Python_for_Data_Science.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3.6",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.9",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "colab": {
      "name": "Python for Data Science.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Brent-Morrison/Misc_scripts/blob/master/Python_for_Data_Science.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nbAoOnI1p1qZ",
        "colab_type": "text"
      },
      "source": [
        "<h1>Analyzing US Economic Data and  Building a Dashboard  </h1>\n",
        "<h2>Description</h2>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pdu71oaFp1qa",
        "colab_type": "text"
      },
      "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>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hPt_HdaYp1qb",
        "colab_type": "text"
      },
      "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>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sAVFttOgp1qb",
        "colab_type": "text"
      },
      "source": [
        "<h2 id=\"Section_1\"> Define Function that Makes a Dashboard  </h2>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SsfvKHZPp1qc",
        "colab_type": "text"
      },
      "source": [
        "We will import the following libraries."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kbWBWscrp1qd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "from bokeh.plotting import figure, output_file, show, output_notebook\n",
        "output_notebook()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kl3BRL-np1qi",
        "colab_type": "text"
      },
      "source": [
        "In this section, we define the function <code>make_dashboard</code>. \n",
        "You 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. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LjGnkJeqp1qj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def make_dashboard(x, gdp_change, unemployment, title, file_name):\n",
        "    output_file(file_name)\n",
        "    p = figure(title=title, x_axis_label='year', y_axis_label='%')\n",
        "    p.line(x.squeeze(), gdp_change.squeeze(), color=\"firebrick\", line_width=4, legend=\"% GDP change\")\n",
        "    p.line(x.squeeze(), unemployment.squeeze(), line_width=4, legend=\"% unemployed\")\n",
        "    show(p)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8RLePUf_p1qp",
        "colab_type": "text"
      },
      "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."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OJVd_qvFp1qp",
        "colab_type": "code",
        "colab": {}
      },
      "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'}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rqQlYyuVp1qt",
        "colab_type": "text"
      },
      "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>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4M5_4SQbp1qu",
        "colab_type": "text"
      },
      "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."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w7lVOHj6p1qv",
        "colab_type": "text"
      },
      "source": [
        "<b>Hint: <code>links[\"GDP\"]</code> contains the path or name of the file.</b>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1sgY796Ap1qw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "gdp_df = pd.read_csv(links[\"GDP\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f6nUxz7Bp1q2",
        "colab_type": "text"
      },
      "source": [
        "Use the method <code>head()</code> to display the first five rows of the GDP data, then take a screen-shot."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dtTRR6uop1q3",
        "colab_type": "code",
        "outputId": "b8a8a9bc-73d4-443c-e0b5-bdcf8117a781",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "gdp_df.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S3ZUXmA1p1q8",
        "colab_type": "text"
      },
      "source": [
        "<h3 id=\"Section_2\"> Question 2: Create a dataframe that contains the unemployment data. Display the first five rows of the dataframe. </h3>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cHtjwKadp1rA",
        "colab_type": "text"
      },
      "source": [
        "Use the dictionary <code>links</code> and the function <code>pd.read_csv</code> to create a Pandas dataframes that contains the unemployment data.\n",
        "\n",
        "*I have appended the `_df` as otherwise the name of the data frame is the same as one of the columns.  I find this confusing.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w8N2uUVop1rB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "unemployment_df = pd.read_csv(links[\"unemployment\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sc1NPEwpp1rE",
        "colab_type": "text"
      },
      "source": [
        "Use the method <code>head()</code> to display the first five rows of the GDP data, then take a screen-shot."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ihc1Ocjap1rF",
        "colab_type": "code",
        "outputId": "d517eea7-d955-4d44-b65e-886d0d29c803",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "unemployment_df.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f_qM1pEep1rI",
        "colab_type": "text"
      },
      "source": [
        "<h3 id=\"Section_3\">Question 3: Display a dataframe where unemployment was greater than 8.5%. Take a screen-shot.</h3>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nJ6qsNEUp1rJ",
        "colab_type": "code",
        "outputId": "f698ae2f-8b1f-4b63-91f2-df2c3f944648",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "unemployment_df.query('unemployment > 8.5')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7iTTxvzEp1rN",
        "colab_type": "text"
      },
      "source": [
        "Note that there are other ways to perform the same operation.  I could have used this....."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tTA2By2nt6Kj",
        "colab_type": "code",
        "outputId": "047cb904-7758-4981-f5d0-fe5d55a7830c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "unemployment_df[unemployment_df['unemployment'] > 8.5].head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qfC00K1Q0c0W",
