group5_visualizations copy.ipynb

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  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "# import necessary libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "import matplotlib_venn as venn\n",
    "import altair as alt\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4a9c03fe",
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year of Record</th>\n",
       "      <th>Total People Enslaved in Record</th>\n",
       "      <th># Male</th>\n",
       "      <th># Female</th>\n",
       "      <th># Black</th>\n",
       "      <th># Native</th>\n",
       "      <th>Name and/or Description</th>\n",
       "      <th>Enslaver Last Name</th>\n",
       "      <th>Enslaver First Name</th>\n",
       "      <th>Town</th>\n",
       "      <th>Unique ID</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1741</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Coelia</td>\n",
       "      <td>Alford</td>\n",
       "      <td>Captain</td>\n",
       "      <td>Charlestown</td>\n",
       "      <td>ID_1</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1751</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Phillis</td>\n",
       "      <td>Alford</td>\n",
       "      <td>John</td>\n",
       "      <td>Charlestown</td>\n",
       "      <td>ID_2</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1756</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Cerado</td>\n",
       "      <td>Alford, Esqr.</td>\n",
       "      <td>John</td>\n",
       "      <td>Charlestown</td>\n",
       "      <td>ID_3</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1753</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Rose</td>\n",
       "      <td>Angier</td>\n",
       "      <td>R</td>\n",
       "      <td>Charlestown</td>\n",
       "      <td>ID_4</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1749</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Belinda</td>\n",
       "      <td>Austin</td>\n",
       "      <td>John</td>\n",
       "      <td>Charlestown</td>\n",
       "      <td>ID_5</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>1730</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Pippwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Charlestown</td>\n",
       "      <td>ID_158</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>1760</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Pomp Miller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Charlestown</td>\n",
       "      <td>ID_159</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>1753</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Prince Green</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Charlestown</td>\n",
       "      <td>ID_160</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>1706</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Robert Comings</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Charlestown</td>\n",
       "      <td>ID_161</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>1704</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Samson Jefferies</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Charlestown</td>\n",
       "      <td>ID_162</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>162 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year of Record  Total People Enslaved in Record  # Male  # Female  \\\n",
       "1              1741                                1       0         1   \n",
       "2              1751                                1       0         1   \n",
       "3              1756                                1       1         0   \n",
       "4              1753                                1       0         1   \n",
       "5              1749                                1       0         1   \n",
       "..              ...                              ...     ...       ...   \n",
       "158            1730                                1       0         1   \n",
       "159            1760                                1       1         0   \n",
       "160            1753                                1       1         0   \n",
       "161            1706                                1       1         0   \n",
       "162            1704                                1       1         0   \n",
       "\n",
       "     # Black  # Native Name and/or Description Enslaver Last Name  \\\n",
       "1          1         0                  Coelia             Alford   \n",
       "2          1         0                 Phillis             Alford   \n",
       "3          1         0                 Cerado       Alford, Esqr.   \n",
       "4          1         0                    Rose             Angier   \n",
       "5          1         0                Belinda              Austin   \n",
       "..       ...       ...                     ...                ...   \n",
       "158        1         0                  Pippwe                NaN   \n",
       "159        1         0             Pomp Miller                NaN   \n",
       "160        1         0            Prince Green                NaN   \n",
       "161        1         0          Robert Comings                NaN   \n",
       "162        1         0        Samson Jefferies                NaN   \n",
       "\n",
       "    Enslaver First Name         Town Unique ID  Gender  \n",
       "1               Captain  Charlestown      ID_1  Female  \n",
       "2                  John  Charlestown      ID_2  Female  \n",
       "3                  John  Charlestown      ID_3    Male  \n",
       "4                     R  Charlestown      ID_4  Female  \n",
       "5                  John  Charlestown      ID_5  Female  \n",
       "..                  ...          ...       ...     ...  \n",
       "158                 NaN  Charlestown    ID_158  Female  \n",
       "159                 NaN  Charlestown    ID_159    Male  \n",
       "160                 NaN  Charlestown    ID_160    Male  \n",
       "161                 NaN  Charlestown    ID_161    Male  \n",
       "162                 NaN  Charlestown    ID_162    Male  \n",
       "\n",
       "[162 rows x 12 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read in data for enslaved people tab\n",
