EDA Covid informacion combinada.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Análisis exploratorio de datos (Cont.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para este análisis usamos el dataset \"csse_covid_19_time_series\" de *Daily reports (csse_covid_19_daily_reports)* en el link https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series \n",
    "\n",
    "Este es el repositorio de datos para el 2019 Novel Coronavirus Visual Dashboard operado por el Centro de Ciencias e Ingeniería de Sistemas de la Universidad Johns Hopkins (JHU CSSE). Además, con el apoyo del equipo de ESRI Living Atlas y el Laboratorio de Física Aplicada de la Universidad Johns Hopkins (JHU APL)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Leemos los tres conjuntos de datos de casos confirmados, recuperados y fallecidos utilizando su url, \n",
    "# así tendremos el dataFrame siempre actualizado\n",
    "# estos datos son actualizados diariamente\n",
    "confirmados = pd.read_csv(\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv\")\n",
    "fallecidos = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Deaths.csv')\n",
    "recuperados = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Recovered.csv')"
   ]
  },
  {
   "cell_type": "code",
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       "      <td>109</td>\n",
       "      <td>114</td>\n",
       "      <td>114</td>\n",
       "      <td>114</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nepal</td>\n",
       "      <td>28.1667</td>\n",
       "      <td>84.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>2.5000</td>\n",
       "      <td>112.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>35</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>49</td>\n",
       "      <td>60</td>\n",
       "      <td>75</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province/State Country/Region      Lat      Long  1/22/20  1/23/20  1/24/20  \\\n",
       "0            NaN       Thailand  15.0000  101.0000        0        0        0   \n",
       "1            NaN          Japan  36.0000  138.0000        0        0        0   \n",
       "2            NaN      Singapore   1.2833  103.8333        0        0        0   \n",
       "3            NaN          Nepal  28.1667   84.2500        0        0        0   \n",
       "4            NaN       Malaysia   2.5000  112.5000        0        0        0   \n",
       "\n",
       "   1/25/20  1/26/20  1/27/20  ...  3/11/20  3/12/20  3/13/20  3/14/20  \\\n",
       "0        0        2        2  ...       34       34       35       35   \n",
       "1        0        1        1  ...      118      118      118      118   \n",
       "2        0        0        0  ...       96       96       97      105   \n",
       "3        0        0        0  ...        1        1        1        1   \n",
       "4        0        0        0  ...       26       26       26       35   \n",
       "\n",
       "   3/15/20  3/16/20  3/17/20  3/18/20  3/19/20  3/20/20  \n",
       "0       35       35       41       42       42       42  \n",
       "1      118      144      144      144      150      191  \n",
       "2      105      109      114      114      114      124  \n",
       "3        1        1        1        1        1        1  \n",
       "4       42       42       49       60       75       87  \n",
       "\n",
       "[5 rows x 63 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Para visualizar todos los DataFrames en una sola celda uso display\n",
    "display(confirmados.head())\n",
    "display(fallecidos.head())\n",
    "display(recuperados.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(477, 63)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(477, 63)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(477, 63)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(confirmados.shape)\n",
    "display(fallecidos.shape)\n",
    "display(recuperados.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combinar todos los DataFrames "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country/Region\n",
       "Afghanistan    24\n",
       "Albania        70\n",
       "Algeria        90\n",
       "Andorra        75\n",
       "Angola          1\n",
       "Name: 3/20/20, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# uso groupby para agrupar por pais/region \n",
    "confirmados.groupby('Country/Region').sum()['3/20/20'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(477, 63)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confirmados.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# para obtener los datos de la ultima columna del DataFrame (ultima fecha registrada)\n",
    "#confirmados.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Funcion para extraer los datos de la ultima columna de un DataFrame\n",
    "def ultimo_por_pais(df):\n",
    "    return df.groupby('Country/Region').sum().iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "info = [ultimo_por_pais(confirmados), ultimo_por_pais(fallecidos), ultimo_por_pais(recuperados)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Country/Region\n",
       " Afghanistan    24\n",