        "colab_type": "text"
      },
      "source": [
        "..... or this."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DP-39Ajv0Z3I",
        "colab_type": "code",
        "outputId": "bcea7418-96ff-4834-cdd2-1b1ef4ea8932",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "unemployment_df[unemployment_df.unemployment > 8.5].head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iSUW4LJ81CpK",
        "colab_type": "text"
      },
      "source": [
        "Note that [this](https://pandas.pydata.org/docs/getting_started/comparison/index.html) section of the pandas documentation is a handy reference if you are familiar with SQL or R.  This sets out how to perform equivalent operations in these languages."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oc8f2BoTp1rO",
        "colab_type": "text"
      },
      "source": [
        "<h3 id=\"Section_4\">Question 4: Use the function make_dashboard to make a dashboard</h3>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q0WM-k6np1rP",
        "colab_type": "text"
      },
      "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."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2g6yeCfKp1rQ",
        "colab_type": "text"
      },
      "source": [
        "Create a new dataframe with the column <code>'date'</code> called <code>x</code> from the dataframe that contains the GDP data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BGgeN62ip1rR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = gdp_df[['date']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yow1ctwep1rU",
        "colab_type": "text"
      },
      "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."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7K9cRMlzp1rV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "gdp_change = gdp_df[['change-current']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YZmTs3cfp1rY",
        "colab_type": "text"
      },
      "source": [
        "Create a new dataframe with the column <code>'unemployment' </code> called <code>unemployment</code>  from the dataframe that contains the  unemployment data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dIDPHn1Hp1rZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "unemployment = unemployment_df[['unemployment']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JSQHE573p1rd",
        "colab_type": "text"
      },
      "source": [
        "Give your dashboard a string title, and assign it to the variable <code>title</code>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D9upcexmp1rd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "title = 'Unemployment and GDP'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "glfDlrDBp1rh",
        "colab_type": "text"
      },
      "source": [
        "Finally, the function <code>make_dashboard</code> will output an <code>.html</code> in your directory, 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>."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cyLq9-Zxp1ri",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "file_name = \"index.html\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xSB6xX5up1ro",
        "colab_type": "text"
      },
      "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>."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QtxS5JKCp1rp",
        "colab_type": "code",
        "outputId": "50edbcb6-d1a6-482c-8e11-5a444eba8209",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 650
        }
      },
      "source": [
        "make_dashboard(x=x, gdp_change=gdp_change, unemployment=unemployment, title=title, file_name=file_name)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "BokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead\n",
            "BokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "(function(root) {\n",
              "  function now() {\n",
              "    return new Date();\n",
              "  }\n",
              "\n",
              "  var force = true;\n",
              "\n",
              "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
              "    root._bokeh_onload_callbacks = [];\n",
              "    root._bokeh_is_loading = undefined;\n",
              "  }\n",
              "\n",
              "  var JS_MIME_TYPE = 'application/javascript';\n",
              "  var HTML_MIME_TYPE = 'text/html';\n",
              "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
              "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
              "\n",
              "  /**\n",
              "   * Render data to the DOM node\n",
              "   */\n",
              "  function render(props, node) {\n",
              "    var script = document.createElement(\"script\");\n",
              "    node.appendChild(script);\n",
              "  }\n",
              "\n",
              "  /**\n",
              "   * Handle when an output is cleared or removed\n",
              "   */\n",
              "  function handleClearOutput(event, handle) {\n",
              "    var cell = handle.cell;\n",
              "\n",
              "    var id = cell.output_area._bokeh_element_id;\n",
              "    var server_id = cell.output_area._bokeh_server_id;\n",
              "    // Clean up Bokeh references\n",
              "    if (id != null && id in Bokeh.index) {\n",
              "      Bokeh.index[id].model.document.clear();\n",
              "      delete Bokeh.index[id];\n",
              "    }\n",
              "\n",
              "    if (server_id !== undefined) {\n",
              "      // Clean up Bokeh references\n",
              "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
              "      cell.notebook.kernel.execute(cmd, {\n",
              "        iopub: {\n",
              "          output: function(msg) {\n",
              "            var id = msg.content.text.trim();\n",
              "            if (id in Bokeh.index) {\n",
              "              Bokeh.index[id].model.document.clear();\n",
