Data Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Data Visualization Lab**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lab we 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": [
    "Downloading database file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-10-04 17:07:52--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DA0321EN-SkillsNetwork/LargeData/m4_survey_data.sqlite\n",
      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104\n",
      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 36679680 (35M) [application/octet-stream]\n",
      "Saving to: ‘m4_survey_data.sqlite’\n",
      "\n",
      "m4_survey_data.sqli 100%[===================>]  34.98M  22.0MB/s    in 1.6s    \n",
      "\n",
      "2023-10-04 17:07:55 (22.0 MB/s) - ‘m4_survey_data.sqlite’ 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\n",
    "conn = sqlite3.connect(\"m4_survey_data.sqlite\") # open a database connection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import pandas module.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "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(*)\n",
       "0     11398"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing how many rows are there in the table named 'master'\n",
    "QUERY = \"\"\"\n",
    "SELECT COUNT(*)\n",
    "FROM master\n",
    "\"\"\"\n",
    "\n",
    "# the read_sql_query runs the sql query and returns the data as a dataframe\n",
    "df = pd.read_sql_query(QUERY,conn)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "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\n",
       "0                 EduOther\n",
       "1                  DevType\n",
       "2                  LastInt\n",
       "3               JobFactors\n",
       "4                 WorkPlan\n",
       "5            WorkChallenge\n",
       "6       LanguageWorkedWith\n",
       "7   LanguageDesireNextYear\n",
       "8       DatabaseWorkedWith\n",
       "9   DatabaseDesireNextYear\n",
       "10      PlatformWorkedWith\n",
       "11  PlatformDesireNextYear\n",
       "12      WebFrameWorkedWith\n",
       "13  WebFrameDesireNextYear\n",
       "14      MiscTechWorkedWith\n",
       "15  MiscTechDesireNextYear\n",
       "16              DevEnviron\n",
       "17              Containers\n",
       "18               SOVisitTo\n",
       "19            SONewContent\n",
       "20                  Gender\n",
       "21               Sexuality\n",
       "22               Ethnicity\n",
       "23                  master"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing all the tables names in the database\n",
    "QUERY = \"\"\"\n",
    "SELECT name as Table_Name FROM\n",
    "sqlite_master WHERE\n",
    "type = 'table'\n",
    "\"\"\"\n",
    "# the read_sql_query runs the sql query and returns the data as a dataframe\n",
    "pd.read_sql_query(QUERY,conn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>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\n",
       "0    NaN    287\n",
       "1   16.0      3\n",
       "2   17.0      6\n",
       "3   18.0     29\n",
       "4   19.0     78\n",
       "5   20.0    109\n",
       "6   21.0    203\n",
       "7   22.0    406\n",
       "8   23.0    581\n",
       "9   24.0    679\n",
       "10  25.0    738\n",
       "11  26.0    720\n",
       "12  27.0    724\n",
       "13  28.0    787\n",
       "14  29.0    697\n",
       "15  30.0    651\n",
       "16  31.0    531\n",
       "17  32.0    489\n",
       "18  33.0    483\n",
       "19  34.0    395\n",
       "20  35.0    393\n",
       "21  36.0    308\n",
       "22  37.0    280\n",
       "23  38.0    279\n",
       "24  39.0    232\n",
       "25  40.0    187\n",
       "26  41.0    136\n",
       "27  42.0    162\n",
       "28  43.0    100\n",
