├── Analyzing US Economic Data and Building Dashboard.ipynb
├── Final Project - Data Visualization.ipynb
├── First Notebook.ipynb
├── House Sales in King County, USA Project.ipynb
├── Machine Learning Final Project.ipynb
├── Neighborhoods in Mumbai to Open a Restaurant.ipynb
├── README.md
├── SQL Assignment - Chicago.ipynb
└── Segmenting and Clustering Neighborhoods in Toronto.ipynb


/Analyzing US Economic Data and Building Dashboard.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |     "cells": [
  3 |         {
  4 |             "cell_type": "markdown",
  5 |             "metadata": {},
  6 |             "source": "<a><img src=\"https://ibm.box.com/shared/static/ugcqz6ohbvff804xp84y4kqnvvk3bq1g.png\" width=\"200\" align=\"center\"></a>"
  7 |         },
  8 |         {
  9 |             "cell_type": "markdown",
 10 |             "metadata": {},
 11 |             "source": "<h1>Analyzing US Economic Data and  Building a Dashboard  </h1>\n<h2>Description</h2>\n"
 12 |         },
 13 |         {
 14 |             "cell_type": "markdown",
 15 |             "metadata": {},
 16 |             "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<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>"
 17 |         },
 18 |         {
 19 |             "cell_type": "markdown",
 20 |             "metadata": {},
 21 |             "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>"
 22 |         },
 23 |         {
 24 |             "cell_type": "markdown",
 25 |             "metadata": {},
 26 |             "source": "<h2 id=\"Section_1\"> Define Function that Makes a Dashboard  </h2>"
 27 |         },
 28 |         {
 29 |             "cell_type": "markdown",
 30 |             "metadata": {},
 31 |             "source": "We will import the following libraries."
 32 |         },
 33 |         {
 34 |             "cell_type": "code",
 35 |             "execution_count": 83,
 36 |             "metadata": {},
 37 |             "outputs": [
 38 |                 {
 39 |                     "data": {
 40 |                         "text/html": "\n    <div class=\"bk-root\">\n        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n        <span id=\"1940\">Loading BokehJS ...</span>\n    </div>"
 41 |                     },
 42 |                     "metadata": {},
 43 |                     "output_type": "display_data"
 44 |                 },
 45 |                 {
 46 |                     "data": {
 47 |                         "application/javascript": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  var JS_MIME_TYPE = 'application/javascript';\n  var HTML_MIME_TYPE = 'text/html';\n  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n  var CLASS_NAME = 'output_bokeh rendered_html';\n\n  /**\n   * Render data to the DOM node\n   */\n  function render(props, node) {\n    var script = document.createElement(\"script\");\n    node.appendChild(script);\n  }\n\n  /**\n   * Handle when an output is cleared or removed\n   */\n  function handleClearOutput(event, handle) {\n    var cell = handle.cell;\n\n    var id = cell.output_area._bokeh_element_id;\n    var server_id = cell.output_area._bokeh_server_id;\n    // Clean up Bokeh references\n    if (id != null && id in Bokeh.index) {\n      Bokeh.index[id].model.document.clear();\n      delete Bokeh.index[id];\n    }\n\n    if (server_id !== undefined) {\n      // Clean up Bokeh references\n      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n      cell.notebook.kernel.execute(cmd, {\n        iopub: {\n          output: function(msg) {\n            var id = msg.content.text.trim();\n            if (id in Bokeh.index) {\n              Bokeh.index[id].model.document.clear();\n              delete Bokeh.index[id];\n            }\n          }\n        }\n      });\n      // Destroy server and session\n      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n      cell.notebook.kernel.execute(cmd);\n    }\n  }\n\n  /**\n   * Handle when a new output is added\n   */\n  function handleAddOutput(event, handle) {\n    var output_area = handle.output_area;\n    var output = handle.output;\n\n    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n      return\n    }\n\n    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n      // store reference to embed id on output_area\n      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n    }\n    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n      var bk_div = document.createElement(\"div\");\n      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n      var script_attrs = bk_div.children[0].attributes;\n      for (var i = 0; i < script_attrs.length; i++) {\n        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n      }\n      // store reference to server id on output_area\n      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n    }\n  }\n\n  function register_renderer(events, OutputArea) {\n\n    function append_mime(data, metadata, element) {\n      // create a DOM node to render to\n      var toinsert = this.create_output_subarea(\n        metadata,\n        CLASS_NAME,\n        EXEC_MIME_TYPE\n      );\n      this.keyboard_manager.register_events(toinsert);\n      // Render to node\n      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n      render(props, toinsert[toinsert.length - 1]);\n      element.append(toinsert);\n      return toinsert\n    }\n\n    /* Handle when an output is cleared or removed */\n    events.on('clear_output.CodeCell', handleClearOutput);\n    events.on('delete.Cell', handleClearOutput);\n\n    /* Handle when a new output is added */\n    events.on('output_added.OutputArea', handleAddOutput);\n\n    /**\n     * Register the mime type and append_mime function with output_area\n     */\n    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n      /* Is output safe? */\n      safe: true,\n      /* Index of renderer in `output_area.display_order` */\n      index: 0\n    });\n  }\n\n  // register the mime type if in Jupyter Notebook environment and previously unregistered\n  if (root.Jupyter !== undefined) {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  }\n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1940\");\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) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"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.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"1940\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1940' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.4.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1940\")).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.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));",
 48 |                         "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(\"1940\");\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) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"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.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"1940\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1940' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.4.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1940\")).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.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
 49 |                     },
 50 |                     "metadata": {},
 51 |                     "output_type": "display_data"
 52 |                 }
 53 |             ],
 54 |             "source": "import pandas as pd\nfrom bokeh.plotting import figure, output_file, show,output_notebook\noutput_notebook()"
 55 |         },
 56 |         {
 57 |             "cell_type": "markdown",
 58 |             "metadata": {},
 59 |             "source": "In this section, we define the function <code>make_dashboard</code>. \nYou don't have to know how the function works, you should only care about the inputs. 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. "
 60 |         },
 61 |         {
 62 |             "cell_type": "code",
 63 |             "execution_count": 84,
 64 |             "metadata": {},
 65 |             "outputs": [],
 66 |             "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)"
 67 |         },
 68 |         {
 69 |             "cell_type": "markdown",
 70 |             "metadata": {},
 71 |             "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."
 72 |         },
 73 |         {
 74 |             "cell_type": "code",
 75 |             "execution_count": 85,
 76 |             "metadata": {},
 77 |             "outputs": [],
 78 |             "source": "links={'GDP':'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/projects/coursera_project/clean_gdp.csv',\\\n       'unemployment':'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/projects/coursera_project/clean_unemployment.csv'}"
 79 |         },
 80 |         {
 81 |             "cell_type": "markdown",
 82 |             "metadata": {},
 83 |             "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>"
 84 |         },
 85 |         {
 86 |             "cell_type": "markdown",
 87 |             "metadata": {},
 88 |             "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."
 89 |         },
 90 |         {
 91 |             "cell_type": "markdown",
 92 |             "metadata": {},
 93 |             "source": "<b>Hint: <code>links[\"GDP\"]</code> contains the path or name of the file.</b>"
 94 |         },
 95 |         {
 96 |             "cell_type": "code",
 97 |             "execution_count": 86,
 98 |             "metadata": {},
 99 |             "outputs": [],
100 |             "source": "# Type your code here\ndf_gdp = pd.read_csv(links['GDP'])"
101 |         },
102 |         {
103 |             "cell_type": "markdown",
104 |             "metadata": {},
105 |             "source": "Use the method <code>head()</code> to display the first five rows of the GDP data, then take a screen-shot."
106 |         },
107 |         {
108 |             "cell_type": "code",
109 |             "execution_count": 87,
110 |             "metadata": {},
111 |             "outputs": [
112 |                 {
113 |                     "data": {
114 |                         "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>",
115 |                         "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"
116 |                     },
117 |                     "execution_count": 87,
118 |                     "metadata": {},
119 |                     "output_type": "execute_result"
120 |                 }
121 |             ],
122 |             "source": "# Type your code here\ndf_gdp.head(5)"
123 |         },
124 |         {
125 |             "cell_type": "markdown",
126 |             "metadata": {},
127 |             "source": "<h3 id=\"Section_2\"> Question 2: Create a dataframe that contains the unemployment data. Display the first five rows of the dataframe. </h3>"
128 |         },
129 |         {
130 |             "cell_type": "markdown",
131 |             "metadata": {},
132 |             "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."
133 |         },
134 |         {
135 |             "cell_type": "code",
136 |             "execution_count": 88,
137 |             "metadata": {},
138 |             "outputs": [],
139 |             "source": "# Type your code here\ndf_unemployment = pd.read_csv(links['unemployment'])"
140 |         },
141 |         {
142 |             "cell_type": "markdown",
143 |             "metadata": {},
144 |             "source": "Use the method <code>head()</code> to display the first five rows of the GDP data, then take a screen-shot."
145 |         },
146 |         {
147 |             "cell_type": "code",
148 |             "execution_count": 89,
149 |             "metadata": {},
150 |             "outputs": [
151 |                 {
152 |                     "data": {
153 |                         "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>",
154 |                         "text/plain": "   date  unemployment\n0  1948      3.750000\n1  1949      6.050000\n2  1950      5.208333\n3  1951      3.283333\n4  1952      3.025000"
155 |                     },
156 |                     "execution_count": 89,
157 |                     "metadata": {},
158 |                     "output_type": "execute_result"
159 |                 }
160 |             ],
161 |             "source": "# Type your code here\ndf_unemployment.head(5)"
162 |         },
163 |         {
164 |             "cell_type": "markdown",
165 |             "metadata": {},
166 |             "source": "<h3 id=\"Section_3\">Question 3: Display a dataframe where unemployment was greater than 8.5%. Take a screen-shot.</h3>"
167 |         },
168 |         {
169 |             "cell_type": "code",
170 |             "execution_count": 90,
171 |             "metadata": {},
172 |             "outputs": [
173 |                 {
174 |                     "data": {
175 |                         "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>",
176 |                         "text/plain": "    date  unemployment\n34  1982      9.708333\n35  1983      9.600000\n61  2009      9.283333\n62  2010      9.608333\n63  2011      8.933333"
177 |                     },
178 |                     "execution_count": 90,
179 |                     "metadata": {},
180 |                     "output_type": "execute_result"
181 |                 }
182 |             ],
183 |             "source": "# Type your code here\ndf_unemployment[df_unemployment['unemployment']>8.5]"
184 |         },
185 |         {
186 |             "cell_type": "markdown",
187 |             "metadata": {},
188 |             "source": "<h3 id=\"Section_4\">Question 4: Use the function make_dashboard to make a dashboard</h3>"
189 |         },
190 |         {
191 |             "cell_type": "markdown",
192 |             "metadata": {},
193 |             "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."
194 |         },
195 |         {
196 |             "cell_type": "markdown",
197 |             "metadata": {},
198 |             "source": "Create a new dataframe with the column <code>'date'</code> called <code>x</code> from the dataframe that contains the GDP data."
199 |         },
200 |         {
201 |             "cell_type": "code",
202 |             "execution_count": 91,
203 |             "metadata": {},
204 |             "outputs": [],
205 |             "source": "x = df_gdp['date'] # Create your dataframe with column date"
206 |         },
207 |         {
208 |             "cell_type": "markdown",
209 |             "metadata": {},
210 |             "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."
211 |         },
212 |         {
213 |             "cell_type": "code",
214 |             "execution_count": 92,
215 |             "metadata": {},
216 |             "outputs": [],
217 |             "source": "gdp_change = df_gdp['change-current'] # Create your dataframe with column change-current"
218 |         },
219 |         {
220 |             "cell_type": "markdown",
221 |             "metadata": {},
222 |             "source": "Create a new dataframe with the column <code>'unemployment' </code> called <code>unemployment</code>  from the dataframe that contains the  unemployment data."
223 |         },
224 |         {
225 |             "cell_type": "code",
226 |             "execution_count": 93,
227 |             "metadata": {},
228 |             "outputs": [],
229 |             "source": "unemployment = df_unemployment['unemployment'] # Create your dataframe with column unemployment"
230 |         },
231 |         {
232 |             "cell_type": "markdown",
233 |             "metadata": {},
234 |             "source": "Give your dashboard a string title, and assign it to the variable <code>title</code>"
235 |         },
236 |         {
237 |             "cell_type": "code",
238 |             "execution_count": 94,
239 |             "metadata": {},
240 |             "outputs": [],
241 |             "source": "title = 'Dashboard for Unemployment and GDP Change' # Give your dashboard a string title"
242 |         },
243 |         {
244 |             "cell_type": "markdown",
245 |             "metadata": {},
246 |             "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>."
247 |         },
248 |         {
249 |             "cell_type": "code",
250 |             "execution_count": 95,
251 |             "metadata": {},
252 |             "outputs": [],
253 |             "source": "file_name = \"index.html\""
254 |         },
255 |         {
256 |             "cell_type": "markdown",
257 |             "metadata": {},
258 |             "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>."
