hist3368-week5-wordcloud-and-bar-plot.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hist 3368 - Week 5 - Wordcloud and Bar Plot\n",
    "\n",
    "#### By Jo Guldi"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Part of text mining is regularly *inspecting* the data. One of the major tools for inspecting data is to visualize it. \n",
    "\n",
    "In this notebook, we'll learn to make two standard visualizations that reduce the major words from a document to a single visualization:\n",
    "a wordcloud and a bar plot.\n",
    "\n",
    "## Load some software packages\n",
    "\n",
    "The command string \"!pip install [softwarepackage] --u\" tells Python to install a new software on your user account on the mainframe.  If you ever get an error in this class which tells you that you don't have a software package installed yet, you can use this string of commands to add the package.  Please note that you will also need to \"import\" the software in the following lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Usage:   \n",
      "  pip install [options] <requirement specifier> [package-index-options] ...\n",
      "  pip install [options] -r <requirements file> [package-index-options] ...\n",
      "  pip install [options] [-e] <vcs project url> ...\n",
      "  pip install [options] [-e] <local project path> ...\n",
      "  pip install [options] <archive url/path> ...\n",
      "\n",
      "ambiguous option: --u (--upgrade, --upgrade-strategy, --use-deprecated, --use-feature, --use-pep517, --user?)\n",
      "\n",
      "Usage:   \n",
      "  pip install [options] <requirement specifier> [package-index-options] ...\n",
      "  pip install [options] -r <requirements file> [package-index-options] ...\n",
      "  pip install [options] [-e] <vcs project url> ...\n",
      "  pip install [options] [-e] <local project path> ...\n",
      "  pip install [options] <archive url/path> ...\n",
      "\n",
      "ambiguous option: --u (--upgrade, --upgrade-strategy, --use-deprecated, --use-feature, --use-pep517, --user?)\n"
     ]
    }
   ],
   "source": [
    "!pip install bs4 --u\n",
    "!pip install urllib --u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'lxml'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[7], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39murllib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mrequest\u001b[39;00m\u001b[39m,\u001b[39m \u001b[39murllib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39merror\u001b[39;00m\u001b[39m,\u001b[39m \u001b[39murllib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mparse\u001b[39;00m\u001b[39m,\u001b[39m \u001b[39mbs4\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mbs\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mlxml\u001b[39;00m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'lxml'"
     ]
    }
   ],
   "source": [
    "import urllib.request, urllib.error, urllib.parse, bs4 as bs"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load some data \n",
    "\n",
    "The following lines use the bs4 (\"Beautiful Soup\") software package to download all the text from a URL on the internet. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "FeatureNotFound",
     "evalue": "Couldn't find a tree builder with the features you requested: lxml. Do you need to install a parser library?",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFeatureNotFound\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m source \u001b[39m=\u001b[39m urllib\u001b[39m.\u001b[39mrequest\u001b[39m.\u001b[39murlopen(\u001b[39m'\u001b[39m\u001b[39mhttp://www.oldbaileyonline.org/browse.jsp?id=t17800628-33&div=t17800628-33\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m soup \u001b[39m=\u001b[39m bs\u001b[39m.\u001b[39;49mBeautifulSoup(source, \u001b[39m'\u001b[39;49m\u001b[39mlxml\u001b[39;49m\u001b[39m'\u001b[39;49m)\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/bs4/__init__.py:248\u001b[0m, in \u001b[0;36mBeautifulSoup.