5-DataVisualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork21426264-2022-01-01\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Data Visualization**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we will be focusing on the visualization of data.\n",
    "\n",
    "The data set will be presented in the form of a RDBMS.\n",
    "\n",
    "Therefore, we will have to use SQL queries to extract the data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, 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": [
    "## Demo: How to work with database\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First at all, we download database file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "--2022-10-30 14:13:53--  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.1'\n",
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      " 10500K .......... .......... .......... .......... .......... 29%  829K 25s\n",
      " 10550K .......... .......... .......... .......... .......... 29%  613K 25s\n",
      " 10600K .......... .......... .......... .......... .......... 29%  754K 25s\n",
      " 10650K .......... .......... .......... .......... .......... 29%  746K 25s\n",
      " 10700K .......... .......... .......... .......... .......... 30%  715K 25s\n",
      " 10750K .......... .......... .......... .......... .......... 30%  722K 25s\n",
      " 10800K .......... .......... .......... .......... .......... 30%  659K 25s\n",
      " 10850K .......... .......... .......... .......... .......... 30%  670K 25s\n",
      " 10900K .......... .......... .......... .......... .......... 30%  621K 25s\n",
      " 10950K .......... .......... .......... .......... .......... 30%  807K 25s\n",
      " 11000K .......... .......... .......... .......... .......... 30%  244K 26s\n",
      " 11050K .......... .......... .......... .......... .......... 30% 1.00M 26s\n",
      " 11100K .......... .......... .......... .......... .......... 31%  771K 26s\n",
      " 11150K .......... .......... .......... .......... .......... 31% 1.02M 26s\n",
      " 11200K .......... .......... .......... .......... .......... 31%  877K 26s\n",
      " 11250K .......... .......... .......... .......... .......... 31% 1.05M 25s\n",
      " 11300K .......... .......... .......... .......... .......... 31% 1.55M 25s\n",
      " 11350K .......... .......... .......... .......... .......... 31%  885K 25s\n",
      " 11400K .......... .......... .......... .......... .......... 31% 1004K 25s\n",
      " 11450K .......... .......... .......... .......... .......... 32% 1.36M 25s\n",
      " 11500K .......... .......... .......... .......... .......... 32% 1.25M 25s\n",
      " 11550K .......... .......... .......... .......... .......... 32%  812K 25s\n",
      " 11600K .......... .......... .......... .......... .......... 32% 1.02M 25s\n",
      " 11650K .......... .......... .......... .......... .......... 32% 1.12M 25s\n",
      " 11700K .......... .......... .......... .......... .......... 32% 1.75M 25s\n",
      " 11750K .......... .......... .......... .......... .......... 32%  961K 25s\n",
      " 11800K .......... .......... .......... .......... .......... 33% 1.09M 25s\n",
      " 11850K .......... .......... .......... .......... .......... 33% 1.14M 25s\n",
      " 11900K .......... .......... .......... .......... .......... 33% 1.11M 25s\n",
      " 11950K .......... .......... .......... .......... .......... 33% 1.25M 24s\n",
      " 12000K .......... .......... .......... .......... .......... 33%  786K 24s\n",
      " 12050K .......... .......... .......... .......... .......... 33% 1.25M 24s\n",
      " 12100K .......... .......... .......... .......... .......... 33% 1.26M 24s\n",
      " 12150K .......... .......... .......... .......... .......... 34%  350K 24s\n",
      " 12200K .......... .......... .......... .......... .......... 34% 41.9M 24s\n",
      " 12250K .......... .......... .......... .......... .......... 34% 1.49M 24s\n",
      " 12300K .......... .......... .......... .......... .......... 34%  409K 24s\n",
      " 12350K .......... .......... .......... .......... .......... 34%  119M 24s\n",
      " 12400K .......... .......... .......... .......... .......... 34%  914K 24s\n",
      " 12450K .......... .......... .......... .......... .......... 34% 1000K 24s\n",
      " 12500K .......... .......... .......... .......... .......... 35%  946K 24s\n",
      " 12550K .......... .......... .......... .......... .......... 35%  937K 24s\n",
      " 12600K .......... .......... .......... .......... .......... 35% 1012K 24s\n",
      " 12650K .......... .......... .......... .......... .......... 35%  970K 24s\n",
      " 12700K .......... .......... .......... .......... .......... 35% 1.29M 24s\n",
      " 12750K .......... .......... .......... .......... .......... 35% 1.07M 24s\n",
      " 12800K .......... .......... .......... .......... .......... 35%  789K 24s\n",
      " 12850K .......... .......... .......... .......... .......... 36%  979K 24s\n",
      " 12900K .......... .......... .......... .......... .......... 36% 1016K 24s\n",
      " 12950K .......... .......... .......... .......... .......... 36%  961K 24s\n",
      " 13000K .......... .......... .......... .......... .......... 36%  957K 23s\n",
      " 13050K .......... .......... .......... .......... .......... 36% 1.32M 23s\n",
      " 13100K .......... .......... .......... .......... .......... 36%  998K 23s\n",
      " 13150K .......... .......... .......... .......... .......... 36%  778K 23s\n",
      " 13200K .......... .......... .......... .......... .......... 36% 1.06M 23s\n",
      " 13250K .......... .......... .......... .......... .......... 37% 1.47M 23s\n",
      " 13300K .......... .......... .......... .......... .......... 37% 1.06M 23s\n",
      " 13350K .......... .......... .......... .......... .......... 37% 1.48M 23s\n",
      " 13400K .......... .......... .......... .......... .......... 37% 1.12M 23s\n",
      " 13450K .......... .......... .......... .......... .......... 37% 1.24M 23s\n",
      " 13500K .......... .......... .......... .......... .......... 37% 1.54M 23s\n",
      " 13550K .......... .......... .......... .......... .......... 37% 1.07M 23s\n",
      " 13600K .......... .......... .......... .......... .......... 38%  932K 23s\n",
      " 13650K .......... .......... .......... .......... .......... 38% 1.04M 23s\n",
      " 13700K .......... .......... .......... .......... .......... 38% 1.59M 23s\n",
      " 13750K .......... .......... .......... .......... .......... 38% 1.18M 22s\n",
      " 13800K .......... .......... .......... .......... .......... 38% 1.19M 22s\n",
      " 13850K .......... .......... .......... .......... .......... 38% 1007K 22s\n",
      " 13900K .......... .......... .......... .......... .......... 38% 1.57M 22s\n",
      " 13950K .......... .......... .......... .......... .......... 39%  845K 22s\n",
      " 14000K .......... .......... .......... .......... .......... 39%  833K 22s\n",
      " 14050K .......... .......... .......... .......... .......... 39% 1019K 22s\n",
      " 14100K .......... .......... .......... .......... .......... 39% 2.20M 22s\n",
      " 14150K .......... .......... .......... .......... .......... 39%  886K 22s\n",
      " 14200K .......... .......... .......... .......... .......... 39% 1.30M 22s\n",
      " 14250K .......... .......... .......... .......... .......... 39% 1.58M 22s\n",
      " 14300K .......... .......... .......... .......... .......... 40% 1.06M 22s\n",
      " 14350K .......... .......... .......... .......... .......... 40% 1.14M 22s\n",
      " 14400K .......... .......... .......... .......... .......... 40%  939K 22s\n",
      " 14450K .......... .......... .......... .......... .......... 40% 1.92M 22s\n",
      " 14500K .......... .......... .......... .......... .......... 40% 1.09M 22s\n",
      " 14550K .......... .......... .......... .......... .......... 40% 1.07M 21s\n",
      " 14600K .......... .......... .......... .......... .......... 40% 2.11M 21s\n",
      " 14650K .......... .......... .......... .......... .......... 41% 1.08M 21s\n",
      " 14700K .......... .......... .......... .......... .......... 41% 1.15M 21s\n",
      " 14750K .......... .......... .......... .......... .......... 41% 1.41M 21s\n",
