Bible_analytics_v2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set working directory\n",
    "import matplotlib as plt\n",
    "import pandas as pd\n",
    "import os \n",
    "os.chdir(\"C:\\\\Users\\\\rohan\\\\Documents\\\\Analytics\\\\Data\\\\Bible\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Gen|1|1| In the beginning God created the heaven and the earth.~\\n', 'Gen|1|2| And the earth was without form, and void; and darkness was upon the face of the deep. And the Spirit of God moved upon the face of the waters.~\\n']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "31102"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#open the file\n",
    "text_file = open(\"kjvdat.txt\", \"r\")\n",
    "\n",
    "#read the lines\n",
    "lines = text_file.readlines()\n",
    "print(lines[0:2])\n",
    "\n",
    "#lines is of list data type\n",
    "type(lines)\n",
    "\n",
    "#Number of lines (verses)\n",
    "len(lines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['In the beginning God created the heaven and the earth.', 'And the earth was without form, and void; and darkness was upon the face of the deep. And the Spirit of God moved upon the face of the waters.']\n"
     ]
    }
   ],
   "source": [
    "#Remove the book, chapter and verse from each line\n",
    "for i in range(0,len(lines)):\n",
    "    lines[i] = lines[i].split('| ')[1][:-2]\n",
    "    \n",
    "#Quick sanity check\n",
    "print(lines[0:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['in', 'the', 'beginning', 'god', 'created', 'the', 'heaven', 'and', 'the', 'earth', 'and', 'the', 'earth', 'was', 'without', 'form', 'and', 'void', 'and', 'darkness', 'was', 'upon', 'the', 'face', 'of', 'the', 'deep', 'and', 'the', 'spirit', 'of', 'god', 'moved', 'upon', 'the', 'face', 'of', 'the', 'waters', 'and', 'god', 'said', 'let', 'there', 'be', 'light', 'and', 'there', 'was', 'light', 'and', 'god', 'saw', 'the', 'light', 'that', 'it', 'was', 'good', 'and', 'god', 'divided', 'the', 'light', 'from', 'the', 'darkness', 'and', 'god', 'called', 'the', 'light', 'day', 'and', 'the', 'darkness', 'he', 'called', 'night', 'and', 'the', 'evening', 'and', 'the', 'morning', 'were', 'the', 'first', 'day', 'and', 'god', 'said', 'let', 'there', 'be', 'a', 'firmament', 'in', 'the', 'midst']\n"
     ]
    }
   ],
   "source": [
    "#Convert list to string\n",
    "bible = ' '.join(lines)\n",
    "\n",
    "#Convert all words to lower case\n",
    "bible = bible.lower()\n",
    "\n",
    "#Split string to separate out each word\n",
    "import re\n",
    "bible2 = re.findall(r\"[\\w']+\", bible)\n",