       "29  44.0     95\n",
       "30  45.0     85\n",
       "31  46.0     66\n",
       "32  47.0     68\n",
       "33  48.0     64\n",
       "34  49.0     66\n",
       "35  50.0     57\n",
       "36  51.0     29\n",
       "37  52.0     41\n",
       "38  53.0     32\n",
       "39  54.0     26\n",
       "40  55.0     13\n",
       "41  56.0     16\n",
       "42  57.0     11\n",
       "43  58.0     12\n",
       "44  59.0     11\n",
       "45  60.0      2\n",
       "46  61.0     10\n",
       "47  62.0      5\n",
       "48  63.0      7\n",
       "49  65.0      2\n",
       "50  66.0      1\n",
       "51  67.0      1\n",
       "52  69.0      1\n",
       "53  71.0      2\n",
       "54  72.0      1\n",
       "55  99.0      1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QUERY = \"\"\"\n",
    "SELECT Age,COUNT(*) as count\n",
    "FROM master\n",
    "group by age\n",
    "order by age\n",
    "\"\"\"\n",
    "pd.read_sql_query(QUERY,conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CREATE TABLE \"master\" (\n",
      "\"index\" INTEGER,\n",
      "  \"Respondent\" INTEGER,\n",
      "  \"MainBranch\" TEXT,\n",
      "  \"Hobbyist\" TEXT,\n",
      "  \"OpenSourcer\" TEXT,\n",
      "  \"OpenSource\" TEXT,\n",
      "  \"Employment\" TEXT,\n",
      "  \"Country\" TEXT,\n",
      "  \"Student\" TEXT,\n",
      "  \"EdLevel\" TEXT,\n",
      "  \"UndergradMajor\" TEXT,\n",
      "  \"OrgSize\" TEXT,\n",
      "  \"YearsCode\" TEXT,\n",
      "  \"Age1stCode\" TEXT,\n",
      "  \"YearsCodePro\" TEXT,\n",
      "  \"CareerSat\" TEXT,\n",
      "  \"JobSat\" TEXT,\n",
      "  \"MgrIdiot\" TEXT,\n",
      "  \"MgrMoney\" TEXT,\n",
      "  \"MgrWant\" TEXT,\n",
      "  \"JobSeek\" TEXT,\n",
      "  \"LastHireDate\" TEXT,\n",
      "  \"FizzBuzz\" TEXT,\n",
      "  \"ResumeUpdate\" TEXT,\n",
      "  \"CurrencySymbol\" TEXT,\n",
      "  \"CurrencyDesc\" TEXT,\n",
      "  \"CompTotal\" REAL,\n",
      "  \"CompFreq\" TEXT,\n",
      "  \"ConvertedComp\" REAL,\n",
      "  \"WorkWeekHrs\" REAL,\n",
      "  \"WorkRemote\" TEXT,\n",
      "  \"WorkLoc\" TEXT,\n",
      "  \"ImpSyn\" TEXT,\n",
      "  \"CodeRev\" TEXT,\n",
      "  \"CodeRevHrs\" REAL,\n",
      "  \"UnitTests\" TEXT,\n",
      "  \"PurchaseHow\" TEXT,\n",
      "  \"PurchaseWhat\" TEXT,\n",
      "  \"OpSys\" TEXT,\n",
      "  \"BlockchainOrg\" TEXT,\n",
      "  \"BlockchainIs\" TEXT,\n",
      "  \"BetterLife\" TEXT,\n",
      "  \"ITperson\" TEXT,\n",
      "  \"OffOn\" TEXT,\n",
      "  \"SocialMedia\" TEXT,\n",
      "  \"Extraversion\" TEXT,\n",
      "  \"ScreenName\" TEXT,\n",
      "  \"SOVisit1st\" TEXT,\n",
      "  \"SOVisitFreq\" TEXT,\n",
      "  \"SOFindAnswer\" TEXT,\n",
      "  \"SOTimeSaved\" TEXT,\n",
      "  \"SOHowMuchTime\" TEXT,\n",
      "  \"SOAccount\" TEXT,\n",
      "  \"SOPartFreq\" TEXT,\n",
      "  \"SOJobs\" TEXT,\n",
      "  \"EntTeams\" TEXT,\n",
      "  \"SOComm\" TEXT,\n",
      "  \"WelcomeChange\" TEXT,\n",
      "  \"Age\" REAL,\n",
      "  \"Trans\" TEXT,\n",
      "  \"Dependents\" TEXT,\n",
      "  \"SurveyLength\" TEXT,\n",
      "  \"SurveyEase\" TEXT\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "table_name = 'master'  # the table you wish to describe\n",
    "\n",
    "QUERY = \"\"\"\n",
    "SELECT sql FROM sqlite_master\n",
    "WHERE name= '{}'\n",
    "\"\"\".format(table_name)\n",
    "\n",
    "df = pd.read_sql_query(QUERY,conn)\n",
    "print(df.iat[0,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "QUERY = \"\"\"\n",
    "SELECT Respondent FROM sqlite_master\n",
    "WHERE name= '{}'\n",
    "\"\"\".format(table_name)\n",
    "\n",
    "df = pd.read_sql_query(QUERY,conn)\n",