              "              delete Bokeh.index[id];\n",
              "            }\n",
              "          }\n",
              "        }\n",
              "      });\n",
              "      // Destroy server and session\n",
              "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
              "      cell.notebook.kernel.execute(cmd);\n",
              "    }\n",
              "  }\n",
              "\n",
              "  /**\n",
              "   * Handle when a new output is added\n",
              "   */\n",
              "  function handleAddOutput(event, handle) {\n",
              "    var output_area = handle.output_area;\n",
              "    var output = handle.output;\n",
              "\n",
              "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
              "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
              "      return\n",
              "    }\n",
              "\n",
              "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
              "\n",
              "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
              "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
              "      // store reference to embed id on output_area\n",
              "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
              "    }\n",
              "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
              "      var bk_div = document.createElement(\"div\");\n",
              "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
              "      var script_attrs = bk_div.children[0].attributes;\n",
              "      for (var i = 0; i < script_attrs.length; i++) {\n",
              "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
              "      }\n",
              "      // store reference to server id on output_area\n",
              "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
              "    }\n",
              "  }\n",
              "\n",
              "  function register_renderer(events, OutputArea) {\n",
              "\n",
              "    function append_mime(data, metadata, element) {\n",
              "      // create a DOM node to render to\n",
              "      var toinsert = this.create_output_subarea(\n",
              "        metadata,\n",
              "        CLASS_NAME,\n",
              "        EXEC_MIME_TYPE\n",
              "      );\n",
              "      this.keyboard_manager.register_events(toinsert);\n",
              "      // Render to node\n",
              "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
              "      render(props, toinsert[toinsert.length - 1]);\n",
              "      element.append(toinsert);\n",
              "      return toinsert\n",
              "    }\n",
              "\n",
              "    /* Handle when an output is cleared or removed */\n",
              "    events.on('clear_output.CodeCell', handleClearOutput);\n",
              "    events.on('delete.Cell', handleClearOutput);\n",
              "\n",
              "    /* Handle when a new output is added */\n",
              "    events.on('output_added.OutputArea', handleAddOutput);\n",
              "\n",
              "    /**\n",
              "     * Register the mime type and append_mime function with output_area\n",
              "     */\n",
              "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
              "      /* Is output safe? */\n",
              "      safe: true,\n",
              "      /* Index of renderer in `output_area.display_order` */\n",
              "      index: 0\n",
              "    });\n",
              "  }\n",
              "\n",
              "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
              "  if (root.Jupyter !== undefined) {\n",
              "    var events = require('base/js/events');\n",
              "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
              "\n",
              "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
              "      register_renderer(events, OutputArea);\n",
              "    }\n",
              "  }\n",
              "\n",
              "  \n",
              "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
              "    root._bokeh_timeout = Date.now() + 5000;\n",
              "    root._bokeh_failed_load = false;\n",
              "  }\n",
              "\n",
              "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
              "     \"<div style='background-color: #fdd'>\\n\"+\n",
              "     \"<p>\\n\"+\n",
              "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
              "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
              "     \"</p>\\n\"+\n",
              "     \"<ul>\\n\"+\n",
              "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
              "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
              "     \"</ul>\\n\"+\n",
              "     \"<code>\\n\"+\n",
              "     \"from bokeh.resources import INLINE\\n\"+\n",
              "     \"output_notebook(resources=INLINE)\\n\"+\n",
              "     \"</code>\\n\"+\n",
              "     \"</div>\"}};\n",
              "\n",
              "  function display_loaded() {\n",
              "    var el = document.getElementById(null);\n",
              "    if (el != null) {\n",
              "      el.textContent = \"BokehJS is loading...\";\n",
              "    }\n",
              "    if (root.Bokeh !== undefined) {\n",
              "      if (el != null) {\n",
              "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
              "      }\n",
              "    } else if (Date.now() < root._bokeh_timeout) {\n",
              "      setTimeout(display_loaded, 100)\n",
              "    }\n",
              "  }\n",
              "\n",
              "\n",
              "  function run_callbacks() {\n",
              "    try {\n",
              "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
              "        if (callback != null)\n",
              "          callback();\n",
              "      });\n",
