Exp_14_Zipf's_Law_nlp.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ec525a0e",
   "metadata": {},
   "source": [
    "# Zipf's Law"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5168f1f0",
   "metadata": {},
   "source": [
    "##### States that given some corpus Of natural language utterances The frequency of any word Is inversely proportional  To its rank In the Frequency table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b745081f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import brown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "65c0e8f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['The', 'Fulton', 'County', 'Grand', 'Jury', 'said', ...]\n"
     ]
    }
   ],
   "source": [
    "Words = brown.words()\n",
    "print(Words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "56074d3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1161192"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Words)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b21a222f",
   "metadata": {},
   "source": [
    "# Over 1 Millon Tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6d78187b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "pun = set((',','.','--','-','!','?',':',';','``',\"''\",'(',')','[',']'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "91b9509d",
   "metadata": {},
   "outputs": [],
   "source": [
    "word_list = (x.lower() for x in Words if x not in pun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "63e7b1a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "token_counts = Counter(word_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f2799108",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('the', 69971),\n",
       " ('of', 36412),\n",
       " ('and', 28853),\n",
       " ('to', 26158),\n",
       " ('a', 23195),\n",
       " ('in', 21337),\n",
       " ('that', 10594),\n",
       " ('is', 10109),\n",
       " ('was', 9815),\n",
       " ('he', 9548),\n",
       " ('for', 9489),\n",
       " ('it', 8760),\n",
       " ('with', 7289),\n",
       " ('as', 7253),\n",
       " ('his', 6996),\n",
       " ('on', 6741),\n",
       " ('be', 6377),\n",
       " ('at', 5372),\n",
       " ('by', 5306),\n",
       " ('i', 5164)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "token_counts.most_common(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cb31ef43",
   "metadata": {},
   "outputs": [],