259 |         },
260 |         {
261 |             "cell_type": "code",
262 |             "execution_count": 96,
263 |             "metadata": {},
264 |             "outputs": [
265 |                 {
266 |                     "data": {
267 |                         "text/html": "\n\n\n\n\n\n  <div class=\"bk-root\" id=\"e7891f1e-642b-4997-aa2f-10cb71655477\" data-root-id=\"1942\"></div>\n"
268 |                     },
269 |                     "metadata": {},
270 |                     "output_type": "display_data"
271 |                 },
272 |                 {
273 |                     "data": {
274 |                         "application/javascript": "(function(root) {\n  function embed_document(root) {\n    \n  var docs_json = {\"7e9ee6d3-2eb7-4009-aecf-268a588f8538\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1952\",\"type\":\"LinearAxis\"}],\"left\":[{\"id\":\"1957\",\"type\":\"LinearAxis\"}],\"renderers\":[{\"id\":\"1952\",\"type\":\"LinearAxis\"},{\"id\":\"1956\",\"type\":\"Grid\"},{\"id\":\"1957\",\"type\":\"LinearAxis\"},{\"id\":\"1961\",\"type\":\"Grid\"},{\"id\":\"1970\",\"type\":\"BoxAnnotation\"},{\"id\":\"1988\",\"type\":\"Legend\"},{\"id\":\"1980\",\"type\":\"GlyphRenderer\"},{\"id\":\"1993\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"1941\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"1968\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"1944\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"1948\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"1946\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"1950\",\"type\":\"LinearScale\"}},\"id\":\"1942\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{},\"id\":\"1948\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"2052\",\"type\":\"Selection\"},{\"attributes\":{\"plot\":null,\"text\":\"Dashboard for Unemployment and GDP Change\"},\"id\":\"1941\",\"type\":\"Title\"},{\"attributes\":{\"data_source\":{\"id\":\"1977\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1978\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1979\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"1981\",\"type\":\"CDSView\"}},\"id\":\"1980\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"source\":{\"id\":\"1977\",\"type\":\"ColumnDataSource\"}},\"id\":\"1981\",\"type\":\"CDSView\"},{\"attributes\":{\"callback\":null,\"data\":{\"x\":[1948,1949,1950,1951,1952,1953,1954,1955,1956,1957,1958,1959,1960,1961,1962,1963,1964,1965,1966,1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016],\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[69]}},\"selected\":{\"id\":\"2001\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"2002\",\"type\":\"UnionRenderers\"}},\"id\":\"1977\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"axis_label\":\"%\",\"formatter\":{\"id\":\"1985\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"1942\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"1958\",\"type\":\"BasicTicker\"}},\"id\":\"1957\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"2053\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"1985\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1950\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1958\",\"type\":\"BasicTicker\"},{\"attributes\":{\"items\":[{\"id\":\"1989\",\"type\":\"LegendItem\"},{\"id\":\"2003\",\"type\":\"LegendItem\"}],\"plot\":{\"id\":\"1942\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"1988\",\"type\":\"Legend\"},{\"attributes\":{},\"id\":\"1953\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"2001\",\"type\":\"Selection\"},{\"attributes\":{\"axis_label\":\"year\",\"formatter\":{\"id\":\"1983\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"1942\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"1953\",\"type\":\"BasicTicker\"}},\"id\":\"1952\",\"type\":\"LinearAxis\"},{\"attributes\":{\"label\":{\"value\":\"% GDP change\"},\"renderers\":[{\"id\":\"1980\",\"type\":\"GlyphRenderer\"}]},\"id\":\"1989\",\"type\":\"LegendItem\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1962\",\"type\":\"PanTool\"},{\"id\":\"1963\",\"type\":\"WheelZoomTool\"},{\"id\":\"1964\",\"type\":\"BoxZoomTool\"},{\"id\":\"1965\",\"type\":\"SaveTool\"},{\"id\":\"1966\",\"type\":\"ResetTool\"},{\"id\":\"1967\",\"type\":\"HelpTool\"}]},\"id\":\"1968\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"1983\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1962\",\"type\":\"PanTool\"},{\"attributes\":{},\"id\":\"1963\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"callback\":null,\"data\":{\"x\":[1948,1949,1950,1951,1952,1953,1954,1955,1956,1957,1958,1959,1960,1961,1962,1963,1964,1965,1966,1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016],\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[69]}},\"selected\":{\"id\":\"2052\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"2053\",\"type\":\"UnionRenderers\"}},\"id\":\"1990\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"overlay\":{\"id\":\"1970\",\"type\":\"BoxAnnotation\"}},\"id\":\"1964\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"line_color\":\"#1f77b4\",\"line_width\":4,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1991\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"1965\",\"type\":\"SaveTool\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"line_width\":4,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1992\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"1966\",\"type\":\"ResetTool\"},{\"attributes\":{\"data_source\":{\"id\":\"1990\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1991\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1992\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"1994\",\"type\":\"CDSView\"}},\"id\":\"1993\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"1942\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"1958\",\"type\":\"BasicTicker\"}},\"id\":\"1961\",\"type\":\"Grid\"},{\"attributes\":{\"callback\":null},\"id\":\"1944\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1967\",\"type\":\"HelpTool\"},{\"attributes\":{\"plot\":{\"id\":\"1942\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"1953\",\"type\":\"BasicTicker\"}},\"id\":\"1956\",\"type\":\"Grid\"},{\"attributes\":{\"source\":{\"id\":\"1990\",\"type\":\"ColumnDataSource\"}},\"id\":\"1994\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"2002\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"plot\":null,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1970\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"label\":{\"value\":\"% unemployed\"},\"renderers\":[{\"id\":\"1993\",\"type\":\"GlyphRenderer\"}]},\"id\":\"2003\",\"type\":\"LegendItem\"},{\"attributes\":{\"line_color\":\"firebrick\",\"line_width\":4,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1978\",\"type\":\"Line\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"line_width\":4,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1979\",\"type\":\"Line\"},{\"attributes\":{\"callback\":null},\"id\":\"1946\",\"type\":\"DataRange1d\"}],\"root_ids\":[\"1942\"]},\"title\":\"Bokeh Application\",\"version\":\"1.0.4\"}};\n  var render_items = [{\"docid\":\"7e9ee6d3-2eb7-4009-aecf-268a588f8538\",\"roots\":{\"1942\":\"e7891f1e-642b-4997-aa2f-10cb71655477\"}}];\n  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n\n  }\n  if (root.Bokeh !== undefined) {\n    embed_document(root);\n  } else {\n    var attempts = 0;\n    var timer = setInterval(function(root) {\n      if (root.Bokeh !== undefined) {\n        embed_document(root);\n        clearInterval(timer);\n      }\n      attempts++;\n      if (attempts > 100) {\n        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n        clearInterval(timer);\n      }\n    }, 10, root)\n  }\n})(window);",
275 |                         "application/vnd.bokehjs_exec.v0+json": ""
276 |                     },
277 |                     "metadata": {
278 |                         "application/vnd.bokehjs_exec.v0+json": {
279 |                             "id": "1942"
280 |                         }
281 |                     },
282 |                     "output_type": "display_data"
283 |                 }
284 |             ],
285 |             "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)"
286 |         },
287 |         {
288 |             "cell_type": "markdown",
289 |             "metadata": {},
290 |             "source": "<h3 id=\"Section_5\"> <b>(Optional not marked)</b>Save the dashboard on IBM cloud and display it  </h3>"
291 |         },
292 |         {
293 |             "cell_type": "markdown",
294 |             "metadata": {},
295 |             "source": "From the tutorial <i>PROVISIONING AN OBJECT STORAGE INSTANCE ON IBM CLOUD</i> copy the JSON object containing the credentials you created. You\u2019ll want to store everything you see in a credentials variable like the one below (obviously, replace the placeholder values with your own). Take special note of your <code>access_key_id</code> and <code>secret_access_key</code>. <b>Do not delete <code># @hidden_cell </code> as this will not allow people to see your credentials when you share your notebook. </b>"
296 |         },
297 |         {
298 |             "cell_type": "markdown",
299 |             "metadata": {},
300 |             "source": "<code>\ncredentials = {<br>\n &nbsp; \"apikey\": \"your-api-key\",<br>\n &nbsp; \"cos_hmac_keys\": {<br>\n &nbsp;  \"access_key_id\": \"your-access-key-here\", <br>\n &nbsp;   \"secret_access_key\": \"your-secret-access-key-here\"<br>\n &nbsp; },<br>\n</code>\n<code>\n   &nbsp;\"endpoints\": \"your-endpoints\",<br>\n &nbsp; \"iam_apikey_description\": \"your-iam_apikey_description\",<br>\n &nbsp; \"iam_apikey_name\": \"your-iam_apikey_name\",<br>\n &nbsp; \"iam_role_crn\": \"your-iam_apikey_name\",<br>\n &nbsp;  \"iam_serviceid_crn\": \"your-iam_serviceid_crn\",<br>\n &nbsp;\"resource_instance_id\": \"your-resource_instance_id\"<br>\n}\n</code>"
301 |         },
302 |         {
303 |             "cell_type": "code",
304 |             "execution_count": 97,
305 |             "metadata": {},
306 |             "outputs": [],
307 |             "source": "# The code was removed by Watson Studio for sharing."
308 |         },
309 |         {
310 |             "cell_type": "markdown",
311 |             "metadata": {},
312 |             "source": "You will need the endpoint make sure the setting are the same as <i> PROVISIONING AN OBJECT STORAGE INSTANCE ON IBM CLOUD </i> assign the name of your bucket to the variable  <code>bucket_name </code> "
313 |         },
314 |         {
315 |             "cell_type": "code",
316 |             "execution_count": 98,
317 |             "metadata": {},
318 |             "outputs": [],
319 |             "source": "endpoint = 'https://s3-api.us-geo.objectstorage.softlayer.net'"
320 |         },
321 |         {
322 |             "cell_type": "markdown",
323 |             "metadata": {},
324 |             "source": "From the tutorial <i> PROVISIONING AN OBJECT STORAGE INSTANCE ON IBM CLOUD </i> assign the name of your bucket to the variable  <code>bucket_name </code> "
325 |         },
326 |         {
327 |             "cell_type": "code",
328 |             "execution_count": 99,
329 |             "metadata": {},
330 |             "outputs": [],
331 |             "source": "bucket_name = 'python-for-ds-and-ai-bucket' # Type your bucket name on IBM Cloud"
332 |         },
333 |         {
334 |             "cell_type": "markdown",
335 |             "metadata": {},
336 |             "source": "We can access IBM Cloud Object Storage with Python useing the <code>boto3</code> library, which we\u2019ll import below:"
337 |         },
338 |         {
339 |             "cell_type": "code",
340 |             "execution_count": 100,
341 |             "metadata": {},
342 |             "outputs": [],
343 |             "source": "import boto3"
344 |         },
345 |         {
346 |             "cell_type": "markdown",
347 |             "metadata": {},
348 |             "source": "We can interact with IBM Cloud Object Storage through a <code>boto3</code> resource object."
349 |         },
350 |         {
351 |             "cell_type": "code",
352 |             "execution_count": 101,
353 |             "metadata": {},
354 |             "outputs": [],
355 |             "source": "resource = boto3.resource(\n    's3',\n    aws_access_key_id = credentials[\"cos_hmac_keys\"]['access_key_id'],\n    aws_secret_access_key = credentials[\"cos_hmac_keys\"][\"secret_access_key\"],\n    endpoint_url = endpoint,\n)"
356 |         },
357 |         {
358 |             "cell_type": "markdown",
359 |             "metadata": {},
360 |             "source": "We are going to use  <code>open</code> to create a file object. To get the path of the file, you are going to concatenate the name of the file stored in the variable <code>file_name</code>. The directory stored in the variable directory using the <code>+</code> operator and assign it to the variable \n<code>html_path</code>. We will use the function <code>getcwd()</code> to find current the working directory."
361 |         },
362 |         {
363 |             "cell_type": "code",
364 |             "execution_count": 102,
365 |             "metadata": {},
366 |             "outputs": [],
367 |             "source": "import os\n\ndirectory = os.getcwd()\nhtml_path = directory + \"/\" + file_name"
368 |         },
369 |         {
370 |             "cell_type": "markdown",
371 |             "metadata": {},
372 |             "source": "Now you must read the html file, use the function <code>f = open(html_path, mode)</code> to create a file object and assign it to the variable <code>f</code>. The parameter <code>file</code> should be the variable <code>html_path</code>, the mode should be <code>\"r\"</code> for read. "
373 |         },
374 |         {
375 |             "cell_type": "code",
376 |             "execution_count": 103,
377 |             "metadata": {},
378 |             "outputs": [],
379 |             "source": "# Type your code here\nf = open(file=html_path, mode='r')"
380 |         },
381 |         {
382 |             "cell_type": "markdown",
383 |             "metadata": {},
384 |             "source": "To load your dataset into the bucket we will use the method <code>put_object</code>, you must set the parameter name to the name of the bucket, the parameter <code>Key</code> should be the name of the HTML file and the value for the parameter Body  should be set to <code>f.read()</code>."
385 |         },
386 |         {
387 |             "cell_type": "code",
388 |             "execution_count": 104,
389 |             "metadata": {},
390 |             "outputs": [
391 |                 {
392 |                     "data": {
393 |                         "text/plain": "s3.Object(bucket_name='python-for-ds-and-ai-bucket', key='index.html')"
394 |                     },
395 |                     "execution_count": 104,
396 |                     "metadata": {},
397 |                     "output_type": "execute_result"
398 |                 }
399 |             ],
400 |             "source": "# Fill up the parameters in the following function:\nresource.Bucket(name=bucket_name).put_object(Key=file_name, Body=f.read())"
401 |         },
402 |         {
403 |             "cell_type": "markdown",
404 |             "metadata": {},
405 |             "source": "In the dictionary <code>Params</code> provide the bucket name  as the value for the key <i>'Bucket'</i>. Also for the value of the key <i>'Key'</i> add the name of the <code>html</code> file, both values should be strings."
406 |         },
407 |         {
408 |             "cell_type": "code",
409 |             "execution_count": 105,
410 |             "metadata": {},
411 |             "outputs": [],
412 |             "source": "# Fill in the value for each key\nParams = {'Bucket': bucket_name,'Key': file_name}"
413 |         },
414 |         {
415 |             "cell_type": "markdown",
416 |             "metadata": {},
417 |             "source": "The following lines of code will generate a URL to share your dashboard. The URL only last seven days, but don't worry you will get full marks if the URL is visible in your notebook.  "
418 |         },
419 |         {
420 |             "cell_type": "code",
421 |             "execution_count": 106,
422 |             "metadata": {},
423 |             "outputs": [
424 |                 {
425 |                     "name": "stdout",
426 |                     "output_type": "stream",
427 |                     "text": "https://s3-api.us-geo.objectstorage.softlayer.net/python-for-ds-and-ai-bucket/index.html?AWSAccessKeyId=58a4eaefd7364ccbba025153bff5738b&Signature=ZnxTgAFOI3kUNeJhfHTRejpMFy8%3D&Expires=1596791728\n"
428 |                 }
429 |             ],
430 |             "source": "import sys\ntime = 7*24*60**2\nclient = boto3.client(\n    's3',\n    aws_access_key_id = credentials[\"cos_hmac_keys\"]['access_key_id'],\n    aws_secret_access_key = credentials[\"cos_hmac_keys\"][\"secret_access_key\"],\n    endpoint_url=endpoint,\n\n)\nurl = client.generate_presigned_url('get_object',Params=Params,ExpiresIn=time)\nprint(url)"
431 |         },
432 |         {
433 |             "cell_type": "markdown",
434 |             "metadata": {},
435 |             "source": "<h2 id=\"Section_5\">  How to submit </h2>"
436 |         },
437 |         {
438 |             "cell_type": "markdown",
439 |             "metadata": {},
440 |             "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>"
441 |         },
442 |         {
443 |             "cell_type": "markdown",
444 |             "metadata": {},
445 |             "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>"
446 |         },
447 |         {
448 |             "cell_type": "markdown",
449 |             "metadata": {},
450 |             "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<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>"
451 |         },
452 |         {
453 |             "cell_type": "markdown",
454 |             "metadata": {},
455 |             "source": "<h2>References :</h2> "
456 |         },
457 |         {
458 |             "cell_type": "markdown",
459 |             "metadata": {},
460 |             "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>"
461 |         }
462 |     ],
463 |     "metadata": {
464 |         "kernelspec": {
465 |             "display_name": "Python 3.6",
466 |             "language": "python",
467 |             "name": "python3"
468 |         },
469 |         "language_info": {
470 |             "codemirror_mode": {
471 |                 "name": "ipython",
472 |                 "version": 3
473 |             },
474 |             "file_extension": ".py",
475 |             "mimetype": "text/x-python",
476 |             "name": "python",
477 |             "nbconvert_exporter": "python",
478 |             "pygments_lexer": "ipython3",
479 |             "version": "3.6.9"
480 |         }
481 |     },
482 |     "nbformat": 4,
483 |     "nbformat_minor": 2
484 | }


--------------------------------------------------------------------------------
/First Notebook.ipynb:
--------------------------------------------------------------------------------
 1 | {
 2 |     "cells": [
 3 |         {
 4 |             "cell_type": "markdown",
 5 |             "metadata": {
 6 |                 "collapsed": true
 7 |             },
 8 |             "source": "# My Jupyter Notebook on IBM Data Science Experience"
 9 |         },
10 |         {
11 |             "cell_type": "markdown",
12 |             "metadata": {},
13 |             "source": "**Raunak Bhutoria**  \nChemical Engineering Student"
14 |         },
15 |         {
16 |             "cell_type": "markdown",
17 |             "metadata": {},
18 |             "source": "_I am interested in data science because I want to develop data analysis and data visualization skills as well as interpret data to gain various insights and predict future decisions_"
19 |         },
20 |         {
21 |             "cell_type": "markdown",
22 |             "metadata": {},
23 |             "source": "The code below is supposed to print \"Hello World\"."