      " 14800K .......... .......... .......... .......... .......... 41%  961K 21s\n",
      " 14850K .......... .......... .......... .......... .......... 41% 1.08M 21s\n",
      " 14900K .......... .......... .......... .......... .......... 41% 1.52M 21s\n",
      " 14950K .......... .......... .......... .......... .......... 41% 1.28M 21s\n",
      " 15000K .......... .......... .......... .......... .......... 42% 1.12M 21s\n",
      " 15050K .......... .......... .......... .......... .......... 42% 1.36M 21s\n",
      " 15100K .......... .......... .......... .......... .......... 42% 1.51M 21s\n",
      " 15150K .......... .......... .......... .......... .......... 42% 1.05M 21s\n",
      " 15200K .......... .......... .......... .......... .......... 42% 1001K 21s\n",
      " 15250K .......... .......... .......... .......... .......... 42% 1.10M 21s\n",
      " 15300K .......... .......... .......... .......... .......... 42% 1.27M 21s\n",
      " 15350K .......... .......... .......... .......... .......... 42% 1.07M 20s\n",
      " 15400K .......... .......... .......... .......... .......... 43% 1.06M 20s\n",
      " 15450K .......... .......... .......... .......... .......... 43%  245K 21s\n",
      " 15500K .......... .......... .......... .......... .......... 43% 1.03M 21s\n",
      " 15550K .......... .......... .......... .......... .......... 43%  343K 21s\n",
      " 15600K .......... .......... .......... .......... .......... 43%  567K 21s\n",
      " 15650K .......... .......... .......... .......... .......... 43% 32.8M 20s\n",
      " 15700K .......... .......... .......... .......... .......... 43%  948K 20s\n",
      " 15750K .......... .......... .......... .......... .......... 44%  238K 21s\n",
      " 15800K .......... .......... .......... .......... .......... 44% 30.3M 20s\n",
      " 15850K .......... .......... .......... .......... .......... 44%  754K 20s\n",
      " 15900K .......... .......... .......... .......... .......... 44%  725K 20s\n",
      " 15950K .......... .......... .......... .......... .......... 44%  406K 20s\n",
      " 16000K .......... .......... .......... .......... .......... 44%  522K 20s\n",
      " 16050K .......... .......... .......... .......... .......... 44%  313K 21s\n",
      " 16100K .......... .......... .......... .......... .......... 45% 1.72M 20s\n",
      " 16150K .......... .......... .......... .......... .......... 45%  752K 20s\n",
      " 16200K .......... .......... .......... .......... .......... 45%  625K 20s\n",
      " 16250K .......... .......... .......... .......... .......... 45%  369K 20s\n",
      " 16300K .......... .......... .......... .......... .......... 45%  788K 20s\n",
      " 16350K .......... .......... .......... .......... .......... 45%  542K 20s\n",
      " 16400K .......... .......... .......... .......... .......... 45%  146K 21s\n",
      " 16450K .......... .......... .......... .......... .......... 46%  535K 21s\n",
      " 16500K .......... .......... .......... .......... .......... 46%  143K 21s\n",
      " 16550K .......... .......... .......... .......... .......... 46%  193K 21s\n",
      " 16600K .......... .......... .......... .......... .......... 46%  398K 21s\n",
      " 16650K .......... .......... .......... .......... .......... 46%  263K 21s\n",
      " 16700K .......... .......... .......... .......... .......... 46%  309K 21s\n",
      " 16750K .......... .......... .......... .......... .......... 46%  354K 21s\n",
      " 16800K .......... .......... .......... .......... .......... 47%  304K 21s\n",
      " 16850K .......... .......... .......... .......... .......... 47%  309K 21s\n",
      " 16900K .......... .......... .......... .......... .......... 47%  378K 22s\n",
      " 16950K .......... .......... .......... .......... .......... 47%  372K 22s\n",
      " 17000K .......... .......... .......... .......... .......... 47%  411K 22s\n",
      " 17050K .......... .......... .......... .......... .......... 47%  403K 22s\n",
      " 17100K .......... .......... .......... .......... .......... 47%  421K 22s\n",
      " 17150K .......... .......... .......... .......... .......... 48%  423K 22s\n",
      " 17200K .......... .......... .......... .......... .......... 48%  340K 22s\n",
      " 17250K .......... .......... .......... .......... .......... 48%  475K 22s\n",
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      " 17350K .......... .......... .......... .......... .......... 48%  468K 22s\n",
      " 17400K .......... .......... .......... .......... .......... 48%  460K 22s\n",
      " 17450K .......... .......... .......... .......... .......... 48%  451K 22s\n",
      " 17500K .......... .......... .......... .......... .......... 48%  563K 22s\n",
      " 17550K .......... .......... .......... .......... .......... 49%  450K 22s\n",
      " 17600K .......... .......... .......... .......... .......... 49%  361K 22s\n",
      " 17650K .......... .......... .......... .......... .......... 49%  469K 22s\n",
      " 17700K .......... .......... .......... .......... .......... 49%  451K 22s\n",
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      " 17800K .......... .......... .......... .......... .......... 49%  737K 22s\n",
      " 17850K .......... .......... .......... .......... .......... 49%  460K 21s\n",
      " 17900K .......... .......... .......... .......... .......... 50%  471K 21s\n",
      " 17950K .......... .......... .......... .......... .......... 50%  459K 21s\n",
      " 18000K .......... .......... .......... .......... .......... 50%  366K 21s\n",
      " 18050K .......... .......... .......... .......... .......... 50%  525K 21s\n",
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      " 18150K .......... .......... .......... .......... .......... 50%  523K 21s\n",
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      " 18250K .......... .......... .......... .......... .......... 51%  836K 21s\n",
      " 18300K .......... .......... .......... .......... .......... 51%  995K 21s\n",
      " 18350K .......... .......... .......... .......... .......... 51%  761K 21s\n",
      " 18400K .......... .......... .......... .......... .......... 51%  836K 21s\n",
      " 18450K .......... .......... .......... .......... .......... 51%  813K 21s\n",
      " 18500K .......... .......... .......... .......... .......... 51%  959K 21s\n",
      " 18550K .......... .......... .......... .......... .......... 51%  938K 21s\n",
      " 18600K .......... .......... .......... .......... .......... 52%  878K 21s\n",
      " 18650K .......... .......... .......... .......... .......... 52% 1.34M 21s\n",
      " 18700K .......... .......... .......... .......... .......... 52%  913K 21s\n",
      " 18750K .......... .......... .......... .......... .......... 52%  960K 21s\n",
      " 18800K .......... .......... .......... .......... .......... 52%  754K 21s\n",
      " 18850K .......... .......... .......... .......... .......... 52%  941K 21s\n",
      " 18900K .......... .......... .......... .......... .......... 52%  959K 20s\n",
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      " 19100K .......... .......... .......... .......... .......... 53% 1.05M 20s\n",
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      " 19550K .......... .......... .......... .......... .......... 54%  963K 20s\n",
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      " 20000K .......... .......... .......... .......... .......... 55%  686K 19s\n",
      " 20050K .......... .......... .......... .......... .......... 56% 1.20M 19s\n",
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      " 20150K .......... .......... .......... .......... .......... 56% 2.50M 19s\n",
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      " 21550K .......... .......... .......... .......... .......... 60% 11.4M 18s\n",
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      " 22900K .......... .......... .......... .......... .......... 64%  827K 16s\n",
      " 22950K .......... .......... .......... .......... .......... 64%  671K 16s\n",
      " 23000K .......... .......... .......... .......... .......... 64%  752K 16s\n",
      " 23050K .......... .......... .......... .......... .......... 64%  669K 16s\n",
      " 23100K .......... .......... .......... .......... .......... 64%  585K 16s\n",
