00_data_acquisition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0c5d5dca",
   "metadata": {},
   "source": [
    "# Data Acquisition\n",
    "* This file is being used to find an appropriate data source\n",
    "* Initial exploration of data from multiple sources"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a65ea46",
   "metadata": {},
   "source": [
    "# Import Helper Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0b93efe8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# importing sys\n",
    "import sys\n",
    "\n",
    "# adding 00_helper_files to the system path\n",
    "sys.path.insert(0, '/Users/qmcbt/codeup-data-science/00_helper_files')\n",
    "\n",
    "# Import Helper Modules\n",
    "import QMCBT_00_quicktips as qt\n",
    "import QMCBT_01_acquire as acq\n",
    "import QMCBT_02_prepare as prep\n",
    "import QMCBT_03_explore as exp\n",
    "import QMCBT_04_visualize as viz\n",
    "import QMCBT_05_model as mod\n",
    "import QMCBT_wrangle as w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b1362f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "url=\"https://upload.wikimedia.org/wikipedia/en/thumb/1/15/The_Elder_Scrolls_V_Skyrim_cover.png/220px-The_Elder_Scrolls_V_Skyrim_cover.png\"\n",
    "pic1 = 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\"\n",
    "pic2 = 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rb2oyfg8kl7g0Vwzh7CQ5tTly26H3pLTxVEiJ+6eQUngvjLNGWs3k3Ui5I9KuXDcVDiIlcjLm1AbSq/8Ao2MaKlm6kbVK8KjvA0ZPmU3UkfSunUmOOZpgogoPHeEY2xaTtZsvmVRtm/CWHpv7gVcMLHkXWmOGYXTM3pvSsXiKxOeXfE4+X7pnSp5W5fqmsbiwKiI8SXfSgONcTAvrQvBcYTr7f9/yqq5utQAAVnXDk61A4bEIskTthzKxaQNGykBmzG1jrc6gWtvfuLS+OxVoHIJBK2FhffS3ppfXpXsThJJIIZWVpEKJDHGnlbNYuzFVBBF84O18oJq2k20+/dlswpDWku0ccupE2NuLzOo0mU1HhrK8gwbfcdXNgvLJYiQgA30BGXTo3QaOwykaMNRofcaGkQ8RxCRPCtuWUQs+WW0GcWy5tTtlHUdqb4SMwsFsqqozLmKlgNbk7Na2mg02rqrBiPwua9J5aah2Nrk2tE5jYidNTM6QTLRC9PPQTMU1omM3quIS9JaobicZzggE5lIsDa7KC6X+K1LTPrag8Wl11Glxe99ri9yASNL7A+xqWm6sZZwIQScYebILt5FyWIAQaZtGvqxykBbbWt1oeXFgtY70bNwh0iWUoBHIqGNzo6SZRYSNlBysbrf2PcmpcZkdMQqspXy217hmG/XYa+tdua43K1YqnS+alAGhA5mdvOORadQToPCJs0du1Hg+lVzwlibvlPUH5jX+Rq2hBTrB1M9IeFvfovFdoU+nXdGhv9f5Q4UUM4N7VJKgpBXWtKxISONutOonU0RkFeVaEIUx+9DZD3NSeWhctCFl/h7xJBHpNOWPUGrKvFsHoVkVTbMLmsADOTe+tFxcOmcjysb9rn5UsFKN07L1tGA47G0mRAWZm7g/H2q04TDZm2qqeAPCgwqZ31mceYnXIu+QfzPU+wq8JMEFY8RWa85W2A18VtweHfTGZ5lx08ErGPlGUaAVWuNcTCKdad4xxUAEk1VXgklOdwQPwj+ZqljDWfAWitWbh6ed/oNyff0Un4ewau3PxADdUjOw/aYdT2H9iRxsKByygAMS1htcm7fMkn41ALhHA0vRGEzA6g260zrYdvQytGl/fokOG7SqHFh1TQ2ibAHT9fyp7DSeZVIUoXXPfrH+NFHW6k39AaFmUEPIGkWIsBAqyKQodmzKyg3Wyh9OmxvfUfGTFE5mUnLexBIKlkZQwI6gsD8Kfn4mgw0RiY8/O4ZjdhJm1aQFhZWvkOwPmXqNFrCC2Cvb4Wm/KHMEyY8tSZ9IEwYBI3KIwPEZkzqsTSxygu4KAPlF0ax8wCg5RYjoNBfV2PFSRqJI4Dy/JFMWVBaZHu2VY9gNtR1prB8clwWIcYpf9MAKljY2XKFN/KDpmOpJAve1NSripftEUfLjUn7S8TEho7HRSw0DWFyDvlNWiYgEze3H8HjieFLqYuXNYGkNOYkwRI4i7RF4gkDcgCUx7jKaF8O4nMH/AGLj4dP51Dz8R/VqSdcg+dqkPCsRVGZv9Tb2F9fmTVKUwQLqaw2Hza0NjOK/ZGRwnMNz5SSvQ9bHXWpPByALUDxeeSTPBlSNVZJBLIXQsxHkWMjTqTc9LnS1dNECRqr8PTbUq5XiW73i2nnvsmuLxywxh8R5TJM0gw9zyWA5bWMY23I1t733gPGKKbOsPKC5HVfvFY5AAQzdAHykX25mmhFpvinH5nvHiYVIi1VAbLm5mUOfMM6BCwFjbQXJvUXxfEQxYRxmYySDy8wgtymSMo2mwzIgANtLWFtassXGNPH3yt9Rrxh/8jfiv8t27GRB2aJJM2JgpvwzjMksZJ0zC/sdDWnECsW4ZN930rY+Gyc2JH3uov7jQ/UVs7Pd8zPVeR7Yp/I8eI/I/KdAr1hTqoB0rjAHpTJJE3mUdRXmHXSlth1P4aSMKNrUKEm470rTvXRAO1OcodqF0FgY8JLmuIgB7mrV4V8PiNs7C1vui99e/wAKtLMo3UUnCb3766etKsY/ptgG5+yedng1nFxFh9/6RRYKKg+K8YVQdaP4nmym1VbgPBpMXiWMykQxkXB/G24Uenf5daW0qTqrsrU3q1WUWGo9F8MgMpE84tFfyKfxHox/Z7d/becODjfa9u4qUx+BUxlbWFvlahPDeGYRanQk29qf0aDaQyheXxGJOIl7tRoNo9/VDR8NAOma3rRS8MTt9amVw46mu/ZAetX5VjVZnwgIeJtmBAPvsf77VHRxkNykR/MsZYyKOYrRsV5kMlyI18iqSTprppVj49hgoRlIuXVNSNma2Y+gJHzqM41g5oAySxpMszJkJNrGOzWuTZbglbAi+UnraktSh06jht++g/C9z2RizUpAE3PJi7YlwmxgfFqJiFH4HF4xpHWWzS5wtpH5bryitjlH3l1sCAdRca61EYrHYuR3z+SRmyvlICF1e4D9Blzv1NsuumzocmB5eVlKNGgVCV8+d3YErZiQAFBBGg1bTU7AYESSMkbx8uWV8jzMVkFmvvYXvcjKcw22vc8gyIk+/wBf7TmBTqF5a0AReOINrkC18o1y7m6a8P8AApsSQ8qOsQPl8rASDoVNhdTpqBrep7G4lL5UZbr0UjS2lrdLVH8PmnWIHLyxzzGLGwdnU/qGXRjdmGo2Gm4qHlwYjUYiSV2bIjuTrcWQEC21l2P7I9q5c0QISoUGioW1HRrEXjgH3tteLA3E7A2sL7FjYA+pOwobF4uXmrPLNEgZIWCKZVVUUqVjkUAnUKzEX0Gva4MkRb9W7EgKrPmjDZIT95nMag6BgRY9B921J4ZgBKsqqqZGYiIPnDXRFZxGkZIzlTETmJ0XewuCmzYH3da6VMUqed4jkwDaRoTa4E6GQPKQ+M+IZJ8WHzKQJAytkJQIG8rMoBJDBdF1Nr9SSRPFSyxxRo75yyhmNrZfMX5IP4gC1yQd9NLazfCfDYxU6q8miayICUYAMyldPvD7vmNjdvxBTe0ePOBI+BIjQDkDMgHRAPMPlr7ir2UXOa5/vxWHtHH06TqeHYALQYi3F4G9zzM7BZBwyexFbB4ExmeJkvqpB+BFv5fWsLWfK1aV/hzxK0oBOjDKfjt9bVzQdkqg+n1S/GU+pQcBqLj0/iVp5X1rthSgor2QGnK8xCbtXRalqlqUTUSohN6V2wpWauZxUqQqpi4mylcm43FAYSexynQjTWrs3teoXj/C+YOYgtIu4H4x29+3ypZjKBqDMNR9k4wGJFF2R2h38ULIuZKiuH4nkTG/3H0b9k9G/r/1RPDMYD5TSeJ4W9LKdQ03B7dU4q0m1Gmm/QojxNxDJEVA8zEKD70fgwqxopB2Gx61VcDG0s8MTm4Uk69QNQKvrYJD0r0NKsKgzjReYxGHNE9M6+4Qqoh/C1OuqgbNTpwS23IpiaaJBdpAPjVsrPkUX4kgJhvHoVZczEsto92+7qNQPlUPxydOScsksmJJIwrFxdY85OYSbkgkg38x0tpQviLxmQTFAAelz1+FBzxNKqMozsFGUOzCwOtgdRb72ltc5uaWYwgOzc2Xq+w6wawU6hIDTIjx2PhOoGoJCJeOKIRrnWUaF1ZSc8gjysofLYJc230Iv0FieKRR4d2YvnW6yRR3XKEmjIJCXALAqmwt5dbXvUbh/DOKkLc2URxMLcuI5vW1yugude9FS+DndyZZ80eVV0Uh8qgALe9hooF7VlL2xYePv+U3GIZPxP0BGmvFwBG48jFxZQ3FMezYSLLK2UyvNGGWzCxXJICNmzWFrm1j0JNOmXnJHAPvNy0NgQOWAGZttrWHY3NTnE+D4dsvkAyWAy6aCwAPcWAqKnk5L84DRVswAuchNvL0Fib/AB96rzTZVOrdabAG54udfDTyvcmSpUtMDMqtk5gVUIyguLH9XmJva6kEjtYnQU1