    "print(df.iat[0,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing distribution of data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ploting a histogram of `ConvertedComp.`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "QUERY1 = \"\"\"\n",
    "SELECT ConvertedComp\n",
    "FROM master\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY1,conn)\n",
    "df.head()\n",
    "df.plot(kind='hist', figsize=(10, 8) )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Box Plots\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ploting 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 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "QUERY2 = \"\"\"\n",
    "SELECT Age\n",
    "FROM master\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY2,conn)\n",
    "df.head()\n",
    "df.plot(kind='box', figsize=(10, 8) )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing relationships in data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Plots\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a scatter plot of `Age` and `WorkWeekHrs.`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jx45VcXGxZs2apf3792vDhg3asGGDpAuPkYqKilRcXKz8/Hzl5+eruLhYycnJmj17tiTJ5XJp7ty5Wrx4sdLT05WWlqaHHnpIw4cP1+TJk+2uMgAAMJDtIebaa6/Vtm3btGzZMj3yyCPKy8vT448/rjvuuCNQZsmSJaqrq9P8+fNVU1Oj0aNHa/fu3UpJSQmUWbt2reLi4jRr1izV1dVp0qRJ2rRpk2JjY+2uMgAAMJDt88REC+aJAQDAPL06TwwAAEBPIMQAAAAjEWIAAICRCDEAAMBIhBgAAGAkQgwAADASIQYAABiJEAMAAIxEiAEAAEYixAAAACMRYgAAgJEIMQAAwEiEGAAAYCRCDAAAMBIhBgAAGIkQAwAAjESIAQAARiLEAAAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMRIgBAABGIsQAAAAjEWIAAICRCDEAAMBIhBgAAGAkQgwAADBSXG9XANGhscnS/sqTqq49q8yURF2Xl6bYGEdvVwsAgDYRYqBd5VVauaNCVd6zgW3ZrkQtn1GgaYXZvVgzAADaxuOkfm5XeZXu3XIwKMBIksd7VvduOahd5VW9VDMAANpHiOnHGpssrdxRISvEe/5tK3dUqLEpVAkAAHoXIaYf2195slULTHOWpCrvWe2vPNlzlQIAIEyEmH6surbtANOZcgAA9CRCTD+WmZJoazkAAHoSIaYfuy4vTdmuRLU1kNqhC6OUrstL68lqAQAQFkJMPxYb49DyGQWS1CrI+H9ePqOA+WIAAFGJENPPTSvM1vo7r5HbFfzIyO1K1Po7r2GeGABA1GKyO2haYbamFLiZsRcAYBRCDCRdeLQ05vL03q4GAABh43ESAAAwEiEGAAAYiRADAACMRIgBAABGIsQAAAAjMToJPa6xyWI4NwCgywgx6FG7yqu0ckdF0OrZ2a5ELZ9RwMR6AICI8DgJPWZXeZXu3XIwKMBIksd7VvduOahd5VW9VDMAgIkIMegRjU2WVu6okBXiPf+2lTsq1NgUqgQAAK0RYtAj9leebNUC05wlqcp7VvsrT/ZcpQAARiPEoEdU17YdYDpTDgAAQgx6RGZKYseFIigHAAAhBj3iurw0ZbsS1dZAaocujFK6Li+tJ6sFADAYIQY9IjbGoeUzCiSpVZDx/7x8RgHzxQAAwtbtIaakpEQOh0NFRUWBbZZlacWKFcrJyVFSUpImTpyow4cPB32uvr5eixYtUkZGhgYMGKCZM2fq2LFj3V1ddKNphdlaf+c1cruCHxm5XYlaf+c1zBMDAIhIt052d+DAAW3YsEFXXnll0PbVq1drzZo12rRpk774xS/q3//93zVlyhS9/fbbSklJkSQVFRVpx44d2rp1q9LT07V48WJNnz5dZWVlio2N7c5qoxtNK8zWlAI3M/YCALqs21piTp06pTvuuEMbN27UwIEDA9sty9Ljjz+uH/zgB/r617+uwsJC/epXv9KZM2f09NNPS5K8Xq9+8Ytf6LHHHtPkyZM1YsQIbdmyRW+++aZeeOGF7qoyekhsjENjLk/XLVdfqjGXpxNgAACd0m0hZsGCBbr55ps1efLkoO2VlZXyeDyaOnVqYJvT6dSECRO0b98+SVJZWZnOnTsXVCYnJ0eFhYWBMi3V19fL5/MFvQAAQN/VLY+Ttm7dqoMHD+rAgQOt3vN4PJKkrKysoO1ZWVk6cuRIoExCQkJQC46/jP/zLZWUlGjlypV2VL/HddeCiHbsN9Q+JIW9367WoScXizRtYUrT6gsAdrM9xBw9elT333+/du/ercTEtuf8cDiC/2drWVarbS21V2bZsmV68MEHAz/7fD7l5uZGUPPe0V0LItqx31D7uDg5XpL02ZlzHe63q3XoycUiTVuY0rT6AkB3sP1xUllZmaqrqzVy5EjFxcUpLi5OpaWl+tnPfqa4uLhAC0zLFpXq6urAe263Ww0NDaqpqWmzTEtOp1OpqalBr2jXXQsi2rHftvbx2ZlzQQGmrf12tQ49uVikaQtTmlZfAOgutoeYSZMm6c0339ShQ4cCr1GjRumOO+7QoUOHNHToULndbu3ZsyfwmYaGBpWWlmrs2LGSpJEjRyo+Pj6oTFVVlcrLywNlTNddCyLasd/29hFKy/12tQ49uVikaQtTmlZfAOhOtj9OSklJUWFhYdC2AQMGKD09PbC9qKhIxcXFys/PV35+voqLi5WcnKzZs2dLklwul+bOnavFixcrPT1daWlpeuihhzR8+PBWHYVNFcmCiGMuTw9sb6sfhH/7n9890an9njp7Xg8887o+rKnTRQmx7e6jvf2u3fOOBibHh1WHtXve0bgvZATOoeF8kza/+oH+YvPvpj2dvQ49qfl5nait79TvFgD6om6dJ6YtS5YsUV1dnebPn6+amhqNHj1au3fvDswRI0lr165VXFycZs2apbq6Ok2aNEmbNm3qM3PEdGZBxLb6Qcy8Klvb36iKKHg03+/Mda/ob8fsGc217qV3Iyq77qV3le1KVOGlqfrTW9WKpAEhnN9NR31Eon1hylDnFY7mv1v6yQDoqxyWZfXJdmefzyeXyyWv1xuV/WNefe9T3b7xtQ7L/fbuL2vM5emBfhB2XSz/fu0MMD2to9+Nv/2hvdmAI70OPcmOax7O7wAAokkk92/WTuolkSyIGGkflY7EOKSRQwbq1NnzxgYY/zl0tY/IyCED1dHTFv+xepJd15x+MgD6MkJML4lkQcSO+m1EqsmSyo7U6IFnXrdtnz3Nfw6R9GkJpexITYePsPzH6kl2XvOOfgcAYCpCTC8Kd0HE7uiPUV17Vh/W1Nm+355UXXu2y31aorVPTHddcwDoS3qlYy8+196CiP5RKf/4uNb242amJGrwwCS97bF/3z0lM6XtyRTDLRvuPiI5lh3CPd6Pbh6mmjMNWvfSe7btEwBMQYiJAv4FEZvr7KiUjjh0oaXnurw0Db/UpcIVf7R1/z2h+TlIF/oOebxnQ/YfaVm2JX/fpM5+vruEW69vj8uTJP3fgx9F3TkAQHfjcVIUamtG1q5q2dfmosQ4XTko+kZutaflOUTStyiUrn6+u0RSr2g9BwDoboSYKBPpqJRsV6Lm3ZCn7Bb9ai5Ojg+sc+TXsq+NJG1feH2bQSYpPvw/D2dccNlsV6KGpCeFLBsf62hVdkpBZqtRQg5JAxKC5wUKdQ7h9i1qS1c/310iqVe0ngMAdCfmiYky4c5bsvArXwiakbWrq003n7F38MAkrb1thC5KjAu5PTbGoeKdFfrg0zO6LD1Z37+pQAlxMSGPFerzSQmxIcv6Z+w9cvKMhqQla86YywKjs/raitmRiKRe0XoOABCuSO7fhJgo87tDH+n+rYc6LPef37xat1x9afdXCACAHsRkdwaL1tEyAABEG0JMlIlkJl8AAPozQkyUYaQJAADhIcREIUaaAADQMSa7i1LtzeQLAAAIMVEt1Ey+AADgAh4nAQAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMRIgBAABGIsQAAAAjEWIAAICRCDEAAMBIhBgAAGAkQgwAADASIQYAABiJEAMAAIxEiAEAAEYixAAAACMRYgAAgJEIMQAAwEiEGAAAYCRCDAAAMBIhBgAAGIkQAwAAjESIAQAARiLEAAAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMRIgBAABGIsQAAAAjEWIAAICRCDEAAMBIhBgAAGAkQgwAADASIQYAABiJEAMAAIxEiAEAAEYixAAAACPZHmJKSkp07bXXKiUlRZmZmbr11lv19ttvB5WxLEsrVqxQTk6OkpKSNHHiRB0+fDioTH19vRYtWqSMjAwNGDBAM2fO1LFjx+yuLgAAMJTtIaa0tFQLFizQa6+9pj179uj8+fOaOnWqTp8+HSizevVqrVmzRuvWrdOBAwfkdrs1ZcoU1dbWBsoUFRVp27Zt2rp1q/bu3atTp05p+vTpamxstLvKAADAQA7LsqzuPMAnn3yizMxMlZaW6oYbbpBlWcrJyVFRUZGWLl0q6UKrS1ZWllatWqV58+bJ6/Xqkksu0ebNm3XbbbdJko4fP67c3Fzt3LlTN954Y4fH9fl8crlc8nq9Sk1N7c5TBAAANonk/t3tfWK8Xq8kKS0tTZJUWVkpj8ejqVOnBso4nU5NmDBB+/btkySVlZXp3LlzQWVycnJUWFgYKNNSfX29fD5f0AsAAPRd3RpiLMvSgw8+qPHjx6uwsFCS5PF4JElZWVlBZbOysgLveTweJSQkaODAgW2WaamkpEQulyvwys3Ntft0AABAFOnWELNw4UL97W9/029/+9tW7zkcjqCfLctqta2l9sosW7ZMXq838Dp69GjnKw4AAKJet4WYRYsWafv27XrppZc0aNCgwHa32y1JrVpUqqurA60zbrdbDQ0NqqmpabNMS06nU6mpqUEvAADQd9keYizL0sKFC/Xss8/qxRdfVF5eXtD7eXl5crvd2rNnT2BbQ0ODSktLNXbsWEnSyJEjFR8fH1SmqqpK5eXlgTIAAKB/i7N7hwsWLNDTTz+t3/3ud0pJSQm0uLhcLiUlJcnhcKioqEjFxcXKz89Xfn6+iouLlZycrNmzZwfKzp07V4sXL1Z6errS0tL00EMPafjw4Zo8ebLdVQYAAAayPcSsX79ekjRx4sSg7b/85S/17W9/W5K0ZMkS1dXVaf78+aqpqdHo0aO1e/dupaSkBMqvXbtWcXFxmjVrlurq6jRp0iRt2rRJsbGxdlcZAAAYqNvniekt/W2emLqGRhXvrNAHn57RZenJ+v5NBUqIi9H+ypOqrj2rzJREXZeXptgYhxrON2nzqx/oyMkzGpKWrDljLlNCXOgni+HuV1LIY0WisclqtY+G802tjp+UEBuybCTHC/X5SM6hq8cHAIQWyf2bENMH3P3rA9pTUd1quzMuRvXnmwI/Z7sSVXhpqv70VrWaml31GId09/V5WnZTQaf2e3FyvCTpszPngo61fEaBphVmh3UOu8qrtHJHhaq8ZwPbEuJi1NDsOH5XDkrVJ7UNQWUjOV6oY0VyDqE+H+n5AgBCI8So74aYlq0o+977VH/6e+ug0Rnzbvg8yLQVYMLlb5NYf+c1Hd7Yd5VX6d4tB9WVP8RwjxfJsfz7/D+zr9HAAQmqrj2rD06c0eMvvNPq85+XHaGBA5y00ABAJxFi1DdDTMnOCm18pTKoFcVOMQ7p7z/+mhqbLA37t1227DPblai9S7/a7mOZ8ateDGrV6K7jdfZYMQ6F/TtvWZYWGgCITFQtOwB7lOys0M9f7r4AI124+W5+9QMV76ywbZ9V3rPaX3myzff3V560LcB0dLzOHiuS33nLsh7vWd275aB2lVdFfFwAQPsIMQZoON+kja9U9six3j9xSh98esbWfX5U0/b+PN46W48lSUdPhj5eda19YSlc/kyzckeFGrszgQJAP0SIMcDmVz/o1haY5qp99bosPdnWff7xcOj1riTp5OkGW48lSc8c+DDk9syURNuPFQ5LHbdIAQAiR4gxwJE2Wha6Q2Zqor7fYpRSV9Wda2zzvbSLnLYeS5Jq68+H3H5dXpqyXYnqrW62vdESBAB9GSHGAEPS7G0Zac/QjAFKSojVlYPs6wydlzGgzffcqfa3jrT1+4qNcWj5jAsBrTeCTG+1BAFAX0WIMcCcMZepJ0bpOhwXjtXYZOmTWvse8yydNqzN9/ytI3b6j29c3eZ70wqztf7Oa+RuccyLk+MDc8V0RnvXx6ELo5T8E+oBAOxh+7IDsF9CXIzuvj5PP3858s69M650a3KBWxkXOXX3rw/oTEPryeP8kuNjFRvjsH3E0JsfeTXm8vSQ7/lbR+7dclCSujRXjF9Fla/N40kXgsyUAnfQjLs1p+v1yO8jH5W18Ctf0LgvZKjmdIMWPN36HPzZZvmMAuaLAQCb0RJjiGU3FWjeDXmtvvHHOC7MYBtq+7wb8vTE7JG65epLFeNwtBtgJOl0Q2Pgxm6njvbXVutIdx1PuhCexlyerluuvlTeugYtePp1eXz1ER8rP+sijbk8XTddGfoc3K7EsCb8AwBEjpYYgyy7qUCLp14Rct2jjtZDCjeY+FsmwvHPV+do26HjHZYLZ3/NW0c8vrP68e8P6+Tpcx1+rrPH82tssrRyR0WnW4CaHytUCw8z9gJA9yHEGCYhLkZzrx8a9na/cG/s/htvtitRHu/ZkDd3hy60MKz6f67Sa5UnOywXbl8Qf+vIq+992qkAE+nxpM5PgNfWsfznAADofjxO6ic6Gl7cvPNpe6N4mvfxSIiLCatcpC0RnXmc1dnj9eSxAAD2IsT0E+EGE/9Nua1+Ki37eIRbLhIZAyKfO6azx+vMsGf6uQBAdOBxUj/iDxwrd1QEPUJxt7FIYbh9PGzvCxLmx35w0zBlpjq7dLxwHp1lpTr12KyrdeJUPf1cACCKEGL6mUgDR7h9POzsC3LiVHijhDJTnbrl6ku7dKzmQ7wdCj08esXML2ncFzK6dBwAgP0IMf1QtHc+jaQTcnONTVanWoMibaECAEQHQgyiTrijo5qPDNpVXtUqhGRHEEIYHg0A5qFjL6JOpJ2Qd5VX6d4tB1sNlfZ4z+reLQe1q7wq7OP6J8Abc3k6AQYAohwhBlEp3FFP7U1W59+2ckeFGpvsWNAAABBNeJyEdoXqZ9LYZLU7O7BdwnnE09FkdZakKu9Z7a88GdQPqLP9Z0zV384XQP9AiEGbQvUzSU6IVd25RlnNGjYe3fmW7r4+T8tuKghrv5HcUDvqhBzJcgp+Xe0/Y5r+dr4A+g9CDELy9zNp+RDmTENjq7JNlgIrbHcUZOy+oUY6kqmt8/L3n+lrk9j1t/MF0L/QJwatdHZRxI2vVKrhfNsrZdvVAbe5SJZT6G/9Z/rb+QLofwgxaKWziyI2WdKS/35Dr773aasbY2dvqI1Nll5971P97tBHIfcbyUimSPrP9AX97XwB9D88TurDOtuZszOLIvo9d+i4njt0vNUjos50wA330dO0wmx994Y8bXylMqivjsMh3X19XqBsZ/rPmKy/nS+A/ocQY5hwg8mu8iqt2H5YHt/nU/i7U51aMfNLHfaB6MyiiC217HMR6Q01kr4cu8qrtOHlylZlmyxpw8uVGjF4oKYVZnd6JuDe1tkwaur5AkC4CDFRrOXNq+Z0vR75/Vvy+JpNjZ+aqBUzg1smdpVX6Z4tB1vtz+Or1z1bDmrdN0fo49qzbQ6R7mjG3HBYuvA4Z/nvynX0ZJ0OfBDeI4u/fnBSBz44qd8dOt7moyf/fv+77KiOnjyryhOn2qynJel7//2G/vzupxqclqyslARV1za0ORNw2oAEebx1evW9Tzs1DDncwBFZGK3o8JqH0pmZjyPBsG0Avc1hWVaf7NXn8/nkcrnk9XqVmpra29WJWKibV3ue+t+WicYmSyP/fY8+O3Mu7GPF/O9jl+Yji9oKQqZzOCTLUqvFHkOJdNRUuI+/IinX3jV4KoyRRf4WLSn04padHZ3EsG0A3SWS+zchJgp1JkAMTI7Xutuv0Z/f+0RP/s/7nTrujCvdmlzgVmZKol78+8fa+Eplp/ZjguSE2JDDxZvz3+j/z+wRGjjA2W6LQ1uPv/xh6YHJ+bosY4A+OHFaa1/4R5vHWh9BGB2YHK+//nBKh60fdgeO9s61+TkAQGcQYmRuiOlMSwoiF+OQlk67Qsdq6vTcoWOqPdt2oIlxXOhf49cyADQ2WRq/6sVOjehqzv94Z+/Sr+q19z/VHf/1lw4/84Obhikz1dnh45yG802tZln2j9hqHs4ktfuIqKNzbX4O4TxaCvVIqqM6AOjbIrl/0ycmyrz2/qcEmB7QZEklz/897LLNtexc3Nkh6S01H6H16nufhvWZR3e+FfjvtlpXQrXEPPHSu5IU9Ld2cXJ8q212jDJrS6h6hVMHAPBjnpgos+/dE71dBXSg5bw2dg9RvrC/yBtI/eHqP1/4R2BenZ1/Cz3B4GdnzrUKy6G2tZyI0K5h221NfBhOHezU0TxEAKIbLTHdKNzFEyUFth08UtO7lUZY/C0O+T/YqUSbF7/86a6/6+Tp+o4LhqiTJK194Z3AthhHZ+JQ632u3FGhKf/bXyocJ2rr9btDH4X8u88dmKz/euX9sOvlH5Hmr0PLx1udfRwVaV8hRmMB0Yc+Md0k3MUTwxklA0SD3979ZV2Xl9Zh/5+WfYhC/d13pQ7tTYYY7uOoSDsnMxoL6Dn0iellkSyeSICBKZ7Z/6GeL6/SkPTkdkNMyycyHY0Ci0RHkyGG6k/Wsg9TR0tg+Ft9UpzxOnG6vs0RZSyiCfQ+QozNOrt4IhDtnnvjeG9XQZkpiRH/G2v5OCrczsl3/KL90WHtPeYC0DMIMTaza6QKgM81n124M//Gmo+asrMjdlujsaKh/0w01AHoboQYm7GYHmA/S9KyG/9JK7aX6y9dWHX7z++eUHc8xG3+776t/jM/unlYyEkTuyNs0IcH3S1aQjIde23Q/GKeqK3Xj//wVscfAtBn+Dsct9VXJ5RsV6JmXpWt7W9U2Ro2mFEZ3a27QzIz9qr7QkzLmU+zUhL14z9U6OPayIfEAjDfxcnxKvvhFEmybeZmqXNhw+4ZlYGWeiIkMzqpm5TsrNDGVypbjb4A0H/5/+dt58zNkcyL0/x9O2dUBloKd2RfT3Z0J8SEqWRnhX7+ct9dEBFA59ScOdcjHYbDacK3a0ZlIJRoDMksOxCGhvNNfXpFZwBdU/VZnTIGOG3fb8t5cVreQFouyRDujMrhlgOai8aQTIgJw+ZXP+AREoA2lX148vPnSjbqaF6clut4jRwyUB214sc4pJFDBtpdVfQD0RiSCTFhOHLyTG9XAUAU8/jO6sQp+zr3O3ThUVE48+I0b8IvO1LT4ReuJksqY402dMJ1eWnKdiW2mdeb/932FEJMGIakJfd2FQBEsfpzTbZ9+/TfIJbPKFBsjCOiJvxobO5H3xEb49DyGQWSWjc8tvy77SmEmDDMGXNZh020APqvKy+9uMNvqS1luxI174Y8ZbuCw4/blRg0TDWSJvxwy/7j41N69b1P1chzckRoWmG21t95jdwd/N32FEYnhSEhLkZ3X5/H6CQAIY37QkbgW+q9Ww62Wp3eH2yKJn9Rl2UkBw2PXjJtWLvDpv3hyOM9G7JfTPMlGSS1W9Zv3Uvvat1L7zKLLzplWmF2YB0yZuztJt0x2R3zxADmSk6ItXVFbT//ZHf+/4F3x2ym/tFJUuhw1PwbcFtlQ2EWX0QjZuxV94SYXeVVWv67w0Gz87oS4+RwOPRZ3TlbjgH0F0PSk/SHRTfogWdeV/lxX8QTxaUnxWjZ1wq0YW+lqmsblJmSoNF5afrN/qNBXzRiHNLd1+cFWjyeL6/Sr1890uH+F37lC8rPukgfnDijtS+802a5p0IEgN5eDylU2bYwiy+iDSFG9oeYSNZEAdDa0Ixk1Z1rUo4rUf/vt6+TKzk+8N6r732q2ze+1uE+fnTzMGWkONsNBi2XBpkz5jIlxH3e/S/cY/nXQ5IutMJueLmyVSvId2/I07KbCjrcl10iCUf+sn9+9xOte+m9Dvfd/HyB3sSyAzZrb54GIFo4JCXGOVR3/vO/1MyLEvTP11yq/z5wRJ/WNQW2x0qy/8HKBanOWPnqP997VkqCVt5S2O7jinD7fXx7XF6HrQUJcTGae/3QLh/L38dkV3lVqwAjXXhUs+HlSo0YPLDHHsXExjjCDhr+soxYQl9GiAmDXWuiAHb613GXKfvipKAWh9gYR8hv6qE6jzacb1LxzgodOvqZ3vzIZ0udHJKSnXFaP2eUTpyqD/tRSjidYu0auhnJscL5AtPTa8VEKhonKAPsEvVDrJ988knl5eUpMTFRI0eO1CuvvNLjdeAbCqLRH8o9+va4PD1yS6HmXj9UCXExgW/ft1x9qcZcnh64sYbanpQQqx/fOlzPLRgf0dDg9liSPL56xTgcrerQkZ4cuhnusSKZaC5aReMEZYBdorol5plnnlFRUZGefPJJjRs3Tj//+c/1ta99TRUVFRo8eHCP1YNvKOguCyZeLodDYfVZaMmuhdbaa5norM4G/54cuhnOsfrCo5iebOUCelpUt8SsWbNGc+fO1b/+679q2LBhevzxx5Wbm6v169f3aD0incQKCJfDIY37wiWd/rxdN8+2WiY6qyvBv63WpO7Q0bH6yqOYaJugDLBL1LbENDQ0qKysTA8//HDQ9qlTp2rfvn2tytfX16u+/vOhzz6fPc/4pe75pgpc4Oiwo2l77Lx5tmyZyBjg1OL/7w197Au/Xi07xZou0k7A0SyaJigD7BK1LTEnTpxQY2OjsrKygrZnZWXJ4/G0Kl9SUiKXyxV45ebm2lofu7+pApIC3/7bWo+kLd3Vj6F5y8S4/AytmBl+vfrio4loXCumK3qylQvoCVEbYvwcjuB/ZJZltdomScuWLZPX6w28jh49antdphVma+/Sr+q3d39Za2+7Whc5Y20/Bsx0cbM5T8I1MDleXx56oT9LJCG5J2+ebdXr4uT4VufcVx9N8CgGiF5R+zgpIyNDsbGxrVpdqqurW7XOSJLT6ZTT6ez2ejWfpyEpPkb3/O/03qG4kuLkrTsf+Dk+1qFzjTyMCiWSx3QJsQ41hPg9xjqk5psHJMTqdIhp5lMT4+Q7e77V9lCuHJSqj731QbM0u1Od+rfpBRo4wBnULL+nwqMV2yvk8YXXT6Xk68ODQkio5v6a0w368R+CZ1519/B6N209hpDUbx5N8CgGiE5RPWPv6NGjNXLkSD355JOBbQUFBbrllltUUlLS7me7Y9mBUHaVV2nF9sPy+IJvcitmfink//T8c3N88OkZXZaerO/fVKCG8036zqb9Ou49GzSb6Ycnzmjaf5aq7lyTkuJjtOv+CfKeOaeZT+6VpQs3/u3zx2v4YJfK3q/Rv2z4vK/Q96Z+Uf/1ynuqqfv8Jp6WFKtvXDtYW16p1OnmN3uHNP/GofrprvcD2/7vd8dq99+rQi56+c9XZ2rboerAz8/dM06XpDr1tZ+V6nR9owY4Y/X8fRP05vHPtGTrQfma5YXUOOmHMwr1s5fe1cnT55Q2IF7b5o/XJalOec+ca/V7iI1x6IFnXteHNXUaPDBJa28boYsS40KWvSgxrtXvu7HJCjl7a6jPJ8TFtLo2SQmxnZol9fMQUq9Hfv9WULCJdB2d7pjCHgCiVZ9ZduCZZ57RnDlz9NRTT2nMmDHasGGDNm7cqMOHD2vIkCHtfranQowUvTeZtuoVSX07msK9s3XoT/gdAED4+kyIkS5Mdrd69WpVVVWpsLBQa9eu1Q033NDh53oyxAAAAHv0qRDTWYQYAADME8n9O+pHJwEAAIRCiAEAAEYixAAAACMRYgAAgJEIMQAAwEiEGAAAYCRCDAAAMBIhBgAAGClqF4DsKv8cfj6fr5drAgAAwuW/b4czF2+fDTG1tbWSpNzc3F6uCQAAiFRtba1cLle7ZfrssgNNTU06fvy4UlJS5HBE/2J7Pp9Pubm5Onr0KMskGIJrZh6umVm4Xuax45pZlqXa2lrl5OQoJqb9Xi99tiUmJiZGgwYN6u1qRCw1NZV/rIbhmpmHa2YWrpd5unrNOmqB8aNjLwAAMBIhBgAAGIkQEyWcTqeWL18up9PZ21VBmLhm5uGamYXrZZ6evmZ9tmMvAADo22iJAQAARiLEAAAAIxFiAACAkQgxAADASISYHlZSUqJrr71WKSkpyszM1K233qq33347qIxlWVqxYoVycnKUlJSkiRMn6vDhw71UYzRXUlIih8OhoqKiwDauV/T56KOPdOeddyo9PV3Jycm6+uqrVVZWFnifaxZdzp8/rx/+8IfKy8tTUlKShg4dqkceeURNTU2BMlyz3vPyyy9rxowZysnJkcPh0HPPPRf0fjjXpr6+XosWLVJGRoYGDBigmTNn6tixY12uGyGmh5WWlmrBggV67bXXtGfPHp0/f15Tp07V6dOnA2VWr16tNWvWaN26dTpw4IDcbremTJkSWA8KvePAgQPasGGDrrzyyqDtXK/oUlNTo3Hjxik+Pl7PP/+8Kioq9Nhjj+niiy8OlOGaRZdVq1bpqaee0rp16/TWW29p9erV+ulPf6onnngiUIZr1ntOnz6tq666SuvWrQv5fjjXpqioSNu2bdPWrVu1d+9enTp1StOnT1djY2PXKmehV1VXV1uSrNLSUsuyLKupqclyu93WT37yk0CZs2fPWi6Xy3rqqad6q5r9Xm1trZWfn2/t2bPHmjBhgnX//fdblsX1ikZLly61xo8f3+b7XLPoc/PNN1vf+c53grZ9/etft+68807Lsrhm0USStW3btsDP4Vybzz77zIqPj7e2bt0aKPPRRx9ZMTEx1q5du7pUH1piepnX65UkpaWlSZIqKyvl8Xg0derUQBmn06kJEyZo3759vVJHSAsWLNDNN9+syZMnB23nekWf7du3a9SoUfrGN76hzMxMjRgxQhs3bgy8zzWLPuPHj9ef/vQnvfPOO5KkN954Q3v37tVNN90kiWsWzcK5NmVlZTp37lxQmZycHBUWFnb5+vXZBSBNYFmWHnzwQY0fP16FhYWSJI/HI0nKysoKKpuVlaUjR470eB0hbd26VQcPHtSBAwdavcf1ij7vv/++1q9frwcffFDf//73tX//ft13331yOp361re+xTWLQkuXLpXX69UVV1yh2NhYNTY26tFHH9Xtt98uiX9n0Syca+PxeJSQkKCBAwe2KuP/fGcRYnrRwoUL9be//U179+5t9Z7D4Qj62bKsVtvQ/Y4ePar7779fu3fvVmJiYpvluF7Ro6mpSaNGjVJxcbEkacSIETp8+LDWr1+vb33rW4FyXLPo8cwzz2jLli16+umn9aUvfUmHDh1SUVGRcnJydNdddwXKcc2iV2eujR3Xj8dJvWTRokXavn27XnrpJQ0aNCiw3e12S1KrdFpdXd0q6aL7lZWVqbq6WiNHjlRcXJzi4uJUWlqqn/3sZ4qLiwtcE65X9MjOzlZBQUHQtmHDhunDDz+UxL+xaPS9731PDz/8sL75zW9q+PDhmjNnjh544AGVlJRI4ppFs3CujdvtVkNDg2pqatos01mEmB5mWZYWLlyoZ599Vi+++KLy8vKC3s/Ly5Pb7daePXsC2xoaGlRaWqqxY8f2dHX7vUmTJunNN9/UoUOHAq9Ro0bpjjvu0KFDhzR06FCuV5QZN25cq2kL3nnnHQ0ZMkQS/8ai0ZkzZxQTE3w7io2NDQyx5ppFr3CuzciRIxUfHx9UpqqqSuXl5V2/fl3qFoyI3XvvvZbL5bL+53/+x6qqqgq8zpw5Eyjzk5/8xHK5XNazzz5rvfnmm9btt99uZWdnWz6frxdrDr/mo5Msi+sVbfbv32/FxcVZjz76qPWPf/zD+s1vfmMlJydbW7ZsCZThmkWXu+66y7r00kut3//+91ZlZaX17LPPWhkZGdaSJUsCZbhmvae2ttZ6/fXXrddff92SZK1Zs8Z6/fXXrSNHjliWFd61ueeee6xBgwZZL7zwgnXw4EHrq1/9qnXVVVdZ58+f71LdCDE9TFLI1y9/+ctAmaamJmv58uWW2+22nE6ndcMNN1hvvvlm71UaQVqGGK5X9NmxY4dVWFhoOZ1O64orrrA2bNgQ9D7XLLr4fD7r/vvvtwYPHmwlJiZaQ4cOtX7wgx9Y9fX1gTJcs97z0ksvhbxv3XXXXZZlhXdt6urqrIULF1ppaWlWUlKSNX36dOvDDz/sct0clmVZXWvLAQAA6Hn0iQEAAEYixAAAACMRYgAAgJEIMQAAwEiEGAAAYCRCDAAAMBIhBgAAGIkQAwAAjESIAQAARiLEAAAAIxFiAACAkQgxAADASP8/gD9f/Cuk+yEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Age = pd.read_sql_query(QUERY2, conn)\n",
    "WWH = pd.read_sql_query('SELECT WorkWeekHrs FROM master', conn)\n",
    "\n",
    "Query3 = \"\"\"\n",
    "SELECT Age, WorkWeekHrs\n",
    "FROM master\n",
    "\"\"\"\n",
