NLTK_Basics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1452d50b-525f-4607-90f8-952a1659dde7",
   "metadata": {},
   "source": [
    "# NLTK Basics\n",
    "\n",
    "Date: 2023/11/02, 2023/11/06\n",
    "\n",
    "Reference: https://realpython.com/nltk-nlp-python/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "219e1742-6593-4e18-b031-31a797e9c7fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Introductory Examples for the NLTK Book ***\n",
      "Loading text1, ..., text9 and sent1, ..., sent9\n",
      "Type the name of the text or sentence to view it.\n",
      "Type: 'texts()' or 'sents()' to list the materials.\n",
      "text1: Moby Dick by Herman Melville 1851\n",
      "text2: Sense and Sensibility by Jane Austen 1811\n",
      "text3: The Book of Genesis\n",
      "text4: Inaugural Address Corpus\n",
      "text5: Chat Corpus\n",
      "text6: Monty Python and the Holy Grail\n",
      "text7: Wall Street Journal\n",
      "text8: Personals Corpus\n",
      "text9: The Man Who Was Thursday by G . K . Chesterton 1908\n"
     ]
    }
   ],
   "source": [
    "#pip3 install nltk==3.5\n",
    "import nltk\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "#nltk.download('punkt')\n",
    "#nltk.download(\"stopwords\")\n",
    "#nltk.download('averaged_perceptron_tagger')\n",
    "#nltk.download(\"maxent_ne_chunker\")\n",
    "#nltk.download(\"words\")\n",
    "#nltk.download('tagsets')\n",
    "#nltk.download(\"book\")\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.book import *\n",
    "from nltk import FreqDist\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9de61b55-98d8-4e6a-9b42-a565df14229f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import sent_tokenize, word_tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1e968dfe-2e7b-440a-bfca-b157ee944554",
   "metadata": {},
   "outputs": [],
   "source": [
    "# I told Bing Chat that \"Give me a sentense in 3000 words about Python\". Bing replied with the following:\n",
    "\n",
    "example_string = '''\\\n",
    "Python is a high-level, interpreted programming language that was first released in 1991 by Guido van Rossum, and has since become one of the most popular programming languages in the world, with a large and active community of developers who use it for a wide range of applications, from web development and data analysis to machine learning and scientific computing.\n",
    "\n",
    "Python is known for its simplicity, readability, and ease of use, which makes it an ideal language for beginners who are just starting to learn programming, as well as experienced developers who need to quickly prototype and test their ideas.\n",
    "\n",
    "One of the key features of Python is its extensive standard library, which provides a wide range of modules and functions that can be used to perform common tasks, such as working with files, networking, and parsing data.\n",
    "\n",
    "Python is also highly extensible, with a large number of third-party libraries and frameworks available that can be used to add additional functionality to the language, such as web frameworks like Django and Flask, data analysis libraries like NumPy and Pandas, and machine learning libraries like TensorFlow and PyTorch.\n",
    "\n",
    "Python is an interpreted language, which means that code written in Python is executed directly by the interpreter, without the need for compilation, which makes it easy to write and test code quickly.\n",
    "\n",
    "Python is also a dynamically typed language, which means that variables do not need to be declared before they are used, and their type is determined at runtime, which can make it easier to write code quickly, but can also lead to errors if variables are not used correctly.\n",
    "\n",
    "Despite its simplicity, Python is a powerful language that can be used to build complex applications, and is widely used in industry, academia, and research.\n",
    "\n",
    "Python has a large and active community of developers who contribute to the language by creating new libraries and frameworks, fixing bugs, and improving the performance of the interpreter.\n",
    "\n",
    "Python is also an open-source language, which means that the source code is freely available and can be modified and distributed by anyone, making it an ideal language for collaborative development and open science.\n",
    "\n",
    "In summary, Python is a high-level, interpreted programming language that is known for its simplicity, readability, and ease of use, and is widely used for a wide range of applications, from web development and data analysis to machine learning and scientific computing. Python has a large and active community of developers who contribute to the language, and is an open-source language that is ideal for collaborative development and open science.\n",
    "'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f2c733a2-67f2-4077-89f2-c9cb229b378a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Python is a high-level, interpreted programming language that was first released in 1991 by Guido van Rossum, and has since become one of the most popular programming languages in the world, with a large and active community of developers who use it for a wide range of applications, from web development and data analysis to machine learning and scientific computing.\\n\\nPython is known for its simplicity, readability, and ease of use, which makes it an ideal language for beginners who are just starting to learn programming, as well as experienced developers who need to quickly prototype and test their ideas.\\n\\nOne of the key features of Python is its extensive standard library, which provides a wide range of modules and functions that can be used to perform common tasks, such as working with files, networking, and parsing data.\\n\\nPython is also highly extensible, with a large number of third-party libraries and frameworks available that can be used to add additional functionality to the language, such as web frameworks like Django and Flask, data analysis libraries like NumPy and Pandas, and machine learning libraries like TensorFlow and PyTorch.\\n\\nPython is an interpreted language, which means that code written in Python is executed directly by the interpreter, without the need for compilation, which makes it easy to write and test code quickly.\\n\\nPython is also a dynamically typed language, which means that variables do not need to be declared before they are used, and their type is determined at runtime, which can make it easier to write code quickly, but can also lead to errors if variables are not used correctly.\\n\\nDespite its simplicity, Python is a powerful language that can be used to build complex applications, and is widely used in industry, academia, and research.\\n\\nPython has a large and active community of developers who contribute to the language by creating new libraries and frameworks, fixing bugs, and improving the performance of the interpreter.\\n\\nPython is also an open-source language, which means that the source code is freely available and can be modified and distributed by anyone, making it an ideal language for collaborative development and open science.\\n\\nIn summary, Python is a high-level, interpreted programming language that is known for its simplicity, readability, and ease of use, and is widely used for a wide range of applications, from web development and data analysis to machine learning and scientific computing. Python has a large and active community of developers who contribute to the language, and is an open-source language that is ideal for collaborative development and open science.\\n'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_string"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7163096c-01b6-42d8-be18-892105662032",
   "metadata": {},
   "source": [
    "## Tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "32033689-fefe-4b5e-bdee-fe7391a6ff6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Python is a high-level, interpreted programming language that was first released in 1991 by Guido van Rossum, and has since become one of the most popular programming languages in the world, with a large and active community of developers who use it for a wide range of applications, from web development and data analysis to machine learning and scientific computing.',\n",
       " 'Python is known for its simplicity, readability, and ease of use, which makes it an ideal language for beginners who are just starting to learn programming, as well as experienced developers who need to quickly prototype and test their ideas.',\n",
       " 'One of the key features of Python is its extensive standard library, which provides a wide range of modules and functions that can be used to perform common tasks, such as working with files, networking, and parsing data.',\n",
       " 'Python is also highly extensible, with a large number of third-party libraries and frameworks available that can be used to add additional functionality to the language, such as web frameworks like Django and Flask, data analysis libraries like NumPy and Pandas, and machine learning libraries like TensorFlow and PyTorch.',\n",
       " 'Python is an interpreted language, which means that code written in Python is executed directly by the interpreter, without the need for compilation, which makes it easy to write and test code quickly.',\n",
       " 'Python is also a dynamically typed language, which means that variables do not need to be declared before they are used, and their type is determined at runtime, which can make it easier to write code quickly, but can also lead to errors if variables are not used correctly.',\n",
       " 'Despite its simplicity, Python is a powerful language that can be used to build complex applications, and is widely used in industry, academia, and research.',\n",
       " 'Python has a large and active community of developers who contribute to the language by creating new libraries and frameworks, fixing bugs, and improving the performance of the interpreter.',\n",
       " 'Python is also an open-source language, which means that the source code is freely available and can be modified and distributed by anyone, making it an ideal language for collaborative development and open science.',\n",
       " 'In summary, Python is a high-level, interpreted programming language that is known for its simplicity, readability, and ease of use, and is widely used for a wide range of applications, from web development and data analysis to machine learning and scientific computing.',\n",
       " 'Python has a large and active community of developers who contribute to the language, and is an open-source language that is ideal for collaborative development and open science.']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentences = sent_tokenize(example_string)\n",
    "sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fc7eef97-2ed4-4aae-a3fd-7e0c51cb7967",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. Python is a high-level, interpreted programming language that was first released in 1991 by Guido van Rossum, and has since become one of the most popular programming languages in the world, with a large and active community of developers who use it for a wide range of applications, from web development and data analysis to machine learning and scientific computing.\n",
      "2. Python is known for its simplicity, readability, and ease of use, which makes it an ideal language for beginners who are just starting to learn programming, as well as experienced developers who need to quickly prototype and test their ideas.\n",
      "3. One of the key features of Python is its extensive standard library, which provides a wide range of modules and functions that can be used to perform common tasks, such as working with files, networking, and parsing data.\n",
      "4. Python is also highly extensible, with a large number of third-party libraries and frameworks available that can be used to add additional functionality to the language, such as web frameworks like Django and Flask, data analysis libraries like NumPy and Pandas, and machine learning libraries like TensorFlow and PyTorch.\n",
      "5. Python is an interpreted language, which means that code written in Python is executed directly by the interpreter, without the need for compilation, which makes it easy to write and test code quickly.\n",
      "6. Python is also a dynamically typed language, which means that variables do not need to be declared before they are used, and their type is determined at runtime, which can make it easier to write code quickly, but can also lead to errors if variables are not used correctly.\n",
      "7. Despite its simplicity, Python is a powerful language that can be used to build complex applications, and is widely used in industry, academia, and research.\n",
      "8. Python has a large and active community of developers who contribute to the language by creating new libraries and frameworks, fixing bugs, and improving the performance of the interpreter.\n",
      "9. Python is also an open-source language, which means that the source code is freely available and can be modified and distributed by anyone, making it an ideal language for collaborative development and open science.\n",
      "10. In summary, Python is a high-level, interpreted programming language that is known for its simplicity, readability, and ease of use, and is widely used for a wide range of applications, from web development and data analysis to machine learning and scientific computing.\n",
      "11. Python has a large and active community of developers who contribute to the language, and is an open-source language that is ideal for collaborative development and open science.\n"
     ]
    }
   ],
   "source": [
    "i = 1\n",
    "for s in sentences:\n",
    "    print('{}. {}'.format(i, s))\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94500f7e-b755-42ee-adbb-8458117a7fda",
   "metadata": {},
   "source": [
    "## Filtering Stop Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a9d884a2-7c1e-421b-ae18-dd24591cefec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Python',\n",
       " 'is',\n",
       " 'a',\n",
       " 'high-level',\n",
       " ',',\n",
       " 'interpreted',\n",
       " 'programming',\n",
       " 'language',\n",
       " 'that',\n",
       " 'was',\n",
       " 'first',\n",
       " 'released',\n",
       " 'in',\n",
       " '1991',\n",
       " 'by',\n",
       " 'Guido',\n",
       " 'van',\n",
       " 'Rossum',\n",
       " ',',\n",
       " 'and',\n",
       " 'has',\n",
       " 'since',\n",
       " 'become',\n",
       " 'one',\n",
       " 'of',\n",
       " 'the',\n",
       " 'most',\n",
       " 'popular',\n",
       " 'programming',\n",
       " 'languages',\n",
       " 'in',\n",
       " 'the',\n",
       " 'world',\n",
       " ',',\n",
       " 'with',\n",
       " 'a',\n",
       " 'large',\n",
       " 'and',\n",
       " 'active',\n",
       " 'community',\n",
       " 'of',\n",
       " 'developers',\n",
       " 'who',\n",
       " 'use',\n",
       " 'it',\n",
       " 'for',\n",
       " 'a',\n",
       " 'wide',\n",
       " 'range',\n",
       " 'of',\n",
       " 'applications',\n",
       " ',',\n",
       " 'from',\n",
       " 'web',\n",
       " 'development',\n",
       " 'and',\n",
       " 'data',\n",
       " 'analysis',\n",
       " 'to',\n",
       " 'machine',\n",
       " 'learning',\n",
       " 'and',\n",
       " 'scientific',\n",
       " 'computing',\n",
       " '.',\n",
       " 'Python',\n",
       " 'is',\n",
       " 'known',\n",
       " 'for',\n",
       " 'its',\n",
       " 'simplicity',\n",
       " ',',\n",
       " 'readability',\n",