LHEUknxBdWM2SJCxcEqixSSA5gzDYluhQDXahuJYt3jEiRscjDKXFshUM2UIdHvnU3O2U6daN4Z4uwJjyYsASRwZFuoY5zzc2UuLAkOhu1gT7VayDZx+um/v6cK8VHNphzRJkA5CA6IH1BgTYwM0WMovwjjEjxpiiBdJI7h8wbKqWuMxUEgFkHTcb9L/NEGBUjQgg+xFjWP4c3bDthJWLcw9coKyWzWB1I8sV7726WFbBAboCdwNfcb1twbvgiIXn/wD0FICuHgySN5kxYEzyOOF8s8XwbrO8Y/C7KT+6xH8quHhGEpY3N+9ReJPNnZ/zOzf8iT/OrTwuHKPhWGqbQFopg6ndazCQyq191B+YvS7DvSeHQ/qox1yJf3yinuTToGRdeXc2CQkD3rxYUvlivWFTKjKmr1y/pTxQUnKKlcwozh3F9lkFvWphYkcEqRWW4DxJcAP9al4vFPLHkqhMC1c4rwfEJI0nLNrkkrqPelYTFiRcrb9DTcvjmbYECoiPiILliQCTfTQX9qX4nDn52+oTHDYgQGP9CjOIYNlYMuhU3BHQjUUrHeMcRYKia2sSB17+lSGGnDjK3wNRvFOHW1FZ6FY0jOx1WmvQbWEHUaKMxHGMS3meQ/uigxiJGYFtB1vTEseU3djXEVpdrgdzTUVARIKUmkWmCEQcEM/NI0qWwakAU34e4RYkykvr5QDv79hUxyoIlzTzZALA5vLr01OlYMS/qQAmGDZkBcd0/gMV0NPTYvSo2KXCPflz2I2J+6e2p0Pwp9MPIFzMPLfLmGoJte3pvWQtcFrMaoad71H4pQRY2s3lNxfRrr8N6OmoeOZfMrIGzC2pPl65hbXMCFIPoagaq2mQCCUFwyXNDZtWsFPuptc+ttPjURxDhkTPdlBo3CFg0gykKWJBItrf1pXIeRsiC7fID1J6CpBINlybi6mPCmEwyMsrpdl8qW0yje/vrVv4xxFUhlKsTeJyNdjkNqqWAwSR2ErAnQAA2zE62A3NJ8RYaVYZGjN1Itk1uFOhPtqa3U6rQ2Euq0HF8qlcIw92J9atICooLaC4v86YwHCygXTpcn1p2bh0uKkEMRHkGZrnr0vVLB1HrRWdkZdXLC+LFAHkuO6/0qawPHoJR5WAPY6VkeMgnwzFWKt+4b2psY+1jY27i96ZB0JQWArclANK5VZZwrxe6CxbMvY71ZeHeLQ5H866zlcdEbK2PlG5A96azx/mX5ioHjeHhxKZtc3oxH8Kqv6Lh/LJ/wAjRnUGihuP8EUAMPKx+VRJ4HLluHUHs2n1qd4jxgYhQHBBHUClx4QyoFvp39KolalWOH8FxEp1YBRpca39qf4l4RkCXSU36jpV2w8UaLlXoKSZ0yMSbKO9daKNVQvDnEpoW5E/mANg3Vf6ir5BIHFm+BrP5OJQtKZS65c2gHYVcMLiAQGU3BAII2I9azVsOD8TbFaaOIIs64QvFeGsG8ux6729adw2G6WuPapSDHxscrEA9j19u9d4hxvD4ZC0hAAFZGte20wthcxwmJXsLh8oLMpttp/Gw1p9dVzALMnwzD+vsbVn0n+K7NJlhw2dL21azsPRQD0qxYfiYmRpsMrxTAeZJFMeY20Dg6MOmYfPpXZYW6oBDtEaUSUkQNqPvRPoy+6nUD6UwuFW9jmGUhsmdgtxqCUBynvtUfjV5vJxcDESBgrgnL5W3B/KQbG503GxvUpiJ+WH+0xyCQKcpQalulxcBlO3brUFh8l0He90nEbVH4TV2/v++lLlxNxfa42O49Kb4NCVWR2a95GKi5sAUjB02BOX6VTErvRdxtwCRvQkfEZzEmHwsAExLGSYsSNSbM2nlAFu+2lPY7E6H+FGYQcyBDh1aFRYTTOQXklNrrCgvmI11OgB6kWq2k2ZhcvIESk8J4aIWLM3PxBHmmkJst+i78tf2Rqba1JJjgG5YOeS12sNFB6t0Ub2vvUBPj3k/UwjkwoTzZ21II3CX+/IbG5J069qMwqSPBfBKjLnYEljnJGhY5h5ye5NdljifFcZmtCJaMQk2IMdiT+wbXt7VAcNxmV2cEhmJudRe9XTw7wSTKWxVtT902N/e2gHpUtxHg+FlTKyqthoV0IrTQYW3Kx4ioHGAs+5PMbKNyL60nBcImW5kby7D1NT3+WTqeZoD5TbW3rXJJGHl0IWrXhxbDdVlKqIK85opAF3s22tDpjshtfb5VZsXg1kN2Av3tS+HcPw6td4g/TXtU0w8tGcXUSovBeIGFtfrUx+n/WrR/kLBNaQAqCL2B0r3+RcN3NdZUSowcM8oVVsK9jeH5ULgt5V+6vWrC7elMuh6WrlWLLoeI40FpCByx+DZrGorxnxSVwuHhBGcAt3F+la++DB1Kr8qjpfDkDOZDGM3eplRlWPQ+BpTHobn361bvDME0EQhkW9tjvV5HDYlGgPzpscPTfzUG6kCFT+IYRmIL6Aa2qucW8ORurTO0hOhVc11FyBpe/S9X7xJEFRQNy2/p/ZqDm1ZV9ifibf1rLUeQ8ALZh6YcwkoXgHBkgAsAgNm0+81wPKxOu96kp8SQbj2oDiuKs1xQ6YwHesz3FxkrYxgaIC0DwXw9J4HJAVllGW3msQAwYg7hr6j9mrgMKDGI5ArgdGGnpa99hpes8/w64llxDxE6SJcfvJqP8A4l/lWgvNTXDf5GT6H0SrEgtfl21HqojF+F8K34HT9xtPk16HTwvhhpmmt2uv/wCKl5MTTJxFXd0p/wDIXAr1f+ihIPD+FU3EJc95WuP+Ox+VSD4VW3AvsAosB7UwMRQfG+L/AGfDyT/iAyRjvIwsPlqfYGusjKTSRZckvebmSs98WSK2IeKNjyorRqtyRdfvm2182au4HH8iLyfe3A71CwnS51J1uaexXDpJUDx9NMvfW9KWuJeSEwqtAYAVfPD3GZJkBfS9TyoDvVM8MStywrrlI6HSrMshFa5WAhN8XmKocv0qkY7ictwIk2NyTpV1nkvvUHxbAHR0HwouiyBw3F1NlYEOdLW61J4PBzsbcsqt+psTUDw/BSPOrSS2UE6WAPprV5CeXMHY270Sd0ZQl4XxIEblMp0FtNdqO/zJB3qn46VM2ZRqDrS/0wn+0vyqM0boyzsrqY6SYaer0dClDtCO9q8Ih3ogrrXGQdqlQUOcKKT9mUUTHTGIoRCqnjPQxj94/wD1/pUFhV1JPW30qd8Y6tH+6f41F4SMdqwVj8ZTLD/6woLjGGJOlV+SUofNp77VouKiW21RGMwkZBuoNcgjQq+ZUPwniJiljmXdGVrDqAdR8RcfGtpaYMAym6sAynuCLj+NYLiIFScogsvbp9dq2Lwec2BhvrbT4BiAPpTHAuyuLfVYcYAQHKQZqQWpbrTRFMlhSkuxCj/z1rNPHniATzCOI3iiuq2/Gx+/J67WHoPWrr4klKYLEMps33bjexNiKyaOMZjp0pdjKhs0ea2YVgMuTkUpJsa1TwHhwcMzEf6hHyVP61muDgXt/GtW8C6YWw/Of4CsdH5ldifkR83DVddhfvTUuAbLUj1olq1rAqwcL0O9BTYV7gDarYv3q7yVvsKJXSpEvh8Sas5WxvcVMxcJGWyyMbdzU1NGLbU3kFtqjVQoZuDRtoykHe4O9K/QEPdvnUxHtXbVEAoX/9k=\"\n",
    "pic3 = 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+tfOtYuq04YvcAmZEhuxx5DvX0nS3lW0ptsGDEgHOC0QI+4qk+Or4G093BkRKhRPzTyTAxOP0hwcs4zUEtfkz6IuLUrpdii6rVuhBlTyVjj1x/X+VQhqjOYM/b9RW/VWlJxIHaTOAAZ9RBntzxiodu1LEHt/WP3NfRwdHi5km2WDpGrvCURwCxkx355MYx5VdPBvRNQDuvFSgYGWYESsEYGTMx7CqN4evorgfwkgMeSMN8ykj8iI5r6/0XUEFbXykFtgnk7D9RBwMMMf+K9TC7hae0fP8zwT6WlT+Gx4k6o9y4tiyFCkE3GI5KwAsTnHnPHlXNvdaSwoZShIYnsZIHc8xBPFcvxn1K1bS4lty9y423ep2hRMn5iDycQO05EZpHTlN29attuK/EQOC0fJuhpxiQDkE1LyRh4UrKY+PLL45Oi4+IfELahwFb5SskLzlIKLIMCQTJHc1w7ustsyqyXBA2D/luSAIiNhPr/IZr6Bq/BejuMwsf5bIQu9XJG4crBY+XJ9Iqsdb6Xbs/EXerNP8coschoALMY/3D71MX1aRbwQ+pX9R0m7ta7buQBnbct3LLn0Eqbf/AO/6Vv6HptXcZYS6AOXVdyxzyO8/muU3UL1lptvbB/220Ij7pmp9vx9rJG42nA/hNsAe8rB/BFZdai/M6njySi+mvh6l1XWJA+KpRnZUuM5KMzAEE7FgCcMCT3NWLrWvsH4alp2hTtLQAGWVJ/r681840PjCw4ZNRptqvEhXLIT57SNy+YIJIjFbr1k3FAsuzIo3K0g/5ZLEq+P4WBlo4ZcZNdHVGdNeR5rwTjcZKr+q+jPoXT+qW2Qi2Yzmc4PJkYPeTWfUtQriC8MsFWA5I7YwZ8vtVY8PaVyhl7eB5qC0YkER2nBkYxFe7b9vcwFt1bG6ft79+DxVlCN2mYtz3HyOjc6gV5wTgOs7SADHBBB+oHy+1e2OrsuwG5JMTPImIk+fb3rg6+1cYAm3E87Vbb2ye/vBHao2msNBncCRG5hyATABOCRJAB7d/PSo+hSMdXZbdN1dX2gbdzhQScRAmB6iRPtUbQ9ctsVTYqKsbpGRsHB9JwD5Vx7Fgh0ZyCpdW4IyCwaPIxn+wKjrp42kc/EXcO+SREeUFlx/r7xRwigtvuWhvEKqdoCiPOlUO/oW3H5i2cGeR2/SKVFR/wBpr0f8j6DZ6MAoa4R8xJ8vqIkAd5gVq1Ot09veNrM6oTtECYGQJz5/g18ps+M9Qj7w0ng5b5xEDcZyY781ddH1D/FW1dLpX4ioGtmBkEh9pUA/Uhzn8Yr4GX6fLG1KbtfyfokOT7VtKX8EQ+I2ZXe1aCAKCSQGb5pCMSzTtPElY4qt63xXqo2tcI7bUgARGQVxme1dnxCEt2ripaO+4u1iCDtUEFZIwsHZ3P0jivndwspyc16WDj4Xbivuc2bPkg11P7HVvdRus8s7Tzn5fbjNWvoHWFdDYDgO6sP8zA3EQAD35Pec1RwxYKOSMCeI7frWsqZ4iOff1rXJxozVPVE4+XLG7StP1Lda6XrBfH+RdQkgM6h3Ro+ksxlYmMk4qG0i4V1KqjMclSCwJP8AEAxG38H3qs3VPJFYg+VXWG+7RkuXKMtJ97/9LHrNGbZzBHIyCCO0EcitdsGcR6T/AC86i9K6y9sfDYBkmYwCG43BuZj8wK7um0BuLvsxc4LFT8ybpgOD9PB58u9ZTTh37ep34s8M3bT9P7HS6KbCCXQkzl2MAH/Vs/qc+Qq26BmuWSqOwSR87B2JBPAI3E/b08qqXSunuvIBPv8AIsc7icftVj0vVU01xUJLs5Cs8nZbWcbRwxB7/r2ryeRcn4dno5MdQ0vF6Get6UgLXbyF0IKoh3KWUqAzZAKSB74HEmanpOo2hd+aDMqrEbdi8KMSB6tH86+n3AbsL/iB8xgEKAYPYiCDPniq/ct9MLMXtDcGKz8Mjeykglf4YJHOKx43JtNSTfy8jljkarVy+T7Ghr5t6EMrYDljB3CRAgkdic+wqidZ6oHtqjGWEwTy0iJMnHPHpX0LWf4a9tsWlZbRXLKxQAZBA3LBJ48/xNUfxDrhvuJaFu3bkqRbgFlU7fnaN5EzidsRiu7gpOTtbuzHlSahdVf8lYa6oYAiYBB+6BR3zxP3rT8dRLAZM/vIzPt27VnrtkgqCPMGY+xOc1EYHn+5r2keHN2Tuj6gI6luJB84iRx7E1f/AAR1V7mqChiF3boEnDfKRzhRgjONp86onSdWbZJtmJEMDDBl77lYFSPQir90g2fgpq1tqHWVuJacqrQT82yCZEn6TEHyGO/jJvR5POqrrfkc3xBqrdvejBX3XDjcD3y0gfLmYPpUvwzZs31YHd8RjtF04gr/AMtdo75+oZ45GKsOpbpDuTct/MYElHMsRkcSD/c10ei2bHw1v6W58FCxQ2yoX5kJknDHjOYkEcTW93K2vkcbk1iqPfzdkTpuhu2ivzlbgUh7ZS4ZCnBD7QrYA4zx5VWep6iyxIuhnaSYZirITnEzA9CI9KtHibxClsJauuNR8WQ21dhtpPKxnmMHJjJqr9X0hIlH+KpXDqR8RQfp3LyPuKvG5Ral3MlSmpLSZTtfbAPyYU8K3I9AeG/78Cofw5Md67f/AAe6SYXeoBJI4hRJLdxAkzx61Cua8WDttsGYZDD5lUx/DIgn14HrXHkj0vxaPYxy6lUdnl3TrbUb8u3CTgDsXIPyj07x2FS10Gra0Ctp2QkgLZBeQRLSV3EDgc5z61WmYkknvmsaweQ29k9b/wA+Be+mu+ltM+oUW921VQk79o5+UkxzxHnNVjqPUizyjMBmJwc+YGK5lZKOCRiryzylFRWkjOHFjGbm9tnZ0PiLUWyAt1x2mY/SrD0nxLqHDb9rjbuyiEbVB3MxlYxPJ8qojNJmu54Su7XYFCyXEKNHZSQT7ZC59KtiyS6qsrnwY+ly6VZ9F8P9fsXFdbtlrYWMgiJj5QA3GDPyniTXdt6Czd2vbPMQGhSYgwMwSDnEjiqhprQtjcjC0pVgTIbEEiMwD7ifSuH4j8WXQ5s22YKu1WMwWZR807YxMj2FduSXs1bZ5EMPtp1BaL/d6HqEJVYgcYPfPnXlfLV8VaoABbzqAAAq3GUAAQAADivay98Oj/Tp+pXa63T/ABBftW/hqUKjgMiPGd2NwPfPvXIr2vMcYyVNWe7GUou4uju6rxRqLiG3cIYHjER+O3pXGa4Scmtc1O06oFDMPYTJP28veqqMYqkjROWSXil9zDU3O3YiRH9+dazdZ8EyfWtusu7xJgRgKBAAMnEfaok0S0Tkm+p70TzaTYSTBWIg7txMz7ARPeoVxQDAM1jurGrJUZymn2Qqf0fqVzTuLlptrcHuGB5Vh3B8vQHkCoFKNJqn2KptO0XK34ls3fl1CvbUkEi3DqD5gEgr3x81djT37V1Alq6lyOB9L4/2HJ58q+bGlcmTgwf7W1/0enh/Vs+N+Kn8+/3PqGhv3NLcW7c3C2rAsT2Xy9Sew5rg3+ofERtu9mucCV2q26VBkcQSPtVZGucwruzLxBJaAeYBNeJe2/SxB9Mfefaqw4ajvzNMv6m8krSrVdyTqXdX2XZlTBDE/LHse3pW7TX0a43xDhlIDGWAYkCZMkQAYPOK1axi9vdtUd8cgH27QO/nXKNdSgjz5ZpMkatVDFVMr2J7itBbtWNK0SMGydZ+GLbEmWaABHBBB57SMT71hcvSBnIwMsSBz5x+Ki1u0glhwffitFK3Rm41tnW6bcdBvuByLikIwKk4IBOQTAEirDoeqtc0/wDhrRLOt17h/wBRQoihvsVMjsCPWqbqLxnbwPIce8D0rVbulcqSGnkYIjiDW0czi9eRz5OOsi33LvY07Wz8S6Qv+5zAjtBPPPFQtZ1LSo3xBdd7k/8AtrCgf9TEfoDVUv3mYyzMx82JJ/JrVUy5baqKopDgpO5O/lpHc6v4he7b+Co2W8bgDLORkb2gSAcwABIByQDXDpSueU5TdyezrhCMI1FUhW7TopJ3NEAkepH8JMiJ860mvKqXZm47j95rwua8otBRk/61M6b1F7TbliYjP6e8eVai4b6onz4mT3I/n+ajMM1PU4u0yjipKmjtN4l1ByShMRJRCfyRP/iuTful2LMZLEknzJya10qZTlL9zsQxxh+1JClKVU0FKUoQKUpUEilKUApSlCBSlKAUpSpAr0nFeUoSS97NbMsfkgR/tPr6GMetRK2W7hWY/iEH2P8A4rXQClKUIPK26dZPMYJJ9s1qrNXIBA78/vRBnlxyTJMmsaUoD2lKVAFKUoSKUpUkClKVAFKUoSKUpQgUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUqQKUpQClKVAFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgP/2Q==\"\n",
    "\n",
    "\n",
    "qt.display_pic(url, width=120, height=74)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f8efed5",
   "metadata": {},
   "outputs": [],
   "source": [
    "qt.display_pic(pic1, width=320, height=175)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17f65fc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "qt.display_pic(pic2, width=220, height=75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aea45280",
   "metadata": {},
   "outputs": [],
   "source": [
    "qt.display_pic(pic3)#, width=420, height=275)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d43de37c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "qt.cs_hypothesis()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b9c567f",
   "metadata": {},
   "source": [
    "**Set Hypothesis**  \n",
    "\n",
    "* One Tail (```<= | >```) or Two Tails (```== | !=```)?\n",
    "    * two_tail (gender, been_manager)  \n",
    "\n",
    "\n",
    "* One Sample or Two Samples?\n",
    "    * two_sample (gender, been_manager)  \n",
    "\n",
    "\n",
    "* Continuous or Discreat?  \n",
    "    * Discreat (gender) vs Discreat (been_manager) = **$Chi^2$**\n",
    "    * T-Test = ```Discreat``` vs ```Continuous```  \n",
    "    * Pearson’s = ```Continuous``` vs ```Continuous``` (linear)  \n",
    "    * $Chi^2$ = ```Discreat``` vs ```Discreat```  \n",
    "\n",
    "\n",
    "* $𝐻_0$: The opposite of what I am trying to prove  \n",
    "    * $H_{0}$: The employee gender **is NOT** ```dependent``` on whether the employee has been a manager  \n",
    "    * ```employees.gender ``` != ```employees.been_manager```  \n",
    "\n",
    "\n",
    "* $𝐻_𝑎$: What am I trying to prove  \n",
    "    * $H_{a}$: The employee gender **is** ```dependent``` on whether the employee has been a manager  \n",
    "    * ```employees.gender ``` == ```employees.been_manager```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "756c0836",
   "metadata": {},
   "outputs": [],
   "source": [
    "qt.cs_train_val_test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f142578",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "qt.cs_confusion_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1535b267",
   "metadata": {},
   "outputs": [],
   "source": [
    "qt.cell_color()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "033f848d",
   "metadata": {},
   "outputs": [],
   "source": [
