NLP_FB_live_coding_soln.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Frequencies of words in novels: a Data Science pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"img/moby-dick.jpg\" width=\"350\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this code-along session, you will use some basic Natural Language Processing to plot the most frequently occurring words in the novel _Moby Dick_. In doing so, you'll also see the efficacy of thinking in terms of the following Data Science pipeline with a constant regard for process:\n",
    "1. State your question;\n",
    "2. Get your data;\n",
    "3. Wrangle your data to answer your question;\n",
    "4. Answer your question;\n",
    "5. Present your solution so that others can understand it.\n",
    "\n",
    "For example, what would the following word frequency distribution be from?\n",
    "\n",
    "<img src=\"img/d-x.png\" width=\"350\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-steps\n",
    "\n",
    "Follow the instructions in the README.md to get your system set up and ready to go."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. State your question\n",
    "\n",
    "What are the most frequent words in the novel _Moby Dick_ and how often do they occur?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Get your data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Your raw data is the text of Melville's novel _Moby Dick_. We can find it at [Project Gutenberg](https://www.gutenberg.org/). \n",
    "\n",
    "**TO DO:** Head there, find _Moby Dick_ and then store the relevant url in your Python namespace:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Store url\n",
    "url = 'https://www.gutenberg.org/files/2701/2701-h/2701-h.htm'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "You're going to use [`requests`](http://docs.python-requests.org/en/master/) to get the web data.\n",
    "You can find out more in DataCamp's [Importing Data in Python (Part 2) course](https://www.datacamp.com/courses/importing-data-in-python-part-2). \n",
    "\n",
    "<img src=\"img/requests.png\" width=\"200\">\n",
    "\n",
    "According to the `requests` package website:\n",
    "\n",
    "> Requests is one of the most downloaded Python packages of all time, pulling in over 13,000,000 downloads every month. All the cool kids are doing it!\n",
    "\n",
    "You'll be making a `GET` request from the website, which means you're _getting_ data from it. `requests` make this easy with its `get` function. \n",
    "\n",
    "**TO DO:** Make the request here and check the object type returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "requests.models.Response"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import `requests`\n",
    "import requests\n",
    "\n",
    "# Make the request and check object type\n",
    "r = requests.get(url)\n",
    "type(r)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a `Response` object. You can see in the [`requests` kickstart guide](http://docs.python-requests.org/en/master/user/quickstart/) that a `Response` object has an attribute `text` that allows you to get the HTML from it! \n",
    "\n",
    "**TO DO:** Get the HTML and print the HTML to check it out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Extract HTML from Response object and print\n",
    "html = r.text\n",
    "#print(html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK! This HTML is not quite what you want. However, it does _contain_ what you want: the text of _Moby Dick_. What you need to do now is _wrangle_ this HTML to extract the novel. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Recap:** \n",
    "\n",
    "* you have now scraped the web to get _Moby Dick_ from Project Gutenberg.\n",
    "\n",
    "**Up next:** it's time for you to parse the html and extract the text of the novel."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Wrangle your data to answer the question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 1: getting the text from the HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Here you'll use the package [`BeautifulSoup`](https://www.crummy.com/software/BeautifulSoup/). The package website says:\n",
    "\n",
    "<img src=\"img/bs4.png\" width=\"550\">\n",
    "\n",
    "\n",
    "**TO DO:** Create a `BeautifulSoup` object from the HTML."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bs4.BeautifulSoup"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import BeautifulSoup from bs4\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "\n",
    "# Create a BeautifulSoup object from the HTML\n",
    "soup = BeautifulSoup(html, \"html5lib\")\n",
    "type(soup)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From these soup objects, you can extract all types of interesting information about the website you're scraping, such as title:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<title>\n",
       "      Moby Dick; Or the Whale, by Herman Melville\n",
       "    </title>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get soup title\n",
    "soup.title"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or the title as a string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n      Moby Dick; Or the Whale, by Herman Melville\\n    '"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get soup title as string\n",
    "soup.title.string"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or all URLs found within a page’s < a > tags (hyperlinks):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<a href=\"#link2H_4_0002\"> ETYMOLOGY. </a>,\n",
       " <a href=\"#link2H_4_0003\"> EXTRACTS (Supplied by a Sub-Sub-Librarian).\n",
       "         </a>,\n",
       " <a href=\"#link2HCH0001\"> CHAPTER 1. Loomings. </a>,\n",
       " <a href=\"#link2HCH0002\"> CHAPTER 2. The Carpet-Bag. </a>,\n",
       " <a href=\"#link2HCH0003\"> CHAPTER 3. The Spouter-Inn. </a>,\n",
       " <a href=\"#link2HCH0004\"> CHAPTER 4. The Counterpane. </a>,\n",
       " <a href=\"#link2HCH0005\"> CHAPTER 5. Breakfast. </a>,\n",
       " <a href=\"#link2HCH0006\"> CHAPTER 6. The Street. </a>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get hyperlinks from soup and check out first 10\n",
