hist3368-short-essay-2-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Looking for a keyword-term pair and keyword in Congress"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How much memory did you allocate for this session?  You will need at least 50 GB."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import software packages and define helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import csv\n",
    "import matplotlib.pyplot as plt\n",
    "from wordcloud import WordCloud\n",
    "import string\n",
    "import numpy, re\n",
    "from nltk.corpus import wordnet as wn\n",
    "from textblob import TextBlob\n",
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package wordnet to /users/jguldi/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download(\"wordnet\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/scratch/group/history/hist_3368-jguldi\n"
     ]
    }
   ],
   "source": [
    "cd /scratch/group/history/hist_3368-jguldi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "congress = pd.read_csv(\"congress-just-1970-2010.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This might take a minute."
   ]
  },
  {
   "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>Unnamed: 0</th>\n",
       "      <th>speech</th>\n",
       "      <th>chamber</th>\n",
       "      <th>date</th>\n",
       "      <th>speaker</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>5yrperiod</th>\n",
       "      <th>index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5369522</td>\n",
       "      <td>The second session of the 91st Congress will n...</td>\n",
       "      <td>S</td>\n",
       "      <td>1970-01-19</td>\n",
       "      <td>The PRESIDENT pro tempore</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1970</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>5369522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5369523</td>\n",
       "      <td>Mr. President. I suggest the absence of a quorum.</td>\n",
       "      <td>S</td>\n",
       "      <td>1970-01-19</td>\n",
       "      <td>Mr. MANSFIELD</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>MANSFIELD</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1970</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>5369523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5369524</td>\n",
       "      <td>The clerk will call the roll.</td>\n",
       "      <td>S</td>\n",
       "      <td>1970-01-19</td>\n",
       "      <td>The PRESIDENT pro tempore</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1970</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>5369524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5369525</td>\n",
       "      <td>I announce that the Senator from Connecticut ....</td>\n",
       "      <td>S</td>\n",
       "      <td>1970-01-19</td>\n",
       "      <td>Mr. KENNEDY</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>KENNEDY</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1970</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>5369525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5369526</td>\n",
       "      <td>I announce that the Senator from Colorado . th...</td>\n",
       "      <td>S</td>\n",
       "      <td>1970-01-19</td>\n",
       "      <td>Mr. GRIFFIN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>GRIFFIN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1970</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>5369526</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5507434</th>\n",
       "      <td>10876956</td>\n",
       "      <td>Madam Speaker. on rollcall Nos. 662 and 661. I...</td>\n",
       "      <td>E</td>\n",
       "      <td>2010-12-22</td>\n",
       "      <td>Ms. GRANGER</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>GRANGER</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>10876956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5507435</th>\n",
       "      <td>10876957</td>\n",
       "      <td>Madam Speaker. as I leave Congress as the peop...</td>\n",
       "      <td>E</td>\n",
       "      <td>2010-12-22</td>\n",
       "      <td>Ms. KILPATRICK of Michigan</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>KILPATRICK</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>10876957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5507436</th>\n",
       "      <td>10876958</td>\n",
       "      <td>Madam Speaker. on rolicall No. 658. I was unav...</td>\n",
       "      <td>E</td>\n",
       "      <td>2010-12-22</td>\n",
       "      <td>Mr. HELLER</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>HELLER</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>10876958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5507437</th>\n",
       "      <td>10876959</td>\n",
       "      <td>Madam Speaker. on rollcall No. 658 my flight w...</td>\n",
       "      <td>E</td>\n",
       "      <td>2010-12-22</td>\n",
       "      <td>Mr. PAULSEN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>PAULSEN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>10876959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5507438</th>\n",
