{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Анализ полученных данных\n",
    "\n",
    "Статистика по вакансиям для удобного отображения вынесена в отдельную тетрадку."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "\n",
    "import nltk\n",
    "# nltk.download('stopwords')\n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "from scipy.sparse import hstack\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Загрузка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Ids</th>\n",
       "      <th>Employer</th>\n",
       "      <th>Name</th>\n",
       "      <th>Salary</th>\n",
       "      <th>From</th>\n",
       "      <th>To</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Schedule</th>\n",
       "      <th>Keys</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39912622</td>\n",
       "      <td>Знаменка Капитал</td>\n",
       "      <td>Data Scientist / Quantitative Researcher</td>\n",
       "      <td>True</td>\n",
       "      <td>217500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>От 3 до 6 лет</td>\n",
       "      <td>Полный день</td>\n",
       "      <td>['Mathematical Statistics', 'Mathematical Prog...</td>\n",
       "      <td>Обязанности:  Разработка и оценка статистическ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40114134</td>\n",
       "      <td>NZT Group</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>True</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>От 1 года до 3 лет</td>\n",
       "      <td>Полный день</td>\n",
       "      <td>['Python', 'Data Mining', 'Математическая стат...</td>\n",
       "      <td>Наша компания занимается разработкой своих вну...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40005967</td>\n",
       "      <td>Адаперио</td>\n",
       "      <td>ML Engineer / Data Scientist</td>\n",
       "      <td>True</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>От 1 года до 3 лет</td>\n",
       "      <td>Полный день</td>\n",
       "      <td>['Анализ данных', 'Статистический анализ', 'Ра...</td>\n",
       "      <td>Адаперио - компания разработчик комплексного р...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40086655</td>\n",
       "      <td>Эверис, Представительство</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>True</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>230000.0</td>\n",
       "      <td>От 3 до 6 лет</td>\n",
       "      <td>Полный день</td>\n",
       "      <td>['R', 'Python', 'SQL']</td>\n",
       "      <td>В подразделение французской компании, специали...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39964742</td>\n",
       "      <td>retailCRM</td>\n",
       "      <td>Senior Data Scientist</td>\n",
       "      <td>True</td>\n",
       "      <td>174000.0</td>\n",
       "      <td>261000.0</td>\n",
       "      <td>От 3 до 6 лет</td>\n",
       "      <td>Удаленная работа</td>\n",
       "      <td>['Python', 'Data Science']</td>\n",
       "      <td>RetailCRM — это крупнейшая CRM для омниканальн...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>38576646</td>\n",
       "      <td>РАБЛЗ</td>\n",
       "      <td>Data Scientist (middle or senior)</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>435000.0</td>\n",
       "      <td>От 3 до 6 лет</td>\n",
       "      <td>Гибкий график</td>\n",
       "      <td>['Python', 'Data Mining', 'SQL', 'Математическ...</td>\n",
       "      <td>Наша компания разрабатывает аналитические реше...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>39602668</td>\n",
       "      <td>Артистраж</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>True</td>\n",
       "      <td>104400.0</td>\n",
       "      <td>147900.0</td>\n",
       "      <td>От 1 года до 3 лет</td>\n",
       "      <td>Полный день</td>\n",
       "      <td>['Python', 'R', 'Machine Learning', 'Computer ...</td>\n",
       "      <td>Обязанности:  Обработка и аналитика входных да...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Ids                   Employer  \\\n",
       "0   39912622           Знаменка Капитал   \n",
       "1   40114134                  NZT Group   \n",
       "2   40005967                   Адаперио   \n",
       "3   40086655  Эверис, Представительство   \n",
       "4   39964742                  retailCRM   \n",
       "7   38576646                      РАБЛЗ   \n",
       "10  39602668                  Артистраж   \n",
       "\n",
       "                                        Name  Salary      From        To  \\\n",
       "0   Data Scientist / Quantitative Researcher    True  217500.0       NaN   \n",
       "1                             Data Scientist    True  150000.0  300000.0   \n",
       "2               ML Engineer / Data Scientist    True  200000.0       NaN   \n",
       "3                             Data Scientist    True  150000.0  230000.0   \n",
       "4                      Senior Data Scientist    True  174000.0  261000.0   \n",
       "7          Data Scientist (middle or senior)    True       NaN  435000.0   \n",
       "10                            Data Scientist    True  104400.0  147900.0   \n",
       "\n",
       "            Experience          Schedule  \\\n",
       "0        От 3 до 6 лет       Полный день   \n",
       "1   От 1 года до 3 лет       Полный день   \n",
       "2   От 1 года до 3 лет       Полный день   \n",
       "3        От 3 до 6 лет       Полный день   \n",
       "4        От 3 до 6 лет  Удаленная работа   \n",
       "7        От 3 до 6 лет     Гибкий график   \n",
       "10  От 1 года до 3 лет       Полный день   \n",
       "\n",
       "                                                 Keys  \\\n",
       "0   ['Mathematical Statistics', 'Mathematical Prog...   \n",
       "1   ['Python', 'Data Mining', 'Математическая стат...   \n",
       "2   ['Анализ данных', 'Статистический анализ', 'Ра...   \n",
       "3                              ['R', 'Python', 'SQL']   \n",
       "4                          ['Python', 'Data Science']   \n",
       "7   ['Python', 'Data Mining', 'SQL', 'Математическ...   \n",
       "10  ['Python', 'R', 'Machine Learning', 'Computer ...   \n",
       "\n",
       "                                          Description  \n",
       "0   Обязанности:  Разработка и оценка статистическ...  \n",
       "1   Наша компания занимается разработкой своих вну...  \n",
       "2   Адаперио - компания разработчик комплексного р...  \n",
       "3   В подразделение французской компании, специали...  \n",
       "4   RetailCRM — это крупнейшая CRM для омниканальн...  \n",
       "7   Наша компания разрабатывает аналитические реше...  \n",
       "10  Обязанности:  Обработка и аналитика входных да...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../../hh_results.csv')\n",
    "df[df['Salary']][0:7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Количество вакансий"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of vacancies: 236\n"
     ]
    }
   ],
   "source": [
    "print('Number of vacancies: {}'.format(df['Ids'].count()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Вакансия с максимальной зарплатой"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
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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>Ids</th>\n",
       "      <th>Employer</th>\n",
       "      <th>Name</th>\n",
       "      <th>Salary</th>\n",
       "      <th>From</th>\n",
       "      <th>To</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Schedule</th>\n",
       "      <th>Keys</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>39835664</td>\n",
       "      <td>Hunt For You</td>\n",
       "      <td>Senior Fullstack Developer (Python, remote)</td>\n",
       "      <td>True</td>\n",
       "      <td>316405.0</td>\n",
       "      <td>553709.0</td>\n",
       "      <td>Более 6 лет</td>\n",
       "      <td>Удаленная работа</td>\n",
       "      <td>['Python', 'PostgreSQL', 'SQL', 'JavaScript', ...</td>\n",
       "      <td>О нас:  Американская компания с русскоговоряще...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>39835664</td>\n",
       "      <td>Hunt For You</td>\n",
       "      <td>Senior Fullstack Developer (Python, remote)</td>\n",
       "      <td>True</td>\n",
       "      <td>316405.0</td>\n",
       "      <td>553709.0</td>\n",
