Female_Emigration/BLN_Parts1-6/Visualization_Frequency_Taxonomy_BLN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyses of the Frequency_Taxonomy for female emigration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import yaml\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import collections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def freq_count(results):\n",
    "    freq_count={}\n",
    "    for year in results:\n",
    "        for i in results[year]:\n",
    "            if year not in freq_count:\n",
    "                freq_count[year]= i['count']\n",
    "            else:    \n",
    "                freq_count[year]+=i['count']\n",
    "    return freq_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_freq(publication, freq_count):\n",
    "    normed_results = {}\n",
    "    for year in freq_count:\n",
    "        if year>0:\n",
    "            normed_results[year] = freq_count[year]/float(publication[year][2])\n",
    "    return normed_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_query_results(filename):\n",
    "    with open('./results_BLN/'+filename, 'r') as f:\n",
    "        query_results = yaml.load(f)\n",
    "    return query_results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_freq_count(freq_count):\n",
    "    plt.plot(freq_count.keys(), freq_count.values())\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Frequencies (without normalised them! ) results using different preprocessing treatments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Preprocessing: Normalized + Lemmatized\n",
    "Plot the frequency of the query results, in which each word has been normalized (lower-case and removing all\n",
    "characters that are not 'a',...,'z') and lemmatized (reducing words to their correct base form)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": [
    "query_results_lem=read_query_results('results_bln_taxonomy_1850_1914')\n",
    "freq_count_lem=freq_count(query_results_lem)\n",
    "plot_freq_count(freq_count_lem)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.b) Example of a normalized + lemmatized query result for 1800"
   ]
  },
  {
   "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>count</th>\n",
       "      <th>target_word</th>\n",
       "      <th>words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[empire, marriage, mother, respectable, servan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[empire, mother, respectable, servant, service...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colonial, colony, mother]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[respectable, success]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colonial, colony, empire, indecent]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colonial, daughter, engagement, marriage, mot...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colony, daughter, guardian, loan, service, su...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[guardian, respectable, servant, success, suit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colony, daughter, engagement, marriage, service]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[alien, colony, genteel, governess, guardian, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colonial, colony, engagement, genteel, govern...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[servant, service, success]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colonial, daughter, failure, genteel, governe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colony, engagement, genteel, loan, respectabl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colonial, colony, daughter, empire, engagemen...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[empire, engagement, genteel, governess, guard...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[bookkeeping, empire, genteel, governess, maid...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[empire, governess, mother, respectable, serva...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[failure, marriage]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colonial, service]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[alien, daughter, genteel, guardian, loan, mai...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[daughter, empire, guardian, mother, respectab...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[engagement, loan, respectable, service]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colony, empire, governess, guardian, happines...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colony, engagement, servant, success]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colony, governess, guardian, happiness, marri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[bookkeeping, colonial, daughter, engagement, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colony, respectable, success, suitable]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colony, daughter, hardship, mother, respectab...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[alien, colonial, colony, genteel, loan, marri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[daughter, guardian, marriage, mother, respect...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[guardian, hardship, loan, maid, respectable, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colonial, colony, daughter, failure, governes...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[guardian, mother]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colony, empire, engagement, genteel, respecta...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[empire, failure, genteel, governess, guardian...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>4</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[marriage]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colony, engagement, guardian, marriage, respe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[daughter, servant, service]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colony, loan, marriage, mother, service, succ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[mother, service]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[daughter, loan, marriage, matron, mother, res...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[daughter, empire, engagement, governess, guar...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colonial, colony, daughter, empire, failure, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[empire, respectable, servant, service, superi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colony, engagement, failure, governess, guard...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colony, maid, servant, success]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colonial, colony, loan, success]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colony, empire, servant]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colony, daughter, engagement, matron, mother,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>2</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[servant]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[daughter, mother, respectable, servant, service]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[service, success, suitable]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[guardian, servant, service]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colony, engagement, genteel, guardian, happin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[daughter, loan]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colonial, daughter, engagement, genteel, gove...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colonial, colony, guardian, service]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[governess, guardian, loan, marriage, respecta...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>1</td>\n",
