CoronaData_Part2.ipynb

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   "source": [
    "<div align=\"left\">\n",
    "    <img src='./businesscard.jpg' width='400' align=\"right\">\n",
    "</div>\n",
    "\n",
    "# Utilizando Python para Analisar Dados sobre a Pandemia Covid-19 - Part 2\n",
    "## Renato Profeta \n",
    "### @Guitars.AI\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/6isr3mEpu44\" frameborder=\"0\" allow=\"accelerometer; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
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       "<IPython.core.display.HTML object>"
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   "source": [
    "%%html\n",
    "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/6isr3mEpu44\" frameborder=\"0\" allow=\"accelerometer; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Download dos Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
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       "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/_rJVTaTWBwE\" frameborder=\"0\" allow=\"accelerometer; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
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       "<IPython.core.display.HTML object>"
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   ],
   "source": [
    "%%html\n",
    "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/_rJVTaTWBwE\" frameborder=\"0\" allow=\"accelerometer; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'requests.models.Response'>\n"
     ]
    }
   ],
   "source": [
    "url = 'https://coronavirus-tracker-api.herokuapp.com/v2/locations?timelines=1'\n",
    "r = requests.get(url)\n",
    "print(type(r))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "### API (Application Programming Interface)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    " - Interface de Programação de Aplicação\n",
    " - Ferramenta de comunicação entre aplicações que desejam compartilhar suas serviços, protocolos, rotinas.\n",
    " - Disponibiliar recurso de uma aplicação para ser utilizado por outras aplicações."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Web API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - Interface de Programação de Aplicação para um servidor ou navegador web.\n",
    " - Aplicação Web Servidor-Cliente.\n",
    " - Mensagens de aquisição e resposta HTTP.\n",
    " - Geralmente nos formatos XML e JSON.\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src='https://developer.spotify.com/documentation/web-api/' width='800' height='400'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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   ],
   "source": [
    "%%html\n",
    "<iframe src='https://developer.spotify.com/documentation/web-api/' width='800' height='400'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src='https://coronavirus-tracker-api.herokuapp.com/v2/locations?country_code=BR' width='800' height='100'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<iframe src='https://coronavirus-tracker-api.herokuapp.com/v2/locations?country_code=BR' width='800' height='100'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "### HTTP GET"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    " - Solicitação HTTP.\n",
    " - Receber dados de uma fonte."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "### JSON (JavaScript Object Notation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "  - Formato compacto de troca de dados entre sistemas.\n",
    "  - Formato Atributo:Valor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Transformar o JSON-Response em Pandas Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/zGtpapHXswc\" frameborder=\"0\" allow=\"accelerometer; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
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       "<IPython.core.display.HTML object>"
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   "source": [
    "%%html\n",
    "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/zGtpapHXswc\" frameborder=\"0\" allow=\"accelerometer; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
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  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dict'>\n"
     ]
    }
   ],
   "source": [
    "# Python Dictionary\n",
    "r=r.json()\n",
    "print(type(r))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(r['locations']).set_index('country')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "### Dictionário"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    " - Estrutuda de dados para guardar grupos de objetos.\n",
