M4DataVisualization-lab.ipynb

{
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   "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 Lab**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated time needed: **45 to 60** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this assignment you will be focusing on the visualization of data.\n",
    "\n",
    "The data set will be presented to you in the form of a RDBMS.\n",
    "\n",
    "You will have to use SQL queries to extract the data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lab you will perform the following:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*   Visualize the distribution of data.\n",
    "\n",
    "*   Visualize the relationship between two features.\n",
    "\n",
    "*   Visualize composition of data.\n",
    "\n",
    "*   Visualize comparison of data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo: How to work with database\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download database file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "--2022-12-09 14:36:11--  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.2'\n",
      "\n",
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      " 10300K .......... .......... .......... .......... .......... 28% 5.57M 5s\n",
      " 10350K .......... .......... .......... .......... .......... 29% 4.62M 5s\n",
      " 10400K .......... .......... .......... .......... .......... 29% 4.21M 5s\n",
      " 10450K .......... .......... .......... .......... .......... 29% 4.96M 5s\n",
      " 10500K .......... .......... .......... .......... .......... 29% 5.87M 5s\n",
      " 10550K .......... .......... .......... .......... .......... 29% 7.02M 5s\n",
      " 10600K .......... .......... .......... .......... .......... 29% 6.64M 5s\n",
      " 10650K .......... .......... .......... .......... .......... 29% 5.45M 5s\n",
      " 10700K .......... .......... .......... .......... .......... 30% 5.98M 5s\n",
      " 10750K .......... .......... .......... .......... .......... 30% 10.3M 5s\n",
      " 10800K .......... .......... .......... .......... .......... 30% 2.81M 5s\n",
      " 10850K .......... .......... .......... .......... .......... 30% 5.66M 5s\n",
      " 10900K .......... .......... .......... .......... .......... 30% 12.7M 5s\n",
      " 10950K .......... .......... .......... .......... .......... 30% 2.64M 5s\n",
      " 11000K .......... .......... .......... .......... .......... 30% 17.7M 5s\n",
      " 11050K .......... .......... .......... .......... .......... 30% 7.40M 5s\n",
      " 11100K .......... .......... .......... .......... .......... 31% 6.64M 5s\n",
      " 11150K .......... .......... .......... .......... .......... 31% 2.80M 5s\n",
      " 11200K .......... .......... .......... .......... .......... 31% 17.6M 5s\n",
      " 11250K .......... .......... .......... .......... .......... 31% 6.58M 5s\n",
      " 11300K .......... .......... .......... .......... .......... 31% 2.14M 5s\n",
      " 11350K .......... .......... .......... .......... .......... 31%  168M 5s\n",
      " 11400K .......... .......... .......... .......... .......... 31% 26.3M 5s\n",
      " 11450K .......... .......... .......... .......... .......... 32% 1.69M 5s\n",
      " 11500K .......... .......... .......... .......... .......... 32% 8.99M 5s\n",
      " 11550K .......... .......... .......... .......... .......... 32%  119M 5s\n",
      " 11600K .......... .......... .......... .......... .......... 32%  630K 5s\n",
      " 11650K .......... .......... .......... .......... .......... 32% 19.1M 5s\n",
      " 11700K .......... .......... .......... .......... .......... 32% 6.48M 5s\n",
      " 11750K .......... .......... .......... .......... .......... 32%  193M 5s\n",
      " 11800K .......... .......... .......... .......... .......... 33%  100M 5s\n",
      " 11850K .......... .......... .......... .......... .......... 33%  219M 5s\n",
      " 11900K .......... .......... .......... .......... .......... 33%  185M 5s\n",
      " 11950K .......... .......... .......... .......... .......... 33% 2.41M 5s\n",
      " 12000K .......... .......... .......... .......... .......... 33% 16.7M 5s\n",
      " 12050K .......... .......... .......... .......... .......... 33% 29.2M 5s\n",
      " 12100K .......... .......... .......... .......... .......... 33% 54.4M 5s\n",
      " 12150K .......... .......... .......... .......... .......... 34% 22.6M 5s\n",
      " 12200K .......... .......... .......... .......... .......... 34% 5.74M 5s\n",
      " 12250K .......... .......... .......... .......... .......... 34% 3.71M 5s\n",
      " 12300K .......... .......... .......... .......... .......... 34% 5.03M 5s\n",
      " 12350K .......... .......... .......... .......... .......... 34% 22.7M 5s\n",
      " 12400K .......... .......... .......... .......... .......... 34% 16.1M 5s\n",
      " 12450K .......... .......... .......... .......... .......... 34% 4.48M 5s\n",
      " 12500K .......... .......... .......... .......... .......... 35% 6.79M 5s\n",
      " 12550K .......... .......... .......... .......... .......... 35% 4.90M 5s\n",
      " 12600K .......... .......... .......... .......... .......... 35% 18.7M 5s\n",
      " 12650K .......... .......... .......... .......... .......... 35% 7.04M 5s\n",
      " 12700K .......... .......... .......... .......... .......... 35% 40.1M 5s\n",
      " 12750K .......... .......... .......... .......... .......... 35% 4.02M 5s\n",
      " 12800K .......... .......... .......... .......... .......... 35% 5.46M 5s\n",
      " 12850K .......... .......... .......... .......... .......... 36% 16.4M 5s\n",
      " 12900K .......... .......... .......... .......... .......... 36% 22.5M 5s\n",
      " 12950K .......... .......... .......... .......... .......... 36% 3.12M 5s\n",
      " 13000K .......... .......... .......... .......... .......... 36% 5.58M 5s\n",
      " 13050K .......... .......... .......... .......... .......... 36%  184M 5s\n",
      " 13100K .......... .......... .......... .......... .......... 36% 6.22M 4s\n",
      " 13150K .......... .......... .......... .......... .......... 36% 3.73M 4s\n",
      " 13200K .......... .......... .......... .......... .......... 36% 4.61M 4s\n",
      " 13250K .......... .......... .......... .......... .......... 37% 36.0M 4s\n",
      " 13300K .......... .......... .......... .......... .......... 37% 6.49M 4s\n",
      " 13350K .......... .......... .......... .......... .......... 37% 4.47M 4s\n",
      " 13400K .......... .......... .......... .......... .......... 37% 3.78M 4s\n",
      " 13450K .......... .......... .......... .......... .......... 37% 10.4M 4s\n",
      " 13500K .......... .......... .......... .......... .......... 37% 5.25M 4s\n",
      " 13550K .......... .......... .......... .......... .......... 37% 4.08M 4s\n",
      " 13600K .......... .......... .......... .......... .......... 38% 2.41M 4s\n",
      " 13650K .......... .......... .......... .......... .......... 38% 49.3M 4s\n",
      " 13700K .......... .......... .......... .......... .......... 38% 4.48M 4s\n",
      " 13750K .......... .......... .......... .......... .......... 38% 5.76M 4s\n",
      " 13800K .......... .......... .......... .......... .......... 38% 3.60M 4s\n",
      " 13850K .......... .......... .......... .......... .......... 38% 7.83M 4s\n",
      " 13900K .......... .......... .......... .......... .......... 38% 5.61M 4s\n",
      " 13950K .......... .......... .......... .......... .......... 39% 4.50M 4s\n",
      " 14000K .......... .......... .......... .......... .......... 39% 3.84M 4s\n",
      " 14050K .......... .......... .......... .......... .......... 39% 10.8M 4s\n",
      " 14100K .......... .......... .......... .......... .......... 39% 10.9M 4s\n",
      " 14150K .......... .......... .......... .......... .......... 39% 4.05M 4s\n",
      " 14200K .......... .......... .......... .......... .......... 39% 3.78M 4s\n",
      " 14250K .......... .......... .......... .......... .......... 39% 9.31M 4s\n",
      " 14300K .......... .......... .......... .......... .......... 40% 10.9M 4s\n",
      " 14350K .......... .......... .......... .......... .......... 40% 5.54M 4s\n",
      " 14400K .......... .......... .......... .......... .......... 40% 2.98M 4s\n",
      " 14450K .......... .......... .......... .......... .......... 40% 44.3M 4s\n",
      " 14500K .......... .......... .......... .......... .......... 40% 4.64M 4s\n",
      " 14550K .......... .......... .......... .......... .......... 40% 4.34M 4s\n",
      " 14600K .......... .......... .......... .......... .......... 40% 6.30M 4s\n",
      " 14650K .......... .......... .......... .......... .......... 41% 29.0M 4s\n",
      " 14700K .......... .......... .......... .......... .......... 41% 2.27M 4s\n",
      " 14750K .......... .......... .......... .......... .......... 41% 37.1M 4s\n",
      " 14800K .......... .......... .......... .......... .......... 41% 3.60M 4s\n",
      " 14850K .......... .......... .......... .......... .......... 41% 3.44M 4s\n",
      " 14900K .......... .......... .......... .......... .......... 41% 71.7M 4s\n",
      " 14950K .......... .......... .......... .......... .......... 41% 5.13M 4s\n",
      " 15000K .......... .......... .......... .......... .......... 42% 2.71M 4s\n",
      " 15050K .......... .......... .......... .......... .......... 42% 58.5M 4s\n",
      " 15100K .......... .......... .......... .......... .......... 42% 4.96M 4s\n",
      " 15150K .......... .......... .......... .......... .......... 42% 2.94M 4s\n",
      " 15200K .......... .......... .......... .......... .......... 42% 8.79M 4s\n",
      " 15250K .......... .......... .......... .......... .......... 42% 9.43M 4s\n",
      " 15300K .......... .......... .......... .......... .......... 42% 2.81M 4s\n",
      " 15350K .......... .......... .......... .......... .......... 42% 12.0M 4s\n",
      " 15400K .......... .......... .......... .......... .......... 43% 7.76M 4s\n",
      " 15450K .......... .......... .......... .......... .......... 43% 5.58M 4s\n",
      " 15500K .......... .......... .......... .......... .......... 43% 5.41M 4s\n",
      " 15550K .......... .......... .......... .......... .......... 43% 1.26M 4s\n",
      " 15600K .......... .......... .......... .......... .......... 43%  847K 4s\n",
      " 15650K .......... .......... .......... .......... .......... 43% 1.36M 4s\n",
      " 15700K .......... .......... .......... .......... .......... 43% 1.30M 4s\n",
      " 15750K .......... .......... .......... .......... .......... 44% 1.54M 4s\n",
      " 15800K .......... .......... .......... .......... .......... 44% 1.02M 4s\n",
      " 15850K .......... .......... .......... .......... .......... 44% 1.63M 4s\n",
      " 15900K .......... .......... .......... .......... .......... 44% 2.12M 4s\n",
      " 15950K .......... .......... .......... .......... .......... 44%  903K 4s\n",
      " 16000K .......... .......... .......... .......... .......... 44% 1.29M 4s\n",
      " 16050K .......... .......... .......... .......... .......... 44% 1.45M 4s\n",
      " 16100K .......... .......... .......... .......... .......... 45% 1.79M 4s\n",
      " 16150K .......... .......... .......... .......... .......... 45% 1.62M 4s\n",
      " 16200K .......... .......... .......... .......... .......... 45% 1.36M 4s\n",
      " 16250K .......... .......... .......... .......... .......... 45% 1.01M 4s\n",
      " 16300K .......... .......... .......... .......... .......... 45% 1.23M 4s\n",
      " 16350K .......... .......... .......... .......... .......... 45%  910K 4s\n",
      " 16400K .......... .......... .......... .......... .......... 45%  540K 4s\n",
      " 16450K .......... .......... .......... .......... .......... 46%  359K 5s\n",
      " 16500K .......... .......... .......... .......... .......... 46%  435K 5s\n",
      " 16550K .......... .......... .......... .......... .......... 46%  348K 5s\n",
