Question 1 & 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17
    },
    "id": "wDpHhrWPilIU",
    "outputId": "cfd30bf6-aac5-4e9c-c368-aa1eca771277"
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "  var py_version = '3.3.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n",
       "  var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n",
       "  var reloading = false;\n",
       "  var Bokeh = root.Bokeh;\n",
       "  var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n",
       "\n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\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, js_modules, js_exports, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "    if (js_modules == null) js_modules = [];\n",
       "    if (js_exports == null) js_exports = {};\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "\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.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    if (!reloading) {\n",
       "      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    }\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",
       "    window._bokeh_on_load = on_load\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    var skip = [];\n",
       "    if (window.requirejs) {\n",
       "      window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n",
       "      require([\"jspanel\"], function(jsPanel) {\n",
       "\twindow.jsPanel = jsPanel\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-modal\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-tooltip\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-hint\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-layout\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-contextmenu\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-dock\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"gridstack\"], function(GridStack) {\n",
       "\twindow.GridStack = GridStack\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"notyf\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      root._bokeh_is_loading = css_urls.length + 9;\n",
       "    } else {\n",
       "      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n",
       "    }\n",
       "\n",
       "    var existing_stylesheets = []\n",
       "    var links = document.getElementsByTagName('link')\n",
       "    for (var i = 0; i < links.length; i++) {\n",
       "      var link = links[i]\n",
       "      if (link.href != null) {\n",
       "\texisting_stylesheets.push(link.href)\n",
       "      }\n",
       "    }\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      if (existing_stylesheets.indexOf(url) !== -1) {\n",
       "\ton_load()\n",
       "\tcontinue;\n",
       "      }\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",
       "    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n",
       "      var urls = ['https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n",
       "      for (var i = 0; i < urls.length; i++) {\n",
       "        skip.push(urls[i])\n",
       "      }\n",
       "    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n",
       "      var urls = ['https://cdn.holoviz.org/panel/1.3.1/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n",
       "      for (var i = 0; i < urls.length; i++) {\n",
       "        skip.push(urls[i])\n",
       "      }\n",
       "    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n",
       "      var urls = ['https://cdn.holoviz.org/panel/1.3.1/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n",
       "      for (var i = 0; i < urls.length; i++) {\n",
       "        skip.push(urls[i])\n",
       "      }\n",
       "    }    var existing_scripts = []\n",
       "    var scripts = document.getElementsByTagName('script')\n",
       "    for (var i = 0; i < scripts.length; i++) {\n",
       "      var script = scripts[i]\n",
       "      if (script.src != null) {\n",
       "\texisting_scripts.push(script.src)\n",
       "      }\n",
       "    }\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n",
       "\tif (!window.requirejs) {\n",
       "\t  on_load();\n",
       "\t}\n",
       "\tcontinue;\n",
       "      }\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",
       "    for (var i = 0; i < js_modules.length; i++) {\n",
       "      var url = js_modules[i];\n",
       "      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n",
       "\tif (!window.requirejs) {\n",
       "\t  on_load();\n",
       "\t}\n",
       "\tcontinue;\n",
       "      }\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",
       "      element.type = \"module\";\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "    for (const name in js_exports) {\n",
       "      var url = js_exports[name];\n",
       "      if (skip.indexOf(url) >= 0 || root[name] != null) {\n",
       "\tif (!window.requirejs) {\n",
       "\t  on_load();\n",
       "\t}\n",
       "\tcontinue;\n",
       "      }\n",
       "      var element = document.createElement('script');\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.type = \"module\";\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      element.textContent = `\n",
       "      import ${name} from \"${url}\"\n",
       "      window.${name} = ${name}\n",
       "      window._bokeh_on_load()\n",
       "      `\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "    if (!js_urls.length && !js_modules.length) {\n",
       "      on_load()\n",
       "    }\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.bokeh.org/bokeh/release/bokeh-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.0.min.js\", \"https://cdn.holoviz.org/panel/1.3.1/dist/panel.min.js\"];\n",
       "  var js_modules = [];\n",
       "  var js_exports = {};\n",
       "  var css_urls = [];\n",
       "  var inline_js = [    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "function(Bokeh) {} // ensure no trailing comma for IE\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\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",
       "      }\n",
       "      // Cache old bokeh versions\n",
       "      if (Bokeh != undefined && !reloading) {\n",
       "\tvar NewBokeh = root.Bokeh;\n",
       "\tif (Bokeh.versions === undefined) {\n",
       "\t  Bokeh.versions = new Map();\n",
       "\t}\n",
       "\tif (NewBokeh.version !== Bokeh.version) {\n",
       "\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n",
       "\t}\n",
       "\troot.Bokeh = Bokeh;\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",
       "    }\n",
       "    root._bokeh_is_initializing = false\n",
       "  }\n",
       "\n",
       "  function load_or_wait() {\n",
       "    // Implement a backoff loop that tries to ensure we do not load multiple\n",
       "    // versions of Bokeh and its dependencies at the same time.\n",
       "    // In recent versions we use the root._bokeh_is_initializing flag\n",
       "    // to determine whether there is an ongoing attempt to initialize\n",
       "    // bokeh, however for backward compatibility we also try to ensure\n",
       "    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n",
       "    // before older versions are fully initialized.\n",
       "    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n",
       "      root._bokeh_is_initializing = false;\n",
       "      root._bokeh_onload_callbacks = undefined;\n",