    "enslaved_tab = pd.read_csv('Census Charlestown 1775 05-14-24 - Enslaved people.csv')\n",
    "\n",
    "# clean up formatting\n",
    "enslaved_tab.columns = ['Year of Record', 'Total People Enslaved in Record', '# Male', '# Female', '# Black', \n",
    "                        '# Native', 'Name and/or Description', 'Enslaver Last Name', 'Enslaver First Name', \n",
    "                        'Town', 'Unique ID']\n",
    "enslaved_tab = enslaved_tab.drop([0])\n",
    "\n",
    "# convert some columns from nan to 0\n",
    "enslaved_tab['# Male'] = enslaved_tab['# Male'].fillna(0)\n",
    "enslaved_tab['# Female'] = enslaved_tab['# Female'].fillna(0)\n",
    "enslaved_tab['# Native'] = enslaved_tab['# Native'].fillna(0)\n",
    "\n",
    "# convert datatypes\n",
    "enslaved_tab['Year of Record'] = enslaved_tab['Year of Record'].astype(int)\n",
    "enslaved_tab['Total People Enslaved in Record'] = enslaved_tab['Total People Enslaved in Record'].astype(int)\n",
    "enslaved_tab['# Male'] = enslaved_tab['# Male'].astype(int)\n",
    "enslaved_tab['# Female'] = enslaved_tab['# Female'].astype(int)\n",
    "enslaved_tab['# Black'] = enslaved_tab['# Black'].astype(int)\n",
    "enslaved_tab['# Native'] = enslaved_tab['# Native'].astype(int)\n",
    "\n",
    "def gender(person):\n",
    "    if person['# Female'] > 0:\n",
    "        return 'Female'\n",
    "    elif person['# Male'] > 0:\n",
    "        return 'Male'\n",
    "    else:\n",
    "        return 'Unknown'\n",
    "\n",
    "# Add a new column indicating if the person is male or female\n",
    "enslaved_tab['Gender'] = enslaved_tab.apply(gender, axis=1)\n",
    "\n",
    "enslaved_tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9d1f747b",
   "metadata": {},
   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 432x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get counts of male and female\n",
    "female_count = enslaved_tab['# Female'].sum()\n",
    "male_count = enslaved_tab['# Male'].sum()\n",
    "total_count = enslaved_tab['Total People Enslaved in Record'].sum()\n",
    "unknown_count = total_count - (female_count + male_count)\n",
    "\n",
    "# make bar chart for gender distribution\n",
    "plt.figure(figsize=(6, 8))\n",
    "plt.bar('Female', female_count, color='pink', label='Female')\n",
    "plt.bar('Male', male_count, color='skyblue', label='Male')\n",
    "plt.bar('Unknown', unknown_count, color='yellowgreen', label='Unknown')\n",
    "plt.title('Gender Distribution of Enslaved People in Charlestown', fontsize=20)\n",
    "plt.xlabel('Gender', fontsize=14)\n",
    "plt.ylabel('Number of Enslaved People', fontsize=14)\n",
    "plt.text(0, 68, female_count)\n",
    "plt.text(1, 92, male_count)\n",
    "plt.text(2, 7, unknown_count)\n",
    "plt.rcParams['axes.spines.right'] = False\n",
    "plt.rcParams['axes.spines.top'] = False\n",
    "plt.legend(bbox_to_anchor=(1.4, 1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4f7c9641",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/yg/vlskscg174zgkdtq09c25czr0000gn/T/ipykernel_35762/3400796750.py:2: DtypeWarning: Columns (0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  census_tab = pd.read_csv('Census Charlestown 1775 05-14-24 - Census.csv')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Consistency of Enslaved People Listed Across Datasets')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# read in data for census tab\n",
    "census_tab = pd.read_csv('Census Charlestown 1775 05-14-24 - Census.csv')\n",
    "census_tab.columns = ['Order', 'Family', 'Original Order', 'Link to Claim', 'Last Name', 'Maiden Name', \n",
    "                      'First Name', 'Relation to Head of Household', 'Age', 'Sex', 'Race', 'Occupation', \n",
    "                      'Household Inventory #', '1375 People', 'Household', 'Family Notes', 'Unique ID', \n",
    "                      'Family V2', 'Household V2']\n",
    "census_tab = census_tab.drop(0)\n",
    "\n",
    "# filter for only people who are not white and enslaved\n",
    "census_race_b_m = census_tab.loc[census_tab['Race'] != 'W']\n",
    "census_enslaved = census_race_b_m.loc[(census_race_b_m['Relation to Head of Household'] == 'Enslaved') | \n",
    "                                      (census_race_b_m['Relation to Head of Household'] == 'enslaved')]\n",
    "\n",
    "# get counts of enslaved people from census tab and enslaved tab\n",
    "census_enslaved_count = census_enslaved['Order'].count()\n",
    "enslaved_tab_count = enslaved_tab['Total People Enslaved in Record'].sum()\n",
    "\n",
    "# look for overlap in tabs based on unique ID\n",
    "overlap_people = []\n",
    "overlap_count = 0\n",
    "census_id_lst = census_enslaved['Unique ID'].to_list()\n",
    "\n",
    "# iterate through the people in the enslaved tab\n",
    "for i in range(len(enslaved_tab['Unique ID'])):\n",
    "    \n",
    "    # if match is found, note their name and update counts\n",
    "    if enslaved_tab['Unique ID'].iloc[i] in census_id_lst:\n",
    "        overlap_people.append(enslaved_tab['Name and/or Description'].iloc[i])\n",
    "        overlap_count += 1\n",
    "        census_enslaved_count -= 1\n",
    "        enslaved_tab_count -= 1\n",
    "\n",
    "# make venn diagram showing consistency across tabs\n",
    "plt.figure(figsize=(6, 8))\n",
    "venn.venn2(subsets=(census_enslaved_count, enslaved_tab_count, overlap_count), \n",
    "      set_labels=('Enslaved People in Census Dataset', 'Enslaved People in Enslaved People Dataset'), \n",
    "      set_colors=('lightcoral', 'skyblue'))\n",
    "venn.venn2_circles(subsets=(census_enslaved_count, enslaved_tab_count, overlap_count))\n",
    "plt.title('Consistency of Enslaved People Listed Across Datasets', fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d9be5646",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# collect number of records per year\n",
    "record_year_dict = {}\n",
    "for i in range(len(enslaved_tab['Year of Record'])):\n",
    "    if enslaved_tab['Year of Record'].iloc[i] in record_year_dict.keys():\n",
    "        record_year_dict[enslaved_tab['Year of Record'].iloc[i]] += 1\n",
    "    else:\n",
    "        record_year_dict[enslaved_tab['Year of Record'].iloc[i]] = 1\n",
    "        \n",
    "# add 0 to years which are not listed in data\n",
    "year_range = [i for i in range(1700, 1776)]\n",
