6. Data Visualization.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "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"
        },
        {
            "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\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"
        },
        {
            "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": 1,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "--2021-05-19 07:41:54--  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)... 169.63.118.104\nConnecting 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.\nHTTP request sent, awaiting response... 200 OK\nLength: 36679680 (35M) [application/octet-stream]\nSaving to: \u2018m4_survey_data.sqlite.1\u2019\n\nm4_survey_data.sqli 100%[===================>]  34.98M  25.2MB/s    in 1.4s    \n\n2021-05-19 07:41:56 (25.2 MB/s) - \u2018m4_survey_data.sqlite.1\u2019 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": 2,
            "metadata": {},
            "outputs": [],
            "source": "import sqlite3\nconn = sqlite3.connect(\"m4_survey_data.sqlite\") # open a database connection"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Import pandas module.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [],
            "source": "import pandas as pd\nimport matplotlib.pyplot as plt"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Demo: How to run an sql query\n"
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        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>",
                        "text/plain": "   COUNT(*)\n0     11398"
                    },
                    "execution_count": 4,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "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()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Demo: How to list ALL TABLES \n"
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {
                "scrolled": true
            },
            "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>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\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"
                    },
                    "execution_count": 5,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# print all the tables names in the database\nQUERY = \"\"\"\nSELECT name as Table_Name \nFROM sqlite_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"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Demo: How to run a group by query\n"
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {
                "scrolled": true
            },
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Age</th>\n      <th>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>",
                        "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"
                    },
                    "execution_count": 6,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "QUERY = \"\"\"\nSELECT Age,COUNT(*) as Count\nFROM master\ngroup by age\norder by age\n\"\"\"\npd.read_sql_query(QUERY,conn)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Demo: How to describe a table\n"
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {
                "scrolled": true
            },
            "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\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])"
        },
        {
            "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": 8,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>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\u2019s 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\u2019s 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  </tbody>\n</table>\n<p>3 rows \u00d7 63 columns</p>\n</div>",
                        "text/plain": "   index  Respondent                      MainBranch Hobbyist  \\\n0      0           4  I am a developer by profession       No   \n1      1           9  I am a developer by profession      Yes   \n2      2          13  I am a developer by profession      Yes   \n\n                                         OpenSourcer  \\\n0                                              Never   \n1                         Once a month or more often   \n2  Less than once a month but more than once per ...   \n\n                                          OpenSource          Employment  \\\n0  The quality of OSS and closed source software ...  Employed full-time   \n1  The quality of OSS and closed source software ...  Employed full-time   \n2  OSS is, on average, of HIGHER quality than pro...  