       "      <td>10876960</td>\n",
       "      <td>Madam Speaker. I wold like to submit the follo...</td>\n",
       "      <td>E</td>\n",
       "      <td>2010-12-22</td>\n",
       "      <td>Mr. HALL of New York</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>HALL</td>\n",
       "      <td>New York</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>10876960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5507439 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0                                             speech  \\\n",
       "0           5369522  The second session of the 91st Congress will n...   \n",
       "1           5369523  Mr. President. I suggest the absence of a quorum.   \n",
       "2           5369524                      The clerk will call the roll.   \n",
       "3           5369525  I announce that the Senator from Connecticut ....   \n",
       "4           5369526  I announce that the Senator from Colorado . th...   \n",
       "...             ...                                                ...   \n",
       "5507434    10876956  Madam Speaker. on rollcall Nos. 662 and 661. I...   \n",
       "5507435    10876957  Madam Speaker. as I leave Congress as the peop...   \n",
       "5507436    10876958  Madam Speaker. on rolicall No. 658. I was unav...   \n",
       "5507437    10876959  Madam Speaker. on rollcall No. 658 my flight w...   \n",
       "5507438    10876960  Madam Speaker. I wold like to submit the follo...   \n",
       "\n",
       "        chamber        date                     speaker first_name  \\\n",
       "0             S  1970-01-19   The PRESIDENT pro tempore    Unknown   \n",
       "1             S  1970-01-19               Mr. MANSFIELD    Unknown   \n",
       "2             S  1970-01-19   The PRESIDENT pro tempore    Unknown   \n",
       "3             S  1970-01-19                 Mr. KENNEDY    Unknown   \n",
       "4             S  1970-01-19                 Mr. GRIFFIN    Unknown   \n",
       "...         ...         ...                         ...        ...   \n",
       "5507434       E  2010-12-22                 Ms. GRANGER    Unknown   \n",
       "5507435       E  2010-12-22  Ms. KILPATRICK of Michigan    Unknown   \n",
       "5507436       E  2010-12-22                  Mr. HELLER    Unknown   \n",
       "5507437       E  2010-12-22                 Mr. PAULSEN    Unknown   \n",
       "5507438       E  2010-12-22        Mr. HALL of New York    Unknown   \n",
       "\n",
       "          last_name     state  year  5yrperiod     index  \n",
       "0           Unknown   Unknown  1970     1970.0   5369522  \n",
       "1         MANSFIELD   Unknown  1970     1970.0   5369523  \n",
       "2           Unknown   Unknown  1970     1970.0   5369524  \n",
       "3           KENNEDY   Unknown  1970     1970.0   5369525  \n",
       "4           GRIFFIN   Unknown  1970     1970.0   5369526  \n",
       "...             ...       ...   ...        ...       ...  \n",
       "5507434     GRANGER   Unknown  2010     2010.0  10876956  \n",
       "5507435  KILPATRICK  Michigan  2010     2010.0  10876957  \n",
       "5507436      HELLER   Unknown  2010     2010.0  10876958  \n",
       "5507437     PAULSEN   Unknown  2010     2010.0  10876959  \n",
       "5507438        HALL  New York  2010     2010.0  10876960  \n",
       "\n",
       "[5507439 rows x 11 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "congress"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look for one word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "word1 = \"environmentalist\"\n",
    "\n",
    "contains_word1 = congress[congress['speech'].str.contains(word1)].copy() # search the text for the presence of our keyword "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look for another word"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's look in contains_word1 for those speeches that also contain another term, which we'll call *word2*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "word2 = \"kook\"\n",
    "\n",
    "contains_2words = contains_word1[contains_word1['speech'].str.contains(word2)].copy() # search the text for the presence of our keyword "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter for a particular period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>speech</th>\n",
       "      <th>chamber</th>\n",
       "      <th>date</th>\n",
       "      <th>speaker</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>5yrperiod</th>\n",
       "      <th>index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>109881</th>\n",
       "      <td>5479403</td>\n",
       "      <td>Mr. Speaker. I believe that my colleagues will...</td>\n",
       "      <td>E</td>\n",
       "      <td>1970-08-06</td>\n",
       "      <td>Mr. BURTON of Utah</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>BURTON</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1970</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>5479403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257847</th>\n",