       "      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n",
       "      load_or_wait();\n",
       "    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n",
       "      setTimeout(load_or_wait, 100);\n",
       "    } else {\n",
       "      Bokeh = root.Bokeh;\n",
       "      bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n",
       "      root._bokeh_is_initializing = true\n",
       "      root._bokeh_onload_callbacks = []\n",
       "      if (!reloading && (!bokeh_loaded || is_dev)) {\n",
       "\troot.Bokeh = undefined;\n",
       "      }\n",
       "      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n",
       "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "\trun_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }\n",
       "  // Give older versions of the autoload script a head-start to ensure\n",
       "  // they initialize before we start loading newer version.\n",
       "  setTimeout(load_or_wait, 100)\n",
       "}(window));"
      ],
      "application/vnd.holoviews_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n  var py_version = '3.3.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n  var reloading = false;\n  var Bokeh = root.Bokeh;\n  var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\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, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\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.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      run_callbacks();\n      return null;\n    }\n    if (!reloading) {\n      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    }\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    window._bokeh_on_load = on_load\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    var skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n      require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n      })\n      require([\"jspanel-modal\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-tooltip\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-hint\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-layout\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-dock\"], function() {\n\ton_load()\n      })\n      require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n      })\n      require([\"notyf\"], function() {\n\ton_load()\n      })\n      root._bokeh_is_loading = css_urls.length + 9;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    var existing_stylesheets = []\n    var links = document.getElementsByTagName('link')\n    for (var i = 0; i < links.length; i++) {\n      var link = links[i]\n      if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n      }\n    }\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n      }\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    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.3.1/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.3.1/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    var existing_scripts = []\n    var scripts = document.getElementsByTagName('script')\n    for (var i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n\texisting_scripts.push(script.src)\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\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    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\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      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      var url = js_exports[name];\n      if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\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.bokeh.org/bokeh/release/bokeh-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.0.min.js\", \"https://cdn.holoviz.org/panel/1.3.1/dist/panel.min.js\"];\n  var js_modules = [];\n  var js_exports = {};\n  var css_urls = [];\n  var inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\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      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t  Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\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    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      Bokeh = root.Bokeh;\n      bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      if (!reloading && (!bokeh_loaded || is_dev)) {\n\troot.Bokeh = undefined;\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
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       "    var nchildren = toinsert.length;\n",
       "    var html_node = toinsert[nchildren-1].children[0];\n",
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       "    var nodelist = html_node.querySelectorAll(\"script\");\n",
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   "source": [
    "#Import dependencies\n",
    "import datetime\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.dates import YearLocator\n",
    "import holoviews as hv\n",
    "import hvplot.pandas\n",
    "from warnings import filterwarnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "UF5VUkG3mSmk"
   },
   "outputs": [],
   "source": [
    "# Set up the environment: Ignore warnings and apply the 'paper' theme in Seaborn\n",
    "filterwarnings(\"ignore\")\n",
    "sns.set_theme('paper')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "ei9GDfPLi6Yg",
    "outputId": "4c3a91f4-058c-45b9-9121-fe29b4e835bd"
   },
   "outputs": [],
   "source": [
    "#Read data from CSV files\n",
    "finance_df = pd.read_csv(\"Datasets/Financial_stock_data.csv\")\n",
    "health_df = pd.read_csv(\"Datasets/Healthcare_Stock_Data4.csv\")\n",
    "auto_df = pd.read_csv(\"Datasets/Auto_stock_data.csv\")\n",
    "tech_df = pd.read_csv(\"Datasets/tech_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "H2d2zs2zj2mY",
    "outputId": "c4d78108-abaf-4cf9-f0ed-8da3c296e7d1"
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   "outputs": [
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       "      <th></th>\n",
       "      <th>Date</th>\n",
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       "      <td>2018-01-02</td>\n",
       "      <td>WD</td>\n",
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       "      <td>2018-01-03</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>WD</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Mortgage</td>\n",
       "      <td>46.110001</td>\n",
       "      <td>48.060001</td>\n",
       "      <td>45.349998</td>\n",
       "      <td>47.980000</td>\n",
       "      <td>336900.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>WD</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Mortgage</td>\n",
       "      <td>48.160000</td>\n",
       "      <td>48.880001</td>\n",
       "      <td>47.369999</td>\n",
       "      <td>47.820000</td>\n",
       "      <td>310100.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>114456</th>\n",
       "      <td>2023-11-22</td>\n",
       "      <td>MRK</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers</td>\n",
       "      <td>102.200000</td>\n",
       "      <td>102.680000</td>\n",
       "      <td>101.110000</td>\n",
       "      <td>101.680000</td>\n",
       "      <td>5724130.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114457</th>\n",
       "      <td>2023-11-24</td>\n",
       "      <td>MRK</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers</td>\n",
       "      <td>102.260000</td>\n",