24 |         },
25 |         {
26 |             "cell_type": "code",
27 |             "execution_count": 3,
28 |             "metadata": {},
29 |             "outputs": [
30 |                 {
31 |                     "name": "stdout",
32 |                     "output_type": "stream",
33 |                     "text": "Hello World\n"
34 |                 }
35 |             ],
36 |             "source": "print(\"Hello World\")"
37 |         },
38 |         {
39 |             "cell_type": "markdown",
40 |             "metadata": {},
41 |             "source": "**Horizontal Rule:**\n\nHi everyone!\n\n---\n\nHope you all are doing well!\n\n***\n\nAll the best for the course!\n\n**Hyperlink:**\n\nhttps://en.wikipedia.org/wiki/Data_science\n\n**Image:**\n\n![alt text](https://github.com/adam-p/markdown-here/raw/master/src/common/images/icon48.png)"
42 |         },
43 |         {
44 |             "cell_type": "code",
45 |             "execution_count": null,
46 |             "metadata": {},
47 |             "outputs": [],
48 |             "source": ""
49 |         }
50 |     ],
51 |     "metadata": {
52 |         "kernelspec": {
53 |             "display_name": "Python 3.6 with Spark",
54 |             "language": "python3",
55 |             "name": "python36"
56 |         },
57 |         "language_info": {
58 |             "codemirror_mode": {
59 |                 "name": "ipython",
60 |                 "version": 3
61 |             },
62 |             "file_extension": ".py",
63 |             "mimetype": "text/x-python",
64 |             "name": "python",
65 |             "nbconvert_exporter": "python",
66 |             "pygments_lexer": "ipython3",
67 |             "version": "3.6.8"
68 |         }
69 |     },
70 |     "nbformat": 4,
71 |     "nbformat_minor": 1
72 | }


--------------------------------------------------------------------------------
/House Sales in King County, USA Project.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |     "cells": [
  3 |         {
  4 |             "cell_type": "markdown",
  5 |             "metadata": {},
  6 |             "source": " <a href=\"https://www.bigdatauniversity.com\"><img src = \"https://ibm.box.com/shared/static/ugcqz6ohbvff804xp84y4kqnvvk3bq1g.png\" width = 300, align = \"center\"></a>\n\n<h1 align=center><font size = 5>Data Analysis with Python</font></h1>"
  7 |         },
  8 |         {
  9 |             "cell_type": "markdown",
 10 |             "metadata": {},
 11 |             "source": "# House Sales in King County, USA"
 12 |         },
 13 |         {
 14 |             "cell_type": "markdown",
 15 |             "metadata": {},
 16 |             "source": "This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015."
 17 |         },
 18 |         {
 19 |             "cell_type": "markdown",
 20 |             "metadata": {},
 21 |             "source": "<b>id</b> : A notation for a house\n\n<b> date</b>: Date house was sold\n\n\n<b>price</b>: Price is prediction target\n\n\n<b>bedrooms</b>: Number of bedrooms\n\n\n<b>bathrooms</b>: Number of bathrooms\n\n<b>sqft_living</b>: Square footage of the home\n\n<b>sqft_lot</b>: Square footage of the lot\n\n\n<b>floors</b> :Total floors (levels) in house\n\n\n<b>waterfront</b> :House which has a view to a waterfront\n\n\n<b>view</b>: Has been viewed\n\n\n<b>condition</b> :How good the condition is overall\n\n<b>grade</b>: overall grade given to the housing unit, based on King County grading system\n\n\n<b>sqft_above</b> : Square footage of house apart from basement\n\n\n<b>sqft_basement</b>: Square footage of the basement\n\n<b>yr_built</b> : Built Year\n\n\n<b>yr_renovated</b> : Year when house was renovated\n\n<b>zipcode</b>: Zip code\n\n\n<b>lat</b>: Latitude coordinate\n\n<b>long</b>: Longitude coordinate\n\n<b>sqft_living15</b> : Living room area in 2015(implies-- some renovations) This might or might not have affected the lotsize area\n\n\n<b>sqft_lot15</b> : LotSize area in 2015(implies-- some renovations)"
 22 |         },
 23 |         {
 24 |             "cell_type": "markdown",
 25 |             "metadata": {},
 26 |             "source": "You will require the following libraries: "
 27 |         },
 28 |         {
 29 |             "cell_type": "code",
 30 |             "execution_count": 7,
 31 |             "metadata": {},
 32 |             "outputs": [],
 33 |             "source": "import pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport seaborn as sns\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler,PolynomialFeatures\nfrom sklearn.linear_model import LinearRegression\n%matplotlib inline"
 34 |         },
 35 |         {
 36 |             "cell_type": "markdown",
 37 |             "metadata": {},
 38 |             "source": "# Module 1: Importing Data Sets "
 39 |         },
 40 |         {
 41 |             "cell_type": "markdown",
 42 |             "metadata": {},
 43 |             "source": " Load the csv:  "
 44 |         },
 45 |         {
 46 |             "cell_type": "code",
 47 |             "execution_count": 8,
 48 |             "metadata": {
 49 |                 "jupyter": {
 50 |                     "outputs_hidden": false
 51 |                 }
 52 |             },
 53 |             "outputs": [],
 54 |             "source": "file_name='https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DA0101EN/coursera/project/kc_house_data_NaN.csv'\ndf=pd.read_csv(file_name)"
 55 |         },
 56 |         {
 57 |             "cell_type": "markdown",
 58 |             "metadata": {},
 59 |             "source": "\nWe use the method <code>head</code> to display the first 5 columns of the dataframe."
 60 |         },
 61 |         {
 62 |             "cell_type": "code",
 63 |             "execution_count": 9,
 64 |             "metadata": {},
 65 |             "outputs": [
 66 |                 {
 67 |                     "data": {
 68 |                         "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>Unnamed: 0</th>\n      <th>id</th>\n      <th>date</th>\n      <th>price</th>\n      <th>bedrooms</th>\n      <th>bathrooms</th>\n      <th>sqft_living</th>\n      <th>sqft_lot</th>\n      <th>floors</th>\n      <th>waterfront</th>\n      <th>...</th>\n      <th>grade</th>\n      <th>sqft_above</th>\n      <th>sqft_basement</th>\n      <th>yr_built</th>\n      <th>yr_renovated</th>\n      <th>zipcode</th>\n      <th>lat</th>\n      <th>long</th>\n      <th>sqft_living15</th>\n      <th>sqft_lot15</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>7129300520</td>\n      <td>20141013T000000</td>\n      <td>221900.0</td>\n      <td>3.0</td>\n      <td>1.00</td>\n      <td>1180</td>\n      <td>5650</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>7</td>\n      <td>1180</td>\n      <td>0</td>\n      <td>1955</td>\n      <td>0</td>\n      <td>98178</td>\n      <td>47.5112</td>\n      <td>-122.257</td>\n      <td>1340</td>\n      <td>5650</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>6414100192</td>\n      <td>20141209T000000</td>\n      <td>538000.0</td>\n      <td>3.0</td>\n      <td>2.25</td>\n      <td>2570</td>\n      <td>7242</td>\n      <td>2.0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>7</td>\n      <td>2170</td>\n      <td>400</td>\n      <td>1951</td>\n      <td>1991</td>\n      <td>98125</td>\n      <td>47.7210</td>\n      <td>-122.319</td>\n      <td>1690</td>\n      <td>7639</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>5631500400</td>\n      <td>20150225T000000</td>\n      <td>180000.0</td>\n      <td>2.0</td>\n      <td>1.00</td>\n      <td>770</td>\n      <td>10000</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>6</td>\n      <td>770</td>\n      <td>0</td>\n      <td>1933</td>\n      <td>0</td>\n      <td>98028</td>\n      <td>47.7379</td>\n      <td>-122.233</td>\n      <td>2720</td>\n      <td>8062</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>2487200875</td>\n      <td>20141209T000000</td>\n      <td>604000.0</td>\n      <td>4.0</td>\n      <td>3.00</td>\n      <td>1960</td>\n      <td>5000</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>7</td>\n      <td>1050</td>\n      <td>910</td>\n      <td>1965</td>\n      <td>0</td>\n      <td>98136</td>\n      <td>47.5208</td>\n      <td>-122.393</td>\n      <td>1360</td>\n      <td>5000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>1954400510</td>\n      <td>20150218T000000</td>\n      <td>510000.0</td>\n      <td>3.0</td>\n      <td>2.00</td>\n      <td>1680</td>\n      <td>8080</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>8</td>\n      <td>1680</td>\n      <td>0</td>\n      <td>1987</td>\n      <td>0</td>\n      <td>98074</td>\n      <td>47.6168</td>\n      <td>-122.045</td>\n      <td>1800</td>\n      <td>7503</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 22 columns</p>\n</div>",
 69 |                         "text/plain": "   Unnamed: 0          id             date     price  bedrooms  bathrooms  \\\n0           0  7129300520  20141013T000000  221900.0       3.0       1.00   \n1           1  6414100192  20141209T000000  538000.0       3.0       2.25   \n2           2  5631500400  20150225T000000  180000.0       2.0       1.00   \n3           3  2487200875  20141209T000000  604000.0       4.0       3.00   \n4           4  1954400510  20150218T000000  510000.0       3.0       2.00   \n\n   sqft_living  sqft_lot  floors  waterfront  ...  grade  sqft_above  \\\n0         1180      5650     1.0           0  ...      7        1180   \n1         2570      7242     2.0           0  ...      7        2170   \n2          770     10000     1.0           0  ...      6         770   \n3         1960      5000     1.0           0  ...      7        1050   \n4         1680      8080     1.0           0  ...      8        1680   \n\n   sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n0              0      1955             0    98178  47.5112 -122.257   \n1            400      1951          1991    98125  47.7210 -122.319   \n2              0      1933             0    98028  47.7379 -122.233   \n3            910      1965             0    98136  47.5208 -122.393   \n4              0      1987             0    98074  47.6168 -122.045   \n\n   sqft_living15  sqft_lot15  \n0           1340        5650  \n1           1690        7639  \n2           2720        8062  \n3           1360        5000  \n4           1800        7503  \n\n[5 rows x 22 columns]"
 70 |                     },
 71 |                     "execution_count": 9,
 72 |                     "metadata": {},
 73 |                     "output_type": "execute_result"
 74 |                 }
 75 |             ],
 76 |             "source": "df.head()"
 77 |         },
 78 |         {
 79 |             "cell_type": "markdown",
 80 |             "metadata": {},
 81 |             "source": "### Question 1 \nDisplay the data types of each column using the attribute dtype, then take a screenshot and submit it, include your code in the image. "
 82 |         },
 83 |         {
 84 |             "cell_type": "code",
 85 |             "execution_count": 10,
 86 |             "metadata": {
 87 |                 "jupyter": {
 88 |                     "outputs_hidden": false
 89 |                 }
 90 |             },
 91 |             "outputs": [
 92 |                 {
 93 |                     "data": {
 94 |                         "text/plain": "Unnamed: 0         int64\nid                 int64\ndate              object\nprice            float64\nbedrooms         float64\nbathrooms        float64\nsqft_living        int64\nsqft_lot           int64\nfloors           float64\nwaterfront         int64\nview               int64\ncondition          int64\ngrade              int64\nsqft_above         int64\nsqft_basement      int64\nyr_built           int64\nyr_renovated       int64\nzipcode            int64\nlat              float64\nlong             float64\nsqft_living15      int64\nsqft_lot15         int64\ndtype: object"
 95 |                     },
 96 |                     "execution_count": 10,
 97 |                     "metadata": {},
 98 |                     "output_type": "execute_result"
 99 |                 }
100 |             ],
101 |             "source": "df.dtypes"
102 |         },
103 |         {
104 |             "cell_type": "markdown",
105 |             "metadata": {},
106 |             "source": "We use the method describe to obtain a statistical summary of the dataframe."
