daily_geographic_distribution_of_covid_19_cases_worldwide.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wuPOJockkOtd",
        "colab_type": "text"
      },
      "source": [
        "# Display charts about the geographic distribution of COVID-19 cases worldwide\n",
        "\n",
        "> Data source is \"today’s data on the geographic distribution of COVID-19 cases worldwide\": http://bit.ly/2U9YcGS\n",
        "\n",
        "> ECDC Dashboard : http://bit.ly/38Ysgum"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "qie5vCvOGQNr",
        "colab_type": "code",
        "outputId": "3ec79ee8-4bfd-4ae1-a825-243f855668a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "# setup the python program\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from plotly.offline import iplot\n",
        "import plotly.graph_objs as go\n",
        "from datetime import datetime, timedelta\n",
        "import locale\n",
        "import urllib.request, urllib.error\n",
        "import ipywidgets as widgets\n",
        "locale.setlocale(locale.LC_ALL, '')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'en_US.UTF-8'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "skvWxnmeeOtw",
        "colab_type": "text"
      },
      "source": [
        "# Load the LATEST data from the European Center for Disease Prevention and Control (ECDC)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kIpPBjERVpxa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def check_if_data_source_exists(url):\n",
        "  \"\"\"\n",
        "  check if an url exists\n",
        "  \"\"\"\n",
        "  url_exists = False\n",
        "  try:\n",
        "    conn = urllib.request.urlopen(url)\n",
        "  except urllib.error.HTTPError as e:\n",
        "      # Return code error (e.g. 404, 501, ...)\n",
        "      # ...\n",
        "      print('HTTPError: {}, the file does not exist'.format(e.code))\n",
        "      url_exists = False\n",
        "  except urllib.error.URLError as e:\n",
        "      # Not an HTTP-specific error (e.g. connection refused)\n",
        "      # ...\n",
        "      print('URLError: {}'.format(e.reason))\n",
        "      url_exists = False\n",
        "  else:\n",
        "      # 200\n",
        "      # ...\n",
        "      print('The url exists')\n",
        "      url_exists = True\n",
        "  return (url_exists), url"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xgHiyT5sWG7P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def find_latest_datasource(url):\n",
        "  \"\"\"\n",
        "  find the latest datasource starting today from the ECDC website\n",
        "\n",
        "  format of the url: \n",
        "    https://www.ecdc.europa.eu/sites/default/files/documents/COVID-19-geographic-disbtribution-worldwide-2020-03-17.xlsx\n",
        "    https://www.ecdc.europa.eu/sites/default/files/documents/COVID-19-geographic-disbtribution-worldwide-%latest_datasource_date%.xlsx\n",
        "\n",
        "  \"\"\"\n",
        "  is_found=False\n",
        "  i=-1\n",
        "  while is_found == False and i<5:\n",
        "    i += 1\n",
        "    latest_datasource_date = (datetime.now() - timedelta(i)).strftime('%Y-%m-%d')\n",
        "    datasource_url = url.replace('%latest_datasource_date%', latest_datasource_date)\n",
        "    print(f'Search for this latest date: {latest_datasource_date}',\n",
        "          f'datasource_url: {datasource_url}')\n",
        "    is_found, existing_datasource_date = check_if_data_source_exists(datasource_url + \".xls\")\n",
        "    if is_found == False:\n",
        "      is_found, existing_datasource_date = check_if_data_source_exists(datasource_url + \".xlsx\")\n",
        "\n",
        "  if is_found == True:\n",
        "    print(f'Download the data until : {latest_datasource_date}')\n",
        "    return existing_datasource_date\n",
        "  else:\n",
        "    return None"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6eIOpLa3oAUp",
        "colab_type": "code",
        "outputId": "d079979a-18be-48af-e0c5-42f790da03f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "# load the data\n",
        "datasource_url_template = \"https://www.ecdc.europa.eu/sites/default/files/documents/COVID-19-geographic-disbtribution-worldwide-%latest_datasource_date%\"\n",
