hist3368-optional-access-data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For citation information, please see the \"Source Information\" section listed in the associated README file: https://github.com/stephbuon/digital-history/tree/master/hist3368-week5-plotting-change-over-time/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Instructions for Accessing Data\n",
    "\n",
    "#### By Rob Kalescky, Eric Godat, Steph Buongiorno, and Jo Guldi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The purpose of this Notebook is to provide access and insight to the many data sets available to this course. \n",
    "\n",
    "You do not need to read every section of this Notebook. Instead, you can use **command + F / control + F** to skip to the data that is relevant for your research. \n",
    "\n",
    "These are the section names for the data:\n",
    "- Project Gutenberg\n",
    "- EDGAR Database\n",
    "- Hansard\n",
    "- US Congress\n",
    "- Reddit Archive\n",
    "- Dallas and Houston City Council Minutes\n",
    "- NovelTM Datasets for English-Language Fiction, 1700-2009\n",
    "\n",
    "You also have a section with examples and code exercises for this week: \n",
    "- Working with Our Data: Counting Top Words\n",
    "\n",
    "\n",
    "Import the modules needed across multiple data sets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/hpc/applications/anaconda/3/lib/python3.6/site-packages/dask/config.py:168: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n",
      "  data = yaml.load(f.read()) or {}\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import dask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project Gutenberg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Project Gutenberg](https://www.gutenberg.org/) is a library of over 60,000 digitized books in the public domain. It is best known for its abundant literary texts.\n",
    "\n",
    "First import the modules for accessing and processing the Project Gutenberg text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gutenberg.acquire import load_etext\n",
    "from gutenberg.query import get_metadata\n",
    "from gutenberg.cleanup import strip_headers\n",
    "\n",
    "gutenberg_mirror = 'https://gutenberg.pglaf.org/' # the mirror from which we will access and download books"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have used Project Gutenberg before, you may have noticed that at the top of each html page is meta data that looks something like this: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The Project Gutenberg EBook of Pride and Prejudice, by Jane Austen\n",
    ">\n",
    "> This eBook is for the use of anyone anywhere at no cost and with\n",
    "> almost no restrictions whatsoever.  You may copy it, give it away or\n",
    "> re-use it under the terms of the Project Gutenberg License included\n",
    "> with this eBook or online at www.gutenberg.org\n",
    "> \n",
    ">Title: Pride and Prejudice\n",
    "> \n",
    "> Author: Jane Austen\n",
    "> \n",
    "> Release Date: August 26, 2008 [EBook #1342]\n",
    "> Last Updated: November 12, 2019\n",
    "> \n",
    "> Language: English\n",
    "> \n",
    "> Character set encoding: UTF-8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Important to note is the EBook #1342 ID tag. Using the gutenberg Python module, you can download books based on this ID. Here is an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "books = [\n",
    "    [1342,\"Pride and Prejudice\",\"Jane Austen\"],\n",
    "    [11,\"Alice's Adventures in Wonderland\", \"Lewis Carroll\"],\n",
    "    [2701,\"Moby Dick; Or, The Whale\",\"Herman Melville\"],\n",
    "    [84,\"Frankenstein; Or, The Modern Prometheus\", \"Mary Wollenstonecraft Shelley\" ],\n",
    "    [345,\"Dracula\", \"Bram Stoker\"]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Title</th>\n",
       "      <th>Author</th>\n",
       "      <th>FullText</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1342</td>\n",
       "      <td>Pride and Prejudice</td>\n",
       "      <td>Jane Austen</td>\n",
       "      <td>THERE IS AN ILLUSTRATED EDITION OF THIS TITLE ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>Alice's Adventures in Wonderland</td>\n",
       "      <td>Lewis Carroll</td>\n",
       "      <td>Alice’s Adventures in Wonderlandby Lewis Carro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2701</td>\n",
       "      <td>Moby Dick; Or, The Whale</td>\n",
       "      <td>Herman Melville</td>\n",
       "      <td>MOBY-DICK;or, THE WHALE.By Herman MelvilleCONT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84</td>\n",
       "      <td>Frankenstein; Or, The Modern Prometheus</td>\n",
       "      <td>Mary Wollenstonecraft Shelley</td>\n",
       "      <td>Frankenstein;or, the Modern Prometheusby Mary ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>345</td>\n",
       "      <td>Dracula</td>\n",
       "      <td>Bram Stoker</td>\n",
       "      <td>DRACULA       ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID                                    Title  \\\n",
       "0  1342                      Pride and Prejudice   \n",
       "1    11         Alice's Adventures in Wonderland   \n",
       "2  2701                 Moby Dick; Or, The Whale   \n",
       "3    84  Frankenstein; Or, The Modern Prometheus   \n",
       "4   345                                  Dracula   \n",
       "\n",
       "                          Author  \\\n",
       "0                    Jane Austen   \n",
       "1                  Lewis Carroll   \n",
       "2                Herman Melville   \n",
       "3  Mary Wollenstonecraft Shelley   \n",
       "4                    Bram Stoker   \n",
       "\n",
       "                                            FullText  \n",
       "0  THERE IS AN ILLUSTRATED EDITION OF THIS TITLE ...  \n",
       "1  Alice’s Adventures in Wonderlandby Lewis Carro...  \n",
       "2  MOBY-DICK;or, THE WHALE.By Herman MelvilleCONT...  \n",
       "3  Frankenstein;or, the Modern Prometheusby Mary ...  \n",
       "4                                  DRACULA       ...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a data frame from the books variable that has columns for the ID, Title, and Author\n",
    "gutenDF = pd.DataFrame(books, columns=['ID','Title','Author']) \n",
    "\n",
    "# Now add a column to the gutenDF data frame for the books' full text. \n",
    "# we are going to replace characters that the machine uses to understand the text (but that are messy to us human readers, \n",
    "# characters like \"\\n\", signifying a linebreak, and \"[Illustration]\", signifying an image\n",
    "gutenDF['FullText'] = gutenDF.apply(lambda row: strip_headers(load_etext(row['ID'], mirror=gutenberg_mirror)).replace(\"\\n\", \"\").replace(\"[Illustration]\", \"\") , axis=1)\n",
    "\n",
    "gutenDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also filter our returns based on criteria, such as the characters a title contains. The following code searches the `Title` column for the word \"Whale\" and returns the full text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    MOBY-DICK;or, THE WHALE.By Herman MelvilleCONT...\n",
       "Name: FullText, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gutenDF[gutenDF['Title'].str.contains(\"Whale\")]['FullText'].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Searching for titles that contains \"Whale\" returns __Moby Dick__."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MetaData and Caching"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you plan on doing a lot of work with Project Gutenberg's metadata functionality, you'll need to cache their metadata first. This can take a very long time but makes it possible to query their metadata quickly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from gutenberg.acquire import get_metadata_cache\n",
    "# cache = get_metadata_cache()\n",
    "# cache.populate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from gutenberg.query import get_etexts\n",
    "# from gutenberg.query import get_metadata\n",
    "\n",
    "# print(get_metadata('title', 11))  # prints frozenset([u'Moby Dick; Or, The Whale'])\n",
    "# print(get_metadata('author', 11)) # prints frozenset([u'Melville, Hermann'])\n",
    "\n",
    "# print(get_etexts('title', 'Moby Dick; Or, The Whale'))  # prints frozenset([2701, ...])\n",
    "# print(get_etexts('author', 'Melville, Hermann'))        # prints frozenset([2701, ...])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDGAR Database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[EDGAR](https://www.sec.gov/edgar.shtml) is a data base for the Securities and Exchange Commission. You can discover the data for a specific company in multiple ways, depending on your needs. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can perform a superficial search of the EDGAR data base by providing the `find_company_name()` method with a company name. The following code returns companies with  \"Cisco System\" in their name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CISCO SYSTEMS (SWITZERLAND) INVESTMENTS LTD',\n",
       " 'CISCO SYSTEMS CAPITAL CORP',\n",
       " 'CISCO SYSTEMS INC',\n",
       " 'CISCO SYSTEMS INTERNATIONAL B.V.',\n",
       " 'CISCO SYSTEMS, INC.',\n",
       " 'L3TV SAN FRANCISCO CABLE SYSTEM, LLC',\n",
       " 'SPANISH BROADCASTING SYSTEM SAN FRANCISCO INC']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from edgar import Edgar\n",
    "edgar = Edgar()\n",
    "possible_companies = edgar.find_company_name(\"Cisco System\")\n",
    "possible_companies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the code below, we're loading EDGAR's data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Content</th>\n",
       "      <th>Period of Report</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EXXON MOBIL CORP</td>\n",
       "      <td>usgaapusgovernmentdebtsecuritiesmember country...</td>\n",
       "      <td>2019-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EXXON MOBIL CORP</td>\n",
       "      <td>united state security exchange commission wash...</td>\n",
       "      <td>2018-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EXXON MOBIL CORP</td>\n",
       "      <td>united state security exchange commission wash...</td>\n",
       "      <td>2017-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EXXON MOBIL CORP</td>\n",
       "      <td>united state security exchange commission wash...</td>\n",
       "      <td>2016-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EXXON MOBIL CORP</td>\n",
       "      <td>united state security exchange commission wash...</td>\n",
       "      <td>2015-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>MARATHON PETROLEUM CORP</td>\n",
       "      <td>table content united state security exchange c...</td>\n",
       "      <td>2015-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>MARATHON PETROLEUM CORP</td>\n",
       "      <td>table content united state security exchange c...</td>\n",
       "      <td>2014-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>MARATHON PETROLEUM CORP</td>\n",
       "      <td>table content united state security exchange c...</td>\n",
       "      <td>2013-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>MARATHON PETROLEUM CORP</td>\n",
       "      <td>table content united state security exchange c...</td>\n",
       "      <td>2012-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>MARATHON PETROLEUM CORP</td>\n",
       "      <td>table content united state security exchange c...</td>\n",
       "      <td>2011-12-31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>126 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Company  \\\n",
       "0           EXXON MOBIL CORP   \n",
       "1           EXXON MOBIL CORP   \n",
       "2           EXXON MOBIL CORP   \n",
       "3           EXXON MOBIL CORP   \n",
       "4           EXXON MOBIL CORP   \n",
       "..                       ...   \n",
       "121  MARATHON PETROLEUM CORP   \n",
       "122  MARATHON PETROLEUM CORP   \n",
       "123  MARATHON PETROLEUM CORP   \n",
       "124  MARATHON PETROLEUM CORP   \n",
       "125  MARATHON PETROLEUM CORP   \n",
       "\n",
       "                                               Content Period of Report  \n",
       "0    usgaapusgovernmentdebtsecuritiesmember country...       2019-12-31  \n",
       "1    united state security exchange commission wash...       2018-12-31  \n",
       "2    united state security exchange commission wash...       2017-12-31  \n",
       "3    united state security exchange commission wash...       2016-12-31  \n",
       "4    united state security exchange commission wash...       2015-12-31  \n",
       "..                                                 ...              ...  \n",
       "121  table content united state security exchange c...       2015-12-31  \n",
       "122  table content united state security exchange c...       2014-12-31  \n",
       "123  table content united state security exchange c...       2013-12-31  \n",
       "124  table content united state security exchange c...       2012-12-31  \n",
       "125  table content united state security exchange c...       2011-12-31  \n",
       "\n",
       "[126 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/scratch/group/history/hist_3368-jguldi/edgar-data.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get a rough sense of what's in the 'Content' Column by creating a wordcloud."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: wordcloud in /users/dbalut/.local/lib/python3.6/site-packages (1.8.1)\n",
      "Requirement already satisfied: pillow in /hpc/applications/anaconda/3/lib/python3.6/site-packages (from wordcloud) (6.2.1)\n",
      "Requirement already satisfied: numpy>=1.6.1 in /hpc/applications/anaconda/3/lib/python3.6/site-packages (from wordcloud) (1.17.3)\n",
      "Requirement already satisfied: matplotlib in /hpc/applications/anaconda/3/lib/python3.6/site-packages (from wordcloud) (2.2.5)\n",
      "Requirement already satisfied: cycler>=0.10 in /hpc/applications/anaconda/3/lib/python3.6/site-packages (from matplotlib->wordcloud) (0.10.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /hpc/applications/anaconda/3/lib/python3.6/site-packages (from matplotlib->wordcloud) (2.8.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /hpc/applications/anaconda/3/lib/python3.6/site-packages (from matplotlib->wordcloud) (1.1.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /hpc/applications/anaconda/3/lib/python3.6/site-packages (from matplotlib->wordcloud) (2.4.2)\n",
      "Requirement already satisfied: pytz in /hpc/applications/anaconda/3/lib/python3.6/site-packages (from matplotlib->wordcloud) (2019.3)\n",
      "Requirement already satisfied: six>=1.10 in /hpc/applications/anaconda/3/lib/python3.6/site-packages (from matplotlib->wordcloud) (1.12.0)\n",
      "Requirement already satisfied: setuptools in /hpc/applications/anaconda/3/lib/python3.6/site-packages (from kiwisolver>=1.0.1->matplotlib->wordcloud) (41.4.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install wordcloud --user\n",
    "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "stop_words = set(STOPWORDS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The wordcloud package has its own built-in function to split a block of text.  It just needs one big block of text assembled from all the rows in the 'Content' column.  We'll use the join() command to paste together all the entries in df['Content'], calling the result 'allwords.'  Then we'l use the WordCloud().generate() command to make a wordcloud from the variable 'allwords'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords # this calls all multilingual stopword lists from NLTK\n",