Employed full-time   \n\n         Country Student                                            EdLevel  \\\n0  United States      No           Bachelor\u2019s degree (BA, BS, B.Eng., etc.)   \n1    New Zealand      No  Some college/university study without earning ...   \n2  United States      No        Master\u2019s degree (MA, MS, M.Eng., MBA, etc.)   \n\n   ...                           SOPartFreq  \\\n0  ...  Less than once per month or monthly   \n1  ...  Less than once per month or monthly   \n2  ...  Less than once per month or monthly   \n\n                                              SOJobs  \\\n0                                                Yes   \n1  No, I knew that Stack Overflow had a job board...   \n2                                                Yes   \n\n                                            EntTeams          SOComm  \\\n0                No, and I don't know what those are  No, not really   \n1                No, and I don't know what those are   Yes, somewhat   \n2  No, I've heard of them, but I am not part of a...         Neutral   \n\n                              WelcomeChange   Age Trans Dependents  \\\n0   Just as welcome now as I felt last year  22.0    No         No   \n1   Just as welcome now as I felt last year  23.0    No         No   \n2  Somewhat more welcome now than last year  28.0    No        Yes   \n\n            SurveyLength                  SurveyEase  \n0  Appropriate in length                        Easy  \n1  Appropriate in length  Neither easy nor difficult  \n2  Appropriate in length                        Easy  \n\n[3 rows x 63 columns]"
                    },
                    "execution_count": 8,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# your code goes here\n\nimport seaborn as sns\n\nQUERY = \"\"\"\nSELECT *\nFROM master\n\"\"\"\n\ndf = pd.read_sql_query(QUERY,conn)\ndf.head(3)\n"
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f364dcc7150>"
                    },
                    "execution_count": 9,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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AklnIU88AAAAAWEEERQAAAAAkERQBAAAAMAiKAAAAAEgiKAIAAABgEBQBAAAAkERQBAAAAMAgKAIAAAAgiaAIAAAAgEFQBAAAAEASQREAAAAAg6AIAAAAgCSCIgAAAAAGQREAAAAASQRFAAAAAAyCIgAAAACSCIoAAAAAGARFAAAAACQRFAEAAAAwCIoAAAAASCIoAgAAAGAQFAEAAACQRFAEAAAAwCAoAgAAACCJoAgAAACAYZ9BUVVdVlWPVdXdU7VnV9VNVfWZ8XrU1LYLq2p7Vd1XVWdP1U+rqrvGtourqhb/cAAAAADYX7NcUXR5knN2q701yc3dvSHJzeN9quqkJJuTnDz6XFJVh40+70yyJcmGsey+TwAAAAAOon0GRd390SRf2K18bpIrxvoVSV41Vb+mu5/o7vuTbE+yqaqOTfLM7r6luzvJlVN9AAAAAFgG9vceRcd09yNJMl6fM+rrkjw01W7HqK0b67vXAQAAAFgmFvtm1nPdd6j3Up97J1VbqmprVW3duXPnog0OAAAAgD3b36Do0fF1sozXx0Z9R5LjptqtT/LwqK+foz6n7r60uzd298a1a9fu5xABAAAAmI/9DYquT3LeWD8vyXVT9c1VdURVnZjJTatvG19Pe7yqzhhPO3vjVB8AAAAAloE1+2pQVVcnOSvJ0VW1I8kvJPmlJNdW1flJHkzymiTp7m1VdW2Se5I8meSC7n5q7OpNmTxB7cgkN44FAAAAgGVin0FRd79uD5teuof2FyW5aI761iSnzGt0AAAAACyZxb6ZNQAAAACHKEERAAAAAEkERQAAAAAMgiIAAAAAkgiKAAAAABgERQAAAAAkERQBAAAAMAiKAAAAAEiSrDnYA2DluurWBxd9n68//fhF3ycAAAAw4YoiAAAAAJIIigAAAAAYBEUAAAAAJBEUAQAAADAIigAAAABIIigCAAAAYBAUAQAAAJBEUAQAAADAICgCAAAAIImgCAAAAIBBUAQAAABAEkERAAAAAIOgCAAAAIAkgiIAAAAABkERAAAAAEkERQAAAAAMgiIAAAAAkiwwKKqqB6rqrqq6o6q2jtqzq+qmqvrMeD1qqv2FVbW9qu6rqrMXOngAAAAAFs9iXFH0vd19andvHO/fmuTm7t6Q5ObxPlV1UpLNSU5Ock6SS6rqsEX4fAAAAAAWwYH46tm5Sa4Y61ckedVU/ZrufqK770+yPcmmA/D5AAAAAOyHhQZFneRDVXV7VW0ZtWO6+5EkGa/PGfV1SR6a6rtj1L5KVW2pqq1VtXXnzp0LHCIAAAAAs1izwP5ndvfDVfWcJDdV1af30rbmqPVcDbv70iSXJsnGjRvnbAMAAADA4lrQFUXd/fB4fSzJ+zL5KtmjVXVskozXx0bzHUmOm+q+PsnDC/l8AAAAABbPfgdFVfX1VfWMXetJXp7k7iTXJzlvNDsvyXVj/fokm6vqiKo6McmGJLft7+cDAAAAsLgW8tWzY5K8r6p27eeq7v5AVX0iybVVdX6SB5O8Jkm6e1tVXZvkniRPJrmgu59a0OgBAAAAWDT7HRR192eTvGCO+ueTvHQPfS5KctH+fiYAAAAAB85Cn3oGAAAAwAohKAIAAAAgiaAIAAAAgEFQBAAAAEASQREAAAAAg6AIAAAAgCSCIgAAAAAGQREAAAAASQRFAAAAAAyCIgAAAACSCIoAAAAAGARFAAAAACRJ1hzsAcB8XHXrg4u6v9effvyi7g8AAAAOZa4oAgAAACCJoAgAAACAQVAEAAAAQBJBEQAAAACDoAgAAACAJIIiAAAAAIY1B3sAAAAAAAfCVbc+uKj7e/3pxy/q/pYjVxQBAAAAkMQVRaxy0mUAFpPzCgBwqHNFEQAAAABJBEUAAAAADIIiAAAAAJIchHsUVdU5Sf5bksOSvKu7f2mpxwAHintTAAAAcChb0iuKquqwJL+Z5BVJTkryuqo6aSnHAAAAAMDclvqKok1Jtnf3Z5Okqq5Jcm6Se5Z4HHBIWOwrlACW0mJfFenfRACAA2+pg6J1SR6aer8jyelLPAYAYAkIdpYfc7I8LfdQ9VD4KrxjXrjVeMyL7UD8b7gaj5mDr7p76T6s6jVJzu7ufz/evyHJpu7+id3abUmyZbx9fpL7lmyQB87RST53sAfBQWHuVy9zv3qZ+9XL3K9e5n51Mu+rl7lfvVbS3H9Ld6/dvbjUVxTtSHLc1Pv1SR7evVF3X5rk0qUa1FKoqq3dvfFgj4OlZ+5XL3O/epn71cvcr17mfnUy76uXuV+9VsPcL+nNrJN8IsmGqjqxqp6WZHOS65d4DAAAAADMYUmvKOruJ6vqzUk+mOSwJJd197alHAMAAAAAc1vqr56lu29IcsNSf+4ysKK+Sse8mPvVy9yvXuZ+9TL3q