        "if datasource_url_template is None:\n",
        "  print(f'There is no datasource on the ECDC website. is https://www.ecdc.europa.eu down?')\n",
        "  exit\n",
        "datasource_url = find_latest_datasource(datasource_url_template)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Search for this latest date: 2020-03-21 datasource_url: https://www.ecdc.europa.eu/sites/default/files/documents/COVID-19-geographic-disbtribution-worldwide-2020-03-21\n",
            "HTTPError: 404, the file does not exist\n",
            "HTTPError: 404, the file does not exist\n",
            "Search for this latest date: 2020-03-20 datasource_url: https://www.ecdc.europa.eu/sites/default/files/documents/COVID-19-geographic-disbtribution-worldwide-2020-03-20\n",
            "HTTPError: 404, the file does not exist\n",
            "The url exists\n",
            "Download the data until : 2020-03-20\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "qhf_nO3HGQN8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_excel(datasource_url)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8hrUN3ojRC7B",
        "colab_type": "text"
      },
      "source": [
        "# What is the range of data that has been collected from the ECDC?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6_r-FL0vQuZm",
        "colab_type": "code",
        "outputId": "01a02d02-0a59-43ed-c47f-722ddf67ade3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "start_date = df.DateRep.min().strftime(\"%b %d %Y\")\n",
        "end_date = df.DateRep.max().strftime(\"%b %d %Y\")\n",
        "\n",
        "print(\n",
        "    f'DATE RANGE:\\n',\n",
        "    f'start date : {start_date}\\n', \n",
        "    f'end date : {end_date}'\n",
        "      )"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DATE RANGE:\n",
            " start date : Dec 31 2019\n",
            " end date : Mar 20 2020\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SsiarKaserk5",
        "colab_type": "text"
      },
      "source": [
        "# Display Totals : Confirmed Cases and Deaths"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xm6f2_Bnev4Q",
        "colab_type": "code",
        "outputId": "43a9de34-06dd-4460-f0a1-0cdc463adb07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "cv19_countries_day = df.groupby(by=['DateRep','Countries and territories']).sum()[['Cases','Deaths']]\n",
        "Total_confirmed = cv19_countries_day.groupby('DateRep').sum()[['Cases','Deaths']].sum()['Cases']\n",
        "Total_deaths = cv19_countries_day.groupby('DateRep').sum()[['Cases','Deaths']].sum()['Deaths']\n",
        "print(f\"{end_date}:\\n Total Confirmed Cases: {Total_confirmed:n}\\n Total Deaths: {Total_deaths:n}\")"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mar 20 2020:\n",
            " Total Confirmed Cases: 242,476\n",
            " Total Deaths: 9,885\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kTZXhPfERRC5",
        "colab_type": "text"
      },
      "source": [
        "# Group the data by daily-New Confirmed Cases (WW)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NTEPU7CBRUsJ",
        "colab_type": "code",
        "outputId": "3dacca46-78bd-43f4-b507-f93c6fa77198",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        }
      },
      "source": [
        "cv19_countries_day_new_confirmed_cases = df.groupby(by=['DateRep'], as_index=False)['Cases'].sum()\n",
        "print(f'WW New Confirmed cases first-5 records:\\n\\n {cv19_countries_day_new_confirmed_cases.head(5)}')\n",
        "print(f'\\nWW New Confirmed cases last-5 records:\\n\\n {cv19_countries_day_new_confirmed_cases.tail(5)}')"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WW New Confirmed cases first-5 records:\n",
            "\n",
            "      DateRep  Cases\n",
            "0 2019-12-31     27\n",
            "1 2020-01-01      0\n",
            "2 2020-01-02      0\n",
            "3 2020-01-03     17\n",
            "4 2020-01-04      0\n",
            "\n",
            "WW New Confirmed cases last-5 records:\n",
            "\n",
            "       DateRep  Cases\n",
            "76 2020-03-16  16051\n",
            "77 2020-03-17  12745\n",
            "78 2020-03-18  14750\n",
            "79 2020-03-19  18345\n",