    "stop = stopwords.words('english') # this command calls only the English stopwords, labeling them \"stop\"\n",
    "stop_set = set(stop) # use the Python native command \"set\" to streamline how the stopwords are stored, improve performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "allwords = \" \".join(df['Content'])\n",
    "wordcloud = WordCloud(stopwords=stop, background_color=\"white\").generate(allwords)\n",
    "plt.figure(figsize=(12, 12))\n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's visualize the most frequent words, breaking the variable 'allwords' down into individual words using split().  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['usgaapusgovernmentdebtsecuritiesmember',\n",
       " 'countryus',\n",
       " 'usgaapforeignplanmember',\n",
       " 'usgaapforeignplanmember',\n",
       " 'usgaapagingofcapitalizedexploratorywellcostsperiodtwomember',\n",
       " 'srtparentcompanymember',\n",
       " 'countryus',\n",
       " 'usgaapforeignplanmember',\n",
       " 'usgaapnonusmember',\n",
       " 'xomchemicalmember']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wordlist = allwords.split()\n",
    "wordlist[:10] # look at the first ten elements of the list only"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, count the individual words using the pandas commands \"Series()\" and \"value_counts()\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "company      35236\n",
       "million      29748\n",
       "financial    27212\n",
       "tax          23999\n",
       "net          22314\n",
       "year         22098\n",
       "product      21980\n",
       "cost         21721\n",
       "asset        20824\n",
       "income       20719\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wordcounts = pd.Series(wordlist).value_counts()[:20]\n",
    "wordcounts[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, plot those values as a well-labeled barchart.  Notice that the axes are well-labeled and that the chart has a title that describes the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2aab04fb55c0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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az0knER8suRztrodPkc6i7yXt895EiZaYiLiNtC/5vqQ3lth3QjqL3Y6ez2YbOrFtlvEP0rpeRNp2PxwRf8qvfQT4ct4G/pMVh/KV0sd6WY20L15E2r++jj5aM5Qv8g4rko4jdQx5z2DXxcxsKJM0ihQ4Xx4R9w5yXXYjtVCM7+u9Q8kqP7GBmZm15XDSPAqDGmRXZp5VyczMWpI0n9SZaEDnZ17VDMumYzMzs4HipmMzM7Mauem4BxtvvHFMnDhxsKthZrZSueWWWx6KiJ4mmhmWHGh7MHHiRObM6Wkkk5mZtSKpysx7w4Kbjs3MzGrkQGtmZlYjB1ozM7MaOdCamZnVyIHWzMysRg60ZmZmNXKgNTMzq5EDrZmZWY0caM3MzGrkQGtmZlYjB1ozM7MaOdCamZnVyIHWzMysRoMeaCWtKWm2pNskzZX0pZx+nKS/S7o1/72pkOcYSfMk3SNp70L6TpLuyK+dLEk5fQ1JF+b0myRNHOjlNDOz4Wko3CbvWWD3iHhS0urA9ZIuz6+dFBHfLL5Z0iRgOrAtsCnwG0kvjojlwKnADOBG4BfANOBy4DDg0YjYStJ04OvAO6tUcuLRP+/zPfOP36dKkWZmNgwM+hltJE/mp6vnv+gly37ABRHxbETcB8wDdpY0FhgdETdERADnAPsX8pydH18E7NE42zUzM6vToAdaAEkjJN0KPAhcERE35Zc+Kul2SWdI2iCnjQMWFLIvzGnj8uPm9G55ImIZ8DiwUYt6zJA0R9KcJUuWdGjpzMxsOBsSgTYilkfEZGA86ex0O1Iz8JbAZGAxcEJ+e6sz0eglvbc8zfU4LSKmRMSUMWPGVFwKMzOzFQ2JQNsQEY8B1wDTIuKBHID/DXwf2Dm/bSEwoZBtPLAop49vkd4tj6SRwHrAIzUthpmZ2fMGPdBKGiNp/fx4FLAn8Kd8zbXhrcCd+fElwPTck3gLYGtgdkQsBpZKmpqvvx4CXFzIc2h+fABwVb6Oa2ZmVquh0Ot4LHC2pBGkwD8rIi6TdK6kyaQm3vnAhwAiYq6kWcBdwDLgiNzjGOBw4CxgFKm3caP38unAuZLmkc5kpw/EgpmZmQ16oI2I24EdW6Qf3EuemcDMFulzgO1apD8DHNheTc3MzKob9KZjMzOzVZkDrZmZWY0caM3MzGrkQGtmZlYjB1ozM7MaOdCamZnVyIHWzMysRg60ZmZmNXKgNTMzq5EDrZmZWY0caM3MzGrkQGtmZlYjB1ozM7MaOdCamZnVyIHWzMysRg60ZmZmNXKgNTMzq5EDrZmZWY0caM3MzGrkQGtmZlYjB1ozM7MaOdCamZnVyIHWzMysRg60ZmZmNXKgNTMzq5EDrZmZWY0caM3MzGrkQGtmZlYjB1ozM7MaOdCamZnVyIHWzMysRg60ZmZmNXKgNTMzq5EDrZmZWY0caM3MzGo06IFW0pqSZku6TdJcSV/K6RtKukLSvfn/BoU8x0iaJ+keSXsX0neSdEd+7WRJyulrSLowp98kaeJAL6eZmQ1Pgx5ogWeB3SNiB2AyME3SVOBo4MqI2Bq4Mj9H0iRgOrAtMA04RdKIXNapwAxg6/w3LacfBjwaEVsBJwFfH4gFMzMzG/RAG8mT+enq+S+A/YCzc/rZwP758X7ABRHxbETcB8wDdpY0FhgdETdERADnNOVplHURsEfjbNfMzKxOgx5oASSNkHQr8CBwRUTcBGwSEYsB8v8X5rePAxYUsi/MaePy4+b0bnkiYhnwOLBRi3rMkDRH0pwlS5Z0avHMzGwYGxKBNiKWR8RkYDzp7HS7Xt7e6kw0eknvLU9zPU6LiCkRMWXMmDF9VdvMzKxPIwe7AkUR8Zika0jXVh+QNDYiFudm4Qfz2xYCEwrZxgOLcvr4FunFPAsljQTWAx6pbUF6MPHon/f6+vzj9xmgmpiZ2UAZ9DNaSWMkrZ8fjwL2BP4EXAIcmt92KHBxfnwJMD33JN6C1Olpdm5eXippar7+ekhTnkZZBwBX5eu4ZmZmtRoKZ7RjgbNzz+HVgFkRcZmkG4BZkg4D7gcOBIiIuZJmAXcBy4AjImJ5Lutw4CxgFHB5/gM4HThX0jzSmez0AVkyMzMb9gY90EbE7cCOLdIfBvboIc9MYGaL9DnACtd3I+IZcqA2MzMbSIPedGxmZrYqc6A1MzOrkQOtmZlZjRxozczMauRAa2ZmViMHWjMzsxo50JqZmdXIgdbMzKxGDrRmZmY1cqA1MzOrkQOtmZlZjRxozczMauRAa2ZmViMHWjMzsxo50JqZmdXIgdbMzKxGDrRmZmY1cqA1MzOrkQOtmZlZjUYOdgWsmolH/7zX1+cfv88A1cTMzMrwGa2ZmVmNHGjNzMxq5EBrZmZWIwdaMzOzGjnQmpmZ1ciB1szMrEYOtGZmZjVyoDUzM6uRA62ZmVmNHGjNzMxq5EBrZmZWIwdaMzOzGjnQmpmZ1ciB1szMrEYOtGZmZjUa9EAraYKkqyXdLWmupCNz+nGS/i7p1vz3pkKeYyTNk3SPpL0L6TtJuiO/drIk5fQ1JF2Y02+SNHGgl9PMzIanQQ+0wDLgkxHxUmAqcISkSfm1kyJicv77BUB+bTqwLTANOEXSiPz+U4EZwNb5b1pOPwx4NCK2Ak4Cvj4Ay2VmZjb4gTYiFkfEH/LjpcDdwLhesuwHXBARz0bEfcA8YGdJY4HREXFDRARwDrB/Ic/Z+fFFwB6Ns10zM7M6DXqgLcpNujsCN+Wkj0q6XdIZkjbIaeOABYVsC3PauPy4Ob1bnohYBjwObNTi82dImiNpzpIlSzqyTGZmNrwNmUAraR3gJ8BREfEEqRl4S2AysBg4ofHWFtmjl/Te8nRPiDgtIqZExJQxY8ZUXAIzM7MVDYlAK2l1UpA9LyJ+ChARD0TE8oj4N/B9YOf89oXAhEL28cCinD6+RXq3PJJGAusBj9SzNGZmZl0GPdDma6WnA3dHxImF9LGFt70VuDM/vgSYnnsSb0Hq9DQ7IhYDSyVNzWUeAlxcyHNofnwAcFW+jmtmZlarkYNdAeDVwMHAHZJuzWmfAw6SNJnUxDsf+BBARMyVNAu4i9Rj+YiIWJ7zHQ6cBYwCLs9/kAL5uZLmkc5kp9e8TGZmZsAQCLQRcT2tr6H+opc8M4GZLdLnANu1SH8GOLCNapqZmfXLoDcdm5mZrcocaM3MzGrkQGtmZlajQb9GawNr4tE/7/M984/fZwBqYmY2PPiM1szMrEYOtGZmZjVyoDUzM6uRA62ZmVmNHGjNzMxq5EBrZmZWIwdaMzOzGjnQmpmZ1ciB1szMrEYOtGZmZjVyoDUzM6uRA62ZmVmNHGjNzMxq5EBrZmZWI98mzyrzrfbMzMrzGa2ZmVmNfEZrg6Kvs2KfEZvZqsKB1lZaDtZmtjJw07GZmVmNfEZrw5Y7dZnZQPAZrZmZWY0caM3MzGrkQGtmZlYjB1ozM7MaOdCamZnVyIHWzMysRg60ZmZmNfI4WrM2eCyumfXFgdZskHkqSbNVm5uOzczMauRAa2ZmVqNBD7SSJki6WtLdkuZKOjKnbyjpCkn35v8bFPIcI2mepHsk7V1I30nSHfm1kyUpp68h6cKcfpOkiQO9nGZmNjwNeqAFlgGfjIiXAlOBIyRNAo4GroyIrYEr83Pya9OBbYFpwCmSRuSyTgVmAFvnv2k5/TDg0YjYCjgJ+PpALJiZmdmgd4aKiMXA4vx4qaS7gXHAfsBu+W1nA9cAn83pF0TEs8B9kuYBO0uaD4yOiBsAJJ0D7A9cnvMcl8u6CPi2JEVE1L18ZgPBHarMhq6hcEb7vNykuyNwE7BJDsKNYPzC/LZxwIJCtoU5bVx+3JzeLU9ELAMeBzZq8fkzJM2RNGfJkiWdWSgzMxvWhkyglbQO8BPgqIh4ore3tkiLXtJ7y9M9IeK0iJgSEVPGjBnTV5XNzMz6NCQCraTVSUH2vIj4aU5+QNLY/PpY4MGcvhCYUMg+HliU08e3SO+WR9JIYD3gkc4viZmZWXeDHmhzz+DTgbsj4sTCS5cAh+bHhwIXF9Kn557EW5A6Pc3OzctLJU3NZR7SlKdR1gHAVb4+a2ZmA2HQO0MBrwYOBu6QdGtO+xxwPDBL0mHA/cCBABExV9Is4C5Sj+UjImJ5znc4cBYwitQJ6vKcfjpwbu449Qip17KZmVntBj3QRsT1tL6GCrBHD3lmAjNbpM8BtmuR/gw5UJuZmQ2kQW86NjMzW5U50JqZmdXIgdbMzKxGDrRmZmY1cqA1MzOrkQOtmZlZjQZ9eI+ZDb6+bkoAvjGBWX/5jNbMzKxGDrRmZmY1cqA1MzOrka/RmllH+ObzZq35jNbMzKxGPqM1syHDZ8W2KvIZrZmZWY0caM3MzGrkQGtmZlYjB1ozM7MaOdCamZnVyIHWzMysRg60ZmZmNXKgNTMzq5EnrDCzVUq7k174loHWaT6jNTMzq5HPaM3MOsxTSVqRz2jNzMxq5EBrZmZWIwdaMzOzGjnQmpmZ1ciB1szMrEYOtGZmZjVyoDUzM6uRA62ZmVmNHGjNzMxq5JmhzMyGIM8uterwGa2ZmVmNBv2MVtIZwJuBByNiu5x2HPBBYEl+2+ci4hf5tWOAw4DlwMcj4lc5fSfgLGAU8AvgyIgISWsA5wA7AQ8D74yI+QOycGZmg8hnxUPDUDijPQuY1iL9pIiYnP8aQXYSMB3YNuc5RdKI/P5TgRnA1vmvUeZhwKMRsRVwEvD1uhbEzMys2aAH2oi4Dnik5Nv3Ay6IiGcj4j5gHrCzpLHA6Ii4ISKCdAa7fyHP2fnxRcAektS5JTAzM+vZoAfaXnxU0u2SzpC0QU4bBywovGdhThuXHzend8sTEcuAx4GNWn2gpBmS5kias2TJklZvMTMzq2SoBtpTgS2BycBi4ISc3upMNHpJ7y3PiokRp0XElIiYMmbMmGo1NjMza2FIBtqIeCAilkfEv4HvAzvnlxYCEwpvHQ8syunjW6R3yyNpJLAe5ZuqzczM2jIkA22+5trwVuDO/PgSYLqkNSRtQer0NDsiFgNLJU3N118PAS4u5Dk0Pz4AuCpfxzUzM6vdUBje8yNgN2BjSQuBY4HdJE0mNfHOBz4EEBFzJc0C7gKWAUdExPJc1OF0De+5PP8BnA6cK2ke6Ux2ev1LZWZmlgx6oI2Ig1okn97L+2cCM1ukzwG2a5H+DHBgO3U0MxuO+hqHCx6LW8aQbDo2MzNbVTjQmpmZ1ciB1szMrEYOtGZmZjVyoDUzM6uRA62ZmVmNHGjNzMxq5EBrZmZWIwdaMzOzGjnQmpmZ1ciB1szMrEYOtGZmZjVyoDUzM6uRA62ZmVmNHGjNzMxq5EBrZmZWIwdaMzOzGjnQmpmZ1ciB1szMrEYOtGZmZjVyoDUzM6uRA62ZmVmNHGjNzMxq5EBrZmZWIwdaMzOzGjnQmpmZ1ciB1szMrEYOtGZmZjVyoDUzM6uRA62ZmVmNHGjNzMxq5EBrZmZWIwdaMzOzGjnQmpmZ1ciB1szMrEaDHmglnSHpQUl3FtI2lHSFpHvz/w0Krx0jaZ6keyTtXUjfSdId+bWTJSmnryHpwpx+k6SJA7l8ZmY2vA16oAXOAqY1pR0NXBkRWwNX5udImgRMB7bNeU6RNCLnORWYAWyd/xplHgY8GhFbAScBX69tSczMzJoMeqCNiOuAR5qS9wPOzo/PBvYvpF8QEc9GxH3APGBnSWOB0RFxQ0QEcE5TnkZZFwF7NM52zczM6jbogbYHm0TEYoD8/4U5fRywoPC+hTltXH7cnN4tT0QsAx4HNmr1oZJmSJojac6SJUs6tChmZjacDdVA25NWZ6LRS3pveVZMjDgtIqZExJQxY8b0s4pmZmZdhmqgfSA3B5P/P5jTFwITCu8bDyzK6eNbpHfLI2kksB4rNlWbmZnVYqgG2kuAQ/PjQ4GLC+nTc0/iLUidnmbn5uWlkqbm66+HNOVplHUAcFW+jmtmZla7kYNdAUk/AnYDNpa0EDgWOB6YJekw4H7gQICImCtpFnAXsAw4IiKW56IOJ/VgHgVcnv8ATgfOlTSPdCY7fQAWy8zMDBgCgTYiDurhpT16eP9MYGaL9DnAdi3SnyEHajMzs4E2VJuOzczMVgn3qA9QAAAfvUlEQVQOtGZmZjVyoDUzM6uRA62ZmVmNHGjNzMxq5EBrZmZWIwdaMzOzGjnQmpmZ1ciB1szMrEYOtGZmZjVyoDUzM6uRA62ZmVmNHGjNzMxq5EBrZmZWIwdaMzOzGjnQmpmZ1ciB1szMrEYOtGZmZjVyoDUzM6uRA62ZmVmNHGjNzMxq5EBrZmZWIwdaMzOzGjnQmpmZ1ciB1szMrEYOtGZmZjVyoDUzM6uRA62ZmVmNHGjNzMxq5EBrZmZWIwdaMzOzGjnQmpmZ1ciB1szMrEYOtGZmZjVyoDUzM6vRkA60kuZLukPSrZLm5LQNJV0h6d78f4PC+4+RNE/SPZL2LqTvlMuZJ+lkSRqM5TEzs+FnSAfa7PURMTkipuTnRwNXRsTWwJX5OZImAdOBbYFpwCmSRuQ8pwIzgK3z37QBrL+ZmQ1jK0OgbbYfcHZ+fDawfyH9goh4NiLuA+YBO0saC4yOiBsiIoBzCnnMzMxqNdQDbQC/lnSLpBk5bZOIWAyQ/78wp48DFhTyLsxp4/Lj5vQVSJohaY6kOUuWLOngYpiZ2XA1crAr0IdXR8QiSS8ErpD0p17e2+q6a/SSvmJixGnAaQBTpkxp+R4zM7MqhvQZbUQsyv8fBH4G7Aw8kJuDyf8fzG9fCEwoZB8PLMrp41ukm5mZ1W7IBlpJa0tat/EYeANwJ3AJcGh+26HAxfnxJcB0SWtI2oLU6Wl2bl5eKmlq7m18SCGPmZlZrYZy0/EmwM/ySJyRwPkR8UtJNwOzJB0G3A8cCBARcyXNAu4ClgFHRMTyXNbhwFnAKODy/GdmZla7IRtoI+KvwA4t0h8G9ughz0xgZov0OcB2na6jmZlZX4Zs07GZmdmqwIHWzMysRg60ZmZmNXKgNTMzq5EDrZmZWY0caM3MzGrkQGtmZlYjB1ozM7MaOdCamZnVyIHWzMysRg60ZmZmNXKgNTMzq5EDrZmZWY0caM3MzGrkQGtmZlYjB1ozM7MaOdCamZnVyIHWzMysRg60ZmZmNXKgNTMzq5EDrZmZWY0caM3MzGrkQGtmZlYjB1ozM7MaOdCamZnVyIHWzMysRg60ZmZmNXKgNTMzq5EDrZmZWY0caM3MzGrkQGtmZlYjB1ozM7MaOdCamZnVyIHWzMysRg60ZmZmNRo2gVbSNEn3SJon6ejBro+ZmQ0PwyLQShoBfAd4IzAJOEjSpMGtlZmZDQfDItACOwPzIuKvEfEccAGw3yDXyczMhoHhEmjHAQsKzxfmNDMzs1opIga7DrWTdCCwd0R8ID8/GNg5Ij7W9L4ZwIz8dBvgnl6K3Rh4qM2qrSplDIU6DJUyhkIdhkoZQ6EOQ6WMoVCHTpRRJv/mETGmjc9Y5Ywc7AoMkIXAhMLz8cCi5jdFxGnAaWUKlDQnIqa0U6lVpYyhUIehUsZQqMNQKWMo1GGolDEU6tCJMjpRh+FouDQd3wxsLWkLSS8ApgOXDHKdzMxsGBgWZ7QRsUzSR4FfASOAMyJi7iBXy8zMhoFhEWgBIuIXwC86WGSpJuZhUsZQqMNQKWMo1GGolDEU6jBUyhgKdehEGZ2ow7AzLDpDmZmZDZbhco3WzMxsUDjQmpmZ1ciB1szMrEYOtBVIerOklX6dtZrnWdJuFcvYokxaD3lXk/SqKp/XS1mvLpPWS/4Dy6TVXcZQIWlzSXvmx6MkrVsx/3b11Kw6SeMkvUrSaxt/A/jZHdnGlbxH0n/m55tJ2rlC/jXKpJUoZ1dJ78uPx5T9rVvizlAVSPohsAvwE+DMiLi7H2WMAT4ITKTQ6zsi3t9HvjuAVl+WUvbYvkId7gTOBb4BrJn/T4mIXSqU8YeIeHlT2i0RsVPJ/DdU+byK9Vghra78HSzjxcCpwCYRsZ2k7YG3RMRXK5TxKlbcrs6pkP+DpJnRNoyILSVtDXw3IvaoUMb1wAuAs4DzI+Kxsnlz/k2A/wI2jYg35oPCXSLi9IrlfB14J3AXsDwnR0S8ZaDq0YltXNKpwL+B3SPipZI2AH4dEa8omb8T2+axwBRgm4h4saRNgR9HROkD2uFu2Azv6YSIeI+k0cBBwJmSAjgT+FFELC1ZzMXAb4Hf0LUDKOPNlSrbu1cCXwd+D6wLnAeU+tFIegmwLbCepLcVXhpNCtpl/VrS24GfRj+O9iTtArwKGCPpE031GFEi/xuBNwHjJJ3clH9ZyTq0XUbB94FPA98DiIjbJZ0PlAq0ks4FtgRupRBYgNKBFjiCdAOOm3Id7pX0wgr5iYhdc4B+PzBH0mzSQekVJYs4i/Sb+nx+/mfgQqBSoAX2JwWGZyvm62Q92trGs1dGxMsl/REgIh7Nk+70StJ/kOZzHyVpR9IBOaRtc62KdXgrsCPwh1yHRVVbOoY7B9qKIuIJST8BRgFHkTbCT0s6OSK+VaKItSLis/343L9VzdOLfwFPk5ZhTeC+iPh3ybzbkIL++sC+hfSlpDP1sj4BrA0sk/QMXWfmo0vmfwGwDmkbLv7onwAOKJF/ETAHeAtwSyF9KfD/StahE2U0rBURsyUV06oE6ynApDZ26ADPRsRzjTpIGknrVpRe5QD9BdK6ORnYUanQz0XET/vIvnFEzJJ0TC5rmaQqB6QNfwVWB/obaDtRj8Y2vlzS01TfxgH+pXSbz4DnW8TK/Fb3Bt5Lmm72xEL6UuBzFT4f4LmIiHxigaS1K+Yf9hxoK5C0L+lIfUtS0+vOEfGgpLWAu4EygfYySW/KE2j0pw5T8+e8lBRsRgBPVfzx3kw6s34FsBHwPUkHRESfASoiLgYulrRLRNxQeQG6ymnriDgirgWulXRWfw5CIuI24DZJ50fEvwBys9yEiHi0ahmk39JmEdHbjSh685CkLenaoR4ALK6Q/07gPyrmaXatpM+RzoL2Aj4CXFqlgNzk/T5gH+AKYN+I+ENubrwB6CvQPiVpI7rWw1Tg8WqLAcA/gVslXUkh2EbEx0vmb7se7W7j2cnAz4AXSppJOoj8QonPPhs4W9LbI+InbdZhlqTvAevnywvvJ7XAWEm+RluBpHOAH0TEdS1e2yMirixRxlLSUe5zpDNLqHCUK2kOaa7mH5POYg4BtoqIz/easXsZUyJiTlPawRFxboUyOnFNcQNgawpNzq3WbR9lXAEc2LgWmMu8ICL2Lpn/GtIZ6UhSs+sS4NqI+ERv+ZrK2Bf4JvCCiNhC0mTgy2WvB+YyXkSadedVwKPAfcB7ImJ+H/kuJQWDdYHJwGy6B5YqdVgNOAx4A+ns61ek7b30TkLSdaSd8EUR8XTTa31uY5JeTjqQ3I508DAGOCAibi9bh1zOoa3ScwAqk7/teuSz+HcDW0TEVyRNAMZGxOyyZeRyXgLsQfpOrqzSN0Sp49PbWfHa/Zcr1mEvCttFhUsBhgPtSkf57hmSbm90gJL0+4io3MMxX38rBrn7K+S9lnxNMSJ2zGl3RkSpXqeSPgAcSWrauhWYCtwQEbuXXwKQ9MfG5/eW1lf+XJ8JEXFscd2WLOMWYHfgmsK6qFRGoay1gdXKXvOX9LreXs9n/lU++5mIWJ6fjwDWiIh/li2jE3KT9Taknfo9jRaHfpTzAuDF+WnlctqtR7sdmXIZm7VKL/tblfRL0pn4LRT6hETECSXzjyAF1j3LvN9ac9NxBbnzz9eBF5J+fP255oKktwCNoQbXRMRlFbL/M+9AbpX0DVJTYaVrJvkM7ERgU+BBYHNS0/e2FYpp95rikaSm6xsj4vX5qP1LFfI3/FvSZo0dj6TNqXZdcaSkscA76Or4UtWyiHi8aV1UIml9UuvExFwnoO+mzkYgzUHy6Yj4d25teAlwecVqXAnsCTyZn48Cfk06yy4ld4T6GjCJ7gdxLyqZ/5CmpJdLqtR7OpezG3A2MJ/0O50g6dC+WkzUvYNf0YtzPfpq+i7qV0emJj8nbc8irc8tSPfJLvtbHR8R0yp+5vMiYrmkf0paLyL604RvONBW9Q3SNafKw3oaJB1PCjDn5aQjJe0aEUeXLOJg0nXZj5I63EwgNQ1V8VXSGeRv8tnc60k9qato95riMxHxjCQkrRERf5K0TcU6QAqO1+czbEgHMDMq5P8yqYn0dxFxc27CvbdiHe6U9C5gRA40Hyf16K7iF8CNwB2U6+zS7DrgNfms6UpSR6R3kpouy1ozIhpBloh4Mvc/qOJM4FjgJOD1pOu1VY5Aimd7a5KaTP9Atd7TACcAb2hcM88HHz8C+hp+tm8vrwV9X2Mu6m9Hpq4PjHhZ8Xlu0v5QhSJ+L+llEXFHlc9t8gxwR75M81ShbmWvdw97bjquQNLvos2xY5JuByY3evnmH+If+9PM2EYdGs3PtwE75rOg2RFRZSB8q2uK7y7bMUnSz0g74aNIza6PAqtHxJsqLg6SNiYdOIjU/PxQ1TLakYPR5+l+bfMrEfFMhTIqjW3sKb+kjwGjIuIbkm6NiMkVyvgd8LGI+EN+vhPw7ag2vvqWiNhJ0h2NICHptxHxmoqL1ChvPeDcKteac74Vmu7725zfX5LeTTrY2Yk0XOgA4AsR8eM2y60yTvwuYCvS7/NZ6Ne4+7aud5vPaKuaI+lC4P/o3uGkylEupKExj+TH65XJIGlWRLxDPUxcUXEH8pikdUhnQedJepCujlll/Z109nI1sCFpWM2hpDPEPkXEW/PD4yRdTVoPv6xYB9Q1288T+f+k3MRXqlOVpPGkTi+vJq3X64EjI2Jh2Trka5ifBz6fD5zWrhJks3OVenReRvdt65Ges3QjpbHF7yZ1aIIS44mbHAX8WNKi/HwsKVBU8UzuVHWv0j2g/0661NJf/yR1mKtqjqTTSaMDIK2XW3p5/wok7UNqoi02gZfuRBQR5+Xr940JP/av2hqm7mPEVwNeTuqwV9Ybq3xeKw6o7XOgrWY06Yf/hkJa1eakrwF/zMFFpKbOY0rkOzL/78TEFbeRluP/kXZA65HGpFZxMfAYqVlvUR/vbUnSrsDWEXFmblYbRzryruLThcdrkiZcaHROKuNM4HygMWXie3LaXmUroDS858Okzia3kCbzODEi/rtsGaRe6P9NCtiNA6kASl3bJG0fxwA/i4i5ucXh6gqfT246fwldHYD+1I+OSEeRJkT4OPAVUvNx83XXHqmrFzWkwDIJmFWxDgCHkybg+DhpWa4DTqlQj++SluP1wA9IZ6OVegtna5EOeIJ0zbuq4hChZaRrtqWH60TE31r8zir91tu97m5uOh4UufPNK0g7gJsi4h8V8m4BLG6cMUkaRRpiM79CGa2mZava07Z0D+Me8tcyrZvSEIpvRESpa86tmlf70eR6a0RMzk2FOwGfBW6puD7/Quo8M6DN3vmzd4+Iq3rqCFSlxUbSFNLBwuakCSNyEeXWhbr3ol4G/K1K60KnNH4Phf/rkGZ4ekOfmbvK+E/SAdxPSL/1/UnbeOkhcO3qxO9MaVrNxnX3fcnX3SPi2DrqvCryGW0FktYkNcs1Nyf1Ok9xzvuS3OGnEeAaO49NJW3auC5Wwo/p3gt0eU7rc8iApMNJkxBsma8VN6wL/K7k5ze028mirmndFpLGPpb1kKT3kDrKQOoU9nDFz1xd0uqkHem3I+JfyrPoVDCX1MrQL/lM5TOsuG2WObN/HXAVrTsCVW2xOY/UytCvTl1VhiO10sFLLI2m/3/m4PQIqcdvFQeR+kA0DoqPJ23vVcaavxj4FCuOgy3bYtOJ39moiLhSknIfjOMk/ZYUfK0EB9pqzgX+RJre7MukZtey11w+SZqisNX4taB8U+fIiHju+YxpyryyQwbOJw35+BpQ7OW8tMK1wIZdgfdK6m8ni45M6ybpW3RvapxMahov6/3At0lH60HqLfy+itX4HmkYyW3AdUpDjJ7oNceKlpOGbF1N/2YyOo80F++bSc3Yh1LyWl7hzOQDkcfQtmFJRFxSNZPSRC693TSj7BC6Tl1iuVRpyNV/k4JUUH02pPmkg55G0F4D+EvFMn4MfJfUfN2f76YTv7NOX3cfdtx0XIG6JjdoNCetThrMXWmShTbrcAXwrcbOTNJ+wMejwh1WOlSPzVulV+h1/ClSJ5e9SIH//aS7vZSZxrJYTrFH5DJgfkSUPjuXdDZwVORpFyVtCHyzTCtFH+WOjIjS44rb7dlZ6O1bnMjk2ojodUKLpjLuJ3VIuxC4Kvqxc5C0B+lMrnnqw6odBgeV0m0OfxkRSyV9kdQJ6StlWp4KB3+bkVqarsjP9wKuj4jpFepR+o5YPeRv+3cm6RWkE4r1Sdfd1yNdnrmxv/UabhxoK1AeAqM0zdxHgH8As8t0Cujp+ldD2R2R0tjV80iTTQhYABwSEfPK5B8qlIah/IPUeWnQpnVTmzNL5fcfSepAtZR05rEjcHRE/LpiXfo9k5GkGyNiqqRfkebHXUSaBnHLCmWMIjUfTycFlstI01leX6GMH5Imy5hLV9NxVD1wUT9nLevUmXHhYHpX0u3yTiDdFOGVJfK2PGhqqNKLV9JxpEllfkb/eqMjT5846BxoK1Capu8nwPakHes6wBcj4nsl8p7Zy8v92RGtQ/r+yt6eb0iR9FXSDv0PwBmkHUCVOXV7uj8vUP5anNJY4t2azmivjaaJAvoqIyJ2kLQ3qafrF0m3hqtyz8/daJrJCDg0yg9TejPp9osTSMOVRgPHRUSlmwIUytsA+F/S2OjSw4RUGD/bz899CymodZu1LCKqzFrWtkLr1deAOyLi/KoHYB2qR6te+FHm4L6pnNF0v8ZbJVC/mHTdfXP6d5142HOgXcmoQ5OEDwWSRDrSfh+pZ+Qs4PSI6PM6VqHp+oj8vzhe8p9l14fSlH/HABeRAvc7gJlR7QYLjbOf/yVNqfmzfpwV3wK8K5pmMirbbJibwI+Mrpsr9KsJPPf6fSdp/OXNwIVR4e4vkr4PnBQRd1X53EL+20j9FbrNWhYRpWb7ysvdo7IBRtJlpGuRe5J6kj9Nar3aoUz+XMabSU2tjQDVrylb2yHpQ6T+JE+TWhgadSgdqPN38l1WnC+50rjk4cyBtgKl22YdR9fkBr8lXbfps5equg88X0FEnNjb64Vy2pokfKiRtAMp0E4jjfucClwREZ8pmX+F2bpapfVRxiTSzr1xd5RKQSK3Vowj9UrdgTRu8poq19bU5kxGHWoCv490g4dZwCUR8VQfWVqVcTfpNpL96iSnNmcty8vQmBu4WekAozTb1zTS2ey9SkPyXlblcoCkecDbchn92tHmenyCdAvGGUpjWreJkvOjS7oX2CXaGDbW7nVic6/jqi4gDXxvzC38blLHkTJ3tujE0BVoc5LwoULSx0k9Yx8iXdf8dKRhMauR5houFWiBtZXmir4+l/sqKt5kIQfWfp2BZYeRejuvTjoz35g05V4V7c5ktJqkDZqawEv/vpVmtDqzAy0j7W6bjVnLfkvXrGWlO5VFRNUhOD2V808Kw5oiYjHV7/W7ALizv0E2O5O0HTSG9C0k9UQueyOSv9DPYWOF1oFLJX2ENq4TD3c+o62g1ZFd4wh8AOtwGqnXcTuThA86SV8mNROv0EtZ0kuj5FR1SvPxnkHXVJaPAe8v0zu0U9SBW/7lSwJHkIZNPT+TUUQ822vGrvydaAK/OiJeX/b9dVCa5OEMUlB7D+l7Pa9Mq1HO3zxevZsB3i5eQWo6vpbuAapU61Uuo3GG/3