3.4. Analysis_Q4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6cc95842",
   "metadata": {},
   "source": [
    "# 3. Analysis\n",
    "## 3.4. Q4: Comparison between the frequency of news stories aired about V. Putin and that of other individuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bae85616",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import copy\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import translators as ts\n",
    "import translators.server as tss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "94f3f743",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run \"3.0. Analysis_Functions.ipynb\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4ebc2252",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run \"0.1. Intro_Visualization style.ipynb\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1013bf71",
   "metadata": {},
   "source": [
    "## Data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "df40de7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tokenized data set\n",
    "file = 'data/df_tokenized.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "acb8f5f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tokenized = pd.read_csv(file)\n",
    "df_tokenized.drop(columns=['Unnamed: 0'], inplace=True)\n",
    "df_tokenized['dat'] = pd.to_datetime(df_tokenized['dat'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b6214f10",
   "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>id_in_source_file</th>\n",
       "      <th>date</th>\n",
       "      <th>tags_top</th>\n",
       "      <th>title</th>\n",
       "      <th>url</th>\n",
       "      <th>body</th>\n",
       "      <th>tags_bottom</th>\n",
       "      <th>video_duration_seconds</th>\n",
       "      <th>file_key</th>\n",
       "      <th>datetime</th>\n",
       "      <th>dat</th>\n",
       "      <th>year</th>\n",
       "      <th>year_month</th>\n",
       "      <th>hour</th>\n",
       "      <th>weekday</th>\n",
       "      <th>whole newscast</th>\n",
       "      <th>body_token_final</th>\n",
       "      <th>title_token_final</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>2023-01-27 21:45:00</td>\n",
       "      <td>Общество</td>\n",
       "      <td>Выпуск программы «Время» в 21:00 от 27.01.2023</td>\n",
       "      <td>https://www.1tv.ru/news/2023-01-27/446104-vypu...</td>\n",
       "      <td>Смотрите в этом выпуске: 79 лет назад была пол...</td>\n",
       "      <td>Общество</td>\n",
       "      <td>2422.0</td>\n",
       "      <td>0-15</td>\n",
       "      <td>2023-01-27 21:45:00</td>\n",
       "      <td>2023-01-27</td>\n",
       "      <td>2023</td>\n",
       "      <td>2023-01</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>['смотреть', 'выпуск', '79', 'год', 'полностью...</td>\n",
       "      <td>['выпуск', 'программа', 'время', '21:00', '27....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>2023-01-27 21:35:00</td>\n",
       "      <td>Культура</td>\n",
       "      <td>В Москве проходит церемония вручения премии «З...</td>\n",
       "      <td>https://www.1tv.ru/news/2023-01-27/446121-v_mo...</td>\n",
       "      <td>Событие в мире отечественного кино. В Москве в...</td>\n",
       "      <td>Культура,Кино,Марина Зудина,Екатерина Климова,...</td>\n",
       "      <td>290.0</td>\n",
       "      <td>0-15</td>\n",
       "      <td>2023-01-27 21:35:00</td>\n",
       "      <td>2023-01-27</td>\n",
       "      <td>2023</td>\n",
       "      <td>2023-01</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>['событие', 'мир', 'отечественный', 'кино', 'м...</td>\n",
       "      <td>['москва', 'проходить', 'церемония', 'вручение...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>2023-01-27 21:34:00</td>\n",
       "      <td>Спорт</td>\n",
       "      <td>В Туле появился новый футбольный манеж</td>\n",
       "      <td>https://www.1tv.ru/news/2023-01-27/446120-v_tu...</td>\n",
       "      <td>Футбольный манеж в Туле — искусственный газон ...</td>\n",
       "      <td>Спорт,Регионы,Футбол,Алексей Дюмин,Тульская об...</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0-15</td>\n",
       "      <td>2023-01-27 21:34:00</td>\n",
       "      <td>2023-01-27</td>\n",
       "      <td>2023</td>\n",
       "      <td>2023-01</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>['футбольный', 'манеж', 'тула', 'искусственный...</td>\n",
       "      <td>['тула', 'появиться', 'новый', 'футбольный', '...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id_in_source_file                 date  tags_top  \\\n",
       "0                 15  2023-01-27 21:45:00  Общество   \n",
       "1                 16  2023-01-27 21:35:00  Культура   \n",
       "2                 20  2023-01-27 21:34:00     Спорт   \n",
       "\n",
       "                                               title  \\\n",
       "0     Выпуск программы «Время» в 21:00 от 27.01.2023   \n",