5e5X53M++pl7levFT/3S3ozawAAAACWr6W+RxEAAAAAy5SgaBFU1TlVdV9Vba+qt86xvarq4rH9zqp60ax9Wd5mmPsfHnN+Z1X9RVW9YGrbA1V1V1XdUVVbl3bkLNQMc39WVf3tmN87qurnZ+3L8jXDvP/M1JzfXVVPVdWzxzY/84ewqrqsqh6rqrv3sN25fgWaYd6d51eoGebeeX6FmmHunetXqKo6rqo+XFX3VtW2qvrJOdqsjvN9d1sWsGRyU+6/SvKtSZ6W5FNJTtqtzSuT3JikkpyR5NZZ+1qW7zLj3H9XkqPG+it2zf14/0CSow/2cVgO2NyfleT9+9PXsjyX+c5dku9P8qdT7/3MH8JLku9O8qIkd+9hu3P9ClxmmHfn+RW6zDD3zvMrdNnX3O/W1rl+BS1Jjk3yorH+jCR/uVr/v70rihZuU5Lt3f3Z7v5/Sa5Jcu5ubc5NcmVPfDzJs6rq2Bn7snztc/66+y+6+2/G248nWb/EY+TAWMjPrp/7Q9d85+51Sa5ekpFxwHX3R5N8YS9NnOtXoH3Nu/P8yjXDz/ye+Jk/xM1z7p3rV5DufqS7PznWH09yb5J1uzVbFed7QdHCrUvy0NT7Hfnq/5j21GaWvixf852/8zNJn3fpJB+qqturassBGB8Hzqxz/+Kq+lRV3VhVJ8+zL8vPzHNXVV+X5JwkfzRV9jO/sjnX4zy/+jjPr2LO9StbVZ2Q5IVJbt1t06o436852ANYAWqO2u6PkttTm1n6snzNPH9V9b2Z/AL5kqnymd39cFU9J8lNVfXp8RcMlr9Z5v6TSb6lu79cVa9M8sdJNszYl+VpPnP3/Un+vLun/yLpZ35lc65fxZznVyXneZzrV6iqenomAeBbuvtLu2+eo8uKO9+7omjhdiQ5bur9+iQPz9hmlr4sXzPNX1V9R5J3JTm3uz+/q97dD4/Xx5K8L5PLFTk07HPuu/tL3f3lsX5DksOr6uhZ+rJszWfuNme3S9H9zK94zvWrlPP86uQ8T5zrV6SqOjyTkOg93f3eOZqsivO9oGjhPpFkQ1WdWFVPy+QfjOt3a3N9kjeOO6SfkeRvu/uRGfuyfO1z/qrq+CTvTfKG7v7LqfrXV9Uzdq0neXmSOZ+swLI0y9x/U1XVWN+Uyb+3n5+lL8vWTHNXVd+Q5HuSXDdV8zO/8jnXr0LO86uX8/zq5ly/Mo2f6Xcnube7f20PzVbF+d5Xzxaou5+sqjcn+WAmdzq/rLu3VdV/GNt/K8kNmdwdfXuSv0/yo3vrexAOg/0w49z/fJJvTHLJ+F3iye7emOSYJO8btTVJruruDxyEw2A/zDj3P5jkTVX1ZJJ/SLK5uzuJn/tD1IzzniSvTvKh7v67qe5+5g9xVXV1Jk85OrqqdiT5hSSHJ871K9kM8+48v0LNMPfO8yvUDHOfONevVGcmeUOSu6rqjlF7W5Ljk9V1vq/Jv2cAAAAArHa+egYAAABAEkERAAAAAIOgCAAAAIAkgiIAAAAABkERAAAAwCGiqi6rqseq6u4Z2/9QVd1TVduq6qp9tRcUAQCHlKr6pqq6pqr+avzSc0NVPe8gjONHquqb59nnhOlf6qpqU1V9tKruq6pPV9W7qurrFn+0AMAKcnmSc2ZpWFUbklyY5MzuPjnJW/bVR1AEABwyqqqSvC/JR7r7ud19UpK3JTlmicdxWJIfSTKvoGi3fRyT5A+S/Ofufn6Sb0vygSTPWIwxAgArU3d/NMkXpmtV9dyq+kBV3V5V/6uq/vXY9GNJfrO7/2b0fWxf+xcUAQCHku9N8k/d/Vu7Ct19R5KPVdUvV9XdVXVXVb02SarqrKr6SFX94bhi5z018YqqunbXPka7/znWX15Vt1TVJ6vqD6rq6aP+QFX9fFV9LMnrkmxM8p6quqOqjqyq06rqz8YvaB+sqmNHv9Oq6lNVdUuSC6aO5YIkV3T3LeM4urv/sLsfrapnV9UfV9WdVfXxqvqOsa+3V9UVVfWhMZ4fqKr/Oo75A1V1+NRY/0tV3TaWf3WgJgQAWBYuTfIT3X1akv+U5JJRf16S51XVn4/fKfZ5JZKgCAA4lJyS5PY56j+Q5NQkL0jysiS/vCuoSfLCTC6zPinJtyY5M8lNSc6oqq8fbV6b5Per6ugkP5fkZd39oiRbk/zU1Of8Y3e/pLt/b2z74e4+NcmTSf57kh8cv6BdluSi0ed3kvzH7n7xjMeSJL+Y5H9393dkcsXUlVPbnpvk3yQ5N8nvJflwd397kn8Y9V2+1N2bkvxGkl/fw+cAAIe48Uet70ryB1V1R5L/kWTX70FrkmxIclYmf+h6V1U9a2/7W3PghgoAsGRekuTq7n4qyaNV9WdJvjPJl5Lc1t07kmT88nRCd3+sqj6Q5Pur6g8zCVh+Nsn3ZBIo/fnkW255WpJbpj7n9/fw+c/PJPi5afQ7LMkjVfUNSZ7V3X822v1uklfMeDz/Nkm6+0+r6hvHvpLkxu7+p6q6a3zOB0b9riQnTO3j6qnXd8zwmQDAoelrknxx/PFqdzuSfLy7/ynJ/VV1XybB0Sf2tjMAgEPFtiSnzVGvvfR5Ymr9qfzzH8p+P8kPJfm+JJ/o7sfHfm7q7lPHclJ3nz/V/+/28BmVZNtUv2/v7pePes/zWPZ0PLv280SSdPdXMvka3q76V/Iv/wjYe1gHAFaQ7v5SJiHQa5LJPR2r6gVj8x9n8tX9jCunn5fks3vbn6AIADiU/GmSI6rqx3YVquo7k/xNktdW1WFVtTbJdye5bR/7+kiSF2Vyk8ddVwp9PMmZu+7pU1Vft5cnqj2ef77x9H1J1lbVi0e/w6vq5O7+YpK/raqXjHY/PNX/N5KcV1WnTx3Lv6uqb0ry0V1tq+qsJJ8bvwTOx2unXm/ZW0MA4NBRVVdncm5/flXtqKrzM/m94fyq+lQmf4w6dzT/YJLPV9U9ST6c5Ge6+/N727+vngEAh4zu7qp6dZJfr6q3JvnHJA9kcg+ipyf5VCZXz/xsd//fqSd+zLWvp6rq/Zk8vey8UdtZVT+S5OqqOmI0/bkkfznHLi5P8ltV9Q9JXpzkB5NcPL4itiaT+wJtS/KjSS6rqr/P5Je1XZ//aFVtTvIrVfWcTK4I+miS9yZ5e5Lfqao7k/z9rvHN0xFVdWsmfxh83X70BwCWoe7e03n9q25UPa48/qn8y3su7lX989XKAACsBFX1QJKN3f25gz0WAODQ4qtnAAAAACRxRREAAAAAgyuKAAAAAEgiKAIAAABgEBQBAAAAkERQBAAAAMAgKAIAAAAgiaAIAAAAgOH/A/8DYS+RfHV8AAAAAElFTkSuQmCC\n",
                        "text/plain": "<Figure size 1440x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# the read_sql_query runs the sql query and returns the data as a dataframe\n\nplt.figure(figsize = (20,5))\nsns.distplot(df['ConvertedComp'],kde = False)"
        },
        {
            "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": 10,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 720x576 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# your code goes here\nplt.figure(figsize=(10,8))\nax = sns.boxplot(df['Age'],orient = 'v')"
        },
        {
            "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": 11,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f364d93ae50>"
                    },