    "acq.TOC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9dcb3369",
   "metadata": {},
   "outputs": [],
   "source": [
    "%who"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f011b30a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "qt.imports()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67e27980",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97071c2e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from xml.etree import ElementTree\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "# Only works inside notebook\n",
    "%matplotlib inline \n",
    "#import matplotlib as mpl\n",
    "#mpl.rcParams['agg.path.chunksize'] = 10000 # assists with processor speed\n",
    "\n",
    "# import preprocessing\n",
    "from sklearn.preprocessing import MinMaxScaler \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.preprocessing import QuantileTransformer\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# import helper files from local environment\n",
    "from env import user, password, host\n",
    "import QMCBT_01_wrangle as w\n",
    "import QMCBT_02_explore as explore\n",
    "import QMCBT_03_model as model\n",
    "\n",
    "# allows import reload without needing to clear kernel and rerun\n",
    "from importlib import reload\n",
    "# reload(packagename) \n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dde0c6f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Left Align Tables in Jupyter Notebook\n",
    "from IPython.core.display import HTML\n",
    "table_css = 'table {align:left;display:block}'\n",
    "HTML('<style>{}</style>'.format(table_css))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9505b420",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "# TX Hate Crimes (by Victim Type)\n",
    "* https://crime-data-explorer.fr.cloud.gov/pages/docApi\n",
    "* https://api.usa.gov/crime/fbi/sapi/api/hatecrime/states/TX/all/VICTIM_TYPE?API_KEY=iiHnOKfno2Mgkt5AynpvPpUQTEyxE77jo1RU8PIv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29b5a4d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://api.usa.gov/crime/fbi/sapi/api/hatecrime/states/TX/all/VICTIM_TYPE?API_KEY=iiHnOKfno2Mgkt5AynpvPpUQTEyxE77jo1RU8PIv'\n",
    "response = requests.get(url)\n",
    "response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b4d63b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "response.ok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7c687a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "response.text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c17b8b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = response.json()\n",
    "print(type(data))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ab0546d",
   "metadata": {},
   "outputs": [],
   "source": [
    "response = requests.get(url)\n",
    "print(response)\n",
    "response.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "033d33aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "response = requests.get(url)\n",
    "data = response.json()\n",
    "data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "debfc577",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Pull it all at once\n",
    "response = requests.get(url)\n",
    "data = response.json()\n",
    "\n",
    "TX_hate_crimes_victim_df = pd.DataFrame(data['data'])\n",
    "TX_hate_crimes_victim_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "134fc24e",
   "metadata": {},
   "outputs": [],
   "source": [
    "TX_hate_crimes_victim_df.key.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc29a773",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "# TX Hate Crimes (by location)\n",
    "* https://crime-data-explorer.fr.cloud.gov/pages/docApi\n",
    "* https://api.usa.gov/crime/fbi/sapi/api/hatecrime/states/TX/all/LOCATION?API_KEY=iiHnOKfno2Mgkt5AynpvPpUQTEyxE77jo1RU8PIv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b709c772",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://api.usa.gov/crime/fbi/sapi/api/hatecrime/states/TX/all/LOCATION?API_KEY=iiHnOKfno2Mgkt5AynpvPpUQTEyxE77jo1RU8PIv'\n",
    "response = requests.get(url)\n",
    "response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "955a616b",
   "metadata": {},
   "outputs": [],
   "source": [
    "response.ok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0078004f",
   "metadata": {},
   "outputs": [],
   "source": [
    "response.text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08c4b776",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = response.json()\n",
    "print(type(data))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "708768f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "response = requests.get(url)\n",
    "print(response)\n",
    "response.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b0ccb10",
   "metadata": {},
   "outputs": [],
   "source": [
    "response = requests.get(url)\n",
    "data = response.json()\n",
    "data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2b0a506",
   "metadata": {},
   "outputs": [],
   "source": [
    "ui_type = data['ui_type']\n",
    "noun = data['noun']\n",
    "category = data['category']\n",
    "title = data['title']\n",
    "short_title = data['short_title']\n",
    "ui_restriction = data['ui_restriction']\n",
    "api_keys_column = data['keys']\n",