       " Albania        70\n",
       " Algeria        90\n",
       " Andorra        75\n",
       " Angola          1\n",
       "                ..\n",
       " Uzbekistan     33\n",
       " Venezuela      42\n",
       " Vietnam        91\n",
       " Zambia          2\n",
       " Zimbabwe        1\n",
       " Name: 3/20/20, Length: 162, dtype: int64, Country/Region\n",
       " Afghanistan     0\n",
       " Albania         2\n",
       " Algeria        11\n",
       " Andorra         0\n",
       " Angola          0\n",
       "                ..\n",
       " Uzbekistan      0\n",
       " Venezuela       0\n",
       " Vietnam         0\n",
       " Zambia          0\n",
       " Zimbabwe        0\n",
       " Name: 3/20/20, Length: 162, dtype: int64, Country/Region\n",
       " Afghanistan     1\n",
       " Albania         0\n",
       " Algeria        32\n",
       " Andorra         1\n",
       " Angola          0\n",
       "                ..\n",
       " Uzbekistan      0\n",
       " Venezuela       0\n",
       " Vietnam        16\n",
       " Zambia          0\n",
       " Zimbabwe        0\n",
       " Name: 3/20/20, Length: 162, dtype: int64]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>3/20/20</th>\n",
       "      <th>3/20/20</th>\n",
       "      <th>3/20/20</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Albania</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Algeria</td>\n",
       "      <td>90</td>\n",
       "      <td>11</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Andorra</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Angola</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Venezuela</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Vietnam</td>\n",
       "      <td>91</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Zambia</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>162 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                3/20/20  3/20/20  3/20/20\n",
       "Country/Region                           \n",
       "Afghanistan          24        0        1\n",
       "Albania              70        2        0\n",
       "Algeria              90       11       32\n",
       "Andorra              75        0        1\n",
       "Angola                1        0        0\n",
       "...                 ...      ...      ...\n",
       "Uzbekistan           33        0        0\n",
       "Venezuela            42        0        0\n",
       "Vietnam              91        0       16\n",
       "Zambia                2        0        0\n",
       "Zimbabwe              1        0        0\n",
       "\n",
       "[162 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# necesito concatenar todas las informaciones, pero necesito atribuir el eje 1\n",
    "info_covid = pd.concat(info, axis=1)\n",
    "info_covid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(162, 3)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_covid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmados</th>\n",
       "      <th>fallecidos</th>\n",
       "      <th>recuperados</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Albania</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Algeria</td>\n",
       "      <td>90</td>\n",
       "      <td>11</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Andorra</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Angola</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                confirmados  fallecidos  recuperados\n",
       "Country/Region                                      \n",
       "Afghanistan              24           0            1\n",
       "Albania                  70           2            0\n",
       "Algeria                  90          11           32\n",
       "Andorra                  75           0            1\n",
       "Angola                    1           0            0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cambiar el nombre de las columnas\n",
    "info_covid.columns = ['confirmados', 'fallecidos', 'recuperados']\n",
    "info_covid.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculando la tasa de letalidad "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Letalidad se expresa porcentaje. La cantidad de casos letales dentro de los casos que fueron letales y no letales\n",
    "\n",
    "*Ejemplo*: sea una enfermedad que se sabe que acabó, si afecto a 100 personas de las cuales 5 murieron, la letalidad fue de 5%\n",
    "\n",
    "*Atención*: si la enfermedad continua existiendo, entonces debemos colocar los datos en un rango de tiempo bien definido\n",
    "Es muy dificil hablar de letalidad intentando ser preciso durante una epidemia\n",
    "\n",
    "*Conclusión*: en el inicio es muy común que solo los casos graves sean reportados pero luego este número aumenta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "272166"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = info_covid.sum()\n",
    "total[\"confirmados\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.151510475224679\n"
     ]
    }
   ],
   "source": [
    "# primer intento (no es lo ideal)\n",