107 |         },
108 |         {
109 |             "cell_type": "code",
110 |             "execution_count": 11,
111 |             "metadata": {
112 |                 "jupyter": {
113 |                     "outputs_hidden": false
114 |                 }
115 |             },
116 |             "outputs": [
117 |                 {
118 |                     "data": {
119 |                         "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>Unnamed: 0</th>\n      <th>id</th>\n      <th>price</th>\n      <th>bedrooms</th>\n      <th>bathrooms</th>\n      <th>sqft_living</th>\n      <th>sqft_lot</th>\n      <th>floors</th>\n      <th>waterfront</th>\n      <th>view</th>\n      <th>...</th>\n      <th>grade</th>\n      <th>sqft_above</th>\n      <th>sqft_basement</th>\n      <th>yr_built</th>\n      <th>yr_renovated</th>\n      <th>zipcode</th>\n      <th>lat</th>\n      <th>long</th>\n      <th>sqft_living15</th>\n      <th>sqft_lot15</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>21613.00000</td>\n      <td>2.161300e+04</td>\n      <td>2.161300e+04</td>\n      <td>21600.000000</td>\n      <td>21603.000000</td>\n      <td>21613.000000</td>\n      <td>2.161300e+04</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>...</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>10806.00000</td>\n      <td>4.580302e+09</td>\n      <td>5.400881e+05</td>\n      <td>3.372870</td>\n      <td>2.115736</td>\n      <td>2079.899736</td>\n      <td>1.510697e+04</td>\n      <td>1.494309</td>\n      <td>0.007542</td>\n      <td>0.234303</td>\n      <td>...</td>\n      <td>7.656873</td>\n      <td>1788.390691</td>\n      <td>291.509045</td>\n      <td>1971.005136</td>\n      <td>84.402258</td>\n      <td>98077.939805</td>\n      <td>47.560053</td>\n      <td>-122.213896</td>\n      <td>1986.552492</td>\n      <td>12768.455652</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>6239.28002</td>\n      <td>2.876566e+09</td>\n      <td>3.671272e+05</td>\n      <td>0.926657</td>\n      <td>0.768996</td>\n      <td>918.440897</td>\n      <td>4.142051e+04</td>\n      <td>0.539989</td>\n      <td>0.086517</td>\n      <td>0.766318</td>\n      <td>...</td>\n      <td>1.175459</td>\n      <td>828.090978</td>\n      <td>442.575043</td>\n      <td>29.373411</td>\n      <td>401.679240</td>\n      <td>53.505026</td>\n      <td>0.138564</td>\n      <td>0.140828</td>\n      <td>685.391304</td>\n      <td>27304.179631</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.00000</td>\n      <td>1.000102e+06</td>\n      <td>7.500000e+04</td>\n      <td>1.000000</td>\n      <td>0.500000</td>\n      <td>290.000000</td>\n      <td>5.200000e+02</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>290.000000</td>\n      <td>0.000000</td>\n      <td>1900.000000</td>\n      <td>0.000000</td>\n      <td>98001.000000</td>\n      <td>47.155900</td>\n      <td>-122.519000</td>\n      <td>399.000000</td>\n      <td>651.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>5403.00000</td>\n      <td>2.123049e+09</td>\n      <td>3.219500e+05</td>\n      <td>3.000000</td>\n      <td>1.750000</td>\n      <td>1427.000000</td>\n      <td>5.040000e+03</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>7.000000</td>\n      <td>1190.000000</td>\n      <td>0.000000</td>\n      <td>1951.000000</td>\n      <td>0.000000</td>\n      <td>98033.000000</td>\n      <td>47.471000</td>\n      <td>-122.328000</td>\n      <td>1490.000000</td>\n      <td>5100.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>10806.00000</td>\n      <td>3.904930e+09</td>\n      <td>4.500000e+05</td>\n      <td>3.000000</td>\n      <td>2.250000</td>\n      <td>1910.000000</td>\n      <td>7.618000e+03</td>\n      <td>1.500000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>7.000000</td>\n      <td>1560.000000</td>\n      <td>0.000000</td>\n      <td>1975.000000</td>\n      <td>0.000000</td>\n      <td>98065.000000</td>\n      <td>47.571800</td>\n      <td>-122.230000</td>\n      <td>1840.000000</td>\n      <td>7620.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>16209.00000</td>\n      <td>7.308900e+09</td>\n      <td>6.450000e+05</td>\n      <td>4.000000</td>\n      <td>2.500000</td>\n      <td>2550.000000</td>\n      <td>1.068800e+04</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>8.000000</td>\n      <td>2210.000000</td>\n      <td>560.000000</td>\n      <td>1997.000000</td>\n      <td>0.000000</td>\n      <td>98118.000000</td>\n      <td>47.678000</td>\n      <td>-122.125000</td>\n      <td>2360.000000</td>\n      <td>10083.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>21612.00000</td>\n      <td>9.900000e+09</td>\n      <td>7.700000e+06</td>\n      <td>33.000000</td>\n      <td>8.000000</td>\n      <td>13540.000000</td>\n      <td>1.651359e+06</td>\n      <td>3.500000</td>\n      <td>1.000000</td>\n      <td>4.000000</td>\n      <td>...</td>\n      <td>13.000000</td>\n      <td>9410.000000</td>\n      <td>4820.000000</td>\n      <td>2015.000000</td>\n      <td>2015.000000</td>\n      <td>98199.000000</td>\n      <td>47.777600</td>\n      <td>-121.315000</td>\n      <td>6210.000000</td>\n      <td>871200.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows \u00d7 21 columns</p>\n</div>",
120 |                         "text/plain": "        Unnamed: 0            id         price      bedrooms     bathrooms  \\\ncount  21613.00000  2.161300e+04  2.161300e+04  21600.000000  21603.000000   \nmean   10806.00000  4.580302e+09  5.400881e+05      3.372870      2.115736   \nstd     6239.28002  2.876566e+09  3.671272e+05      0.926657      0.768996   \nmin        0.00000  1.000102e+06  7.500000e+04      1.000000      0.500000   \n25%     5403.00000  2.123049e+09  3.219500e+05      3.000000      1.750000   \n50%    10806.00000  3.904930e+09  4.500000e+05      3.000000      2.250000   \n75%    16209.00000  7.308900e+09  6.450000e+05      4.000000      2.500000   \nmax    21612.00000  9.900000e+09  7.700000e+06     33.000000      8.000000   \n\n        sqft_living      sqft_lot        floors    waterfront          view  \\\ncount  21613.000000  2.161300e+04  21613.000000  21613.000000  21613.000000   \nmean    2079.899736  1.510697e+04      1.494309      0.007542      0.234303   \nstd      918.440897  4.142051e+04      0.539989      0.086517      0.766318   \nmin      290.000000  5.200000e+02      1.000000      0.000000      0.000000   \n25%     1427.000000  5.040000e+03      1.000000      0.000000      0.000000   \n50%     1910.000000  7.618000e+03      1.500000      0.000000      0.000000   \n75%     2550.000000  1.068800e+04      2.000000      0.000000      0.000000   \nmax    13540.000000  1.651359e+06      3.500000      1.000000      4.000000   \n\n       ...         grade    sqft_above  sqft_basement      yr_built  \\\ncount  ...  21613.000000  21613.000000   21613.000000  21613.000000   \nmean   ...      7.656873   1788.390691     291.509045   1971.005136   \nstd    ...      1.175459    828.090978     442.575043     29.373411   \nmin    ...      1.000000    290.000000       0.000000   1900.000000   \n25%    ...      7.000000   1190.000000       0.000000   1951.000000   \n50%    ...      7.000000   1560.000000       0.000000   1975.000000   \n75%    ...      8.000000   2210.000000     560.000000   1997.000000   \nmax    ...     13.000000   9410.000000    4820.000000   2015.000000   \n\n       yr_renovated       zipcode           lat          long  sqft_living15  \\\ncount  21613.000000  21613.000000  21613.000000  21613.000000   21613.000000   \nmean      84.402258  98077.939805     47.560053   -122.213896    1986.552492   \nstd      401.679240     53.505026      0.138564      0.140828     685.391304   \nmin        0.000000  98001.000000     47.155900   -122.519000     399.000000   \n25%        0.000000  98033.000000     47.471000   -122.328000    1490.000000   \n50%        0.000000  98065.000000     47.571800   -122.230000    1840.000000   \n75%        0.000000  98118.000000     47.678000   -122.125000    2360.000000   \nmax     2015.000000  98199.000000     47.777600   -121.315000    6210.000000   \n\n          sqft_lot15  \ncount   21613.000000  \nmean    12768.455652  \nstd     27304.179631  \nmin       651.000000  \n25%      5100.000000  \n50%      7620.000000  \n75%     10083.000000  \nmax    871200.000000  \n\n[8 rows x 21 columns]"
121 |                     },
122 |                     "execution_count": 11,
123 |                     "metadata": {},
124 |                     "output_type": "execute_result"
125 |                 }
126 |             ],
127 |             "source": "df.describe()"
128 |         },
129 |         {
130 |             "cell_type": "markdown",
131 |             "metadata": {},
132 |             "source": "# Module 2: Data Wrangling"
133 |         },
134 |         {
135 |             "cell_type": "markdown",
136 |             "metadata": {},
137 |             "source": "### Question 2 \nDrop the columns <code>\"id\"</code>  and <code>\"Unnamed: 0\"</code> from axis 1 using the method <code>drop()</code>, then use the method <code>describe()</code> to obtain a statistical summary of the data. Take a screenshot and submit it, make sure the <code>inplace</code> parameter is set to <code>True</code>"
138 |         },
139 |         {
140 |             "cell_type": "code",
141 |             "execution_count": 12,
142 |             "metadata": {
143 |                 "jupyter": {
144 |                     "outputs_hidden": false
145 |                 }
146 |             },
147 |             "outputs": [
148 |                 {
149 |                     "data": {
150 |                         "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>price</th>\n      <th>bedrooms</th>\n      <th>bathrooms</th>\n      <th>sqft_living</th>\n      <th>sqft_lot</th>\n      <th>floors</th>\n      <th>waterfront</th>\n      <th>view</th>\n      <th>condition</th>\n      <th>grade</th>\n      <th>sqft_above</th>\n      <th>sqft_basement</th>\n      <th>yr_built</th>\n      <th>yr_renovated</th>\n      <th>zipcode</th>\n      <th>lat</th>\n      <th>long</th>\n      <th>sqft_living15</th>\n      <th>sqft_lot15</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>2.161300e+04</td>\n      <td>21600.000000</td>\n      <td>21603.000000</td>\n      <td>21613.000000</td>\n      <td>2.161300e+04</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>5.400881e+05</td>\n      <td>3.372870</td>\n      <td>2.115736</td>\n      <td>2079.899736</td>\n      <td>1.510697e+04</td>\n      <td>1.494309</td>\n      <td>0.007542</td>\n      <td>0.234303</td>\n      <td>3.409430</td>\n      <td>7.656873</td>\n      <td>1788.390691</td>\n      <td>291.509045</td>\n      <td>1971.005136</td>\n      <td>84.402258</td>\n      <td>98077.939805</td>\n      <td>47.560053</td>\n      <td>-122.213896</td>\n      <td>1986.552492</td>\n      <td>12768.455652</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>3.671272e+05</td>\n      <td>0.926657</td>\n      <td>0.768996</td>\n      <td>918.440897</td>\n      <td>4.142051e+04</td>\n      <td>0.539989</td>\n      <td>0.086517</td>\n      <td>0.766318</td>\n      <td>0.650743</td>\n      <td>1.175459</td>\n      <td>828.090978</td>\n      <td>442.575043</td>\n      <td>29.373411</td>\n      <td>401.679240</td>\n      <td>53.505026</td>\n      <td>0.138564</td>\n      <td>0.140828</td>\n      <td>685.391304</td>\n      <td>27304.179631</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>7.500000e+04</td>\n      <td>1.000000</td>\n      <td>0.500000</td>\n      <td>290.000000</td>\n      <td>5.200000e+02</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>290.000000</td>\n      <td>0.000000</td>\n      <td>1900.000000</td>\n      <td>0.000000</td>\n      <td>98001.000000</td>\n      <td>47.155900</td>\n      <td>-122.519000</td>\n      <td>399.000000</td>\n      <td>651.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>3.219500e+05</td>\n      <td>3.000000</td>\n      <td>1.750000</td>\n      <td>1427.000000</td>\n      <td>5.040000e+03</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>3.000000</td>\n      <td>7.000000</td>\n      <td>1190.000000</td>\n      <td>0.000000</td>\n      <td>1951.000000</td>\n      <td>0.000000</td>\n      <td>98033.000000</td>\n      <td>47.471000</td>\n      <td>-122.328000</td>\n      <td>1490.000000</td>\n      <td>5100.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>4.500000e+05</td>\n      <td>3.000000</td>\n      <td>2.250000</td>\n      <td>1910.000000</td>\n      <td>7.618000e+03</td>\n      <td>1.500000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>3.000000</td>\n      <td>7.000000</td>\n      <td>1560.000000</td>\n      <td>0.000000</td>\n      <td>1975.000000</td>\n      <td>0.000000</td>\n      <td>98065.000000</td>\n      <td>47.571800</td>\n      <td>-122.230000</td>\n      <td>1840.000000</td>\n      <td>7620.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>6.450000e+05</td>\n      <td>4.000000</td>\n      <td>2.500000</td>\n      <td>2550.000000</td>\n      <td>1.068800e+04</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>4.000000</td>\n      <td>8.000000</td>\n      <td>2210.000000</td>\n      <td>560.000000</td>\n      <td>1997.000000</td>\n      <td>0.000000</td>\n      <td>98118.000000</td>\n      <td>47.678000</td>\n      <td>-122.125000</td>\n      <td>2360.000000</td>\n      <td>10083.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>7.700000e+06</td>\n      <td>33.000000</td>\n      <td>8.000000</td>\n      <td>13540.000000</td>\n      <td>1.651359e+06</td>\n      <td>3.500000</td>\n      <td>1.000000</td>\n      <td>4.000000</td>\n      <td>5.000000</td>\n      <td>13.000000</td>\n      <td>9410.000000</td>\n      <td>4820.000000</td>\n      <td>2015.000000</td>\n      <td>2015.000000</td>\n      <td>98199.000000</td>\n      <td>47.777600</td>\n      <td>-121.315000</td>\n      <td>6210.000000</td>\n      <td>871200.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
151 |                         "text/plain": "              price      bedrooms     bathrooms   sqft_living      sqft_lot  \\\ncount  2.161300e+04  21600.000000  21603.000000  21613.000000  2.161300e+04   \nmean   5.400881e+05      3.372870      2.115736   2079.899736  1.510697e+04   \nstd    3.671272e+05      0.926657      0.768996    918.440897  4.142051e+04   \nmin    7.500000e+04      1.000000      0.500000    290.000000  5.200000e+02   \n25%    3.219500e+05      3.000000      1.750000   1427.000000  5.040000e+03   \n50%    4.500000e+05      3.000000      2.250000   1910.000000  7.618000e+03   \n75%    6.450000e+05      4.000000      2.500000   2550.000000  1.068800e+04   \nmax    7.700000e+06     33.000000      8.000000  13540.000000  1.651359e+06   \n\n             floors    waterfront          view     condition         grade  \\\ncount  21613.000000  21613.000000  21613.000000  21613.000000  21613.000000   \nmean       1.494309      0.007542      0.234303      3.409430      7.656873   \nstd        0.539989      0.086517      0.766318      0.650743      1.175459   \nmin        1.000000      0.000000      0.000000      1.000000      1.000000   \n25%        1.000000      0.000000      0.000000      3.000000      7.000000   \n50%        1.500000      0.000000      0.000000      3.000000      7.000000   \n75%        2.000000      0.000000      0.000000      4.000000      8.000000   \nmax        3.500000      1.000000      4.000000      5.000000     13.000000   \n\n         sqft_above  sqft_basement      yr_built  yr_renovated       zipcode  \\\ncount  21613.000000   21613.000000  21613.000000  21613.000000  21613.000000   \nmean    1788.390691     291.509045   1971.005136     84.402258  98077.939805   \nstd      828.090978     442.575043     29.373411    401.679240     53.505026   \nmin      290.000000       0.000000   1900.000000      0.000000  98001.000000   \n25%     1190.000000       0.000000   1951.000000      0.000000  98033.000000   \n50%     1560.000000       0.000000   1975.000000      0.000000  98065.000000   \n75%     2210.000000     560.000000   1997.000000      0.000000  98118.000000   \nmax     9410.000000    4820.000000   2015.000000   2015.000000  98199.000000   \n\n                lat          long  sqft_living15     sqft_lot15  \ncount  21613.000000  21613.000000   21613.000000   21613.000000  \nmean      47.560053   -122.213896    1986.552492   12768.455652  \nstd        0.138564      0.140828     685.391304   27304.179631  \nmin       47.155900   -122.519000     399.000000     651.000000  \n25%       47.471000   -122.328000    1490.000000    5100.000000  \n50%       47.571800   -122.230000    1840.000000    7620.000000  \n75%       47.678000   -122.125000    2360.000000   10083.000000  \nmax       47.777600   -121.315000    6210.000000  871200.000000  "
