OTUS_NLP_HW1_Kirill_N_18_Feb_2024.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OTUS, Курс NLP\n",
    "## ДЗ №01: Парсинг данных\n",
    "### Выполнил: Кирилл Н., ibnkir@yandex.ru, 18.02.2024 г."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Описание: собираем новости на Хабре (дата публикации, ссылка, название, краткое содержание, категории, время на прочтение, среднее кол-во просмотров в день). В конце сравниваем категории по частоте и популярности."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Парсинг"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import numpy as np   \n",
    "import pandas as pd  \n",
    "import time   \n",
    "from tqdm import tqdm\n",
    "from fake_useragent import UserAgent\n",
    "import re\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting fake_useragent\n",
      "  Downloading fake_useragent-1.4.0-py3-none-any.whl.metadata (13 kB)\n",
      "Downloading fake_useragent-1.4.0-py3-none-any.whl (15 kB)\n",
      "Installing collected packages: fake_useragent\n",
      "Successfully installed fake_useragent-1.4.0\n"
     ]
    }
   ],
   "source": [
    "#!pip install fake_useragent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Парсинг веб-страницы по переданной ссылке с заполнением выходного датафрейма\n",
    "def parsePage(page_url, start_dtm, df_out):\n",
    "    \n",
    "    response = requests.get(page_url, headers={'User-Agent': UserAgent().chrome})\n",
    "    if not response.ok:\n",
    "        return response.status_code, df_out\n",
    "    \n",
    "    soup = BeautifulSoup(response.text, 'lxml')\n",
    "\n",
    "    dates_arr = []\n",
    "    urls_arr = []\n",
    "    titles_arr = []\n",
    "    bodies_arr = []\n",
    "    minutes2read_arr = []\n",
    "    daily_views_arr = []\n",
    "    categs_arr = []\n",
    "    \n",
    "    for article in soup.find_all('article'):\n",
    "        tag = article.find('a', attrs={'class':'tm-article-datetime-published'})\n",
    "\n",
    "        date = tag.time.attrs['title']\n",
    "        date = re.match(r'\\d{4}-\\d{2}-\\d{2}', date).group(0)\n",
    "        dates_arr.append(date)\n",
    "        period_days = abs((start_dtm - datetime.strptime(date, \"%Y-%m-%d\")).days)\n",
    "    \n",
    "        href = tag.attrs['href']\n",
    "        urls_arr.append('https://habr.com' + href)\n",
    "      \n",
    "        title = article.find('h2', attrs={'class': 'tm-title tm-title_h2'}).span.text.strip()\n",
    "        titles_arr.append(title)\n",
    "\n",
    "        body = article.find('div', attrs={'class': 'article-formatted-body'}).text.strip()\n",
    "        bodies_arr.append(body)\n",
    "        \n",
    "        minutes2read = article.find('span', attrs={'class': 'tm-article-reading-time__label'}).text.strip()\n",
    "        minutes2read_arr.append(re.search(r'\\d+', minutes2read).group())\n",
    "   \n",
    "        views = article.find('span', attrs={'class': 'tm-icon-counter__value'}).text.strip()\n",
    "        if views.endswith('K') or views.endswith('К'):\n",
    "            views = int(float(views[:-1]) * 1e3)\n",
    "        elif views.endswith('M') or views.endswith('М'):\n",
    "            views = int(float(views[:-1]) * 1e6)\n",
    "        else:\n",
    "            views = int(views)\n",
    "        daily_views = views / (1 if period_days == 0 else period_days)\n",
    "        daily_views_arr.append(daily_views)\n",
    "        \n",
    "        curr_categs = []\n",
    "        for categ in article.find_all('a', attrs={'class': 'tm-publication-hub__link'}):\n",
    "            curr_categs.append(categ.span.text.strip())\n",