    "for i in year_range:\n",
    "    if i not in record_year_dict.keys():\n",
    "        record_year_dict[i] = 0\n",
    "        \n",
    "# convert dictionary to list of tuples sorted by year\n",
    "record_year_lst = [(key, value) for key, value in record_year_dict.items()]\n",
    "record_year_lst.sort(key=lambda tup: tup[0])\n",
    "\n",
    "# make separate lists for x and y\n",
    "years = []\n",
    "num_records = []\n",
    "for year in record_year_lst:\n",
    "    \n",
    "    # do not include records after 1775\n",
    "    if year[0] > 1775:\n",
    "        break\n",
    "        \n",
    "    years.append(year[0])\n",
    "    num_records.append(year[1])\n",
    "    \n",
    "# plot number of records over time\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(years, num_records)\n",
    "plt.title('Number of Slave Records Over Time', fontsize=20)\n",
    "plt.xlabel('Year', fontsize=14)\n",
    "plt.ylabel('Number of Records', fontsize=14)\n",
    "\n",
    "# add average line\n",
    "record_year_avg = sum(num_records) / len(num_records)\n",
    "plt.axhline(y=record_year_avg, color='red', linestyle='--', label=f'Average: {round(record_year_avg, 2)}')\n",
    "\n",
    "plt.legend(bbox_to_anchor=(0.35, 1))\n",
    "plt.rcParams['axes.spines.right'] = False\n",
    "plt.rcParams['axes.spines.top'] = False\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6f0878fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get number of common names \n",
    "# count first time attached to ID to make sure we are not double counting the same person\n",
    "names_id_dict = {}\n",
    "for i in range(len(enslaved_tab['Name and/or Description'])):\n",
    "    name_id = enslaved_tab['Name and/or Description'].iloc[i] + '_' + enslaved_tab['Unique ID'].iloc[i] + '_' + enslaved_tab['Gender'].iloc[i]\n",
    "    if name_id in names_id_dict.keys():\n",
    "        names_id_dict[name_id] += 1\n",
    "    else:\n",
    "        names_id_dict[name_id] = 1\n",
    "        \n",
    "# count second time after distinct people have been identified\n",
    "names_dict = {}\n",
    "for key in names_id_dict.keys():\n",
    "    name_gender = key.split('_')[0] + '_' + key.split('_')[3]\n",
    "    if name_gender in names_dict.keys():\n",
    "        names_dict[name_gender] += 1\n",
    "    else:\n",
    "        names_dict[name_gender] = 1\n",
    "        \n",
    "# make list of names that appear more than once\n",
    "common_names = []\n",
    "for key, value in names_dict.items():\n",
    "    if value > 1:\n",
    "        common_names.append((key, value))\n",
    "        \n",
    "# sort the list from greatest to least\n",
    "common_names.sort(key=lambda tup: tup[1])\n",
    "\n",
    "# create color dictionary\n",
    "gender_colors = {'Female': 'pink', 'Male': 'skyblue', 'Unknown': 'lightgreen'}\n",
    "\n",
    "# plot most common names \n",
    "plt.figure(figsize=(6, 8))\n",
    "\n",
    "for name in common_names:\n",
    "    \n",
    "    # do not include descriptions\n",
    "    if name[0].split('_')[0] == 'servant' or name[0].split('_')[0] == 'man':\n",
    "        pass\n",
    "    \n",
    "    else:\n",
    "        if name[0].split('_')[0] == 'Nanne' or name[0].split('_')[0] == 'Wenham':\n",
    "            plt.barh(name[0].split('_')[0], name[1], color=gender_colors[name[0].split('_')[1]], \n",
    "                     label=name[0].split('_')[1])\n",
    "        else:\n",
    "            plt.barh(name[0].split('_')[0], name[1], color=gender_colors[name[0].split('_')[1]])\n",
    "        \n",
    "plt.title('Common Names of Enslaved People in Charlestown', fontsize=20)\n",
    "plt.xlabel('Number of People', fontsize=14)\n",
    "plt.ylabel('Name', fontsize=14)\n",
    "plt.legend()\n",
    "plt.text(6.5, 15, f'*Did not include descriptions such \\nas \"servant\" or \"man\"', color='firebrick')\n",
    "plt.rcParams['axes.spines.right'] = False\n",
    "plt.rcParams['axes.spines.top'] = False\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "88d950f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Occupations of Black People in 1775 Charlestown Census')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show occupation of black people in census tab\n",
    "\n",
    "# make dictionary to get occupation counts\n",
    "census_race_b = census_race_b_m.loc[census_race_b_m['Race'] == 'B']\n",
    "occupations = {'None': 2}\n",
    "\n",
    "for i in range(len(census_race_b['First Name'])):\n",
    "    \n",
    "    # identify people who were free\n",
    "    if census_race_b['Relation to Head of Household'].iloc[i] != 'Enslaved' and census_race_b['Relation to Head of Household'].iloc[i] != 'enslaved':\n",
    "        if census_race_b['Occupation'].iloc[i] == 'Enslaved?':\n",
    "            occupations['Unknown'] = 1\n",
    "        elif census_race_b['Occupation'].iloc[i] == 'Shoemaker':\n",
    "            occupations[census_race_b['Occupation'].iloc[i]] = 1\n",
    "    \n",
    "    # get counts of slaves/servants\n",
    "    elif census_race_b['Occupation'].iloc[i] == 'servant' or census_race_b['Occupation'].iloc[i] == 'Servant':\n",
    "        if 'Servant (Enslaved)' in occupations.keys():\n",
    "            occupations['Servant (Enslaved)'] += 1\n",
    "        else:\n",
    "            occupations['Servant (Enslaved)'] = 1\n",
    "        \n",
    "# make a pie chart showing data\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "_, _, autotexts = ax.pie(occupations.values(), labels=occupations.keys(), \n",
    "                         colors=['lightblue', 'lightcoral', 'yellowgreen', 'orange'], autopct='%1.1f%%')\n",
    "plt.title('Occupations of Black People in 1775 Charlestown Census', fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "044085a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get number of enslaved people by enslaver family\n",
    "enslaver_names_dict = {}\n",
    "for i in range(len(enslaved_tab['Enslaver Last Name'])):\n",
    "    if enslaved_tab['Enslaver Last Name'].iloc[i] in enslaver_names_dict.keys():\n",
    "        enslaver_names_dict[enslaved_tab['Enslaver Last Name'].iloc[i]] += 1\n",
    "    else:\n",
    "        enslaver_names_dict[enslaved_tab['Enslaver Last Name'].iloc[i]] = 1\n",
    "        \n",
    "# make list of enslaver families that enslaved more than one person\n",
