Text Collection - Demo and Visualisation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building a text collection class and graphing the distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text Collection Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "import inflect\n",
    "import re\n",
    "import copy\n",
    "# 'txt-for-assignment-data-science.txt'\n",
    "\n",
    "class TextCollect(object):\n",
    "    def __init__(self, filepath):\n",
    "        self.path = copy.deepcopy(filepath)\n",
    "        self.list_of_lists = []\n",
    "        self.lol_no_punc = []\n",
    "        self.lol_no_plural = []\n",
    "        self.hash_table = {}\n",
    "\n",
    "    def read_data(self, lower = True):\n",
    "        '''\n",
    "        :param lower: True to convert all words to lowercase, otherwise \n",
    "        False. True by default\n",
    "        :return: A list of all words in file. If run first, tags, \n",
    "        punctuation, plural words and empty spaces remain.\n",
    "        '''\n",
    "        with open(self.path, 'r') as text:\n",
    "            string_obj = text.read()\n",
    "            if lower:\n",
    "                # At this point, self.data is a list\n",
    "                self.list = string_obj.lower().split()\n",
    "            else:\n",
    "                # At this point, self.data is a list\n",
    "                self.list = string_obj.split()\n",
    "        return self.list\n",
    "\n",
    "    def split_list(self):\n",
    "        '''\n",
    "        Splits the list into n lists where n = number of documents\n",
    "        :return: Returns a list of lists. Each list represents \n",
    "        words in each document\n",
    "        '''\n",
    "        split_indexes = [idx for idx,\n",
    "                         word in enumerate(self.list) if word == \n",
    "                         '</doc>']\n",
    "        for idx, i in enumerate(split_indexes):\n",
    "            if idx == 0:\n",
    "                self.list_of_lists.append(self.list[\n",
    "                    1:split_indexes[idx]])\n",
    "            else:\n",
    "                self.list_of_lists.append(self.list[\n",
    "                    split_indexes[idx - 1] + 2:split_indexes[idx]])\n",
    "        return self.list_of_lists\n",
    "\n",
    "    def remove_tags(self):\n",
    "        '''\n",
    "        Removes the tags, e.g. '<docno>'\n",
    "        :return: Returns a list of lists after removing tags.\n",
    "        '''\n",
    "        num_doc = len(self.list_of_lists)\n",
    "\n",
    "        remove = list(set(['<docno>', '<docid>', '<date>', '<p>', '</p>',\n",
    "                           '</date>', '<section>', '</section>',\n",
    "                           '<length>','</length>','<headline>', \n",
    "                           '</headline>', '<byline>', '</byline>',\n",
    "                           '<text>', '</text>', '<graphic>', \n",
    "                           '</graphic>','<type>','</type>', '</docno>',\n",
    "                           '</docid>']))\n",
    "\n",
    "        for i in range(num_doc):\n",
    "            for each_tag in remove:\n",
