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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Analysis of Pronoun Usage In Presidential News Conferences\n",
      "\n",
      "This notebook analyzes how presidents have used first person vs. second person pronouns during their official news conferences.\n",
      "\n",
      "The analysis relies on this parsing library: https://github.com/BuzzFeedNews/whtranscripts"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import sys\n",
      "import whtranscripts\n",
      "import re"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<strong>press_conference_data</strong> is a directory that includes a full set of press conferences pulled down using whtranscripts.download "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "conferences = whtranscripts.Conference.from_dir(\"../president_speech_notebooks/press_conference_data\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_passages = [x for b in conferences for x in b.passages]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "passages = pd.DataFrame(all_passages, columns=[\"passage\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "passages[\"date\"] = passages[\"passage\"].apply(lambda x: x.transcript.date)\n",
      "passages[\"speaker\"] = passages[\"passage\"].apply(lambda x: x.speaker)\n",
      "passages[\"text\"] = passages[\"passage\"].apply(lambda x: x.text)\n",
      "passages[\"president\"] = passages[\"passage\"].apply(lambda x: x.transcript.president)\n",
      "passages[\"tokens\"] = passages[\"passage\"].apply(lambda x: x.tokens)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Presidents Only DataFrame\n",
      "\n",
      "This eliminates everything but the words spoken by the President. Unfortunately there are a lot of special cases so we have to do some fancy filtering in the <strong>is_president</strong> function."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def is_president(row):\n",
      "    if row[\"speaker\"] and \"The President\" in row[\"speaker\"]\\\n",
      "        and \"Secretary\" not in row[\"speaker\"]:\n",
      "        return True\n",
      "    elif row[\"speaker\"] == \"The. President\" or row[\"speaker\"] == \"Mr. President\":\n",
      "        return True\n",
      "    elif row[\"speaker\"] and row[\"president\"].split()[-1] in row[\"speaker\"]\\\n",
      "        and \"Mrs.\" not in row[\"speaker\"] and \"Governor\" not in row[\"speaker\"]:\n",
      "        return True\n",
      "    else:\n",
      "        return False"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "passages[\"is_president\"] = passages.apply(lambda x: is_president(x), axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "president_passages = passages[passages[\"is_president\"]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## First Person References"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First person singular references"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "president_passages[\"i\"] = president_passages[\"passage\"].apply(lambda x: x.count_token_occurrences(\"i\"))\n",
      "president_passages[\"me\"] = president_passages[\"passage\"].apply(lambda x: x.count_token_occurrences(\"me\"))\n",
      "president_passages[\"my\"] = president_passages[\"passage\"].apply(lambda x: x.count_token_occurrences(\"my\"))\n",
      "president_passages[\"mine\"] = president_passages[\"passage\"].apply(lambda x: x.count_token_occurrences(\"mine\"))\n",
      "president_passages[\"myself\"] = president_passages[\"passage\"].apply(lambda x: x.count_token_occurrences(\"myself\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "president_passages[\"first_person_singular\"] = president_passages.apply(lambda x: x[\"i\"] + x[\"me\"] + x[\"my\"] +\\\n",
      "                                                                       x[\"mine\"] + x[\"myself\"], axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First person plural references"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "president_passages[\"we\"] = president_passages[\"passage\"].apply(lambda x: x.count_token_occurrences(\"we\"))\n",
      "president_passages[\"our\"] = president_passages[\"passage\"].apply(lambda x: x.count_token_occurrences(\"our\"))\n",
      "president_passages[\"ours\"] = president_passages[\"passage\"].apply(lambda x: x.count_token_occurrences(\"ours\"))\n",
      "president_passages[\"ourselves\"] = president_passages[\"passage\"].apply(lambda x: x.count_token_occurrences(\"ourselves\"))\n",
