duke_history.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6d02db72",
   "metadata": {},
   "source": [
    "# Duke History in the Rubenstein Library Card Catalog"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a232c05",
   "metadata": {},
   "source": [
    "Duke University has a long and storied past, with many individuals shaping the school through leadership and monetary support. We plan to dig deeper into what of Duke history is present in the card catalog files and see what that can tell us about the institution."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5776a331",
   "metadata": {},
   "source": [
    "## Duke Presidential Last Names in the Card Catalog"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc07b3cb",
   "metadata": {},
   "source": [
    "*Packages used:* Like most of our code, we have used the Python pandas package as a way to work with the dataset in the format of a pandas dataframe. The plots are created using the Matplotlib package and scipy, which is used to perform the linear regression. Re, or regular expression is used to detect patterns in text, allowing us to search for instances of presidential names in the catalog."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5791cd6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "from scipy import stats\n",
    "import operator\n",
    "pd.set_option('display.max_colwidth', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e475ba21",
   "metadata": {},
   "source": [
    "First we will be exploring the presidents of Duke University and their prevalence within the card catalog. The current President, Vincent Price, will be excluded since his last name is commonly found in the cards with another meaning and his time at Duke is well past the time of the catalog. Interestingly enough, the penultimate President's name appears in the cards (Richard Brodhead) despite his not coming to Duke until after the card catalog was digitized. Perhaps this eponymous figure was an ancestor of the one we know in relation to Duke today. We will be searching by last name, to see the frequency of the Presidents' last names in the catalog. A few of the presidents have surnames that have alternate meanings or uses (e.g., York, Wood) and we will attempt to remove the non-name occuraces of these. A disclamer to this analysis is that this method only captures names that were both properly spelled in the catloging process and translated into text accurately by our OCR software; it is likely that there are some misspelled names that did not make it thorugh the calculations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "72589019",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in dataset\n",
    "df = pd.read_csv(\"main_file_dataset.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b162bbc3",
   "metadata": {},
   "source": [
    "# First Approach"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "461cff0d",
   "metadata": {},
   "source": [
    "### Name Counting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "05cd61f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'York': 55, 'Craven': 97, 'Gannaway': 5, 'Wood': 192, 'Crowell': 10, 'Kilgo': 19, 'Few': 65, 'Flowers': 87, 'Edens': 3, 'Hart': 139, 'Knight': 81, 'Sanford': 145, 'Brodie': 15, 'Keohane': 0, 'Brodhead': 5}\n"
     ]
    }
   ],
   "source": [
    "# Duke University presidents in chronological order\n",
    "presidents = {\"York\": 0, \"Craven\": 0, \"Gannaway\": 0, \"Craven\": 0, \"Wood\": 0, \"Crowell\": 0, \"Kilgo\": 0, \"Few\": 0, \"Flowers\": 0,\n",
    "    \"Edens\": 0, \"Hart\": 0, \"Knight\": 0,\"Sanford\": 0, \"Brodie\": 0, \"Keohane\": 0, \"Brodhead\": 0}\n",
    "\n",
    "# Check for occurances of presidental last names in the cards\n",
    "for index, row in df.iterrows():\n",
    "    for pres in presidents.keys():\n",
    "        if pres in str(row['Text']):\n",
    "            # Check for homonyms\n",
    "            x = str(row['Text'])\n",
    "            if pres == \"York\" and (\"New\" in x or \"Yorktown\" in x or \"Yorkshire\" in x or \"England\" in x or \"Yorkville\" in x or \"in York\" in x or \"York Co\" in x.title() or \"of York\" in x or \"York City\" in x or \"York Dist\" in x.title() or \"Yorke\" in x):\n",
    "                continue\n",
    "            elif pres == \"Craven\" and \"Craven Co\" in x.title():\n",
    "                continue\n",
    "            elif pres == \"Wood\" and (re.search(r\"Wood[a-z-]\", x) or \"Wood County\" in x):\n",
    "                continue\n",
    "            elif pres == \"Kilgo\" and re.search(r\"Kilgo[a-z]\", x):\n",
    "                continue\n",
    "            elif pres == \"Hart\" and re.search(r\"Hart[a-z]\", x):\n",
    "                continue\n",
    "            elif pres == \"Knight\" and (re.search(r\"Knight[a-z]\", x) or re.search(r\"[a-z]Knight\", x)):\n",
    "                continue\n",
    "            elif pres == \"Sanford\" and (re.search(r\"Sanford,.*North Carolina\", x) or \"in Sanford\" in x or \"of Sanford\" in x or \"at Sanford\" in x or \"Railway\" in x):\n",
    "                continue\n",
    "            else:\n",
    "                presidents[pres] = presidents.get(pres) + 1\n",
    "\n",
    "print(presidents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a9454504",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Counts of last name frequency after running above code\n",
    "president_counts = {'York': 55, 'Craven': 97, 'Gannaway': 5, 'Wood': 192, 'Crowell': 10, 'Kilgo': 19, 'Few': 65, \n",
    "    'Flowers': 87, 'Edens': 3, 'Hart': 139, 'Knight': 81, 'Sanford': 145, 'Brodie': 15, 'Keohane': 0, 'Brodhead': 5}\n",
    "\n",
    "# Frequency of presidential last names as of the 1990 US census\n",
    "census_freqs = {'York': 0.019, 'Craven': 0.006, 'Gannaway': 0.001, 'Wood': 0.098, 'Crowell': 0.007, 'Kilgo': 0.001, \n",
    "    'Few': 0.001, 'Flowers': 0.028, 'Edens': 0.002, 'Hart': 0.054, 'Knight': 0.060, 'Sanford': 0.015, 'Brodie': 0.002, \n",
    "    'Keohane': 0.000, 'Brodhead': 0.000}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65de4d5e",
   "metadata": {},
   "source": [
    "### Frequency Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "920d2f0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sNl2r4jYzM7OejdoStJmZWZ05QJuZmdWQ31g0AIsuumhMnjy528kwM3tVufHGGx+OiIl9T2ngAD0gkydP5oYbbuh2MszMXlUktdvFszGCqrglTZN0u6SZkg5vMf7tkm6S9KKk7ZrG7S7pjvIZaJeDZmZmHTMiArSksWSPNZuS/SHvJGlK02T/IHvY+l7T/y5Cdj/5VrL70U+pvKTczMysW0ZEgCYD68yIuLN0mXcu2ZH5/0TE3RHxJ17Z/+o7gStLR/KPke+OnTYciTYzM+vJSAnQk5j9lW730v6rIAfzv2ZmZkNipAToISdpH0k3SLrhoYce6nZyzMxshBspAfo+Zn/n8BK0/37Vtv43Is6IiKkRMXXiRD8lYGZmQ2ukBOjrgeXLezvnBHYELmrzf68ANpG0cGkctkkZZmZm1jUjIkBHxIvA/mRg/QtwfkTcKuloSVsBSFpD0r3A9sDXJN1a/vdR4BgyyF8PHF2GmZmZdY1fljEAU6dODXdU0j/a7uuD+v+YsXeHUmJm3SLpxoiY2u10vFqMiBK0mZnZSOMAbWZmVkMO0GZmZjXkAG1mZlZDDtBmZmY15ABtZmZWQw7QZmZmNeQAbWZmVkMO0GZmZjXkAG1mZlZDDtBmZmY15ABtZmZWQw7QZmZmNeQAbWZmVkMO0GZmZjXkAG1mZlZDDtBmZmY15ABtZmZWQw7QZmZmNeQAbWZmVkMO0GZmZjXkAG1mZlZDDtBmZmY15ABtZmZWQw7QZmZmNeQAbWZmVkMO0GZmZjXkAG1mZlZDDtBmZmY15ABtZmZWQw7QZmZmNeQAbWZmVkMO0GZmZjXkAG1mZlZDIyZAS5om6XZJMyUd3mL8XJLOK+OvlTS5DJ9D0jmSbpH0F0lHDHvizczMmoyIAC1pLHAqsCkwBdhJ0pSmyfYCHouI5YCTgBPK8O2BuSJiZWB1YN9G8DYzM+uWERGggTWBmRFxZ0Q8D5wLTG+aZjpwTvk+A9hIkoAA5pM0DpgHeB54YniSbWZm1tpICdCTgH9Wft9bhrWcJiJeBP4DTCCD9X+B+4F/AJ+PiEebFyBpH0k3SLrhoYce6vwamJmZVYyUAD0YawIvAYsDywAfkfT65oki4oyImBoRUydOnDjcaTQzs1FmpATo+4AlK7+XKMNaTlOqsxcCHgF2Bi6PiBci4kHgN8DUIU+xmZlZL0ZKgL4eWF7SMpLmBHYELmqa5iJg9/J9O+DnERFktfaGAJLmA9YC/josqTYzM+vBiAjQ5Z7y/sAVwF+A8yPiVklHS9qqTHYWMEHSTOBgoPEo1qnA/JJuJQP9NyPiT8O7BmZmZrMb1+0EdEpEXApc2jTsyMr3Z8lHqpr/76lWw83MzLppRJSgzczMRhoHaDMzsxpygDYzM6shB2gzM7MacoA2MzOrIQdoMzOzGnKANjMzqyEHaDMzsxpygDYzM6shB2gzM7MacoA2MzOrIQdoMzOzGnKANjMzqyEHaDMzsxpygDYzM6shB2gzM7MacoA2MzOrIQdoMzOzGnKANjMzqyEHaDMzsxpygDYzM6shB2gzM7MacoA2MzOrIQdoMzOzGqpVgJb0I0mbS6pVuszMzIZb3QLhacDOwB2Sjpe0YrcTZGZm1g21CtARcVVE7AKsBtwNXCXpt5L2lDRHd1NnZmY2fGoVoAEkTQD2APYG/gCcTAbsK7uYLDMzs2E1rtsJqJL0Y2BF4NvAlhFxfxl1nqQbupcyMzOz4VWrAA18OSKubjUiIqYOd2LMzMy6pW5V3FMkjW/8kLSwpA90MT1mZmZdUbcA/b6IeLzxIyIeA97XveSYmZl1R90C9FhJavyQNBaYs4vpMTMz64q6BejLyQZhG0naCPh+GdYnSdMk3S5ppqTDW4yfS9J5Zfy1kiZXxr1Z0u8k3SrpFklzd2qFzMzMBqJujcQOA/YF3l9+Xwl8va9/KiXtU4GNgXuB6yVdFBG3VSbbC3gsIpaTtCNwArCDpHHAd4DdIuKP5TGvFzq2RmZmZgNQqwAdES8DXy2f/lgTmBkRdwJIOheYDlQD9HTgqPJ9BvCVUp2+CfCniPhjScMjA14BMzOzDqlVFbek/5N0paS/SbpT0l2S7mzjXycB/6z8vrcMazlNRLwI/AeYAKwAhKQrJN0k6dDBr4mZmdng1KoEDZwFHATcCLw0TMscB6wLrAE8DfxM0o0R8bPqRJL2AfYBWGqppYYpaWYGoO36vNPVq5ixd4dSYjZ8alWCBv4TEZdFxIMR8Ujj08b/3QcsWfm9RBnWcppy33kh4BGytP3LiHg4Ip4GLiW7Fp1NRJwREVMjYurEiRP7v2ZmZmb9ULcAfbWkz0laW9JqjU8b/3c9sLykZSTNCewIXNQ0zUXA7uX7dsDPIyKAK4CVJc1bAvd6zH7v2szMbNjVrYr7reVvtVvPADbs7Z8i4kVJ+5PBdizwjYi4VdLRwA0RcRFZff5tSTOBR8kgTkQ8JumLZJAP4NKI+EknV8rMzKy/ahWgI2KDQfzvpWT1dHXYkZXvzwLb9/C/3yEftTIzM6uFWlVxS3qtpLMkXVZ+T5G0V7fTZWZmNtxqFaCBs8lq6sXL778BB3YrMWZmZt1StwC9aEScD7wM/3teebgetzIzM6uNugXo/5auNgNA0lpkhyJmZmajSq0aiQEHk49DLSvpN8BE8pEoMzOzUaVWAToibpK0HrAiIOD2iPCLK8zMbNSpVYCW9J6mQatJIiK+1ZUEmZmZdUmtAjTZH3bD3MBGwE2AA7TZq8Rg+80G951tBjUL0BHxoepvSeOBc7uTGjMzs+6pWyvuZv8Flul2IszMzIZbrUrQki6mPGJFZh6mAOd3L0VmZmbdUasADXy+8v1F4J6IuLdbiTEzM+uWWgXoiPhFt9NgZmZWB7UK0JKeZFYV92yjgIiIBYc5SWZmZl1RqwANfAm4H/g2GZR3AV5XfW2kmZnZaFC3VtxbRcRpEfFkRDwREV8Fpnc7UWZmZsOtbgH6v5J2kTRW0hhJu5CPWpmZmY0qdQvQOwPvBv5dPtuXYWZmZqNKre5BR8TduErbzMysXiVoSStI+pmkP5ffb5b0iW6ny8zMbLjVKkADZwJHAC8ARMSfgB27miIzM7MuqFuAnjcirmsa9mJXUmJmZtZFdQvQD0taltJZiaTtyOeizczMRpVaNRIDPgicAbxB0n3AXWRnJWZmZqNKbQK0pLHAByLiHZLmA8ZExJPdTpeZmVk31CZAR8RLktYt3905iZmZjWq1CdDFHyRdBPyASg9iEfGj7iXJzMxs+NUtQM8NPAJsWBkWgAO0mZmNKrUI0JKOi4iPRcSekjaOiCu7nSYzM7NuqstjVtMq30/oWirMzMxqoi4B2szMzCpqUcUNvEbSwYAq3/8nIr7YnWSZmZl1R10C9JnAAi2+m5mZjUq1CNAR8enBzkPSNOBkYCzw9Yg4vmn8XMC3gNXJluI7lNdbNsYvBdwGHBURnx9seszMzAZjRNyDLr2QnQpsCkwBdpI0pWmyvYDHImI54CRe2Rjti8BlQ51WMzOzdoyIAA2sCcyMiDsj4nngXGB60zTTgXPK9xnARpIEIGlrst/vW4cnuWZmZr0bKQF6EvDPyu97y7CW00TEi8B/gAmS5gcOAwZdzW5mZtYptQzQktaSdLmka0rpdigdBZwUEU/1kaZ9JN0g6YaHHnpoiJNkZmajXS0aiUlaLCIeqAw6GHgX+djVtcAFfcziPmDJyu8lyrBW09wraRywENlY7K3AdpJOBMYDL0t6NiK+Uv3niDiDfBUmU6dOjbZXzszMbABqEaCB0yXdBJwYEc8CjwPbAS8DT7Tx/9cDy0tahgzEOwI7N01zEbA78Lsy759HRABva0wg6SjgqebgbGZmNtxqUcUdEVsDfwAukfQe4EBgLmACsHUb//8isD9wBfAX4PyIuFXS0ZK2KpOdRd5znkmW0A/v8GqYmZl1TF1K0ETExZIuBT4A/Bj4TET8sh//fylwadOwIyvfnwW272MeR/UnzWZmZkOlFiVoSVtJuhq4HPgzsAMwXdK5kpbtburMzMyGX11K0MeSzzLPA1wREWsCH5G0PPAZ8p6ymZnZqFGXAP0fYBtgXuDBxsCIuAMHZzMzG4VqUcVNPlI1gcwwNLe+NjMzG3VqUYKOiIeBU7qdDjMzs7qoSwnazMzMKhygzczMasgB2szMrIYcoM3MzGrIAdrMzKyGHKDNzMxqyAHazMyshhygzczMasgB2szMrIYcoM3MzGrIAdrMzKyGHKDNzMxqyAHazMyshhygzczMasgB2szMrIZq8T5oM2uPtvv6oOcRM/buQErMbKi5BG1mZlZDDtBmZmY15ABtZmZWQw7QZmZmNeQAbWZmVkMO0GZmZjXkAG1mZlZDDtBmZmY15ABtZmZWQw7QZmZmNeQAbWZmVkMO0GZmZjU0YgK0pGmSbpc0U9LhLcbPJem8Mv5aSZPL8I0l3SjplvJ3w2FPvJmZWZMREaAljQVOBTYFpgA7SZrSNNlewGMRsRxwEnBCGf4wsGVErAzsDnx7eFJtZmbWsxERoIE1gZkRcWdEPA+cC0xvmmY6cE75PgPYSJIi4g8R8a8y/FZgHklzDUuqzczMejBSAvQk4J+V3/eWYS2niYgXgf8AE5qm2Ra4KSKeG6J0mpmZtWVctxNQF5JWIqu9N+lh/D7APgBLLbXUMKbMzMxGo5FSgr4PWLLye4kyrOU0ksYBCwGPlN9LAD8G3hMRf2+1gIg4IyKmRsTUiRMndjj5ZmZmsxspJejrgeUlLUMG4h2BnZumuYhsBPY7YDvg5xERksYDPwEOj4jfDHVCtd3XBz2PmLF3B1JiZmZ1NiJK0OWe8v7AFcBfgPMj4lZJR0vaqkx2FjBB0kzgYKDxKNb+wHLAkZJuLp/XDPMqmJmZzWaklKCJiEuBS5uGHVn5/iywfYv/OxY4dsgTaGZm1g8jogRtZmY20jhAm5mZ1ZADtJmZWQ05QJuZmdWQA7SZmVkNOUCbmZnV0Ih5zMrMrF3uMMheDVyCNjMzqyEHaDMzsxpygDYzM6shB2gzM7MacoA2MzOrIQdoMzOzGnKANjMzqyEHaDMzsxpygDYzM6shB2gzM7MacoA2MzOrIQdoMzOzGnKANjMzqyEHaDMzsxpygDYzM6shB2gzM7MaGtftBJgNlLb7+qD+P2bs3aGUmJl1nkvQZmZmNeQS9AjgkqSZ2cjjErSZmVkNOUCbmZnVkAO0mZlZDTlAm5mZ1ZADtJmZWQ05QJuZmdWQA7SZmVkN+Tlos1HOz9F3xmC3I3hb2uxGTAla0jRJt0uaKenwFuPnknReGX+tpMmVcUeU4bdLeuewJtzMzKyFEVGCljQWOBXYGLgXuF7SRRFxW2WyvYDHImI5STsCJwA7SJoC7AisBCwOXCVphYh4aXjXoj5Ga0lgKNbbpVMzG6iRUoJeE5gZEXdGxPPAucD0pmmmA+eU7zOAjSSpDD83Ip6LiLuAmWV+ZmZmXaOI6HYaBk3SdsC0iNi7/N4NeGtE7F+Z5s9lmnvL778DbwWOAn4fEd8pw88CLouIGU3L2AfYp/xcEbh9iFZnUeDhUThPp7Ge83u1zNNprOf8mi0dEROHcP4jyoio4h4OEXEGcMZQL0fSDRExdbTN02ms5/xeLfN0Gus5PxuckVLFfR+wZOX3EmVYy2kkjQMWAh5p83/NzMyG1UgJ0NcDy0taRtKcZKOvi5qmuQjYvXzfDvh5ZP3+RcCOpZX3MsDywHXDlG4zM7OWRkQVd0S8KGl/4ApgLPCNiLhV0tHADRFxEXAW8G1JM4FHySBOme584DbgReCDXW7BPRTV6K+GeTqN9Zzfq2WeTmM952eDMCIaiZmZmY00I6WK28zMbERxgLZRqTwDb2ZWWw7QNeUAMuTmAJDkc8AGZbQfQ5Lm6nYaRqpRfWDVWWlhjqS5h2oZ3coElMfc+ppmNUlvG6LlrwD8UtISEfFy3S6wQ7lfnPHrHElvkTSmk8fQq23/SHoz8GVJS3c7LSNRrS5MBpJeU/m+NXnwd/yklaRKJmCRTs+/l+WuCOxavo/tYZo5yV7ePi1pnU6nISL+BlwLfFfS4gO5wLbaJ4O9SEvaUdIyERHDsM83lTSgDimqaaserwOYz3hJS5TvUyQtMNB59TD/uSVNKt+XkjRHB+c9DjgQ+OlggnTzfo4haLU7xEH/r8B44IMO0p3nAF0j5WJ1nKR3l0EvA3d2+qQtF5TGhfpA4ApJJ1SWO5TeBmwB0OpxthJEngfOJB+b+8RAA0mLea8m6bdl2R8Gfg/M6G+QbgQ65RvUPifpDElvKPNo+2LYYto3kY8CLjgUF+rKPv8w8BnyccN+aQry7wcOkjR+APMZB0wB3ifpK8CRQKfXeXVgO0mHApeT3VgOWjl/XoyI3cnOjr5btku/g3RlW+4m6ZiSSXtDJ9JZ5lvdX1tKmi5pzXZqsfqab9kOzwM/AjYDTpW0eAeSbYUDdL28CNwMrCdpK0DA051eSES8DCBpXWBl4FDgTmBTSbv39r8D1bhPFRFfB8ZLel8PaWtcpN9f0rYIcHQnqrsj4ibgOUnXlN+HAb+in0G6BOe3A8eTneQ8DFwk6S1lXFvnVeXCuUMpiR5Fdpyzy2AvoFVNJd7VgD2BdSPiTknrSNqm3YxFJc17A+8FTouIx9WPWzElaLxI9j2wGrANMCMinupkaS8ifgOsRQb/UyPi/g7Nt3H+7A48QGYEzhtoSVrSh4C9gVuAw4GNOpHOktbG/joEOLik9QTgHYOdb1nXD5DvKPgY8Bqy1muJwaXaGhyga6BxUYqIByivyyRP0r2BLSStVy7iO6ryHutBLG+MpLWAXwK3RMTVZGC4jMwc7DvYZTQtbyXg0EoJ/XRgwTKuVVXxGsD+wAHAfsCFwGGDqZJVqU6PiA2ApyX9pvxuBOnvS1qycfHtbV7l65rAxRFxfkR8DPgS8ANJE9udR9kPcwNfAH4IHEJ2tDNP+XSi2rxagtocuIesOThN0hfJTMGuQFv7vGzLeYANgKPJDM/+ZCnyiP6khwwSY4FvAOtKWr+S1jn7sZrN6aseU98EvgssIentjfl2YLtuRq7/qeR5+l/gh+2UpJVtIBrfFwBeT74qd0HgQeB0SXNKmn8waawsY0lgajn2nytp/WnZj4OZ75zA+mTm5yJgHWAC8NWyTBskB+gaqFyU9gT2AG4AbgXmBSaTJclNyGqkAd1Hq160IuLliPg98Dngk8oq1fuBq4FrgFUlLTTA1WllHjLjcbikw8ngtmelxNkIWI00zkG+PvTRiLiZzDg8R96PX6M/C24EhIh4SdKiABGxGfBgU5C+BThT0tg+SnGN0sED5MWoUeV5GvALoM/7qJUA9aaIeJa8l3kjWaLclizpfLxM22uwb3dZyje+nVjSfAHwLHA2ebvh10CPAbFpeygingEuBU4Bvg4sTAbBt/R13FTSswWZKdgW+DLZ//1OynvRGwJbDqAkOkfZ1yHpbWUZ/4qIfYHngR2AFSWtD7ynP6X1plqIZcjbT+eU9gy/I0uQiwEXNoJ0D/OZk6wRWqRsjyfLqN8A20TEJuXWz+7AoNsIFC8AL0n6JrB2Wc7LwOb9CaTN8y3V27cBS0taoNSKfBBYD9hLHawFGrUiwp8afID/IwPRm8vvxcmc+ankazIHM29Vvm9PnkTrl98nAncBi5bfiwILdGJ5ZEOvacCK5fckYBfgGPL+54lkMG5MP778HQf8FPhEZZ7HAF8Elhxgmj5ABqTPAmuVYZcA11SmeU0f67M8cBWwE1na+Q1wGFkCWodsMLNiG9tFZKD8I/BJYBUy4K1WlnER2Yhtkeq+G8T+WAP4C7BZi+NhNzJzMKWNY2dnsnp0jfL7jcCC5ft0skZm/jbSsxp53/LsyrAVyIzJVeV4XL6f67hI2f5LlP1xD/A94NtlHccAnya7snwA2GqA58/7gNPIzPL9jWOpjPs88BNgUg/zWbKy/zcATi6/pwE/A3Yqv3clM+jLDfTcK98Xa+yPcv5c39jP5O2JPwGvG8B81yXbD0wA3g5cDGwKTCx/vw9MHuxx6084QHdlo+cJWv2MI6tzbywXkTnLdJPK8JPJktmgLtblAng1s0ps25XhJwJPAIt0cB03Jd+ZfVCZ91ZN43cqF7MFyu99yu+vkhmINchS2feADwF/BpYYYFp2JquxFwf+Bny2Mu6XwOW97avydzrwY7Lh2i+Ad5GZmXOBc8hq4817mc+YyveJ5e94snr3ILKv+J+VeS4ymH3RfJyQQf+Sku7GBXtusoR2IbByG/PchXyJzGfLsbNj0767paf5tEjPa8mMzSXVbUZmelYg3xk8kPU+lgyapwJvKcO2Kvtn1/J7CeANrdLVxvzfWfZTI8P5XjIzsROZAbyKFpk88hxfuGz/A8hM6SQyQ/FJYD7yVs5Pyza5CVhpMPsd+EhZ3i1kDdxqwHHAb8kai1sHuIz9y3FwJPDzsl6bA98iz9+bgTcO9Nj1p2l7dzsBo/FTvQCRr7pslEJ2KSfPTsC4MmwxSslyAMupnrCLkFVyAB8mW7XOVVnOMQwgx97DcieXi8zy5L21f5CN0HZsmu6nwNZkI6FbyICxa7nANqpjP1suYn0GkV7ScziZ09+tLHOOMryREXpFqZy8vdAY/1qytLEKGUC3LBfSLcv4OYClmrd5D2n5IFla/gqZ8RhX5vdpstp030Fu++aSzgZk4FuWrIE4i1lBegHaqC0hq0WvYVZg2r6s//ZkoNmKEvT6SM/WZT+8mbz3fChwErDpINd5bOX7/mU7blV+L1y273nA/gPZlmTpew5gBplRfEflvNmerOb/DrBKD/MZU/6uRmbw9im/X1eOq4+V3/OTGZRFB7k93kGe3+PLNv492ThsPmBD8nbZ5AHMdxp5O2Q+MtjfRp63ryvjJwOLDSbt/jRt824nYLR9yJaOV5bvB5WT56py4ZwH2IssMe9ZvfAMcpmNC+s3yoX1ksoFZg9ghQ6u3ySydLZUubDfWIbvVS6cW5YL3uvIktiyJQ0HlunmLReT79JDlXM/0rIGmQHanszZX1UZ9xHgsB7+bzzZ6GsLMhOzCFnymKeMX5jMQPwe2LmPNCxW+b4TWYOxRLmgf79p2iPoXCbpILLW4KxyUV21XPw/D/yAXqqimT2oji3b7ybgpMrwbcnah22o1A70NC8yU/hbZtXibFbmfQj5SN07Brm+KzOr1Hw4eQtl+cr+2ppy+6jN+VVrPCaUv3OQ1dunluO8up36LI2TtS5Xkvf/DyvDXke2Oflyh/b7W8r+/UZl2IfLsbp+P+fVXPOxXFnvvcjX9ULWIN1DB68h/sz6uJHY8JsDWFDSTuR7qbchSyCLk60hzwJmkhfT+Qa7sNKg5eOSliKrphYFTox8RefuwEfJBliDVhphHUZWcf2DvBd4cxn9VzIj8khkI7X7gQ0i4u/Av8mODlaKiKcj4kaydLd0P5dfbcizIHkPfxVyve8BfixpsqQdydqKi1vMY76IeBz4D1l19/aIeBT4A/A1SfNExGPk/eObgQ0kLdZDejYnH7+aWAa9TG7vzcmM2m5lulUBIuKzETGzP+vcw3JXIS/GbyOrMp+JbGw3kwyGf6eHxmxNrb5XJGsGfgB8AphD+dw8EfFDsvX59dGiQZSk5SQtFBGhfJxvM7JEP5HMoB1atsPJZCnslgGsZ6Nx4TpkRuRHklaNiOPJR+B+IWlK2V+XRMSf2p13zHqUal/gXElfJ6unP0hmpA8jM3+N6aOPtG4DfIpsqLYz2Qjuw+U8mE42zOx3py8tGoT9gwzGiygf1SQiTibv+R/VbsvtpuNgsrLXvZkRcR9ZM3ZymfQPZEatm6/oHbm6nUMYjR+yOvnjZE58XGX4deTJOjewcIeWNQk4n7wYzluW+zuy8cyfGcB9qB6Ws3D5+yngu+X7amQO+6tkoNisMv3OZCvyfckqz13Je7AbkCW239NDY5s20vJmMnOzMRlIFyWrVo8jSzAX06LKnCxpHQrMW34fRD6msyF5Ufpc2Ud7kVX2W5ANYpZuMa9pZAl2WmXYFuT9+GpJ/n1kMJm7Q/thTjIIfqps98uZVaW/dRk/rsX/NZeWDi774wryHu4SZGbyFOCIvo4Fsur6RDIjsAgZzHYl74vOXdb5LsptgkGs74ZkTcw0shr7p8wqSX8SeKwcCz2W8pvmtwawZvn+LrIKeir5ONHF5H3usWRDvuNps5YLeA9wRuX3+iVtnyi/+11bxuwl+G3IRzNXIzNAHyFrgbaqTNPvawrZXuWqsu7fKcM+R96iOZos/b+2E8euPy22f7cTMBo+ZHD4Alm9uBJZ1TyDzNWuVJnuOPIRiE4s843MasSzBRmoligXlxXKhWjxDi1rdbJF84Hl9w+BQ8v3DcrFYg/yHuhryFLITWSJ5Phy8m9ZLuCXlG3TdnVkU1rWJEvkF1aWdRYwVxm/ADBfi/9bvEy/BBlMNi3DDyZb/m5ClpwOIKtlVy3Luo6mlrBkQHoZ2Lr8Xo5sQT6+HAeXkVX7+5X90qlM0ttLeseTjeuuodwmIB/b+WNPF9PG9infNwYuLd+PBX5avs8BvJsMvq9oxMbsrdQ3Lf/7KWZleA4Btq18P4Z+Nggja2U2qPw+ATi68vuzZIv1VcrvtluDky3x/8CszOa2wEGVdVq8HKtLkveLe2qt/YrqbrLDlLNL+hu3l75djoWFBrnfG7cPDiQzQFuX9B5I3h9/Rev9Nue7JZnhGUuep1eX4a8lz+kvM8Dz1J8290G3EzAaPsCK5WQ/vRzod5C5/e+Xi+j+ZG7/FgZ4L4fZc9PLk1WZvyWreMeXC+KgSiu9LHsx8j7n3WQQ3qms21KVaVYoJ/se5cKxehm+CNkatvHIydy0KOH1ZxuQLUx/X5Z3IFkKXK+X/xtXttV65ffB5cK2SeX3GWTgGluGrUs+CrVKD/PcnMyEvJksiTYu9OPJwDSDrNnoSHAu816ZvJUwlcxAXEiWok8tx1bLZZEZiBmUYFn+/71kgL2CWSXwtcvflvevmRV4Go2idiAf7TmSLMW+nyztHk1Wsy89gHVcj3x8b6Hyezcy0zOhMs31VO6z02ZgKsfmlczKLO5K3hpZojLN94F12pzffmSm+zQyuJ9FVg3vVcb9hEE+jlS2xWVkqfk48v7+1WQt1FgygzqgEm45xjcn20ZUG1eu2qlj1p8+9kG3EzAaP+XieRiZ8z2JrOY9iR5awrYxv2pwnqfy/SNkFfPnyRLueR1ejzXKRe01ZM3APmSJ6fNkCfJoYJnKxXRjslvMB4EvNM3nB1RKcQNIy8bMKumuUi6yO5QL1CNkVW/Lak6y2vdqSgAjg+gBZAnhnWXY4WQJqPG8+Kr08Uw2We36MnB4i/0kBpgRabGcvcu2nkD2df5TMuPzRrL25L3A63s6dsgS4XFkqXsS+YzrdWRrc1WW8Rt6KO2RtxHuZlaJffEy/WlkqfbjZfh25K2DAWdMyFqQ28iS4mvJQLc7mQlchazBuYLK43T9mPdfyfYHjVb5nyJL5OuVY/062shYkFXaN5Gl8h+RVcJvLufIF8jM05sGkL4xTb+XLJ+dyXvBc5EZq3soj1G2Mc//PfLZNHwj8p72JZVhe5X1WbATx64/vX8aJ58NsUaji8rfN5ABZH7y3s4fO7CMA8kL1MvAkRFxn6TlyWrbz5P3JteKiH8NdllleeuSja2eJkuht5Edf9yu7KtZ5POht5PV3w8ou/vci7zIXhERn5a0LZlJ2SGyQU87y652GYnyTUXvIVuPr0HeI/5pRFxUGt8sENkgrTqP1wOPRcRjki4EPhTZuI3SI9ZeZEO1KyPiEkmTI+Lufm6jjcn7tm+NiP9ImjOyB6YBa7HunyGD8B1kKW0i2Rjvm33MZ0zMagy1Dbn9HidrdLYhg8n3ySC4NdmRxq29zG9LMhjvSJYUfxQRp5aeu6aTrZePjYj/dmCddyIzufuTmb6Pkve+VyzLnwIsGxHHtTNfZVewc5M1WyKD9C5l3CFk7cS8wKcj4s9tpO9E4G+Rfc8j6RSySnyb8nvuyF7kBkTSW8gM0TMR8aykg8he086T1LjNcVZE3NPGvJaPiDvK9/2BN5A1Hd8l20gcTNZErULe4uj1OLAO6nYOYTR/yBLOEQzycaIyrw+SpcAFyOrDyyg9PpXxizLIxhzMKk2tSQbBZSu/LyFLqv9rNEIG7e8A/yQbGm1GZkqOYdb9sovJ1tADes6ZrCo8kSztTiRLkR8muxZ9Btiil/99B9lQZzylq8qm8ePIRmMnUzoXGWAaG522dKwjmDLfzcj74guSpbs9yABzdVn/tnqEI2taflP2ywVlW8xT0n0w+eartm690FRrUIaNJTv5+MxAtmPluFudrCFplG63Jxs6rlc9xslag5vpo4QKs9VovJ7Z78P/APhh5fcc9FDj0TSfw8gq/Q+Rz7YvVBl3MbM6qenv/eANgYPL9w+QJduzyQzR+LLv/0iWnu+izapzssT9QPm/VciGjYeU8+nLZb13I2toTmGAtXz+DOzT9QSM9g/lvs4A/q+5OupYsqrrQDJYnkTei1uHDrUQLsvZnGyR/eFyEWx01jG2pOHX5cK+Qhm+aLlQHUOWkj9P3h9uPPc9DwMMXMxejTiDbHSzbCWdX6KP6kgyoNxN3qM9kqzK3qVc/Dcjq85bVg/3M63TS1rH9Pfi3LzPK3+/XS7S7ycbS72LLAVuTFZ39lStvTqzujudiwxGjaC3DFkFexYDzDiW5f+VpupwSmOxAa7zRsC/yAaWD1KCL9m24y5g+/J7PrJXq1XaPX/ITMitZMvs48qwecgq/yv7uY9/RN5eeEs5D3chS/PbkqXShdqdX9O8VyL7E/8iGZSXIrsH/mzZXyrH/AfpodvWFvNsdD26FFn7ch2z2oasVuZ9MrMy3IPudtaffu73bifAn0HuwMyxH1pO0DcAP6uMu4sM1J16hGd5MugvQwbHP5FVybuV8WPKuDPJEvPWZfjuZMl54XKh+QlZyvpqP5ffnCk5Edi78vskZn+Eac4257t+Sc85ZCbjm+T9198C/9fBfdVnP9XtrDtZ0plUvq9NZpauLQFsjzK85WM7ZObpRmb1+T5nWc8DKtPsSJZMv07WIrT1iFKL5Qy41oC8LdNo/f1GsrHbuuX3wWS1dqPNwPbA2yr/2/YjS2Qjq++U43bFEqROLOPmJTMELbuYJWtrGi2+FyBvB9xRGT+9pHsG2VBwoLVEjYaJbyz7uNGqfixZk3Ui2f6irceomL3r0Uap/HVkN6nVDk5WLefUF8tx4AA9zJ+uJ8Cffu6wLP2sSZaUNidz7I2L9WvJ6s1NyXuG5zPAfo17WPbEchH7P7L0PD/ZmOoJSpAu0y1GNlq5lyzdbUw23Fm9ks5PMsBes8hSbk/ViBcygK4SySrE2/tzcR+m/d2cKdmfrMr8IRmUG90sbkl2RPJDem5lPY2sym60Tp9QAsu65C2RXcrwncpFeVDdNjLAWoMSDH5NlprHkVWt15OtqhstxA8iO9hZufJ//VmGyIzOn8gg3AiCS1Fex9nH/29GBvPzgWPKsJUo3bhWpptQzpMJ7aat1fowqwX1CmQ7gUMq49Yp50Fbt7DouevRJSnV3ZVp38wgbu/4M7hP1xPgTz92Vl5gryfvCa1NVuX9rTJ+frJhzyX08lhNP5bXqF5cisrLOsgGSY2WydsDXwM2bPH/q5GPrRxKBs5rmFUF3XapjNaZkqGoRtyULJk1SkVdLzFQqWYmH3+6nvL8OvnI3o3MegvYm+nh7US88tnsZcn7jeuTQXBzsqr/e2QL4LaqSdtIf79rDZjVqr5RjT2BLCX+701aZfihwMb9PZ6bhr2PvB2wTiUILlOW/5oe/qfRJ/V0spT5HWb12/4G8vbASR08Bhp99H+IDKJLk4G0GqT7XUvG7F2PNs7nSWTN25e6fez74wD9qvmQj3nc0XSBeiNZXfzlyrB5yUZDHendhywp3EOWMr5SLubvKss9hKziXq1M2+pitgTZGvrUEiAOoh8lKlpnSjpejdi0vut3e3+XtCxGlsjeXdmW32L2GoNvUV620Mb8qs9mXwl8pGn8omQNyYB6cOvA+r6eWZmjC5n9OfqJZCOl4ynPY1fG9bfB1XZkRnaV8ntf8tG0dStBuqcGYY2MzrvK7zXJquFGH91jyjb8AeV+9iC3yd5kKX+Pcv6dRWZ8ly3p6NcLQCrz3YasBVukfP81szoaWoq8Jz+xv9vWn85+up4Af9rcUXnf7cPleyO3PoYsOZ4JnDAEy5xCVnVuSFbfnQ58rYzbkaym7rGVdGU+c5Al8DPo5X3JLf6vt0xJR6oRe1l2Vy9M5G2ABciMyQXlIjo32e3izpXpjqBy/7iN+TY/m93oXGRzeunMZZjWua9W9fORtTWfpR+1JFQap5GNKH9FPt98NeXtYSUQXktT8O9hfpuTPY6tQmZ0Pl2C2rXM6g5zCm2+a7mX5Ywjaw7eXn4vTmYsPldZRtvnU9O8O971qD+d/3Q9Af70sYNmVSufQrk3RKVTgfJ9l3LhHnSOvcxzbAl6/yBbto4pw5YuQfYcZu9DfEiCGX1nSk7s9v4ZovXegry/uTCwEJkZuoy89z+FLAV/jmwR/wf6+f5dZrWyHl9+71GWt0wN1r23VvUbl+DYdgcfzGrNP4lsEHZeGX5I2Y5nVIL0e6iU2ttIZ/PjZPOT70heaIDr3qoG6jgyg9Z4k9pKZKag7fvCPcx3yLoe9adzH7/NquainDnAj4G1JK1ehknS2PL9NeTJ9uXBLKvxZpyIeCkiHiHvNb+BrGJ9KbLTg88CL5IXiuY0dkTlDT3LkNVsAC+UziBeJnt2ugZYTVKvHVG82kiaRrbM/1Rkpy1PRMS55MXzE+Tti8Zzvo+Qnbv8pT/LiIgrKa+jlPR+8hbEnhFxV8dWZIAi4nIyw7ASWY07P1my3pN8FvexaNFRSCuStiAD3DWRb2H6E3BwecvYFmT19L+AD0naNyK+FaWjmjbT+U5gT0njy+DtyVqOV7zdq420Vt8eta2kvSW9nawq/zPwSUljyIAa5DnYlsp895N0nKTTyMz3S+QTALtL2o+s7n5/RPynv+m3IdLtHII/7X3I6r2jyCqv1SvDdyJbnbaV829jORuQpbP1yu/1yEYjO1Sm6dhz1X2kZUOyZqDR+nsMs1rbHkS26h0xL4in9Us2ziGD8kJlX1/cGN+B5W1BPlvbsf7AO7zvB9yqnrx/fzXl9gj5XPNc5H38Qyj37clM6BkM/JnvTckA+gGysdmguu9k1vuyDyFb3O9MNmA7uwz/HX08493DMoak61F/hvbT9QT404+dldV0R5LPL36BfGb3rwyyYVRl/muTrYJPIfuu3p+sQn878BCVe5/DtL7Dkimp04ceXrJRxs1Pli7PpdKqfpDL63fnIcO4LQbcqp68PfBTsovOuctxdFU5rq8rGaFvkI+mDap3rMFkdMh7v43GZa8Fzi7DDya7woVZb2J7LW1WPzdvK17ZZ8ApZFesjd/Dkun2p38f98X9KlNeuL46We13P/kKuL91YL4rkh1THBkRV5fqwenkM7enkiVpRcTVg11WP9M1iayC3YjsRvQZshXu9hFxy3CmZbiUau5LyVLe8aWf6JcjIiTNT+6HJ7ubyuEhaTPg6Yi4pp//JzLIbUJWl19FtlT+C9lS/z6yD/lfR8TMDqRz3oh4uj/pK/tzW2BqRBxR9u1pZI3JOLKl+AuSdgOubfc8b6ouP4ysOXiMbKX/xShV2JIuBt4bEQ819yVu9TCu2wmw/omIZ8gLza87Nc9yMRtPVqO+jwz6l0h6iaxGHhcRX2pMO5wncuQLPz5HNr5pZEq26kSmpK4i4nJJ7wROkfTVyJdszAG8EBFPdTt9wykiLoX+H3cl+H2N7CVtSeDCiHiuzGsf4KaI+GEH09l2cC7GAS+Q592iZR5PSbqWbLl/cAnOe5AvApnWj7Q0gvN0smHc3mQDz2OALST9gXwaYnGy5I+Dcz25BD1KVXLw44HnIuIZSauSj+3cERGfKNNtAdwzUkurdSZpU7IF8toR8WiXkzMiSNqebBn+7mh6u9kwpmFRsjZoFfL20a4RsUMZ90aysdk08h78W8naoj7fHiVpAlnT8pikxmONUyNi+TJ+epnvRPIWwIE+r+vNAXoUK68H/CjZkvWmiDhR0upkA6wHIuKQribQGhfVT5G9iIVLOgMj6XVkf/DvIxs8ttUSfAjTsxX5dq/jyYZr3ydfcfkM2aBtVbKV9V2RLdD7mt9m5H32u4HbI+KTklYiG3zeGRH7l+kmkF2kzhX5pIbVmAP0KFJy1XNExKOS3kE+R7sN+ZztsWRnJ0dIWpMM3B8fyVXJrxaS5h9tVdudVtpubEgGr0Hfc+4ESZuQjdaeI1tWv4lslPkwec9413JLq6/5TCMfwfsc2evfIeS95eeV753/KPm43kFDsiI2ZBygR4nSCOxzZG9HM8hnTO8je0A6hHwn8FVkD12fkLRgRDzRrfSajQaS3kb2jPd6MiivADxKtq7v87l0SYuQAX3biPhxyVxfSPabEGT/3cuTGfA7IuJjQ7IiNiQcoEcBSVPI52lPJx+teKwMF/k2nm9FxMWSTiGfi5zSrftzZqNNqZ4+EdgoIv49gP/fnAzAe5C1Yr8l++z+ARmUdy3XgMci4v6OJdyGnFtxj3ClWvsU4PSIOKsyfDfyHtdvgKUkbUe2KF3Nwdls+ETEpaWV/uWlp8B+9UQWET8pT1z8gfJoHoCkjYCLJC0UEbd1PuU21NzV58j3DPle5hmNAeXRjcPJl13sRT53+UmydH1rmUavmJOZDYmIuBB4W3+Dc+X/O9r1qNWDA/QIVoLs/OTr6f6vMmw+8vGO/yN7CDuXfMXijyv9cfveh9kwGmxDwMg+1g8Efi3pA2T/5fuMlk5tRiJXcY9gJcg+LukrwHaSHoiImySdHhEvSVqH7E3pv4370g7MZq9eEXFZ6XnuR+TrOvt8ftrqy43ERgFJE8mc9QSyUdgvydfNnUy+//Wy7qXOzDqtv12PWj05QI8Skl4LvBt4P9m/9jLA8RFxQTfTZWZmrTlAjzIlUL9E9iR0nzvJNzOrJwdoMzOzGnIrbjMzsxpygDYzM6shB2gzM7MacoA2MzOrIQdoMzOzGnKANqspSSHpC5Xfh0g6qotJMrNh5ABtVl/PAdtIWrTbCTGz4ecAbVZfLwJnAAc1j5C0paRrJf1B0lWlAxokHSXpHEm/knSPpG0knSjpFkmXl9caIml1Sb+QdKOkKyS9bnhXzcz64gBtVm+nArtIWqhp+K+BtSLiLeTbyA6tjFsW2BDYCvgOcHVErEy+enTzEqRPAbaLiNWBbwCfGdrVMLP+8tuszGosIp6Q9C3gADLANiwBnFdKvnMCd1XGXRYRL0i6BRgLXF6G3wJMBlYE3gRcWd4uOha4fyjXw8z6zyVos/r7ErAX+R7vhlOAr5SS8b7A3JVxzwFExMvAC5W+1l8mM+UCbo2IVctn5YjYZIjXwcz6yQHarOYi4lHyNaF7VQYvBNxXvu/ez1neDkyUtDaApDkkrTTohJpZRzlAm706fAGotuY+CviBpBuBh/szo4h4HtgOOEHSH4GbgXU6k0wz6xS/zcrMzKyGXII2MzOrIQdoMzOzGnKANjMzqyEHaDMzsxpygDYzM6shB2gzM7MacoA2MzOrIQdoMzOzGvp/nwqEH/FX94sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Disply bar chart of last name occurances\n",
    "plt.bar(*zip(*president_counts.items()), color='#00539B')\n",
    "plt.xticks(rotation = 45)\n",
    "plt.title(\"Occurances of Duke Presidental Names in the Card Catalog\")\n",
    "plt.xlabel(\"Name\")\n",
    "plt.ylabel(\"# of Cards\")\n",
    "plt.show()\n",
    "\n",
    "# Display bar chart of last name frequencies based on the 1990 census data\n",
    "plt.bar(*zip(*census_freqs.items()), color='#00539B')\n",
    "plt.xticks(rotation = 45)\n",
    "plt.title(\"Frequency of Duke Presidental Last Names in America From 1990 Census Data\")\n",
    "plt.xlabel(\"Name\")\n",
    "plt.ylabel(\"% Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc6a8dc3",
   "metadata": {},
   "source": [
    "The first bar chart shows the frequencies of the Duke presidential last names within the card catalog, in order of presidental appointment. There does not appear to be any trend over time amongst the names. When comparing to the 1990 census data of last name frequency in the United States, some names are similarly more frequent than others (e.g., Wood and Hart) or similarly infrequent (e.g. Gannaway, Edens, and Keohane). Some presidents, however, have statistically uncommon last names but a large amount of occurances in the card catalog (Craven, Few, Sanford). Are there other factors at play? Let's see how this compares to the amount of time each person spent in office."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee0e00e7",