       "      <td>5627369</td>\n",
       "      <td>Mr. Speaker. in the S. Thompson. is Secretary ...</td>\n",
       "      <td>E</td>\n",
       "      <td>1971-06-30</td>\n",
       "      <td>Mr. HATHAWAY</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>HATHAWAY</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1971</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>5627369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403421</th>\n",
       "      <td>5772943</td>\n",
       "      <td>Mr. President. according to the Atomic Industr...</td>\n",
       "      <td>S</td>\n",
       "      <td>1972-05-01</td>\n",
       "      <td>Mr. GRAVEL</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>GRAVEL</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1972</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>5772943</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0                                             speech chamber  \\\n",
       "109881     5479403  Mr. Speaker. I believe that my colleagues will...       E   \n",
       "257847     5627369  Mr. Speaker. in the S. Thompson. is Secretary ...       E   \n",
       "403421     5772943  Mr. President. according to the Atomic Industr...       S   \n",
       "\n",
       "              date             speaker first_name last_name    state  year  \\\n",
       "109881  1970-08-06  Mr. BURTON of Utah    Unknown    BURTON     Utah  1970   \n",
       "257847  1971-06-30        Mr. HATHAWAY    Unknown  HATHAWAY  Unknown  1971   \n",
       "403421  1972-05-01          Mr. GRAVEL    Unknown    GRAVEL  Unknown  1972   \n",
       "\n",
       "        5yrperiod    index  \n",
       "109881     1970.0  5479403  \n",
       "257847     1970.0  5627369  \n",
       "403421     1970.0  5772943  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bothwords_1period = contains_2words[(contains_2words['year'] < 1975) & (contains_2words['year'] >= 1970)].copy() # search the text for the presence of our keyword \n",
    "bothwords_1period.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>speech</th>\n",
       "      <th>chamber</th>\n",
       "      <th>date</th>\n",
       "      <th>speaker</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>5yrperiod</th>\n",
       "      <th>index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1016481</th>\n",
       "      <td>6386003</td>\n",
       "      <td>Everyone knows the Senator from California is ...</td>\n",
       "      <td>S</td>\n",
       "      <td>1975-10-01</td>\n",
       "      <td>Mr. HOLLINGS</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>HOLLINGS</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1975</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>6386003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1119641</th>\n",
       "      <td>6489163</td>\n",
       "      <td>Mr. President. I thank my distinguished collea...</td>\n",
       "      <td>S</td>\n",
       "      <td>1976-04-13</td>\n",
       "      <td>Mr. HOLLINGS</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>HOLLINGS</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1976</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>6489163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0                                             speech  \\\n",
       "1016481     6386003  Everyone knows the Senator from California is ...   \n",
       "1119641     6489163  Mr. President. I thank my distinguished collea...   \n",
       "\n",
       "        chamber        date       speaker first_name last_name    state  year  \\\n",
       "1016481       S  1975-10-01  Mr. HOLLINGS    Unknown  HOLLINGS  Unknown  1975   \n",
       "1119641       S  1976-04-13  Mr. HOLLINGS    Unknown  HOLLINGS  Unknown  1976   \n",
       "\n",
       "         5yrperiod    index  \n",
       "1016481     1975.0  6386003  \n",
       "1119641     1975.0  6489163  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bothwords_1period = contains_2words[(contains_2words['year'] < 1980) & (contains_2words['year'] >= 1975)].copy() # search the text for the presence of our keyword \n",
    "bothwords_1period.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare the results to another search\n",
    "\n",
    "What if we look for how people talked about \"kooks\" when they *weren't* talked about environmentalists in the same period?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>speech</th>\n",
       "      <th>chamber</th>\n",
       "      <th>date</th>\n",
       "      <th>speaker</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>5yrperiod</th>\n",
       "      <th>index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>874720</th>\n",
       "      <td>6244242</td>\n",
       "      <td>OF THE UNITED STATES. The White House. Washing...</td>\n",
       "      <td>E</td>\n",
       "      <td>1975-02-06</td>\n",
       "      <td>The PRESIDENT</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1975</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>6244242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998022</th>\n",