       "1  В Москве проходит церемония вручения премии «З...   \n",
       "2             В Туле появился новый футбольный манеж   \n",
       "\n",
       "                                                 url  \\\n",
       "0  https://www.1tv.ru/news/2023-01-27/446104-vypu...   \n",
       "1  https://www.1tv.ru/news/2023-01-27/446121-v_mo...   \n",
       "2  https://www.1tv.ru/news/2023-01-27/446120-v_tu...   \n",
       "\n",
       "                                                body  \\\n",
       "0  Смотрите в этом выпуске: 79 лет назад была пол...   \n",
       "1  Событие в мире отечественного кино. В Москве в...   \n",
       "2  Футбольный манеж в Туле — искусственный газон ...   \n",
       "\n",
       "                                         tags_bottom  video_duration_seconds  \\\n",
       "0                                           Общество                  2422.0   \n",
       "1  Культура,Кино,Марина Зудина,Екатерина Климова,...                   290.0   \n",
       "2  Спорт,Регионы,Футбол,Алексей Дюмин,Тульская об...                    48.0   \n",
       "\n",
       "  file_key             datetime        dat  year year_month  hour  weekday  \\\n",
       "0     0-15  2023-01-27 21:45:00 2023-01-27  2023    2023-01    21        4   \n",
       "1     0-15  2023-01-27 21:35:00 2023-01-27  2023    2023-01    21        4   \n",
       "2     0-15  2023-01-27 21:34:00 2023-01-27  2023    2023-01    21        4   \n",
       "\n",
       "   whole newscast                                   body_token_final  \\\n",
       "0            True  ['смотреть', 'выпуск', '79', 'год', 'полностью...   \n",
       "1           False  ['событие', 'мир', 'отечественный', 'кино', 'м...   \n",
       "2           False  ['футбольный', 'манеж', 'тула', 'искусственный...   \n",
       "\n",
       "                                   title_token_final  \n",
       "0  ['выпуск', 'программа', 'время', '21:00', '27....  \n",
       "1  ['москва', 'проходить', 'церемония', 'вручение...  \n",
       "2  ['тула', 'появиться', 'новый', 'футбольный', '...  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tokenized.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f2c09935",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose a period for analysis (before 2018, quality of bottom_tags is not so good)\n",
    "df = df_tokenized[df_tokenized['dat'] >= pd.to_datetime('2018-01-01')].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf29d325",
   "metadata": {},
   "source": [
    "## Measuring news story frequency for individuals' names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5160e296",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              Марина Зудина\n",
       "1          Екатерина Климова\n",
       "2             Никита Ефремов\n",
       "3              Алексей Дюмин\n",
       "4        Республика Дагестан\n",
       "                ...         \n",
       "79629         Владимир Путин\n",
       "79630       Кирилл Клейменов\n",
       "79631          Анна Шатилова\n",
       "79632         Сергей Собянин\n",
       "79633         Сергей Собянин\n",
       "Name: 0, Length: 79634, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract all names from the tags\n",
    "names = df['tags_bottom'].str.extractall(\"([А-Я][а-я]* [А-Я][а-я]*)\")\n",
    "names.reset_index(inplace=True)\n",
    "names[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8bfacdec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Владимир Путин', 11386),\n",
       " ('Михаил Мишустин', 2388),\n",
       " ('Дональд Трамп', 1914),\n",
       " ('Дмитрий Медведев', 1754),\n",
       " ('Сергей Лавров', 1728)]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Trasform series with names into Counter\n",
    "names_count = Counter(names[0])\n",
    "names_count.most_common(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fb4ffce8",
   "metadata": {},
   "outputs": [],
   "source": [
    "names_df = pd.DataFrame.from_dict(dict(names_count), \n",
    "                                  orient='index', \n",
    "                                  columns=['frequency'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "50f1f6ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>index</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Владимир Путин</td>\n",
       "      <td>11386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Михаил Мишустин</td>\n",
       "      <td>2388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Дональд Трамп</td>\n",
       "      <td>1914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Дмитрий Медведев</td>\n",
       "      <td>1754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Сергей Лавров</td>\n",
       "      <td>1728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Сергей Собянин</td>\n",
       "      <td>1138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Игорь Конашенков</td>\n",
