Processed_Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "22a2291a",
   "metadata": {},
   "source": [
    "# Data Processing\n",
    "This is the script that processes my data files.\n",
    "\n",
    "### Table of Contents\n",
    "- [Part 1](#1-Importing-libraries-and-packages)\n",
    "- [Part 2](#2.-Reading-in-and-Parsing-the-Data)\n",
    "- [Part 3](#3.-String-Manipulation)\n",
    "- [Part 4](#4.-Some-Descriptive-Analysis)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b8a4ef4",
   "metadata": {},
   "source": [
    "## 1. Importing libraries and packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b46cd28e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pretty printing has been turned OFF\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "import spacy\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import figure\n",
    "import seaborn as sns\n",
    "import dataframe_image as dfi\n",
    "%pprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "387dc00c",
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load('ru_core_news_sm') #loads the russian nlp model from spacy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4148e355",
   "metadata": {},
   "source": [
    "## 2. Reading in and Parsing the Data\n",
    "- Processes the raw plain text data into parsed data frame object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f7374edd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Авария 13 – traffic accident\\nавтобус 12 – bus\\nавтомобиль 12 – car\\nавтомойка 13 – car wash\\nаксессуары 13, 15 – accessories\\nалкогольный напиток 16 – alcoholic drink\\nаллергия 16 – allergy\\nальпинизм 10 – mountain climbing\\nангина 16 – strep throat\\nанкета 14 – application form\\nапельсин 5 – orange\\nарбуз 5 – watermelon\\nаренда 11 – rent n.\\nаспирантура 1 – graduate school\\nафиша 10 – poster\\nБанан 5 – banana\\nбанкомат 13 – ATM\\nбарабаны 10 – drums\\nбаскетбол 10 – basketball\\nбег 10 – running\\nбежать ~ бегать 12 – to run\\nбез (+ gen.) 3 – without\\nбезветренный 15 – windless, calm\\nбелок (pl. белки) 5 – protein\\nбензин 13 – gasoline\\nберег (prep.: на берегу) 1 – сoast, shore\\nбесплатный 14 – free\\nбеспокоить (impf. кого?) 16 – to bother, disturb\\nбеспокоиться (impf. о ком? о чём? за кого? за что? colloq.) 16 – to worry, be worried\\nбижутерия 13 – jewelry\\nблин (pl. -ы) 6 – pancake\\nблокнот 7 – notebook\\nблондин 2 – blond (man) \\nблондинка 2 – blonde (woman)\\nблузка 15 – blouse\\nблюдо 5 – dish\\nбоевик 17 – action movie\\nбокал 6 – wine glass\\nбокс 10 – boxing\\nболе́льщик 10 – fan\\nболе́ть (impf. за кого́ ? за что?) 10 – to be a fan (of a sports team)\\nболе́ть/заболе́ть (чем?) 16 – to get sick\\nбо́льно 16 – here: it is painful\\nбо́льше всего́ 12 – most of all\\nбо́льше не 16 – no longer, not anymore\\nбольшинство́ (кого́ ? чего́ ?) 10 – majority\\nборода́ 2 – beard\\nборо́ться (impf. c кем? с чем?) 16 – to fight\\nботи́нки 15 – boots\\nбоя́ться (impf. + inf.) 14 – to be afraid\\nброни́ровать/заброни́ровать (что?) 14 – to book, reserve\\nбронхи́т 16 – bronchitis\\nброса́ть/бро́сить курс (по чему́? по исто́рии) 7 – to drop a course (a history course)\\nбрю́ки (pl. only, gen.: брюк) 15 – trousers, pants\\nбу́дущий (-ая, -ее, -ие) 8 – future adj.\\nбу́лочка 5 – roll, bun\\nбуты́лка 6 – bottle\\nбы́вший муж 4 – ex-husband\\nбытова́я те́хника 11 – appliances\\nбыть (impf. в чём?) 15 – to be wearing (literally: to be “in”) something\\nВо́бщем 12 – in general\\nв-тре́тьих 7, 12 – thirdly\\nва́жно 8 – important adv.\\nвари́ть/свари́ть (что? где? в кастрю́ле)'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I read in my txt version of the textbook dictionary using utf-8 encoding and I'm able to read it in as a list of\n",
    "# lowercased russian words their page number and definition.\n",
    "nti = open('./textbook_vocab_data/novice_to_intermediate.txt', encoding= 'utf8')\n",
    "nti = nti.read() \n",
    "nti[:2000] # very messy ugh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "be18ad72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['авария 13 – traffic accident', 'автобус 12 – bus', 'автомобиль 12 – car', 'автомойка 13 – car wash', 'аксессуары 13, 15 – accessories', 'алкогольный напиток 16 – alcoholic drink', 'аллергия 16 – allergy', 'альпинизм 10 – mountain climbing', 'ангина 16 – strep throat', 'анкета 14 – application form', 'апельсин 5 – orange', 'арбуз 5 – watermelon', 'аренда 11 – rent n.', 'аспирантура 1 – graduate school', 'афиша 10 – poster', 'банан 5 – banana', 'банкомат 13 – atm', 'барабаны 10 – drums', 'баскетбол 10 – basketball', 'бег 10 – running', 'бежать ~ бегать 12 – to run', 'без (+ gen.) 3 – without', 'безветренный 15 – windless, calm', 'белок (pl. белки) 5 – protein', 'бензин 13 – gasoline', 'берег (prep.: на берегу) 1 – сoast, shore', 'бесплатный 14 – free', 'беспокоить (impf. кого?) 16 – to bother, disturb', 'беспокоиться (impf. о ком? о чём? за кого? за что? colloq.) 16 – to worry, be worried', 'бижутерия 13 – jewelry']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nti_txt = nti.split('\\n') # I turn the file into a list, splitting on linebreaks to divide most of the vocab items\n",
    "nti_txt_low = [x.lower() for x in nti_txt] # Stress marking was encoded with capital letters, so I lower everything\n",
    "ntiplay = nti_txt_low[:30] # let't take a look at some of the entries and make it a play set\n",
    "ntiplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ca8dccda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['авария – traffic accident', 'автобус – bus', 'автомобиль – car', 'автомойка – car wash', 'аксессуары  – accessories', 'алкогольный напиток – alcoholic drink', 'аллергия – allergy', 'альпинизм – mountain climbing', 'ангина – strep throat', 'анкета – application form', 'апельсин – orange', 'арбуз – watermelon', 'аренда – rent n.', 'аспирантура – graduate school', 'афиша – poster', 'банан – banana', 'банкомат – atm', 'барабаны – drums', 'баскетбол – basketball', 'бег – running', 'бежать ~ бегать – to run', 'без (+ gen.) – without', 'безветренный – windless, calm', 'белок (pl. белки) – protein', 'бензин – gasoline', 'берег (prep.: на берегу) – сoast, shore', 'бесплатный – free', 'беспокоить (impf. кого?) – to bother, disturb', 'беспокоиться (impf. о ком? о чём? за кого? за что? colloq.) – to worry, be worried', 'бижутерия – jewelry']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First, We need to get rid of any page numbers and extraneous white spaces\n",
    "ntiplay = [re.sub('(\\s\\d+,|\\s\\d+\\s|\\|)', ' ', x) for x in ntiplay]\n",
    "ntiplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "32d5f7da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Entry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>авария – traffic accident</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>автобус – bus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>автомобиль – car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>автомойка – car wash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>аксессуары  – accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>алкогольный напиток – alcoholic drink</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>аллергия – allergy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>альпинизм – mountain climbing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ангина – strep throat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>анкета – application form</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>апельсин – orange</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>арбуз – watermelon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>аренда – rent n.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>аспирантура – graduate school</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>афиша – poster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>банан – banana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>банкомат – atm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>барабаны – drums</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>баскетбол – basketball</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>бег – running</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>бежать ~ бегать – to run</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>без (+ gen.) – without</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>безветренный – windless, calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>белок (pl. белки) – protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>бензин – gasoline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>берег (prep.: на берегу) – сoast, shore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>бесплатный – free</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>беспокоить (impf. кого?) – to bother, disturb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>беспокоиться (impf. о ком? о чём? за кого? за ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>бижутерия – jewelry</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Entry\n",
       "0                           авария – traffic accident\n",
       "1                                       автобус – bus\n",
       "2                                    автомобиль – car\n",
       "3                                автомойка – car wash\n",
       "4                           аксессуары  – accessories\n",
       "5               алкогольный напиток – alcoholic drink\n",
       "6                                  аллергия – allergy\n",
       "7                       альпинизм – mountain climbing\n",
       "8                               ангина – strep throat\n",
       "9                           анкета – application form\n",
       "10                                  апельсин – orange\n",
       "11                                 арбуз – watermelon\n",
       "12                                   аренда – rent n.\n",
       "13                      аспирантура – graduate school\n",
       "14                                     афиша – poster\n",
       "15                                     банан – banana\n",
       "16                                     банкомат – atm\n",
       "17                                   барабаны – drums\n",
       "18                             баскетбол – basketball\n",
       "19                                      бег – running\n",
       "20                           бежать ~ бегать – to run\n",
       "21                             без (+ gen.) – without\n",
       "22                      безветренный – windless, calm\n",
       "23                        белок (pl. белки) – protein\n",
       "24                                  бензин – gasoline\n",
       "25            берег (prep.: на берегу) – сoast, shore\n",
       "26                                  бесплатный – free\n",
       "27      беспокоить (impf. кого?) – to bother, disturb\n",
       "28  беспокоиться (impf. о ком? о чём? за кого? за ...\n",
       "29                                бижутерия – jewelry"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's make this into a dataframe and see what we can do\n",
    "df_ntiplay=pd.DataFrame(ntiplay,columns=['Entry'])\n",
    "df_ntiplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "97d93eb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Entry</th>\n",
       "      <th>Russian</th>\n",
       "      <th>English</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>авария – traffic accident</td>\n",
       "      <td>авария</td>\n",
       "      <td>traffic accident</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>автобус – bus</td>\n",
       "      <td>автобус</td>\n",
       "      <td>bus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>автомобиль – car</td>\n",
       "      <td>автомобиль</td>\n",
       "      <td>car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>автомойка – car wash</td>\n",
       "      <td>автомойка</td>\n",
       "      <td>car wash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>аксессуары  – accessories</td>\n",
       "      <td>аксессуары</td>\n",
       "      <td>accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>алкогольный напиток – alcoholic drink</td>\n",
       "      <td>алкогольный напиток</td>\n",
       "      <td>alcoholic drink</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>аллергия – allergy</td>\n",
       "      <td>аллергия</td>\n",
       "      <td>allergy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>альпинизм – mountain climbing</td>\n",
       "      <td>альпинизм</td>\n",
       "      <td>mountain climbing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ангина – strep throat</td>\n",
       "      <td>ангина</td>\n",
       "      <td>strep throat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>анкета – application form</td>\n",
       "      <td>анкета</td>\n",
       "      <td>application form</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>апельсин – orange</td>\n",
       "      <td>апельсин</td>\n",
       "      <td>orange</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>арбуз – watermelon</td>\n",