       " ',',\n",
       " 'and',\n",
       " 'ease',\n",
       " 'of',\n",
       " 'use',\n",
       " ',',\n",
       " 'which',\n",
       " 'makes',\n",
       " 'it',\n",
       " 'an',\n",
       " 'ideal',\n",
       " 'language',\n",
       " 'for',\n",
       " 'beginners',\n",
       " 'who',\n",
       " 'are',\n",
       " 'just',\n",
       " 'starting',\n",
       " 'to',\n",
       " 'learn',\n",
       " 'programming',\n",
       " ',',\n",
       " 'as',\n",
       " 'well',\n",
       " 'as',\n",
       " 'experienced',\n",
       " 'developers',\n",
       " 'who',\n",
       " 'need',\n",
       " 'to',\n",
       " 'quickly',\n",
       " 'prototype',\n",
       " 'and',\n",
       " 'test',\n",
       " 'their',\n",
       " 'ideas',\n",
       " '.',\n",
       " 'One',\n",
       " 'of',\n",
       " 'the',\n",
       " 'key',\n",
       " 'features',\n",
       " 'of',\n",
       " 'Python',\n",
       " 'is',\n",
       " 'its',\n",
       " 'extensive',\n",
       " 'standard',\n",
       " 'library',\n",
       " ',',\n",
       " 'which',\n",
       " 'provides',\n",
       " 'a',\n",
       " 'wide',\n",
       " 'range',\n",
       " 'of',\n",
       " 'modules',\n",
       " 'and',\n",
       " 'functions',\n",
       " 'that',\n",
       " 'can',\n",
       " 'be',\n",
       " 'used',\n",
       " 'to',\n",
       " 'perform',\n",
       " 'common',\n",
       " 'tasks',\n",
       " ',',\n",
       " 'such',\n",
       " 'as',\n",
       " 'working',\n",
       " 'with',\n",
       " 'files',\n",
       " ',',\n",
       " 'networking',\n",
       " ',',\n",
       " 'and',\n",
       " 'parsing',\n",
       " 'data',\n",
       " '.',\n",
       " 'Python',\n",
       " 'is',\n",
       " 'also',\n",
       " 'highly',\n",
       " 'extensible',\n",
       " ',',\n",
       " 'with',\n",
       " 'a',\n",
       " 'large',\n",
       " 'number',\n",
       " 'of',\n",
       " 'third-party',\n",
       " 'libraries',\n",
       " 'and',\n",
       " 'frameworks',\n",
       " 'available',\n",
       " 'that',\n",
       " 'can',\n",
       " 'be',\n",
       " 'used',\n",
       " 'to',\n",
       " 'add',\n",
       " 'additional',\n",
       " 'functionality',\n",
       " 'to',\n",
       " 'the',\n",
       " 'language',\n",
       " ',',\n",
       " 'such',\n",
       " 'as',\n",
       " 'web',\n",
       " 'frameworks',\n",
       " 'like',\n",
       " 'Django',\n",
       " 'and',\n",
       " 'Flask',\n",
       " ',',\n",
       " 'data',\n",
       " 'analysis',\n",
       " 'libraries',\n",
       " 'like',\n",
       " 'NumPy',\n",
       " 'and',\n",
       " 'Pandas',\n",
       " ',',\n",
       " 'and',\n",
       " 'machine',\n",
       " 'learning',\n",
       " 'libraries',\n",
       " 'like',\n",
       " 'TensorFlow',\n",
       " 'and',\n",
       " 'PyTorch',\n",
       " '.',\n",
       " 'Python',\n",
       " 'is',\n",
       " 'an',\n",
       " 'interpreted',\n",
       " 'language',\n",
       " ',',\n",
       " 'which',\n",
       " 'means',\n",
       " 'that',\n",
       " 'code',\n",
       " 'written',\n",
       " 'in',\n",
       " 'Python',\n",
       " 'is',\n",
       " 'executed',\n",
       " 'directly',\n",
       " 'by',\n",
       " 'the',\n",
       " 'interpreter',\n",
       " ',',\n",
       " 'without',\n",
       " 'the',\n",
       " 'need',\n",
       " 'for',\n",
       " 'compilation',\n",
       " ',',\n",
       " 'which',\n",
       " 'makes',\n",
       " 'it',\n",
       " 'easy',\n",
       " 'to',\n",
       " 'write',\n",
       " 'and',\n",
       " 'test',\n",
       " 'code',\n",
       " 'quickly',\n",
       " '.',\n",
       " 'Python',\n",
       " 'is',\n",
       " 'also',\n",
       " 'a',\n",
       " 'dynamically',\n",
       " 'typed',\n",
       " 'language',\n",
       " ',',\n",
       " 'which',\n",
       " 'means',\n",
       " 'that',\n",
       " 'variables',\n",
       " 'do',\n",
       " 'not',\n",
       " 'need',\n",
       " 'to',\n",
       " 'be',\n",
       " 'declared',\n",
       " 'before',\n",
       " 'they',\n",
       " 'are',\n",
       " 'used',\n",
       " ',',\n",
       " 'and',\n",
       " 'their',\n",
       " 'type',\n",
       " 'is',\n",
       " 'determined',\n",
       " 'at',\n",
       " 'runtime',\n",
       " ',',\n",
       " 'which',\n",
       " 'can',\n",
       " 'make',\n",
       " 'it',\n",
       " 'easier',\n",
       " 'to',\n",
       " 'write',\n",
       " 'code',\n",
       " 'quickly',\n",
       " ',',\n",
       " 'but',\n",
       " 'can',\n",
       " 'also',\n",
       " 'lead',\n",
       " 'to',\n",
       " 'errors',\n",
       " 'if',\n",
       " 'variables',\n",
       " 'are',\n",
       " 'not',\n",
       " 'used',\n",
       " 'correctly',\n",
       " '.',\n",
       " 'Despite',\n",
       " 'its',\n",
       " 'simplicity',\n",
       " ',',\n",
       " 'Python',\n",
       " 'is',\n",
       " 'a',\n",
       " 'powerful',\n",
       " 'language',\n",
       " 'that',\n",
       " 'can',\n",
       " 'be',\n",
       " 'used',\n",
       " 'to',\n",
       " 'build',\n",
       " 'complex',\n",
       " 'applications',\n",
       " ',',\n",
       " 'and',\n",
       " 'is',\n",
       " 'widely',\n",
       " 'used',\n",
       " 'in',\n",
       " 'industry',\n",
       " ',',\n",
       " 'academia',\n",
       " ',',\n",
       " 'and',\n",
       " 'research',\n",
       " '.',\n",
       " 'Python',\n",
       " 'has',\n",
       " 'a',\n",
       " 'large',\n",
       " 'and',\n",
       " 'active',\n",
       " 'community',\n",
       " 'of',\n",
       " 'developers',\n",
       " 'who',\n",
       " 'contribute',\n",
       " 'to',\n",
       " 'the',\n",
       " 'language',\n",
       " 'by',\n",
       " 'creating',\n",
       " 'new',\n",
       " 'libraries',\n",
       " 'and',\n",
       " 'frameworks',\n",
       " ',',\n",
       " 'fixing',\n",
       " 'bugs',\n",
       " ',',\n",
       " 'and',\n",
       " 'improving',\n",
       " 'the',\n",
       " 'performance',\n",
       " 'of',\n",
       " 'the',\n",
       " 'interpreter',\n",
       " '.',\n",
       " 'Python',\n",
       " 'is',\n",
       " 'also',\n",
       " 'an',\n",
       " 'open-source',\n",
       " 'language',\n",
       " ',',\n",
       " 'which',\n",
       " 'means',\n",
       " 'that',\n",
       " 'the',\n",
       " 'source',\n",
       " 'code',\n",
       " 'is',\n",
       " 'freely',\n",
       " 'available',\n",
       " 'and',\n",
       " 'can',\n",
       " 'be',\n",
       " 'modified',\n",
       " 'and',\n",
       " 'distributed',\n",
       " 'by',\n",
       " 'anyone',\n",
       " ',',\n",
       " 'making',\n",
       " 'it',\n",
       " 'an',\n",
       " 'ideal',\n",
       " 'language',\n",
       " 'for',\n",
       " 'collaborative',\n",
       " 'development',\n",
       " 'and',\n",
       " 'open',\n",
       " 'science',\n",
       " '.',\n",
       " 'In',\n",
       " 'summary',\n",
       " ',',\n",
       " 'Python',\n",
       " 'is',\n",
       " 'a',\n",
       " 'high-level',\n",
       " ',',\n",
       " 'interpreted',\n",
       " 'programming',\n",
       " 'language',\n",
       " 'that',\n",
       " 'is',\n",
       " 'known',\n",
       " 'for',\n",
       " 'its',\n",
       " 'simplicity',\n",
       " ',',\n",
       " 'readability',\n",
       " ',',\n",
       " 'and',\n",
       " 'ease',\n",
       " 'of',\n",
       " 'use',\n",
       " ',',\n",
       " 'and',\n",
       " 'is',\n",
       " 'widely',\n",
       " 'used',\n",
       " 'for',\n",
       " 'a',\n",
       " 'wide',\n",
       " 'range',\n",
       " 'of',\n",
       " 'applications',\n",
       " ',',\n",
       " 'from',\n",
       " 'web',\n",
       " 'development',\n",
       " 'and',\n",
       " 'data',\n",
       " 'analysis',\n",
       " 'to',\n",
       " 'machine',\n",
       " 'learning',\n",
       " 'and',\n",
       " 'scientific',\n",
       " 'computing',\n",
       " '.',\n",
       " 'Python',\n",
       " 'has',\n",
       " 'a',\n",
       " 'large',\n",
       " 'and',\n",
       " 'active',\n",
       " 'community',\n",
       " 'of',\n",
       " 'developers',\n",
       " 'who',\n",
       " 'contribute',\n",
       " 'to',\n",
       " 'the',\n",
       " 'language',\n",
       " ',',\n",
       " 'and',\n",
       " 'is',\n",
       " 'an',\n",
       " 'open-source',\n",
       " 'language',\n",
       " 'that',\n",
       " 'is',\n",
       " 'ideal',\n",
       " 'for',\n",
       " 'collaborative',\n",
       " 'development',\n",
       " 'and',\n",
       " 'open',\n",
       " 'science',\n",
       " '.']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words = word_tokenize(example_string)\n",
    "words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c9ea5050-a73d-42c5-b974-1b5e51d2b582",
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words = set(stopwords.words(\"english\"))\n",
    "filtered_list = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6b3dbb56-d77d-45ad-b981-efc928b09b37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a',\n",
       " 'about',\n",
       " 'above',\n",
       " 'after',\n",
       " 'again',\n",
       " 'against',\n",
       " 'ain',\n",
       " 'all',\n",
       " 'am',\n",
       " 'an',\n",
       " 'and',\n",
       " 'any',\n",
       " 'are',\n",
       " 'aren',\n",
       " \"aren't\",\n",
       " 'as',\n",
       " 'at',\n",
       " 'be',\n",
       " 'because',\n",
       " 'been',\n",
       " 'before',\n",
       " 'being',\n",
       " 'below',\n",
       " 'between',\n",
       " 'both',\n",
       " 'but',\n",
       " 'by',\n",
       " 'can',\n",
       " 'couldn',\n",
       " \"couldn't\",\n",
       " 'd',\n",
       " 'did',\n",
       " 'didn',\n",
       " \"didn't\",\n",
       " 'do',\n",
       " 'does',\n",
       " 'doesn',\n",
       " \"doesn't\",\n",
       " 'doing',\n",
       " 'don',\n",
       " \"don't\",\n",
       " 'down',\n",
       " 'during',\n",
       " 'each',\n",
       " 'few',\n",
       " 'for',\n",
       " 'from',\n",
       " 'further',\n",
       " 'had',\n",
       " 'hadn',\n",
       " \"hadn't\",\n",
       " 'has',\n",
       " 'hasn',\n",
       " \"hasn't\",\n",
       " 'have',\n",
       " 'haven',\n",
       " \"haven't\",\n",
       " 'having',\n",
       " 'he',\n",
       " 'her',\n",
       " 'here',\n",
       " 'hers',\n",
       " 'herself',\n",
       " 'him',\n",
       " 'himself',\n",
       " 'his',\n",
       " 'how',\n",
       " 'i',\n",
       " 'if',\n",
       " 'in',\n",
       " 'into',\n",
       " 'is',\n",
       " 'isn',\n",
       " \"isn't\",\n",
       " 'it',\n",
       " \"it's\",\n",
       " 'its',\n",
       " 'itself',\n",
       " 'just',\n",
       " 'll',\n",
       " 'm',\n",
       " 'ma',\n",
       " 'me',\n",
       " 'mightn',\n",
       " \"mightn't\",\n",
       " 'more',\n",
       " 'most',\n",
       " 'mustn',\n",
       " \"mustn't\",\n",
       " 'my',\n",
       " 'myself',\n",
       " 'needn',\n",
       " \"needn't\",\n",
       " 'no',\n",
       " 'nor',\n",
       " 'not',\n",
       " 'now',\n",
       " 'o',\n",
       " 'of',\n",
       " 'off',\n",
       " 'on',\n",
       " 'once',\n",
       " 'only',\n",
       " 'or',\n",
       " 'other',\n",
       " 'our',\n",
       " 'ours',\n",
       " 'ourselves',\n",
       " 'out',\n",
       " 'over',\n",
       " 'own',\n",
       " 're',\n",
       " 's',\n",
       " 'same',\n",
       " 'shan',\n",
       " \"shan't\",\n",
       " 'she',\n",
       " \"she's\",\n",
       " 'should',\n",
       " \"should've\",\n",
       " 'shouldn',\n",
       " \"shouldn't\",\n",
       " 'so',\n",
       " 'some',\n",
       " 'such',\n",
       " 't',\n",
       " 'than',\n",
       " 'that',\n",
       " \"that'll\",\n",
       " 'the',\n",
       " 'their',\n",
       " 'theirs',\n",
       " 'them',\n",
       " 'themselves',\n",
       " 'then',\n",
       " 'there',\n",
       " 'these',\n",
       " 'they',\n",
       " 'this',\n",
       " 'those',\n",
       " 'through',\n",
       " 'to',\n",
       " 'too',\n",
       " 'under',\n",
       " 'until',\n",
       " 'up',\n",
       " 've',\n",
       " 'very',\n",
       " 'was',\n",
       " 'wasn',\n",
       " \"wasn't\",\n",
       " 'we',\n",
       " 'were',\n",
       " 'weren',\n",
       " \"weren't\",\n",
       " 'what',\n",
       " 'when',\n",
       " 'where',\n",
       " 'which',\n",
       " 'while',\n",
       " 'who',\n",
       " 'whom',\n",
       " 'why',\n",
       " 'will',\n",
       " 'with',\n",
       " 'won',\n",
       " \"won't\",\n",
       " 'wouldn',\n",
       " \"wouldn't\",\n",
       " 'y',\n",
       " 'you',\n",
       " \"you'd\",\n",
       " \"you'll\",\n",
       " \"you're\",\n",
       " \"you've\",\n",
       " 'your',\n",
       " 'yours',\n",
       " 'yourself',\n",
       " 'yourselves'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(stopwords.words(\"english\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "34f7225b-4b57-48ef-a1ea-c5b5c92239ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'word'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Word'.casefold()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5132aa83-7af9-404f-b4f6-cf1eddefd36f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Python',\n",
       " 'high-level',\n",
       " ',',\n",
       " 'interpreted',\n",
       " 'programming',\n",
       " 'language',\n",
       " 'first',\n",
       " 'released',\n",
       " '1991',\n",
       " 'Guido',\n",
       " 'van',\n",
       " 'Rossum',\n",
       " ',',\n",
       " 'since',\n",
       " 'become',\n",
       " 'one',\n",
       " 'popular',\n",
       " 'programming',\n",
       " 'languages',\n",
       " 'world',\n",
       " ',',\n",
       " 'large',\n",
       " 'active',\n",
       " 'community',\n",
       " 'developers',\n",
       " 'use',\n",
       " 'wide',\n",
       " 'range',\n",
       " 'applications',\n",
       " ',',\n",
       " 'web',\n",
       " 'development',\n",
       " 'data',\n",
       " 'analysis',\n",
       " 'machine',\n",
       " 'learning',\n",
       " 'scientific',\n",
       " 'computing',\n",
       " '.',\n",
       " 'Python',\n",
       " 'known',\n",
       " 'simplicity',\n",
       " ',',\n",
       " 'readability',\n",
       " ',',\n",
       " 'ease',\n",
       " 'use',\n",
       " ',',\n",
       " 'makes',\n",
       " 'ideal',\n",
       " 'language',\n",
       " 'beginners',\n",
       " 'starting',\n",
       " 'learn',\n",
       " 'programming',\n",
       " ',',\n",
       " 'well',\n",
       " 'experienced',\n",
       " 'developers',\n",
       " 'need',\n",
       " 'quickly',\n",
       " 'prototype',\n",
       " 'test',\n",
       " 'ideas',\n",
       " '.',\n",
       " 'One',\n",
       " 'key',\n",
       " 'features',\n",
       " 'Python',\n",
       " 'extensive',\n",
       " 'standard',\n",
       " 'library',\n",
       " ',',\n",
       " 'provides',\n",
       " 'wide',\n",
       " 'range',\n",
       " 'modules',\n",
       " 'functions',\n",
       " 'used',\n",
       " 'perform',\n",
       " 'common',\n",
       " 'tasks',\n",
       " ',',\n",
       " 'working',\n",
       " 'files',\n",
       " ',',\n",
       " 'networking',\n",
       " ',',\n",
       " 'parsing',\n",
       " 'data',\n",
       " '.',\n",
       " 'Python',\n",
       " 'also',\n",
       " 'highly',\n",
       " 'extensible',\n",
       " ',',\n",
       " 'large',\n",
       " 'number',\n",
       " 'third-party',\n",
       " 'libraries',\n",
       " 'frameworks',\n",
       " 'available',\n",
       " 'used',\n",
       " 'add',\n",
       " 'additional',\n",
       " 'functionality',\n",