                    "execution_count": 11,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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96JxnBoZ3DPlQ356WRvjtnz6rXI1Uq0Njob58/2NV4/7y/Y/p0Fj0Yw6PRY/5sKf9oVxBB0fyVVXnDo7kdShXmJcxj+Si+zOS8495eCyMvL6HPWOI26di0ZMK6Wk/XohOkatV7WxwJB/5evNVzxsai662N1Sj+t+Xpl0XX7r/sRrvAS4y1XIiPBfd/7jV/+KOIW4616qutG79/TOqUiFv/f0zvO3rqdqYDKJTUn1B5rgVD81crFRlKV5Kaj3pZXEfE/faq+c8NzvNb2gkr8/ft7dqDJ+/b++Cpe3VO2srTlrdUk09BADgaMIMpiUqdkpAzOBMsY6KaoGk8197zIzZM74oaLGkGTOYJCff5910ItBV/+vxuqj/uKoUPO9i1J50tJInvSwwaefZr9SBkUJlfzvPfqVq/eHVV8HMt27TeCHUCauq07mWZwMd9gSkEkF0FSzfX4OTCYs8B8kaZbPMpD/+3ZOVDBIKTFrVndEf/+7J3hvSYuj0gbNP0lkb+iopcj98fNB7w1sMXWSg784dW71jjko79M5s8y467r8Rilt5LpXwzDzzHNdC6KJT3moco7+66DU6dmVX5bp45sWRmpXwkoF09dknaTQXqhg6ZZKBrj77JBU8Ad1izD5JTn/05pM1cHjipj6dCPRHbz5Z5gkYjRdD7X7yRf3dlVsr5+3eR/bp5SuiF/r3VTysteZZPRUD0wnTpy54dWX2nG+dLWniZnqsUKqaxfjVS7d402qLodO7t6zXBZvXV425Vn/G8iWZqtMzn3lxRGP5UnQVxmKoT5z/Kp368uWV9o8+d8ib5ufC6FTlEzypynFnC9UTqIj/GKfLzzpBf/qNl2ZtffYdm7zXXhCYTlpVnW7ZVyM1KwhMJ/R2zkjbnd9UsVDv+08n6kP/8NIM2r9+52sVhvOTnhlXPbO24s5IakXqISl4c8NxAgDUiwDTEhX3w2LcKlhx0zIkKV90umbaLIxrbn9Ed+3wpBElpP1HijPSS9aviN5HV4dFpuB1dUSPIeEbsydykjDTWL40I0XO116KX4FteUegJ4dyM27mTlgVnaKQTQWRVbCyngW1C6Xoc+AL5kyOIV90+oPbH67ah69S3YpsQqces0IXT0svW5GNPm/eQJ/nJnx5NjrtcLknfS1uapY0UdkuKnDnO29d5Zln06/VLs/Ms+5MdKqiL+VtZVdC+VKHHts/XJVGuLLL/3rLpk3/MTQzNfB4z7XUlfak4Xle08kg0OGxwozXQ59ncfbuTEJvPGVN1XnbdclmdXvGnJ3Hioe+a3XwSE6X3/yvM9p/4+qztHb5zPTJF0ZyumpaeuZVNdIzl3UkdPar1laN+YbtW7SsRkrqsmygro50VXrm9du3aJnn+u7JBnrZis4Z7X3pnNl0EPk+6Zsl6Zvd+g9Xnal1EcFBX1VI32unrsc4VYJLk33602/8THd53scKhZIeGxyZ8b66cU23UhEVOnO56FTLk1d3KZOZn49VoZMe+OVvZwT63rvtxMj29RzXOCZnbU0PFtWatRW3Smo968LFQQre3HCcAACNIMC0RE2mcjw1NFq5IX3Fqk7vh8VkYPr8u147Yz0iX1pGYUq62OQHxZsfelJ/8bbTvH0qhtGzpIqev9iO5sNKis/UD+CXnXWCeiP+kn/Ek5p1546tWhax1I6Z9JX3vF4HRgqVY9TblfLOSCqELnLMf15jzIFJ1120acZNsm8fh8ZC/WPEmC8/6wR1RYwhVwj1yH8MVc0KeeDR57X6tHWRzx9/lspE+ldUepYvKDWSCyvpK5PH6Uv3P6a/eNtpWh6xQLY30Oc5SL4UvDt3bFXEPY0Spshru8ZEFWVSpg+ce/KMG9KMp3LecO6lNMLJMX/5/sf0Cc+YxwuhvvzA49XtH3hcn/BcS6M5NyOY88WLX6dlmWTkmOs5Trli9LW0ynMt5Uthpari1Gv12JURA5Y/zc93HflSv3xBBGni9RZ1rn2vt/FiKXKGUc4zoyJuKuRY3v9aiHoPkybOm+99L+q8xX3fG837r9WoPvlmt/oKAyQC6fpLNlcC2ZPB09niIHfs+F8kWWXWoy91UvLPbvO9jw2O5CODZHdddWbkDLoXRqNTLe/csVXH1AgwFQolDQznqha9jwpgSVIiIb112mzSXZdsVsITa3HORf4ucTVmYsaZpVLPovo5T5XUnCd1tzebinxf7a2xjmEc9a4j1U6LrbdCK9bbAoClZrH/boiDAFObiluBTYr34VWSxqelctx46RZv21BOr1rXNSM164hnnRoLFJ0eU6uimiedyxdISCVMO88+ScNTUnx2nn2Sd82mUui06z2v06rujsoYhobHa6S8mdLTjnk6GXgXu02YdO3ZG3Tt37104/Tf37O5ZqBivBjqmaER3bFja1W6mC8tKJkw7XjjiRovuMqYd7zxRPkyDzPpQKefWD2b56uXblHGN3MmndAfnvtK5cvH0Mz0h+e+Ul01rqOSJ+XNm6pkik5f8RynwKTPvmPTjPa+9+S4qVMl5/SK1dU3TqmkqegpRS9JuYKLtWB8KXT6q3e9RkfGJq7V9b1ZnXnSa3RwNDpQYZ5ryXeMSs7p3585MCP4sy5ils3U4xQnmNiRCrTt5DX69cBLs6S2nbxGHZ7ZcOmE6aL+Y7XvwEuLs1/Uf6zSSf9aQXFSJ+NWVJQmjtNJa6rfx7LpwHvD29OR0H/etK5qzP950zrvovfpmDPbLJDef84GvTCcrzz+/edsqPk+mUqYzn/ttBlGl/irYcZNCzST/utbX6XAApWc08tXZHXKW1/lvfbiFh4YL4QqFItV52D/odGa64WlEtJ4cWJdvMAmZlqmkybf21I6Gegz73i1ElPSdj/zjlfPeD+fFPePG/WkWhYKJT1/ZFz5oquM4fkj41rX0xH5e7pQdPqn8np40wOJUcaLoT73nb1V73uf+85efeHi10W2j52+Vsei+oFnwXjfe/eBsULk++p8BTbiVlSU2m+x9VZoVZVEAFgqjobfDXEQYGpD9VRgKxRK+tXA8IwPf6d4pvgPHsnpC/9c/VfqL/zzY/rUBa+JTP1Ylgn0m4h0mhM96TQJi5ceI0mppOn952yY8ZftlOeGNG6KT29XQofGEzNSRXo7PX9BDkyHRuc+BjOrBASkiQ9k1/6dv1KYJC3PJvSqaeliE+lc0X3qSJmeenHmmF+xMrpPhaLTF6ed5y/+82P6ywteHT2GQDo4WpiRUtfd6w8wLe/0jMFzXJ0nfcU3U8XMdMsPq2eG3fLDJ71j6IyZntmVDvR0RBW543r912ohjF5jrOC5IV3VlYiV2hjEvJYyqei0wIwn+CP5K8/5UtJKJacXIqokLvfM2HBOemE4P7OqYkf0bIQVnutohec6ihvYkKTujkBPRbxnvMJzHoqliffKuY4hm7bIynzZtCcoLdNwrjjj+dfWuJkulpyuuX3a7Jnb93hnbsVNC8ymAg2NFHTNbQ9XjWH9iujzvCwbnf7pS9nr7gj04mhqxvtwt6dypjSRLnZotDDjd4MvPTMVWDnl+uGq9inPtZHwBEJ86c1Jzx9Dal17h3OFyPfWrnRCqyJ+R6eSFjmDyff7MJ0MNDg8kaI5dQy+oFrc9DXfovp37tiqY7wBJs8fBzynOldHACiOelL3487maVW1vWb+FbwVVRIBYClZajNDqSLXhuqpwDYwnIv88Od7TMmFuvysE/Spbz+qd9/4I33q24/q8rNOUOiib5APetIsDnqqZuVL0VWwfGkTklT0rMFU9MxIiluNLG77nKeSl68qXNz0GCl+BbO4VedC5yLPsy9tYiwfnaY0VqOK3LCnT8OePsVdU8kkXbGtegxXbDtBvo/ThVJ06lTBMyNp2HMOhmtUkcskJtb/mdqnj5y3URnPTfshT9U5X6W9uNfSeD56DOM1zlvcynN5T0pavsYMo8jz4Gkf9zrKJANdf8nmqkpe11+yWZkaMz196WJH5ul9LO7rOe4xlSaOa193Rl+9dIvu3LFVX710i/q6M97jOpmGO/U41UrDHfVcS6Oea+mwp4qk73017nmWJq7VqN8Nvmt1vOhp73n9WDkQMvUY1ZpV2ZkO9P5zT656/b//3JPV6ZkZOjmGOK+3QtFFtvctwp8KTLumvR521QiqjXvS13z9qWfWVuhU+ePA5LVxyw+flCcOr1Rg+rPfO6XquP7Z751SM3AXR9yKilIdBVGaPPtn8q/gF+56SNs++z1duOsh7d1/pGZ1vrh6syndMK1a7XymKgLAUrPUZoYyg6kNFTxrWtSqwBb3w5+b8sFv6qwQ3xovcZ8/bhBB8qe81Kqc1cz2ccfgWyuo1l8Wmz2GuLOF6rmJiPuYuGsqxU398L1+vBUP6xxz1I2K77jGTeeKey214rz5Xg++G5u47eP2J18KlUqavnbFGQps4ma2GJZqB7Gb/B7QivfJjpiLm8d9/dTznvTdRwf03UcHqrb/17eeOi/PX2+fmvn7cMQThLtjx1bv2lnNHsN4MdRXpq3b9pUHHveuAeiftRXd/3pmDAZBdDp0rYqeURVD77rKv65aHAVP4N5XUVGKP5un2bN/WvFX8APlteCmrw336Qs3HZV/aQeAZltqM0OZwdSGJqvBTLW+N6tkjVVQJz/8zXiMb8HO8jo402e2+D4rxn3+tGcMvrQM6aWb6umP8X0YjdunuO1Tnva+vwgnA4ucKVDrA3izxxD3Bjbu89fzmA7PzBPfDXIysErqx7tv/JGu+voeDQ7n5u0Y1TNm383fbMHH6fvwBdU6kkHkbIRax6jZ5y3u+1Lc9nH745z0B7fs0Zv/5vs656+/rzf/zff1B7fsUY01jVvyHtDM55cmbsKjgpu+teTa7fXTims