      " 23150K .......... .......... .......... .......... .......... 64%  874K 16s\n",
      " 23200K .......... .......... .......... .......... .......... 64%  183K 16s\n",
      " 23250K .......... .......... .......... .......... .......... 65%  968K 16s\n",
      " 23300K .......... .......... .......... .......... .......... 65%  796K 16s\n",
      " 23350K .......... .......... .......... .......... .......... 65% 1.01M 16s\n",
      " 23400K .......... .......... .......... .......... .......... 65% 1.98M 15s\n",
      " 23450K .......... .......... .......... .......... .......... 65% 1005K 15s\n",
      " 23500K .......... .......... .......... .......... .......... 65%  956K 15s\n",
      " 23550K .......... .......... .......... .......... .......... 65% 1.09M 15s\n",
      " 23600K .......... .......... .......... .......... .......... 66% 1.07M 15s\n",
      " 23650K .......... .......... .......... .......... .......... 66% 1.86M 15s\n",
      " 23700K .......... .......... .......... .......... .......... 66%  936K 15s\n",
      " 23750K .......... .......... .......... .......... .......... 66% 1.20M 15s\n",
      " 23800K .......... .......... .......... .......... .......... 66%  145K 15s\n",
      " 23850K .......... .......... .......... .......... .......... 66%  101K 15s\n",
      " 23900K .......... .......... .......... .......... .......... 66% 43.2K 16s\n",
      " 23950K .......... .......... .......... .......... .......... 67% 23.5M 16s\n",
      " 24000K .......... .......... .......... .......... .......... 67%  917K 16s\n",
      " 24050K .......... .......... .......... .......... .......... 67% 1.80M 15s\n",
      " 24100K .......... .......... .......... .......... .......... 67% 1.43M 15s\n",
      " 24150K .......... .......... .......... .......... .......... 67% 2.49M 15s\n",
      " 24200K .......... .......... .......... .......... .......... 67% 1.99M 15s\n",
      " 24250K .......... .......... .......... .......... .......... 67% 1.42M 15s\n",
      " 24300K .......... .......... .......... .......... .......... 67%  242K 15s\n",
      " 24350K .......... .......... .......... .......... .......... 68%  309K 15s\n",
      " 24400K .......... .......... .......... .......... .......... 68% 11.3M 15s\n",
      " 24450K .......... .......... .......... .......... .......... 68% 43.8M 15s\n",
      " 24500K .......... .......... .......... .......... .......... 68% 56.6M 15s\n",
      " 24550K .......... .......... .......... .......... .......... 68%  510K 15s\n",
      " 24600K .......... .......... .......... .......... .......... 68%  623K 15s\n",
      " 24650K .......... .......... .......... .......... .......... 68%  608K 15s\n",
      " 24700K .......... .......... .......... .......... .......... 69%  633K 15s\n",
      " 24750K .......... .......... .......... .......... .......... 69% 47.6M 14s\n",
      " 24800K .......... .......... .......... .......... .......... 69% 38.8M 14s\n",
      " 24850K .......... .......... .......... .......... .......... 69%  721K 14s\n",
      " 24900K .......... .......... .......... .......... .......... 69% 45.6M 14s\n",
      " 24950K .......... .......... .......... .......... .......... 69%  707K 14s\n",
      " 25000K .......... .......... .......... .......... .......... 69% 4.58M 14s\n",
      " 25050K .......... .......... .......... .......... .......... 70% 8.71M 14s\n",
      " 25100K .......... .......... .......... .......... .......... 70%  239K 14s\n",
      " 25150K .......... .......... .......... .......... .......... 70% 53.6M 14s\n",
      " 25200K .......... .......... .......... .......... .......... 70% 44.3M 14s\n",
      " 25250K .......... .......... .......... .......... .......... 70% 50.8M 14s\n",
      " 25300K .......... .......... .......... .......... .......... 70% 52.5M 14s\n",
      " 25350K .......... .......... .......... .......... .......... 70% 1.23M 14s\n",
      " 25400K .......... .......... .......... .......... .......... 71% 9.18M 13s\n",
      " 25450K .......... .......... .......... .......... .......... 71% 45.7M 13s\n",
      " 25500K .......... .......... .......... .......... .......... 71% 1.17M 13s\n",
      " 25550K .......... .......... .......... .......... .......... 71% 4.41M 13s\n",
      " 25600K .......... .......... .......... .......... .......... 71%  335K 13s\n",
      " 25650K .......... .......... .......... .......... .......... 71% 1.19M 13s\n",
      " 25700K .......... .......... .......... .......... .......... 71% 4.73M 13s\n",
      " 25750K .......... .......... .......... .......... .......... 72% 10.2M 13s\n",
      " 25800K .......... .......... .......... .......... .......... 72%  889K 13s\n",
      " 25850K .......... .......... .......... .......... .......... 72% 1.85M 13s\n",
      " 25900K .......... .......... .......... .......... .......... 72%  957K 13s\n",
      " 25950K .......... .......... .......... .......... .......... 72% 1.04M 13s\n",
      " 26000K .......... .......... .......... .......... .......... 72% 1.51M 13s\n",
      " 26050K .......... .......... .......... .......... .......... 72% 1.19M 12s\n",
      " 26100K .......... .......... .......... .......... .......... 73% 1.24M 12s\n",
      " 26150K .......... .......... .......... .......... .......... 73% 1.37M 12s\n",
      " 26200K .......... .......... .......... .......... .......... 73% 1.10M 12s\n",
      " 26250K .......... .......... .......... .......... .......... 73% 1.69M 12s\n",
      " 26300K .......... .......... .......... .......... .......... 73% 1.15M 12s\n",
      " 26350K .......... .......... .......... .......... .......... 73%  996K 12s\n",
      " 26400K .......... .......... .......... .......... .......... 73% 1.85M 12s\n",
      " 26450K .......... .......... .......... .......... .......... 73% 1.17M 12s\n",
      " 26500K .......... .......... .......... .......... .......... 74% 1.44M 12s\n",
      " 26550K .......... .......... .......... .......... .......... 74% 1.58M 12s\n",
      " 26600K .......... .......... .......... .......... .......... 74% 1.13M 12s\n",
      " 26650K .......... .......... .......... .......... .......... 74% 1.29M 12s\n",
      " 26700K .......... .......... .......... .......... .......... 74% 1.88M 12s\n",
      " 26750K .......... .......... .......... .......... .......... 74%  950K 11s\n",
      " 26800K .......... .......... .......... .......... .......... 74% 1.47M 11s\n",
      " 26850K .......... .......... .......... .......... .......... 75%  465K 11s\n",
      " 26900K .......... .......... .......... .......... .......... 75% 4.21M 11s\n",
      " 26950K .......... .......... .......... .......... .......... 75% 3.86M 11s\n",
      " 27000K .......... .......... .......... .......... .......... 75% 1.16M 11s\n",
      " 27050K .......... .......... .......... .......... .......... 75% 1.03M 11s\n",
      " 27100K .......... .......... .......... .......... .......... 75%  859K 11s\n",
      " 27150K .......... .......... .......... .......... .......... 75% 1.45M 11s\n",
      " 27200K .......... .......... .......... .......... .......... 76% 1.11M 11s\n",
      " 27250K .......... .......... .......... .......... .......... 76% 1.03M 11s\n",
      " 27300K .......... .......... .......... .......... .......... 76% 1.05M 11s\n",
      " 27350K .......... .......... .......... .......... .......... 76%  294K 11s\n",
      " 27400K .......... .......... .......... .......... .......... 76% 1.19M 11s\n",
      " 27450K .......... .......... .......... .......... .......... 76% 2.81M 11s\n",
      " 27500K .......... .......... .......... .......... .......... 76% 2.17M 10s\n",
      " 27550K .......... .......... .......... .......... .......... 77%  624K 10s\n",
      " 27600K .......... .......... .......... .......... .......... 77%  849K 10s\n",
      " 27650K .......... .......... .......... .......... .......... 77%  874K 10s\n",
      " 27700K .......... .......... .......... .......... .......... 77%  800K 10s\n",
      " 27750K .......... .......... .......... .......... .......... 77%  225K 10s\n",
      " 27800K .......... .......... .......... .......... .......... 77%  174K 10s\n",
      " 27850K .......... .......... .......... .......... .......... 77%  362K 10s\n",
      " 27900K .......... .......... .......... .......... .......... 78%  102K 10s\n",