       " 'language',\n",
       " ',',\n",
       " 'web',\n",
       " 'frameworks',\n",
       " 'like',\n",
       " 'Django',\n",
       " 'Flask',\n",
       " ',',\n",
       " 'data',\n",
       " 'analysis',\n",
       " 'libraries',\n",
       " 'like',\n",
       " 'NumPy',\n",
       " 'Pandas',\n",
       " ',',\n",
       " 'machine',\n",
       " 'learning',\n",
       " 'libraries',\n",
       " 'like',\n",
       " 'TensorFlow',\n",
       " 'PyTorch',\n",
       " '.',\n",
       " 'Python',\n",
       " 'interpreted',\n",
       " 'language',\n",
       " ',',\n",
       " 'means',\n",
       " 'code',\n",
       " 'written',\n",
       " 'Python',\n",
       " 'executed',\n",
       " 'directly',\n",
       " 'interpreter',\n",
       " ',',\n",
       " 'without',\n",
       " 'need',\n",
       " 'compilation',\n",
       " ',',\n",
       " 'makes',\n",
       " 'easy',\n",
       " 'write',\n",
       " 'test',\n",
       " 'code',\n",
       " 'quickly',\n",
       " '.',\n",
       " 'Python',\n",
       " 'also',\n",
       " 'dynamically',\n",
       " 'typed',\n",
       " 'language',\n",
       " ',',\n",
       " 'means',\n",
       " 'variables',\n",
       " 'need',\n",
       " 'declared',\n",
       " 'used',\n",
       " ',',\n",
       " 'type',\n",
       " 'determined',\n",
       " 'runtime',\n",
       " ',',\n",
       " 'make',\n",
       " 'easier',\n",
       " 'write',\n",
       " 'code',\n",
       " 'quickly',\n",
       " ',',\n",
       " 'also',\n",
       " 'lead',\n",
       " 'errors',\n",
       " 'variables',\n",
       " 'used',\n",
       " 'correctly',\n",
       " '.',\n",
       " 'Despite',\n",
       " 'simplicity',\n",
       " ',',\n",
       " 'Python',\n",
       " 'powerful',\n",
       " 'language',\n",
       " 'used',\n",
       " 'build',\n",
       " 'complex',\n",
       " 'applications',\n",
       " ',',\n",
       " 'widely',\n",
       " 'used',\n",
       " 'industry',\n",
       " ',',\n",
       " 'academia',\n",
       " ',',\n",
       " 'research',\n",
       " '.',\n",
       " 'Python',\n",
       " 'large',\n",
       " 'active',\n",
       " 'community',\n",
       " 'developers',\n",
       " 'contribute',\n",
       " 'language',\n",
       " 'creating',\n",
       " 'new',\n",
       " 'libraries',\n",
       " 'frameworks',\n",
       " ',',\n",
       " 'fixing',\n",
       " 'bugs',\n",
       " ',',\n",
       " 'improving',\n",
       " 'performance',\n",
       " 'interpreter',\n",
       " '.',\n",
       " 'Python',\n",
       " 'also',\n",
       " 'open-source',\n",
       " 'language',\n",
       " ',',\n",
       " 'means',\n",
       " 'source',\n",
       " 'code',\n",
       " 'freely',\n",
       " 'available',\n",
       " 'modified',\n",
       " 'distributed',\n",
       " 'anyone',\n",
       " ',',\n",
       " 'making',\n",
       " 'ideal',\n",
       " 'language',\n",
       " 'collaborative',\n",
       " 'development',\n",
       " 'open',\n",
       " 'science',\n",
       " '.',\n",
       " 'summary',\n",
       " ',',\n",
       " 'Python',\n",
       " 'high-level',\n",
       " ',',\n",
       " 'interpreted',\n",
       " 'programming',\n",
       " 'language',\n",
       " 'known',\n",
       " 'simplicity',\n",
       " ',',\n",
       " 'readability',\n",
       " ',',\n",
       " 'ease',\n",
       " 'use',\n",
       " ',',\n",
       " 'widely',\n",
       " 'used',\n",
       " 'wide',\n",
       " 'range',\n",
       " 'applications',\n",
       " ',',\n",
       " 'web',\n",
       " 'development',\n",
       " 'data',\n",
       " 'analysis',\n",
       " 'machine',\n",
       " 'learning',\n",
       " 'scientific',\n",
       " 'computing',\n",
       " '.',\n",
       " 'Python',\n",
       " 'large',\n",
       " 'active',\n",
       " 'community',\n",
       " 'developers',\n",
       " 'contribute',\n",
       " 'language',\n",
       " ',',\n",
       " 'open-source',\n",
       " 'language',\n",
       " 'ideal',\n",
       " 'collaborative',\n",
       " 'development',\n",
       " 'open',\n",
       " 'science',\n",
       " '.']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for word in words:\n",
    "    if word.casefold() not in stop_words:\n",
    "        filtered_list.append(word)\n",
    "\n",
    "filtered_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a4cf520-efb4-439e-b9aa-4390985bb60a",
   "metadata": {},
   "source": [
    "## Stemming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "590a45ba-2854-4f5f-aa82-44c46324c10b",
   "metadata": {},
   "outputs": [],
   "source": [
    "stemmer = PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fff9c577-6dac-4e37-83fc-ee3247a4c3b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "words = word_tokenize(example_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c43699ac-ea20-49fe-bfde-755f93352ad0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['python',\n",
       " 'is',\n",
       " 'a',\n",
       " 'high-level',\n",
       " ',',\n",
       " 'interpret',\n",
       " 'program',\n",
       " 'languag',\n",
       " 'that',\n",
       " 'wa',\n",
       " 'first',\n",
       " 'releas',\n",
       " 'in',\n",
       " '1991',\n",
       " 'by',\n",
       " 'guido',\n",
       " 'van',\n",
       " 'rossum',\n",
       " ',',\n",
       " 'and',\n",
       " 'ha',\n",
       " 'sinc',\n",
       " 'becom',\n",
       " 'one',\n",
       " 'of',\n",
       " 'the',\n",
       " 'most',\n",
       " 'popular',\n",
       " 'program',\n",
       " 'languag',\n",
       " 'in',\n",
       " 'the',\n",
       " 'world',\n",
       " ',',\n",
       " 'with',\n",
       " 'a',\n",
       " 'larg',\n",
       " 'and',\n",
       " 'activ',\n",
       " 'commun',\n",
       " 'of',\n",
       " 'develop',\n",
       " 'who',\n",
       " 'use',\n",
       " 'it',\n",
       " 'for',\n",
       " 'a',\n",
       " 'wide',\n",
       " 'rang',\n",
       " 'of',\n",
       " 'applic',\n",
       " ',',\n",
       " 'from',\n",
       " 'web',\n",
       " 'develop',\n",
       " 'and',\n",
       " 'data',\n",
       " 'analysi',\n",
       " 'to',\n",
       " 'machin',\n",
       " 'learn',\n",
       " 'and',\n",
       " 'scientif',\n",
       " 'comput',\n",
       " '.',\n",
       " 'python',\n",
       " 'is',\n",
       " 'known',\n",
       " 'for',\n",
       " 'it',\n",
       " 'simplic',\n",
       " ',',\n",
       " 'readabl',\n",
       " ',',\n",
       " 'and',\n",
       " 'eas',\n",
       " 'of',\n",
       " 'use',\n",
       " ',',\n",
       " 'which',\n",
       " 'make',\n",
       " 'it',\n",
       " 'an',\n",
       " 'ideal',\n",
       " 'languag',\n",
       " 'for',\n",
       " 'beginn',\n",
       " 'who',\n",
       " 'are',\n",
       " 'just',\n",
       " 'start',\n",
       " 'to',\n",
       " 'learn',\n",
       " 'program',\n",
       " ',',\n",
       " 'as',\n",
       " 'well',\n",
       " 'as',\n",
       " 'experienc',\n",
       " 'develop',\n",
       " 'who',\n",
       " 'need',\n",
       " 'to',\n",
       " 'quickli',\n",
       " 'prototyp',\n",
       " 'and',\n",
       " 'test',\n",
       " 'their',\n",
       " 'idea',\n",
       " '.',\n",
       " 'one',\n",
       " 'of',\n",
       " 'the',\n",
       " 'key',\n",
       " 'featur',\n",
       " 'of',\n",
       " 'python',\n",
       " 'is',\n",
       " 'it',\n",
       " 'extens',\n",
       " 'standard',\n",
       " 'librari',\n",
       " ',',\n",
       " 'which',\n",
       " 'provid',\n",
       " 'a',\n",
       " 'wide',\n",
       " 'rang',\n",
       " 'of',\n",
       " 'modul',\n",
       " 'and',\n",
       " 'function',\n",
       " 'that',\n",
       " 'can',\n",
       " 'be',\n",
       " 'use',\n",
       " 'to',\n",
       " 'perform',\n",
       " 'common',\n",
       " 'task',\n",
       " ',',\n",
       " 'such',\n",
       " 'as',\n",
       " 'work',\n",
       " 'with',\n",
       " 'file',\n",
       " ',',\n",
       " 'network',\n",
       " ',',\n",
       " 'and',\n",
       " 'pars',\n",
       " 'data',\n",
       " '.',\n",
       " 'python',\n",
       " 'is',\n",
       " 'also',\n",
       " 'highli',\n",
       " 'extens',\n",
       " ',',\n",
       " 'with',\n",
       " 'a',\n",
       " 'larg',\n",
       " 'number',\n",
       " 'of',\n",
       " 'third-parti',\n",
       " 'librari',\n",
       " 'and',\n",
       " 'framework',\n",
       " 'avail',\n",
       " 'that',\n",
       " 'can',\n",
       " 'be',\n",
       " 'use',\n",
       " 'to',\n",
       " 'add',\n",
       " 'addit',\n",
       " 'function',\n",
       " 'to',\n",
       " 'the',\n",
       " 'languag',\n",
       " ',',\n",
       " 'such',\n",
       " 'as',\n",
       " 'web',\n",
       " 'framework',\n",
       " 'like',\n",
       " 'django',\n",
       " 'and',\n",
       " 'flask',\n",
       " ',',\n",
       " 'data',\n",
       " 'analysi',\n",
       " 'librari',\n",
       " 'like',\n",
       " 'numpi',\n",
       " 'and',\n",
       " 'panda',\n",
       " ',',\n",
       " 'and',\n",
       " 'machin',\n",
       " 'learn',\n",
       " 'librari',\n",
       " 'like',\n",
       " 'tensorflow',\n",
       " 'and',\n",
       " 'pytorch',\n",
       " '.',\n",
       " 'python',\n",
       " 'is',\n",
       " 'an',\n",
       " 'interpret',\n",
       " 'languag',\n",
       " ',',\n",
       " 'which',\n",
       " 'mean',\n",
       " 'that',\n",
       " 'code',\n",
       " 'written',\n",
       " 'in',\n",
       " 'python',\n",
       " 'is',\n",
       " 'execut',\n",
       " 'directli',\n",
       " 'by',\n",
       " 'the',\n",
       " 'interpret',\n",
       " ',',\n",
       " 'without',\n",
       " 'the',\n",
       " 'need',\n",
       " 'for',\n",
       " 'compil',\n",
       " ',',\n",
       " 'which',\n",
       " 'make',\n",
       " 'it',\n",
       " 'easi',\n",
       " 'to',\n",
       " 'write',\n",
       " 'and',\n",
       " 'test',\n",
       " 'code',\n",
       " 'quickli',\n",
       " '.',\n",
       " 'python',\n",
       " 'is',\n",
       " 'also',\n",
       " 'a',\n",
       " 'dynam',\n",
       " 'type',\n",
       " 'languag',\n",
       " ',',\n",
       " 'which',\n",
       " 'mean',\n",
       " 'that',\n",
       " 'variabl',\n",
       " 'do',\n",
       " 'not',\n",
       " 'need',\n",
       " 'to',\n",
       " 'be',\n",
       " 'declar',\n",
       " 'befor',\n",
       " 'they',\n",
       " 'are',\n",
       " 'use',\n",
       " ',',\n",
       " 'and',\n",
       " 'their',\n",
       " 'type',\n",
       " 'is',\n",
       " 'determin',\n",
       " 'at',\n",
       " 'runtim',\n",
       " ',',\n",
       " 'which',\n",
       " 'can',\n",
       " 'make',\n",
       " 'it',\n",
       " 'easier',\n",
       " 'to',\n",
       " 'write',\n",
       " 'code',\n",
       " 'quickli',\n",
       " ',',\n",
       " 'but',\n",
       " 'can',\n",
       " 'also',\n",
       " 'lead',\n",
       " 'to',\n",
       " 'error',\n",
       " 'if',\n",
       " 'variabl',\n",
       " 'are',\n",
       " 'not',\n",
       " 'use',\n",
       " 'correctli',\n",
       " '.',\n",
       " 'despit',\n",
       " 'it',\n",
       " 'simplic',\n",
       " ',',\n",
       " 'python',\n",
       " 'is',\n",
       " 'a',\n",
       " 'power',\n",
       " 'languag',\n",
       " 'that',\n",
       " 'can',\n",
       " 'be',\n",
       " 'use',\n",
       " 'to',\n",
       " 'build',\n",
       " 'complex',\n",
       " 'applic',\n",
       " ',',\n",
       " 'and',\n",
       " 'is',\n",
       " 'wide',\n",
       " 'use',\n",
       " 'in',\n",
       " 'industri',\n",
       " ',',\n",
       " 'academia',\n",
       " ',',\n",
       " 'and',\n",
       " 'research',\n",
       " '.',\n",
       " 'python',\n",
       " 'ha',\n",
       " 'a',\n",
       " 'larg',\n",
       " 'and',\n",
       " 'activ',\n",
       " 'commun',\n",
       " 'of',\n",
       " 'develop',\n",
       " 'who',\n",
       " 'contribut',\n",
       " 'to',\n",
       " 'the',\n",
       " 'languag',\n",
       " 'by',\n",
       " 'creat',\n",
       " 'new',\n",
       " 'librari',\n",
       " 'and',\n",
       " 'framework',\n",
       " ',',\n",
       " 'fix',\n",
       " 'bug',\n",
       " ',',\n",
       " 'and',\n",
       " 'improv',\n",
       " 'the',\n",
       " 'perform',\n",
       " 'of',\n",
       " 'the',\n",
       " 'interpret',\n",
       " '.',\n",
       " 'python',\n",
       " 'is',\n",
       " 'also',\n",
       " 'an',\n",
       " 'open-sourc',\n",
       " 'languag',\n",
       " ',',\n",
       " 'which',\n",
       " 'mean',\n",
       " 'that',\n",
       " 'the',\n",
       " 'sourc',\n",
       " 'code',\n",
       " 'is',\n",
       " 'freeli',\n",
       " 'avail',\n",
       " 'and',\n",
       " 'can',\n",
       " 'be',\n",
       " 'modifi',\n",
       " 'and',\n",
       " 'distribut',\n",
       " 'by',\n",
       " 'anyon',\n",
       " ',',\n",
       " 'make',\n",
       " 'it',\n",
       " 'an',\n",
       " 'ideal',\n",
       " 'languag',\n",
       " 'for',\n",
       " 'collabor',\n",
       " 'develop',\n",
       " 'and',\n",
       " 'open',\n",
       " 'scienc',\n",
       " '.',\n",
       " 'in',\n",
       " 'summari',\n",
       " ',',\n",
       " 'python',\n",
       " 'is',\n",
       " 'a',\n",
       " 'high-level',\n",
       " ',',\n",
       " 'interpret',\n",
       " 'program',\n",
       " 'languag',\n",
       " 'that',\n",
       " 'is',\n",
       " 'known',\n",
       " 'for',\n",
       " 'it',\n",
       " 'simplic',\n",
       " ',',\n",
       " 'readabl',\n",
       " ',',\n",
       " 'and',\n",
       " 'eas',\n",
       " 'of',\n",
       " 'use',\n",
       " ',',\n",
       " 'and',\n",
       " 'is',\n",
       " 'wide',\n",
       " 'use',\n",
       " 'for',\n",
       " 'a',\n",
       " 'wide',\n",
       " 'rang',\n",
       " 'of',\n",
       " 'applic',\n",
       " ',',\n",
       " 'from',\n",
       " 'web',\n",
       " 'develop',\n",
       " 'and',\n",
       " 'data',\n",
       " 'analysi',\n",
       " 'to',\n",
       " 'machin',\n",
       " 'learn',\n",
       " 'and',\n",
       " 'scientif',\n",
       " 'comput',\n",
       " '.',\n",
       " 'python',\n",
       " 'ha',\n",
       " 'a',\n",
       " 'larg',\n",
       " 'and',\n",
       " 'activ',\n",
       " 'commun',\n",
       " 'of',\n",
       " 'develop',\n",
       " 'who',\n",
       " 'contribut',\n",
       " 'to',\n",
       " 'the',\n",
       " 'languag',\n",
       " ',',\n",
       " 'and',\n",
       " 'is',\n",
       " 'an',\n",
       " 'open-sourc',\n",
       " 'languag',\n",
       " 'that',\n",
       " 'is',\n",
       " 'ideal',\n",
       " 'for',\n",
       " 'collabor',\n",
       " 'develop',\n",
       " 'and',\n",
       " 'open',\n",
       " 'scienc',\n",
       " '.']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stemmed_words = [stemmer.stem(word) for word in words]\n",
    "stemmed_words"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28d0425a-591b-4bf6-96c0-43e7e437bbd0",
   "metadata": {},
   "source": [
    "## Tagging Parts of Speech"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "03c58362-43ad-4119-aaf0-835a8acaa768",
   "metadata": {},
   "outputs": [],
   "source": [
    "words = word_tokenize(example_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1da8ca1f-2013-4c4a-a148-8758bd9ca7d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Python', 'NNP'),\n",
       " ('is', 'VBZ'),\n",
       " ('a', 'DT'),\n",
       " ('high-level', 'JJ'),\n",
       " (',', ','),\n",
       " ('interpreted', 'JJ'),\n",
       " ('programming', 'NN'),\n",
       " ('language', 'NN'),\n",
       " ('that', 'WDT'),\n",
       " ('was', 'VBD'),\n",
       " ('first', 'RB'),\n",
