Hail_1KGenome.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hail as hl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Running on Apache Spark version 2.4.1\n",
      "SparkUI available at http://100.64.10.24:4040\n",
      "Welcome to\n",
      "     __  __     <>__\n",
      "    / /_/ /__  __/ /\n",
      "   / __  / _ `/ / /\n",
      "  /_/ /_/\\_,_/_/_/   version 0.2.31-6060f9c971cc\n",
      "LOGGING: writing to /Users/pargol_gheissari/Documents/Documents/Cornell_Tech/SDL_Project/hail-20200324-0841-0.2.31-6060f9c971cc.log\n"
     ]
    }
   ],
   "source": [
    "hl.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1001\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1001\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };var element = document.getElementById(\"1001\");\n",
       "  if (element == null) {\n",
       "    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.2.0.min.js\"];\n",
       "  var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.2.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.2.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.2.0.min.css\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {} // ensure no trailing comma for IE\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };var element = document.getElementById(\"1001\");\n  if (element == null) {\n    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n    return false;\n  }\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.2.0.min.js\"];\n  var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.2.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.2.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.2.0.min.css\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from hail.plot import show\n",
    "from pprint import pprint\n",
    "hl.plot.output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-03-24 08:42:00 Hail: INFO: 1KG files found\n"
     ]
    }
   ],
   "source": [
    "#Download data\n",
    "hl.utils.get_1kg('data/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-03-24 08:42:03 Hail: INFO: Coerced sorted dataset\n",
      "2020-03-24 08:42:06 Hail: INFO: wrote matrix table with 10961 rows and 284 columns in 2 partitions to data/1kg.mt\n"
     ]
    }
   ],
   "source": [
    "#Import data from vcf \n",
    "hl.import_vcf('data/1kg.vcf.bgz').write('data/1kg.mt', overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "mt = hl.read_matrix_table('data/1kg.mt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><thead style=\"font-weight: bold;\"><tr><td>locus</td><td>alleles</td></tr>\n",
       "<tr><td>locus&lt;GRCh37&gt;</td><td>array&lt;str&gt;</td></tr>\n",
       "</thead><tbody><tr><td>1:904165</td><td>[&quot;G&quot;,&quot;A&quot;]</td></tr>\n",
       "<tr><td>1:909917</td><td>[&quot;G&quot;,&quot;A&quot;]</td></tr>\n",
       "<tr><td>1:986963</td><td>[&quot;C&quot;,&quot;T&quot;]</td></tr>\n",
       "<tr><td>1:1563691</td><td>[&quot;T&quot;,&quot;G&quot;]</td></tr>\n",
       "<tr><td>1:1707740</td><td>[&quot;T&quot;,&quot;G&quot;]</td></tr>\n",
       "</tbody></table><p style=\"background: #fdd; padding: 0.4em;\">showing top 5 rows</p>\n"
      ],
      "text/plain": [
       "+---------------+------------+\n",
       "| locus         | alleles    |\n",
       "+---------------+------------+\n",
       "| locus<GRCh37> | array<str> |\n",
       "+---------------+------------+\n",
       "| 1:904165      | [\"G\",\"A\"]  |\n",
       "| 1:909917      | [\"G\",\"A\"]  |\n",
       "| 1:986963      | [\"C\",\"T\"]  |\n",
       "| 1:1563691     | [\"T\",\"G\"]  |\n",
       "| 1:1707740     | [\"T\",\"G\"]  |\n",
       "+---------------+------------+\n",
       "showing top 5 rows"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Show data\n",
    "mt.rows().select().show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-03-24 08:42:06 Hail: INFO: Reading table to impute column types\n",
      "2020-03-24 08:42:06 Hail: INFO: Finished type imputation\n",
      "  Loading column 'Sample' as type 'str' (imputed)\n",
      "  Loading column 'Population' as type 'str' (imputed)\n",
      "  Loading column 'SuperPopulation' as type 'str' (imputed)\n",
      "  Loading column 'isFemale' as type 'bool' (imputed)\n",
      "  Loading column 'PurpleHair' as type 'bool' (imputed)\n",
      "  Loading column 'CaffeineConsumption' as type 'int32' (imputed)\n"
     ]
    }
   ],
   "source": [
    "table = (hl.import_table('data/1kg_annotations.txt', impute=True)\n",
    "         .key_by('Sample'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n",
      "Global fields:\n",
      "    None\n",
      "----------------------------------------\n",
      "Row fields:\n",
      "    'Sample': str \n",
      "    'Population': str \n",
      "    'SuperPopulation': str \n",
      "    'isFemale': bool \n",
      "    'PurpleHair': bool \n",
      "    'CaffeineConsumption': int32 \n",
      "----------------------------------------\n",
      "Key: ['Sample']\n",
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "table.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><thead style=\"font-weight: bold;\"><tr><td>Sample</td><td>Population</td><td>SuperPopulation</td><td>isFemale</td><td>PurpleHair</td><td>CaffeineConsumption</td></tr>\n",
       "<tr><td>str</td><td>str</td><td>str</td><td>bool</td><td>bool</td><td>int32</td></tr>\n",
       "</thead><tbody><tr><td>&quot;HG00096&quot;</td><td>&quot;GBR&quot;</td><td>&quot;EUR&quot;</td><td>false</td><td>false</td><td>4</td></tr>\n",
       "<tr><td>&quot;HG00097&quot;</td><td>&quot;GBR&quot;</td><td>&quot;EUR&quot;</td><td>true</td><td>true</td><td>4</td></tr>\n",
       "<tr><td>&quot;HG00098&quot;</td><td>&quot;GBR&quot;</td><td>&quot;EUR&quot;</td><td>false</td><td>false</td><td>5</td></tr>\n",
       "<tr><td>&quot;HG00099&quot;</td><td>&quot;GBR&quot;</td><td>&quot;EUR&quot;</td><td>true</td><td>false</td><td>4</td></tr>\n",
       "<tr><td>&quot;HG00100&quot;</td><td>&quot;GBR&quot;</td><td>&quot;EUR&quot;</td><td>true</td><td>false</td><td>5</td></tr>\n",
       "<tr><td>&quot;HG00101&quot;</td><td>&quot;GBR&quot;</td><td>&quot;EUR&quot;</td><td>false</td><td>true</td><td>1</td></tr>\n",
       "<tr><td>&quot;HG00102&quot;</td><td>&quot;GBR&quot;</td><td>&quot;EUR&quot;</td><td>true</td><td>true</td><td>6</td></tr>\n",
       "<tr><td>&quot;HG00103&quot;</td><td>&quot;GBR&quot;</td><td>&quot;EUR&quot;</td><td>false</td><td>true</td><td>5</td></tr>\n",
       "<tr><td>&quot;HG00104&quot;</td><td>&quot;GBR&quot;</td><td>&quot;EUR&quot;</td><td>true</td><td>false</td><td>5</td></tr>\n",
       "<tr><td>&quot;HG00105&quot;</td><td>&quot;GBR&quot;</td><td>&quot;EUR&quot;</td><td>false</td><td>false</td><td>4</td></tr>\n",
       "</tbody></table><p style=\"background: #fdd; padding: 0.4em;\">showing top 10 rows</p>\n"
      ],
      "text/plain": [
       "+-----------+------------+-----------------+----------+------------+---------------------+\n",
       "| Sample    | Population | SuperPopulation | isFemale | PurpleHair | CaffeineConsumption |\n",
       "+-----------+------------+-----------------+----------+------------+---------------------+\n",
       "| str       | str        | str             |     bool |       bool |               int32 |\n",
       "+-----------+------------+-----------------+----------+------------+---------------------+\n",
       "| \"HG00096\" | \"GBR\"      | \"EUR\"           |    false |      false |                   4 |\n",
       "| \"HG00097\" | \"GBR\"      | \"EUR\"           |     true |       true |                   4 |\n",
       "| \"HG00098\" | \"GBR\"      | \"EUR\"           |    false |      false |                   5 |\n",
       "| \"HG00099\" | \"GBR\"      | \"EUR\"           |     true |      false |                   4 |\n",
       "| \"HG00100\" | \"GBR\"      | \"EUR\"           |     true |      false |                   5 |\n",
       "| \"HG00101\" | \"GBR\"      | \"EUR\"           |    false |       true |                   1 |\n",
       "| \"HG00102\" | \"GBR\"      | \"EUR\"           |     true |       true |                   6 |\n",
       "| \"HG00103\" | \"GBR\"      | \"EUR\"           |    false |       true |                   5 |\n",
       "| \"HG00104\" | \"GBR\"      | \"EUR\"           |     true |      false |                   5 |\n",
       "| \"HG00105\" | \"GBR\"      | \"EUR\"           |    false |      false |                   4 |\n",
       "+-----------+------------+-----------------+----------+------------+---------------------+\n",
       "showing top 10 rows"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table.show(width=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------\n",
      "Type:\n",
      "        struct {\n",
      "        s: str, \n",
      "        pheno: struct {\n",
      "            Population: str, \n",
      "            SuperPopulation: str, \n",
      "            isFemale: bool, \n",
      "            PurpleHair: bool, \n",
      "            CaffeineConsumption: int32\n",
      "        }\n",
      "    }\n",
      "--------------------------------------------------------\n",
      "Source:\n",
      "    <hail.matrixtable.MatrixTable object at 0x1a256a4350>\n",
      "Index:\n",
      "    ['column']\n",
      "--------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "mt = mt.annotate_cols(pheno = table[mt.s])\n",
    "mt.col.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'AFR': 1018, 'AMR': 535, 'EAS': 617, 'EUR': 669, 'SAS': 661}\n"
     ]
    }
   ],
   "source": [
    "#Aggregate rows of table, counts the number of occurrences of each unique element\n",
    "pprint(table.aggregate(hl.agg.counter(table.SuperPopulation)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max': 10.0,\n",
      " 'mean': 3.9837142857142855,\n",
      " 'min': -1.0,\n",
      " 'n': 3500,\n",
      " 'stdev': 1.7021055628070711,\n",
      " 'sum': 13943.0}\n"
     ]
    }
   ],
   "source": [
    "#See distribution\n",
    "pprint(table.aggregate(hl.agg.stats(table.CaffeineConsumption)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max': 9.0,\n",
      " 'mean': 4.415492957746479,\n",
      " 'min': 0.0,\n",
      " 'n': 284,\n",
      " 'stdev': 1.577763427465917,\n",
      " 'sum': 1254.0}\n"
     ]
    }
   ],
   "source": [
    "#Fewer samples in our dataset than in the full 1000genomes cohort, look at annotations on the dataset\n",
    "mt.aggregate_cols(hl.agg.counter(mt.pheno.SuperPopulation))\n",
    "pprint(mt.aggregate_cols(hl.agg.stats(mt.pheno.CaffeineConsumption)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{Struct(ref='A', alt='G'): 1944,\n",
      " Struct(ref='C', alt='A'): 496,\n",
      " Struct(ref='A', alt='C'): 454,\n",
      " Struct(ref='C', alt='T'): 2436,\n",
      " Struct(ref='C', alt='G'): 150,\n",
      " Struct(ref='A', alt='T'): 76,\n",
      " Struct(ref='G', alt='A'): 2387,\n",
      " Struct(ref='G', alt='C'): 112,\n",
      " Struct(ref='T', alt='G'): 468,\n",
      " Struct(ref='G', alt='T'): 480,\n",
      " Struct(ref='T', alt='C'): 1879,\n",
      " Struct(ref='T', alt='A'): 79}\n"
     ]
    }
   ],
   "source": [
    "#Count each 12 possible unique SNPS\n",
    "snp_counts = mt.aggregate_rows(hl.agg.counter(hl.Struct(ref=mt.alleles[0], alt=mt.alleles[1])))\n",
    "pprint(snp_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(Struct(ref='C', alt='T'), 2436),\n",
       " (Struct(ref='G', alt='A'), 2387),\n",
       " (Struct(ref='A', alt='G'), 1944),\n",
       " (Struct(ref='T', alt='C'), 1879),\n",
       " (Struct(ref='C', alt='A'), 496),\n",
       " (Struct(ref='G', alt='T'), 480),\n",
       " (Struct(ref='T', alt='G'), 468),\n",
       " (Struct(ref='A', alt='C'), 454),\n",
       " (Struct(ref='C', alt='G'), 150),\n",
       " (Struct(ref='G', alt='C'), 112),\n",
       " (Struct(ref='T', alt='A'), 79),\n",
       " (Struct(ref='A', alt='T'), 76)]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#counts in descending order\n",
    "from collections import Counter\n",
    "counts = Counter(snp_counts)\n",
    "counts.most_common()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"988f2c53-44c5-470e-9a70-047186b43277\" data-root-id=\"1002\"></div>\n"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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Application\",\"version\":\"1.2.0\"}};\n",
       "  var render_items = [{\"docid\":\"4c2e6e25-2176-4aaf-aaa4-d7f73912ca89\",\"roots\":{\"1002\":\"988f2c53-44c5-470e-9a70-047186b43277\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1002"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = hl.plot.histogram(mt.DP, range=(0,30), bins=30, title='DP Histogram', legend='DP')\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"c21c7d89-2510-49a6-b6ab-f3fa079d2377\" data-root-id=\"1133\"></div>\n"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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Application\",\"version\":\"1.2.0\"}};\n",
       "  var render_items = [{\"docid\":\"573c6bce-5f56-4687-a22a-cd7779fbc521\",\"roots\":{\"1133\":\"c21c7d89-2510-49a6-b6ab-f3fa079d2377\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1133"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting QC metrics\n",
    "mt = hl.sample_qc(mt)\n",
    "p = hl.plot.histogram(mt.sample_qc.call_rate, range=(.88,1), legend='Call Rate')\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add health predisposition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows in the table:  3500\n"
     ]
    }
   ],
   "source": [
    "num_rows = table.count()\n",
    "print(\"Number of rows in the table: \", num_rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of health predispositions:  13\n"
     ]
    }
   ],
   "source": [
    "health_pred = [\"Type_2_Diabetes\", \"Age-Related_Macular_Degeneration\", \"Alpha-1_Antitrypsin_Deficiency\", \"BRCA1/BRCA2\",\n",
    "              \"Celiac_Disease\", \"Familial_Hypercholesterolemia\", \"G6PD_Deficiency\", \"Hereditary_Amyloidosis(TTR-Related)\",\n",
    "              \"Hereditary_Hemochromatosis(HFE‑Related)\", \"Hereditary_Thrombophilia\", \"Late-Onset_Alzheimers_Disease\", \"MUTYH-Associated_Polyposis\",\n",
    "              \"Parkinsons_Disease\"]\n",
    "print(\"Number of health predispositions: \", len(health_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "rand_health_pred = [random.choice(health_pred) for i in range(num_rows)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# turn hail table into pandas df \n",
    "genome_pd_df = table.to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add predisposition list to pandas table\n",
    "#genome_pd_df['Health Pred'] = rand_health_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>Population</th>\n",
       "      <th>SuperPopulation</th>\n",
       "      <th>isFemale</th>\n",
       "      <th>PurpleHair</th>\n",
       "      <th>CaffeineConsumption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>HG00096</td>\n",
       "      <td>GBR</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>1</td>\n",
       "      <td>HG00097</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>HG00098</td>\n",
       "      <td>GBR</td>\n",
       "      <td>EUR</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>HG00099</td>\n",
       "      <td>GBR</td>\n",
       "      <td>EUR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>HG00100</td>\n",
       "      <td>GBR</td>\n",
       "      <td>EUR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>3495</td>\n",
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       "      <td>True</td>\n",
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       "      <td>3496</td>\n",
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       "      <td>GIH</td>\n",
       "      <td>SAS</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <td>3497</td>\n",
       "      <td>NA21142</td>\n",
       "      <td>GIH</td>\n",
       "      <td>SAS</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <td>3498</td>\n",
       "      <td>NA21143</td>\n",
       "      <td>GIH</td>\n",
       "      <td>SAS</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3499</td>\n",
       "      <td>NA21144</td>\n",
       "      <td>GIH</td>\n",
       "      <td>SAS</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3500 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sample Population SuperPopulation  isFemale  PurpleHair  \\\n",
       "0     HG00096        GBR             EUR     False       False   \n",
       "1     HG00097        GBR             EUR      True        True   \n",
       "2     HG00098        GBR             EUR     False       False   \n",
       "3     HG00099        GBR             EUR      True       False   \n",
       "4     HG00100        GBR             EUR      True       False   \n",
       "...       ...        ...             ...       ...         ...   \n",
       "3495  NA21137        GIH             SAS      True       False   \n",
       "3496  NA21141        GIH             SAS      True        True   \n",
       "3497  NA21142        GIH             SAS      True        True   \n",
       "3498  NA21143        GIH             SAS      True        True   \n",
       "3499  NA21144        GIH             SAS      True       False   \n",
       "\n",
       "      CaffeineConsumption  \n",
       "0                       4  \n",
       "1                       4  \n",
       "2                       5  \n",
       "3                       4  \n",
       "4                       5  \n",
       "...                   ...  \n",
       "3495                    1  \n",
       "3496                    2  \n",
       "3497                    2  \n",
       "3498                    5  \n",