      "president_passages[\"us\"] = president_passages[\"passage\"].apply(lambda x: x.count_token_occurrences(\"us\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "president_passages[\"first_person_plural\"] = president_passages.apply(lambda x: x[\"we\"] + x[\"our\"] + x[\"ours\"] + x[\"ourselves\"] + x[\"us\"], axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "president_passages[\"first_person\"] = president_passages.apply(lambda x: x[\"first_person_singular\"] + \n",
      "                                                                x[\"first_person_singular\"], axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Second Person References"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "president_passages[\"you\"] = president_passages[\"passage\"].apply(lambda x: x.count_token_occurrences(\"you\"))\n",
      "president_passages[\"your\"] = president_passages[\"passage\"].apply(lambda x: x.count_token_occurrences(\"your\"))\n",
      "president_passages[\"yours\"] = president_passages[\"passage\"].apply(lambda x: x.count_token_occurrences(\"yours\"))\n",
      "president_passages[\"yourself\"] = president_passages[\"passage\"].apply(lambda x: x.count_token_occurrences(\"yourself\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "president_passages[\"second_person\"] = president_passages.apply(lambda x: x[\"you\"] + x[\"your\"] + + x[\"yours\"] + x[\"yourself\"], axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Third Person References"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "president_passages[\"they\"] = president_passages[\"passage\"].apply(lambda x: x.count_token_occurrences(\"they\"))\n",
      "president_passages[\"their\"] = president_passages[\"passage\"].apply(lambda x: x.count_token_occurrences(\"their\"))\n",
      "president_passages[\"theirs\"] = president_passages[\"passage\"].apply(lambda x: x.count_token_occurrences(\"theirs\"))\n",
      "president_passages[\"themselves\"] = president_passages[\"passage\"].apply(lambda x: x.count_token_occurrences(\"themselves\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "president_passages[\"third_person\"] = president_passages.apply(lambda x: x[\"they\"] + x[\"their\"] + x[\"theirs\"] + x[\"themselves\"], axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "president_passages[\"word_count\"] = president_passages[\"passage\"].apply(lambda x: x.get_word_count())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "president_analysis = president_passages[[\"word_count\", \"tokens\", \"date\", \"speaker\", \"president\",\n",
      "                                         \"passage\", \"first_person\", \"first_person_singular\",\n",
      "                                         \"first_person_plural\", \"second_person\", \"third_person\"]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "presidents = pd.DataFrame(president_analysis.groupby(\"president\").sum())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Analysis of the Presidential Pronoun Dataframe"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "round(100.0 * presidents[\"first_person_singular\"].sum() / presidents[\"word_count\"].sum())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "3.0"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "presidents[\"pct_first\"] = presidents.apply(lambda x: round(100.0 * x[\"first_person\"] / x[\"word_count\"], 2), axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "presidents[\"pct_first_singular\"] = presidents.apply(lambda x: round(100.0 * x[\"first_person_singular\"] / x[\"word_count\"], 2), axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "presidents[\"pct_first_plural\"] = presidents.apply(lambda x: round(100.0 * x[\"first_person_plural\"] / x[\"word_count\"], 2), axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "presidents[[\"pct_first_singular\", \"pct_first_plural\", \"pct_first\", \"word_count\"]].sort(\"pct_first_singular\", ascending=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>pct_first_singular</th>\n",
        "      <th>pct_first_plural</th>\n",
        "      <th>pct_first</th>\n",
        "      <th>word_count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>president</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Harry S. Truman</th>\n",
        "      <td> 4.85</td>\n",
        "      <td> 0.90</td>\n",
        "      <td> 9.70</td>\n",
        "      <td> 366974</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>George Bush</th>\n",
        "      <td> 4.65</td>\n",
        "      <td> 2.38</td>\n",
        "      <td> 9.30</td>\n",