   "metadata": {},
   "source": [
    "#### Occurances versus Time in Office"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23206e48",
   "metadata": {},
   "source": [
    "Each of these Presidents served varied time spans in office, let's see if there is a relationship between the length of time they spent in office and the amount of times they were mentioned in cards in the catalog. Preseident Craven served two nonconsecutive terms, so his time in office will be the addition of the two terms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a6f2e3ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+VWZndcYYY4IklK3FFlN6d+Z+u89W1VF4nQ+G1NSMdTw4LZMfd+TQqnEco5ISuL5r21DP1hhjjhihPHKpkqZmrCMlfRF787yDog3bc0hJXwRgCcYYY4LkiOtb7MFpmUWJpdDevHwenFbyepmbbrqJrKwssrOzadq0KQcOHGD9+vUMHjw44Pn17h2SO4gaY0yVdsQdufy4Iyfg8gsuuICMjAwiIiLo3bs3S5cu5euvv6ZBgwYMGjSI7OxsXnrpJf71r38xZ84cfvvtN9LS0jhw4ADDhw/npJNOIifH//yMMaYmO+KSS6vGcWzYXnKH36pxyVtcX3TRRYwdO5aYmBiGDRvG5MmTWbp0KQ0aNOC1117j9ddfZ/78+UyaNIl33nmHDRs28Pzzz6OqjB49mpYtW3LZZZdVxmIZY0yVcsSdFhuVlEBsrciDymJrRTIqKaHEsG3atGH9+vXs27ePFi1asGXLFnz7YvNaW1PivapSq1YtIiMjiYo64vK3McYceUcuhZX2gbYWi4+Pp3HjxkXv27VrR0xMDEOGDCErK4uXXnqJ/fv3c8cdd7B3714efvhh8vPzGTVqFMcdd1ylLJMxxlQ11fp+LgkJCWodVxpjTPmIyDJVLXm6JoiOuNNixhhjQs+SizHGmKCz5GKMMSboLLkYY4wJOksuxhhjgs6SizHGmKCz5GKMMSboLLkYY4wJOksuxhhjgs6SizHGmKALWXIRkVdFZKuIrPIpe1NEVrjH+sLbH4tIGxHZ5/NZeqjiMsYYE3qh7LhyIvACMKmwQFWvK3wtIs8A2T7Df6eqHUIYjzHGmEoSsuSiqhki0sbfZ+L1TX8tcFGo5m+MMSZ8wlXncj6wRVX/51N2nIgsF5GFInJ+aSOKSIqIZIpI5rZt20IfqTHGmHILV3LpB7zh834z0EpVOwKDgWkiUt/fiKo6XlUTVDUhPj6+EkI1xhhTXpWeXEQkCugFvFlYpqq5qrrDvV4GfAecVNmxGWOMCY5wHLlcAqxV1U2FBSISLyKR7vXxwInA92GIzRhjTBCEsinyG8CnQDsR2SQit7mP+nLwKTGArsBXIvIl8DaQqqpZoYrNGGNMaIWytVi/Uspv9lP2DvBOqGIxxhhTuewKfWOMMUFnycUYY0zQWXIxxhgTdJZcjDHGBJ0lF2OMMUFnycUYY0zQWXIxxhgTdJZcjDHGBJ0lF2OMMUFnycUYY0zQWXIxxhgTdJZcjDHGBJ0lF2OMMUFnycUYY0zQWXIxxhgTdJZcjDHGBF0o70T5qohsFZFVPmVpIvKTiKxwj+4+n90vIutE5BsRuTxUcRljjAm9UB65TASu8FP+rKp2cI/3AUSkPd7tj09x44wTkcgQxmaMMSaEQpZcVDUDyApw8GuA6aqaq6o/AOuAzqGKzRhjTGiFo85loIh85U6bNXJlzYGNPsNscmUliEiKiGSKSOa2bdtCHasxxpgKqOzk8iJwAtAB2Aw848rFz7DqbwKqOl5VE1Q1IT4+PiRBGmOMOTyVmlxUdYuq5qtqATCB3099bQJa+gzaAvi5MmMzxhgTPJWaXESkmc/bPwKFLclmA31FpLaIHAecCCypzNhMzTc1Yx1tUqcT0edl2qROZ2rGunCHZEyNFRWqCYvIG0Ai0ERENgHDgUQR6YB3yms9cAeAqq4WkRnAGuAA8BdVzQ9VbObIMzVjHSnpi9ib521WG7bnkJK+CIDru7YNZ2jG1Eii6rdqo1pISEjQzMzMcIdhqoE2qdPZsD2nRHnrJnGsT+9bonz16tUMHz6cpk2b0qpVK+67774yp79792769u1Lz549SUlJ8TtMQUEBERHeyYIFCxawatUqBg4cWIGlMebwiMgyVU0I5TxCduRiTFXy446SiaWs8vnz53PbbbfRrVs3AEaOHMn27dtp2rQp999/Pz169CAxMZFVq1YxePBgMjIy2LVrFy1atOCll17iq6++Yvfu3YwdO5bnn3+erKwsOnbsyK5du9iwYQPZ2dmcddZZIVteY8LNkos5IrRqHOf3yKVV4zi/w9922208+eSTvP3223Tu3JmCggLq16/PzJkzuf/++wEYPHgwq1evZu7cufTr14/169fTvXt3evXqxcyZM1m4cCFvvPEGANdddx1dunThj3/8I7NmzeLNN9/EmtKbmsz6FjNHhFFJCcTWOrjTh9hakYxK8n9moH79+owaNYpXXnmFd999FxFh5MiRxMV5ySgmJoaIiAiio6PJzc09aFwROegZoEGDBgDUqlULgNq1awdnwYypouzIpQKmZqzjwWmZ/Lgjh1aN4xiVlGCVwlVc4fcT6Pf27rvvMm/ePKKiojj55JP58ssvefrppwM62rj44ou566672LlzJ88++ywvvPBC0WddunThiSee4LvvvqNDhw5BWTZjqiKr0C+n4q2OwPsHPD71fEswxphqoTIq9O20WDk9OC3zoMQCsDcvnwenlZ7kJk6cyNy5cwFYu3YtaWlppQ5bUFBA7969gxKrMcaEiyWXcipvqyN/9u7dy0MPPcTAgQOZMGEC4J0uGT16NIsXL2bNmjU89thjQYnXGGPCwepcyqm8rY4KjRs3jrlz57Jr1y5at27NgQMHOOqoo5gxYwbJycnExsYybNgwANq3b89DDz0UkviNMaYy2JFLOZW31VGhAQMGkJ6eTlpaGu+//z7t27fn0Ucf5cCBA8DvrYng4FZGxhhTHVlyKafru7ZlfOr5tG4Sh4h3hXd5K/M7duzI3LlzefLJJ8nPL9nLTXR0NA8//HAwwzbGmEplrcWMMeYIY63FjDHGVEuWXIwxxgSdJRdjjDFBZ8nFGGNM0FlyMcYYE3SWXIwxxgRdyJKLiLwqIltFZJVP2VMislZEvhKRWSLS0JW3EZF9IrLCPdJDFZcxxpjQC+WRy0TgimJlHwCnqurpwLfA/T6ffaeqHdwjNYRxGWOMCbGQJRdVzQCyipXNV9UD7u1nQItQzd8YY0z4hLPO5VbgXz7vjxOR5SKyUETOL20kEUkRkUwRybTbxBpjTNUUluQiIg8CB4Cprmgz0EpVOwKDgWkiUt/fuKo6XlUTVDUhPj6+cgI2xhhTLpWeXETkJqAHcL26js1UNVdVd7jXy4DvgJMqOzZjjDHBUanJRUSuAIYCV6vqXp/yeBGJdK+PB04Evq/M2IwxxgRPyG4WJiJvAIlAExHZBAzHax1WG/jA3bPkM9cyrCvwqIgcAPKBVFXN8jthY4wxVd4hk4uI1AX2qWqBiJwEnAz8S1X3lzWeqvbzU/xKKcO+A7wTQLzGGGOqgUBOi2UAMSLSHPgQuAXvGhZjjDHGr0CSi7j6kV7A86r6R6B9aMMyxhhTnQWUXETkXOB64J+uLGR1NcYYY6q/QJLLILyK+Fmqutq15voopFEZY4yp1g55BKKqC4GFPu+/B+4KZVDGGGOqt1KTi4jMAbS0z1X16pBEZIwxptor68jlaffcC2gKTHHv+wHrQxiTMcaYaq7U5OJOhyEiI1W1q89Hc0QkI+SRGWOMqbYCqdCPd5X4AIjIcYD1GGmMMaZUgTQpHgQsEJHCvr7aACmhCsgYY0z1V2ZyEZEIoAFeR5Inu+K1qpob6sCMMcZUX2WeFlPVAmCg6xL/S/ewxGKMMaZMgdS5fCAi94hISxE5qvAR8siMMcZUW4HUudzqnv/iU6bA8X6GNcYYYwK6Qv+4ygjEGGNMzRFQB5QicipeT8gxhWWqOilUQRljjKneArlZ2HC8O0q2B94HugGLAUsuxhhj/AqkQr83cDHwi6reApyBd6viMonIqyKyVURW+ZQdJSIfiMj/3HMjn8/uF5F1IvKNiFxegWUxxhhTRQSSXPa5JskHRKQ+sJXAKvMnAlcUKxsGfKiqJ+Ld1XIYgIi0B/oCp7hxxolIZEBLYIwxpsoJJLlkikhDYAKwDPgCWHKokVQ1A8gqVnwN8Lp7/TrQ06d8urue5gdgHdA5gNiMMcZUQYG0FhvgXqaLyL+B+qr6VQXnd4yqbnbT3SwiR7vy5sBnPsNtcmUliEgKrvuZVq1aVTAMY4ypnqZmrOPBaZn8uCOHVo3jGJWUwPVd24Y7rBJKPXIRkctFpLdvmaquB04TkUuDHIf4KfN7LxlVHa+qCaqaEB9v/WcaY44cUzPWkZK+iA3bc1CFDdtzSElfxNSMdeEOrYSyjlxGAFf5Kf8QmAV8UIH5bRGRZu6opRle/Q14RyotfYZrAfxcgekbY0yN9eC0TPbm5R9UtjcvnwenZfo9esnLy2PIkCGoKvv37ycpKYkLLrigUmItK7nEquq24oWq+ouI1K3g/GYDNwGj3fN7PuXTRGQMcCxeR5mHrNcxxpgjyY87cspVPmHCBLp37063bt0ASEpK4qOPPgJoKCL9gQ5AfbweWCYD/YE44G/AKGAI3pml74B38S5BmY3X+OpWVS31bsVlVejHiEiJ5CMi0UCdMsYrHO4N4FOgnYhsEpHb8JLKpSLyP+BS9x5VXQ3MANYA/wb+oqr5/qdsjDFHplaN48pVvnr1ajp16lT0vlatWiQnJwPsckUH8Oq3O+KdjboU7/KTt4ABwD5gB3Ba4SRV9Wm8xlpNy4q1rOQyE5jge5TiXqe7z8qkqv1UtZmqRqtqC1V9RVV3qOrFqnqie87yGX6Uqp6gqu1U9V+Hmr4xxhxpRiUlEFvr4Ks0YmtFMiopwe/wp5xyCsuWLSt6n5eXR4MGDQrfXquqw/DOEsUCbwN/Ai4E/ouXH6aqapqq3ubG2eOe93OI6x3LOi32EPAYsEFENriyVsArwMNlTdQYY0zwFdarBNpaLDk5mcGDBzNnzhzy8/PZvHmz78ebReQ+vMs+Fqpqtjsz9bOq5ovIC8DjIrIZ+JXfLyMJiJRxyswbQKQOUBj5OlXdV54ZhFJCQoJmZmaGOwxjjKlWRGSZqvo/3AmSQK5z2QesDGUQxhhjapZArtA3xhhjyqWsiyi7uOdDdlJpwmNqxjrapE4nos/LtEmdXiUvpDLGHJnKOnJ5zj1/WhmBmPKpTlfqGmOOPGXVuewXkdeA5iLyXPEPVfWu0IVlDqW8V+pOnDiRGTNm0KpVK04++WTatm1Ljx49DjsOVUXEX+89xpgjWVnJpQdwCXARXm/Ipgop75W6AAMGDKBHjx5MnDgRgHXr1pGWlkZMTAxXXXUVeXl5xMbGsnXrVjIzM0lLS2P06NHcddddPPPMM6gqJ5xwAj179uT666/nqquu4oQTTmD27Nk0bdqUwYMH06xZs1AsrjGmmin1tJiqblfV6cDVqvp68Uclxmj8KO+VugDjxo0jNTWV7OxsANLT0xk5ciQvv/wyU6ZMITExkYyMDNasWUNMTAwLFiyga9eujBs3jjp16tC4cWNWrvQaDrZv355hw4ahqrRp04bk5GRLLMaYIoG0FtshIrPcXSW3iMg7ItIi5JGZMpX3Sl3wjlzS09OLrtD1PaUlIsTHx7NlyxYAWrRowRtvvEHXrl0pKCjg+uuvJy0tjVdeeQWgaBrXXnstN954I2PHji3ss8gYYw59nQvwGjAN6OPe93dlwe5235RDea/U9eeOO+7g4YcfJjY2ln79+gFQr149TjjhBM455xwmT55Mo0aNGDhwIA888ADNmjWjXr163HTTTUXTePvtt/nss8/Izs6mRQv7z2GM8QRyhf6XqnpGsbIVqtohlIEFoqZfoV9dbgpkjKleKuMK/UBOi20Tkf4iEuke/fF6yTQhVN6mxhMnTmTu3Lls3LiRPn36FO9DiNmzZ7No0SK/4/bufdA94UhLS2PVqlXBWRBjzBEpkORyK3At8AuwGa875ltDGZQpu6lxadauXcu9997L+PHj6dOnD2PGjCEpKYmff/6ZrKwssrOz+frrr+nbty/Dhw/niiuuALxWY8OGDeOaa65h3759LF68mHHjxrF48eKQLqMxpuYKpG+xH4GrKyEW46MiTY2ffvppZs6cSaNGjahXr15Rb6gLFy4sGubll1/mySefpHnz5lx++eUAxMfHM3r0aMaOHcuKFSs477zz6N27N6eeempwF8oYc8QIpELfhEGrxnFs2F4ykZTV1Pi5555jzJgxxMTEULeudxue6OhocnNzi4YpbCHme+FjYcuvmJgYcnNziYiwLueMMYfH9iJVVEWaGsfGxjJ58mSeeOIJsrKy/A6TnJzM0KFDefTRR4mL85+oOnXqxFNPPXXQEY8xxpTHIVuLBX2GIu2AN32KjgceARoCycA2V/6Aqr5f1rSstVj5ZWVlMXbsWHbs2MHFF19Mr169ghStMaa6qIzWYoE0RX5IVR9zr2uram6ZI5Rn5iKRwE/A2cAtQI67P3NAanpyMcaYUAhrU2QRuU9EzsVrHVYo2D0kXwx8p6obDjmkMcaYaqOsOpdv8K7KP15EFonIeKCxO60VLH2BN3zeDxSRr0TkVRFp5G8EEUkRkUwRydy2bZu/QYwxxoRZWcllJ/AAsA5I5Pf7uwwTkU8Od8YiUguvifNbruhF4ASgA971NM/4G09Vx6tqgqomxMfHH24YxhhjQqCspshXAMPxdvhjgC+BPap6S5Dm3Q34QlW3ABQ+A4jIBGBukOZjjDGmkpXV5f4DqnoxsB6YgpeI4kVksYjMCcK8++FzSkxEfPtr/yNg/Y8YY0w1FchFlPNUdSmwVET+rKrniUiTw5mpiMTi9ap8h0/x30SkA6B4Ce2OkmMaY4ypDgLp/uU+n7c3u7LthzNTVd0LNC5WdsPhTNMYY0zVUa4r9FX1y1AFYowxpuaw7l+MMdXe1Ix1tEmdTkSfl2mTOr3UW1OYymMdVxpjqrXCex8V3qKi8N5HgN1cL4zsyMUYU62V995HEydOpHv37qSmpvLuu+9WQoRHJksuxphqrSL3PhowYADp6enk5uYyaNAgbr31VlasWMEtt9yCqnLJJZfw8ccf89RTT/Hll1bVXBGWXIwx1Vpp9zgq695H48aNIzU1ldTUVBo2bMgxxxzDkiVLaN++PcuWLaNdu3ZkZGSwcuVKTj/99FCFXqMdkXUu2XvyaHjTpHCHUWU0axRL86NiaX5UXVo0rlv0uvlRsbRoXJeWTeKIrX1EbiqmGhiVlHBQnQsc+t5HAwYMoEePHnz//fekpaUVlS9dupTHH3+clJQU5s2bR0RExEE31jOBOyL3GLWjI+h4XGOW/7Aj3KFUCZt37mXzzr1kfndYly/VWLWiIoiLiXaPKOJioqlXJ9rnOaro88Jy37Lir+vVqUV0lJ00CJbCSvuK3Puof//+pKSkUKdOHa688kouvvhiFi1axKRJk5g3bx4dO3YMdfg1VqXfLCyYqvv9XFSVvbkH2LUnj+y9xR/7D37vhtlVbJhde3Kpxl+hOQJFFyVrn6RclHijD0rUlqxDozLu53JEHrlUFSJC3Zho6sZE07xx3XCHUyPsyz3Ajpxcsn79jaycXPc6l6yc3x8NYmsRWzuKnN/2u8eBMl/vP1AQ7sWqUfYfKGBnTi47c4J238FqIyJCqBUV4R6RAb2uHRVB7ejIg8rjYqJ5pE9H6sZEh3uRSmXJxdQodWpH0aJ2FC0sWQdF3v58v0l39748ft23n1/3BZagLVl7CgqU3/Ly+S0vH9h/WNMSgdH9OwcnsBCw5BIiUzPWVegcsDFVSa3oSI6KjuSoeuGOpGbIzy8g70DhI/8Qr/PJ3V9Ars/7vP3eMAC3XxLM+zYGnyWXELArho0x/kRGRlAnMoI6tcMdSehZLVgIlPeK4f79+7N582by8vLo06cPv/32W4lhCgoK6N27d0jiNcaYYLMjlxAo7xXDTz31FEOHDqV169bk5OTwwAMPsH//fp5//nl69OjBH/7wBzp16gTA9u3bue+++xg1ahTNmjXzOz1jjAk3O3IJgfJeMdysWTMSExNZs2YNnTt3ZsyYMTRp0oRVq1ZRUFDAfffdx6WXXsqOHTv461//yjPPPGOJxRhTpYUluYjIehFZKSIrRCTTlR0lIh+IyP/cc6NwxBYMo5ISiK0VeVDZoa4YPv7440lMTCy6GrjwOTY2lqgo7wAzLi6OyMhItm7dGqLIjTEmOMJ55HKhqnbwuZBnGPChqp4IfOjeV0vXd23L+NTzad0kDhFo3SSO8annH7IyX0TYvn079957L1u3buXUU0896PPatWuTnp7O448/ztq1a0O5CMYYc1jCcoW+iKwHEnxvlywi3wCJqrpZRJoBC1S1zLZ21f0KfWOMCYfKuEI/XEcuCswXkWUikuLKjlHVzQDu+Wh/I4pIiohkikjmtm3bKilcY4wx5RGu1mJdVPVnETka+EBEAj7Ho6rjgfHgHbmEKkBjjDEVF5YjF1X92T1vBWYBnYEt7nQY7tlqrY0xppqq9OQiInVFpF7ha+AyYBUwG7jJDXYT8F5lx2aMMSY4wnFa7BhglmtqGwVMU9V/i8hSYIaI3Ab8CPQJQ2zGGGOCoNKTi6p+D5zhp3wHcHFlx2OMMSb47Ap9Y4wxQWfJxRhjTNBZcjHGGBN0llyMMcYEnSUXY4wxQWfJxRhjTNBZcjHGGBN0llyMMcYEnSUXY4wxQWfJxRhjTNBZcjHGGBN0llyMMcYEnSUXY4wxQWfJxRhjTNBZcjHGGBN0llyMMcYEnSUXY4wxQVfpyUVEWorIRyLytYisFpG7XXmaiPwkIivco3tlx2aMMSY4Kv02x8ABYIiqfiEi9YBlIvKB++xZVX06DDEZY4wJokpPLqq6GdjsXv8qIl8DzSs7DmOMMaET1joXEWkDdAQ+d0UDReQrEXlVRBqVMk6KiGSKSOa2bdsqK1RjjDHlELbkIiJxwDvAIFXdDbwInAB0wDuyecbfeKo6XlUTVDUhPj6+ssI1xhhTDmFJLiISjZdYpqrqTABV3aKq+apaAEwAOocjNmOMMYcvHK3FBHgF+FpVx/iUN/MZ7I/AqsqOzdRsUzPW0SZ1OhF9XqZN6nSmZqwLd0jG1FjhaC3WBbgBWCkiK1zZA0A/EekAKLAeuCMMsZkaamrGOlLSF7E3Lx+ADdtzSElfBMD1XduGMzRjaqRKP3JR1cWqKqp6uqp2cI/3VfUGVT3NlV/tWpUZExQPTsssSiyF9ubl8+C0TL/DT5w4kblz57Jx40b69OlDcnIyAL179w55rMbUBOE4cjGm0v24I6dc5QBr165lypQpjB8/vii5FLr//vtRVb755htuu+02mjRpwosvvoiqMmDAAM4555ygxm9MdWPJpYabmrGOB6dl8uOOHFo1jmNUUsIReRqoVeM4NmwvmUhaNY4rdZynn36amTNn0qjRwa3is7Oz2bJlC6+++ipPPfUUAM8//zyvvPIKAMnJyZZczBHP+harwQrrGTZsz0H193qGI7Eie1RSArG1Ig8qi60VyaikhFLHee655xgzZgxffPFFic+8dim/P6sqIlL03pgjnR251GBl1TP4O3qZOHEib7/9Nq1atSIyMpLnn38egIKCAiIi/P8PWb9+PS+88AJ9+/blu+++47rrrgv+ggRB4fKW5yguNjaWyZMnc+ONN5KVlVVU3qBBA44++miGDRvG6tWrSUhIYODAgaSmpgLw5z//ObQLY0w1IKoa7hgqLCEhQTMz/VfIGojo8zL+vl4RKHjr9hLlEydOpEmTJvTo0YOePXvSsGFD2rRpw2mnncaqVavYuXMn2dnZvPTSS8yaNYv//Oc/xMXFERkZSY8ePVi1ahU33HADw4cPR1WpV68ejz32WCUsaeWbOXMmS5cuZdu2bYwbN45atWqFOyRjAiYiy1S19MP2ILAjlxqsIvUMEyZM4N1336Vly5b8+uuvJCcnU69ePebOnctrr73G66+/zvz585k+fTqzZs3i888/56233ioaf9q0aezbt49mzZrx7bffkpeXVyN3vL169aJXr17hDsOYKsvqXGqwitQzJCcn8/LLL3PssceSnZ1NgwYNDvq8sE4hOjoagNq1ax/0eUFBAVdeeSVpaWlMmzatRiYWY8yh2ZFLNXaolmAVqWdIT09n3rx57Nixg8hILzHVr1+f1q1bM2TIELKysnjppZfYs2cPjzzyCLm5uQeN379/fwYOHMiiRYvIy8vj73//ewiW3BhT1VmdSzVV/Ipz8I5Kxqeef0Q2NTbGBK4y6lzstFg1Vd4rzvPy8rjzzjsZOHAgd9xxBwsXLgS801gVdfPNN5OTk1P0bIwxhey0WDVV3ivOJ0yYQPfu3enWrRsASUlJfPTRR35bgqWkpDBx4kROPPFEFixYwJNPPsmIESOYPn063377LTt37qyxrcCMMcFhRy7VVGktvkorX716NZ06dSp6X6tWLZKTk7n00ktZv349Y8eOJTExkfnz59OgQQOWL19OYmIiCxYsICsri+joaCZNmkSDBg1o1KiR3wsLjTGmkCUXP6pD1+zlbQl2yimnsGzZsqL3eXl5pbYEO++88xg5ciR//etf+c9//sMxxxyDqtK8eXPS0tJ49tlnufrqq4O6PNVhnRtjAmenxYqpLl2zl7clWHJyMoMHD2bOnDnk5+ezebPX6bS/lmDZ2dkMGjSI9u3bs2vXLnr27Em9evXo3Lkzd955J6rKrbfeGrRlqS7r3BgTOGstVkyb1Ol+Lzxs3SSO9el9S5Tn5eVxzz33UFBQgKpy1llnBXXHWx0cbueY5V3nh9NNTWFPAgMHDgw4Pl+9e/fm7bffrtC4xlQVdoV+GFSkorxbt25FFeUHDhxg5MiRbN++naZNm3L//ffTo0cPEhMTWbVqFYMHDyYrK4sxY8bQpUsXfvnlF5599lmmTJnCihUr2L17N//4xz9ISUnh1Vdf5dJLL2XEiBF88sknXHbZZaxcufKg4ZKTk3nxxRfJzs7m8ccf57nnngvZuvEnGEcdFekOPzU1taibmptvvjngbmoA5s+fzw8//EBubi4vvPACb775Jp9++im7d+/mrrvuYs+ePcyZM4dffvmFhx56iPr163PnnXfStm1bNm3aVJ7VY8wRy+pciqloRXlBQQGDBg1iwIABFBQUUL9+fWbOnFk03ODBgxkyZAj//Oc/AejSpQtDhw7ll19+KRomKiqKn376ieXLl9O+fXuWLVtGu3btyMjIYOXKlZx++uklhrvxxhuZOnUq48ePJyUlJVirIWAVuQlX9+7dSU1NZeTIkYBbt2tmQ/ZPBw17qG5qbr/9dlq2bAlQauOE6dOnM2HCBPr2/f0I6Oyzz+aZZ55h27ZtALzwwgs0bNiQY445hiVLllCrVi3y8vKIjY1l5syZvPnmm6SmpjJq1KiingmMMWWrckcuInIF8HcgEnhZVUdX5vxHJSX4vTixrIrypUuX0q1bN8aOHcuFF15IixYtGDlyJIsXLwYgJiaGiIgIoqOji65or1u3LuB11Q4wY8YMZs+ezYgRI9i7dy+JiYk8/vjjpKSkMG/ePCIiIhCREsNddNFFvPbaa6gqp556aihXjV8VOeoYMGAAPXr0ALykcMZv0Wzc8T8Kmp8FW9bAL18RpQfo++ebmDhxIgsXLuT4448nIiKCe++9lxdffJHmzZtz1VVXsWbNGpYsWUKDBg0OumanrG5qChsyFA5Tp04d0tLSij7v3bs3b7zxBp988gkfffQRMTExRd3YFO/uxhjjX5VKLiISCfwDuBTYBCwVkdmquqayYqhIRfk999zD3LlziYqKIjExkS+//JKnn3666J9xIJo1a8bf/vY3lixZwgUXXMCZZ57JokWLmDRpEvPmzaNjx45+hwMvwbVr1+4wl7xijqpbmx05uX7LSzNu3Djmzp3LKaecwrHHHsuEESO45Jok1tSOZfPimTQ49kQuOu1Y4vb9BLTgiiuu4LrrrqNfv37k5+ezf/9+fv75Z+bPn8/OnTsr1E2Nr/79+5OSkkKdOnW48sor6dq1K8OHD2fPnj00atSIvn378tBDD7FkyRJ27dp1uKvMmCNClarQF5FzgTRVvdy9vx9AVZ/wN/yR3P1LoTlz5jBjxgxef/31UiuzQ6nJLZPZ8WvJHXfjerXZ/toNJcpvv/12jj32WJKTk7nttts488wzGT16NIMGDeL2229n6NChvPfee0RFef97OnXqxPDhw+nRowd9+/Zl+vTpZGdnM3/+fD788EPS09NDvozG1DRHYvcvzYGNPu83ubIiIpIiIpkiklmeI4Oa6qqrrmLy5MlhSSwAWX6OWsoqB+8U4IUXXkjbtm2ZN28eXbt2ZdasWYBXn3TGGWfQsWNHpk2bxoEDB5g6dSrXXHMNu3btYvny5Vx88cU8+uij5Od7py5PO+00nnnmGZKSkti3bx/Lly/n7rvv5i9/+QtTpkwJ/kIbYw6pqiUXf/eIPejQSlXHq2qCqibEx8dXUlimNOVtAHHeeeexa9cuJk2axGmnnUbnzp256KKL6NKlCwUFBXTo0IHVq1fTs2dPTj/9dJo1a8bkyZMZMWIEF1xwAc2bNycxMZFevXoVnaJq0aIFQ4YM4dxzz2XFihWMGTOGRo0aER8fz/Lly0O16MaYMlSpOhe8I5WWPu9bAD+HKRYTgPI2gIDf703fsWNHrrzyyqKr/VeuXFni3vSxsbFERUURExNDbm4ukydP5uqrr+bss8/mmmuuAX5vHFHYYCIvL4+7776bRo0ahWSZjTGHVtWSy1LgRBE5DvgJ6AskhTckU5bDuTd9UlISixcvPujeLy+99BL33nsv+/bt89v67Q9/+APp6el8/PHHpd6IbOjQodx5550cc8wxtGnThjvvvDM4C2uMCViVqtAHEJHuwFi8psivquqo0oa1Cn1jjCm/I/IKfVV9H3g/3HEYY4ypuKpWoW+MMaYGsORijDEm6Cy5GGOMCTpLLsYYY4KuyrUWKw8R2QZsKOXjJsD2SgynoqpLnFB9YrU4g6u6xAnVJ9Zwx9laVUN6FXq1Ti5lEZHMUDe1C4bqEidUn1gtzuCqLnFC9Ym1usR5OOy0mDHGmKCz5GKMMSboanJyGR/uAAJUXeKE6hOrxRlc1SVOqD6xVpc4K6zG1rkYY4wJn5p85GKMMSZMLLkYY4wJuhqZXETkChH5RkTWiciwcMdTGhFZLyIrRWSFiFSZ7p1F5FUR2Soiq3zKjhKRD0Tkf+65StwspZRY00TkJ7deV7ietsNKRFqKyEci8rWIrBaRu115lVqvZcRZpdapiMSIyBIR+dLFOcKVV7X1WVqcVWp9hkKNq3MRkUjgW+BSvJuPLQX6qeqasAbmh4isBxJUtUpd9CUiXYEcYJKqnurK/gZkqepol7AbqerQcMbp4vIXaxqQo6pPhzM2XyLSDGimql+ISD1gGdATuJkqtF7LiPNaqtA6Fe9ucnVVNUdEooHFwN1AL6rW+iwtziuoQuszFGrikUtnYJ2qfq+qecB04Jowx1StqGoGkFWs+Brgdff6dbwdTtiVEmuVo6qbVfUL9/pX4GugOVVsvZYRZ5Winhz3Nto9lKq3PkuLs8aricmlObDR5/0mquCPw1FgvogsE5GUcAdzCMeo6mbwdkDA0WGO51AGishX7rRZlTiFV0hE2gAdgc+pwuu1WJxQxdapiESKyApgK/CBqlbJ9VlKnFDF1mew1cTkIn7Kquo/hS6qeibQDfiLO8VjDt+LwAlAB2Az8ExYo/EhInHAO8AgVd0d7nhK4yfOKrdOVTVfVTsALYDOIlLyvthVQClxVrn1GWw1MblsAlr6vG8B/BymWMqkqj+7563ALLxTelXVFnc+vvC8/NYwx1MqVd3iftAFwASqyHp159zfAaaq6kxXXOXWq784q+o6BVDVXcACvHqMKrc+C/nGWZXXZ7DUxOSyFDhRRI4TkVpAX2B2mGMqQUTqugpTRKQucBmwquyxwmo2cJN7fRPwXhhjKVPhzsX5I1VgvbqK3VeAr1V1jM9HVWq9lhZnVVunIhIvIg3d6zrAJcBaqt769BtnVVufoVDjWosBuGZ9Y4FI4FVVHRXeiEoSkePxjlYAooBpVSVOEXkDSMTrFnwLMBx4F5gBtAJ+BPqoatgr0kuJNRHvdIMC64E7Cs/Dh4uInAcsAlYCBa74Abz6jCqzXsuIsx9VaJ2KyOl4FfaReH+SZ6jqoyLSmKq1PkuLczJVaH2GQo1MLsYYY8KrJp4WM8YYE2aWXIwxxgSdJRdjjDFBZ8nFGGNM0FlyMcYYE3SWXEyNIZ7FItLNp+xaEfl3iOf7qIhcUs5xznO95a51jxSfz+JF5HMRWS4i54tIH9dL8UcikiAizwV/KYwJLmuKbGoU17XGW3h9YkUCK/CuiP6uAtOKVNX84EYIItIUWAL0dL0PNwHmAY+o6j9FpC/QTVVvcsP/G3hSVT8KdizGhIolF1PjuNsD7AHquufWwGl4F6umqep7rlPGyW4YgIGq+omIJOJdiLkZ7yK3TngX5bXAS1YjVfXNYvObCMxV1bfdbRReB67C6wG3j6quLTb8SLwOcx/xKbsYSAPuxLvKvA7wE96Ftve617OBfwL3qGoP1//X80AC3sV4I1T1HRG5DBgB1Aa+A27x6ZnXmEoRFe4AjAmBEcAXQB4wF/ivqt7quuFYIiL/wetz6lJV/U1ETgTewNtJg9fP06mq+oOI/An4WVWvBBCRBgHMf7uqnikiA4B7gNuLfX4Kv3cLXygTOEVVV4jII3j3+Rno5nkhXkLJdMmv0MNAtqqe5oZr5I6CHgIuUdU9IjIUGAw8GkDcxgSNJRdT47id6pt4NxG7FrhKRO5xH8fgdQ3yM/CCiHQA8oGTfCaxRFV/cK9XAk+LyJN4RyeLAgihsFPKZXg3rypO8N9Td3lPI1yC13eeN7LqThHpAbQHPva6CaMW8Gk5p2vMYbPkYmqqAvcQ4E+q+o3vh+LdrXILcAZew5bffD7eU/hCVb8VkbOA7sATIjJfVQ91FJDrnvPx/xtbjXeU5Nuh6llAee+W6i9JCd49Q/qVc1rGBJW1FjM13TzgTtfbLyLS0ZU3ADa7Ls9vwKtPKUFEjgX2quoU4GngzCDE9A/gZnfUhOts8Ungb+WcznxgoE+sjYDPgC4i0taVxYrISaWMb0zIWHIxNd1IvIr1r0RklXsPMA64SUQ+wzsltqeU8U/Dq6dZATwIPHa4Abneb/sDE0RkLfAJXu/dc8o5qceARiKySkS+BC5U1W3AzcAbIvIVXrI5+XBjNqa8rLWYMcaYoLMjF2OMMUFnycUYY0zQWXIxxhgTdJZcjDHGBJ0lF2OMMUFnycUYY0zQWXIxxhgTdP8P/hDll8Tc6OoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "terms = [4, 37, 2, 1, 7, 16, 30, 7, 11, 3, 6, 16, 8, 11, 13]\n",
    "counts = [55, 97, 5, 192, 10, 19, 65, 87, 3, 139, 81, 145, 15, 0, 5]\n",
    "last = [\"York\", \"Craven\", \"Gannaway\", \"Wood\", \"Crowell\", \"Kilgo\", \"Few\", \"Flowers\", \"Edens\", \"Hart\", \"Knight\", \"Sanford\", \"Brodie\", \"Keohane\", \"Brodhead\"]\n",
    "\n",
    "# Regression code adapted from https://www.w3schools.com/python/python_ml_linear_regression.asp\n",
    "slope, intercept, r, p, std_err = stats.linregress(terms, counts)\n",
    "def myfunc(x):\n",
    "  return slope * x + intercept\n",
    "mymodel = list(map(myfunc, terms))\n",
    "\n",
    "# Plot points with name labels, regression line\n",
    "plt.scatter(terms, counts, color='#00539B')\n",
    "for i in range(len(terms)):\n",
    "    plt.annotate(last[i], (terms[i] + 1, counts[i] - 3), fontsize=7)   \n",
    "plt.plot(terms, mymodel, color='#00539B')\n",
    "plt.title(\"Card Catalog Presidential Mentions versus Time in Office\")\n",
    "plt.xlabel(\"Years in Office\")\n",
    "plt.ylabel(\"# of Cards\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e66e141e",
   "metadata": {},
   "source": [
    "Shown above is a scatterplot of the number of cards on which a presidential last name is mentioned versus the amount of time they spent in office. We ran a linear regression on the data and found **no correlation** between these two variables. Time in office appears not to impact the prevalence of presidential last names in the catalog. What else is going on here? Let's check the catalog for both first *and* last names."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba20e1b3",
   "metadata": {},
   "source": [
    "# Second Approach"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7caaf5e7",
   "metadata": {},
   "source": [
    "### Presidental First and Last Names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7b382d0",
   "metadata": {},
   "source": [
    "A common occurrance in the card catalog is the presence of multiple generations of family members. By looking at the *last* names of presidents, we were able to look for more general trends in regards to the presidents' names, but are unable to determine just how many of the last names gleaned are actually related to the president or their family members. Next we will look into instances where the specific presidents' *first and last* names occur in the files, with hopes of learning more about the history and prevalence of these figures within the card catalog."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8440b225",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in dataframe and pull out wanted, non-null columns\n",
    "df = df[~df.Name.isnull()]\n",
    "df = df[~df.Text.isnull()]\n",
    "df = df.iloc[:,[2,3,8,9,10]]\n",
    "\n",
    "# Using these regexes, we can find if both words occur in a row, e.g. Craven, Braxton\n",
    "base = r'^{}'\n",
    "expr = '(?=.*{})'\n",
    "\n",
    "# Names to look for occuring together\n",
    "full_names = [[\"York\",\"Brantley\"], [\"Craven\", \"Braxton\"], [\"Gannaway\", \"William\"], [\"Wood\", \"Marquis\"], [\"Crowell\", \"John\"],\n",
    "    [\"Kilgo\", \"John\"], [\"Few\", \"William\"], [\"Flowers\", \"Robert\"], [\"Edens\", \"Arthur\"], [\"Hart\", \"Julian\"],\n",
    "    [\"Knight\", \"Douglas\"], [\"Sanford\", \"Terry\"], [\"Brodie\", \"Keith\"], [\"Keohane\", \"Nannerl\"], [\"Brodhead\", \"Richard\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c91368b",
   "metadata": {},
   "source": [
    "York was found 55 times according to the previous method, but most talk about cities & counties or unrelated people with the same last name. There are probably very few talking about the president. This better extracts the president names by checking for the occurence of both last and first names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "020917c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4, 36, 5, 5, 5, 10, 34, 15, 0, 1, 4, 17, 0, 0, 3]\n"
     ]
    }
   ],
   "source": [
    "name_counts = []\n",
    "\n",
    "for name in full_names:\n",
    "    name_counts.append(len(df[df.Text.str.contains(base.format(''.join(expr.format(w) for w in name)),case=True)]))\n",
    "\n",
    "print(name_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "89319068",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dictionary storing indices of president mentioning cards\n",
    "president_indices = {\"York\": [7072, 14590, 50164, 50170], \"Craven\": [2004, 2005, 9541, 10235, 11146, 11155, 11156, 11161, 11167, 11168, 11169, 11170, 11171, 11172, 11173, 11174, 11175, 11176, 11177, 11178, 11179, 11180, 11181, 11182, 11183, 11197, 11200, 14590, 15131, 19231, 20678, 20732, 21211, 25196, 27216, 48485], \n",
    "              \"Gannaway\": [6266, 6267, 6269, 16522, 41864], \"Wood\": [13320, 30929, 30984, 49501, 49528], \n",
    "              \"Crowell\": [127, 11387, 19234, 32886, 46573], \"Kilgo\": [158, 6815, 21326, 25715, 31143, 31930, 42066, 42069, 45697, 45808], \n",
    "              \"Few\": [4293, 4295, 5490, 5535, 6660, 7338, 7356, 13399, 13678, 13680, 14742, 15288, 15290, 15291, 15292, 15293, 15294, 15726, 17493, 17627, 17628, 17632, 18218, 21323, 26808, 28326, 32409, 34437, 34637, 35247, 42069, 46891, 47138, 47141], \n",
    "              \"Flowers\": [10743, 15710, 16296, 19004, 20519, 22959, 26399, 27309, 28173, 30657, 34052, 42059, 42065, 44500, 45697], \n",
    "            \"Edens\": [], \"Hart\": [14123], \"Knight\": [5490, 25741, 30171, 47829],\n",
    "              \"Sanford\": [5490, 9533, 9541, 9554, 19873, 19875, 22266, 39311, 39312, 39313, 39314, 39363, 39364, 39365, 42007, 44948, 45317], \n",
    "              \"Brodie\": [], \"Keohane\": [], \"Brodhead\": [2619, 34442, 35593]}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66370396",
   "metadata": {},
   "source": [
    "When we check for both the first and last names of the Duke presidents, we get markedly fewer results, but more accurate ones. Let's take a look at the frequency of each president's mentions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6b5b1a8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "last = [\"York\", \"Craven\", \"Gannaway\", \"Wood\", \"Crowell\", \"Kilgo\", \"Few\", \"Flowers\", \"Edens\", \"Hart\", \"Knight\", \"Sanford\", \"Brodie\", \"Keohane\", \"Brodhead\"]\n",
    "name_counts = [4, 36, 5, 5, 5, 10, 34, 15, 0, 1, 4, 17, 0, 0, 3]\n",
    "\n",
    "# Disply bar chart of first and last name occurances\n",
    "plt.bar(last, name_counts, color='#00539B')\n",
    "plt.xticks(rotation = 45)\n",
    "plt.title(\"Occurances of Duke Presidental Names in the Card Catalog\")\n",
    "plt.xlabel(\"Name\")\n",
    "plt.ylabel(\"# of Cards\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d666d210",
   "metadata": {},
   "source": [
    "It looks like some of the presidents with more common last names, like Wood, Knight, and Hart, have gone down in frequency when we also check for first names. Let's compare with the length of time in office."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8fa6f456",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "terms = [4, 37, 2, 1, 7, 16, 30, 7, 11, 3, 6, 16, 8, 11, 13]\n",
    "\n",
    "# Linear regression code adapted from https://www.w3schools.com/python/python_ml_linear_regression.asp\n",
    "slope, intercept, r, p, std_err = stats.linregress(terms, name_counts)\n",
    "def myfunc(x):\n",
    "  return slope * x + intercept\n",
    "mymodel = list(map(myfunc, terms))\n",
    "\n",
    "# Plot labeled points, regression line\n",
    "plt.scatter(terms, name_counts, color='#00539B')\n",
    "for i in range(len(terms)):\n",
    "    plt.annotate(last[i], (terms[i] - 1, name_counts[i] + 1), fontsize=7)\n",
    "plt.plot(terms, mymodel, color='#00539B')\n",
    "plt.title(\"Card Catalog Presidential Mentions versus Time in Office\")\n",
    "plt.xlabel(\"Years in Office\")\n",
    "plt.ylabel(\"# of Cards\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4781b686",
   "metadata": {},
   "source": [
    "When we run a linear regression on first and last name occurrances versus years a president was in office, we find a **positive** correlation between the variables. So, as the number of years in office a president had increases, the number of cards on which they are mentioned increases."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "665ef0d8",
   "metadata": {},
   "source": [
    "## Qualitative Analysis of Duke Presidents in the Catalog"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d8ecd42",
   "metadata": {},
   "source": [
    "Let's see what the collections containing the Duke Presidents' full names are talking about and whether they are indeed mentioning the presidents. To do this, we searched through the instances where a president's first and last name appeared in the catalog, briefly summarized the mention, and provided relevant links."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adcdf23e",
   "metadata": {},
   "source": [
    "### Keith Brodie, Nannerel Keohane, and Arthur Edens"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71ae0204",
   "metadata": {},
   "source": [
    "Presidents Brodie, Keohane, and Edens were not mentioned in the card catalog."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "776dc2f3",
   "metadata": {},
   "source": [
    "### Julian Hart"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1495b0ce",
   "metadata": {},
   "source": [
