2.2. DataManipulation_Data quality.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1e9e8d5b",
   "metadata": {},
   "source": [
    "# 2. Data Manipulation\n",
    "# 2.2. Quality of scraping data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "5e147f3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "6dc3dcf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run \"0.1. Intro_Visualization style.ipynb\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b63a4422",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run \"2.0. DataManipulation_Functions.ipynb\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "4d4a7309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0-15_output_scraping_cleaned.csv',\n",
       " '105-120_output_scraping_cleaned.csv',\n",
       " '120-135_output_scraping_cleaned.csv',\n",
       " '135-150_output_scraping_cleaned.csv',\n",
       " '15-30_output_scraping_cleaned.csv',\n",
       " '150-165_output_scraping_cleaned.csv',\n",
       " '165-180_output_scraping_cleaned.csv',\n",
       " '180-195_output_scraping_cleaned.csv',\n",
       " '195-210_output_scraping_cleaned.csv',\n",
       " '210-225_output_scraping_cleaned.csv',\n",
       " '225-240_output_scraping_cleaned.csv',\n",
       " '240-255_output_scraping_cleaned.csv',\n",
       " '255-270_output_scraping_cleaned.csv',\n",
       " '270-285_output_scraping_cleaned.csv',\n",
       " '285-300_output_scraping_cleaned.csv',\n",
       " '30-45_output_scraping_cleaned.csv',\n",
       " '300-315_output_scraping_cleaned.csv',\n",
       " '315-330_output_scraping_cleaned.csv',\n",
       " '330-345_output_scraping_cleaned.csv',\n",
       " '345-360_output_scraping_cleaned.csv',\n",
       " '45-60_output_scraping_cleaned.csv',\n",
       " '60-75_output_scraping_cleaned.csv',\n",
       " '75-90_output_scraping_cleaned.csv',\n",
       " '90-105_output_scraping_cleaned.csv']"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folder = 'C:/University of Michigan/SIADS 593 Milestone 1/Project/Primary data'\n",
    "files = os.listdir(folder)\n",
    "files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "028973f3",
   "metadata": {},
   "source": [
    "## Data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "7ddb9371",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scraped = load_clean_scraped_data(folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "3ee9f2f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>weekday</th>\n",
       "      <th>whole newscast</th>\n",
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       "  </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",
       "    </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",
       "    </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",
       "    </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  \n",
       "0            True  \n",
       "1           False  \n",
       "2           False  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scraped.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "5a667204",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scraped['dat'] = pd.to_datetime(df_scraped['dat'])\n",
    "df_scraped['video_duration_min'] = df_scraped['video_duration_seconds'] / 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "b5253e1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(359041, 17)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scraped.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "feb71ed0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_in_source_file</th>\n",
       "      <th>video_duration_seconds</th>\n",
       "      <th>year</th>\n",
       "      <th>hour</th>\n",
       "      <th>weekday</th>\n",
       "      <th>video_duration_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>359041.000000</td>\n",
       "      <td>314073.000000</td>\n",
       "      <td>359041.000000</td>\n",
       "      <td>359041.000000</td>\n",
       "      <td>359041.000000</td>\n",
       "      <td>314073.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7508.671405</td>\n",
       "      <td>157.577331</td>\n",
       "      <td>2014.384154</td>\n",
       "      <td>14.459062</td>\n",
       "      <td>2.579984</td>\n",
       "      <td>2.626289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4320.757624</td>\n",
       "      <td>285.130063</td>\n",
       "      <td>4.563075</td>\n",
       "      <td>4.777690</td>\n",
       "      <td>1.828797</td>\n",
       "      <td>4.752168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3768.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7508.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.116667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11248.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15014.000000</td>\n",
       "      <td>11399.000000</td>\n",
       "      <td>2023.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>189.983333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id_in_source_file  video_duration_seconds           year  \\\n",
       "count      359041.000000           314073.000000  359041.000000   \n",
       "mean         7508.671405              157.577331    2014.384154   \n",
       "std          4320.757624              285.130063       4.563075   \n",
       "min             0.000000                0.000000    2006.000000   \n",
       "25%          3768.000000               38.000000    2011.000000   \n",
       "50%          7508.000000               67.000000    2014.000000   \n",
       "75%         11248.000000              201.000000    2018.000000   \n",
       "max         15014.000000            11399.000000    2023.000000   \n",
       "\n",
       "                hour        weekday  video_duration_min  \n",
       "count  359041.000000  359041.000000       314073.000000  \n",