      " 27950K .......... .......... .......... .......... .......... 78%  799K 10s\n",
      " 28000K .......... .......... .......... .......... .......... 78%  505K 10s\n",
      " 28050K .......... .......... .......... .......... .......... 78%  485K 10s\n",
      " 28100K .......... .......... .......... .......... .......... 78%  503K 10s\n",
      " 28150K .......... .......... .......... .......... .......... 78%  639K 10s\n",
      " 28200K .......... .......... .......... .......... .......... 78%  649K 10s\n",
      " 28250K .......... .......... .......... .......... .......... 79%  533K 10s\n",
      " 28300K .......... .......... .......... .......... .......... 79%  381K 10s\n",
      " 28350K .......... .......... .......... .......... .......... 79%  401K 10s\n",
      " 28400K .......... .......... .......... .......... .......... 79%  340K 10s\n",
      " 28450K .......... .......... .......... .......... .......... 79%  712K 10s\n",
      " 28500K .......... .......... .......... .......... .......... 79%  462K 10s\n",
      " 28550K .......... .......... .......... .......... .......... 79%  172K 10s\n",
      " 28600K .......... .......... .......... .......... .......... 79%  141K 10s\n",
      " 28650K .......... .......... .......... .......... .......... 80%  160K 10s\n",
      " 28700K .......... .......... .......... .......... .......... 80% 51.3K 10s\n",
      " 28750K .......... .......... .......... .......... .......... 80%  229K 10s\n",
      " 28800K .......... .......... .......... .......... .......... 80%  132K 10s\n",
      " 28850K .......... .......... .......... .......... .......... 80% 35.2K 10s\n",
      " 28900K .......... .......... .......... .......... .......... 80%  730K 10s\n",
      " 28950K .......... .......... .......... .......... .......... 80%  503K 10s\n",
      " 29000K .......... .......... .......... .......... .......... 81% 2.55M 10s\n",
      " 29050K .......... .......... .......... .......... .......... 81%  998K 10s\n",
      " 29100K .......... .......... .......... .......... .......... 81%  932K 10s\n",
      " 29150K .......... .......... .......... .......... .......... 81% 2.71M 9s\n",
      " 29200K .......... .......... .......... .......... .......... 81% 1.76M 9s\n",
      " 29250K .......... .......... .......... .......... .......... 81% 1.57M 9s\n",
      " 29300K .......... .......... .......... .......... .......... 81%  221K 9s\n",
      " 29350K .......... .......... .......... .......... .......... 82% 25.5M 9s\n",
      " 29400K .......... .......... .......... .......... .......... 82%  310K 9s\n",
      " 29450K .......... .......... .......... .......... .......... 82% 37.5M 9s\n",
      " 29500K .......... .......... .......... .......... .......... 82% 49.3M 9s\n",
      " 29550K .......... .......... .......... .......... .......... 82%  862K 9s\n",
      " 29600K .......... .......... .......... .......... .......... 82% 5.72M 9s\n",
      " 29650K .......... .......... .......... .......... .......... 82% 30.4M 9s\n",
      " 29700K .......... .......... .......... .......... .......... 83% 26.0M 9s\n",
      " 29750K .......... .......... .......... .......... .......... 83%  216K 9s\n",
      " 29800K .......... .......... .......... .......... .......... 83% 5.10M 8s\n",
      " 29850K .......... .......... .......... .......... .......... 83% 12.0M 8s\n",
      " 29900K .......... .......... .......... .......... .......... 83% 48.2M 8s\n",
      " 29950K .......... .......... .......... .......... .......... 83%  292K 8s\n",
      " 30000K .......... .......... .......... .......... .......... 83% 20.5M 8s\n",
      " 30050K .......... .......... .......... .......... .......... 84%  638K 8s\n",
      " 30100K .......... .......... .......... .......... .......... 84% 54.4M 8s\n",
      " 30150K .......... .......... .......... .......... .......... 84% 31.3M 8s\n",
      " 30200K .......... .......... .......... .......... .......... 84% 2.77M 8s\n",
      " 30250K .......... .......... .......... .......... .......... 84%  598K 8s\n",
      " 30300K .......... .......... .......... .......... .......... 84% 1.55M 8s\n",
      " 30350K .......... .......... .......... .......... .......... 84%  680K 8s\n",
      " 30400K .......... .......... .......... .......... .......... 85% 7.72M 8s\n",
      " 30450K .......... .......... .......... .......... .......... 85%  781K 7s\n",
      " 30500K .......... .......... .......... .......... .......... 85% 6.71M 7s\n",
      " 30550K .......... .......... .......... .......... .......... 85% 39.5M 7s\n",
      " 30600K .......... .......... .......... .......... .......... 85% 1.90M 7s\n",
      " 30650K .......... .......... .......... .......... .......... 85%  380K 7s\n",
      " 30700K .......... .......... .......... .......... .......... 85% 44.2M 7s\n",
      " 30750K .......... .......... .......... .......... .......... 85% 1.42M 7s\n",
      " 30800K .......... .......... .......... .......... .......... 86%  836K 7s\n",
      " 30850K .......... .......... .......... .......... .......... 86%  733K 7s\n",
      " 30900K .......... .......... .......... .......... .......... 86% 10.5M 7s\n",
      " 30950K .......... .......... .......... .......... .......... 86% 3.35M 7s\n",
      " 31000K .......... .......... .......... .......... .......... 86%  870K 7s\n",
      " 31050K .......... .......... .......... .......... .......... 86%  930K 7s\n",
      " 31100K .......... .......... .......... .......... .......... 86%  953K 7s\n",
      " 31150K .......... .......... .......... .......... .......... 87%  903K 6s\n",
      " 31200K .......... .......... .......... .......... .......... 87% 1.18M 6s\n",
      " 31250K .......... .......... .......... .......... .......... 87%  990K 6s\n",
      " 31300K .......... .......... .......... .......... .......... 87%  980K 6s\n",
      " 31350K .......... .......... .......... .......... .......... 87% 1015K 6s\n",
      " 31400K .......... .......... .......... .......... .......... 87% 1.02M 6s\n",
      " 31450K .......... .......... .......... .......... .......... 87% 1.34M 6s\n",
      " 31500K .......... .......... .......... .......... .......... 88% 1.19M 6s\n",
      " 31550K .......... .......... .......... .......... .......... 88%  884K 6s\n",
      " 31600K .......... .......... .......... .......... .......... 88%  982K 6s\n",
      " 31650K .......... .......... .......... .......... .......... 88% 1.11M 6s\n",
      " 31700K .......... .......... .......... .......... .......... 88% 1.10M 6s\n",
      " 31750K .......... .......... .......... .......... .......... 88% 1.06M 6s\n",
      " 31800K .......... .......... .......... .......... .......... 88% 1.15M 5s\n",
      " 31850K .......... .......... .......... .......... .......... 89% 1.14M 5s\n",
      " 31900K .......... .......... .......... .......... .......... 89% 1.13M 5s\n",
      " 31950K .......... .......... .......... .......... .......... 89%  831K 5s\n",
      " 32000K .......... .......... .......... .......... .......... 89% 1.16M 5s\n",
      " 32050K .......... .......... .......... .......... .......... 89% 1.33M 5s\n",
      " 32100K .......... .......... .......... .......... .......... 89% 1.26M 5s\n",
      " 32150K .......... .......... .......... .......... .......... 89% 1.19M 5s\n",
      " 32200K .......... .......... .......... .......... .......... 90% 1.42M 5s\n",
      " 32250K .......... .......... .......... .......... .......... 90% 1.31M 5s\n",
      " 32300K .......... .......... .......... .......... .......... 90%  396K 5s\n",
      " 32350K .......... .......... .......... .......... .......... 90%  435K 5s\n",
      " 32400K .......... .......... .......... .......... .......... 90%  161M 5s\n",
      " 32450K .......... .......... .......... .......... .......... 90%  803K 5s\n",
      " 32500K .......... .......... .......... .......... .......... 90% 16.1M 4s\n",
      " 32550K .......... .......... .......... .......... .......... 91%  931K 4s\n",
      " 32600K .......... .......... .......... .......... .......... 91%  879K 4s\n",
      " 32650K .......... .......... .......... .......... .......... 91% 1003K 4s\n",
      " 32700K .......... .......... .......... .......... .......... 91%  828K 4s\n",
      " 32750K .......... .......... .......... .......... .......... 91%  844K 4s\n",