zrRKNPQMn8O5KC9U1UHDbWQ+vA8wGj6nXi4cxntNVcLWk6XVPCHUCaEq1Pkj4TaaL34rjP55XZ8LN2x68OCRHxn728Vno+2HydaIfc2UMxOLfy6sQt/0YC/9vYCeczzDXKZo6IcyTNoasJ/G39uE76e0nfJrXSFO/SMmDBCZ6/KcMjpBakC8sG2ewTpLs3nUD335moNl69E2aSbjm4JlD19ngNz+Xe4I1xsFtSCJglfI80GUnlCUQarQOS3kEa6vSECkOdqpQ13PmMtoI8dGBtujbY1ejaIfXayUHSvhFxae763yrQlroNmNocv7qqUbq7y7F03d/3WuDLAxlwJd0cEa+QdCtpGsVnVX0axxuBPSPfpi43n/46IkrfC7ZdSpNlNIvB6F0qaXtSp6y3Awuj4o3Hc3D6COnAtNGf4tSofrOHfutEa5ekN5CmtJxEujfwq4H3RsQ1JfP/vt1tSG0MdbLEZ7QVRES/r7NG1zCLu4DP0b3XcFDyfpuNgKqmcYbD2BnAnaSmUkj36z2T1AlloCxUmkXo/4ArJD1K9RstdOJesG0Z7GbjJg+Sxlk/TP9mITqbNDvXyfn5QaTf2Dt6zNF5v5H0hiodqJpFxK9zj/TGbSCPrNix6WpJM4BL6f/11Uany32A70bExUrje60kn9FWlI+0J9J9aE2VSdfvocVcsGXPSIfKOMOhotWZY9WzyQ7X53XkW/5FYarMEvnavhdsuyRtQjpj2TQi3ph7Y+8SEacPYB0OJ53JjiFdb76wH03gLa9jVrm22QmFFrDn8l/l4T2SLiHNw93fXuBtj8PtxFCn4c5ntBVIOoM0WUW3WW+oNul6v+aCLfgK6ei22zjDNspb2T3d1Ov41aQdwaCI/k+K34l7wbbrLFJrwOfz8z+TrtcOWKAlHTgeFRG3tlnOHyVNjTxNoKRXUv3GGW1ppwWs4ATSdnC8pNmk7+Oysk3gHeqF/Q5Sb/JvRsRjeajTp/vIYwU+o61A0l0RManNMtqaC7bdcYarmjwO9xy6eh0/SppR6faecw1NSnNnt3Mv2HY/v3GtudjDddBaB/pDXTOGNdbl/fn55sBd0catHftRF5GGaW0REV9RuoXj2IiofF/b3Dlud9KNSaaVPStudxyudYbPaKu5QdKk/jRlFbyPNBfs6vTvrLgxzvA6+jHOcFWSx9xuE2n6w9EAEVH1rjlDySvouiyxo6TSneQ65CmlSVkaPVynkiZHWZm0e9eeTjqF9BvfndQS9STwHUrc0rJIXXNQv5PU47f0XMm0Pw7XOsBntBVIei2pU8E/6OfQGrU/F+zapNtuNY6WK40zXNVIui4iXtv3O4c2SeeSZlS6la7OJ1Fh2Fcn6vBy0jzJ25E6mI0BDlgZWweGAkl/iIiX93cMbH7/hcArSXdVmkWacaz0MJ12x+FaZ/iMtpozSL1a+3VT6+zGds6KmzpEVDmyXVVdoXQrsOaxnyvbrDVTgEkxuEe+W5LmOJ5AGlbzSryPaMe/cpNvo4VgDNX3G2eS5sDu732C2x2Hax3gM9oKJF3V7phCtT8X7NuAr5OGPKiQf8AmKh9KCrPXdFOlV+VQIOnHpPsKV53mr5N18HjJDpL0bro39x4AfCEiflyhjHbnOm41Dvd9EdFqzLTVxIG2AkmnkG5+3DwmrcrwnnZvmD4P2DcqzJ60KuthYoLvRsSg9TzujzxZxGRgNt23rbcMYB2GxK3hViV5lrA94PkbVlT63eam41tI95zeLm/vN1ScDGUjusbh3lhxHK51gANtBWp9T9mIircia7MOle5Ms6qTNIs0McF5OekgYP2IGMiJCdqWx9+uoI3hQv2pg8dLdlDuTDY38j2jJa1LujxwU4Uy2p3r+MqI2KOvNKuXr79UEBHvG+w6kO7yciFpFqJ+nVWvYrZp2ulcnYc+rVQGMqD2wuMlO+tUUrNxw1Mt0vrSr2usktYE1gI2lrQBXTcGGE2a7MYGkANtBZLGk3plNu5Hez1pSrSFA1iN0aTbXr2hkFZ10oxVyaBPTNAOSddHxK55FqEVJsEfyGvv0Zlbw1kXFTu35THvVW5dKNIN138JTJB0Hnmu4xLZP0SaBGVTUtNzI9A+QRpiZAPITccVSLoCOJ+ue4a+B3h3ROw1eLUa3nLnssbEBACbAXeTendWGnpl1kmSfgpcQzqLhdSX4PURsX+FMm4hHVT36xqrpI9FxLdKV9pq4UBbwWDOq6vO3WZvldJT57KGsp3MzDpN6cYfJ5MmrAjSbHBHRcSDFcr4DnBWRNzcRj22I/U6fv4mJAM8Ecqw50BbgaTfkOaD/VFOOojUVb72jgWSHo6IjSQdRZpmsJuI8Jhas1WMpLuAFwN/I13jrToc8FhgN1Kg/QVpnPT1EXFALRW2lhxoK5C0GfBtYBfSEervSWMf7+81Y2c++y7Sj+QSYIXbma2EEzSYrdIkvZjUbLxJHpqzPfCWiPhqhTLaHQ54B7AD8Mc8VekmwA8iYt+ydbD2uTNUNV8hTVj/KICkDYFvAgMxvOdUUqeIFwFzCukiBf2VaoIGs2Hg+6Re298DiIjbJZ0PlA60Hbj08XTuhLUszwf+IN5XDDgH2mq2bwRZSGeRkgZkMH/u0PAtSadGxOED8Zlm1pa1ImJ26jz8vIG+AcgcSeuTgv4tpBsbVL57kLXHgbaa1SRt0HRGO6Dr0EHWbKXxUB732hgDewADPFwqIj6SH35X0i+B0b5JxMBzoK3mBOD3ki4i/XjeAcwc3CqZ2RB1BHAa8BJJfyfNb/7ugaxAcRaoiJjfnGYDw52hKpI0idRdvzF3aTv3pjWzVYykTzQljQJWI99dKiJOHIA6NGaGuprU67g4M9TlEfHSuutgXXxGW1EOrA6uZtaTdfP/bUg3eb+YFOgOBq4boDq0mhkqgKWkkRM2gHxGa2ZWA0m/Bt7edFOBH0fEtAGsw38C/xMRT0j6Imme5a9ExB8Gqg6WmjPMzKzzNgOeKzx/Dpg4wHU4IAfZXYG9SBPunNp7Fus0Nx2bmdXjXGC2pJ+Rmm3fSroB/EBanv/vQ7pP88WSjhvgOgx7bjo2M6uJpJcDr8lPr4uIPw7w5/sew0OAA62Z2SpK0lqkewzfERH35nsMvywifj3IVRtWHGjNzMxq5M5QZmZmNXKgNTMzq5EDrZmZWY0caM3MzGr0/wES56f4q2vWSAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wordcounts.plot(kind='bar', \n",
    "                title='Most frequent words in the CONTENT column of EDGAR for 8 key companies',\n",
    "                 figsize=(6, 6)\n",
    "               )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hansard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hansard is a data set of the 19th-century British Parliamentary debates. Please note: to use Hansard you must significantly increase the memory requested for your JupyterLab session. Per the README, you are suggested to request `64G`. This might result in a longer wait for launching your job but will allow you to hold all of the data in dataframe in memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "hansard = pd.read_csv('/scratch/group/history/hist_3368-jguldi/hansard_justnine_12192019.csv')\n",
    "\n",
    "hansard.head(5) # view just the first five rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After loading the Hansard data we can take a subset so its is smaller and easier to work with. A subset can be thought of as a slice of the data containing just the criteria in which you are interested. \n",
    "\n",
    "This section will demonstrate two ways to take a subset: 1) by date and 2) by the contents of a column.\n",
    "\n",
    "To subset the data by date, first convert the speechdate column from integers to datetimes objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hansard['speechdate']=pd.to_datetime(hansard['speechdate'], errors='coerce') # any errors are forced to NaN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you can filter for debates from before 1900 . . ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hansard1800s = hansard[hansard['speechdate']<dt.datetime(1900,1,1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hansard1800s.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ". . . or debates from just the 1870s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hansard_1870 = hansard[(hansard['speechdate'] >= dt.datetime(1870,1,1)) & (hansard['speechdate'] <= dt.datetime(1879,12,31))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hansard_1870.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also filter for speakers with the name \"Gladstone.\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hansard1800s = hansard1800s[(hansard1800s['speaker'].str.contains('Gladstone'))].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hansard1800s.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you wanted to be more specific, you could return instances where speaker \"Gladstone\" utters the word \"Dublin.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hansard1800s = hansard1800s[(hansard1800s['speaker'].str.contains('Gladstone'))&(hansard1800s['text'].str.contains('Dublin'))].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hansard1800s.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hansard is a large file, so it's wise to remove it from memory after you have taken your subset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "del hansard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## US Congress"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "US Congress data ([Stanford's Congressional Record](https://data.stanford.edu/congress_text)) is available on M2. If you use this data, please refer to the link for proper citation for the dataset.\n",
    "\n",
    "The data lives within multiple text files. The following code merges these files into a single data frame.\n",
    "\n",
    "> Note: as you read in the data you will see many `skipped line` warnings. This is okay. Jupyter is just telling you which lines we are skipping due to formatting errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import glob\n",
    "import pandas as pd\n",
    "\n",
    "directory = '/scratch/group/oit_research_data/stanford_congress/hein-bound/'\n",
    "file_type = 'txt'\n",
    "delim ='|'\n",
    "\n",
    "speeches_df = pd.concat([pd.read_csv(f, sep=delim, encoding=\"ISO-8859-1\", error_bad_lines=False, quoting=csv.QUOTE_NONE) \n",
    "                         for f in glob.glob(directory + \"speeches_*\"+file_type)])\n",
    "\n",
    "descr_df = pd.concat([pd.read_csv(f, sep=delim, encoding=\"ISO-8859-1\", error_bad_lines=False, quoting=csv.QUOTE_NONE) \n",
    "                      for f in glob.glob(directory + \"descr_*\"+file_type)])\n",
    "\n",
    "all_congressional_data = pd.merge(speeches_df, descr_df, on='speech_id').fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_congressional_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After loading the data, you can take a subset. The following code filters the speaker column for just the speaker \"Mr. Dole.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bob_dole = all_congressional_data[(all_congressional_data.speaker == \"Mr. DOLE\")].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bob_dole"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reddit Archive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**DISCLAIMER**: The Reddit data is broken into 92 (yes, 92!) files totalling ~250G of Reddit data. This is big data!!! You can access individual files using ~`6-15G`, but please see Steph if you need help."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reddit data is separated by month (Oct. 2007 to May 2015). Later dates are larger in file size, so you may need to allocated more memory in your JupyterLab session before reading them. September 2010, for example, is only 1G while May 2015 is over 12G."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create a data frame of reddit data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "        Unnamed: 0                                               body  \\\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import glob\n",
    "\n",
    "reddit_files = glob.glob('/scratch/group/oit_research_data/reddit/*.tsv') # you can replace astrict with a specific reddit file if you want to read just one file\n",
    "\n",
    "# Add or remove entries to this list to change which months are loaded\n",
    "DATES_TO_LOAD = [\n",
    "    # (year, month),\n",
    "    (2008, 10),\n",
    "    (2008, 11),\n",
    "]\n",
    "\n",
    "list_dfs = []\n",
    "for file in reddit_files:\n",
    "    filename = file.rsplit('/', maxsplit=1)[-1].rsplit('.', maxsplit=1)[0]\n",
    "    filetime = datetime.strptime(filename, 'RC_%Y-%m')\n",
    "    if (filetime.year, filetime.month) in DATES_TO_LOAD:\n",
    "        list_dfs.append(pd.read_csv(file, sep='\\t'))\n",
    "\n",
    "reddit_df = pd.concat(list_dfs)\n",
    "del list_dfs[:]\n",
    "\n",
    "# Recommended: drop deleted comments from your dataset    \n",
    "reddit_df = reddit_df[reddit_df['body'] != '[deleted]']\n",
    "\n",
    "# Drop instances of NaN (empty comments)\n",
    "reddit_df = reddit_df[~reddit_df['body'].isna()]\n",
    "\n",
    "# Subreddit filtering example:\n",
    "subreddit_filter = ('politics', 'worldnews', 'news')\n",
    "reddit_df=reddit_df[reddit_df['subreddit'].isin(subreddit_filter)]\n",
    "\n",
    "display(reddit_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Note that `reddit_files = glob.glob('/scratch/group/oit_research_data/reddit/*.tsv')` can be replaced with a specific reddit file to avoid loading ALL of the data. Consider: `reddit_files = glob.glob('/scratch/group/oit_research_data/reddit/RC_2008-10.tsv')` for just October 2008."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After reading the data, we can take a subset through various ways.\n",
    "\n",
    "For example, we can filter the reddit data frame for specific subreddits, like those containing the word \"climate.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "subset_by_keyword = reddit_df[(reddit_df['body'].str.contains('climate'))].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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       "        Unnamed: 0                                               body  \\\n",
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       "\n",
       "        created_utc  downs       id   link_id   parent_id  subreddit  \\\n",
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       "...             ...    ...      ...       ...         ...        ...   \n",
       "769587   1225466210      0  c0646gf  t3_7ad8k  t1_c0644p8   politics   \n",
       "773417   1225474301      0  c0649fv  t3_7ajoj  t1_c0649fr   politics   \n",
       "777958   1225484234      0  c064czd  t3_7akwu    t3_7akwu  worldnews   \n",
       "779100   1225486720      0  c064dv7  t3_7aklv    t3_7aklv   politics   \n",
       "782092   1225494930      0  c064g6v  t3_7ajy0  t1_c064ezb   politics   \n",
       "\n",
       "       subreddit_id  ups  \n",
       "6607       t5_2qh13    1  \n",
       "14953      t5_2cneq    1  \n",
       "54316      t5_2cneq    3  \n",
       "72058      t5_2cneq    3  \n",
       "85022      t5_2cneq    4  \n",
       "...             ...  ...  \n",
       "769587     t5_2cneq    1  \n",
       "773417     t5_2cneq    1  \n",
       "777958     t5_2qh13   16  \n",
       "779100     t5_2cneq   91  \n",
       "782092     t5_2cneq    1  \n",
       "\n",
       "[233 rows x 10 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_by_keyword"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dallas and Houston City Council Minutes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section gives access infomration for two separate datasets: the Dallas City Council minutes and the Houston City Council minutes. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Filename</th>\n",
       "      <th>Date</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>010219Min</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>OFFICE OF THE CITY SECRETARY      CITY OF DALL...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>010318Min</td>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>OFFICE OF THE CITY SECRETARY      CITY OF DALL...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>010417Min</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>OFFICE OF THE CITY SECRETARY      CITY OF DA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>010616Min</td>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>OFFICE OF THE CITY SECRETARY      CITY OF DALL...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>010814Min</td>\n",
       "      <td>2014-01-08</td>\n",
       "      <td>OFFICE OF THE CITY SECRETARY      CITY OF DALL...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1029</th>\n",
       "      <td>cc121698</td>\n",
       "      <td>1998-12-16</td>\n",
       "      <td>1/6/99  9:22 am  F:\\98MINCC121698.WPD    OFF...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030</th>\n",
       "      <td>cc121703</td>\n",
       "      <td>2003-12-17</td>\n",
       "      <td>1/13/04   10:10 AM  G:\\2003MIN\\AG121503.doc   ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031</th>\n",
       "      <td>cc121797</td>\n",
       "      <td>1997-12-17</td>\n",
       "      <td>5/31/11   14:38  N:\\DATA\\MINUTES\\1997\\CC1217...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>cc122004</td>\n",
       "      <td>2004-12-20</td>\n",
       "      <td>1/24/2005     1:05 PM  G:\\2004MIN\\CC122004.doc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>cc122397</td>\n",
       "      <td>1997-12-23</td>\n",
       "      <td>May 31, 2011   14:39  N:\\DATA\\MINUTES\\1997\\C...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1034 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Filename       Date                                               Text\n",
       "index                                                                         \n",
       "0      010219Min 2019-01-02  OFFICE OF THE CITY SECRETARY      CITY OF DALL...\n",
       "1      010318Min 2018-01-03  OFFICE OF THE CITY SECRETARY      CITY OF DALL...\n",
       "2      010417Min 2017-01-04    OFFICE OF THE CITY SECRETARY      CITY OF DA...\n",
       "3      010616Min 2016-01-06  OFFICE OF THE CITY SECRETARY      CITY OF DALL...\n",
       "4      010814Min 2014-01-08  OFFICE OF THE CITY SECRETARY      CITY OF DALL...\n",
       "...          ...        ...                                                ...\n",
       "1029    cc121698 1998-12-16    1/6/99  9:22 am  F:\\98MINCC121698.WPD    OFF...\n",
       "1030    cc121703 2003-12-17  1/13/04   10:10 AM  G:\\2003MIN\\AG121503.doc   ...\n",
       "1031    cc121797 1997-12-17    5/31/11   14:38  N:\\DATA\\MINUTES\\1997\\CC1217...\n",
       "1032    cc122004 2004-12-20  1/24/2005     1:05 PM  G:\\2004MIN\\CC122004.doc...\n",