              "    } finally {\n",
              "      delete root._bokeh_onload_callbacks\n",
              "    }\n",
              "    console.debug(\"Bokeh: all callbacks have finished\");\n",
              "  }\n",
              "\n",
              "  function load_libs(css_urls, js_urls, callback) {\n",
              "    if (css_urls == null) css_urls = [];\n",
              "    if (js_urls == null) js_urls = [];\n",
              "\n",
              "    root._bokeh_onload_callbacks.push(callback);\n",
              "    if (root._bokeh_is_loading > 0) {\n",
              "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
              "      return null;\n",
              "    }\n",
              "    if (js_urls == null || js_urls.length === 0) {\n",
              "      run_callbacks();\n",
              "      return null;\n",
              "    }\n",
              "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
              "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
              "\n",
              "    function on_load() {\n",
              "      root._bokeh_is_loading--;\n",
              "      if (root._bokeh_is_loading === 0) {\n",
              "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
              "        run_callbacks()\n",
              "      }\n",
              "    }\n",
              "\n",
              "    function on_error() {\n",
              "      console.error(\"failed to load \" + url);\n",
              "    }\n",
              "\n",
              "    for (var i = 0; i < css_urls.length; i++) {\n",
              "      var url = css_urls[i];\n",
              "      const element = document.createElement(\"link\");\n",
              "      element.onload = on_load;\n",
              "      element.onerror = on_error;\n",
              "      element.rel = \"stylesheet\";\n",
              "      element.type = \"text/css\";\n",
              "      element.href = url;\n",
              "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
              "      document.body.appendChild(element);\n",
              "    }\n",
              "\n",
              "    for (var i = 0; i < js_urls.length; i++) {\n",
              "      var url = js_urls[i];\n",
              "      var element = document.createElement('script');\n",
              "      element.onload = on_load;\n",
              "      element.onerror = on_error;\n",
              "      element.async = false;\n",
              "      element.src = url;\n",
              "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
              "      document.head.appendChild(element);\n",
              "    }\n",
              "  };\n",
              "\n",
              "  function inject_raw_css(css) {\n",
              "    const element = document.createElement(\"style\");\n",
              "    element.appendChild(document.createTextNode(css));\n",
              "    document.body.appendChild(element);\n",
              "  }\n",
              "\n",
              "  \n",
              "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n",
              "  var css_urls = [];\n",
              "  \n",
              "\n",
              "  var inline_js = [\n",
              "    function(Bokeh) {\n",
              "      Bokeh.set_log_level(\"info\");\n",
              "    },\n",
              "    function(Bokeh) {\n",
              "    \n",
              "    \n",
              "    }\n",
              "  ];\n",
              "\n",
              "  function run_inline_js() {\n",
              "    \n",
              "    if (root.Bokeh !== undefined || force === true) {\n",
              "      \n",
              "    for (var i = 0; i < inline_js.length; i++) {\n",
              "      inline_js[i].call(root, root.Bokeh);\n",
              "    }\n",
              "    } 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(null)).parents('.cell').data().cell;\n",
              "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
              "    }\n",
              "\n",
              "  }\n",
              "\n",
              "  if (root._bokeh_is_loading === 0) {\n",
              "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
              "    run_inline_js();\n",
              "  } else {\n",
              "    load_libs(css_urls, js_urls, function() {\n",
              "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
              "      run_inline_js();\n",
              "    });\n",
              "  }\n",
              "}(window));"
            ],
            "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(null);\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    } 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(null)).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "\n",
              "\n",
              "\n",
              "\n",
              "\n",
              "  <div class=\"bk-root\" id=\"85b977a5-3544-48ea-a533-071dbc99cc54\" data-root-id=\"1001\"></div>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "(function(root) {\n",
              "  function embed_document(root) {\n",
              "    \n",
              "  var docs_json = {\"e442590e-b9a6-4386-b557-73c1de088c6c\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1012\",\"type\":\"LinearAxis\"}],\"center\":[{\"id\":\"1016\",\"type\":\"Grid\"},{\"id\":\"1021\",\"type\":\"Grid\"},{\"id\":\"1046\",\"type\":\"Legend\"}],\"left\":[{\"id\":\"1017\",\"type\":\"LinearAxis\"}],\"renderers\":[{\"id\":\"1038\",\"type\":\"GlyphRenderer\"},{\"id\":\"1051\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"1002\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"1028\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"1004\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"1008\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"1006\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"1010\",\"type\":\"LinearScale\"}},\"id\":\"1001\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{},\"id\":\"1010\",\"type\":\"LinearScale\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1022\",\"type\":\"PanTool\"},{\"id\":\"1023\",\"type\":\"WheelZoomTool\"},{\"id\":\"1024\",\"type\":\"BoxZoomTool\"},{\"id\":\"1025\",\"type\":\"SaveTool\"},{\"id\":\"1026\",\"type\":\"ResetTool\"},{\"id\":\"1027\",\"type\":\"HelpTool\"}]},\"id\":\"1028\",\"type\":\"Toolbar\"},{\"attributes\":{\"source\":{\"id\":\"1035\",\"type\":\"ColumnDataSource\"}},\"id\":\"1039\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1071\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1042\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1044\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"callback\":null},\"id\":\"1006\",\"type\":\"DataRange1d\"},{\"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},\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1045\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"items\":[{\"id\":\"1047\",\"type\":\"LegendItem\"},{\"id\":\"1061\",\"type\":\"LegendItem\"}]},\"id\":\"1046\",\"type\":\"Legend\"},{\"attributes\":{\"label\":{\"value\":\"% GDP change\"},\"renderers\":[{\"id\":\"1038\",\"type\":\"GlyphRenderer\"}]},\"id\":\"1047\",\"type\":\"LegendItem\"},{\"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\":\"1071\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1070\",\"type\":\"UnionRenderers\"}},\"id\":\"1048\",\"type\":\"ColumnDataSource\"},{\"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\":\"1060\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1059\",\"type\":\"UnionRenderers\"}},\"id\":\"1035\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1022\",\"type\":\"PanTool\"},{\"attributes\":{\"data_source\":{\"id\":\"1035\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1036\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1037\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"1039\",\"type\":\"CDSView\"}},\"id\":\"1038\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1008\",\"type\":\"LinearScale\"},{\"attributes\":{\"text\":\"Unemployment and GDP\"},\"id\":\"1002\",\"type\":\"Title\"},{\"attributes\":{\"line_color\":\"#1f77b4\",\"line_width\":4,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1049\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"1018\",\"type\":\"BasicTicker\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"line_width\":4,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1050\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"1013\",\"type\":\"BasicTicker\"},{\"attributes\":{\"data_source\":{\"id\":\"1048\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1049\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1050\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"1052\",\"type\":\"CDSView\"}},\"id\":\"1051\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"line_color\":\"firebrick\",\"line_width\":4,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1036\",\"type\":\"Line\"},{\"attributes\":{\"source\":{\"id\":\"1048\",\"type\":\"ColumnDataSource\"}},\"id\":\"1052\",\"type\":\"CDSView\"},{\"attributes\":{\"ticker\":{\"id\":\"1013\",\"type\":\"BasicTicker\"}},\"id\":\"1016\",\"type\":\"Grid\"},{\"attributes\":{\"overlay\":{\"id\":\"1045\",\"type\":\"BoxAnnotation\"}},\"id\":\"1024\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"axis_label\":\"%\",\"formatter\":{\"id\":\"1044\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1018\",\"type\":\"BasicTicker\"}},\"id\":\"1017\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"1059\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"1018\",\"type\":\"BasicTicker\"}},\"id\":\"1021\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"1025\",\"type\":\"SaveTool\"},{\"attributes\":{},\"id\":\"1060\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1026\",\"type\":\"ResetTool\"},{\"attributes\":{\"axis_label\":\"year\",\"formatter\":{\"id\":\"1042\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1013\",\"type\":\"BasicTicker\"}},\"id\":\"1012\",\"type\":\"LinearAxis\"},{\"attributes\":{\"label\":{\"value\":\"% unemployed\"},\"renderers\":[{\"id\":\"1051\",\"type\":\"GlyphRenderer\"}]},\"id\":\"1061\",\"type\":\"LegendItem\"},{\"attributes\":{},\"id\":\"1027\",\"type\":\"HelpTool\"},{\"attributes\":{},\"id\":\"1023\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"1070\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"line_width\":4,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1037\",\"type\":\"Line\"},{\"attributes\":{\"callback\":null},\"id\":\"1004\",\"type\":\"DataRange1d\"}],\"root_ids\":[\"1001\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
              "  var render_items = [{\"docid\":\"e442590e-b9a6-4386-b557-73c1de088c6c\",\"roots\":{\"1001\":\"85b977a5-3544-48ea-a533-071dbc99cc54\"}}];\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",
              "        clearInterval(timer);\n",
              "        embed_document(root);\n",
              "      } else {\n",
              "        attempts++;\n",
              "        if (attempts > 100) {\n",
              "          clearInterval(timer);\n",
              "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
              "        }\n",
              "      }\n",
              "    }, 10, root)\n",
              "  }\n",
              "})(window);"
            ],
            "application/vnd.bokehjs_exec.v0+json": ""
          },
          "metadata": {
            "tags": [],
            "application/vnd.bokehjs_exec.v0+json": {
              "id": "1001"
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DXuUo8NVp1sc",
        "colab_type": "text"
      },
      "source": [
        "<h2>References :</h2> "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VZOq8r4_p1sd",
        "colab_type": "text"
      },
      "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>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UU6fEWAY23Xq",
        "colab_type": "text"
      },
      "source": [
        "# Lets look at something in an Irish context (should we have time)\n",
        "\n",
        "Lets see if we can observe a relationship between the irish unemployment rate and the year over year change in total stock of bank credit issued to the non-financial sector. \n",