    "tasa_letalidad_1 = total['fallecidos']/total['confirmados']\n",
    "print(tasa_letalidad_1 * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Interpretación*: 4.15% de las personas de todos los casos confirmados (del conjunto de datos que estamos estudiando) fallecieron.\n",
    "\n",
    "Esta tasa no es absoluta, por que la epidemia todavía continua. \n",
    "\n",
    "*ATENCIÓN*: esta información va cambiar a medida que la pandemia se expanda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Letalidad por pais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country/Region\n",
       "Afghanistan     0.000000\n",
       "Albania         2.857143\n",
       "Algeria        12.222222\n",
       "Andorra         0.000000\n",
       "Angola          0.000000\n",
       "                 ...    \n",
       "Uzbekistan      0.000000\n",
       "Venezuela       0.000000\n",
       "Vietnam         0.000000\n",
       "Zambia          0.000000\n",
       "Zimbabwe        0.000000\n",
       "Length: 162, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_covid['fallecidos']/info_covid['confirmados']* 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmados</th>\n",
       "      <th>fallecidos</th>\n",
       "      <th>recuperados</th>\n",
       "      <th>tasa_letalidad_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Albania</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Algeria</td>\n",
       "      <td>90</td>\n",
       "      <td>11</td>\n",
       "      <td>32</td>\n",
       "      <td>12.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Andorra</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Angola</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                confirmados  fallecidos  recuperados  tasa_letalidad_1\n",
       "Country/Region                                                        \n",
       "Afghanistan              24           0            1          0.000000\n",
       "Albania                  70           2            0          2.857143\n",
       "Algeria                  90          11           32         12.222222\n",
       "Andorra                  75           0            1          0.000000\n",
       "Angola                    1           0            0          0.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tasa_letalidad_1 = info_covid['fallecidos']/info_covid['confirmados']* 100\n",
    "# agregando un nueva columna, tasa de letalidad 1\n",
    "info_covid['tasa_letalidad_1'] = tasa_letalidad_1\n",
    "info_covid.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Es dificil resumir esta informacion, esto es solo una estimación que puede ser alta o puede ser baja. Puede ser que varios de los casos reportados son de pacientes muy graves que irán a óbito y entonces esta tasa aumentara. \n",
    "\n",
    "Queda claro: esto es una estimación!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.9860300213430224\n"
     ]
    }
   ],
   "source": [
    "# segundo intento \n",
    "# Siendo que mis datos son hasta la fecha actual, la cantidad de recuperados + fallecidos es el total de los reportados\n",
    "# puedo realizar otro intento sobre esta muestra\n",
    "# Ejemplo: si 5 se recuperaron y 1 fallecio entonces la tasa de letalidad es 1/6\n",
    "tasa_letalidad_2 = total['fallecidos']/(total['fallecidos'] + total['confirmados']) * 100\n",
    "print(tasa_letalidad_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La tasa de mortalidad en este grupo es de 3.9 %. Esta estimacion es cercana a la anterior.\n",
    "\n",
    "También tener en cuenta que puede ser que la recuperación sea lenta."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmados</th>\n",
       "      <th>fallecidos</th>\n",
       "      <th>recuperados</th>\n",
       "      <th>tasa_letalidad_1</th>\n",
       "      <th>tasa_letalidad_2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</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",
       "      <td>Afghanistan</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Albania</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2.857143</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Algeria</td>\n",
       "      <td>90</td>\n",
       "      <td>11</td>\n",
       "      <td>32</td>\n",
       "      <td>12.222222</td>\n",
       "      <td>25.581395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Andorra</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Angola</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                confirmados  fallecidos  recuperados  tasa_letalidad_1  \\\n",
       "Country/Region                                                           \n",
       "Afghanistan              24           0            1          0.000000   \n",
       "Albania                  70           2            0          2.857143   \n",
       "Algeria                  90          11           32         12.222222   \n",
       "Andorra                  75           0            1          0.000000   \n",
       "Angola                    1           0            0          0.000000   \n",
       "\n",
       "                tasa_letalidad_2  \n",