    " - Composto por Keys : Values\n",
    " - Um tipo de mapeamento."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "### Python Dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hide_input": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src='https://docs.python.org/3/tutorial/datastructures.html#dictionaries' width='800' height='400'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
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     "output_type": "display_data"
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   ],
   "source": [
    "%%html\n",
    "<iframe src='https://docs.python.org/3/tutorial/datastructures.html#dictionaries' width='800' height='400'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "### Pandas "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hide_input": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src='https://pandas.pydata.org/docs/' width='800' height='400'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<iframe src='https://pandas.pydata.org/docs/' width='800' height='400'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src='https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html?highlight=dataframe' width='800' height='400'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "%%html\n",
    "<iframe src='https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html?highlight=dataframe' width='800' height='400'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coordinates</th>\n",
       "      <th>country_code</th>\n",
       "      <th>id</th>\n",
       "      <th>last_updated</th>\n",
       "      <th>latest</th>\n",
       "      <th>province</th>\n",
       "      <th>timelines</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Thailand</th>\n",
       "      <td>{'latitude': '15', 'longitude': '101'}</td>\n",
       "      <td>TH</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-03-22T15:54:41.375723Z</td>\n",
       "      <td>{'confirmed': 411, 'deaths': 1, 'recovered': 42}</td>\n",
       "      <td></td>\n",
       "      <td>{'confirmed': {'latest': 411, 'timeline': {'20...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>{'latitude': '36', 'longitude': '138'}</td>\n",
       "      <td>JP</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-03-22T15:54:41.383890Z</td>\n",
       "      <td>{'confirmed': 1007, 'deaths': 35, 'recovered':...</td>\n",
       "      <td></td>\n",
       "      <td>{'confirmed': {'latest': 1007, 'timeline': {'2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>{'latitude': '1.2833', 'longitude': '103.8333'}</td>\n",
       "      <td>SG</td>\n",
       "      <td>2</td>\n",
       "      <td>2020-03-22T15:54:41.392349Z</td>\n",
       "      <td>{'confirmed': 432, 'deaths': 2, 'recovered': 140}</td>\n",
       "      <td></td>\n",
       "      <td>{'confirmed': {'latest': 432, 'timeline': {'20...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nepal</th>\n",
       "      <td>{'latitude': '28.1667', 'longitude': '84.25'}</td>\n",
       "      <td>NP</td>\n",
       "      <td>3</td>\n",
       "      <td>2020-03-22T15:54:41.397230Z</td>\n",
       "      <td>{'confirmed': 1, 'deaths': 0, 'recovered': 1}</td>\n",
       "      <td></td>\n",
       "      <td>{'confirmed': {'latest': 1, 'timeline': {'2020...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malaysia</th>\n",
       "      <td>{'latitude': '2.5', 'longitude': '112.5'}</td>\n",
       "      <td>MY</td>\n",
       "      <td>4</td>\n",
       "      <td>2020-03-22T15:54:41.401656Z</td>\n",
       "      <td>{'confirmed': 1183, 'deaths': 4, 'recovered': ...</td>\n",
       "      <td></td>\n",
       "      <td>{'confirmed': {'latest': 1183, 'timeline': {'2...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               coordinates country_code  id  \\\n",
       "country                                                                       \n",
       "Thailand            {'latitude': '15', 'longitude': '101'}           TH   0   \n",
       "Japan               {'latitude': '36', 'longitude': '138'}           JP   1   \n",
       "Singapore  {'latitude': '1.2833', 'longitude': '103.8333'}           SG   2   \n",
       "Nepal        {'latitude': '28.1667', 'longitude': '84.25'}           NP   3   \n",
       "Malaysia         {'latitude': '2.5', 'longitude': '112.5'}           MY   4   \n",
       "\n",
       "                          last_updated  \\\n",