       "      <td>6367544</td>\n",
       "      <td>Mr. Speaker. in recent months Congress and the...</td>\n",
       "      <td>E</td>\n",
       "      <td>1975-09-03</td>\n",
       "      <td>Mr. TEAGUE</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>TEAGUE</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1975</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>6367544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1001325</th>\n",
       "      <td>6370847</td>\n",
       "      <td>Mr. Speaker. I would like to take one moment t...</td>\n",
       "      <td>H</td>\n",
       "      <td>1975-09-10</td>\n",
       "      <td>Mr. SYMMS</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>SYMMS</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1975</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>6370847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002486</th>\n",
       "      <td>6372008</td>\n",
       "      <td>Mr. Speaker. I would like to take a moment tod...</td>\n",
       "      <td>E</td>\n",
       "      <td>1975-09-10</td>\n",
       "      <td>Mr. SYMMS</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>SYMMS</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1975</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>6372008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009638</th>\n",
       "      <td>6379160</td>\n",
       "      <td>Mr. Speaker. the obscene and sad gesture by th...</td>\n",
       "      <td>E</td>\n",
       "      <td>1975-09-23</td>\n",
       "      <td>Mr. NIX</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NIX</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1975</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>6379160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0                                             speech  \\\n",
       "874720      6244242  OF THE UNITED STATES. The White House. Washing...   \n",
       "998022      6367544  Mr. Speaker. in recent months Congress and the...   \n",
       "1001325     6370847  Mr. Speaker. I would like to take one moment t...   \n",
       "1002486     6372008  Mr. Speaker. I would like to take a moment tod...   \n",
       "1009638     6379160  Mr. Speaker. the obscene and sad gesture by th...   \n",
       "\n",
       "        chamber        date        speaker first_name last_name    state  \\\n",
       "874720        E  1975-02-06  The PRESIDENT    Unknown   Unknown  Unknown   \n",
       "998022        E  1975-09-03     Mr. TEAGUE    Unknown    TEAGUE  Unknown   \n",
       "1001325       H  1975-09-10      Mr. SYMMS    Unknown     SYMMS  Unknown   \n",
       "1002486       E  1975-09-10      Mr. SYMMS    Unknown     SYMMS  Unknown   \n",
       "1009638       E  1975-09-23        Mr. NIX    Unknown       NIX  Unknown   \n",
       "\n",
       "         year  5yrperiod    index  \n",
       "874720   1975     1975.0  6244242  \n",
       "998022   1975     1975.0  6367544  \n",
       "1001325  1975     1975.0  6370847  \n",
       "1002486  1975     1975.0  6372008  \n",
       "1009638  1975     1975.0  6379160  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word2alone_bothperiods = bothwords_1period = congress[(congress['speech'].str.contains(word2)) & (congress['year'] < 1980) & (congress['year'] >= 1975)].copy() # search the text for the presence of our keyword \n",
    "\n",
    "word2alone_bothperiods.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving the data as a file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before, we can save the data as a file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/users/jguldi\n"
     ]
    }
   ],
   "source": [
    "cd ~/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "contains_word1.to_csv('kwic2.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "YOU decide which file to save -- bothwords_1period from 1970-75? from 1975-80? the other context?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From CSV file to Microsoft Word Table\n",
    "\n",
    "In the left-hand navigation pane, you should be able to see the new file.  \n",
    "\n",
    "If you do not see a left-hand navigation pane, you probably loaded \"JUPYTER NOTEBOOK\" rather than \"JUPYTER LAB\" in the launch pane on hpc.smu.edu.  Just start a new JUPYTER LAB session from hpc.smu.edu and you should see the file. \n",
    "\n",
    "You can use LEFT CLICK / CONTROL+CLICK with your mouse to select the new file from the left pane.  \n",
    "\n",
    "One of your options is \"download.\"  Select this option to download the file.\n",
    "\n",
    "Next, open Microsoft Excel on your own computer.  Choose FILE > IMPORT.  Tell your computer that you want to import a CSV file.  Then navigate to your downloads folder and find your new file.  If you are asked for any other parameters, choose the default. \n",
    "\n",
    "Once you have the data in Excel, select CTRL+A to highlight everything.  Select CTRL+C to copy it.  \n",
    "\n",
