M4DataVisualization-lab.ipynb

{
    "cells": [
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "<center>\n    <img src=\"https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n</center>\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "# **Data Visualization Lab**\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Estimated time needed: **45 to 60** minutes\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "In this assignment you will be focusing on the visualization of data.\n\nThe data set will be presented to you in the form of a RDBMS.\n\nYou will have to use SQL queries to extract the data.\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Objectives\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "In this lab you will perform the following:\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "<hr>\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Demo: How to work with database\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Download database file.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "!wget https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DA0321EN-SkillsNetwork/LargeData/m4_survey_data.sqlite",
            "execution_count": 1,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "--2021-04-03 20:26:54--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DA0321EN-SkillsNetwork/LargeData/m4_survey_data.sqlite\nResolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 198.23.119.245\nConnecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|198.23.119.245|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 36679680 (35M) [application/octet-stream]\nSaving to: \u2018m4_survey_data.sqlite.6\u2019\n\nm4_survey_data.sqli 100%[===================>]  34.98M  35.7MB/s    in 1.0s    \n\n2021-04-03 20:26:56 (35.7 MB/s) - \u2018m4_survey_data.sqlite.6\u2019 saved [36679680/36679680]\n\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Connect to the database.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "import sqlite3\nconn = sqlite3.connect(\"m4_survey_data.sqlite\") # open a database connection",
            "execution_count": 2,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Import pandas module.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "import pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n%matplotlib inline",
            "execution_count": 3,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Demo: How to run an sql query\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# print how many rows are there in the table named 'master'\nQUERY = \"\"\"\nSELECT COUNT(*)\nFROM master\n\"\"\"\n\n# the read_sql_query runs the sql query and returns the data as a dataframe\ndf = pd.read_sql_query(QUERY,conn)\ndf.head()",
            "execution_count": 4,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 4,
                    "data": {
                        "text/plain": "   COUNT(*)\n0     11398",
                        "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>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>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Demo: How to list all tables\n"
        },
        {
            "metadata": {
                "scrolled": true
            },
            "cell_type": "code",
            "source": "# print all the tables names in the database\nQUERY = \"\"\"\nSELECT name as Table_Name FROM\nsqlite_master WHERE\ntype = 'table'\n\"\"\"\n# the read_sql_query runs the sql query and returns the data as a dataframe\npd.read_sql_query(QUERY,conn)\n",
            "execution_count": 5,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 5,
                    "data": {
                        "text/plain": "                Table_Name\n0                 EduOther\n1                  DevType\n2                  LastInt\n3               JobFactors\n4                 WorkPlan\n5            WorkChallenge\n6       LanguageWorkedWith\n7   LanguageDesireNextYear\n8       DatabaseWorkedWith\n9   DatabaseDesireNextYear\n10      PlatformWorkedWith\n11  PlatformDesireNextYear\n12      WebFrameWorkedWith\n13  WebFrameDesireNextYear\n14      MiscTechWorkedWith\n15  MiscTechDesireNextYear\n16              DevEnviron\n17              Containers\n18               SOVisitTo\n19            SONewContent\n20                  Gender\n21               Sexuality\n22               Ethnicity\n23                  master",
                        "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>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>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Demo: How to run a group by query\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "QUERY = \"\"\"\nSELECT Age,COUNT(*) as count\nFROM master\ngroup by age\norder by age\n\"\"\"\npd.read_sql_query(QUERY,conn)",
            "execution_count": 6,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 6,
                    "data": {
                        "text/plain": "     Age  count\n0    NaN    287\n1   16.0      3\n2   17.0      6\n3   18.0     29\n4   19.0     78\n5   20.0    109\n6   21.0    203\n7   22.0    406\n8   23.0    581\n9   24.0    679\n10  25.0    738\n11  26.0    720\n12  27.0    724\n13  28.0    787\n14  29.0    697\n15  30.0    651\n16  31.0    531\n17  32.0    489\n18  33.0    483\n19  34.0    395\n20  35.0    393\n21  36.0    308\n22  37.0    280\n23  38.0    279\n24  39.0    232\n25  40.0    187\n26  41.0    136\n27  42.0    162\n28  43.0    100\n29  44.0     95\n30  45.0     85\n31  46.0     66\n32  47.0     68\n33  48.0     64\n34  49.0     66\n35  50.0     57\n36  51.0     29\n37  52.0     41\n38  53.0     32\n39  54.0     26\n40  55.0     13\n41  56.0     16\n42  57.0     11\n43  58.0     12\n44  59.0     11\n45  60.0      2\n46  61.0     10\n47  62.0      5\n48  63.0      7\n49  65.0      2\n50  66.0      1\n51  67.0      1\n52  69.0      1\n53  71.0      2\n54  72.0      1\n55  99.0      1",
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Age</th>\n      <th>count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>NaN</td>\n      <td>287</td>\n    </tr>\n    <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>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Demo: How to describe a table\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "table_name = 'master'  # the table you wish to describe\n\nQUERY = \"\"\"\nSELECT sql FROM sqlite_master\nWHERE name= '{}'\n\"\"\".format(table_name)\n\ndf = pd.read_sql_query(QUERY,conn)\nprint(df.iat[0,0])",
            "execution_count": 7,
            "outputs": [
                {
                    "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",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "# Hands-on Lab\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Visualizing distribution of data\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### Histograms\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Plot a histogram of `ConvertedComp.`\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# your code goes here\nQUERY=\"\"\"\nSELECT ConvertedComp\nFROM master\n\"\"\"\n\ndf=pd.read_sql_query(QUERY,conn)\ndf.hist()",