1bvtMcmLtq6oZSeds8M6Gixs8bcUYSuVA39Tz/N1HB7x9yqYTkb+vfOliGc97Uq0Zg4kgiJzB5HvfK5Q8QbUa6+HFMfkBf6rZPuBPVs+bOu5a1fPito+rFX8FzxdLkdfS0fqXdgBotmb/bmg3BJja0JruTOT05DU11i9a052ZWCdj6k17jceEnpktvs/HqYRn+r3nz52T1Zqmtq9VrUmKH6DpKJd+nz7mDt8C1h3R7X1rfyzPBpHnwVfuPhGYOtMJfeqCV+vOHVv1qQterc50wvthenIfUX3y7aPTM2ZfakYQM2jX4+mPr6S5JK3wPGaF5zHjxZcqpFVSah543Jt62JWJfv6ujC+wEe9a7Uh5gjk11i/yBVB9izmnE0Hkte0LuBZDV5mNMHmMvvLA497ZC3HPgRT/WjJz+sK7X1fV/gvvfp3Movu0rMMiXz/LOuK9nrOe/oQuOlUsrBFhint9Z1LR7wG+ta3ivid1pKOf39demliDKc5N+DLPmH1rJMV9PcR9X437epYm1heK6pNvfaG4wZBlmaSWdab0tSvO0AMfeqO+dsUZWtaZ0jLP+mIdnkBFpkagYkU24XmNRj9mVTYd2X5VNvrDaCbp+aOOZ8wrsmmtXdZR9ftq7bIOrfA8/7JMUqt70vr7K7fqwQ+/SX9/5Vat7kl7j5Ekre7K6IO/U51K/MHf2ajVXdGfS3xBtVq/Q+Oo5wN+EJg2ru3RPTu36aE/PVv37NxWc1HWuO3jqidI1o77AIClpNm/G9qN1Sphu5j19/e73bt3L3Q36jZZRa5YCpVsQhW5p4dG9IbrHpyx/QcffpOOWzVzjv++A6P6+g+f1EX9x1VVtLn0rBO0vndm2fGnhkb03x94YkaFqmvPeaVeEfH8kjRweFzPHxrTiyOFSsWmlV0prVue1ZqIetr7Dozqn3/xvM45dZ2cc7Jy5aw3n7Yusk/PHRxVYFIxVKVKVTKYmAmxbkVE+wOjKrmSAktUjmnoSkpYQi+PeP6h4XEdHC3o6Rdfqpp13MqsVnSmtKo7uprXcwfHNJ7PK5NKVfaRKxTUkU5HVpL77aGJVEknq1QLMzklE4Fetnxm+4FDY/r1CyMz0mlOWt2lNRHtnz80pucPjGjt8s6qCk/reru0LqK9JA2Pj8tpYi2WyccsywYySd0dM8f91NCI3hjj2ntqaEQ//vULM6qLbT1pdWT75w6O6ZaHfjPjWr1824neYzqSL8o5q6RamTl1pZORx1SSDoyM69mDMxftPqY3o97OmWPef2hMB0bzev5QrnJtrFueUW9nWmsj9vH0iyN6w+cijtFH3qTjVs4c87MHRtWTDSpV6pKBVX4+JuJanXzMI08N6fWvWFU5rj95akibX7Eq8jHPHhjVJ//xFzMWNv7E207zts8VCjOu7UwqFdn+uQOj+m7E6/l3T1sX+Xrbf2hMv4m4tk9c3RV5TKWJ10MiKY3nXaVPHWlTqajI18OzB0Z1X0Sffue0dd4xx2m//9CYcqWSTEHl9ewUKpNIeMfw/MExvfOr/zJjmvU/XHWm1kVc34NHxpVMSKO5l66NzkygYknqi1jMeWh4XCP5UqXaWeikdNImFqOOeB/bd2BUvxk4rJPWLKs8/68HDuvENcsi34cHjoyrFIYqllQZczIxMdslanHpyeN0cCxfqQoXOqkUliaCJBHHKZcr6rcjORWmjCGVNL2sK6OMJyCSzxc1OJKvjKGvK+2tjlYshto7cERXff2l1/9XL92ijWv8hTjC0OlILqfh8ZfOQ3dHoJ5MxvsBc3y8qKGxl/q0KptWR8f89mmui0WHodPe3x7WlVOe/6ZLt2jjy5bV/IAcZx8vjuS097dHZrymN76sRys9Qam4FnuZ6FZUIqpnH4v9uAIA4jOzPc65/unbWYOpTSWTgbdUvU8qlfDeTE6XTnoqC3k+iKYD05tOWVtV0ea6izYp7fkAkU0GunDzMTPaZ2sEyXzBTt/2VGDauG55VeWs6y7a5E0vMTP9+b0/n3GD/ElPNbKOdKAXR0p65sWXSpQfuzKrlV2emTmFUP/vYwORN5c+yztML45Il938o6pAxdpl/r+ODh3OzwzCea6VroxpdU+mMptqNF/S6p6MujLRz78sY3oxXV3h6auXbtEyT3tJipo4UWu90UzMay+TDPSl7/1aH/nmz6vav2Hjmsj2PR2mt72uuoz76CFHmwAAFMVJREFUDdu3qMczcyaQNDxenHFMezw3l9LEQshPDh7WHTu2VgVnejtXKeolmM2YDueqx5dIBMp6jmvcyl8dyUBPvTA+o8rWyyMqQk7qTAc6vm/ZjKptvhlMPR1BZIW0Hs9MlXQy0HMHi/rgXS+1//y7XqvjV0ffKHZ3RFfC81YXM4tMFfvGNWd5x7wsk/r/27v3KLnL+o7jn+/c90KS3Q0BckeMScUikEiD9FgK9sBpVbTCCahUaTFYxdvRI2qPt/ZoD8fSarV6wAtQUC6CLRwOai1aFSm3iFauARKSbAi57S3Z3bk//WNmNzOZ3zPsb2d3Z2f3/foHZvL7zTzPM78nmd93nuf7Ve/QqHZVBIFXdLdp+YLg+dOWCG6Tb1VVezKi9at7qo9/5+lq96zOWZiMa9dQXr0Vf8cs727TknZ/Mt2uVFzXXrq+JpDQ5alsJ0n7hjLjwZlcwelwJue9YR/OFvT1+0o/DshMzjl9/b7n9cFzX6kezxjFY7GqvzNKW62C+9zdltALfcM1n8Hqbv+1moiZImba1TdyZJy6Ukp4KqolkzEdL+nASCk4k4yYFrcnvMElSUokYt5qaEfrL1fjPLo6Z70cNZGI6ZhkUtl8VirfhB+TrH8TnkrFtMwTUDpaLBbR2iXH6PYrzpzwD1ORiE04p87B4ex4cEkq5/65acvL5v4J8x6Vq6rGPud6q6omI0x7ZqPKX8GnK5gT9j3mW/ltAEB9rGCapSbza1CYc8L++r93cFQvDaVrbsKPX5AKPD7sr+yStKtvWB+65bd639knVZUo/tdLTtWKgFUbBw6l1TeS1e7+9HiblnWl1N2e0OKAX8L3Do5qZ9/IeBLRsRveld3tgX3oG05rz0BGV1TcUF/7rvU6YVFS3R21r98/nFbvQG0J9OWLkuoKOF4qrXh4PuDX/5OWLAgMFvYNlz6Do2/OujvigW3q7R/R3wesOvnsm08OXF0wnE6rqNrVSBFJHQGrkSRpz+CITFKhqPFzohHJSTphYe17HDiU1t6h2nE9bkEy8HPrH07rxcFMzQ310oXB4zqZFRt9w1n1VlxHy7tS6u5IeFeeDafT2hZQ7v4VPcnAccpk8to7nKlZFXKcZ0VF2Gs1nc5r30jt6y9pT3pXPGQyefWlszUrSbpTwTfiL/aP6KXBkZrVbccvbA9cYZTLFbSjf6TmWl3V1R64snJ3/4iefHFQr166cPz1xx77VguddfXPa57/9VV/WjfQHmZVyN7BUY3k8opFouNjlC8W1B6PBX4OxaLT7oERZSo+h2TMtGxR+5SsUpnMOfl80RPQ6QgMPoT9u7tYdHrh4LB2HDwS/FnV067VPR3+pPQhV+hOZlyn02SvvVY2U31mJUzr2X8oo7d949c1f2fM1fLbAIASVjC1kMkuTw5zTtjKQul8UZ+/+0m97+yT1K6osoXSY9/xWU8lr3oVniJ2JBntmOVdbYp4akUPZwv69i+3B27DW+zpw5fufbqqz1+692l/n7PF8SDIWPuvuHmLbt+8UQrY5TeULuip3QNVq1oeeHa/FqYW160s9C///XxNUO2rnjYdShf0o//bowtOXz6+Suqu3/TqLactU0AMbjzx60QrPA2MFscDXGMe2zGgk5YskCdGpmJR3q1TQYazBT0RME6dqcWBn9tItqhMNqfbNm+sCmyMZOOecTXdv3WfTltVWm/hJN2/db82nhS84mk4W9C3Aq6jK89dE7hiQ5JGcqbjFsSr2hSPlZ4PGqehbEHv+NZDwV/AA4I5Ya/VVCqmJdJ40CE5gUBFMhlTlyudU3SlvGldSf8qD4tIRUWqVqp8ZdOpMl8C63hUKxe1qS0erdp25Nu2m4hF9YV7ngocI9/xwSvh6ucJSSSiSuSi46tIEp6kxpIUiUR060M7x7dbZotF3fHoTl3+hld6ji8FPcLcIIdZpTKZc2KxiFZ3d6g9EZtQQCdi0jUXvVYf+8GR4OY1F/nz50UippVd7UrFo1WvX6/PkYgpHo3IOad4NPKyAYTJjOt0mm/VYKT52WdMzHwrvw0AqI8A0yw0mTK0Yc+prCw0ZnlX/WpHQcf7tqOFLUUvlZIz33DZ62p+afclZ45FTA9sO6jbt/RWvceH37jGe3yYPoctE92eiGpFT0fVlqN6VXkkqS0e1af/fF3NSpWU5yY8GYtow4ndNdsCfclrw261KjpXc8OQiEXrJk6ORKRPnL9OUimHUU9nUp84f50ingUJncmoTl3VpWf3HtkWdOqqLnUmg/u8sC2q/pHqbXulZOue45NRrVu6qOr11y1dpIWe10/FooHX0cfPW+vt86JUTC/0Z9Vbca0u727T6q7