            "80 2020-03-20  29222\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yc1jubCxf3d2",
        "colab_type": "text"
      },
      "source": [
        "# Group the data by daily Deaths (WW)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cAd_-14GcAKk",
        "colab_type": "code",
        "outputId": "d33766db-5213-4f34-dcef-f0932be9c9b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        }
      },
      "source": [
        "cv19_countries_day_new_deaths = df.groupby(by=['DateRep'], as_index=False)['Deaths'].sum()\n",
        "print(f'WW New Deaths first-5 records:\\n\\n {cv19_countries_day_new_deaths.head(5)}')\n",
        "print(f'nWW New Deaths last-5 records:\\n\\n {cv19_countries_day_new_deaths.tail(5)}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WW New Deaths first-5 records:\n",
            "\n",
            "      DateRep  Deaths\n",
            "0 2019-12-31       0\n",
            "1 2020-01-01       0\n",
            "2 2020-01-02       0\n",
            "3 2020-01-03       0\n",
            "4 2020-01-04       0\n",
            "nWW New Deaths last-5 records:\n",
            "\n",
            "       DateRep  Deaths\n",
            "74 2020-03-14     438\n",
            "75 2020-03-15     354\n",
            "76 2020-03-16     746\n",
            "77 2020-03-17     596\n",
            "78 2020-03-18     773\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HR8wAWsLf_O3",
        "colab_type": "text"
      },
      "source": [
        "# Draw a chart with daily New (WW) Confirmed Cases and Deaths"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ayeoT2tkWZvP",
        "colab_type": "code",
        "outputId": "81fdcfa7-9348-4e33-a45e-5701d028c5a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "data = [\n",
        "        go.Scatter(\n",
        "        name=\"WW New Confirmed cases\",\n",
        "        x=cv19_countries_day_new_confirmed_cases['DateRep'],\n",
        "        y=cv19_countries_day_new_confirmed_cases['Cases']\n",
        "        ),\n",
        "        go.Scatter(\n",
        "        name=\"WW New Deaths\",\n",
        "        x=cv19_countries_day_new_deaths['DateRep'],\n",
        "        y=cv19_countries_day_new_deaths['Deaths']\n",
        "        ),\n",
        "]\n",
        "\n",
        "# plot titles and axis labels\n",
        "layout = go.Layout(\n",
        "    title=f'Worldwide New Confirmed cases and Deaths',\n",
        "    xaxis=dict(\n",
        "        title='Date'\n",
        "    ),\n",
        "    yaxis=dict(\n",
        "        title='New Confirmed cases/New Deaths'\n",
        "    )\n",
        ")\n",
        "\n",
        "# plot the Figure object using the 'iplot' method\n",
        "fig = go.Figure(data=data, layout=layout)\n",
        "iplot(fig)\n"
      ],
      "execution_count": 0,
      "outputs": [
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              "                        [{\"name\": \"WW New Confirmed cases\", \"type\": \"scatter\", \"x\": [\"2019-12-31T00:00:00\", \"2020-01-01T00:00:00\", \"2020-01-02T00:00:00\", \"2020-01-03T00:00:00\", \"2020-01-04T00:00:00\", \"2020-01-05T00:00:00\", \"2020-01-06T00:00:00\", \"2020-01-07T00:00:00\", \"2020-01-08T00:00:00\", \"2020-01-09T00:00:00\", \"2020-01-10T00:00:00\", \"2020-01-11T00:00:00\", \"2020-01-12T00:00:00\", \"2020-01-13T00:00:00\", \"2020-01-14T00:00:00\", \"2020-01-15T00:00:00\", \"2020-01-16T00:00:00\", \"2020-01-17T00:00:00\", \"2020-01-18T00:00:00\", \"2020-01-19T00:00:00\", \"2020-01-20T00:00:00\", \"2020-01-21T00:00:00\", \"2020-01-22T00:00:00\", \"2020-01-23T00:00:00\", \"2020-01-24T00:00:00\", \"2020-01-25T00:00:00\", \"2020-01-26T00:00:00\", \"2020-01-27T00:00:00\", \"2020-01-28T00:00:00\", \"2020-01-29T00:00:00\", \"2020-01-30T00:00:00\", \"2020-01-31T00:00:00\", \"2020-02-01T00:00:00\", \"2020-02-02T00:00:00\", \"2020-02-03T00:00:00\", \"2020-02-04T00:00:00\", \"2020-02-05T00:00:00\", \"2020-02-06T00:00:00\", \"2020-02-07T00:00:00\", \"2020-02-08T00:00:00\", \"2020-02-09T00:00:00\", \"2020-02-10T00:00:00\", \"2020-02-11T00:00:00\", \"2020-02-12T00:00:00\", \"2020-02-13T00:00:00\", \"2020-02-14T00:00:00\", \"2020-02-15T00:00:00\", \"2020-02-16T00:00:00\", \"2020-02-17T00:00:00\", \"2020-02-18T00:00:00\", \"2020-02