I was in law school, I had the opportunit...   \n",
       "3443    Mrs. Kim, here is what I would do as president.   \n",
       "3445  [applause]\\n\\nSecondly, I would use every lega...   \n",
       "3634  So I think when you were running for the Senat...   \n",
       "4006  I spoke out against Wall Street when I was a S...   \n",
       "4194   ... And, our President led the effort to prot...   \n",
       "4195   Well, I'm a little bewildered about how to re...   \n",
       "4197  [applause]\\n\\nAnd, you know, I am getting a li...   \n",
       "4232  Senator sanders voted for that, and that's exa...   \n",
       "4242   There was also in that a reference to the Sec...   \n",
       "4281  I did it in concert with... [applause]\\n\\nPres...   \n",
       "4363  You know, we are in vigorous agreement here, S...   \n",
       "\n",
       "                                                   Tree  \n",
       "2     [(You, PRP), (know, VBP), (,, ,), (I, PRP), (r...  \n",
       "132   [(And, CC), (,, ,), (you, PRP), (know, VBP), (...  \n",
       "375   [(I, PRP), (flew, VBP), (from, IN), [(Cambodia...  \n",
       "453   [(I, PRP), (did, VBD), (it, PRP), (as, IN), (a...  \n",
       "491   [(And, CC), (,, ,), (at, IN), (the, DT), (end,...  \n",
       "669   [(There, EX), ('s, VBZ), (no, DT), (doubt, NN)...  \n",
       "1178  [(He, PRP), (went, VBD), (after, IN), [(Mr., N...  \n",
       "1400  [(He, PRP), (never, RB), (apologized, VBD), (t...  \n",
       "1839  [(Well, RB), (,, ,), (first, RB), (of, IN), (a...  \n",
       "1840  [(And, CC), (my, PRP$), (reading, NN), (of, IN...  \n",
       "1926  [(But, CC), (wait, NN), (,, ,), (I, PRP), (jus...  \n",
       "2173  [(And, CC), (,, ,), (you, PRP), (know, VBP), (...  \n",
       "2310  [[(Senator, NNP)], (,, ,), (I, PRP), (have, VB...  \n",
       "2332  [(I, PRP), (would, MD), (like, VB), (the, DT),...  \n",
       "2409  [(Last, JJ), (week, NN), (in, IN), (a, DT), [(...  \n",
       "2508  [(I, PRP), (think, VBP), (what, WP), [(Preside...  \n",
       "2552  [(Well, RB), (,, ,), (that, DT), (just, RB), (...  \n",
       "2559  [(I, PRP), ('ve, VBP), (said, VBD), (I, PRP), ...  \n",
       "2569  [(Well, RB), (,, ,), [(Senator, NNP)], (,, ,),...  \n",
       "2598  [(But, CC), (,, ,), (the, DT), (real, JJ), (is...  \n",
       "2745  [(You, PRP), (know, VBP), (,, ,), [(Senator, N...  \n",
       "2805  [(Under, IN), (his, PRP$), (definition, NN), (...  \n",
       "2806  [(Even, RB), (the, DT), (late, JJ), (,, ,), (g...  \n",
       "2824  [(It, PRP), (is, VBZ), (fair, JJ), (to, TO), (...  \n",
       "2826  [(Let, VB), ('s, POS), (talk, VB), (about, IN)...  \n",
       "2829  [(You, PRP), (know, VBP), (,, ,), (the, DT), (...  \n",
       "2856  [(And, CC), (I, PRP), (just, RB), (absolutely,...  \n",
       "2870  [(Well, RB), (,, ,), (you, PRP), (know, VBP), ...  \n",
       "2937  [(I, PRP), (probably, RB), (described, VBD), (...  \n",
       "2946  [(..., :), (Well, RB), (,, ,), [(Senator, NNP)...  \n",
       "3009  [[(Well, NNP)], [(Senator, NNP)], (,, ,), (let...  \n",
       "3021  [(That, DT), ('s, VBZ), (exactly, RB), (the, D...  \n",
       "3173  [(I, PRP), (agree, VBP), (,, ,), (the, DT), [(...  \n",
       "3189  [(So, RB), (I, PRP), (will, MD), (make, VB), (...  \n",
       "3404  [(It, PRP), (was, VBD), (also, RB), (important...  \n",
       "3406  [(When, WRB), (I, PRP), (was, VBD), (in, IN), ...  \n",
       "3443  [[(Mrs., NNP)], [(Kim, NNP)], (,, ,), (here, R...  \n",
       "3445  [([, NN), (applause, NN), (], NNP), (Secondly,...  \n",
       "3634  [(So, RB), (I, PRP), (think, VBP), (when, WRB)...  \n",
       "4006  [(I, PRP), (spoke, VBD), (out, RP), (against, ...  \n",
       "4194  [(..., :), (And, CC), (,, ,), (our, PRP$), [(P...  \n",
       "4195  [(Well, RB), (,, ,), (I, PRP), ('m, VBP), (a, ...  \n",
       "4197  [([, NN), (applause, NN), (], NN), (And, CC), ...  \n",
       "4232  [[(Senator, NNP)], (sanders, NNS), (voted, VBD...  \n",
       "4242  [(There, EX), (was, VBD), (also, RB), (in, IN)...  \n",
       "4281  [(I, PRP), (did, VBD), (it, PRP), (in, IN), (c...  \n",
       "4363  [(You, PRP), (know, VBP), (,, ,), (we, PRP), (...  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_trees_for(clinton, 'TITLE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We see that Hillary Clinton often refers to one of the other candidates as Senator. Since all of these instances were in the primary debates, and it's clear that she is talking directly to someone instead of about them, it is clear that she much be talking about Senator Bernie Sanders or Senator Jim Webb. Based on the dates of the debates, she is always talking about Bernie Sanders, because Jim Webb withdrew from the race on October 20, 2015.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Debate Type</th>\n",
       "      <th>Speaker</th>\n",
       "      <th>Sents</th>\n",
       "      <th>Tree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>10-19-16</td>\n",
       "      <td>general</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>And, you know, look, I understand that Donald'...</td>\n",
       "      <td>[(And, CC), (,, ,), (you, PRP), (know, VBP), (...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>964</th>\n",
       "      <td>10-19-16</td>\n",
       "      <td>general</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>Donald has said he's in favor of defunding Pla...</td>\n",
       "      <td>[[(Donald, NNP)], (has, VBZ), (said, VBD), (he...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>10-19-16</td>\n",
       "      <td>general</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>I don't want to see the deportation force that...</td>\n",
       "      <td>[(I, PRP), (do, VBP), (n't, RB), (want, VB), (...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005</th>\n",
       "      <td>10-19-16</td>\n",
       "      <td>general</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>When it comes to the wall that Donald talks ab...</td>\n",
       "      <td>[(When, WRB), (it, PRP), (comes, VBZ), (to, TO...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>10-19-16</td>\n",
       "      <td>general</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>But it is clear, when you look at what Donald ...</td>\n",
       "      <td>[(But, CC), (it, PRP), (is, VBZ), (clear, JJ),...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date Debate Type  Speaker  \\\n",
       "955   10-19-16     general  CLINTON   \n",
       "964   10-19-16     general  CLINTON   \n",
       "991   10-19-16     general  CLINTON   \n",
       "1005  10-19-16     general  CLINTON   \n",
       "1013  10-19-16     general  CLINTON   \n",
       "\n",
       "                                                  Sents  \\\n",
       "955   And, you know, look, I understand that Donald'...   \n",
       "964   Donald has said he's in favor of defunding Pla...   \n",
       "991   I don't want to see the deportation force that...   \n",
       "1005  When it comes to the wall that Donald talks ab...   \n",
       "1013  But it is clear, when you look at what Donald ...   \n",
       "\n",
       "                                                   Tree  \n",
       "955   [(And, CC), (,, ,), (you, PRP), (know, VBP), (...  \n",
       "964   [[(Donald, NNP)], (has, VBZ), (said, VBD), (he...  \n",
       "991   [(I, PRP), (do, VBP), (n't, RB), (want, VB), (...  \n",
       "1005  [(When, WRB), (it, PRP), (comes, VBZ), (to, TO...  \n",
       "1013  [(But, CC), (it, PRP), (is, VBZ), (clear, JJ),...  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinton_trump_tree = get_trees_for(clinton, 'Donald Trump')\n",
    "clinton_trump_tree.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald Trump', 'Donald Trump', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald Trump', 'Donald', 'Donald Trump', 'Donald Trump', 'Donald', 'Donald', 'Donald', 'Donald Trump', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald Trump', 'Donald Trump', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald Trump', 'Donald', 'Trump', 'Donald', 'Donald', 'Donald Trump', 'Donald', 'Donald Trump', 'Donald Trump', 'Mr. Trump', 'Trump', 'Donald Trump', 'Donald Trump', 'Donald Trump', 'Donald Trump', 'Donald Trump', 'Donald Trump', 'Donald Trump', 'Donald Trump', 'Trump', 'Donald Trump', 'Donald', 'Donald Trump', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Trump', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald Trump', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald', 'Donald']"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinton_REtrump = get_RE_for_label(clinton['Tree'], 'Donald Trump')\n",
    "clinton_REtrump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Donald': 81, 'Donald Trump': 23, 'Trump': 4, 'Mr. Trump': 1}"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinton_trump_freq = {re: clinton_REtrump.count(re) for re in clinton_REtrump}\n",
    "clinton_trump_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 1, 'FULL NAME': 23, 'FIRST NAME': 81, 'LAST NAME': 4}"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinton_trump_REtype_freq = get_re_type_freq(trump_re_maps, clinton_trump_freq)\n",
    "clinton_trump_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Bernie Sanders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Bernie Sanders', 'Bernie', 'Senator', 'Senator Sanders'}"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinton_REsanders = get_RE_for_label(clinton['Tree'], 'Bernie Sanders')\n",
    "clinton_senator = get_RE_for_label(clinton['Tree'], 'TITLE')\n",
    "clinton_senator = [x for x in clinton_senator if x == 'Senator']\n",
    "clinton_REsanders = clinton_REsanders + clinton_senator\n",
    "set(clinton_REsanders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Senator Sanders': 111, 'Bernie': 2, 'Bernie Sanders': 3, 'Senator': 21}"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinton_sanders_freq = {re: clinton_REsanders.count(re) for re in clinton_REsanders}\n",
    "clinton_sanders_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FIRST NAME': 2, 'FULL NAME': 3, 'PROFESSIONAL': 132}"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sanders_re_maps['Senator'] = 'PROFESSIONAL'\n",
    "clinton_sanders_REtype_freq = get_re_type_freq(sanders_re_maps, clinton_sanders_freq)\n",
    "clinton_sanders_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Martin O'Malley"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Martin', \"Governor O'Malley\"}"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinton_REomalley = get_RE_for_label(clinton['Tree'], \"Martin O'Malley\")\n",
    "set(clinton_REomalley)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{\"Governor O'Malley\": 10, 'Martin': 2}"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinton_omalley_freq = {re: clinton_REomalley.count(re) for re in clinton_REomalley}\n",
    "clinton_omalley_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 10, 'FIRST NAME': 2}"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "omalley_re_maps = {\"Governor O'Malley\" : \"PROFESSIONAL\", \"Martin\": \"FIRST NAME\"}\n",
    "clinton_omalley_REtype_freq = get_re_type_freq(omalley_re_maps, clinton_omalley_freq)\n",
    "clinton_omalley_REtype_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIRST NAME</th>\n",
       "      <th>FULL NAME</th>\n",
       "      <th>LAST NAME</th>\n",
       "      <th>PROFESSIONAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>O'MALLEY</th>\n",
       "      <td>16.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>83.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SANDERS</th>\n",
       "      <td>1.459854</td>\n",
       "      <td>2.189781</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>96.350365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRUMP</th>\n",
       "      <td>74.311927</td>\n",
       "      <td>21.100917</td>\n",
       "      <td>3.669725</td>\n",
       "      <td>0.917431</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          FIRST NAME  FULL NAME  LAST NAME  PROFESSIONAL\n",
       "O'MALLEY   16.666667   0.000000   0.000000     83.333333\n",
       "SANDERS     1.459854   2.189781   0.000000     96.350365\n",
       "TRUMP      74.311927  21.100917   3.669725      0.917431"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distribution_clinton = pd.DataFrame([clinton_omalley_REtype_freq, clinton_sanders_REtype_freq, clinton_trump_REtype_freq, ], index = [\"O'MALLEY\", 'SANDERS', 'TRUMP'])\n",
    "distribution_clinton.fillna(0, inplace=True)\n",
    "\n",
    "distribution_clinton['TOTAL']=distribution_clinton.sum(axis=1)\n",
    "\n",
    "#Get the percentage of each type of RE for each candidate.\n",
    "distribution_clinton['FIRST NAME'] = distribution_clinton['FIRST NAME']/distribution_clinton['TOTAL']*100\n",
    "distribution_clinton['FULL NAME'] = distribution_clinton['FULL NAME']/distribution_clinton['TOTAL']*100\n",
    "distribution_clinton['LAST NAME'] = distribution_clinton['LAST NAME']/distribution_clinton['TOTAL']*100\n",
    "distribution_clinton['PROFESSIONAL'] = distribution_clinton['PROFESSIONAL']/distribution_clinton['TOTAL']*100\n",
    "del distribution_clinton['TOTAL']\n",
    "distribution_clinton.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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evXpelyUbGxuFh4fr5MmTjx17//59bdiwQcOHD1dwcHCG8yUkJCgyMtKoPSQk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\nX7p0qc6cOZObtQAAAAAAgL/JMLiPHz9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dAAAAAAATo7gDAAAAAGBiXJwOAAATK9PvCaMj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\nMWpHOCs1EkcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure object at 0x118dc2b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_clinton.plot(kind='bar', figsize=(15,6))\n",
    "plt.xlabel('CANDIDATE')\n",
    "plt.ylabel('% of RE')\n",
    "plt.title('CLINTON DISTRIBUTION OF RE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some Conclusions\n",