       "      <td>арбуз</td>\n",
       "      <td>watermelon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>аренда – rent n.</td>\n",
       "      <td>аренда</td>\n",
       "      <td>rent n.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>аспирантура – graduate school</td>\n",
       "      <td>аспирантура</td>\n",
       "      <td>graduate school</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>афиша – poster</td>\n",
       "      <td>афиша</td>\n",
       "      <td>poster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>банан – banana</td>\n",
       "      <td>банан</td>\n",
       "      <td>banana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>банкомат – atm</td>\n",
       "      <td>банкомат</td>\n",
       "      <td>atm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>барабаны – drums</td>\n",
       "      <td>барабаны</td>\n",
       "      <td>drums</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>баскетбол – basketball</td>\n",
       "      <td>баскетбол</td>\n",
       "      <td>basketball</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>бег – running</td>\n",
       "      <td>бег</td>\n",
       "      <td>running</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>бежать ~ бегать – to run</td>\n",
       "      <td>бежать ~ бегать</td>\n",
       "      <td>to run</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>без (+ gen.) – without</td>\n",
       "      <td>без (+ gen.)</td>\n",
       "      <td>without</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>безветренный – windless, calm</td>\n",
       "      <td>безветренный</td>\n",
       "      <td>windless, calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>белок (pl. белки) – protein</td>\n",
       "      <td>белок (pl. белки)</td>\n",
       "      <td>protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>бензин – gasoline</td>\n",
       "      <td>бензин</td>\n",
       "      <td>gasoline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>берег (prep.: на берегу) – сoast, shore</td>\n",
       "      <td>берег (prep.: на берегу)</td>\n",
       "      <td>сoast, shore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>бесплатный – free</td>\n",
       "      <td>бесплатный</td>\n",
       "      <td>free</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>беспокоить (impf. кого?) – to bother, disturb</td>\n",
       "      <td>беспокоить (impf. кого?)</td>\n",
       "      <td>to bother, disturb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>беспокоиться (impf. о ком? о чём? за кого? за ...</td>\n",
       "      <td>беспокоиться (impf. о ком? о чём? за кого? за ...</td>\n",
       "      <td>to worry, be worried</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>бижутерия – jewelry</td>\n",
       "      <td>бижутерия</td>\n",
       "      <td>jewelry</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Entry  \\\n",
       "0                           авария – traffic accident   \n",
       "1                                       автобус – bus   \n",
       "2                                    автомобиль – car   \n",
       "3                                автомойка – car wash   \n",
       "4                           аксессуары  – accessories   \n",
       "5               алкогольный напиток – alcoholic drink   \n",
       "6                                  аллергия – allergy   \n",
       "7                       альпинизм – mountain climbing   \n",
       "8                               ангина – strep throat   \n",
       "9                           анкета – application form   \n",
       "10                                  апельсин – orange   \n",
       "11                                 арбуз – watermelon   \n",
       "12                                   аренда – rent n.   \n",
       "13                      аспирантура – graduate school   \n",
       "14                                     афиша – poster   \n",
       "15                                     банан – banana   \n",
       "16                                     банкомат – atm   \n",
       "17                                   барабаны – drums   \n",
       "18                             баскетбол – basketball   \n",
       "19                                      бег – running   \n",
       "20                           бежать ~ бегать – to run   \n",
       "21                             без (+ gen.) – without   \n",
       "22                      безветренный – windless, calm   \n",
       "23                        белок (pl. белки) – protein   \n",
       "24                                  бензин – gasoline   \n",
       "25            берег (prep.: на берегу) – сoast, shore   \n",
       "26                                  бесплатный – free   \n",
       "27      беспокоить (impf. кого?) – to bother, disturb   \n",
       "28  беспокоиться (impf. о ком? о чём? за кого? за ...   \n",
       "29                                бижутерия – jewelry   \n",
       "\n",
       "                                              Russian                English  \n",
       "0                                             авария        traffic accident  \n",
       "1                                            автобус                     bus  \n",
       "2                                         автомобиль                     car  \n",
       "3                                          автомойка                car wash  \n",
       "4                                        аксессуары              accessories  \n",
       "5                                алкогольный напиток         alcoholic drink  \n",
       "6                                           аллергия                 allergy  \n",
       "7                                          альпинизм       mountain climbing  \n",
       "8                                             ангина            strep throat  \n",
       "9                                             анкета        application form  \n",
       "10                                          апельсин                  orange  \n",
       "11                                             арбуз              watermelon  \n",
       "12                                            аренда                 rent n.  \n",
       "13                                       аспирантура         graduate school  \n",
       "14                                             афиша                  poster  \n",
       "15                                             банан                  banana  \n",
       "16                                          банкомат                     atm  \n",
       "17                                          барабаны                   drums  \n",
       "18                                         баскетбол              basketball  \n",
       "19                                               бег                 running  \n",
       "20                                   бежать ~ бегать                  to run  \n",
       "21                                      без (+ gen.)                 without  \n",
       "22                                      безветренный          windless, calm  \n",
       "23                                 белок (pl. белки)                 protein  \n",
       "24                                            бензин                gasoline  \n",
       "25                          берег (prep.: на берегу)            сoast, shore  \n",
       "26                                        бесплатный                    free  \n",
       "27                          беспокоить (impf. кого?)      to bother, disturb  \n",
       "28  беспокоиться (impf. о ком? о чём? за кого? за ...   to worry, be worried  \n",
       "29                                         бижутерия                 jewelry  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creates new Russian and English columns that expand the full entry line into\n",
    "# respective parts\n",
    "df_ntiplay = df_ntiplay.join(df_ntiplay['Entry'].str.split('–', 1, expand=True).rename(columns={0:'Russian', 1:'English'}))\n",
    "df_ntiplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3393bcda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Entry</th>\n",
       "      <th>Russian</th>\n",
       "      <th>English</th>\n",
       "      <th>Extra</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>авария – traffic accident</td>\n",
       "      <td>авария</td>\n",
       "      <td>traffic accident</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>автобус – bus</td>\n",
       "      <td>автобус</td>\n",
       "      <td>bus</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>автомобиль – car</td>\n",
       "      <td>автомобиль</td>\n",
       "      <td>car</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>автомойка – car wash</td>\n",
       "      <td>автомойка</td>\n",
       "      <td>car wash</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>аксессуары  – accessories</td>\n",
       "      <td>аксессуары</td>\n",
       "      <td>accessories</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>алкогольный напиток – alcoholic drink</td>\n",
       "      <td>алкогольный напиток</td>\n",
       "      <td>alcoholic drink</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>аллергия – allergy</td>\n",
       "      <td>аллергия</td>\n",
       "      <td>allergy</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>альпинизм – mountain climbing</td>\n",
       "      <td>альпинизм</td>\n",
       "      <td>mountain climbing</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ангина – strep throat</td>\n",
       "      <td>ангина</td>\n",
       "      <td>strep throat</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>анкета – application form</td>\n",
       "      <td>анкета</td>\n",
       "      <td>application form</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>апельсин – orange</td>\n",
       "      <td>апельсин</td>\n",
       "      <td>orange</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>арбуз – watermelon</td>\n",
       "      <td>арбуз</td>\n",
       "      <td>watermelon</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>аренда – rent n.</td>\n",
       "      <td>аренда</td>\n",
       "      <td>rent n.</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>аспирантура – graduate school</td>\n",
       "      <td>аспирантура</td>\n",
       "      <td>graduate school</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>афиша – poster</td>\n",
       "      <td>афиша</td>\n",
       "      <td>poster</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>банан – banana</td>\n",
       "      <td>банан</td>\n",
       "      <td>banana</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>банкомат – atm</td>\n",
       "      <td>банкомат</td>\n",
       "      <td>atm</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>барабаны – drums</td>\n",
       "      <td>барабаны</td>\n",
       "      <td>drums</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>баскетбол – basketball</td>\n",
       "      <td>баскетбол</td>\n",
       "      <td>basketball</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>бег – running</td>\n",
       "      <td>бег</td>\n",
       "      <td>running</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>бежать ~ бегать – to run</td>\n",
       "      <td>бежать ~ бегать</td>\n",
       "      <td>to run</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>без (+ gen.) – without</td>\n",
       "      <td>без (+ gen.)</td>\n",
       "      <td>without</td>\n",
       "      <td>(+ gen.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>безветренный – windless, calm</td>\n",
       "      <td>безветренный</td>\n",
       "      <td>windless, calm</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>белок (pl. белки) – protein</td>\n",
       "      <td>белок (pl. белки)</td>\n",
       "      <td>protein</td>\n",
       "      <td>(pl. белки)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>бензин – gasoline</td>\n",
       "      <td>бензин</td>\n",
       "      <td>gasoline</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>берег (prep.: на берегу) – сoast, shore</td>\n",
       "      <td>берег (prep.: на берегу)</td>\n",
       "      <td>сoast, shore</td>\n",
       "      <td>(prep.: на берегу)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>бесплатный – free</td>\n",
       "      <td>бесплатный</td>\n",
       "      <td>free</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>беспокоить (impf. кого?) – to bother, disturb</td>\n",
       "      <td>беспокоить (impf. кого?)</td>\n",
       "      <td>to bother, disturb</td>\n",
       "      <td>(impf. кого?)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>беспокоиться (impf. о ком? о чём? за кого? за ...</td>\n",
       "      <td>беспокоиться (impf. о ком? о чём? за кого? за ...</td>\n",
       "      <td>to worry, be worried</td>\n",
       "      <td>(impf. о ком? о чём? за кого? за что? colloq.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>бижутерия – jewelry</td>\n",