       "      <td>102.513600</td>\n",
       "      <td>101.440000</td>\n",
       "      <td>101.870000</td>\n",
       "      <td>3167952.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114458</th>\n",
       "      <td>2023-11-27</td>\n",
       "      <td>MRK</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers</td>\n",
       "      <td>101.910000</td>\n",
       "      <td>102.150000</td>\n",
       "      <td>100.700000</td>\n",
       "      <td>101.430000</td>\n",
       "      <td>7356536.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114459</th>\n",
       "      <td>2023-11-28</td>\n",
       "      <td>MRK</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers</td>\n",
       "      <td>101.310000</td>\n",
       "      <td>101.350000</td>\n",
       "      <td>100.010000</td>\n",
       "      <td>100.180000</td>\n",
       "      <td>8738483.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114460</th>\n",
       "      <td>2023-11-29</td>\n",
       "      <td>MRK</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers</td>\n",
       "      <td>100.280000</td>\n",
       "      <td>101.170000</td>\n",
       "      <td>99.800000</td>\n",
       "      <td>101.130000</td>\n",
       "      <td>10286779.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>114461 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Date Ticker      Sector            Industry        Open  \\\n",
       "0       2018-01-02     WD     Finance            Mortgage   47.700001   \n",
       "1       2018-01-03     WD     Finance            Mortgage   47.230000   \n",
       "2       2018-01-04     WD     Finance            Mortgage   46.189999   \n",
       "3       2018-01-05     WD     Finance            Mortgage   46.110001   \n",
       "4       2018-01-08     WD     Finance            Mortgage   48.160000   \n",
       "...            ...    ...         ...                 ...         ...   \n",
       "114456  2023-11-22    MRK  Healthcare  Drug Manufacturers  102.200000   \n",
       "114457  2023-11-24    MRK  Healthcare  Drug Manufacturers  102.260000   \n",
       "114458  2023-11-27    MRK  Healthcare  Drug Manufacturers  101.910000   \n",
       "114459  2023-11-28    MRK  Healthcare  Drug Manufacturers  101.310000   \n",
       "114460  2023-11-29    MRK  Healthcare  Drug Manufacturers  100.280000   \n",
       "\n",
       "              High         Low       Close      Volume  Unnamed: 0  \n",
       "0        47.919998   47.259998   47.320000    181300.0         NaN  \n",
       "1        47.230000   46.090000   46.130001    134800.0         NaN  \n",
       "2        46.810001   46.090000   46.099998    140200.0         NaN  \n",
       "3        48.060001   45.349998   47.980000    336900.0         NaN  \n",
       "4        48.880001   47.369999   47.820000    310100.0         NaN  \n",
       "...            ...         ...         ...         ...         ...  \n",
       "114456  102.680000  101.110000  101.680000   5724130.0         NaN  \n",
       "114457  102.513600  101.440000  101.870000   3167952.0         NaN  \n",
       "114458  102.150000  100.700000  101.430000   7356536.0         NaN  \n",
       "114459  101.350000  100.010000  100.180000   8738483.0         NaN  \n",
       "114460  101.170000   99.800000  101.130000  10286779.0         NaN  \n",
       "\n",
       "[114461 rows x 10 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merge the dataframes\n",
    "merge1_df = pd.merge(finance_df, tech_df, on = [\"Date\", \"Ticker\", \"Sector\", \"Industry\", \"Open\", \"High\", \"Low\", \"Close\",\"Volume\"], how = \"outer\")\n",
    "merge2_df = pd.merge(merge1_df, auto_df, on = [\"Date\", \"Ticker\", \"Sector\", \"Industry\", \"Open\", \"High\", \"Low\", \"Close\",\"Volume\"], how = \"outer\")\n",
    "merged_df = pd.merge(merge2_df, health_df, on = [\"Date\", \"Ticker\", \"Sector\", \"Industry\", \"Open\", \"High\", \"Low\", \"Close\",\"Volume\"], how = \"outer\")\n",
    "\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "71oODHWwQakC"
   },
   "source": [
    "# **Overall Performance**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this part of Script, Percentage of change was calculated for each stock from Open price of the earliest date to the Close price of the latest date available for that ticker. And creating a Dataframe with Open Price on earliest date, Close price on latest date and change occurred in price in percentage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "RxeOeVLWlzcc",
    "outputId": "216a6cbd-e6f3-4e96-d3d0-72a01611ea18"
   },
   "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>Ticker</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Initial Open</th>\n",
       "      <th>Final Close</th>\n",
       "      <th>Total Change in Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WD</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Mortgage</td>\n",
       "      <td>47.700001</td>\n",
       "      <td>89.870003</td>\n",
       "      <td>88.406711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PFSI</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Mortgage</td>\n",
       "      <td>22.350000</td>\n",
       "      <td>79.779999</td>\n",
       "      <td>256.957483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LDI</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Mortgage</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.980000</td>\n",
       "      <td>-85.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GHI</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Mortgage</td>\n",
       "      <td>18.150000</td>\n",
       "      <td>17.040001</td>\n",
       "      <td>-6.115695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>COOP</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Mortgage</td>\n",
       "      <td>10.320000</td>\n",
       "      <td>62.709999</td>\n",
       "      <td>507.655048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>PFE</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers</td>\n",
       "      <td>36.380000</td>\n",
       "      <td>30.080000</td>\n",
       "      <td>-17.317207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>JNJ</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers</td>\n",
       "      <td>139.660000</td>\n",
       "      <td>152.110000</td>\n",
       "      <td>8.914507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>LLY</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers</td>\n",
       "      <td>84.460000</td>\n",
       "      <td>591.860000</td>\n",
       "      <td>600.757755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>AZN</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers</td>\n",
       "      <td>35.240000</td>\n",
       "      <td>64.120000</td>\n",
       "      <td>81.952327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>MRK</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers</td>\n",
       "      <td>56.590000</td>\n",
       "      <td>101.130000</td>\n",
       "      <td>78.706485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ticker      Sector            Industry  Initial Open  Final Close  \\\n",
       "0      WD     Finance            Mortgage     47.700001    89.870003   \n",
       "1    PFSI     Finance            Mortgage     22.350000    79.779999   \n",
       "2     LDI     Finance            Mortgage     14.000000     1.980000   \n",
       "3     GHI     Finance            Mortgage     18.150000    17.040001   \n",
       "4    COOP     Finance            Mortgage     10.320000    62.709999   \n",
       "..    ...         ...                 ...           ...          ...   \n",
       "75    PFE  Healthcare  Drug Manufacturers     36.380000    30.080000   \n",
       "76    JNJ  Healthcare  Drug Manufacturers    139.660000   152.110000   \n",
       "77    LLY  Healthcare  Drug Manufacturers     84.460000   591.860000   \n",
       "78    AZN  Healthcare  Drug Manufacturers     35.240000    64.120000   \n",
       "79    MRK  Healthcare  Drug Manufacturers     56.590000   101.130000   \n",
       "\n",
       "    Total Change in Percent  \n",
       "0                 88.406711  \n",
       "1                256.957483  \n",
       "2                -85.857143  \n",
       "3                 -6.115695  \n",
       "4                507.655048  \n",
       "..                      ...  \n",
       "75               -17.317207  \n",
       "76                 8.914507  \n",
       "77               600.757755  \n",
       "78                81.952327  \n",
       "79                78.706485  \n",
       "\n",
       "[80 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Define lists to append calculated data\n",
    "open_price = []\n",
    "close_price = []\n",
    "change_price = []\n",
    "ticker = []\n",
    "sectors = []\n",
    "industry = []\n",
    "\n",
    "#Loop through each sector\n",
    "for sector in merged_df[\"Sector\"].unique():\n",
    "\n",
    "    sector_df = merged_df.loc[merged_df[\"Sector\"] == sector]\n",
    "\n",
    "  #Loop through each ticker\n",
    "    for symbols in sector_df[\"Ticker\"].unique():\n",
    "\n",
    "        #Dataframe for each iteration of loop\n",
    "        con_df = sector_df.loc[sector_df[\"Ticker\"] == symbols]\n",
    "\n",
    "        #Convert the \"date\" column to datetime\n",
    "        con_df[\"Date\"] = pd.to_datetime(con_df[\"Date\"])\n",
    "\n",
    "        #Find the Open price with the earliest date\n",
    "        earliest_date_row = con_df.loc[con_df[\"Date\"].idxmin(), \"Open\"]\n",
    "\n",
    "        #Find the Close price with the latest date\n",
    "        latest_date_row = con_df.loc[con_df[\"Date\"].idxmax(), \"Close\"]\n",
    "\n",
    "        #Calculate change in price\n",
    "        percent_change_price = ((latest_date_row - earliest_date_row)*100)/earliest_date_row\n",
    "\n",
    "        #Appending data in lists\n",
    "        ticker.append(symbols)\n",
    "        sectors.append(con_df.loc[con_df[\"Date\"].idxmin(), \"Sector\"])\n",
    "        industry.append(con_df.loc[con_df[\"Date\"].idxmin(), \"Industry\"])\n",
    "        open_price.append(earliest_date_row)\n",
    "        close_price.append(latest_date_row)\n",
    "        change_price.append(percent_change_price)\n",
    "\n",
    "#Create a dataframe with calculated values\n",
    "analysis_df = pd.DataFrame({\n",
    "    \"Ticker\" : ticker,\n",
    "    \"Sector\" : sectors,\n",
    "    \"Industry\" : industry,\n",
    "    \"Initial Open\" : open_price,\n",
    "    \"Final Close\" : close_price,\n",
    "    \"Total Change in Percent\" : change_price\n",
    "\n",
    "})\n",
    "analysis_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5tENmhy0G5oc"
   },
   "source": [
    "# Performance by Sector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, it group the data by Sector and Industry to get the average change in price for each Industry in each Sector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 426
    },
    "id": "Z_mwhzt0G5oc",
    "outputId": "f4aa52d4-4876-481b-de04-fa5a52479ae6"
   },
   "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></th>\n",
       "      <th>Total Change in Percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sector</th>\n",
       "      <th>Industry</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Automobiles</th>\n",
       "      <th>Auto &amp; Truck Dealerships</th>\n",
       "      <td>111.703054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Auto Manufacturers</th>\n",
       "      <td>-4.721549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Auto Parts</th>\n",
       "      <td>7.787522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Semiconductors</th>\n",
       "      <td>311.366607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Finance</th>\n",
       "      <th>Asset Management</th>\n",
       "      <td>18.186490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Credit Services</th>\n",
       "      <td>81.657920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insurance</th>\n",
       "      <td>66.003138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mortgage</th>\n",
       "      <td>152.209281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Healthcare</th>\n",
       "      <th>Biotechnology</th>\n",
       "      <td>178.293249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug Manufacturers</th>\n",
       "      <td>150.602773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthcare plans</th>\n",
       "      <td>72.534892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medical Instruments &amp; supplies</th>\n",
       "      <td>72.354319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Technology</th>\n",
       "      <th>Consumer</th>\n",
       "      <td>72.043735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infotech</th>\n",
       "      <td>26.190427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Semiconductors</th>\n",
       "      <td>69.240697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Software</th>\n",
       "      <td>161.812711</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Total Change in Percent\n",
       "Sector      Industry                                               \n",
       "Automobiles Auto & Truck Dealerships                     111.703054\n",
       "            Auto Manufacturers                            -4.721549\n",
       "            Auto Parts                                     7.787522\n",
       "            Semiconductors                               311.366607\n",
       "Finance     Asset Management                              18.186490\n",
       "            Credit Services                               81.657920\n",
       "            Insurance                                     66.003138\n",
       "            Mortgage                                     152.209281\n",
       "Healthcare  Biotechnology                                178.293249\n",
       "            Drug Manufacturers                           150.602773\n",
       "            Healthcare plans                              72.534892\n",
       "            Medical Instruments & supplies                72.354319\n",
       "Technology  Consumer                                      72.043735\n",
       "            Infotech                                      26.190427\n",
       "            Semiconductors                                69.240697\n",
       "            Software                                     161.812711"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Group the data by Sector and Industry to calculate average change in price for each industry under each sector\n",
    "perf_by_sector = pd.DataFrame(analysis_df.groupby([\"Sector\", \"Industry\"])[\"Total Change in Percent\"].mean())\n",