    "enslaver_names_lst = []\n",
    "for key, value in enslaver_names_dict.items():\n",
    "    if value > 1:\n",
    "        enslaver_names_lst.append((key, value))\n",
    "        \n",
    "# sort the list from greatest to least\n",
    "enslaver_names_lst.sort(key=lambda tup: tup[1])\n",
    "\n",
    "# make separate lists for x and y\n",
    "enslaver_names_x = []\n",
    "enslaver_names_y = []\n",
    "for name in enslaver_names_lst:\n",
    "    \n",
    "    # skip nan values\n",
    "    if enslaver_names_lst.index(name) == 26:\n",
    "        pass\n",
    "    \n",
    "    else:\n",
    "        enslaver_names_x.append(name[0])\n",
    "        enslaver_names_y.append(name[1])\n",
    "        \n",
    "# plot the data\n",
    "plt.figure(figsize=(8, 10))\n",
    "plt.barh(enslaver_names_x, enslaver_names_y)\n",
    "plt.title('Enslaver Families with the Most Slaves', fontsize=20)\n",
    "plt.xlabel('Number of Enslaved People', fontsize=14)\n",
    "plt.ylabel('Enslaver Last Name', fontsize=14)\n",
    "plt.text(8.65, 25, '*28 enslaved people did not \\nhave an enslaver listed.', color='firebrick')\n",
    "plt.rcParams['axes.spines.right'] = False\n",
    "plt.rcParams['axes.spines.top'] = False\n",
    "plt.grid(True, axis='x')\n",
    "plt.rc('axes', axisbelow=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e69bfc7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter data for just families with more than one slave\n",
    "multi_slaves_lst = []\n",
    "\n",
    "for i in range(len(enslaved_tab['Enslaver Last Name'])):\n",
    "    if enslaved_tab['Enslaver Last Name'].iloc[i] in enslaver_names_x:\n",
    "        multi_slaves_lst.append(enslaved_tab.iloc[i])\n",
    "        \n",
    "multi_slaves = pd.DataFrame(multi_slaves_lst, columns=['Year of Record', 'Total People Enslaved in Record', \n",
    "                                                       '# Male', '# Female', '# Black', '# Native', \n",
    "                                                       'Name and/or Description', 'Enslaver Last Name', \n",
    "                                                       'Enslaver First Name', 'Town', 'Unique ID'])\n",
    "\n",
    "def gender(person):\n",
    "    if person['# Female'] > 0:\n",
    "        return 'Female'\n",
    "    elif person['# Male'] > 0:\n",
    "        return 'Male'\n",
    "    else:\n",
    "        return 'Unknown'\n",
    "\n",
    "# Add a new column indicating if the person is male or female\n",
    "multi_slaves['Gender'] = multi_slaves.apply(gender, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1630b250",
   "metadata": {},
   "outputs": [
    {
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       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
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Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Grace\", \"Enslaver Last Name\": \"Austin\", \"Enslaver First Name\": \"Ebenezer\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_7\", \"Gender\": \"Female\"}, {\"Year of Record\": 1751, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Nanne\", \"Enslaver Last Name\": \"Austin\", \"Enslaver First Name\": \"Ebenezer\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_8\", \"Gender\": \"Female\"}, {\"Year of Record\": 1741, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Jupiter\", \"Enslaver Last Name\": \"Cary\", \"Enslaver First Name\": \"Samuel\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_16\", \"Gender\": \"Male\"}, {\"Year of Record\": 1741, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Marcah\", \"Enslaver Last Name\": \"Cary\", \"Enslaver First Name\": \"Samuel\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_17\", \"Gender\": \"Female\"}, {\"Year of Record\": 1741, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Tartar\", \"Enslaver Last Name\": \"Cary\", \"Enslaver First Name\": \"S.\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_18\", \"Gender\": \"Male\"}, {\"Year of Record\": 1741, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Wenham\", \"Enslaver Last Name\": \"Cary\", \"Enslaver First Name\": \"Samuel\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_19\", \"Gender\": \"Male\"}, {\"Year of Record\": 1755, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Mark\", \"Enslaver Last Name\": \"Codman\", \"Enslaver First Name\": \"John\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_24\", \"Gender\": \"Male\"}, {\"Year of Record\": 1755, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Phebe \", \"Enslaver Last Name\": \"Codman\", \"Enslaver First Name\": \"John\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_25\", \"Gender\": \"Female\"}, {\"Year of Record\": 1755, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Phillis\", \"Enslaver Last Name\": \"Codman\", \"Enslaver First Name\": \"John\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_26\", \"Gender\": \"Female\"}, {\"Year of Record\": 1770, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Pompey \", \"Enslaver Last Name\": 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\"Unique ID\": \"ID_38\", \"Gender\": \"Male\"}, {\"Year of Record\": 1729, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Jack \", \"Enslaver Last Name\": \"Frothingham\", \"Enslaver First Name\": \"Mary\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_41\", \"Gender\": \"Male\"}, {\"Year of Record\": 1710, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Robin\", \"Enslaver Last Name\": \"Frothingham\", \"Enslaver First Name\": \"Mary\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_42\", \"Gender\": \"Female\"}, {\"Year of Record\": 1742, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Flora \", \"Enslaver Last Name\": \"Gowen\", \"Enslaver First Name\": \"Joseph\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_45\", \"Gender\": \"Female\"}, {\"Year of Record\": 1762, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Fortune\", \"Enslaver Last Name\": \"Gowen\", \"Enslaver First Name\": \"Hammond\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_46\", \"Gender\": \"Male\"}, {\"Year of Record\": 1762, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Jane\", \"Enslaver Last Name\": \"Kent\", \"Enslaver First