       "3499                    3  \n",
       "\n",
       "[3500 rows x 6 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genome_pd_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "for item in health_pred:\n",
    "    genome_pd_df[item] = [random.choice([True, False]) for i in range(num_rows)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>HG00099</td>\n",
       "      <td>GBR</td>\n",
       "      <td>EUR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>HG00100</td>\n",
       "      <td>GBR</td>\n",
       "      <td>EUR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3495</td>\n",
       "      <td>NA21137</td>\n",
       "      <td>GIH</td>\n",
       "      <td>SAS</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3496</td>\n",
       "      <td>NA21141</td>\n",
       "      <td>GIH</td>\n",
       "      <td>SAS</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3497</td>\n",
       "      <td>NA21142</td>\n",
       "      <td>GIH</td>\n",
       "      <td>SAS</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3498</td>\n",
       "      <td>NA21143</td>\n",
       "      <td>GIH</td>\n",
       "      <td>SAS</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3499</td>\n",
       "      <td>NA21144</td>\n",
       "      <td>GIH</td>\n",
       "      <td>SAS</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3500 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sample Population SuperPopulation  isFemale  PurpleHair  \\\n",
       "0     HG00096        GBR             EUR     False       False   \n",
       "1     HG00097        GBR             EUR      True        True   \n",
       "2     HG00098        GBR             EUR     False       False   \n",
       "3     HG00099        GBR             EUR      True       False   \n",
       "4     HG00100        GBR             EUR      True       False   \n",
       "...       ...        ...             ...       ...         ...   \n",
       "3495  NA21137        GIH             SAS      True       False   \n",
       "3496  NA21141        GIH             SAS      True        True   \n",
       "3497  NA21142        GIH             SAS      True        True   \n",
       "3498  NA21143        GIH             SAS      True        True   \n",
       "3499  NA21144        GIH             SAS      True       False   \n",
       "\n",
       "      CaffeineConsumption  Type_2_Diabetes  Age-Related_Macular_Degeneration  \\\n",
       "0                       4             True                             False   \n",
       "1                       4            False                             False   \n",
       "2                       5             True                             False   \n",
       "3                       4            False                             False   \n",
       "4                       5             True                             False   \n",
       "...                   ...              ...                               ...   \n",
       "3495                    1             True                              True   \n",
       "3496                    2             True                             False   \n",
       "3497                    2            False                             False   \n",
       "3498                    5            False                              True   \n",
       "3499                    3            False                              True   \n",
       "\n",
       "      Alpha-1_Antitrypsin_Deficiency  BRCA1/BRCA2  Celiac_Disease  \\\n",
       "0                               True        False           False   \n",
       "1                              False         True            True   \n",
       "2                               True         True           False   \n",
       "3                               True         True           False   \n",
       "4                              False        False           False   \n",
       "...                              ...          ...             ...   \n",
       "3495                            True         True            True   \n",
       "3496                           False        False            True   \n",
       "3497                           False        False            True   \n",
       "3498                           False         True           False   \n",
       "3499                            True         True            True   \n",
       "\n",
       "      Familial_Hypercholesterolemia  G6PD_Deficiency  \\\n",
       "0                             False             True   \n",
       "1                              True             True   \n",
       "2                             False             True   \n",
       "3                              True            False   \n",
       "4                              True            False   \n",
       "...                             ...              ...   \n",
       "3495                           True             True   \n",
       "3496                           True             True   \n",
       "3497                          False             True   \n",
       "3498                           True            False   \n",
       "3499                          False             True   \n",
       "\n",
       "      Hereditary_Amyloidosis(TTR-Related)  \\\n",
       "0                                   False   \n",
       "1                                    True   \n",
       "2                                   False   \n",
       "3                                   False   \n",
       "4                                    True   \n",
       "...                                   ...   \n",
       "3495                                False   \n",
       "3496                                False   \n",
       "3497                                False   \n",
       "3498                                 True   \n",
       "3499                                False   \n",
       "\n",
       "      Hereditary_Hemochromatosis(HFE‑Related)  Hereditary_Thrombophilia  \\\n",
       "0                                        True                      True   \n",
       "1                                       False                      True   \n",
       "2                                        True                     False   \n",
       "3                                        True                      True   \n",
       "4                                        True                     False   \n",
       "...                                       ...                       ...   \n",
       "3495                                     True                      True   \n",
       "3496                                     True                      True   \n",
       "3497                                     True                     False   \n",
       "3498                                    False                      True   \n",
       "3499                                    False                      True   \n",
       "\n",
       "      Late-Onset_Alzheimers_Disease  MUTYH-Associated_Polyposis  \\\n",
       "0                              True                       False   \n",
       "1                             False                       False   \n",
       "2                             False                       False   \n",
       "3                              True                        True   \n",
       "4                             False                       False   \n",
       "...                             ...                         ...   \n",
       "3495                          False                        True   \n",
       "3496                           True                        True   \n",
       "3497                          False                       False   \n",
       "3498                          False                        True   \n",
       "3499                           True                        True   \n",
       "\n",
       "      Parkinsons_Disease  \n",
       "0                   True  \n",
       "1                  False  \n",
       "2                  False  \n",
       "3                   True  \n",
       "4                   True  \n",
       "...                  ...  \n",
       "3495                True  \n",
       "3496               False  \n",
       "3497                True  \n",
       "3498                True  \n",
       "3499               False  \n",
       "\n",
       "[3500 rows x 19 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genome_pd_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><thead style=\"font-weight: bold;\"><tr><td>locus</td><td>alleles</td></tr>\n",
       "<tr><td>locus&lt;GRCh37&gt;</td><td>array&lt;str&gt;</td></tr>\n",
       "</thead><tbody><tr><td>1:904165</td><td>[&quot;G&quot;,&quot;A&quot;]</td></tr>\n",
       "<tr><td>1:909917</td><td>[&quot;G&quot;,&quot;A&quot;]</td></tr>\n",
       "<tr><td>1:986963</td><td>[&quot;C&quot;,&quot;T&quot;]</td></tr>\n",
       "<tr><td>1:1563691</td><td>[&quot;T&quot;,&quot;G&quot;]</td></tr>\n",
       "<tr><td>1:1707740</td><td>[&quot;T&quot;,&quot;G&quot;]</td></tr>\n",
       "<tr><td>1:2252970</td><td>[&quot;C&quot;,&quot;T&quot;]</td></tr>\n",
       "<tr><td>1:2284195</td><td>[&quot;T&quot;,&quot;C&quot;]</td></tr>\n",
       "<tr><td>1:2779043</td><td>[&quot;T&quot;,&quot;C&quot;]</td></tr>\n",
       "<tr><td>1:2944527</td><td>[&quot;G&quot;,&quot;A&quot;]</td></tr>\n",
       "<tr><td>1:3761547</td><td>[&quot;C&quot;,&quot;A&quot;]</td></tr>\n",
       "</tbody></table><p style=\"background: #fdd; padding: 0.4em;\">showing top 10 rows</p>\n",
       "<p style=\"background: #fdd; padding: 0.4em;\">showing the first 0 of 284 columns</p>\n"
      ],
      "text/plain": [
       "+---------------+------------+\n",
       "| locus         | alleles    |\n",
       "+---------------+------------+\n",
       "| locus<GRCh37> | array<str> |\n",
       "+---------------+------------+\n",
       "| 1:904165      | [\"G\",\"A\"]  |\n",
       "| 1:909917      | [\"G\",\"A\"]  |\n",
       "| 1:986963      | [\"C\",\"T\"]  |\n",
       "| 1:1563691     | [\"T\",\"G\"]  |\n",
       "| 1:1707740     | [\"T\",\"G\"]  |\n",
       "| 1:2252970     | [\"C\",\"T\"]  |\n",
       "| 1:2284195     | [\"T\",\"C\"]  |\n",
       "| 1:2779043     | [\"T\",\"C\"]  |\n",
       "| 1:2944527     | [\"G\",\"A\"]  |\n",
       "| 1:3761547     | [\"C\",\"A\"]  |\n",
       "+---------------+------------+\n",
       "showing top 10 rows\n",
       "showing the first 0 of 284 columns"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SuperPopulations:  ['EUR' 'EAS' 'AMR' 'SAS' 'AFR']\n",
      "Number of Super Populations:  5\n",
      "Populations:  ['GBR' 'FIN' 'CHS' 'PUR' 'CDX' 'CLM' 'IBS' 'PEL' 'PJL' 'KHV' 'ACB' 'GWD'\n",
      " 'ESN' 'BEB' 'MSL' 'STU' 'ITU' 'CEU' 'YRI' 'CHB' 'JPT' 'LWK' 'ASW' 'MXL'\n",
      " 'TSI' 'GIH']\n",
      "Number of Populations:  26\n"
     ]
    }
   ],
   "source": [
    "superPopulations = genome_pd_df.SuperPopulation.unique()\n",
    "print('SuperPopulations: ', superPopulations)\n",
    "print('Number of Super Populations: ', len(superPopulations))\n",
    "populations = genome_pd_df.Population.unique()\n",
    "print('Populations: ', populations)\n",
    "print('Number of Populations: ', len(populations))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Sample</th>\n",
       "      <th>Population</th>\n",
       "      <th>SuperPopulation</th>\n",
       "      <th>isFemale</th>\n",
       "      <th>PurpleHair</th>\n",
       "      <th>CaffeineConsumption</th>\n",
       "      <th>Type_2_Diabetes</th>\n",
       "      <th>Age-Related_Macular_Degeneration</th>\n",
       "      <th>Alpha-1_Antitrypsin_Deficiency</th>\n",
       "      <th>BRCA1/BRCA2</th>\n",
       "      <th>Celiac_Disease</th>\n",
       "      <th>Familial_Hypercholesterolemia</th>\n",
       "      <th>G6PD_Deficiency</th>\n",
       "      <th>Hereditary_Amyloidosis(TTR-Related)</th>\n",
       "      <th>Hereditary_Hemochromatosis(HFE‑Related)</th>\n",
       "      <th>Hereditary_Thrombophilia</th>\n",
       "      <th>Late-Onset_Alzheimers_Disease</th>\n",
       "      <th>MUTYH-Associated_Polyposis</th>\n",
       "      <th>Parkinsons_Disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>HG00096</td>\n",
       "      <td>GBR</td>\n",
       "      <td>EUR</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>HG00097</td>\n",
       "      <td>GBR</td>\n",
       "      <td>EUR</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>HG00098</td>\n",
       "      <td>GBR</td>\n",
       "      <td>EUR</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>HG00099</td>\n",
       "      <td>GBR</td>\n",
       "      <td>EUR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>HG00100</td>\n",
       "      <td>GBR</td>\n",
       "      <td>EUR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3382</td>\n",
       "      <td>NA20827</td>\n",
       "      <td>TSI</td>\n",
       "      <td>EUR</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3383</td>\n",
       "      <td>NA20828</td>\n",
       "      <td>TSI</td>\n",
       "      <td>EUR</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3384</td>\n",
       "      <td>NA20829</td>\n",
       "      <td>TSI</td>\n",
       "      <td>EUR</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3385</td>\n",
       "      <td>NA20831</td>\n",
       "      <td>TSI</td>\n",
       "      <td>EUR</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3386</td>\n",
       "      <td>NA20832</td>\n",
       "      <td>TSI</td>\n",
       "      <td>EUR</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>669 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sample Population SuperPopulation  isFemale  PurpleHair  \\\n",
       "0     HG00096        GBR             EUR     False       False   \n",
       "1     HG00097        GBR             EUR      True        True   \n",
       "2     HG00098        GBR             EUR     False       False   \n",
       "3     HG00099        GBR             EUR      True       False   \n",
       "4     HG00100        GBR             EUR      True       False   \n",
       "...       ...        ...             ...       ...         ...   \n",
       "3382  NA20827        TSI             EUR     False       False   \n",
       "3383  NA20828        TSI             EUR      True        True   \n",
       "3384  NA20829        TSI             EUR     False        True   \n",
       "3385  NA20831        TSI             EUR     False       False   \n",
       "3386  NA20832        TSI             EUR      True        True   \n",
       "\n",
       "      CaffeineConsumption  Type_2_Diabetes  Age-Related_Macular_Degeneration  \\\n",
       "0                       4             True                             False   \n",
       "1                       4            False                             False   \n",
       "2                       5             True                             False   \n",
       "3                       4            False                             False   \n",
       "4                       5             True                             False   \n",
       "...                   ...              ...                               ...   \n",
       "3382                    6             True                             False   \n",
       "3383                    4            False                              True   \n",
       "3384                    5             True                              True   \n",
       "3385                    2             True                              True   \n",
       "3386                    5             True                              True   \n",
       "\n",
       "      Alpha-1_Antitrypsin_Deficiency  BRCA1/BRCA2  Celiac_Disease  \\\n",
       "0                               True        False           False   \n",
       "1                              False         True            True   \n",
       "2                               True         True           False   \n",
       "3                               True         True           False   \n",
       "4                              False        False           False   \n",
       "...                              ...          ...             ...   \n",
       "3382                            True         True            True   \n",
       "3383                           False         True           False   \n",
       "3384                           False         True            True   \n",
       "3385                            True        False           False   \n",
       "3386                            True        False            True   \n",
       "\n",
       "      Familial_Hypercholesterolemia  G6PD_Deficiency  \\\n",
       "0                             False             True   \n",
       "1                              True             True   \n",
       "2                             False             True   \n",
       "3                              True            False   \n",
       "4                              True            False   \n",
       "...                             ...              ...   \n",
       "3382                           True            False   \n",
       "3383                          False            False   \n",
       "3384                           True             True   \n",
       "3385                           True             True   \n",
       "3386                           True            False   \n",
       "\n",
       "      Hereditary_Amyloidosis(TTR-Related)  \\\n",
       "0                                   False   \n",