    "print(bible2[0:100])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of words in the bible :  789651\n"
     ]
    }
   ],
   "source": [
    "#Number of words in the bible\n",
    "print(\"Number of words in the bible : \", len(bible2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Count the frequency of each word\n",
    "from collections import Counter\n",
    "#Word Frequency\n",
    "counts = Counter(bible2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique words in the bible :  12843\n"
     ]
    },
    {
     "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>Word</th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>the</td>\n",
       "      <td>63924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>and</td>\n",
       "      <td>51696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>of</td>\n",
       "      <td>34617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>to</td>\n",
       "      <td>13562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>that</td>\n",
       "      <td>12912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>in</td>\n",
       "      <td>12667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>he</td>\n",
       "      <td>10420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>shall</td>\n",
       "      <td>9838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>unto</td>\n",
       "      <td>8997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>for</td>\n",
       "      <td>8971</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Word  Frequency\n",
       "1      the      63924\n",
       "6      and      51696\n",
       "15      of      34617\n",
       "76      to      13562\n",
       "26    that      12912\n",
       "0       in      12667\n",
       "33      he      10420\n",
       "143  shall       9838\n",
       "51    unto       8997\n",
       "78     for       8971"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert counter object to data frame\n",
    "word_count = pd.DataFrame.from_dict(counts, orient='index').reset_index()\n",
    "word_count.rename(columns = {'index' : 'Word',0:'Frequency'},inplace = True)\n",
    "word_count = word_count.sort_values('Frequency',ascending=False)\n",
    "\n",
    "#Number of unique words\n",
    "print(\"Number of unique words in the bible : \", len(word_count))\n",
    "\n",
    "#Top ten words\n",
    "word_count.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Word</th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10863</th>\n",
       "      <td>jesus</td>\n",
       "      <td>973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Word  Frequency\n",