       "      <td>1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Сергей Шойгу</td>\n",
       "      <td>1015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Джозеф Байден</td>\n",
       "      <td>952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Владимир Зеленский</td>\n",
       "      <td>925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Вячеслав Володин</td>\n",
       "      <td>899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Петр Порошенко</td>\n",
       "      <td>687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Эммануэль Макрон</td>\n",
       "      <td>665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Дмитрий Песков</td>\n",
       "      <td>643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Мария Захарова</td>\n",
       "      <td>598</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 index  frequency\n",
       "0       Владимир Путин      11386\n",
       "1      Михаил Мишустин       2388\n",
       "2        Дональд Трамп       1914\n",
       "3     Дмитрий Медведев       1754\n",
       "4        Сергей Лавров       1728\n",
       "5       Сергей Собянин       1138\n",
       "6     Игорь Конашенков       1097\n",
       "7         Сергей Шойгу       1015\n",
       "8        Джозеф Байден        952\n",
       "9   Владимир Зеленский        925\n",
       "10    Вячеслав Володин        899\n",
       "11      Петр Порошенко        687\n",
       "12    Эммануэль Макрон        665\n",
       "13      Дмитрий Песков        643\n",
       "14      Мария Захарова        598"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names_df.sort_values(by='frequency', ascending=False, inplace=True)\n",
    "\n",
    "# Exclude rows that are not people's names\n",
    "names_df['delete'] = names_df.index.str.contains('Республика|День|Чемпионат') \n",
    "names_df = names_df[names_df['delete']==False]\n",
    "names_df.drop(columns=['delete'], inplace=True)\n",
    "\n",
    "names_df = names_df.head(15)\n",
    "names_df.reset_index(inplace=True)\n",
    "names_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "62a0a20c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Translate names into English\n",
    "names_df['eng_name'] = names_df['index'].apply(lambda x: tss.google(x, 'ru', 'en'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d61b8be8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>index</th>\n",
       "      <th>frequency</th>\n",
       "      <th>eng_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Владимир Путин</td>\n",
       "      <td>11386</td>\n",
       "      <td>Vladimir Putin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Михаил Мишустин</td>\n",
       "      <td>2388</td>\n",
       "      <td>Mikhail Mishustin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Дональд Трамп</td>\n",
       "      <td>1914</td>\n",
       "      <td>Donald Trump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Дмитрий Медведев</td>\n",
       "      <td>1754</td>\n",
       "      <td>Dmitry Medvedev</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Сергей Лавров</td>\n",
       "      <td>1728</td>\n",
       "      <td>Sergey Lavrov</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Сергей Собянин</td>\n",
       "      <td>1138</td>\n",
       "      <td>Sergey Sobyanin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Игорь Конашенков</td>\n",
       "      <td>1097</td>\n",
       "      <td>Igor Konashenkov</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Сергей Шойгу</td>\n",
       "      <td>1015</td>\n",
       "      <td>Sergey Shoigu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Джозеф Байден</td>\n",
       "      <td>952</td>\n",
       "      <td>Joseph Biden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Владимир Зеленский</td>\n",
       "      <td>925</td>\n",
       "      <td>Vladimir Zelensky</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Вячеслав Володин</td>\n",
       "      <td>899</td>\n",
       "      <td>Vyacheslav Volodin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Петр Порошенко</td>\n",
       "      <td>687</td>\n",
       "      <td>Petro Poroshenko</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Эммануэль Макрон</td>\n",
       "      <td>665</td>\n",
       "      <td>Emmanuel Macron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Дмитрий Песков</td>\n",
       "      <td>643</td>\n",
       "      <td>Dmitry Peskov</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Мария Захарова</td>\n",
       "      <td>598</td>\n",
       "      <td>Maria Zakharova</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 index  frequency            eng_name\n",
       "0       Владимир Путин      11386      Vladimir Putin\n",
       "1      Михаил Мишустин       2388   Mikhail Mishustin\n",
       "2        Дональд Трамп       1914        Donald Trump\n",
       "3     Дмитрий Медведев       1754     Dmitry Medvedev\n",
       "4        Сергей Лавров       1728       Sergey Lavrov\n",
