M4DataVisualization-lab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork21426264-2022-01-01\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Data Visualization Lab**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated time needed: **45 to 60** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this assignment you will be focusing on the visualization of data.\n",
    "\n",
    "The data set will be presented to you in the form of a RDBMS.\n",
    "\n",
    "You will have to use SQL queries to extract the data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lab you will perform the following:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*   Visualize the distribution of data.\n",
    "\n",
    "*   Visualize the relationship between two features.\n",
    "\n",
    "*   Visualize composition of data.\n",
    "\n",
    "*   Visualize comparison of data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo: How to work with database\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download database file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: wget in c:\\users\\mark\\anaconda3\\lib\\site-packages (3.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install wget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Saved under m4_survey_data (1).sqlite\n"
     ]
    }
   ],
   "source": [
    "!python -m wget https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DA0321EN-SkillsNetwork/LargeData/m4_survey_data.sqlite"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Connect to the database.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "conn = sqlite3.connect(\"m4_survey_data.sqlite\") # open a database connection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import pandas module.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo: How to run an sql query\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COUNT(*)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   COUNT(*)\n",
       "0     11398"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print how many rows are there in the table named 'master'\n",
    "QUERY = \"\"\"\n",
    "SELECT COUNT(*)\n",
    "FROM master\n",
    "\"\"\"\n",
    "\n",
    "# the read_sql_query runs the sql query and returns the data as a dataframe\n",
    "df = pd.read_sql_query(QUERY,conn)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo: How to list all tables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Table_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EduOther</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DevType</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LastInt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JobFactors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WorkPlan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>WorkChallenge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LanguageWorkedWith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>LanguageDesireNextYear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DatabaseWorkedWith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DatabaseDesireNextYear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PlatformWorkedWith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>PlatformDesireNextYear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>WebFrameWorkedWith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>WebFrameDesireNextYear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MiscTechWorkedWith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MiscTechDesireNextYear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DevEnviron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Containers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>SOVisitTo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>SONewContent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Gender</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Sexuality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Ethnicity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>master</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Table_Name\n",
       "0                 EduOther\n",
       "1                  DevType\n",
       "2                  LastInt\n",
       "3               JobFactors\n",
       "4                 WorkPlan\n",
       "5            WorkChallenge\n",
       "6       LanguageWorkedWith\n",
       "7   LanguageDesireNextYear\n",
       "8       DatabaseWorkedWith\n",
       "9   DatabaseDesireNextYear\n",
       "10      PlatformWorkedWith\n",
       "11  PlatformDesireNextYear\n",
       "12      WebFrameWorkedWith\n",
       "13  WebFrameDesireNextYear\n",
       "14      MiscTechWorkedWith\n",
       "15  MiscTechDesireNextYear\n",
       "16              DevEnviron\n",
       "17              Containers\n",
       "18               SOVisitTo\n",
       "19            SONewContent\n",
       "20                  Gender\n",
       "21               Sexuality\n",
       "22               Ethnicity\n",
       "23                  master"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print all the tables names in the database\n",
    "QUERY = \"\"\"\n",
    "SELECT name as Table_Name FROM\n",
    "sqlite_master WHERE\n",
    "type = 'table'\n",
    "\"\"\"\n",
    "# the read_sql_query runs the sql query and returns the data as a dataframe\n",
    "pd.read_sql_query(QUERY,conn)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo: How to run a group by query\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>count</th>\n",
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       "      <td>16.0</td>\n",
       "      <td>3</td>\n",
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       "      <th>2</th>\n",
       "      <td>17.0</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.0</td>\n",
       "      <td>29</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19.0</td>\n",
       "      <td>78</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20.0</td>\n",
       "      <td>109</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>21.0</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>22.0</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>23.0</td>\n",
       "      <td>581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>24.0</td>\n",
       "      <td>679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>25.0</td>\n",
       "      <td>738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>26.0</td>\n",
       "      <td>720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>27.0</td>\n",
       "      <td>724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>28.0</td>\n",
       "      <td>787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>29.0</td>\n",
       "      <td>697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>30.0</td>\n",
       "      <td>651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>31.0</td>\n",
       "      <td>531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>32.0</td>\n",
       "      <td>489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>33.0</td>\n",
       "      <td>483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>34.0</td>\n",
       "      <td>395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>35.0</td>\n",
       "      <td>393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>36.0</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>37.0</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>38.0</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>39.0</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>40.0</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>41.0</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>42.0</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>43.0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>44.0</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>45.0</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>46.0</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>47.0</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>48.0</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>49.0</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>50.0</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>51.0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>52.0</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>53.0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>54.0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>55.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>56.0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>57.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>58.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>59.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>60.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>61.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>62.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>63.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>65.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>66.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>67.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>69.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>71.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>72.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>99.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  count\n",
       "0    NaN    287\n",
       "1   16.0      3\n",
       "2   17.0      6\n",
       "3   18.0     29\n",
       "4   19.0     78\n",
       "5   20.0    109\n",
       "6   21.0    203\n",
       "7   22.0    406\n",
       "8   23.0    581\n",
       "9   24.0    679\n",
       "10  25.0    738\n",
       "11  26.0    720\n",
       "12  27.0    724\n",
       "13  28.0    787\n",
       "14  29.0    697\n",
       "15  30.0    651\n",
       "16  31.0    531\n",
       "17  32.0    489\n",
       "18  33.0    483\n",
       "19  34.0    395\n",
       "20  35.0    393\n",
       "21  36.0    308\n",
       "22  37.0    280\n",
       "23  38.0    279\n",
       "24  39.0    232\n",
       "25  40.0    187\n",
       "26  41.0    136\n",
       "27  42.0    162\n",
       "28  43.0    100\n",
       "29  44.0     95\n",
       "30  45.0     85\n",
       "31  46.0     66\n",
       "32  47.0     68\n",
       "33  48.0     64\n",
       "34  49.0     66\n",
       "35  50.0     57\n",
       "36  51.0     29\n",
       "37  52.0     41\n",
       "38  53.0     32\n",
       "39  54.0     26\n",
       "40  55.0     13\n",
       "41  56.0     16\n",
       "42  57.0     11\n",
       "43  58.0     12\n",
       "44  59.0     11\n",
       "45  60.0      2\n",
       "46  61.0     10\n",
       "47  62.0      5\n",
       "48  63.0      7\n",
       "49  65.0      2\n",
       "50  66.0      1\n",
       "51  67.0      1\n",
       "52  69.0      1\n",
       "53  71.0      2\n",
       "54  72.0      1\n",
       "55  99.0      1"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QUERY = \"\"\"\n",
    "SELECT Age,COUNT(*) as count\n",
    "FROM master\n",
    "group by age\n",
    "order by age\n",
    "\"\"\"\n",