       "10863  jesus        973"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of times Jesus appears\n",
    "word_count[word_count['Word'] == \"jesus\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['the', 'and', 'of', 'to', 'that', 'in', 'he', 'shall', 'unto', 'for', 'i', 'his', 'a', 'lord', 'they', 'be', 'is', 'him', 'not']\n"
     ]
    }
   ],
   "source": [
    "#Number of times god appears\n",
    "word_count[word_count['Word'] == \"god\"]\n",
    "ab = word_count['Word'][0:19]\n",
    "abc = ab.tolist()\n",
    "print(abc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rohan\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1460: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  stat_data = remove_na(group_data)\n"
     ]
    },
    {
     "data": {
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A7ASWA1cBLxF/JbuUU1VTx/QHF7Kj3OvJr65T/v72es64601eWJq0id7GJE2ikx3rgb+5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\ngbEAY4wJjAUYY0xg/j8BAUQGQ8byOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2078f5ca3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Line plot with seaborn\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "g = sns.factorplot(x='Word',y='Frequency',data=word_count[0:19])\n",
    "g.set_xticklabels(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\rohan\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import stop words\n",
    "import io\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "nltk.download(\"stopwords\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'when', 'in', 'those', 'into', 'me', 'herself', 'we', 'if', \"aren't\", 'out', 'such', \"needn't\", 'our', 'has', 'again', 'how', 'who', 't', 'below', 'further', 'shan', \"you'll\", 'i', 'hasn', 'ma', 'above', 'she', 'this', 'an', 'doing', 'nor', 'myself', 'it', 'what', 'by', \"isn't\", 'from', 'down', 'mightn', 'their', 'mustn', 'up', 'or', \"it's\", 'until', 'no', 'these', 'so', \"shan't\", 'isn', 'during', 'here', 'are', 'once', 'can', 'does', 'over', 'theirs', 'themselves', 'to', 'own', 'about', 'd', 'didn', \"she's\", 'with', 'needn', 'the', 'through', 'some', 'than', 'other', \"you'd\", 'ours', 'wouldn', 'after', 'ourselves', 'where', 'they', \"don't\", 'be', 'weren', \"you've\", 'there', 'yours', 'shouldn', 'being', 'at', \"mustn't\", 'same', 'its', 'doesn', 'each', 're', 'on', 'is', 'between', 'now', 'my', 's', 'that', 've', \"haven't\", 'have', 'few', 'her', 'against', 'won', 'which', 'm', \"you're\", 'hadn', 'were', 'having', \"mightn't\", 'for', 'more', 'a', 'too', 'under', 'his', 'hers', \"that'll\", 'was', 'll', \"doesn't\", 'off', 'y', 'all', 'because', 'am', 'why', 