            "execution_count": 8,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 8,
                    "data": {
                        "text/plain": "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f57b6e60050>]],\n      dtype=object)"
                    },
                    "metadata": {}
                },
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x288 with 1 Axes>",
                        "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEVCAYAAADgh5I1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAZLUlEQVR4nO3dfbRddX3n8fdHoohEIBib0oAEXdGWh+qQuxCx2puCi6g4QZe0aVHR4mRKUenUOoJ16ZpZpZN2li5FRVeWOMCApvERqsaHQbJ0lICEogEiY5QUI0gqTxKkVOh3/ti/6PFyb3LPObkPwPu11ll3799v//b+npOd87l773P2TVUhSdITZroASdLsYCBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQpBmVZH2SN850HRIYCJrFkvxJkmuT7Ehye5J1SX5vpuvaKcmiJJVkzh5c50FJLmjP974k30vy35Lsu6e2IU3EQNCslOQvgfcBfwssAJ4BnA8sn8m6dtqTIdCzzgOBq4B9gBdU1VOBlwAHAM/a09uTxjIQNOsk2R/478CZVfWZqrq/qn5RVf9YVW9LsneS9yW5rT3el2TvNnY0ybYkb02yvf2m/YbWd2ySnyTZq2dbr0zy3Tb9hCRnJ/lBkjuTrG1v0r1HA6cnuRX4GvD1tpp72lHMC9qyf5pkc5K7k3w5yaE923tJ+63/3iQfBNLz1P8SuA94TVVtBaiqH1XVWVW1s8bjkny7jf92kuN61r0+yd8k+Var5x+TPC3JpUl+1pZf1LN8JXlLkh8m+WmS/5nE94THMf/xNRu9AHgy8NkJ+v8aOBZ4HvBc4BjgnT39vwnsDywETgc+lGReVW0A7gf+oGfZPwE+3qbfApwM/D7wW8DdwIfGbPv3gd8BTgRe3NoOqKq5VXVVkpOBdwCvAp4OfAP4BECS+cCnW63zgR8AL+xZ9wnAZ6rq38d70i2cvgCcBzwNeC/whSRP61lsBfDa9tyfRXfE8b+AA4HNwLvHrPaVwAhwNN3R15+Ot209TlSVDx+z6gGcCvxkF/0/AF7WM38isLVNjwIPAHN6+rcDx7bpvwE+1qafShcQh7b5zcDxPeMOAn4BzAEWAQU8s6d/Z1vvttYBp/fMPwH4OXAo8DpgQ09fgG3AG9v894E/28Xzfi1wzZi2q4DXt+n1wF/39L0HWNcz/wrg+p75Apb1zP85cMVM//v7mLmHRwiaje4E5u/iPP1vAf/cM//Pre2X46vqoZ75nwNz2/THgVe1U0yvAq6rqp3rOhT4bJJ7ktxDFxAP013D2OlHu6n9UOD9Peu4i+6Nf2Gr8Zfjq6rGrO9OuhCayNjnTZtf2DN/R8/0A+PMz+XX9W5/7OuoxxkDQbPRVcC/0p2+Gc9tdG+8Oz2jte1WVd1E98b3Un79dBF0b44vraoDeh5Prqof965igunedfznMevYp6q+BdwOHLJzwSTpnQf+D/DKXZzHH/u8oXvuPx5n2cnq3f6kX0c9NhkImnWq6l7gXXTn/k9O8pQkT0zy0iR/T3dO/p1Jnt7Oy78LuKSPTXyc7nrBi4FP9rR/BDh350Xgtv5dfarpX4B/B545Zh3nJDmirWP/JKe0vi8ARyR5VTv6eQvd9Y6d3gvsB1zUU8PCJO9N8rvAF4Fnt4/jzknyR8DhwOf7eO5jvS3JvCSHAGcB/zDEuvQoZyBoVqqq99J96uaddG+8PwLeBHyO7jrAtcB3gU3Ada1tsj5Bd63ha1X105729wOXA19Jch+wAXj+Lmr8OXAu8M12iujYqvos8HfAmiQ/A26gOxqhbesUYBXd6aHFwDd71ncXcBzddYurWw1XAPcCW6rqTuAk4K1t/H8FThrzHPp1GbARuJ4usC4YYl16lEt3GlPS402SAhZX1ZaZrkWzg0cIkiTAQJAkNZ4ykiQBHiFIkprd3qArycfoPtmwvaqObG0H0n08bRGwFfjDqrq79Z1Dd7uAh4G3VNWXW/sS4EK6G3d9ETirqqp9QehiYAndJyf+qNp9XHZl/vz5tWjRosk/0x73338/++47+24eaV39sa7+zdbarKs/w9S1cePGn1bV08ft3N1Xmek+q300cENP298DZ7fps4G/a9OHA98B9gYOo7vFwF6t7xq6e9SE7uv9L23tfw58pE2vAP5hMl+xXrJkSQ3qyiuvHHjsVLKu/lhX/2ZrbdbVn2HqAq6tQW9dUVVfp/v6fa/lwEVt+iJ+9Y3S5cCaqnqwqm4BtgDHJDkI2K+qrmoFXTxmzM51fQo4vn2DU5I0jSZ1UbndMvfz9atTRvdU1QE9/XdX1bx2O98NVXVJa7+A7mhgK7Cqqk5o7S8C3l5VJyW5ge4GW9ta3w+A59c4X7ZJshJYCbBgwYIla9asGehJ79ixg7lzx97SZeZZV3+sq3+ztTbr6s8wdS1dunRjVY2M17en/8jHeL/Z1y7adzXmkY1Vq4HVACMjIzU6OjpAibB+/XoGHTuVrKs/1tW/2VqbdfVnquoa9FNGd7TTQLSf21v7Nn79ZlkH090sa1ubHtv+a2Pa/V3255GnqCRJU2zQQLgcOK1Nn0Z3P5Sd7SvaX7Q6jO5eLddU1e3Afe0vVoXuvvCXjbOuV9PdX8YvR0jSNJvMx0533ghsfpJtdH9xaRWwNsnpwK10N+yiqm5Msha4CXiI7k8gPtxWdQa/+tjpuvaA7mZa/zvJFrojgxV75JlJkvqy20Coqj+eoOv4CZY/l+4OkGPbrwWOHKf9X2mBIkmaOX5TWZIEGAiSpGZPf+z0UWHTj+/l9Wd/YUa2vXXVy2dku5K0Ox4hSJIAA0GS1BgIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNQaCJAkwECRJjYEgSQKGDIQk/yXJjUluSPKJJE9OcmCSryb5fvs5r2f5c5JsSXJzkhN72pck2dT6zkuSYeqSJPVv4EBIshB4CzBSVUcCewErgLOBK6pqMXBFmyfJ4a3/CGAZcH6SvdrqPgysBBa3x7JB65IkDWbYU0ZzgH2SzAGeAtwGLAcuav0XASe36eXAmqp6sKpuAbYAxyQ5CNivqq6qqgIu7hkjSZom6d6DBxycnAWcCzwAfKWqTk1yT1Ud0LPM3VU1L8kHgQ1VdUlrvwBYB2wFVlXVCa39RcDbq+qkcba3ku5IggULFixZs2bNQHVvv+te7nhgoKFDO2rh/hP27dixg7lz505jNZNjXf2ZrXXB7K3NuvozTF1Lly7dWFUj4/XNGbSgdm1gOXAYcA/wySSv2dWQcdpqF+2PbKxaDawGGBkZqdHR0X5K/qUPXHoZ79k08FMfytZTRyfsW79+PYM+p6lkXf2ZrXXB7K3NuvozVXUNc8roBOCWqvqXqvoF8BngOOCOdhqI9nN7W34bcEjP+IPpTjFta9Nj2yVJ02iYQLgVODbJU9qngo4HNgOXA6e1ZU4DLmvTlwMrkuyd5DC6i8fXVNXtwH1Jjm3reV3PGEnSNBn4vElVXZ3kU8B1wEPAP9GdzpkLrE1yOl1onNKWvzHJWuCmtvyZVfVwW90ZwIXAPnTXFdYNWpckaTBDnUivqncD7x7T/CDd0cJ4y59LdxF6bPu1wJHD1CJJGo7fVJYkAQaCJKkxECRJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqRkqEJIckORTSb6XZHOSFyQ5MMlXk3y//ZzXs/w5SbYkuTnJiT3tS5Jsan3nJckwdUmS+jfsEcL7gS9V1W8DzwU2A2cDV1TVYuCKNk+Sw4EVwBHAMuD8JHu19XwYWAksbo9lQ9YlSerTwIGQZD/gxcAFAFX1b1V1D7AcuKgtdhFwcpteDqypqger6hZgC3BMkoOA/arqqqoq4OKeMZKkaZLuPXiAgcnzgNXATXRHBxuBs4AfV9UBPcvdXVXzknwQ2FBVl7T2C4B1wFZgVVWd0NpfBLy9qk4aZ5sr6Y4kWLBgwZI1a9YMVPv2u+7ljgcGGjq0oxbuP2Hfjh07mDt37jRWMznW1Z/ZWhfM3tqsqz/D1LV06dKNVTUyXt+cIWqaAxwNvLmqrk7yftrpoQmMd12gdtH+yMaq1XQhxMjISI2OjvZV8E4fuPQy3rNpmKc+uK2njk7Yt379egZ9TlPJuvozW+uC2VubdfVnquoa5hrCNmBbVV3d5j9FFxB3tNNAtJ/be5Y/pGf8wcBtrf3gcdolSdNo4ECoqp8AP0rynNZ0PN3po8uB01rbacBlbfpyYEWSvZMcRnfx+Jqquh24L8mx7dNFr+sZI0maJsOeN3kzcGmSJwE/BN5AFzJrk5wO3AqcAlBVNyZZSxcaDwFnVtXDbT1nABcC+9BdV1g3ZF2SpD4NFQhVdT0w3sWJ4ydY/lzg3HHarwWOHKYWSdJw/KayJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEnN0IGQZK8k/5Tk823+wCRfTfL99nNez7LnJNmS5OYkJ/a0L0myqfWdlyTD1iVJ6s+eOEI4C9jcM382cEVVLQauaPMkORxYARwBLAPOT7JXG/NhYCWwuD2W7YG6JEl9GCoQkhwMvBz4aE/zcuCiNn0RcHJP+5qqerCqbgG2AMckOQjYr6quqqoCLu4ZI0maJunegwccnHwK+B/AU4G/qqqTktxTVQf0LHN3Vc1L8kFgQ1Vd0tovANYBW4FVVXVCa38R8PaqOmmc7a2kO5JgwYIFS9asWTNQ3dvvupc7Hhho6NCOWrj/hH07duxg7ty501jN5FhXf2ZrXTB7a7Ou/gxT19KlSzdW1ch4fXMGLSjJScD2qtqYZHQyQ8Zpq120P7KxajWwGmBkZKRGRyez2Uf6wKWX8Z5NAz/1oWw9dXTCvvXr1zPoc5pK1tWf2VoXzN7arKs/U1XXMO+KLwT+Y5KXAU8G9ktyCXBHkoOq6vZ2Omh7W34bcEjP+IOB21r7weO0S5Km0cDXEKrqnKo6uKoW0V0s/lpVvQa4HDitLXYacFmbvhxYkWTvJIfRXTy+pqpuB+5Lcmz7dNHresZIkqbJVJw3WQWsTXI6cCtwCkBV3ZhkLXAT8BBwZlU93MacAVwI7EN3XWHdFNQlSdqFPRIIVbUeWN+m7wSOn2C5c4Fzx2m/FjhyT9QiSRqM31SWJAEGgiSpMRAkSYCBIElqDARJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkZOBCSHJLkyiSbk9yY5KzWfmCSryb5fvs5r2fMOUm2JLk5yYk97UuSbGp95yXJcE9LktSvYY4QHgLeWlW/AxwLnJnkcOBs4IqqWgxc0eZpfSuAI4BlwPlJ9mrr+jCwEljcHsuGqEuSNICBA6Gqbq+q69r0fcBmYCGwHLioLXYRcHKbXg6sqaoHq+oWYAtwTJKDgP2q6qqqKuDinjGSpGmS7j14yJUki4CvA0cCt1bVAT19d1fVvCQfBDZU1SWt/QJgHbAVWFVVJ7T2FwFvr6qTxtnOSrojCRYsWLBkzZo1A9W7/a57ueOBgYYO7aiF+0/Yt2PHDubOnTuN1UyOdfVnttYFs7c26+rPMHUtXbp0Y1WNjNc3Z6iqgCRzgU8Df1FVP9vF6f/xOmoX7Y9srFoNrAYYGRmp0dHRvusF+MCll/GeTUM/9YFsPXV0wr7169cz6HOaStbVn9laF8ze2qyrP1NV11CfMkryRLowuLSqPtOa72ingWg/t7f2bcAhPcMPBm5r7QeP0y5JmkbDfMoowAXA5qp6b0/X5cBpbfo04LKe9hVJ9k5yGN3F42uq6nbgviTHtnW+rmeMJGmaDHPe5IXAa4FNSa5vbe8AVgFrk5wO3AqcAlBVNyZZC9xE9wmlM6vq4TbuDOBCYB+66wrrhqhLkjSAgQOhqv4v45//Bzh+gjHnAueO034t3QVpSdIM8ZvKkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUzJnpAh5vFp39hQn73nrUQ7x+F/3D2Lrq5VOyXkmPHR4hSJKAWXSEkGQZ8H5gL+CjVbVqhkt6TNnVkcnuTOWRyzB2V9dMHRVt+vG9M/Z6eSQ4fYb5PzWsC5ftOyXrnRWBkGQv4EPAS4BtwLeTXF5VN81sZXo0m6n/sG89akY2Kw1tVgQCcAywpap+CJBkDbAcMBCkPuwuBB+tR3szZbbWNVVSVTNdA0leDSyrqje2+dcCz6+qN41ZbiWwss0+B7h5wE3OB3464NipZF39sa7+zdbarKs/w9R1aFU9fbyO2XKEkHHaHpFUVbUaWD30xpJrq2pk2PXsadbVH+vq32ytzbr6M1V1zZZPGW0DDumZPxi4bYZqkaTHpdkSCN8GFic5LMmTgBXA5TNckyQ9rsyKU0ZV9VCSNwFfpvvY6ceq6sYp3OTQp52miHX1x7r6N1trs67+TElds+KisiRp5s2WU0aSpBlmIEiSgMdgICRZluTmJFuSnD1Of