       "country                                  \n",
       "Thailand   2020-03-22T15:54:41.375723Z   \n",
       "Japan      2020-03-22T15:54:41.383890Z   \n",
       "Singapore  2020-03-22T15:54:41.392349Z   \n",
       "Nepal      2020-03-22T15:54:41.397230Z   \n",
       "Malaysia   2020-03-22T15:54:41.401656Z   \n",
       "\n",
       "                                                      latest province  \\\n",
       "country                                                                 \n",
       "Thailand    {'confirmed': 411, 'deaths': 1, 'recovered': 42}            \n",
       "Japan      {'confirmed': 1007, 'deaths': 35, 'recovered':...            \n",
       "Singapore  {'confirmed': 432, 'deaths': 2, 'recovered': 140}            \n",
       "Nepal          {'confirmed': 1, 'deaths': 0, 'recovered': 1}            \n",
       "Malaysia   {'confirmed': 1183, 'deaths': 4, 'recovered': ...            \n",
       "\n",
       "                                                   timelines  \n",
       "country                                                       \n",
       "Thailand   {'confirmed': {'latest': 411, 'timeline': {'20...  \n",
       "Japan      {'confirmed': {'latest': 1007, 'timeline': {'2...  \n",
       "Singapore  {'confirmed': {'latest': 432, 'timeline': {'20...  \n",
       "Nepal      {'confirmed': {'latest': 1, 'timeline': {'2020...  \n",
       "Malaysia   {'confirmed': {'latest': 1183, 'timeline': {'2...  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Seleção de Informações"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/Lf-FGnjqI4U\" frameborder=\"0\" allow=\"accelerometer; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
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   ],
   "source": [
    "%%html\n",
    "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/Lf-FGnjqI4U\" frameborder=\"0\" allow=\"accelerometer; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "### Apenas Informações Desejadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timelines</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Thailand</th>\n",
       "      <td>{'confirmed': {'latest': 411, 'timeline': {'20...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>{'confirmed': {'latest': 1007, 'timeline': {'2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>{'confirmed': {'latest': 432, 'timeline': {'20...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nepal</th>\n",
       "      <td>{'confirmed': {'latest': 1, 'timeline': {'2020...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malaysia</th>\n",
       "      <td>{'confirmed': {'latest': 1183, 'timeline': {'2...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   timelines\n",
       "country                                                     \n",
       "Thailand   {'confirmed': {'latest': 411, 'timeline': {'20...\n",
       "Japan      {'confirmed': {'latest': 1007, 'timeline': {'2...\n",
       "Singapore  {'confirmed': {'latest': 432, 'timeline': {'20...\n",
       "Nepal      {'confirmed': {'latest': 1, 'timeline': {'2020...\n",
       "Malaysia   {'confirmed': {'latest': 1183, 'timeline': {'2..."
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame(df['timelines'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "### Apenas Países Desejados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "countries = ['Latvia','Italy', 'Germany', 'Spain', 'Brazil']\n",
    "data=data.loc[countries,: ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timelines</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Latvia</th>\n",
       "      <td>{'confirmed': {'latest': 124, 'timeline': {'20...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>{'confirmed': {'latest': 53578, 'timeline': {'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>{'confirmed': {'latest': 22213, 'timeline': {'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>{'confirmed': {'latest': 25374, 'timeline': {'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>{'confirmed': {'latest': 1021, 'timeline': {'2...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 timelines\n",
       "country                                                   \n",
       "Latvia   {'confirmed': {'latest': 124, 'timeline': {'20...\n",
       "Italy    {'confirmed': {'latest': 53578, 'timeline': {'...\n",
       "Germany  {'confirmed': {'latest': 22213, 'timeline': {'...\n",
       "Spain    {'confirmed': {'latest': 25374, 'timeline': {'...\n",
       "Brazil   {'confirmed': {'latest': 1021, 'timeline': {'2..."