152 |                     },
153 |                     "execution_count": 12,
154 |                     "metadata": {},
155 |                     "output_type": "execute_result"
156 |                 }
157 |             ],
158 |             "source": "df.drop(['id', 'Unnamed: 0'], axis=1, inplace=True)\ndf.describe()"
159 |         },
160 |         {
161 |             "cell_type": "markdown",
162 |             "metadata": {},
163 |             "source": "We can see we have missing values for the columns <code> bedrooms</code>  and <code> bathrooms </code>"
164 |         },
165 |         {
166 |             "cell_type": "code",
167 |             "execution_count": 13,
168 |             "metadata": {
169 |                 "jupyter": {
170 |                     "outputs_hidden": false
171 |                 }
172 |             },
173 |             "outputs": [
174 |                 {
175 |                     "name": "stdout",
176 |                     "output_type": "stream",
177 |                     "text": "number of NaN values for the column bedrooms : 13\nnumber of NaN values for the column bathrooms : 10\n"
178 |                 }
179 |             ],
180 |             "source": "print(\"number of NaN values for the column bedrooms :\", df['bedrooms'].isnull().sum())\nprint(\"number of NaN values for the column bathrooms :\", df['bathrooms'].isnull().sum())\n"
181 |         },
182 |         {
183 |             "cell_type": "markdown",
184 |             "metadata": {},
185 |             "source": "\nWe can replace the missing values of the column <code>'bedrooms'</code> with the mean of the column  <code>'bedrooms' </code> using the method <code>replace()</code>. Don't forget to set the <code>inplace</code> parameter to <code>True</code>"
186 |         },
187 |         {
188 |             "cell_type": "code",
189 |             "execution_count": 14,
190 |             "metadata": {},
191 |             "outputs": [],
192 |             "source": "mean=df['bedrooms'].mean()\ndf['bedrooms'].replace(np.nan,mean, inplace=True)"
193 |         },
194 |         {
195 |             "cell_type": "markdown",
196 |             "metadata": {},
197 |             "source": "\nWe also replace the missing values of the column <code>'bathrooms'</code> with the mean of the column  <code>'bathrooms' </code> using the method <code>replace()</code>. Don't forget to set the <code> inplace </code>  parameter top <code> True </code>"
198 |         },
199 |         {
200 |             "cell_type": "code",
201 |             "execution_count": 15,
202 |             "metadata": {},
203 |             "outputs": [],
204 |             "source": "mean=df['bathrooms'].mean()\ndf['bathrooms'].replace(np.nan,mean, inplace=True)"
205 |         },
206 |         {
207 |             "cell_type": "code",
208 |             "execution_count": 16,
209 |             "metadata": {
210 |                 "jupyter": {
211 |                     "outputs_hidden": false
212 |                 }
213 |             },
214 |             "outputs": [
215 |                 {
216 |                     "name": "stdout",
217 |                     "output_type": "stream",
218 |                     "text": "number of NaN values for the column bedrooms : 0\nnumber of NaN values for the column bathrooms : 0\n"
219 |                 }
220 |             ],
221 |             "source": "print(\"number of NaN values for the column bedrooms :\", df['bedrooms'].isnull().sum())\nprint(\"number of NaN values for the column bathrooms :\", df['bathrooms'].isnull().sum())"
222 |         },
223 |         {
224 |             "cell_type": "markdown",
225 |             "metadata": {},
226 |             "source": "# Module 3: Exploratory Data Analysis"
227 |         },
228 |         {
229 |             "cell_type": "markdown",
230 |             "metadata": {},
231 |             "source": "### Question 3\nUse the method <code>value_counts</code> to count the number of houses with unique floor values, use the method <code>.to_frame()</code> to convert it to a dataframe.\n"
232 |         },
233 |         {
234 |             "cell_type": "code",
235 |             "execution_count": 28,
236 |             "metadata": {
237 |                 "jupyter": {
238 |                     "outputs_hidden": false
239 |                 }
240 |             },
241 |             "outputs": [
242 |                 {
243 |                     "data": {
244 |                         "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>floors</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1.0</th>\n      <td>10680</td>\n    </tr>\n    <tr>\n      <th>2.0</th>\n      <td>8241</td>\n    </tr>\n    <tr>\n      <th>1.5</th>\n      <td>1910</td>\n    </tr>\n    <tr>\n      <th>3.0</th>\n      <td>613</td>\n    </tr>\n    <tr>\n      <th>2.5</th>\n      <td>161</td>\n    </tr>\n    <tr>\n      <th>3.5</th>\n      <td>8</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
245 |                         "text/plain": "     floors\n1.0   10680\n2.0    8241\n1.5    1910\n3.0     613\n2.5     161\n3.5       8"
246 |                     },
247 |                     "execution_count": 28,
248 |                     "metadata": {},
249 |                     "output_type": "execute_result"
250 |                 }
251 |             ],
252 |             "source": "floor_count = df['floors'].value_counts().to_frame()\nfloor_count"
253 |         },
254 |         {
255 |             "cell_type": "markdown",
256 |             "metadata": {},
257 |             "source": "### Question 4\nUse the function <code>boxplot</code> in the seaborn library  to  determine whether houses with a waterfront view or without a waterfront view have more price outliers."
258 |         },
259 |         {
260 |             "cell_type": "code",
261 |             "execution_count": 30,
262 |             "metadata": {
263 |                 "jupyter": {
264 |                     "outputs_hidden": false
265 |                 }
266 |             },
267 |             "outputs": [
268 |                 {
269 |                     "data": {
270 |                         "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f01c4a56128>"
271 |                     },
272 |                     "execution_count": 30,
273 |                     "metadata": {},
274 |                     "output_type": "execute_result"
275 |                 },
276 |                 {
277 |                     "data": {
278 |                         "image/png": "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\n",
279 |                         "text/plain": "<Figure size 432x288 with 1 Axes>"
280 |                     },
281 |                     "metadata": {
282 |                         "needs_background": "light"
283 |                     },
284 |                     "output_type": "display_data"
285 |                 }
286 |             ],
287 |             "source": "sns.boxplot(x='waterfront', y='price', data=df)"
288 |         },
289 |         {
290 |             "cell_type": "markdown",
291 |             "metadata": {},
292 |             "source": "### Question 5\nUse the function <code>regplot</code>  in the seaborn library  to  determine if the feature <code>sqft_above</code> is negatively or positively correlated with price."
293 |         },
294 |         {
295 |             "cell_type": "code",
296 |             "execution_count": 31,
297 |             "metadata": {
298 |                 "jupyter": {
299 |                     "outputs_hidden": false
300 |                 }
301 |             },
302 |             "outputs": [
303 |                 {
304 |                     "data": {
305 |                         "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f01c447be80>"
306 |                     },
307 |                     "execution_count": 31,
308 |                     "metadata": {},
309 |                     "output_type": "execute_result"
310 |                 },
311 |                 {
312 |                     "data": {
313 |                         "image/png": "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\n",
314 |                         "text/plain": "<Figure size 432x288 with 1 Axes>"
315 |                     },
316 |                     "metadata": {
317 |                         "needs_background": "light"
318 |                     },
319 |                     "output_type": "display_data"
320 |                 }
321 |             ],
322 |             "source": "sns.regplot(x='sqft_above', y='price', data=df)"
323 |         },
324 |         {
325 |             "cell_type": "markdown",
326 |             "metadata": {},
327 |             "source": "\nWe can use the Pandas method <code>corr()</code>  to find the feature other than price that is most correlated with price."
328 |         },
329 |         {
330 |             "cell_type": "code",
331 |             "execution_count": 32,
332 |             "metadata": {
333 |                 "jupyter": {
334 |                     "outputs_hidden": false
335 |                 }
336 |             },
337 |             "outputs": [
338 |                 {
339 |                     "data": {
340 |                         "text/plain": "zipcode         -0.053203\nlong             0.021626\ncondition        0.036362\nyr_built         0.054012\nsqft_lot15       0.082447\nsqft_lot         0.089661\nyr_renovated     0.126434\nfloors           0.256794\nwaterfront       0.266369\nlat              0.307003\nbedrooms         0.308797\nsqft_basement    0.323816\nview             0.397293\nbathrooms        0.525738\nsqft_living15    0.585379\nsqft_above       0.605567\ngrade            0.667434\nsqft_living      0.702035\nprice            1.000000\nName: price, dtype: float64"
341 |                     },
342 |                     "execution_count": 32,
343 |                     "metadata": {},
344 |                     "output_type": "execute_result"
345 |                 }
346 |             ],
347 |             "source": "df.corr()['price'].sort_values()"
348 |         },
349 |         {
350 |             "cell_type": "markdown",
351 |             "metadata": {},
352 |             "source": "# Module 4: Model Development"
353 |         },
354 |         {
355 |             "cell_type": "markdown",
356 |             "metadata": {},
357 |             "source": "\nWe can Fit a linear regression model using the  longitude feature <code>'long'</code> and  caculate the R^2."
358 |         },
359 |         {
360 |             "cell_type": "code",
361 |             "execution_count": 33,
362 |             "metadata": {
363 |                 "jupyter": {
364 |                     "outputs_hidden": false
365 |                 }
366 |             },
367 |             "outputs": [
368 |                 {
369 |                     "data": {
370 |                         "text/plain": "0.00046769430149007363"
371 |                     },
372 |                     "execution_count": 33,
373 |                     "metadata": {},
374 |                     "output_type": "execute_result"
375 |                 }
376 |             ],
377 |             "source": "X = df[['long']]\nY = df['price']\nlm = LinearRegression()\nlm.fit(X,Y)\nlm.score(X, Y)"
378 |         },
379 |         {
380 |             "cell_type": "markdown",
381 |             "metadata": {},
382 |             "source": "### Question  6\nFit a linear regression model to predict the <code>'price'</code> using the feature <code>'sqft_living'</code> then calculate the R^2. Take a screenshot of your code and the value of the R^2."
383 |         },
384 |         {
385 |             "cell_type": "code",
386 |             "execution_count": 35,
387 |             "metadata": {
388 |                 "jupyter": {
389 |                     "outputs_hidden": false
390 |                 }
391 |             },
392 |             "outputs": [
393 |                 {
394 |                     "data": {
395 |                         "text/plain": "0.49285321790379316"
396 |                     },
397 |                     "execution_count": 35,
398 |                     "metadata": {},
399 |                     "output_type": "execute_result"
400 |                 }
401 |             ],
402 |             "source": "X1 = df[['sqft_living']]\nY1 = df[['price']]\nlm1 = LinearRegression().fit(X1, Y1)\nlm1.score(X1, Y1)"
403 |         },
404 |         {
405 |             "cell_type": "markdown",
406 |             "metadata": {},
407 |             "source": "### Question 7\nFit a linear regression model to predict the <code>'price'</code> using the list of features:"
408 |         },
409 |         {
410 |             "cell_type": "code",
411 |             "execution_count": 37,
412 |             "metadata": {},
413 |             "outputs": [
414 |                 {
415 |                     "data": {
416 |                         "text/plain": "0.657679183672129"
417 |                     },
418 |                     "execution_count": 37,
419 |                     "metadata": {},
420 |                     "output_type": "execute_result"
421 |                 }
422 |             ],
423 |             "source": "features =[\"floors\", \"waterfront\",\"lat\" ,\"bedrooms\" ,\"sqft_basement\" ,\"view\" ,\"bathrooms\",\"sqft_living15\",\"sqft_above\",\"grade\",\"sqft_living\"]\nlm2 = LinearRegression().fit(df[features], df[['price']])\nlm2.score(df[features], df[['price']])"
424 |         },
425 |         {
426 |             "cell_type": "markdown",
427 |             "metadata": {},
428 |             "source": "Then calculate the R^2. Take a screenshot of your code."
429 |         },
430 |         {
431 |             "cell_type": "markdown",
432 |             "metadata": {
433 |                 "jupyter": {
434 |                     "outputs_hidden": false
435 |                 }
436 |             },
437 |             "source": ""
438 |         },
439 |         {
440 |             "cell_type": "markdown",
441 |             "metadata": {},
442 |             "source": "### This will help with Question 8\n\nCreate a list of tuples, the first element in the tuple contains the name of the estimator:\n\n<code>'scale'</code>\n\n<code>'polynomial'</code>\n\n<code>'model'</code>\n\nThe second element in the tuple  contains the model constructor \n\n<code>StandardScaler()</code>\n\n<code>PolynomialFeatures(include_bias=False)</code>\n\n<code>LinearRegression()</code>\n"
443 |         },
444 |         {
445 |             "cell_type": "code",
446 |             "execution_count": 38,
447 |             "metadata": {},
448 |             "outputs": [],
449 |             "source": "Input=[('scale',StandardScaler()),('polynomial', PolynomialFeatures(include_bias=False)),('model',LinearRegression())]"
450 |         },
451 |         {
452 |             "cell_type": "markdown",
453 |             "metadata": {},
454 |             "source": "### Question 8\nUse the list to create a pipeline object to predict the 'price', fit the object using the features in the list <code>features</code>, and calculate the R^2."