    "**It's a little harder to compare this graph to Trump's since Clinton didn't refer to all of the same candidates Trump did, but we can see that she refers to O'Malley and Sanders by a professional title the majority of the time, yet she almost never calls Trump by a professional title, and mostly refers to him by his first name.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Carly Fiorina"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Donald Trump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Mr. Trump'}"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fiorina_REtrump = get_RE_for_label(fiorina['Tree'], \"Donald Trump\")\n",
    "set(fiorina_REtrump)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Mr. Trump': 12}"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fiorina_trump_freq = {re: fiorina_REtrump.count(re) for re in fiorina_REtrump}\n",
    "fiorina_trump_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 12}"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fiorina_trump_REtype_freq = get_re_type_freq(trump_re_maps, fiorina_trump_freq)\n",
    "fiorina_trump_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Hillary Clinton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Hillary Clinton', 'Clinton', 'Mrs. Clinton'}"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fiorina_REclinton = get_RE_for_label(fiorina['Tree'], \"Hillary Clinton\")\n",
    "set(fiorina_REclinton)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Mrs. Clinton': 7, 'Hillary Clinton': 14, 'Clinton': 3}"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fiorina_clinton_freq = {re: fiorina_REclinton.count(re) for re in fiorina_REclinton}\n",
    "fiorina_clinton_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FULL NAME': 14, 'LAST NAME': 3, 'GENDERED': 7}"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fiorina_clinton_REtype_freq = get_re_type_freq(clinton_re_maps, fiorina_clinton_freq)\n",
    "fiorina_clinton_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Ted Cruz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Senator Cruz'}"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fiorina_REcruz = get_RE_for_label(fiorina['Tree'], \"Ted Cruz\")\n",
    "set(fiorina_REcruz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Senator Cruz': 1}"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fiorina_cruz_freq = {re: fiorina_REcruz.count(re) for re in fiorina_REcruz}\n",
    "fiorina_cruz_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 1}"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fiorina_cruz_REtype_freq = get_re_type_freq(cruz_re_maps, fiorina_cruz_freq)\n",
    "fiorina_cruz_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Marco Rubio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Senator Rubio'}"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fiorina_RErubio = get_RE_for_label(fiorina['Tree'], \"Marco Rubio\")\n",
    "set(fiorina_RErubio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Senator Rubio': 2}"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fiorina_rubio_freq = {re: fiorina_RErubio.count(re) for re in fiorina_RErubio}\n",
    "fiorina_rubio_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 2}"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fiorina_rubio_REtype_freq = get_re_type_freq(rubio_re_maps, fiorina_rubio_freq)\n",
    "fiorina_rubio_REtype_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FULL NAME</th>\n",
       "      <th>GENDERED</th>\n",
       "      <th>LAST NAME</th>\n",
       "      <th>PROFESSIONAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CLINTON</th>\n",
       "      <td>58.333333</td>\n",
       "      <td>29.166667</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRUMP</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RUBIO</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRUZ</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         FULL NAME   GENDERED  LAST NAME  PROFESSIONAL\n",
       "CLINTON  58.333333  29.166667       12.5           0.0\n",
       "TRUMP     0.000000   0.000000        0.0         100.0\n",
       "RUBIO     0.000000   0.000000        0.0         100.0\n",
       "CRUZ      0.000000   0.000000        0.0         100.0"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distribution_fiorina = pd.DataFrame([fiorina_clinton_REtype_freq, fiorina_trump_REtype_freq, fiorina_rubio_REtype_freq, fiorina_cruz_REtype_freq], index = [\"CLINTON\", 'TRUMP', 'RUBIO', 'CRUZ'])\n",
    "distribution_fiorina.fillna(0, inplace=True)\n",
    "\n",
    "distribution_fiorina['TOTAL']=distribution_fiorina.sum(axis=1)\n",
    "\n",
    "#Get the percentage of each type of RE for each candidate.\n",
    "distribution_fiorina['FULL NAME'] = distribution_fiorina['FULL NAME']/distribution_fiorina['TOTAL']*100\n",
    "distribution_fiorina['LAST NAME'] = distribution_fiorina['LAST NAME']/distribution_fiorina['TOTAL']*100\n",
    "distribution_fiorina['GENDERED'] = distribution_fiorina['GENDERED']/distribution_fiorina['TOTAL']*100\n",
    "distribution_fiorina['PROFESSIONAL'] = distribution_fiorina['PROFESSIONAL']/distribution_fiorina['TOTAL']*100\n",
    "del distribution_fiorina['TOTAL']\n",
    "distribution_fiorina.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eXl6ZPodAmzZtVK9ePf38889pemICACDtCO4AgHTp37+/ypYtqwMHDsjf3z/ZI8ru37+v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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure object at 0x118dc2390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_fiorina.plot(kind='bar', figsize=(15,6))\n",
    "plt.xlabel('CANDIDATE')\n",
    "plt.ylabel('% of RE')\n",
    "plt.title('FIORINA DISTRIBUTION OF RE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some Conclusions\n",
    "**While Fiorina referred to Rubio and Cruz as Senator, she never referred to Clinton as Secretary, always Mrs., her full name, or her last name. This could be a way of making Hillary Clinton and herself appear more similar by taking the political references out of the way Hillary Clinton is addressed. Using Mrs. also draws attention to Clinton's husband, former President Bill Clinton. It's possible that drawing attention to their relationship using this form of address can cause audience members to associate characteristics or decisions made by President Bill Clinton with Hillary Clinton. Fiorina always referred to Trump as Mr. Trump, which is interesting because he only ever referred to her by her first name. Perhaps by showing respect by using this title when referring to Trump, Fiorina is attempting to get respect back from him. Fiorina never referred to Ben Carson. Fiorina referred to Clinton 24 times, which is interesting because Clinton did not refer to Fiorina at all. It is also important to notice that Fiorina only referred to Marco Rubio two times, and Ted Cruz one time, so seeing in the graph that she referred to them by professional titles 100% of the time doesn't mean very much.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Ted Cruz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Donald Trump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Donald J. Trump', 'Donald', 'Donald Trump', 'Mr. Trump'}"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cruz_REtrump = get_RE_for_label(cruz['Tree'], \"Donald Trump\")\n",
    "set(cruz_REtrump)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Donald': 116, 'Donald J. Trump': 1, 'Donald Trump': 27, 'Mr. Trump': 2}"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cruz_trump_freq = {re: cruz_REtrump.count(re) for re in cruz_REtrump}\n",
    "cruz_trump_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 2, 'FULL NAME': 28, 'FIRST NAME': 116}"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cruz_trump_REtype_freq = get_re_type_freq(trump_re_maps, cruz_trump_freq)\n",
    "cruz_trump_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Hillary Clinton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Hillary Clinton', 'Clinton', 'Hillary', 'Madam Secretary'}"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cruz_REclinton = get_RE_for_label(cruz['Tree'], \"Hillary Clinton\")\n",
    "set(cruz_REclinton)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Hillary Clinton': 53, 'Hillary': 11, 'Clinton': 5, 'Madam Secretary': 1}"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cruz_clinton_freq = {re: cruz_REclinton.count(re) for re in cruz_REclinton}\n",
    "cruz_clinton_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FULL NAME': 53, 'FIRST NAME': 11, 'LAST NAME': 5, 'GENDERED': 1}"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cruz_clinton_REtype_freq = get_re_type_freq(clinton_re_maps, cruz_clinton_freq)\n",
    "cruz_clinton_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Carly Fiorina"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Carly Fiorina'}"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cruz_REfiorina = get_RE_for_label(cruz['Tree'], \"Carly Fiorina\")\n",
    "set(cruz_REfiorina)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Carly Fiorina': 1}"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cruz_fiorina_freq = {re: cruz_REfiorina.count(re) for re in cruz_REfiorina}\n",
    "cruz_fiorina_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Debate Type</th>\n",
       "      <th>Speaker</th>\n",
       "      <th>Sents</th>\n",
       "      <th>Tree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>3-10-16</td>\n",
       "      <td>primary_rep</td>\n",
       "      <td>CRUZ</td>\n",
       "      <td>It's why Carly Fiorina endorsed me yesterday.</td>\n",
       "      <td>[(It, PRP), ('s, VBZ), (why, WRB), [(Carly, NN...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Debate Type Speaker  \\\n",
       "1699  3-10-16  primary_rep    CRUZ   \n",
       "\n",
       "                                              Sents  \\\n",
       "1699  It's why Carly Fiorina endorsed me yesterday.   \n",
       "\n",
       "                                                   Tree  \n",
       "1699  [(It, PRP), ('s, VBZ), (why, WRB), [(Carly, NN...  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's take a peek at this one instance Ted Cruz referred to Carly Fiorina\n",
    "get_trees_for(cruz, 'Carly Fiorina')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FULL NAME': 1}"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cruz_fiorina_REtype_freq = get_re_type_freq(fiorina_re_maps, cruz_fiorina_freq)\n",
    "cruz_fiorina_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Marco Rubio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Marco Rubio', 'Senator Rubio', 'Marco'}"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cruz_RErubio = get_RE_for_label(cruz['Tree'], \"Marco Rubio\")\n",
    "set(cruz_RErubio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Marco Rubio': 2, 'Marco': 32, 'Senator Rubio': 6}"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cruz_rubio_freq = {re: cruz_RErubio.count(re) for re in cruz_RErubio}\n",
    "cruz_rubio_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FULL NAME': 2, 'PROFESSIONAL': 6, 'FIRST NAME': 32}"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cruz_rubio_REtype_freq = get_re_type_freq(rubio_re_maps, cruz_rubio_freq)\n",
    "cruz_rubio_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Bernie Sanders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Bernie Sanders'}"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cruz_REsanders = get_RE_for_label(cruz['Tree'], \"Bernie Sanders\")\n",
    "set(cruz_REsanders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Bernie Sanders': 3}"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cruz_sanders_freq = {re: cruz_REsanders.count(re) for re in cruz_REsanders}\n",
    "cruz_sanders_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FULL NAME': 3}"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cruz_sanders_REtype_freq = get_re_type_freq(sanders_re_maps, cruz_sanders_freq)\n",
    "cruz_sanders_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Ben Carson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Ben Carson', 'Ben'}"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cruz_REcarson = get_RE_for_label(cruz['Tree'], \"Ben Carson\")\n",
    "set(cruz_REcarson)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Ben': 9, 'Ben Carson': 2}"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cruz_carson_freq = {re: cruz_REcarson.count(re) for re in cruz_REcarson}\n",
    "cruz_carson_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FULL NAME': 2, 'FIRST NAME': 9}"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cruz_carson_REtype_freq = get_re_type_freq(carson_re_maps, cruz_carson_freq)\n",
    "cruz_carson_REtype_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIRST NAME</th>\n",
       "      <th>FULL NAME</th>\n",
       "      <th>GENDERED</th>\n",
       "      <th>LAST NAME</th>\n",
       "      <th>PROFESSIONAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CLINTON</th>\n",
       "      <td>15.714286</td>\n",
       "      <td>75.714286</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>7.142857</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRUMP</th>\n",
       "      <td>79.452055</td>\n",
       "      <td>19.178082</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.369863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RUBIO</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FIORINA</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SANDERS</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CARSON</th>\n",
       "      <td>81.818182</td>\n",
       "      <td>18.181818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         FIRST NAME   FULL NAME  GENDERED  LAST NAME  PROFESSIONAL\n",
       "CLINTON   15.714286   75.714286  1.428571   7.142857      0.000000\n",
       "TRUMP     79.452055   19.178082  0.000000   0.000000      1.369863\n",
       "RUBIO     80.000000    5.000000  0.000000   0.000000     15.000000\n",
       "FIORINA    0.000000  100.000000  0.000000   0.000000      0.000000\n",
       "SANDERS    0.000000  100.000000  0.000000   0.000000      0.000000\n",