    "\n",
    "#Show the result\n",
    "perf_by_sector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, Plot the data using Seaborn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 650
    },
    "id": "QGoK1JzQ-Jkq",
    "outputId": "b9a4ebcd-4d21-4d74-e4c1-ca2b3007bbd9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot grouped bar chart using seaborn\n",
    "plt.figure(figsize=(12, 5.2))\n",
    "ax = sns.barplot(analysis_df, x=\"Sector\", y=\"Total Change in Percent\", hue =\"Industry\")\n",
    "\n",
    "ax.legend(loc = \"best\", bbox_to_anchor=(1, 1))\n",
    "ax.set_title(\"Performance by Sector\")\n",
    "\n",
    "#Save the Output\n",
    "# plt.savefig(\"Finances/fig1.png\")\n",
    "\n",
    "#Show the Output\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, the code get the best performing stock per Sector and Close price for those stocks will be retrieved into one dataframe along with Ticker and Date to plot the line graph for those tickers over the years available in dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A_xa9nwBQpTh"
   },
   "source": [
    "# **Best Performers by Sector**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "Cm3x--aaue37",
    "outputId": "e7f6599f-e375-497a-d0ed-e93cb20d4f6d"
   },
   "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>Ticker</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Initial Open</th>\n",
       "      <th>Final Close</th>\n",
       "      <th>Total Change in Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AMD</td>\n",
       "      <td>Automobiles</td>\n",
       "      <td>Semiconductors</td>\n",
       "      <td>10.42</td>\n",
       "      <td>118.570000</td>\n",
       "      <td>1037.907869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>COOP</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Mortgage</td>\n",
       "      <td>10.32</td>\n",
       "      <td>62.709999</td>\n",
       "      <td>507.655048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LLY</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers</td>\n",
       "      <td>84.46</td>\n",
       "      <td>591.860000</td>\n",
       "      <td>600.757755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NOW</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Software</td>\n",
       "      <td>131.00</td>\n",
       "      <td>687.620000</td>\n",
       "      <td>424.900763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Ticker       Sector            Industry  Initial Open  Final Close  \\\n",
       "0    AMD  Automobiles      Semiconductors         10.42   118.570000   \n",
       "1   COOP      Finance            Mortgage         10.32    62.709999   \n",
       "2    LLY   Healthcare  Drug Manufacturers         84.46   591.860000   \n",
       "3    NOW   Technology            Software        131.00   687.620000   \n",
       "\n",
       "   Total Change in Percent  \n",
       "0              1037.907869  \n",
       "1               507.655048  \n",
       "2               600.757755  \n",
       "3               424.900763  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#idxmax returns the index of the max value\n",
    "best_performer_df = analysis_df.iloc[analysis_df.groupby(\"Sector\")[\"Total Change in Percent\"].idxmax()].reset_index(drop = True)\n",
    "\n",
    "#Show the result\n",
    "best_performer_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "uW1aoCpqTR1H",
    "outputId": "9c16276e-eec2-496e-d5ee-168f8dae021f"
   },
   "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>Ticker</th>\n",
       "      <th>Date</th>\n",
       "      <th>Close</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AMD</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>10.98</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AMD</td>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>11.55</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AMD</td>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>12.12</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AMD</td>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>11.88</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AMD</td>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>12.28</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5955</th>\n",
       "      <td>NOW</td>\n",
       "      <td>2023-11-28</td>\n",
       "      <td>665.24</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5956</th>\n",
       "      <td>NOW</td>\n",
       "      <td>2023-11-29</td>\n",
       "      <td>678.93</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5957</th>\n",
       "      <td>NOW</td>\n",
       "      <td>2023-11-30</td>\n",
       "      <td>685.74</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5958</th>\n",
       "      <td>NOW</td>\n",
       "      <td>2023-12-01</td>\n",
       "      <td>690.79</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5959</th>\n",
       "      <td>NOW</td>\n",
       "      <td>2023-12-04</td>\n",
       "      <td>687.62</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5960 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Ticker       Date   Close  Year\n",
       "0       AMD 2018-01-02   10.98  2018\n",
       "1       AMD 2018-01-03   11.55  2018\n",
       "2       AMD 2018-01-04   12.12  2018\n",
       "3       AMD 2018-01-05   11.88  2018\n",
       "4       AMD 2018-01-08   12.28  2018\n",
       "...     ...        ...     ...   ...\n",
       "5955    NOW 2023-11-28  665.24  2023\n",
       "5956    NOW 2023-11-29  678.93  2023\n",
       "5957    NOW 2023-11-30  685.74  2023\n",
       "5958    NOW 2023-12-01  690.79  2023\n",
       "5959    NOW 2023-12-04  687.62  2023\n",
       "\n",
       "[5960 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_performers_by_sector = best_performer_df[\"Ticker\"]\n",
    "ls= []\n",
    "for ticker in top_performers_by_sector:\n",
    "    line_df = merged_df.loc[(merged_df[\"Ticker\"] == ticker),[\"Ticker\",\"Date\", \"Close\"]]\n",
    "    line_df[\"Year\"] = pd.to_datetime(line_df[\"Date\"]).dt.year\n",
    "\n",
    "    ls.append(line_df)\n",
    "plot_df = pd.concat(ls, ignore_index = True)\n",
    "plot_df[\"Date\"] = pd.to_datetime(plot_df[\"Date\"])\n",
    "plot_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 458
    },
    "id": "9l-HtGqPG5oY",
    "outputId": "ad9226f9-a580-491f-e4a8-4e7436c63237"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot line graph using seaborn\n",
    "plt.figure(figsize=(12, 5.2))\n",
    "fig2 = sns.lineplot(data=plot_df, x = \"Date\", y = \"Close\", hue = \"Ticker\")\n",
    "fig2.set_title(\"Performance of Top Performers Per Sector\")\n",
    "\n",
    "fig2.legend(loc = \"right\", bbox_to_anchor=(1.1, 1))\n",
    "\n",
    "#Set xtick labels as years\n",
    "fig2.xaxis.set_major_locator(YearLocator())\n",
    "\n",
    "#Save the Output\n",
    "# plt.savefig(\"Finances/fig2.png\")\n",
    "\n",
    "#Show the Output\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IGWdejQKMgzB"
   },
   "source": [
    "# **Performance by Year**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the script will calculate the Percentage Change in Close price on latest date of year and Open Price on earliest date of year, for each year of each ticker."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "8PlsQcNgsXrn",