-19T00:00:00\", \"2020-02-20T00:00:00\", \"2020-02-21T00:00:00\", \"2020-02-22T00:00:00\", \"2020-02-23T00:00:00\", \"2020-02-24T00:00:00\", \"2020-02-25T00:00:00\", \"2020-02-26T00:00:00\", \"2020-02-27T00:00:00\", \"2020-02-28T00:00:00\", \"2020-02-29T00:00:00\", \"2020-03-01T00:00:00\", \"2020-03-02T00:00:00\", \"2020-03-03T00:00:00\", \"2020-03-04T00:00:00\", \"2020-03-05T00:00:00\", \"2020-03-06T00:00:00\", \"2020-03-07T00:00:00\", \"2020-03-08T00:00:00\", \"2020-03-09T00:00:00\", \"2020-03-10T00:00:00\", \"2020-03-11T00:00:00\", \"2020-03-12T00:00:00\", \"2020-03-13T00:00:00\", \"2020-03-14T00:00:00\", \"2020-03-15T00:00:00\", \"2020-03-16T00:00:00\", \"2020-03-17T00:00:00\", \"2020-03-18T00:00:00\"], \"y\": [27, 0, 0, 17, 0, 15, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 5, 17, 136, 20, 153, 142, 97, 266, 453, 673, 797, 1767, 1480, 1756, 2003, 2120, 2608, 2818, 3243, 3907, 3751, 3218, 3442, 2619, 2988, 2565, 2072, 15151, 4215, 2560, 2162, 2067, 1995, 1864, 532, 996, 1085, 1008, 527, 793, 863, 1106, 1264, 1838, 1821, 2044, 1595, 2413, 2239, 2856, 3961, 3691, 3871, 4537, 4378, 6887, 8352, 9375, 8140, 16051, 12745, 14750]}, {\"name\": \"WW New Deaths\", \"type\": \"scatter\", \"x\": [\"2019-12-31T00:00:00\", \"2020-01-01T00:00:00\", \"2020-01-02T00:00:00\", \"2020-01-03T00:00:00\", \"2020-01-04T00:00:00\", \"2020-01-05T00:00:00\", \"2020-01-06T00:00:00\", \"2020-01-07T00:00:00\", \"2020-01-08T00:00:00\", \"2020-01-09T00:00:00\", \"2020-01-10T00:00:00\", \"2020-01-11T00:00:00\", \"2020-01-12T00:00:00\", \"2020-01-13T00:00:00\", \"2020-01-14T00:00:00\", \"2020-01-15T00:00:00\", \"2020-01-16T00:00:00\", \"2020-01-17T00:00:00\", \"2020-01-18T00:00:00\", \"2020-01-19T00:00:00\", \"2020-01-20T00:00:00\", \"2020-01-21T00:00:00\", \"2020-01-22T00:00:00\", \"2020-01-23T00:00:00\", \"2020-01-24T00:00:00\", \"2020-01-25T00:00:00\", \"2020-01-26T00:00:00\", \"2020-01-27T00:00:00\", \"2020-01-28T00:00:00\", \"2020-01-29T00:00:00\", \"2020-01-30T00:00:00\", \"2020-01-31T00:00:00\", \"2020-02-01T00:00:00\", \"2020-02-02T00:00:00\", \"2020-02-03T00:00:00\", \"2020-02-04T00:00:00\", \"2020-02-05T00:00:00\", \"2020-02-06T00:00:00\", \"2020-02-07T00:00:00\", \"2020-02-08T00:00:00\", \"2020-02-09T00:00:00\", \"2020-02-10T00:00:00\", \"2020-02-11T00:00:00\", \"2020-02-12T00:00:00\", \"2020-02-13T00:00:00\", \"2020-02-14T00:00:00\", \"2020-02-15T00:00:00\", \"2020-02-16T00:00:00\", \"2020-02-17T00:00:00\", \"2020-02-18T00:00:00\", \"2020-02-19T00:00:00\", \"2020-02-20T00:00:00\", \"2020-02-21T00:00:00\", \"2020-02-22T00:00:00\", \"2020-02-23T00:00:00\", \"2020-02-24T00:00:00\", \"2020-02-25T00:00:00\", \"2020-02-26T00:00:00\", \"2020-02-27T00:00:00\", \"2020-02-28T00:00:00\", \"2020-02-29T00:00:00\", \"2020-03-01T00:00:00\", \"2020-03-02T00:00:00\", \"2020-03-03T00:00:00\", \"2020-03-04T00:00:00\", \"2020-03-05T00:00:00\", \"2020-03-06T00:00:00\", \"2020-03-07T00:00:00\", \"2020-03-08T00:00:00\", \"2020-03-09T00:00:00\", \"2020-03-10T00:00:00\", \"2020-03-11T00:00:00\", \"2020-03-12T00:00:00\", \"2020-03-13T00:00:00\", \"2020-03-14T00:00:00\", \"2020-03-15T00:00:00\", \"2020-03-16T00:00:00\", \"2020-03-17T00:00:00\", \"2020-03-18T00:00:00\"], \"y\": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 3, 11, 0, 9, 15, 15, 25, 25, 26, 38, 43, 46, 46, 57, 65, 66, 72, 73, 86, 89, 97, 108, 97, 255, 13, 144, 142, 106, 98, 139, 116, 119, 112, 104, 156, 79, 64, 38, 57, 64, 58, 67, 72, 84, 80, 103, 103, 97, 229, 209, 269, 326, 351, 438, 354, 746, 596, 773]}],\n",
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\"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Worldwide New Confirmed cases and Deaths\"}, \"xaxis\": {\"title\": {\"text\": \"Date\"}}, \"yaxis\": {\"title\": {\"text\": \"New Confirmed cases/New Deaths\"}}},\n",
              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('0988533e-18bf-421a-9af7-d0624c03e4ee');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IbhL2OezKikl",
        "colab_type": "text"
      },
      "source": [
        "# ACTION : Choose the countries you want to compare or update the list of countries you want to study"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FJGAMAQBEd8Z",
        "colab_type": "code",
        "outputId": "21480db7-da21-433f-a223-4198b38326f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 188,