       "CARSON    81.818182   18.181818  0.000000   0.000000      0.000000"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distribution_cruz = pd.DataFrame([cruz_clinton_REtype_freq, cruz_trump_REtype_freq, cruz_rubio_REtype_freq, cruz_fiorina_REtype_freq, cruz_sanders_REtype_freq, cruz_carson_REtype_freq], index = [\"CLINTON\", 'TRUMP', 'RUBIO', 'FIORINA', 'SANDERS', 'CARSON'])\n",
    "distribution_cruz.fillna(0, inplace=True)\n",
    "\n",
    "distribution_cruz['TOTAL']=distribution_cruz.sum(axis=1)\n",
    "\n",
    "#Get the percentage of each type of RE for each candidate.\n",
    "distribution_cruz['FIRST NAME'] = distribution_cruz['FIRST NAME']/distribution_cruz['TOTAL']*100\n",
    "distribution_cruz['FULL NAME'] = distribution_cruz['FULL NAME']/distribution_cruz['TOTAL']*100\n",
    "distribution_cruz['LAST NAME'] = distribution_cruz['LAST NAME']/distribution_cruz['TOTAL']*100\n",
    "distribution_cruz['GENDERED'] = distribution_cruz['GENDERED']/distribution_cruz['TOTAL']*100\n",
    "distribution_cruz['PROFESSIONAL'] = distribution_cruz['PROFESSIONAL']/distribution_cruz['TOTAL']*100\n",
    "del distribution_cruz['TOTAL']\n",
    "distribution_cruz.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cHByqOyyIRCJs374d169fL7FtdnY2duzYgW+++QYRERFFjpeRkYH4+Hi58sjI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\nAABMjOIOAAAAAICJUdwBAAAAADCx/wcDLzFkrMmYXwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure object at 0x119aa5c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_cruz.plot(kind='bar', figsize=(15,6))\n",
    "plt.xlabel('CANDIDATE')\n",
    "plt.ylabel('% of RE')\n",
    "plt.title('CRUZ DISTRIBUTION OF RE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some Conclusions\n",
    "**Ted Cruz also preferred to use the other candidates first names while addressing them, except for Hillary Clinton, Bernie Sanders, and Carly Fiorina, where he preferred their full names. This is most likely because he never spoke to those three, only about them. He refers to Marco Rubio and Donald Trump by professional titles, but no one else.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Marco Rubio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Donald Trump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Mr. Trump', 'Donald', 'Donald Trump', 'Trump'}"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rubio_REtrump = get_RE_for_label(rubio['Tree'], \"Donald Trump\")\n",
    "set(rubio_REtrump)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Donald': 27, 'Donald Trump': 11, 'Trump': 3, 'Mr. Trump': 4}"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rubio_trump_freq = {re: rubio_REtrump.count(re) for re in rubio_REtrump}\n",
    "rubio_trump_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 4, 'FULL NAME': 11, 'FIRST NAME': 27, 'LAST NAME': 3}"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rubio_trump_REtype_freq = get_re_type_freq(trump_re_maps, rubio_trump_freq)\n",
    "rubio_trump_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Hillary Clinton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Hillary Clinton', 'Hillary'}"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rubio_REclinton = get_RE_for_label(rubio['Tree'], \"Hillary Clinton\")\n",
    "set(rubio_REclinton)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Hillary Clinton': 37, 'Hillary': 2}"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rubio_clinton_freq = {re: rubio_REclinton.count(re) for re in rubio_REclinton}\n",
    "rubio_clinton_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FULL NAME': 37, 'FIRST NAME': 2}"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rubio_clinton_REtype_freq = get_re_type_freq(clinton_re_maps, rubio_clinton_freq)\n",
    "rubio_clinton_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Ted Cruz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Senator Cruz', 'Ted Cruz', 'Ted'}"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rubio_REcruz = get_RE_for_label(rubio['Tree'], \"Ted Cruz\")\n",
    "set(rubio_REcruz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Ted': 14, 'Ted Cruz': 8, 'Senator Cruz': 8}"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rubio_cruz_freq = {re: rubio_REcruz.count(re) for re in rubio_REcruz}\n",
    "rubio_cruz_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FULL NAME': 8, 'PROFESSIONAL': 8, 'FIRST NAME': 14}"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rubio_cruz_REtype_freq = get_re_type_freq(cruz_re_maps, rubio_cruz_freq)\n",
    "rubio_cruz_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Bernie Sanders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Bernie Sanders'}"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rubio_REsanders = get_RE_for_label(rubio['Tree'], \"Bernie Sanders\")\n",
    "set(rubio_REsanders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Bernie Sanders': 8}"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rubio_sanders_freq = {re: rubio_REsanders.count(re) for re in rubio_REsanders}\n",
    "rubio_sanders_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FULL NAME': 8}"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rubio_sanders_REtype_freq = get_re_type_freq(sanders_re_maps, rubio_sanders_freq)\n",
    "rubio_sanders_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Ben Carson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Dr. Carson', 'Ben Carson'}"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rubio_REcarson = get_RE_for_label(rubio['Tree'], \"Ben Carson\")\n",
    "set(rubio_REcarson)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Dr. Carson': 1, 'Ben Carson': 1}"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rubio_carson_freq = {re: rubio_REcarson.count(re) for re in rubio_REcarson}\n",
    "rubio_carson_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FULL NAME': 1, 'PROFESSIONAL': 1}"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rubio_carson_REtype_freq = get_re_type_freq(carson_re_maps, rubio_carson_freq)\n",
    "rubio_carson_REtype_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIRST NAME</th>\n",
       "      <th>FULL NAME</th>\n",
       "      <th>LAST NAME</th>\n",
       "      <th>PROFESSIONAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CLINTON</th>\n",
       "      <td>5.128205</td>\n",
       "      <td>94.871795</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRUMP</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>24.444444</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>8.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRUZ</th>\n",
       "      <td>46.666667</td>\n",
       "      <td>26.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SANDERS</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CARSON</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         FIRST NAME   FULL NAME  LAST NAME  PROFESSIONAL\n",
       "CLINTON    5.128205   94.871795   0.000000      0.000000\n",
       "TRUMP     60.000000   24.444444   6.666667      8.888889\n",
       "CRUZ      46.666667   26.666667   0.000000     26.666667\n",
       "SANDERS    0.000000  100.000000   0.000000      0.000000\n",
       "CARSON     0.000000   50.000000   0.000000     50.000000"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distribution_rubio = pd.DataFrame([rubio_clinton_REtype_freq, rubio_trump_REtype_freq, rubio_cruz_REtype_freq, rubio_sanders_REtype_freq, rubio_carson_REtype_freq], index = [\"CLINTON\", 'TRUMP', 'CRUZ', 'SANDERS', 'CARSON'])\n",
    "distribution_rubio.fillna(0, inplace=True)\n",
    "\n",
    "distribution_rubio['TOTAL']=distribution_rubio.sum(axis=1)\n",
    "\n",
    "#Get the percentage of each type of RE for each candidate.\n",
    "distribution_rubio['FIRST NAME'] = distribution_rubio['FIRST NAME']/distribution_rubio['TOTAL']*100\n",
    "distribution_rubio['FULL NAME'] = distribution_rubio['FULL NAME']/distribution_rubio['TOTAL']*100\n",
    "distribution_rubio['LAST NAME'] = distribution_rubio['LAST NAME']/distribution_rubio['TOTAL']*100\n",
    "distribution_rubio['PROFESSIONAL'] = distribution_rubio['PROFESSIONAL']/distribution_rubio['TOTAL']*100\n",
    "\n",
    "del distribution_rubio['TOTAL']\n",
    "distribution_rubio.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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upepbo0YNVa9eXZs2bZIkrV69Wr1790537BdffNHk2JjDLQRPK++vpQAAAAAA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\n5xQdHa1WrVoZ17G1tVXjxo2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bRYsW1fz58+Xv72/dHhAQoCNHjmj9+vUGpgMA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure object at 0x1137bacf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_rubio.plot(kind='bar', figsize=(15,6))\n",
    "plt.xlabel('CANDIDATE')\n",
    "plt.ylabel('% of RE')\n",
    "plt.title('RUBIO DISTRIBUTION OF RE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some Conclusions\n",
    "**Marco Rubio was also a fan of using first names except in the case of Bernie Sanders, Hillary Clinton, and Ben Carson. Like Cruz, this is perhaps because Rubio only ever spoke about Sanders and Clinton and not to them. Rubio never referred to Carly Fiorina. Rubio also never used professional titles for the Democratic candidates, but did for the three Republican candidates. It is interesting that the three male, Republican candidates we have looked at so far mostly use first names. This is most likely a power move. If another candidate is trying to strip you of a title that provides insight into your experience and professionalism, you have to match them and refer to them the same way to avoid looking like they are more competent than you are.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ben Carson"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Donald Trump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Mr. Trump', 'Donald'}"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carson_REtrump = get_RE_for_label(carson['Tree'], \"Donald Trump\")\n",
    "set(carson_REtrump)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Donald': 1, 'Mr. Trump': 1}"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carson_trump_freq = {re: carson_REtrump.count(re) for re in carson_REtrump}\n",
    "carson_trump_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 1, 'FIRST NAME': 1}"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carson_trump_REtype_freq = get_re_type_freq(trump_re_maps, carson_trump_freq)\n",
    "carson_trump_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Hillary Clinton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Hillary Clinton', 'Hillary'}"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carson_REclinton = get_RE_for_label(carson['Tree'], \"Hillary Clinton\")\n",
    "set(carson_REclinton)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Hillary Clinton': 5, 'Hillary': 1}"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carson_clinton_freq = {re: carson_REclinton.count(re) for re in carson_REclinton}\n",
    "carson_clinton_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FULL NAME': 5, 'FIRST NAME': 1}"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carson_clinton_REtype_freq = get_re_type_freq(clinton_re_maps, carson_clinton_freq)\n",
    "carson_clinton_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Ted Cruz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Senator Cruz', 'Ted'}"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carson_REcruz = get_RE_for_label(carson['Tree'], \"Ted Cruz\")\n",
    "set(carson_REcruz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Ted': 1, 'Senator Cruz': 1}"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carson_cruz_freq = {re: carson_REcruz.count(re) for re in carson_REcruz}\n",
    "carson_cruz_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 1, 'FIRST NAME': 1}"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carson_cruz_REtype_freq = get_re_type_freq(cruz_re_maps, carson_cruz_freq)\n",
    "carson_cruz_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Marco Rubio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Marco'}"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carson_RErubio = get_RE_for_label(carson['Tree'], \"Marco Rubio\")\n",
    "set(carson_RErubio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Marco': 1}"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carson_rubio_freq = {re: carson_RErubio.count(re) for re in carson_RErubio}\n",
    "carson_rubio_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FIRST NAME': 1}"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carson_rubio_REtype_freq = get_re_type_freq(rubio_re_maps, carson_rubio_freq)\n",
    "carson_rubio_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Bernie Sanders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Bernie Sanders'}"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carson_REsanders = get_RE_for_label(carson['Tree'], \"Bernie Sanders\")\n",
    "set(carson_REsanders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Bernie Sanders': 4}"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carson_sanders_freq = {re: carson_REsanders.count(re) for re in carson_REsanders}\n",
    "carson_sanders_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FULL NAME': 4}"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carson_sanders_REtype_freq = get_re_type_freq(sanders_re_maps, carson_sanders_freq)\n",
    "carson_sanders_REtype_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIRST NAME</th>\n",
       "      <th>FULL NAME</th>\n",
       "      <th>PROFESSIONAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CLINTON</th>\n",
       "      <td>16.666667</td>\n",
       "      <td>83.333333</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRUMP</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRUZ</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RUBIO</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SANDERS</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         FIRST NAME   FULL NAME  PROFESSIONAL\n",
       "CLINTON   16.666667   83.333333           0.0\n",