       "      <td>бижутерия</td>\n",
       "      <td>jewelry</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Entry  \\\n",
       "0                           авария – traffic accident   \n",
       "1                                       автобус – bus   \n",
       "2                                    автомобиль – car   \n",
       "3                                автомойка – car wash   \n",
       "4                           аксессуары  – accessories   \n",
       "5               алкогольный напиток – alcoholic drink   \n",
       "6                                  аллергия – allergy   \n",
       "7                       альпинизм – mountain climbing   \n",
       "8                               ангина – strep throat   \n",
       "9                           анкета – application form   \n",
       "10                                  апельсин – orange   \n",
       "11                                 арбуз – watermelon   \n",
       "12                                   аренда – rent n.   \n",
       "13                      аспирантура – graduate school   \n",
       "14                                     афиша – poster   \n",
       "15                                     банан – banana   \n",
       "16                                     банкомат – atm   \n",
       "17                                   барабаны – drums   \n",
       "18                             баскетбол – basketball   \n",
       "19                                      бег – running   \n",
       "20                           бежать ~ бегать – to run   \n",
       "21                             без (+ gen.) – without   \n",
       "22                      безветренный – windless, calm   \n",
       "23                        белок (pl. белки) – protein   \n",
       "24                                  бензин – gasoline   \n",
       "25            берег (prep.: на берегу) – сoast, shore   \n",
       "26                                  бесплатный – free   \n",
       "27      беспокоить (impf. кого?) – to bother, disturb   \n",
       "28  беспокоиться (impf. о ком? о чём? за кого? за ...   \n",
       "29                                бижутерия – jewelry   \n",
       "\n",
       "                                              Russian                English  \\\n",
       "0                                             авария        traffic accident   \n",
       "1                                            автобус                     bus   \n",
       "2                                         автомобиль                     car   \n",
       "3                                          автомойка                car wash   \n",
       "4                                        аксессуары              accessories   \n",
       "5                                алкогольный напиток         alcoholic drink   \n",
       "6                                           аллергия                 allergy   \n",
       "7                                          альпинизм       mountain climbing   \n",
       "8                                             ангина            strep throat   \n",
       "9                                             анкета        application form   \n",
       "10                                          апельсин                  orange   \n",
       "11                                             арбуз              watermelon   \n",
       "12                                            аренда                 rent n.   \n",
       "13                                       аспирантура         graduate school   \n",
       "14                                             афиша                  poster   \n",
       "15                                             банан                  banana   \n",
       "16                                          банкомат                     atm   \n",
       "17                                          барабаны                   drums   \n",
       "18                                         баскетбол              basketball   \n",
       "19                                               бег                 running   \n",
       "20                                   бежать ~ бегать                  to run   \n",
       "21                                      без (+ gen.)                 without   \n",
       "22                                      безветренный          windless, calm   \n",
       "23                                 белок (pl. белки)                 protein   \n",
       "24                                            бензин                gasoline   \n",
       "25                          берег (prep.: на берегу)            сoast, shore   \n",
       "26                                        бесплатный                    free   \n",
       "27                          беспокоить (impf. кого?)      to bother, disturb   \n",
       "28  беспокоиться (impf. о ком? о чём? за кого? за ...   to worry, be worried   \n",
       "29                                         бижутерия                 jewelry   \n",
       "\n",
       "                                             Extra  \n",
       "0                                                   \n",
       "1                                                   \n",
       "2                                                   \n",
       "3                                                   \n",
       "4                                                   \n",
       "5                                                   \n",
       "6                                                   \n",
       "7                                                   \n",
       "8                                                   \n",
       "9                                                   \n",
       "10                                                  \n",
       "11                                                  \n",
       "12                                                  \n",
       "13                                                  \n",
       "14                                                  \n",
       "15                                                  \n",
       "16                                                  \n",
       "17                                                  \n",
       "18                                                  \n",
       "19                                                  \n",
       "20                                                  \n",
       "21                                        (+ gen.)  \n",
       "22                                                  \n",
       "23                                     (pl. белки)  \n",
       "24                                                  \n",
       "25                              (prep.: на берегу)  \n",
       "26                                                  \n",
       "27                                   (impf. кого?)  \n",
       "28  (impf. о ком? о чём? за кого? за что? colloq.)  \n",
       "29                                                  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data data was manipulated manually to including extraneous grammatical\n",
    "# information in parentheses \n",
    "# duplicates grammatical information in its own column\n",
    "df_ntiplay['Extra'] = df_ntiplay['Russian'].str.extract('(\\(.*?\\))', expand=True)\n",
    "df_ntiplay = df_ntiplay.fillna('')\n",
    "df_ntiplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e000a034",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Entry</th>\n",
       "      <th>Russian</th>\n",
       "      <th>English</th>\n",
       "      <th>Extra</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>авария – traffic accident</td>\n",
       "      <td>авария</td>\n",
       "      <td>traffic accident</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>автобус – bus</td>\n",
       "      <td>автобус</td>\n",
       "      <td>bus</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>автомобиль – car</td>\n",
       "      <td>автомобиль</td>\n",
       "      <td>car</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>автомойка – car wash</td>\n",
       "      <td>автомойка</td>\n",
       "      <td>car wash</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>аксессуары  – accessories</td>\n",
       "      <td>аксессуары</td>\n",
       "      <td>accessories</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>алкогольный напиток – alcoholic drink</td>\n",
       "      <td>алкогольный напиток</td>\n",
       "      <td>alcoholic drink</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>аллергия – allergy</td>\n",
       "      <td>аллергия</td>\n",
       "      <td>allergy</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>альпинизм – mountain climbing</td>\n",
       "      <td>альпинизм</td>\n",
       "      <td>mountain climbing</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ангина – strep throat</td>\n",
       "      <td>ангина</td>\n",
       "      <td>strep throat</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>анкета – application form</td>\n",
       "      <td>анкета</td>\n",
       "      <td>application form</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>апельсин – orange</td>\n",
       "      <td>апельсин</td>\n",
       "      <td>orange</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>арбуз – watermelon</td>\n",
       "      <td>арбуз</td>\n",
       "      <td>watermelon</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>аренда – rent n.</td>\n",
       "      <td>аренда</td>\n",
       "      <td>rent n.</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>аспирантура – graduate school</td>\n",
       "      <td>аспирантура</td>\n",
       "      <td>graduate school</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>афиша – poster</td>\n",
       "      <td>афиша</td>\n",
       "      <td>poster</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>банан – banana</td>\n",
       "      <td>банан</td>\n",
       "      <td>banana</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>банкомат – atm</td>\n",
       "      <td>банкомат</td>\n",
       "      <td>atm</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>барабаны – drums</td>\n",
       "      <td>барабаны</td>\n",
       "      <td>drums</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>баскетбол – basketball</td>\n",
       "      <td>баскетбол</td>\n",
       "      <td>basketball</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>бег – running</td>\n",
       "      <td>бег</td>\n",
       "      <td>running</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>бежать ~ бегать – to run</td>\n",
       "      <td>бежать ~ бегать</td>\n",
       "      <td>to run</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>без (+ gen.) – without</td>\n",
       "      <td>без</td>\n",
       "      <td>without</td>\n",
       "      <td>(+ gen.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>безветренный – windless, calm</td>\n",
       "      <td>безветренный</td>\n",
       "      <td>windless, calm</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>белок (pl. белки) – protein</td>\n",
       "      <td>белок</td>\n",
       "      <td>protein</td>\n",
       "      <td>(pl. белки)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>бензин – gasoline</td>\n",
       "      <td>бензин</td>\n",
       "      <td>gasoline</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>берег (prep.: на берегу) – сoast, shore</td>\n",
       "      <td>берег</td>\n",
       "      <td>сoast, shore</td>\n",
       "      <td>(prep.: на берегу)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>бесплатный – free</td>\n",
       "      <td>бесплатный</td>\n",
       "      <td>free</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>беспокоить (impf. кого?) – to bother, disturb</td>\n",
       "      <td>беспокоить</td>\n",
       "      <td>to bother, disturb</td>\n",
       "      <td>(impf. кого?)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>беспокоиться (impf. о ком? о чём? за кого? за ...</td>\n",
       "      <td>беспокоиться</td>\n",
       "      <td>to worry, be worried</td>\n",
       "      <td>(impf. о ком? о чём? за кого? за что? colloq.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>бижутерия – jewelry</td>\n",
       "      <td>бижутерия</td>\n",
       "      <td>jewelry</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Entry               Russian  \\\n",
       "0                           авария – traffic accident               авария    \n",
       "1                                       автобус – bus              автобус    \n",
       "2                                    автомобиль – car           автомобиль    \n",
       "3                                автомойка – car wash            автомойка    \n",
       "4                           аксессуары  – accessories          аксессуары     \n",
       "5               алкогольный напиток – alcoholic drink  алкогольный напиток    \n",