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'latest': 53578,\n",
       " 'timeline': {'2020-01-22T00:00:00Z': 0,\n",
       "  '2020-01-23T00:00:00Z': 0,\n",
       "  '2020-01-24T00:00:00Z': 0,\n",
       "  '2020-01-25T00:00:00Z': 0,\n",
       "  '2020-01-26T00:00:00Z': 0,\n",
       "  '2020-01-27T00:00:00Z': 0,\n",
       "  '2020-01-28T00:00:00Z': 0,\n",
       "  '2020-01-29T00:00:00Z': 0,\n",
       "  '2020-01-30T00:00:00Z': 0,\n",
       "  '2020-01-31T00:00:00Z': 2,\n",
       "  '2020-02-01T00:00:00Z': 2,\n",
       "  '2020-02-02T00:00:00Z': 2,\n",
       "  '2020-02-03T00:00:00Z': 2,\n",
       "  '2020-02-04T00:00:00Z': 2,\n",
       "  '2020-02-05T00:00:00Z': 2,\n",
       "  '2020-02-06T00:00:00Z': 2,\n",
       "  '2020-02-07T00:00:00Z': 3,\n",
       "  '2020-02-08T00:00:00Z': 3,\n",
       "  '2020-02-09T00:00:00Z': 3,\n",
       "  '2020-02-10T00:00:00Z': 3,\n",
       "  '2020-02-11T00:00:00Z': 3,\n",
       "  '2020-02-12T00:00:00Z': 3,\n",
       "  '2020-02-13T00:00:00Z': 3,\n",
       "  '2020-02-14T00:00:00Z': 3,\n",
       "  '2020-02-15T00:00:00Z': 3,\n",
       "  '2020-02-16T00:00:00Z': 3,\n",
       "  '2020-02-17T00:00:00Z': 3,\n",
       "  '2020-02-18T00:00:00Z': 3,\n",
       "  '2020-02-19T00:00:00Z': 3,\n",
       "  '2020-02-20T00:00:00Z': 3,\n",
       "  '2020-02-21T00:00:00Z': 20,\n",
       "  '2020-02-22T00:00:00Z': 62,\n",
       "  '2020-02-23T00:00:00Z': 155,\n",
       "  '2020-02-24T00:00:00Z': 229,\n",
       "  '2020-02-25T00:00:00Z': 322,\n",
       "  '2020-02-26T00:00:00Z': 453,\n",
       "  '2020-02-27T00:00:00Z': 655,\n",
       "  '2020-02-28T00:00:00Z': 888,\n",
       "  '2020-02-29T00:00:00Z': 1128,\n",
       "  '2020-03-01T00:00:00Z': 1694,\n",
       "  '2020-03-02T00:00:00Z': 2036,\n",
       "  '2020-03-03T00:00:00Z': 2502,\n",
       "  '2020-03-04T00:00:00Z': 3089,\n",
       "  '2020-03-05T00:00:00Z': 3858,\n",
       "  '2020-03-06T00:00:00Z': 4636,\n",
       "  '2020-03-07T00:00:00Z': 5883,\n",
       "  '2020-03-08T00:00:00Z': 7375,\n",
       "  '2020-03-09T00:00:00Z': 9172,\n",
       "  '2020-03-10T00:00:00Z': 10149,\n",
       "  '2020-03-11T00:00:00Z': 12462,\n",
       "  '2020-03-12T00:00:00Z': 12462,\n",
       "  '2020-03-13T00:00:00Z': 17660,\n",
       "  '2020-03-14T00:00:00Z': 21157,\n",
       "  '2020-03-15T00:00:00Z': 24747,\n",
       "  '2020-03-16T00:00:00Z': 27980,\n",
       "  '2020-03-17T00:00:00Z': 31506,\n",
       "  '2020-03-18T00:00:00Z': 35713,\n",
       "  '2020-03-19T00:00:00Z': 41035,\n",
       "  '2020-03-20T00:00:00Z': 47021,\n",
       "  '2020-03-21T00:00:00Z': 53578}}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc['Italy','timelines']['confirmed']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "### Casos Confirmados desde 14 Dias antes da Confirmação de Mortes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/tcbHKpT1bKg\" frameborder=\"0\" allow=\"accelerometer; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/tcbHKpT1bKg\" frameborder=\"0\" allow=\"accelerometer; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Italy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "Italy_confirmed = pd.Series(data.loc['Italy','timelines']['confirmed']['timeline'])\n",
    "Italy_deaths = pd.Series(data.loc['Italy','timelines']['deaths']['timeline'])\n",
    "\n",
    "day_first_death=(Italy_deaths == 0).sum()\n",
    "\n",
    "Italy_confirmed = Italy_confirmed.iloc[day_first_death-14:].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Germany"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "Germany_confirmed = pd.Series(data.loc['Germany','timelines']['confirmed']['timeline'])\n",