    "The card containing \"Julian\" and \"Hart\" is not referring to the Duke president."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1c1c173",
   "metadata": {},
   "source": [
    "### Richard Brodhead"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fafb99e9",
   "metadata": {},
   "source": [
    "On two cards, a Richard Brodhead is mentioned, but not the one who was president of Duke. Upon further inspection, this man was a [U.S. Democratic Senator from Pennsylvania](https://en.wikipedia.org/wiki/Richard_Brodhead). The cards upon which he is mentioned can be viewed [here](https://archive.org/details/rubensteinmanuscriptcatalog_P_to_Peo/page/44/mode/2up) and [here](https://archive.org/details/rubensteinmanuscriptcatalog_Pep_to_Pn/page/n393/mode/2up)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca210221",
   "metadata": {},
   "source": [
    "### Douglas Knight"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02d66fb6",
   "metadata": {},
   "source": [
    "President Douglas Knight is mentioned as having [correspondence with Herbert Clarence Bradshaw](https://archive.org/details/rubensteinmanuscriptcatalog_R._Boyd_to_I._Brown/page/n125/mode/2up), being the recipient of a [letter from a Mr. Matton](https://archive.org/details/rubensteinmanuscriptcatalog_Mani_to_Maw/page/3/mode/2up) and [letters from William Murray Werber](https://archive.org/details/rubensteinmanuscriptcatalog_Ware_to_H._White/page/n567/mode/2up). The first two mention religion and all involve letters."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e65c7acf",
   "metadata": {},
   "source": [
    "### Brantley York"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27ae27cf",
   "metadata": {},
   "source": [
    "One [card](https://archive.org/details/rubensteinmanuscriptcatalog_X_to_Z/page/n83/mode/2up) simply prompts a look to the Duke University Archives. York is mentioned here as [correspondent to Tod Robinson Caldwell](https://archive.org/details/rubensteinmanuscriptcatalog_M._Butler_to_Caq/page/n202/mode/2up). \n",
    "\n",
    "[This collection](https://archive.org/details/rubensteinmanuscriptcatalog_Durh_to_Els/page/n586/mode/2up) mentions early foundations of Duke Univesity and President Craven.\n",
    "\n",
    "Here is [York's son's collection](https://archive.org/details/rubensteinmanuscriptcatalog_X_to_Z/page/n89/mode/2up)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdc552d2",
   "metadata": {},
   "source": [
    "### William Gannaway"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06d449cd",
   "metadata": {},
   "source": [
    "Four cards mention William Gannaway Brownlow, former Governor of Tennesee and one is a prompt to [see the archives](https://archive.org/details/rubensteinmanuscriptcatalog_Fro_to_Geq/page/n322/mode/2up)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8179fcd9",
   "metadata": {},
   "source": [
    "### Marquis Wood"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6614476c",
   "metadata": {},
   "source": [
    "Like many of the other presidents, Wood has a [card](https://archive.org/details/rubensteinmanuscriptcatalog_S._Williams_to_Wood/page/n717/mode/2up) prompting a check of the archives.\n",
    "\n",
    "Marquis Wood also has two collections of [papers](https://archive.org/details/rubensteinmanuscriptcatalog_Meth_to_Mh/page/59/mode/2up) associated with the Methodist Episcopal [Church](https://archive.org/details/rubensteinmanuscriptcatalog_Meth_to_Mh/page/n344/mode/2up).\n",
    "\n",
    "In William Clark Doub's [collection](https://archive.org/details/rubensteinmanuscriptcatalog_Del_to_Dov/page/19/mode/2up), Wood's manuscript on the introduction of Methodism into the Yadkin Valley is mentioned."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f846ad9e",
   "metadata": {},
   "source": [
    "### John Crowell"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01f6cf08",
   "metadata": {},
   "source": [
    "We, of course, have the entry under Crowell's name to [See Duke University Archives](https://archive.org/details/rubensteinmanuscriptcatalog_Cre_to_I._Davis/page/n119/mode/2up).\n",
    "\n",
    "This [card](https://archive.org/details/rubensteinmanuscriptcatalog_A_to_Amer/page/n127/mode/2up) mentions a quarrel between Crowell and his faculty.\n",
    "\n",
    "[This](https://archive.org/details/rubensteinmanuscriptcatalog_Mus_to_Nn/page/n105/mode/2up) and [this](https://archive.org/details/rubensteinmanuscriptcatalog_Gri_to_Hand/page/n131/mode/2up) mention letters to and from John Crowell.\n",
    "\n",
    "[Here](https://archive.org/details/rubensteinmanuscriptcatalog_V_to_Ward/page/n85/mode/2up) Crowell is part of a list of unpublished sketches of well-known North Carolinians."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65ccec17",
   "metadata": {},
   "source": [
    "### John Kilgo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2cb3958",
   "metadata": {},
   "source": [
    "Here we have the boilerplate John Kilgo [card](https://archive.org/details/rubensteinmanuscriptcatalog_K_to_Kira/page/n489/mode/2up).\n",
    "\n",
    "Kilgo is listed as a correspondent in the Hemphill Family [Collection](https://archive.org/details/rubensteinmanuscriptcatalog_Harw_to_Hem/page/23/mode/2up).\n",
    "\n",
    "Kilgo appears to have been involved in the Methodist Episcopal Church [here](https://archive.org/details/rubensteinmanuscriptcatalog_Meth_to_Mh/page/n503/mode/2up) and [here](https://archive.org/details/rubensteinmanuscriptcatalog_Mi_to_Mord/page/n686/mode/2up).\n",
    "\n",
    "Correspondence with President Kilgo is mentioned [here](https://archive.org/details/rubensteinmanuscriptcatalog_Ton_to_Tz/page/n668/mode/2up) and [here](https://archive.org/details/rubensteinmanuscriptcatalog_Wi._Smith_to_So/page/n642/mode/2up) in relation to the Southgates.\n",
    "\n",
    "Kilgo seems to be something of a controversial and outspoken character, as [here](https://archive.org/details/rubensteinmanuscriptcatalog_J._Brown_to_L._Butler/page/n675/mode/2up) and [here](https://archive.org/details/rubensteinmanuscriptcatalog_Ton_to_Tz/page/81/mode/2up) he is spoken of positively and [here](https://archive.org/details/rubensteinmanuscriptcatalog_A_to_Amer/page/n157/mode/2up) and [here](https://archive.org/details/rubensteinmanuscriptcatalog_Wi._Smith_to_So/page/n639/mode/2up) he is said to be involved in a court case."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d26ca18",
   "metadata": {},
   "source": [
    "### Robert Flowers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85da5af5",
   "metadata": {},
   "source": [
    "In addition to the requisite Flowers [card](https://archive.org/details/rubensteinmanuscriptcatalog_Fif_to_Frn/page/n276/mode/2up) we have correspondence between Flowers and others [here](https://archive.org/details/rubensteinmanuscriptcatalog_Conl_to_Crd/page/n265/mode/2up), [here](https://archive.org/details/rubensteinmanuscriptcatalog_Gre_to_Grh/page/n605/mode/2up), [here](https://archive.org/details/rubensteinmanuscriptcatalog_No_to_Oz/page/151/mode/2up), [here](https://archive.org/details/rubensteinmanuscriptcatalog_Wi._Smith_to_So/page/n632/mode/2up), [here](https://archive.org/details/rubensteinmanuscriptcatalog_Wi._Smith_to_So/page/n638/mode/2up), and [here](https://archive.org/details/rubensteinmanuscriptcatalog_J._Thomas_to_Tom/page/n73/mode/2up).\n",
    "\n",
    "President Flowers is praised [here](https://archive.org/details/rubensteinmanuscriptcatalog_Ton_to_Tz/page/81/mode/2up) along with some other notable Dukies and had gifted some items related to the Methodist Church [here](https://archive.org/details/rubensteinmanuscriptcatalog_Meth_to_Mh/page/n17/mode/2up).\n",
    "\n",
    "He is also in a [photograph](https://archive.org/details/rubensteinmanuscriptcatalog_Lowr_to_Mack/page/n115/mode/2up) that is cataloged and is said to have written a biography of [Edwin W. Fuller](https://archive.org/details/rubensteinmanuscriptcatalog_Fro_to_Geq/page/n95/mode/2up)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d80de58f",
   "metadata": {},
   "source": [
    "### Terry Sanford"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5af14f7d",
   "metadata": {},
   "source": [
    "As Terry Sanford was a US Senator and NC Governor as well as a President of Duke, there appear to be many cards mentioning him. Cards mentioning correspondence with Sanford can be found [here](https://archive.org/details/rubensteinmanuscriptcatalog_R._Boyd_to_I._Brown/page/n126/mode/2up), [here](https://archive.org/details/rubensteinmanuscriptcatalog_Claw_to_Com/page/n498/mode/2up), [here](https://archive.org/details/rubensteinmanuscriptcatalog_Claw_to_Com/page/n506/mode/2up), [here](https://archive.org/details/rubensteinmanuscriptcatalog_Hold_to_Huba/page/n149/mode/2up), [here](https://archive.org/details/rubensteinmanuscriptcatalog_J._Thomas_to_Tom/page/80/mode/2up), and [here](https://archive.org/details/rubensteinmanuscriptcatalog_Ton_to_Tz/page/n177/mode/2up).\n",
    "\n",
    "[This](https://archive.org/details/rubensteinmanuscriptcatalog_Wi._Smith_to_So/page/n580/mode/2up) talks about the Southern Rural Poverty Project, directed by members of the Sanford Institute of Public Policy.\n",
    "\n",
    "[Here](https://archive.org/details/rubensteinmanuscriptcatalog_Claw_to_Com/page/n519/mode/2up) and [here](https://archive.org/details/rubensteinmanuscriptcatalog_Hane_to_Harv/page/n9/mode/2up) are collections that catalog items that talk about Sanford.\n",
    "\n",
    "[Here] and on subsequent pages we have a restricted collection relating to manuscripts created by Terry Sanford, related to his time as Governor and Duke President. And [here](https://archive.org/details/rubensteinmanuscriptcatalog_San_to_Sem/page/n73/mode/2up) there are documents related to his time as a US Senator."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0733fc8a",
   "metadata": {},
   "source": [
    "### William Few"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ecf31a8",
   "metadata": {},
   "source": [
    "Few is mentioned as a professor [here](https://archive.org/details/rubensteinmanuscriptcatalog_J._Brown_to_L._Butler/page/1873/mode/2up).\n",
    "\n",
    "He is included in collections with other important Duke figures [here](https://archive.org/details/rubensteinmanuscriptcatalog_Ben_to_Blac/page/n481/mode/2up), [here](https://archive.org/details/rubensteinmanuscriptcatalog_Dow_to_Durg/page/n255/mode/2up), [here](https://archive.org/details/rubensteinmanuscriptcatalog_Fif_to_Frn/page/n292/mode/2up), [here](https://archive.org/details/rubensteinmanuscriptcatalog_Harw_to_Hem/page/21/mode/2up), [here](https://archive.org/details/rubensteinmanuscriptcatalog_Pep_to_Pn/page/n47/mode/2up), and [here](https://archive.org/details/rubensteinmanuscriptcatalog_Wi._Smith_to_So/page/n641/mode/2up).\n",
    "\n",
    "Correspondece with William Few is mentioned [here](https://archive.org/details/rubensteinmanuscriptcatalog_Lanp_to_Ler/page/n223/mode/2up), [here](https://archive.org/details/rubensteinmanuscriptcatalog_P_to_Peo/page/39/mode/2up), and [here](https://archive.org/details/rubensteinmanuscriptcatalog_V_to_Ward/page/n403/mode/2up).\n",
    "\n",
    "Few's son, Lynne Few, appears [here](https://archive.org/details/rubensteinmanuscriptcatalog_Ger_to_Gorl/page/n536/mode/2up).\n",
    "\n",
    "He also has a [collection](https://archive.org/details/rubensteinmanuscriptcatalog_Elt_to_Fie/page/n625/mode/2up) of papers related to war and money.\n",
    "\n",
    "Ella Howerton Parks remembers Few as the [\"prince of all hat doffers.\"](https://archive.org/details/rubensteinmanuscriptcatalog_P_to_Peo/page/n295/mode/2up).\n",
    "\n",
    "[This collection](https://archive.org/details/rubensteinmanuscriptcatalog_V_to_Ward/page/n649/mode/2up) talks about a treatise Few signed related to the relations between the northern and southern colonies."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e8fb13b",
   "metadata": {},
   "source": [
    "### Braxton Craven"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86ef87aa",
   "metadata": {},
   "source": [
    "President Craven's great grandson has a rather extensive [collection](https://archive.org/details/rubensteinmanuscriptcatalog_Conl_to_Crd/page/n689/mode/2up) and his grandson is mentioned [here](https://archive.org/details/rubensteinmanuscriptcatalog_Conl_to_Crd/page/n677/mode/2up).\n",
    "\n",
    "Braxton Craven is cataloged in the 1850 census of Randolph County [here](https://archive.org/details/rubensteinmanuscriptcatalog_Armi_to_Bal/page/n486/mode/2up) along with many transactions under his name. Another money-related [card](https://archive.org/details/rubensteinmanuscriptcatalog_Con_to_Conk/page/n403/mode/2up) mentions a statement for what Trinity College owed a company.\n",