    "outputId": "0c0fa4b6-6e53-4870-a137-fb5c8fc2dc67"
   },
   "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>Ticker</th>\n",
       "      <th>Year</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Initial Open</th>\n",
       "      <th>Final Close</th>\n",
       "      <th>Total Change in Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WD</td>\n",
       "      <td>2018</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Mortgage</td>\n",
       "      <td>47.700001</td>\n",
       "      <td>43.250000</td>\n",
       "      <td>-9.329142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PFSI</td>\n",
       "      <td>2018</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Mortgage</td>\n",
       "      <td>22.350000</td>\n",
       "      <td>21.260000</td>\n",
       "      <td>-4.876958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GHI</td>\n",
       "      <td>2018</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Mortgage</td>\n",
       "      <td>18.150000</td>\n",
       "      <td>16.860001</td>\n",
       "      <td>-7.107433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>COOP</td>\n",
       "      <td>2018</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Mortgage</td>\n",
       "      <td>10.320000</td>\n",
       "      <td>11.670000</td>\n",
       "      <td>13.081399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFL</td>\n",
       "      <td>2018</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Insurance</td>\n",
       "      <td>44.180000</td>\n",
       "      <td>45.560001</td>\n",
       "      <td>3.123588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>460</th>\n",
       "      <td>PFE</td>\n",
       "      <td>2023</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers</td>\n",
       "      <td>51.010000</td>\n",
       "      <td>30.080000</td>\n",
       "      <td>-41.031170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>JNJ</td>\n",
       "      <td>2023</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers</td>\n",
       "      <td>176.160000</td>\n",
       "      <td>152.110000</td>\n",
       "      <td>-13.652361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>LLY</td>\n",
       "      <td>2023</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers</td>\n",
       "      <td>366.260000</td>\n",
       "      <td>591.860000</td>\n",
       "      <td>61.595588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>AZN</td>\n",
       "      <td>2023</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers</td>\n",
       "      <td>68.650000</td>\n",
       "      <td>64.120000</td>\n",
       "      <td>-6.598689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>MRK</td>\n",
       "      <td>2023</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers</td>\n",
       "      <td>110.410000</td>\n",
       "      <td>101.130000</td>\n",
       "      <td>-8.405036</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>465 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Ticker  Year      Sector            Industry  Initial Open  Final Close  \\\n",
       "0       WD  2018     Finance            Mortgage     47.700001    43.250000   \n",
       "1     PFSI  2018     Finance            Mortgage     22.350000    21.260000   \n",
       "2      GHI  2018     Finance            Mortgage     18.150000    16.860001   \n",
       "3     COOP  2018     Finance            Mortgage     10.320000    11.670000   \n",
       "4      AFL  2018     Finance           Insurance     44.180000    45.560001   \n",
       "..     ...   ...         ...                 ...           ...          ...   \n",
       "460    PFE  2023  Healthcare  Drug Manufacturers     51.010000    30.080000   \n",
       "461    JNJ  2023  Healthcare  Drug Manufacturers    176.160000   152.110000   \n",
       "462    LLY  2023  Healthcare  Drug Manufacturers    366.260000   591.860000   \n",
       "463    AZN  2023  Healthcare  Drug Manufacturers     68.650000    64.120000   \n",
       "464    MRK  2023  Healthcare  Drug Manufacturers    110.410000   101.130000   \n",
       "\n",
       "     Total Change in Percent  \n",
       "0                  -9.329142  \n",
       "1                  -4.876958  \n",
       "2                  -7.107433  \n",
       "3                  13.081399  \n",
       "4                   3.123588  \n",
       "..                       ...  \n",
       "460               -41.031170  \n",
       "461               -13.652361  \n",
       "462                61.595588  \n",
       "463                -6.598689  \n",
       "464                -8.405036  \n",
       "\n",
       "[465 rows x 7 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert \"date\" column to respective year using datetime\n",
    "merged_df[\"Year\"] = pd.to_datetime(merged_df[\"Date\"]).dt.year\n",
    "\n",
    "#Defining lists to append calculated data\n",
    "open_price = []\n",
    "close_price = []\n",
    "change_price = []\n",
    "ticker = []\n",
    "sector = []\n",
    "industry = []\n",
    "year_ls = []\n",
    "\n",
    "#Loop thorugh data based on year\n",
    "for year in merged_df[\"Year\"].unique():\n",
    "\n",
    "    #Dataframe for each iteration of loop\n",
    "    yearly_df = merged_df.loc[merged_df[\"Year\"] == year]\n",
    "\n",
    "    #Loop through each ticker for that specific year\n",
    "    for symbols in yearly_df[\"Ticker\"].unique():\n",
    "\n",
    "        #Dataframe for each iteration of loop\n",
    "        con_df = yearly_df.loc[yearly_df[\"Ticker\"] == symbols]\n",
    "\n",
    "        #Convert the \"date\" column to datetime\n",
    "        con_df[\"Date\"] = pd.to_datetime(con_df[\"Date\"])\n",
    "\n",
    "        #Find the Open price with the earliest date\n",
    "        earliest_date_row = con_df.loc[con_df[\"Date\"].idxmin(), \"Open\"]\n",
    "\n",
    "        #Find the Close price with the latest date\n",
    "        latest_date_row = con_df.loc[con_df[\"Date\"].idxmax(), \"Close\"]\n",
    "\n",
    "        #Calculate change in price\n",
    "        percent_change_price = ((latest_date_row - earliest_date_row)*100)/earliest_date_row\n",
    "\n",
    "        #Appending data in lists\n",
    "        ticker.append(symbols)\n",
    "        sector.append(con_df.loc[con_df[\"Date\"].idxmin(), \"Sector\"])\n",
    "        industry.append(con_df.loc[con_df[\"Date\"].idxmin(), \"Industry\"])\n",
    "        open_price.append(earliest_date_row)\n",
    "        close_price.append(latest_date_row)\n",
    "        change_price.append(percent_change_price)\n",
    "        year_ls.append(year)\n",
    "\n",
    "#Creating a dataframe with calculated data\n",
    "top_by_year = pd.DataFrame({\n",
    "    \"Ticker\" : ticker,\n",
    "    \"Year\" : year_ls,\n",
    "    \"Sector\" : sector,\n",
    "    \"Industry\" : industry,\n",
    "    \"Initial Open\" : open_price,\n",
    "    \"Final Close\" : close_price,\n",
    "    \"Total Change in Percent\" : change_price\n",
    "\n",
    "})\n",
    "#Show the dataframe\n",
    "top_by_year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, it groups the dataframe made in last cell by year and sector to calculate average change in prices during that year and plot the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 645
    },
    "id": "A0px2cNVG5oZ",
    "outputId": "1c1e7e34-c786-4931-9c88-084e8e19f0ae"
   },