      " 32800K .......... .......... .......... .......... .......... 91% 1.05M 4s\n",
      " 32850K .......... .......... .......... .......... .......... 91%  883K 4s\n",
      " 32900K .......... .......... .......... .......... .......... 91% 1.47M 4s\n",
      " 32950K .......... .......... .......... .......... .......... 92%  960K 4s\n",
      " 33000K .......... .......... .......... .......... .......... 92%  986K 4s\n",
      " 33050K .......... .......... .......... .......... .......... 92%  932K 4s\n",
      " 33100K .......... .......... .......... .......... .......... 92%  979K 4s\n",
      " 33150K .......... .......... .......... .......... .......... 92% 1.01M 4s\n",
      " 33200K .......... .......... .......... .......... .......... 92%  800K 4s\n",
      " 33250K .......... .......... .......... .......... .......... 92%  776K 3s\n",
      " 33300K .......... .......... .......... .......... .......... 93% 1.14M 3s\n",
      " 33350K .......... .......... .......... .......... .......... 93% 1.48M 3s\n",
      " 33400K .......... .......... .......... .......... .......... 93%  977K 3s\n",
      " 33450K .......... .......... .......... .......... .......... 93%  923K 3s\n",
      " 33500K .......... .......... .......... .......... .......... 93%  843K 3s\n",
      " 33550K .......... .......... .......... .......... .......... 93%  969K 3s\n",
      " 33600K .......... .......... .......... .......... .......... 93%  907K 3s\n",
      " 33650K .......... .......... .......... .......... .......... 94% 1.13M 3s\n",
      " 33700K .......... .......... .......... .......... .......... 94% 1.15M 3s\n",
      " 33750K .......... .......... .......... .......... .......... 94% 1.18M 3s\n",
      " 33800K .......... .......... .......... .......... .......... 94%  975K 3s\n",
      " 33850K .......... .......... .......... .......... .......... 94% 1.02M 3s\n",
      " 33900K .......... .......... .......... .......... .......... 94%  945K 3s\n",
      " 33950K .......... .......... .......... .......... .......... 94%  913K 2s\n",
      " 34000K .......... .......... .......... .......... .......... 95% 1.01M 2s\n",
      " 34050K .......... .......... .......... .......... .......... 95% 1.31M 2s\n",
      " 34100K .......... .......... .......... .......... .......... 95%  291K 2s\n",
      " 34150K .......... .......... .......... .......... .......... 95% 12.9M 2s\n",
      " 34200K .......... .......... .......... .......... .......... 95%  927K 2s\n",
      " 34250K .......... .......... .......... .......... .......... 95% 1.67M 2s\n",
      " 34300K .......... .......... .......... .......... .......... 95% 1.32M 2s\n",
      " 34350K .......... .......... .......... .......... .......... 96%  695K 2s\n",
      " 34400K .......... .......... .......... .......... .......... 96% 1.89M 2s\n",
      " 34450K .......... .......... .......... .......... .......... 96%  399K 2s\n",
      " 34500K .......... .......... .......... .......... .......... 96%  462K 2s\n",
      " 34550K .......... .......... .......... .......... .......... 96% 22.9M 2s\n",
      " 34600K .......... .......... .......... .......... .......... 96% 3.89M 2s\n",
      " 34650K .......... .......... .......... .......... .......... 96%  977K 2s\n",
      " 34700K .......... .......... .......... .......... .......... 97% 2.15M 1s\n",
      " 34750K .......... .......... .......... .......... .......... 97%  363K 1s\n",
      " 34800K .......... .......... .......... .......... .......... 97% 8.05M 1s\n",
      " 34850K .......... .......... .......... .......... .......... 97% 1.52M 1s\n",
      " 34900K .......... .......... .......... .......... .......... 97%  327K 1s\n",
      " 34950K .......... .......... .......... .......... .......... 97%  429K 1s\n",
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      " 35100K .......... .......... .......... .......... .......... 98%  889K 1s\n",
      " 35150K .......... .......... .......... .......... .......... 98%  734K 1s\n",
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      " 35250K .......... .......... .......... .......... .......... 98% 1.09M 1s\n",
      " 35300K .......... .......... .......... .......... .......... 98%  891K 1s\n",
      " 35350K .......... .......... .......... .......... .......... 98%  903K 1s\n",
      " 35400K .......... .......... .......... .......... .......... 98%  890K 1s\n",
      " 35450K .......... .......... .......... .......... .......... 99%  857K 0s\n",
      " 35500K .......... .......... .......... .......... .......... 99%  631K 0s\n",
      " 35550K .......... .......... .......... .......... .......... 99%  803K 0s\n",
      " 35600K .......... .......... .......... .......... .......... 99%  351K 0s\n",
      " 35650K .......... .......... .......... .......... .......... 99% 1.19M 0s\n",
      " 35700K .......... .......... .......... .......... .......... 99%  884K 0s\n",
      " 35750K .......... .......... .......... .......... .......... 99%  909K 0s\n",
      " 35800K .......... ..........                                 100%  900K=49s\n",
      "\n",
      "2022-10-30 14:14:42 (737 KB/s) - 'm4_survey_data.sqlite.1' 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": [
    "Then we connect to the database.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "conn = sqlite3.connect(\"m4_survey_data.sqlite\") # We open a database connection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We import pandas module.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo: How to run an sql query\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we wiil print how many rows are there in the table named 'master'\n",
    "QUERY = \"\"\"\n",
    "SELECT COUNT(*)\n",
    "FROM master\n",
    "\"\"\"\n",
    "\n",
    "# the read_sql_query runs the sql query and returns the data as a dataframe\n",
    "df = pd.read_sql_query(QUERY,conn)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo: How to list all tables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we print all the tables names in the database\n",
    "QUERY = \"\"\"\n",
    "SELECT name as Table_Name FROM\n",
    "sqlite_master WHERE\n",
    "type = 'table'\n",
    "\"\"\"\n",
    "# the read_sql_query runs the sql query and returns the data as a dataframe\n",
    "pd.read_sql_query(QUERY,conn)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo: How to run a group by query\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QUERY = \"\"\"\n",
    "SELECT Age,COUNT(*) as count\n",
    "FROM master\n",
    "group by age\n",
    "order by age\n",
    "\"\"\"\n",
    "pd.read_sql_query(QUERY,conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo: How to describe a table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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 we wish to describe\n",
    "\n",
    "QUERY = \"\"\"\n",
    "SELECT sql FROM sqlite_master\n",
    "WHERE name= '{}'\n",
    "\"\"\".format(table_name)\n",
    "\n",
    "df = pd.read_sql_query(QUERY,conn)\n",
    "print(df.iat[0,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing distribution of data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot a histogram of `ConvertedComp.`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x20871f85460>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 872.5x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "QUERY = \"\"\"\n",
    "SELECT ConvertedComp\n",
    "FROM master\n",
    "\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY,conn)\n",
    "\n",
    "sns.displot(df, height = 7, aspect = 1.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Box Plots\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot a box plot of `Age.`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Age\n",
      "0      22.0\n",
      "1      23.0\n",
      "2      28.0\n",
      "3      26.0\n",
      "4      29.0\n",
      "...     ...\n",
      "11393  36.0\n",
      "11394  25.0\n",
      "11395  34.0\n",
      "11396  25.0\n",
      "11397  30.0\n",
      "\n",
      "[11398 rows x 1 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "QUERY = \"\"\"\n",
    "SELECT Age\n",
    "FROM master\n",
    "\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY,conn)\n",
    "\n",
    "print(df)\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [15,8]\n",
    "\n",