    "soup.findAll('a')[:8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What you want to do is to extract the text from the `soup` and there's a souper helpful `.get_text()` method precisely for this. \n",
    "\n",
    "**TO DO:** Get the text, print it out and have a look at it. Is it what you want?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get the text out of the soup and print it\n",
    "text = soup.get_text()\n",
    "#print(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that this is now nearly what you want. You'll need to do a bit more work."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Recap:** \n",
    "\n",
    "* you have scraped the web to get _Moby Dick_ from Project Gutenberg;\n",
    "* you have also now parsed the html and extracted the text of the novel.\n",
    "\n",
    "**Up next:** you'll use Natural Language Processing, tokenization and regular expressions to extract the list of words in _Moby Dick_."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2: Extract words from your text using NLP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll now use `nltk`, the Natural Language Toolkit, to\n",
    "\n",
    "1. Tokenize the text (fancy term for splitting into tokens, such as words);\n",
    "2. Remove stopwords (words such as 'a' and 'the' that occur a great deal in ~ nearly all English language texts.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1: Tokenize\n",
    "\n",
    "You want to tokenize your text, that is, split it into a list a words.\n",
    "\n",
    "To do this, you're going to use a powerful tool called _regular expressions_, or _regex_.\n",
    "\n",
    "* Example: you have the string 'peter piper picked a peck of pickled peppers' and you want to extract from the list of _all_ words in it that start with a 'p'. \n",
    "\n",
    "The regular expression that matches all words beginning with 'p' is 'p\\w+'. Let's unpack this: \n",
    "\n",
    "* the 'p' at the beginning of the regular expression means that you'll only match sequences of characters that start with a 'p';\n",
    "* the '\\w' is a special character that will match any alphanumeric A-Z, a-z, 0-9, along with underscores;\n",
    "* The '+' tells you that the previous character in the regex can appear as many times as you want in strings that you're trying to match. This means that '\\w+' will match arbitrary sequences of alphanumeric characters and underscores.\n",
    "\n",
    "**You'll now use the built-in Python package `re` to extract all words beginning with 'p' from the sentence 'peter piper picked a peck of pickled peppers' as a warm-up.**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['peter', 'piper', 'pick', 'peck', 'pickled', 'peppers']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import regex package\n",
    "import re\n",
    "\n",
    "# Define sentence\n",
    "sentence = 'peter piper pick a peck of pickled peppers'\n",
    "\n",
    "# Define regex\n",
    "ps = 'p\\w+'\n",
    "\n",
    "\n",
    "# Find all words in sentence that match the regex and print them\n",
    "re.findall(ps, sentence)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks pretty good. Now, if 'p\\w+' is the regex that matches words beginning with 'p', what's the regex that matches all words?\n",
    "\n",
    "**It's your job to now do this for our toy Peter Piper sentence above.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['peter', 'piper', 'pick', 'a', 'peck', 'of', 'pickled', 'peppers']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find all words and print them\n",
    "re.findall('\\w+', sentence)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TO DO:** use regex to get all the words in _Moby Dick_:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Moby', 'Dick', 'Or', 'the', 'Whale', 'by', 'Herman', 'Melville']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find all words in Moby Dick and print several\n",
    "tokens = re.findall('\\w+', text)\n",
    "tokens[:8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Recap:** \n",
    "\n",
    "* you have scraped the web to get _Moby Dick_ from Project Gutenberg;\n",
    "* you have parsed the html and extracted the text of the novel;\n",
    "* you have used tokenization and regular expressions to extract the list of words in _Moby Dick_.\n",
    "\n",
    "**Up next:** extract the list of words in _Moby Dick_ using `nltk`, the Natural Language Toolkit.\n",
    "\n",
    "Go get it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Moby', 'Dick', 'Or', 'the', 'Whale', 'by', 'Herman', 'Melville']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import RegexpTokenizer from nltk.tokenize\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "\n",
    "# Create tokenizer\n",
    "tokenizer = RegexpTokenizer('\\w+')\n",
    "\n",
    "\n",
    "\n",
    "# Create tokens\n",
    "tokens = tokenizer.tokenize(text)\n",
    "tokens[:8]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TO DO:** Create a list containing all the words in _Moby Dick_ such that all words contain only lower case letters. You'll find the string method `.lower()` handy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['moby', 'dick', 'or', 'the', 'whale', 'by', 'herman', 'melville']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize new list\n",
    "words = []\n",
    "\n",
    "\n",
    "# Loop through list tokens and make lower case\n",
    "for word in tokens:\n",
    "    words.append(word.lower())\n",
    "\n",
    "\n",
    "# Print several items from list as sanity check\n",
    "words[:8]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Recap:** \n",
    "\n",
    "* you have scraped the web to get _Moby Dick_ from Project Gutenberg;\n",
    "* you have parsed the html and extracted the text of the novel;\n",
    "* you have used tokenization and regular expressions to extract the list of words in _Moby Dick_.\n",
    "\n",
    "**Up next:** remove common words such as 'a' and 'the' from the list of words."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2: Remove stop words\n",
    "\n",
    "It is common practice to remove words that appear alot in the English language such as 'the', 'of' and 'a' (known as stopwords) because they're not so interesting. For more on all of these techniques, check out our [Natural Language Processing Fundamentals in Python course](https://www.datacamp.com/courses/nlp-fundamentals-in-python). \n",