       "6                                  аллергия – allergy             аллергия    \n",
       "7                       альпинизм – mountain climbing            альпинизм    \n",
       "8                               ангина – strep throat               ангина    \n",
       "9                           анкета – application form               анкета    \n",
       "10                                  апельсин – orange             апельсин    \n",
       "11                                 арбуз – watermelon                арбуз    \n",
       "12                                   аренда – rent n.               аренда    \n",
       "13                      аспирантура – graduate school          аспирантура    \n",
       "14                                     афиша – poster                афиша    \n",
       "15                                     банан – banana                банан    \n",
       "16                                     банкомат – atm             банкомат    \n",
       "17                                   барабаны – drums             барабаны    \n",
       "18                             баскетбол – basketball            баскетбол    \n",
       "19                                      бег – running                  бег    \n",
       "20                           бежать ~ бегать – to run      бежать ~ бегать    \n",
       "21                             без (+ gen.) – without                 без     \n",
       "22                      безветренный – windless, calm         безветренный    \n",
       "23                        белок (pl. белки) – protein               белок     \n",
       "24                                  бензин – gasoline               бензин    \n",
       "25            берег (prep.: на берегу) – сoast, shore               берег     \n",
       "26                                  бесплатный – free           бесплатный    \n",
       "27      беспокоить (impf. кого?) – to bother, disturb          беспокоить     \n",
       "28  беспокоиться (impf. о ком? о чём? за кого? за ...        беспокоиться     \n",
       "29                                бижутерия – jewelry            бижутерия    \n",
       "\n",
       "                  English                                           Extra  \n",
       "0        traffic accident                                                  \n",
       "1                     bus                                                  \n",
       "2                     car                                                  \n",
       "3                car wash                                                  \n",
       "4             accessories                                                  \n",
       "5         alcoholic drink                                                  \n",
       "6                 allergy                                                  \n",
       "7       mountain climbing                                                  \n",
       "8            strep throat                                                  \n",
       "9        application form                                                  \n",
       "10                 orange                                                  \n",
       "11             watermelon                                                  \n",
       "12                rent n.                                                  \n",
       "13        graduate school                                                  \n",
       "14                 poster                                                  \n",
       "15                 banana                                                  \n",
       "16                    atm                                                  \n",
       "17                  drums                                                  \n",
       "18             basketball                                                  \n",
       "19                running                                                  \n",
       "20                 to run                                                  \n",
       "21                without                                        (+ gen.)  \n",
       "22         windless, calm                                                  \n",
       "23                protein                                     (pl. белки)  \n",
       "24               gasoline                                                  \n",
       "25           сoast, shore                              (prep.: на берегу)  \n",
       "26                   free                                                  \n",
       "27     to bother, disturb                                   (impf. кого?)  \n",
       "28   to worry, be worried  (impf. о ком? о чём? за кого? за что? colloq.)  \n",
       "29                jewelry                                                  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removes grammatical information from the Russian column\n",
    "df_ntiplay['Russian'] = df_ntiplay['Russian'].replace('(\\(.*?\\))', '', regex=True)\n",
    "df_ntiplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "867e0c8b",
   "metadata": {
    "scrolled": true
   },
   "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>Russian</th>\n",
       "      <th>English</th>\n",
       "      <th>Extra</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>яблоко</td>\n",
       "      <td>apple</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>являться</td>\n",
       "      <td>to be (in official or formal contexts)</td>\n",
       "      <td>(impf. кем? чем?)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>ягода</td>\n",
       "      <td>berry</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>яичница</td>\n",
       "      <td>fried eggs</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>яркий</td>\n",
       "      <td>bright</td>\n",
       "      <td>(-ая, -ое, -ие)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Russian                                  English              Extra\n",
       "914     яблоко                                     apple                   \n",
       "915  являться     to be (in official or formal contexts)  (impf. кем? чем?)\n",
       "916      ягода                                     berry                   \n",
       "917    яичница                                fried eggs                   \n",
       "918     яркий                                     bright    (-ая, -ое, -ие)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# does above for full intermediate dataset\n",
    "# removes accents \n",
    "# removes full entry column\n",
    "nti_sub = [re.sub('(\\s\\d+,|\\s\\d+\\s|\\|| 3| 3)', ' ', x) for x in nti_txt_low]\n",
    "df_nti=pd.DataFrame(nti_sub,columns=['Entry'])\n",
    "df_nti=df_nti.join(df_nti['Entry'].str.split('–', 1, expand=True).rename(columns={0:'Russian', 1:'English'}))\n",
    "df_nti['Extra'] = df_nti['Russian'].str.extract('(\\(.*?\\))', expand=True)\n",
    "df_nti = df_nti.fillna('')\n",
    "df_nti['Russian'] = df_nti['Russian'].replace('(\\(.*?\\))', '', regex=True)\n",
    "df_nti['Russian'] = df_nti['Russian'].replace('а́', 'а', regex=True)\n",
    "df_nti['Russian'] = df_nti['Russian'].replace('я́', 'я', regex=True)\n",
    "df_nti['Russian'] = df_nti['Russian'].replace('э́', 'э', regex=True)\n",
    "df_nti['Russian'] = df_nti['Russian'].replace('е́', 'е', regex=True)\n",
    "df_nti['Russian'] = df_nti['Russian'].replace('ы́', 'ы', regex=True)\n",
    "df_nti['Russian'] = df_nti['Russian'].replace('и́', 'и', regex=True)\n",
    "df_nti['Russian'] = df_nti['Russian'].replace('о́', 'о', regex=True)\n",
    "df_nti['Russian'] = df_nti['Russian'].replace('у́', 'у', regex=True)\n",
    "df_nti['Russian'] = df_nti['Russian'].replace('ю́', 'ю', regex=True)\n",
    "df_nti=df_nti[['Russian','English','Extra']]\n",
    "df_nti.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "da8c7e00",
   "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>Russian</th>\n",
       "      <th>English</th>\n",
       "      <th>Extra</th>\n",
       "      <th>Level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>авария</td>\n",
       "      <td>traffic accident</td>\n",
       "      <td></td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>автобус</td>\n",
       "      <td>bus</td>\n",
       "      <td></td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>автомобиль</td>\n",
       "      <td>car</td>\n",
       "      <td></td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>автомойка</td>\n",
       "      <td>car wash</td>\n",
       "      <td></td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>аксессуары</td>\n",
       "      <td>accessories</td>\n",
       "      <td></td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>яблоко</td>\n",
       "      <td>apple</td>\n",
       "      <td></td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>являться</td>\n",
       "      <td>to be (in official or formal contexts)</td>\n",
       "      <td>(impf. кем? чем?)</td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>ягода</td>\n",
       "      <td>berry</td>\n",
       "      <td></td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>яичница</td>\n",
       "      <td>fried eggs</td>\n",
       "      <td></td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>яркий</td>\n",
       "      <td>bright</td>\n",
       "      <td>(-ая, -ое, -ие)</td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>919 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Russian                                  English              Extra  \\\n",
       "0         авария                          traffic accident                      \n",
       "1        автобус                                       bus                      \n",
       "2     автомобиль                                       car                      \n",
       "3      автомойка                                  car wash                      \n",
       "4    аксессуары                                accessories                      \n",
       "..            ...                                      ...                ...   \n",
       "914       яблоко                                     apple                      \n",
       "915    являться     to be (in official or formal contexts)  (impf. кем? чем?)   \n",
       "916        ягода                                     berry                      \n",
       "917      яичница                                fried eggs                      \n",
       "918       яркий                                     bright    (-ая, -ое, -ие)   \n",
       "\n",
       "    Level  \n",
       "0     Int  \n",
       "1     Int  \n",
       "2     Int  \n",
       "3     Int  \n",
       "4     Int  \n",
       "..    ...  \n",
       "914   Int  \n",
       "915   Int  \n",
       "916   Int  \n",
       "917   Int  \n",
       "918   Int  \n",
       "\n",
       "[919 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Broadcasts 'Int' as a lable for intermediate textbook level\n",
    "df_nti['Level'] = 'Int'\n",
    "df_nti"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6ecde83",
   "metadata": {},
   "source": [
    "- Below I repeat the process on the advanced textbook data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b8690427",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"АбсолЮтно 3 - absolutely \\nаварИйное состоЯние 5 - unsafe condition \\nакварель (f) 3 - watercolor \\nаккуратный 2 - neat, tidy, well organized; orderly \\nактуально 1 - timely \\nакция (акции) 9 - share (stocks) \\nальтернатИва 6 - alternative \\nамфитеатр 3 - (raised) rear part in orchestra section (theater) \\nанализИровать/проанализИровать (что?) 1 - to analyze \\nангина 7 - strep throat \\nантракт 3 - intermission \\nаполитИчность 6 - indifference towards politics \\nаттестат 1 - high school diploma \\nБаловать/избаловать (кого?) 4 - to indulge, to pamper \\nбедность 4 - poverty \\nбездельничать 2 - to loaf, not do anything \\nбездомный 5 - homeless \\nбезопасность 4 - security \\nбезработица 2 - unemployment, joblessness \\nбезработный (n.) 2 - unemployed (person) \\nбезразлИчный 6 - indifferent \\nбезусловно 1 - unconditionally; for sure \\nбелкИ (sing. белoк) 7 - proteins \\nбельэтаж 3 - dress circle; first tier \\nбензИн 9 - gas (gasoline) \\nберечь  (impf кого?) 4 - to take good care of \\nбеспокоить  (impf кого?) 5 - to bother \\nбеспокойство 8 - concern \\nбессонница 7 - insomnia \\nбИржа труда 6 - job center \\nблагодарИть/поблагодарИть (кого?) 7 - to thank, express gratitude \\nблагодарность 9 - gratitude \\nблагодарный 1 - grateful \\nблагополучие (финансовое) 9 -financial stabllity \\nблагополучный 4 - happy, successful \\nблагосостоЯние 10 - prosperity \\nблаготворИтельный 6 - charitable \\nблестящий 4 - brilliant \\nблизнецы 4 - twins \\nбогатеть/разбогатеть 9 - to Ьесоте rich \\nбогач 9 - wealthy person \\nбогослужение 8 - religious service \\nбодрый 7 - healthy; hale and hearty \\nбольшинство (кого? чего?) 2, 9 - majority \\nбороться  (impf за что? права) 4 - to fight for (one's rights) \\nборьба (sing. оnlу) (за что?) 4 - fight \\nботаник 1 - botanist (slang for а nerd) \\nбрать/взять кредИт 9 - take out а loan \\nбрать/взять пример (с кого?) 6 - to follow someone's example \\nбронхит 7 - bronchitis \\nбуддИйский 8 - Buddhist (adj.) \\nбыт 3 - everyday life \\nВакансия (вакантное место) 2 - job орепiпg \\nвведение полИтики 10 - estaЬlishing, introd\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ita = open('./textbook_vocab_data/intermediate_to_advanced.txt', encoding= 'utf8')\n",