       " ('released', 'VBN'),\n",
       " ('in', 'IN'),\n",
       " ('1991', 'CD'),\n",
       " ('by', 'IN'),\n",
       " ('Guido', 'NNP'),\n",
       " ('van', 'NN'),\n",
       " ('Rossum', 'NNP'),\n",
       " (',', ','),\n",
       " ('and', 'CC'),\n",
       " ('has', 'VBZ'),\n",
       " ('since', 'IN'),\n",
       " ('become', 'VBN'),\n",
       " ('one', 'CD'),\n",
       " ('of', 'IN'),\n",
       " ('the', 'DT'),\n",
       " ('most', 'RBS'),\n",
       " ('popular', 'JJ'),\n",
       " ('programming', 'NN'),\n",
       " ('languages', 'NNS'),\n",
       " ('in', 'IN'),\n",
       " ('the', 'DT'),\n",
       " ('world', 'NN'),\n",
       " (',', ','),\n",
       " ('with', 'IN'),\n",
       " ('a', 'DT'),\n",
       " ('large', 'JJ'),\n",
       " ('and', 'CC'),\n",
       " ('active', 'JJ'),\n",
       " ('community', 'NN'),\n",
       " ('of', 'IN'),\n",
       " ('developers', 'NNS'),\n",
       " ('who', 'WP'),\n",
       " ('use', 'VBP'),\n",
       " ('it', 'PRP'),\n",
       " ('for', 'IN'),\n",
       " ('a', 'DT'),\n",
       " ('wide', 'JJ'),\n",
       " ('range', 'NN'),\n",
       " ('of', 'IN'),\n",
       " ('applications', 'NNS'),\n",
       " (',', ','),\n",
       " ('from', 'IN'),\n",
       " ('web', 'NN'),\n",
       " ('development', 'NN'),\n",
       " ('and', 'CC'),\n",
       " ('data', 'NNS'),\n",
       " ('analysis', 'NN'),\n",
       " ('to', 'TO'),\n",
       " ('machine', 'NN'),\n",
       " ('learning', 'NN'),\n",
       " ('and', 'CC'),\n",
       " ('scientific', 'JJ'),\n",
       " ('computing', 'NN'),\n",
       " ('.', '.'),\n",
       " ('Python', 'NNP'),\n",
       " ('is', 'VBZ'),\n",
       " ('known', 'VBN'),\n",
       " ('for', 'IN'),\n",
       " ('its', 'PRP$'),\n",
       " ('simplicity', 'NN'),\n",
       " (',', ','),\n",
       " ('readability', 'NN'),\n",
       " (',', ','),\n",
       " ('and', 'CC'),\n",
       " ('ease', 'NN'),\n",
       " ('of', 'IN'),\n",
       " ('use', 'NN'),\n",
       " (',', ','),\n",
       " ('which', 'WDT'),\n",
       " ('makes', 'VBZ'),\n",
       " ('it', 'PRP'),\n",
       " ('an', 'DT'),\n",
       " ('ideal', 'JJ'),\n",
       " ('language', 'NN'),\n",
       " ('for', 'IN'),\n",
       " ('beginners', 'NNS'),\n",
       " ('who', 'WP'),\n",
       " ('are', 'VBP'),\n",
       " ('just', 'RB'),\n",
       " ('starting', 'VBG'),\n",
       " ('to', 'TO'),\n",
       " ('learn', 'VB'),\n",
       " ('programming', 'NN'),\n",
       " (',', ','),\n",
       " ('as', 'RB'),\n",
       " ('well', 'RB'),\n",
       " ('as', 'IN'),\n",
       " ('experienced', 'JJ'),\n",
       " ('developers', 'NNS'),\n",
       " ('who', 'WP'),\n",
       " ('need', 'VBP'),\n",
       " ('to', 'TO'),\n",
       " ('quickly', 'RB'),\n",
       " ('prototype', 'VB'),\n",
       " ('and', 'CC'),\n",
       " ('test', 'VB'),\n",
       " ('their', 'PRP$'),\n",
       " ('ideas', 'NNS'),\n",
       " ('.', '.'),\n",
       " ('One', 'CD'),\n",
       " ('of', 'IN'),\n",
       " ('the', 'DT'),\n",
       " ('key', 'JJ'),\n",
       " ('features', 'NNS'),\n",
       " ('of', 'IN'),\n",
       " ('Python', 'NNP'),\n",
       " ('is', 'VBZ'),\n",
       " ('its', 'PRP$'),\n",
       " ('extensive', 'JJ'),\n",
       " ('standard', 'NN'),\n",
       " ('library', 'NN'),\n",
       " (',', ','),\n",
       " ('which', 'WDT'),\n",
       " ('provides', 'VBZ'),\n",
       " ('a', 'DT'),\n",
       " ('wide', 'JJ'),\n",
       " ('range', 'NN'),\n",
       " ('of', 'IN'),\n",
       " ('modules', 'NNS'),\n",
       " ('and', 'CC'),\n",
       " ('functions', 'NNS'),\n",
       " ('that', 'WDT'),\n",
       " ('can', 'MD'),\n",
       " ('be', 'VB'),\n",
       " ('used', 'VBN'),\n",
       " ('to', 'TO'),\n",
       " ('perform', 'VB'),\n",
       " ('common', 'JJ'),\n",
       " ('tasks', 'NNS'),\n",
       " (',', ','),\n",
       " ('such', 'JJ'),\n",
       " ('as', 'IN'),\n",
       " ('working', 'VBG'),\n",
       " ('with', 'IN'),\n",
       " ('files', 'NNS'),\n",
       " (',', ','),\n",
       " ('networking', 'NN'),\n",
       " (',', ','),\n",
       " ('and', 'CC'),\n",
       " ('parsing', 'VBG'),\n",
       " ('data', 'NNS'),\n",
       " ('.', '.'),\n",
       " ('Python', 'NNP'),\n",
       " ('is', 'VBZ'),\n",
       " ('also', 'RB'),\n",
       " ('highly', 'RB'),\n",
       " ('extensible', 'JJ'),\n",
       " (',', ','),\n",
       " ('with', 'IN'),\n",
       " ('a', 'DT'),\n",
       " ('large', 'JJ'),\n",
       " ('number', 'NN'),\n",
       " ('of', 'IN'),\n",
       " ('third-party', 'JJ'),\n",
       " ('libraries', 'NNS'),\n",
       " ('and', 'CC'),\n",
       " ('frameworks', 'NNS'),\n",
       " ('available', 'JJ'),\n",
       " ('that', 'WDT'),\n",
       " ('can', 'MD'),\n",
       " ('be', 'VB'),\n",
       " ('used', 'VBN'),\n",
       " ('to', 'TO'),\n",
       " ('add', 'VB'),\n",
       " ('additional', 'JJ'),\n",
       " ('functionality', 'NN'),\n",
       " ('to', 'TO'),\n",
       " ('the', 'DT'),\n",
       " ('language', 'NN'),\n",
       " (',', ','),\n",
       " ('such', 'JJ'),\n",
       " ('as', 'IN'),\n",
       " ('web', 'JJ'),\n",
       " ('frameworks', 'NNS'),\n",
       " ('like', 'IN'),\n",
       " ('Django', 'NNP'),\n",
       " ('and', 'CC'),\n",
       " ('Flask', 'NNP'),\n",
       " (',', ','),\n",
       " ('data', 'NN'),\n",
       " ('analysis', 'NN'),\n",
       " ('libraries', 'NNS'),\n",
       " ('like', 'IN'),\n",
       " ('NumPy', 'NNP'),\n",
       " ('and', 'CC'),\n",
       " ('Pandas', 'NNP'),\n",
       " (',', ','),\n",
       " ('and', 'CC'),\n",
       " ('machine', 'NN'),\n",
       " ('learning', 'VBG'),\n",
       " ('libraries', 'NNS'),\n",
       " ('like', 'IN'),\n",
       " ('TensorFlow', 'NNP'),\n",
       " ('and', 'CC'),\n",
       " ('PyTorch', 'NNP'),\n",
       " ('.', '.'),\n",
       " ('Python', 'NNP'),\n",
       " ('is', 'VBZ'),\n",
       " ('an', 'DT'),\n",
       " ('interpreted', 'JJ'),\n",
       " ('language', 'NN'),\n",
       " (',', ','),\n",
       " ('which', 'WDT'),\n",
       " ('means', 'VBZ'),\n",
       " ('that', 'IN'),\n",
       " ('code', 'NN'),\n",
       " ('written', 'VBN'),\n",
       " ('in', 'IN'),\n",
       " ('Python', 'NNP'),\n",
       " ('is', 'VBZ'),\n",
       " ('executed', 'VBN'),\n",
       " ('directly', 'RB'),\n",
       " ('by', 'IN'),\n",
       " ('the', 'DT'),\n",
       " ('interpreter', 'NN'),\n",
       " (',', ','),\n",
       " ('without', 'IN'),\n",
       " ('the', 'DT'),\n",
       " ('need', 'NN'),\n",
       " ('for', 'IN'),\n",
       " ('compilation', 'NN'),\n",
       " (',', ','),\n",
       " ('which', 'WDT'),\n",
       " ('makes', 'VBZ'),\n",
       " ('it', 'PRP'),\n",
       " ('easy', 'JJ'),\n",
       " ('to', 'TO'),\n",
       " ('write', 'VB'),\n",
       " ('and', 'CC'),\n",
       " ('test', 'VB'),\n",
       " ('code', 'NN'),\n",
       " ('quickly', 'RB'),\n",
       " ('.', '.'),\n",
       " ('Python', 'NNP'),\n",
       " ('is', 'VBZ'),\n",
       " ('also', 'RB'),\n",
       " ('a', 'DT'),\n",
       " ('dynamically', 'RB'),\n",
       " ('typed', 'JJ'),\n",
       " ('language', 'NN'),\n",
       " (',', ','),\n",
       " ('which', 'WDT'),\n",
       " ('means', 'VBZ'),\n",
       " ('that', 'IN'),\n",
       " ('variables', 'NNS'),\n",
       " ('do', 'VBP'),\n",
       " ('not', 'RB'),\n",
       " ('need', 'VB'),\n",
       " ('to', 'TO'),\n",
       " ('be', 'VB'),\n",
       " ('declared', 'VBN'),\n",
       " ('before', 'IN'),\n",
       " ('they', 'PRP'),\n",
       " ('are', 'VBP'),\n",
       " ('used', 'VBN'),\n",
       " (',', ','),\n",
       " ('and', 'CC'),\n",
       " ('their', 'PRP$'),\n",
       " ('type', 'NN'),\n",
       " ('is', 'VBZ'),\n",
       " ('determined', 'VBN'),\n",
       " ('at', 'IN'),\n",
       " ('runtime', 'NN'),\n",
       " (',', ','),\n",
       " ('which', 'WDT'),\n",
       " ('can', 'MD'),\n",
       " ('make', 'VB'),\n",
       " ('it', 'PRP'),\n",
       " ('easier', 'JJR'),\n",
       " ('to', 'TO'),\n",
       " ('write', 'VB'),\n",
       " ('code', 'NN'),\n",
       " ('quickly', 'RB'),\n",
       " (',', ','),\n",
       " ('but', 'CC'),\n",
       " ('can', 'MD'),\n",
       " ('also', 'RB'),\n",
       " ('lead', 'VB'),\n",
       " ('to', 'TO'),\n",
       " ('errors', 'NNS'),\n",
       " ('if', 'IN'),\n",
       " ('variables', 'NNS'),\n",
       " ('are', 'VBP'),\n",
       " ('not', 'RB'),\n",
       " ('used', 'VBN'),\n",
       " ('correctly', 'RB'),\n",
       " ('.', '.'),\n",
       " ('Despite', 'IN'),\n",
       " ('its', 'PRP$'),\n",
       " ('simplicity', 'NN'),\n",
       " (',', ','),\n",
       " ('Python', 'NNP'),\n",
       " ('is', 'VBZ'),\n",
       " ('a', 'DT'),\n",
       " ('powerful', 'JJ'),\n",
       " ('language', 'NN'),\n",
       " ('that', 'WDT'),\n",
       " ('can', 'MD'),\n",
       " ('be', 'VB'),\n",
       " ('used', 'VBN'),\n",
       " ('to', 'TO'),\n",
       " ('build', 'VB'),\n",
       " ('complex', 'JJ'),\n",
       " ('applications', 'NNS'),\n",
       " (',', ','),\n",
       " ('and', 'CC'),\n",
       " ('is', 'VBZ'),\n",
       " ('widely', 'RB'),\n",
       " ('used', 'VBN'),\n",
       " ('in', 'IN'),\n",
       " ('industry', 'NN'),\n",
       " (',', ','),\n",
       " ('academia', 'NN'),\n",
       " (',', ','),\n",
       " ('and', 'CC'),\n",
       " ('research', 'NN'),\n",
       " ('.', '.'),\n",
       " ('Python', 'NNP'),\n",
       " ('has', 'VBZ'),\n",
       " ('a', 'DT'),\n",
       " ('large', 'JJ'),\n",
       " ('and', 'CC'),\n",
       " ('active', 'JJ'),\n",
       " ('community', 'NN'),\n",
       " ('of', 'IN'),\n",
       " ('developers', 'NNS'),\n",
       " ('who', 'WP'),\n",
       " ('contribute', 'VBP'),\n",
       " ('to', 'TO'),\n",
       " ('the', 'DT'),\n",
       " ('language', 'NN'),\n",
       " ('by', 'IN'),\n",
       " ('creating', 'VBG'),\n",
       " ('new', 'JJ'),\n",
       " ('libraries', 'NNS'),\n",
       " ('and', 'CC'),\n",
       " ('frameworks', 'NNS'),\n",
       " (',', ','),\n",
       " ('fixing', 'VBG'),\n",
       " ('bugs', 'NNS'),\n",
       " (',', ','),\n",
       " ('and', 'CC'),\n",
       " ('improving', 'VBG'),\n",
       " ('the', 'DT'),\n",
       " ('performance', 'NN'),\n",
       " ('of', 'IN'),\n",
       " ('the', 'DT'),\n",
       " ('interpreter', 'NN'),\n",
       " ('.', '.'),\n",
       " ('Python', 'NNP'),\n",
       " ('is', 'VBZ'),\n",
       " ('also', 'RB'),\n",
       " ('an', 'DT'),\n",
       " ('open-source', 'JJ'),\n",
       " ('language', 'NN'),\n",
       " (',', ','),\n",
       " ('which', 'WDT'),\n",
       " ('means', 'VBZ'),\n",
       " ('that', 'IN'),\n",
       " ('the', 'DT'),\n",
       " ('source', 'NN'),\n",
       " ('code', 'NN'),\n",
       " ('is', 'VBZ'),\n",
       " ('freely', 'RB'),\n",
       " ('available', 'JJ'),\n",
       " ('and', 'CC'),\n",
       " ('can', 'MD'),\n",
       " ('be', 'VB'),\n",
       " ('modified', 'VBN'),\n",
       " ('and', 'CC'),\n",
       " ('distributed', 'VBN'),\n",
       " ('by', 'IN'),\n",
       " ('anyone', 'NN'),\n",
       " (',', ','),\n",
       " ('making', 'VBG'),\n",
       " ('it', 'PRP'),\n",
       " ('an', 'DT'),\n",
       " ('ideal', 'JJ'),\n",
       " ('language', 'NN'),\n",
       " ('for', 'IN'),\n",
       " ('collaborative', 'JJ'),\n",
       " ('development', 'NN'),\n",
       " ('and', 'CC'),\n",
       " ('open', 'JJ'),\n",
       " ('science', 'NN'),\n",
       " ('.', '.'),\n",
       " ('In', 'IN'),\n",
       " ('summary', 'JJ'),\n",
       " (',', ','),\n",
       " ('Python', 'NNP'),\n",
       " ('is', 'VBZ'),\n",
       " ('a', 'DT'),\n",
       " ('high-level', 'JJ'),\n",
       " (',', ','),\n",
       " ('interpreted', 'JJ'),\n",
       " ('programming', 'NN'),\n",
       " ('language', 'NN'),\n",
       " ('that', 'WDT'),\n",
       " ('is', 'VBZ'),\n",
       " ('known', 'VBN'),\n",
       " ('for', 'IN'),\n",
       " ('its', 'PRP$'),\n",
       " ('simplicity', 'NN'),\n",
       " (',', ','),\n",
       " ('readability', 'NN'),\n",
       " (',', ','),\n",
       " ('and', 'CC'),\n",
       " ('ease', 'NN'),\n",
       " ('of', 'IN'),\n",
       " ('use', 'NN'),\n",
       " (',', ','),\n",
       " ('and', 'CC'),\n",
       " ('is', 'VBZ'),\n",
       " ('widely', 'RB'),\n",
       " ('used', 'VBN'),\n",
       " ('for', 'IN'),\n",
       " ('a', 'DT'),\n",
       " ('wide', 'JJ'),\n",
       " ('range', 'NN'),\n",
       " ('of', 'IN'),\n",
       " ('applications', 'NNS'),\n",
       " (',', ','),\n",
       " ('from', 'IN'),\n",
       " ('web', 'NN'),\n",
       " ('development', 'NN'),\n",
       " ('and', 'CC'),\n",
       " ('data', 'NNS'),\n",
       " ('analysis', 'NN'),\n",
       " ('to', 'TO'),\n",
       " ('machine', 'NN'),\n",
       " ('learning', 'NN'),\n",
       " ('and', 'CC'),\n",
       " ('scientific', 'JJ'),\n",
       " ('computing', 'NN'),\n",
       " ('.', '.'),\n",
       " ('Python', 'NNP'),\n",
       " ('has', 'VBZ'),\n",
       " ('a', 'DT'),\n",
       " ('large', 'JJ'),\n",
       " ('and', 'CC'),\n",
       " ('active', 'JJ'),\n",
       " ('community', 'NN'),\n",
       " ('of', 'IN'),\n",
       " ('developers', 'NNS'),\n",
       " ('who', 'WP'),\n",
       " ('contribute', 'VBP'),\n",
       " ('to', 'TO'),\n",
       " ('the', 'DT'),\n",
       " ('language', 'NN'),\n",
       " (',', ','),\n",
       " ('and', 'CC'),\n",
       " ('is', 'VBZ'),\n",
       " ('an', 'DT'),\n",
       " ('open-source', 'JJ'),\n",
       " ('language', 'NN'),\n",
       " ('that', 'WDT'),\n",
       " ('is', 'VBZ'),\n",
       " ('ideal', 'JJ'),\n",
       " ('for', 'IN'),\n",
       " ('collaborative', 'JJ'),\n",
       " ('development', 'NN'),\n",
       " ('and', 'CC'),\n",
       " ('open', 'JJ'),\n",
       " ('science', 'NN'),\n",
       " ('.', '.')]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.pos_tag(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "84a01c4b-d220-4355-bb7c-3753d6803e33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$: dollar\n",
      "    $ -$ --$ A$ C$ HK$ M$ NZ$ S$ U.S.$ US$\n",
      "'': closing quotation mark\n",
      "    ' ''\n",
      "(: opening parenthesis\n",
      "    ( [ {\n",
      "): closing parenthesis\n",
      "    ) ] }\n",
      ",: comma\n",
      "    ,\n",
      "--: dash\n",
      "    --\n",
      ".: sentence terminator\n",
      "    . ! ?\n",
      ":: colon or ellipsis\n",
      "    : ; ...\n",
      "CC: conjunction, coordinating\n",
      "    & 'n and both but either et for less minus neither nor or plus so\n",
      "    therefore times v. versus vs. whether yet\n",
      "CD: numeral, cardinal\n",
      "    mid-1890 nine-thirty forty-two one-tenth ten million 0.5 one forty-\n",
      "    seven 1987 twenty '79 zero two 78-degrees eighty-four IX '60s .025\n",
      "    fifteen 271,124 dozen quintillion DM2,000 ...\n",
      "DT: determiner\n",
      "    all an another any both del each either every half la many much nary\n",
      "    neither no some such that the them these this those\n",
      "EX: existential there\n",
      "    there\n",
      "FW: foreign word\n",
      "    gemeinschaft hund ich jeux habeas Haementeria Herr K'ang-si vous\n",
      "    lutihaw alai je jour objets salutaris fille quibusdam pas trop Monte\n",
      "    terram fiche oui corporis ...\n",
      "IN: preposition or conjunction, subordinating\n",
      "    astride among uppon whether out inside pro despite on by throughout\n",
      "    below within for towards near behind atop around if like until below\n",
      "    next into if beside ...\n",
      "JJ: adjective or numeral, ordinal\n",
      "    third ill-mannered pre-war regrettable oiled calamitous first separable\n",
      "    ectoplasmic battery-powered participatory fourth still-to-be-named\n",