          "referenced_widgets": [
            "f015575161644ec4b8f4b6c20c6f8dcb",
            "2387db7780b24924adfebc8247eae90f",
            "4c83649853f24b9d8a58d96669dad360"
          ]
        }
      },
      "source": [
        "countries = ['China', 'Belgium', 'Spain', 'France', 'Italy', 'United_States_of_America']\n",
        "countries_widget = widgets.SelectMultiple(\n",
        "    options=df['Countries and territories'].unique().tolist(),\n",
        "    value=countries,\n",
        "    rows=10,\n",
        "    description='Countries and territories',\n",
        "    disabled=False\n",
        ")\n",
        "\n",
        "try:\n",
        "  display(countries_widget)\n",
        "  #print('you are running a jupyter notebook')\n",
        "except:\n",
        "  print('you are not running a jupyter notebook')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f015575161644ec4b8f4b6c20c6f8dcb",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "SelectMultiple(description='Countries and territories', index=(31, 15, 128, 50, 70, 144), options=('Afghanista…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "925R89IjE_oL",
        "colab_type": "code",
        "outputId": "6810b5b4-06e9-48cc-96d3-4c8785be1e6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "if countries_widget is not None:\n",
        "    if countries_widget.value is not None:\n",
        "      countries = countries_widget.value\n",
        "      \n",
        "print(f'Selected countries : {countries}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Selected countries : ('China', 'Belgium', 'Spain', 'France', 'Italy', 'United_States_of_America')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pkkTc5YuK0AY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def display_bar_chart(countries, \n",
        "                      bar_name=\"New confirmed cases in\", \n",
        "                      bar_x='DateRep', \n",
        "                      bar_y='Cases',\n",
        "                      layout_x_title='Date', \n",
        "                      layout_y_title='New confirmed cases'\n",
        "                      ):\n",
        "  # use the DataFrame columns for generating data\n",
        "\n",
        "  data = []\n",
        "  for country in countries:\n",
        "    # print(f\"{country}\")\n",
        "    data.append(\n",
        "        go.Bar(\n",
        "          name=f\"{bar_name} {country}\",\n",
        "          x=df[df['Countries and territories']==country][bar_x],\n",
        "          y=df[df['Countries and territories']==country][bar_y]\n",
        "        )\n",
        "      )\n",
        "  # plot titles and axis labels\n",
        "  layout = go.Layout(\n",
        "      title=f'{bar_name} {countries}',\n",
        "      xaxis=dict(\n",
        "          title=layout_x_title\n",
        "      ),\n",
        "      yaxis=dict(\n",
        "          title=layout_y_title\n",
        "      )\n",
        "  )\n",
        "\n",
        "  # plot the Figure object using the 'iplot' method\n",
        "  fig = go.Figure(data=data, layout=layout)\n",
        "  iplot(fig)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M_OYOKg_Kcd9",
        "colab_type": "text"
      },
      "source": [
        "# Draw a chart including New Confirmed Cases in Italy and Belgium\n",
        "\n",
        "## Some hints regarding the dynamic charts\n",
        "\n",
        "> functions are available on the top-left of the screen. Click on the buttons\n",
        "\n",
        "> the user can zoom in/zoom out\n",
        "\n",
        "> click on a legend e.g., new confirmed cases (on the right) and focus on one single dataset\n",
        "\n",
        "> click-and-drag your mouse to zoom on a dedicated area\n",
        "\n",
        "> double click on the chart to reset the zoom"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "skB83-UgMquJ",
        "colab_type": "code",
        "outputId": "7b27df74-56e8-463f-c93f-0f508a8a6a4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "display_bar_chart(\n",
        "    countries, \n",
        "    bar_name=\"New confirmed cases in\", \n",
        "    bar_x='DateRep', \n",
        "    bar_y='Cases',\n",
        "    layout_x_title='Date', \n",