    "ax = sns.boxplot(data = df, orient = 'h')\n",
    "ax.set_title('Age Distribution', size = 18)\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": [
    "We create a scatter plot of `Age` and `WorkWeekHrs.`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Age  WorkWeekHrs\n",
      "0      22.0         80.0\n",
      "1      23.0         32.0\n",
      "2      28.0         40.0\n",
      "3      26.0         40.0\n",
      "4      29.0         45.0\n",
      "...     ...          ...\n",
      "11393  36.0         40.0\n",
      "11394  25.0         40.0\n",
      "11395  34.0         40.0\n",
      "11396  25.0         42.5\n",
      "11397  30.0         80.0\n",
      "\n",
      "[11398 rows x 2 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "QUERY = \"\"\"\n",
    "SELECT Age, WorkWeekHrs\n",
    "FROM master\n",
    "\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY,conn)\n",
    "print(df)\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [15,8]\n",
    "\n",
    "sns.scatterplot(data = df, x = 'Age', y = 'WorkWeekHrs')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bubble Plots\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a bubble plot of `WorkWeekHrs` and `CodeRevHrs`, use `Age` column as bubble size.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Age  WorkWeekHrs  CodeRevHrs\n",
      "0      22.0         80.0         NaN\n",
      "1      23.0         32.0        12.0\n",
      "2      28.0         40.0         5.0\n",
      "3      26.0         40.0         NaN\n",
      "4      29.0         45.0         5.0\n",
      "...     ...          ...         ...\n",
      "11393  36.0         40.0         NaN\n",
      "11394  25.0         40.0         5.0\n",
      "11395  34.0         40.0         5.0\n",
      "11396  25.0         42.5         2.0\n",
      "11397  30.0         80.0         1.0\n",
      "\n",
      "[11398 rows x 3 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "\n",
    "QUERY = \"\"\"\n",
    "SELECT Age, WorkWeekHrs, CodeRevHrs\n",
    "FROM master\n",
    "\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY,conn)\n",
    "\n",
    "print(df)\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [15,15]\n",
    "\n",
    "sns.scatterplot(data = df, y = 'WorkWeekHrs', x = 'CodeRevHrs', size = 'Age', alpha = 0.6, sizes=(1, 1000))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing composition of data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>type</th>\n",
       "      <th>name</th>\n",
       "      <th>tbl_name</th>\n",
       "      <th>rootpage</th>\n",
       "      <th>sql</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>table</td>\n",
       "      <td>EduOther</td>\n",
       "      <td>EduOther</td>\n",
       "      <td>2</td>\n",
       "      <td>CREATE TABLE \"EduOther\" (\\n\"Respondent\" INTEGE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>table</td>\n",
       "      <td>DevType</td>\n",
       "      <td>DevType</td>\n",
       "      <td>741</td>\n",
       "      <td>CREATE TABLE \"DevType\" (\\n\"Respondent\" INTEGER...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>table</td>\n",
       "      <td>LastInt</td>\n",
       "      <td>LastInt</td>\n",
       "      <td>1024</td>\n",
       "      <td>CREATE TABLE \"LastInt\" (\\n\"Respondent\" INTEGER...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>table</td>\n",
       "      <td>JobFactors</td>\n",
       "      <td>JobFactors</td>\n",
       "      <td>1355</td>\n",
       "      <td>CREATE TABLE \"JobFactors\" (\\n\"Respondent\" INTE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>table</td>\n",
       "      <td>WorkPlan</td>\n",
       "      <td>WorkPlan</td>\n",
       "      <td>1808</td>\n",
       "      <td>CREATE TABLE \"WorkPlan\" (\\n\"Respondent\" INTEGE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>table</td>\n",
       "      <td>WorkChallenge</td>\n",
       "      <td>WorkChallenge</td>\n",
       "      <td>2088</td>\n",
       "      <td>CREATE TABLE \"WorkChallenge\" (\\n\"Respondent\" I...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>table</td>\n",
       "      <td>LanguageWorkedWith</td>\n",
       "      <td>LanguageWorkedWith</td>\n",
       "      <td>2378</td>\n",
       "      <td>CREATE TABLE \"LanguageWorkedWith\" (\\n\"Responde...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>table</td>\n",
       "      <td>LanguageDesireNextYear</td>\n",
       "      <td>LanguageDesireNextYear</td>\n",
       "      <td>2638</td>\n",
       "      <td>CREATE TABLE \"LanguageDesireNextYear\" (\\n\"Resp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>table</td>\n",
       "      <td>DatabaseWorkedWith</td>\n",
       "      <td>DatabaseWorkedWith</td>\n",
       "      <td>2877</td>\n",
       "      <td>CREATE TABLE \"DatabaseWorkedWith\" (\\n\"Responde...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>table</td>\n",
       "      <td>DatabaseDesireNextYear</td>\n",
       "      <td>DatabaseDesireNextYear</td>\n",
       "      <td>3028</td>\n",
       "      <td>CREATE TABLE \"DatabaseDesireNextYear\" (\\n\"Resp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>table</td>\n",
       "      <td>PlatformWorkedWith</td>\n",
       "      <td>PlatformWorkedWith</td>\n",
       "      <td>3170</td>\n",
       "      <td>CREATE TABLE \"PlatformWorkedWith\" (\\n\"Responde...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>table</td>\n",
       "      <td>PlatformDesireNextYear</td>\n",
       "      <td>PlatformDesireNextYear</td>\n",
       "      <td>3341</td>\n",
       "      <td>CREATE TABLE \"PlatformDesireNextYear\" (\\n\"Resp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>table</td>\n",
       "      <td>WebFrameWorkedWith</td>\n",
       "      <td>WebFrameWorkedWith</td>\n",
       "      <td>3540</td>\n",
       "      <td>CREATE TABLE \"WebFrameWorkedWith\" (\\n\"Responde...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>table</td>\n",
       "      <td>WebFrameDesireNextYear</td>\n",
       "      <td>WebFrameDesireNextYear</td>\n",
       "      <td>3655</td>\n",
       "      <td>CREATE TABLE \"WebFrameDesireNextYear\" (\\n\"Resp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>table</td>\n",
       "      <td>MiscTechWorkedWith</td>\n",
       "      <td>MiscTechWorkedWith</td>\n",
       "      <td>3769</td>\n",
       "      <td>CREATE TABLE \"MiscTechWorkedWith\" (\\n\"Responde...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>table</td>\n",
       "      <td>MiscTechDesireNextYear</td>\n",
       "      <td>MiscTechDesireNextYear</td>\n",
       "      <td>3853</td>\n",
       "      <td>CREATE TABLE \"MiscTechDesireNextYear\" (\\n\"Resp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>table</td>\n",
       "      <td>DevEnviron</td>\n",
       "      <td>DevEnviron</td>\n",
       "      <td>3977</td>\n",
       "      <td>CREATE TABLE \"DevEnviron\" (\\n\"Respondent\" INTE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>table</td>\n",
       "      <td>Containers</td>\n",
       "      <td>Containers</td>\n",
       "      <td>4152</td>\n",
       "      <td>CREATE TABLE \"Containers\" (\\n\"Respondent\" INTE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>table</td>\n",
       "      <td>SOVisitTo</td>\n",
       "      <td>SOVisitTo</td>\n",
       "      <td>4290</td>\n",
       "      <td>CREATE TABLE \"SOVisitTo\" (\\n\"Respondent\" INTEG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>table</td>\n",
       "      <td>SONewContent</td>\n",
       "      <td>SONewContent</td>\n",
       "      <td>4581</td>\n",
       "      <td>CREATE TABLE \"SONewContent\" (\\n\"Respondent\" IN...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>table</td>\n",
       "      <td>Gender</td>\n",
       "      <td>Gender</td>\n",
       "      <td>4885</td>\n",
       "      <td>CREATE TABLE \"Gender\" (\\n\"Respondent\" INTEGER,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>table</td>\n",
       "      <td>Sexuality</td>\n",
       "      <td>Sexuality</td>\n",
       "      <td>4924</td>\n",
       "      <td>CREATE TABLE \"Sexuality\" (\\n\"Respondent\" INTEG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>table</td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>5012</td>\n",
       "      <td>CREATE TABLE \"Ethnicity\" (\\n\"Respondent\" INTEG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>table</td>\n",