      "    multilingual multi-disciplinary ...\n",
      "JJR: adjective, comparative\n",
      "    bleaker braver breezier briefer brighter brisker broader bumper busier\n",
      "    calmer cheaper choosier cleaner clearer closer colder commoner costlier\n",
      "    cozier creamier crunchier cuter ...\n",
      "JJS: adjective, superlative\n",
      "    calmest cheapest choicest classiest cleanest clearest closest commonest\n",
      "    corniest costliest crassest creepiest crudest cutest darkest deadliest\n",
      "    dearest deepest densest dinkiest ...\n",
      "LS: list item marker\n",
      "    A A. B B. C C. D E F First G H I J K One SP-44001 SP-44002 SP-44005\n",
      "    SP-44007 Second Third Three Two * a b c d first five four one six three\n",
      "    two\n",
      "MD: modal auxiliary\n",
      "    can cannot could couldn't dare may might must need ought shall should\n",
      "    shouldn't will would\n",
      "NN: noun, common, singular or mass\n",
      "    common-carrier cabbage knuckle-duster Casino afghan shed thermostat\n",
      "    investment slide humour falloff slick wind hyena override subhumanity\n",
      "    machinist ...\n",
      "NNP: noun, proper, singular\n",
      "    Motown Venneboerger Czestochwa Ranzer Conchita Trumplane Christos\n",
      "    Oceanside Escobar Kreisler Sawyer Cougar Yvette Ervin ODI Darryl CTCA\n",
      "    Shannon A.K.C. Meltex Liverpool ...\n",
      "NNPS: noun, proper, plural\n",
      "    Americans Americas Amharas Amityvilles Amusements Anarcho-Syndicalists\n",
      "    Andalusians Andes Andruses Angels Animals Anthony Antilles Antiques\n",
      "    Apache Apaches Apocrypha ...\n",
      "NNS: noun, common, plural\n",
      "    undergraduates scotches bric-a-brac products bodyguards facets coasts\n",
      "    divestitures storehouses designs clubs fragrances averages\n",
      "    subjectivists apprehensions muses factory-jobs ...\n",
      "PDT: pre-determiner\n",
      "    all both half many quite such sure this\n",
      "POS: genitive marker\n",
      "    ' 's\n",
      "PRP: pronoun, personal\n",
      "    hers herself him himself hisself it itself me myself one oneself ours\n",
      "    ourselves ownself self she thee theirs them themselves they thou thy us\n",
      "PRP$: pronoun, possessive\n",
      "    her his mine my our ours their thy your\n",
      "RB: adverb\n",
      "    occasionally unabatingly maddeningly adventurously professedly\n",
      "    stirringly prominently technologically magisterially predominately\n",
      "    swiftly fiscally pitilessly ...\n",
      "RBR: adverb, comparative\n",
      "    further gloomier grander graver greater grimmer harder harsher\n",
      "    healthier heavier higher however larger later leaner lengthier less-\n",
      "    perfectly lesser lonelier longer louder lower more ...\n",
      "RBS: adverb, superlative\n",
      "    best biggest bluntest earliest farthest first furthest hardest\n",
      "    heartiest highest largest least less most nearest second tightest worst\n",
      "RP: particle\n",
      "    aboard about across along apart around aside at away back before behind\n",
      "    by crop down ever fast for forth from go high i.e. in into just later\n",
      "    low more off on open out over per pie raising start teeth that through\n",
      "    under unto up up-pp upon whole with you\n",
      "SYM: symbol\n",
      "    % & ' '' ''. ) ). * + ,. < = > @ A[fj] U.S U.S.S.R * ** ***\n",
      "TO: \"to\" as preposition or infinitive marker\n",
      "    to\n",
      "UH: interjection\n",
      "    Goodbye Goody Gosh Wow Jeepers Jee-sus Hubba Hey Kee-reist Oops amen\n",
      "    huh howdy uh dammit whammo shucks heck anyways whodunnit honey golly\n",
      "    man baby diddle hush sonuvabitch ...\n",
      "VB: verb, base form\n",
      "    ask assemble assess assign assume atone attention avoid bake balkanize\n",
      "    bank begin behold believe bend benefit bevel beware bless boil bomb\n",
      "    boost brace break bring broil brush build ...\n",
      "VBD: verb, past tense\n",
      "    dipped pleaded swiped regummed soaked tidied convened halted registered\n",
      "    cushioned exacted snubbed strode aimed adopted belied figgered\n",
      "    speculated wore appreciated contemplated ...\n",
      "VBG: verb, present participle or gerund\n",
      "    telegraphing stirring focusing angering judging stalling lactating\n",
      "    hankerin' alleging veering capping approaching traveling besieging\n",
      "    encrypting interrupting erasing wincing ...\n",
      "VBN: verb, past participle\n",
      "    multihulled dilapidated aerosolized chaired languished panelized used\n",
      "    experimented flourished imitated reunifed factored condensed sheared\n",
      "    unsettled primed dubbed desired ...\n",
      "VBP: verb, present tense, not 3rd person singular\n",
      "    predominate wrap resort sue twist spill cure lengthen brush terminate\n",
      "    appear tend stray glisten obtain comprise detest tease attract\n",
      "    emphasize mold postpone sever return wag ...\n",
      "VBZ: verb, present tense, 3rd person singular\n",
      "    bases reconstructs marks mixes displeases seals carps weaves snatches\n",
      "    slumps stretches authorizes smolders pictures emerges stockpiles\n",
      "    seduces fizzes uses bolsters slaps speaks pleads ...\n",
      "WDT: WH-determiner\n",
      "    that what whatever which whichever\n",
      "WP: WH-pronoun\n",
      "    that what whatever whatsoever which who whom whosoever\n",
      "WP$: WH-pronoun, possessive\n",
      "    whose\n",
      "WRB: Wh-adverb\n",
      "    how however whence whenever where whereby whereever wherein whereof why\n",
      "``: opening quotation mark\n",
      "    ` ``\n"
     ]
    }
   ],
   "source": [
    "nltk.help.upenn_tagset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b04b8448-9843-4e70-a157-8d72812f626f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(nltk.pos_tag(words), columns=['word', 'tag'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e34b9267-2f3a-4f34-bb30-681777dab492",
   "metadata": {},
   "outputs": [
    {
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       "      <td>13</td>\n",
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       "      <td>5</td>\n",
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      ],
      "text/plain": [
       "      word\n",
       "tag       \n",
       ",       38\n",
       ".       11\n",
       "CC      31\n",
       "CD       3\n",
       "DT      27\n",
       "IN      50\n",
       "JJ      45\n",
       "JJR      1\n",
       "MD       6\n",
       "NN      73\n",
       "NNP     20\n",
       "NNS     29\n",
       "PRP      6\n",
       "PRP$     6\n",
       "RB      20\n",
       "RBS      1\n",
       "TO      14\n",
       "VB      17\n",
       "VBD      1\n",
       "VBG      8\n",
       "VBN     17\n",
       "VBP      8\n",
       "VBZ     26\n",
       "WDT     13\n",
       "WP       5"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_counts = df.groupby(by='tag').count()\n",
    "tag_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "034cd998-9a3e-4991-8046-d2cc4d94f0ab",
   "metadata": {},
   "outputs": [
    {
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       "      <th>RBS</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JJR</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      word\n",
       "tag       \n",
       "NN      73\n",
       "IN      50\n",
       "JJ      45\n",
       ",       38\n",
       "CC      31\n",
       "NNS     29\n",
       "DT      27\n",
       "VBZ     26\n",
       "RB      20\n",
       "NNP     20\n",
       "VBN     17\n",
       "VB      17\n",
       "TO      14\n",
       "WDT     13\n",
       ".       11\n",
       "VBG      8\n",
       "VBP      8\n",
       "PRP      6\n",
       "PRP$     6\n",
       "MD       6\n",
       "WP       5\n",
       "CD       3\n",
       "VBD      1\n",
       "RBS      1\n",
       "JJR      1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_counts = tag_counts.sort_values(by='word', ascending=False)\n",
    "tag_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7628bdfc-6fb8-4d19-9d23-ffdd0d165ccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(x=tag_counts.index.to_list(), height=tag_counts['word'])\n",
    "plt.xticks(rotation = 90)\n",
    "plt.xlabel('Category')\n",
    "plt.title('Word Counts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dec3b78e-4822-4d9d-9b43-b251baa73189",
   "metadata": {},
   "source": [
    "## Using Named Entity Recognition (NER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "284fadaa-39a8-4f89-8be0-17ec63f8a068",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_ne(quote):\n",
    "    words = word_tokenize(quote, language='English')\n",
    "    tags = nltk.pos_tag(words)\n",
    "    tree = nltk.ne_chunk(tags, binary=True)\n",
    "    return set(\n",
    "        \" \".join(i[0] for i in t)\n",
    "        for t in tree\n",
    "            if hasattr(t, \"label\") and t.label() == \"NE\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "42e2fe74-3c65-4544-9a18-08b31e724901",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Django',\n",
       " 'Flask',\n",
       " 'Guido',\n",
       " 'NumPy',\n",
       " 'Pandas',\n",
       " 'PyTorch',\n",
       " 'Python',\n",
       " 'Rossum',\n",
       " 'TensorFlow'}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extract_ne(example_string)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dab98c5-940d-492b-8483-b20621d56cfc",
   "metadata": {},
   "source": [
    "## Using Concrdance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ab641f76-b380-4eed-8ee1-5ca90370a79c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Displaying 12 of 12 matches:\n",
      " Python is a high-level , interpreted progr\n",
      "learning and scientific computing . Python is known for its simplicity , reada\n",
      " ideas . One of the key features of Python is its extensive standard library ,\n",
      "s , networking , and parsing data . Python is also highly extensible , with a \n",
      "aries like TensorFlow and PyTorch . Python is an interpreted language , which \n",
      " , which means that code written in Python is executed directly by the interpr\n",
      "sy to write and test code quickly . Python is also a dynamically typed languag\n",
      "orrectly . Despite its simplicity , Python is a powerful language that can be \n",
      "ndustry , academia , and research . Python has a large and active community of\n",
      "he performance of the interpreter . Python is also an open-source language , w\n",
      "ent and open science . In summary , Python is a high-level , interpreted progr\n",
      "learning and scientific computing . Python has a large and active community of\n"
     ]
    }
   ],
   "source": [
    "words = word_tokenize(example_string)\n",
    "text = nltk.Text(words)\n",
    "text.concordance(\"Python\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "76eda3e4-0678-4dc4-bd24-4d4e7b8a703d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Displaying 12 of 12 matches:\n",
      "gh-level , interpreted programming language that was first released in 1991 by\n",
      "e of use , which makes it an ideal language for beginners who are just startin\n",
      "dd additional functionality to the language , such as web frameworks like Djan\n",
      "PyTorch . Python is an interpreted language , which means that code written in\n",
      "Python is also a dynamically typed language , which means that variables do no\n",
      " simplicity , Python is a powerful language that can be used to build complex \n",
      "f developers who contribute to the language by creating new libraries and fram\n",
      "er . Python is also an open-source language , which means that the source code\n",
      "ted by anyone , making it an ideal language for collaborative development and \n",
      "gh-level , interpreted programming language that is known for its simplicity ,\n",
      "f developers who contribute to the language , and is an open-source language t\n",
      "e language , and is an open-source language that is ideal for collaborative de\n"
     ]
    }
   ],
   "source": [
    "text.concordance(\"language\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b92e10e2-3501-49af-a7e7-28e8cd8ed175",
   "metadata": {},
   "source": [
    "## Making a Dispersion Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3a2f869a-2237-4e28-a52b-e605fa21dbdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "text.dispersion_plot(\n",
    "    [\"Python\", \"language\", \"popular\", \"library\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e03972ac-166b-41ed-bf3c-7d143eed8f76",
   "metadata": {},
   "source": [
    "## Making a Frequency Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "26566b21-4e66-4477-9bfd-07c366d8df14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FreqDist({',': 38, 'and': 30, 'is': 17, 'to': 14, 'of': 13, 'Python': 12, 'language': 12, 'a': 11, 'the': 11, '.': 11, ...})"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequency_distribution = FreqDist(words)\n",
    "frequency_distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "402a3886-83be-4331-8583-ac0d22498a98",
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words = stop_words.union([',', '.', '’', '!'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "029774c2-add7-4348-9345-a0b5d5f0d4e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "meaningful_words = lambda text: [word for word in text if word not in stop_words]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "aea33889-570b-4020-8d53-270a58142ed2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FreqDist({'Python': 12, 'language': 12, 'used': 7, 'programming': 4, 'large': 4, 'developers': 4, 'development': 4, 'data': 4, 'also': 4, 'libraries': 4, ...})"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequency_distribution = FreqDist(meaningful_words(words))\n",