       "mean       14.459062       2.579984            2.626289  \n",
       "std         4.777690       1.828797            4.752168  \n",
       "min         0.000000       0.000000            0.000000  \n",
       "25%        10.000000       1.000000            0.633333  \n",
       "50%        15.000000       3.000000            1.116667  \n",
       "75%        18.000000       4.000000            3.350000  \n",
       "max        23.000000       6.000000          189.983333  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scraped.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "743047eb",
   "metadata": {},
   "source": [
    "## Checking outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "dc68144e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>weekday</th>\n",
       "      <th>whole newscast</th>\n",
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       "      <th>16946</th>\n",
       "      <td>1857</td>\n",
       "      <td>2022-05-09 17:12:00</td>\n",
       "      <td>Общество</td>\n",
       "      <td>Выпуск новостей в 13:50 от 09.05.2022</td>\n",
       "      <td>https://www.1tv.ru/news/2022-05-09/428383-vypu...</td>\n",
       "      <td>Смотрите в этом выпуске: в Кремле президент вр...</td>\n",
       "      <td>Общество</td>\n",
       "      <td>11399.0</td>\n",
       "      <td>15-30</td>\n",
       "      <td>2022-05-09 17:12:00</td>\n",
       "      <td>2022-05-09</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-05</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>189.983333</td>\n",
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      ],
      "text/plain": [
       "       id_in_source_file                 date  tags_top  \\\n",
       "16946               1857  2022-05-09 17:12:00  Общество   \n",
       "\n",
       "                                       title  \\\n",
       "16946  Выпуск новостей в 13:50 от 09.05.2022   \n",
       "\n",
       "                                                     url  \\\n",
       "16946  https://www.1tv.ru/news/2022-05-09/428383-vypu...   \n",
       "\n",
       "                                                    body tags_bottom  \\\n",
       "16946  Смотрите в этом выпуске: в Кремле президент вр...    Общество   \n",
       "\n",
       "       video_duration_seconds file_key            datetime        dat  year  \\\n",
       "16946                 11399.0    15-30 2022-05-09 17:12:00 2022-05-09  2022   \n",
       "\n",
       "      year_month  hour  weekday  whole newscast  video_duration_min  \n",
       "16946    2022-05    17        0            True          189.983333  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.reset_option('display.max_colwidth')\n",
    "df_scraped[df_scraped['video_duration_seconds'] > 10000]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edb5b918",
   "metadata": {},
   "source": [
    "After examining the outlier in the dataset, we concluded that it is not a mistake and should be kept in the analysis. The First Channel had a 3 hours newscast on 2022-05-09, which is a Victory Day over Nazi Germany in Russia. This outliers reflects an important characteristic of Russian propaganda that seeks to use national pride and historical events to unite the population. Therefore, removing the outlier would lead to a loss of valuable information and potentially bias the analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "012f5be0",
   "metadata": {},
   "outputs": [
    {
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       "      <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>video_duration_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>314474</th>\n",
       "      <td>408</td>\n",
       "      <td>2009-03-01 21:01:00</td>\n",
       "      <td>Экономика</td>\n",
       "      <td>Владимир Путин обсудил с руководством «Единой ...</td>\n",
       "      <td>https://www.1tv.ru/news/2009-03-01/169871-vlad...</td>\n",
       "      <td>Экономика остается в центре внимания политиков...</td>\n",
       "      <td>Экономика</td>\n",
       "      <td>0.0</td>\n",
       "      <td>315-330</td>\n",
       "      <td>2009-03-01 21:01:00</td>\n",
       "      <td>2009-03-01</td>\n",
       "      <td>2009</td>\n",
       "      <td>2009-03</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337018</th>\n",
       "      <td>7984</td>\n",
       "      <td>2008-02-03 21:43:00</td>\n",
       "      <td>В мире</td>\n",
       "      <td>Европейские целостности: Сербия выбирает между...</td>\n",
       "      <td>https://www.1tv.ru/news/2008-02-03/196380-evro...</td>\n",
       "      <td>В Сербии второй тур выборов президента, борьбу...</td>\n",
       "      <td>В мире</td>\n",
       "      <td>0.0</td>\n",
       "      <td>330-345</td>\n",
       "      <td>2008-02-03 21:43:00</td>\n",
       "      <td>2008-02-03</td>\n",
       "      <td>2008</td>\n",
       "      <td>2008-02</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348766</th>\n",
       "      <td>4711</td>\n",
       "      <td>2007-06-13 14:28:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Как правильно наказывать ребенка? Советы психо...</td>\n",
       "      <td>https://www.1tv.ru/news/2007-06-13/209008-kak_...</td>\n",
       "      <td>В Америке пятилетний ребенок сдал отца полицей...</td>\n",
       "      <td>Другие новости</td>\n",
       "      <td>0.0</td>\n",
       "      <td>345-360</td>\n",
       "      <td>2007-06-13 14:28:00</td>\n",
       "      <td>2007-06-13</td>\n",
       "      <td>2007</td>\n",
       "      <td>2007-06</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id_in_source_file                 date   tags_top  \\\n",
       "314474                408  2009-03-01 21:01:00  Экономика   \n",
       "337018               7984  2008-02-03 21:43:00     В мире   \n",