'not', 'itself', 'couldn', 'as', 'will', 'he', 'them', 'yourselves', 'yourself', 'been', 'only', \"didn't\", 'aren', \"wasn't\", 'then', \"wouldn't\", 'ain', 'himself', 'do', 'haven', \"shouldn't\", \"weren't\", 'most', 'did', 'your', 'don', \"hasn't\", 'before', 'o', 'wasn', 'both', 'him', \"hadn't\", \"won't\", 'whom', 'had', 'should', 'just', 'you', 'but', 'very', 'any', \"should've\", \"couldn't\", 'of', 'and', 'while'}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "set"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View the stop words\n",
    "stop = set(stopwords.words('english'))\n",
    "print(stop)\n",
    "type(stop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Word</th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>shall</td>\n",
       "      <td>9838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>unto</td>\n",
       "      <td>8997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>lord</td>\n",
       "      <td>7830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>thou</td>\n",
       "      <td>5474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>thy</td>\n",
       "      <td>4600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Word  Frequency\n",
       "143  shall       9838\n",
       "51    unto       8997\n",
       "166   lord       7830\n",
       "231   thou       5474\n",
       "321    thy       4600"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove stop words from the dataframe\n",
    "word_count2 = word_count[~word_count['Word'].isin(stop)]\n",
    "word_count2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rohan\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1460: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  stat_data = remove_na(group_data)\n"
     ]
    },
    {
     "data": {
      "image/png": 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sgFZeKY8X56ynvKIyxSMyEkUqst6rqu71Npt7D8UqB2YMrXKyOcer0bN59wFm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\nMEyhGIaRMEyhGIaRMP4/OwGKINlKngoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20792b19cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot top 10 words after removing stop words\n",
    "g = sns.factorplot(x='Word',y='Frequency',data=word_count2[0:19])\n",
    "g.set_xticklabels(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to plot a dispersion graph\n",
    "def dispersion(word):\n",
    "    d = list()\n",
    "    for i in range(0,len(bible2)):\n",
    "        if bible2[i] == word:\n",
    "            d.append(i)\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.vlines(d, 0, 1, edgecolor=\"black\") # <-- ANSWER\n",