    "ita = ita.read()\n",
    "ita[:2000] # very messy ugh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "03314537",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['абсолютно 3 - absolutely ', 'аварийное состояние 5 - unsafe condition ', 'акварель (f) 3 - watercolor ', 'аккуратный 2 - neat, tidy, well organized; orderly ', 'актуально 1 - timely ', 'акция (акции) 9 - share (stocks) ', 'альтернатива 6 - alternative ', 'амфитеатр 3 - (raised) rear part in orchestra section (theater) ', 'анализировать/проанализировать (что?) 1 - to analyze ', 'ангина 7 - strep throat ', 'антракт 3 - intermission ', 'аполитичность 6 - indifference towards politics ', 'аттестат 1 - high school diploma ', 'баловать/избаловать (кого?) 4 - to indulge, to pamper ', 'бедность 4 - poverty ', 'бездельничать 2 - to loaf, not do anything ', 'бездомный 5 - homeless ', 'безопасность 4 - security ', 'безработица 2 - unemployment, joblessness ', 'безработный (n.) 2 - unemployed (person) ', 'безразличный 6 - indifferent ', 'безусловно 1 - unconditionally; for sure ', 'белки (sing. белoк) 7 - proteins ', 'бельэтаж 3 - dress circle; first tier ', 'бензин 9 - gas (gasoline) ', 'беречь  (impf кого?) 4 - to take good care of ', 'беспокоить  (impf кого?) 5 - to bother ', 'беспокойство 8 - concern ', 'бессонница 7 - insomnia ', 'биржа труда 6 - job center ']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ita_txt = ita.split('\\n') # I turn the file into a list, splitting on linebreaks to divide most of the vocab items\n",
    "ita_txt_low = [x.lower() for x in ita_txt] # Stress marking was encoded with capital letters, so I lower everything\n",
    "itaplay = ita_txt_low[:30] # let't take a look at some of the entries and make it a play set\n",
    "itaplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ac9cf5e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Russian</th>\n",
       "      <th>English</th>\n",
       "      <th>Extra</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>абсолютно</td>\n",
       "      <td>absolutely</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>аварийное состояние</td>\n",
       "      <td>unsafe condition</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>акварель</td>\n",
       "      <td>watercolor</td>\n",
       "      <td>(f)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>аккуратный</td>\n",
       "      <td>neat, tidy, well organized; orderly</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>актуально</td>\n",
       "      <td>timely</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>язва</td>\n",
       "      <td>ulcer</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>яма</td>\n",
       "      <td>pothole</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>яркий</td>\n",
       "      <td>bright</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>яркие огни</td>\n",
       "      <td>bright lights</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199</th>\n",
       "      <td>ясно</td>\n",
       "      <td>clear</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1200 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Russian                                English Extra\n",
       "0               абсолютно                             absolutely       \n",
       "1     аварийное состояние                       unsafe condition       \n",
       "2               акварель                              watercolor    (f)\n",
       "3              аккуратный    neat, tidy, well organized; orderly       \n",
       "4               актуально                                 timely       \n",
       "...                    ...                                    ...   ...\n",
       "1195                 язва                                  ulcer       \n",
       "1196                  яма                                pothole       \n",
       "1197                яркий                                 bright       \n",
       "1198           яркие огни                          bright lights       \n",
       "1199                 ясно                                  clear       \n",
       "\n",
       "[1200 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ita_sub = [re.sub('(\\s\\d+,|\\s\\d+\\s|\\|| 3| 3)', ' ', x) for x in ita_txt_low]\n",
    "df_ita=pd.DataFrame(ita_sub,columns=['Entry'])\n",
    "df_ita=df_ita.join(df_ita['Entry'].str.split('-', 1, expand=True).rename(columns={0:'Russian', 1:'English'}))\n",
    "df_ita['Extra'] = df_ita['Russian'].str.extract('(\\(.*?\\))', expand=True)\n",
    "df_ita = df_ita.fillna('')\n",
    "df_ita['Russian'] = df_ita['Russian'].replace('(\\(.*?\\))', '', regex=True)\n",
    "df_ita=df_ita[['Russian','English','Extra']]\n",
    "df_ita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "931fa8d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Russian</th>\n",
       "      <th>English</th>\n",
       "      <th>Extra</th>\n",
       "      <th>Level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>абсолютно</td>\n",
       "      <td>absolutely</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>аварийное состояние</td>\n",
       "      <td>unsafe condition</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>акварель</td>\n",
       "      <td>watercolor</td>\n",
       "      <td>(f)</td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>аккуратный</td>\n",
       "      <td>neat, tidy, well organized; orderly</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>актуально</td>\n",
       "      <td>timely</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>язва</td>\n",
       "      <td>ulcer</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>яма</td>\n",
       "      <td>pothole</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>яркий</td>\n",
       "      <td>bright</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>яркие огни</td>\n",
       "      <td>bright lights</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199</th>\n",
       "      <td>ясно</td>\n",
       "      <td>clear</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1200 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Russian                                English Extra Level\n",
       "0               абсолютно                             absolutely          Adv\n",
       "1     аварийное состояние                       unsafe condition          Adv\n",
       "2               акварель                              watercolor    (f)   Adv\n",
       "3              аккуратный    neat, tidy, well organized; orderly          Adv\n",
       "4               актуально                                 timely          Adv\n",
       "...                    ...                                    ...   ...   ...\n",
       "1195                 язва                                  ulcer          Adv\n",
       "1196                  яма                                pothole          Adv\n",
       "1197                яркий                                 bright          Adv\n",
       "1198           яркие огни                          bright lights          Adv\n",
       "1199                 ясно                                  clear          Adv\n",
       "\n",
       "[1200 rows x 4 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ita['Level'] = 'Adv'\n",
    "df_ita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9f7fdda0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2119 entries, 0 to 1199\n",
      "Data columns (total 4 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   Russian  2119 non-null   object\n",
      " 1   English  2119 non-null   object\n",
      " 2   Extra    2119 non-null   object\n",
      " 3   Level    2119 non-null   object\n",
      "dtypes: object(4)\n",
      "memory usage: 82.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# combines both textbook datasets into a single dataframe sorted by Russian column\n",
    "df_vocab = pd.concat([df_nti,df_ita], axis=0)\n",
    "df_vocab = df_vocab.sort_values(by='Russian')\n",
    "df_vocab.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "09f9ab90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Russian</th>\n",
       "      <th>English</th>\n",
       "      <th>Extra</th>\n",
       "      <th>Level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>дружная семья</td>\n",
       "      <td>happy family</td>\n",
       "      <td></td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>опоздание</td>\n",
       "      <td>beiпg late, tardiпess</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>инфаркт</td>\n",
       "      <td>heart attack</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>перекус</td>\n",
       "      <td>snack</td>\n",
       "      <td></td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1194</th>\n",
       "      <td>ядерное оружие</td>\n",
       "      <td>nuclear weapons</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>продажа</td>\n",
       "      <td>sale</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>льготный</td>\n",
       "      <td>preferential</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>баловать/избаловать</td>\n",
       "      <td>to indulge, to pamper</td>\n",
       "      <td>(кого?)</td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>соцзащита</td>\n",
       "      <td>social services</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>знаменитый</td>\n",
       "      <td>renowned</td>\n",
       "      <td>(-ая, -ое, -ые)</td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>обещать/пообещать</td>\n",
       "      <td>to promise</td>\n",
       "      <td>(кому́? что?)</td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>персонаж</td>\n",
       "      <td>character</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>высылка</td>\n",
       "      <td>expulsioп, exile</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083</th>\n",
       "      <td>труба</td>\n",
       "      <td>pipe</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>827</th>\n",
       "      <td>уверен</td>\n",
       "      <td>(to be) sure (of), confident in</td>\n",
       "      <td>(-а, -ы в ком? в чём?)</td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>многие</td>\n",
       "      <td>many (but not all)</td>\n",
       "      <td>(pl.)</td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>футбол</td>\n",
       "      <td>soccer</td>\n",
       "      <td></td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1187</th>\n",
       "      <td>энергоноситель</td>\n",
       "      <td>energy-producing material</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>зависеть</td>\n",
       "      <td>to depeпd оп</td>\n",
       "      <td>(impf от кого? чего?)</td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>пора</td>\n",
       "      <td>it’s time to</td>\n",
       "      <td>(кому́? + inf.)</td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Russian                           English  \\\n",
       "208          дружная семья                       happy family   \n",
       "576              опоздание             beiпg late, tardiпess    \n",
       "337                инфаркт                      heart attack    \n",
       "503                перекус                              snack   \n",
       "1194        ядерное оружие                   nuclear weapons    \n",
       "818                продажа                              sale    \n",
       "420               льготный                      preferential    \n",
       "13    баловать/избаловать              to indulge, to pamper    \n",
       "1013             соцзащита                   social services    \n",
       "262            знаменитый                            renowned   \n",
       "430     обещать/пообещать                          to promise   \n",
       "653               персонаж                         character    \n",
       "150                высылка                  expulsioп, exile    \n",
       "1083                 труба                              pipe    \n",
       "827                уверен     (to be) sure (of), confident in   \n",
       "390                многие                  many (but not all)   \n",
       "863                 футбол                             soccer   \n",
       "1187        энергоноситель         energy-producing material    \n",
       "264              зависеть                       to depeпd оп    \n",
       "555                  пора                        it’s time to   \n",
       "\n",
       "                       Extra Level  \n",