    "Then create a Microsoft Word document and select CTRL+V to paste the new data as a table. \n",
    "\n",
    "You can now format the data however you want."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze the speeches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look just at the 'speech' column from our dataframe *contains_2words*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "109881     Mr. Speaker. I believe that my colleagues will...\n",
       "257847     Mr. Speaker. in the S. Thompson. is Secretary ...\n",
       "403421     Mr. President. according to the Atomic Industr...\n",
       "1016481    Everyone knows the Senator from California is ...\n",
       "1119641    Mr. President. I thank my distinguished collea...\n",
       "3557892    Mr. Speaker. we have been through a very painf...\n",
       "3910393    Mr. President. I thank the distinguished senio...\n",
       "Name: speech, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contains_2words['speech']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(contains_2words['speech'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A 'Series' is just a fancy list with 'index' numbers (which you see to the left). We can turn the series into a normal list with the command \"to_list()\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "listed_speeches = contains_2words['speech'].to_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "look at the first speech."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mr. Speaker. I believe that my colleagues will find the following finely detailed article by Theodor'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listed_speeches[0][:100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the code from our counting notebook to remove stopwords, lowercase the text, remove punctuation, and count the words. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load stopwords:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "\n",
    "stopwords = stopwords.words('english')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am making an extra stopwords list. You will want to add your own to increase the interpretability of the results. The extra word list should contain all words that are likely to occur in other speeches that aren't specifically about kooks and the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "extrawords = ['there', 'say', 'year', 'over', 'get', 'an', 'he', 'up', 'no', 'go', 'about', 'one', 'house', 'gas', 'come', 'state', 'some',\n",
    "              'senator', 'new', 'know', 'more', 'here', 'president', 'make', 'american', 'right', 'want' ,'take', 'think', 'going', 'like', 'let', 'government', 'mr.', 'unite', 'first', 'many', 'committee', 'believe', 'call', 'way', 'see', 'world', 'congress', 'vote', 'tax', 'need', 'senate', \n",
    "              'america', 'member', 'tell', 'back', 'also', 'today', 'point', 'every', 'ask', 'two', 'work,' 'may','must', 'put', 'control',\n",
    "             'house', 'be', 'in', 'have', 'it', 'are', 'on', 'not', 'by', 'wa', 'but', 'do', 'as', 'ha', 'or',\n",
    "              'kind', 'speaker', 'thing', 'much', 'attempt', 'may', 'group', 'chairman', 'raise', 'well', 'look', 'legislation', 'statement', 'expenditure',\n",
    "              'all', 'at', 'have', 'say', 'know', 'go', 'time', 'bill', 'federal', 'country', 'national', 'last', 'get', 'go', 'one', 'come', 'new', 'try', 'say', 'mr', 'state', 'us', 'mr', 'say', 'know', 'use', 'even', 'last', 'come', 'president', 'people', 'years', 'year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "lemmas = []\n",
    "\n",
    "for speech in listed_speeches:\n",
    "    speech = speech.replace('\\n', '') # get rid of extra whitespace\n",
    "    for word in speech.lower().split(): # loop through every word\n",
    "        if len(word) > 1: # get rid of short words\n",
    "            for c in string.punctuation: # strip punctuation\n",
    "                word = word.replace(c, \"\") \n",
    "                lemma = wn.morphy(word) # lemmatize\n",
    "                if lemma != None: # get rid of nonetype elements generated by wordnet\n",
    "                    lemmas.append(lemma) # save the good results\n",
    "\n",
    "lemma_count = pd.Series.value_counts(lemmas) # count them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "be                9720\n",
       "in                8793\n",
       "have              5189\n",
       "it                3963\n",
       "are               2681\n",
       "                  ... \n",
       "forum               17\n",
       "ms.                 13\n",
       "attitude            12\n",
       "limb                12\n",
       "misinformation      12\n",
       "Length: 2145, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lemma_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It might take a minute. let it run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tax              1644\n",
       "billion          1551\n",
       "regulation        932\n",
       "oil               908\n",
       "carolina          785\n",
       "senate            767\n",