        "      <td> 400470</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Dwight D. Eisenhower</th>\n",
        "      <td> 4.55</td>\n",
        "      <td> 1.75</td>\n",
        "      <td> 9.10</td>\n",
        "      <td> 564562</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Gerald R. Ford</th>\n",
        "      <td> 4.18</td>\n",
        "      <td> 2.13</td>\n",
        "      <td> 8.36</td>\n",
        "      <td> 126528</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Jimmy Carter</th>\n",
        "      <td> 3.50</td>\n",
        "      <td> 2.38</td>\n",
        "      <td> 6.99</td>\n",
        "      <td> 220024</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>William J. Clinton</th>\n",
        "      <td> 3.40</td>\n",
        "      <td> 3.03</td>\n",
        "      <td> 6.79</td>\n",
        "      <td> 637070</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Lyndon B. Johnson</th>\n",
        "      <td> 3.11</td>\n",
        "      <td> 3.04</td>\n",
        "      <td> 6.22</td>\n",
        "      <td> 404830</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Richard Nixon</th>\n",
        "      <td> 3.09</td>\n",
        "      <td> 2.32</td>\n",
        "      <td> 6.19</td>\n",
        "      <td> 171527</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>George W. Bush</th>\n",
        "      <td> 3.03</td>\n",
        "      <td> 2.87</td>\n",
        "      <td> 6.07</td>\n",
        "      <td> 615121</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ronald Reagan</th>\n",
        "      <td> 3.00</td>\n",
        "      <td> 3.10</td>\n",
        "      <td> 6.00</td>\n",
        "      <td> 173451</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>John F. Kennedy</th>\n",
        "      <td> 2.80</td>\n",
        "      <td> 3.09</td>\n",
        "      <td> 5.60</td>\n",
        "      <td> 245266</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Barack Obama</th>\n",
        "      <td> 2.45</td>\n",
        "      <td> 3.61</td>\n",
        "      <td> 4.90</td>\n",
        "      <td> 473680</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Franklin D. Roosevelt</th>\n",
        "      <td> 2.14</td>\n",
        "      <td> 1.30</td>\n",
        "      <td> 4.29</td>\n",
        "      <td> 314211</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Herbert Hoover</th>\n",
        "      <td> 1.94</td>\n",
        "      <td> 1.13</td>\n",
        "      <td> 3.88</td>\n",
        "      <td> 133108</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "                       pct_first_singular  pct_first_plural  pct_first  \\\n",
        "president                                                                \n",
        "Harry S. Truman                      4.85              0.90       9.70   \n",
        "George Bush                          4.65              2.38       9.30   \n",
        "Dwight D. Eisenhower                 4.55              1.75       9.10   \n",
        "Gerald R. Ford                       4.18              2.13       8.36   \n",
        "Jimmy Carter                         3.50              2.38       6.99   \n",
        "William J. Clinton                   3.40              3.03       6.79   \n",
        "Lyndon B. Johnson                    3.11              3.04       6.22   \n",
        "Richard Nixon                        3.09              2.32       6.19   \n",
        "George W. Bush                       3.03              2.87       6.07   \n",
        "Ronald Reagan                        3.00              3.10       6.00   \n",
        "John F. Kennedy                      2.80              3.09       5.60   \n",
        "Barack Obama                         2.45              3.61       4.90   \n",
        "Franklin D. Roosevelt                2.14              1.30       4.29   \n",
        "Herbert Hoover                       1.94              1.13       3.88   \n",
        "\n",
        "                       word_count  \n",
        "president                          \n",
        "Harry S. Truman            366974  \n",
        "George Bush                400470  \n",
        "Dwight D. Eisenhower       564562  \n",
        "Gerald R. Ford             126528  \n",
        "Jimmy Carter               220024  \n",
        "William J. Clinton         637070  \n",
        "Lyndon B. Johnson          404830  \n",
        "Richard Nixon              171527  \n",
        "George W. Bush             615121  \n",
        "Ronald Reagan              173451  \n",
        "John F. Kennedy            245266  \n",
        "Barack Obama               473680  \n",
