data_eda.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('salary_data_cleaned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Job Title</th>\n",
       "      <th>Salary Estimate</th>\n",
       "      <th>Job Description</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Headquarters</th>\n",
       "      <th>Size</th>\n",
       "      <th>Founded</th>\n",
       "      <th>Type of ownership</th>\n",
       "      <th>...</th>\n",
       "      <th>avg_salary</th>\n",
       "      <th>company_txt</th>\n",
       "      <th>job_state</th>\n",
       "      <th>same_state</th>\n",
       "      <th>age</th>\n",
       "      <th>python_yn</th>\n",
       "      <th>R_yn</th>\n",
       "      <th>spark</th>\n",
       "      <th>aws</th>\n",
       "      <th>excel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>$53K-$91K (Glassdoor est.)</td>\n",
       "      <td>Data Scientist\\nLocation: Albuquerque, NM\\nEdu...</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Tecolote Research\\n3.8</td>\n",
       "      <td>Albuquerque, NM</td>\n",
       "      <td>Goleta, CA</td>\n",
       "      <td>501 to 1000 employees</td>\n",
       "      <td>1973</td>\n",
       "      <td>Company - Private</td>\n",
       "      <td>...</td>\n",
       "      <td>72.0</td>\n",
       "      <td>Tecolote Research\\n</td>\n",
       "      <td>NM</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Healthcare Data Scientist</td>\n",
       "      <td>$63K-$112K (Glassdoor est.)</td>\n",
       "      <td>What You Will Do:\\n\\nI. General Summary\\n\\nThe...</td>\n",
       "      <td>3.4</td>\n",
       "      <td>University of Maryland Medical System\\n3.4</td>\n",
       "      <td>Linthicum, MD</td>\n",
       "      <td>Baltimore, MD</td>\n",
       "      <td>10000+ employees</td>\n",
       "      <td>1984</td>\n",
       "      <td>Other Organization</td>\n",
       "      <td>...</td>\n",
       "      <td>87.5</td>\n",
       "      <td>University of Maryland Medical System\\n</td>\n",
       "      <td>MD</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>$80K-$90K (Glassdoor est.)</td>\n",
       "      <td>KnowBe4, Inc. is a high growth information sec...</td>\n",
       "      <td>4.8</td>\n",
       "      <td>KnowBe4\\n4.8</td>\n",
       "      <td>Clearwater, FL</td>\n",
       "      <td>Clearwater, FL</td>\n",
       "      <td>501 to 1000 employees</td>\n",
       "      <td>2010</td>\n",
       "      <td>Company - Private</td>\n",
       "      <td>...</td>\n",
       "      <td>85.0</td>\n",
       "      <td>KnowBe4\\n</td>\n",
       "      <td>FL</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>$56K-$97K (Glassdoor est.)</td>\n",
       "      <td>*Organization and Job ID**\\nJob ID: 310709\\n\\n...</td>\n",
       "      <td>3.8</td>\n",
       "      <td>PNNL\\n3.8</td>\n",
       "      <td>Richland, WA</td>\n",
       "      <td>Richland, WA</td>\n",
       "      <td>1001 to 5000 employees</td>\n",
       "      <td>1965</td>\n",
       "      <td>Government</td>\n",
       "      <td>...</td>\n",
       "      <td>76.5</td>\n",
       "      <td>PNNL\\n</td>\n",
       "      <td>WA</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>$86K-$143K (Glassdoor est.)</td>\n",
       "      <td>Data Scientist\\nAffinity Solutions / Marketing...</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Affinity Solutions\\n2.9</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>51 to 200 employees</td>\n",
       "      <td>1998</td>\n",
       "      <td>Company - Private</td>\n",
       "      <td>...</td>\n",
       "      <td>114.5</td>\n",
       "      <td>Affinity Solutions\\n</td>\n",
       "      <td>NY</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Job Title              Salary Estimate  \\\n",
       "0             Data Scientist   $53K-$91K (Glassdoor est.)   \n",
       "1  Healthcare Data Scientist  $63K-$112K (Glassdoor est.)   \n",
       "2             Data Scientist   $80K-$90K (Glassdoor est.)   \n",
       "3             Data Scientist   $56K-$97K (Glassdoor est.)   \n",
       "4             Data Scientist  $86K-$143K (Glassdoor est.)   \n",
       "\n",
       "                                     Job Description  Rating  \\\n",
       "0  Data Scientist\\nLocation: Albuquerque, NM\\nEdu...     3.8   \n",
       "1  What You Will Do:\\n\\nI. General Summary\\n\\nThe...     3.4   \n",
       "2  KnowBe4, Inc. is a high growth information sec...     4.8   \n",
       "3  *Organization and Job ID**\\nJob ID: 310709\\n\\n...     3.8   \n",
       "4  Data Scientist\\nAffinity Solutions / Marketing...     2.9   \n",
       "\n",
       "                                 Company Name         Location  \\\n",
       "0                      Tecolote Research\\n3.8  Albuquerque, NM   \n",
       "1  University of Maryland Medical System\\n3.4    Linthicum, MD   \n",
       "2                                KnowBe4\\n4.8   Clearwater, FL   \n",
       "3                                   PNNL\\n3.8     Richland, WA   \n",
       "4                     Affinity Solutions\\n2.9     New York, NY   \n",
       "\n",
       "     Headquarters                    Size  Founded   Type of ownership  ...  \\\n",
       "0      Goleta, CA   501 to 1000 employees     1973   Company - Private  ...   \n",
       "1   Baltimore, MD        10000+ employees     1984  Other Organization  ...   \n",
       "2  Clearwater, FL   501 to 1000 employees     2010   Company - Private  ...   \n",
       "3    Richland, WA  1001 to 5000 employees     1965          Government  ...   \n",
       "4    New York, NY     51 to 200 employees     1998   Company - Private  ...   \n",
       "\n",
       "  avg_salary                              company_txt job_state same_state  \\\n",
       "0       72.0                      Tecolote Research\\n        NM          0   \n",
       "1       87.5  University of Maryland Medical System\\n        MD          0   \n",
       "2       85.0                                KnowBe4\\n        FL          1   \n",
       "3       76.5                                   PNNL\\n        WA          1   \n",
       "4      114.5                     Affinity Solutions\\n        NY          1   \n",
       "\n",
       "   age  python_yn  R_yn  spark  aws excel  \n",
       "0   47          1     0      0    0     1  \n",
       "1   36          1     0      0    0     0  \n",
       "2   10          1     0      1    0     1  \n",
       "3   55          1     0      0    0     0  \n",
       "4   22          1     0      0    0     1  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Job Title', 'Salary Estimate', 'Job Description', 'Rating',\n",
       "       'Company Name', 'Location', 'Headquarters', 'Size', 'Founded',\n",
       "       'Type of ownership', 'Industry', 'Sector', 'Revenue', 'Competitors',\n",
       "       'hourly', 'employer_provided', 'min_salary', 'max_salary', 'avg_salary',\n",
       "       'company_txt', 'job_state', 'same_state', 'age', 'python_yn', 'R_yn',\n",
       "       'spark', 'aws', 'excel'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def title_simplifier(title):\n",
    "    if 'data scientist' in title.lower():\n",
    "        return 'data scientist'\n",
    "    elif 'data engineer' in title.lower():\n",
    "        return 'data engineer'\n",
    "    elif 'analyst' in title.lower():\n",
    "        return 'analyst'\n",
    "    elif 'machine learning' in title.lower():\n",
    "        return 'mle'\n",
    "    elif 'manager' in title.lower():\n",
    "        return 'manager'\n",
    "    elif 'director' in title.lower():\n",
    "        return 'director'\n",
    "    else:\n",
    "        return 'na'\n",
    "    \n",
    "def seniority(title):\n",
    "    if 'sr' in title.lower() or 'senior' in title.lower() or 'sr' in title.lower() or 'lead' in title.lower() or 'principal' in title.lower():\n",
    "            return 'senior'\n",
    "    elif 'jr' in title.lower() or 'jr.' in title.lower():\n",
    "        return 'jr'\n",
    "    else:\n",
    "        return 'na'\n",
    "\t\t\n",
    "## Job title and seniority \n",
    "\t\t\n",
    "##  Fix state Los Angeles \n",
    "\n",
    "##  Job description length \n",
    "\n",
    "##  Competitor count\n",
    "\n",
    "## hourly wage to annual \n",
    "\n",
    "#remove new line from job title\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['job_simp'] = df['Job Title'].apply(title_simplifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "data scientist    279\n",
       "na                184\n",
       "data engineer     119\n",
       "analyst           102\n",
       "manager            22\n",
       "mle                22\n",
       "director           14\n",
       "Name: job_simp, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.job_simp.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "na        520\n",
       "senior    220\n",
       "jr          2\n",
       "Name: seniority, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['seniority'] = df['Job Title'].apply(seniority)\n",
    "df.seniority.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA    152\n",
       "MA    103\n",
       "NY     72\n",
       "VA     41\n",
       "IL     40\n",
       "MD     35\n",
       "PA     33\n",
       "TX     28\n",
       "WA     21\n",
       "NC     21\n",
       "NJ     17\n",
       "FL     16\n",
       "OH     14\n",
       "TN     13\n",
       "DC     11\n",
       "CO     11\n",
       "IN     10\n",
       "UT     10\n",
       "WI     10\n",
       "AZ      9\n",
       "MO      9\n",
       "AL      8\n",
       "KY      6\n",
       "GA      6\n",
       "MI      6\n",
       "DE      6\n",
       "CT      5\n",
       "IA      5\n",
       "OR      4\n",
       "NE      4\n",
       "LA      4\n",
       "KS      3\n",
       "NM      3\n",
       "MN      2\n",
       "ID      2\n",
       "SC      1\n",
       "RI      1\n",
       "Name: job_state, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fix state Los Angeles \n",
    "df['job_state']= df.job_state.apply(lambda x: x.strip() if x.strip().lower() != 'los angeles' else 'CA')\n",
    "df.job_state.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2536\n",
       "1      4783\n",
       "2      3461\n",
       "3      3883\n",
       "4      2728\n",
       "       ... \n",
       "737    6162\n",
       "738    6130\n",
       "739    3078\n",
       "740    1642\n",
       "741    3673\n",
       "Name: desc_len, Length: 742, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Job description length \n",
    "df['desc_len'] = df['Job Description'].apply(lambda x: len(x))\n",
    "df['desc_len']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Competitor count\n",
    "df['num_comp'] = df['Competitors'].apply(lambda x: len(x.split(',')) if x != '-1' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                                     -1\n",
       "1                                                     -1\n",
       "2                                                     -1\n",
       "3      Oak Ridge National Laboratory, National Renewa...\n",
       "4                   Commerce Signals, Cardlytics, Yodlee\n",
       "                             ...                        \n",
       "737                           Pfizer, AstraZeneca, Merck\n",
       "738                      See Tickets, TicketWeb, Vendini\n",
       "739                                                   -1\n",
       "740                                                   -1\n",
       "741                                                   -1\n",
       "Name: Competitors, Length: 742, dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Competitors']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hourly wage to annual \n",
    "\n",
    "df['min_salary'] = df.apply(lambda x: x.min_salary*2 if x.hourly ==1 else x.min_salary, axis =1)\n",
    "df['max_salary'] = df.apply(lambda x: x.max_salary*2 if x.hourly ==1 else x.max_salary, axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hourly</th>\n",
       "      <th>min_salary</th>\n",
       "      <th>max_salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>165</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>177</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>197</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>202</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>210</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>246</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>271</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>337</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>345</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>357</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>363</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>408</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>409</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>492</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>509</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>533</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>542</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>617</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>618</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>645</td>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>666</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>682</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>711</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>724</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     hourly  min_salary  max_salary\n",
       "165       1          34          48\n",
       "177       1          42          68\n",
       "197       1          36          50\n",
       "202       1          42          68\n",
       "210       1          30          50\n",
       "246       1          34          48\n",
       "271       1          42          68\n",
       "337       1          36          50\n",
       "345       1          48          78\n",
       "357       1          42          68\n",
       "363       1          50          56\n",
       "408       1          42          58\n",
       "409       1          20          34\n",
       "492       1          36          50\n",
       "509       1          48          78\n",
       "533       1          42          68\n",
       "542       1          50          56\n",
       "617       1          42          58\n",
       "618       1          20          34\n",
       "645       1          54          94\n",
       "666       1          36          50\n",
       "682       1          48          78\n",
       "711       1          42          68\n",
       "724       1          50          56"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.hourly ==1][['hourly','min_salary','max_salary']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['company_txt'] = df.company_txt.apply(lambda x: x.replace('\\n', ''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                          Tecolote Research\n",
       "1      University of Maryland Medical System\n",
       "2                                    KnowBe4\n",
       "3                                       PNNL\n",
       "4                         Affinity Solutions\n",
       "                       ...                  \n",
       "737                                      GSK\n",
       "738                               Eventbrite\n",
       "739           Software Engineering Institute\n",
       "740                             Numeric, LLC\n",
       "741             Riverside Research Institute\n",
       "Name: company_txt, Length: 742, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['company_txt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>Rating</th>\n",
       "      <th>Founded</th>\n",
       "      <th>hourly</th>\n",
       "      <th>employer_provided</th>\n",
       "      <th>min_salary</th>\n",
       "      <th>max_salary</th>\n",
       "      <th>avg_salary</th>\n",
       "      <th>same_state</th>\n",
       "      <th>age</th>\n",
       "      <th>python_yn</th>\n",
       "      <th>R_yn</th>\n",
       "      <th>spark</th>\n",
       "      <th>aws</th>\n",
       "      <th>excel</th>\n",
       "      <th>desc_len</th>\n",
       "      <th>num_comp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>3.618868</td>\n",
       "      <td>1837.154987</td>\n",
       "      <td>0.032345</td>\n",
       "      <td>0.022911</td>\n",
       "      <td>74.719677</td>\n",
       "      <td>128.149596</td>\n",
       "      <td>100.626011</td>\n",
       "      <td>0.557951</td>\n",
       "      <td>46.591644</td>\n",
       "      <td>0.528302</td>\n",
       "      <td>0.002695</td>\n",
       "      <td>0.225067</td>\n",
       "      <td>0.237197</td>\n",
       "      <td>0.522911</td>\n",
       "      <td>3869.545822</td>\n",
       "      <td>1.053908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.801210</td>\n",
       "      <td>497.183763</td>\n",
       "      <td>0.177034</td>\n",
       "      <td>0.149721</td>\n",
       "      <td>30.980593</td>\n",
       "      <td>45.220324</td>\n",
       "      <td>38.855948</td>\n",
       "      <td>0.496965</td>\n",
       "      <td>53.778815</td>\n",
       "      <td>0.499535</td>\n",
       "      <td>0.051882</td>\n",
       "      <td>0.417908</td>\n",
       "      <td>0.425651</td>\n",
       "      <td>0.499812</td>\n",
       "      <td>1521.495868</td>\n",
       "      <td>1.384239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>407.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>1939.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>73.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2801.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>1988.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>69.500000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>97.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3731.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>122.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4740.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>306.000000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>276.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10051.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Rating      Founded      hourly  employer_provided  min_salary  \\\n",
       "count  742.000000   742.000000  742.000000         742.000000  742.000000   \n",
       "mean     3.618868  1837.154987    0.032345           0.022911   74.719677   \n",
       "std      0.801210   497.183763    0.177034           0.149721   30.980593   \n",
       "min     -1.000000    -1.000000    0.000000           0.000000   15.000000   \n",
       "25%      3.300000  1939.000000    0.000000           0.000000   52.000000   \n",
       "50%      3.700000  1988.000000    0.000000           0.000000   69.500000   \n",
       "75%      4.000000  2007.000000    0.000000           0.000000   91.000000   \n",
       "max      5.000000  2019.000000    1.000000           1.000000  202.000000   \n",
       "\n",
       "       max_salary  avg_salary  same_state         age   python_yn        R_yn  \\\n",
       "count  742.000000  742.000000  742.000000  742.000000  742.000000  742.000000   \n",
       "mean   128.149596  100.626011    0.557951   46.591644    0.528302    0.002695   \n",
       "std     45.220324   38.855948    0.496965   53.778815    0.499535    0.051882   \n",
       "min     16.000000   13.500000    0.000000   -1.000000    0.000000    0.000000   \n",
       "25%     96.000000   73.500000    0.000000   11.000000    0.000000    0.000000   \n",
       "50%    124.000000   97.500000    1.000000   24.000000    1.000000    0.000000   \n",
       "75%    155.000000  122.500000    1.000000   59.000000    1.000000    0.000000   \n",
       "max    306.000000  254.000000    1.000000  276.000000    1.000000    1.000000   \n",
       "\n",
       "            spark         aws       excel      desc_len    num_comp  \n",
       "count  742.000000  742.000000  742.000000    742.000000  742.000000  \n",
       "mean     0.225067    0.237197    0.522911   3869.545822    1.053908  \n",
       "std      0.417908    0.425651    0.499812   1521.495868    1.384239  \n",
       "min      0.000000    0.000000    0.000000    407.000000    0.000000  \n",
       "25%      0.000000    0.000000    0.000000   2801.000000    0.000000  \n",
       "50%      0.000000    0.000000    1.000000   3731.000000    0.000000  \n",
       "75%      0.000000    0.000000    1.000000   4740.000000    3.000000  \n",
       "max      1.000000    1.000000    1.000000  10051.000000    4.000000  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Job Title', 'Salary Estimate', 'Job Description', 'Rating',\n",
       "       'Company Name', 'Location', 'Headquarters', 'Size', 'Founded',\n",
       "       'Type of ownership', 'Industry', 'Sector', 'Revenue', 'Competitors',\n",
       "       'hourly', 'employer_provided', 'min_salary', 'max_salary', 'avg_salary',\n",
       "       'company_txt', 'job_state', 'same_state', 'age', 'python_yn', 'R_yn',\n",
       "       'spark', 'aws', 'excel', 'job_simp', 'seniority', 'desc_len',\n",
       "       'num_comp'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2814125b888>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DXHEE/5v8Cnh7Vb0FOBM4L8k5Y65pEFcCO8ddxJDMV9WZR9J17odVVe2sqn6/4ToJmrldQ1XdA/xi3HUMqqoeq6oHuudPsxQm68ZbVX9qyYFu8pju54i8kiLJKcB7gX8edy2Trolwb8A64GfLpvdwhAZJi5LMAG8FvjPeSvrXncp4ENgP3FlVR2ovfw98HHh+3IUMQQH/lmR7dzuWoTpi/kB2km8Dv73CrL+uqlsPdz1DturtGjQeSaaArwMfq6qnxl1Pv6rqOeDMJCcAtyQ5o6qOqM9FkvwhsL+qtieZG3c9Q3BuVe1N8nrgziQ/7H7zHYojJtyr6p3jrmGEvF3DBEpyDEvB/pWqunnc9QxDVT2RZJGlz0WOqHAHzgX+KMn5wKuB30ry5ar6szHX1Zeq2ts97k9yC0unZ4cW7p6WmQzermHCJAlwHbCzqj477noGkeR13RE7SY4F3gn8cLxVrV1VfaKqTqmqGZb+j/z7kRrsSY5L8ppDz4F3MeQ32ybCPcn7k+wBfh/YmuRb465pLarqIHDodg07gZtGeLuGkUryVeA/gTcm2ZPk8nHX1KdzgUuAt3eXqj3YHTEeiU4G7k7yfZYOJO6sqiP6MsIGTAP3JvkecD+wtaq+OcwdePsBSWpQE0fukqQXMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSg/4PanHf0dH2kosAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Rating.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28141405a48>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df.avg_salary.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28141481e48>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df.age.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28141510c88>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df.desc_len.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x281415baec8>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df.boxplot(column = ['age','avg_salary','Rating'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28141703408>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAD4CAYAAADxeG0DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAANYklEQVR4nO3dfWyd113A8e/PcZpYadVJtFzWNZqRmFBfBp24KqBU4HRLUtIxxCiik6g0BLKS1mNSmKZESAiYRhqUoP3RJl0C1aryksFG2ehrAvihKhkbDulQQjY0jU6Udi0RwqujOGuXH3/kNkozJ77OPfb1Sb4fyarv48fnnEpX3z4997nXkZlIkuo10O8FSJJ6Y8glqXKGXJIqZ8glqXKGXJIqN9iPSa+55pocHh7ux9TSBR0/fpwVK1b0exnSjA4ePHgsM68993hfQj48PMzExEQ/ppYuqGkaRkZG+r0MaUYR8a2Zjru1IkmVM+SSVDlDLkmVM+SSVDlDLkmVK3LXSkS8ALwGfA94IzPbJcaVJM2u5O2HqzPzWMHxJEldcGtFkipX6oo8gX0RkcCnM3P3uSdExCgwCtBqtWiaptDU0vmtXr16QeYZHx9fkHmkmUSJPywREddl5ksR8YPAfuAjmfns+c5vt9vpOzu1GA1vfoIX7r+z38uQZhQRB2d6DbLI1kpmvtT556vAY8CtJcaVJM2u55BHxIqIuOrN74G1wOFex5UkdafEHnkLeCwi3hzvzzPz6QLjSpK60HPIM/ObwI8XWIsk6SJ4+6EkVc6QS1LlDLkkVc6QS1LlDLkkVc6QS1LlDLkkVc6QS1LlDLkkVc6QS1LlDLkkVc6QS1LlDLkkVc6QS1LlDLkkVc6QS1LlDLkkVc6QS1LlDLkkVc6QS1LlDLkkVc6QS1LlDLkkVc6QS1LlioU8IpZExKGIeLzUmJKk2ZW8Iv8ocLTgeJKkLhQJeURcD9wJ/HGJ8SRJ3RssNM6ngI8DV53vhIgYBUYBWq0WTdMUmlqXi/v+/jjHX5//eYY3PzGv469YCg++d8W8zqHLS88hj4j3A69m5sGIGDnfeZm5G9gN0G63c2TkvKdKMzr+9BO8cP+d8zpH0zTM93NzePMT8z6HLi8ltlZWAR+IiBeAvcDtEfGnBcaVJHWh55Bn5pbMvD4zh4G7gX/IzF/teWWSpK54H7kkVa7Ui50AZGYDNCXHlCRdmFfkklQ5Qy5JlTPkklQ5Qy5JlSv6Yqc0n666YTPvfmTz/E/0yPwOf9UNcPoTLaQyDLmq8drR+y+Zd3ZKJbm1IkmVM+SSVDlDLkmVM+SSVDlDLkmVM+SSVDlDLkmVM+SSVDlDLkmVM+SSVDlDLkmV87NWVJUF+ZySp+d3jquHls7r+Lr8GHJVY74/MAtO/4diIeaRSnJrRZIqZ8glqXKGXJIqZ8glqXKGXJIq13PII2J5RHwlIr4aEUci4vdKLEyS1J0Stx+eBG7PzKmIWAo8FxFPZeY/FxhbkjSLnkOemQlMdR4u7Xxlr+NKkrpT5A1BEbEEOAj8CPBgZn55hnNGgVGAVqtF0zQlppaK87mp2sTpC+pCg0W8DXgM+EhmHj7fee12OycmJorNK5XiOzu1mEXEwcxsn3u86F0rmfl/QAPcUXJcSdL5lbhr5drOlTgRMQS8D/har+NKkrpTYo/87cAjnX3yAeAvM/PxAuNKkrpQ4q6VfwPeU2AtkqSL4Ds7JalyhlySKmfIJalyhlySKmfIJalyhlySKmfIJalyhlySKmfIJalyhlySKmfIJalyhlySKmfIJalyhlySKmfIJalyhlySKmfIJalyJf7Um7RoRcTcf2fb3OfJzLn/klSIV+S6pGXmrF9jY2MMDg6yY8cOnnrqKXbs2MHg4CBjY2Nd/b4RV78Zcl329uzZw7Zt29i0aRPLly9n06ZNbNu2jT179vR7aVJXDLkueydPnmTDhg1vObZhwwZOnjzZpxVJc+MeuS57y5Yt47rrrmNycvLMsauvvpply5b1cVVS97wi12VvcHCQyclJhoeHefTRRxkeHmZycpLBQa9zVAefqbrsHT9+nFarxcsvv8w999zDsmXLaLVavPLKK/1emtSVnq/II2JlRIxHxNGIOBIRHy2xMGkhHTp0iOnpacbHx5menubQoUP9XpLUtRJbK28Av5WZNwA/BdwXETcWGFdaMHfdddcFH0uLWc8hz8yXM/NfO9+/BhwF3tHruNJCWblyJQcOHGDVqlUcO3aMVatWceDAAVauXNnvpUldiZJvZoiIYeBZ4ObM/M45PxsFRgFardZP7N27t9i8Uq/Wrl3L66+/fubx0qVL2bdvXx9XJH2/1atXH8zM9rnHi4U8Iq4E/hH4ZGb+9YXObbfbOTExUWReqVfr1q1j3759bNy4kfXr1/Pkk0+ya9cu1q5dyzPPPNPv5UlnRMSMIS9y10pELAU+D/zZbBGXFpv9+/ezceNGdu7cSdM07Ny5E4CHHnqozyuTulPirpUA/gQ4mpl/1PuSpIWVmWzduvUtx7Zu3epnqKgaJe5aWQXcA9weEc93vtYXGFdaEBHBli1b3nJsy5YtF/XJiVI/9Ly1kpnPAT7jVa01a9awa9cuANavX8+99957Zo9cqkHRu1a65YudWmzWrVvH/v37yUwigjVr1vhCpxadeX2xU6rdm9FumoaRkZH+LkaaIz80S5IqZ8glqXKGXJIqZ8glqXKGXJIqZ8glqXKGXJIqZ8glqXKGXJIqZ8glqXKGXJIqZ8glqXKGXJIqZ8glqXKGXJIqZ8glqXKGXJIqZ8glqXKGXJIqZ8glqXKGXJIqZ8glqXJFQh4RD0fEqxFxuMR4kqTulboi/wxwR6GxJElzUCTkmfks8L8lxpIkzc3gQk0UEaPAKECr1aJpmoWaWura1NSUz01VZ8FCnpm7gd0A7XY7R0ZGFmpqqWtN0+BzU7XxrhVJqpwhl6TKlbr98C+ALwE/GhEvRsSvlxhXkjS7InvkmfmhEuNIkubOrRVJqpwhl6TKGXJJqpwhl6TKGXJJqpwhl6TKGXJJqpwhl6TKGXJJqpwhl6TKGXJJqpwhl6TKGXJJqpwhl6TKGXJJqpwhl6TKGXJJqpwhl6TKGXJJqpwhl6TKGXJJqpwhl6TKGXJJqpwhl6TKFQl5RNwREV+PiG9ExOYSY0qSutNzyCNiCfAg8HPAjcCHIuLGXseVJHWnxBX5rcA3MvObmfldYC/wCwXGlSR1YbDAGO8A/uusxy8CP3nuSRExCowCtFotmqYpMLVU1tTUlM9NVadEyGOGY/l9BzJ3A7sB2u12joyMFJhaKqtpGnxuqjYltlZeBFae9fh64KUC40qSulAi5P8CvCsifjgirgDuBr5YYFxJUhd63lrJzDciYgx4BlgCPJyZR3pemSSpKyX2yMnMJ4EnS4wlSZob39kpSZUz5JJUOUMuSZUz5JJUOUMuSZUz5JJUOUMuSZUz5JJUOUMuSZUz5JJUOUMuSZUz5JJUOUMuSZUz5JJUOUMuSZUz5JJUOUMuSZUz5JJUOUMuSZUz5JJUOUMuSZUz5JJUOUMuSZUz5JJUucFefjkifhn4XeAG4NbMnCixKGmhDQwMkJlnHkcEp06d6uOKpO71ekV+GPgg8GyBtUh98WbEly9fzgMPPMDy5cvJTAYG/B9W1aGnZ2pmHs3Mr5dajNQPb0b8xIkT3HTTTZw4ceJMzKUa9LS1MhcRMQqMArRaLZqmWaippVlt376dpmmYmpqiaRq2b9/O2NiYz1NVIWa76oiIvwN+aIYf/XZmfqFzTgN8rNs98na7nRMTbqdrcYiIM1fkTdMwMjLC0NAQ09PTXpVrUYmIg5nZPvf4rFsrmfm+zLx5hq8vzM9SpYUVEUxPTzM0NMSRI0fORDwi+r00qSsLtrUiLVanTp1iYGCA6elpxsbGAO9aUV16erEzIn4xIl4Efhp4IiKeKbMsaWGdOnWKzGR8fJzMNOKqSk9X5Jn5GPBYobVIki6CN8pKUuUMuSRVzpBLUuUMuSRVbtY3BM3LpBH/A3xrwSeWZncNcKzfi5DO452Zee25B/sScmmxioiJmd45Jy1mbq1IUuUMuSRVzpBLb7W73wuQ5so9ckmqnFfkklQ5Qy5JlTPkuuRExPci4vmIOBwRfxsRb5vl/FsiYv1Zjz8QEZvnf6VSGe6R65ITEVOZeWXn+0eA/8jMT17g/A8D7cwcW6AlSkX5hyV0qfsS8GMAEXEr8ClgCDgB/Brwn8DvA0MRcRuwtfPzdmaORcRngO8AbU7/ycOPZ+bnImIAeAD42c4YA8DDmfm5Bfx3kwC3VnQJi4glwHuBL3YOfQ34mcx8D/A7wB9k5nc73382M2/JzM/OMNTbgduA9wP3d459EBgG3g38Bqf/uIrUF16R61I0FBHPczq0B4H9neNXA49ExLuABJZ2Od7fZOYp4N8jotU5dhvwV53j346I8WKrl+bIK3Jdik5k5i3AO4ErgPs6xz8BjGfmzcDPA8u7HO/kWd/HOf+U+s6Q65KVmZPAbwIfi4ilnL4i/+/Ojz981qmvAVfNcfjngF+KiIHOVfpIb6uVLp4h1yUtMw8BXwXuBv4Q2BoR/wQsOeu0ceDGzi2Lv9Ll0J8HXgQOA58GvgxMFlu4NAfefihdpIi4MjOnIuIHgK8AqzLz2/1ely4/vtgpXbzHO282ugL4hBFXv3hFLkmVc49ckipnyCWpcoZckipnyCWpcoZckir3/597gLuAhYkuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column = 'Rating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>age</th>\n",