       "      <td>Более 6 лет</td>\n",
       "      <td>Удаленная работа</td>\n",
       "      <td>['Python', 'PostgreSQL', 'SQL', 'JavaScript', ...</td>\n",
       "      <td>О нас:  Американская компания с русскоговоряще...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Ids      Employer                                         Name  \\\n",
       "223  39835664  Hunt For You  Senior Fullstack Developer (Python, remote)   \n",
       "223  39835664  Hunt For You  Senior Fullstack Developer (Python, remote)   \n",
       "\n",
       "     Salary      From        To   Experience          Schedule  \\\n",
       "223    True  316405.0  553709.0  Более 6 лет  Удаленная работа   \n",
       "223    True  316405.0  553709.0  Более 6 лет  Удаленная работа   \n",
       "\n",
       "                                                  Keys  \\\n",
       "223  ['Python', 'PostgreSQL', 'SQL', 'JavaScript', ...   \n",
       "223  ['Python', 'PostgreSQL', 'SQL', 'JavaScript', ...   \n",
       "\n",
       "                                           Description  \n",
       "223  О нас:  Американская компания с русскоговоряще...  \n",
       "223  О нас:  Американская компания с русскоговоряще...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[df[['From', 'To']].idxmax()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Вакансия с минимальной зарплатой"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ids</th>\n",
       "      <th>Employer</th>\n",
       "      <th>Name</th>\n",
       "      <th>Salary</th>\n",
       "      <th>From</th>\n",
       "      <th>To</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Schedule</th>\n",
       "      <th>Keys</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>39755183</td>\n",
       "      <td>Платформа НТИ</td>\n",
       "      <td>Data scientist</td>\n",
       "      <td>True</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>Нет опыта</td>\n",
       "      <td>Полный день</td>\n",
       "      <td>['Анализ данных', 'Python', 'Веб-аналитика', '...</td>\n",
       "      <td>Платформа НТИ — некоммерческая организация, со...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>39585034</td>\n",
       "      <td>Раксел Телематикс</td>\n",
       "      <td>Андроид Разработчик (Android Developer)</td>\n",
       "      <td>True</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>130000.0</td>\n",
       "      <td>От 3 до 6 лет</td>\n",
       "      <td>Удаленная работа</td>\n",
       "      <td>['Java', 'Android', 'Kotlin', 'DI', 'Room', 'R...</td>\n",
       "      <td>Международная технологическая компания Раксел ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Ids           Employer                                     Name  \\\n",
       "138  39755183      Платформа НТИ                           Data scientist   \n",
       "234  39585034  Раксел Телематикс  Андроид Разработчик (Android Developer)   \n",
       "\n",
       "     Salary      From        To     Experience          Schedule  \\\n",
       "138    True  100000.0  150000.0      Нет опыта       Полный день   \n",
       "234    True  110000.0  130000.0  От 3 до 6 лет  Удаленная работа   \n",
       "\n",
       "                                                  Keys  \\\n",
       "138  ['Анализ данных', 'Python', 'Веб-аналитика', '...   \n",
       "234  ['Java', 'Android', 'Kotlin', 'DI', 'Room', 'R...   \n",
       "\n",
       "                                           Description  \n",
       "138  Платформа НТИ — некоммерческая организация, со...  \n",
       "234  Международная технологическая компания Раксел ...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[df[['From', 'To']].idxmin()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Статистика по зарплатам\n",
    "\n",
    "Здесь отображается количество вакансий с указанными зарплатами \"От\" и \"До\", производится подсчет таких вакансий, а также некоторые свойства: среднее, медиана, минимальное и максимальное значение."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>From</th>\n",
       "      <th>To</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>185859</td>\n",
       "      <td>295746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>65462</td>\n",
       "      <td>122663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>100000</td>\n",
       "      <td>130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>316405</td>\n",
       "      <td>553709</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         From      To\n",
       "count      19      17\n",
       "mean   185859  295746\n",
       "std     65462  122663\n",
       "min    100000  130000\n",
       "max    316405  553709"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stat = df[['From', 'To']].describe().applymap(np.int32)\n",
    "df_stat.iloc[list(range(4))+[-1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### График зарплат\n",
    "\n",
    "Приводятся графики и распределения зарплат \"От\" и \"До\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R7VLe4S/l/um4avx2skyWAP8FiAqD4hMAAAAAn2nRuLZaNK7t7zAA+EDekR3ehadLuNOPKy/5KwW07FLOUaEi4m53AAAAAACgxFyp316xPy91dzlFgoqO4hMAAAAAACi5q66iZZkt8lF8AgAAAAAAJWa9Pu7K/b+6cj+qD4pPAAAAAACgxCy/ukWWhpFF9plDw2UNb1POEaGiovgEAAAAAABKzGQyK+jOMQq46XYpIDC/0WpTwI23qUafp2Qyc48z5OM3AQAAAAAAlIrJFqTAro/I3ulBGTmZMgXVkslq93dYqGAoPgEAAAAAgGtistpkqtXA32GggmLZHQAAAAAAAHyG4hMAAAAAAAB8huITAAAAAAAAfIbiEwAAAAAAAHyG4hMAAAAAAAB8huITAAAAAAAAfIbiEwAAAAAAAHyG4hMAAAAAAAB8pkTFp1WrVmn48OFKSEhQbGys7r77bi1dulSGYXiNW7JkiXr27Kno6Gj169dPa9euLTRXVlaWJk6cqA4dOiguLk6jRo3S6dOnC43bsWOH+vfvr5iYGHXv3l3z588vdDzDMDR//nzddtttiomJUf/+/bVr165Cc6WlpWnkyJGKi4tThw4d9Mwzz+j8+fMlOQUAAADFRu6E6s6Z59aX35/S8k0p2vjNSV105Pk7JAA+YOQ55DywWbnbP5TzwCYZebn+DgkVTImKT//7v/+roKAgjR8/XnPnzlVCQoImTZqk119/3TNmxYoVmjRpknr16qWkpCTFxsZqxIgRhRKa0aNHa/PmzZoyZYpmzJihlJQUDRkyRHl5P/+HdPToUQ0aNEihoaGaN2+eHn300f9j787Doyrv//+/zmQy2VcgIbImAcK+CMoqaISyaGv1iyAKVUFK+VQsKipSK7TSYqnS/hAXRFyotqIWtSooggqCaKugiOwkYQmSSEI2ksySmd8faUaGhJCEnEwSno/rygU59z3nvOeQhDuvc5/7aOnSpXr++ed99rVixQotXbpUt912m5YvX65WrVpp6tSpOnr0qLeP0+nUHXfcoYyMDD3++ONasGCBtmzZonvvvbdWJwwAAKCmGDvhYnYoM1/3Pf2Znn1nt97ekq4X1u3VvU9+pm8OnvR3aQDqkev7fTr9j3tV+skKOba/rdJPnlPRK/fIdew7f5eGRsRam85PP/20YmNjvZ8PHjxYeXl5euGFF/R///d/slgsWrp0qa655hrNnj1bkjRo0CDt379fTz75pFasWCFJ2rFjh7Zs2aKVK1dq2LBhkqTExESNGzdO69ev17hx4yRJK1euVExMjJYsWSKbzabBgwcrNzdXzzzzjKZMmSKbzSa73a7ly5dr6tSpuu222yRJ/fv315gxY7Ry5UotWLBAkvTBBx/owIEDWrt2rZKSkiRJkZGRmjZtmnbu3KnevXvX/SwCAABUgbETLlYldpf+9vo3Ol3qqrT9qbd26Y/TB6plVIifqgNQXzylRSp5/2+Ss8S3wX5aJeuXKuymP8sSGu2f4tCo1Grm05mDpwrdunVTUVGRiouLdfToUWVkZGjs2LE+fcaNG6dt27bJ4XBIkjZv3qzIyEgNHTrU2ycpKUndunXT5s2bvds2b96sq6++WjabzWdfBQUF2rFjh6TyqeVFRUU+x7TZbBo1alSlfaWkpHgHT5I0dOhQRUdHa9OmTbU5DQCAGnCUeZr8h93llt3l9nsdF/oB/2HshIvVZ7tOVAqeKjhdbm36+ngDVwTADM59n1YOniq47HLu3Vx1Gy46tZr5VJWvvvpK8fHxCg8P11dffSWp/ErcmZKTk+V0OnX06FElJycrLS1NiYmJMgzDp19SUpLS0tIkScXFxfr+++99BjwVfQzDUFpamgYOHOjtf3a/5ORkvfTSSyotLVVwcLDS0tIq9TEMQ4mJid59XAirlbXbq1NWxvlpzKxWC1/DqBdnfq8/9sUPfqwEVeF7vXFg7FSOr8Xm7XjO6WrbM0+e5msAaAbseceqbfecOsb3OiRdYPj05Zdfau3atXrggQckSfn5+ZLKp2SfqeLzivaCggJFRERU2l9UEVxOxQAAIABJREFUVJR27dolqXxRzar2ZbPZFBIS4rMvm82moKCgSsf0eDzKz89XcHBwtces2FddWSyGYmLCLmgfzV1paYC/S0A1oqPDFBwc7O8y0Azwvd648b3uf4ydyjF2av7iWlT/79sqJpSvAaAZcMe0kKOa9pDoWL7XIekCwqcTJ07o7rvv1sCBA/WLX/yiPmtqctxujwoKiv1dRqNmt5f6uwRUIy/vtIKCyvxdBpoBj8ejFSte9HcZ9cJut+vOO2dIkpYtW17pF/WmqLjYpZKS6mcjXOwiI0MUEGDOFVrGTj9i7NT8DejcUq99uF/nuvH38q6tdOoUP4+Aps7TYZD0+b/P3Z44iO/1Zq6mY6c6hU8FBQWaPn26oqOj9cQTT8hiKT9QVFSUpPIrb61atfLpf2Z7ZGSkTpw4UWm/+fn53j4VV9oqruJVcDgcKikp8dmXw+GQ3W73+cWgoKBAhmH49Kvq0cD5+flKSEiow1nw5XK5L3gfzRnnp3FzudwKCODfCPUjIMB2/k5NwJnfEwEBgc3ifZWVeaRz/ioIMzF2qoyxQfPWIjJY469K1usfH6rUNmpAOyVfEsXXANAcRF0iW//r5fjqzUpNtr7XSLEd+V6HpDqET6WlpZoxY4YKCwu1evVqn+nYFesCnL1GQFpamgIDA9WuXTtvv23btsnj8fisXZCenq4uXbpIkkJDQ5WQkFBpTYH09HR5PB7v/iv+TE9PV9euXX2Oeckll3hvL0hKStL+/ft99uXxeJSenu6zeCcAAEB9YuyEi9XYgR2UlBCpj3dkKutUiWIjgjSi7yXqndzS36UBqEdB/a9TQOvOcu75WO6CbFkiWimw25Wytu3p79LQiNRqXrnL5dLs2bOVlpam5557TvHx8T7t7dq1U8eOHfX+++/7bF+7dq0GDx7sffLK8OHDlZ+fr23btnn7pKena/fu3Ro+fLh32/Dhw7Vx40Y5nU6ffUVGRqpfv36SpEsvvVTh4eFat26dt4/T6dT69esr7Wvv3r3KyMjwbtu2bZvy8vI0YsSI2pwGAACAGmHshItdSvsY/eq6npp/22Wa9f96EzwBzZS1TXeFjPy1wm74vUJG3UnwhEoCFixYsKCmnefPn6/33ntPs2fPVosWLXTixAnvR2xsrAICAhQTE6Nly5bJ7S6fWrdixQp9/PHHWrRokXeKdkJCgr7++mu98cYbio+P19GjRzV//ny1atVK8+bN805FT0pK0gsvvKC9e/cqOjpaH330kZYtW6ZZs2bpsssukyRZrVYZhqHly