4-DataVisualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"380\" 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": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-08-05 08:09:19--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DA0321EN-SkillsNetwork/LargeData/m4_survey_data.sqlite\n",
      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104, 169.63.118.104\n",
      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 36679680 (35M) [application/octet-stream]\n",
      "Saving to: ‘m4_survey_data.sqlite.2’\n",
      "\n",
      "m4_survey_data.sqli 100%[===================>]  34.98M  51.6MB/s    in 0.7s    \n",
      "\n",
      "2024-08-05 08:09:20 (51.6 MB/s) - ‘m4_survey_data.sqlite.2’ saved [36679680/36679680]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!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": 3,
   "metadata": {
    "tags": []
   },
   "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": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo: How to run an sql query\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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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>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",
       "</div>"
      ],
      "text/plain": [
       "   COUNT(*)\n",
       "0     11398"
      ]
     },
     "execution_count": 5,
     "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": 6,
   "metadata": {
    "tags": []
   },
   "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": 6,
     "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": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>287</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19.0</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20.0</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <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": 7,
     "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": 8,
   "metadata": {
    "tags": []
   },
   "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": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# your code goes here\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "query= \"\"\" Select * from master \"\"\"\n",
    "df= pd.read_sql_query(query,conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'Respondent', 'MainBranch', 'Hobbyist', 'OpenSourcer',\n",
       "       'OpenSource', 'Employment', 'Country', 'Student', 'EdLevel',\n",
       "       'UndergradMajor', 'OrgSize', 'YearsCode', 'Age1stCode', 'YearsCodePro',\n",
       "       'CareerSat', 'JobSat', 'MgrIdiot', 'MgrMoney', 'MgrWant', 'JobSeek',\n",
       "       'LastHireDate', 'FizzBuzz', 'ResumeUpdate', 'CurrencySymbol',\n",
       "       'CurrencyDesc', 'CompTotal', 'CompFreq', 'ConvertedComp', 'WorkWeekHrs',\n",
       "       'WorkRemote', 'WorkLoc', 'ImpSyn', 'CodeRev', 'CodeRevHrs', 'UnitTests',\n",
       "       'PurchaseHow', 'PurchaseWhat', 'OpSys', 'BlockchainOrg', 'BlockchainIs',\n",
       "       'BetterLife', 'ITperson', 'OffOn', 'SocialMedia', 'Extraversion',\n",
       "       'ScreenName', 'SOVisit1st', 'SOVisitFreq', 'SOFindAnswer',\n",
       "       'SOTimeSaved', 'SOHowMuchTime', 'SOAccount', 'SOPartFreq', 'SOJobs',\n",
       "       'EntTeams', 'SOComm', 'WelcomeChange', 'Age', 'Trans', 'Dependents',\n",
       "       'SurveyLength', 'SurveyEase'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Converted Comp')"
      ]
     },
     "execution_count": 11,
     "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": [
    "plt.hist(df['ConvertedComp'],bins=10)\n",
    "plt.xlabel('Converted Comp')"
   ]
  },
  {
   "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": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age'>"
      ]
     },
     "execution_count": 12,
     "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": [
    "# your code goes here\n",
    "import seaborn as sns\n",
    "sns.boxplot(df['Age'],data=df)"
   ]
  },
  {
   "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": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "plt.scatter(df['Age'], df['WorkWeekHrs'], cmap='viridis', alpha=0.5, marker='o')\n",
    "\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Work Week Hours')\n",
    "plt.title('Age vs work week hours')\n",
    "plt.show()"
   ]
  },
  {
   "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": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "size = [100, 200, 300, 400, 500]  # Third variable for bubble size\n",
    "\n",
    "# your code goes here\n",
    "plt.scatter(df['CodeRevHrs'], df['WorkWeekHrs'],s= df['Age'] ,cmap='viridis', alpha=0.5, marker='o')\n",
    "\n",
    "plt.xlabel('CodeRevHrs')\n",
    "plt.ylabel('Work Week Hours')\n",
    "plt.title('CodeRevHrs vs work week hours')\n",
    "plt.show()"
   ]
  },
  {
   "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": 15,
   "metadata": {
    "tags": []
   },
   "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>type</th>\n",
       "      <th>name</th>\n",
       "      <th>tbl_name</th>\n",
       "      <th>rootpage</th>\n",
       "      <th>sql</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>table</td>\n",
       "      <td>EduOther</td>\n",
       "      <td>EduOther</td>\n",
       "      <td>2</td>\n",
       "      <td>CREATE TABLE \"EduOther\" (\\n\"Respondent\" INTEGE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>table</td>\n",
       "      <td>DevType</td>\n",
       "      <td>DevType</td>\n",
       "      <td>741</td>\n",
       "      <td>CREATE TABLE \"DevType\" (\\n\"Respondent\" INTEGER...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>table</td>\n",
       "      <td>LastInt</td>\n",
       "      <td>LastInt</td>\n",
       "      <td>1024</td>\n",
       "      <td>CREATE TABLE \"LastInt\" (\\n\"Respondent\" INTEGER...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>table</td>\n",
       "      <td>JobFactors</td>\n",
       "      <td>JobFactors</td>\n",
       "      <td>1355</td>\n",
       "      <td>CREATE TABLE \"JobFactors\" (\\n\"Respondent\" INTE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>table</td>\n",
       "      <td>WorkPlan</td>\n",
       "      <td>WorkPlan</td>\n",
       "      <td>1808</td>\n",
       "      <td>CREATE TABLE \"WorkPlan\" (\\n\"Respondent\" INTEGE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>table</td>\n",
       "      <td>WorkChallenge</td>\n",
       "      <td>WorkChallenge</td>\n",
       "      <td>2088</td>\n",
       "      <td>CREATE TABLE \"WorkChallenge\" (\\n\"Respondent\" I...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>table</td>\n",
       "      <td>LanguageWorkedWith</td>\n",
       "      <td>LanguageWorkedWith</td>\n",
       "      <td>2378</td>\n",
       "      <td>CREATE TABLE \"LanguageWorkedWith\" (\\n\"Responde...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>table</td>\n",
       "      <td>LanguageDesireNextYear</td>\n",
       "      <td>LanguageDesireNextYear</td>\n",
       "      <td>2638</td>\n",
       "      <td>CREATE TABLE \"LanguageDesireNextYear\" (\\n\"Resp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>table</td>\n",
       "      <td>DatabaseWorkedWith</td>\n",
       "      <td>DatabaseWorkedWith</td>\n",
       "      <td>2877</td>\n",
       "      <td>CREATE TABLE \"DatabaseWorkedWith\" (\\n\"Responde...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>table</td>\n",
       "      <td>DatabaseDesireNextYear</td>\n",
       "      <td>DatabaseDesireNextYear</td>\n",
       "      <td>3028</td>\n",
       "      <td>CREATE TABLE \"DatabaseDesireNextYear\" (\\n\"Resp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>table</td>\n",
       "      <td>PlatformWorkedWith</td>\n",
       "      <td>PlatformWorkedWith</td>\n",
       "      <td>3170</td>\n",
       "      <td>CREATE TABLE \"PlatformWorkedWith\" (\\n\"Responde...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>table</td>\n",
       "      <td>PlatformDesireNextYear</td>\n",
       "      <td>PlatformDesireNextYear</td>\n",
       "      <td>3341</td>\n",
       "      <td>CREATE TABLE \"PlatformDesireNextYear\" (\\n\"Resp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>table</td>\n",
       "      <td>WebFrameWorkedWith</td>\n",
       "      <td>WebFrameWorkedWith</td>\n",
       "      <td>3540</td>\n",
       "      <td>CREATE TABLE \"WebFrameWorkedWith\" (\\n\"Responde...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>table</td>\n",
       "      <td>WebFrameDesireNextYear</td>\n",
       "      <td>WebFrameDesireNextYear</td>\n",
       "      <td>3655</td>\n",
       "      <td>CREATE TABLE \"WebFrameDesireNextYear\" (\\n\"Resp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>table</td>\n",
       "      <td>MiscTechWorkedWith</td>\n",
       "      <td>MiscTechWorkedWith</td>\n",
       "      <td>3769</td>\n",
       "      <td>CREATE TABLE \"MiscTechWorkedWith\" (\\n\"Responde...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>table</td>\n",
       "      <td>MiscTechDesireNextYear</td>\n",
       "      <td>MiscTechDesireNextYear</td>\n",
       "      <td>3853</td>\n",
       "      <td>CREATE TABLE \"MiscTechDesireNextYear\" (\\n\"Resp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>table</td>\n",
       "      <td>DevEnviron</td>\n",
       "      <td>DevEnviron</td>\n",
       "      <td>3977</td>\n",
       "      <td>CREATE TABLE \"DevEnviron\" (\\n\"Respondent\" INTE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>table</td>\n",
       "      <td>Containers</td>\n",
       "      <td>Containers</td>\n",
       "      <td>4152</td>\n",
       "      <td>CREATE TABLE \"Containers\" (\\n\"Respondent\" INTE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>table</td>\n",
       "      <td>SOVisitTo</td>\n",
       "      <td>SOVisitTo</td>\n",
       "      <td>4290</td>\n",
       "      <td>CREATE TABLE \"SOVisitTo\" (\\n\"Respondent\" INTEG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>table</td>\n",
       "      <td>SONewContent</td>\n",
       "      <td>SONewContent</td>\n",
       "      <td>4581</td>\n",
       "      <td>CREATE TABLE \"SONewContent\" (\\n\"Respondent\" IN...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>table</td>\n",