       "208                            Int  \n",
       "576                            Adv  \n",
       "337                            Adv  \n",
       "503                            Int  \n",
       "1194                           Adv  \n",
       "818                            Adv  \n",
       "420                            Adv  \n",
       "13                   (кого?)   Adv  \n",
       "1013                           Adv  \n",
       "262          (-ая, -ое, -ые)   Int  \n",
       "430            (кому́? что?)   Int  \n",
       "653                            Adv  \n",
       "150                            Adv  \n",
       "1083                           Adv  \n",
       "827   (-а, -ы в ком? в чём?)   Int  \n",
       "390                    (pl.)   Int  \n",
       "863                            Int  \n",
       "1187                           Adv  \n",
       "264    (impf от кого? чего?)   Adv  \n",
       "555          (кому́? + inf.)   Int  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vocab.sample(n=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5b97343",
   "metadata": {},
   "source": [
    "## 3. String Manipulation \n",
    "- Uses regular expressions and splitting to explode slash-, tilde-, and comma-separated entries into their own rows and duplicating the associated values from the other columns\n",
    "- Separates all collocations into tokens in their own rows \n",
    "- Lemmatizes all tokens\n",
    "- Tags all lemmas for part of speech\n",
    "- Removes rows with non-meaningful POS tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "86af6301",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vocab['Russian'] = df_vocab['Russian'].str.split('/| ~ |,')\n",
    "df_vocab = df_vocab.explode('Russian').reset_index(drop=True)\n",
    "cols = list(df_vocab.columns)\n",
    "cols.append(cols.pop(cols.index('English')))\n",
    "df_vocab = df_vocab[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "40b696a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vocab['Russian'] = df_vocab.Russian.str.strip(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9035eb7c",
   "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>Russian</th>\n",
       "      <th>Extra</th>\n",
       "      <th>Level</th>\n",
       "      <th>English</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1879</th>\n",
       "      <td>рассуждение</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "      <td>coпsideratioп, discussioп</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1329</th>\n",
       "      <td>оценивать</td>\n",
       "      <td>(кого? что?)</td>\n",
       "      <td>Adv</td>\n",
       "      <td>to value, appraise</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>повесить</td>\n",
       "      <td>(что? куда́? где?)</td>\n",
       "      <td>Int</td>\n",
       "      <td>to hang, put (on a wall, ceiling)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>банан</td>\n",
       "      <td></td>\n",
       "      <td>Int</td>\n",
       "      <td>banana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1040</th>\n",
       "      <td>намерен</td>\n",
       "      <td>(намерена)</td>\n",
       "      <td>Adv</td>\n",
       "      <td>to intend (he intends, she intends)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1826</th>\n",
       "      <td>равняться impf</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "      <td>to ье equal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1577</th>\n",
       "      <td>постоянное место жительство</td>\n",
       "      <td></td>\n",
       "      <td>Int</td>\n",
       "      <td>permanent place of residence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2109</th>\n",
       "      <td>сокращать</td>\n",
       "      <td>(кого?)</td>\n",
       "      <td>Adv</td>\n",
       "      <td>to lау off; to fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1502</th>\n",
       "      <td>показывать</td>\n",
       "      <td>(что? по телевизору)</td>\n",
       "      <td>Adv</td>\n",
       "      <td>to show; to broadcast (ье оп tv)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1010</th>\n",
       "      <td>мысль</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "      <td>thought</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1752</th>\n",
       "      <td>проза</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "      <td>prose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>космос</td>\n",
       "      <td></td>\n",
       "      <td>Int</td>\n",
       "      <td>space</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1541</th>\n",
       "      <td>поощрять</td>\n",
       "      <td>(кого?)</td>\n",
       "      <td>Adv</td>\n",
       "      <td>to reward</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1753</th>\n",
       "      <td>проиграть</td>\n",
       "      <td>(выборы)</td>\n",
       "      <td>Adv</td>\n",
       "      <td>to lose аn election</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1629</th>\n",
       "      <td>предприятие</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "      <td>enterprise,factory, plant, shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>гастарбайтер</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "      <td>migrant worker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2283</th>\n",
       "      <td>термометр</td>\n",
       "      <td></td>\n",
       "      <td>Int</td>\n",
       "      <td>thermometer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1380</th>\n",
       "      <td>перекрывать</td>\n",
       "      <td>(что?)</td>\n",
       "      <td>Adv</td>\n",
       "      <td>to shut off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>баскетбол</td>\n",
       "      <td></td>\n",
       "      <td>Int</td>\n",
       "      <td>basketball</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>вести</td>\n",
       "      <td>(кого́? куда́?)</td>\n",
       "      <td>Int</td>\n",
       "      <td>to take, lead (by foot)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1628</th>\n",
       "      <td>предпринять шаги</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "      <td>to take measures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1348</th>\n",
       "      <td>памятник архитектуры</td>\n",
       "      <td></td>\n",
       "      <td>Int</td>\n",
       "      <td>architectural landmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2271</th>\n",
       "      <td>телевизионная сеть</td>\n",
       "      <td></td>\n",
       "      <td>Int</td>\n",
       "      <td>tv network</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>сессия</td>\n",
       "      <td></td>\n",
       "      <td>Int</td>\n",
       "      <td>finals week</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2111</th>\n",
       "      <td>сокращаться</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "      <td>to decrease</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2205</th>\n",
       "      <td>стимул</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "      <td>iпcentive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>без сознания</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "      <td>unconscious</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>отвыкать</td>\n",
       "      <td>(от кого? чего?)</td>\n",
       "      <td>Adv</td>\n",
       "      <td>to getout of the hablt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>нехватка</td>\n",
       "      <td></td>\n",
       "      <td>Adv</td>\n",
       "      <td>lack of</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>гостеприимный</td>\n",
       "      <td>(-ая, -ое, -ые)</td>\n",
       "      <td>Int</td>\n",
       "      <td>hospitable</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Russian                 Extra Level  \\\n",
       "1879                  рассуждение                         Adv   \n",
       "1329                    оценивать          (кого? что?)   Adv   \n",
       "165                      повесить    (что? куда́? где?)   Int   \n",
       "31                          банан                         Int   \n",
       "1040                      намерен            (намерена)   Adv   \n",
       "1826               равняться impf                         Adv   \n",
       "1577  постоянное место жительство                         Int   \n",
       "2109                    сокращать               (кого?)   Adv   \n",
       "1502                   показывать  (что? по телевизору)   Adv   \n",
       "1010                        мысль                         Adv   \n",
       "1752                        проза                         Adv   \n",
       "860                        космос                         Int   \n",
       "1541                     поощрять               (кого?)   Adv   \n",
       "1753                    проиграть              (выборы)   Adv   \n",
       "1629                  предприятие                         Adv   \n",
       "353                  гастарбайтер                         Adv   \n",
       "2283                    термометр                         Int   \n",
       "1380                  перекрывать                (что?)   Adv   \n",
       "34                      баскетбол                         Int   \n",
       "150                         вести       (кого́? куда́?)   Int   \n",
       "1628             предпринять шаги                         Adv   \n",
       "1348         памятник архитектуры                         Int   \n",
       "2271           телевизионная сеть                         Int   \n",
       "2015                       сессия                         Int   \n",
       "2111                  сокращаться                         Adv   \n",
       "2205                       стимул                         Adv   \n",
       "40                   без сознания                         Adv   \n",
       "1280                     отвыкать      (от кого? чего?)   Adv   \n",
       "1112                     нехватка                         Adv   \n",
       "396                 гостеприимный       (-ая, -ое, -ые)   Int   \n",
       "\n",
       "                                    English  \n",
       "1879             coпsideratioп, discussioп   \n",
       "1329                    to value, appraise   \n",
       "165       to hang, put (on a wall, ceiling)  \n",
       "31                                   banana  \n",
       "1040   to intend (he intends, she intends)   \n",
       "1826                           to ье equal   \n",
       "1577           permanent place of residence  \n",
       "2109                   to lау off; to fire   \n",
       "1502      to show; to broadcast (ье оп tv)   \n",
       "1010                               thought   \n",
       "1752                                 prose   \n",
       "860                                   space  \n",
       "1541                             to reward   \n",
       "1753                   to lose аn election   \n",
       "1629       enterprise,factory, plant, shop   \n",
       "353                         migrant worker   \n",
       "2283                            thermometer  \n",
       "1380                           to shut off   \n",
       "34                               basketball  \n",
       "150                 to take, lead (by foot)  \n",
       "1628                      to take measures   \n",
       "1348                 architectural landmark  \n",
       "2271                             tv network  \n",
       "2015                            finals week  \n",
       "2111                           to decrease   \n",
       "2205                             iпcentive   \n",
       "40                             unconscious   \n",
       "1280                to getout of the hablt   \n",
       "1112                               lack of   \n",
       "396                              hospitable  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vocab.sample(n=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5e60be9",
   "metadata": {},
   "source": [
    "The method above leaves the collocations from the original glossary entries intact, but separates out synonomous forms (i.e. perfective and imperfective forms, synonyms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7f103a5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ADJ', 'NOUN']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# defines a function to return part of speech for a string based on spacy's nlp\n",
    "# model for russian\n",
    "def get_spacy_POS(ru_str):\n",
    "    tiny_doc1 = nlp(ru_str)\n",
    "    out_list1 = []\n",
    "    for token in tiny_doc1:\n",
    "        out_list1.append((token.pos_))\n",
    "    return out_list1\n",
    "\n",
    "get_spacy_POS('худо́жественный фильм')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "01af5297",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['художественный', 'фильм']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# defines a function to return lemma for a string based on spacy's nlp model \n",
