Python for Data Science and AI Assignment.ipynb

{"cells":[{"metadata":{},"cell_type":"markdown","source":"<center>\n    <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n</center>\n"},{"metadata":{},"cell_type":"markdown","source":"<h1>Analyzing US Economic Data and  Building a Dashboard  </h1>\n<h2>Description</h2>\n"},{"metadata":{},"cell_type":"markdown","source":"Extracting essential data from a dataset and displaying it is a necessary part of data science; therefore individuals can make correct decisions based on the data. In this assignment, you will extract some essential economic indicators from some data, you will then display these economic indicators in a Dashboard. You can then share the dashboard via an URL.\n\n<p>\n<a href=\"https://en.wikipedia.org/wiki/Gross_domestic_product\"> Gross domestic product (GDP)</a> is a measure of the market value of all the final goods and services produced in a period. GDP is an indicator of how well the economy is doing. A drop in GDP indicates the economy is producing less; similarly an increase in GDP suggests the economy is performing better. In this lab, you will examine how changes in GDP impact the unemployment rate. You will take screen shots of every step, you will share the notebook and the URL pointing to the dashboard.</p>\n"},{"metadata":{},"cell_type":"markdown","source":"<h2>Table of Contents</h2>\n<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n    <ul>\n        <li><a href=\"#Section_1\"> Define a Function that Makes a Dashboard </a></li>\n    <li><a href=\"#Section_2\">Question 1: Create a dataframe that contains the GDP data and display it</a> </li>\n    <li><a href=\"#Section_3\">Question 2: Create a dataframe that contains the unemployment data and display it</a></li>\n    <li><a href=\"#Section_4\">Question 3: Display a dataframe where unemployment was greater than 8.5%</a></li>\n    <li><a href=\"#Section_5\">Question 4: Use the function make_dashboard to make a dashboard</a></li>\n        <li><a href=\"#Section_6\"><b>(Optional not marked)</b> Save the dashboard on IBM cloud and display it</a></li>\n    </ul>\n<p>\n    Estimated Time Needed: <strong>180 min</strong></p>\n</div>\n\n<hr>\n"},{"metadata":{},"cell_type":"markdown","source":"<h2 id=\"Section_1\"> Define Function that Makes a Dashboard  </h2>\n"},{"metadata":{},"cell_type":"markdown","source":"We will import the following libraries.\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"pip install bokeh","execution_count":1,"outputs":[{"output_type":"stream","text":"Requirement already satisfied: bokeh in /srv/conda/envs/notebook/lib/python3.7/site-packages (2.2.3)\nRequirement already satisfied: numpy>=1.11.3 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from bokeh) (1.19.5)\nRequirement already satisfied: PyYAML>=3.10 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from bokeh) (5.3.1)\nRequirement already satisfied: pillow>=7.1.0 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from bokeh) (8.1.0)\nRequirement already satisfied: typing-extensions>=3.7.4 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from bokeh) (3.7.4.3)\nRequirement already satisfied: tornado>=5.1 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from bokeh) (6.0.4)\nRequirement already satisfied: packaging>=16.8 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from bokeh) (20.4)\nRequirement already satisfied: Jinja2>=2.7 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from bokeh) (2.11.2)\nRequirement already satisfied: python-dateutil>=2.1 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from bokeh) (2.8.1)\nRequirement already satisfied: six in /srv/conda/envs/notebook/lib/python3.7/site-packages (from packaging>=16.8->bokeh) (1.15.0)\nRequirement already satisfied: pyparsing>=2.0.2 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from packaging>=16.8->bokeh) (2.4.7)\nRequirement already satisfied: MarkupSafe>=0.23 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from Jinja2>=2.7->bokeh) (1.1.1)\nNote: you may need to restart the kernel to use updated packages.\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"import pandas as pd\nfrom bokeh.plotting import figure, output_file, show,output_notebook\noutput_notebook()","execution_count":2,"outputs":[{"output_type":"display_data","data":{"text/html":"\n    <div class=\"bk-root\">\n        <a href=\"https://bokeh.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; 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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    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.3.min.js\": \"T2yuo9Oe71Cz/I4X9Ac5+gpEa5a8PpJCDlqKYO0CfAuEszu1JrXLl8YugMqYe3sM\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.3.min.js\": \"98GDGJ0kOMCUMUePhksaQ/GYgB3+NH9h996V88sh3aOiUNX3N+fLXAtry6xctSZ6\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.3.min.js\": \"89bArO+nlbP3sgakeHjCo1JYxYR5wufVgA3IbUvDY+K7w4zyxJqssu7wVnfeKCq8\"};\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      if (url in hashes) {\n        element.crossOrigin = \"anonymous\";\n        element.integrity = \"sha384-\" + hashes[url];\n      }\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\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  \n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.3.