    "Germany_deaths = pd.Series(data.loc['Germany','timelines']['deaths']['timeline'])\n",
    "\n",
    "day_first_death=(Germany_deaths == 0).sum()\n",
    "\n",
    "Germany_confirmed = Germany_confirmed.iloc[day_first_death-14:].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Spain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "Spain_confirmed = pd.Series(data.loc['Spain','timelines']['confirmed']['timeline'])\n",
    "Spain_deaths = pd.Series(data.loc['Spain','timelines']['deaths']['timeline'])\n",
    "\n",
    "day_first_death=(Spain_deaths == 0).sum()\n",
    "\n",
    "Spain_confirmed = Spain_confirmed.iloc[day_first_death-14:].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Brazil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "Brazil_confirmed = pd.Series(data.loc['Brazil','timelines']['confirmed']['timeline'])\n",
    "Brazil_deaths = pd.Series(data.loc['Brazil','timelines']['deaths']['timeline'])\n",
    "\n",
    "day_first_death=(Brazil_deaths == 0).sum()\n",
    "\n",
    "Brazil_confirmed = Brazil_confirmed.iloc[day_first_death-14:].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Ajuste de Curvas e Previsões Casos Confirmados desde 14 dias antes da Confirmação de Mortes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/5xZy9VcTloI\" frameborder=\"0\" allow=\"accelerometer; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/5xZy9VcTloI\" frameborder=\"0\" allow=\"accelerometer; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "def exponential_func(x, a, c, d):\n",
    "    return a*np.exp(c*x)+d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "from scipy.optimize import curve_fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Italy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "days_to_predict=Italy_confirmed.size+5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "y = np.array(Italy_confirmed.tolist())\n",
    "x = np.arange(y.size)\n",
    "\n",
    "popt, pcov = curve_fit(exponential_func, x, y)\n",
    "Italy_cases_prediction = exponential_func(np.arange(days_to_predict), *popt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Germany"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "y = np.array(Germany_confirmed.tolist())\n",
    "x = np.arange(y.size)\n",
    "\n",
    "popt, pcov = curve_fit(exponential_func, x, y)\n",
    "Germany_cases_prediction = exponential_func(np.arange(days_to_predict), *popt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Spain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array(Spain_confirmed.tolist())\n",
    "x = np.arange(y.size)\n",
    "\n",
    "popt, pcov = curve_fit(exponential_func, x, y)\n",
    "Spain_cases_prediction = exponential_func(np.arange(days_to_predict), *popt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Brazil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "y = np.array(Brazil_confirmed.tolist()) \n",
    "x = np.arange(y.size)\n",
    "\n",
    "popt, pcov = curve_fit(exponential_func, x, y)\n",
    "Brazil_cases_prediction = exponential_func(np.arange(days_to_predict), *popt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "### Mortes e Mortes/Casos (Ajuste de Curvas e Previsões)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/kbYlAgqZhfQ\" frameborder=\"0\" allow=\"accelerometer; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/kbYlAgqZhfQ\" frameborder=\"0\" allow=\"accelerometer; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Italy\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "Italy_deaths = np.trim_zeros(np.array(Italy_deaths.tolist()))\n",