    "    ax.set_xlim([0, len(bible2)]) # set the lower and upper limits of graph\n",
    "    ax.set_xlabel('narrative time')\n",
    "    ax.set_xticks([0],minor=True) # turn off: ax.set_xticks([])\n",
    "    ax.set_ylabel(word) # turn off by droping this line\n",
    "    ax.set_yticks([])\n",
    "    fig.set_figheight(1)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAABnCAYAAAAg2qjzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAC/pJREFUeJzt3XuQVnUdx/H3F7kpooBQkZQLo2hY\niYgJyTiMec8hTVPQvGU6Vo6hWUleGmecabqMmHmjsdTpYqhpouUgIVniLVa5eEMXoRFFEJ1FUsqA\nb3/8fsseludydn0Oe376ec3sPOf57Tm/89k9z373eX7POb/H3B0REUlHj+4OICIinaPCLSKSGBVu\nEZHEqHCLiCRGhVtEJDEq3CIiiVHhFhFJjAq3iEhiVLhFRBLTs4hOBw8e7E1NTUV0LSLygdXc3LzW\n3YfUW6+Qwt3U1MSCBQuK6FpE5APLzP6VZz0NlYiIJKaQZ9wiImbW3RFKz90ZMGAA69at69R2esYt\nIpIYFW4RkcSocIuIJEaFW0QkMSrcIiKJUeEWEUmMCreISGJUuEVEEqPCLSKSGBVuEZHEqHCLiCRG\nhVtEJDF1C7eZ9TOzHnF5pJlNMrNexUcTEZFK8jzj/jvQ18x2B+YCZwG3FhlKRESqy1O4zd3fBb4M\n/MLdjwdGFRtLRESqyVW4zWw8cCrw59imebxFRLpJnsI9FZgG3OPuz5rZCGBesbFERKSaus+c3f1h\n4OHM/ZeBC4oMJSIi1dUt3GY2D/CO7e5+aCGJRESkpjxj1RdnlvsCJwAbi4kjIiL15Bkqae7QNN/M\nHq64soiIFC7PUMmgzN0ewAHAxwpLJCIiNeUZKmkmjHEbYYhkOXB2kaFERKS6PEMlw7dHEBERySfP\nXCVfMbP+cfkyM7vbzMYUH01ERCrJcwHO5e6+3swmAEcCtwE3FhtLRESqyVO4N8XbLwI3uvu9QO/i\nIomISC15CverZjYDOAn4i5n1ybmdiIgUIE8BPgmYDRzl7q3AIOC7haYSEZGq6hbuOKXrGmBCbNoI\nvFRkKBERqS7PWSU/BL5PmCEQoBfw2yJDiYhIdXmGSo4HJgHvALj7a0D/IkOJiEh1eQr3e+7uxBkC\nzaxfsZFERKSWPIX7jnhWyQAzO4fwuZM3FxtLRESqyXPJ+8/M7HDgbWAkcJm7/7XwZCIiUlHVwm1m\nj7j7BDNbT/skUwDnmdlm4C3gp+5+w3bIKSIiUdXC7e4T4m3FNyLNbDfgUUCFW0RkO+ryFZDu/iYw\nsXFRREQkj/d16bq7r2pUEBERyUdzjoiIJEaFW0QkMSrcIiKJUeEWEUmMCreISGJUuEVEEqPCLSKS\nGBVuEZHEqHCLiCSm7uyAIiJdEabxl3paW1u3LJtZjTXb6Rm3iEhiVLhFRBJjRbycMbMNwLMN77gY\ng4G13R0iB+VsvFSyppIT0sla1px7uPuQeisVVbjfyLPzMjCzBe4+trtz1KOcjZdK1lRyQjpZU8lZ\nTVFDJa31VxERka4oqnCvK6hfEZEPvaIK9y8L6rcIqWRVzsZLJWsqOSGdrKnkrKiQMW4RESmOTgcU\nEUmNuzf0CzgKWAq0AJc0uv/Mfn4NrAGeybQNAuYAL8XbgbHdgGtjpsXAmMw2Z8T1XwLOyLQfACyJ\n21xL+6uTivuokfMTwDzgecIpkt8uY1agL/AksCjmvDK2DweeiH3MBHrH9j7xfkv8flOmr2mxfSlw\nZL3HRrV91Pm97gA8Ddxf8pwr4rFZCCwo47HP9DUAuAt4gfB4HV+2rMDe8XfZ9vU2MLVsOYv+anQx\n3QFYBowAehOKwKhCgsMhwBi2Ltw/aftDAy4BfhyXjwEeiAdxHPBE5kC8HG8HxuW2A/5kfOBa3Pbo\nWvuokXNo24MF6A+8CIwqW9a47c5xuRehQI0D7gAmx/abgG/E5W8CN8XlycDMuDwqHvc+hEK3LD4u\nqj42qu2jzu/1IuD3tBfusuZcAQzu0FaqY5/JdRvw9bjcm1DIS5k1U29eB/Yoc85C6l9DOws/7OzM\n/WnAtMLCQxNbF+6lwNC4PBRYGpdnAFM6rgdMAWZk2mfEtqHAC5n2LetV20cnMt8LHF7mrMBOwFPA\nQYSLFHp2PL7AbGB8XO4Z17OOx7xtvWqPjbhNxX3UyDcMmAscCtxfq4/uzBnXW8G2hbt0xx7YBVhO\nfHZZ5qyZPo4A5pc9ZxFfjR7j3h14JXN/ZWzbXj7q7qsA4u1H6uSq1b6yQnutfdRlZk3A/oRns6XL\namY7mNlCwhDUHMIzz1Z331ih7y154vfXAbt1If9uNfZRzTXA94DN8X6tProzJ4ADD5pZs5mdG9tK\nd+wJrzDeAG4xs6fN7GYz61fSrG0mA7fX6aMMORuu0YW70tRW3uB9dEW1XJ1t73oAs52BPwJT3f3t\nWqt2MlPDsrr7JncfTXhG+zngUzX6blTOTuU3s2OBNe7enG0uW86Mg919DHA08C0zO6TGut35OO1J\nGHq80d33B94hDAdU061/U2bWG5gE3Flv1U7mKWsN20qjC/dKwptxbYYBrzV4H7WsNrOhAPF2TZ1c\ntdqHVWivtY+qzKwXoWj/zt3vLnNWAHdvBf5GGBMcYGZt0/9m+96SJ35/V+CtLuRfW2MflRwMTDKz\nFcAfCMMl15QwJwDu/lq8XQPcQ/iHWMZjvxJY6e5PxPt3EQp5GbNC+Ef4lLuvrtNHd+csRKML9z+B\nvcxsePyPOBmY1eB91DKL8E4x8fbeTPvpFowD1sWXOrOBI8xsoJkNJIyZzY7fW29m4yxMkHt6h74q\n7aOiuP2vgOfd/eqyZjWzIWY2IC7vCBxGOLNgHnBilZxtfZ8IPORh8G8WMNnM+pjZcGAvwps9FR8b\ncZtq+9iGu09z92Hu3hT7eMjdTy1bzvh77Gdm/duWCcfsGUp27AHc/XXgFTPbOzZ9AXiujFmjKbQP\nk9Tqo7tzFqPRg+aEd3FfJIyPXlrU4DzhoK0C/kf4L3k2YRxyLuF0nbnAoLiuAdfHTEuAsZl+vkY4\n7acFOCvTPpbwR7YMuI72U4Iq7qNGzgmEl1qLaT+F6ZiyZQU+Szi9bnHs64rYPoJQ0FoIL0v7xPa+\n8X5L/P6ITF+XxixLie/I13psVNtHjsfARNrPKildzrj+ItpPsby01nHprmOf6Ws0sCA+Bv5EONui\ndFkJb56/CeyaaStdziK/dOWkiEhidOWkiEhiVLhFRBKjwi0ikhgVbhGRxKhwi4gkRoVbPlDMrMnM\nTsncH2tm1zag39Fmdkzm/iQzq3VloUhhdDqglJ6Z9fT2OUK2ud9h3YnAxe5+bIMznEk4B/j8RvYr\n0hUq3FK4OLnWA8AjwOeBV4EvufsGMzsHOJcwjWgLcJq7v2tmtxIuTd+fMFPheuDjhBkh1wI/AH4D\n9Iu7Od/dHzWzxwlzrCwnTFP6NHAxYV6Ll4HRHi7px8xaCJfQbyZM0/rJ2NdUd5+fyd+WbceY/Udx\neay7nx+zbgD2IUwxehbhyrrxhGlEz4z9HAFcSZhKdhnhoo9/d/X3Kh9eGiqR7WUv4Hp33xdoBU6I\n7Xe7+4Huvh/hEvuzM9uMBA5z9+/E+wcQCv4phHkiDvcwgdPJhAnvIUyM9A93H+3u09s6cvfNhEuU\njwcws4OAFR7muvg5MN3dD4y5bs4Gd/f3gCsIc3mPdveZFX6+gYR5Uy4E7gOmA/sCn4nDLIOBy+LP\nM4ZwheJFeX95Ilk9668i0hDL3X1hXG4mPHMG+LSZXUWYtH9nwhwSbe50902Z+7PcfUNc7gVcZ2aj\ngU2EIl/PTEIBvoX4oQqx/TBgVJiaAoBdzKy/u6/P+8MB97m7m9kSYLW7LwEws2cJP+swwoc3zI/7\n6Q081on+RbZQ4Zbt5b+Z5U2EoQaAW4Hj3H1RHEeemFnvnQ59ZO9fCKwG9iO8cvxPjgyPAXua2RDg\nOOCq2N6D8GELG6puWV/bz7eZrX/WzYS/s03AHHef8j72IQJoqES6X39gVZz69tRObLcrsCoOgZxG\n+BgrCGPh/Stt4OENnXuAqwmzNb4Zv/UgsOVNx/gsvqOq/eb0OHCwme0Z97GTmeV5lSCyDRVu6W6X\nEz4RaA7hQ2rzugE4I74ZOZL2Z+OLgY1mtsjMLqyw3Uzgq7QPkwBcAIw1s8Vm9hxwXoXt5hGGUxaa\n2cmdyAmAu78BnAncbmaLCYV8n872IwI6q0REJDl6xi0ikhgVbhGRxKhwi4gkRoVbRCQxKtwiIolR\n4RYRSYwKt4hIYlS4RUQS838OsGJACabGGwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x207fa05f240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Dispersion plot for Jesus\n",
    "dispersion('jesus')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAABnCAYAAAAg2qjzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x207fa02ff28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Dispersion plot for God\n",
    "dispersion('god')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAABnCAYAAAAg2qjzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2078fc97668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Dispersion plot for Satan\n",