5crLCxMJSUlevzxx7V//34tXrzYO3U8MTFRGzZs0Lp165SQkKA9e/boD3/4gwYMGKA77rjjgk6ix+NRSYnz/B0vYmVlLr333tuSJFvL7jIMnnjgdx63HDm7JUnXXnudrNYLfvgl0Kyc+XOL75GLR0iITRaLcf6ONcTYqWqMnQAAaB5qOnYyPB5PjRd/SE1NVWZmZpVtGzduVNu2bSVJr7/+ulasWKHjx48rMTFR99xzj6666iqf/oWFhVq0aJE+/PBDuVwuDRs2TA899FClK4Lbt2/Xo48+qj179ig2Nla33HKLpk+f7jPl3OPx6Nlnn9U//vEP5ebmqlu3bnrwwQe9V/gqZGVlaeHChdqyZYusVqtGjRqlefPmKTw8vKanoEplZW7l5rKIWnXs9lLNnDlVkhSeMl6GhV/i/M3jdqlo3xuSpKeffl5BQTwBCzjTmT+3+B65eMTGhtXrguOMnarG2AkAgOahpmOnWoVPqBoDqPMjfGp8CJ+A6hE+XZzqO3xC1Rg7AQDQPNR07MToCgAAAAAAAKYhfAIAAAAAAIBpCJ8AAAAAAABgGhbeAQAAAFAnRSVOHczMlzXAUEq7GAVaubYNAKiM8AkAAABArbg9Hr3+8UF9tD1TTpdbkhQeEqgbRiTpyr5t/FwdAKCx4dIEAAAAgFp5c3OaPvjPUW/wJJXPglr1/j59uTfbj5UBABojZj4BAAAAqLESu0sbvzp2zvb3Pj+sAV3jJEm7M3L18fZMZZ0qVmxksIb3uUSXdmnVUKUCABoJwicAAAAANXY0u0iljrJzth8+USi7s0wf/veo1mxO824/9sNp7TyUo9RL22jyT1IaolQAQCNB+IQG53G7/F0CxL8DAACom/MtKh5gMXQyv0RvnhE8nemj7Zm6rGucUtrHmFEeAKARInxCg/B4PN6/nz7wlh8rQVXO/PcBAACoTsfWEYqLDlF2XkmV7f26tNIXu7NV3ehi67cnCJ8A4CLCguMAAAAAaswwDN10dWcFWIxKbWHBVl1/RaKKih3V7qOoxGlWeQCARoiZT2gQhvHj4CSs889lWPjS8zeP2+WdhXbmvw8AAMD59O3cUvdN6qe1nx/W7oxcBQRYNKBLK107pKPiY0PVLi682te3jQtroEoBAI0BCQAanGGxEj4BAAA0cV3aRatLu+gq2wb1aK03P02vcoaTLdCiK/u2Mbs8AEAjwm13AAAAAOpVSJBVd0/oo+hwm8/2sGCr7ry+l2Ijg/1UGQDAH5h+AgAAAKDeJSZEavHMIdpx4KSycosVGxmkASlxsgUG+Ls0AEADI3wCAAAAYAprgEWXdY3zdxkAAD/jtjsAAAAAAACYhvAJAAAAAAAApuG2OwAAAAC1YneU6du0HDldbnVuG6WW0SHn7HswM/9/az4Fq2v7aBmG0YCVArhQZdmH5M47ISMsRgGXdJVhMIcFtUf4BAAAAKDGPt6RqTc+OagSe5kkyTCkgd3iddvYrj6LiWflFuvpt3bpSHaRd1tcTIhm/KyHEhMiG7xuALXjLshWyYan5D6Z4d1mRLRSSOoMBcR3qtTf43ZLzhIpMESGhYAKvviKAAAAAFAjXx84qb9/sM8bPEmSxyN9vjtLf1+/z7vN6SrTY69+7RM8SVL2qRItWf21Coo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i5lfov5dMS/p0ICK/UGPbAIerOwFYVZXPzHxdqZGfZ1smv3HHPMoL03lkxzmO1nbyl48c5HfftZiiHN3pRURE5Eq54UFi9QeI1ezDaa/9yRO2f+ROdZXrsYoXaJj1BGcYBlbhXMyMQmLnduMOdBCv3o3Z3Yw9Zx2GL+B1RJFxpfJJRH5OZ2+IPccvAlBVlkVVWZbHiWSi27CkiOK8FL7ygxO0dQ/zxYcP8pEtC1gxN8/raCIiIhOem4gTbz5OvHov8aZj4MRHnjAMrBkL8FWux561EsOnVeiTjRFMx7d0M4nmEyTOH8XpbCTafwnf/JswMwq8jicyblQ+icjPCEXivHS0FcdxKclLYeV8lQdyeWYVpfO5rat56ImTnGvu5Ss/OMF9G2Zxj+ZAiYiI/BzXdXE6zxOr3kO8bv/PznHKmYlvznrsOWsxU3QRcLIzDBO7dClmVjHxc6/ghvqJndg+Mhi+aL624cm0oPJJRN7kOC6vHGtlOBwnPcXPhqVFKg3kiqSn+PnUe5bx2K5adh5q4ck9DTS1j8yBCgb0K0dERMQZ6hkZHF6zF6fnwpuPG8EM7Mp1+Cqvx8qZ6WFCGStmWi6+5fcQr96L09lIvG4/5kDnyDY8S+dJMrXpO1xE3nS4uoP27hC2ZbBx+Qz8tmYJyJWzLZP33T6XmQWpfHPHOY7UdPLX3zzE775rMflZyV7HExERGXduIkb8/FFi53aTaDkBrjvyhGVjl63AN3cDVslCzXGaBgzLhz3/JhIX8kg0HBy5i+FQD74FN2MkpXkdT2TMqHwSEQAaL/ZzurEHgOsXF5GZqiGIcm1uWDKDGTkpfOWHJ7jQOcQXHz7Ib9+3iIWzsr2OJiIiMi4S3c3Ezu4mXrMPNzL45uNWQSX23OvxVazGCOgGHdONYRjYJQsxU7OJnXkJd6ib6JEfjcyByprhdTyRMaHySUToGYiw72QbAAtnZVNWqKsuMjpmF2fwFx9czb/98AT1rf3802NH+bWb53D76pmabyAiIlOSGxkiVref2LndOB0Nbz5uJGfim7sB37wNmBmFHiaUicLMLMK//B5iZ17EHewidvJ5rPIVWCWLdJ4kU47KJ5FpLhpL8NKRC8QTLkU5ySyvzPU6kkwxWWkBPvO+5Tyy4xx7T7Tx3V21nG8fZOvmefi0tVNERKYA13WJt9cROrGLeN1+SERHnjAs7LJl+ObfOFIoaFudvIWRlIpv6Wbita/htNeSaDyEO9iFPXeD5kDJlKLvZpFpzHVd9hy/yMBwjJQkmxuWFmGausoio89nW/zmXVWUFqTx6Au1vHqqjbbuIT754BKy0rTFU0REJic3FiZSvZ8LZ14i2v6TVU5m1gx8827ErlyPGUz3MKFMBoZpY1dej5OWS7zuAE5nI7HIEL4Ft2L4k7yOJzIqVD6JTGMn6rpo6RjCNA02Li8mya+3BBk7hmFw+6qZFOem8NATJ2m4OMAXHn6dTz64mNkzMryOJyIictkS3S3ETr9IrGYfxEIjD1o+7IrV+KtuxiyYo21TckUMw8Aqmo8RzCR2ZhfuQAfRY9vwLbwNM1nnSTL56ZOmyDTV2jnE0douANYuKCAnQ1dVZHwsKM/mz7eu5l+/f5wLnUP83bcP88FN87l+cZHX0URERN6W6zjEm44QO/EciYvn3nzczCggc9UmEmVrcGwND5drY2YW4lt6F7FTOyE8QOzYM/gW3IKZUeB1NJFrovJJZBoKReLsOX4RgMqSDOaU6GqKjK/8zCCfff9K/vtHpzlS08n/bDtD86VBfuXm2Vim6XU8ERGRN7mxMLFzu4mefB63/9LIg4aFXb4c34JbCJQuIDM7jZ6eIZy4421YmRLM5Ez8S+8mdvoF3IFOYid2YM/dgJVf4XU0kaum8klkmnFdl70nLhKOJshM9bO6Kt/rSDJNBQM2n3hwMU/taeCpvY0893ozFzoG+a37FpEa9HkdT0REpjlnsIvYqReInnkJosMjDwZS8FfdjG/hrZgpWQAYhi6ayOgz/EF8izcRP/cKTlcT8XOv4EYGsUoWa0unTEoqn0SmmVONPbR2DmOZBjcunYFt6YRJvGMaBvffUEFJXir/ve00pxp7+MtHDvK771pCca62LoiIyPhLdDUTPbqNeP0BcEdWMhnpBfgX34Fv7gYMn26UIePDsGzsqo0k6g+SaD1NovEwbngQe/ZaDK0Ul0lG5ZPINNLZG+JIdQcAq6vyydRdxmSCWDU/n/ysIP/6+Aku9YT4q0cO8rF7FrKsMtfraCIiMk3Eu1oYPv4I8cZDbz5mFc3Hv+ROrNKlWuEknjAME3v2GkhKJVF/AKetmnh0GHv+RgxLH+dl8tB3q8g0EY0leOXYRVwXygrTqNScJ5lgSgvS+POtq/jqEyc529TLvz5+nPtvrGDLujItLxcRkTHjDHQSazlBqPP8G48Y2BWr8C+7Gyu33MtoIm+yixdgJKUSP/syTncLsZPP41t4C4ati8kyOai+F5kGXNfltVPtDIZipCTZrFtYoA/zMiGlJ/v5o19bxi0rinGBH75Sz0NPniISTXgdTUREphhnoIPYyZ3Ejv4Ip/M8YGDPWUvyu/+S4G2fUPEkE46VU4pv0e1g+XD724kd34EbDXkdS+SyaOWTyDRQe6GPxrYBDANuXDYDv8/yOpLI27Itk9+4Yx4z81P51nPVHDx7ifbuYX73wcXkZga9jiciIpOcM9RDouEgTs+FNx4xsApmk7b+V3Hy5+G6rqf5RN6JmVGIb8kmYiefxx3qJnbsGXyL78BISvM6msg70sonkSmudzDCgdMjtwVeXplLnj68yyRx07JiPv3e5aQn+2i+NMgXHj7I2fM9XscSEZFJyo0OE6vZS+zwU28UTwZmwRx8qx7Av+AWrMwCryOKXBYzNQff0rsgkIIbHiB67FmcIZ0jycSm8klkCkskHF452krCcSnKSWbhrGyvI4lckbkzM/mLraspK0hjMBTjS989yguHWnRVWkRELpubiBFvOkb09R/gtNUALmZOGb5VD+CbuwEzmO51RJErZgbT8S+9CyM5E6LDxI5vx+nv8DqWyNvStjuRKexITSe9g1GS/BYblhRpztMUYxgjf2D8/rv++Fto5LUv/3WvpSzKTk/iT35jBQ9vP8trp9r59vPVNLb184E75+Gzx2YL6Vj9rFzt8Xs7k62Eu5Z/82gfOxGZ+lzXwblUR7zxCESHATDScrFnrcbM+AWrnAzjqn6vTpT3J9d1J+374ySN7TkjkIJvyWZip57HHegkdmIHvgW3YGbN8DqayM9R+SQyRbV1DXO6cWT57bpFhQQD+nGfSizLwDRNBkJxYPwKCMM0iDrDhMIxXOfyXzcpYHMtNVHAZ/HRLQsoK0jjsRdr2XuijdbOIT7xwGKy05Ou4Sv/vAQQDsdG9Wv+2NUev7dzrcd1PF3rcR3tY/d2Mlx30hxTEXl7Tl878br9uEPdIw8EUrFnrcTMLf+FBY1hWtgmGNFerrTXN+MGsd4hrHAMYwzfn96RYWD7fMSjUW9e/xoZpolp6OYiV8PwBfAtvpPY6Rdxe1uJndqJPf8mrNwyr6OJ/Ax9GhWZgqKxBHtPXARgTkkGM/NTPU4ko80yDULROHXNfUTj43eyZpkGwaCfUChK4jJPsH22yYLybFKTfNe0UscwDO5cU0pJfipffeIkDRcH+MLDB/nEA4uoLMm86q/71tcIh2OcbuwmFndG5Wv+tKs5fm9ntI7reBiN4zqax+6d5OemjtmKOhEZe24sQrzx4Bvb6wDLh1W6FGtGFYb5Dj/bpokbCxNqbcSJx6/oNU0TCPoZDkVxRv9Xx+VlSAqSkl9M+CryTwQ/zq8FUFfHsHz4Ft5K/NwrOJ3niZ95CebfiJU3y+toIm9S+SQyBb1+5hJD4TipQR+r5+d7HUfGUCzuEI2Nb/lk+0ZecywLgHeysDybv9i6mn99/AQtHYP8/XeO8Ou3z2Xj8uJRe42xOq4T4fh56VqO63gduwne44nI23BdF6ejgXj9AYiFATAL52KXr8DwXf4KWTcex4lf2eohwzRwEwZuPIbj0Xu7EfcBV5d/Ivhxfrl6hmlhz7+JePXeke2mZ18BJ45VUOl1NBFAA8dFppzzbQPUtfZjABuWFOKz9WMuU09eZpD/+/6VrJ6fT8JxeWTHOb7+zBli47gKTEREJgY3PEDs1E7i516BWBgjmIFvyWZ8leuvqHgSmewMw8SeuwGzcC7gEq/eS6L1rNexRACtfBKZUkKROK+dagdg4axs8rOSPU4kMnYCfovfvm8hZYVpPP5yHbuPX6SlY3BM5kCJiMjE47oOiQunSZw/Ak4CDHNki13JonfeYicyhRmGgT1nHQnTItF6hnjda7huArt4odfRZJrTkgiRKcJ1XfadbCMSS5CVFmBpZa7XkUTGnGEY3LW2jD/61WWkJNk0XBzg819/nTON3V5HExGRMeQM9RA78iMSDQfBSWBkFOBbcR926VIVTzLtGYaBVbEGq2QxAIn614k3HfM4lUx3Kp9Epoialj4udAxhGgYblhRhmRrZKNPHwlnZfG7rakoLUhkMxfjSo0fZvr9pwg/iFhGRK+O6LonWM8SO/mjkTna2H7vyenyLN2EmZ3gdT2TCMAwDq3wFVtlyABLnjxBvPKxzI/GMyieRKWBgOMrBs5cAWD43l6y0gMeJRMZfbmaQz/7GSq5fVIjrwmMv1vLQk6cIRSbfXX9EROTnudEQ8dMvEK/bP7LaKasY/8r7sQorMQxddBN5K8MwsEuXYs1aBUCi+TiJhoMqoMQTmvkkMsk5rsue423EEy4F2UEWlGd5HUnEM36fxW/eXUXFjHS+s7OGg2cv0do5xCceWERRTorX8URE5Co53S3EqveM3MnOMLFmrcKaUaXSSeQy2G/MQYvX7Sdx4RS4DlbFGv38yLjSyieRSe5kfRcdvSF8lsn1i4v0S0SmPcMwuHlFCZ953woyUv20dg7xhYcPcuBMu9fRRETkCrlOnHjdfmKndo7cyS45E9/ye7CLF+icR+QKWDOqsCvXA4wMIq99TSugZFypfBKZxNq7hzhwemS73cr5eaQGfR4nEpk45pRk8Pmtq5lfmkkkmuCrT57iO89XE084XkcTEZHLMDJUfBuJ1jPAyIdn37ItmCla5S1yNazCudhzNwDgtJ0jXrMP19V5kYwPlU8ik5TjuHzt6dMkHJeinGQqSzRkU+StMlIDfOo9y7hrbRkAOw+18LffPkx3f9jjZCIi8k4SHQ3Ejm7DHe4BXxL2wtuwZ1+HYWlqiMi1sArmYM+7ATBw2muIV+9VASXjQuWTyCT1/OvN1Lb04rNM1i0q1NJzkbdhmSbv3jib33vXEpIDNvWt/Xz+669zsr7L62giIvIWrusQbzhI/OzL4MQxMovwr7gPK7vE62giU4aVPxt7/o2AgXOpjtjpF3GdhNexZIpT+SQyCbV3D/PYrloA1i4q0HY7kcuwrDKXv/jQasoK0hgMxfjnx47xxO56HEfzDkREJgI3FiF2aieJlpMAWCWL8C26HcMf9DiZyNRj5c3CrtoIhkHiUh2DLz+Cm9AdgmXsqHwSmWQc1+Vrz5whGneoKs+mqkxzD0QuV35mkM++fwUbl83ABZ7a28iXvnuE3sGI19FERKY1Z6iH6NEf4fa0gmlhz7sRe9YqDEMfV0TGipVbhl11Mxgm0cajhHb+mwooGTN6NxeZZF441EJNSx9Jfoutd+tOLyJXymdbfGDTfD66ZQEBn8XZpl4+97UDnGzQNjwRES8kOhqJHd0G4QEIpOJbejdWfoXXsUSmBSunFP/iO8CyiTceJvT8v+LGo17HkilI5ZPIJNLeM8zjL9UB8J7bKsnN1DJ0kau1blEhf7F1FSV5qQwMx/inR4/x/ZdqSWgbnojIuHBdh3jjIeJnX/rJfKflWzBTs72OJjKtWDmlpN36UbB8JJqOEXruyyqgZNSpfBKZJBzX5evPnB3ZbleWxc0rir2OJDLpFeWk8GcfWMnNy0d+nra9ep4vf+8Yg6GYx8lERKY2NxEnfuYlEs0nALCKF47Md/IleZxMZHryF88nefOnwPaTaDnJ4DP/hBPTWAIZPSqfRCaJFw9foLq5l4DPYuvm+ZjabicyKvw+i/ffOY/fuX8RwYBFfWs/P3y5nuZLg15HExGZktxYhNjJ53C6msAwR+Y7VazWfCcRj9nFVQQ3fwp8ScRbTtP23b/CjYW9jiVThN7hRSaBzt4Q33tp5O527944mzxttxMZdavn5/P5D11HaUEqkViCFw9f4LVT7cQTjtfRRESmDDc8SOzYM7j9l8D241t0h+Y7iUwgdtE8ku/6P+APEm46xcDT/4AbDXkdS6YAlU8iE5zrujy84xzRmMPcmZnabicyhvKzgvzhry1jUcXIvJHq5l627TtPd7+u+omIXCtnsIvosW24oT7wJ+Nbshkzs9DrWCLyFlbBHNLu+WPMpBQSbTUMP/MPuJEhr2PJJKfySWSC23eyjVMN3diWqe12IuPAtkzWLizktlUlBAMWfUNRnnm1idMN3biuhpGLiFwNp6eV2PHtEA1hJGfhX3Y3ZkqW17FE5G3YBbMpet/nMQIpOJfqGd72D7hhjSSQq6fySWQC6xuM8N0XagC4/4ZZFGYne5xIZPqYkZvCPdeXMzM/Fcd1OXiug50HWxgOx72OJiIyqSTa64ideh4SMYyMQnxLN2MEUryOJSK/RKCogtT7/hQjKQ2ns5HhbX+HE+r3OpZMUiqfRCawbz9fzVA4TllBGneumel1HJFpJ8lvs3H5DNYuLMAyDS52DfP03kaa2ge8jiYiMinEm08Qr94NrouZN2vkjna23+tYInKZ7NxSglv+BCOYjtPVTOjpv8EZ6vE6lkxCKp9EJqhD5y5x8FwHpmHwobvmY5n6cRXxgmEYzJ2ZyZb1ZWSnB4jEErx0pJU9xy8SjSW8jiciMiG5rku88TCJxkMAWMULsefdiGFaHicTkStlZReTfM9nMVKycXovMvzUX+MMdHgdSyYZfZoVmYCGwjG+9Vw1AJvXllJakOZxIhHJSA2weW0Zi2ZlYwD1rf08taeR1k4N4BQR+Wmu65JoOEii+TgA1qxV2BWrMTS3UmTSMjMLSb73TzHS8nAHOhh+6m9w+tq8jiWTiMonkQnosV219A1FKcxO5t7ry72OIyJvsEyDFfPy2HRdKWnJPoYjcXYebOG1U23E4o7X8UREPOe6LvG6/SQunALArrgOu2SRx6lEZDSYaXkk3/tZzMwi3KFuhp/6GxLdF7yOJZOEyieRCeZ0Yze7j18EYOvm+fhsLU8XmWjysoJsWV/O/NJMAKqb+3h6byPt3cMeJxMR8Y7rOsTO7ca5eBYAe846rOIqj1OJyGgyU7II3vOnmNkzcUN9hJ7+GxKdjV7HkklA5ZPIBBKJJvjGsyMnbLesKGbuzEyPE4nI2/HZJmsWFHD76hJSkmwGQzF2HGjm9TOXtApKRKYd13EY2v0dEhfPAgb23A1YRfO8jiUiY8AMppO85TOYebNwI4MM/+jvSLTXeh1LJjiVTyITyA9319PZFyY7PcC7bprtdRwRuQxFOSncs6GcOSUZAJw538PTezULSkSmD9eJE9r1VSJ1r4NhYM+7AatgjtexRGQMGUmpJN/9x1iFcyEaYnjbPxBvPeN1LJnAVD6JTBD1rf08f7AZgA/cOZ9gwPY4kYhcLr9tsX5RIbeu/MkqqJ0HW9h74iIR3RFPRKYwNxEnvPMh4nX7wbTwL7wNK7/C61giMg4Mf5Dg5k9hFS+EeITQM/9I7I07XIq8lconkQkgnnD4xrNncF1Yu7CAJbNzvI4kIlehOC+FezfMenMWVN2Ffp7c3cD5tgGPk4mIjD7XiRN+4d+JNx4C0ybtlt/EypvldSwRGUeGL0Dwzt/HLl8JTpzw818hdm6317FkAlL5JDIBbN/fREvHEKlBH++5tdLrOCJyDX48C2rTdaVkpPgJRxO8fLSVl45cYDgc8zqeiMiocB2H8Iv/RbzxMFg2yZv+AP9M3dVOZDoybD9Jt30c37wbwHUJv/w/RI8/63UsmWBUPol47GLXEE/tbQTgvbdWkp7s9zaQiIyK/KwgW64vY8nsHAwDmtoH+cErDRyr6cBxXK/jiYhcNdd1CL/y9Te32gVv/yT2zMVexxIRDxmmReDG38S3ZBMAkdceJXLge7iuznlkhMonEQ85rsvD288RTzgsmpXN2oUFXkcSkVFkmSbLKnPZsr6M3IwkYnGHPcdaeXpfIx29Ia/jiYhcMdd1iez9NvHq3WAYJN3y29ily7yOJSITgGEYJK19D/41vwJA9Og2Iru/gevoLsCi8knEU68ca6W6uRe/z+QDd87DMAyvI4nIGMhKS2Lz2lLWLyok4LPo7o/w7GtNvHqyjUhUA8lFZHJwXZfI/seInX4BMEja+FF8Fau9jiUiE0xg2d0EbtgKGMTOvkx410O4CY0emO5UPol4pGcgwvderAXgwRtnk5sZ9DiRiIwlwzCYV5rJr2+aT2VJBgA1LX08sbuB2pY+LUsXkQkvevhJYm/McQnc8EF8les9TiQiE5W/aiNJt30cTIt4/euEtv8LblSrvqczlU8iHvn289WEIglmFaVx28oSr+OIyDgJBmw2LCnizutmkpnqJxJLsO9kG8++1qSteCIyYUWPPUP00BMABNa9F3/VRm8DiciE56tYTXDTH4IdIHHhFMNP/w3OUI/XscQjKp9EPHDo3CUOV3dgmQZbN1dhmtpuJzLdFGQls2V9OSvn5WFbBp19YZ59rYk9xy8yHI57HU9E5E3RUzuJ7H8MAP/qd+NffKfHiURksrBLFpG85TMYwXScriaGn/xLEj0XvI4lHlD5JDLOhsMxvvVcNQCb15YyMz/V40Qi4hXTNFg4K5v7b6hgdnE6APWt/Tyxu54TdV0kEhrQKSLeitXsI7L3WwD4l99DYPkWjxOJyGRj5VeQfN+fYWQU4g52MfzkXxFvPet1LBlnKp9ExtljL9bRNxSlIDuZe9aXex1HRCaA5CSb6xcXcde6UvIyk4gnXI7UdPLknkbOtw1oHpSIeCLedJzwS/8DgG/R7fhXPehxIhGZrMz0fFLu+zPMgjkQHSb0zJeI1e33OpaMI5VPIuPo7PkeXjnWCsDWTfPw2ZbHiURkIsnNCLLpulI2LCkiOclmMBTj5aOtbN/fxKUezYMSkfGTuFRHaOdXwE1gz1lLYN17dVdeEbkmRlIqyXf/MXb5SnDihF94iOixZ3WRbZpQ+SQyTqKxBN/YPrK8dOPyYuaVZnmcSEQmIsMwqJiRzn0bZrFkdg6WadDRG2b7/iZePHyBvsGI1xFFZIpL9LQy/Ow/QTyKVbKIpJs+gmHoY4OIXDvD9pN02yfwLbodgMj+R4ns+zauo1EDU51+i4iMkyf3NnCpJ0Rmqp933zTb6zgiMsH5bJNllbk8cOMs5pRkYADNlwZ5am8jr51qYzgc8zqiiExBzmA3oWe+BJEhzLwKgrd/EsOyvY4lIlOIYZoE1r2PwNr3ABA7tZPQc1/GjWqV91Sm8klkHJxvG2DH/mYA3n/nPJKTdBInIpcnOcnH+kWF3LOhnJL8VFwXqpv7eGxXLdv2NerOeCIyatzwIKFnv4Q71I2ZUUhw8x9i+JK8jiUiU5BhGPiXbCLp1o+DZZNoOsrwU3+FM9DhdTQZIyqfRMZYwnH4+rNncFyX1fPzWV6Z53UkEZmEMlMD3LKimDvXzCQ3Y2Qo+fb9TfzxQ3vZ9mojkWjC64giMom58QjDO/4Fp6cVIzmT4F3/BzMpzetYIjLF+WavIfmeP8UIZuB0tzD8wy8Qv3jO61gyBlQ+iYyx5w4009Q+SEqSzftun+t1uqw3GQAAIABJREFUHBGZ5Aqyk9m8tpRbV5ZQkB1kKBzn8Zfr+cxX9/Hc683E4iqhROTKuE6c0M5/x2mvBX/ySPGUlut1LBGZJqz82SQ/8DnM3DLc8AChbX9P9OzLXseSUabySWQMtXcP88SeBgB+7ZZKMlL8HicSkanAMAxmzUjns+9fxUe3LCAvM4n+4RjffaGGP/mP13jxcAvxhAZ3isgv57ou4VceJtF0DCwfwU1/gJVd4nUsEZlmzNRsku/9LHbFanASRF75OuFX/xfX0UW1qULlk8gYcV2Xh7efJRZ3WFCexfWLC72OJCJTjGkarF9cxF99dC0f3DSP7PQAPQMRvvlcNX/6H6/x0pELxOIqoUTk7UUPPUG8ejcYBsFbP45dqFXaIuINww6QdOvH8a+8H4DYiR2EdvwLbnTY42QyGlQ+iYyR3ccvcrapF7/P5AOb5mMYhteRRGSKsi2Tm5YV8zcfW8ev3z6XjBQ/Xf1hHtlxjj/5j1fZebCZaExXDkXkZ8XO7SZ6+EkAAhs+iF2+3ONEIjLdGYZBYOX9JN32cbD8JJpPMPzEF0n0tnodTa6RyieRMdA7GOHRXbUAPHBDBfmZQY8Tich04LNNbl1Zwt/99jree1slmal+egYifGdnDZ/56qvsONCkweQiAkC85SThV74BgH/ZFvxVGz3NIyLy03wVa0i+77MYKVk4vRcZ/uEXiNUd8DqWXAOVTyKjzHVdvrnjHKFInPLCNG5fNdPrSCIyzfh9Frevmsnf/fY63n/HXLLTA/QNRXl0Vy1//NV9bHu1keFw3OuYIuKRROd5Qs9/BdwE9px1+Fe/y+tIIiI/x8otJ/mBz2MVzYdYmPAL/05433dwHZ3DTEYqn0RG2etnL3GkphPLNPjNu6owTW23ExFv+GyLm1eU8Le/tY6tm+eTm5HEwHCMx1+u59MP7ePxl+voH4p6HVNExpEz2EVo+z9DLIw1o4qkmz6s0QAiMmGZyRkE7/40/qV3ARA7+Ryhp/8OZ6jH42RypVQ+iYyi/uEo33quGoAt68spyU/1OJGIyMhMqBuXzuCvP7aWD99dRVFOMqFInG2vnufTD+3jW8+do7M35HVMERljbmSI0LP/hDvci5lVTPD2T2JYttexRETekWFaBK77VZLu+F3wBUm01zD8g88Rbz3jdTS5AvptIzKKvvN8NYOhGCV5Kdy9rszrOCIiP8O2TK5fXMS6RYUcrelk26vnabjYz67DF3jpSCvXLchn89oySvJUnItMNW4iTuj5r+D0XMBIziS4+Y8wAilexxIRuWy+8pVYD5aMvJd1NxPa9vf4V78L/9K7MAytq5noVD6JjJIj1R0cOHMJw4AP3VWFbekNUEQmJtMwWDE3j+WVuZw938Mzr53nVGMPr55q59VT7SyuyGHzdaXMK83UdhyRKcB1XcIv/w+J1jPgSyK46Q8xU3O8jiUicsXMjAKS7/8zwnseIV69l+iB75NoqyHppg9jBtO9jifvQOWTyCgYCsd45LlzAGy6rpRZRXrjE5GJzzAMqsqzqSrPpuFiP8++dp5D1R2cqO/iRH0X5YVpbF5bxsq5eZpfJzKJhV//AfHaV8EwCd72Caxcrc4WkcnLsAMk3fQRYgWVRPZ9i0TTMYa//+ck3fxR7JJFXseTt6HySWQUPPpCLX2DUQqzk7nv+llexxERuWKzitL5+AOLae8Z5rkDzew5cZHGtgEeeuIkeZlJ3LmmlOsXFxHwWV5HFZErMHBsF+GDTwKQdMNW7JmLPU4kInLtDMPAX7URK7+C8K6v4vS0EnrmS/gW30lgzbsxLJ/XEeUttC9I5BqdbOhiz4mLGMCH7pqPXx/MRGQSK8hK5v13zuMffmc9915fTkqSTUdvmG89V82n/30fT+yu1x3yRCaJWMtpOp75KgD+ZVvwzb/R40QiIqPLyikl+YHP41twCwCxEzsYfuKLJHpbPU4mb6XySeQahCJxHn72LAC3riyhsiTT40QiIqMjPcXP/TdU8KWPX8+v3z6X3IwkBkMxntrbyKcf2scj28/S1j3sdUwReRuJngsMbf8yOAl8c9biX/2g15FERMaEYftJ2vABgnf8PkZSGk5XE8OPf57o6RdxXdfrePIGbbsTuQbff7mOrv4IuRlJvOum2V7HEREZdQG/xa0rS9i4fAaHznWwfX8TjW0DvHS0lZePtrKsMpdN15WqfBeZQJzhPkLb/xk3OkygZD7BWz5CQtecRWSKs8uXk5z/RcIv/heJC6eI7HmYRPNxAjd+SMPIJwCVTyJX6ez5Hl48fAGArZvnE/Bru52ITF2WabKmqoDV8/Opbu5lx4FmjtZ2cqRm5M/s4nQ2rSlleaWGk4t4yY1HCO34f7gDnZgZBRT+ymfoj1gQd7yOJiIy5szkTIJ3fYrYieeIHPg+8fNHSHyvlsD1v4FdsUZ38fWQyieRqxCKxPnaM2cAuHHpDBaUZ3ucSERkfBiGwbzSLOaVZtHaOcRzrzex72QbdRf6+bcfnqQgK8gda0q5flGhZuCJjDPXdQjv+k+cjnoIpJB696ewktMhMuR1NBGRcWMYJv4lm7BmVBF+6b9xupsJv/AQdt0BAhvej5ms1dpe0Ppbkavw2Iu1dPaFyc1I4tdumeN1HBERT8zITWHr5ir+4XfWc/e6MlKSbNp7Qnxzxzk+/dA+ntrTwGAo5nVMkWkjsv8x4o2HwLQJ3vn7WJmFXkcSEfGMlVtG8gOfw7/yfjAs4o2HGPre/yVWs0+zoDyglU8iV+hkfRcvHx25e8KH7qoiGNCPkYhMbxmpAd5102zuXlfG7mMXee71Zrr6wzyxp4Fn9p/nxqUzuGHpDK9jikxp0dO7iB3fDkDSxo9gF871OJGIiPcMyyaw8n7s8hWEX/ofnK7zhF/8T6y6AyTd8EHMlCyvI04b+tQscgWGwzG+/sbd7W5bWUJVmd6sRER+LMlvc/vqmdyyspjXz15i+/4mmtoH2XmwhV2HL1AxI52qsiyy0gJeRxWZUuJNx4js/SYA/lUP4puz1uNEIiITi5VTSvIDf0706DNEDz9FoukoQ987R9La92LPu0GzoMaByieRK/CdnTX0DEQoyAryro26u52IyC9imSZrFxRyXVUBp8/38Oxr5znd2ENtSx+1LX2U5KWwqCKb/Kxkr6OKTHqJzvOEXngIXBd77gb8y+/xOpKIyIRkmDaBFfdil68k/PJ/43Q0EH7la1jVewhseD9W9kyvI05pKp9ELtOR6g72nWzDMODDWxYQ0CBdEZF3ZBgGC8uzWTQrh9ON3Xz/pVoaLg7Q0jFES8cQ+VlBFlfkMCM3WVccRa6CM9hNaPs/QyyMNaOKpBu26mdJROSXsLKLSb7vz4idfI7IwSdItFUz/Pjn8C2+g8CK+zD8Qa8jTkkqn0Quw8BwlIe3j2y323RdKXOKMzxOJCIyuZQWpnHrqpl09oY42dBN/YV+LvWEeOFQCznpSSyenc3M/FR9cBa5TG40RGjHP+MO92JmzSB4+ycxLJ3ai4hcDsO08C/ZjF2xhsir/0u84SCx49uJ1+0nsO592LNW6ZxklOludyKX4ZvPVdM/HKM4N4X7N1R4HUdEZNJKT/GzflEhD95UwYLyLGzLoKs/zEtHWnl6byMNF/txdAcakXfkOglCL/w7TlczRjCd4KY/xAikeB1LRGTSMVNzCN7+SYKb/ggjLQ93qIfwzn8j9Ow/4vS1eR1vSlH5JPJLHDjTzsGzl7BMgw9vqcJn68dGRORaJSfZrJqfz4M3VbCoIhufZdI7GGX3sYs8tbuBugt9OI5KKJG3cl2XyN5vkWg+AZaf4J1/gJmW53UsEZFJzS5dQsqv/BX+FfeBZZNoOcnQ9/6MyIHv40ZDXsebEvQpWuQd9AxE+OaOcwDcva6M8sJ0jxOJiEwtSX6bFXPzeHBjBUvn5OD3mfQPx9h7oo0n9zRQ36qVUCI/LXZ8O7EzLwIGSbf+Fla+VmSLiIwGw/YTWPUAKe/+S6ySReDEiR79EUOPfobomZdwHcfriJOayieRt+G4Ll/bdpqhcJyygjS2rC/3OpKIyJQV8FksnZPLu26azYq5uQR8FgPDMfYcv8jTexqpb+3HVQkl01ys/nUi+x8FILD2PfjKV3qcSERk6jEzCglu/hRJd/weRkYBbqifyO5vMPyDvyDectLreJOWphKKvI2drzdzqrEHv23ysXsXYFvqakWulmGM/IGJPbhxss2VnIrH1WebLKrIYV5pFmfP93CqsZu+oSgvH23lRH03S2dnU6LB5DINJdprCb/4nwD4FtyKb/EdHicSkSnFMH7mnOLHv2ZHzjUm/u9c13VHNadhGPhnrcRXupTo6V1EDj2B091C6JkvYZcuJbD2PVhZM0bhdX72NacylU8iv0BT+wDff7kOgPfcWklRjoZ4ilwtyzIwTZOBUByY2CtXTNNgsiyonurH1WebLJ6dw7yyTM429nC6sYfu/jAvHmklKy3A8rm5FOemTPkTNREAp6+d0I7/B4kYVulSAuvfp+99ERk1hmlhm2BEe/nxImMzbhDrHcIKxzAm+gxGw8D2+YhHo2Py5f3z1pBcvpDQ0R2Ez+wm3nSMePMJAvPWk7z0Tszkqx/NMnKchwm4EItGRjH1+DHcJC5nU53KJ5G3iMQS/MdTp4gnXJbNyeWmZdfeaItMZ5ZpEIrGqWvuIxpPeB3nHSUn2ZQVpWNM8JVEMH2Oq9+2WDInlwWzsqlp6edoTQc9AxF2HbpAQVaQFXPzyMsKjkFqkYnBCfUz/Ow/4oYHMHPLCN76Oxim5XUsEZlKTBM3FibU2ogTj//4IQj6GQ5FmeijjsykICn5xYR/Kv9YMArmEUgrIFZ3AKezkcjZPUSqX8MuWYRduhTDF7jir2maYGSmY6TlE74wtvnHSkp2Dqb9y6sllU8ib/G9F2u52DVMRoqfrXfN15VFkVESiztEYxO7JPH7Jt/22ulyXAM+izULC6ksTudobSdnm3pp7wnx7P4mZuansnxuLpmpV37SJzKRufEIoR3/gtt/CSMtl+CmP8TwJXkdS0SmKDcex4mPrB4yTAM3YeDGYxP+7rNG3Af8bP4x40/GV7URp/ci8cZDuAOdxJuOEr9wCqtkMVZxFYblu+wvZ5gGvHERcVzyj4nL+/5Q+STyU47WdrLr8AUAPnx3FenJfo8TiYjITwv4LVbNz6eqLItjdV3UtfTRfGmQlkuDVBSns3ROLqnByz/pE5moXMch/MJXcS7VQyCF4OY/wkzO9DqWiIgAZmYRvqV343Q3k2g8jDvcS+L8YRKtp7FmLsEqmqdVqm+h8knkDX1DUb7+zBkAbl81k0UVOR4nEhGRt5MS9LF+USELyrM4WtNJU/sgdRf6abw4wILyLBZV5OCzJ99KNhEYGZwb2fdt4uePgGUTvPP3sTI1BkBEZCIxDAMrpxQzuwSno3HkPTs8QKL+AIkLp7BLl2Lmz1YJ9QaVTyKMnOR9bdsZBoZjlOSl8O6NFV5HEhGRy5CZGmDj8mI6ekMcPtdBe0+IE/Xd1LT0sawylznFGZimtk/L5BI7/iyx0y8ABkk3/xZ24VyvI4mIyNswDBMrvwIztxynvYZ40zGIDBGv2QdNx7BnLsYsqJz2JZQuCYoAuw5f4ER9F7Zl8rF7F+Kzp/cbg4jIZJOXGeSONTPZuHwGack+wtEEr51q50f7GmntHPI6nshli9W+RmT/YwAE1r4HX8VqjxOJiMjlMEwTq2ge/lUPYlWsBl9wpISqfY3o64+TaD2D60y+geKjRSufZNo73zbAo7tqAfjVm2dTkpfqcSIREbkahmFQWpBGcV4q1U29HKvrpHcwys6DLRTnprByfp6GksuEFm89S/il/wbAt+h2/Evu9DiRiIhcKcOysYsXYhXOw2mrId5yAqLDxOv2Q/PxkcHkhXMxrOlVx0yvf63IWwyH4zz0xEniCYels3O4dWWJ15FEROQaWaZBVXkWFTPSOV7XxdmmHi50DtG6d4j5pVksnZOD36cVrjKxJLpbCD33ZXDi2LNWEVj7Xq8jiYjINTAsG6u4CrOoEqetdqSEigyNzIRqPo5VvACzuMrrmONG5ZNMW67r8vVnz3CpN0ROeoAPb1mAYWguiIjIVBHwW6yuymdeaSYHz3XQcmmQM+d7aLjYz4q5ecwuTtf7vkwIzkAHoWe+BNFhrIJKkm7+GIap6RgiIlOBYdpYM+ZjFlbitNcRbz4OkUESjYcJNZ/AmLWU5BWbvI455lQ+ybS181ALh851YJkGv33/It2aW0RkikpP8XPLimIudAzx+pl2+odj7DvZRnVzL2sWFJCbkeR1RJnGnFA/w898CXe4FzOrhOCmP8Cw/V7HEhGRUWaYFlbRXMyCOTgdDSRaTuAO9zJce5Dh+iPYhZWYMxZgBNO9jjomVD7JtFTX2sdjP57zdMscZs/I8DiRiIiMteK8FApzZnHmfA/Hazvp7AvzzKvnmVOSwYq5uST5dVok48uNhgg9+4+4fe0YqTkE7/oURiDF61giIjKGDNPEKpiNmV8BPS24F08T675IvPUstJ7DzC3DKlmEmZbrddRRpbMsmXYGQzG++sRJEo7Lynl53KY5TyIi04ZlGiyalU1FUTqHqzuob+2ntqWPpvYBVszN4y5cryPKNOEmYoSe+zJO53mMpDSS7/o0ZkqW17FERGScGIaBlVtK6pwlxKJh+g5sw+lqwulsxOn8/+3deXSUVZ7/8c9TlcrCEgIBAgSQBEkICSGRTcREUEGBtPSoNB7FBdxb4KeOMzb2oN32zFE52jitrYILiodpFBd6bBYVEKNsKjsIYiBAICTImoQktd7fH0hNFwmypVKV5P06Jyfk1s1T3+fLTd1b33qW3bJadZA9MV22Np0bxWUCKD6hSfEZozf/8b0OlznVvnWMxo9IaxR/yACA89MsOkJXZnZUSpdWWvP9QR0td2r11lJVVnvUPIZTnhBcxudT9bIZ8hZvkxzRihnxqGxxHUIdFgAgRCITkhSdeb3cx0vl3bdZvp8KZY6XyHO8RFZMq5NFqIRkWbaGW8LhSoZoUj5ds1cbdx5WhN2m3/46Q82iG+4fLwDg4rVv3UyjBl2ifj3bKcJuyevjyCcElzFGzhWz5Sn8TrJFKGb4ZNnbJYU6LABAGLA1by1Haq4i+98se+cMye6QqTouT8FKub75QJ49G2Rc1aEO84LwzhtNxo6iY/rwy12SpNuG9VDXhJYhjggAEA5sNku9urVRtw4tFWHnczkEl2vtfLm3LZdkKfrq+xSR2CvUIQEAwowV1VwRSf1k79JH3tId8u7/XnKekHfvBnn3bZatfXfZO6U1qNO1KT6hSTha7tSr87fIZ4wGpScot0+nUIcEAAgzzaIdioliaYTgcW35XK51f5ckRV15uxzJA0IcEQAgnFkRDkUkpsveKU2+Q3vk3bdVpuKQfCU75CvZISuuk+yJvWRrnRj2l5NhhYVGz+X26uWPNun4CZcS2zXX7delhv0fJgAAaFxc25bLuXKOJCmy768V2evqEEcEAGgoLMsme7sk2dp2kyk7KO/+7+U7vFfmWLE8x4plxcTK3qmXbAndZdkdoQ63VhSf0KgZY/T24u0qPFCu5tERmnRTJrfSBgAA9cr940o5v3pHkuTofZ0iLxsd4ogAAA2RZVmyWiXI1ipBprpc3uLt8pbskKkqk2fnamnPOtk7pMjesaes6BahDjcA78LRqC1es1ert5bKZln67b/0Vvu4mFCHBAAAmhD3rm9Uvfx1SUaOXlcr6vJbOAIbAHDRrOiWikjuL3vXLPlKC+Qp/l6qLpd33xZ5922VLb6r7IlpsmITwmLeofiERmvTzkP6YPlOSdKtw3oo7ZKGczE2AADQ8Hl2r1f10hmSMXKk5ihq8LiweAMAAGg8rAiH7IlpsnVKle/IPnmLt8kcOyDf4T3yHd4jq3mbk4+3S5JlC10JiOITGqXiQyc043+3ykgaktVJQ7MTQx0SAABoQjxFm1W15K+S8Sri0ssVlTNelsXdFAEAwWFZNtnju8oe31W+E0flLd4m38GdMieOyLNjhVS49v9OyYtqVu/xUXxCo3Oi2q2/fLhJVU6vUjq30q3DUviUEQAA1BtP8TZVffYXyedRRFI/RQ+5V5aNwhMAoH7YmreWrccVMt36yluyQ94D2yXnCXmLNsm7b7NsbbvJ3ilNVst29fZemeITGhWvz6fX5m/RwaNVio+N1m9v7K0IO4s9AABQPzwlP6pq8YuS1y171yxFX/2ALJs91GEBAJogyxGliC69Ze+cLt/hvfLu3yZTVirfT4Xy/VQoq0W87J1OnZIX3LmK4hMaDWOM5i4t0NbdRxXpsGnSTb0V2ywy1GEBAIAmwltaoKpFL0gep+ydMxRz7W9l2VluAwBCy7JssrftJnvbbvJVHJa3eLt8P+2SqTgsz46vpcLvgn5KHrMhGo3Fa/Zq6dp9kqR7RvVS14SWIY4IAAA0FZ7ibSePePI4Ze/YUzHDJ8mK4EMwAEB4sbWIly1lsEzSz6fkFW+XXJX/d0pe/CWyJ/aq81PyKD6hUVix+YDm/Xxnu1uuvlT9erYPcUQAAKCp8BRtPnmNJ69b9sR0xQyfLCsiKtRhAQBwRpYjWhFdMmXvnCHfob0n75JXVirfod3yHdr9T6fkdauTu+RRfEKDt3nXYb29aLsk6foBXTV8QNcQRwQAAJoKz+71J+9q5/PI3rWPYq59iCOeAAANhmXZZG/XTfZ2Zzglb9e3sndMlb1jqqyo5hf8PBSf0KAVHijTKx9vkddndHl6gm4e2j3UIQEAgCbCvXONqpfNlIz35F3trn6AazwBABqsXzwlr2izbG0vOXmXvNj2531KHrMjGqzSI5V6cd5GOd1epXdrrQkj02Srp9tEAgCAps2942tVf/mmZIwiLh2k6CH3cFc7AECjEHBK3j/fJe/UKXnN28ieePIueeeK4hMapOMVTr3w3gaVV7p1SUJL/fZfeivCbgt1WAAAoAlwfb9Mzq9nS5IcPXMVdeVdsmysQwAAjUvgXfKOyFu87eQpeSeOyLNjhbTrO6nvECn67HfIo/iEBqfK6dH0eRt16Hi12sVF6+Hf9FFMFEMZAAAEn2vTYjlXz5UkOTKGKWrQrXV6NyAAAMKRrUWbwFPyDvwgOU/IeFzn9Pu8Y0eDUu3y6L/nbdTe0gq1bObQo2Oz1Ko5F/UEAADBZYxPztXvyb35U0lSZJ+RihwwhsITAKBJOf2UPJ3jKecUn9BgVLs8evH9jdqx77hioiL0yG/6KKH12Q/vAwAAuBjG41L1FzPlKfxOkhQ5YIwi+4yk8AQAaLJOnZJnRZ3be3KKT2gQTi88/evYLHXrEBvqsAAAQCNnqitU9el/y1v6o2SzK3rIPXJcOijUYQEA0KBQfELYq63wlNyJwhMAAAguX9lBVS36s3zHS6TIGMUMn6yITmmhDgsAgAaH4hPCGoUnAAAQCt6Du1T16YsyVWWymrdRzIh/lb1NYqjDAgCgQaL4hLBF4QkAAISCZ88GVS19RfK4ZIvvqpjrH5GteetQhwUAQINF8Qlhqcp58q52FJ4AAEB9McbIveUzOVfPlYyRvXOGYq59SFZkTKhDAwCgQaP4hLBzvMKpF+dt0p7ScgpPAACgXhi3U9X5s+TZuVqS5EjNUVTOnbJsLJcBALhYzKYIKwcOn9D09zfq0PFqtWzm0MNj+iipI4UnAAAQPL7jpar6/CX5juyTLLuiBt0iR/q1siwr1KEBANAoUHxC2Phx3zH95YNNOlHtUfvWMXrkN32U0LpZqMMCAACNmGfvBlUtmyG5qmTFxCr62ocU0TE11GEBANCoUHxCWPhu+0HN/OR7ebw+JXWM1f8bk6nYZpGhDgsAADRSxvjkWve/cq2dL0myJVyqmGsf4sLiAAAEAcUnhNxn3+zVnM92yEjKurSt7h+driiHPdRhAQCARso4T6jqi5ny7t0oSXL0ulpRg26VZWdpDABAMDDDImR8xuitT7bq4+UFkqQh2Ym6bVgP2W22EEcGAAAaK29pgaqWzZAp/0myRyj6yjvlSM0JdVgAADRqFJ8QEhVVbr3xj++1aedhSdJNVyVr5OWXcGFPAAAQFMbnkWvt3+Xa8A/JGFkt4hUzfJLsbbuFOjQAABo9ik+od4UHyvTKx5t1uMypyAibJoxK04C0hFCHBQAAGinvsWJVL5sp36HdkqSISwcpevA4WVHNQxsYAABNBMUn1BtjjJav36+/Lf1RHq9RQusY/X7CQMXFRMjj8YU6PAAA0MgYY+TeulTONe9JXrcU1fzkaXbdB4Q6NAAAmhSKT6gXTpdX73y6Xau3lkqSsnu01f2jM5TYsZWOHj0R4ugAAEBj4ztxVNVfvinvvi2SJHvnDEVfdTd3swMAIAQoPiHoDhw+oVc+3qL9h07IZlm6eUh3XTegixzc0Q4AANQxY4w8P65U9ar/kZwnJLtDUZePlaPXNVxbEgCAEKH4hKDxGaMvNxTr/S8K5HR51ap5pB4Yna7UrnziCAAA6p73yD45V7wr74EfJEm2dkmKHnqv7HGdQhwZAABNG8UnBMVPx6o0a+E2bd97TJKU2iVOD4xOV6sWUSGODAAANDbGVSXn2vlyb/lcMj4pIlKRl92gyMzrZdlY7gIAEGrMxqhTPmP0xbr9+mD5TjndXkVG2HTTVd11Td/Ostk41B0AANQdY4w8O9fIuXquTOXJD7wiuvVV1BW3ytYiPsTRAQCAUyg+oc6UHq3UrIXbtaPo5OIvpUucxo/sqYTWzUIcGQAAaGy8R4tPnmJXvE2SZMW2V/TgcYrokhniyAAAwOkoPuGiebw+LV27Tx/n75LL41OUw66bh3TX0MsSZePCngAAoA75Kg7Lte4TuX/4SjJeye5QZHaeIjNHyIqIDHV4AACgFhSfcMGMMdrw4yG9/0Uakv4eAAAZWklEQVSBSo9WSZLSLmmtu0b0VLu4mBBHBwAAGhNf5TG5NiyQ+/svJJ9HkmTvmqXoK26VLbZ9iKMDAAC/hOITLsjukjK9t7RAP/x8il1sM4duvKq7cjI7chtjAABQZ0x1hVwbF8q1ZYnkdUmS7B1TFdn/JkV0SAlxdAAA4FxQfMJ5OVJWrQ+/3KVVW0skSY4Im4b376KRl1+imCiGEwAAqBvGeUKuzZ/JtflTyV0tSbK1T1ZU/5tl75TGh10AADQgVAtwTo6fcOnzb4u05LsiuTw+SdKg9ATdmNtd8a2iQxwdAABoLHzHSuTa8rncO76WPE5Jki2+q6L63yh7lz4UnQAAaIAoPuEXHTxaqcXfFOnrTQfk8Z4sOqV0bqWx1/RQUsfYEEcHAAAaA2N88u7bKteWz+Ut2uRvt7XurMi+oxWR1FeWZQthhAAA4GJQfEKt9pSUa9GaPfp2+0EZc7Kte6dYjRx0ibIubcunjgAA4KIZd7XcO1bIvXWJfMcO/Nxqyd61jyIzhsme2Is1BwAAjQDFJ/j5fEZbCo/o8++KtLXwiL+9d3K8Rl7eVSld4lgAAgCAi+bzuHRi+Sy5ClZLrpN3zJUjWo7UHEWmXytbq4TQBggAAOoUxSeo9GilVmw+oBWbS3S0/OdrK1iWBvRqrxEDL1GX9i1CHCEAAGhMvOVH5fr+C0mSFZugyIxr5Ui5UlZkTIgjAwAAwUDxqYlyurz67oeD+nrTAf1QdMzf3iLGoUHpHTSsX2e1jWMBCAAAgsCyFNkzV/bul8veqSfXcwIAoJGj+NSEVDk92lJ4ROt//EkbfjykapdXkmRZUkZSvHIyO6rPpW3liGABCAAAgieiVTs1v/oeeX6+gy4AAGjcKD41cscqnNrw4yGt//GQtu05Io/X+B9rFxetKzM7aXBGB7WJjQ5hlAAAoCnhGpIAADQtFJ8aGafLq4L9x/VD0VFtLTyqwgNlAY8ntI5Rdo92yk5pq+6JrWRj8QcAAAAAAIKI4lMDV+X0nCw27T2mH/Ye1e6Scnl9JqBPcqdYZfdoq+we7dQxvhmfNgIAAAAAgHpD8akBqax2a09phfaWlmtPabn2lJSr5EilTGCtSfGxUUrp0lqpXePUOzlerVtGhSZgAAAAAADQ5FF8CkMVVW6VHKlU6ZFKlRypVMnhSu09WK6fjlXX2r9tq2ildo1TapfW6tk1jrvUAQAAAACAsEHxKQSqnB4dLqvWkTKnjpRX68jP/z54tEolRypVUeU+4++2bRWtSxJaqmtCC13SoaW6JrRUXAuObAIAAAAAAOGJ4lMd8PmMDh2rUtkJlyqrPaqocqu80qXySrfKKl0qq3Sr/IRLZZUuHatwqsrpPes2W7eMUoc2zdShTTMltGmmzu2aq2tCS7WIcdTDHgEAAAAAANQNik914ODRSj368orz+p3m0RFqExutNi2jTn6PjVK7uJiTxabWzRQVaQ9StAAAAAAAAPXHMub0y1XjfHm8Ph06ViWbZcmydPK7zZLNkmw2SzbLCmiz2yzuOPczu90mr9cX6jAaNKfbW+Oi8+HGsqQIu00ery9sYrVZlny1BBOOsdYmlHGeKXdn0lByKtVPrOebvzNpinmtq9z9kpioCNlszNH1gfm/ftTnWssyPvk8bklh/qJUG8uSzR4hn8ejC4m/Pl6fftFFxh9y5xF/yHNdm0aa/7DMdW0aeP5tNptkszfY+O2R0bJstrP2o/gEAAAAAACAoDl7eQoAAAAAAAC4QBSfAAAAAAAAEDQUnwAAAAAAABA0FJ8AAAAAAAAQNBSfAAAAAAAAEDQUnwAAAAAAABA0FJ8AAAAAAAAQNBSfAAAAAAAAEDQUnwAAAAAAABA0FJ8AAAAAAAAQNBSfAAAAAAAAEDQUnwAAAAAAABA0FJ8AAAAAAAAQNBSfLtDOnTs1fvx4ZWVlafDgwZo2bZpcLleowwqKjz76SKmpqTW+nn/++YB+8+bN03XXXafevXvrhhtu0BdffFFjW+Xl5XriiSc0YMAAZWdna/LkyTp48GCNfuvWrdPYsWOVmZmpoUOHaubMmTLGBPQxxmjmzJkaMmSIMjMzNXbsWG3YsKFud/4C7NmzR08++aRGjx6tXr16KS8vr9Z+4Zqv0tJSTZo0SdnZ2RowYIB+//vfq6Ki4gKzcX7OJXe33357reNx586dAf2aWu4WLVqkBx98ULm5ucrKytLo0aP1wQcf1NgXxl1N55I7xl3tvvzyS40bN06XX365MjIydM011+iZZ55ReXl5QL9ly5bphhtuUO/evXXdddfpww8/rLEtl8ul5557ToMHD1ZWVpbGjx+vXbt21eh3rvNvXY511I2mtHY6G9YK9YO5sX4wF4TOiRMnlJubq9TUVG3evDngMcb1xWno74HDMs8G5+3YsWNm8ODB5rbbbjP5+flm3rx5pm/fvuaPf/xjqEMLig8//NCkpKSY/Px8s379ev9XcXGxv88//vEPk5qaaqZPn25WrVplpk6danr16mXWr18fsK0JEyaY3Nxcs2DBArNkyRKTl5dnbrjhBuN2u/19du/ebbKyssxDDz1kVq5caWbNmmXS09PNG2+8EbCtGTNmmPT0dDNr1iyzcuVK89BDD5ns7Gyzd+/e4CbkLD7//HOTm5trJk2aZPLy8syoUaNq9AnXfLlcLpOXl2fy8vLM0qVLzYIFC0xubq6577