g+ZkJ+QU89LWaL1atYMoVnPa5jJbGIt5AQi5X0LMHh2tWYa1b0hkYBErEIopHrWr7Sjxq3m1KxaJT72C6amVLps7Klp6OhG557x/VrFJ5uWpNRwfV6yXsDRuI72qLB2637Grzb0ebjVtwwmy5jkQi+s7926qCm9+5f5u++LZTAo8vFp2e3X94wmM62e00s2lcJ3PttbqZ6DNbrVoTwUcAQKWWqSJnZueb2TNm9pyZfbLZ7ZlOk/k1KOwNbHsiuMqOL/9KMh5c+cdXTSkZC66aVa+kcSxqSueK+sxdj2vTdQ/qM3c9rnSuqJgnwBS2OlI8ZvrmUX3+5jtPV9xzg5wIWZUnly8G5oTJeaqjSaUVRmPBpbFzPnr777zl7otOge/hy3mUjAdXVEvGg/u8sD2qjlRCm657UH/y5f/RpuseVEcqoYXt/i+K7fGIRnNFvef6h3XONb/Qe65/WKO5oto910a+4HTwcLbqcz54OOutgjWULo4n0x7r8/tu3qKhdPC49o/mdOBQpur1DxzKqH80F3h8KiFP5S9vl0O/h5Vft9LRjyuFrTg1mM5qYCRX9RkMjOQ0mM5632Pf4SNb/KTSuP7tzVu073Am8PhCUfrA9x/TZTc8ok3XPajLbnhEH/j+Y/ItShwYzWrvULpqjPYOpTUwGtymYtFpcDRf1YfB0by3zPpkKnL4AvEHh4Pb1D+aC7z2fJ/zXNDTkQis/OULJIQd07DHz0bzrRqMNDN9ngvXxnw038pvAwDqa4kVTGYWlfRvkv5MUq+kR8zsbufck81t2fSYzK9BYzewR5/jcyhT0M3/u0PXv+d1NduCgrYd5QpOiah0w2VnVFXxyXmCAs5JPZ2JqtUOPZ0J1Uv5NZoNDiTc5tuSNlrU1+6rTrT6tfu26nNvPllBxYhyead7fre7qs93PLpTf/X6EwPbY5K+fOEpNXmqfF+nc54VT7k6Ga/DbiXMe47Pe44fyRbHy92PjdHXf/asPvvmkwO31B0aLQYGHW7bvFEBhfwkSYcz/nMCUjApV3SBQbJbN28MfP2cp885T5/Dvv6hOteRr8/ZQnAw0fceZtLVbz9FV9155Fq6+u2nyLP7UwOjBd3z297AazVoC146V9T7ywm+x9rz/u/9pjR3PMKu0MvlPZ+DJ4A6mi0EjpFvPu87nAmc/7dfcaZ39U3YVS1hg/fzcetH2AS/83VMZ9OKqpky3X2eK9fGfDMTiccBAK2jJQJMks6Q9Jxzbpskmdmtki6QNCcDTJNZiu67gfX9+x61cNvLMvmiPnnn4zW5gnw5YUZzBV11x++rcjZddcfvvcdL4W948yHzC5lJb1hbXQmv3k1+2DxVMc+2QN+2Jin8VsKI53jfF7mwOZjCfgaTOafgOd63UiXsGIV9/bDX0WTewznpxge2V11LNz6w3ZunKl90uvZXL+jaX71Q9fw7Nq72Hh/2cwt7vYYNfBdccJs8MWlvINEXPJ2MsH2Yr1s/wgQSGFNMFa6N1jUfA64AgGCtskVumaRdFY97y8/NSZNbim7jN7C3bd6oz7zp1brxge1ynvU2bYlo4BY2X76gRDQynhNm03UP6oqbtmj/4Yw3l09lDpnK4+sFW8ZueCvVu+ENe3zlTX7VGHlueMP2oS0RvGXPV65bKo1r0OfgG9d4xAKP9+XCinvGyHd82DGdzDnJkFsPE54+J3yBkGjw68frXKth+9wWjwae48udlYpHdOU5a6q2HV15zhqlPNsIw7YpHrLPkrSkMxl4vS7pDL5JCLsNIuUdI8+17elDrE4fwgrbB7Z+vDzGFFOFawMAgNZnrt6epVnCzC6SdJ5z7vLy40slneGc++BRx22WtFmSVq5cuX7Hjh0z3tZmyeeLenrvofEtJmPJaNcdd0xgkt+wpaXDvn4mk9fWA7UJhF+1uMNbpSqdzgcmHV7T0xFYDWu6jx8cTWtnX20p+pXdSS1sq92nlE7n1ZfJqlBR9j0albqT/mpe6XRevUOjNYnNly9oCzxnOJ3Wi0O5muTSSxfE1ZEKbtNzB4drPrdXTtEYldqU0baD6ZpzXtGTUkeqNliRTuf1fN+wrrjpyPHXXrpeJ3UHv0cmk9euwdoxWrGwLfBaymbz2rq/ts+vOrZDiUTw8c/sr+3zWs/xUjkZ7UuH9N6bKlYZXrpBa48PDgTn80W9ODQ6noS76KREzLR0QduUzJ+w83NMLlcolYsvV3lb0pn0Vnkb6/dEK3mFTdg72T6EFbYMOmXTXx5jiqnCtQEAQGswsy3OuQ01z7dIgOlMSZ93zp1XfvwpSXLO/aPvnA0bNrhHH310hlo4O+TzxdLN4gRKUUvhv8iFff1MJq8DI9nxm9fF7f4S6GPS6fx4qfXYBEqtT+fxxaLTgeG0snmnQtEpGilVzFrckfKOU9j2TKZNo9mMBkaL48cvaouoLeEvCz7dYzoTbQp7LWWzee0fPnL8sR0Jb7BoMseP9Xs2zZ+wrz8TpnuMAAAAAMy8Vg8wxSRtlXSupN2SHpH0DufcE75z5mOACVNvNv6aSpsAAAAAAM3iCzC1RJJv51zezK6U9BNJUUnfrRdcAqbKbExcSZsAAAAAALNNSwSYJMk5d6+ke5vdDgAAAAAAAFQjuQUAAAAAAAAaQoAJAAAAAAAADSHABAAAAAAAgIYQYAIAAAAAAEBDCDABAAAAAACgIQSYAAAAAAAA0BACTAAAAAAAAGgIASYAAAAAAAA0hAATAAAAAAAAGmLOuWa3YVqY2X5JO5rdDkiSFks60OxGAJgyzGlg7mA+A3MLcxqYO2bzfF7lnDv26CfnbIAJs4eZPeqc29DsdgCYGsxpYO5gPgNzC3MamDtacT6zRQ4AAAAAAAANIcAEAAAAAACAhhBgwky4rtkNADClmNPA3MF8BuYW5jQwd7TcfCYHEwAAAAAAABrCCiYAAAAAAAA0hAATppSZrTCzn5vZU2b2hJl9uPx8t5n91MyeLf+3q9ltBTAxZhY1s8fM7J7yY+Yz0KLMbJGZ3WFmT5f/rT6TOQ20JjP7aPn79uNmdouZpZjPQOsws++a2T4ze7ziOe8cNrNPmdlzZvaMmZ3XnFbXR4AJUy0v6WPOuT+QtFHSB8zs1ZI+Kek+59waSfeVHwNoDR+W9FTFY+Yz0Lq+KunHzrl1kl6r0txmTgMtxsyWSfqQpA3OuddIikq6WMxnoJXcIOn8o54LnMPle+qLJZ1cPucbZhaduaZODAEmTCnn3B7n3G/K/39IpS+uyyRdIOnG8mE3Snprc1oIIAwzWy7pLyR9u+Jp5jPQgsxsgaQ3SPqOJDnnss65ATGngVYVk9RmZjFJ7ZJeFPMZaBnOuV9K6jvqad8cvkDSrc65jHNuu6TnJJ0xIw0NgQATpo2ZrZZ0mqSHJB3nnNsjlYJQkpY0r2UAQviKpE9IKlY8x3wGWtMrJO2XdH152+u3zaxDzGmg5Tjndkv6J0k7Je2RNOic+y8xn4FW55vDyyTtqjiut/zcrEKACdPCzDol3SnpI865oWa3B0B4ZvYmSfucc1ua3RYAUyIm6XRJ33TOnSZpWGyfAVpSOS/LBZJOlLRUUoeZvau5rQIwjSzgOTfjrXgZBJgw5cwsrlJw6XvOuR+Wn95rZieU//wESfua1T4AE3aWpLeY2QuSbpV0jpndLOYz0Kp6JfU65x4qP75DpYATcxpoPW+UtN05t985l5P0Q0mvF/MZaHW+OdwraUXFcctV2hY7qxBgwpQyM1Mpt8NTzrl/rvijuyW9u/z/75Z010y3DUA4zrlPOeeWO+dWq5RU8GfOuXeJ+Qy0JOfcS5J2mdna8lPnSnpSzGmgFe2UtNHM2svfv89VKfcp8xlobb45fLeki80saWYnSloj6eEmtK8uc27WrapCCzOzP5b0K0m/15GcLZ9WKQ/T7ZJWqvQP4kXOuaMTmgGYpczsbEkfd869ycx6xHwGWpKZnapS0v6EpG2SLlPpB0fmNNBizOwLkjapVMX5MUmXS+oU8xloCWZ2i6SzJS2WtFfS5yT9pzxz2Mz+TtJfqzTnP+Kc+1ETml0XASYAAAAAAAA0hC1yAAAAAAAAaAgBJgAAAAAAADSEABMAAAAAAAAaQoAJAAAAAAAADSHABAAAAAAAgIYQYAIAAJhmZvY2M3Nmtq7ZbQEAAJgOBJgAAACm3yWS7pd0cbMbAgAAMB0IMAEAAEwjM+uUdJakv1E5wGRmETP7hpk9YWb3mNm9ZnZh+c/Wm9kvzGyLmf3EzE5oYvMBAAAmhAATAADA9HqrpB8757ZK6jOz0yX9paTVkv5Q0uWSzpQkM4tL+pqkC51z6yV9V9IXm9FoAACAMGLNbgAAAMAcd4mkr5T//9by47ikHzjnipJeMrOfl/98raTXSPqpmUlSVNKemW0uAABAeASYAAAApomZ9Ug6R9JrzMypFDBykv7Dd4qkJ5xzZ85QEwEAAKYEW+QAAACmz4WS/t05t8o5t9o5t0LSdkkHJL29nIvpOElnl49/RtKxZja+Zc7MTm5GwwEAAMIgwAQAADB9LlHtaqU7JS2V1CvpcUnXSnpI0qBzLqtSUOpqM/udpN9Kev3MNRcAAGByzDnX7DYAAADMO2bW6Zw7XN5G97Cks5xzLzW7XQAAAJNBDiYAAIDmuMfMFklKSPoHgksAAKCVsYIJAAAAAAAADSEHEwAAAAAAABpCgAkAAAAAAAANIcAEAAAAAACAhhBgAgAAAAAAQEMIMAEAAAAAAKAhBJgAAAAAAADQkP8HNPmNxAFqEUMAAAAASUVORK5CYII=\n",