       "      <td>Gender</td>\n",
       "      <td>Gender</td>\n",
       "      <td>4885</td>\n",
       "      <td>CREATE TABLE \"Gender\" (\\n\"Respondent\" INTEGER,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>table</td>\n",
       "      <td>Sexuality</td>\n",
       "      <td>Sexuality</td>\n",
       "      <td>4924</td>\n",
       "      <td>CREATE TABLE \"Sexuality\" (\\n\"Respondent\" INTEG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>table</td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>5012</td>\n",
       "      <td>CREATE TABLE \"Ethnicity\" (\\n\"Respondent\" INTEG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>table</td>\n",
       "      <td>master</td>\n",
       "      <td>master</td>\n",
       "      <td>5111</td>\n",
       "      <td>CREATE TABLE \"master\" (\\n\"index\" INTEGER,\\n  \"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>index</td>\n",
       "      <td>ix_master_index</td>\n",
       "      <td>master</td>\n",
       "      <td>5114</td>\n",
       "      <td>CREATE INDEX \"ix_master_index\"ON \"master\" (\"in...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     type                    name                tbl_name  rootpage  \\\n",
       "0   table                EduOther                EduOther         2   \n",
       "1   table                 DevType                 DevType       741   \n",
       "2   table                 LastInt                 LastInt      1024   \n",
       "3   table              JobFactors              JobFactors      1355   \n",
       "4   table                WorkPlan                WorkPlan      1808   \n",
       "5   table           WorkChallenge           WorkChallenge      2088   \n",
       "6   table      LanguageWorkedWith      LanguageWorkedWith      2378   \n",
       "7   table  LanguageDesireNextYear  LanguageDesireNextYear      2638   \n",
       "8   table      DatabaseWorkedWith      DatabaseWorkedWith      2877   \n",
       "9   table  DatabaseDesireNextYear  DatabaseDesireNextYear      3028   \n",
       "10  table      PlatformWorkedWith      PlatformWorkedWith      3170   \n",
       "11  table  PlatformDesireNextYear  PlatformDesireNextYear      3341   \n",
       "12  table      WebFrameWorkedWith      WebFrameWorkedWith      3540   \n",
       "13  table  WebFrameDesireNextYear  WebFrameDesireNextYear      3655   \n",
       "14  table      MiscTechWorkedWith      MiscTechWorkedWith      3769   \n",
       "15  table  MiscTechDesireNextYear  MiscTechDesireNextYear      3853   \n",
       "16  table              DevEnviron              DevEnviron      3977   \n",
       "17  table              Containers              Containers      4152   \n",
       "18  table               SOVisitTo               SOVisitTo      4290   \n",
       "19  table            SONewContent            SONewContent      4581   \n",
       "20  table                  Gender                  Gender      4885   \n",
       "21  table               Sexuality               Sexuality      4924   \n",
       "22  table               Ethnicity               Ethnicity      5012   \n",
       "23  table                  master                  master      5111   \n",
       "24  index         ix_master_index                  master      5114   \n",
       "\n",
       "                                                  sql  \n",
       "0   CREATE TABLE \"EduOther\" (\\n\"Respondent\" INTEGE...  \n",
       "1   CREATE TABLE \"DevType\" (\\n\"Respondent\" INTEGER...  \n",
       "2   CREATE TABLE \"LastInt\" (\\n\"Respondent\" INTEGER...  \n",
       "3   CREATE TABLE \"JobFactors\" (\\n\"Respondent\" INTE...  \n",
       "4   CREATE TABLE \"WorkPlan\" (\\n\"Respondent\" INTEGE...  \n",
       "5   CREATE TABLE \"WorkChallenge\" (\\n\"Respondent\" I...  \n",
       "6   CREATE TABLE \"LanguageWorkedWith\" (\\n\"Responde...  \n",
       "7   CREATE TABLE \"LanguageDesireNextYear\" (\\n\"Resp...  \n",
       "8   CREATE TABLE \"DatabaseWorkedWith\" (\\n\"Responde...  \n",
       "9   CREATE TABLE \"DatabaseDesireNextYear\" (\\n\"Resp...  \n",
       "10  CREATE TABLE \"PlatformWorkedWith\" (\\n\"Responde...  \n",
       "11  CREATE TABLE \"PlatformDesireNextYear\" (\\n\"Resp...  \n",
       "12  CREATE TABLE \"WebFrameWorkedWith\" (\\n\"Responde...  \n",
       "13  CREATE TABLE \"WebFrameDesireNextYear\" (\\n\"Resp...  \n",
       "14  CREATE TABLE \"MiscTechWorkedWith\" (\\n\"Responde...  \n",
       "15  CREATE TABLE \"MiscTechDesireNextYear\" (\\n\"Resp...  \n",
       "16  CREATE TABLE \"DevEnviron\" (\\n\"Respondent\" INTE...  \n",
       "17  CREATE TABLE \"Containers\" (\\n\"Respondent\" INTE...  \n",
       "18  CREATE TABLE \"SOVisitTo\" (\\n\"Respondent\" INTEG...  \n",
       "19  CREATE TABLE \"SONewContent\" (\\n\"Respondent\" IN...  \n",
       "20  CREATE TABLE \"Gender\" (\\n\"Respondent\" INTEGER,...  \n",
       "21  CREATE TABLE \"Sexuality\" (\\n\"Respondent\" INTEG...  \n",
       "22  CREATE TABLE \"Ethnicity\" (\\n\"Respondent\" INTEG...  \n",
       "23  CREATE TABLE \"master\" (\\n\"index\" INTEGER,\\n  \"...  \n",
       "24  CREATE INDEX \"ix_master_index\"ON \"master\" (\"in...  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query= '''Select * from sqlite_master'''\n",
    "df1= pd.read_sql_query(query,conn)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "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>Respondent</th>\n",
       "      <th>DatabaseDesireNextYear</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>PostgreSQL</td>\n",
       "      <td>4328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>MongoDB</td>\n",
       "      <td>3649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>Redis</td>\n",
       "      <td>3331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>MySQL</td>\n",
       "      <td>3281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>Elasticsearch</td>\n",
       "      <td>2856</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Respondent DatabaseDesireNextYear  count\n",
       "0           9             PostgreSQL   4328\n",
       "1          16                MongoDB   3649\n",
       "2           9                  Redis   3331\n",
       "3           4                  MySQL   3281\n",
       "4          16          Elasticsearch   2856"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query='''SELECT *, COUNT(*) as count FROM DatabaseDesireNextYear GROUP BY DatabaseDesireNextYear ORDER BY count DESC Limit 5''' \n",
    "df2= pd.read_sql_query(query,conn)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Respondent', 'DatabaseDesireNextYear', 'count'], dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df2 = pd.read_sql_query(QUERY,conn)\n",
    "\n",
    "data = df2['count']\n",
    "labels = df2['DatabaseDesireNextYear']\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [8,8]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.pie(data, labels = labels, autopct='%1.1f%%', explode = (0.1, 0, 0, 0, 0), shadow = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "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>Respondent</th>\n",
       "      <th>LanguageDesireNextYear</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>JavaScript</td>\n",
       "      <td>6630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>HTML/CSS</td>\n",
       "      <td>5328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>Python</td>\n",
       "      <td>5239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>SQL</td>\n",
       "      <td>5012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>TypeScript</td>\n",
       "      <td>4088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Respondent LanguageDesireNextYear  count\n",
       "0           4             JavaScript   6630\n",
       "1           9               HTML/CSS   5328\n",
       "2          20                 Python   5239\n",
       "3           4                    SQL   5012\n",
       "4           9             TypeScript   4088"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query='''SELECT *, COUNT(*) as count FROM LanguageDesireNextYear GROUP BY LanguageDesireNextYear ORDER BY count DESC Limit 5''' \n",
    "df5= pd.read_sql_query(query,conn)\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df2 = pd.read_sql_query(QUERY,conn)\n",
    "\n",
    "data = df5['count']\n",
    "labels = df5['LanguageDesireNextYear']\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [8,8]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.pie(data, labels = labels, autopct='%1.1f%%', explode = (0.1, 0, 0, 0, 0), shadow = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "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>index</th>\n",
       "      <th>Respondent</th>\n",
       "      <th>CompTotal</th>\n",
       "      <th>ConvertedComp</th>\n",
       "      <th>WorkWeekHrs</th>\n",
       "      <th>CodeRevHrs</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11398.000000</td>\n",
       "      <td>11398.000000</td>\n",
       "      <td>1.058900e+04</td>\n",
       "      <td>1.058200e+04</td>\n",
       "      <td>11276.000000</td>\n",
       "      <td>8972.000000</td>\n",
       "      <td>11111.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5698.500000</td>\n",
       "      <td>12490.392437</td>\n",
       "      <td>7.570477e+05</td>\n",
       "      <td>1.315967e+05</td>\n",
       "      <td>42.064606</td>\n",
       "      <td>4.781071</td>\n",
       "      <td>30.779318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3290.463519</td>\n",
       "      <td>7235.461999</td>\n",
       "      <td>9.705598e+06</td>\n",
       "      <td>2.947865e+05</td>\n",
       "      <td>24.672741</td>\n",
       "      <td>4.567060</td>\n",
       "      <td>7.393680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2849.250000</td>\n",
       "      <td>6264.250000</td>\n",
       "      <td>2.500000e+04</td>\n",
       "      <td>2.686800e+04</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5698.500000</td>\n",
       "      <td>12484.000000</td>\n",
       "      <td>6.500000e+04</td>\n",
       "      <td>5.774500e+04</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8547.750000</td>\n",