   "source": [
    "from operator import itemgetter\n",
    "import pandas as pd\n",
    "frequency = token_counts\n",
    "rank = 1\n",
    "column_header = ['Rank', 'Frequency']\n",
    "df = pd.DataFrame( columns = column_header )\n",
    "collection = sorted(frequency.items(), key=itemgetter(1), reverse = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "069f3a9b",
   "metadata": {},
   "source": [
    "# itemgetter is a pointer "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5f98496",
   "metadata": {},
   "source": [
    "import operator\n",
    "sampleDict = {'Product':'iPhone 13 Pro Max','Company':'Apple'} #Dictionary\n",
    "operator.itemgetter('Product')(sampleDict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5107423b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Rank Frequency\n",
      "the     1     69971\n",
      "of      2     36412\n",
      "and     3     28853\n",
      "to      4     26158\n",
      "a       5     23195\n",
      "in      6     21337\n",
      "that    7     10594\n",
      "is      8     10109\n",
      "was     9      9815\n",
      "he     10      9548\n",
      "for    11      9489\n",
      "it     12      8760\n",
      "with   13      7289\n",
      "as     14      7253\n",
      "his    15      6996\n",
      "on     16      6741\n",
      "be     17      6377\n",
      "at     18      5372\n",
      "by     19      5306\n",
      "i      20      5164\n"
     ]
    }
   ],
   "source": [
    "for word , freq in token_counts.most_common(20):\n",
    "    df.loc[word] = [rank, freq]\n",
    "    rank = rank + 1\n",
    "print (df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "472d852d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a850a46a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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AGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBmND5V1Ver6p6q+lJV7V5mp1TV7VX1wPL+ynX7X1VVe6rq/qp617r5ecv37Kmqa6uqlvmLq+rmZX5XVW2fPB8AAAAADs/RuPLpJ7r7zd29Y/l8ZZI7uvvsJHcsn1NV5yTZmeSNSS5K8pGqOmE55roklyc5e3ldtMwvS/LN7n5dkmuSfPgonA8AAAAAKzoWt91dnOT6Zfv6JO9ZN7+pu5/u7geT7ElyflWdnuSk7r6zuzvJDQccs++7bkly4b6rogAAAAA49qbjUyf571X1haq6fJmd1t2PJcnyfuoyPyPJw+uOfWSZnbFsHzjf75jufibJk0ledeAiquryqtpdVbv37t17RE4MAAAAgEPbMvz9b+/uR6vq1CS3V9VXDrLvRlcs9UHmBztm/0H3R5N8NEl27NjxA38HAAAAYMbolU/d/ejy/kSSTyU5P8njy610Wd6fWHZ/JMmZ6w7fluTRZb5tg/l+x1TVliQnJ/nGxLkAAAAAcPjG4lNVvayqXr5vO8nfSPJ7SW5Ncumy26VJPrNs35pk5/ILdmdl7cHidy+35j1VVRcsz3N63wHH7Puu9yb53PJcKAAAAACeByZvuzstyaeW539vSfIb3f1bVfW7SXZV1WVJHkpySZJ0971VtSvJfUmeSXJFdz+7fNf7k3wiyYlJblteSfLxJDdW1Z6sXfG0c/B8AAAAADhMY/Gpu38/yZs2mH89yYXPcczVSa7eYL47ybkbzL+XJV4BAAAA8Pwz/Wt3AAAAALyAiU8AAAAAjBGfAAAAABgjPgEAAAAwRnwCAAAAYIz4BAAAAMAY8QkAAACAMeITAAAAAGPEJwAAAADGiE8AAAAAjBGfAAAAABgjPgEAAAAwRnwCAAAAYIz4BAAAAMAY8QkAAACAMeITAAAAAGPEJwAAAADGiE8AAAAAjBGfAAAAABgjPgEAAAAwRnwCAAAAYIz4BAAAAMAY8QkAAACAMeITAAAAAGPEJwAAAADGiE8AAAAAjBGfAAAAABgjPgEAAAAwRnwCAAAAYIz4BAAAAMAY8QkAAACAMeITAAAAAGPEJwAAAADGiE8AAAAAjBGfAAAAABgjPgEAAAAwRnwCAAAAYIz4BAAAAMAY8QkAAACAMeITAAAAAGPEJwAAAADGiE8AAAAAjBGfAAAAABgjPgEAAAAwRnwCAAAAYIz4BAAAAMAY8QkAAACAMeITAAAAAGPEJwAAAADGiE8AAAAAjBGfAAAAABgjPgEAAAAwRnwCAAAAYIz4BAAAAMAY8QkAAACAMeITAAAAAGPEJwAAAADGiE8AAAAAjBGfAAAAABgjPgEAAAAwRnwCAAAAYIz4BAAAAMAY8QkAAACAMeITAAAAAGPEJwAAAADGiE8AAAAAjBGfAAAAABgjPgEAAAAwRnwCAAAAYIz4BAAAAMAY8QkAAACAMeITAAAAAGPEJwAAAADGiE8AAAAAjBGfAAAAABgjPgEAAAAwRnwCAAAAYIz4BAAAAMAY8QkAAACAMeITAAAAAGPEJwAAAADGiE8AAAAAjBGfAAAAABgjPgEAAAAwRnwCAAAAYIz4BAAAAMAY8QkAAACAMeITAAAAAGPEJwAAAADGiE8AAAAAjBGfAAAAABgjPgEAAAAwRnwCAAAAYIz4BAAAAMAY8QkAAACAMeITAAAAAGPEJwAAAADGiE8AAAAAjBGfAAAAABgjPgEAAAAwRnwCAAAAYIz4BAAAAMAY8QkAAACAMeITAAAAAGPEJwAAAADGiE8AAAAAjBGfAAAAABgjPgEAAAAwRnwCAAAAYIz4BAAAAMAY8QkAAACAMeITAAAAAGPEJwAAAADGiE8AAAAAjBGfAAAAABgjPgEAAAAwRnwCAAAAYIz4BAAAAMAY8QkAAACAMeITAAAAAGPEJwAAAADGiE8AAAAAjBGfAAAAABgjPgEAAAAwRnwCAAAAYIz4BAAAAMAY8QkAAACAMeITAAAAAGPEJwAAAADGiE8AAAAAjBGfAAAAABgjPgEAAAAwRnwCAAAAYIz4BAAAAMAY8QkAAACAMeITAAAAAGPEJwAAAADGiE8AAAAAjBmPT1V1QlV9sap+c/l8SlXdXlUPLO+vXLfvVVW1p6rur6p3rZufV1X3LH+7tqpqmb+4qm5e5ndV1fbp8wEAAABgdUfjyqd/luTL6z5fmeSO7j47yR3L51TVOUl2JnljkouSfKSqTliOuS7J5UnOXl4XLfPLknyzu1+X5JokH549FQAAAAAOx2h8qqptSX4qya+tG1+c5Ppl+/ok71k3v6m7n+7uB5PsSXJ+VZ2e5KTuvrO7O8kNBxyz77tuSXLhvquiAAAAADj2pq98+uUk/yLJn6ybndbdjyXJ8n7qMj8jycPr9ntkmZ2xbB843++Y7n4myZNJXnVkTwEAAACAH9ZYfKqqv5Xkie7+wqqHbDDrg8wPdsyBa7m8qnZX1e69e/euuBwAAAAA/qwmr3x6e5J3V9VXk9yU5B1V9etJHl9upcvy/sSy/yNJzlx3/LYkjy7zbRvM9zumqrYkOTnJNw5cSHd/tLt3dPeOrVu3HpmzAwAAAOCQxuJTd1/V3du6e3vWHiT+ue7+h0luTXLpstulST6zbN+aZOfyC3ZnZe3B4ncvt+Y9VVUXLM9zet8Bx+z7rvcu/+MHrnwCAAAA4NjYcgz+54eS7Kqqy5I8lOSSJOnue6tqV5L7kjyT5IrufnY55v1JPpHkxCS3La8k+XiSG6tqT9aueNp5tE4CAAAAgEM7KvGpuz+f5PPL9teTXPgc+12d5OoN5ruTnLvB/HtZ4hUAAAAAzz/Tv3YHAAAAwAuY+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjVopPVXXu9EIAAAAA2HxWvfLp31fV3VX1T6rqFaMrAgAAAGDTWCk+dfdfTvIPkpyZZHdV/UZVvXN0ZQAAAAAc91Z+5lN3P5DkA0l+MclfS3JtVX2lqv721OIAAAAAOL6t+synH6+qa5J8Ock7kvx0d//FZfuawfUBAAAAcBzbsuJ+/y7Jx5L8Und/d9+wux+tqg+MrAwAAACA496q8elvJvludz+bJFX1oiQv6e7vdPeNY6sDAAAA4Li26jOfPpvkxHWfX7rMAAAAAOA5rRqfXtLdf7Tvw7L90pklAQAAALBZrBqfvl1Vb933oarOS/Ldg+wPAAAAACs/8+kXknyyqh5dPp+e5O/NLAkAAACAzWKl+NTdv1tVb0jy+iSV5Cvd/cejKwMAAADguLfqlU9J8rYk25dj3lJV6e4bRlYFAAAAwKawUnyqqhuT/IUkX0ry7DLuJOITAAAAAM9p1SufdiQ5p7t7cjEAAAAAbC6r/trd7yX5c5MLAQAAAGDzWfXKp1cnua+q7k7y9L5hd797ZFUAAAAAbAqrxqcPTi4CAAAAgM1ppfjU3b9dVX8+ydnd/dmqemmSE2aXBgAAAMDxbqVnPlXVzyW5JcmvLqMzknx6alEAAAAAbA6rPnD8iiRvT/KtJOnuB5KcOrUoAAAAADaHVePT0939/X0fqmpLkp5ZEgAAAACbxarx6ber6peSnFhV70zyyST/ZW5ZAAAAAGwGq8anK5PsTXJPkp9P8t+SfGBqUQAAAABsDqv+2t2fJPnY8gIAAACAlawUn6rqwWzwjKfufu0RXxEAAAAAm8ZK8SnJjnXbL0lySZJTjvxyAAAAANhMVnrmU3d/fd3r/3b3Lyd5x/DaAAAAADjOrXrb3VvXfXxR1q6EevnIimAz+ODJx3oFm8cHnzzWKwAAAODPYNXb7v7Nuu1nknw1yd894qsBAAAAYFNZ9dfufmJ6IQAAAABsPqvedvfPD/b37v63R2Y5AAAAAGwmh/Nrd29Lcuvy+aeT/I8kD08sCgAAAIDNYdX49Ookb+3up5Kkqj6Y5JPd/Y+mFgYAAADA8e9FK+73miTfX/f5+0m2H/HVAAAAALCprHrl041J7q6qTyXpJD+T5IaxVQEAAACwKaz6a3dXV9VtSf7KMvrZ7v7i3LIAAAAA2AxWve0uSV6a5Fvd/StJHqmqs4bWBAAAAMAmsVJ8qqp/leQXk1y1jH4kya9PLQoAAACAzWHVK59+Jsm7k3w7Sbr70SQvn1oUAAAAAJvDqvHp+93dWXvYeKrqZXNLAgAAAGCzWDU+7aqqX03yiqr6uSSfTfKxuWUBAAAAsBkc8tfuqqqS3JzkDUm+leT1Sf5ld98+vDYAAAAAjnOHjE/d3VX16e4+L4ngBAAAAMDKVr3t7neq6m2jKwEAAABg0znklU+Ln0jyj6vqq1n7xbvK2kVRPz61MAAAAACOfweNT1X1mu5+KMlPHqX1AAAAALCJHOrKp08neWt3f62q/nN3/52jsSgAAAAANodDPfOp1m2/dnIhAAAAAGw+h4pP/RzbAAAAAHBIh7rt7k1V9a2sXQF14rKd/OkDx08aXR0AAAAAx7WDxqfuPuFoLQQAAACAzedQt90BAAAAwA9NfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwJix+FRVL6mqu6vqf1XVvVX1r5f5KVV1e1U9sLy/ct0xV1XVnqq6v6retW5+XlXds/zt2qqqZf7iqrp5md9VVdunzgcAAACAwzd55dPTSd7R3W9K8uYkF1XVBUmuTHJHd5+d5I7lc6rqnCQ7k7wxyUVJPlJVJyzfdV2Sy5OcvbwuWuaXJflmd78uyTVJPjx4PgAAAAAcprH41Gv+aPn4I8urk1yc5Pplfn2S9yzbFye5qbuf7u4Hk+xJcn5VnZ7kpO6+s7s7yQ0HHLPvu25JcuG+q6IAAAAAOPZGn/lUVSdU1ZeSPJHk9u6+K8lp3f1Ykizvpy67n5Hk4XWHP7LMzli2D5zvd0x3P5PkySSvmjkbAAAAAA7XaHzq7me7+81JtmXtKqZzD7L7Rlcs9UHmBztm/y+uuryqdlfV7r179x5q2QAAAAAcIUfl1+66+w+TfD5rz2p6fLmVLsv7E8tujyQ5c91h25I8usy3bTDf75iq2pLk5CTf2OD/f7S7d3T3jq1btx6hswIAAADgUCZ/7W5rVb1i2T4xyV9P8pUktya5dNnt0iSfWbZvTbJz+QW7s7L2YPG7l1vznqqqC5bnOb3vgGP2fdd7k3xueS4UAAAAAM8DWwa/+/Qk1y+/WPeiJLu6+zer6s4ku6rqsiQPJbkkSbr73qraleS+JM8kuaK7n12+6/1JPpHkxCS3La8k+XiSG6tqT5kP1Y8AABziSURBVNaueNo5eD4AAAAAHKax+NTd/zvJWzaYfz3Jhc9xzNVJrt5gvjvJDzwvqru/lyVeAQAAAPD8c1Se+QQAAADAC5P4BAAAAMAY8QkAAACAMeITAAAAAGPEJwAAAADGiE8AAAAAjBGfAAAAABgjPgEAAAAwRnwCAAAAYIz4BAAAAMAY8QkAAACAMeITAAAAAGO2HOsFABxtP3b9jx3rJWwa91x6z7FeAgAA8DznyicAAAAAxohPAAAAAIwRnwAAAID/3969B3t+1/Udf73J0nAx4SIRbIIgmALhLhcxotymA1pbqA1yhyo0Y8AC1nYE26lohxFsgQpVKIIIWAUqVpASGRoo1xCI3BIkQCogERRoEFKGVRPf/eP3PeRk2WxI2Pd+f7v7eMycOef3+V3yPnxn2XOe+/19vjBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAxe9YeAAB2fPS2t1t7hCPG7S746NojAABAEmc+AQAAADBIfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwJg9aw8AABwefu2n3rL2CEeMJ73o/muPAABwyDjzCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMbsWXsAAAC+Nc952I+uPcIR42df/Ya1RwCAI44znwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxozFp6q6eVW9tao+WlUfqaqnLOs3rqo3V9Unls832vWcp1fVhVX1sap64K71u1XVect9z6+qWtaPrapXL+vnVNUtp74fAAAAAK6+yTOfLk3ys919uyT3SvKkqjolydOSnNXdJyc5a7md5b6HJ7l9kgcl+fWqOmZ5rRcmOT3JycvHg5b1xyf5Und/T5LnJXn24PcDAAAAwNU0Fp+6+3Pd/f7l60uSfDTJiUkenOTly8NenuQhy9cPTvKq7v7r7v5kkguT3LOqvjPJ8d19dnd3klfs85yd1/q9JA/YOSsKAAAAgPUdkj2flrfD3TXJOUlu2t2fSzaBKsl3LA87Mclndj3tomXtxOXrfdev8JzuvjTJl5N8+8T3AAAAAMDVNx6fqurbkrw2yVO7+ysHeuh+1voA6wd6zr4znF5V51bVuV/4wheuamQAAAAADpLR+FRV184mPP237v79Zfkvl7fSZfn8+WX9oiQ33/X0k5J8dlk/aT/rV3hOVe1JcoMkF+87R3e/uLvv3t13P+GEEw7GtwYAAADAN2HyaneV5KVJPtrdz9111+uTPG75+nFJXrdr/eHLFey+O5uNxd+7vDXvkqq61/Kaj93nOTuvdVqStyz7QgEAAACwBfYMvvYPJHlMkvOq6oPL2s8neVaS11TV45P8WZKHJkl3f6SqXpPkT7K5Ut6Tuvuy5XlnJPmtJNdNcubykWzi1iur6sJsznh6+OD3AwAAAMDVNBafuvud2f+eTEnygCt5zjOTPHM/6+cmucN+1vdmiVcAAAAAbJ9DcrU7AAAAAI5O4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBmz9oDAADAkeyip71j7RGOGCc96wfXHgGAa8CZTwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgzFh8qqrfrKrPV9X5u9ZuXFVvrqpPLJ9vtOu+p1fVhVX1sap64K71u1XVect9z6+qWtaPrapXL+vnVNUtp74XAAAAAK6ZyTOffivJg/ZZe1qSs7r75CRnLbdTVackeXiS2y/P+fWqOmZ5zguTnJ7k5OVj5zUfn+RL3f09SZ6X5Nlj3wkAAAAA18ieqRfu7rfv52ykBye57/L1y5P87yQ/t6y/qrv/Osknq+rCJPesqk8lOb67z06SqnpFkockOXN5zjOW1/q9JP+lqqq7e+Y7AgAAjiTPeMYz1h7hiOF/S+BADvWeTzft7s8lyfL5O5b1E5N8ZtfjLlrWTly+3nf9Cs/p7kuTfDnJt49NDgAAAMDVti0bjtd+1voA6wd6zje+eNXpVXVuVZ37hS984RqOCAAAAMDVdajj019W1XcmyfL588v6RUluvutxJyX57LJ+0n7Wr/CcqtqT5AZJLt7ff7S7X9zdd+/uu59wwgkH6VsBAAAA4KqM7fl0JV6f5HFJnrV8ft2u9d+pqucm+fvZbCz+3u6+rKouqap7JTknyWOTvGCf1zo7yWlJ3mK/JwAAgCPDWW+59dojHDEecP//s/YIHOXG4lNV/W42m4vfpKouSvIL2USn11TV45P8WZKHJkl3f6SqXpPkT5JcmuRJ3X3Z8lJnZHPlvOtms9H4mcv6S5O8ctmc/OJsrpYHAAAAwBaZvNrdI67krgdcyeOfmeSZ+1k/N8kd9rO+N0u8AgAAAGA7bcuG4wAAAAAcgQ71nk8AAADAYe5mb/