       "      <td>emigration</td>\n",
       "      <td>[colony, failure, marriage, service, teacher, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>451 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     count target_word                                              words\n",
       "0        1  emigration  [empire, marriage, mother, respectable, servan...\n",
       "1        1  emigration  [empire, mother, respectable, servant, service...\n",
       "2        1  emigration                         [colonial, colony, mother]\n",
       "3        2  emigration                             [respectable, success]\n",
       "4        1  emigration               [colonial, colony, empire, indecent]\n",
       "5        1  emigration  [colonial, daughter, engagement, marriage, mot...\n",
       "6        1  emigration  [colony, daughter, guardian, loan, service, su...\n",
       "7        1  emigration  [guardian, respectable, servant, success, suit...\n",
       "8        1  emigration  [colony, daughter, engagement, marriage, service]\n",
       "9        1  emigration  [alien, colony, genteel, governess, guardian, ...\n",
       "10       1  emigration  [colonial, colony, engagement, genteel, govern...\n",
       "11       1  emigration                        [servant, service, success]\n",
       "12       1  emigration  [colonial, daughter, failure, genteel, governe...\n",
       "13       1  emigration  [colony, engagement, genteel, loan, respectabl...\n",
       "14       1  emigration  [colonial, colony, daughter, empire, engagemen...\n",
       "15       1  emigration  [empire, engagement, genteel, governess, guard...\n",
       "16       1  emigration  [bookkeeping, empire, genteel, governess, maid...\n",
       "17       1  emigration  [empire, governess, mother, respectable, serva...\n",
       "18       1  emigration                                [failure, marriage]\n",
       "19       2  emigration                                [colonial, service]\n",
       "20       1  emigration  [alien, daughter, genteel, guardian, loan, mai...\n",
       "21       1  emigration  [daughter, empire, guardian, mother, respectab...\n",
       "22       1  emigration           [engagement, loan, respectable, service]\n",
       "23       1  emigration  [colony, empire, governess, guardian, happines...\n",
       "24       1  emigration             [colony, engagement, servant, success]\n",
       "25       1  emigration  [colony, governess, guardian, happiness, marri...\n",
       "26       1  emigration  [bookkeeping, colonial, daughter, engagement, ...\n",
       "27       1  emigration           [colony, respectable, success, suitable]\n",
       "28       1  emigration  [colony, daughter, hardship, mother, respectab...\n",
       "29       1  emigration  [alien, colonial, colony, genteel, loan, marri...\n",
       "..     ...         ...                                                ...\n",
       "421      1  emigration  [daughter, guardian, marriage, mother, respect...\n",
       "422      1  emigration  [guardian, hardship, loan, maid, respectable, ...\n",
       "423      1  emigration  [colonial, colony, daughter, failure, governes...\n",
       "424      1  emigration                                 [guardian, mother]\n",
       "425      1  emigration  [colony, empire, engagement, genteel, respecta...\n",
       "426      1  emigration  [empire, failure, genteel, governess, guardian...\n",
       "427      4  emigration                                         [marriage]\n",
       "428      1  emigration  [colony, engagement, guardian, marriage, respe...\n",
       "429      1  emigration                       [daughter, servant, service]\n",
       "430      1  emigration  [colony, loan, marriage, mother, service, succ...\n",
       "431      1  emigration                                  [mother, service]\n",
       "432      1  emigration  [daughter, loan, marriage, matron, mother, res...\n",
       "433      1  emigration  [daughter, empire, engagement, governess, guar...\n",
       "434      1  emigration  [colonial, colony, daughter, empire, failure, ...\n",
       "435      1  emigration  [empire, respectable, servant, service, superi...\n",
       "436      1  emigration  [colony, engagement, failure, governess, guard...\n",
       "437      1  emigration                   [colony, maid, servant, success]\n",
       "438      1  emigration                  [colonial, colony, loan, success]\n",
       "439      1  emigration                          [colony, empire, servant]\n",
       "440      1  emigration  [colony, daughter, engagement, matron, mother,...\n",
       "441      2  emigration                                          [servant]\n",
       "442      1  emigration  [daughter, mother, respectable, servant, service]\n",
       "443      1  emigration                       [service, success, suitable]\n",
       "444      1  emigration                       [guardian, servant, service]\n",
       "445      1  emigration  [colony, engagement, genteel, guardian, happin...\n",
       "446      1  emigration                                   [daughter, loan]\n",
       "447      1  emigration  [colonial, daughter, engagement, genteel, gove...\n",
       "448      1  emigration              [colonial, colony, guardian, service]\n",
       "449      1  emigration  [governess, guardian, loan, marriage, respecta...\n",
       "450      1  emigration  [colony, failure, marriage, service, teacher, ...\n",
       "\n",
       "[451 rows x 3 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example of the normalized + lemmatized query results for 1800\n",
    "tdf_lem = pd.DataFrame.from_dict(query_results_lem[1850])\n",
    "tdf_lem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Normalised frequencies results using different preprocessing treatments\n",
    "\n",
    "Here, we are examining the total number of isses, articles and words published over the period to see how much our search terms are affected by the way that the number of issues (and articles, and words!) published increases over the measurement period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_publication=read_query_results('results_bln_normalized_1850_1914')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We count the total number of words per year, and then count total instances of that search phrase per year, and plot normalised frequency on a graph (x axis is years), to see whether it is a thing that is becoming more or less popular with time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot of normalized frequencies in which each word has been preprocessed using: normalization + lemmatization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": [
    "normed_results_lem=normalize_freq(norm_publication, freq_count_lem)\n",
    "plot_freq_count(normed_results_lem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}