    "\n",
    "The package `nltk` has a list of stopwords in English which you'll now store as `sw` and print the first several elements of.\n",
    "\n",
    "If you get an error here, run the command `nltk.download('stopwords')` to install the stopwords on your system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['i', 'me', 'my', 'myself', 'we']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import nltk\n",
    "import nltk\n",
    "\n",
    "# Get English stopwords and print some of them\n",
    "sw = nltk.corpus.stopwords.words('english')\n",
    "sw[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You want the list of all words in `words` that are *not* in `sw`. One way to get this list is to loop over all elements of `words` and add the to a new list if they are *not* in `sw`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['moby', 'dick', 'whale', 'herman', 'melville']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize new list\n",
    "words_ns = []\n",
    "\n",
    "# Add to words_ns all words that are in words but not in sw\n",
    "for word in words:\n",
    "    if word not in sw:\n",
    "        words_ns.append(word)\n",
    "\n",
    "# Print several list items as sanity check\n",
    "words_ns[:5]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Recap:** \n",
    "\n",
    "* you have scraped the web to get _Moby Dick_ from Project Gutenberg;\n",
    "* you have parsed the html and extracted the text of the novel;\n",
    "* you have used tokenization and regular expressions to extract the list of words in _Moby Dick_.\n",
    "* you have removed common words such as 'a' and 'the' from the list of words.\n",
    "\n",
    "**Up next:** plot the word frequency distribution of words in _Moby Dick_."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Answer your question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Our question was 'What are the most frequent words in the novel Moby Dick and how often do they occur?' \n",
    "\n",
    "You can now plot a frequency distribution of words in _Moby Dick_ in two line of code using `nltk`. To do this,\n",
    "\n",
    "* Create a frequency distribution object using the function `nltk.FreqDist()`;\n",
    "* Using the plot method of the resulting object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yFh/mOn/+fKxbtw5jx46FVCrF4MGD4eLigsjISERERGDSpEmYPXs2BAKBpUMl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\nzqliaFOGse7evYt79+5hz5496mNRUVF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      "text/plain": [
       "<matplotlib.figure.Figure at 0x111fe4908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Import datavis libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Figures inline and set visualization style\n",
    "%matplotlib inline\n",
    "sns.set()\n",
    "\n",
    "# Create freq dist and plot\n",
    "freqdist1 = nltk.FreqDist(words_ns)\n",
    "freqdist1.plot(25)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Recap:** \n",
    "\n",
    "* you have scraped the web to get _Moby Dick_ from Project Gutenberg;\n",
    "* you have parsed the html and extracted the text of the novel;\n",
    "* you have used tokenization and regular expressions to extract the list of words in _Moby Dick_.\n",
    "* you have removed common words such as 'a' and 'the' from the list of words.\n",
    "* you have plotted the word frequency distribution of words in _Moby Dick_.\n",
    "\n",
    "**Up next:** adding more stopwords."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add more stop words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CpZW1KK3UviT9r1fPYaCHE3w8ndGrS3twOJQsSNtGCYI8NyyFPNhZc5skDl3q\nFjrrb2D5pwujSaRyiKulqKiSorJaiorqWlSqfpeiskqKW5kFKKmQ4FRyDk4l56C9lQADPJzg09sJ\nHi624HJoQCFpeyhBEPKMhHwuhHwu7G0sNJZJTU2GhV0vJN8uwKVb+Sgsq0HslYeIvfIQ1pZ8DPBw\nhE9vZ3h2twOPS8mCtA2UIAhpBQzDwK1ze7h1bo/pI93wIK8Sl27n49KtfOSVVCPu2mPEXXsMKwse\n+vdyxEBPZ4gr5SgsrQaHw4DLYdT+5DAMdZATo6EEQUgrYxgG3TuK0L2jCFP8XPGwQIxLt/ORfLsA\nDwvFSLiRi4QbuXWFzyTqfDwOU5csGCgg/P0seFwGfB4HfB4XfC7nye9PLk+u855cl1XXwLlzNRxt\naYgwaYoSBCEmxDAMujpbo6uzNfx9XfG4SIxLtwtw9U4hSsoqweUJoGBZyBUsFIqmPxXsk4u8bnFD\nabXU4BjO3kyEp4sthvfthIG9nWAhoI8FUofeCYS0IZ0crDDxZStMfLmHXruEscoEoWCRmnYVbr36\nQipTQCpX1P1UXp5cl9W7XiuV48rNTNzNk+PWg1LcelCK/f9Lh09vJ7zctxN6u9jSsFwzRwmCkL8w\nhmHAZRhwOYCAx0DUTmDQ8Z3bFaCHaz9cup2PhOuPcSenTNXE5WBjgeF9O+LlFzvC2a6dkZ4Bacso\nQRBi5tpZ8OD3Umf4vdQZecVVSLiRi8Qbj1FUXoOfEzLxc0ImPLq2x/C+nRpMWCTPP0oQhBCVDvbt\nMMXPFf6+PXE7qwQJN3Jx6XY+0nPKkJ5Thqjf09GhPQcu99Pg2N4Sju0t4NjeAg7tLWAnEtJ8jucM\nJQhCSBMchkGfHvbo08Mes17zeNIElYv07FJkFymQXZSr9hh7GyEcbJ4mDcf2lrC3EeJhsQwWj8qf\nDs+tP2SXYcDl1ruNYSCVs5DJFaqhvMQ0KEEQQrSyFPLg268zfPt1RnF5DZIuX0M7264oerLSbt3P\napRW1qpW372dreaB4i+puVGL438AABhA/RyQegmFy2HAY2rhlnUTHe3rloTv5NAOju0taUmTZ0AJ\nghCiN3sbC/Rw5sPLq0uT+6QyOYrLJU+SRHVd8iivQXG5BBWVFRAK29UN0WXVD9lV/WRZyGRyAAzk\nChYsAPmT+3V5WPy4wXUel0EH+3boZN8OHR3aoZO9FTo6tENH+3awFNLHny70FyKEtAg+j4sO9u3Q\nwb7piCcBos8/AAAgAElEQVR9huyqK8+yLFgWDZNIvXkhyoQilytw9cYNCKy74HGRGI+L6paDL6mQ\nPFnxV9zkHLbWAlgJ5HDJSIVDvf4Ux/YWsLexoCVPQAmCENKGMQwDhoFezUQlTnx4eXVtcFu1RIa8\nkio8Lqq75BaJn+wlUtckVgrgYXFe0/MCsBUJn/SjKC+WqCiRQmhbhnZCHtpZ8NFOyAOf9/wmEkoQ\nhJDnlqWQhx4dbdCjY8Pl3BUKFkXlNUhOSYGVbTdVf0pdn0o1iiskKHlyuZtT1vBBk5IbXOXzOE8S\nBg/thDxYPvmpTCDlpTV4UPFA7ZIn9S887tP7qiQK1NTKwOdxTDoyjBIEIcTscDgMnGwt4eLIh5dX\n5yb3y+QKlFZIVEmjsKwaRWU1yMktBIdviaoaGaokMlTVyCCVKVAmq0WZWMt+ILfuGh7kybi6WBlG\n63pafC4HPLYKvdzlEPBbdq92ShCEENIIj8uBo61lk0UMG/elsCyLWplClTCqa2SokkgbJJCHjx/C\n1s65ybInmpZBqZXJIamVQsFyUCuTQ8GydZtfSeVaYy4sq0FnR6uW/Tu06KMRQogZYRhGtR+InUio\ntkxaWjG8vNwNetz6iUiuUJ9Q6l8veJzR4skBoARBCCFtGpfDAVfAgYWWZbbSqjKNcu7nt/udEELI\nM6EEQQghRC1KEIQQQtRq1T4IqVSKtWvX4uHDh6itrcWCBQvQq1cvrFmzBgzDwN3dHRs2bACHw8Hh\nw4cRHR0NHo+HBQsWYNSoUa0ZKiGEmL1WTRA///wzbG1t8emnn6K0tBT+/v7w9PTE0qVLMWTIEISG\nhuL06dPo378/IiMjcfToUUgkEgQFBWH48OEQCAzbDIUQQkjztWqCeP311zFu3DgAdeOHuVwuUlNT\nMXjwYACAn58fEhISwOFw4O3tDYFAAIFAABcXF9y6dQv9+vVrzXAJIcSsMSzL6l4isYVVVlZiwYIF\nCAgIwPbt2xEfHw8ASExMxNGjR+Hr64v09HSsXLkSALBq1Sr4+/vj5Zdf1vq4SUnnIZHUGD1+Qgh5\nnowYMULt7a0+D+Lx48dYtGgRgoKCMHHiRHz66aeq+8RiMWxsbGBtbQ2xWNzgdpFIpPOxhw4dYpSY\nCSHEHLXqKKbCwkK89957WLlyJaZNmwYA8PLywvnz5wEAcXFx8PHxQb9+/ZCcnAyJRIKKigpkZGTA\nw8OjNUMlhBCz16pNTFu2bMFvv/0GV1dX1W3r1q3Dli1bIJVK4