    "                self.list_of_lists[i] = list(filter(lambda x: \n",
    "                    x != each_tag, self.list_of_lists[i]))\n",
    "        return self.list_of_lists\n",
    "\n",
    "    def punctuation(self):\n",
    "        '''\n",
    "        Removes all non numerical/alphabetical characters\n",
    "        :return: Returns a list of lists with no punctuation and no\n",
    "        empty values\n",
    "        '''\n",
    "        num_doc = len(self.list_of_lists)\n",
    "        for i in range(num_doc):\n",
    "            current_list = self.list_of_lists[i]\n",
    "            for idx, each_word in enumerate(current_list):\n",
    "                current_list[idx] = re.sub('[^a-zA-Z0-9]+', '', \n",
    "                                           each_word)\n",
    "            current_list = list(filter(None, current_list))\n",
    "            self.lol_no_punc.append(current_list)\n",
    "        return self.lol_no_punc\n",
    "\n",
    "    def singular(self):\n",
    "        '''\n",
    "        Tries to remove plural words and make them singular\n",
    "        :return: Returns a list of lists with no plural words\n",
    "        '''\n",
    "        p = inflect.engine()\n",
    "        for each_list in self.lol_no_punc:\n",
    "            current_list = each_list\n",
    "            for idx, each_word in enumerate(current_list):\n",
    "                singular = p.singular_noun(each_word)\n",
    "                if singular is False:\n",
    "                    current_list[idx] = each_word.lower()\n",
    "                elif singular is not False:\n",
    "                    current_list[idx] = singular.lower()\n",
    "            self.lol_no_plural.append(current_list)\n",
    "        return self.lol_no_plural\n",
    "\n",
    "    def word_dict(self):\n",
    "        '''\n",
    "        Returns a dictionary where the 'key' is a word. Using indexing,\n",
    "        you can access the count of the word in\n",
    "        each document.\n",
    "        :return: A hash table of words\n",
    "        '''\n",
    "        num_doc = len(self.lol_no_plural)\n",
    "\n",
    "        for idx, current_list in enumerate(self.lol_no_plural):\n",
    "            for current_word in current_list:\n",
    "                if current_word not in self.hash_table:\n",
    "                    self.hash_table[current_word] = [Counter()\n",
    "                    for i in range(num_doc)]\n",
    "                    self.hash_table[current_word][idx].update([\n",
    "                            current_word])\n",
    "                elif current_word in self.hash_table:\n",
    "                    self.hash_table[current_word][idx].update([\n",
    "                            current_word])\n",
    "        return self.hash_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the hash table "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "os.chdir('C:\\\\Users\\\\yeachan153\\\\Desktop\\\\Text-Collection')\n",
    "\n",
    "# from TextCollect import *\n",
    "\n",
    "path = 'txt-for-assignment-data-science.txt'\n",
    "\n",
    "# Run in this order\n",
    "hash_table = TextCollect(path)\n",
    "hash_table.read_data()\n",