    "frequency_distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "76c72085-ccd5-4f5a-ac66-4ccafc7cdd68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RFWAsZAqgjK0KUjUCj2OTjJwNERGR4bCQKYB05slw5RIRERVgLGQKIJ0l2JwnQ0REBRgLmQJIdwk2R2SIiKjgYiFTAOk2xeOIDBERFVwsZAogjsgQEVFhwUKmAHKwtoCrnSUA9pIhIqKCjYVMAZUxKvMwJhGpmjQjZ0NERGQYLGQKqIx5MmkCeBjNXjJERFQwsZApoDhPhoiICgMWMgWUt2vmXjIsZIiIqGBiIVNAZey3BHDCLxERFVwsZAoonREZblNAREQFFAuZAirzHBluU0BERAUVC5kCysbSHO4O1gA42ZeIiAouFjIFWMYS7MexyUhK1Rg5GyIiIvmxkCnAMp9eehDN00tERFTwsJApwHSXYLOQISKigoeFTAHGpnhERFTQsZApwDLmyADA3ecckSEiooKHhUwBxhEZIiIq6FjIFGAeLjZQqdIvc44MEREVRCxkCjBrC3OUcLIBANzniAwRERVALGQKOK9/Vy49jUtBQorayNkQERHJy6QLGY1Gg8mTJ8PX1xe2trYoU6YMvvjiCwghjJ2aYni7cvNIIiIquPQqZM6ePYsLFy5or2/btg0dO3bEZ599hpSUFNmSmzNnDpYtW4YlS5bgypUrmDNnDr766issXrxYtvso6Lwy9ZLhhF8iIipo9CpkBg8ejPDwcADAzZs30bVrV9jZ2WHjxo0YN26cbMkdPXoUQUFBaNu2LXx8fPD++++jZcuWOHnypGz3UdB5Fcm0eSSXYBMRUQFjoc8fhYeHIzAwEACwceNGNG7cGOvWrcORI0fQtWtXLFiwQJbk6tevj++//x7h4eEoV64cwsLCcPjwYcybNy/Hv0lOTkZycrL2emxsLABArVZDrZZvjkhGLDljGiJ+SScr7eU7z+KzxDX1/PM7NuMbLzbjGy+20uMrOXelx8+P2G+iEnpMOHFycsKZM2fw1ltv4Z133kG7du0wfPhw3LlzB/7+/khMlOebf1paGj777DN89dVXMDc3h0ajwcyZMzFx4sQc/2bq1KmYNm1aluM7duyAvb29LHkpSVRCGsYcSD+lVLO4OYZWt33DXxARERlffHw82rZti5iYGDg5OeV4O71GZGrWrIkZM2agRYsWOHDgAJYtWwYAiIyMRPHixfXLOBsbNmzAL7/8gnXr1qFSpUoIDQ3FiBEj4OHhgd69e2f7NxMnTsSoUaO012NjY+Ht7Y26deu+9oHIK7VajRMnTqBOnTqwsNDrYcyX+GpNGsYf2gNNmkCiuT0aNKgna/wc79eA8ZWcu9LjKzl3pcdXcu6Gjq/k3JUe35CxM86ovIle9zp//nz07NkTW7duxf/+9z+ULVsWALBp0ybUr19fn5DZGjt2LCZMmICuXbsCAAICAnD79m3MmjUrx0LG2toa1tbWWY5bWFgY5AliqLhyxbewAEo62+Dei0Tce5GYJZap52+s2IxvvNiMb7zYSo+v5NyVHt8QsXMbT697rVq1qs6qpQxff/21rP8jCQkJMDPTnY9sbm6OtLQ02e6jMPBytcW9F4mISUxFbFIqnGwsjZ0SERGRLPRateTn54dnz55lOZ6UlIRy5cpJTipD+/btMXPmTOzYsQO3bt3Cli1bMG/ePAQHB8t2H4WBTi8ZrlwiIqICRK/hk1u3bkGj0WQ5npycjHv37klOKsPixYsxefJkfPzxx3jy5Ak8PDwwePBgTJkyRbb7KAy8i+huHlnRQ765QkRERMaUp0Jm+/bt2su7du2Cs7Oz9rpGo8HevXvh6+srW3KOjo5YsGCBbMu5C6vMTfG4eSQRERUkeSpkOnbsCABQqVRZJttaWlrCx8cHc+fOlS05kserIzJEREQFRZ4KmYxJtr6+vjh16hTc3d0NkhTJS2dEhnNkiIioANFrjkxkZKTceZABFXe0gaW5CqkawREZIiIqUPReK713717s3bsXT548ybIc+qeffpKcGMnHzEwFTxdb3HqWgHsvErl7OBERFRh6Lb+eNm0aWrZsib179+Lp06d48eKFzg+Znox5MnHJakQnpBo5GyIiInnoNSKzfPlyrFq1Ch9++KHc+ZCBZJ4nc+9FIiqUKHz7ThERUcGj14hMSkqKrFsRkOF5ZWqKd5fzZIiIqIDQq5AZMGAA1q1bJ3cuZEBcgk1ERAWRXqeWkpKS8P3332PPnj2oUqUKLC119+6ZN2+eLMmRfLgEm4iICiK9Cpnz588jMDAQAHDx4kWd36lUKslJkfx09lviiAwRERUQehUy+/fvlzsPMjB3ByvYWJohKTWN2xQQEVGBodccGVIelUqlnfB770UCe8kQEVGBoNeITNOmTV97Cmnfvn16J0SG4+Vqi4gncUhKTcOz+BRjp0NERCSZXoVMxvyYDKmpqQgNDcXFixezbCZJpsNbZwk2Ty8REZHy6VXIzJ8/P9vjU6dORVxcnKSEyHAyr1y6/yIRrkbMhYiISA6yzpHp2bMn91kyYZl7yXBEhoiICgJZC5ljx47BxsZGzpAko1dHZIiIiJROr1NLnTp10rkuhMDDhw9x+vRpTJ48WZbESH5Z5sgUNWIyREREMtCrkHF2dta5bmZmBn9/f0yfPh0tW7aUJTGSn4udJRysLRCXrMb96ERw9T0RESmdXoXMypUr5c6D8kF6LxlbXH30EvejE5Em7N78R0RERCZMr0Imw5kzZ3DlyhUAQKVKlVCtWjVZkiLD8XK1w9VHL5GqEYhOZlM8IiJSNr0KmSdPnqBr164ICQmBi4sLACA6OhpNmzbFr7/+iqJFOfnCVGWe8Ps0gYUMEREpm16TJIYOHYqXL1/i0qVLeP78OZ4/f46LFy8iNjYWw4YNkztHklHmJdhRiWlGzISIiEg6vUZkdu7ciT179qBChQraYxUrVsTSpUs52dfE6YzIJHJEhoiIlE2vEZm0tDRYWlpmOW5paYm0NH7LN2WZl2BHJfDfioiIlE2vQqZZs2YYPnw4Hjx4oD12//59jBw5Es2bN5ctOZKfVxGOyBARUcGhVyGzZMkSxMbGwsfHB2XKlEGZMmXg6+uL2NhYLF68WO4cSUZONpZwtk0fTeMcGSIiUjq95sh4e3vj7Nmz2LNnD65evQoAqFChAlq0aCFrcmQYXq62iElMxfMkAbUmDRaSFuETEREZT55GZPbt24eKFSsiNjYWKpUK77zzDoYOHYqhQ4eiVq1aqFSpEg4dOmSoXEkmGfNk0gTwKDbZyNkQERHpL0+FzIIFCzBw4EA4OTll+Z2zszMGDx6MefPmyZYcGYZ3pnkyd58nGDETIiIiafJUyISFhaF169Y5/r5ly5Y4c+aM5KTIsPyKOmgvX3n00oiZEBERSZOnQubx48fZLrvOYGFhgaioKMlJkWFV9XLRXg67F2O8RIiIiCTKUyHj6emJixcv5vj78+fPo2TJkpKTIsMqV9wBtpbmAIDzLGSIiEjB8lTIvPvuu5g8eTKSkpKy/C4xMRGff/452rVrJ1tyZBgW5mao7JE+z+nui0Q8i+OEXyIiUqY8LbydNGkSNm/ejHLlyuHTTz+Fv78/AODq1atYunQpNBoN/ve//xkkUZJXFS9nnLr9AkD6qEzT8sWMnBEREVHe5amQKV68OI4ePYqPPvoIEydOhBDpnWFVKhVatWqFpUuXonjx4gZJlORVxctZe/nc3WgWMkREpEh5boVWunRp/PXXX3jx4gUiIiIghMBbb70FV1dXQ+RHBlI1UyETdjfaeIkQERFJoHdPV1dXV9SqVUvOXCgfebrYwMlKhdgUgbB70RBCQKVSGTstIiKiPNFrryVSPpVKBT/n9H/+6IRU3GFjPCIiUiAWMoWYn4u59nIoTy8REZECsZApxDJGZAAWMkREpEwsZAoxX+f/RmQ44ZeIiJSIhUwh5mClgo9b+k7YFx/EIlWTZuSMiIiI8oaFTCGX0U8mRZ2Gqw+5gSQRESkLC5lCLnM/mdB70cZLhIiISA8sZAo5NsYjIiIlYyFTyFUo4QhL8/RGeCxkiIhIaVjIFHLWluaoUDJ9J+yIqDi8TEo1ckZERES5x0KGUNXLBQAgBHDhXoxxkyEiIsoDFjKEqt4u2suc8EtERErCQoYQmKmQ4TwZIiJSEhYyBD93ezhap2+Ezq0KiIhISVjIEMzMVKjinb4M+3FsMh7FJBk5IyIiotxhIUMA/pvwC3BUhoiIlIOFDAF4ZZ4MJ/wSEZFCsJAhALqFTOidaKPlQURElBcsZAgAUMzJBiWdbQAAF+7HQJMmjJwRERHRm7GQIa2MeTJxyWrcjIozbjJERES5wEKGtAJLuWgvc8IvEREpAQsZ0uLKJSIiUhoWMqQV4OUMVfpG2Fy5REREisBChrQcrC3wVjEHAMDVhy+RlKoxckZERESvx0KGdGQsw1anCVx6EGvcZIiIiN7A5AuZ+/fvo2fPnnBzc4OtrS0CAgJw+vRpY6dVYOnshM15MkREZOIsjJ3A67x48QINGjRA06ZN8ffff6No0aK4fv06XF1djZ1agZV5wi93wiYiIlNn0oXMnDlz4O3tjZUrV2qP+fr6GjGjgs+/hCOsLcyQrE7jhF8iIjJ5Jl3IbN++Ha1atULnzp1x4MABeHp64uOPP8bAgQNz/Jvk5GQkJydrr8fGps/zUKvVUKvVsuWWEUvOmKYQXwWgsocTztyJxu1nCYiKTYCrnZVs8eVQUB97JcRXcu5Kj6/k3A0dX8m5Kz1+fsR+E5UQwmR70dvYpLfMHzVqFDp37oxTp05h+PDhWL58OXr37p3t30ydOhXTpk3LcnzHjh2wt7c3aL4Fxborydh1KxUAMKqmDaoWNel6l4iICqD4+Hi0bdsWMTExcHJyyvF2Jl3IWFlZoWbNmjh69Kj22LBhw3Dq1CkcO3Ys27/JbkTG29sbz549e+0DkVdqtRonTpxAnTp1YGEh/we9MeP/ef4hRmw4DwAY1qwMhjUrK2t8qQryY2/q8ZWcu9LjKzl3Q8dXcu5Kj2/I2LGxsXBzc3tjIWPSX7VLliyJihUr6hyrUKECfv/99xz/xtraGtbW1lmOW1hYGOQJYqi4xoxfvbSb9vKF+7GS7t+Q+RfEx14p8ZWcu9LjKzl3Q8dXcu5Kj2+I2LmNZ9LLrxs0aIBr167pHAsPD0fp0qWNlFHh4F3EFkXs0+fFhN2LgQkP2hERUSFn0oXMyJEjcfz4cXz55ZeIiIjAunXr8P333+OTTz4xdmoFmkqlQlUvZwDA8/gU3H2eaOSMiIiIsmfShUytWrWwZcsWrF+/HpUrV8YXX3yBBQsWoEePHsZOrcDTaYzHZdhERGSiTHqODAC0a9cO7dq1M3YahU7mQibsbjQ6VPUwXjJEREQ5MOkRGTKeQHb4JSIiBWAhQ9lytbdCaTc7AMCF+zFI1aQZOSMiIqKsWMhQjjL2XUpWp+Hao5fGTYaIiCgbLGQoRzrzZDjhl4iITBALGcpRoLez9jLnyRARkSliIUM5quThDAszFQAg7G6MkbMhIiLKioUM5cjG0hzlSzoCAMKfvERcsmF2ZiUiItIXCxl6rYwJv0IAF+5xVIaIiEwLCxl6LU74JSIiU8ZChl6r2isdfomIiEwJCxl6Lb+iDnCwTt/JIpSFDBERmRgWMvRa5mYqBHimL8N+GJOEx7FJRs6IiIjoPyxk6I1e3UCSiIjIVLCQoTcK5IRfIiIyUSxk6I0yFzKcJ0NERKaEhQy9UQlnGxR3sgYAnL8bg7Q0YeSMiIiI0rGQoVzJaIz3MlmNm0/jjZsMERHRv1jIUK4ElnLRXuaEXyIiMhUsZChXAv8dkQE4T4aIiEwHCxnKlcpezlClb4TNlUtERGQyWMhQrjjZWKJMUQcAwJWHsUhK1Rg5IyIiIhYylAcZy7BTNQJXHsYaNxkiIiKwkKE8qMp+MkREZGJYyFCuZZ7wy5VLRERkCljIUK75l3CElUX6UybsXoyRsyEiImIhQ3lgZWGGyh5OAIDIp/GITkgxckZERFTYsZChPNHZCZujMkREZGQsZChPdHbC5jwZIiIyMhYylCdVOeGXiIhMCAsZypPSbnZwsbMEkN7hVwjuhE1ERMbDQobyRKVSaUdlnsal4N6LROMmREREhRoLGcoz3Qm/0UbLg4iIiIUM5Vmgt7P2MufJEBGRMbGQoTzTnfDLJdhERGQ8LGQoz9wcrOFdxBYAcOF+DNSaNCNnREREhRULGdJLxqhMYqoG4Y/jjJsMEREVWixkSC+BnPBLREQmgIUM6aUqO/wSEZEJYCFDeqns4QxzMxUAIJSFDBERGQkLGdKLrZU5/Is7AgDCH79EfLLayBkREVFhxEKG9JZxeilNABfvcxk2ERHlPxYypDedxnic8EtEREbAQob0Fujtqr3MeTJERGQMLGRIb2WLOcDOyhwAO/wSEZFxsJAhvZmbqRDgmX566X50Ip68TDJyRkREVNiwkCFJMjfGO89RGSIiymcsZEiSzIUM58kQEVF+YyFDklTlVgVERGRELGRIkpLONijqaA0gfauCtDRh5IyIiKgwYSFDkqhUKu1O2LFJatx6Fm/chIiIqFBhIUOSVSvlor3MeTJERJSfWMiQZBkjMgB3wiYiovzFQoYkC/D6b6uC0Htcgk1ERPmHhQxJ5mxrCb+i9gCAKw9ikazWGDkjIiIqLFjIkCwy+smkaNJw5eFL4yZDRESFBgsZkkXmxnicJ0NERPmFhQzJghN+iYjIGFjIkCzKl3SElXn60ymUHX6JiCifsJAhWVhbmKOihxMA4GZUPGISU42cERERFQYsZEg2mefJXLjPZdhERGR4LGRINlW9/+snc579ZIiIKB+wkCHZ6Ez4ZSFDRET5gIUMycbHzR5ONhYA0gsZIbgTNhERGRYLGZKNmZkKVf+dJ/M0LgXPk1jIEBGRYSmqkJk9ezZUKhVGjBhh7FQoB5kn/N6MTjNeIkREVCgoppA5deoUvvvuO1SpUsXYqdBrZJ4nczOGey4REZFhWRg7gdyIi4tDjx498MMPP2DGjBnGTodeo2qmEZnQKA3WHLsNMzN56+W0tDTcvJWCG2byx2Z848XOl/giDdYvWWATFSSKKGQ++eQTtG3bFi1atHhjIZOcnIzk5GTt9djYWACAWq2GWq2WLaeMWHLGLAjxXW3N4elig/vRSXgQl4bpO67KGl/HFQPGZnzjxTZwfCszoFpgDMqVcH7zjfPIkK9bpb4n5Ed8Jeeu9Pj5EftNVMLEl5b8+uuvmDlzJk6dOgUbGxs0adIEgYGBWLBgQba3nzp1KqZNm5bl+I4dO2Bvb2/gbAkA1l9Jxs5b7OxLpsvf1QwT6tjCTKUydipElIP4+Hi0bdsWMTExcHJyyvF2Jl3I3L17FzVr1sQ///yjnRvzpkImuxEZb29vPHv27LUPRF6p1WqcOHECderUgYWF/ANbSo6fqknD4etROHPhCsqUKQtzc3lPEWg0abhxI8IgsRnfeLHzI/78PRG4+yIRADCzYyV0qekla3xDvq6U/J5g6PhKzl3p8Q0ZOzY2Fm5ubm8sZEz61NKZM2fw5MkTVK9eXXtMo9Hg4MGDWLJkCZKTk2Fubq7zN9bW1rC2ts4Sy8LCwiBPEEPFVXJ8CwugafnisHoWgQbVvQzywjmSeNsgsRnfeLHzI34Reyv0WXUGADBn5zW8U6kEijnayH4/hnzdKvE9Ib/iKzl3pcc3zGdJ7uKZ9Kql5s2b48KFCwgNDdX+1KxZEz169EBoaGiWIoaI6HUalnVHA8/0N8fYJDWmbb9s5IyISCqTHpFxdHRE5cqVdY7Z29vDzc0ty3EiotzoVt4al1+o8CIhFTsuPETw5cdoUbG4sdMiIj2Z9IgMEZHcHK1U+N+75bXXJ2+7iLhkw6wWISLDU1whExISkuNEXyKi3AiqWhKNyxUFADyMScI3u64ZOSMi0pfiChkiIqlUKhVmdqwMG8v0t8DVx27h7J0XRs6KiPTBQoaICiXvInYY9U45AIAQwMTfLyBVw/3BiJSGhQwRFVr9Gviismd6f4prj1/i+4M3jZwREeUVCxkiKrQszM0wu1MVmP3b4Hfh3uu4GRVn3KSIKE9YyBBRoVbZ0xn9G/oCAFLUafhsywWYcMNzInoFCxkiKvRGvlMOXq62AIDjN59j4+l7Rs6IiHKLhQwRFXp2Vhb4MjhAe33mX1cQ9TL5NX9BRKaChQwREYDG5YoiuJonACAmMRXT/rhk5IyIKDdYyBAR/WtS2wpwtbMEAPx5/iH2XX1s5IyI6E1YyBAR/cvNwRqT2lbUXp+89RLiuX0BkUljIUNElEmn6p5oWNYdAHA/OhHf7Ob2BUSmjIUMEVEmKpUKM4P/275g1dFbCL0bbdykiChHLGSIiF5R2s0eI1r8t33BhN/Pc/sCIhPFQoaIKBsDGvqiYsn07QuuPnqJHw5x+wIiU8RChogoGxbmZpj9XsB/2xfsuY5bT+ONmxQRZcFChogoB1W8XNC3Qfr2BcncvoDIJLGQISJ6jVHvlIOnS/r2BUdvPMOmM9y+gMiUsJAhInoNe2sLzAiurL0+868reBrH7QuITAULGSKiN2jqXwwdqnoAAKITUjH9j8tGzoiIMrCQISLKhSntK8Ll3+0Ltoc9wP5rT4ycEREBLGSIiHLF3cEa/3u3gvb6pC0XuX0BkQlgIUNElEvv1/BC/TJuANK3L5j3T7iRMyIiFjJERLmkUqnwZXAArC3S3zpXHolEGLcvIDIqFjJERHng426P4S3eAgCkCWDC5gvcvoDIiFjIEBHl0cBGfihfwhEAcOVhLFYcjjRyRkSFFwsZIqI8sjQ3w+z3qkD17/YF8/8Jx+1n3L6AyBhYyBAR6SHQ2wV96vsA4PYFRMbEQoaISE9jWvprty84EvEMW0MfGDkjosLHwtgJEBEplb21Bb7oWAn9Vp0GAMz86xom1rLE07hkmJtrZL0vjUaNmOQ0g8RWenwl5670+BmxoxNS4O5knJKChQwRkQTNyhdHuyol8ef5h4hOTMX4g6nAwRDD3eE+A8ZWenwl567w+HUiQ/Hb4PoGi/86PLVERCTR5+0rwdnW0thpEBVKHJEhIpKoqKM1fuxdE6uOROLh4ygUcXODKmNJk0yEEHj+7JlBYis9vpJzV3r8jNg1SrnKGjcvWMgQEcmglk8RVPNywpEjR9CgQTVYWMj79qpWqw0WW+nxlZy70uP/F/stWePmBU8tERERkWKxkCEiIiLFYiFDREREisVChoiIiBSLhQwREREpFgsZIiIiUiwWMkRERKRYLGSIiIhIsVjIEBERkWKxkCEiIiLFYiFDREREisVChoiIiBSLhQwREREpVoHf/VoIAQCIjY2VNa5arUZ8fDxiY2MNtlsp4+d/bMY3XmzGN15spcdXcu5Kj2/I2Bmf2xmf4zkp8IXMy5cvAQDe3t5GzoSIiIjy6uXLl3B2ds7x9yrxplJH4dLS0vDgwQM4OjpCpVLJFjc2Nhbe3t64e/cunJycZIvL+MaNzfjGi834xout9PhKzl3p8Q0ZWwiBly9fwsPDA2ZmOc+EKfAjMmZmZvDy8jJYfCcnJ4M88RjfuLEZ33ixGd94sZUeX8m5Kz2+oWK/biQmAyf7EhERkWKxkCEiIiLFYiGjJ2tra3z++eewtrZm/HyOr+TclR5fybkrPb6Sczd0fCXnrvT4hs49Nwr8ZF8iIiIquDgiQ0RERIrFQoaIiIgUi4UMERERKRYLGSIiIlIsFjJERESkWCxkiHIQHR0tW6yDBw9CrVZnOa5Wq3Hw4EHZ7seQoqKicPjwYRw+fBhRUVHGTodI0RITE5GQkKC9fvv2bSxYsAC7d+82YlbKxELGBKWkpODatWvZfvCRYcyZMwe//fab9voHH3wANzc3eHp6IiwsTHL8pk2b4vnz51mOx8TEoGnTppLjA8Dly5exc+dObN++XedHqvj4ePTr1w8eHh5o3LgxGjduDA8PD/Tv31/njdiUHTp0CD179kS9evVw//59AMDatWtx+PBhI2f2Zjdu3MCkSZPQrVs3PHnyBADw999/49KlS5Jj9+7dWzGFdHbWrl2LBg0awMPDA7dv3wYALFiwANu2bTNyZm8WFBSENWvWAEj/0lSnTh3MnTsXQUFBWLZsmeT4e/fuRbt27VCmTBmUKVMG7dq1w549eyTHBYC7d+/i3r172usnT57EiBEj8P3338sSP88E5cmjR49Ez549RcmSJYW5ubkwMzPT+ZEiPj5e9OvXT5ibmwtzc3Nx48YNIYQQn376qZg1a5Yc6RvUtm3bsv3Zvn272L17t7h586bk+4iIiBD/+9//RNeuXcXjx4+FEEL89ddf4uLFi5Li+vj4iCNHjgghhNi9e7dwcXERu3btEv379xfvvPOO5LxVKpV48uRJluPXrl0Tjo6OkmLfuHFDVKlSRahUKmFmZiZUKpX2stTnpBBCDBo0SPj5+Ym//vpLxMTEiJiYGLFjxw5RpkwZMWTIEMnx79y5I+7evau9fuLECTF8+HDx3XffSY4thBCbNm0Stra2YsCAAcLa2lr7ulq8eLFo06aNXjEDAwNFtWrVcvUjRUhIiLC1tRUtWrQQVlZW2txnzZol3nvvPUmxhRAiKChIWFpairJly4qZM2eKe/fuSY75KkO9Zr/99lvh7u4uZsyYIWxtbbWPzcqVK0WTJk0k5y2EEAcPHhQ9evQQdevW1T42a9asEYcOHZIc283NTfsY/PDDD6JKlSpCo9GIDRs2iPLly0uKvXTpUmFhYSG6du0qFi5cKBYuXCi6desmLC0txZIlSyTn3rBhQ7FmzRohhBAPHz4UTk5Ool69esLd3V1MmzZNcvy8YiGTR61btxYVK1YU3377rdiyZYvYunWrzo8Uw4YNEzVq1BCHDh0S9vb22hfm1q1bRWBgoF4xXVxchKura65+pHr1gzTzB2rGfxs3biyeP3+uV3xDvqnb2NiIO3fuCCHS/x0GDRokhEgvNFxcXPSOGxwcLIKDg4WZmZl49913tdeDg4NFhw4dhI+Pj2jVqpWk3Nu1ayeCgoJEVFSUcHBwEJcvXxaHDh0StWvXFgcPHpQUW4j0N9z9+/dnOb5v3z7h7u4uOb6h3xQDAwPF6tWrhRBCODg4aJ83Z8+eFcWLF9cr5tSpU7U/EyZMEE5OTqJu3bpi5MiRYuTIkaJevXrCyclJTJgwQVLudevWFXPnzs2S+4kTJ4Snp6ek2BmePHki5s6dK6pUqSIsLCxE69atxcaNG0VKSork2IZ8zVaoUEFs2bJFCKH72Fy4cEG4ublJii2EYQrgzGxtbcXt27eFEEJ07txZTJ06VQiRXtjb2tpKiu3p6SkWL16c5fiSJUuEh4eHpNhCpH+uXL16VQghxMKFC0X9+vWFEELs2rVL+Pr6So6fVyxk8sjBwUGcO3fOILFLlSoljh07pr2fjBfO9evX9f7WvmrVKu3P3Llzhaurq06V3rVrV+Hq6irmzZsnOf89e/aIOnXqiD179ojY2FgRGxsr9uzZI+rVqyd27NghDh8+LCpVqiT69eunV3xDvqmXLFlSOyJTrlw5sWHDBiGEEFevXpU0YtKnTx/Rp08foVKpRJcuXbTX+/TpIwYNGiS+/PJLERUVJSl3Nzc3ERYWJoQQwsnJSfsGs3fvXr0L4MxsbW3F5cuXsxy/ePGisLOzkxzf0G+Ktra2IjIyUgih+7y5ceOGsLa2lhy/f//+YtKkSVmOT5kyRfTt21dSbHt7e+1IZubcIyMjZcn9VWfOnBGffvqpsLGxEe7u7mLEiBEiPDxc73iGfM3a2NiIW7duZYkdHh4ubGxsJMUWwjAFcGYBAQFi4cKF4s6dO8LJyUkcPXpUCCHE6dOnJce3t7cX169fz3I8PDxc2NvbS4qdET/jNdW+fXsxe/ZsIYQQt2/fluWxzysWMnlUoUIFcfbsWYPEzjw8mvmFExoaKpycnCTH79SpU7ZV+uLFi0VQUJDk+JUqVdIWA5kdPnxYVKxYUQghxD///CO8vb31im/IN/VPPvlElC5dWrRo0UK4ubmJly9fCiGEWL9+veTTA0Kkf4OPi4uTHCc7Li4u2sfFz89P7Nu3TwiRPqQv9ZudEEI0a9ZMdO7cWSQmJmqPJSQkiM6dO4vmzZtLjm/oN0VfX1/xzz//CCF0nzerV68WFSpUkBzfyckp2w/78PBwya9bT09P7Wsqc+6bN28Wfn5+kmK/6sGDB2L27NnC399f2Nvbi169eonmzZsLCwsLvb/oGPI1W6FCBe0oeObYixYtkuU1a+gCeOPGjcLS0lKYmZnpnL7+8ssvRevWrSXF7tatm/jqq6+yHP/6669Fly5dJMUWQojatWuL8ePHi4MHDwobGxsRGhoqhBDi2LFjso0U5gULmTzatWuXaNmypfYJLqdGjRqJRYsWCSHSXzgZbwCffvqp5NMPQuRcpV+/fl2WKt3GxkZcuHAhy/Hz589rP5Bu3bql94erId/UU1JSxDfffCOGDRumU6jOmzdP/PDDD5JiG1rDhg21Q+zdunUTrVu3FocPHxa9evUSlSpVkhz/woULwsPDQ7i5uYlmzZqJZs2aCTc3N+Hp6Sl5noMQhn9T/PLLL0XFihXF8ePHhaOjozh06JD4+eefRdGiRbWvNymKFy8uVq5cmeX4ypUrRbFixSTFHj16tGjYsKF4+PChcHR0FNevXxeHDx8Wfn5+2lMRUqSkpIhNmzaJtm3bCktLS1GjRg2xbNkyERMTo73N5s2b9T69asjX7A8//CA8PT3Fr7/+Kuzt7cX69evFjBkztJelMnQBLET6qdSzZ88KjUajPXbixAlx5cqVPMfKGGVfuHCh+OKLL4Szs7N49913xRdffCG++OIL0bZtW+Hi4iK++OILyXnv379fuLi4CDMzM51Rx4kTJ4rg4GDJ8fOKhUweubi4CCsrK2FmZiYcHBxknWdy6NAh4eDgIIYMGSJsbGzE8OHDxTvvvCPs7e3F6dOnJedeqlQp8c0332Q5/s0334hSpUpJjt+gQQPRunVrnUmtT548Ea1btxaNGjUSQqSPyJQrV06v+IZ6U09JSRF9+/aVZTJyTgw5SXznzp3i999/F0KkF6X+/v5CpVIJd3d3sWfPHjnSF/Hx8eL7778Xo0aNEqNGjRI//PCDSEhIkCW2od8U09LStB9wGfO2bGxssj0dpI9Zs2YJGxsbMXToULF27Vqxdu1a8emnnwo7OzvJk/STk5PFgAEDhIWFhVCpVNpv8D179hRqtVpy7m5ubsLV1VV8/PHHOZ4yf/HihfDx8dErvqELsZ9//lmULVtW++/q6ekpfvzxR8lxhTB8ASw3Hx+fXP3INYdFrVZnme8YGRmpndCdn7j7dR6tXr36tb/v3bu3pPg3btzA7NmzERYWhri4OFSvXh3jx49HQECApLgAsGrVKgwYMABt2rRBnTp1AAAnTpzAzp078cMPP6BPnz6S4l+7dg1BQUGIjIyEt7c3gPRlen5+fti2bRvKlSuHrVu34uXLl/jwww/zHD8lJQWffPIJVq1aBY1GAwsLC2g0GnTv3h2rVq2Cubm53rk7OzsjNDQUvr6+esd4nTZt2uDOnTv49NNPUbJkSahUKp3fBwUFyXp/z58/h6ura5b7MVUajQaxsbFwdXXVHrt16xbs7OxQrFgxWe4jJSUFERERiIuLQ8WKFeHg4CBLXADYsGEDFi5ciCtXrgAAKlSogOHDh+ODDz6QJf6dO3dw8eJFxMXFoVq1anjrrbdkibt27Vp07twZNjY2ssR7lSFfs5klJCQgLi5OtucKAAgh8OWXX2LWrFnaNgPW1tYYM2YMvvjiC8nx4+PjMXv2bOzduxdPnjxBWlqazu9v3rwp+T4KCxYyhcyJEyewaNEinTfcYcOGaQsbqdLS0rB7926Eh4cDAPz9/fHOO+/AzEy+lkWGeFPv3bs3AgMDMXLkSBkyzMrR0RGHDh1CYGCg7LH79euHhQsXwtHRUed4fHw8hg4dip9++klS/NWrV8Pd3R1t27YFAIwbNw7ff/89KlasiPXr16N06dKS4gPpjQFDQkJw48YNdO/eHY6Ojnjw4AGcnJxkLTiU5vDhw2jYsKHB4hv6uZPh7t27uHDhguyFWH4wVAHcrVs3HDhwAB9++GG2X26GDx8u+T5SUlIQGRmJMmXKwMLCQlKs6tWrY+/evXB1dUW1atVe+yXp7Nmzku4rr1jI6EGj0WDr1q3aYqBSpUro0KGD5G8XsbGx2R5XqVSwtraGlZWVpPiUsxkzZmDu3Llo3rw5atSoAXt7e53fDxs2TFL8ihUr4pdffkG1atUkxcmOubk5Hj58mOXb6NOnT1GiRAnJjRX9/f2xbNkyNGvWDMeOHUPz5s2xYMEC/Pnnn7CwsMDmzZslxb99+zZat26NO3fuIDk5GeHh4fDz88Pw4cORnJyM5cuX5zlmp06dcn1bqfkD6Q3NNm3ahJs3b2LMmDEoUqQIzp49i+LFi8PT01PvuFZWVvD09ES3bt3Qs2dPVKxYUXKumRn6ufMqjUaDCxcuoHTp0jqjb7n1pg/QzPL7wzSvXFxcsGPHDjRo0ED22AkJCRg6dKj2DELGa2ro0KHw9PTEhAkT8hxz2rRpGDt2LOzs7DBt2rTX3vbzzz/XK299SSvRCqGIiAi8++67uH//Pvz9/QEAs2bNgre3N3bs2IEyZcroHdvFxeW1L1IvLy/06dMHn3/+ud4jHDdu3MDKlStx8+ZNLFiwAMWKFcPff/+NUqVKoVKlSvqmrrV3794ch0qlfrsbNWpUtsdVKhVsbGxQtmxZBAUFoUiRInmOvWLFCri4uODMmTM4c+ZMlvhSC5kFCxZgwoQJ+O677+Dj4yMpVobY2FiI9HluePnypc7pAY1Gg7/++kuWofa7d++ibNmyAICtW7fi/fffx6BBg9CgQQM0adJEcvzhw4ejZs2aCAsLg5ubm/Z4cHAwBg4cqFdMZ2dn7WUhBLZs2QJnZ2fUrFkTAHDmzBlER0fnqeDJyfnz59GiRQs4Ozvj1q1bGDBgAIoUKYLNmzfjzp072u6t+njw4AF+/fVXrF+/HrNnz0aVKlXQo0cPdOvWDV5eXnrHza/nzogRIxAQEID+/ftDo9Hg7bffxtGjR2FnZ4c///wzz8+fjh07ai8nJSXh22+/RcWKFVGvXj0AwPHjx3Hp0iV8/PHHknMPDg7O9v048/tN9+7dtZ8DeeXq6qrXe1VuTJw4EWFhYQgJCUHr1q21x1u0aIGpU6fqVchkLk7yu1B5o3yflaNwbdq0Ea1btxbPnj3THnv69Klo3bq1ePfddyXFXr16tfDy8hKTJk0S27dvF9u3bxeTJk0S3t7e4rvvvhMzZswQLi4uYubMmXrFN3SX0KlTpwozMzNRu3ZtERQUJDp27KjzI1WTJk2Ek5OTsLe3F9WrVxfVq1cXDg4OwtnZWdSpU0fb/O/SpUuS70tuhpgknrl7b3Y/5ubmYsaMGZJzL1q0qHYlV2BgoLZ5XUREhCyr3YoUKaLtI/PqEl05lo+PGzdODBgwQGdyrFqtFoMGDRJjxoyRHL958+Zi7NixQgjd/I8cOSJKly4tOX6GmzdvihkzZohKlSoJc3Nz0bRpU71j5ddzx9PTU5w6dUoIIcSWLVtEyZIlxbVr18SkSZO0/YL0Zcj+PUII0bt3b+Hs7CxKly4tOnXqJDp16iR8fHyEi4uL+OCDD4S/v7+wtrYWhw8f1iv+2rVrxfvvvy/i4+Ml5/oqQ/Qky8zQ3bjzioVMHtnZ2Ynz589nOR4aGir5Tb1Zs2bit99+y3L8t99+E82aNRNCpLfH9vf31yu+obuElihRQvshZwjz588XnTp10lkaGh0dLd5//32xYMECER8fL4KCgkTLli31vo/k5GRx9epVkZqaKkfKWpkbE2b3o4+QkBCxf/9+oVKpxObNm0VISIj25+jRo+L+/fuy5N69e3dRvXp10b9/f2FnZyeePn0qhEjfkkKO5d0uLi7a4jPz8/LQoUOSly8LIYS7u7u2UMrs6tWrokiRIpLjOzk5iYiICCGEbv63bt2SvWmdWq0Wf/zxhwgMDJS02i2/njvW1tbaD7yBAweK4cOHCyHSizKpH6iG7N8jhBDjx48XH330kc7SaI1GIz799FMxceJEkZaWJgYNGiQaNGigV/zAwEDh6OgoHBwcROXKlWXd2sLQPcle7cbt6OjILQqUxNXVNcemb1KXX9vY2OT4wsz4Znrz5k29v6UauktokSJFtG/ohuDh4ZHtaMvFixe1bbfPnDmjV3tyJe9zdevWLZ03W7m9ePFCfPLJJ6JDhw7i77//1h6fMmWKLN/aP/jgAzFw4EAhxH/9k16+fCmaNWsm+vTpIzm+i4tLttuHbN26VdL2Exkyj1hlfl3t3r1beHl5SY4vRPr7y0cffSSKFi0qHB0dRc+ePXX+LfR169YtkZaWJkOG2StVqpTYtWuXUKvVwtvbW/z5559CiPTXrNTH3pD9e4RIL4CvXbuW5fi1a9e07zHnz58Xzs7OesXPvM1Fdj9SGLonGbcoULgPP/xQVKpUSRw/flykpaWJtLQ0cezYMVG5cmXRu3dvSbHfeustMX78+CzHx48fr+29curUKb33yjB0l9Bx48aJ6dOnS46TE3t7+2z3/Nm/f79wcHAQQqR33dTnm54h9rl6laE2z8sQHx8vrly5IsLCwnR+TN3du3dFxYoVRYUKFYSFhYWoW7eucHNzE/7+/rL0pBg5cqRwc3MTc+fOFYcOHRKHDh0S33zzjXB3dxcjR46UHL9///6iY8eOIiUlRfuhcfv2bVGtWjXtCIS+JkyYIHx8fISVlZVo27atWLduneRTEWFhYdrC99XnitzPnc8//1w4OzuL8uXLi1KlSomkpCQhhBArVqwQdevWlRTbkP17hEj/sN62bVuW49u2bdMWYeHh4bIUw3IzdE8yblGgcC9evBAdOnQQKpVKWFlZaec9dOzYUURHR0uKvW3bNmFlZSWqVKki+vfvL/r37y+qVq0qrK2txR9//CGESN/xVd83X0M3pxo2bJhwcXERjRs3Fp9++ql2A72MH6m6d+8ufH19xebNm8Xdu3fF3bt3tUVYz549hRDpWwrUqFEjz7ENfU7ZkPOTnjx5Itq2bZvjfAd9vOkDTu5CKTU1Vaxdu1aMHTtWfPTRR7I23NNoNGLOnDnCw8ND2zjNw8NDzJkzR5amctHR0aJFixbCxcVFmJubC29vb2FpaSkaN24seVuK+vXri6VLl0rejyszlUqlLRBz2ug147gcNm3aJObNm6czp2LVqlXZFgl59dtvv4n69etr55rVr18/29Pz+hg6dKhwd3cX8+bN0xbA8+bNE+7u7mLYsGFCiPTuwvqeWspw+vRpbSEm5/Y3N27cEAMGDBC1atUSFSpUED169Mh2WoQ+TG2LAi6/1tP169dx9epVAOm9WDJWdUh169YtfPfdd7h27RqA9KWvgwcPlmWli6GbUzVt2jTH36lUKuzbt09S/Li4OIwcORJr1qzRLgu1sLBA7969MX/+fNjb2yM0NBQA8tyvxc7ODhcvXoSfnx8cHR0RFhYGPz8/hIWFoXHjxoiJiZGUe7169dC5c2eMGjVKJ/7JkyfRqVMn3Lt3T+/YPXr0wO3bt7FgwQI0adIEW7ZswePHj7VLyjP6v+SFmZkZVCoV3vT2oFKpoNFo9E0932W0OHBycpI99pEjR3QaWbZo0UL2+5DD7du3UapUKahUKty+ffu1t5XaI2j69Omv/f2UKVMkxTckjUaD2bNnY8mSJXj8+DEAoHjx4hg6dCjGjx8Pc3Nz3LlzB2ZmZnqtIHvy5Am6du2KkJAQuLi4AEhfxt+0aVP8+uuvKFq0qN659+rVC02bNkXjxo0lraTNSUhICIKDgxEbG4vevXtrV6R+9tlnuHr1qiwtDfKChUwhpMTmVBqNBkeOHEFAQAAsLS21XS/9/PxkaVDVuHFjdO7cGUOHDoWjoyPOnz8PX19fDB06FNevX8fOnTslxXdwcMCFCxfg6+urU8jcunUL5cuXR1JSkt6xS5YsiW3btqF27dpwcnLC6dOnUa5cOWzfvh1fffUVDh8+nOeYb/qAy0yfD7vt27ejTZs2sLS0xPbt21972w4dOuQ5fnaioqK0XxDKly8Pd3d3WeKuWbMGXbp0gbW1tc7xlJQU/Prrr+jVq1ee4hnjsTGUV/smpaamIjIyEhYWFihTpozJ93rJYIgCuEuXLrh58ybWrFmDChUqAAAuX76M3r17o2zZsli/fr3esQcMGICDBw/ixo0b8PDwwNtvv40mTZrg7bfflu39Pj+6cecWC5k80mg0WLVqVY69UqSOOgDpzYzu3LmDlJQUneNVqlSRHDszqc2p8puNjQ2uXLlikG0EDh8+jDZt2qBnz55YtWoVBg8ejMuXL+Po0aM4cOAAatSoISm+l5cXNmzYgPr16+sUMlu2bMGYMWNw48YNvWM7OTnh/Pnz8PHxQenSpbFu3To0aNAAkZGRqFSpkra9ulSXL1/O8rxUqVRo3759nmOZmZnh0aNHKFas2Gt7Iskx4pPRpXbNmjXa16u5uTl69eqFxYsXw87OTlL8nJrKPXv2DMWKFctz/vn52ADpo8v79+/P9v3MECMmsbGx6NOnD4KDg/O8VUmRIkUQHh4Od3f3N27B8fz5c6mpGpSzszP27NmDWrVq6Rw/efIkWrZsiejoaMn3cf/+fRw8eBAHDhzAgQMHEB4ejpIlS0oaAQbS+8j069dPlq7ecmBDvDwaPnw4Vq1ahbZt26Jy5cqy7mUTFRWFvn374u+//87291LftORuTgWkd1BdtWoVnJyc3thcTOpwY+XKlXHz5k2DFDINGzZEaGgoZs+ejYCAAOzevRvVq1fHsWPHZNnnqmvXrhg/fjw2btwIlUqFtLQ0HDlyBGPGjMnzN/ZX+fv749q1a/Dx8UHVqlW1TfeWL1+OkiVLSs795s2bCA4OxoULF3RON2U89/V5Xmb+wHz1w1Nuo0aNwoEDB/DHH39ou6gePnwYw4YNw+jRo7Fs2TJJ8YUQ2b4P3Lt3T6cxX27l52Pzww8/4KOPPoK7uztKlCih8/+hUqkMUsg4OTlh2rRpaN++fZ4Lmfnz52u3U1iwYIHsuWX2+PFjjBkzRvul9dXv/FLfj9PS0mBpaZnluKWlpWz/7q6urnBzc4OrqytcXFxgYWEh6ZRVhm3btmHmzJl4++230b9/f7z33ntZRiTzVb7PylE4Nzc3sWPHDoPE7t69u2jQoIE4deqUsLe3F7t37xZr164V/v7+2mWLUhiiOVWfPn1EbGys9vLrfqT6+++/RWBgoPjjjz/EgwcPRExMjM6PKTPkLsZr167VLkM9ffq0cHd3FyqVSlhbW4tff/1Vcu7t2rUTQUFBIioqSjg4OIhLly6JQ4cOidq1a4uDBw9Kip2SkiKaNWuWbdsBubi5uWW72m3fvn3C3d1d77iBgYGiWrVqwszMTAQEBOj0AKlSpYpwdHQUnTt3lpB5epPMjJU+mSUnJ4vVq1dLii1E+iT3jBUn+enQoUOSV/t8+OGHYsWKFQZr+dC6dWtRsWJF8e2334otW7aIrVu36vxI1aFDB9G4cWOdnj337t0Tb7/9tuQGohMnThT16tUTNjY2olq1amLEiBFi69atWXarluLs2bPaCdEuLi5iyJAh4uTJk7LFzwueWsojDw8PhISEoFy5crLHNsRch8xsbGwQEREBLy8vDBo0CHZ2dliwYAEiIyNRtWrVHPd6MhWZh9kzf3MU/34jlvoNSaPRYMuWLdo9tCpWrIigoCDJm61lZqhdjDNLSEjA1atXUapUKVnmgbi7u2Pfvn2oUqUKnJ2dcfLkSfj7+2Pfvn0YPXo0zp07Jyl+0aJFcfToUYPN1bKzs8OZM2e08xAyXLp0CbVr10Z8fLxecTP2m5k2bRpGjx6tM1fLysoKPj4+eO+99yTtkSb3aatXOTk5ITQ0FH5+fpLi5GTRokU614UQePjwIdauXYu3334b69at0zv2wIEDceDAAYPNAzHkRq9A+lzFDh064NKlS/D29tYeq1y5MrZv3y5pCwozMzMULVoUI0eORKdOnQzyeZUhNTUVf/zxB1auXIldu3ahfPny6N+/P/r06aPXiKQ+eGopj0aPHo2FCxdiyZIlsp5WAtLP5We8Ybm6uiIqKgrlypVDQECALJPiihcvjsuXL6NkyZLYuXOndkg9ISFB8oql/LB//36Dxb506RI6dOiAR48eafdOmTNnDooWLYo//vgDlStXluV+SpUqhVKlSkmOk9O+U9mZN2+epPvSaDTa4Xx3d3c8ePAA/v7+KF26tHbyrBQ9e/bEihUrMHv2bMmxslOvXj18/vnnWLNmjXZPocTEREybNk27R48+Mvab8fHxQZcuXXT2K5KLkPm01as6d+6M3bt3Y8iQIZJjZWf+/Pk61zM+YHv37o2JEydKiv3DDz8A0J0HMnfuXAwePFiWeSDe3t5vXLUnNf7Zs2exZ88enRWwcqx2O3fuHA4cOICQkBDMnTsXVlZW2kKvSZMmshY2QgikpqYiJSUFQgi4urpiyZIlmDx5Mn744Qd06dJFtvvKCQuZXHh17se+ffvw999/o1KlSlnOcUqZB2LouQ59+/bFBx98oN0yPuMFc+LECZQvX15y/GfPnmHKlCk5ThyUOvnu7bfflvT3rzNgwABUqlQJp0+f1k58fvHiBfr06YNBgwbh6NGjkuILIbBp06YcH5u8Pm9eHQU5e/Ys1Gq1tggLDw+Hubm55EnKQPrcpLCwMPj6+qJOnTr46quvYGVlhe+//16Wb/JqtRo//fQT9uzZk+3O41ILsQULFqB169bw8vJC1apVAQBhYWGwtrbG7t27JcUGgN69eyM6Oho///wzbty4gbFjx0re/Tpjl2eVSoXmzZvrjApqNBpERkbqbAaor7Jly2Ly5Mk4fvy4dkVgZlI3S42MjJT097lhqHkghtjo9VUqlQrvvPMO3nnnHVnjVq1aFVWrVtX++4WFhWH+/Pn45JNPkJaWJssk8TNnzmDlypVYv349rK2t0atXLyxdulTbimTx4sUYNmxYvhQyPLWUC3379s31bVeuXKn3/fz8889Qq9Xo06cPzpw5g9atW+P58+ewsrLCqlWrZHlC/P7777hz5w46d+6sHbpcvXo1XF1dJS/lfPfddxEREYH+/fujePHiWb5J9u7dW1L8DIZY1WVra4vTp09n2QH84sWLqFWrFhITE/WODaRPEv/uu+/QtGnTbB8bKc+befPmISQkRPvvCKQXYX379kWjRo0wevRoSbnv2rUL8fHx6NSpEyIiItCuXTuEh4fDzc0Nv/32G5o1ayYpvqH7DwHpz5lffvlF55tvjx49YGtrKzn2q7tfX7t2DX5+fpg0aZLeu1/nx2krAK+dOK9SqbRtDkzRZ599hpCQEJw7dw4VKlTQjjg0btxYllWYrq6uSEhIgFqthp2dXZYiT58vZosWLcKgQYNgY2OT5bTbq6QUkUIInDt3DiEhIQgJCcHhw4cRGxuLKlWq4O23384yUpZXAQEBuHr1Klq2bImBAweiffv2WUb1nz59imLFihl8wjrAQsakyT3XwdDNqRwdHXH48GHtt165GXJVV9WqVTF//vwsH8r79u3D8OHDceHCBb1jA+nLRn/++We8++67kuJkx9PTE7t37862CGvZsiUePHgg+30+f/78jctfTcWsWbNQvHhx9OvXT+f4Tz/9hKioKIwfP15S/ObNm6NGjRr46quvdJbWHz16FN27d8etW7f0jr169Wp07drVuCtCTJSh54GsXr36tb/X54uZr68vTp8+DTc3N4MWka6uroiLi0PVqlW1BV6jRo20jfek+uKLL9CvXz+9RhsNwhgzjJWsadOm4sWLF1mOx8TEiKZNm+Z/QnkQGBio81OpUiVhZ2cnnJycJO+2KoQQNWvW1Lb5NwRDrurasWOHqFSpkti4caN2+4ONGzeKgIAAsWPHDsmro3x8fMSVK1ck5ZgTBweHHFflZOxBVZiVLl06241ejx8/Lnx8fCTHN+Tu1ydPnhTHjx/Pcvz48ePaFYiFVWhoqFi4cKEIDg4W7u7uwsPDQ3Tr1k1899132W72WJj8+eefJr+SU04ckcmjzM2qMnvy5Ak8PT2Rmpqqd+xXvzG+KqMNtJykNKd61alTpzBhwgRMmTIFlStXzjIUK7UrpiFXdWW3Ikq80i9FSFgdtXr1auzcuRM//fSTLKczMuvVqxcOHTqEuXPnonbt2gDS5z2NHTsWjRo1euM3S1Nw+vRpbNiwIdtThlL7D+XUSPHmzZuoWLGipK7KAFCsWDHs2rUL1apV0xmR+eeff9CvXz/cvXtX79i1a9fGuHHj8P777+sc37x5M+bMmYMTJ05Iyt0Y7zmGkjEP5JdffpFtHkiGpKSkLM9Lqe9n06dPx5gxY7I0ZExMTMTXX39tcts35OcCg7ziZN9cOn/+vPby5cuX8ejRI+11jUaDnTt3Sh5me/Hihc711NRUXLx4EdHR0ZLnIeRESnOqV7m4uCA2NjZLrlIKgMwMuarLkCuiAOCDDz7A+vXrUaxYMfj4+GQp8qTkv3z5cowZMwbdu3fXFtIWFhbo378/vv76a0l554eMNv6tWrXC7t270bJlS4SHh+Px48cIDg6WHN/b2xtHjhzJUsgcOXIEHh4ekuN36NAB06dPx4YNGwCkF7537tzB+PHj8d5770mKffnyZVSvXj3L8WrVquHy5cuSYgPGec+Ri3jDPBCp4uPjMX78eGzYsAHPnj3L8nup72fTpk3DkCFDshQyCQkJmDZtmskVMrlts2CM080sZHIpMDBQu4oguxe4ra0tFi9eLOk+tmzZkuVYWloaPvroI4Ns/JUhJiZG8qaIQPrmhZaWlli3bl22E1qlMuSqLkOuiALSz6efOXMGPXv2lP2xsbOzw7fffouvv/5au9VBmTJlsqz+MVVffvmldkWFo6MjFi5cCF9fX+0yWqkGDhyIESNGIDU1Vfva3bt3L8aNGyd5IjQAzJ07F++//z6KFSuGxMREvP3223j06BHq1auHmTNnSoptbW2Nx48fZ1kd9vDhQ1n6GxnrPUcORYoU0ZkHMnDgQFnngYwbNw779+/HsmXL8OGHH2Lp0qW4f/8+vvvuO1laBYgcltaHhYWhSJEikuPLzdBf9qTgqaVcun37NoQQ2h2LMy/vs7KyQrFixQzWi+XatWto0qQJHj58KCmOIZtTAekfqOfOndMuAZZbdqu6nj17BisrK6xevVryqq7o6GisWLFC2xCvUqVK6Nevnyz9Ouzt7bFr1y40bNhQcqyCxt7eHpcuXYKPjw/c3NwQEhKCgIAAXLlyBc2aNZP8vBdCYMKECVi0aJH29ICNjQ3Gjx8v67deQ+x+3a1bNzx8+BDbtm3TPg+jo6PRsWNHFCtWTDsKJDe53nMMaceOHWjUqJFBdjIH0ns+rVmzBk2aNIGTkxPOnj2LsmXLYu3atVi/fj3++usvveJmTJKPiYmBk5OTTjGj0WgQFxeHIUOGYOnSpXL9rxR4HJHJpYzNsV6+fJnv33Rv3LgBtVotOY4hm1MBQM2aNXH37l2DFTI9e/bUXq5RowZu374t26qu06dPo1WrVrC1tdXOM5k3bx5mzpyp3XdJCm9vb4O94Sqdq6srXr58CSB9BdbFixcREBCA6OhoWTa8VKlUmDNnDiZPnowrV67A1tYWb731liwrgVJTU2Fra4vQ0FA0aNBAu5eTXL755hs0btwYpUuX1u4kHRoaiuLFi2Pt2rWy3ldmcr3nGFLbtm0NGv/58+fakTAnJyftcuuGDRvio48+0jvuggULIIRAv379MG3aNJ0vShlL66U0aswvhpzXllcsZPKoePHi+OCDD9CvXz/Zv12/OpkqY8Rkx44dsvRgMXRzqqFDh2L48OEYO3Zsts219Onzkl8TzEaOHIkOHTrghx9+0A7Zq9VqDBgwACNGjMDBgwf1jg2kn34YN24cli9fbrDmWkrVuHFj/PPPPwgICEDnzp0xfPhw7Nu3D//88w+aN28u2/04ODhk2WlYKktLS5QqVUrWiaWZeXp64vz58/jll18QFhYGW1tb9O3bF926dct2w8G8MvR7jpL5+fkhMjISpUqVQvny5bFhwwbUrl0bf/zxh6TTVxmPq6+vL+rXry/Lv2N+M/S8trziqaU82rp1K1atWoW//voLPj4+6NevH3r16iXLpMFXG4NljJg0a9YM/fr1k3XPH0PIvPInQ8ZuyfpO9n1ds7RX70dK4zRbW1ucO3cuS4fjy5cvo2bNmpJHBgzRXKugeP78OZKSkuDh4YG0tDR89dVX2r2XJk2aJEtzM0NasWIFNm/ejLVr1xpsbsPly5ez/eYrtYml0t9zDGn+/PkwNzfHsGHDsGfPHrRv317bjn/evHkYPny4bPdliFVRhlSlShUMHjxYO68to/N3xry2jIaO+YWFjJ6ioqKwdu1arFq1CleuXEGrVq3Qr18/dOjQodC++G/fvv3a32ecnjNFGUP1LVu21Dm+a9cu9OrVC48fP5YU3xDNtQoCtVqNdevWoVWrVihevLix09FLtWrVEBERgdTUVJQuXTrLqWcpK9Ju3ryJ4OBgXLhwQedLQQZDjQQVdqmpqWjdujWWL1+u3YDy9u3bOHPmDMqWLSupi3iGhIQEjBs3zmCrogzJ0PPa8qpwfuLKoGjRohg1ahRGjRqFxYsXY+zYsfjrr7/g7u6OIUOGYMKECVmW1RV0plyovEmXLl3Qv39/fPPNN6hfvz6A9MmbY8eORbdu3STHL6yFyptYWFhgyJAh2gnWStSxY0eDxR4+fDh8fX2xd+9e+Pr64sSJE3j+/DlGjx6Nb775xmD3W9hZWlrqtNwA0t/f5HyPGzt2rEFXRRmSoee15RVHZPT0+PFjrF69GqtWrcLt27cRHByM/v374969e5gzZw48PDzyvCFdxkZxr1KpVLCxsUHZsmXRp0+fXJ9uMYYHDx7g8OHD2W6MKHUDOkNKSUnB2LFjsXz5cu0kR0tLS3z00UeYPXu2LBNDNRoNtmzZov3QrlixIoKCggrtCF6GJk2aYOTIkQgKCjJ2KibH3d0d+/btQ5UqVeDs7IyTJ0/C398f+/btw+jRo3Pd2yMnht7oVclGjhwJa2trgxUVhloVlR+6d++OmjVrYtSoUfjiiy+wePFiBAUF4Z9//kH16tU52dfUbd68GT/99BN27dqFSpUq4eOPP0bPnj11Jn/Vr18fFSpUyHPs1q1bY9myZQgICNCunDl16hTOnz+PPn364PLly2jRogU2b95skm/6q1atwuDBg2FlZQU3NzedokylUpl0IWNlZYWFCxdi1qxZOr1Y5BpVu3TpEjp06IBHjx5pV3XNmTMHRYsWxR9//IHKlSvLcj9K9PHHH2PUqFG4e/dutrtfyzGMnx9Onz6tU6TKsfO4RqOBo6MjgPSi5sGDB/D390fp0qVx7do1yfE//PDD1270WpgZeld2Q62Kyg9LlizRdsT+3//+B0tLSxw9ehTvvfceJk2alP8J5d9uCAWDk5OTGDx4sDh58mSOt0lISBBTp07Nc+wBAwaI6dOnZzn+xRdfiAEDBgghhJgyZYqoUaNGnmPnBy8vLzFjxgyh0WiMnYrJqVu3rmjfvr14/vy59tjz589Fhw4dRL169YyYmfGpVKosP2ZmZtr/mrq7d++Khg0bCpVKJVxdXYWrq6tQqVSiQYMG4u7du5JiN2zYUGzZskUIIUS3bt1E69atxeHDh0WvXr1EpUqVJOfu4OAgQkNDJccpiJo0aZLjjxz76gUEBIiQkBAhhBDNmzcXo0ePFkIIsXDhQuHp6Sk5fmHCU0u5lJaWhq+//hq//fYbzM3N0bx5c3z++eey7pvj7OysnUyWWUREBGrUqIGYmBhcvXoVtWrV0p6fNCVubm44efKkyXcEzdCpU6dc31bqUKmtrS1Onz6d7Q7VtWrVQmJioqT4SqbkSeJA+khqdHQ0Vq9erR1tu3btGvr27QsnJyfs3LlT79i7du1CfHw8OnXqhIiICLRr1w7h4eFwc3PDb7/9JnkbgVq1amHx4sWoW7eupDiUd/m5KsoQ0tLSEBERke0pycaNG+drLjy1lEszZ87E1KlT0aJFC9ja2mLhwoV48uSJrJuq2djY4OjRo1kKmaNHj8LGxgZA+pMn47Kp6d+/PzZu3IgJEyYYO5VckaNjb26VK1cOjx8/zlLIPHnyJMu/d2Fj6oXKmxw4cABHjx7VaQTp7++PxYsXo1GjRpJit2rVSnu5bNmyuHr1Kp4/f67tDivVt99+a9CNXilnI0eO1F5u0aIFrl69KuuqKEM6fvw4unfvru14n5kc++rlFQuZXFqzZg2+/fZbDB48GACwZ88etG3bFj/++GO2/VP0MXToUAwZMgRnzpzRNu46deoUfvzxR3z22WcA0r+hBQYGynJ/cps1axbatWuHnTt3ZtsQL793RH2TlStX5tt9zZo1C8OGDcPUqVO1336PHz+O6dOnY86cOYiNjdXetjB+eFy7dg2LFy/WzjGpUKEChg4darAu0XLy9vbOdtd7jUYjS3+pV8nZq8bQG71S7sm9KsqQhgwZgpo1a2LHjh0oWbKk0edW8dRSLllbWyMiIgLe3t7aYzY2NoiIiICXl5ds9/PLL79gyZIl2ol8/v7+GDp0KLp37w4gfYv3jFVMpmbGjBmYMmUK/P39s0wclNqwLj+o1WqEhITgxo0b6N69OxwdHfHgwQM4OTnBwcFBUuzMxW7G45Lx0st8vTB+ePz+++/o2rUratasqW3Nfvz4cZw6dQq//vqr5B2kDW3btm348ssvsXTpUtSsWRNA+sTfoUOHYvz48QZdni1V7dq1YWFhgeHDh2c72dfQm6kWZsOGDUPZsmWzLIJYsmQJIiIisGDBAuMklgv29vYICwszmdFkFjK5ZG5ujkePHulsFuno6Ijz58/D19fXiJmZDldXV8yfPx99+vQxdip5dvv2bbRu3Rp37txBcnIywsPD4efnh+HDhyM5ORnLly+XFP/AgQO5vm1h+/AoU6YMevTogenTp+sc//zzz/Hzzz9rV5GZqsxdmzNvb2FhYZFlpYupLWc29EavlDNPT09s3749y+q2s2fPokOHDrh3756RMnuzZs2aYdy4cWjdurWxUwHAU0u5JoRAnz59dPqJJCUlYciQITpvVnKsn09JScl2AlWpUqUkxzYka2tr2TfNyy/Dhw9HzZo1ERYWBjc3N+3x4OBgDBw4UHL8wlac5MXDhw/Rq1evLMd79uyJr7/+2ggZ5Y0pf3N+E0Nv9Eo5e/bsWbbz9JycnPD06VMjZJR7Q4cOxejRo/Ho0SPZ9tWTgoVMLmXXmTXzbsxyuH79Ovr164ejR4/qHFfKKYfhw4dj8eLFWLRokbFTybNDhw7h6NGjsLKy0jnu4+OD+/fvy3If0dHRWLFihXYeSKVKldCvX798nXRsipo0aYJDhw5lGaY+fPiw5MmyhqZWq6FSqRS7xYIhNnql3Clbtix27tyJTz/9VOf433//re0vY6oyTvf269cvy+842deE5cfE0D59+sDCwgJ//vmnSUygyquTJ09i3759+PPPP1GpUqUsb4r53e0xL9LS0rJ98d27d0/bkEyK06dPo1WrVrC1tdU2O5w3bx5mzpyJ3bt3o3r16pLvQ0m2b9+uvdyhQweMHz8eZ86c0ZkIvXHjxnzffC6vlL7FQpcuXQDofiBJ3eiVcmfUqFH49NNPERUVpZ1svXfvXsydO9fkR/kiIyONnYIOzpExIfb29jhz5kyWHZiVom/fvq/9fX6uEsqrLl26wNnZGd9//7127lPRokURFBSEUqVKSc69UaNGKFu2LH744QedeRQDBgzAzZs3cfDgQTn+NxQjtyv9lPBh2qRJE4wYMcKkJ/XmROk9fJRu2bJlmDlzJh48eAAgfQR46tSp2Z5qNUXZ7cquUqnQvn37fM2DhYwJqVWrFubPn4+GDRsaO5VC5969e2jVqhWEELh+/Tpq1qyJ69evw93dHQcPHkSxYsUkxbe1tcW5c+eyFKmXL19GzZo1jbLRGsljw4YNmDhxIkaOHKnoLRbIeKKiomBrayt5dWR+yW5XduC/FZj5/eWDhYwJ2bdvHyZNmoQvv/wy2/PVhbG/SH5Sq9X49ddfcf78ecTFxaF69ero0aOHLN2bixcvjrVr16Jly5Y6x3ft2oVevXrh8ePHku+DjCO70SUlnZ5R6kavZDzt27eHubk5fvzxx2x3Zc/vuW0sZExIxhviq3NjlPKGCACbNm3Chg0bsgw3AunLCk1VUlKSQXvzDBs2DFu2bME333yD+vXrAwCOHDmCsWPH4r333jP5c+JyW7RoEQYNGgQbG5s3Tg439Q9TJZ+eedNGrzdv3jRidgVP9erVsXfvXri6uqJatWqvnQdpyu+Xht6VPa842deE7N+/39gpSLJo0SL873//Q58+fbBt2zb07dsXN27cwKlTp/DJJ58YO73XKlasGIKDg9GzZ080b95ctm7NGb755huoVCr06tULarUaAGBpaYmPPvoIs2fPlvW+lGD+/Pno0aMHbGxsMH/+/BxvZ+q7pgOmXai8yeTJkzFlyhRMnDhR9uc8ZRUUFKRt4aHEOVUZDL0re15xRIZkU758eXz++efo1q0bHB0dERYWBj8/P0yZMgXPnz/HkiVLjJ1ijrZs2YJ169Zhx44dcHZ2RpcuXdCzZ09tp1a5JCQkaBu8lSlTBnZ2drLGJ+NYu3Ytli9fjsjISBw7dgylS5fGggUL4Ovri6CgIGOnlyOlbfRKpqFRo0YYPXo0OnbsiO7du+PFixeYNGkSvv/+e5w5cwYXL17M13xYyJighISEbE/NmPqkQTs7O1y5cgWlS5dGsWLF8M8//6Bq1aq4fv066tati2fPnhk7xTd6+fIlNm3ahPXr12Pfvn3w8/NDz549MWXKFGOnVqCMGjUqV7dTqVSYO3eugbORZtmyZZgyZQpGjBiBmTNn4uLFi/Dz88OqVauwevVqkx5pHTduHIoUKaKYjV7JNBh6V/a8YiFjQqKiotC3b1/8/fff2f7e1OfI+Pn54ffff0e1atVQs2ZNDBw4EIMHD8bu3bvRtWtXk2vP/iaXL19Gjx49cP78eb0e+06dOuX6tqbcY8cQmjZtmqvbKWGProoVK+LLL79Ex44ddUYiL168iCZNmph0l1aNRoN27dohMTFRERu9Kl1edi1X2vulnLuy5xXnyJiQESNGIDo6GidOnECTJk2wZcsWPH78GDNmzDD5b6VA+v4b27dvR7Vq1dC3b1+MHDkSmzZtwunTp/P0oW5MSUlJ2L59O9atW4edO3eiePHiGDt2rF6xCnvH3tcx5VGKvIqMjES1atWyHLe2tkZ8fLwRMsq9WbNmYdeuXdotCl6d7EvyKsiT+uXclT3PBJmMEiVKiBMnTgghhHB0dBTXrl0TQgixbds20aBBA2OmlisajUakpqZqr69fv14MHTpULFq0SCQnJxsxszfbuXOn6NWrl3BychJFihQRgwYNEgcOHDB2WqQAFSpUEFu3bhVCCOHg4CBu3LghhBBi0aJFolq1asZM7Y1cXFzEypUrjZ0GkSQckTEh8fHx2sZrrq6uiIqKQrly5RAQEGDSS/GA9B4sX375Jfr16wcvLy8AQNeuXdG1a1cjZ5Y7wcHBaNeuHdasWYN33303yxC7HNRqNUJCQnDjxg10794djo6OePDgAZycnBTTCIuyGjVqFD755BMkJSVBCIGTJ09i/fr1mDVrFn788Udjp/daSt7otSDQaDTYsmWLdouLihUrIigoSNv9m3LJ2JUU/admzZpi586dQggh2rdvLz788ENx7949MW7cOOHn52fk7N7M3t5eREZGGjsNvcTGxho0/q1bt0T58uWFnZ2dMDc3135rHzZsmBg8eLBB75sM7+effxZly5YVKpVKqFQq4enpKX788Udjp/VGX375pRg6dKix0yiULl68KPz8/ISdnZ2oVq2aqFatmrC3txc+Pj7iwoULxk5PUTjZ14T8/PPPUKvV6NOnD86cOYPWrVvj2bNnsLKywurVq7UbvJmqoKAgdOrUKdudwpXgxo0bWLlyJW7cuIGFCxeiWLFi+Pvvv1GqVClUqlRJUuyMiaArVqyAm5ubdkJoSEgIBg4ciOvXr8v0f0HGlJCQgLi4OMlbWuSX4OBg7Nu3D25uborb6FXp6tWrh6JFi2L16tVwdXUFALx48QJ9+vRBVFQUjh49auQMlYOFjAlLSEjA1atXUapUKbi7uxs7nTdavnw5pk2bhh49emS750yHDh2MlNmbHThwAG3atEGDBg1w8OBBXLlyBX5+fpg9ezZOnz6NTZs2SYrv5uaGo0ePwt/fX2dly61bt1CxYkXutaRgzZo1w+bNm+Hi4qJzPDY2Fh07djTpVVdK3uhV6WxtbXH69OksX5IuXryIWrVqITEx0UiZKQ9PxBlZbvtpAKa/FPLjjz8GkH2epr7FwoQJEzBjxgyMGjVK27ESSP+QkqORX1paWrb///fu3dO5P1KekJCQLD2fgPQVcIcOHTJCRrnHQsV4ypUrh8ePH2cpZJ48eYKyZcsaKStlYiFjZLndk0IJSyFf3XBOSS5cuIB169ZlOV6sWDFZ+oC0bNkSCxYswPfffw8g/d8zLi4On3/+Od59913J8Sn/nT9/Xnv58uXLePTokfa6RqPBzp074enpaYzU8iwqKkrbWt7f3x9FixY1ckYF36xZszBs2DBMnToVdevWBQAcP34c06dPx5w5cxAbG6u9LTcMfj2eWiIC4OXlhQ0bNqB+/fo6p362bNmCMWPGaLcV0Ne9e/fQqlUrCCFw/fp11KxZE9evX4e7uzsOHjyomDkV9B8zMzPtF4zs3kZtbW2xePFi9OvXL79Ty7X4+HgMHToUa9as0X4RMTc3R69evbB48WJuoWFAmfe2evV5lPm6qY9mmwKOyJBsctrFWKVSwcbGBmXLlkXjxo1hbm6ez5m9WdeuXTF+/Hhs3LgRKpUKaWlpOHLkCMaMGYNevXpJju/l5YWwsDD8+uuvOH/+POLi4tC/f3/06NEDtra2MvwfUH6LjIyEEAJ+fn44efKkziiGlZUVihUrZpLP9cxGjRqFAwcO4I8//tAuwz58+DCGDRuG0aNHY9myZUbOsOAqSE0hjY0jMiQbX19fREVFISEhQWcWvp2dHRwcHPDkyRP4+flh//798Pb2NnK2ulJSUvDJJ59g1apV0Gg0sLCwgFqtRo8ePbBq1SrJH0hJSUmwsbGRKVsiebi7u2PTpk1o0qSJzvH9+/fjgw8+QFRUlHESI8oDFjIkm/Xr1+P777/Hjz/+qN1NNyIiAoMHD8agQYPQoEEDdO3aFSVKlJC8CshQ7t69iwsXLiAuLg7VqlXDW2+9JUtcJycnBAcHo2fPnmjevLnOsDIp3/Xr17F//348efIky1wxU95w1M7ODmfOnEGFChV0jl+6dAm1a9c2+S0WlO7FixdYsWKFTkO8vn37GrfdvwKxkCHZlClTBr///jsCAwN1jp87dw7vvfcebt68iaNHj+K9997Dw4cPjZNkJvm5YmzLli1Yt24dduzYAWdnZ3Tp0gU9e/ZEzZo1JcUl4/vhhx/w0Ucfwd3dHSVKlMiyX5Epd+Vu3rw53NzcsGbNGu2IYWJiInr37o3nz59jz549Rs6w4Dp48CDat28PZ2dn7fvAmTNnEB0djT/++AONGzc2cobKwUKGZGNnZ4eDBw9m+XA+deoU3n77bSQkJODWrVuoXLky4uLijJTlf17dgfns2bNQq9XaDfTCw8Nhbm6OGjVqyNYL5OXLl9i0aRPWr1+Pffv2wc/PDz179jTpb+30eqVLl8bHH3+M8ePHGzuVPLtw4QJat26N5ORkVK1aFQAQFhYGa2tr7N69W3IjSMpZQEAA6tWrh2XLlmlPXWs0Gnz88cc4evQoLly4YOQMlYOFDMmmbdu2ePToEX788UftbsDnzp3DwIEDUaJECfz555/4448/8Nlnn5nci3TevHkICQnJ0mWzb9++aNSoEUaPHi37fV6+fBk9evTA+fPnuSpBwZycnBAaGgo/Pz9jp6KXhIQE/PLLL7h69SoAoEKFCpyEng9sbW0RGhqq/eKU4dq1awgMDGRDvDzgqiWSzYoVK/Dhhx+iRo0a2lbnarUazZs3x4oVKwAADg4OmDt3rjHTzNbcuXOxe/dubREDpG/cOWPGDLRs2VK2QiYpKQnbt2/HunXrsHPnThQvXhxjx46VJTYZR+fOnbF7924MGTLE2Knk2axZs1C8eHEMHDhQ5/hPP/2EqKgoRY4yKUX16tVx5cqVLIXMlStXtKNjlDssZEg2JUqUwD///IOrV68iPDwcQHpzrcwv1FdP55iK2NjYbFdoREVF4eXLl5Lj79q1C+vWrcPWrVthYWGB999/H7t37+Z58AKgbNmymDx5Mo4fP46AgIAs+xUNGzbMSJm92XfffZdtI8hKlSppWxKQYQwbNgzDhw9HRESETkO8pUuXYvbs2ToNF6tUqWKsNBWBp5bIIF5t7GTqevXqhUOHDmHu3LmoXbs2AODEiRMYO3YsGjVqhNWrV0uKb2dnh3bt2qFHjx549913s3zYkXL5+vrm+DuVSoWbN2/mYzZ5Y2NjgytXrmT5f7h58yYqVqyIpKQkI2VW8L1p5aJKpWJDvFziiAzJas2aNfj666+1uzmXK1cOY8eOxYcffmjkzF5v+fLlGDNmDLp3747U1FQAgIWFBfr374+vv/5acvzHjx9zT6UCKjIy0tgp6M3b2xtHjhzJUsgcOXIEHh4eRsqqcFDy88bUsJAh2cybNw+TJ0/Gp59+qtMldMiQIXj69ClGjhxp5AxzZmdnh2+//RZff/21djuCMmXKZNnBW1+Ojo64ceMGVq5ciRs3bmDhwoUoVqwY/v77b5QqVYqrQxRm1KhR+OKLL2Bvb//aZfwqlcok54RlGDhwIEaMGIHU1FQ0a9YMALB3716MGzfOIBPc6T+lS5cGkD7p/86dOzobj6pUKrRv395YqSkOTy2RbHx9fTFt2rQsLf1Xr16NqVOnFupvIAcOHECbNm3QoEEDHDx4EFeuXIGfnx9mz56N06dPm2yDQMpe06ZNsWXLFri4uLx23pdKpZJt6b4hCCEwYcIELFq0SPtBamNjg/Hjx7MlgIHdvHkTwcHBuHDhgvY0EvDf6XieTso9FjIkGxsbG1y8eDHLFvTXr