      " 16600K .......... .......... .......... .......... .......... 46%  442K 5s\n",
      " 16650K .......... .......... .......... .......... .......... 46%  440K 5s\n",
      " 16700K .......... .......... .......... .......... .......... 46%  561K 5s\n",
      " 16750K .......... .......... .......... .......... .......... 46%  879K 5s\n",
      " 16800K .......... .......... .......... .......... .......... 47%  554K 5s\n",
      " 16850K .......... .......... .......... .......... .......... 47%  333K 5s\n",
      " 16900K .......... .......... .......... .......... .......... 47%  404K 5s\n",
      " 16950K .......... .......... .......... .......... .......... 47%  331K 6s\n",
      " 17000K .......... .......... .......... .......... .......... 47%  826K 6s\n",
      " 17050K .......... .......... .......... .......... .......... 47%  439K 6s\n",
      " 17100K .......... .......... .......... .......... .......... 47%  464K 6s\n",
      " 17150K .......... .......... .......... .......... .......... 48%  544K 6s\n",
      " 17200K .......... .......... .......... .......... .......... 48%  347K 6s\n",
      " 17250K .......... .......... .......... .......... .......... 48%  520K 6s\n",
      " 17300K .......... .......... .......... .......... .......... 48%  813K 6s\n",
      " 17350K .......... .......... .......... .......... .......... 48%  965K 6s\n",
      " 17400K .......... .......... .......... .......... .......... 48%  355K 6s\n",
      " 17450K .......... .......... .......... .......... .......... 48% 1.15M 6s\n",
      " 17500K .......... .......... .......... .......... .......... 48% 1.19M 6s\n",
      " 17550K .......... .......... .......... .......... .......... 49% 1.75M 6s\n",
      " 17600K .......... .......... .......... .......... .......... 49% 1.23M 6s\n",
      " 17650K .......... .......... .......... .......... .......... 49% 1.98M 6s\n",
      " 17700K .......... .......... .......... .......... .......... 49% 1020K 6s\n",
      " 17750K .......... .......... .......... .......... .......... 49% 1.43M 6s\n",
      " 17800K .......... .......... .......... .......... .......... 49%  830K 6s\n",
      " 17850K .......... .......... .......... .......... .......... 49% 1.47M 6s\n",
      " 17900K .......... .......... .......... .......... .......... 50% 1.09M 6s\n",
      " 17950K .......... .......... .......... .......... .......... 50% 1024K 6s\n",
      " 18000K .......... .......... .......... .......... .......... 50%  824K 6s\n",
      " 18050K .......... .......... .......... .......... .......... 50% 1.35M 6s\n",
      " 18100K .......... .......... .......... .......... .......... 50% 1.72M 6s\n",
      " 18150K .......... .......... .......... .......... .......... 50% 1.20M 6s\n",
      " 18200K .......... .......... .......... .......... .......... 50% 1.35M 6s\n",
      " 18250K .......... .......... .......... .......... .......... 51%  944K 6s\n",
      " 18300K .......... .......... .......... .......... .......... 51%  444K 6s\n",
      " 18350K .......... .......... .......... .......... .......... 51%  412K 7s\n",
      " 18400K .......... .......... .......... .......... .......... 51%  604K 7s\n",
      " 18450K .......... .......... .......... .......... .......... 51%  577K 7s\n",
      " 18500K .......... .......... .......... .......... .......... 51%  446K 7s\n",
      " 18550K .......... .......... .......... .......... .......... 51%  598K 7s\n",
      " 18600K .......... .......... .......... .......... .......... 52%  364K 7s\n",
      " 18650K .......... .......... .......... .......... .......... 52%  568K 7s\n",
      " 18700K .......... .......... .......... .......... .......... 52%  618K 7s\n",
      " 18750K .......... .......... .......... .......... .......... 52%  565K 7s\n",
      " 18800K .......... .......... .......... .......... .......... 52%  299K 7s\n",
      " 18850K .......... .......... .......... .......... .......... 52%  581K 7s\n",
      " 18900K .......... .......... .......... .......... .......... 52%  891K 7s\n",
      " 18950K .......... .......... .......... .......... .......... 53%  449K 7s\n",
      " 19000K .......... .......... .......... .......... .......... 53%  770K 7s\n",
      " 19050K .......... .......... .......... .......... .......... 53%  879K 7s\n",
      " 19100K .......... .......... .......... .......... .......... 53%  452K 7s\n",
      " 19150K .......... .......... .......... .......... .......... 53%  580K 7s\n",
      " 19200K .......... .......... .......... .......... .......... 53%  640K 7s\n",
      " 19250K .......... .......... .......... .......... .......... 53%  884K 7s\n",
      " 19300K .......... .......... .......... .......... .......... 54%  999K 7s\n",
      " 19350K .......... .......... .......... .......... .......... 54%  978K 7s\n",
      " 19400K .......... .......... .......... .......... .......... 54% 1.19M 7s\n",
      " 19450K .......... .......... .......... .......... .......... 54% 3.08M 7s\n",
      " 19500K .......... .......... .......... .......... .......... 54% 1.71M 7s\n",
      " 19550K .......... .......... .......... .......... .......... 54% 1.40M 7s\n",
      " 19600K .......... .......... .......... .......... .......... 54%  917K 7s\n",
      " 19650K .......... .......... .......... .......... .......... 54% 1.15M 7s\n",
      " 19700K .......... .......... .......... .......... .......... 55%  753K 7s\n",
      " 19750K .......... .......... .......... .......... .......... 55% 1.51M 7s\n",
      " 19800K .......... .......... .......... .......... .......... 55% 1.36M 7s\n",
      " 19850K .......... .......... .......... .......... .......... 55%  551K 7s\n",
      " 19900K .......... .......... .......... .......... .......... 55%  402K 7s\n",
      " 19950K .......... .......... .......... .......... .......... 55%  342K 7s\n",
      " 20000K .......... .......... .......... .......... .......... 55%  568K 7s\n",
      " 20050K .......... .......... .......... .......... .......... 56%  569K 7s\n",
      " 20100K .......... .......... .......... .......... .......... 56%  345K 8s\n",
      " 20150K .......... .......... .......... .......... .......... 56%  450K 8s\n",
      " 20200K .......... .......... .......... .......... .......... 56%  428K 8s\n",
      " 20250K .......... .......... .......... .......... .......... 56%  343K 8s\n",
      " 20300K .......... .......... .......... .......... .......... 56% 1.16M 8s\n",
      " 20350K .......... .......... .......... .......... .......... 56%  800K 8s\n",
      " 20400K .......... .......... .......... .......... .......... 57% 1.21M 8s\n",
      " 20450K .......... .......... .......... .......... .......... 57%  635K 8s\n",
      " 20500K .......... .......... .......... .......... .......... 57%  615K 8s\n",
      " 20550K .......... .......... .......... .......... .......... 57% 1.59M 8s\n",
      " 20600K .......... .......... .......... .......... .......... 57% 1.24M 8s\n",
      " 20650K .......... .......... .......... .......... .......... 57% 1.03M 8s\n",
      " 20700K .......... .......... .......... .......... .......... 57% 1.21M 8s\n",
      " 20750K .......... .......... .......... .......... .......... 58%  913K 8s\n",
      " 20800K .......... .......... .......... .......... .......... 58%  872K 8s\n",
      " 20850K .......... .......... .......... .......... .......... 58% 1.37M 8s\n",
      " 20900K .......... .......... .......... .......... .......... 58%  898K 8s\n",
      " 20950K .......... .......... .......... .......... .......... 58% 1.22M 8s\n",
      " 21000K .......... .......... .......... .......... .......... 58% 1.15M 8s\n",
      " 21050K .......... .......... .......... .......... .......... 58%  721K 8s\n",
      " 21100K .......... .......... .......... .......... .......... 59% 1.76M 8s\n",
      " 21150K .......... .......... .......... .......... .......... 59% 1.32M 8s\n",
      " 21200K .......... .......... .......... .......... .......... 59% 1.85M 8s\n",
      " 21250K .......... .......... .......... .......... .......... 59% 1.42M 7s\n",
      " 21300K .......... .......... .......... .......... .......... 59% 1.68M 7s\n",
      " 21350K .......... .......... .......... .......... .......... 59%  872K 7s\n",
      " 21400K .......... .......... .......... .......... .......... 59%  940K 7s\n",
      " 21450K .......... .......... .......... .......... .......... 60% 1.29M 7s\n",
      " 21500K .......... .......... .......... .......... .......... 60%  910K 7s\n",
      " 21550K .......... .......... .......... .......... .......... 60%  849K 7s\n",
      " 21600K .......... .......... .......... .......... .......... 60%  835K 7s\n",
      " 21650K .......... .......... .......... .......... .......... 60% 1.22M 7s\n",
      " 21700K .......... .......... .......... .......... .......... 60%  545K 7s\n",
      " 21750K .......... .......... .......... .......... .......... 60%  597K 7s\n",
      " 21800K .......... .......... .......... .......... .......... 60%  805K 7s\n",
      " 21850K .......... .......... .......... .......... .......... 61%  411K 7s\n",
      " 21900K .......... .......... .......... .......... .......... 61%  349K 8s\n",
      " 21950K .......... .......... .......... .......... .......... 61% 1.30M 7s\n",
      " 22000K .......... .......... .......... .......... .......... 61% 1.46M 7s\n",
      " 22050K .......... .......... .......... .......... .......... 61% 1.00M 7s\n",
      " 22100K .......... .......... .......... .......... .......... 61%  774K 7s\n",
      " 22150K .......... .......... .......... .......... .......... 61%  839K 7s\n",
      " 22200K .......... .......... .......... .......... .......... 62%  867K 7s\n",
      " 22250K .......... .......... .......... .......... .......... 62%  469K 7s\n",
      " 22300K .......... .......... .......... .......... .......... 62%  350K 7s\n",
      " 22350K .......... .......... .......... .......... .......... 62%  436K 8s\n",
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      " 22450K .......... .......... .......... .......... .......... 62%  599K 8s\n",
      " 22500K .......... .......... .......... .......... .......... 62%  631K 8s\n",
      " 22550K .......... .......... .......... .......... .......... 63%  426K 8s\n",
      " 22600K .......... .......... .......... .......... .......... 63%  439K 8s\n",
      " 22650K .......... .......... .......... .......... .......... 63%  611K 8s\n",
      " 22700K .......... .......... .......... .......... .......... 63%  921K 8s\n",
      " 22750K .......... .......... .......... .......... .......... 63% 1.27M 8s\n",
      " 22800K .......... .......... .......... .......... .......... 63%  727K 8s\n",
      " 22850K .......... .......... .......... .......... .......... 63%  468K 8s\n",
      " 22900K .......... .......... .......... .......... .......... 64%  926K 8s\n",
      " 22950K .......... .......... .......... .......... .......... 64%  921K 7s\n",
      " 23000K .......... .......... .......... .......... .......... 64%  799K 7s\n",
      " 23050K .......... .......... .......... .......... .......... 64%  922K 7s\n",
      " 23100K .......... .......... .......... .......... .......... 64%  646K 7s\n",
      " 23150K .......... .......... .......... .......... .......... 64%  453K 7s\n",
      " 23200K .......... .......... .......... .......... .......... 64%  354K 8s\n",
      " 23250K .......... .......... .......... .......... .......... 65%  921K 7s\n",
      " 23300K .......... .......... .......... .......... .......... 65% 1.39M 7s\n",
      " 23350K .......... .......... .......... .......... .......... 65%  845K 7s\n",
      " 23400K .......... .......... .......... .......... .......... 65%  577K 7s\n",
      " 23450K .......... .......... .......... .......... .......... 65%  809K 7s\n",
      " 23500K .......... .......... .......... .......... .......... 65%  969K 7s\n",
      " 23550K .......... .......... .......... .......... .......... 65%  914K 7s\n",