5Kc1/q/m+ToyY6d4rpObfV8N8m3kjy3p29rkk1Jrk9y7TTXNZrk3rbt65O8a7Jjp7iut/XUdEOSh5Mc2Pqm5PVK8rEk25PcMEH/TO1bu6trRvatSdY2U/vX7uqaif3rkCRXJtmc5MYkZ42zzNTuY1X1mHnQXZD+AfBM4EnAd4DDxyzzMmAd3XcfjgWunuzYKa7rOGBem37pzrra/FZg/gy9XqPA5wcZO5V1jVn+FcDXpuH1ejFwNHDDBP3Tvm9Nsq5p37f6qG3a96/J1DVD+9dBwNFt+qnA/5vu96/H2hHCL2+BUVX/Buy8BUav5cDF1dkAHJDkoEmOnbK6qupbVXV3m91A912MqTbMc57R12uMPwY+sYe2PaGq+jpw1y4WmYl9a7d1zdC+tXPbu3vNJjKjr9kY07V/3V5V17Xp+4DNwMIxi03pPvZYC4SFwI965rfxyBd0omUmM3Yq6+p1Ot1vATsV8JUkG9PdvmNPmWxdL0jynSTrkhzR59iprIskTwGWAZ/uaZ6q12t3ZmLf6td07Vv9mO79a9Jmav9Ksgj4D8DVY7qmdB+bFd9D2IMmcwuMiZaZ1O0zBjTpdSdZSvef9vd6ml9YVbcl+Q3gq0m+137DmY66rqO798mOJC8DPgcsnuTYqaxrp1cA36yq3t/2pur12p2Z2LcmbZr3rcmaif2rH9O+fyWZSxdAf1FVPxvbPc6QPbaPPdaOECZzC4yJlpnK22dMat1Jfhf4KLC8qu7c2V5Vt7Wf24HP0h0eTktdVfWzqtrRpr8IPDHJ/MmMncq6eqxgzOH8FL5euzMT+9akzMC+NSkztH/1Y1r3ryRPpAuDS6vqM+MsMrX72J6+MDKTD7ojnh8Ch/GrCytHjFnm5fz6RZlrJjt2iut6BrAFOG5M+77AU3umv0V3Z9jpqus3+dUXGI8Bbm2v3Yy+Xm25/enOA+87Ha9XW+ciJr5AOu371iTrmvZ9q4/apn3/mkxdM7F/ted9MfC+XSwzpfvYY+qUUU1wC4wkf9b6PwJ8ke5K/Rbg58AbdjV2Gut6F/A04PwkAA9VdzfDBcBnW9sc4ONV9aVprOvVwBlJHgIeAFZUtwfO9OsF8ErgK1V1f8/wKXu9knyC7lMx85NsA94NPLGnpmnftyZZ17TvW33UNu371yTrgmnev4AXAq8FNiW5vrW9gy7Qp2Uf89YVkiTgsXcNQZI0IANBkgQYCJKkxkCQJAEGgiQ9KuzuhnzjLP+HSW5qN8r7+KTG+CkjSZr9krwY2EF3L6Mjd7PsYmAt8AdVdXeS36jui3S75BGCJD0K1Dg35EvyrCRfavdV+kaS325d/wn4ULWbGk4mDMBAkKRHs9XAm6tqCfBXwPmt/dnAs5N8M8mGJMsms7LH1DeVJenxot0E7zjgk+2b0wB7t59z6G4SOEp3X6NvJDmyqu7Z1ToNBEl6dHoCcE9VPW+cvm3Ahqr6BXBLkpvpAuLbu1uhJOlRprpbY9+S5BT45Z/X3PnnUT8HLG3t8+lOIf1wd+s0ECTpUaDdkO8q4DlJtiU5HTgVOD3Jd4Ab+dVfSfsycGeSm4ArgbdVz23PJ9yGHzuVJIFHCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEmAgSJKa/w9PqoR44c9gnwAAAABJRU5ErkJggg==\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### Box Plots\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Plot a box plot of `Age.`\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# your code goes here\nQUERY=\"\"\"\nSELECT Age\nFROM master\n\"\"\"\n\ndf1=pd.read_sql_query(QUERY,conn)\ndf1.boxplot()",
            "execution_count": 9,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 9,
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f57b669d310>"
                    },
                    "metadata": {}
                },
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x288 with 1 Axes>",
                        "image/png": "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\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Visualizing relationships in data\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### Scatter Plots\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Create a scatter plot of `Age` and `WorkWeekHrs.`\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# your code goes here\nQUERY=\"\"\"\nSELECT Age, WorkWeekHrs\nFrom master\n\"\"\"\n\ndf2=pd.read_sql_query(QUERY, conn)\ndf2.plot.scatter(x='Age', y='WorkWeekHrs', grid=True)",
            "execution_count": 10,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 10,
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f57b65d4bd0>"
                    },
                    "metadata": {}
                },
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x288 with 1 Axes>",
                        "image/png": 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\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### Bubble Plots\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Create a bubble plot of `WorkWeekHrs` and `CodeRevHrs`, use `Age` column as bubble size.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# your code goes here\n\nQUERY=\"\"\"\nSELECT WorkWeekHrs,CodeRevHrs, Age\nFrom master\n\"\"\"\n\ndf3=pd.read_sql_query(QUERY,conn)\nsns.scatterplot(x='WorkWeekHrs', y='CodeRevHrs', alpha=0.5, color='red', size='Age', sizes=(10,200), data=df3)",
            "execution_count": 11,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 11,
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f57b64f6710>"
                    },
                    "metadata": {}
                },
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x288 with 1 Axes>",
                        "image/png": 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djZ49ezbrvB49ejTpeJ9p6ODBg7z99tvcc889VjrbMAyjo5Gens7atWubZEKKjo4mPT29SfeZNWsWhYWFRERE8Nvf/pbevXvz1FNPWelswzCMjsT06dN5/vnnm3xeRkZGk47/9NNPz9rWp08fK51tGIbRkYiKimL27NlBl7CIiori3nvvpVu3bm3csrbBRMEwDKMRHnnkESZPntyoMERFRTF58mQefvjh89Sy1qfJoiAiYSIS2xaNMQzD6IiEhYXx85//nPvuu4/o6Giio6NP2+/bdt999/Hzn/+8U9c+CsqnICKvA98GaoCNQJyI/Jdz7hdt2TjDMIyOQlhYGN/+9rd54IEHWLp0KZ988gnFxcXExcVx9dVXh1yV1DHOuWIRuRdYDDyJioOJgmEYIUNlZSUff/wxf/7zn9m7d2/dymvZ2dmEhYUxffp0IiMj27uZLSJYUYgQkQjgduA3zrkqEXFt2K6OweHDsGMH7N8PPXvChRfC0KHQyX90wzCaTmZmJt/73veorq6mrKwMgOrqagCysrJ47rnn+OUvf8mLL75IWlpaeza1RQRr+Po9sB/oDqwUkQuA4ubeVER6icibIvKViOwQkSkiEi8iH4nIbu/f3s29fquwdy/Mnw+ffgrZ2bB9OyxcCCtWQFVVuzbNMIzzy7Zt2/j2t79NcXFxnSCcSVlZGcXFxTzyyCNs27atyff45je/Sd++fRk7dmzdth/+8IeMGjWKiy66iDvuuIOioqK6fc8++yzDhg1j5MiRfPDBB01/qAZoVBREJAzIc84NdM7NcM454CBwTQvu+2tgiXNuFDAO2AE8BSxzzg0Hlnk/tw+VlbB8OZw8efp252DtWsjJaZdmGYZx/qmsrOSxxx5rUpXUxx57jMrKyibd54EHHmDJkiWnbcvIyCAzM5Mvv/ySESNG8OyzzwKwfft23njjDbZt28aSJUv4zne+Q01NTZPu1xCNioJzrhb47hnbnHOuujk39EYuXQW84r1WpXOuCLgNmOc9bB5qqmofjh6FQ4fq31dTo+YkwzBCgqVLl9aZiYKlurq6yUlnV111FfHx8adtu+666+oK402ePJkc74B00aJF3H333XTr1o0hQ4YwbNgw1q9f36T7NUSw5qOPRORfRGSQ18wTLyLxjZ9WL6lAPjBXRDaLyB9FpDvQzzmXC+D9W2/lJxF5WEQ2iMiG/Pz8ZjahEWpr9dUQTfwHYhhG52XevHkNmowaoqysjNdee61V2/Hqq69y4403AnDo0CEGDRpUty85OZlDDQ1km0iwovBN4FFgJRp1tBHY0Mx7eoAJwO+ccxcDJ2mCqcg597JzbqJzbmJiYmIzm9AI8fGQkFD/PhEI+DEMw+i61NTUsHfv3madu3fv3lYz6TzzzDN4PB7uvfdeANSKfzoi0ir3CkoUnHND6nmlNvOeOUCOc26d9/ObqEjkiUgSgPdv268m0RA9esDll0N99cyHD4fBg89/mwzDOO+Ul5cTHh7erHPDw8ObvA5DfcybN4/333+f+fPn13X8ycnJZGdn1x2Tk5PDgAEDWnwvaCQkVURmnmu/c+7tpt7QOXdERLJFZKRzbicwDdjufc0BnvP+XdTUa7cq48ZBt26wfj0UFGgY6oUXwoQJEBPTrk0zDOP8EB0d3ezRfk1NzVmZz01lyZIlPP/886xYsYKYgH7n1ltvZfbs2Tz++OMcPnyY3bt3c9lll7XoXj4ay1O45Yz3/xPw2QFNFgUvjwHzRSQS2As8iM5aForIQ2h0053NvHbrEBYGY8bozKCkREWhifXRDcPo3ISHh5OamkpWVlaTz01NTW3SLOOee+5h+fLlFBQUkJyczM9+9jOeffZZTp06VVdxdfLkyfz+978nLS2Nu+66izFjxuDxePjtb3/b7BnNmUh9tql6DxTZ7PUBdBgmTpzoNmxormvDMAxD2bFjB6NHj6533+LFi3nuueea5GyOiYnhRz/6UZ1juD2p79lEZKNzbmJ9xzelalPXz2A2DMM4g+nTpzd5vWSPx8O0adPaqEVtS+ct5WcYhnEeiIyM5MUXX2zSegovvvhip62B1Jij+X/QGYIAqSLyXuB+59ytbdi2tufECSgqUodyv34abmoYRkjinGswrDMtLY2XXnqJxx577LTaR4HExMTg8Xg6VO2jYN0DgTQ2J/plA+87N7W1sHkzrFypwhAVBRddBFddZc5kwwhBoqKiKCwspE+fPucUhiVLlrBs2TJee+2106qkpqam8sADDzBt2rQOM0NwzlFYWNjkct5BOZpF5A5gsXPuVDPb1yY029G8dy8sWHB2Ybtp0+DKK1uncYZhdBqqqqrIyckJur4RQG1tLRUVFURFRXXYRXWioqJITk4mIiLitO3ncjQH6z25FXhBRFYCbwAfNLf2UYcgK6v+SqdbtsDEidDC2GLDMDoXERERDBkypL2b0SEINqP5QWAY8DdgNpAlIn9sy4a1KQ2Vvq6p0ZdhGEaIEvScxzlXBfwDnSlsRKuadk6GDq3fqTxypPkUDMMIaYISBRG5QUReA/YAXwP+CCS1YbvaliFDtLaRz84motsuvbR922UYhtHOBOtTeACdITzS0ZzNzSIyEq69FkaNguPH1YcwcKDVNDIMI+QJShScc3d7l+C8ElgqItGAxzlX0qata0vCw7UEtq8MdlUV5OerQNRnQqqp0VDWM7z4hmEYXYmgREFE/gl4GIgHhgLJ6LrNnTOP+0z27dO1l48eVVGYNAkuvlhLZ+fkwMGD+qquhtRUGDsW4uL03JMnITMTduzQbRdfDCkp7fo4hmEYzSVY89GjwGXAOgDn3G4RqXdltE5HQQG88w4UF+vnsjJYskRNSWFhsHMnfPihvqKiIC0NHnoIpk/X/StXwrp1/uvt3g2zZ0Nycvs8j2EYRgsINvrolHOubhVqEfHQVQrkHT7sFwQfvozngwehsBA+/ljNRydP6rZNm1RMCgth69bTzy0r0zwIwzCMTkiworBCRH4MRItIBpqv8D+NnNM5aCgvobLSvz9wTWbndF9trd/PcCYN5UEYhmF0cIIVhaeAfGAr8Aha8uLpNmvV+aR///ozmMeN0319+vhDVaOidP3m8eN1DeeEBPUxBBIRcfY2wzCMTkKw0Ue1wB+8L0TkOhH5yDmX0ZaNOy8kJcGNN6qJqKhIO/7x43XVtYgI/RwTA1dcoTOEUaO0FIYvCmn6dOjeHfbs0eOmTNGcB8MwjE5IY6Wzr0WjjAYA7wL/CfwJLaX9TJu37nxx0UVwwQWasxAVdXoZ7VGj9FVaCqdOQe/e6mD2ER8PN92k+yMj9WUYhtFJaWym8Cs0FHUNcCOwFvg359yv27ph5524OH+YaX306HHuEhhWHsMwjC5AY6LgnHPLve/fFZH8LikIhmEYBtC4KPQSkZkBnyXws3Pu7bZplmEYhtEeNCYKK4BbGvjsgK4vCkeOaPmLmBgtiWE+A8MwujDnFAXvOgqhy/798Le/adJaWBhMnuzPZDYMw+iCBFs6u5+IvCIi//B+HiMiD7Vt0zoAmzapIIAmqW3cqPWRDMMwuijBDnlfAz5AQ1MBdgE/aIsGdSjOzEyura0/g9kwDKOLEKwoJDjnFgK1AN71mbv+upXjx5/uQ0hLg75dow6gYRhGfQRbJfWkiPTBWwRPRCYDJ9qsVR2FESO04umhQxAbq+UrPMF+ZYZhGJ2PYHu4x4H3gKEisgpIRJfl7NqI6NoItj6CYRghQrC1jzaJSDowEi1xsdM5Z6VADcMwuhiN1T6a2cCuESJiyWuGYRhdjMZmCr5Etb7A5cDH3s/XAMvp6slrp075F9qpqdEEtuRkSEwM7vwTJ/TciAgtw23rOxuG0cEJKnlNRN4Hxjjncr2fk4DftuTGIhIObAAOOeduFpF44K9ACrAfuMs5d7wl92gQ57SzP3pUF9Dp1UvLXUdF+Y8pLYV334XcXFi+HL76SqunpqfDHXdo+exzJbFt3QrLlmk5bo9HndTXX6/rMxiGYXRQgnU0p/gEwUseMKKF9/4+sAOI9X5+CljmnHtORJ7yfn6yhfeon507YfVq2LZNl91MSIA774RbbvELw5dfwmefQXY2rF0L5eVa7iIsDMLDNTS1IQf04cPw979DRYV+rq6GXbt0MZ/bb/eX5TYMw+hgBJunsFxEPhCRB0RkDvB34JPm3lREkoGbgD8GbL4NmOd9Pw+4vbnXb5TPP9e/GzdqMtrRo7BqlXbmoDOJNWu0HPaWLdq519ZqMptvhrF/f8PXz8nxC0Ige/bo2s6GYRgdlGCjj74rIncAV3k3veyce6cF930BeALoGbCtn2824pzLFZF6s8RE5GF0jQcGDx7cvLvX1GjHH4hzp2+rrdURfW3t6dt9x50rs7mhfWdeyzAMo4PRlMpuq1FH8zJgVXNvKCI3A0edcxubc75z7mXn3ETn3MTEYB2+Z3LJJWoGGjVKP8fFwYQJujSnNlLXZT55UpfljIrSbR4PDByoq62dK3dh4MD6q6mmpJhPwTCMDk1QMwURuQv4BRpxJMCLIvJD59ybzbjnVOBWEZkBRAGxIvIXIE9EkryzhCSg7SrPpaVpRz90qJqE4uNh+HCNLvJx8cXqXB40SNdm3r9f/QiTJsGMGXCuWUpyslZTXb4cyspUUAYOVCd1eHibPZZhGEZLEReEOUNEtgAZzrmj3s+JwFLn3LgW3VzkauBfvNFHvwAKAxzN8c65J851/sSJE92GDRta0oRzU1oK+/ZBXp6afrp317Wck5KC69yPHlUfQmQkDBhwuugYhmG0EyKy0Tk3sb59wUYfhfkEwUshTTM9BcNzwEJvSe6DwJ2tfP2m06MHXHihvppD375WQM8wjE5FsKKwREQ+ABZ4P38dWNzSm3vXf17ufV8ITGvpNVuVsjLYsUPDSfv0UXHw+R0MwzC6II2VuRiGRgX90Fvy4grUp7AGmH8e2te+rFoF69drhdRDh9THMHu2JrsdPgzHjkG3buoviI1t/HqGYRgdnMZmCi8APwZ8dY7eBhCRid59tzR8aifn2DHt+Hv10tlCQoJmPefl6Ypsn3/uX4QnMRFuvVWd0oZhGJ2YxkQhxTn35ZkbnXMbRCSlTVrUURBRAfjTn9SMFB4OF12kjuY1a07PN8jPh48+gvvuqz8U1TAMo5PQmLM46hz7oluzIR2O2loth1Fa6s9mrqjQ0hj1RWwdOmTrNxuG0elpTBQ+F5F/OnOjN0KoWclnnYrERE1w69NHTUNDhzZcBO/MjGjDMIxOSGPmox8A74jIvfhFYCIQCdzRlg1rd+LjYcoUzVOIiFBzUny8ZkMvrifwasAACz81DKPT01jp7DzgchG5Bhjr3fx359zH5zitayAC06ZB795aSbVfP5g6VR3OR4/qtpoaPbZXLz22W7f2bbNhGEYLCSqjuaPS5hnNDVFZqZVQ8/O1HHZyss4iDMMwOgGtkdFsBBIZqYvmpKa2d0sMwzBaldYuVWEYhmF0YmymcPy4+g969fJvy8/XtZUrKtRvUFOj+5OTgy9q51tTISxM8xwOHFCTU1SUltBOTm7eCmy+FeB8q7/Zus+GYbQioSsKWVmacLZtm/oDpk7Vstg7dmjJ65wcXWOhoEA74JIS7cSTkqBnTxg/HkaPPvu6lZWQmalZzyKa8HbkiH72+W8iI+GGG/T6TeHAAfjgA71eWJiarzIyNHTWMAyjFQhNUTh8GP78Z+38QTv948c1Ue3gQcjNhREjNHO5slIjjUCX00xN1fUUVq6EH/7wbL/Cl1/q+sw+AdiwQddmCA/XtZpBr7l0qeY+BNuhl5TA++/rLAF0JrJrly78M2uWrdNgGEarEJo+hawsf0cPah4qKIDPPtM1E0pKNKpo1Sp/NrNv3eWyMj3X44Hdu0+/bnW1rvscGNF1+DB88cXZ0UllZTriD5YjR/yCEMjevbbus2EYrUZoikJDYbi+rGSfrT/w85nrNJ/rOsHeryXU1MCJE1q4r6Sk9a9vGEZIEpqikJoK4wIWjQsP11IWU6fqCL57d3XoTp6sjtzwcHUMd+umM4iLL9bZw/Dhp1/X49F9gQ7k/v3V/3D8+OnHxsQ0LQO6Xz//+s4VFer72LJFxWHFCjV1+ZLpDGMcHMUAACAASURBVMMwmklo+hSSk+H++zU7eft2/TtliorC9u0qCDt3wpVXqtlmzBgdlTun5SxiY7XzHzr07GuPG6eisGmTOoOvv17NPrm5/mMiIuCaa7SjD5bYWLj5ZliyRAXgxAlda3rcOC3FkZur17PcCcMwWkBoZzQ7p+YXX10jH3l5aqevrFQ/QU2NlrsYNCj4kFSfU9njUQf2gQP6io7WjnvQoIaL652LggJ1UldUqHgdPeo3T115pZbbMAzDOAeW0dwQIn6TTCD9+jVtFF8fnoCvtkcPHdWnpbXsmqAmrCNHoKjo7H2dWOANw+gYhKZPoTPTs6eW8z6T8HAzHRmG0WJCc6ZQXa35CHv2qPkoKQlGjlSnMOiIu7RUR+UREXDypM4qCgv1fWysnlNbqyYgnxmoogKys3UkHxWlJiLfNVuTSZPUcb1nj5q2YmLgiit0VTjDMIwWEHqiUFurTuD33oO1a/22/wsvhO9/X0fiq1apaPTpox1uZqaag5yD9eu1s09L046/ulod1EOGaLbx1q3+Ehfdu6tzuL7M55bQuzd87WuaA1FRoSU4WmruMgzDIBRFwVdy4rPPTt++dSusXg2nTunSmjExGvK5bZvOJr78EgYO1DDVTZs0mW3UKPjqK01Yu+cePT6Qkye1lMbAgTq7aE0iImxmYBhGqxN6PoVjx1QA6uPIEe3kQesTrV2r4aTV1ZqXkJWlM4mdO3Xb3r3a4ZeV6bEN3S8vr22exTAMo5UJPVE4F4FJZxbJYxhGCBJ6ohAfr/6D+ujXzx/ZU1WlpqLERPUnREZqslpJiRbL83j0s8/UNHlyw/cze79hGJ2E0PMp9O+vJasPHdLM4EBH8+WXq3lo9WpNNBs/XrdlZsJtt6kDed06LYd90UWaf5CaqsekpmpE0JmO5oyM1vcnGIZhtBGhmdEcGJJ6/LgKRX0hqadOaYRPbKy+91VKrapSIYiP1/wAn9mpokKPyc3VzOVBg2yWYBhGh8Myms/E4zn3Gssi2qkvXqyF5yIjtXidx6PhpcnJGo4aWFG1slKPGzZMX4ZhGJ2Q0BQF0JF/fr6aeuLj1RQUSH6+CkN6OhQXq98gPl6jid5/Xz9HR+vSmmVlajbq2VMTy0aObJdHMgzDaCmhKQo7d8Knn6qZxycKkyerD8G35nFBga5sNnCgVkatqdFs5Z491XS0YYPmJbz5porD1Klqkjp0CGbPbrscgqNHVZgiItSMBZrMZmYqwzBagfMuCiIyCPgT0B+oBV52zv1aROKBvwIpwH7gLufc8Yau02z27IG339bEsoICrTRaWKgdbU2NisPhw7qk5sSJmry2eLHOGHz+gvBwrUh64ADExalAHDyoCWw5OZrP0Nqi4JyK0KpV2uZ9+7TN3burcF13nbbdluU0DKMFtEdIajXwv51zo4HJwKMiMgZ4CljmnBsOLPN+buU7V2uZiooKnQUsW6ZrLX/yiXb2a9ao43njRl0vYcUKePxxTWr75S/h5Zc1JPXAAXj1Vbj6avjHP7Qz3rrV72AOzHdoLfLytD3Ll8P+/VpS48MPNYFu9Wp45x3dbhiG0QLOuyg453Kdc5u870uAHcBA4DZgnvewecDtrX7zoiIdyZeVqShUVekIvKRER94nTqiJ6Msvdfs776h5adAgNTnl5qp/oaZGBebQIV19rbBQR+xbt+qCPfUtvtNS8vJU0JKSdCGgEye0HUePaluOHNFnMAzDaAHtmrwmIinAxcA6oJ9zLhdUOIB616oUkYdFZIOIbMivbyH74G+uHXmvXho15Bvdi/jfBy6CE7g/cFtkpApBWpqKx/TpMHhw89vVWJvPDCH2tTew3YZhGM2k3URBRHoAbwE/cM4VB3uec+5l59xE59zExMTEpt00cPW0sWPV1FNRoY7kIUNUIAYN8i+pOWuWCsOBAxqK6itsFx6ujt7+/XX2MX68XnvMGC233Rb066dmqtxcFaC4OA2R9YXKJiXpMxiGYbSAdok+EpEIVBDmO+fe9m7OE5Ek51yuiCQBR1v9xuHhcNllWvQuP9+fj1BermahKVNUGCZMgD//WdcoePFFrYp6333qgM7J0WilK6/U0NSZM+F3v1PxSEzUa917r4pEa9K/vzq7fes73HyzOspjYlTIMjI0PNYwDKMFtEf0kQCvADucc/8VsOs9YA7wnPfvojZpwNChcMMN2qGC2uUHDjx9kZqkJLjxRnXk9usH3/iG5jX07q3HJSZqVdSLL1bndK9eOnqPjdXrHjrU+qIAWopjwAB1hns8p4ekJiS0/v0Mwwg52mOmMBW4H9gqIl94t/0YFYOFIvIQcBC4s81aEBennfhEb5Z3VZXOFAJ9CPHxWt+oqkodyc7pTKOoSMNP4+I0s/nECRUC3yI8bW3b79On/nWlDcMwWoHzLgrOuc+AhnrNaeelEQMGaMTOxo3qU+jRA6ZN0xmAj6Ii7exra7XDj4rS7R6PisfQoRr5M3KkXufUKb3uxIk68zAMw+iEhGZGc1yczgJKStS3MHy4dvIi2rnv2aPOZdCEsepqdSIPGqSj9IoKnT0cOAC7d2sGc3GxziquuUbNSYZhGJ2Q0FtPAVQI1q/3r5Fw4oRmJZ88qbkJb7yhHf7zz2u11OpqePZZdSC/8ora7xcs0FnD2LHw7/+uOQIXX6zRQYZhGJ2U0BQFn82/ulqFoLJSo3p27YL33tMZwa5dcNVVGom0aZNGHh0/rpnEGzbA3XdrOYvoaHjkEfj97zXBzWYJhmF0YkLTfJSQoDH/n3+uwjB8uIajfvKJmpQGD9ZyEgcPat2jmBg1EX35pR47d67mMqxbp5FKzqljeutWFRHDMIxOSmjOFI4eVfPRgQNaO+irrzTpLDxcBeHQIa1pVFysorF1q1ZVvfVWWLhQZwygpSc++URDUSMi9BUd3b7PZhiG0QJCc6Zw4ID6ChISVAyqqlQYxo9XJ/LatRpump2tuQEHDvirqYLODsLCtExGZKSKSL9+6lNITm7fZzMMw2gBoSkKYWFatiIyUmcNvlH+8OEqFkuX6ozh+HEVju9+V2cN3btrRvSoUWp6mjRJZxelpXD77SoK3buffb/aWr1PVZVez2YThmF0UEJTFLp313UJ3n1X/QFDh+oiOUlJ6hO46Sb405/g0Uc1JHX1ahgxQktbnDihZqMRI3T2kJkJM2ZofsLw4Wffq7hY/RPbtqko9Ounax9YnSLDMDogoedTOHFCZwKvv64j/JMnNdJo82ZdX6G2VnMR5sxRM9LGjZq45vFoqOqECbr6mq/09gMPqF9h1y71P5zJxo0avXTqlF47Nxf+53+0HYZhGB2M0Jsp5OdrTSNfSQrQzvzgQV3JbO9enQkMHao1hYYOVXPTkSM6E4iMhPvvV3PR1q1aqG71ahWU7t21llJpqfoqBg9WEejbVyOYTp1Sc1NYmGZLx8W173dhGIZxBqEnCmFhOuqPjtYsZd8s4Kab1LH86qvqWL7uOvUR9OmjZiKPR2cOf/ubdvjjx2vimsejZqXsbK2aGhurzuaePdW0FB2tiW2LFqkogIav9u+vouFbE7opnDih4nbwoIbQJidDamrbFOHrDPh+n2C+y/Jyf62qvn1t+VLDOIPQEoWqKk0wu+sunQFs3qyO36uu0kzl2lrtZOPitOP+9FPdPmGCJq2NGKHbq6pUHD79VJ3N+/apqBw4AL/6lc44rr5ar3vsmC75OXiwzk4qK1UcVq9WYWhKXkNRkeZGHDmiIhMXp3WWNm/Wa82adXr9pq7IyZOaL5KZqVVtS0o0sbB7d/0txoxpuCChbxnTvDwVhZEjdVEkm7EZRh2h5VPIyVGHb0yMjvq3bFFRWLJEo4PCwtTcM2KEhp++9RZccokeO3iw+gcOH9a1kg8cUPNRba121vv26Szh+HGdHezfr07mpUtVBHbt0sJ7vXtrZ+TxaK5EWVlwbXdOxSU7W3Mojh3Te+bl6f4jR1QcujqrV2vHXl6uv8Pvf6/f9cGDGjjQ0JKkJSU6k8vN1d+sslJ/v3XrzmvzDaOjE1qikJurnfKaNdqp+Batyc5Wv0JxsUYH+WYRERH+xLSCAvUXVFX5hSAsDHbs0P2LFmlHf//9ek5RkX8UGx+vJqeICDU5xcZqe/LztXMPhmPHVFh8/g0fR45oe0BnQeXlrfuddSROnNAZAujMIDNTfw+f076qSosZ1kdenn8NjUB27FAfkGEYQKiZj8LC9BUVBZdfruad4mLNORDRfdHROpMID9c1FyZN8i9gc/CgzhCOHtUO33dNn1noiy+0o3rkER3N++7nnP6NjDzdhn2mmSM/X18ej5bh7tFDz83N1XsPHKgCc801ur2gQLefOqXt7urrNPueLzZWxT01VWd+gc/ckI/Ad25Da1wbhgGEmigMGKCZyzNmaBjp/PlqU05P144+IUE7jePHdfuBA2py2r1bR6a9e6sdu7TUXw4jLU1DTNPTteMfPlwL5o0YoaanzZv1+B49zl4cp39/vw9g82Y1NZ08qZ1U375aViMnBz7+2B+1tHChzmyys7UNM2f6/RPp6f51H7oisbFas2rpUjUFdeum+SXFxToT6N+//lwR0ByUpCSduQUyblz9CYeGEaKElvlowADtRN56C/7zP9WX8OyzOvoeOlQFoX9/He1HRWknvm+fjsKrqrSDLijQ65w6pbkMPXtqMb34eDXjvPOO2rz37VNfxE03aQc/fvzpFVQ9Hu3gunXTmcCHH6oggLYjL0/F5cMP9V4JCX6fgm/1taIif0mO7t21umtXpqbGL9QlJfravl1nDD17qgj361f/uTExuq718OH6e/bsqf8WLrnk/D6DYXRwQmum4PFoR/Luuzq69Hh0VvDii7omwvHjmqcQGanvf/UrtTlHRWmne911Otq84AINQ62q0n033qgd9oYNOvKPiFAByMxUU09ysnb8FRXajj59dK3nMWP0c17e2b6AsDCdiRQVaQfm83nExOjxEyZoGysqtF0+09OwYef3Oz2fFBSo2Pbvr6+sLN1WVaXP/cUXWqvKt9b2mQwYAF//uvpnIiJCN4TXMM5BaIkCqBkmIkI73cGDdYRfWamd+nXXqVAMGgT//d/aeVRVaYdcXa0mixkzNDLpuefUxt+rl5qQLr5YHZ7HjqkAlJbqzCE/X0WjZ0/dHx6uo9mePf1tCmtgwhZoHw88pqpK21xdraam4mI1i4wb1zbfWUfB56MB/Q7Cw3VWdeqUX3Abyzvw5ScYhlEvoScKPXtqhNDu3WqKKSiAW27RIndxcToKP3xYO/fkZH8hu+pqFYtTp3SNhehoPSY6WkeoI0boyHPmTDUfRUbqaDYlRUUgNlY/10dSkvocAqNgamv1/seO6T1FdIaxc6d2bEeO6Czloos0rDU6uuubjxIS1Pzz7rt+U9uFF6oZ7uhR/X4aMh8ZhhEUoSUKpaVq0rnuOu34o6K0k9m1S80PeXna+UZG6uh77FidGfhKZOflqXAMH64isW2bjkxXrYJrr9XCd0OHwhNPqP170CDt2BuaCfhITFRh+vBDFYGwMO3cUlJULFav1ntPneqPZAI1QU2Y4HdMDxjQ1t9g+1Jaqt/3pZeqGWn0aP0+PvtMRXT0aDUpjRxpEUVG1+TUKfWrHTigg8MhQ7SfacXM/NASBZ8NfvduHWleeql29lVVaub54gs1y3zrWzr6TE7WBKecHBWG4cN1f79+euzgwfojjRmjtv+YGJ1F9OmjnVN8vK7rnJ2tJqYJExouxTBypJqj8vN11Lt9O7z5pgrXsGHalkOH4PHHtb1VVRqFs3ixmq5mztRtzSmb0RmorVXRPXxYw3D794e//13F2zn9HT/9FO64Q533VoXW6GqcOqU11j7/3B9avWoVTJum/VUrDYRCSxR8uQiRkRoxlJ2tppfevbXjPX5cO/raWi1497vfaUfjSw7Lz1cTRm2tOpATE/WHWrtWxaO83N9hHzyo4a/x8RqBtGOHmq58zuX68JmQli/3O57Ly1XEEhJ0/x/+oKaiDRt0VnLBBVq9tbAQfvxjjcTpiuTmqpBv2qSi6ZwKZ1GRimJpqb5ftkxnWBdc0PgMzTA6E/v3ny4IoBaL5cv133srWQpC63+Nb0GcgQPVNr94sTqP33hDO99bb1Xbf2Wldjh79+r24mJ1Em/bprMGEX/Gc0KC1uKJiNDt/frpbOTwYRUbX8bsokUNZ9sGcvTo6ZFIiYlqstq/H156Sc0mu3apGWzRIi3TcOWVmiy3f39bfXPtQ1mZCnd+vgp2eLiKbJ8+Gk58+LB+P7W1+p8jOlqn1YWFVprc6Hrs33928iXowNRX7qYVCC1R6NZNO+otW7TuUGamdrA1NSoO1dU66szNVbNNba120DU1+r62Vn+ApCT9W1CgHdXjj2vkz7Rp2kGvXKmzhYICNSlFRGintnx54yUVAqeA3brpP4KsLJ11VFXpbAG0TadO6QwFVMxKSvyVWDs7JSUqenPnwmuv6SwgLk79K0eOaNRXZKSKe3i436Yqou/Np2B0Nc41821Fn0JoiUJlpY7sP/9cO37fl1xY6C8bUVqqs4DERP/6CT5Gj1Zbdnm5bo+LU5GpqNAZQna2dmQVFXpcZaWe4wulrK7WUe+5SEryh6v27q2dfGKiP3GtslI7xMAlPXfuhNtuUyd1ffV9OiPZ2fpctbVqNtq8Wb/D9HSNuLriCp0uT57sF8uTJ9Wk5ptdtSY1NTrrW7hQExT37m3d6xtGY6Sm1t/5d+/ecGRjMwgtn4LHo51MeLh23H36aEfTo4d2phUV8NFH/vLW11yjnUt+vnbWgwerwzcyEu65R81Bw4apqWLMGP2blqYd2ujReu748TrtGzXKf79zkZCgkUgffaTisHu3jporK9W05PNj3HijmkrKynTd6HHj1OTVVUbIYWGn1yrq2VN/J59vZuJErUu1cqX6EPr00f033KDfdWt/Dzt3wttv+1fX27kTZs/WfxOGcT644AIdFH32mfYHoJaI669v1dyb0BKFhAQ1C02erJ3LV1/5ncwej4449+3T8NKkJO2Mp0xRM87x4zpDWLRIR+qxsdrx5+friDUqSn+YRx/VjsPj0VLb3brpCN4XMhpMHP2IEXrswYNqcjp+XDvBrCxt144dajbq3Vs7zeHD9XNKStdZTyElRaPDtm3TkdD06eo3GTNG/SjPPqvmusceU7H0rXUxdqwKd2uzdevpy61WVOhswUTBOF94PDpDTk1VH0J4uPYHrZybE1qiUF2tnXN1teYfdOvmX1jnqqt05J2XpwlR77+vnU+vXjoqPHBAO+FbbtHOIDdXheEvf1GRyc5WUamp0RHsqVNaa8cX3VRbq6PYYArWHT+us4tNm3Q2UlSkM4HwcG3Tzp3aKRUVac5FZqbOUK64ouuEpEZFQUaGfpe++lB5eToz+OwzFcO//EUT2Z58Un+LadPariBgfd9rV/mujc5DWJgOetpi4OMltETBF8r45Zfa2c6cqZ1NWZlGskybpvkHRUVaRXXIEP3riyiqrdXPEybo8R99pCP6khI1V0yYoBFCf/+72r0vueT0pTsDS1s0xLFjaqbweLQ6alqaRkmVlWm5b+d0X1SUjo7j4nR24/GcO9y1MxIR4a8se/Kk+lHWrfOXtxDR2duaNfrsWVn6fbUF48ZpUIKvnEZsrPovDKOLEVqi4Cs/7fGos3nHDhWA9HTtdBcuVHNReLgeU1Oj51VX+0tdiOgs4NQp7bRqavT4Cy5Qkdi+XTuMLVt0ZHv33TqLmDIluDZ+9ZWGvV5wgQpEfLyKV2ysXm/3bm1LRYU6PKdM0VFDV89m9lWw7dbt9HBTEZ3NeTyauxEX1zajqKFD1YeQlaW/+7BhrercM4yOQoeLPhKRG0Rkp4jsEZGnWvXicXHqUPbV5d+9W//Gx6u9es8eHXXGxqrpZ9cuNWHU1qo/IjJSR+t5eSoKF17oT2jzCUF0tHbYpaXqBFq1SjuyYNm2Tf/6/BmZmf6wyxEj9J5btuhxsbFqb+/eXWciXZlevVQo77jj7O0336y/SXGxCnBbMXiwBh9ccYUJgtFl6VAzBREJB34LZAA5wOci8p5zbnur3MC3ClphoXawDz2kI81LLlFHblycjs4PHdIIlkcf1c7m2mtVGPLytEMePNhfIXXKFB25Vlbq3x499Hzfgi4DB/pXbgsGj/cn8Tmn779fBWLIEG1DRoZ2SOvXq1D07q2+iq5uykhO1hnUrFn6261dq6ala6/1V7rNyWnVeG3DCEU6lCgAlwF7nHN7AUTkDeA2oHVEITJSR9fR0RpFNGqUjuZLSjTpbPNmFYjSUh1919bq/vx87exHj9b93btrZFFNjc4K1q/3j9Y3b1YR6N1bR64ZGXqNYLnoInVqg/pAfKU5jh/3ry42fry+Tp7UZ0pP94tJVyUiQhML16zR7/Ouu9SMlp2tYu5z/KektHdLDaNT09F6koFA4Pw/B5gUeICIPAw8DDC4OeGAQ4aoCWn2bPUnHD6sHcuIEeqsTEmB3/5WS17s2KEjz7g4FY5Nm7ST379fZwXFxbp91y7tnG+/XSsWZmVp2NiUKdqhN6Ue0ciRGt20fbvOanwO1REjtErq0qU6Sg4LU3PK9deHThRMjx46M+jfX019PXtq2GpWlor1mDG2VoJhtBBx9dXSaCdE5E7geufct7yf7wcuc849Vt/xEydOdBs2bGj6jYqLdcRZXKymhwMHdCQ+cKC+nFNTRO/eWlJh/359n5amx+3apeISH6/7165V88Xhw3DnneqELCtTMRk9Wo9rCuXlmqNw8KB2+Ckp/vK4ZWU6cwkL084xVATBMIxWQ0Q2Oucm1revo80UcoDAlWKSgcMNHNt8YmN1hF1SogJw4YVqMhJRB255uSaE1dZq55uYqJ1xaanOCG65RY/ZtUudwNOnqympVy8dzYOacwJLUTSF6GidMYwcefa+mJiGl5s0DMNoIR1NFD4HhovIEOAQcDcwu83u5ssbiI31mx369NER+ooVKhYDBqi5yePR4xMSdObgW5ktLU3Fo1cvHc23VfKUYRjGeaBDiYJzrlpEvgt8AIQDrzrntp3XRsTGqsM4NVXt+r6ibL17qzPZOY30mTJFjzUMw+hCdChRAHDOLQYWt3c7iInRMNWJ9ZrdDMMwuiQdLnnNMAzDaD9MFAzDMIw6TBQMwzCMOkwUDMMwjDo6VPJaUxGRfOBAM09PALrI2pVBYc/b9Qm1Z7bnbT4XOOfqXZGrU4tCSxCRDQ1l9HVF7Hm7PqH2zPa8bYOZjwzDMIw6TBQMwzCMOkJZFF5u7wacZ+x5uz6h9sz2vG1AyPoUDMMwjLMJ5ZmCYRiGcQYmCoZhGEYdIScKInKDiOwUkT0i8lR7t6c1EJFBIvKJiOwQkW0i8n3v9ngR+UhEdnv/9g4450fe72CniFzffq1vPiISLiKbReR97+eu/ry9RORNEfnK+1tP6crPLCL/y/vvOVNEFohIVFd7XhF5VUSOikhmwLYmP6OIXCIiW737/p+ISLMb5ZwLmRdajjsLSAUigS3AmPZuVys8VxIwwfu+J7ALGAP8HHjKu/0p4Hnv+zHeZ+8GDPF+J+Ht/RzNeO7HgdeB972fu/rzzgO+5X0fCfTqqs+MLs27D4j2fl4IPNDVnhe4CpgAZAZsa/IzAuuBKYAA/wBubG6bQm2mcBmwxzm31zlXCbwB3NbObWoxzrlc59wm7/sSYAf6n+o2tCPB+/d27/vbgDecc6ecc/uAPeh302kQkWTgJuCPAZu78vPGoh3IKwDOuUrnXBFd+JnR0v7RIuIBYtBVGLvU8zrnVgLHztjcpGcUkSQg1jm3xqlC/CngnCYTaqIwEMgO+Jzj3dZlEJEU4GJgHdDPOZcLKhyAb1X7rvA9vAA8AdQGbOvKz5sK5ANzvSazP4pId7roMzvnDgG/BA4CucAJ59yHdNHnPYOmPuNA7/sztzeLUBOF+uxsXSYmV0R6AG8BP3DOFZ/r0Hq2dZrvQURuBo465zYGe0o92zrN83rxoGaG3znnLgZOoqaFhujUz+y1o9+GmkkGAN1F5L5znVLPtk7zvEHS0DO26rOHmijkAIMCPiejU9JOj4hEoIIw3zn3tndznndqiffvUe/2zv49TAVuFZH9qAnwWhH5C133eUGfIcc5t877+U1UJLrqM08H9jnn8p1zVcDbwOV03ecNpKnPmON9f+b2ZhFqovA5MFxEhohIJHA38F47t6nFeCMNXgF2OOf+K2DXe8Ac7/s5wKKA7XeLSDcRGQIMRx1VnQLn3I+cc8nOuRT0N/zYOXcfXfR5AZxzR4BsERnp3TQN2E7XfeaDwGQRifH++56G+sq66vMG0qRn9JqYSkRksve7+kbAOU2nvb3v7eDtn4FG52QBT7d3e1rpma5Ap4tfAl94XzOAPsAyYLf3b3zAOU97v4OdtCBSob1fwNX4o4+69PMC44EN3t/5XaB3V35m4GfAV0Am8Gc06qZLPS+wAPWZVKEj/oea84zARO/3lAX8Bm+1iua8rMyFYRiGUUeomY8MwzCMc2CiYBiGYdRhomAYhmHUYaJgGIZh1GGiYBiGYdRhomB0KUTk/4rIDwI+fyAifwz4/CsReTzIay0XkYlnbPu+iLwQ8PklEVka8PkxEfl/zWj3/xGRf6ln+2si8rUztpU29fqGESwmCkZXYzWa+YqIhAEJQFrA/suBVY1dRETCG7u+l/FAXMDxQV2/tTlHew2jSZgoGF2NVfg77TQ0oadERHqLSDdgNNDLW1Ruq7eefTcAEdkvIj8Rkc+AO30XFJEwEZknIv8BbAZGiEi0iMQBZWiy4IXewy8HVovIUBFZIiIbReRTERnlvVaiiLwlIp97X1PPfAAR+ScR+YeIRJ/rQUXkatF1NF4HtopIdxH5u4hsEV2D4OvN/haNkMXT3g0wjNbEOXdYRKpFZDDaQa9BK0ZOqyfwSgAAAl5JREFUAU6g2ex/BKY553aJyJ+Af0arrgJUOOeuABCRb6P/R+aj9e6f8W7/ArgUiEar0e4GLheRo2gmabaILAO+7ZzbLSKTgP8GrgV+Dfxf59xn3jZ+gAoV3mt/F7gOuN05d8q7VsovRORfG3jky4Cxzrl9IjILOOycu8l7rbiWfJdGaGKiYHRFfLOFy4H/QkXhclQUDqEd/y7vsfOAR/GLwl/PuNZLwEKfIJxx/WhUdHYDP0ZLW6/2Vqu9HPhbwAJY3bx/pwNjArbHikhP7/v70VIHtzstAufjh865N30fzvAprHdaWx9gK/BLEXkeLf3xaf1fj2E0jJmPjK6Iz+5/IWo+WovOFC4HNjVy7sl6rnWNiETVc/0pqCjsQFfF8vkTwoAi59z4gJdvNhAGTAnYPtDpwkh425rC6RUvG6OuvV6huwQVh2dF5CdNuI5hACYKRtdkFXAzcMw5V+OcO4YuXTkFmAukiMgw77H3AyvOca1XgMXoqN83s14NTAYSnXNHnRYQy0fr/692upbFPhG5E7SKrYiM8577IfBd38VFZHzAvTYDjwDviciApj6095wy59xf0AVqJjT1GoZhomB0RbaiUUdrz9h2wjmXAzyIdvJb0ZXbfn+uizktR74J+LOIhDnnjqMisC3gsDXoCllbvJ/vBR4SkS3e43zLvn4PmCgiX4rIduDbZ9zrM+BfgL+LSELTHpsLgfVen8fTwH808XzDsCqphmEYhh+bKRiGYRh1mCgYhmEYdZgoGIZhGHWYKBiGYRh1mCgYhmEYdZgoGIZhGHWYKBiGYRh1/H/h660yA+lAFwAAAABJRU5ErkJggg==\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Visualizing