       "maine             757\n",
       "power             697\n",
       "natural           640\n",
       "american          595\n",
       "increase          582\n",
       "budget            574\n",
       "environmental     544\n",
       "million           537\n",
       "percent           531\n",
       "nuclear           530\n",
       "south             499\n",
       "point             492\n",
       "environment       486\n",
       "warming           485\n",
       "much              480\n",
       "clean             480\n",
       "north             480\n",
       "free              480\n",
       "want              480\n",
       "disc              473\n",
       "moratorium        465\n",
       "public            454\n",
       "water             454\n",
       "control           454\n",
       "industry          453\n",
       "global            448\n",
       "make              448\n",
       "plant             440\n",
       "two               435\n",
       "enterprise        435\n",
       "yankee            420\n",
       "concern           416\n",
       "put               416\n",
       "many              416\n",
       "cause             403\n",
       "back              403\n",
       "revenue           390\n",
       "air               384\n",
       "price             383\n",
       "good              371\n",
       "temperature       371\n",
       "ask               371\n",
       "like              371\n",
       "world             364\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopworded = []\n",
    "\n",
    "for i in range(0, len(lemmas)-1):\n",
    "    lemma = lemmas[i]\n",
    "    if lemma not in stopwords:\n",
    "        if lemma not in extrawords:\n",
    "    #for stopword in stopwords: #eliminate stopwords\n",
    "       # if lemma != stopword:  \n",
    "        #    for extraword in extrawords: #eliminate extra words\n",
    "         #       if lemma != extrawords:\n",
    "                    stopworded.append(lemmas[i])\n",
    "\n",
    "stopworded_count = pd.Series.value_counts(stopworded) # count them\n",
    "stopworded_count[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a well-labeled barplot\n",
    "f, ax = plt.subplots(figsize=(18,5))\n",
    "plt.bar(list(stopworded_count[:50].index), stopworded_count[:50])\n",
    "plt.xticks(rotation=90) # this tells Python to 'rotate' the labels on the x axis so that the words fit on the screen\n",
    "plt.savefig('bar.png') # this line tells Python to save a copy in your digital-history folder\n",
    "plt.xlabel('Word') # this line labels the x axis\n",
    "plt.ylabel('Count') # this line labels the y axis\n",
    "plt.suptitle('Words Collocated With KOOK and ENVIRONMENTALIST') #This line labels the title\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do these words give you a thumbnail understanding of in what context the terms 'kook' and 'environmentalist' appeared together?  If not, adjust the extrawords list and try again!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare Your Results to Another Query"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also ask: how did speakers use the word \"kook\" when they *weren't* talking about environmentalists?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "listed_speeches = word2alone_bothperiods['speech'].to_list()\n",
    "\n",
    "lemmas = []\n",
    "\n",
    "for speech in listed_speeches:\n",
    "    speech = speech.replace('\\n', '') # get rid of extra whitespace\n",
    "    for word in speech.lower().split(): # loop through every word\n",
    "        if len(word) > 1: # get rid of short words\n",
    "            for c in string.punctuation: # strip punctuation\n",
    "                word = word.replace(c, \"\") \n",
    "                lemma = wn.morphy(word) # lemmatize\n",
    "                if lemma != None: # get rid of nonetype elements generated by wordnet\n",
    "                    lemmas.append(lemma) # save the good results\n",
    "\n",
    "lemma_count = pd.Series.value_counts(lemmas) # count them\n",
    "\n",
    "stopworded = []\n",
    "\n",
    "for i in range(0, len(lemmas)-1):\n",
    "    lemma = lemmas[i]\n",
    "    if lemma not in stopwords:\n",
    "        if lemma not in extrawords:\n",
    "    #for stopword in stopwords: #eliminate stopwords\n",
    "       # if lemma != stopword:  \n",
    "        #    for extraword in extrawords: #eliminate extra words\n",
    "         #       if lemma != extrawords:\n",
    "                    stopworded.append(lemmas[i])\n",
    "\n",
    "stopworded_count = pd.Series.value_counts(stopworded) # count them\n",
    "\n",
    "# a well-labeled barplot\n",
    "f, ax = plt.subplots(figsize=(18,5))\n",
    "plt.bar(list(stopworded_count[:50].index), stopworded_count[:50])\n",
    "plt.xticks(rotation=90) # this tells Python to 'rotate' the labels on the x axis so that the words fit on the screen\n",
    "plt.savefig('bar.png') # this line tells Python to save a copy in your digital-history folder\n",
    "plt.xlabel('Word') # this line labels the x axis\n",