    "hash_table.split_list()\n",
    "hash_table.remove_tags()\n",
    "hash_table.punctuation()\n",
    "hash_table.singular()\n",
    "hash1 = hash_table.word_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accessing the hash table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\noindent \n",
    "The hashtable can be accessed as follows. Enter the word you are interested in as the key, for example: hash1['and']. This will bring up a list of counters, how many counters there are depends on how many documents there were. If you wish to see how many times 'and' appeared in a particular document number, simply add an index after entering your key (zero-indexed). For example, hash1['and'][0] will give you the count of 'and' in your first document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'and': 35})\n",
      "Counter({'and': 3})\n",
      "Counter({'and': 53})\n"
     ]
    }
   ],
   "source": [
    "print(hash1['and'][0]) # 'and' appears 35 times in the first document\n",
    "print(hash1['and'][1]) # 'and' appears 35 times in the second document\n",
    "print(hash1['and'][2]) # 'and' appears 35 times in the third document"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reformating the hash table so it can be plotted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "keys = [key for key in hash1]\n",
    "\n",
    "counter_list = list(hash1.values())\n",
    "num_list = len(counter_list[0])\n",
    "\n",
    "for idx, current_list in enumerate(counter_list):\n",
    "    counter_list[idx] = sum((current_list[0:num_list]), Counter())\n",
    "\n",
    "total = np.array([counter_list[i][keys[i]] for i in \n",
    "                  range(len(counter_list))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting as a histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median number of times a word appeared: 1.0\n",
      "Mean number of times a word appeared: 2.4968152866242037\n",
      "Max number of times a word appeared: 187\n",
      "Total unique words: 1099\n"
     ]
    },
    {
     "data": {
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AEARBKCgoEFatWiWVxcXFCYIgCEuXLhXkcrnQr18/tdv10UcfqW2r6ty6dUtQKBSCXC6X1l/ckSNHBLlcLnTq1El4+vSpIAiCsHDhQkEulwsffPCBtG8FoWgffvLJJ4JcLhd8fHye+9naEL/TTz/9VBAEQcjMzBQ6duwobf+z7dbPz0+Qy+VCUFCQynTxWHn2mHz2cz7//HOV6cWPkTVr1kifl5KSIrRt21aQy+WCo6Oj4O3tLfz333/ScqGhoULjxo1LnBuKtzm5XC4sXbpUUCqVgiAUHZezZ88W5HK50KRJE5X9kZ2dLXTt2lWQy+XC+PHjhbt370plqampwtixYwW5XC689957wpMnT6Qy8RgQ26R47Dx8+FDan6X5+OOPBblcLrRp00bleM/LyxN+/PFHad3Hjh3T6vt8nmf3t6j495aYmChNL37O6d69u5CQkCCVLVu2TCpzdHQU5s+fL52TcnJyhNGjR0ttu7hTp04JCoVCaNKkibBlyxbpeBQEQQgLCxO8vLykfSd6/Pix4OLiIsjlcuHw4cMq6zt9+rTg6uqqtqw0YltesmSJyvSzZ89K2+Xq6irs379fOrdnZWUJEyZMkNpQZmamtNyLtiFN4uLi1H6OIAjSeV4ulwu9e/dWKcvKyhKaNGkiODo6Cvfv3xcEoeg8JF7nxo0bJ6SlpUnz37x5U1pfx44dpTYsCM+/5uTn5wu+vr6CXC4Xhg8fLn2eIAjCX3/9JTRt2lRaXtO54Vnp6elC8+bNBblcLsyaNUvIycmRyrKzs1WurbGxsVJZ8f2mUCiETZs2SfstPT1dGD9+vCCXywUvLy+VbSx+DDs7OwuHDh2Syu7evSt9Nz169FA5H77odUz8Tlu2bKlyThMEQfjvv/+ENm3aSNfi4sT2KpfLhbVr10rTxX1x9+5doUWLFoJcLhdmzJghPH78WJonKSlJGDhwoCCXy4UhQ4aorLeifscQ0duFQ/mIqMpQl2eqoKBAGmZVvBeU+P/iw/mSk5ORkpICQ0NDNGvWTJqel5cnvRp90qRJGDp0KKpV+//TZ/fu3aUhTD///LPa4TDNmjWDr6+v9Le5uTkASOvt168fPvroI2