       "TRUMP     50.000000    0.000000          50.0\n",
       "CRUZ      50.000000    0.000000          50.0\n",
       "RUBIO    100.000000    0.000000           0.0\n",
       "SANDERS    0.000000  100.000000           0.0"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distribution_carson = pd.DataFrame([carson_clinton_REtype_freq, carson_trump_REtype_freq, carson_cruz_REtype_freq, carson_rubio_REtype_freq, carson_sanders_REtype_freq], index = [\"CLINTON\", 'TRUMP', 'CRUZ', 'RUBIO', 'SANDERS'])\n",
    "distribution_carson.fillna(0, inplace=True)\n",
    "\n",
    "distribution_carson['TOTAL']=distribution_carson.sum(axis=1)\n",
    "\n",
    "#Get the percentage of each type of RE for each candidate.\n",
    "distribution_carson['FIRST NAME'] = distribution_carson['FIRST NAME']/distribution_carson['TOTAL']*100\n",
    "distribution_carson['FULL NAME'] = distribution_carson['FULL NAME']/distribution_carson['TOTAL']*100\n",
    "distribution_carson['PROFESSIONAL'] = distribution_carson['PROFESSIONAL']/distribution_carson['TOTAL']*100\n",
    "\n",
    "del distribution_carson['TOTAL']\n",
    "distribution_carson.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Sq1evSkr+DPrMYG1tbbyOf9u2bWn2vXbtmg4ePChJaT7LXXr4ezBkyBDduHHjqR47mDT+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure object at 0x11595efd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_carson.plot(kind='bar', figsize=(15,6))\n",
    "plt.xlabel('CANDIDATE')\n",
    "plt.ylabel('% of RE')\n",
    "plt.title('CARSON DISTRIBUTION OF RE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some Conclusions\n",
    "\n",
    "**Ben Carson follows a similar pattern as the other male, Republican candidates. The data for Carson isn't too telling, becuase he only referred to Trump and Cruz twice, and Rubio once. He didn't refer to Carly Fiorina at all. In fact, he referred to Clinton and Sanders more than any of those Republican candidates. It seems he spent more time talking about Clinton and Sanders and their respective policies or ideas than addressing the candidates in the debate with him. We might interpret this as confidence; Carson is more focused on the Democratic candidates (the general election) than the Republican candidates (the primary election he has to win first). It could also be that the moderators just asked him many questions about the Democrats. With this little information, it is hard to draw many conclusions.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bernie Sanders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Donald Trump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Donald Trump', 'Trump'}"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sanders_REtrump = get_RE_for_label(sanders['Tree'], \"Donald Trump\")\n",
    "set(sanders_REtrump)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Donald Trump': 13, 'Trump': 13}"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sanders_trump_freq = {re: sanders_REtrump.count(re) for re in sanders_REtrump}\n",
    "sanders_trump_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FULL NAME': 13, 'LAST NAME': 13}"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sanders_trump_REtype_freq = get_re_type_freq(trump_re_maps, sanders_trump_freq)\n",
    "sanders_trump_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Hillary Clinton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Clinton', 'Madam Secretary', 'Hillary Clinton', 'Secretary', 'Hillary', 'Secretary Clinton'}"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sanders_REclinton = get_RE_for_label(sanders['Tree'], \"Hillary Clinton\")\n",
    "sanders_REclinton.remove('Secretary Secretary Clinton')\n",
    "sanders_REclinton.append('Secretary Clinton')\n",
    "set(sanders_REclinton)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Secretary Clinton': 116, 'Secretary': 25, 'Clinton': 10, 'Hillary Clinton': 21, 'Hillary': 3, 'Madam Secretary': 3}"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sanders_clinton_freq = {re: sanders_REclinton.count(re) for re in sanders_REclinton}\n",
    "sanders_clinton_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 116, 'FULL NAME': 21, 'FIRST NAME': 3, 'LAST NAME': 10, 'GENDERED': 3}"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sanders_clinton_REtype_freq = get_re_type_freq(clinton_re_maps, sanders_clinton_freq)\n",
    "sanders_clinton_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Martin O'Malley"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{\"Martin O'Malley\", 'Martin', \"Governor O'Malley\"}"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sanders_REomalley = get_RE_for_label(sanders['Tree'], \"Martin O'Malley\")\n",
    "set(sanders_REomalley)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{\"Governor O'Malley\": 7, \"Martin O'Malley\": 1, 'Martin': 1}"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sanders_omalley_freq = {re: sanders_REomalley.count(re) for re in sanders_REomalley}\n",
    "sanders_omalley_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 7, 'FIRST NAME': 1}"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sanders_omalley_REtype_freq = get_re_type_freq(omalley_re_maps, sanders_omalley_freq)\n",
    "sanders_omalley_REtype_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIRST NAME</th>\n",
       "      <th>FULL NAME</th>\n",
       "      <th>GENDERED</th>\n",
       "      <th>LAST NAME</th>\n",
       "      <th>PROFESSIONAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CLINTON</th>\n",
       "      <td>1.960784</td>\n",
       "      <td>13.72549</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.535948</td>\n",
       "      <td>75.816993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRUMP</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O'MALLEY</th>\n",
       "      <td>12.500000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>87.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          FIRST NAME  FULL NAME  GENDERED  LAST NAME  PROFESSIONAL\n",
       "CLINTON     1.960784   13.72549       3.0   6.535948     75.816993\n",
       "TRUMP       0.000000   50.00000       0.0  50.000000      0.000000\n",
       "O'MALLEY   12.500000    0.00000       0.0   0.000000     87.500000"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distribution_sanders = pd.DataFrame([sanders_clinton_REtype_freq, sanders_trump_REtype_freq, sanders_omalley_REtype_freq], index = [\"CLINTON\", 'TRUMP', \"O'MALLEY\"])\n",
    "distribution_sanders.fillna(0, inplace=True)\n",
    "\n",
    "distribution_sanders['TOTAL']=distribution_sanders.sum(axis=1)\n",
    "\n",
    "#Get the percentage of each type of RE for each candidate.\n",
    "distribution_sanders['FIRST NAME'] = distribution_sanders['FIRST NAME']/distribution_sanders['TOTAL']*100\n",
    "distribution_sanders['FULL NAME'] = distribution_sanders['FULL NAME']/distribution_sanders['TOTAL']*100\n",
    "distribution_sanders['LAST NAME'] = distribution_sanders['LAST NAME']/distribution_sanders['TOTAL']*100\n",
    "distribution_sanders['PROFESSIONAL'] = distribution_sanders['PROFESSIONAL']/distribution_sanders['TOTAL']*100\n",
    "\n",
    "del distribution_sanders['TOTAL']\n",
    "distribution_sanders.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ut2PHDrm5uRm39u3bWzoko+rVqyswMFBjx47NsM2ZM2cUHR1tjDswMFCrVq3S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\neXh4qGfPnjpx4kSidex2u2bOnCkvLy9VrFhRHTp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8uOlYAJBubm5u2rt3r6pVq6b4+HiV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure object at 0x116bdccf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_sanders.plot(kind='bar', figsize=(15,6))\n",
    "plt.xlabel('CANDIDATE')\n",
    "plt.ylabel('% of RE')\n",
    "plt.title('SANDERS DISTRIBUTION OF RE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some Conclusions\n",
    "\n",
    "**Like Hillary Clinton, Bernie Sanders did not refer to any of the Republican candidates besides Donald Trump. Out of the 26 times Sanders referred to Trump, there  was an even split between using his full name and his last name. Sanders never used a professional title for Trump. He referred to Clinton and Martin O'Malley most often by a professional title. He called Hillary Clinton a gendered term- Madam Secretary a handful of times, but as I mentioned before, this still maintains information about her profession. Sanders also used Cliton's full name a decent amount of the time, indicating that even though they were always participating in the same debates, Sanders talked *about* Clinton sometimes instead of to her. This is not true of how he referred to Martin O'Malley. He only ever referred to O'Malley by his first name or professional title. I think there is some interesting information regarding who is talking *about* who, and who is not being talked *about*. We will discuss this more later.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Moderators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Next, we'll look at how moderators who moderated multiple debates (preferably both a rebuplican and democratic debate) referred to the same candidates. Specifically, we will look at Dana Bash and David Muir, because they were the only two moderators to refer to all of the candidates mentioned above, except David Muir does not moderate a debate where Carly Fiorina is participating. Also, Dana Bash is a woman while David Muir is a man, so we can see if gender plays any role here as well.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Jeb Bush', 'Barack Obama', 'Hillary Clinton', 'Rand Paul', 'Donald Trump', 'Carly Fiorina', 'Ben Carson', 'Bernie Sanders', 'Chris Christie', 'Lindsey Graham', 'Ted Cruz', 'Marco Rubio', 'TITLE', 'John Kasich'}"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bash = speaker_dfs[23]\n",
    "get_entities(bash['Entities'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Jeb Bush', 'Barack Obama', 'Hillary Clinton', 'Ben Carson', 'Donald Trump', 'Bernie Sanders', 'Martha Raddatz', 'Chris Christie', 'Rick Santorum', 'Bashar al-Assad', 'George W. Bush', 'Ted Cruz', 'Marco Rubio', \"Martin O'Malley\", 'TITLE', 'John Kasich'}"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "muir = speaker_dfs[17]\n",
    "get_entities(muir['Entities'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dana Bash"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Donald Trump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Mr. Trump', 'Donald Trump'}"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bash_REtrump = get_RE_for_label(bash['Tree'], \"Donald Trump\")\n",
    "set(bash_REtrump)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Donald Trump': 5, 'Mr. Trump': 27}"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bash_trump_freq = {re: bash_REtrump.count(re) for re in bash_REtrump}\n",
    "bash_trump_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 27, 'FULL NAME': 5}"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bash_trump_REtype_freq = get_re_type_freq(trump_re_maps, bash_trump_freq)\n",
    "bash_trump_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Hillary Clinton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Secretary', 'Hillary Clinton', 'Secretary Clinton', 'Madam Secretary'}"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bash_REclinton = get_RE_for_label(bash['Tree'], \"Hillary Clinton\")\n",
    "bash_REclinton.remove('Secretary Secretary Clinton')\n",
    "bash_REclinton.append('Secretary Clinton')\n",
    "set(bash_REclinton)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Secretary Clinton': 20, 'Hillary Clinton': 1, 'Secretary': 2, 'Madam Secretary': 5}"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bash_clinton_freq = {re: bash_REclinton.count(re) for re in bash_REclinton}\n",
    "bash_clinton_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 20, 'FULL NAME': 1, 'GENDERED': 5}"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bash_clinton_REtype_freq = get_re_type_freq(clinton_re_maps, bash_clinton_freq)\n",
    "bash_clinton_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Ted Cruz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Senator Cruz'}"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bash_REcruz = get_RE_for_label(bash['Tree'], \"Ted Cruz\")\n",
    "set(bash_REcruz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Senator Cruz': 30}"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bash_cruz_freq = {re: bash_REcruz.count(re) for re in bash_REcruz}\n",
    "bash_cruz_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 30}"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bash_cruz_REtype_freq = get_re_type_freq(cruz_re_maps, bash_cruz_freq)\n",
    "bash_cruz_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Marco Rubio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Senator Rubio'}"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bash_RErubio = get_RE_for_label(bash['Tree'], \"Marco Rubio\")\n",
    "bash_RErubio.remove('Senator Senator Rubio')\n",
    "bash_RErubio.append('Senator Rubio')\n",
    "set(bash_RErubio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Senator Rubio': 23}"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bash_rubio_freq = {re: bash_RErubio.count(re) for re in bash_RErubio}\n",
    "bash_rubio_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 23}"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bash_rubio_REtype_freq = get_re_type_freq(rubio_re_maps, bash_rubio_freq)\n",
    "bash_rubio_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Carly Fiorina"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Fiorina', 'Carly Fiorina'}"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bash_REfiorina = get_RE_for_label(bash['Tree'], \"Carly Fiorina\")\n",
    "set(bash_REfiorina)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Carly Fiorina': 2, 'Fiorina': 3}"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bash_fiorina_freq = {re: bash_REfiorina.count(re) for re in bash_REfiorina}\n",
    "bash_fiorina_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FULL NAME': 2, 'LAST NAME': 3}"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bash_fiorina_REtype_freq = get_re_type_freq(fiorina_re_maps, bash_fiorina_freq)\n",
    "bash_fiorina_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Ben Carson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Dr. Carson'}"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bash_REcarson = get_RE_for_label(bash['Tree'], \"Ben Carson\")\n",
    "set(bash_REcarson)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Dr. Carson': 3}"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bash_carson_freq = {re: bash_REcarson.count(re) for re in bash_REcarson}\n",
    "bash_carson_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 3}"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bash_carson_REtype_freq = get_re_type_freq(carson_re_maps, bash_carson_freq)\n",
    "bash_carson_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Bernie Sanders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Senator Sanders', 'President Sanders'}"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bash_REsanders = get_RE_for_label(bash['Tree'], \"Bernie Sanders\")\n",
    "set(bash_REsanders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Senator Sanders': 20, 'President Sanders': 1}"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bash_sanders_freq = {re: bash_REsanders.count(re) for re in bash_REsanders}\n",
    "bash_sanders_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 21}"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bash_sanders_REtype_freq = get_re_type_freq(sanders_re_maps, bash_sanders_freq)\n",
    "bash_sanders_REtype_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FULL NAME</th>\n",
       "      <th>GENDERED</th>\n",
       "      <th>LAST NAME</th>\n",
       "      <th>PROFESSIONAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CLINTON</th>\n",
       "      <td>3.846154</td>\n",
       "      <td>19.230769</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.923077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRUMP</th>\n",
       "      <td>15.625000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRUZ</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SANDERS</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CARSON</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FIORINA</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         FULL NAME   GENDERED  LAST NAME  PROFESSIONAL\n",
       "CLINTON   3.846154  19.230769        0.0     76.923077\n",
       "TRUMP    15.625000   0.000000        0.0     84.375000\n",
       "CRUZ      0.000000   0.000000        0.0    100.000000\n",
       "SANDERS   0.000000   0.000000        0.0    100.000000\n",
       "CARSON    0.000000   0.000000        0.0    100.000000\n",
       "FIORINA  40.000000   0.000000       60.0      0.000000"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distribution_bash = pd.DataFrame([bash_clinton_REtype_freq, bash_trump_REtype_freq, bash_cruz_REtype_freq, bash_sanders_REtype_freq, bash_carson_REtype_freq, bash_fiorina_REtype_freq], index = [\"CLINTON\", 'TRUMP', 'CRUZ', 'SANDERS', 'CARSON', \"FIORINA\"])\n",
    "distribution_bash.fillna(0, inplace=True)\n",
    "\n",
    "distribution_bash['TOTAL']=distribution_bash.sum(axis=1)\n",
    "\n",
    "#Get the percentage of each type of RE for each candidate.\n",
    "distribution_bash['FULL NAME'] = distribution_bash['FULL NAME']/distribution_bash['TOTAL']*100\n",
    "distribution_bash['LAST NAME'] = distribution_bash['LAST NAME']/distribution_bash['TOTAL']*100\n",
    "distribution_bash['GENDERED'] = distribution_bash['GENDERED']/distribution_bash['TOTAL']*100\n",
    "distribution_bash['PROFESSIONAL'] = distribution_bash['PROFESSIONAL']/distribution_bash['TOTAL']*100\n",
    "\n",
    "del distribution_bash['TOTAL']\n",
    "distribution_bash.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8PNwsLBcrVkx+fn4yGo2mNicnpzSD63vvvadFixapR48eKliwYIr97O3tTWMV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\n5bKqCWDiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure object at 0x117fd30b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_bash.plot(kind='bar', figsize=(15,6))\n",
    "plt.xlabel('CANDIDATE')\n",
    "plt.ylabel('% of RE')\n",
    "plt.title('BASH DISTRIBUTION OF RE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some Conclusions\n",
    "**Unsurprisingly, Bash refers to all of the candidates by a professional title. She often refers to Hillary Clinton by Madam Secretary, which genders her title. For some reason, Carly Fiorina is only called by her full name and last name. Only Clinton, Trump and Fiorina are referred to by their full names, indicating the other candidates were probably asked questions about them or were asked to respond to Clinton or Trump's answers. Bash never uses a candidates first name only**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### David Muir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Donald Trump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Mr. Trump', 'Donald Trump', 'Trump'}"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "muir_REtrump = get_RE_for_label(muir['Tree'], \"Donald Trump\")\n",
    "set(muir_REtrump)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Donald Trump': 6, 'Mr. Trump': 21, 'Trump': 1}"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "muir_trump_freq = {re: muir_REtrump.count(re) for re in muir_REtrump}\n",
    "muir_trump_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 21, 'FULL NAME': 6, 'LAST NAME': 1}"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "muir_trump_REtype_freq = get_re_type_freq(trump_re_maps, muir_trump_freq)\n",
    "muir_trump_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Hillary Clinton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Clinton', 'Hillary Clinton', 'Secretary Clinton', 'Secretary'}"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "muir_REclinton = get_RE_for_label(muir['Tree'], \"Hillary Clinton\")\n",
    "set(muir_REclinton)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Secretary Clinton': 30, 'Clinton': 2, 'Secretary': 7, 'Hillary Clinton': 1}"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "muir_clinton_freq = {re: muir_REclinton.count(re) for re in muir_REclinton}\n",
    "muir_clinton_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 30, 'FULL NAME': 1, 'LAST NAME': 2}"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "muir_clinton_REtype_freq = get_re_type_freq(clinton_re_maps, muir_clinton_freq)\n",
    "muir_clinton_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Ted Cruz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Senator Cruz', 'Cruz', 'Senator Ted Cruz'}"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "muir_REcruz = get_RE_for_label(muir['Tree'], \"Ted Cruz\")\n",
    "set(muir_REcruz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Senator Ted Cruz': 1, 'Senator Cruz': 17, 'Cruz': 1}"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "muir_cruz_freq = {re: muir_REcruz.count(re) for re in muir_REcruz}\n",
    "muir_cruz_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 18, 'LAST NAME': 1}"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "muir_cruz_REtype_freq = get_re_type_freq(cruz_re_maps, muir_cruz_freq)\n",
    "muir_cruz_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Marco Rubio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Senator Marco Rubio', 'Senator Rubio'}"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "muir_RErubio = get_RE_for_label(muir['Tree'], \"Marco Rubio\")\n",
    "set(muir_RErubio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Senator Marco Rubio': 1, 'Senator Rubio': 21}"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "muir_rubio_freq = {re: muir_RErubio.count(re) for re in muir_RErubio}\n",
    "muir_rubio_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 22}"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "muir_rubio_REtype_freq = get_re_type_freq(rubio_re_maps, muir_rubio_freq)\n",
    "muir_rubio_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Bernie Sanders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Senator Senator Sanders', 'Senator Sanders', 'President Sanders'}"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "muir_REsanders = get_RE_for_label(muir['Tree'], \"Bernie Sanders\")\n",
    "set(muir_REsanders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Senator Sanders': 22, 'Senator Senator Sanders': 1, 'President Sanders': 1}"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "muir_sanders_freq = {re: muir_REsanders.count(re) for re in muir_REsanders}\n",
    "muir_sanders_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 23}"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "muir_sanders_REtype_freq = get_re_type_freq(sanders_re_maps, muir_sanders_freq)\n",
    "muir_sanders_REtype_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Ben Carson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Dr. Ben Carson', 'Dr. Carson', 'Carson'}"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "muir_REcarson = get_RE_for_label(muir['Tree'], \"Ben Carson\")\n",
    "set(muir_REcarson)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Dr. Ben Carson': 2, 'Dr. Carson': 8, 'Carson': 1}"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "muir_carson_freq = {re: muir_REcarson.count(re) for re in muir_REcarson}\n",
    "muir_carson_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 10, 'LAST NAME': 1}"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "muir_carson_REtype_freq = get_re_type_freq(carson_re_maps, muir_carson_freq)\n",
    "muir_carson_REtype_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FULL NAME</th>\n",
       "      <th>LAST NAME</th>\n",
       "      <th>PROFESSIONAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CLINTON</th>\n",
       "      <td>3.030303</td>\n",
       "      <td>6.060606</td>\n",
       "      <td>90.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRUMP</th>\n",
       "      <td>21.428571</td>\n",
       "      <td>3.571429</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRUZ</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.263158</td>\n",
       "      <td>94.736842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SANDERS</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CARSON</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.090909</td>\n",
       "      <td>90.909091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         FULL NAME  LAST NAME  PROFESSIONAL\n",
       "CLINTON   3.030303   6.060606     90.909091\n",
       "TRUMP    21.428571   3.571429     75.000000\n",
       "CRUZ      0.000000   5.263158     94.736842\n",
       "SANDERS   0.000000   0.000000    100.000000\n",
       "CARSON    0.000000   9.090909     90.909091"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distribution_muir = pd.DataFrame([muir_clinton_REtype_freq, muir_trump_REtype_freq, muir_cruz_REtype_freq, muir_sanders_REtype_freq, muir_carson_REtype_freq], index = [\"CLINTON\", 'TRUMP', 'CRUZ', 'SANDERS', 'CARSON'])\n",
    "distribution_muir.fillna(0, inplace=True)\n",
    "\n",
    "distribution_muir['TOTAL']=distribution_muir.sum(axis=1)\n",
    "\n",
    "#Get the percentage of each type of RE for each candidate.\n",
    "distribution_muir['FULL NAME'] = distribution_muir['FULL NAME']/distribution_muir['TOTAL']*100\n",
    "distribution_muir['LAST NAME'] = distribution_muir['LAST NAME']/distribution_muir['TOTAL']*100\n",
    "distribution_muir['PROFESSIONAL'] = distribution_muir['PROFESSIONAL']/distribution_muir['TOTAL']*100\n",
    "\n",
    "del distribution_muir['TOTAL']\n",
    "distribution_muir.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vrjfffNNq/IcffqidO3dq69atqlGjhho3bqyiRYsqNjZWP/74o86fP6/69eurX79+Nt/G\nexo0aKA5c+YoICBAPXr00JQpU1SnTh05OTnpxIkT2rRpk27duqVOnTpp4sSJmZ7XZDJp9OjReu21\n13Ty5Mks1eTi4qJly5apc+fO+vbbb7V582a9/PLL8vLyUmxsrDZv3qyzZ8+qZMmS+vbbby33xee0\n3r17KyQkREuXLlW/fv1UoUIFq/6MXgfn6OioDz/8MCfKBIA8heAOAMhRw4YNU926dTV79mxFRkZq\n5cqVcnR0VMmSJfXRRx+pT58+KlKkiM3W16JFC1WpUkX79u3TDz/8oNdeey1Ty9174rbJZJKzs7Pc\n3NxUtmxZtW/fXh07dlTJkiUzXUPHjh1VsGBBbd26Vf/73/8UFRWlGzduyNXVVRUqVFDTpk3Vs2dP\nubi4ZHMrM8fLy0tvvvmmvv76a02cOFHjxo3L1HL/fEq9q6ur/Pz81K9fP/Xq1SvV5dwODg5asWKF\nQkNDtWzZMq1YsULXr1+Xq6urKlasqAEDBqhr167Zeh96VrRs2VI1a9bU9OnTtWnTJoWHh+v27dsq\nVqyYmjZtqq5du2bpy5x76tWrp6ZNm6Z6cn9meHt7a/PmzVq8eLGWL1+ujRs3Ki4uTq6uripfvrz6\n9Omj7t27Z+qVcY+Ks7Oz+vfvr48//lifffaZFi5caNWf0evgChYsSHAHgEfAFBcXl/FjXwEAAAAA\nQK7hHncAAAAAAAyM4A4AAAAAgIER3AEAAAAAMDCCOwAAAAAABkZwBwAAAADAwAjuAAAAAAAYGMEd\nAAAAAAADI7jjkYuOjs7tEoAcw/GOvITjHXkJxzvyEo534yG4AwAAAABgYAR3AAAAAAAMjOAOAAAA\nAICBEdwBAAAAADAw+9wuAAAAAAByS3JyshISEnK7DENxdHRUfHx8bpfxr+Pi4iJ7++xFcII7AAAA\ngDwpOTlZ169fl5ubm0wmU26XYxgODg5ydHTM7TL+Vcxms+Li4uTq6pqt8M6l8gAAAADypISEBEI7\ncoTJZJKbm1u2r+4guAMAAADIswjtyCkPc6wR3AEAAAAAMLBcDe47d+6Uv7+/KlSoIDc3N4WHh1v1\nm81mjR07VuXLl5enp6eaNWumo0ePWo25ffu2AgMDVapUKXl5ecnf31/nz5/Pyc0AAAAAAOCRydXg\nnpCQoIoVK2rcuHFycnJK1T916lQFBwdr/Pjx2rJli9zd3dW6dWtdv37dMiYoKEjff/+9Zs+erbVr\n1+r69evq2LGjUlJScnJTAAAAAAB4JHL1qfKNGzdW48aNJUl9+vSx6jObzQoJCVG/fv3UsmVLSVJI\nSIj8/Py0bNky9ejRQ/Hx8QoLC1NwcLBeeOEFSdL06dNVqVIlbd26VS+++GLObhAAAACAx5rb3Jy9\nejeuh3eWlwkICNCiRYtStW/fvl2VK1dWs2bNVLFiRU2YMMGqPzw8XAMHDrRcoTx27FitXr1aUVFR\naa4nvXnSM3bsWI0fP15dunTRV199ZWk/ffq0qlSpooiICFWrVs1qmYEDB2rWrFn64osv1K1bt1T1\nvvvuuypVqpR+/vlnq76NGzeqffv2cnFxsWxPZGSkmjdvnmZte/bsUdmyZTO1HUZk2HvcT58+rZiY\nGDVq1MjS5uTkpLp162r37t2SpIMHDyopKclqTPHixVWuXDnLGAAAAAD4t2nYsKGOHz9u9alYsWJu\nlyVHR0ctWrRIx44de+DY27dva+nSpfrwww8VGhqa7nzx8fHasWOHVXtYWJiKFy+e5jK7du1KtW9K\nly6d9Y0xEMMG95iYGEmSu7u7Vbu7u7v++usvSdJff/0lOzs7FS1aNN0xAAAAAPBv4+DgIA8PD6tP\ndt4PbmtPPfWUXnzxRX366acPHPv999/L19dXAwYM0PHjx/Xrr7+mGmNnZ6eOHTtqwYIFlrbY2Fht\n2LBBnTp1SnNed3f3VPvGzs4u29tkBLn/m80F0dHRuV1CnsM+R17C8Y68hOMdecnDHO9eZ9+3YSVZ\n86fPtFxbt9E5OjrKwcEhV2tITEzM8jIpKSlKSUlJd9k7d+4oOTk5VX9SUpLMZrOlPTk5WXfu3Elz\nnsTExHTnSc+9+YKCgvTiiy9q69atql27tm7fvi3p7hn2++eaN2+e2rRpo3z58qlp06aaO3euPvvs\ns1T1dujQQU2bNtWoUaNUoEABLViwQM8++6y8vb2ttufvv/+21J6d/ZoTrl27luZJZj8/