3g2iMcEf7ifndZe4RDwplPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIw57ONTVT2oqj5WVRdW1dPWngcAAACAyx3W8amqjknya0l+OMkpSR5RVaesOxUAAAAAOw7r+JTknkku7O4/7e6/SfKqJA9eeSYAAAAAFod7fDoxyWd23b5oWQMAAABgC1R3rz3DNVZVD03ywO5+wnL7MUnu2d3/cp/HnZ7k9OXmbZJ87JAOenS7SZIvrj0EV8rx2V6OzXZzfLaXY7PdHJ/t5dhsN8dnezk2283xObRu0d0n7O+OPYd6koPsoiQ333X7pCSf3fdB3f3iJC8+VENxuao6t7vvvvYc7J/js70cm+3m+Gwvx2a7OT7by7HZbo7P9nJstpvjsz0O97fdvS/JyVX13VX195I8PMnrV54JAAAAgMVhfeZTd19aVT+d5E1Jjknym939kZXHAgAAAGBxWMenJOnuNyZ549pzcKW83XG7OT7by7HZbo7P9nJstpvjs70cm+3m+Gwvx2a7OT5b4rDecBwAAACA7Xa47/kEAAAAwBYTnwAAAAAYIz4BAAAAMEZ8gqNAVb1y+fyUtWeBw1VV3bSqfnT5+I615+GKqupGVXXPqvqhnY+1Z4JtV1XHfjNrAPCtEp84qJZfzl5aVWcut0+pqsevPRe5W1XdIslPLr+g3Xj3x9rDHe2q6pKq+sqVfaw9H0lV/XiS9yZ5aJIfT3JOVZ227lTsqKonJHl7kjcl+cXl8zPWnIkrqqpTq+qRVfXYnY+1ZyJJcvY3ucYKquoHqur6y9ePrqrnLj/PsbKq+pWqOr6qrl1VZ1XVF6vq0WvPxUZV/YPluJy/3L5TVf27tec62rnaHQfVEp1eluTfdvedq2pPkg909x1XHu2oVlVPTnJGklsl+fPddyXp7r7VKoNxBVX1S0n+Iskrszk2j0pyXHf/yqqDkar6UJJ/2N2fX26fkOR/dfed152MJKmq85LcI8l7uvsuVXXbJL/Y3Q9beTTy9bNvb53kg0kuW5a7u5+83lRHt6q6WZITk/x2kkdm83dOkhyf5EXdfdu1ZuNyVfXhJHdOcqdsfjZ4aZIf6+77rDoYqaoPLn/f/NMkD0nyM0ne6ueC7VBVb0vyb5L81+6+67J2fnffYd3Jjm571h6AI85Nuvs1VfX0JOnuS6vqsqt6ErO6+/lJnl9VL0zyoiQ7b0d5e3d/aL3J2McDu/v7dt1+YVWdk0R8Wt+1dsLT4v/G2cPbZG93762qVNWx3X1BVd1m7aH4ursnOaX9i+c2eWCSf57kpCTP3bV+SZKfX2Mg9uvS7u6qenCSX+3ul1bV49YeiiTJtZfPP5Lkd7v74qo60OM5tK7X3e/d55hcutYwbIhPHGxfrapvT9JJUlX3SvLldUdilwuy+VfO38/mXzlfWVW/0d0vWHcsFpdV1aOSvCqbP0OPyOVnCbCuM6vqTUl+d7n9sCRvXHEeruiiqrphkj9I8uaq+lKSz648E5c7P8nNknxu7UHY6O6XJ3l5Vf2z7n7t2vNwpS5Z/kH30Ul+qKqOyeXRg3X9YVVdkORrSZ64nBG9d+WZuNwXq+rWufx30tPi76DVedsdB1VVfW+SFyS5QzY/bJ6Q5LTu/vCqg5Hk66dvf393f3W5ff0kZ3f3ndadjCSpqlsm+dUkP5DNX5bvSvLU7v7UelORJFX17CTnJLl3NuH27Unu1d0/t+pgfIOquk+SGyT5o+7+m7XnIamqtya5Szb7pv31znp3/5PVhuLrquofJbl9kuvsrHX3L603ETuWt0c+Msn7uvsdVfVdSe7b3a9YeTSyudBFkq9092VVdb0kx3f3X6w9F0lV3SrJi5OcmuRLST6Z5FHd/elVBzvKiU8cdMs+T7fJ5he0j3X33648EoudfVG6e+9y+zrZ/EBjTy44gKp6f3d/7z5rHxZu4aotQfAbdPfbDvUsXFFVvSjJ9ZLcL8lLkpyW5L3d7WIxcBWq6g5JTskVw60wuAWWq3aeluSWSW6c5CvZ7DUorK/I2+6YcM9s/qDvSfK9VeX/iLfHy7K5Stf/WG4/JJvNK9kCyynb/yKX//lJknT3T64109Guqs5I8sQkt1rOHNxxXDZnpgFXQWTaaqd2952WmP6LVfWcbN6az4qq6p3dfe+quiTL24Z27srmF+jjVxqNRVX9QpL7ZhOf3pjkh5O8M4nfebbD65L8VZL3x9vwt4YznzioXNFm+y1vjfz6W4e6+wMrj8Siqt6d5B1J/ji79nqyH8d6quoGSW6U5JeTPG3XXZd098XrTAWHB79Ab7+qOqe7v6+q3pPkx7K5mML53X3yyqPBVlveTXDnbK7qfeeqummSl3T3P155NOLKdtvKmU8cbK5os+W6+/3Z/CsA2+d69hDaLt