      " 23600K .......... .......... .......... .......... .......... 66%  431K 7s\n",
      " 23650K .......... .......... .......... .......... .......... 66%  450K 7s\n",
      " 23700K .......... .......... .......... .......... .......... 66%  588K 7s\n",
      " 23750K .......... .......... .......... .......... .......... 66%  432K 7s\n",
      " 23800K .......... .......... .......... .......... .......... 66%  816K 7s\n",
      " 23850K .......... .......... .......... .......... .......... 66%  882K 7s\n",
      " 23900K .......... .......... .......... .......... .......... 66%  357K 7s\n",
      " 23950K .......... .......... .......... .......... .......... 67%  442K 7s\n",
      " 24000K .......... .......... .......... .......... .......... 67%  458K 7s\n",
      " 24050K .......... .......... .......... .......... .......... 67%  442K 7s\n",
      " 24100K .......... .......... .......... .......... .......... 67%  418K 7s\n",
      " 24150K .......... .......... .......... .......... .......... 67%  340K 7s\n",
      " 24200K .......... .......... .......... .......... .......... 67%  584K 7s\n",
      " 24250K .......... .......... .......... .......... .......... 67%  786K 7s\n",
      " 24300K .......... .......... .......... .......... .......... 67%  903K 7s\n",
      " 24350K .......... .......... .......... .......... .......... 68% 1.66M 7s\n",
      " 24400K .......... .......... .......... .......... .......... 68% 1.76M 7s\n",
      " 24450K .......... .......... .......... .......... .......... 68% 1.71M 7s\n",
      " 24500K .......... .......... .......... .......... .......... 68%  784K 7s\n",
      " 24550K .......... .......... .......... .......... .......... 68%  546K 7s\n",
      " 24600K .......... .......... .......... .......... .......... 68%  442K 7s\n",
      " 24650K .......... .......... .......... .......... .......... 68%  443K 7s\n",
      " 24700K .......... .......... .......... .......... .......... 69%  443K 7s\n",
      " 24750K .......... .......... .......... .......... .......... 69%  602K 7s\n",
      " 24800K .......... .......... .......... .......... .......... 69%  576K 7s\n",
      " 24850K .......... .......... .......... .......... .......... 69% 1.12M 7s\n",
      " 24900K .......... .......... .......... .......... .......... 69% 1.17M 7s\n",
      " 24950K .......... .......... .......... .......... .......... 69% 1.37M 7s\n",
      " 25000K .......... .......... .......... .......... .......... 69% 1.49M 7s\n",
      " 25050K .......... .......... .......... .......... .......... 70% 1.93M 7s\n",
      " 25100K .......... .......... .......... .......... .......... 70% 1.50M 7s\n",
      " 25150K .......... .......... .......... .......... .......... 70%  384K 7s\n",
      " 25200K .......... .......... .......... .......... .......... 70%  334K 7s\n",
      " 25250K .......... .......... .......... .......... .......... 70%  733K 7s\n",
      " 25300K .......... .......... .......... .......... .......... 70%  772K 7s\n",
      " 25350K .......... .......... .......... .......... .......... 70% 1.31M 7s\n",
      " 25400K .......... .......... .......... .......... .......... 71%  829K 7s\n",
      " 25450K .......... .......... .......... .......... .......... 71%  993K 7s\n",
      " 25500K .......... .......... .......... .......... .......... 71% 1.55M 7s\n",
      " 25550K .......... .......... .......... .......... .......... 71%  346K 7s\n",
      " 25600K .......... .......... .......... .......... .......... 71%  455K 7s\n",
      " 25650K .......... .......... .......... .......... .......... 71%  600K 7s\n",
      " 25700K .......... .......... .......... .......... .......... 71%  903K 7s\n",
      " 25750K .......... .......... .......... .......... .......... 72%  948K 7s\n",
      " 25800K .......... .......... .......... .......... .......... 72%  942K 7s\n",
      " 25850K .......... .......... .......... .......... .......... 72% 1.69M 7s\n",
      " 25900K .......... .......... .......... .......... .......... 72% 1.10M 7s\n",
      " 25950K .......... .......... .......... .......... .......... 72% 1.47M 7s\n",
      " 26000K .......... .......... .......... .......... .......... 72%  905K 7s\n",
      " 26050K .......... .......... .......... .......... .......... 72%  872K 7s\n",
      " 26100K .......... .......... .......... .......... .......... 73%  419K 7s\n",
      " 26150K .......... .......... .......... .......... .......... 73%  360K 7s\n",
      " 26200K .......... .......... .......... .......... .......... 73%  444K 7s\n",
      " 26250K .......... .......... .......... .......... .......... 73%  406K 7s\n",
      " 26300K .......... .......... .......... .......... .......... 73%  292K 7s\n",
      " 26350K .......... .......... .......... .......... .......... 73%  459K 7s\n",
      " 26400K .......... .......... .......... .......... .......... 73%  452K 7s\n",
      " 26450K .......... .......... .......... .......... .......... 73%  357K 7s\n",
      " 26500K .......... .......... .......... .......... .......... 74%  436K 7s\n",
      " 26550K .......... .......... .......... .......... .......... 74% 1.25M 7s\n",
      " 26600K .......... .......... .......... .......... .......... 74%  995K 7s\n",
      " 26650K .......... .......... .......... .......... .......... 74%  385K 7s\n",
      " 26700K .......... .......... .......... .......... .......... 74%  572K 7s\n",
      " 26750K .......... .......... .......... .......... .......... 74%  576K 7s\n",
      " 26800K .......... .......... .......... .......... .......... 74%  798K 7s\n",
      " 26850K .......... .......... .......... .......... .......... 75%  444K 7s\n",
      " 26900K .......... .......... .......... .......... .......... 75%  428K 7s\n",
      " 26950K .......... .......... .......... .......... .......... 75%  786K 7s\n",
      " 27000K .......... .......... .......... .......... .......... 75%  623K 6s\n",
      " 27050K .......... .......... .......... .......... .......... 75% 1.33M 6s\n",
      " 27100K .......... .......... .......... .......... .......... 75%  658K 6s\n",
      " 27150K .......... .......... .......... .......... .......... 75%  459K 6s\n",
      " 27200K .......... .......... .......... .......... .......... 76%  950K 6s\n",
      " 27250K .......... .......... .......... .......... .......... 76%  878K 6s\n",
      " 27300K .......... .......... .......... .......... .......... 76% 1.46M 6s\n",
      " 27350K .......... .......... .......... .......... .......... 76% 2.57M 6s\n",
      " 27400K .......... .......... .......... .......... .......... 76% 1.31M 6s\n",
      " 27450K .......... .......... .......... .......... .......... 76%  994K 6s\n",
      " 27500K .......... .......... .......... .......... .......... 76% 1.07M 6s\n",
      " 27550K .......... .......... .......... .......... .......... 77% 1.44M 6s\n",
      " 27600K .......... .......... .......... .......... .......... 77% 1.40M 6s\n",
      " 27650K .......... .......... .......... .......... .......... 77% 1.84M 6s\n",
      " 27700K .......... .......... .......... .......... .......... 77% 1.33M 6s\n",
      " 27750K .......... .......... .......... .......... .......... 77% 1.33M 6s\n",
      " 27800K .......... .......... .......... .......... .......... 77% 1.49M 6s\n",
      " 27850K .......... .......... .......... .......... .......... 77% 1.43M 6s\n",
      " 27900K .......... .......... .......... .......... .......... 78% 1.05M 6s\n",
      " 27950K .......... .......... .......... .......... .......... 78% 1.24M 6s\n",
      " 28000K .......... .......... .......... .......... .......... 78% 1.99M 6s\n",
      " 28050K .......... .......... .......... .......... .......... 78% 1.15M 6s\n",
      " 28100K .......... .......... .......... .......... .......... 78%  912K 6s\n",
      " 28150K .......... .......... .......... .......... .......... 78%  766K 6s\n",
      " 28200K .......... .......... .......... .......... .......... 78% 1.26M 6s\n",
      " 28250K .......... .......... .......... .......... .......... 79%  868K 6s\n",
      " 28300K .......... .......... .......... .......... .......... 79%  794K 6s\n",
      " 28350K .......... .......... .......... .......... .......... 79% 1.20M 6s\n",
      " 28400K .......... .......... .......... .......... .......... 79% 1.84M 6s\n",
      " 28450K .......... .......... .......... .......... .......... 79%  805K 5s\n",
      " 28500K .......... .......... .......... .......... .......... 79%  890K 5s\n",
      " 28550K .......... .......... .......... .......... .......... 79%  421K 5s\n",
      " 28600K .......... .......... .......... .......... .......... 79%  427K 5s\n",
      " 28650K .......... .......... .......... .......... .......... 80%  558K 5s\n",
      " 28700K .......... .......... .......... .......... .......... 80%  356K 5s\n",
      " 28750K .......... .......... .......... .......... .......... 80%  447K 5s\n",
      " 28800K .......... .......... .......... .......... .......... 80%  902K 5s\n",
      " 28850K .......... .......... .......... .......... .......... 80%  827K 5s\n",
      " 28900K .......... .......... .......... .......... .......... 80%  362K 5s\n",
      " 28950K .......... .......... .......... .......... .......... 80%  439K 5s\n",
      " 29000K .......... .......... .......... .......... .......... 81%  467K 5s\n",
      " 29050K .......... .......... .......... .......... .......... 81%  922K 5s\n",
      " 29100K .......... .......... .......... .......... .......... 81%  908K 5s\n",
      " 29150K .......... .......... .......... .......... .......... 81%  943K 5s\n",
      " 29200K .......... .......... .......... .......... .......... 81% 1.53M 5s\n",
      " 29250K .......... .......... .......... .......... .......... 81% 1.48M 5s\n",
      " 29300K .......... .......... .......... .......... .......... 81% 1.01M 5s\n",
      " 29350K .......... .......... .......... .......... .......... 82% 1.45M 5s\n",
      " 29400K .......... .......... .......... .......... .......... 82% 1.43M 5s\n",
      " 29450K .......... .......... .......... .......... .......... 82%  447K 5s\n",
      " 29500K .......... .......... .......... .......... .......... 82%  454K 5s\n",
      " 29550K .......... .......... .......... .......... .......... 82%  291K 5s\n",
      " 29600K .......... .......... .......... .......... .......... 82%  561K 5s\n",
      " 29650K .......... .......... .......... .......... .......... 82%  603K 5s\n",
      " 29700K .......... .......... .......... .......... .......... 83%  634K 5s\n",
      " 29750K .......... .......... .......... .......... .......... 83% 1.51M 5s\n",
      " 29800K .......... .......... .......... .......... .......... 83% 1.03M 5s\n",
      " 29850K .......... .......... .......... .......... .......... 83% 1.48M 5s\n",
      " 29900K .......... .......... .......... .......... .......... 83% 1.32M 5s\n",
      " 29950K .......... .......... .......... .......... .......... 83% 2.16M 5s\n",
      " 30000K .......... .......... .......... .......... .......... 83% 1.17M 5s\n",
      " 30050K .......... .......... .......... .......... .......... 84% 1.42M 5s\n",
      " 30100K .......... .......... .......... .......... .......... 84% 1.18M 4s\n",
      " 30150K .......... .......... .......... .......... .......... 84% 1.83M 4s\n",
      " 30200K .......... .......... .......... .......... .......... 84% 1.34M 4s\n",
      " 30250K .......... .......... .......... .......... .......... 84%  778K 4s\n",
      " 30300K .......... .......... .......... .......... .......... 84%  782K 4s\n",
      " 30350K .......... .......... .......... .......... .......... 84%  290K 4s\n",
      " 30400K .......... .......... .......... .......... .......... 85%  416K 4s\n",
      " 30450K .......... .......... .......... .......... .......... 85%  937K 4s\n",
      " 30500K .......... .......... .......... .......... .......... 85%  337K 4s\n",
      " 30550K .......... .......... .......... .......... .......... 85%  399K 4s\n",