       "Country/Region                    \n",
       "Afghanistan             0.000000  \n",
       "Albania               100.000000  \n",
       "Algeria                25.581395  \n",
       "Andorra                 0.000000  \n",
       "Angola                       NaN  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Siendo que mis datos son hasta la fecha actual, la cantidad de recuperados + fallecidos es el total de los reportados\n",
    "# puedo realizar otro intento sobre esta muestra\n",
    "# Ejemplo: si 5 se recuperaron y 1 fallecio entonces la tasa de letalidad es 1/6\n",
    "tasa_letalidad_2 = info_covid['fallecidos'] / (info_covid['recuperados'] + info_covid['fallecidos'])*100\n",
    "info_covid['tasa_letalidad_2'] = tasa_letalidad_2\n",
    "info_covid.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Varios casos que no tienen fallecidos y recuperados serán NaN, o puede ser que no tengan recuperados lo que resultará en una tasa de 100%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmados</th>\n",
       "      <th>fallecidos</th>\n",
       "      <th>recuperados</th>\n",
       "      <th>tasa_letalidad_1</th>\n",
       "      <th>tasa_letalidad_2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</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",
       "      <td>Sudan</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Gabon</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Guyana</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14.285714</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Algeria</td>\n",
       "      <td>90</td>\n",
       "      <td>11</td>\n",
       "      <td>32</td>\n",
       "      <td>12.222222</td>\n",
       "      <td>25.581395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Ukraine</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>10.344828</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>San Marino</td>\n",
       "      <td>144</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>9.722222</td>\n",
       "      <td>77.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Indonesia</td>\n",
       "      <td>369</td>\n",
       "      <td>32</td>\n",
       "      <td>15</td>\n",
       "      <td>8.672087</td>\n",
       "      <td>68.085106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Italy</td>\n",
       "      <td>47021</td>\n",
       "      <td>4032</td>\n",
       "      <td>4440</td>\n",
       "      <td>8.574892</td>\n",
       "      <td>47.592068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Guatemala</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Iraq</td>\n",
       "      <td>208</td>\n",
       "      <td>17</td>\n",
       "      <td>49</td>\n",
       "      <td>8.173077</td>\n",
       "      <td>25.757576</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                confirmados  fallecidos  recuperados  tasa_letalidad_1  \\\n",
       "Country/Region                                                           \n",
       "Sudan                     2           1            0         50.000000   \n",
       "Gabon                     3           1            0         33.333333   \n",
       "Guyana                    7           1            0         14.285714   \n",
       "Algeria                  90          11           32         12.222222   \n",
       "Ukraine                  29           3            0         10.344828   \n",
       "San Marino              144          14            4          9.722222   \n",
       "Indonesia               369          32           15          8.672087   \n",
       "Italy                 47021        4032         4440          8.574892   \n",
       "Guatemala                12           1            0          8.333333   \n",
       "Iraq                    208          17           49          8.173077   \n",
       "\n",
       "                tasa_letalidad_2  \n",
       "Country/Region                    \n",
       "Sudan                 100.000000  \n",
       "Gabon                 100.000000  \n",
       "Guyana                100.000000  \n",
       "Algeria                25.581395  \n",
       "Ukraine               100.000000  \n",
       "San Marino             77.777778  \n",
       "Indonesia              68.085106  \n",
       "Italy                  47.592068  \n",
       "Guatemala             100.000000  \n",
       "Iraq                   25.757576  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_covid.sort_values('tasa_letalidad_1', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Existen algunos paises con pocos datos entonces no es una muestra representativa. \n",
    "\n",
    "Comparar con la tasa de la OMS. Coincide? que puede estar pasando?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogramas y mortalidad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Como podemos visualizar esta informacion?\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20229049508>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cual es la relacion entre confirmados y letalidad?\n",