        "\n",
        "We will use the quandl package to retrieve data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Zro6X8WD5hm",
        "colab_type": "code",
        "outputId": "62aeed34-c974-4320-a6b9-6cfcc9ad6fec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "!pip install quandl"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: quandl in /usr/local/lib/python3.6/dist-packages (3.5.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from quandl) (1.12.0)\n",
            "Requirement already satisfied: requests>=2.7.0 in /usr/local/lib/python3.6/dist-packages (from quandl) (2.23.0)\n",
            "Requirement already satisfied: more-itertools in /usr/local/lib/python3.6/dist-packages (from quandl) (8.3.0)\n",
            "Requirement already satisfied: pandas>=0.14 in /usr/local/lib/python3.6/dist-packages (from quandl) (1.0.3)\n",
            "Requirement already satisfied: numpy>=1.8 in /usr/local/lib/python3.6/dist-packages (from quandl) (1.18.4)\n",
            "Requirement already satisfied: inflection>=0.3.1 in /usr/local/lib/python3.6/dist-packages (from quandl) (0.4.0)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.6/dist-packages (from quandl) (2.8.1)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.7.0->quandl) (2020.4.5.1)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.7.0->quandl) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.7.0->quandl) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.7.0->quandl) (2.9)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.14->quandl) (2018.9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eJ1DXnBVK8OB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import quandl as ql\n",
        "\n",
        "# Use Matplotlib instead of Bokeh this time\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-XaVdE4RvzQF",
        "colab_type": "text"
      },
      "source": [
        "Grabbing the data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HqUTtCbzLy5w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ql.ApiConfig.api_key = 'hpbPcsfGudN3viBgh8th'\n",
        "\n",
        "# Credit to Private Non-Financial Sector by Banks\n",
        "irl_banks = ql.get('BIS/CN_QIEPBM770A') \n",
        "\n",
        "# Ireland Standardised unemployment, Rate, Total , Total ; unspecified; Eurostat; Seasonally adjusted\n",
        "irl_unemp = ql.get('ECB/STS_M_IE_S_UNEH_RTT000_4_000')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q-umikn5v2uy",
        "colab_type": "text"
      },
      "source": [
        "Viewing the last entries in each series."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4A4Z1lfOLE4H",
        "colab_type": "code",
        "outputId": "745e0d0e-0ae2-47db-f580-2d0f53218e5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        }
      },
      "source": [
        "irl_banks.tail()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Value</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-12-31</th>\n",
              "      <td>40.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-31</th>\n",
              "      <td>39.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-30</th>\n",
              "      <td>39.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-09-30</th>\n",
              "      <td>38.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31</th>\n",
              "      <td>37.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Value\n",
              "Date             \n",
              "2018-12-31   40.9\n",
              "2019-03-31   39.6\n",
              "2019-06-30   39.2\n",
              "2019-09-30   38.8\n",
              "2019-12-31   37.7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OQOyd4POdcu-",
        "colab_type": "code",
        "outputId": "28b9eee9-d264-4f2f-8a56-0138ad212504",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        }
      },
      "source": [
        "irl_unemp.tail()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Percent</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-12-31</th>\n",
              "      <td>4.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-31</th>\n",
              "      <td>4.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-29</th>\n",
              "      <td>4.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-31</th>\n",
              "      <td>5.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-30</th>\n",
              "      <td>5.37</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Percent\n",
              "Date               \n",
              "2019-12-31     4.74\n",
              "2020-01-31     4.86\n",
              "2020-02-29     4.84\n",
              "2020-03-31     5.28\n",
              "2020-04-30     5.37"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ppq0TL9yv-6p",
        "colab_type": "text"
      },
      "source": [
        "To get a sense of the data downloaded, we look at the length and frequency of each series."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iigkvQbSfSJq",
        "colab_type": "code",
        "outputId": "dd1a32bf-4efb-471f-fddc-688ee3b0d71a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "print('Length of banking series :'.ljust(45)+str(len(irl_banks)))\n",
        "print('Frequency of banking series :'.ljust(45)+pd.infer_freq(irl_banks.index))\n",
        "print('\\n')\n",
        "print('Length of unemployment series :'.ljust(45)+str(len(irl_unemp)))\n",
        "print('Frequency of unemployment series :'.ljust(45)+pd.infer_freq(irl_unemp.index))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Length of banking series :                   195\n",
            "Frequency of banking series :                Q-DEC\n",
            "\n",
            "\n",
            "Length of unemployment series :              448\n",
            "Frequency of unemployment series :           M\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1jwOTzh3wZLr",
        "colab_type": "text"
      },
      "source": [