    "Age_WWH = pd.read_sql_query(Query3, conn)\n",
    "plt.scatter(x = Age, y = WWH)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bubble Plots\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating 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 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CRH = pd.read_sql_query('SELECT CodeRevHrs FROM master', conn)\n",
    "\n",
    "Query4 = \"\"\"\n",
    "SELECT WorkWeekHrs, CodeRevHrs\n",
    "FROM master\n",
    "\"\"\"\n",
    "Age_WWH = pd.read_sql_query(Query4, conn)\n",
    "plt.scatter(x = WWH, y = CRH, s = Age, alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing composition of data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pie Charts\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a pie chart of the top 5 databases that respondents wish to learn next year. Labeling the pie chart with database names. Displaying percentages of each database on the pie chart.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Database  No_of_respondents\n",
      "0     PostgreSQL               4328\n",
      "1        MongoDB               3649\n",
      "2          Redis               3331\n",
      "3          MySQL               3281\n",
      "4  Elasticsearch               2856\n"
     ]
    }
   ],
   "source": [
    "Query5 = \"\"\"\n",
    "SELECT DatabaseDesireNextYear as Database, count(*) as No_of_respondents\n",
    "FROM DatabaseDesireNextYear\n",
    "GROUP BY DatabaseDesireNextYear \n",
    "ORDER BY No_of_respondents DESC LIMIT 5\"\"\"\n",
    "\n",
    "db_df = pd.read_sql_query(Query5, conn)\n",
    "print(db_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "db_df.plot(kind = 'pie', labels = db_df['Database'], y = 'No_of_respondents',\n",
    "figsize = (7,10), #dimension of the chart window\n",
    "autopct='%1.1f%%', #autopct displays percentages in pie chart. so .1f% rounds to 1 decimal place.\n",
    "colors = ['blue', 'green', 'red', 'yellow', 'pink'], #colours of the pie chart items\n",
    "startangle = 60) #start Angle implies that we can rotate the pie chart according to the degree angle we specify  \n",
    "plt.title('Top 5 databases that respondents wish to learn next year')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stacked Charts\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a stacked chart of median `WorkWeekHrs` and `CodeRevHrs` for the age group 30 to 35.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      WorkWeekHrs  CodeRevHrs   Age\n",
      "0             8.0         3.0  31.0\n",
      "1            40.0         2.0  34.0\n",
      "2            40.0         2.0  32.0\n",
      "3            42.0        15.0  34.0\n",
      "4            40.0         2.0  33.0\n",
      "...           ...         ...   ...\n",
      "2937         42.0         4.0  32.0\n",
      "2938         50.0         2.0  32.0\n",
      "2939         56.0        14.0  32.0\n",
      "2940         40.0         5.0  34.0\n",
      "2941         80.0         1.0  30.0\n",
      "\n",
      "[2942 rows x 3 columns]\n"
     ]
    },
    {
     "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",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2917.000000</td>\n",
       "      <td>2379.000000</td>\n",
       "      <td>2942.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>42.909479</td>\n",
       "      <td>4.784443</td>\n",
       "      <td>32.210401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24.753388</td>\n",
       "      <td>4.135441</td>\n",
       "      <td>1.710495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>42.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>425.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       WorkWeekHrs   CodeRevHrs          Age\n",
       "count  2917.000000  2379.000000  2942.000000\n",