    "\n",
    "Correspondences involving Craven are cataloged [here](https://archive.org/details/rubensteinmanuscriptcatalog_Durh_to_Els/page/n586/mode/2up), [here](https://archive.org/details/rubensteinmanuscriptcatalog_Harw_to_Hem/page/n34/mode/2up), and [here](https://archive.org/details/rubensteinmanuscriptcatalog_Harw_to_Hem/page/n88/mode/2up).\n",
    "\n",
    "Craven is mentioned, but not in much detail [here](https://archive.org/details/rubensteinmanuscriptcatalog_Elt_to_Fie/page/n469/mode/2up), [here](https://archive.org/details/rubensteinmanuscriptcatalog_Gri_to_Hand/page/n128/mode/2up), [here](https://archive.org/details/rubensteinmanuscriptcatalog_Harw_to_Hem/page/167/mode/2up), [here](https://archive.org/details/rubensteinmanuscriptcatalog_J._Johnson_to_Jz/page/n676/mode/2up), [here](https://archive.org/details/rubensteinmanuscriptcatalog_Lanp_to_Ler/page/27/mode/2up), and [here](https://archive.org/details/rubensteinmanuscriptcatalog_I._White_to_R._Williams/page/n408/mode/2up)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b7eec38",
   "metadata": {},
   "source": [
    "## Duke Building Names in the Catalog"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed8fd6b4",
   "metadata": {},
   "source": [
    "While going through the card catalog, it was striking to see how many of the last names present in the cards were names of buildings at Duke's Durham campus today. Let's explore the prevalence of these names in the catalog."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c0b597b",
   "metadata": {},
   "source": [
    "Taking the names from buildings on Duke's East and West campuses--including dorms, academic buildings, and public spaces--we will check for the building names in the collection header author name columns of our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "e1a2d9eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Alspaugh': 4, 'Baldwin': 148, 'Bassett': 37, 'Bivins': 1, 'Blackwell': 86, 'Branson': 58, 'Brodie': 15, 'Brown': 826, 'Crowell': 10, 'Epworth': 7, 'Friedl': 2, 'Gilbert-Addoms': 0, 'Giles': 80, 'Lilly': 12, 'Biddle': 133, 'Pegram': 62, 'Southgate': 55, 'White': 613, 'Wilson': 580, 'Allen': 402, 'Bostock': 3, 'Brodhead': 5, 'Fitzpatrick': 10, 'Flowers': 87, 'Gray': 239, 'Gross': 16, 'Hart': 357, 'Karsh': 2, 'Levine': 0, 'Perkins': 177, 'Reuben-Cooke': 0, 'Rubenstein': 0, 'Sanford': 174, 'Teer': 1, 'Wilkinson': 84, 'Craven': 161, 'Edens': 3, 'Few': 65, 'Keohane': 0, 'Kilgo': 25, 'Wannamaker': 2}\n"
     ]
    }
   ],
   "source": [
    "buildings = {\n",
    "'Alspaugh': 0, 'Baldwin': 0, 'Bassett': 0, 'Bivins': 0, 'Blackwell': 0, 'Branson': 0, 'Brodie': 0, 'Brown': 0, 'Crowell': 0,\n",
    "'Epworth': 0, 'Friedl': 0, 'Gilbert-Addoms': 0, 'Giles': 0, 'Lilly': 0, 'Biddle': 0, 'Pegram': 0, 'Southgate': 0,\n",
    "'White': 0, 'Wilson': 0, 'Allen': 0, 'Bostock': 0, 'Brodhead': 0, 'Fitzpatrick': 0, 'Flowers': 0, 'Gray': 0, \n",
    "'Gross': 0, 'Hart': 0, 'Karsh': 0, 'Levine': 0, 'Perkins': 0, 'Reuben-Cooke': 0,  'Rubenstein': 0, 'Sanford': 0, \n",
    "'Teer': 0, 'Wilkinson': 0, 'Craven': 0, 'Crowell': 0, 'Edens': 0, 'Few': 0, 'Keohane': 0,  'Kilgo': 0, 'Wannamaker': 0\n",
    "}\n",
    "\n",
    "# Check each collection author's name for matches\n",
    "for index, row in df.iterrows():\n",
    "    for b in buildings.keys():\n",
    "        if b in str(row['Text']):\n",
    "            buildings[b] = buildings.get(b) + 1\n",
    "                \n",
    "print(buildings)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18586c8f",
   "metadata": {},
   "source": [
    "When we look in the dataset at large, nearly all of the names are present. However, this is certainly not entirely accurate when considering what we are looking for. As we saw with the presidents deep dive, many names have double meanings or can also be first names. Let's check in just the name column to see if we can yield more accurate results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "05616dce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Alspaugh': 1, 'Baldwin': 9, 'Bassett': 2, 'Bivins': 0, 'Blackwell': 36, 'Branson': 0, 'Brodie': 1, 'Brown': 74, 'Crowell': 1, 'Epworth': 0, 'Friedl': 0, 'Gilbert-Addoms': 0, 'Giles': 7, 'Lilly': 2, 'Biddle': 19, 'Pegram': 5, 'Southgate': 4, 'White': 42, 'Wilson': 49, 'Allen': 46, 'Bostock': 0, 'Brodhead': 0, 'Fitzpatrick': 0, 'Flowers': 4, 'Gray': 13, 'Gross': 0, 'Hart': 10, 'Karsh': 0, 'Levine': 0, 'Perkins': 6, 'Reuben-Cooke': 0, 'Rubenstein': 0, 'Sanford': 10, 'Teer': 0, 'Wilkinson': 5, 'Craven': 10, 'Edens': 1, 'Few': 4, 'Keohane': 0, 'Kilgo': 1, 'Wannamaker': 0}\n",
      "{'Alspaugh': [644], 'Baldwin': [2093, 2094, 20279, 21958, 21987, 21988, 33105, 33124, 33125], 'Bassett': [2902, 2903], 'Blackwell': [4524, 4525, 4529, 4530, 4532, 4534, 4537, 4549, 4550, 4551, 4552, 4553, 4554, 4555, 4556, 4557, 4558, 4559, 4560, 4561, 4562, 4563, 4564, 4565, 4566, 4567, 4568, 4569, 4570, 4571, 4572, 4573, 4574, 4575, 4577, 4578], 'Brodie': [5869], 'Brown': [672, 5635, 6019, 6023, 6045, 6046, 6047, 6049, 6059, 6060, 6062, 6065, 6068, 6069, 6073, 6076, 6077, 6079, 6081, 6082, 6083, 6084, 6123, 6125, 6127, 6134, 6136, 6137, 6138, 6140, 6142, 6145, 6146, 6148, 6150, 6153, 6156, 6157, 6158, 6159, 6160, 6161, 6163, 6164, 6165, 6166, 6167, 6168, 6169, 6170, 6171, 6172, 6173, 6174, 6175, 6176, 6184, 6188, 6192, 6195, 6196, 6198, 6201, 6202, 6206, 6215, 6223, 6224, 6225, 6226, 6240, 6241, 9600, 9605], 'Crowell': [11387], 'Giles': [17234, 17236, 17242, 17243, 43415, 43419, 47180], 'Lilly': [10994, 27597], 'Biddle': [4287, 4291, 4292, 4293, 4296, 4299, 4300, 4314, 4315, 4316, 4317, 4318, 4319, 4320, 4321, 4323, 4324, 38118, 40075], 'Pegram': [35032, 35033, 35034, 35035, 35036], 'Southgate': [42029, 42030, 42067, 42068], 'White': [27817, 48046, 48048, 48050, 48051, 48053, 48054, 48056, 48058, 48059, 48060, 48061, 48062, 48063, 48064, 48065, 48067, 48077, 48086, 48090, 48091, 48092, 48094, 48100, 48101, 48110, 48112, 48114, 48118, 48119, 48120, 48122, 48125, 48126, 48129, 48131, 48133, 48134, 48141, 48143, 48144, 48146], 'Wilson': [7645, 15252, 27146, 33430, 35342, 48955, 48958, 48960, 48961, 48964, 48967, 48968, 48969, 48970, 48971, 48980, 48986, 48987, 48990, 48996, 48998, 49000, 49004, 49007, 49008, 49009, 49019, 49021, 49024, 49028, 49030, 49031, 49034, 49035, 49036, 49038, 49040, 49042, 49043, 49045, 49047, 49049, 49052, 49053, 49056, 49059, 49062, 49064, 49066], 'Allen': [486, 487, 499, 503, 505, 507, 508, 509, 515, 518, 519, 520, 521, 522, 523, 524, 525, 529, 530, 533, 535, 538, 540, 544, 547, 548, 549, 550, 552, 553, 554, 563, 566, 14979, 15511, 17472, 22453, 34964, 35032, 35033, 35034, 35035, 35292, 35293, 37048, 49468], 'Flowers': [15700, 15708, 15710, 15711], 'Gray': [18368, 18369, 18370, 18372, 18375, 18381, 18383, 18386, 18388, 18390, 18391, 18392, 23022], 'Hart': [20569, 20574, 20575, 20577, 20579, 20580, 20582, 20584, 20588, 20593], 'Perkins': [35223, 35226, 35228, 35241, 35243, 35246], 'Rubenstein': [], 'Sanford': [37109, 39311, 39349, 39350, 39355, 39357, 39359, 39360, 39363, 39367], 'Wilkinson': [48566, 48572, 48574, 48575, 48577], 'Craven': [11142, 11146, 11147, 11153, 11154, 11167, 11184, 11192, 11193, 14234], 'Edens': [14394], 'Few': [15285, 15288, 15290, 15293], 'Kilgo': [25715]}\n"
     ]
    }
   ],
   "source": [
    "# Dictionary storing important Duke building names (excluding those not named after people)\n",
    "buildings = {\n",
    "'Alspaugh': 0, 'Baldwin': 0, 'Bassett': 0, 'Bivins': 0, 'Blackwell': 0, 'Branson': 0, 'Brodie': 0, 'Brown': 0, 'Crowell': 0,\n",
    "'Epworth': 0, 'Friedl': 0, 'Gilbert-Addoms': 0, 'Giles': 0, 'Lilly': 0, 'Biddle': 0, 'Pegram': 0, 'Southgate': 0,\n",
    "'White': 0, 'Wilson': 0, 'Allen': 0, 'Bostock': 0, 'Brodhead': 0, 'Fitzpatrick': 0, 'Flowers': 0, 'Gray': 0, \n",
    "'Gross': 0, 'Hart': 0, 'Karsh': 0, 'Levine': 0, 'Perkins': 0, 'Reuben-Cooke': 0,  'Rubenstein': 0, 'Sanford': 0, \n",
    "'Teer': 0, 'Wilkinson': 0, 'Craven': 0, 'Crowell': 0, 'Edens': 0, 'Few': 0, 'Keohane': 0,  'Kilgo': 0, 'Wannamaker': 0\n",
    "}\n",
    "\n",
    "# Store indices of building names in dictionary\n",
    "building_indices = {'Alspaugh': [], 'Baldwin': [], 'Bassett': [], 'Blackwell': [], 'Brodie': [], 'Brown': [], 'Crowell': [], \n",
    "                    'Giles': [], 'Lilly': [], 'Biddle': [], 'Pegram': [], 'Southgate': [], 'White': [], 'Wilson': [], 'Allen': [],\n",
    "                    'Flowers': [], 'Gray': [], 'Hart': [], 'Perkins': [], 'Rubenstein': [], 'Sanford': [], 'Wilkinson': [], \n",
    "                    'Craven': [], 'Edens': [], 'Few': [], 'Kilgo': []}\n",
    "\n",
    "# Check each collection author's name for matches\n",
    "for index, row in df.iterrows():\n",
    "    if row['Coll_head'] == 1:\n",
    "        for b in buildings.keys():\n",
    "            if (b + ',') in str(row['Name']) or (b + ' ') in str(row['Name']):\n",
    "                buildings[b] = buildings.get(b) + 1\n",
    "                building_indices[b].append(index)\n",
    "                \n",
    "print(buildings)\n",
    "print(building_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e749d9cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "non_zero = {}\n",
    "\n",
    "for x, y in buildings.items():\n",
    "    if y != 0:\n",
    "        non_zero[x] = y\n",
    "    \n",
    "non_zero = sorted(non_zero.items(), key=operator.itemgetter(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49c52c0e",
   "metadata": {},
   "source": [
    "**Alspaugh: 1, \n",
    "Brodie: 1, \n",
    "Crowell: 1, \n",
    "Edens: 1, \n",
    "Kilgo: 1, \n",
    "Bassett: 2, \n",
    "Lilly: 2, \n",
    "Southgate: 4, \n",
    "Flowers: 4, \n",
    "Few: 4, \n",
    "Pegram: 5, \n",
    "Wilkinson: 5, \n",
    "Perkins: 6, \n",
    "Giles: 7, \n",
    "Baldwin: 9, \n",
    "Hart: 10, \n",
    "Sanford: 10, \n",
    "Craven: 10, \n",
    "Gray: 13, \n",
    "Biddle: 19, \n",
    "Blackwell: 36, \n",
    "White: 42, \n",
    "Allen: 46, \n",
    "Wilson: 49, \n",
    "Brown: 74**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae43cf94",
   "metadata": {},
   "source": [
    "Above, we can see the building names found in the author names of the collections of the card catalog, sorted in ascending order. Not all of the names were present in the files, but many had at least a couple instances in the files. As we've already looked into some of these in the presidents section, let's see what we can learn from some of the other notable Duke names."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef73c029",
   "metadata": {},
   "source": [
    "### Qualitative Analysis of Duke Building Names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "515be543",
   "metadata": {},
   "source": [
    "Let's see if any of mentions of Duke building names in the dataset are actually related to the people that the building was named after."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d05aada9",
   "metadata": {},
   "source": [
    "#### Lilly, Pegram, Wilkinson, Gray, White, Allen, Wilson, Brown"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88619fe3",
   "metadata": {},
   "source": [
    "Some of these names, unfortunately, do not have any hits that are directly relevant to the history of the building, but it is still interesting to see the prevalence of Duke-related names, regardless if it is the same specific individuals. Additionally, the library's history of East Campus buildings is found [here](https://library.duke.edu/rubenstein/uarchives/history/exhibits/building-names/east)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e086e9bb",
   "metadata": {},
   "source": [
    "#### Alspaugh, Bassett, Baldwin"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59789205",
   "metadata": {},
   "source": [
    "Similar to the presidents, some of these names' only relevant cards are the \"See Duke University Archives\" cards assoicated with the person the building is named after. See [Alspaugh](https://archive.org/details/rubensteinmanuscriptcatalog_A_to_Amer/page/n644/mode/2up), [Bassett](https://archive.org/details/rubensteinmanuscriptcatalog_Bam_to_Bedh/page/n449/mode/2up), and [Baldwin](https://archive.org/details/rubensteinmanuscriptcatalog_Armi_to_Bal/page/n575/mode/2up).\n",
    "\n",