776jhLtTuX3I0bN87ccsstAWNx/fr1prq6OqBfU8vdb37zG/PII4+YBQsWmJUrV5rnn3/e9OzZ07z00kv+Poy72p1L7hh3tZs/f7557rnnzOLFi83q1avNu+++awYMGGDGjx/v7/Ptt9+atLQ0M3XqVLNq1Sozffp0k5qaahYtWhSwralTp5q+ffuaefPmmfz8fHPrrbeanJwcU1ZW5u9zrvNvXY511I2mtnY6G9YK9YO5sX4wF4TOtGnTzBVXXGFSUlLMpk2b/O2M64vXkN8Dh2ueKT5dgNdee81kZWWZo0eP+tvmzp1r0tLSTElJSQgjC45Tf3iHDx8+Y5/hw4ebRx99NKBt7Nix5p577vH/vG7dOpOSkmK++uorf9vOnTtNamqqWbBggb9t6tSpZujQocbpdPrbXnjhBdOvXz9/W3V1tbnsssvMCy+84O/jdDrN0KFDzVNPPXXB+1oXvF6v/9+PP/54rQvKcM3XJ598YlJTU83OnTv9bV999ZVJSUkxGzduPJ80XJBzyd24cePO+sLZFHNX29/nf/zHf5jLLrvMn1fGXe3OJXeMu3P33nvvmZSUFP98OGHCBDN27NiAPo8++qgZMWKE/+cDBw6YtLQ0M3fuXH/b0aNHTVZWlpk5c6a/7Vzn37oc66gbTW3tdDasFeoHc2PoMBcEX0FBgcnKyjJ/+9vfahSfGNcXryG/Bw7XPHPa3QXIz8/XoEGDFBcX528bMWKEfD6fVqxYEcLIQqOoqEi7d+/WiBEjAtpHjhypVatW+Q+Bzc/PV2xsrAYPHuzvk5ycrLS0NOXn5/vb8vPzdc011ygyMjJgW2VlZVq/fr2kk4ckVlRUBDxnZGSkhg0bFrCtULDZfvnPKpzzlZ+fr9TUVCUnJ/vbBg8erLi4OH355Zfnk4YLcrbcnaummLs2bdrUaEtLS1NFRYUqKysZd7/gbLk7V00xd7U5NTe63W65XC6tWbNG119/fUCfkSNHaufOndq3b58k6euvv5bP5wvoFxcXp8GDB9fY37PNv3U91lE3WDsFYq1QP5gbQ4e5IPj+8z//U7fccouSkpIC2hnX9YM8nz+KTxdg165dAf+RkhQbG6t27drVek5yY5GXl6e0tDRdc801mjFjhrxeryT59/n0F77u3bvL7XarqKjI3y8pKUmWZQX0S05O9m+jsrJSBw4cqJHf5ORkWZbl73fq++n9unfvruLiYlVXV9fFLgdFOOertrFtWZaSkpLCamx/8803ysrKUu/evTVu3Dh9++23AY+Tu5PWrl2rhIQEtWjRgnF3nv45d6cw7s7M6/XK6XRq69at+utf/6qrr75anTt31t69e+V2u2vdj1Pxn/oeHx+vVq1a1ej3z/txLvNvXY511J2muna6ULxmBw9zY/AwF9SfxYsXa8eOHXrooYdqPMa4rlsN8T1wuOY5ImTP3ICVlZUpNja2RnurVq10/PjxEEQUXO3atdOkSZPUp08fWZalZcuW6cUXX1RpaamefPJJ/z6fnpNTP596vKysTC1btqyx/VatWmnLli2S5L8w4enbioyMVExMTMC2IiMjFRUVVeM5jTE6fvy4oqOjL3bXgyKc8/VLzxkuY7t///4aPXq0unXrpoMHD+rNN9/U+PHj9e677yo7O1sSuZOk7777TgsXLtTjjz8uiXF3Pk7PncS4O5uhQ4eqtLRUkpSTk6MXXnhB0sWPu9jY2ID9OJf5ty7HOupOU1s7XSxes4ODuTG4mAvqR1VVlZ599lk98sgjAR+SncK4rhsN+T1wuOaZ4hPOKicnRzk5Of6fr7zySkVFRemdd97RAw88EMLI0BRNnjw54OchQ4YoLy9Pr7zyil5//fUQRRVeSkpK9Mgjj2jgwIG64447Qh1Og3Km3DHuftnMmTNVVVWlgoICvfrqq3rggQc0a9asUIcFAH7MjcHHXFA/Xn31VcXHx+umm24KdSiNGu+B6x6n3V2A2NjYGrcOlU5WN08/TLSxGjFihLxer7Zt2+bf59NzUlZWJkn+x2NjY2u9veM/5+1Uhfb0bblcLlVVVQVsy+Vyyel01nhOy7LC+v8hnPN1Ls8Zbpo1a6arrrpKW7du9bc15dyVlZXp3nvvVVxcnF566SX/dUUYd2d3ptzVhnEXqGfPnsrOztaYMWP0yiuvaM2aNfr8888vetyVlZUF7Me5zL91OdZRd1g7nR9es+sWc2P9YC4Ivv379+utt97S5MmTVV5errKyMv/1KSsrK3XixAnGdRA1lPfA4Zpnik8XoLZzgMvLy/XTTz/VOLeyKTi1z6fnZNeuXXI4HOrSpYu/X2FhoYwxAf0KCwv922jWrJk6duxYY1unfu9Uv1PfCwsLazxnp06dwvaUOym881Xb2DbGBDxnQ9BUc1ddXa37779f5eXleuONNwIOt2Xc/bJfyt25aqq5O11qaqocDof27t2rrl27yuFw1DruJAXs76FDh2ocCn76NQvOZf6ty7GOusPa6fzwml13mBtDg7kgOPbt2ye326377rtP/fv3V//+/f1H4dxxxx0aP34847qekOfzR/HpAuTm5mrlypX+qqZ08qJvNpst4Cr2jdnChQtlt9vVq1cvdenSRd26ddPixYtr9Bk0aJD/iv25ubk6fvy4Vq1a5e9TWFio77//Xrm5uf623NxcLV26VG63O2BbsbGx/murXHbZZWrRooUWLVrk7+N2u/XZZ58FbCschXO+cnNztX37du3evdvftmrVKh07dkxXXXVV3SSgjlVWVmr58uXq3bu3v60p5s7j8ejhhx/Wrl279MYbbyghISHgccbdmZ0td7Vh3J3Zxo0b5Xa71blzZ0VGRmrgwIH69NNPA/osXLhQ3bt3V+fOnSWdPJTdZrPps88+8/c5fvy4vv766xr7e7b5t67HOuoGa6fzw2t23WBuDB3mguBIS0vT7NmzA76mTJkiSfrjH/+op556inEdRA3lPXC45tn+hz/84Q8he/YGqkePHpo3b55Wrlyp9u3b69tvv9Vzzz2nm266SaNGjQp1eHXu7rvvVmlpqSoqKrRnzx699dZbmjNnjm6//Xb/rVBbt26tl19+WT6fT5L0+uuv64svvtAzzzyjjh07SpI6duyoDRs26IMPPlBCQoKKior01FNPqV27dnriiSf8h0AnJydr1qxZ2r59u+Li4rRs2TK9/PLLmjRpkvr37y9JioiIkGVZmjFjhpo3b66qqiq98MIL2rFjh6ZNmxbSwwmrqqq0dOlSFRQUaMWKFTp06JA6dOiggoICtWnTRjExMWGbr6SkJC1ZskSLFi1Sx44dtW3bNj399NPq16+f7rnnnpDnbuvWrXr66afldDpVVlamdevWaerUqSoqKtJzzz3nX1Q2xdw99dRTWrBggR5++GHFx8erpKTE/9WmTRvZ7XbG3QXmbv369Yy7M5g4caL27t2r8vJylZSUaMmSJfqv//ovdenSRb/73e9kt9uVmJioV199VT/99JNiYmL00Ucfac6cOXryySfVo0cPSVKLFi1UWlqqd955R/Hx8Tpy5Ij+9Kc/qaqqSs8884z/wprnOv/W5VhH3Whqa6ezYa1QP5gb6wdzQf2JiopS586dA76cTqc+/vhjTZw4URkZGZLC971ZQxrXDfk9cNjm2eCCFBQUmDvvvNNkZmaaQYMGmWeffdY4nc5QhxUUf/rTn8zw4cNNZmamycjIMHl5eeadd94xPp8voN/7779vhg0bZtLT001eXp5ZtmxZjW2VlZWZKVOmmH79+pmsrCwzceJEU1JSUqPf2rVrzZgxY0xGRobJzc01M2bMqPF8Pp/PvPbaayY3N9dkZGSYMWPGmHXr1tXtzl+AoqIik5KSUuvX6tWr/f3CNV8lJSVm4sSJJisry/Tr189MmTLFlJeX10Fmzu5sudu9e7eZMGGCGTx4sElPTzf9+vUz9957r9m4cWONbTW13A0dOvSMuSsqKvL3Y9zVdLbcMe7ObMaMGWb06NEmOzvbZGVlmVGjRpkXX3yxxvMvWbLE5OXlmfT0dDNs2DAzb968GttyOp3m2WefNYMGDTKZmZnmrrvuMgUFBTX6nev8W5djHXWjKa2dzoa1Qv1gbqwfzAWhtXr1apOSkmI2bdoU0M64vjgN/T1wOObZMua0kw8BAAAAAACAOhJexxECAAAAAACgUaH4BAAAAAAAgKCh+AQAAAAAAICgofgEAAAAAACAoKH4BAAAAAAAgKCh+AQAAAAAAICgofgEAAAAAACAoKH4BAAAAAAAgKCh+AQAAAAAAICgiQh1AAAQTB999JGmTJlS62P33nuvHnvssXqOCAAAoOFLTU09p36zZ8/WwIEDgxwNgHBH8QlAkzB58mR17tw5oC0lJSVE0QAAADRs06ZNC/j573//u1asWFGjvXv37vUZFoAwRfEJQJOQm5ur3r17n7Wf0+mUw+GQzcZZyQAAAGcyevTogJ83btyoFStW1GgHAIlrPgFowtasWaPU1FQtWLBA06dPV05Ojvr06aOKigpJ0qJFi3TjjTcqMzNTAwcO1GOPPabS0tKAbfzud79Tdna2iouLdf/99ys7O1s5OTmaM2eOJOmHH37QHXfcoaysLA0dOlSffPJJve8nAABAKFRWVurZZ5/VVVddpYyMDF133XV68803ZYwJdWgA6hlHPgFoEioqKnTkyJFaH3vllVfkcDh09913y+VyyeFw+K8V1bt3bz366KM6fPiwZs+erXXr1mn+/PmKjY31/77X69W9996rfv366bHHHtMnn3yip59+WjExMZo+fbp+9atfafjw4Zo7d64ef/xxZWVlqUuXLvW16wAAAPXOGKMHH3xQa9as0c0336y0tDR99dVXmjZtmkpLS/XEE0+EOkQA9YjiE4Am4a677qrRNnv2bEknT7X78MMPFR0dLUlyu916/vnnlZKSojlz5igqKkqS1LdvX91///16++23NXnyZP92nE6nbrjhBt1///2SpF/96lfKycnRE088oT//+c8aOXKkJOmKK67QiBEjNH/+fE2aNCmYuwsAABBSS5cu1erVq/Xwww/rwQcflCTddtttmjx5smbPnq1x48apa9euIY4SQH2h+ASgSXjyySeVlJRU62O//vWv/YUnSdqyZYsOHz6siRMn+gtPkjRkyBAlJydr+fLlAcUnSRozZoz/37GxsUpKStKePXs0YsQIf3tycrJiY2NVVFRUV7sFAAAQlvLz82W323X77bcHtE+YMEGffvqp8vPzNW7cuBBFB6C+UXwC0CRkZmbWuOD4mjVrJKnGXfCKi4slqdZiVXJystauXRvQFhUVpTZt2gS0tWzZUh06dJBlWTXay8rKLmwnAAAAGoj9+/erffv2atGiRUD7qbvf7d+/PxRhAQgRLjgOoMn756OeLoTdbj+vdi6yCQAAAKApofgEAKfp1KmTJKmwsLDGY4WFhf7HAQAAULvExEQdPHjQfxfhU3bt2uV/HEDTQfEJAE6TkZGh+Ph4zZ07Vy6Xy9/+5ZdfaufOnRoyZEjoggMAAGgAcnNz5fV6NWfOnID2t99+W5ZlKTc3N0SRAQgFrvkEAKdxOBx67LHHNGXKFI0bN06jRo3S4cOHNXv2bCUmJtZ65zwAAAD8n6uvvloDBw7U9OnTtX//fqWmpmrFihVaunSp7rzzTu50BzQxFJ8AoBY33nijoqOj9frrr+v5559Xs2bNdO211+rf/u3fFBsbG+rwAAAAwprNZtOrr76qv/zlL1q4cKE++ugjJSYm6t///d81YcKEUIcHoJ5ZhivfAgAAAAAAIEi45hMAAAAAAACChuITAAAAAAAAgobiEwAAAAAAAIKG4hMAAAAAAACChuITAAAAAAAAgobiEwAAAAAAAIKG4hMAAAAAAACChuITAAAAAAAAgobiEwAAAAAAAIKG4hMAAAAAAACChuITAAAAAAAAgobiEwAAAAAAAILm/wMUV4hinIINJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fz = plt.figure('Salary plots', figsize=(12, 8), dpi=100)\n",
    "\n",
    "fz.add_subplot(2, 2, 1)\n",
    "plt.title('From / To: Boxplot')\n",
    "sns.boxplot(data=df[['From', 'To']].dropna(), width=0.4)\n",
    "\n",
    "fz.add_subplot(2, 2, 2)\n",
    "plt.title('From / To: Swarmplot')\n",
    "sns.swarmplot(data=df[['From', 'To']].dropna(), size=6)\n",
    "\n",
    "fz.add_subplot(2, 2, 3)\n",
    "plt.title('From: Distribution ')\n",
    "sns.histplot(df['From'].dropna(), bins=8, color='C0', kde=True)\n",
    "plt.grid(True)\n",
    "plt.xlim([0, df['From'].max()])\n",
    "plt.yticks([], [])\n",
    "\n",
    "fz.add_subplot(2, 2, 4)\n",
    "plt.title('To: Distribution')\n",
    "sns.histplot(df['To'].dropna(), bins=8, color='C1', kde=True)\n",
    "plt.grid(True)\n",
    "plt.xlim([0, df['To'].max()])\n",
    "plt.yticks([], [])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Зависимость зарплаты от опыта работы "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1920x960 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mapping = {'Нет опыта': 0, 'От 1 года до 3 лет': 1, 'От 3 до 6 лет': 2, 'Более 6 лет': 3}\n",
    "dummy_experience = df['Experience'].map(mapping)\n",
    "\n",
    "plt.figure('Linear regression')\n",
    "fig = plt.figure(figsize=(16, 8), dpi=120)\n",
    "plt.suptitle('Linear regression: Salary vs Experience', y=1.02, fontsize=18)\n",
    "\n",
    "fig.add_subplot(1, 2, 1)\n",
    "sns.regplot(x=dummy_experience, y=df['To'], color=\"C0\", x_jitter=.1)\n",
    "plt.xticks(list(range(4)), labels=mapping.keys(), rotation='vertical')\n",
    "\n",
    "fig.add_subplot(1, 2, 2)\n",
    "sns.regplot(x=dummy_experience, y=df['From'], color=\"C1\", x_jitter=.1)\n",
    "plt.xticks(list(range(4)), labels=mapping.keys(), rotation='vertical')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Усредненная статистика\n",
    "\n",
    "Поскольку не у всех вакансий указаны поля \"От\" и \"До\", производится расчет средней зарплаты, а на основании этих данных рассчитывается статистика."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average statistics (average filter for \"From\"-\"To\" parameters):\n",
      "Min    : 120000\n",
      "Max    : 435057\n",
      "Mean   : 239038\n",
      "Median : 217500\n"
     ]
    }
   ],
   "source": [
    "comb_from_to = np.nanmean(df[df['Salary']][['From', 'To']].to_numpy(), axis=1)\n",