    "api_data_column = data['data']\n",
    "\n",
    "\n",
    "print(f'ui_type: {ui_type}')\n",
    "print(f'noun: {noun}')\n",
    "print(f'category: {category}')\n",
    "print(f'title: {title}')\n",
    "print(f'short_title: {short_title}')\n",
    "print(f'ui_restriction: {ui_restriction}')\n",
    "print(f'api_keys_column: {api_keys_column}')\n",
    "print(f'api_data_column: {api_data_column}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d01f63a7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(data['data'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "761c42ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.month_num.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7eebc2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.data_year.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20493ec4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.data_year.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32669a95",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.value.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f73640a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d3bd1f4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Pull it all at once\n",
    "response = requests.get(url)\n",
    "data = response.json()\n",
    "\n",
    "TX_hate_crimes_df = pd.DataFrame(data['data'])\n",
    "TX_hate_crimes_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4000aa4d",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "# National Hate Crimes (by Location)\n",
    "* https://crime-data-explorer.fr.cloud.gov/pages/docApi\n",
    "* https://api.usa.gov/crime/fbi/sapi/api/hatecrime/national/all/LOCATION?API_KEY=iiHnOKfno2Mgkt5AynpvPpUQTEyxE77jo1RU8PIv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d40282f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://api.usa.gov/crime/fbi/sapi/api/hatecrime/national/all/LOCATION?API_KEY=iiHnOKfno2Mgkt5AynpvPpUQTEyxE77jo1RU8PIv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6494ecc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "response = requests.get(url)\n",
    "response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3077a28",
   "metadata": {},
   "outputs": [],
   "source": [
    "response.ok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae953368",
   "metadata": {},
   "outputs": [],
   "source": [
    "response.status_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07e266fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "response.text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed24e32c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = response.json()\n",
    "print(type(data))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "342a30f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "response = requests.get(url)\n",
    "print(response)\n",
    "response.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f51b04c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "response = requests.get(url)\n",
    "\n",
    "data = response.json()\n",
    "data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "386d3666",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99d6769a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Pull it all at once\n",
    "response = requests.get(url)\n",
    "data = response.json()\n",
    "\n",
    "national_hate_crimes_df = pd.DataFrame(data['data'])\n",
    "national_hate_crimes_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0dec553",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "# Life Expectancy\n",
    "* **GHO Selections:** https://apps.who.int/gho/athena/api/GHO\n",
    "* **Homepage:** https://www.who.int/data/gho/info/athena-api-examples\n",
    "* **CSV Data Pull:** https://apps.who.int/gho/athena/api/GHO/WHOSIS_000001,WHOSIS_000002,WHOSIS_000003,WHOSIS_000004,WHOSIS_000005,WHOSIS_000006,WHOSIS_000007,WHOSIS_000008,WHOSIS_000009,WHOSIS_000010,WHOSIS_000012,WHOSIS_000013,WHOSIS_000014,WHOSIS_000015,WHOSIS_000016?format=csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33570c61",
   "metadata": {},
   "outputs": [],
   "source": [
    "life_Expectancy_df = pd.read_csv('../../my_notes/smoking_WHOSIS000001-16.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e362ec25",
   "metadata": {},
   "outputs": [],
   "source": [
    "life_Expectancy_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be720eab",
   "metadata": {},
   "outputs": [],
   "source": [
    "life_Expectancy_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d762d181",
   "metadata": {},
   "outputs": [],
   "source": [
    "life_Expectancy_df.YEAR.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d66bc43",
   "metadata": {},
   "outputs": [],
   "source": [
    "life_Expectancy_df.GHO.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3029a3c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "life_Expectancy_df.GHO.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33454196",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "life_Expectancy_df.GHO.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf39c1f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "life_Expectancy_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2a35d2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f'WHOSIS_000004    {round(9690/39880*100,2)}%')\n",