       "      <td>18784.750000</td>\n",
       "      <td>1.200000e+05</td>\n",
       "      <td>1.000000e+05</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11397.000000</td>\n",
       "      <td>25142.000000</td>\n",
       "      <td>7.000000e+08</td>\n",
       "      <td>2.000000e+06</td>\n",
       "      <td>1012.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              index    Respondent     CompTotal  ConvertedComp   WorkWeekHrs  \\\n",
       "count  11398.000000  11398.000000  1.058900e+04   1.058200e+04  11276.000000   \n",
       "mean    5698.500000  12490.392437  7.570477e+05   1.315967e+05     42.064606   \n",
       "std     3290.463519   7235.461999  9.705598e+06   2.947865e+05     24.672741   \n",
       "min        0.000000      4.000000  0.000000e+00   0.000000e+00      3.000000   \n",
       "25%     2849.250000   6264.250000  2.500000e+04   2.686800e+04     40.000000   \n",
       "50%     5698.500000  12484.000000  6.500000e+04   5.774500e+04     40.000000   \n",
       "75%     8547.750000  18784.750000  1.200000e+05   1.000000e+05     43.000000   \n",
       "max    11397.000000  25142.000000  7.000000e+08   2.000000e+06   1012.000000   \n",
       "\n",
       "        CodeRevHrs           Age  \n",
       "count  8972.000000  11111.000000  \n",
       "mean      4.781071     30.779318  \n",
       "std       4.567060      7.393680  \n",
       "min       0.000000     16.000000  \n",
       "25%       2.000000     25.000000  \n",
       "50%       4.000000     29.000000  \n",
       "75%       5.000000     35.000000  \n",
       "max      99.000000     99.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df.describe()"
   ]
  },
  {
   "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": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "QUERY = \"\"\"\n",
    "SELECT WorkWeekHrs, CodeRevHrs, Age\n",
    "FROM master\n",
    "WHERE Age BETWEEN 30 AND 35\n",
    "\"\"\"\n",
    "df3=pd.read_sql_query(QUERY,conn)\n",
    "\n",
    "# Plotting\n",
    "work_week = df['WorkWeekHrs'].median()\n",
    "code_rev = df['CodeRevHrs'].median()\n",
    "\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [8,8]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.bar('Age 30 - 35', work_week, label='WorkWeekHrs')\n",
    "ax.bar('Age 30 - 35', code_rev, bottom=work_week, label='CodeRevHrs')\n",
    "ax.legend()\n",
    "\n",
    "plt.show()\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": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# your code goes here\n",
    "\n",
    "QUERY= \"\"\"\n",
    "SELECT ConvertedComp, Age FROM master Where Age Between 45 and 60\"\"\"\n",
    "df4= pd.read_sql_query(QUERY,conn)\n",
    "median_conv=df4.groupby('Age')['ConvertedComp'].median().reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(median_conv['Age'],median_conv['ConvertedComp'] ,  marker='o', linestyle='-', color='b', label='Series 1')\n",
    "#plt.plot(x, y2, marker='s', linestyle='--', color='r', label='Series 2')\n",
    "\n",
    "# Adding titles and labels\n",
    "plt.title('Line Chart ')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Converted Comp')\n",
    "plt.legend()\n",
    "\n",
    "# Show plot\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0kSSlpqbq+eefV2xsrEqUKKFWrVpp4sSJWd5vyZIlevLJJ+3HPXv2lCSNGDFCI0eOdPCnAQAAgKwkJ0tHjmQMW1dfnzx563uUKXMtcN34Z1BQxnUQUlOlb7+99WM9N5l85RQsNlt2d6XCrSQlJcnHx0eJiYkqUaJEumuXL1/WoUOHVLlyZXl4eOR6LWlpaUpKSlKJEiVU5E4fIisk8vq/kbOxWq1avny52rRpI1dXV7PLQT5An4Gj6DNwFH3GeVy5YszSuvE5rqt/Hj9+671uS5TIPGxVqmR83fDX52y5usqilPljPWausnizbHA9RsgAAACAQi4tzQhVWU0pPHr01qsVenllPcJVubJUsuS1oJRTzHisJ6cRyAAAAIACzmYzpg1mtWjG339LVuvN7+Hmdm0062rIuj54lSuX84ErO64+1rN27RWtWLFLrVs/oGbNiubZgnd3ikAGAAAA5HM2m3TmTOZh6+rry5dvfo+iRaWKFbMe5fLzu/PtlHKLi4vUtKlNFy4cU9OmtfJNGJMIZAAAAEC+kJiYddg6fNhYOv5mri5ykdVzXHfdZYQy5C1+5HmMNVScF/9tAACAmS5cyHpK4eHDUkLCre/h75/581uVKkmBgca0QzgXAlkecfn/cdOUlBR5enqaXA0yk5KSIunafysAAICcdPmy8axWVqHr1Klb36Ns2cyf36pc2ZhuyF8z8x8CWR4pWrSovLy8dOrUKbm6uub6UvRpaWlKSUnR5cuXWfY+G9LS0nTq1Cl5eXmpKGP1AADgNlitxmqEWT3HdeLEre9RsuTNl4YvViwXPwBMwd8884jFYpG/v78OHTqkv//+O9ffz2az6dKlS/L09JTFjOVu8qEiRYqoYsWK/LwAAECmUlONTYizeo4rNtZYPv5mvL2zHuGqVMkIZChcCGR5yM3NTVWqVLFPjctNVqtVGzZsUJMmTdhIMZvc3NwYTQQAoBBLSzOWhs9q8+MjR4wNkm/Gw+PmS8OXKWPO0vBwXgSyPFakSBF5eHjk+vu4uLjoypUr8vDwIJABAIB8JzVVWr/eog0b7pK3t0XNmumOlzK32aR//sl60YzDh6Xk5Jvfw9XVeFYrq1EuX18CFxxDIAMAAIBTWbRIGjxYio0tKqmeJkwwlmv/+GNjE+CbSUi4+UqFFy7c/PuLFDFWI8xqSmFAwJ0HQ+B6BDIAAAA4jUWLpK5djdGs6x07ZpyfM0eqWTPr57gSE29+f4vFCFVZbX5coYIxCgbkFQIZAAAAnEJqqjEyltnWoFfPPf74re9Tvnzm+3BdXRre3T0nqwbuDIEMAAAATmHjRmOlwlspXlyqUiXz57gqVZK8vHK5UCAHEcgAAADgFI4fz167qVOlXr1ytxYgr7DGNwAAAEyXmGgErezw98/dWoC8RCADAACAqXbskGrXljZsuHk7i8VYAfHhh/OmLiAvEMgAAABgCpvNWMq+USNjlcTKlaX33zeC1417eV09/ugjlp1HwUIgAwAAQJ5LSDD2FBsyRLJapS5dpJ07pVdflb7/XrrrrvTtK1Qwzt9qHzIgv2FRDwAAAOSpLVuknj2lv/+W3NykCROkgQOvjYJ17ix17CitXXtFK1bsUuvWD6hZs6KMjKFAIpABAAAgT6SlGeFr+HDpyhXpnnukb7+V6tTJ2NbFRWra1KYLF46padNahDEUWAQyAAAA5LrTp6W+faVly4zjHj2kadOkEiXMrQswG8+QAQAAIFdt2iQ98IARxtzdjeXtv/6aMAZIBDIAAADkkrQ0aexY6ZFHpNhYqWpVads2acCAjKsoAoUVUxYBAACQ4+LjpSeekH7+2Th+/HHp88+lYsXMrQtwNgQyAAAA5Kj166VevaQTJyRPT2nyZOnJJxkVAzLDlEUAAADkiNRU6Z13pEcfNcJYtWrS9u3SU08RxoCsmBrIRo4cKYvFku7Lz8/Pfr1fv34Zrjdo0CDdPZKTk/Xiiy+qbNmy8vb2VocOHRQbG5uuTUJCgsLDw+Xj4yMfHx+Fh4fr7Nmz6docOXJE7du3l7e3t8qWLatBgwYpJSUl1z47AABAQRIXJ4WFSSNGGM+OPfmk8bxY9epmVwY4N9OnLFavXl2rVq2yH7vcsMlEq1at9NVXX9mP3dzc0l0fMmSIli5dqgULFqhMmTIaOnSo2rVrp5iYGPu9evfurdjYWEVGRkqSBgwYoPDwcC1dulSSlJqaqrZt26pcuXLatGmTTp8+rb59+8pms2nSpEm58rkBAAAKitWrpT59pJMnJW9v41mx8HCzqwLyB9MDWdGiRdONit3I3d09y+uJiYmaMWOG5syZoxYtWkiS5s6dq8DAQK1atUphYWHav3+/IiMjtWXLFtWvX1+SNH36dDVs2FAHDhxQcHCwoqKitG/fPh09elQBAQGSpPHjx6tfv34aPXq0SrAmKwAAQAZXrhhTFEeNkmw2KSTE2Oj5vvvMrgzIP0wPZAcPHlRAQIDc3d1Vv359jRkzRnfffbf9+rp161S+fHmVLFlSTZs21ejRo1W+fHlJUkxMjKxWq0JDQ+3tAwICVKNGDW3evFlhYWGKjo6Wj4+PPYxJUoMGDeTj46PNmzcrODhY0dHRqlGjhj2MSVJYWJiSk5MVExOjZs2aZVp7cnKykpOT7cdJSUmSJKvVKqvVmjM/oNt09f3NrgP5B30GjqLPwFH0mYLl+HEpPNxFGzcaT8A8/XSqxo9Pk6enlFP/iekzcISz9Zfs1mFqIKtfv75mz56tqlWr6uTJkxo1apQaNWqkvXv3qkyZMmrdurW6deumoKAgHTp0SG+++aYeffRRxcTEyN3dXXFxcXJzc1OpUqXS3dfX11dxcXGSpLi4OHuAu1758uXTtfH19U13vVSpUnJzc7O3yczYsWP19ttvZzgfFRUlLy8vh38euWHlypVml4B8hj4DR9Fn4Cj6TP7366/lNHFiXSUlucrD44qef36XHn74mNauzZ33o8/AEc7SXy5evJitdqYGstatW9tfh4SEqGHDhrrnnns0a9Ysvfzyy+rRo4f9eo0aNVSvXj0FBQVp2bJl6ty5c5b3tdlssly3lI8lk2V9bqfNjYYPH66XX37ZfpyUlKTAwECFhoaaPs3RarVq5cqVatmypVxdXU2tBfkDfQaOos/AUfSZ/O/KFWnEiCIaN854Tr9WLZvmz7epSpVakmrl+PvRZ+AIZ+svV2fP3YrpUxav5+3trZCQEB08eDDT6/7+/goKCrJf9/PzU0pKihISEtKNksXHx6tRo0b2NidPnsxwr1OnTtlHxfz8/LR169Z01xMSEmS1WjOMnF3P3d1d7u7uGc67uro6RSeQnKsW5A/0GTiKPgNH0Wfyp6NHjb3FfvnFOB44UBo/3iIPj9z/b0mfgSOcpb9ktwan2ocsOTlZ+/fvl7+/f6bXT58+raNHj9qv161bV66urumGJU+cOKE9e/bYA1nDhg2VmJiobdu22dts3bpViYmJ6drs2bNHJ06csLeJioqSu7u76tatm+OfEwAAID9Ztkx64AEjjJUoIX33nfTpp5KHh9mVAfmfqYFs2LBhWr9+vQ4dOqStW7eqa9euSkpKUt++fXX+/HkNGzZM0dHROnz4sNatW6f27durbNmyeuyxxyRJPj4+6t+/v4YOHarVq1fr119/1eOPP66QkBD7qov333+/WrVqpYiICG3ZskVbtmxRRESE2rVrp+DgYElSaGioqlWrpvDwcP36669avXq1hg0bpoiICNOnHgIAAJjFapWGDZPatZPOnJHq1ZN+/VXq2tXsyoCCw9Qpi7GxserVq5f++ecflStXTg0aNNCWLVsUFBSkS5cuaffu3Zo9e7bOnj0rf39/NWvWTN98842KFy9uv8fEiRNVtGhRde/eXZcuXVLz5s01c+bMdPuZzZs3T4MGDbKvxtihQwdNnjzZft3FxUXLli3TwIED1bhxY3l6eqp379768MMP8+6HAQAA4EQOH5Z69pSuPtUxeLD0/vtSJk9rALgDpgayBQsWZHnN09NTP//88y3v4eHhoUmTJt10A+fSpUtr7ty5N71PxYoV9dNPP93y/QAAAAq6H36QnnxSOntWKllS+uorqVMnc2sCCiqneoYMAAAA5klOloYMkR57zAhj9etLu3YRxoDcRCADAACA/vc/qXFj6eOPjeNhw6SNG6WgIHPrAgo6p1r2HgAAAHnv+++l/v2lpCSpdGlp9mypbVuzqwIKB0bIAAAACqnLl6Xnn5e6dTPCWOPGxhRFwhiQdwhkAAAAhdDBg1LDhtJnnxnHw4dL69ZJgYGmlgUUOkxZBAAAKGS+/loaMEA6f14qV06aM0cKCzO7KqBwYoQMAACgkLh0yQhivXsbYaxpU2OKImEMMA+BDAAAoBD473+lhx6Spk+XLBbpzTelVaukgACzKwMKN6YsAgAAFHCzZ0vPPSddvCj5+kpz50otWphdFQCJETIAAIAC68IF6cknpb59jTDWvLkxRZEwBjgPAhkAAEABtHevMUVx5kypSBHpnXekn3+W/PzMrgzA9ZiyCAAAUIDYbNJXX0kvvGAs4uHvb6yq2LSp2ZUByAyBDAAAoIA4f1569llp3jzjOCzMeH6sfHlz6wKQNaYsAgAAFAC//SbVrWuEMRcXaexYaflywhjg7BghAwAAyMdsNmnaNGnwYCk5WapQwZii+K9/mV0ZgOwgkAEAAORTSUnGRs/ffGMct20rzZollSljbl0Aso8piwAAAPnQzp1SnTpGGCtaVPrwQ2nJEsIYkN8wQgYAAJCP2GzSp59KQ4dKKSlSUJC0YIHUoIHZlQG4HQQyAACAfOLsWal/f2nRIuO4Y0djiftSpUwtC8AdYMoiAABAPrBtm1S7thHGXF2ljz6SFi8mjAH5HYEMAADAidls0sSJxqqJhw9LlStLv/xirKposZhdHYA7xZRFAAAAJ3XmjPTkk8ZiHZLUtav0xReSj4+5dQHIOYyQAQAAOKHoaOmBB4ww5uZmLOTx7beEMaCgIZABAAA4kbQ06YMPpIcflo4ele69V9qyRRo4kCmKQEHElEUAAAAn8c8/0hNPSCtWGMe9eklTp0rFi5tbF4DcwwgZAACAE9i40ZiiuGKF5OEhTZsmzZtHGAMKOgIZAACAidLSpDFjpGbNpGPHpOBgaetWKSKCKYpAYcCURQAAAJPEx0uPPy6tXGkch4dLn30mFStmbl0A8g6BDAAAwARr10q9e0txcZKnpxHE+vUzuyoAeY0piwAAAHkoNVV6+22pRQsjjFWrJu3YQRgDCitGyAAAAPLIiRPGFMU1a4zjp56SJk2SvLzMrQuAeQhkAAAAeWDlSiOMxcdL3t7SlCnGMYDCjSmLAAAAuejKFek//5HCwowwVrOmMUWRMAZAYoQMAAAg1xw7ZmzuvHGjcfzMM9LEicYiHgAgEcgAAAByxYoV0hNPSP/8Y2zuPG2a1LOn2VUBcDZMWQQAAMhBVqv0739LbdoYYax2bWnnTsIYgMwxQgYAAJBDjhwxpihu3mwcv/CCNG6c5OFhbl0AnBeBDAAAIAcsXSr17SslJEg+PtKMGVKXLmZXBcDZMWURAADgDqSkSEOHSh06GGHswQeNKYqEMQDZwQgZAADAbTp0yHg2bNs243jIEOn99yU3N1PLApCPEMgAAABuw+LF0pNPSomJUqlS0syZxigZADiCKYsAAAAOSE6WBg2SOnc2wliDBtKvvxLGANweAhkAAEA2/fWX1LixNGmScfzKK9KGDVJQkLl1Aci/mLIIAACQDd9+Kz39tHTunFSmjDRrltS2rdlVAcjvGCEDAAC4icuXpeeek3r0MMLYv/4l7dpFGAOQMwhkAAAAWfjjD+MZsSlTJItFev11ae1aqUIFsysDUFAwZREAACAT8+ZJzzwjXbgglSsnzZ0rhYaaXRWAgoYRMgAAgOtcvGg8K/b440YYe+QRY4oiYQxAbiCQAQAA/L/9+6X69aUZM4wpiiNGSKtWSQEBZlcGoKBiyiIAAICMVRMHDjRGyPz8jCmLjz5qdlUACjpGyAAAQKF24YLUt6/Ur58Rxlq0MKYoEsYA5AUCGQAAKLR275bq1ZNmz5aKFJFGjZIiIyVfX7MrA1BYMGURAAAUOjab8ZzYiy8a+4wFBEhffy01aWJ2ZQAKG1NHyEaOHCmLxZLuy8/PT5JktVr173//WyEhIfL29lZAQICeeOIJHT9+PN09HnnkkQz36NmzZ7o2CQkJCg8Pl4+Pj3x8fBQeHq6zZ8+ma3PkyBG1b99e3t7eKlu2rAYNGqSUlJRc/fwAACDvnTtnrKAYEWGEsVatjCmKhDEAZjB9hKx69epatWqV/djFxUWSdPHiRe3cuVNvvvmmatWqpYSEBA0ZMkQdOnTQjh070t0jIiJC77zzjv3Y09Mz3fXevXsrNjZWkZGRkqQBAwYoPDxcS5culSSlpqaqbdu2KleunDZt2qTTp0+rb9++stlsmjRpUq58bgAAkPd27ZK6d5cOHpRcXKTRo6VXXjGmKwKAGUwPZEWLFrWPil3Px8dHK1euTHdu0qRJeuihh3TkyBFVrFjRft7LyyvTe0jS/v37FRkZqS1btqh+/fqSpOnTp6thw4Y6cOCAgoODFRUVpX379uno0aMK+P91bcePH69+/fpp9OjRKlGiRE59XAAAYAKbTZoyRXrpJSk5WapQQVqwQGrc2OzKABR2pgeygwcPKiAgQO7u7qpfv77GjBmju+++O9O2iYmJslgsKlmyZLrz8+bN09y5c+Xr66vWrVtrxIgRKl68uCQpOjpaPj4+9jAmSQ0aNJCPj482b96s4OBgRUdHq0aNGvYwJklhYWFKTk5WTEyMmjVrlmk9ycnJSk5Oth8nJSVJMqZbWq3W2/p55JSr7292Hcg/6DNwFH0GjjKrzyQmSs8956LvvzeGwdq0SdOMGakqU0ai+zo3fs/AEc7WX7Jbh6mBrH79+po9e7aqVq2