        "The unemployment series is quarterly, while the banking series is monthly.  In order to plot these series, we will join the data, rename columns and interpolate missing values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dpVO3y0Hi_0F",
        "colab_type": "code",
        "outputId": "f6036a11-a143-4acc-aa58-d6471f3fdf3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        }
      },
      "source": [
        "# Join series\n",
        "irl = pd.merge(irl_unemp, irl_banks, how='left', left_index=True, right_index=True)\n",
        "# Rename columns\n",
        "irl.columns = ['Unemployment', 'Bank_credit']\n",
        "irl.tail(10)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Unemployment</th>\n",
              "      <th>Bank_credit</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-07-31</th>\n",
              "      <td>5.05</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-08-31</th>\n",
              "      <td>4.95</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-09-30</th>\n",
              "      <td>4.85</td>\n",
              "      <td>38.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-31</th>\n",
              "      <td>4.73</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-11-30</th>\n",
              "      <td>4.68</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31</th>\n",
              "      <td>4.74</td>\n",
              "      <td>37.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-31</th>\n",
              "      <td>4.86</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-29</th>\n",
              "      <td>4.84</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-31</th>\n",
              "      <td>5.28</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-30</th>\n",
              "      <td>5.37</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Unemployment  Bank_credit\n",
              "Date                                 \n",
              "2019-07-31          5.05          NaN\n",
              "2019-08-31          4.95          NaN\n",
              "2019-09-30          4.85         38.8\n",
              "2019-10-31          4.73          NaN\n",
              "2019-11-30          4.68          NaN\n",
              "2019-12-31          4.74         37.7\n",
              "2020-01-31          4.86          NaN\n",
              "2020-02-29          4.84          NaN\n",
              "2020-03-31          5.28          NaN\n",
              "2020-04-30          5.37          NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YqQxak31k8D5",
        "colab_type": "text"
      },
      "source": [
        "Interpolate missing values returned due to the differing frequencies.  Note that the `merge_ordered` function could have been used above to perform this in one step."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4X4pAhyfj4GI",
        "colab_type": "code",
        "outputId": "526e2126-adaf-4bd8-b323-154e03f61dcf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        }
      },
      "source": [
        "irl.interpolate(method='time', inplace=True)\n",
        "irl.tail(10)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Unemployment</th>\n",
              "      <th>Bank_credit</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-07-31</th>\n",
              "      <td>5.05</td>\n",
              "      <td>39.065217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-08-31</th>\n",
              "      <td>4.95</td>\n",
              "      <td>38.930435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-09-30</th>\n",
              "      <td>4.85</td>\n",
              "      <td>38.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-31</th>\n",
              "      <td>4.73</td>\n",
              "      <td>38.429348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-11-30</th>\n",
              "      <td>4.68</td>\n",
              "      <td>38.070652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31</th>\n",
              "      <td>4.74</td>\n",
              "      <td>37.700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-31</th>\n",
              "      <td>4.86</td>\n",
              "      <td>37.700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-29</th>\n",
              "      <td>4.84</td>\n",
              "      <td>37.700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-31</th>\n",
              "      <td>5.28</td>\n",
              "      <td>37.700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-30</th>\n",
              "      <td>5.37</td>\n",
              "      <td>37.700000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Unemployment  Bank_credit\n",
              "Date                                 \n",
              "2019-07-31          5.05    39.065217\n",
              "2019-08-31          4.95    38.930435\n",
              "2019-09-30          4.85    38.800000\n",
              "2019-10-31          4.73    38.429348\n",
              "2019-11-30          4.68    38.070652\n",
              "2019-12-31          4.74    37.700000\n",
              "2020-01-31          4.86    37.700000\n",
              "2020-02-29          4.84    37.700000\n",
              "2020-03-31          5.28    37.700000\n",
              "2020-04-30          5.37    37.700000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tSorVtfonm-M",
        "colab_type": "text"
      },
      "source": [
        "We can see the `NaN` values have now been interpolated.  Note that the values post December 2019 have been filled forward.\n",
        "\n",
        "Opening `NaN` values are not interpolated since there is no initial reference value."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RqmFXyOinmDJ",
        "colab_type": "code",
        "outputId": "9b8dfb7d-616d-4e02-c6c1-a50fba7205c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        }
      },
      "source": [
        "irl.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Unemployment</th>\n",
              "      <th>Bank_credit</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1983-01-31</th>\n",
              "      <td>12.94</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1983-02-28</th>\n",