vwyXM2xw\n9/DwkCRdunRJPj4+lvZLly6pWLFikqRixYopJSVFsbGxeuKJJ6zG1KlTJ925H7RTYFvR0dHsc+QZ\nHO/ISzjekZc87PGecNaGxWQRf6fpi4+Pl6OjY67WkJ3129nZKSIiwury7zp16mjZsmWSpHz58sne\n3j7V3Pnz55fJZLK029vbK1++fKnGJSYmytHRMd150nNvvmrVqsnf31+jR4/Wxo0bLV+OODg4WOY6\ndeqUdu/erdmzZ8vR0VGvv/66evTooVGjRlnG36u3atWqqlChgtasWaNu3bpp8eLF6tevn5KTk622\n5z//+Y8kqWbNmlZ1FSpUKM2z+bmhYMGCVvk2swx7qXyJEiXk4eGhiIgIS1tiYqKioqJUq1YtSVLV\nqlWVP39+qzHnz5/X8ePHLWMAAAAA4N+mbt26ioyMtHy+/PLL3C7JyuDBg3XkyBGtXr06zf4FCxao\nQYMGlhO2zz//vJydnbVmzZo0x7/xxhtasGCB9u3bp/Pnz6tFixbprvv777+32jdr1659+A3KZbl6\nxv3GjRs6efKkpLuXc5w7d06HDx9W4cKF5ePjo4CAAE2ePFl+fn4qU6aMJk6cKBcXF7Vr107S3W9O\n3njjDQ0fPlzu7u4qXLiwhgwZoqeffloNGzbMxS0DAAAAgEfH2dlZpUqVSrPP1dVV8fHxqdrj4+NV\nsGDBR12apLsPDe/Vq5dGjhypJUuWWPWlpKRo4cKFunDhgtXzyu7cuaPQ0FC1adMm1Xxt2rTR4MGD\n9emnn6pdu3Zpvk78nhIlSqR6DtrjLleD+4EDB6we1z927FiNHTtWnTp1UkhIiPr27atbt24pMDBQ\ncXFxqlGjhpYvXy5XV1erZezs7NSjRw8lJiaqfv36+uabbx77hw8AAAAAQHb4+flp48aNMpvNMplM\nlvZDhw6pTJkyOVZH//79FRoamuqJ8Zs2bdKVK1cUERFhubxdks6dO6eOHTvq9OnTKlGihNUyBQsW\nVIsWLbR48WKr++DzilwN7s8//7zi4uLS7TeZTAoKClJQUFC6YxwcHDRhwoRMv1sQAAAAAP7Nevbs\nqZkzZ2rgwIHq2rWrHB0d9eOPP+q7775L9f73xMREHT582KrNzs5OTz/9tKS7T3D/Z7+7u7uefPLJ\nB9bh5uamAQMGaPTo0VbtoaGheumll1S1alWr9ooVK8rPz08LFizQkCFDUs03ZcoUjRkzRkWKFMlw\nvZcuXVJycrJVW+HCha2+JHjcGPbhdAAAAACArCtZsqTWrl2rUaNGqU2bNrp9+7b8/Pw0b948vfzy\ny1Zj//jjD9WvX9+qrXLlytq+fbskafny5Vq+fLlV/3vvvadRo0ZlqpZevXpp+vTpOnfunKS7r/Te\nsGGDQkJC0hzfsmVLLVy4MM2Tt46Ojpl6UF7t2rVTta1cufKxvp3aFBcXZ87tIvDvxlOHkZdwvCMv\n4XhHXvLQT5Xf0sSG1WSNS6P1ubZuo4uPj1ehQoVyuwzDufdUedhedo85wz5VHgAAAAAAENwBAAAA\nADA0gjsAAAAAAAZGcAcAAAAAwMAI7gAAAAAAGBjBHQAAAAAAAyO4AwAAAABgYAR3AAAAAAAMjOAO\nAAAAAICB2ed2AQAAIG9K2NIk19bt0mh9rq0bAICsIrgDAAAAwP9XoFvDHF3fjflbs7xMQECArly5\nom+//TbDcV999ZU++eQTffjhhxo2bFiq/tDQUM2cOVMnT56UnZ2dihcvrqZNm+qjjz5Ss2bNtHPn\nznTn9vHx0ZEjR1K1R0ZGqnnz5vLz81NUVJTs7f8vclaqVEm9evXS+++/b7XM6tWr1b17d7Vt21Yz\nZ8606jt9+rSqVKmifPny6dChQ/Lx8bH0xcXFqXz58kpMTFRERISqVasmSXJzc0uz5smTJ+vNN99M\nd5uMjEvlAQAAAOBfaMGCBfrwww+1cOFCpaSkWPWFhYVp0KBBevPNNxUZGamNGzdq4MCBunnzpmXZ\n48eP6/jx49q1a5eku0H/XltERESG6z579qzCwsIyVWdYWJj69u2rNWvWKC4uLs0xXl5eCg8Pt2pb\nsmSJnnjiiTTHf/nll5Za7306deqUqXqMiOAOAAAAAP8ye/bsUWxsrD7++GM5Ojpq48aNVv3r1q1T\n8+bN1aNHD5UqVUrlypVTq1atNGbMGElS4cKF5eHhIQ8PD7m7u6dqSy8w39O7d2+NGzdOCQkJGY47\nf/68IiMj9f777+vZZ5/VkiVL0hzXqVMnLVy4UGaz2dIWFhamzp07pzm+UKFCllrvfZycnDKsxcgI\n7gAAAADwLxMaGqo2bdoof/786tChg0JDQ636PTw8tH//fp06deqRrL9Xr17Knz+/goODMxwXHh6u\nF154QUWKFFHHjh3TPUvfuHFj3b59W9u3b5ckHTp0SKdOnVLr1q1tXrsREdwBAAAA4F/kxo0bWrly\npfz9/SVJ/v7+2rhxo2JiYixjBg0apCJFiqhq1aqqXr26evXqpUWLFikpKckmNTg6Omrw4MGaNm2a\nLl++nOYYs9ms8PBwS50tW7bU77//rgMHDqQaa29vL39/fy1YsEDS3Uv5W7VqJWdn5zTn7t27t7y9\nva0+v/zyi022LTcQ3AEAAADgX2T58uXy8vKyPKztqaeeUvXq1bVo0SLLGE9PT23cuFE//fSTAgIC\nZDab9eGHH6pRo0aW+9wflr+/v3x8fPT555+n2b9t2zbFxcWpSZO7bxkpUKCAmjVrlu5Z9y5duuiH\nH35QTEyMli5dqjfeeCPddY8cOVKRkZFWHz8/v4ffqFzCU+UBAAAA4F8kNDRU0dHRKlq0qKXtzp07\nunz5svr162c1tmLFiqpYsaLefvttRUVF6dVXX7U85f1h5cuXT59++qlef/11BQQEpFlnfHy8vLy8\nLG1ms1kFChTQqFGjUp1N9/PzU5UqVdSzZ095eHioZs2aOn36dJrr9vDwUKlSpR56G4yC4A4AAAAA\n/xJHjx7Vvn37tHLlShUrVszSnpiYqFdeeUU7d+7Uc889l+ay5cuXl6QHPlAuKxo3bqxatWrps88+\ns2q/evWq1qxZo5CQEFWpUsWqr2XLllq1alWaT4Hv0qWL3nvvvVTz/dsR3AEAAADgMXPt2jUdPnzY\nqq1QoUIKDQ1V5cqV1bBhw1TLNGjQQKGhoXruuefUv39/eXp6qn79+vLy8lJMTIwmTpwoZ2fnNJd9\nGCNHjtRLL72k/PnzW9oWL14sV1dXdejQQXZ2dlbjmzdvrtDQ0DSDe6dOnfTqq6+qUKFCGa4zPj7e\n6p5+SXJxcVGBAgUeYktyD8EdAAAAAB4zUVFRql+/vlXba6+9pqioKPXp0yfNZVq2bKmBAwfq888/\nV8OGDRUeHq65c+cqNjZWhQsXVtWqVbVixQqVLl3aprVWr15dLVu21IoVKyxtYWFhatasWarQfq/O\nOXPm6MSJE1ZhX5Ls7OysbgFIzwcffJCq7aOPPtLQoUOzsQW5zxQXF2d+8DAg+6Kjox/rB0EAWcHx\njrzkYY/3hC1NbFhN1rg0Wp9r68bjieP93yk+Pv6BZ27zosTERDk6OuZ2Gf9K2T3meKo8AAAAAAAG\nxqXyMDy+oQYAAAAeHynXfsu1ddsVLJtr636UOOMOAAAAAICBEdwBAAAAADAwgjsAAAAAAAZGcAcA\nAAAAwMAI7gAAAAAAGBjBHQAAAAAAAyO4AwAAAABgYAR3AAAAAAAMzD63CwAAAAAAo0jY0iRH1+fS\naH2WlwkICNCiRYskSfb29vL29lbz5s0VFBSky5cvq0qVKpaxBQsWVNmyZTVgwAC9+uqrVvMkJibq\nyy+/1LJly3T69Gk5OTmpVq1a6tu3r+rWrWsZFx4ernfffTdVHWPGjFGfPn3S7Zek879FytHRQZdj\nr2rc5Bkn0o+5AAAgAElEQVTaFPGTYv66rEIFXVW+bCn17dNNL9SvJUk6fea8xkyarp1RPyv2ylUV\ndiukys+U0+CP3lHlZ8pJkqrWbam3urXXe727WNbx24lTmjBlliJ/2qf4azfk6emp5s2bKzAwUG5u\nbpZxzZo1086dO/XNN9/I39/favsGDhyo8+fPp6r/ueee07Fjx7R7926VKVMm1e/hypUr+vbbb9Pc\ndlsiuAMAAADAY6Zhw4aaPn26kpKSFBUVpQ8++EC3bt3SBx98IEn67rvv9Mwzzyg+Pl6zZs1S165d\ntW3bNlWsWFGS9Pfff6t169Y6deqURowYodq1a+vq1auaMWOGWrZsqdDQUKug7+zsrAMHDljV4Orq\nmmZ/yvXfLe2Ojg6SpO69B+nmrURN/XyISpX00aXYq/pp18+6GhcvSUpKSlbb199XyRLemvP1GD3p\n6a6LMZcUsX23ZUxa9h/8Ra07vavnalVT2MzP5V2mln755RcNHz5cGzdu1I8//mgV3h0dHTV69Gi1\nbt1aDg4OGe7j/fv36/Lly/L391dYWJhGjBiR4fhHieAOAAAAAI8ZBwcHeXh4SJLat2+vHTt2aM2a\nNZbgXqRIEXl4eMjDw0PDhg3TjBkzFBkZaQnuISEh2rVrlyIiIlS1alVJkq+vr4KDgxUbG6v3339f\nhw8flrOzsyTJZDJZ1peW+/tTnKyDdnz8dUXtOajvwr9Sg3o1JUk+xZ9U9SoVLWOO/XZSf5w+pyVh\nU1WqpI9lzH9rVE53nWazWR989JnKPOWr8DmTlC9fPtkV9JGPj4+qVKmi6tWra9SoUZo4caJlmdat\nW2vTpk2aOXOm3nvvvQz3cVhYmNq1a6dmzZqpR48eGjZsmOztcydCc487AAAAADzmHBwcdPv27VTt\nSUlJmj9/viQpf/78lvYlS5aoYcOGltB+vz59+ujy5cuKiIiwSW0uLk5ycXHW+o3blZiYukZJKlrU\nTfny5dP3a7coOTk5U/Me+eU3HfvtpPr06qx8+ayj7ZNPPqn27dtr2bJlMpvNlvYCBQpo0KBBmjRp\nkuLi4tKdOyEhQcuXL1eHDh1Up04dOTo6av36rN/WYCsEdwAAAAB4jO3fv19Lly5Vw4YNLW1NmzaV\nt7e3PDw8NHToUJUoUUKtW7e29P/+++8qW7ZsmvPdaz9x4oSlLSEhQd7e3laf+93f71u+gXzLN1CT\n1j0l3b0P/6tJn2jpivUqVelFvdLqTX0yaqr2HfifZXkvz2IaO2KAJk6drVLPvKjm7XtrzMRvdOz4\n70rP7yfP3K23zFNp9pcrV05xcXG6fPmyVXv37t1VpEgRTZkyJd25V65cKW9vb1WpUkUmk0kdOnRQ\nWFhYuuMfNS6VBwAAAIDHzKZNm+Tt7a3k5GQlJSWpadOm+vzzz3Xz5k1J0syZM1WhQgWdOHFCgwcP\n1pQpU1S4cOFsr8/Z2VmRkZGZ6k+58YckyeE//7H0t2jaSI0bPaeoPQe17+cj2rwtSsEzwjVkYID6\nv9dDkvRWt/bq2Lapdvy0X/sP/k/rftyuqV/P15cThqlj26bZrv2f7O3tNWzYMAUEBKhXr15pjgkL\nC1PHjh0tP/v7+2vy5Mm6cOGCnnzySZvVklmccQcAAACAx0zdunUVGRmpvXv3KiYmRgsWLJC7u7ul\n39vbW6VLl9Yrr7yiqVOnqkePHoqNjbX0ly5dWsePH09z7t9++80y5h6TyaRSpUpZfe5n1V/SR6VK\n+sjby/qeeEdHB71Qv5YC+72l9Stmq4t/C33+xUz9/XeSZYxrARe92ri+hg7so+0bwlWvzrMaO2l6\nmnWWLuUrSToe/Uea/cePH5ebm5ueeOKJVH2tWrVSxYoVNWbMmDS3f9euXRo5cqSKFi2qokWLqmbN\nmkpJSVF4eHia63rUCO4AAAAA8JhxdnZWqVKl5Ovra3Xvelrq1auncuXKafz48Za29u3ba9u2bTp4\n8GCq8cHBwSpatKgaNWpk87rvV87vKSUnpygxjXvzpbtfBviVLqGEhJtp9ld6uqzKlnlKX88M1507\nd6z6Lly4oKVLl6pdu3YymUxpLj9ixAgtWrRIx44ds2oPCwvTs88+qx07digyMtLy+fjjj7VgwQKr\ne+ZzCsEdAAAAAP7l3nvvPc2fP1/nzp2TdPcd5DVr1lTnzp21dOlSnTlzRocPH9a7776rLVu2aNq0\naZYnymeG2WxWTEzM3c9fly2flJQUXbkap5b+AVqyfJ1+ORqt02fOa9UPmzTtmzDVf+6/KuhaQEd+\n+U2v9/xIq9ds1rHfTurkqbNasHiVFi75Xs1eaZjmOk0mk76cOFS/nzyjLj0/0t79h3Xu3Dlt2LBB\nrVq1ko+Pj4YOHZpuzfXq1dNLL72kmTNnWtqSkpK0ePFitWvXThUrVrT6dO/eXWfOnNH27dst469d\nu6bDhw9bfU6fPp3p/ZZZ3OMOAAAAAP9yTZo0ka+vryZMmKCpU6fKwcFBK1eu1NSpUzV+/HidOXNG\njo6Oql27tlauXKnnnnsuS/PfvHlT5cqVS9W+d/t38n7SQ89Wq6Tpcxbrj1Pn9Pfff+tJz2Jq2/IV\nDfjgTUmS15PFVNLXSxOmztaZcxdkvnNH3l6eerfX6+rbp1u66/1v9Ur6cfVcTZw6W13eClT8tRvy\n9PRU8+bNNXDgQKt3uKdl+PDhqlevnpycnCRJ69at0+XLl9WiRYtUYz09PVWrVi2FhYWpQYMGkqSo\nqCjVr1/falyLFi0UGhqa8Q7LIlNcXFzOn+dHnhIdHS0/P79sL5+wpYkNq8kal0a598oHPJ4e9ngH\nHif8+468hOP93yk+Pl6FChXK7TIMJzExUY6OjtlePuXabzasJmvsCqb9pHyjyO4xx6XyAAAAAAAY\nGMEdAAAAAAADI7gDAAAAAGBgBHcAAAAAAAyM4A4AAAAAgIER3AEAAAAAMDCCOwAAAIA8yd7eXgkJ\nCTKbeUM2Hi2z2ayEhATZ29tna/nsLQUAAAAAjzkXFxfdvn1b165dy+1SDOXatWsqWLBgtpdPvvg/\nG1aTNfZmj1xb94M4OjrKwcEhW8sS3AEAAADkWQ4ODtkOU/9Wf/31l3x8fLK9fML+GTasJmtcyrXJ\ntXU/SlwqDwAAAACAgRHcAQAAAAAwMII7AAAAAAAGZujgnpKSolGjRqly5cry8PBQ5cqVNWrUKCUn\nJ1vGmM1mjR07VuXLl5enp6eaNWumo0eP5mLVAAAAAADYjqGD+5QpUzRr1iyNHz9ee/bs0bhx4zRr\n1ixNnjzZMmbq1KkKDg7W+PHjtWXLFrm7u6t169a6fv16LlYOAAAAAIBtGDq479mzR02aNNGrr76q\nEiVKqGnTpmrSpIn2798v6e7Z9pCQEPXr108tW7ZUxYoVFRISohs3bmjZsmW5XD0AAAAAAA/P0MG9\ndu3a2rFjh3777TdJ0rFjxxQZGamXX35ZknT69GnFxMSoUaNGlmWcnJxUt25d7d69O1dqBgAAAADA\nlgz9Hvd+/frpxo0bqlWrluzs7JScnKyPPvpIb731liQpJiZGkuTu7m61nLu7uy5cuJDuvNHR0Y+u\naKTpYfa5lw3ryCqOFWQHxw3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5/fr1rdpPnz6tmJgYNWrUyNLm5OSkunXravfu3TldJgAA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\nAwAAAABgxyjuAAAAAADYsf8HlMEOtG1vvgoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure object at 0x116be2320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_muir.plot(kind='bar', figsize=(15,6))\n",
    "plt.xlabel('CANDIDATE')\n",
    "plt.ylabel('% of RE')\n",
    "plt.title('MUIR DISTRIBUTION OF RE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some Conclusions\n",
    "\n",
    "**Like Bash, Muir refers to all of the candidates most often by their professional titles. Unlike Bash however, Muir does not refer to Carly Fiorina at all, and he never refers to Clinton by a gendered title. Both Bash and Muir refer to Donald Trump by his full name a larger percent of the time than they refer to Hillary Clinton by her full name. This could indicate that Trump is talked about more often then Clinton.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Differences Across Debates\n",
    "**The last avenue we're going to explore is looking at changes over time. Let's see if there are any changes between the way Donald Trump and Hillary Clinton refer to each other from one general debate to the next.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Debate 1\n",
    "##### Hillary Clinton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clinton_1 = clinton.loc[clinton['Date'] == '9-26-16', :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Donald', 'Donald Trump', 'Trump'}"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinton_trump_1 = get_RE_for_label(clinton_1['Tree'], 'Donald Trump')\n",
    "set(clinton_trump_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Donald': 28, 'Donald Trump': 2, 'Trump': 1}"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinton_trump_freq1 = {re: clinton_trump_1.count(re) for re in clinton_trump_1}\n",
    "clinton_trump_freq1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FULL NAME': 2, 'FIRST NAME': 28, 'LAST NAME': 1}"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinton_trump_REtype1 = get_re_type_freq(trump_re_maps, clinton_trump_freq1)\n",
    "clinton_trump_REtype1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Donald Trump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trump_1 = trump.loc[trump['Date'] == '9-26-16', :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Secretary', 'Secretary Clinton', 'Hillary'}"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_clinton_1 = get_RE_for_label(trump_1['Tree'], 'Hillary Clinton')\n",
    "set(trump_clinton_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Hillary': 8, 'Secretary': 3, 'Secretary Clinton': 21}"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_clinton_freq1 = {re: trump_clinton_1.count(re) for re in trump_clinton_1}\n",
    "trump_clinton_freq1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PROFESSIONAL': 21, 'FIRST NAME': 8}"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_clinton_REtype1 = get_re_type_freq(clinton_re_maps, trump_clinton_freq1)\n",
    "trump_clinton_REtype1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Debate 2\n",
    "##### Hillary Clinton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clinton_2 = clinton.loc[clinton['Date'] == '10-9-16', :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Donald', 'Donald Trump', 'Trump'}"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinton_trump_2 = get_RE_for_label(clinton_2['Tree'], 'Donald Trump')\n",
    "set(clinton_trump_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Donald Trump': 8, 'Donald': 25, 'Trump': 1}"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinton_trump_freq2 = {re: clinton_trump_2.count(re) for re in clinton_trump_2}\n",
    "clinton_trump_freq2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FULL NAME': 8, 'FIRST NAME': 25, 'LAST NAME': 1}"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinton_trump_REtype2 = get_re_type_freq(trump_re_maps, clinton_trump_freq2)\n",
    "clinton_trump_REtype2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Donald Trump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trump_2 = trump.loc[trump['Date'] == '10-9-16', :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Hillary Clinton', 'Clinton', 'Hillary'}"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_clinton_2 = get_RE_for_label(trump_2['Tree'], 'Hillary Clinton')\n",
    "set(trump_clinton_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Hillary': 16, 'Hillary Clinton': 10, 'Clinton': 1}"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_clinton_freq2 = {re: trump_clinton_2.count(re) for re in trump_clinton_2}\n",
    "trump_clinton_freq2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FULL NAME': 10, 'FIRST NAME': 16, 'LAST NAME': 1}"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_clinton_REtype2 = get_re_type_freq(clinton_re_maps, trump_clinton_freq2)\n",
    "trump_clinton_REtype2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Debate 3\n",
    "##### Hillary Clinton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clinton_3 = clinton.loc[clinton['Date'] == '10-19-16', :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Donald', 'Donald Trump'}"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinton_trump_3 = get_RE_for_label(clinton_3['Tree'], 'Donald Trump')\n",
    "set(clinton_trump_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Donald': 28, 'Donald Trump': 2}"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinton_trump_freq3 = {re: clinton_trump_3.count(re) for re in clinton_trump_3}\n",
    "clinton_trump_freq3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FULL NAME': 2, 'FIRST NAME': 28}"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinton_trump_REtype3 = get_re_type_freq(trump_re_maps, clinton_trump_freq3)\n",
    "clinton_trump_REtype3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Donald Trump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trump_3 = trump.loc[trump['Date'] == '10-19-16', :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Hillary Clinton', 'Hillary'}"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_clinton_3 = get_RE_for_label(trump_3['Tree'], 'Hillary Clinton')\n",
    "set(trump_clinton_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Hillary': 13, 'Hillary Clinton': 6}"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_clinton_freq3 = {re: trump_clinton_3.count(re) for re in trump_clinton_3}\n",