        "Franklin D. Roosevelt      314211  \n",
        "Herbert Hoover             133108  "
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "presidents[[\"pct_first_singular\", \"pct_first_plural\", \"pct_first\", \"word_count\"]].sort(\"pct_first_plural\", ascending=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>pct_first_singular</th>\n",
        "      <th>pct_first_plural</th>\n",
        "      <th>pct_first</th>\n",
        "      <th>word_count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>president</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Barack Obama</th>\n",
        "      <td> 2.45</td>\n",
        "      <td> 3.61</td>\n",
        "      <td> 4.90</td>\n",
        "      <td> 473680</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ronald Reagan</th>\n",
        "      <td> 3.00</td>\n",
        "      <td> 3.10</td>\n",
        "      <td> 6.00</td>\n",
        "      <td> 173451</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>John F. Kennedy</th>\n",
        "      <td> 2.80</td>\n",
        "      <td> 3.09</td>\n",
        "      <td> 5.60</td>\n",
        "      <td> 245266</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Lyndon B. Johnson</th>\n",
        "      <td> 3.11</td>\n",
        "      <td> 3.04</td>\n",
        "      <td> 6.22</td>\n",
        "      <td> 404830</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>William J. Clinton</th>\n",
        "      <td> 3.40</td>\n",
        "      <td> 3.03</td>\n",
        "      <td> 6.79</td>\n",
        "      <td> 637070</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>George W. Bush</th>\n",
        "      <td> 3.03</td>\n",
        "      <td> 2.87</td>\n",
        "      <td> 6.07</td>\n",
        "      <td> 615121</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>George Bush</th>\n",
        "      <td> 4.65</td>\n",
        "      <td> 2.38</td>\n",
        "      <td> 9.30</td>\n",
        "      <td> 400470</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Jimmy Carter</th>\n",
        "      <td> 3.50</td>\n",
        "      <td> 2.38</td>\n",
        "      <td> 6.99</td>\n",
        "      <td> 220024</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Richard Nixon</th>\n",
        "      <td> 3.09</td>\n",
        "      <td> 2.32</td>\n",
        "      <td> 6.19</td>\n",
        "      <td> 171527</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Gerald R. Ford</th>\n",
        "      <td> 4.18</td>\n",
        "      <td> 2.13</td>\n",
        "      <td> 8.36</td>\n",
        "      <td> 126528</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Dwight D. Eisenhower</th>\n",
        "      <td> 4.55</td>\n",
        "      <td> 1.75</td>\n",
        "      <td> 9.10</td>\n",
        "      <td> 564562</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Franklin D. Roosevelt</th>\n",
        "      <td> 2.14</td>\n",
        "      <td> 1.30</td>\n",
        "      <td> 4.29</td>\n",
        "      <td> 314211</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Herbert Hoover</th>\n",
        "      <td> 1.94</td>\n",
        "      <td> 1.13</td>\n",
        "      <td> 3.88</td>\n",
        "      <td> 133108</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Harry S. Truman</th>\n",
        "      <td> 4.85</td>\n",
        "      <td> 0.90</td>\n",
        "      <td> 9.70</td>\n",
        "      <td> 366974</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "                       pct_first_singular  pct_first_plural  pct_first  \\\n",
        "president                                                                \n",
        "Barack Obama                         2.45              3.61       4.90   \n",
        "Ronald Reagan                        3.00              3.10       6.00   \n",
        "John F. Kennedy                      2.80              3.09       5.60   \n",
        "Lyndon B. Johnson                    3.11              3.04       6.22   \n",
        "William J. Clinton                   3.40              3.03       6.79   \n",
        "George W. Bush                       3.03              2.87       6.07   \n",
        "George Bush                          4.65              2.38       9.30   \n",
        "Jimmy Carter                         3.50              2.38       6.99   \n",
        "Richard Nixon                        3.09              2.32       6.19   \n",
        "Gerald R. Ford                       4.18              2.13       8.36   \n",