       "      <th>avg_salary</th>\n",
       "      <th>Rating</th>\n",
       "      <th>desc_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>age</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.019655</td>\n",
       "      <td>0.021655</td>\n",
       "      <td>0.163911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>avg_salary</td>\n",
       "      <td>0.019655</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013492</td>\n",
       "      <td>0.078808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Rating</td>\n",
       "      <td>0.021655</td>\n",
       "      <td>0.013492</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>desc_len</td>\n",
       "      <td>0.163911</td>\n",
       "      <td>0.078808</td>\n",
       "      <td>-0.012281</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 age  avg_salary    Rating  desc_len\n",
       "age         1.000000    0.019655  0.021655  0.163911\n",
       "avg_salary  0.019655    1.000000  0.013492  0.078808\n",
       "Rating      0.021655    0.013492  1.000000 -0.012281\n",
       "desc_len    0.163911    0.078808 -0.012281  1.000000"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['age','avg_salary','Rating','desc_len']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x281414022c8>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "sns.heatmap(df[['age','avg_salary','Rating','desc_len','num_comp']].corr(),vmax=.3, center=0, cmap=cmap,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Job Title', 'Salary Estimate', 'Job Description', 'Rating',\n",
       "       'Company Name', 'Location', 'Headquarters', 'Size', 'Founded',\n",
       "       'Type of ownership', 'Industry', 'Sector', 'Revenue', 'Competitors',\n",
       "       'hourly', 'employer_provided', 'min_salary', 'max_salary', 'avg_salary',\n",
       "       'company_txt', 'job_state', 'same_state', 'age', 'python_yn', 'R_yn',\n",
       "       'spark', 'aws', 'excel', 'job_simp', 'seniority', 'desc_len',\n",
       "       'num_comp'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cat = df[['Location', 'Headquarters', 'Size','Type of ownership', 'Industry', 'Sector', 'Revenue', 'company_txt', 'job_state','same_state', 'python_yn', 'R_yn',\n",
    "       'spark', 'aws', 'excel', 'job_simp', 'seniority']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph for Location: total = 200\n"
     ]
    },
    {
     "data": {
      "image/png": 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p0w+uyWwFQbAWEz2CIAiCGicMQRAEQY0ThiAIgqDGiTmCGuXK6w8DNG9w1qce5A83HPbaeUqtwDmfeJCLbizXWoAffOxBfnZz5VpK/OzkgfzfrYfTbNeaElxy4kDOuuNwGu3a8gR/PW4gp9x9OIs9jryRpWQDHjj2eo64+3Sgly7lXjxw3O87L/NBELQhegRBEAQ1ThiCIAiCGieGhoK1liPv+hZFRHvwj+N+wZF3/hjobte684/jf8SRd16A+zSJ7tx//Hf46B0XU/ycbtx/wtl89I5L2137Ch+940rIfi1x/4ln8NHb/wwku9KN+078HEfdft1r18jduO+kT3PUbdeXayTuO+kTHHXbTe2uncJRt95KSiWOe086gaNvu4v02n3duOekoznmtvtIlPvuPukIjr3tQe4+6TCOu+3hSrjduOukQzj+9ifa5OfOEw/ihNv/XSmLbtx+4v6cePvgSrjdue3Ed3Py7cNJVo63nLh7B2ojeDMTPYIgCIIaJwxBEARBjROGIAiCoMaJOYIgqHFOuX00t5z4Nk69YyzJVMIGdOMvJ+zIWXdOZn2bm1iPxAXHb8dP7pxGL7vWi8S3j9+aS+6cQU+71iMnvnRCX665o54er12DT564FTffPrvM8GQ4/qQtuefW2XTL5doRp27JQzfNplurLnbLcMgnt2LQ9bPa3Pe+z2zFM9fNolvOr117z+f78tKf69n7C30Z+qd6unsYrbD7mf0YffnMEkZrZpevb83E382gm53N0r0ls+13t2H6r6eCHdiSUgtbf2cnZlw0DpIf4tLC1t96OzN++3Llvlb6ffNdzPzdS5X7Wun3jf2Y+ftnX7sPWul39oHMvPSflWuZfl//APV/eKxcS630/epHqL9sYJtn+371KOovv6fttbNOoP6K29BCb0i0stVXPr76yje6pEeQUjo8pTQ6pTQ2pfT9rkhDEARBINa4IUgpdQcuB44Adgc+nlKKZQtBEARdRFf0CPYHxuacx+ecG4GbgGO7IB1BEAQBkLKNr62xCFM6CTg85/wF+/5p4L0556+2u+8M4Az7+jZgjv0/G9iy8ndtutbV8a+L17o6/nXxWlfHv7Zf6+r416ZrG+Wct2J15JzX6Ac4Gfhz5fungT904LnBwGD/f2281tXxr4vXujr+dfFaV8e/tl/r6vjXpms5d0wvd8XQ0BRgh8r37YFpXZCOIAiCgK6ZI3ge2C2ltHNKqRfwMeCeLkhHEARBQBfsI8g5N6eUvgo8iA6NuTbnPLIDj169gv/XxmtdHf+6eK2r418Xr3V1/Gv7ta6Of227tkrW+GRxEARBsHYRR0wEQRDUOGEIgiAIapy1zhCkcnD7in5bP6V0sv2/n/29OaW0le9OtnsOtf/fmVLayv7vm1JavxJWb/t7QPtwU0pXtIu35wrSckm77zuklL5j/+/s6Wv/20rytUNK6TuryvtKnutR+f+1PKwiXdetJJz+bzDee+3vftW/K7l3hemq/H7KCq71W8G1Hu3i7JNS+tRKwlxpeVfLbHW0D6ddnfZOKW1U+b5+SmmPlNKpVTlb0bMr+G1b+/uNyrWeFbno1+7+fpXftu1APk5cUbire24V4fWzNrdVNezK7/39Pvu7fkrp5I7IWeWZft7uVtL+bm7XblcnZ/1SSqeklC6qXNuv3d+VhrGK9tWvWj+rS0e7Z19XDymlA9v/llJ678rut+s7muy9c0Wy16G0rG1zBCmlIcCZOeen7Xt34CPAx4EjgXqgJ7AAOBV4AU06PwvsgybA63POb0kpHQacho60+Ik9OwqtVNoQGI6OumgBJgKbAhnYAtgA+CDwZeDDwO+AX9r9bwd+jSa87wNOAbYDngQOBHYFFgGzgJ3RG0VagCZgIbAR0ABsbr81A8OATYBLgZMsrI8By4CROefTrDxOAGYC37V4vgPcaeXxT+Bgy/OxwDvsMwC4G7gS+BDwG2A9YCxwpqVpAHBZznlWSmkzy/N3gUNzzgvM0H4HONHKZr6Vew9gKXCzVeGmwFuBdwJTrSw3Ahotzsn21wV2Y2A58HvgW8AYoD9wG3Cj3fdptP9kspXR5hZvMzAd+Ivlua+lDcqS5FeBRTnnY1JKhwN/RrvZv4Q2KW6AZGdzYBckB5tZHTcC8yx9fe37JOAtlvfu6OSv54G9kWPlb4/5vd1/ktXTNOARYD/gMrtnYM55UkppktXTRGCCxbeflfFSy+vulsczLZ1TUL1/CRgI7Gn3vg3JzPUWR3fg85au5ZXynm/5f9q+d7OyeAeS5x2BP1n5H2z5u8Du6Y/aSba01SPZW4ja5kkWXnfUDnpZfJtaHY4H/gX8r8XRHcnMh4H/sXA2R21mHGpDf7b8fBPpg1uBxZavXhbGeCTHXwP+YL+9G9gLtc8RwHvRJOqxwLaUk9uWAr0tTy12bTmwxMqgN2q/PS2e9YB+ls/ZwBeQjjoKyaXrkh7A7cCPc86jU0pnAp+wdK0HvAwcjur7p2hpfYvV4VTgfuAHSJY2tuuzgRmobe1qcSyy3zZC8nwJcH/OuZmO0NENB2vqYxX1HHAvcC1qQMOtEl5Ewns+8JJda7UCb7IKaLHn9kXK2ivW7/N7XJBbkTJpsYqfCsxFArsIqEONfwBSUk+jhufPNiGlPtTStLSSliYkSHciRd+AGs08e7bR4v2G5bmlkranLS1LreL/AFxkaZyHGkuzpTEjBTKrUhZNJhgjrFw/Yffndp/Wdt/9nmWW72X2abZ7/fcGS3+2PD5lefM8jLF7xtk9yy2cFqvDAcB1dl+1Pp5DBnZpJXyPeyFqAE3IkG8M/N1+a0XKptHS02pxzkANf6DF/zySo1ZkUJdY+U6wOJfbd5ebJRbfCOAfyOA22vNLLE6v7wa0FPoFu+Z5cjldbvU1155tAY6zdFXvr9aJl71/n2P1vBdycFyOqvK9EHgFGYPGSpgzLI9TkBKdZuFX463WRTUtjZb+UVbPnv/qPdV21lr5fBE5NkOsjFotH2NQO1tGaUut7cJcaPUxz9LebHXh98yjrXx5fqr5mAOMtHtaK9dcHr0umu36HCsfz4+38WcrefeyakFydLulpaGStjr77tdHWByN9rmNYvi9/l0/zaJte2tBx/M8b2VSrdOHLQ/NSB5nWx2PBrbtkN7tasW/EmMwBSmQRtRohllhzbTMTqMIfzPQZM9NtkJwb+dVK7AbkDLO9vsC+/8pu9cVzcoawjRKA26155pMWObbtQcrYcxESny63fcs8lJa7f959vu9lcqbQxHmbIKwyD7zLC5X9OOQl9wf6GPXlgL/tv/rTDBnUxRUVdCes/sbgMctjtmW3lbLp5dvK6XBtCBPrhV4zO6ZXymzZvvMRgqjxYRxkdXP9VaWP0Ze1WTgGqTQXqEYmKpRarH0eeP4rl1/gNJAhtm1vhbudsCgSl00VsrhbiQPrZaXyyycQchD/TdSGi3ALchTXWyfVuAhC6+pUr43VspnOXIKHrZwFwFHAxOsDHayel9g5Vpt1C2WxkPtr5fnUOSVT7aycuPrSv5q1GZaUW/lF8iIZyRn9UCdxd8A/BU5S4uQR3qZ5acFKZYHKErZ5eE+i+8R4APAixbeYovDDeSLSLYmWn25olxWuXcpxTBW21wzxfF5i4X3QQtnlt2fKY7UREuD5+0GigN3v5WVG4cpSDY8Tpetaai9tCIvfQqq/0H220OV+llkZXNMpWzGUdq7G8w/A42WpvF2bal9vmXfZyG5ccM0s5KfVot/HPAEapcNlPYyrpLmhByqbyDZGmrpHwN8HRiwLhuCP1qhN1IsajZBykj5+LWpVtFHVn77hBXSMIq3MYQyPOOK5VkTiAGUxnU2agzuaTdZoTfa3zmVMBtNWLzBj7TfllbSu8TSOMHidKXkRsGVUKs9N87ue4biEf4BNcYXK2magbaV32/XXqAoht+hLqN7Sv63CRhfEVBvzCMsPveqvfHPRAplJkU5X04Rck/7e+3+KZX7/s/ie9Ty4w3CvS5v+IuRt+iez3wLqwkpqEWUxtsA/JzSAxpk982keEdNqGHca/eNp21D9kbWihrLGIqx856UKybvzbhsLEUG0H+fCkyrlIUr0ox6h/7cD+3vK5Xw2nu+1XgOsjwvR4ZrVqXMZlTurQdaLX6vezeKiymGxMu6fX4a0ZCpK+BJSIa9PTQipfwKRc6bLI6lqKfu7WwWxdNdjpwwd2rm2fcRqA25p7+A0jvwIbaZFMXeihyKxkrc8yiOyXw0RNSAHJ/5FCfPf3/NWbS/nqZ6uza+ct9lFMU/zz6eXi/DecjIeb2NrfyWgcmWdndO51D0jLdTl5/pyGh778MNrff6/dpSisNzo6V9soXxGaubzyDn8GVgG+BVi3/0OmkI0Jj8OPt7CGrU82jr2c6xysioK+5KromicP1+L0i34I+jMXIfh3NlOx81Ou8O3owU0kKkVIa0S+dUq5iRFCXlcSxESscrf56F1YC8Bff83Uuvs/CqSuHfdl9LJfzJ9v8kiufkYSy3cmlBjcENgHvF3pBb0FjvZGRYfmTXRqHGup3FXY+UziCKUvO8uBKcD1yIFPlye84bnefFhyL6IEUwC/iVlc8CSuP0BnsbRWE1VuL1vHi4/7ZwZ6HG1IQUk9e3G49Zdq8P2XnPMlue77f8X9UuDi9zr4Pq0GOzhfEP2iqZBai3OIvitDRThhOWIsXztJX9TCSvY+w+70VUP9WemZdnpm3PdjalZ/EzZCz/Thkic8XpQ17uNbZYeC7/7qA8Y3mabPU9gzKEtwy1SS+fqpy7fHk5Nln6R1g8XudLUO/wOYr8VodK3UnLyAPeFc1R3Gj59Pz4MIsbCTee7dOzG5onWULp2U219C+opGFe5buXt8vmPNSTWloJu8Hq8FXKMFkLcn4akFy6s/WChfVkJb/th5GrIxMvIONWNWS/p7SN+RRHbBbShz7X+CngHov3pY7o3bVxsvgG4Js55/rKtZORIByCum9TkdXrSelyeQ9iczR23A0JTT9UiC8jZbQREobNkaC/igrwMItuMfK0/4Imf95vz6yPDMJGaGKuD5q4m4smZq5HPZGtKBOWboi2oEwiLrO09aQMLwyxvK2PKnRjC6MXUlY3Au9CE3j7WNpeQj2F6Uix7IImFH9q6doOKaUt0MTnFCujd1EUwEZIwDdAntWxqEeULN3no97FcWi4YhkS/H7IsPRFDaCPhfcw6tJvYN93RpN4GyJFe4rVw54W1gVWJpdgXX1Ur8uQotrevj+F6n8isAcSfp/Q7WXpTRb2WyzuDaw8fUz7UTREszGq7z3tt+3t3plWLhOsXJ5Bk3CHIwfhNLveatd97mCGxbnc8tmK5KoHqv/dUYPFrjdauW2MJkrvRBPwR1s6m63uxiAZ2QfJe69Kmc9HzsOHK3F6GbiScYV8ERryerelfynyYvtbGh62fBxrz7lx2dzS7HMSYy2NVRrtby/7W+3hpEo6llpd/MP+HoKM867IGNyJJks/iWSrStWoZOR47IfkMSP5GoXaxgbA34DjLf4fopGC/axsz0c64W/Imdqt8uyWlh+fFO6GekIL7Xkok7LnAe9Bi1VcaR+M6mUDSltvtmcWU3TCfDThvQD4KCp312FvQ215mJXNIRRnoCrnY9Gkck/73t3C7UUZYv4Vatu/zjnvympYGw3Bjkh59UGNASTMoBUjC1HB9kOrB7pRxqU3QhXlyudwyuqZPZCy2w41/v5opcMeFO+7Lyq8j1SSNA4V+k6ooHvZtZHIeExCjfIIilJvtLg2RJVehwRsORpGcSM1BVX+clT5u6FVQ4ciZf45pLh3Rgrxr2h1ggtkPWqwc5AS3BQpUSwv1yOB2h0NAfzRym0DS1sTZWXHQiubJnt+PUvXevb7EiS8fVHj9onk/6F4aLOQgp5v4c9CDW0/y/P2FvY0pMzfZmn2FUTD7W8fijf+IjqOvI+V1zeQfOyL6u4Au6+H5WFLC/8xq6df5JxbUkqnot5Yg+Wph6VxqcXRHXXnD7FloccCX0Wrn8agFVPjkcE4E40TvxMZji9a+u5BhqEX8D6kJH6N6nxHq69tKPMePlzVw+JPaAz6bOTV7Y9WofShGIlnrC6+nXOuTyltgVbpXGxlcpwjj+YAACAASURBVBZwfM55h5TSHLv/38iYzkbj6O+iDHW1IoPQZPW7MXIudkG954ScIZeLnlaXrRQGotVyLyJHZDe7/jY0R7QjksHjrQ57W76TpW8MMvIDLK7PIll0x2gvJLNbW919AcnSpnbf25FDdoDF66vyRqO2PxgNtzVRFiE8gJR3b0qvabaF94CVE8iQnIxkc2Mrl78hI340Zf7mVxb/p5A8dkc6Y28rl/Us3FfQPNceqN7nWDn/HDkcl6G6bLL4/PcrK+XdnbKSbhmS48Wo3qbZ39fIOX+O1bA2GoLhSPHOpZyxnZEg9KQowL6oQl5GDe1IpBTdg+hO6T4utM/GyIp/EnlT5yLBS5XnhyLl9VH7Xo+E5RGk5D6MhAx7rielIWdLdx0yWjtSrPb9qAF+HhmLY4BzUNf3Y8gwjUWCOwMp0Y2RIDWiBuRd3u0sjrda2AOQUG6ClF9CgvYPJESnW5q3R5N+M9F47AjUiF2RPl6pig1Qw93cym59C3cqMiaPWni9kODdjJTcF+wZHzJopiyTnYEa0scsTQdZnvtSls9iZdAdNWiQ0d0ZKaBewLfRhPNGlvYp9mwf1Oj2A3bJOc8ASCldixrO01aeG1CWev7Rfluf4rli8Ts+Rl+PDPsPUc/vnVbefa08EjKMfZGsdENG8WbggJzzXpaeMWgOZUekfI+nLAS4Hy0J3QdNVn8eTbZvmVL6LOB7XOZRVmstQr2KX1jZnWm/QVlptRuS4xYr8xdRTzOhHs0HLJ31qD4voPSG56PJ5dtRWzsRKd8bLX3D7f6zkQP0YSSbGyJF5UrbjZ2v+lnfymkL+22plcdkJKM9kYHpbuFeh8bw6+yZXhZHsvz2Qkp/Lyv7Fyk9yJ5WNltUysENmqdrBpKNb9uzD9h9Uyi9vj4ULxxK76GH5elci/N79vvLls/qiqX5qA232rOjKfI71MprrJX/FMrCDpDMdUPt9lzU7vsiA7wlxYEi5zyXjtLVcwIrmScYaX9nIaHx1TC+PPFBJOB322c0Zcnct9AwxBOUJZbVZW3PUCaVHrFnT0DKdoL9NhNZ+MbK89WlaT5u10rb1S6+aqQ6Ptna7rsvgVsATKnk+WlLj0+g3Yw8FF+F8h3KuP4oNNY4DCmiEZVnGizfHrePcfvysu8hL8uv+VjvMjT0dQpqbHOsnE+lTMZ6F92XgDag7vaPLE07WJgn270/QHM90yydPonp45zes5hLWQbqnvI8y291ZVN1aZ6XtxtH92h9Ys5XNA2iDPl42dajhracMqzYgozcHMtDvdVtg90zpF181f+9jr2sfey8uqzTx6B9NdVDVk+9rP58ks/TOgM5Cj73dY6leSllQnIn1APwsX8fI29FyvxqyiS9z1dUV8VVewX1qOc91errMuThjgLuoO18TXVcvzqv0kKZgPcl2ctQW/L5jTF27ZXKs0uRjF1gdeRzMq2WFo9nlD37FNIJrheq833eLqvzazOQDN2A5GxmpZ693BbTtu16fjyvXg/T0NBsM2qLL6M2VWf1WS2b6hCd6waXwYGU1VlPV+JttHp+3L57b3xF5V2dQ6rqKV8Cew5wzjo5WWwC/iryYAYgRfFvJNA+MXsuUia+8sMngZYgT/d5NDTw2YpA+Xi8TzIvQsp0LmU5oVfYZNTNH4iUsDd4F+7HKsIx3YRgBG33CXj3f6ilbaCF8xJtFcSTVmHTkaUfYXn2OP3eZUig6ygTai0mfN74F1Amiebbb+4hLDYBG2K/VZfjVSfA/OPe/OMWxqB2v/skWgNFKfvy0/GURniqCeXtlXDHUbroPnm3zNIzkaKI3SM6wcJ+liL0X7L69NVd99gzl1EaXVUJe4PPqDGfR5koHU2ZfPPhg9motzDbPpcCdyFHYibygofab0Pt+Ufs2XORB9nb6sAbclWZurIabn/d+ZlpafO/rki8jD3/PgTQgHqVfp87GrMq8Vbr1JWIl9uLqA3Ms2eaLZ/XIy+8CfW8P0VZXdOCepvX2nPX2meBpfk8Vqys3EB6u10A/Ba170VoPsqN82DUA/ANn66ol6H5mt5WXqPttyGU1TNPUlaQzbDr7uzMR+1rHBrbX05ZHeUrn9wxeBkNRY6xOh6DehRbUBao+IKEkbRtRwuRQZ9jaTjLro1BG16xsm9E7b7e7ndd5av+hqB222zpGoR0wGjUY3zZ7huP5gzrK3VwHnDeOmkIULdqqmX2U0B3u74eGjvLJqSzKN5cM/IwfCJsvlX8jEoF30SZtR9h9/dEguwba6ZZhR9V+XzKKui3lBUzy+y+ZykbYqoezkxL685oGKGRojjnUoT9fuRtnUfZZFTtPVSVxsJKGZ1MaZTea1lsQjCOoly8R1SHupBfsniep6w48MlmX80x1OL+NOqqnowazHzK6hff3NOKhpq+Z+U91srD8+z7ExpQt3UKUi5jLG3nIEPTStmY5b27Jvu7FPX6XrHnvHfiHrevKPN17F5u1U1lLZZvV37NlPHURmSkbrdy+SVShIsp3uM04ItW9ptaWqoe2EALezpliaHvX3jB7ndl6HX/C7v+HstfHRq6GIOG3hYiJeA7RsdaGN6z8pVCy9BKK4/zW8BcS2tGCvWTwCi7Ngw5Wj3QkNRNFOPhhqqOsmTajYtvQpuF5k+utvRNBz5oYd+PhljPsudduR2BvN+zLO56NK4+1sLx4S1fKdRo6RyGFJ33klwmGiz/T6K9INchL71hJZ86pCjvpMhWg9XNU0g2WpBeqEeG4no0R/cjyhLmZchR8bbn9TkJDe32R0M0vvKoxeKdYuU0FemcsWgCf76VaZ098w3KPpt6igPgGyDHWbomVepzNyubIRTH5lPYsH+H9W5XK/4VGAIfk5tvQjDIKsiV2j+RsroDNaRPWMH65rFmypimL++bZJX+GbvvPrvua3H9ORf6VtpW9GIr4H+i8bsfIyPj63zHm4D4vS+bILyAlPJstDKmGU0sPo/GV3+BJoavRYprAsUzm2DxLUFKah/7uPc51ASjv8V3sAnDSxanD1E0ouGeaylGY5al7zHK5pwZlsdhlO56roRzhwnYdPt/HGVT1L3IUx5AaSS+y9e9dt8NPR41hsnIg/EG44ZrMmVzjndzr0Ne7xKk7JZaGbrxuAUpvFuQYmhGS+0ORgZtoD03zurnFnt+NjJMp6Fllw9bGV4G/K/J41XI038A2Mmu7U+RieVInq6jNERX1K7UnkPjzrujXuoSK68vWLqPo6xem2b1Mx21g6locvFgZDD+hmRtKjKiC5GcDLb7r0CORTf7fo7lazGSo1moBzwAvc8WNITnSxkbkfG/nDIB+bDF6YZgazS2/2WKg/BZ5DhNoMiN77lxR817XHUUD9c9/eqQzjgrq1OtPn6J5GYRansXoTbdBJzdAZ2yHmXZpvfUfWOb70T3ucC3Wl08Yfc9StmLcgNlX5Av9X0V9UZdHpvREO3nUU/HdYTvYn+EtpvPvkvZcf0Uasu+0fBuiqP7ANJzV9j3qchQvYqM72ZWTt6uRwHHdFTvro2TxafZvz2Qp9kPCZ+vg9/Uvu9BOXOmG1JKyX73SRj3pl+161sho7KFhTsXKcBJSHkejoRjD3t2JJocm0HxOM6lnA8E8n6Ho8m245EgbYBWFO2JGvsedu9vc87fTikdgZSNHxUwHlXgHrQdLumNlGYzZbJoE+SxfgEpgD+hFUG+CqM/bceA17PrM62M3oKEfDs0If4pJODvQEvzvoIEexry0E5AE6SH2Wc0mozcAU36HoyEcCOLxzf9rI/Gv7tZ+bniy8gLPABNcvlQQX80efox5HHuSlkW6XmZYvW4DWoo/7L8P2d5+TgaN58MfC3nPJUKKaV9UQP9GJrsew7V502oEd+EGlFz5bH10a5wX6rpiwOypWei3ePKcZHV263o3dxPp5TWa5e++5AsfszKamvKOTeNlrf5qKd2Uc55uKV/I7TyaX8km31Qj8Lnqqag1Up/tOuNyDE4DdXzFCuv91pe5lv6uyE5bK3k2/cLXIkm379v3w9Cu32bUb1Otby+F8mvn6s1EbWvCZamPZGcg3rKdage+1tcdUh5HUdZ0TYNydqTyJHZx8qpASniH+ScF1TSTErpWGBGzvlZ+z6AMsrw85zzcymlQWghxi2oN+WTqtugNnwcOofIV749hAzwmchxAxmO7kg5H2VlOQ45HsejeREfyluA6nlTVGcLK9+bUW85WxgPA8/knC+19PdAOuUI1LPbyuLdEtX5i2jifjvUBm9GbbkJODfn/AwdYK0zBFVSSq1IKUJZ5tVi3zMSwN1Q4/onasj35ZyX2fMboob/DSR8vZEiBBibc17agTT8xOLaCwnU9pT5iGYk8O+neKuXolUV76+kswk1yF9Xgn4VTQDvgwzHROTpjEYrA0DjxuNTSru0v2Zpuww1nrcD/bMOVvP0VtkcCRDIy/gNUjL/ixrD+mgV093IG/RewraUnZP7UJYQdqdsctnQrn2fsrpiR9TQWpAxOAQ19D4W5gwrx+mUFUkNlP0FCynnRFX5Oq9X2D9FRuPfwCU55xGsgJTS55CHuT5q/JNzznfbb09U4nLD699z1pLSK1CPcgHyrK+3+55CjsRbkdIb1S7du1i+rwZuqqYvpbQBFXlEDfm9Oef7V5SHynNVeRiLnJHjUb2sR/FaR6IyfhXJ7mGovuYgxTEVKZRNLA8ft2f2RErvnZRVPhejMh6DerO+r2Fzy/vAnPMd7dK5DRqmWr4iGbb2PR45X1CW9G5r/7cgb7m/pXMYcoJ881yVd1u+uyOF3iPnfEQ7HeLP9K48N8H+boMM4iLL00g0ouALFKq64klgujsaKaUNc85LzMDsYfe+gtpYdSnnYlTuR1L2CdVbnh5DBvYbwM455+4W9iBLz82UTa5YeYywPPuSeT948ik0lNymPlbFWm0IqlR6Cnuj9ckgD34SUrL/RI2gP1L6O6EGOBZbR51zvq0S3lewSczcgRP6TKCGW3zQVhD3Ql3Ij1O6mZtRJpT+2S64nHP+/Ori7Czs2NyzkKe2GxLIWUgBjEYCt5DieYCGOi5EjfFflF7HhytBP9suqpxz/nxF4Q1FwjsRNYTdUaNcgsrWlxWChLo9H6z8npGS+gDFOVhSuTdZ/JtYnldVf+Scj7H7HkGG5fac87dfCyyl45Ch3oEyQTgOGWGQgRnO6zmItrtdV5i+1VH1dFNKx6C6OxAp15GodzMRlfHzppj8/ChfYXMAMsA9UE9vMVJac+y5unZl8vV2aXgWDV1U2d2u7YPmVKYCYyydKyzLSnj9kfOxGWWp6WDk5Q6jKOmxaAhlVcrqH8gB2T/n/D2Luwm4POd8n8W3L3KIhq2gtzihEv5mqE24E5fRUKOzC1oK/mrO+dR2YfRn5elcVJHHTdFS4/fZZ29WoKsszH6UjWluCBJqm7ehYTk/3mMRxWEbAkW2V8U6YwgAUkq/RN1i3+zxQ8rxviBhmY4axETgrJV5/Smls6h40h2I++DK1y0ox10AkHMelFLatH1XtSO0786u5t71UI9jJyrvnM45n59S8uV3f845z/kP0vETVt3YJqzq+ZzzgNWEf/BKftoADV0Mp4Npt0Y9vX2Dtt9eV56riNvTPqhy7wVIsf8i5zynvdOQUkrA7rlj79peWfoPQHMkvuYdYPHKDIOlaU/Ksd8LkCc+AvjDinpCpmx8aODynPNlld8eQUNzzyDlvStyZl6jfX2mlF7MOe/T7tpLOee9zVDuipTSBIpH3qas2itoO0lgGrBBzvmrKymu9vlqU/cppffRrj0gnXAIsGPO+QK7bwAaxukGPFRV4iuIY5X6wfLVO+fc8LqHO5aHWZTNfv8GnlvVCMWqZM5k+23IYLwDDQ2+RlW2Vxr+OmYIhgF75Zxb7Xt3dJbGuyr3rNIL6UAcA5Bnebk3Lu/iIgMwF62OWJWns1IltaJ40IT3nljjWcG9rzUetNt1AfJYGigHf41FiqEVeHfO+TMrife17vqq0rsy4+T3oq50+2deV3btnk1onHMXM1w7AlvbuK0rkpWmvV1YA2jnlVUUtg/j+Vn5PmSwFRqfH2AK/QLkRfmxBHPRxPf7UTnPsLwej8bxR7VXhCtI1+uM8QoU17God3U0kqUfoYm/l3LOP+xg+FujeYDewMM55+9Vwvaew8loonw9tOqtN5oUfwXtQl6CNrpdvpo4P45k9CDK+UCtaEipJef8oXb370A5ZoGc86TKb9si2Xl/zvl3q4p3FekZgHogPdEQZG/Um2hEwyw55/z1lNKjlDP7p2LGMKW0saWrYSWy395YnZ9z/nHl927A33LOn0wpjbLLrxnajrT/DuTxINRb/wAq65ssH3Ow86yqbfj/l3XGEFgj/zWaF7g45zwiaXv9Eznnd1ULf0XWM6V0Lpr4uolyrMOP0JKyqynKYjnqnv4UjfcORmOrW1F2It6OxvdfZ6FNSA+w+4atzOtIeivSncjz/EnVW7Pfq8r/RdR4PgJ8Kuf8zpTSV9GY9zybG6h6jOdWyqK9UL9mKJHCbEJGZQvaKtUL0MRXBo6sGEVXwH6URPWZ/VD3/Bg0kde+++3LbxtzzlunlDZHntl+tMMa2FZoGOp088wvsHK9POc83e4b7dcs7DZeXCVNn7Tfd0cG9GtWptdaXrz+10cGYFM0h/J5NG8yE43VvtN7fStQ8BegMeCxwG5u0NqV2VMW1yloDHk7y9/zKaXnkbI93NJwmaVxCBoCaEVDdrsDe+ScP7GCcqvKwXbWNg5CK0ouosxT9co575xS2gs432SojZNg+TuUslnz50j57kI5uO5jaHEBlKNYdkHzTFtQ2tASNPbfgsbze+acd0wpvRsNRT6Ohh9HUBmubWfYqm38IWSI9kO71C8CHsk5fyyl9HY01PIDNFF+nKXtJaub2Tnn5yp1cyoyIneg+ZTNkXHZIed8VNKb/fZERvBqpBteRE7GHLT44ixk1L9Caf8Zrc56XS/X8vVDe/614Sv77TzkiGyIdFQDWhDyS7T6bFvKEmg/RXY6MoCrHd1YEeuSITgLDR/sgyrrReS5/R8yDjeiBlKHhoQmVa01KsTZSHg3Rg3yqxQLeydSFk8hQf0+aoR+suVWyFu8AI3p7Y4Uva9O8F6DHw63P1ql0GC/uyc9P+e8OKW0h4WxJVLMQ5Hw/QBV/I2o8V2BxqMvRwK9P5V19zlnP9elWlaueHZFCuwOpNj+Yg1qFFL+1yKP8YCc8+UppY2rXd2KEvVx1+rk38bmUbV5pl38r6J5Gl8RtSUS6DPt90+jRuWKhFyZO0kpfRqNZfdEywY/gI4x2BIZsktyzi8mzR/thmTCWQAMz5XDCyvhVpXlRNTA/cCzcyhHL0xAyuNWpGi2ouxsPdDKtAH4d8751NX1aqwL/xe0FHBPNL9yL5LpC9Au7dmozrZHcupHWDSisfh32vWEFPRRyEiAZGQaWrHSD00+L0FDb49bOb7f8rwtMDXnvIv1tA9DE+C7YD1dq8dDKMtiL7Ky+SmauPfls+7Zn4OWlN6C2lVvykKInkhx+zlAvVFbvRytSvsu2ucymraG3OvKDzjsb3ncBhmhJ6xMf4smsvdAQyO+2sw3Zm2O6rk7GoZq43wkLRjohnqMPudEzvlicywfsLo5ELg05/y76vARMmB+GOXFqJ2+hAz9a/LQrtf6HiuPyUj//MTyudSefwwN+12eUhpmRr06xLmThbMj6iHW8QaGV9vkf201BCml9SqeycaoMntRDm9qQQJ4NFpa5Uc0d0Pd1XfZs1uipW2/RAr+r8AdOec7rQF8BlnmA5HiBQnvB5ARGIG8twTsmnN+q3Vvn0WeWi80QbML8vhuBy7MOc+uKP99kaDvY+HcgypxD7v/UOT5Po4Ea3804T3K7u+P1rNPRPsR+lB2mC5B3tcpXnY55xct7+6hbWZx7I4awmlImRyBGgfoYLe+SKCWoAnuW9Fwk08Q/hl5Vr5+GtS7akVKc6bFf4zl3Y/j3gZ5SZ9Hwn6DpeNlZBDOwDY9Ubzzy63X9zhS/MspS2hB3tIStBzyfNTgxlt9+FEX/rrA5Za+za3sG5EsjEK9Fz99dAOkzG6gvNaxBY2d72Fx1iFFdxAycLeiBu1nCzl1SPHVoWXQtyJ5egsy7mciZX8+WjKY7Lqvob8D9UQ+hTzxbyEnobvVjR/oth7yRHdDnvUVSNZPQg7LGFSvPZCSOMDK+ydohdIcawf1lJ7iX9r1pjdFHu85yGH5PurNzLN8+0GLeyLF/C7UfjIaB78JOR2gY0uuQsNtH0GyfkHO+d2V+NoMMSadFXWSldFEpGAPyzn3TSk1WJkMQb2DRqu78agtbWjpIJeVOCNR2+mJPPZlVjd+LpifTXZS5flmZAR3sPChLLddgGTpaitHLL4nK3nqbnE9zet7rVWDshky0icgw/y8xbk16m38D/Za2OqQm4XjjsghwBlvaGgqrwWbyFb0QcK9N6r0iZTDl05CAvRJ5E0tQQrqYeS5gtahvxc1/M+gxuhnBfk6d9/U4et8x6P3+b7XKvN2yisym5BBGFpJ3zD768u/bkWN+BHKizUGWPpftjh9R2qrVWYTEuB+yBA8TDllcgrlJTIHICV4Lxou+DlawlZPmWx62fKwAHWVP4yM42Qrt5GUDVV+hoofzXGvhbUYKY47Kacx+gtO/NwjPyrgKUvjE5bup1Hv4hpk8O61+NwY+M7aJit7Py5jIvKGr2h3n6+p9x2tyylnEvlmNl/Cmu23J5C3+F3K2UMtVu67UF5L+TVLwxOUPRv3WxzPW7xzLMyRloexaMhmc6S83k850M13QE9ERuNBi9OPBZlGOdtpJloi2EB5iVJ/ylEev0LerZfTIqSAfV3+CKSM34mckUXIQx+B5G0G8IrJzYaoB7Wb3buN1ZEvwRyEehOP2/1borkKP07hg8hxaqC8HXC4/T8ELYOcY2U2xcL8PuUYDl/muczKei+rO5eLpahdzAYeq6T5Kgt3vJXVDCvvT1g4D1kaB1F2eFfPBWql7ORfjmTGd7L/k7a71ocjGZiN9sTshnoaB9H2HSPLKLvvfRf5q5R3Pvtu9zp7bhLl6I1RlCXnFyOd9Bm0r6ab5ftZNETlL2nyDbQPInn09ultw/PZgOp+GEUnVfXO94A+q9W3Xa3wV2EIvmjC8CHk1Q6nvLxlMOU1eL6N+wTKgVfLaHtEwxTUGJcgL2oq8lLG2PWfU5SNK8crkIJ9hvK2rmakEI5ADf0YNIY3zYTi3kqYQ5HXdZQJ1XCLe4iF797mEmTBl1u4T1oe76Icj5ArH98Z6YLgu0FdgfqW9TrK6wWbTCD8uI1FyND1Abax8h6KPKrnkXdyJOV4h79ZuE+g3tN19sx8pMDGU94I5RvEvHE02f/7W5ldi+ZXJqEVDs+iYYapFsZM5PleRtkEdwplkrwZKecTgEmWjuHIk3sn2s2dLO2jLf8/RSutGrAXDKGG48cA++5Nn2v4vdXDn1BD39DivNTimkc5FMx3bp+IlNaJlJeRLERenCsCf6GQ79r2OL1eM2rw91LeGuabxZZbWqfaxx2j5VbXMy3tQyk7xBuszJ5HztIfUA/6GsqO4DFouSeoXflhep62RovnXqvL/7E6881SriRdCc+s1FuL5XkBUuYbUl6h6XLhy4aHo57K7ciQ+5yEHynykv0djWTpPfb5qP39HBo/P9LK+32UM4hGW37+RTmuohf20hbLj082j7ayWwyMs9/7IAO32O49DDlYQygvlPHd+r+yPJ5on1ORARtFORRwsuV5OVL0/qKsO63OZlGOcxlhv/WxMliAjPfdyOEdgpwE32dwbUWHfgz1JP+6zhoCy8gUE8AJltlFVlH/R1mf79utp9n/FyLLP9oqe6iF5XML77O/+1hBzrdw/NS/RRbWrSaEPlPvDWQB5eX2rpT8MLoWyqaOuZRXzPmJp4vQOPZsZECmUoZ35iNv4R60g3OgCcV0yoF5Z5og+pLZiz0ey+MyK7PhFu5jFENwHlJgT1FedH8pUiKXmeD58jo/c2gC5dgMP7tnvl37NfLsdkFGdU801tuEeiTuobnHVkfpts+yz1sox3M0WZn6WUnvs/w0IKU8nvLmrDloXHgsGvL6q5XLaagxDaPs+l1C8exc+frRG14+u1fu8XR7vlspb4LzvQienwVWF7dRzpG/2OrMvfpzkBKciIaUllkdX2p150eT9KesYHrIyuJgyqsR56OG/U/KTuvZlu8dKErXN0C5M+TG2PO/1MrRjzZOSMH0oCjD6ZRdwYvt+Vcphw/+3r4fbenbBym6cZSNXe7oXEcxaBMs/Jkmr66I97U69rON/FWlzcir9XpxB8PPEvOegc9hjKK8K/xsysZEnx+cafc9j3on3gP23sMSi+dIJG87I2fvm1YvLZTXbi5AQ4F+kN1QSjt0nbOB5etmi3ugfSZRTrN9jOKk+o75Y9GihUXIACxBznBdJY7nkaz/jDJM/grw+/9I13a1sl+B8j+n8hlulTrPKtQ9xpsp3aZmEwj3RAda4dehCUY/k2WyFehgC288xav2M0/qTNCmoYZ6v13z0xl/Y2HcaWnxyWc/K8cPilpMeX9vI6VH8DLlpeSPWFgLKUMHjZbe9yLl5uPifriZN4bhSPgXI4PSihqTD0/4IWI9KOefXGnCO47yNq7ZFGXvm56aafvqz2mW9xFIec9DCngkbd+L22B58fcZe7d1iMX3G8p7audZ+V5jYc608von5YUibjjmWz3PQQ1yEuUMnPbdZD/1dablrdqbWkZ5PeH1lLHhRUi5TbL4/LyXCcibb0S9oOetXA+0uLajrFSqflrbfZ9Tif+nlHcD+7DSHZafDSm7bH01yH5ISd+Mltli941F4/CPoWHSQZSzcJajXfNY3EciA7SAchqmb25zI+HzKK70Z1u+N6b0wlzhjKH0zF+inCXUTHl/82I0d+Fnxi3fOwAAIABJREFUJ42yZ11uh6E2OQe1Vx8C9GOm/d0MyylHjZyA5sf8kLdnKceTP4F673dS3kh3AeVFUb+3uO6hOF3PWRwPW5omVNL0LGVoaiBl/msp5dBI77G5o+c94OowlcuCt/kHvQdrf5eiHtZoyqthqyfiPmpl7O3f6+1Wimwvpu3Gt4nA298MhuC8yueXVsiNFI9/LvKUZiJlscQqv8kKawTqzntF+UFr3ugXWWE9SxmjHI0a/3wk+JNQw3dv7BATtA0opx5ONAEZi4Zx7rFw/gcJsr83YD7FULjH7oKxvJImn/z1CvYhi82QYniB0ijPoZysOIjitbpCXo4U2lOUc1peobw0Z46V1yjL94OU47x/QVkmtxx53NNM4F6gLPF7ATUUP9WzFXnFS9GQis8NTKnU1VLkYbkHNYhyttIy2irQqmJygzmRsi/gPsoBco+hrvIINJT4RWRUNkWrMrZF47HnIm/u9Ep9+emoN6CG+Dvkzc+hvKS8DjkSLVZmDZQjsd3L9mG5ZqRAj7Yy97OoGipldqHF7+cS3VXJl8u6b4z0YSJ3El6hzF/MRTIxgvKOXFeMp1NeI9pg975kdfF3ysGMftjdZCtrN7oug42W1gmUYaCMemRjKO3M683nuLw9NlEU42Iri0WUF9a707LM6s57fj53N8nK4klKD62u8nHH4zGL53EkK/cgp69aP/4ZanlvQe38GiSXnp+qofT8XILmUCZTXvi0GDlWc2l7xLgP03ovc0+7v5lyxPU99nwD0hVNFofPbb1E2SQ31spnY6Rbplq5uDF+AS1m8PY3kzI0vf86aQisUrsDv6l8vwaNsw23jE82IfgV5bwNVyqTKMMYz1khnY8E/kdozOzPVpnfsnDPN8H0IYCvU04XrAqQC4gr64lIgUxDXeBmtDKmzirLu8uno4bUjJTPvy1tdUjwpyFB3g2tTLgLLQnbyMpiVxMWHxNeirzaCVYmU9C4/m8sD3Noe367D7FUj7KuR175TWjMfqI98xcL4wQrx95obHke8qxGoYY2zNLeF3Vjvdwb0HzEflaWUygT5QtRI3fFNtru970O8+z7NORxb2hp9S72KUjAB1N22O6FDE8TxSC5ZzaMcvSCDy0uQ2O8PsTkw1V1FGPzb4oRarX73EkYSZn0bURe6Bct3C2QnG1t/w+3/1+18vkZxYD70ccjLb+PU15If7XF/SfK5L4PT/qQ0ATK/NCjls9XKF77XIriaabM8yxESm+ElcPZFs5NaI0+Vra3Wd39AjllIy3ez1ImWde3fL8fyfl01HN/CsnnhbQ7Gx+N+59n8Rxkfz9kYX8cGePPUObSfmbhvWpl5AbtOaRMx1LmOlqQgfYD4Lz+hlm5XUdpl5fZ9ZtR77veymAR6qW5s3cJMhanUIbAvtDucylaJXVWpX35UKef6jrN0jLR6uOjyDHJVm4z0cqsesp5W7Mpq+EWWDm7nO1sz+2M9Iu/w8QXeYy0+pu3zhoCK8hHkTL2c0h+gZShd73qKEc8tyDlXmcC6Ecg/82++3KxOVaRM+yae1buCdxif2eiNftuzWcgo7OXxXULanQvUbyNJRRvwA1FC229ken2fZQJYYtdewV53rdRFHcz8uwGIkXpk2rnI0UxjfJyjybL+1jUgD6MlNsTqPG4AlliYbtn5cNRyykrE9zjq05ivkKZNPTyn2xl9x3kfZ+BGuccqyuflD8FONnq9B6k7ButjEci43IGZaLez2paSDnG2j2sSynvH3CPsjrEUfUu3Uvz+Aby+jeS3WdlVofGZy+0zxS7/0LkYNxiYZyJlNTFaK36+Wi46vpKmfokq0/0u4fpnvF8ylzIUjQ5+hQaxvQNTb+z+0+zfDyAxqMXV9rH+kgh+CFpWyDjPYfSk9sarcDxoQxPzzLKUMdjlPdrLLQ8+udKpBB9ZY/vZVhk1z5geb0GOTBD0JLYwWil29/Q/EtPpEy93n5LebFUPZLhRWi4bjly3qba9evt3mGUSVwfKhlnZTfK6miE1eXvkAL3HunXrFwvqzz/a4vvL5Q5Ap8LG4HajQ/3tB/+87J053EBGiZ058pHMeotbSdQhit3QnuJoPT2/kHbd03MqcThQz7VoabqfJcv1GhEsrJNOz06eF03BBdbAX/aCvIEZDEXIAXo3v9ypKinUGbkvbCa7Z7ZyOr7CgT38lwRv4i8tM+hxjHL4skWtiv3EWh8biJqwNMs/O8ij/zPSDkMRMJ8NWogg0zY+iOlOhito3ZvrhkpiNGUyaf5FG/R5wY8HddSPCAfK29td1+mKJv5yPN5qhLf3ymnkJ6JDM1YtJLnUMqE4l1Wpq8gj20e8kbdIDRW4vT3Kn8VNbyXKa/N9P0OL1i+mpDyuY3i/cxAjWei1YnPXVxs4X+lkp8xqKHfYtfebXXg5dwfGG2ytBj1rJYhw/QVykqzeqvvhZQeTYulbTtkLNzIe0/T5ceHHR63MnKnZBnqKdyIvL7JSKn7395oXmu2lcdzyGg/b/UxlzIMs4Syk7XB6upXlFceVhWUv8fDJ/PrrN4+jYaIPoqGC6ZYuF4P7Vemefvx6w2UyeVXKSvXRlbq3RcoVD8+jOOO1h4UmXWnwod1h1PG9X2RhDslvpzXh5Imo81tA5GsHIwMz/Pt6sjDHWXpHkkZg/fexOOUXtoIpJx9wt17ig9j8oTk7RbLvw/TNVPePDjewn8MyfOL9ttcqxuXJR8uHmf1Mq0S1ni7z1dVueP4F8oS1afs/hst7HOQM/EschzWQ0b49HXdEPgQxS1I2brSX2QV5YK81ArAZ9evsQobTBHy55Fn44LcipT0afb8Cxb2bEpDmEHx7LzRTaSt0WkyYRhBee/sY8gbuRgNMc1CnlUj8lRepMxLLLP/XRntipRWouxh+LLlaxbaOzEeLUvzSeHuqKH1p0xG/6+F+yxl7fM5yMvax+J90f6fSHkfwkTk0YxF8zNz0SqivdBRElg5vMview6t/Jlv975K2bMwGynToRRPdKnVi/d4qp5NpoyZtqC5lqctD30o3l+L3TfT8r2+lc0jqGfRjBr3QHvmLsrqnmbKENhwq6vHrPyHUOaPPL2NFv8rlN7UdZQXvlxt9z9jYXyLsiJtX+RxXmJx3YSU261Wvq5k3WFZYGHMRcMh+6JhS3/5zxzKeHUr5bWStyFF8zMkZyMs377ufYzVrw9X+mS8jzW/2+7fsuKl9kNe9OxKOC/b/zcgWalH3rovzfTy8h6Dj6HPt7haKvLjzpuvZvMlk+dYWtdDRsNX+kxE7d+H9u6lOE6tFu5IJAPee73Ryt03e/l82uRK3fvy0ln23PNI4X7V0vkYZR/TCZaGM+wzGe2EPhfJ6cG0HUZ2B6hqUOfT1siOxdb4o7a0HmofgyyNjZaueZQ5oWbKaISvoJqL9M1i5OgNRe27BzYxvc4aAiscP2Z6FBLkJtSNarGKde+/wSrRh1++jlbq/Bg14BPsuWlot+ZY2r7r18eLTzLhm4a8tgtRw3bF5b2DOkpDepIVe1TVbl37Lp0rPx828uWU0y2vp1DGf/9u97xQEazllfAa0ETp2Sawk5Hy8k0zPtxyreVrGsWIuXfnRs+XjfoyOV9B4l6W18FoNCQyEyk2Xxvvk6Uu8L6R6BU0xNaCPLlRyMt6n4X3I6TEZlCWC05HcxOLKml4mdK1X0jp0nuZzqG8dnR+5bc5lCWjno8JFt5k++0Ee+7HaBzcVx8tQsr8l5TXQTYjY+Arw7xhu+Phm46aKXMpoy0/1QnDe1HP4S7UkOvRcMpjlHmIwci5ORjJwoeRcn835chmX4b5K8o8150U79Xr3N9850atFU0Oz0T7Zu6jvDt5LBp/9s1hh1vZ32X1OYryatLJlGWZu6Bds6D2sxQprXloBZOXj08K700Z9nAnwHsLyyycW1GP3odvvM2NsfL3OQAPx50RX3U3jNKj8RVbvmz8OcqErfc6vDdyP2XxhYftTov3Bt04PWfle4/F7Q7HbEo7/Jt9r8qs96xbKW9d8+HaQZQl7b5QxjfC+RsS/9fqYm/76++M8E2FQ9ZpQ4DGGu+kbNR4ygRqGFJ8EyuFuoiykca7gb4apbqqwVdeHI42/3jF+sTdc5R3p55jlfccZefe3agRfNsEZz2rqA8hD/lZ+zvG0tEfNaI/osY40u4Zisakj0bKYBra7DKH0kCbUEPxVTozkcc5hOKJ+dhhewPUfknnXZXy8FVU3hgWIKW+FPXC/mL59o0q85GHW4fWqz9J8RJ9medSNP56J/JkelodPGN1ORFtAlpC2STUSNk8t8Dq+XHKXoHr7NptlBU1gymvMHUP+nHkVU4B9rb41kO9lgnIs36XfX5on68gBeh7NZqsjF2B+nDb5rTdTT4INWbvdYy2OnvInnuR0sgn2m8LUQP34c3xaDXT0kq4Ey0872G6gzCUMga/noVzFWUJZwNF2VyHlKUvM15G2ZQ01sptAFJu8ykrx3w1zzzKMso61CY+R9kM5sMlvuJtmd3rBtHb2mTklT5D6RleYmHcRDHkrrg9rGVI5mZbeAssjz4E0p+28xyuiJuQU/EsUp63IPm6mjI57iuOfBVRI20dwUbK0J7vLp9O2/c2exu8CTkLvlfFncSX0bDw11Av4UkrnwaKsfUhInckmq0ub0EO01OUVYQnoNGAH1kez0Dt8JcWnsuI9zzGUJyx+fb8AcCgdd0QPIwmHTdBSvUGyrbugyyzd1hFvIqUz47IIvvY9XPAyxaerx75g90/xir9VqssnwDzSR5fjvkKRcgfp61HUJ1wvZay0cl7DxtYvJ+lNLrnkCfiq1ImogYwAg1VfcnyNBwpmgcpyt4nHV3Bv4Ia/+Fo0ra6B+NcS+/LltfHLZ73oZ27/UzIJtm1Jagn9W3KOGSdpcXX2O+IVpq0oobTiDyQz9n/gygT+WeYYPpqien2uxvdWRaG779YRNn57ROm45Cn6ktIf25hXogaawNqeA8jZTfIwvkjxbtzReO9lvmVen7c4nmYsunI14n7cEwdGi67njKX4L2nv6L5oZst/hHIcP0WzWc9U4lvFpKJZiQDvp59hNWtN2hX4j7mX8fre5ytlAUR1WvuuQ6m7Tr3RiuXmZSjJsZZuT9g6fun3fMkaiNTKEdcTEdOyGIrdz964xz7bRjlnbzuxEy3eH3DVgtl+PB4ZNg+h1bZfRjJ+7HIofimhXkvZbOnzyt8wz7L0JxCHVq15S/F2RMZxXGUzXq+lHUJMjB1lN7cdEvj75Hy/AxS2N7Of045MmaWlcM5qE2cgza31lOGr8dT5GcB5Z3b77Pfbrf0vIDk9wE0/DoO6Y45qA34MSALLfwbLd2TkNPYSOn1zEG9yFmW5wWUlVbvWtcNwRAThoQaVSOlQTdSXsjtwwSzrMA/g5TfbKuoevt7kVWAW+SXkPJ0Be0C7BM4j6Ix2uNNGCairvdFyDJ/j+IBeYWNoEz0PWLP++oV99J9ZY5X4GKKx3USGvP+DTIAE5AReQAdxUulTHxs9u+oEfqGtZeRgZyJhsOWIS/2AcqqlblISX3XyvMGikJxL81X4fi6ZF9Z5Cs93m9l7EtbF1DmX3wyv9GuNyKv/SLUWCdafZ1HWUr4CyuPmUiZepm5knuIMtz1Y8rk5E8tbfciJZcpZ0f5QgFv8J6/afbXh8TGIe9rNGVlUqYs633Iyu2XyIBOonh07qV6z7QONWLfbOVe2hjUcB9Asv0PylLQFuRNenntb+HuZ/n0oZdRSHleYGXWHc0XbQRsbNcGI+M8FjkKb7H6nmV/fcjONzIOomymvB8ZsV2RF//0/yPvvePtKqr+//ekEJDeFFGI0kSlFwstghURVLoNRFFsNEEe0UdCER5RQARBeqjSJEAgQCCQAEkgIe2mkJ7c9N7rTW7u/P74rJU15+TcmwuiwPe3X6/9Oufss/fsmTVrVl9riL2iO9szdxI7m3W1ufqunQfYOd/64n4it3e7Tdtzf4YavJ8q5nsuIWh0R7g1BQViHEusla6IBjiOuI/CfU2u8fS16/cgxr2SKHs938boOOZztdL6WG998KRDN+u9bvd5tJivK48Ic5/HZxHhvxHRhlWI0ZUCSmnedG3HKw64WdPzh1wr8/tX2jvmIo30WiTIXY2CBdq3mt6+2wS/BUbQ05CzrSHhnYQ54nOGHCMKAHmGnduCGwkJ1RO83CThjqub7P5Po0VxLwrD84y/ywlbXAMiNCusrT7Wr1cRUt9LSE8ZIX8vwhb8faKS51IURnYcImquslZLd6WjyRlVJmy8K4noFa/V4xqDS+ZNyKTlkrlnP68hshZ72XiWGSLtQRDYOYjZzEeLYgpiLC6hOSJ7DsAcFP/uRcdm2ly8iqT7YWhx+IJ8hVDdPQLoAmTS8WgOb8vn1SNqMhGm6eF8Xr7gMXv3kdbmAahezLNEPZq7idow/QjmPBURiEmI4Jxl135JSNmfJGrPHG3verpowxPO5hlsGwhzwp0IDwajKqLzUZj0z2xexiGcmG2//4yI+HhsQyBEQJciYl+P7ciH8HW5tb8SSbrjbIyn2zwNtGd+T0T+5Bpn6ey8hdgn2s0rbprwxKkZRMa+27U9Pt99Ba419CMI/SJCanezjTNFF/zc5l5GJfnaaDAYzyK0mEcJbaABaexXE/h6u8HgWru/zK73/rhpdDaS1l2I9H6utvu62Tw8iyLuPKLPfUZlMIRrlK4tOF40Wp89cu4eQpO7H+HRxXb/6YgOXoaS3BYiBj7X4HA9JmC9bxkBIhZbIDOEO0bWEpKB28/q7f4hNgEXGOB+jWy//YgQ0KlEzO9Iu9aXqONzIuEwdkenE99RdnroY6O14ZFCK6yPpxFFqjzzbwKxaFzrcPuox/16uJ8vnrk2pjsMQR4iHE6+WNxU8iYRFnmvtd0HaTMrrC8rDAGdUTxLJG89jhbvACLi5yq0ALoRG3l0AeZYe0sMdtcTGZzjrK0vo9jyB20c85EZahYiku7I7YqYtIdrusTjUpU7zl0aGoQWh49/ISL07uh2qXYilYstE1K/myvcJDfE/nPC4BKlS4OehV0u5CZktliBzGqLbEwjEIG/nFjwve3ZfgZ7JzIeZODChTMbL4/ghM3t327DLgUCl149EGCCXXuDqFsznsg1eYPYk3i2jds130fs+i8Ip+mjSEt8nsj5eNPun0Iwm5GIuP6ayPJ32/9IonzJUoPHE0X/e9nZm0g8rMP2I7B2BqH6SrsjjaPR3luH8O4Jm4Or0Dq4H2WRe2i5S9T7Eevbgy7GWT9mI3yahsItL6JSS3Dn7SuIOHv5mXn2uQzRjylEGZMlNnYvjjmSiDb0xNCHCXPjS3b/znbvQLRupqG17CbCZ+3cGgUPuKnR8cjXwyA05xeirTrfl4zgIpuk7yIiOAAtpoVEKKQXxHqNqMviMbieePKcAWIq8qj3tO+fIJI45tmkLCFMCq7yf8YQoKOdeyOpyDP3srUxnpCeFiB11zn+QoSoj9p/vYjy08/bhDcgQrsGhUlugxbcNHvmQWvvamLj+G6GeCPRIshU1g063NrYEpkz3Ab9GxQJshARbc/Gfo6wKT9uffFY/TJXwZ3zK5EPYqH1dwdExGYSqn+T9c+J3k9svq5GRDgjjehEFPr4BjKH/YvKyBvXXrzY3yKimJxLt0vR/F+P1PYxaHG9iXwVTqBc9R9D2Lmd8L6AmM0daHFOs3kbSoTmusnJI656GNy+i4jSg/a7HuGta2AuMZ+LNMUT7PcYRNR6EFrcxGIt7IEkzT2Q1voI0i7vRJFlJxPRIauRMJBtLp6gUrhptO+vGow9QewLCFduRwxpNWGGm2Qwm4i0yrlICj3A4HOLtf0SIahMsTFeTURrPUPYtZ1Y7YuEvq2JjPb7Cdv3aqIw4mQi87sRCX0XoPBId8afi4h0vd1zMbHBUw9r74eIkA5GGpgzShc87iHMPrOoLB1xH2IarpEvs7aWIpowyPpyBxJSFhoM/mLndIRLnQyGrm00Fm06HfGS3ssQrnglVxcAXQhYbHDwqLEjkZP8fPu8F7N+vO8YgQ3oI4hg9kSEYiBCXpecXVJyCXulAdZrrngBq0FEIac1iBGMNQTd3H4/ZMjoCDXB3j2QKLy1ORGTPxUt3ucQtx5NLICRds2J5c0oAehMe+/JyJzUHyX59DdE6ECllOeEu5FIgltNlJPwJJwGxAguIjSLUl13n8NSQ8xXqDR1PYMW2Dfs3Z9ATCajRbAKEYg7CYJWOq49pt+l7On2/QRr/3ZDVHfse9LYIqL4mIfCzSLKECw02HVFhO86ZEaYbvBYRMS4ryBMO28gG+w29vujiEnNL+A6lihu5qYnjxxzp6TbbgcjPBiGmGY90mxcy6uW0h0f1xocPYzVi6g9anPzAKFZDjP4Ty3a9byXl61/E2yu/oiI3+BirQyx+cmIUcyjshT5fggnlyHGW2oZ5Vq6m8hs9b4PJMI6fd49g3qVtbmAKJY3DxFd9xm5tvV3xEgPRut0NWKCLsA9TJgQ3UflfXza/jvL4OeM2wliU9Wz7gxebLB/hahD5LkGjxNhtpOsL8vR2nrB3uNrbDKhcS23dvtY278gymPPRUzWibvjgQsBrp10QHTgaMIcVZrhSmtEE7Er3mIiocxh14QEhDpiX4OhyH+5EgkjF7yfGUEfZGpxZPBPJ37H2gR5Esz29vkUURzMnUXTicJoK9GCqrbFux1yIcEMatlN70JSww12ziUKlnkNpC6GCJMQk5mPnEUewVBWxVxr76onGJYXunsZIfCzRLXH5USlVPdV7I4WegekAbyAED0TtseViJAvtD55RqQzu6fsvs8h5jGAykiTeuBIg/GbhM22kcikHI6knTdRDoYjspvn3O67KZKwnyEcvo/a+TAiag6XB5A9/2BkP3/A3jW0wJXLClgeSkRWuK/ECYTD82RCkuuBFuQ1xVy6FtLT5uQ5RIymG7zKEskLkYmoDmkjl6KFeA1RqtlxwYnbIoJA+uL3uHonsl5KxYmVR/4sJUKhSxxyidFLG7jkOp/QlMcTmwh1Jcx6WxPmCnf01xNmrDVIYPGkyD8gPPY2HD9WEWYNZ5JlVF0Z6ugBGqsR8fSksVVIy+iIfGe3EQx6FIFLq4vTia3H+z9FRAVNsX6ejvwsblGYhLSBwQaTqcgE1dnedy5RkXYUwutFSMJfiZXuNvxrg/DgUaQ1OZ1aZX24lMjdKAMuZti4f2v/OyEfiHCoLcKJ8QTueLTgyTZ3TQhH56M163kvF2MJoK2mue820a/BBD6NuPhDxKYp96NF6xPlsbKziFBAtzV7FcVj7b9rkHTphMkdTS6BOxcebkg00d7xGZtIN0tNQ2Grp9s5lpCIFhRtr6l6l0uHI63P9db/bjaBU2wSXSOpt7GPMWTpQpTGvtjafBARJo+eWoAWje8aVmYwriYcY2Pt+1TC9+HmEu/zEsIk5Hb5IQaf4UQyUUd7b0eiJPUuaDH8jkpHndveneh55qNX9/SqmM6MXcPwPq2y8R9OSNBDCOnL53CV9c8JkNunPfnMieoiJEX+hTAr9SvePxwR4f9D5rpNEGOahExrXex8xmDZs4CdOxIX2/g+jsIiT0D46wXN/oEY22g7HyDCIZfb+zy/Y0sbrxcSa0JmjnsQzp6Ighv2Qrg0zObaHabVAo3DzM2ZrkE2ICn3w4gwdrR+LLN7fo8EIJ9Hr5N1NsLdA+30LPSBQDf7vghFmi1HRPY2JDQ8RDjqXXr22Hz32zguLbexDSd249rD2nqRyH3xMGGvNXQzEfnmJrne9r5ehDDoIdETCWl9prU/z743GFyGIKb4hrVd5judb3O1q41lSyJB8CrrVw/r67XI2uG1nnycbir8F1F6/WIqQ8SHEiXxnQ4+hPKaWlVjyM/33J7FtrH6uTnn51NKn0MAvRZJZjeg8L0JCMC722MNRLZlO8SlT0MTsjtayO0IdbIdIqZr7PlrkAnj+0hiXYXifnvknHdKKV1K2HV3QRveLEIq7yREXNsQPobOaGei7ZGUPRGF8d2IkKcu57xXSmklIm5dUJ0cLy/RiArttbXTGdVK5O/4G6o4OMiufRMhQTJY/BP5APZHC+Rs+2xv7bWxd91i/TvO3tcbIdcsg/PXkGmpESFjG4R0H0J24jHW78cQ8ViNJMz5KPlsNRG18lXr0xMIUV/B/CZZ+0ePsb7/Ay3IfRDj3d/61mDwPtze/T0U9fUyMgW1RZE8O6DFnayvlyEnXBMiUp4R/STCo5/b3PwQ+HTOuXNK6UK0YBPClTtsPmcg4v9tFNDQ3uCzBC32NugYhRYo1odt0IJuh4jD3gg3eiMN6AEUBtjf2t8bOS4/ioj7BITDGRGcfXLO7Wwv39Ntbrx21k7WpzXE/sZHG0z3s7Y+g7SjM7CigAafTW3MIA1zlfV5I5vDXyDmuRshCXekMvRxjMF7nvXldhvfWdbmqfbsDKKMylGEiXAna3cHtPZ3MthCJGK1RfjWZP27iqhYWofwfVt7ZrXBY63Ny05one5KbA97KMK3RxE+OOx8Pjd0TEXm3645581SSr9GAS3b2XsGoii4EdaPA4itSrcx+LYn9m3YHs2F06LVhH9uBPJv7GxnW7SO7kb4dJWNZQISNi/POS/e4AjebQ2ghkbQofg+BMSsCjXMo2EuQcSwE5J+OiGp98uI8DYhp5fbuxsQt74UIdyVdv6cqFEzDy20FWjhLUQq5XSEYF8lJIClSJKZYxP1MeBB69ujyAE6AXHxyTbJI4mt+9zEsBIh5FDCIeb9Lc0bXobCneWrbazPIKnwJnvPaCQt9ESLcLy9czwhnbu919/vccv9rf/PIMl1BbLNz7bnL6Yy5rkkAG6SWG7vu5DQNibZcx7a6fWj+hHZp65NvWTt9kcMbg/ExCYQIbBeDnoCMslsTWSAu8nKQxe9b3MIM4VLhtU2WTcRZqIG/NWIUbvNtx9lv3WfAAAgAElEQVTCs0uQWciJ5VykQbgJ7yqbVzcjjSfKiHscfz/r/ySiFIX7stYSZio3s5R9LvM0RhLmkUlEjfwGRLhn2O/jiVILXuNqtPXlTBuPZ0ovsU///gqxV/T5yFy3j/0/GOFL6dB3zcPDuj0AwDW3wYRPaBlak9vbHO6HiOByhAO/Q/jiJtLhKELrMqKm1hFIsPE6YNmeOwLhRB0SQtws59qfWxEaCPPTeMKv8LDN0xiE/53sHI0Y7FJkkvMgkUX2/ADCZzbUPm+38U23uV5K0LO+9jkc0bk6m7cn7NmX7HR/qIeOT0Xrboq9eygScnYx2HZ9X5qGqpjCMvt09d6je9xscDYRU3s9klrcwbOiANgWRAaxx7s7l/VU+bVEHPg/kBTh0UfLENN4xYDtyU8LDOglQexN7E/gal6ZNerll702SZN974vUxWVEka86whntxMPVRydgq+19tyNNoDSV1DrdudtIIL2HWI4niqMdRGTXeiKQ25uXEsTbTWVLkG11AuEE9vsnI8LpxOh0G/OTRJSEE+lZNp99rD8rCAJQV7T7vI37HmIXKQ/RdIb3JJF4dzQR9unS55VEIIHPjzvhRqHFeh1R0tgZmwsXPg8rqz49xX8+ijbZxvpzPlq40xCjHI0I4jDr195E9VT//ITBzwuiXW1j9vILVyLGfyMiIm5amWTvetWed8J8DbGH7nLC7PEb5GRciLSsaSgI4S7C3n2Dwbw7UThuNmEamUtk2HZEkWJrCNv1TGvnHwjPhiCB5UGDmWs3x9s419qYPRu9LArpoZi32rhGWd+mI0m8kSjO5j4IL5fuwthKxEycGfVAhPQcpPXNtz5/r6BJmyJC3c/ma46dHiHlZt4mJEDcY3PkUT4eRn0lgaeT0Hp3PHRTrftTnOZ1QfjUFrik6NM+1t4qoGcVDX1/1xqyQXS1SWlP1DRfgBbAvxDy9yEiAuqJzU/mISl5BRE37QwkE+UMvowQfBEiFr6N3NfsfR4BMZ4ghlcSiT6PEYlXmbBzuhTvWZ5rDFn3Qyrb59Hif8R+n4vMRvPQYuxiY52DJIExSCM5BqnS09Ai8dh7l3SXEWGCXVAImfs8lhLVOF1idqm5lPJz8b/H8ruz2eP1vfTwEcih2x+ZAC5DtuRpRJz3o9YXT3Q63MZzisHlR4R91m2t7lNoMph4lUl3jHa1eXWnWz2R2eyS3QRstyt7diYiHB6aO5vYQ+EMRLy+ZDD0hKyeKOy1wd7xHGIMrpUtIZLalhKhqgPs+p3EpvA3E07mG5BgMdjm8x7CMfhVg/HtyNc1oWpddK46bybsyquQD8VzEjw2/037PZko7LcGaZMvEMRxqrV1H7G5ULY2PELGJf3uiNl2sTlyaXYEkpqHE7uwdbHxDrU59kJ4XtplKOFvG0HkOfwB4dXPEVP2NTEd4b8zPmfE7oR2PC5xuoyWGk/QjVVERF+Jd2VUmBPttchUeSrhd1pM0IBGYvMl1wbXrS+bv3mIfnmuTPk+76OX7PBsYl+T7mz28T6GzKdnI1wcX+DJocBr72tGgBZHRyQ5eBjlYrQj1ocQsn/DJtSLjZ2OJJ6FCNnvpjICyO3f/yBMEIuRJP1FIh29lKDX2AQ/ieyQ89HCmWXvW4x8GG2AadaPKYYMnYiqj9OIfUg9Sc5NDWOIEhouRT9NSKEeLVFmFK8hInG+jWzx3uedrM3jEYLPNMRdgRb6IuvHLHvuRkSQRiN75beJVPs1RB2gLYnaRz2JiBXXKDzk1RnNJGv7QXvPYoTYXkxtIRH+N90+9ye0BC/Qd7j1YRPCXPIIkYU5CzFtT/hzx54XEFyJJN9HEEG5h8h0/ZE943kMrhGUWpUv6tXWr5cRriw1eF5isHsa4Yab99y+6+GO9axvTnM8cE1tPCKStaLWHF8XExsblcRuKbG3cm9rz+fpYkJo8LGVocClyc+d8+7Ad7OTaxqeNbzaxukRK032Pt9ToK/NcR2RYfw64ahdSwR3DEO287H2Hq+rP5HQ8jwvws1i63Iv7P6jiBBlr1r7rMFkus3BDKTl/MPau8men0kkgHrAxDTEiE4vzsG2xs+2e8fZ2E5HJtXphGDWRJht3GS5goiKO9n6cBaxT8koIiLpE0jovNnm/EUb/612/zTC+nEr8nPWEZnmQ4B93++M4F5DitcQUT8DSZg/tUE+Q9iMPY5/qd3vReP+bgDrbwi6FfIRvIIIey9DzP2INP41BvCNDYF+Q9iluxCF5ZYRWYRDbRKHI0IwjzBvTCeKWLkJpIlYAMuI+O2ZaOF4XPVypOZOIHY6mobqpU8gpAaPkJlnMPPEE69lNBhJeJ68dg9aCO7fmIIW59wCOV8gEo88VNbDZlchp+IaourrLGSiGEHsFfwJxHCnIc3ucJuz0YgwbY3MER5DPwZJaDcgpluPkLsnUZ/JibLX/qkndrP7X4KglRFHZbRYo82XL3iXuNYUz2Wbt/2BzQwfXcIeQxQtHIuEiFn2/xsITwaiPJE/W/89O9pNiy4petSZx+4vRETJ4f2QjfkPRALZyUjjGmd9/BUiFGsJDcEJ0OvWpuPIbIP/i9bH7yGnqmsMryGbvOc9dLX3ez38xTZfi4ly0EMJ34czNofzKsIM6ZEtYw3uR9nvNwwOQxDjqENSbg+i6mwpMXdEa2MgMpGsROuiH8KpiURF1kXYVo3IZt7L3j2cqCowkxC0XKv1JMhG5DvZuqBLS1EQw+vIxPYaIdQsRvju+S6NNnedDE6/RUEnPyO23XQT2zIiS3uOzcccIhl1KmK4Hn3lIb9efLEX8JL1cQtgi7dEb99tgr8BZrA9WuylTTwboq5FRMATgGYWAFtIpMi79DIV2MbaPQ5FdXi6+WybUGcop9k5Gnn9fWE9aUh0jN13AVHIqlQpPfzRHX7unOztyElsZDEMLYIlSIWeiIi0S2Rn2jjuR0R8FjIZeF1/X+RTkOrc2cZeSrQudTZZ+2Otvz9HROBAIkzVJZmy3MNQxFRuI3Zc6mpjvNvaHoQIzALry2y0GB8mylNsSUicUw0eDyHiv7Z4Zg1hzptk7zwWEYFjqC0pu2SbrR3X3LoV51C0cNyPcZD1eTVRY+pSIgLHtYC5iPBORoR4CsHInYl4SOVSe5cnuo0ncjJ6IGLXFxGuPxl81qKFfzMitHvZvd8kwmvbYvZfRLB2suvHIcLZhHDkEuvT9kgL+7795zZsN7U5zFayPgwdd+YSUVw+vlGI0L2GtEwnrEuIGP5FNsaVSHuagIhcPaHxrkHr6XaCyXe3szdRkXYlQTSbiP2evfxCA8LBfxI78vmcuBZTnquJCMRXESNdaPe7oON+h0wIEqV/qDQ1NSHcGIPw7myi2mofhK+fJQTTs5Dm2I3Q6hsQrXmI0DA8m9+Za097zoW8envu+waPi5HfycPaH8DCTN/XjMAG6EWkJiNu+JQN+hybvLsQAZtdIJQvXI8G8kiQgUjauJSQQMda2ycghHVJ/XmbtMFUMiGXLJfZuxbYBE9BXHgJMls9b+8aiZD9r/b8IEPWF5A5qQkt1mXETmELbCw9qLQHetKWl5J4gag/cgPSmK5E5rIdUM2VzyNi8XlERL3cgpsS3La9NULkL9nzbr55weBwN7Gl50j77VVXvQ6SZ4uuJPwqDUhlnmxz6vWB6ojSzEuLexsQwjciM8F3iHIE0w2ORxC1b7zI2YkIXy5BfocexCY8fjph6k9UA/W4/yYiG3WZ3T8MaYouwS8yWPrcezG52YiBv4gIVne7tzexu52bURYRESD9Ef5+AGm3fa2N/7P/xxHx7b+hUtvy6K7ZiCD2LebTCZjjqocsrkYa0wIbx91EUuKjds9wojLtGkQwuyGCuanB9m7CBOX+msdQlFRfJLh4nsZyhCevI3zsSJh0yrh4F5SWUBkU4ucyJKxMRMTwSRvDWIQvd1TRjpfsHS8SeRqjiSKRi9Ea8uzb0sfg5tBqIcM1Kzfz/h+hhf+ISCRdgJjkBCLfybWLBiIp9At2rTsSBrrYuRC4y8YxgAj1dh+Qz2s2WLrm1BlpaEuI/ZPHVsPm/cgIPFX+DCQ9H4ukn67EgvNaPlOIfYFdGpiFFrfXVHHkciA6EczEBhNNCJm72YTPM6TzFPn2xD64zxhS7mBt/c7e05uwE7qz9QbCeXyQIYovTld5vUTEcrvuCWMeEjiYyFBdYwg3jtisxSN7yt3GvJTDPMTsZiKH+C5Io3jEELaBYGqj0cLujAiimxv+iCRYl9KfQgz5AaI+y2B7byeboyZ7xxyDwRLCxDbAzuVV390HsAViktMQc1prc/xUMXd1iDAtI2riT0QO2tUohPdSg5GHcno9nZdRXfu5aAHda/29lJBkhxq8v2HP9CVCDR8zmL2MmPhSxMB9zs+19s4iQjaHEeG0K2x8uyPi1GB9d22hiahPMx0RNTcdzje4uj/kQoPHxwlzaX/Cge3aoOP4LELzugUx96cQMfdooXFIa3bTXkLSc73By31GXlrBNcQFCA9WU8mUXJpfThSAfIoIshiN8OsWZK5yxumC0WuEr+IlwgxyLxGK+zqxIc6dxM5unYBOdv9oYCP7fiVa2zcionkrwpUV9nkLCg3ujwIEBhI+Gvdv+el+v1J7cKFrho33ZBT2OtPevxxp8YcSdcTGI1ozFNG1BwhH/hyET8m+TyOCE17GKiRb23WoNM5z72tGYIO5xSbKs+YmIPu2LzZXM+sMcE4gRqOFMg2p2vsYMMchqeRGlED0Jlr4fyDsotUhn2U9mn8SEpATcZ9sr2Tp9tA6Aumvs+/1SPoehRZbG+vXo0j6eoLYG/b3KFlrL5Rt/SJiijejhTeRyoqmLm2usbZLyb/W6VLwHELlbUKL80nrv+cROKNxNXUFWpjLDW7uvHvREPYpYltPd3ouxAqYFfN7qLXnknm1VOvSoNfmmYSkf3d6TyccfA7/2UjbytbuRMIk1ogY9hi0qJ9GUvkRSDJ1R3cDsud+xsZ4nLVxALFd50X2vtORFLyUysiXoXafS8HuIFyLksWWEsTMw5pvRcT3cWCu/de5OLva+TQifG7yu99gewphOjkTEaCLCML+A4RnTxIE2ENz3YTokrH73pYReRgeKTTN/ruL2LthBZXRXo5PM208nvG+EBHqUihYSmjELvEOQev0JcJm/iBaQ99EjNazwZcis8rDhNY9Hws/Nzh+1Pqxytp7zJ7vZecwwl/kZsE1RPmMhYQmONZ+u0nJLQmutXvW8w5UBh6U2k5pZioj/pZam67JNthYxiMmN4TIJ/J73ATWhCLOtkfMqwNWlPB9ywiQRLiiAJZPzhLCwbWdId7HEWH3hBbnyg1EBIZL+/sWvx3Z3S7qQPfaL+70GUdlFMhKe+Zhm6AhRJXSU+36DELCHYsIvvsq1iKpqptd8zC4xUS0izsRPXs4IwR2c4MT2J7Wh46IAfZDmsvwApaXIwJYbzDoafBz/8rPkbQ3n5BmPJY5E1mQTtzXFv9fbmNaYP87IWlEkupNyD68OQot9VDPesJZthox/b2sv20IRuaM2CVKV7MTEerbYGMZauP7ILF3xQ9sjOcR8fuPEbHxCxEhcJPAYKJs8jhi1zJfsGuL7764NzHYn0jEy3uUS1fCbzHRnt+LqOrZjiieN4woobDC5nuJtdnbThcMNkams0VIQBlLEN96hAeLCEI3D+HbU8QeAK8j09FKZD58hNgOdTYi9G5m8YidAQbbAfZ/A1E73520Y4kw4I42RysRYf+AwXWhzeNYa899IpOAHe2ZyXauJcpGOMFcQYSZeh2sHoQFwPNPnNm+gAQp3yVvqrXTzf4bg4TOWUjQGmnnI0StKTdhzSP2ah5ssO5n87YnWot3EKZTX0dlMmdj0d4FRMnq59Fa7YTCst8ghL2Z1uZYhBvPG3yWIQHTc0KuRImEQ4Hfvd8ZwTAk/X7XkOAwtBC6ovhmjz92KX4kYQJySckl+Z3s3iHILjkTSYA9kLT+bfv086rivJxw0HwQEd2tEaPqhyTj65Fz+buEo3kEKt8wEplitkYLYCKVBbP89BC6KcV/E+2aS9397T53Nq0iNvuYZ3Dy+PZ5RJ38edanxQa/BfbMjQbTE6x/9xsCnoPs7Z7wNdb60RcRkjftvlGGfC4xfoGITc+IiQ2x//9s/XF/ynNoIVyGTC+vAoOK+T/exn2tzY+H3rrTrY/19yxCOp6GiNNYxNi86umtKGJsFFroM4lqqq6RuEmqPMvQybWEP6g0MZa5KS5RdjN4nYeYg2ePL0aL1xnL3YjArESCQ5mM9QpREtzxuImocus4uQDhxU9RyYEtCxiWiZhuQnBJtwmZKv6JiEk7RDj2Ihj1adbu/yKC56GTQ1DEkSdFuTTqiVRNxbsWI7x3oeYla6sTwr1f2Hx9z35PI/Ycd0m8CeHO15Bk75VU90WM/Vy0BpvQmv4W0uZGAQdan4faZyekeT9u7+hOaEU32RjqbMyvFrD0/Q56YKal4r/DEJMZhqTxo63vF9tYNyM2rJoLnFA8u8Lmrz9irq5ReajpAuSrOsz6+zQSqq4hcP7viHFPt//vt3P/VtPbd5vgb4AZ/BqZbqYbUvUgdkmaSKiGLpndiRbReEJa7I8W5GJCxZ6FnD1LCPXKJdWJiJB5THg3QmVzT/+OhFffs4PdBru2+O2S8dKqdjy34M/FWAfY+C42BOhYnAORxNaNIDjLCa2hVOtXIYL5JiIYHne9kGBE/YHlBbFwzcgjetyM4ZKmEz+Pve+JJI4L0cLyCK4xBufuNta/IiK+CjGMuURKfUlonXm7VLkaEcxnqSx25xKnv2uazUN/tOinId+N23z/jsw5HhO+0sY1gpDAVyKJ9hf2ewukIV1k/TzXru1E7Fa1DBELl3onE7Xty/lYgpysc4itI09ExGGUjeVaKuvJzCa2dnzGnvkrkjDfICrsTjYYzUWO8kGIkE5ATPQcRNiOt/NmIsegj82Ra8GlrdsFk1rmRBeuHAdftWd8b+aNkM3+FiKM1Z9rKtpZhPCwntj/w9ewC3PzkA9lL8LcWq6thSgip7ddd0vAJMz8SGW57p4Gp80N/t+3dq8mwlzbGzw8A3yMveN1wpc2EOFPT2ITrLF2OnwcjqV52U3Yj6A1eTuVGxktt348b3PuplI/y3L67n9x0113IrntboRv3bF6RO9rRoAInycolXbHRcQOX68R0R8nGxDbokXZxwDRnajfvREiXgPQYivNAkvs/+2JxBd3gDrxHkSonk1ocXW0Puxp175l1zyc7FrEvZ1IdzfkGEAwNZc4/dNtrisNFiOAb9n31fac1/B3dfk+RGzmIcfu7wj79V2EQ7KMy15mp++zuprY6cyziD1TOyOC+SqhJbjZwZHTpf95hFmuq8HCd1Jbjcwas1GRN5/vUYh5OUNwE0k90tLmI6ZylI31PuRc/BWqOfUUUvtHoYXgKrw7RRfZPdOJzdH3RkS2yZ4bizShSxCBuoRw7I2zPhxIBADcZmPazsa7L/I3NVq/ZxB+KC/FsBaVXvgUscvebILpDyBKTGyMhAPPaZiAzFArkZDi+NKtgHsTYXN2jWU1YtBnWrsHInxdStSg8uiTmUSpiCuI/Z19s5XlRN6KaxkTiL0atkBM4Dpgt2J+Sy3bAzkWW/uNQIPd12DwbI+YRS/Cx5SJfQ5mEPH/bjasxbw8V6QU5DwiqonQusYhyX0l8knNRLjrQQS3ERnVL1p7zxHC5BCEf92J6KEZBp97ER15tYDT9wgNagzSAIdbX75m7U0j9ldwk6gn7q0gzNLDCUuDB3x4WO+w9zsj6G8DGmrnYMImNxUYZfctN0B6lIQT0jEGiJGE5LySyrhgj6A4m9gCcygiLKMM0HchybiTnU5InbC7GWcXiN2AENFZbBN3H5GjMKB4vzM1d74+XUzmEHv/P5GJxk0Ba6hUxwcYPMYR4Xw+tsH2/n9Zn3ojVb8BLQr3V8y2vrjT0qW4nnZfb4J5vYEYw52IGPYwJOxO5Ybtbld2qciR3qWb2wtYnWTPOzF8FlV/3ZMoe70ELaLSjj4DES3PAXHb/COICZ5ic9YD4cOUYrwjkFBwqT3bGwUWXEhEakz2OUXS3J1IQBmLpN77EUH+nY35imIOHJ5uNvP6Uc7c2yHcbWP3no3CECcgAWYmisrZi9i28Th7dj4KJphkff+HweEbBt8zkA9lqX12JpK5xiPG4v2sB64otNKDia1Nf0lkU59ObBU7D+HqDxBuvmSwugetw6eRCcel2BEoc3875MxssGdnGlx7EqYb96FsZ2Nrg/D0LLt/OMK77oS57V57dlKNc2IVXRmMiPBwIkcjE6W2nZF6xN0koNGenWUw7Wx9udHmYgJaAz+y8Xju0Eh73wiCWe5X9GU4yl85y2C4MZGtvYRINC21Kdc2qn97tFhHm/ORb4nevtsEfwOM4A2bqDGsz+mdKzqQFtoEXGCI+qgh31GIKNfb5HnJXt/k5CEUubDaEHKevb/e3v8GIaV6xMxpRMq479/qdr3phIP3KiQl3IUI9TBif+TJdu9oomLqoUSdm4GEA3kSkXTzWyLdfSSSYhYh4tmPMEc5Ark/wiW5iYZof0AmEDetZST1nEmYityO7lE98wgH61giausZIs9gERHfvYQwZ5X9cru1tz2UyOB9jMr9jL1EhEdkrSH2UvDFNpww0XmW6s2I4A+05x+2+ZiJGIXbk3+NfBtPEhrEm/bsdShSZTiVETUucCwjdi7zENzHET4db3P0LSLowWHQm8gmfYWodLrEvg+3sX/RYPIEcJp995LNk6jcec8ZcEZr4G82FyOsX54jMA05IT28cRIRuroY+VKuJkx3T6Ky050NNgcgBvugwWkFsUeCmyrcr/FHIqHT2yszvd035oKDZ+AvtnY9K3geUVByjd3XA/m1HidCPz0h7IaCjtxYXkc47SHNHjbujMADMUYQUXnjCULr4d2nUrnvgF8fZO32RkT8MGSea4vWzOZVguJ4u38oscGTC4lD7NnzCd/RvUirmIRyS14jys64j8YrGXR5K/T2PbcfgR8ppX8hM81WaOtKR6ZRSOL5MiKcxyGk2B9lAS9E3HUnhMDtiRpFmZjsA4lY3YVI+trY2pmDCOxiZGt1ibwtUa89IRVxst37OTQh29u9GyPV8ViEZNvZc8uQnbLBfm9k390++QHEfG5DZovfoQXlGs2diMh8DCFBHyT1bkVUP2yPtJqXUDTVP2zseyAz1ffsvUfb/QuJvWObrK3lCJG9vK7vReDfs326RHQwsZf0J5BtfBrSHvbEaiLlnKdTdaSUhuSc9/fvyKcDsqP+xMa7ETITXI1MRAuRVHiM3fsMYQr5HFqoByA77Gdsng4lUvq3BX6Ucx5t790GMYXvIcn2JCSZfhcx8sU2pulImt8Rk8hzzvuklJ5GONVg8MtonpvsmQ8XcPsKEkTWIEf9I8Bnc87zU0qvIXzfEuFVGUzQ1uD4BNJAN7X5+YZ93xjhZSK2klyDcH8kylmYiwjG3kS5dah0es9HiZGLDe6+J8YcG9uWaK63tmfaIxw7LOe8X0ppC8Sc7kZO1+8ixtQOMafNEWPaBYU4erG2pWj9NVqfkvWxvY0NRDz/gojgCCSUlcduaK29Zr8PQ7gA0tgeRYmTbna6zP57yJ6ZYfC+D+H1/yIt5jcGi1IK97XbaP2ba2PshQQAN2MtNNiPyznvA5BS6mTvvR5J8bOQ8NTH2jwIRVHNQhri3ag0RR0S1noifPsEkVfQFYXKd0H4DkDO+To2cLyXGcF2CHm+hBCuN1qgE4FJOedZdt+VRITIYKSSghbxHGIXrSb7vT1h8vgzAt6WRJjap5BEtTWSsMchorHS/nPJ9yuIQB2NJKwVOedP2mY6X0RI/kW0ADexs409dxVSjTdD0k4btMB8IwwvpdsWqec3IRPE/tYPjy66ADkzryEINIT2sjGxx+nXEVJNQ0h1iT2T0OLaCSG22/D9cFNURovhlJzzUQb7EXb/55Hk3QMR4Y8azG7Aym7nnF+kmSOlNDjnfECN7y4VXYc0g4vQYl1ksNkEMbvZiOG2IQjLx4gw3c8YrBsJHNnB7l+KCJNLhU/ZfcflnLdLKa1GxOBhg/s+1u3h1idsTo4wOG6C8OR0u7cfMu/NRATZq+gehKqKfiql1Av4cs650d53A2KoFyGN48OEhvcCYnDTkUT+J8TMP4McoA9af+4jCOoh1t5XEQ7sYm3da9c+aTDdBBHw+fZ7DGKw/zB4jEkp/RJptYcgAvsF68fVwPnlPBpcPcpna8Kpu73BfRQSILwultdW8iPnnC9PKW2FhINdbM4+bGcZNfWwwWJbJBGvLdo4x/rkSZcr7L8dDY7bIwGsERHuDoigj/I2rJ9fQL7ItYjQT0cCU0cklHwH0acPWf9ce0xoTa0sxva1nHPflFJT1fUmm4e29n2BjWkY8MOc89CUku9XcBbC808i+vV16+uHkQC22ABwGRs63m0TUAumoTbACPv+RRQ14YA+DRHhI4iImJeJyCCP1XWHqqvSr6BF445Zrzc+FSGy1zapR8SnLxEJMRoRzzLqxdVdl6QaCVPMakTE+iD1tY4oqOXvnIckz0U20Z0QEWiwdz+IpOJXc9g3hxbff0KYrroQsdljCDNEf0Q8Pc7cncm9DF4XIeLiNs8x1ie3XS5oYY7GWz+2RtKOX+9A1Kg/u+qZBOxUdc19CLWcfQ7jFdbfR9BinG7z1cfeM5uIpupq43/EYDqTcPK749jrKHlilTM898N4RE1Gzru2mH3f+jwWMekRxJ62bxTtTSO2Ol2DtLvhCM/cTOemOzc9DLJ3/trOIc3A/UY73fdURox5tvGtCP96EU5q16oXIRPDXwifx1GIkNQh4jMTEcK7rW+/tjEMJXb66279fZyodzSHCM6ojvS5AxUyXIbWpZuQPJ/F/TzuB3rRxtsPrcczkGB1nuNwDeoAACAASURBVI3tb0Top5eImYR2FqwFt8F2HmjnKEK4ckbuYZvua3OGexNiBJ5dvbjAg3Lte0WAhhbWzdP23j/bXLkpucwtGFPAwc0+P7TzWYNDP2QCnWf48E+7/1zg5bdEb99tgl8DSJcUZx2KuqkjbF8LENF7ChFLrxtyoyHRpYYMc4kInhFYYou94wGkbnkG4DBEmEcgDeB5xFGfRcg+iCjJu8LuyTY52xPRHDMI+/1K5Jitt34NsXMmlWGTfzSEHGl9ORRx91OsX068yjA/X9BNiHGsNAQZSTgjxyM/wKsEU8xExFUXovTDngjB7wY+WcDpg8hE9QJafB6K6KqxM8Dq0MMVhP9kFFWRCxT5Aq3Ah3Kjeg8QqLO+ebne7avG1kBlyr87BfexuTwCmRE/RJQz/2CVENKHqIOzltAYXaL0GG53Xi4p5t7LEt+FmEMDirkfb33pj0x88+w+zxh280RfJACMQtL/Ac3Apg4JRkeidfGc9cnDC1da/xqQUOHhpr8g7Pl1VXNZEqPy2mAkOOUac++2+xU250vQGvFIsI5IiOtJ2OS7E4ERU9Ba29We2cPmqg4JGXXIN9aHYJ5LCea+p8FjMVpPDQSu+nmFzcts65/DaaaNzyN2ViBh0kOV59lzbodfbrD0Mh0u0ZflJJZbH1x4XdcP6+cIIiR4c7vWESUKLrL+dCzOEYiO3IZw5Pf2+whncPb5J4QDz1BViuP9yAguKM5xhJPUicsSpLZ1Qkg9H6muXmNngSGTO0FdynfE6UZlIajRxA5IHeyd7TDp297zTHEuQETBkbGUoCcU41iGFr5vEuPnBYYos4hNKVYgwryfXW9E0v0SRDTmE0zmzuI5lzwXImJ0OOGUdEblkRB+rZpolwv/Sev7cQYHL3SVic1yuiA1vCOSqgYiaWyu3T/DYHIjYuYdKZiwtX8TcHALOHC9fT5lc9WtmLduRI6IL4A2FNJ60c7TSOLdwn6fhwSFPyHiMhUt6oew8hV23yeIbG13QLt24IEDDkvPMPX/XDov78/EJu1rCIn+7zX63Ks4l1KUF65x7yDExLa1/tcVeJGrvu+PCIr3vZ5glrOQifNwe+YZ66c7Vb088iIk/Z+P1tyliHjVE4EITxJ7W0yx7xcSuHocWtNX2bXnEIMai0JPRxDbu7oT2zPe1yCpdw/ENLaliI6xdznc3cHqjMM1JxeknK70svYfR3j7KMLh/gaXW61/PQmm6AmeU6ydcTbG7ZB57lm7z7N/fd14Mbnx9kyqMafjqdSCSzxyptsXMbQe9szLBrPlNjejDG53AVe/LxmBDaydfX4Ncc0VaFF/E0lKfYkKhB3t3t0MEJciCbZM7nBAltE0rxClgf9AmE08lG4Mks52Aw4t+raUiNn3kgRdEJL7nrw/NkQ6HTGO31ifnkchh57F6tLzWiqJSEYL7yJDqtXI1ul9aIucyP+yPk9AzvPHEWJ7EtYWhiBz7P9ELP6haKGPQAthCjIVHIQIyraExH0kkka2aeY8jjBNrCAYkDsAl1TNr+cLTCBCQYcV/3s2aCeinIULAi5t+Vz+0GA0mCrmgpkWi99TiLDCJkRs/HuZZTwWETovWd1EJDBuj0qaeAawn32pDH+9zGC8i8FlKRIwvDT3d2wO6wgNxpl1Y3F6Lsc6OCIG2Q3hqJc+qUcE+yQio9XrW72JpOWv2jhG27MLi/cOJ8xaZXkP74fD5xrEKDykdQVaP1NQCOw5iFBug8Jxz7PnJxPBCEuQuXclwj2Xxj36aZL18xwb7y+JfBmfP08auwsJR19AJpGnCDNrJ6zYXLFuHjB4NBJ1qqo121IbmmDzeBVmvrO23JqwP5VFD2cTjPAsajBxTKOocf3HCDcmEzlGC5DZbSwSVD1fZp7NXw87RxBb+44iosxaZSJ6TzqLU0p1aMDfIyIVPk6Eun0AmQb2QIRvY4QoeyMk86iG8Ui1vg5LEss5b5JSGoaA2Rl4KOf8lZSSO5fKqKA1aPE2orhxkF2+D0L0TdHkrCWSyjZHE/R/1qcTkKS10vq8BVr8zyGkvxQxtH8hf8TPrP/T7fdk++8SJA35cTwyJxyGHOO9kWS8CZISXW3FrrtjuC2Ki26fUmpr9+2MTFLXIefTxvbeHZHj83rElNsQ5bfdEenHR2zcv845P5BSOgS4Oee8X9V9pJQ6Vl8DyDlPrnW91pFSOt7G7hETP0CSfD0iOJsCH805dyiemZpz3sm+N1T91waoyznvbb+HEtFG09DcdkAS477ApjnnjxfPX4siQ4Yi4vdD69MbyKyxGZpTjy4Bwdd9Vn+xax2QSXFrRAyORw7XcYRW6VrKQ3ZfImzULkEusv+WIFv0zXbtUsQoPkQkDroWOQutly8gfHsTmU7+aPcPsT7ugNbFgzaW24F7cs6HGixW55w3su83IXx51Pp5v/3+FBI27kSRX3OyIo7a5Zwb7dnBOecDUkoTUE3/C5Gf4FuEU7k9wl3HhVesPz9C0WU7IV/CLTnnNSml55AZdA0y122CtMaHkLA0AOHygUSkX3XE4Fr7PR2t6Y0Iv8mfkCN9f+CYnPObVB0ppQ8RG888Y5cPR07e9vbe84FrCzg+i5j8gXZ/F4NFedycc/50SqkHsb/0v3LOu1b3Yb3j3Zb+a3DFv6BJ/h+0eAZTKXn5eQzSCp5FUkovwkk7DC1Gd/iMIUwk3YiEjcUUTk57/1CblIMRAZ6NFoWXZPb4di9ZMBxJDWcjv8JMYn9eL4JXVuh8lUr1f7i9YxVC4Nts/APsfV9A4Xf1hA263sbREyHzHYTDfONiLMMJs4jHMq+oGq+bV+qR5FFPMIfb7XsPJA23Q6pv/xrzdiBheqg3ONa0bdv9hwFn2PftgY9X9XtYjXMMxZ6sdu/OSFo6nPXtqk8CP7H7jkJS64k2PzORT6ULMtO8gIICjrDzTXtuBSIAnsHpGaErkNbwFWTS89j/mfbZHUmrPyEk0Aa7z52WC2vA5Rbrxyqb/+GIWO6IQmFnYQlOxTOHs76tv9a5hMq8kVIKLv1OLxOC0nUFLAahqLB6QiJ+GQlkBxT98RDeHZBkfAQK8HgSaQDbIoazt8G90WB0LRKCdqDSmfsyIq5D0Loq189LiBDvjQQi12g9n+QGm+M7rG+3Iub8B4Pl5QjHHyCq57rmshdam6NtvlYhTeVYYiMszwnaFti2gIEnm7rTt1rr/YiNeZy915l3HRLwdkahyX7/Y0iwfRAx1UVYvgQSFqYS2vhyorbTsa2iu+824a+xEBwx3Znq6rqnie9s5+6IOP8dqWAb2eDd+TIMEaOjkIp4k7V7qiGHv8cJwcPIfj+a2MHLy732Iuy1TYiADEMLu5pYfdjef5tNhEcwuV3ek6LW2HdX9zdFRMaTae4gNmspF3JL0TX+njIJzCMyHJa1nimTpbayvrRBhH8CUvdLJH+9hfk7maLwWTP3dEYa31j7vSPQt/i/FuPviBbMV4v7Po8Y8iybkzrC9ltt2igdx41E5q9HhkymsuTIZCJj+Mt2/5UGm2FIUOiGFl0fJAj4Bjz7sf7esR7B9FhxjjEceQIx/C8YvL8KrLK2fowWeT8b53XA5wsY7IU0jiHINHITkfyXiZ251hIOTzeZHmZt1GEbnSONZV2FU/vfczJet9+vEUR6Qo35Lc2CXmqkEZl4piKGcCgi4l2Q0FeP/E3jkQl1GrHmfB3WIwa7LmnMYDbd/vPkqrk+F0Wf6grc+yfhZ3ONaBaRZ/J1IuhisLX/d7t/kV0vNweagMzWbir9ms3rejhcA1ZHIdy5w8bq5dXdFDoU4dlgJAQORYyiP3C6tTEeCThP1Wj/vNbQ3feqacjraCTkFJpDqE2NiEC5zfJi5OiajAjWSkTEPPnDia7H8Y9AKvOORJKUE0PfMGMRcG5WzO6DCGF/nqWm9kNS8YlIEvtV0fWdkG34AHvna3aPb3KzDZJ+PdoHJBWAFtVWiJC+jMwPOxEmmOtzzpcYfEYQiVTVxzNZ6uH3kC9jGSJenjzkEm7OOW+RUvoZ0nwuRFpDVypjuU9HzOQhg9MpyHxxE2pkgfXpklqdyTlfXn3NzC77I23EE8mGZUu2ae5IKU0Ens05/9J+d7J+QZi/bkG48VTOeZzddyQibpcgSXB4zrlv0e5wpIrfkBXXvyeax55ofjcnkqA+iQSFI4gNYxYjk99JCL6enLcMwXqePf8hhMMLkeDxJevCrkTc+0bW5kcQAxqGNOOfA7/NOX/C+rzU2mpP5I+A5ggkaW+BTKpDEJ4di3Dws9ZmNzTnOyBCOAOZbBZavzsgQt2VyMRfjExVH7H3uL9mHTgJM1gjIor3Iq3hSCTx90TrowPC9U6I8HdGpivHpWU558dSSqcX7e9r726PiOLPrP+vIqbZxq7PyznvbLDaBZlIPMehDvm1Xkfz+SZae15248/I3zLf3uNJomuRNL4YaSgb2/OeEJoIYezgnPOwlNIHKfJycs5TaOFIKW2KGPsXER1ZjuZjEJqjnyDG/e0cuVR9c86HppSGZzNtFu1NcTi0dLTb0A3v0vHJqt/PInvtA4gA7oIkqi0J2+qmaBLa2enS9ygkaZyGpPRriXCuAxDy+4L0ks2H5ZyX2rXzkBP2Uyml3mhBD0EL9k/IB0DO+bqU0sMIuY9CRONUhLSPI4K6K5Ig1th3is+tkGR4HFo07swdhvaqXVjAIyF7+NcQQQItqBeA9iml9siOemTO+eWUUl3Oed8acCbnfEtKaWuDwWZoYTUiRvxJIlv1PCoTX35k8N7Ffi8v/tsY+SlGUftYnXPOKaUM65C/5pFS2g+Zxk5G9tjRRd9fTinNRLbgF5EkegUqV7GkuK8X0Mv8CscCbVNKhxILdBvE/D+SUuqQcx6dUvoEYgJPIAn/K0hKvhcR+u0RYfQomI0QAbgazeVeSEC5xfp1LMKFVYgh/xyZ6baoGu8fkIb6RWKr1C7W1x1TSgPQLnmbGwN7AeFyRuaYVxBO70fs27A853xBSulSYkvNiYiAdUDMdFdE4P9GmI+uR2bX7yAfiWexjs45r7H+npdzvr5qDIOs3Wyw3ALokHMeklI6025zZunjHWvnjJzzPUVbx6KEvvsMhu2RGeYOxHg3RmvwVBvXczaXO9p6TUgaP8Pa2wvh9evWTm+0Po+xOf4LYkyz0do6GdGelxBTPRPRmrXWF98lbBZiQsfmnJeklI5LKT2GBM451odRCIdqHimljRGh3xolz34Tze1ZNqZTUQnsz6eU9ksp3ZZzPg4YaLRne8NxEPC7En6Nlo9/15Tz3ziBgYUK28a+L6+6ZzBFXoB9HooIx1QUuTMFEfEGm5wHETL+jshdmFG06SYW9y+4duFJQFchKeYSVxeJJLjbkEq9CEmXRyPJ44fUSLQq3tkeMZtvGSJs1kJfGojS1p0Rso5A0tgzxCJ4tQXYnklUYl1C2LEXAp+ze/akSG5CRKNDDtX8HGCrqnY70Hxiz4XIVjsRIf5rJTwQI7wELZx1Zhebr59UtVWa20oT0CrC7PZrO+8kIr0WIqIxE5kVtkKL/xXDGZfs/LOBME+utrn+IiIKpxV48QZiFp3tmc52riR2ueptc3QecgxfZON9zMZ8A2JOHRBxPhoJHXMRwbnHxjXAPuvtWS8t4Zmno5FW+ZS96xC71gHF6e9bzOM4YEoB1/OQ+WtHzI9G2PlvALaxa1NqzG9fRLznI0HGmc6HkeTuSZmr7H+PnR+OfEduYnF/idft8hIrvr5d8/Cooa+hNeD28oeRYNOh6Jub2F6nMh9mNcKxmYSNfY6N5ZmqORmF8gA8qu4k4LHiHVfRTOTdBujcw8QeG08Afyv+G0QEwbjfy8Oou6B1Mon1Q1XXm5+a7363iXwrGUFPm/QbETH4G5U28+owt0Zk7vC8ALcPPoCkn3/Yf89RaUd1W2pFuF5VX06yz0cQY7mzQJSBSKP4GFrsXi31AsRsliFCcSmyl5YM6BIiEsPt28vs+z+B9kUfRlAkWhXXhzpyVF1v1wJshyOpygtf/YVgBOV+CfUEERhufd0N2Uf/ikxSZbsV2cY13vtle9c1qLxC+Z87LMsyxhORaaWfwfZaO19GC3cH5GO5DjHT0jHXuTgbiT0pOqOImOftvvHIFjwU2L14fhfkMD/ffo9BJr8RxVjnI4b/e7QgL7JrRxjMViGJ1Qmyl+r2TOZu9vki0moGEIXw7kRa0X5V4x+KCMBEO4cQW1wOQo7QOoKgeRhub0SsjiEI+lhgajFmzwHYhjCFzUDS9xVERdupNeb2YLRej7XxPIFMuMMQLvtOgW8S+x97ATw/JxIEf5H1Zy5BrO9Ea3g8YvJdiRyHDhRVPav6Vkf4wDYhKsY+ZeNbVcDEx3y23dcPSegTiTLankU9G/ONGdxqCa8DNkDnyl0F21G5n4L7JIYXOOAbN5W0bwlVdPH/JUZQOi9Pp8p5Wdz3ByTZnUCkq1+OCOdGNnkzkNPvNKTWjWjhvS/WuOZS5ulURvGcbhPjZXodoV26aCR2qBqPuP9FVCbQ/dWeLSMw9rd2S+lgPJKiDyuuubNvIiKGA+28lhact8Ab9jmP2MJvJiLG9xULYjVBBLwq5G8wSZ6ogTQMOW7nAL9q5p3no9DO5vr0bYPPVBS59EVUX8r/PxItzrORGW4kIv6PEpuU19Vo9yQb50+QRLgjMnuMI2K4h6DwvSlVz25PSHdnIILUD2VjT7I58jn3QmNrER7OQwvzYWRS604kOw0kIsuuM/w4gihIdwUSajYt+vK43Xs2cJRd+xiS8m9Ga+BnNq7RSHs513BpI8SILkRahO+Z8AQwv3jHVETEvo+Y1E3ApcX/XuqklkZwUjOw3w9pLSWBexmZ1zaq8YwTv/HW/8cRwb0RMYeZwOVvgY7sZu/7g8HrVJuTKYg5TUKC4TOIkdyEBACPmvIowIVEaZmXEENfCHS1+4ZQW3jtt4H+DW7uNyEMDEOBMl0JbfmrSLC8GUWq3YVpBK2Gzb9DoP/TJ1XJXMX1I4Bdi9+uxrZDkodrBc4ZPXPWI4E8s3MsNeq5IAl5GyLFfRtD5Lut3RuK824KTs/6UmumUv10c0OtRKtx1NhIwia/rOXzIJI+PTKl3pCvM1rAlyEpdhe71rUFGD9OmEUykdDknx5yWO61sBwR0BFY2CeSjDra+RFa1kI6I+L9qiHwh5q5zyOpnqYI16xx3zm0whyGJLU/YSY8ouZPPWKiOxBSfi1Jd0TxfQdkuvsmsEPVfdn681ObI993djmxG1ZvIvDhdYSjXzQcuAnF1YMio1ZhBBdJ0q9af+uRT2I/+297FD65lf3+GBJQyjDd5VQm5XmmtCc59kZ462GIAwjN1JPbPAKtpsRJFUGza65leuTWm4iwuba+F7LHn1acpWA3CxF+T/gcYWetMOOam7EgPDrUnvMxvYGilvojpjgd0QW3/T+JCLpn2DttyTVOtyb45waF16r++XOllWOddQIJsG8g4eFKIqrrUSQwTLB3PU8hOLbmfE9GDfmRUuqLgLktAqh75U8GOuecj7X7riE2MhmGCPFuwM9yRLVMQirk80gyuRQ5hdoTIZ5eQOpOxFx2RMgBkqTWIqQ4HxH8kxBBaGttrM0575JSOs3a34xINnmRqCPzS+DGnPPwqvGORVw+I+cYiBC2Qwxxj5TSzkShr9VogYGkwY1Q+OxeVe0OzTUSu6ru8eqetyGn2TNISts9pTQa+GnO+RW7dxxy4L+Wc34wpfRxFE30PLLxZqBPznlIjVeV79zHnjsBmJZz/lIL9zozXlf9dANtl4lJXiH2ZCQBgpjVTmj+fphzfsnu9SSm9aItqiqjfgQxnHVVbnPOr9jc34jwqi0yD61ATHkaIoBdES5ch/xBbZAgMwtpQN9DdvS9bV62ROGMHmE1Iue8lyXmnWrnDtbWOGSL/ynSAA5CTt7eCO93RwLM68i8WU0A2trZHgk5exJ5JQfknHNKaTeKBLICPrXgDIpe+jZaU/9CDLmOKJ8+H62tdgjvjsbqCuWcT04peRa/C1Rb2fkY0qIXV43h0wbL8liJpPfHkD+hATl/f4vmojeatzVIa/s4IsRHZlUJ3Q0xrU1trBnN68YGQ6dNqxCBrgjESSkdAUzPOU/gHT6SlXH3yDsLFunRmnXix3s1asiPg5DTdBARXkdW3faPFb8vBEgpbWTPHILVYE8pLUT20DVIylueUroTEfP/KdtF6iZZGa5/SymdnXO+seyQE5iUUh+ERH9FC7k7sMom/E+Ia69GSPENe3Y4UtnbAWdYOGQDgURvogWwFZIgPFNyIWKGAE8YMTokpfQKYl4Aj+ecX0opvZZSOizn3MfeeSiV0T7rHSmlw5AkVFf0fWNgYUrpSXv+Vbt3NyStnlPAf5LB/h5E5ADuTik9mnP+YwuvnkPUXPpgS300hn6rndX9/xCa4x1zzkenlD6FJOk77ZYZxN7Ng+zaxoj4TgM+b9FJAPullFYBHVJK6yKPiFLCpJSuRgxsJJIwXwdWp5RA8PsRIrp7IcKwOWIaixCBPQURxW2RVD8JmSC+jCT+BiTZgezd81E2vR9rDSaTkUZwtRHLeUgg2B35wzbBwnQRIff9EKbbuLfIG5AEk8qqfxj5UfzeNsgkUX3MQLhzHAFnkER7YM65i7W5CxKkNrP+fIiQqk+w/t8B/DQpQ93DWJ2hnozMkh9FgRonWrvbGaweRhGBw9C87UWUwvaIuHrEoNoQZee/juZkDBKqjgdusnV2G9A9ad+JR5G28hsivHMbhAtXId9f9bECRWEdW+O/Zo+U0lOsz6zXHVlRQ2vs5yKLipqFtMHWv+c9rhGsyjlv3Mx/43POu1Vd2xIRgEPRQhuDEH4XtCg+hqSCzyDCtgmyDVcfB9W4dgFSmV0C2Q0h1O4AWaUrXkXINzfnfKn1aZ00npoprVAcjchW67sdZUSsNgW+mXOenqo2cfHvBQz2RZEWW9qlhSjxZFitF6aUOtt4P2Eax45Iajsahdk5EVieUnoERYN0Z33msgcySayydjdBJoLqUGBSSj9HxHB7e9fDuUYqfmuPpPT7LsDvc877ppTaIZPf3sU9g1Ey2lxj1g8hJvth5BN4pLrd3Ewd95TSGGCfnHNDGZqbVE5hbs750pTSMUi674Xw8GBEqD+LtKltkFayK5LmeyIG8BQy5S23Np+zvp6DwkPPAQ7KOZ9qkt/XkEZwMmLCD+acn7DxNuacP1NoOZsipnAtEpb2Qjbv13LOnd8S0Fs4Ukrts4WXFtd6GhxAzG5rhNfnI2K+v4WdHokYxwjr664Itx60Z49ChPg5xHT3s2fuQ/PYBglUv8w5j7R3fwoJWH3s/l2QyesxJGhuh/BgOpqLfTGNx3Dp4pzzFUmc/niinEUfpAWTFTJ6HnBmtUZewGC9OP9WwLJTS/9nhVCfaWPZG2lxmwF/yDmvJzS11NB79kQT2gMR9wOK88eIePh9Hqr5HBGq6bbHS+z+Tkh67oSkpQcJe+A45DBqJMoOVJ8PUVl5cyBiLM8i+/oJCNFHY+Vh8/p25Y0Rg/Js6PXs6Ei6/DpRwOsYCicTlQ6k9WyxxX9bUFTdbOG+MQj53RF6GmJ060IEi3s9a7pjjbMXRQgp0mqebuadf6LYu/UdwBN3eJchrkOr7qkjolEWIwbp0TbrRWBt4H3PEmG9I4giiV5OwcMe1xBlIl4zHJqKCP7DqBz0GdSIxinetZ3h62ykQd2PiNFddq2syVX6ruYgYWe4jXddmC6SpE9BDswJWLbsOzgfh6L8hrFEhM1kIsx3if2/s91f7eAegtbaJGSyWYyElRFEZd6xyIQ5hfVDnVfU6NMKpHksJ6oae/b9YiTh74sYSUXJjBbGeRhwU/F7ClUlUKrub/a/twnnlgIuWlVaws/3ukbQB3E5T4sHqdmjqcysew4tmBHIP/AaIrbPoYV2BHISD82yu/aq8bqMIinOyjmf1Yq+HYziibdCDqWPISayOc3YUi3pYw1aoEejNPhzq9pdz55fpVW40y4RmcJQZAvX6Guz2YUppeXIVt4TMamHEaG6D+1NcOIG4HAjgt3OSOp9wX5/GfkJTi3u3aaltrL5c97qkZQ4dALwQpbk+zlUfrdTcc8IJE2uRfjxW+vzNqhOfE0prpn3PYaIxosIPz9GEJQDENM53j5fRDb3IxF+LECS/cvW3FSa0SBbeH8vFIb5WA4f2OnN3P5pJPl3QERuN6KUsfushuecm1o7/g0d5lM6nyqTLjKN7Yu0kUPQfAxEAt9zWabNjyEBZlhKaQFilt9E/qF+aH19B2kL+yBz5Da50DzNHPwQkXV+CmIynmXfZLDwxLlGXzdJGdtOFDdCprxlOect7f/97P2nIEbVNZv5OKU0FWkJL+Wcb6+CyY9RoMMpbxGW30QE/yb73R9p0qA5PTTnXF/1zBnA/+bWFJvzZ97LjMCPFCUCQPXHX6pxT0JI70i2HyKUuyAzRkK2eVdZaxLO0iFov4+xdt1E9VFkd/0ksZ/rciSdrDOj2LN7IMlxsP1epxqayjmgfJdd74tCMv2ZA1Hd+s+3GmDrw2Zd1c0a/81AUuWXkdS2G8ofuLGaKFUtEgiY+vGz6vZzZZbopOL56ozHnHPehbdxGIxuQDgyAi2UE3NhDksp/R5pWtWOz4FoN6lD12+52fdVE91dkSnud1kmtP6o8ui/kPlnCsKbE9C4u6BEvrXI51E64t0RXLNkhx0553xFC/3bDUVilWU0rkOS8ws559dbO9a3c6SU+uecP9uK+6ahKr3XojyZtnbd7eJHIg1gAJK+RyKmeUxSGZr9kBnkjqo1OwRpTqUJ52bErD+Qc172FsbyLWR+m4EYgPsgLsw5d6y6dwoShjyQw/0kByFasU54fQvv7wucmnOear+HEomM3ezz6znKqVyMorGOzjlPa/V73uuMoAYhJteoX1Pc/1Gkmh6CWQpT2AAAFrtJREFUbdqRc96qmXbdcQtCmAV2/1ftnluQk+5I5Lw6EamNr6IJfxlJ05tipQ+ynDfN9a2ayVT8tmsHI0nGo5U+jCJlSufbWzo2oBGMQP6PLyEn55XZNrt2ovR23/ufPswm25coj+wlEMbkKhu13X86sbH5arRAzwV+k3N+uurej+ecJ73NfpVlIm5CRGAqkkS7I6n2IFqIxkkpXVCj6U2RmXPbnPNmLbz/acSUhlVdP4gi2u4/daSU/kTtulWNhKB2CNKkvAzD7Tnne+151+RuRyatiu9ZdnEP5aylGW+cc27/Do4nI3/SjxGuZcSASoaSgE2yRQu1Rnht5bvfyDkfXPz+e875V/b9dZTAeCvydZyJ6NI3cmVJmg2/573MCJohxANyzj+uuu8chFiHEmrvSpRosRWapLLQmrf7TcS1P40W6l+Ruu0OTw/H8s/NkF1xKEagrd2bUEgoOWdX+WuNx806UInAFdqJOQHXq+uyAVhVS+vr/qJA0BrPlZLyx4F9a5m1ajy3L8qu9Z2RVtV4b85FIbmU0p5ZtWcOoMbhWlBrj1Q7bLgvcn6uZ2aqMgk2osiRa5CDtZohD8o5H0iNI6W0O5JkP0WlgLKeRpNS6oAI02L7/h2UUX0v5mxsToMs2tgcMawfI6f2tTnnOc2ApUUG/nYclm/1qIKz411GWlN/JOG/AXTPOX80pfQmlXtJdECM5ONUmkE9HwhaIPZJkXKXIt/VOrxvjcaZilo9yHx0EAp9HYpwzZ33d+RiP4r/1JFqBMUU/03IOe+aFPX3BML/k51+vaUjv4POi3f6ZP3aQZth5QCq7rsOMYkPF9fGU+y/u4F2d6B2FrFnNnoWagfEYL6BnGyzUdTA6HdgrAdTJCZRo67LOwzbzyFHXFd712TCIfk1WnCYIaI0AmVt/w1pQ5ewgbK7SOqDynryfrZqb9Vm+lMzW7YVz+2JzDUTqNzf9ocUWyDWeK4PkvaH2TgvBS6ruucQpKKfhjStv6FoF6+F/5FW9G8bVAJjkr1j61Y8cyjr79lQ7rD3jjosW9Efd9BPqnF62e5qvKkoN160dRA1Si3XuG808sF9ENsngA0kcxXPlgEityOJ+4P2X6sSHN9h+D1AVX0tu34W4Xj3+lfr7WbX6vf8N5HibQChFiFutn5N1bN9W9FuHYqqmGxAHQTsVdxXK7Pxb0gK3AIRwueQ3bBmEbm3MNbBrF/jpGYkyTsE24HIr3ESLRSYa+bZYVSWPNiU9TeoP5QiouI/jCdbIuZ1BXJ6DwS6NHNfWX7jCWRLnl9FAG4ADmnhfb5dYVkb5tXi+31IOrsZBRTMQVLwXm9hTH9BDOp/sAilt4BHFcX5iM2HKqLt/sNzcgxRTO8SrDBjdV+L72XRtpbKvqxXS6vGPettnPQW+l2rkkGtazVrGf0H4PhBw6VeRH2t3si8WTMj/+2c7/WEsqdTSluhRTEYqZe3t/zIusNLsz5BYafMKs3q7XZAgG5Aqv5LKBT1ELvXHXKPmd21VPG/hBzG26JEEU+kertH2xzmjFNQpcLH7N1D/822ax3tcs7PA6SULs/mQMwy3Wzo2URlNMhaNVNRMnoSVTAx/8fUHNFepyFmNxnVsXlLUUMppduQWW8pMjn0A67LzdtH70KazMn2+wcoiuXanPNVb+HVq5K2thyXUvoVsWWhHwcBn8o555SSl5b4JNCvgG2zUV52XIDw8n+B32/ouZTS5xHebo9w/ncppf9BTOijKaWXMYflWxjn2zqaM+nWurX4XpptauYO2bFJK7rQK6X0F6p8FLl1pscbUeRXi9dyCwmO7+SRZQI8JKV0FFHCunt+mz6H5o73NCNojhC38vEtkPr2lbJJFO51BUBKyUvjrms3pbRpMwTrl8CKlNIsZDbZgSgHPSrnPP3fGCqoRr6XRfgiKhHgx39inspwwerksA05jroA/VNKjyNGeCJa1H9HERUp53xkjeduxfZ+SJGBfTaK/rjN2nkrx85EhVnPll3Uwv275pxPKH5fZkx2a5QR2trjPETozkFayFFEJjCI2ewAzMw5t1n/8Q0fb+O5jZDp1Eux3I2csb4RTO93mni0cBySw7d2WdJ+zuuEgpTSSTnnR6nEsy2L72+klH6Sa4dgtiZowiOWysTQjOap5lEy0pTSr4u/tkD+inf1sLn7j83fe9JZ/E5Lji20OxCZNV4n0r8PQgTmSznnBSmyUJeg5LZzULTAB4hIhU0QMW1JwttQ35oLb2zRaft2j9RyPsIGoy7M4XsYgtsQFNk03v6bmGs7Tmtm4drvDcbPN9OP6rDhZrNlU0qvoQihsvzGNWiBbYmY2LoNdlopQZbte9jj5oi5DaBSIm02ouydOlJKHbNKT2BaS8UmPf+NI1n4qEW1HI9MbyNyzrvb/57pXOKgF3YDOWk3Rr6YfzsEs5V97oRqEP0MJQT6UbHb3f+rx3uVEQxmfULskmOLSU4ppYtyzn9OkehUHqcCexbtPoxMTgcgxLsXOeV6VxMs4Jic88FOsKrDuHLOn3sHxl3WdWkxkuTdPowRHI4I784ogqbFiIqkUNX9smo1VRey+7dCVVMrwobNdHUPIvoeLvxDxMyqj5yrinalDdR9QfbbZo/cQkTZO3WklP6JiNlaYjOT63LOf2nxwXe2D9Xhs27SfY3mi9J9Kuf8map23lIIZkrp+znn+6sk+nVHtrDoDbSxjpH+/+l4r5qG/h17uW+POLDGf9+qavfWKon0XPtey0zzfXuuHYAzATu25x04co1En5zz2Fr3vptHUrLTSUSEzmeQ/X0sVpk1pfQPVAjv+eLRB4GXU0rzWL+QXWtNfmU/aoUNv2Z9GV59f855KLBv0taJFJJyLTNWreOaDfw/napELuvnEfbff+P4VFbdm++hSp7/gxjCf40RtOBb25coSufCXE/k0zi/RjseUdbaw7c83bxWt1rZxgrzL1TnLrW6kuf78vh3vc3/iZMa9VvK/zbw7Lq9CZprF2XkLUWqZzc7lxTff4+IypPI7JFQGNfvqIpGQpEDD77bMPsvz88orBa6/d4E+Un8d7MRFShs9dtURh21qrZLjbbWCxvewP1e1fU6KuvyVEcTbWgzn29gu05VXX8aFaOrvt6qsMd3aG5atUnPf+jdGwyBRvb2B+z7tkjTfqfe/2/X3kHh4D82HO+EhIqr/xvwezfP96pp6G3by1tKMkIbhn8dLcy1qCBYf5T+fzHarJyszMUKM01K6YPI9LEWZRkCHIicld/KOc9+5yDw3j6Sqn1+J+e8yH5vBdyfrdz2e/VIKfVD/qDhVDrLj0NCgpfD+AFKrDueGkdK6X5UCPExFKY6yq6/q4lc9p5zkBZQh0I4d0Zzc/h/4d0tmXT3Ak7IOTcllXW+HjHHDSZLvoX3j0H5B/VV11tdeydZIqE5uvexay/nom7V/4vHe5IRwP/X3r3HSFpWeRz//hiIDBIQFQJekHXGGyIaDcjFDOKwustN4w2DV/ASL8sqQbO72awiGCWQLAmbiEDcEQiLt1FRI4rhMiqKUUZEcFBE4y7LqgFlggwOMHv847zV/XZ3dXVXd7/1vFXv75N0uurt7qozPZ166rmcc5a/Xq6ZvQkOrz7uJ5N7nkxmlr6aLFz1A/Ld6xcX8bj1Y1xLTh0fZ5K+Qp8Cc+RRRaLWq6BN1KekR3V9YKG/eR5rDzJL+BTyd7AB+Kf5Xmw0IEO0abVlzqafZ9BhgG1keY+9yIq3j5A1mKZqUcUi1vAXeP5jyTyfJdfe6e33SfoWOZO5h8zjWXQBt3HU1j0CYvnr5avJTag9q497yGSU+uNepem0/wsl7RezGtH0iaHRY1xj4svVR88NheIY1uWS3kku4dRr4DykIZv5RK7DbyT/zj5ALnftI+m/IuLk+vcOcexx2bRwk54mDToCrYjYVv0uerP0d9N/PX9JIuIbyiPhVyuLxfVq76yLxdfe+Ziyr8kZ5Ib3HvTZv5g0rZ0RLJXmJhndBNw0+w+hGgCOIweBA8i9gf+M5ecDWEtJeh/Z6/V+pjcPg3wRH6aZz4nkTGANmUV8aUT8QVlC+Q7yb24kxx77xLZgk54Gn3vQku4t5MzxfODtEXG7pNuBF8cQ1UAXGcfQtXck7UoOTGvJpcNPj2IW1RaTOBD0601wW9T+oZIuJdcsrwY+GxG3lYh1XGmIomttIuku8oXn3ur+/hHx37Wvzz5NNN/jXEq+UHynz9fWk/sPy648uRSqqlVqZie7JeVoLPH551vSXUfuxdxIVmD9HFm590/kwPGWqDqKLeO5e4UXRe7d9focL5TFjRbRK2SSTdxAAAsnGWk67R/m1tcf+AdjoGwY9BHy3d0J5LtjxQq2O2yCpK+Std23Vffrzeg3xsys4/keYxXZGPyYZqNdGi2iSU9p1ab9v0YeD0XSS4GPR8QRBWNasFfIJGvtHsFyVO/+b5N0P3k+fSt55O9Q8gXstWTT731gRonqFVuvnHCrI+JaSYpMvjlT2a+51QMB+e7wFmWZ5O1kDZ4Lqs3tRc1mImKHpG2S9ozFlzsZpTPIZc41yqYmezN86Y4VNysZ77nA6ZJOJY/rXsR0DkApU6eXIhMeS8YychM3ECwyyehc8lzxlr4PYgtZqOhaW32l+ug5kem1/GGmxn8Bfibp28wsSVHstJRmNuk5igWa9BTwa3JQurL6vD9ZguNF5J7Kb8qFBmSiYW9JUMDq6n4nVgkmbmlI2ZLv+2Ti1//N8z03DspFsME0s1/z2eQm67n9Tnq1iaTjgW9E1Z9XS+j/XP3cW/tdj1pbzlEblD8TS6zNtZIkfSci1lW39wI+Si4p3kn2IFg7xMkeW2ETNxAMounuQ0eR1SH7lai2CTVfItgkmS9/JiIOLBzXFnIGdhx5MuducoP4wPrGtpUxcUtDC6j3au1bonq04Yyn6hTIh5jbCrDV9Vgi4k21RLANyl60G8gSIQ8M/ulpLT811Td/pmhE6QzyCOkD5J7dU4FrJD2WWlKZldGpGUGPpCNjbmGwOdesP0k/JUv13kytQU1EjCRparkkPZEsIvgBcolrLXDBQsmEtZ9v3ampxebPlKSsPvtGcuntTrKrW2dO5rRZ12YEPYvqQmTzejQiLiwdxLAknUCWGOklgh1aJYLtRg4IixoIaOepqWGb9JSwCzlY7UyWdnl82XCsp1MDgVrehWiMfE3Se8kyE/U9luKbkgt4HXD+7ESwqvTBqUM8TutOTUXE383KnzkDOEhS3yY9oybpcrLK7Gdql3fu0smcNuvU0pA63oVopUjqd9QvWrJG3ri2n5rSIpr0FIhpC1Uf55JxWH+dGgh61NEuRF1VKz0w50tMyDvRAfkzN5LFFv9/wI83TtIXgH+c70i3ldWppaGaz1QnRmZo+6mXNpF0BFmsr35q6LJiAQ2wUhnjVYmKQc/TeE/iAQ4Avgic3tIX2ycCP5c08j7OtrCuDgQfrN3elazN0plKg8tVrfeuIY8D9k4NBVnBs3VmVZa8lawyu5T/78OB/yGzY39IzihaISL69ultkTNLB2Dz6+TSUD/qQBeilTJu670rVVmyKjj3t2QewsFkFc0rl1s106y0Ts4IJNWPre1E1jvZt1A44+g28vfVxiWIfg6sVZb8NFnjZmgRsYNsV/pNTTc0ukHSWYvNQegaSd+LiJf02aeZmP2ZSdDJgYBMhOrVLX+ULHj19qIRjZdxW+9dscqSfRoaXYAz0ucVES+pPruyb4t5aciGVh3DnSMiNo06lsWoFZeDmQXmhnpX6oZGS1PlXNwaEQct+M1WRCcHAkm7AO8huyZB9ty9qCXleseOssfvyRHxvtKxNMkNjZZO0hXAv0StI5y1R1eXhi4k090/Wd1/c3XtHcUiGjOSXgCcDLyeXFrbWDai5kXETqVjGGP7AbdXy4n1Hg5tXU7slK4OBIdExPNr96+rCqnZAFXV0TeQ6+P3kX1nFRFHFw3MxsFHSwdg8+vqQLBD0pqIuAtA0tOpVdG0ed1BHsE8ISJ+BSDp9LIh2TiIiE2S9iXbxQbwo4j4XeGwrNLVqe6HgOsl3SBpE3AdWaTLBnsN8Dvyd3eJpPW0KKnK2kvSO8hju68meyjfNGShP2tQJzeLYeoYYK+v6x0RsX2BH7FK1UzkVeQS0cvIxiJfjohrigZmrSXpF8AREXFfdf8JwPcj4lllIzPo2IxA0iHV9JTqhf8FwFnAebOSzGyAiHgwIq6IiOOBp5ClJv65cFjWbneTVX57HiDLdVgLdGpGIGkzcExE/FHSOuCzwGnkgPCciHht0QDNJpSky4DnAVeRewSvJJeKfgkQEf9eLjrr2mbxqlrzlJOAiyNiI7BR0i0F4zKbdHdVHz1XVZ+dcdwCnRsIJO1cVZ5cD7yr9rWu/S7MRiYipo6PStoLuH9cihZ2Qaf2CMjywZskXQU8RB6FRNJaYGvJwMwmkaQPS3p2dfsxkq4jZwa/l3RM2eisp1N7BACSDiOzHK+JiAera88Edo+IzUWDM5swkm4HDoqIkPQuMht9Pdm/+NKIOLRogAZ0cDmkX1/ZiPhliVjMOuDh2hLQK8j+DTuALZI69/rTVl1bGjKz0dou6SBJewNHA/Vck90KxWSzeEQ2sya9n+ylvDdwfkT8BkDSscBPSgZm0zq3R2BmZjN5acjMrOM8EJiZdZwHAjOzjvNAYGaNk7SbpH+TdEl1/xmSji8dlyUPBGY2ChuA7cDh1f27gY+VC8fqPBCY2SisiYhzgUcAIuIh3NSoNTwQmNkoPCxpNVmCGklryBmCtYATysxsFD4CfBN4qqQrgCOBtxWNyKY4oczMGlMr+95rT3kYuSR0U0TcWzQ4m+KBwMwaI2lzRLywuv0fEXFa6ZhsLu8RmFmT6hvCRxaLwgbyQGBmTfKSwxjw0pCZNUbSNuBX5MxgTXWb6n5ExMGlYrNpPjVkZk16TukAbGGeEZiZdZz3CMzMOs4DgZlZx3kgMDPrOG8Wm1njJD0D+ARwILBr73pEPL1YUDbFMwIzG4UNwIXAo8DRwGXA5UUjsikeCMxsFFZHxLXkScXfRsSZwMsKx2QVLw2Z2Sj8RdJOwJ2S/gH4X2CfwjFZxXkEZtY4SYcAW4DHAWcDewDnRsQPiwZmgJeGzGw0DoiIP0fE3RFxSkS8Bti/dFCWPCMws8bVy1EPumZleI/AzBoj6e+BY4EnS7qg9qU9yBNE1gIeCMysSfcAPwZOBG6uXX8AOL1IRDaHl4bMrHGSdomIR0rHYf15RmBmo3CopDOBp5GvO71+BM4sbgHPCMyscZLuIJeCbgZ29K5HxH3FgrIpnhGY2ShsjYirSwdh/XlGYGaNk3QOsAr4ErC9dz0iNhcLyqZ4IDCzxkm6vs/liAjXG2oBDwRmZh3nPQIzGwlJxwHPZWY/grPKRWQ9rjVkZo2T9CngJOA08ujo68ijpNYCXhoys8ZJujUiDq593h34UkS8vHRs5hmBmY3GQ9XnbZKeBDwC/E3BeKzGewRmNgpfl/Q44DxgMxDAJWVDsh4vDZnZSEl6DLBrRGwtHYslLw2ZWWMkHSJp39r9twCfB86W9PhykVmdBwIza9JFwMMAktYB5wCXAVuBiwvGZTXeIzCzJq2KiD9Wt08CLo6IjcBGSbcUjMtqPCMwsyatktR7w7keuK72Nb8RbQn/R5hZk64ENkm6lzxC+l0ASWvJ5SFrAZ8aMrNGSToM2A+4JiIerK49E9jd1UfbwQOBmVnHeY/AzKzjPBCYmXWcBwIzs47zQGBm1nF/BdUofAweyUdZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph for Headquarters: total = 198\n"
     ]
    },
    {
     "data": {
      "image/png": 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+B3w+pTQS+Lz9D4IgCNYibbbWUM75uJXs+mxbXXNNmXzVT+hxyiVMuupUcl4MwLan/nkt5yoIgqB9eccOFgdBEATtQxiCIAiCGicMQRAEQY0ThqAFb17xNQDG/eFIRl/Rm9FX9GbElb0BGHpV77WZtSAIgjYhDEEQBEGNE4YgCIKgxglDEARBUOO8q99Z3FYMvvoImpJ+N6XMR79/PwOv/SLNtm2fk+9fe5kLgiBYQ6JHEARBUOOEIQiCIKhxwhAEQRDUODFG0Aa89MfD2ffkB3jhusOXjRs0k9n/ew/y9J8OW27b5777EI9efyjNdu4h33loreQ5CILaJXoEQRAENU4YgiAIghonDEEQBEGNE2ME7zAevP4QAHKCw7/9MP1uOGTZ+EFzyhz1rUfoc+PBy2376jcf5babDuL4bz66VvIcBMG6TfQIgiAIapwwBEEQBDVOGIIgCIIaJ8YI3kXcetNBgJ5POPGbj3HjzV8g277mlOwbTjrhUa699aDyPEOCU49/lP+7bfltPznuUS6646Bl6yqd/dUYgwiCdyPRIwiCIKhxwhAEQRDUOGEIgiAIapwYIwhazXl/PYj//sqjnPvXg2iyMYemBOcd+whn9jmYC495hDP6HkyDHd+Q4NqjHuFb9xzMYhtnWJjgvt6PcEi/IyFvaEd24eHeN3DIfd+H3BmARGceOvJSDr335zx05EUceu8veOjI89v3hoOgRogeQRAEQY0ThiAIgqDGCUMQBEFQ48QYQbDOcOi9v9KP3JGHvvRrDr3nt0BH29uBh750FofdcxHkDsu2PXjUGRx29+UUn6cDDx51KofdfTVgAxfLjk88ePRJHNb3z8v2JTrwwNHf4vC+Ny13/APHnMDhff5StpF44JivcXifO5fbpuMTDxx7LF/s07eyrwP3H3MkR/S5r5K3RL9jDqd3n4eW23bfMQdxZJ/Hy7aUuPfoz/Clvk9zz9Gf5kt9nyVV7u/uo/fjqL4vLbet79F7c0zfVyvbOvK3oz/Cl/sO5a6jd25F6QfvZqJHEARBUOOEIQiCIKhxwhAEQRDUOGtljCCldDDwfyjA++ec8+/WRj6CIIAv9x0BQEod+etR23Pc3eO546iefO/uCWxgz4t0oQMXfWkbfnnPJNa3cY7OJM76UncuuWcqnX1bhh8c1Y0/3V23TLl0InHCUVtxR9/pdLTjOtkiWB0yHHnsltx/1ww6VrYd/NUtefyO6XSwbR0zfPr4rXj21unLHfeJE7ei/011dGwu2wA6NsPu39uaN66ro0NzXrZtpx92Y8SV0+hY2dbr9Pcy4dKpdGzStg7Nme5n9mDKRW+SkiWcmnjvT7dj6sUjee9Pd2DqJcPB99HEe8/4CNMuex38TSGpmW6n78m0ywcsv+20fZn2fy9Uzm2m24/3Z9ofnqbbjz9N3RV/L8fTzNY/+gJ1Vz6y/LZTD6fuqn7LbzvlKOqu7rNsW6KJrX543GpqvtDuPYKUUkfgKuAQYBfguJTSLu2djyAIgkCsjdDQx4BROecxOeelwJ1A77WQjyAIgoC1Ywi2Ad6s/J9o24IgCIK1QMo5r/6ot/OCKR0LHJRz/q79PwH4WM75Ry2OOwk4yf7uCMy03zOALSvfK9q2qn1v97ZIN8r83Zbuu+Ee1rV02+paG+Wct2J15Jzb9QN8Ani08v9s4OxWnDcAGOC/V7VtTY//T7ZFulHm77Z03w33sK6l21bXyrl1enlthIb+CeyQUvpASmk94KtAv7WQjyAIgoC1MH0059yYUjoVeBRNH70h5zy4vfMRBEEQiLXyHEHO+SHgoTU87boV/F7VtjU9/j/ZFum2bbrtea1It/2vFem2/bVWSbsPFgdBEATvLGKJiSAIghrnHWcIUkorzFNKqUtKaVubfkpK6a/2/YWUUpdWpNstpbRVSunLKaUbbVtn+/5wSmn1U6x07L6V3+9LKf1sJdfq4nltxfE7ppR+Vj2nkrftV3D8CtNZE1pc6/CVlfsqzu+2hsfv04pjOq/uWisq15ZppJTOb7GtS0rpI1bPb5GVlNLF9n30yvKdUjrMy3xV97KqcqmmsTo8Hbuf95l89LBtPUyWVymzKaWNU0obraxc/x0q+drYJnz0rJbbmlxrdXKcUvprSmkj+71hSqmT11WL4z5g36uUjcrx+6SUTl/F/k4t/r8lnytp32/J2wrS7pJSOrbSvr+8gmNcV21l//3YZfmwdHY3mf7z6q672ny900JDKaV/AT/IOb9o/zsCX0BrE20NPAH8D/AiUIcGnB8F/gL8CpgH7Af8AvgIsBewIbAr4EKa0HSrXYFz0HMKm+Scv5ZS2gP4JXAosD4wH1hi6W5ieVho6UwD/m7pfhb4NXCqpTcfGAcMt+OOR4b3b8AtwHuAs4D1LL9T7PcwYCTwJeB+4HBgKHAX8Fu77vrAZGAu8GHLWxe7r79XinNz4PWc8ykppe/affzIrnE8cA9wGXADsDHQYHmYDmxk6WXgu8DrwEGWry/acZOALez+tgKetuveBAwBbrRjDrH8/hE4wO5/JzSD7FFgNLCZlclSK7cOqN7ei+p4AbCBbW8A5ti1/mHnfx/JxP52zPNoHvV77XuhHbctkpWOwCeB81As9V927leRzOwKfBTV+fqW5gRLawmq8x5WJ+sjWeti500Arrdr1wM/sfIFeAU4F/g2sC/Q1cr7UPs+FzjKjh0O7IMewLwH+LLV1a/sfrpYOVyRc/5NSumTwNdQ3WJ5b7C8Xmnl/RPgGeD/AXcjeT4aeAlosmt2QvUMkq3xqJ4/ZMdj5dHJzgF4AXif7T8M2B34OnpYtLOVw0aWn/HAB1F9TgJes9/rAz+0Y76JppY3oHbwNdSmmoDLge2snna3PL9g112M5HFDoLul6fLj9Tjd6uMVYKCVC8B/5Zz3Sym9gp5huhnoBUy18j8T6aLrrTzmWl1dCGyP2sBc1G4WW5oJmAU8iWRjf7uHDpbv99vvpagut7K8z0ZtzF/svdjKcax9b2vlPw+1m6moLTXlnL+TUro153wCreSdaAg+DlyBKnM68HngZbQMxbnAMUAjsAdSSIcBJ6PVljohpfkeoBsq4IQKuROlwNdHg9UjUKPeCPiKfXvBL7TjJyPh3tjyhF2/B1JY2yJl2YQa5j+QktsMVZgr0+o3lAaagf6owTdaOn9HyrNDi+PqUYOZj5R8gx3TEViEBGeppbEACcdo4HNIKSxGCm9HipFLllfsOh2sLP2esHSbrAyz7V9oaXjebrXj5gEn2L5s5yy1e+tseZ2K6vbDdkyHFtftZMcvQUZrF0uvERn4BstXT6QQPkdRtC3LuNHOPRjJxFdRY65HSuBNy+/LSDHPszJez873tLwsfoca6e9s24aUht0EnG/7f2DljN3XXKSEf4QMTLb0X7K872Zl4+XrDs5uOedtUkrzkXxiefJ7y0juMpLPTewz38rxT0iJbmHndrB961Hq28vN0wY5JutbWp3t3uYAm9p1D0WG4dJKvvog5buXXTtV7snTdblyuZhj+d+0sq+j7ets1xqCDIfLWxNakWDbSp6zfWbauVsDP0NGsyuq703sGNcBK6JlXkH6aH2ka2agulpiZficpT0cOUxdkKFYhIzgTshJcP2UkXw02rEb2bEb2jF1SP5AeszvrQH4FHAJcmDqrMwW2fHzkHzPzDlvnVJ6Jee850rucQV33coHDtrzgyp5NKrUvkhpZOSV/gC4xv7PsQJpQla3ySpovu2vQ8pvnBVYIzDY9o2xSl1KUUAN9ns2UsYj7ZrZ0p1s20fYcX5etvSbUW8lo4Z/OdBs9zS7ctxC5MHMtXPG2r28QhHUqfZ7pN3bDch7Wow8qGYTirNRo/2dndNg5y9GxqAZKbtZwP9aufp9Ntv/WchLe8nKer59FtqnCTWGCZb/JXatV+z/YNQbWGx5bgSerZTPj5GgXow88HmVdIfZd73l+3rksTdbOqOQx3YtMNbK8jwkI5OtXO+y60wAXrVzZ1TKcqGltQApywl231sgJdFMkQuvD9822+7r81aO/2P15oY5W36m2rELKQqpGfi05WWJndeEDNBSijKfRZGPpUjh+L1OodR9E1LqjZaPoUg+RlfKfZEdd5t9j6I4GNNRWzjS6negbdsSeaIvWb69Pq5HnmwjRY42RLI60u7nXMub99IWW914e3IZd7l153MMMiZz7Z7nIyfKy/RlK5OZSJYW2zGLbP90u95vkRzMsvyfYvXnbX0qMNmuuRh4xX7PR7LQAfgeatfebhfbdX9CaSdv2r5Ftv+Xtn0CcLuVpeuYo6zOJ9r1n0ZGYqnd01Ckm2Yjmfw88GeKfsiUtrQ7cnaarMwaUA/keaSbPk+R7dlWJoMsnTdQRGD1OndtK/2VGII/2o01mJC5YvcGOMe+nwG+URGMBiucQVZJft5rVukLUWimwa7jRmCUCeCGts3T3ht5a0sr28daGtnytsiu+6Bt+4dVSH/LR0ZhrYwU4EATktl27aUmZFUhmIEU5AJkdNzb+6X9HgwstXvoQ1Fg41GoK1eu9xJqJJ6G72sEJlgaU+3+x9ixP0VKwI+dgrr9zZbHV5CXNsLu3e+zoXLOq/a/WtadKEZ9ieW3U+WcZtQAJ7C8kW2kGI5p9u1pjEUN1o37aNs+vFK/z1tdjanUo1/PvWrP+zzLg9fHYrveGbZtdKUcR1saO1SOf8Lq1g3RLyrXGwiMtPLw6w+1en7ZrtNo12+0fdn2D7Xjr6hc+zw7zg3BDEobeabyPY7Su6pDbWaa1fs04Cmr96UoXOmK/Q6KjHt5u2PkhntQpZwvRj1Or7uDUPhpHqVX+CAy6u5MLKjUp7ftZru+10mDHePy12ifecC9SD4X2b1MQaHJRZU0q/XbXPk9G7WfBsv3TNRuM5K3y1H9u4y4gWxAcufbllCMpbeX6RSn8nLUQ/HrT7BzvNxcn2UU8fhZJR+/sG0uB55v7/kvtTq91vb1BT5g9dIT6LlOGgIU6/WY72dQ13aSVcA8q2z3vqdagXoFeSW8afsXUEIMbtnd0i6leE9eQTPs/2TU83gWKZRZFcFsqqT1IjI2M+2YJZW8PWcV7mm70CygjDtklm8EE+3/1Mq20SaUUyvXdm9zmv1ebHl1D8Pz0Gj36Z77rErePa0HLf3JyBMfRulV1Nk+94hGWfruPVfzvrByvWpjc+90idVvRzSO4r2SyfY9ERnYcRSF6D2G+ZQGOIFi9P3aHjteYud7KKaJ4iU1AI8DP7dy+DuKYV9m5eMN2RV/o/32ulto9zba0h6KlHdVuSy2enquUtbeU3WPOSMD2GjpTEAGak4lrdfsWm4QJlfu1cu3qcW3O0ou64Ps3HEUmZ9o9/GgHdto+ZpPUTJX2GdeJU/1dp1FVh8zKb28RRSvvhNwNfL0vWdUlf+Wv11+l9inDxq7a7R6G2l570NRtm4oOqAYvit87/HMRHLq5Tqd5WW1KqNVp6Bl/qrfDwF/oPR0qrpiwxb6axrwAFLgfStlU3VqppoMuDNYT5G1u+w6t1rdDKC05zOR3E6hhJG2QsZ2JMVBuwIYvEZ6d20r/hUYgtuAre33UfY5FsVeXzSBGIAEfKYV0iIk+BMpXbLFqEvWx/6Po1jxRiv8oWjQrosV4O8rlTPXCvRjlma9VfLfLG+fQ93mJZbO65YPNzDVhvs8EuCnLc/no7DEFijeN9Mq9x+W3izU+N3qN1q5TLXKdyPSbL/dSC1ESsS9kUkohDDC0noYOA15bq5cplMU1Tz7vheF3660zywru2F2D7Pt99X2ewoS/s8hL/C3FI/GQw0ZGdUnLF+zkcK4qnKfk5FSmo6U9k2oQb9kdTMVGSE3/K9Z3f13pbwXW36HoEbj3vmtVl9u1F9GobI9rY43tf8TLP2zUEjK78899ReQPJ1m+VmKvO6qkZ1A8ciHINmYbnVVT1HWDcjAPmTXmYvk1J2LaUhpn48MkHuAbuSrIb6MBuH/ZmXg8uoOlCvDRkvfz5tt/5+za15odevtxA1jS4dmFiWMONPKts7qtz8at+uFeh9XWhnOofSgPZ0mirOxEHgMte9XrZzm2LYxlBDIa6jNzbN0h1kZ+b0eBRxV0SldkZc9myLri9BYzZ6Vz/Fo4CuPsdMAACAASURBVNydiqWoV3Q+MNrS+iklxp9ROxlrdfyG3YMbq6rT+Ahyaj0EOJES4h2B2owf6z3gqnHy47ysqj2opZbfCZaXq7CQdGs/78TB4vdTYrcnt9i9KaroeiDnnL+dUuqJvIMvoIGrP9r570XxtQ6okj+Yc34mpfQiKugOKDa6PirAjyLhqEfhoM3tGG8QPsDVAXUj56LGviEaFNoGKfI+yGDNRI18PBqMPQhVVkLKawkS6nrL60dyzqNSShvYdY5Cyusw5I0stGv0tvu4EQ1Y72X7NkLe9hDUIL9LUVAnI6N2GXB0zvlbKaWPWF72Q97d1lbmyfLVGbjAyn03JOjHIgEGeV1jgCPQWlGfQgaEnPMtVpcfBP4K7Gzl15kyoPyGlSt2zadR+MUNRDfL706U3sn7kEK8Atgs57xsmmhK6QC0oOHuyCA9bmUBirEebNfZyuppKzSYOxUNOtbZ9jFWdh2RbByXc74npfS4Hb+x3cds1LjfRHK0K5o5djywfc753JTSD9EkgI8jBdsVycX7LD97Uzy73laPf7e8f8p+v2hlfKbJx2bAfyFDNQ4ZyYXIGB6LJj0camV5EiX2fap9jkae43GUwVyfSfNeysSACfZ7ltXVV1D78OP9k+zjM+k6IKfl0/a/Dsmmt8kRtu+LKKTWZHW6K5Kzvez4k5GC/60d3wm1g79Zmh9FodKhVr8ZDSiDZHMH5HBg+atH7Wdzu6celF5aJ9vWy+6vHjlQDUBXq8tjrK7OyznvbFM7n0fjdN9A7fxDqO38Czmm+1lZn5Rz3g2WTQX1scsHkRxuicI5j1h+P4VkJFn59EGy/T3bf6OVRbP970GRdec9SEcta4+r4p1oCN5AhTALVQhoitXGSMhcga5XOW1DO35DyhRDkKJ5ClXmXkmL3E1ASq8eNWifveCDLc+jSlqCFANIsIegSvkIUhBzUcx3SyRweyOF/xhqMHOQQpqDlJN7gGMsnz4GsoPldyqK7z+CKrwjGgjuRJlt4/eUkZBcjsIbcyw/vZCy8mcP+iFFdT1Sqg3Iox5sed45a8rsKGREz7Gy8cHLLeycP+Wcf5lS6lsp709YnjaiDAhWp7r5eAN2fxsjBdfdjn8h59wHIKU0Gwmu93BeRo3hFTSzyGfFzEGKrDeaXvgQmgXUTBlraKLM+AF5up1Qvf8Y9Tp6UCYKfMfK+QoUc98CGbvulJ7jC2ja30+tnDpamiPt2t0tvWTbe1EaqStMdyiWWP3sadfeHikwH2jeAs1iugjJ1gPIEAxCMvglq7+DkcLrbvc7E8n6xXa945H8bY8MRj/krX8BORkT0Kykg1CPaD7yUC9ARmUgCjdehep4GGUiRjcruyF2z92tnHphU7EtTx2tvLsjJ2E08lj7W9nsjAbCp1l5bWXfG1XKb0jlfj3EshmlnjtYfd0NfAs5i7PsO1XKfhZqc70oY02e5yVIPhMKN32e4njNRXLXg+JsuTe+smdvRiCZ3dDysR5lIH8jO+9i1OZ3tWPeZ9e61cqkzsrAp97OyznvnlLaArXd6jtc/FW/6yGnZkdk8GYC2+ScV7+U0NoOBa0kPDS48rsXZabKU5Rwgzcs74I1IQW0uLLdu4LzkXL9DaWb/hukLH6PLPgdyMpWB6ieAO6zNPrbuTegRjkNNRyPOY5HwvYE8njHULrUQ2ybdxXvQAI3B3kNHj7xmOwMyoCYKzcPC3lXehGK6d9mv/1+3AOcVSmLp6wMnkVCNYcSn90fGb8PosbfhBTfC5SZLkspYYCjkSCPQo16KJqT7iGrJXbd6ZRYps9i8pCZb2tCSslj/H6vz1a+n6V0iT1sVp2dtIgSNphheapHYzxuWMbZMWMpg+rTbN99lkY/ZDAnoZCX53EmCm3U2f38yq5VZ+nehLzrRXbNxZRpyQ2WFw/H+Kydlyke6Gt2b7+hhBbnWh5ftfP9ZbgNdp7LRgMl1FCPPMXquE1zi/+r+nh4cTIlRu+K12esLEBKdwoKqT6IxvL2RnP916eML3mdjbOyPgM5GouQAV1AGVD3cE0DcoRyZXvL+L6HRQahOfMgGXoEDY4Ot2/XEeORRz0cha/mI3kfZnXnYZUZyGnwMYpplse/WBoDkFPSiHpIcyytXyKDPo6iCyZV8u0hRW+7PqbZYOU4mTJgnSljNdNZfkZjpow5elnUWT7nUORsDnJo3kC98d3WyTECq9gRyFO+CSma76EQyc0sP91uth37K+ANO/d15HnMoAzALLZCHYUU71BsXr2ds7EV7htWSe7pZYpC/71V3BlW0QsoYxSv2zGehwkVwWgwIcyVtIegUEgzUtIe6/XpaT7LZx5lsHQ8RZleZ+m8ihrbGEun0SrfFaMLzTgTwH6W5jw0XjAVKaL5dt54y+OLlGmmV6OG87xdeypqUDMr5eQD9S0HWJuRoXnOfvugrM/8GISUVx0Km7jimV45/mX7uKAPoUwGyKghvGL59pjvQORx1Veu82cUpvHeyli73ljUeBZSBpjdgLQc5Gyw+nCZmgycY3W+CIUoGpFM/peVq9ftdMv7OPRQ3eiKvC6y325EVzRg6cp6GiXGvRjJVrPdQz8ko9U0TrC6u83K7WmrZ48734LaxH6UQWafgPAkMo5umBYgj3We5XcI5cGyiZRZTn4fPjPJB0j9c47VzSuWZh3qLTaiXsosNA30OUovNltdfRUY4eVV0RmvoMiBj9kNrZRF1eA123af3OD/x6D29Kqdc03lHJ8G6+3c9U9/inMz1cr4NeSV+0C1G+0nK98DgBsreffQaXXG3BUUg+Jl6gPmrktcJmejHteTtv0mYMc10rlrW+mvwAh8xApgLAp7dKzs+2+76cEmQD6X9jmrWJ8+t6hSUE3IADxn5wyyby/k11GYo46igOYiZfkVNG/blYKHCnzMYARlzvyRlqYPkk5EPY3XLO/T7fM6apw7Uaa7Ntg1PmLHdrP8PEVpgD5I1Wz374NrPrPEG74/BX056r00WbkMQZ6Lz7/vQxnHGIwUsjca90p84PN+1PVehJTydNvux/kspn72GYXi7G9Wjj2eMs10ETaN0u73IjQgt5jykJUb8rGUZx0a7Fqj0ayMI6yOrrN056IBXu9WV8MxPpf/VhSP7YyU8reRkZhCmdvfYPf2kpXLvRSv3D1m9/Z8KrCXfTM2dxuFEQ6p5GUhxQCMQCGOYXbda+24MZbmIcj5WYrCNU8gZd3T9t+NDE1fu/481B4mWFkNtHp1gzefEn7yAeBmuy//jGhRdlPsfP94+WekqAfb9wgr+70oEw4WWR5us3J+GD15P7JSNpdZuovRQP149PR0X2REbkey9HNKT2OJ3cOFlfz7vHnvZVajAqPQmMlAiuL0XteTaJbOjaitdEYzc6pTWG9COqknalO3ox7wU5QZU3NQ2PFOSvjXy3op6q34NO//sfu/H7WVyUinNKPe/COU6aw+Q7LejnuN0kO5AMnoUqsnH7Cf8W/p3bWt+FdgCJqQsppD8ZyfpkyhGowUwc2oS3g+pWs8B1nmSchDmIIeDR+J4sDTTEjmUWZwuPBkExoX/gloYOpUyvSsKSzfbXXPsDpzYxFFGT1C8UBfRzNNPNbq4Syf9XQtUlIHI2V7IopLuqGYQ1HqIyyPPVEDG0zxunpic4fRwz4Pm5AsoAwee2P5BmWc6FxK+OVcK4uTLV1fjmMC6jUtsTz4A2yvI0VzMgoVvIA8ssWUmVhjkKD7DKpGZDQPRoOB4ygzax5FDeF65Cn+FBnr9VYiMx+mTHWdZ2k/ZOkNRw1nHJo5Mh0pLw9f3Up5kKoPmrPdBcXNxyHl9UX73EuZMeUOwHDKg41zrHxOQbH366yMvKH6DI+Lkcc5z/LhoTpXxG+iHpqfVwd8oHK/37O6fBAZ0WcpvaVr0esJQYOX/6AY9XrU2xtJmRL6cUxmKp/trV6vsHo8AvW0hlIeKhxE6R3NRfLljok/vzMRyfGPrIyXItm+xPL/IhpA/jtS7AvtuI1Q3Px3dtz1dp93WTlVQ4mjrXwGIhn7tN3DALu3R1E7/ielJ1Rn9+GOy4uUsMssZLz6IVnsa5/RdtyjlsZ8y/eNVo+D7P8QykNj3iOcVvl/ERqLOMA+UyjTyZei9j3V8uOh4husPr9i+yfa/80ozw35TDqXA5/F1KoHyt6Jg8Un2s9OqPFvg5RQN8r0uu1RaCdRQkWXooE0fwT+BVQQOyFPZUfKrJjFlJ7DNsgTqI4pbECZWfACEsbjUKXegpTMXLvWz5BCdaXuXWUP73SjzGXvgeKob1KWAvgbarDb2XU7o8bUBQ2g+gM3d6PBJzcOJyMB9FlIs+3+HkQNyRdGexYplT+hhjnS0tjJ9s+161SXBLgFrXF0GGoUWyLD82FkID5EeQy/o6XZBQ32edd5MPJg30N5+GcCUqbfo6wh5IOCbuyWovVmHsk5+6yit5BS2g6FCXpbee6FBr8Xp5T655w/nlJ62e7lNDQGMsny1h15Vj9HhtI92SWVcshoyYlTkEeIlcF05K2vZ2X+Odu3FNX9epT3fGSk3OstDZ91hpWTzwqZSZFNjzN7uORuu8Z3kSIDhUCuRyHRx5CXOhs5RT7gfSJlkkK2e78BKR3QrKPFSD5fzTnva+W6ETJgn0Ky6GHVAUjRXYDqqj8K+21JUfp/tDL8YeV+Rtr1fGbPuRSH7nnKEjEHIAW2ABkdX/plKVLwWDkOQt5xR+D0StmdDtyec15s9zHLyrCb5Wc8CnOexlu5DHnTQ3LON/rGlJJPxwU5ng8jr38fVG9HVurDPfNFyHB82/I1BUUyPoWci71QWxmHjMPuVrbfRfV8AdJvCbWXF1G46kuoju9DTu0gyjMEz9l1dkVG+zBkTI4CyDm73KyUd5whWBEpJfdmQPH8ZspsgCbUGLZCBTETFeIGmKDnnDtW0roH9RZutU1fR4Ndv0Te4CGowuopU+KWIIW2gW3bBBiac/6wpXlAiyx3tzTejxpld9Rg6iz//0QC9YALbiV/9UixPYy8yqYWaa9v6foUxoXA73POV5hy/DASlmFIiH0OeWdsJk3O+VI79jOW1qFI4I6ze5uBlPIOVlabIa/cx2oeQwr1s0jwt6asXQNFuafKt/eWuiChfQh5Snch4T8UNfhFdk628m5ETyfv1qKcmpHicC/9e5S1mTalGO9mS/NxyiywqejJ7LstrWFIGdxTLe+c80zb/4CVxXRUb3flnOtYAVau/ZC8dKUsDDYWKblOSFn3RsYQK4c7gJdzzotapmnpbkBleuSKjrMpwe5RNyBl86Ll3Zln2w5FPdbPoHYz2vLbw8rnUjvvh5SQlxu/+ZSZXJcAB1o6w5DCn47qfFuKURwCfCvn/Irl9WZkHF25LkAzmnZEcjQDOTX/pIR/vTfuHJBz3r1y/2dU9l2ygmL0saycc950BftXSkppw5zzwsr/m1Hbu4qyyOCeqCebUf36JItmZLi6UGYTucF8Oec8LKW0N2VixB3I4foNagPPozq7E+mGMcjYvNGiPMg5351SSsD+OednWn1/64IhAEgpHYE8kIMpC2I5HZGl9e53T1SQ70dzePtU0tkceSWfQsL6LOr+z0QCdx/yeKsFcwiy6mei+cUT0Pzyk1aS1xmWR5998zyq4NeRZ+bXcoVJzvlSO/emFteuknPO37bjNrEN81selFL69SrSIOd8bovjX805fzSl9DryKi9Byt3jwYORAhmMFPd2dqovAvg3ZEy/gQa+3MiOyjkvMiVWb2XwFGXAy/kSmu7mM2icuajnMjbn/MUV3OMJyND6APJuSAF3RIpjF6RIOlKM0rJiQHFdgMdyznuvrLzM6PiT3d7QS0I5H7GK89yBqZ6T+DeU0Uqu0ZuyFMJVqD56Vq5Zve8M3LyC+t8AKZkjkAc/3HbtYefPQMb0m8i4LvOac87jU0qnIeM4xZ7rqbIx0jODWuT5HpYvmw2RIW9Ekzg2tWNPXD45fo7q4Qk0/nZzJd1zKsf5U9WPIaM/l1ZgCnkKkuepOef+KzluH6RfPpZzPtPOW4/SExqMnKb90EOjDRSF/jxS4o8h778vcmQPR+3sabvMMZVLZlQPW1W29bHrLcIeQHP9sKasE4YgpXQB8rBvs03HAQNyzmfb/umUubkvoDjkYuCZnPP+lXS6A7NyzktWcI1fs3LleVLWCpCvAR/NOTdb2OE8ViAsKaX3tBS8Svr7oMp8D7L8wFuV88qwh5RmAn1XFTpZTRrnIyX755zzzJTSQDR3+gHk+dehrvL+wPyc86zKudsgRdMJhRBOAq7M5YGZF3LO+63gmi0bdJUdUex1OdyjSSlt4gbPG2rOeZL9TygkMbpyP11QSOdM1At4DoUEmlEjfT/ybH3lVY85z8c8fru+e68HoMY8izKLbQHyWOetief171LxQHug0MhVOedBVpe7Is/xMsvnsrpdg/T3QU7Wzqg8QfXSzbdlPcj4llUt3ZFosW19NNW4F5VX4mYtl+157pRzPqRyTgJ2yTkPblnPtr87cgC7AvvmnK9ayb3sh3pP+6K21gE5GeScf1M5zsv0aeC5nPMk27abXWd0yzyujMp5I3LOX7Ftl2KOYM55Sov73Dbn/Gb1nm3fMllfwTV600LfrOi6/w7riiF4Hdgj59xs/zuiuOZulWOuQQrKV6I8Fnk2z6PGfjQyJhsii7/ck3g55yNWZihSSk+geOAFlBDPPkiA3iLQ/8F9LlfR1e6ne1QppVOQl9xzZZ7oys6t7DsSeSK755y/YYbUH5LypXobKOGn8yycdCEasPJZS/shQZ9NGRT9ZtaDL28R2tXcezdUpp9G88PPTHrK/L0555etbjJSdh9GhnQ0CultjeLMu6B6Ohs1/k+g0N9xqMcxEoUwLss5+4uNDqEYks4onNEFyc7PUe9oJyRHPv7zxxblt8w4oxDBCsu9taxIDiseqD/J/bGc85ktzvshmhFVB+yac/5GK661CfIkF6zmuOOQ4d8Q1f8427Up0Jhz/pwddxQa+O1l+z3U9t/oQpfYcV1sPGdrVN7Y/gm23xXc9JzzF2zbE6jcX805H1XJ2+U559NTSvejXsxGlqZPA/YQIjnnS1JKQ+3U+9FYx6+xab8VJb4JCkkutnJdigzizsi4LEE9jc+0LM+qIrc8NyB5eCCl9CkUcjsVhX43yTmPrRy/nJPWIu1lBtTyvswhXJUBaQ3rkiG4Bc3cGGq/r8057+aNBs2WWBlfQI34GSRImyLvdzIKKY3LOf+iImjPIUFegLrbT6EeRkLTIN8D3FaJIT+BBHAc6o08g7qFPpPpAuRt/hnNdT8LdQ1n5ZyXVIRlPvLseqD4YTNSXFvlnH9aKY8P5JzHtohTeuy9u333QIrj42jgcBYSxistDT+3I1KCm6OYvz+K38/K4EA0INULeVhft7RnorDLFGRgv0h58c4raDZDq4xk0luarkSNrR4ps6+hXsljOed9rIwOsPzOQkp7UyuzKcgDXEIJGY5GBmEv1LBOQEbjClSfZ1bKf4CHhqyxnYIMXBekjJ4FtjMD95aGWjHOB1Diu3u3VNQruO+9kaIc4krf5PkvKPz2LzTQfzIW+jFlciWaZulPw4Lk52zLR0+ksJcpoBbXvblyb12tHBejQUl/en/Z2JU5ST2tXn6PQhIv2e75aGZKoxn/K1F7ewH1rq4Cbsk571k1cCml0ZSnomcgY75s3K2S13+hZzJesPOOA86u9EBvRnJ2O+rV7oeM97dRnS/rWdrxPnaxEMnzQ6hNX2x5vc/KZjAKN/ZH7fowy2NCstUNyeELKDTzgOXj72jsZ2rOuX/SW+W6o2iGy+7maMzqAeCXOedPVvLnTtqvkFw/iwb552aNJXhv7MuoTXZHL9R5pWLg/Jwf0kqnZF0xBMchAZuHPNZOwPdzzndWlHffqrKsnNsRVVonZImvQR7dN1EFd0INeDwShKtQ/HJ7VBH32ffBSOhfpBLbN15CQnAgxSPbHQ0UHYQMjr9N6gQUY51FiQ9eiip035zzVS0svy9Kt+z+LET1MdSQ3UvcECnDzSkP2nmPyWdgfTzn/KCl4R7m8Wjq3X8jI+QPlL2ac/5x0tua6lDD+htSAkup9Eqq3VOkWJbVR4vG74J6K/K4J1pjeQ0p51MoT+PORrOC7rRv0LMDQy2/P0LjPFfZfX8y59w1pfQqCgNca/nZ3+rl4JzzjimlZ1Fs9ng7zxWpz/o5H3lrV9o9DUcN/ZdIYZ1q1/3sCsIhy3prlAFarw9/OPDynPOrdvzNaHykA5oFcg8axH0/ciYOR2Mg/uBbX9Rr+jzlLVxjkFwNpeJZW367o1lt91AJJVrd32r1OsbyujmS64lojGw88pRbKtLt0Pr+i1NKH7L7PY3yrMLnUHupR0q2F3JGvmZ1si1ytHojI90XGechqP5voYSSfmTpHmBlPxJ5+5/30Kvdy0looLbezt0cee0DkAJ/NOf8Ocvvz1CvdoBd40OojX8dKf8Jtm0xkvdnkcH4dtYyNYNRT3F/u+/BlBfrLELysovdzzInyAzCU2j1gu8ih2VHlh8vm5lzXrZ0REqpK3IKtkJ6xdeY8hdkOdl7JnbO1Ug2uiKj+JbeY0vesYYgpbR+xUvaBFnjj6PK7p9znlrtdiEvvSdSvM1IkD+CPKsZlAdEdkAxuTFJ7xh9DL0v4P9RUZR2XZ8PvQEsW7fEp/h1xFYUbdFNXdbVNut+NRrYWoRCVJei3sxHzTv5HhLcKciTOBQJ/6ZI6LdHQrofZf76lcjIuLK9GSn6bihm/b6U0ieQ0fmV5WE6ZaB6MHqa1WPgLyBjdghquHcgxbQBaoTvzznvlLTW0IFI8P2JSpChA4XNNrT7HY4UycPImE1Bg7Mv2v4PWF5GoHjpriml/nafb1g+XkUGz6eVboV6aZsgb/mjFCXbAynoh6yOtrSymoLWcRmKehHvR4rxKIpnNwLJyxGo9/MFu0YjZfB6C2BBznnzpMHRk5GxHmt1NdTKGNRjGIAUyUetbE9AxvT9qDfx06R1Y0Ce/XGWv48gT+5ppGgWWP1dgB662g49wb6PxeFftnLcw87ZDXgw53ya1e0pKJS2PQrtuPEdhHp9y3ptKaXXrNfzBDKSPaj0DCp5XYSU0dZ2b/OQZ/wh1Mb2RUrOeyWdK+X4T/t+X875g+YEfBT1lr+CHA13tD6BjMsOSMYWoFU1920Rjtss5zwjaZ2yD1Be29qhktZIpB/GoXZ4u33+krWI3KtIF7wHTXp4EYV+Dkkp7YLk+Dg0MN8HrTBQn3P2db2w8v4kkvuelPDYvJzzlknjipvY9vORfDyPjEoX5PRsjQxiX2TM90N1+nfkAEzExrCMW5Dceoi27t8JEb2TDcGDqCBuQAqyA2pwJ1DeqZtRA90VeY1dkFIdi4QSSpywHjXy3VCjmWT7XDhGIUveSHlF3xxkhb2b9wZqeB9CwrvYjltAeS/qhkgIxyCB+g1qvJujyt0ZGauzkKB/kfIOgc1Rw7ofjWlsggS7DzJUrjiG2bU3s3t4GCkmXzHVH77biPLCHS8Ln0HTiBrlcOAIE9QtbFsPinflq6G6cD1k+7ekDPA+iQaZF9v+GZSpr79FiqEZhZyuzznPTyltiGLJB9i+BXZ+d6S0/MHCfqjON6U8Pn+Q1cuNSNF/mbKY3nusHHzaZifUqG9Dg/tnIyNwBGr4P0ONcT6Ss5dRQ90W1fFfLb0XkBf7FzQOMQEpy47IYPWyOvid1ccGwF6mtC5E4yzzkmatPYtk8oNISc5DCsBl1V9VOBzJ9PpW3iBPcxhSyNsjw3K35WkKkpGNsXda5Jy3M7ldhOR2FuWZj98g5XG23cemqN5nIAXuhuBOy+N8K6/RlvcnKK+mXExZOPBvdu6OyCB2R57wflZOO9j3NMvvQ8j4Dqcss7wr8miH2fELLO87IKM9xeqos133TcoCb9fZ7+5I1vdEBn8hcgKOQW3LFztchNqIr666vpXB5qid+KKP/hDbEjv+aUtzY6vTLZEMzKK8hrMj5VWSXVHb/gbFq98cybfLpK/tdRV6+Oxk+91MWR6mB2rn8yiLHt6OHK4elIUWAWg5jrFC8jvgaeIVfZCnPBPFrV+1G36Tsh7NPKu4fyFr+aqdNwgpda/4K1E87ikkVD6o6at/+iPx2Sq9L+VF2V2R5fa1eQbZNYYhL9K7wE9apb9OeZ3cQUgxzaWsi98JTUtbigY9F6NZLZMo6wh5d7MONdTRqIvcESmpXyIl9JTd3wLkLfmSF0ssnSuRZ70pWgxtHPLmfeGrJXbP/sCQr13zBBqw9bcefQYZy9OtHOZS1iGaZ2Xt87vHUnpj3ax8Blm9bYPCcq6sHrO6W2xlMwA1vumoIc+2+76ZsnLlOMqLdIbZsTugpTN8tch/IgPzZTvf1zXy5wl8YcIG2z7QyvBoq4cpFCfBn76dZXlaasfVIXnwBwt9QbFL7J6eRTI7n/KuaH+3wAKWfxI9V67xIopV+5oyS5F8jOatax/508JLkDw0oqe6J1qePoFW3QUp1X9a2jMp7wN/gbIcxANWlxdaXexm99ATKalzUdtYSFkMr4/VzxjLg0//vcA+i5HB+b3V3RQrq0VWZ+ciGT/R7v0kJMPzKU/g32K/B6Aw1Bgkd/0s72/Y/fgCbW9Snnz+qtXvaZXyvxmNX01ATqYvX7KE8prKBUjmmlEYeTqK0YN6Fi9TnDcfT3EDuhA5Kzfy1ie2eyKvvg7pCn+a+R7KoLbrpmmU1QJm275NrGzuRkZilpWfLwb5GtIF/uDaXsgZWfeWmGhhDCYiCzoHxXS/aJUzDcX1fmyF8Avbdo0J3Bw033+eVdodVlHjTFDmIyUxDnkHkykLj52JFHGDCdtvUOO+yIR1mAmPLzvR3wRrqqX3MUtnJ8o6InebsPS2fE9HVrwBjT0MMMFwJbSd7dsCCasLXTMlzNWMPJJGysJ1+6GBWKyyfAAAIABJREFUWh/4m4+8s4uRshlq6e9CWefkZWQ0fHG0esraJ+MoT8bOoDxo5tf3tV8+afnYHo01tFw10T3+WfZ7LJV1eShG/DXL+7WW3+ftvDmUBfbutPwsqlzDF/fz34+jBu9LYSxE8vGE3fNz9nnIPuOsbPze3Uj4U7/zKmlPsrSeRfU/DPXyqmv0NLP8S2SWIoXlv9+kvLZ0hN3jPynLIMxi+RcG+XpSX0Vt4jJkmMcj+TjMynQMZdkMD/f9AXnx11OWRbnCymY2ZS2m2Xau180YJO83WL2Ppnjr/mlA4arJdl/+NL2PFzRT1qGaSVm8bSTqZQP8w773pyjyZiuvhkodzLF6utvy6etifRDJwmuUl964PCys1JvPhJtEWVrGZbo3CseMtrp8GjmB7mRNseP/YPX2GOWtbt+28jrR9rtRcVlahBT+XMq7wP15lDMsjWG27XW7r/7IkM1BTukoyutKZ1l59UU9qA6VenWnYj62MGCrde3aVvYrUP5nVD5vUN5s9FvKq/gWUBpsrgjoLNTAfR2fabbflfzllPfsPkxZfMu9gbnIG36lkq4LozfwOsqSEWeiuKZ7CBORYm+wSvP1Q3yZiCEs/0o9bxgLKV69N4YmJLTT7bgfmkCcaEIyACn+xai38DoKf7hCcoH3HkqjCUh/JLxLkdezteXzdpZ/h2z1vr2MJlIEfC/LQ6YomfkUJbCEMk3Xn3LdDDWMr1Fe19hg192e0jNqRoP5j9r/91mZzKG8+N49/dlI6d1DafTVNYfGUt4JPYnyPt4hlJVKB1ie7rV68zX05yBF90273mWo670eJTbvi/H5E+Fez64YH0Qyt6vly9+u12Tp+EuObqes6OqLps23Y29EsXGQwnsdDSS7fA23e5qOZGRPZCh8HKfa85iJFM9CFBbpTzGYCyjhBk87U4zuCNQ+/oB6A9NQm5pqeR5v6W5ueZpp5dqMQpyT7Zi7Le0v2fGbIjmtGoCeSM6XoPZYj4z7K5afa1i+nb1BWUtovKXvC+ktpSwGN4Zi5G9HRnGQlcMY1KvbHxnRqsPTZNeoR4Zgtv3/sJXbNpS3yDVWPi0dlTGVsvXVSzenLNvu6441sbwRqzpVrt8WWxnPRG16KNJr3SlvQNxiXTUE51Q+F1iFeENbiLp5vq7PM1Y4w1AcbQxqSP5Er6+6+QDF0/FFvupRb+NkO8c9GPcWvJC3sv2fRo2yvwnjcIrnPIDymrxBts27nL4o3F9QA6pHsw6Go26nDwD9y/I1nBJf9Aa5xNIYjxrvEMpbjl6jLJU93rYNQd5lowmYr1rqQpRX8HEPaykKaz1FeQrYPwtQvHQaGty7ACmxfyBl0J/yhqbX7Z4foKyUupCy1PCjFANV9RinURbpe9nq9norx0l23uNo0N3fqXCG1fU4ln89YDUstNDu53WKYnsWedM+pe9h1JN7AYVW/J0FVS/Yw0qDKa/79Cc7fZxjAaUBP4UGsQdZPQynhHImUda88mczfO2sJygy76/EfJ7yEns32nOQbDdRPNjBFGU3EU0YOMfydhZylhot/QVIhu5D4yHHIPl+zfIwArWzyagXeSeSrwlWdu4tL6S8U6MOtYVPUGT+MbvfhyjLsnso5nWKzO9n5fsnSm+syc7pZ+X4jJVFPZLzBmTQJ1Pe/PWY6ZP+lEXYullZeDvwOvUen/e4F1XKdCiSh7GUJSBchlyO3Cn1eukH9LPrP2/l7cux9KQsvf4Skq9rkGy8SFkY8yErk2EotPwMmkjyW8oKsvNZ3pl1J/NNygrOY1qjd30BrHcMWU/Y/i+wcc757KzFsLZAoZ46NBi3AN2wLwH8KlK8PVBXto99ptu2hAp4AvLCNkAe5N5oIHNLiqB2RFM/fWXFCyhdzLmoy/gpJDw7odk3e1PGDHzmyGZoUO5Bu97dyFvrhASnB5ry+Lil/SAK0fSx+38ENcaBdrxPDR2OvPiBaPyiL1KM9VZOU3LOu6AY7AsopPYaamD/ylp36ROogfRDjW0pZWG4eaiBv4F6H01IMb9h+d/YyqHe7n8IEvT/sjTdMGyNeks7UsIkVMp3f6R0zkEK/z2om/uK3cd7UIhsR7uHz6DB3PehgfMdUUNotHwsRIpkAmU5iFGWvyVIBrpaOb4XIOe8f9YssfWQPOyLDJePlWxJURreu/TYd1dLv96uMRH12vyBxilINj+GwnP3V8pgJ+R5bmLlfz8afPw5Uhj+FrJH7bujpd8VDQ43WDldSXnV6ZXoIcouSPZGIpmbiwaqz0WK6FeoN9eEZNF7k3ujXsZpVj9zrVz6U96OtzUllPpeFAvvRVm19gyKYro85/wi8rhHIQP0CzTz5ivI6O5G8WZ9BdGLLF/fRfLuk0K2Q7PadqSMUwxFbyU7Gynpi60udgdmp5TusONHW/6vQD0UD7PcjIzu1kiOHsg5+yJ6X7W0Rtm9dkXyNQzJncvqgXb9n1hduIHeyGbZdbPy/gCSwe9bfQ9Hyr6BMutnIJKfDkhn+LNFCU0bvhJFIf6IZKSX/Z9JCWHdiRzCyTnnD+ScfTmYVbO2ewCr6Bk8iRTcZpVtPSjLPM80YfDlmd06T0MGYgplQNS9pecpnnV/ygvd70LCM5kSmnFl4PFT9/6W2Lnu/TQj73gExTLXmUC45fZunCuVqchaL7TzWsZdPXY9ys4bgAbxxlMWzppHiVf/Awno51i+0blnWu1euoL8FFI0OyNP8DzKeup1qPF4TNMV2yzL92DUCL5IeSvYjcirfNPSfxg1zh9j3VPUmF5G8dgtUWMcZddaZPmdS1kzfpz9/jLFM77crjeREurxgVV3EHxw93+RF/WK1dmdlv6frBy+grrhK+p6L0Xy9xIyQnvaPY61su6MpjO7N1yHQg2L7NrPUl7MMo2yfLJPmR1HMaSno7j+EuT9LbVPP8oLXOYh5fEmcIaV52mUqZwX2r1Ps3PGU167OovizCyysluCjJSHj1oORHtdeD481OUe8Gy7l59QQoIzrG695zeT0qN1L/r1SnvuaPl0ObgLGc1xlHcEnGif/vbtE0bc+3/D8uJO4u+QAXzRPnehHuVgO95f4LIYKdQGS8u969/affqzI4Mo4eVnkIH4jZ33mP3+H6Tsp1vZjKL05r1dz6codu+NzLbj+9m5P0OG/GFkHOqQEzeesjruaDvWxzw8nOTl6yvszkHPvHReJ0NDFSG5hPJEqD/p+SYlLjwJeQD+9qzfoHDBEKvYG5EwX2bnLUTTAV9BCrCqGJts/3Qr8CmUmPJS236GVcQBJhBLTQDeZPlXZ3rs71WrkFvR4NMs5BWMsmt4ZS62a8+3fY0t0sqogXkM9EQ71w2gh7G8i+tCdiElRv4VyjLSrsS2oSj6ZuRJT7CyuYOitAbY9V5FCuQcpJDeQB7M1ynLent82VenfIPyatBFln9vHG7Q/NNAedvSOCuvz9txe1CM6mTKO6GbKA8uLbBvj5lOsc9TVv6voka91OpkIkWJD7S0T0A9nsfQeMuTlF6ST5+cSVkZc4jd2xjUePehrDD6K2SI9rU8T7JrevinqfJZateajgzyLCuHa6zeDrB6GI5m4My0a/tMs+lIaZxvaf2fXftZZMRchpsoL6uZQgmTupyMRGG971g9jaSETeuRXMylOAKzKb3AJZQXy8+2Y+agaaFPIC94OuXF609TZH9G5fMHK39P0+PzufK7HinI6y1tH8+aTXG8Jloef4Pk7im7D3/F5BuUcPE0ynu8+yPH6uxK+c61/P/L8uDRBR938LEtN7ZNsOw1ml2RM7Exmu56I1LmYyxfc5FszkDh7UHIeXjI7nMoZfxlLHIU+lPeYfFbu/4H7P77oN7CSLvWn9d1Q3CjFY6/jOIRSkzZ483+EodxyKs6FVnsSchbGoOUtXvzL9h5Hnu9HjXacSYcP0ezibpSXmD/mhX89nZet0ol+oCZD7BeSnkP6bWWvxtQrNwHERegRvAdpBS2tLQeRoI7FcXo/wXL1qufhTzpZsor+OYh5eEeyTgktK/YvR1DeSPaK0jZvZ/y9iz32r5mv09AU9D2s21TrHxcQfn9XIsUkntkS0wor7LzfGbGuShkdBHlncwL7FzvqcxCDW4XNGB4kp37NdRA/JmIaq/KH/66wrYNorwUph8K7fSye9wX9R59yqYr3vNse7Odd6/dx6+RV+ahDp+Y4HFvV8D+mWfbXXG7gq9n+bGVpsr3myj0Vc/yU0J9LMOnkfrMlMEUJd5EmTbtz5J4r9cVkDsXPubza2ChlcdsZBzOtvMuQA7OPWiSg/cmHrN67416dG9YXiYjufTeYUa9oKfsvGRldLBdz9/ON7qSx9mUQfVvoN7TY0ihD6UM7Fen+XovwA3B+Uhel1Ict9eQwm1EbeFSFFJ0L3koZQxtit3TTCTPe1qdzGzxqc5283GRbMf70iUfsTraCcmij6U1IYMy2cr6RhQ+/Q5S6P9jn4mUXpk7N6NQyGk6ckh8tt5M1Jb/lzKedg7SQ/0sv+ehNnZstQ7WWUNgN3GwFf5M5MW5MhlLmX/uSsW7yG9YxVxM8eKnUQZTz6cMJPrsk+o0vfOsoqdXBG20XWsGZQBxouXjGPQwVT0KjfhUuvF2rL9xqpHyjt9nKdPkJlm6/lBTA/LoplJmqyykvA7xPsq88nqkGEeixuQhMvf8HqKELfravbjHnZGRuJLyRrZRFIXnHph7+wPt+ynUKE6keIXzLL8+YOXd7aUUI+BdZffqM2pgu7eo83r7HoG8qMmUqZbzKF18z+ck5HUuRF35c6w8jrLrTaqU1UTKG9WG2bbfISM2B8nSfMr7i++0z18tvb9XPjdaenVWt88gZVRv9XMppWc6BSnJ4ZT4crN9T0BycoqV1+DKxx8Ec0V4hpXLvVb+t9s1n7N79Gmm51B6k25EXS4GIgXVZPdWLVefFz8OxZ8fsPp9iRLS8CVAPCw6izIjpmWIycedvOfp0ykPRIq5EyX08zKSbQ+nPYbawYCK4+JjYU2Va7oBfY0yzdrbzOuUcOtxqMfSz+q7CTmXbmB9dpCHJT20fJ6V0XD0bNNStKAflpcLUfv5RqXMh1CcAR9A9t6yG+tpKKx2I3K4qgbd0/Hwm09SWEJ5Q5qHqRdQ3g7n9b2z5W874JV12hCgbtQ9lBdAP28FcyES/Ho0Ar8IzV0fgSysv+t2Bv+fu/cO07Oq9v4/dxJIIBB6b4JIUcqheaRIAEURj6g0FTwgTUGkCIqCkFDkKCIgIL0EpLcAoSUBEhJIJ5My6WVm0nubSTKZun9/fNdi7Xkyk4TX83uB97mu+3raXfZee/W2I4/7Y0Sw19jvjUjSDkcBrxG0TPtyoLukdobShBjETQb4mYSmmqealjLDKuTzu5VwN92D/PRfQQTgvvhaIoNiEpHdMYQopHGz1N1HY4i9ip3J5nEH1xZdOHndhGs7AxDhOSN8kshkcM1/BbJketkxAmnP5UTu9Fu2ThOQ5jMBuXemIELy1EzPrHKf6QcGj7dtXKcgzXMPW68KpN1/jKyzZTbG+Xb+sQaHmUjI1iBmXYMsyuXI4vKUQCdyd9O4m2qSPfM9xHwnI0KdWXLMQsx+LEHwi+37RShoewhRHDea2ORnJbKSmpGl6O0zXOt3RlaNGHEN0iTnImZThnDB0wNPIITxXBvHQuRuGGbXDycySVYifHTBvwoJzhGI4c0xGLrmXm//ldv6eDbKHoRiU2UwbSYEWDnqHAuyRHdHgd3p9uznbYwTCCE0DuFZPlbPsluJGHcjwuOuNv4/2PllBsNJCC+GoGrrOju/wu59DoE3Xgx3jv1XjlzSdyA+09PuXYdw9TWiAHMxa+fvN9tzK2w9zrPxzEX4V4vw8iV7vscIlqMg8haEstAbCazeiH7GGbwWEUkSy7PjHaTUzEb8cZCtywkbwm8/zy0m3kUIfBtyHVyEFmwZAvQ29n0AsYn85iijZAgyaW9EGRQvILfD3ajK1rWa3ex9MJLOm6AshalIQLxrx0mIUfZEZvXhdr+7kFB4CZnRS5DZdhkKat6GBMS5CBl/hJhAQojiry0Rsl+MMnr+ipjJImIrwCsQg6yx9/ZEOfw7KCj6dTv/O8hU3Tupj8ppCPnHIuH6BHJZdSBcFqAskTkppd2LotgTMZrZNuatiQ1ptkSZFC5o6m1dypDm24B8lrsRbS+aEIPaHiG7t7xegnLsnbgakPutI7GtY5XNawEizN2QUKkwWHuMpz1iDtsjzel7iAGNtnmWI230fpSVshnRYqDe5gXRU8pjGSvtdy9Wc+brzdhWIYG3NYq9tDdYOoMYhoTdhUR21xZEwkGDwWI2kXXW3uaxHbKGz0buMx+bv1wrXmhjOxMJ5lFIQC616/5g9/4Zsdve14ld5bywDDt/BaKXnyLL4yl77liU4XQX0R4DRFfl9nmGzetHRP8pH3MVsjbqkUD5ElrXHRGObonW/iO0VtWI+W5lc9kRxbq+T+yP/S2D3eGEJr2NPS+HFQhPOtqzfo5cQz9APKMToost7TcQfu6I1mMo8ha8izKsXrbvlyFm/yHiHYuR8PwucrU2ISttD+DelFJ5URS32zkdEW3vZ589Q+tRhL9zkRLRESm6v0RK07U2R7c6ltt1iWgv8WWA1Mr+K2u9PmvNfx0WgeeiFyijZwxRVduf0NrdZzqQaDkxG7mAJiFkc43fgyxVKGPgGqTxvUO4A9yUHYpV8hGFOK8j4eT+YA+kdbdxHW0LVoG0gXok1ecR1kIzYiD3Ef4/N+cXI4GzEcEYhyIi92Kf2Tb33oR/fxnR4+cyxOzHICJ+DiF5HWKS7kJ7G23cA0L6yYQ2WmHjfQ8xj+5II5mIELsOIWQD0u6OJJipWypeFDXDnjsZaclViACPs/XpZDCebuN3+DcZfJ4mCuPmICKsQ/GgWsSUh9o4vChvX5vXHna4+8bdQo/ZWN3F9oLdYxmhPb5hR29b5/l2n4E2xqVE3YL74/19lc33CaJmxTNuGpGVsj0q/DkFBfNvJdKe/07UzjjeuPupHilFd9l4JtlcJyImUoW05cfseS/Zs2fZvF4lcthn2ufZyK3mhZwTUfo2iAHfY7Byl4UrACuRwJ1h45yMBJ7Xb3gcaiFh2Vegde9l45xu739DzP1yW8eFiNGfgATJtggfPH7jSQ7uIqpGWVzuenGr3OMq7nZ1YdtMWKc+RncVNyBrbLbN7zcGv15E1fUKxKArbb3cG+HxKHdfepzD8c+zheYTCQvd7H4f0ZIH1SBLeAZqeIg9700bu2dm5dXnFSV89AvvGnrPJu398p9EAawapP02IS1qMjJpLzegu+k0kAg4eXqbV8Y2G8AXE31TXKh4QNKRYxmRtuV+da9wvQ0x7qnZQruP0RHS0/W8mOZ6W0ivhv6djXlLWgbFElYXYL97gdGQ7PM9iGgbsmv8cKRz4eNBLHdlTCYYRJM9x11jHo/w+oL5hozjbW3qkJbomTjuA33B7uXtP/oRgTOPh7hf04PM7r6rteeONBjNJRjTbHvW75F7aDVyKSSkhV6FiPEDu+9rKNPoBft/us11ld23u13b045GJJjdUvGeUjsiHJxu8OmJet+sQYz2ZiTkHrV5dDe4t0Oa6jSiOOvLhO94jo3ZYbgE4dUJtqYr7Zwag/ubRIO2CcjqdJ+yM0O3XnqieIanQjdn5/m7a//OpJYheuiNNOIqgkk5bjXbbx4Yf9rW4SYii+YuhOPtbT172Tzutc8LkPLiMZoym99CpKS5X7+cSAtuNDgsRMLiPYPXyza2JwxeC+1eHmz9uq3hTxHNTLd7TUO8YazdZw2i30XIsjiJSGTwau3XDLYfEOnpS2mZlutrUE8U1E1BdDDbnu/KkQsoV3hWEFXHXjv0UbZWpfEXp6dpxL7kL9u6/MxgfBambH0hBQEy27sgk94lbDKkuRgR5ET7zassD0IEvBgRgufXrkGazwOIKcyzcxJKu3JG4znem6MWvyAGV4OyE7zS8D0iMN2AiMEtkWZkebjf95+I0H+AiHsy4ff0XjINyCJZjpjfeCJA3UxUXHrQzRvmuTvBfYWeWVNj50y2az1bxLUT943/jkhl9LG7hfW8jckFwkLE/BYiU/dQIothNiKE8YacbxBZTr1tHKMJn7u3zHjAYDIeWSyeejiQqBfw57v15Zqgv+eZRG4Vet77PFtTT2Wtz66tQRalZ/d8YNdeDiw3eOUBWg9qe1znGSKnPg8+j0Auq0qbV2+COdcQVoQz1SYirbYZWTp/QRala9K+PhWEpbGKSGt0bdcZhT/DrZXv2JpNRYrGSuSy7GPzOwUxqNuIliRvIOFUhlyaVfZeabBxy9HbOYxE+PAH+38UYkCrCe11hMHcGb5rsW8SVokX4HlR4BAibdPTopcQsbQtCFeSx9IWIQF4Iy1pLFeQJtoaDEFCbAnyOPQjUsibiBiSW3TuTnYr91giA28JUgaG2DPmIzfRm0SccbCN6TQbY4HcfsOQG3EJirE5PuS1ApUG40m2bgcgV2S9ne/xGRcuPu5ewKlfVEFwDULcs5BGdpUhxIvIIliFXDk5MzgREfRwQ6JmZAJfTDCJpYYETjgjkZAYagtXi/zyS2xBPXPG3SrtiD78zwDJxluJMkS+jIimI2JqOyFiehfllDcRvmqvOPVeLK6Rl9n/4228hxhyeoHUbMSo3ex9nciZHmbnH5QdY5EWudwQyKsPOxJB7NF2rwdoaVI7I1pDpDCuJISsazYezJxHaDaeKbGCELa7GZwmEdZQFWIai1B+/DFo7U82uL1oRz+U9eRCZily/fVBjGdM9p8Hf51wvbrWx+64sQLhy40Gl94IN94i+k69jpjUb5CF4cWCrqU5fJxh50LVtb0KW4f+REDXBd1xSKurQ26GPLXXM0RWIKWiDmmru9o8eyJ30jTki29EcZlRSJC/hpj7EwSOuJXsadTe+MwzhdYYLA9D/ul7iYZ/1YQLtR7hSw0t9/rw1hmuyXpevwtDD7Kfjbks7H77GvxrkCJYj2J2c5Eb8REifdW1bhfGPezzKqLNxHgbzxtEfc237PMLhGL0J1uXPkSa9FIkuO4w2HnDvX2Jduz727OmIbdODYpPeMfSxTaesUSB5oMGjzfs/J1QgezHiK+4xe7tajzVfJrd72UUM3na5vAIigfdb+dPQjygExIUT3wqvvtZM/42hMEuNtn3UHrmzUT3vsHE5uq5tHft0InRC0RcUlbb9xF2n0rETH6FUvcqCM17qiHEFEJI3IgIZxYikEZEXE2GfNNoWSTkxyrEcEYiRujB2ffs3vcRFsZyu9f19pw+RMFQuSHGh0Q14lY2Rvdvuhm9OntfYfdcwNpNsdwK8GrIOvt9DBKaNYi43af7HiLMHyGmcIzNrb3d4wgkJL1qc7XN7yaD59gMJu7acDO3jMjt7w/0M1w4kGg7cbbBfjUS/Lch5tjR7vcPm2slEiCeHeI+a2dQk4lCrmpEwN6baaAdzUTmWO5icZfZQMQ8vSZljcGwB7KgzkX48WekTNyKrMzvEh1i70aCfQxRjzKYiF/8l8H/fmI70eNszU+xNViOkibcIn2HSMm8xw7PY3+T2IvZW0Z7zOgde+6zRBM61y49r36swWsWkYzhmSxVyHXjxXMPIWa6xuDgRWaedbbU3htRbGCa/ebW5aWEVeCWV5V9n2Tv3oolIZq91eDh9QSj7b/xSOtuQvjiKZk3IEHYA+HTQhSE9Ww/16xXZOOuI7IW3a21lPDRDyDSXIfQMgW0mai/SLamrhw02P3G2Xo4/bxLZNmVEy5nT4e+x+D/HcIzcBXhfrwA7ff+hRQEHxHInReSeIbEs7Z4PYic3NMRo/JCrYFEFeCpiCn0J5rKlRMppo123stIwt+EtOujUKuGJiSp3zHgD6FlQNQrA1cTra89JbHa/n8bZV4MIrTGRoTc3QkCnkK4rzxjZR5ibO6r9lYTDdkzXfC4f9+1wMkoiO3uCQ9Cucb8LpGyNx9tlwkSxCPtvGmIIa0ktNn/QAztQqSlrkEM2C0GJ6TcLJ+Jud7sGTNtbK8S2vpSgji9gCf3b1faeF2guvnsdQVuqQwlCoHuQoTR1Z71S8Q4voW1wLbxuFvQ3WZewVpv834YxSIWIKb6PYN/D6SYnEEQ/3wbR7WN6UMbj9fF+JzqCCHknUnvIVwgpUeuZHhMyn+fiLTcq5EL73UUw/KUR4ddja2l+80dT6fbPNzP7fUNX0JBd98MZQHq2fQWcoksJrbKrEGMq9x+85x+x9EyO6Yhy28gkc58N9J0RyEh4u4Z98N7avhTdo63j/C6Hk+8uBsx9s2IGJ93nW0g0nO7Z8dbhGLksQPHLz/c+mvMvs9DTNur1pcgwXMEykAchxSUY4j9BtagTKEHiNqoRbZmlxOKq+P7k0Cd4aYrLv0RrlyIeEYl4Qr1GMwkg9UI4JovlCBAqWEDkab0TfvtUDtesMXubshYTXRfdAboQcRdEVJXISabkHthpiGJZzF4ib03scs1+Uaija375724xtst3EbshrWxLfJGKDaQd/1M2Wf37yaEoB8h0/MdojW1+4Z9UQfb706oiWgcN8hgUIkY3R8Qs/sQuanmIe2oD9pI3GF9DBK4NTbWXkTB1FLEBD3jyYvi3Aq62n5z188Koq/Ox0TvlipEtOXIPzrInn00Ypo7Erne7k543j73tWfvS+RkTyeyrqYiQeaV3LOIAht31XiGiGvu+VqMJfq0dEDMybOSvBVBNbJylhCZRauRlTqBsDBqCCFfRmToVBAdJV9BFs1K1A/qLKJHVY3BvILIEPs2kY0zHLkFutrxbaLh2GRaBt1XEIzPU2tX2+cXkVtpCmHJjkDxif8hXCSP0bIddl8igJwLeKebZmIzmcOQO8vjee7mWowye860YyPEKL1osBkJIK83WW3/f4/YRMYzZZbYep9OFJdeZ3MeQhRarrH7/RXhYj2RKOGqJns9AAAgAElEQVRuxF9n81poz94fpdhOQq6WqwxuryJX6MdEweMjyN+/hugR9iKKxdUavh9s164kMr+G2DM/JGJX/QlrdLmti3++1p5xJMIxz4B8BOFGA6Lh3yKhvBmymDYBJnzRBMFE4Dv2+TUUwHUADUTMbhHhw12EtIeTEBE8TstWFKuQpeBMfqkBs7CFGYSETxVRGOWaiLsBVhJ7nD6LiOwwotfJPXZvj0+4m8e1B7dsHkfa4ylI8v/RFtf7Ej1oY74HMVUPTi+3Z1aguMc0Iod+icHlZoTQ3g4gT2lcTDD4+Qi5X7U5ewW1C5eLiR7yTyJiO4jIe881WbdsPrJneivtqXbv/0II+jDKZphE1BwsJ+IIzUT5fyUijEaDwyxCEZiCfLET7PskJPDnI8IbSvjlH0RC+mFEHFWEQPPeRO8YLM83eCxGGtYsVMuwDSIkTz925uda6vtEsN4trWai8Ossw+PniUCv49QqYne9FYjJ3ISUi3m23hcixvRVJKwGIzfcj5CA90aKryNGdY1dd4fB+C6k6XovIrf87rJ16UL0waon2jW7+9AFiCdneN1DN0SXe9gxkyjo8zYJrkw1EPsuu2XiWXQrbC18F7rRSLmZZp89mO7V0+NRLMm132b771+EwHJ3zkt23xU2/sOz8R5o8OhLxAKnI2F1BJE8kLdCOQ6lcn5ocFpMZHZ5DNKLTh9AAt9jJMMJwevJL443bvk3AYcavtSW8ES37OcRFrlnlPVH/GOKwX/j7LqOwET7PGqdfPezZvytCIKO2edR2L7K9r2dIda/DHjdDZiPGXLeTfTr9+6P1yIf5F/tfg7Et5GGtjHKUKpHhD8KaaBjDRHnG3LlZuQFRH97TzFbSqT91SFNZiXyYy5CGoR31pyFEH0GIu4JKHvmZwiJz7VxuyA4HzG9ciLDqcKeNQq5vnrYGK6xubxhz38B7VVbi1LpXDCV+hors99d63em/YQ970Jk7j6FCGc40bZijM3JBbX713PXhTPRZYTLwIOPoxChlNOynUUdoTEnolipMfvNXWy97dxbkevGNeBRBnPfuvEYJETnIya6ArlAyoguqGvsulHZ/auRS8TdWLnboCH7fUoGg1mEe2IlihNNQb2xHiPiIq8AV9j4ypFQc2vnOXsfg/B2PpFt8wGRQj0O4cBi+9+z3tyyaZG2aM/6sd2zP+Ei8ljRWzauV2yM9xJJAfMRjv0NWUzzgHPsnt2J7Li/IiH7tq3zq0hh+TXC1fmEVbMU4fcYZJ1MRAHwJ4hgtLvdquxwJcuVlFoiCD/Txu54OAxZsk/YuW8TPv5FCBec3l1BmWDPHE0ok48hPB+H8KYZWah72Zi8DfpDdp6n6zrsPzb49Cc8G01EbzKfk+N4Tjue2NHXYDaDls0BG4iEio8RX+wMPPOFEgQlQmG0vX+E/PUuQV179e/eNuF8pDFNRozTi6Kqid29jiH66X9kxyzE+L0C81zCj+umumeSjCVcCj+0Bd45+729Lcg+9t+vsnGOJLRpZ4DepsKFhAeC3zQkWojM6Ho75xkiNuDmrFtAq5AGnGwOf0EMe7qddwXSpNsZXH3XK3fv/IGIfbiWXocYfp8MXnNQBsMoG8cbRLqab6XXhyioa7T7329zuhoxDm+nfKS990cIfgFC7BMRYbmVdrI90/23eaqdE4vHOgbZd093XGlwfB4x37kIN3a2/88iOrw+SWiM7xItDf5JBHZvNHj/iShaHE4kHHxExAgqCIHhKb+e9up1LQuI4rAzEBN8yN7/YfDYG9vS1M4bh9wLtxD77M5ESsFDBn93J/jhgtPfHX96EvjTHzHqi2xtPcmgElUQr0YKjLtkKoi2JysR7XiA1xMT6ohUyEpggc1hjc2rirDYXXFosGc7bfQnNgByC8zTXgcjbf8uG3sdyuTbGlkSO9OSobYmxBci/FiDFMkme1+CGHRng+k0IiV0a/vcG+GTF1YmW7MxhIByQesWk8eP3kK4NwvxrT4IV71pXwfCwnKa93UbTLh1HyW6H1wHHL7BvPazZvbrEQQ9kYn2NEL4D4lt6Zpt0QYagGfbgj9O+IKdYT6ZIdaiDBnn0rIT6UiiaZh3Hf2nXe/HLYZwI4nik5eJQOWLtJTka4ht+LYlkH0zQtAdRDS28xxmT49dTQSIfcGXE0zju4Y8v0ZMz1Mp86BWTgD1SEsabp9nIQ21mkj18yIir6pejTTocsRgR9v1Pj5nKKuz31w7WWyfH0ea4jJio/pRiGHNI1JUnTBWoAIr9797TYT3p9mD6FGzP3LH5EVxbuE44U2zNaolhPxTRMqrF7d5xedKFIuYb/Odggi0koglvG/nevWzj/VvwHs23q/Ztb2J9MwjieKpZ5ALy+sDPKBfjSzguhKauM1geJI9z+HivugyRBP9iD017kKWSBlidtchnK0kuvhebrA5146NDc6zCL/1XKIA8XWigdzzyFp4hIhFuNI0nijQexzR2XEGk0eIvYsftLF5mxGvynXLdAGRrdeAmN40hFNzUQbQl2x8jrdO5+5aTdk9c4HgiQceu2uwMS1DQngRoTysJCzaGwiLt96e48HwRmRxeJryIoPnW/bfX5BQnVuyvmXACMd1e9/O4PG2rWt/omnlZcCw7Pr2SOjt7seG8NrPc6+hE4hil9MR8XZECPNXZP6fRmiBX0FMcndEaNcgX+kVKaX7i6IYj7SO7xLthLdHQD0FMadyws8OyvXdnGCWJxOm+W+I3iC579xz9T3Y9TNkDp9OBI+3RYjjFZgJIdgCG1Nh5wwg+q28j3zDfo8ONs5tUNBtXxvHALvvV4j4ygDkM/2H3cP7mTRm46+33zxTaSjq21SFfNRDkAVxSlEUTyFtayXKHtrCrp1r79sjhP2GjfFoIkZyocH4D8jsr0Aa4RyDd3tEXJvauHystSg+8y0i7rGDXesV4HsRWuq9iEl8YP9vZvc5DcUGuhJ9aBrQ+v6nzalAwba85xCI0IchHKpD1s5VBuODU0rbF0XxiMFkns3zQJt3hd2/DuHa0QQTAWneJ9o4HR8SShn1ugKvin6FtXvogITh75FghKhP2RRprt6DJiFL4xiEV30RLu9s/89DLqVOSMnY0+A6GfnaPVnCLZTmlNIJRVH80MZ2D0r1nYMEn7/aoTU+yMYz1N43Q3i8GUFLHVqZ4+1o/eqR8LnA5vAWsSXo9gbruYgmdrCxb4os/gcQTXqvq43tewXCyX1RcHUbBHtfi3qktPwI8aWjie1NByAX0U7Efhle3Hk8Eqx72v3fRe64jRGebov4EUhoHmb/zbX1mG3vM2wsT9u8OyG6Oxq1KGlv8D2fEIR1qN7pINb3+qy1/nVYA/9CxDkEIeM5iJntbv+fisynexGhlxEbjYxGjMozeU4nAoHvGzCnGqA8qLOK2C1sJhGQ9hx8j/B7BeQA5GaZjAK1H9jz3Lf5NRvnClT9eDeR2eTFI3ciwv4YIc0T9vtIwgRtsP/6IYR5hNhX9WNa3zv1JqxVgI3hDsTAvNDmXIT4ZyEGcD7RdfF8hGST7b99kNBcQewJXY8Y4L9sPt65si9yT3jPnFcRU+hh119uYyiI7Jfjic1Xutr8+tsa9rc1cRO+mbVN+2ZkpbgGdgvSwLz74kB7pgfLPYPFO3UuAf6UrdUkxFgWIGVjHtLItiMKvsajoHp/G8PdBvvvE24gz4LxzJuX7N3xcSpa+xeJ9iaecOD9cEYSFdI+Nnc71NnazEAM6TyiNsOtol8hobQCMbZ5KGb0D2QNfde+zzc4ryaCvZ5mOpEIcueatM+tzsa4mmg5fjOR/eaBzFpiL21vm3wuUbfhVcMvGUxG2P1fxiwfg10tYW3V2NhWIBdJL6Lo8nnkQfjQPt9pa74ZEgbe4qIKMfNlCA+/Z/f5u53ve44sQEJuDmK+mxLpxrvZmk5B1pe7Kt0rMM7gvRDh3tU2nidtPlPsKDPYuLu4kSjSG05U4I8jaoS8TsHjF0vI6nA2mN9+1gx/PcKgDyIYN98dCb10PDftWmMQ/u6BtTJkUUxEzGglsQnNSJTyOcbOm0i0UNjNxrPGEMJNyW7E5irnEMSf7JmevlaNNKdz7PdtkGbVEwUNpyMCn4MY2GU2Ng/SucbuPsH3bcF72P3/RQgHd9N4BsUEm8dU5LKoQwx6NuanbQP27yCiGYO01YeJ9sIrDEYeX9nLkNO1OQ+yVhOuhoVE240ZiEmNsTlPQ8T3to1vgs3rDqT1bIO0tulIg1qDhNUolHlVS2xy743nnHE500pE/5pPtCUbi+fN53EHh3lCxOafPV50gMFpCbb5C5GjfgFSCB5FAvNvRKHUPINbN8SEvaVGlZ3jTMoLs2bY0ZNok+J4nbfZyGMAN2Zz93HnxZetHdVEKxV3M3VFguFvdv1wG1sVkfAw3c6pI1o9rEEM2IubZgLXZq6PMqKRoG+g8n0i4WKRPWseckFVEpl4XqfgtSpTEf1NJrawnUuk0Hq670xEI1cjt4wrfh8SmTeeLl5nz6xHeP8uERfxbCrHrwEGuwkZvK8mAsiPGxwOtTm+TtQ7DEcK5qVIMOwBjDR4lCO34gFENwR39zq/82QLzyysYgPbTn9hBAGqYF1C9BivRn7Wx5AG1oBcRMuQ1N7BgPE7A8yDiBAfIxpjvYKI5CXk2nHNsRER4xrErO4mfH61SJOcSqSmeRaCB8BqDPHWEL3Wv0UE3SagtLQ97bxhyE+7Crm+nMEcAvwoYzAVhM//z0iDm5+NtcLG0tXG52PxfHdHHg8w5YFg94euQgT4kB2+v/AriEEvIipU30dM3bXEaUTqm2vmg23M99j4HrbzvaCvkZYM2hlyfngpfj+E3I8Sm45XEUH8OnvWWPvey+BQZr/vioSLa1ce4Kwk4hnucvH2BZcjBuL+cdfGZxJZGt75cSLRqmNq9vsxRNFaFRIyGxFbYvZBTN/3TrgZWQeeT+6+7PNsLY4ggqtPEcqIJ0VMQrTRHQn4boixrURa52rk1pyAGPfd9lth3/dHdLLEnvG8wfQXRL+geuQuOhXRzXY2/j0MNlMQHi5AisYAwnJJ9uw7ieyyiSjOMYPYYMj3FfBsNV83L/Dy+I6nlHqSwRiDwdeRa+UwhL9ej9BA9Dfqbkc56hf0ZcSkK5Elcp/d63h7zovIOnC8cbz1oLjjbC2x13knO6c/YWF9oqUjz0Qz8gAsJCq6ByAaaoc8DE/YWi1FitMBdp+RCN8723o8iRTKOluLd5EidxWWkLG+4/McI1hM9D1/BHg9pXRwURQ7IkT7GjKNr0ZAuhwxnUpkqt2NfJEfI4C4D3xzIgo/Bknnr6HCprlEC+idCHPUz5+IBEwnhNRXIp/l4WhRXMN1oH4TEeb+iGibUd/xWuTT2xQRjvv7mhFxLkfm/oTsmqMQUl5N5Mx3QgJwF3tmGQqYbYr8nHXIWmhCQuQEG6+//mTvnbIx1yGXyFH2fV8il39j5Bc+CxU0HYcEwSjkC78VCbi+CKF/jQitg43DX19GiP9aSmkVQFEUV6N1SmjNMZjeSDDjTnaAGKbHETYi6hJ2J5j6KuTmuZbIvvkTWtf9EP68h/zUlyH/+lWIEXZBa+b+9j5I0HSy89qj9T4A4Uo7ZFH+CzGIzmhd5ti7uxO7onRQEMM5A7kO+xB9p15C8YL9CQuvPaKDhBjyRkgD9teAlNJNRVE8hgS2JzW8jlx8tyM30XUGiwMQ894BCYNHkZv0JVT/sRXC060Qju5KVMFuavDfxsbWzt6vQYLvcURn9UjRuZrIjnoKCUm3Vg8hsqumIzzqYmu4HWJqZcgV96j9dx6ib++9tCMRy2mw8/dDeJkQHZ6CLMy/ovV1he27CK93Q3g+29ZzERLoLsyqgG4ppZeKohiVUjoEoCiKMQhHf4poxuG1p63BrwwWj9h9QdmGBVK8foKER2d7fwnhSLP9thHhMtoKuaUS4hdTEH/zuOgmSLAUCO9eB0gp3cT6Xp+15r8eq2AGIhrvQz6EyI92s+hRRHhuyrlfzrNsVhGmrfs+m2hZ+FJH+OlWE+br7fZeiRDoj8g8d7/dd2zBZtGyJ03ujnA3QzUKng5AmvQ2QL3NszfSgG+38U21a59HltFUQosemz3zXEOem5BvfgKR93wJQnyvvnZ3URW2qbw9+wdIuC0g8t3zdEx/d6uinshoqkKE83eiUrOR0NDmIjdIhY31TsSk5hEFad624lSCgbu27nD0NW2w+76HmMtCu/dkG4fneudWipvx1Qb7RsRAq4kityYia8sLdxoNDo8gC+56YjMgT+N7H+HGpWj9NyGqYT2NsDtRh+Bbl05BWt0b9vlHNqbVSNvuR2wH6hljCTjX1mwKbRQIISbtPXeSje99Ir/caxvc3eBwqrL32UStSpnN5UQknGvsfl5h/EekKDyFrINpxD4atYiJ9zJ4nUZ01ByHNk2C6O9fiywjV5g8tvIyov/lRO+gRKSZuuXwAmL8KxA/mI40b09j9eKwqUhbHkh0KHBr7CPEUI+zowdSZnZCysQIokXITwg8W5kdbnm7K9Kz6LxTgWc9TUNMvwbR6XQbywsGp7x+6gyD5XlIqepAWPSjiD3CPT66GdD3/xXXUBeCmeZZOQ1Ey4JfZMD3c7zHS2V2bR+75p8GwNeR6+J5RFQPIE1wjV3fCZWEl9kCeMZEa26MUmY5yRbyeiKrKT/PNTxHluk2h70QsT1h869F2kQ/oiJyFdL4VyBN9ywi0NhoyOWdKhcSWzf2zOCXp7Y2E1tRzskQdiIiPK8jqLff1tg4vCTf3VGO3HkQ0VsPLEfm9iOI8TUgxuFFdBcQDe/GEJrjGiT0DjJ4uMnc08b6Y1uTdsha2RJpQgciQr/c7nctsgwXEEqAB94fR4SzxJ5bSeyf4IE37z3vKb1eKDaF2CLSi+MGEAy32b6fYPfa2q5dQmQE5YqIF8l5gVDeduElbMMRpP16TclkgrlUlxzuYvKWKp4NNs3u0xUFOMfb/e6ye16DGLIrLCvsnOMRQ3zH7jMDaemvEO2Qr7KxdEWa6jjkF+9HxBGeQMxuFmK+1Qb3GkSjP7R7X0UULD6bjaWWiDe4IlVv93Dlwt19j9r5yxGND0bMfBrCx762bv1sLZcSsY2cTlcgQVlp8JxOBKorkHXePzsOtbmPQzi4FxI279t4/4X4Vx/kkhuNaMHbyLyEvAy+p0IZlmqe8UdXPj0j7zAihvKmrVEvoNcXXRCMNaCcBexqv41A0rGzAcL9kBMQUXj+sRfc1CJNuQqZkq6xuL+xlsiIcJdEPUE4uebRx543C2Ur/Rr57QcTlZKjkeskb2T2OLGf6u8Qo/ZS/seRZliLBNBfgM52XSLyyfPCI9eUlyGm+L7d2wPTqxGjnG3ItjWxocn2RH7xA4hYn7V7jSJqBgYiIfSw3XsZYrzj7B7LiW6Rj9i5E5BlMcnu/QARkHNraTmR9/6J1oKEzBMEIe9C5J7fSwjtvILUi++qifRK16xGo2Dte0gAuCb6mo3xOhvniTaHgUjz7o4EbF6x7MV/HhPy/zrZc96x+yxDVp6vh8dq3BqrtfU53K6rRRqe12/8k9h/tp4oNnQLZxnResMtEsePCURblqOIOFKZPe8Fg/E4++3Ldm5BxBhGGmwX2P3/SLRX8apkF4xeeZ2IDq+jsNROu/fuiH4XEbjrzLWe0M7HG3zdMluMhMDW9v8CxIBvRC6xvsgle6rd50xEr68StUEzkCUxgbDGhgHPZnzhEkS/fYm4go/9FaRhexJEd6BnKzzqEJt3U/ZbN0R38+0e4w22LnBrDF4TEMN24ebKZEK064qHV1MvQt4Bt0oX2z0/NjhdTOyLvATFDboCXb/QgsCA+hzyx96FCKU3YiyvE2Xwng3hGq9r7Xlptv/vaVn1xH7EE2nZldBdES+QFesQKYdTEDPcE2MGds4myIQdYUjwMnJf9CbM/qGIOfWjZcsKrzadTOzQVGHj9e6BHlC62cYzx577EPLDeuOvvKhrKWJwzrzqaZnWeKzdw7VEh89SG5e7WsptbktpmX3iLq92hpQVSCA6A/U2EZfbb68RfWeORQh/Gy2tPddu/d4usM9FuelHE32V3Pc6AcVNzkNMYCbSBo9DxPAuLRnnGnvWy7Y+LhjeQ0Q0kthZ6x7kWuyCtDt3s/zc4HMtYjLNSAkZh+IQvm7eI2cOUQdQi4LYT9h1bxnsKpBQWk6kSbdD8Ys1hEtmD8Qgb0bMtw7h8SSEnw+xNl2UWtb5kbsy7yCywjwx4gbCxehWzEqksb9GSwGdWzqJ6F00AVnJc2w+J2R0vjXRcXcZrY+xdJy5i8i7zA4lhL4rC3n1tAvzRqTUDQF2zBh4E2Lei23Nb7a1u8pgcg+i/R/YvBcjXHkY1ZFAdGEdgmIMfYjU2jWout8V0WbCInIrrxkJtv8h6NTd4LnV3YyU0AXIheU8pyOwxafmtZ81s1+HEDiS6PvhObmuWXknzIWIsLyx0lQkMBqQdLyWltV5f0eE6f37VyMinGUL5z12fomY8mI7v9buVYqQntLpDGYO4XecgjQhZ2QXo+CRVwfnRzViMi6cvoQY3bYocFeJNKxyFGcYRfhM65GfNtnzfpjB8EFkhtYTfuY6Ioj2MUL8FxBiu8DI55mISuEmFGB1d8ajxIYiTvjue74MmJGNZTEieM+0cg3RtZ8ZiDF6vOendl0prHplMJxG1CUMQ9rw7Gyebv25BdVIaFIziWZhuUY2CQnqVcBeNobRSPDXI03rTVuLG4lYgjf/82wSZ+zdiFTRZUgAvIEYjcNqBVGEWMq419gYRiL8Xo2EZxWx3/IsO2dnol3xDFQT4jj3gb3PQe64PyLhWoaY9Sib805EkeOjhKXUiBiju/c8FrTAYDKUqGMZjZhSP6IB4gMGlw9L6HwH5B55BwVDnyZwNXdnNmTPrSHcpXug2FN/IrbhiQN3IyvqPcKynWjjGGBzdYv5eoNthT1jjt3vAVr2B1tjMPB20zfbUY5w/h3kpjzY4ONWrldw72f3abb7J6LjanuiAeV2tPQs3FYCt3IklLzieDQt29YvRHRUsUH89rNm+OsQBMMQUY5GBH+ZTa6//b+SYPK+29FYxBTKkCWxBBH8qXaf+xAxv0lUp5YT2y16fq8zqgb7/WGCOfyWSEn8LdDJnt0eWGafx2TzeAz5t0cRWo+7STx1zt1RpxpCfki0oXZtoRPyf48gfP4LkVbm1owz2UbkSvHyfQ9gupviFVp2QXQBuxAx9heRxr6XzX0ZSoedTLTQWEW09/Y4zTKbo/cwcr/2z1HWF0hAPokCpN7zqIzYyH068oOX23o2EtvxTTeYrEEMroxoQ7IYaUivGEy998+fERF74Ny1Tk8Y6IYYxXX2/4+Ry+5Fwr+eC4yHiAZ/Yw0OzyEXxSMEE7m65OhNNJ7bk2j7cCtiGDciQVCHhFl7ZOG4m3OQwW6wnfO0retI4JQM34YjgViGFIlHEA6/hwT/UmA7O3dHIvYzjWgY6EKoN5Hp5NXZHQn/9ApiU5bVyL9/A/JxT83mPoTYjOh0hOenZnR+JhJspyLXb72ddwtRaHgistB62jPeQ4L374QbxTP79icUx2mElT02+z1X6PxoRAVl3oTOYx/uxrof0ewvDGadM7g/gHCwD7Ez3BIb1zKioeErRALDVFuD/RFu9zI4u5Lpca0Vdu97smO+jaXK/h9n101FAvxh5Krc5v8FQTACMdDRhoBTiH4eudbqvm1ngk1E98+ZiIlXEz7WJkRsnhXh7iLXCuchf33O0Psa0NvZNdW0JMA/2D2mEIy5HhHAY7aQV2RI5VpLI0LqZYhgPV95AWJSnpY5DmmCzxNukyvsvpcgxj0GEdMdBIO/k+j7cr9d+yYimCqUXz4VuVzuILZHrEEm8Gxig5ZVSPOqtefOINwEHjzzNRlu9xlgn3dB1sdcoqPlOzY3N5G9R9QaYvOUGlSI97LB4F7kIvJeNzkhjyME/Fwiy8WtArcgf4mUjG8iHGgmtKlqQqA5nCoIt9hNRDBxEZFVtoRwazTZ2lbYczzLrTGbUx7g9SysZoPvbCSM3rDzetl9FiBB4DD8z1boZgKhhbqAn0EoOf7clYTv3mmpNrvW42eHopjca4ipzrB7vIGY3eGIyU5G7kMP1q6ipaU4FlnfHyFB+riN112cnunzNOFWXA383v5/wf77FbEhzPIMfk8gAeXKkGeZTTSY9LDDGem4dfCecUiIeqdVx+Metsb32Fi/QbRIv5noU3UesrZWIRrO2603EtmMC5FicQ+xn7PTrVu7vuHOYGL/6HORi9ID9s3ETnED/o/47WfN8NexGC8b8McgzaDCFnYkYoDltmCrDGA9kCayyhbQGfET9t9CA/hKwp/qVbmNiMG8S8ssoMTaFZkVRJqlm8J5kZZrJvkYigx5q+35m9s8nekvJmsgRTTM8sNT0aaiTIyHiZROZ2ZNhliPopz5GwxGZxEZUb4ZiaeaXoWY/xs2vveIVs9nEFlY3W3uDyHh+jTSglbab5OJzUwasrF5mpyb7SOI0v8apKU+jnLcvfLzSeDRVnBiZ6SNLTJY9UDW2dv2zPftcy3y0y+zsXq6qgc93QVWQ2y8s9Dm7QJ9jsFvvs11CtEt9XlgajYu74+0lLCUvNLTmdJ4Iv13LLEhzliEi5cZrE4itNFRhKvFhdRyg9Ni++6fx9o4XkVWzFiD+x+Ri3AywpMaYqc+t3K8jcsi5Js+hJbK0RoigO3Zbr+wtRqLBaoNFlsQPvUmpAS467YD2Q519tsHSHMts3l+gwiG+7OcBvO4kbu0phEW+8V2rhf9uXtwKbLef0sw0smILs6xYxYh5PJYh1u6r6NEEN9Y6SoifdT7irmlXInccvXIM/E7pMTNRZbg/YTyMxQJuRWINuqQ0nmQHXsQRZ6jSmA31N69t9bjCD+OJATUoRvCbz/PBWXbIh/ftxEjHYrG1gcAACAASURBVI2AUoUm61kOvwP+nlK6oyiKWxDQLi2KYmOEgF8uiuL7KKi2GzKbm5CWU4MQ6BTkCjgR+SndtbKF/VeLGOH+xM5jXyYapY1BvvMHbfjnIm3yNBRw3rUoCkcsb2RWa+duYu83IgZ+GSLonggp8tcNNt7DEBJ7euJ8hFS7IzfJA6iF8DPIbXZQURRfQc3jdkTW1fVIC/sAaciFHU4IGxPFQ9jnzRBD64wIbjvgHymlW4qiKEdM9L8RM/kZ0oy8J1NH4EspQ7iiKMaiqtleWEfZlNI1RVG0Q4x3NtHT/mXkE22PCPZjRHz++m8igHkiUbS3bUppZVEUTXb9eymlQ4qiGIG0vo1Q0NnX/KvIFfE8CjZvjoTFnkjQzESurk0Ii6QKFSV2R4VFXQjt/3s2l4sRsR5o176OBO3ZSLG522B9PBKE1yFB8JHB8DGkyIAY2l3ZO8ArKaUZRVH0QTjagKzE0xE+zrB5LE8pnVkUxcc2tpkI509FzKo38nE7nkLLxnEgN+FeNvcHEFPFxvkisrQ8zfZ9FJN4BQnu/0kp/YffqCiKQw0uBxhc3IXkMbqD7fpHUGrr1w1ekxGj7YysknEppa9Yc8l/Ifz2Bn4+D7f8NkG48SCiSezcnyAFpR+qEdoG8YhOKaUjiqLYB9gspVRmY+9D9IMaaOMeauPexWC4HNHZczbOk+1390psiXBwIyQMNrLxNNp537ZzKtEaH2rPPprojHo8ch27Z8SLZ8uBlFI6gfW9PmvNfx0WQTtKzDeEgN7R8y07qpGWfA9C/ipC02om/MU1SOtpRkjkGkM/xNg9u6Eei8DbMycSmUHu6+9qC/qiXTPJzrsbSeOhRE/5/0Iah/c0GUmkk3paaD9k+l1LbDIxldiYfiUSOjfb4rvG6gVM7g/3DAMPbtXac7yHT6XNo9zmcYbN3fcpvi2D4ULE9N8gNsvJsy7mGcw+RMxonUEppJ3uVPJbOWJGCwwmZwJ72n+rETF+YM/rgZjFfkQBkiN9W1kmpYefe489c4StVYN93w8xpbnE5i81KM6wDBHq1URKcUN2JEJzLo295O4r18C9QZv/lmytKmy97zA43ELEdvJAe19geCtwfsjm9YKNczThavFUz7OI1F6PiXnBlTee+9BgMdXu68HsXPutQ3g3nMivzzVpD+ym7JpapCjsk425A8LHYcQmR+cirXsIYuRuDbiV4C5I/7wgg1ONHQMRA52ClJGHMpou3fBqHLCqxMpbhhSBW1ESyjfJNG2D7c5kLZ9pmTDhsGgirIf/RvziViSsxqBg9YH23t/GvJjY/OojxHvKsvGNRXzofIP7PTaernyKtFE/vC3t5+ZVFEW37GtTURSezgbSWhqQ1rW5/eZZRNva5+8ire4opMF3JhAJtLhd0OK3R8RxDGI0tQhpOiMzEDvPtffOwCYppQFFUbyCEM+rUb+NzOULEWI/b9c+hpj62SmlGTZHzyLw+3e1MR6GkOcvyBIZh7TDHkRL6k6IGLaw+a0h9ktdaM+7BGm/cxBSHm/z37UoiuUGw8LguDFwYVEUdxlMvaBuK7vfvvabF315te0vkJAoUMvrjrTyKoriDZvT5sCEoig6IGJ1eL5p990KMeXniqI4HSCl1Lcoit52/elEe4zOBr9OyFrwwrqFCFc+QC6S4wy2Bxps3SL7tb1vZ791MDgMsLkdiRjh2YjhPotcVycZzC5CxHmEjWc4EprDkMC7CeFFYfc8GWmxx9u6dEJCbZyN4cs25sOR9tcBOKcoihWIIf4Vrek0pGnuiCyUF1sB+Vxk+Z6C1nl3m+OtSLMejYK6IAtkpT3vMURfPwceTil9syiK7YD2RVG4681fvew5ZyBXYjtkVVYi/NgBCfYLEYPyTLftkLJxM3BpURSjkeuqsHn/C9FmA2J0ryO8WY5oogNaz5/aODra+c8g/DgGWdS/Ae5LKR1rWvzolFJZURS+XuMMhvMAUkrN1ipix6IoPIPOg7wnEu1Wbs5gsAvyUIwj1roLEr47I7pzzbxAwvkIg9l1KB7n9xlp18xJKe1lcNmLqANJCOdTURTVCFc3RrhZbsdihBMn2u+XGk3/MaXUl/W9PmvNvxWNJs+08CKp6USkvYbw+40lOhHeTeTydkVuFq/W7I6Exc0IWatt0cYj6byYKP3vw9oah/tnPRhaRWiRjjBTiJLvfsiMPQkx0XvtvvcQGrb7EpttHJtkMPg+IjRPPZyCCKXWEMADT8/Y2F+x+45C7pg5hNXiLY1dS3GtzzXVFTaeF+ycUchMnoJ84wtsrh8QPleHlwvpIZgfuJX17FpynJkdlYiJzwZ2yTSdQ2jpgy8ruadniXnW2GQii2IeYiiViDm0R607OiOG1YFozZFrrzW0LAz6F2I+ntpZZXD7yOa7mGhL4t1xG7L7rSYqfJtsHSfa9ZcRaZ5X2XpNN1iPtu97YRvh2HkbIVxvjxjEplinyhLYnIESBGZgFhihTTdln/Pg/GJkSfRBrs77ENPqi4THRFu7x5H151qsu/auIrKb6hBj8hTILoiO/4IY3ltI4HRATG6lrZVng00idpWbhIRsTxvj/Vh7d3uWbwjkRWO+sZHXw+TWWp566gHbPkRKcl5/4IdbkTVAdQmcXShfjZSO15Cb+gmkQIyz+b0NPGjX9CA25jkOKR097L/7gCMyfL6G4AG3YC1hMh7p+LIUuZW725hXIkE4H+uOsEF897Nm/G0wjw72fhKRy/ysIeZvkSQeROQRfw35z59F2lonRGyzDaG2RNrkfFtcT6t8kZbmpZvzTrzVhrBn2/n72MJMtnPuwnLlUdbNGhQoHIZ8o92IVLQyxIBmIo3UmdjxtuD97Jhj820mCrpmEEHbHjbOSXbv1UjAedX0SsSkPetmPiIkb//sefa5+V5qznYjqkw9E8YzZX6ONLJpKHVves6UW1nLvQ02W5cc30fEsjXheutM9EBy+Jdm2LiroTo7x6upPcW3F/J1ryAysnqVjOtYe34vFH+4iBAwFyENrhYxqXeIamHHjZXEpvC/sXU9F+sHVPKsZOu/d/bbasRQ9yAC2Z4M4IHk7ez+LxCFks0l7y2YVDaH2xGj+wXRc/9vGY74XPJ19/t6skQTUfnq8Rp3TbkLzCuvpxhMm+x5nqG0wtbkKax1t43vSnv3TK43icCsp/cuBbo7fhkMt87Wby6i6962hveiBIjxxK57S5EAOptI9S5VTrqi2Np/lf5u52+BhOvHdtyB3E4dWllr7/U0ws69FaVKX4MsxXdsjK8huvwQCRLvb+XZUk1Enc0C4LKS57hF8x3kYppp890eKcXOl9a5af3nXRCMQX7ZSsRUt7LfJxJtf2uI/HLvTe5FI01En51cG3AEd8bmqZSVhjzX2n1/gXyAv7FneQ58M9J2XjHEm2/3WEhkEDUhaV1N5P/+Rza3j7M5eh79OKLMfZoh3TJiU29nOkOR68Gbubmf+RdEb6U37Lnu+52LEHwiYjJLUJbFOKKNQz1R4p8Ia2EOUZW7DBFHuT2rCjGrryMGfoOtVQVR/1Bh8D0o+15JZEF5pXMjItipwOWfAk8uRC6lroT/fybK1OhqzxuMGMUg5J91a/MftBT+zdl3f69FpvahNrZD7bmlmS/tiB5BK8i2m7T/vZ11I1FsVo+0Q8fdp5EltDdicnvYtcuQ8PeqXndHTiC2u/Q1uRcxDU+RfAvh9Xzgx3a/Cfa8ycQezSsR3j2P0jO9ZcoUZLkNsHtcSbRnX4QEbzNiVl8hekidiATRQuQS+x2imbnABBvHTHt/CVkh04k9eu8lsvz8WGjPvMfOuQ+40YUfoUx0RBbKMkQL44CO68Gjo+1+U208P6VlDCFvOeF9g6oQDU+1sS3xtS25txd1zrK1KUdWw8cG375EmuoEO+9AhMcPIab+cStj3hrRTCWyCLZCeNLXxjQdudXWshpbOz53WUNFUdyOpHM3tDgDU0TK90Cuj58hooJoc/wcYnwXISR4BQHiIPvtYOTn/SFyrxRo4V5GTPLvSDofizSprey8K1JKo+35vVFAcXdi97MO9qzTiC3xliNTvgvRtM5fRyJztwLFNRaiYO1/2jNmIJfG+zb2bZDmsA8itHdtrJsRu2d5R8zVKaWdzMf4dYSo2yFmfKbB7CnEUIYghnkaQqSjkY8xGWzaIeKvRMwpISTFrr09pTQzW7dJiCHsgJjKhfbXgJTSAbTyskyjq5BWsykSWPuklEqzpVq7divEfLwt9SWIOMahLSEPInzwH2WXnpR93jybEyj43onY8HxvRLAHo7WuREzgAYQfs+26zvb9I2BlSukUG+NXEBzXZPf/DsLBoxBDWI0sCbc4vAvmpimlZ4qiKEspHVoUhRdetfXaH8H+fxBDmGq/e7rogUj4tkfa8dk219eQ9ropqmLuYbGB45Cb6VaEx71s/l1Q0sYbSdlo41Am0jGGA79MKQ20+U9GguBohPdbIiF6XlEUs1JKuxVF0Z/YInQ7hGf+8vjNGkRP84EbbIyTUHxhFhKiGxl87rLPB9u8DrP5A5CtzX8gt+R/GxxmIN7SE2nbGyHe0A14MWWZTnb9PBtbb8KCx66/EHUH6IDocDii9XJbn/ft/KUppQOKotjExrqVndceCfhfIuE5IKX0lezZt9u4tyIsh8JgdxzCrW8iYb5LSmks63t91tp/K5LOtTM3W3MXQbUB6EtElL4K21Epu8coW0DPEz+ciPT/hCiMyo+V2edPMgZK7jsWEcJ8LGvDgP8hUS08HGlQ30Zm893ItP+BHS8gJrMVElC+DV43xIC8otrh0EQUm3mzuomEW8AtnFyTbUaE6+6f1vKjHYFyl8tqZI3sbf/X0jJLw/sYNdhYuqEe7dByA+3Ns8/T1rHWpX7XM7AOjevBEd+pazXRAXUqYoAfIGZwBrKUmigxq9c3NuRC8f5DJ9qcbyWKktya9KKx6xGR5+6EH9h5lfb9PTL3FPKtD6blNpB5/UoT4Ya6Bwmkkcid+Rxt5IgjK+CgVn4/HGnILxBxMK/3cH+6P7MOaZsz7JqdkZtyNNFKeyaiPY+1rCC2V32FltunXo9Z9dl43CLoiizxgUgxK0NMsa8deZuSPyHL7nVE49cQlbil8FtDNAm8zJ5zNq3EapBwLs2gO4fo/7MC+FU2jqORMjSsFTiPQUrJ15EQKrf3ofZMH6d3Ci1Dmr/XH+UWuePBKuCkVnikx0c8g8trL8qRO+pYrJ/Y+o7PnUUAUBTFaoRQBdL4p9lfXRDz95L/XbPLGgnNqzMCVHv77prbNKQ5zbT/N8/O6ZJS2jkbQxlyT+Wvv6IF3Bch46aoJUEt0vZ8g4522TUJI/aUUhe798qU0mZFUfzK7vVjRGy+w1Ul0hy2RwzjYmLXIy9U2xppHFsjBHYrpzfKnvCMCq8N8Lxwz2TwsU1F5q5npniWlSMbds2NREdPL+q7057RB/Xkb4cIelj2jN+jnZkeyQFZFMUFyBoYmFK6xDT8t1AA7TjE5O9LKY0rWQOKoliA3Fu/Qa63D5GWvQQR25vIajwGadpvoSydb9ich6B1PByZ/J2Qpu/1BJ7h41rgPig2tTki9MmIWQ1Elke5waGOyDi5nWg7ckhRFKOA9sk2ErcsFZBCUKD18s6RFwE7p5TeKIriXDvvjyVgmE8rOeJFUVQgt8AexAbwKSkbpRzh1HCEd19FDPWrdm6ZjXURgQPtUHzpEmRhgejuLrS27RDOnYpSLDdC7qWZNoZG+21VPkyUHNHBxnwlshZmEDjnryuSeQTs3G8gV1JfhHfdUWbdLxEz/wEKah+AeMVAJJy/Ztl6HwIXpJSm2f3qUkod7fMU5IX4byTw9kdrfway3ubZMJYhN9nfkBXjbcdBdQZfzcZ7A3J1fQvh7BZ2/u2InrymaDNCYbvW5tAPCaSdgOeSbYZj9x2UUjraPh9v8z0S4fhWRHHekFIcafX1f0vT/zQHLRtK5ccE5Bbx4JUXcTyLgphelVtu33vZIpYBh2UayOsIiRYawCuISr88QOlN5eqRJugdGfshRuVVqe73rbbPT6GMna62uHvZonyAiKaeaIewDLkrpmCdEG2cv0IMy1s1jEWC4rsZPM5GyDWMSFfrhZhhP7tmOCr0Oio7Lkbup6PsPmU2348JYTQHIaz7N48hYgquEXnzs7eJCtfFhGXVD7krBtvc77Djk86Pdo8HEWM9LdOWT6Ok0VYGmxH2Xo31UrH1eMZg9WdEGGPt+TVIC7zTjom23iOI7o0NBru7kOn+rB09iI6gK2yuC5HW2w0JkhuR5jaUsBp8P4ZP+mHRMvNjJMLTV239nkDM8kD7/0qDw1oByfXQTj2qzdge6zWTwWgasUVlPi7vDeWB5kpb8yrC3VpHVOWOR8zdd9O6kswqJ/YEPwAx6CUIt/oCN5WM91RiW1W3aOtQMsHWrCPrhZb7+34fWQjeTaAbYroX2z0vQwqX74XwCGLOddn91qBY167ZfT2o/Qck7LvY977IYlxlMJxv961ELpludv/tECO/EmU9LUcWwHSidYsHiL025ilEh97OYmvWrip2T8PPDIanIl6Td0TeD3hhg/Dms2b6nxLJPdA6xZD0CluMYYjRzEWCoobob9PRFuc6Ih3Ts4Y8Q2WDd/QhiKWjIdokpM2fZAs4E2mMT9sCnkQUJo1BTMRz/gdhDBa5pi6xeS22c15EzGiePau0wO5opDU70nZG6X7VSFtZp5sg+/48lpZnMDsLMbyfIIvBOynWIAExCmnCk1lPIC57xvGIGG9CWU6nIqJ7zMb+MFkzsvXc61WkQS5GzK200MhdYh6MdvP6Mjt8s5h2RIDyMeQGuZEQ7p7x1Q1lpq0iNlW/GRFeJdF6fJdsjI8RLgtvyufjcffLTKID5jeIzep917fBNoYPkPZ4I9KAe6CUwm6twGYxcFErv1+AGEfKYOIKT+5SvAjh4EfEXgJDkOL0SxvvHxGtLUXC3osMPfMtL3zaFSlJd2Mb3ZeMy630Xsg/fhThfnT3SK6g5RlSvllRBeIBNRnMR9pv9YhR52vTGSlRbxIpol7wVpqd5i6sV9oQQs5DKokss0piD3N3wY5DFtMag+dfEY94gWjk6EkZc4lC1VtQrKI0hbpHK8ci+280RpeUbGjT1vG5dA219SqK4j1C0/oWApoHehYhJvYR8ue6KbopQpa8uKMZSe/dEUNuRDnknwRkSp7rQbidkT+xAZn9jfb8GqJMfA3y4Z4FTEkytTsiRvoTrKNqSml/cz99G2mgmxN7GO9jz7gGIc1Ae8aWhAvKy9Q9V3ozggjqkanfETH555BAyRe7s8GoQIHJ9kVR3IY0jFoUOF6ENJqnU0rnW4BuL2I3qD2RD/cAg9P3EcP0AC4ppbwIBwtkj7bx/tCetZE9y1+j7dpTWluP7F5/QgxunI2lHRJ+9wE1KaWldp679J43GPyEcL31Q77UGkSA4xD890FWySHI3zoNxQv2QpbGTJQK+M2U0lFFUUxH1obPvz0SoF6I1Qf4c0ppjY2pEsW6EtECAbKGaSmlLtYq5XBkSe2MGGs90b7gSWKnL894Ow3h0Xi751ftmh8jS/krNpe/oMrUNxBj3Nyuq7L5b4tcjmchQV362git4VsITzxo2Z6WrlmQG28QChZ/4v5xF0dRFC8i18Z4JBi9FcrbKaXzWnk2ViA2kagQPwclU5xg8x1qc98ztZGAUBTF1shr8JO0tpttFGpRslu+T7H9NxRZ52MIr8EuKNtrRXbexjbGB1CMozNa30dTSpdZm5WPUKLELogmnWd5/Kgzstq8/USrr6IoXkUB7ysNBsuAjVJKJ6/rOuALZxF0RtK9A9EDfgaxVZ0fFdk1pVr0JOTu2Bsh3QKkzd1s/68VrEQEfybSgAbbojYSO3h5TrO3jliKtN3xdv1RNq5z7JiBmMZqZDJ7YcjN2RhnIA3sCaIlbYON9SlgsZ37JCLc8Xbsm417NmJYM4HGUk0s+/yCzWmm3aPM5lmBGOBpyBXiLqknkAbyjj37IaTVT0TMqLt9f6wVWLpF9XukJXZFVkfX0mMDceI45FNdbte1qFfItLXSw7V+391tlH2/CgnO3gaXa5DLyTXnvJW4B3M9uOedO89FroO7yVoVtzH++5Bg2rmN/7dAVuUCFJ9ZbbDfDLnYLidiXv2zYxRR8FbqVvD0zkcx14f97lbbACzATiuuGbI9BOz7XOR3x669E7ked1rX3O18txTmIZwfhly3l9BKTUbJtWfYu7uEc4vAXb0tkk0+Jb8pIyyCUo38v2yc9yNlbRrKGOpp/59j/z+I6OgWouZlCUEHXmcznqifmoQEp+8kt1anVKRsXGrPfw25uB/P/u+Kqss33qC5/rvM+f/GYcB6kLWLi1YjqfeXdVz7MC39ruOQQNkaSfMdsA5/SENbq0KW8EmvgU+sqFpfJGKj9sWICYzO/nsKMVV3DeSFOO42aCIqMj0jYhzS/i4nMglqMqTzLoWp5D7VRNfTZoPRNcCR2XwuMOTxzKuvoNx6b5f7O6SVjELBLwif8jGIgZxryHq1fZ7l73Zeq+42ROg/s/nta8i8xBE5R+YNxI25hFU3g/DTtlAISq7ZDjHDYYhpPoIEqrtBdiAjPsOTiQafYYQgWYkE4RCstoBwFXzT7ueM5GBEtNeUMrLs+/+U4O0gJJBuQjjmbTh+Y2OuRS6VFuv7KWDXh9Zdh3m/JHchJVq63hy/nCYnIA158qccQw+i1fJUm/OrZHn8rVxzqp3r+O60tCIbo/eGWkAWm2njfp6RWHr4vVoraqwmc7tgxW7Imj3W1n4SotVE9Brye+WfpyNFZpDh4yIiy+uTjqclY34JCZfpxJ4lM5H78DtYLGOD1+F/k2H/bx9IUr6LCG420R7Ct5c7HDGr17GgSiv3yAtoFhG+R0eeJUgbqUQE/5tW7vEBCrotQz5Sb5XbAZl7OdHk/ldPFStK7ueCIk+HW233fRRp2nVIk3gNMQOvLG4g2l6PJfNdIkb6GFGcMhgR2AdIy51By171Dgu3XF5BlsnbRBGPF/CUIU3/DeTyAmV0eVveYcgVMBy5MDqStYnIxvhVu/fPDJnvJYryVtvYK9iAnZXy8RgR7ExGNCjYmgfgzzF88UZ9820NPHYwJTs3VyA+tvmPQSb78QbL32NbLmIN4JAb7wDko55By12mxhGaYF697kf+vbc99wlkGf6TqJB3peJ11NG1hTABtsy+b4WC556s0BO5u8YR7bdPagW25SgOdCtiaNOJXcAWEcWUXiToisnRn5LGX8yeN5moYF5tMLqplWvmEu1PvOCsh+HTTIPd2yjAuwdWnPe/zJv2snEuJ3pcnYGUgvuQAPD18yKxvADQPzcbfGttjX5M0GM5ctG1libsipkrHl0Qjl6HYh8LEL7ev0Hz+f+Tkf8vAHsSyoCYSGRATCOi+rcYMv7KPr/cyj32KDkG22KNJFoE1GEVj22MY6AtuufsriI2K5lnCPxjolndIF9AWu+6OQQ4xj5/A9v31r7fiZhS3uP9IxQTKSf2TW5AVlLek8erKnsiN8JLSABMNES7H2mwexsSboOY2utI4PrWoKmVo1TQVSPt1DOJbkA+zkl2n3mYq2sdcHVkHm9j2cHmemVr19rc8mMOCrrNQYR4KNFp8lCbd2lLgpnI3/4yIuYh2f2bSo6UvSckVJ9DmvRSMuWDllXOFbY+F9NSEIwp+T6Klvsvew76NfZ/nnnzBJF582dUhHUJclsNB57KYVoCtzK7tkW+PKKl/dq4Zhry01+G8LkWC64jgbIfFvi1ew2wz6dtIG37HB8jGHkP5I7rj5SaGZQElzO4nWv/n2vHLSjj62LCBbst67As/k3eNBQx3TFES41VSHhOMnzz9fMdy8YiXvIPgh/V2XsLC9TXYB3PL1U8tjW864x4RTdbwy/2VpU2SWeOuT+71gB8MCLMVRkSrhUhp2WLWD+uQAT4ki3kNBQ0bDVrBRH3CYZwv7DPXREhXk/LLetaSHBD6tIGVx9kCFRliL2WiZ7dI0+Tc41yCBKANdl5nZFW3gUJ0NvsvMlERkFrLS5qaSPlsA3ky7cGHVtyju8mt0XJ77nmN9YO14pW5shs533UyvO7lxyziZTXSuSfzgsDc9jch8xmH/vofC7ZeaWKwx5IUB2MAtK3IqHjzGct5cPu8zKxZeTGyN32PC0zaspoWYjnY8t/OwJp8D9B/nSPFb2OGMogW98nszXpmF2/iY0/d2P4RjHPoSyh0hjCU0Qb8puQFedrPxIxuUFISPoWmgfZ/zM3kLZ/YO/nIu1+KLLOfaeuXxvM22XXeJqkp07+nEg/9U2hVhPWsxdZrWXxtDIedzet1SJkXbzJPndB/KSGaPHuLuS9kfXp69cicwopJn9tBU8uYB2pn4TicazhhNdevW/3+zGZNby+43OdNWTZHp59MQz5Dgcj//N1SCssQ6bwoUVRjEsl7QysiCYhv/CWRGHWfKTVTUeI5K+UUjq/5B6XAZNSSu8WRXE90jb/bOOYk1Kavo45dG3t96RW1l3sc3VRFFemlP7Rxj0GIZ/zyyiDZQ1itquJPQkK+7ylwegjhICXppQOz2B5HNK4L7NzJyOfc+kcliHG2i1FpsvvaJlxcgdakz+klOYXRXEYQuIJSFu7MUXmzk4ppXnWJsRfP0FusJNtPJsiK2cmcElK6eA2wOpwKR3P+cCzKaV77f9xqM9To7c/MBg+j7JknkF9ZZ7PbnsN0ir3RsRWT2RYgfAnIWYxHblmdiyK4qqS4XVGa7UrWpu+SAHxvlS+XhDVvZ2QonGpjekoxKDmosygRYj5+T4TTUgD94JJiqK4BgUJe9g4z0fKx09TtGrxthU7oPU7kMgKOtzmfk5K6f3svvcjF0cXFAj2+MgNKC7QYOfNSintxga+rEDsJWJHuLuQQtDOxtA7O7dHyeXbo6K/He27budl1QAAIABJREFUt4PeCCUwnImlFqcs46eNcUxDwmniBo67tUy0LyPX3X+gdTsKKYxNSDEZQknmVFEU+yOtfguii+oym/+PU0rzS567a77e9ttKZIUMBv6ZUpqyIXNocY/PuSDobx83QsjajJjvakRo9Qhp+yAJ+GSyartW7tUdEdEWCMiDEZIcjhjW861dZ9dWoyBpF8Rw/o4E0aWoO+IP/q2J6hkzU0q7t/Gfp8ltibTQLYC/pZSGlpw3OrXc/Wlz5DM/qCiKwUhD8urnAhHSg0jQnkTLCskO9n8H4PgU/ZZORC6GAmnhdxIFaXsiAXUxIob9U0qnb8Dc+2dfnRj+nlKa3PoVLa718Xg65QobMyg1dwtkOe6OBPg5Nq+LkHZf+joZaZMfIitpBjK1SwXKIGTFfJxS2sjw65NhIXzsmVK6aT3j/xLSFI9GDGUQEtSXIxy9KaX0NTv3PmTZ3WjfxyarVC655/dszAUK2PcptENbLoB8Pwz//lskiCfZ3EtfOyMhdRSyEKYid+Ig5F5zgd8mHpeMsZd9PBbhdgdC4VuGBPJzrTHwoiiOTikNsrX4LbLMD0KtHI7CLC8UfH+nNPWzjfEMaot3tHF+ZRt/7YYUmdfQ+g1KKc1r49z8fl4dDIoR9GvjvMnAd1NKVdlv7ZEQvRStyb7INTsErU2r92px38+zICh9GbCORBrgdKRhbYO03KFk28it4x4FMnn/QTRn2gUBrT+0mvtem1LapCiKvyBp/qwjV1EU5SmlA1t5jmdarPUXWbuJ7PxPpUm1MbdBSLOuR+b9jkQjtk2Slb4XRfFKSum07Lr+rdwupZROMGJbmFI6tuRZ2yJTfjTSrgtkzi/IGNUngqkEHhu38rzrsgffuZ557g3skFIalP32NlIOxti4/OU99vumlFbZufvQBq7k61loE53hKIh+MplASSklG0dvRHiXp5Tuyu7TB2mY9aXP+DSvNqyaBuRb3zultHFRFAejXji//jefVUmsUVHy984pWjGMQ9bLpgjmmyKtdypqGtjqJkUlz1qELIwdUOJAgdK6z4FPLOZWGXhm0SxDQmtzxAN8/5FNUFuTU/Lz2xjHqfaxK6IXbxGCjaPn+uZScr8NFYTd1vF3Sind0sZ1JyPF4eSU0lT77VpU6/G9lNJss/ROR0Jyz5RS+9bulb8+dzuUlb7aKFI6PjtlFUKgtq7PTfZ2SCuchbIKzkBMsyMKeG2LCL/01VwUxUNIw7zNCsS8n9AmrZxPSmnz1n5fx2stoZFpTa1fsHbB1ZXIzN4OadWrUL7z5sCbGSyPckRMKd1cAs/SVx2wfVEUHyCL4RYUjN7EjjoUOGxGfV4ezK79BL9yeJRozyD4n4788RRFsQsKFq+g9ddDCMnz156oR0z3lNLl65gP6zGdG7LzGouiIKV0a1EU7xMCxdeqHXBmSqmpKIpTiP2DQfAfZGu4KrtnCyFnQukBJNgOKIriIOCUlJL3uXoOGFAUxWLkEvoQKT2/REKIlNKYoiiOtft9A2Vi7Y8Ebnu0BWMLxaMNuOzZ1n9FUdxXFMURKaURyJ12KNHMsQuyJH6/vmdkrx2RQvcMYmxv2TgH5EMqGcORSHnbzui6Arne9rJTvKiyA3CiuUwKWlc8/JVb8749av78tQRBptR0oGVfMdgAnlqoK+1pSAjmRWJ3o9jANojO1nqllN4uiqIOeKcoih8h92VXbD/ooiiOQvMdgvBgUGv3KX19rgVBURQPEht6P4qYxYKiKKYi876gDQ07e+UM+QgE5F2RX24rlD++B0KiTWll4RGSNqGg0/KiKHYCfl+ocdrITzGfdVkJrQmUI5HQeg6ZzKVaWotXSmlEURT7IQvgbBTXcN9tJ+THPN7GcAbSnG8utB1o6WtTJHxHII3tcuRm6YdSVi8siqIncindhwTCJhhRmrbcFiN/DVlie6Fsiu7IpH0SmfdfRYh9ahvXH5XWbq27FVEL8u+8DjZXINi62Pe18KxEoAwuiuKfKIjpFaEjEaNwHGxt7R9BDPQhu+fYoiiexRoetiaEZNSykGimCLEV6z8Ro34JMepziG1X1/kqimK/lNKkQhvKl76+B1xiTKgRMc3VyDpa1sr563yllJqA3kVR+ObtVwOdiqKoJbZS7VRy2caITjsgmG6PBN3GyM3rGW9VCB/X0EpjvpJxnGdzPzq3MP23Nq7Z3CzQNSg43Jz9t05XoL16oPjmXUgQ/Qopoucht1Zr9Jg///2iKH6Bkk4G2897onjbDcm2xP1Ur7SBUeXP4iByZPOeQKuR7/nT3ms8sduXt3OuQxaAb0LdVu67N04bhIJqDyOtbAifIjL/fzDm9ojRPokyEf5MtlVfdl5pvvzHyO/aA/ldr6dlj5lG1q53WFFyLCH2TcgrtT3jpB0Kxo0i8p/bzJ7Kfr8CCaqbkR+zHJid/d8iq6fk2v2QJtVAZJCcinzXMxED9orS9WV9rJXv3tpvG7BGfe29f8kxDrkn8nPPaOV6L1bM00rX2R+GNjKSfO1zmrHPgzdwLo+0MpfB9j4IMZ6xSMj8EYsV/Rv43dHW7yWkcNzA/9femUfLUVVr/LdvAAORhCiDTCEEmUEevCAE4YkMKg6AD1CZcSHroTLJoEsRUVBfDIKIoktAYuShKMbIE8QhQJJHMMyEBBARYhBEGQ2RMZD9/vhO3arurqquvreHe2/Vt1av29236tSp6qqzz9nD9yU4gXKeiZ+H99dSgEurYF/SKqjzCO9yi9SaHCvKAryfWIBrKXVU3Rn7RsVsy5E344Ui93uz15BeERCLxr9oZhugwcm8YGQfwMy2Q/7ycciCP4+s8YNoWXUBCqzdhQbLS1Oa2QW5WzZCg9F4lCY3sfVTKg4PsyY0c3oDmpnPMbNzPGTGBERuK4KLYEN0Pu9FmSs/QjffZBNHyn+ia7nY3TcPM5/6WeNbkbG7PfQhwkuhbyvN7AS9rQ1ah/9nuV+ORXGMnZBBuhKtSr4d+jQq9OellH23RK6ulWgGGR1jORogpgHbuPtHMo6dxLdDH5p91wzrQIO7MvJL189EP4cGvSSeNrPNCKsFMzuYmO44C8cjN8KGaFXwOxQoBD0rqwH3mNm00NaYIifi7sfVn0s4j93C+5loRbktMkSHIXfEsygoWe/yy4SZzUDB0etRQLyBbjyjj6+b+IFAq7+Nzey91PFbZc3mU/pR726KMJZarqR6XG9m7/YiwvCNeNkkLjMBXYPPo4SVpisrb93lXAhD3RBcG5aO5xEP1Pea2U8pHtS5BDjV3W8CMLM9UTbBbqjoAmCmmV2LNE3T3BnnolnvbFeA+F1oUO44ggF4fzjeRFR8U3+uozxkbSD3z/e9NmB7spmdlXItI6P3ba8LpoUB4KzwPnKXJF0loBndKma2MbV+8GfJhqH7Llrij0ED+SFodvsicm8cU7+ju18TfqcnUHBwU2LX3OrI8H0o59iDefCzMC4RcATl/O8ETDKz64jTcscSq+ol8Sl0j25lZo+j2eHheQd096dztjkSrdZOQHGUjdEqqilM2Wl/9ZCyaGZHhfO4CNVgTHJNSxebWZShtQwZ57cjF19RHInumS2Ak4K7C5q7egHuDrGXScitth0qoNyWOMa3RsF+1LubIjyPXNFZWADMMrM+YldWs35HOAW5eiO67fcDr2a5ILuBIW0IPI6c9w/UKNsHCgR1AsZERiC0OcfM1jazt9Td8AcBS83sSykD2Qp3f8bM+sysz91vMjF1dhQtzJpGmTJcZqEBcmHCzzspCjq7+z+pvZbbmNlpNBkUPSPrwOIUunmJr504eJeG6Sj9dhaxctu5yMCtj4rAns/aOcwIp7gYP5Mpd7ehh/9jwY8dbV+fgTTQBz8L49BAGI1k40O7UfuRoVpOY4Abd38E2MfMxqAB/CVkzBv8vFYg08Tdl5qkD9f3Yv7qJOpXllNRgsAyZKzWNrOrUKrrCrQi2wTdK8+G+8HdPe/3jzpbH2RtBW8irB5RfKEPTSCmAL8IsbtCcQtXdtLNiE6klet1fjjeomAcW8HE+vM3s0PcvX612BLS2ija7pBMH82YmRxEXZFSwbZmoRnwFeGrI5BPdZK7Pxtu+KuQuyI1991Ef30gqiGIdIZ3jpbMnYJJUSmaaSd/qJpZg4mO+X1hm/WI9YI3RL74M5A+6ty6a/k7FJw9ntpsn+XIx/oQHUAwUrujQOYpxBoHo5Gf+DpEovaDjP2/igbgKDALyqJpcKlkPdxmtomHoFqY1b0xzwDlnEtqaqKZreohUJ+x31i0GtgQVQnPDp9PRxQDB6Tsc1pKU5EGxZtdqncfRIZ2NXff1KTNe443ofQO7S/0UMRnoWYB/R4vECvXRRz7EOtM35n4DndPpu92DGZ2J5pVb4do1l9G98FCUoqxmrR1Y4orL2/736J0zZVNN27ct+GeybqPutXuUDUEdwH71A3Us9HgdShK/ayBZ6QMmiQQv4wGHkOz17095tCvL9KpKcoK340h5lo5HA1CV3brhi8CU9rg9qiyNpkvfw0KgmYaPTP7jLtPq2vvEOSmeMLdH8845lFp37t7Zjpv2G8nlDV0BqqCvRz5ujdGv9FOKHjaUJ8R9s+se8g7bl0bP0YGMBIxGQdc4O7nFW0jtJOV6/4O5E7ZhDrJyPD/a9Cs9Q8oRjUerVZO9lC81+S4a6LA+7FI2OR8d38yDI57AXOifllG4VlKmw01Cx4L0adV7d/q7rs0a7fdMLONUDxnX7QSeAr52x1l2UVKXQAUuS9MmXOboxhO0s2Z6mkwsx+ilW9EEBltn1kDYyr0ex9Ksvhp4l9jUWzr7c362al2h6prqN7nfQkKdv4qDD6FUzZDAKbGSJjZYjNbxd1fQ77oRWZ2h7tfS8o1iQZWVE9wHfDMAJaDHYW7LzCz73rQBrZQNGZmr9ZfS3efiVxE0YDzURRoTeJzKKPnPSaN4L1SDN/OifejiYPueXUdX0TxgJlo4JuOHsCTkduvLwxEr2e14Sl1D2a2jikAV19zkjUIbOOi9jicmKnyThRDaQVHZnz/A+QKqpktJzDJ48K1ywjFau6+PO9gpkDpqWhCMoPG9M3X3H1ZwufeCtJqFvJSgW8K1/wX1A6GuUWdbcB0tAI4JHw+Irz6UPLB6cjIH02t4FEeIndT8n7JczkvCa/VyK9TSOJvKKNvf2rHsFS3YQsYdLtD1hAkBuq90XJvp+AyWu7uM5o1YPnFWKOIb/hH0JJ81/obPsyyIz/puci9tDbQZ2Y1PChDBMmnP/LTpl3LCGuZsnU2NAUEI4xFA8rRpqKVbVDOc80KwN1PrDm42ThiF1wWDgV2dPeXTdkmn0VB58XI3/tcuO5ZNQjRcc5G9AQgTqQd0SDwAYoNAqua2arI5fcdd19hZi0b95y4zTJ3vz5n12Th2utmtqSAETgPZXxFFNn/StlssZkdhn73zYmpKprC8wvnTkzZJVoNTE42Q+1g2glMQKI4r0G/C3VnZIxmoVXVXPSMz81uJoZnqKDlbP/lcOw19TH1t6jfZyGK3/04z23YKlwFhYsRY3HTsTENQ9U1FPm8kxwx56EbbHuU0xzl9ffznNS1EZWwpxVjrYaW4ivIoR4wsztQatc49PDtF2beW5HBg9JLJP2BFpfhN1xL9356hKvRLHwqqm6N8AKaYSyH4jO8MLDe6+5b52xzPXCoK3CNKZPpf5Fx3g4ZhHWAg72xaCxqY2bYLrrpj0RKW+OTbhAzm+vu78xo4yRkhBairI0JSJJzjyLnmnN+kT/2w+GcUmfLFnP/ADX8P5lZI2HAiwq6UmNGZrYG0liIkilqJDJHAkzZNWegItM90OD/VzSrX4CC9BehmfLP3X2zAm1G7qaI8+lmZFAey9g+SkuPUlmfRiR596VtX7dvrttwoDCz36Cq9JZpTYakIYD+2XgaR8xa6OLthqL2UxCt6zZ1+49CPsRDESHVdcgoTKJgMM1quXIeSA5wWb7hXsKyicVGhe/WSTN6JvoI0LXdFgXDV0NV2E8B13kdI2to41fEA1IfWjlc7e6fzenjL9Hs7fdoJbA/mv3PROmxtwFT82ZMGXGcF9x9TAjitTQIJNqIVk4DRkb8IkJLcYwWjzuamDU1kgkd1LkUPG5TneoOHPN+RLk8BQWxH0XqaNsjYZ7D0KA+FmXb5VK1hDZ/j9xNyaSSw91934ztbwHO9PS09GbHisjy2hpkN9Hg7IQmVpm0JmkYqq4hPKNIKbgFpqBZ+jj0wC9K2Ta1GAv5PrcP73H3e0wMkGlIZgTUFzgNOQvqBcilEtsmC77ORn79iDtoDHIFLSMWJ0/DNxLvX0NC9qkzqARmhReo4vlnyGV1BxpMzkDprHnZYS+Z2e7ufjP0z7AeDffGacSDQKZ/1ETM9TVEprafmW2D7qvUTKVmSMzyNqSNs7wWMINa1tStUUZWx2DpFDBpXF3txkqkG5IMai9F6d9Tg7sujz8rDeu4+/TE5x+aWd71S0tLL1S4R3O34UDxt/BK0poUgw+wJLnbLxo1XPejSUk26SXsEYVBsqQ/tVycWuHres3SFb2+Jm28tneiqt17wmsL4jL4NKqH9+S01UCjkLPtC2hpfTexgtgScgRfwn470IKwT0Yb1yP3TaQGtQpBd3iA1zBS04vEfb4JbJr4/3jkounUb7go8X4VcugR2njMNAqYBp3qNh7vi+E1G60CHkApwxHdy4xwD12UfBVsezZaBYwKryOAG3K2nxXGk4nh9QXglwWPNRW5uqcQq+k10LEM4jqtSdAaL/oasiuCFEwgcAGhvPPHEO1sKiyjGMvMJhYNpnkLM+xhjlXd/cEwu9oSVeeuZWaXooetHr82s3nAEd6YWppGo9CPcM3/G7mRVkdZRuujjKZfA5u7+1mJjKbkvhPc/VFX0G0HUy7+NMS58vG0TBnPZiJd291/ZqLwxZtkKhVAzSzPzPZ09/4Vibs/Z6IQ/sIgjpGHBtbULiCNAiaTwbQNiNwd1yPusLFoHPgEMkKzUCV366RrEvD5DjLgjsaEBndo3fZfJs4qmodI44qgI0H2+rhFSIYpFLfo6IyhAzOCLA3XNIHrlWjmHpE0JWfzr6AVwh1InGR0r8+tx9c1Er1/NxpUPbxeJoXMCs3Aj0MBukPq/9fkWJH+8r1oNv9oOM6Lob1I8nBxyr5JmceZ4e/Riddf6j4fndOPOWjmHq0Q+3V3W7x20Yyufpb3J2CXxHarE0TJO/QbJoXvkyvYQZGRNTnmWSiudBAFdarbeOw1kVFdgiRZ1637f0fIIJH78hRkNP4LTaI6fr4F+3YLEpGKPu9JQcLBIRsszkOI8L8DBYw/gKoq1+ptr4Y2THw4XyeFvjvEUE5AWTTz0ezmu+7+SkZbUUbSFihlczGSxHzRmlQymgqergjHm4H4gbZFg8jbXGmcbyVFbS4ZoE8L1rcSwDfJal5EwUylnHaygsMbo1Tj0yCWjPS6wr2RgnAPZXF1tfM49XUU3/IUsrZm92Fiu5YEYkw8Z8lYzF/cveVYTCeC7JaoDM/7Lg3DxjUU0v12I+Y5mY/8gpeTEizOaSeZ6RJhGVodfN9HUJpdHaaRockaBvzzTfrKxyChi68G98IcV6FdA1zB+ymISvduy6g0rsPLiMV1PKIy/jvyyZ6JZnnPkp237hnv876rQQgAzkermnciV5iR0N1tBZ4j6mO1kpHnuvtvW21/KMJyKGCaBPkHe9widRT9mxds9oWU7/ppO2gUiNnG40LAHzCA4HgHg+yPmNlZ1GY+LcnZPkavlzMtLHsuCBds/UG28y2UJvbB8PoflP1yMXBFr8+zg9dvfsp3B6CZfPT5GeRSew25bqKg3NS6/RrcP2gZ+ggq+Mvrx87In7sRmtHNQTPzmSjQd3/OvnnB++cpECANv/UtyODMQZlD7wfeNMjr+zVgrcTnjgaHe3wv3RVdL+Ig/0E0CfK34bgrUVwizd1b77785ADaz3U3Reee97ngcToSZA/33EXh97kL1Qg11ThwH6auocHAzOZ5o/7uPHf/DzO7z4NQ+EiB5WuyfhVVI/41bLsUrRLPRzOiaWhmdaonuGrM7EB3/2XKscYj7dypBfsWpQK/I/xdC2W/FK7ytFrVtzWoFWV3z6DzNXH2T6ZJPUoL/ahxS4U6mBtC31qSjBzqsBRyOs/h6upy30Yh4sWkTOqjTfYp5G4K27ZcCJjSxq3uvoulaIM02zejvfoaksu9xdXtsHENtRHrRNknoEwU5MsFBUpHGvI0WdeOjEDAr5DBWICCrT9Gs6QkRTVpRiB8/xwKmmbCYiK8txCriS1DQa6BSB4OVKhjdZR1kluP0gJGmdkbPI6rXBza/BctSkYOA+TRlvRsTDGzE1FNzD/Q6tGQIc4k3GvR3YS3J5MwTWclTRCrKAZdQ1LGFcH7EOXyw+hG2RT4JHITHOfuF2bvPXxh6Zqsj7n7RonPe6LUz4hPP1qGn+ruV7WpHwuJC64eRvUAi4CfeRduRjO7BAXpliPqkQXAgoEYobp2P4OqpKejB/tbyFVyhMeUF7d4h6nLuwHLpy1pCPJ3sV9/RplahSt0rQBtR3t72XD8QQfZzWyRx3GLVYDbvEVK69IZAui/+FuhH/uPPnIDxP1Iy6KwQPrmMWPpnag8f3+0kvgJcLwXyDpooR93uvu/myLRkeThbih7p2XJwwEc/zdoBbgYxQr+gJblA34QwrlshM5nH3RfvRtpPVxGnFp5TDuvZS9h2RQw/VxdPejTTcC+3gVqjYEgL8hOizorde3WPNtFM6Zq2iiLIbBaOcEGeLbU5bCGxdKMp6BimQhjEY3vMjQjegyl4j6MfNqnooH5KuCj7Xq4zexLiMtoVjjuBkjmcAe6lArcCSMUGbjE502Qi2I1RHUxDqXk/nmQ3a+QgZDFsyWiRCmkEdBNWLrOSqYgVgvtDjpuUaYYQeQrXxc9/DegC/Uu5BYakYaAfGnGA939ITPbC7nL+oDNwnYXowrFN6NMhN3b1J+jkRGahnKoo8K1z9JiKvBAEWb/7dDdTWKBme3s7reHYyy1gUtGVhgYHg2vVjQCuokGnRVv1AZpGe2IW5RmRRDBpNd7nLs/ET6vD1zs7rkrhuEOS0gz5mzzBuQzfwb50OchI3myux/Txr5cQKARj36HbiGnHmU+ylhqWXowtHs/4mhaimZnY1FA/AlvUTKywsiEtagA102UaUUQYWLd4PMP9ACPdPzQUoRX3H0vM7vB3fdG+sbm7vsktwmDWDvxc0TUtW8oWsObyFu2ERPD8T/dZiO0X93na9Eq4wpoynJbYRAwswvd/ZSMYlGGkPFtVQGuayijIZhj4qz/CbppPgrkcciPFJyeeD8aBamioNr6oap4f1QdOgsRwDnKDkkjnhsQzOwK5H66h5iL3cmRt2wn3P3U5lsNqN2lAGa2Lrq+UcFbhc4jqqT9Ru5WPYa3rgDXNZTONQT9geNIiWqeu8/K236kwoKCl5kdjArIdkd5zRug4i4Qw+v27cqsMrMHUJn+iLrxzGx/VIi3AQqGT0Qsuc8io3sSIig7vld9rFAhC6U0BGVEqJ6M0IdSGy9y9y0T25zldSRbHejH1cBJ3Y4NdBqhPmIvYLa772hm70W0E9GqZ8RJRg41WC3FeZLMrVviQMMWpXENmdnN7r57HSUBdKlwZAjgTnTehtwWS9AqIIk9zexGavVU10cBz3Y9TGsD95vZbdSm+A0VP+5AscLdnzGzUWb2aeT+Go/0FYZkXvsIxHSU9fVNlA34MYqTz5Ua1YqgQsRVMga4EeUgn4kqftdAUpJTWqnWbHKsd6Z97+5z29F+r2Bms4EDEavpqqge498QkWFHJSMrCIlixWSl7f+5+x7N9i07SrMiADCzPsT417M0rV7BzFZFSk4R4d4cRLu9AglsnIL8245y/EHBzovaZQSgccA3af0eBgxrQ4CYXF9CNRFfQQVkhyJp1QrdwcvhGX/IzE5AMZp1e9ynYYHSrQjM7Ergc96EkXCkwcwuQzPVGeGrI4HX3f3jiW1OROLro1CBXdJ10zbagJCOehjSDF6C1Ma+0672e4VQTTzb3Tc3szXQdZzbarl/hYEhUDg8gBIdzkW1HOe5+4KedmwYoIyG4EbEiX8bCVGKEeCjzoUVVC8KfEOjqfWt/t3dB6unugVK1T0UFaz9FDjd3TcZTLtDBWZ2HGLhnIyK8fqIA5YtlftXGBjMbEd3v7vX/RiOKJVrKKCs5f6vm9lm7v4wgJlNIs5oIXx3BVoF3Jr4n3u2AHwr+CMqoPlgxLcTgqojBZ9CFBW3eiyn2e+rrtAVXBCYAq4GrvIiou0VAM1aSoXgo34Q+XDHIonC4e6fLoIzgJvMbI6ZzUWB4dPqtpmMKmRfJhZrWWESkBksIoHzm8zsUjOLJBxHCl5x9349i0AHXK7ldo/hkg3dE3gKuMTMFpnZF3rbq+GBMrqGPg58EQ2EhoRYznH3y3vasS4gcAlFGr1/9Dpx+pDjvwbSb07GEnZoFxeTmY1B2TWHorz7GcAsd/9dO9rvFcxsGiq+OwpViX4SyW6e2dOOlRRmtj3wGeAj7j4UCeiGFMpoCB4EdosyYczszcAtycKqkYRWONADn/seiJk1aSQmeAfkB0OR2yHoYR1UDKLXCNkqxyIdAkMFZJeNtArqoQwz2xqxeh6M4lBXoUSEJ3vasWGAMhqCG4D9omW8Sbv21/VEayMFrXCghxz/7wLfI6aD3g6pbE3pbs+HL4KB28jd7+11X8oEkwbwVUjt7m+97s9wQmmCxWYWkY09DtxqZtcgH+4BKINopKIwB7q7zzWz05AWwSg0s/0ncg9VyIGZzUGkfasgQr2nApdTR0juKjTC3XftdR+GK0pjCIhFWR4OrwjX9KAv3URhofFECiRI8HsSIlLbC6hmt/kY5+7PhxjUdHc/28yqa9ZFmNkS0mmoK66hJiiNISjIwh2vAAAEJElEQVSxSlQrHOjJFMjngXsCrfKpwIXd6/KwxCohdfHDiKKjQvcxOfF+NIo/vSlj2woJlMYQRDCzyehBjUjVAHD3t/WsUx1Eixzor7j7q5FYTCIFciSleXYK56AA8Xx3vz3UaTzU4z6VCilUKBea2c0oS7BCDsoYLH4Q5dQvAvplCZvJOJYBWSmQwJHuPqGXfatQoRnMLEnl0YdWCJ+or56v0IgyGoKb3b1dQuwjBgl67lWJV0qvhdfq7l661WMrCBQa3wPWc/ftzOxtwP7u/pUed600COnPEV4D/gJ8w90f7E2Phg/KaAj2RsVMNbny7v6LnnVqCKDMzKztQKjWPgMxukYUEz0VJK9QoSjKOMv7GLAVmvlGriFHbJulhbuvNLOFZjahbMysbcIa7n5bFF8JqARpuohAhXI2MdX6XMQa0FNh+OGAMhqCHSoisEysD9wX1MNKw8zaJjxtZpsR0heDDvSIkuMcBrgcWIwyt0D1L9OBttCjjGSU0TV0KfBNd7+/130Zahip6mHdQMgSugTYDXgO6SwcXiUhdA9mdk89FUradxUaUcYVwe7A0aH45BVinvgRmT7aCpIDvpmtDTxTceUUg7s/AuwTSPX6UM3GRxCnU4Xu4CUz293db4Z+9buXetynYYEyrghShVDKPHMzs12BqcCzSNnpCiQy3wcc5e6V3GIGzGwsKsTbEFWpzw6fTwcWuvsBPexeqWBmOwA/QhTzoJXZ0RXnU3OUzhBECBWzkYIUZQ6QmtkdwOfRA3QJIuVbYGZbAT+JsmAqNCJwVj0H/AFReIwHVgNOdvd78vat0BkE40yg/DjF3auq+CYonSEws/0Rf84GwJOowvgBd9+2px3rIZJ+VDN7wN23Tvzv7soQZCOpQmZmo4CnEW338t72rAKAmT1aFUM2R+kUypDrY1fgT+6+KZrFze9tl3qOlYn39T7Vcs0UWseK6I27vw4sqYzAkEJFj1IAZQwWr3D3Z8ysz8z63P0mM/t6rzvVY+xgZs+jh2b18J7weXT2bhWIrx3UXr9KrH5ooJrIFEAZDcE/zeyNwDzgSjN7kpIX/rj7qF73Ybiiuna9R4IepeFfwOpd7s6wRBljBGOQ+6MPOBwFSK9MYS6sUKFChVKgNIYg8O+v5+7z677/D+Bxd384fc8KFSpUGNkoU7D4QiAtiPcilehKhQoVSowyGYKJaYUl7n4HMLH73alQoUKFoYEyGYK87JcqoFShQoXSokyG4PYgzl4DMzsWuLMH/alQoUKFIYEyBYvXA2YBrxIP/JMRHcCH3P3vvepbhQoVKvQSpTEEEczsXUCkGnWfu9/Yy/5UqFChQq9ROkNQoUKFChVqUaYYQYUKFSpUSEFlCCpUqFCh5KgMQYUKFSqUHJUhqFChQoWSozIEFSpUqFBy/D+P0Tn78Ctg8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph for Size: total = 9\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph for Type of ownership: total = 11\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph for Industry: total = 60\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph for Sector: total = 25\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph for Revenue: total = 14\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph for company_txt: total = 343\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph for job_state: total = 37\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph for same_state: total = 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph for python_yn: total = 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph for R_yn: total = 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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6WyrOAb416SZOBfnrLZNuQU/k3+bJ9ZLFiqdaKGSRWh1VqNoObB++naUnye6qmp50H9JC/m2Ox6l2+mgWWDNvvBo4MKFeJGnJOdVC4QvA+iQvTXI6cCmwc8I9SdKScUqdPqqqx5K8Hfg34DTgQ1V174TbWmo8LadTlX+bY5Cqo07ZS5KWqFPt9JEkaYIMBUlSZyhIkrpT6kKzxivJKxl9Y3wVo++DHAB2VtXeiTYmaWI8UliikryX0WNEAnye0e3AAW7wQYQ6lSW5YtI9PJt599ESleS/gJ+rqh8tqJ8O3FtV6yfTmfTkktxfVWsn3cezlaePlq4fAz8DfHNBfWXbJk1MknuOtQlYMc5elhpDYel6F7AryT6OPIRwLfBy4O0T60oaWQH8GvDQgnqAz46/naXDUFiiqupTSX6W0ePKVzH6j20W+EJVPT7R5iT4JPDCqtqzcEOSO8bfztLhNQVJUufdR5KkzlCQJHWGgiSpMxQkSZ2hIEnq/h+Lwe37MnBEagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph for spark: total = 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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1K+buIy2vJM9i/mtE1jL/kOAeYGtV7ZhoY5ImyiOFVSjJm5j/bqkAn2f+GZEA1/rttFrJklw66R4Od96Sugol+XfgF6vqB0vqRwL3VNWGyXQmHVqS+6rq5En3cTjz9NHq9EPg6cA3ltRPatukiUly98E2AScuZy+rkaGwOr0W2JZkJz/6ZtqTgWcAr5pYV9K8E4HfAB5YUg/wL8vfzupiKKxCVfWpJL/A/N+wWMv8f7ZZ4M6qemSizUlwE/CUqrpr6YYkty9/O6uL1xQkSZ13H0mSOkNBktQZCpKkzlCQJHX/DwvZsPmA/C0nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph for aws: total = 2\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph for excel: total = 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph for job_simp: total = 7\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAE0CAYAAAAogpSbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAdBklEQVR4nO3debhcVZ3u8e8LxJFZAhchIWBHbFAZDDQKilMj4EUcQKEdaECjj6B44fG2aCtpFa+NiveKYxAx4NQgIthNMwhIGhEwCWEy0KYBNYaGoIgRRAy894+9y9Q+55BT55CqVZV6P89znqq9alfOS3I4v9prrb2WbBMREdGyXukAERHRX1IYIiKiIYUhIiIaUhgiIqIhhSEiIho2KB3gidpiiy08Y8aM0jEiIgbKwoUL77M9dazXBr4wzJgxgwULFpSOERExUCT94vFeS1dSREQ0pDBERERDCkNERDSkMEREREMKQ0RENKQwREREQwpDREQ0pDBERERDCkNERDQM/J3PY3nB+88qHWFMCz/1ttIRIiLGlSuGiIhoSGGIiIiGFIaIiGhIYYiIiIYUhoiIaEhhiIiIhhSGiIhoSGGIiIiGFIaIiGhIYYiIiIYUhoiIaEhhiIiIhhSGiIhoSGGIiIiGFIaIiGhIYYiIiIYUhoiIaOhqYZA0TdKVkpZIulXScXX7HEm/lrS4/jqw7T0nSloq6XZJr+pmvoiIGK3bW3uuAk6wvUjSRsBCSZfVr33W9qfbT5a0E3AYsDPwTOCHkp5t+9Eu54yIiFpXrxhs3217Uf18JbAE2GYNbzkY+I7tP9m+E1gK7NnNjBER0dSzMQZJM4DdgOvqpmMl3STpa5I2q9u2AX7V9rZljFFIJM2WtEDSghUrVnQxdUTE8OlJYZC0IXAe8D7bvwe+BDwL2BW4G/hM69Qx3u5RDfZc27Nsz5o6dWqXUkdEDKeuFwZJU6iKwjdtfw/A9j22H7X9GHA6q7uLlgHT2t6+LbC82xkjImK1bs9KEnAGsMT2qW3tW7ed9jrglvr5hcBhkp4saXtgJnB9NzNGRERTt2cl7Q28FbhZ0uK67YPA4ZJ2peomugt4J4DtWyWdA/yMakbTMZmRFBHRW10tDLavZuxxg4vW8J6TgZO7FioiItYodz5HRERDCkNERDSkMEREREMKQ0RENKQwREREQwpDREQ0pDBERERDCkNERDSkMEREREMKQ0RENKQwREREQwpDREQ0pDBERERDCkNERDSkMEREREMKQ0RENKQwREREQwpDREQ0pDBERERDCkNERDSkMEREREMKQ0RENKQwREREQwpDREQ0pDBERERDCkNERDSkMEREREMKQ0RENHS1MEiaJulKSUsk3SrpuLp9c0mXSfp5/bhZ3S5Jn5O0VNJNknbvZr6IiBit21cMq4ATbP81sBdwjKSdgA8Al9ueCVxeHwMcAMysv2YDX+pyvoiIGKGrhcH23bYX1c9XAkuAbYCDgXn1afOA19bPDwbOcuVaYFNJW3czY0RENPVsjEHSDGA34DpgK9t3Q1U8gC3r07YBftX2tmV128g/a7akBZIWrFixopuxIyKGTk8Kg6QNgfOA99n+/ZpOHaPNoxrsubZn2Z41derUtRUzIiLoQWGQNIWqKHzT9vfq5ntaXUT14711+zJgWtvbtwWWdztjRESs1u1ZSQLOAJbYPrXtpQuBI+rnRwAXtLW/rZ6dtBfwQKvLKSIiemODLv/5ewNvBW6WtLhu+yDwSeAcSUcDvwQOrV+7CDgQWAo8BBzZ5XwRETFCVwuD7asZe9wA4BVjnG/gmG5mioiINcudzxER0ZDCEBERDSkMERHRkMIQERENKQwREdGQwhAREQ3dvo8hJuGXH31e6Qhjmv6Rm0tHiIgeyBVDREQ0pDBERERDCkNERDSkMEREREMKQ0RENKQwREREQwpDREQ0dHwfg6TXA/tQbbV5te3zu5YqIiKK6eiKQdIXgXcBNwO3AO+U9IVuBouIiDI6vWLYF3huvZEOkuZRFYmIiFjHdDrGcDswve14GnDT2o8TERGldXrF8AxgiaTr6+M9gJ9IuhDA9mu6ES4iInqv08Lwka6miIiIvtFRYbB9FYCkjdvfY/u3XcoVERGFdFQYJM0GPgb8EXgMENW01R26Fy0iIkrotCvp/cDOtu/rZpiIiCiv01lJ/wU81M0gERHRHzq9YjgRuEbSdcCfWo2239uVVBERUUynheErwBVUN7U91r04ERFRWqeFYZXt47uaJCIi+kKnYwxXSpotaWtJm7e+uposIiKK6LQw/B31OAOwsP5aMN6bJH1N0r2SbmlrmyPp15IW118Htr12oqSlkm6X9KqJ/adERMTa0OkNbttP8s//OvB54KwR7Z+1/en2Bkk7AYcBOwPPBH4o6dm2H53k946IiElYY2GQ9HLbV9R7MYxi+3trer/t+ZJmdJjlYOA7tv8E3ClpKbAn8JMO3x8REWvBeFcM+1LNRjpojNcMrLEwrMGxkt5G1R11gu37gW2Aa9vOWVa3jVLfiT0bYPr06WOdEhERk7TGwmD7pPrxyLX4Pb9EtbyG68fPAEdRLbMxKsLj5JoLzAWYNWvWmOdERMTkdLqD23GSNlblq5IWSdpvMt/Q9j22H7X9GHA6VXcRVFcI09pO3RZYPpnvERERk9fprKSjbP8e2A/YEjgS+ORkvqGkrdsOX0e1VSjAhcBhkp4saXtgJnD9yPdHRER3dXqDW6ub50DgTNs3Shqr66f5JunbwEuBLSQtA04CXippV6puoruAdwLYvlXSOcDPgFXAMZmRFBHRe50WhoWSLgW2B06UtBEdLI1h+/Axms9Yw/knAyd3mCkiIrqg08JwNLArcIfthyQ9g6o7CQBJO9u+tRsBIyKitzq9we0xYFHb8W+A37Sdcjaw+9qNFhERJXQ6+DyecccbIiJiMKytwpB7CSIi1hFrqzBERMQ6Ym0VhkfW0p8TERGFdToriXohvX2ouo2utn1+6zXbe3UhW0REFNDpkhhfBN5FtbXnLcA7JX2hm8EiIqKMTq8Y9gWea9sAkuZRFYmIiFjHdDrGcDvQvr71NOCmtR8nIiJKG2+jnh9QjSlsAiyR1FrUbk+qbT4jImIdM15X0qfHeT0iItYx423Uc1XruaStgD3qw+tt39vNYBERUUans5LeSLU3wqHAG4HrJB3SzWAREVFGp7OSPgTs0bpKkDQV+CHw3W4Fi4iIMjqdlbTeiK6j30zgvRERMUA6vWK4WNIlwLfr48OAf+9OpIiIKKnT/RjeXy+JsTfVEttftv39riaLgbX3aXuXjjCmH7/nx6UjRAyE8e5juNr2PpJWUt3P0Np34R2SHgN+C3zK9he7nDMiInpkvOmq+9SPG431er3F5zVACkNExDriCQ0g11t8vnTtRImIiH7whGcW2b57bQSJiIj+kCmnERHRkMIQERENKQwREdGQwhAREQ0pDBER0ZDCEBERDV0tDJK+JuleSbe0tW0u6TJJP68fN6vbJelzkpZKuknS7t3MFhERY+v2FcPXgf1HtH0AuNz2TODy+hjgAGBm/TUb+FKXs0VExBg6XV11UmzPlzRjRPPBrL5beh7wI+Af6vazbBu4VtKmkrbODXTRS1e9ZN/SER7XvvOvGv+kiLWgxBjDVq1f9vXjlnX7NsCv2s5bVreNImm2pAWSFqxYsaKrYSMihk0/DT5rjDaPdaLtubZn2Z41derULseKiBguJQrDPZK2BqgfWzvDLQOmtZ23LbC8x9kiIoZeicJwIXBE/fwI4IK29rfVs5P2Ah7I+EJERO91dfBZ0repBpq3kLQMOAn4JHCOpKOBXwKH1qdfBBwILAUeAo7sZraIiBhbt2clHf44L71ijHMNHNPNPBERMb5+GnyOiIg+kMIQERENKQwREdGQwhAREQ0pDBER0ZDCEBERDSkMERHRkMIQERENKQwREdGQwhAREQ0pDBER0ZDCEBERDSkMERHRkMIQERENKQwREdGQwhAREQ0pDBER0ZDCEBERDSkMERHRkMIQERENKQwREdGQwhAREQ0pDBER0ZDCEBERDSkMERHRkMIQERENKQwREdGQwhAREQ0pDBER0bBBqW8s6S5gJfAosMr2LEmbA/8CzADuAt5o+/5SGSMihlHpK4aX2d7V9qz6+APA5bZnApfXxxER0UOlC8NIBwPz6ufzgNcWzBIRMZRKFgYDl0paKGl23baV7bsB6sctx3qjpNmSFkhasGLFih7FjYgYDsXGGIC9bS+XtCVwmaTbOn2j7bnAXIBZs2a5WwEjIoZRsSsG28vrx3uB84E9gXskbQ1QP95bKl9ExLAqUhgkPV3SRq3nwH7ALcCFwBH1aUcAF5TIFxExzEp1JW0FnC+pleFbti+W9FPgHElHA78EDi2ULyJiaBUpDLbvAHYZo/03wCt6nygiIlr6bbpqREQUlsIQERENKQwREdGQwhAREQ0pDBER0ZDCEBERDSkMERHRkMIQERENKQwREdGQwhAREQ0pDBER0ZDCEBERDSkMERHRkMIQERENJbf2jIi17PMn/KB0hDEd+5mDOjrv5Lcc0uUkk/Ohb3y3dISeyhVDREQ0pDBERERDCkNERDSkMEREREMKQ0RENGRWUkTEWrLk5CtKRxjTX3/o5RM6P1cMERHRkMIQERENKQwREdGQwhAREQ0pDBER0ZDCEBERDSkMERHR0HeFQdL+km6XtFTSB0rniYgYNn1VGCStD3wBOADYCThc0k5lU0VEDJe+KgzAnsBS23fYfgT4DnBw4UwREUNFtktn+AtJhwD72357ffxW4G9sHzvivNnA7PpwR+D2LsbaArivi39+tyV/WYOcf5CzQ/KPZzvbU8d6od/WStIYbaMql+25wNzuxwFJC2zP6sX36obkL2uQ8w9ydkj+J6LfupKWAdPajrcFlhfKEhExlPqtMPwUmClpe0lPAg4DLiycKSJiqPRVV5LtVZKOBS4B1ge+ZvvWwrF60mXVRclf1iDnH+TskPyT1leDzxERUV6/dSVFRERhKQwREdGQwjCCpEM7aYu1T5Vp458ZEd2UwjDaiR22xVrmasDr+6VzPBGS9u6kLbpL0tNLZ5gISetL+kbpHC19NSupJEkHAAcC20j6XNtLGwOryqSaHElPAY4Gdgae0mq3fVSxUJ27VtIetn9aOsgknQbs3kFb36nXKrvE9itLZ5ksSS8CvgpsCEyXtAvwTtvvLptszWw/KmmqpCfVywEVlcKw2nJgAfAaYGFb+0rgfxVJNHlnA7cBrwI+CrwZWFI0UedeBrxL0l3Ag1R3w9v284umGoekFwIvAqZKOr7tpY2ppl73vfqX00OSNrH9QOk8k/RZqp/7CwFs3yjpJWUjdewu4MeSLqT62QfA9qm9DpLCULN9I3CjpG/Z/jOApM2AabbvL5tuwv7K9qGSDrY9T9K3qO4NGQQHlA4wSU+i+pS6AbBRW/vvgUOKJJqch4GbJV1G85fTe8tFmhjbv5Iaq+s8WirLBC2vv9aj+TPUcykMo10m6TVUfzeLgRWSrrJ9/Djv6yd/rh9/J+m5wH8DM8rF6ZztX0jaB5hp+0xJU6l+4fY121cBV0n6uu1fAEhaD9jQ9u/LppuQf6u/BtWv6u4k16snvJcBuVq2/U8AkjaqDv2HUllyg9sIkm6wvZukt1NdLZwk6aZ+78poV2c/D3ge8HWqX6wftv2Vkrk6IekkYBawo+1nS3omcK7tgRjAra/O3kX1KXUhsAlwqu1PFQ02AZKeCky33c1Vi7tC0hbA/wNeSdUNeSlwnO3fFA3WgfpD3NnA5nXTfcDbSqz+kFlJo20gaWvgjcC/lg4zSWdTdcnsA8yj2vxoq6KJOvc6qnGeBwFsL6fwZfUE7VRfIbwWuAiYDry1bKTOSTqI6kr54vp417rPeyDYvs/2m21vZXtL228ZhKJQmwscb3s729sBJwCnlwiSrqTRPkrVH3+17Z9K2gH4eeFME3UB8ADVJ9Y/Fc4yUY/YtiTD4E07BKZImkJVGD5v+8+t/5YBMYdqw6wfAdheLGn7koE6Iek0xliiv2VAxkiebvvK1oHtH5X6+U9hGMH2ucC5bcd3AG8ol2hStrW9f+kQk3SOpK8Am0p6B3AUhT41TdJXqGaX3AjMl7Qd1QD0oFhl+4ERg7eDUNgWjDgehMwj3SHpw1RX/ABvAe4sESRjDDVJ/9v2KY/3yWNAPnEAIGkucJrtm0tnmQxJfwvsR9VHfIntywpH6pik9W0/2nYsYH3bA3EvjKQzgMuBD1B9IHovMMX2u4oG65CkPYAPUk22aH3w7fvpzvCXWZD/RNUFDDAfmGP7d73OkiuG1VozF0Z+8hhE+wB/L+lOqq6kgbgXoM1/UuX9oaSnSdrI9srSoTq0VNK5wJm2l9R3cw9EUai9B/gQ1c/Nt6m6VT9WNNHEfAN4P3Az8FjhLBP1ypEfQOvleM59nPO7JlcMI0g6tO5OWmNbP6u7L0ZpTaPsZ3X30Wxgc9vPkjQT+LLtVxSO1pF6quFhwJFUkzu+BnxnwKasDixJV9veZ/wz+4+kRbZ3H6+tJ1lSGJr66R9nGElaTDX4eZ3t3eq2m20/r2yyiavvuP02sCnwXeBjtpeWTbVmkn7A6K7UB6iupL9i++Hep+qcpFcAh1N1h/1l4oXt7xULNY625XjeCPxL20sbU81y27PXmdKVVFuX1koacH+y/Uhr8FPSBgzQQGK93tCrqa4YZgCfAb4JvJhq+uqzi4XrzB3AVKqCBvAm4B6q3KfT/1NvjwSeA0xhdVeSgb4tDPThcjwpDKv13T/OkLpK0geBp9aD0O8GflA400T8HLgS+JTta9ravzsga/bsZrs95w8kzbf9Ekmlt9ntxC6DdnXZthzP+cCDrckL9YeMJ5fIlK6kESRNaa2VFL1XLyNxNG2zkoCvekB+UCVtWHIpgydK0hLgVbZ/WR9PBy62vVNrVYCyCddM0unAZ23/rHSWiZJ0LdUA9B/q4w2BS22/qNdZcsUw2p6S5gDbUf39tGb07FA01ZCw/RhVl8Ug3bvQuMFqxD0AwEBNdz4BuFrSf1H97G8PvLu+0Wpe0WSd2Qc4YkBn5D2l/UOF7T9IelqJICkMo51B1XW0kMFZlXGdUW9qM4fBK8zrwjRnbF9UzwR7DtXf/W1tA87/t1yyjg3qjZ0AD0ra3fYiAEkvAP5YIki6kkaQdJ3tvymdY1hJuo0xCvMArXcz8OrF3HaiucnTWeUSDYf65rzvUI13AmwNvMn2wsd/V5eypDA0Sfok1cYq36M53W1RsVBDZNALc71M+D8w+hfry4uFmoB6dduXUuW/iGoxxqttD9KeEgOrXmdrR1ZfrRUZ70xX0mitX0qz2toMDMT/2OuAKyV9isEtzN+kmov+aqrlt48AVhRNNDGHALsAN9g+UtJWVFtlRpfV4wnHA9vZfoekmZJ2tN3zVZ5TGEaw/bLSGYbcoBfmZ9g+Q9JxXr15z1WlQ03AH20/JmmVpI2Be4F+H99ZV5xJ1YX6wvp4GdVyGCkMpdWfkD4BPNP2AZJ2Al5o+4zC0YbCOlCYW5f+d0t6NVV/8bYF80zUAkmbUs0KWwj8Abi+bKSh8Szbb5J0OIDtP2qsKW49kMIw2tepKveH6uP/pOoaSGHoIklvsf0NSWNuoeoCG6JP0sclbUI17fM0qjvnB+YGSdvvrp9+WdLFwMa2byqZaYg8Uu+e15r2/CwK7aeSwjDaFrbPkXQigO1VkjJttftaG5IM0m5to7T1Bz8ADOTVj6RtWD1dGEkvsT2/bKqhcBLVznnTJH0T2Bv4+xJBUhhGe1DSM1hdtfei+p88usj1ftSuN0QfVPWspHfQ3A8A20eVyjQRkv6Zan2kn7F6urCp9gaILqnv+N8MeD2wF9WspONs31ckT6arNknanaoL4LnALVQLih2Sy+neGLGAYcsDwALbF/Q6z0RJugb4D0bfh3FesVATIOl24Pm2B21L2IHXWpOqdA5IYRhTvaJnay7x7Vk7qXfq3eeew+rNSd4A3ApMA+6w/b5S2TohabHtXUvnmCxJ/w4cOsjrPQ2qelvPP1KNaT7Yarf9255nSWGoSHq57SskvX6s1/t5Pfd1iaQrgP1aW2HWRfpS4G+Bm23vVDLfeCR9HLjG9kWls0yGpPOo7mMYuZ/BoKz1NLDq9Z1GKrIcTMYYVtsXuAI4aIzX+n0993XJNlQD0a1xnadTTR1+VNIgdG8cB3ywzvpnVq/1tHHZWB27sP6KHrO9fekMLSkMNdsn1Y9Hls4y5E4BFkv6EdUv1ZcAn6hX9/xhyWCdsL2RpM2BmbQtiTEobA/CCqrrlH7srUhX0giSPgGcYvt39fFmwAm2/7FssuEhaWuq7T0FXG97+Thv6RuS3k511bAtsJhqhsk1A7Rn9Uzg/zB6rafc/dwlkubYniPpTKreCbU/lpjRtl6vv+EAOKBVFABs30+15Wf0znpU6wv9FvirAdn5rOU4YA/gF/Vd3LsBRaYcTtKZwJeotrN9GXAWcHbRROu+lfWNnbe0fd0K3Fw/77l0JY22vqQnt6br1XciFtlebxi1zaO/leaevYMyj/5h2w9Lov45uk3SjqVDTcBTbV8uSbZ/AcyR9B9UN19Fd2xYP+5I9aHiAqqrhYMo9HOfwjDaN4DL2y7rjmIwdq5aV7wW2HGA59Evq9ca+j5wmaT7Wb2+/iB4uL7Z6ueSjgV+DWxZONM6rXVTp6RLgd1tr6yP57B62nZPZYxhDJL2B15JVbUvtX1J4UhDY12aRy9pX2ATqj2THymdpxP1ZjFLgE2Bj1Gt9XSK7euKBhsC9SZVu7T1VjwZuNH2c3qdJVcMI9SzXy61fXHdBbCjpCm5ya1nHqKalTTw8+jrZbcHjanGFLYDptRtpwODsGfyoDsbuF7S+VT/Dq+jUG9FrhhGkLQQeDHVuiXXUu3l+5DtNxcNNiQkHTFWe6ZR9ka9JMb7qQY+W2M81OMN0WX1kjwvrg/n276hSI4UhiZJi2zvLuk9VANxp0i6wfZupbMNi3rAf7rt20tnGTaSrra9T+kcUVa6kkaTpBcCbwaOrtvy99Qjkg4CPg08Cdhe0q7AR22/pmyyoXGSpK8yekmM3Pk/RPILb7TjgBOB823fKmkH4MrCmYbJHKqb234EYHuxpL5ZKmAIHEm1iOEUmtOFUxiGSArDCPWGJPPbju8ABm7gc4Ctsv3AiB0N09/ZO7vYfl7pEFFW7nyOfnOLpL+jutFwpqTTgGtKhxoi19b7nMcQy+Bz9BVJT6Pab3u/uukS4OO2Hy6XanhIWgI8C7iTaoyhtV5PpqsOkRSGiPgLSduN1Z7pqsMlhWEESU+hmo20M83VJQdiz96IiCcqYwyjnQ38D+BVwFVUyyevLJooIqKHcsUwQutmNkk32X6+pCnAJbZfXjpbREQvZLrqaK01kX4n6bnAfwMzysUZLunKiygvXUmjza13bftHqr1vfwb8c9lIQyVdeRGFpStpBEnb275zvLbojnTlRZSXK4bRzhuj7bs9TzG8RnblbUK68iJ6KmMMNUnPoerX3kTS69te2pi2vu7oupFdeRsCHy4bKWK4pDCstiPwP6l2rjqorX0l8I4iiYbT5bbvp1qvageouvLKRooYLhljGEHSC23/pHSOYdXaD2NE20LbLyiVKWLY5IphtBskHUOmS/ZUuvIi+kcKw2hnA7dRTZf8KNWGPUuKJhoO6cqL6BPpShoh0yXLSldeRHm5Yhgtdz6Xla68iMJyH8NoremSH2b1nc+nlI00VHLnc0Rh6UqKvpKuvIjy0pVUk3T8ml63fWqvsgy5dOVFFJbCsNpG9eOOwB5U3UhQzZCZXyTRcBrZlbch8JGykSKGS7qSRpB0KfAG2yvr442Ac23vXzZZRERv5IphtOnAI23Hj5CujK5LV15E/0hhGO1s4HpJ5wMGXgfMKxtpKKQrL6JPpCtpDJJ2B15cH863fUPJPMMkXXkR5eWKYQy2FwGLSucYUunKiygshSH6TbryIgpLV1L0nXTlRZSVwhAREQ1ZKykiIhpSGCIioiGFISIiGlIYIiKi4f8DLd9GFgAOe7MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph for seniority: total = 3\n"
     ]
    },
    {
     "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": [
    "for i in df_cat.columns:\n",
    "    cat_num = df_cat[i].value_counts()\n",
    "    print(\"graph for %s: total = %d\" % (i, len(cat_num)))\n",
    "    chart = sns.barplot(x=cat_num.index, y=cat_num)\n",
    "    chart.set_xticklabels(chart.get_xticklabels(), rotation=90)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph for Location: total = 20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph for Headquarters: total = 20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph for company_txt: total = 20\n"
     ]
    },
    {
     "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": [
    "for i in df_cat[['Location','Headquarters','company_txt']].columns:\n",
    "    cat_num = df_cat[i].value_counts()[:20]\n",
    "    print(\"graph for %s: total = %d\" % (i, len(cat_num)))\n",
    "    chart = sns.barplot(x=cat_num.index, y=cat_num)\n",
    "    chart.set_xticklabels(chart.get_xticklabels(), rotation=90)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Job Title', 'Salary Estimate', 'Job Description', 'Rating',\n",
       "       'Company Name', 'Location', 'Headquarters', 'Size', 'Founded',\n",
       "       'Type of ownership', 'Industry', 'Sector', 'Revenue', 'Competitors',\n",
       "       'hourly', 'employer_provided', 'min_salary', 'max_salary', 'avg_salary',\n",
       "       'company_txt', 'job_state', 'same_state', 'age', 'python_yn', 'R_yn',\n",
       "       'spark', 'aws', 'excel', 'job_simp', 'seniority', 'desc_len',\n",
       "       'num_comp'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>avg_salary</th>\n",
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       "    <tr>\n",
       "      <th>job_simp</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>analyst</td>\n",
       "      <td>65.857843</td>\n",
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       "      <td>data engineer</td>\n",
       "      <td>105.403361</td>\n",
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       "    <tr>\n",
       "      <td>data scientist</td>\n",
       "      <td>117.564516</td>\n",
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       "      <td>director</td>\n",
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       "      <td>126.431818</td>\n",
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      ],
      "text/plain": [
       "                avg_salary\n",
       "job_simp                  \n",
       "analyst          65.857843\n",
       "data engineer   105.403361\n",
       "data scientist  117.564516\n",
       "director        168.607143\n",
       "manager          84.022727\n",
       "mle             126.431818\n",
       "na               84.853261"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, index = 'job_simp', values = 'avg_salary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <td rowspan=\"2\" valign=\"top\">na</td>\n",
       "      <td>na</td>\n",
       "      <td>73.988189</td>\n",
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       "    <tr>\n",
       "      <td>senior</td>\n",
       "      <td>109.061404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          avg_salary\n",
       "job_simp       seniority            \n",
       "analyst        jr          56.500000\n",
       "               na          61.155405\n",
       "               senior      79.092593\n",
       "data engineer  na          96.701220\n",
       "               senior     124.689189\n",
       "data scientist jr         106.500000\n",
       "               na         107.043011\n",
       "               senior     138.956522\n",
       "director       na         168.607143\n",
       "manager        na          84.022727\n",
       "mle            na         119.133333\n",
       "               senior     142.071429\n",
       "na             na          73.988189\n",
       "               senior     109.061404"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, index = ['job_simp','seniority'], values = 'avg_salary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>data scientist</td>\n",
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       "      <td rowspan=\"2\" valign=\"top\">AZ</td>\n",
       "      <td>analyst</td>\n",
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       "    <tr>\n",
       "      <td>na</td>\n",
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       "      <td rowspan=\"3\" valign=\"top\">AL</td>\n",
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       "    </tr>\n",
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       "      <td>data engineer</td>\n",
       "      <td>65.000000</td>\n",
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       "    <tr>\n",
       "      <td>analyst</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>114 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          avg_salary\n",
       "job_state job_simp                  \n",
       "WI        na               54.000000\n",
       "          data scientist  113.500000\n",
       "          analyst          58.833333\n",
       "WA        na               97.500000\n",
       "          data scientist   99.764706\n",
       "...                              ...\n",
       "AZ        analyst          55.000000\n",
       "          na              124.500000\n",
       "AL        na               43.750000\n",
       "          data engineer    65.000000\n",
       "          analyst          62.200000\n",
       "\n",
       "[114 rows x 1 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, index = ['job_state','job_simp'], values = 'avg_salary').sort_values('job_state', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_rows\n",
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>RI</td>\n",
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       "      <td rowspan=\"6\" valign=\"top\">PA</td>\n",
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       "      <td rowspan=\"2\" valign=\"top\">MI</td>\n",
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       "      <td>data scientist</td>\n",
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       "      <td>na</td>\n",
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       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"6\" valign=\"top\">MA</td>\n",
       "      <td>na</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>manager</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>director</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>data scientist</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>data engineer</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>analyst</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">LA</td>\n",
       "      <td>analyst</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>data engineer</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>na</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">KY</td>\n",
       "      <td>na</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>data scientist</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>KS</td>\n",
       "      <td>mle</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">IN</td>\n",
       "      <td>na</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>data scientist</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>data engineer</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"6\" valign=\"top\">IL</td>\n",
       "      <td>analyst</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>na</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mle</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>director</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>data scientist</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>data engineer</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ID</td>\n",
       "      <td>analyst</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">IA</td>\n",
       "      <td>analyst</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>data engineer</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">GA</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>na</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">FL</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>data engineer</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>analyst</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DE</td>\n",
       "      <td>na</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">DC</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>data engineer</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>analyst</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">CT</td>\n",
       "      <td>na</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>data scientist</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">CO</td>\n",
       "      <td>mle</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>data engineer</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>analyst</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>na</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>data scientist</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"6\" valign=\"top\">CA</td>\n",
       "      <td>na</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mle</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>manager</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>data scientist</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>data engineer</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>analyst</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">AZ</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>data engineer</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>analyst</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>na</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">AL</td>\n",
       "      <td>na</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>data engineer</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>analyst</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          avg_salary\n",
       "job_state job_simp                  \n",
       "WI        na                       4\n",
       "          data scientist           3\n",
       "          analyst                  3\n",
       "WA        na                       1\n",
       "          data scientist          17\n",
       "          analyst                  3\n",
       "VA        na                       5\n",
       "          mle                      2\n",
       "          data scientist          18\n",
       "          data engineer           10\n",
       "          analyst                  6\n",
       "UT        data engineer            3\n",
       "          analyst                  4\n",
       "          data scientist           3\n",
       "TX        na                       7\n",
       "          data scientist          13\n",
       "          data engineer            8\n",
       "TN        data scientist           1\n",
       "          data engineer           10\n",
       "          analyst                  2\n",
       "SC        na                       1\n",
       "RI        analyst                  1\n",
       "PA        manager                  3\n",
       "          data engineer            1\n",
       "          data scientist           6\n",
       "          analyst                  8\n",
       "          mle                      7\n",
       "          na                       8\n",
       "OR        data scientist           3\n",
       "          data engineer            1\n",
       "OH        na                       3\n",
       "          data scientist           7\n",
       "          analyst                  4\n",
       "NY        na                      12\n",
       "          mle                      2\n",
       "          data scientist          40\n",
       "          data engineer            4\n",
       "          analyst                 14\n",
       "NM        data scientist           3\n",
       "NJ        data scientist           4\n",
       "          analyst                  6\n",
       "          data engineer            2\n",
       "          manager                  2\n",
       "          director                 2\n",
       "          na                       1\n",
       "NE        na                       4\n",
       "NC        na                      12\n",
       "          data scientist           3\n",
       "          data engineer            6\n",
       "MO        na                       3\n",
       "          manager                  1\n",
       "          data scientist           3\n",
       "          analyst                  2\n",
       "MN        data engineer            1\n",
       "          analyst                  1\n",
       "MI        manager                  2\n",
       "          data scientist           4\n",
       "MD        na                      16\n",
       "          data scientist          13\n",
       "          data engineer            3\n",
       "          analyst                  3\n",
       "MA        na                      46\n",
       "          manager                  5\n",
       "          director                 6\n",
       "          data scientist          30\n",
       "          data engineer           12\n",
       "          analyst                  4\n",
       "LA        analyst                  1\n",