qkydPatSoUWrUqJH27t2rMmXKpGt7+fJlvfbaa+rdu3e6Eas+ffqocuXK8vPz0549ezR8+HD99ttv9tG1uLg4lS9fPsN7ly9fXnFxcfY2vjcsp1SqVCm5ubnZ22Rm7NixevvttzOcj4qKkpeXV/Z/ELnoxlFG4FboM3AUfQaOyss+8+efPho37kGdPOktF5c0PfHEPnXo8Je2bs2zEpAD+D0DRzhLf7l48WK22pkayFq3bm1/HRISooYNG+qee+7RrFmz9PLLL9uvWa1W9ezZU2lpafrss8/S3SMiIsL+ukaNGqpSpYrq1aunnTt3qk6dOpIki8WS4b1tNlu689lpc6Phw4enqzMpKUmBgYEKDQ01fZqj1WrVypUr1bJlS7m6uppaC/IH+gwcRZ+Bo/Kyz9hs0mefFdHrrxdRSopFQUE2zZuXpoceCpYUnKvvjZzD7xk4wtn6y9XZc7di+pTF63l7eyskJEQHDx60n7NarerevbsOHTqkNWvW3DLo1KlTR66urjp48KDq1KkjPz8/nTx5MkO7U6dO2UfF/Pz8tPWGfypLSEiQ1WrNMHJ2PXd3d7m7u2c47+rq6hSdQHKuWpA/0GfgKPoMHJXbfSYhQerfX1q82Dju1En68kuLSpVyqr/2wAH8noEjnKW/ZLcGp1pTKDk5Wfv375e/v7+ka2Hs4MGDWrVqVYZpjJnZu3evrFar/R4NGzZUYmKitm3bZm+zdetWJSYmqlGjRvY2e/bs0YkTJ+xtoqKi5O7urrp16+bkRwQAALlo61apTh0jjLm5SZ98Ii1aJJUqZXZlAJA5U/+paNiwYWrfvr0qVqyo+Ph4jRo1SklJSerbt6+uXLmirl27aufOnfrpp5+Umppqf56rdOnScnNz019//aV58+apTZs2Klu2rPbt26ehQ4eqdu3aavz/yybdf//9atWqlSIiIjR16lRJxrL37dq1U3CwMWUhNDRU1apVU3h4uMaNG6czZ85o2LBhioiIMH3qIQAAuDWbTZo4Ufr3v6UrV6S775a+/Vbi31UBODtTR8hiY2PVq1cvBQcHq3PnznJzc9OWLVsUFBSk2NhYLVmyRLGxsXrggQfk7+9v/9q8ebMkyc3NTatXr1ZYWJiCg4M1aNAghYaGatWqVfb9zCRjFcaQkBCFhoYqNDRUNWvW1Jw5c+zXXVxctGzZMnl4eKhx48bq3r27OnXqpA8//DDPfyYAAMAxp09LHTpIQ4caYaxbN2nnTsIYgPzB1BGyBQsWZHmtUqVKstlsN/3+wMBArV+//pbvU7p0ac2dO/embSpWrKiffvrplvcCAADOY/NmqWdP6ehRyd1d+ugj6ZlnpJusyQUATsWpniEDAADIjrQ06f33pSZNjDBWpYq0ZYv07LOEMQD5C8sNAQCAfOXUKemJJ6TISOO4d29pyhSpeHFz6wKA20EgAwAA+caGDVKvXtLx45KHhzRpkrHEPaNiAPIrpiwCAACnl5oqjRolNWtmhLH77pO2bZOefpowBiB/Y4QMAAA4tZMnpccfl1atMo779pU+/VTy9ja3LgDICQQyAADgtNasMZ4RO3lS8vKSPvvMCGQAUFAwZREAADid1FRpxAipRQsjjFWvLm3fThgDUPAwQgYAAJzK8eNSnz7SunXG8dNPSx9/bIyQAUBBQyADAABOIyrKeF7s1CmpWDFp6lRjyiIAFFRMWQQAAKa7ckV64w2pVSsjjNWqJcXEEMYAFHyMkAEAAFPFxhp7i23aZBw/+6w0caKxzxgAFHQEMgAAYJrly6UnnpBOn5aKF5e++ELq3t3sqgAg7zBlEQAA5LrUVGn9eos2bLhL69dbdPmy9OqrUtu2RhirU0fauZMwBqDwYYQMAADkqkWLpMGDpdjYopLqacIEyc1NSkkxrr/4ojRunOTubmqZAGAKAhkAAMg1ixZJXbtKNlv681fD2CuvSB98kPd1AYCzYMoiAADIFampxsjYjWHsKotFWrDAaAcAhRWBDAAA5IqoKGMFxazYbNLRo9LGjXlXEwA4G6YsAgCAHJOSYgSx+fOlhQuz9z0nTuRuTQDgzAhkAADgjqSlGXuIzZ8vffeddOaMY9/v7587dQFAfkAgAwAADrPZpF27jBC2YEH6qYl+flKPHsZX9+7SsWOZP0dmsUgVKkgPP5xnZQOA0yGQAQCAbPvzT+nrr40g9t//Xjvv4yN16SL17i098ojk4mKc//hjY5VFiyV9KLNYjD8/+uhaWwAojAhkAADgpk6ckL791ghh27ZdO+/uLrVvb4Sw1q0lD4+M39u5s/T991f3Ibt2vkIFI4x17pzr5QOAUyOQAQCADBITjT3E5s+X1qwxnhOTpCJFpBYtjBDWqZMxMnYrnTtLHTtKa9de0YoVu9S69QNq1qwoI2MAIAIZAAD4f5cuScuXGyFs2TIpOfnatQYNjBDWvbvk6+v4vV1cpKZNbbpw4ZiaNq1FGAOA/0cgAwCgELtyxRgBmz/fGBE7d+7atWrVpD59pJ49pbvvNq9GACjICGQAABQyNpu0dasRwr75RoqPv3atYkWpVy9jNCwk5NriGwCA3EEgAwCgkNi3zwhh8+dLhw5dO1+mjDEVsXdvqVEj4zkxAEDeIJABAFCA/f23sU/Y119Lv/127by3t7EoR+/eUsuWkquraSUCQKFGIAMAoID55x/pu++MkbBNm66dd3U1lqfv3Vtq184IZQAAcxHIAAAoAM6fl3780QhhUVHGYh2S8QxY06ZGCOvSRSpd2tw6AQDpEcgAAMinUlKkn382QtiPPxrL1l9Vp44Rwnr0MDZhBgA4JwIZAAD5SFqatGGDEcK+/15KSLh27d57jWXqe/WSgoPNqxEAkH0EMgAAnJzNJv36qxHCFiyQjh27ds3f39gnrHdvqW5dlqkHgPyGQAYAgJM6eNBYHXH+fOnAgWvnfXykrl2NENa0qeTiYl6NAIA7QyADAMCJnDhhbNY8f760ffu18x4eUvv2Rghr3VpydzevRgBAziGQAQBgsrNnpYULjdGwNWuMKYqSMfLVooURwjp1kkqUMLNKAEBuIJABAGCCS5ekn34yRsKWLzdWTLyqUSMjhHXrJpUvb16NAIDcRyADACCPXLkirV5thLDFi6Vz565dq17dWCGxZ0+pcmXzagQA5C0CGQAAuchmk7ZsMULYN99Ip05duxYUZCxR37u3FBJiXo0AAPMQyAAAyAV79hgh7OuvpcOHr50vW9bYrLlXL6lhQ6lIEdNKBAA4AQIZAAA55PBhY5+w+fOl3buvnS9WTHrsMWMkrHlzydXVtBIBAE6GQAYAwB04dUr67jsjhP3yy7Xzrq5SmzZGCGvXTvLyMq9GAIDzIpABAOCgc+ekH380QlhUlJSaapy3WKRHHjFCWJcuUqlSppYJAMgHCGQAAGRDcrIUGWk8E7ZkibFs/VX16hkhrHt36a67zKsRAJD/EMgAAMhCaqq0YYMxEvb998YGzldVqWIsU9+rl1S1qmklAgDyOQIZAADXsdmknTuNELZggXT8+LVrAQHGPmG9e0t16hhTFAEAuBMEMgAAJP3xhzEdcf584/VVJUtKXbsaIaxJE8nFxbQSAQAF0G0FstTUVC1evFj79++XxWLRfffdp06dOqloUfIdACD/OHbM2Kx5/nwpJubaeU9PqUMHYzpiq1aSu7t5NQIACjaHE9SePXvUsWNHxcXFKTg4WJL0xx9/qFy5clqyZIlCQkJyvEgAAHJKQoK0cKERwtatM6YoSsbIV2ioMRLWsaNUvLipZQIACgmHA9nTTz+t6tWra8eOHSr1/+v5JiQkqF+/fhowYICio6NzvEgAAO7ExYvSTz8ZIWz5cslqvXatcWMjhHXrJpUrZ16NAIDCyeFA9ttvv6ULY5JUqlQpjR49Wg8++GCOFgcAwO2yWqVVq4znwhYvls6fv3YtJMQIYT17SpUqmVYiAACOB7Lg4GCdPHlS1atXT3c+Pj5e9957b44VBgCAo9LSpOhoYyTs22+lf/65dq1SJSOE9eol1ahhWokAAKTjcCAbM2aMBg0apJEjR6pBgwaSpC1btuidd97R+++/r6SkJHvbEiVK5FylAABkYfduI4R9/bX099/XzpcrJ/XoYQSxBg1Yph4A4HyKOPoN7dq10759+9S9e3cFBQUpKChI3bt31549e9S+fXuVKlVKJUuWTDelMSsjR46UxWJJ9+Xn52e/brPZNHLkSAUEBMjT01OPPPKI9u7dm+4eycnJevHFF1W2bFl5e3urQ4cOio2NTdcmISFB4eHh8vHxkY+Pj8LDw3X2+t09JR05ckTt27eXt7e3ypYtq0GDBiklJcXRHw8AII8cPiyNHWtMP6xZU3rvPSOMFSsmPfGEFBlp7CE2aZLUsCFhDADgnBweIVu7dm2OFlC9enWtWrXKfuxy3QYvH3zwgSZMmKCZM2eqatWqGjVqlFq2bKkDBw6o+P8vfzVkyBAtXbpUCxYsUJkyZTR06FC1a9dOMTEx9nv17t1bsbGxioyMlCQNGDBA4eHhWrp0qSRjGf+2bduqXLly2rRpk06fPq2+ffvKZrNp0qRJOfp5AQC3Lz5e+u47YzRs8+Zr593cpLZtjemI7doZy9YDAJAfOBzImjZtmrMFFC2ablTsKpvNpo8++khvvPGGOnfuLEmaNWuWfH19NX/+fD3zzDNKTEzUjBkzNGfOHLVo0UKSNHfuXAUGBmrVqlUKCwvT/v37FRkZqS1btqh+/fqSpOnTp6thw4Y6cOCAgoODFRUVpX379uno0aMKCAiQJI0fP179+vXT6NGjs5x6mZycrOTkZPvx1emaVqtV1uuX8DLB1fc3uw7kH/QZOCqv+kxSkvTjjxZ9800RrV5tUWqqMdRlsdjUrJlNPXumqVMnm0qWvL62XC0Jt4nfM3AUfQaOcLb+kt06bmsn58uXL+v3339XfHy80tLS0l3r0KGDQ/c6ePCgAgIC5O7urvr162vMmDG6++67dejQIcXFxSk0NNTe1t3dXU2bNtXmzZv1zDPPKCYmRlarNV2bgIAA1ahRQ5s3b1ZYWJiio6Pl4+NjD2OS1KBBA/n4+Gjz5s0KDg5WdHS0atSoYQ9jkhQWFqbk5GTFxMSoWbNmmdY+duxYvf322xnOR0VFycvLy6GfQ25ZuXKl2SUgn6HPwFG50Wes1iKKiSmvDRsqaMcOP6WkXJs9UaVKgh5+OFb/+tdxlS59WVL60TI4P37PwFH0GTjCWfrLxYsXs9XO4UAWGRmpJ554Qv9cv3TV/7NYLEpNTc32verXr6/Zs2eratWqOnnypEaNGqVGjRpp7969iouLkyT5+vqm+x5fX1/9/f9PbMfFxcnNzS3D82q+vr7274+Li1P58uUzvHf58uXTtbnxfUqVKiU3Nzd7m8wMHz5cL7/8sv04KSlJgYGBCg0NNX1BE6vVqpUrV6ply5ZydXU1tRbkD/QZOCqn+0xqqrRhg0ULFhTRokUWJSZee+iralVjJKxHjzRVqVJM0n3//4X8hN8zcBR9Bo5wtv5y/WKHN+NwIHvhhRfUrVs3vfXWWxlCjKNat25tfx0SEqKGDRvqnnvu0axZs+wrOFpueArbZrNlOHejG9tk1v522tzI3d1d7u7uGc67uro6RSeQnKsW5A/0GTjqTvqMzSbt2GGsjrhggXTixLVrd91l7BPWu7dUu7ZFFouLJJcs74X8g98zcBR9Bo5wlv6S3RocDmTx8fF6+eWX7ziMZcbb21shISE6ePCgOnXqJMkYvfL390/3/lff28/PTykpKUpISEg3ShYfH69GjRrZ25w8eTLDe506dSrdfbZu3ZruekJCgqxWa658TgAo7A4cMBbmmD9f+vPPa+dLlZK6dTNC2MMPS0UcXgsYAID8xeH/q+vatavWrVuXC6UYi2Ts379f/v7+qly5svz8/NLNAU1JSdH69evtYatu3bpydXVN1+bEiRPas2ePvU3Dhg2VmJiobdu22dts3bpViYmJ6drs2bNHJ677p9moqCi5u7urbt26ufJZAaCwiY2Vxo+X6taV7rtPeucdI4x5ehojYUuWSHFx0tSpUtOmhDEAQOHg8AjZ5MmT1a1bN23cuFEhISEZhuIGDRqU7XsNGzZM7du3V8WKFRUfH69Ro0YpKSlJffv2lcVi0ZAhQzRmzBhVqVJFVapU0ZgxY+Tl5aXevXtLknx8fNS/f38NHTpUZcqUUenSpTVs2DCFhITYV128//771apVK0VERGjq1KmSjGXv27Vrp+DgYElSaGioqlWrpvDwcI0bN05nzpzRsGHDFBERYfqzYADgjFJTpfXrLdqw4S55e1vUrJnkkslswjNnpIULjZGw9euNKYqS0TYszBgJ69jR2DsMAIDCyOFANn/+fP3888/y9PTUunXrMjyH5Uggi42NVa9evfTPP/+oXLlyatCggbZs2aKgoCBJ0quvvqpLly5p4MCBSkhIUP369RUVFWXfg0ySJk6cqKJFi6p79+66dOmSmjdvrpkzZ6bbz2zevHkaNGiQfTXGDh06aPLkyfbrLi4uWrZsmQYOHKjGjRvL09NTvXv31ocffujojwcACrxFi6TBg6XY2KKS6mnCBKlCBenjj6XOnaWLF6WlS40QtmJF+iXoH37Y2Cusa1epXDnTPgIAAE7DYrNd/ffK7PHz89OgQYP02muvqQjzSdJJSkqSj4+PEhMTTR9Zs1qtWr58udq0aeMUDzXC+dFnkB2LFhlh6sb/57BYjHNNmkgxMdKFC9eu1apljIT17ClVrJi39cK58HsGjqLPwBHO1l+ymw0cHiFLSUlRjx49CGMAUMikphojY5n9M97Vcxs2GH9WrmyEsF69pOrV865GAADyG4dTVd++ffXNN9/kRi0AACe2caOxMMetfPqp9Ndf0qhRhDEAAG7F4RGy1NRUffDBB/r5559Vs2bNDMOBEyZMyLHiAADO4/o9wm6mVCljCiMAALg1hwPZ7t27Vbt2bUnSnj170l271YbNAID867otIXOkHQAAuI1Atnbt2tyoAwDg5CpVMparT03N/LrFYqy2+PDDeVoWAAD52h2tzBEbG6tjx47lVC0AACcVFyeFhl4LYzdOiLh6/NFHme9HBgAAMudwIEtLS9M777wjHx8fBQUFqWLFiipZsqTeffddpaWl5UaNAAATnT4ttWwpHTwoBQVJU6dKd92Vvk2FCtL33xv7kAEAgOxzeMriG2+8oRkzZui9995T48aNZbPZ9Msvv2jkyJG6fPmyRo8enRt1AgBMkJQktWol7dljPBu2apV0771S//7S2rVXtGLFLrVu/YCaNSvKyBgAALfB4UA2a9YsffHFF+rQoYP9XK1atXTXXXdp4MCBBDIAKCAuXJDatpV27JDKlr0WxiRjWmLTpjZduHBMTZvWIowBAHCbHJ6yeObMGd13330Zzt933306c+ZMjhQFADDX5cvSY49JmzZJPj5SVJRUrZrZVQEAUPA4HMhq1aqlyZMnZzg/efJk1apVK0eKAgCYx2qVevSQVq6UvL2lFSuk/9/tBAAA5DCHpyx+8MEHatu2rVatWqWGDRvKYrFo8+bNOnr0qJYvX54bNQIA8khqqhQeLi1ZInl4SEuXSg0bml0VAAAFl8MjZE2bNtUff/yhxx57TGfPntWZM2fUuXNnHThwQA+z+QwA5FtpaVJEhPTNN5Krq7RwodSsmdlVAQBQsDk8QiZJAQEBLN4BAAWIzSYNGSJ99ZVUpIj09ddSmzZmVwUAQMGX7RGygwcPqlevXkpKSspwLTExUb1799b//ve/HC0OAJA33nhDmjTJeD1zptSli6nlAABQaGQ7kI0bN06BgYEqUaJEhms+Pj4KDAzUuHHjcrQ4AEDuGz1aGjvWeP3558YzZAAAIG9kO5Bt2LBB3bp1y/J69+7dtWbNmhwpCgCQNz76SPrPf4zX48dLzz5rajkAABQ62Q5kf//9t8qXL5/l9bJly+ro0aM5UhQAIPdNny699JLx+u23pZdfNrceAAAKo2wHMh8fH/31119ZXv/zzz8znc4IAHA+8+dLzzxjvH7lFenNN82tBwCAwirbgaxJkyaadPWJ70x88sknLHsPAPnA4sXSE08YKysOHCi9/75ksZhdFQAAhVO2A9nw4cO1YsUKde3aVdu2bVNiYqISExO1detWdenSRT///LOGDx+em7UCAO5QZKTUo4exAXTfvsbKioQxAADMk+19yGrXrq3vv/9eTz31lBYvXpzuWpkyZfTtt9+qTp06OV4gACBnrF8vPfaYZLVK3bpJX3xh7DkGAADM49DG0O3atdPff/+tyMhI/fnnn7LZbKpatapCQ0Pl5eWVWzUCAO7Q1q1Su3bS5ctS27bS3LlSUYf+HwAAAOQGh//v2NPTU4899lhu1AIAyAW//Sa1aiWdPy89+qj0/feSm5vZVQEAAMmBZ8gAAPnPf/8rtWwpnT0rNWok/fij5OFhdlUAAOAqAhkAFFD/+5/UvLl06pRUp460bJlUrJjZVQEAgOsRyACgAIqNNcLY8eNS9erSzz9LJUuaXRUAALgRgQwACpiTJ40wdviwdO+90sqVUtmyZlcFAAAyk61FPZKSkrJ9wxIlStx2MQCAO3PmjBQaKv3xh1SxorR6teTvb3ZVAAAgK9kKZCVLlpQlmzuHpqam3lFBAIDbk5QktW4t/f675OcnrVplhDIAAOC8shXI1q5da399+PBhvfbaa+rXr58aNmwoSYqOjtasWbM0duzY3KkSAHBTFy8a+4xt2yaVKWOEsSpVzK4KAADcSrYCWdOmTe2v33nnHU2YMEG9evWyn+vQoYNCQkI0bdo09e3bN+erBABkKTlZeuwxaeNGqUQJKSrKWMgDAAA4P4cX9YiOjla9evUynK9Xr562bduWI0UBALLHapV69DBCmJeXtGKFscQ9AADIHxwOZIGBgZoyZUqG81OnTlVgYGCOFAUAuLXUVKlvX2OzZ3d3ackSY/NnAACQf2RryuL1Jk6cqC5duujnn39WgwYNJElbtmzRX3/9pYULF+Z4gQCAjNLSpGeekb7+WipaVFq40FjqHgAA5C8Oj5C1adNGf/zxhzp06KAzZ87o9OnT6tixo/744w+1adMmN2oEAFzHZpNeekmaMUMqUkSaP19q29bsqgAAwO1weIRMMqYtjhkzJqdrAQBkw5tvSp98Yrz+8kupWzdz6wEAALfP4REySdq4caMef/xxNWrUSMeOHZMkzZkzR5s2bcrR4gAA6Y0dK40ebbz+9FPjGTIAAJB/ORzIFi5cqLCwMHl6emrnzp1KTk6WJJ07d45RMwDIRZ98Ir3+uvF63Dhp4EBz6wEAAHfO4UA2atQoTZkyRdOnT5erq6v9fKNGjbRz584cLQ4AYJgxQxo82Hg9YoQ0bJi59QAAgJzhcCA7cOCAmjRpkuF8iRIldPbs2ZyoCQBwna+/liIijNdDhxqBDAAAFAwOBzJ/f3/9+eefGc5v2rRJd999d44UBQAw/PijFB5urKz47LPGVEWLxeyqAABATnE4kD3zzDMaPHiwtm7dKovFouPHj2vevHkaNmyYBvJAAwDkmKgoqXt3YwPo8HBjEQ/CGAAABYvDy96/+uqrSkxMVLNmzXT58mU1adJE7u7uGjZsmF544YXcqBEACp2NG6VOnaSUFKlLF2N5+yK3tS4uAABwZre1D9no0aP1xhtvaN++fUpLS1O1atVUrFixnK4NAAqlbduMjZ4vXZLatDE2fi56W7+tAQCAs3P431ufeuopnTt3Tl5eXqpXr54eeughFStWTBcuXNBTTz2VGzUCQKHx++9Sq1bSuXNSs2bS999Lbm5mVwUAAHKLw4Fs1qxZunTpUobzly5d0uzZs3OkKAAojA4ckFq2lBISpIYNpSVLJE9Ps6sCAAC5KduTYJKSkmSz2WSz2XTu3Dl5eHjYr6Wmpmr58uUqX758rhQJAAXdoUNS8+ZSfLz0wAPS8uUSM8EBACj4sh3ISpYsKYvFIovFoqpVq2a4brFY9Pbbb+docQBQGBw7ZoSxY8ekatWM1RVLljS7KgAAkBeyHcjWrl0rm82mRx99VAsXLlTp0qXt19zc3BQUFKSAgIBcKRIACqr4eKlFC2OE7J57pJUrpXLlzK4KAADklWwHsqZNm+rKlSt64oknVK9ePQUGBuZmXQBQ4CUkSKGh0n//KwUGSqtXS/y7FgAAhYtDi3oULVpUCxcuVGpqam7VAwCFwrlzUuvW0m+/Sb6+0qpVUlCQ2VUBAIC85vAqi82bN9e6detyvJCxY8fKYrFoyJAh9nNXn1m78WvcuHH2