    "Italy_death_rate=Italy_deaths/np.array(Italy_confirmed.tolist()[14:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "days_to_predict=Italy_deaths.size+5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "y = Italy_deaths\n",
    "x = np.arange(y.size)\n",
    "\n",
    "popt, pcov = curve_fit(exponential_func, x, y)\n",
    "Italy_deaths_prediction = exponential_func(np.arange(days_to_predict), *popt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Spain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "Spain_deaths = np.trim_zeros(np.array(Spain_deaths.tolist()))\n",
    "Spain_death_rate=Spain_deaths/np.array(Spain_confirmed.tolist()[14:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "y = Spain_deaths\n",
    "x = np.arange(y.size)\n",
    "\n",
    "popt, pcov = curve_fit(exponential_func, x, y)\n",
    "Spain_deaths_prediction = exponential_func(np.arange(days_to_predict), *popt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Germany"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "Germany_deaths = np.trim_zeros(np.array(Germany_deaths.tolist()))\n",
    "Germany_death_rate=Germany_deaths/np.array(Germany_confirmed.tolist()[14:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "y = Germany_deaths\n",
    "x = np.arange(y.size)\n",
    "\n",
    "popt, pcov = curve_fit(exponential_func, x, y)\n",
    "Germany_deaths_prediction = exponential_func(np.arange(days_to_predict), *popt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Brazil\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "Brazil_deaths = np.trim_zeros(np.array(Brazil_deaths.tolist()))\n",
    "Brazil_death_rate=Brazil_deaths/np.array(Brazil_confirmed.tolist()[14:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "Brazil_deaths[4]=18\n",
    "y = Brazil_deaths\n",
    "x = np.arange(y.size)\n",
    "\n",
    "popt, pcov = curve_fit(exponential_func, x, y)\n",
    "Brazil_deaths_prediction = exponential_func(np.arange(days_to_predict), *popt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Visualização de Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/kbYlAgqZhfQ\" frameborder=\"0\" allow=\"accelerometer; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
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    "### Casos Confirmados e Mortes"
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    "fig=plt.figure(figsize=(12,8))\n",
    "plt.yscale('log')\n",
    "plt.xlabel('Number of Days since 1st Death in each Country')\n",
    "plt.ylabel('Number of Cases')\n",
    "plt.title('Confirmed Cases and Curve Fit since 1st Death in each Country')\n",
    "plt.grid()\n",
    "## Italy\n",
    "# Italy Case\n",
    "plt.plot(Italy_confirmed.tolist()[14:], 'r', label='Italy Cases')\n",
    "plt.plot(Italy_cases_prediction[14:], 'rx',label = 'Italy Cases Prediction', markersize=3)\n",
    "# Italy Deaths\n",
    "plt.plot(Italy_deaths, 'r', label='Italy Deaths',linestyle='dashed')\n",
    "plt.plot(Italy_deaths_prediction, 'r.',label = 'Italy Deaths Prediction', markersize=3)\n",
    "\n",
    "## Spain\n",
    "# Spain Cases\n",
    "plt.plot(Spain_confirmed.tolist()[14:], 'k', label='Spain Cases')\n",
    "plt.plot(Spain_cases_prediction[14:], 'kx',label = 'Spain Cases Prediction', markersize=3)\n",
    "#Spain Deaths\n",
    "plt.plot(Spain_deaths, 'k', label='Spain Deaths',linestyle='dashed')\n",
    "plt.plot(Spain_deaths_prediction, 'k.',label = 'Spain Deaths Prediction', markersize=3)\n",
    "\n",
    "## Germany\n",
    "# Germany Cases\n",