erqii1btoDL5eLw4cM4dOgQWJbF\nvHnzVH0XhBBCWodJ+iAIIYS0fTRRjhBCiFqUIAghhKhFCYIQQohalCAIIYSoRQmCEEKIWpQgzFxp\naampQ3hmsbGxDa7/+uuvJork2SUlJZk6hOdeSUkJbty4gfLyclOH0uaZ7Y5ylZWV2LNnD/Lz8zFq\n1Cj07t0b3bt313mcXC5HTEwMHj16hKFDh8Ld3R329vYayx87dgxfffUVamtrwbIsGIbB6dOntZ7j\n8uXL2LRpE4qKiuDs7IwtW7bAy8tL6zHZ2dn45JNPkJmZCXd3d6xcuRKdOnXSWP7ChQvYvHkz5HI5\nXn/9dXTu3BnTp0/Xeo6srCycOHECUqkUAJCfn4/NmzerLRscHAyGYdTe9/333ze57YsvvtB43sWL\nF6u9PTY2FpcvX8bx48dx5coVAHWvz5kzZ/Dmm29qfS6Nz8fn89GxY0e8+eab4PP5Tcob+roDQEJC\nAvbt24fa2qeb2at77vVFRERg6NChWsvUt3nzZoSGhqqur1q1Cp988onasq+++mqD14TH40Emk0Eg\nEOC3337TeI709HRs3LgR5eXlmDRpEtzd3XWurrx582ZMnz4dffr00fu53Lp1C9XV1eBwOAgPD8f8\n+fMxbNgwrcc0fk7W1tb46aefNJb/4YcfsGfPHvTq1Qv37t3DkiVLdL5XfH19UVxcDDs7O5SWlkIg\nEMDR0REbNmzA8OHD1R5jyPvl0aNHGs/duXNnjffJ5XLcuXOnwfurpderM9sEsXbtWvj5+eHixYtw\ndHTEunXrsH//fp3HhYaGwtnZGefOnUPfvn2xevVq7NmzR2P5PXv2YPfu3Vo/rBvbsmULwsLC0KtX\nL6SnpyM0NBTR0dE6n8/cuXMxYMAAXLx4EWvXrsW+ffs0lv/ss8+wf/9+LFmyBPPnz0dgYKDOBLFi\nxQq89tpruHz5MpydnVFVVaWx7KZNmwAAX375JUaPHo2BAwciJSWlybd9JUdHRwDAqVOn0LVrVwwY\nMADXr1/H48ePNZ7D09MTpaWlEAqF6NmzJ4C6PYInTJig9XkAwO3btyEUCuHj44Nr167h8ePHcHJy\nQnx8fIPlX5QMfd0BYNu2bVi7di06duyoMx4lhmGwaNEi9OzZExxOXQV/+fLlTcpFRUVh165dKC0t\nxf/+9z8AdQtg9urVS+NjnzhxAizLYtOmTZg5cyb69euHtLQ0HDhwQGtMW7duxbZt27B+/XpMmzYN\nc+fO1ZkgRo4cid27dyMvLw+TJk3CpEmTYG1trfWYjRs3IiQkBBEREVi2bBk+/fRTnQnixIkTAOqe\n+40bN3Dy5Emt5aOjo/Hzzz9DKBSiqqoKc+bM0ZkgBg0ahMWLF8PV1RUPHjzAF198gUWLFmHlypUa\nE4Qh75dly5YBqKvNi8ViuLu74+7du3B0dMSPP/6oMa4PPvgAtbW1sLGxAVD33tH2RatZWDMVHBzc\n4GdgYKBex82ePbvBcTNmzNBaft68eQbH9vbbbze4PmfOHJ3HNC7T+DEamzVrFsuyT5+H8nlpoyy7\nZs0almX1+5s1jkP5GJq8++67Da6/8847Os8hl8sbXM/LyzM4LuV5Z86cqba8oa87y7Ls3LlzdZZp\nLCYmpslFm127dhl8jsavdVBQkNbyyr+VIe8VpaKiInb58uVs//792dWrV7NZWVkaywYHB7MSiYR9\n7733DD6Pkq7n8v7777MymYxl2br3jfJc2jR+nyvj0vReqV/GkPfLwoUL2YqKCpZlWVYsFuv87FD+\nDxuT2dYgACAjIwMAkJubCy6Xq9cxcrkcxcXFAOqaqZTf8jSxsLDA3Llz0adPH1VVWN03wvocHByw\nbt06DB06FKmpqVAoFDh06BAAYMaMGQ3KKlfCtbS0xJ49ezBo0CCkpKSovpFr0r17d4SFhaGkpARf\nf/211qqsEsMwKCgogFgsRlVVldYaRH1HjhxBv379cOXKFbXNN/WVlpbiwYMHcHFxwb1791BRUaHz\n8SMiInDw4EFIpVLU1NSgR48eOH78uNZjKioqUFxcDHt7e5SUlKCiokJ1vDqGvu5A3esYGhoKLy8v\n1Wvf+PVrbOLEifjxxx8bNE1oM3PmTPz3v/+FTCYDy7LIz8/HvHnztB4jEomwY8cO1Wvi5OSktXz7\n9u0RHR2N6upqHD9+XPWNVZuMjAzExMQgNjYWgwcPRlRUFGQyGZYuXYqYmBi1xzAMg1WrVsHPzw+/\n/vqrzvcKAISFhan+tvn5+Rpfl+XLl4NhGBQXF2PKlCl46aWXkJaWBgsLC53ncHJywr/+9S94e3vj\nypUrcHR0REJCgtb4mvN+yc3NVdWy2rVrh4KCAq3lfXx8cPbsWbi5ualu0+f/2BBmu9TG7du3ERoa\nioyMDLi6umLDhg144YUXdB534cIFhISEoKCgAJ06dcK6deu0LkOuror41ltvaT2HIe3xH3/8scay\n27Zt03jfvHnzMGDAAOTm5sLNzQ0BAQE6N2S6ePEi7ty5gw4dOiAkJASTJ0/G6tWrtR5TUFCA3bt3\nIzMzE7169cL8+fNhZ2ensfylS5dU/S8dO3bExo0bdbarTp48GUeOHME//vEPvPvuu9i0aRP+85//\naD0mNjYWW7duhbW1NaqqqrB+/XrcunULVlZWmDVrVpPyjV/3tWvXamxeUFL3OmrqT1Fat26dqmli\n3rx5OHjwoNamrNmzZ8PV1RXp6ekQCoWwtLTE7t27tZ6jqqoK0dHRqtdk5syZWl/7yspK7N69G+np\n6XBzc8P8+fPRvn17recIDAxEQEAAXn/9dVhaWqpuj4qKUvv3BYDi4mJcv34dI0aMQFJSEjw9PWFr\na6v1PPX/v4RCIXx9fdWu/HzhwoUG1xmGgfKjT7kfjSYSiQSHDh3CvXv34O7ujmnTpiEtLQ3dunXT\n+EWsOe+Xf//730hOTsaLL76IlJQU+Pr6YsGCBRrLh4SE4NKlSw2amHQ1RRvKbBNEc0mlUvD5fFWn\nVXZ2NlxcXDSWl8lkuH79eoNveJrayHNzc9GxY0fcv3+/yX3KNnZ95efnw9nZWeP9GRkZOHr0KBIS\nEvDKK69g+vTp6NGjh87HraysRE5ODrp16wYrKyuN5dQ9ByVDnovy763N3Llz8c0332DlypX49NNP\nERwcjMjISJ2PrVAoUFxcDAcHB40d6o0pax3aPMvrqIxd+XPmzJla/+lnzZqFqKgofPzxx9i6dSuC\ngoI0lr9+/Tr69u2rqnXW98orr2g8x86dO7Fw4ULV9bCwMKxYsULr8wDq3oP13/fe3t5ayzdn4IhM\nJsOhQ4dw9+5d9OjRA4GBgVqTXUlJCb766ivVYI4PPvhA51YCzRmgoKT8nND3/XXjxg1V4vb09NRa\nVvnaG5PZNTFp+0dQ94/T2IoVK/D555/D3t4e0dHR2Ldvn9aOscWLF0MqlSI/Px9yuRzOzs4aE8S+\nffvw8ccfNxiVAtR9M9A1+uWzzz4zqJnFzc0Nq1atQnFxMbZu3YoJEyZg0KBB+PDDDzX+I588eRK7\ndu1SjXxiGKbBB0d9yufAMAxKSkqQnZ2Nrl27wt7eXutzUf5NlR8sPB5P1QmrSceOHfHDDz/A0tIS\nYWFheg1fTEhIwLfffguJRKK6TVdchw4dalBe03DaZ3kdlU0TDMPo1TTB5XIhkUhQXV0NhmEgl8s1\nlk1MTETfvn3Vvi/U/V8cOXIEP/zwAzIyMhAXFwegLqlKpVKdCWLt2rW4evUqqqurUV1dDRcXFxw+\nfFjnMYYOHAkNDYWNjQ2GDx+OCxcuYP369RpHcQHA6tWrMXLkSPj7++PSpUtYvXo1du7cqfMc+nY4\nz5gxQ2My0PXt/vHjx0hMTIREIkFmZiZOnTqltcbZu3dvXL16tcEIxxbfltnovRzPmQMHDrAfffQR\nO2/ePPajjz5iS0tLtZYPCAhgWZZl165dy1ZXV2vt2HoWkyZNYiUSCbthwwY2MzOzSWdvY3/88Qf7\nf//3f+yECRPYPXv2sIWFhWxubi47ceJEjcfMmDGDlUgk7OzZs1mFQsG+9dZbOuM6fvw4O3bsWHbB\nggXs6NGj2R9//FFr+QkTJrB5eXnsxo0b2aSkJHbBggU6zyGXy9mHDx+yFRUV7Pfff8/euXNH5zHj\nx49nExIS2IyMDNVFmzfeeIPNzs5my8vLVRdDSSQSnWUuXLjAjh07lvX29mbffPNNNiEhQWv5EydO\nsLt372YPHz7M+vr6skuXLjU4Lk2d+hKJhM3OzmbXr1/P5uTksDk5OeyjR4/0eh5vvfUWq1Ao2PXr\n17NFRUUGDYIwZOBI405pXZ3BjQcn6BOXsozyp7ZzKP9O9+/fV/2ek5PDpqam6jzP9OnT2fDwcPbg\nwYOqizYTJkxgR40axb766quqny3N7GoQSlevXkVMTEyDMf179+7VWF451njq1KmoqqpCYmIitm7d\nqvM8yk6w6upqvTrEgLq266ioqAYd57pqN05OThAIBBCLxejevbvqeWny888/IzAwEEOGNNyFb8mS\nJRqP4XK5EAgEYBgGDMM0aFvW5LvvvkNMTAysrKxQWVmJOXPmwN/fX2N5Z2dnODs7QywWY8iQIXoN\n26uqqkJkZCQyMjLQo0cPTJw4UecxnTp10rmFbX29e/dGp06d9B7MADStDfH5fJ3DMC0sLHDy5ElV\n08TFixe1llfuk1JaWoo33nhD51BSQP/a5u3bt9G3b1+MHTu2QXNZRkaG1po4ANja2oJhGFRVVend\nHKN8bED/gSPK2pOlpSVqamo01qCU8dvZ2eG3336Dj48PUlJS0LVrV53nMKRWJxAIUFlZidWrV+OT\nTz4By7JQKBQIDQ3FDz/8oPU8VlZWqiGv+pg7dy4mT56sd/nmMNsEsXHjRsydOxcnT56Eh4dHg8km\n6iibVNh6XTbK27RNfBs7diy+/PJLeHp6YsaMGXp9qMbGxiI2NlbvhAIY3swSFham9vbXXntN4zED\nBw7EihUrkJeXh9DQUPTt21dnXAzDqPoqrK2tIRQKtZYXiUQ4deqUqsNNn5nea9euhY+PDyZNmoQL\nFy5gzZo1OjtqDR1hNHToUIwZMwbdunVTTXjU1VwUFRWFyMhI7Nq1C6+//jq+++47jWUvXbqEu3fv\n4ttvv8W7774LoK45JyoqCv/97381Hnfx4kVs2rTJoAmPZ86cQVxcXINOfXWUTVLqmtJ0JYgXX3wR\ne/fuhbOzM5YtW4bq6mqt5YG6Dvq1a9ciIyMDH374ITZs2KDzmLfffhuTJ09WzR3Q9AWnfnPfgQMH\nVHM/9OkbWLp0KQIDA1FQUIAZM2Zg7dq1Gsteu3YN3333He7fv4/Q0FCwLAsOh6Pz7wUA7u7uOH78\neIMRj9r6rI4cOUIJwljs7OwwYcIEJCQkYMmSJZg9e7bW8mfOnGnWeTp27Ij4+HhIpVJYWFjo9a3I\nwcEBPJ5hL83mzZuRm5uL119/HT/++CPCw8ObFa82QUFBOHXqFFxdXRETE4OIiAidx3Tr1g3//Oc/\n4ePjg0uXLmnt0AfqJglmZ2dj+fLl2LdvH9avX6/zHCUlJXj77bcBAH369NH5LR2A6ptjYWGhzrIA\ncOjQIezYsUOvvdGVDKkN2djYoLCwELW1tarhjQzDYOXKlVrPsWPHDoMnPOpb2/zggw8A1I2GKy4u\n1jgEWJ34+Hh07doV48ePB5/Px0svvaTzmN69e6uGc+tr0qRJ8PPzU/VxaRohp8+gBU0GDx7coFan\nLamMGTMGY8aMwbFjxxrUlO/du6fzPDdv3sTNmzdV13V9CamtrYW/v3+DSZWavvg1l9kmCA6Hgzt3\n7qC6uhr37t1DWVmZ1vLKJQ1mzpzZ5D5tnU+ffPIJNm/erHNYIPB0rHZhYSHeeustuLu7q96Mul74\nR48e4eTJk6pvar/99pvOIZWG+uijj7B48WIcOHAAy5cvx7Zt23T+423btg2HDh3CuXPn4ObmprNz\n09LSEjdu3MCjR48watQonfMAgLpmhoKCAjg5OaGgoAAKhULnMYsXL8Yff/yBO3fuoGfPnhgzZozW\n8h06dEDfvn31Gs+uZEhtyMPDAx4eHpg+fTr4fD6ys7PRvXt3ncM8ORyOqjlHKBRqHVmmZGhtMzQ0\nFImJiXBwcFDVnnR1uMbExCAjIwOnT5/GmTNn4OjoqDFBPsvAkT/++AMHDx5sUEPR9qHaeGkOkUiE\nY8eOaT2HumVjNJ0jPT0d+fn52Ldvn2oIrEKhQFhYmNYlQADDk9hHH31kUPnmMNsEsWbNGty5cwfB\nwcH46KOPMHXqVK3l5XI5wsPDm0xE0VVFdXd3b9LOr4ky+Tx+/BiVlZXgcrnYs2cPgoODdR67YsUK\n+Pr66pwg9ywYhsGgQYOwe/dujB8/XueoFKBuvR9N497Vac6SFsomAEtLS1RXV+Pvf/+7zvOEhYUh\nKysLAwYMwLFjx5CcnKx1Tkdtba2qKUPfpL1lyxY8ePBAVRsKCQnRGdfp06fx3XffoVevXrh79y4W\nLlyotRnBxcUFYWFhKC0t1XvC48KFC1FVVYW+ffti8uTJOHjwoNbyt27dwv/+9z+9h2oCdd+Gz507\nh/PnzwNAg33oG9Nn9KAmn332GT7++GO93/eNl+ZQXtdG2QTHsixSU1MbfMtvrLy8HMePH0dRUZGq\nX4dhGAQFBek8j6HJy8vLq8mw4JZmtgnC3d0dDg4OqKmpQUREhM43f//+/QEYPh9h9OjRmDFjRoN/\nEE0T2JQTdmbPnt3gm3p0dDTeeecdreexsLBo8RpDYzKZDJ9++il8fHyQlJSksyO8OR48eICtW7ci\nOTkZr776Kr7++mudx0ilUrAsq/p2q88H2cWLF1XfgufMmYOAgACt5XXNTlYnPDxc1fa9Zs0arFq1\nSuekrMOHD6vWCqqursbs2bO1JogNGzbg6NGjGDhwICwtLfVKjqtWrVK9v8LCwhAeHq7126uTkxPE\nYrFeHeBKs2fPRrdu3bBs2TKMGDFCr2MMHTgC1M3y1vU3ra/+MNCBAwfq1RRb/3/Xzc1Na2ezj48P\nfHx8kJqaqtfE2/oMTV7NXU/OEGabIEJCQpCYmAhHR0e9qs26Zj9rEhkZiblz5xrUdm3IN3Xl6AxH\nR0f88ssveOGFF/Tq4GqObdu2ISEhAdOnT8epU6ewffv2Fn18oHlLFHzxxRc4cuQI7O3tUVBQgEWL\nFums3chkMigUCnA4HCgUCo1JJTY2FqNGjVI76U3TB5O6hfQANFgSQRMHBwdVP5WFhYXOJqb58+fr\nnDXemL7vL+WY/sLCQowdOxbdunVTHa+rien8+fNITk5GfHw8/vOf/8DBwUHnh7EhA0eUfRUCgQAh\nISEN3vfaBhvUX5qjoKBAr/dX/X6RgoICrUvMKJuiN2/e3OQ9petvZmjyKi0txbRp0/Dzzz9jwIAB\nejWtGspsE8Tt27fx+++/G1Rtbg5HR0edq0U2Zsg39fqjM44cOYIjR44AqGsSaelp9z169FDNtjb0\nOelr2bJlCAoKQkFBAWbOnKl1xIiSlZWVaiilk5OTXiPFxo8fj8DAQLz00ktISUnR+HyUfVObNm1q\nMCmw/oS5xmbNmoVZs2Zh9+7dmD9/vs5Y6mNZFv7+/vD29kZaWhpkMpmq30Zdk5aNjQ1OnTrVoKNS\n1xcDfd9f4eHhYFkWU6dOxfDhw/HCCy9g5MiReo2uKy8vR15eHh49eoTq6mq9mr4MGTii7MiPj4/H\nwoULVYMNtL0uQMPagKenJ/z8/HTGVX9NJIFAgB07dmgsq2yK7tKlS4Pb9fmc0Xddqfqas56cIcw2\nQTSn2twcFhYWeP/99xsMp9S1WJ8h39SVTQMHDx7Et99+q/pnN3QUVFtx//59cDgcyOVyiMVihISE\naBxGrPyGJZfLMW/ePNWS4tpmk9b/J+zQoQNiY2PRp08fVa2lMalUqhqefPbsWQB1nY71P7g1mTZt\nGrZu3Yr79+/D3d1drzWM6icUfeZzFBUVNekw1TX8Vt/3l/JD7vz586oO53Xr1sHBwQFffvml1nPM\nnTsXY8aMwfz58/UaaAAYNnCkQ4cOqo52Q16X69ev671/htKUKVOa3Kbcw6Fx4mtuUzTQNHn5+vpq\nLb9+/XqDhwUbyuzWYqpfba6qqjKo2twczVmsrzkmTpyIvXv3qsbcf//99zr/iduiKVOmICIiosEK\no5o+8LWtla/pb2zoMbW1tcjPz8dXX32l+vDmcDhwcHDQuaxBcHAw3njjDQwYMADJycmIi4vDV199\npfWY0tJSxMfH6706q6+vL4qKilSr0goEAjg5OWndzMZQyg7nc+fOQSwWY/DgwTq/5DTHnTt3VItB\nbt26FZMmTdLY96YcDly/lqbtdanf7Fe/2c7NzU3r/BSgbvBITk4OPD09cffuXfD5fNjb27f4Z4ah\n60oBdWs9PXjwAD169NDZHNksLT43u43Lyclhs7Oz2cGDB7PLli1jv/nmG/bu3btsTk6OqUN7Jsp1\n7VeuXMmybPPW0m8L9Fmf/6+i8Wugz2sya9YsNiQkhJ0xYwb79ttv69wTYNmyZaplQrKysthVq1ax\nmZmZ7PTp05sfeCMDBgxgJ0+ezP7xxx8t9piaFBUVsQ8fPmRzcnLYhw8ftvjjR0REsOnp6WxGRga7\nZs0aNi0tTecxCxYsUC2tUlVV1aw9XvTx8ccfs9u2bWN///13duvWrar/ZU3279/Pjh07ll24cCE7\nduxY9tixYy0e01+zHeIZPEu1uS1rzgzktkTZXFRbW2twk1xb5erqip9//hlDhgxBamoqbG1tVZ3d\nmpogWJbF5s2bG6zOqk1ubq6qacLFxQWPHj1C9+7dW7Q9ujkdzs1h6MCR5khKSsKgQYMQFRWFcePG\n4R//+IfO+Qd5eXmqQSYWFhYoKipq0ZiUsrKyVKuzjhkzRu2cq/qOHDli0Ii35jC7BKHUeJy2PiNM\n2rLGY+71mYHclig/MFt65JUp3bt3D/fu3VMNHADqBhVomyFryOqsQPM2szFUczqcm6M1Bo4wDAMf\nHx/s2rVL77k8w4cPx+zZs1X7NIwfP94osem7rpSSoSPemsPs+iCUBg4caPA4bUKehT57W5w8eRKZ\nmZmwt7dHREQEBg4ciH//+98ayys3s8nIyICHh4dem9kYasqUKRgzZgxee+01vTucm2PRokXYvn27\nUQeOKEeuWVtbw8fHB59//rnOPbkBICUlBQ8fPoSHh4fRvkz+8ssviIiIaLCulLb91d977z3VPhs3\nb96EVCpV7UneUktumG2CkMlkqmpzSkqK0arNxHw1ZzXX+iorK40+yq4taM2BI5mZmQ1GcPXt21d1\nPk3y8vLw6aefori4GK+//jp69+6t19pShpo1axZ27tyJBw8eaF1XSumnn35qsuKCcm8IQyYPamO2\nTUytVW0m5suQ1VyVjhw5gm+//bbBwnjaVgt+HjzLfAtDNWcuT0hICN59913s3LkTPj4+WLNmjV5N\nU4ZiGAZr165tMKdFW//bkSNHDF5xwVBmmyCaM06bEEM0Z2+LgwcP4uuvv24wzPd519YHjtTU1GDY\nsGHYtWsXXF1ddS5Z31y61oNrrDlroxnKbBNETEyMqUMgz7nmjCyzs7NrMgvXXLTVgSNCoRBnz56F\nQqHA1atXW35bzycMnR/VGmujmW0fBCHGdvHiRaSmpuKNN97Ali1b4O3tjffee09tWWX/15UrVyAQ\nCDrjSgIAAAXwSURBVJ6LYb6GaqsDR3Jzc7F9+3akp6fDzc0NK1eu1Nlv0Rqa059iKEoQhBjJ1KlT\n8e9//xsuLi7Izs7GmjVrVOPcG1PO8C4oKIBQKISNjQ3Cw8Px3nvvqXaYe9615YEj9+/fR1ZWFjw9\nPdGhQwejr+HWVphtExMhxsbn81U76HXr1k3r4mvK5oX6SUXZIWouCaKtDhzZv38/fv/9d5SVleGt\nt95CVlZWg/WcnmeUIAgxks6dOyM8PBz9+/dHSkoKnJ2ddR5jSFJ53rTVgSPHjx9HVFQU5syZgzlz\n5hjcmfxXRgmCECPZtm0bDh48iD///BNubm4NlgvXpDlJ5XnRVgeOsI02ojJWJ3VbRH0QhLQhEokE\nBw8exP379+Hm5oaZM2ea1QdSW7R//378+uuvePToEdzd3TF06FC8//77pg6rVVCCIIQQHTIyMpCe\nno6ePXvC09PT1OG0GkoQhBCihboJjsbe/72toD4IQgjRQrnoIcuySEtLM8rez20VJQhCCNGi8b4M\nc+fONVEkrY8SBCGEaKHc5AkA8vPzVftRmwNKEIQQooVykyegbl2mNWvWmDii1kOd1IQQosWkSZNQ\nWVkJoVAIiUQChmFUcyOe96XYqQZBCCFaeHt7w9/fH97e3rh9+zb27t2LLVu2mDqsVkEJghBCtMjI\nyIC3tzcAoHfv3nj8+LHZTF6kBEEIIVqIRCLs2LED/fr1w6VLl9rMIoKtgfogCCFEi6qqKhw4cAA5\nOTno3bs3pk+fDh7PPL5bU4IghBCilvmsJUwIIcQglCAIIYSoRQmCEDVOnDiBKVOmYNKkSZg4cSK+\n+eYbo50rJibGrCZfkb8O8+hpIcQAeXl52L59O2JiYmBnZwexWIzg4GD07NkTo0ePNnV4hLQaShCE\nNFJSUgKpVIqamhoAgJWVFf75z39CKBTit99+w759+1BTUwOJRIItW7Zg0KBBCA4ORp8+fZCYmIia\nmhqsX78ekZGRuHv3Lt555x288847iIiIQGZmJh48eIDS0lLMmDGjycJvKSkp2LZtG2pqamBnZ4dN\nmzahW7du2LdvH3788UdwOBz069cPmzdvNsWfhpgZShCENOLp6YnRo0djzJgx6NOnD4YMGYKJEyei\nW7duCA0Nxe7du2Fvb48ffvgBe/fuxaBBg1TH/vLLL/jiiy+wZcsW/PzzzyguLoa/vz/eeecdAEB6\nejqio6OhUCgwZcoUDBs2THVsbW0t1q9fj927d6Nz5844e/YsQkJC8M033+Crr77C2bNnweVysWnT\nJuTl5aFDhw6t/achZoYSBCFqbNq0CQsXLkR8fDzi4+MREBCAf/3rX/jyyy9x5swZ3L9/HxcuXACH\n87Qbz8/PD0DdvtIvvfQSLC0t0aVLF5SXl6vKTJgwAVZWVgCAV199FUlJSbCzswMAZGZmIjs7GwsW\nLFCVr6ysBI/Hg7e3N6ZNm4bRo0dj1qxZlBxIq6AEQUgjf/zxB6qqqvDmm29i6tSpmDp1Kg4fPoyo\nqCiEhYVh8uTJGDRoEHr37o2oqCjVcXw+X/W7polUXC5X9btCoWhyvWvXrvjpp58AAHK5HIWFhQCA\nnTt34urVq4iLi8PcuXPxr3/9C4MHD27R501IYzSKiZBGLCwsEBYWhpycHAB1O4ndvXsXAoEAHA4H\n8+fPx9ChQxEXFwe5XG7QY586dQq1tbUoKytDbGwsXnnlFdV9rq6uKCsrw6VLlwAAR48exUcffYTi\n4mK88cYb8PDwwP/93/9h+PDhuH37dss9YUI0oBoEIY0MHToUixcvxvz58yGVSgEAvr6++PLLL7Fm\nzRq88cYbsLCwwKBBgwzePEYoFCIoKAiVlZWYN28eevXqhZSUFACAQCDAZ599hq1bt0IikcDa2hrb\nt2+Hvb09Zs6ciWnTpsHS0hKdOnXCW2+91eLPm5DGaKkNQlpJREQEAGDJkiUmjoQQ/VATEyGEELWo\nBkEIIUQtqkEQQghRixIEIYQQtShBEEIIUYsSBCGEELUoQRBCCFGLEgQhhBC1/h9nPcKSgYjhswAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12089c7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import stopwords from sklearn\n",
    "from sklearn.feature_extraction.stop_words import ENGLISH_STOP_WORDS\n",
    "\n",
    "# Add sklearn stopwords to words_sw\n",
    "sw = set(sw + list(ENGLISH_STOP_WORDS))\n",
    "\n",
    "# Initialize new list\n",
    "words_ns = []\n",
    "\n",
    "\n",
    "# Add to words_ns all words that are in words but not in sw\n",
    "for word in words:\n",
    "    if word not in sw:\n",
    "        words_ns.append(word)\n",
    "\n",
    "\n",