    "print(f'WHOSIS_000003    {round(9648/39880*100,2)}%')\n",
    "print(f'WHOSIS_000016     {round(6262/39880*100,2)}%')\n",
    "print(f'WHOSIS_000014     {round(4353/39880*100,2)}%')\n",
    "print(f'WHOSIS_000002     {round(2328/39880*100,2)}%')\n",
    "print(f'WHOSIS_000007     {round(2328/39880*100,2)}%')\n",
    "print(f'WHOSIS_000015     {round(2328/39880*100,2)}%')\n",
    "print(f'WHOSIS_000001     {round(2328/39880*100,2)}%')\n",
    "print(f'WHOSIS_000006     {round(615/39880*100,2)}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "888ada37",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "#### About 24% of data\n",
    "* 'WHOSIS_000004': 'Adult mortality rate (probability of dying between 15 and 60 years per 1000 population)',\n",
    "* 'WHOSIS_000003': 'Neonatal mortality rate (0 to 27 days) per 1000 live births)',\n",
    "\n",
    "#### Between 10-15% of data\n",
    "* 'WHOSIS_000016': 'Mortality rate among children ages 5 to 9 years (per 1000 children aged 5)', \n",
    "* 'WHOSIS_000014': 'Stillbirth rate (per 1000 total births)', \n",
    "\n",
    "#### These are all exactly the same record count at 5.84%\n",
    "* 'WHOSIS_000001': 'Life expectancy at birth (years)', \n",
    "* 'WHOSIS_000002': 'Healthy life expectancy (HALE) at birth (years)', \n",
    "* 'WHOSIS_000007': 'Healthy life expectancy (HALE) at age 60 (years)', \n",
    "* 'WHOSIS_000015': 'Life expectancy at age 60 (years)', \n",
    "\n",
    "#### Very little representation at only 1.54% with just 615 records\n",
    "* 'WHOSIS_000006': 'Infants exclusively breastfed for the first six months of life (%)', "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21be01ba",
   "metadata": {},
   "source": [
    "'WHOSIS_000001': 'Life expectancy at birth (years)', \n",
    "'WHOSIS_000002': 'Healthy life expectancy (HALE) at birth (years)', \n",
    "'WHOSIS_000003': 'Neonatal mortality rate (0 to 27 days) per 1000 live births) (SDG 3.2.2)',\n",
    "'WHOSIS_000004': 'Adult mortality rate (probability of dying between 15 and 60 years per 1000 population)',\n",
    "'WHOSIS_000006': 'Infants exclusively breastfed for the first six months of life (%)', \n",
    "'WHOSIS_000007': 'Healthy life expectancy (HALE) at age 60 (years)', \n",
    "'WHOSIS_000014': 'Stillbirth rate (per 1000 total births)', \n",
    "'WHOSIS_000015': 'Life expectancy at age 60 (years)', \n",
    "'WHOSIS_000016': 'Mortality rate among children ages 5 to 9 years (per 1000 children aged 5)', "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52d1bc06",
   "metadata": {},
   "source": [
    "|[GHO Code](https://apps.who.int/gho/athena/api/GHO)      |[Documentation](https://www.who.int/data/gho/indicator-metadata-registry)|[Global Health Observatory](https://www.who.int/data/gho) (GHO) Code Description|\n",
    "|:-------------|:-----------:|:------------------------------------------------|\n",
    "|WHOSIS_000001|[📖](https://www.who.int/data/gho/indicator-metadata-registry/imr-details/65)|Life expectancy at birth (years)| \n",
    "|WHOSIS_000002|[📖](https://www.who.int/data/gho/indicator-metadata-registry/imr-details/66)|Healthy life expectancy (HALE) at birth (years)| \n",
    "|WHOSIS_000003|[📖](https://www.who.int/data/gho/indicator-metadata-registry/imr-details/67)|Neonatal mortality rate (0 to 27 days) per 1000 live births)|\n",
    "|WHOSIS_000004|[📖](https://www.who.int/data/gho/indicator-metadata-registry/imr-details/64)|Adult mortality rate (probability of dying between 15 and 60 years per 1000 population)|\n",
    "|WHOSIS_000006|[📖](https://www.who.int/data/gho/indicator-metadata-registry/imr-details/130)|Infants exclusively breastfed for the first six months of life (%)| \n",
    "|WHOSIS_000007|[📖](https://www.who.int/data/gho/indicator-metadata-registry/imr-details/3443)|Healthy life expectancy (HALE) at age 60 (years)| \n",
    "|WHOSIS_000014|[📖](https://www.who.int/data/gho/indicator-metadata-registry/imr-details/2444)|Stillbirth rate (per 1000 total births)| \n",
    "|WHOSIS_000015|[📖](https://www.who.int/data/gho/indicator-metadata-registry/imr-details/2977)|Life expectancy at age 60 (years)| \n",
    "|WHOSIS_000016|[📖](https://www.who.int/data/gho/indicator-metadata-registry/imr-details/5669)|Mortality rate among children ages 5 to 9 years (per 1000 children aged 5)| \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9304b7f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "life_Expectancy_df.PUBLISHSTATE.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9194fd62",
   "metadata": {},
   "outputs": [],
   "source": [
    "life_Expectancy_df.Numeric.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db111fd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "life_Expectancy_df.COUNTRY.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "687222e1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "life_Expectancy_df.COUNTRY.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cde5983e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "life_Expectancy_df.COUNTRY.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8db593f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "life_Expectancy_df.COUNTRY.isnull().sum() / life_Expectancy_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f0dceec",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "life_Expectancy_df.SEX.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81dfcfa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "life_Expectancy_df.SEX.isnull().sum() / life_Expectancy_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a84fbe9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "life_expectancy_df = life_Expectancy_df[(life_Expectancy_df['GHO'] == 'WHOSIS_000001') |\n",