       "5       Сергей Собянин       1138     Sergey Sobyanin\n",
       "6     Игорь Конашенков       1097    Igor Konashenkov\n",
       "7         Сергей Шойгу       1015       Sergey Shoigu\n",
       "8        Джозеф Байден        952        Joseph Biden\n",
       "9   Владимир Зеленский        925   Vladimir Zelensky\n",
       "10    Вячеслав Володин        899  Vyacheslav Volodin\n",
       "11      Петр Порошенко        687    Petro Poroshenko\n",
       "12    Эммануэль Макрон        665     Emmanuel Macron\n",
       "13      Дмитрий Песков        643       Dmitry Peskov\n",
       "14      Мария Захарова        598     Maria Zakharova"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e051308c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 280x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot bar chart\n",
    "names_df.sort_values(by='frequency', inplace=True)\n",
    "\n",
    "plt.figure(figsize=(2.8, 3))\n",
    "plt.barh(names_df['eng_name'], names_df['frequency'], color=color_1)\n",
    "plt.title('Number of News Stories Featuring\\n Officials in Tags (Jan 2018 - Jan 2023)', loc='right');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e096e4ac",
   "metadata": {},
   "source": [
    "## Analysis of news coverage during presidential elections"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d2e3523",
   "metadata": {},
   "source": [
    "### Useful functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "aa74a4da",
   "metadata": {},
   "outputs": [],
   "source": [
    "def election(df, dat1, dat2, candidates, tokens):\n",
    "    \"\"\"\n",
    "    Calculates for presidential candidates the number \n",
    "    of mentions in the news and the duration of videos \n",
    "    during the campaign period\n",
    "    \n",
    "    Args: \n",
    "    df - dataframe with tokenized data\n",
    "    dat1, dat2 - datetime, date of start and date of end\n",
    "                 of the campaign\n",
    "    candidates - list of candidates names\n",
    "    tokens - list of candidates tokens\n",
    "    \n",
    "    Returns: candidates_df - dataframe\n",
    "    \"\"\"\n",
    "    df_elect = df[(df['dat']>=dat1)&(df['dat']<=dat2)].copy()\n",
    "    \n",
    "    news_number = []\n",
    "    news_duration = []\n",
    "    \n",
    "    for i in range(len(tokens)):\n",
    "        # Mark news in which the candidate is mentioned\n",
    "        token_dict = {'body_token_final': [tokens[i]],\n",
    "                      'title_token_final': [tokens[i]]}\n",
    "        df_c = find_tokens(df_elect, token_dict)\n",
    "        \n",
    "        # Number of news where the candidate was mentioned\n",
    "        news_number_c = df_c.loc[df_c['contain_token']==1, 'url'].count()\n",
    "        # Duration of news where the candidate was mentioned (minutes)\n",
    "        news_duration_c = round(df_c.loc[df_c['contain_token']==1, 'video_duration_seconds'].sum() / 60)\n",
    "        \n",
    "        news_number.append(news_number_c)\n",
    "        news_duration.append(news_duration_c)\n",
    "    \n",
    "    candidates_df = pd.DataFrame(candidates, columns =['candidates'])\n",
    "    candidates_df['news_number'] = news_number\n",
    "    candidates_df['news_duration'] = news_duration\n",
    "    \n",
    "    # Translate candidate names into English\n",
    "    candidates_df['eng_name'] = candidates_df['candidates'].apply(lambda x: tss.google(x, 'ru', 'en'))\n",
    "    \n",
    "    candidates_df.sort_values(by='news_duration', inplace=True)\n",
    "    \n",
    "    \n",
    "    return candidates_df "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d740e473",
   "metadata": {},
   "source": [
    "### Presidential election 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7a47b86e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The presidential campaign\n",
    "dat1 = pd.to_datetime('2018-02-17')\n",
    "dat2 = pd.to_datetime('2018-03-17')\n",
    "\n",
    "# Candidates\n",
    "candidates = ['путин', 'грудинин', 'жириновский',\n",
    "              'собчак', 'явлинский', 'титов',\n",
    "              'сурайкин', 'бабурин']\n",
    "\n",
    "tokens = ['путин', 'грудинин', 'жириновск',\n",
    "          'собчак', 'явлинск', 'титов',\n",
    "          'сурайкин', 'бабурин']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "828a1d30",
   "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>candidates</th>\n",
       "      <th>news_number</th>\n",
       "      <th>news_duration</th>\n",
       "      <th>eng_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>сурайкин</td>\n",
       "      <td>33</td>\n",
       "      <td>239</td>\n",
       "      <td>Suraykin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>собчак</td>\n",
       "      <td>33</td>\n",
       "      <td>247</td>\n",
       "      <td>Sobchak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>титов</td>\n",