       "1                                    True   \n",
       "2                                   False   \n",
       "3                                   False   \n",
       "4                                    True   \n",
       "...                                   ...   \n",
       "3382                                False   \n",
       "3383                                False   \n",
       "3384                                False   \n",
       "3385                                False   \n",
       "3386                                False   \n",
       "\n",
       "      Hereditary_Hemochromatosis(HFE‑Related)  Hereditary_Thrombophilia  \\\n",
       "0                                        True                      True   \n",
       "1                                       False                      True   \n",
       "2                                        True                     False   \n",
       "3                                        True                      True   \n",
       "4                                        True                     False   \n",
       "...                                       ...                       ...   \n",
       "3382                                     True                      True   \n",
       "3383                                    False                     False   \n",
       "3384                                    False                      True   \n",
       "3385                                     True                      True   \n",
       "3386                                     True                     False   \n",
       "\n",
       "      Late-Onset_Alzheimers_Disease  MUTYH-Associated_Polyposis  \\\n",
       "0                              True                       False   \n",
       "1                             False                       False   \n",
       "2                             False                       False   \n",
       "3                              True                        True   \n",
       "4                             False                       False   \n",
       "...                             ...                         ...   \n",
       "3382                           True                       False   \n",
       "3383                          False                       False   \n",
       "3384                          False                        True   \n",
       "3385                           True                        True   \n",
       "3386                           True                        True   \n",
       "\n",
       "      Parkinsons_Disease  \n",
       "0                   True  \n",
       "1                  False  \n",
       "2                  False  \n",
       "3                   True  \n",
       "4                   True  \n",
       "...                  ...  \n",
       "3382                True  \n",
       "3383                True  \n",
       "3384               False  \n",
       "3385                True  \n",
       "3386                True  \n",
       "\n",
       "[669 rows x 19 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eur = genome_pd_df.loc[genome_pd_df['SuperPopulation'] == 'EUR']\n",
    "eas = genome_pd_df.loc[genome_pd_df['SuperPopulation'] == 'EAS']\n",
    "amr = genome_pd_df.loc[genome_pd_df['SuperPopulation'] == 'AMR']\n",
    "sas = genome_pd_df.loc[genome_pd_df['SuperPopulation'] == 'SAS']\n",
    "afr = genome_pd_df.loc[genome_pd_df['SuperPopulation'] == 'AFR']\n",
    "eur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbr = genome_pd_df.loc[genome_pd_df['Population'] == 'GBR']\n",
    "fin = genome_pd_df.loc[genome_pd_df['Population'] == 'FIN']\n",
    "chs = genome_pd_df.loc[genome_pd_df['Population'] == 'CHS']\n",
    "pur = genome_pd_df.loc[genome_pd_df['Population'] == 'PUR']\n",
    "cdx = genome_pd_df.loc[genome_pd_df['Population'] == 'CDX']\n",
    "clm = genome_pd_df.loc[genome_pd_df['Population'] == 'CLM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "eur_true, eur_false = eur.Type_2_Diabetes.value_counts()\n",
    "eas_true, eas_false = eas.Type_2_Diabetes.value_counts()\n",
    "amr_true, amr_false = amr.Type_2_Diabetes.value_counts()\n",
    "sas_true, sas_false = sas.Type_2_Diabetes.value_counts()\n",
    "afr_true, afr_false = afr.Type_2_Diabetes.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_occurence_country(df, continent, disease):\n",
    "    continent = df.loc[df['SuperPopulation'] == continent]\n",
    "    unique_countries = continent.Population.unique()\n",
    "    disease_dict = {}\n",
    "    for country in unique_countries:\n",
    "        df_temp = df.loc[df['Population'] == country]\n",
    "        true_freq, false_freq = df_temp[disease].value_counts()\n",
    "        disease_dict[country] = true_freq\n",
    "    return disease_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_bar(input_dict):\n",
    "    keys = input_dict.keys()\n",
    "    values = input_dict.values()\n",
    "    plt.bar(keys, values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_occurence_country = count_occurence_country(genome_pd_df, 'EUR', 'Type_2_Diabetes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Count of Type 2 Diabetes for Countries in Europe\")\n",
    "plot_bar(count_occurence_country)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_occurence_continents(df, disease):\n",
    "    unique_continents = df.SuperPopulation.unique()\n",
    "    disease_dict = {}\n",
    "    for continent in unique_continents:\n",
    "        df_temp = df.loc[df['SuperPopulation'] == continent]\n",
    "        true_freq, false_freq = df_temp[disease].value_counts()\n",
    "        disease_dict[continent] = true_freq\n",
    "    return disease_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_occurence_continents = count_occurence_continents(genome_pd_df, 'Type_2_Diabetes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'EUR': 338, 'EAS': 310, 'AMR': 280, 'SAS': 331, 'AFR': 512}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_occurence_continents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Count of Type 2 Diabetes for all Continents\")\n",
    "plot_bar(count_occurence_continents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_occurence_allDisease(df, health_pred):\n",
    "    unique_continents = df.SuperPopulation.unique()\n",
    "    disease_dict = {}\n",
    "    for continent in unique_continents: \n",
    "            df_temp = df.loc[df['SuperPopulation'] == continent]\n",
    "            disease_dict[continent] = {}\n",
    "            for disease in health_pred:\n",
    "                true_freq, false_freq = df_temp[disease].value_counts()\n",
    "                disease_dict[continent][disease] = true_freq\n",
    "    return disease_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_allDisease = count_occurence_allDisease(genome_pd_df, health_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Type_2_Diabetes</th>\n",
       "      <th>Age-Related_Macular_Degeneration</th>\n",
       "      <th>Alpha-1_Antitrypsin_Deficiency</th>\n",
       "      <th>BRCA1/BRCA2</th>\n",
       "      <th>Celiac_Disease</th>\n",
       "      <th>Familial_Hypercholesterolemia</th>\n",
       "      <th>G6PD_Deficiency</th>\n",
       "      <th>Hereditary_Amyloidosis(TTR-Related)</th>\n",
       "      <th>Hereditary_Hemochromatosis(HFE‑Related)</th>\n",
       "      <th>Hereditary_Thrombophilia</th>\n",
       "      <th>Late-Onset_Alzheimers_Disease</th>\n",
       "      <th>MUTYH-Associated_Polyposis</th>\n",
       "      <th>Parkinsons_Disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>EUR</td>\n",
       "      <td>338</td>\n",
       "      <td>347</td>\n",
       "      <td>344</td>\n",
       "      <td>365</td>\n",
       "      <td>343</td>\n",
       "      <td>336</td>\n",
       "      <td>338</td>\n",
       "      <td>344</td>\n",
       "      <td>337</td>\n",
       "      <td>340</td>\n",
       "      <td>338</td>\n",
       "      <td>347</td>\n",
       "      <td>336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>EAS</td>\n",
       "      <td>310</td>\n",
       "      <td>314</td>\n",
       "      <td>326</td>\n",
       "      <td>312</td>\n",
       "      <td>317</td>\n",
       "      <td>320</td>\n",
       "      <td>310</td>\n",
       "      <td>312</td>\n",
       "      <td>310</td>\n",
       "      <td>311</td>\n",
       "      <td>309</td>\n",
       "      <td>331</td>\n",
       "      <td>310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AMR</td>\n",
       "      <td>280</td>\n",
       "      <td>290</td>\n",
       "      <td>290</td>\n",
       "      <td>277</td>\n",
       "      <td>286</td>\n",
       "      <td>269</td>\n",
       "      <td>278</td>\n",
       "      <td>268</td>\n",
       "      <td>281</td>\n",
       "      <td>268</td>\n",
       "      <td>281</td>\n",
       "      <td>273</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SAS</td>\n",
       "      <td>331</td>\n",
       "      <td>340</td>\n",
       "      <td>340</td>\n",
       "      <td>332</td>\n",
       "      <td>333</td>\n",
       "      <td>344</td>\n",
       "      <td>332</td>\n",
       "      <td>336</td>\n",
       "      <td>334</td>\n",
       "      <td>340</td>\n",
       "      <td>351</td>\n",
       "      <td>336</td>\n",
       "      <td>337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AFR</td>\n",
       "      <td>512</td>\n",
       "      <td>511</td>\n",
       "      <td>512</td>\n",
       "      <td>556</td>\n",
       "      <td>522</td>\n",
       "      <td>519</td>\n",
       "      <td>534</td>\n",
       "      <td>525</td>\n",
       "      <td>517</td>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>523</td>\n",
       "      <td>515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Type_2_Diabetes  Age-Related_Macular_Degeneration  \\\n",
       "EUR              338                               347   \n",
       "EAS              310                               314   \n",
       "AMR              280                               290   \n",
       "SAS              331                               340   \n",
       "AFR              512                               511   \n",
       "\n",
       "     Alpha-1_Antitrypsin_Deficiency  BRCA1/BRCA2  Celiac_Disease  \\\n",
       "EUR                             344          365             343   \n",
       "EAS                             326          312             317   \n",
       "AMR                             290          277             286   \n",
       "SAS                             340          332             333   \n",
       "AFR                             512          556             522   \n",
       "\n",
       "     Familial_Hypercholesterolemia  G6PD_Deficiency  \\\n",
       "EUR                            336              338   \n",
       "EAS                            320              310   \n",
       "AMR                            269              278   \n",
       "SAS                            344              332   \n",
       "AFR                            519              534   \n",
       "\n",
       "     Hereditary_Amyloidosis(TTR-Related)  \\\n",
       "EUR                                  344   \n",
       "EAS                                  312   \n",
       "AMR                                  268   \n",
       "SAS                                  336   \n",
       "AFR                                  525   \n",
       "\n",
       "     Hereditary_Hemochromatosis(HFE‑Related)  Hereditary_Thrombophilia  \\\n",
       "EUR                                      337                       340   \n",
       "EAS                                      310                       311   \n",
       "AMR                                      281                       268   \n",
       "SAS                                      334                       340   \n",
       "AFR                                      517                       512   \n",
       "\n",
       "     Late-Onset_Alzheimers_Disease  MUTYH-Associated_Polyposis  \\\n",
       "EUR                            338                         347   \n",
       "EAS                            309                         331   \n",
       "AMR                            281                         273   \n",
       "SAS                            351                         336   \n",
       "AFR                            512                         523   \n",
       "\n",
       "     Parkinsons_Disease  \n",
       "EUR                 336  \n",
       "EAS                 310  \n",
       "AMR                 275  \n",
       "SAS                 337  \n",
       "AFR                 515  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diseaseGlobe_df = pd.DataFrame.from_dict(count_allDisease, orient=\"index\")\n",
    "diseaseGlobe_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = diseaseGlobe_df.plot.bar(figsize = (15,15), rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a26be9a50>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x2880 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = diseaseGlobe_df.plot.bar(figsize = (5,40), rot=0, subplots=True)\n",
    "axes[1].legend(loc=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>isFemale</th>\n",
       "      <th>PurpleHair</th>\n",
       "      <th>CaffeineConsumption</th>\n",
       "      <th>Type_2_Diabetes</th>\n",
       "      <th>Age-Related_Macular_Degeneration</th>\n",
       "      <th>Alpha-1_Antitrypsin_Deficiency</th>\n",
       "      <th>BRCA1/BRCA2</th>\n",
       "      <th>Celiac_Disease</th>\n",
       "      <th>Familial_Hypercholesterolemia</th>\n",
       "      <th>G6PD_Deficiency</th>\n",
       "      <th>Hereditary_Amyloidosis(TTR-Related)</th>\n",
       "      <th>Hereditary_Hemochromatosis(HFE‑Related)</th>\n",
       "      <th>Hereditary_Thrombophilia</th>\n",
       "      <th>Late-Onset_Alzheimers_Disease</th>\n",
       "      <th>MUTYH-Associated_Polyposis</th>\n",
       "      <th>Parkinsons_Disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>isFemale</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014095</td>\n",
       "      <td>-0.004478</td>\n",
       "      <td>-0.007406</td>\n",
       "      <td>0.005701</td>\n",
       "      <td>0.006342</td>\n",
       "      <td>0.028311</td>\n",
       "      <td>0.027846</td>\n",
       "      <td>0.010325</td>\n",
       "      <td>-0.002332</td>\n",
       "      <td>0.006270</td>\n",
       "      <td>0.009098</td>\n",
       "      <td>0.011396</td>\n",
       "      <td>-0.013120</td>\n",
       "      <td>-0.014263</td>\n",
       "      <td>-0.004076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PurpleHair</td>\n",
       "      <td>0.014095</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.009908</td>\n",
       "      <td>-0.003291</td>\n",
       "      <td>-0.024666</td>\n",
       "      <td>0.016349</td>\n",
       "      <td>0.030800</td>\n",
       "      <td>0.004703</td>\n",
       "      <td>0.000811</td>\n",
       "      <td>-0.013430</td>\n",
       "      <td>-0.011535</td>\n",
       "      <td>0.006011</td>\n",
       "      <td>-0.007633</td>\n",
       "      <td>-0.007865</td>\n",
       "      <td>-0.012440</td>\n",
       "      <td>-0.009608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CaffeineConsumption</td>\n",
       "      <td>-0.004478</td>\n",
       "      <td>0.009908</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.016244</td>\n",
       "      <td>0.024362</td>\n",
       "      <td>-0.002611</td>\n",
       "      <td>0.006051</td>\n",
       "      <td>-0.031460</td>\n",
       "      <td>-0.023734</td>\n",
       "      <td>-0.017214</td>\n",
       "      <td>0.011945</td>\n",
       "      <td>-0.028628</td>\n",
       "      <td>-0.026635</td>\n",
       "      <td>0.014901</td>\n",
       "      <td>-0.008599</td>\n",
       "      <td>-0.007429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Type_2_Diabetes</td>\n",
       "      <td>-0.007406</td>\n",
       "      <td>-0.003291</td>\n",
       "      <td>0.016244</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.026867</td>\n",
       "      <td>-0.012611</td>\n",
       "      <td>0.019094</td>\n",
       "      <td>-0.039901</td>\n",
       "      <td>0.008544</td>\n",
       "      <td>0.006318</td>\n",
       "      <td>0.002297</td>\n",
       "      <td>-0.016540</td>\n",
       "      <td>0.008594</td>\n",
       "      <td>0.025127</td>\n",
       "      <td>-0.041160</td>\n",
       "      <td>-0.003376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Age-Related_Macular_Degeneration</td>\n",
       "      <td>0.005701</td>\n",
       "      <td>-0.024666</td>\n",
       "      <td>0.024362</td>\n",
       "      <td>0.026867</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.014264</td>\n",
       "      <td>-0.001265</td>\n",
       "      <td>-0.008642</td>\n",
       "      <td>-0.021699</td>\n",
       "      <td>-0.000018</td>\n",
       "      <td>0.022279</td>\n",
       "      <td>0.007982</td>\n",
       "      <td>0.001130</td>\n",
       "      <td>0.001723</td>\n",
       "      <td>0.014295</td>\n",
       "      <td>-0.008602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Alpha-1_Antitrypsin_Deficiency</td>\n",
       "      <td>0.006342</td>\n",
       "      <td>0.016349</td>\n",
       "      <td>-0.002611</td>\n",
       "      <td>-0.012611</td>\n",
       "      <td>-0.014264</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003494</td>\n",
       "      <td>0.012861</td>\n",
       "      <td>-0.000638</td>\n",
       "      <td>0.017793</td>\n",
       "      <td>-0.001115</td>\n",