   "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></th>\n",
       "      <th>Total Change in Percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th>Sector</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2018</th>\n",
       "      <th>Automobiles</th>\n",
       "      <td>-13.393407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finance</th>\n",
       "      <td>-11.083345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthcare</th>\n",
       "      <td>8.175083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>-8.901781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2019</th>\n",
       "      <th>Automobiles</th>\n",
       "      <td>47.608147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finance</th>\n",
       "      <td>33.834788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthcare</th>\n",
       "      <td>25.103388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>41.431462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2020</th>\n",
       "      <th>Automobiles</th>\n",
       "      <td>35.298993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finance</th>\n",
       "      <td>18.944464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthcare</th>\n",
       "      <td>38.152534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>29.633250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2021</th>\n",
       "      <th>Automobiles</th>\n",
       "      <td>33.697632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finance</th>\n",
       "      <td>24.180602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthcare</th>\n",
       "      <td>28.331568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>24.984831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2022</th>\n",
       "      <th>Automobiles</th>\n",
       "      <td>-33.253297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finance</th>\n",
       "      <td>-13.455324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthcare</th>\n",
       "      <td>3.133066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>-31.578574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2023</th>\n",
       "      <th>Automobiles</th>\n",
       "      <td>34.696392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finance</th>\n",
       "      <td>11.664808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthcare</th>\n",
       "      <td>-0.712506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>23.342853</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Total Change in Percent\n",
       "Year Sector                              \n",
       "2018 Automobiles               -13.393407\n",
       "     Finance                   -11.083345\n",
       "     Healthcare                  8.175083\n",
       "     Technology                 -8.901781\n",
       "2019 Automobiles                47.608147\n",
       "     Finance                    33.834788\n",
       "     Healthcare                 25.103388\n",
       "     Technology                 41.431462\n",
       "2020 Automobiles                35.298993\n",
       "     Finance                    18.944464\n",
       "     Healthcare                 38.152534\n",
       "     Technology                 29.633250\n",
       "2021 Automobiles                33.697632\n",
       "     Finance                    24.180602\n",
       "     Healthcare                 28.331568\n",
       "     Technology                 24.984831\n",
       "2022 Automobiles               -33.253297\n",
       "     Finance                   -13.455324\n",
       "     Healthcare                  3.133066\n",
       "     Technology                -31.578574\n",
       "2023 Automobiles                34.696392\n",
       "     Finance                    11.664808\n",
       "     Healthcare                 -0.712506\n",
       "     Technology                 23.342853"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Group the data by Year and Sector to calculate average change in price for each Sector in each year\n",
    "perf_by_year = pd.DataFrame(top_by_year.groupby([\"Year\", \"Sector\"])[\"Total Change in Percent\"].mean())\n",
    "\n",
    "#Show the dataframe\n",
    "perf_by_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 480
    },
    "id": "1jUJXcDaB8c0",
    "outputId": "8afcdd5c-8b0a-4f5c-c45c-27ab2e1cd3b6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot data by Year and Sector\n",
    "plt.figure(figsize=(12, 5.2))\n",
    "sns.barplot(top_by_year, x=\"Year\", y=\"Total Change in Percent\", hue=\"Sector\").set(title='Performance of Sectors by Year')\n",
    "\n",
    "plt.legend(loc = \"right\", bbox_to_anchor=(1.14, 1))\n",
    "\n",
    "#Save the Output\n",
    "# plt.savefig(\"Finances/fig3.png\")\n",
    "\n",
    "#Show the Output\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, it locate the stock with most positive change in price for each year and plots the data using hvplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "id": "EYL7g61fsXrp",
    "outputId": "b87fc931-340a-4caf-ac7e-0f7712356c16"
   },
   "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>Ticker</th>\n",
       "      <th>Year</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Initial Open</th>\n",
       "      <th>Final Close</th>\n",
       "      <th>Total Change in Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>AMD</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automobiles</td>\n",
       "      <td>Semiconductors</td>\n",
       "      <td>10.42</td>\n",
       "      <td>18.46</td>\n",
       "      <td>77.159309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>AMD</td>\n",
       "      <td>2019</td>\n",
       "      <td>Automobiles</td>\n",
       "      <td>Semiconductors</td>\n",
       "      <td>18.01</td>\n",
       "      <td>45.86</td>\n",
       "      <td>154.636313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>MRNA</td>\n",
       "      <td>2020</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Biotechnology</td>\n",
       "      <td>19.57</td>\n",
       "      <td>104.47</td>\n",
       "      <td>433.827287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>MRNA</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Biotechnology</td>\n",
       "      <td>107.23</td>\n",
       "      <td>253.98</td>\n",
       "      <td>136.855358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>MRK</td>\n",
       "      <td>2022</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers</td>\n",
       "      <td>76.58</td>\n",
       "      <td>110.95</td>\n",
       "      <td>44.881170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>NVDA</td>\n",
       "      <td>2023</td>\n",
       "      <td>Automobiles</td>\n",
       "      <td>Semiconductors</td>\n",
       "      <td>148.51</td>\n",
       "      <td>455.10</td>\n",
       "      <td>206.444011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Ticker  Year       Sector            Industry  Initial Open  Final Close  \\\n",
       "54     AMD  2018  Automobiles      Semiconductors         10.42        18.46   \n",
       "129    AMD  2019  Automobiles      Semiconductors         18.01        45.86   \n",
       "222   MRNA  2020   Healthcare       Biotechnology         19.57       104.47   \n",
       "300   MRNA  2021   Healthcare       Biotechnology        107.23       253.98   \n",