        "Dwight D. Eisenhower                 4.55              1.75       9.10   \n",
        "Franklin D. Roosevelt                2.14              1.30       4.29   \n",
        "Herbert Hoover                       1.94              1.13       3.88   \n",
        "Harry S. Truman                      4.85              0.90       9.70   \n",
        "\n",
        "                       word_count  \n",
        "president                          \n",
        "Barack Obama               473680  \n",
        "Ronald Reagan              173451  \n",
        "John F. Kennedy            245266  \n",
        "Lyndon B. Johnson          404830  \n",
        "William J. Clinton         637070  \n",
        "George W. Bush             615121  \n",
        "George Bush                400470  \n",
        "Jimmy Carter               220024  \n",
        "Richard Nixon              171527  \n",
        "Gerald R. Ford             126528  \n",
        "Dwight D. Eisenhower       564562  \n",
        "Franklin D. Roosevelt      314211  \n",
        "Herbert Hoover             133108  \n",
        "Harry S. Truman            366974  "
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "presidents[[\"pct_first_singular\", \"pct_first_plural\", \"pct_first\", \"word_count\"]].sort(\"pct_first\", ascending=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>pct_first_singular</th>\n",
        "      <th>pct_first_plural</th>\n",
        "      <th>pct_first</th>\n",
        "      <th>word_count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>president</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Harry S. Truman</th>\n",
        "      <td> 4.85</td>\n",
        "      <td> 0.90</td>\n",
        "      <td> 9.70</td>\n",
        "      <td> 366974</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>George Bush</th>\n",
        "      <td> 4.65</td>\n",
        "      <td> 2.38</td>\n",
        "      <td> 9.30</td>\n",
        "      <td> 400470</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Dwight D. Eisenhower</th>\n",
        "      <td> 4.55</td>\n",
        "      <td> 1.75</td>\n",
        "      <td> 9.10</td>\n",
        "      <td> 564562</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Gerald R. Ford</th>\n",
        "      <td> 4.18</td>\n",
        "      <td> 2.13</td>\n",
        "      <td> 8.36</td>\n",
        "      <td> 126528</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Jimmy Carter</th>\n",
        "      <td> 3.50</td>\n",
        "      <td> 2.38</td>\n",
        "      <td> 6.99</td>\n",
        "      <td> 220024</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>William J. Clinton</th>\n",
        "      <td> 3.40</td>\n",
        "      <td> 3.03</td>\n",
        "      <td> 6.79</td>\n",
        "      <td> 637070</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Lyndon B. Johnson</th>\n",
        "      <td> 3.11</td>\n",
        "      <td> 3.04</td>\n",
        "      <td> 6.22</td>\n",
        "      <td> 404830</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Richard Nixon</th>\n",
        "      <td> 3.09</td>\n",
        "      <td> 2.32</td>\n",
        "      <td> 6.19</td>\n",
        "      <td> 171527</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>George W. Bush</th>\n",
        "      <td> 3.03</td>\n",
        "      <td> 2.87</td>\n",
        "      <td> 6.07</td>\n",
        "      <td> 615121</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ronald Reagan</th>\n",
        "      <td> 3.00</td>\n",
        "      <td> 3.10</td>\n",
        "      <td> 6.00</td>\n",
        "      <td> 173451</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>John F. Kennedy</th>\n",
        "      <td> 2.80</td>\n",
        "      <td> 3.09</td>\n",
        "      <td> 5.60</td>\n",
        "      <td> 245266</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Barack Obama</th>\n",
        "      <td> 2.45</td>\n",
        "      <td> 3.61</td>\n",
        "      <td> 4.90</td>\n",
        "      <td> 473680</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Franklin D. Roosevelt</th>\n",
        "      <td> 2.14</td>\n",
        "      <td> 1.30</td>\n",
        "      <td> 4.29</td>\n",
        "      <td> 314211</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Herbert Hoover</th>\n",
        "      <td> 1.94</td>\n",
        "      <td> 1.13</td>\n",
        "      <td> 3.88</td>\n",
        "      <td> 133108</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "                       pct_first_singular  pct_first_plural  pct_first  \\\n",