Name\": \"Mrs\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_53\", \"Gender\": \"Female\"}, {\"Year of Record\": 1753, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Jenny\", \"Enslaver Last Name\": \"Kent\", \"Enslaver First Name\": \"Benjamin\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_54\", \"Gender\": \"Female\"}, {\"Year of Record\": 1753, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Violet\", \"Enslaver Last Name\": \"Kent\", \"Enslaver First Name\": \"Stephen\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_55\", \"Gender\": \"Female\"}, {\"Year of Record\": 1749, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Charlestown\", \"Enslaver Last Name\": \"Luist\", \"Enslaver First Name\": \"R\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_57\", \"Gender\": \"Male\"}, {\"Year of Record\": 1759, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Jack\", \"Enslaver Last Name\": \"Luist\", \"Enslaver First Name\": \"Robert\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_58\", \"Gender\": \"Male\"}, {\"Year of Record\": 1771, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Boston\", \"Enslaver Last Name\": \"Lynde\", \"Enslaver First Name\": \"Thomas\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_59\", \"Gender\": \"Male\"}, {\"Year of Record\": 1771, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Cimbo\", \"Enslaver Last Name\": \"Lynde\", \"Enslaver First Name\": \"Thomas\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_60\", \"Gender\": \"Male\"}, {\"Year of Record\": 1713, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"servant\", \"Enslaver Last Name\": \"Lynde\", \"Enslaver First Name\": \"Col. 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\"Charlestown\", \"Unique ID\": \"ID_64\", \"Gender\": \"Male\"}, {\"Year of Record\": 1730, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Glocester\", \"Enslaver Last Name\": \"Marshall\", \"Enslaver First Name\": \"John\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_65\", \"Gender\": \"Male\"}, {\"Year of Record\": 1730, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Phillis\", \"Enslaver Last Name\": \"Marshall\", \"Enslaver First Name\": \"John\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_66\", \"Gender\": \"Female\"}, {\"Year of Record\": 1730, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Toney\", \"Enslaver Last Name\": \"Marshall\", \"Enslaver First Name\": \"John\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_67\", \"Gender\": \"Male\"}, {\"Year of Record\": 1752, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Phillis\", \"Enslaver Last Name\": \"Miller\", \"Enslaver First Name\": \"John\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_68\", \"Gender\": \"Female\"}, {\"Year of Record\": 1752, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Smith\", \"Enslaver Last Name\": \"Miller\", \"Enslaver First Name\": \"John\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_69\", \"Gender\": \"Male\"}, {\"Year of Record\": 1751, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Jenny\", \"Enslaver Last Name\": \"Phillips\", \"Enslaver First Name\": \"Joanna\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_74\", \"Gender\": \"Female\"}, {\"Year of Record\": 1751, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Middlesex\", \"Enslaver Last Name\": \"Phillips\", \"Enslaver First Name\": \"Samuel J\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_75\", \"Gender\": \"Male\"}, {\"Year of Record\": 1707, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Nelle\", \"Enslaver Last Name\": \"Phillips\", \"Enslaver First Name\": \"Elizabeth\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_76\", \"Gender\": \"Female\"}, {\"Year of Record\": 1757, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Phebe \", \"Enslaver Last Name\": \"Phillips\", \"Enslaver First Name\": \"Joanna\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_77\", \"Gender\": \"Female\"}, {\"Year of Record\": 1755, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Cato\", \"Enslaver Last Name\": \"Rand\", \"Enslaver First Name\": \"Dr. \", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_80\", \"Gender\": \"Male\"}, {\"Year of Record\": 1760, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Dinah\", \"Enslaver Last Name\": \"Rand\", \"Enslaver First Name\": \"Nathaniel\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_81\", \"Gender\": \"Female\"}, {\"Year of Record\": 1770, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Negro Man\", \"Enslaver Last Name\": \"Rand\", \"Enslaver First Name\": \"Joseph\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_82\", \"Gender\": \"Male\"}, {\"Year of Record\": 1708, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Charles Dungeon\", \"Enslaver Last Name\": \"Rows\", \"Enslaver First Name\": \"Captain\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_84\", \"Gender\": \"Male\"}, {\"Year of Record\": 1708, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Maudlin Ward\", \"Enslaver Last Name\": \"Rows\", \"Enslaver First Name\": \"Captain\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_85\", \"Gender\": \"Male\"}, {\"Year of Record\": 1721, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Herriford \", \"Enslaver Last Name\": \"Russell\", \"Enslaver First Name\": \"Danil.\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_87\", \"Gender\": \"Male\"}, {\"Year of Record\": 1741, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Phillis\", \"Enslaver Last Name\": \"Russell\", \"Enslaver First Name\": \"James\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_88\", \"Gender\": \"Female\"}, {\"Year of Record\": 1764, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Quace\", \"Enslaver Last Name\": \"Russell\", \"Enslaver First Name\": \"Daniel\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_89\", \"Gender\": \"Male\"}, {\"Year of Record\": 1725, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Zilphah \", \"Enslaver Last Name\": \"Russell\", \"Enslaver First Name\": \"Mr.