    "info_covid[['confirmados', 'tasa_letalidad_1']].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Este grafico no tiene ningun sentido, por que la cantidad de confirmados es superior a 80mil y la tasa de letalidad es menor a 5.\n",
    "\n",
    "El grafico anterior es horrible! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Usemos seaborn\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2022c1b7ec8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data = info_covid, x=\"confirmados\", y=\"tasa_letalidad_1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cada punto muestra la tasa de letalidad de un pais, por ejemplo China tiene mas de 80mil confirmados aparece al final. De todas formas el grafico esta mejor pero no deja de distinguir mas informacion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2022c1737c8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# puedo utilizar un filtro para visualizar los paises con mas de 40 casos confirmados, usamos \"query\"\n",
    "sns.scatterplot(data = info_covid.query(\"confirmados > 40\"), x=\"confirmados\", y=\"tasa_letalidad_1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2022c764648>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# usando la tasa de letalidad 2\n",
    "sns.scatterplot(data = info_covid.query(\"recuperados > 40\"), x=\"confirmados\", y=\"tasa_letalidad_2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2022c8f0fc8>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cual es la distribucion de la tasa de mortalidad por pais\n",
    "sns.distplot(info_covid[\"tasa_letalidad_1\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2022c576fc8>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# filtrando confirmados > 40\n",
    "sns.distplot(info_covid.query(\"confirmados > 40\")[\"tasa_letalidad_1\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2022c4ad1c8>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# desactivando kde\n",
    "sns.distplot(info_covid.query(\"confirmados > 40\")[\"tasa_letalidad_1\"], kde = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2022ccd8288>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# usando tasa de mortalidad 2\n",
    "sns.distplot(info_covid.query(\"recuperados > 40\")[\"tasa_letalidad_2\"], kde = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmados</th>\n",
       "      <th>fallecidos</th>\n",
       "      <th>recuperados</th>\n",
       "      <th>tasa_letalidad_1</th>\n",
       "      <th>tasa_letalidad_2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</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",
       "      <td>San Marino</td>\n",
       "      <td>144</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>9.722222</td>\n",
       "      <td>77.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Indonesia</td>\n",
       "      <td>369</td>\n",
       "      <td>32</td>\n",
       "      <td>15</td>\n",
       "      <td>8.672087</td>\n",
       "      <td>68.085106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Italy</td>\n",
       "      <td>47021</td>\n",
       "      <td>4032</td>\n",
       "      <td>4440</td>\n",
       "      <td>8.574892</td>\n",
       "      <td>47.592068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Iraq</td>\n",
       "      <td>208</td>\n",
       "      <td>17</td>\n",
       "      <td>49</td>\n",
       "      <td>8.173077</td>\n",
       "      <td>25.757576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Philippines</td>\n",
       "      <td>230</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>7.826087</td>\n",
       "      <td>69.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Iran</td>\n",
       "      <td>19644</td>\n",
       "      <td>1433</td>\n",
       "      <td>6745</td>\n",
       "      <td>7.294848</td>\n",
       "      <td>17.522622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Spain</td>\n",
       "      <td>20410</td>\n",
       "      <td>1043</td>\n",
       "      <td>1588</td>\n",
       "      <td>5.110240</td>\n",
       "      <td>39.642721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>4014</td>\n",
       "      <td>178</td>\n",
       "      <td>67</td>\n",
       "      <td>4.434479</td>\n",
       "      <td>72.653061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>China</td>\n",
       "      <td>81250</td>\n",
       "      <td>3253</td>\n",
       "      <td>71266</td>\n",
       "      <td>4.003692</td>\n",
       "      <td>4.365330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Netherlands</td>\n",
       "      <td>3003</td>\n",
       "      <td>107</td>\n",
       "      <td>2</td>\n",
       "      <td>3.563104</td>\n",
       "      <td>98.165138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                confirmados  fallecidos  recuperados  tasa_letalidad_1  \\\n",
       "Country/Region                                                           \n",
       "San Marino              144          14            4          9.722222   \n",
       "Indonesia               369          32           15          8.672087   \n",
       "Italy                 47021        4032         4440          8.574892   \n",
       "Iraq                    208          17           49          8.173077   \n",
