Analyzing US Economic Data.ipynb

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    "<center>\n",
    "    <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
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   "source": [
    "<h1>Analyzing US Economic Data and  Building a Dashboard  </h1>\n",
    "<h2>Description</h2>\n"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extracting essential data from a dataset and displaying it is a necessary part of data science. In this assignment, you will extract some essential economic indicators from some data, you will then display these economic indicators in a Dashboard. You can then share the dashboard via an URL.\n",
    "\n",
    "<p>\n",
    "<a href=\"https://en.wikipedia.org/wiki/Gross_domestic_product\"> Gross domestic product (GDP)</a> is a measure of the market value of all the final goods and services produced in a period. GDP is an indicator of how well the economy is doing. A drop in GDP indicates the economy is producing less; similarly an increase in GDP suggests the economy is performing better.</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"Section_1\"> Define Function that Makes a Dashboard  </h2>\n"
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   "source": [
    "We will import the following libraries.\n"
   ]
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from bokeh.plotting import figure, output_file, show,output_notebook\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we define the function <code>make_dashboard</code>. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dictionary  <code>links</code> contain the CSV files with all the data. The value for the key <code>GDP</code> is the file that contains the GDP data. The value for the key <code>unemployment</code> contains the unemployment data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "links={'GDP':'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/FinalModule_Coursera/data/clean_gdp.csv',\\\n",
    "       'unemployment':'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/FinalModule_Coursera/data/clean_unemployment.csv'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 id=\"Section_2\"> Question 1: Create a dataframe that contains the GDP data and display the first five rows of the dataframe.</h3>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_GDP = pd.read_csv(links[\"GDP\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>"
      ],
      "text/plain": [
       "   date  level-current  level-chained  change-current  change-chained\n",
       "0  1948          274.8         2020.0            -0.7            -0.6\n",
       "1  1949          272.8         2008.9            10.0             8.7\n",
       "2  1950          300.2         2184.0            15.7             8.0\n",
       "3  1951          347.3         2360.0             5.9             4.1\n",
       "4  1952          367.7         2456.1             6.0             4.7"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_GDP.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 id=\"Section_2\"> Question 2: Create a dataframe that contains the unemployment data. Display the first five rows of the dataframe. </h3>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_unemployment = pd.read_csv(links[\"unemployment\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
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       "<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>"
      ],
      "text/plain": [
       "   date  unemployment\n",
       "0  1948      3.750000\n",
       "1  1949      6.050000\n",
       "2  1950      5.208333\n",
       "3  1951      3.283333\n",
       "4  1952      3.025000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_unemployment.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 id=\"Section_3\">Question 3: Display a dataframe where unemployment was greater than 8.5%.</h3>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "<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>"
      ],
      "text/plain": [
       "    date  unemployment\n",
       "34  1982      9.708333\n",
       "35  1983      9.600000\n",
       "61  2009      9.283333\n",
       "62  2010      9.608333\n",
       "63  2011      8.933333"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_unemployment[df_unemployment[\"unemployment\"] > 8.5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 id=\"Section_4\">Question 4: Use the function make_dashboard to make a dashboard</h3>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "x = df_GDP[[\"date\"]]\n",
    "gdp_change = df_GDP[[\"change-current\"]]\n",
    "unemployment = df_unemployment[[\"unemployment\"]]\n",
    "title = 'Unemployment'\n",
    "file_name = \"index.html\"\n",
    "make_dashboard(x= x, gdp_change= gdp_change, unemployment= unemployment, title= title, file_name= file_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<p>Copyright &copy; 2019 IBM Developer Skills Network. This notebook and its source code are released under the terms of the <a href=\"https://cognitiveclass.ai/mit-license/\">MIT License</a>.</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>About the Authors:</h2> \n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n",
    "\n",
    "<p>\n",
    "Other contributors: <a href=\"https://www.linkedin.com/in/yi-leng-yao-84451275/\">Yi leng Yao</a>, <a href=\"www.linkedin.com/in/jiahui-mavis-zhou-a4537814a\">Mavis Zhou</a> \n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>References :</h2> \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ul>\n",
    " <il>\n",
    "     1) <a href=\"https://research.stlouisfed.org/\">Economic Research at the St. Louis Fed </a>:<a href=\"https://fred.stlouisfed.org/series/UNRATE/\"> Civilian Unemployment Rate</a>\n",
    "   </il>   \n",
    "    <p>\n",
    "     <il>\n",
    "    2) <a href=\"https://github.com/datasets\">Data Packaged Core Datasets\n",
    "       </a>\n",
    "   </il> \n",
    "    </p>\n",
    "    \n",
    "</ul>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By    | Change Description        |\n",
    "| ----------------- | ------- | ------------- | ------------------------- |\n",
    "| 2020-11-10        | 1.1     | Malika Singla | Deleted the Optional part |\n",
    "| 2020-08-27        | 1.0     | Malika Singla | Added lab to GitLab       |\n",
    "\n",
    "<hr>\n",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
    "\n",
    "<p>\n"
   ]
  }
 ],
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