        "    layout_y_title='New confirmed cases'\n",
        "    )\n",
        "display_bar_chart(\n",
        "    countries,\n",
        "    bar_name=\"New deaths in\", \n",
        "    bar_x='DateRep', \n",
        "    bar_y='Deaths',\n",
        "    layout_x_title='Date', \n",
        "    layout_y_title='New deaths'\n",
        "    )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>\n",
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              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ixdD0hNinUYM",
        "colab_type": "text"
      },
      "source": [
        "# Are they Correlations between the fields 'Deaths' and 'New cases'?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6tvejO5znaXW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def display_correlation(df, selected_citizenship= 'Belgian', method='pearson'):\n",
        "  correlation = df['Cases'].corr(df['Deaths'], method=method)\n",
        "  print(f\"correlation using '{method}'' method between '{selected_citizenship}' 'New cases' and 'Deaths': {correlation}\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab_type": "code",
        "outputId": "e1396bff-2db7-410c-ac2f-94b0a44b22d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        }
      },
      "source": [
        "for country in countries:\n",
        "  display_correlation(df[df['Countries and territories']==country], country, method='pearson')\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "correlation using 'pearson'' method between 'China' 'New cases' and 'Deaths': 0.6967850705676589\n",
            "correlation using 'pearson'' method between 'Belgium' 'New cases' and 'Deaths': 0.3122780416204782\n",
            "correlation using 'pearson'' method between 'Spain' 'New cases' and 'Deaths': 0.8413012488777472\n",
            "correlation using 'pearson'' method between 'France' 'New cases' and 'Deaths': 0.9351254311146221\n",
            "correlation using 'pearson'' method between 'Italy' 'New cases' and 'Deaths': 0.9344236685737289\n",
            "correlation using 'pearson'' method between 'United_States_of_America' 'New cases' and 'Deaths': 0.9615831512522206\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u5okD9Fhv1fm",
        "colab_type": "text"
      },
      "source": [
        "# CHANGELOG\n",
        "\n",
        "v.0.0.2\n",
        "\n",
        "> fix: ECDC has changed the format of the file\n",
        "\n",
        "  > filename is renamed *.xlsx into *.xls\n",
        "\n",
        "> feat: add widget to select the countries you want a analysis about\n",
        "\n",
        "> refactor: one function displays diverse bart charts\n",
        "\n",
        "v.0.0.1 \n",
        "  \n",
        "> fix: ECDC has changed the format of the file\n",
        "\n",
        "    > filename is renamed *.xls into *.xlsx\n",
        "  \n",
        "    > renamed fields: CountryExp -> Countries and territories, NewConfCases -> Cases, Newdeaths -> Deaths\n",
        "  \n",
        "    > removed fields: Gaul1Nuts1, EU\n",
        "\n",
        "v.0.0.0 \n",
        "\n",
        "    > feat: display charts for the World, Belgium and Italy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VJMD-mvtvwuL",
        "colab_type": "text"
      },
      "source": [
        "# LICENSE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TdqJdsi1haaY",
        "colab_type": "text"
      },
      "source": [
        "(c) 2010-2020 Abdelkrim Boujraf, alt-f1 sprl <http://www.alt-f1.be>, Abdelkrim Boujraf <http://www.alt-f1.be/literature.html>\n",
        "\n",
        "This work is licensed under a Creative Commons Attribution 4.0 International License: <http://creativecommons.org/licenses/by/4.0>\n",
        "\n",
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        "* Adapt — remix, transform, and build upon the material for any purpose, even commercially\n",
        "\n",
        "Vous êtes autorisé à <https://creativecommons.org/licenses/by/4.0/deed.fr>: \n",
        "\n",
        "* Partager : copier, distribuer et communiquer le matériel par tous moyens et sous tous formats\n",
        "* Adapter : remixer, transformer et créer à partir du matériel pour toute utilisation, y compris commerciale"
      ]
    }
  ]
}