m9BgYGmDFjhtqnsuXl888/l/LLiPU5ceIELl68CEtLS6xfvx7vvPOONH/16tUxd+5cuLm54dGjRyrDjHbt2oWHDx/C1tYWixYtQvXq1QEA1apVg5+fX7m/pW358uVQKpXw8/PDlClTVHp6ODk5ISgoCDo6Ojh06JDaRNx6enpYsWIFGjVqJE0Th1gCJXOVrVixAgAwatQojBgxQupRoaOjg/Hjx6NNmzYoLCyUhh29//77kMlkiIqKUhkiARQNhQkLC4OOjg769Onz3G21traW2sHBgwdLlAcHBwMA+vbtKyUgv3btGgDAx8dH2rdA0T6cMWMGWrdujebNmyM3N/e5n19WpqamUq+UqKgorFu3rtw/Q5127dph7Nix0NUt6gxet25d9OvXD0DRkLy5c+fC1dVVmr9NmzZSr79nh7uJevbsiSlTpki9IA0MDDBz5kx4enoiPz8fGzdulObds2cPkpKS0KRJEyxfvhxWVlZSmYWFBZYtWwZra2skJiZi3759aj9vypQp0rFTq1YtaX9q8u+//0rDsIKCgtCyZUupTF9fH5MnT8bgwYMBAIsWLSp1Xa/C/PnzVXoVjRkzRjqWPD09ERgYKJ2TjIyMpPPG7du38fjxY2k5sWfGZ599huHDh6v0cPLy8sL8+fMBAJs2bZKSvCckJCAvLw81a9ZUORcDQNu2bTF27Fj4+PiUe+L28ePHo2/fvtK53cTEBDNnzgQA5Ofn499//5XmLY82VJy9vT3s7OyQn58v9a4BgMzMTFy6dAn29vYwNzdHTEyM9D0BRb2p8vPz4e7uLg1pX7NmDXJyciCXy7Fs2TLUqVNHmt/GxgZr1qyBhYUF7ty5g23btqmtj7przu+//464uDjUrFkTQUFBKkPoO3TogK+//vq52/msiIgI5Ofnw8LCAjNmzFDpDWlsbIzAwEBpP8fGxqpdx6hRozBy5Ehpv9WsWRNLly5FgwYN8ODBA+zcuVPjNvbs2VP628rKCqtWrUL16tVx/fp1lWGmL3odCwsLQ7Vq1fDJJ5+onNMAwNXVFR9++GGp22ZtbQ1/f3/pb3FfbNiwAenp6fD29sacOXNQo0YNaR5bW1usWrUKpqamiIiIwKlTp6SyyvwdQ0RVBwNTRFRliHmmigcVLl68iKysLCgUClhaWkrT27ZtC6BoCIOYBFgcxtesWTOVhMARERHIyMiArq4uhg4dqvazfX19YWVlhcLCQmkIRHHPDnsDgCdPnkg3C+IN9LOeHb5SXiwsLGBjY1Ni+vHjxwEAXbp0gbGxcYlymUyG3r17A4BKXpKTJ08CAHr37q02mfKIESOee5OtLaVSKQ3PFOvyLIVCAUdHRwiCoDZ/irOzc4mhCACkQFxmZqY0LSkpCTdu3ACgeX/MnTtXGjoCFN2oNW/eHABK5MgJDg7G06dP0b59e7V1UEcMmD2br+Phw4cIDQ2VhvyJxJv/9evX4+DBgyrbY2VlhU2bNmHOnDnl8jY2dVq3bi0FRFauXFkhOa2e1bFjxxLTrK2tARQFOdQNJxHPCdnZ2WrX6efnV2KaTCbDoEGDAEDl5kw8dnx9fdUOBTM0NISPjw8AqG2T1apVg4eHh9p6aCKux9XVVWX4cnEfffQRgKJ2rOlG9VUwMzMrMWzVyMhIuilWN6y0+DlbzDV069YtREdHA9B8/Hfo0AG1atVCbm6u9GCiQYMG0NXVxePHjxEYGCgFb0UBAQEICgrCe++994JbqJ669VlZWUlDuR8+fChNf9k2pI44RO/MmTPStPDwcBQWFqJ169Zwd3eHIAhSvixA/TA+cdqHH36o9hxfs2ZNaWiruB3FabrmFP+smjVrlijv0aOHFKzVVufOnXHx4kUcP35cClQXl5eXJ33/moYJqhuCbWBgIJ1nT5w4UaLc2NgYAwcOLDHd3NwcXbt2VVnuZa5jO3fuxKVLlzRej8RAnKYHDx4eHmqvx+J+01SfOnXqSA/1xPpU5u8YIqpamGOKiKoMMWH5gwcPkJCQgEaNGkk9osRAlMjZ2RlmZmZIT0/HtWvX4OTkpDG/lBiUaNiwIUxNTdV+tkwmg5OTE+7du6c26ai6AMSdO3ekZLvvvvuu2vWKOTjKW/EbvuLEG9c///yzxI2bSExwm5iYCEEQIJPJcPPmTQDQmG/G3Nwctra2ahPHllViYqL0vX3zzTca3yp2584dAP+//4or3hOhODFQUzwfllhnMU+JOs/mowKKek2dO3cOhw4dwv/+9z/pRkDs4SQGm7Tx3nvv4dtvv8WtW7cQGRkpBTpDQkKQn5+PFi1aqNRt9OjR+O2335CWloZp06ZBV1dXSt7dvn17uLm5lVugUJPp06cjNDQUt2/fxhdffIHdu3ervUksL8VzxYnEXhFmZmYqvRyfLRcEQe06mzRpona6mCcmLS0NGRkZqFGjhnTs7NmzR+1NK1CUVBhQ3yZr1KhR5kChuB5N9QSKgpSmpqbIyspCQkLCK8v59Sx1xwiAEj1oiiveXsR9dP36dWlaQECAxs8TkzSL31Ht2rXh7++P1atX48CBAzhw4AAsLCzQqlUrtG3bFu3bt1dbh5el6VxjYmKC9PR0lWTSL9uG1PH29samTZtUAlOhoaEAgFatWiEpKQknT57E2bNn4ePjA0EQpIBrly5dABQFBcWXBDg7O2v8LLEdqrsGarrmiPNqugaKuazEHGplYWhoiGvXruHatWtITk7GzZs3ERcXh+vXryM/Px+A+mPf0tJS7fkEABwdHQEUXYeepVAoNOZpE88Z4nIvex3T09NDZmYmLly4gMTERCQnJyMxMRHR0dFSGxEfuj1L3e+R7Oxs3L59GwCwatUqbN26Ve2y4jxifSrzdwwRVS0MTBFRlSGTydCqVSuEhITgwoULaNSokfQD/NnAVLVq1eDl5YWjR48iPDwcTk5OiIiIAFAyMCU+qX/eU1sxaKWu94W6G87iQ1PUJegGoNKVvjxp+vEsbmtKSgpSUlJKXUdhYSGys7Olm14AantZiczMzMolMFW898+VK1fKNL+oLMN1xLcnatpHmnTr1g1z5sxBSkoKwsPD0apVK1y+fBlxcXGoVatWiWTDpTE0NISvry927dqFQ4cOSYEpTUGuevXqITg4GGvWrMFvv/2Ge/fu4eLFi7h48SJWrlwJa2trfPnll9KNZ0UQh/SNHDkSUVFRWLNmTamBhJdVWttTF5R6Hj09PY03i8XbwpMnT1CjRg3pGBCTD5dGXZssLfG4Jtqem0xMTJCVlaWxZ9iroC65fHHa7qPi392zQ26fN//UqVPh7OyM7du3IyIiAmlpaTh06BAOHToEXV1d+Pr6YubMmWXuoVOasuzXl21D6nh6esLMzEwKXtjY2EhDiVu2bIn69esDKOo9DBSdU9PS0uDg4CD1vCzebjQ9nClelpOTIz20EGn6HsQHHaUdv+p6Uj3PqVOn8OOPP+Lq1asq0y0tLdGtWzf8/fffKtfg4sTeVOqI9VT3/Ze2nHjOELf3Za5jWVlZWLJkCfbv34+cnBxpup6eHpo0aYLGjRurfeuwSN2+KP72Q216Vor1qczfMURUtTAwRURVipeXF0JCQvDff//B29sbV69ehZGRkUrOKFGbNm1w9OhRREREoEePHrh58yYsLCxKvDVH/LH1vBsB8QentgGM4j9is7Ky1D6tL+3VzBVBvHn8+uuv1Q5j0sTU1BSPHj0q9dXepb1ZSVOPFXXLFL+BuXDhQpkDRmUlfl5Zb+oNDQ3Ro0cPKZjUqlUrKZDUq1evMueyef/997Fr1y4cPXoUX331FZKTk3H58mUYGxtLw3uKq127Nr788kt8+eWXiImJwblz53D27FmpF9OkSZPwyy+/lMhRUp68vLykt/T99NNPKkODNNHUForfgL0K+fn5UCqVaoNTxc8F4k2XkZERMjMzsXr16jIFHV+Gtucmsbyij5VXQTwezczMVPImaatr167o2rUrsrKypLfxnTp1Cjdu3JCGva5evbq8q62VimhDOjo66NChA4KDg3HmzBm0bdsWSUlJcHV1RfXq1dG4cWPUqlULN27cQGpqqtRbqvixWrzdlHaOF4MUxsbGWvfIFK+Dpa23rLnwzp49i3HjxuHp06dwd3dHr169IJfLYW9vL+WwateuncblSzvXiMeSuuu1NsuJn/8y17EJEyYgPDwchoaGGDVqFNzc3PDuu++iYcOG0NPTw+7du0sNTKlTPHB86NAhrXtWvo6/Y4jozcQcU0RUpYh5pi5fvozIyEgIgoDmzZurvbkUf5hGR0cjMjISQMneUsD/5x1KSkrS+OP56dOn0pNZ8dXrz1O/fn2pJ9WzT3VFxYetvApiMvDSPjclJQX//vsvUlNTpWnij1gx98uzlEql2t5SYh4VcSjAs4p/hsjGxkZaTl1ic9GlS5cQExPz0r1ExF4DOTk5ahPbA0V5Q4YNG4YffvhBZbqYc+XEiRMoLCyUhueI08vCzc0NDg4OSE9Pl4YIAkU9s57tbXDv3j2cPXtWuqFTKBQYNmwYVq5ciRMnTsDa2hqFhYUlclZVhOnTp8Pa2hr5+fkIDAyUhtA860XaQkXTNFxKPF5tbW2lGzptjp3ExERcvnxZJa/QyxDPTZqStwNAfHy8dMOs7bnpdSZ+z+np6UhLS9M4X0REBOLj46VjIDc3VxrWBRQF0729vREYGIijR4/i008/BVA0jFnb3kjlraLakLe3N4CiIXxiME98WYjY0xgoyj0l5g4q3pvS1NRUGopXWu8esax4gvvnEbdZ07VDEIQSL5B4nnXr1uHp06do1aoVfv75Z/j5+aFFixZSUEipVKoke39WSkqKxmu9WE8HB4cSZfHx8RoD688u96LXsX///Vfah2vWrEFgYCC6d+8OBwcH6WHH3bt3Na5Pkxo1akgJ7UurT0xMDKKjo6Ug5Ov4O4aI3kwMTBFRldKgQQPY2NggNjZWyqnx7DA+Ud26dWFvb4/bt29LTxfFH+vFeXp6ombNmigoKMCOHTvUriskJATT9WqXAAAgAElEQVRpaWmQyWSlPoktztDQUEr4q+kNP3v27NFqXeVFfEp/5MgRPHjwQO08X375JQYPHoz//e9/0jSxx05wcLDaQFBwcLDap6a1atUCUPSkXd3n/fHHHyWmmZqaSomsNeXBSE5OxpAhQ9C7d2/89ttvaufRlr29vZRE+9dff1U7z/79+3Hu3LkSN4vik+xHjx5h27ZtuHPnDpycnKQ8JWUlDtn7448/pO16NshVUFCAvn37YsSIEWoT8depU0cKJGrKQVKexCF9MpkM0dHRKm8hK05sC+qCQXfv3tVquEt5U7e/CwsLpeNVvOEH/v/Y2bt3r9oeHgUFBZgwYQIGDBiABQsWlEv9xM+8dOmSxmFt4tsz69atW6I36IsQe8JougGvaPb29lKAbfv27WrniYyMxNChQ+Hr6yu1t127dqFPnz6YNm2a2roXP/cXzzFXmvLO01ZRbahdu3bQ19fH2bNnERYWBgAqb0oTt/3w4cOIioqCpaUlXFxc1NZt586daoPHjx8/ll70oC6RvSZicviTJ09KeayK+/PPP0sNQKojPkBwdHRUm0T+wIEDUoBc3b4uLCzE/v37S0x/8uSJ1Ou1W7duJcrv37+vNil9amqqlFhcXO5Fr2PFH46oy/f15MkThISESNtRFuILJLZv36722pCZmYkRI0agb9++2LJlC4DX83cMEb2ZGJgioiqndevWKCgokH5AagpMAf/fQ+ro0aOQyWRqe0wVf215UFAQduzYofKj7dixY9LrvwcNGiQ9AdbGxIkToaenh+PHj2PhwoXSD/78/HwsW7YMv//+u9brKg++vr6Qy+XIyMjA6NGjVZ50ZmVlYfbs2QgLC4NMJpO+E6AoYGJnZ4e7d+9i0qRJUvJVoOiGY968eWo/z83NDXp6ehAEAfPmzZNuxvLz87Flyxbs3r1b7XITJ06Ejo4ODh8+jPnz56sEw2JjYzF27Fjk5+fD2toavXr1eqnvRCaTYcKECQCKnsTv2bNHurEtLCzE2rVr8ccff0BXVxcjR44ssbwYOFq2bBmAsiU9f1afPn2gq6uLgwcP4saNG7C1tS0xTFVXVxc9evQAUPS2wEuXLqmU//7771LuteI3kPn5+YiPj0d8fHy59xgRh/QBmgMaYt6s/fv3Sz0YgaIeIhMmTNDY06oibdu2TeV4z8rKwvTp0xEVFYWaNWtKb7wDgKFDh8LCwgJJSUkYP368lLQYKHrz2pQpUxAfHw89PT2V5V6Gh4cHOnToAACYNGmSytA2pVKJoKAg6RiaPn16uQRSxCFHxbfvVZs8eTIAYO3atVi3bp1KoCQiIkIqd3d3lwIw3bt3h56eHmJjYzFv3jyVYVcPHz7E0qVLARSdk8Qg6fOIPRXFhNAvq6LakImJCVq0aIHMzEwcO3YMBgYGKm9xFHsa//XXXxAEAd7e