    "        categs_arr.append(';'.join(curr_categs))\n",
    "            \n",
    "\n",
    "    if len(dates_arr) > 0:\n",
    "        df_row = pd.DataFrame(\n",
    "            {\n",
    "            'date': dates_arr, \n",
    "            'url': urls_arr, \n",
    "            'title': titles_arr, \n",
    "            'body': bodies_arr, \n",
    "            'minutes2read': minutes2read_arr, \n",
    "            'daily_views': daily_views_arr,\n",
    "            'categs': categs_arr\n",
    "            }\n",
    "        )\n",
    "\n",
    "        df_out = pd.concat([df_out, df_row], ignore_index=True)\n",
    "    \n",
    "    return response.status_code, df_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Начальный дататайм\n",
    "start_dtm = datetime.now()\n",
    "\n",
    "# Выходной датафрейм для результатов парсинга\n",
    "df_out = pd.DataFrame(columns=['date', 'url', 'title', 'body', 'minutes2read', 'daily_views', 'categs'])\n",
    "\n",
    "# Сколько новостных страниц парсить (макс кол-во на данный момент - 2148, см. https://habr.com/ru/news/page2149)\n",
    "pages_num = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Pages: 100%|███████████████████████████████████████████████████████████████████████| 1000/1000 [24:46<00:00,  1.49s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parsing completed successfully\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Парсим заданное кол-во веб-страниц с новостями\n",
    "for num in tqdm(range(1, pages_num + 1), desc='Pages'):\n",
    "    url = f'https://habr.com/ru/news/page{num}/'\n",
    "    \n",
    "    # Делаем максимум 3 попытки скачать одну страницу. Если ни одна не удалась, прекращаем парсинг.\n",
    "    for i in range(3):\n",
    "        resp_code, df_out = parsePage(url, start_dtm, df_out)\n",
    "        \n",
    "        if resp_code == 200:\n",
    "            break    \n",
    "        else:\n",
    "            print(f'Warning! Parsing once again: {url}')\n",
    "            time.sleep(0.3)       \n",
    "    else:\n",
    "        print(f'Warning! Parsing terminated abnormally, fatal error on the page: {url}')\n",
    "        break\n",
    "    \n",
    "    time.sleep(0.3)\n",
    " \n",
    "else:\n",
    "    print('Parsing completed successfully')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "    .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>date</th>\n",
       "      <th>url</th>\n",
       "      <th>title</th>\n",
       "      <th>body</th>\n",
       "      <th>minutes2read</th>\n",
       "      <th>daily_views</th>\n",
       "      <th>categs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-02-18</td>\n",
       "      <td>https://habr.com/ru/news/794518/</td>\n",
       "      <td>Пользователь обнаружил и поделился зловредом д...</td>\n",
       "      <td>Пользователь Reddit под ником VegetableLuck об...</td>\n",
       "      <td>2</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>Информационная безопасность;Антивирусная защит...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-02-18</td>\n",
       "      <td>https://habr.com/ru/news/794496/</td>\n",
       "      <td>Релиз LineageOS 21 на основе Android 14</td>\n",
       "      <td>В середине февраля 2024 года состоялся релиз п...</td>\n",
       "      <td>4</td>\n",
       "      <td>5300.0</td>\n",
       "      <td>Open source;Разработка под Android;Смартфоны;С...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-02-18</td>\n",
       "      <td>https://habr.com/ru/news/794482/</td>\n",
       "      <td>Сбой в облачном сервисе Wyze открыл некоторым ...</td>\n",
       "      <td>Wyze Labs расследует проблему безопасности во ...</td>\n",