       "      <td>32</td>\n",
       "      <td>247</td>\n",
       "      <td>Titov</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>грудинин</td>\n",
       "      <td>38</td>\n",
       "      <td>250</td>\n",
       "      <td>Grudinin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>явлинский</td>\n",
       "      <td>34</td>\n",
       "      <td>252</td>\n",
       "      <td>Yavlinsky</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>бабурин</td>\n",
       "      <td>33</td>\n",
       "      <td>252</td>\n",
       "      <td>Baburin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>жириновский</td>\n",
       "      <td>40</td>\n",
       "      <td>268</td>\n",
       "      <td>Zhirinovsky</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>путин</td>\n",
       "      <td>300</td>\n",
       "      <td>1260</td>\n",
       "      <td>Putin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    candidates  news_number  news_duration     eng_name\n",
       "6     сурайкин           33            239     Suraykin\n",
       "3       собчак           33            247      Sobchak\n",
       "5        титов           32            247        Titov\n",
       "1     грудинин           38            250     Grudinin\n",
       "4    явлинский           34            252    Yavlinsky\n",
       "7      бабурин           33            252      Baburin\n",
       "2  жириновский           40            268  Zhirinovsky\n",
       "0        путин          300           1260        Putin"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidates_df_2018 = election(df_tokenized, dat1, dat2, candidates, tokens)\n",
    "candidates_df_2018"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7051714",
   "metadata": {},
   "source": [
    "### Presidential election 2012"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "86a3d075",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The presidential campaign\n",
    "dat1 = pd.to_datetime('2012-02-05')\n",
    "dat2 = pd.to_datetime('2012-03-03')\n",
    "\n",
    "# Candidates\n",
    "candidates = ['путин', 'зюганов', 'прохоров',\n",
    "              'жириновский', 'миронов']\n",
    "\n",
    "tokens = ['путин', 'зюганов', 'прохоров',\n",
    "              'жириновск', 'миронов']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "35dc337a",
   "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>candidates</th>\n",
       "      <th>news_number</th>\n",
       "      <th>news_duration</th>\n",
       "      <th>eng_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>зюганов</td>\n",
       "      <td>78</td>\n",
       "      <td>513</td>\n",
       "      <td>Zyuganov</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>прохоров</td>\n",
       "      <td>70</td>\n",
       "      <td>528</td>\n",
       "      <td>Prokhorov</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>жириновский</td>\n",
       "      <td>87</td>\n",
       "      <td>554</td>\n",
       "      <td>Zhirinovsky</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>миронов</td>\n",
       "      <td>73</td>\n",
       "      <td>554</td>\n",
       "      <td>Mironov</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>путин</td>\n",
       "      <td>233</td>\n",
       "      <td>1162</td>\n",
       "      <td>Putin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    candidates  news_number  news_duration     eng_name\n",
       "1      зюганов           78            513     Zyuganov\n",
       "2     прохоров           70            528    Prokhorov\n",
       "3  жириновский           87            554  Zhirinovsky\n",
       "4      миронов           73            554      Mironov\n",
       "0        путин          233           1162        Putin"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidates_df_2012 = election(df_tokenized, dat1, dat2, candidates, tokens)\n",
    "candidates_df_2012"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5576ec78",
   "metadata": {},
   "source": [
    "### Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c93646eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(8,3))\n",
    "\n",
    "ax[0].barh(candidates_df_2018['eng_name'], candidates_df_2018['news_duration'], color=color_1)\n",
    "ax[1].barh(candidates_df_2012['eng_name'], candidates_df_2012['news_duration'], color=color_3)\n",
    "ax[0].title.set_text('2018')\n",
    "ax[1].title.set_text('2012')\n",
    "\n",
    "# Add more space between subplots\n",
    "plt.subplots_adjust(left=0.1,\n",
    "                    bottom=0.23,\n",
    "                    right=0.9,\n",
    "                    top=0.77,\n",
    "                    wspace=0.5)\n",
    "\n",
    "plt.suptitle('Duration of News Stories Featuring Presidential Candidates\\n During the Campaign Period (minutes)',\n",
    "             size=12, weight='bold');"
   ]
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}