455 |         },
456 |         {
457 |             "cell_type": "code",
458 |             "execution_count": 40,
459 |             "metadata": {
460 |                 "jupyter": {
461 |                     "outputs_hidden": false
462 |                 }
463 |             },
464 |             "outputs": [
465 |                 {
466 |                     "name": "stderr",
467 |                     "output_type": "stream",
468 |                     "text": "/opt/conda/envs/Python36/lib/python3.6/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n  return self.partial_fit(X, y)\n/opt/conda/envs/Python36/lib/python3.6/site-packages/sklearn/base.py:467: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n  return self.fit(X, y, **fit_params).transform(X)\n/opt/conda/envs/Python36/lib/python3.6/site-packages/sklearn/pipeline.py:511: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n  Xt = transform.transform(Xt)\n"
469 |                 },
470 |                 {
471 |                     "data": {
472 |                         "text/plain": "0.7513408553309376"
473 |                     },
474 |                     "execution_count": 40,
475 |                     "metadata": {},
476 |                     "output_type": "execute_result"
477 |                 }
478 |             ],
479 |             "source": "pipe = Pipeline(Input)\npipe.fit(df[features], df[['price']])\npipe.score(df[features], df[['price']])"
480 |         },
481 |         {
482 |             "cell_type": "markdown",
483 |             "metadata": {},
484 |             "source": "# Module 5: Model Evaluation and Refinement"
485 |         },
486 |         {
487 |             "cell_type": "markdown",
488 |             "metadata": {},
489 |             "source": "Import the necessary modules:"
490 |         },
491 |         {
492 |             "cell_type": "code",
493 |             "execution_count": 41,
494 |             "metadata": {
495 |                 "jupyter": {
496 |                     "outputs_hidden": false
497 |                 }
498 |             },
499 |             "outputs": [
500 |                 {
501 |                     "name": "stdout",
502 |                     "output_type": "stream",
503 |                     "text": "done\n"
504 |                 }
505 |             ],
506 |             "source": "from sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import train_test_split\nprint(\"done\")"
507 |         },
508 |         {
509 |             "cell_type": "markdown",
510 |             "metadata": {},
511 |             "source": "We will split the data into training and testing sets:"
512 |         },
513 |         {
514 |             "cell_type": "code",
515 |             "execution_count": 42,
516 |             "metadata": {
517 |                 "jupyter": {
518 |                     "outputs_hidden": false
519 |                 }
520 |             },
521 |             "outputs": [
522 |                 {
523 |                     "name": "stdout",
524 |                     "output_type": "stream",
525 |                     "text": "number of test samples: 3242\nnumber of training samples: 18371\n"
526 |                 }
527 |             ],
528 |             "source": "features =[\"floors\", \"waterfront\",\"lat\" ,\"bedrooms\" ,\"sqft_basement\" ,\"view\" ,\"bathrooms\",\"sqft_living15\",\"sqft_above\",\"grade\",\"sqft_living\"]    \nX = df[features]\nY = df['price']\n\nx_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.15, random_state=1)\n\n\nprint(\"number of test samples:\", x_test.shape[0])\nprint(\"number of training samples:\",x_train.shape[0])"
529 |         },
530 |         {
531 |             "cell_type": "markdown",
532 |             "metadata": {},
533 |             "source": "### Question 9\nCreate and fit a Ridge regression object using the training data, set the regularization parameter to 0.1, and calculate the R^2 using the test data. \n"
534 |         },
535 |         {
536 |             "cell_type": "code",
537 |             "execution_count": 43,
538 |             "metadata": {},
539 |             "outputs": [],
540 |             "source": "from sklearn.linear_model import Ridge"
541 |         },
542 |         {
543 |             "cell_type": "code",
544 |             "execution_count": 44,
545 |             "metadata": {
546 |                 "jupyter": {
547 |                     "outputs_hidden": false
548 |                 }
549 |             },
550 |             "outputs": [
551 |                 {
552 |                     "data": {
553 |                         "text/plain": "0.6478759163939121"
554 |                     },
555 |                     "execution_count": 44,
556 |                     "metadata": {},
557 |                     "output_type": "execute_result"
558 |                 }
559 |             ],
560 |             "source": "RR = Ridge(alpha=0.1).fit(x_train, y_train)\nRR.score(x_test, y_test)"
561 |         },
562 |         {
563 |             "cell_type": "markdown",
564 |             "metadata": {},
565 |             "source": "### Question 10\nPerform a second order polynomial transform on both the training data and testing data. Create and fit a Ridge regression object using the training data, set the regularisation parameter to 0.1, and calculate the R^2 utilising the test data provided. Take a screenshot of your code and the R^2."
566 |         },
567 |         {
568 |             "cell_type": "code",
569 |             "execution_count": 51,
570 |             "metadata": {
571 |                 "jupyter": {
572 |                     "outputs_hidden": false
573 |                 }
574 |             },
575 |             "outputs": [
576 |                 {
577 |                     "data": {
578 |                         "text/plain": "0.7002744279699229"
579 |                     },
580 |                     "execution_count": 51,
581 |                     "metadata": {},
582 |                     "output_type": "execute_result"
583 |                 }
584 |             ],
585 |             "source": "from sklearn.preprocessing import PolynomialFeatures\npoly = PolynomialFeatures(degree=2)\nx_train_poly = poly.fit_transform(x_train)\nx_test_poly = poly.transform(x_test)\nRR1 = Ridge(alpha=0.1).fit(x_train_poly, y_train)\nRR1.score(x_test_poly, y_test)"
586 |         },
587 |         {
588 |             "cell_type": "markdown",
589 |             "metadata": {},
590 |             "source": "<p>Once you complete your notebook you will have to share it. Select the icon on the top right a marked in red in the image below, a dialogue box should open, and select the option all&nbsp;content excluding sensitive code cells.</p>\n        <p><img width=\"600\" src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DA0101EN/coursera/project/save_notebook.png\" alt=\"share notebook\"  style=\"display: block; margin-left: auto; margin-right: auto;\"/></p>\n        <p></p>\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 width=\"600\"  src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DA0101EN/coursera/project/url_notebook.png\" alt=\"HTML\" style=\"display: block; margin-left: auto; margin-right: auto;\" /></p>\n        <p>&nbsp;</p>"
591 |         },
592 |         {
593 |             "cell_type": "markdown",
594 |             "metadata": {},
595 |             "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."
596 |         },
597 |         {
598 |             "cell_type": "markdown",
599 |             "metadata": {},
600 |             "source": "Other contributors: <a href=\"https://www.linkedin.com/in/michelleccarey/\">Michelle Carey</a>, <a href=\"www.linkedin.com/in/jiahui-mavis-zhou-a4537814a\">Mavis Zhou</a> "
601 |         },
602 |         {
603 |             "cell_type": "code",
604 |             "execution_count": null,
605 |             "metadata": {},
606 |             "outputs": [],
607 |             "source": ""
608 |         }
609 |     ],
610 |     "metadata": {
611 |         "kernelspec": {
612 |             "display_name": "Python 3.6",
613 |             "language": "python",
614 |             "name": "python3"
615 |         },
616 |         "language_info": {
617 |             "codemirror_mode": {
618 |                 "name": "ipython",
619 |                 "version": 3
620 |             },
621 |             "file_extension": ".py",
622 |             "mimetype": "text/x-python",
623 |             "name": "python",
624 |             "nbconvert_exporter": "python",
625 |             "pygments_lexer": "ipython3",
626 |             "version": "3.6.9"
627 |         },
628 |         "widgets": {
629 |             "state": {},
630 |             "version": "1.1.2"
631 |         }
632 |     },
633 |     "nbformat": 4,
634 |     "nbformat_minor": 4
635 | }


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # IBM Data Science Professional Certificate Projects
 2 | 
 3 | This repository contains the projects/assignments for courses in the *IBM Data Science Professional Certificate* on Coursera. The professional certificate contains 9 courses. These are as follows:
 4 | 1. What is Data Science?
 5 | 2. Tools for Data Science
 6 | 3. Data Science Methodology
 7 | 4. Python for Data Science and AI
 8 | 5. Databases and SQL for Data Science
 9 | 6. Data Analysis with Python
10 | 7. Data Visualization with Python
11 | 8. Machine Learning with Python
12 | 9. Applied Data Science Capstone
13 | 
14 | Project/assignment notebooks for courses 2, 4, 5, 6, 7, 8 and 9 are included in this repository. Courses 1 and 3 only have quizzes as part of their assignments. Hence, there are no notebooks for them.
15 | 
16 | The last course, *Applied Data Science Capstone*, has multiple submissions. These include:
17 | 1. A basic introduction to the capstone project notebook for week 1 of the course
18 | 2. An assignment notebook for week 3 of the course
19 | 3. A PDF document highlighting the introduction and data collection for the final project for week 4 of the course
20 | 4. The final project notebook, project report, and project presentation for week 5 of the course
21 | 
22 | All of the files mentioned above can be found at https://github.com/raunakbhutoria/Coursera_Capstone. The Coursera_Capstone repository was the one used for making all submissions for the capstone course. Thus, in this repository I will only be including the Week 3 assignment notebook and the final project code notebook. In order to view the report, presentation, and other materials of the capstone course, please visit my Coursera_Capstone repository.
23 | 
24 | # Thank You!
25 | 


--------------------------------------------------------------------------------
/SQL Assignment - Chicago.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |     "cells": [
  3 |         {
  4 |             "cell_type": "markdown",
  5 |             "metadata": {},
  6 |             "source": "<a href=\"https://cognitiveclass.ai\"><img src = \"https://ibm.box.com/shared/static/ugcqz6ohbvff804xp84y4kqnvvk3bq1g.png\" width = 300, align = \"center\"></a>\n\n<h1 align=center><font size = 5>Assignment: Notebook for Peer Assignment</font></h1>"
  7 |         },
  8 |         {
  9 |             "cell_type": "markdown",
 10 |             "metadata": {},
 11 |             "source": "# Introduction\n\nUsing this Python notebook you will:\n1. Understand 3 Chicago datasets  \n1. Load the 3 datasets into 3 tables in a Db2 database\n1. Execute SQL queries to answer assignment questions "
 12 |         },
 13 |         {
 14 |             "cell_type": "markdown",
 15 |             "metadata": {},
 16 |             "source": "## Understand the datasets \nTo complete the assignment problems in this notebook you will be using three datasets that are available on the city of Chicago's Data Portal:\n1. <a href=\"https://data.cityofchicago.org/Health-Human-Services/Census-Data-Selected-socioeconomic-indicators-in-C/kn9c-c2s2\">Socioeconomic Indicators in Chicago</a>\n1. <a href=\"https://data.cityofchicago.org/Education/Chicago-Public-Schools-Progress-Report-Cards-2011-/9xs2-f89t\">Chicago Public Schools</a>\n1. <a href=\"https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2\">Chicago Crime Data</a>\n\n### 1. Socioeconomic Indicators in Chicago\nThis dataset contains a selection of six socioeconomic indicators of public health significance and a \u201chardship index,\u201d for each Chicago community area, for the years 2008 \u2013 2012.\n\nFor this assignment you will use a snapshot of this dataset which can be downloaded from:\nhttps://ibm.box.com/shared/static/05c3415cbfbtfnr2fx4atenb2sd361ze.csv\n\nA detailed description of this dataset and the original dataset can be obtained from the Chicago Data Portal at:\nhttps://data.cityofchicago.org/Health-Human-Services/Census-Data-Selected-socioeconomic-indicators-in-C/kn9c-c2s2\n\n\n\n### 2. Chicago Public Schools\n\nThis dataset shows all school level performance data used to create CPS School Report Cards for the 2011-2012 school year. This dataset is provided by the city of Chicago's Data Portal.\n\nFor this assignment you will use a snapshot of this dataset which can be downloaded from:\nhttps://ibm.box.com/shared/static/f9gjvj1gjmxxzycdhplzt01qtz0s7ew7.csv\n\nA detailed description of this dataset and the original dataset can be obtained from the Chicago Data Portal at:\nhttps://data.cityofchicago.org/Education/Chicago-Public-Schools-Progress-Report-Cards-2011-/9xs2-f89t\n\n\n\n\n### 3. Chicago Crime Data \n\nThis dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. \n\nThis dataset is quite large - over 1.5GB in size with over 6.5 million rows. For the purposes of this assignment we will use a much smaller sample of this dataset which can be downloaded from:\nhttps://ibm.box.com/shared/static/svflyugsr9zbqy5bmowgswqemfpm1x7f.csv\n\nA detailed description of this dataset and the original dataset can be obtained from the Chicago Data Portal at:\nhttps://data.cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2\n"
 17 |         },
 18 |         {
 19 |             "cell_type": "markdown",
 20 |             "metadata": {},
 21 |             "source": "### Download the datasets\nIn many cases the dataset to be analyzed is available as a .CSV (comma separated values) file, perhaps on the internet. Click on the links below to download and save the datasets (.CSV files):\n1. __CENSUS_DATA:__ https://ibm.box.com/shared/static/05c3415cbfbtfnr2fx4atenb2sd361ze.csv\n1. __CHICAGO_PUBLIC_SCHOOLS__  https://ibm.box.com/shared/static/f9gjvj1gjmxxzycdhplzt01qtz0s7ew7.csv\n1. __CHICAGO_CRIME_DATA:__ https://ibm.box.com/shared/static/svflyugsr9zbqy5bmowgswqemfpm1x7f.csv\n\n__NOTE:__ Ensure you have downloaded the datasets using the links above instead of directly from the Chicago Data Portal. The versions linked here are subsets of the original datasets and have some of the column names modified to be more database friendly which will make it easier to complete this assignment."