__init__\u001b[0;34m(self, markup, features, builder, parse_only, from_encoding, exclude_encodings, element_classes, **kwargs)\u001b[0m\n\u001b[1;32m    246\u001b[0m     builder_class \u001b[39m=\u001b[39m builder_registry\u001b[39m.\u001b[39mlookup(\u001b[39m*\u001b[39mfeatures)\n\u001b[1;32m    247\u001b[0m     \u001b[39mif\u001b[39;00m builder_class \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 248\u001b[0m         \u001b[39mraise\u001b[39;00m FeatureNotFound(\n\u001b[1;32m    249\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mCouldn\u001b[39m\u001b[39m'\u001b[39m\u001b[39mt find a tree builder with the features you \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    250\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mrequested: \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m. Do you need to install a parser library?\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    251\u001b[0m             \u001b[39m%\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m,\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mjoin(features))\n\u001b[1;32m    253\u001b[0m \u001b[39m# At this point either we have a TreeBuilder instance in\u001b[39;00m\n\u001b[1;32m    254\u001b[0m \u001b[39m# builder, or we have a builder_class that we can instantiate\u001b[39;00m\n\u001b[1;32m    255\u001b[0m \u001b[39m# with the remaining **kwargs.\u001b[39;00m\n\u001b[1;32m    256\u001b[0m \u001b[39mif\u001b[39;00m builder \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
      "\u001b[0;31mFeatureNotFound\u001b[0m: Couldn't find a tree builder with the features you requested: lxml. Do you need to install a parser library?"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "source = urllib.request.urlopen('http://www.oldbaileyonline.org/browse.jsp?id=t17800628-33&div=t17800628-33')\n",
    "soup = bs.BeautifulSoup(source, 'lxml')\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning the data\n",
    "\n",
    "The following lines are cleaning steps.  They define a set of stopwords, remove punctuation, lowercase and split the text, and remove standard stopwords."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IF YOU GET AN ERROR MESSAGE for the line beginning with \"import,\" please review the instructions for \"installing additional packages\" found in hist3368-week1-first-time-setup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'soup' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[6], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39m# lowercasing \u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m text \u001b[39m=\u001b[39m soup\u001b[39m.\u001b[39mget_text()\u001b[39m.\u001b[39mlower() \n\u001b[1;32m      4\u001b[0m \u001b[39m# stopwording\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[39m# we can define a set of words that seem to come from the webpage that we don't want to use\u001b[39;00m\n\u001b[1;32m      7\u001b[0m extrawords \u001b[39m=\u001b[39m [\u001b[39m\"\u001b[39m\u001b[39myes\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mmr\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m[]\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m_gaq.push([\u001b[39m\u001b[39m'\u001b[39m\u001b[39m_setaccount\u001b[39m\u001b[39m'\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mbrowse\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m-\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mcentral\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mcriminal\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mcourt\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mvar\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m_gaq\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m_gaq\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m||\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m[];\u001b[39m\u001b[39m\"\u001b[39m, \n\u001b[1;32m      8\u001b[0m               \u001b[39m\"\u001b[39m\u001b[39m_gaq.push([\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m_setaccount\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mua-19174022-1]\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m_gaq.push([\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m\\\"\u001b[39;00m\u001b[39m_gaq.push([\u001b[39m\u001b[39m'\u001b[39m\u001b[39m_setaccount\u001b[39m\u001b[39m'\u001b[39m\u001b[39m\\\"\u001b[39;00m\u001b[39m\"\u001b[39m,\n\u001b[1;32m      9\u001b[0m               \u001b[39m\"\u001b[39m\u001b[39m_trackpageview\u001b[39m\u001b[39m\"\u001b[39m,\u001b[39m\"\u001b[39m\u001b[39m(function()\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m{\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mvar\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mga\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mdocument.createelement\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     47\u001b[0m               \u001b[39m\"\u001b[39m\u001b[39mbackgrounddatathe\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m(trial\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39maccount)\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mprojectcontact\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m(t17800628-33).close\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m(www.oldbaileyonline.org,\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mnumbert17800628-33verdictguiltysentencedeathrelated\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m     48\u001b[0m              ]\n",
      "\u001b[0;31mNameError\u001b[0m: name 'soup' is not defined"
     ]
    }
   ],
   "source": [
    "# lowercasing \n",
    "text = soup.get_text().lower() \n",
    "\n",
    "# stopwording\n",
    "# we can define a set of words that seem to come from the webpage that we don't want to use\n",
    "\n",
    "extrawords = [\"yes\", \"mr\", \"[]\", \"_gaq.push(['_setaccount'\", \"browse\", \"-\", \"central\", \"criminal\", \"court\", \"var\", \"_gaq\", \"=\", \"_gaq\", \"||\", \"[];\", \n",
    "              \"_gaq.push([\", \"_setaccount\", \"ua-19174022-1]\", \"_gaq.push([\", \"\\\"_gaq.push(['_setaccount'\\\"\",\n",
    "              \"_trackpageview\",\"(function()\", \"{\", \"var\", \"ga\", \"=\", \"document.createelement\",\n",
    "              \"'ua-19174022-1'\", \"_gaq.push(['_trackpageview'\",\n",
    "              \"script\", \"ga.type\", \"=\", \"text/javascript\", \"ga.async\", \"=\", \"true;\", \"ga.src\", \"=\", \"https:\",\n",
    "              \"==\", \"document.location.protocol\", \"?\", \"https://ssl\", \":\", \"http://www\", \"+\", \n",
    "              \".google-analytics.com/ga.js\", \"var\", \"s\", \"=\", \"document.getelementsbytagname\",\n",
    "              \"s.parentnode.insertbefore(ga\", \"s\", \"})();\", \"jump\", \"to\", \"contentjump\", \"to\", \"main\",\n",
    "              \"navigationjump\", \"section\", \"navigation\",  \"proceedings\", \n",
    "              '(www.oldbaileyonline.org', '8.0', '2020)', '1780',\n",
    "              \"'ua-19174022-1'])\", \"_gaq.push(['_trackpageview'])\", \"document.createelement('script')\", \n",
    "              \"'text/javascript'\", 'true', \"('https:'\", \"'https://ssl'\", '', \"'http://www')\",\n",
    "              \"'.google-analytics.com/ga.js'\", \"document.getelementsbytagname('script')[0]\", 's)', '})()',\n",
    "              \"bailey\",  \"1674\", \"to\", \"1913\", \"main\", \"navigationhomesearchabout\",\n",
    "              \"proceedingshistorical\", \"backgrounddatathe\", \"projectcontact\", \"benjamin\", \"bowsey\", \n",
    "              \"breaking\", \"peace:\", \"riot.\", \"28th\", \"june\", \"1780reference\", \n",
    "              \"numbert17800628-33verdictguiltysentencedeathrelated\", \"material\", \"associated\", \n",
    "              \"recordsactionscite\", \"this\", \"textold\", \"bailey\", \"proceedings\", \"online\", \n",
    "              '1674-1834', 'api', 'demonstrator', '<!--', 'google_ad_client', 'pub-6166712890256554', \n",
    "              '/*', '180x150', 'created', '21/11/08', '*/', 'google_ad_slot', '3829571269', 'google_ad_width',\n",
    "              '180', 'google_ad_height', '150', '//-->', '<!--', 'google_ad_client', 'pub-6166712890256554', '/*',\n",