    "trump_clinton_freq3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FULL NAME': 6, 'FIRST NAME': 13}"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_clinton_REtype3 = get_re_type_freq(clinton_re_maps, trump_clinton_freq3)\n",
    "trump_clinton_REtype3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIRST NAME</th>\n",
       "      <th>FULL NAME</th>\n",
       "      <th>LAST NAME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Debate 1</th>\n",
       "      <td>90.322581</td>\n",
       "      <td>6.451613</td>\n",
       "      <td>3.225806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Debate 2</th>\n",
       "      <td>73.529412</td>\n",
       "      <td>23.529412</td>\n",
       "      <td>2.941176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Debate 3</th>\n",
       "      <td>93.333333</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          FIRST NAME  FULL NAME  LAST NAME\n",
       "Debate 1   90.322581   6.451613   3.225806\n",
       "Debate 2   73.529412  23.529412   2.941176\n",
       "Debate 3   93.333333   6.666667   0.000000"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinton_debates = pd.DataFrame([clinton_trump_REtype1, clinton_trump_REtype2, clinton_trump_REtype3], index = [\"Debate 1\", \"Debate 2\", \"Debate 3\"])\n",
    "clinton_debates.fillna(0, inplace=True)\n",
    "\n",
    "clinton_debates['TOTAL']=clinton_debates.sum(axis=1)\n",
    "\n",
    "#Get the percentage of each type of RE for each candidate.\n",
    "clinton_debates['FULL NAME'] = clinton_debates['FULL NAME']/clinton_debates['TOTAL']*100\n",
    "clinton_debates['FIRST NAME'] = clinton_debates['FIRST NAME']/clinton_debates['TOTAL']*100\n",
    "clinton_debates['LAST NAME'] = clinton_debates['LAST NAME']/clinton_debates['TOTAL']*100\n",
    "\n",
    "del clinton_debates['TOTAL']\n",
    "clinton_debates.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WLVsUGxurIkWKaNCgQSl5y01mnb93754uX76syMhIXbx4UQ4ODho3bpx69uyZ\n6PpJnRt8fX315ptvmpQ9+0g06emQi/379+vw4cNydHQ0dt2X/v94SLg4YEmmTJnUunVrBQYGKjg4\nWIMHD05qd18oqUeiSVK/fv2UKVMmxcbGatCgQRo5cqSqVKmiEiVKyNnZWVevXtXOnTt17tw5OTs7\nW3zaBIDUISkHkGpNmzZVlSpVNG/ePIWFhSk0NFQPHjxQ7ty51aBBA3Xo0CHJO9sGg0G1atUyPgfb\n0ozZZcuWlaurq2JiYlSyZMlUTcjz5ptv6sCBAwoKCtJ3332nrVu3KiYmRo6OjvLy8lK3bt3UsWNH\nFSlSxGzdvXv3JjrW19PT86W6PY4bN05vvfWWVbstd+vWTb6+vvrqq6+0c+dOLVq0SE+ePJG7u7ta\nt26tLl26qHz58ilu18/PT1999VWij9NJrSxZsqhv374aPHiwxo4dq2XLlpksT+qRaC4uLi+dlCf1\nSDTp6YWj5CblBoNBw4YNk7+/v8aOHStfX18ZDAZ988032rZtm3bv3q1t27bp6tWrMhgMyps3r1q3\nbq3OnTun+d3pl/2uppf+/fsrNDRU8+fPV/fu3Y0XKry8vBQeHq65c+fq+++/V3BwsO7fv68cOXKo\nTp066ty5s9555x0bR5+42NhYrVmzRhkzZpS/v3+Sdf38/PTZZ59p7dq1mjhxovFO5MucxxLj4eGh\njz76SHPnztXUqVNNeiLUrFlTUVFRWrBggb7//nt98803unfvntzc3FSzZk21adNGjRs3NrvAUKdO\nHX399dfavn27Dh06pCNHjujGjRtycnJS4cKF1aFDB3Xv3t3kcVqtW7eWi4uLduzYoZ9//lmRkZG6\nffu2nJ2dVaJECTVo0ECdO3c2DvVIroRZ5w0Gg7Jmzars2bOrVKlS6tatm1q1amWcRC8xSZ0bsmXL\nZpaUP/9ItEyZMilv3rxq3769Pv30U+PjPhMmeMuRI8cL/w/p2LGjAgMDFRISooEDB77Uc9uTeiSa\n9LRXVKZMmVSyZEmFhIRox44dOnDggNauXavr16/L0dFRBQsWVM+ePdW9e3erPaoNgGSIiYmx/iA2\nAAAAAADwQowpBwAAAADARkjKAQAAAACwEZJyAAAAAABshKQcAAAAAAAbISkHAAAAAMBGSMoBAAAA\nALARknIAAAAAAGyEpBxIpdOnT9s6BABIM5zjALzKOMfhn4SkHAAAAAAAGyEpBwAAAADARkjKAQAA\nAACwEZJaE4dnAAAgAElEQVRyAAAAAABsxN7WAQAAAADAv8njx491584dW4eBfxAnJyfZ26cuvSYp\nBwAAAIBkevz4sW7duiVXV1cZDAZbh4N/gPj4eMXExMjZ2TlViTnd1wEAAAAgme7cuUNCDhMGg0Gu\nrq6p7j1BUg4AAAAAKUBCjue9zDFBUg4AAAAAgI2QlAMAAAAAYCMk5QAAAAAA2AizrwMAAADAS3Bd\nfCldtxfTKV+K1wkICNDy5cvNynft2qWyZcsqICBA169f1zfffCNJmjhxoiZPnizp6Xhpd3d31apV\nS6NGjVL+/PmN6589e1YTJkxQRESErl27Jjc3N5UtW1bDhg3TzZs31bhx4yTjmjNnjtq2bWtW3rBh\nQ+3evVtfffWV/Pz8jOWhoaEaOHCgLl0yf89r1qypX3/9Vfv27VORIkUs7n+7du00e/Zsk2WjRo3S\nzJkzVa9ePYv7/6zcuXPr1KlTSe5TSpGUAwAAAMBroG7dupo3b55JWY4cORKt7+3trY0bN+rJkyf6\n448/1L9/f3Xq1Elbt26VJD169EjNmjXTG2+8ocWLFytv3rz666+/9OOPPyomJkbVq1fXyZMnje2N\nGTNGp0+fVnBwsLHMxcUl0e07ODho/PjxatasmTJnzpzkvkVFRenatWvy8/NTcHCwRo8ebVYnf/78\nWrt2rSZPniwnJydJTx9xt2LFCpMLDc/v/7Ps7OySjCM1SMoBAAAA4DWQOXNmubu7J7u+vb29sX7e\nvHnVsWNHDRo0SDdv3pSLi4tOnDihP/74Q99++60KFSokSfL09FSVKlWMbTy7vSxZsihjxozJjqFZ\ns2YKCwvTggUL1LNnzyTrBgcHq2XLlmrYsKE6deqkESNGmD0zvFSpUrp8+bLWrFmjdu3aSZK2bNmi\nzJkzq0aNGrpx40ai+5+WSMoBAACAf6n07jad1lLTLRvp48qVK9qwYYPs7OyMd4tz5sypDBkyaP36\n9erZs6dZEvyysmbNqkGDBmn8+PFq166dXF1dLda7c+eOVq9erQ0bNqhs2bJycHDQ999/r0aNGpnV\nbd++vUJDQ41JeUhIiNq2bauzZ89aNfaUYKI3AAAAAHgNhIWFKV++fMZXy5Ytk6x/8uRJ5cuXT3nz\n5lWxYsUUERGhjz/+2Nj128PDQ5MnT9bnn3+uggULqkGDBho3bpxOnDhhtZg//PBDubm5acaMGYnW\nWbt2rfLly6dy5crJYDCoVatWJl3kn/XBBx/o0KFD+u2333TlyhVt27ZN/v7+Fusm7P+zr86dO1tl\nv57FnXIAAAAAeA3UqFFDM2fONP7t4OCQZP033nhDq1at0oMHD7R582atX79eI0eONKnTpUsX+fn5\nKTw8XFFRUdq8ebNmzJih2bNnm0zQllr29vYaMWKEAgIC1LVrV4t1goOD1bp1a+Pffn5+mj59ui5f\nvqy8efOa1HV1dVWjRo0UEhKibNmyycfHRwUKFLDYbsL+PyvhgoQ1caccAAAAAF4DWbJkUaFChYwv\nDw+PJOtnypRJhQoVUokSJdSvXz+VKlVK/fv3N6vn7OysBg0aaMSIEdq9e7dq1aql8ePHWy3u999/\nXyVLltSECRPMlp06dUp79+7VmDFjlCNHDuXIkUNVqlRRXFycQkNDLbbXrl07rVixQiEhIcZu7JYk\n7P+zr7QYY05SDgAAAAB4oQEDBmjlypU6fPhwonUMBoO8vb11584dq2579OjRWr58uX799VeT8uDg\nYFWuXFkREREKDw83vgYPHqyQkBDFx8ebtVWnTh1lzJhR0dHRatiwoVXjTA26rwMAAAAAXuiNN95Q\ngwYNNH78eK1atUpHjx7VxIkT5efnp2LFiilTpkyKiIhQaGioWrRoYdVt+/j4yNfXVwsWLDBONPfo\n0SOtWLFC/fr1U8mSJU3qu7m56fPPP9euXbtUp04dk2UGg0G7d+9WfHx8ko9ae/z4sa5cuWJWbu27\n5STlAAAAAPASXqdZ43v27Kl69epp3759KlKkiLy8vDR58mRduHBBT548Uf78+dWzZ0/16dPH6tse\nNWqUfHx85OjoKEn67rvvdO3aNTVp0sSsbp48eVS1alUFBwebJeXS0y73L3L69GkVK1bMrPzatWtW\nnWneEBMTY34/H8ALnT59Wt7e3rYOAwDSBOc44N+BR6Klzsuc42JjY5UtWzYrR4RXQWqPDcaUAwAA\nAABgIyTlAAAAAADYCGPKQbcnAAAAALAR7pQDAAAAAGAjJOUAAAAAANgISTkAAAAAADZCUg4AAAAA\ngI2QlAMAAAAAYCMk5QAAAAAA2AhJOQAAAAAANsJzygEAAADgJWTtWDddt3d76Y4UrxMQEKDly5eb\nle/atUtly5ZVw4YNVbJkSU2ZMsVkeWhoqAYOHKhLly5JkiZOnKj169crMjLS4nYSaycxEydO1OTJ\nk9WuXTvNnj3bWH7u3DmVK1dO27dvV4UKFUzWGThwoBYuXKgvvvhCHTt2NIv3v//9rwoVKqSffvrJ\nZNnWrVv1wQcfyMnJybg/4eHhaty4scXY9u/fr6JFiyZrP14Gd8oBAAAA4DVQt25dnTx50uRVsmRJ\nW4clBwcHLV++XL/++usL6z548ECrVq1Snz59FBQUlGh7sbGxioiIMCkPDg5W/vz5La6zd+9es/em\ncOHCKd+ZVCApBwAAAIDXQObMmeXu7m7ysre3fefpN954Q++8844+++yzF9bdsGGDPD091a9fP508\neVLHjx83q2NnZ6fWrVsrJCTEWBYdHa0tW7aoTZs2FtvNlSuX2XtjZ2eX6n1KCZJyAAAAAIBNjRo1\nSj/88IP27NmTZL2goCC1atVKWbJkUePGjRO9W96+fXtt2LBBt27dkiStWLFCVapUkZeXl7VDf2kk\n5QAAAADwGggLC1O+fPmMr5YtW9o6JKNSpUrJz89Po0aNSrTO2bNnFRkZaYzbz89PK1eu1IMHD8zq\nlihRQsWLF9fq1aslSSEhIWrXrl2ibZctW9bkvUnPbv2276sAAAAAAEhzNWrU0MyZM41/Ozg42DAa\nc0OHDlXlypW1fv16lStXzmx5SEiI6tSpI3d3d0lSrVq1lCVLFm3atEnNmzc3q9++fXuFhISoVKlS\nunTpkpo0aWJM0p+3YcMGubq6Gv/OkCH97l+TlAMAAADAayBLliwqVKiQxWXOzs6KjY01K4+NjZWL\ni0tahyZJyp8/v7p27aoxY8Zo5cqVJsvi4uK0bNkyXb58WTly5DCWP3nyREFBQRaT8ubNm2vo0KH6\n7LPP1LJlSzk6Oia67YIFC5q0m55IygEAAADgNeft7a2tW7cqPj5eBoPBWH7kyBEVKVIk3eLo27ev\ngoKCzMaKh4WF6fr169q+fbsyZcpkLL948aJat26tc+fOqWDBgibruLi4qEmTJlqxYoXGjh2bLvGn\nBkk5AAAAALzmOnfurAULFmjgwIHq0KGDHBwc9MMPP+jbb781e775/fv3dfToUZOyLFmyGJP36Oho\ns+W5cuVS3rx5XxiHq6ur+vXrp/Hjx5uUBwUFydfXV+XLlzcpL1mypLy9vRUSEqJhw4aZtTdjxgxN\nmDBBbm5uSW736tWrevz4sUlZ9uzZTS4ApBWScgAAAAB4CbeX7rB1CC/Ny8tLmzdv1rhx49S8eXM9\nePBA3t7eWrJkif7zn/+Y1P3jjz9Uu3Ztk7Ly5ctrx44dkqTVq1ebjd3u2bOnxo0bl6xYunbtqnnz\n5unixYuSpL///ltbtmxRYGCgxfpNmzbVsmXLNGTIELNlDg4OyRo7X61aNbOytWvXqm7dusmK+WUY\nYmJi4tN8K/hHc118ydYhWFVMp3zpsp3Tp0/L29s7XbYFAOmNcxzw78DvuNR5mXNcbGyssmXLZuWI\n8CpI7bHBI9EAAAAAALARknIAAAAAAGyEpBwAAAAAABshKQcAAAAAwEZIygEAAAAAsBGScgAAAAAA\nbISkHAAAAAAAGyEpBwAAAADARkjKAQAAAACwEZJyAAAAAABsxN7WAQAAAADAv9mdH+un6/ac3v4+\nxesEBATo+vXr+uabb5KsN3v2bI0cOVJ9+vTRiBEjzJYHBQVpwYIF+v3332VnZ6f8+fOrQYMGGj58\nuBo2bKjdu3cn2naBAgV07Ngxs/Lw8HA1btxY3t7eioyMlL39/6epZcqUUdeuXfXJJ5+YrLN+/Xp9\n+OGHatGihRYsWGCy7Ny5cypXrpwyZMigI0eOqECBAsZlMTExKl68uO7fv6/t27erQoUKkiRXV1eL\nMU+fPl0fffRRovtkDdwpBwAAAABIkkJCQtSnTx8tW7ZMcXFxJsuCg4M1aNAgffTRRwoPD9fWrVs1\ncOBA3b1717juyZMndfLkSe3du1fS0yQ+oWz79u1JbvvChQsKDg5OVpzBwcHq1auXNm3apJiYGIt1\nPDw8FBoaalK2cuVK5cyZ02L9L7/80hhrwqtNmzbJiudlkJQDAAAAALR//35FR0dr8ODBcnBw0Nat\nW02Wf/fdd2rcuLE6deqkQoUKqVixYnr//fc1YcIESVL27Nnl7u4ud3d35cqVy6wssWQ4Qbdu3TRp\n0iTduXMnyXqXLl1SeHi4PvnkE1WuXFkrV660WK9NmzZatmyZ4uPjjWXBwcHy9/e3WD9btmzGWBNe\njo6OScZiDSTlAAAAAAAFBQWpefPmypgxo1q1aqWgoCCT5e7u7oqKitLZs2fTZPtdu3ZVxowZNWfO\nnCTrhYaG6q233pKbm5tat26d6N31d999Vw8ePNCuXbskSUeOHNHZs2fVrFkzq8f+MmyWlMfFxWnc\nuHEqW7as3N3dVbZsWY0bN06PHz821omPj9fEiRNVvHhx5cmTRw0bNtSJEydsFTIAAAAAvJJu376t\ntWvXys/PT5Lk5+enrVu36sqVK8Y6gwYNkpubm8qXL6+KFSuqa9euWr58uR49emSVGBwcHDR06FDN\nmjVL165ds1gnPj5eoaGhxjibNm2q3377TYcOHTKra29vLz8/P4WEhEh62r3+/fffV5YsWSy23a1b\nN+XLl8/k9csvv1hl35Jis6R8xowZWrhwoSZPnqz9+/dr0qRJWrhwoaZPn26sM3PmTM2ZM0eTJ0/W\njz/+qFy5cqlZs2a6deuWrcIGAAAAgFfO6tWr5eHhYZz47I033lDFihW1fPlyY508efJo69at2rNn\njwICAhQfH68+ffro7bffNo4rf1l+fn4qUKCAPv/8c4vLd+7cqZiYGNWv/3RyvaxZs6phw4aJ3i1v\n166dNm7cqCtXrmjVqlVq3759otseM2aMwsPDTV7e3t4vv1MvYLPZ1/fv36/69evrvffekyQVLFhQ\n9evXV1RUlKSnV0ACAwPVu3dvNW3aVJIUGBgob29v/e9//1OnTp1sFToAAAAAvFKCgoJ0+vRp5ciR\nw1j25MkTXbt2Tb179zapW7JkSZUsWVJdunRRZGSk3nvvPa1Zs0Zt27Z96TgyZMigzz77TG3btlVA\nQIDFOGNjY+Xh4WEsi4+PV9asWTVu3Dizu+De3t4qV66cOnfuLHd3d1WpUkXnzp2zuG13d3cVKlTo\npfchpWx2p7xatWqKiIjQqVOnJEm//vqrwsPD9Z///EfS02nsr1y5orffftu4jqOjo2rUqKF9+/bZ\nJGYAAAAAeNWcOHFCBw8e1Jo1a0zuEm/btk3nz59P8jFnxYsXl6QXTs6WEu+++66qVq2qsWPHmpTf\nuHFDmzZtUmBgoEmcERERypw5s9atW2exvXbt2ikiIkLt2rWzWozWZLM75b1799bt27dVtWpV2dnZ\n6fHjx+rfv78+/vhjSTKOXUiYtS9Brly5dPny5UTbPX36dNoF/cqyPKbi3yo9jwGONwCvMs5xwL8B\nv+PSe1sODg7KnDmzlaNJmfv376d4nbi4OMXGxurgwYMm5S4uLlq8eLHKlCmjatWqma3n4+OjxYsX\nq1KlSho4cKDy5MkjHx8f5c2bV3///be++OILOTo6qmbNmiZxJfz74cOHL4z34cOHxnUS6g4bNkwN\nGzZUxowZ9ejRI92/f18hISHKmjWrmjRpIjs7O5M23nvvPS1dulTNmjXTgwcPJEkPHjzQ/fv31axZ\nM7311lvKli2b7t+/b7Y8wbVr13T+/HmTdp2cnOTk5JRk/Alu3rypv//+26z8RV3gbZaUr169WitW\nrNDChQtVvHhxHTt2TIMHD5anp6c6dOiQ6nbTo8//Kyfikq0jsKr0OgZOnz7N8QbglcU5DviX4Hdc\nqrzMOS42NlYODg6mhW9/b4Wo0padnZ327t0rX19fk/JGjRopMjJSPXr0MN8vSc2bN9fAgQM1bdo0\n+fr6KjQ0VMHBwYqOjlb27NlVvnx5rV27VqVKlTJZL6GtTJkyWWz3WZkyZTKuk1C3evXqatq0qdas\nWaOMGTPKwcFBy5cvV6NGjSwmyS1atFDTpk118eJF40WTzJkzG9t7dh1LyyWpX79+Zu32799fw4cP\nTzL+BC4uLipQoECy6j7LEBMTE//iatZXqlQp9ezZ02ScwJQpU7Rs2TIdOnRIZ8+eVfny5fXjjz+q\nYsWKxjqtWrWSm5ubvvrqK1uE/UpyXfxqncxjOuVLl+3wgxXAq4xzHPDvwO+41HnZpDxbtmxWjgiv\ngtQeGzYbU3737l2zLgd2dnZ68uSJpKcTv7m7u2v79u3G5ffv31dkZKSqVq2arrECAAAAAJAWbNZ9\nvX79+poxY4YKFiyo4sWL6+jRo5ozZ47xeXMGg0EBAQGaPn26vL29VaRIEU2dOlVOTk5q2bKlrcIG\nAAAAAMBqbJaUf/755xo/frz69euna9euyd3dXR07dtTAgQONdXr16qV79+5pwIABiomJUaVKlbR6\n9Wo5OzvbKmwAAAAAAKzGZmPK8c/BWKTUYbwlgFcZ5zjg34HfcanDmHKkhX/dmHIAAAAAAF53JOUA\nAAAAkALx8XQ2hqmXOSZIygEAAAAgmZycnBQTE0NiDqP4+HjFxMRYfH56cthsojcAAAAA+Lext7eX\ns7Ozbt68aetQ8A/i7Owse/vUpdck5QAAAACQAvb29kz2Bquh+zoAAAAAADZCUg4AAAAAgI2QlAMA\nAAAAYCMk5QAAAAAA2AhJOQAAAAAANkJSDgAAAACAjZCUAwAAAABgIyTlAAAAAADYCEk5AAAAAAA2\nQlIOAAAAAICNkJQDAAAAAGAj9rYOAACAtOS6+JKtQ7CqmE75bB0CAACwIu6UAwAAAABgIyTlAAAA\nAADYCEk5AAAAAAA2QlIOAAAAAICNkJQDAAAAAGAjJOUAAAAAANgISTkAAAAAADZCUg4AAAAAgI2Q\nlAMAAAAAYCMk5QAAAAAA2AhJOQAAAAAANkJSDgAAAACAjZCUAwAAAABgIyTlAAAAAADYCEk5AAAA\nAAA2QlIOAAAAAICNkJQDAAAAAGAjJOUAAAAAANgISTkAAAAAADZCUg4AAAAAgI2QlAMAAAAAYCMk\n5QAAAAAA2AhJOQAAAAAANkJSDgAAAACAjZCUAwAAAABgIyTlAAAAAADYCEk5AAAAAAA2QlIOAAAA\nAICNkJQDAAAAAGAjJOUAAAAAANgISTkAAAAAADZCUg4AAAAAgI2QlAMAAAAAYCMk5QAAAAAA2AhJ\nOQAAAAAANkJSDgAAAACAjZCUAwAAAABgIyTlAAAAAADYCEk5AAAAAAA2QlIOAAAAAICNkJQDAAAA\nAGAjJOUAAAAAANgISTkAAAAAADZi06T8r7/+Uvfu3VW4cGG5u7uratWqioiIMC6Pj4/XxIkTVbx4\nceXJk0cNGzbUiRMnbBgxAAAAAADWY7OkPCYmRvXq1VN8fLxWrlypffv26fPPP1euXLmMdWbOnKk5\nc+Zo8uTJ+vHHH5UrVy41a9ZMt27dslXYAAAAAABYjb2tNvzll18qT548mjdvnrHMy8vL+O/4+HgF\nBgaqd+/eatq0qSQpMDBQ3t7e+t///qdOnTqld8gAAAAAAFiVze6Ub9q0SZUqVVKnTp1UpEgR+fj4\naP78+YqPj5cknTt3TleuXNHbb79tXMfR0VE1atTQvn37bBU2AAAAAABWY7Ok/OzZs/r666/l5eWl\nb7/9Vt27d9fo0aO1YMECSdKVK1ckyaQ7e8Lff//9d7rHCwAAAACAtdms+/qTJ09UoUIFjRo1SpJU\nrlw5/f7771q4cKG6du2a6nZPnz5trRBfI1lsHYBVpecxwPEG/Btwjvs3bAtAanGO+zdsC683b2/v\nJJfbLCl3d3dXsWLFTMqKFi2qixcvGpdL0tWrV1WgQAFjnatXryp37tyJtvuiHYYFEZdsHYFVpdcx\ncPr0aY434N+Ac1yqcI4D/iU4x6UK5zj8k9is+3q1atV05swZk7IzZ84YE/CCBQvK3d1d27dvNy6/\nf/++IiMjVbVq1XSNFQAAAACAtGCzpLxHjx46cOCApk6dqt9//11r167V/Pnz9fHHH0uSDAaDAgIC\nNHPmTK1fv17Hjx9Xjx495OTkpJYtW9oqbAAAAAAArMZm3dcrVqyo0NBQjRkzRlOmTFH+/Pk1dOhQ\nY1IuSb169dK9e/c0YMAAxcTEqFKlSlq9erWcnZ1tFTYAAAAAAFZjs6RckurVq6d69eolutxgMGjI\nkCEaMmRIOkYFAAAAAED6sFn3dQAAAAAAXnck5QAAAAAA2EiSSfmSJUv0xx9/GP9+8uSJzpw5o/v3\n75vVPXLkiEaOHGn9CAEAAAAAeEUlmZT37dtXBw8eNP4dExOjKlWqaN++fWZ1T548qdmzZ1s/QgAA\nAAAAXlFJJuXx8fHJKgMAAAAAACnHmHIAAAAAAGyEpBwAAAAAABshKQcAAAAAwEbsX1Th4MGDsrd/\nWu3WrVsyGAzavXu3rl+/blLvwIEDaRMhAAAAAACvqBcm5fPnz9f8+fNNyqZMmWKxrsFgsE5UAAAA\nAAC8BpJMyjds2JBecQAAAAAA8NpJMin38fFJrzgAAAAAAHjtWG2it8ePHyskJMRazQEAAAAA8Mp7\n6aT83r17CgwMVPny5fXpp59aIyYAAAAAAF4LL0zKg4KCVL16deXJk0fFixfXoEGD9ODBA0nSV199\npTJlymjo0KFydXXV3Llz0zxgAAAAAABeFUmOKV+xYoV69eqlrFmzqmTJkvrzzz+1YMEC3b9/X7Gx\nsVq3bp3q1KmjXr166a233kqvmAEAAAAAeCUkmZQvWLBA3t7e+u6775QjRw7FxcWpR48eCg4Olqur\nq7799lu9/fbb6RUrAAAAAACvlCS7r//6669q3769cuTIIUmys7NT7969FR8fr/79+5OQAwAAAADw\nEpJMyu/evas8efKYlLm7u0uSSpQokXZRAQAAAADwGnjhRG8Gg8FiuZ2dndWDAQAAAADgdZLkmHJJ\nmjlzpr755hvj348ePZIkjR49Wm5ubiZ1DQaDVq5caeUQAQAAAAB4NSWZlOfPn1+xsbGKjY01KS9Q\noICuXr2qq1evmpQndlcdAAAAAACYSzIpP3bsWHrFAQAAAADAa+eFY8oBAAAAAEDaICkHAAAAAMBG\nSMoBAAAAALARknIAAAAAAGyEpBwAAAAAABtJNCn/6quvdObMmfSMBQAAAACA10qiSfnQoUN16NAh\n499ubm5atWpVugQFAAAAAMDrINGkPHv27Prrr7+Mf8fHx6dLQAAAAAAAvC7sE1tQu3ZtTZo0Sfv3\n75eLi4skacmSJdqxY0eijRkMBs2ePdvqQQIAAAAA8CpKNCmfNm2aRo0apd27d+vatWsyGAyKiorS\nkSNHEm2MpBwAAAAAgORLNCl3c3PTrFmzjH9nz55ds2bN0gcffJAugQEAAAAA8KpL9iPR5syZoypV\nqqRlLAAAAAAAvFYSvVP+PH9/f0lSXFycjhw5ovPnz0uSPD09Vb58eWXIwCPPAQAAAABIiWQn5ZK0\nevVqDRs2TFeuXDHOxm4wGOTu7q4JEyaoWbNmaRIkAAAAAACvomQn5Zs2bdLHH3+sokWLqm/fvipa\ntKgk6dSpU1q0aJE+/vhjZc6cWQ0aNEizYAEAAAAAeJUkOymfNm2aypcvr82bN8vBwcFYXqdOHXXo\n0EH169fX1KlTScoBAAAAAEimZA8EP3HihFq1amWSkCfInDmzWrdurRMnTlg1OAAAAAAAXmXJTsod\nHR0VHR2d6PJr167J0dHRKkEBAAAAAPA6SHZSXqdOHc2bN0979uwxW7Z3717Nnz9fdevWtWZsAAAA\nAAC80pI9pnz06NGKjIxUo0aNVK5cOXl7e0uSTp8+rSNHjsjd3V2fffZZWsUJAAAAAMArJ9l3yj09\nPRUREaHu3bvr9u3bWr9+vdavX6/bt2+rR48eCg8Pl6enZ1rGCgAAAADAKyVFzynPmTOnJkyYoAkT\nJqRVPAAAAAAAvDaSfaccAAAAAABYF0k5AAAAAAA2QlIOAAAAAICNkJQDAAAAAGAjJOUAAAAAANgI\nSTkAAAAAADaSokeiPWvZsmVatWqVLl++rNy5c6t58+bq2LGjDAaDNeMDAAAAAOCVlaqk/PPPP9fX\nX3+tDz/8UHnz5tWvv/6qIUOG6Pfff9eYMWOsHSMAAAAAAK+kJJPy8+fPy9PT06x82bJl+vrrr+Xj\n42Msc3d315w5c0jKAQAAAABIpiTHlFevXl2TJk3S/fv3TcqzZs2qCxcumJRdvHhRWbNmtX6EAAAA\nAAC8opJMyjds2KCwsDBVqVJF69atM5YPHDhQvXr1UuPGjdW1a1fVrVtXixcv1uDBg9M8YAAAAAAA\nXhVJJuUVK1ZUWFiYBgwYoAEDBqhJkyb69ddf1aRJE+3evVu1a9eWs7Oz6tevr4iICPn5+aVX3AAA\nAAAA/Osla6K39u3bq0mTJpo4caLq1q2rDz/8UEOHDtWAAQPSOj4AAAAAAF5ZyX5OebZs2TRp0iRt\n375dJ06cUKVKlRQUFJSWsQEAAAAA8Ep7YVJ+4cIFLV26VIGBgTpw4IBKlCihdevWacqUKZoyZYrq\n1pKwf4kAABrqSURBVK2rAwcOpEesAAAAAAC8UpJMyr///ntVqVJFX3zxhVasWKH69etr2LBhkqT3\n339f+/fvl6+vr5o0aaJu3brpypUr6RI0AAAAAACvgiST8lGjRqlJkyY6fPiwdu7cqcDAQAUGBurP\nP/+UJDk6Omr48OGKjIzUzZs3Vbly5VQHMn36dLm6upqMU4+Pj9fEiRP/r737D7Kqvs8A/K4gCiiz\ni8CCKBKBhGCIqDUKVCE4NkaqqMWIpmnGGrUkzWhVouhUbTJGzTpOkgmlyWiKY4WIQgVrGjojCCUV\ntRJJGrHSSqIhiIRfCrIqsP0jw043IK7syvfu9Xlmdsb7PWfveWGun+G9595zMnTo0PTt2zfjx4/P\nypUr9/sYAAAAUEn2WcrXrl2bU089tfnxKaeckqamprz66qst9hs4cGBmzZqVGTNm7FeIZ555JjNm\nzMhxxx3XYv073/lOpk2bljvvvDMLFy5M7969c/755+eNN97Yr+MAAABAJdlnKR85cmS+//3vZ9my\nZVm5cmW+8Y1vpK6uLkOHDt3r/mecccb7DrBly5Zcfvnl+d73vpfa2trm9aampkyfPj1XX311JkyY\nkGHDhmX69OnZunVrHn744fd9HAAAAKg0+yzl3/72t1NfX5+zzz47o0aNys9+9rPMmDEj3bp1a7cA\nu0v36aef3mL917/+ddatW5dx48Y1r3Xt2jWjRo3KU0891W7HBwAAgFL2eZ/yfv36Zd68edm+fXve\neuutFmey28N9992Xl156KT/4wQ/22Lb7onG9e/dusd67d++sXbv2XZ9z1apV7Zrxw6H93mSpBAfy\nNeD1Bh2BGdcRjgXsLzOuIxyLD7chQ4bsc/s+S/luXbt2TdeuXdsl0G6rVq3K17/+9fzkJz/JwQcf\n3G7P+15/YPZi6ZrSCdrVgXoNrFq1yusNOgIzbr+YcdBBmHH7xYyjkrznfco/KE8//XQ2bNiQU089\nNUcccUSOOOKI/PSnP80999yTI444Ij179kySrF+/vsXvrV+/Pn369CkRGQAAANpVq86UfxDGjx+f\nE044ocXaV77ylQwaNCjXXHNNBg8enPr6+ixatCgnnnhikqSxsTFPPvlkvv71r5eIDAAAAO2qWCmv\nra3d4zvq3bp1S11dXYYNG5YkmTx5cu6+++4MGTIkgwcPzl133ZXu3btn4sSJJSIDAABAuypWylvj\nqquuyvbt2zNlypRs3rw5J510UubOnZvDDz+8dDQAAABos4oq5Y899liLxzU1NZk6dWqmTp1aKBEA\nAAB8cIpd6A0AAAA+7JRyAAAAKEQpBwAAgEKUcgAAAChEKQcAAIBClHIAAAAoRCkHAACAQpRyAAAA\nKEQpBwAAgEKUcgAAAChEKQcAAIBClHIAAAAoRCkHAACAQpRyAAAAKEQpBwAAgEKUcgAAAChEKQcA\nAIBClHIAAAAoRCkHAACAQpRyAAAAKEQpBwAAgEKUcgAAAChEKQcAAIBClHIAAAAoRCkHAACAQpRy\nAAAAKEQpBwAAgEKUcgAAAChEKQcAAIBClHIAAAAoRCkHAACAQpRyAAAAKEQpBwAAgEKUcgAAAChE\nKQcAAIBClHIAAAAoRCkHAACAQpRyAAAAKEQpBwAAgEKUcgAAAChEKQcAAIBClHIAAAAoRCkHAACA\nQpRyAAAAKEQpBwAAgEKUcgAAAChEKQcAAIBClHIAAAAoRCkHAACAQpRyAAAAKEQpBwAAgEKUcgAA\nACikc+kAAEDrHfbFsQfkOCcckKMkW+974gAdCQAqkzPlAAAAUIhSDgAAAIUo5QAAAFCIUg4AAACF\nKOUAAABQiFIOAAAAhSjlAAAAUEixUn733Xfn05/+dI4++ugMGjQoF110UZ5//vkW+zQ1NeX222/P\n0KFD07dv34wfPz4rV64slBgAAADaV7FSvnTp0lx22WVZsGBB5s+fn86dO+e8887Lpk2bmvf5zne+\nk2nTpuXOO+/MwoUL07t375x//vl54403SsUGAACAdtO51IHnzp3b4vH3v//9DBgwIMuWLctnP/vZ\nNDU1Zfr06bn66qszYcKEJMn06dMzZMiQPPzww7n00ktLxAYAAIB2UzHfKd+6dWt27dqV2traJMmv\nf/3rrFu3LuPGjWvep2vXrhk1alSeeuqpUjEBAACg3RQ7U/6HbrjhhgwfPjyf+tSnkiTr1q1LkvTu\n3bvFfr17987atWvf9XlWrVr1wYWsWt1KB2hXB/I14PUGHUF1zbhqY45CW1XXjPPvOKrRkCFD9rm9\nIkr5jTfemGXLluUnP/lJOnXq1Kbneq8/MHuxdE3pBO3qQL0GVq1a5fUGHUGVzbhqY45CG1XZjPPv\nOD6Min98ferUqZkzZ07mz5+fgQMHNq/X19cnSdavX99i//Xr16dPnz4HMiIAAAB8IIqW8uuvv765\nkH/0ox9tse2YY45JfX19Fi1a1LzW2NiYJ598MqeccsqBjgoAAADtrtjH16+77ro8+OCD+ad/+qfU\n1tY2f4e8e/fuOeyww1JTU5PJkyfn7rvvzpAhQzJ48ODcdddd6d69eyZOnFgqNgAAALSbYqX8nnvu\nSZLm253tdv3112fq1KlJkquuuirbt2/PlClTsnnz5px00kmZO3duDj/88AOeFwAAANpbsVK+efPm\n99ynpqYmU6dObS7pAAAAUE2KX+gNAAAAPqyUcgAAAChEKQcAAIBClHIAAAAoRCkHAACAQpRyAAAA\nKEQpBwAAgEKUcgAAAChEKQcAAIBClHIAAAAoRCkHAACAQpRyAAAAKEQpBwAAgEKUcgAAAChEKQcA\nAIBClHIAAAAoRCkHAACAQpRyAAAAKEQpBwAAgEKUcgAAAChEKQcAAIBClHIAAAAoRCkHAACAQpRy\nAAAAKEQpBwAAgEKUcgAAAChEKQcAAIBClHIAAAAoRCkHAACAQpRyAAAAKEQpBwAAgEKUcgAAAChE\nKQcAAIBClHIAAAAoRCkHAACAQpRyAAAAKEQpBwAAgEKUcgAAAChEKQcAAIBClHIAAAAoRCkHAACA\nQjqXDgAAAJAkh31x7AE5zgkH5Ci/t/W+Jw7g0eiIlHKqTrUNc4McAACql4+vAwAAQCFKOQAAABSi\nlAMAAEAhSjkAAAAUopQDAABAIUo5AAAAFKKUAwAAQCFKOQAAABSilAMAAEAhSjkAAAAU0rl0AGDf\nti08q3SEdtV93E9KRwAAgIqhlAMAxVTbG4+JNx8BeH98fB0AAAAKUcoBAACgEKUcAAAAClHKAQAA\noBClHAAAAApRygEAAKCQDlHK77nnnnzyk59MfX19xowZk//4j/8oHQkAAADarOJL+dy5c3PDDTfk\n2muvzZIlS/KpT30qF154YV555ZXS0QAAAKBNKr6UT5s2LZdcckm++MUv5mMf+1gaGhpSX1+fH/7w\nh6WjAQAAQJvUbN68ual0iHfz9ttvp1+/frn33ntz3nnnNa9fd911ef755/PjH/+4YDoAAABom4o+\nU75hw4bs3LkzvXv3brHeu3fvvPbaa4VSAQAAQPuo6FIOAAAA1ayiS/kRRxyRTp06Zf369S3W169f\nnz59+hRKBQAAAO2jokt5ly5dMmLEiCxatKjF+qJFi3LKKacUSgUAAADto3PpAO/lK1/5Sq688sqc\ndNJJOeWUU/LDH/4wr776ai699NLS0QAAAKBNKr6UX3DBBdm4cWMaGhqybt26fPzjH8/s2bMzYMCA\n0tEg27Zty3PPPZfRo0eXjgLQ7nbt2pU1a9bk6KOPLh0FYL+9/fbb6dKlS/PjZcuW5a233srIkSNb\nrEMpFX1LNKh0v/jFLzJmzJhs3LixdBSA962xsTFTp07N/PnzU1tbmy996UuZPHly8/bXXnstQ4cO\nNeOADmnt2rX5whe+kOXLl+fkk0/Oj370o1x++eV5/PHHkyQDBw7Mj3/84/Tr169wUj7sKvo75QDA\nB+db3/pWFixYkBtvvDF//ud/nrvuuitXXHFFdu3a1bxPU5P37oGO6ZZbbkmnTp3ywAMPpH///pk0\naVK2bduWX/7yl/n5z3+e+vr63H333aVjgjPlsC89e/Zs1X7OIgEd0YgRI9LQ0JAzzzwzSfLyyy/n\nwgsvzHHHHZd77rknv/vd75wpBzqsoUOH5v7778/JJ5+cTZs25dhjj80jjzySMWPGJEkWL16cq666\nKs8991zhpHzYVfx3yqGkrl27ZvLkyRk+fPhet7/88su55ZZbDnAqgPaxbt26fPSjH21+PGDAgDz6\n6KM599xz85d/+Ze57bbbCqYDaJvNmzc3fzS9rq4u3bp1a3GNjGOPPTbr1q0rFQ+aKeWwD8OHD09d\nXV0mTJiw1+2/+MUvDnAigPZTX1+f1atX55hjjmle69OnT+bNm5dzzjknf/VXf1UwHUDb9OrVK+vW\nrctRRx2VJLn88stTV1fXvH3Lli3p1q1bqXjQzHfKYR/OPPPMvP766++6va6uLpMmTTqAiQDaz2mn\nnZaHHnpoj/X6+vrMnz8/v/3tbwukAmgfw4cPzzPPPNP8+NZbb21RypctW5Zhw4aViAYt+E45AHxI\nvfzyy1m1alXOOOOMvW5/9dVXs3DhwlxyySUHOBlA2+2+UGVNTc1etz/zzDM59NBD3/VrinCgKOUA\nAABQiI+vAwAAQCFKOQAAABSilAMAAEAhSjkAAAAUopTD+/Czn/0sc+fOzbZt25Ik27Zty44dOwqn\nAmgfZhxQzcw4KlXn0gGgI3jttddyySWX5Nlnn01NTU2WL1+e7t2756abbsohhxySO++8s3REgP1m\nxgHVzIyj0jlTDq1w4403pnfv3lm9enW6devWvH7eeedl0aJFBZMBtJ0ZB1QzM45K50w5tMLixYsz\nb9681NbWtlgfOHBgfvOb3xRKBdA+zDigmplxVDpnyqEVGhsb06VLlz3WN2zYkEMOOaRAIoD2Y8YB\n1cyMo9Ip5dAKo0aNysyZM1us7dy5M9/+9rczZsyYQqkA2ocZB1QzM45KV7N58+am0iGg0r3wwgsZ\nP358hg8fnp/+9Kf5zGc+kxdeeCGvv/56FixYkI985COlIwLsNzMOqGZmHJVOKYdWWrduXe69996s\nWLEiu3btyvHHH58vfelL6du3b+loAG1mxgHVzIyjkinl0AqvvPJKjjrqqNTU1Ox129FHH10gFUD7\nMOOAambGUel8pxxa4fjjj8/vfve7PdY3btyY448/vkAigPZjxgHVzIyj0inl0ApNTU17fXd169at\nOfTQQwskAmg/ZhxQzcw4Kp37lMM+fO1rX0uS1NTU5O/+7u/StWvX5m27du3Ks88+m+HDh5eKB9Am\nZhxQzcw4OgqlHPbh+eefT/L7d1hffPHFHHzwwc3bunTpkuOPPz5f/epXS8UDaBMzDqhmZhwdhQu9\nQSt8+ctfzh133JEePXqUjgLQ7sw4oJqZcVQ6pRwAAAAK8fF1aKUlS5Zkzpw5+c1vfpO33367xbZH\nH320UCqA9mHGAdXMjKOSufo6tMIDDzyQCy+8MFu3bs3SpUvTq1evbN68OStWrMjQoUNLxwNoEzMO\nqGZmHJVOKYdW+N73vpeGhobce++9Ofjgg3PLLbfk3//933PRRRele/fupeMBtIkZB1QzM45Kp5RD\nK/zqV7/KmDFjkvz+ap1bt25Nklx++eWZOXNmyWgAbWbGAdXMjKPSKeXQCj179mwe4P369cvKlSuT\nJBs3bkxjY2PJaABtZsYB1cyMo9K50Bu0wsiRI7Nw4cIcd9xxOf/883P99ddn0aJFWbJkScaOHVs6\nHkCbmHFANTPjqHRuiQatsGnTpjQ2NqZfv37ZtWtXvvvd72bZsmUZPHhwrrvuutTW1paOCLDfzDig\nmplxVDqlHAAAAArx8XVopcbGxjz00EP57//+7yTJxz72sUycODFdu3YtnAyg7cw4oJqZcVQyZ8qh\nFZ577rlcfPHFefPNNzNs2LAkycqVK3PIIYfkwQcfzIgRIwonBNh/ZhxQzcw4Kp1SDq0wduzYDBw4\nMNOmTWu+n+W2bdvy13/911m9enWeeOKJsgEB2sCMA6qZGUelU8qhFfr27ZsnnngiQ4cObbG+cuXK\nfPrTn86rr75aKBlA25lxQDUz46h07lMOrTBkyJC9Dux169Zl0KBBBRIBtB8zDqhmZhyVrtMNN9xw\na+kQUIl23z6jsbExgwYNyk033ZSePXume/fu2bJlSx5//PF84xvfyM0335zBgweXjgvwvphxQDUz\n4+hIfHwd3kVdXV1qamqaHzc1/f5/ld1r///xxo0bD3xAgDYw44BqZsbRkbglGryLRx99tHQEgA+M\nGQdUMzOOjsSZcgAAACjEhd6glX75y19mypQpmThxYvPFQv7lX/4lK1asKJwMoO3MOKCamXFUMqUc\nWmHhwoUZN25cfvvb32bJkiVpbGxMkqxevTp33nln4XQAbWPGAdXMjKPSKeXQCrfddltuu+22PPDA\nA+nSpUvz+mmnnZbly5cXTAbQdmYcUM3MOCqdUg6tsHLlypx55pl7rNfW1mbTpk0FEgG0HzMOqGZm\nHJVOKYdWqKury9q1a/dYX7FiRY488sgCiQDajxkHVDMzjkqnlEMrTJw4MTfffHPWrFmTmpqa7Nix\nI0uXLs3f/u3fZtKkSaXjAbSJGQdUMzOOSueWaNAK77zzTr785S9nzpw5aWpqykEHHZSmpqZMnDgx\n06dPT6dOnUpHBNhvZhxQzcw4Kp1SDu/D6tWr8/Of/zy7du3KJz/5yQwaNKh0JIB2Y8YB1cyMo1Ip\n5QAAAFCI75TDe9i+fXvuuOOOjBo1Kv37989RRx2V0aNHp6GhIdu3by8dD6BNzDigmplxdATOlMM+\n7NixI5/97GezYsWKnHHGGRk6dGiamprywgsv5PHHH8+JJ56Yxx57LJ07dy4dFeB9M+OAambG0VF4\nBcI+zJgxIy+99FIWL16cj3/84y22Pf/88znnnHNy33335bLLLiuUEGD/mXFANTPj6Ch8fB32Yd68\nebn22mv3GORJMmzYsPzN3/xNHnnkkQLJANrOjAOqmRlHR6GUwz688MILOf300991+9ixY7Ny5coD\nmAig/ZhxQDUz4+golHLYh82bN6dXr17vur1Xr17ZsmXLAUwE0H7MOKCamXF0FEo57MPOnTv3efGP\ngw46KDt37jyAiQDajxkHVDMzjo7Chd5gH5qamnLFFVekS5cue93+9ttvH+BEAO3HjAOqmRlHR6GU\nwz5cfPHF77nPpEmTDkASgPZnxgHVzIyjo3CfcgAAACjEd8oBAACgEKUcAAAAClHKAQAAoBClHAAA\nAApRygGgAj3wwAOpra1t/qmvr8/QoUNzwQUX5B/+4R/yxhtvtNj/9ttvb7H/H/68+OKLzfv+4bZ+\n/frl5JNPzje/+c09nvf/u/LKK1NbW5urr756j21nnXXWPo+/++erX/3qe+4/bNiwdvpbBIDK55Zo\nAFDBbrjhhnzkIx/JO++8k9deey1Lly7N1KlTM23atMyaNSuf+MQnWuzf0NCQHj167PE8ffv2bfH4\n9NNPz+c///kkyZtvvpmnn346DQ0NWb58eR5++OE9fv/NN9/MY489lgEDBuSRRx7Jt771rRb3/v3a\n176W9evXNz9eunRp7r///tx444055phjmtePPfbY5v/u379/br755j2O1a1bt/f6awGAqqGUA0AF\nO+OMM3LyySc3P77mmmuyePHiTJo0KRdffHGefvrpdO3atXn7ueeem/r6+vd83kGDBuWiiy5qfnzp\npZempqYmM2fOzKZNm1JXV9di/8ceeyxvvvlmHnzwwYwfPz4LFizIOeec07x93LhxLfZ/6623cv/9\n9+fMM8/MCSecsNcMPXr0aJEBAD6MfHwdADqYMWPGZMqUKXnllVcye/bsdnve3WW+c+c937OfPXt2\n/viP/zijR4/OiBEj8tBDD7XbcQHgw0wpB4AOaPcZ5oULF7ZY37RpUzZs2NDiZ9OmTXv8fmNjY/P2\nV155JY888kj+8R//MX/6p3+aww8/vMW+69evz6JFizJx4sQkyZ/92Z9lwYIF2bJlS5v+DDt37twj\n64YNG7J169Y2PS8AdCQ+vg4AHVD//v3To0ePrF69usX6qaeeuse+ffr0aXGhtySZNWtWZs2a1WLt\nT/7kT/KDH/xgj9+fM2dOampqcu655yZJLrjggtx8882ZN29e/uIv/mK//wwvvvhiBg0atMf65z73\nub3mAIBqpJQDQAd12GGH7XFWecaMGamtrW2x9v8vyLbbZz7zmUyePDnJ7y/i9uyzz+bv//7vc9ll\nl2XmzJmpqalp3nf27NkZN25c8/P2798/I0eOzOzZs9tUygcMGJDvfve7e6y35jvxAFAtlHIA6KC2\nbt2aXr16tVgbOXJkq0rtkUcembFjxzY/Pvvss9OzZ8/cdNNN+dd//decffbZSZL/+Z//yfLly3Pr\nrbfmpZdeat5/9OjRueuuu7JmzZr0799/v/J37969RQYA+DDynXIA6IDWrFmT119/vcUtxtrq9NNP\nT5I8+eSTzWsPPvhgkuTWW2/NiSee2PzT0NCQpqYmF3wDgDZyphwAOqDdZfkPb0XWFjt27EiSbNu2\nrXntoYceyujRo3PFFVfssf+0adMye/bsXH311e2WAQA+bJRyAOhgFi9enIaGhhxzzDH53Oc+127P\n+2//9m9Jkk984hNJkqeeeiq/+tWvct1112XChAl77L9hw4Zcc801+a//+q/m3wEA3h+lHAAq2OOP\nP56XXnopO3bsyPr167NkyZIsWrQoRx99dGbNmpVDDz20xf7z589Pjx499nie0047LUceeWTz4//9\n3/9tPtve2NiY//zP/8zMmTNz7LHHNt9ubfbs2TnooINy1lln7TXbWWedlWuvvTazZ8/er1L++uuv\nN2f4QxdeeGEOOsi37ACofko5AFSwO+64I8nvr6BeV1eXYcOG5fbbb8/nP//5Pe4nniRTpkzZ6/P8\n6Ec/alHKlyxZkiVLliRJOnXqlL59++YLX/hCbrrppnTv3j3vvPNO/vmf/zl/9Ed/tMfF5HY78sgj\nM2LEiMyZMye33nrr+y7Ra9asyZVXXrnXbRMmTNjjDQcAqEY1mzdvbiodAgAAAD6MfC4MAAAAClHK\nAQAAoBClHAAAAApRygEAAKAQpRwAAAAKUcoBAACgEKUcAAAAClHKAQAAoBClHAAAAApRygEAAKCQ\n/wPnbXzyzcfKCAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure object at 0x119812668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clinton_debates.plot(kind='bar', figsize=(15,6))\n",
    "plt.xlabel('DEBATE')\n",
    "plt.ylabel('% of RE')\n",
    "plt.title('HOW CLINTON REFERS TO TRUMP ACROSS DEBATES')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some Conclusions\n",
    "\n",
    "**Clinton consistently referred to Trump by his first name most often. In the second debate, she uses his full name more often than she does in the other two debates. Since they are both participating in all three of these debates, the use of Trump's full name seems more confrontational, or as if Clinton were talking like Trump wasn't even there, than another form of address. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIRST NAME</th>\n",
       "      <th>FULL NAME</th>\n",
       "      <th>LAST NAME</th>\n",
       "      <th>PROFESSIONAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Debate 1</th>\n",
       "      <td>27.586207</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>72.413793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Debate 2</th>\n",
       "      <td>59.259259</td>\n",
       "      <td>37.037037</td>\n",
       "      <td>3.703704</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Debate 3</th>\n",
       "      <td>68.421053</td>\n",
       "      <td>31.578947</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          FIRST NAME  FULL NAME  LAST NAME  PROFESSIONAL\n",
       "Debate 1   27.586207   0.000000   0.000000     72.413793\n",
       "Debate 2   59.259259  37.037037   3.703704      0.000000\n",
       "Debate 3   68.421053  31.578947   0.000000      0.000000"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_debates = pd.DataFrame([trump_clinton_REtype1, trump_clinton_REtype2, trump_clinton_REtype3], index = [\"Debate 1\", \"Debate 2\", \"Debate 3\"])\n",
    "trump_debates.fillna(0, inplace=True)\n",
    "\n",
    "trump_debates['TOTAL']=trump_debates.sum(axis=1)\n",
    "\n",
    "#Get the percentage of each type of RE for each candidate.\n",
    "trump_debates['FULL NAME'] = trump_debates['FULL NAME']/trump_debates['TOTAL']*100\n",
    "trump_debates['FIRST NAME'] = trump_debates['FIRST NAME']/trump_debates['TOTAL']*100\n",
    "trump_debates['LAST NAME'] = trump_debates['LAST NAME']/trump_debates['TOTAL']*100\n",
    "trump_debates['PROFESSIONAL'] = trump_debates['PROFESSIONAL']/trump_debates['TOTAL']*100\n",
    "\n",
    "del trump_debates['TOTAL']\n",
    "trump_debates.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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J6LV5enqiffv2WL9+PeLj4xEZGYmioiLUq1cPvXr1wtChQ8t9si0SieDi4oLY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\nz2XLlpVuvoVFB9i4kn3NKF8lPONKnd9X2AaYcZvMjKNUlOz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      "text/plain": [
       "<matplotlib.figure.Figure object at 0x1198122e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trump_debates.plot(kind='bar', figsize=(15,6))\n",
    "plt.xlabel('DEBATE')\n",
    "plt.ylabel('% of RE')\n",
    "plt.title('HOW TRUMP REFERS TO CLINTON ACROSS DEBATES')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some Conclusions\n",
    "\n",
    "**Trump makes a significant change in the way he refers to Clinton from the first to the second debate. He changes from calling her by a professional title most often to calling her by her first name most often. This is most likely a response to Clinton referring to him by his first name throughout the entire debate. By the second and third debate, Trump does not use a professional title for Clinton at all. In the second and third debate, Trump refers to Clinton by her first name more often than Clinton refers to Trump by his first name. Maintaining the same possible analysis from above, this could indicate a confrontational strategy or Trump could be talking as if Clinton isn't even there. Trump does not refer to Clinton by any gendered titles.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Please view [my final report](https://github.com/Data-Science-for-Linguists/2016-Election-Project/blob/master/final_report.md) for a summary of this entire project and some of my own interpretations of what the results and conclusions above could mean.**"
   ]
  }
 ],
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