       "      <td>-0.003923</td>\n",
       "      <td>0.007485</td>\n",
       "      <td>0.010247</td>\n",
       "      <td>0.018248</td>\n",
       "      <td>-0.007873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BRCA1/BRCA2</td>\n",
       "      <td>0.028311</td>\n",
       "      <td>0.030800</td>\n",
       "      <td>0.006051</td>\n",
       "      <td>0.019094</td>\n",
       "      <td>-0.001265</td>\n",
       "      <td>-0.003494</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012412</td>\n",
       "      <td>-0.008795</td>\n",
       "      <td>-0.006144</td>\n",
       "      <td>-0.015600</td>\n",
       "      <td>0.019035</td>\n",
       "      <td>0.014577</td>\n",
       "      <td>-0.001506</td>\n",
       "      <td>-0.022106</td>\n",
       "      <td>0.006748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Celiac_Disease</td>\n",
       "      <td>0.027846</td>\n",
       "      <td>0.004703</td>\n",
       "      <td>-0.031460</td>\n",
       "      <td>-0.039901</td>\n",
       "      <td>-0.008642</td>\n",
       "      <td>0.012861</td>\n",
       "      <td>-0.012412</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.020952</td>\n",
       "      <td>-0.016812</td>\n",
       "      <td>-0.017234</td>\n",
       "      <td>0.011772</td>\n",
       "      <td>-0.003025</td>\n",
       "      <td>-0.028467</td>\n",
       "      <td>-0.007887</td>\n",
       "      <td>0.006477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Familial_Hypercholesterolemia</td>\n",
       "      <td>0.010325</td>\n",
       "      <td>0.000811</td>\n",
       "      <td>-0.023734</td>\n",
       "      <td>0.008544</td>\n",
       "      <td>-0.021699</td>\n",
       "      <td>-0.000638</td>\n",
       "      <td>-0.008795</td>\n",
       "      <td>-0.020952</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.025089</td>\n",
       "      <td>-0.031410</td>\n",
       "      <td>0.019485</td>\n",
       "      <td>0.008040</td>\n",
       "      <td>-0.001742</td>\n",
       "      <td>-0.015456</td>\n",
       "      <td>0.006377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>G6PD_Deficiency</td>\n",
       "      <td>-0.002332</td>\n",
       "      <td>-0.013430</td>\n",
       "      <td>-0.017214</td>\n",
       "      <td>0.006318</td>\n",
       "      <td>-0.000018</td>\n",
       "      <td>0.017793</td>\n",
       "      <td>-0.006144</td>\n",
       "      <td>-0.016812</td>\n",
       "      <td>-0.025089</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002880</td>\n",
       "      <td>0.003365</td>\n",
       "      <td>-0.022904</td>\n",
       "      <td>0.014318</td>\n",
       "      <td>-0.022255</td>\n",
       "      <td>0.015325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hereditary_Amyloidosis(TTR-Related)</td>\n",
       "      <td>0.006270</td>\n",
       "      <td>-0.011535</td>\n",
       "      <td>0.011945</td>\n",
       "      <td>0.002297</td>\n",
       "      <td>0.022279</td>\n",
       "      <td>-0.001115</td>\n",
       "      <td>-0.015600</td>\n",
       "      <td>-0.017234</td>\n",
       "      <td>-0.031410</td>\n",
       "      <td>-0.002880</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.008595</td>\n",
       "      <td>0.001698</td>\n",
       "      <td>0.006869</td>\n",
       "      <td>0.013726</td>\n",
       "      <td>-0.013753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hereditary_Hemochromatosis(HFE‑Related)</td>\n",
       "      <td>0.009098</td>\n",
       "      <td>0.006011</td>\n",
       "      <td>-0.028628</td>\n",
       "      <td>-0.016540</td>\n",
       "      <td>0.007982</td>\n",
       "      <td>-0.003923</td>\n",
       "      <td>0.019035</td>\n",
       "      <td>0.011772</td>\n",
       "      <td>0.019485</td>\n",
       "      <td>0.003365</td>\n",
       "      <td>-0.008595</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005760</td>\n",
       "      <td>0.017747</td>\n",
       "      <td>-0.015397</td>\n",
       "      <td>0.022183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hereditary_Thrombophilia</td>\n",
       "      <td>0.011396</td>\n",
       "      <td>-0.007633</td>\n",
       "      <td>-0.026635</td>\n",
       "      <td>0.008594</td>\n",
       "      <td>0.001130</td>\n",
       "      <td>0.007485</td>\n",
       "      <td>0.014577</td>\n",
       "      <td>-0.003025</td>\n",
       "      <td>0.008040</td>\n",
       "      <td>-0.022904</td>\n",
       "      <td>0.001698</td>\n",
       "      <td>-0.005760</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.009692</td>\n",
       "      <td>0.049738</td>\n",
       "      <td>-0.004076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Late-Onset_Alzheimers_Disease</td>\n",
       "      <td>-0.013120</td>\n",
       "      <td>-0.007865</td>\n",
       "      <td>0.014901</td>\n",
       "      <td>0.025127</td>\n",
       "      <td>0.001723</td>\n",
       "      <td>0.010247</td>\n",
       "      <td>-0.001506</td>\n",
       "      <td>-0.028467</td>\n",
       "      <td>-0.001742</td>\n",
       "      <td>0.014318</td>\n",
       "      <td>0.006869</td>\n",
       "      <td>0.017747</td>\n",
       "      <td>-0.009692</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.015984</td>\n",
       "      <td>0.012625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>MUTYH-Associated_Polyposis</td>\n",
       "      <td>-0.014263</td>\n",
       "      <td>-0.012440</td>\n",
       "      <td>-0.008599</td>\n",
       "      <td>-0.041160</td>\n",
       "      <td>0.014295</td>\n",
       "      <td>0.018248</td>\n",
       "      <td>-0.022106</td>\n",
       "      <td>-0.007887</td>\n",
       "      <td>-0.015456</td>\n",
       "      <td>-0.022255</td>\n",
       "      <td>0.013726</td>\n",
       "      <td>-0.015397</td>\n",
       "      <td>0.049738</td>\n",
       "      <td>0.015984</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.019378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Parkinsons_Disease</td>\n",
       "      <td>-0.004076</td>\n",
       "      <td>-0.009608</td>\n",
       "      <td>-0.007429</td>\n",
       "      <td>-0.003376</td>\n",
       "      <td>-0.008602</td>\n",
       "      <td>-0.007873</td>\n",
       "      <td>0.006748</td>\n",
       "      <td>0.006477</td>\n",
       "      <td>0.006377</td>\n",
       "      <td>0.015325</td>\n",
       "      <td>-0.013753</td>\n",
       "      <td>0.022183</td>\n",
       "      <td>-0.004076</td>\n",
       "      <td>0.012625</td>\n",
       "      <td>-0.019378</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         isFemale  PurpleHair  \\\n",
       "isFemale                                 1.000000    0.014095   \n",
       "PurpleHair                               0.014095    1.000000   \n",
       "CaffeineConsumption                     -0.004478    0.009908   \n",
       "Type_2_Diabetes                         -0.007406   -0.003291   \n",
       "Age-Related_Macular_Degeneration         0.005701   -0.024666   \n",
       "Alpha-1_Antitrypsin_Deficiency           0.006342    0.016349   \n",
       "BRCA1/BRCA2                              0.028311    0.030800   \n",
       "Celiac_Disease                           0.027846    0.004703   \n",
       "Familial_Hypercholesterolemia            0.010325    0.000811   \n",
       "G6PD_Deficiency                         -0.002332   -0.013430   \n",
       "Hereditary_Amyloidosis(TTR-Related)      0.006270   -0.011535   \n",
       "Hereditary_Hemochromatosis(HFE‑Related)  0.009098    0.006011   \n",
       "Hereditary_Thrombophilia                 0.011396   -0.007633   \n",
       "Late-Onset_Alzheimers_Disease           -0.013120   -0.007865   \n",
       "MUTYH-Associated_Polyposis              -0.014263   -0.012440   \n",
       "Parkinsons_Disease                      -0.004076   -0.009608   \n",
       "\n",
       "                                         CaffeineConsumption  Type_2_Diabetes  \\\n",
       "isFemale                                           -0.004478        -0.007406   \n",
       "PurpleHair                                          0.009908        -0.003291   \n",
       "CaffeineConsumption                                 1.000000         0.016244   \n",
       "Type_2_Diabetes                                     0.016244         1.000000   \n",
       "Age-Related_Macular_Degeneration                    0.024362         0.026867   \n",
       "Alpha-1_Antitrypsin_Deficiency                     -0.002611        -0.012611   \n",
       "BRCA1/BRCA2                                         0.006051         0.019094   \n",
       "Celiac_Disease                                     -0.031460        -0.039901   \n",
       "Familial_Hypercholesterolemia                      -0.023734         0.008544   \n",
       "G6PD_Deficiency                                    -0.017214         0.006318   \n",
       "Hereditary_Amyloidosis(TTR-Related)                 0.011945         0.002297   \n",
       "Hereditary_Hemochromatosis(HFE‑Related)            -0.028628        -0.016540   \n",
       "Hereditary_Thrombophilia                           -0.026635         0.008594   \n",
       "Late-Onset_Alzheimers_Disease                       0.014901         0.025127   \n",
       "MUTYH-Associated_Polyposis                         -0.008599        -0.041160   \n",
       "Parkinsons_Disease                                 -0.007429        -0.003376   \n",
       "\n",
       "                                         Age-Related_Macular_Degeneration  \\\n",
       "isFemale                                                         0.005701   \n",
       "PurpleHair                                                      -0.024666   \n",
       "CaffeineConsumption                                              0.024362   \n",
       "Type_2_Diabetes                                                  0.026867   \n",
       "Age-Related_Macular_Degeneration                                 1.000000   \n",
       "Alpha-1_Antitrypsin_Deficiency                                  -0.014264   \n",
       "BRCA1/BRCA2                                                     -0.001265   \n",
       "Celiac_Disease                                                  -0.008642   \n",
       "Familial_Hypercholesterolemia                                   -0.021699   \n",
       "G6PD_Deficiency                                                 -0.000018   \n",
       "Hereditary_Amyloidosis(TTR-Related)                              0.022279   \n",
       "Hereditary_Hemochromatosis(HFE‑Related)                          0.007982   \n",
       "Hereditary_Thrombophilia                                         0.001130   \n",
       "Late-Onset_Alzheimers_Disease                                    0.001723   \n",
       "MUTYH-Associated_Polyposis                                       0.014295   \n",
       "Parkinsons_Disease                                              -0.008602   \n",
       "\n",
       "                                         Alpha-1_Antitrypsin_Deficiency  \\\n",
       "isFemale                                                       0.006342   \n",
       "PurpleHair                                                     0.016349   \n",
       "CaffeineConsumption                                           -0.002611   \n",
       "Type_2_Diabetes                                               -0.012611   \n",
       "Age-Related_Macular_Degeneration                              -0.014264   \n",
       "Alpha-1_Antitrypsin_Deficiency                                 1.000000   \n",
       "BRCA1/BRCA2                                                   -0.003494   \n",
       "Celiac_Disease                                                 0.012861   \n",
       "Familial_Hypercholesterolemia                                 -0.000638   \n",
       "G6PD_Deficiency                                                0.017793   \n",
       "Hereditary_Amyloidosis(TTR-Related)                           -0.001115   \n",
       "Hereditary_Hemochromatosis(HFE‑Related)                       -0.003923   \n",
       "Hereditary_Thrombophilia                                       0.007485   \n",
       "Late-Onset_Alzheimers_Disease                                  0.010247   \n",
       "MUTYH-Associated_Polyposis                                     0.018248   \n",
       "Parkinsons_Disease                                            -0.007873   \n",
       "\n",
       "                                         BRCA1/BRCA2  Celiac_Disease  \\\n",
       "isFemale                                    0.028311        0.027846   \n",
       "PurpleHair                                  0.030800        0.004703   \n",
       "CaffeineConsumption                         0.006051       -0.031460   \n",
       "Type_2_Diabetes                             0.019094       -0.039901   \n",
       "Age-Related_Macular_Degeneration           -0.001265       -0.008642   \n",
       "Alpha-1_Antitrypsin_Deficiency             -0.003494        0.012861   \n",
       "BRCA1/BRCA2                                 1.000000       -0.012412   \n",
       "Celiac_Disease                             -0.012412        1.000000   \n",
       "Familial_Hypercholesterolemia              -0.008795       -0.020952   \n",
       "G6PD_Deficiency                            -0.006144       -0.016812   \n",
       "Hereditary_Amyloidosis(TTR-Related)        -0.015600       -0.017234   \n",
       "Hereditary_Hemochromatosis(HFE‑Related)     0.019035        0.011772   \n",
       "Hereditary_Thrombophilia                    0.014577       -0.003025   \n",
       "Late-Onset_Alzheimers_Disease              -0.001506       -0.028467   \n",
       "MUTYH-Associated_Polyposis                 -0.022106       -0.007887   \n",
       "Parkinsons_Disease                          0.006748        0.006477   \n",
       "\n",
       "                                         Familial_Hypercholesterolemia  \\\n",
       "isFemale                                                      0.010325   \n",
       "PurpleHair                                                    0.000811   \n",
       "CaffeineConsumption                                          -0.023734   \n",
       "Type_2_Diabetes                                               0.008544   \n",
       "Age-Related_Macular_Degeneration                             -0.021699   \n",
       "Alpha-1_Antitrypsin_Deficiency                               -0.000638   \n",
       "BRCA1/BRCA2                                                  -0.008795   \n",
       "Celiac_Disease                                               -0.020952   \n",
       "Familial_Hypercholesterolemia                                 1.000000   \n",
       "G6PD_Deficiency                                              -0.025089   \n",
       "Hereditary_Amyloidosis(TTR-Related)                          -0.031410   \n",
       "Hereditary_Hemochromatosis(HFE‑Related)                       0.019485   \n",
       "Hereditary_Thrombophilia                                      0.008040   \n",
       "Late-Onset_Alzheimers_Disease                                -0.001742   \n",
       "MUTYH-Associated_Polyposis                                   -0.015456   \n",
       "Parkinsons_Disease                                            0.006377   \n",
       "\n",
       "                                         G6PD_Deficiency  \\\n",
       "isFemale                                       -0.002332   \n",
       "PurpleHair                                     -0.013430   \n",
       "CaffeineConsumption                            -0.017214   \n",
       "Type_2_Diabetes                                 0.006318   \n",
       "Age-Related_Macular_Degeneration               -0.000018   \n",
       "Alpha-1_Antitrypsin_Deficiency                  0.017793   \n",
       "BRCA1/BRCA2                                    -0.006144   \n",
       "Celiac_Disease                                 -0.016812   \n",
       "Familial_Hypercholesterolemia                  -0.025089   \n",
       "G6PD_Deficiency                                 1.000000   \n",
       "Hereditary_Amyloidosis(TTR-Related)            -0.002880   \n",
       "Hereditary_Hemochromatosis(HFE‑Related)         0.003365   \n",
       "Hereditary_Thrombophilia                       -0.022904   \n",
       "Late-Onset_Alzheimers_Disease                   0.014318   \n",
       "MUTYH-Associated_Polyposis                     -0.022255   \n",
       "Parkinsons_Disease                              0.015325   \n",
       "\n",
       "                                         Hereditary_Amyloidosis(TTR-Related)  \\\n",
       "isFemale                                                            0.006270   \n",
       "PurpleHair                                                         -0.011535   \n",
       "CaffeineConsumption                                                 0.011945   \n",
       "Type_2_Diabetes                                                     0.002297   \n",
       "Age-Related_Macular_Degeneration                                    0.022279   \n",
       "Alpha-1_Antitrypsin_Deficiency                                     -0.001115   \n",
       "BRCA1/BRCA2                                                        -0.015600   \n",
       "Celiac_Disease                                                     -0.017234   \n",
       "Familial_Hypercholesterolemia                                      -0.031410   \n",
       "G6PD_Deficiency                                                    -0.002880   \n",
       "Hereditary_Amyloidosis(TTR-Related)                                 1.000000   \n",