    "print('Average statistics (average filter for \"From\"-\"To\" parameters):')\n",
    "print('Min    : %d' % np.min(comb_from_to))\n",
    "print('Max    : %d' % np.max(comb_from_to))\n",
    "print('Mean   : %d' % np.mean(comb_from_to))\n",
    "print('Median : %d' % np.median(comb_from_to))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Список часто используемых слов\n",
    "\n",
    "В этом разделе производится поиск наиболее употребляемых слов для параметра **Ключевые навыки**. Результа функции - список в порядке убывания."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def top_keys(df):\n",
    "    # Collect keys from dataframe\n",
    "    keys_df = df['Keys'].to_list()\n",
    "    \n",
    "    # Create a list of keys for all vacancies\n",
    "    lst_keys = []\n",
    "    for keys_elem in keys_df:\n",
    "        for el in keys_elem[1:-1].split(', '):\n",
    "            if el != '':\n",
    "                lst_keys.append(re.sub('\\'', '', el.lower()))\n",
    "\n",
    "    # Unique keys and their counter\n",
    "    set_keys = set(lst_keys)\n",
    "    # Dict: {Key: Count}\n",
    "    dct_keys = {el:lst_keys.count(el) for el in set_keys}\n",
    "    # Sorted dict\n",
    "    srt_keys = dict(sorted(dct_keys.items(), key=lambda x: x[1], reverse=True))\n",
    "    # Return pandas series\n",
    "    return pd.Series(srt_keys, name='Keys')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python                          134\n",
      "sql                              95\n",
      "data mining                      49\n",
      "математическая статистика        24\n",
      "английский язык                  19\n",
      "ms sql                           17\n",
      "linux                            15\n",
      "hadoop                           15\n",
      "анализ данных                    15\n",
      "git                              13\n",
      "data science                     13\n",
      "статистический анализ            12\n",
      "java                             12\n",
      "математическое моделирование     12\n",
      "big data                         11\n",
      "Name: Keys, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "ds_keys = top_keys(df)\n",
    "\n",
    "print(ds_keys[:15])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Описание вакансий\n",
    "\n",
    "Поиск наиболее употребляемых слов для параметра **Описание вакансий**.  \n",
    "Результат функции - список наиболее часто употребляемых слов (на английском языке) в порядке убывания."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def top_words(df):\n",
    "    # Collect keys from dataframe\n",
    "    words_df = df['Description'].to_list()\n",
    "    # Long string - combine descriptions\n",
    "    words_ls = ' '.join([re.sub(' +', ' ', re.sub('\\d+', '', el.strip().lower())) for el in words_df])\n",
    "    # Find all words\n",
    "    words_re = re.findall('[a-zA-Z]+', words_ls)\n",
    "    # Filter words with length < 3\n",
    "    words_l2 = [el for el in words_re if len(el) > 2]\n",
    "    # Unique words\n",
    "    words_st = set(words_l2)\n",
    "    # Remove 'stop words'\n",
    "    stop_words = set(stopwords.words('english'))\n",
    "    # XOR for dictionary\n",
    "    words_st ^= stop_words\n",
    "    # Dictionary - {Word: Counter}\n",
    "    words_cnt = {el : words_l2.count(el) for el in words_st}\n",
    "    # Pandas series\n",
    "    return pd.Series(dict(sorted(words_cnt.items(), key=lambda x: x[1], reverse=True)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data          505\n",
      "python        273\n",
      "sql           188\n",
      "science       126\n",
      "scientist     110\n",
      "spark         107\n",
      "quot          104\n",
      "hadoop        101\n",
      "learning       91\n",
      "experience     88\n",
      "pandas         83\n",
      "numpy          79\n",
      "team           69\n",
      "tensorflow     60\n",
      "nlp            59\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "ds_words = top_words(df)\n",
    "\n",
    "print(ds_words.head(15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Предсказание зарплаты\n",
    "\n",
    "На этом этапе происходит предсказание зарплаты для вакансий, у которых она не указана. Ниже пошагово представлен алгоритм.\n",
    "___\n",
    "\n",
    "**Шаг 1: Предобработка данных**:\n",
    "- Удаление NaN значений из таблицы,\n",
    "- Поиск среднего значение для From / To столбцов,\n",
    "- Замена столбцов зарплаты на усредненое значение"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace NaN from Salary into From / To fields,\n",
    "# Find mean values for each elemen and drop NaNs\n",
    "df_num = df[df['From'].notna() | df['From'].notna()]\n",
    "df_avg = df_num[['From', 'To']].mean(axis=1)\n",
    "df_num = df_num.drop(['Salary', 'From', 'To'], axis=1)\n",
    "df_num.insert(3, 'Average', df_avg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Шаг 2: Подготовка модели**:\n",
    "- Формирование вектора признаков из столбца ключевых навыков со списком исключений `stop_words`\n",
    "- Поиск наиболее часто используемых слов в ключевых навыках\n",
    "- One-Hot кодирование признаков таблицы\n",
    "- Объединение в матрицу \"объекты-признаки\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top words used in keys: ['python', 'data', 'sql']\n"
     ]
    }
   ],
   "source": [
    "def text_replace(text) -> pd.Series:\n",
    "    return text.str.lower().replace(\"[^a-zA-Z]\\bqout\\b|\\bamp\\b\", \" \", regex=True)\n",
    "\n",
    "# Set TF-IDF features\n",
    "min_df = 5 # ignore terms w/ frequency < than threshold\n",
    "tf_idf = TfidfVectorizer(min_df=min_df, stop_words='english')\n",
    "\n",
    "# Training set\n",
    "X_train_text = tf_idf.fit_transform(text_replace(df_num['Keys']))\n",
    "# Dict: {key: counter}\n",
    "# print(pd.Series(tf_idf.vocabulary_)[:10])\n",
    "\n",
    "# Print top words used in keys\n",
    "idx = np.ravel(X_train_text.sum(axis=0).argsort(axis=1))[::-1][:7]\n",
    "top_words = np.array(tf_idf.get_feature_names_out())[idx].tolist()\n",
    "print('Top words used in keys: {}'. format(top_words))\n",
    "\n",
    "# One-hot-encoding for data frame features\n",
    "dct_enc = DictVectorizer()\n",
    "X_train_cat = dct_enc.fit_transform(df_num[['Experience', 'Name']].to_dict('Records'))\n",
    "\n",
    "# Stack vectors\n",
    "X_train = hstack([X_train_text, X_train_cat])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Шаг 3: Обучение модели**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1, random_state=255)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train model\n",
    "y_train = df_num['Average']\n",
    "model = Ridge(alpha=1, random_state=255)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Шаг 4: Применение к тестовым данным**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average salary for vacancies with NaN is 219877\n"
     ]
    }
   ],
   "source": [
    "# Frame with NaNs\n",
    "x_test = df[df['From'].isna() & df['To'].isna()]\n",
    "\n",
    "# Test vectors\n",
    "X_test_text = tf_idf.transform(text_replace(x_test['Description']))\n",
    "X_test_cat = dct_enc.transform(x_test[['Experience', 'Name']].to_dict('Records'))\n",
    "X_test = hstack([X_test_text, X_test_cat])\n",
    "\n",
    "# Prediction model - result\n",
    "y_test = model.predict(X_test)\n",
    "\n",
    "print('Average salary for vacancies with NaN is {}'.format(y_test.mean(dtype=int)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Шаг 5: Замена отсутствующих зарплат на предсказанные значения**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Ids</th>\n",
       "      <th>Employer</th>\n",
       "      <th>Name</th>\n",
       "      <th>Average</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Schedule</th>\n",
       "      <th>Keys</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>40117400</td>\n",
       "      <td>Билайн</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>196144</td>\n",
       "      <td>От 1 года до 3 лет</td>\n",
       "      <td>Полный день</td>\n",
       "      <td>['Python', 'Data Mining', 'Java', 'Git', 'SCAL...</td>\n",
       "      <td>Наша команда каждый день работает над повышени...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>40012491</td>\n",