      " 30600K .......... .......... .......... .......... .......... 85%  508K 4s\n",
      " 30650K .......... .......... .......... .......... .......... 85%  601K 4s\n",
      " 30700K .......... .......... .......... .......... .......... 85%  398K 4s\n",
      " 30750K .......... .......... .......... .......... .......... 85%  897K 4s\n",
      " 30800K .......... .......... .......... .......... .......... 86% 1.12M 4s\n",
      " 30850K .......... .......... .......... .......... .......... 86% 1.41M 4s\n",
      " 30900K .......... .......... .......... .......... .......... 86% 1.64M 4s\n",
      " 30950K .......... .......... .......... .......... .......... 86% 1.20M 4s\n",
      " 31000K .......... .......... .......... .......... .......... 86% 1.24M 4s\n",
      " 31050K .......... .......... .......... .......... .......... 86% 1.70M 4s\n",
      " 31100K .......... .......... .......... .......... .......... 86% 1.32M 4s\n",
      " 31150K .......... .......... .......... .......... .......... 87% 1.50M 4s\n",
      " 31200K .......... .......... .......... .......... .......... 87% 1.26M 4s\n",
      " 31250K .......... .......... .......... .......... .......... 87% 1.96M 4s\n",
      " 31300K .......... .......... .......... .......... .......... 87% 1.56M 4s\n",
      " 31350K .......... .......... .......... .......... .......... 87% 2.53M 4s\n",
      " 31400K .......... .......... .......... .......... .......... 87% 1.05M 4s\n",
      " 31450K .......... .......... .......... .......... .......... 87%  844K 3s\n",
      " 31500K .......... .......... .......... .......... .......... 88%  592K 3s\n",
      " 31550K .......... .......... .......... .......... .......... 88%  442K 3s\n",
      " 31600K .......... .......... .......... .......... .......... 88%  615K 3s\n",
      " 31650K .......... .......... .......... .......... .......... 88% 1.34M 3s\n",
      " 31700K .......... .......... .......... .......... .......... 88%  952K 3s\n",
      " 31750K .......... .......... .......... .......... .......... 88%  650K 3s\n",
      " 31800K .......... .......... .......... .......... .......... 88%  628K 3s\n",
      " 31850K .......... .......... .......... .......... .......... 89%  612K 3s\n",
      " 31900K .......... .......... .......... .......... .......... 89%  701K 3s\n",
      " 31950K .......... .......... .......... .......... .......... 89% 1022K 3s\n",
      " 32000K .......... .......... .......... .......... .......... 89%  944K 3s\n",
      " 32050K .......... .......... .......... .......... .......... 89%  939K 3s\n",
      " 32100K .......... .......... .......... .......... .......... 89% 1.32M 3s\n",
      " 32150K .......... .......... .......... .......... .......... 89% 1.57M 3s\n",
      " 32200K .......... .......... .......... .......... .......... 90% 1.87M 3s\n",
      " 32250K .......... .......... .......... .......... .......... 90% 1.01M 3s\n",
      " 32300K .......... .......... .......... .......... .......... 90%  725K 3s\n",
      " 32350K .......... .......... .......... .......... .......... 90%  437K 3s\n",
      " 32400K .......... .......... .......... .......... .......... 90%  434K 3s\n",
      " 32450K .......... .......... .......... .......... .......... 90%  843K 3s\n",
      " 32500K .......... .......... .......... .......... .......... 90%  838K 3s\n",
      " 32550K .......... .......... .......... .......... .......... 91% 1.54M 3s\n",
      " 32600K .......... .......... .......... .......... .......... 91% 1.36M 3s\n",
      " 32650K .......... .......... .......... .......... .......... 91% 1.02M 3s\n",
      " 32700K .......... .......... .......... .......... .......... 91%  953K 3s\n",
      " 32750K .......... .......... .......... .......... .......... 91% 1.40M 2s\n",
      " 32800K .......... .......... .......... .......... .......... 91%  732K 2s\n",
      " 32850K .......... .......... .......... .......... .......... 91% 1.81M 2s\n",
      " 32900K .......... .......... .......... .......... .......... 91% 1.34M 2s\n",
      " 32950K .......... .......... .......... .......... .......... 92% 1013K 2s\n",
      " 33000K .......... .......... .......... .......... .......... 92% 1.64M 2s\n",
      " 33050K .......... .......... .......... .......... .......... 92%  818K 2s\n",
      " 33100K .......... .......... .......... .......... .......... 92%  599K 2s\n",
      " 33150K .......... .......... .......... .......... .......... 92%  467K 2s\n",
      " 33200K .......... .......... .......... .......... .......... 92%  446K 2s\n",
      " 33250K .......... .......... .......... .......... .......... 92%  439K 2s\n",
      " 33300K .......... .......... .......... .......... .......... 93%  932K 2s\n",
      " 33350K .......... .......... .......... .......... .......... 93% 1.18M 2s\n",
      " 33400K .......... .......... .......... .......... .......... 93% 1.09M 2s\n",
      " 33450K .......... .......... .......... .......... .......... 93%  709K 2s\n",
      " 33500K .......... .......... .......... .......... .......... 93%  946K 2s\n",
      " 33550K .......... .......... .......... .......... .......... 93%  412K 2s\n",
      " 33600K .......... .......... .......... .......... .......... 93%  455K 2s\n",
      " 33650K .......... .......... .......... .......... .......... 94% 1.37M 2s\n",
      " 33700K .......... .......... .......... .......... .......... 94% 1.52M 2s\n",
      " 33750K .......... .......... .......... .......... .......... 94%  925K 2s\n",
      " 33800K .......... .......... .......... .......... .......... 94%  687K 2s\n",
      " 33850K .......... .......... .......... .......... .......... 94% 1.54M 2s\n",
      " 33900K .......... .......... .......... .......... .......... 94%  360K 2s\n",
      " 33950K .......... .......... .......... .......... .......... 94%  454K 2s\n",
      " 34000K .......... .......... .......... .......... .......... 95%  604K 1s\n",
      " 34050K .......... .......... .......... .......... .......... 95%  379K 1s\n",
      " 34100K .......... .......... .......... .......... .......... 95%  561K 1s\n",
      " 34150K .......... .......... .......... .......... .......... 95% 1.02M 1s\n",
      " 34200K .......... .......... .......... .......... .......... 95% 1.27M 1s\n",
      " 34250K .......... .......... .......... .......... .......... 95% 1.01M 1s\n",
      " 34300K .......... .......... .......... .......... .......... 95% 1.26M 1s\n",
      " 34350K .......... .......... .......... .......... .......... 96% 1.08M 1s\n",
      " 34400K .......... .......... .......... .......... .......... 96% 1.33M 1s\n",
      " 34450K .......... .......... .......... .......... .......... 96% 1.46M 1s\n",
      " 34500K .......... .......... .......... .......... .......... 96% 1.46M 1s\n",
      " 34550K .......... .......... .......... .......... .......... 96% 1.25M 1s\n",
      " 34600K .......... .......... .......... .......... .......... 96% 1.69M 1s\n",
      " 34650K .......... .......... .......... .......... .......... 96% 1.89M 1s\n",
      " 34700K .......... .......... .......... .......... .......... 97%  900K 1s\n",
      " 34750K .......... .......... .......... .......... .......... 97% 2.92M 1s\n",
      " 34800K .......... .......... .......... .......... .......... 97% 1.01M 1s\n",
      " 34850K .......... .......... .......... .......... .......... 97%  656K 1s\n",
      " 34900K .......... .......... .......... .......... .......... 97%  467K 1s\n",
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      " 35050K .......... .......... .......... .......... .......... 97%  369K 1s\n",
      " 35100K .......... .......... .......... .......... .......... 98%  356K 1s\n",
      " 35150K .......... .......... .......... .......... .......... 98%  895K 1s\n",
      " 35200K .......... .......... .......... .......... .......... 98%  823K 0s\n",
      " 35250K .......... .......... .......... .......... .......... 98%  456K 0s\n",
      " 35300K .......... .......... .......... .......... .......... 98%  469K 0s\n",
      " 35350K .......... .......... .......... .......... .......... 98%  880K 0s\n",
      " 35400K .......... .......... .......... .......... .......... 98%  291K 0s\n",
      " 35450K .......... .......... .......... .......... .......... 99% 9.78M 0s\n",
      " 35500K .......... .......... .......... .......... .......... 99% 4.06M 0s\n",
      " 35550K .......... .......... .......... .......... .......... 99% 3.94M 0s\n",
      " 35600K .......... .......... .......... .......... .......... 99% 1.77M 0s\n",
      " 35650K .......... .......... .......... .......... .......... 99% 1.44M 0s\n",
      " 35700K .......... .......... .......... .......... .......... 99% 1.77M 0s\n",
      " 35750K .......... .......... .......... .......... .......... 99% 1.23M 0s\n",
      " 35800K .......... ..........                                 100% 1.57M=31s\n",
      "\n",
      "2022-12-09 14:36:42 (1.13 MB/s) - 'm4_survey_data.sqlite.2' saved [36679680/36679680]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DA0321EN-SkillsNetwork/LargeData/m4_survey_data.sqlite"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Connect to the database.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "connected\n"
     ]
    }
   ],
   "source": [
    "import sqlite3\n",
    "conn = sqlite3.connect(\"m4_survey_data.sqlite.2\")\n",
    "print(\"connected\")   # open a database connection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import pandas module.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "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": 38,
   "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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print how many rows are there in the table named 'master'\n",
    "QUERY = \"\"\"\n",
    "SELECT COUNT(*)\n",
    "FROM master\n",
    "\"\"\"\n",
    "\n",
    "# the read_sql_query runs the sql query and returns the data as a dataframe\n",
    "df = pd.read_sql(QUERY,conn)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo: How to list all tables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print all the tables names in the database\n",
    "QUERY = \"\"\"\n",
    "SELECT name as Table_Name FROM\n",
    "sqlite_master WHERE\n",
    "type = 'table'\n",
    "\"\"\"\n",
    "# the read_sql_query runs the sql query and returns the data as a dataframe\n",
    "pd.read_sql(QUERY,conn)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo: How to run a group by query\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": 40,
     "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": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CREATE TABLE \"master\" (\n",
      "\"index\" INTEGER,\n",
      "  \"Respondent\" INTEGER,\n",
      "  \"MainBranch\" TEXT,\n",
      "  \"Hobbyist\" TEXT,\n",
      "  \"OpenSourcer\" TEXT,\n",
      "  \"OpenSource\" TEXT,\n",
      "  \"Employment\" TEXT,\n",
      "  \"Country\" TEXT,\n",
      "  \"Student\" TEXT,\n",
      "  \"EdLevel\" TEXT,\n",
      "  \"UndergradMajor\" TEXT,\n",
      "  \"OrgSize\" TEXT,\n",
      "  \"YearsCode\" TEXT,\n",
      "  \"Age1stCode\" TEXT,\n",
      "  \"YearsCodePro\" TEXT,\n",
      "  \"CareerSat\" TEXT,\n",
      "  \"JobSat\" TEXT,\n",
      "  \"MgrIdiot\" TEXT,\n",
      "  \"MgrMoney\" TEXT,\n",
      "  \"MgrWant\" TEXT,\n",
      "  \"JobSeek\" TEXT,\n",
      "  \"LastHireDate\" TEXT,\n",
      "  \"FizzBuzz\" TEXT,\n",
      "  \"ResumeUpdate\" TEXT,\n",
      "  \"CurrencySymbol\" TEXT,\n",
      "  \"CurrencyDesc\" TEXT,\n",
      "  \"CompTotal\" REAL,\n",
      "  \"CompFreq\" TEXT,\n",
      "  \"ConvertedComp\" REAL,\n",
      "  \"WorkWeekHrs\" REAL,\n",
      "  \"WorkRemote\" TEXT,\n",
      "  \"WorkLoc\" TEXT,\n",
      "  \"ImpSyn\" TEXT,\n",
      "  \"CodeRev\" TEXT,\n",
      "  \"CodeRevHrs\" REAL,\n",
      "  \"UnitTests\" TEXT,\n",
      "  \"PurchaseHow\" TEXT,\n",
      "  \"PurchaseWhat\" TEXT,\n",
      "  \"OpSys\" TEXT,\n",
      "  \"BlockchainOrg\" TEXT,\n",
      "  \"BlockchainIs\" TEXT,\n",
      "  \"BetterLife\" TEXT,\n",