       "348766               4711  2007-06-13 14:28:00        NaN   \n",
       "\n",
       "                                                    title  \\\n",
       "314474  Владимир Путин обсудил с руководством «Единой ...   \n",
       "337018  Европейские целостности: Сербия выбирает между...   \n",
       "348766  Как правильно наказывать ребенка? Советы психо...   \n",
       "\n",
       "                                                      url  \\\n",
       "314474  https://www.1tv.ru/news/2009-03-01/169871-vlad...   \n",
       "337018  https://www.1tv.ru/news/2008-02-03/196380-evro...   \n",
       "348766  https://www.1tv.ru/news/2007-06-13/209008-kak_...   \n",
       "\n",
       "                                                     body     tags_bottom  \\\n",
       "314474  Экономика остается в центре внимания политиков...       Экономика   \n",
       "337018  В Сербии второй тур выборов президента, борьбу...          В мире   \n",
       "348766  В Америке пятилетний ребенок сдал отца полицей...  Другие новости   \n",
       "\n",
       "        video_duration_seconds file_key            datetime        dat  year  \\\n",
       "314474                     0.0  315-330 2009-03-01 21:01:00 2009-03-01  2009   \n",
       "337018                     0.0  330-345 2008-02-03 21:43:00 2008-02-03  2008   \n",
       "348766                     0.0  345-360 2007-06-13 14:28:00 2007-06-13  2007   \n",
       "\n",
       "       year_month  hour  weekday  whole newscast  video_duration_min  \n",
       "314474    2009-03    21        6           False                 0.0  \n",
       "337018    2008-02    21        6           False                 0.0  \n",
       "348766    2007-06    14        2           False                 0.0  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scraped[df_scraped['video_duration_seconds']==0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ed651c1",
   "metadata": {},
   "source": [
    "Although the values for the \"video_duration_seconds\" column were not loaded properly for these three rows, this will not have a significant impact on our analysis. We plan to use data on video duration starting from 2011, and these three rows fall outside of that range. Therefore, the mistake will not affect our analysis, and we can proceed with confidence that our results will not be skewed by this issue."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2038c23f",
   "metadata": {},
   "source": [
    "### Plotting time series to check for strange values or anomalies in a dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "579377fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate total news duration per day\n",
    "news_duration = df_scraped.groupby('dat')[['video_duration_min']].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "81070b2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw timeseries of daily news duration\n",
    "\n",
    "plt.figure(figsize=(8, 3))\n",
    "plt.plot(news_duration.index, news_duration['video_duration_min'], \n",
    "         c=color_1, label='total duration');\n",
    "plt.title('Time series of daily total news duration');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "46c8812f",
   "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>video_duration_min</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dat</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>21.766667</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-02</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-03</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-27</th>\n",
       "      <td>113.966667</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-28</th>\n",
       "      <td>109.733333</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-29</th>\n",
       "      <td>110.416667</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-30</th>\n",
       "      <td>103.650000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-31</th>\n",
       "      <td>57.050000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            video_duration_min  year\n",
       "dat                                 \n",
       "2010-01-01           21.766667  2010\n",
       "2010-01-02            0.000000  2010\n",
       "2010-01-03            0.000000  2010\n",
       "2010-01-04            0.000000  2010\n",
       "2010-01-05            0.000000  2010\n",
       "...                        ...   ...\n",
       "2010-12-27          113.966667  2010\n",
       "2010-12-28          109.733333  2010\n",
       "2010-12-29          110.416667  2010\n",
       "2010-12-30          103.650000  2010\n",
       "2010-12-31           57.050000  2010\n",
       "\n",
       "[365 rows x 2 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "news_duration['year'] = news_duration.index.year\n",
    "news_duration[news_duration['year']==2010]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "0b2b6bd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2010-01-02', '2010-01-03', '2010-01-04', '2010-01-05',\n",
       "               '2010-01-06', '2010-01-07', '2010-01-08', '2010-01-09',\n",
       "               '2010-01-10', '2010-01-11',\n",
       "               ...\n",
       "               '2010-04-09', '2010-04-10', '2010-04-11', '2010-04-12',\n",
       "               '2010-04-13', '2010-04-14', '2010-04-15', '2010-04-16',\n",
       "               '2010-04-17', '2010-04-18'],\n",
       "              dtype='datetime64[ns]', name='dat', length=107, freq=None)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find for what period we don't have video duration\n",
    "news_duration[(news_duration['year']==2010)&(news_duration['video_duration_min']==0)].index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c157de2f",
   "metadata": {},
   "source": [