       "      <td>1</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>Информационная безопасность;Умный дом</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-02-18</td>\n",
       "      <td>https://habr.com/ru/news/794474/</td>\n",
       "      <td>Новая функция Google Chrome блокирует атаки на...</td>\n",
       "      <td>Google тестирует новую функцию, предотвращающу...</td>\n",
       "      <td>2</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>Информационная безопасность;Сетевые технологии...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-02-18</td>\n",
       "      <td>https://habr.com/ru/news/794472/</td>\n",
       "      <td>Microsoft представила обновление Sticky Notes ...</td>\n",
       "      <td>Microsoft представила «большое обновление» для...</td>\n",
       "      <td>1</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>Управление продуктом;Разработка под Windows;Софт</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date                               url  \\\n",
       "0  2024-02-18  https://habr.com/ru/news/794518/   \n",
       "1  2024-02-18  https://habr.com/ru/news/794496/   \n",
       "2  2024-02-18  https://habr.com/ru/news/794482/   \n",
       "3  2024-02-18  https://habr.com/ru/news/794474/   \n",
       "4  2024-02-18  https://habr.com/ru/news/794472/   \n",
       "\n",
       "                                               title  \\\n",
       "0  Пользователь обнаружил и поделился зловредом д...   \n",
       "1            Релиз LineageOS 21 на основе Android 14   \n",
       "2  Сбой в облачном сервисе Wyze открыл некоторым ...   \n",
       "3  Новая функция Google Chrome блокирует атаки на...   \n",
       "4  Microsoft представила обновление Sticky Notes ...   \n",
       "\n",
       "                                                body minutes2read  \\\n",
       "0  Пользователь Reddit под ником VegetableLuck об...            2   \n",
       "1  В середине февраля 2024 года состоялся релиз п...            4   \n",
       "2  Wyze Labs расследует проблему безопасности во ...            1   \n",
       "3  Google тестирует новую функцию, предотвращающу...            2   \n",
       "4  Microsoft представила «большое обновление» для...            1   \n",
       "\n",
       "   daily_views                                             categs  \n",
       "0       3800.0  Информационная безопасность;Антивирусная защит...  \n",
       "1       5300.0  Open source;Разработка под Android;Смартфоны;С...  \n",
       "2       2300.0              Информационная безопасность;Умный дом  \n",
       "3       3300.0  Информационная безопасность;Сетевые технологии...  \n",
       "4       2400.0   Управление продуктом;Разработка под Windows;Софт  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Результаты парсинга\n",
    "df_out.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Сохраняем датасет в excel-файл\n",
    "df_out.to_excel('habr_news_dataset_20240218.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Exploratory data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим основные статистики среднего кол-ва просмотров за день по всем загруженным новостям."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    20000.000000\n",
       "mean        48.395558\n",
       "std        299.571305\n",
       "min          0.212237\n",
       "25%          3.333333\n",
       "50%          7.636364\n",
       "75%         21.741848\n",
       "max      12307.692308\n",