 22 |         },
 23 |         {
 24 |             "cell_type": "markdown",
 25 |             "metadata": {},
 26 |             "source": "### Store the datasets in database tables\nTo analyze the data using SQL, it first needs to be stored in the database.\n\nWhile it is easier to read the dataset into a Pandas dataframe and then PERSIST it into the database as we saw in Week 3 Lab 3, it results in mapping to default datatypes which may not be optimal for SQL querying. For example a long textual field may map to a CLOB instead of a VARCHAR. \n\nTherefore, __it is highly recommended to manually load the table using the database console LOAD tool, as indicated in Week 2 Lab 1 Part II__. The only difference with that lab is that in Step 5 of the instructions you will need to click on create \"(+) New Table\" and specify the name of the table you want to create and then click \"Next\". \n\n<img src = \"https://ibm.box.com/shared/static/uc4xjh1uxcc78ks1i18v668simioz4es.jpg\">\n\n##### Now open the Db2 console, open the LOAD tool, Select / Drag the .CSV file for the first dataset, Next create a New Table, and then follow the steps on-screen instructions to load the data. Name the new tables as folows:\n1. __CENSUS_DATA__\n1. __CHICAGO_PUBLIC_SCHOOLS__\n1. __CHICAGO_CRIME_DATA__"
 27 |         },
 28 |         {
 29 |             "cell_type": "markdown",
 30 |             "metadata": {},
 31 |             "source": "### Connect to the database \nLet us first load the SQL extension and establish a connection with the database"
 32 |         },
 33 |         {
 34 |             "cell_type": "code",
 35 |             "execution_count": 1,
 36 |             "metadata": {},
 37 |             "outputs": [],
 38 |             "source": "%load_ext sql"
 39 |         },
 40 |         {
 41 |             "cell_type": "markdown",
 42 |             "metadata": {},
 43 |             "source": "In the next cell enter your db2 connection string. Recall you created Service Credentials for your Db2 instance in first lab in Week 3. From the __uri__ field of your Db2 service credentials copy everything after db2:// (except the double quote at the end) and paste it in the cell below after ibm_db_sa://\n\n<img src =\"https://ibm.box.com/shared/static/hzhkvdyinpupm2wfx49lkr71q9swbpec.jpg\">"
 44 |         },
 45 |         {
 46 |             "cell_type": "code",
 47 |             "execution_count": 2,
 48 |             "metadata": {},
 49 |             "outputs": [
 50 |                 {
 51 |                     "data": {
 52 |                         "text/plain": "'Connected: pgq43854@BLUDB'"
 53 |                     },
 54 |                     "execution_count": 2,
 55 |                     "metadata": {},
 56 |                     "output_type": "execute_result"
 57 |                 }
 58 |             ],
 59 |             "source": "# Remember the connection string is of the format:\n# %sql ibm_db_sa://my-username:my-password@my-hostname:my-port/my-db-name\n# Enter the connection string for your Db2 on Cloud database instance below\n%sql ibm_db_sa://pgq43854:3js4p2w4dzrc85%5Ev@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.bluemix.net:50000/BLUDB"
 60 |         },
 61 |         {
 62 |             "cell_type": "markdown",
 63 |             "metadata": {},
 64 |             "source": "## Problems\nNow write and execute SQL queries to solve assignment problems\n\n### Problem 1\n\n##### Find the total number of crimes recorded in the CRIME table"
 65 |         },
 66 |         {
 67 |             "cell_type": "code",
 68 |             "execution_count": 10,
 69 |             "metadata": {},
 70 |             "outputs": [
 71 |                 {
 72 |                     "name": "stdout",
 73 |                     "output_type": "stream",
 74 |                     "text": " * ibm_db_sa://pgq43854:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.bluemix.net:50000/BLUDB\nDone.\n"
 75 |                 },
 76 |                 {
 77 |                     "data": {
 78 |                         "text/html": "<table>\n    <tr>\n        <th>count_of_crimes</th>\n    </tr>\n    <tr>\n        <td>533</td>\n    </tr>\n</table>",
 79 |                         "text/plain": "[(Decimal('533'),)]"
 80 |                     },
 81 |                     "execution_count": 10,
 82 |                     "metadata": {},
 83 |                     "output_type": "execute_result"
 84 |                 }
 85 |             ],
 86 |             "source": "# Rows in Crime table\n%sql SELECT COUNT(*) as count_of_crimes FROM CHICAGO_CRIME_DATA"
 87 |         },
 88 |         {
 89 |             "cell_type": "markdown",
 90 |             "metadata": {},
 91 |             "source": "### Problem 2\n\n##### Retrieve first 10 rows from the CRIME table\n"
 92 |         },
 93 |         {
 94 |             "cell_type": "code",
 95 |             "execution_count": 11,
 96 |             "metadata": {},
 97 |             "outputs": [
 98 |                 {
 99 |                     "name": "stdout",
100 |                     "output_type": "stream",
101 |                     "text": " * ibm_db_sa://pgq43854:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.bluemix.net:50000/BLUDB\nDone.\n"
102 |                 },
103 |                 {
104 |                     "data": {
105 |                         "text/html": "<table>\n    <tr>\n        <th>id</th>\n        <th>case_number</th>\n        <th>DATE</th>\n        <th>block</th>\n        <th>iucr</th>\n        <th>primary_type</th>\n        <th>description</th>\n        <th>location_description</th>\n        <th>arrest</th>\n        <th>domestic</th>\n        <th>beat</th>\n        <th>district</th>\n        <th>ward</th>\n        <th>community_area_number</th>\n        <th>fbicode</th>\n        <th>x_coordinate</th>\n        <th>y_coordinate</th>\n        <th>YEAR</th>\n        <th>updatedon</th>\n        <th>latitude</th>\n        <th>longitude</th>\n        <th>location</th>\n    </tr>\n    <tr>\n        <td>3512276</td>\n        <td>HK587712</td>\n        <td>08/28/2004 05:50:56 PM</td>\n        <td>047XX S KEDZIE AVE</td>\n        <td>890</td>\n        <td>THEFT</td>\n        <td>FROM BUILDING</td>\n        <td>SMALL RETAIL STORE</td>\n        <td>FALSE</td>\n        <td>FALSE</td>\n        <td>911</td>\n        <td>9</td>\n        <td>14</td>\n        <td>58</td>\n        <td>6</td>\n        <td>1155838</td>\n        <td>1873050</td>\n        <td>2004</td>\n        <td>2018-02-10 15:50:01</td>\n        <td>41.80744050</td>\n        <td>-87.70395585</td>\n        <td>(41.8074405, -87.703955849)</td>\n    </tr>\n    <tr>\n        <td>3406613</td>\n        <td>HK456306</td>\n        <td>06/26/2004 12:40:00 PM</td>\n        <td>009XX N CENTRAL PARK AVE</td>\n        <td>820</td>\n        <td>THEFT</td>\n        <td>$500 AND UNDER</td>\n        <td>OTHER</td>\n        <td>FALSE</td>\n        <td>FALSE</td>\n        <td>1112</td>\n        <td>11</td>\n        <td>27</td>\n        <td>23</td>\n        <td>6</td>\n        <td>1152206</td>\n        <td>1906127</td>\n        <td>2004</td>\n        <td>2018-02-28 15:56:25</td>\n        <td>41.89827996</td>\n        <td>-87.71640551</td>\n        <td>(41.898279962, -87.716405505)</td>\n    </tr>\n    <tr>\n        <td>8002131</td>\n        <td>HT233595</td>\n        <td>04/04/2011 05:45:00 AM</td>\n        <td>043XX S WABASH AVE</td>\n        <td>820</td>\n        <td>THEFT</td>\n        <td>$500 AND UNDER</td>\n        <td>NURSING HOME/RETIREMENT HOME</td>\n        <td>FALSE</td>\n        <td>FALSE</td>\n        <td>221</td>\n        <td>2</td>\n        <td>3</td>\n        <td>38</td>\n        <td>6</td>\n        <td>1177436</td>\n        <td>1876313</td>\n        <td>2011</td>\n        <td>2018-02-10 15:50:01</td>\n        <td>41.81593313</td>\n        <td>-87.62464213</td>\n        <td>(41.815933131, -87.624642127)</td>\n    </tr>\n    <tr>\n        <td>7903289</td>\n        <td>HT133522</td>\n        <td>12/30/2010 04:30:00 PM</td>\n        <td>083XX S KINGSTON AVE</td>\n        <td>840</td>\n        <td>THEFT</td>\n        <td>FINANCIAL ID THEFT: OVER $300</td>\n        <td>RESIDENCE</td>\n        <td>FALSE</td>\n        <td>FALSE</td>\n        <td>423</td>\n        <td>4</td>\n        <td>7</td>\n        <td>46</td>\n        <td>6</td>\n        <td>1194622</td>\n        <td>1850125</td>\n        <td>2010</td>\n        <td>2018-02-10 15:50:01</td>\n        <td>41.74366532</td>\n        <td>-87.56246276</td>\n        <td>(41.743665322, -87.562462756)</td>\n    </tr>\n    <tr>\n        <td>10402076</td>\n        <td>HZ138551</td>\n        <td>02/02/2016 07:30:00 PM</td>\n        <td>033XX W 66TH ST</td>\n        <td>820</td>\n        <td>THEFT</td>\n        <td>$500 AND UNDER</td>\n        <td>ALLEY</td>\n        <td>FALSE</td>\n        <td>FALSE</td>\n        <td>831</td>\n        <td>8</td>\n        <td>15</td>\n        <td>66</td>\n        <td>6</td>\n        <td>1155240</td>\n        <td>1860661</td>\n        <td>2016</td>\n        <td>2018-02-10 15:50:01</td>\n        <td>41.77345530</td>\n        <td>-87.70648047</td>\n        <td>(41.773455295, -87.706480471)</td>\n    </tr>\n    <tr>\n        <td>7732712</td>\n        <td>HS540106</td>\n        <td>09/29/2010 07:59:00 AM</td>\n        <td>006XX W CHICAGO AVE</td>\n        <td>810</td>\n        <td>THEFT</td>\n        <td>OVER $500</td>\n        <td>PARKING LOT/GARAGE(NON.RESID.)</td>\n        <td>FALSE</td>\n        <td>FALSE</td>\n        <td>1323</td>\n        <td>12</td>\n        <td>27</td>\n        <td>24</td>\n        <td>6</td>\n        <td>1171668</td>\n        <td>1905607</td>\n        <td>2010</td>\n        <td>2018-02-10 15:50:01</td>\n        <td>41.89644677</td>\n        <td>-87.64493868</td>\n        <td>(41.896446772, -87.644938678)</td>\n    </tr>\n    <tr>\n        <td>10769475</td>\n        <td>HZ534771</td>\n        <td>11/30/2016 01:15:00 AM</td>\n        <td>050XX N KEDZIE AVE</td>\n        <td>810</td>\n        <td>THEFT</td>\n        <td>OVER $500</td>\n        <td>STREET</td>\n        <td>FALSE</td>\n        <td>FALSE</td>\n        <td>1713</td>\n        <td>17</td>\n        <td>33</td>\n        <td>14</td>\n        <td>6</td>\n        <td>1154133</td>\n        <td>1933314</td>\n        <td>2016</td>\n        <td>2018-02-10 15:50:01</td>\n        <td>41.97284491</td>\n        <td>-87.70860008</td>\n        <td>(41.972844913, -87.708600079)</td>\n    </tr>\n    <tr>\n        <td>4494340</td>\n        <td>HL793243</td>\n        <td>12/16/2005 04:45:00 PM</td>\n        <td>005XX E PERSHING RD</td>\n        <td>860</td>\n        <td>THEFT</td>\n        <td>RETAIL THEFT</td>\n        <td>GROCERY FOOD STORE</td>\n        <td>TRUE</td>\n        <td>FALSE</td>\n        <td>213</td>\n        <td>2</td>\n        <td>3</td>\n        <td>38</td>\n        <td>6</td>\n        <td>1180448</td>\n        <td>1879234</td>\n        <td>2005</td>\n        <td>2018-02-28 15:56:25</td>\n        <td>41.82387989</td>\n        <td>-87.61350386</td>\n        <td>(41.823879885, -87.613503857)</td>\n    </tr>\n    <tr>\n        <td>3778925</td>\n        <td>HL149610</td>\n        <td>01/28/2005 05:00:00 PM</td>\n        <td>100XX S WASHTENAW AVE</td>\n        <td>810</td>\n        <td>THEFT</td>\n        <td>OVER $500</td>\n        <td>STREET</td>\n        <td>FALSE</td>\n        <td>FALSE</td>\n        <td>2211</td>\n        <td>22</td>\n        <td>19</td>\n        <td>72</td>\n        <td>6</td>\n        <td>1160129</td>\n        <td>1838040</td>\n        <td>2005</td>\n        <td>2018-02-28 15:56:25</td>\n        <td>41.71128051</td>\n        <td>-87.68917910</td>\n        <td>(41.711280513, -87.689179097)</td>\n    </tr>\n    <tr>\n        <td>3324217</td>\n        <td>HK361551</td>\n        <td>05/13/2004 02:15:00 PM</td>\n        <td>033XX W BELMONT AVE</td>\n        <td>820</td>\n        <td>THEFT</td>\n        <td>$500 AND UNDER</td>\n        <td>SMALL RETAIL STORE</td>\n        <td>FALSE</td>\n        <td>FALSE</td>\n        <td>1733</td>\n        <td>17</td>\n        <td>35</td>\n        <td>21</td>\n        <td>6</td>\n        <td>1153590</td>\n        <td>1921084</td>\n        <td>2004</td>\n        <td>2018-02-28 15:56:25</td>\n        <td>41.93929582</td>\n        <td>-87.71092344</td>\n        <td>(41.939295821, -87.710923442)</td>\n    </tr>\n</table>",
106 |                         "text/plain": "[(3512276, 'HK587712', '08/28/2004 05:50:56 PM', '047XX S KEDZIE AVE', '890', 'THEFT', 'FROM BUILDING', 'SMALL RETAIL STORE', 'FALSE', 'FALSE', 911, 9, 14, 58, '6', 1155838, 1873050, 2004, datetime.datetime(2018, 2, 10, 15, 50, 1), Decimal('41.80744050'), Decimal('-87.70395585'), '(41.8074405, -87.703955849)'),\n (3406613, 'HK456306', '06/26/2004 12:40:00 PM', '009XX N CENTRAL PARK AVE', '820', 'THEFT', '$500 AND UNDER', 'OTHER', 'FALSE', 'FALSE', 1112, 11, 27, 23, '6', 1152206, 1906127, 2004, datetime.datetime(2018, 2, 28, 15, 56, 25), Decimal('41.89827996'), Decimal('-87.71640551'), '(41.898279962, -87.716405505)'),\n (8002131, 'HT233595', '04/04/2011 05:45:00 AM', '043XX S WABASH AVE', '820', 'THEFT', '$500 AND UNDER', 'NURSING HOME/RETIREMENT HOME', 'FALSE', 'FALSE', 221, 2, 3, 38, '6', 1177436, 1876313, 2011, datetime.datetime(2018, 2, 10, 15, 50, 1), Decimal('41.81593313'), Decimal('-87.62464213'), '(41.815933131, -87.624642127)'),\n (7903289, 'HT133522', '12/30/2010 04:30:00 PM', '083XX S KINGSTON AVE', '840', 'THEFT', 'FINANCIAL ID THEFT: OVER $300', 'RESIDENCE', 'FALSE', 'FALSE', 423, 4, 7, 46, '6', 1194622, 1850125, 2010, datetime.datetime(2018, 2, 10, 15, 50, 1), Decimal('41.74366532'), Decimal('-87.56246276'), '(41.743665322, -87.562462756)'),\n (10402076, 'HZ138551', '02/02/2016 07:30:00 PM', '033XX W 66TH ST', '820', 'THEFT', '$500 AND UNDER', 'ALLEY', 'FALSE', 'FALSE', 831, 8, 15, 66, '6', 1155240, 1860661, 2016, datetime.datetime(2018, 2, 10, 15, 50, 1), Decimal('41.77345530'), Decimal('-87.70648047'), '(41.773455295, -87.706480471)'),\n (7732712, 'HS540106', '09/29/2010 07:59:00 AM', '006XX W CHICAGO AVE', '810', 'THEFT', 'OVER $500', 'PARKING LOT/GARAGE(NON.RESID.)', 'FALSE', 'FALSE', 1323, 12, 27, 24, '6', 1171668, 1905607, 2010, datetime.datetime(2018, 2, 10, 15, 50, 1), Decimal('41.89644677'), Decimal('-87.64493868'), '(41.896446772, -87.644938678)'),\n (10769475, 'HZ534771', '11/30/2016 01:15:00 AM', '050XX N KEDZIE AVE', '810', 'THEFT', 'OVER $500', 'STREET', 'FALSE', 'FALSE', 1713, 17, 33, 14, '6', 1154133, 1933314, 2016, datetime.datetime(2018, 2, 10, 15, 50, 1), Decimal('41.97284491'), Decimal('-87.70860008'), '(41.972844913, -87.708600079)'),\n (4494340, 'HL793243', '12/16/2005 04:45:00 PM', '005XX E PERSHING RD', '860', 'THEFT', 'RETAIL THEFT', 'GROCERY FOOD STORE', 'TRUE', 'FALSE', 213, 2, 3, 38, '6', 1180448, 1879234, 2005, datetime.datetime(2018, 2, 28, 15, 56, 25), Decimal('41.82387989'), Decimal('-87.61350386'), '(41.823879885, -87.613503857)'),\n (3778925, 'HL149610', '01/28/2005 05:00:00 PM', '100XX S WASHTENAW AVE', '810', 'THEFT', 'OVER $500', 'STREET', 'FALSE', 'FALSE', 2211, 22, 19, 72, '6', 1160129, 1838040, 2005, datetime.datetime(2018, 2, 28, 15, 56, 25), Decimal('41.71128051'), Decimal('-87.68917910'), '(41.711280513, -87.689179097)'),\n (3324217, 'HK361551', '05/13/2004 02:15:00 PM', '033XX W BELMONT AVE', '820', 'THEFT', '$500 AND UNDER', 'SMALL RETAIL STORE', 'FALSE', 'FALSE', 1733, 17, 35, 21, '6', 1153590, 1921084, 2004, datetime.datetime(2018, 2, 28, 15, 56, 25), Decimal('41.93929582'), Decimal('-87.71092344'), '(41.939295821, -87.710923442)')]"
107 |                     },
108 |                     "execution_count": 11,
109 |                     "metadata": {},
110 |                     "output_type": "execute_result"
111 |                 }
112 |             ],
113 |             "source": "%sql SELECT * FROM CHICAGO_CRIME_DATA LIMIT 10"
114 |         },
115 |         {
116 |             "cell_type": "markdown",
117 |             "metadata": {},
118 |             "source": "### Problem 3\n\n##### How many crimes involve an arrest?"