       "Hereditary_Hemochromatosis(HFE‑Related)                            -0.008595   \n",
       "Hereditary_Thrombophilia                                            0.001698   \n",
       "Late-Onset_Alzheimers_Disease                                       0.006869   \n",
       "MUTYH-Associated_Polyposis                                          0.013726   \n",
       "Parkinsons_Disease                                                 -0.013753   \n",
       "\n",
       "                                         Hereditary_Hemochromatosis(HFE‑Related)  \\\n",
       "isFemale                                                                0.009098   \n",
       "PurpleHair                                                              0.006011   \n",
       "CaffeineConsumption                                                    -0.028628   \n",
       "Type_2_Diabetes                                                        -0.016540   \n",
       "Age-Related_Macular_Degeneration                                        0.007982   \n",
       "Alpha-1_Antitrypsin_Deficiency                                         -0.003923   \n",
       "BRCA1/BRCA2                                                             0.019035   \n",
       "Celiac_Disease                                                          0.011772   \n",
       "Familial_Hypercholesterolemia                                           0.019485   \n",
       "G6PD_Deficiency                                                         0.003365   \n",
       "Hereditary_Amyloidosis(TTR-Related)                                    -0.008595   \n",
       "Hereditary_Hemochromatosis(HFE‑Related)                                 1.000000   \n",
       "Hereditary_Thrombophilia                                               -0.005760   \n",
       "Late-Onset_Alzheimers_Disease                                           0.017747   \n",
       "MUTYH-Associated_Polyposis                                             -0.015397   \n",
       "Parkinsons_Disease                                                      0.022183   \n",
       "\n",
       "                                         Hereditary_Thrombophilia  \\\n",
       "isFemale                                                 0.011396   \n",
       "PurpleHair                                              -0.007633   \n",
       "CaffeineConsumption                                     -0.026635   \n",
       "Type_2_Diabetes                                          0.008594   \n",
       "Age-Related_Macular_Degeneration                         0.001130   \n",
       "Alpha-1_Antitrypsin_Deficiency                           0.007485   \n",
       "BRCA1/BRCA2                                              0.014577   \n",
       "Celiac_Disease                                          -0.003025   \n",
       "Familial_Hypercholesterolemia                            0.008040   \n",
       "G6PD_Deficiency                                         -0.022904   \n",
       "Hereditary_Amyloidosis(TTR-Related)                      0.001698   \n",
       "Hereditary_Hemochromatosis(HFE‑Related)                 -0.005760   \n",
       "Hereditary_Thrombophilia                                 1.000000   \n",
       "Late-Onset_Alzheimers_Disease                           -0.009692   \n",
       "MUTYH-Associated_Polyposis                               0.049738   \n",
       "Parkinsons_Disease                                      -0.004076   \n",
       "\n",
       "                                         Late-Onset_Alzheimers_Disease  \\\n",
       "isFemale                                                     -0.013120   \n",
       "PurpleHair                                                   -0.007865   \n",
       "CaffeineConsumption                                           0.014901   \n",
       "Type_2_Diabetes                                               0.025127   \n",
       "Age-Related_Macular_Degeneration                              0.001723   \n",
       "Alpha-1_Antitrypsin_Deficiency                                0.010247   \n",
       "BRCA1/BRCA2                                                  -0.001506   \n",
       "Celiac_Disease                                               -0.028467   \n",
       "Familial_Hypercholesterolemia                                -0.001742   \n",
       "G6PD_Deficiency                                               0.014318   \n",
       "Hereditary_Amyloidosis(TTR-Related)                           0.006869   \n",
       "Hereditary_Hemochromatosis(HFE‑Related)                       0.017747   \n",
       "Hereditary_Thrombophilia                                     -0.009692   \n",
       "Late-Onset_Alzheimers_Disease                                 1.000000   \n",
       "MUTYH-Associated_Polyposis                                    0.015984   \n",
       "Parkinsons_Disease                                            0.012625   \n",
       "\n",
       "                                         MUTYH-Associated_Polyposis  \\\n",
       "isFemale                                                  -0.014263   \n",
       "PurpleHair                                                -0.012440   \n",
       "CaffeineConsumption                                       -0.008599   \n",
       "Type_2_Diabetes                                           -0.041160   \n",
       "Age-Related_Macular_Degeneration                           0.014295   \n",
       "Alpha-1_Antitrypsin_Deficiency                             0.018248   \n",
       "BRCA1/BRCA2                                               -0.022106   \n",
       "Celiac_Disease                                            -0.007887   \n",
       "Familial_Hypercholesterolemia                             -0.015456   \n",
       "G6PD_Deficiency                                           -0.022255   \n",
       "Hereditary_Amyloidosis(TTR-Related)                        0.013726   \n",
       "Hereditary_Hemochromatosis(HFE‑Related)                   -0.015397   \n",
       "Hereditary_Thrombophilia                                   0.049738   \n",
       "Late-Onset_Alzheimers_Disease                              0.015984   \n",
       "MUTYH-Associated_Polyposis                                 1.000000   \n",
       "Parkinsons_Disease                                        -0.019378   \n",
       "\n",
       "                                         Parkinsons_Disease  \n",
       "isFemale                                          -0.004076  \n",
       "PurpleHair                                        -0.009608  \n",
       "CaffeineConsumption                               -0.007429  \n",
       "Type_2_Diabetes                                   -0.003376  \n",
       "Age-Related_Macular_Degeneration                  -0.008602  \n",
       "Alpha-1_Antitrypsin_Deficiency                    -0.007873  \n",
       "BRCA1/BRCA2                                        0.006748  \n",
       "Celiac_Disease                                     0.006477  \n",
       "Familial_Hypercholesterolemia                      0.006377  \n",
       "G6PD_Deficiency                                    0.015325  \n",
       "Hereditary_Amyloidosis(TTR-Related)               -0.013753  \n",
       "Hereditary_Hemochromatosis(HFE‑Related)            0.022183  \n",
       "Hereditary_Thrombophilia                          -0.004076  \n",
       "Late-Onset_Alzheimers_Disease                      0.012625  \n",
       "MUTYH-Associated_Polyposis                        -0.019378  \n",
       "Parkinsons_Disease                                 1.000000  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_df = genome_pd_df.corr()\n",
    "correlation_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.plotting.scatter_matrix(correlation_df, figsize=(50, 50), alpha=1)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RsH1789zRgqRDWe+CYPt2WLFieLu774ajj144OOaPFiRpOes8CJKckeSWJNuSXLDA9iOTfLjdfl2S6S7rmZ6GXbuGt9u1Cx7/eHj3u/cOjsHRgiQtd50GQZLDgEuAM4G1wLoka+c1Ww/8sKpOAt4B/Ncua5qagg0b4KijhrfdtQv+4i/gnnseuH737iZQJOlQ0PWI4FRgW1XdWlW7gCuAc+a1OQd4X/v8o8BpSdJlUevWwbe+Bc961vC299wDv/mbTXAcc0zzuGFDEyiSNAldn6xyeDcv+zPHAbcNLO8AnrhYm6q6N8lO4JHA9wYbJTkPOA9g9erVYynui18crd0nPwlf+hLceWczEjAEJE3Kxo3NcckVK5q9FBs2NF9mx6nrEcFC3+zrANpQVZdV1UxVzUyN4ZN41IPG0LS78054whMMAUmTM6mTVboOgh3A8QPLq4DbF2uT5HDgWOAHHdc18kFj8JiApKWx0BfWLk5W6ToItgBrkpyYZAVwLrBpXptNwIva588HPldVe40Ixm3woPHDHrZ4uyOP9JiApKWx0BfWLr6YdhoEVXUvcD6wGbgZuLKqbkpyYZKz22YbgEcm2Qa8AtjrFNOu7Dlo/LGPNR/48x15JGzdOv79cZI0isEvrF2erJIJfPkeu5mZmZqdnT3g35+ba4ZWgwd+N26EF72oSVtohmPvfe/9IbDQ70jSJIzr8yfJ9VU1M399764sXmy6iHXr4Dvfgc2bm58dO+4PAaeYkLSUpqa6PVmlVyOCubnmg3xwXqGjjmp2Dy32B3wgvyNJD0aOCDiwI/CTOmovSUulV0FwIEfgJ3XUXpKWSq+C4ECOwE/qqL0kLZVeHSPY40COwHvWkKTlbrFjBF3PNfSgNDW1/x/mB/I7krQc9GrXkCRpbwaBJPWcQSBJPWcQSFLPGQSS1HMGgST1nEEgST23LC8oSzIHfGvML7uSefdJPsT1qb/29dDUp77CePp7QlXtdUXUsgyCLiSZXeiKu0NVn/prXw9NfeordNtfdw1JUs8ZBJLUcwbB/S5b6gImrE/9ta+Hpj71FTrsr8cIJKnnHBFIUs8ZBJLUc70LgiRnJLklybYkFyyw/cgkH263X5dkevJVjscIfX1Fkq8luSHJZ5OcsBR1jsuw/g60e36SSrJsTz0cpa9JXtD+/d6U5EOTrnFcRvh3vDrJ1Um2tv+Wz1qKOschyeVJ7khy4yLbk+Sv2z+LG5KcMpY3rqre/ACHAf8P+PfACuArwNp5bV4KXNo+Pxf48FLX3WFfnwn8m/b5Hy/Xvo7a37bdw4FrgGuBmaWuu8O/2zXAVuDftss/t9R1d9jXy4A/bp+vBbYvdd0H0d+nAacANy6y/Szg00CAJwHXjeN9+zYiOBXYVlW3VtUu4ArgnHltzgHe1z7/KHBakkywxnEZ2tequrqq7moXrwVWTbjGcRrl7xbgzcBFwN2TLG7MRunrS4BLquqHAFV1x4RrHJdR+lrAMe3zY4HbJ1jfWFXVNcAP9tHkHOD91bgWeESSRx3s+/YtCI4DbhtY3tGuW7BNVd0L7AQeOZHqxmuUvg5aT/NNY7ka2t8kJwPHV9WnJllYB0b5u3008Ogk/5Tk2iRnTKy68Rqlr28CXphkB3AV8LLJlLYk9vf/9Uj6ds/ihb7Zzz9/dpQ2y8HI/UjyQmAGeHqnFXVrn/1N8hDgHcCLJ1VQh0b5uz2cZvfQM2hGev+Y5LFV9aOOaxu3Ufq6DnhvVf23JE8GPtD29b7uy5u4Tj6f+jYi2AEcP7C8ir2HkT9rk+RwmqHmvoZqD1aj9JUkpwOvA86uqnsmVFsXhvX34cBjgc8n2U6zf3XTMj1gPOq/409U1e6q+iZwC00wLDej9HU9cCVAVX0ReCjNBG2HopH+X++vvgXBFmBNkhOTrKA5GLxpXptNwIva588HPlftUZplZmhf210l76YJgeW6D3mPffa3qnZW1cqqmq6qaZpjImdX1ezSlHtQRvl3/HGakwFIspJmV9GtE61yPEbp67eB0wCSPIYmCOYmWuXkbAJ+vz176EnAzqr67sG+aK92DVXVvUnOBzbTnI1weVXdlORCYLaqNgEbaIaW22hGAucuXcUHbsS+XgwcDXykPR7+7ao6e8mKPggj9veQMGJfNwPPSfI14KfAq6rq+0tX9YEZsa+vBP42yX+m2U3y4mX65Y0kG2l2561sj3m8ETgCoKoupTkGchawDbgL+IOxvO8y/fOSJI1J33YNSZLmMQgkqecMAknqOYNAknrOIJCknjMI1FtJvjBk+/YkX03y5fbnVzus5RlJlvvUF1qmenUdgTSoqkb5YH9mVX2v82KkJeSIQL2V5M728VFJrmm/9d+Y5NeG/N6rkmxp54P/83bddJKvJ3lP+xofTHJ6O+nbN5Kc2rY7NckX2rnzv5DkFxZ4/Ye189JvadstNIuqNDYGgQS/C2yuqscDvwx8eWDb1W1AXAeQ5Dk0c/acCjwe+JUkT2vbngS8C3gc8Ivt6z4V+DPgtW2brwNPq6qTgTcAb1mgntfRTG3yBJppIi5O8rBxdVaaz11DUjOfzeVJjgA+XlWDQTB/19Bz2p+t7fLRNMHwbeCbVfVVgCQ3AZ+tqkryVWC6bX8s8L4ka2imQzhigXqeA5yd5M/a5YcCq4GbD66b0sIMAvVeVV3Tfqv/dZp5pi6uqvcv0jzAX1XVux+wsrml6eDsrfcNLN/H/f/X3gxcXVX/of2dzy/yHr9VVbfsd2ekA+CuIfVemns131FVf0sz6eC+7gO7GfjDJEe3v3tckp/bj7c7FvhO+/zF+3iPl+25M147S6zUGUcEUjPb46uS7AbuBH5/sYZV9b/bqY6/2H5O3wm8kGaGz1FcRLNr6BXA5xZp82bgncANbRhsB35jxNeX9puzj0pSz7lrSJJ6ziCQpJ4zCCSp5wwCSeo5g0CSes4gkKSeMwgkqef+P0yfxFkGy2jrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = correlation_df.plot.scatter(x='isFemale', y='Type_2_Diabetes', c='blue')\n",
    "ax2 = correlation_df.plot.scatter(x='isFemale', y='Parkinsons_Disease', c='red')\n",
    "ax3 = correlation_df.plot.scatter(x='CaffeineConsumption', y='Type_2_Diabetes', c='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#diseaseGlobe_df.plot.hist(alpha=0.5, figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a27c98e90>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(20,15))\n",
    "sns.heatmap(correlation_df, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add more conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_rows = table.count()\n",
    "#print(\"Number of rows in the table: \", num_rows)\n",
    "\n",
    "# Add asthma\n",
    "genome_pd_df[\"Asthma\"] = [random.choice([True, False]) for i in range(num_rows)]\n",
    "\n",
    "# Add HIV\n",
    "genome_pd_df[\"HIV\"] = [random.choice([True, False]) for i in range(num_rows)]\n",
    "\n",
    "#Add Age\n",
    "genome_pd_df[\"Age\"] = [random.randint(0,99) for i in range(num_rows)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>Sample</th>\n",
       "      <th>Population</th>\n",
       "      <th>SuperPopulation</th>\n",
       "      <th>isFemale</th>\n",
       "      <th>PurpleHair</th>\n",
       "      <th>CaffeineConsumption</th>\n",
       "      <th>Type_2_Diabetes</th>\n",
       "      <th>Age-Related_Macular_Degeneration</th>\n",
       "      <th>Alpha-1_Antitrypsin_Deficiency</th>\n",
       "      <th>BRCA1/BRCA2</th>\n",
       "      <th>...</th>\n",
       "      <th>G6PD_Deficiency</th>\n",
       "      <th>Hereditary_Amyloidosis(TTR-Related)</th>\n",
       "      <th>Hereditary_Hemochromatosis(HFE‑Related)</th>\n",
       "      <th>Hereditary_Thrombophilia</th>\n",
       "      <th>Late-Onset_Alzheimers_Disease</th>\n",
       "      <th>MUTYH-Associated_Polyposis</th>\n",
       "      <th>Parkinsons_Disease</th>\n",
       "      <th>Asthma</th>\n",
       "      <th>HIV</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>HG00096</td>\n",
       "      <td>GBR</td>\n",
       "      <td>EUR</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>HG00097</td>\n",
       "      <td>GBR</td>\n",
       "      <td>EUR</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>HG00098</td>\n",
       "      <td>GBR</td>\n",
       "      <td>EUR</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>HG00099</td>\n",
       "      <td>GBR</td>\n",
       "      <td>EUR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>HG00100</td>\n",
       "      <td>GBR</td>\n",
       "      <td>EUR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3495</td>\n",
       "      <td>NA21137</td>\n",
       "      <td>GIH</td>\n",
       "      <td>SAS</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3496</td>\n",
       "      <td>NA21141</td>\n",
       "      <td>GIH</td>\n",
       "      <td>SAS</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3497</td>\n",
       "      <td>NA21142</td>\n",
       "      <td>GIH</td>\n",
       "      <td>SAS</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3498</td>\n",
       "      <td>NA21143</td>\n",
       "      <td>GIH</td>\n",
       "      <td>SAS</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3499</td>\n",
       "      <td>NA21144</td>\n",
       "      <td>GIH</td>\n",
       "      <td>SAS</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3500 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sample Population SuperPopulation  isFemale  PurpleHair  \\\n",
       "0     HG00096        GBR             EUR     False       False   \n",
       "1     HG00097        GBR             EUR      True        True   \n",
       "2     HG00098        GBR             EUR     False       False   \n",
       "3     HG00099        GBR             EUR      True       False   \n",
       "4     HG00100        GBR             EUR      True       False   \n",
       "...       ...        ...             ...       ...         ...   \n",
       "3495  NA21137        GIH             SAS      True       False   \n",
       "3496  NA21141        GIH             SAS      True        True   \n",
       "3497  NA21142        GIH             SAS      True        True   \n",
       "3498  NA21143        GIH             SAS      True        True   \n",
       "3499  NA21144        GIH             SAS      True       False   \n",
       "\n",
       "      CaffeineConsumption  Type_2_Diabetes  Age-Related_Macular_Degeneration  \\\n",
       "0                       4             True                             False   \n",
       "1                       4            False                             False   \n",
       "2                       5             True                             False   \n",
       "3                       4            False                             False   \n",
       "4                       5             True                             False   \n",
       "...                   ...              ...                               ...   \n",
       "3495                    1             True                              True   \n",
       "3496                    2             True                             False   \n",
       "3497                    2            False                             False   \n",
       "3498                    5            False                              True   \n",
       "3499                    3            False                              True   \n",
       "\n",
       "      Alpha-1_Antitrypsin_Deficiency  BRCA1/BRCA2  ...  G6PD_Deficiency  \\\n",
       "0                               True        False  ...             True   \n",
       "1                              False         True  ...             True   \n",
       "2                               True         True  ...             True   \n",
       "3                               True         True  ...            False   \n",
       "4                              False        False  ...            False   \n",
       "...                              ...          ...  ...              ...   \n",
       "3495                            True         True  ...             True   \n",
       "3496                           False        False  ...             True   \n",
       "3497                           False        False  ...             True   \n",
       "3498                           False         True  ...            False   \n",
       "3499                            True         True  ...             True   \n",
       "\n",
       "      Hereditary_Amyloidosis(TTR-Related)  \\\n",
       "0                                   False   \n",
       "1                                    True   \n",
       "2                                   False   \n",
       "3                                   False   \n",