       "Name: daily_views, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_out.daily_views.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Найдем кол-во новостей и среднее кол-во просмотров за день по каждой категории. Если новость относится к нескольким категориям, то будем считать ее соответствующее кол-во раз."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Заполняем словарь с кол-вом новостей и просмотрами по каждой категории\n",
    "ht = {}\n",
    "for i in range(len(df_out)):\n",
    "    categs = df_out.loc[i, 'categs'].split(';')\n",
    "    daily_views = df_out.loc[i, 'daily_views']\n",
    "    for categ in categs:\n",
    "        if categ not in ht:\n",
    "            ht[categ] = {'news_num': 1, 'daily_views': daily_views}\n",
    "        else:\n",
    "            ht[categ]['news_num'] += 1\n",
    "            ht[categ]['daily_views'] += daily_views           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Дополнительно усредняем кол-во просмотров по кол-ву новостей, чтобы получить среднее кол-во \n",
    "# просмотров в день на 1 новость внутри каждой категории\n",
    "for categ in ht:\n",
    "    ht[categ]['daily_views'] /= ht[categ]['news_num']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Возмьем 10 самых крупных категорий по кол-ву опубликованных новостей\n",
    "ht_news_sorted = sorted(ht.items(), key=lambda x:x[1]['news_num'])[-10:]\n",
    "k, v = map(list, zip(*ht_news_sorted)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A305FZwBTI2Jb4CfAlbnqM4BvpOUjgSm5dYsiol9E9AMOz5XPBnaJiO2B04FftCC2vsA2ZVafmdoutd2xkhokNSx+t7HS3ZmZWZV52GXVNBjYA/hsRHyYynYGDgKIiPskrSupW1p3K7CfpGuB9sCrFeyjG3CFpD5ApO0KhkjaOS33AhqKtj0L+Blwdr5Q0o5kCXNxfVLcY4AxAB169IkKYjQzsxpwz0cbJ+m43LBIz1T8aeAI4DxJKlQtsXnhAv4f4Bng18BfKtz1mcD9EbENsC/QMbduXK63ZFzRdoOAhcD0Em2eBfy0wv2bmVmdcvLRxkXE7wsX+oh4JRWPiYjrgBf4eNjlQdKwSZrPMT8iFuSaugTYHritwl13A+am5aEtCHkk2TBNsV2BeRHxVAvaMjOzOuRhl1XbD4FHJd1GdtG/TNIM4F3gqHzFiJgG9Af4uLOkSeeQDbucDNzXgpgmRsRzknoXlfcBWsePFpiZWZMU4aFxa3s69OgTPY46v9ZhmJlV1fL+sJykKRHRfwWFU5aHXczMzKyqnHyYmZlZVXnOh7VJfXt1o2E5ux/NzGzlcM+HmZmZVZWTDzMzM6sqJx9mZmZWVZ7zYW3SzLmN9B5xR63DsBVgeb86aGb1xz0fZmZmVlVOPszMzKyqnHyYmZlZVTn5MDMzs6py8tHKSFpN0h6SnpfUs9bxrCySdpN0u6SjJU1Lt/clzUzLv6p1jGZmtmz8bZdWRNLewO+BV4F1gTslvQkcHhHzahrcShIRlwGXAUiaA+weEfNrGpSZmS0X93y0EpI+CVwK7Ad8A3gpIvoB9wMXpjojJQ1Ly3tKCkn90/2Fubb6SxqflneS9IikqenvFql8qKTRuW1GSxqalgekutMlTZLUtbh+8X6KyodKej1t/6ykw1bUeTIzs/rn5KP1GAhMjIhZReUXAnuUqP8z4NkK2p0N7BIR2wOnA79oqrKkNYBxwAkRsR0wGFhUwX6KjUvb/xj4+jJsXyq2YyU1SGpY/G7jimjSzMxWAg+7tB7/ATqWKO+Y1v2XpIOAycCOueJOkqYVloHCME034ApJfYAA2ue2GSJp57TcC2gAtgDmRcRkgIhYkPaZr/8BcAbZ8FA5QyTtAvQGDmqiXsUiYgwwBqBDjz6xIto0M7MVzz0frccjwJaSDiwUSOoE/Bq4IVevHXAK8Mui7RdFRL80VHN4rvxM4P6I2AbYlyUTnHG5bcYVdkuWpJQyLtX9BvCnZo5nXERsS5YgjW6mrpmZtSFOPlqJiHgb+ApwLFki0gd4CngD+FGu6hHAHS2YlNkNmJuWh1ZQfzbQU9IAgDTfo7gH7U0q71V7m2zyrJmZrSI87NKKRMQTwJclbQLcHhFbl6i2PjCqBc2eQzbscjJwXwUxvC9pCHBh6nlZRDbvA+BASf2ALsDwZpoqDNF0AH7YgnjNzKyVU4SHxq3t6dCjT/Q46vxah2ErgH9Yzqx6JE2JiP4rez8edjEzM7OqcvJhZmZmVeXkw8zMzKrKE06tTerbqxsNnitgZlaX3PNhZmZmVeXkw8zMzKrKyYeZmZlVled8WJs0c24jvUfcUeswVhn+Xxxm1hLu+TAzM7OqcvJhZmZmVeXkw8zMzKrKyYeZmZlVVbPJh6SFueX1Jb0raWSlO5C0m6TblzG+VZ6kjSRdJWmSpFmSutc6JjMzs+XR0m+7/BCYvzICsaVJ6giMBU4FHgj/BLGZmbUBFQ+7SFoHOAS4NFd2uaSDc/dnSeqdli+UNBP4X6CHpPslTZfUJ63fWNK9kmakvxvl2rxI0kOS/iFpn1Q+VFJI+ky6v2W6PzTdP13S5BTDGElK5eMl9c/FmO/JmVPoSZB0taRZabmjpMskzZQ0VdLuuRhG57Yfndt/vq3ukuak5d7pWB5Pt0G57S+S9JSkaZIWlzjtewCdgNHATEm/zm27l6RHU5vXS+qSyv+aYn5K0v8WHjtJN6dz/ZikbVP5SElz0/6nSWqUtFta90dJDZKekHRGbr8DJD2SHstJkro2c172TPHMlHSppA5NtHN/imOhpKfT8n4pzmElzo+ZmbVCLZnzcSJwCbCwmXpI2hnoC2wHTADWBL4C/BT4Vao2GrgyIrYFrgEuyDXRG9gV+CpwUeoBAJgEHJOWjwEm5rYZHREDImIbsgv2PpUemKS+wDa5ouMAIqIvcBhwRS6GlnoN+GJE7AAMIR1n2ucgYOuI6AcsKrHtekAvYHegHzBA0gEpyTkNGJzabQBOTjEfFBHbA/uTnW+AM4Cp6Vz/BLgyt49REdEvxfBQrvzUiOgPbAvsKmlbSWsA44ATImI7YHCZuEnH2BG4HBiSzuXqwPfKtRMRu6c4GoDDU1y3lmu/xP6OTQlTw+J3GyvdzMzMqqyi5EPSWsCRwIUlVp9b+OQMbJrKBgD3RcRHwAzg2YhYBNwLfDbV+Rzwl7R8FbBzrs3rIuKjiHgGeB74TCqfDGyfLmr9yC5SBbtLmqist2UPYOtKji05C/hZ7v7OKSYiYjbwT2DztG5I7niHFLVzfyq/P1fWHvhziut6YKtUvhhYI93KEXBXRLweER+SJWm7AANTOw+n/R0FbPzfjaSpwHTg9BLHcx+wrqRuTewX4BBJjwNTyc7lVsAWwLyImJzaWpDiKndetgBeiIh/pPtXpPibaqeck1L7D0saWKpCRIyJiP4R0b9d5+YOz8zMaqXSOR/HAddExFtpNCNveETcANmwSypbqlISzawrtVx8/29kSdD/AZ9O++0I/AHoHxEvKZsQW2lPxSCy3pzpubJyMQKMi4jj035HF63bPSLmp56JQmJ0EvAvsl6g1YD3ACLiSUnXAa9Jep6st6bYgjIxCLgnIg4rtTIitpf0aWCcpIvLHE/Z+SOSNgGGAQMi4t+SLic7n2piu1Lnpdx5bKqdckZFxG8kDQbOI3vczMysFaqk52N14FhgVAvabQD2kLQaWbf9ZpI6kXWvT051HgEOTcuHkw3PFHxd0mqSNiVLMJ7OrbuK7MJzda6skGjMT3MfDqZyI/m4h6DgwRQTkjYHNiqKoSW6kX3K/wj4H6Bdbl0j8Lsmhl2mkJ3H7pLakQ0BPQA8Bnxe0mYpxs6SNk/nrGva9j9kvTXti45nN2B+RJRLbADWAt4BGiWtD+ydymcDPSUNSG11ldRUAjsb6F2IMx3/A8vQTt4bNN1bZGZmda6SN/wOwI0RUfG3XCLiQUlPkfUmPEnWs3An0B34eqr2A+BSScOB14Gjc008TXaRWh/4bkS8V+hxiYjXSEMqubK3JP0ZmAnM4eMEp+BifTzRtJOkCyPi++n+xIh4TmmibPIHsrkmM4EPgaER8Z8SvT6V+APwV0lfJxuOeSfFPgjYi2wuTEkR8c/Ui/Mg2TDNHRFxS9p+KDC2MIGTbA7Ii8ADKVHpCPwoIt5PbVwmaQbwLtkwTVkRMT0N3TxBNuz1cCp/X9IQ4MKUTC4iSyjLtfOepKOB61NyMRm4qIl2mppPdJykA4DOwI+bit/MzOqbVva3N9Mn7WERUdEE0NTFf3thKGclxTQ+InZbWe1b7XXo0Sd6HHV+rcNYZfiH5czaBklT0pcNVqpV9T+cXlLrAMzMzFZVLf0nYy0WEeOB8S2oP3RlxZLbx1Urex9mZmZW2kpPPsxqoW+vbjR4KMDMrC6tqsMuZmZmViNOPszMzKyqnHyYmZlZVXnOh7VJM+c20nvEHbUOY5Xhr9qaWUu458PMzMyqysmHmZmZVZWTDzMzM6sqJx9mZmZWVU4+mpH7QbrC/aG5n4w3MzOzFnLyYWZmZlXl5GM5SNpX0kRJUyX9XdL6klaT9Iyk9VKd1SQ9K6m7pPGS+qfy//agSLpc0sG5dmdJ6p2WT073Z0k6sUwcIyXNlTQt3RrTrwkj6TBJM9P2v85tszhX/+kUW9nYc3G+kLZ5P1d+kaSnUvniVLabpAcl3STpyVRntbRuYS6O/pLGp+V1JN0saYakxyRtm8rXlHSppMnpXO+/3A+emZnVjJOP5nXKXaSnAT/PrZsADIyI7YFrgVMi4iPgauDwVGcwMD0i5gMfAap0x5J2BI4GPgsMBL4tafsy1UdFRL+I6Ac8lLbvCfwa2APoBwyQdECqvyhX/3CAZmIHaAf8MG3zStpHX2AQsHUqX5SLaSfgh0BfYFPgwGYO+QxgakRsC/wEuDKVnwrcFxEDgN2BcyWtWbyxpGMlNUhqWPxuYzO7MjOzWnHy0bz/XqTTxfX03LoNgLskzQSGA1un8kuBI9PyMcBlaflloFzycG4uwdk0le0M3BQR70TEQuBG4AstiH0AMD4iXo+ID4FrgF2a2aZc7ACdgPeK6i8G1ki3YpMi4vmIWAyMTccDuYQuxVSwM3AVQETcB6wrqRuwFzAi1R8PdAQ2Kt5ZRIyJiP4R0b9d527NHKaZmdWKk4/lcyEwOiL6At8huygSES8B/5K0B1mvxf+l+r8AjpE0nSV7UACG5xKc51JZyV4SScflemN6NhFfxb0sBU3EDtCT1OORq/8kcB3wWkoOOuVXFzef/i7V69JEvJHKD8olgRtFxFMtOzIzM6sXTj6WTzdgblo+qmjdxWRDGNelT/5ExOyIGBgR27FkD0o5DwIHSOqchhm+BjwUEb/PXYhfaWL7icCuab5JO+Aw4IEK9rtU7JI2A3oDT5ao3wj8rtSwi6RN0lyPIWTDVE15kJSMpDkr8yNiAXAX8H1JSuvK9R6ZmVkr4ORj+YwErpf0EDC/aN2tQBeWHLZokYh4HLgcmESWSFwcEVNbsP084MfA/cB04PGIuKWCTZeIPfWu3AIcGxHv5ytKGkQ2LDKyRDuPAr8CZgEvADc1s9+RQH9JM9J2hYTuTKA9MEPSrHTfzMxaKUUU94zbipC+1TIqIloyR6MurIjYU8/FsIjYZ0XF1RIdevSJHkedX4tdr5L8w3JmbYOkKRHRf2Xvx79quxJIGgF8jyXnM7QKrTl2MzNrHTzsshJExK8iYuOIaG6OQ91ZUbFHxPha9XqYmVl9c8+HtUl9e3WjwUMBZmZ1yT0fZmZmVlVOPszMzKyqnHyYmZlZVXnOh7VJM+c20nvEHbUOo274q7BmVk/c82FmZmZV5eTDzMzMqsrJh5mZmVWVkw8zMzOrqmaTD0mfknStpOckPSnpTkmbVyM4qy5J35L0kKQGST+rdTxmZtY2Nfltl/QT5jcBV0TEoamsH7A+8I+VHp1VjaRvAgOBfSKisdbxmJlZ29Vcz8fuwAcRcVGhICKmRcRDypwraZakmZKGQPZrppJuL9SXNEzSyLQ8VNLotLyFpA8lHZzuny5pcmpvTEp8Cm2Ml/S0pGmSFha3lSdppKRhufu3p19YpbBtUf1Zknqn5SMkTUr7+ZOkdiXqD5D0iKTpqW5XSe3SuZgsaYak76S616S23pT0Qlr+blPnoWhf49MvzCLprFLxp3VHpv1Ol3RVKttY0r2p/F5JGzVVDhwLbAhMkPSYpG2Lz6ekgyVdnpa7S5qTi2EvSY9KelzS9ZK6NHG+7i88lrnHdb+0r7kpttmS9kht7CtpoqSpkv4uaf1S58HMzFqH5pKPbYApZdYdCPQDtgMGA+dK6tGCfZ8JzM7dHx0RAyJiG6ATkP9RsnbAYRHRrwXtt4ikLYEhwOfTfhZT9MuuktYAxgEnREThuBcB3wQaI2IAMAD4tqRNIuLw1NatwPCI6JdP5JLi81Aqtk8Ce5ZZtzVwKrBHiumEtGo0cGVEbAtcA1zQTPkngUcioi/wE+DKpmIqiqE7cBowOCJ2ABqAk8udr4jYPZ2XBuDwdF5uTc2NSrFdwsfPgQnAwIjYHrgWOKVMHMcqGzJqWPyuO2/MzOrV8vyTsZ2BsRGxGPiXpAfILrwLmttQ0o5kiU9Drnh3SacAnYF1gCeA29K6TsB7JZoaImln4APgjIgo9LicJOmItLwJ8JtCO5KmAQIeAE7MtbUnsCMwOXW6dAJeK9rfFsC8iJgMEBEL0vHsBWyb673