HB_person_distribution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distributions of Person Across the Hebrew Bible"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we can see the counts for first, second, and third person markers in the Hebrew Bible. They are broken down by narrative (`N`) and domain (`D`). Interestingly, the third person dominates accross both domains."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The total counts per person is shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "p3    59785\n",
       "p2    22992\n",
       "p1    17577\n",
       "Name: person, dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "person_results.person.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<hr>\n",
    "*Python code*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.00s loading features ...\n",
      "   |     0.10s B ps                   from /Users/cody/github/etcbc/bhsa/tf/c\n",
      "   |     0.97s T prs_ps               from /Users/cody/github/etcbc/bhsa/tf/c\n",
      "   |     0.01s B domain               from /Users/cody/github/etcbc/bhsa/tf/c\n",
      "  5.26s All features loaded/computed - for details use loadLog()\n"
     ]
    },
    {
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   ],
   "source": [
    "# ETCBC's BHSA data\n",
    "from tf.fabric import Fabric\n",
    "from tf.extra.bhsa import Bhsa\n",
    "\n",
    "# stats & data-containers\n",
    "import collections\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "\n",
    "# data visualizations\n",
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\")\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# prep the data\n",
    "name = 'HB_person_distribution'\n",
    "hebrew_data = ['~/github/etcbc/{}/tf/c'.format(direc) for direc in ('bhsa',)] # data dirs\n",
    "load_features = '''\n",
    "ps prs_ps domain\n",
    "''' \n",
    "\n",
    "# TF load statements\n",
    "TF = Fabric(locations=hebrew_data, silent=True)\n",
    "api = TF.load(load_features)\n",
    "B = Bhsa(api=api, hoist=globals(), silent=True, name=name) # Bhsa functions for search and visualizing text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done with 100354 results...\n",
      "Sample:\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "\n",
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       "        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>node</th>\n",
       "      <th>person</th>\n",
       "      <th>book</th>\n",
       "      <th>domain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33</td>\n",
       "      <td>p3</td>\n",
       "      <td>Genesis</td>\n",
       "      <td>N</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35</td>\n",
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       "      <td>N</td>\n",
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      ],
      "text/plain": [
       "   node person     book domain\n",
       "0    33     p3  Genesis      N\n",
       "1    35     p3  Genesis      Q\n",
       "2    38     p3  Genesis      N\n",
       "3    41     p3  Genesis      N\n",
       "4    47     p3  Genesis      N"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "person_results = list()\n",
    "\n",
    "header = ('node', 'person', 'book', 'domain')\n",
    "\n",
    "for word in F.otype.s('word'):\n",
    "    \n",
    "    domain = F.domain.v(L.u(word, 'clause')[0])\n",
    "    person = F.ps.v(word)\n",
    "    person_suffix = F.prs_ps.v(word)\n",
    "    persons = [p for p in (person, person_suffix) if all([p not in {'unknown', 'NA'},\n",
    "                                                          domain in {'Q', 'N'}\n",
    "                                                         ])]\n",
    "    \n",
    "    for person in persons:\n",
    "        book, chapter, verse = T.sectionFromNode(word)\n",
    "        person_results.append([word, person, book, domain])\n",
    "        \n",
    "person_results = pd.DataFrame(person_results, columns=header)\n",
    "        \n",
    "print(f'Done with {len(person_results)} results...')\n",
    "print('Sample:')\n",
    "person_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_data():\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    sns.set(font_scale=1.6)\n",
    "    sns.countplot(x='domain', hue='person', data=person_results).set_title('Person Counts Across the Hebrew Bible by Narrative/Quotation')"
   ]
  }
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