       "4                                    True   \n",
       "...                                   ...   \n",
       "3495                                False   \n",
       "3496                                False   \n",
       "3497                                False   \n",
       "3498                                 True   \n",
       "3499                                False   \n",
       "\n",
       "      Hereditary_Hemochromatosis(HFE‑Related)  Hereditary_Thrombophilia  \\\n",
       "0                                        True                      True   \n",
       "1                                       False                      True   \n",
       "2                                        True                     False   \n",
       "3                                        True                      True   \n",
       "4                                        True                     False   \n",
       "...                                       ...                       ...   \n",
       "3495                                     True                      True   \n",
       "3496                                     True                      True   \n",
       "3497                                     True                     False   \n",
       "3498                                    False                      True   \n",
       "3499                                    False                      True   \n",
       "\n",
       "      Late-Onset_Alzheimers_Disease  MUTYH-Associated_Polyposis  \\\n",
       "0                              True                       False   \n",
       "1                             False                       False   \n",
       "2                             False                       False   \n",
       "3                              True                        True   \n",
       "4                             False                       False   \n",
       "...                             ...                         ...   \n",
       "3495                          False                        True   \n",
       "3496                           True                        True   \n",
       "3497                          False                       False   \n",
       "3498                          False                        True   \n",
       "3499                           True                        True   \n",
       "\n",
       "      Parkinsons_Disease  Asthma    HIV  Age  \n",
       "0                   True   False   True   88  \n",
       "1                  False    True  False   95  \n",
       "2                  False    True   True   92  \n",
       "3                   True   False   True   27  \n",
       "4                   True    True  False    2  \n",
       "...                  ...     ...    ...  ...  \n",
       "3495                True   False  False   85  \n",
       "3496               False    True   True   27  \n",
       "3497                True   False  False   85  \n",
       "3498                True    True  False   98  \n",
       "3499               False   False  False   28  \n",
       "\n",
       "[3500 rows x 22 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genome_pd_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isFemale</th>\n",
       "      <th>PurpleHair</th>\n",
       "      <th>CaffeineConsumption</th>\n",
       "      <th>Type_2_Diabetes</th>\n",
       "      <th>Age-Related_Macular_Degeneration</th>\n",
       "      <th>Alpha-1_Antitrypsin_Deficiency</th>\n",
       "      <th>BRCA1/BRCA2</th>\n",
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       "      <td>isFemale</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014095</td>\n",
       "      <td>-0.004478</td>\n",
       "      <td>-0.007406</td>\n",
       "      <td>0.005701</td>\n",
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       "      <td>0.027846</td>\n",
       "      <td>0.010325</td>\n",
       "      <td>-0.002332</td>\n",
       "      <td>0.006270</td>\n",
       "      <td>0.009098</td>\n",
       "      <td>0.011396</td>\n",
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       "      <td>-0.014263</td>\n",
       "      <td>-0.004076</td>\n",
       "      <td>0.045783</td>\n",
       "      <td>-0.010332</td>\n",
       "      <td>-0.009973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PurpleHair</td>\n",
       "      <td>0.014095</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.009908</td>\n",
       "      <td>-0.003291</td>\n",
       "      <td>-0.024666</td>\n",
       "      <td>0.016349</td>\n",
       "      <td>0.030800</td>\n",
       "      <td>0.004703</td>\n",
       "      <td>0.000811</td>\n",
       "      <td>-0.013430</td>\n",
       "      <td>-0.011535</td>\n",
       "      <td>0.006011</td>\n",
       "      <td>-0.007633</td>\n",
       "      <td>-0.007865</td>\n",
       "      <td>-0.012440</td>\n",
       "      <td>-0.009608</td>\n",
       "      <td>-0.042487</td>\n",
       "      <td>0.019734</td>\n",
       "      <td>-0.007031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CaffeineConsumption</td>\n",
       "      <td>-0.004478</td>\n",
       "      <td>0.009908</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.016244</td>\n",
       "      <td>0.024362</td>\n",
       "      <td>-0.002611</td>\n",
       "      <td>0.006051</td>\n",
       "      <td>-0.031460</td>\n",
       "      <td>-0.023734</td>\n",
       "      <td>-0.017214</td>\n",
       "      <td>0.011945</td>\n",
       "      <td>-0.028628</td>\n",
       "      <td>-0.026635</td>\n",
       "      <td>0.014901</td>\n",
       "      <td>-0.008599</td>\n",
       "      <td>-0.007429</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>-0.011506</td>\n",
       "      <td>0.016866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Type_2_Diabetes</td>\n",
       "      <td>-0.007406</td>\n",
       "      <td>-0.003291</td>\n",
       "      <td>0.016244</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.026867</td>\n",
       "      <td>-0.012611</td>\n",
       "      <td>0.019094</td>\n",
       "      <td>-0.039901</td>\n",
       "      <td>0.008544</td>\n",
       "      <td>0.006318</td>\n",
       "      <td>0.002297</td>\n",
       "      <td>-0.016540</td>\n",
       "      <td>0.008594</td>\n",
       "      <td>0.025127</td>\n",
       "      <td>-0.041160</td>\n",
       "      <td>-0.003376</td>\n",
       "      <td>-0.014332</td>\n",
       "      <td>0.037176</td>\n",
       "      <td>0.033214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Age-Related_Macular_Degeneration</td>\n",
       "      <td>0.005701</td>\n",
       "      <td>-0.024666</td>\n",
       "      <td>0.024362</td>\n",
       "      <td>0.026867</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.014264</td>\n",
       "      <td>-0.001265</td>\n",
       "      <td>-0.008642</td>\n",
       "      <td>-0.021699</td>\n",
       "      <td>-0.000018</td>\n",
       "      <td>0.022279</td>\n",
       "      <td>0.007982</td>\n",
       "      <td>0.001130</td>\n",
       "      <td>0.001723</td>\n",
       "      <td>0.014295</td>\n",
       "      <td>-0.008602</td>\n",
       "      <td>-0.009117</td>\n",
       "      <td>-0.000018</td>\n",
       "      <td>-0.017367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Alpha-1_Antitrypsin_Deficiency</td>\n",
       "      <td>0.006342</td>\n",
       "      <td>0.016349</td>\n",
       "      <td>-0.002611</td>\n",
       "      <td>-0.012611</td>\n",
       "      <td>-0.014264</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003494</td>\n",
       "      <td>0.012861</td>\n",
       "      <td>-0.000638</td>\n",
       "      <td>0.017793</td>\n",
       "      <td>-0.001115</td>\n",
       "      <td>-0.003923</td>\n",
       "      <td>0.007485</td>\n",
       "      <td>0.010247</td>\n",
       "      <td>0.018248</td>\n",
       "      <td>-0.007873</td>\n",
       "      <td>-0.007540</td>\n",
       "      <td>0.013222</td>\n",
       "      <td>0.010624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BRCA1/BRCA2</td>\n",
       "      <td>0.028311</td>\n",
       "      <td>0.030800</td>\n",
       "      <td>0.006051</td>\n",
       "      <td>0.019094</td>\n",
       "      <td>-0.001265</td>\n",
       "      <td>-0.003494</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012412</td>\n",
       "      <td>-0.008795</td>\n",
       "      <td>-0.006144</td>\n",
       "      <td>-0.015600</td>\n",
       "      <td>0.019035</td>\n",
       "      <td>0.014577</td>\n",
       "      <td>-0.001506</td>\n",
       "      <td>-0.022106</td>\n",
       "      <td>0.006748</td>\n",
       "      <td>-0.007410</td>\n",
       "      <td>-0.011866</td>\n",
       "      <td>-0.007276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Celiac_Disease</td>\n",
       "      <td>0.027846</td>\n",
       "      <td>0.004703</td>\n",
       "      <td>-0.031460</td>\n",
       "      <td>-0.039901</td>\n",
       "      <td>-0.008642</td>\n",
       "      <td>0.012861</td>\n",
       "      <td>-0.012412</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.020952</td>\n",
       "      <td>-0.016812</td>\n",
       "      <td>-0.017234</td>\n",
       "      <td>0.011772</td>\n",
       "      <td>-0.003025</td>\n",
       "      <td>-0.028467</td>\n",
       "      <td>-0.007887</td>\n",
       "      <td>0.006477</td>\n",
       "      <td>0.007765</td>\n",
       "      <td>-0.022529</td>\n",
       "      <td>0.003601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Familial_Hypercholesterolemia</td>\n",
       "      <td>0.010325</td>\n",
       "      <td>0.000811</td>\n",
       "      <td>-0.023734</td>\n",
       "      <td>0.008544</td>\n",
       "      <td>-0.021699</td>\n",
       "      <td>-0.000638</td>\n",
       "      <td>-0.008795</td>\n",
       "      <td>-0.020952</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.025089</td>\n",
       "      <td>-0.031410</td>\n",
       "      <td>0.019485</td>\n",
       "      <td>0.008040</td>\n",
       "      <td>-0.001742</td>\n",
       "      <td>-0.015456</td>\n",
       "      <td>0.006377</td>\n",
       "      <td>-0.013794</td>\n",
       "      <td>0.012627</td>\n",
       "      <td>-0.023507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>G6PD_Deficiency</td>\n",
       "      <td>-0.002332</td>\n",
       "      <td>-0.013430</td>\n",
       "      <td>-0.017214</td>\n",
       "      <td>0.006318</td>\n",
       "      <td>-0.000018</td>\n",
       "      <td>0.017793</td>\n",
       "      <td>-0.006144</td>\n",
       "      <td>-0.016812</td>\n",
       "      <td>-0.025089</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002880</td>\n",
       "      <td>0.003365</td>\n",
       "      <td>-0.022904</td>\n",
       "      <td>0.014318</td>\n",
       "      <td>-0.022255</td>\n",
       "      <td>0.015325</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>-0.021780</td>\n",
       "      <td>-0.021804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hereditary_Amyloidosis(TTR-Related)</td>\n",
       "      <td>0.006270</td>\n",
       "      <td>-0.011535</td>\n",
       "      <td>0.011945</td>\n",
       "      <td>0.002297</td>\n",
       "      <td>0.022279</td>\n",
       "      <td>-0.001115</td>\n",
       "      <td>-0.015600</td>\n",
       "      <td>-0.017234</td>\n",
       "      <td>-0.031410</td>\n",
       "      <td>-0.002880</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.008595</td>\n",
       "      <td>0.001698</td>\n",
       "      <td>0.006869</td>\n",
       "      <td>0.013726</td>\n",
       "      <td>-0.013753</td>\n",
       "      <td>-0.007396</td>\n",
       "      <td>0.021121</td>\n",
       "      <td>0.022990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hereditary_Hemochromatosis(HFE‑Related)</td>\n",
       "      <td>0.009098</td>\n",
       "      <td>0.006011</td>\n",
       "      <td>-0.028628</td>\n",
       "      <td>-0.016540</td>\n",
       "      <td>0.007982</td>\n",
       "      <td>-0.003923</td>\n",
       "      <td>0.019035</td>\n",
       "      <td>0.011772</td>\n",
       "      <td>0.019485</td>\n",
       "      <td>0.003365</td>\n",
       "      <td>-0.008595</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005760</td>\n",
       "      <td>0.017747</td>\n",
       "      <td>-0.015397</td>\n",
       "      <td>0.022183</td>\n",
       "      <td>-0.001052</td>\n",
       "      <td>-0.026351</td>\n",
       "      <td>-0.009289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hereditary_Thrombophilia</td>\n",
       "      <td>0.011396</td>\n",
       "      <td>-0.007633</td>\n",
       "      <td>-0.026635</td>\n",
       "      <td>0.008594</td>\n",
       "      <td>0.001130</td>\n",
       "      <td>0.007485</td>\n",
       "      <td>0.014577</td>\n",
       "      <td>-0.003025</td>\n",
       "      <td>0.008040</td>\n",
       "      <td>-0.022904</td>\n",
       "      <td>0.001698</td>\n",
       "      <td>-0.005760</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.009692</td>\n",
       "      <td>0.049738</td>\n",
       "      <td>-0.004076</td>\n",
       "      <td>-0.001078</td>\n",
       "      <td>0.050242</td>\n",
       "      <td>-0.042636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Late-Onset_Alzheimers_Disease</td>\n",
       "      <td>-0.013120</td>\n",
       "      <td>-0.007865</td>\n",
       "      <td>0.014901</td>\n",
       "      <td>0.025127</td>\n",
       "      <td>0.001723</td>\n",
       "      <td>0.010247</td>\n",
       "      <td>-0.001506</td>\n",
       "      <td>-0.028467</td>\n",
       "      <td>-0.001742</td>\n",
       "      <td>0.014318</td>\n",
       "      <td>0.006869</td>\n",
       "      <td>0.017747</td>\n",
       "      <td>-0.009692</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.015984</td>\n",
       "      <td>0.012625</td>\n",
       "      <td>-0.005189</td>\n",
       "      <td>0.002889</td>\n",
       "      <td>-0.018686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>MUTYH-Associated_Polyposis</td>\n",
       "      <td>-0.014263</td>\n",
       "      <td>-0.012440</td>\n",
       "      <td>-0.008599</td>\n",
       "      <td>-0.041160</td>\n",
       "      <td>0.014295</td>\n",
       "      <td>0.018248</td>\n",
       "      <td>-0.022106</td>\n",
       "      <td>-0.007887</td>\n",
       "      <td>-0.015456</td>\n",
       "      <td>-0.022255</td>\n",
       "      <td>0.013726</td>\n",
       "      <td>-0.015397</td>\n",
       "      <td>0.049738</td>\n",
       "      <td>0.015984</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.019378</td>\n",
       "      <td>0.021099</td>\n",
       "      <td>-0.006254</td>\n",
       "      <td>0.005514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Parkinsons_Disease</td>\n",
       "      <td>-0.004076</td>\n",
       "      <td>-0.009608</td>\n",
       "      <td>-0.007429</td>\n",
       "      <td>-0.003376</td>\n",
       "      <td>-0.008602</td>\n",
       "      <td>-0.007873</td>\n",
       "      <td>0.006748</td>\n",
       "      <td>0.006477</td>\n",
       "      <td>0.006377</td>\n",
       "      <td>0.015325</td>\n",
       "      <td>-0.013753</td>\n",
       "      <td>0.022183</td>\n",
       "      <td>-0.004076</td>\n",
       "      <td>0.012625</td>\n",
       "      <td>-0.019378</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014438</td>\n",
       "      <td>-0.020108</td>\n",
       "      <td>-0.004713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Asthma</td>\n",
       "      <td>0.045783</td>\n",
       "      <td>-0.042487</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>-0.014332</td>\n",
       "      <td>-0.009117</td>\n",
       "      <td>-0.007540</td>\n",
       "      <td>-0.007410</td>\n",
       "      <td>0.007765</td>\n",
       "      <td>-0.013794</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>-0.007396</td>\n",
       "      <td>-0.001052</td>\n",
       "      <td>-0.001078</td>\n",
       "      <td>-0.005189</td>\n",
       "      <td>0.021099</td>\n",
       "      <td>0.014438</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.021625</td>\n",
       "      <td>0.022735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>HIV</td>\n",
       "      <td>-0.010332</td>\n",
       "      <td>0.019734</td>\n",
       "      <td>-0.011506</td>\n",
       "      <td>0.037176</td>\n",
       "      <td>-0.000018</td>\n",
       "      <td>0.013222</td>\n",
       "      <td>-0.011866</td>\n",
       "      <td>-0.022529</td>\n",
       "      <td>0.012627</td>\n",
       "      <td>-0.021780</td>\n",
       "      <td>0.021121</td>\n",
       "      <td>-0.026351</td>\n",
       "      <td>0.050242</td>\n",
       "      <td>0.002889</td>\n",
       "      <td>-0.006254</td>\n",
       "      <td>-0.020108</td>\n",
       "      <td>-0.021625</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Age</td>\n",
       "      <td>-0.009973</td>\n",
       "      <td>-0.007031</td>\n",
       "      <td>0.016866</td>\n",
       "      <td>0.033214</td>\n",
       "      <td>-0.017367</td>\n",
       "      <td>0.010624</td>\n",
       "      <td>-0.007276</td>\n",
       "      <td>0.003601</td>\n",
       "      <td>-0.023507</td>\n",
       "      <td>-0.021804</td>\n",
       "      <td>0.022990</td>\n",
       "      <td>-0.009289</td>\n",
       "      <td>-0.042636</td>\n",
       "      <td>-0.018686</td>\n",
       "      <td>0.005514</td>\n",
       "      <td>-0.004713</td>\n",
       "      <td>0.022735</td>\n",