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\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; 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The function will produce a dashboard as well as an html file. You can then use this html file to share your dashboard. If you do not know what an html file is don't worry everything you need to know will be provided in the lab. \n"},{"metadata":{"trusted":true},"cell_type":"code","source":"def make_dashboard(x, gdp_change, unemployment, title, file_name):\n    output_file(file_name)\n    p = figure(title=title, x_axis_label='year', y_axis_label='%')\n    p.line(x.squeeze(), gdp_change.squeeze(), color=\"firebrick\", line_width=4, legend=\"% GDP change\")\n    p.line(x.squeeze(), unemployment.squeeze(), line_width=4, legend=\"% unemployed\")\n    show(p)","execution_count":3,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"The dictionary  <code>links</code> contain the CSV files with all the data. The value for the key <code>GDP</code> is the file that contains the GDP data. The value for the key <code>unemployment</code> contains the unemployment data.\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"links={'GDP':'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/FinalModule_Coursera/data/clean_gdp.csv',\\\n       'unemployment':'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/FinalModule_Coursera/data/clean_unemployment.csv'}","execution_count":4,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<h3 id=\"Section_2\"> Question 1: Create a dataframe that contains the GDP data and display the first five rows of the dataframe.</h3>\n"},{"metadata":{},"cell_type":"markdown","source":"Use the dictionary <code>links</code> and the function <code>pd.read_csv</code> to create a Pandas dataframes that contains the GDP data.\n"},{"metadata":{},"cell_type":"markdown","source":"<b>Hint: <code>links[\"GDP\"]</code> contains the path or name of the file.</b>\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"df_GDP = pd.read_csv(links['GDP'])","execution_count":5,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Use the method <code>head()</code> to display the first five rows of the GDP data, then take a screen-shot.\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"df_GDP.head()","execution_count":6,"outputs":[{"output_type":"execute_result","execution_count":6,"data":{"text/plain":"   date  level-current  level-chained  change-current  change-chained\n0  1948          274.8         2020.0            -0.7            -0.6\n1  1949          272.8         2008.9            10.0             8.7\n2  1950          300.2         2184.0            15.7             8.0\n3  1951          347.3         2360.0             5.9             4.1\n4  1952          367.7         2456.1             6.0             4.7","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>date</th>\n      <th>level-current</th>\n      <th>level-chained</th>\n      <th>change-current</th>\n      <th>change-chained</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1948</td>\n      <td>274.8</td>\n      <td>2020.0</td>\n      <td>-0.7</td>\n      <td>-0.6</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1949</td>\n      <td>272.8</td>\n      <td>2008.9</td>\n      <td>10.0</td>\n      <td>8.7</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1950</td>\n      <td>300.2</td>\n      <td>2184.0</td>\n      <td>15.7</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1951</td>\n      <td>347.3</td>\n      <td>2360.0</td>\n      <td>5.9</td>\n      <td>4.1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1952</td>\n      <td>367.7</td>\n      <td>2456.1</td>\n      <td>6.0</td>\n      <td>4.7</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"<h3 id=\"Section_2\"> Question 2: Create a dataframe that contains the unemployment data. Display the first five rows of the dataframe. </h3>\n"},{"metadata":{},"cell_type":"markdown","source":"Use the dictionary <code>links</code> and the function <code>pd.read_csv</code> to create a Pandas dataframes that contains the unemployment data.\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"df_UnEmp = pd.read_csv(links['unemployment'])","execution_count":7,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Use the method <code>head()</code> to display the first five rows of the unemployment data, then take a screen-shot.\n"},{"metadata":{"trusted":true,"scrolled":true},"cell_type":"code","source":"df_UnEmp.head()","execution_count":8,"outputs":[{"output_type":"execute_result","execution_count":8,"data":{"text/plain":"   date  unemployment\n0  1948      3.750000\n1  1949      6.050000\n2  1950      5.208333\n3  1951      3.283333\n4  1952      3.025000","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>date</th>\n      <th>unemployment</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1948</td>\n      <td>3.750000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1949</td>\n      <td>6.050000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1950</td>\n      <td>5.208333</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1951</td>\n      <td>3.283333</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1952</td>\n      <td>3.025000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"<h3 id=\"Section_3\">Question 3: Display a dataframe where unemployment was greater than 8.5%. Take a screen-shot.