\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_90\", \"Gender\": \"Female\"}, {\"Year of Record\": 1771, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Cato\", \"Enslaver Last Name\": \"Sheaf\", \"Enslaver First Name\": \"Edward\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_93\", \"Gender\": \"Male\"}, {\"Year of Record\": 1771, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Molly\", \"Enslaver Last Name\": \"Sheaf\", \"Enslaver First Name\": \"E\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_94\", \"Gender\": \"Female\"}, {\"Year of Record\": 1751, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Dick\", \"Enslaver Last Name\": \"Skinner\", \"Enslaver First Name\": \"John\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_95\", \"Gender\": \"Male\"}, {\"Year of Record\": 1751, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Duke\", \"Enslaver Last Name\": \"Skinner\", \"Enslaver First Name\": \"John\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_96\", \"Gender\": \"Male\"}, {\"Year of Record\": 1727, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"London \", \"Enslaver Last Name\": \"Smith\", \"Enslaver First Name\": \"Willm.\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_97\", \"Gender\": \"Male\"}, {\"Year of Record\": 1717, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Marca \", \"Enslaver Last Name\": \"Smith\", \"Enslaver First Name\": null, \"Town\": \"Charlestown\", \"Unique ID\": \"ID_98\", \"Gender\": \"Female\"}, {\"Year of Record\": 1717, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Meria\", \"Enslaver Last Name\": \"Smith\", \"Enslaver First Name\": \"Elizabeth\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_99\", \"Gender\": \"Female\"}, {\"Year of Record\": 1770, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Dinah \", \"Enslaver Last Name\": \"Stevens\", \"Enslaver First Name\": \"Abigail\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_101\", \"Gender\": \"Female\"}, {\"Year of Record\": 1756, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Sambo \", \"Enslaver Last Name\": \"Stevens\", \"Enslaver First Name\": \"Abigail\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_102\", \"Gender\": \"Male\"}, {\"Year of Record\": 1741, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Boston \", \"Enslaver Last Name\": \"Symmes\", \"Enslaver First Name\": \"Thomas\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_105\", \"Gender\": \"Male\"}, {\"Year of Record\": 1735, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Cesar\", \"Enslaver Last Name\": \"Symmes\", \"Enslaver First Name\": \"Thomas\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_106\", \"Gender\": \"Male\"}, {\"Year of Record\": 1741, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Cimbo \", \"Enslaver Last Name\": \"Symmes\", \"Enslaver First Name\": \"Thomas\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_107\", \"Gender\": \"Female\"}, {\"Year of Record\": 1754, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Bandon\", \"Enslaver Last Name\": \"Temple\", \"Enslaver First Name\": \"Mehetable\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_108\", \"Gender\": \"Male\"}, {\"Year of Record\": 1725, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Dutches \", \"Enslaver Last Name\": \"Temple\", \"Enslaver First Name\": \"Capt. Robert\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_109\", \"Gender\": \"Female\"}, {\"Year of Record\": 1754, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Jack\", \"Enslaver Last Name\": \"Temple\", \"Enslaver First Name\": \"William\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_110\", \"Gender\": \"Male\"}, {\"Year of Record\": 1754, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Jumbo\", \"Enslaver Last Name\": \"Temple\", \"Enslaver First Name\": \"R\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_111\", \"Gender\": \"Male\"}, {\"Year of Record\": 1754, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Kerry\", \"Enslaver Last Name\": \"Temple\", \"Enslaver First Name\": \"Mehetable\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_112\", \"Gender\": \"Male\"}, {\"Year of Record\": 1754, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Limerick\", \"Enslaver Last Name\": \"Temple\", \"Enslaver First Name\": \"John\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_113\", \"Gender\": \"Male\"}, {\"Year of Record\": 1754, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Mallow\", \"Enslaver Last Name\": \"Temple\", \"Enslaver First Name\": \"Mehetable\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_114\", \"Gender\": \"Female\"}, {\"Year of Record\": 1754, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Sambo\", \"Enslaver Last Name\": \"Temple\", \"Enslaver First Name\": \"John\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_115\", \"Gender\": \"Male\"}, {\"Year of Record\": 1732, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Scipio\", \"Enslaver Last Name\": \"Tucker\", \"Enslaver First Name\": \"John\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_118\", \"Gender\": \"Male\"}, {\"Year of Record\": 1732, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Violet\", \"Enslaver Last Name\": \"Tucker\", \"Enslaver First Name\": \"John\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_119\", \"Gender\": \"Female\"}, {\"Year of Record\": 1771, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Jack \", \"Enslaver Last Name\": \"Tufts\", \"Enslaver First Name\": \"Wm.\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_120\", \"Gender\": \"Male\"}, {\"Year of Record\": 1748, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Nanne\", \"Enslaver Last Name\": \"Tufts\", \"Enslaver First Name\": \"Sarah\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_121\", \"Gender\": \"Female\"}, {\"Year of Record\": 1748, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Reuben\", \"Enslaver Last Name\": \"Tufts\", \"Enslaver First Name\": \"Sarah\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_122\", \"Gender\": \"Male\"}, {\"Year of Record\": 1734, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Cuffee \", \"Enslaver Last Name\": \"Wyer\", \"Enslaver First Name\": \"William\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_130\", \"Gender\": \"Male\"}, {\"Year of Record\": 1748, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Ephraim\", \"Enslaver Last Name\": \"Wyer\", \"Enslaver First Name\": \"Thomas\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_131\", \"Gender\": \"Male\"}, {\"Year of Record\": 1748, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Grace \", \"Enslaver Last Name\": \"Wyer\", \"Enslaver First Name\": \"William\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_132\", \"Gender\": \"Female\"}, {\"Year of Record\": 1734, \"Total People Enslaved in Record\": 1, \"# Male\": 0, \"# Female\": 1, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Jenny \", \"Enslaver Last Name\": \"Wyer\", \"Enslaver First Name\": \"William\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_133\", \"Gender\": \"Female\"}, {\"Year of Record\": 1748, \"Total People Enslaved in Record\": 1, \"# Male\": 1, \"# Female\": 0, \"# Black\": 1, \"# Native\": 0, \"Name and/or Description\": \"Quash\", \"Enslaver Last Name\": \"Wyer\", \"Enslaver First Name\": \"Edward\", \"Town\": \"Charlestown\", \"Unique ID\": \"ID_134\", \"Gender\": \"Male\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.VConcatChart(...)