       "      <td>0.034289</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         isFemale  PurpleHair  \\\n",
       "isFemale                                 1.000000    0.014095   \n",
       "PurpleHair                               0.014095    1.000000   \n",
       "CaffeineConsumption                     -0.004478    0.009908   \n",
       "Type_2_Diabetes                         -0.007406   -0.003291   \n",
       "Age-Related_Macular_Degeneration         0.005701   -0.024666   \n",
       "Alpha-1_Antitrypsin_Deficiency           0.006342    0.016349   \n",
       "BRCA1/BRCA2                              0.028311    0.030800   \n",
       "Celiac_Disease                           0.027846    0.004703   \n",
       "Familial_Hypercholesterolemia            0.010325    0.000811   \n",
       "G6PD_Deficiency                         -0.002332   -0.013430   \n",
       "Hereditary_Amyloidosis(TTR-Related)      0.006270   -0.011535   \n",
       "Hereditary_Hemochromatosis(HFE‑Related)  0.009098    0.006011   \n",
       "Hereditary_Thrombophilia                 0.011396   -0.007633   \n",
       "Late-Onset_Alzheimers_Disease           -0.013120   -0.007865   \n",
       "MUTYH-Associated_Polyposis              -0.014263   -0.012440   \n",
       "Parkinsons_Disease                      -0.004076   -0.009608   \n",
       "Asthma                                   0.045783   -0.042487   \n",
       "HIV                                     -0.010332    0.019734   \n",
       "Age                                     -0.009973   -0.007031   \n",
       "\n",
       "                                         CaffeineConsumption  Type_2_Diabetes  \\\n",
       "isFemale                                           -0.004478        -0.007406   \n",
       "PurpleHair                                          0.009908        -0.003291   \n",
       "CaffeineConsumption                                 1.000000         0.016244   \n",
       "Type_2_Diabetes                                     0.016244         1.000000   \n",
       "Age-Related_Macular_Degeneration                    0.024362         0.026867   \n",
       "Alpha-1_Antitrypsin_Deficiency                     -0.002611        -0.012611   \n",
       "BRCA1/BRCA2                                         0.006051         0.019094   \n",
       "Celiac_Disease                                     -0.031460        -0.039901   \n",
       "Familial_Hypercholesterolemia                      -0.023734         0.008544   \n",
       "G6PD_Deficiency                                    -0.017214         0.006318   \n",
       "Hereditary_Amyloidosis(TTR-Related)                 0.011945         0.002297   \n",
       "Hereditary_Hemochromatosis(HFE‑Related)            -0.028628        -0.016540   \n",
       "Hereditary_Thrombophilia                           -0.026635         0.008594   \n",
       "Late-Onset_Alzheimers_Disease                       0.014901         0.025127   \n",
       "MUTYH-Associated_Polyposis                         -0.008599        -0.041160   \n",
       "Parkinsons_Disease                                 -0.007429        -0.003376   \n",
       "Asthma                                              0.000059        -0.014332   \n",
       "HIV                                                -0.011506         0.037176   \n",
       "Age                                                 0.016866         0.033214   \n",
       "\n",
       "                                         Age-Related_Macular_Degeneration  \\\n",
       "isFemale                                                         0.005701   \n",
       "PurpleHair                                                      -0.024666   \n",
       "CaffeineConsumption                                              0.024362   \n",
       "Type_2_Diabetes                                                  0.026867   \n",
       "Age-Related_Macular_Degeneration                                 1.000000   \n",
       "Alpha-1_Antitrypsin_Deficiency                                  -0.014264   \n",
       "BRCA1/BRCA2                                                     -0.001265   \n",
       "Celiac_Disease                                                  -0.008642   \n",
       "Familial_Hypercholesterolemia                                   -0.021699   \n",
       "G6PD_Deficiency                                                 -0.000018   \n",
       "Hereditary_Amyloidosis(TTR-Related)                              0.022279   \n",
       "Hereditary_Hemochromatosis(HFE‑Related)                          0.007982   \n",
       "Hereditary_Thrombophilia                                         0.001130   \n",
       "Late-Onset_Alzheimers_Disease                                    0.001723   \n",
       "MUTYH-Associated_Polyposis                                       0.014295   \n",
       "Parkinsons_Disease                                              -0.008602   \n",
       "Asthma                                                          -0.009117   \n",
       "HIV                                                             -0.000018   \n",
       "Age                                                             -0.017367   \n",
       "\n",
       "                                         Alpha-1_Antitrypsin_Deficiency  \\\n",
       "isFemale                                                       0.006342   \n",
       "PurpleHair                                                     0.016349   \n",
       "CaffeineConsumption                                           -0.002611   \n",
       "Type_2_Diabetes                                               -0.012611   \n",
       "Age-Related_Macular_Degeneration                              -0.014264   \n",
       "Alpha-1_Antitrypsin_Deficiency                                 1.000000   \n",
       "BRCA1/BRCA2                                                   -0.003494   \n",
       "Celiac_Disease                                                 0.012861   \n",
       "Familial_Hypercholesterolemia                                 -0.000638   \n",
       "G6PD_Deficiency                                                0.017793   \n",
       "Hereditary_Amyloidosis(TTR-Related)                           -0.001115   \n",
       "Hereditary_Hemochromatosis(HFE‑Related)                       -0.003923   \n",
       "Hereditary_Thrombophilia                                       0.007485   \n",
       "Late-Onset_Alzheimers_Disease                                  0.010247   \n",
       "MUTYH-Associated_Polyposis                                     0.018248   \n",
       "Parkinsons_Disease                                            -0.007873   \n",
       "Asthma                                                        -0.007540   \n",
       "HIV                                                            0.013222   \n",
       "Age                                                            0.010624   \n",
       "\n",
       "                                         BRCA1/BRCA2  Celiac_Disease  \\\n",
       "isFemale                                    0.028311        0.027846   \n",
       "PurpleHair                                  0.030800        0.004703   \n",
       "CaffeineConsumption                         0.006051       -0.031460   \n",
       "Type_2_Diabetes                             0.019094       -0.039901   \n",
       "Age-Related_Macular_Degeneration           -0.001265       -0.008642   \n",
       "Alpha-1_Antitrypsin_Deficiency             -0.003494        0.012861   \n",
       "BRCA1/BRCA2                                 1.000000       -0.012412   \n",
       "Celiac_Disease                             -0.012412        1.000000   \n",
       "Familial_Hypercholesterolemia              -0.008795       -0.020952   \n",
       "G6PD_Deficiency                            -0.006144       -0.016812   \n",
       "Hereditary_Amyloidosis(TTR-Related)        -0.015600       -0.017234   \n",
       "Hereditary_Hemochromatosis(HFE‑Related)     0.019035        0.011772   \n",
       "Hereditary_Thrombophilia                    0.014577       -0.003025   \n",
       "Late-Onset_Alzheimers_Disease              -0.001506       -0.028467   \n",
       "MUTYH-Associated_Polyposis                 -0.022106       -0.007887   \n",
       "Parkinsons_Disease                          0.006748        0.006477   \n",
       "Asthma                                     -0.007410        0.007765   \n",
       "HIV                                        -0.011866       -0.022529   \n",
       "Age                                        -0.007276        0.003601   \n",
       "\n",
       "                                         Familial_Hypercholesterolemia  \\\n",
       "isFemale                                                      0.010325   \n",
       "PurpleHair                                                    0.000811   \n",
       "CaffeineConsumption                                          -0.023734   \n",
       "Type_2_Diabetes                                               0.008544   \n",
       "Age-Related_Macular_Degeneration                             -0.021699   \n",
       "Alpha-1_Antitrypsin_Deficiency                               -0.000638   \n",
       "BRCA1/BRCA2                                                  -0.008795   \n",
       "Celiac_Disease                                               -0.020952   \n",
       "Familial_Hypercholesterolemia                                 1.000000   \n",
       "G6PD_Deficiency                                              -0.025089   \n",
       "Hereditary_Amyloidosis(TTR-Related)                          -0.031410   \n",
       "Hereditary_Hemochromatosis(HFE‑Related)                       0.019485   \n",
       "Hereditary_Thrombophilia                                      0.008040   \n",
       "Late-Onset_Alzheimers_Disease                                -0.001742   \n",
       "MUTYH-Associated_Polyposis                                   -0.015456   \n",
       "Parkinsons_Disease                                            0.006377   \n",
       "Asthma                                                       -0.013794   \n",
       "HIV                                                           0.012627   \n",
       "Age                                                          -0.023507   \n",
       "\n",
       "                                         G6PD_Deficiency  \\\n",
       "isFemale                                       -0.002332   \n",
       "PurpleHair                                     -0.013430   \n",
       "CaffeineConsumption                            -0.017214   \n",
       "Type_2_Diabetes                                 0.006318   \n",
       "Age-Related_Macular_Degeneration               -0.000018   \n",
       "Alpha-1_Antitrypsin_Deficiency                  0.017793   \n",
       "BRCA1/BRCA2                                    -0.006144   \n",
       "Celiac_Disease                                 -0.016812   \n",
       "Familial_Hypercholesterolemia                  -0.025089   \n",
       "G6PD_Deficiency                                 1.000000   \n",
       "Hereditary_Amyloidosis(TTR-Related)            -0.002880   \n",
       "Hereditary_Hemochromatosis(HFE‑Related)         0.003365   \n",
       "Hereditary_Thrombophilia                       -0.022904   \n",
       "Late-Onset_Alzheimers_Disease                   0.014318   \n",
       "MUTYH-Associated_Polyposis                     -0.022255   \n",
       "Parkinsons_Disease                              0.015325   \n",
       "Asthma                                          0.000091   \n",
       "HIV                                            -0.021780   \n",
       "Age                                            -0.021804   \n",
       "\n",
       "                                         Hereditary_Amyloidosis(TTR-Related)  \\\n",
       "isFemale                                                            0.006270   \n",
       "PurpleHair                                                         -0.011535   \n",
       "CaffeineConsumption                                                 0.011945   \n",
       "Type_2_Diabetes                                                     0.002297   \n",
       "Age-Related_Macular_Degeneration                                    0.022279   \n",
       "Alpha-1_Antitrypsin_Deficiency                                     -0.001115   \n",
       "BRCA1/BRCA2                                                        -0.015600   \n",
       "Celiac_Disease                                                     -0.017234   \n",
       "Familial_Hypercholesterolemia                                      -0.031410   \n",
       "G6PD_Deficiency                                                    -0.002880   \n",
       "Hereditary_Amyloidosis(TTR-Related)                                 1.000000   \n",
       "Hereditary_Hemochromatosis(HFE‑Related)                            -0.008595   \n",
       "Hereditary_Thrombophilia                                            0.001698   \n",
       "Late-Onset_Alzheimers_Disease                                       0.006869   \n",
       "MUTYH-Associated_Polyposis                                          0.013726   \n",
       "Parkinsons_Disease                                                 -0.013753   \n",
       "Asthma                                                             -0.007396   \n",
       "HIV                                                                 0.021121   \n",
       "Age                                                                 0.022990   \n",
       "\n",
       "                                         Hereditary_Hemochromatosis(HFE‑Related)  \\\n",
       "isFemale                                                                0.009098   \n",
       "PurpleHair                                                              0.006011   \n",
       "CaffeineConsumption                                                    -0.028628   \n",
       "Type_2_Diabetes                                                        -0.016540   \n",
       "Age-Related_Macular_Degeneration                                        0.007982   \n",
       "Alpha-1_Antitrypsin_Deficiency                                         -0.003923   \n",
       "BRCA1/BRCA2                                                             0.019035   \n",
       "Celiac_Disease                                                          0.011772   \n",
       "Familial_Hypercholesterolemia                                           0.019485   \n",
       "G6PD_Deficiency                                                         0.003365   \n",
       "Hereditary_Amyloidosis(TTR-Related)                                    -0.008595   \n",
       "Hereditary_Hemochromatosis(HFE‑Related)                                 1.000000   \n",
       "Hereditary_Thrombophilia                                               -0.005760   \n",
       "Late-Onset_Alzheimers_Disease                                           0.017747   \n",
       "MUTYH-Associated_Polyposis                                             -0.015397   \n",
       "Parkinsons_Disease                                                      0.022183   \n",
       "Asthma                                                                 -0.001052   \n",
       "HIV                                                                    -0.026351   \n",
       "Age                                                                    -0.009289   \n",
       "\n",
       "                                         Hereditary_Thrombophilia  \\\n",
       "isFemale                                                 0.011396   \n",
       "PurpleHair                                              -0.007633   \n",
       "CaffeineConsumption                                     -0.026635   \n",
       "Type_2_Diabetes                                          0.008594   \n",
       "Age-Related_Macular_Degeneration                         0.001130   \n",
       "Alpha-1_Antitrypsin_Deficiency                           0.007485   \n",
       "BRCA1/BRCA2                                              0.014577   \n",
       "Celiac_Disease                                          -0.003025   \n",
       "Familial_Hypercholesterolemia                            0.008040   \n",
       "G6PD_Deficiency                                         -0.022904   \n",
       "Hereditary_Amyloidosis(TTR-Related)                      0.001698   \n",
       "Hereditary_Hemochromatosis(HFE‑Related)                 -0.005760   \n",
       "Hereditary_Thrombophilia                                 1.000000   \n",
       "Late-Onset_Alzheimers_Disease                           -0.009692   \n",
       "MUTYH-Associated_Polyposis                               0.049738   \n",
       "Parkinsons_Disease                                      -0.004076   \n",
       "Asthma                                                  -0.001078   \n",
       "HIV                                                      0.050242   \n",
       "Age                                                     -0.042636   \n",
       "\n",
       "                                         Late-Onset_Alzheimers_Disease  \\\n",
       "isFemale                                                     -0.013120   \n",
       "PurpleHair                                                   -0.007865   \n",
       "CaffeineConsumption                                           0.014901   \n",
       "Type_2_Diabetes                                               0.025127   \n",
       "Age-Related_Macular_Degeneration                              0.001723   \n",
       "Alpha-1_Antitrypsin_Deficiency                                0.010247   \n",