    "pd.read_sql_query(QUERY,conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo: How to describe a table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CREATE TABLE \"master\" (\n",
      "\"index\" INTEGER,\n",
      "  \"Respondent\" INTEGER,\n",
      "  \"MainBranch\" TEXT,\n",
      "  \"Hobbyist\" TEXT,\n",
      "  \"OpenSourcer\" TEXT,\n",
      "  \"OpenSource\" TEXT,\n",
      "  \"Employment\" TEXT,\n",
      "  \"Country\" TEXT,\n",
      "  \"Student\" TEXT,\n",
      "  \"EdLevel\" TEXT,\n",
      "  \"UndergradMajor\" TEXT,\n",
      "  \"OrgSize\" TEXT,\n",
      "  \"YearsCode\" TEXT,\n",
      "  \"Age1stCode\" TEXT,\n",
      "  \"YearsCodePro\" TEXT,\n",
      "  \"CareerSat\" TEXT,\n",
      "  \"JobSat\" TEXT,\n",
      "  \"MgrIdiot\" TEXT,\n",
      "  \"MgrMoney\" TEXT,\n",
      "  \"MgrWant\" TEXT,\n",
      "  \"JobSeek\" TEXT,\n",
      "  \"LastHireDate\" TEXT,\n",
      "  \"FizzBuzz\" TEXT,\n",
      "  \"ResumeUpdate\" TEXT,\n",
      "  \"CurrencySymbol\" TEXT,\n",
      "  \"CurrencyDesc\" TEXT,\n",
      "  \"CompTotal\" REAL,\n",
      "  \"CompFreq\" TEXT,\n",
      "  \"ConvertedComp\" REAL,\n",
      "  \"WorkWeekHrs\" REAL,\n",
      "  \"WorkRemote\" TEXT,\n",
      "  \"WorkLoc\" TEXT,\n",
      "  \"ImpSyn\" TEXT,\n",
      "  \"CodeRev\" TEXT,\n",
      "  \"CodeRevHrs\" REAL,\n",
      "  \"UnitTests\" TEXT,\n",
      "  \"PurchaseHow\" TEXT,\n",
      "  \"PurchaseWhat\" TEXT,\n",
      "  \"OpSys\" TEXT,\n",
      "  \"BlockchainOrg\" TEXT,\n",
      "  \"BlockchainIs\" TEXT,\n",
      "  \"BetterLife\" TEXT,\n",
      "  \"ITperson\" TEXT,\n",
      "  \"OffOn\" TEXT,\n",
      "  \"SocialMedia\" TEXT,\n",
      "  \"Extraversion\" TEXT,\n",
      "  \"ScreenName\" TEXT,\n",
      "  \"SOVisit1st\" TEXT,\n",
      "  \"SOVisitFreq\" TEXT,\n",
      "  \"SOFindAnswer\" TEXT,\n",
      "  \"SOTimeSaved\" TEXT,\n",
      "  \"SOHowMuchTime\" TEXT,\n",
      "  \"SOAccount\" TEXT,\n",
      "  \"SOPartFreq\" TEXT,\n",
      "  \"SOJobs\" TEXT,\n",
      "  \"EntTeams\" TEXT,\n",
      "  \"SOComm\" TEXT,\n",
      "  \"WelcomeChange\" TEXT,\n",
      "  \"Age\" REAL,\n",
      "  \"Trans\" TEXT,\n",
      "  \"Dependents\" TEXT,\n",
      "  \"SurveyLength\" TEXT,\n",
      "  \"SurveyEase\" TEXT\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "table_name = 'master'  # the table you wish to describe\n",
    "\n",
    "QUERY = \"\"\"\n",
    "SELECT sql FROM sqlite_master\n",
    "WHERE name= '{}'\n",
    "\"\"\".format(table_name)\n",
    "\n",
    "df = pd.read_sql_query(QUERY,conn)\n",
    "print(df.iat[0,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hands-on Lab\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing distribution of data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot a histogram of `ConvertedComp.`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "QUERY = \"\"\"\n",
    "SELECT ConvertedComp from master\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY, conn)\n",
    "df.plot(kind='hist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Box Plots\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot a box plot of `Age.`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'BoxPlot of Programmer Ages'}>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "QUERY = \"\"\"\n",
    "SELECT Age from master\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY, conn)\n",
    "df.plot(kind='box', title='BoxPlot of Programmer Ages')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing relationships in data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Plots\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a scatter plot of `Age` and `WorkWeekHrs.`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'ScatterPlot of Work Week Hours vs Age'}, xlabel='Age', ylabel='WorkWeekHrs'>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "QUERY = \"\"\"\n",
    "SELECT Age,WorkWeekHrs from master\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY, conn)\n",
    "df.plot(kind='scatter', x='Age', y='WorkWeekHrs', title='ScatterPlot of Work Week Hours vs Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bubble Plots\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a bubble plot of `WorkWeekHrs` and `CodeRevHrs`, use `Age` column as bubble size.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Work Week Hours')"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "QUERY = \"\"\"\n",
    "SELECT Age, WorkWeekHrs, CodeRevHrs from master\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY, conn)\n",
    "#df = df[df.WorkWeekHrs < 600]\n",
    "#df = df[df.CodeRevHrs < 80]\n",
    "plt.figure(figsize=(15,8))\n",
    "sns.scatterplot(data=df, x='WorkWeekHrs', y='CodeRevHrs', size='Age', sizes=(10,200))\n",
    "plt.title('BubblePlot of Code Revision Hours vs Work Week Hours scaled by Age')\n",
    "plt.ylabel('Code Revision Hours')\n",
    "plt.xlabel('Work Week Hours')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing composition of data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pie Charts\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a pie chart of the top 5 databases that respondents wish to learn next year. Label the pie chart with database names. Display percentages of each database on the pie chart.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        Lang   CNT\n",
      "Lang                              \n",
      "PostgreSQL        PostgreSQL  4328\n",
      "MongoDB              MongoDB  3649\n",
      "Redis                  Redis  3331\n",
      "MySQL                  MySQL  3281\n",
      "Elasticsearch  Elasticsearch  2856\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'PieChart of DataBases Desired to Learn Next Year'}, ylabel='CNT'>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "QUERY = \"\"\"\n",
    "SELECT DatabaseDesireNextYear as Lang, count(DatabaseDesireNextYear) as CNT from DatabaseDesireNextYear \n",
    "    GROUP BY Lang\n",
    "    ORDER by CNT DESC\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY, conn)\n",
    "df.index = df.Lang\n",
    "print(df.head())\n",
    "df.head(5).plot(kind='pie', y='CNT', x='Lang', figsize=(12,5), legend=False, autopct='%1.1f%%',\n",
    "                title='PieChart of DataBases Desired to Learn Next Year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Lang   CNT\n",
      "Lang                        \n",
      "JavaScript  JavaScript  6630\n",
      "HTML/CSS      HTML/CSS  5328\n",
      "Python          Python  5239\n",
      "SQL                SQL  5012\n",
      "TypeScript  TypeScript  4088\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'PieChart of Languages Desired to Learn Next Year'}, ylabel='CNT'>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ASExMhJeXl7Z2lUqFRYsWVTsM/1FNn3zyCWxsbLBq1Sr89NNPEAqFiI6OxpdffqkdUFedql5naWlpuHPnDsRi8RO3m5WVBZFIVOl14OHh8eQd1cEY74Ga7mNiYiKioqIQEhKChQsXwt/fH1KpFKdOncKbb75Z6T3xpPd/TT5vaqJjx44YMGAA5s2bh9dee63KfQOgPSb8X49/AXlUV//+/fHDDz9g4MCBeh1g9mgfJRJJhfqM/dwbisHCMiwsTOeb93EuLi7gOA5HjhzRPumPe3RbmzZt4OTkhK1bt2Lu3LnabzD1lZeXhx07dmD69On4+OOPtbeXlZUhOzu7ysfUd9uP3lApKSmV7nv48CEEAgEcHR3rtY3qrFu3DmKxGDt27KgwircmQ8ar4+zsrH3zPi41NbXSbVZWVigrK6t0+6MPz0dWrVqFrl27YsmSJRVuLygoqLTtU6dO6dz2o1bvJ598ghdeeKHK/QgJCQEA2NjYYObMmZg5cybS0tK0rcznnnsON27cqPKxNXHq1CmkpqbilVdeAVD+LVkoFGLUqFF48803q3xMQEAAAEAkEuGDDz7ABx98gNzcXOzfvx9TpkxBr1698ODBgyeGSlWvVxcXF8hksioHaz26Hyh/flUqFbKysioEQVV/29owxnugpvsYGxuLoqIibN68GX5+ftr7qzvdRV+fPbrMnTsXERERmDNnTqX7HtW+cePGCjVXZ9++fViyZAnatWuHLVu2YNOmTfXuIXsSc3/uH2ewsKytfv36Yd68eUhOTn5it51YLMbkyZMxefJkzJo1q9KpIwCQnp6O27dvo1OnTjXePsdxYIxVCurffvutRqPRHqnNN8iQkBB4e3tjzZo1mDhxovYFUFRUhE2bNmlHBxrCowkjHu8qKikpwR9//FHndXbp0gU7d+5EZmam9k2g0WiwYcOGSsv6+/tXOln5wIEDKCwsrFTnf/8mly5dwokTJ+Dr61th2+vXr8euXbvw7LPPam9ft25dhceGhIQgODgYFy9erPLDpzru7u6IiYnBxYsX8d1339V5qHp2djYmTJgAsViM999/HwBgbW2Nbt264fz582jevHmFb+ZP4uDggBdffBHJycl47733kJCQgPDw8FrV069fP8yZMwfOzs7aQK5Kt27d8NVXX2H16tV45513tLevWbOmRtup7n1hjPdATffx0bYff70xxvDrr7/Wa/v1FRoairFjx2LRokXo2LFjhft69eoFkUiEu3fv6gy9lJQU7aln+/btwwsvvIBXXnkFrVq1qvC8/LfHqD7M/bl/nMmEZadOnfDaa69hzJgxOHPmDKKjo2FjY4OUlBQcPXoUzZo1w+uvvw4A+Oijj3D9+nVMnz4dp06dwvDhw+Hr64u8vDz8/fff+OWXXzBz5sxahaWdnR2io6Px9ddfw8XFBf7+/jh8+DCWLl0KBweHGq/nUbfGwoUL8fLLL0MsFiMkJAS2traVlhUIBPjqq68wYsQI9OvXD+PHj0dZWRm+/vpr5ObmYt68eTXeblWqG6repUsX9O3bF/Pnz8fw4cPx2muvISsrC998802Vrfqa+vTTT7F9+3b06NEDn376KWQyGX766SftEPHHu4RGjRqFqVOnYtq0aejSpQuuXbuGxYsXw97evsI6+/Xrh1mzZmH69Ono0qULbt68ic8//xwBAQFQqVTa5V5++WUsWLAAI0eOxBdffIGgoCDs2rULe/bsqbTtn3/+Gc8++yx69eqFmJgYeHt7Izs7G9evX8e5c+e04d6+fXv069cPzZs3h6OjI65fv44//vijxh/gt2/fxsmTJ6HRaJCVlYV//vkHS5cuRX5+Pn7//Xc0bdpUu+zChQvRuXNnREVF4fXXX4e/vz8KCgpw584dbN++HQcOHABQfh7xo3OYXV1dcf/+fXz33Xfw8/OrU9fwe++9h02bNiE6Ohrvv/8+mjdvDo1Gg8TEROzduxcffvgh2rdvj2eeeQbR0dGYNGkSioqK0KZNGxw7dqzGX64aN24MmUyG1atXIywsDHK5HF5eXvDy8tLLe+Dy5ctVzljVtm3bGu9jz549IZFIMGzYMEyaNAmlpaVYsmQJcnJyavWcGsKMGTOwevVqHDx4sMIxRn9/f3z++ef49NNPce/ePfTu3RuOjo5IS0vDqVOntL0jarUaw4YNA8dxWLNmDYRCIVasWIHIyEgMHToUR48e1X5Ja9asGQ4dOoTt27fD09MTtra22t6W2rKE515L3yOGqjt15L+qOnWEMcaWLVvG2rdvz2xsbJhMJmONGzdmo0ePZmfOnKm07NatW1nfvn2Zq6srE4lEzNHRkXXr1o399NNPrKysjDH25NFy+M8ovqSkJDZo0CDm6OjIbG1tWe/evdmVK1cqjdDUtY+ffPIJ8/LyYgKBgAGocIpDVWJjY1n79u2ZVCplNjY2rEePHuzYsWMVlqnLaNjqfh7Vs2zZMhYSEsKsrKxYYGAgmzt3Llu6dGmlkZR+fn6sb9++lbbTpUsX1qVLlwq3HTlyhLVv355ZWVkxDw8P9tFHH7Evv/ySAagwbL6srIxNmjSJ+fr6MplMxrp06cIuXLhQ6bkuKytjEydOZN7e3kwqlbJWrVqx2NjYKl8/iYmJ7IUXXmByuZzZ2tqyQYMGsZ07d1YYLv/IxYsX2ZAhQ5ibmxsTi8XMw8ODde/evcIo648//pi1adOGOTo6ap+j999/n2VmZtbq+ReJRMzZ2Zl16NCBTZkyhSUkJFT5uPj4eDZ27Fjm7e3NxGIxc3V1ZR07dmRffPGFdplvv/2WdezYkbm4uDCJRMIaNWrEXnnllQrrrG40bNOmTavcbmFhIfvss89YSEgIk0gk2lNY3n//fe3odcYYy83NZWPHjmUODg7M2tqa9ezZk924caNGo2EZY2zt2rUsNDSUicXiSo+pyXuguufsSa/1R6Nva7qP27dvZy1atGBSqZR5e3uzjz76SDsS+PH3cXXPZ20+b6rypPf5lClTGIBKp44wVv78devWjdnZ2TErKyvm5+fHXnzxRbZ//37GGGOffvopEwgElUaQHj9+nIlEIvbuu+9qb7tw4QLr1KkTs7a2ZgAqvcerU93norGee0PjGPvPGeiE6NkzzzyDhISEGp3Arm9z5szBZ599hsTERPj4+Bh9+4QQy2Ay3bDEMnzwwQdo2bIlfH19kZ2djdWrV2Pfvn3aGUIMafHixQDKj/EolUocOHAA33//PUaOHElBSQipFwpLoldqtRrTpk1DamoqOI5DeHg4/vjjD4wcOdLg27a2tsaCBQuQkJCAsrIyNGrUCJMnT8Znn31m8G0TQiwbdcMSQgghOtDFnwkhhBAdKCwJIYQQHSgsCSGEEB0oLAkhhBAdKCwJIYQQHSgsCSGEEB0oLAkhhBAdKCwJIYQQHSgsCSGEEB0oLAkhhBAdKCwJIYQQHSgsCSGEEB0oLAkhhBAdKCwJIYQQHSgsCSGEEB0oLAkhhBAdKCwJIYQQHSgsCSGEEB0oLAkhhBAdKCwJIYQQHSgsCSGEEB0oLAkhhBAdKCwJIYQQHSgsCSGEEB0oLAkhhBAdKCwJIYQQHSgsCSGEEB0oLAkhhBAdKCwJIYQQHSgsCSGEEB0oLAkhhBAdKCwJIYQQHSgsCSGEEB0oLAkhhBAdKCwJIYQQHSgsCSGEEB0oLAkhhBAdKCwJIYQQHSgsCSGEEB0oLAkhhBAdKCwJIYQQHSgsCSGEEB1EfBdAiMVSKYDCNKAwHSjKABSFgFoBqMrK/1UrUMIk+KWkOxgYGCt/mEQkgFQshFQsgFQk/Pf/YiFsrERwtpHARW4FmUTI7/4R0oBQWBJSWyoFkH0XyH9YHoSPArEwreL/S3N1rkps640FGT51KkMmFsJZLoGz3Aou/w9QLwcZGjnL0MjJBn7O1nCRW9Vp3YSQiigsCakOY0BOPJB+HUi7BqRfLf83+y6gUellExzT1PmxJUo1knJKkJRTUu0yNhIhfJ2s4edsDX8XG4R52CHcyw6NXeUQCrg6b5uQhobCkhAAKMoE0q78G4rp14H0G4CyyLDbZWqDrr5IocaN1ALcSC2ocLuVSIAm7rYI9ywPz6ZedojwtodUTF27hFSFwpI0TMXZQMIRIP7v8p/MW7yUwWkMG5bVKVNpcDk5D5eT87S3iYUcIrzt0c7fCW3//2NvLealPkJMDcfYo2EFhFiwskLg/nEg/nB5OKZdAerRBaovGqkDAnN/5LuMKnEc0MTNFm0DHPFUoDOigl1hL6PwJA0ThSWxTMpS4ME//7YcH57T23FGfWISOQLyf+G7jBoRCTi0auSIbqFu6B7qhhAPW75LIsRoKCyJ5VApgDv7gSubgFu7y0/VMHFMbI2Agt/4LqNOvB1k6Briih5hbugc5AqJiE7bJpaLwpKYN40auHcIuLIZuLEdKM3T+RBTwoRWCChazncZ9WYnFaFXUw/0j/RCx8YuNNKWWBwKS2J+GAMST5S3IK9tLT/h30wxToiAkj/4LkOvXORW6NPMA/1beKG1nyM4joKTmD8KS2I+ks+VB+TVLUB+Mt/V6I1/6Rq+SzAYbwcZBrXyxpC2vvBxtOa7HELqjMKSmDZVGXB5I/DPEiD1Mt/VGERQ2SqomGUf7xNwQKcgFwxv1wg9w90hElr2/hLLQ2FJTFNBGnD6N+DscrPuZq2JpspVKFI3nPBwt7PC0LaNMLxdI3jYS/kuh5AaobAkpuXheeDkkvKuVrWC72qMooX6d+QpG978ICIBh94RHpjQpTEivO35LoeQJ7Lor7MxMTEYMGBApdsPHToEjuMwYMAAcBz3xJ9H6+E4DhMmTKi0rjfeeAMcxyEmJkbndv8rICAAu3fvBgAwxvDLL7+gffv2kMvlcHBwQJs2bfDdd9+huLgYAFBUVITJkycjMDAQUqkUrq6u6Nq1K3bs2KFd57179zBs2DB4eXlBKpXCx8cHzz//PG7d4meGmhrRqIGrscDSXsAvXYFLfzaYoAQACcf/5Ah8UGkYdlxKQb9FRzHyt39w5LZl9yAQ89bwvs4+ZuHChfjpp5+0v3t6emL58uXo3bt3pWV9fX2xbt06LFiwADKZDABQWlqKtWvXolGjRrXe9qVLl5CVlYVu3boBAEaNGoXNmzfjs88+w+LFi+Hq6oqLFy/iu+++g7+/PwYMGIAJEybg1KlTWLx4McLDw5GVlYXjx48jKysLAKBQKNCzZ0+EhoZi8+bN8PT0RFJSEnbu3Im8PBM8paI0Dzi7Ajj1K5D3gO9qeCMRUOfO0TuZOHonE0297PBadCD6Nfei00+ISWnQYWlvbw8HB4cKtzk4OMDDw6PSsq1atcK9e/ewefNmjBgxAgCwefNm+Pr6IjAwsNbb3rp1K3r16gUrKyusX78eq1evRmxsLJ5//nntMv7+/ujfvz/y8/MBANu3b8fChQvRp08f7f2tW7fWLn/t2jXcu3cPBw4cgJ+fHwDAz88PnTp1qnV9BqUsBU79DBxdAJTk8F0N7xpqy7IqVx/m4911F/DN3pt4q1sQXmztS6FJTIJFd8Pq25gxY7B8+b8nkC9btgxjx46t07q2bdumDcbVq1cjJCSkQlA+wnEc7O3Lj+d4eHhg586dKCgoqLQcALi6ukIgEGDjxo1Qq/mZoPuJNGrg3O/AolbAvmkUlP8npndhJQ+ySzB502X0XHAY2y8+BA2tIHyz+Lfpjh07IJfLK/w8++yzdVrXqFGjcPToUSQkJOD+/fs4duwYRo4cWev1JCcn4+LFi9oW4u3btxESEqLzcb/88guOHz8OZ2dntG3bFu+//z6OHTumvd/b2xvff/89pk2bBkdHR3Tv3h2zZs3CvXv3al2j3l3bBvz4FLDtbYs6R1IfJAIT/GJjIu5lFOHttefR9/ujOHAjje9ySANm8WHZrVs3XLhwocLPb7/VbS5OFxcX9O3bFytXrsTy5cvRt29fuLi41Ho927ZtQ6dOneDk5ASgfHBPTWY5iY6Oxr179xAXF4dBgwbh6tWriIqKwqxZs7TLvPnmm0hNTcWqVavQoUMHbNiwAU2bNsW+fftqXadexP8N/NodWD+Kt8tgmTqJxb8L6+9aSj7GrjiDF5ccxz/3svguhzRAFv82tbGxQVBQUIUfb2/vOq9v7NixWLFiBVauXKmXLlgAaNKkCa5fv16jx4rFYkRFReHjjz/G3r178fnnn2PWrFlQKP4dPWpra4v+/ftj9uzZuHjxIqKiovDFF1/UqdY6S7kI/DEQWPkckHzWuNs2MyI6ZlljZ+7nYOgvJ/HG6rNIzi3huxzSgFh8WOpb7969oVAooFAo0KtXr1o/vrCwEAcPHkT//v21tw0fPhy3bt3C1q1bKy3PGHviSNbw8HCoVCqUlpZWeT/HcQgNDUVRUVGta62TvCRg41jg5y7A3QPG2aaZE1NY1trOy6l4+tvDWLj/NkqV1I1NDK9Bj4atC6FQqG0FCoXCapfLy8vDhQsXKtzm5OSEU6dOITg4uMII2iFDhmDLli0YNmwYpk6dip49e8LV1RWXL1/GggUL8Pbbb2PAgAHo2rUrhg0bhjZt2sDZ2RnXrl3DlClT0K1bN9jZ2eHChQuYPn06Ro0ahfDwcEgkEhw+fBjLli3D5MmTDfJ8aGk05TPuxH0OKKoegESqJuZo8EpdlCjVWLD/FjacfYDP+oajd0TlUeyE6AuFZR3Y2dnpXObQoUNo2bJlhdtefvllqNXqSqNeOY7DmjVr8Msvv2DZsmX44osvIBKJEBwcjNGjR2tbsL169cLKlSsxZcoUFBcXw8vLC/369cO0adMAAD4+PvD398fMmTORkJAAjuO0v7///vt62vsqpN8Atr9TfrFlUmsSAbUs6yMppwQTVp1F5yAXzHy+KRq7yvkuiVggmu7OiNRqNdzc3LBr1y60a9eO73LqT6UAjnwLHJ3foGbc0bc3bL/HzozaDxQjlVmJBHj36WCMj25M52cSvaJjlkaUlZWF999/H23btuW7lPp7cAr4OQo4PI+Csp7E1LLUmzKVBl/tvomBPx7DjdR8vsshFoRalqR2ygqA/TOBM0sBRh/y+jDRfj42ptHxNn0TCzm80TUIb3UPgpguCUbqiV5BpOZu7QF+eAo4/SsFpR6JaG5Yg1CqGRbG3cZzi47icpIJzo1sph6Nh/jvAEZLR2FJdCsrBLa8DqwZAuQn8V2NxaFTRwzrRmoBXlhyDD8dvsvrtHk1vRqRPm3atAnt27eHvb09bG1t0bRpU3z44Yf1Wqevry9SUlIQERFR48esWLGi0jzc5obCkjxZ6mXgly7AxTV8V2KxKCwNT6lmmLfrBkYvO4WMgjK+yzGK/fv346WXXsKLL76IU6dO4ezZs5g9e3aFCUxqS6FQQCgUwsPDAyJRwzqZgsKSVO/Ur8CvPYCsO3xXYtFEdJ6l0Ry5nYlnF/6Nw7f4vXbm7t270blzZzg4OMDZ2Rn9+vXD3bt3tfd36NABH3/8cYXHZGRkQCwW4+DBgwCAVatWoU2bNrC1tYWHhweGDx+O9PR07fI7duxA586d8dFHHyEkJARNmjTBgAEDsGjRogrr3bZtG9q0aQOpVAoXFxe88MIL2vv8/f3xxRdfICYmBvb29hg3blylbthH1wf+66+/0KJFC0ilUrRv3x6XL1/W3j9mzBjk5eVprxM8Y8YMfT6dRkFhSSoryQX+HAnsnAioG8a3cD6JQC1LY8osVCBm+SnM2XkdSjU/z31RURE++OADnD59GnFxcRAIBBg4cCA0mvJ6RowYgbVr11boNv7zzz/h7u6OLl26AChv5c2aNQsXL15EbGws4uPjK1yE3sPDA1evXsWVK1eqreOvv/7CCy+8gL59++L8+fOIi4tDmzZtKizz9ddfIyIiAmfPnsXUqVOrXddHH32Eb775BqdPn4abmxv69+8PpVKJjh074rvvvoOdnR1SUlKQkpKCiRMn1uVp4xWNhiUVpV4uD8qcBL4raTAWuH2BhYm1vyYqqb8WPvZYMrI1vBxkBt9WTEwMcnNzERsbW+m+jIwMuLm54fLly4iIiEBGRga8vLxw4MABREVFAQA6duyIzp0746uvvqpy/adPn0a7du1QUFAAuVyOoqIiDBkyBDt37oSfnx+eeuopPPPMMxgxYgSsrKy06wwMDMSqVauqXKe/vz9atmyJLVu2aG9LSEhAQEAAzp8/j8jISBw6dAjdunXDunXrMHToUABAdnY2fHx8sGLFCgwZMgQrVqzAe++9h9zc3Ho8g/yiliX518V1wG89KSiNjFqW/LmYlIf+i4/idEK2Ubd79+5dDB8+HIGBgbCzs0NAQAAAIDExEUD5tWl79uyJ1atXAwDi4+Nx4sQJ7YXnAeD8+fN4/vnn4efnB1tbW3Tt2rXCOmxsbPDXX3/hzp07+OyzzyCXy/Hhhx+iXbt2KC4uBgBcuHABPXr0eGKt/21pVqdDhw7a/zs5OSEkJKTGF4gwBxSWpHwmnh0fAFvGAyq6koOx0TFLfmUWKjD815NYdfK+0bb53HPPISsrC7/++iv++ecf/PNP+VSRjw++GTFiBDZu3AilUok1a9agadOmaNGiBYDybtxnnnkGcrkcq1atwunTp7Wtv/8O4GncuDFeffVV/Pbbbzh37hyuXbuGP//8EwAgk+luUdvY2NR5P2ty6UFzQWHZ0BWkAiv6lE8yQHhBl+jin1LN8FnsFXyy+bLBj2NmZWXh+vXr+Oyzz9CjRw+EhYUhJyen0nIDBgxAaWkpdu/ejTVr1lS40PyNGzeQmZmJefPmISoqCqGhoRUG91TH398f1tbW2qsQNW/eHHFxcXrZr5MnT2r/n5OTg1u3biE0NBQAIJFIoFab99VhGtbYX1JRxi1g1SAgL5HvSho06oY1HWtPJeJ2WgGWjGwNV1srg2zD0dERzs7O+OWXX+Dp6YnExMRKI1+B8hbd888/j6lTp+L69esYPny49r5GjRpBIpFg0aJFmDBhAq5cuVLhIvAAMGPGDBQXF6NPnz7w8/NDbm4uvv/+eyiVSvTs2RMAMH36dPTo0QONGzfGSy+9BJVKhV27dmHSpEm13q/PP/8czs7OcHd3x6effgoXFxfteaX+/v4oLCxEXFwcWrRoAWtra1hbW9d6G3yilmVD9eAUsOwZCkoTQC1L03Lmfg6eX3wUt9L0e6k5jUYDkUgEgUCAdevW4ezZs4iIiMD777+Pr7/+usrHjBgxQnsB90aNGmlvd3V1xYoVK7BhwwaEh4dj3rx5+Oabbyo8tkuXLrh37x5Gjx6N0NBQPPvss0hNTcXevXsREhICAOjatSs2bNiAbdu2ITIyEt27d9d2CdfWvHnz8O6776J169ZISUnBtm3bIJFIAJQPJJowYQKGDh0KV1fXagcpmTIaDdsQ3dhZfoFmOj5pEtZ4fYIp95rxXQb5D3uZGL+OboN2AU56WV/v3r0RFBSExYsX62V9puLRaNicnByzn6XnSahl2dCcWV5+aggFpcmglqVpyitRYuTSf7Drckq91pOTk4O//voLhw4dwtNPP62n6oixUVg2JAdmAzveA5h5H2i3NHTM0nQpVBq8ueYcVh5PqPM6xo4di/Hjx+PDDz+sdOF3Yj6oG7Yh0KiB7e8C5//guxJShW0+H+KdO635LoPoMKFLY0zuHWJRp0OQmqOWpaVTFAPrhlNQmjAhtSzNwk+H72LSxkvQaKh90RDRqSOWrDgbWD0YSD7DdyXkCSgszceGs0lQqjX4dkgkhAJqYTYk1LK0VCW5wO/9KSjNAA3wMS+xFx7i7bXneJuEnfCDwtISlRUCq18snxSdmDwhaMCVudl5ORVvrzkPFQVmg0FhaWmUJcDal4Ck03xXQmpICDoGZo52X03FWxSYDQaFpSVRKYA/RwEJR/iuhNQCHbM0X7uvpuKddeehpkE/Fo/C0lJo1MCmV4A7+/iuhNQSdcOat52XUzFlMx3ysHQUlpaAMSD2DeD6Nr4rIXVALUvz9+eZB5i36wbfZRADorC0BH99AFxax3cVpI6oZWkZfjp8F7/+fY/vMoiBUFiau72fAWeW8V0FqQchnTpiMebsuo5NZ5P4LoMYAIWlOTv8FXB8Ed9VkHoSMApLS8EYMHnTJcRdT+O7FKJnFJbm6vJG4OBsvqsgekDHLC2LSsPw5ppzuPggl+9SiB5RWJqjhxeArW/xXQXREwpLy1Oq1GDc72eQmlfKdylETygszU1RJl2P0sIIaICPRUovKMO438+gVEl/X0tAYWlO1Epg/Wgg7wHflRA9opal5bqcnIcPN1wEXQnR/FFYmpNdk4D7x/iugugZRwN8LNpfl1KwMO4232WQeqKwNBdnltEpIhaKzrO0fAvjbmPn5RS+yyD1QGFpDu6fAHZO4rsKYiAC6oa1eIwBH66/iNtpBXyXQuqIwtLU5SUB60cBGiXflRADETBqWTYEJUo13lh9DiUK+nubIwpLU6YsAdaNAIoy+K6EGBCNhm04bqcX4rPYK3yXQeqAwtKU7fwISLnAdxXEwKhl2bBsOpeEDWdoRLu5obA0VTd2Auf/4LsKYgR0zLLhmbb1Kh2/NDMUlqaoKBPY/g7fVRAjoVNHGh46fml+KCxN0Y736DhlA0LdsA3T7fRCfL7jGt9lkBqisDQ1F9YC17fzXQUxIgrLhmvtqUQcvkVfjM0BhaUpyUsCdk3muwpiZByFZYP28aZLyC+lU8NMHYWlqWAMiH0dKMvjuxJiZDTAp2FLySvFzG3UHWvqKCxNxT8/AfF/810F4QG1LMmmc0nYf40uGG3KKCxNQcYtYP9MvqsgPKGwJADwyZbLyC1W8F0GqQaFJd/UKmDLeLo+ZQNGp44QAMgoKMOMbVf5LoNUg8KSbycWAQ/P8V0F4RHHVHyXQExE7IWHOH4nk+8ySBUoLPlUkAr8/Q3fVRCeCahlSR4zbdtVKNX0mjA1FJZ82j8TUBTyXQXhGx2zJI+5k16I347E810G+Q8KS74knwUuruW7CoOZe6QMbX8thO3cfLh9XYAB64pxM7NiKDDGMONQKby+LYBsdj66rijC1fQnB8fm60q0+aUQDvPyYTMnH5E/FeKPixUHRay+pITvggI4fZmPj/aWVrgvIVeDJosKkV/G9LOjesBpKCxJRYsO3MbDXBrHYEooLPnA2P8nHzCdD2x9O3xfhTfbSnDyFRvsG2UNlQZ4ZlUxihT/7vNXxxSYf0KBxX2kOD3OBh5yDj3/KEbBE4LMScbh0ygrnHjFBpcmyDEmUowxW0ux5075cb/MYg1e3V6Cb3pKsWekDVZeVOKvW/+e8P36XyWY97QV7Kw4w+18LdFoWPJfxQo1Pt9O516aEgpLPlxaDySd5rsKg9o90gYxkRI0dROihYcQy5+XIjGP4WxKeTAwxvDdPwp8GmWFF8LEiHATYuUAGYqVDGsuVz+bSVd/EQaGiRHmKkRjJwHefcoKzd0FOJpYHpb3chjsrTgMjRCjrbcQ3QKEuJZRfvxnzWUlJEIOL4SJDf8E1AKFJanK7qupOHgzne8yyP9RWBqbogjYP4PvKowur6z8XydZeYsuPpchtZDhmcYi7TJWIg5d/EU4nlSz8GCMIe6eCjezNIj2K19PsJMAxUqG8ylqZJcwnE5Wo7m7ENklDNMOlmLxs1L97pg+UFiSanyx4xrUGsvtgTInIt2LEL06ugAoeMh3FUbFGMMHe0rRuZEQEW5CAEBqYXlrz11esTvU3YbD/bwnjwTMK2Xwnl+AMjUg5IAf+0rR8/+h6yjjsHKADKNjS1CiZBjdQoxeQSKM3VqCt9tJEJ+rQf91xVCqgRldrfBiOP+tTDpmSapzN6MI6888wLB2jfgupcGjsDSm3ETg+CK+qzC6t3aW4lKaGkfH2lS6779HDhmrfNt/2VoBFybIUagob1l+sKcUgY4CdPUvfzkPDBNj4GNdrYcSVLicrsbiPlIEfV+ItYNk8JBzaPdbEaL9hHCz4bmDhVqW5Am+238LAyK9IZMI+S6lQaNuWGPa+xmgKtW9nAV5e2cJtt1S4eDLNvCx+/fl5iEv/39qYcUupvRiBnf5k1+WAo5DkJMAkR5CfNixvHU492hZlcuWqRje+KsUP/eT4U62BioN0MVfhBAXIZo4C/BPDbt8DYmOWZInScsvw7JjdCoJ3ygsjSXhGHBtK99VGA1jDG/tLMHmGyocGG2NAMeKL7UABw4ecg777v07e41CzXA4QYWOPrX7Bs0YUFbNJDiz/i7Ds0EitPIUQq0BVI8d/1GqAbUpHA6ibliiw0+H7yKniOaN5RN1wxrLwdl8V2BUb+4sxZrLSmx9yRq2Vpz2GKW9FQeZmAPHcXivvQRzjpQh2EmAYGcB5hwpg7WYw/Bm/3ahjt5SAm9bDnOfLh+YM/dIGdp4lY+EVagZdt5W4fdLSizpW3ngztV0Nf68qsKF8eXdv6EuAgg4DkvPKeAh53AjU4O2XibQtWWmYZl3Yj2Kb52AMjsJnEgCK+8wOHaJgdjZR7tM5l8LUHQlrsLjJJ4h8Bz9bbXrLbiwG0VXD0CZcb98eY8gOESPhpVXiHaZwqsHkXt4JZiyFPLmz8Cx21jtfaq8NKT9ORWeL38HgZW1vnaXVwWlKiw6cAfTngvnu5QGi8LSGBJPAveP8V2FUS05U376R9eVxRVuX/68FDGREgDApE4SlKgY3thZipwShvY+QuwdVR6ujyTmaSDg/m2VFinLl0/K10AmAkJdhFg1UIahERUH6jDG8NqOUizoZQUbSfn6ZGIOKwZI8ebOUpSpgMV9pPC2M4HOFY15zg1b+uAKbFv1hcQjGGBq5P79B9LWT4XXK0sgkPz75UUa0Boufd7794HCJ3/slD64DJuwLrB6OgycSIy8fzYhbf00eL3yA0S2LlAX5yF79yI493kPIgcPpG+cCatGzWDduC0AIGvPj3DsEmMxQfnIqpP3MbazP3wcLWu/zAXHGDOFjijLtnowcHsv31UQE+ZfuobvEupNXZyHpEUj4D58HqS+EQDKW5aasiK4vfBZndfLNGo8WPgSnHpOgDyiB8oe3kT65lnwfWsVACBj65eQeATBvv0gFF07hKLrR+A2aKpe9snUjHyqEb4Y0IzvMhokE/habeFSL1NQEp2EnPlPnK0pKwIACKTyCreXJl7Gg0UjkPzLa8ja9T3URbm1Wi9TlgEaNQRSWwCAyMkbTFkGRdpdqEsKoEi5BYmrP9QlBcg9shpOPSfoZX9M0YYzSUgvaFiDBE0FdcMa2pHqj80Q8oiVgKHYPA9dAijv9s458BusfMIhcfXX3i4LbA3r0M4Q2blClZeG3COrkLZuCjxfXghOVLNzXHMOr4RQ7gyZfyQAQCiVw6Xv+8jcMR9MpYBNRHfIAlsjc+d3sG3dD6q8NKRvmgVoVLDvNBw2oZ0NsMf8KFNp8NuReEzpE8Z3KQ0OhaUhZd1tUCNgSd2JBQww47DM3vcTFOkJ8BjxVYXbbcKitf+XuPpD4hGM5CVjUXL3NKxDOupcb94/G1F8/TDch80FJ5Job7du0hHWTf59fGniJSgz7sOp5wQ8/OU1uDz3EYQ2jkj5/QNIfSMgtHGo/06aiNUn7+PNrkGwt+Z/Qo2GhLphDenoAoCuVUhqQGLG3bDZ+35CyZ1/4D5sDkR2Lk9cViR3gsjeFcoc3bNY5f2zGXknNsBtyCxI3AKqXY6plMjeuwROvd6EKicFTKOGtFEziJ19IHbyRlnKzVrvkykrUqix/Didd2lsFJaGkpcMXPqT7yqImZAIzG+cHWMM2fuWoPjWcbi/NBtiBw+dj1GX5EOVnwmh3PGJy+X9swl5x9fBffBMWHkGP3HZ3OPrIA1sDSuPoPIvp4+disM0KkBjvl9EqrPieAKKqju5mBgEhaWhHF8EqOkkYlIzYs78wjJ73xIUXj0El+c+gkBiDXVhDtSFOdAoy2dT0ihKkHNgKcqSr0OVl4bSxEvI2Pg5hDI7WAd30K4nc8e3yDm8Qvt73j8bkXvkDzj3eRcie/d/16uofH1HRcZ9FN/4Gw6dRwIARE4+ACdAwcW9KL57GsqsJEh0hK05yi1WYs0/iXyX0aDQMUtDKMoCzq3kuwpiRiQC82v9FJ7fCQBIW/tJhdud+7wHebOnAU4ARUYCCq8egKa0CEK5I6SNmsPl+ckVzoFU5WcAj51LW3BuJ6BWITN2boX12ncaBofOI7S/M8aQvWcxHLuP057XKRBbwbnPe8jetwRMrYRTzwkQ2T65a9hcrTyRgFc6B0AgMJ1rs1oyOs/SEA58Afz9Nd9VEDPSV/wrrhZUnmiekCf5eVRr9Gqqu/ub1B91w+qbWgmcXcF3FcTMmPMAH8KflccT+C6hwaCw1Ldbu4GiDL6rIGZGbIYDfAj/jt/Nwp30Ar7LaBAoLPXt/Gq+KyBmyBwH+BDTsOokDfQxBgpLfSpIA+7s47sKYoZE1A1L6mjzuSSUKMx4RgszQWGpTxfXmu0VJAi/zPE8S2Ia8ktV2H5J9yQPpH4oLPXpAnXBkrqhliWpj01nk/guweJRWOrLg1NA5i2+qyBmyhzPsySm41RCNpJzK0/aQPSHwlJfzv/BdwXEjIlBYUnqjjEg9nwy32VYNApLfVAUA1e28F0FMWMiOmZJ6mkLhaVBUVjqw7WtgILOdSJ1J6ZjlqSe7qQX4kpyHt9lWCwKS304v4rvCoiZE1E3LNGDzeeodWkoFJb1lZsI3D/GdxXEzNEMPkQftl18CLWGXkuGQGFZXzd2AqAXJ6kfalkSfcgsLMOZhGy+y7BIFJb1dXMn3xUQCyCmU0eInuy/nsZ3CRaJwrI+SnKpC5bohYjmhiV6su8ahaUhUFjWx539NL0d0QshdcMSPUnIKqYrkRgAhWV93PiL7wqIhaBTR4g+7buWzncJFofCso5UGhXmWAMHgzujWEJXuCf1QwN8iD7RcUv9E/FdgLm6lHEJazPPYC0Aia8n2tgFIFolRPTDG/DNSuC7PGJmaCJ1ok/nE3OQWVgGF7kV36VYDArLOjr28N+BPQqNAsdzb+I4gHl2gL9nB0SJnRCdm4HW989DrFHyVygxC0IKS6JHGgYcuZ2BgS19+C7FYlBY1tHx5OPV3pdQlIwEJOMPADaNG6ODbQCiS5WIenAZLgXUPUIqo25Yom8n7mZRWOoRhWUd5Jbm4lr2tRotW6Qqxv6cq9gPgHORIiwgGlECW0RnJiEi+TIEjD4kCXXDEv07cS+L7xIsCoVlHZxIOQFNHUKOgeFaQQKuAfhZDDiFRKCztS+iigrR6f4F2JbSJMgNFZ06QvTtQXYJknNL4O0g47sUi0BhWQenU0/rZT3ZZbnYVpaLbQBEXs5oYdcG0RoJotPuISjtpl62QcwDtSyJIZy4m4UXW1NXrD5QWNbB5czLel+niqlwNu82zgJYYA14N22LzlZuiM7LQbvE85Aq6SroloxalsQQKCz1h8KylkpVpbiTc8fg20kuTsOfxWn4E4DUzwdtbQMQrQSik2/AKyfR4NsnxkVhSQzhJB231BsKy1q6kX0DKmbcKe5K1WU4knsDRwDMdgCCfDohSuSA6OxURD64CBFNuWf26NQRYgjJuSV4kF0MXydrvksxexSWtXQl8wrfJeBO4QPcwQMsFwC2