       "      <td>ФГУП ГосНИИАС</td>\n",
       "      <td>Data Scientist (Computer Vision) - Junior</td>\n",
       "      <td>145117</td>\n",
       "      <td>Нет опыта</td>\n",
       "      <td>Гибкий график</td>\n",
       "      <td>['Python', 'ML', 'Pytorch', 'Computer Vision']</td>\n",
       "      <td>Ведущий индустриальный центр по развитию и вне...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>39713985</td>\n",
       "      <td>Mail.ru Group</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>168177</td>\n",
       "      <td>От 1 года до 3 лет</td>\n",
       "      <td>Полный день</td>\n",
       "      <td>['Python', 'Linux', 'SQL']</td>\n",
       "      <td>MY.GAMES — международный игровой бренд (входит...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>40046412</td>\n",
       "      <td>Айтаргет</td>\n",
       "      <td>Data Scientist (AiData.me product)</td>\n",
       "      <td>225712</td>\n",
       "      <td>От 1 года до 3 лет</td>\n",
       "      <td>Полный день</td>\n",
       "      <td>['Data Mining', 'Python', 'Математическая стат...</td>\n",
       "      <td>Мы ищем в команду aidata.me (DMP платформа для...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>39597321</td>\n",
       "      <td>Mail.ru Group</td>\n",
       "      <td>Data Scientist (Predictive Analytic Solutions)</td>\n",
       "      <td>234071</td>\n",
       "      <td>От 3 до 6 лет</td>\n",
       "      <td>Гибкий график</td>\n",
       "      <td>['Python', 'Data Mining', 'Mathematical Analys...</td>\n",
       "      <td>Команда направления Big Data и Predictive Anal...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>40138097</td>\n",
       "      <td>Константа Сервисез</td>\n",
       "      <td>Аналитик данных/Data scientist</td>\n",
       "      <td>188726</td>\n",
       "      <td>От 1 года до 3 лет</td>\n",
       "      <td>Полный день</td>\n",
       "      <td>['SQL', 'Python', 'Transact-SQL', 'PostgreSQL']</td>\n",
       "      <td>Сфера деятельности Мы занимаемся проектами по ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>40061077</td>\n",
       "      <td>ОКБ</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>168177</td>\n",
       "      <td>От 1 года до 3 лет</td>\n",
       "      <td>Полный день</td>\n",
       "      <td>['Python', 'SQL', 'Управление рисками', 'Анали...</td>\n",
       "      <td>Обязанности:  Поведенческие модели PD, LGD, EA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>40143339</td>\n",
       "      <td>Лига Цифровой Экономики</td>\n",
       "      <td>Data Scientists</td>\n",
       "      <td>226866</td>\n",
       "      <td>От 1 года до 3 лет</td>\n",
       "      <td>Полный день</td>\n",
       "      <td>['Data Mining', 'SQL', 'Python', 'MATLAB', 'Ма...</td>\n",
       "      <td>Проект направлен на создание продуктивных моде...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>40137655</td>\n",
       "      <td>Неофлекс</td>\n",
       "      <td>Data Scientist/ Аналитик данных</td>\n",
       "      <td>148903</td>\n",
       "      <td>От 1 года до 3 лет</td>\n",
       "      <td>Полный день</td>\n",
       "      <td>['Python', 'Финансовое моделирование', 'Управл...</td>\n",
       "      <td>О компании Неофлекс - ведущий поставщик програ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>39444384</td>\n",
       "      <td>«Газпромбанк» (Акционерное общество)</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>243420</td>\n",
       "      <td>От 3 до 6 лет</td>\n",
       "      <td>Удаленная работа</td>\n",
       "      <td>['Python', 'SQL', 'Oracle Pl/SQL', 'Sas', 'MS ...</td>\n",
       "      <td>В Департаменте анализа и моделирования данных ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Ids                              Employer  \\\n",
       "5   40117400                                Билайн   \n",
       "6   40012491                         ФГУП ГосНИИАС   \n",
       "8   39713985                         Mail.ru Group   \n",
       "9   40046412                              Айтаргет   \n",
       "11  39597321                         Mail.ru Group   \n",
       "13  40138097                    Константа Сервисез   \n",
       "14  40061077                                   ОКБ   \n",
       "15  40143339               Лига Цифровой Экономики   \n",
       "16  40137655                              Неофлекс   \n",
       "17  39444384  «Газпромбанк» (Акционерное общество)   \n",
       "\n",
       "                                              Name  Average  \\\n",
       "5                                   Data Scientist   196144   \n",
       "6        Data Scientist (Computer Vision) - Junior   145117   \n",
       "8                                   Data Scientist   168177   \n",
       "9               Data Scientist (AiData.me product)   225712   \n",
       "11  Data Scientist (Predictive Analytic Solutions)   234071   \n",
       "13                  Аналитик данных/Data scientist   188726   \n",
       "14                                  Data Scientist   168177   \n",
       "15                                 Data Scientists   226866   \n",
       "16                 Data Scientist/ Аналитик данных   148903   \n",
       "17                                  Data Scientist   243420   \n",
       "\n",
       "            Experience          Schedule  \\\n",
       "5   От 1 года до 3 лет       Полный день   \n",
       "6            Нет опыта     Гибкий график   \n",
       "8   От 1 года до 3 лет       Полный день   \n",
       "9   От 1 года до 3 лет       Полный день   \n",
       "11       От 3 до 6 лет     Гибкий график   \n",
       "13  От 1 года до 3 лет       Полный день   \n",
       "14  От 1 года до 3 лет       Полный день   \n",
       "15  От 1 года до 3 лет       Полный день   \n",
       "16  От 1 года до 3 лет       Полный день   \n",
       "17       От 3 до 6 лет  Удаленная работа   \n",
       "\n",
       "                                                 Keys  \\\n",
       "5   ['Python', 'Data Mining', 'Java', 'Git', 'SCAL...   \n",
       "6      ['Python', 'ML', 'Pytorch', 'Computer Vision']   \n",
       "8                          ['Python', 'Linux', 'SQL']   \n",
       "9   ['Data Mining', 'Python', 'Математическая стат...   \n",
       "11  ['Python', 'Data Mining', 'Mathematical Analys...   \n",
       "13    ['SQL', 'Python', 'Transact-SQL', 'PostgreSQL']   \n",
       "14  ['Python', 'SQL', 'Управление рисками', 'Анали...   \n",
       "15  ['Data Mining', 'SQL', 'Python', 'MATLAB', 'Ма...   \n",
       "16  ['Python', 'Финансовое моделирование', 'Управл...   \n",
       "17  ['Python', 'SQL', 'Oracle Pl/SQL', 'Sas', 'MS ...   \n",
       "\n",
       "                                          Description  \n",
       "5   Наша команда каждый день работает над повышени...  \n",
       "6   Ведущий индустриальный центр по развитию и вне...  \n",
       "8   MY.GAMES — международный игровой бренд (входит...  \n",
       "9   Мы ищем в команду aidata.me (DMP платформа для...  \n",
       "11  Команда направления Big Data и Predictive Anal...  \n",
       "13  Сфера деятельности Мы занимаемся проектами по ...  \n",
       "14  Обязанности:  Поведенческие модели PD, LGD, EA...  \n",
       "15  Проект направлен на создание продуктивных моде...  \n",
       "16  О компании Неофлекс - ведущий поставщик програ...  \n",
       "17  В Департаменте анализа и моделирования данных ...  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tst = x_test.drop(['Salary', 'From', 'To'], axis=1)\n",
    "df_tst.insert(3, 'Average', y_test.astype(int))\n",
    "df_tst.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x640 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fp = plt.figure('Predicted salaries', figsize=(12, 8), dpi=80)\n",
    "fp.add_subplot(2, 2, 1)\n",
    "plt.title('Average Boxplot')\n",
    "sns.boxplot(data=df_tst[['Average']], width=0.4)\n",
    "\n",
    "fp.add_subplot(2, 2, 2)\n",
    "plt.title('Average Swarmplot')\n",
    "sns.swarmplot(data=df_tst[['Average']].dropna(), size=6)\n",
    "\n",
    "fp.add_subplot(2, 2, 3)\n",
    "plt.title('Average: Distribution ')\n",
    "sns.histplot(df_tst[['Average']].dropna(), bins=12)\n",
    "plt.grid(False)\n",
    "plt.yticks([], [])\n",
    "plt.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}