    "plt.ylabel('Count') # this line labels the y axis\n",
    "plt.suptitle('Words Collocated With KOOK, 1970-80') #This line labels the title\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does this allow you to answer the question: did members of Congress *only* or *disproportionately* apply the term 'kook' to environmentalists?   In what other contexts was the word 'kook' used?\n",
    "    \n",
    "What next steps would you take to answer that question? Feel free to tweak the code or add more code here. \n",
    "\n",
    "You might also want to answer the question by looking at the KWIC file generated from the dataframe *word2alone_bothperiods*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ngrams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In what phrases does the term 'kook' appear alongside the keyword 'environmentalist'?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "kookie environmentalists    1\n",
       "kookout Representative      1\n",
       "kooks No                    1\n",
       "kookie regulation           1\n",
       "kooks When                  1\n",
       "gone kooky                  1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigrams = []\n",
    "\n",
    "for speech in listed_speeches:\n",
    "    speech = speech.replace('\\n', '') # get rid of extra whitespace\n",
    "    speech_bigrams = TextBlob(speech).ngrams(n=2)\n",
    "    for bigram in speech_bigrams:\n",
    "            if bigram[0] not in stopwords:\n",
    "                if bigram[1] not in stopwords:\n",
    "                    bigram2 = bigram[0] + ' ' + bigram[1] # call the first word, a space, and the second word into a new string\n",
    "                    if word2 in bigram2:            \n",
    "                        bigrams.append(bigram2)\n",
    "\n",
    "bigram_count = pd.value_counts(bigrams)\n",
    "bigram_count[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could make a barplot, but it won't be very interesting because the bigrams only appear once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a well-labeled barplot\n",
    "plt.bar(list(bigram_count[:20].index), bigram_count[:20])\n",
    "plt.xticks(rotation=90) # this tells Python to 'rotate' the labels on the x axis so that the words fit on the screen\n",
    "plt.savefig('bar.png') # this line tells Python to save a copy in your digital-history folder\n",
    "plt.xlabel('Word') # this line labels the x axis\n",
    "plt.ylabel('Count') # this line labels the y axis\n",
    "plt.suptitle('Bigrams where KOOK is used in speeches referring to ENVIRONMENTALIST, 1970-74') #This line labels the title\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the results to how members of Congress used the word 'kook' overall during the period 1970-1975:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "kook and            4\n",
       "every kook          4\n",
       "of kooks            3\n",
       "skookum paper       2\n",
       "kooky but           2\n",
       "these kooks         2\n",
       "kooks and           2\n",
       "kooks or            2\n",
       "a skookum           1\n",
       "kooks as            1\n",
       "schooner skookum    1\n",
       "as skookum          1\n",
       "kooks is            1\n",
       "nuts kooks          1\n",
       "some kookie         1\n",
       "gone kooky          1\n",
       "for kooky           1\n",
       "kooks for           1\n",
       "few kooks           1\n",
       "kooky when          1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_congress_bigrams = []\n",
    "\n",
    "for speech in word2alone_bothperiods['speech']:\n",
    "    speech = speech.lower().replace('\\n', '') # get rid of extra whitespace\n",
    "    speech_bigrams = TextBlob(speech).ngrams(n=2)\n",
    "    for bigram in speech_bigrams:\n",
    "            bigram2 = bigram[0] + ' ' + bigram[1] # call the first word, a space, and the second word into a new string\n",
    "            if word2 in bigram2:            \n",
    "                    all_congress_bigrams.append(bigram2)\n",
    "\n",
    "all_congress_bigram_counts = pd.value_counts(all_congress_bigrams)\n",
    "all_congress_bigram_counts[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can adjust the dates (1970, 1975) and term (\"kook\") in the code above to search for bigrams containing your term in different periods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a well-labeled barplot\n",
    "f, ax = plt.subplots(figsize=(18,5))\n",
    "plt.bar(list(all_congress_bigram_counts[:50].index), all_congress_bigram_counts[:50])\n",
    "plt.xticks(rotation=90) # this tells Python to 'rotate' the labels on the x axis so that the words fit on the screen\n",
    "plt.savefig('bar.png') # this line tells Python to save a copy in your digital-history folder\n",
    "plt.xlabel('Word') # this line labels the x axis\n",
    "plt.ylabel('Count') # this line labels the y axis\n",
    "plt.suptitle('Top Bigrams where KOOK is used, 1970-80') #This line labels the title\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}