oBvQBXliG81DKT4FfAGNLrNsDuCEi5qeY3kzlnyNLEAGuAs5pplzpPhFxn6R1JXVrJq6CgcBWwMPp3K0BPEqZ89WMkyQdQ5YM7Z7KNgDGpeR2Dcqc14gYA4wB6NCjT1QYu5mZVVlzPR9PkF2QS1GZ8kqcRXZBzRqSOgJ/AA5On7z/DHTM1e8JzCvRzrj0CfobwJ9y5aPSp+l+wEO58kWpbEdgW7JP4v8Ng2xuS7902yIiRhbtT0Cpi5qA7+e23SQi7i596EtY4jyU0RvYJiJuK7O+XEzFytUplJdKDCq9gAu4J3f8W0XEN1sQW96oiNgKOBT4bSq7kKxnrC/wHZZ8bpiZWSvTXPJxH9BB0rcLBWkMf1fgQbKeh3aS1gN2ASZVsM9dyT4NP5UrK1xM5qe5Av+d/5B6Nv4dEf9uos03aUEvTkR8CDSSfYouuBc4OA1xIGkdSRsXbTob6ClpQKrTVdLqwF3A9yS1T+WbS1qzmTBKnYdSfpZu5dwLHCJp3ULcqfwRsgs4ZMNHE5opn5juo2yOzPwKeyoAHgM+L2mztH1nZd+IKne+KrEA6J6WuwFz0/JRFW5vZmZ1qskLQUSEpK8B50saQTb0MYdsuOJBsi786WSfbk+JiFclfQYYJKlwUesFtJN0S7rfB1jihyYi4i1JfwZmpvYnQ5bokM1JOKZMiAcq+/ZNF2B4BcfbKcXVPu3nLuBXKYYnJZ0G3C1pNbKhnOOAf+bifF/ZxNoLJXUim+8xGLiYrIficWXjDq8DBzQTy1LnoYyXI+LBcisj4glJZwMPSFoMTAWGAj8ALpU0PMVzdNqkXPlPgcslzQDeYcmL/HGSDgDWBdZJ53B14FOSfhIRv5A0FBgrqUPa5rSI+EeZ81Vy4mxSGDJbHShMHB4JXC9pLlmis0kT25uZWZ1TxMofGlf2bZfxETF+pe/MqkbZt4RGRsTQGoeylA49+kSPo86vdRh1wz8sZ2aVkDQlIvqv7P1U61dt7yPXg2Btxr+Bv9Y6CDMza12qknw0NWxgrVf6Z2TlJsKamZmVVK2eD7Oq6turGw0eajAzq0v+YTkzMzOrKicfZmZmVlVOPszMzKyqPOfD2qSZcxvpPeKOWodRc/6KrZnVI/d8mJmZWVU5+TAzM7OqcvJhZmZmVeXkw8zMzKqq7pIPSQtzy+tLejf9NowVkXSYpImSJkjaqtbxmJmZVaLev+3yQ2B+rYOoVxExFhhb6zjMzMxaou56PgokrQMcAlyaK1tf0k2SpqfbIEm9Jc1K67dM5RtKOlPSCbltz5b0g7R8iqSZqe6vUtlmkv6eyh6XtKmkayRNk/SmpBfS8nclDZX0eqr7rKTDUhtrSrpU0mRJUyXtn8qHShqdi2V0+gl6JM2R1D0td5c0p9Q2qay/pPFpeaSkYWl5T0khaalfIixq/+rcuSoZk6Qh6TifldSYlu9MdfaS9Gg6P9dL6lK0n5mSnizsIxfn3NTOwkKMko6QNCmV/0lSu1S+MP39VFq3naST0vKL6bxPk3Rxs08iMzOrS3WbfAAnApcAC3NlFwAPRMR2wA7AE4UVknoB1wLfiIiX0rZHpXWrAYcC10jaGzgA+Gxq55zUxDXA71PZIGBeRBweEf2AW4HhEdEvIi5K9celuj8Gvp7KTgXui4gBwO7AuZLWXEHnoyk/A55tqoKkvsA2zTUUEePSMX8LeCgd81dSAnMaMDgidgAagJNzm7YDdgW+UtRkO+C3qc2GFMuWwBDg86l8MXB4Lta1gJuBkyJiekSMSvVOJzvv/SLiWyWO8VhJDZIaFr/b2NyhmplZjdTlsEu6+BwJ9CO7CBbskcqJiMVAo6RPAF2Av5Fd+J9I6+dIekPS9sD6wNSIeEPSYOCyiHg31XtTUlegV0TclMreqyDMIZJ2AXoDB6WyvYD9Cj0SQEdgo1z9ndNyL9KFOLlf0mKyC3XxPnYGPgDOAF4tDkLSQcBkYMdm4j2LLEk5u0T7pWIqNhDYCnhYEsAawKO59Z2A94C1irbrBMwrKtszxTs5tdUJeC2tWw24CfhXRNzfzDEtISLGAGMAOvToEy3Z1szMqqcukw/gOOCaiHgrXZyasyHwP8AISVtGxFOp/GJgKPApPh6+EVB8YapoJ0XGRcTxkvoAtwNbpHYOioinl2hc+myhfro/uqit3SNifupdyCcA+X2MB/Yv2q4dcArwVeCGJmIdRNaDNL3UMZSJqZiAeyLisKVWSB2B1SLi3RKPV09gQom2roiIH5fYTyfgNmB/SXtExH3NxGVmZq1MPQ67rA4cC4wqse5e4HsAktqlHhKApyLiL8D3gT/p4yvgTcCXgQHAXansbuAYSZ1TO+tExALgZUkHpLIOhfUVeBtYNy3fBXy/sP/U67IivEnpRPEI4I6IaG5S7kiyIYvl8RjweUmbAUjqLGnztO5gluwFIdXpDnwBmFi06l7gYEmfTPXWkbRxWvdORJwPfAe4QFKn5YzbzMzqTD0mHx2AG8tcUE8Adpc0E5gCbJ1fGREPALNJCUpEvA/cD1yXhmmIiL+RzeFokDQNKAyR/A/wA0kzgEfIekuaMiRtfz/Zt3IAzgTaAzPSpMszKzzmcg6UNIHsYj28xPr1KZ2kFZsYEc8tTyAR8TpZL9LYdI4eAz4j6Wtk5/vEEptNAEZGxBLDLhHxJNn8kbtTW/cAPYrq/AP4C9lwk5mZtSGKaLtD42mi6ePA1yPimVrHY9XToUef6HHU+bUOo+b8w3Jm1hKSpkTEUt+cXNHqsedjhVD2T7eeBe514mFmZlY/6nXC6XJLXfufrnUcZmZmtqQ2m3zYqq1vr240eMjBzKwutdlhFzMzM6tPTj7MzMysqpx8mJmZWVV5zoe1STPnNtJ7xB21DqPq/NVaM2sN3PNhZmZmVeXkw8zMzKrKyYeZmZlVlZMPMzMzq6pWm3xIWphbXl/Su5JG1jAkMzMzq0CrTT6K/BBo7mflzczMrA60+uRD0jrAIcClubLLJb0gaVq6DZI0VNItkv4m6WlJP0t1e0uaVaLdhSXK/ltXUntJz0saXaLeSEnD0vKekkJS/3R/cS6uv6ey8ZLOl/SIpFmSdiocm6SbJc2Q9JikbXPtz03lsyXtkTvug4tiGVboEZK0aTr+KZIekvSZZmIfIemyCmIZltv+dkm7peUvS3pc0nRJ9zbVTtFxTZO0UFL/Ss+5mZm1Hm3h/3ycCFwCLAK65MqHR8QNhTuSNgd2ArYB3gUmS7qDZe8xORZYKkEp4Wdkv65bsCgi+pWot2ZEDJK0C1kitQ1wBjA1Ig5ICcaVQGHbURHxG0nDgX2A+yqIZQzw3Yh4RtJngT8Ae5SqKOlI4AvA/qmoqVhKbb8e8Gdgl4h4ISWJzbXTDvhtRJwnaXyJZps855KOTXVot9Z65aqZmVmNteqeD0lrAUcCF1a4yT0R8UZELAJuBHZO5ZvmeiNOrWC/nYGjgT82U+8gYDIwt4LYxgJExIPAWpLWTvFdlcrvA9aV1C3VP0nSk8CPgMty7ZybjuPelHAVYukCDAKulzQN+BPQo0wsg4GLgdMi4sNU1lws01K7X0hlA4EHI+KFtM2bFbTTCXivVECVnPOIGBMR/SOif7vO3cpVMzOzGmvVyQdwHHBNRLxVYf0oc/+51BsxCDhK0hbNtHMiWS/CoibqtANOAX65HLGpiXqjImIr4FDgt7n1w9OxjAVG5spXA96KiH6525ZlYvk0cARwnqRCDM3F0i/t96Fc/eJjaq6dnsArZWI6kebPuZmZtQKtOflYnayLfVQLtvlimnPQCTgAeLho/SKyIZn2TbTRLW17aRN1ILt43xERlQ7rDAGQtDPQGBGNwIPA4al8N2B+RCwo2m4B0L1Ee28AaxTupO1ekPT11J4kbVcmljERcR3wAvDtVFZJLHmPArtK2iRtUxh2KdmOpO5kvSYTS7RV6Tk3M7NWoDXP+egA3NiCizvABLIu/82Av0REg6TewCaSJpB1+z8YEbM+/sC/lA2AYRHxYRN1ANanZYnRvyU9AqwFHJPKRgKXSZpBlhQdlat/kqQjyB7DYbnyMyWdSHZ+vsOSczoOB/4o6TSyBOtaYHoTMf0QeFTSbc3EspSIeD3NwbhR0mrAa8AXm2hnAjAyIuaVaK7Sc25mZq2AIkr1jLc9koYC/SPi+FrHUixNrhwWEQ21jqWt6NCjT/Q46vxah1F1/mE5M1sekqZERP+VvZ/WPOxiZmZmrVBrHnZpkYi4HLi8xmGUFBG71ToGMzOzalllkg9btfTt1Y0GD0GYmdUlD7uYmZlZVTn5MDMzs6py8mFmZmZV5Tkf1ibNnNtI7xF31DqMqvNXbc2sNXDPh5mZmVWVkw8zMzOrKicfZmZmVlVOPszMzKyqnHy0kKQNJN0i6RlJz0n6naQ1cut3kXSnpEmSbq9lrGZmZvXIyUcLKPtJ1RuBmyOiD7A50AU4O63fCjgLOD4idoqIfWoWrJmZWZ1y8tEyewDvRcRlABGxGDgJOEZSZ2AoEMDtkmZKGlLYUNJukholTZP0qqRhqfx0SZMlzZI0JiU4SJojqXtaHpmrP15S/1y7C3PtL9HTIqm7pDlpuZ2kc9O+Zkj6TqkDlHRkWj9d0lWpbD1Jf03bTpb0+VxcV0m6L/UEfbvEsT4v6eRUPlTS6LS8haQPJR2c7g+Q9Eja7yRJXfP1c/H1T78CbGZmrZT/z0fLbA1MyRdExAJJLwKbAesB7wN9ge7AZEkPRsQ8oB3wQETsJ2lkronREfFzgHSx3we4DfgI0AqM/ZtAY0QMkNQBeFjS3RHxQqGCpK2BU4HPR8R8SeukVb8DRkXEBEkbAXcBW6Z12wIDgTWBqZIK/1zjoYjYR9IA4E/AeUXxnAnMTvtdAxgHDImIyZLWAhatwGM3M7M64uSjZUTWs1GuXMDY1CPyL0kPAAOAW4FOwHsltt1d0ilAZ2Ad4Amy5ONlYHvg7hLbXCOpcHHulCv/gqRpKZYL034L9gK2LfQ0AN2APsALuTp7ADdExHyAiHgzlQ8GtkqdMgBrSeqalm+JiEXAIkn3AzsBb+Vi2Qw4Ph+8pB3Jet0aUtEWwLyImJz2uyDVAxgiaWfgA+AM4NUS56PQ7rHAsQDt1lqvXDUzM6sxJx8t8wRwUL4gfUrfEHgOWNDEtj2BV4q27Qj8AegfES+lHpGOafVPgNGSzgE+Bfwmt+nhEdGQ2liYKy/0NnQn61X4e353wPcj4q4mYiyXXK0GfC4lGfn4KVG/cD8fyxRJ1+bqnAWcDPyomf0CjIuI4yX1AcYD+5cLPiLGAGMAOvToU649MzOrMc/5aJl7gc6SjoRsHgXwW+DyiHgXmEj2Sb2dpPWAXYBJqd6BwMNF7RUSjfmSugCFXgkiYkJE9IuIfsBFLYzzbeBDsqGegruA70lqn2LfXNKaJY7vEEnrpjqFYZe7yfVeSOqX22Z/SR3TNrsBk4vafJesd6ZDur8rWS/HU7k6s4GeaYiGNN+jODF+EyfLZmZtgt/MWyAiQtLXgD9I+ilZ8nYnWS8FwLXAIGAGsBg4JSJelfQX4Bngr0XtvSXpz8BMYA5LX7hbapCkCWTzL0aRJSEFFwO9gcfTpNbXgQOK4nlC0tnAA5IWA1PJJtH+APi9pBlkz5kHge+mzSYBdwAbAWdGxCuSNufjYZeOwHkR0Zh6SvoAS/wASUS8r2xy7oWSOpHN9xicVh+Ykp0uwPBlPjNmZlY3FOHeaVs2aZhoYUT8prm61dahR5/ocdT5tQ6j6vzDcma2PCRNiYj+zddcPh52MTMzs6rysIsts4gYWesYzMys9XHPh5mZmVWVez6sTerbqxsNnv9gZlaX3PNhZmZmVeXkw8zMzKrKyYeZmZlVled8WJs0c24jvUfc0XzFNsL/38PMWhP3fJiZmVlVOfkwMzOzqnLyYWZmZlXl5MPMzMyqqs1POJX0GeAKQMAawBTg+xHxbk0DsyZJGgr0B+YBX0/Ffcl+ARjg0oi4oAahmZnZcmrzyQfZxetLEfEWgKRRwInAL2oYk1UoIs4GzgaQtDAi+tU2IjMzW15tftglIhpzicdqQEegcP/bkiZLmi7pr5I6p/LLJR2clv+YfjoeSRtLulfSjPR3o+L66f4sSb3T8snp/ixJJ+bqdJf0vqRpkp6VdHsq75LaflzSTEn7p/JzU91XJc1Nyz9P64an45gh6YzcPnpLWpTqvihpdCofL2mpn0yWNEDSI+l8TJLUVdL9afuFkp5Oy/tJGpnimCFptqQ9mjpHRfspua2Zma0a2nzyASCpk6RpwOvAdsCf06obI2JARGwHPAV8s2i704F2uV9vHQ1cGRHbAtcATXb7S9oROBr4LDAQ+Lak7dPqdsDL6ZP8t3KbvQd8LSJ2AHYHfitJETE81b0IGBUR/SLidEl7AX2AnYB+wI6Sdsnt45m03enNxLoGMA44IZ2PwcCiiNg9bd8AHJ72e2vabFQ6F5cA+7TwHJXadrlIOlZSg6SGxe82rogmzcxsJVgVhl2IiEVAP0mrAxcCpwIjgW0knQWsDXQB7sptNhT4IrBhruxzwIFp+SrgnNy6cyWdlpY3TX93Bm6KiHcAJN0IfAGYmvb3ZolwBfwiJRAfAb2A9YFXyxzeXuk2Nd3vQpaMPAh0IktmSrlG0iLgRbLkZ31gXkRMBoiIBWW2yztJ0jHAJ8kSJWj6HDW37XKJiDHAGIAOPfrEimjTzMxWvFWi56MgIj4ErgUGpKLLgeMjoi9wBtmQTME6wEnAb5pqMrc8PPUK9AOeS2VqYttNgJdLlB8OrAfsmNr6V1FcxQT8srDviNgsIi5J63oCr5TZ7vDU/gyyOTAqOp5KjIqIrYBDgd+WqVOuzUq2NTOzNqjNJx+S+uTmZgjYD5iUVncF5klqT3bRzzsvIv4A9ExDGwCPkF0sSfUnNLP7B4EDJHWWtCbwNeChtO7rwO0ltukGvBYRH0jaHdi4mX3cBRwjqUs6xl6SPpnbx8PNbP8G2beAZpMd64DUTtfUU1SJBUD3tNzSc5Tf1szMVgGrwrBLF7IhhjXS/QeAX6blnwITgX+SfYWza4ntvwPcmi7KPwAulTScbP7I0U3tOCIel3Q5Hyc7F0fEVEn/CxwL7Crp+BTjepL2I5sncZukBmAaWVLQ1D7ulrQl8GiWW7EQOELSMGBN4PdlNr1Y0sK0fHhEvC9pCHChpE7AIrJ5HwvLbA/Z0MkRZM+jYams0nNUalszM1sFKMJD49WWvj0zPiLG58r2AbpHxOU1CqtN6dCjT/Q46vxah1E1/mE5M1sRJE2JiKW+DbmirQo9H/XoBuC1orLHgQ41iMXMzKyqnHzUQETMKlFWbmKomZlZm+Lkw9qkvr260eChCDOzutTmv+1iZmZm9cXJh5mZmVWVkw8zMzOrKs/5sDZp5txGeo+4o9ZhrFD+Oq2ZtRXu+TAzM7OqcvJhZmZmVeXkw8zMzKrKyYeZmZlVlSecruIkLSb7Ub2CiyLiolrFY2ZmbZ+TD1sUEf1qHYSZma06POxiJUn6o6QGSU9IOqNo3SxJT0qaJmlhKltL0kRJO6T7cyR1l7ReaqdPKt9L0qOSHpd0vaQukoaktp6V1JiW70z1F5aIbZak3iv9JJiZ2Urh5MPKOTX9rPK2wK6Sts2tawd8Od9jEhELgCHAJZI2SMUdgeuBYRHxjKTuwGnA4IjYAWgATo6IcamtbwEPRUS/iPhKSwOWdGxKdBoWv9vY4gM2M7Pq8LCLlXOIpGPJniM9gK2AGWldF+DN4g0iYo6kvwG3kSUeFwMfRMT4VGVgaudhSQBrAI82E0cnSdMAAQ8AJ5arGBFjgDEAHXr0ieYO0MzMasPJhy1F0ibAMGBARPxb0uVkyQSSOgKdIqLUcMiGwFeA84DLgOnAZyR9MSLuIUsg7omIw1oQzqKI6CdpdeDvwODlODQzM6sDHnaxUtYC3gEaJa0P7J1b9zXgb2W2Ox8YERFXAC8C5wInAOdK6gA8Bnxe0mYAkjpL2rySgCLiQ6CRrLfEzMxaMScftpSImA5MBZ4ALgUeBpDUH7gE2C1NCp1GNizyc0l7p23/r6itOcB1wCkR8TowFBgraQZZMvKZZsLpJGmCpInAe8BdK+QgzcysZhThoXGrjKTdgN0iYmSurAswOiKG1iaq0jr06BM9jjq/1mGsUP5hOTNb2SRNSV82WKk858Na4klgflHZe8AfaxCLmZm1Uk4+rGIR8RrwWlHZh8DE2kRkZmatkZMPa5P69upGg4cpzMzqkiecmpmZWVU5+TAzM7OqcvJhZmZmVeXkw8zMzKrKyYeZmZlVlZMPMzMzqyonH2ZmZlZVTj7MzMysqpx8mJmZWVX5h+WsTZL0NvB0reNoRneW/q2celLv8YFjXFHqPcZ6jw/aTowbR8R6KzsQ/3t1a6uersYvMy4PSQ31HGO9xweOcUWp9xjrPT5wjC3lYRczMzOrKicfZmZmVlVOPqytGlPrACpQ7zHWe3zgGFeUeo+x3uMDx9ginnBqZmZmVeWeDzMzM6sqJx9mZmZWVU4+rE2R9GVJT0t6VtKIKu/7UkmvSZqVK1tH0j2Snkl/P5Fb9+MU59OSvpQr31HSzLTuAklaQfFtKOl+SU9JekLSCXUYY0dJkyRNTzGeUW8x5tpvJ2mqpNvrMUZJc1Lb0yQ11FuMktaWdIOk2ek5+bk6i2+LdO4KtwWSTqynGFPbJ6XXyixJY9NrqK5iLCkifPOtTdyAdsBzwKeBNYDpwFZV3P8uwA7ArFzZOcCItDwC+HVa3irF1wHYJMXdLq2bBHwOEPB/wN4rKL4ewA5puSvwjxRHPcUooEtabg9MBAbWU4y5WE8G/gLcXm+PdWp7DtC9qKxuYgSuAL6VltcA1q6n+IpibQe8CmxcTzECvYAXgE7p/nXA0HqKsdzNPR/WluwEPBsRz0fE+8C1wP7V2nlEPAi8WVS8P9mbLOnvAbnyayPiPxHxAvAssJOkHsBaEfFoZO8IV+a2Wd745kXE42n5beApsjeveooxImJhuts+3aKeYgSQtAHwVeDiXHFdxVhGXcQoaS2yZP0SgIh4PyLeqpf4StgTeC4i/lmHMa4OdJK0OtAZeKUOY1yKkw9rS3oBL+Xuv5zKamn9iJgH2cUf+GQqLxdrr7RcXL5CSeoNbE/Ws1BXMabhjGnAa8A9EVF3MQLnA6cAH+XK6i3GAO6WNEXSsXUW46eB14HL0tDVxZLWrKP4ih0KjE3LdRNjRMwFfgO8CMwDGiPi7nqKsRwnH9aWlBqjrNfvkpeLdaUfg6QuwF+BEyNiQVNVy8SyUmOMiMUR0Q/YgOxT2TZNVK96jJL2AV6LiCmVblImlpX9WH8+InYA9gaOk7RLE3WrHePqZEOUf4yI7YF3yIYHyqnl62UNYD/g+uaqlollZT4XP0HWm7EJ0BNYU9IRTW1SJpaqv3c6+bC25GVgw9z9Dci6IGvpX6lLk/T3tVReLtaX03Jx+QohqT1Z4nFNRNxYjzEWpG748cCX6yzGzwP7SZpDNrS3h6Sr6yxGIuKV9Pc14CayYcl6ifFl4OXUqwVwA1kyUi/x5e0NPB4R/0r36ynGwcALEfF6RHwA3AgMqrMYS3LyYW3JZKCPpE3Sp5VDgVtrHNOtwFFp+Sjgllz5oZI6SNoE6ANMSl2kb0samGabH5nbZrmk9i4BnoqI8+o0xvUkrZ2WO5G9uc6upxgj4scRsUFE9CZ7jt0XEUfUU4yS1pTUtbAM7AXMqpcYI+JV4CVJW6SiPYEn6yW+Iofx8ZBLIZZ6ifFFYKCkzqntPcnmctVTjKWtzNmsvvlW7RvwFbJvcTwHnFrlfY8lG3f9gOyTxDeBdYF7gWfS33Vy9U9NcT5NbmY50J/sQvEcMJr0n4hXQHw7k3WlzgCmpdtX6izGbYGpKcZZwOmpvG5iLIp3Nz7+tkvdxEg2p2J6uj1ReC3UWYz9gIb0WN8MfKKe4kttdwbeALrlyuotxjPIEvRZwFVk32SpqxhL3fzv1c3MzKyqPOxiZmZmVeXkw8zMzKrKyYeZmZlVlZMPMzMzqyonH2ZmZlZVTj7MzMysqpx8mJmZWVX9PwbsFIgZlFMzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Построим горизонтальную гистограмму\n",
    "plt.barh(k, [x['news_num'] for x in v])\n",
    "plt.title('Первые 10 категорий по кол-ву опубликованных новостей')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Возмьем 10 самых крупных категорий по среднему кол-ву просмотров одной новости в день\n",
    "ht_views_sorted = sorted(ht.items(), key=lambda x:x[1]['daily_views'])[-10:]\n",
    "k, v = map(list, zip(*ht_views_sorted)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Построим горизонтальную гистограмму\n",
    "plt.barh(k, [x['daily_views'] for x in v])\n",
    "plt.title('Первые 10 категорий по среднему кол-ву просмотров одной новости в день')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Таким образом, можно заметить, что новостные категории сильно отличаются по кол-ву публикаций и уровню популярности. "
   ]
  }
 ],
 "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}