                        "text/plain": "<Figure size 1440x432 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# your code goes here\n\nplt.figure(figsize=(20,6))\nsns.scatterplot(data=df,x='Age',y='WorkWeekHrs')"
        },
        {
            "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": 12,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 1440x432 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# your code goes here\n\nplt.figure(figsize=(20,6))\nsns.scatterplot(data=df,size ='Age',y = 'CodeRevHrs',x = 'WorkWeekHrs')\nplt.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": 13,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>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\n0           9             PostgreSQL   4328\n1          16                MongoDB   3649\n2           9                  Redis   3331\n3           4                  MySQL   3281\n4          16          Elasticsearch   2856"
                    },
                    "execution_count": 13,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# your code goes here\n\nQUERY = \"\"\"\nSELECT *,count(1) as Count\nFROM DatabaseDesireNextYear\n\ngroup by DatabaseDesireNextYear\norder by Count desc\nlimit 5\n\"\"\"\n# the read_sql_query runs the sql query and returns the data as a dataframe\ndf_new = pd.read_sql_query(QUERY,conn)\ndf_new"
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 1080x576 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "import matplotlib as mpl\nimport matplotlib.pyplot as plt\ncolors_list = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'lightgreen', 'pink']\n# dfpie\n\ndf_new['Count'].plot(kind='pie',\n                          figsize = (15,8),\n                          autopct='%1.1f%%',\n                          pctdistance= 1.13,\n                          labels = None,\n                          colors = colors_list,\n                          shadow= True,\n                          startangle =120)\n\nplt.title('Top 5 databases that respondents wish to learn next year',y=1.12)\nplt.axis('equal')\nplt.legend(labels=df_new['DatabaseDesireNextYear'], loc='upper right') \nplt.show()"
        },
        {
            "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": 15,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>WorkWeekHrs</th>\n      <th>CodeRevHrs</th>\n    </tr>\n    <tr>\n      <th>Age</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>30.0</th>\n      <td>40.0</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>31.0</th>\n      <td>40.0</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>32.0</th>\n      <td>40.0</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>33.0</th>\n      <td>40.0</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>34.0</th>\n      <td>40.0</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>35.0</th>\n      <td>40.0</td>\n      <td>4.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "      WorkWeekHrs  CodeRevHrs\nAge                          \n30.0         40.0         4.0\n31.0         40.0         4.0\n32.0         40.0         4.0\n33.0         40.0         4.0\n34.0         40.0         4.0\n35.0         40.0         4.0"
                    },
                    "execution_count": 15,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# your code goes here\n\nQuery=\"\"\"\n    Select WorkWeekHrs, CodeRevHrs, Age\n    from master \n    where Age between 30 and 35    \n    \"\"\"\ndfage = pd.read_sql_query(Query,conn)\ndfage = dfage.groupby('Age').median()\ndfage"
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 1080x432 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "dfage.plot(kind='bar',figsize=(15,6),stacked = True)\n\nplt.xlabel('Age') # add to x-label to the plot\nplt.ylabel('Number of hours') # add y-label to the plot\nplt.title('Stacked Bar plot of Work week Hours and CodeRevHrs') # add title to the plot\n\nplt.show()"
        },
        {
            "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": 17,
            "metadata": {
                "scrolled": false
            },