    "# Create freq dist and plot\n",
    "freqdist2 = nltk.FreqDist(words_ns)\n",
    "freqdist2.plot(25)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Present your solution so that others can understand it.\n",
    "\n",
    "The cool thing is that, in using `nltk` to answer our question, we actually already presented our solution in a manner that can be communicated to other: a frequency distribution plot! You can read off the most common words, along with their frequency. For example, 'whale' is the most common word in the novel (go figure), excepting stopwords, and it occurs a whopping >1200 times! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## BONUS MATERIAL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you have seen that there are lots of novels on Project Gutenberg we can make these word frequency distributions of, it makes sense to write your own function that does all of this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_word_freq(url):\n",
    "    \"\"\"Takes a url (from Project Gutenberg) and plots a word frequency\n",
    "    distribution\"\"\"\n",
    "    # Make the request and check object type\n",
    "    r = requests.get(url)\n",
    "    # Extract HTML from Response object and print\n",
    "    html = r.text\n",
    "    # Create a BeautifulSoup object from the HTML\n",
    "    soup = BeautifulSoup(html, \"html5lib\")\n",
    "    # Get the text out of the soup and print it\n",
    "    text = soup.get_text()\n",
    "    # Create tokenizer\n",
    "    tokenizer = RegexpTokenizer('\\w+')\n",
    "    # Create tokens\n",
    "    tokens = tokenizer.tokenize(text)\n",
    "    # Initialize new list\n",
    "    words = []\n",
    "    # Loop through list tokens and make lower case\n",
    "    for word in tokens:\n",
    "        words.append(word.lower())\n",
    "    # Get English stopwords and print some of them\n",
    "    sw = nltk.corpus.stopwords.words('english')\n",
    "    # Initialize new list\n",
    "    words_ns = []\n",
    "    # Add to words_ns all words that are in words but not in sw\n",
    "    for word in words:\n",
    "        if word not in sw:\n",
    "            words_ns.append(word)\n",
    "    # Create freq dist and plot\n",
    "    freqdist1 = nltk.FreqDist(words_ns)\n",
    "    freqdist1.plot(25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now use the function to plot word frequency distributions from other texts on Project Gutenberg:\n",
    "\n",
    "* Pride and Prejudice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3Z+nSpezZs4eOHTsSFRXFtm3b0Gq1REZG0rt3bzQaGSUiqs7bw4W+9zWh731N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\nERHgB1+Riq551Zd0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121daacf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_word_freq('https://www.gutenberg.org/files/42671/42671-h/42671-h.htm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Robinson Crusoe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WLTf3bBoQWxs1/QOkI1kIcWuYlQwGDhxIXFwcH374IQArV67kjjvuMFqmrKyMqKgoHBwc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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121add828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_word_freq('https://www.gutenberg.org/files/521/521-h/521-h.htm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The King James Bible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VOVVYxr7cc+zPPcf+o+coKKqgoAg0/9rPY3/og0YjyUMIUU0Sh0BRFLp10NOt\ng57fhfXAalXZlVPAB1/tYdOeU5irLEy9NxgXrVxLIYSQq6rEFWg0CgMDOzJhmBfuOi0/7zvDB1/9\nKjPQhRCAJA5Rjx7tXZgTNRC9uws7DhSwbM1uubugEEISh6ifX1dv5kaHYvB0Ze/hs7yzehflpipn\nhyWEcCJJHKJBPTt5Me/hUNp66cg+dp74z3ZirKh0dlhCCCeRxCEapWt7PfMeDqW9tzuHThbzdvIO\nisvMzg5LCOEEkjhEo3Vq58n8R0Lp3M6Do2dKeSt5B+dLTc4OSwjhYJI4RJP4eLsz7+FQunfQc7LA\nyJufbqewSBZQFOJGIolDNFkbLzfmRg/Et7OBM+fKefPTTE6fK3N2WEIIB3FK4igsLGTUqFHk5OSQ\nm5tLVFQU0dHRLFq0CKu1eq7A6tWrGT9+PJGRkaxfvx6AiooKZsyYQXR0NE899RRnz551RvgCMHjq\nmBM1gIDu3hQWm3jz0+2cKDA6OywhhAM4fOZ4ZWUlCxcuxN3dHYAlS5Ywc+ZMhg4dysKFC1m3bh0D\nBgwgKSmJNWvWYDKZiI6O5vbbbyclJYWgoCBmzJjB2rVrWb58OQsWLHD0LogLPN1dmT1pAMu+2M3+\no+dZ+ul2+nTT8FvBIXSuGly1GlxdNLi6aC/8rv7RuWhwcal+vaRcJhUK0dooqqo69MYMf/7znxk1\nahSJiYm88sorPPbYY6SlpaEoCj/88AObN29mxIgRbNy4kddeew2AZ599lqeffprExESmTp3KgAED\nKCkpYfLkyaxdu7be7W3dmoHJJOfg7anSovL1NiOHz1zd/I72Bg2BXVy5qasrndtoZXl3IVqAUaNG\n1fmaQ3scX375JT4+PoSHh5OYmAiAqqq2Lwq9Xk9JSQmlpaUYDAZbO71eT2lpaa3ymroNCQgIvqaY\ns7K2Exwcarf6jmpj7230Dbby877TZOccxqd9V8xVViqrrFRarFRWWqp/XyizvVZlJf9cKYUlVgpL\nTGw9YMLH242BgR0JDepIUM82aDWXn029Hj4vievGjutq2jgqrsZwaOJYs2YNiqKQnp7Ovn37iI2N\nrTVOYTQa8fb2xsvLC6PRWKvcYDDUKq+pK1oGF62G2/p1pa3mFMHBfo1ut+fXTDR6P7Zn57P9QD5n\ni02syzzOuszjeHm40v+m9oQGdaRvbx90rlo77oEQorEcmjg+/fRT2+OYmBheeeUV3n77bTIyMhg6\ndChpaWnoHmOFAAAgAElEQVQMGzaMkJAQ3n33XUwmE2azmZycHIKCgggNDWXjxo2EhISQlpZGWFiY\nI8MXdqDVKAT7+dDXz4eHfx/E4ZPFbD+Qz/bsAk6fLWPznjw278lD56rhVr/qJOJukdveCuFMTl9W\nPTY2lri4OBISEvD392fs2LFotVpiYmKIjo5GVVVmzZqFm5sbUVFRxMbGEhUVhaurK/Hx8c4OXzQj\njaIQ0L0NAd3bMGFUACcLy6p7Itn55OaVkJmdT2Z2Pu30GvoGW6QHIoSTOC1xJCUl2R6vWrXqstcj\nIyOJjIysVebh4cGyZcvsHptwPkVR6N5BT/cOeu67rTeFRRVsP5DPf7Ydo6Cogo07T3LX4J7ODlOI\nG5JMABStQvs27tw1qCdRvwsE4F8ZuVRWyRLvQjiDJA7RqgwI7EAnby1FpWY27jzp7HCEuCFJ4hCt\niqIoDL/ZDYB/bZVehxDOIIlDtDo3dXGlR0cvzpeaSdt1ytnhCHHDkcQhWh1FUbj/9t5ATa9Dli0R\nwpEkcYhWKfTmjnTvqOdciYlNe6TXIYQjSeIQrZJGUbj/9uoZ6v9KP0KVRXodQjiKJA7RaoXd3JFu\nHfQUFkuvQwhHksQhWi2NonDfbb0BWLslV3odQjiIJA7Rqg3u04mu7T0pLK5gy695zg5HiBuCJA7R\nqmk0/+11fLNFxjqEcARJHKLVG3JLZ7r4eFJQVEG69DqEsDtJHKLVq9XrSD+CxSq9DiHsSRKHuC4M\nCe5E53Ye5J+vYOve084OR4jrmiQOcV3QajTce6HX8c8t0usQwp4kcYjrxrC+nenUzoMz58rJyJJe\nhxD2IolDXDe0Gg33Du8NwD+35GK1yi1mhbAHh94B0GKxsGDBAg4fPoyiKLz66qu4ubkxb948FEUh\nMDCQRYsWodFoWL16Nampqbi4uDBt2jTGjBlDRUUFc+bMobCwEL1ez9KlS/Hx8XHkLogWbni/zvxz\ny2FOny0jY99phvft4uyQhLjuOLTHsX79egBSU1OZOXMm77zzDkuWLGHmzJkkJyejqirr1q0jPz+f\npKQkUlNTWblyJQkJCZjNZlJSUggKCiI5OZmIiAiWL1/uyPBFK1Cr17H5iPQ6hLADhyaOO++8k8WL\nFwNw8uRJvL292bt3L0OGDAFg5MiRbNmyhd27dzNw4EB0Oh0Gg4FevXqxf/9+MjMzCQ8Pt9VNT093\nZPiilRjerwsd2riTd7aMn/fLWIcQzU1RVdXhh2SxsbH85z//YdmyZcybN49NmzYBkJ6ezpo1awgP\nDyc7O5s5c+YAMHfuXCIiIkhMTCQuLo6AgACsViujR48mLS2t3m1t3ZqByVRh930SLcvuXBP/3lWO\nj5eGx8cYUBTF2SEJ0aqMGjWqztccOsZRY+nSpbz44otERkZiMpls5UajEW9vb7y8vDAajbXKDQZD\nrfKaug0JCAi+plizsrYTHBxqt/qOanOjxRV0s5XMw+kUFpswanrgpZ5oEXFdaxuJ6/qI62raOCqu\nxnDoqaqvvvqKDz/8EAAPDw8URaFfv35kZGQAkJaWxqBBgwgJCSEzMxOTyURJSQk5OTkEBQURGhrK\nxo0bbXXDwsIcGb5oRVy0Gu65aKzDCR1rIa5bDu1x/P73v2f+/Pk8/PDDVFVV8dJLLxEQEEBcXBwJ\nCQn4+/szduxYtFotMTExREdHo6oqs2bNws3NjaioKGJjY4mKisLV1ZX4+HhHhi9amREhXfkm/Qgn\nCoxszHJD9SykW3s97QxucupKiGvg0MTh6enJe++9d1n5qlWrLiuLjIwkMjKyVpmHhwfLli2zW3zi\n+uKirb7C6m/f/8YvOSZ+ydkFgLtOS7cOerq111f/7uBJt/Z6fNq4o5GEIkSDnDLGIYSjjBrQDXed\nlozdBym3enKywEhpeSWHThZz6GRxrbo6Vw1d21cnFE1VBcdKj+LqosFVq6n+7aLB5aLHtZ5rNRhN\nVkrLK9EoClqNgkZTvQCjRlGkhyOuK5I4xHVNURSG9e2Ct3LSNkhYXGbmVIGRkwVGThaUcbKw+nGR\n0UxuXgm5eSXVjX872PQNfv9THXFwIZkotsSi1Sj46FXCK44zILAj7QxuV7ubQjiUJA5xw/H21OHd\nS8fNvdrVKi8tr+TUhSSyP+cI3m06UWmxUlVlpdJipbKq5sdClUWtfmypfl5ZZcVkrkSj0WKxqlhV\nFatVxWoFq6qiqlBlUcFSe5C+uAyO/DubpH9n07uLgYFBHRkY2IHuHfTSSxEtliQOIS7w8nAlsEdb\nAnu0paPuNMHBgU1qX9elj6pak0ioTiaqisWqYq608MOWHZw26tl7+CxH8ko4klfC39MO0bGtOwMD\nq5NIYI+2aDSSRETLIYlDCDtTFAWtoqC99OJ3D1f69XIjMjgEU6WFrCNn2ZFdwM6DBeSfr+Df247x\n723H8PJwpf9N7RkY2BFtlVxWLJxPEocQLYCbq/ZCD6MjVqvKwRNF7DiQz47sAs6cL2fznjw276m+\nLa72u/UoSvXgu6IoaBRQUFCUi54r/31utVTitSUDnasGnYsWnasWnYum+rmr9kJZ9WM3l+rfJecr\nad+1jA5t3NFqZBFtUZskDiFaGI1GIahnW4J6tiVyzE2cLDCy40ABOw4UcPhUMRarCqhgafx7FpUZ\nG650ib9nbMVFq9CpnSddfTzp0t6TLhd+d/XxxNPdtcnvKa4PkjiEaMEURaF7Ry+6d/Ti3tt68+uv\nmdzcZyDqhQH3moF3lUueX/R73/49+Pa+BVOVBXNl9WC+udKKqdKCucqKudJS/VNVXWYyW8g9mU+J\nScu5EtOFq88uTzzeel11IvHxRFNVgdW9kO4dvWjrpZOB/eucJA4hWhGNRsHVpWmnjtp5aenRyatJ\nbbKyygkODqXcVMXpc2XkFZaRd7aMUxd+550to9hopthoJvvYeQA27K2eYOnh5kL3jnq6d7jop6MX\n3npdk2IQLZckDiFEnTzcXOjdxZveXWovKGpVVc4WV9iSSdbBXMotnpzIL8VYUcXB40UcPF5Uq43B\n0/VCIvGiW0c9puJKvPNLMXjq8PJwkbGUVkQShxCiyTSKQoc2HnRo40E/v/Z098wnODgUVVUpMpo5\nUWDkRL6RkwWlnMg3cqLASElZJfuPnmf/0fO291md/jMACuDp7oK3XofBwxWDpw6D7XH1c29PV/KL\nLRQZzZJonEwShxCi2SiKQlsvN9p6udG3939v66yqKmeLTZwoKLUllaMn87EobpSUVWIsr8RYUYWx\noopTDWzjkw2bUAC9hyve+uqE4nUhsXhfSDjeF5JNYYmFgvPluFxYHkarUXDRanDRyjIw10IShxDC\n7hRFoX0bd9q3cSckoANQe8KkxWrFWF5FSZmZ4rJKSsrMlFzhd8H5YkxVWozllZRe+DnZ0MbXX/lO\noRcnEa1Wg+uF39YqEz47tqN3d8HT3QW9u+slv13wdHet9ftGc+PtsRCixdFqNNW9B72O7vXUq0k2\nFquV0vIqSoxmW7IpLrvw2PjfRFN4vgSNVkeVtXrpmCqrisVipcpSPXvfYrVgqrx8OwUl5y8vrIe7\nq0KH9AzaGdxpZ3DDx+BGO4Mb7bzdaGdwx8fghofb9fN1e/3siRDihqHVaGij19GmgSu16lsGxmJV\nqapJIhd+V1ms7PvtV7p0C8RYUX36rKyiCmNFpe13dVnt1yoqVY7nGzmeX/d8GXed1pZU2hrcKCst\n49fTB9HZVlvW2h7rXLW2FZh1LtUTN8+VWig3VeGu0zr9NJskDiHEDUdRFFy01aeqLlXYxoU+vu2u\n0OrKrKpK5s5MOnW9mbMlFZwrMXGuxMTZYhPnLnpeYbZwqrD6KjSbw0ebFPfKH9PQuWpoq3fD26s6\ncdY8bqvX0cZLRxu9G228dBg87TdBUxKHEEJcA42ioHfT4NvFgG8XwxXrqKpKmamKc8UmzpaYOF9q\n4uixXHw6dMNcaaleZbnSivnC6svmqv+uxmyuslBZaaWotIxyM5grrZw5X86Z8+X1xqUA/XvrCA5u\n/n2WxCGEEHamKAp6d1f07q62yZhZrnkEB/s2+j2ysrZzyy0DqTBXX5JcVGq68Ntc+/mFxyVllZSb\n7bMopkMTR2VlJS+99BInTpzAbDYzbdo0brrpJubNm4eiKAQGBrJo0SI0Gg2rV68mNTUVFxcXpk2b\nxpgxY6ioqGDOnDkUFhai1+tZunQpPj4+DW9YCCGuA4qi4OHmgoebC118POuta7Wq7N+/wy5xOHQG\nzddff03btm1JTk7mf//3f1m8eDFLlixh5syZJCcno6oq69atIz8/n6SkJFJTU1m5ciUJCQmYzWZS\nUlIICgoiOTmZiIgIli9f7sjwhRCi1bDnPVwc2uMYN24cY8eOBarP+Wm1Wvbu3cuQIUMAGDlyJJs3\nb0aj0TBw4EB0Oh06nY5evXqxf/9+MjMzmTp1qq2uJA4hhHA8RVVVh98ZprS0lGnTphEZGcnSpUvZ\ntGkTAOnp6axZs4bw8HCys7OZM2cOAHPnziUiIoLExETi4uIICAjAarUyevRo0tLS6t3W1q0ZmEwV\ndt8nIYS4nowaNarO1xw+OH7q1CmeffZZoqOjue+++3j77bdtrxmNRry9vfHy8sJoNNYqNxgMtcpr\n6jYkIODaLimo6zrw5qrvqDYSl8QlcbXuNo6KqzEcOsZRUFDAE088wZw5c5gwYQIAwcHBZGRkAJCW\nlsagQYMICQkhMzMTk8lESUkJOTk5BAUFERoaysaNG211w8LCHBm+EEIIHNzjWLFiBcXFxSxfvtw2\nPvHyyy/z5z//mYSEBPz9/Rk7dixarZaYmBiio6NRVZVZs2bh5uZGVFQUsbGxREVF4erqSnx8vCPD\nF0IIgYMTx4IFC1iwYMFl5atWrbqsLDIyksjIyFplHh4eLFu2zG7xCSGEaJgsaC+EEKJJnHJVlRBC\niNZLehxCCCGaRBKHEEKIJpHEIYQQokkkcQghhGgSSRxCCCGaRBKHEEKIJpHEIYQQokkkcQjhZGaz\n2W7vXVFRYdf3BygsLLTL+5aWll51W6vVyunTp7FarU1qd/bsWew5te1q39tsNlNR0XJW+ZbEcYHZ\nbK7zpzEOHDjAt99+y759++wcaePl5+c7O4R6nT9/3tkhONSPP/7ImDFjuOuuu/jXv/5lK6+5x0xz\nOHjwIM888wzz589ny5Yt3H333dx9992sX7++2bZx+PDhWj/Tpk2zPW5Ot99+O59//nmj67/00ksA\n7Nq1i7FjxzJ9+nTuvfdedu7cWWebNWvW8P7777N3717GjRvH448/zrhx49iyZcs1x1/j6NGjPPnk\nk4wZM4Z+/foRGRnJ7Nmz6/3/efjwYZ577jlmz57Nzp07ue+++7jnnntq/d04k9xz/IJx48ahKMpl\nRwSKorBu3bp62/7tb3/jm2++oX///qxcuZI//OEPPPnkk1ese/LkyTrfp1u3bnW+dscdd6Ao/72j\nl5eXF//4xz/qjeu5557Dx8eHCRMmMGrUKDSauo8TmhrXpfG4uLhQVVWFTqfj22+/rTeun3/+mdde\new2LxcK4cePo1q0bEydOrLdNdnY2r7zyCsXFxdx///0EBgYyZsyYets01b59+/jss88wmUy2siVL\nltTb5quvvuLDDz/EbDajqmq9fy8rVqzgq6++wmq18vzzz2MymXjwwQfrPQr97LPP6nxt0qRJl5Ut\nWrSI559/nhMnTvDcc8/x/fff4+bmxtSpUxv8vH7/+99jsVhsz11cXOjatStz5syhb9++tvLHH38c\nd3d3OnXqhKqqHD58mIULF6IoCn/729/q3UZTPuM+ffqwb98+pkyZwvTp0203fKvL8ePHAXjnnXf4\n6KOP6N27N6dPn2b27NlXXA8PIDk5maSkJKZNm8YHH3yAn58fp0+f5plnnuG2226rd3urV6/mk08+\noaKiot5/+1dffZUFCxbg5+fHzp07WbduHWPHjuXll18mMTHxiu8dFxfHM888Q0lJCU8//TRff/01\nBoOBxx9/nLvvvrveuKD63kZHjx6lf//++Pn54ebm1mCbppDEccGPP/541W3Xrl1LcnIyLi4uVFZW\nMnny5DoTx6xZs4Dqo22j0UhgYCAHDx6kQ4cO/P3vf69zG9999x1Q3dX99ddf+f777xuMKyUlhYMH\nD7JmzRo++OADhg8fzoQJE+jZs+c1x/Xdd9+hqiqvvvoqkydPJiQkhKysLJKTkxuM67333mPVqlXM\nmDGDP/3pT0RFRTWYOF5//XWWLFnCggULmDBhQr1fhDExMVRWVtYqq/mPnZqaWuc25s2bxyOPPEKX\nLl0a3IcaH330EStWrKBr164N1nV1daVNmzYALF++nEcffZSuXbvWSsCXamqv0Wq12r5gMzIyaN++\nPVCdBBoybNgwxo0bx6BBg9ixYweff/45Dz30EH/+859JSUmx1VuzZg2LFi0iKiqK22+/nZiYGJKS\nkhoVX1M+Yzc3NxYuXMiePXtITExk8eLFDBs2jJ49ezJlypQ622m1Wnr37g1A586d6z1d5erqiqen\nJ3q93vb/onPnzvX+m9RITU0lMTGRjh071luvtLQUPz8/AAYMGMDbb7/N7NmzKS4urrNNVVUVt912\nG6qqkpCQQOfOnYHG/TsmJCSQl5dHTk4OOp2OxMREEhISGmzXFJI4Lpg0aVKdfyz1fdlA9ZdSzT+o\nq6srrq6uddatOYJ89tlnWbp0KV5eXpSVlfHCCy/Uuw2dTmd7HBYW1ug/hM6dO9OzZ0/27t1LdnY2\nr7/+OjfddBMvvvjiNcVVE8+xY8cICQkBqu+t0pjTFYqi0LZtWxRFwc3NDb1e36h98fX1RVEUfHx8\n6m3z4osvsmDBAv7617+i1Wob9d4AHTp0aDCBXapnz574+vo2qm737t1ZsmQJzz//PF5eXrz//vs8\n+eST9X6BTJ8+3fZ4y5YtHDt2zHYUeSV+fn68/PLLLF68mDfffBOAxMREOnTo0GB8hw8fth1lDx06\nlOXLlzN8+HDef//9WvXat2/Pu+++y9KlS9mzZ0+D73uxpnzGNT2xW2+9lb/85S+UlJSwbdu2Ov/G\nSktLGT9+PGVlZXz++efcf//9vPnmmw325KdNm0ZQUBBPP/004eHh/PTTTwwbNqzB+Nq1a0f37t0b\nrNejRw8WLlzIyJEj2bBhA/369WPDhg14eHjU2aZ79+7MmjULi8WCXq/nnXfewcvLq8EkBZCZmcmn\nn35KTEwMDz74YK2k31wkcVxwLRk5NDSU5557jrCwMDIzMxk4cGCDbfLy8vDy8gLA09OzwSPL+Ph4\nW2I7c+ZMvaedajz//PMcOHCA+++/n7ffftt21DJ+/Phmi8tgMPDuu+8SEhLCjh07GvWH7evrS3x8\nPOfOnSMxMbHe/9g12rRpQ2pqKuXl5axdu7beuz/279+fBx54gN9++4277rqrwfeu0b17dxITE7nl\nlltsn/WIESPqbePu7s7UqVNrtakr2b7xxht8/fXXtnpdu3blb3/7Gx9++GGDsTX2KPLPf/4zP/74\nY62/j86dOxMTE9PgNnQ6HSkpKQwcOJAdO3ag0+n49ddfa52+quHi4sLLL7/Ml19+2aQB36Z8xpf+\nnRoMBu6444463/vLL7/EbDazf/9+3N3dURSFoKAg203jruSPf/wjP//8M5s2baJbt24UFhYSExPD\n6NGj62xT87mbzWaefPJJgoOD6/23X7JkCZ9//jmbN28