       "      <td>master</td>\n",
       "      <td>master</td>\n",
       "      <td>5111</td>\n",
       "      <td>CREATE TABLE \"master\" (\\n\"index\" INTEGER,\\n  \"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>index</td>\n",
       "      <td>ix_master_index</td>\n",
       "      <td>master</td>\n",
       "      <td>5114</td>\n",
       "      <td>CREATE INDEX \"ix_master_index\"ON \"master\" (\"in...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     type                    name                tbl_name  rootpage  \\\n",
       "0   table                EduOther                EduOther         2   \n",
       "1   table                 DevType                 DevType       741   \n",
       "2   table                 LastInt                 LastInt      1024   \n",
       "3   table              JobFactors              JobFactors      1355   \n",
       "4   table                WorkPlan                WorkPlan      1808   \n",
       "5   table           WorkChallenge           WorkChallenge      2088   \n",
       "6   table      LanguageWorkedWith      LanguageWorkedWith      2378   \n",
       "7   table  LanguageDesireNextYear  LanguageDesireNextYear      2638   \n",
       "8   table      DatabaseWorkedWith      DatabaseWorkedWith      2877   \n",
       "9   table  DatabaseDesireNextYear  DatabaseDesireNextYear      3028   \n",
       "10  table      PlatformWorkedWith      PlatformWorkedWith      3170   \n",
       "11  table  PlatformDesireNextYear  PlatformDesireNextYear      3341   \n",
       "12  table      WebFrameWorkedWith      WebFrameWorkedWith      3540   \n",
       "13  table  WebFrameDesireNextYear  WebFrameDesireNextYear      3655   \n",
       "14  table      MiscTechWorkedWith      MiscTechWorkedWith      3769   \n",
       "15  table  MiscTechDesireNextYear  MiscTechDesireNextYear      3853   \n",
       "16  table              DevEnviron              DevEnviron      3977   \n",
       "17  table              Containers              Containers      4152   \n",
       "18  table               SOVisitTo               SOVisitTo      4290   \n",
       "19  table            SONewContent            SONewContent      4581   \n",
       "20  table                  Gender                  Gender      4885   \n",
       "21  table               Sexuality               Sexuality      4924   \n",
       "22  table               Ethnicity               Ethnicity      5012   \n",
       "23  table                  master                  master      5111   \n",
       "24  index         ix_master_index                  master      5114   \n",
       "\n",
       "                                                  sql  \n",
       "0   CREATE TABLE \"EduOther\" (\\n\"Respondent\" INTEGE...  \n",
       "1   CREATE TABLE \"DevType\" (\\n\"Respondent\" INTEGER...  \n",
       "2   CREATE TABLE \"LastInt\" (\\n\"Respondent\" INTEGER...  \n",
       "3   CREATE TABLE \"JobFactors\" (\\n\"Respondent\" INTE...  \n",
       "4   CREATE TABLE \"WorkPlan\" (\\n\"Respondent\" INTEGE...  \n",
       "5   CREATE TABLE \"WorkChallenge\" (\\n\"Respondent\" I...  \n",
       "6   CREATE TABLE \"LanguageWorkedWith\" (\\n\"Responde...  \n",
       "7   CREATE TABLE \"LanguageDesireNextYear\" (\\n\"Resp...  \n",
       "8   CREATE TABLE \"DatabaseWorkedWith\" (\\n\"Responde...  \n",
       "9   CREATE TABLE \"DatabaseDesireNextYear\" (\\n\"Resp...  \n",
       "10  CREATE TABLE \"PlatformWorkedWith\" (\\n\"Responde...  \n",
       "11  CREATE TABLE \"PlatformDesireNextYear\" (\\n\"Resp...  \n",
       "12  CREATE TABLE \"WebFrameWorkedWith\" (\\n\"Responde...  \n",
       "13  CREATE TABLE \"WebFrameDesireNextYear\" (\\n\"Resp...  \n",
       "14  CREATE TABLE \"MiscTechWorkedWith\" (\\n\"Responde...  \n",
       "15  CREATE TABLE \"MiscTechDesireNextYear\" (\\n\"Resp...  \n",
       "16  CREATE TABLE \"DevEnviron\" (\\n\"Respondent\" INTE...  \n",
       "17  CREATE TABLE \"Containers\" (\\n\"Respondent\" INTE...  \n",
       "18  CREATE TABLE \"SOVisitTo\" (\\n\"Respondent\" INTEG...  \n",
       "19  CREATE TABLE \"SONewContent\" (\\n\"Respondent\" IN...  \n",
       "20  CREATE TABLE \"Gender\" (\\n\"Respondent\" INTEGER,...  \n",
       "21  CREATE TABLE \"Sexuality\" (\\n\"Respondent\" INTEG...  \n",
       "22  CREATE TABLE \"Ethnicity\" (\\n\"Respondent\" INTEG...  \n",
       "23  CREATE TABLE \"master\" (\\n\"index\" INTEGER,\\n  \"...  \n",
       "24  CREATE INDEX \"ix_master_index\"ON \"master\" (\"in...  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We print the master table so we can get a better understanding of the tables we have\n",
    "\n",
    "QUERY = \"\"\"\n",
    "SELECT *\n",
    "FROM sqlite_master\n",
    "\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY,conn)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pie Charts\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a pie chart of the top 5 databases that respondents wish to learn next year. We label the pie chart with database names and display percentages of each database on the pie chart.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "QUERY = \"\"\"\n",
    "SELECT *, COUNT(*) AS count\n",
    "FROM DatabaseDesireNextYear\n",
    "GROUP BY DatabaseDesireNextYear\n",
    "ORDER BY count DESC\n",
    "LIMIT 5\n",
    "\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY,conn)\n",
    "\n",
    "data = df['count']\n",
    "labels = df['DatabaseDesireNextYear']\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [8,8]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.pie(data, labels = labels, autopct='%1.1f%%', explode = (0.1, 0, 0, 0, 0), shadow = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the list of most popular languages respondents wish to learn next year, what is the rank of Python?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Respondent LanguageDesireNextYear  count\n",
      "0           4             JavaScript   6630\n",
      "1           9               HTML/CSS   5328\n",
      "2          20                 Python   5239\n",
      "3           4                    SQL   5012\n",
      "4           9             TypeScript   4088\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "QUERY = \"\"\"\n",
    "SELECT *, COUNT(*) AS count\n",
    "FROM LanguageDesireNextYear\n",
    "GROUP BY LanguageDesireNextYear\n",
    "ORDER BY count DESC\n",
    "LIMIT 5\n",
    "\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY,conn)\n",
    "\n",
    "print(df)\n",
    "\n",
    "data = df['count']\n",
    "labels = df['LanguageDesireNextYear']\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [8,8]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.pie(data, labels = labels, autopct='%1.1f%%', explode = (0, 0.1, 0, 0, 0), shadow = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many respondents indicated that they currently work with 'SQL'? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Respondent     LanguageWorkedWith  count\n",
      "0           9             JavaScript   8687\n",
      "1           9               HTML/CSS   7830\n",
      "2           4                    SQL   7106\n",
      "3           9  Bash/Shell/PowerShell   4642\n",
      "4           4                 Python   4542\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "\n",
    "QUERY = \"\"\"\n",
    "SELECT *, COUNT(*) AS count\n",
    "FROM LanguageWorkedWith\n",
    "GROUP BY LanguageWorkedWith\n",
    "ORDER BY count DESC\n",
    "LIMIT 5\n",
    "\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY,conn)\n",
    "\n",
    "print(df)\n",
    "\n",
    "data = df['count']\n",
    "labels = df['LanguageWorkedWith']\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [8,8]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.pie(data, labels = labels, autopct='%1.1f%%', explode = (0, 0.1, 0, 0, 0), shadow = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many respondents indicated that they work on 'MySQL' only? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "474"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QUERY = \"\"\"\n",
    "SELECT * \n",
    "FROM DatabaseWorkedWith\n",
    "ORDER BY Respondent\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY,conn)\n",
    "\n",
    "counts = df.groupby('Respondent').count() #counts: number databases each respondent has worked with\n",