        "president                                                                \n",
        "Harry S. Truman                      4.85              0.90       9.70   \n",
        "George Bush                          4.65              2.38       9.30   \n",
        "Dwight D. Eisenhower                 4.55              1.75       9.10   \n",
        "Gerald R. Ford                       4.18              2.13       8.36   \n",
        "Jimmy Carter                         3.50              2.38       6.99   \n",
        "William J. Clinton                   3.40              3.03       6.79   \n",
        "Lyndon B. Johnson                    3.11              3.04       6.22   \n",
        "Richard Nixon                        3.09              2.32       6.19   \n",
        "George W. Bush                       3.03              2.87       6.07   \n",
        "Ronald Reagan                        3.00              3.10       6.00   \n",
        "John F. Kennedy                      2.80              3.09       5.60   \n",
        "Barack Obama                         2.45              3.61       4.90   \n",
        "Franklin D. Roosevelt                2.14              1.30       4.29   \n",
        "Herbert Hoover                       1.94              1.13       3.88   \n",
        "\n",
        "                       word_count  \n",
        "president                          \n",
        "Harry S. Truman            366974  \n",
        "George Bush                400470  \n",
        "Dwight D. Eisenhower       564562  \n",
        "Gerald R. Ford             126528  \n",
        "Jimmy Carter               220024  \n",
        "William J. Clinton         637070  \n",
        "Lyndon B. Johnson          404830  \n",
        "Richard Nixon              171527  \n",
        "George W. Bush             615121  \n",
        "Ronald Reagan              173451  \n",
        "John F. Kennedy            245266  \n",
        "Barack Obama               473680  \n",
        "Franklin D. Roosevelt      314211  \n",
        "Herbert Hoover             133108  "
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Singular vs. Plural Pronouns Over Time"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import mplstyle, mplstyle.styles.simple\n",
      "mplstyle.set(mplstyle.styles.simple)\n",
      "mplstyle.set({ \n",
      "    \"figure.figsize\": (10, 6),\n",
      "    \"axes\": {\n",
      "        \"color_cycle\": [ \"teal\", \"red\" ],\n",
      "    },\n",
      "    \"lines\": {\n",
      "        \"linewidth\": 2\n",
      "    }\n",
      "})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import datetime\n",
      "president_analysis[\"datetime\"] = president_analysis[\"date\"].apply(lambda x: datetime.datetime(x.year, x.month, x.day))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_term_freq(df, term, resampler=\"AS\"):\n",
      "    _ = df.set_index(\"datetime\")\n",
      "    total_words = _[\"word_count\"].resample(resampler, how=\"sum\")\n",
      "    freq_count = _[term].resample(resampler, how=\"sum\")\n",
      "    return (100.0 * freq_count / total_words)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = get_term_freq(president_analysis, \"first_person_singular\").plot(kind=\"line\", label=\"singular\", color=\"r\")\n",
      "get_term_freq(president_analysis, \"first_person_plural\").plot(kind=\"line\", label=\"plural\", color=\"b\")\n",
      "ax.legend(bbox_to_anchor=(0.2, 1))\n",
      "pass"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NVN1tu2R3WceO3m3gzkny1tUmmTePnKD0dODpp2ki3KQk4LffgPnzScgaBpPi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\nLTcXGDxYHOT79wPNm4umzspIBAtGJ+/7LfZWb2RlSXny2FjbgmOV4Ub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CiAN8EttbtUpy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\nEEKIHWgkEUIIIYTYgUYSIYQQQogd/h95gH0UO0WoJwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x135c3c950>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "terms_df = pd.DataFrame(get_term_freq(president_analysis, \"first_person_singular\"))\n",
      "terms_df.columns = [\"singular\"]\n",
      "terms_df[\"plural\"] = get_term_freq(president_analysis, \"first_person_plural\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "terms_df[\"date\"] = terms_df.index\n",
      "terms_df[\"year\"] = terms_df[\"date\"].apply(lambda x: int(x.year))\n",
      "terms_df.set_index(\"year\")\n",
      "terms_df[[\"singular\", \"plural\"]].to_csv(\"singularVsPlural2.csv\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    }
   ],
   "metadata": {}
  }
 ]
}