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# base chart to build on top of\n",
    "base = (\n",
    "    alt.Chart(data=multi_slaves)\n",
    "    .transform_aggregate(count_=\"count()\", groupby=[\"Enslaver Last Name\", \"Enslaver First Name\"])\n",
    "    .transform_stack(\n",
    "        stack=\"count_\",\n",
    "        as_=[\"stack_count_Enslaver_Last_Name1\", \"stack_count_Enslaver_Last_Name2\"],\n",
    "        offset=\"normalize\",\n",
    "        sort=[alt.SortField(\"Enslaver Last Name\", \"ascending\")],\n",
    "        groupby=[],\n",
    "    )\n",
    "    .transform_window(\n",
    "        x=\"min(stack_count_Enslaver_Last_Name1)\",\n",
    "        x2=\"max(stack_count_Enslaver_Last_Name2)\",\n",
    "        rank_First=\"dense_rank()\",\n",
    "        distinct_First=\"distinct(Enslaver First Name)\",\n",
    "        groupby=[\"Enslaver Last Name\"],\n",
    "        frame=[None, None],\n",
    "        sort=[alt.SortField(\"Enslaver First Name\", \"ascending\")],\n",
    "    )\n",
    "    .transform_window(\n",
    "        rank_Last=\"dense_rank()\",\n",
    "        frame=[None, None],\n",
    "        sort=[alt.SortField(\"Enslaver Last Name\", \"ascending\")],\n",
    "    )\n",
    "    .transform_stack(\n",
    "        stack=\"count_\",\n",
    "        groupby=[\"Enslaver Last Name\"],\n",
    "        as_=[\"y\", \"y2\"],\n",
    "        offset=\"normalize\",\n",
    "        sort=[alt.SortField(\"Enslaver First Name\", \"ascending\")],\n",
    "    )\n",
    "    .transform_calculate(\n",
    "        ny=\"datum.y + (datum.rank_First - 1) * datum.distinct_First * 0.01 / 3\",\n",
    "        ny2=\"datum.y2 + (datum.rank_First - 1) * datum.distinct_First * 0.01 / 3\",\n",
    "        nx=\"datum.x + (datum.rank_Last - 1) * 0.01\",\n",
    "        nx2=\"datum.x2 + (datum.rank_Last - 1) * 0.01\",\n",
    "        xc=\"(datum.nx+datum.nx2)/2\",\n",
    "        yc=\"(datum.ny+datum.ny2)/2\",\n",
    "    ).properties(\n",
    "        height=300,\n",
    "        width=2000\n",
    "    )\n",
    ")\n",
    "\n",
    "# get geometry for rectangles\n",
    "rect = base.mark_rect().encode(\n",
    "    x=alt.X(\"nx:Q\").axis(None),\n",
    "    x2=\"nx2\",\n",
    "    y=alt.Y(\"ny:Q\").axis(None),\n",
    "    y2=\"ny2\",\n",
    "    color=alt.Color(\"Enslaver Last Name:N\").legend(None),\n",
    "    opacity=alt.Opacity(\"Enslaver First Name:N\").legend(None),\n",
    "    tooltip=[\"Enslaver Last Name:N\", \"Enslaver First Name:N\"],\n",
    ")\n",
    "\n",
    "# add labels to rectangles\n",
    "text = base.mark_text(baseline=\"middle\").encode(\n",
    "    alt.X(\"xc:Q\").axis(None),\n",
    "    alt.Y(\"yc:Q\").title(\"Enslaver First Name\"),\n",
    "    text=\"Enslaver First Name:N\"\n",
    ")\n",
    "\n",
    "# combine to create mosaic\n",
    "mosaic = rect + text\n",
    "\n",
    "# add enslaver name labels\n",
    "origin_labels = base.mark_text(baseline=\"middle\", align=\"center\").encode(\n",
    "    alt.X(\"min(xc):Q\").title(\"Enslaver Last Name\").axis(orient=\"top\", labelColor='white'),\n",
    "    alt.Color(\"Enslaver Last Name\").legend(None),\n",
    "    text=\"Enslaver Last Name\",\n",
    ").properties(height=10, width=2000, title='Enslaver Families in 1775 Charlestown')\n",
    "\n",
    "# combine chart together\n",
    "mosaic_w_labels = (\n",
    "    (origin_labels & mosaic)\n",
    "    .resolve_scale(x=\"shared\")\n",
    "    .configure_view(stroke=\"\")\n",
    "    .configure_concat(spacing=1)\n",
    "    .configure_axis(domain=False, ticks=False, labels=True, grid=False)\n",
    ")\n",
    "\n",
    "# create brush to link to other chart\n",
    "brush = alt.selection_interval(empty=True)\n",
    "mosaic_w_labels = mosaic_w_labels.add_params(brush)\n",
    "\n",
    "# make gender chart connected by brush\n",
    "family_bar = alt.Chart(data=multi_slaves, title='Gender of Enslaved People').transform_aggregate(count_=\"count()\",\n",
    "                                                              groupby=[\"Enslaver Last Name\",\n",
    "                                                                       \"Enslaver First Name\",\n",
    "                                                                       \"Name and/or Description\",\n",
    "                                                                      \"Gender\"]).transform_stack(\n",
    "        stack=\"count_\",\n",
    "        as_=[\"stack_count_Enslaver_Last_Name1\", \"stack_count_Enslaver_Last_Name2\"],\n",
    "        offset=\"normalize\",\n",
    "        sort=[alt.SortField(\"Enslaver Last Name\", \"ascending\")],\n",
    "        groupby=[],\n",
    "    ).transform_window(\n",
    "        x=\"min(stack_count_Enslaver_Last_Name1)\",\n",
    "        x2=\"max(stack_count_Enslaver_Last_Name2)\",\n",
    "        rank_First=\"dense_rank()\",\n",
    "        distinct_First=\"distinct(Enslaver First Name)\",\n",
    "        groupby=[\"Enslaver Last Name\"],\n",
    "        frame=[None, None],\n",
    "        sort=[alt.SortField(\"Enslaver First Name\", \"ascending\")],\n",
    "    ).transform_window(\n",
    "        rank_Last=\"dense_rank()\",\n",
    "        frame=[None, None],\n",
    "        sort=[alt.SortField(\"Enslaver Last Name\", \"ascending\")],\n",
    "    ).transform_stack(\n",
    "        stack=\"count_\",\n",
    "        groupby=[\"Enslaver Last Name\"],\n",