       "BRCA1/BRCA2                                                  -0.001506   \n",
       "Celiac_Disease                                               -0.028467   \n",
       "Familial_Hypercholesterolemia                                -0.001742   \n",
       "G6PD_Deficiency                                               0.014318   \n",
       "Hereditary_Amyloidosis(TTR-Related)                           0.006869   \n",
       "Hereditary_Hemochromatosis(HFE‑Related)                       0.017747   \n",
       "Hereditary_Thrombophilia                                     -0.009692   \n",
       "Late-Onset_Alzheimers_Disease                                 1.000000   \n",
       "MUTYH-Associated_Polyposis                                    0.015984   \n",
       "Parkinsons_Disease                                            0.012625   \n",
       "Asthma                                                       -0.005189   \n",
       "HIV                                                           0.002889   \n",
       "Age                                                          -0.018686   \n",
       "\n",
       "                                         MUTYH-Associated_Polyposis  \\\n",
       "isFemale                                                  -0.014263   \n",
       "PurpleHair                                                -0.012440   \n",
       "CaffeineConsumption                                       -0.008599   \n",
       "Type_2_Diabetes                                           -0.041160   \n",
       "Age-Related_Macular_Degeneration                           0.014295   \n",
       "Alpha-1_Antitrypsin_Deficiency                             0.018248   \n",
       "BRCA1/BRCA2                                               -0.022106   \n",
       "Celiac_Disease                                            -0.007887   \n",
       "Familial_Hypercholesterolemia                             -0.015456   \n",
       "G6PD_Deficiency                                           -0.022255   \n",
       "Hereditary_Amyloidosis(TTR-Related)                        0.013726   \n",
       "Hereditary_Hemochromatosis(HFE‑Related)                   -0.015397   \n",
       "Hereditary_Thrombophilia                                   0.049738   \n",
       "Late-Onset_Alzheimers_Disease                              0.015984   \n",
       "MUTYH-Associated_Polyposis                                 1.000000   \n",
       "Parkinsons_Disease                                        -0.019378   \n",
       "Asthma                                                     0.021099   \n",
       "HIV                                                       -0.006254   \n",
       "Age                                                        0.005514   \n",
       "\n",
       "                                         Parkinsons_Disease    Asthma  \\\n",
       "isFemale                                          -0.004076  0.045783   \n",
       "PurpleHair                                        -0.009608 -0.042487   \n",
       "CaffeineConsumption                               -0.007429  0.000059   \n",
       "Type_2_Diabetes                                   -0.003376 -0.014332   \n",
       "Age-Related_Macular_Degeneration                  -0.008602 -0.009117   \n",
       "Alpha-1_Antitrypsin_Deficiency                    -0.007873 -0.007540   \n",
       "BRCA1/BRCA2                                        0.006748 -0.007410   \n",
       "Celiac_Disease                                     0.006477  0.007765   \n",
       "Familial_Hypercholesterolemia                      0.006377 -0.013794   \n",
       "G6PD_Deficiency                                    0.015325  0.000091   \n",
       "Hereditary_Amyloidosis(TTR-Related)               -0.013753 -0.007396   \n",
       "Hereditary_Hemochromatosis(HFE‑Related)            0.022183 -0.001052   \n",
       "Hereditary_Thrombophilia                          -0.004076 -0.001078   \n",
       "Late-Onset_Alzheimers_Disease                      0.012625 -0.005189   \n",
       "MUTYH-Associated_Polyposis                        -0.019378  0.021099   \n",
       "Parkinsons_Disease                                 1.000000  0.014438   \n",
       "Asthma                                             0.014438  1.000000   \n",
       "HIV                                               -0.020108 -0.021625   \n",
       "Age                                               -0.004713  0.022735   \n",
       "\n",
       "                                              HIV       Age  \n",
       "isFemale                                -0.010332 -0.009973  \n",
       "PurpleHair                               0.019734 -0.007031  \n",
       "CaffeineConsumption                     -0.011506  0.016866  \n",
       "Type_2_Diabetes                          0.037176  0.033214  \n",
       "Age-Related_Macular_Degeneration        -0.000018 -0.017367  \n",
       "Alpha-1_Antitrypsin_Deficiency           0.013222  0.010624  \n",
       "BRCA1/BRCA2                             -0.011866 -0.007276  \n",
       "Celiac_Disease                          -0.022529  0.003601  \n",
       "Familial_Hypercholesterolemia            0.012627 -0.023507  \n",
       "G6PD_Deficiency                         -0.021780 -0.021804  \n",
       "Hereditary_Amyloidosis(TTR-Related)      0.021121  0.022990  \n",
       "Hereditary_Hemochromatosis(HFE‑Related) -0.026351 -0.009289  \n",
       "Hereditary_Thrombophilia                 0.050242 -0.042636  \n",
       "Late-Onset_Alzheimers_Disease            0.002889 -0.018686  \n",
       "MUTYH-Associated_Polyposis              -0.006254  0.005514  \n",
       "Parkinsons_Disease                      -0.020108 -0.004713  \n",
       "Asthma                                  -0.021625  0.022735  \n",
       "HIV                                      1.000000  0.034289  \n",
       "Age                                      0.034289  1.000000  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_df2 = genome_pd_df.corr()\n",
    "correlation_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a286d02d0>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(20,15))\n",
    "sns.heatmap(correlation_df2, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add blood type\n",
    "blood_types = [\"A+\", \"A-\", \"B+\", \"B-\", \"O+\", \"O-\", \"AB+\", \"AB-\"]\n",
    "genome_pd_df[\"BloodType\"] = [random.choice(blood_types) for i in range(num_rows)]\n",
    "\n",
    "#Add continuous feature - vitamin D level, from blood test\n",
    "# glucose \t70-99 mg/dL (normal range)\n",
    "#Adding normal range\n",
    "genome_pd_df[\"Glucose(mg/dL)\"] = [random.uniform(70, 99) for i in range(num_rows)]\n",
    "# Adding unnormal range\n",
    "for i in range(583):\n",
    "    genome_pd_df.loc[i ,'Glucose(mg/dL)'] = random.uniform(10, 70) \n",
    "for i in range(583, 1166):\n",
    "    genome_pd_df.loc[i ,'Glucose(mg/dL)'] = random.uniform(100, 300) \n",
    "\n",
    "# HDL \t >60mg/dL(high)  men:<40mg/dL; women:<50mg/dL (low) \n",
    "# LDL \t >160mg/dL(high)  <100mg/dL(low)\n",
    "# Adding normal range for LDL and HDL\n",
    "genome_pd_df[\"HDL(mg/dL)\"] = [random.uniform(45, 60) for i in range(num_rows)]\n",
    "genome_pd_df[\"LDL(mg/dL)\"] = [random.uniform(100, 160) for i in range(num_rows)]\n",
    "# Adding unnormal range \n",
    "for i in range(583):\n",
    "    genome_pd_df.loc[i ,'HDL(mg/dL)'] = random.uniform(5, 45) \n",
    "for i in range(583, 1166):\n",
    "    genome_pd_df.loc[i ,'HDL(mg/dL)'] = random.uniform(60, 100) \n",
    "    \n",
    "for i in range(583):\n",
    "    genome_pd_df.loc[i ,'LDL(mg/dL)'] = random.uniform(160, 400) \n",
    "for i in range(583, 1166):\n",
    "    genome_pd_df.loc[i ,'LDL(mg/dL)'] = random.uniform(0, 100) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>Sample</th>\n",
       "      <th>Population</th>\n",
       "      <th>SuperPopulation</th>\n",
       "      <th>isFemale</th>\n",
       "      <th>PurpleHair</th>\n",
       "      <th>CaffeineConsumption</th>\n",
       "      <th>Type_2_Diabetes</th>\n",
       "      <th>Age-Related_Macular_Degeneration</th>\n",
       "      <th>Alpha-1_Antitrypsin_Deficiency</th>\n",
       "      <th>BRCA1/BRCA2</th>\n",
       "      <th>...</th>\n",
       "      <th>Late-Onset_Alzheimers_Disease</th>\n",
       "      <th>MUTYH-Associated_Polyposis</th>\n",
       "      <th>Parkinsons_Disease</th>\n",
       "      <th>Asthma</th>\n",
       "      <th>HIV</th>\n",
       "      <th>Age</th>\n",
       "      <th>BloodType</th>\n",
       "      <th>Glucose(mg/dL)</th>\n",
       "      <th>HDL(mg/dL)</th>\n",
       "      <th>LDL(mg/dL)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>HG00096</td>\n",
       "      <td>GBR</td>\n",
       "      <td>EUR</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>88</td>\n",
       "      <td>A-</td>\n",
       "      <td>18.430868</td>\n",
       "      <td>13.178005</td>\n",
       "      <td>257.086911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>HG00097</td>\n",
       "      <td>GBR</td>\n",
       "      <td>EUR</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>95</td>\n",
       "      <td>A+</td>\n",
       "      <td>56.482967</td>\n",
       "      <td>30.654960</td>\n",
       "      <td>258.163207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>HG00098</td>\n",
       "      <td>GBR</td>\n",
       "      <td>EUR</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>92</td>\n",
       "      <td>A+</td>\n",
       "      <td>33.493171</td>\n",
       "      <td>21.558603</td>\n",
       "      <td>246.029115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>HG00099</td>\n",
       "      <td>GBR</td>\n",
       "      <td>EUR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>27</td>\n",
       "      <td>A+</td>\n",
       "      <td>23.594134</td>\n",
       "      <td>17.035567</td>\n",
       "      <td>313.395370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>HG00100</td>\n",
       "      <td>GBR</td>\n",
       "      <td>EUR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>O+</td>\n",
       "      <td>57.684244</td>\n",
       "      <td>39.418264</td>\n",
       "      <td>253.325376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3495</td>\n",
       "      <td>NA21137</td>\n",
       "      <td>GIH</td>\n",
       "      <td>SAS</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>85</td>\n",
       "      <td>O+</td>\n",
       "      <td>97.060290</td>\n",
       "      <td>47.549795</td>\n",
       "      <td>148.207369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3496</td>\n",
       "      <td>NA21141</td>\n",
       "      <td>GIH</td>\n",
       "      <td>SAS</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>27</td>\n",
       "      <td>O+</td>\n",
       "      <td>78.702526</td>\n",
       "      <td>48.649258</td>\n",
       "      <td>131.590865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3497</td>\n",
       "      <td>NA21142</td>\n",
       "      <td>GIH</td>\n",
       "      <td>SAS</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>85</td>\n",
       "      <td>AB-</td>\n",
       "      <td>83.107839</td>\n",
       "      <td>50.776724</td>\n",
       "      <td>114.930695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3498</td>\n",
       "      <td>NA21143</td>\n",
       "      <td>GIH</td>\n",
       "      <td>SAS</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>98</td>\n",
       "      <td>A-</td>\n",
       "      <td>76.628276</td>\n",
       "      <td>56.343075</td>\n",
       "      <td>154.900122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3499</td>\n",
       "      <td>NA21144</td>\n",
       "      <td>GIH</td>\n",
       "      <td>SAS</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>28</td>\n",
       "      <td>A+</td>\n",
       "      <td>78.604511</td>\n",
       "      <td>53.620269</td>\n",
       "      <td>150.223170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3500 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sample Population SuperPopulation  isFemale  PurpleHair  \\\n",
       "0     HG00096        GBR             EUR     False       False   \n",
       "1     HG00097        GBR             EUR      True        True   \n",
       "2     HG00098        GBR             EUR     False       False   \n",
       "3     HG00099        GBR             EUR      True       False   \n",
       "4     HG00100        GBR             EUR      True       False   \n",
       "...       ...        ...             ...       ...         ...   \n",
       "3495  NA21137        GIH             SAS      True       False   \n",
       "3496  NA21141        GIH             SAS      True        True   \n",
       "3497  NA21142        GIH             SAS      True        True   \n",
       "3498  NA21143        GIH             SAS      True        True   \n",
       "3499  NA21144        GIH             SAS      True       False   \n",
       "\n",
       "      CaffeineConsumption  Type_2_Diabetes  Age-Related_Macular_Degeneration  \\\n",
       "0                       4             True                             False   \n",
       "1                       4            False                             False   \n",
       "2                       5             True                             False   \n",
       "3                       4            False                             False   \n",
       "4                       5             True                             False   \n",
       "...                   ...              ...                               ...   \n",
       "3495                    1             True                              True   \n",
       "3496                    2             True                             False   \n",
       "3497                    2            False                             False   \n",
       "3498                    5            False                              True   \n",
       "3499                    3            False                              True   \n",
       "\n",
       "      Alpha-1_Antitrypsin_Deficiency  BRCA1/BRCA2  ...  \\\n",
       "0                               True        False  ...   \n",
       "1                              False         True  ...   \n",
       "2                               True         True  ...   \n",
       "3                               True         True  ...   \n",
       "4                              False        False  ...   \n",
       "...                              ...          ...  ...   \n",
       "3495                            True         True  ...   \n",
       "3496                           False        False  ...   \n",
       "3497                           False        False  ...   \n",
       "3498                           False         True  ...   \n",
       "3499                            True         True  ...   \n",
       "\n",
       "      Late-Onset_Alzheimers_Disease  MUTYH-Associated_Polyposis  \\\n",
       "0                              True                       False   \n",
       "1                             False                       False   \n",
       "2                             False                       False   \n",
       "3                              True                        True   \n",
       "4                             False                       False   \n",
       "...                             ...                         ...   \n",
       "3495                          False                        True   \n",
       "3496                           True                        True   \n",
       "3497                          False                       False   \n",
       "3498                          False                        True   \n",
       "3499                           True                        True   \n",
       "\n",
       "      Parkinsons_Disease  Asthma    HIV  Age  BloodType  Glucose(mg/dL)  \\\n",
       "0                   True   False   True   88         A-       18.430868   \n",
       "1                  False    True  False   95         A+       56.482967   \n",
       "2                  False    True   True   92         A+       33.493171   \n",
       "3                   True   False   True   27         A+       23.594134   \n",
       "4                   True    True  False    2         O+       57.684244   \n",
       "...                  ...     ...    ...  ...        ...             ...   \n",
       "3495                True   False  False   85         O+       97.060290   \n",
       "3496               False    True   True   27         O+       78.702526   \n",
       "3497                True   False  False   85        AB-       83.107839   \n",
       "3498                True    True  False   98         A-       76.628276   \n",
       "3499               False   False  False   28         A+       78.604511   \n",
       "\n",
       "      HDL(mg/dL)  LDL(mg/dL)  \n",
       "0      13.178005  257.086911  \n",
       "1      30.654960  258.163207  \n",
       "2      21.558603  246.029115  \n",
       "3      17.035567  313.395370  \n",
       "4      39.418264  253.325376  \n",
       "...          ...         ...  \n",
       "3495   47.549795  148.207369  \n",
       "3496   48.649258  131.590865  \n",
       "3497   50.776724  114.930695  \n",
       "3498   56.343075  154.900122  \n",
       "3499   53.620269  150.223170  \n",
       "\n",
       "[3500 rows x 26 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genome_pd_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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