    "plt.plot(Germany_confirmed.tolist()[14:], 'b', label='Germany Cases')\n",
    "plt.plot(Germany_cases_prediction[14:], 'bx',label = 'Germany Cases Prediction', markersize=3)\n",
    "# Germany Deaths\n",
    "plt.plot(Germany_deaths, 'b', label='Germany Deaths',linestyle='dashed')\n",
    "plt.plot(Germany_deaths_prediction, 'b.',label = 'Germany Deaths Prediction', markersize=3)\n",
    "\n",
    "## Brazil\n",
    "# Brazil Cases\n",
    "plt.plot(Brazil_confirmed.tolist()[14:], 'g', label='Brazil Cases')\n",
    "plt.plot(Brazil_cases_prediction[14:], 'gx',label = 'Brazil Cases Prediction', markersize=3)\n",
    "# Brazil Deaths\n",
    "plt.plot(Brazil_deaths,label = 'Brazil Deaths', linestyle='dashed', color='g')\n",
    "plt.plot(Brazil_deaths_prediction, 'gx',label = 'Brazil Deaths Prediction', markersize=3)\n",
    "\n",
    "\n",
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    "                \n",
    "plotly_fig.layout.update(showlegend=True, shapes=shapes)\n",
    "plotly.offline.iplot(plotly_fig)"
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   "source": [
    "### Mortes / Casos Confirmados"
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       "                        [{\"line\": {\"color\": \"rgba (255, 0, 0, 1)\", \"dash\": \"solid\", \"width\": 1.5}, \"mode\": \"lines\", \"name\": \"Italy Deaths/Cases\", \"type\": \"scatter\", \"uid\": \"49f238b8-28c5-4df4-b916-21746abc3172\", \"x\": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0], \"xaxis\": \"x\", \"y\": [0.05, 0.03225806451612903, 0.01935483870967742, 0.03056768558951965, 0.031055900621118012, 0.026490066225165563, 0.025954198473282442, 0.02364864864864865, 0.025709219858156027, 0.020070838252656435, 0.025540275049115914, 0.03157474020783373, 0.03463904176108773, 0.0383618455158113, 0.042493528904227786, 0.039605643379228284, 0.0496271186440678, 0.050479720889664195, 0.062173613163858506, 0.06636173968865351, 0.06636173968865351, 0.07168742921857305, 0.06810984544122513, 0.0730997696690508, 0.0771265189421015, 0.07944518504411859, 0.08338700193206955, 0.08297794565614719, 0.08574892069500861, 0.0900556198439658], \"yaxis\": \"y\"}, {\"line\": {\"color\": \"rgba (0, 0, 0, 1)\", \"dash\": \"solid\", \"width\": 1.5}, \"mode\": \"lines\", \"name\": \"Spain Deaths/Cases\", \"type\": \"scatter\", \"uid\": \"76703bc3-22c4-4f2f-989d-e613e1e8b780\", \"x\": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0], \"xaxis\": \"x\", \"y\": [0.006060606060606061, 0.009009009009009009, 0.011583011583011582, 0.0125, 0.02, 0.02526002971768202, 0.0260950605778192, 0.02064896755162242, 0.023715415019762844, 0.024154589371980676, 0.025420489296636085, 0.030511657017681115, 0.037060784816619646, 0.0343995171997586, 0.04536942458290773, 0.04478792235801582, 0.046206090296721035, 0.05110240078392945, 0.05418932765823284], \"yaxis\": \"y\"}, {\"line\": {\"color\": \"rgba (0, 0, 255, 1)\", \"dash\": \"solid\", \"width\": 1.5}, \"mode\": \"lines\", \"name\": \"Germany Deaths/Cases\", \"type\": \"scatter\", \"uid\": \"d8b79877-b13f-4e94-b9d6-9a71c3e0ccb6\", \"x\": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0], \"xaxis\": \"x\", \"y\": [0.0017006802721088435, 0.0013726835964310226, 0.0015723270440251573, 0.0014436958614051972, 0.0019047619047619048, 0.0019629225736095966, 0.0018981880931837791, 0.0023377337733773377, 0.0025926326023549746, 0.002271436683702442, 0.0028720626631853785, 0.0033756549778315194, 0.0037815693512807816], \"yaxis\": \"y\"}, {\"line\": {\"color\": \"rgba (0, 127, 0, 1)\", \"dash\": \"solid\", \"width\": 1.5}, \"mode\": \"lines\", \"name\": \"Brazil Deaths/Cases\", \"type\": \"scatter\", \"uid\": \"cb39252e-6fc5-401b-b57a-6beb3a1507df\", \"x\": [0.0, 1.0, 2.0, 3.0, 4.0], \"xaxis\": \"x\", \"y\": [0.003115264797507788, 0.008064516129032258, 0.00966183574879227, 0.013871374527112233, 0.014691478942213516], \"yaxis\": \"y\"}],\n",