    "              '180x150', 'created', '21/11/08', '*/', 'google_ad_slot', '1983343858', 'google_ad_width', '180', \n",
    "              'google_ad_height', '150', '//-->', '<!--', 'google_ad_client', 'pub-6166712890256554', '/*', \n",
    "              '180x150', 'created', '21/11/08', '*/', 'google_ad_slot', '9176171409', 'google_ad_width', '180', \n",
    "              'google_ad_height', '150', '//-->', 'footer', 'march', '2018', '©', '2003-2018',  \n",
    "              'www.oldbaileyonline.org', '2020', 't17800628-33).close', \"'324'\",\n",
    "              \"_gaq.push(['_setaccount\", \"_gaq.push(['_trackpageview\", \"document.createelement('script\", \"document.getelementsbytagname('script')[0\",\n",
    "              \"_gaq.push(['_setaccount',\", \"ua-19174022-1']);\", \"_gaq.push(['_trackpageview']);\", \n",
    "              \"document.createelement('script');\", \"text/javascript';\", \"('https:\", \"http://www')\", \n",
    "              \".google-analytics.com/ga.js';\", \"document.getelementsbytagname('script')[0];\", 's.parentnode.insertbefore(ga,', 's);',\n",
    "               'web', 'site', 'sitemap', 'copyright', '&', 'citation', 'guide', 'visual', 'design', 'technical',\n",
    "              'design', 'xml', 'feedback', 'ua-19174022-1', \"'www.oldbaileyonline.org'\", \"'2020'\", \"'t17800628-33).close'\", \n",
    "              \"'ua-19174022-1']\", \"_gaq.push(['_trackpageview']\", 'function', \"document.createelement('script'\", \"'https:'\", \"'http://www'\", '}', \n",
    "              \"(www.oldbaileyonline.org,\", \"version\", \"8.0,\", \"08\", \"september\", \"2020),\", \"june\", \"1780,\", \n",
    "              '\"pub-6166712890256554\";', '180x150,', '\"3829571269\";', '180;', '150;', '\"pub-6166712890256554\";', '180x150,', \n",
    "              '\"1983343858\";', '180;', '150;', '\"pub-6166712890256554\";', '180x150,', '\"9176171409\";', '180;', '150;',\n",
    "              \"trial\", \"of\", \"benjamin\", \"bowsey\", \"(t17800628-33).close\", \"|\", \"print-friendly\", \"version\", \n",
    "              \"|\", \"report\", \"errornavigation<\", \"previous\", \"text\", \"(trial\", \"account)\", \"|\", \"next\", \"recordsactionscite\", \n",
    "              \"text\", \"(trial\", \"account)\", \">see\", \"original\", \"324.\", \"navigationjump\", \"contentjump\", \"browse\", \"navigationhomesearchabout\",\n",
    "             \"proceedingshistorical\", \"1780reference\", \"navigationjump\", \"navigationhomesearchabout\", \"navigationhomesearchabout\", \"proceedingshistorical\", \"backgrounddatathe\", \"projectcontact\",\n",
    "              \"backgrounddatathe\", \"(trial\", \"account)\", \"projectcontact\", \"(t17800628-33).close\", \"(www.oldbaileyonline.org,\", \"numbert17800628-33verdictguiltysentencedeathrelated\",\n",
    "             ]\n",
    "#print(extrawords)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following lines recapitulate the code we learned in our last notebook for cleaning text.\n",
    "\n",
    "Notice how brief the code is after you lump together many commands into a single for loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "house       23\n",
       "yes         20\n",
       "mr          19\n",
       "prisoner    18\n",
       "man         16\n",
       "mob         14\n",
       "black       13\n",
       "night       12\n",
       "no          12\n",
       "him         11\n",
       "saw         11\n",
       "akermans    10\n",
       "see          9\n",
       "face         9\n",
       "went         9\n",
       "fire         9\n",
       "sworn        9\n",
       "know         9\n",
       "room         8\n",
       "believe      8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import string\n",
    "import pandas as pd \n",
    "from nltk.corpus import stopwords\n",
    "stopwords = stopwords.words('english')\n",
    "\n",
    "stopworded = []\n",
    "\n",
    "for word in text.lower().split(): # loop through every word\n",
    "    if word not in stopwords: # remove stopwords \n",