    "# for russian\n",
    "def get_spacy_lemma(ru_str):\n",
    "    tiny_doc2 = nlp(ru_str)\n",
    "    out_list2 = []\n",
    "    for token in tiny_doc2:\n",
    "        out_list2.append((token.lemma_))\n",
    "    return out_list2\n",
    "\n",
    "get_spacy_lemma('художественного фильма')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ccd922b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# gets lemmas for each Russian entry\n",
    "df_vocab['Lemma'] = df_vocab.Russian.map(get_spacy_lemma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "da152093",
   "metadata": {},
   "outputs": [],
   "source": [
    "# makes lemma lists into comma-separated string\n",
    "df_vocab['Lemma'] = df_vocab.Lemma.apply(lambda x: ','.join(map(str, x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a71aec34",
   "metadata": {},
   "outputs": [],
   "source": [
    "# splits lemmas lists and explodes lemmas into rows\n",
    "df_vocab['Lemma'] = df_vocab['Lemma'].str.split(',')\n",
    "df_vocab = df_vocab.explode('Lemma').reset_index(drop=True)\n",
    "cols = list(df_vocab.columns)\n",
    "cols.append(cols.pop(cols.index('Russian')))\n",
    "df_vocab = df_vocab[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4e86f912",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vocab = df_vocab.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "043502b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creats a column for POS tags based on lemma column\n",
    "df_vocab['POS'] = df_vocab.Lemma.map(get_spacy_POS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4cc68069",
   "metadata": {},
   "outputs": [],
   "source": [
    "# converts POS tag to string\n",
    "df_vocab['POS'] = df_vocab['POS'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b0f8a4fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2972 entries, 0 to 2993\n",
      "Data columns (total 6 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   Extra    2972 non-null   object\n",
      " 1   Level    2972 non-null   object\n",
      " 2   English  2972 non-null   object\n",
      " 3   Lemma    2972 non-null   object\n",
      " 4   Russian  2972 non-null   object\n",
      " 5   POS      2972 non-null   object\n",
      "dtypes: object(6)\n",
      "memory usage: 162.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# drops rows with non-meaningful POS tags for white space and punctuation\n",
    "df_vocab.drop(df_vocab[df_vocab.POS.isin([\"['SPACE']\",\"['PUNCT']\"])].index, inplace=True)\n",
    "df_vocab.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "bfb217bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'dataframe_image' from 'C:\\\\Users\\\\obrim\\\\anaconda3\\\\lib\\\\site-packages\\\\dataframe_image\\\\__init__.py'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removes the English colum from data frame and drops duplicate rows\n",
    "df_vocab = df_vocab[['Russian','Lemma','POS','Level']].drop_duplicates()\n",
    "df_vocab.sample(20)\n",
    "dfi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1c6647ed",
   "metadata": {},
   "outputs": [
    {
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       "        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>Russian</th>\n",
       "      <th>Lemma</th>\n",
       "      <th>POS</th>\n",
       "      <th>Level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1821</th>\n",
       "      <td>принять лекарство</td>\n",
       "      <td>лекарство</td>\n",
       "      <td>['NOUN']</td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>мудрость</td>\n",
       "      <td>мудрость</td>\n",
       "      <td>['NOUN']</td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>горячее</td>\n",
       "      <td>горячий</td>\n",
       "      <td>['ADJ']</td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>вести денежные дела</td>\n",
       "      <td>денежный</td>\n",
       "      <td>['ADJ']</td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1145</th>\n",
       "      <td>нормализоваться</td>\n",
       "      <td>нормализоваться</td>\n",
       "      <td>['VERB']</td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1742</th>\n",
       "      <td>предоставить возможность</td>\n",
       "      <td>предоставить</td>\n",
       "      <td>['VERB']</td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2309</th>\n",
       "      <td>спортивная передача</td>\n",
       "      <td>спортивный</td>\n",
       "      <td>['ADJ']</td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>выдвинуть   свою кандидатуру</td>\n",
       "      <td>кандидатура</td>\n",
       "      <td>['NOUN']</td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>разводиться</td>\n",
       "      <td>разводиться</td>\n",
       "      <td>['VERB']</td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2415</th>\n",
       "      <td>такси</td>\n",
       "      <td>такси</td>\n",
       "      <td>['NOUN']</td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1025</th>\n",
       "      <td>наблюдаться</td>\n",
       "      <td>наблюдаться</td>\n",
       "      <td>['VERB']</td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1593</th>\n",
       "      <td>полный</td>\n",
       "      <td>полный</td>\n",
       "      <td>['ADJ']</td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>допустить</td>\n",
       "      <td>допустить</td>\n",
       "      <td>['VERB']</td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>веровать</td>\n",
       "      <td>веровать</td>\n",
       "      <td>['VERB']</td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2440</th>\n",
       "      <td>течь impf</td>\n",
       "      <td>impf</td>\n",
       "      <td>['PROPN']</td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2205</th>\n",
       "      <td>служебная лестница</td>\n",
       "      <td>лестница</td>\n",
       "      <td>['NOUN']</td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2154</th>\n",
       "      <td>семестр</td>\n",
       "      <td>семестр</td>\n",
       "      <td>['NOUN']</td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1815</th>\n",
       "      <td>принять</td>\n",
       "      <td>принять</td>\n",
       "      <td>['VERB']</td>\n",
       "      <td>Both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>городская дума</td>\n",
       "      <td>дума</td>\n",
       "      <td>['NOUN']</td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2161</th>\n",
       "      <td>серёжки</td>\n",
       "      <td>серёжка</td>\n",
       "      <td>['NOUN']</td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Russian            Lemma        POS Level\n",
       "1821             принять лекарство        лекарство   ['NOUN']   Int\n",
       "1012                      мудрость         мудрость   ['NOUN']   Adv\n",
       "407                        горячее          горячий    ['ADJ']   Int\n",
       "146            вести денежные дела         денежный    ['ADJ']   Adv\n",
       "1145               нормализоваться  нормализоваться   ['VERB']   Adv\n",
       "1742      предоставить возможность     предоставить   ['VERB']   Adv\n",
       "2309           спортивная передача       спортивный    ['ADJ']   Int\n",
       "302   выдвинуть   свою кандидатуру      кандидатура   ['NOUN']   Adv\n",
       "1971                   разводиться      разводиться   ['VERB']   Int\n",
       "2415                         такси            такси   ['NOUN']   Int\n",
       "1025                   наблюдаться      наблюдаться   ['VERB']   Adv\n",
       "1593                        полный           полный    ['ADJ']   Int\n",
       "514                      допустить        допустить   ['VERB']   Adv\n",
       "136                       веровать         веровать   ['VERB']   Adv\n",
       "2440                     течь impf             impf  ['PROPN']   Adv\n",
       "2205            служебная лестница         лестница   ['NOUN']   Adv\n",
       "2154                       семестр          семестр   ['NOUN']   Int\n",
       "1815                       принять          принять   ['VERB']  Both\n",
       "404                 городская дума             дума   ['NOUN']   Adv\n",
       "2161                       серёжки          серёжка   ['NOUN']   Int"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# groups by Russian token and combines any items that appear at both levels\n",
    "# under a 3rd level category: 'both'\n",
    "df_vocab = df_vocab.groupby(['Russian','Lemma','POS'], as_index=False).agg({'Level': ' '.join})\n",
    "df_vocab['Level'] = df_vocab['Level'].replace('Int Adv|Adv Int', 'Both', regex=True)\n",
    "df_vocab.sample(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a86c51b",
   "metadata": {},
   "source": [
    "## 4. Some Descriptive Analysis\n",
    "- Visualizes and plots distributions of tokens by book level\n",
    "- Visualizes proportions of POS according to level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e6eb78ac",
   "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>Russian</th>\n",
       "      <th>Lemma</th>\n",
       "      <th>POS</th>\n",
       "      <th>Level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2757</td>\n",
       "      <td>2757</td>\n",
       "      <td>2757</td>\n",
       "      <td>2757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2370</td>\n",
       "      <td>2426</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>система скидок для клиентов</td>\n",
       "      <td>на</td>\n",
       "      <td>['NOUN']</td>\n",
       "      <td>Adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>1209</td>\n",
       "      <td>1519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Russian Lemma       POS Level\n",
       "count                          2757  2757      2757  2757\n",
       "unique                         2370  2426        13     3\n",
       "top     система скидок для клиентов    на  ['NOUN']   Adv\n",
       "freq                              4    11      1209  1519"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vocab.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "daec4a89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Adv     1519\n",
       "Int     1091\n",
       "Both     147\n",
       "Name: Level, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vocab['Level'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e998f730",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2000x4000 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = sns.color_palette('muted')\n",
    "fig, axes = plt.subplots(2, 1, figsize=(5,10), dpi=400, sharex=False, sharey=False)\n",
    "sns.countplot(x='Level', data=df_vocab, ax=axes[0], palette='muted')\n",
    "df_vocab['Level'].value_counts().plot.pie(ax=axes[1], autopct='%.0f%%', colors=colors)\n",
    "\n",
    "filename1 = 'Item_Breakdown_by_Level'\n",
    "plt.savefig(filename1+'.png', facecolor=('#e8f4f0'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "12106394",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NOUN']     1209\n",
       "['VERB']     1041\n",
       "['ADJ']       308\n",
       "['ADV']        73\n",
       "['ADP']        66\n",
       "['PROPN']      11\n",
       "['SCONJ']      10\n",
       "['NUM']         8\n",
       "['PART']        8\n",
       "['PRON']        8\n",
       "['DET']         8\n",
       "['CCONJ']       5\n",
       "['AUX']         2\n",
       "Name: POS, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vocab['POS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2d963dd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_c6331_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >POS</th>\n",
       "      <th class=\"col_heading level0 col0\" >['ADJ']</th>\n",
       "      <th class=\"col_heading level0 col1\" >['ADP']</th>\n",
       "      <th class=\"col_heading level0 col2\" >['ADV']</th>\n",
       "      <th class=\"col_heading level0 col3\" >['AUX']</th>\n",
       "      <th class=\"col_heading level0 col4\" >['CCONJ']</th>\n",
       "      <th class=\"col_heading level0 col5\" >['DET']</th>\n",
       "      <th class=\"col_heading level0 col6\" >['NOUN']</th>\n",
       "      <th class=\"col_heading level0 col7\" >['NUM']</th>\n",
       "      <th class=\"col_heading level0 col8\" >['PART']</th>\n",
       "      <th class=\"col_heading level0 col9\" >['PRON']</th>\n",
       "      <th class=\"col_heading level0 col10\" >['PROPN']</th>\n",
       "      <th class=\"col_heading level0 col11\" >['SCONJ']</th>\n",
       "      <th class=\"col_heading level0 col12\" >['VERB']</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Level</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "      <th class=\"blank col10\" >&nbsp;</th>\n",
       "      <th class=\"blank col11\" >&nbsp;</th>\n",
       "      <th class=\"blank col12\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c6331_level0_row0\" class=\"row_heading level0 row0\" >Adv</th>\n",