       "1033    cc122397 1997-12-23    May 31, 2011   14:39  N:\\DATA\\MINUTES\\1997\\C...\n",
       "\n",
       "[1034 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Filename</th>\n",
       "      <th>Date</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20140107</td>\n",
       "      <td>2014-01-07</td>\n",
       "      <td>NO. 2014-0000-1    City Council Chamber, City ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20140114</td>\n",
       "      <td>2014-01-14</td>\n",
       "      <td>NO. 2014-0017-1    City Council Chamber, City ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20140122</td>\n",
       "      <td>2014-01-22</td>\n",
       "      <td>NO. 2014-0056-1    City Council Chamber, City ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20140129</td>\n",
       "      <td>2014-01-29</td>\n",
       "      <td>NO. 2014-0088-1    City Council Chamber, City ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20140204</td>\n",
       "      <td>2014-02-04</td>\n",
       "      <td>NO. 2014-0109-1    City Council Chamber, City ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>20201013</td>\n",
       "      <td>2020-10-13</td>\n",
       "      <td>No. 2020-518-1  10/13-10/14, 2020 Page 1    Ci...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>20201020</td>\n",
       "      <td>2020-10-20</td>\n",
       "      <td>No.2020-526-1  10/20-10/21, 2020 Page 1    Cit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>20201027</td>\n",
       "      <td>2020-10-27</td>\n",
       "      <td>No. 2020-535-1  10/27-10/28, 2020 Page 1    Ci...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>20201104</td>\n",
       "      <td>2020-11-04</td>\n",
       "      <td>No. 2020-546-1  11/4, 2020 Page 1    Pat J. Da...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>20201110</td>\n",
       "      <td>2020-11-10</td>\n",
       "      <td>No. 2020-553-1  11/10, 2020 Page 1    Pat J. D...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>328 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Filename       Date                                               Text\n",
       "index                                                                        \n",
       "0      20140107 2014-01-07  NO. 2014-0000-1    City Council Chamber, City ...\n",
       "1      20140114 2014-01-14  NO. 2014-0017-1    City Council Chamber, City ...\n",
       "2      20140122 2014-01-22  NO. 2014-0056-1    City Council Chamber, City ...\n",
       "3      20140129 2014-01-29  NO. 2014-0088-1    City Council Chamber, City ...\n",
       "4      20140204 2014-02-04  NO. 2014-0109-1    City Council Chamber, City ...\n",
       "...         ...        ...                                                ...\n",
       "323    20201013 2020-10-13  No. 2020-518-1  10/13-10/14, 2020 Page 1    Ci...\n",
       "324    20201020 2020-10-20  No.2020-526-1  10/20-10/21, 2020 Page 1    Cit...\n",
       "325    20201027 2020-10-27  No. 2020-535-1  10/27-10/28, 2020 Page 1    Ci...\n",
       "326    20201104 2020-11-04  No. 2020-546-1  11/4, 2020 Page 1    Pat J. Da...\n",
       "327    20201110 2020-11-10  No. 2020-553-1  11/10, 2020 Page 1    Pat J. D...\n",
       "\n",
       "[328 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "council_dir = '/scratch/group/history/hist_3368-jguldi/city_council'\n",
    "\n",
    "dallas_minutes = pd.read_csv(f'{council_dir}/dallas.csv', sep='|', index_col='index') # read in the Dallas City Council minutes\n",
    "dallas_minutes['Date'] = pd.to_datetime(dallas_minutes['Date'], format='%Y-%m-%d') # cast the data column to datetime objects \n",
    "\n",
    "houston_minutes = pd.read_csv(f'{council_dir}/houston.csv', sep='|', index_col='index')# read in the Houston City Council minutes\n",
    "houston_minutes['Date'] = pd.to_datetime(houston_minutes['Date'], format='%Y-%m-%d') # cast the data column to datetime objects \n",
    "\n",
    "display(dallas_minutes)\n",
    "display(houston_minutes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like the previous examples, you can take a subset of the data. The following code filters for data from before January 1, 2000. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Filename</th>\n",
       "      <th>Date</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>cc010697</td>\n",
       "      <td>1997-01-06</td>\n",
       "      <td>5/31/11   14:01  N:\\DATA\\MINUTES\\1997\\CC0106...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>cc010699</td>\n",
       "      <td>1999-01-06</td>\n",
       "      <td>1/7/99  12:23 pm  D:\\99MIN\\CC010699.WPD    O...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>cc010798</td>\n",
       "      <td>1998-01-07</td>\n",
       "      <td>5/31/11   14:40  N:\\DATA\\MINUTES\\1998\\CC0107...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>cc010897</td>\n",
       "      <td>1997-01-08</td>\n",
       "      <td>5/31/11   14:02  N:\\DATA\\MINUTES\\1997\\CC0108...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>cc011397</td>\n",
       "      <td>1997-01-13</td>\n",
       "      <td>5/31/11   14:03  N:\\DATA\\MINUTES\\1997\\CC0113...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017</th>\n",
       "      <td>cc121097</td>\n",
       "      <td>1997-12-10</td>\n",
       "      <td>5/31/11   14:37  N:\\DATA\\MINUTES\\1997\\CC1210...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1028</th>\n",
       "      <td>cc121599</td>\n",
       "      <td>1999-12-15</td>\n",
       "      <td>5/31/11     3:57 PM    G:\\99MIN\\CC121599.WPD...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1029</th>\n",
       "      <td>cc121698</td>\n",
       "      <td>1998-12-16</td>\n",
       "      <td>1/6/99  9:22 am  F:\\98MINCC121698.WPD    OFF...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031</th>\n",
       "      <td>cc121797</td>\n",
       "      <td>1997-12-17</td>\n",
       "      <td>5/31/11   14:38  N:\\DATA\\MINUTES\\1997\\CC1217...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>cc122397</td>\n",
       "      <td>1997-12-23</td>\n",
       "      <td>May 31, 2011   14:39  N:\\DATA\\MINUTES\\1997\\C...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Filename       Date                                               Text\n",
       "index                                                                        \n",
       "332    cc010697 1997-01-06    5/31/11   14:01  N:\\DATA\\MINUTES\\1997\\CC0106...\n",
       "333    cc010699 1999-01-06    1/7/99  12:23 pm  D:\\99MIN\\CC010699.WPD    O...\n",
       "336    cc010798 1998-01-07    5/31/11   14:40  N:\\DATA\\MINUTES\\1998\\CC0107...\n",
       "338    cc010897 1997-01-08    5/31/11   14:02  N:\\DATA\\MINUTES\\1997\\CC0108...\n",
       "350    cc011397 1997-01-13    5/31/11   14:03  N:\\DATA\\MINUTES\\1997\\CC0113...\n",
       "...         ...        ...                                                ...\n",
       "1017   cc121097 1997-12-10    5/31/11   14:37  N:\\DATA\\MINUTES\\1997\\CC1210...\n",
       "1028   cc121599 1999-12-15    5/31/11     3:57 PM    G:\\99MIN\\CC121599.WPD...\n",
       "1029   cc121698 1998-12-16    1/6/99  9:22 am  F:\\98MINCC121698.WPD    OFF...\n",
       "1031   cc121797 1997-12-17    5/31/11   14:38  N:\\DATA\\MINUTES\\1997\\CC1217...\n",
       "1033   cc122397 1997-12-23    May 31, 2011   14:39  N:\\DATA\\MINUTES\\1997\\C...\n",
       "\n",
       "[144 rows x 3 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dallas_minutes[dallas_minutes['Date'] < datetime(year=2000, month=1, day=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code filters for data from just the month of September."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Filename</th>\n",
       "      <th>Date</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>20140903</td>\n",
       "      <td>2014-09-03</td>\n",
       "      <td>NO. 2014-0835-1    City Council Chamber, City ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>20140909</td>\n",
       "      <td>2014-09-09</td>\n",
       "      <td>NO. 2014-0835-1    City Council Chamber, City ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>20140930</td>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>NO. 2014-0871-1    City Council Chamber, City ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>20150901</td>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>No.2015-588-1  9/1-9/2, 2015 Page 1    City Co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>20150909</td>\n",
       "      <td>2015-09-09</td>\n",
       "      <td>No. 2015-609-1  9/9/2015 Page 1    City Counci...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>20150915</td>\n",
       "      <td>2015-09-15</td>\n",
       "      <td>No.2015-624-1  9/15-9/16, 2015 Page 1    City ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>20150922</td>\n",
       "      <td>2015-09-22</td>\n",
       "      <td>No.2015-640-1  9/22-9/23, 2015 Page 1    City ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>20150929</td>\n",
       "      <td>2015-09-29</td>\n",
       "      <td>No.2015-654-1  9/29-9/30, 2015 Page 1    City ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>20160907</td>\n",
       "      <td>2016-09-07</td>\n",
       "      <td>No. 2016-419-1  09/7, 2016 Page 1    City Coun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>20160913</td>\n",
       "      <td>2016-09-13</td>\n",
       "      <td>No.2016-427-1  9/13-9/14, 2016 Page 1    City ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>20160920</td>\n",
       "      <td>2016-09-20</td>\n",
       "      <td>No.2016-439-1  9/20-9/21, 2016 Page 1    City ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>20160927</td>\n",
       "      <td>2016-09-27</td>\n",
       "      <td>No.2016-454-1  9/27-9/28, 2016 Page 1    City ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>20170906</td>\n",
       "      <td>2017-09-06</td>\n",
       "      <td>No. 2017-543-1  9/6, 2017 Page 1    City Counc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>20170912</td>\n",
       "      <td>2017-09-12</td>\n",
       "      <td>No. 2017-549-1  9/12-9/13, 2017 Page 1    City...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>20170919</td>\n",
       "      <td>2017-09-19</td>\n",
       "      <td>No. 2017-567-1  9/19-9/20, 2017 Page 1    City...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>20170925</td>\n",
       "      <td>2017-09-25</td>\n",
       "      <td>No. 2017-585-1  9/25-9/27, 2017 Page 1    City...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>20170926</td>\n",
       "      <td>2017-09-26</td>\n",
       "      <td>No. 2017-585-1  9/25-9/27, 2017 Page 1    City...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>20180905</td>\n",
       "      <td>2018-09-05</td>\n",
       "      <td>No. 2018-445-1  9/05/18 Page 1    City Council...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>20180911</td>\n",
       "      <td>2018-09-11</td>\n",
       "      <td>No. 2018-460-1  9/11-9/12, 2018 Page 1    City...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>20180918</td>\n",
       "      <td>2018-09-18</td>\n",
       "      <td>No. 2018-460-1  9/18-9/19, 2018 Page 1    City...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>20180925</td>\n",
       "      <td>2018-09-25</td>\n",
       "      <td>No.2018-492-1  9/25-9/26, 2018 Page 1    City ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>20190904</td>\n",
       "      <td>2019-09-04</td>\n",
       "      <td>No. 2019-0426-1  9/04, 2019 Page 1      Pat J....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>20190910</td>\n",
       "      <td>2019-09-10</td>\n",
       "      <td>No. 2019-450-1  9/10-9/11, 2019 Page 1    City...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>20190917</td>\n",
       "      <td>2019-09-17</td>\n",
       "      <td>No. 2019-0461-1  09/17-09/18, 2019 Page 1    C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>20190924</td>\n",
       "      <td>2019-09-24</td>\n",
       "      <td>No. 2019-476-1  09/24-09/25, 2019 Page 1    Ci...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>20200901</td>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>No. 2020-442-1  9/1-9/2, 2020 Page 1    City C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>20200909</td>\n",
       "      <td>2020-09-09</td>\n",
       "      <td>No. 2020-459-1   9/9, 2020 Page 1    Pat J. Da...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>20200915</td>\n",
       "      <td>2020-09-15</td>\n",
       "      <td>No. 2020-481-1  9/15-9/16, 2020 Page 1    City...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>20200922</td>\n",
       "      <td>2020-09-22</td>\n",
       "      <td>No. 2020-490-1  9/22-9/23, 2020 Page 1    City...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>20200929</td>\n",
       "      <td>2020-09-29</td>\n",
       "      <td>No. 2020-447-1  9/29-9/30, 2020 Page 1    City...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Filename       Date                                               Text\n",
       "index                                                                        \n",
       "27     20140903 2014-09-03  NO. 2014-0835-1    City Council Chamber, City ...\n",
       "28     20140909 2014-09-09  NO. 2014-0835-1    City Council Chamber, City ...\n",
       "29     20140930 2014-09-30  NO. 2014-0871-1    City Council Chamber, City ...\n",
       "68     20150901 2015-09-01  No.2015-588-1  9/1-9/2, 2015 Page 1    City Co...\n",
       "69     20150909 2015-09-09  No. 2015-609-1  9/9/2015 Page 1    City Counci...\n",
       "70     20150915 2015-09-15  No.2015-624-1  9/15-9/16, 2015 Page 1    City ...\n",
       "71     20150922 2015-09-22  No.2015-640-1  9/22-9/23, 2015 Page 1    City ...\n",
       "72     20150929 2015-09-29  No.2015-654-1  9/29-9/30, 2015 Page 1    City ...\n",
       "117    20160907 2016-09-07  No. 2016-419-1  09/7, 2016 Page 1    City Coun...\n",
       "118    20160913 2016-09-13  No.2016-427-1  9/13-9/14, 2016 Page 1    City ...\n",
       "119    20160920 2016-09-20  No.2016-439-1  9/20-9/21, 2016 Page 1    City ...\n",
       "120    20160927 2016-09-27  No.2016-454-1  9/27-9/28, 2016 Page 1    City ...\n",
       "166    20170906 2017-09-06  No. 2017-543-1  9/6, 2017 Page 1    City Counc...\n",
       "167    20170912 2017-09-12  No. 2017-549-1  9/12-9/13, 2017 Page 1    City...\n",
       "168    20170919 2017-09-19  No. 2017-567-1  9/19-9/20, 2017 Page 1    City...\n",
       "169    20170925 2017-09-25  No. 2017-585-1  9/25-9/27, 2017 Page 1    City...\n",
       "170    20170926 2017-09-26  No. 2017-585-1  9/25-9/27, 2017 Page 1    City...\n",
       "218    20180905 2018-09-05  No. 2018-445-1  9/05/18 Page 1    City Council...\n",
       "219    20180911 2018-09-11  No. 2018-460-1  9/11-9/12, 2018 Page 1    City...\n",
       "220    20180918 2018-09-18  No. 2018-460-1  9/18-9/19, 2018 Page 1    City...\n",
       "221    20180925 2018-09-25  No.2018-492-1  9/25-9/26, 2018 Page 1    City ...\n",
       "267    20190904 2019-09-04  No. 2019-0426-1  9/04, 2019 Page 1      Pat J....\n",
       "268    20190910 2019-09-10  No. 2019-450-1  9/10-9/11, 2019 Page 1    City...\n",
       "269    20190917 2019-09-17  No. 2019-0461-1  09/17-09/18, 2019 Page 1    C...\n",
       "270    20190924 2019-09-24  No. 2019-476-1  09/24-09/25, 2019 Page 1    Ci...\n",
       "317    20200901 2020-09-01  No. 2020-442-1  9/1-9/2, 2020 Page 1    City C...\n",
       "318    20200909 2020-09-09  No. 2020-459-1   9/9, 2020 Page 1    Pat J. Da...\n",
       "319    20200915 2020-09-15  No. 2020-481-1  9/15-9/16, 2020 Page 1    City...\n",
       "320    20200922 2020-09-22  No. 2020-490-1  9/22-9/23, 2020 Page 1    City...\n",
       "321    20200929 2020-09-29  No. 2020-447-1  9/29-9/30, 2020 Page 1    City..."