    "        as_=[\"y\", \"y2\"],\n",
    "        offset=\"normalize\",\n",
    "        sort=[alt.SortField(\"Enslaver First Name\", \"ascending\")],\n",
    "    ).transform_calculate(\n",
    "        ny=\"datum.y + (datum.rank_First - 1) * datum.distinct_First * 0.01 / 3\",\n",
    "        ny2=\"datum.y2 + (datum.rank_First - 1) * datum.distinct_First * 0.01 / 3\",\n",
    "        nx=\"datum.x + (datum.rank_Last - 1) * 0.01\",\n",
    "        nx2=\"datum.x2 + (datum.rank_Last - 1) * 0.01\",\n",
    "        xc=\"(datum.nx+datum.nx2)/2\",\n",
    "        yc=\"(datum.ny+datum.ny2)/2\",\n",
    "    ).mark_bar().encode(\n",
    "    x=alt.X('Gender:N', axis=alt.Axis(labelAngle=0)),\n",
    "    y=alt.Y('Name and/or Description:N', title='Name and/or Description'),\n",
    "    color='Enslaver Last Name:N',\n",
    "    tooltip=['Enslaver First Name', 'Enslaver Last Name']\n",
    ").transform_filter(brush).properties(\n",
    "        height=550,\n",
    "        width=300\n",
    ")\n",
    "\n",
    "interactive_enslavers = ((mosaic_w_labels & family_bar).configure_concat(spacing=50).configure_axis(domain=False, ticks=False, labels=True, grid=False))\n",
    "interactive_enslavers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d461fac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in data\n",
    "df_nb = gpd.read_file(\"1775Charlestown.geojson\")\n",
    "\n",
    "# from \"Group 5 Census Charlestown 1775 05-14-24\"\n",
    "overlap = pd.read_csv('overlap.csv')\n",
    "\n",
    "# clean data\n",
    "df_overlap = overlap.drop(columns=[\"which tab\"])\n",
    "df_overlap.rename(columns={\"Name\": \"Name of Enslaved Person\"}, inplace=True)\n",
    "df_overlap['Enslaver Name'] = df_overlap['Enslaver First Name'] + ' ' + df_overlap['Enslaver Last Name']\n",
    "df_overlap['Sex'] = df_overlap['Sex'].replace({'?': 'Unknown'}).fillna('Unknown')\n",
    "df_overlap['Sex'] = df_overlap['Sex'].replace({'M': 'Male'})\n",
    "df_overlap['Sex'] = df_overlap['Sex'].replace({'F': 'Female'})\n",
    "\n",
    "# count number of enslaved people and gender per family and make a new df for these counts\n",
    "family_counts = df_overlap['Family'].value_counts().reset_index()\n",
    "family_counts.columns = ['F1775_Clai', 'Count']\n",
    "family_info = df_overlap[['Family', 'Enslaver Name']].drop_duplicates()\n",
    "family_info = pd.merge(family_info, family_counts, left_on='Family', right_on='F1775_Clai', how='left')\n",
    "\n",
    "gender_counts = df_overlap.groupby(['Family', 'Sex']).size().unstack(fill_value=0).reset_index()\n",
    "gender_counts.columns = ['Family', 'Female', 'Male', 'Unknown']\n",
    "family_info = pd.merge(family_info, gender_counts, on='Family', how='left')\n",
    "\n",
    "family_info = family_info.dropna()\n",
    "family_info['Count'] = family_info['Count'].astype(int)\n",
    "family_info['Female'] = family_info['Female'].astype(int)\n",
    "family_info['Male'] = family_info['Male'].astype(int)\n",
    "family_info['Unknown'] = family_info['Unknown'].astype(int)\n",
    "\n",
    "family_info.rename(columns={\"Count\": \"Enslaved People Count\"}, inplace=True)\n",
    "\n",
    "# blank map of charlestown\n",
    "res_map = alt.Chart(df_nb).mark_geoshape(\n",
    "    fill = \"#f5f5f5\",\n",
    "    stroke='#ccc'\n",
    ").project(\n",
    "    type='identity',\n",
    "    reflectY=True\n",
    ").properties(\n",
    "    width=800,\n",
    "    height=600\n",
    ")\n",
    "\n",
    "# color layer with color scale based on how many enslaved people\n",
    "\n",
    "click = alt.selection_point(fields=['F1775_Clai'])\n",
    "\n",
    "layer = alt.Chart(df_nb).mark_geoshape(\n",
    "    stroke='black'\n",
    ").encode(\n",
    "    tooltip=[alt.Tooltip(\"Enslaved People Count:Q\", title=\"Enslaved People\"),\n",
    "             alt.Tooltip(\"Enslaver Name:N\", title=\"Enslaver Name\"),\n",
    "             alt.Tooltip(\"F1775_Clai:N\", title=\"Family ID (1775)\")],\n",
    "    color=alt.condition(click,\n",
    "        alt.Color(\"Enslaved People Count:Q\", scale=alt.Scale(scheme='reds', reverse=True)),\n",
    "        alt.value('lightgrey')),\n",
    "    opacity=alt.condition(click, alt.value(1), alt.value(0.4)),\n",
    ").transform_lookup(\n",
    "    lookup='F1775_Clai',\n",
    "    from_=alt.LookupData(family_info, 'F1775_Clai', ['F1775_Clai', 'Enslaved People Count', 'Enslaver Name' ])\n",
    ").add_params(click\n",
    ").project(\n",
    "    type='identity',\n",
    "    reflectY=True\n",
    ").properties(\n",
    "    width=800,\n",
    "    height=600\n",
    ")\n",
    "\n",
    "gender_bars = alt.Chart(family_info).mark_bar().encode(\n",
    "    y=alt.Y(\"Sex:N\", title='Gender'),\n",
    "    x=alt.X(\"Enslaved People Count:Q\", title='Number of Enslaved People'),\n",
    "    color=alt.Color(\"Sex:N\", scale=alt.Scale(domain=['Female', 'Male', 'Unknown'], range=['pink', 'lightskyblue', 'darkseagreen']), legend=None),\n",
    "    tooltip=[alt.Tooltip(\"Enslaved People Count:Q\")]\n",
    ").transform_filter(click\n",
    ").transform_aggregate(\n",
    "    Female='sum(Female)',\n",
    "    Male='sum(Male)',\n",
    "    Unknown='sum(Unknown)',\n",
    "    groupby=[]\n",
    ").transform_fold(['Female', 'Male', 'Unknown'], as_=['Sex', 'Enslaved People Count']\n",
    ").properties(\n",
    "    width=400,\n",
    "    height=100\n",
    ")\n",
    "\n",
    "# note\n",
    "note = alt.Chart(pd.DataFrame({'text': [\n",
    "    'Note: This visualization is based solely on the data \\n'\n",
    "    'provided, where the slaveholder was identifiable and \\n'\n",
    "    'their household IDs matched the provided mapping data.']})\n",
    ").mark_text(\n",
    "    align=\"left\",\n",
    "    font='Helvetica',\n",
    "    fontSize=12,\n",
    "    color='grey',\n",
    "    lineBreak='\\n'\n",
    ").encode(text='text'\n",
    ")\n",
    "\n",
    "layered_maps = res_map + layer\n",
    "gender_and_note = gender_bars | note\n",
    "\n",
    "final = (layered_maps & gender_and_note).properties(title='Geographic Distribution and Gender Demographics of Enslaved People in Charlestown'\n",
    ").configure_title(\n",
    "    fontSize=18,\n",
    "    font='Helvetica',\n",
    "    anchor='middle')\n",
    "\n",
    "final"
   ]
  }
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