       "      <td id=\"T_c6331_row0_col0\" class=\"data row0 col0\" >5.73%</td>\n",
       "      <td id=\"T_c6331_row0_col1\" class=\"data row0 col1\" >1.02%</td>\n",
       "      <td id=\"T_c6331_row0_col2\" class=\"data row0 col2\" >1.34%</td>\n",
       "      <td id=\"T_c6331_row0_col3\" class=\"data row0 col3\" >0.04%</td>\n",
       "      <td id=\"T_c6331_row0_col4\" class=\"data row0 col4\" >0.11%</td>\n",
       "      <td id=\"T_c6331_row0_col5\" class=\"data row0 col5\" >0.04%</td>\n",
       "      <td id=\"T_c6331_row0_col6\" class=\"data row0 col6\" >24.88%</td>\n",
       "      <td id=\"T_c6331_row0_col7\" class=\"data row0 col7\" >0.15%</td>\n",
       "      <td id=\"T_c6331_row0_col8\" class=\"data row0 col8\" >0.15%</td>\n",
       "      <td id=\"T_c6331_row0_col9\" class=\"data row0 col9\" >0.07%</td>\n",
       "      <td id=\"T_c6331_row0_col10\" class=\"data row0 col10\" >0.25%</td>\n",
       "      <td id=\"T_c6331_row0_col11\" class=\"data row0 col11\" >0.04%</td>\n",
       "      <td id=\"T_c6331_row0_col12\" class=\"data row0 col12\" >21.29%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6331_level0_row1\" class=\"row_heading level0 row1\" >Both</th>\n",
       "      <td id=\"T_c6331_row1_col0\" class=\"data row1 col0\" >0.44%</td>\n",
       "      <td id=\"T_c6331_row1_col1\" class=\"data row1 col1\" >0.04%</td>\n",
       "      <td id=\"T_c6331_row1_col2\" class=\"data row1 col2\" >0.07%</td>\n",
       "      <td id=\"T_c6331_row1_col3\" class=\"data row1 col3\" >0.00%</td>\n",
       "      <td id=\"T_c6331_row1_col4\" class=\"data row1 col4\" >0.00%</td>\n",
       "      <td id=\"T_c6331_row1_col5\" class=\"data row1 col5\" >0.00%</td>\n",
       "      <td id=\"T_c6331_row1_col6\" class=\"data row1 col6\" >1.41%</td>\n",
       "      <td id=\"T_c6331_row1_col7\" class=\"data row1 col7\" >0.00%</td>\n",
       "      <td id=\"T_c6331_row1_col8\" class=\"data row1 col8\" >0.00%</td>\n",
       "      <td id=\"T_c6331_row1_col9\" class=\"data row1 col9\" >0.00%</td>\n",
       "      <td id=\"T_c6331_row1_col10\" class=\"data row1 col10\" >0.07%</td>\n",
       "      <td id=\"T_c6331_row1_col11\" class=\"data row1 col11\" >0.00%</td>\n",
       "      <td id=\"T_c6331_row1_col12\" class=\"data row1 col12\" >3.30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6331_level0_row2\" class=\"row_heading level0 row2\" >Int</th>\n",
       "      <td id=\"T_c6331_row2_col0\" class=\"data row2 col0\" >5.01%</td>\n",
       "      <td id=\"T_c6331_row2_col1\" class=\"data row2 col1\" >1.34%</td>\n",
       "      <td id=\"T_c6331_row2_col2\" class=\"data row2 col2\" >1.23%</td>\n",
       "      <td id=\"T_c6331_row2_col3\" class=\"data row2 col3\" >0.04%</td>\n",
       "      <td id=\"T_c6331_row2_col4\" class=\"data row2 col4\" >0.07%</td>\n",
       "      <td id=\"T_c6331_row2_col5\" class=\"data row2 col5\" >0.25%</td>\n",
       "      <td id=\"T_c6331_row2_col6\" class=\"data row2 col6\" >17.56%</td>\n",
       "      <td id=\"T_c6331_row2_col7\" class=\"data row2 col7\" >0.15%</td>\n",
       "      <td id=\"T_c6331_row2_col8\" class=\"data row2 col8\" >0.15%</td>\n",
       "      <td id=\"T_c6331_row2_col9\" class=\"data row2 col9\" >0.22%</td>\n",
       "      <td id=\"T_c6331_row2_col10\" class=\"data row2 col10\" >0.07%</td>\n",
       "      <td id=\"T_c6331_row2_col11\" class=\"data row2 col11\" >0.33%</td>\n",
       "      <td id=\"T_c6331_row2_col12\" class=\"data row2 col12\" >13.17%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler object at 0x00000125000BEC40>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab_ct = pd.crosstab(df_vocab['Level'],df_vocab['POS'], normalize=True)\n",
    "df_vocab_ct = vocab_ct.style.format('{:.2%}')\n",
    "df_vocab_ct\n",
    "#dfi.export(df_vocab_ct, \"LevelPosPercent.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a16d60ce",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vocab_ct.plot.bar(stacked=True, figsize=(10,10))\n",
    "plt.title('Percentages of POS by Level')\n",
    "plt.xlabel('Level')\n",
    "plt.ylabel('Percent POS')\n",
    "filename4 = 'PercentagePOSbar'\n",
    "plt.savefig(filename4+'.png', facecolor=('#e8f4f0'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bf417241",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = sns.color_palette('muted')\n",
    "plt.figure(figsize=(10,10))\n",
    "chart1 = sns.countplot(x='POS', data=df_vocab, palette='muted', order=df_vocab['POS'].value_counts().index)\n",
    "chart1.set_xticklabels(chart1.get_xticklabels(), rotation=45)\n",
    "\n",
    "filename2 = 'Total_POS'\n",
    "plt.savefig(filename2+'.png', facecolor=('#e8f4f0'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1f6ab5ac",
   "metadata": {
    "scrolled": false
   },
   "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>Level</th>\n",
       "      <th>POS</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adv</td>\n",
       "      <td>['ADJ']</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adv</td>\n",
       "      <td>['ADP']</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adv</td>\n",
       "      <td>['ADV']</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adv</td>\n",
       "      <td>['AUX']</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adv</td>\n",
       "      <td>['CCONJ']</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Adv</td>\n",
       "      <td>['DET']</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Adv</td>\n",
       "      <td>['NOUN']</td>\n",
       "      <td>686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Adv</td>\n",
       "      <td>['NUM']</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Adv</td>\n",
       "      <td>['PART']</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Adv</td>\n",
       "      <td>['PRON']</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Adv</td>\n",
       "      <td>['PROPN']</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Adv</td>\n",
       "      <td>['SCONJ']</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Adv</td>\n",
       "      <td>['VERB']</td>\n",
       "      <td>587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Both</td>\n",
       "      <td>['ADJ']</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Both</td>\n",
       "      <td>['ADP']</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Both</td>\n",
       "      <td>['ADV']</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Both</td>\n",
       "      <td>['NOUN']</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Both</td>\n",
       "      <td>['PROPN']</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Both</td>\n",
       "      <td>['VERB']</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Int</td>\n",
       "      <td>['ADJ']</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Int</td>\n",
       "      <td>['ADP']</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Int</td>\n",
       "      <td>['ADV']</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Int</td>\n",
       "      <td>['AUX']</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Int</td>\n",
       "      <td>['CCONJ']</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Int</td>\n",
       "      <td>['DET']</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Int</td>\n",
       "      <td>['NOUN']</td>\n",
       "      <td>484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Int</td>\n",
       "      <td>['NUM']</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Int</td>\n",
       "      <td>['PART']</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Int</td>\n",
       "      <td>['PRON']</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Int</td>\n",
       "      <td>['PROPN']</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Int</td>\n",
       "      <td>['SCONJ']</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Int</td>\n",
       "      <td>['VERB']</td>\n",
       "      <td>363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Level        POS    0\n",
       "0    Adv    ['ADJ']  158\n",
       "1    Adv    ['ADP']   28\n",
       "2    Adv    ['ADV']   37\n",
       "3    Adv    ['AUX']    1\n",
       "4    Adv  ['CCONJ']    3\n",
       "5    Adv    ['DET']    1\n",
       "6    Adv   ['NOUN']  686\n",
       "7    Adv    ['NUM']    4\n",
       "8    Adv   ['PART']    4\n",
       "9    Adv   ['PRON']    2\n",
       "10   Adv  ['PROPN']    7\n",
       "11   Adv  ['SCONJ']    1\n",
       "12   Adv   ['VERB']  587\n",
       "13  Both    ['ADJ']   12\n",
       "14  Both    ['ADP']    1\n",
       "15  Both    ['ADV']    2\n",
       "16  Both   ['NOUN']   39\n",
       "17  Both  ['PROPN']    2\n",
       "18  Both   ['VERB']   91\n",
       "19   Int    ['ADJ']  138\n",
       "20   Int    ['ADP']   37\n",
       "21   Int    ['ADV']   34\n",
       "22   Int    ['AUX']    1\n",
       "23   Int  ['CCONJ']    2\n",
       "24   Int    ['DET']    7\n",
       "25   Int   ['NOUN']  484\n",
       "26   Int    ['NUM']    4\n",
       "27   Int   ['PART']    4\n",
       "28   Int   ['PRON']    6\n",
       "29   Int  ['PROPN']    2\n",
       "30   Int  ['SCONJ']    9\n",
       "31   Int   ['VERB']  363"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_by_level = df_vocab.groupby(['Level','POS']).size().reset_index()\n",
    "pos_by_level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1ab58a3f",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"13\" halign=\"left\">0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POS</th>\n",
       "      <th>['ADJ']</th>\n",
       "      <th>['ADP']</th>\n",
       "      <th>['ADV']</th>\n",
       "      <th>['AUX']</th>\n",
       "      <th>['CCONJ']</th>\n",
       "      <th>['DET']</th>\n",
       "      <th>['NOUN']</th>\n",
       "      <th>['NUM']</th>\n",
       "      <th>['PART']</th>\n",
       "      <th>['PRON']</th>\n",
       "      <th>['PROPN']</th>\n",
       "      <th>['SCONJ']</th>\n",
       "      <th>['VERB']</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Level</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adv</th>\n",
       "      <td>158</td>\n",
       "      <td>28</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>686</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Both</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Int</th>\n",
       "      <td>138</td>\n",
       "      <td>37</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>484</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0                                                             \\\n",
       "POS   ['ADJ'] ['ADP'] ['ADV'] ['AUX'] ['CCONJ'] ['DET'] ['NOUN'] ['NUM']   \n",
       "Level                                                                      \n",
       "Adv       158      28      37       1         3       1      686       4   \n",
       "Both       12       1       2       0         0       0       39       0   \n",
       "Int       138      37      34       1         2       7      484       4   \n",
       "\n",
       "                                                      \n",
       "POS   ['PART'] ['PRON'] ['PROPN'] ['SCONJ'] ['VERB']  \n",
       "Level                                                 \n",
       "Adv          4        2         7         1      587  \n",
       "Both         0        0         2         0       91  \n",
       "Int          4        6         2         9      363  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table1 = pd.pivot_table(pos_by_level, index=['Level'], columns=['POS'])\n",
    "table1 = table1.fillna(0).astype(int)\n",
    "table1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "16bed9bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "font_color = '#525252'\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(10,10), facecolor='#e8f4f0')\n",
    "fig.delaxes(ax= axes[1,1])\n",
    "\n",
    "for i, (idx,row) in enumerate(table1.iterrows()):\n",
    "    ax = axes[i // 2, i % 2]\n",
    "    row = row[row.gt(row.sum()*.0002)]\n",
    "    ax.pie(row,\n",
    "           labels=row.values,\n",
    "           startangle=30,\n",
    "           colors=sns.color_palette('muted', 13),\n",
    "           textprops={'color':font_color})\n",
    "    ax.set_title(idx, fontsize=16, color=font_color)\n",
    "    \n",
    "    legend = plt.legend([x[1] for x in row.index],\n",
    "                        bbox_to_anchor=(1.3, .87),\n",
    "                        loc='upper left',\n",
    "                        ncol=2,\n",
    "                        fancybox=True)\n",
    "    for text in legend.get_texts():\n",
    "        plt.setp(text, color=font_color)\n",
    "        \n",
    "fig.subplots_adjust(wspace=.2)\n",
    "\n",
    "title = fig.suptitle('Part of Speech Proportions According to Textbook Level',\n",
    "                     y=.95, fontsize=20, color=font_color)\n",
    "\n",
    "plt.subplots_adjust(top=0.85, bottom=0.15)\n",
    "\n",
    "filename3 = 'POS_Level_Comparisons'\n",
    "plt.savefig(filename3+'.png', facecolor=('#e8f4f0'))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}