No888kiG6z179kx374SEBIWHh8vHx0c+Pj4KDw/X2bNn07U5cuSI2rdvL29vb5UtW1aDBg1SSkpKjn9OALh4UWrfXtq6VSpd2ghjmTyaCwAACgGHtxpt3bq1hg8frj179qhu3bry9vZOd71Dhw4OF7F9+3ZNmzZNNWvWTHf+xIkT6Y5XrFih/v37q0uXLunOR0RE6J133rEfe96wTnTv3r0VGxuryMhISdKAAQMUHh6upUuXSjJWiWzbtq3KlSunTZs26fTp0+rbt69sNpsmTZrk8OcBgKwkJ0tdukjr10slShgLeNSoYXZVAADALA4Hsueee06SNGHChAzXLBaLw9MZz58/rz59+mj69OkaNWpUumt+fn7pjn/88Uc1a9ZMd999d7rzXl5eGdpetX//fkVGRmrLli2qX7++JGn69Olq2LChDhw4oODgYEVFRWnfvn06evSofWGS8ePHq1+/fho9erRKlCjh0GcCgMxcuSL16iVFRkpeXtKyZVLdumZXBQAAzORwIEtLS8vRAp5//nm1bdtWLVq0yBDIrnfy5EktW7ZMs2bNynBt3rx5mjt3rnx9fdW6dWuNGDFCxYsXlyRFR0fLx8fHHsYkqUGDBvLx8dHmzZsVHBys6Oho1ahRI90qkWFhYUpOTlZMTIyaNWuWaU3JyclKTk62HyclJUmSrFarrFarYz+IHHb1/c2uA/kHfSZ3paZKTz3losWLi8jNzaaFC1NVv75N+fnHTZ+Bo+gzcBR9Bo5wtv6S3TocDmTXu3z5croNoh21YMEC7dy5U9u3b79l21mzZql48eLq3LlzuvN9+vRR5cqV5efnpz179mj48OH67bfftHLlSklSXFxcphtWly9fXnFxcfY2vr6+6a6XKlVKbm5u9jaZGTt2bKZ7r0VFRcnLy+uWnykvXP05ANlFn8l5Npv02We1tHJlJbm4pGnYsG1KTj6p5cvNrixn0GfgKPoMHEWfgSOcpb9cvHgxW+0cDmSpqakaM2aMpkyZopMnT+qPP/7Q3XffrTfffFOVKlVS//79s3Wfo0ePavDgwYqKispWqPvyyy/Vp0+fDG0jIiLsr2vUqKEqVaqoXr162rlzp+rUqSPJmEp5I5vNlu58dtrcaPjw4Xr55Zftx0lJSQoMDFRoaKjp0xytVqtWrlypli1bytXV1dRakD/QZ3KHzSa98koRrVzpoiJFbJo1K03duxeMeYr0GTiKPgNH0WfgCGfrL1dnz92Kw4Fs9OjRmjVrlj744IN0YSgkJEQTJ07MdiCLiYlRfHy86l73AEVqaqo2bNigyZMnKzk5WS4uLpKkjRs36sCBA/rmm29ued86derI1dVVBw8eVJ06deTn56eTJ09maHfq1Cn7qJifn5+2bt2a7npCQoKsVmuGkbPrubu7y93dPcN5V1dXp+gEknPVgvyBPpOz3npL+uQT4/UXX1jUp88dTUxwSvQZOIo+A0fRZ+AIZ+kv2a3B4WXvZ8+erWnTpqlPnz72wCRJNWvW1H//+99s36d58+bavXu3du3aZf+qV6+e+vTpo127dqW794wZM1S3bl3VqlXrlvfdu3evrFar/P39JUkNGzZUYmKitm3bZm+zdetWJSYmqlGjRvY2e/bsSbeqY1RUlNzd3dMFRgBwxPvvS+++a7yePFl68klz6wEAAM7H4X+qPXbsmO69994M59PS0hx6gK548eKqccNaz97e3ipTpky680lJSfruu+80fvz4DPf466+/NG/ePLVp00Zly5bVvn37NHToUNWuXVuNGzeWJN1///1q1aqVIiIiNHXqVEnGsvft2rVTcHCwJCk0NFTVqlVTeHi4xo0bpzNnzmjYsGGKiIgwfeohgPxp8mTptdeM1++/Lz3/vLn1AAAA5+TwCFn16tW1cePGDOe/++471a5dO0eKut6CBQtks9nUq1evDNfc3Ny0evVqhYWFKTg4WIMGDVJoaKhWrVqVboRt3rx5CgkJUWhoqEJDQ1WzZk3NmTPHft3FxUXLli2Th4eHGjdurO7du6tTp0768MMPc/zzACj4vvpKevFF4/Wbb0qvvmpuPQAAwHk5PEI2YsQIhYeH69ixY0pLS9OiRYt04MABzZ49Wz/99NMdFbNu3boM5wYMGKABAwZk2j4wMFDr16+/5X1Lly6tuXPn3rRNxYoV77h+APjmG+npp43XL70kZbIQKwAAgJ3DI2Tt27fXN998o+XLl8tiseitt97S/v37tXTpUrVs2TI3agSAfGHpUunxx6W0NGnAAGn8eOkmC7UCAADc3j5kYWFhCgsLy+laACDfWrVK6tpVunLFCGWff04YAwAAt+bwCNmTTz6p1atXy2az5UY9AJDvbNokdewopaRIjz1mPENWxOHfrgAAoDBy+K8Mp0+fVtu2bVWhQgUNHTpUv/76a27UBQD5wo4dUps20sWLUqtW0tdfS0UL3lZjAAAglzgcyJYsWaK4uDiNGDFCMTExqlevnqpVq6YxY8bo8OHDuVAiADin3bulsDDp3DmpaVNp4UIpk73iAQAAsnRbk2pKliypAQMGaN26dfr777/15JNPas6cOZnuTwYABdEff0gtW0pnzkj16xsLenh5mV0VAADIb+7oKQer1aodO3Zo69atOnz4sHx9fXOqLgBwWocPS82bSydPSrVqSStWSMWLm10VAADIj24rkK1du1YRERHy9fVV3759Vbx4cS1dulRHjx7N6foAwKkcP26EsdhY6b77pKgoqVQps6sCAAD5lcOPnleoUEGnT59WWFiYpk6dqvbt28vDwyM3agMAp3LqlNSihfS//0l3320sdV++vNlVAQCA/MzhQPbWW2+pW7duKsU/CQMoRM6elUJDpf37pQoVpNWrpbvuMrsqAACQ3zkcyAYMGJAbdQCA0zp/XmrdWtq1yxgRW7VKqlTJ7KoAAEBB4HAgu3Dhgt577z2tXr1a8fHxSktLS3f9f//7X44VBwBmu3RJ6tBB2rLFeFZs5UopONjsqgAAQEHhcCB7+umntX79eoWHh8vf318WiyU36gIA06WkSF26SGvXGqso/vyzVLOm2VUBAICCxOFAtmLFCi1btkyNGzfOjXoAwClcuSL17m0sae/pKS1bJj34oNlVAQCAgsbhZe9LlSql0qVL50YtAOAU0tKkp56SFi6U3NykH36QHn7Y7KoAAEBB5HAge/fdd/XWW2/p4sWLuVEPAJjKZpMGDpTmzJFcXKRvvzVWVwQAAMgNDk9ZHD9+vP766y/5+vqqUqVKcnV1TXd9586dOVYcAOQlm00aNkyaOlWyWKS5c6WOHc2uCgAAFGQOB7JOnTrlQhkAYL6335YmTDBef/GF1LOnufUAAICCz+FANmLEiNyoAwBMNW6cEcgk6ZNPjGfIAAAAcpvDgeyqmJgY7d+/XxaLRdWqVVPt2rVzsi4AyDOffSa9+qrxeuxY6cUXza0HAAAUHg4Hsvj4ePXs2VPr1q1TyZIlZbPZlJiYqGbNmmnBggUqV65cbtQJALli1izp+eeN12+8Ib32mrn1AACAwsXhVRZffPFFJSUlae/evTpz5owSEhK0Z88eJSUladCgQblRIwDkiu++uzY1cfBg6d13za0HAAAUPg6PkEVGRmrVqlW6//777eeqVaumTz/9VKGsDQ0gn/jpJ2Pj57Q06emnpYkTjZUVAQAA8pLDI2RpaWkZlrqXJFdXV6WlpeVIUQCQm1avlrp2la5cMULZlCmEMQAAYA6HA9mjjz6qwYMH6/jx4/Zzx44d00svvaTmzZvnaHEAkNN++UXq0EFKTpY6dZJmzjQ2gAYAADCDw4Fs8uTJOnfunCpVqqR77rlH9957rypXrqxz585p0qRJuVEjAOSImBipTRvp4kUpNFRasEDKZMAfAAAgzzj8DFlgYKB27typlStX6r///a9sNpuqVaumFi1a5EZ9AJAj9uwxQlhSktSkibR4seTubnZVAACgsLvtfchatmypli1b5mQtAJArDh6UWraUzpyRHnrIWNDDy8vsqgAAAByYsrhmzRpVq1ZNSUlJGa4lJiaqevXq2rhxY44WBwB36u+/pebNpbg4qWZNacUKqXhxs6sCAAAwZDuQffTRR4qIiFCJEiUyXPPx8dEzzzyjCRMm5GhxAHAnTpyQWrSQjh6VgoOlqCipdGmzqwIAALgm24Hst99+U6tWrbK8HhoaqpiYmBwpCgDu1D//GGHszz+lypWlVaskX1+zqwIAAEgv24Hs5MmTme4/dlXRokV16tSpHCkKAO7E2bPGAh779kl33WXsO1ahgtlVAQAAZJTtQHbXXXdp9+7dWV7//fff5e/vnyNFAcDtOn9eattW+vVXqVw5Y2SscmWzqwIAAMhctgNZmzZt9NZbb+ny5csZrl26dEkjRoxQu3btcrQ4AHDEpUtSx47S5s1SqVLSypXSffeZXRUAAEDWsr3s/X/+8x8tWrRIVatW1QsvvKDg4GBZLBbt379fn376qVJTU/XGG2/kZq0AkKWUFKlbN2nNGqlYMSkyUqpVy+yqAAAAbi7bgczX11ebN2/Wc889p+HDh8tms0mSLBaLwsLC9Nlnn8mXJ+YBmODKFenxx6VlyyRPT+PPhx4yuyoAAIBbc2hj6KCgIC1fvlwJCQn6888/ZbPZVKVKFZUqVSq36gOAm0pLk/r3l777TnJ1lRYvlpo0MbsqAACA7HEokF1VqlQpPfjggzldCwA4xGaTXnhBmj1bcnGRvv1WCgszuyoAAIDsy/aiHgDgTGw26dVXpc8/lywWI5R16mR2VQAAAI4hkAHIl959V/rwQ+P1tGlS797m1gMAAHA7CGQA8p3x46URI4zXEydKTz9tbj0AAAC3i0AGIF/5/HNp2DDj9ahR0pAhppYDAABwRwhkAPKN2bOlgQON18OHS2x9CAAA8jsCGYB8YeFC6cknjdcvviiNHm1uPQAAADmBQAbA6S1fLvXqZew59tRT0kcfGSsrAgAA5HcEMgBObc0aqXNnyWqVevY0VlQswm8uAABQQPDXGgBOKzpa6tBBSk42/ry6ATQAAEBBQSAD4JR27pRat5YuXJBatpS++UZydTW7KgAAgJxFIAPgdPbulUJDpcRE6V//khYvljw8zK4KAAAg5xHIADiVP/80RsROn5bq1ZOWLZO8vc2uCgAAIHcQyAA4jSNHpObNpRMnpJAQ6eefpRIlzK4KAAAg9xDIADiFuDipRQsjlFWtKq1cKZUubXZVAAAAuYtABsB0p08bYezgQSkoSFq1SvL1NbsqAACA3Oc0gWzs2LGyWCwaMmSI/Vy/fv1ksVjSfTVo0CDd9yUnJ+vFF19U2bJl5e3trQ4dOig2NjZdm4SEBIWHh8vHx0c+Pj4KDw/X2bNn07U5cuSI2rdvL29vb5UtW1aDBg1SSkpKbn1cAP8vMVEKCzMW8ggIkFavlgIDza4KAAAgbzhFINu+fbumTZummjVrZrjWqlUrnThxwv61fPnydNeHDBmixYsXa8GCBdq0aZPOnz+vdu3aKTU11d6md+/e2rVrlyIjIxUZGaldu3YpPDzcfj01NVVt27bVhQsXtGnTJi1YsEALFy7U0KFDc+9DA9CFC1LbtlJMjFS2rDEyds89ZlcFAACQd4qaXcD58+fVp08fTZ8+XaNGjcpw3d3dXX5+fpl+b2JiombMmKE5c+aoRYsWkqS5c+cqMDBQq1atUlhYmPbv36/IyEht2bJF9evXlyRNnz5dDRs21IEDBxQcHKyoqCjt27dPR48eVUBAgCRp/Pjx6tevn0aPHq0SrCoA5LjLl6VOnaRffpFKljSeGbv/frOrAgAAyFumB7Lnn39ebdu2VYsWLTINZOvWrVP58uVVsmRJNW3aVKNHj1b58uUlSTExMbJarQoNDbW3DwgIUI0aNbR582aFhYUpOjpaPj4+9jAmSQ0aNJCPj482b96s4OBgRUdHq0aNGvYwJklhYWFKTk5WTEyMmjVrlmntycnJSk5Oth8nJSVJkqxWq6xW6539YO7Q1fc3uw7kH3nZZ6xWqVs3F61aVUTFitm0dGmqqle3ie6av/B7Bo6iz8BR9Bk4wtn6S3brMDWQLViwQDt37tT27dszvd66dWt169ZNQUFBOnTokN588009+uijiomJkbu7u+Li4uTm5qZSpUql+z5fX1/FxcVJkuLi4uwB7nrly5dP18b3hhUESpUqJTc3N3ubzIwdO1Zvv/12hvNRUVHy8vK6+YfPIytXrjS7BOQzud1nUlOlCRPq6Zdf7pKbW6r+/e9onT59WjfMRkY+wu8ZOIo+A0fRZ+AIZ+kvFy9ezFY70wLZ0aNHNXjwYEVFRcnDwyPTNj169LC/rlGjhurVq6egoCAtW7ZMnTt3zvLeNptNFovFfnz96ztpc6Phw4fr5Zdfth8nJSUpMDBQoaGhpk9ztFqtWrlypVq2bClXV1dTa0H+kBd9Ji1NGjDARb/8UkSurjZ9/71NrVrVv/U3winxewaOos/AUfQZOMLZ+svV2XO3Ylogi4mJUXx8vOrWrWs/l5qaqg0bNmjy5MlKTk6Wi4tLuu/x9/dXUFCQDh48KEny8/NTSkqKEhIS0o2SxcfHq1GjRvY2J0+ezPD+p06dso+K+fn5aevWremuJyQkyGq1Zhg5u567u7vc3d0znHd1dXWKTiA5Vy3IH3Krz9hs0osvSrNnSy4u0oIFFrVvb/qsaeQAfs/AUfQZOIo+A0c4S3/Jbg2mrbLYvHlz7d69W7t27bJ/1atXT3369NGuXbsyhDFJOn36tI4ePSp/f39JUt26deXq6ppuWPLEiRPas2ePPZA1bNhQiYmJ2rZtm73N1q1blZiYmK7Nnj17dOLECXubqKgoubu7pwuMAG6PzSa99pr06aeSxSLNnCndZJAbAACg0DDtn6eLFy+uGjVqpDvn7e2tMmXKqEaNGjp//rxGjhypLl26yN/fX4cPH9brr7+usmXL6rHHHpMk+fj4qH///ho6dKjKlCmj0qVLa9iwYQoJCbGvunj//ferVatWioiI0NSpUyVJAwYMULt27RQcHCxJCg0NVbVq1RQeHq5x48bpzJkzGjZsmCIiIkyfeggUBKNHSx98YLyeMkV6/HFz6wEAAHAWTrEPWWZcXFy0e/dudezYUVWrVlXfvn1VtWpVRUdHq3jx4vZ2EydOVKdOndS9e3c1btxYXl5eWrp0aboRtnnz5ikkJEShoaEKDQ1VzZo1NWfOnHTvtWzZMnl4eKhx48bq3r27OnXqpA8//DBPPzNQEE2cKL35pvF6wgRpwABz6wEAAHAmTvUAx7p16+yvPT099fPPP9/yezw8PDRp0iRNmjQpyzalS5fW3Llzb3qfihUr6qeffsp2rQBubdo06eq6N++8I730krn1AAAAOBunHSEDkL/NnSs9+6zx+t//lv7zH3PrAQAAcEYEMgA5btEiqV8/YzGP55+Xxo41FvMAAABAegQyADlqxQqpZ09jA+h+/aRPPiGMAQAAZIVABiDHrFtnLGdvtUo9ekhffCEV4bcMAABAlvirEoAcsWWL1K6ddPmy1L69NGeOsQE0AAAAskYgA3DHdu2SWreWLlyQmjeXvv1Wyubm9AAAAIUagQzAHdm/X2rZUjp7VmrcWPrxR8nDw+yqAAAA8gcCGYDb9tdfxojYP/9IdetKy5ZJ3t5mVwUAAJB/EMgA3JajR40wduKEVKOG9PPPko+P2VUBAADkLwQyAA47eVJq0UL6+2/p3nullSulMmXMrgoAACD/IZABcMjp00YY++MPqWJFafVqyc/P7KoAAADyJwIZgGxLSpJatZL27JH8/Y0wVrGi2VUBAADkXwQyANly4YLUtq20Y4cxPXHVKmO6IgAAAG4fgQzALV2+LD32mLRpk7FwR1SUVK2a2VUBAADkfwQyADdltUo9ehgLd3h7SytWSHXqmF0VAABAwUAgA5Cl1FTpiSekJUuMzZ6XLpUaNjS7KgAAgIKDQAYgU2lp0oAB0oIFkqurtHCh1KyZ2VUBAAAULAQyABnYbNKQIdKXX0pFikjz50tt2phdFQAAQMFT1OwCAJgvNVVav96iDRvukre3RatWSZMmGddmzpS6djW1PAAAgAKLQAYUcosWSYMHS7GxRSXV04QJ1659/rkUHm5aaQAAAAUegQwoxBYtMka/bLbMr5cvn7f1AAAAFDY8QwYUUqmpxshYVmHMYjGeI0tNzdOyAAAAChUCGVBIbdwoxcZmfd1mk44eNdoBAAAgdxDIgELqxImcbQcAAADHEciAQshmk7ZuzV5bf//crQUAAKAwY1EPoJCJj5eeekpatuzm7SwWqUIF6eGH86YuAACAwogRMqAQWb5cCgkxwpi7uxHMLBbj63pXjz/6SHJxyfMyAQAACg0CGVAIXLokvfCC1LatMUIWEiJt3y7NmCF9/710113p21eoYJzv3NmcegEAAAoLpiwCBdyuXVKfPtK+fcbxkCHS2LGSh4dx3Lmz1LGjtHbtFa1YsUutWz+gZs2KMjIGAACQBwhkQAGVliZNmCC98YaUkiL5+UmzZkmhoRnburhITZvadOHCMTVtWoswBgAAkEcIZEABdOyY1LevtHq1cdyxo/TFF1LZsubWBQAAgPR4hgwoYBYuNJ4RW71a8vKSpk2TFi8mjAEAADgjRsiAAuL8eWnwYOnLL43junWl+fOlqlXNrQsAAABZY4QMKAC2bpUeeMAIYxaLNHy4tHkzYQwAAMDZMUIG5GNXrhgrJr79tpSaKlWsKM2ZIzVpYnZlAAAAyA4CGZBPHTokhYdLv/xiHPfqJX32mVSypKllAQAAwAFMWQTyGZtNmjtXqlXLCGMlShjH8+cTxgAAAPIbRsiAfOTsWem556QFC4zjxo2NMFapkplVAQAA4HYxQgbkExs2GKNiCxYYGzm/+660bh1hDAAAID9jhAxwcikp0ogR0vvvG9MV77lHmjdPql/f7MoAAABwpwhkgBM7cEDq00eKiTGO+/eXPvpIKlbM1LIAAACQQ5iyCDghm02aNk2qU8cIY6VKSd9/L33xBWEMAACgIGGEDHAyp05JERHSjz8ax82bS7NmSXfdZW5dAAAAyHmMkAFO5OefpZo1jTDm5iZ9+KEUFUUYAwAAKKgYIQOcwOXL0muvSR9/bBxXq2bsK1arlrl1AQAAIHcRyACT7d4t9e4t7dljHL/wgvTBB5Knp7l1AQAAIPcxZREwSVqaMSL24INGGCtfXlq2TJo0iTAGAABQWDBCBpjgxAmpXz/j+TBJatdOmjHDCGUAAAAoPBghA/LYjz9KISFGGPP0lD77TFqyhDAGAABQGDFCBuSRCxekl1829heTpNq1pXnzpPvvN7cuAAAAmIcRMiAP7NhhbPI8bZpksUivvipt2UIYAwAAKOwYIQNyUWqqsWLiW29JV64Y+4nNmSM1a2Z2ZQAAAHAGBDIgl/z9t/TEE9KGDcZxt27SlClS6dLm1gUAAADn4TRTFseOHSuLxaIhQ4ZIkqxWq/79738rJCRE3t7eCggI0BNPPKHjx4+n+75HHnlEFosl3VfPnj3TtUlISFB4eLh8fHzk4+Oj8PBwnT17Nl2bI0eOqH379vL29lbZsmU1aNAgpaSk5OZHRgH29dfGps4bNkjFikkzZ0rffEMYAwAAQHpOMUK2fft2TZs2TTVr1rSfu3jxonbu3Kk333xTtWrVUkJCgoYMGaIOHTpox44d6b4/IiJC77zzjv3Y84ZNnHr37q3Y2FhFRkZKkgYMGKDw8HAtXbpUkpSamqq2bduqXLly2rRpk06fPq2+ffvKZrNp0qRJufWxUQAlJhobO8+daxw3aGC8vucec+sCAACAczI9kJ0/f159+vTR9OnTNWrUKPt5Hx8frVy5Ml3bSZMm6aGHHtKRI0dUsWJF+3kvLy/5+fllev/9+/crMjJSW7ZsUf369SVJ06dPV8OGDXXgwAEFBwcrKipK+/bt09GjRxUQECBJGj9+vPr166fRo0erRIkSOf2xUQBt2iQ9/rgxVbFIEeO5sTfekIqa/r8yAAAAOCvT/6r4/PPPq23btmrRokW6QJaZxMREWSwWlSxZMt35efPmae7cufL19VXr1q01YsQIFS9eXJIUHR0tHx8fexiTpAYNGsjHx0ebN29WcHCwoqOjVaNGDXsYk6SwsDAlJycrJiZGzbJYgSE5OVnJycn246SkJEnGdEur1erQzyGnXX1/s+soDKxWadSoInr//SJKS7OocmWbZs1KVYMGNtlsxvX8gD4DR9Fn4Cj6DBxFn4EjnK2/ZLcOUwPZggULtHPnTm3fvv2WbS9fvqzXXntNvXv3Tjdi1adPH1WuXFl+fn7as2ePhg8frt9++80+uhYXF6fymey4W758ecXFxdnb+Pr6prteqlQpubm52dtkZuzYsXr77bcznI+KipKXl9ctP1NeuHGUETnrxAlvTZhQVwcPlpIkNWt2RBERu3XmzBUtX25ycbeJPgNH0WfgKPoMHEWfgSOcpb9cvHgxW+1MC2RHjx7V4MGDFRUVJQ8Pj5u2tVqt6tmzp9LS0vTZZ5+luxYREWF/XaNGDVWpUkX16tXTzp07VadOHUmSxWLJcE+bzZbufHba3Gj48OF6+eWX7cdJSUkKDAxUaGio6dMcrVarVq5cqZYtW8rV1dXUWgoim02aNcuiYcNcdOGCRSVL2vTpp6nq1s1fkr/Z5d0W+gwcRZ+Bo+gzcBR9Bo5wtv5ydfbcrZgWyGJiYhQfH6+6devaz6WmpmrDhg2aPHmykpOT5eLiIqvVqu7du+vQoUNas2bNLYNOnTp15OrqqoMHD6pOnTry8/PTyZMnM7Q7deqUfVTMz89PW7duTXc9ISFBVqs1w8jZ9dzd3eXu7p7hvKurq1N0Asm5aikoTp+WnnlGWrjQOH7kEWn2bIsCA02fAZwj6DNwFH0GjqLPwFH0GTjCWfpLdmswbdn75s2ba/fu3dq1a5f9q169eurTp4927dqVLowdPHhQq1atUpkyZW55371798pqtcrf3xilaNiwoRITE7Vt2zZ7m61btyoxMVGNGjWyt9mzZ49OnDhhbxMVFSV3d/d0gRFYtUqqWdMIY66u0vvvG+cCA82uDAAAAPmRaf+kX7x4cdWoUSPdOW9vb5UpU0Y1atTQlStX1LVrV+3cuVM//fSTUlNT7c9zlS5dWm5ubvrrr780b948tWnTRmXLltW+ffs0dOhQ1a5dW40bN5Yk3X///WrVqpUiIiI0depUScay9+3atVNwcLAkKTQ0VNWqVVN4eLjGjRunM2fOaNiwYYqIiDB96iGcQ3KysWLi+PHGcXCwNH++9P+zYgEAAIDb4jQbQ98oNjZWS5YsUWxsrB544AH5+/vbvzZv3ixJcnNz0+rVqxUWFqbg4GANGjRIoaGhWrVqlVxcXOz3mjdvnkJCQhQaGqrQ0FDVrFlTc+bMsV93cXHRsmXL5OHhocaNG6t79+7q1KmTPvzwwzz/3HA+e/dK9etfC2PPPivt3EkYAwAAwJ1zqode1q1bZ39dqVIl2Wy2m7YPDAzU+vXrb3nf0qVLa+7VnXqzULFiRf3000/ZqhOFg80mffqp9Mor0uXLUtmy0pdfSu3bm10ZAAAACgqnCmSAszh5UnrqKdmXrm/VSvrqKymL/ccBAACA2+K0UxYBs/z0kxQSYoQxd3fpk0+M14QxAAAA5DRGyID/d/GiNGyY9PnnxnHNmsbCHdWrm1sXAAAACi5GyABJv/4q1a17LYy9/LK0bRthDAAAALmLQIZCLS1NGjfOWEXxv/+V/P2lqChjRcVM9vwGAAAAchRTFlFoxcZKfftKa9YYx489Jk2fLmVj/3EAAAAgRzBChkLpu++MZ8TWrJG8vKQvvpAWLiSMAQAAIG8xQoZC5dw56cUXpVmzjOMHH5TmzZOqVDG3LgAAABROjJCh0IiOlh54wAhjRYpIb7wh/fILYQwAAADmYYQMBd6VK9Lo0dK770qpqVJQkDRnjvTww2ZXBgAAgMKOQIYC7X//kx5/3Bgdk6Q+faRPP5V8fMytCwAAAJCYsogCymaTZs82pihGR0slShjPis2dSxgDAACA82CEDAVOQoL07LPSt98axw8/bISzSpVMLQsAAADIgBEyFCjr1hnL2X/7rVS0qPHs2Nq1hDEAAAA4J0bIUCCkpEhvvSV98IExXbFKFWOK4oMPml0ZAAAAkDUCGfK9//7XWKxj507jOCJCmjBBKlbM3LoAAACAW2HKIvItm02aMkWqU8cIY2XKSIsWSdOmEcYAAACQPzBChnzp1Cmpf39p6VLjuGVLaeZMKSDA1LIAAAAAhzBChnxnxQopJMQIY25u0sSJUmQkYQwAAAD5DyNkyDcuXZL+/W9p0iTjuHp1af58Y1VFAAAAID9ihAz5wm+/GSsmXg1jgwZJ27cTxgAAAJC/Ecjg1NLSjCmJDz0k7d0r+foaUxY//ljy9DS7OgAAAODOMGURTuv4calvX2nVKuO4Qwfpiy+kcuXMrQsAAADIKYyQwSktXmws3LFqlTESNmWK9MMPhDEAAAAULIyQwamcPy+99JIxEiYZe4zNmyfdd5+5dQEAAAC5gREyOI3t26XatY0wZrFIr70mRUcTxgAAAFBwEchgutRUafRoqVEj6c8/pcBAac0aaexYY58xAAAAoKBiyiJMdfiwFB4ubdpkHPfoIX3+uVSqlKllAQAAAHmCETKYZt48qVYtI4wVLy7Nni19/TVhDAAAAIUHI2TIc2fPSs8/L82fbxw3aiTNnStVrmxqWQAAAECeY4QMeWrjRmNUbP58ycVFevttaf16whgAAAAKJ0bIkCesVmnkSOm996S0NOnuu40piw0amF0ZAAAAYB4CGXLdwYNSnz7GsvaS1K+f9MknxnNjAAAAQGHGlEXkGpvN2FPsgQeMMFaqlPTdd9JXXxHGAAAAAIkRMuSS06eliAhp8WLj+NFHpVmzpAoVzK0LAAAAcCaMkCHHrVwphYQYYczVVRo3zjhHGAMAAADSY4QMOebyZen116WJE43j++83Fu6oXdvcugAAAABnRSBDjti7V+rdW/r9d+N44EBjZMzLy9y6AAAAAGfGlEXcEZvNWDGxbl0jjJUrJy1dKn36KWEMAAAAuBVGyHDb4uKkJ5+UIiON4zZtpC+/lHx9za0LAAAAyC8YIcNtWbLEWLgjMlLy8JAmT5Z++okwBgAAADiCETI45MIFaehQaepU47hWLWn+fKlaNXPrAgAAAPIjRsiQbTExxrNiV8PYsGHS1q2EMQAAAOB2EchwS6mp0vvvSw0aSAcOSHfdJa1aZayi6O5udnUAAABA/sWURdzU0aNSeLi0fr1x3KWLNG2aVLq0uXUBAAAABQEjZMjSN99INWsaYczb21hB8bvvCGMAAABATmGEDBkkJUkvvijNnm0c168vzZ0r3XuvuXUBAAAABQ0jZEhn82bpgQeMMFakiPTmm9LGjYQxAAAAIDcwQgZJ0pUr0rvvSqNGSWlpUqVKxqhY48ZmVwYAAAAUXAQy6K+/pD59jCXsJWMRj0mTJB8fc+sCAAAACjqmLBZiNps0c6YxRXHrViOAff21MV2RMAYAAADkPkbICqkzZ6RnnpG+/944btJEmjNHqljR3LoAAACAwoQRskJozRpjOfvvv5eKFpXGjjXOEcYAAACAvOU0gWzs2LGyWCwaMmSI/ZzNZtPIkSMVEBAgT09PPfLII9q7d2+670tOTtaLL76osmXLytvbWx06dFBsbGy6NgkJCQoPD5ePj498fHwUHh6us2fPpmtz5MgRtW/fXt7e3ipbtqwGDRqklJSU3Pq4pkhOll59VWrRQjp2TKpaVYqOll57TXJxMbs6AAAAoPBxikC2fft2TZs2TTVr1kx3/oMPPtCECRM0efJkbd++XX5+fmrZsqXOnTtnbzNkyBAtXrxYCxYs0KZNm3T+/Hm1a9dOqamp9ja9e/fWrl27FBkZqcjISO3atUvh4eH266mpqWrbtq0uXLigTZs2acGCBVq4cKGGDh2a+x8+F6SmSuvXW7Rhw11av96i1FRp/36pQQNp3Djj2bEBA6SdO6V69cyuFgAAACi8TH+G7Pz58+rTp4+mT5+uUaNG2c/bbDZ99NFHeuONN9S5c2dJ0qxZs+Tr66v58+frmWeeUWJiombMmKE5c+aoRYsWkqS5c+cqMDBQq1atUlhYmPbv36/IyEht2bJF9evXlyRNnz5dDRs21IEDBxQcHKyoqCjt27dPR48eVUBAgCRp/Pjx6tevn0aPHq0SJUrk8U/l9i1aJA0eLMXGFpVUTxMmSCVLShcuSFarVLas9MUXUseOZlcKAAAAwPRA9vzzz6tt27Zq0aJFukB26NAhxcXFKTQ01H7O3d1dTZs21ebNm/XMM88oJiZGVqs1XZuAgADVqFFDmzdvVlhYmKKjo+Xj42MPY5LUoEED+fj4aPPmzQoODlZ0dLRq1KhhD2OSFBYWpuTkZMXExKhZs2aZ1p6cnKzk5GT7cVJSkiTJarXKarXe+Q/HQYsXW9Szp4tsNkmy2M9fnZ1Zq1aalixJlb+/Ec6A613ts2b0XeRP9Bk4ij4DR9Fn4Ahn6y/ZrcPUQLZgwQLt3LlT27dvz3AtLi5OkuTr65vuvK+vr/7++297Gzc3N5UqVSpDm6vfHxcXp/Lly2e4f/ny5dO1ufF9SpUqJTc3N3ubzIwdO1Zvv/12hvNRUVHy8vLK8vtyQ2qqNHBgqGw2F10fxq6xKTb2snbsWMnzYriplStXml0C8hn6DBxFn4Gj6DNwhLP0l4sXL2arnWmB7OjRoxo8eLCioqLk4eGRZTuLJX24sNlsGc7d6MY2mbW/nTY3Gj58uF5++WX7cVJSkgIDAxUaGprn0xzXr7fo9Omb/ee06PRpL5Uo0VZNm9ryrC7kH1arVStXrlTLli3l6upqdjnIB+gzcBR9Bo6iz8ARztZfrs6euxXTAllMTIzi4+NVt25d+7nU1FRt2LBBkydP1oEDByQZo1f+/v72NvHx8fbRLD8/P6WkpCghISHdKFl8fLwaNWpkb3Py5MkM73/q1Kl099m6dWu66wkJCbJarRlGzq7n7u4ud3f3DOddXV3zvBOcOpXddkXlBP0TTsyM/ov8jT4DR9Fn4Cj6DBzhLP0luzWYtspi8+bNtXv3bu3atcv+Va9ePfXp00e7du3S3XffLT8/v3RDjikpKVq/fr09bNWtW1eurq7p2pw4cUJ79uyxt2nYsKESExO1bds2e5utW7cqMTExXZs9e/boxIkT9jZRUVFyd3dPFxid2XWZNUfaAQAAAMh9po2QFS9eXDVq1Eh3ztvbW2XKlLGfHzJkiMaMGaMqVaqoSpUqGjNmjLy8vNS7d29Jko+Pj/r376+hQ4eqTJkyKl26tIYNG6aQkBD7qov333+/WrVqpYiICE2dOlWSNGDAALVr107BwcGSpNDQUFWrVk3h4eEaN26czpw5o2HDhikiIiLfrLD48MNShQrG/mK2TGYkWizG9YcfzvvaAAAAAGTO9FUWb+bVV1/VpUuXNHDgQCUkJKh+/fqKiopS8eLF7W0mTpyookWLqnv37rp06ZKaN2+umTNnyuW6lSvmzZunQYMG2Vdj7NChgyZPnmy/7uLiomXLlmngwIFq3LixPD091bt3b3344Yd592HvkIuL9PHHUteuRvi6PpRdfQzuo4/YABoAAABwJk4VyNatW5fu2GKxaOTIkRo5cmSW3+Ph4aFJkyZp0qRJWbYpXbq05s6de9P3rlixon766SdHynU6nTtL339/dR+ya+crVDDC2P9v5wYAAADASThVIMOd69zZ2PR57dorWrFil1q3fkDNmhVlZAwAAABwQgSyAsjFRWra1KYLF46padNahDEAAADASZm2yiIAAAAAFHYEMgAAAAAwCYEMAAAAAExCIAMAAAAAkxDIAAAAAMAkBDIAAAAAMAmBDAAAAABMQiADAAAAAJMQyAAAAADAJAQyAAAAADAJgQwAAAAATEIgAwAAAACTEMgAAAAAwCRFzS6gILHZbJKkpKQkkyuRrFarLl68qKSkJLm6uppdDvIB+gwcRZ+Bo+gzcBR9Bo5wtv5yNRNczQhZIZDloHPnzkmSAgMDTa4EAAAAgDM4d+6cfHx8srxusd0qsiHb0tLSdPz4cRUvXlwWi8XUWpKSkhQYGKijR4+qRIkSptaC/IE+A0fRZ+Ao+gwcRZ+BI5ytv9hsNp07d04BAQEqUiTrJ8UYIctBRYoUUYUKFcwuI50SJUo4RYdE/kGfgaPoM3AUfQaOos/AEc7UX242MnYVi3oAAAAAgEkIZAAAAABgEgJZAeXu7q4RI0bI3d3d7FKQT9Bn4Cj6DBxFn4Gj6DNwRH7tLyzqAQAAAAAmYYQMAAAAAExCIAMAAAAAkxDIAAAAAMAkBDIAAAAAMAmBLB8bO3asHnzwQRUvXlzly5dXp06ddODAgXRt+vXrJ4vFku6rQYMGJlUMs2Wnz0jS/v371aFDB/n4+Kh48eJq0KCBjhw5YkLFMFt2+syNv2Oufo0bN86kqmGm7PSZ8+fP64UXXlCFChXk6emp+++/X59//rlJFcNs2ekzJ0+eVL9+/RQQECAvLy+1atVKBw8eNKlimO3zzz9XzZo17RtAN2zYUCtWrLBft9lsGjlypAICAuTp6alHHnlEe/fuNbHimyOQ5WPr16/X888/ry1btmjlypW6cuWKQkNDdeHChXTtWrVqpRMnTti/li9fblLFMFt2+sxff/2lf/3rX7rvvvu0bt06/fbbb3rzzTfl4eFhYuUwS3b6zPW/X06cOKEvv/xSFotFXbp0MbFymCU7feall15SZGSk5s6dq/379+ull17Siy++qB9//NHEymGWW/UZm82mTp066X//+59+/PFH/frrrwoKClKLFi0y/J0HhUOFChX03nvvaceOHdqxY4ceffRRdezY0R66PvjgA02YMEGTJ0/W9u3b5efnp5YtW+rcuXMmV54FGwqM+Ph4myTb+vXr7ef69u1r69ixo3lFwall1md69Ohhe/zxx02sCs4ssz5zo44dO9oeffTRPKwKziyzPlO9enXbO++8k65dnTp1bP/5z3/yujw4oRv7zIEDB2ySbHv27LG3uXLliq106dK26dOnm1UmnEypUqVsX3zxhS0tLc3m5+dne++99+zXLl++bPPx8bFNmTLFxAqzxghZAZKYmChJKl26dLrz69atU/ny5VW1alVFREQoPj7ejPLghG7sM2lpaVq2bJmqVq2qsLAwlS9fXvXr19cPP/xgYpVwJln9nrnq5MmTWrZsmfr375+XZcGJZdZn/vWvf2nJkiU6duyYbDab1q5dqz/++ENhYWFmlQkncmOfSU5OlqR0MzVcXFzk5uamTZs25X2BcCqpqalasGCBLly4oIYNG+rQoUOKi4tTaGiovY27u7uaNm2qzZs3m1hp1ghkBYTNZtPLL7+sf/3rX6pRo4b9fOvWrTVv3jytWbNG48eP1/bt2/Xoo4/af7mh8Mqsz8THx+v8+fN677331KpVK0VFRemxxx5T586dtX79epMrhtmy+j1zvVmzZql48eLq3LlzHlcHZ5RVn/nkk09UrVo1VahQQW5ubmrVqpU+++wz/etf/zKxWjiDzPrMfffdp6CgIA0fPlwJCQlKSUnRe++9p7i4OJ04ccLkimGW3bt3q1ixYnJ3d9ezzz6rxYsXq1q1aoqLi5Mk+fr6pmvv6+trv+ZsippdAHLGCy+8oN9//z3DvxT16NHD/rpGjRqqV6+egoKCtGzZMv7CVMhl1mfS0tIkSR07dtRLL70kSXrggQe0efNmTZkyRU2bNjWlVjiHrH7PXO/LL79Unz59eOYQkrLuM5988om2bNmiJUuWKCgoSBs2bNDAgQPl7++vFi1amFQtnEFmfcbV1VULFy5U//79Vbp0abm4uKhFixZq3bq1iZXCbMHBwdq1a5fOnj2rhQsXqm/fvun+8dhisaRrb7PZMpxzFgSyAuDFF1/UkiVLtGHDBlWoUOGmbf39/RUUFMTKRIVcVn2mbNmyKlq0qKpVq5au/f3338+0kEIuO79nNm7cqAMHDuibb77J4+rgjLLqM5cuXdLrr7+uxYsXq23btpKkmjVrateuXfrwww8JZIXYzX7P1K1bV7t27VJiYqJSUlJUrlw51a9fX/Xq1TOpWpjNzc1N9957rySpXr162r59uz7++GP9+9//liTFxcXJ39/f3j4+Pj7DqJmzYMpiPmaz2fTCCy9o0aJFWrNmjSpXrnzL7zl9+rSOHj2aroOi8LhVn3Fzc9ODDz6YYbnhP/74Q0FBQXlZKpyEI79nZsyYobp166pWrVp5WCGcza36jNVqldVqVZEi6f8K4uLiYh+lR+HiyO8ZHx8flStXTgcPHtSOHTvUsWPHPKwUzsxmsyk5OVmVK1eWn5+fVq5cab+WkpKi9evXq1GjRiZWmDVGyPKx559/XvPnz9ePP/6o4sWL2+fF+vj4yNPTU+fPn9fIkSPVpUsX+fv76/Dhw3r99ddVtmxZPfbYYyZXDzPcqs9I0iuvvKIePXqoSZMmatasmSIjI7V06VKtW7fOxMphluz0GUlKSkrSd999p/Hjx5tVKpzErfpMiRIl1LRpU73yyivy9PRUUFCQ1q9fr9mzZ2vChAkmVw8zZOf3zHfffady5cqpYsWK2r17twYPHqxOnTqlW7gBhcfrr7+u1q1bKzAwUOfOndOCBQu0bt06RUZGymKxaMiQIRozZoyqVKmiKlWqaMyYMfLy8lLv3r3NLj1zpqztiBwhKdOvr776ymaz2WwXL160hYaG2sqVK2dzdXW1VaxY0da3b1/bkSNHzC0cprlVn7lqxowZtnvvvdfm4eFhq1Wrlu2HH34wp2CYLrt9ZurUqTZPT0/b2bNnzSkUTiM7febEiRO2fv362QICAmweHh624OBg2/jx421paWnmFQ7TZKfPfPzxx7YKFSrY/z7zn//8x5acnGxe0TDVU089ZQsKCrK5ubnZypUrZ2vevLktKirKfj0tLc02YsQIm5+fn83d3d3WpEkT2+7du02s+OYsNpvNlhfBDwAAAACQHs+QAQAAAIBJCGQAAAAAYBICGQAAAACYhEAGAAAAACYhkAEAAACASQhkAAAAAGASAhkAAAAAmIRABgAAAAAmIZABAAAAgEkIZAAAZNPmzZvl4uKiVq1amV0KAKCAsNhsNpvZRQAAkB88/fTTKlasmL744gvt27dPFStWNLskAEA+xwgZAADZcOHCBX377bd67rnn1K5dO82cOTPd9SVLlqhKlSry9PRUs2bNNGvWLFksFp09e9beZvPmzWrSpIk8PT0VGBioQYMG6cKFC3n7QQAAToVABgBANnzzzTcKDg5WcHCwHn/8cX311Ve6Osnk8OHD6tq1qzp16qRdu3bpmWee0RtvvJHu+3fv3q2wsDB17txZv//+u7755htt2rRJL7zwghkfBwDgJJiyCABANjRu3Fjdu3fX4MGDdeXKFfn7++vrr79WixYt9Nprr2nZsmXavXu3vf1//vMfjR49WgkJCSpZsqSeeOIJeXp6aurUqfY2mzZtUtOmTXXhwgV5eHiY8bEAACZjhAwAgFs4cOCAtm3bpp49e0qSihYtqh49eujLL7+0X3/wwQfTfc9DDz2U7jgmJkYzZ85UsWLF7F9hYWFKS0vToUOH8uaDAACcTlGzCwAAwNnNmDFDV65c0V133WU/Z7PZ5OrqqoSEBNlsNlkslnTfc+MElLS0ND3zzDMaNGhQhvuzOAgAFF4EMgAAbuLKlSuaPXu2xo8fr9DQ0HTXunTponnz5um+++7T8uXL013bsWNHuuM6depo7969uvfee3O9ZgBA/sEzZAAA3MQPP/ygHj16KD4+Xj4+PumuvfHGG1q+fLkWLVqk4OBgvfTSS+rfv7927dqloUOHKjY2VmfPnpWPj49+//13NWjQQE8++aQiIiLk7e2t/fv3a+XKlZo0aZJJnw4AYDaeIQMA4CZmzJihFi1aZAhjkjFCtmvXLiUkJOj777/XokWLVLNmTX3++ef2VRbd3d0lSTVr1tT69et18OBBPfzww6pdu7befPNN+fv75+nnAQA4F0bIAADIBaNHj9aUKVN09OhRs0sBADgxniEDACAHfPbZZ3rwwQdVpkwZ/fLLLxo3bhx7jAEAbolABgBADjh48KBGjRqlM2fOqGLFiho6dKiGDx9udlkAACfHlEUAAAAAMAmLegAAAACASQhkAAAAAGASAhkAAAAAmIRABgAAAAAmIZABAAAAgEkIZAAAAABgEgIZAAAAAJiEQAYAAAAAJvk/cJ/asAXrDg0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting\n",
    "\n",
    "# your code goes here\n",
    "\n",
    "QUERY= \"\"\"\n",
    "SELECT ConvertedComp, Age FROM master Where Age Between 25 and 30\"\"\"\n",
    "df4= pd.read_sql_query(QUERY,conn)\n",