395WwumvCItWeBw01333v5fNzas3Cl3rBs1v8fs/nZoLN5+x1wYHu7+++q6tKqOj7J55Pcau2h+Lp3V9Udu/u8tQfhcuITB5sr2sA194aq+pHudhU1AMZ1939YvnxtVb0hyXWW6A4c2PuWcPsb2Zyx/v+yuagCK1rOSOtsOsdPVNWfZnOhi50zbu3VuSJvu+OgqKo/zOYP+nFxRRu4Rpa3plw/mz87fxtvTQFgWFWdmm/ca9C+NXAAy1Yjb89mu4S92VzpztW9V1ZVtzjQ/a52ty5nPnGw/KdsflF+djabWO/YWQOuQncfV1U3TnJydl05BQAmXNlenbFpMlyVl2Wzh+oLsnm73Qer6u3d/avrjnV0E5e2mzOfOKhcjhyuuap6QpKnJDkpm18E7pXk3d39gFUHA+CIVFUfjb064RqpqmOS3CPJ/ZL8VJKvdfdt150Ktte11h6AI0NVnbG8x/Y2VfXhXR+fTOIUVPjmPCWbH2I+3d33S3LXJF9cdyQAjmA7e3UCV0NVnZXkXUkeluRjSe4hPMGBedsdB8vvJDkzLkcO34q93b23qlJVx3b3BVV1m7WHAuDIss9enX9SVfbqhKvnw0nuluQO2VwV96+q6uzu/tq6Y8H2Ep84KFyOHA6Ki5Yrp/xBkjdX1ZeSfHblmQA48tirE74F3f0zSVJV35bkJ7LZA+pmSY5dcy7YZvZ8AthCVXWfJDdI8kfd/TdrzwPAkcdenXDNVNVPJ/nBbM5++nSWK99191tWHQy2mDOfALZQd79t7RkAODJV1RlJnpjkVlW1e2/O47LZxwY4sOsmeW6SP+7uS9ceBg4HznwCAICjSFXdIMmNYq9OAA4R8QkAAACAMddaewAAAAAAjlziEwAAAABjxCcAgEFV9byqeuqu22+qqpfsuv2cqvpX1+B171tVbzhYcwIATBGfAABmvTvJqUlSVddKcpMkt991/6n5Jq4wVlXHjEwHADBMfAIAmPWuLPEpm+h0fpJLqupGVXVsktsluWFVfaCqzquq31zWU1Wfqqp/X1XvTPLQqnpQVV2w3P6xnf9AVd2nqj64fHygqo47tN8iAMCV27P2AAAAR7Lu/mxVXVpV35VNhDo7yYlJvj/Jl5N8PMlLkjyguz9eVa9IckaS/7y8xN7uvndVXSfJJ5LcP8mFSV696z/zr5M8qbvfVVXflmTvofjeAAC+Gc58AgCYt3P20058OnvX7T9P8snu/vjy2Jcn+aFdz92JTLddHveJ7u4kv73P6z+3qp6c5IbdfenYdwIAcDWJTwAA83b2fbpjNm+7e082Zz6dmuT9V/Hcr+76uvf3gO5+VpInJLlukvdU1W2/1YEBAA4W8QkAYN67kvxokou7+7LuvjjJDbMJUC9Lcsuq+p7lsY9J8rb9vMYFSb67qm693H7Ezh1VdevuPq+7n53k3GzOkgIA2AriEwDAvPOyucrde/ZZ+3J3X5TkJ5L896o6L8nfJXnRvi/Q3XuTnJ7kfy4bjn96191Prarzq+pDSb6W5MyZbwMA4OqrzZYBAAAAAHDwOfMJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGDM/wccTq+yMYdntAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))  #to increase the plot resolution\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.xlabel(\"Words\")\n",
    "plt.xticks(rotation=90)    #to rotate x-axis values\n",
    "\n",
    "for word , freq in collection[:10]:\n",
    "    plt.bar(word, freq)    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8c83ab94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))  #to increase the plot resolution\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.xlabel(\"Words\")\n",
    "plt.xticks(rotation=90)    #to rotate x-axis values\n",
    "for word , freq in collection[:120]:\n",
    "    plt.bar(word, freq)    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82868fa3",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}