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houston_minutes[houston_minutes['Date'].dt.month == 9]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code filters for data from just Tuesday."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Filename</th>\n",
       "      <th>Date</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20140107</td>\n",
       "      <td>2014-01-07</td>\n",
       "      <td>NO. 2014-0000-1    City Council Chamber, City ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20140114</td>\n",
       "      <td>2014-01-14</td>\n",
       "      <td>NO. 2014-0017-1    City Council Chamber, City ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20140204</td>\n",
       "      <td>2014-02-04</td>\n",
       "      <td>NO. 2014-0109-1    City Council Chamber, City ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20140211</td>\n",
       "      <td>2014-02-11</td>\n",
       "      <td>NO. 2014-0117-1    City Council Chamber, City ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20140218</td>\n",
       "      <td>2014-02-18</td>\n",
       "      <td>NO. 2014-0133-1    City Council Chamber, City ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>20201006</td>\n",
       "      <td>2020-10-06</td>\n",
       "      <td>No. 2020-510-1  10/6-10/7, 2020 Page 1      Ci...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>20201013</td>\n",
       "      <td>2020-10-13</td>\n",
       "      <td>No. 2020-518-1  10/13-10/14, 2020 Page 1    Ci...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>20201020</td>\n",
       "      <td>2020-10-20</td>\n",
       "      <td>No.2020-526-1  10/20-10/21, 2020 Page 1    Cit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>20201027</td>\n",
       "      <td>2020-10-27</td>\n",
       "      <td>No. 2020-535-1  10/27-10/28, 2020 Page 1    Ci...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>20201110</td>\n",
       "      <td>2020-11-10</td>\n",
       "      <td>No. 2020-553-1  11/10, 2020 Page 1    Pat J. D...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>273 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Filename       Date                                               Text\n",
       "index                                                                        \n",
       "0      20140107 2014-01-07  NO. 2014-0000-1    City Council Chamber, City ...\n",
       "1      20140114 2014-01-14  NO. 2014-0017-1    City Council Chamber, City ...\n",
       "4      20140204 2014-02-04  NO. 2014-0109-1    City Council Chamber, City ...\n",
       "5      20140211 2014-02-11  NO. 2014-0117-1    City Council Chamber, City ...\n",
       "6      20140218 2014-02-18  NO. 2014-0133-1    City Council Chamber, City ...\n",
       "...         ...        ...                                                ...\n",
       "322    20201006 2020-10-06  No. 2020-510-1  10/6-10/7, 2020 Page 1      Ci...\n",
       "323    20201013 2020-10-13  No. 2020-518-1  10/13-10/14, 2020 Page 1    Ci...\n",
       "324    20201020 2020-10-20  No.2020-526-1  10/20-10/21, 2020 Page 1    Cit...\n",
       "325    20201027 2020-10-27  No. 2020-535-1  10/27-10/28, 2020 Page 1    Ci...\n",
       "327    20201110 2020-11-10  No. 2020-553-1  11/10, 2020 Page 1    Pat J. D...\n",
       "\n",
       "[273 rows x 3 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houston_minutes[houston_minutes['Date'].dt.day_name() == 'Tuesday']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NovelTM Datasets for English-Language Fiction, 1700-2009\n",
    "\n",
    "The NovelTM datasets are a collection of metadata for 210,305 volumes of fiction. Performing quantitative measures with these datasets can lend insight into literary history. The types of questions a researcher might ask include: how does the proportion of fiction written by British authors or by women change across time?\n",
    "\n",
    "The NovelTM datasets are vast. They include files that can be used to determine authorial gender, standardize author names, detect nationality, and more. These files are spread across different folders. This section will only focus on a few of these folders and their data sets. You are welcome to explore the data that isn't covered in this Notebook by reading the `readme.md` file. \n",
    "\n",
    "For citation information, please see [this link](https://culturalanalytics.org/article/13147-noveltm-datasets-for-english-language-fiction-1700-2009)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reading Datasets of Interest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.The Title Metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>docid</th>\n",
       "      <th>oldauthor</th>\n",
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       "      <th>authordate</th>\n",
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       "      <th>datetype</th>\n",
       "      <th>startdate</th>\n",
       "      <th>enddate</th>\n",
       "      <th>imprint</th>\n",
       "      <th>...</th>\n",
       "      <th>allcopiesofwork</th>\n",
       "      <th>copiesin25yrs</th>\n",
       "      <th>enumcron</th>\n",
       "      <th>volnum</th>\n",
       "      <th>title</th>\n",
       "      <th>parttitle</th>\n",
       "      <th>earlyedition</th>\n",
       "      <th>shorttitle</th>\n",
       "      <th>nonficprob</th>\n",
       "      <th>juvenileprob</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mdp.39015031913893</td>\n",
       "      <td>Spencer, Louise Reid</td>\n",
       "      <td>Spencer, Louise Reid</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2100</td>\n",
       "      <td>|</td>\n",
       "      <td>||||</td>\n",
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       "      <td>Thomas Y. Crowell company|1945</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Guerrilla wife | $c: [by] Louise Reid Spencer.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>Guerrilla wife</td>\n",
       "      <td>0.809395</td>\n",
       "      <td>0.134542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mdp.39015003936864</td>\n",
       "      <td>Baker, Robert H</td>\n",
       "      <td>Baker, Robert H</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2100</td>\n",
       "      <td>n</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Port Washington, N.Y.|Ashley Books|197-?].</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The suburbs : | a novel / | $c: by Robert H. B...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>The suburbs : a novel</td>\n",
       "      <td>0.047942</td>\n",
       "      <td>0.012300</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mdp.39015068342305</td>\n",
       "      <td>Dickens, Charles</td>\n",
       "      <td>Dickens, Charles</td>\n",
       "      <td>1812-1870.</td>\n",
       "      <td>0</td>\n",
       "      <td>1870</td>\n",
       "      <td>n</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>New York|The American news company|n.d.</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Edwin Drood. | $c: By Charles Dickens. With il...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>Edwin Drood</td>\n",
       "      <td>0.395749</td>\n",
       "      <td>0.021590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mdp.39015055066586</td>\n",
       "      <td>Stretton, Hesba</td>\n",
       "      <td>Stretton, Hesba</td>\n",
       "      <td>1832-1911.</td>\n",
       "      <td>0</td>\n",
       "      <td>1911</td>\n",
       "      <td>n</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>New York|Dodd, Mead &amp; co.|n.d.</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Carola, | $c: by Hesba Stretton.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>Carola</td>\n",
       "      <td>0.056825</td>\n",
       "      <td>0.012471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mdp.39015055066594</td>\n",
       "      <td>Stretton,Hesba</td>\n",
       "      <td>Stretton, Hesba</td>\n",
       "      <td>1832-1911.</td>\n",
       "      <td>0</td>\n",
       "      <td>1911</td>\n",
       "      <td>n</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>New York|Dodd, Mead &amp; co.|n.d.</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>In prison &amp; out. | $c: By Hesba Stretton.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>In prison &amp; out</td>\n",
       "      <td>0.029148</td>\n",
       "      <td>0.155111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                docid             oldauthor                author  authordate  \\\n",
       "0  mdp.39015031913893  Spencer, Louise Reid  Spencer, Louise Reid         NaN   \n",
       "1  mdp.39015003936864       Baker, Robert H       Baker, Robert H         NaN   \n",
       "2  mdp.39015068342305      Dickens, Charles      Dickens, Charles  1812-1870.   \n",
       "3  mdp.39015055066586       Stretton, Hesba       Stretton, Hesba  1832-1911.   \n",
       "4  mdp.39015055066594        Stretton,Hesba       Stretton, Hesba  1832-1911.   \n",
       "\n",
       "   inferreddate  latestcomp datetype startdate enddate  \\\n",
       "0             0        2100        |      ||||    ||||   \n",
       "1             0        2100        n                     \n",
       "2             0        1870        n                     \n",
       "3             0        1911        n                     \n",
       "4             0        1911        n                     \n",
       "\n",
       "                                      imprint  ... allcopiesofwork  \\\n",
       "0              Thomas Y. Crowell company|1945  ...             1.0   \n",
       "1  Port Washington, N.Y.|Ashley Books|197-?].  ...             1.0   \n",
       "2     New York|The American news company|n.d.  ...             1.0   \n",
       "3              New York|Dodd, Mead & co.|n.d.  ...             1.0   \n",
       "4              New York|Dodd, Mead & co.|n.d.  ...             1.0   \n",
       "\n",
       "  copiesin25yrs enumcron volnum  \\\n",
       "0           1.0      NaN    NaN   \n",
       "1           1.0      NaN    NaN   \n",
       "2           1.0      NaN    NaN   \n",
       "3           1.0      NaN    NaN   \n",
       "4           1.0      NaN    NaN   \n",
       "\n",
       "                                               title parttitle  earlyedition  \\\n",
       "0     Guerrilla wife | $c: [by] Louise Reid Spencer.       NaN          True   \n",
       "1  The suburbs : | a novel / | $c: by Robert H. B...       NaN          True   \n",
       "2  Edwin Drood. | $c: By Charles Dickens. With il...       NaN          True   \n",
       "3                   Carola, | $c: by Hesba Stretton.       NaN          True   \n",
       "4          In prison & out. | $c: By Hesba Stretton.       NaN          True   \n",
       "\n",
       "              shorttitle  nonficprob  juvenileprob  \n",
       "0         Guerrilla wife    0.809395      0.134542  \n",
       "1  The suburbs : a novel    0.047942      0.012300  \n",
       "2            Edwin Drood    0.395749      0.021590  \n",
       "3                 Carola    0.056825      0.012471  \n",
       "4        In prison & out    0.029148      0.155111  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_to_noveltm_metadata = \"/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/metadata\"\n",
    "\n",
    "title_metadata = pd.read_csv(f'{path_to_noveltm_metadata}/titlemeta.tsv', sep='\\t', low_memory=False)\n",
    "\n",
    "title_metadata[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.The Gender-Balanced Subset\n",
    "\n",
    "The gender-balanced subset was taken from a fiction titles data frame. It has been reduced in size to ensure equal representation of writers who were identified as men or women in each 5-year segment of time. The curators also included a proportional sample of texts where gender was marked \"unknown.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>docid</th>\n",
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       "      <th>imprint</th>\n",
       "      <th>genres</th>\n",
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       "      <th>shorttitle</th>\n",
       "      <th>category</th>\n",
       "      <th>hathiadvent</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>uiuo.ark+=13960=t8nc6b618</td>\n",
       "      <td>Neri, Mary Anne</td>\n",
       "      <td>Neri, Mary Anne</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
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       "      <td>1804</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>v.1</td>\n",
       "      <td>London;Printed for T. Cadell and W. Davies by ...</td>\n",
       "      <td>Novel|Fiction</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The eve of San-Pietro</td>\n",
       "      <td>longfiction</td>\n",
       "      <td>contemporary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dul1.ark+=13960=t89g6ct7k</td>\n",
       "      <td>Edgeworth, Maria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>uk</td>\n",
       "      <td>1767-1849.</td>\n",
       "      <td>1805</td>\n",
       "      <td>1804</td>\n",
       "      <td>1805</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>London;J. Johnson;1805.</td>\n",
       "      <td>NotFiction</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The modern Griselda</td>\n",
       "      <td>longfiction</td>\n",
       "      <td>contemporary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dul1.ark+=13960=t4vh67297</td>\n",
       "      <td>Grafigny</td>\n",
       "      <td>Graffigny, Mme de (Françoise d'Issembourg d'Ha...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>fr</td>\n",
       "      <td>1695-1758</td>\n",
       "      <td>1802</td>\n",
       "      <td>1802</td>\n",
       "      <td>1802</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>v.2</td>\n",
       "      <td>Paris;printed for Durand Bookseller;1802.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Utopias</td>\n",
       "      <td>Letters of a Peruvian princess</td>\n",
       "      <td>longfiction</td>\n",
       "      <td>contemporary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>uiuo.ark+=13960=t2z32gn5j</td>\n",
       "      <td>West</td>\n",
       "      <td>West, Jane</td>\n",
       "      <td>West, Mrs.</td>\n",
       "      <td>f</td>\n",
       "      <td>uk</td>\n",
       "      <td>1758-1852</td>\n",
       "      <td>1802</td>\n",
       "      <td>1802</td>\n",
       "      <td>1802</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>v.1</td>\n",
       "      <td>London;Printed by A. Strahan, for T.N. Longman...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The infidel father</td>\n",
       "      <td>longfiction</td>\n",
       "      <td>contemporary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>uva.x000099740</td>\n",
       "      <td>Hamilton, Elizabeth</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>uk</td>\n",
       "      <td>1756?-1816.</td>\n",
       "      <td>1800</td>\n",
       "      <td>1800</td>\n",
       "      <td>1800</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>v.2</td>\n",
       "      <td>Bath;Printed by R. Cruttwell for G.G. and J. R...</td>\n",
       "      <td>Fiction|NotFiction</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Memoirs of modern philosophers</td>\n",
       "      <td>longfiction</td>\n",
       "      <td>contemporary</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                       docid               author  \\\n",
       "0  uiuo.ark+=13960=t8nc6b618      Neri, Mary Anne   \n",
       "1  dul1.ark+=13960=t89g6ct7k     Edgeworth, Maria   \n",
       "2  dul1.ark+=13960=t4vh67297             Grafigny   \n",
       "3  uiuo.ark+=13960=t2z32gn5j                 West   \n",
       "4             uva.x000099740  Hamilton, Elizabeth   \n",
       "\n",
       "                                            realname   pseudonym gender  \\\n",
       "0                                    Neri, Mary Anne         NaN      f   \n",
       "1                                                NaN         NaN      f   \n",
       "2  Graffigny, Mme de (Françoise d'Issembourg d'Ha...         NaN      f   \n",
       "3                                         West, Jane  West, Mrs.      f   \n",
       "4                                                NaN         NaN      f   \n",
       "\n",
       "  nationality   authordate  inferreddate  firstpub  latestcomp  \\\n",
       "0          it          NaN          1804      1804        1804   \n",
       "1          uk   1767-1849.          1805      1804        1805   \n",
       "2          fr    1695-1758          1802      1802        1802   \n",
       "3          uk    1758-1852          1802      1802        1802   \n",
       "4          uk  1756?-1816.          1800      1800        1800   \n",
       "\n",
       "   allcopiesofwork  copiesin25yrs enumcron  \\\n",
       "0         1.000000       1.000000      v.1   \n",
       "1         2.000000       2.000000      NaN   \n",
       "2         1.000000       1.000000      v.2   \n",
       "3         1.000000       1.000000      v.1   \n",
       "4         5.333333       4.333333      v.2   \n",
       "\n",
       "                                             imprint              genres  \\\n",
       "0  London;Printed for T. Cadell and W. Davies by ...       Novel|Fiction   \n",
       "1                            London;J. Johnson;1805.          NotFiction   \n",
       "2          Paris;printed for Durand Bookseller;1802.                 NaN   \n",
       "3  London;Printed by A. Strahan, for T.N. Longman...                 NaN   \n",
       "4  Bath;Printed by R. Cruttwell for G.G. and J. R...  Fiction|NotFiction   \n",
       "\n",
       "  subjects                      shorttitle     category   hathiadvent  \n",
       "0      NaN           The eve of San-Pietro  longfiction  contemporary  \n",
       "1      NaN             The modern Griselda  longfiction  contemporary  \n",
       "2  Utopias  Letters of a Peruvian princess  longfiction  contemporary  \n",
       "3      NaN              The infidel father  longfiction  contemporary  \n",
       "4      NaN  Memoirs of modern philosophers  longfiction  contemporary  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_balanced_subset = pd.read_csv(f'{path_to_noveltm_metadata}/gender_balanced_subset.tsv', sep='\\t')\n",
    "\n",
    "gender_balanced_subset[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.The Frequently Reprinted Subset\n",
    "\n",
    "The frequently reprinted subset was taken from a fiction titles data frame. It is made up of volumes where the curators had the largest number of editions and instances attested within 25 years of a title's first appearance in HathiTrust."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>hvd.rsmd2m</td>\n",
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       "      <td>Madame Cottin, (Sophie)</td>\n",
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       "      <td>Poughkeepsie [N.Y.;Printed by Paraclete Potter...</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Elizabeth, | or, The exiles of Siberia. A tale...</td>\n",
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       "      <td>Porter, Anna Maria</td>\n",
       "      <td>Porter, Anna Maria</td>\n",
       "      <td>1780-1832.</td>\n",
       "      <td>1807</td>\n",
       "      <td>1807</td>\n",
       "      <td>s</td>\n",
       "      <td>1807</td>\n",
       "      <td></td>\n",
       "      <td>London;Longman, Hurst, Rees, and Orme;1807.</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>London;Longman, Hurst, Rees, and Orme;1807.</td>\n",
       "      <td>...</td>\n",
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       "      <td>The Hungarian brothers</td>\n",
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       "      <td>Porter, Anna Maria</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>nyp.33433074916226</td>\n",
       "      <td>Lamb, Charles</td>\n",
       "      <td>Lamb, Charles</td>\n",
       "      <td>1775-1834.</td>\n",
       "      <td>1809</td>\n",
       "      <td>1809</td>\n",
       "      <td>s</td>\n",
       "      <td>1809</td>\n",
       "      <td></td>\n",
       "      <td>London;Printed for M.J. Godwin;1809.</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>v. 1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tales from Shakespear : | designed for the use...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>Tales from Shakespear : designed for the use o...</td>\n",