    "                                (life_Expectancy_df['GHO'] == 'WHOSIS_000002') |\n",
    "                                (life_Expectancy_df['GHO'] == 'WHOSIS_000007') |\n",
    "                                (life_Expectancy_df['GHO'] == 'WHOSIS_000015')\n",
    "                               ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d74e7961",
   "metadata": {},
   "outputs": [],
   "source": [
    "life_expectancy_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ace882e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "life_expectancy_df.GHO.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f980450",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "# Infant Mortality\n",
    "* **GHO Selections:** https://apps.who.int/gho/athena/api/GHO\n",
    "* **Homepage:** https://www.who.int/data/gho/info/athena-api-examples\n",
    "* **CSV Data Pull:** https://apps.who.int/gho/athena/api/GHO/MDG_0000000001,MDG_0000000003,MDG_0000000005,MDG_0000000007,MDG_0000000025,MDG_0000000026?format=csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8675d611",
   "metadata": {},
   "outputs": [],
   "source": [
    "infant_mortality_df = pd.read_csv('../../my_notes/infant_mortality.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a9860a2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "infant_mortality_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29f8ab81",
   "metadata": {},
   "outputs": [],
   "source": [
    "infant_mortality_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1965c77",
   "metadata": {},
   "outputs": [],
   "source": [
    "infant_mortality_df.YEAR.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49e2a05e",
   "metadata": {},
   "outputs": [],
   "source": [
    "infant_mortality_df.GHO.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21823c80",
   "metadata": {},
   "outputs": [],
   "source": [
    "infant_mortality_df.GHO.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a33cf98",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "infant_mortality_df.GHO.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97aafb20",
   "metadata": {},
   "outputs": [],
   "source": [
    "infant_mortality_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdf358d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f'MDG_0000000007    {round(36084/82935*100,2)}%')\n",
    "print(f'MDG_0000000001    {round(35933/82935*100,2)}%')\n",
    "print(f'MDG_0000000003     {round(5345/82935*100,2)}%')\n",
    "print(f'MDG_0000000026     {round(3420/82935*100,2)}%')\n",
    "print(f'MDG_0000000025     {round(2153/82935*100,2)}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bb47274",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "#### Over 35K value counts\n",
    "* 'MDG_0000000007': 'Under-five mortality rate (per 1000 live births)', \n",
    "* 'MDG_0000000001': 'Infant mortality rate (between birth and 11 months per 1000 live births)', \n",
    "\n",
    "#### Between 2-5k value counts\n",
    "* 'MDG_0000000003': 'Adolescent birth rate (per 1000 women)', \n",
    "* 'MDG_0000000026': 'Maternal mortality ratio (per 100 000 live births)', \n",
    "* 'MDG_0000000025': 'Births attended by skilled health personnel (%)', \n",
    "\n",
    "#### MDG...05 returned no data\n",
    "* 'MDG_0000000005': 'Contraceptive prevalence (%)', "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b17ed4d5",
   "metadata": {},
   "source": [
    "'MDG_0000000001': 'Infant mortality rate (between birth and 11 months per 1000 live births)', \n",
    "'MDG_0000000003': 'Adolescent birth rate (per 1000 women)', \n",
    "'MDG_0000000005': 'Contraceptive prevalence (%)', \n",
    "'MDG_0000000007': 'Under-five mortality rate (per 1000 live births)', \n",
    "'MDG_0000000025': 'Births attended by skilled health personnel (%)', \n",
    "'MDG_0000000026': 'Maternal mortality ratio (per 100 000 live births)', "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91f802b1",
   "metadata": {},
   "source": [
    "|[GHO Code](https://apps.who.int/gho/athena/api/GHO)      |[Documentation](https://www.who.int/data/gho/indicator-metadata-registry)|[Global Health Observatory](https://www.who.int/data/gho) (GHO) Code Description|\n",
    "|:-------------|:-----------:|:------------------------------------------------|\n",
    "|MDG_0000000001|[📖](https://www.who.int/data/gho/indicator-metadata-registry/imr-details/1)|Infant mortality rate (between birth and 11 months per 1000 live births)| \n",
    "|MDG_0000000003|[📖](https://www.who.int/data/gho/indicator-metadata-registry/imr-details/4669)|Adolescent birth rate (per 1000 women)| \n",
    "|MDG_0000000005|[📖](https://www.who.int/data/gho/indicator-metadata-registry/imr-details/5)|Contraceptive prevalence (%)| \n",