composition of data\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### Pie Charts\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# your code goes here\nQUERY=\"\"\"\nSELECT DatabaseDesireNextYear, count(*) as count\nFROM DatabaseDesireNextYear\nGROUP BY DatabaseDesireNextYear\nORDER BY count DESC LIMIT 5\n\"\"\"\n\n#df4=pd.read_sql_query(QUERY,conn)\n#x=df4.DatabaseDesireNextYear['counts']\n#title=df4['DatabaseDesireNextYear']\n#plt.pie(x, labels=title, autopct='%1,1f%%', radius=1)\n\nDatabaseDesireNextYear= pd.read_sql_query(QUERY, conn)\nx = DatabaseDesireNextYear['count']\nname = DatabaseDesireNextYear['DatabaseDesireNextYear']\nplt.pie(x, labels=name, autopct='%1.1f%%',radius=1)\nplt.title('The 5 Most Desired Databases Next Year')\nplt.show",
            "execution_count": 12,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 12,
                    "data": {
                        "text/plain": "<function matplotlib.pyplot.show(*args, **kw)>"
                    },
                    "metadata": {}
                },
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x288 with 1 Axes>",
                        "image/png": 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brZKpAKq6C9ghIkfZ6c7H6jbtgVhciTUg7+1W9vcHhgNru3Bs1hicQvcDwGe039IKN8JdeJo5EpifU1D8Sk5B8f5OG2PoHiHvN6vqeuDeVnYVArNFZAlQC0zvoJxFIvIasBhLHL4Adtm7pwNFIpIKrAYu9Mv6TxH5K5CK9Tr9WL83WmD5eLxAAlCgqp0f5Gb5ce4DBnc6T/gQKcLTzCnAT3MKiq8H7jFvwCKLiB5AKCLpqlptC8xc4Heq+lXIDSl0DwEewLoZIhJVKoc3/CeYo5aDyVzgwrIZU1c7bYihc0R6PJ5HReQbrDdTLzokOpcCy4lg0QFQwta53BmOBhbnFBRf5rQhhs4R0S0eRyl09wL+jfUqP+LxqWzbt+HpPk7bEQDeBS4umzF1XU8LsrvcJVguiVJguqrWdiF/DnCkqv6np7b4lXkRcBXWixcXcL2qvmoPiL0ey82gWLMCrrDfJDePyp9kvxl2nEhv8ThDofsI4BuiRHQg4ls8/pwAlOQUFJ8dgLLqVHWiPbyiEehqiyoH+HVXKxWRVsdTicgQLHGZYo9ROxxrbBhAPpbjfYKqjgRuA14XkbAcgGmEp6sUuq/Eeks21GlTAkykOZfbww08k1NQfFtOQXGghl3MA/ZrZ4T9T0TkG/vztYhkADOAo+xtV4lIqog8b+d9TkQW2gNhEZFqEblFRBYCR4jIeSLyuZ33EVuM+mPNJawGUNVqVV1j23ctVgun1t73Dn5visMNIzydpdCdSqH7Gaw3cglOmxNoFImWFo8/fwFe7Om0C3tC88+xul1tjbC/GshX1YlYAdrqgAJgnt1quhv4PbDDzvs34GC/atKApap6GLAN+BUw2S7PiyUgi7HGlq0Rkdki8kvbvkwgzR575s8XhOkoeSM8naHQ3Q/4EAhE8z0sUSSaWjz+nAp8klNQ3J0Waor98uIL4AdgJm2PsP8E+Jc9NixLVT2tlDcFa9oQqrqUH7tJYInLi/b347BEaZFd/3HAvqrqBX6GFT7lO+BuESlsx/5AtfYCjhGejrBCjX6KNU8saokiH09rTAA+zykoPqKL+Zp9PBNV9Qp7vFerI+xVdQZWaJMUYIGIjG4lXXtCUG8LS3O6OX51j1LVwuaKVPVzVb0d60F4uj3QtkZE9m1R5kFYohl2GOFpj0L3JCzRCVhc43AlSrta/gwAPswpKD6/h+W0OsJeREaoaomq3oF1s4/G8sdk+OWdD5xl5z0Aaz5ga7wPnGGPnm+O3DBMRLJF5CC/dBP5cWT9P4H7RCTFznM81tzAF3p4vEHBzPhti0L3z7D+aWH5ViDQxIDwACQBT+QUFPctmzH17m6WUUjrI+z/KCLHYnWZlmOFgPEBHhFZjBXo7CFgjp33a6yu1i5aoKrLReQG4B0RcQFNWG+tNgJ3ikg2UA9U8OObtvuxVhZZIiIJWBEsx6pqvV/RS0R2/5+fV9U/dfMc9Bgzjqc1Ct3nYl0oMSPMdZq4Mrfh8Via+3Rt2Yyp/whlhfabqQRVrReREVgtm5EtpuwEop50rOD6i1T1L4EsO1DEzI3VaQrdZ2KF0gin2DRBJ0ZaPP7ckVNQnFg2Y+qtIawzFWsuYAKWH+fyQIsOWK/ZscYzhS2mxeNPoXsq1pMi6l6Xd0SNJpeOaZiV67QdDnBd2YypM5w2ItYwzuVmrBjILxCDogPgi70WTzO35xQUX+G0EbGGER5ongLxGiFavyocicGulj/35hQUX+S0EbGEEZ5C9wSsMKwx8faqLXxILPe5BXgsp6D4Z04bEivEtvAUuvtjxWrOctoUp4nxFg9Y98IzJqphaIhd4Sl0J2D5dPZx2pRwIMZbPM1kAa/mFBRndJjS0CNiV3jgHqzJfAbAhyvWWzzN5AJPB3BWu6EVYlN4Ct2XYM0UNtiYFs8e/BJr9rghSMSe8FhvsB502oxww2taPC25Pqeg+AynjYhWYkt4Ct29sfw6iU6bEm74cJkWz948nlNQPNZpI6KR2BIeq6WT7bQR4YhPjfC0QhowxyyfHHhiR3isOVhRG8irp3iNj6ctDgKucdqIaCM2hKfQPQArJIGhDUxXq11uzCkoDssQopFKbAgPPAb0ddqIcMZrhKc9koDZOQXFMRWxIJhEv/AUui/Aej1qaAfT4umQQwHHAmdFG9EtPIXuPkB3I811i3W7fBw7p4bcB6sZ81A19y5oAGB7nXLCkzXsf381JzxZw4661u/zexc0MPYhK+89dl6Aa9+tZ/zD1fzm5brd255c3Li7/J7ijfJLIUDcklNQPMppI6KBaL/abibE87DiXXDXT5MpzU9nwcVpPLioieUVXmbMb+C44fGsvCKd44bHM2P+3oKxdIuXx75q4vPfprH4sjTe+M7Dym1edtUrn673suTydLyqlGz2UtekPL64id8fEpiRAaar1SmSgZlmVHPPiV7hKXTnApeGutpBGS4OGmS5AjKShNx+LjZUKq9+62H6BCvUz/QJCbzy7d6rn5RW+Dh8SBypCUK8S/jJsHheXuHBJdDoVVSVuiZIiIN/ftrIlYcmkhAXmHvACE+nmYy1vIyhB0Sv8MBdOBzatWynj683ejlsSBybq30MyrBO96AMF1tq9h4oPLa/i7lrvWyr9VHbpLz5vYd1u3xkJAmn5yZw4CM1DM9y4U4SFpV7OWV04GKWeWMr0mtPudWM7ekZ0XnyCt0nYq386BjVjcrpz9dyz8+SyUzqXKskt18c105O5IQna0lPFCYMcBHvsvJeMzmJayYnAXDJa3XcckwS//6qkXdWeRg/II4bjk7qkb2mxdMlRgIXAP922I6IJfpaPIXuOKzWjmM0eS3ROXdcAqflWq2SAekuNlZZrZyNVT76p7V+6i8+KJGvLk1n7oVp9E4R9u+zZ7qvN1prvo3s4+KJxU08f2YqS7d4WbmtZwuBejU4l4KnsoJNz1zHhscuo/zfv6fyi1f32L9r4UusveMXeGv3WuUFgMpFr1D+799TPvP3VLz2D9RjxUbf8dFsymf9ga1v/Pivrl76wV7lB5GbcgqKYzZiZU+JPuGxnkRjnKpcVbn4tXpy+8bxpyN+bIWcPDKeOYubAJizuIlTRrXe2Gzugv2wy8dLpR7OGbtnd+qvHzZwy7FJNPnAa7dRXAK1TT2z2xOsrpYrjl7HXszg3xYx8Pw7qfqqmMatP1h1VlZQX/Y1cZn9WrepaiuVX77OwOl3k33xQ+DzUVM6F19DDQ0bSsm+6AFUfTRWlOFraqBm6XtkHDg1OMexN0Ow1roydIPoEp5CtwuHh7d/ss7Lk0ua+GCNh4lF1UwsqubNlU0UTEnk3dUe9r+/mndXeyiYYolSeZWPk56u3Z3/9OfrOODBan75TC0PnpRMr5Qfu2mvrGjikOw4sjNcZCULRwyJY9zD1YjAhIE9Ew4PcUHpasWn9yZpoLUQqysplYQ+++Ct2gbAjvcfo9exF9Luyr4+L+ppRH1e1NNAXHpvQFCvB1VFPY2IK47Kz18i4+CTkbiQeg+uyykozgxlhdFCtPl4pmH1vx1jytB49KbWr8X3f7N3WOfsDBdvnpu6+/e8C9sO/TxtdALT/FbkvvOnydzZfVP3IBTjeDy7NtO4eTVJ2aOoXbmQuIw+JPZvudz3j8Rn9CXz0FPZ8PCFSHwiycMPJGW4tYJv6qgj2fj4lSQPm4AkpdG48TuyJp8T9GNoQR/gauDGUFcc6URXi8dM5us2wX6r5Wuso+Llv9P7uN+Cy8Wuz54j66jz2repvpralQsZfNlMhuQ/gTY1UL3sQwDch51B9oX30zvvEnbNe4qso86javH/qHhlBjs/fTaox9KCP5pQqV0neoSn0H00cJjTZkQqniBeCur1UPHy30k74BhSRx2JZ+cmPLs2Uz7rCtY/fBHeqq1sfPyPeKt37JGvvuwb4t0DiEt1I3HxpI48goYNpXukady8CoD4XoOpWfoB/aYV0FSxlqbtG4J2PC3IAM4NVWXRQjR1ta512oBIJlgtHlVl21v3ktBnHzIPPRWAxH457HPF07vTrH/4IgZNv5u4VPceeeMz+9FY/i2+pnokPon6tYtJHLjnIhA75z1F7xP/AD4PqD02SlyoJzBTSTrJ5UBRKCuMdKKjxVPoHoPD43YinWC91WrYsJyaZR9S/8MSymdfQfnsK6hbtahtO6q2sfm/NwGQlD2K1FGT2fj4H9k4Kx9UyZjw49JXtd99RuLA/YnP6IMrOZ2k7NGUz8wHoV3fURAYn1NQPDmUFUY60bF2eqH7XuBKp82IZJ7z/OSjaz2XHuO0HRHM02UzprbvtDLsJvJbPIXueCDkrzOiDS8BmvQVu5yRU1BsYj51ksgXHquL1foINEOnCdoAwtghCTDrr3eSaBCe3zhtQDRghCcgXJpTUBwN91TQieyTVOjOwkQXDAhW8A1DD9kXONxpIyKByBYeOAuriWvoISYsRsAwD8JOEOnCYwZuBQiPcS4HCiM8nSByhcdaFdSMnQgQTWqEJ0CMySkoHu60EeFO5AoPnAimfxAoPMQb4QkcptXTAZEsPCELvBILmLdaAeVkpw0IdyJSeMbNGScH5uxz0LTBAz99zJ35SUWcq8JpmyId4+MJKEfnFBS7O04Wu0TqJNGxHpHcVYmJ3Nc7kft6uTVZ9duD6xs2nV5VnfGT2roxieZtV5cwr9MDSgKWK+B5pw0JVyJVeI7b45eI1IuM+iQ1ZdQnqSmgWtvP6/3iuNq6mtOqqofkNjaNcMjOiMG0eALOsRjhaZNIFZ6j290rkloRHz/p2cwMns3MIE61fGRj0+qTq2viflFdMzrL5+sVIjsjBg9xEdntDmMOdtqAcCZShadL/1SvSHZpUmJ2aVIid/TO8qWpLj+srn7L6VXVvY+sqz8gPnLPQ8DwmtfpgWZcTkFxfNmMqXuv3GiIvBtu3JxxfYCh3S5AxFUjcsAHaakHfJCWCqqV2R7vihNqa+tPr6oeNrzJMyxw1kYOpqsVcJKxVjtZ7LQh4UjECQ9wUEBLE8ksT4g/dI47kznuTOJVfxjT0Fg2rbo6+cTq2tEZqjGxioARnqBwMEZ4WsUITws8IkMXJycNXZycxM19ensyfb4lk+vqd5xRVd13Un1DritChyB0hAdXVB6XwxwMzHLaiHDECE97iMRXxsWNfys9jbfS0xDVHUM9nhU/r671nlpdPSLb4x0UMluCjGnxBIXQXasRRiQ+5SY4VbGK9FqbkHBEUS/3lBP3GTzo4GH7rLpoYP+5b6SlflEnUttxCeGL17zVCgYTcgqKzZDwVoioFs+4OeMECJsJeI0uGbEoJXnEopRkUG3o7fN99ZPauqozqqoHjmtoHCntLpEZXpgWT1BIAfYDvnXakHAjooQHGAgkOm1Eq4gkbY+LO+jljHRezkjHpbpl36amlb+oruWU6uqRfb2+sA7Panw8QWMwRnj2ItKEp/uv0UOMT6T/94mJ/e/pncg99pSOSdaUjsyjrSkdYSWgXo0zXYLgEDV+wEASacITmWNs7Ckd81NTRs23p3T093qX2FM69hnd2BTSRaBao8mExQgWRnhaIdKEJ2JaPO0ikrolPn7SM5kZPGNN6dgwqrFxjTWlozbX7fNlhdokr+lqBYtspw0IR4zwhAFekcHLk5IGL09KYkbvXr401WVH1NVvPa2quvcRdfW5oZjSYeZqBQ3T4mmFSBOe/k4bEHSsKR1j3ktL5T1rSseubI93xU9rahtPr6oeluPxBEV8TYsnaBjhaYVIE540pw0IOSLu8oT4wx7PyuTxLGtKx9iGxrJpVdXJJ9bU5qarZgSiGtPiCRpGeFrBCE+E4REZ+k1y0tBvkpMo7Nu7ye3zLZlSV7/99Krq/gfXN4zu7pQOLy7zVis4GOFpBSM8kYxIwq64uPHF6WkUW1M6tg/1eFacVF3rO7WqesQgb+endJiRy0HDXLOtIKrqtA2dZtyccUuxQg0YOkGiT1cd2NCw/tSq6vTjausOSFZNaSvtEfX3b9pIn4GhtC+GiCubMdXntBHhhGnxRDGNLhmxMCV5xEK/KR3H1NZVnVFZPWhcY+NI/7SmqxVU4oFGp40IJyJNeNp8Yhs6wJ7S8VJGOi9ZUzo2j2hqWvmL6hrXydU1+5spEw6yNt8AABnzSURBVEHFCE8LIk14TBjJAOETGbAyMXHA3b0TmVfh/ip/g3dXXVqyYzP/oxlBIsefESIiTXjqnTYg2nDX6NarX/YNbkgqGvjZYbckIWK6s4GnyWkDwo1Ia14b4QkkqvqPmd4ylzIgpX579sBNC79w2qQoRPOL8kxLvQVGeGKY/Dd8c3vVMKn59+jvnj5SfE2rnbQpCjGtnVYwwhOjTFjlW3L0Up3sv82lvoQxpXN2OmVTlGKEpxWM8MQgGbW6veC/vn7Sio+vf8XXB6XUbvnMCbuilAanDQhHIk14djhtQMSjqnfM8n4fp20P5T9w8X05qFaH0qwoZoPTBoQjkSY85p/YQ377tm9u3yoObS9NcsOOQYM2fvplqGyKctY4bUA4YoQnhhiz1rfs+G/0yM6kHbXy2SNd3qZVwbYpBjDC0wqRJjzrnTYgUkmr013XP+tzCyR0Jr1LfQljls+qCrZdMYARnlaINOExLZ5uMuNxb2m8jyFdydNv25KJqTWbPg2WTTGCEZ5WMMITA0x/z/vxgJ0c3p28ExffPwJV0/LpPmZcVCtEovCY8AJdYNQ6LT1pkXZLdACSG3cOyC6f/1UgbYoxTIunFSIqHg/AuDnjvgVGdpiwi6yfuZ6qb6qIz4xn/9v2B6DuhzrK55Tja/CR2CeRIZcNIS5l7+gRreUF2PT8JqqWVJEyNIUhv7N6OTs+2YG3xkvfn/YN9CHsRUqDVs28x7s93tezZYF84vLMnXLXGl9c4v4dpzb4UZFflBf9ccK7QaS1eACWBKPQXlN6kfN/OXtsK59dzsAzB7L/rfuTeXAmW9/c2um83lovtd/Xsv+t+6M+pX5dPb5GHzvn76RPXp9gHMJe3P64t6SnogPgUl/82OUzI3pteIcwrZ02iEThWRyMQtNGpRGXtmdrpmFjA6mjUq39Y9Ko/LKy03kRUI+iqmiTInHC1re20ueEPkgI1s475yPvvOztdOrVeWfou23phLSa8k8CVV6MYISnDYzwtEPSkCSqvrb8qpWLKmna3vlpN3EpcWROymTVjatI6JuAK9VF3eo6Mg/KDJa5uxmxUVdO+0wndZyya0xcfP9IVHcFutwoxjiW2yDS4vFACIVnyEVDKH+6nC2vbiHzwEwkrmstlX4n9aPfSf0A2DBrA/1P68/2j7dTvbSa5H2S6X9y4Lv/SY1ac8uT3jgJQrTGpMbKfkM2fDx3/ZBjjg502VGKmfPWBhHX4imZXvIDIZqzlZSdxPA/D2e/m/fDfbibxP6J3Sqnbm2dVd7AJHZ+spOh+UNpWN9Aw6bAzx+89Qnv1wlegrYW+/7fvzDZ5W38LljlRxEe4COnjQhXIk54bELyJPFUWvGb1KdUvFZB72N7d6ucLS9tof+p/VGP/jgYwAW+xsCODDh9vu+TYRVMCWihLRA0btzSR+uJtNehoWdRflGeGf/UBpHY1QJ4HzgpkAWue3gdNStq8FR7WHHVCvpP64+vwcf297cDkHlwJllHZQHQtKOJDbM3kPOnnDbz9v6JJVKVX1aSMjyFhF7WTIWU/VJYecNKkockkzI0cL2hYZt11VnzfBMDVmA79NlROj69ZsMn1elDJnecOmZ532kDwpmIG8cDMG7OuAnAN07bES4kNmndzHu865M8hGycTUNCRsUnR96eiIg7VHVGGMfkF+V97LQR4UqktniWABVAP6cNCQdufsr7RZKHo0JZZ1JTVb991n84d90+eT12ND/10T9ZunYBGSlZXH/WzN3bP1r6MnOXvoLLFcfYoYcx7fBLW83v83n5x0u/x53Wh8t//ncAXlnwKMvXfc6QPvvxm7wCAD7/7l1qGio5dtzpPTW5I2oxjuV2iUgfT8n0EgU+dNqOcOAXC32fjtgUWtFpZr9VL012eRtW9LScw0eeSP5Jt++x7bsNX1NS9inXnfkYN5w1i+MmnNVm/g+XvsSAXkN3/65rqGbN5mX85cx/41MfG7atptHTwIJv/8fRB5zSU3M7w/z8ojyzjlY7RKTw2MR8H3pIhZad/4FvnFP1Cxo3vuQRT08dzftljyc1ec/xTfOWv84JE88mIc56k5iR0qvVvDuqK1i2diFHjv7R5SfiwuOzzGryNBDniuf9xc9xzLhTiYsLSSP/vVBUEslEsvD8z2kDnCTeow1/n+NtEMhw0o7eO78dm169LuAjmrfsWs+qjSX88+V87nntKtZuab1h9eKnDzLt8N8h8uMYq+TEVCYOP4oZL15Kn8yBpCSmsXbLt4zPCZkvPOYfih0RscJTMr1kLbDIaTuc4sb/eBcmNzHKaTsAJi55MBfVgK5O4fN5qW2s5uppDzDt8EuZ9d7f9mpYlaz9jIyUXgztt/ec4RMmns11ZzzKaUdczhtfzGbqIRfwaWkxM9+9hbe/eiqQprZkG+bFR4d0W3hExCsi34jIUhF5XUSyupi/2v6bLSIvdNOM/3YzX0Tz0y99C0ZvIGxGDyc2VfcZuu69kkCWmZXWjwnDpyAi5PQfjYhQXb/nbI3Vm5ZRsvZTbnz618x+71a+K/+GOe//fY8067auBKC/ewgLv3uXi0+4kfLta9iyK2jBLN/LL8ozoVs6oCctnjpVnaiqY4HtQH53ClHVclU9o5s2/BeIvPEAPWDgdl138Tu+XKftaMmI1a9OjvPUlwaqvPHDJ/Pdhq8B2LxzHR6vh/TkPd/cn3LYJdx63nPccu5/uPD4GxiZPZHpx/1ljzRvLJrN1EkX4PV5UbX0QMRFoydoq87MClbB0USgulqfAYMBRGSEiLwtIl+KyDwRGW1vHy4in4nIIhH5W3NGEckRkaX29zEi8rndkloiIu2OSymZXlIGxExozjivNt0x21slEHZjZwR1jV9a5Nt9d3eB2e/dyl2vXMHmXeu44alf8emKNzli1M/YVrmR256/mNnv38r5x16LiLCzZisPvXldp8pdvGY+w/qNJiutL6lJ6eQMOIDb/nsJgjCkz4guH2MnWAW8G4yCo41uDyAUkWpVTReROOBZYKaqvi0i7wOXqepKETkMuF1V80TkNeAFVX1CRPKBO+z8OcAbqjpWRO4HFqjq0yKSCMSpal17doybM+5y4KFuHUSE8df/eD8et1Z/4rQd7bHooD/Pq8rMceT1fhhwbX5R3j+cNiIS6EmLJ0VEvsFypvUG3hWRdOBI4L/2vkdg98Jxk4Fn7O9PtlHmZ8BfRORaYFhHomPzHDGwwugxi32fh7voAEwoeWgMqtudtsMBGjHdrE7TYx8PMAxIxPLxuICdtu+n+ePvj2i3eaWq/wFOBuqA/4lIXkdGlEwv2c6PghaV9N+pGy5/0xcRYUcTm2p6D/vhnWVO2+EAL+YX5bUeotKwFz328agVGOpK4GoswVgjImcCiMUEO+knwNn293NbK0tE9gVWq+p9wGvA+E6acV83zQ97XD713DHLu12g9RF0Yci+a16fHOepizXxecRpAyKJgDiXVfVrrABdZ2OJysUishhYBjSPUf9/QL6ILKJt5+ivgKV2N2008ERn6i+ZXvINMK/7RxC+XPOCb35aA46NTu4OgromlDzs6o6jOUIpNRNCu0a3x4+ranqL37/0+/mzVtKvAY7w2zTD3l4GjLW/3w7c3jJvJ7kXnJmzFCymLPN9ceCq8PfrtEbWrlW5mZVr5lW6942q/0kbmNZOF4nYkcut8Arwg9NGBIo+lbrpD6/5cgSCHxk+SEwoeXgc6tvmtB1Bpg6Y47QRkUbUCE/J9BIvUeLrcfnU+49Z3k0uCP7iW0EkwVOblbP27YANKgxTnssvygvodJFYIGqEx+YhYKPTRvSUq17xzc+oIyTRBIPN8LLiyfGe2oBOpwgjvNguA0PXiCrhKZleUgfc6rQdPeGwFb6vDv1Wo8YvIiDjlzyUgKrXaVuCwKz8orxvnTYiEokq4bF5jAhdSC2rWiuuesU3RKLs/5JVuWa0e9eqaFsMsA4odNqISCWqLnCAkuklTcDNTtvRVUTV94+Z3nUuJSrX2h6/tGgC6qtw2o4Acm9+UV6500ZEKlEnPDZPAsudNqIr/OE137ysWg5y2o5gkeCpcw8vK46W9bi2A3c4bUQkE5XCUzK9xAdc47QdneXA732LpyzXoK6HFQ7krH37yPim2iVO2xEAbuzoTZZfvKrmT4G9/SMR6fLy0iIyTUQO8Pt9i4gc33XTA4+IFIrI1V3JE5XCA1AyvaQYeMlpOzois0a3XfOCr79AnNO2BBsBmbjkgeQIdzR/AxR1Il1dizmLPX37NQ3YLTyqeqOqhjS2sx2JIiBErfDYXAmE72qOqnrHLO/qON09gz/qyaxaOzJr18pIdjT/Ib8oLyDCKSIPi8gXIrJMRG722z5DRJbbManuFJEjsSZP/9NuPY0QkcdF5Aw7/SEi8qmILLbjWWW0FdtKRM7z2/5Is5i0Y0uZiNwoIvOBM0XkZyLylV2Xf2zpA+zW3GoRubKjY49q4SmZXrIB+KvTdrTFpW/5Pu5TzSFO2xFqxpU8OgH1bXHajm7wVH5RXmdFM6VFV+tXraS5XlUnYU2G/omIjBeR3sCpwBhVHQ/cqqqfYk2a/rPdelrVXIAdt+o54P+p6gTgeKw3bpcB99oRJCYB60UkF2s+5GR7u5cfJ2zvZYufnfWqOgUriP1jwOl2XWf6pRkNnAgcCtwkIgntnZyoFh6b+4EvnTaiJePW+EryFke/X6c1Erx17n3XvL7SaTu6yC7gz11I37Kr9Vwrac4Ska+Ar4ExWF2pSqz4Uv8WkdOwFgdsj1HARlVdBKCqlarqofXYVscBBwOL7InYxwH7tmNLM822Hw7MteddonvGXSpW1QZV3QpsAQa0Z3TUC4/taL4US93DgvQ63fmX5329JEAruV6/cSNTvl/JyWtW795WWl/P2WvLOLVsDWeWlbGkrvWYanO2b+eXa1Zz8prVXF2+gQafNaH8rootTFuzhoKNP74xfm3XLp7cEZgYXzk/vDM5vqlmcUAKCw0X5xflbQpUYSIyHCuUzHF2y6YYSLZF41DgRSy/ztsdFUUrca7aiG0lwBw/MRylqoVt2eJXXE17ddn4B7H20sG1HfXCA1AyveRLwmhszx2zvN/G+RgSqPJOdbt5dMg+e2y7q2ILv+/Tl5dzhvOHvn25q2Lvns3mpiae2rmD/w7L4bXh++IF3qyqpMrr5eu6Ol4ZPhyvwncN9dT7fLxcuYuzswIXFmji4vtTsW60cOeB/KK8FwNcZibWDb1LRAYAPwewo3i6VfVN4I+we+pMFa2vobYCyBaRQ+z8GSIS30Zsq/eBM0Skv522t4gMa8uWVvgMqxs2vDl/dw8+UtdO7w63AcfaH8e48B3v3H6VgV2aZlJqKhua9lwxVxBq7NZLtc9H//jWu9xeVepViVel3k7nEmhSRVVpUB/xCLO2b+e8rF4kSOAmy2dWr9u/185vP97Ra3Q4h/74Evi/buRrDg3czNuqWtD8Q1UXi8jXWDGrVmMFygNLXF4VkWSsFsZV9vZngcdsx+0ZfuU02v6j+0UkBauFczyWL+c8EWkCNgG3qOp2EbkBeEdEXEATkK+qC9qwZQ9UtUJEfge8ZOffApzQjXPT/WDvkci4OeOysQKWOTLrO/cHXV74tHc/sULFBpQNTY1cvn49rw23uuyrGhr47fp1APiAp4cOY3DC3uLz5I7t3FNRQbLLxZGpafwzOxuAmdu28UZVJYenpnJR7z7ctGkjD7VoVQUCT1xS1dwpd9YirnZ9Ag6xCzgovyhvdYcpDV0iJrpazZRMLykHLnSi7pR6rfzrM970YIhOazy7cycF/fvzwYj9uLZff/66ae9J+7u8Xj6orubdfUfw0Yj9qFMfr+2yFs27uE8fXs4ZzrX9B3Df1gr+0LcfL+zcyVXlGyjaFrjQwvHehowRq19Z1XFKR7jEiE5wiCnhASiZXvIGVrTCkDLjce+yeB9DQ1Xfq5W7OCHdcgn8LCODkvq9F+L4rLaGwQkJ9I6PJ0GEE9Iz+KZ+Tyf0cjtfTmIir1bu4u7swaxsaKCssXGv8rrLsHXvH5nQWPV1wAoMDA/mF+V1d4VbQwfEnPDY/Bn4KFSVnfuBd96gHXuEfQ06/ePjWVRnvYldUFvLsFa6WYPiE1hcV0edz4eqsqC2hn0T92yQ3b+1giv69sWjis/ulbsQ6n2BDac8cckDmag2BbTQ7tNdv46hk8Sk8Ngz2E8Hgj5pcb8N+u3JCzWogwSvLt/AOWvXUtbYyLGrvufFnTu5ecBA/rFlC6eWreGerRXcPNAaHL3F08Sltu9nQkoKP83I4Iy1ZZxStgYfcJY7a3e571VVMTY5hf7xCWTGxTEhJYVT1lgRR0YnJ+9lR0/IqF4/oveO0nBYFXYXcFZ+UV7Q1jg2xJhzuSXj5ozbD1gA9AlG+cmNWj3zHm9FgpfhwSg/2vDEJVXPnXJnFeJyagqJDzgzvygv7Of4RTox2eJppmR6yfdYw9MD57Dw47Y53sVGdDpPvLchff9VL5U5VL0ClxrRCQ0xLTwAJdNL5gEXB7rcM+d55+2zlcmBLjfa2Wf9h0ckNFZ95UDVf8gvyvu3A/XGJDEvPAAl00uewholGhCGb9Lvz5ivBweqvFjjwMX39UI1KK3QNvhTflHeQyGsL+YxwmNTMr3kXn4cJdptEpu09m9PeEUgNQBmxSTpNeXD+2xf9lmIqrsuvyjv7hDVZbAxwuNHyfSSe7Amy3Wbvz3p/SrRy4gAmRSzjF02cxLqDXZM48L8ojyzPI0DGOFpQcn0krvoZtjUUz7zfTJ8MzEZ6iLQxPka00aufCGYK8Penl+UFzYTh2MNIzytUDK95J90UXz22aJrfv2Rb0KQTIpJhpTPPTyxYVcwYin9K78o7y9BKNfQSYzwtIEtPufRiVftCR6t//scb6NAevAtiy0OXHxfnwA7mu/OL8ozo5IdxghPO5RML3ka+Cmwo710Nz3t/TzJw6jQWBVbpNVuyum7rSQQjuYGrEmffwpAWYYeYoSnA0qml3wMHEkbq5P+fJHvs5HlgY2vY9iTMctnHyI+7/oeFLEeODq/KG9moGwy9AwjPJ2gZHrJCqx4s5/7b8/epmsveM83xhmrYoc4X2PqyJXPbehm9o+Bg/OL8j7vMKUhZBjh6SQl00u2AEcDjwDEe7Xx9tneWrHCRhqCzOCNnxyW1LBzURez3QMcn1+UF4krWkQ1MT1JtLuMmzPu3Buf9k4f+4N2K+yjoXvUpAxYu/DQvw5EJKmDpHXAb/OL8p4OhV2GrmOEp5uUjs4diRUH90CnbYklSsZc8nFFvwPbi9G8Bjgtvyjvm3bSGBzGdLW6Se6K0u+w/D53Y4VTMISAA0rnHCo+77o2dr8OTDKiE/6YFk8AKB2dewTWCovG0RwCygce/vmK0ecf6rdpE/D/8ovynnfKJkPXMC2eAJC7ovQzrC7XX9lzYTNDEMjetODQpPrti7Bi6DwG5BrRiSxMiyfA2L6fR4FwXisq4qlOHfT+54fecHN+Ud48p20xdB0jPEGgdHSuAL8GbgVynLUm6tgJ3AQ8lLuiNBJWITW0ghGeIFI6OjcRuAy4AejnsDmRTh3wMDAjd0VphdPGGHqGEZ4QUDo6NwMrzs+fMBNJu0o91qDNGbkrSjc5bYwhMBjhCSGlo3P7A1ditYKCsrJFFNGA5SubkbuiNNgBwQwhxgiPA5SOzk0BpmPFeTaz2vekHOtN1SO5K0r3XnfZEBUY4XEQ2wl9EpYAHQeIsxY5ykfAQ8DLxmkc/RjhCRNKR+cOBc7FCj52gMPmhIpNwPNYrZvlThtjCB1GeMKQ0tG5E7EE6Bwg22FzAs1G4EXgv8D83BWlZrpJDGKEJ4wpHZ3rAg4CTrQ/RwDxjhrVdRRYDrwHvAB8asTGYIQngigdnZsJ5GGJ0GSsLlmco0a1znIsn81HwEdm3I2hJUZ4Ihj77dgE4GC/zwGErlXUAJQCJfZnKfBV7orSzSGq3xChGOGJMkpH58YBQ4BhWNM1cuzvw4DeQJb9SQMSWynCgyUojVijhTdjveLe2OLvSmCleQNl6A5GeGIYW6RSsKIUNAKNxv9iCAVGeAwGQ8gx8XgMBkPIMcJjMBhCjhGeECMiKiJP+v2OF5EKEXmjg3yjROQjEflGREpF5FG/fVNE5HMRWSEi34pIvt++QhG5OjhHYzB0j0gbjBYN1ABjRSRFVeuAE4DOLFZ3H3C3qr4KICLj7L8Dgf8A01T1KxHpC/xPRMpV9eXgHILB0DNMi8cZ3gKm2t/PAZ4BEBGXiKwUkX5+v7+3xWQQ1lK8AKhqif01H3hcVb+yt28FrgH+HJIjMRi6gREeZ3gWOFtEkoHxwEIAVfUBT2FNFgU4Hlhsi8ndwAci8paIXCUiWXaaMcCXLcr/gtiZaGqIQIzwOICqLsEa2HcO8GaL3bOA39jfLwJm23lmA7lYkyuPARaItaKmYM2HMhgiBiM8zvEacCd2N6sZVV0HbBaRPOAwrG5Z875yVZ2lqqdgjTAeCywDJrUo+2CsVo/BEJYY4XGOWcAtfr4af/6N1eV6XlW9ACLyMxFJsL8PxAqdugF4ELhARCba+/oAtwF/C/4hGAzdwwiPQ6jqelW9t43dr2EFhZ/tt+2nwFIRWQz8D/izqm5S1Y1YsXseFZFvseZR3aeqH/vlvUFE1jd/An80BkPXMFMmwhARmYT16vyobuTNxwomf7Sq7gi4cQZDADDCE2aISAFwOXCuqs532h6DIRgY4TEYDCHH+HgMBkPIMcJjMBhCjhEeg8EQcozwGAyGkGOEx2AwhBwjPAaDIeQY4TEYDCHHCI/BYAg5RngMBkPIMcJjMBhCjhEeg8EQcozwGAyGkGOEx2AwhBwjPAaDIeT8f/rkvkx8Ndm6AAAAAElFTkSuQmCC\n"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### Stacked Charts\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Create a stacked chart of median `WorkWeekHrs` and `CodeRevHrs` for the age group 30 to 35.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# your code goes here\nQUERY=\"\"\"\nSELECT Avg(WorkWeekHrs), Avg(CodeRevHrs), Age \nFROM master \nwhere Age between 30 and 35\ngroup by Age\n\"\"\"\n\ndf5 = pd.read_sql_query(QUERY, conn)\nWorkWeekHrs= df5['Avg(WorkWeekHrs)']\nCodeRevHrs= df5['Avg(CodeRevHrs)']\nAge= df5['Age']\n\nfig, ax = plt.subplots()\nax.bar(Age, WorkWeekHrs, label='WorkWeekHrs')\nax.bar(Age, CodeRevHrs, bottom=WorkWeekHrs, label='CodeRevHrs')\nplt.show()",
            "execution_count": 13,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x288 with 1 Axes>",
                        "image/png": "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\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Visualizing comparison of data\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### Line Chart\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Plot the median `ConvertedComp` for all ages from 45 to 60.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# your code goes here\nQUERY=\"\"\"\nSELECT Age, ConvertedComp\nFrom master\nWHERE Age BETWEEN 45 AND 60\n\"\"\"\n\ndf6=pd.read_sql_query(QUERY,conn)\ndf6=df6.groupby(by='Age')['ConvertedComp'].median()\ndf6.plot(grid=True, title='The Median Converted Compensation for Ages Between 45 and 60.')",
            "execution_count": 14,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 14,
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f57b642a450>"
                    },
                    "metadata": {}
                },
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x288 with 1 Axes>",
                        "image/png": 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\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### Bar Chart\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Create a horizontal bar chart using column `MainBranch.`\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# your code goes here\nQUERY=\"\"\"\nSELECT MainBranch\nFROM master\n\"\"\"\n\ndf7=pd.read_sql_query(QUERY,conn)\ndf7=df7.groupby('MainBranch') ['MainBranch'].count()\ndf7.plot(kind='barh', figsize=(8,4), color='Orange')\nplt.show()",
            "execution_count": 15,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 576x288 with 1 Axes>",
                        "image/png": 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\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "",
            "execution_count": null,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Close the database connection.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "#conn.close()",
            "execution_count": 16,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Authors\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Ramesh Sannareddy\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### Other Contributors\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Rav Ahuja\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Change Log\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": " Copyright \u00a9 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&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"
        }
    ],
    "metadata": {
        "kernelspec": {
            "name": "python3",
            "display_name": "Python 3.7",
            "language": "python"
        },
        "language_info": {
            "name": "python",
            "version": "3.7.10",
            "mimetype": "text/x-python",
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "pygments_lexer": "ipython3",
            "nbconvert_exporter": "python",
            "file_extension": ".py"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 4
}