</h3>\n"},{"metadata":{"trusted":true,"scrolled":true},"cell_type":"code","source":"df_UnEmp85 = df_UnEmp[df_UnEmp['unemployment']>8.5]\ndf_UnEmp85.head(10)","execution_count":9,"outputs":[{"output_type":"execute_result","execution_count":9,"data":{"text/plain":"    date  unemployment\n34  1982      9.708333\n35  1983      9.600000\n61  2009      9.283333\n62  2010      9.608333\n63  2011      8.933333","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>date</th>\n      <th>unemployment</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>34</th>\n      <td>1982</td>\n      <td>9.708333</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>1983</td>\n      <td>9.600000</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>2009</td>\n      <td>9.283333</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>2010</td>\n      <td>9.608333</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>2011</td>\n      <td>8.933333</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"<h3 id=\"Section_4\">Question 4: Use the function make_dashboard to make a dashboard</h3>\n"},{"metadata":{},"cell_type":"markdown","source":"In this section, you will call the function  <code>make_dashboard</code> , to produce a dashboard. We will use the convention of giving each variable the same name as the function parameter.\n"},{"metadata":{},"cell_type":"markdown","source":"Create a new dataframe with the column <code>'date'</code> called <code>x</code> from the dataframe that contains the GDP data.\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"x = df_GDP['date']","execution_count":10,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Create a new dataframe with the column <code>'change-current'</code> called <code>gdp_change</code>  from the dataframe that contains the GDP data.\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"gdp_change = df_GDP['change-current']","execution_count":11,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Create a new dataframe with the column <code>'unemployment' </code> called <code>unemployment</code>  from the dataframe that contains the  unemployment data.\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"unemployment = df_UnEmp['unemployment']","execution_count":12,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Give your dashboard a string title, and assign it to the variable <code>title</code>\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"title = 'My Title'","execution_count":13,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Finally, the function <code>make_dashboard</code> will output an <code>.html</code> in your direictory, just like a <code>csv</code> file. The name of the file is <code>\"index.html\"</code> and it will be stored in the varable  <code>file_name</code>.\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"file_name = \"index.html\"","execution_count":14,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Call the function <code>make_dashboard</code> , to produce a dashboard.  Assign the parameter values accordingly take a the <b>, take a screen shot of the dashboard and submit it</b>.\n"},{"metadata":{"trusted":true,"scrolled":true},"cell_type":"code","source":"# Fill up the parameters in the following function:\nmake_dashboard(x=x, gdp_change=gdp_change, unemployment=unemployment, title=title, file_name=file_name)","execution_count":15,"outputs":[{"output_type":"stream","text":"BokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead\nBokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead\n","name":"stderr"},{"output_type":"display_data","data":{"text/html":"\n\n\n\n\n\n  <div class=\"bk-root\" id=\"af606130-6206-4803-b412-ecc8771272ac\" data-root-id=\"1002\"></div>\n"},"metadata":{}},{"output_type":"display_data","data":{"application/javascript":"(function(root) {\n  function embed_document(root) 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</h2>\n"},{"metadata":{},"cell_type":"markdown","source":"<p>Once you complete your notebook you will have to share it to be marked. Select the icon on the top right a marked in red in the image below, a dialogue box should open, select the option all&nbsp;content excluding sensitive code cells.</p>\n\n<p><img height=\"440\" width=\"700\" src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/projects/EdX/ReadMe%20files/share_noteook1.png\" alt=\"share notebook\" /></p>\n<p></p>\n\n<p>You can then share the notebook&nbsp; via a&nbsp; URL by scrolling down as shown in the following image:</p>\n<p style=\"text-align: center;\"> <img height=\"308\" width=\"350\" src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/projects/EdX/ReadMe%20files/link2.png\"  alt=\"share notebook\" /> </p>\n"},{"metadata":{},"cell_type":"markdown","source":"<hr>\n<p>Copyright &copy; 2019 IBM Developer Skills Network. This notebook and its source code are released under the terms of the <a href=\"https://cognitiveclass.ai/mit-license/\">MIT License</a>.</p>\n"},{"metadata":{},"cell_type":"markdown","source":"<h2>About the Authors:</h2> \n\n<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n\n<p>\nOther contributors: <a href=\"https://www.linkedin.com/in/yi-leng-yao-84451275/\">Yi leng Yao</a>, <a href=\"www.linkedin.com/in/jiahui-mavis-zhou-a4537814a\">Mavis Zhou</a> \n</p>\n"},{"metadata":{},"cell_type":"markdown","source":"<h2>References :</h2> \n"},{"metadata":{},"cell_type":"markdown","source":"<ul>\n <il>\n     1) <a href=\"https://research.stlouisfed.org/\">Economic Research at the St. Louis Fed </a>:<a href=\"https://fred.stlouisfed.org/series/UNRATE/\"> Civilian Unemployment Rate</a>\n   </il>   \n    <p>\n     <il>\n    2) <a href=\"https://github.com/datasets\">Data Packaged Core Datasets\n       </a>\n   </il> \n    </p>\n    \n</ul>\n</div>\n"},{"metadata":{},"cell_type":"markdown","source":"## Change Log\n\n| Date (YYYY-MM-DD) | Version | Changed By    | Change Description        |\n| ----------------- | ------- | ------------- | ------------------------- |\n| 2020-11-10        | 1.1     | Malika Singla | Deleted the Optional part |\n| 2020-08-27        | 1.0     | Malika Singla | Added lab to GitLab       |\n\n<hr>\n\n## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n\n<p>\n"}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.8","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":2}