    "        if word not in extrawords: #remove extrawords\n",
    "            for c in string.punctuation: # strip punctuation\n",
    "                word = word.replace(c, \"\") \n",
    "            stopworded.append(word) # save the cood results\n",
    "\n",
    "count = pd.Series.value_counts(stopworded) # count them\n",
    "\n",
    "count[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wordclouds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a wordcloud\n",
    "import matplotlib.pyplot as plt\n",
    "from wordcloud import WordCloud\n",
    "\n",
    "wc = WordCloud(background_color=\"white\",width=1000,height=1000, max_words=100,relative_scaling=0.5,normalize_plurals=False).generate_from_frequencies(count)\n",
    "plt.imshow(wc)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<wordcloud.wordcloud.WordCloud at 0x2aaab7743588>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import wordcloud\n",
    "wordcloud.WordCloud.to_file(wc, 'wordcloud.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much better!   Now, we're getting a visualization that draws attention to the more and less common words in the text.\n",
    "\n",
    "One component of a good *inspection* practice is iteration: looking again and again in different ways.  In the present context, iterative inspection means looking at the data using different visualizations.  We've just used a word cloud, but what might we learn from a different way of visualizing the same data, perhaps one that has numbers, not just relative size, as an indicator of how prominent each word is?  \n",
    "\n",
    "Numbers assigned to words can give us more accuracy, which can allow us to think in terms of comparison.  \n",
    "\n",
    "Let's try a relatively common graph: a bar plot. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the x axis we'll plot all of the counts of words -- how frequently each word appears. This information is in \"count\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "house       23\n",
       "yes         20\n",
       "mr          19\n",
       "prisoner    18\n",
       "man         16\n",
       "mob         14\n",
       "black       13\n",
       "night       12\n",
       "no          12\n",
       "him         11\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to tell Python to use the labels that are part of the Series index for count. We access those with .index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['house', 'yes', 'mr', 'prisoner', 'man', 'mob', 'black', 'night', 'no', 'him']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(count.index)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make a Bar Graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the Python built-in function plt.bar to make a bar graph. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/g/jwBP0XmvvOaf8M+CE1kNblynuImrSj1I+rXg3sberK23HUS/4eAT4/SZtvol4vvBBYkMNemRuj3ZYFwBD1ix6oH6GNAW9h/FroH1Avx1wPnJzTPT+ff5d6/fLFwPIctyjLHMo2zAee1jHfkROsw+Nynj8CvkcN6NOAu6kf3z1C7a9wNPBO6qnqZ4CPZJ0PAH8F/H2rDS+nhvs64HTq5XUj1IsHmssI3we8iHp2eF+rXcfl9vhC1reAemHBO7L8e4F9HcuwiPqO6DeplwOupV7yOgzckcu+Ndfnj7PO3wBW5/wXA5dMsO+dnm0cBb5MfSOzgtq3ZFOW11zGfGJu69XZnuYKvaXAtTlsM/VSxE9Rr3v/AfA32Z4NuW7Hcv38L/VFvpC6P64GXpv3v5TTfYR69cg72X8//xK1j8Sv5HRvyvoXALdm+dfTej0CJ+R98zoYzu26ATib+mZuR467D/haLvdbqftL089lTQ5/eZa3jLpvXJDLfRz1stRLqFfELKR+KvCTXE+rW6+7hcAfU/vzfDnr30G9VPl46qXUd2f5zZerP6Be2nwf8NIc3uy3G3M9rKf+FP57gGtb66DZ547LbfJaxl9Dp1OPG+vy9tfUy7mbbf5B6v5wJnUfvbbj2NDsU++nvp4ubK3zZ1D3o6uA3yL7XpH7JfX1fEuvj9E/beuhFAa5cz0IXDrJ+A/nir2Hei3/oicJg/a011AvaR1i/Lr2ba0daWuW0Q6Dm3KavXnbCCw+QBicSj2Q3kG9Pv2t2ZaPZ1u2Us+MvpHDh3MnfwPjHWfW5M71UM5zBzW43kwNg8fz+dU5/vvAn7TacEwu50/n62jrVTnfQ1nWvpx2R66bpg/BxdmWW3KatbnOl2dbN1IPGHe12nBajttOfeG1OxA1HXb2ZHnDwJnZpmdRX8wfyDrXZZn3tZbxs61tc1Vr+HpqcI1SD7pN/e0ORDvyfntulzOpnXfubLYprX0P+BDj/QT2UU/P9zFxR6U7WmW+prXed1L3m33Z1tuzvT/KddVcGti0tynzz/P5vbk91ub9A1nWbbltrqAewNr7+Y8Y79D1bep+d3+247Es/7XsHwa3t5bp8VzGR6n7cbtDW2mtgz3A59m/Q1bTeXN1bsOV1D4+FwCX5/rYQD0Y3814R7Q9WVezL7w7t9dYax3uzHXwQ/bv79NuW9OZselMOZzr5GtZ32ty2N3AzcALWuvgQeqbhSeowd9+DTWdBp/Iabbn9LsZ7yS6Pe9Xd5Tb7FPNuryNGnS3ZrnDOXxTrr+LGN8v/4L6Gn11v8LA3yaSJPm5vCTJMJAkYRhIkjAMJEkYBpIkDANJEvD/2TV3wCrrK+8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now prepare the data for a bar graph\n",
    "plt.bar(list(count.index), count)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice about the code that the entire plot is generated by one line:\n",
    "\n",
    "    plt.bar(x, y) — \n",
    "\n",
    "which generates the bar plot.  Then we call **plt.show()** to make it show up on the Jupyter screen.\n",
    "\n",
    "**plt.bar()** takes two arguments: \n",
    "\n",
    "   * the list of data for the x axis, and \n",
    "   * the list for the y axis.\n",
    "\n",
    "In the code above, those inputs are **list(count.index)** — which is a list of the most common words — and **count,** which is the frequency counts of those words.  \n",
    "\n",
    "By swapping out other variables in the place of the x and y information, you can use the same code to visualize other data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Next step: think about making the bar plot more readable \n",
    "\n",
    "We learn from the above visualization that some words occur all the time, but most of the 500 unique words in our text only occur fewer than five times.  \n",
    "\n",
    "In general, however, we should be dissatisfied with such a plot.  No one can read the words! \n",
    "\n",
    "Remember: a good graph is a lucid graph.  \n",
    "\n",
    "Often, we will want to zoom in on just the top words."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Zoom in on just the top words so that we can read them\n",
    "\n",
    "It would be also  nice to be able to *see* the names of the top words and compare precisely how much their wordcounts vary.\n",
    "\n",
    "Remember that we can use square brackets and a colon to choose only the first few items in a list.  In **count**, the list is already ordered with the biggest numbers at the top, so if we use **[:10]**, we get the top ten words overall."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a more useful barplot would show only the most common words.\n",
    "mostcommon = count[:10] \n",
    "\n",
    "# here's the barplot code\n",
    "plt.bar(list(mostcommon.index), mostcommon)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's better, but it's still hard to read.  Let's add some visualization magic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a well-labeled barplot\n",
    "plt.bar(list(mostcommon.index), mostcommon)\n",
    "plt.xticks(rotation=90) # this tells Python to 'rotate' the labels on the x axis so that the words fit on the screen\n",
    "plt.savefig('bar.png') # this line tells Python to save a copy in your digital-history folder\n",
    "plt.xlabel('Word') # this line labels the x axis\n",
    "plt.ylabel('Count') # this line labels the y axis\n",
    "plt.suptitle('Top Ten Words') #This line labels the title\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Takeaway: Notice the good labeling and formatting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how nicely we have labeled the chart.  Do you know exactly what it shows?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the future, we will expect any visualizations you generate for this class to be well labeled, just like this one.  You may return here for the formatting instructions that will help you to label each axis, to rotate the words, and to supply a title."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment\n",
    "\n",
    "Save your wordcount and your final bar plot using the following command:\n",
    "\n",
    "    plt.savefig('filename.png')\n",
    "\n",
    "You should see the new file in your left-hand sidebar in the Jupyter notebook.  \n",
    "\n",
    "* Mouse to the file, and you should be able to download it.  \n",
    "* Upload it to Canvas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": "3.9.10"
  },
  "vscode": {
   "interpreter": {
    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}