QU3QUe6H6BIFOidehFNRJt/lkTqgU0eIoVxKyqOw1AMKy1q6ksV/WD6uQFmIPTlXsQeAwF2OCNsIRMEa0Rn3EfbwGji61qZZoG5YYiiXk/PQt7kn32WYPQrLWjKFlmV1NEyDS/n3cAnAD1aAa1gLdJZ5I7owHx3uX4BNGV2JwFQJoea7BGKhriTTKWn6QGFZC/mKfCTmm8/gmozSbGwpzcYWAGIfN7Sya49olQhRqbcRkHGX7/LIY6hlSQzlMoWlXlBY1sKVzCtgZtqtqdQo8U/uLfwD4Gs54OvWDtESN0TnZaLN/fOQqMv4LrFBowE+xFDySpRIzCpGI2c6blkfFJa1cDXzKt8l6M2D4lSsLk7FagCyQH88ZRuA6DI1opKuwj3vId/lNThCRt2wxHAuJ+dRWNYThWUtGGIyAlNQoirBwZxrOAgATiKE+EUhWmCH6KyHaJ50keavNQIBdcMSA6JBPvVHYVkLltSyfJKbBfdxE8CvIsChSVN0svFFVHExOidehH1xDt/lWSQ6ZkkM6WZqPt8lmD0KyxoqUBQgvSSd7zKMLleRh78UefgLgNDDAc3tWiKaSRGVFo+Q1Ot8l2cxKCyJIcVnFvFdgtmjsKyh5MJkvkvgnZqpcT7vDs4DWCgDPMJbI0rqgaj8XDyVeB4yRTHfJZot6oYlhpSUUwKlWgOxUMB3KWaLwrKGkgsoLP8rtSQDG0oysAGAxNcLbe0CEaXiEP3wBnyz7vNdnlkR0AAfYkAqDcP9rGIEucn5LsVsUVjWUFJhEt8lmDSFRoFjuTdwDMA8OyDAswOixE6Izk1Hq/sXINYo+S7RpFHLkhhafGYRhWU9UFjW0MNCOp2iNuKLkhGPZPwOQB4UhA5yf0SVKhGVeAkuhQ3v2K8uNIMPMbR7GYUA3Pkuw2xRWNYQHbOsu0JlEfblXMU+AJyrDOGNuyCKkyM68wEiki7T/LWgliUxPBrkUz8UljVEYakfDAxX8+NxFcBPYsAptDk6W/sguqgQHe9fgG1pw5yai45ZEkNLyKKwrA8KyxqisDSM7LIcbCvLwTYAIi9nRNq1QbRGgujUu2icfovv8oyGwpIYWlo+TWlZHxSWNZBVkoUSVQnfZVg8FVPhTN5tnAEw3wbwbtoWUVbuiM7LQrv752GlKuW7RIOhblhiaOn5lvv+MQYKyxqgViU/kovTsK44DesASP190c42ENFKhujk6/DMecB3eXpFLUtiaEUKNQrLVJBb0cd+XdCzVgM0EpZ/peoy/J17HX8DgAOHIJ9OiBY5IDorFZEPLpj9ROQctSyJEaTll0LuSqeP1AWFZQ3QOZam507hA9zBAywTArbBIegk90N0SRk6J16EY1EW3+XVGrUsiTGk55ehMYVlnVBY1kBOKU0ebsoKlIXYnXMVuwEI3G0RYdsc0ZAhOj0BYSnX+C6vRigsiTGkF9Bxy7qqVVh2794dmzdvhoODg4HKMU1FShpybS40TINL+XdxCcBiKeAW1hKdZV6ILshHh8TzsC4r5LvEKlE3LDGGdBoRW2e1CstDhw5BoVAYqhaTVayiCcLNVXppFjaXZmEzALGPO1rbdUC0SojolFvwy7zHd3la1LIkxpBd3PA+v/WFumFroFhJYWkJlBolTubexEkAX9kCjdzbI9rKFVE5GWibeB5iNX8fJByFJTGCojIV3yWYrVqHZUFBAaRS6ROXsbOzq3NBpoi6YS1TYnEKVhWnYBUA68BAPGXrj6gyFaKSrsI9L8WotVBYEmMoKqPXWV3VOiybNGlS7X2MMXAcB7Xasv4g1A1r+YpVxTiQcw0HAMBJjFC/KEQJ7BCdlYzmSZcgYIY9psgZeP2EANSyrI9ah+XGjRvh5ORkiFpMFnXDNjw3Cu7jBoBfRYBjk6boZOOLqOJidLp/AfYluXrfHrUsiTEUKSgs66rWYdmpUye4ubkZohaTRd2wDVuOIg87FHnYAUDo6YgWdq0QxaSITr2HJmk39LINCktiDNSyrDsa4FMD1A1LHlEzNc7l3cE5AAutAY/w1oiSeiA6Pw/tE89Bpqjba4XCkhgDHbOsu1qFpZ+fH4RCoaFqMUmMMeqGJdVKLcnAhpIMbABg1cgbbWwDEK3iEJ18HT7ZiTVeD6ehDzFieNQNW3e1Csv4+HhD1WGySlQlYHRxYlIDZeoyHMu9gWMA5toDAV4dEC12RnROGlomXoBYo6z2sdSyJMagUtNnWV3VegYfXTiOQ1xcXJ0LMjXUBUvqKr4oGfFIxkoOkAcFoYPcH9GlSnROvASXwvQKy9JoWGIMGkZhWVe1CssWLVpUe19+fj7Wrl2LsjLLmk6J0YuL6EGhsgj7cq5iHwDOVYbwxl0QzckRnZGIpslXAEbdY8TwNPRxVme1CssFCxZUuk2lUuGHH37A7Nmz4e3tjVmzZumtOFMgEUr4LoFYGAaGq/nxuApgiQRwDm2OEZom+FDiDIaGNSaAGJfYil5fdVWv0bCrV6/GtGnTUFxcjOnTp2P8+PEQiSxrgC2FJTG03LJcdNiRibyAi7iQH8x3OcSCSe3o86yuBHV50O7duxEZGYk33ngDMTExuHPnDt58802LC0oAkAjoxUUMa3p8Swgv34TTtu/g6cnxXQ6xYBy9vOqsVmF56tQpdOvWDQMHDkS3bt1w9+5dTJ06FTY2Noaqj3dCgRAizvK+BBDT0LnUF6Gbzmt/Dz66EBIpdZURw+AElJZ1VasUeOqppyCTyfD666/D398fa9asqXK5d955Ry/FmQqxUAyVigZgEP2SMhHe2c4A5b+nlIge3ERE88s4h3AeKyOWisKy7jhWi+Ge/v7+4HS04zmOw717pnOdQH3ovK4z8sry+C6DWJj511rCZ+vpKu+7OfwHJD80ckHE4jm4W2PEzKf4LsMs1aplmZCQYKAyTJuVwIrvEoiF6VcYBJ/tZ6u9P+jgN8iMnIyyEpqsgOiPREaHlOqqVscsDxw4gPDwcOTn51e6Ly8vD02bNsWRI0f0VpypEAvFfJdALIijRoaXt+QDmuonIhCmxCOCnTNiVaQhsJLR8fC6qlVYfvfddxg3blyVF3e2t7fH+PHjMX/+fL0VZyro9BGiT3MuNAFL0t3Har/7F/h60VnkRH8kMvriX1e1CsuLFy+id+/e1d7/zDPP4OzZ6ruWzJWVkLphiX6MzA2H856av0cC938FqQ11nRH9sLKm11Jd1Sos09LSIBZX/81EJBIhIyOj3kWZGmpZEn3wVtthwIakWj1GmJ6IZmUnDFQRaWjomGXd1Sosvb29cfny5Wrvv3TpEjw9PetdlKlxsHLguwRiAb440QiazOxaP852/wr4edGpS6T+rCgs66xWYdmnTx9MmzYNpaWlle4rKSnB9OnT0a9fP70VZyrcrN34LoGYubfSm8PmyIU6Pz5g91xYy+mDjtQPdcPWXa2euc8++wybN29GkyZN8NZbbyEkJAQcx+H69ev44YcfoFar8emnnxqqVt5QWJL6CFG6oMv62/W6KqogOxURhYdxCp30VhdpeKgbtu5q9cy5u7vj+PHjeP311/HJJ59oL1/FcRx69eqFH3/8Ee7u7gYplE8e1h58l0DMFMeAaQedwApS670u+aE1CBzeFvce0jF0UjfUsqy7Wj9zfn5+2LlzJ3JycnDnzh0wxhAcHAxHR0dD1GcSqGVJ6mpKUkuIz1Y9S09dNPprNtK6zEZRPh3DJLUnd6SR/XVV568Zjo6OaNu2rT5rMVkUlqQu2pZ5IXLj5Xp1v/6XIC8TETn78I+wmx7XShoKW2cZ3yWYrTpdoquhobAktSViAkzcJQGrYjBcfdkc2YjGXvpfL7FsVtYiGg1bDxSWNWBvZQ+ZiL6RkZqbdTcS3PU7Blu/7/bZkNvTBx+pObmTlO8SzBqFZQ1R65LUVI8SfzTeYth5XQUF2YhI/wugKy6RGrJzprCsDwrLGqKwJDUhZxJMiC0DjHD9U+sT2xDsUWTw7RDLYEsty3qhsKwhd2vLOyWG6N+cK03BEh4YbXve276AnSNNjk10s6WWZb1QWNYQhSXRZVBBCDz+OmPUbQqK8tE0aTN0XJOdEArLeqKwrKEA+wC+SyAmzEVjg5c2ZgDM+JfUkp3ejSbueUbfLjEvdnTaSL1QWNZQqFMo3yUQEzb3TGOw1HTetu8ZOxsOztQdS6rGcYC9G4VlfVBY1lCgfSBEAhqqTyp7JTsC9nGGHf2qi6C0COH3/gRH72hSBTtXGSRS+vyqD3r2akgsFKOxfWPczLnJdyk1VnSzCJk7M1FyvwSqXBUavd0Idq3ttPer8lRIXZ+KwquFUBerYdPEBp4jPWHlUf2UWEzFkPFXBnKO5kCVo4KVpxXcB7vDtrmtdpnc47lI3ZgKVsbgGOUIj5f+nVtXkaFAwjcJaDyjMYQyoWF23Ij8VQ7ovT5Br7P01JX0fBxCh3bF9TQnvkshJsbFR853CWaPvofWQohTCN8l1IqmTANpIyk8R1a+xihjDPe/vw9FhgKN3mmEoJlBELuIkfB1AjRlmmrXmbY5DdkHs+E10gvBc4Lh2M0RiYsSUXK/BACgKlAheXkyPId6wu9DP+Qcy0HBhQLt4x/+/hDug90tIigB4POjXmA5uXyXoeWxZTYcXag7llTk4mOreyHyRBSWtWBuxy1tm9vCfZA77NvYV7pPkaZAyd0SeL3sBetAa1h5WsFrtBc0pRrknsytdp25x3Ph2s8Vti1sIXGTwLm7M+QRcmTuzixfb4YCQpkQ9u3tYR1oDZswG5Q+LJ+aLfdELjgRV2U95uiDlBaQnrjEdxkVcIpShN36AwIhDY8l/6KWZf1RWNZCiKN5tSyfhCn/f3k18b8fqpyAAyfiUHyr+ImPe/wxACCQCLSPsXK3gkahKe/6LVShJL4EUl8pVIUqpG9Jr7KVa44ilO7osOEG32VUSXrpCMKc0vgug5gQZwrLeqOwrAVz64Z9EitPK4idxUjbkAZ1kRoalQYZOzKgylNBlVf97DPyZnJk7clCWWoZmIah8Eoh8s/nax8jtBHCZ5wPkn5Nwr3P78GhowNsm9ki9c9UOD3tBGWmEnem3cHtT28j77R5nu4gBIdP98nBikx39hzXLXPh7EpDEghgZSOi2Xv0gN5NtWBvZQ9PG0+kFKXwXUq9cSIOjd5uhOSlybj+5nVAAMjD5ZA3f/I3UM/hnkhenozbn9wGOEDiJoFjZ0fkHM3RLmPX2q7CQKLC64UoSyqD10gv3Jp8C74TfCGyF+Hu53dhE2IDkZ15vQynJrSE8OIpvst4IoFKgbAry3HcazQ0alMYfkT4Ql2w+mFen1ImIMQpxCLCEgBk/jIEzQqCulgNpmIQ2ZUHmMy/+vOxRHYi+L3rB41CA3WRGiIHEdI2pEHiIqlyeY1Sg5Q/UuDzmg8U6QowNYNNqA0AwMrDCsV3i2HX0q7Kx5qijqW+aLrpgkmMftVFcv0kmob3wOUsL75LITyiwT36Qd2wtWRJxy0fEVoLIbIToSy1DCXxJbBtpfvNJZAIIHYUA2og/0x+tY/J2JYBeTM5ZP4yMA0DHhtoy1QVfzd1VkyI93YATKHgu5Qac4n9Eq5u9J24IXPzp7DUB3oX1VKYcxjfJdSYulQNRdq/H+yKTAVK7pdAKBdC4ixB3qk8CG3L/1+aVIqU1Smwa2UH24h/31xJvyRB5CiCx+DycyWL7xZDmaOErJEMyhwl0mPTwRiD67OulbZfmlyKvFN5CPo8CED5cVJwQPbhbIjtxShLKYMs0HxmFZl9swVw27S7X/+LU6sQcv5n5PiPg0ppRt9MiN54BzvyXYJFoLCspTbubSDgBNAw0//gKYkvQcKXCdrfU9emAgAcOjnAZ5wPVHkqpKxLgTqvvDvVoaMDXJ+vGHqKLEWFayYyJUP65nQo0hUQSAWwbW4Ln9d8ILSpeN4kYwwPlz+ExzAPCKzKOzAEEgG8X/VGyh8pYEoGz1Ge5a1TM9CnqDEabTvLdxl1Irl9DuER8biU48d3KcTI7N1ksHGofpIRUnMcYzzM/GzmXtrxEq5mXeW7DGIk9hopfltjB/bgId+l1BkTCHFtyA9IS1XzXQoxovDOXug20rzODzdVdMyyDjp4deC7BGJEcy+GmnVQAgCnUaPJP4shktBbviHxbuLAdwkWg7ph66CDZwf8dvk3vssgRjA8Lwwuu417jUpDEcdfQUTz27igaMx3KVp3Hl7C/ot/IjHzNvKLszDumZloEdBZe/8fB7/EP7f2VniMv1sYJg5cXO06T97cjVWHvq50+4JXdkEsKh+1ffr2fmz95zcoVKXoEPIsBnYYr10uqyAVi/+ahEkvLIFMYlPfXeSVFx2v1BsKyzpo6dYSMpEMJaoSvkshBuShlmPghhSzOE2kphy3LYDnSz8iJcU0jrmXqUrg7dwYT4X0xm/7ZlS5TLhvW4zsOkn7u7AGV/+RSmwwbeiKCrc9CsrCkjysOfwtRnadBBc7TyzZ9SmCvVogwu8pAMCfR77D8+3GmX1Q2rvKIHek45X6QmFZB2KhGK3cW+FY8jG+SyEGNOefALCM83yXoVccYwg+thCZYe9C+YQJ842laaP2aNqo/ROXEQnFsLOu3ZVUOKDax2QWpEAqsUHroG4AgCZekUjNuY8Iv6dw+nYchAIxIgOjarU9U0RdsPpFYVlHHTw7UFhasNczmkF+2LKC8hFR4g1ENL+G82XmMfDj9sOL+HjlIMisbBDs2QLPtRsLW9mTuxfLlCWYunoYGNPA27kx+rUdA1+XYACAm703lKoyPMi8DSe5O+5n3MRTob1RVJqPv86swLvPfWuM3TI4rybUBatPFJZ1RIN8LFew0hnd19+1qO7X/3LcsQjew35Ecopp72W4bzu0DOwCJ1t3ZOWnYMeZFfh++0RMGrQEYmHVs0a5OzTCyK6T4OUciFJFEQ5d3oz5W9/FJy/+Ajd7H1hb2WJUt8n4/eCXUKrK0K5JT4T7tsWqQ1+jS8QAZOWn4ufdU6HWqNCnzWi0DOxi5L3WAw7wCaWw1CcKyzpq4tgELjIXZJZk8l0K0bPph13A8i3/1KDGf89HRvOJUJSY7ukkj7pKAcDLKQCNXEMwbc1wXL3/T7VdpQHu4QhwD9f+HugRgS83TcDhK7EY3OktAECLgM4VBhLdengBD7PjMaTT25ixbjTG9PgUdtZO+HrLmwjybK6zJWtq3P3tYGNPxyv1icaR10N7zycfayHm55PklpCctvygBABR8h00g3l1NdvbOMNJ7o6M/KQaP0bACeDnGoKMvKofo1QrsP7IQgyLeg8Z+cnQaNQI9moBdwdfuNn7ICHtur7KN5rAyMozapH6obCshw6e1BVrSVoqPNFqw2W+yzAq+10/w9fLtLtiH1dYmoeconTYWTvX+DGMMSRl3a32MbvPrkJ4o3bwdW0CDdNAw/5taas1KrOYreu/KCz1j7ph66GTdyezmfqOPJmICTB5txSspJTvUowuIO5rZLT+BKXFxu+OLVOWICMvWft7VkEqkjLvwNrKFjZSO/x1ZiUiA6Jgb+OMrIJUbD+1FHKpPVr4/9uF+vuBebC3ccHz7V8FAOw88zv83cPgZu+NUkUxDl3ZgqSsOxjS+Z1K20/JTsC5u4fw8Ys/Ayg/3slxHI7f2Ak7mRPSchPh52ZeF09w9LSBg7s132VYHArLenCRuaC1e2ucTj3Ndymknj6/FwnBVfOaJF1fRGn3EaH8B2fQxujbvp9xE99v/1D7++YTSwAA7Zs8g6FR7+FhdjxO3dqHEkUh7Kyd0MQrEmOfngqp5N8wyC5MB8f9O4FxiaIQa/+ej4LiHEglNvBxCcJ7zy2Av1vF0b+MMaz9ez5e6Pg6rMTlE/pLRFYY2XUS1h/9Hiq1EkM6vQ0HG/NqpQW2cOG7BItEc8PW0/qb6zHr5Cy+yyD10K3EH68vTgBUKr5L4dXd4d/j/kOh7gWJSRv8SRu4+ZnPNWLNBR2zrKen/Z6GkKMPGHNlrRHj9W2KBh+UAOC/Zy5kcupsMmdyRysKSgOhsKwnJ6kT2nm047sMUkdzrzcD7iXyXYZJEGalIKL4CN9lkHoIaGFeXcbmxCzCcsaMGYiMjOS7jGr1DujNdwmkDgYWNIHnDsuYJF1fbA+sQoCXku8ySB0FtXbjuwSLpfewjImJAcdx4DgOYrEYgYGBmDhxIoqKimr0eI7jEBsbq++yDOppv6chEVQ9mwgxTc4aawzfnA1oaCTzfzXaNQfWttQda27s3WTwCnbguwyLZZCWZe/evZGSkoJ79+7hiy++wI8//oiJEycaYlMmwU5ih66+Xfkug9TC3HPBYA9T+S7DJAlz0hGRF8d3GaSWQjt48l2CRTNIWFpZWcHDwwO+vr4YPnw4RowYgdjYWAQFBeGbb76psOyVK1cgEAhw9+5d+Pv7AwAGDhwIjuO0vz/yxx9/wN/fH/b29njppZdQUFCgva+srAzvvPMO3NzcIJVK0blzZ5w+/e8pHYcOHQLHcYiLi0ObNm1gbW2Njh074ubNm3rZ5+eDntfLeojhxeQ0hcO+s3yXYdLkf69HY6+Gd86pueIEHMIoLA3KKMcsZTIZlEolxo4di+XLl1e4b9myZYiKikLjxo214bZ8+XKkpKRUCLu7d+8iNjYWO3bswI4dO3D48GHMmzdPe/+kSZOwadMmrFy5EufOnUNQUBB69eqF7OzsCtv79NNP8e233+LMmTMQiUQYO3asXvaxo1dHOEtrPqsI4UcjtQP6rqcBPTXhu2MO5PbUHWsOGoU7wcaB5oI1JIOH5alTp7BmzRr06NEDY8aMwc2bN3HqVPnJ30qlEqtWrdIGlqtr+UguBwcHeHh4aH8HAI1GgxUrViAiIgJRUVEYNWoU4uLKu4qKioqwZMkSfP3113j22WcRHh6OX3/9FTKZDEuXLq1Qz+zZs9GlSxeEh4fj448/xvHjx1FaWv9v0CKBCH0D+9Z7PcSwZh31AsvO4bsMsyDIz0JExq7yi0MSkxbWkVqVhmaQsNyxYwfkcjmkUik6dOiA6OhoLFq0CJ6enujbty+WLVumXa60tBSDBw/WuU5/f3/Y2tpqf/f09ER6ejqA8lanUqlEp06dtPeLxWK0a9cO169XnAS5efPmFdYBQLue+qKuWNP2XmoLyI5f4rsMs2J9PBbBHjUbnEf4IZWL4U+z9hicQcKyW7duuHDhAm7evInS0lJs3rwZbm7lQ5pfffVVrFu3DiUlJVi+fDmGDh0Ka2vd8xiKxeIKv3McB83/RzI+moTo8SmvHt3+39seX8+j+zR6GhHZxLEJWru31su6iH6FK13RacMNvsswS97bZ8POUax7QcKLkHYeEArN4ixAs2aQZ9jGxgZBQUHw8/OrFHJ9+vSBjY0NlixZgl27dlU6ZigWi6FW125C56CgIEgkEhw9elR7m1KpxJkzZxAWFlb3HamD0eGjjbo9ohvHgM/iHMAKqYVUF4LCPDRNjgVH3bEmKawTdcEag9G/jgiFQsTExOCTTz5BUFAQOnSoeJkrf39/xMXFITU1FTk5NTu2ZGNjg9dffx0fffQRdu/ejWvXrmHcuHEoLi7GK6+8YojdqFZX367ws/Mz6jbJk01NbAnRefO7JqEpkZ3aiWD3fL7LIP/hFewAZ28532U0CLy03V955RUoFIoqR6J+++232LdvH3x9fdGyZcsar3PevHkYNGgQRo0ahVatWuHOnTvYs2cPHB2Ne4VzASfAyLCRRt0mqV6HUh8020THKfXBa+ts2DtRd6wpadmzEd8lNBi8XHXk2LFj6Nq1K5KSkuDu7m7szRtciaoEPTf2RF5ZHt+lNGhWTIjft3iDu3mP71IsRmmrnjjhMAB0CVf+OXpYY9j09pXGZRDDMGrLsqysDHfu3MHUqVMxZMgQiwxKAJCJZBjSZAjfZTR4s263oKDUM+m5fQhxpVNvTEFkz0YUlEZk1LBcu3YtQkJCkJeXh6+++sqYmza6YaHDIBZQlxVfehUHwn/rOb7LsEiesbPh6EyTFfDJ2k6CkHYefJfRoBg1LGNiYqBWq3H27Fl4e3sbc9NG52rtimcDnuW7jAbJVmOFV7cU0TUqDYQrK0H47dUQCKhVw5dm3XwgFNPpIsZEz7YB0Wkk/Jh3ORwsMZnvMiya1aW/Eeqin8k8SO2IrISIiLbsxoYporA0oBCnELT3bM93GQ3K0LxQuO6ia1Qag1vsXDi7UnessYV39ITUhg7xGBuFpYG9HP4y3yU0GG4aG7y4KQ0w/gDvBkmgKEPY1RUQCKk71lgEQg4tevjyXUaDRGFpYFE+UYh0jeS7jAZh7j+BYGkZfJfRoEiunUC440O+y2gwQjt6ws5FxncZDRKFpRF82OZDvkuweOMzm8H20Hm+y2iQXLfMg4sbdccamlAkQNs+/nyX0WBRWBpBpFskuvt257sMi9VY5YSnN9D5lHzh1CqEXvyNRmcaWNNoL8gdpXyX0WDRq9tI3mv9HkQcffs2hBmH3cByabYkPklunka4LV1U21BEVkK07u3PdxkNGoWlkQTYB2Bg8EC+y7A4k5IjYXXqCt9lEAAusV/BzV3IdxkWKfJpX1jbSfguw+TMmDEDkZGRRtkWhaURvRH5BmQiOjivL5EKD7TdcJXvMsj/cRo1mpz5ESIJfazok8xWXK8J0zmOe+JPTEyM/oqtRlFRESZPnozAwEBIpVK4urqia9eu2LFjR73WO3HiRMTFxdXqMV27dsV7771X621Rv6ARuchc8HLTl/HTxZ/4LsXsCcHh473WYCVJfJdCHiO5ewlNm93FRUUA36VYjLZ9AyCR1v2jOiUlRfv/P//8E9OmTcPNmze1t8lkhv8CP2HCBJw6dQqLFy9GeHg4srKycPz4cWRlZdVpfYwxqNVqyOVyyOXGuUQZfQU0sjFNx8BJ6sR3GWZvxr2WEFy+xXcZpApOW7+Fhwd9tOiDvZsMTaO86rUODw8P7Y+9vT04joOHhwfc3d3RuXNn/PrrrxWWv3LlCgQCAe7evQugvGW6ZMkSPPvss5DJZAgICMCGDRsqPCY5ORlDhw6Fo6MjnJ2d8fzzzyMhIUF7//bt2zFlyhT06dMH/v7+aN26Nd5++228/PK/56GXlZVh0qRJ8PX1hZWVFYKDg7F06VIAwKFDh8BxHPbs2YM2bdrAysoKR44cqdQNGxMTgwEDBmDmzJlwc3ODnZ0dxo8fD4VCob3/8OHDWLhwobZl/XidT0KvaCOzFltjQosJfJdh1rqU+CFkM50mYqo4xtDkxCKIrejjpb6ihjSBQGiY55HjOIwdOxbLly+vcPuyZcsQFRWFxo0ba2+bOnUqBg0ahIsXL2LkyJEYNmwYrl8vv6B6cXExunXrBrlcjr///htHjx6FXC5H7969tSHl4eGBnTt3oqCgoNp6Ro8ejXXr1uH777/H9evX8dNPP1VqNU6aNAlz587F9evX0bx58yrXExcXh+vXr+PgwYNYu3YttmzZgpkzZwIAFi5ciA4dOmDcuHFISUlBSkoKfH1rNskDvZp5MLjJYPjZ+fFdhlmSMhHe3K4ClEq+SyFPILp/DRGSG3yXYdYCW7rCL8LZoNsYM2YMbt68iVOnTgEAlEolVq1ahbFjx1ZYbvDgwXj11VfRpEkTzJo1C23atMGiRYsAAOvWrYNAIMBvv/2GZs2aISwsDMuXL0diYiIOHToEAPjll19w/PhxODs7o23btnj//fdx7Ngx7fpv3bqF9evXY9myZRg4cCACAwPRo0cPDB06tEIdn3/+OXr27InGjRvD2bnq50YikWDZsmVo2rQp+vbti88//xzff/89NBoN7O3tIZFIYG1trW1tC4U1G5RGYckDkUCED1vTRAV1Mfd6c+Dufb7LIDXguH0hvDxpKry6EFsJETUk2ODb8fT0RN++fbFs2TIAwI4dO1BaWorBgwdXWK5Dhw6Vfn/Usjx79izu3LkDW1tb7TFEJycnlJaWartyo6Ojce/ePcTFxWHQoEG4evUqoqKiMGvWLADAhQsXIBQK0aVLlyfW26ZNG5371KJFC1hbW1eotbCwEA8ePND52CehsORJt0bd8IzfM3yXYVb6FwbDeztNkm5Ogo4sgERKp5PUVtt+AUabgODVV1/FunXrUFJSguXLl2Po0KEVwqY6jy48rdFo0Lp1a1y4cKHCz61btzB8+HDt8mKxGFFRUfj444+xd+9efP7555g1axYUCkWNBxnZ2NjUbScfq7euKCx5NKX9FDhYOfBdhllw1MgwanMuoNHwXQqpBVHSbURwF/kuw6w4e9ugRXcfo22vT58+sLGxwZIlS7Br165KXbAAcPLkyUq/h4aGAgBatWqF27dvw83NDUFBQRV+7O3tq91ueHg4VCoVSktL0axZM2g0Ghw+fLje+3Px4kWUlJRUqFUul8PHp/w5lUgkUKvVtV4vhSWPnGXOmNR2Et9lmIV555uAJafoXpCYHIddS+BTvwGdDQcHdBkWYrBBPVURCoWIiYnBJ598gqCgoEpdrgCwYcMGLFu2DLdu3cL06dNx6tQpvPXWWwCAESNGwMXFBc8//zyOHDmC+Ph4HD58GO+++y6SkspP7eratSt+/vlnnD17FgkJCdi5cyemTJmCbt26wc7ODv7+/nj55ZcxduxYxMbGIj4+HocOHcL69etrvT8KhQKvvPIKrl27hl27dmH69Ol46623IBCUP6f+/v74559/kJCQgMzMTGhq+AWcwpJnzzV+Dp29O/NdhkkbndMUjnvP8l0GqYfAg1/Dypq6Y3UJ6+AJzyAHo2/3lVdegUKhqLJVCQAzZ87EunXr0Lx5c6xcuRKrV69GeHg4AMDa2hp///03GjVqhBdeeAFhYWEYO3YsSkpKYGdnBwDo1asXVq5ciWeeeQZhYWF4++230atXrwphuGTJErz44ot44403EBoainHjxqGoqKjW+9KjRw8EBwcjOjoaQ4YMwXPPPYcZM2Zo7584cSKEQiHCw8Ph6uqKxMSaTdPIMUYX/+NbalEqBmwdgCJl7V8Yls5HZY8FyxhYVjbfpZB6yu/1Ks6UteS7DJMltRFjxMynIJUb/8LOx44dQ9euXZGUlAR3d/cK93Echy1btmDAgAFGr6u2YmJikJubi9jYWL2vm1qWJsDDxgPvtXqP7zJM0hcnfCgoLYTdnt/QyIuOOVcnelgTowdlWVkZ7ty5g6lTp2LIkCGVgpL8i8LSRAwNGYrW7q35LsOkvJ3eAtZHaXCIJQnY9yWkNjTL5n81ae+O4DbGD6q1a9ciJCQEeXl5+Oqrr4y+fXNC3bAm5H7+fQzaNghl6jK+S+FdiNIFX/xcCFZQyHcpVTpTXIxl2Vm4WlqGDLUK33t542lbW+39mSoV5mek41hRMQo0arSRWWOKuzv8JdVfOeJ2WRkWZ2bgamkpHqpU+NjVDaOdKk6NuD0/DwsyMlCs0WCQvQM+cnPT3pesVODVBw+wwc8f8hqeaM2Hgh6jcVrdnu8yTIbcyQovTW0PKxl9iTBl1LI0IX52fngj8g2+y+Adx4DpB5xMNigBoFijQYiVFJ9V0W3FGMPbyUl4oFRisbc3Nvn7w1MsxisPElH8hJF3pRoNfMQSfODqBpcqwi5HpcK01FR85OqGX318sTU/D4cL/32OZqal4QNXN5MOSgCwjfsd/l4qvsswCRwHPB0TTkFpBigsTczL4S+juWvVcx42FJ8+aAnRuWt8l/FE0XI53nV1Rc/HWpOP3FcqcbG0FNPcPdBMJkOAxArT3N1RrNFgZ35+tetsJpPhIzc39LGzg6SKE6gfKJWQCwR41s4OzWQytLO2xh1FeS/Ejvw8iDmuynpMkd/uObC2pYCI7NkI3k0c+S6D1ACFpYkRCoT4tsu3cLRqmG+gdmXeaLHpMt9l1IuClbcerR4LPCHHQcxxOFdSXOf1+kkkKGUM10pLkatW40ppKUKsrJCrVmNRZiY+czOfwRnC7DQ0yz/Idxm8cvGVo33/QL7LIDVEYWmCPGw8MDdqLgRcw/rzSJgQH+4SgZWW8l1KvQRIrOAlEmFBZgby1GooGMOvWVnIVKuRoar9zCGP2AuFmOvhiU9SUjD0fgL629mhs40cX6enY6SjI5KVSryQEI/+8fewp6D6FqypsDm8DoFeDfP4vFAswNNjwiEUNaz3uDmjv5SJ6uTdCeObj+e7DKOadacFuOt3+S6j3sQch4XePkhQKNDhzm20vnUTp4uLEWVjA0E95xV/2tYWWwMCsCewMd5yccWp4iLcVpThRXsHfPjwIT5xc8dCb29MTU1Flsr0jws2+msObOwaXndsh4GN4exlnIsWE/2gsDRhE1pMQEevjnyXYRRPFwcgIPYc32XoTVOpFFv8A/BPUDAONw7CL76+yFWr4SPW33l0Co0Gn6elYYa7BxIVCqjB0NbaGgESK/hLJLhUWqJ7JTwT5GUiInsv32UYVXBbd7ToXrNrKBLTQWFpwgScAPOi5sHd2nyORdWFrcYK47eWAGbQEqotW6EQTiIREhQKXC0tRXe5/gbgLMnKQpSNDcKlUqgBqB47C0zJGNRmclKYzdFNCPI0/WDXB2dvObqNCuW7DFIHFJYmzlHqiG+7fguRwHK7quZcCQdLSOK7jFop0mhwvbQU1/9/fDVZqcT10lI8/P9FqXcX5ONUcREeKBSIKyjAqw8S0UMuR6fHLjH0ccpDzM9I1/6uYEy7TiUD0lQqXC8txf3/X23+cbfLyrCrIB9vu7gCAAIlEgg4Dptyc3G4sBDxCgWaSY1ziSd98Nn+BWwdjD/NmzFZWYvw7IRmEEtM+9QeUjWalMBMrLq2Cl+e/pLvMvTuxfwQDPnxGmBmL8NTxUWIqeJisgPs7DDH0wt/5GRjeXY2MlUquIpEeN7eHhOcXSqcEvJy4n14i8WY41l+SY5kpQI9792rtM62MhlWNvLT/s4Yw8jERIxzdkZX+b/HvQ4VFmJWWioUjOFdF1e86OCgxz02vOKnnsNJWW/AvF4KNcJxQL+3WqBRU2e+SyF1RGFpRiYenog9CXv4LkNv3NRy/LBSApaWrnth0iAkDfsSt1Isb+DLUwMC0bq3P99lkHqgblgzMrPjTPjb+fNdht7MORNIQUkq8No2G3aOltUd27ilKwWlBaCwNCM2Yhv80OMHOEmddC9s4l7NioDdAcsZ/Ur0Q1CUj6YPNqGKCYzMkqOnDbq/HMZ3GUQPKCzNTCO7Rvixx4+QiWR8l1Jn/ioH9NqQwHcZxETJzuxBiHsu32XUm7WdBP3ebA6J1HIH5zUkFJZmqKlLUyzousBsR8jOOuIJlpPLdxnEhHlu+QIOzubbHSuRCtHv7RawczHfL7WkIgpLM9XJuxM+7/g5OJhXf9XElEhYnTTvuV+J4XFlJQi/uxaC+k55xAOBiEPvCc3g6msek9qTmqGwNGPPNX4O77Z6l+8yaqyZwg3tN1znuwxiJqQXDiLUJZPvMmrn/5fc8g01/3EFpCIKSzP3SrNXMDJsJN9l6CQEhyn7bMGKivguhZgR99g5cHIxn8MNnV8MRnAby55xq6GisLQAk9pOQm//3nyX8UTT41tCeOkm32UQM8MpShF24w8IhKbfHdvymUZo0YPmfLVUFJYWgOM4zOk8B+082vFdSpU6l/oibPMFvssgZsrqylGEOaXyXcYThTzlgQ4DG/NdBjEgCksLIRaKsbDbQoQ6mdYkzVImwjs7GFgV85sSUlOuW+bBxc00u2OD27ih+6hQcJZyciipEoWlBZFL5Pi1568Idw7nuxSt2TebA7cT+C6DmDmBSoHQS79BKDKtQGrSzh1Pj20KgZA+Si0d/YUtjIPUAUufWYqWbi35LgV9C4Pgu+0s32UQCyG5cRrh9qZzdZqQpzzwdEy4WZ7eQmqPwtICySVy/NzzZzzl+RRvNdhrpIjZkg+o1bzVQCyPy5Yv4erO/yWuwjp6osfoMHAUlA0GhaWFkolk+KHHD+jm242X7c+7EAqW9JCXbRPLxWnUCD37M0Ri/j66wjt7oduoUArKBobC0oJJhBLM7zofzwY8a9TtjsgNg/OeM0bdJmk4xHfOI9ym8nU/jSEi2htdR4TQYJ4GiMLSwokEIsyLmodBwYOMsj1PtS0GbqAWJTEs563fwN3DuB9fLXr4ostwCsqGisKyARBwAkzvMN0oM/3MOekHTWaWwbdDGjaOMTQ5uRgiieE/wjgO6Dw4GJ0HBxt8W8R0UVg2EBzHYXK7yRjXbJzBtvFmejPY/H3BYOsn5HHihKuIkN4y6DZEYgF6v9aMZuYhFJYNzTut3sGU9lMg4vR7gnewyhldN9zV6zoJ0cVx+3fw9DTMx5jMVoznP2iJwJauBlk/MS8Ulg3QsNBh+OWZX+Bo5aiX9XEMmHHAGSw/Xy/rI6SmOMYQfPQ7SKT6PZ3Ewd0agya1gUeAvV7XS8wXhWUD1dajLdb2W4sQx5B6r+vjpEiIz17TQ1WE1J7owU00FervGqmeQfYYNKk17F3pws3kXxSWDZi33Bu/P/s7evr1rPM62pR5odWmK3qsipDac/zrB3h71X89wW3d8fy7LSG1Edd/ZcSicIwxxncRhH8/X/wZP1z4AQw1fzmImACrtjWC4NodA1ZGSM2oPQNwMnIyykpqP2uUQMih4wtBNJCHVItalgQAML7FeHzf/XvIxfIaP2bW3UgKSmIyhCnxiGDnav04uaMVBn7YioKSPBG1LEkF93Lv4Z2D7+B+/v0nLte92B8TfkgAVCrjFEZIDd0evhgPHtZs4gCfUEc880pTyGwlBq6KmDtqWZIKAh0CsabvGnTy7lTtMnImwevbFBSUxCQF7v8KUhsdp0ZxQJs+/uj/TiQFJakRCktSiZ3EDkt6LMHENhMhEVT+IJlzpSlYfCIPlRGimzA9Ec3KTlR7v9RGjH5vtUD7/oE0GTqpMeqGJU90M/smJv89GXfzyicceKGgCV764TpALxti4u4OX4j7Dyu2MD0b2+PpseGwc6bTQkjtUFgSncrUZfj2zLfYf207fvzdCiwlje+SCNFJ7eyJUx1moKRQBYGIQ/vnAtGyZyNqTZI6obAkNZZ64QQK3v0EqjQKS2IeCruNwF2vnnh6TDhcfGz5LoeYMTpmSWrMI7IDArdvg/2AAXyXQohuIhH8msgx+OPWFJSk3qhlSeqk4OBBpE6bDlVGBt+lEFKJVWgovObMhjQ8nO9SiIWgsCR1ps7LQ9q8L5EXG0sDfohJ4MRiOL8+AS7jxoET05R1RH8oLEm9lVy8iNTZc1B66RLfpZAGzPqpp+Dx6RRYBdNFmon+UVgSvWCMIW9LLNIXzIc6I5PvckgDIvZrBPdJk2DbowffpRALRmFJ9EpdWITMH39E9h9/AEol3+UQCyawtYXL66/DaeQIcBKahYcYFoUlMYiy+HikzZ2Lor+P8F0KsTRCIRwGvwjXd96ByMmJ72pIA0FhSQyq4NAhpM+dB8X9J0/MTkhN2HTsALfJH0Ma0oTvUkgDQ2FJDI4pFMhZuxZZvy2lU01InUj8/OA2eRJsu3fnuxTSQFFYEqPRlJUhd+NGZC1dCtXDFL7LIWZAEhgI51dfhf1z/ehUEMIrCktidEypRG5sLLJ++RXKBw/4LoeYIGl4OJxfew22z/QEJ6CJxgj/KCwJb5hKhbwdO5D18y9QxMfzXQ4xAdZt2sB5/HjIozrzXQohFVBYEt4xjQYFu3cjc8lPKLt9m+9yCA/kXbrAefx4WLdqyXcphFSJwpKYDMYYCg8cQM7qNSg6cYKm0LN0AgHseveC82uvQRoaync1hDwRhSUxSYoHD5C7YSNyt2ymGYEsjNjbG/aDXoDDwIEQe3ryXQ4hNUJhSUwaU6lQcPAgctdvQNGxY4BGw3dJpA44iQS2PXvC4cVBsH7qKXAcXYCZmBcKS2I2lMnJyNm4EXmbNkOVns53OaQGrMLC4DBoEOyf6wehvT3f5RBSZxSWxOwwtRqFhw8jd/NmFB09BlZayndJ5DECOzvY9+sL+0GDIGvalO9yCNELCkti1jQlJSg8cgSFcXEoOHQYmrw8vktqkIQuLpB3iYZt9+6w6dQJAqmU75II0SsKS2IxmEqF4tOnUbA/DgVxcVClpvJdkkWzCg6GvFs32HbvBmmLFnQcklg0CktisUouX0FB3H4U7N8PxZ27fJdj/sRiWLduDdvu3SDv3h0SHx++KyLEaCgsSYOgSEpGydkzKD57DiXnz6Hszl06j1MXgQBWQUGQRUbCun07yKOjIbS15bsqQnhBYUkaJHVeHorPnUPJuXMoPnsOpVeugCkUfJfFK4GdHWQtWkAW2QKyyEjIWrSAUC7nuyxCTAKFJSEANAoFSq9cQfHZsyi5cBFld25DmZQMqNV8l2YYHAdJ48DyVmNkJGSRkZA0bkzHHQmpBoUlIdVgCgUU9++j7F48FPH3UHbvHhT34qGIj4emqIjv8mpE6OICib8fJH5+kPj7//tvo0Y0YpWQWqCwJKQOlGlpUNy7h7L4eKhS06DOyYYqJwfq7Byoc3Kgzs6GOj/fcMdFhUIIbW0htLeHwMEeQnt7iBydIG7kC4mff3kg+vtRNyohekJhSYiBMLUa6txcqHNyoMrOhjonF+q8XDCVClCpwFTq8v+rVWBqDSDgyq/dyAn+/38hOJkUQnt7CO0dyv/9fzAKbG2py5QQI6KwJIQQQnSgS5ATQgghOlBYEkIIITpQWBJCCCE6UFgSQgghOlBYEkIIITpQWBJCCCE6UFgSQgghOlBYEkIIITpQWBJCCCE6UFgSokN6ejrGjx+PRo0awcrKCh4eHujVqxdOnDihXeb48ePo06cPHB0dIZVK0axZM3z77bdQ/+eqJRzHITY21sh7QAipLxHfBRBi6gYNGgSlUomVK1ciMDAQaWlpiIuLQ3Z2NgBgy5YtGDJkCMaMGYODBw/CwcEB+/fvx6RJk3Dy5EmsX7+e5nElxMzR3LCEPEFubi4cHR1x6NAhdOnSpdL9RUVF8PPzQ5cuXbBp06YK923fvh39+/fHunXrMHToUADlLcstW7ZgwIABxiifEKIn1A1LyBPI5XLI5XLExsairKys0v179+5FVlYWJk6cWOm+5557Dk2aNMHatWuNUSohxIAoLAl5ApFIhBUrVmDlypVwcHBAp06dMGXKFFy6dAkAcOvWLQBAWFhYlY8PDQ3VLkMIMV8UloToMGjQIDx8+BDbtm1Dr169cOjQIbRq1QorVqzQLlPd0QzGGCQSiZEqJYQYCoUlITUglUrRs2dPTJs2DcePH0dMTAymT5+O4OBgAMD169erfNyNGzfQpEkTY5ZKCDEACktC6iA8PBxFRUXo1asXnJyc8O2331ZaZtu2bbh9+zZiYmKMXyAhRK/o1BFCniArKwuDBw/G2LFj0bx5c9ja2uLMmTP46quv8Pzzz8PGxgY///wzXnrpJbz22mt46623YGdnh7i4OHz00Ud49dVX0adPnwrrjI+Px4ULFyrcFhQUBLlcbsQ9I4TUBp06QsgTlJWVYcaMGdi7dy/u3r0LpVIJX19fDB48GFOmTIFMJgMAHDlyBLNnz8aJEyeQn58PAJg3bx4mT55cYX3VnW958OBBdO3a1aD7QgipOwpLQvSstLQUzz//PB48eIDDhw/D1dWV75IIIfVEYUmIAZSWluK7775DcHAwBg0axHc5hJB6orAkhBBCdKDRsIQQQogOFJaEEEKIDhSWhBBCiA4UloQQQogOFJaEEEKIDhSWhBBCiA4UloQQQogOFJaEEEKIDhSWhBBCiA4UloQQQogOFJaEEEKIDhSWhBBCiA4UloQQQogOFJaEEEKIDhSWhBBCiA4UloQQQogOFJaEEEKIDhSWhBBCiA4UloQQQogOFJaEEEKIDhSWhBBCiA4UloQQQogOFJaEEEKIDhSWhBBCiA4UloQQQogO/wP+6uu9mt9AqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "QUERY = \"\"\"\n",
    "SELECT LanguageDesireNextYear as Lang, count(LanguageDesireNextYear) as CNT from LanguageDesireNextYear \n",
    "    GROUP BY Lang\n",
    "    ORDER by CNT DESC\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY, conn)\n",
    "df.index = df.Lang\n",
    "print(df.head())\n",
    "df.head(5).plot(kind='pie', y='CNT', x='Lang', figsize=(12,5), legend=False, autopct='%1.1f%%',\n",
    "                title='PieChart of Languages Desired to Learn Next Year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "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>Lang</th>\n",
       "      <th>CNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>JavaScript</td>\n",
       "      <td>8687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HTML/CSS</td>\n",
       "      <td>7830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SQL</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bash/Shell/PowerShell</td>\n",
       "      <td>4642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Python</td>\n",
       "      <td>4542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Java</td>\n",
       "      <td>4506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>C#</td>\n",
       "      <td>4288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TypeScript</td>\n",
       "      <td>3232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PHP</td>\n",
       "      <td>2913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>C++</td>\n",
       "      <td>1946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>C</td>\n",
       "      <td>1578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Ruby</td>\n",
       "      <td>1149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Go</td>\n",
       "      <td>1114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Other(s):</td>\n",
       "      <td>840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kotlin</td>\n",
       "      <td>751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Swift</td>\n",
       "      <td>707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>VBA</td>\n",
       "      <td>628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>R</td>\n",
       "      <td>585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Objective-C</td>\n",
       "      <td>518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Scala</td>\n",
       "      <td>492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Assembly</td>\n",