    "\n",
    "for i in range(len(counts)):\n",
    "    if counts.values[i] == 1:\n",
    "        uniques.append(counts.index[i]) #uniques: respondents who have worked with one database\n",
    "\n",
    "uniq_mysql = 0\n",
    "\n",
    "for j in range(len(df)):\n",
    "    if df['Respondent'][j] in uniques:\n",
    "        if df['DatabaseWorkedWith'][j] == 'MySQL':\n",
    "            uniq_mysql = uniq_mysql + 1 #uniq_mysql: respondents who have worked with MySQL only\n",
    "\n",
    "uniq_mysql    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stacked Charts\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a stacked chart of median `WorkWeekHrs` and `CodeRevHrs` for the age group 30 to 35.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "QUERY = \"\"\"\n",
    "SELECT WorkWeekHrs, CodeRevHrs, Age\n",
    "FROM master\n",
    "WHERE Age BETWEEN 30 AND 35\n",
    "\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY,conn)\n",
    "\n",
    "work_week = df['WorkWeekHrs'].quantile(0.5)\n",
    "code_rev = df['CodeRevHrs'].quantile(0.5)\n",
    "\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [8,8]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.bar('Age 30 - 35', work_week, label='WorkWeekHrs')\n",
    "ax.bar('Age 30 - 35', code_rev, bottom=work_week, label='CodeRevHrs')\n",
    "ax.legend()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing comparison of data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line Chart\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot the median `ConvertedComp` for all ages from 45 to 60.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "QUERY = \"\"\"\n",
    "SELECT ConvertedComp, Age\n",
    "FROM master\n",
    "WHERE Age BETWEEN 45 AND 60\n",
    "\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY,conn)\n",
    "\n",
    "medians = df.groupby('Age')['ConvertedComp'].median()\n",
    "\n",
    "data = pd.DataFrame(list(zip(medians.index, medians.values)),\n",
    "               columns =['Age', 'MedConvertedComp'])\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [8,8]\n",
    "\n",
    "sns.lineplot(data=data, x='Age', y = 'MedConvertedComp')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Chart\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a horizontal bar chart using column `MainBranch.`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "QUERY = \"\"\"\n",
    "SELECT MainBranch, COUNT(*) AS Frequency\n",
    "FROM master\n",
    "GROUP BY MainBranch\n",
    "\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY,conn)\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [14, 8]\n",
    "\n",
    "sns.barplot(data = df, y = 'MainBranch', x = 'Frequency', orient = 'h')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the Line Chart of Age and median ConvertedComp for the age group 25-30, what kind of correlation do you see between Age and median ConvertedComp? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "QUERY = \"\"\"\n",
    "SELECT ConvertedComp, Age\n",
    "FROM master\n",
    "WHERE Age BETWEEN 25 AND 30\n",
    "\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(QUERY,conn)\n",
    "\n",
    "medians = df.groupby('Age')['ConvertedComp'].median()\n",
    "\n",
    "data = pd.DataFrame(list(zip(medians.index, medians.values)),\n",
    "               columns =['Age', 'MedConvertedComp'])\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [8,8]\n",
    "\n",
    "sns.lineplot(data=data, x='Age', y = 'MedConvertedComp')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Positive correlation between Age and median ConvertedComp for the age group 25-30"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What developer type are the majority of the survey responders?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>DevType</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Developer, full-stack</td>\n",
       "      <td>6928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Developer, back-end</td>\n",
       "      <td>6290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Developer, front-end</td>\n",
       "      <td>3920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Developer, desktop or enterprise applications</td>\n",
       "      <td>2575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Developer, mobile</td>\n",
       "      <td>1959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DevOps specialist</td>\n",
       "      <td>1639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Database administrator</td>\n",
       "      <td>1413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>System administrator</td>\n",
       "      <td>1202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Designer</td>\n",
       "      <td>988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Developer, QA or test</td>\n",
       "      <td>911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Developer, embedded applications or devices</td>\n",
       "      <td>854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Engineer, data</td>\n",
       "      <td>832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Data scientist or machine learning specialist</td>\n",
       "      <td>803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Data or business analyst</td>\n",
       "      <td>802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Student</td>\n",
       "      <td>766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Academic researcher</td>\n",
       "      <td>556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Educator</td>\n",
       "      <td>514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Product manager</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Developer, game or graphics</td>\n",
       "      <td>472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Engineer, site reliability</td>\n",
       "      <td>449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Engineering manager</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Scientist</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Senior executive/VP</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Marketing or sales professional</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          DevType  Count\n",
       "0                           Developer, full-stack   6928\n",
       "1                             Developer, back-end   6290\n",
       "2                            Developer, front-end   3920\n",
       "3   Developer, desktop or enterprise applications   2575\n",
       "4                               Developer, mobile   1959\n",
       "5                               DevOps specialist   1639\n",
       "6                          Database administrator   1413\n",
       "7                            System administrator   1202\n",
       "8                                        Designer    988\n",
       "9                           Developer, QA or test    911\n",
       "10    Developer, embedded applications or devices    854\n",
       "11                                 Engineer, data    832\n",
       "12  Data scientist or machine learning specialist    803\n",
       "13                       Data or business analyst    802\n",
       "14                                        Student    766\n",
       "15                            Academic researcher    556\n",
       "16                                       Educator    514\n",
       "17                                Product manager    480\n",
       "18                    Developer, game or graphics    472\n",
       "19                     Engineer, site reliability    449\n",
       "20                            Engineering manager    386\n",
       "21                                      Scientist    354\n",
       "22                            Senior executive/VP    160\n",
       "23                Marketing or sales professional     61"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QUERY = \"\"\"\n",
    "SELECT DevType, COUNT(*) as Count\n",
    "FROM DevType\n",
    "GROUP BY DevType\n",
    "ORDER BY Count DESC\n",
    "\n",
    "\"\"\"\n",
    "pd.read_sql_query(QUERY,conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally , we close the database connection.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn.close()"
   ]
  }
 ],
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