       "          data engineer            2\n",
       "          na                       1\n",
       "KY        na                       4\n",
       "          data scientist           2\n",
       "KS        mle                      3\n",
       "IN        na                       4\n",
       "          data scientist           2\n",
       "          data engineer            4\n",
       "IL        analyst                  3\n",
       "          na                       6\n",
       "          mle                      2\n",
       "          director                 6\n",
       "          data scientist          15\n",
       "          data engineer            8\n",
       "ID        analyst                  2\n",
       "IA        analyst                  2\n",
       "          data engineer            3\n",
       "GA        data scientist           3\n",
       "          na                       3\n",
       "FL        data scientist           7\n",
       "          data engineer            3\n",
       "          analyst                  6\n",
       "DE        na                       6\n",
       "DC        data scientist           5\n",
       "          data engineer            4\n",
       "          analyst                  2\n",
       "CT        na                       3\n",
       "          data scientist           2\n",
       "CO        mle                      1\n",
       "          data engineer            3\n",
       "          analyst                  1\n",
       "          na                       3\n",
       "          data scientist           3\n",
       "CA        na                      27\n",
       "          mle                      5\n",
       "          manager                  9\n",
       "          data scientist          68\n",
       "          data engineer           25\n",
       "          analyst                 18\n",
       "AZ        data scientist           1\n",
       "          data engineer            5\n",
       "          analyst                  1\n",
       "          na                       2\n",
       "AL        na                       2\n",
       "          data engineer            1\n",
       "          analyst                  5"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, index = ['job_state','job_simp'], values = 'avg_salary', aggfunc = 'count').sort_values('job_state', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>avg_salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_state</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>DC</td>\n",
       "      <td>149.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CA</td>\n",
       "      <td>142.522059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>UT</td>\n",
       "      <td>140.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>MO</td>\n",
       "      <td>127.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>IL</td>\n",
       "      <td>117.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>NC</td>\n",
       "      <td>117.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>NY</td>\n",
       "      <td>115.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>MA</td>\n",
       "      <td>113.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WI</td>\n",
       "      <td>113.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PA</td>\n",
       "      <td>113.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>MD</td>\n",
       "      <td>109.115385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CO</td>\n",
       "      <td>108.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>VA</td>\n",
       "      <td>108.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>NJ</td>\n",
       "      <td>106.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>MI</td>\n",
       "      <td>106.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>OH</td>\n",
       "      <td>105.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TX</td>\n",
       "      <td>100.730769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WA</td>\n",
       "      <td>99.764706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>OR</td>\n",
       "      <td>98.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>FL</td>\n",
       "      <td>97.357143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TN</td>\n",
       "      <td>96.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>IN</td>\n",
       "      <td>84.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>KY</td>\n",
       "      <td>84.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CT</td>\n",
       "      <td>84.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GA</td>\n",
       "      <td>81.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>NM</td>\n",
       "      <td>74.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AZ</td>\n",
       "      <td>69.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           avg_salary\n",
       "job_state            \n",
       "DC         149.000000\n",
       "CA         142.522059\n",
       "UT         140.500000\n",
       "MO         127.666667\n",
       "IL         117.233333\n",
       "NC         117.000000\n",
       "NY         115.250000\n",
       "MA         113.750000\n",
       "WI         113.500000\n",
       "PA         113.333333\n",
       "MD         109.115385\n",
       "CO         108.666667\n",
       "VA         108.416667\n",
       "NJ         106.875000\n",
       "MI         106.625000\n",
       "OH         105.285714\n",
       "TX         100.730769\n",
       "WA          99.764706\n",
       "OR          98.500000\n",
       "FL          97.357143\n",
       "TN          96.000000\n",
       "IN          84.500000\n",
       "KY          84.000000\n",
       "CT          84.000000\n",
       "GA          81.333333\n",
       "NM          74.333333\n",
       "AZ          69.500000"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df[df.job_simp == 'data scientist'], index = 'job_state', values = 'avg_salary').sort_values('avg_salary', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Job Title', 'Salary Estimate', 'Job Description', 'Rating',\n",
       "       'Company Name', 'Location', 'Headquarters', 'Size', 'Founded',\n",
       "       'Type of ownership', 'Industry', 'Sector', 'Revenue', 'Competitors',\n",
       "       'hourly', 'employer_provided', 'min_salary', 'max_salary', 'avg_salary',\n",
       "       'company_txt', 'job_state', 'same_state', 'age', 'python_yn', 'R_yn',\n",
       "       'spark', 'aws', 'excel', 'job_simp', 'seniority', 'desc_len',\n",
       "       'num_comp'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rating, industry, sector, revenue, number of comp, hourly, employer provided, python, r, spark, aws, excel, desc_len, Type of onwership"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pivots = df[['Rating', 'Industry', 'Sector', 'Revenue', 'num_comp', 'hourly', 'employer_provided', 'python_yn', 'R_yn', 'spark', 'aws', 'excel', 'Type of ownership','avg_salary']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rating\n",
      "        avg_salary\n",
      "Rating            \n",
      "-1.0    136.181818\n",
      " 5.0    134.700000\n",
      " 2.5    120.000000\n",
      " 4.5    117.714286\n",
      " 4.1    116.368421\n",
      " 3.9    113.071429\n",
      " 2.6    109.083333\n",
      " 4.4    108.606061\n",
      " 3.2    105.500000\n",
      " 4.0    105.340426\n",
      " 3.5    104.897959\n",
      " 3.7    104.131148\n",
      " 4.7    103.983871\n",
      " 4.3    102.468750\n",
      " 3.3    101.858974\n",
      " 4.2    100.442308\n",
      " 2.2     97.750000\n",
      " 4.6     97.250000\n",
      " 3.8     96.811475\n",
      " 2.1     95.900000\n",
      " 3.4     93.011364\n",
      " 1.9     87.500000\n",
      " 3.1     86.580000\n",
      " 3.0     85.588235\n",
      " 3.6     85.467391\n",
      " 2.8     83.928571\n",
      " 2.7     83.250000\n",
      " 2.3     81.500000\n",
      " 2.9     81.138889\n",
      " 4.8     80.277778\n",
      " 2.4     60.214286\n",
      "Industry\n",
      "                                          avg_salary\n",
      "Industry                                            \n",
      "Other Retail Stores                       163.500000\n",
      "Motion Picture Production & Distribution  146.000000\n",
      "Financial Analytics & Research            145.125000\n",
      "Health, Beauty, & Fitness                 139.500000\n",
      "Telecommunications Services               131.500000\n",
      "Brokerage Services                        129.000000\n",
      "Auctions & Galleries                      128.000000\n",
      "Internet                                  123.810345\n",
      "Investment Banking & Asset Management     118.400000\n",
      "TV Broadcast & Cable Networks             117.750000\n",
      "Enterprise Software & Network Solutions   115.369048\n",
      "Computer Hardware & Software              115.194915\n",
      "-1                                        114.650000\n",
      "Accounting                                113.500000\n",
      "Biotech & Pharmaceuticals                 111.767857\n",
      "Insurance Agencies & Brokerages           109.250000\n",
      "K-12 Education                            108.875000\n",
      "Consulting                                108.689655\n",
      "Transportation Management                 107.500000\n",
      "Metals Brokers                            107.000000\n",
      "Staffing & Outsourcing                    106.200000\n",
      "Video Games                               106.166667\n",
      "Insurance Carriers                        105.626984\n",
      "Real Estate                               104.750000\n",
      "Transportation Equipment Manufacturing    104.500000\n",
      "Advertising & Marketing                   104.300000\n",
      "Wholesale                                 103.166667\n",
      "IT Services                               102.840000\n",
      "Colleges & Universities                   102.687500\n",
      "Aerospace & Defense                        99.060000\n",
      "Logistics & Supply Chain                   98.250000\n",
      "Gas Stations                               98.000000\n",
      "Mining                                     98.000000\n",
      "Consumer Products Manufacturing            95.350000\n",
      "Security Services                          93.500000\n",
      "Department, Clothing, & Shoe Stores        92.666667\n",
      "Stock Exchanges                            87.000000\n",
      "Industrial Manufacturing                   86.750000\n",
      "Consumer Product Rental                    86.666667\n",
      "Beauty & Personal Accessories Stores       85.500000\n",
      "Sporting Goods Stores                      85.000000\n",
      "Lending                                    83.000000\n",
      "Farm Support Services                      80.500000\n",
      "Energy                                     80.464286\n",
      "Education Training Services                79.500000\n",
      "Trucking                                   79.000000\n",
      "Religious Organizations                    78.833333\n",
      "Federal Agencies                           78.545455\n",
      "Research & Development                     78.394737\n",
      "Financial Transaction Processing           76.500000\n",
      "Health Care Products Manufacturing         73.000000\n",
      "Banks & Credit Unions                      70.625000\n",
      "Travel Agencies                            69.500000\n",
      "Health Care Services & Hospitals           67.622449\n",
      "Food & Beverage Manufacturing              53.250000\n",
      "Architectural & Engineering Services       50.500000\n",
      "Gambling                                   48.500000\n",
      "Social Assistance                          48.166667\n",
      "Telecommunications Manufacturing           44.000000\n",
      "Construction                               26.500000\n",
      "Sector\n",
      "                                    avg_salary\n",
      "Sector                                        \n",
      "Media                               116.666667\n",
      "-1                                  114.650000\n",
      "Accounting & Legal                  113.500000\n",
      "Information Technology              113.191667\n",
      "Biotech & Pharmaceuticals           111.767857\n",
      "Insurance                           105.942029\n",
      "Real Estate                         104.750000\n",
      "Mining & Metals                     104.000000\n",
      "Telecommunications                  102.333333\n",
      "Education                           100.739130\n",
      "Consumer Services                    99.875000\n",
      "Retail                               99.666667\n",
      "Transportation & Logistics           99.312500\n",
      "Aerospace & Defense                  99.060000\n",
      "Business Services                    97.701031\n",
      "Finance                              97.369048\n",
      "Manufacturing                        84.044118\n",
      "Agriculture & Forestry               80.500000\n",
      "Oil, Gas, Energy & Utilities         80.464286\n",
      "Government                           78.545455\n",
      "Travel & Tourism                     69.500000\n",
      "Non-Profit                           68.611111\n",
      "Health Care                          67.622449\n",
      "Arts, Entertainment & Recreation     48.500000\n",
      "Construction, Repair & Maintenance   26.500000\n",
      "Revenue\n",
      "                                  avg_salary\n",
      "Revenue                                     \n",
      "$5 to $10 million (USD)           126.111111\n",
      "$1 to $5 million (USD)            119.312500\n",
      "-1                                117.500000\n",
      "$10+ billion (USD)                115.165323\n",
      "Less than $1 million (USD)        108.625000\n",
      "$1 to $2 billion (USD)            104.533333\n",
      "Unknown / Non-Applicable          104.310345\n",
      "$50 to $100 million (USD)         100.565217\n",
      "$10 to $25 million (USD)          100.359375\n",
      "$2 to $5 billion (USD)             94.730769\n",
      "$5 to $10 billion (USD)            94.184211\n",
      "$500 million to $1 billion (USD)   86.991228\n",
      "$100 to $500 million (USD)         83.153846\n",
      "$25 to $50 million (USD)           82.837500\n",
      "num_comp\n",
      "          avg_salary\n",
      "num_comp            \n",
      "2         106.865854\n",
      "1         106.208333\n",
      "3         105.504386\n",
      "0          97.602174\n",
      "4          56.500000\n",
      "hourly\n",
      "        avg_salary\n",
      "hourly            \n",
      "0         103.1539\n",
      "1          25.0000\n",
      "employer_provided\n",
      "                   avg_salary\n",
      "employer_provided            \n",
      "1                  119.970588\n",
      "0                  100.172414\n",
      "python_yn\n",
      "           avg_salary\n",
      "python_yn            \n",
      "1          112.653061\n",
      "0           87.155714\n",
      "R_yn\n",
      "      avg_salary\n",
      "R_yn            \n",
      "0     100.706757\n",
      "1      70.750000\n",
      "spark\n",
      "       avg_salary\n",
      "spark            \n",
      "1      113.347305\n",
      "0       96.931304\n",
      "aws\n",
      "     avg_salary\n",
      "aws            \n",
      "1    112.559659\n",
      "0     96.915194\n",
      "excel\n",
      "       avg_salary\n",
      "excel            \n",
      "0      102.953390\n",
      "1       98.502577\n",
      "Type of ownership\n",
      "                                avg_salary\n",
      "Type of ownership                         \n",
      "-1                              117.500000\n",
      "Company - Public                110.893782\n",
      "Subsidiary or Business Segment  110.573529\n",
      "College / University            107.615385\n",
      "Company - Private               101.776829\n",
      "Government                       84.300000\n",
      "School / School District         77.750000\n",
      "Other Organization               77.500000\n",
      "Nonprofit Organization           68.281818\n",
      "Hospital                         54.000000\n",
      "Unknown                          39.500000\n",
      "avg_salary\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Grouper for 'avg_salary' not 1-dimensional",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-114-ffbf8dbbfab7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdf_pivots\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpivot_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_pivots\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'avg_salary'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'avg_salary'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mascending\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\pivot.py\u001b[0m in \u001b[0;36mpivot_table\u001b[1;34m(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed)\u001b[0m\n\u001b[0;32m     93\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     94\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 95\u001b[1;33m     \u001b[0mgrouped\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobserved\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobserved\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     96\u001b[0m     \u001b[0magged\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrouped\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0magg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maggfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     97\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mdropna\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0magged\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mABCDataFrame\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0magged\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mgroupby\u001b[1;34m(self, by, axis, level, as_index, sort, group_keys, squeeze, observed, **kwargs)\u001b[0m\n\u001b[0;32m   7892\u001b[0m             \u001b[0msqueeze\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   7893\u001b[0m             \u001b[0mobserved\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobserved\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 7894\u001b[1;33m             \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   7895\u001b[0m         )\n\u001b[0;32m   7896\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\groupby.py\u001b[0m in \u001b[0;36mgroupby\u001b[1;34m(obj, by, **kwds)\u001b[0m\n\u001b[0;32m   2520\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"invalid type: {}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2521\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2522\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mklass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mby\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\groupby.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, observed, **kwargs)\u001b[0m\n\u001b[0;32m    389\u001b[0m                 \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    390\u001b[0m                 \u001b[0mobserved\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobserved\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 391\u001b[1;33m                 \u001b[0mmutated\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmutated\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    392\u001b[0m             )\n\u001b[0;32m    393\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\grouper.py\u001b[0m in \u001b[0;36m_get_grouper\u001b[1;34m(obj, key, axis, level, sort, observed, mutated, validate)\u001b[0m\n\u001b[0;32m    650\u001b[0m                 \u001b[0min_axis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0min_axis\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    651\u001b[0m             )\n\u001b[1;32m--> 652\u001b[1;33m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgpr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mGrouping\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    653\u001b[0m             \u001b[1;32melse\u001b[0m \u001b[0mgpr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    654\u001b[0m         )\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\grouper.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, index, grouper, obj, name, level, sort, observed, in_axis)\u001b[0m\n\u001b[0;32m    345\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"ndim\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    346\u001b[0m                     \u001b[0mt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 347\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Grouper for '{}' not 1-dimensional\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    348\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrouper\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    349\u001b[0m                 if not (\n",
      "\u001b[1;31mValueError\u001b[0m: Grouper for 'avg_salary' not 1-dimensional"
     ]
    }
   ],
   "source": [
    "for i in df_pivots.columns:\n",
    "    print(i)\n",
    "    print(pd.pivot_table(df_pivots,index =i, values = 'avg_salary').sort_values('avg_salary', ascending = False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>python_yn</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Revenue</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>$1 to $2 billion (USD)</td>\n",
       "      <td>16.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>$1 to $5 million (USD)</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>$10 to $25 million (USD)</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>$10+ billion (USD)</td>\n",
       "      <td>66.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>$100 to $500 million (USD)</td>\n",
       "      <td>47.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>$2 to $5 billion (USD)</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>$25 to $50 million (USD)</td>\n",
       "      <td>24.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>$5 to $10 billion (USD)</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>$5 to $10 million (USD)</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>$50 to $100 million (USD)</td>\n",
       "      <td>21.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>$500 million to $1 billion (USD)</td>\n",
       "      <td>29.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>-1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Less than $1 million (USD)</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Unknown / Non-Applicable</td>\n",
       "      <td>93.0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "python_yn                            0      1\n",
       "Revenue                                      \n",
       "$1 to $2 billion (USD)            16.0   44.0\n",
       "$1 to $5 million (USD)             1.0    7.0\n",
       "$10 to $25 million (USD)          16.0   16.0\n",
       "$10+ billion (USD)                66.0   58.0\n",
       "$100 to $500 million (USD)        47.0   44.0\n",
       "$2 to $5 billion (USD)            17.0   22.0\n",
       "$25 to $50 million (USD)          24.0   16.0\n",
       "$5 to $10 billion (USD)            9.0   10.0\n",
       "$5 to $10 million (USD)            9.0    9.0\n",
       "$50 to $100 million (USD)         21.0   25.0\n",
       "$500 million to $1 billion (USD)  29.0   28.0\n",
       "-1                                 1.0    NaN\n",
       "Less than $1 million (USD)         1.0    3.0\n",
       "Unknown / Non-Applicable          93.0  110.0"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df_pivots, index = 'Revenue', columns = 'python_yn', values = 'avg_salary', aggfunc = 'count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "words = \" \".join(df['Job Description'])\n",
    "\n",
    "def punctuation_stop(text):\n",
    "    \"\"\"remove punctuation and stop words\"\"\"\n",
    "    filtered = []\n",
    "    stop_words = set(stopwords.words('english'))\n",
    "    word_tokens = word_tokenize(text)\n",
    "    for w in word_tokens:\n",
    "        if w not in stop_words and w.isalpha():\n",
    "            filtered.append(w.lower())\n",
    "    return filtered\n",
    "\n",
    "\n",
    "words_filtered = punctuation_stop(words)\n",
    "\n",
    "text = \" \".join([ele for ele in words_filtered])\n",
    "\n",
    "wc= WordCloud(background_color=\"white\", random_state=1,stopwords=STOPWORDS, max_words = 2000, width =800, height = 1500)\n",
    "wc.generate(text)\n",
    "\n",
    "plt.figure(figsize=[10,10])\n",
    "plt.imshow(interpolation=\"bilinear\")\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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