    "dispersion('satan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAABnCAYAAAAg2qjzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxitR+k4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2078fc85b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Dispersion plot for moses\n",
    "dispersion('moses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAABnCAYAAAAg2qjzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAADJFJREFUeJzt3XuwVWUZx/Hvo0dQAdkoVBTpgUQM\nNQGPiWl6xmuakZqToHlBzS46JWaN5KWcbJqaRs1RU8dSx8pQ0wRnDEnUyrx0kJs3FIRGEkFsUCIS\nhac/3nefs9jsyzq4F2ct/H1m9uy13r3W+z57r3Wes/a71n6XuTsiIlIc2/R0ACIi0j1K3CIiBaPE\nLSJSMErcIiIFo8QtIlIwStwiIgWjxC0iUjBK3CIiBaPELSJSMC1ZVDpw4EBvbW3NomoRka3WrFmz\nVrr7oEbLZZK4W1tb6ejoyKJqEZGtlpn9M81y6ioRESkYJe6cK5VKlEqljcrMLLMHQHt7O+3t7Z3t\ntbS0bLJcS0sLpVKp6mvv91GuO/nek/GlVfk+Ksvb29ur1lsuK5VKdddPTpc/i/I65fnK5RrFlnw9\nGVvlZ11v3bRKpVLn+2ykMt5q+2VZ5fZsb2+npaWl8/NIbudqdSc//3rvt1b81WJLtluuq9q2L2+7\n5D6yOdLUUxlnd9pS4hYRKRglbhGRglHiFhEpGCVuEZGCUeIWESkYJW4RkYJR4hYRKRglbhGRglHi\nFhEpGCVuEZGCUeIWESkYJW4RkYJR4hYRKZi643Gb2TTAa73u7uOaHpGIiNTV6EYKP4/PJwIfAX4T\n5ycASzKKSURE6qibuN39MQAz+5G7H5J4aZqZ/SXTyEREpKq0fdyDzGxYecbMhgIN74smIiLNl/ae\nk5OAR83slTjfCnwtk4hERKSuVInb3f9kZsOBPWPRi+7+TnZhiYhILY2uKjnM3Wea2YkVL33CzHD3\nezOMTUREqmh0xH0oMBP4QpXXHFDiFhHZwhpdVfKDOHmOu6/fAvGIiEgDaa8qWWxmN5vZ4VZ5P3sR\nEdmi0ibuEcCfgfMISfw6Mzs4u7BERKSWVInb3de6+13ufiIwGtgJeCzTyEREpKrUg0yZ2aFmdgPw\nDLA98OXMohIRkZpSXcdtZouBOcBdwHfdfU2mUYmISE1pfzm5r7u/nWkkIiKSStrEvc7MzgP2InST\nAODuZ2USlYiI1JS2j/sOwrCuRxNOSg4BVmcVlIiI1JY2ce/u7pcBa9z9duDzwD7ZhSUiIrWkTdzv\nxudVZrY30J8wQqCIiGxhafu4bzazAcClwFSgL3BZZlGJiEhNjUYHvDAxOzE+Xx+f+2QSkYiI1NXo\niLtffB4B7E842oYwWqBuXSYi0gMajQ54BYCZPQSMcffVcf6HwN2ZRyciIptIe3JyV2BdYn4dOjkp\nItIj0p6cvAN42szuI9xA4QTg9syiEhGRmtLec/LHZvYg8NlYNNHdZ2cXloiI1JL2iBt3f4YwMqCI\niPSg1MO6iohIPihxi4gUjBK3iEjBKHGLiBSMEreISMEocYuIFIwSt4hIwShxi4gUjLl70ytta2vz\njo6OptcrIrI1M7NZ7t7WaDkdcYuIFIwSt4hIwWTSVWJma4Hnml5xNgYCK3s6iBQUZ/MVJdaixAnF\niTWvce7m7oMaLZRV4n4jTeN5YGYdafqUepribL6ixFqUOKE4sRYlzlqy6ipZlVG9IiIfeFkl7rcy\nqldE5AMvq8R9c0b1ZqEosSrO5itKrEWJE4oTa1HirCqTPm4REcmOLgcUESkad2/qA/gcsABYCFzc\n7PoT7fwaWAE8myjbGZgBvByfB8RyA66NMc0DxiTWOSMu/zJwRqJ8P2B+XOdaur6dVG2jTpwfBx4B\nXiBcIvntPMYKbA88DcyNcV4Ry4cCT8U6pgC9YnnvOL8wvt6aqGtyLF8AHN1o36jVRoPPdVtgNvBA\nzuNcErfNHKAjj9s+UVcJuAd4kbC/Hpi3WIER8bMsP94GLshbnFk/mp1MtwUWAcOAXoQkMDKTwOEQ\nYAwbJ+6flf/QgIuBn8bpY4EH40YcCzyV2BCvxOcBcbq8wZ+OO67FdY+p10adOAeXdxagH/ASMDJv\nscZ1+8bp7QgJaixwFzA+lt8IfCNOfxO4MU6PB6bE6ZFxu/cmJLpFcb+ouW/UaqPB53oh8Du6Ende\n41wCDKwoy9W2T8R1O3BOnO5FSOS5jDWRb14HdstznJnkv6ZWFt7s9MT8ZGByZsFDKxsn7gXA4Dg9\nGFgQp28CJlQuB0wAbkqU3xTLBgMvJso7l6vVRjdivh84Ms+xAjsSbgx9AOFHCi2V2xeYDhwYp1vi\ncla5