    "There is a gap in the video duration data for the period from 2010-01-02 to 2010-04-18. The reason is that the First Channel did not upload videos during this time. However, since we are planning to use information on video duration starting from 2011, this mistake should not impact our results. We can proceed with the understanding that our analysis will not be affected by this gap in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b9550cc",
   "metadata": {},
   "source": [
    "## Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "037367f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 359041 entries, 0 to 359040\n",
      "Data columns (total 17 columns):\n",
      " #   Column                  Non-Null Count   Dtype         \n",
      "---  ------                  --------------   -----         \n",
      " 0   id_in_source_file       359041 non-null  int64         \n",
      " 1   date                    359041 non-null  object        \n",
      " 2   tags_top                335525 non-null  object        \n",
      " 3   title                   359041 non-null  object        \n",
      " 4   url                     359041 non-null  object        \n",
      " 5   body                    359041 non-null  object        \n",
      " 6   tags_bottom             356653 non-null  object        \n",
      " 7   video_duration_seconds  314073 non-null  float64       \n",
      " 8   file_key                359041 non-null  object        \n",
      " 9   datetime                359041 non-null  datetime64[ns]\n",
      " 10  dat                     359041 non-null  datetime64[ns]\n",
      " 11  year                    359041 non-null  int64         \n",
      " 12  year_month              359041 non-null  period[M]     \n",
      " 13  hour                    359041 non-null  int64         \n",
      " 14  weekday                 359041 non-null  int64         \n",
      " 15  whole newscast          359041 non-null  bool          \n",
      " 16  video_duration_min      314073 non-null  float64       \n",
      "dtypes: bool(1), datetime64[ns](2), float64(2), int64(4), object(7), period[M](1)\n",
      "memory usage: 44.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df_scraped.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "f89c5232",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can see that the column 'video_duration_seconds' has missing values\n",
    "df_scraped['video_missing'] = np.where(df_scraped['video_duration_seconds'].isnull(),\n",
    "                                      1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "ebb5a18f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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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>sum</th>\n",
       "      <th>len</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>74</td>\n",
       "      <td>4333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1147</td>\n",
       "      <td>16134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>2840</td>\n",
       "      <td>20598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>4175</td>\n",
       "      <td>22675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>8495</td>\n",
       "      <td>21736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>4031</td>\n",
       "      <td>26277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>3571</td>\n",
       "      <td>26907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>3444</td>\n",
       "      <td>25345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>3206</td>\n",
       "      <td>24584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>2840</td>\n",
       "      <td>23366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>2582</td>\n",
       "      <td>22826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>2588</td>\n",
       "      <td>20967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2340</td>\n",
       "      <td>18654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2206</td>\n",
       "      <td>19371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>1272</td>\n",
       "      <td>20346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>110</td>\n",
       "      <td>18883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>44</td>\n",
       "      <td>24572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>3</td>\n",
       "      <td>1467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sum    len\n",
       "year             \n",
       "2006    74   4333\n",
       "2007  1147  16134\n",
       "2008  2840  20598\n",
       "2009  4175  22675\n",
       "2010  8495  21736\n",
       "2011  4031  26277\n",
       "2012  3571  26907\n",
       "2013  3444  25345\n",
       "2014  3206  24584\n",
       "2015  2840  23366\n",
       "2016  2582  22826\n",
       "2017  2588  20967\n",
       "2018  2340  18654\n",
       "2019  2206  19371\n",
       "2020  1272  20346\n",
       "2021   110  18883\n",
       "2022    44  24572\n",
       "2023     3   1467"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scraped.groupby('year')['video_missing'].agg([sum, len])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e09551d",
   "metadata": {},
   "source": [
    "Upon conducting a random check, it was discovered that the missing values were not caused by errors in our scraping, but rather by the First Channel's failure to post videos. \n",
    "\n",
    "The number of missing values for the years 2021-2023 is negligible, which means that our analysis results during these years should be reliable. However, for previous years, the number of missing values is significant, which could potentially result in biased outcomes. It's important to be aware of this fact and approach the results with caution."
   ]
  }
 ],
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