119 |         },
120 |         {
121 |             "cell_type": "code",
122 |             "execution_count": 12,
123 |             "metadata": {},
124 |             "outputs": [
125 |                 {
126 |                     "name": "stdout",
127 |                     "output_type": "stream",
128 |                     "text": " * ibm_db_sa://pgq43854:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.bluemix.net:50000/BLUDB\nDone.\n"
129 |                 },
130 |                 {
131 |                     "data": {
132 |                         "text/html": "<table>\n    <tr>\n        <th>count_of_arrest</th>\n    </tr>\n    <tr>\n        <td>163</td>\n    </tr>\n</table>",
133 |                         "text/plain": "[(Decimal('163'),)]"
134 |                     },
135 |                     "execution_count": 12,
136 |                     "metadata": {},
137 |                     "output_type": "execute_result"
138 |                 }
139 |             ],
140 |             "source": "%sql SELECT COUNT(*) as count_of_arrest FROM CHICAGO_CRIME_DATA WHERE ARREST = 'TRUE'"
141 |         },
142 |         {
143 |             "cell_type": "markdown",
144 |             "metadata": {},
145 |             "source": "### Problem 4\n\n##### Which unique types of crimes have been recorded at GAS STATION locations?\n"
146 |         },
147 |         {
148 |             "cell_type": "code",
149 |             "execution_count": 64,
150 |             "metadata": {},
151 |             "outputs": [
152 |                 {
153 |                     "name": "stdout",
154 |                     "output_type": "stream",
155 |                     "text": " * ibm_db_sa://pgq43854:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.bluemix.net:50000/BLUDB\nDone.\n"
156 |                 },
157 |                 {
158 |                     "data": {
159 |                         "text/html": "<table>\n    <tr>\n        <th>unique_types_of_crimes</th>\n    </tr>\n    <tr>\n        <td>CRIMINAL TRESPASS</td>\n    </tr>\n    <tr>\n        <td>NARCOTICS</td>\n    </tr>\n    <tr>\n        <td>ROBBERY</td>\n    </tr>\n    <tr>\n        <td>THEFT</td>\n    </tr>\n</table>",
160 |                         "text/plain": "[('CRIMINAL TRESPASS',), ('NARCOTICS',), ('ROBBERY',), ('THEFT',)]"
161 |                     },
162 |                     "execution_count": 64,
163 |                     "metadata": {},
164 |                     "output_type": "execute_result"
165 |                 }
166 |             ],
167 |             "source": "%%sql\nSELECT PRIMARY_TYPE as unique_types_of_crimes FROM CHICAGO_CRIME_DATA \nWHERE LOCATION_DESCRIPTION = 'GAS STATION' GROUP BY PRIMARY_TYPE"
168 |         },
169 |         {
170 |             "cell_type": "markdown",
171 |             "metadata": {},
172 |             "source": "Hint: Which column lists types of crimes e.g. THEFT?"
173 |         },
174 |         {
175 |             "cell_type": "markdown",
176 |             "metadata": {},
177 |             "source": "### Problem 5\n\n##### In the CENUS_DATA table list all Community Areas whose names start with the letter \u2018B\u2019."
178 |         },
179 |         {
180 |             "cell_type": "code",
181 |             "execution_count": 16,
182 |             "metadata": {},
183 |             "outputs": [
184 |                 {
185 |                     "name": "stdout",
186 |                     "output_type": "stream",
187 |                     "text": " * ibm_db_sa://pgq43854:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.bluemix.net:50000/BLUDB\nDone.\n"
188 |                 },
189 |                 {
190 |                     "data": {
191 |                         "text/html": "<table>\n    <tr>\n        <th>community_area_name</th>\n    </tr>\n    <tr>\n        <td>Belmont Cragin</td>\n    </tr>\n    <tr>\n        <td>Burnside</td>\n    </tr>\n    <tr>\n        <td>Brighton Park</td>\n    </tr>\n    <tr>\n        <td>Bridgeport</td>\n    </tr>\n    <tr>\n        <td>Beverly</td>\n    </tr>\n</table>",
192 |                         "text/plain": "[('Belmont Cragin',),\n ('Burnside',),\n ('Brighton Park',),\n ('Bridgeport',),\n ('Beverly',)]"
193 |                     },
194 |                     "execution_count": 16,
195 |                     "metadata": {},
196 |                     "output_type": "execute_result"
197 |                 }
198 |             ],
199 |             "source": "%sql SELECT COMMUNITY_AREA_NAME FROM CENSUS_DATA WHERE COMMUNITY_AREA_NAME LIKE 'B%'"
200 |         },
201 |         {
202 |             "cell_type": "markdown",
203 |             "metadata": {},
204 |             "source": "### Problem 6\n\n##### Which schools in Community Areas 10 to 15 are healthy school certified?"
205 |         },
206 |         {
207 |             "cell_type": "code",
208 |             "execution_count": 29,
209 |             "metadata": {},
210 |             "outputs": [
211 |                 {
212 |                     "name": "stdout",
213 |                     "output_type": "stream",
214 |                     "text": " * ibm_db_sa://pgq43854:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.bluemix.net:50000/BLUDB\nDone.\n"
215 |                 },
216 |                 {
217 |                     "data": {
218 |                         "text/html": "<table>\n    <tr>\n        <th>community_area_number</th>\n        <th>community_area_name</th>\n        <th>name_of_school</th>\n        <th>healthy_school_certified</th>\n    </tr>\n    <tr>\n        <td>10</td>\n        <td>NORWOOD PARK</td>\n        <td>Rufus M Hitch Elementary School</td>\n        <td>Yes</td>\n    </tr>\n</table>",
219 |                         "text/plain": "[(10, 'NORWOOD PARK', 'Rufus M Hitch Elementary School', 'Yes')]"
220 |                     },
221 |                     "execution_count": 29,
222 |                     "metadata": {},
223 |                     "output_type": "execute_result"
224 |                 }
225 |             ],
226 |             "source": "%%sql\nSELECT CPS.COMMUNITY_AREA_NUMBER, CPS.COMMUNITY_AREA_NAME, NAME_OF_SCHOOL, CPS.HEALTHY_SCHOOL_CERTIFIED \n    FROM CHICAGO_PUBLIC_SCHOOLS as CPS JOIN CENSUS_DATA as CD \n    ON CD.COMMUNITY_AREA_NUMBER = CPS.COMMUNITY_AREA_NUMBER \n    WHERE CD.COMMUNITY_AREA_NUMBER BETWEEN 10 AND 15 AND CPS.HEALTHY_SCHOOL_CERTIFIED = 'Yes'"
227 |         },
228 |         {
229 |             "cell_type": "markdown",
230 |             "metadata": {},
231 |             "source": "### Problem 7\n\n##### What is the average school Safety Score? "
232 |         },
233 |         {
234 |             "cell_type": "code",
235 |             "execution_count": 31,
236 |             "metadata": {},
237 |             "outputs": [
238 |                 {
239 |                     "name": "stdout",
240 |                     "output_type": "stream",
241 |                     "text": " * ibm_db_sa://pgq43854:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.bluemix.net:50000/BLUDB\nDone.\n"
242 |                 },
243 |                 {
244 |                     "data": {
245 |                         "text/html": "<table>\n    <tr>\n        <th>average_school_safety_score</th>\n    </tr>\n    <tr>\n        <td>49.504873</td>\n    </tr>\n</table>",
246 |                         "text/plain": "[(Decimal('49.504873'),)]"
247 |                     },
248 |                     "execution_count": 31,
249 |                     "metadata": {},
250 |                     "output_type": "execute_result"
251 |                 }
252 |             ],
253 |             "source": "%sql SELECT AVG(SAFETY_SCORE) as average_school_safety_score FROM CHICAGO_PUBLIC_SCHOOLS"
254 |         },
255 |         {
256 |             "cell_type": "markdown",
257 |             "metadata": {},
258 |             "source": "### Problem 8\n\n##### List the top 5 Community Areas by average College Enrollment [number of students] "
259 |         },
260 |         {
261 |             "cell_type": "code",
262 |             "execution_count": 68,
263 |             "metadata": {},
264 |             "outputs": [
265 |                 {
266 |                     "name": "stdout",
267 |                     "output_type": "stream",
268 |                     "text": " * ibm_db_sa://pgq43854:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.bluemix.net:50000/BLUDB\nDone.\n"
269 |                 },
270 |                 {
271 |                     "data": {
272 |                         "text/html": "<table>\n    <tr>\n        <th>community_area_name</th>\n        <th>avg_college_enrollment</th>\n    </tr>\n    <tr>\n        <td>ARCHER HEIGHTS</td>\n        <td>2411.500000</td>\n    </tr>\n    <tr>\n        <td>MONTCLARE</td>\n        <td>1317.000000</td>\n    </tr>\n    <tr>\n        <td>WEST ELSDON</td>\n        <td>1233.333333</td>\n    </tr>\n    <tr>\n        <td>BRIGHTON PARK</td>\n        <td>1205.875000</td>\n    </tr>\n    <tr>\n        <td>BELMONT CRAGIN</td>\n        <td>1198.833333</td>\n    </tr>\n</table>",
273 |                         "text/plain": "[('ARCHER HEIGHTS', Decimal('2411.500000')),\n ('MONTCLARE', Decimal('1317.000000')),\n ('WEST ELSDON', Decimal('1233.333333')),\n ('BRIGHTON PARK', Decimal('1205.875000')),\n ('BELMONT CRAGIN', Decimal('1198.833333'))]"
274 |                     },
275 |                     "execution_count": 68,
276 |                     "metadata": {},
277 |                     "output_type": "execute_result"
278 |                 }
279 |             ],
280 |             "source": "%%sql\nSELECT COMMUNITY_AREA_NAME, AVG(COLLEGE_ENROLLMENT) as AVG_COLLEGE_ENROLLMENT FROM \nCHICAGO_PUBLIC_SCHOOLS GROUP BY COMMUNITY_AREA_NAME ORDER BY AVG_COLLEGE_ENROLLMENT DESC LIMIT 5"
281 |         },
282 |         {
283 |             "cell_type": "markdown",
284 |             "metadata": {},
285 |             "source": "### Problem 9\n\n##### Use a sub-query to determine which Community Area has the least value for school Safety Score? "
286 |         },
287 |         {
288 |             "cell_type": "code",
289 |             "execution_count": 55,
290 |             "metadata": {},
291 |             "outputs": [
292 |                 {
293 |                     "name": "stdout",
294 |                     "output_type": "stream",
295 |                     "text": " * ibm_db_sa://pgq43854:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.bluemix.net:50000/BLUDB\nDone.\n"
296 |                 },
297 |                 {
298 |                     "data": {
299 |                         "text/html": "<table>\n    <tr>\n        <th>community_area_name</th>\n        <th>safety_score</th>\n    </tr>\n    <tr>\n        <td>WASHINGTON PARK</td>\n        <td>1</td>\n    </tr>\n</table>",
300 |                         "text/plain": "[('WASHINGTON PARK', 1)]"
301 |                     },
302 |                     "execution_count": 55,
303 |                     "metadata": {},
304 |                     "output_type": "execute_result"
305 |                 }
306 |             ],
307 |             "source": "%%sql\nSELECT COMMUNITY_AREA_NAME, SAFETY_SCORE FROM CHICAGO_PUBLIC_SCHOOLS\nWHERE SAFETY_SCORE = (SELECT MIN(SAFETY_SCORE) FROM CHICAGO_PUBLIC_SCHOOLS)"
308 |         },
309 |         {
310 |             "cell_type": "markdown",
311 |             "metadata": {},
312 |             "source": "### Problem 10\n\n##### [Without using an explicit JOIN operator] Find the Per Capita Income of the Community Area which has a school Safety Score of 1."
313 |         },
314 |         {
315 |             "cell_type": "code",
316 |             "execution_count": 59,
317 |             "metadata": {},
318 |             "outputs": [
319 |                 {
320 |                     "name": "stdout",
321 |                     "output_type": "stream",
322 |                     "text": " * ibm_db_sa://pgq43854:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.bluemix.net:50000/BLUDB\nDone.\n"
323 |                 },
324 |                 {
325 |                     "data": {
326 |                         "text/html": "<table>\n    <tr>\n        <th>community_area_name</th>\n        <th>safety_score</th>\n        <th>per_capita_income</th>\n    </tr>\n    <tr>\n        <td>WASHINGTON PARK</td>\n        <td>1</td>\n        <td>13785</td>\n    </tr>\n</table>",
327 |                         "text/plain": "[('WASHINGTON PARK', 1, 13785)]"
328 |                     },
329 |                     "execution_count": 59,
330 |                     "metadata": {},
331 |                     "output_type": "execute_result"
332 |                 }
333 |             ],
334 |             "source": "%%sql\nSELECT CPS.COMMUNITY_AREA_NAME, CPS.SAFETY_SCORE, CD.PER_CAPITA_INCOME \n    FROM CENSUS_DATA as CD, CHICAGO_PUBLIC_SCHOOLS as CPS \n    WHERE CPS.SAFETY_SCORE = 1 AND CPS.COMMUNITY_AREA_NUMBER = CD.COMMUNITY_AREA_NUMBER"
335 |         },
336 |         {
337 |             "cell_type": "markdown",
338 |             "metadata": {},
339 |             "source": "Copyright &copy; 2018 [cognitiveclass.ai](cognitiveclass.ai?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/).\n"
340 |         }
341 |     ],
342 |     "metadata": {
343 |         "kernelspec": {
344 |             "display_name": "Python",
345 |             "language": "python",
346 |             "name": "conda-env-python-py"
347 |         },
348 |         "language_info": {
349 |             "codemirror_mode": {
350 |                 "name": "ipython",
351 |                 "version": 3
352 |             },
353 |             "file_extension": ".py",
354 |             "mimetype": "text/x-python",
355 |             "name": "python",
356 |             "nbconvert_exporter": "python",
357 |             "pygments_lexer": "ipython3",
358 |             "version": "3.6.11"
359 |         },
360 |         "widgets": {
361 |             "state": {},
362 |             "version": "1.1.2"
363 |         }
364 |     },
365 |     "nbformat": 4,
366 |     "nbformat_minor": 4
367 | }


--------------------------------------------------------------------------------