zcvL1do34jpV26gT3xDgYeAw4IF6dfRknHG5JWyauHO37YGdgMXEo8s8x5qo4yjg8bzHmcWj\n2X3cHwNeTcwvjWVbyofdfRlAfP5Qg7jqlS+tUl6vjYbMrBUYTTiazV2sZratmc0hdEHNIBx5rnL3\n96rU3RlPfP0tYJfNiH+XOm3Ucg3wPWBDnK9XR0/GCeDAQ2Y2y8zOjWW52/aEbxhvALea2Wwzu8XM\n+uQ01rLxwJ0N6shDnE3X7MRtVcq8yW1sjlpxdbd88wMw6wv8AbjA3d+ut2g3Y2parO6+3t1HEY5o\nPw18sk7dzYqzW/Gb2XHACneflSzOW5wJB7n7GOAY4DwzO6TOsj25n7YQuh5/6e6jgTWE7oBaevRv\nysx6AeOAuxst2s148prDNtLsxL2UcDKubAjwWpPbqGe5mQ0GiM8rGsRVr3xIlfJ6bdRkZtsRkvZv\n3f3ePMcK4O6rgEcJfYIlM2upUndnPPH1/sC/NyP+lXXaqOYgYJyZLQF+T+guuSaHcQLg7q/F5xXA\nfYR/iHnc9kuBpe7+VJy/h5DI8xgrhH+Ez7j78gZ19HScmWh24v4HMNzMhsb/iOOBqU1uo56phDPF\nxOf7E+WnWzAWeCt+1ZkOHGVmA8xsAKHPbHp8bbWZjTUzA06vqKtaG1XF9X8FvODuV+U1VjMbZGal\nOL0DcAThyoJHgJNqxFmu+yRgpofOv6nAeDPrbWZDgeGEkz1V9424Tq02NuHuk919iLu3xjpmuvup\neYszfo59zKxfeZqwzZ4lZ9sewN1fB141sxGx6HDg+TzGGk2gq5ukXh09HWc2mt1pTjiL+xKhf/SS\nrDrnCRttGfAu4b/k2YR+yIcJl+s8DOwclzXg+hjTfKAtUc9ZhMt+FgITE+VthD+yRcB1dF0SVLWN\nOnEeTPiqNY+uS5iOzVuswKcIl9fNi3VdHsuHERLaQsLX0t6xfPs4vzC+PixR1yUxlgXEM/L19o1a\nbaTYB9rpuqokd3HG5efSdYnlJfW2S09t+0Rdo4COuA/8kXC1Re5iJZw8fxPonyjLXZxZPvTLSRGR\ngtEvJ0VECkaJW0SkYJS4RUQKRolbRKRglLhFRApGiVu2KmbWamanJObbzOzaJtQ7ysyOTcyPM7N6\nvywUyYwuB5TcM7MW7xojZJP5imXbgYvc/bgmx3Am4Rrg85tZr8jmUOKWzMXBtR4E/gZ8BvgX8EV3\nX2tmXwXOJQwjuhA4zd3/a2a3EX6aPpowUuFq4KOEESFXAt8H7gD6xGbOd/e/m9mThDFWFhOGKZ0N\nXEQY1+IVYJSHn/RjZgsJP6HfQBimdddY1wXu/ngi/nJsO8TYfxKn29z9/BjrWmBPwhCjEwm/rDuQ\nMIzombGeo4ArCEPJLiL86OM/m/u5ygeXukpkSxkOXO/uewGrgC/F8nvdfX9335fwE/uzE+vsARzh\n7t+J8/sREv4phHEijvQwgNPJhAHvIQyM9Fd3H+XuV5crcvcNhJ8onwBgZgcASzyMdfEL4Gp33z/G\ndUsycHdfB1xOGMt7lLtPqfL+BhDGTZkETAOuBvYC9ondLAOBS+P7GUP4heKFaT88kaSWxouINMVi\nd58Tp2cRjpwB9jazKwmD9vcljCFRdre7r0/MT3X3tXF6O+A6MxsFrCck+UamEBLwrcSbKsTyI4CR\nYWgKAHYys37uvjrtmwOmubub2XxgubvPBzCz5wjvdQjh5g2Px3Z6AU90o36RTkrcsqW8k5heT+hq\nALgNON7d58Z+5PbEcmsq6kjOTwKWA/sSvjn+L0UMTwC7m9kg4Hjgyli+DeFmC2trrtlY+f1tYOP3\nuoHwd7YemOHuE95HGyKAukqk5/UDlsWhb0/txnr9gWWxC+Q0wm2sIPSF96u2gocTOvcBVxFGa3wz\nvvQQ0HnSMR7FV6pZb0pPAgeZ2e6xjR3NLM23BJFNKHFLT7uMcEegGYSb1KZ1A3BGPBm5B11H4/OA\n98xsrplNqrLeFOArdHWTAHwLaDOzeWb2PPD1Kus9QuhOmWNmJ3cjTgDc/Q3gTOBOM5tHSOR7drce\nEdBVJSIihaMjbhGRglHiFhEpGCVuEZGCUeIWESkYJW4RkYJR4hYRKRglbhGRglHiFhEpmP8D3g2E\nUtA7vgkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2078fc96ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Dispersion plot for david\n",
    "dispersion('david')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAABnCAYAAAAg2qjzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2078fd33748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Dispersion plot for Elisha\n",
    "dispersion('elisha')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}