       "      <td>0.127081</td>\n",
       "      <td>0.689162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nyp.33433074916218</td>\n",
       "      <td>Lamb, Charles</td>\n",
       "      <td>Lamb, Charles</td>\n",
       "      <td>1775-1834.</td>\n",
       "      <td>1809</td>\n",
       "      <td>1809</td>\n",
       "      <td>s</td>\n",
       "      <td>1809</td>\n",
       "      <td></td>\n",
       "      <td>London;Printed for M.J. Godwin;1809.</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>v. 2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Tales from Shakespear : | designed for the use...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>Tales from Shakespear : designed for the use o...</td>\n",
       "      <td>0.176754</td>\n",
       "      <td>0.007584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>uc2.ark+=13960=t3513xp37</td>\n",
       "      <td>Grant, Anne MacVicar</td>\n",
       "      <td>Grant, Anne MacVicar</td>\n",
       "      <td>1755-1838.</td>\n",
       "      <td>1807</td>\n",
       "      <td>1807</td>\n",
       "      <td>m</td>\n",
       "      <td>1807</td>\n",
       "      <td></td>\n",
       "      <td>London;Longman Hurst Rees &amp; Orme;1807-</td>\n",
       "      <td>...</td>\n",
       "      <td>5.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>v.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Letters from the mountains</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>Letters from the mountains</td>\n",
       "      <td>0.458376</td>\n",
       "      <td>0.125661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>njp.32101064790809</td>\n",
       "      <td>Stanhope, Louisa Sidney</td>\n",
       "      <td>Stanhope, Louisa Sidney</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1807</td>\n",
       "      <td>1807</td>\n",
       "      <td>s</td>\n",
       "      <td>1807</td>\n",
       "      <td></td>\n",
       "      <td>London;Printed at the Minerva-Press, for Lane,...</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>vol. 2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>The bandit's bride; | or, The maid of Saxony. ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>The bandit's bride; or, The maid of Saxony. A ...</td>\n",
       "      <td>0.062363</td>\n",
       "      <td>0.008489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>njp.32101064790791</td>\n",
       "      <td>Stanhope, Louisa Sidney</td>\n",
       "      <td>Stanhope, Louisa Sidney</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1807</td>\n",
       "      <td>1807</td>\n",
       "      <td>s</td>\n",
       "      <td>1807</td>\n",
       "      <td></td>\n",
       "      <td>London;Printed at the Minerva-Press, for Lane,...</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>vol. 3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>The bandit's bride; | or, The maid of Saxony. ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>The bandit's bride; or, The maid of Saxony. A ...</td>\n",
       "      <td>0.081406</td>\n",
       "      <td>0.007475</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       docid                oldauthor  \\\n",
       "0                 hvd.rsmd2m             More, Hannah   \n",
       "1         nyp.33433075814735  Madame Cottin, (Sophie)   \n",
       "2         mdp.39015014117546       Porter, Anna Maria   \n",
       "3  uiuo.ark+=13960=t2t449b4t       Porter, Anna Maria   \n",
       "4  uiuo.ark+=13960=t3vt28143       Porter, Anna Maria   \n",
       "5         nyp.33433074916226            Lamb, Charles   \n",
       "6         nyp.33433074916218            Lamb, Charles   \n",
       "7   uc2.ark+=13960=t3513xp37     Grant, Anne MacVicar   \n",
       "8         njp.32101064790809  Stanhope, Louisa Sidney   \n",
       "9         njp.32101064790791  Stanhope, Louisa Sidney   \n",
       "\n",
       "                    author  authordate  inferreddate  latestcomp datetype  \\\n",
       "0             More, Hannah  1745-1833.          1809        1809        s   \n",
       "1  Madame Cottin, (Sophie)  1770-1807.          1810        1807        s   \n",
       "2       Porter, Anna Maria  1780-1832.          1807        1807        s   \n",
       "3       Porter, Anna Maria  1780-1832.          1807        1807        s   \n",
       "4       Porter, Anna Maria  1780-1832.          1807        1807        s   \n",
       "5            Lamb, Charles  1775-1834.          1809        1809        s   \n",
       "6            Lamb, Charles  1775-1834.          1809        1809        s   \n",
       "7     Grant, Anne MacVicar  1755-1838.          1807        1807        m   \n",
       "8  Stanhope, Louisa Sidney         NaN          1807        1807        s   \n",
       "9  Stanhope, Louisa Sidney         NaN          1807        1807        s   \n",
       "\n",
       "  startdate enddate                                            imprint  ...  \\\n",
       "0      1809                              New-York;David Carlisle;1809.  ...   \n",
       "1      1810          Poughkeepsie [N.Y.;Printed by Paraclete Potter...  ...   \n",
       "2      1807                London;Longman, Hurst, Rees, and Orme;1807.  ...   \n",
       "3      1807                London;Longman, Hurst, Rees, and Orme;1807.  ...   \n",
       "4      1807                London;Longman, Hurst, Rees, and Orme;1807.  ...   \n",
       "5      1809                       London;Printed for M.J. Godwin;1809.  ...   \n",
       "6      1809                       London;Printed for M.J. Godwin;1809.  ...   \n",
       "7      1807                     London;Longman Hurst Rees & Orme;1807-  ...   \n",
       "8      1807          London;Printed at the Minerva-Press, for Lane,...  ...   \n",
       "9      1807          London;Printed at the Minerva-Press, for Lane,...  ...   \n",
       "\n",
       "  allcopiesofwork copiesin25yrs enumcron volnum  \\\n",
       "0             9.0           8.0      NaN    NaN   \n",
       "1             8.0           6.0      NaN    NaN   \n",
       "2             5.0           5.0      v.1    1.0   \n",
       "3             5.0           5.0      v.2    2.0   \n",
       "4             5.0           5.0      v.3    3.0   \n",
       "5             5.0           5.0     v. 1    1.0   \n",
       "6             5.0           5.0     v. 2    2.0   \n",
       "7             5.5           5.5      v.2    2.0   \n",
       "8             5.0           5.0   vol. 2    2.0   \n",
       "9             5.0           5.0   vol. 3    3.0   \n",
       "\n",
       "                                               title parttitle  earlyedition  \\\n",
       "0  Cœlebs in search of a wife : | comprehending o...       NaN          True   \n",
       "1  Elizabeth, | or, The exiles of Siberia. A tale...       NaN          True   \n",
       "2  The Hungarian brothers ... | $c: By Miss Anna ...       NaN          True   \n",
       "3                             The Hungarian brothers       NaN          True   \n",
       "4                             The Hungarian brothers       NaN          True   \n",
       "5  Tales from Shakespear : | designed for the use...       NaN          True   \n",
       "6  Tales from Shakespear : | designed for the use...       NaN          True   \n",
       "7                         Letters from the mountains       NaN          True   \n",
       "8  The bandit's bride; | or, The maid of Saxony. ...       NaN          True   \n",
       "9  The bandit's bride; | or, The maid of Saxony. ...       NaN          True   \n",
       "\n",
       "                                          shorttitle  nonficprob  juvenileprob  \n",
       "0  Cœlebs in search of a wife : comprehending obs...    0.000000      0.000000  \n",
       "1  Elizabeth, or, The exiles of Siberia. A tale, ...    0.231656      0.008057  \n",
       "2                             The Hungarian brothers    0.076883      0.616757  \n",
       "3                             The Hungarian brothers    0.208313      0.158856  \n",
       "4                             The Hungarian brothers    0.224745      0.221614  \n",
       "5  Tales from Shakespear : designed for the use o...    0.127081      0.689162  \n",
       "6  Tales from Shakespear : designed for the use o...    0.176754      0.007584  \n",
       "7                         Letters from the mountains    0.458376      0.125661  \n",
       "8  The bandit's bride; or, The maid of Saxony. A ...    0.062363      0.008489  \n",
       "9  The bandit's bride; or, The maid of Saxony. A ...    0.081406      0.007475  \n",
       "\n",
       "[10 rows x 30 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequently_reprinted_subset = pd.read_csv(f'{path_to_noveltm_metadata}/frequently_reprinted_subset.tsv', sep='\\t')\n",
    "\n",
    "frequently_reprinted_subset.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.Author Set (a list of authors and their standardized names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Robinson, Mary Stephens</th>\n",
       "      <th>Robinson, Mary S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Howes, Edith Annie</td>\n",
       "      <td>Howes, Edith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Williams, Elizabeth Whitney, Mrs</td>\n",
       "      <td>Williams, Elizabeth Whitney</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Somerville, E. Œ. (Edith Œnone)</td>\n",
       "      <td>Somerville, E. &amp;#xbf;. (Edith &amp;#xbf;none)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pearce, Donn</td>\n",
       "      <td>Pearce, Donald</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Read, Charles Anderson</td>\n",
       "      <td>Read, Charles A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Anderson, Kenneth</td>\n",
       "      <td>Anderson, Ken</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Heydrick, Benjamin A. (Benjamin Alexander)</td>\n",
       "      <td>Heydrick, Benjamin Alexander</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Copping, Arthur E. (Arthur Edward)</td>\n",
       "      <td>Copping, Arthur Edward</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Woods, Margaret L. (Margaret Louisa)</td>\n",
       "      <td>Woods, Margaret L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Shane, Peggy (Smith)</td>\n",
       "      <td>Shane, Peggy (Smith)], Mrs</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Robinson, Mary Stephens  \\\n",
       "0                          Howes, Edith Annie   \n",
       "1            Williams, Elizabeth Whitney, Mrs   \n",
       "2             Somerville, E. Œ. (Edith Œnone)   \n",
       "3                                Pearce, Donn   \n",
       "4                      Read, Charles Anderson   \n",
       "5                           Anderson, Kenneth   \n",
       "6  Heydrick, Benjamin A. (Benjamin Alexander)   \n",
       "7          Copping, Arthur E. (Arthur Edward)   \n",
       "8        Woods, Margaret L. (Margaret Louisa)   \n",
       "9                        Shane, Peggy (Smith)   \n",
       "\n",
       "                            Robinson, Mary S  \n",
       "0                               Howes, Edith  \n",
       "1                Williams, Elizabeth Whitney  \n",
       "2  Somerville, E. &#xbf;. (Edith &#xbf;none)  \n",
       "3                             Pearce, Donald  \n",
       "4                            Read, Charles A  \n",
       "5                              Anderson, Ken  \n",
       "6               Heydrick, Benjamin Alexander  \n",
       "7                     Copping, Arthur Edward  \n",
       "8                          Woods, Margaret L  \n",
       "9                 Shane, Peggy (Smith)], Mrs  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_to_noveltm_dedup = \"/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/dedup/\"\n",
    "\n",
    "authorsets = pd.read_csv(f'{path_to_noveltm_dedup}/authorsets.tsv', sep='\\t')\n",
    "\n",
    "authorsets.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exploring NovelTM \n",
    "\n",
    "If you want to further explore NovelTM you can read the `readme.md` file and open files in different directories. \n",
    "\n",
    "Here's a little knowledge that can help you explore the data:\n",
    "\n",
    "The different directories (main directories and sub directories) make up a \"file hierarchy.\" You can view NovelTM's file hierarchy like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/LICENSE',\n",
       " '/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/plans',\n",
       " '/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/finalpolish',\n",
       " '/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/eda',\n",
       " '/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/readme.md',\n",
       " '/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/manuallists',\n",
       " '/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/incopyrightpagepredicts',\n",
       " '/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/missing',\n",
       " '/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/makemaster',\n",
       " '/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/metadata',\n",
       " '/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/get_EF',\n",
       " '/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/dedup']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import glob \n",
    "\n",
    "path_to_noveltm = \"/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/\"\n",
    "\n",
    "glob.glob('{}/*'.format(path_to_noveltm))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each of the names at the end of path represent a folder containing data (minus LICENSE and `readme.md`). You can open a folder by adding it to the directory path defined above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/metadata/recordmeta.tsv',\n",
       " '/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/metadata/gender_balanced_subset.tsv',\n",
       " '/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/metadata/frequently_reprinted_subset.tsv',\n",
       " '/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/metadata/readme.md',\n",
       " '/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/metadata/titlemeta.tsv',\n",
       " '/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/metadata/recordmeta_data_dictionary.md',\n",
       " '/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/metadata/manual_title_data_dictionary.md',\n",
       " '/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/metadata/manual_title_subset.tsv',\n",
       " '/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/metadata/weighted_subset.tsv',\n",
       " '/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/metadata/frequently_reprinted_datadict.md',\n",
       " '/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/metadata/volumemeta_data_dictionary.md',\n",
       " '/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/metadata/titlemeta_data_dictionary.md',\n",
       " '/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/metadata/volumemeta.tsv']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_to_noveltm = \"/scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72/metadata/\"\n",
    "\n",
    "glob.glob('{}/*'.format(path_to_noveltm))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that from the `metadata` folder we only opened `titlemeta.tsv`, `gender_balanced_subset.tsv` and `frequently_reprinted_data.tsv`, but there are many more files!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jupyter does not offer an easy interface to navigate and understand files. For purposes of exploration, you should consider opening a Shell and using the command `cd /scratch/group/history/hist_3368-jguldi/tedunderwood-noveltmmeta-451ae72\"` followed by `ls`. This interface will show all the files in a directory. The folders you can change into are in blue font, whereas data is in white font and ends with `.tsv`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with Our Data: Counting Top Words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have practiced reading in the data, let's do something with it! \n",
    "\n",
    "For this first exercise we will just count top words in the reddit data from November 2008.\n",
    "\n",
    "First read in the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>body</th>\n",
       "      <th>created_utc</th>\n",
       "      <th>downs</th>\n",
       "      <th>id</th>\n",
       "      <th>link_id</th>\n",
       "      <th>parent_id</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>subreddit_id</th>\n",
       "      <th>ups</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>[deleted]</td>\n",
       "      <td>1225497606</td>\n",
       "      <td>0</td>\n",
       "      <td>c064gt3</td>\n",
       "      <td>t3_7ajem</td>\n",
       "      <td>t3_7ajem</td>\n",
       "      <td>reddit.com</td>\n",
       "      <td>t5_6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Dude, your phone is ringing Dude... Dude, your...</td>\n",
       "      <td>1225497628</td>\n",
       "      <td>0</td>\n",
       "      <td>c064gt4</td>\n",
       "      <td>t3_7ajhj</td>\n",
       "      <td>t1_c0648hy</td>\n",
       "      <td>news</td>\n",
       "      <td>t5_2qh3l</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>What with incest being illegal in most countri...</td>\n",
       "      <td>1225497629</td>\n",
       "      <td>0</td>\n",
       "      <td>c064gt5</td>\n",
       "      <td>t3_7a9re</td>\n",
       "      <td>t1_c0645i0</td>\n",
       "      <td>WTF</td>\n",
       "      <td>t5_2qh61</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>[deleted]</td>\n",
       "      <td>1225497640</td>\n",
       "      <td>0</td>\n",
       "      <td>c064gt6</td>\n",
       "      <td>t3_7ahln</td>\n",
       "      <td>t3_7ahln</td>\n",
       "      <td>funny</td>\n",
       "      <td>t5_2qh33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>&amp;gt; Also, a blue-ring's bite is totally painl...</td>\n",
       "      <td>1225497654</td>\n",
       "      <td>0</td>\n",
       "      <td>c064gt7</td>\n",
       "      <td>t3_7ahln</td>\n",
       "      <td>t1_c064838</td>\n",
       "      <td>funny</td>\n",
       "      <td>t5_2qh33</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784067</th>\n",
       "      <td>792304</td>\n",
       "      <td>Putting up alarmist information, then hedging ...</td>\n",
       "      <td>1228089583</td>\n",
       "      <td>0</td>\n",
       "      <td>c06lhq3</td>\n",
       "      <td>t3_7gfs1</td>\n",
       "      <td>t1_c06lho1</td>\n",
       "      <td>Health</td>\n",
       "      <td>t5_2qh9z</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784068</th>\n",
       "      <td>792306</td>\n",
       "      <td>When I was 16 I smoked a joint with three budd...</td>\n",
       "      <td>1228089587</td>\n",
       "      <td>0</td>\n",
       "      <td>c06lhq5</td>\n",
       "      <td>t3_7gj5b</td>\n",
       "      <td>t3_7gj5b</td>\n",
       "      <td>AskReddit</td>\n",
       "      <td>t5_2qh1i</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784069</th>\n",
       "      <td>792307</td>\n",
       "      <td>That's just retarded. They may not be barred f...</td>\n",
       "      <td>1228089588</td>\n",
       "      <td>0</td>\n",
       "      <td>c06lhq6</td>\n",
       "      <td>t3_7gi2i</td>\n",
       "      <td>t3_7gi2i</td>\n",
       "      <td>politics</td>\n",
       "      <td>t5_2cneq</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784070</th>\n",
       "      <td>792308</td>\n",
       "      <td>If you know how to sew, even a little, [these ...</td>\n",
       "      <td>1228089588</td>\n",
       "      <td>0</td>\n",
       "      <td>c06lhq7</td>\n",
       "      <td>t3_7gj0i</td>\n",
       "      <td>t3_7gj0i</td>\n",
       "      <td>AskReddit</td>\n",
       "      <td>t5_2qh1i</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784071</th>\n",
       "      <td>792309</td>\n",
       "      <td>[deleted]</td>\n",
       "      <td>1228089593</td>\n",
       "      <td>0</td>\n",
       "      <td>c06lhq8</td>\n",
       "      <td>t3_7gjgq</td>\n",
       "      <td>t3_7gjgq</td>\n",
       "      <td>reddit.com</td>\n",
       "      <td>t5_6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>784072 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0                                               body  \\\n",