       "      <td>437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Rust</td>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Dart</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Elixir</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Clojure</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>F#</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>WebAssembly</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Erlang</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Lang   CNT\n",
       "0              JavaScript  8687\n",
       "1                HTML/CSS  7830\n",
       "2                     SQL  7106\n",
       "3   Bash/Shell/PowerShell  4642\n",
       "4                  Python  4542\n",
       "5                    Java  4506\n",
       "6                      C#  4288\n",
       "7              TypeScript  3232\n",
       "8                     PHP  2913\n",
       "9                     C++  1946\n",
       "10                      C  1578\n",
       "11                   Ruby  1149\n",
       "12                     Go  1114\n",
       "13              Other(s):   840\n",
       "14                 Kotlin   751\n",
       "15                  Swift   707\n",
       "16                    VBA   628\n",
       "17                      R   585\n",
       "18            Objective-C   518\n",
       "19                  Scala   492\n",
       "20               Assembly   437\n",
       "21                   Rust   324\n",
       "22                   Dart   237\n",
       "23                 Elixir   187\n",
       "24                Clojure   164\n",
       "25                     F#   158\n",
       "26            WebAssembly   133\n",
       "27                 Erlang    98"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QUERY = \"\"\"\n",
    "SELECT LanguageWorkedWith as Lang, count(LanguageWorkedWith) as CNT from LanguageWorkedWith \n",
    "    GROUP BY Lang\n",
    "    ORDER by CNT DESC\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY, conn)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "474"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QUERY = \"\"\"\n",
    "SELECT Respondent, DatabaseWorkedWith as DB, Count(Respondent) as CNT from DatabaseWorkedWith \n",
    "GROUP BY Respondent\n",
    "HAVING count(Respondent)==1\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY, conn)\n",
    "len(df[df.DB=='MySQL'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stacked Charts\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a stacked chart of median `WorkWeekHrs` and `CodeRevHrs` for the age group 30 to 35.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age'>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "QUERY = \"\"\"\n",
    "SELECT WorkWeekHrs, CodeRevHrs, Age from master \n",
    "    WHERE Age BETWEEN 30 AND 35\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY, conn)\n",
    "df_grouped1 = df.groupby('Age').median().reset_index()\n",
    "df_grouped1.plot(kind='bar', x='Age', stacked=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing comparison of data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line Chart\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the median `ConvertedComp` for all ages from 45 to 60.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'ScatterPlot of Annual Compensation vs Age')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "QUERY = \"\"\"\n",
    "SELECT ConvertedComp, Age from master \n",
    "    WHERE Age BETWEEN 45 AND 60\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY, conn)\n",
    "df_grouped1 = df.groupby('Age').median().reset_index()\n",
    "df_grouped1.plot(kind='scatter', x='Age', y='ConvertedComp')\n",
    "plt.ylabel('Annual Compensation ($)')\n",
    "plt.xlabel('Age')\n",
    "plt.title('ScatterPlot of Annual Compensation vs Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Chart\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a horizontal bar chart using column `MainBranch.`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                           MainBranch  \\\n",
      "MainBranch                                                                                              \n",
      "I am a developer by profession                                         I am a developer by profession   \n",
      "I am not primarily a developer, but I\\nwrite co...  I am not primarily a developer, but I write co...   \n",
      "\n",
      "                                                      CNT  \n",
      "MainBranch                                                 \n",
      "I am a developer by profession                      10618  \n",
      "I am not primarily a developer, but I\\nwrite co...    780  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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q3LixJKly5crKmDGj9u7dq+7du9+1jmbNmql3796aOHGiDh06pJw5c6pmzZrW+gIFCihfvnxWW/eiQIECypkzp6ZOnarXX3/dOi6XL1/WrFmzrCdX3WrKlCkqU6aM9XrGjBmKi4uzgll4eLhcXV118ODBfz29736rXr26ZsyYoblz5+r555+3ln/zzTfW+pTUq1dP06dPV3x8vJ566qlUtZkuXTo99dRTKliwoKZMmaJt27ZZwSWRl5eXateurevXr6thw4bas2dPssHlnxwv/DcRXAAAtrF7927FxcVJujl9ZPbs2Vq2bJmef/55PfHEE5KkunXratSoUWrevLk6deqk06dP68MPP7zn7xL55ptvVKtWLdWuXVvt27dX7dq1lSlTJsXGxmrBggWaNm2aoqKi7im4VKpUSZkyZVLnzp3Vr18/pU+fXlOmTNGOHTvuqW/FihXT7NmzNW7cOJUpU0YuLi53/CS+QIEC6tSpk8aOHSsXFxfVrl1bMTEx6tu3r4KCgv71k5aioqLk5+enGzduWF9A+e233yp79uxasGCBNb3I29tbY8eOVZs2bXTmzBm9+OKLyp49u/766y/t2LFDf/31l8aNG2fVmzFjRj3//POaOHGizp07p9dffz3JdKsvvvhCtWvXVnh4uNq2baucOXPqzJkzio6O1rZt2zRz5sxk++zi4qIRI0aoRYsWqlevnl555RVdu3ZNH3zwgc6dO6dhw4Yl2Wb27NlydXXVs88+qz179qhv374qUaKEFcxCQkI0cOBAvfPOOzp06JD1HUN//vmnNm/eLC8vr3/05LD7oXXr1vr000/Vpk0bxcTEqFixYlq3bp2GDBmiOnXqqEaNGilu37RpU02ZMkV16tRRr169VL58eaVPn16///67Vq1apQYNGuj555/X559/rpUrV6pu3brKnTu3/v77b02YMEGSrDY6duwoDw8PVa5cWTly5NCJEyc0dOhQ+fn5pTjN7l6PF/6j0vrpAAAAJPdUMT8/P1OyZEkzatQo8/fffzuVnzBhgilQoIBxc3MzefLkMUOHDjVff/21kWQOHz5slQsODjZ169a9Y7tXr141H3/8salYsaLx9fU1rq6uJjAw0DRq1Mj88MMPSfq3ZcsWp+1vfzqSMcZs2LDBVKxY0Xh6epps2bKZDh06mG3bthlJJjIy0irXpk0b4+XllWy/zpw5Y1588UWTMWNG43A4zN3+XMfHx5vhw4eb/Pnzm/Tp05usWbOali1bmmPHjjmV+ydPFUv8cXNzMzly5DA1a9Y0H330kblw4UKy261Zs8bUrVvXZM6c2aRPn97kzJnT1K1b18ycOTNJ2aVLl1r179+/P9n6duzYYRo3bmyyZ89u0qdPbwICAky1atXM559/bpVJ7jgYY8zcuXPNU089Zdzd3Y2Xl5epXr26Wb9+fbL7GRUVZerXr2+8vb2Nj4+Padasmfnzzz+T9Gfu3LmmatWqxtfX17i5uZng4GDz4osvmuXLl1tlUjq2ybnT+XW7lOo9ffq06dy5s8mRI4dxdXU1wcHB5u23307yu5PcU8WMMebGjRvmww8/NCVKlDDu7u7G29vbFCxY0LzyyivmwIEDxhhjNm7caJ5//nkTHBxs3NzcTJYsWUxoaKiZP3++Vc+kSZNM1apVjb+/v8mQIYMJDAw0jRs3Njt37rTK3I/jdfs5nDiGt/7+4/HjMOa2x4IAAAD8R/Tv318DBgzQX3/99cDuBwJwf/A4ZAAAAAC2R3ABAAAAYHtMFQMAAABge1xxAQAAAGB7BBcAAAAAtkdwAQAAAGB7fAElgMdCQkKCjh8/Lh8fH+tblwEAgL0ZY3Tx4kUFBgYm+RLa2xFcADwWjh8/rqCgoLTuBgAA+AeOHTumXLlypViG4ALgseDj4yPp5n98vr6+adwbAACQGhcuXFBQUJD1dzwlBBcAj4XE6WG+vr4EFwAAHjGpmebNzfkAAAAAbI/gAgAAAMD2CC4AAAAAbI/gAgAAAMD2CC4AAAAAbI/gAgAAAMD2CC4AAAAAbI/gAgAAAMD2CC4AAAAAbI/gAgAAAMD2CC4AAAAAbI/gAgAAAMD2CC4AAAAAbI/gAgAAAMD2CC4AAAAAbI/gAgAAAMD2CC4AAAAAbI/gAgAAAMD2CC4AAAAAbI/gAgAAAMD2CC4AAAAAbI/gAgAAAMD2CC4AAAAAbI/gAgAAAMD2CC4AAAAAbI/gAgAAAMD2CC4AAAAAbI/gAgAAAMD2CC4AAAAAbI/gAgAAAMD2CC4AAAAAbI/gAgAAAMD2CC4AAAAAbI/gAgAAAMD2CC4AAAAAbI/gAgAAAMD2CC4AAAAAbI/gAgAAAMD2CC4AAAAAbI/gAgAAAMD2CC4AAAAAbI/gAgAAAMD2CC4AAAAAbM81rTsAAPdT0X5L5OLmmdbdUMywumndBQAAHitccQEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewSX/5iYmBg5HA5t3749TdqfOHGiMmbMaL3u37+/SpYsmSZ9uZ3D4dDcuXMfapshISEaM2bMQ23z32jbtq0aNmyY1t0AAAD/QY98cHnc3kg96P0JCgpSbGysihYt+sDaSEmTJk20f//+NGkb9pHakJgWYRIAANiTa1p3AA/P9evXlSFDBgUEBKRJ+zdu3JCHh4c8PDzSpH3c2Y0bN5Q+ffq07gYAAMAdPfJXXO7m9OnTatasmXLlyiVPT08VK1ZM06ZNcyoTFhamHj16KCIiQpkyZZK/v7++/PJLXb58We3atZOPj4/y5s2rH3/8McW2QkJCNGTIELVv314+Pj7KnTu3vvzyS6cyu3btUrVq1eTh4aEsWbKoU6dOunTpkqSb06YmTZqkefPmyeFwyOFwaPXq1cm2FRYWpu7du6t79+7KmDGjsmTJonfffVfGGKf+vP/++2rbtq38/PzUsWPHJFPFVq9eLYfDoSVLlqhUqVLy8PBQtWrVdPLkSf34448qVKiQfH191axZM125csWqe/HixXr66aettuvVq6eDBw9a6xPbmTFjhsLCwuTu7q7JkycnmSp2q59++knp06fXiRMnnJa/9tprqlKlyh3HfdSoUSpWrJi8vLwUFBSkrl27WmN6JwcOHFCVKlXk7u6uwoULa9myZUnK/PHHH2rSpIkyZcqkLFmyqEGDBoqJiZEkLVmyRO7u7jp37pzTNj179lRoaKj1esOGDapSpYo8PDwUFBSknj176vLly3fs19GjR9WgQQN5e3vL19dXjRs31p9//mmtT5xa98UXXygoKEienp566aWXkvQjMjJShQoVkru7uwoWLKjPPvvMWnenY3MvBgwYoOzZs8vX11evvPKKrl+/bq1LbvpbyZIl1b9/f2u9JD3//PNyOBzWawAAgJQ89sHl77//VpkyZbRw4ULt3r1bnTp1UqtWrbRp0yancpMmTVLWrFm1efNm9ejRQ126dNFLL72kSpUqadu2bQoPD1erVq2c3rwnZ+TIkSpbtqx++eUXde3aVV26dNGvv/4qSbpy5Ypq1aqlTJkyacuWLZo5c6aWL1+u7t27S5Jef/11NW7cWLVq1VJsbKxiY2NVqVKlO7Y1adIkubq6atOmTfr44481evRoffXVV05lPvjgAxUtWlRRUVHq27fvHevq37+/PvnkE23YsEHHjh1T48aNNWbMGE2dOlU//PCDli1bprFjx1rlL1++rN69e2vLli1asWKFXFxc9PzzzyshIcGp3jfffFM9e/ZUdHS0wsPDUxy7KlWqKE+ePPr222+tZXFxcZo8ebLatWt3x+1cXFz08ccfa/fu3Zo0aZJWrlypN954447lExIS1KhRI6VLl04///yzPv/8c7355ptOZa5cuaKqVavK29tbP/30k9atWydvb2/VqlVL169fV40aNZQxY0bNmjXL2iY+Pl4zZsxQixYtJN0MqeHh4WrUqJF27typ7777TuvWrbOO9+2MMWrYsKHOnDmjNWvWaNmyZTp48KCaNGniVO63337TjBkztGDBAi1evFjbt29Xt27drPXjx4/XO++8o8GDBys6OlpDhgxR3759NWnSJKd67uXY3GrFihWKjo7WqlWrNG3aNM2ZM0cDBgxI9fZbtmyRdDNcxcbGWq8BAABS8thPFcuZM6def/1163WPHj20ePFizZw5U0899ZS1vESJEnr33XclSW+//baGDRumrFmzqmPHjpKk9957T+PGjdPOnTtVoUKFO7ZXp04dde3aVdLNN4ajR4/W6tWrVbBgQU2ZMkVXr17VN998Iy8vL0nSJ598ovr162v48OHy9/eXh4eHrl27lqrpXEFBQRo9erQcDocKFCigXbt2afTo0VafJalatWpO+594xeB277//vipXrixJevnll/X222/r4MGDypMnjyTpxRdf1KpVq6w3+C+88ILT9l9//bWyZ8+uvXv3Ot0/ExERoUaNGt11XxK9/PLLioyMVJ8+fSRJP/zwg65cuaLGjRvfcZuIiAjr30888YQGDRqkLl26OF1luNXy5csVHR2tmJgY5cqVS5I0ZMgQ1a5d2yozffp0ubi46KuvvpLD4ZB08412xowZtXr1atWsWVNNmjTR1KlT9fLLL0u6+Yb+7NmzeumllyTdDI3Nmze3+pcvXz59/PHHCg0N1bhx4+Tu7p6kXzt37tThw4cVFBQkSfr2229VpEgRbdmyReXKlZN0M4xPmjTJ6vvYsWNVt25djRw5UgEBARo0aJBGjhxpjfsTTzyhvXv36osvvlCbNm2cxu1ejk2iDBkyaMKECfL09FSRIkU0cOBA9enTR4MGDZKLy90/C8mWLZskKWPGjGk2bREAADx6HvsrLvHx8Ro8eLCKFy+uLFmyyNvbW0uXLtXRo0edyhUvXtz6d7p06ZQlSxYVK1bMWubv7y9JOnnyZIrt3VqPw+FQQECAtU10dLRKlChhhRZJqly5shISErRv37573rcKFSpYb6olqWLFijpw4IDi4+OtZWXLlk1VXbf229/fX56enlZoSVx2674fPHhQzZs3V548eeTr66snnnhCkpKMa2rbT9S2bVv99ttv+vnnnyVJEyZMUOPGjZ3G7HarVq3Ss88+q5w5c8rHx0etW7fW6dOn7zglKzo6Wrlz57be+Es3x+5WUVFR+u233+Tj4yNvb295e3src+bM+vvvv60pcS1atNDq1at1/PhxSdKUKVNUp04dZcqUyapj4sSJ1vbe3t4KDw9XQkKCDh8+nGy/goKCrNAiSYULF1bGjBkVHR1tLUuu74nn0F9//aVjx47p5Zdfdmr3/fffd5rKJ937sUlUokQJeXp6OrV/6dIlHTt27B/VBwAAkBqP/RWXkSNHavTo0RozZox1H0RERITTnHxJSW5MdjgcTssSA8LtU6Ful1w9idsYY5yCxu3lHoSU3vDf6vZ9TWk/JKl+/foKCgrS+PHjFRgYqISEBBUtWjTJuKa2/UTZs2dX/fr1FRkZqTx58mjRokV3vM9Hko4cOaI6deqoc+fOGjRokDJnzqx169bp5Zdf1o0bN5Ld5tb7gG7dv1slJCSoTJkymjJlSpKyiVcMypcvr7x582r69Onq0qWL5syZo8jISKc6XnnlFfXs2TNJHblz5062X8mdBymdN7f2/dZjNH78eKcritLNQH6rez02d5PYDxcXlyRjfKdjAQAAkFqPfXBZu3atGjRooJYtW0q6+WbywIEDKlSo0EPvS+HChTVp0iRdvnzZetO4fv16ubi4KH/+/JJuTsO59YpJShKvStz6Ol++fEneoN5vp0+fVnR0tL744gs988wzkqR169bdt/o7dOigpk2bKleuXMqbN681hS05W7duVVxcnEaOHGlNU5oxY0aK9RcuXFhHjx7V8ePHFRgYKEnauHGjU5nSpUvru+++s25Av5PmzZtrypQpypUrl1xcXFS3bl2nOvbs2aMnn3zyrvt8a7+OHTtmXXXZu3evzp8/73S+Jtf3xHPI399fOXPm1KFDh6x7be63HTt26OrVq9bT4X7++Wd5e3tbV4GyZcum2NhYq/yFCxeSXGFKnz59qs9zAAAA6T8wVezJJ5/UsmXLtGHDBkVHR+uVV15J8tSqh6VFixZyd3dXmzZttHv3bq1atUo9evRQq1atrKloISEh2rlzp/bt26dTp06l+En1sWPH1Lt3b+3bt0/Tpk3T2LFj1atXrwe+H4lP2fryyy/122+/aeXKlerdu/d9qz88PFx+fn56//33U7wpX5Ly5s2ruLg4jR07VocOHdK3336rzz//PMVtatSooQIFCqh169basWOH1q5dq3feecepTIsWLZQ1a1Y1aNBAa9eu1eHDh7VmzRr16tVLv//+u1O5bdu2afDgwXrxxRed7lt58803tXHjRnXr1k3bt2/XgQMHNH/+fPXo0eOO/SpevLhV5+bNm9W6dWuFhoY6TetKPIcS+96zZ081btzYul+kf//+Gjp0qD766CPt379fu3btUmRkpEaNGpXiuKTW9evX9fLLL2vv3r368ccf1a9fP3Xv3t0KjtWqVdO3336rtWvXavfu3WrTpk2SMB0SEqIVK1boxIkTOnv27H3pFwAAeLw99sGlb9++Kl26tMLDwxUWFqaAgIA0+8JKT09PLVmyRGfOnFG5cuX04osvqnr16vrkk0+sMh07dlSBAgVUtmxZZcuWTevXr79jfa1bt9bVq1dVvnx5devWTT169FCnTp0e+H64uLho+vTpioqKUtGiRfXqq6/qgw8+uK/1t23bVvHx8WrdunWKZUuWLKlRo0Zp+PDhKlq0qKZMmaKhQ4fetf45c+bo2rVrKl++vDp06KDBgwc7lfH09NRPP/2k3Llzq1GjRipUqJDat2+vq1evOl2ByZcvn8qVK6edO3cmucJRvHhxrVmzRgcOHNAzzzyjUqVKqW/fvsqRI0ey/Ur8ssVMmTKpSpUqqlGjhvLkyaPvvvvOqdyTTz6pRo0aqU6dOqpZs6aKFi3q9CCCDh066KuvvtLEiRNVrFgxhYaGauLEidZ9SClxOByaOHFiimWqV6+ufPnyqUqVKmrcuLHq169vPepYuvlwiypVqqhevXqqU6eOGjZsqLx58zrVMXLkSC1btkxBQUEqVarUXfsFAADgMMlN+IfthYWFqWTJkkm+L+Nx0bFjR/3555+aP39+WnfFVvr376+5c+da38NzP8XExChfvnzau3ev8uXLd9/rf9AuXLggPz8/BUXMkIub5903eMBihtW9eyEAAP7jEv9+nz9/PsXp+dJ/4B4XPFrOnz+vLVu2aMqUKZo3b15ad+c/ZfHixerUqdMjE1quXbuma9euWa8vXLiQhr0BAAAPGsEFttKgQQNt3rxZr7zyip599tm07s5/SufOndO6C/dk6NCh9/TFlwAA4NHGVDEAj6TkrrgEBQUxVQwAgEcIU8UAPPbc3Nzk5uaW1t0AAAAPyUN/qljbtm3T7Kle92r16tVyOBw6d+5cWnfloZg4caIyZsyY1t3A/7d+/XoVK1ZM6dOnf2R+Z/6NmJgYORyOB/LgAQAA8Oh76MHlo48+cnrcalhYmCIiIh52N/7zQkJCkjyRrEmTJtq/f3/adOgxcT+Dee/evVWyZEkdPnz4ro8oBgAAeNw9tKli8fHxcjgc8vPze1hN4h55eHhY34aOe5N4ft9PBw8eVOfOna1vpH+cXb9+Pa27AAAAbC7VV1wWLFigjBkzKiEhQZK0fft2ORwO9enTxyrzyiuvqFmzZpL+b9rRwoULVbhwYbm5uenIkSNOn0i3bdtWa9as0UcffSSHwyGHw6GYmBhJ0t69e1WnTh15e3vL399frVq10qlTp1Ls4/r16xUaGipPT09lypRJ4eHh1rdyX7t2TT179lT27Nnl7u6up59+Wlu2bHHaftGiRcqfP788PDxUtWpVqy+32rBhg6pUqSIPDw8FBQWpZ8+eunz58h37tGPHDlWtWlU+Pj7y9fVVmTJltHXrVmv9rFmzVKRIEbm5uSkkJEQjR4502j4kJETvv/++WrduLW9vbwUHB2vevHn666+/1KBBA3l7e6tYsWJOdd6tn2FhYTpy5IheffVVa9xvPWaJ+vfvr5IlS2rChAnKnTu3vL291aVLF8XHx2vEiBEKCAhQ9uzZk3x54/nz59WpUydlz55dvr6+qlatmnbs2JHqMbndqFGjVKxYMXl5eSkoKEhdu3bVpUuXrPVHjhxR/fr1lSlTJnl5ealIkSJatGjRHesLCQnRoEGD1Lx5c3l7eyswMFBjx469pzaTO7/btWunSZMmad68eda4rl69Otk+pHQ+Jk6ZOn36tNq3b5/il0Le6/lx+fJl+fr66vvvv3eqZ8GCBfLy8tLFixeTtHGvv/tS6s/rtm3bys/PTx07dkzSbkJCgjp27Kj8+fPryJEjye4/AAD470h1cKlSpYouXryoX375RZK0Zs0aZc2aVWvWrLHKrF69WqGhodbrK1euaOjQofrqq6+0Z88eZc+e3anOjz76SBUrVlTHjh0VGxur2NhYBQUFKTY2VqGhoSpZsqS2bt2qxYsX688//1Tjxo3v2L/t27erevXqKlKkiDZu3Kh169apfv36io+PlyS98cYbmjVrliZNmqRt27bpySefVHh4uM6cOSNJOnbsmPVt5Nu3b1eHDh301ltvObWxa9cuhYeHq1GjRtq5c6e+++47rVu3Tt27d79jv1q0aKFcuXJpy5YtioqK0ltvvaX06dNLkqKiotS4cWM1bdpUu3btUv/+/dW3b98kb1JHjx6typUr65dfflHdunXVqlUrtW7dWi1btrT2pXXr1kp8QNzd+jl79mzlypVLAwcOtMb9Tg4ePKgff/xRixcv1rRp0zRhwgTVrVtXv//+u9asWaPhw4fr3Xff1c8//yxJMsaobt26OnHihBYtWqSoqCiVLl1a1atXt8Y6pTFJjouLiz7++GPt3r1bkyZN0sqVK/XGG29Y67t166Zr167pp59+0q5duzR8+HB5e3vfsT5J+uCDD1S8eHFt27ZNb7/9tl599VUtW7Ys1W1KSc/vjz/+WI0bN1atWrWsca1UqVKy7ad0Pib+Dvj6+mrMmDGKjY1VkyZN7rgv93J+eHl5qWnTpoqMjHSqIzIyUi+++KJ8fHyS1H+vv/upPa8/+OADFS1aVFFRUerbt6/TuuvXr6tx48baunWr1q1bp+Dg4DvuPwAA+I8w96B06dLmww8/NMYY07BhQzN48GCTIUMGc+HCBRMbG2skmejoaGOMMZGRkUaS2b59u1Mdbdq0MQ0aNLBeh4aGml69ejmV6du3r6lZs6bTsmPHjhlJZt++fcn2rVmzZqZy5crJrrt06ZJJnz69mTJlirXs+vXrJjAw0IwYMcIYY8zbb79tChUqZBISEqwyb775ppFkzp49a4wxplWrVqZTp05Oda9du9a4uLiYq1evJtu2j4+PmThxYrLrmjdvbp599lmnZX369DGFCxe2XgcHB5uWLVtarxPHuW/fvtayjRs3GkkmNjY21f0MDg42o0ePdioTGRlp/Pz8rNf9+vUznp6e5sKFC9ay8PBwExISYuLj461lBQoUMEOHDjXGGLNixQrj6+tr/v77b6e68+bNa7744ou7jklqzJgxw2TJksV6XaxYMdO/f/9Ubx8cHGxq1arltKxJkyamdu3aqW4zted3clJzPhpjjJ+fn4mMjLzrvtzr+bFp0yaTLl0688cffxhjjPnrr79M+vTpzerVq+/Yzr387qf2vG7YsKFTmcOHDxtJZu3ataZGjRqmcuXK5ty5cynu/63Onz9vJJmgiBkm+M2Faf4DAADuLvHv9/nz5+9a9p5uzg8LC9Pq1atljNHatWvVoEEDFS1aVOvWrdOqVavk7++vggULWuUzZMig4sWL33OYioqK0qpVq+Tt7W39JNZ78ODBZLdJvOKSnIMHD+rGjRuqXLmytSx9+vQqX768oqOjJUnR0dGqUKGC030KFStWTNKviRMnOvUrPDxcCQkJOnz4cLJt9+7dWx06dFCNGjU0bNgwp/5HR0c79UmSKleurAMHDlhXiiQ5jaG/v78kqVixYkmWnTx58h/3805CQkKcPoX39/dX4cKF5eLi4rTs1rYvXbqkLFmyOLV/+PBha99TGpPkrFq1Ss8++6xy5swpHx8ftW7dWqdPn7amvvXs2VPvv/++KleurH79+mnnzp133a/bj23FihWtcyE1bUr//PxOzfl4L+71/ChfvryKFCmib775RpL07bffKnfu3KpSpcod27iX3/3Untdly5ZNtq1mzZrp0qVLWrp0KffEAQAAyz0Hl7Vr12rHjh1ycXFR4cKFFRoaqjVr1iSZJibdvNn7n9ywnJCQoPr162v79u1OPwcOHLjjm6uUbio3/38K1e19McZYy0wqvoczISFBr7zyilOfduzYoQMHDihv3rzJbtO/f3/t2bNHdevW1cqVK1W4cGHNmTMnSfu39/VWt06jSiyf3LLEexD+ST/v5PYpXA6HI9llt7adI0eOJMdu37591j0RKY3J7Y4cOaI6deqoaNGimjVrlqKiovTpp59Kkm7cuCFJ6tChgw4dOqRWrVpp165dKlu2bJJ7VlIjcRxT06b0z8/v1JyP9+Jezw/p5pglTheLjIxUu3btUmz7Xn73U3tee3l5JdtWnTp1tHPnTmv6IQAAgHSPwSVxrvuYMWMUGhoqh8Oh0NBQrV69OtngkhoZMmRw+hRWkkqXLq09e/YoJCRETz75pNPPnd7sFC9eXCtWrEh23ZNPPqkMGTJo3bp11rIbN25o69atKlSokCSpcOHCSd4o3f46sV+39ymx/jvJnz+/Xn31VS1dulSNGjWy3jAWLlzYqU/SzZvq8+fPr3Tp0t2xvrtJTT+TG/f7oXTp0jpx4oRcXV2TtJ01a1ar3J3G5HZbt25VXFycRo4cqQoVKih//vw6fvx4knJBQUHq3LmzZs+erddee03jx49PsZ/JHevEKwapbTM5qRnX1JyPD1rLli119OhRffzxx9qzZ4/atGmTYvl7+d3/t+d1ly5dNGzYMD333HNO99EAAID/tnsKLn5+fipZsqQmT56ssLAwSTff0Gzbtk379++3lt2LkJAQbdq0STExMTp16pQSEhLUrVs3nTlzRs2aNdPmzZt16NAhLV26VO3bt7/jm8K3335bW7ZsUdeuXbVz5079+uuvGjdunE6dOiUvLy916dJFffr00eLFi7V371517NhRV65c0csvvyxJ6ty5sw4ePKjevXtr3759mjp1apKbid98801t3LhR3bp1s64AzZ8/Xz169Ei2T1evXlX37t21evVqHTlyROvXr9eWLVusN6evvfaaVqxYoUGDBmn//v2aNGmSPvnkE73++uv3PI732s+QkBD99NNP+uOPP+76tLZ7UaNGDVWsWFENGzbUkiVLFBMTow0bNujdd9/V1q1b7zomt8ubN6/i4uI0duxYHTp0SN9++60+//xzpzIRERFasmSJDh8+rG3btmnlypV3DQDr16/XiBEjtH//fn366aeaOXOmevXqleo27yQkJEQ7d+7Uvn37dOrUKacrNIlScz4+aJkyZVKjRo3Up08f1axZ866PXL6X3/37cV736NFD77//vurVq5ckBAEAgP+me/4CyqpVqyo+Pt56o5IpUyYVLlxY2bJl+0efFr/++utKly6dVcfRo0cVGBio9evXKz4+XuHh4SpatKh69eolPz8/p3srbpU/f34tXbpUO3bsUPny5VWxYkXNmzdPrq43v6pm2LBheuGFF9SqVSuVLl1av/32m5YsWaJMmTJJknLnzq1Zs2ZpwYIFKlGihD7//HMNGTLEqY3ixYtrzZo1OnDggJ555hmVKlVKffv2VY4cOZLtU7p06XT69Gm1bt1a+fPnV+PGjVW7dm0NGDBA0s2rEzNmzND06dNVtGhRvffeexo4cKDatm17z+N4r/0cOHCgYmJilDdvXmXLlu1ftXcrh8OhRYsWqUqVKmrfvr3y58+vpk2bKiYmRv7+/ncdk9uVLFlSo0aN0vDhw1W0aFFNmTJFQ4cOdSoTHx+vbt26qVChQqpVq5YKFCigzz77LMV+vvbaa4qKilKpUqU0aNAgjRw5UuHh4alu8046duyoAgUKqGzZssqWLZvWr1+fbLm7nY8Pw8svv6zr16+rffv2qSqf2t/9+3VeR0REaMCAAapTp442bNhwT9sCAIDHj8Ok5uYO4DESEhKiiIgIRUREpHVX0tSUKVPUq1cvHT9+PMWpjo+KCxcuyM/PT0ERM+Ti5pnW3VHMsLpp3QUAAGwv8e/3+fPn5evrm2JZ14fUJwA2ceXKFR0+fFhDhw7VK6+88liEFgAA8Pi756liAB5tI0aMUMmSJeXv76+33347rbsDAACQKkwVA/BYYKoYAACPnnuZKsYVFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHuuad0BALifdg8Il6+vb1p3AwAA3GdccQEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABge65p3QEAuJ+K9lsiFzfPtO4GAACPlZhhddO6C1xxAQAAAGB/BBcAAAAAtkdwAQAAAGB7BBcAAAAAtkdwAQAAAGB7BBcAAAAAtkdwAQAAAGB7BBcAAAAAtkdwAQAAAGB7BBcAAAAAtkdwAQAAAGB7BBcAAAAAtkdwAQAAAGB7BBcAAAAAtkdwAQAAAGB7BBcAAAAAtkdwAQAAAGB7BBcAAAAAtkdwAQAAAGB7BBcAAAAAtkdwAQAAAGB7BBcAAAAAtkdwAQAAAGB7BBcAAAAAtkdwAQAAAGB7BBcAAAAAtkdwAQAAAGB7BBcAAAAAtkdwAQAAAGB7BBcAAAAAtkdwAQAAAGB7BBcAAAAAtkdwAQAAAGB7BBcAAAAAtkdwAQAAAGB7BBcAAAAAtkdwAQAAAGB7BBcAAAAAtkdwAQAAAGB7BBcAAAAAtkdwQaqEhIRozJgxD7XNsLAwRUREPNQ270VajMm/9eWXXyooKEguLi4PvO92P34AAODR4prWHXgctW3bVufOndPcuXPTuiuA5cKFC+revbtGjRqlF154QX5+fg+0vdmzZyt9+vQPtA0AAPDfQXAB7uLGjRu2fgOe2v4dPXpUN27cUN26dZUjR44H3q/MmTM/8DYAAMB/B1PF0sDp06fVrFkz5cqVS56enipWrJimTZvmVCYsLEw9evRQRESEMmXKJH9/f3355Ze6fPmy2rVrJx8fH+XNm1c//vhjim1NnjxZZcuWlY+PjwICAtS8eXOdPHkyxW1Onjyp+vXry8PDQ0888YSmTJmSpMz58+fVqVMnZc+eXb6+vqpWrZp27NghSdq3b58cDod+/fVXp21GjRqlkJAQGWMkSXv37lWdOnXk7e0tf39/tWrVSqdOnbpjv86ePavWrVsrU6ZM8vT0VO3atXXgwAFr/cSJE5UxY0bNnTtX+fPnl7u7u5599lkdO3bMqZ4FCxaoTJkycnd3V548eTRgwADFxcVZ6x0Ohz7//HM1aNBAXl5eev/99+/Yp4sXL6p58+by9vZWYGCgxo4da61r37696tWr51Q+Li5OAQEBmjBhQrL1pWYf+vfvr5IlS2rChAnKkyeP3NzcZIzR0aNH1aBBA3l7e8vX11eNGzfWn3/+adVbrFgxSVKePHnkcDgUExOTqvHo37+/cufOLTc3NwUGBqpnz57Wus8++0z58uWTu7u7/P399eKLL1rrbp8qltrjt2TJEhUqVEje3t6qVauWYmNj7zj+AADgv4Pgkgb+/vtvlSlTRgsXLtTu3bvVqVMntWrVSps2bXIqN2nSJGXNmlWbN29Wjx491KVLF7300kuqVKmStm3bpvDwcLVq1UpXrly5Y1vXr1/XoEGDtGPHDs2dO1eHDx9W27ZtU+xf27ZtFRMTo5UrV+r777/XZ5995hR2jDGqW7euTpw4oUWLFikqKkqlS5dW9erVdebMGRUoUEBlypRJEnimTp2q5s2by+FwKDY2VqGhoSpZsqS2bt2qxYsX688//1Tjxo1T7NfWrVs1f/58bdy4UcYY1alTRzdu3LDKXLlyRYMHD9akSZO0fv16XbhwQU2bNrXWL1myRC1btlTPnj21d+9effHFF5o4caIGDx7s1Fa/fv3UoEED7dq1S+3bt79jnz744AMVL15c27Zt09tvv61XX31Vy5YtkyR16NBBixcvdnrjvWjRIl26dCnF/bzbPkjSb7/9phkzZmjWrFnavn27JKlhw4Y6c+aM1qxZo2XLlungwYNq0qSJJKlJkyZavny5JGnz5s2KjY1VUFDQXcfj+++/1+jRo/XFF1/owIEDmjt3rhWAtm7dqp49e2rgwIHat2+fFi9erCpVqtxxv1J7/D788EN9++23+umnn3T06FG9/vrrydZ37do1XbhwwekHAAA8vhwm8eNv3Df/5B6XunXrqlChQvrwww8l3fy0Oj4+XmvXrpUkxcfHy8/PT40aNdI333wjSTpx4oRy5MihjRs3qkKFCqlqZ8uWLSpfvrwuXrwob2/vJOv379+vAgUK6Oeff9ZTTz0lSfr1119VqFAhjR49WhEREVq5cqWef/55nTx5Um5ubta2Tz75pN544w116tRJo0eP1ieffKKDBw861btnzx4VLlxY7733njZt2qQlS5ZY2//+++8KCgrSvn37lD9/foWFhalkyZIaM2aMDhw4oPz582v9+vWqVKmSpJtXroKCgjRp0iS99NJLmjhxotq1a5ds3zdt2qTy5curSpUqql27tt5++22r3cmTJ+uNN97Q8ePHJd284hIREaHRo0enOJYhISEqVKiQ01Wvpk2b6sKFC1q0aJEkqUiRImrTpo3eeOMNSdLzzz+vjBkzKjIyMtk6U7MP/fv315AhQ/THH38oW7ZskqRly5apdu3aOnz4sIKCgiTdvKJVpEgRbd68WeXKldP27dtVqlQpHT58WCEhIZJ01/EYNWqUvvjiC+3evTvJdLTZs2erXbt2+v333+Xj45NkX/7p8fvtt9+UN29eSTev6AwcOFAnTpxIUn///v01YMCAJMuDImbIxc0z2fEFAAD/TMywug+k3gsXLsjPz0/nz5+Xr69vimW54pIG4uPjNXjwYBUvXlxZsmSRt7e3li5dqqNHjzqVK168uPXvdOnSKUuWLNan3ZLk7+8vSSlO/frll1/UoEEDBQcHy8fHR2FhYZKUpK1E0dHRcnV1VdmyZa1lBQsWVMaMGa3XUVFRunTpktX3xJ/Dhw9bQaVp06Y6cuSIfv75Z0nSlClTVLJkSRUuXNiqY9WqVU7bFyxYUJKsOpLrV+KbeUnKkiWLChQooOjoaGvZnfqeWCYqKkoDBw50ardjx46KjY11unJ1ax0pqVixYpLXt/anQ4cOVkg5efKkfvjhhxSv4KRmHyQpODjYCi3SzfEJCgqyQoskFS5cOMl2t7vbeLz00ku6evWq8uTJo44dO2rOnDnWNLJnn31WwcHBypMnj1q1aqUpU6bc8epfao+fp6enFVokKUeOHHc8v99++22dP3/e+rl9SiAAAHi8cHN+Ghg5cqRGjx6tMWPGqFixYvLy8lJERISuX7/uVO72T7gdDofTMofDIUlKSEhItp3Lly+rZs2aqlmzpiZPnqxs2bLp6NGjCg8PT9JWosQLcIl1JychIUE5cuTQ6tWrk6xLDDg5cuRQ1apVNXXqVFWoUEHTpk3TK6+84lRH/fr1NXz48CR1JHfj+J0uDBpjkvQ1ub7fOlYDBgxQo0aNkpRxd3e3/u3l5ZVse6lxa/utW7fWW2+9pY0bN2rjxo0KCQnRM888c091JLfs9v4lNw4pLU90t/FIvAK2bNkyLV++XF27dtUHH3ygNWvWyMfHR9u2bdPq1au1dOlSvffee+rfv7+2bNniFHQT+5Gc2/uX3Dl/p23d3NycrvgBAIDHG8ElDaxdu1YNGjRQy5YtJd1883jgwAEVKlTovrbz66+/6tSpUxo2bJj1SfzWrVtT3KZQoUKKi4vT1q1bVb58eUk3b7Y/d+6cVaZ06dI6ceKEXF1drSlHyWnRooXefPNNNWvWTAcPHnS6T6N06dKaNWuWQkJC5Op699OwcOHCiouL06ZNm5ymGu3fv99p3O7U98SrOaVLl9a+ffv05JNP3rXN1Ei8onTr68S2pJtXFRo2bKjIyEht3LhR7dq1u2udd9uH5BQuXFhHjx7VsWPHnKaKnT9/PsXzKjXj4eHhoeeee07PPfecunXrpoIFC2rXrl0qXbq0XF1dVaNGDdWoUUP9+vVTxowZtXLlyiRBKLXHDwAA4E6YKpYGnnzySS1btkwbNmxQdHS0XnnllWTn8P9buXPnVoYMGTR27FgdOnRI8+fP16BBg1LcpkCBAqpVq5Y6duyoTZs2KSoqSh06dJCHh4dVpkaNGqpYsaIaNmyoJUuWKCYmRhs2bNC7777rFIwaNWqkCxcuqEuXLqpatapy5sxprevWrZvOnDmjZs2aafPmzTp06JCWLl2q9u3bKz4+Pkm/8uXLpwYNGqhjx45at26dduzYoZYtWypnzpxq0KCBVS59+vTq0aOHNm3apG3btqldu3aqUKGCFQLee+89ffPNN+rfv7/27Nmj6Ohofffdd3r33Xf/0RivX79eI0aM0P79+/Xpp59q5syZ6tWrl1OZDh06aNKkSYqOjlabNm3uWufd9iE5NWrUUPHixdWiRQtt27ZNmzdvVuvWrRUaGpritLe7jcfEiRP19ddfa/fu3Tp06JC+/fZbeXh4KDg4WAsXLtTHH3+s7du368iRI/rmm2+UkJCgAgUKJGkntccPAADgTgguaaBv374qXbq0wsPDFRYWpoCAADVs2PC+t5MtWzZNnDhRM2fOVOHChTVs2DDr5v+UREZGKigoSKGhoWrUqJH12ONEDodDixYtUpUqVdS+fXvlz59fTZs2VUxMjHXfjST5+vqqfv362rFjh1q0aOHURmBgoNavX6/4+HiFh4eraNGi6tWrl/z8/OTikvxpGRkZqTJlyqhevXqqWLGijDFatGiR0/QiT09Pvfnmm2revLkqVqwoDw8PTZ8+3VofHh6uhQsXatmyZSpXrpwqVKigUaNGKTg4ONXjeqvXXntNUVFRKlWqlAYNGqSRI0cqPDzcqUyNGjWUI0cOhYeHKzAw8K513m0fkuNwODR37lxlypRJVapUUY0aNZQnTx599913KW53t/HImDGjxo8fr8qVK6t48eJasWKFFixYoCxZsihjxoyaPXu2qlWrpkKFCunzzz/XtGnTVKRIkWTbSs3xAwAAuBOeKobHxsSJExUREeE0rc0Orly5osDAQE2YMCHZe0luZdd9eBQkPpWEp4oBAHD/2eGpYtzjAjwgCQkJOnHihEaOHCk/Pz8999xzad0lAACARxbBBXhAjh49qieeeEK5cuXSxIkTU/UQAgAAACSPqWIAHgtMFQMA4MGxw1Qxbs4HAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHsEFwAAAAC2R3ABAAAAYHuuad0BALifdg8Il6+vb1p3AwAA3GdccQEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALZHcAEAAABgewQXAAAAALbnmtYdAID7wRgjSbpw4UIa9wQAAKRW4t/txL/jKSG4AHgsnD59WpIUFBSUxj0BAAD36uLFi/Lz80uxDMEFwGMhc+bMkqSjR4/e9T8+/DMXLlxQUFCQjh07Jl9f37TuzmOJMX7wGOMHjzF+8B6nMTbG6OLFiwoMDLxrWYILgMeCi8vNW/b8/Pwe+f/E7c7X15cxfsAY4wePMX7wGOMH73EZ49R+4MjN+QAAAABsj+ACAAAAwPYILgAeC25uburXr5/c3NzSuiuPLcb4wWOMHzzG+MFjjB+8/+oYO0xqnj0GAAAAAGmIKy4AAAAAbI/gAgAAAMD2CC4AAAAAbI/gAgAAAMD2CC4AHgufffaZnnjiCbm7u6tMmTJau3ZtWnfJloYOHapy5crJx8dH2bNnV8OGDbVv3z6nMsYY9e/fX4GBgfLw8FBYWJj27NnjVObatWvq0aOHsmbNKi8vLz333HP6/fffncqcPXtWrVq1kp+fn/z8/NSqVSudO3fuQe+irQwdOlQOh0MRERHWMsb3/vjjjz/UsmVLZcmSRZ6enipZsqSioqKs9YzzvxMXF6d3331XTzzxhDw8PJQnTx4NHDhQCQkJVhnG+N789NNPql+/vgIDA+VwODR37lyn9Q9zPI8ePar69evLy8tLWbNmVc+ePXX9+vUHsdv3lwGAR9z06dNN+vTpzfjx483evXtNr169jJeXlzly5Ehad812wsPDTWRkpNm9e7fZvn27qVu3rsmdO7e5dOmSVWbYsGHGx8fHzJo1y+zatcs0adLE5MiRw1y4cMEq07lzZ5MzZ06zbNkys23bNlO1alVTokQJExcXZ5WpVauWKVq0qNmwYYPZsGGDKVq0qKlXr95D3d+0tHnzZhMSEmKKFy9uevXqZS1nfP+9M2fOmODgYNO2bVuzadMmc/jwYbN8+XLz22+/WWUY53/n/fffN1myZDELFy40hw8fNjNnzjTe3t5mzJgxVhnG+N4sWrTIvPPOO2bWrFlGkpkzZ47T+oc1nnFxcaZo0aKmatWqZtu2bWbZsmUmMDDQdO/e/YGPwb9FcAHwyCtfvrzp3Lmz07KCBQuat956K4169Og4efKkkWTWrFljjDEmISHBBAQEmGHDhlll/v77b+Pn52c+//xzY4wx586dM+nTpzfTp0+3yvzxxx/GxcXFLF682BhjzN69e40k8/PPP1tlNm7caCSZX3/99WHsWpq6ePGiyZcvn1m2bJkJDQ21ggvje3+8+eab5umnn77jesb536tbt65p376907JGjRqZli1bGmMY43/r9uDyMMdz0aJFxsXFxfzxxx9WmWnTphk3Nzdz/vz5B7K/9wtTxQA80q5fv66oqCjVrFnTaXnNmjW1YcOGNOrVo+P8+fOSpMyZM0uSDh8+rBMnTjiNp5ubm0JDQ63xjIqK0o0bN5zKBAYGqmjRolaZjRs3ys/PT0899ZRVpkKFCvLz8/tPHJdu3bqpbt26qlGjhtNyxvf+mD9/vsqWLauXXnpJ2bNnV6lSpTR+/HhrPeP87z399NNasWKF9u/fL0nasWOH1q1bpzp16khijO+3hzmeGzduVNGiRRUYGGiVCQ8P17Vr15ymW9qRa1p3AAD+jVOnTik+Pl7+/v5Oy/39/XXixIk06tWjwRij3r176+mnn1bRokUlyRqz5MbzyJEjVpkMGTIoU6ZMScokbn/ixAllz549SZvZs2d/7I/L9OnTFRUVpa1btyZZx/jeH4cOHdK4cePUu3dv/e9//9PmzZvVs2dPubm5qXXr1ozzffDmm2/q/PnzKliwoNKlS6f4+HgNHjxYzZo1k8S5fL89zPE8ceJEknYyZcqkDBky2H7MCS4AHgsOh8PptTEmyTI46969u3bu3Kl169YlWfdPxvP2MsmVf9yPy7Fjx9SrVy8tXbpU7u7udyzH+P47CQkJKlu2rIYMGSJJKlWqlPbs2aNx48apdevWVjnG+Z/77rvvNHnyZE2dOlVFihTR9u3bFRERocDAQLVp08YqxxjfXw9rPB/VMWeqGIBHWtasWZUuXboknxKdPHkyySdK+D89evTQ/PnztWrVKuXKlctaHhAQIEkpjmdAQICuX7+us2fPpljmzz//TNLuX3/99Vgfl6ioKJ08eVJlypSRq6urXF1dtWbNGn388cdydXW19p3x/Xdy5MihwoULOy0rVKiQjh49Konz+H7o06eP3nrrLTVt2lTFihVTq1at9Oqrr2ro0KGSGOP77WGOZ0BAQJJ2zp49qxs3bth+zAkuAB5pGTJkUJkyZbRs2TKn5cuWLVOlSpXSqFf2ZYxR9+7dNXv2bK1cuVJPPPGE0/onnnhCAQEBTuN5/fp1rVmzxhrPMmXKKH369E5lYmNjtXv3bqtMxYoVdf78eW3evNkqs2nTJp0/f/6xPi7Vq1fXrl27tH37duunbNmyatGihbZv3648efIwvvdB5cqVkzzGe//+/QoODpbEeXw/XLlyRS4uzm8T06VLZz0OmTG+vx7meFasWFG7d+9WbGysVWbp0qVyc3NTmTJlHuh+/msP+WEAAHDfJT4O+euvvzZ79+41ERERxsvLy8TExKR112ynS5cuxs/Pz6xevdrExsZaP1euXLHKDBs2zPj5+ZnZs2ebXbt2mWbNmiX7SM5cuXKZ5cuXm23btplq1aol+0jO4sWLm40bN5qNGzeaYsWKPZaPOL2bW58qZgzjez9s3rzZuLq6msGDB5sDBw6YKVOmGE9PTzN58mSrDOP877Rp08bkzJnTehzy7NmzTdasWc0bb7xhlWGM783FixfNL7/8Yn755RcjyYwaNcr88ssv1qP7H9Z4Jj4OuXr16mbbtm1m+fLlJleuXDwOGQAelk8//dQEBwebDBkymNKlS1uP94UzScn+REZGWmUSEhJMv379TEBAgHFzczNVqlQxu3btcqrn6tWrpnv37iZz5szGw8PD1KtXzxw9etSpzOnTp02LFi2Mj4+P8fHxMS1atDBnz559CHtpL7cHF8b3/liwYIEpWrSocXNzMwULFjRffvml03rG+d+5cOGC6dWrl8mdO7dxd3c3efLkMe+88465du2aVYYxvjerVq1K9v/fNm3aGGMe7ngeOXLE1K1b13h4eJjMmTOb7t27m7///vtB7v594TDGmLS51gMAAAAAqcM9LgAAAABsj+ACAAAAwPYILgAAAABsj+ACAAAAwPYILgAAAABsj+ACAAAAwPYILgAAAABsj+ACAMBjLCYmRg6HQ9u3b0/rrlh+/fVXVahQQe7u7ipZsmRadydNhISEaMyYMWndDeCRQnABAOABatu2rRwOh4YNG+a0fO7cuXI4HGnUq7TVr18/eXl5ad++fVqxYkWyZRLHzeFwyNXVVblz51aXLl109uzZh9zbR0dYWJgiIiLSuhvAA0NwAQDgAXN3d9fw4cMfqzfd169f/8fbHjx4UE8//bSCg4OVJUuWO5arVauWYmNjFRMTo6+++koLFixQ165d/3G7AB5tBBcAAB6wGjVqKCAgQEOHDr1jmf79+yeZNjVmzBiFhIRYr9u2bauGDRtqyJAh8vf3V8aMGTVgwADFxcWpT58+ypw5s3LlyqUJEyYkqf/XX39VpUqV5O7uriJFimj16tVO6/fu3as6derI29tb/v7+atWqlU6dOmWtDwsLU/fu3dW7d29lzZpVzz77bLL7kZCQoIEDBypXrlxyc3NTyZIltXjxYmu9w+FQVFSUBg4cKIfDof79+99xTNzc3BQQEKBcuXKpZs2aatKkiZYuXepUJjIyUoUKFZK7u7sKFiyozz77zFp3/fp1de/eXTly5JC7u7tCQkKcjoHD4dC4ceNUu3ZteXh46IknntDMmTOd6t+1a5eqVasmDw8PZcmSRZ06ddKlS5eSHJMPP/xQOXLkUJYsWdStWzfduHHDKnPy5EnVr1/famPKlClJ9vX8+fPq1KmTsmfPLl9fX1WrVk07duyw1ieeH99++61CQkLk5+enpk2b6uLFi1Y/1qxZo48++si6UhUTE6OzZ8+qRYsWypYtmzw8PJQvXz5FRkbeccwBOyO4AADwgKVLl05DhgzR2LFj9fvvv/+rulauXKnjx4/rp59+0qhRo9S/f3/Vq1dPmTJl0qZNm9S5c2d17txZx44dc9quT58+eu211/TLL7+oUqVKeu6553T69GlJUmxsrEJDQ1WyZElt3bpVixcv1p9//qnGjRs71TFp0iS5urpq/fr1+uKLL5Lt30cffaSRI0fqww8/1M6dOxUeHq7nnntOBw4csNoqUqSIXnvtNcXGxur1119P1X4fOnRIixcvVvr06a1l48eP1zvvvKPBgwcrOjpaQ4YMUd++fTVp0iRJ0scff6z58+drxowZ2rdvnyZPnuwUBCWpb9++euGFF7Rjxw61bNlSzZo1U3R0tCTpypUrqlWrljJlyqQtW7Zo5syZWr58ubp37+5Ux6pVq3Tw4EGtWrVKkyZN0sSJEzVx4kRrfdu2bRUTE6OVK1fq+++/12effaaTJ09a640xqlu3rk6cOKFFixYpKipKpUuXVvXq1XXmzBmr3MGDBzV37lwtXLhQCxcu1Jo1a6wpiB999JEqVqyojh07KjY2VrGxsQoKClLfvn21d+9e/fjjj4qOjta4ceOUNWvWVI05YDsGAAA8MG3atDENGjQwxhhToUIF0759e2OMMXPmzDG3/hnu16+fKVGihNO2o0ePNsHBwU51BQcHm/j4eGtZgQIFzDPPPGO9jouLM15eXmbatGnGGGMOHz5sJJlhw4ZZZW7cuGFy5cplhg8fbowxpm/fvqZmzZpObR87dsxIMvv27TPGGBMaGmpKlix51/0NDAw0gwcPdlpWrlw507VrV+t1iRIlTL9+/VKsp02bNiZdunTGy8vLuLu7G0lGkhk1apRVJigoyEydOtVpu0GDBpmKFSsaY4zp0aOHqVatmklISEi2DUmmc+fOTsueeuop06VLF2OMMV9++aXJlCmTuXTpkrX+hx9+MC4uLubEiRNWP4ODg01cXJxV5qWXXjJNmjQxxhizb98+I8n8/PPP1vro6GgjyYwePdoYY8yKFSuMr6+v+fvvv536kjdvXvPFF18YY26eH56enubChQvW+j59+pinnnrKeh0aGmp69erlVEf9+vVNu3btkt1/4FHjmnaRCQCA/5bhw4erWrVqeu211/5xHUWKFJGLy/9NmPD391fRokWt1+nSpVOWLFmcPtGXpIoVK1r/dnV1VdmyZa0rC1FRUVq1apW8vb2TtHfw4EHlz59fklS2bNkU+3bhwgUdP35clStXdlpeuXJlp2lPqVW1alWNGzdOV65c0VdffaX9+/erR48ekqS//vpLx44d08svv6yOHTta28TFxcnPz0/SzSsdzz77rAoUKKBatWqpXr16qlmzplMbt45L4uvEJ7BFR0erRIkS8vLyctqXhIQE7du3T/7+/pJuHpN06dJZZXLkyKFdu3ZZdSSOd6KCBQsqY8aM1uuoqChdunQpyf0+V69e1cGDB63XISEh8vHxcWrn9uN8uy5duuiFF17Qtm3bVLNmTTVs2FCVKlVKcRvArgguAAA8JFWqVFF4eLj+97//qW3btk7rXFxcZIxxWnbrfRKJbp0qJd28TyO5ZQkJCXftT+JTzRISElS/fn0NHz48SZkcOXJY/771DXxq6k1kjPlHT1Dz8vLSk08+KenmtK+qVatqwIABGjRokLV/48eP11NPPeW0XWKIKF26tA4fPqwff/xRy5cvV+PGjVWjRg19//33qep/Sv2+dXlK4594TFPa/4SEBOXIkSPJfUeSnALOPznOtWvX1pEjR/TDDz9o+fLlql69urp166YPP/wwxe0AO+IeFwAAHqKhQ4dqwYIF2rBhg9PybNmy6cSJE07h5X5+98rPP/9s/TsuLk5RUVEqWLCgpJtv8Pfs2aOQkBA9+eSTTj+pDSuS5Ovrq8DAQK1bt85p+YYNG1SoUKF/vQ/9+vXThx9+qOPHj8vf3185c+bUoUOHkvT5iSeecOpTkyZNNH78eH333XeaNWuW030jt45L4uvEcSlcuLC2b9+uy5cvW+vXr18vFxcX6yrU3RQqVEhxcXHaunWrtWzfvn06d+6c9bp06dI6ceKEXF1dk+zLvdyPkiFDBsXHxydZni1bNrVt21aTJ0/WmDFj9OWXX6a6TsBOCC4AADxExYsXV4sWLTR27Fin5WFhYfrrr780YsQIHTx4UJ9++ql+/PHH+9bup59+qjlz5ujXX39Vt27ddPbsWbVv316S1K1bN505c0bNmjXT5s2bdejQIS1dulTt27dP9o1wSvr06aPhw4fru+++0759+/TWW29p+/bt6tWr17/eh7CwMBUpUkRDhgyRdPNJW0OHDtVHH32k/fv3a9euXYqMjNSoUaMkSaNHj9b06dP166+/av/+/Zo5c6YCAgKcrmLMnDlTEyZM0P79+9WvXz9t3rzZuvm+RYsWcnd3V5s2bbR7926tWrVKPXr0UKtWraxpYneTOE2tY8eO2rRpk6KiotShQwd5eHhYZWrUqKGKFSuqYcOGWrJkiWJiYrRhwwa9++67ToHnbkJCQrRp0ybFxMTo1KlTSkhI0Hvvvad58+bpt99+0549e7Rw4cL7EiKBtEBwAQDgIRs0aFCSaWGFChXSZ599pk8//VQlSpTQ5s2bU/3ErdQYNmyYhg8frhIlSmjt2rWaN2+e9Wl+YGCg1q9fr/j4eIWHh6to0aLq1auX/Pz8nO6nSY2ePXvqtdde02uvvaZixYpp8eLFmj9/vvLly3df9qN3794aP368jh07pg4dOuirr77SxIkTVaxYMYWGhmrixInWFRdvb28NHz5cZcuWVbly5RQTE6NFixY57dOAAQM0ffp0FS9eXJMmTdKUKVNUuHBhSZKnp6eWLFmiM2fOqFy5cnrxxRdVvXp1ffLJJ/fU58jISAUFBSk0NFSNGjWyHnucyOFwaNGiRapSpYrat2+v/Pnzq2nTpoqJiUl1QJKk119/XenSpVPhwoWVLVs2HT16VBkyZNDbb7+t4sWLq0qVKkqXLp2mT59+T/0H7MJhbv+fEwAA4D/A4XBozpw5atiwYVp3BUAqcMUFAAAAgO0RXAAAAADYHo9DBgAA/0nMlgceLVxxAQAAAGB7BBcAAAAAtkdwAQAAAGB7BBcAAAAAtkdwAQAAAGB7BBcAAAAAtkdwAQAAAGB7BBcAAAAAtkdwAQAAAGB7/w/WK8TaFM2hsAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def add_break(sentence):\n",
    "    parts = sentence.split(' ')\n",
    "    half = len(parts)//2\n",
    "    first_half = ' '.join(parts[:half])\n",
    "    second_half = ' '.join(parts[half:])\n",
    "    return first_half + '\\n' + second_half\n",
    "\n",
    "QUERY = \"\"\"\n",
    "SELECT MainBranch, COUNT(MainBranch) as CNT from master\n",
    "    GROUP BY MainBranch\n",
    "    ORDER BY CNT DESC\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY, conn)\n",
    "df.index = df.MainBranch\n",
    "\n",
    "old_index = df.index[1]\n",
    "new_index = add_break(old_index)\n",
    "\n",
    "df.rename(index={old_index : new_index}, inplace=True)\n",
    "print(df.head())\n",
    "\n",
    "df.plot(kind='barh', legend=False)\n",
    "plt.title('BarChart of Developer Profession')\n",
    "plt.xlabel('Number of Respondents')\n",
    "plt.ylabel(None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Developer, full-stack                            6928\n",
       "Developer, back-end                              6290\n",
       "Developer, front-end                             3920\n",
       "Developer, desktop or enterprise applications    2575\n",
       "Developer, mobile                                1959\n",
       "DevOps specialist                                1639\n",
       "Database administrator                           1413\n",
       "System administrator                             1202\n",
       "Designer                                          988\n",
       "Developer, QA or test                             911\n",
       "Developer, embedded applications or devices       854\n",
       "Engineer, data                                    832\n",
       "Data scientist or machine learning specialist     803\n",
       "Data or business analyst                          802\n",
       "Student                                           766\n",
       "Academic researcher                               556\n",
       "Educator                                          514\n",
       "Product manager                                   480\n",
       "Developer, game or graphics                       472\n",
       "Engineer, site reliability                        449\n",
       "Engineering manager                               386\n",
       "Scientist                                         354\n",
       "Senior executive/VP                               160\n",
       "Marketing or sales professional                    61\n",
       "Name: DevType, dtype: int64"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DevType, PlatformWorkedWith, JobFactors from master\n",
    "QUERY = \"\"\"\n",
    "SELECT * from DevType\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY, conn)\n",
    "df['DevType'].value_counts().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Close the database connection.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ramesh Sannareddy\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other Contributors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rav Ahuja\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Date (YYYY-MM-DD) | Version | Changed By        | Change Description                 |\n",
    "| ----------------- | ------- | ----------------- | ---------------------------------- |\n",
    "| 2020-10-17        | 0.1     | Ramesh Sannareddy | Created initial version of the lab |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © 2020 IBM Corporation. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork21426264-2022-01-01&cm_mmc=Email_Newsletter-\\_-Developer_Ed%2BTech-\\_-WW_WW-\\_-SkillsNetwork-Courses-IBM-DA0321EN-SkillsNetwork-21426264&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}