            "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>ConvertedComp</th>\n    </tr>\n    <tr>\n      <th>Age</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>45.0</th>\n      <td>90330.0</td>\n    </tr>\n    <tr>\n      <th>46.0</th>\n      <td>91593.0</td>\n    </tr>\n    <tr>\n      <th>47.0</th>\n      <td>91606.0</td>\n    </tr>\n    <tr>\n      <th>48.0</th>\n      <td>105750.0</td>\n    </tr>\n    <tr>\n      <th>49.0</th>\n      <td>85248.0</td>\n    </tr>\n    <tr>\n      <th>50.0</th>\n      <td>95000.0</td>\n    </tr>\n    <tr>\n      <th>51.0</th>\n      <td>100000.0</td>\n    </tr>\n    <tr>\n      <th>52.0</th>\n      <td>105500.0</td>\n    </tr>\n    <tr>\n      <th>53.0</th>\n      <td>100463.0</td>\n    </tr>\n    <tr>\n      <th>54.0</th>\n      <td>121000.0</td>\n    </tr>\n    <tr>\n      <th>55.0</th>\n      <td>119000.0</td>\n    </tr>\n    <tr>\n      <th>56.0</th>\n      <td>99530.5</td>\n    </tr>\n    <tr>\n      <th>57.0</th>\n      <td>117578.0</td>\n    </tr>\n    <tr>\n      <th>58.0</th>\n      <td>108000.0</td>\n    </tr>\n    <tr>\n      <th>59.0</th>\n      <td>110000.0</td>\n    </tr>\n    <tr>\n      <th>60.0</th>\n      <td>132287.5</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "      ConvertedComp\nAge                \n45.0        90330.0\n46.0        91593.0\n47.0        91606.0\n48.0       105750.0\n49.0        85248.0\n50.0        95000.0\n51.0       100000.0\n52.0       105500.0\n53.0       100463.0\n54.0       121000.0\n55.0       119000.0\n56.0        99530.5\n57.0       117578.0\n58.0       108000.0\n59.0       110000.0\n60.0       132287.5"
                    },
                    "execution_count": 17,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# your code goes here\n\nQUERY = \"\"\"\n        Select ConvertedComp,Age \n        from master\n        where age between 45 and 60 \"\"\"\n\ndfcomp = pd.read_sql_query(QUERY,conn)\ndfcomp = dfcomp.groupby('Age').median()\ndfcomp"
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 1296x576 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "dfcomp.plot(kind='line',figsize = (18,8))\n\nplt.ylabel('Converted Comp')\nplt.title('Plot of median ConvertedComp for all ages from 45 to 60')\nplt.legend(loc = 'lower right')\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Create a horizontal bar chart using column `MainBranch.`\n"
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>MainBranch</th>\n      <th>Branch_Count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>I am a developer by profession</td>\n      <td>10618</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>I am not primarily a developer, but I write co...</td>\n      <td>780</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "                                          MainBranch  Branch_Count\n0                     I am a developer by profession         10618\n1  I am not primarily a developer, but I write co...           780"
                    },
                    "execution_count": 19,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# your code goes here\n\nQUERY = \"\"\"\n        Select MainBranch,count(1) as Branch_Count\n        from master\n        group by MainBranch \n        \"\"\"\n\ndf = pd.read_sql_query(QUERY,conn)\ndf.set_index('MainBranch')\n\ndf"
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 1440x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "\ndf.plot(kind='barh',figsize = (20,5))\nplt.ylabel(\"Main Branch\")\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Close the database connection.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 21,
            "metadata": {},
            "outputs": [],
            "source": "conn.close()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Authors\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Ramesh Sannareddy\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Other Contributors\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Rav Ahuja\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Change Log\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "| Date (YYYY-MM-DD) | Version | Changed By        | Change Description                 |\n| ----------------- | ------- | ----------------- | ---------------------------------- |\n| 2020-10-17        | 0.1     | Ramesh Sannareddy | Created initial version of the lab |\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": " Copyright \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": {
            "display_name": "Python 3.7",
            "language": "python",
            "name": "python3"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.7.10"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 4
}