    "median_conv=df4.groupby('Age')['ConvertedComp'].median().reset_index()\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(median_conv['Age'],median_conv['ConvertedComp'] ,  marker='o', linestyle='-', color='b', label='Series 1')\n",
    "#plt.plot(x, y2, marker='s', linestyle='--', color='r', label='Series 2')\n",
    "\n",
    "# Adding titles and labels\n",
    "plt.title('Line Chart ')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Converted Comp')\n",
    "plt.legend()\n",
    "\n",
    "# Show plot\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "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": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "\n",
    "plt.barh(df['MainBranch'],2, color='skyblue')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "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>index</th>\n",
       "      <th>Respondent</th>\n",
       "      <th>MainBranch</th>\n",
       "      <th>Hobbyist</th>\n",
       "      <th>OpenSourcer</th>\n",
       "      <th>OpenSource</th>\n",
       "      <th>Employment</th>\n",
       "      <th>Country</th>\n",
       "      <th>Student</th>\n",
       "      <th>EdLevel</th>\n",
       "      <th>...</th>\n",
       "      <th>SOPartFreq</th>\n",
       "      <th>SOJobs</th>\n",
       "      <th>EntTeams</th>\n",
       "      <th>SOComm</th>\n",
       "      <th>WelcomeChange</th>\n",
       "      <th>Age</th>\n",
       "      <th>Trans</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>SurveyLength</th>\n",
       "      <th>SurveyEase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>I am a developer by profession</td>\n",
       "      <td>No</td>\n",
       "      <td>Never</td>\n",
       "      <td>The quality of OSS and closed source software ...</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
       "      <td>...</td>\n",
       "      <td>Less than once per month or monthly</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No, and I don't know what those are</td>\n",
       "      <td>No, not really</td>\n",
       "      <td>Just as welcome now as I felt last year</td>\n",
       "      <td>22.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>I am a developer by profession</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Once a month or more often</td>\n",
       "      <td>The quality of OSS and closed source software ...</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>No</td>\n",
       "      <td>Some college/university study without earning ...</td>\n",
       "      <td>...</td>\n",
       "      <td>Less than once per month or monthly</td>\n",
       "      <td>No, I knew that Stack Overflow had a job board...</td>\n",
       "      <td>No, and I don't know what those are</td>\n",
       "      <td>Yes, somewhat</td>\n",
       "      <td>Just as welcome now as I felt last year</td>\n",
       "      <td>23.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Neither easy nor difficult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>I am a developer by profession</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Less than once a month but more than once per ...</td>\n",
       "      <td>OSS is, on average, of HIGHER quality than pro...</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Master’s degree (MA, MS, M.Eng., MBA, etc.)</td>\n",
       "      <td>...</td>\n",
       "      <td>Less than once per month or monthly</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No, I've heard of them, but I am not part of a...</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Somewhat more welcome now than last year</td>\n",
       "      <td>28.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>I am a developer by profession</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Never</td>\n",
       "      <td>The quality of OSS and closed source software ...</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>No</td>\n",
       "      <td>Master’s degree (MA, MS, M.Eng., MBA, etc.)</td>\n",
       "      <td>...</td>\n",
       "      <td>Less than once per month or monthly</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No, I've heard of them, but I am not part of a...</td>\n",
       "      <td>Yes, somewhat</td>\n",
       "      <td>Just as welcome now as I felt last year</td>\n",
       "      <td>26.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Neither easy nor difficult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>I am a developer by profession</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Less than once a month but more than once per ...</td>\n",
       "      <td>The quality of OSS and closed source software ...</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Australia</td>\n",
       "      <td>No</td>\n",
       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
       "      <td>...</td>\n",
       "      <td>A few times per week</td>\n",
       "      <td>No, I didn't know that Stack Overflow had a jo...</td>\n",
       "      <td>No, I've heard of them, but I am not part of a...</td>\n",
       "      <td>Yes, somewhat</td>\n",
       "      <td>Just as welcome now as I felt last year</td>\n",
       "      <td>29.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11393</th>\n",
       "      <td>11393</td>\n",
       "      <td>25136</td>\n",
       "      <td>I am a developer by profession</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Never</td>\n",
       "      <td>OSS is, on average, of HIGHER quality than pro...</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Master’s degree (MA, MS, M.Eng., MBA, etc.)</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>No, I knew that Stack Overflow had a job board...</td>\n",
       "      <td>No, I've heard of them, but I am not part of a...</td>\n",
       "      <td>Yes, somewhat</td>\n",
       "      <td>Just as welcome now as I felt last year</td>\n",
       "      <td>36.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Difficult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11394</th>\n",
       "      <td>11394</td>\n",
       "      <td>25137</td>\n",
       "      <td>I am a developer by profession</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Never</td>\n",
       "      <td>The quality of OSS and closed source software ...</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Poland</td>\n",
       "      <td>No</td>\n",
       "      <td>Master’s degree (MA, MS, M.Eng., MBA, etc.)</td>\n",
       "      <td>...</td>\n",
       "      <td>I have never participated in Q&amp;A on Stack Over...</td>\n",
       "      <td>No, I knew that Stack Overflow had a job board...</td>\n",
       "      <td>No, and I don't know what those are</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>A lot more welcome now than last year</td>\n",
       "      <td>25.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Neither easy nor difficult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11395</th>\n",
       "      <td>11395</td>\n",
       "      <td>25138</td>\n",
       "      <td>I am a developer by profession</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Less than once per year</td>\n",
       "      <td>The quality of OSS and closed source software ...</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Master’s degree (MA, MS, M.Eng., MBA, etc.)</td>\n",
       "      <td>...</td>\n",
       "      <td>I have never participated in Q&amp;A on Stack Over...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No, I've heard of them, but I am not part of a...</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>A lot more welcome now than last year</td>\n",
       "      <td>34.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Too long</td>\n",
       "      <td>Easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11396</th>\n",
       "      <td>11396</td>\n",
       "      <td>25141</td>\n",
       "      <td>I am a developer by profession</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Less than once a month but more than once per ...</td>\n",
       "      <td>OSS is, on average, of LOWER quality than prop...</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>No</td>\n",
       "      <td>Secondary school (e.g. American high school, G...</td>\n",
       "      <td>...</td>\n",
       "      <td>Daily or almost daily</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No, I've heard of them, but I am not part of a...</td>\n",
       "      <td>Yes, somewhat</td>\n",
       "      <td>Somewhat less welcome now than last year</td>\n",
       "      <td>25.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11397</th>\n",
       "      <td>11397</td>\n",
       "      <td>25142</td>\n",
       "      <td>I am a developer by profession</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Less than once a month but more than once per ...</td>\n",
       "      <td>OSS is, on average, of HIGHER quality than pro...</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>No</td>\n",
       "      <td>Other doctoral degree (Ph.D, Ed.D., etc.)</td>\n",
       "      <td>...</td>\n",
       "      <td>A few times per month or weekly</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No, and I don't know what those are</td>\n",
       "      <td>Yes, somewhat</td>\n",
       "      <td>Just as welcome now as I felt last year</td>\n",
       "      <td>30.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Easy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11398 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index  Respondent                      MainBranch Hobbyist  \\\n",
       "0          0           4  I am a developer by profession       No   \n",
       "1          1           9  I am a developer by profession      Yes   \n",
       "2          2          13  I am a developer by profession      Yes   \n",
       "3          3          16  I am a developer by profession      Yes   \n",
       "4          4          17  I am a developer by profession      Yes   \n",
       "...      ...         ...                             ...      ...   \n",
       "11393  11393       25136  I am a developer by profession      Yes   \n",