    "|MDG_0000000007|[📖](https://www.who.int/data/gho/indicator-metadata-registry/imr-details/7)|Under-five mortality rate (per 1000 live births)| \n",
    "|MDG_0000000025|[📖](https://www.who.int/data/gho/indicator-metadata-registry/imr-details/25)|Births attended by skilled health personnel (%)| \n",
    "|MDG_0000000026|[📖](https://www.who.int/data/gho/indicator-metadata-registry/imr-details/26)|Maternal mortality ratio (per 100 000 live births)| "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "214a350f",
   "metadata": {},
   "outputs": [],
   "source": [
    "infant_mortality_df.PUBLISHSTATE.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ca6230d",
   "metadata": {},
   "outputs": [],
   "source": [
    "infant_mortality_df.Numeric.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9cd58a8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "infant_mortality_df.COUNTRY.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a30c95df",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "infant_mortality_df.COUNTRY.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d71580e9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "infant_mortality_df.COUNTRY.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1283a7bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "infant_mortality_df.COUNTRY.isnull().sum() / life_Expectancy_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da4e8f47",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "infant_mortality_df.SEX.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fb860b9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "infant_mortality_df.SEX.isnull().sum() / life_Expectancy_df.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29c6a955",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "# Life Expectancy from Online Example\n",
    "* **GHO Selections:** https://apps.who.int/gho/athena/api/GHO\n",
    "* **Homepage:** https://www.who.int/data/gho/info/athena-api-examples\n",
    "* **CSV Data Pull:** https://apps.who.int/gho/athena/api/GHO/WHOSIS_000001,WHOSIS_000015?format=csv\n",
    "* **Online Example:** https://apps.who.int/gho/athena/api/GHO/WHOSIS_000001,WHOSIS_000015?filter=COUNTRY:*&x-sideaxis=COUNTRY;YEAR&x-topaxis=GHO;SEX&profile=verbose&format=csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24f0df43",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load WHO longevity data\n",
    "# http://apps.who.int/gho/data/node.main.688\n",
    "who_list = pd.read_csv('online_example.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cba7cc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "who_list.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ea1d865",
   "metadata": {},
   "outputs": [],
   "source": [
    "who_list['YEAR (CODE)'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56d8f033",
   "metadata": {},
   "outputs": [],
   "source": [
    "who_list['REGION (DISPLAY)'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90b4e642",
   "metadata": {},
   "outputs": [],
   "source": [
    "who_list['COUNTRY (DISPLAY)'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4a026b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "who_list['SEX (DISPLAY)'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d979775",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "# Checkpoint #1\n",
    "\n",
    "1. a brief (one-sentence) description of your project\n",
    "* Predict future Life Expectancy or Healthy Life Expectancy\n",
    "\n",
    "2. a link to the data source\n",
    "* **GHO Selections:** https://apps.who.int/gho/athena/api/GHO\n",
    "* **Homepage:** https://www.who.int/data/gho/info/athena-api-examples\n",
    "* **CSV Data Pull:** https://apps.who.int/gho/athena/api/GHO/WHOSIS_000001,WHOSIS_000002,WHOSIS_000003,WHOSIS_000004,WHOSIS_000005,WHOSIS_000006,WHOSIS_000007,WHOSIS_000008,WHOSIS_000009,WHOSIS_000010,WHOSIS_000012,WHOSIS_000013,WHOSIS_000014,WHOSIS_000015,WHOSIS_000016?format=csv \n",
    "\n",
    "3. your target variable\n",
    "* Numeric (Expected Years of continued Life)\n",
    "\n",
    "4. what one observation represents\n",
    "* Numeric for one year in specified country according to category of life expectancy\n",
    "    * 'WHOSIS_000001': 'Life expectancy at birth (years)', \n",
    "    * 'WHOSIS_000002': 'Healthy life expectancy (HALE) at birth (years)', \n",
    "    * 'WHOSIS_000007': 'Healthy life expectancy (HALE) at age 60 (years)', \n",
    "    * 'WHOSIS_000015': 'Life expectancy at age 60 (years)', \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c49ebdc6",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger\">\n",
    "\n",
    "# Sometimes it is faster to read the unreadable data than it is to read about how to make the data readable!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa9e9ef5",
   "metadata": {},
   "source": [
    "# Additional Web Resources on Life Expectancy\n",
    "* https://towardsdatascience.com/time-left-to-live-modeling-life-expectancy-and-prototyping-it-on-the-web-with-flask-and-68e3a8fa0fe4\n",
    "* https://www.freecodecamp.org/news/regression-analysis-on-life-expectancy/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "199d95d1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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