19HQEBAoT7fXq9ePXTu3BmjRo3S2ZTy5MmT6NSpE+7du2fsFKkQi4uLw5UrV2Bra4u33noL1tbWxk6pwGvfvj3Mzc3x448/wtfXFydOnMDz588xevRofPPNN2jUqJGxU1QM9kkn2ZQtWxYbNmzIcvy3336TrdW/Ul24cAHBwcFZjhcrVgxPnz41QkZE/3FwcECtWrVQuXJlFjH55NixY5g+fTrc3d1hZmYGc3NzNGzYELNmzTLplW6miHNkSDbTpk1Dly5dcPDgQe0cmSNHjmDv3r3ZFjiFiYuLCx4+fJhlUuW5c+fg6elppKyIyFg0Go12AYC7uzsePHgAf39/lC5dGteuXTNydsrCERmSzXvvvYeTJ0/C3d0dW7duxdatW+Hu7o6TJ09mOxpRmGT05Hj06BFUKhXS0tJw5MgRjBkzJsucIiIq+CpXroywsDAAQJ06dfDVV1/hyJEjmD59umK7RBsL58iQLFJTUzF48GBMnjz5tX01CquUlBR88sknWLVqFTQaDSwsLKBWq9GjRw+sWrVKu9cKERUOu3btQnx8PDp16oSIiAi0a9cO4eHhcHNzw2+//aZdRUZvxkKGZOPs7IzQ0FAWMq9x9+5dXLhwAXFxcahWrVqhnztERP95/vw5XF1dFdNI1FSwkCHZ9O7dG4GBgSbdLyY/va6/yKvmzZtnwEyIiAouTvYl2bz11luYPn06jhw5gho1amRpJlfYZuKfO3dO5/rZs2ehVqu1e0+Fh4fD3NwcNWrUMEZ6REQFAkdkSDZK3nPG0ObNm4eQkBCsXr1au8P2ixcv0LdvXzRq1IhdVImI9MRChgxCaZtGGpqnpyd2796dZSuCixcvomXLlnjw4IGRMiMiUjYuvyZZrVixApUrV4aNjQ1sbGxQuXJl/Pjjj8ZOy+hiY2MRFRWV5XhUVBRevnxphIyIiAoGzpEh2UyZMgXz5s3D0KFDUa9ePQDp3StHjhyJO3fuYPr06UbO0HiCg4PRt29fzJ07F7Vr1wYAnDhxAmPHjkWnTp2MnB0RkXLx1BLJpmjRoli0aBG6deumc3z9+vUYOnRooW7Fn5CQgDFjxuCnn35CamoqAMDCwgL9+/fH119/Ldsu20REhQ0LGZKNi4sLTp06laU3Snh4OGrXro3o6GjjJGZC4uPjcePGDQBAmTJlWMAQEUnEQoZkM3ToUFhaWmbpiTJmzBgkJiZi6dKlRsqMiIgKKhYyJJuhQ4dizZo18Pb2Rt26dQGkzwO5c+cOevXqBUtLS+1t2QCOiIjkwEKGZNO0adNc3U6lUmHfvn0GzoaIiAoDFjJERESkWOwjQ0RERIrFQoaIiIgUi4UMERERKRYLGSIiIlIsFjJEVGioVCps3brV2GkQkYxYyBCRrKKiovDRRx+hVKlSsLa2RokSJdCqVSscOXLE2KkRUQHETSOJSFbvvfceUlJSsHr1avj5+eHx48fYu3cvnj17ZuzUiKgA4ogMEckmOjoahw4dwpw5c9C0aVOULl0atWvXxsSJE9GhQwcA6V2dAwICYG9vD29vb3z88ceIi4vTxli1ahVcXFzw559/wt/fH3Z2dnj//feRkJCA1atXw8fHB66urhg2bBg0Go3273x8fPDFF1+gW7dusLe3h6en5xu3xbh79y4++OADuLi4oEiRIggKCsKtW7e0vw8JCUHt2rVhb28PFxcXNGjQALdv35b3QSMiSVjIEJFsHBwc4ODggK1btyI5OTnb25iZmWHRokW4dOkSVq9ejX379mHcuHE6t0lISMCiRYvw66+/YufOnQgJCUFwcDD++usv/PXXX1i7di2+++47bNq0Sefvvv76a1StWhXnzp3DhAkTMHz4cPzzzz/Z5pGamopWrVrB0dERhw4dwpEjR+Dg4IDWrVsjJSUFarUaHTt2xNtvv43z58/j2LFjGDRoEFQqlTwPFhHJQxARyWjTpk3C1dVV2NjYiPr164uJEyeKsLCwHG+/ceNG4ebmpr2+cuVKAUBERERojw0ePFjY2dmJly9fao+1atVKDB48WHu9dOnSonXr1jqxu3TpItq0aaO9DkBs2bJFCCHE2rVrhb+/v0hLS9P+Pjk5Wdja2opdu3aJZ8+eCQAiJCQk7w8CEeUbjsgQkazee+89PHjwANu3b0fr1q0REhKC6tWrY9WqVQCAPXv2oHnz5vD09ISjoyM+/PBDPHv2DAkJCdoYdnZ2KFOmjPZ68eLF4ePjAwcHB51jT5480bnvevXqZbl+5cqVbPMMCwtDREQEHB0dtSNJRYoUQVJSEm7cuIEiRYqgT58+aNWqFdq3b4+FCxfi4cOHUh8eIpIZCxkikp2NjQ3eeecdTJ48GUePHkWfPn3w+eef49atW2jXrh2qVKmC33//HWfOnNHOY0lJSdH+fead0oH0ZdPZHUtLS9M7x7i4ONSoUQOhoaE6P+Hh4ejevTsAYOXKlTh27Bjq16+P3377DeXKlcPx48f1vk8ikh8LGSIyuIoVKyI+Ph5nzpxBWloa5s6di7p166JcuXJ48OCBbPfzapFx/PhxVKhQIdvbVq9eHdevX0exYsVQtmxZnR9nZ2ft7apVq4aJEyfi6NGjqFy5MtatWydbvkQkHQsZIpLNs2fP0KxZM/z88884f/48IiMjsXHjRnz11VcICgpC2bJlkZqaisWLF+PmzZtYu3Ytli9fLtv9HzlyBF999RXCw8OxdOlSbNy4EcOHD8/2tj169IC7uzuCgoJw6NAhREZGIiQkBMOGDcO9e/cQGRmJiRMn4tixY7h9+zZ2796N69ev51gYEZFxsI8MEcnGwcEBderUwfz583Hjxg2kpqbC29sbAwcOxGeffQZbW1vMmzcPc+bMwcSJE9G4cWPMmjULvXr1kuX+R48ejdOnT2PatGlwcnLCvHnz0KpVq2xva2dnh4MHD2L8+PHo1KkTXr58CU9PTzRv3hxOTk5ITEzE1atXsXr1ajx79gwlS5bEJ598gsGDB8uSKxHJQyWEEMZOgohIKh8fH4wYMQIjRowwdipElI94aomIiIgUi4UMERERKRZPLREREZFicUSGiIiIFIuFDBERESkWCxkiIiJSLBYyREREpFgsZIiIiEixWMgQERGRYrGQISIiIsViIUNERESK9X/M8r6+DGxc0gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Samples', ylabel='Counts'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequency_distribution.plot(20, cumulative=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7b737602-f212-4685-9ea0-9da36fdade32",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = sorted(frequency_distribution.items(), key=lambda item: item[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "418b9243-28d8-4411-a652-d9ddb3f20be7",
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = [item[0] for item in d][:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "aae05ea3-4a3b-4a87-b975-11a959867535",
   "metadata": {},
   "outputs": [],
   "source": [
    "values = [item[1] for item in d][:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "91e6f61f-1eb4-447c-92f1-f73c38be7950",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(idx, values)\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('Word Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bddd4f82-063d-4248-a35e-1166d2356b7b",
   "metadata": {},
   "source": [
    "## Finding Collocations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "33b172d6-fdaa-4cbb-ac30-4283174b16b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "words = word_tokenize(example_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ee476ae8-5c03-49ac-a405-946a4bf79e52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Python',\n",
       " 'is',\n",
       " 'a',\n",
       " 'high-level',\n",
       " ',',\n",
       " 'interpreted',\n",
       " 'programming',\n",
       " 'language',\n",
       " 'that',\n",
       " 'wa',\n",
       " 'first',\n",
       " 'released',\n",
       " 'in',\n",
       " '1991',\n",
       " 'by',\n",
       " 'Guido',\n",
       " 'van',\n",
       " 'Rossum',\n",
       " ',',\n",
       " 'and',\n",
       " 'ha',\n",
       " 'since',\n",
       " 'become',\n",
       " 'one',\n",
       " 'of',\n",
       " 'the',\n",
       " 'most',\n",
       " 'popular',\n",
       " 'programming',\n",
       " 'language',\n",
       " 'in',\n",
       " 'the',\n",
       " 'world',\n",
       " ',',\n",
       " 'with',\n",
       " 'a',\n",
       " 'large',\n",
       " 'and',\n",
       " 'active',\n",
       " 'community',\n",
       " 'of',\n",
       " 'developer',\n",
       " 'who',\n",
       " 'use',\n",
       " 'it',\n",
       " 'for',\n",
       " 'a',\n",
       " 'wide',\n",
       " 'range',\n",
       " 'of',\n",
       " 'application',\n",
       " ',',\n",
       " 'from',\n",
       " 'web',\n",
       " 'development',\n",
       " 'and',\n",
       " 'data',\n",
       " 'analysis',\n",
       " 'to',\n",
       " 'machine',\n",
       " 'learning',\n",
       " 'and',\n",
       " 'scientific',\n",
       " 'computing',\n",
       " '.',\n",
       " 'Python',\n",
       " 'is',\n",
       " 'known',\n",
       " 'for',\n",
       " 'it',\n",
       " 'simplicity',\n",
       " ',',\n",
       " 'readability',\n",
       " ',',\n",
       " 'and',\n",
       " 'ease',\n",
       " 'of',\n",
       " 'use',\n",
       " ',',\n",
       " 'which',\n",
       " 'make',\n",
       " 'it',\n",
       " 'an',\n",
       " 'ideal',\n",
       " 'language',\n",
       " 'for',\n",
       " 'beginner',\n",
       " 'who',\n",
       " 'are',\n",
       " 'just',\n",
       " 'starting',\n",
       " 'to',\n",
       " 'learn',\n",
       " 'programming',\n",
       " ',',\n",
       " 'a',\n",
       " 'well',\n",
       " 'a',\n",
       " 'experienced',\n",
       " 'developer',\n",
       " 'who',\n",
       " 'need',\n",
       " 'to',\n",
       " 'quickly',\n",
       " 'prototype',\n",
       " 'and',\n",
       " 'test',\n",
       " 'their',\n",
       " 'idea',\n",
       " '.',\n",
       " 'One',\n",
       " 'of',\n",
       " 'the',\n",
       " 'key',\n",
       " 'feature',\n",
       " 'of',\n",
       " 'Python',\n",
       " 'is',\n",
       " 'it',\n",
       " 'extensive',\n",
       " 'standard',\n",
       " 'library',\n",
       " ',',\n",
       " 'which',\n",
       " 'provides',\n",
       " 'a',\n",
       " 'wide',\n",
       " 'range',\n",
       " 'of',\n",
       " 'module',\n",
       " 'and',\n",
       " 'function',\n",
       " 'that',\n",
       " 'can',\n",
       " 'be',\n",
       " 'used',\n",
       " 'to',\n",
       " 'perform',\n",
       " 'common',\n",
       " 'task',\n",
       " ',',\n",
       " 'such',\n",
       " 'a',\n",
       " 'working',\n",
       " 'with',\n",
       " 'file',\n",
       " ',',\n",
       " 'networking',\n",
       " ',',\n",
       " 'and',\n",
       " 'parsing',\n",
       " 'data',\n",
       " '.',\n",
       " 'Python',\n",
       " 'is',\n",
       " 'also',\n",
       " 'highly',\n",
       " 'extensible',\n",
       " ',',\n",
       " 'with',\n",
       " 'a',\n",
       " 'large',\n",
       " 'number',\n",
       " 'of',\n",
       " 'third-party',\n",
       " 'library',\n",
       " 'and',\n",
       " 'framework',\n",
       " 'available',\n",
       " 'that',\n",
       " 'can',\n",
       " 'be',\n",
       " 'used',\n",
       " 'to',\n",
       " 'add',\n",
       " 'additional',\n",
       " 'functionality',\n",
       " 'to',\n",
       " 'the',\n",
       " 'language',\n",
       " ',',\n",
       " 'such',\n",
       " 'a',\n",
       " 'web',\n",
       " 'framework',\n",
       " 'like',\n",
       " 'Django',\n",
       " 'and',\n",
       " 'Flask',\n",
       " ',',\n",
       " 'data',\n",
       " 'analysis',\n",
       " 'library',\n",
       " 'like',\n",
       " 'NumPy',\n",
       " 'and',\n",
       " 'Pandas',\n",
       " ',',\n",
       " 'and',\n",
       " 'machine',\n",
       " 'learning',\n",
       " 'library',\n",
       " 'like',\n",
       " 'TensorFlow',\n",
       " 'and',\n",
       " 'PyTorch',\n",
       " '.',\n",
       " 'Python',\n",
       " 'is',\n",
       " 'an',\n",
       " 'interpreted',\n",
       " 'language',\n",
       " ',',\n",
       " 'which',\n",
       " 'mean',\n",
       " 'that',\n",
       " 'code',\n",
       " 'written',\n",
       " 'in',\n",
       " 'Python',\n",
       " 'is',\n",
       " 'executed',\n",
       " 'directly',\n",
       " 'by',\n",
       " 'the',\n",
       " 'interpreter',\n",
       " ',',\n",
       " 'without',\n",
       " 'the',\n",
       " 'need',\n",
       " 'for',\n",
       " 'compilation',\n",
       " ',',\n",
       " 'which',\n",
       " 'make',\n",
       " 'it',\n",
       " 'easy',\n",
       " 'to',\n",
       " 'write',\n",
       " 'and',\n",
       " 'test',\n",
       " 'code',\n",
       " 'quickly',\n",
       " '.',\n",
       " 'Python',\n",
       " 'is',\n",
       " 'also',\n",
       " 'a',\n",
       " 'dynamically',\n",
       " 'typed',\n",
       " 'language',\n",
       " ',',\n",
       " 'which',\n",
       " 'mean',\n",
       " 'that',\n",
       " 'variable',\n",
       " 'do',\n",
       " 'not',\n",
       " 'need',\n",
       " 'to',\n",
       " 'be',\n",
       " 'declared',\n",
       " 'before',\n",
       " 'they',\n",
       " 'are',\n",
       " 'used',\n",
       " ',',\n",
       " 'and',\n",
       " 'their',\n",
       " 'type',\n",
       " 'is',\n",
       " 'determined',\n",
       " 'at',\n",
       " 'runtime',\n",
       " ',',\n",
       " 'which',\n",
       " 'can',\n",
       " 'make',\n",
       " 'it',\n",
       " 'easier',\n",
       " 'to',\n",
       " 'write',\n",
       " 'code',\n",
       " 'quickly',\n",
       " ',',\n",
       " 'but',\n",
       " 'can',\n",
       " 'also',\n",
       " 'lead',\n",
       " 'to',\n",
       " 'error',\n",
       " 'if',\n",
       " 'variable',\n",
       " 'are',\n",
       " 'not',\n",
       " 'used',\n",
       " 'correctly',\n",
       " '.',\n",
       " 'Despite',\n",
       " 'it',\n",
       " 'simplicity',\n",
       " ',',\n",
       " 'Python',\n",
       " 'is',\n",
       " 'a',\n",
       " 'powerful',\n",
       " 'language',\n",
       " 'that',\n",
       " 'can',\n",
       " 'be',\n",
       " 'used',\n",
       " 'to',\n",
       " 'build',\n",
       " 'complex',\n",
       " 'application',\n",
       " ',',\n",
       " 'and',\n",
       " 'is',\n",
       " 'widely',\n",
       " 'used',\n",
       " 'in',\n",
       " 'industry',\n",
       " ',',\n",
       " 'academia',\n",
       " ',',\n",
       " 'and',\n",
       " 'research',\n",
       " '.',\n",
       " 'Python',\n",
       " 'ha',\n",
       " 'a',\n",
       " 'large',\n",
       " 'and',\n",
       " 'active',\n",
       " 'community',\n",
       " 'of',\n",
       " 'developer',\n",
       " 'who',\n",
       " 'contribute',\n",
       " 'to',\n",
       " 'the',\n",
       " 'language',\n",
       " 'by',\n",
       " 'creating',\n",
       " 'new',\n",
       " 'library',\n",
       " 'and',\n",
       " 'framework',\n",
       " ',',\n",
       " 'fixing',\n",
       " 'bug',\n",
       " ',',\n",
       " 'and',\n",
       " 'improving',\n",
       " 'the',\n",
       " 'performance',\n",
       " 'of',\n",
       " 'the',\n",
       " 'interpreter',\n",
       " '.',\n",
       " 'Python',\n",
       " 'is',\n",
       " 'also',\n",
       " 'an',\n",
       " 'open-source',\n",
       " 'language',\n",
       " ',',\n",
       " 'which',\n",
       " 'mean',\n",
       " 'that',\n",
       " 'the',\n",
       " 'source',\n",
       " 'code',\n",
       " 'is',\n",
       " 'freely',\n",
       " 'available',\n",
       " 'and',\n",
       " 'can',\n",
       " 'be',\n",
       " 'modified',\n",
       " 'and',\n",
       " 'distributed',\n",
       " 'by',\n",
       " 'anyone',\n",
       " ',',\n",
       " 'making',\n",
       " 'it',\n",
       " 'an',\n",
       " 'ideal',\n",
       " 'language',\n",
       " 'for',\n",
       " 'collaborative',\n",
       " 'development',\n",
       " 'and',\n",
       " 'open',\n",
       " 'science',\n",
       " '.',\n",
       " 'In',\n",
       " 'summary',\n",
       " ',',\n",
       " 'Python',\n",
       " 'is',\n",
       " 'a',\n",
       " 'high-level',\n",
       " ',',\n",
       " 'interpreted',\n",
       " 'programming',\n",
       " 'language',\n",
       " 'that',\n",
       " 'is',\n",
       " 'known',\n",
       " 'for',\n",
       " 'it',\n",
       " 'simplicity',\n",
       " ',',\n",
       " 'readability',\n",
       " ',',\n",
       " 'and',\n",
       " 'ease',\n",
       " 'of',\n",
       " 'use',\n",
       " ',',\n",
       " 'and',\n",
       " 'is',\n",
       " 'widely',\n",
       " 'used',\n",
       " 'for',\n",
       " 'a',\n",
       " 'wide',\n",
       " 'range',\n",
       " 'of',\n",
       " 'application',\n",
       " ',',\n",
       " 'from',\n",
       " 'web',\n",
       " 'development',\n",
       " 'and',\n",
       " 'data',\n",
       " 'analysis',\n",
       " 'to',\n",
       " 'machine',\n",
       " 'learning',\n",
       " 'and',\n",
       " 'scientific',\n",
       " 'computing',\n",
       " '.',\n",
       " 'Python',\n",
       " 'ha',\n",
       " 'a',\n",
       " 'large',\n",
       " 'and',\n",
       " 'active',\n",
       " 'community',\n",
       " 'of',\n",
       " 'developer',\n",
       " 'who',\n",
       " 'contribute',\n",
       " 'to',\n",
       " 'the',\n",
       " 'language',\n",
       " ',',\n",
       " 'and',\n",
       " 'is',\n",
       " 'an',\n",
       " 'open-source',\n",
       " 'language',\n",
       " 'that',\n",
       " 'is',\n",
       " 'ideal',\n",
       " 'for',\n",
       " 'collaborative',\n",
       " 'development',\n",
       " 'and',\n",
       " 'open',\n",
       " 'science',\n",
       " '.']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lemmatizer = WordNetLemmatizer()\n",
    "lemmatized_words = [lemmatizer.lemmatize(word) for word in words]\n",
    "lemmatized_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "39f11063-7c43-4f8e-96a4-083050b84758",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_text = nltk.Text(lemmatized_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8a305e9b-8867-4596-8b40-966661d220a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "active community; machine learning; wide range; data analysis; open\n",
      "science; scientific computing; collaborative development; programming\n",
      "language; widely used; code quickly; interpreted programming; web\n",
      "development; library like; open-source language; ideal language\n"
     ]
    }
   ],
   "source": [
    "new_text.collocations()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c5e7aa3-187b-4f98-a78c-76b0c5848352",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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