       "Philippines             230          18            8          7.826087   \n",
       "Iran                  19644        1433         6745          7.294848   \n",
       "Spain                 20410        1043         1588          5.110240   \n",
       "United Kingdom         4014         178           67          4.434479   \n",
       "China                 81250        3253        71266          4.003692   \n",
       "Netherlands            3003         107            2          3.563104   \n",
       "\n",
       "                tasa_letalidad_2  \n",
       "Country/Region                    \n",
       "San Marino             77.777778  \n",
       "Indonesia              68.085106  \n",
       "Italy                  47.592068  \n",
       "Iraq                   25.757576  \n",
       "Philippines            69.230769  \n",
       "Iran                   17.522622  \n",
       "Spain                  39.642721  \n",
       "United Kingdom         72.653061  \n",
       "China                   4.365330  \n",
       "Netherlands            98.165138  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ordenar\n",
    "info_covid.sort_values(\"tasa_letalidad_1\", ascending=False).query(\"confirmados > 100\").head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "en algunos paises todavia no hay muchos recuperados, necesitan mas tiempo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparando informes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# definiendo una funcion para calcular la tasa de mortalidad en un dia especifico\n",
    "def ultimo_por_pais_en(data, fecha):\n",
    "    return data.groupby(\"Country/Region\").sum()[fecha]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "info = [ultimo_por_pais_en(confirmados, '2/20/20'), ultimo_por_pais_en(fallecidos, '2/20/20'), ultimo_por_pais_en(recuperados, '2/20/20')]\n",
    "info_covid_2_20_20 = pd.concat(info, axis=1)\n",
    "info_covid_2_20_20.columns = [\"confirmados\", \"fallecidos\", \"recuperados\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.980939568709458\n",
      "2.4040992147468607\n"
     ]
    }
   ],
   "source": [
    "# comparar con https://www.worldometers.info/coronavirus/coronavirus-death-rate/\n",
    "total_2_20_20 = info_covid_2_20_20.loc['China']\n",
    "tasa_mortalidad_1 = total_2_20_20[\"fallecidos\"]/total_2_20_20[\"confirmados\"] * 100\n",
    "tasa_mortalidad_2 = total_2_20_20[\"fallecidos\"]/(total_2_20_20[\"confirmados\"] + total_2_20_20[\"recuperados\"]) * 100\n",
    "print(tasa_mortalidad_1)\n",
    "print(tasa_mortalidad_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.6717708476096735\n",
      "2.3075537737548055\n"
     ]
    }
   ],
   "source": [
    "# comparar el 3 de marzo\n",
    "info = [ultimo_por_pais_en(confirmados, '3/3/20'), ultimo_por_pais_en(fallecidos, '3/3/20'), ultimo_por_pais_en(recuperados, '3/3/20')]\n",
    "info_covid_3_3_20 = pd.concat(info, axis=1)\n",
    "info_covid_3_3_20.columns = [\"confirmados\", \"fallecidos\", \"recuperados\"]\n",
    "\n",
    "total_3_3_20 = info_covid_3_3_20.loc['China']\n",
    "tasa_mortalidad_1 = total_3_3_20[\"fallecidos\"]/total_3_3_20[\"confirmados\"] * 100\n",
    "tasa_mortalidad_2 = total_3_3_20[\"fallecidos\"]/(total_3_3_20[\"confirmados\"] + total_3_3_20[\"recuperados\"]) * 100\n",
    "print(tasa_mortalidad_1)\n",
    "print(tasa_mortalidad_2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analizando fechas diferentes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How to calculate the mortality rate during an outbreak (https://www.worldometers.info/coronavirus/coronavirus-death-rate/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.079208996577389\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "confirmados    36814\n",
       "fallecidos      2238\n",
       "recuperados    18014\n",
       "Name: China, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# en un rango de dias\n",
    "# def ultimo_por_pais_en(data, fecha):\n",
    "#    return data.groupby(\"Country/Region\").sum()[fecha]\n",
    "\n",
    "# comparar segun el dia de aparicion de los sintomas\n",
    "info = [ultimo_por_pais_en(confirmados, '2/8/20'), ultimo_por_pais_en(fallecidos, '2/20/20'), ultimo_por_pais_en(recuperados, '2/20/20')]\n",
    "info_covid_12 = pd.concat(info, axis=1)\n",
    "info_covid_12.columns = [\"confirmados\", \"fallecidos\", \"recuperados\"]\n",
    "\n",
    "total_12 = info_covid_12.loc['China']\n",
    "tasa_mortalidad_3 = total_12[\"fallecidos\"]/total_12[\"confirmados\"] * 100\n",
    "#tasa_mortalidad_2 = total_12[\"fallecidos\"]/(total_12[\"confirmados\"] + total_12[\"recuperados\"]) * 100\n",
    "print(tasa_mortalidad_3)\n",
    "#print(tasa_mortalidad_2)\n",
    "display(total_12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explicar https://www.worldometers.info/coronavirus/coronavirus-age-sex-demographics/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Conclusion*: Tenemos que ser responsables con la informacion que mostramos. Por que no son los datos, por eso debemos explicar muy bien todo lo que mostramos."
   ]
  }
 ],
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