       "0                0                                          [deleted]   \n",
       "1                1  Dude, your phone is ringing Dude... Dude, your...   \n",
       "2                2  What with incest being illegal in most countri...   \n",
       "3                3                                          [deleted]   \n",
       "4                4  &gt; Also, a blue-ring's bite is totally painl...   \n",
       "...            ...                                                ...   \n",
       "784067      792304  Putting up alarmist information, then hedging ...   \n",
       "784068      792306  When I was 16 I smoked a joint with three budd...   \n",
       "784069      792307  That's just retarded. They may not be barred f...   \n",
       "784070      792308  If you know how to sew, even a little, [these ...   \n",
       "784071      792309                                          [deleted]   \n",
       "\n",
       "        created_utc  downs       id   link_id   parent_id   subreddit  \\\n",
       "0        1225497606      0  c064gt3  t3_7ajem    t3_7ajem  reddit.com   \n",
       "1        1225497628      0  c064gt4  t3_7ajhj  t1_c0648hy        news   \n",
       "2        1225497629      0  c064gt5  t3_7a9re  t1_c0645i0         WTF   \n",
       "3        1225497640      0  c064gt6  t3_7ahln    t3_7ahln       funny   \n",
       "4        1225497654      0  c064gt7  t3_7ahln  t1_c064838       funny   \n",
       "...             ...    ...      ...       ...         ...         ...   \n",
       "784067   1228089583      0  c06lhq3  t3_7gfs1  t1_c06lho1      Health   \n",
       "784068   1228089587      0  c06lhq5  t3_7gj5b    t3_7gj5b   AskReddit   \n",
       "784069   1228089588      0  c06lhq6  t3_7gi2i    t3_7gi2i    politics   \n",
       "784070   1228089588      0  c06lhq7  t3_7gj0i    t3_7gj0i   AskReddit   \n",
       "784071   1228089593      0  c06lhq8  t3_7gjgq    t3_7gjgq  reddit.com   \n",
       "\n",
       "       subreddit_id  ups  \n",
       "0              t5_6    1  \n",
       "1          t5_2qh3l    0  \n",
       "2          t5_2qh61    1  \n",
       "3          t5_2qh33    1  \n",
       "4          t5_2qh33   10  \n",
       "...             ...  ...  \n",
       "784067     t5_2qh9z    0  \n",
       "784068     t5_2qh1i    1  \n",
       "784069     t5_2cneq   -3  \n",
       "784070     t5_2qh1i   18  \n",
       "784071         t5_6    1  \n",
       "\n",
       "[784072 rows x 10 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reddit_df = pd.read_csv('/scratch/group/oit_research_data/reddit/RC_2008-11.tsv', sep='\\t')\n",
    "\n",
    "reddit_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great. Now let's do a little cleaning for a better outcome:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove comments that have been deleted and thus have \"deleted\" as their text body\n",
    "reddit_df = reddit_df[reddit_df['body'] != '[deleted]']\n",
    "\n",
    "# filter for just the subreddits we wish to operate on\n",
    "subreddit_filter = ('politics', 'worldnews', 'news')\n",
    "reddit_df = reddit_df[reddit_df['subreddit'].isin(subreddit_filter)]\n",
    "\n",
    "# clean, clean, clean \n",
    "reddit_df['body'] = reddit_df['body'].str.lower() # transform words to lowercase\n",
    "reddit_df['body'] = reddit_df['body'].str.replace('[^\\w\\s]','') # remove punctuation\n",
    "reddit_df = reddit_df[~reddit_df['body'].isna()] # remove blank comments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at our cleaned data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
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       "      <th>29</th>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>784047</th>\n",
       "      <td>792284</td>\n",
       "      <td>i think it was books like confessions of an ec...</td>\n",
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       "      <td>c06lhpj</td>\n",
       "      <td>t3_7gga6</td>\n",
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       "      <td>worldnews</td>\n",
       "      <td>t5_2qh13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784056</th>\n",
       "      <td>792293</td>\n",
       "      <td>sorry i meant the video i was unaware there wa...</td>\n",
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       "      <td>c06lhps</td>\n",
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       "      <td>politics</td>\n",
       "      <td>t5_2cneq</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784059</th>\n",
       "      <td>792296</td>\n",
       "      <td>hah thats your proof lets see the neocon bible...</td>\n",
       "      <td>1228089546</td>\n",
       "      <td>0</td>\n",
       "      <td>c06lhpv</td>\n",
       "      <td>t3_7gcxz</td>\n",
       "      <td>t1_c06lfr5</td>\n",
       "      <td>politics</td>\n",
       "      <td>t5_2cneq</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784061</th>\n",
       "      <td>792298</td>\n",
       "      <td>yeah the darpa prize hasnt yielded any results</td>\n",
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       "      <td>0</td>\n",
       "      <td>c06lhpx</td>\n",
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       "      <td>t1_c06lfjz</td>\n",
       "      <td>politics</td>\n",
       "      <td>t5_2cneq</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>784069</th>\n",
       "      <td>792307</td>\n",
       "      <td>thats just retarded they may not be barred fro...</td>\n",
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       "      <td>0</td>\n",
       "      <td>c06lhq6</td>\n",
       "      <td>t3_7gi2i</td>\n",
       "      <td>t3_7gi2i</td>\n",
       "      <td>politics</td>\n",
       "      <td>t5_2cneq</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>130680 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0                                               body  \\\n",
       "1                1  dude your phone is ringing dude dude your phon...   \n",
       "7                7  ask her to do it with her mouth full nudge kno...   \n",
       "22              23  theres a difference between subtle tasteful ad...   \n",
       "23              24  holy shit not only was my ad a floater mine wa...   \n",
       "29              30  do you know what they call proposition 8 in ho...   \n",
       "...            ...                                                ...   \n",
       "784047      792284  i think it was books like confessions of an ec...   \n",
       "784056      792293  sorry i meant the video i was unaware there wa...   \n",
       "784059      792296  hah thats your proof lets see the neocon bible...   \n",
       "784061      792298     yeah the darpa prize hasnt yielded any results   \n",
       "784069      792307  thats just retarded they may not be barred fro...   \n",
       "\n",
       "        created_utc  downs       id   link_id   parent_id  subreddit  \\\n",
       "1        1225497628      0  c064gt4  t3_7ajhj  t1_c0648hy       news   \n",
       "7        1225497666      0  c064gta  t3_7ak8x  t1_c064dd3  worldnews   \n",
       "22       1225497747      0  c064gtq  t3_7aklv  t1_c064c5m   politics   \n",
       "23       1225497749      0  c064gtr  t3_7aklv  t1_c064b44   politics   \n",
       "29       1225497763      0  c064gtx  t3_7ajk1    t3_7ajk1   politics   \n",
       "...             ...    ...      ...       ...         ...        ...   \n",
       "784047   1228089500      0  c06lhpj  t3_7gga6  t1_c06lfnb  worldnews   \n",
       "784056   1228089540      0  c06lhps  t3_7gfkn  t1_c06lhp6   politics   \n",
       "784059   1228089546      0  c06lhpv  t3_7gcxz  t1_c06lfr5   politics   \n",
       "784061   1228089554      0  c06lhpx  t3_7ghb5  t1_c06lfjz   politics   \n",
       "784069   1228089588      0  c06lhq6  t3_7gi2i    t3_7gi2i   politics   \n",
       "\n",
       "       subreddit_id  ups  \n",
       "1          t5_2qh3l    0  \n",
       "7          t5_2qh13    2  \n",
       "22         t5_2cneq    8  \n",
       "23         t5_2cneq    2  \n",
       "29         t5_2cneq    1  \n",
       "...             ...  ...  \n",
       "784047     t5_2qh13    1  \n",
       "784056     t5_2cneq    1  \n",
       "784059     t5_2cneq    1  \n",
       "784061     t5_2cneq    3  \n",
       "784069     t5_2cneq   -3  \n",
       "\n",
       "[130680 rows x 10 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reddit_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To count top words we need to operate on the `body` column. We can't go straight to counting words from here, however.\n",
    "\n",
    "To a human reader, every word in the body column appears as a separate, individual word. These words are easy for us to count. But, this isn't the case to for a machine reader. At its present state, a machine reads the text in each row as an entire unit. For this reason, we need to split the words of each row on the spaces. \n",
    "\n",
    "The following code splits the words into individual tokens that a computer can read."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "reddit_df['body'] = reddit_df['body'].str.split(\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>created_utc</th>\n",
       "      <th>downs</th>\n",
       "      <th>id</th>\n",
       "      <th>link_id</th>\n",
       "      <th>parent_id</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>subreddit_id</th>\n",
       "      <th>ups</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <th>784047</th>\n",
       "      <td>792284</td>\n",
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       "      <td>c06lhpj</td>\n",
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       "      <td>t1_c06lfnb</td>\n",
       "      <td>worldnews</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>784056</th>\n",
       "      <td>792293</td>\n",
       "      <td>[sorry, i, meant, the, video, i, was, unaware,...</td>\n",
       "      <td>1228089540</td>\n",
       "      <td>0</td>\n",
       "      <td>c06lhps</td>\n",
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       "      <td>t1_c06lhp6</td>\n",
       "      <td>politics</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>784059</th>\n",
       "      <td>792296</td>\n",
       "      <td>[hah, thats, your, proof, lets, see, the, neoc...</td>\n",
       "      <td>1228089546</td>\n",
       "      <td>0</td>\n",
       "      <td>c06lhpv</td>\n",
       "      <td>t3_7gcxz</td>\n",
       "      <td>t1_c06lfr5</td>\n",
       "      <td>politics</td>\n",
       "      <td>t5_2cneq</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>784061</th>\n",
       "      <td>792298</td>\n",
       "      <td>[yeah, the, darpa, prize, hasnt, yielded, any,...</td>\n",
       "      <td>1228089554</td>\n",
       "      <td>0</td>\n",
       "      <td>c06lhpx</td>\n",
       "      <td>t3_7ghb5</td>\n",
       "      <td>t1_c06lfjz</td>\n",
       "      <td>politics</td>\n",
       "      <td>t5_2cneq</td>\n",
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       "      <td>politics</td>\n",
       "      <td>t5_2cneq</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>130680 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0                                               body  \\\n",
       "1                1  [dude, your, phone, is, ringing, dude, dude, y...   \n",
       "7                7  [ask, her, to, do, it, with, her, mouth, full,...   \n",
       "22              23  [theres, a, difference, between, subtle, taste...   \n",
       "23              24  [holy, shit, not, only, was, my, ad, a, floate...   \n",
       "29              30  [do, you, know, what, they, call, proposition,...   \n",
       "...            ...                                                ...   \n",
       "784047      792284  [i, think, it, was, books, like, confessions, ...   \n",
       "784056      792293  [sorry, i, meant, the, video, i, was, unaware,...   \n",
       "784059      792296  [hah, thats, your, proof, lets, see, the, neoc...   \n",
       "784061      792298  [yeah, the, darpa, prize, hasnt, yielded, any,...   \n",
       "784069      792307  [thats, just, retarded, they, may, not, be, ba...   \n",
       "\n",
       "        created_utc  downs       id   link_id   parent_id  subreddit  \\\n",
       "1        1225497628      0  c064gt4  t3_7ajhj  t1_c0648hy       news   \n",
       "7        1225497666      0  c064gta  t3_7ak8x  t1_c064dd3  worldnews   \n",
       "22       1225497747      0  c064gtq  t3_7aklv  t1_c064c5m   politics   \n",
       "23       1225497749      0  c064gtr  t3_7aklv  t1_c064b44   politics   \n",
       "29       1225497763      0  c064gtx  t3_7ajk1    t3_7ajk1   politics   \n",
       "...             ...    ...      ...       ...         ...        ...   \n",
       "784047   1228089500      0  c06lhpj  t3_7gga6  t1_c06lfnb  worldnews   \n",
       "784056   1228089540      0  c06lhps  t3_7gfkn  t1_c06lhp6   politics   \n",
       "784059   1228089546      0  c06lhpv  t3_7gcxz  t1_c06lfr5   politics   \n",
       "784061   1228089554      0  c06lhpx  t3_7ghb5  t1_c06lfjz   politics   \n",
       "784069   1228089588      0  c06lhq6  t3_7gi2i    t3_7gi2i   politics   \n",
       "\n",
       "       subreddit_id  ups  \n",
       "1          t5_2qh3l    0  \n",
       "7          t5_2qh13    2  \n",
       "22         t5_2cneq    8  \n",
       "23         t5_2cneq    2  \n",
       "29         t5_2cneq    1  \n",
       "...             ...  ...  \n",
       "784047     t5_2qh13    1  \n",
       "784056     t5_2cneq    1  \n",
       "784059     t5_2cneq    1  \n",
       "784061     t5_2cneq    3  \n",
       "784069     t5_2cneq   -3  \n",
       "\n",
       "[130680 rows x 10 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reddit_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can count the words in each row. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def countWords(dataframe):\n",
    "    ngrams_dict = {}\n",
    "    for ls in dataframe['body']:\n",
    "        for ngram in ls:\n",
    "            if ngram in ngrams_dict:\n",
    "                ngrams_dict[ngram] += 1\n",
    "            else:\n",
    "                ngrams_dict[ngram] = 1\n",
    "    return ngrams_dict\n",
    "            \n",
    "reddit_count = countWords(reddit_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can view our results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ask': 1138,\n",
      " 'do': 18175,\n",
      " 'dude': 588,\n",
      " 'full': 1065,\n",
      " 'her': 4586,\n",
      " 'i': 78471,\n",
      " 'is': 81884,\n",
      " 'it': 60742,\n",
      " 'know': 9123,\n",
      " 'mean': 3807,\n",
      " 'mouth': 316,\n",
      " 'nudge': 13,\n",
      " 'phone': 320,\n",
      " 'ringing': 11,\n",
      " 'theres': 2683,\n",
      " 'to': 146269,\n",
      " 'what': 20476,\n",
      " 'wink': 46,\n",
      " 'with': 29656,\n",
      " 'your': 18159}\n"
     ]
    }
   ],
   "source": [
    "import pprint\n",
    "\n",
    "def printDict(dict_object):\n",
    "    truncated_dict = dict(list(dict_object.items())[0:20]) # print just 20 lines from the dictionary\n",
    "    pprint.pprint(truncated_dict)\n",
    "\n",
    "printDict(reddit_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great--we have counted every word.\n",
    "\n",
    "Looking at this output, however, reveals a problem: words like \"what,\" \"with,\" or \"to\" have high word counts! If we were to visualize the top words without some additional cleaning we might end up with a visualization of \"the\" or \"and,\" which doesn't tell us anything interesting about our data: the subreddits for politics, world news, and news.\n",
    "\n",
    "Data scientists often call these bloat words \"stop words\" and remove them from the data. We can do this by defining a list of stop words and removing items from `reddit_count` that match a stop word. \n",
    "\n",
    "> Note: removing stop words requires careful consideration. The following list removes pronouns like \"he\" or \"she,\" which is useful here, but would be detrimental for an analysis on gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "stopwords = ['right', 'hon', 'general', 'mr', 'shall', 'majesty', 'exchequer', 'address', 'motion', 'bill',\n",
    "             'earl', 'friend', 'chancellor', 'sense', 'object', 'suppose', 'amidst', 'noble', 'lord', 'agree',\n",
    "             'speech', 'kind', 'january', 'february', 'march', 'april', 'may', 'june', 'july', 'august', 'september',\n",
    "             'october', 'november', 'december', 'order', 'held', 'hold', 'assembly', '£', 'humble', 'about', 'above',\n",
    "             'across', 'address', 'after', 'afterwards', 'again', 'against', 'agree', 'all', 'almost', 'alone', 'along',\n",
    "             'already', 'also', 'although', 'always', 'am', 'amidst', 'among', 'amongst', 'amount', 'an', 'and', 'another',\n",
    "             'any', 'anyhow', 'anything', 'anyway', 'anywhere', 'april', 'are', 'around', 'as', 'at', 'back', 'be', 'was',\n",
    "             'becames', 'because', 'become', 'becomes', 'becoming', 'been', 'before', 'beforehand', 'behind', 'being',\n",
    "             'beside', 'besides', 'between', 'beyond', 'both', 'bottom', 'but', 'by', 'call', 'can', 'cannot', 'do',\n",
    "             'could', 'did', 'does', 'doing', 'done', 'down', 'due', 'during', 'each', 'either', 'else', 'elsewhere',\n",
    "             'empty', 'enough', 'even', 'ever', 'everyone', 'everything', 'everywhere', 'except', 'few', 'first', 'for',\n",
    "             'from', 'front', 'full', 'further', 'general', 'get', 'give', 'go', 'had', 'has', 'have', 'held', 'hence',\n",
    "             'here', 'hereby', 'herein', 'hold', 'however', 'if', 'in', 'indeed', 'into', 'is', 'it', 'its', 'itself',\n",
    "             'just', 'keep', 'kind', 'last', 'latter', 'least', 'less', 'made', 'make', 'many', 'me', 'meanwhile', 'might',\n",
    "             'mine', 'more', 'most', 'mostly', 'move', 'much', 'must', 'name,' 'neither', 'never', 'nevertheless', 'next',\n",
    "             'no', 'noble', 'nobody', 'none', 'nor', 'nothing', 'now', 'nowhere', 'of', 'off', 'often', 'on', 'one', 'once',\n",
    "             'only', 'onto', 'or', 'other', 'others', 'otherwise', 'out', 'over', 'own', 'part', 'perhaps', 'please', 'put',\n",
    "             'quite', 'rather', 'really', 'regarding', 'same', 'say', 'see', 'seem', 'seemed', 'seeming', 'seems', 'sense',\n",
    "             'several', 'shall', 'should', 'show', 'side', 'since', 'so', 'some', 'someone', 'something', 'sometime',\n",
    "             'still', 'such', 'suppose', 'take', 'than', 'that', 'the', 'then', 'there', 'thereby', 'therefore', 'these',\n",
    "             'they', 'this', 'those', 'though', 'thus', 'to', 'together', 'too', 'top', 'toward', 'towards', 'under',\n",
    "             'unless', 'until', 'up', 'upon', 'us', 'used', 'using', 'various', 'very', 'was', 'we', 'well', 'were', 'what',\n",
    "             'whatever', 'when', 'when', 'whereby', 'whether', 'which', 'while', 'who', 'why', 'will', 'with', 'within',\n",
    "             'without', 'would', 'yet', 'bright', 'mr.', 'hansard', 'lancashire', '[]', '£1', '000', 'russell', 'committee',\n",
    "             'reading', 'learned', 'deal', 'time', 'royal', 'gentlemen', 'gentleman', 'year', 'years', 'affairs', 'affair',\n",
    "             'academy', 'sir', 'thought', 'took', 'bring', 'brings', 'brought', 'forward', 'great', 'good', 'department',\n",
    "             'treasury', 'second', 'take', 'taken', 'privy', 'member', 'robert', 'large', 'session', 'secretary', 'notice',\n",
    "             'moment', 'think', 'believe', 'hope', 'ask', 'hear', 'beg', 'support', 'state', 'find', 'admit', 'wish',\n",
    "             'refer', 'reply', 'know', 'feel', 'propose', 'understand', 'let', 'allow', 'like', 'receive', 'consider',\n",
    "             'begin', 'tell', 'like', 'send', 'ought', 'come', 'intend', 'add', 'want', 'stand', 'suggest', 'remind',\n",
    "             'use', 'mean', 'suggest', 'would', 'like', 'one', 'a', 'i', 'you', 'not', '', 'he', 'your', 'their', \n",
    "             'dont', 'my', 'them', 'he', 'she', 'we', 'his', 'her', 'thats', 'way', 'im', 'how', 'our', 'him', 'going', \n",
    "             'your', 'youre', 'gt', 'doesnt', 'said', 'actually', 'where', 'cant', 'didnt', 'point', 'things', 'isnt', \n",
    "             'yes', 'no', 'every', 'got', 'hes', 'two', 'lot', 'sure', 'thing', 'things', 'saying']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "for word in stopwords:\n",