    "There are, however, a couple mentions of the Bassett Affair [here](https://archive.org/details/rubensteinmanuscriptcatalog_Wi._Smith_to_So/page/n639/mode/2up) and [here](https://archive.org/details/rubensteinmanuscriptcatalog_J._Brown_to_L._Butler/page/n81/mode/2up). [The Bassett Affair](https://library.duke.edu/rubenstein/uarchives/history/articles/bassett-affair) was when John Spencer Bassett added a sentence in his journal praising Booker T. Washington as one of the best southerners in the past 100 years, enraging many Southern Democrats. President Kilgo and other faculty and students supported Bassett and the Trinity Board of Trustees voted not to accept his resignation, leading to favorable publicity for the college and setting a precident for academic freedom."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abb5f8d0",
   "metadata": {},
   "source": [
    "#### Southgate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f9e8f9a",
   "metadata": {},
   "source": [
    "James Southgate had numerous items recorded in the catalog. Starting with [this card](https://archive.org/details/rubensteinmanuscriptcatalog_Wi._Smith_to_So/page/n602/mode/2up), there is a lot of information about James and family. Southgate's son, James Haywood was a Trinity College trustee and is discussed in his father's collections. The senior's letters are described being more of interest than Haywood's, who wrote anout the insurance business and family stress. Kilgo, apparently was a friend of J.H."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4abeb660",
   "metadata": {},
   "source": [
    "#### Giles"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b568c073",
   "metadata": {},
   "source": [
    "The Giles sisters were the first women to recieve degrees from Trinity college, both undergraduate and graduate. Mary Giles' [collection](https://archive.org/details/rubensteinmanuscriptcatalog_Ger_to_Gorl/page/n279/mode/2up) includes papers concerning her and her sisters' education, international travels, and their lives after college."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8f810a3",
   "metadata": {},
   "source": [
    "#### Blackwell"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b103c51",
   "metadata": {},
   "source": [
    "William Thomas Blackwell was the founder of the Bull Durham Tobacco Company and has many associated collections in the catalog, starting [here](https://archive.org/details/rubensteinmanuscriptcatalog_Ben_to_Blac/page/n725/mode/2up). The cards discuss his tobacco business and his financial woes. There are many money-related logs, ledgers, and journals."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f545746",
   "metadata": {},
   "source": [
    "#### Biddle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ee10a37",
   "metadata": {},
   "source": [
    "The card corresponding to Mary Duke Biddle's collection is found [here](https://archive.org/details/rubensteinmanuscriptcatalog_Ben_to_Blac/page/n481/mode/2up). It contains a variety of documents relating to various aspects of her life."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a03a73f2",
   "metadata": {},
   "source": [
    "#### Perkins"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ad51bae",
   "metadata": {},
   "source": [
    "William Robertson Perkins was a judge who was counsel to James B. Duke and a trustee of the Duke Endowment. Starting [here](https://archive.org/details/rubensteinmanuscriptcatalog_Pep_to_Pn/page/n45/mode/2up), his collection discusses his connection to the university and employment."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "540795aa",
   "metadata": {},
   "source": [
    "## Duke's Nomenclature"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ca2bf73",
   "metadata": {},
   "source": [
    "The institution we now know as Duke University has gone through many naming iterations over the years. Starting with Brown School in the nineteenth century, it has also been called Union Institute, Normal College, and, finally, Trinity College before gaining the moniker which we have today. Are these names present in the catalog? And, if so, *when* are these names mentioned? Let's find out. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa15fbb4",
   "metadata": {},
   "source": [
    "### Finding Name Matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7fbbef11",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lists to hold the dates of Duke name mentions\n",
    "brown, union, normal, trinity, duke = [], [], [], [], []\n",
    "counts = [0, 0, 0, 0, 0]\n",
    "\n",
    "name_indices = {'brown': [], 'union': [], 'normal': [], 'trinity': [], 'duke': []}\n",
    "\n",
    "# Check each card for a college name\n",
    "curr_col = 0\n",
    "for index, row in df.iterrows():\n",
    "    if row['Coll_head'] == 1:\n",
    "        curr_col = index\n",
    "    if not pd.isnull(df.iloc[curr_col]['Year']):\n",
    "        if \"Brown School\" in row['Text'].title():\n",
    "            brown.append(df.iloc[curr_col]['Year'])\n",
    "            name_indices['brown'].append(index)\n",
    "            counts[0] += 1\n",
    "        if \"Union Institute\" in row['Text'].title():\n",
    "            union.append(df.iloc[curr_col]['Year'])\n",
    "            name_indices['union'].append(index)\n",
    "            counts[1] += 1\n",
    "        if \"Normal College\" in row['Text'].title():\n",
    "            normal.append(df.iloc[curr_col]['Year'])\n",
    "            name_indices['normal'].append(index)\n",
    "            counts[2] += 1\n",
    "        if \"Trinity College\" in row['Text'].title():\n",
    "            trinity.append(df.iloc[curr_col]['Year'])\n",
    "            name_indices['trinity'].append(index)\n",
    "            counts[3] += 1\n",
    "        if \"Duke University\" in row['Text'].title():\n",
    "            duke.append(df.iloc[curr_col]['Year'])\n",
    "            name_indices['duke'].append(index)\n",
    "            counts[4] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "cde8a56f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'brown': [], 'union': [31358, 50170], 'normal': [410, 2004, 4821, 5148, 5149, 6904, 7031, 9828, 15130, 15131, 15133, 20074, 20075, 25093, 25094, 25195, 44683, 47523, 47901, 48481, 48488], 'trinity': [127, 158, 932, 949, 1950, 2004, 2726, 4207, 4294, 4855, 4859, 5861, 5864, 5955, 6085, 6659, 6815, 6917, 6918, 6921, 7684, 7685, 7687, 7688, 7698, 7700, 8356, 9180, 9645, 10235, 10741, 10906, 10920, 11155, 11156, 11158, 11161, 11164, 11197, 11199, 11200, 11731, 11822, 12078, 13261, 13302, 13303, 13317, 13671, 13674, 14005, 15133, 16211, 17237, 17238, 17239, 17306, 17967, 19231, 19295, 19298, 19384, 20729, 20731, 21237, 21765, 22038, 22129, 22147, 22150, 22158, 23045, 24107, 24513, 24523, 24837, 25004, 25621, 25748, 27216, 27397, 27816, 27836, 28173, 28657, 28659, 28828, 30321, 30375, 30376, 30480, 30706, 30943, 30945, 30947, 30951, 31012, 31013, 31015, 31016, 31017, 31018, 31019, 31247, 31977, 32804, 32886, 34437, 34637, 34654, 35029, 35460, 37321, 37405, 37500, 37748, 37801, 38323, 38324, 39272, 39480, 39481, 39702, 40312, 40454, 40468, 40616, 40644, 40703, 41457, 41590, 41594, 41642, 41914, 42030, 42061, 42065, 42066, 42069, 42408, 42531, 42532, 42919, 42927, 43242, 43348, 43352, 43354, 43616, 43619, 44460, 44474, 44507, 44696, 45276, 45662, 45663, 45693, 45696, 45697, 45808, 46887, 47503, 47523, 49746, 49750, 49751, 50170, 50219], 'duke': [59, 60, 258, 415, 509, 515, 568, 570, 571, 572, 575, 577, 596, 749, 751, 752, 800, 878, 881, 888, 1084, 1085, 1408, 1432, 1594, 1619, 1941, 1950, 2095, 2168, 2178, 2388, 2820, 2829, 3592, 3885, 3912, 4139, 4143, 4273, 4294, 4463, 4465, 4466, 4467, 4471, 4472, 4504, 4664, 4665, 4860, 5148, 5149, 5231, 5488, 5489, 5610, 5730, 6085, 6086, 6090, 6106, 6116, 6122, 6185, 6186, 6236, 6265, 6434, 6551, 6565, 6630, 6773, 6777, 6844, 6986, 6990, 7021, 7337, 7484, 7683, 7688, 7701, 7974, 8023, 8085, 8090, 8093, 8185, 8191, 8251, 8356, 9038, 9179, 9180, 9260, 9273, 9404, 9405, 9407, 9410, 9412, 9414, 9417, 9419, 9420, 9645, 9663, 10329, 10354, 10386, 10504, 10662, 10743, 10752, 10753, 11037, 11045, 11168, 11175, 11197, 11337, 11735, 11737, 11823, 12145, 12152, 12533, 12561, 12679, 13053, 13141, 13302, 13318, 13655, 13669, 13692, 13693, 13694, 13695, 13696, 13741, 13743, 13745, 13746, 13747, 13750, 13751, 13752, 13753, 13754, 13755, 13756, 13757, 13758, 13759, 13760, 13761, 13762, 13763, 13764, 13765, 13766, 13767, 13768, 13769, 13770, 13771, 13772, 13773, 13774, 13775, 13776, 13777, 13778, 13779, 13780, 13781, 13782, 13783, 13793, 13795, 13796, 13797, 13798, 13799, 13800, 13801, 13806, 13807, 13808, 13809, 14399, 14484, 14512, 14516, 14517, 14585, 14623, 14861, 14979, 14983, 15294, 15511, 15697, 15751, 15752, 15927, 15939, 16184, 16278, 16529, 16542, 16556, 16557, 16797, 16842, 16922, 17179, 17472, 17493, 17494, 17497, 17498, 17754, 17756, 18432, 18666, 18712, 18713, 18952, 19185, 19309, 19311, 19334, 19384, 19444, 19616, 19647, 19648, 19650, 19651, 19652, 19653, 19658, 19660, 19662, 19666, 19771, 19865, 19909, 19912, 19920, 20156, 20210, 20212, 20214, 20318, 20523, 20542, 20549, 20823, 21005, 21396, 21508, 21566, 21616, 21723, 21815, 21954, 22050, 22265, 22266, 22267, 22272, 22285, 22594, 22595, 22597, 22618, 22772, 22868, 22869, 22871, 22893, 22895, 22905, 22921, 22922, 22923, 22926, 22943, 23288, 23343, 23775, 23909, 24166, 24237, 24525, 24619, 24838, 25049, 25074, 25251, 25262, 25263, 25264, 25665, 26306, 26307, 26393, 26398, 26399, 26603, 26801, 26854, 27338, 27340, 27457, 27486, 27719, 27720, 27790, 27802, 27889, 28247, 28561, 28601, 28658, 28718, 28828, 29203, 29456, 29573, 29623, 30375, 30376, 30650, 30666, 30677, 31230, 31322, 31509, 31813, 31892, 31899, 32183, 32297, 32343, 32851, 32877, 32971, 33042, 33092, 33642, 33672, 33917, 33922, 34078, 34080, 34136, 34437, 34537, 34539, 34654, 34850, 34882, 35029, 35030, 35031, 35234, 35237, 35251, 35367, 35373, 35418, 35620, 35990, 36033, 36215, 36299, 36493, 36495, 36496, 36542, 36652, 36711, 36804, 36868, 36869, 37052, 37205, 37214, 37221, 37260, 37261, 37263, 37405, 37612, 37613, 37656, 37657, 37801, 37847, 37849, 38056, 38063, 38064, 38071, 38235, 38401, 38412, 38679, 38866, 39017, 39135, 39255, 39258, 39271, 39294, 39295, 39297, 39311, 39313, 39314, 39673, 39675, 39679, 39680, 39681, 39744, 39745, 39747, 39748, 40073, 40076, 40077, 40078, 40080, 40082, 40083, 40235, 40319, 40337, 40338, 40616, 40703, 40878, 40952, 40978, 41173, 41174, 41568, 41644, 41703, 41736, 41775, 42007, 42051, 42069, 42377, 42533, 42780, 42991, 42994, 43032, 43238, 43242, 43243, 43244, 43247, 43338, 43348, 43352, 43369, 43370, 43417, 43598, 43599, 43616, 43760, 43762, 43763, 43996, 44012, 44200, 44245, 44447, 44459, 44513, 44535, 44812, 44857, 44859, 45317, 45318, 45704, 45710, 45866, 46093, 46469, 46470, 46473, 46685, 47102, 47152, 47173, 47272, 47601, 47703, 47864, 47880, 47979, 48169, 48439, 48644, 48709, 49487, 49585, 49586, 49592, 49716, 50170]}\n"
     ]
    }
   ],
   "source": [
    "# Dictionary storing indices of cards that mention Duke names\n",
    "print(name_indices)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c686969",
   "metadata": {},
   "source": [
    "It looks like we have 0 mentions of Brown School, 2 mentions of Union Institute, 21 mentions of Normal College, 169 mentions of Trinity College, and 523 mentions of Duke University. All mentions are associated with valid dates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8cd0447c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Values stored after running above code\n",
    "counts = [0, 2, 21, 169, 523]\n",
    "names = [\"Brown School\", \"Union Institute\", \"Normal College\", \"Trinity College\", \"Duke University\"]\n",
    "\n",
    "brown = []\n",
    "union = ['1863-1890', '1879-1889']\n",
    "normal = ['1833-1985', '1852-1853', '1881-1919', '1846-1933', '1846-1933', '1820-1907', '1783-1940', '1840-1925', '1851-1861', '1851-1861', '1851-1861', '1870-1900', '1870-1900', '1757-1978', '1757-1978', '1852-1857', '1896-1902', '1856-1866', '1830-1850', '1853-1862', '1853-1862']\n",