       "384    MRK  2022   Healthcare  Drug Manufacturers         76.58       110.95   \n",
       "440   NVDA  2023  Automobiles      Semiconductors        148.51       455.10   \n",
       "\n",
       "     Total Change in Percent  \n",
       "54                 77.159309  \n",
       "129               154.636313  \n",
       "222               433.827287  \n",
       "300               136.855358  \n",
       "384                44.881170  \n",
       "440               206.444011  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Group the data by Year to ticker data with th max positive change in price for that year\n",
    "grouped_df1 = top_by_year.groupby([\"Year\"])[\"Total Change in Percent\"].idxmax()\n",
    "best_by_year = top_by_year.iloc[grouped_df1, :]\n",
    "\n",
    "#Show the result\n",
    "best_by_year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZcfKrTN7L7pO"
   },
   "source": [
    "# **Top Performers by Year**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 480
    },
    "id": "EofpQHxt-Ryv",
    "outputId": "3e72f488-69b0-4fe7-b3f0-1ec93e1901f0"
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
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      "text/plain": [
       ":Bars   [Year]   (Total Change in Percent,Ticker,Sector,Industry,Initial Open,Final Close)"
      ]
     },
     "execution_count": 15,
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   "source": [
    "# Configure the map plot\n",
    "best_yearly = best_by_year.hvplot.barh(\n",
    "                            \"Year\",\n",
    "                            \"Total Change in Percent\",\n",
    "                            frame_width = 600,\n",
    "                            frame_height = 300,\n",
    "                            color = \"Ticker\",\n",
    "                            colorbar = True,\n",
    "                            clabel=\"Year\", \n",
    "                            cmap=\"Greens\",\n",
    "                            hover_cols = [\"Ticker\", \"Year\", \"Sector\", \"Industry\", \"Initial Open\", \"Final Close\", \"Total Change in Percent\"],\n",
    "                            title = \"Top Performers by Year\"\n",
    "\n",
    ")\n",
    "    \n",
    "#Show the Output\n",
    "best_yearly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, it locate the stock with most negative change in price for each year and plots the data using hvplot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1K6YRtl5MHOG"
   },
   "source": [
    "# **Bottom Performers by Year**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "id": "rraWO6ii8aL2",
    "outputId": "40d28cc6-123f-46b5-f9d7-dd1d892ad2f7"
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   "outputs": [
    {
     "data": {
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       "    }\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>Ticker</th>\n",
       "      <th>Year</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Initial Open</th>\n",
       "      <th>Final Close</th>\n",
       "      <th>Total Change in Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>ALV</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automobiles</td>\n",
       "      <td>Auto Parts</td>\n",
       "      <td>127.51</td>\n",
       "      <td>70.23</td>\n",
       "      <td>-44.921967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>LPL</td>\n",
       "      <td>2019</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>8.00</td>\n",
       "      <td>6.94</td>\n",
       "      <td>-13.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>2020</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>296.24</td>\n",
       "      <td>132.69</td>\n",
       "      <td>-55.208615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>LDI</td>\n",
       "      <td>2021</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Mortgage</td>\n",
       "      <td>14.00</td>\n",
       "      <td>4.80</td>\n",
       "      <td>-65.714284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>TSLA</td>\n",
       "      <td>2022</td>\n",
       "      <td>Automobiles</td>\n",
       "      <td>Auto Manufacturers</td>\n",
       "      <td>1147.75</td>\n",
       "      <td>123.18</td>\n",
       "      <td>-89.267698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>MRNA</td>\n",
       "      <td>2023</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Biotechnology</td>\n",
       "      <td>180.85</td>\n",
       "      <td>79.01</td>\n",
       "      <td>-56.311861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Ticker  Year       Sector            Industry  Initial Open  Final Close  \\\n",
       "45     ALV  2018  Automobiles          Auto Parts        127.51        70.23   \n",
       "105    LPL  2019   Technology            Consumer          8.00         6.94   \n",
       "179   AAPL  2020   Technology            Consumer        296.24       132.69   \n",
       "230    LDI  2021      Finance            Mortgage         14.00         4.80   \n",
       "355   TSLA  2022  Automobiles  Auto Manufacturers       1147.75       123.18   \n",
       "459   MRNA  2023   Healthcare       Biotechnology        180.85        79.01   \n",
       "\n",
       "     Total Change in Percent  \n",
       "45                -44.921967  \n",
       "105               -13.250000  \n",
       "179               -55.208615  \n",
       "230               -65.714284  \n",
       "355               -89.267698  \n",
       "459               -56.311861  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Group the data by Year to ticker data with th max negative change in price for that year\n",
    "grouped_df2 = top_by_year.groupby([\"Year\"])[\"Total Change in Percent\"].idxmin()\n",
    "worst_by_year = top_by_year.iloc[grouped_df2, :]\n",
    "\n",
    "#Show the Output\n",
    "worst_by_year"
   ]
  },
  {
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   "execution_count": 17,
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    "outputId": "d72e51d6-6818-4f5e-f3f5-2f555757b77d",
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      ],
      "text/plain": [
       ":Bars   [Year]   (Total Change in Percent,Ticker,Sector,Industry,Initial Open,Final Close)"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1068"
      }
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x520 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Configure the map plot\n",
    "plt.figure(figsize = (12, 5.2))\n",
    "worst_yearly = worst_by_year.hvplot.barh(\n",
    "                            \"Year\",\n",
    "                            \"Total Change in Percent\",\n",
    "                            frame_width = 600,\n",
    "                            frame_height = 300,\n",
    "                            color = \"Ticker\",\n",
    "                            colorbar = True,\n",
    "                            clabel=\"Year\", \n",
    "                            cmap=\"Oranges\",\n",
    "                            hover_cols = [\"Ticker\", \"Year\", \"Sector\", \"Industry\", \"Initial Open\", \"Final Close\", \"Total Change in Percent\"],\n",
    "                            title = \"Bottom Performers by Year\"\n",
    "\n",
    ")\n",
    "\n",
    "\n",
    "# Display the results\n",
    "worst_yearly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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