       "                        {\"autosize\": false, \"height\": 576, \"hovermode\": \"closest\", \"margin\": {\"b\": 72, \"l\": 108, \"pad\": 0, \"r\": 86, \"t\": 69}, \"shapes\": [{\"line\": {\"color\": \"LightSeaGreen\", \"dash\": \"dash\", \"width\": 1}, \"type\": \"line\", \"x0\": 11, \"x1\": 11, \"xref\": \"x\", \"y0\": 0, \"y1\": 10000000, \"yref\": \"y\"}, {\"line\": {\"color\": \"LightSeaGreen\", \"dash\": \"dash\", \"width\": 1}, \"type\": \"line\", \"x0\": 18, \"x1\": 18, \"xref\": \"x\", \"y0\": 0, \"y1\": 10000000, \"yref\": \"y\"}], \"showlegend\": true, \"title\": {\"font\": {\"color\": \"#000000\", \"size\": 12.0}, \"text\": \"Deaths / Cases since 1st Death in each Country\"}, \"width\": 864, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"mirror\": \"ticks\", \"nticks\": 9, \"range\": [-1.4500000000000002, 30.45], \"showgrid\": true, \"showline\": true, \"side\": \"bottom\", \"tickfont\": {\"size\": 10.0}, \"ticks\": \"inside\", \"title\": {\"font\": {\"color\": \"#000000\", \"size\": 10.0}, \"text\": \"Number of Days since 1st Death in each Country\"}, \"type\": \"linear\", \"zeroline\": false}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"mirror\": \"ticks\", \"nticks\": 6, \"range\": [-2.9532765749001633, -0.9546421614287719], \"showgrid\": true, \"showline\": true, \"side\": \"left\", \"tickfont\": {\"size\": 10.0}, \"ticks\": \"inside\", \"title\": {\"font\": {\"color\": \"#000000\", \"size\": 10.0}, \"text\": \"Number of Deaths / Cases\"}, \"type\": \"log\", \"zeroline\": false}},\n",
       "                        {\"showLink\": false, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('63cd3425-a514-4c44-aa22-bca631e73ce0');\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>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(12,8))\n",
    "plt.yscale('log')\n",
    "plt.xlabel('Number of Days since 1st Death in each Country')\n",
    "plt.ylabel('Number of Deaths / Cases')\n",
    "plt.title('Deaths / Cases since 1st Death in each Country')\n",
    "plt.grid()\n",
    "\n",
    "## Italy\n",
    "plt.plot(Italy_death_rate, 'r', label='Italy Deaths/Cases')\n",
    "\n",
    "## Spain\n",
    "plt.plot(Spain_death_rate, 'k', label='Spain Deaths/Cases')\n",
    "\n",
    "## Germany\n",
    "plt.plot(Germany_death_rate, 'b', label='Germany Deaths/Cases')\n",
    "\n",
    "## Brazil\n",
    "plt.plot(Brazil_death_rate, 'g', label='Brazil Deaths/Cases')\n",
    "\n",
    "# Transfomr to Plotlly\n",
    "plotly_fig = tls.mpl_to_plotly(fig)\n",
    "shapes = list()\n",
    "for i in (11, 18):\n",
    "    shapes.append({'type': 'line',\n",
    "                   'xref': 'x',\n",
    "                   'yref': 'y',\n",
    "                   'x0': i,\n",
    "                   'y0': 0,\n",
    "                   'x1': i,\n",
    "                   'y1': 10000000,\n",
    "                   'line' : dict(color=\"LightSeaGreen\", \n",
    "                            width=1,\n",
    "                            dash=\"dash\",)},\n",
    "                 )\n",
    "                \n",
    "plotly_fig.layout.update(showlegend=True, shapes=shapes)\n",
    "plotly.offline.iplot(plotly_fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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