    "trinity = ['1891-1913', '1901-1922', '1847-1890', '1865', '1833-1967', '1852-1853', '1924-1971', '1859', '1887-1960', '1886-1888', '1886-1888', '1842-1864', '1842-1864', '1951', '1912-1974', '1896-1899', '1905', '1806-1909', '1806-1909', '1806-1909', '1885-1976', '1885-1976', '1885-1976', '1885-1976', '1855-1885', '1855-1885', '1914-1946', '1902-1961', '1912-1976', '1861-1865', '1752-1927', '1913-1914', '1854-1869', '1893-1898', '1893-1898', '1893-1898', '1893-1898', '1893-1898', '1785-1966', '1785-1966', '1785-1966', '1816-1876', '1889-1894', '1859-1905', '1909', '1820-1869', '1820-1869', '1820-1869', '1855-1929', '1855-1929', '1783-1984', '1851-1861', '1818-1894', '1846-1942', '1846-1942', '1846-1942', '1836-1932', '1325-1408', '1892-1910', '1848-1984', '1848-1984', '1788-1952', '1767-1965', '1767-1965', '1848-1893', '1796-1891', '1884-1886', '1841-1929', '1841-1929', '1841-1929', '1841-1929', '1903', '1870', '1810-1929', '1915-1955', '1912-1955', '1797-1919', '1840-1856', '1918-1973', '1869', '1881-1935', '1935-1936', '1831-1879', '1881-1916', '1772-1899', '1772-1899', '1860-1927', '1873-1882', '1788-1797', '1788-1797', '1884-1887', '1900-1911', '1861', '1875-1887', '1875-1887', '1860-1877', '1885-1890', '1885-1890', '1885-1890', '1888-1892', '1888-1892', '1888-1892', '1875-1887', '1893-1971', '1805-1881', '1919-1973', '1890-1953', '1889-1917', '1977', '1900-1961', '1881-1959', '1889-1890', '1894-1912', '1918', '1893-1897', '1910-1935', '1864-1868', '1889-1958', '1889-1958', '1924-1952', '1767-1905', '1767-1905', '1747-1751', '1920', '1875-1912', '1837-1893', '1886-1967', '1913-1917', '1854-1940', '1855-1869', '1856-1937', '1856-1937', '1856-1871', '1884-1887', '1851-1935', '1851-1935', '1851-1935', '1851-1935', '1912-1933', '1803-1891', '1915-1954', '1915-1954', '1866-1891', '1866-1891', '1888-1975', '1748-1989', '1748-1989', '1748-1989', '1829-1901', '1829-1901', '1905-1922', '1905-1922', '1905-1922', '1774-1777', '1694', '1891-1969', '1891-1969', '1835-1961', '1835-1961', '1835-1961', '1884-1939', '1890-1948', '1842', '1856-1866', '1828-1969', '1828-1969', '1828-1969', '1879-1889', '1784-1837']\n",
    "duke = ['1973-1989', '1973-1989', '1967-1995', '1962', '1990-1995', '1923-1960', '1843-1971', '1843-1971', '1843-1971', '1843-1971', '1843-1971', '1843-1971', '1866-1969', '1987', '1987', '1987', '1814', '1977-1982', '1977-1982', '1955-1984', '1925-1968', '1925-1968', '1844', '1964-1992', '1913-1962', '1856-1950', '1901-1970', '1833-1967', '1913-1966', '1805-1952', '1805-1952', '1805-1952', '1809-1824', '1866-1868', '1799-1870', '1872-1904', '1820-1962', '1755-1967', '1755-1967', '1216', '1887-1960', '1858-1936', '1967-1984', '1967-1984', '1967-1984', '1899-1972', '1963', '1798-1813', '1840-1865', '1840-1865', '1939-1963', '1846-1933', '1846-1933', '1930-1950', '1922-1976', '1922-1976', '1894', '1868-1928', '1912-1974', '1912-1974', '1912-1974', '1912-1974', '1912-1974', '1912-1974', \"1930-1950\", \"1930-1950\", '1929', '1959-1966', '1851-1907', '1928-1986', '1854-1857', '1811-1899', '1680', '1680', '1822-1888', '1879-1922', '1884-1917', '1975-1979', '1731-1969', '1864', '1885-1976', '1885-1976', '1855-1885', '1962-1972', '1980-1985', '1821-1946', '1821-1946', '1821-1946', '1886-1978', '1862', '1936', '1914-1946', '1942-1944', '1902-1961', '1902-1961', '1981', '1924-1926', '1927-1938', '1927-1938', '1927-1938', '1927-1938', '1927-1938', '1927-1938', '1927-1938', '1927-1938', '1927-1938', '1912-1976', '1912-1976', '1946-1953', '1940-1953', '1941-1953', '1897-1910', '1847-1916', '1752-1927', '1931-1934', '1931-1934', '1881-1968', '1821-1973', '1918-1977', '1918-1977', '1785-1966', '1987', '1816-1876', '1816-1876', '1889-1894', '1927-1961', '1927-1961', '1889-1893', '1900-1982', '1846-1854', '1852-1854', '1865-1887', '1820-1869', '1820-1869', '1915', '1925-1968', '1973-1978', '1973-1978', '1973-1978', '1973-1978', '1973-1978', '1803-1883', '1920-1970', '1920-1970', '1920-1970', '1920-1970', '1979-1994', '1979-1994', '1979-1994', '1979-1994', '1979-1994', '1979-1994', '1979-1994', '1979-1994', '1979-1994', '1979-1994', '1979-1994', '1979-1994', '1979-1994', '1979-1994', '1979-1994', '1979-1994', '1979-1994', '1979-1994', '1979-1994', '1979-1994', '1979-1994', '1979-1994', '1979-1994', '1979-1994', '1979-1994', '1979-1994', '1992-1993', '1992-1993', '1992-1993', '1992-1993', '1992-1993', '1992-1993', '1992-1993', '1992-1993', '1974-1988', '1974-1988', '1974-1988', '1974-1988', '1974-1988', '1974-1988', '1974-1988', '1974-1988', '1971-1992', '1971-1992', '1971-1992', '1971-1992', '1943', '1772-1795', '1728-1984', '1896-1961', '1896-1961', '1827-1858', '1935', '1976', '1862-1902', '1837-1971', '1802', '1969', '1939', '1928-1983', '1928-1983', '1778-1783', '1765-1850', '1943-1948', '1786-1796', '1800-1981', '1800-1981', '1800-1981', '1800-1981', '1938-1939', '1727-1905', '1785-1900', '1860-1920', '1931-1942', '1905-1985', '1905-1985', '1905-1985', '1905-1985', '1967-1987', '1967-1987', '1707-1948', '1863-1909', '1914-1979', '1914-1979', '1814-1864', '1832', '1938-1996', '1938-1996', '1953-1964', '1788-1952', '1939', '1350-1995', '1930-1979', '1930-1979', \"1700-1972\", \"1700-1972\", \"1700-1972\", \"1700-1972\", \"1700-1972\", \"1700-1972\", \"1700-1972\", \"1700-1972\", '1799-1862', '1955-1980', '1927-1983', '1927-1983', '1907-1978', '1941-1942', '1971-1989', '1971-1989', '1971-1989', '1817-1844', '1913-1989', '1913-1989', '1913-1989', ' 1959-1979', '1921-1930', '1951', '1837-1874', '1958-1985', '1854-1855', '1930-1940', '1916-1928', '1967', '1759-1792', '1818-1982', '1818-1982', '1818-1982', '1842-1969', '1936-1983', '1770-1773', '1777-1783', '1727-1805', '1930-1969', '1972-1983', '1837-1841', '1839', '1819-1830', '1905-1986', '1905-1986', '1905-1986', '1905-1986', '1905-1986', '1905-1986', '1905-1986', '1905-1977', '1954-1962', '1871-1872', '1918-1986', '1780-1813', '1861-1991', '1889-1970', '1915-1955', '1940-1941', '1912-1955', '1757-1978', '1757-1978', '1858-1991', '1913-1970', '1913-1970', '1913-1970', '1837', '1997', '1997', '1901-1970', '1901-1970', '1901-1970', '1900-1927', '1755', '1983-1984', '1879-1969', '1879-1969', '1905-1949', '1947-1985', '1963-1980', '1963-1980', '1926-1947', '1940', '1848-1904', '1788-1789', '1911-1984', '1969', '1772-1899', '1797-1800', '1860-1927', '1784-1847', '1826-1895', '1834-1890', '1790-1820', '1788-1797', '1788-1797', '1764-1981', '1764-1981', '1764-1981', '1782-1821', '1860-1868', '1934-1977', '1852-1933', '1942', '1981-1985', '1972-1975', '1878-1934', '1806-1863', '1912-1913', '1917-1970', '1980', '1824-1859', '1899-1947', '1602-1677', '1923-1926', '1840-1949', '1840-1949', '1918-1975', '1956-1985', '1903', '1889-1917', '1957-1976', '1957-1963', '1900-1961', '1942-1970', '1969-1990', '1881-1959', '1881-1959', '1881-1959', '1932-1941', '1932-1941', '1903-1967', '1580-1892', '1952', '1961-1990', '1892-1921', '1972-1996', '1891-1976', '1962-1995', '1892-1959', '1861-1871', '1861-1871', '1968-1986', '1933', '1766', '1925-1938', '1887-1944', '1968-1964', '1968-1964', '1483-1974', '1934-1984', '1964-1982', '1852-1986', '1956-1976', '1956-1976', '1956-1976', '1918', '1922-1982', '1922-1982', '1891-1983', '1891-1983', '1864-1868', '1928-1995', '1928-1995', '1914', '1928-1971', '1928-1971', '1949', '1855-1907', '1776-1794', '1760-1790', '1934', '1760-1845', '1919-1962', '1941-1991', '1937-1990', '1937-1990', '1924-1952', '1926-1980', '1926-1980', '1926-1980', '1985-1992', '1985-1992', '1985-1992', '1983', '1983', '1946-1994', '1946-1994', '1946-1994', '1939-1986', '1939-1986', '1939-1986', '1939-1986', '1859-1959', '1925-1991', '1925-1991', '1925-1991', '1878-1991', '1878-1991', '1878-1991', '1933-1941', '1972-1974', '1812-1815', '1812-1815', '1886-1967', '1854-1940', '1983', '1887-1949', '1964-1965', '1916-1973', '1916-1973', '1887-1953', '1864', '1900-1976', '1900-1976', '1900-1976', '1992', '1851-1935', '1912-1933', '1931-1980', '1915-1954', '1743-1838', '1955-1984', '1904', '1937-1938', '1972-1981', '1888-1975', '1888-1975', '1888-1975', '1888-1975', '1764-1792', '1748-1989', '1748-1989', '1861-1865', '1861-1865', '1921-1953', '1914-1997', '1914-1997', '1829-1901', '1898-1954', '1898-1954', '1898-1954', '1975-1982', '1832-1837', '1596-1816', '1985-1987', '1905-1922', '1905-1922', '1851', '1814-1898', '1839-1880', '1924-1939', '1924-1939', '1971', '1971', '1918-1976', '1833-1834', '1888-1891', '1830-1880', '1843-1888', '1841-1977', '1841-1977', '1990-1992', '1779-1854', '1730-1975', '1750-1762', '1802-1883', '1828-1875', '1963-1979', '1726-1889', '1759-1828', '1879-1915', '1889-1968', '1812-1885', '1839-1961', '1954-1955', '1930-1961', '1957-1976', '1957-1976', '1957-1976', '1844-1955', '1879-1889']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca08af89",
   "metadata": {},
   "source": [
    "### Name Frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "59dbbc5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create bar chart of Duke name frequencies\n",
    "plt.bar(names, counts, color='#00539B')\n",
    "plt.xticks(rotation = 45)\n",
    "plt.title(\"Occurances of Duke's Names in the Catalog\")\n",
    "plt.xlabel(\"Name\")\n",
    "plt.ylabel(\"# of Cards\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8040bc3",
   "metadata": {},
   "source": [
    "### Average Collection Dates by Name"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c96846e",
   "metadata": {},
   "source": [
    "Let's find the average date of a collection that mentions each of these college names. For this, we will find the average date for each instance of a found name--either a single date or the simple average of the collection's start and end dates--and then the mean of all the instances of the college name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b72a0239",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1880, 1871, 1884, 1917]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def find_mean(list):\n",
    "    total, num = 0, 0\n",
    "    for date in list:\n",
    "        dates = date.split(\"-\")\n",
    "        if len(dates) == 1:\n",
    "            total += int(dates[0])\n",
    "        else:\n",
    "            total += ((int(dates[0]) + int(dates[1])) / 2)\n",
    "        num += 1\n",
    "    return round(total / num)\n",
    "\n",
    "averages = []\n",
    "averages.append(find_mean(union))\n",
    "averages.append(find_mean(normal))\n",
    "averages.append(find_mean(trinity))\n",
    "averages.append(find_mean(duke))\n",
    "averages"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97e9675b",
   "metadata": {},
   "source": [
    "#### *Actual Dates of Institutions:*\n",
    "Brown School: 1838-1841\n",
    "\n",
    "Union Institution: 1841-1851\n",
    "\n",
    "Normal College: 1851-1859\n",
    "\n",
    "Trinity College: 1859-1924\n",
    "\n",
    "Duke University: 1924-present\n",
    "\n",
    "\n",
    "#### *Average Associated Date in the Catalog:*\n",
    "Brown School: N/A\n",
    "\n",
    "Union Institution: 1880\n",
    "\n",
    "Normal College: 1871\n",
    "\n",
    "Trinity College: 1884\n",
    "\n",
    "Duke University: 1917"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9401745b",
   "metadata": {},
   "source": [
    "### Names Time Series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e514216",
   "metadata": {},
   "source": [
    "Brown School will not be included in the following analysis, as there were no instances of it in the catalog. But, let's see how the other four names' instances are distributed over time in the catalog. For simplicity's sake, for year ranges of catalog collections, each year in the range was counted once (e.g., for 1940-1942, 1940, 1941, and 1941 each got a quantity increase of 1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f540c4c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def start(college, dic):\n",
    "    for i in range(1838, 2000):\n",
    "        dic[i] = 0\n",
    "    \n",
    "    for date in college:\n",
    "        dates = str(date).split(\"-\")\n",
    "        if int(dates[0]) >= 1838:\n",
    "            dic[int(dates[0])] = dic.get(int(dates[0])) + 1\n",
    "                    \n",
    "    return dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "28198ee2",
   "metadata": {},
   "outputs": [],
   "source": [
    "dic1, dic2, dic3, dic4 = {}, {}, {}, {}\n",
    "dic1 = start(union, dic1)\n",
    "dic2 = start(normal, dic2)\n",
    "dic3 = start(trinity, dic3)\n",
    "dic4 = start(duke, dic4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1a46e1c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dic4.keys(), dic4.values(), label='Duke', color='#00539B')\n",
    "plt.plot(dic3.keys(), dic3.values(), label='Trinity', color='#FFD960')\n",
    "plt.plot(dic2.keys(), dic2.values(), label='Normal', color='#E89923')\n",
    "plt.plot(dic1.keys(), dic1.values(), label='Union', color='#C84E00')\n",
    "plt.legend(loc='upper left')\n",
    "plt.title(\"Mentions of Duke's Names over Time\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"# of Mentions\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ea1a2d9",
   "metadata": {},
   "source": [
    "This plot shows the quantity of the dates of the collections that mention one of Duke's names, for each of the names.\n",
    "\n",
    "A major caveat to this is that we were only able to pull out the dates of when a collection was writen, not when it was acquired by the library, and this plot shows the dates when the collections that mention one of these full names were written."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}