       "11394  11394       25137  I am a developer by profession      Yes   \n",
       "11395  11395       25138  I am a developer by profession      Yes   \n",
       "11396  11396       25141  I am a developer by profession      Yes   \n",
       "11397  11397       25142  I am a developer by profession      Yes   \n",
       "\n",
       "                                             OpenSourcer  \\\n",
       "0                                                  Never   \n",
       "1                             Once a month or more often   \n",
       "2      Less than once a month but more than once per ...   \n",
       "3                                                  Never   \n",
       "4      Less than once a month but more than once per ...   \n",
       "...                                                  ...   \n",
       "11393                                              Never   \n",
       "11394                                              Never   \n",
       "11395                            Less than once per year   \n",
       "11396  Less than once a month but more than once per ...   \n",
       "11397  Less than once a month but more than once per ...   \n",
       "\n",
       "                                              OpenSource          Employment  \\\n",
       "0      The quality of OSS and closed source software ...  Employed full-time   \n",
       "1      The quality of OSS and closed source software ...  Employed full-time   \n",
       "2      OSS is, on average, of HIGHER quality than pro...  Employed full-time   \n",
       "3      The quality of OSS and closed source software ...  Employed full-time   \n",
       "4      The quality of OSS and closed source software ...  Employed full-time   \n",
       "...                                                  ...                 ...   \n",
       "11393  OSS is, on average, of HIGHER quality than pro...  Employed full-time   \n",
       "11394  The quality of OSS and closed source software ...  Employed full-time   \n",
       "11395  The quality of OSS and closed source software ...  Employed full-time   \n",
       "11396  OSS is, on average, of LOWER quality than prop...  Employed full-time   \n",
       "11397  OSS is, on average, of HIGHER quality than pro...  Employed full-time   \n",
       "\n",
       "              Country Student  \\\n",
       "0       United States      No   \n",
       "1         New Zealand      No   \n",
       "2       United States      No   \n",
       "3      United Kingdom      No   \n",
       "4           Australia      No   \n",
       "...               ...     ...   \n",
       "11393   United States      No   \n",
       "11394          Poland      No   \n",
       "11395   United States      No   \n",
       "11396     Switzerland      No   \n",
       "11397  United Kingdom      No   \n",
       "\n",
       "                                                 EdLevel  ...  \\\n",
       "0               Bachelor’s degree (BA, BS, B.Eng., etc.)  ...   \n",
       "1      Some college/university study without earning ...  ...   \n",
       "2            Master’s degree (MA, MS, M.Eng., MBA, etc.)  ...   \n",
       "3            Master’s degree (MA, MS, M.Eng., MBA, etc.)  ...   \n",
       "4               Bachelor’s degree (BA, BS, B.Eng., etc.)  ...   \n",
       "...                                                  ...  ...   \n",
       "11393        Master’s degree (MA, MS, M.Eng., MBA, etc.)  ...   \n",
       "11394        Master’s degree (MA, MS, M.Eng., MBA, etc.)  ...   \n",
       "11395        Master’s degree (MA, MS, M.Eng., MBA, etc.)  ...   \n",
       "11396  Secondary school (e.g. American high school, G...  ...   \n",
       "11397          Other doctoral degree (Ph.D, Ed.D., etc.)  ...   \n",
       "\n",
       "                                              SOPartFreq  \\\n",
       "0                    Less than once per month or monthly   \n",
       "1                    Less than once per month or monthly   \n",
       "2                    Less than once per month or monthly   \n",
       "3                    Less than once per month or monthly   \n",
       "4                                   A few times per week   \n",
       "...                                                  ...   \n",
       "11393                                               None   \n",
       "11394  I have never participated in Q&A on Stack Over...   \n",
       "11395  I have never participated in Q&A on Stack Over...   \n",
       "11396                              Daily or almost daily   \n",
       "11397                    A few times per month or weekly   \n",
       "\n",
       "                                                  SOJobs  \\\n",
       "0                                                    Yes   \n",
       "1      No, I knew that Stack Overflow had a job board...   \n",
       "2                                                    Yes   \n",
       "3                                                    Yes   \n",
       "4      No, I didn't know that Stack Overflow had a jo...   \n",
       "...                                                  ...   \n",
       "11393  No, I knew that Stack Overflow had a job board...   \n",
       "11394  No, I knew that Stack Overflow had a job board...   \n",
       "11395                                                Yes   \n",
       "11396                                                Yes   \n",
       "11397                                                Yes   \n",
       "\n",
       "                                                EntTeams          SOComm  \\\n",
       "0                    No, and I don't know what those are  No, not really   \n",
       "1                    No, and I don't know what those are   Yes, somewhat   \n",
       "2      No, I've heard of them, but I am not part of a...         Neutral   \n",
       "3      No, I've heard of them, but I am not part of a...   Yes, somewhat   \n",
       "4      No, I've heard of them, but I am not part of a...   Yes, somewhat   \n",
       "...                                                  ...             ...   \n",
       "11393  No, I've heard of them, but I am not part of a...   Yes, somewhat   \n",
       "11394                No, and I don't know what those are         Neutral   \n",
       "11395  No, I've heard of them, but I am not part of a...         Neutral   \n",
       "11396  No, I've heard of them, but I am not part of a...   Yes, somewhat   \n",
       "11397                No, and I don't know what those are   Yes, somewhat   \n",
       "\n",
       "                                  WelcomeChange   Age Trans Dependents  \\\n",
       "0       Just as welcome now as I felt last year  22.0    No         No   \n",
       "1       Just as welcome now as I felt last year  23.0    No         No   \n",
       "2      Somewhat more welcome now than last year  28.0    No        Yes   \n",
       "3       Just as welcome now as I felt last year  26.0    No         No   \n",
       "4       Just as welcome now as I felt last year  29.0    No         No   \n",
       "...                                         ...   ...   ...        ...   \n",
       "11393   Just as welcome now as I felt last year  36.0    No         No   \n",
       "11394     A lot more welcome now than last year  25.0    No         No   \n",
       "11395     A lot more welcome now than last year  34.0    No        Yes   \n",
       "11396  Somewhat less welcome now than last year  25.0    No         No   \n",
       "11397   Just as welcome now as I felt last year  30.0    No         No   \n",
       "\n",
       "                SurveyLength                  SurveyEase  \n",
       "0      Appropriate in length                        Easy  \n",
       "1      Appropriate in length  Neither easy nor difficult  \n",
       "2      Appropriate in length                        Easy  \n",
       "3      Appropriate in length  Neither easy nor difficult  \n",
       "4      Appropriate in length                        Easy  \n",
       "...                      ...                         ...  \n",
       "11393  Appropriate in length                   Difficult  \n",
       "11394  Appropriate in length  Neither easy nor difficult  \n",
       "11395               Too long                        Easy  \n",
       "11396  Appropriate in length                        Easy  \n",
       "11397  Appropriate in length                        Easy  \n",
       "\n",
       "[11398 rows x 63 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "474"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uniques=[]\n",
    "QUERY = \"\"\"\n",
    "SELECT * \n",
    "FROM DatabaseWorkedWith\n",
    "ORDER BY Respondent\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY,conn)\n",
    "\n",
    "counts = df.groupby('Respondent').count() #counts: number databases each respondent has worked with\n",
    "\n",
    "for i in range(len(counts)):\n",
    "    if counts.values[i] == 1:\n",
    "        uniques.append(counts.index[i]) #uniques: respondents who have worked with one database\n",
    "\n",
    "uniq_mysql = 0\n",
    "\n",
    "for j in range(len(df)):\n",
    "    if df['Respondent'][j] in uniques:\n",
    "        if df['DatabaseWorkedWith'][j] == 'MySQL':\n",
    "            uniq_mysql = uniq_mysql + 1 #uniq_mysql: respondents who have worked with MySQL only\n",
    "\n",
    "uniq_mysql "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query='''SELECT * FROM DevType'''\n",
    "sql= pd.read_sql_query(query,conn)\n",
    "sql['DevType'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Close the database connection.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": [
    " Copyright © 2020 IBM Corporation. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license?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&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&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&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"
   ]
  },
  {
   "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"
   ]
  }
 ],
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    "name": "ipython",
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