              "      <td>13.17</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1983-03-31</th>\n",
              "      <td>13.37</td>\n",
              "      <td>51.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1983-04-30</th>\n",
              "      <td>13.54</td>\n",
              "      <td>51.735165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1983-05-31</th>\n",
              "      <td>13.72</td>\n",
              "      <td>51.564835</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Unemployment  Bank_credit\n",
              "Date                                 \n",
              "1983-01-31         12.94          NaN\n",
              "1983-02-28         13.17          NaN\n",
              "1983-03-31         13.37    51.900000\n",
              "1983-04-30         13.54    51.735165\n",
              "1983-05-31         13.72    51.564835"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "npx3kXyhoR57",
        "colab_type": "text"
      },
      "source": [
        "Compute the year over year change in bank credit and drop NA's.  \n",
        "\n",
        "The YOY change is computed first.  This is because we want to drop NA's arising from the initial months when there is no prior values to calculate the YOY change."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fjHnc1TkoXc5",
        "colab_type": "code",
        "outputId": "1dfcaa3a-c699-415e-8120-ab6974b98c56",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        }
      },
      "source": [
        "irl['Bank_credit_YOY']=(irl.Bank_credit.pct_change(periods=12)*100)\n",
        "irl.dropna(axis=0, inplace=True)\n",
        "irl.tail(10)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Unemployment</th>\n",
              "      <th>Bank_credit</th>\n",
              "      <th>Bank_credit_YOY</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-07-31</th>\n",
              "      <td>5.05</td>\n",
              "      <td>39.065217</td>\n",
              "      <td>-6.166780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-08-31</th>\n",
              "      <td>4.95</td>\n",
              "      <td>38.930435</td>\n",
              "      <td>-6.338912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-09-30</th>\n",
              "      <td>4.85</td>\n",
              "      <td>38.800000</td>\n",
              "      <td>-6.506024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-31</th>\n",
              "      <td>4.73</td>\n",
              "      <td>38.429348</td>\n",
              "      <td>-6.945834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-11-30</th>\n",
              "      <td>4.68</td>\n",
              "      <td>38.070652</td>\n",
              "      <td>-7.375575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31</th>\n",
              "      <td>4.74</td>\n",
              "      <td>37.700000</td>\n",
              "      <td>-7.823961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-31</th>\n",
              "      <td>4.86</td>\n",
              "      <td>37.700000</td>\n",
              "      <td>-6.803637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-29</th>\n",
              "      <td>4.84</td>\n",
              "      <td>37.700000</td>\n",
              "      <td>-5.862442</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-31</th>\n",
              "      <td>5.28</td>\n",
              "      <td>37.700000</td>\n",
              "      <td>-4.797980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-30</th>\n",
              "      <td>5.37</td>\n",
              "      <td>37.700000</td>\n",
              "      <td>-4.479898</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Unemployment  Bank_credit  Bank_credit_YOY\n",
              "Date                                                  \n",
              "2019-07-31          5.05    39.065217        -6.166780\n",
              "2019-08-31          4.95    38.930435        -6.338912\n",
              "2019-09-30          4.85    38.800000        -6.506024\n",
              "2019-10-31          4.73    38.429348        -6.945834\n",
              "2019-11-30          4.68    38.070652        -7.375575\n",
              "2019-12-31          4.74    37.700000        -7.823961\n",
              "2020-01-31          4.86    37.700000        -6.803637\n",
              "2020-02-29          4.84    37.700000        -5.862442\n",
              "2020-03-31          5.28    37.700000        -4.797980\n",
              "2020-04-30          5.37    37.700000        -4.479898"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ue1ZPHfzyWmE",
        "colab_type": "text"
      },
      "source": [
        "Now on to the plotting."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xm6QySS80Kgp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "d86636cc-73d9-49f1-faac-0e29a08d7d35"
      },
      "source": [
        "plt.style.use('ggplot')\n",
        "fig1, (ax1, ax2) = plt.subplots(nrows = 2, figsize=(11, 9))\n",
        "fig1.suptitle('Unemployment and changes in Bank credit', size = 16).set_y(0.925)\n",
        "fig1.text(.5, .05, 'Bank credit is Credit to Private Non-Financial Sector by Banks', ha='left')\n",
        "ax1.plot(irl.index, irl['Unemployment'], 'k-')\n",
        "ax1.set_ylabel('Unemployment rate (percent)', size = 11)\n",
        "ax1.get_yaxis().set_label_coords(-0.05,0.5)\n",
        "ax2.plot(irl.index, irl['Bank_credit_YOY'], 'k-')\n",
        "ax2.set_ylabel('YOY change in bank credit (percent)', size = 11)\n",
        "ax2.get_yaxis().set_label_coords(-0.05,0.5)\n",
        "ax2.hlines(y=0, xmin=irl.index.min(), xmax=irl.index.max(), linestyles='dashed', colors = 'gray', linewidths = 1)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.LineCollection at 0x7f980c0da198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 792x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yuRlm5SDJmFX",
        "colab_type": "text"
      },
      "source": [
        "..... This is were you write up the interesting insights from your analysis! .....\n",
        "\n"
      ]
    }
  ]
}