mJCSEhx56iD179tT7nbN06VI2btxI7969\n0ev1fPzxx7i7u/PGG2/U2aaGxWLBZDKhKAoWi6VR3xVNJavjXiI3N5fvvvvOdqrjzJkzvPbaaw22\n27BhAzk5OQQEBNT7R1fjnXfeITMzk379+rF7927Cw8OZNm1anfUvPl3k5uZGeHg4BoOh3m1s3ryZ\n22+//bJyk8lU5znPi+PatWsXI0eOrDeusrIyUlNTOXLkCDfddBOTJ0+u1Tu6kqeffprQ0FDy8vII\nCAggMjKywTalpaWsWLGC7OxsAgICePrpp2nbtm29bZpq/vz5l5U1NMZxpdN4Dz74YLPFVOPhhx+2\nHUUmJSURGRnJ6tWrm3Ub586dY8WKFbY7bj711FPs3r2bHj16EBAQ0CzbuJrPuKWp65SyoihEREQ4\nOJrLffvtt7z//vucPXuWrl278thjj3H//fc36zYkcVxiwoQJ3HXXXWRkZNCpUyfKysrqvHmUxWLB\nYrHwwgsv8M4776CqKlarlT/+8Y8NDhIeOnSIsrIy2xdunz596q1fVVXFZ599xsGDB+nduzdRUVEN\nftmuX7/edpReo6G4AH799Vdyc3MJCAioM649e/Zw6623smnTpstea+goPScnhzVr1rB582ZGjBjB\nxIkTbeek63PpqZrmHvCD6kH4gwcP4ufnxy233NJg/aqqKvbs2UNVVRWqqnLmzBnuvffeZo9r8uTJ\nfPLJJzz11FP83//9Hw8//HCDp1CvRmFhYa2B68b0BpuqqZ9xS/Xaa6+xcOFC2/O5c+fy1ltvOTGi\n/yoqKiI3N5cePXrg4+PT7O8vp6ou4enpydNPP82RI0dYsmQJ0dHRddZds2YNK1asoKCggHHjxqGq\nKlqttlH3Qn/55ZdJSUmhX79+jYpr4cKFeHt7c/vtt/Pzzz+zYMGCBv9Ily1bxvz58xt1frtGXl4e\niYmJtv/Y8+fPp0ePHpfVS09P59Zbb2Xt2rWXvdZQ4ggICGDu3LmcPXuW119/nXvvvZfBgwfz3HPP\n1XmazxEDfklJSXzzzTeEhITw//7f/6v36rga06dPp7KykjNnzmCxWOjUqZNdEsejjz7K+PHjOXv2\nLBMnTuSxxx5r9m288sorpKWl2a6WauhigqtxNZ9xS/Ppp5/ywQcfUFRUxL///W9beXP1yq7WCy+8\nUOc4bbPfZlsVtUyZMkU9c+aMOmPGDNVoNKoPPPBAg20+//zzJm/niSeeUF9//XU1OTlZTU1NVVNT\nU+utHx0dXev5pEmTGtzGo48+2uS4nnzySfWHH35Qi4qK1P/85z/qlClTmtT+9OnTDdbZsGGD+vzz\nz6v33nuv+tFHH6kFBQVqXl6eet9999XZpmb/H3nkEVVVVXXixIlNiqsxIiMj1crKSlVVVdVsNqvj\nx49vVBtVVdWXXnpJLS8vVydPntzscdU4f/68umvXLrWwsNAu7//ggw+qFovFLu9d42o+45bqgw8+\ncHYItWRkZNT6+fnnn22Pm5v0OC4xffp0/vOf//DAAw9w55138sADDzTY5vbbb+ejjz6q1cW/+GqY\nK6k5sm7srFuTyUR5eTkeHh5UVFRcccCyRs0VUjqdjri4OPr27Ws7ErnStf+Xbud3v/sdAHfeeScf\nf/xxvfXfe+89UlJSqKyspKKigt69e1+xF3Kxr7/+mqioKIYOHVqrfMaMGXW2ccSAn9qEq+NquLu7\nA1BeXm57bA87d+7kyy+/rDX2tnLlymbdhq+vLyaTqd6rfa7V1XzGLdXkyZP55ptvap2mfPrpp50W\nT81l2IWFhXzwwQccOXKEwMBA/vSnPzX7tiRxXGLw4MEMHjwYwPYF2pDnn3+e4cOHN+pa/hrTp09n\nw4YNHDhwAD8/P+68885660+ZMoUHHnjANr+ivi/ZmiuhNm3axDPPPENBQQFArcRWF4vFwm+//cbN\nN9/Mb7/91mD9H3/8kbS0NN544w0ef/xxXn311Qbb1NVtru8KKEecqgkLC2vy1XG///3v+etf/0qf\nPn2YNGmS3b50X3nlFaZOncr3339PUFCQXZYROXXqFGPGjLFdXmyPU1WXfsahoaHN+v6ONH36dPz9\n/cnOzsbNzc2uCbcpZs6cyd13382ECRPIzMxk7ty5jbpyrykkcVzi/fffZ9WqVbUm2lxpAPhier3e\nNoGuseLj48nNzSU0NJSvvvqKzMxMYmNj66x///33M3LkSI4dO0aPHj1o165dnXU7d+7MF198gYeH\nBz/99BNQPTGsqqqK2bNn1xtXXFwcL7/8MmfOnKFz584sXry43vodO3ZEp9NhNBrx9fW9bOJdc/nD\nH/7AbbfdRm5uLj179qx3/69WbGwsGzZs4NChQzz00EOMGjWqwTZdunRh06ZNVFZW4u7u3qR5I03R\nrl077r33XjZv3syMGTN45JFHmn0bzX4e/AoeffRR1q1bR1FREXv37q33ir2WTlVVXnvtNebPn8/r\nr79e73ioo0VFRQHVs+9rJg83J0kcl1i/fj0bNmxo0mmHwMBA1q5dW+va9LomaNXYtm2b7Wju0Ucf\nJTIyst76GzZsICUlpVFXSD3wwAPcdtttrFixwtZN1Wg0tlnE9fnjH/9IYWEhPj4+ZGVlER0dTYcO\nHVi0aNEVL+3t0qWLLUnFx8fXO5ntWjRl/69WYWEhmzZt4vDhw+Tn5zNgwADbTO+6vPXWW7z22msN\n1rtWGo2GAwcOUF5ezqFDhygqKmq29/7888+ZOHEiqamptQZXFUVp8gFRQ1588UWmT59OcnIyL7zw\nAm+88UajZ523NFqtFpPJRFlZme0Uakvg7+/P119/zdChQ9m7dy9t27a1TZps6HupsSRxXKJ9+/aN\nmtZ/sX379rF///5aZQ19qVVVVWG1WtFoNFit1gaXOHjvvfcafYWUTqeje/fuDfYWrmTw4MG2LvjR\no0d5//33efbZZ5kzZ84VE8czzzxDWVkZt956Kw888IBdZqlC0/b/al1NFz8wMPCysRp7mDdvHgcO\nHCAmJoYXX3yRhx56qNneu2b5D19fX1uPqaysjDfffLPZE4eiKAwePJgPP/yQe+65p9nnojjSww8/\nzCeffMKIESMYPXp0izntdujQIQ4dOsQXX3xhm5zZ2LXEGksSxwU1l7IVFBTw4IMPEhgYaPsyb6gL\nf+LEiVrPG5qYB3DPPfcQFRVF//792b17d4MLl7Vp06bBRd6aQ15eHv7+/gD06tWLU6dO1fpCudTc\nuYms+dMAAAfhSURBVHNtR5Dx8fEkJCTY5QjSUfvf1C7+7373OyZNmmT7zMA+E9rWrFnDvHnzgOoZ\n0s0pPDzcto358+djtVpZsGBBveNoV6uqqoq3336bsLAwtm7dardTm45QVFTEP/7xD8rLyykvL2fX\nrl3ODgmovuT53LlzttPaMo/DjiZPngxUDxCWlpai1Wr56KOPGrVUQ1MWILx46ZDOnTuzfv16brnl\nFs6ePXvF+tdyhdTV6NixI//zP/9jW3aiQ4cObN68uc6rX2qOIFesWGGXI0hH7r+/vz//+Mc/GDZs\nWKO7+ElJSUydOrVRBwvX4uDBgxQXF9e71Mq1+utf/8ozzzyD2Wzmvffes8u8hCVLlrB582YmTpzI\nDz/8wNKlS5t9G47S2EUOHe3bb7/l3XffJSAggAMHDjB9+vRGXR3aFJI4Lqg5mn3kkUdqnYNNTU1t\n8AqepixAePGRqZ+fX4NLXV/LFVJX46233uKzzz4jLS2NoKAgZsyYQVZWVp37VHMEOWjQILscQTpy\n/w8dOsThw4dZs2aNrayhLn6HDh0atcz1tcrJyWHYsGG0a9fOljgbumijsS4+mPHz8+Onn36yLdnf\n0OKbTdW7d2/bKgGO+NzsqbGLHDraxx9/zJdffoler6e0tJRHH31UEoe9Xc0R9MX/8fLz8+udY9DU\ndYyu5Qqpq+Hm5nbZktX1XZZq7yNIR+7/lU45NnTPE3d390YtdHetmvNGTJe69GDGEacEW7OmLnLo\naIqi2FaP9vLyssvSPJI4LnE1R9AX/8fr06eP7Zxxc7iWK6Qcwd5HkI7c/6u550lz30yqLlu2bLFN\nNFu8eDHPP/889913X7O8tz0WZbye1Zy2bK4rlJpbz549efPNNxk0aBC//PILvXr1avZtyCKHlzhy\n5EitI+hbb731ijc+Ete/mhVpW4KJEycSHx/Pq6++yptvvsnMmTNbTGyiZalZEPXQoUP4///27iYk\nqj2M4/jXF8aRhmisiCypGYbKTdHikBW2cNpMZIWKRSG4GGh6o02LEBMmlBJ0EaZYGC5CiF6MalGL\nqFCpKBchuCikZApDkjFrjKMy0110G/K+dOdcxhym32c1A+ec/38GDg//t+dxu6msrEz6CX2NOP4i\nneZgxRorU46/mt1uj28VX7p0aUIV6uT3lJGRgc1mY+HChXg8Hj5//pz0nVWp82aIzDO3243L5cLl\nclFcXExbW9t8dynO4XDg9/vx+Xx0dXWlzFSlpJ66ujpGRkZ4/Pgxk5OTP81I8X9pxCHyp1Sc6/9+\nqtvj8RAKhXj9+jXDw8MJ1S+R31MoFKKhoYH+/n5KSkq4ePFi0ttQ4BBJYd9Pdbvd7vgmjFRdlJXU\nEI1GCYfDZGRkEIlEVDpWRER+7vnz59TU1PD+/XtWr15NTU0NW7ZsSWobWuMQEUkj4+PjRKNRVq1a\nhWmaxGKxpLehqSoRkTTS1tbGtWvXWLx4MWNjYwQCgf8s52yVRhwiImlk0aJF8V13S5YsweFwJL0N\nrXGIiKSRI0eOYJomhmEwODjIhw8f4mlkkpUSRVNVIiJp5Mcy1MuWLZuTNjTiEBERS7TGISIilihw\niIiIJQocIhbcu3ePsrIydu3aRWlpKR0dHXPWVnd3d7xcrEgq0eK4SIJGR0dpbGyku7sbp9PJ5OQk\nVVVVuFwuvF7vfHdP5JdR4BBJ0Pj4ODMzM5imCcCCBQs4e/YsOTk53L17l87OTkzTZGpqivr6egzD\noKqqisLCQp48eYJpmtTW1nL58mWGhoaorq6murqalpYWhoeHCYVCfPz4kb179+L3+2e1PTAwwJkz\nZzBNE6fTSTAYpKCggM7OTm7evElmZibr16/n9OnT8/HXyG9GgUMkQevWrcPr9bJ9+3YKCwvZtGkT\npaWlFBQUUFdXR3t7O3l5eVy/fp1Lly5hGEb83jt37nD+/Hnq6+u5ffs24XCYPXv2xOvZv3r1iitX\nrhCLxSgrK2Pz5s3xe6enp6mtraW9vZ38/Hx6e3s5deoUHR0dXLhwgd7eXrKysggGg4yOjs7ZFkyR\n7xQ4RCwIBoMcPnyYvr4++vr6qKyspKmpidbWVh48eMCbN2949uzZrIyk27ZtAyA/P58NGzaQm5vL\nihUr+PTpU/yanTt3xutEl5SU8PTpU5xOJ/CtKuXbt285dOhQ/PpIJEJ2djYbN26koqICr9fLgQMH\nFDTkl1DgEEnQo0eP+PLlCzt27KC8vJzy8nKuXr1KV1cXzc3N7N69G8MwWLt27ayyrj+W7czO/udX\nLisrK/45Fov97fvKlSu5desW8C1t9tjYGPAtL9GLFy/o6enB7/fT1NQUPyUsMle0q0okQXa7nebm\nZt69ewfA169fGRoawmazkZmZSSAQoKioiJ6eHqLRqKVn379/n+npaSYmJnj48OGspHRut5uJiQn6\n+/sBuHHjBidOnCAcDuPz+VizZg3Hjx9n69atvHz5Mnk/WORfaMQhkqCioiKOHj1KIBBgZmYGgOLi\nYlpbWzl58iQ+nw+73Y5hGIyMjFh6dk5ODvv37ycSiXDw4EE8Hg8DAwMA2Gw2zp07R0NDA1NTUzgc\nDhobG8nLy2Pfvn1UVFSQm5vL8uXLU7KKoaQfpRwRmWctLS0AHDt2bJ57IpIYTVWJiIglGnGIiIgl\nGnGIiIglChwiImKJAoeIiFiiwCEiIpYocIiIiCV/AEmGK6gboGBcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12af29c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_word_freq('https://www.gutenberg.org/files/10/10-h/10-h.htm')"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}