    "    if word in reddit_count.keys():\n",
    "        reddit_count.pop(word)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's print `reddit_count` again and look at the difference: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ads': 174,\n",
      " 'afterschool': 5,\n",
      " 'close': 956,\n",
      " 'dare': 163,\n",
      " 'difference': 1293,\n",
      " 'dude': 588,\n",
      " 'face': 1008,\n",
      " 'jump': 163,\n",
      " 'mouth': 316,\n",
      " 'needs': 1474,\n",
      " 'nudge': 13,\n",
      " 'ones': 1499,\n",
      " 'phone': 320,\n",
      " 'ringing': 11,\n",
      " 'somebody': 488,\n",
      " 'special': 568,\n",
      " 'subtle': 83,\n",
      " 'tasteful': 2,\n",
      " 'theres': 2683,\n",
      " 'wink': 46}\n"
     ]
    }
   ],
   "source": [
    "printDict(reddit_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use matplotlib to visualize our top words. matplotlib works best with data frames, so lets cast `reddit_count` (a dictionary) to a data frame with columns for word and count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Word</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dude</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>phone</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ringing</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mouth</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nudge</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97109</th>\n",
       "      <td>outerspace</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97110</th>\n",
       "      <td>âüàôæöôúß</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97111</th>\n",
       "      <td>peole</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97112</th>\n",
       "      <td>vies</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97113</th>\n",
       "      <td>generalizes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97114 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Word  Count\n",
       "0             dude    588\n",
       "1            phone    320\n",
       "2          ringing     11\n",
       "3            mouth    316\n",
       "4            nudge     13\n",
       "...            ...    ...\n",
       "97109   outerspace      1\n",
       "97110    âüàôæöôúß      1\n",
       "97111        peole      1\n",
       "97112         vies      1\n",
       "97113  generalizes      1\n",
       "\n",
       "[97114 rows x 2 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reddit_count = pd.DataFrame(reddit_count.items(),columns = ['Word','Count'])\n",
    "\n",
    "reddit_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number on the far left side represents the row's index, a numerical representation of the row's sequence.\n",
    "\n",
    "\n",
    "Now let's arrange the data frame from the highest word count to the lowest for viewing purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Word</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>people</td>\n",
       "      <td>24954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>obama</td>\n",
       "      <td>10886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>government</td>\n",
       "      <td>6992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>vote</td>\n",
       "      <td>6162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>country</td>\n",
       "      <td>4952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97004</th>\n",
       "      <td>kuntawala</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97005</th>\n",
       "      <td>gtflavius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97006</th>\n",
       "      <td>sulpicianus</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97007</th>\n",
       "      <td>sestertii</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97113</th>\n",
       "      <td>generalizes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97114 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Word  Count\n",
       "430         people  24954\n",
       "55           obama  10886\n",
       "196     government   6992\n",
       "82            vote   6162\n",
       "780        country   4952\n",
       "...            ...    ...\n",
       "97004    kuntawala      1\n",
       "97005    gtflavius      1\n",
       "97006  sulpicianus      1\n",
       "97007    sestertii      1\n",
       "97113  generalizes      1\n",
       "\n",
       "[97114 rows x 2 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reddit_count.sort_values(by=['Count'], inplace=True, ascending=False)\n",
    "\n",
    "reddit_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't want to visualize every row, so let's take just the first 25:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_reddit_words = reddit_count[:25].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib as plt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [15, 10] # specify the plot's size for viewing\n",
    "\n",
    "top_reddit_words.sort_values(by=['Count'], inplace=True, ascending=True) # sort values by count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2aac10dea908>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_reddit_words.plot.barh(x = \"Word\", y = \"Count\", # visualize the count. \n",
    "            title = \"Top Words in the Politics, Worldnews, and News Subreddit \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also plot our data using a scatter plot. We will substitute `barh()` (for horizontal bar chart) with `scatter()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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": [
    "%matplotlib inline\n",
    "plt.scatter(top_reddit_words.Count, top_reddit_words.Word, color='blue')\n",
    "plt.xlabel(\"Count\")\n",
    "plt.ylabel(\"Word\")\n",
    "plt.xticks(rotation=90)\n",
    "#plt.xticks(xt)\n",
    "plt.title(\"Top Words in the Politics, Worldnews, and News Subreddit\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with Our Data: Implementing KeyWords in Context (KWIC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this last exercise we will find keywords in context (KWIC). You can think of KWIC's output like the lines of a concordance. \n",
    "\n",
    "To produce this output, we will split the text into n-grams where \"n\" stands for any number. For example, a text split into single words (like our data frame in the previous example) is split into 1-grams (also just called \"tokens\"). 3-grams split the text into units that are 3 words long. 5-grams split the text into units that are 5 words long. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our example will use reddit data from November 2008. We will create a dictionary too see keyword \"Obama\" in context.\n",
    "\n",
    "First read in the data and do a little cleaning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "reddit_df = pd.read_csv('/scratch/group/oit_research_data/reddit/RC_2008-11.tsv', sep='\\t') # read in the data \n",
    "\n",
    "reddit_df = reddit_df[reddit_df['body'] != '[deleted]'] # drop comments that have been deleted \n",
    "\n",
    "subreddit_filter = ('politics', 'worldnews', 'news') # specify the subreddits you want to use\n",
    "\n",
    "reddit_df = reddit_df[reddit_df['subreddit'].isin(subreddit_filter)] # filter for the specified subreddits\n",
    "\n",
    "reddit_df['body'] = reddit_df['body'].str.lower() # transform all words to lower case for consistency \n",
    "\n",
    "reddit_df['body'] = reddit_df['body'].str.replace('[^\\w\\s]','') # remove punctuation\n",
    "\n",
    "reddit_df = reddit_df[~reddit_df['body'].isna()] # remove posts without commentary "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>body</th>\n",
       "      <th>created_utc</th>\n",
       "      <th>downs</th>\n",
       "      <th>id</th>\n",
       "      <th>link_id</th>\n",
       "      <th>parent_id</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>subreddit_id</th>\n",
       "      <th>ups</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>dude your phone is ringing dude dude your phon...</td>\n",
       "      <td>1225497628</td>\n",
       "      <td>0</td>\n",
       "      <td>c064gt4</td>\n",
       "      <td>t3_7ajhj</td>\n",
       "      <td>t1_c0648hy</td>\n",
       "      <td>news</td>\n",
       "      <td>t5_2qh3l</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>ask her to do it with her mouth full nudge kno...</td>\n",
       "      <td>1225497666</td>\n",
       "      <td>0</td>\n",
       "      <td>c064gta</td>\n",
       "      <td>t3_7ak8x</td>\n",
       "      <td>t1_c064dd3</td>\n",
       "      <td>worldnews</td>\n",
       "      <td>t5_2qh13</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>theres a difference between subtle tasteful ad...</td>\n",
       "      <td>1225497747</td>\n",
       "      <td>0</td>\n",
       "      <td>c064gtq</td>\n",
       "      <td>t3_7aklv</td>\n",
       "      <td>t1_c064c5m</td>\n",
       "      <td>politics</td>\n",
       "      <td>t5_2cneq</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>holy shit not only was my ad a floater mine wa...</td>\n",
       "      <td>1225497749</td>\n",
       "      <td>0</td>\n",
       "      <td>c064gtr</td>\n",
       "      <td>t3_7aklv</td>\n",
       "      <td>t1_c064b44</td>\n",
       "      <td>politics</td>\n",
       "      <td>t5_2cneq</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>do you know what they call proposition 8 in ho...</td>\n",
       "      <td>1225497763</td>\n",
       "      <td>0</td>\n",
       "      <td>c064gtx</td>\n",
       "      <td>t3_7ajk1</td>\n",
       "      <td>t3_7ajk1</td>\n",
       "      <td>politics</td>\n",
       "      <td>t5_2cneq</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784047</th>\n",
       "      <td>792284</td>\n",
       "      <td>i think it was books like confessions of an ec...</td>\n",
       "      <td>1228089500</td>\n",
       "      <td>0</td>\n",
       "      <td>c06lhpj</td>\n",
       "      <td>t3_7gga6</td>\n",
       "      <td>t1_c06lfnb</td>\n",
       "      <td>worldnews</td>\n",
       "      <td>t5_2qh13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784056</th>\n",
       "      <td>792293</td>\n",
       "      <td>sorry i meant the video i was unaware there wa...</td>\n",
       "      <td>1228089540</td>\n",
       "      <td>0</td>\n",
       "      <td>c06lhps</td>\n",
       "      <td>t3_7gfkn</td>\n",
       "      <td>t1_c06lhp6</td>\n",
       "      <td>politics</td>\n",
       "      <td>t5_2cneq</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784059</th>\n",
       "      <td>792296</td>\n",
       "      <td>hah thats your proof lets see the neocon bible...</td>\n",
       "      <td>1228089546</td>\n",
       "      <td>0</td>\n",
       "      <td>c06lhpv</td>\n",
       "      <td>t3_7gcxz</td>\n",
       "      <td>t1_c06lfr5</td>\n",
       "      <td>politics</td>\n",
       "      <td>t5_2cneq</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784061</th>\n",
       "      <td>792298</td>\n",
       "      <td>yeah the darpa prize hasnt yielded any results</td>\n",
       "      <td>1228089554</td>\n",
       "      <td>0</td>\n",
       "      <td>c06lhpx</td>\n",
       "      <td>t3_7ghb5</td>\n",
       "      <td>t1_c06lfjz</td>\n",
       "      <td>politics</td>\n",
       "      <td>t5_2cneq</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784069</th>\n",
       "      <td>792307</td>\n",
       "      <td>thats just retarded they may not be barred fro...</td>\n",
       "      <td>1228089588</td>\n",
       "      <td>0</td>\n",
       "      <td>c06lhq6</td>\n",
       "      <td>t3_7gi2i</td>\n",
       "      <td>t3_7gi2i</td>\n",
       "      <td>politics</td>\n",
       "      <td>t5_2cneq</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>130680 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0                                               body  \\\n",
       "1                1  dude your phone is ringing dude dude your phon...   \n",
       "7                7  ask her to do it with her mouth full nudge kno...   \n",
       "22              23  theres a difference between subtle tasteful ad...   \n",
       "23              24  holy shit not only was my ad a floater mine wa...   \n",
       "29              30  do you know what they call proposition 8 in ho...   \n",
       "...            ...                                                ...   \n",
       "784047      792284  i think it was books like confessions of an ec...   \n",
       "784056      792293  sorry i meant the video i was unaware there wa...   \n",
       "784059      792296  hah thats your proof lets see the neocon bible...   \n",
       "784061      792298     yeah the darpa prize hasnt yielded any results   \n",
       "784069      792307  thats just retarded they may not be barred fro...   \n",
       "\n",
       "        created_utc  downs       id   link_id   parent_id  subreddit  \\\n",
       "1        1225497628      0  c064gt4  t3_7ajhj  t1_c0648hy       news   \n",
       "7        1225497666      0  c064gta  t3_7ak8x  t1_c064dd3  worldnews   \n",
       "22       1225497747      0  c064gtq  t3_7aklv  t1_c064c5m   politics   \n",
       "23       1225497749      0  c064gtr  t3_7aklv  t1_c064b44   politics   \n",
       "29       1225497763      0  c064gtx  t3_7ajk1    t3_7ajk1   politics   \n",
       "...             ...    ...      ...       ...         ...        ...   \n",
       "784047   1228089500      0  c06lhpj  t3_7gga6  t1_c06lfnb  worldnews   \n",
       "784056   1228089540      0  c06lhps  t3_7gfkn  t1_c06lhp6   politics   \n",
       "784059   1228089546      0  c06lhpv  t3_7gcxz  t1_c06lfr5   politics   \n",
       "784061   1228089554      0  c06lhpx  t3_7ghb5  t1_c06lfjz   politics   \n",
       "784069   1228089588      0  c06lhq6  t3_7gi2i    t3_7gi2i   politics   \n",
       "\n",
       "       subreddit_id  ups  \n",
       "1          t5_2qh3l    0  \n",
       "7          t5_2qh13    2  \n",
       "22         t5_2cneq    8  \n",
       "23         t5_2cneq    2  \n",
       "29         t5_2cneq    1  \n",
       "...             ...  ...  \n",
       "784047     t5_2qh13    1  \n",
       "784056     t5_2cneq    1  \n",
       "784059     t5_2cneq    1  \n",
       "784061     t5_2cneq    3  \n",
       "784069     t5_2cneq   -3  \n",
       "\n",
       "[130680 rows x 10 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reddit_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's write a function that returns our keyword in context."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "keyword = 'obama'\n",
    "\n",
    "keyword_df = reddit_df[reddit_df['body'].str.contains(keyword)].copy() # search the text for the presence of our keyword \n",
    "\n",
    "def KWIC(body):\n",
    "    n = 5 # specify the number of surrounding words to use before and after the keyword\n",
    "    \n",
    "    words = body.split() # split the words into tokens\n",
    "    keyword_index = 0\n",
    "    \n",
    "    # The following for loop iterates through each word in the text. If our keyword is found, the for loop stores its index. \n",
    "    # But, why are we storing the index? As you might remember, the index is a numerical representation of the row's position.\n",
    "    # It can also be thought of as a unique name the computer uses to identify a row. We can call the index to return JUST these rows.\n",
    "    for index, word in enumerate(words): \n",
    "        if keyword in word: \n",
    "            keyword_index = index\n",
    "            break\n",
    "    \n",
    "    before_keyword = words[max(0, keyword_index - n):keyword_index] # store the words that come before the keyword, up to our specified number \n",
    "    after_keyword = words[keyword_index:keyword_index + n] # store the words that come after the keyword, up to our specified number \n",
    "    return ' '.join(before_keyword + after_keyword) # return the keyword in its context\n",
    "\n",
    "keyword_df['context'] = keyword_df['body'].apply(KWIC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>context</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>that obama voted and lobbied for</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>off without a hitch and obama wins nothing wil...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>and remember why you elected obama because hes...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>they are afraid of ners obama is the most set</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>state of hawaii is helping obama you show them...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783439</th>\n",
       "      <td>to invent some cynical crap obama isnt even in...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783540</th>\n",
       "      <td>effort not connected to the obama campaign or ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783654</th>\n",
       "      <td>accounts were ever touched by obama just as ba...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783722</th>\n",
       "      <td>sitting president at this point obama holds du...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784059</th>\n",
       "      <td>say what you will of obama hes not proven himself</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9309 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  context\n",
       "45                       that obama voted and lobbied for\n",
       "67      off without a hitch and obama wins nothing wil...\n",
       "112     and remember why you elected obama because hes...\n",
       "237         they are afraid of ners obama is the most set\n",
       "285     state of hawaii is helping obama you show them...\n",
       "...                                                   ...\n",
       "783439  to invent some cynical crap obama isnt even in...\n",
       "783540  effort not connected to the obama campaign or ...\n",
       "783654  accounts were ever touched by obama just as ba...\n",
       "783722  sitting president at this point obama holds du...\n",
       "784059  say what you will of obama hes not proven himself\n",
       "\n",
       "[9309 rows x 1 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keyword_df[['context']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data frame is a little difficult to look at, so I am going to export the contents to a file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'keyword_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-d58d2c4750af>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mkeyword_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'context'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'KWIC_example_keyword_obama.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m' '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'keyword_df' is not defined"
     ]
    }
   ],
   "source": [
    "keyword_df['context'].to_csv('KWIC_example_keyword_obama.csv', sep = ' ', index=False, header = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the first 18 lines from the file. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> that obama voted and lobbied for\n",
    "\n",
    "> off without a hitch and obama wins nothing will change\n",
    "\n",
    "> and remember why you elected obama because hes the best\n",
    "\n",
    "> they are afraid of ners obama is the most set\n",
    "\n",
    "> state of hawaii is helping obama you show them the\n",
    "\n",
    "> remaining 80 percent how are obamas policies different here we\n",
    "\n",
    "> sounds pretty futile to me obama wont change anything but\n",
    "\n",
    "> with his statement that an obama or mccain vote would\n",
    "\n",
    "> would make shitty picks but obama will make equally shitty\n",
    "\n",
    "> camped out there thats why obama has to keep his\n",
    "\n",
    "> with the model being suggested obama just needs about 40\n",
    "\n",
    "> i just saw an obama on mccain attack ad\n",
    "\n",
    "> i get the impression that obama gets the problems he\n",
    "\n",
    "> is president i dont expect obama to be any different\n",
    "\n",
    "> ways dont you worry if obama falls down reddit will\n",
    "\n",
    "> it is not senator obamas responsibility nor the democrats\n",
    "\n",
    "> people to go away after obama wins and you possibly\n",
    "\n",
    "> dear lord i remember michelle obama parading her kids around\n"
   ]
  }
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