       "mean     42.909479     4.784443    32.210401\n",
       "std      24.753388     4.135441     1.710495\n",
       "min       4.000000     0.000000    30.000000\n",
       "25%      40.000000     2.000000    31.000000\n",
       "50%      40.000000     4.000000    32.000000\n",
       "75%      42.000000     5.000000    34.000000\n",
       "max     425.000000    54.000000    35.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Query6 = \"\"\"\n",
    "SELECT WorkWeekHrs, CodeRevHrs, Age\n",
    "FROM master\n",
    "WHERE Age BETWEEN 30 AND 35\n",
    "\"\"\"\n",
    "stacked_df = pd.read_sql_query(Query6, conn)\n",
    "print(stacked_df)\n",
    "\n",
    "stacked_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "stacked_df = stacked_df.groupby('Age')[['WorkWeekHrs', 'CodeRevHrs']].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stacked_df.plot(kind = 'bar', stacked=True, figsize = (7,7))\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Hours')\n",
    "plt.title('Median hours by age')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing comparison of data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line Chart\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ploting the median `ConvertedComp` for all ages from 45 to 60.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>ConvertedComp</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.012000e+03</td>\n",
       "      <td>4317.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.155598e+05</td>\n",
       "      <td>27.448923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.786517e+05</td>\n",
       "      <td>1.683755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.199600e+04</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.950000e+04</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.483875e+04</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000e+06</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ConvertedComp          Age\n",
       "count   4.012000e+03  4317.000000\n",
       "mean    1.155598e+05    27.448923\n",
       "std     2.786517e+05     1.683755\n",
       "min     0.000000e+00    25.000000\n",
       "25%     2.199600e+04    26.000000\n",
       "50%     4.950000e+04    27.000000\n",
       "75%     8.483875e+04    29.000000\n",
       "max     2.000000e+06    30.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Query7 = \"\"\"SELECT ConvertedComp, Age\n",
    "FROM master\n",
    "WHERE Age BETWEEN 25 AND 30\n",
    "\"\"\"\n",
    "\n",
    "line_df = pd.read_sql_query(Query7, conn)\n",
    "line_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "line_df.plot(kind = 'line', figsize = (5,5))\n",
    "plt.xlabel('cc')\n",
    "plt.ylabel('age')\n",
    "plt.title('Median ConvertedComp for all ages from 45 to 60')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Chart\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a horizontal bar chart using column `MainBranch.`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Frequency</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  Frequency\n",
       "0                     I am a developer by profession      10618\n",
       "1  I am not primarily a developer, but I write co...        780"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Query8 = \"\"\"SELECT MainBranch, count(*) as Frequency FROM master GROUP BY MainBranch\"\"\"\n",
    "bar_df = pd.read_sql_query(Query8, conn)\n",
    "bar_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_df.plot(kind = 'barh', figsize=(10,6))\n",
    "plt.xlabel('Frequency')\n",
    "plt.ylabel('MainBranch')\n",
    "plt.title('Frequency of Mainbranch')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Close the database connection.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}