      "  \"ITperson\" TEXT,\n",
      "  \"OffOn\" TEXT,\n",
      "  \"SocialMedia\" TEXT,\n",
      "  \"Extraversion\" TEXT,\n",
      "  \"ScreenName\" TEXT,\n",
      "  \"SOVisit1st\" TEXT,\n",
      "  \"SOVisitFreq\" TEXT,\n",
      "  \"SOFindAnswer\" TEXT,\n",
      "  \"SOTimeSaved\" TEXT,\n",
      "  \"SOHowMuchTime\" TEXT,\n",
      "  \"SOAccount\" TEXT,\n",
      "  \"SOPartFreq\" TEXT,\n",
      "  \"SOJobs\" TEXT,\n",
      "  \"EntTeams\" TEXT,\n",
      "  \"SOComm\" TEXT,\n",
      "  \"WelcomeChange\" TEXT,\n",
      "  \"Age\" REAL,\n",
      "  \"Trans\" TEXT,\n",
      "  \"Dependents\" TEXT,\n",
      "  \"SurveyLength\" TEXT,\n",
      "  \"SurveyEase\" TEXT\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "table_name = 'master'  # the table you wish to describe\n",
    "\n",
    "QUERY = \"\"\"\n",
    "SELECT sql FROM sqlite_master\n",
    "WHERE name= '{}'\n",
    "\"\"\".format(table_name)\n",
    "\n",
    "df = pd.read_sql_query(QUERY,conn)\n",
    "print(df.iat[0,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hands-on Lab\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import matplot library\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "# %matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing distribution of data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot a histogram of `ConvertedComp.`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Converted Comp histogram')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "# df.head()\n",
    "Query = \"\"\"Select * From master\"\"\"\n",
    "\n",
    "df1 = pd.read_sql_query(Query, conn)\n",
    "df1.head()\n",
    "plt.hist(df1[\"ConvertedComp\"])\n",
    "plt.title(\"Converted Comp histogram\")\n",
    "# plt.xlim(0,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Box Plots\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot a box plot of `Age.`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Boxplot for Age')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df1.boxplot(column = \"Age\", grid = False, vert=False)\n",
    "plt.title(\"Boxplot for Age\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing relationships in data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Plots\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a scatter plot of `Age` and `WorkWeekHrs.`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Age vs Work week Hours')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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L73//++K9994TAMRbb70V99ovv/xSXHXVVSIzM1Po9XqRnZ0tLrjgAu1qtuOJRCIiPz+/1VAihBCPP/64mDBhgnA4HMJgMIj+/fuLW265RRw8eLBd+4/1W1EU4XQ6455raGgQsizHjT4c6a9//asYNWqUMBgMwm63i8suu6zFqFRHApEQQnz11VciOztbjB079rijFm+99ZYAIC688MK47T/96U8FALF06dIWrwmFQuL3v/+9OOOMM4TJZBJJSUmiqKhIzJ49W+zduzeu7ZtvvimmTp0qbDabMBqNYsCAAeLKK6/URqPaOr6KigpRVFQkBg4cKL7++us2+9/RQCSEEPX19eJnP/uZyMnJETqdTgwYMEDce++9wu/3x7VzOp3iJz/5icjKyhJWq1V873vfEwcPHmx3IDp8+LD40Y9+JIqKioTVahVJSUli1KhR4oknnhDhcLjNYyLqbJIQQnT9xBwRdZaFCxfi/vvvR1lZGfLy8nq6O0REpwQWVRP1YsuWLQMAFBUVIRQK4cMPP8TSpUtx/fXXMwwREXUiBiKiXsxiseCJJ57AwYMHEQgE0L9/fyxYsAD3339/T3eNiOiUwikzIiIiSnhcmJGIiIgSHgMRERERJTwGIiIiIkp4LKpuJ1VVUVlZieTk5GPeHoCIiIh6DyEE3G43cnNzIcttjwMxELVTZWUl8vPze7obREREdALKy8uPuVwJA1E7xW7GWF5e3ubdtImIiKh3cblcyM/P177H28JA1E6xaTKbzcZARERE1Mccr9yFRdVERESU8BiIiIiIKOExEBEREVHCYyAiIiKihMdARERERAmPgYiIiIgSHgMRERERJTwGIiIiIkp4DERERESU8LhSNVEHqEJFmbMM7oAbycZk9Lf3hyzx7woior6OgYionUpqS7CydCVK60rhD/th0plQ5CjC5UWXY2jG0J7uHhERnQQGIqJ2KKktwdJNS1HnrUO+PR9WvRWekAfbqrah3FmOOePnMBQREfVhHOsnOg5VqFhZuhJ13joMyxgGm9EGRVZgM9owLGMY6rx1eLP0TahC7emuEhHRCWIgIjqOMmcZSutKkW/Pb3G3ZEmSkGfLQ0ldCcqcZT3UQyIiOlk9Gog++ugjfO9730Nubi4kScKbb74Z97wQAg888AByc3NhNpsxZcoU7Nq1K65NIBDAHXfcAYfDAavViksvvRQVFRVxbRobG3HDDTfAbrfDbrfjhhtuQFNTUxcfHZ0q3AE3/GE/rHprq89bDVb4w364A+5u7hkREXWWHg1EHo8HZ5xxBpYtW9bq84sXL8aSJUuwbNkyfP7558jOzsaFF14It/vbL565c+di5cqVeOWVV7BhwwY0Nzdj5syZiEQiWptrr70W27dvx/vvv4/3338f27dvxw033NDlx0enhmRjMkw6EzwhT6vPe4IemHQmJBuTu7lnRETUWSQhhOjpTgDRqYeVK1di1qxZAKKjQ7m5uZg7dy4WLFgAIDoalJWVhUcffRSzZ8+G0+lERkYGXnjhBVx99dUAgMrKSuTn5+O9997DRRddhJKSEgwbNgyfffYZxo8fDwD47LPPcO6556K0tBSFhYXt6p/L5YLdbofT6YTNZuv8D4B6LVWoeGTDI9hWtQ3DMobFTZsJIVBcW4yxOWOxYNICXoJPRNTLtPf7u9f+1/vAgQOorq7GjBkztG1GoxGTJ0/Gxo0bAQBbtmxBKBSKa5Obm4sRI0ZobT799FPY7XYtDAHAOeecA7vdrrVpTSAQgMvlivuhxCRLMi4vuhwOiwPFtcVw+p0Iq2E4/U4U1xbDYXFgVtEshiEioj6s1/4XvLq6GgCQlZUVtz0rK0t7rrq6GgaDAampqcdsk5mZ2WL/mZmZWpvWLFq0SKs5stvtyM/PP6njob5taMZQzBk/B2NyxqDeV4899XtQ76vH2JyxvOSeiOgU0OvXITr6qh4hRIttRzu6TWvtj7efe++9F/PmzdMeu1wuhqIENzRjKAodhVypmojoFNRrA1F2djaA6AhPTk6Otr2mpkYbNcrOzkYwGERjY2PcKFFNTQ0mTJigtTl8+HCL/dfW1rYYfTqS0WiE0WjslGOhU4csyRiYMrCnu0FERJ2s1/5pO2jQIGRnZ2P16tXatmAwiPXr12thZ9y4cdDr9XFtqqqq8NVXX2ltzj33XDidTmzevFlrs2nTJjidTq0NERERJbYeHSFqbm7Gvn37tMcHDhzA9u3bkZaWhv79+2Pu3LlYuHAhCgoKUFBQgIULF8JiseDaa68FANjtdtxyyy2YP38+0tPTkZaWhrvvvhsjR47E9OnTAQBDhw7FxRdfjJ/+9Kf485//DAD4v//7P8ycObPdV5gRERHRqa1HA9EXX3yBqVOnao9jNTs33XQTnn/+edxzzz3w+Xy49dZb0djYiPHjx2PVqlVITv52vZcnnngCOp0OV111FXw+H6ZNm4bnn38eiqJobf75z39izpw52tVol156aZtrHxEREVHi6TXrEPV2XIeIiIio7+nz6xARERERdRcGIiIiIkp4DERERESU8BiIiIiIKOExEBEREVHCYyAiIiKihMdARERERAmPgYiIiIgSHgMRERERJTwGIiIiIkp4DERERESU8BiIiIiIKOExEBEREVHCYyAiIiKihMdARERERAmPgYiIiIgSHgMRERERJTwGIiIiIkp4DERERESU8BiIiIiIKOExEBEREVHCYyAiIiKihMdARERERAmPgYiIiIgSHgMRERERJTwGIiIiIkp4DERERESU8BiIiIiIKOExEBEREVHCYyAiIiKihMdARERERAmPgYiIiIgSHgMRERERJTwGIiIiIkp4DERERESU8BiIiIiIKOExEBEREVHCYyAiIiKihMdARERERAmPgYiIiIgSHgMRERERJTwGIiIiIkp4DERERESU8BiIiIiIKOExEBEREVHCYyAiIiKihMdARERERAmPgYiIiIgSHgMRERERJTwGIiIiIkp4DERERESU8BiIiIiIKOExEBEREVHCYyAiIiKihMdARERERAmvVweicDiM+++/H4MGDYLZbMbgwYPx4IMPQlVVrY0QAg888AByc3NhNpsxZcoU7Nq1K24/gUAAd9xxBxwOB6xWKy699FJUVFR09+EQERFRL9WrA9Gjjz6KZ555BsuWLUNJSQkWL16Mxx57DE899ZTWZvHixViyZAmWLVuGzz//HNnZ2bjwwgvhdru1NnPnzsXKlSvxyiuvYMOGDWhubsbMmTMRiUR64rCIiIiol5GEEKKnO9GWmTNnIisrC8uXL9e2ff/734fFYsELL7wAIQRyc3Mxd+5cLFiwAEB0NCgrKwuPPvooZs+eDafTiYyMDLzwwgu4+uqrAQCVlZXIz8/He++9h4suuqhdfXG5XLDb7XA6nbDZbJ1/sERERNTp2vv93atHiCZNmoT//ve/2LNnDwDgyy+/xIYNG3DJJZcAAA4cOIDq6mrMmDFDe43RaMTkyZOxceNGAMCWLVsQCoXi2uTm5mLEiBFaGyIiIkpsup7uwLEsWLAATqcTRUVFUBQFkUgEDz/8MK655hoAQHV1NQAgKysr7nVZWVn45ptvtDYGgwGpqakt2sRe35pAIIBAIKA9drlcnXJMRERE1Pv06hGiV199FS+++CJeeuklbN26FX//+9/x+9//Hn//+9/j2kmSFPdYCNFi29GO12bRokWw2+3aT35+/okfCBEREfVqvToQ/eIXv8Avf/lL/PCHP8TIkSNxww034K677sKiRYsAANnZ2QDQYqSnpqZGGzXKzs5GMBhEY2Njm21ac++998LpdGo/5eXlnXloRERE1Iv06kDk9Xohy/FdVBRFu+x+0KBByM7OxurVq7Xng8Eg1q9fjwkTJgAAxo0bB71eH9emqqoKX331ldamNUajETabLe6HiIiITk29uoboe9/7Hh5++GH0798fw4cPx7Zt27BkyRLcfPPNAKJTZXPnzsXChQtRUFCAgoICLFy4EBaLBddeey0AwG6345ZbbsH8+fORnp6OtLQ03H333Rg5ciSmT5/ek4dHREREvUSvDkRPPfUUfv3rX+PWW29FTU0NcnNzMXv2bPzmN7/R2txzzz3w+Xy49dZb0djYiPHjx2PVqlVITk7W2jzxxBPQ6XS46qqr4PP5MG3aNDz//PNQFKUnDouIiIh6mV69DlFvwnWIiIiI+p5TYh0iIiIiou7AQEREREQJj4GIiIiIEh4DERERESU8BiIiIiJKeAxERERElPAYiIiIiCjhMRARERFRwmMgIiIiooTHQEREREQJj4GIiIiIEh4DERERESU8BiIiIiJKeAxERERElPAYiIiIiCjhMRARERFRwmMgIiIiooTHQEREREQJj4GIiIiIEh4DERERESU8BiIiIiJKeAxERERElPAYiIiIiCjhMRARERFRwmMgIiIiooTHQEREREQJj4GIiIiIEh4DERERESU8BiIiIiJKeAxERERElPAYiIiIiCjhdTgQvf/++9iwYYP2+I9//CNGjx6Na6+9Fo2NjZ3aOSIiIqLu0OFA9Itf/AIulwsAsHPnTsyfPx+XXHIJ9u/fj3nz5nV6B4mIiIi6mq6jLzhw4ACGDRsGAHjjjTcwc+ZMLFy4EFu3bsUll1zS6R0kIiIi6modHiEyGAzwer0AgDVr1mDGjBkAgLS0NG3kiIiIiKgv6fAI0cSJEzFv3jxMnDgRmzdvxquvvgoA2LNnD/Ly8jq9g0RERERdrcMjRH/84x+h1+vx+uuv4+mnn0a/fv0AAP/5z39w8cUXd3oHiYiIiLqaJIQQ7W0cDofxz3/+EzNmzEBOTk5X9qvXcblcsNvtcDqdsNlsPd0dIiIiaof2fn93aIRIp9Ph5z//OYLB4El3kIiIiKi36PCU2fjx47Ft27au6AsRERFRj+hwUfWtt96K+fPno6KiAuPGjYPVao17ftSoUZ3WOSIiIqLu0KEaIgCQ5ZaDSpIkQQgBSZIQiUQ6rXO9CWuIiIiI+p72fn+f0MKMRERERKeSDgeiAQMGdEU/iIiIiHpMhwPRP/7xj2M+f+ONN55wZ4iIiIh6QodriFJTU+Meh0IheL1eGAwGWCwWNDQ0dGoHewvWEBEREfU9XbIOEQA0NjbG/TQ3N2P37t2YNGkSXn755ZPqNBEREVFP6HAgak1BQQEeeeQR3HnnnZ2xOyIiIqJu1SmBCAAURUFlZWVn7Y6IiIio23S4qPrtt9+OeyyEQFVVFZYtW4aJEyd2WseIiIiIukuHA9GsWbPiHkuShIyMDFxwwQV4/PHHO6tfRERERN2mw4FIVdWu6AcRERFRjzmpGiIhBDp41T4RERFRr3NCgegf//gHRo4cCbPZDLPZjFGjRuGFF17o7L4RERERdYsOT5ktWbIEv/71r3H77bdj4sSJEELgk08+wc9+9jPU1dXhrrvu6op+EhEREXWZDq9UPWjQIPzud79rcYuOv//973jggQdO2Zu/cqVqIiKivqfLVqquqqrChAkTWmyfMGECqqqqOrq74zp06BCuv/56pKenw2KxYPTo0diyZYv2vBACDzzwAHJzc2E2mzFlyhTs2rUrbh+BQAB33HEHHA4HrFYrLr30UlRUVHR6X4mIiKhv6nAgGjJkCF577bUW21999VUUFBR0SqdiGhsbMXHiROj1evznP/9BcXExHn/8caSkpGhtFi9ejCVLlmDZsmX4/PPPkZ2djQsvvBBut1trM3fuXKxcuRKvvPIKNmzYgObmZsycORORSKRT+0tERER9U4enzN544w1cffXVmD59OiZOnAhJkrBhwwb897//xWuvvYbLL7+80zr3y1/+Ep988gk+/vjjVp8XQiA3Nxdz587FggULAERHg7KysvDoo49i9uzZcDqdyMjIwAsvvICrr74aAFBZWYn8/Hy89957uOiii9rVF06ZERER9T1dNmX2/e9/H5s2bYLD4cCbb76JFStWwOFwYPPmzZ0ahoDoqthnnnkmfvCDHyAzMxNjxozBX/7yF+35AwcOoLq6GjNmzNC2GY1GTJ48GRs3bgQAbNmyBaFQKK5Nbm4uRowYobVpTSAQgMvlivshIiKiU1OHrzIDgHHjxuHFF1/s7L60sH//fjz99NOYN28efvWrX2Hz5s2YM2cOjEYjbrzxRlRXVwMAsrKy4l6XlZWFb775BgBQXV0Ng8GA1NTUFm1ir2/NokWL8Lvf/a6Tj4iIiIh6o3YHovaOkHTmdJKqqjjzzDOxcOFCAMCYMWOwa9cuPP3003FXuUmSFPc6IUSLbUc7Xpt7770X8+bN0x67XC7k5+efyGEQERFRL9fuQJSSknLMABELGJ1ZqJyTk4Nhw4bFbRs6dCjeeOMNAEB2djaA6ChQTk6O1qampkYbNcrOzkYwGERjY2PcKFFNTU2rV8vFGI1GGI3GTjsWIiIi6r3aHYjWrl2r/f9CCFxyySX461//in79+nVJxwBg4sSJ2L17d9y2PXv2YMCAAQCiayJlZ2dj9erVGDNmDAAgGAxi/fr1ePTRRwFEp/f0ej1Wr16Nq666CkB06YCvvvoKixcv7rK+ExERUd/R7kA0efLkuMeKouCcc87B4MGDO71TMXfddRcmTJiAhQsX4qqrrsLmzZvx7LPP4tlnnwUQnSqbO3cuFi5ciIKCAhQUFGDhwoWwWCy49tprAQB2ux233HIL5s+fj/T0dKSlpeHuu+/GyJEjMX369C7rOxEREfUdJ1RU3V3OOussrFy5Evfeey8efPBBDBo0CE8++SSuu+46rc0999wDn8+HW2+9FY2NjRg/fjxWrVqF5ORkrc0TTzwBnU6Hq666Cj6fD9OmTcPzzz8PRVF64rCIiIiol+nwOkQxycnJ+PLLL7t0hKg34TpEREREfU+XrUN0pONdyUVERETUF7R7yuyKK66Ie+z3+/Gzn/0MVqs1bvuKFSs6p2dERERE3aTdgchut8c9vv766zu9M0REREQ9od2B6LnnnuvKfhARERH1mJOqISIiIiI6FTAQERERUcJjICIiIqKEx0BERERECa/DgaipqanN5/bt23cyfSEiIiLqER0ORJdccgn8fn+L7bt378aUKVM6o09ERERE3arDgSg1NRWzZs1COBzWtpWUlGDKlCn4/ve/36mdIyIiIuoOHQ5Eb7zxBjweD6699loIIfDVV19hypQpuOaaa/CHP/yhK/pIRERE1KU6HIhMJhPeeecd7N27Fz/4wQ8wbdo03HjjjViyZElX9I+IiIioy7VrpWqXyxX3WJIkvPrqq5g+fTq+//3v49e//rXWhneCJyIior5GEkKI4zWSZbnVO9vHXipJEoQQkCQJkUik83vZC7hcLtjtdjidToY+IiKiPqK939/tGiFau3Ztp3WMiIiIqLdpVyCaPHlyV/eDiIiIqMec0ErVH3/8Ma6//npMmDABhw4dAgC88MIL2LBhQ6d2joiIiKg7nNBl9xdddBHMZjO2bt2KQCAAAHC73Vi4cGGnd5CIiIioq3U4ED300EN45pln8Je//AV6vV7bPmHCBGzdurVTO0dERETUHTociHbv3o3zzz+/xXabzXbM+5wRERER9VYdDkQ5OTmt3sR1w4YNGDx4cKd0ioiIiKg7dTgQzZ49G3feeSc2bdoESZJQWVmJf/7zn7j77rtx6623dkUfiYiIiLpUuy67P9I999wDp9OJqVOnwu/34/zzz4fRaMTdd9+N22+/vSv6SERERNSl2rVSdWu8Xi+Ki4uhqiqGDRuGpKSkzu5br8KVqomIiPqe9n5/n9A6RABQWVmJ+vp6jBw5EklJSTjBXEVERETU4zociOrr6zFt2jScfvrpuOSSS1BVVQUA+MlPfoL58+d3egeJiIiIulqHA9Fdd90FvV6PsrIyWCwWbfvVV1+N999/v1M7R0RERNQdOlxUvWrVKnzwwQfIy8uL215QUIBvvvmm0zpGRERE1F06PELk8XjiRoZi6urqYDQaO6VTRERERN2pw4Ho/PPPxz/+8Q/tsSRJUFUVjz32GKZOndqpnaO+SxUqDjYdxM7DO3Gw6SBUofZ0l4iIiNrU4Smzxx57DFOmTMEXX3yBYDCIe+65B7t27UJDQwM++eSTrugj9TEltSVYWboSpXWl8If9MOlMKHIU4fKiyzE0Y2hPd4+IiKiFDo8QDRs2DF9++SXOOussXHjhhfB4PLjiiiuwbds2nHbaaV3RR+pDSmpLsHTTUmyr2gaHxYHC9EI4LA5sq9qGpZuWoqS2pKe7SERE1EK7R4gmTZqECy64AFOmTMGECRPw4IMPdmW/qA9ShYqVpStR563DsIxhkCQJAGAz2jAsYxiKa4vxZumbKHQUQpZOeAksIiKiTtfub6XCwkK89NJLmD59OlJTUzFlyhT8v//3/7BhwwaEQqGu7CP1EWXOMpTWlSLfnq+FoRhJkpBny0NJXQnKnGU91EMiIqLWtTsQLV++HPv27UNZWRn+/Oc/47TTTsPzzz+P888/HykpKbjwwguxaNGiruwr9XLugBv+sB9WvbXV560GK/xhP9wBdzf3jIiI6Ng6PG+Rl5eHG2+8EcuXL8fXX3+Nb775BnfddRc2b96M+++/vyv6SH1EsjEZJp0JnpCn1ec9QQ9MOhOSjcnd3DMiIqJj6/BVZgDw9ddfY926ddpPU1MTzj33XEyePLmz+0d9SH97fxQ5irCtaltcDREACCFQ4arA2Jyx6G/v34O9JCIiaqndgei5557D2rVrsW7dOjidTkycOBGTJ0/GbbfdhjPPPBM63QllKzqFyJKMy4suR7mzHMW1xciz5cFqsMIT9KDCVQGHxYFZRbNYUE1ERL2OJNp5m3pZltG/f3/ce++9uPnmm6HX67u6b72Ky+WC3W6H0+mEzWbr6e70aq2tQzTUMRSzimZxHSIiIupW7f3+bncgevrpp7F+/XqsX78efr8fkyZNwpQpUzB58mSMGzeuxVVFpxoGoo5RhYoyZxncATeSjcnob+/PkSEiIup2nR6IjlRcXIz169dj3bp1WkCaOHEipk6dirvvvvukOt5bMRARERH1PV0aiI5UWVmJP/3pT3jqqafQ3NyMSCRyMrvrtRiIiIiI+p72fn93uBL68OHDcVeY7dmzBwaDAePHj+fNXYmIiKhPancguu2227B27Vrs3r0bOp0OZ511Fq688kpMnToVEyZMgMlk6sp+EhEREXWZdgeirVu3YtasWZg6dSomTpwIi8XSlf0iIiIi6jbtDkSffvppV/aD6ITxijYiIjpZXE2R+rTW1jwqchTh8qLLueYRERG1GwMR9VkltSVYumkp6rx1yLfnw6q3whPyYFvVNpQ7yzFn/ByGIiIiahfOK1CfpAoVK0tXos5bh2EZw2Az2qDICmxGG4ZlDEOdtw5vlr4JVag93VUiIuoDOjUQneSSRkTtVuYsQ2ldKfLt+S1WSZckCXm2PJTUlaDMWdZDPSQior6kw4Fo0aJFrW6PRCK49tprT7pDRO3hDrjhD/th1Vtbfd5qsMIf9sMdcHdzz4iIqC/qcCB68skn8eyzz8Zti0Qi+OEPf4jt27d3Vr+IjinZmAyTzgRPyNPq856gByadCcnG5G7uGRER9UUdDkTvvfceFixYgNdeew0AEAqF8IMf/AC7du3C2rVrO72DRK3pb++PIkcRyp3lLaZqhRCocFVgqGMo+tv791APiYioL+nwVWbjxo3DypUrcdlll8FoNGL58uX4+uuvsXbtWmRlZXVFH4lakCUZlxddjnJnOYpri5Fny4PVYIUn6EGFqwIOiwOzimZxPSIiImqXE/q2mDJlCl544QVceeWVOHjwINavX98tYWjRokWQJAlz587Vtgkh8MADDyA3NxdmsxlTpkzBrl274l4XCARwxx13wOFwwGq14tJLL0VFRUWX95e61tCMoZgzfg7G5IxBva8ee+r3oN5Xj7E5Y3nJPRERdUi7RoiuuOKKVrdnZGQgJSUF//d//6dtW7FiRef07Ciff/45nn32WYwaNSpu++LFi7FkyRI8//zzOP300/HQQw/hwgsvxO7du5GcHK0fmTt3Lv7973/jlVdeQXp6OubPn4+ZM2diy5YtUBSlS/pL3WNoxlAUOgq5UjUREZ2UdgUiu93e6vaLLrqoUzvTlubmZlx33XX4y1/+goceekjbLoTAk08+ifvuu08LbX//+9+RlZWFl156CbNnz4bT6cTy5cvxwgsvYPr06QCAF198Efn5+VizZk23HQN1HVmSMTBlYE93g4iI+rB2BaLnnnsOQDSAlJWVISMjo1tv7nrbbbfhu9/9LqZPnx4XiA4cOIDq6mrMmDFD22Y0GjF58mRs3LgRs2fPxpYtWxAKheLa5ObmYsSIEdi4cSMDEREREXWsqFoIgYKCAuzatQsFBQVd1ac4r7zyCrZs2YIvvviixXPV1dUA0KJ+KSsrC998843WxmAwIDU1tUWb2OtbEwgEEAgEtMcul+uEj4GIiIh6tw4FIlmWUVBQgPr6+m4JROXl5bjzzjuxatUqmEymNtsdvVKxEKLFtqMdr82iRYvwu9/9rmMd7iW66u7vJ7vftl7f3v12xnF11WfT0+91svpSX4mIukKHL7tfvHgxfvGLX+Dpp5/GiBEjuqJPmi1btqCmpgbjxo3TtkUiEXz00UdYtmwZdu/eDSA6CpSTk6O1qamp0UaNsrOzEQwG0djYGDdKVFNTgwkTJrT53vfeey/mzZunPXa5XMjPz++0Y+sqXXX395Pdb1uvH5U5Cjtqdhx3v51xXF312fT0e52svtRXIqKuIokO3oAsNTUVXq8X4XAYBoMBZrM57vmGhoZO65zb7damvmJ+/OMfo6ioCAsWLMDw4cORm5uLu+66C/fccw8AIBgMIjMzE48++qhWVJ2RkYEXX3wRV111FQCgqqoKeXl5eO+999pdQ+RyuWC32+F0OmGz2TrtGDtTW3d/L3eWw2FxnPCl6Ce737Zev6tmFypcFciz5WF45vA299sZx9VVn01Pv9fJ6kt9JSI6Ee39/u7wCNGTTz55Mv3qkOTk5BajUFarFenp6dr2uXPnYuHChSgoKEBBQQEWLlwIi8Wi3VfNbrfjlltuwfz585Geno60tDTcfffdGDlypHbV2ang6Lu/x6YDY3d/L64txpulb6LQURg3FdLaVAkAbZvVYMUbJW+gzluHoY6hcAVdqPPWwagzYqhjKErqSlrdrz/sx/Kty3Gg8QB21+2GTtJhWOYwbKvehqZAE+wGOwLhAJwBJzLCGdr72Yw2FKYXorSuFM9tew7XjLwGL+18CbWeWhSmFWLb4ejrU4wpGJM1Bnsa92BFyQpco7sGnqBHOwZVqPis4jMcbj6MDGsGPvrmow4fw4lMIx15HjryXt0pdlxOvxMv7ngRtZ5aDM8c3urvTGufLafSiOhU1OFAdNNNN3VFP07YPffcA5/Ph1tvvRWNjY0YP348Vq1apa1BBABPPPEEdDodrrrqKvh8PkybNg3PP//8KbUGUUfu/h67RL21qZI0cxoAoMHXAH/Yj7AaxoHGA+if0h+flH+COm8dwmoYOlkHh8XR6n5/8+FvsOzzZXAFXFCFCgEBGTL+vfffUBF9DAAyZFj1Vmys2AghRLSdJMOsMyPdnI6t1VvxedXn+KbpG4QiIfx7z78RUkMQEJAgYe3BtShIK8C+hn3YUrUFOlkHky5aa7avYR+qm6sRioQAAGERxlDH0FaPoZ+tX7s+m/ZMI8XOg0Vvafd7dacjj6veW4+SuhLkJOcgy5uFDGuG1k6SJFj0Frxe/HrcZ8upNCI6VXU4EAHROp4333wTJSUlkCQJw4YNw6WXXtotAWPdunVxjyVJwgMPPIAHHnigzdeYTCY89dRTeOqpp7q2cz2oPXd/P+Q+pN39vbWpkjJnGd7e/TYkSJg0YBIK0wtxsOkgqtxVqHBVwGa0IdOaCb2iRygSQlVzFRr9jci0Zmr7/c2Hv8GjGx9FOBKGQTFAggRfxAcVqtYXvaRHRESgQoU7FH2dSTFBL+sRUkNwB91oDjYjzZKGdHM6vqz+Eo3+RgDREKWTdFCFCn/Ej521O5FsSMaIzBEYmDIQOw/vxIcHP0RERDDAPgC5ybmo99Zjf+N+bCzbiAxrBvJsecc8hramkbZVbUO5s/yY00jugBs1nhrUeGrgD/thN9qP+V7d6ejjMikm7K3fiwZvAzYd2oTx/cZroajWU4udNTtR563TPtv2fgZERH1RhwPRvn37cMkll+DQoUMoLCyEEAJ79uxBfn4+3n33XZx22mld0U86jiPv/m4ztpwjPfLu761Nr8VuiGpUjJAkCRXOCgy0D9RGjHxhH6x6azTkSBKMOiMylAxUuishhIDVYIU/7Meyz5chHAnDqrdClmVE1AgQie+LENERniPpZF20H/8bTVKhwuV3wagY4fQ7AQASJEhS9EeRFAhVQIWK5mAz7CY7JEnCzpqdEELArDPDF/JBsSiwGW3QKTr4Q340B5uPeQwnOvUYYzVYcbj5MJqDzchNztVe39p7dafWjksVKsx6M8w6M1xBF0rrSuGwOAAApXWlcAfcSDWlIt2SDkVW2v0ZEBH1RR3+r9mcOXNw2mmnoby8HFu3bsW2bdtQVlaGQYMGYc6cOV3RR2qHjtz9vbXpNWfAiTpvHVJMKbAZbajz1sEZiAYRSNHA4gv7EIgEjn5rzfKty+EKuGBQDJDl6K9WWA23aBebMjtSWA1DQCAiIpBlGRIkhNUwdtXsihtdOvLYYvsREPi64WtUuCrQ4GtAkiEJJp0J3pBX668ECTpZB2/IC1eg7TWlOjL1eCzHW/ahu7V2XHajHQ6LA66gC8mGZO2cOwNO1HprAQAZ1gzYjd+uVN+Rz4CIqC/p8AjR+vXr8dlnnyEtLU3blp6ejkceeQQTJ07s1M5R+3Xk7u+tTa8FwgGE1TD0ih5AdOonEI6GiSR9EiAAb9gLb8gLg2JAMBKEK+BCsjEZmdZMeIIeHGw6CCEEdPK3v1aqUHG01gJRRESgCCWulkgIgSZ/E4BooIm9LrbPI/fjDDiRbExGRESgV/SQICEgAlog08vR4/KH/VqBcOwYbEYbMqwZ8AQ9Wpv2Tj0ezRP0ICspC7WeWtR6a2Ez2uI+r6Pfq7u0ds4lSUKRo0gLQapQ4Q154Qv50OBrQKY1E0WOohbh7nifARFRX9ThQGQ0GuF2t/wPYXNzMwwGQ6d0ik5M7O7vsaLZQ+5DMOlMGJszFrOKZmk1H61Nrxl1RuhkHUKRaNGyTtbBqDMCiH4B6hU94AdCkRDqvfXQyTrkJOWgn62fts+BKQMhSdGRHUWO1pO1usAiVMitDE5G1OjcmiIp2pROqjkVZa6y6HQZpGhRtvi2KDsWilJMKUgyJEGRFIQiISiyAlmStXBmUKK/m2E5DBVq3DHk2fIgIJBsjBbit3fqsTWxgJiVlIUKZwXqvHVwB9xtvld3aWtKNcOagfH9xmN79XZUNVfhkOtQdHrPkoGRmSPjCq1jjvcZEBH1RR0ORDNnzsT//d//Yfny5Tj77LMBAJs2bcLPfvYzXHrppZ3eQeqYtu7+DgAHmw5ql9IXphdie/V2rZ4kNn1S6a6EJEnIScrRpkrSLenYU78HwxzDMDJrJIKRIIw6I2wGG0rqSjA2Zyz62/vjlrG34Ndrfw2n3wm9rIcsyzAoBvgj/rg+mmUzJEmCN+LVtmVYMqAKFZ6QB6FICCE1BJvBhouHXBy90ivihyIpMEgG6GU9JElCMBJEUA3CKBsxNmcsZElGmjkNNZ4aGHVGJBuSYVSioc6sN8MVcCHHmoNLCi5BKBJq9RgAoMhRhG1V2+JqiIBvpx6PbHu02NTltqptmJg/Ea6gC4FwoM336i5H9uvo43JYHMhJzsHE/hNx3cjrkGxMxss7X8b26u0tVnRvz2dARNQXdTgQLV26FDfddBPOPfdc6PXRaYhwOIxLL70Uf/jDHzq9g9RxR9/9va3L6xVZiZte62frhwNNByAhWicSERF4gh7oZb12tZQsyUi3pMMT9KCkriRuKs6kM+H2s27HoxsfhSfkgV7RQy/roUBB5H+V1QoUKIqCkBrS+qdICoKRIEw6ExRJgVf1QpZkjM4eDUVWMCprFD6v/DxaXyTJkCQJITUammRJxuDUwfAEPbAarBiROQJrD66FP+xHhiVDOwZfyAez3oxcWy4USYHNYmv1GAC0e+qxrc8+9vqSuhLk2fLa/Ly60/GmVDMsGfjx6B9ro4hXDL0CFa6KE/oMiIj6onavVL1v3z4MGTIk7nFxcTEAYNiwYXHPnYr6wkrVrTnWSsSKpCA7OVtbc8ikMyHdnA4BEbdtqGMoRmaObHGLjaGOoXFTcTFHrkMUG2GI1fCE1JC2zW60Y3y/8djTsAfVzdWIqBEosoJ0czpGZ4+G3WTX3qvCWYEtVVvgCXniXn/V8KuQb8+P65cECXsb9mrrEOkVPQrSCjCrcBYaA43tOobWQmRbbdv63E/m9V2lI/3qrcdARNQR7f3+bncgkmUZ/fr1w9SpU3HBBRdgypQpGDhwYGf1t9fri4FIFSoe2fBIm9M/xbXFGJ09GteMjF+JGMBJ3YQV+Hal6oNNBzEwZSBuGXsLALTYZtKZEIwE8VbpWyh3lSPflo/Lii6DTta1eK9gJNjq61vr15ErVWclZeGcvHOgk3UdOoauupltT+vOz4CIqKd1eiD6+OOPsX79eqxbtw6ffvop/H4/+vfvjwsuuABTp07F1KlT0a9fv047gN6mLwaig00H8Zu1v4HD4mi1QNjpd6LeV48Hpz7YI6smExERdbVOv5fZeeedh/POOw/3338/QqEQPv30U6xbtw7r1q3Dyy+/jEAggCFDhmh3oKee19HVq4mIiBLVCd26Q6/X4/zzz8dZZ52Fc889Fx988AH+8pe/YN++fZ3dPzoJHVm9moiIKJF1qBjA7/fjww8/xK9//Wucd955SE1NxZw5c9Dc3Iynn34aZWVcubY36cjq1URERIms3SNEkydPxueff47TTjsN559/Pu644w5MnjwZWVlZXdk/OgkdWb2aiIgokbW7qFqv1yMnJwezZs3ClClTcP7558PhcHR1/3qNvlhUHcPLp4mIKFF1+lVmHo8HH3/8MdatW4e1a9di+/btOP300zF58mRMmTIFkydPRkZGy2X+TxV9ORABvHyaiIgSU6cHoqO53W5s2LABa9euxbp16/Dll1+ioKAAX3311Ql3ujfr64GIiIgoEbX3+/uEhwisVivS0tKQlpaG1NRU6HQ6lJSUnOjuiIiIiHpMu4uqVVXFF198oU2ZffLJJ/B4PNrq1X/84x8xderUruwrERERUZdodyBKSUmBx+NBTk4OpkyZgiVLlmDq1Kk47bTTurJ/RERERF2u3YHosccew9SpU3H66ad3ZX+IiIiIul27A9Hs2bO7sh9EREREPYbXXRMREVHCYyAiIiKihMdARERERAmPgYiIiIgSHgMRERERJTwGIiIiIkp4DERERESU8BiIiIiIKOExEBEREVHCYyAiIiKihMdARERERAmPgYiIiIgSHgMRERERJTwGIiIiIkp4DERERESU8BiIiIiIKOExEBEREVHCYyAiIiKihMdARERERAmPgYiIiIgSHgMRERERJTwGIiIiIkp4DERERESU8BiIiIiIKOExEBEREVHCYyAiIiKihMdARERERAmPgYiIiIgSHgMRERERJTwGIiIiIkp4DERERESU8BiIiIiIKOExEBEREVHCYyAiIiKihMdARERERAmvVweiRYsW4ayzzkJycjIyMzMxa9Ys7N69O66NEAIPPPAAcnNzYTabMWXKFOzatSuuTSAQwB133AGHwwGr1YpLL70UFRUV3XkoRERE1Iv16kC0fv163Hbbbfjss8+wevVqhMNhzJgxAx6PR2uzePFiLFmyBMuWLcPnn3+O7OxsXHjhhXC73VqbuXPnYuXKlXjllVewYcMGNDc3Y+bMmYhEIj1xWERERNTLSEII0dOdaK/a2lpkZmZi/fr1OP/88yGEQG5uLubOnYsFCxYAiI4GZWVl4dFHH8Xs2bPhdDqRkZGBF154AVdffTUAoLKyEvn5+Xjvvfdw0UUXteu9XS4X7HY7nE4nbDZblx0jERERdZ72fn/36hGiozmdTgBAWloaAODAgQOorq7GjBkztDZGoxGTJ0/Gxo0bAQBbtmxBKBSKa5Obm4sRI0ZobVoTCATgcrnifoiIiOjU1GcCkRAC8+bNw6RJkzBixAgAQHV1NQAgKysrrm1WVpb2XHV1NQwGA1JTU9ts05pFixbBbrdrP/n5+Z15OERERNSL9JlAdPvtt2PHjh14+eWXWzwnSVLcYyFEi21HO16be++9F06nU/spLy8/sY4TERFRr9cnAtEdd9yBt99+G2vXrkVeXp62PTs7GwBajPTU1NRoo0bZ2dkIBoNobGxss01rjEYjbDZb3A8RERGdmnp1IBJC4Pbbb8eKFSvw4YcfYtCgQXHPDxo0CNnZ2Vi9erW2LRgMYv369ZgwYQIAYNy4cdDr9XFtqqqq8NVXX2ltiIiIKLHperoDx3LbbbfhpZdewltvvYXk5GRtJMhut8NsNkOSJMydOxcLFy5EQUEBCgoKsHDhQlgsFlx77bVa21tuuQXz589Heno60tLScPfdd2PkyJGYPn16Tx4eERER9RK9OhA9/fTTAIApU6bEbX/uuefwox/9CABwzz33wOfz4dZbb0VjYyPGjx+PVatWITk5WWv/xBNPQKfT4aqrroLP58O0adPw/PPPQ1GU7joUIiIi6sX61DpEPSmR1iEKRoJ4q/QtlLvKkW/Lx2VFl8GgGKAKFWXOMrgDbiQbk9Hf3h+qUPFZxWc43HwYWUlZOCfvHOjk1nN2R/YLoMU2WWr/DG9r+5QludU+6GRdl7xXW9s7sg8iIjo57f3+7tUjRNT9nt78NB7/7HFUN1cjokagyAqy/5uNa0dcC5PehNK6UvjDfph0JgDAvoZ9qG6uRigSgl7RY0jaEMw5ew5mFs484f2mmaPrTDX4GrRtRY4iXF50OYZmDD3uMZTUlmBl6cq4fRY5iuAP+fHSzpdQ2VwJVVUhyzIcFgfGZI+B3WTv1PcalTkKO2p2tNje2n7b2kd7+0BERCePI0TtlAgjRE9vfhr3rb0PvpAPyYZkGHVGBMIBOANOQAKGO4ZjyqApsOqt2Hl4Jz48+CEiIoIB9gFIM6fBG/KixlMDu9GOR6c/qoWijuy3zFWGDd9sgIDAef3PQ397f3hCHpQ7y+GwODBn/JxjhoSS2hIs3bQUdd465NvzYdVb4Ql5sPbAWuyq3QVVqNBJOkiShIgaQViEocgKzss/DxP7T+yU99pVswsVrgrk2fIwPHO4tr21/ba1j/b2gYiIju2UXKmaOl9YDWND2Qa8vPNlPPzxw/CFfEg3pUOWZfjDfkiSBJNiQjgSxoGmA7DqrZAkCTtrdkIIAZNiQqO/EQ3eBgghMMg+CM6AE09tfgphNYxgJIjHP3scvpAPGZYMWAwWKLICs94Ms86McCSM/Y37EYqEUOupxd76vTAqRhhkA7ZWbcWmQ5tQ7ixHYXoh6rx1eLP0TahCbfVYVKFiZelK1HnrMNQxFKpQUeetQygcwteNXyOkhgARXbdKCAEV0f1E1Ag2VmzE6n2rUVpXitPTT+/Qew3LGAab0QZFVpBsSEZIDcEZcCIUCSHZkAxFVmAz2jAsYxhqvbV4bvtz+LL6S+xv3I8VJSta7ENr66nFc9uibQ82HWyzL0REdPI4ZZbA3tn9DpZuXop9DfvQ5GtCY6ARMmTU+GqgChVHDh4qkgJPyIPSulIkG5PR4GuAIitoDjYj7A+j3lMPWZZh1pmRakrF3oa9+KziM1S5q1DdXI1kQzJk+dv8HRERhEQIOlkHd9CNt3e/DYNiQJ23DgDgCXkQVsMori2GIitIMiRhdNZolNSVoMxZhoEpA1scT5mzDKV1pbAYLPik/BPUeesQVsNwB9xwB6M3+40g0upNfQORADZVbYIECWsPrMWorFEw683Hfa98e37cAp/OgBP13npkWDNQ76uHM+BEiikFAFDnrUOVuwpbq7ZiV80uGHVGHGg8gFFZo1osElrnrUNVcxW2Vm/FrtpdSLekcxqNiKgLMRAlqHd2v4MFaxbAGXAi05oJoQo0BhqhQo3W10CGIikQQiCMMFShQhYyXAEXJElCIBJAKBLSRi30ih6SJMEb8iIQCcCit+Bw82GUu8oRUSMw6oxx768KFRE1En2//43U2Iw2VLor4Q17AQAyogFLQMAVcGFj+UYMDwyHO+Bu9ZjcATdqPDWo9dTCF/bBbrRDr+jR5Gtq12ciQYIsyfBH/Pi88nO4g+5jvpc/7IdVb43bHggHEFbDSDWmotHXiEA4AACo9dRi06FNaA42QyfrkGfLgy/sQ623FjtrdiLJkIQMa0ZcW0/QA52kQ7/kfkgyJmFb1TaUO8s5jUZE1AU4ZZaAwmoYSzcvhTPgxOCUwbAZbS0Ci0D01iayLENCdPRChYokQxIsOguCkSAiIgJZkiHLMhRZgU7WwagYEYqE4Al6kG5JR74tH4qsIBAORMPV/6bRImoEqhoNXxIkWA1WKLICf9gPIBpOBKLTWoqswKq3IqSGsKd+T4u+xlgNVhxuPgx30I0MSwaMOiNkSYZBMbTrcxEQUCQFekkPAYGvG75u87XJxmSYdCZ4Qp647UadETpZB0/IE/08dEYIIVBaVwpvyAu70Q5FUrRglGZKgzvgRmldKYQQcW1tRhvMejMsBos2jXa8qTwiIjoxDEQJ6LOKz7CvYR8yrZnaNJZBjv/iF//7HwAtEAFAkiEJsYex5xVJ0docPfVzWdFlyE7Khivoik7L+RrR5G+C0+9ERERHiAyKAQ6LA+6AWxstir1/IByAL+SDP+yHIikIRALYVrXtmMd39HUCHbl8PayGERHRKbWQGsKqr1e12q6/vT+KHEUod5bHvZ/daEe6JR21nlqkm9NhN9rhDDhR562DTtbhQNMBNPobsa1qG7449AU8IQ+CkSBqPbVwBpxaW5vBBnfQDYfFAbvRrn22ebY8bdqQiIg6DwNRAjrcfBihSAgWvUXbFkEEihS/UKUq1OjP/0KKXtKj3leP5kB0dEOCpI1URNQIgpEg/GE/dLIOVoMV9d56GBQDrh1xLQQEmkPNiIjo+xw5VZZsSEYwEkQgEmjRVxnREaqwCGsB7JD7UKvH5Ql6kJWUhWRjMmq9tfCH/VCFGi2mbqdYEIuNULUVPGRJxuVFl8NhcaC4thhOvxNhNQxXwAW9rNem61wBF7xBL5wBJ8qd5QipIWRbs+GwOmA1RKfbPCEPDnsPo95bD2/IC1/IB2fACYvegiJHUVzItBqs8If9bU7lERHRiWENUQLKSsqCXtFr0zIAYFAM0Mk6KEJBUA0CiBY+y5BhUkywG+0IRKL1MfXBem16zB/2IxQJIYggJEjaaI9e0SMrKQuqUGHSmzDMMQwHmw7CE/LAF/ZBggS9rIfVYIXNaIM35EUwEtT6GBtxElJ06k4PvRa++iX3a/W4ko3JyLRmIsuahQpXBeq8dVpwiAWc49He93+hKLZIZGuGZgzFnPFztDWEDrkPwaQzYcrAKRiZORJfHv4SW6q2oNZTi0ZfI3SyDoNTB2tByKgzIs+Wh3BTdBqxOdSMBl8DwiKMDHMGRmeP1uqKYjxBD0w6E5KNya11iYiIThADUQI6J+8cDEkbguLaYiTpkyDLMpINyTDrzPCEPFCgwKgzIjspG0adEemmdBxwRq+GWnbJMrgDbvzk7Z+gtK5Ue30sQKiqikZ/I87IOgPn5J2jXY01ddBUWHVWlNaXwhVwIdmQjHpfPSpcFTDpTDgz90z4Qj68Xvw6wiIMCRJ0sg56WR/dvyrgi/hgVswYkzOm1eOKTWNtq9qGifkT4Qq6EAgHoJN1eHHHi2gKNMUFnqMpUKDI0ULykAjBIBsw47QZx/wsh2YMRaGjsMUq07vrdmP74e3435tF/08bS34ZFANSTam465y7YDfa8eKOF3Gg6QAcFkdcOyEEKlwVGJsz9phBjYiIOo5TZglIJ+sw5+w5sBvt2N+0H06/EypUpJpSo0FBArKsWchJzoFRMeKA8wDsRjvmjJ+DIWlDcEb2GRieORyKrCCkhqDICiw6i/ZYlmQMSRsCWZLjrsZSFAXDM4fj3PxzMSJrBIZlDEOyMRlN/iZtzZ5UU6o2miNJ0au+wmoYvogPOlmHgvQC7cqtox05jVVSVwIJEtIt6dDJOuQk5UCWosFNL+thlFsWZsuyrC0HIEHCaWmnxY1atUWWZAxMGYiRWSMxMGUgdtftxtJNS/Fl9ZcYmDIQwzKHIdWcClWo2NewD42+RqhChT/sR623FsnGZGQnZyPVlIozss/AzWNuRoYlI24qzul3ori2GA6LA7OKZvG2HkREnYwjRAkqtop0bB2iOm8d9IoeIzJGAIiup/NN0zfQK3oMzxiOO86+Q3tNrK5m6sCp+KrmKzT4GuAVXiiSggxrBkZkjtDqb468Gis2PReTYc3AyMyR2HF4BzwhDxp8DUgyJiHJkIR6bz18ER+8YS9kSYbNYMPo7NHIs+cdc7qotWkso2JEvj0fAgLVzdXwhDyIiAik//0vVssUUqNByKSYMCprFAodhR2emjp6wUZJitZZ2Y122I12VDVX4bDnMEJqCHpZj5ykHPSzRacAY+/V1lTc2JyxmFU0i5fcExF1AQaiBDazcCYuLri4xc1ZARzzhq2xUZ9RWaNwRtYZqHBXoDnYjCRDEvKS86BCxZ76PXAH3BieOVybxooFhBghBLwhL64cdiWuGXkN3AF3dLqo8QCK0ouwu2E3XAEXbEYbCtMKsbthN4Y6hh53uujoaSxnwIk/f/FnDM8cDotiwdbqrWj0N6LeWw8ZMpKNyahsrkR2UjaykrIwJnsM9tTvadd7Ha21BRvtRjscFgeqmqswyD4IrqALY3PGwmFxwGawoaSupMU0WFtTcRwZIiLqGgxECU4n6zCp/6QW21vbFnP0qM/RocHtd2uFv7FprHJnOYpri5Fny4PVYIUn6EGFqwIOiwNXDL0Cg1MHA4jW0yzdtBS7G3bHtd3dsLtD00WxaSwA2Hl4JwKR6OX7X1Z/qa1gLUkS3EE3giIIq96KcTnjYDVYsad+zwlPTbW2YKMkSShyFMEZcMIVdCGiRmDRWSBBQkldSZvvdeQxEBFR1+Kfm9Rhba3BA3xb+Hvk6EpsCmhMzhjU++qxp34P6n31GJsztsWqyx1p217JxmQEwgF8Uv4JqpqrYNFbkG5JR5o5DSadKXqpe9iHQ+5DnfJerS3YmGHNwPh+45FmTkNYhDvlvYiIqPNwhIg6rD2jPkePeHRkCqizp4tit8lo8DVgcMpgbTFKo86Ifsn98HXj1xiUMggPXfAQUs2pJ/VeR17pdvQUocPiQE5SDibmT8R1o66D3WTnNBgRUS/BQEQn5EQKfzsyBdSZ00UVrgqYdWakmdNQ56uDzWiDQTEgGAnCFXAh3ZKOVHMqUs2pJ/2exwuLGdYM/HjMjzkiRETUyzAQ0QnrK4W/7oAbRp0RE/ImYG/DXm3Bxtjl+AVpBWjwN7RY/VkV6gkdG68SIyLqexiI6KT0hcLfWF2PxWDBpP6T4Aw4EQgHYNQZYTfa4Qq4YArHr/5cUluiBRp/2A+TzoQiRxEuL7q8XYGmr4RFIiKKYiCiU97RdT0pphTtudZWfy6pLcHSTUtR561Dvj0fVr0VnpAH26q2odxZ3u4i6L4QFomIKIp/rtIpr60bsba2+vPRCyvajDYosgKb0YZhGcNQ563Dm6VvavdVIyKiUwMDESWE9l7O39rCijGSJCHPloeSuhJttW4iIjo1cMqMuk1bRcphNXzMlbFPdL9Ha09dT2sLKx7JarDikPtQXAH2iRZf91WJdrxElBgYiKhbtFWknGpMxcrdK7GvYR9CkRD0ih5D0oZgztlztHunnch+2yp+Pl5dz7HuvQYAnqBHW4X7RN6/r0u04yWixMFARF2urSLld/e8i5K6EugVPfol94NFb4E35EVxbTEWrFkAAMcMRZ1V/HykYy2seHQBdle8f2+WaMdLRImF49zUpdoqUk7SJ6GquQq+kA8mxQS9rIc/7IdBMWBwymA4A04s3rgY26q24WDTwRZFzCda/KwKFQebDmLn4Z2t7vfIAuxdtbtQ7ixHpasS5c5y7KrdpRVgA0io4msWmxPRqY4jRNSl2ipSrnBXoNHXCJPehHpfPTwhDyRIkCUZekWPQDiArVVbMe+Deci357eYlulI8XNsiqy90z1DM4biuwXfxdLNS7G1aqs2lVeQVoAfnfEjDM0YioNNBzv8/n3ZiXzeRER9CQNRgutIgeyJFD+3VaTcHGxGKBKCEAIhNQSTYoJO0SEUCaHJ3wQIQFEUOCwOOCyOFtMyHS1+7sh0T0ltCd7d+y5sBhumDpwKnazTLtN/d++7OC3tNITVcIeLr3uLEymKPpFicyKivoSBKEG09iW4u243VpSswJaqLWgONiPJkIRxOeNwxdArWtSCvLP7Hfxh0x9QWleqjZgUOYpw+9m3I92S3mZIOrJIOUmfhAp3BZqDzXAH3AhGggAAAQF3MPpFGhERQAAyZIQiIRxsPAgAKEwrREldCZ789ElceNqFEBAwyAZ4Qh4kG5JbrD7t9rvh9DuxoWwDGv2N+Oibj7TpntgIR2y6Z8fhHViwegFOTz8dA1IGoM5bp7V1BV0IhANINiajX3I/7KrZhSc/fRJn5JwBp98Jd8CNFHNKi8/bE/TAqBjhDDix8/DOE7oaqyPBpb1tS2pL2n3Oj9TRYvMTwavXiKgnSUII0dOd6AtcLhfsdjucTidstpZfCL1Za1NFaeY07KnfgzJnGSJqBBIkCAgosoIRmSPw28m/1b4g39n9Dua+Pxe13loosqItYOgL+wAAdoMdekXf6hViqlDxyIZH8O6ed1HVXIVGX2M09CA6SiRw7F8/CRJ0sg4GxQC9rEdIDSHZkAyz3gxZkpFsSEaKKQXVzdUIRoIwKAboJB0qmyshhIDFYIEECWERxvjc8RidMzpu/2sPrMWmQ5sQiASiU3aQocgKCtMLkWHNQJ23DmE1HA15Aqjx1sAddCPJkIRgOAi9oscFgy5AoaNQ26cQAp9VfAYAyLRmIhAJdPhqrI5czdXetiW1Jfjd+t/hq5qvjnvOjxY7j20VmxfXFmNszlgsmLTghEIMr14joq7S3u9vjhCd4mJTRTXeGugkHVShwh/y453yd1DjqYHD7EBWUhb0il6brtp8aDN+v/H3mDN+DqwGKxZ+vBBVniqYdWaYdCYokoLmYDOC4SBUqJAlGaNTR8MX8WlXiKlCxajsUXAH3IioERTXFcMf8iPJkIQkXVKLMCQh+gV7dEAyySZERASekCf6WDEhOykbkiThG+c3KHOWQSfrYNKZIEsy/GE/vCEvJEgYmDIQOck5qPfW42DTQWwo2wCL3oLTHacDiIahT8o/QUREw4FJib5XUA3iq9qvkOZOQ0F6AfSKHoebD2tF2AbFgFRTKkKREKo8VXh377vwhrwYmTUSnqAHxbXFqHBVIM+WhwxrRtz0XFlTGa4cdiWykrLaHAXp6PRee9qqQsWft/wZmw9thlExIsWU0uKcP7vlWTx+0eOtBppYsXm5sxzFtcXIs+XBarDCE/SgwlURt9r3if6O8uo1IupJDESnsNiVQTsO70CVuwqN/kbty98b8kKRlejUk2KAJEkw6oxIMiThcMNhvF78OvbU7QEAbK3eCoNsgFVvhSRJEELAE/JERxckBYFwAJ6QBynmFCTpk7C7fjfmr5qPc/LOgS/kwyflnyAUCSHFlAJ/2A9nwImjBybbGikSEIggoj0ORoLQy3qY9WYYZSOaRBMQASQl2q9AOAAA0Mk6+MN+KJICu8mOZGMyvCEvNh3ahCHpQ6CqKjYf2oyIiEAHHSABOkUHRSgIqkEICDT5m6CT/jcy5KmBKlSoQkUgEsDh5sPQK3rYDXa4gi5sPrRZ6xcA5NnycE7eOXHTcxnWDHz0zUfRPqQNgVlvbjEKcvTVXEdP7xXXFmNFyQpco7sG7oAbL+54EbWeWgzPHN5q2zdL30ShoxAHmw5i/cH1UCQFmdZMra1RZ0SmNROV7kqsO7gOB5sOYnDq4FbPRWy179hIziH3IZh0JozNGYtZRbNOKLS053hjx8DpMyLqSgxEp7AyZxnWfL0GJXUlCKthGBUjjLIRvpAPYRGGGlHR6GtEiikFiqwgGAmiyl0FT9CDiIhgb+NeRCKR6HSSkBBSQzAoBgQiAW0aSQiBsAjjsOcwJEmCIimIiAgq3ZVo8DVAhozmYDNUoaLOUwcVaqvhR4HS6nMBNRC3TYWKOl8dbKoNzoATOkkHCRKykrMQUSNoDjXDIBugQoUz4NSmX6x6K4KRIOq99ahwVaDaXY1AJAAFCiABiqxoU0gyZKhQEUEEZa4yrXbmyBCnyAokKfqZGGUjImoEo7NHo9BRiLdK30JmUiaEECh3laM52IywGsY3Td8gEA4gIkeQnZQNvaJvMQpyvKu5LHoLXi9+HVuqtiAQDqCkrgQ5yTnI8mYhw5oR1/bIK7/21O9Bg68B2UnZAAB/2K+dw9iIUWVzJT7Y9wG+U/CdY672XZBe0GpxfWs1QLHfw7bqgjrz6rW2apBYm0RE7cFAdApr9DViV+2u6Po+sgHekBcCQlsrRoUKV9CF/Q37IcsyfGEfgpGgFkCcfqe2L7/qR3OwGammVETUCIQQEIiGISA6glLvrYcsydrVYturtyOiRuANeaHi2OvTHDkKdKTWwlOFqyJu3SJVqAir4Wi/IKAoCmQhIxgJwhvywqw3w2F1wBf2ocnXhJLaEtT76qOfBVToJB0USdFGgCRJQuxtDzcfhizLCKkh7f0lIcEZcEKRFCiSgpAaQkRE8K/if8FhceCw5zD6+/pjd/1uNPgaEFGj03CKrGCAfUC0pkkNI92S3mIU5FhXc9V6arGzZifqvHUYkTkCDrMDe+v3osHbgE2HNmF8v/FxoSjuyq//HY8v5EONpyZ6ToSqna9IJAJ30I2Xdr6ETys+1Uaujr7ViSfowVu734qr9fnom48wKnMUdtTsaFGnJiBQ7ixvs4C7s65ea6sGqbV+dWVtEsMXUd/FQHQK29ewD+6gG0IVCCIInRwdTYmoEQQQnVoSEPCFfZBlGYFIIO71Rz/2BD3QydGwo0KNW4QvFo6EEAio0QJlT8gTDRnHCUMdFVJDWkDxR/yQIaPWUwshBCJqBP6wPzrVBWijDla9FTpJh7AIY3v1doQjYa3fqlC1K94AxB1XSA1BUuNHLmKvUVUVfvi17XXeOniCHlQ1V2Fv/V7oFB1sBhskSGj0NyIUCeFA0wFkWbNg1Bm1/h05CtLW1VxCCJTWlcIdcCPVlIp0SzoAwKw3w6wzwxV0obSuFA6LQzvmI6/8Ot1xOix6C8pcZdDLehh1RiiSAn/Yj3pvPSIigiR9Es7IOgMGnQHbqrZhx+EdyE7KRoOvAf6wH4FwAFXNVUg1pWJ45nCt1mf9wfX4545/Is+Wp20vc5XhjeI3olOpphSYFBMEBPbU78HOmp1aAXdnXL3WVg1Sa/3qytokFoYT9W0MRH1ER26MCgCfVXyGTys+RTgSDSkm2aTt5+ipibAIo7UBmqNHZ1SoaPQ3ttq/o9sKCHgDXoQQarV9Z1Khwh1wayM+qqpq77u3bi/MOjPSTek47D2sjZQcGdJiV721RoLU6uiVKlpO7wkhACk6HRUbefKH/AiqQUTUCPSSHoFIAA2+BlQ4K+AKuJCXHC1O/rrua9yw4gY0eBsQVIMoSi3C6NzRONR8KFqALgRqPbUAgFRzKrZXbUejvxGugAuBUAA2ow2ldaVo8jUhKykLY7LHYH/9ftT56jDnvTkYlDIINqMN5a5yLSzGwrCqRj+LJGMS0sxpkGUZGZYMvP/1+zArZgywD0BIDaHMWYbmUDMikQjqPHXwGXwwKAaEIiE4A05khjMRUSOo9dRiR/UObarQH/IjxZQCIQT8YX9cAfeRt0oZ6hiqLXNg1BlhM9hQ7izH4NTBcPqdONh0sMXvfbW7Gqu+XoV6Xz1GZI7QfreTDclav7LVbCQbkiFJ0jFrk05myu1ECsM5mkTUu/Cy+3bqycvuj3Vj1BWlK7C7frf2JZJmTov2N+BCk78Jjf5GSP/7X+w/tkKINqeoqHuYdWYYlej5qvPUwRVytWijQIHVYI2Gr/8tYGlUjPCGvccMcQC0eqijWfVWWPQWbXrQE/RAlmRYdBY4rA58p+A7sBvt2FC2ATsO79AK4GMBUCfpYNAZkGSIhichBBr9jbDoLPCGvdrVdxXuiujimnK0psyit0Av67X3TjOn4Y2r38Dg1MHR5QDW/Q5f1UaXA4gJq2GYdWYMSh0Eo87Y6g2BfSEf3EE30s3pmJA/QVv+oMnfhLUH1mo1bVMHTUWKKUXbt9PvRL2vHg9OfRADUwae1JTbiSxJwNEkou7Dy+5PEce6MepXNV8hIiIwKAbIkKMjD64KyJKM3ORcZFoy0eRvgvjf/1ShtvlFSd1LhgwJEvY37W+zTQQRuIIu2A12hBFGUA0iqH47tXesc9nWdk/IA4vOArvRjuZQM3SyDg6LA+mWdDT6GlFSWwKb0YaS2hI0+BqgQoVRMcIgGeAL+xASIYRCIUBEb4QbCAfgDrrhCrggSzJSTanRKwPVCCIiAlnIkCUZihQtQm8ONgMiOhX2t61/w8UFF8NutANHDFpKkOCP+NHkb4JVb8UY8xj0t/WPuyGwTtYhw5KhLc7Z4G3AmgNrAACFjkIEwtHC/2RzMhp9jdrVhzFH1ibF/o3VemphN9lhNpkRVsNYd2Bdu6bcjlcY3i+5Hz6v/Byr9q1CUUYRPEEPlm1exmUGiHoZBqJerK1LkpP0SShzlsET8kAv6aHoFO2ScwGBiIig3FXe6j4ZhnoHT9gDT9jTrrbOoLPV7Sd6Lmt9tWjyN2kF8e6AG3XeOvjDfi1Qx4JXbMXwWM3Zkf0vrimGUTEiHA4jgmgA2lu/N270MSKi25t8TZDlaDAKRAJQoeIPm/6A5duWw6K3IDspG5cVXoZt1dvQ5GtCva8emZbo8gBfHf4K3qAXVr0Vle5KeIIemHVmVLgqovVkkRBMOhO8QS8+PPAh6rx10Mv66PISQS+gAtuqtsEX8SHVmIqJeRPhjXijVx8arHjlq1ewv3E/QpEQdtXu0pZ2CEaC8IQ9cFgc2tSWzWhDYXohdtfvbnch/K7aXdhbvxdVzVXITspGc6AZsiS3WJbhWMsM9PT0Wk+/P1F3YCDqxdr6y7PcVY5ab7SeJKgG0eBr0EaBiNojJL6t7WoMfFsXpgoVR/4aHasgvrVQ19ZUbBhhHL0rX8gHf9iPiIigwl2B7Ye3IxAOaNNziqRol/Pvrt+trZ8lQWoREptDzQAAb9iL/x74r3YFoCrUaLH/EQe1oXwD+tv646fjfgoA2FSxCQedB9HgbYjW3P1vNDUQCUCWZGws3wggGkBlSUaSIQmjs0YftxC+1lOL9d+sR6WrEiE1hApnBSpdlWgKNCE7KRtD0oYcc6mEjt6UuKv09PsTdRcGol7syL88hRDa/boONh3UrvQi6qtUqJBEdNovGAnGXekH/G90KRINWKqqakXzxxO74jE2Ana0iIjggPMA/l36b9R767Hp0Ca4A+64QnlVqHFLLRhkgxbOXAEXPin/BAXeAmyp3IIxOWNQmF6I7dXbcXra6dhWvQ2NgUZUuapQ561DSA1BlmSt8N8X8qHKXYUtlVswPm88gpGgdg++jtyUuLVVz4Fjr/vUUVxFnLpDbxmBZCDqhWK/HIfchxBWwyhzlqHMWYb9jfvhD/sZhuiU0JFRza64CODTyk+xqXKT9m8pdvFBrG9x7y8iUCPRdjKiU4qldaX4y5a/IDMpE2nmNOyq3YW3dr8VF6Ri+zXIBgQj0VvdqFDhDXqxo2YHmgJN2kiYw+JAni1PW2bgWKt4t7bqeeyCitgyCSc7ksNVxKk79KYRSAaiHtTaJfN76/fi9eLXsaFsA1xBFyqdlajx1mj/MSWiznPkv6ljBbTYLW9i7YBoYAiGo/VG7+97H3vr97b6bzQ2BadTdFpNlorozZGdfifMOjMUWcEh1yEcbDqISwsvRX97/7gpcyB65VwgHEBzsBnFtcVxq567Ai68vfttSJAwacAkFKYXnvRITmeuIk7Umt42AslA1EPe2f0Olm5ein0N+xCKhKBX9MhOykYgHECFqyK6lo1Q27XKMxF1vdbW2tpVtws13hrsa9h3zH+nIRFCJBRpMZJS7akGEB1FkiEjxZyiPRebMveFfPiy+ktt+q3B1wBVqMi35UdHjNUQKlwVMCpGSJKECmcFBtoHnnShdltT9m1N7xF1RG8cgWQg6gHv7H4HC9YsiC5mZ82ERW+BJ+TB9upoUalBMUTXTBHfFosSUe+TZEhCc7D5uOtCxUiSFLcSekzsJsbOgBMbyzdqxdqBcACflH+CsBqG3WhHRERQ01wDAYEKVwVSTCkIRoKo89ZFF7+EQJ23Ds6AEymmlJMq1I4Vi5c5y1DhqkCdt067/53D4kA/W7/jriJO1JbeOALJid9uFlbDWLp5KZwBJwanDIbNaINO1kEv6RGOhLVbYhiU6PoqRNR7mSUzQpH2rcYeW0X9SDJk6CU99LIeChRE1Ah2Ht6Jem898mx58IV9aPA1wGF2wKgzaivNW/QW+MI+hNUw9LI++n8VPQyKAWE1HLfuktVghT/sb1Gova1qGxwWBwrTC+GwOLCtahuWblqKktoSANF1ptLMafi47GNUuith0VuQbkmHRW9BpbsSG8o2IN2crhVzE3VEe+5jeOTvbXdgIOpmn1V8hn0N+5BpzYQsf/vxu4IuhEUYMmRERPQO8wxERL1bQ7ChxT3/juXoaTVZkiHL/1u8UlYARO/Pt7F8IypcFTDrosXSdb7oOlGyJENAwBOKrsWkk3UIqSHtHoPBSPSehbF75QHx94M7eprCZrRBkRVtmqLOW4c3S9+MG8WSIEGSvl0EVEBAkr4tQCc6EUcuV9Ga9tzHsLMxEHWzw82HEYqEYNFb4rbHblkgQ9ZuUsraIaLeLRQOwSAb2t3+6BDRYqrgf6uPVzVXwR1ww6gzYkLeBOQk5cAX8qE5GF1dXCfrkGfLg07WwaAY4LA40ORvgivggsPiiK7+jeitQypcFRjqGNqiUPt40xRlzjI0+BowacAk7f3rvfXwhXzIScrBpP6TUO+rR5mz7AQ/PUpksfsYljvLcfQdxI7+ve0urCHqZllJWdArenhD3rhF3Kx6K2QpOjok49u/Fomo91IUBXqdvl1tTUr0Bsv+iD9ue+zLIDYqI0NGmjlN+wvaYrBgUv9JWlGzJ+RBcW0xnH6nFo762frhQNMBSIiGmoiIwBPwoMJVAYfFgVlFs7S1kI43TXFkobQ/7EdheiEG2ge2KKqOiAh21+1GaW1pj68fQ32PLMm4vOhylDvLUVxbjDxb9EbXnmDL39vuwkDUzc7JOwdD0oaguLYYSfokbdrMZrTBrDNrRdSKpERrCngTVqJey6yYYdFbIEM+7ohuljULsiTjoPNgdNXr/40Gx6aiZEmGKlRYDVbt0vsiR5F209gjb06bpE/CR2UfQS/rcbj5MMx6My4rvAwCAg2+Buyp3wOTzoSxOWMxq2hWi0Lpo1fVjjl6muLItke+PwCUN5VjX8M+PLv1WehkHVewpg4bmjEUc8bP0Qr8D7kPtfp7210YiLqZTtZhztlzsGDNAuxv2o8MS4aWig06A/QRPXSyDt6QVxsxIqKed/TNdB1mB6YPmY6wGkaNpwZ1/jqtXUysfbY1GxcOvhAREcF/9v0HNZ4aAIAiR28xEhERhNUwFFnB1IFTMSRtyDH/gq711mJS/0nRlaqt7V+p+uiQdeS0WWyaYmzOWG1fbbWt8dRgQ9kGmPVmDLAPQJIhiStY0wkZmjEUhY7CXrFStSSOnryjVrlcLtjtdjidTthsLf+y6qh3dr+DP2z6A3bX70YwHIRBZ0BRehHOyTsHG8s3oriuGI3eRvhV//F3RkSa2IrTMqIFy7Eb1XaUDFlbKO7I0R8JEjIsGTgn7xwYdUaE1TAONB7Qbm4biERvsixBgkExIMOSAb2ix1DHUKRb0pFuTsdH33yE3fW7tRsyS5Bg1BkxNnss/vy9P8eFidYukR/qGHrCf0EfuRhea9MUR4aZ1to2B5qx5sAaeENeXHzaxchMytT2LYRAcW0xxuaMxYJJCzh9Rr1Ce7+/OULUQ05LOw3nDzgfsiTDFXTBZrBhUv9JGJM9BgadATpFh331+/B109c93VVKUEePiHQVh8GBkBSCM+A8fmMAekkPg2yAJ/Lt1SkDkgfgrgl3QSfrcLDpIPSKHqU1pchKzkJxTTF21e3SwsQIxwgMThuMDw98iAxLBkZmj8STFz8JWZLx6w9/jf0N+zE4bTD+3wX/D+XOcry26zWsLFmJpkATUowpuHzo5bhy2JWwGqxwB9w45D6E5VuXo8hRBAGBrVVb0eRvQoopBWNzxkIIge3V23HzmJsxLncc+tv7Y3fdbry26zW8t/c9re13T/8ufjDsBy1CTmf/Bd2RaYrW2sYu9T+v/3lxYQjgCtbUtzEQ9YAj/+oanjlc+yt0Q9kGvPLVK+iX3A+p5lRYDJbj74wSjgEGBPHtqIcMWas/UWQFelkfrUcLNiOgfntJuA66Y46Y6CU9LAYLTIoJ6ZZ0DEgZgIG2gVi1fxWcASfsRjtmnDYDoVAIb+19C82hZiTpk3B+3vn4pOoT1Hvqo/f8OqqWRi/pIcvRepmQGtLqZ9LN6TDrzLAarHBYHAipIXzj/AaBcPQu84qkQBIS/JHoasx6WY8McwbG5o3FvZPuxZ76PSh3lSPflo/Lii6DQfn2aq+DTQfxm7W/gVlvxpRBUzAmd0xcQbAr4ML5A8/Hg1MfjPvSfuLiJ+L6PjRjKH49+de4afRNbYaRZGMyzHqzVmtzdr+z4/bh9DuRbknHuNxxce+lyAr62fohxZQCq8F6zIAjS3KnhouOhKyj28YCYFtX/3AFa+qrGIi6WVvLlScbkhFSQ6j2VKPSXQkA8IV9PdlV6oW0u65HVK2+TJEU5CTlQKfoUOQoglEx4mDTQRz2HkaaKQ2np50OWZaxrWobKpsrYZJM0WUdhApZkqGTo0Epy5qF09JOQ5IhCeNyxuGKoVe0+qUJAPc574vb9t6e9/DkpidRXFsMX8gHb8irhTNZOqJ4OAJAAHpFD5vJBoNiQCgSQqW7EoNTB+M7Q74DnaxDWA2j3FmOpkAT7EY7ZEmG1WDFmblnakW7Z2Sf0ebndKyC5NZqZY7leGGko3U5R/5BNDBloPYH0ZfVX+KQ61C31d90JGQd2fboAHi0nlg/hqgzMBB1s7bWAXEGnNjfuB+egAcREUGKKQUpxhQc9h6Oe313TWNQ15EgaTf09Aa92pWEekkPAYGwCGttzYoZ2UnZMOlNqG2uRb2/HmE1jFRTKnSyTlvA81DzIRSkFeCM7DOw/uB61HhrkJOUg/H9xiPDmgEhBMqd5aj2VMOkM0WnOkR0iiOiRhBUg5gycArmjJ8Du8keN1rQ2pfm0dtmFs7ExQUX47OKz1Dtrsaqr1dpdTVVzVUIRoJwBVwwKAYkGZKQk5SDUdmjYFSM2HF4B/Y27EVYDSPPlqf9u8iz5WFXzS4MTh2M60Zd16Jfx9Kdl/R25L164/2bOqqjAZCor+id/+K6yJ/+9CcMGjQIJpMJ48aNw8cff9ztfWhrHRBfyIdaTy0iIgK9rIde0bcafOTEOmU9Ti/poWvl74b2rtJrkAww68xx2wQEkgxJsBlt0Ov02r70ih4mxaQVBJsUE8x6MzKTMpFhyUBADWh3ZJekaBGu1WCFRWeBLMmo89Zhe/V2VDVXYWDKQC0MAdHA7Qv7kG/Lh07RQYjoPmJr2EzqPwkhNQS7yY6BKQNP6MtYJ+uiVz4NvxJ3nXsXhqQPQZo5Defmn4uz+50Nm9EGu9GOdEs6xuSMQXZSNiRJQoOvARnWDNT76uPqiCRJQr49H9We6hPqV6z+ZUzOGNT76rGnfg/qffUYmzO200dh2vteHVkYsbeKBUCHxaGthxRWw3D6nSiuLe6R9WOIOkPCjBC9+uqrmDt3Lv70pz9h4sSJ+POf/4zvfOc7KC4uRv/+3feXTFvrgDT4GrQl+CVJgizJCCOsBaBYXQbXJepeekUPCCAcCcdtb+8oXUREIImW4SkQDkCRlehokWKGL+LTFueM/f8WvQVhNazdmyoYCcIoGxFSo7doAKJfTqnmVGQr2fCEPJiQPwESJIzJHgOdoot7v7AaRrolHZIkYWz2WCQbk+MW2dtTv6fT6j6OLsZt8DUgrIYxOHUwhmUM04JarF+pxlQ0+hrj7sEFnHw9Snde0tue9+rowoi9VW9bP4aoMyRMIFqyZAluueUW/OQnPwEAPPnkk/jggw/w9NNPY9GiRd3Wj2MNN0uQEBERGBQDFClaf6HI0Rs+xhZ+yzRnIiIiqPfXd1ufE8nRi2EqshINpSeYQ2VJbv3LV4o+J0FCBBHoZB3OzD0TBp0Baw+shUVvgSIrUIUKnayDL+SDEAI6WQdIQD9bP1j0luh9qxRjdGXiJg/SzelIt6TDG/bCpnwbuI06I3SyDp6QB3pZD4fVEVdX4wl0ft3HkQGhtLYUz259FgPsA2A32Vvt19H34AI6px6lswuST+a9OrowYm/Wm9aPIeoMCfGbGwwGsWXLFsyYMSNu+4wZM7Bx48ZWXxMIBOByueJ+OkNbw80SJO3LM7YgoyIpMMgGbTRCL+mRnZyNwamDO6UvFC92Do5k1plb3HcOaH3qMrb+zZH0sh46Sddiu0kxQZEU7aacVr0V43LHYVTmKDgsDniCHvhDflj0FhgVo3YFVSgSgllnhsPsQJIhCSadCZIkwRP0QK/oMTxzeKv3B4pNVdV6apFuTtfudQV07X2DYgFhxpAZODP3TFS4KnpFv3pKb7x/08mInd+RWSNPeKqVqLdIiN/euro6RCIRZGVlxW3PyspCdXV1q69ZtGgR7Ha79pOfn99p/Wmt3sCgGLRiTLvJjlAkBF/YB6POqNWV6BU9AuEA3EE3UowpndYfitLLLeu2ImoEsiRDL317vyoFSouRjJgWU2kStFqdGBmythp5IBKATtFhYMpANAeboULFiMwRgBS955VJZ0JERLS6ISEJ5Cbnard8AQBVVVHrrUVBWgEm5E9oNXC7Ai7oZT3sRjv0ih6ugKtb6z7a+kOgp/vV3Vh/Q9R7JcRK1ZWVlejXrx82btyIc889V9v+8MMP44UXXkBpaWmL1wQCAQQC39YzuFwu5Ofnd9pK1UD0Evwjh5t3HN6Be9fciyZ/E+wmO4yKEYFIAHXe6C0BhqQOQbIxGVaDFeNyxmHn4Z14s/TNuLVm6OQpkqLV8LiDbkTUCCRJgqqqEBBQhaoFn9ZurSJBiq6hI0mAgLYSsSpUQIoWH0uQoMjRy+WvGXENTHpT3ErEEiTsbdiL6uZqhCIh6BU9UowpqPPVQRVq3C1far21sBvteHT6o5hZOBNA26sbj8wciR01Ozpt1eOO6q396m6dvfo0EbWNK1UfweFwQFGUFqNBNTU1LUaNYoxGI4zG1kcBOsvR9QYDUwZChoylm5diX8M+uANu6BU9xmSPwW1n34ZRWaNazNV7Q178/pPfY3/jfgxOHYyZhTPxVulbeG3Xa3D6nbCb7MiwZmBLxRZ4Va/2Xsm6ZJzuOB07qncghNBx+5ptzUazrxnNarO2ra0lAM7LPw9f13+NSm+ltk0HHSyyBS7126nHfHM+RvQbgXQ5Ha/seQVhhKGDDhOzJmKPew+qvdXa/i2yBXnWPFT5q6IjJ4oJ52adi1RbKpJ0SXi99HX4wj6YdWZMGzANZe4ylNSXIBgJwqAYcFbOWThvwHn4+ODH2Hp4qxYy8pPyodPrUOWuQiASgFExYlTWKNw5/k7MGDIDb5W+pS3+d3r66VhZuhIf7v8QzqATdoMdZ2SdAVWo+KT8E23F4VlDZyHLkoUVpSuwq3aXtiDgiIwR+PlZP4+usXPUgoJHh+P+9v5QhYrPKj7D4ebDyErKwjl55+D9ve9rvx913rroNFnGcNxx9h1aGAKOXd9xyemX9FjdR2/tV3dj/Q1R75MQI0QAMH78eIwbNw5/+tOftG3Dhg3DZZdd1q6i6s6+l9mxhNVwiy/CI6dcjqe1L9dgJIjlW5fjYNNBDEwZiFvG3gKTztQiUN098W6oQm31FgZvlLyBDWUb4Aq4YDPacF7/8/Cdgu/gnd3vxL3eorcgGAm2CBP/3vNvbK3aCk/Qo41ytbX4X1gNt3j9u3vfbfUv6tZe31qYiC34d/R2AO3+vFv7bIHWb6h5suexq34/iIgSSXu/vxMmEL366qu44YYb8Mwzz+Dcc8/Fs88+i7/85S/YtWsXBgwYcNzXd2cg6q1aCwMd+Yu2p19PRESJh1NmR7n66qtRX1+PBx98EFVVVRgxYgTee++9doUhijrZy5d7+vVERERtSZgRopPFESIiIqK+p73f35xvICIiooTHQEREREQJj4GIiIiIEh4DERERESU8BiIiIiJKeAxERERElPAYiIiIiCjhMRARERFRwkuYlapPVmz9SpfLdZyWRERE1FvEvrePtw41A1E7ud1uAEB+fn4P94SIiIg6yu12w263t/k8b93RTqqqorKyEsnJyZAkqae7c1wulwv5+fkoLy/nrUb6CJ6zvoXnq+/hOet7OuOcCSHgdruRm5sLWW67UogjRO0kyzLy8vJ6uhsdZrPZ+A+/j+E561t4vvoenrO+52TP2bFGhmJYVE1EREQJj4GIiIiIEh4D0SnKaDTit7/9LYxGY093hdqJ56xv4fnqe3jO+p7uPGcsqiYiIqKExxEiIiIiSngMRERERJTwGIiIiIgo4TEQERERUcJjIOrDFi1ahLPOOgvJycnIzMzErFmzsHv37rg2Qgg88MADyM3NhdlsxpQpU7Br164e6jEdbdGiRZAkCXPnztW28Zz1LocOHcL111+P9PR0WCwWjB49Glu2bNGe5/nqXcLhMO6//34MGjQIZrMZgwcPxoMPPghVVbU2PGc966OPPsL3vvc95ObmQpIkvPnmm3HPt+f8BAIB3HHHHXA4HLBarbj00ktRUVFxUv1iIOrD1q9fj9tuuw2fffYZVq9ejXA4jBkzZsDj8WhtFi9ejCVLlmDZsmX4/PPPkZ2djQsvvFC7Nxv1nM8//xzPPvssRo0aFbed56z3aGxsxMSJE6HX6/Gf//wHxcXFePzxx5GSkqK14fnqXR599FE888wzWLZsGUpKSrB48WI89thjeOqpp7Q2PGc9y+Px4IwzzsCyZctafb4952fu3LlYuXIlXnnlFWzYsAHNzc2YOXMmIpHIiXdM0CmjpqZGABDr168XQgihqqrIzs4WjzzyiNbG7/cLu90unnnmmZ7qJgkh3G63KCgoEKtXrxaTJ08Wd955pxCC56y3WbBggZg0aVKbz/N89T7f/e53xc033xy37YorrhDXX3+9EILnrLcBIFauXKk9bs/5aWpqEnq9Xrzyyitam0OHDglZlsX7779/wn3hCNEpxOl0AgDS0tIAAAcOHEB1dTVmzJihtTEajZg8eTI2btzYI32kqNtuuw3f/e53MX369LjtPGe9y9tvv40zzzwTP/jBD5CZmYkxY8bgL3/5i/Y8z1fvM2nSJPz3v//Fnj17AABffvklNmzYgEsuuQQAz1lv157zs2XLFoRCobg2ubm5GDFixEmdQ97c9RQhhMC8efMwadIkjBgxAgBQXV0NAMjKyoprm5WVhW+++abb+0hRr7zyCrZs2YIvvviixXM8Z73L/v378fTTT2PevHn41a9+hc2bN2POnDkwGo248cYbeb56oQULFsDpdKKoqAiKoiASieDhhx/GNddcA4D/xnq79pyf6upqGAwGpKamtmgTe/2JYCA6Rdx+++3YsWMHNmzY0OI5SZLiHgshWmyj7lFeXo4777wTq1atgslkarMdz1nvoKoqzjzzTCxcuBAAMGbMGOzatQtPP/00brzxRq0dz1fv8eqrr+LFF1/ESy+9hOHDh2P79u2YO3cucnNzcdNNN2nteM56txM5Pyd7Djlldgq444478Pbbb2Pt2rXIy8vTtmdnZwNAi8RcU1PTIn1T99iyZQtqamowbtw46HQ66HQ6rF+/HkuXLoVOp9POC89Z75CTk4Nhw4bFbRs6dCjKysoA8N9Yb/SLX/wCv/zlL/HDH/4QI0eOxA033IC77roLixYtAsBz1tu15/xkZ2cjGAyisbGxzTYngoGoDxNC4Pbbb8eKFSvw4YcfYtCgQXHPDxo0CNnZ2Vi9erW2LRgMYv369ZgwYUJ3d5cATJs2DTt37sT27du1nzPPPBPXXXcdtm/fjsGDB/Oc9SITJ05ssZTFnj17MGDAAAD8N9Ybeb1eyHL8V5uiKNpl9zxnvVt7zs+4ceOg1+vj2lRVVeGrr746uXN4wuXY1ON+/vOfC7vdLtatWyeqqqq0H6/Xq7V55JFHhN1uFytWrBA7d+4U11xzjcjJyREul6sHe05HOvIqMyF4znqTzZs3C51OJx5++GGxd+9e8c9//lNYLBbx4osvam14vnqXm266SfTr10+888474sCBA2LFihXC4XCIe+65R2vDc9az3G632LZtm9i2bZsAIJYsWSK2bdsmvvnmGyFE+87Pz372M5GXlyfWrFkjtm7dKi644AJxxhlniHA4fML9YiDqwwC0+vPcc89pbVRVFb/97W9Fdna2MBqN4vzzzxc7d+7suU5TC0cHIp6z3uXf//63GDFihDAajaKoqEg8++yzcc/zfPUuLpdL3HnnnaJ///7CZDKJwYMHi/vuu08EAgGtDc9Zz1q7dm2r31033XSTEKJ958fn84nbb79dpKWlCbPZLGbOnCnKyspOql+SEEKc+PgSERERUd/HGiIiIiJKeAxERERElPAYiIiIiCjhMRARERFRwmMgIiIiooTHQEREREQJj4GIiIiIEh4DERERESU8BiIiOmVt3LgRiqLg4osv7umuEFEvx5WqieiU9ZOf/ARJSUn461//iuLiYvTv37+nu0REvRRHiIjolOTxePDaa6/h5z//OWbOnInnn38+7vm3334bBQUFMJvNmDp1Kv7+979DkiQ0NTVpbTZu3Ijzzz8fZrMZ+fn5mDNnDjweT/ceCBF1CwYiIjolvfrqqygsLERhYSGuv/56PPfcc4gNiB88eBBXXnklZs2ahe3bt2P27Nm477774l6/c+dOXHTRRbjiiiuwY8cOvPrqq9iwYQNuv/32njgcIupinDIjolPSxIkTcdVVV+HOO+9EOBxGTk4OXn75ZUyfPh2//OUv8e6772Lnzp1a+/vvvx8PP/wwGhsbkZKSghtvvBFmsxl//vOftTYbNmzA5MmT4fF4YDKZeuKwiKiLcISIiE45u3fvxubNm/HDH/4QAKDT6XD11Vfjb3/7m/b8WWedFfeas88+O+7xli1b8PzzzyMpKUn7ueiii6CqKg4cONA9B0JE3UbX0x0gIupsy5cvRzgcRr9+/bRtQgjo9Xo0NjZCCAFJkuJec/RguaqqmD17NubMmdNi/yzOJjr1MBAR0SklHA7jH//4Bx5//HHMmDEj7rnvf//7+Oc//4mioiK89957cc998cUXcY/Hjh2LXbt2YciQIV3eZyLqeawhIqJTyptvvomrr74aNTU1sNvtcc/dd999eO+997BixQoUFhbirrvuwi233ILt27dj/vz5qKioQFNTE+x2O3bs2IFzzjkHP/7xj/HTn/4UVqsVJSUlWL16NZ566qkeOjoi6iqsISKiU8ry5csxffr0FmEIiI4Qbd++HY2NjXj99dexYsUKjBo1Ck8//bR2lZnRaAQAjBo1CuvXr8fevXtx3nnnYcyYMfj1r3+NnJycbj0eIuoeHCEiIgLw8MMP45lnnkF5eXlPd4WIegBriIgoIf3pT3/CWWedhfT0dHzyySd47LHHuMYQUQJjICKihLR371489NBDaGhoQP/+/TF//nzce++9Pd0tIuohnDIjIiKihMeiaiIiIkp4DERERESU8BiIiIiIKOExEBEREVHCYyAiIiKihMdARERERAmPgYiIiIgSHgMRERERJTwGIiIiIkp4/x/bTusrSRAEVQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "\n",
    "plt.scatter(df1[\"Age\"], df1[\"WorkWeekHrs\"], c='green',alpha=0.5)\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Work Week Hours\")\n",
    "plt.title(\"Age vs Work week Hours\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bubble Plots\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a bubble plot of `WorkWeekHrs` and `CodeRevHrs`, use `Age` column as bubble size.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'CodeRev Hours')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "\n",
    "plt.scatter( df1[\"WorkWeekHrs\"],df1[\"CodeRevHrs\"], s=df1[\"Age\"],alpha=0.5)\n",
    "plt.xlabel(\"WorkWeek Hrs\")\n",
    "plt.ylabel(\"CodeRev Hours\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing composition of data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pie Charts\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a pie chart of the top 5 databases that respondents wish to learn next year. Label the pie chart with database names. Display percentages of each database on the pie chart.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Respondent</th>\n",
       "      <th>DatabaseWorkedWith</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>MySQL</td>\n",
       "      <td>5469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>Microsoft SQL Server</td>\n",
       "      <td>4110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>PostgreSQL</td>\n",
       "      <td>4097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>SQLite</td>\n",
       "      <td>3248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>MongoDB</td>\n",
       "      <td>3016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Respondent    DatabaseWorkedWith  Count\n",
       "0           4                 MySQL   5469\n",
       "1          16  Microsoft SQL Server   4110\n",
       "2           9            PostgreSQL   4097\n",
       "3           4                SQLite   3248\n",
       "4          16               MongoDB   3016"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "# Query = \"\"\"Select * , COUNT(*) as Count from DatabaseDesireNextYear\n",
    "#              Group by DatabaseDesireNextYear\n",
    "#              order by Count desc\n",
    "# \"\"\"\n",
    "Query = \"\"\"Select * , COUNT(*) as Count from DatabaseWorkedWith\n",
    "             Group by DatabaseWorkedWith\n",
    "             order by Count desc\n",
    "\"\"\"\n",
    "\n",
    "database_df = pd.read_sql_query(Query, conn)\n",
    "database_top5 = database_df.head()\n",
    "database_top5\n",
    "# databse_df.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x1fafa1a6e50>,\n",
       "  <matplotlib.patches.Wedge at 0x1fafa199820>,\n",
       "  <matplotlib.patches.Wedge at 0x1fafa04a1f0>,\n",
       "  <matplotlib.patches.Wedge at 0x1fafa04aac0>,\n",
       "  <matplotlib.patches.Wedge at 0x1fafa055490>],\n",
       " [Text(-0.8434320948332932, 0.8535937566577692, 'PostgreSQL'),\n",
       "  Text(-0.8789062256395302, -0.6614558538043755, 'MongoDB'),\n",
       "  Text(0.3578595814912377, -1.0401617758478325, 'Redis'),\n",
       "  Text(1.0986951210330855, -0.05356333651009644, 'MySQL'),\n",
       "  Text(0.5411414937298409, 0.9576877798968915, 'Elasticsearch')],\n",
       " [Text(-0.492002055319421, 0.49792969138369864, '24.8%'),\n",
       "  Text(-0.4794033958033801, -0.36079410207511386, '20.9%'),\n",
       "  Text(0.1951961353588569, -0.5673609686442722, '19.1%'),\n",
       "  Text(0.5992882478362284, -0.02921636536914351, '18.8%'),\n",
       "  Text(0.2951680874890041, 0.5223751526710316, '16.4%')])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.pie(database_top5[\"Count\"], labels = database_top5[\"DatabaseDesireNextYear\"], autopct = \"%1.1f%%\", explode = [0.1,0,0,0,0],\n",
    "               shadow=True, startangle = 90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stacked Charts\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a stacked chart of median `WorkWeekHrs` and `CodeRevHrs` for the age group 30 to 35.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "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>WorkWeekHrs</th>\n",
       "      <th>CodeRevHrs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  WorkWeekHrs  CodeRevHrs\n",
       "0  30.0         40.0         5.0\n",
       "1  31.0          8.0         3.0\n",
       "2  32.0         40.0         2.0\n",
       "3  33.0         40.0         2.0\n",
       "4  34.0         40.0         2.0\n",
       "5  35.0         38.0         2.0"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "Query = \"\"\"Select Age,WorkWeekHrs, CodeRevHrs\n",
    "from master where Age Between 30 AND 35\n",
    "group by Age\"\"\"\n",
    "\n",
    "df_stack = pd.read_sql_query(Query, conn)\n",
    "df_stack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Work week and Code Rev Hours For Age Group 30-35')"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(df_stack[\"Age\"],df_stack[\"WorkWeekHrs\"].median())\n",
    "plt.bar(df_stack[\"Age\"],df_stack[\"CodeRevHrs\"].median())\n",
    "\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Hours\")\n",
    "plt.legend([\"WorkWeekHrs\", \"CodeRevHrs\"],bbox_to_anchor=(1.1, 1.0))  # \n",
    "plt.title(\"Work week and Code Rev Hours For Age Group 30-35\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing comparison of data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line Chart\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the median `ConvertedComp` for all ages from 45 to 60.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Median Salary')"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "Query = \"\"\"Select Age, ConvertedComp from master where Age Between 45 AND 60\"\"\"\n",
    "\n",
    "df_conv = pd.read_sql_query(Query, conn)\n",
    "df_conv_median = df_conv.groupby(\"Age\").median()\n",
    "df_conv_median.plot(kind = 'line')\n",
    "plt.title(\"Median Salary for Age 45-60\")\n",
    "plt.ylabel(\"Median Salary\")\n",
    "\n",
    "# plt.plot(df_conv[\"ConvertedComp\"].median())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Chart\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a horizontal bar chart using column `MainBranch.`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Main Branch of Developers')"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "Query = \"\"\"Select MainBranch from master\"\"\"\n",
    "df_mainbranch= pd.read_sql_query(Query, conn)\n",
    "df_mbv = df_mainbranch[\"MainBranch\"].value_counts().rename_axis('MainBranch').reset_index(name='Counts')\n",
    "df_mbv.plot(kind = 'barh', legend = None)\n",
    "plt.yticks([0,1],[\"Professional Developer\", \"Code as Hobby\"])\n",
    "plt.xlabel(\"No.of Developers\")\n",
    "plt.title(\"Main Branch of Developers\")\n",
    "\n",
    "# plt.bar(df1[\"MainBranch\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Close the database connection.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ramesh Sannareddy\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other Contributors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rav Ahuja\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Date (YYYY-MM-DD) | Version | Changed By        | Change Description                 |\n",
    "| ----------------- | ------- | ----------------- | ---------------------------------- |\n",
    "| 2020-10-17        | 0.1     | Ramesh Sannareddy | Created initial version of the lab |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © 2020 IBM Corporation. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork21426264-2022-01-01&cm_mmc=Email_Newsletter-\\_-Developer_Ed%2BTech-\\_-WW_WW-\\_-SkillsNetwork-Courses-IBM-DA0321EN-SkillsNetwork-21426264&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}