3iXaypgxY2BiYoLY2FhMnjxZ5UFCcnIyPv74Y9y/fx9WVlZq32inSceOHdG0aVPk5ORg3LhxSE5OlsoiIiLw1Vdfab0ukdiTMCQkRKW3VV5eHrZv347vvvtOmqZpmODixYtVHow8fPgQkyZNwp07d9CoUSON5/GvvvpKylcJFAWSxo8fj5ycHLRo0ULl98iLXMfEbQOK3nhaPLAWFxeHMWPGSPnJShs+r87YsWNhbGyMyMhITJs2TeVBy+3btzF27Fg8evQI1atXV3lD8ev2O4aI3kzMMUVEVU6rVq2wa9cuZGdno379+rC3t9c4b9u2bbF161bk5OTA0dFR6sr+rNGjR+PWrVvYuXMnvv32Wyxfvhw2Nja4e/euNMTIx8enzD+i5XI55s2bhy+//BLr16/Hnj17YGtri+TkZKSnp6Nr165qew1VFD09PaxatQr+/v6Ijo5Gz5490ahRIxgZGSExMVG6mfviiy9UghpGRkb46aefMHr0aISGhqJjx46Qy+V4/Pgxbt26BScnJ7Xd/GvWrInRo0dj9erVOHz4ME6fPo0GDRrg9u3bSE9Px4cffqj2SbqnpyfmzJmDWbNmYfPmzfjll19gb2+P7OxsJCUlQRAE1KlTBxs2bNCYwLosBgwYgLi4OGzatAkzZszAjz/+iLp16+LWrVtIT0+Hjo4OZs+erbYnVJ8+fbB48WLk5ORAT08PPXv2fOF61KlTB+3atZOeymt6PffUqVMRGRmJq1evYuDAgbC2tkatWrWQmpoqtdcPP/xQZR/eu3cPvr6+AID58+e/VABNnenTp+P06dMab+JHjBiBgwcP4v79+xgyZIg05OXGjRuoUaMGRo4cKfX+eRX09PTQtm1bfPvtt/jpp59gZWWFGzduICcnByYmJli2bJnKG9dq1qyJn376CePHj0dYWBg6d+4MBwcHyGQyJCQkQKlUQldXF0uWLCmXnkuiH374AePGjcPFixcxfPhwWFtbw9zcHAkJCcjKyoKOjg6mTJkiBStflviGrQsXLqBbt25wcHDAihUrymXd2urRowcSExOxfPlyLFq0CGvWrIGdnR0ePnwota9GjRph1apV0jKWlpaYN28epk2bhq1bt2Lv3r2wtbVFYWEhbt68iby8PNSqVQtz587Vuh5OTk74888/cejQIcTExKBZs2aYNWvWC29XRbYhb29vhIaGIj8/H15eXioJsG1sbNCgQQOpN466lyLY2NggKCgIkydPxsmTJ9GhQwc4ODigsLAQcXFxePr0KerXr48VK1aU6e2G1apVw+LFi+Hv74+rV6/Cx8cHcrkcT548QWJiIho0aAArKyuNQ/3UCQgIQFhYGNLS0tCrVy/Y2dlBX18fSUlJyMnJgbm5ORo1aoRr165pHPZmY2ODTz75BNbW1jAzM8P169ehVCpRv359BAUFqb2uVK9eHQYGBhg6dCgaNWoEQ0NDxMbGorCwEI6Ojli4cKFKwO9FrmNOTk7o3r07jh49io0bN2L//v2wtrZGenq6tP/atGmDM2fOIDs7G1lZWaUmrC+uYcOG+PHHHzF16lQcPnwYx44dg4ODA/Lz85GYmIiCggIYGxtj7dq10rBI4PX7HUNEbyb2mCKiKsfLy0v68aeuB1RxLVq0kH7wlTavTCbD7NmzsWHDBnTp0gU6OjrSD+VOnTph5cqVCAoKeqE3a/Xu3Rt79uxBz549YWhoiJiYGFhYWODrr79+oafFL8vGxgb79+/H9OnT4ebmhrS0NMTGxsLExAQ+Pj7Yvn272ifi77zzDg4cOIAxY8agXr160g/yjz76CNu2bdP4eVOnTsWiRYvg6emJ/Px8JCQkoFGjRli4cCFmz56tcbn3338fwcHBGDx4MCwsLHD9+nWkpKTA3t4e/v7+OHjwYJl6rz1PYGAgNm3ahM6dO0MQBFy7dg06Ojro1q0bdu3ahYEDB6pdztzcXAoAeXt7a90bQxOxB1a1atXQt29ftfOYmJhg27ZtmDRpEpo0aYL09HRcu3YNgiCgc+fOWLNmTanfbUUwMTGRhvSpU7duXfz666/44IMPUK9ePSQlJSEzMxMDBgzAwYMH1eZ0qUgymQzLly/H5MmTYWRkhJiYGJiammLAgAEIDg6WepkU5+LigkOHDiEgIAAKhQK3bt3CjRs3UKdOHfTt2xe//vqrNHSpvJiZmWHbtm2YM2cOmjdvjszMTMTExKBWrVoYMGAA9u7dq9K78WX17dsX/v7+sLCwwK1btxAdHf1KhoQ+KyAgALt27UKvXr1gamqKa9eu4dGjR3BycsLkyZPx66+/qtw8A0Xn2m3btsHHxwc1atRAfHw8bt++jYYNG+Ljjz/GkSNHNL7yXp0xY8Zg4MCB0lvvYmJiXnq7KqoNFU9mrq7tisP5TE1N0bJlS7XraNu2LUJCQjBy5Eg0aNAACQkJSElJQePGjfHpp58iODgYTZo0KXPd6tevj127dmHSpElo1KiRFFQVX/hQ2tvu1HF2dkZwcDB69+6N+vXr4+bNm7h58yZsbW0xbtw4HD58GMOHDwfw/73EnrVt2zaMGjVKCrxZW1tj/Pjx2Ldvn8bE4MbGxtizZw8GDhyIjIwMJCYmwsHBAdOmTcPOnTtRt27dEsu8yHVs8eLFmDNnDlxcXPD06VPExMRAqVSiU6dOWLNmDTZu3CgNPz958mSZvrsOHTpI+9jW1hYJCQlISkqCtbU1hgwZgoMHD6r0thO9br9jiOjNIxMqK0kAERG9VcQn/Js2bVKby6sq69+/P6KiorB27Vpp6BUREb0+wsPDpYBVVFQUdHW1G1iyb98+fPHFF7CyssLff/9dkVUkIqqy2GOKiIioAkVFRSEqKgr16tUrNd8ZEREREdHbiDmmiIiIypmYwPf+/fsIDAwEAAwfPlztG6KIiIiIiN5mDEwRERGVsyNHjmDJkiXS33K5HH5+fpVYIyIiIiKi1xOH8hEREZUzR0dHWFpawtjYGF26dMHGjRvL5e2ARERERERVDZOfExERERERERFRpXirhvKlpWVWdhXKnampAbKy8iq7GvSaYvug0rB9kCZsG1Qatg/ShG2DSsP2QaVh+3jzWVhUf+FlOZTvDaery0S6pBnbB5WG7YM0Ydug0rB9kCZsG1Qatg8qDdvH263SA1M5OTmYM2cO2rZti2bNmsHf3x9xcXFSeWhoKPr06QNXV1f06tULp06dUln+wYMHmDx5Mpo1awYvLy8sXLgQBQUFr3oziIiIiIiIiIiojCo9MDV37lyEhYVh2bJl2LVrFwwMDODv74+8vDzExcVh/Pjx6NatG/bv34/OnTsjICAA169fl5afOHEi7t+/j+3bt+P777/Hvn37sHz58krcIiIiIiIiIiIi0kalB6aOHz+OIUOGwNPTE/b29pg6dSpSUlIQFxeHrVu3wt3dHePHj4e9vT2mTJkCDw8PbN26FQBw8eJFREZG4vvvv4ejoyM6dOiA6dOnY9u2bVAqlZW8ZUREREREREREVJpKD0yZm5vjyJEjePDgAZRKJfbu3YuaNWvCxsYGERERaNGihcr8LVu2REREBAAgIiIC1tbWsLGxkcpbtGiB7OxsREdHv9LtICIiIiIiIiKisqn0wNScOXNw9+5dtG7dGu7u7ti9ezfWrl2LGjVq4O7du7CyslKZ39LSEnfv3gUA3Lt3D5aWliXKASAlJeXVbAAREREREREREb0Q3cquQFJSEurUqYPZs2fDzMwMGzZswKRJk7B7927k5uZCX19fZX59fX3k5RW9RvLJkycwMDBQKdfT04NMJpPmKc7U1KDKZfvX0akGMzPjyq4GvabYPqg0bB+kCdsGlYbtgzRh26DSsH1Qadg+3m6VGphKTk7G119/jZ9//hnu7u4AgMWLF8PX1xebN2+GgYEB8vPzVZZRKpUwMjICABgaGpbIJZWfnw9BEGBsXLJRZ2WVDFa96czMjJGenlPZ1aDXFNsHlYbtgzRh26DSsH2QJmwbVBq2DyoN28ebz8Ki+gsvW6lD+a5cuYLCwkI4OztL0/T09NC4cWMkJSWhXr16SE1NVVkmNTVVGt5Xt25dpKWllSgHUGIIIBERERERERERvV4qNTBVt25dAEBMTIw0TRAExMfHw87ODp6enjh//rzKMuHh4WjWrBkAwNPTE8nJySr5pMLDw2FiYgJHR8dXsAVERERERERERPSiKjUw5erqCg8PDwQGBiIiIgLx8fGYNWsW7ty5Az8/P/j5+SEiIgJBQUGIj4/HsmXL8N9//2HEiBEAAA8PD7i7u2Pq1KmIiorCqVOnsGjRIowaNapEbioiIiIiIiIiInq9yARBECqzAg8fPsSSJUvw999/IycnB87OzggMDJR6PP31119YuHAhbt68iXfeeQeff/45WrduLS2flpaG2bNn48yZMzAxMcH777+PKVOmoFq1kjG3tLTMV7ZdrwrH4lJp2D6oNGwfpAnbBpWG7YM0Ydug0rB9UGnYPt58L5NjqtIDU68SA1P0tmH7oNKwfZAmbBtUGrYP0oRtg0rD9kGlYft4872xyc+JiIiIiIiIiOjtxcAUERERERERERFVCgamiIiIiIiIiIioUjAwRURERERERERElYKBKSIiIiIiIiIiqhS6mgp8fX1faIUymQwhISEvXCEiIiIiIiIiIno7aAxMJScnQyaTqUwrLCxEYWEhAMDS0hI1a9ZETk4OUlJS8PTpU5iZmaFWrVoVW2MiIiIiIiIiIqoSNAamLl++rPJ3fHw8hg0bhmbNmiEwMBD169eXyh4+fIiFCxfixIkTWLRoUcXVloiIiIiIiIiIqgytc0zNnz8fFhYWWLp0qUpQCgDMzc0xb948NGzYEPPmzSv3ShIRERERERERUdWjdWAqMjISXl5e0NHRUVsuk8ng6emJqKiocqscERERERERERFVXRqH8j3L1NQU8fHxpc5z5coVmJubv3Sl6MXsu5RSYlp/13qVUBMiIiIiIiIioufTusdUp06dEBoaig0bNkAQBJWygoICLF68GJGRkejevXu5V5KIiIiIiIiIiKoerXtMTZkyBeHh4Vi0aBE2bdqExo0bw8TEBJmZmbhy5QoeP34MFxcXfPLJJxVZXyIiIiIiIiIiqiK0DkyZm5tj7969WL16NY4ePYrTp09LZQ0aNMDw4cMxZswY6OvrV0hFiYiIiIiIiIioatE6MAUA1atXx7Rp0zBt2jRkZGTg8ePHqFmzJmrUqFFR9SMiIiIiIiIioipK6xxTxSUnJyM8PBxnz55FjRo1cO/ePeTl5ZV33YiIiIiIiIiIqAorU2AqMTERw4YNw3vvvYdJkyZh1qxZAIA9e/agffv2OH78eIVUkoiIiIiIiIiIqh6tA1N37tzBkCFDcOHCBXTt2hXNmjWT3s5Xt25d5OXlYcqUKfj3338rrLJERERERERERFR1aB2YWrZsGbKysrBjxw4EBQWhZcuWUtmAAQPwyy+/QE9PD6tXr66QihIRERERERERUdWidWAqNDQU3bp1g7u7u9pyR0dHdOvWDVeuXCm3yhERERERERERUdWldWAqMzMTFhYWpc5Ts2ZNZGZmvnSliIiIiIiIiIio6tM6MGVtbY2LFy+WOk9ERASsra1fulJERERERERERFT1aR2Y6tmzJy5evIgVK1ZISc9FBQUFWLRoEaKiotC9e/dyryQREREREREREVU9utrOOGbMGJw5cwYrVqyQEp0DwOjRoxETE4P79+/DyckJY8aMqbDKEhERERERERFR1aF1jyl9fX1s3rwZEydOhL6+PlJSUiAIAs6cOQMA8Pf3x44dO2BoaFhhlSUiIiIiIiIioqpD6x5TV65cgaOjIwICAhAQEID09HRkZmbC2NgYtWvXrsg6EhERERERERFRFaR1j6kJEybgk08+kf42MzODjY0Ng1JERERERERERPRCtA5Mpaenw8HBoSLrQkREREREREREbxGtA1MdO3bEyZMnkZGRUZH1ISIiIiIiIiKit4TWOabatWuHCxcuwNvbGy1btoS1tbXaROcymQxTp04t10oSEREREREREVHVo3Vg6uuvv5b+f+LECY3zMTBFRERERERERETa0DowtW7duoqsBxERERERERERvWXKNJSPiIiIiIiIiIiovGgdmFIqlVqvVF9f/4UqQ0REREREREREbw+tA1Ourq6QyWTPnU8mk+Hq1asvVSkiIiIiIiIiIqr6Xjow9eTJE9y+fRvZ2dlwcXGBk5NTuVaQiIiIiIiIiIiqJq0DU7t379ZY9vTpU2zZsgXLli3DzJkzy6ViRERERERERERUtVUrl5VUq4ZRo0bB09MTS5YsKY9VEhERERERERFRFVcugSmRk5MT/v333/JcJRERERERERERVVHlGpi6cOEC38hHRERERERERERa0TrH1K5du9ROFwQBOTk5OHXqFC5cuIBevXqVW+WIiIiIiIiIiKjq0jowNWvWLMhkMgiCoHGed999F5999lm5VIyIiIiIiIiIiKo2rQNTs2fPVjtdJpNBT08P77zzDtzd3curXkREREREREREVMVpHZj64IMPNJYVFBRAV1frVREREREREREREZUt+fmNGzcwdepU7Nu3T2V6+/btMWnSJNy7d69cK0dERERERERERFWX1oGp+Ph4DB48GEePHkVaWpo0PTc3F3Z2djh+/Dj69++PmzdvVkhFiYiIiIiIiIioatE6MLVs2TLk5+djy5Yt+Pjjj6XphoaG+Pnnn7F582ZkZGRg6dKlFVJRIiIiIiIiIiKqWrQOTF24cAE9evRAy5Yt1Za3aNECvr6+CAsLK7fKERERERERERFR1aV1YCo7OxtGRkalzlOjRg3k5ua+dKWIiIiIiIiIiKjq0zowZW9vj7///ltj4EmpVOLMmTOws7PT+sPDw8OhUCjU/hs+fDgAIDQ0FH369IGrqyt69eqFU6dOqazjwYMHmDx5Mpo1awYvLy8sXLgQBQUFWteBiIiIiIiIiIgqh9aBqUGDBuHmzZsICAjAtWvXVMpiY2MxadIkJCQkYODAgVp/uIeHB0JDQ1X+LViwANWqVcOYMWMQFxeH8ePHo1u3bti/fz86d+6MgIAAXL9+XVrHxIkTcf/+fWzfvh3ff/899u3bh+XLl2tdByIiIiIiIiIiqhy62s44aNAgREZGIjg4GGFhYTAwMICxsTFycnKQl5cHQRDQq1cv+Pn5af3h+vr6sLCwkP7OzMzEokWLMHr0aLRr1w4zZ86Eu7s7xo8fDwCYMmUKIiMjsXXrVsyZMwcXL15EZGQkjh8/DhsbGzg6OmL69OmYM2cOAgICoK+vX4avgoiIiIiIiIiIXiWtA1MAsGDBAvj6+uLw4cOIiYlBRkYGzMzMIJfL0bt3b3h7e79UZVatWgV9fX0EBAQAACIiItC9e3eVeVq2bImQkBCp3NraGjY2NlJ5ixYtkJ2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x28ab2de0710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (20,20))\n",
    "\n",
    "for i in range(2):\n",
    "    ax = fig.add_subplot(2,1, i+1)\n",
    "    sns.distplot(total,kde = False, bins = np.arange(188)-.5)\n",
    "    sns.set_style('darkgrid')\n",
    "    plt.xlabel('Total number of times a word appeared in all documents', \n",
    "               fontsize = 20)\n",
    "    plt.ylabel('Total number of times each word frequency occured',\n",
    "               fontsize = 20)\n",
    "    plt.title('Word frequency vs. Number of times the word appeared',\n",
    "              fontsize = 25)\n",
    "    plt.xticks(fontsize = 15)\n",
    "    plt.yticks(fontsize = 15)\n",
    "    if i == 1:\n",
    "        plt.xlim(0,10)\n",
    "        plt.xticks(np.arange(11))\n",
    "\n",
    "print('Median number of times a word appeared: ' + \n",
    "      str(np.median(total)))\n",
    "\n",
    "print('Mean number of times a word appeared: ' + \n",
    "      str(np.mean(total)))\n",
    "\n",
    "print('Max number of times a word appeared: ' +\n",
    "     str(np.max(total)))\n",
    "\n",
    "print('Total unique words: ' + str(len(total)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\noindent \n",
    "The first graph shows the entire range of the total number of times a word appeared in all the documents. The second graph 'zooms in' and only shows from 0-10 for the total number of times a word appeared in all the documents. We can describe both graphs as discrete distributions that are positively skewed. Thus, as expected the mean value (2.50) is higher than the median value (1.00). The mean is higher as it is more influenced by extreme values, or in other words by the few words that appeared in all the documents very frequently (one word appeared 187 times). Overall, out of 1099 unique words across 3 documents, we can see that the vast majority of words only appeared a few times in all documents - these are likely words that are relatively specific to the article. Words that appear more frequently are generally connective words or general words such as 'and' and 'to'."
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}