api-covid-pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fd4b9529",
   "metadata": {},
   "source": [
    "# AD4: conexión con la API de la COVID19 y análisis con Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75b27b82",
   "metadata": {},
   "source": [
    "Me conecto a la API https://api.covid19api.com/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1a2251f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d8cdbfe",
   "metadata": {},
   "source": [
    "### Casos globales de la Covid19\n",
    "Establezco una variable con el  valor  de la url."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6d15e0a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://api.covid19api.com/countries'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3d3c6cb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://api.covid19api.com/countries'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25975239",
   "metadata": {},
   "source": [
    "### Creo el dataframe\n",
    "Creamos un dataframe con la función de panda.\n",
    "Luego definimos el dataframe, la estructura de tablas/datos en Phyton y  emplearemos la funcion de pd.read_json. Si lo llamo me sale el json en formato de tabla."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fc90a6ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_json(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0bbdb8a6",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Slug</th>\n",
       "      <th>ISO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gibraltar</td>\n",
       "      <td>gibraltar</td>\n",
       "      <td>GI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Oman</td>\n",
       "      <td>oman</td>\n",
       "      <td>OM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>France</td>\n",
       "      <td>france</td>\n",
       "      <td>FR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jersey</td>\n",
       "      <td>jersey</td>\n",
       "      <td>JE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mali</td>\n",
       "      <td>mali</td>\n",
       "      <td>ML</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>puerto-rico</td>\n",
       "      <td>PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>papua-new-guinea</td>\n",
       "      <td>PG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>Saint Pierre and Miquelon</td>\n",
       "      <td>saint-pierre-and-miquelon</td>\n",
       "      <td>PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>timor-leste</td>\n",
       "      <td>TL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>Montenegro</td>\n",
       "      <td>montenegro</td>\n",
       "      <td>ME</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>248 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Country                       Slug ISO2\n",
       "0                    Gibraltar                  gibraltar   GI\n",
       "1                         Oman                       oman   OM\n",
       "2                       France                     france   FR\n",
       "3                       Jersey                     jersey   JE\n",
       "4                         Mali                       mali   ML\n",
       "..                         ...                        ...  ...\n",
       "243                Puerto Rico                puerto-rico   PR\n",
       "244           Papua New Guinea           papua-new-guinea   PG\n",
       "245  Saint Pierre and Miquelon  saint-pierre-and-miquelon   PM\n",
       "246                Timor-Leste                timor-leste   TL\n",
       "247                 Montenegro                 montenegro   ME\n",
       "\n",
       "[248 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "295f9c7c",
   "metadata": {},
   "source": [
    "### Datos de la covid19 en España\n",
    "Para que me salgan los datos de España creo una lista nueva donde le indico  que el país que voy a seleccionar es == a Spain.  Esto gracias a que tengo una lista que se llama  Country."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0154267e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Slug</th>\n",
       "      <th>ISO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Spain</td>\n",
       "      <td>spain</td>\n",
       "      <td>ES</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    Country   Slug ISO2\n",
       "141   Spain  spain   ES"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Country'] == 'Spain']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbce73c3",
   "metadata": {},
   "source": [
    "Procedo a buscar los datos de España en tiempo real para lo que defino una url que se llama rt (de real time). También creo el objeto df_rt_es, el cual leerá la información de la url."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "da132607",
   "metadata": {},
   "outputs": [],
   "source": [
    "url_rt_es = 'https://api.covid19api.com/country/spain/status/confirmed/live'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "06fb25d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rt_es = pd.read_json(url_rt_es)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f75d9ae2",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>Province</th>\n",
       "      <th>City</th>\n",
       "      <th>CityCode</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
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       "      <td>confirmed</td>\n",
       "      <td>2020-01-22 00:00:00+00:00</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-23 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-24 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-25 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-26 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>12818184</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-07-03 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>12818184</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-07-04 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>12890002</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-07-05 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>12890002</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-07-06 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>12890002</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-07-07 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>898 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country CountryCode Province City CityCode    Lat   Lon     Cases  \\\n",
       "0     Spain          ES                         40.46 -3.75         0   \n",
       "1     Spain          ES                         40.46 -3.75         0   \n",
       "2     Spain          ES                         40.46 -3.75         0   \n",
       "3     Spain          ES                         40.46 -3.75         0   \n",
       "4     Spain          ES                         40.46 -3.75         0   \n",
       "..      ...         ...      ...  ...      ...    ...   ...       ...   \n",
       "893   Spain          ES                         40.46 -3.75  12818184   \n",
       "894   Spain          ES                         40.46 -3.75  12818184   \n",
       "895   Spain          ES                         40.46 -3.75  12890002   \n",
       "896   Spain          ES                         40.46 -3.75  12890002   \n",
       "897   Spain          ES                         40.46 -3.75  12890002   \n",
       "\n",
       "        Status                      Date  \n",
       "0    confirmed 2020-01-22 00:00:00+00:00  \n",
       "1    confirmed 2020-01-23 00:00:00+00:00  \n",
       "2    confirmed 2020-01-24 00:00:00+00:00  \n",
       "3    confirmed 2020-01-25 00:00:00+00:00  \n",
       "4    confirmed 2020-01-26 00:00:00+00:00  \n",
       "..         ...                       ...  \n",
       "893  confirmed 2022-07-03 00:00:00+00:00  \n",
       "894  confirmed 2022-07-04 00:00:00+00:00  \n",
       "895  confirmed 2022-07-05 00:00:00+00:00  \n",
       "896  confirmed 2022-07-06 00:00:00+00:00  \n",
       "897  confirmed 2022-07-07 00:00:00+00:00  \n",
       "\n",
       "[898 rows x 10 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rt_es"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed58e1ce",
   "metadata": {},
   "source": [
    "#### Casos de España de la parte superior de la tabla de datos\n",
    "Esta visualización se logra colocando df_rt_es.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4462090a",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
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       "      <td>confirmed</td>\n",
       "      <td>2020-01-23 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
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       "      <td>confirmed</td>\n",
       "      <td>2020-01-24 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-25 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-26 00:00:00+00:00</td>\n",
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      ],
      "text/plain": [
       "  Country CountryCode Province City CityCode    Lat   Lon  Cases     Status  \\\n",
       "0   Spain          ES                         40.46 -3.75      0  confirmed   \n",
       "1   Spain          ES                         40.46 -3.75      0  confirmed   \n",
       "2   Spain          ES                         40.46 -3.75      0  confirmed   \n",
       "3   Spain          ES                         40.46 -3.75      0  confirmed   \n",
       "4   Spain          ES                         40.46 -3.75      0  confirmed   \n",
       "\n",
       "                       Date  \n",
       "0 2020-01-22 00:00:00+00:00  \n",
       "1 2020-01-23 00:00:00+00:00  \n",
       "2 2020-01-24 00:00:00+00:00  \n",
       "3 2020-01-25 00:00:00+00:00  \n",
       "4 2020-01-26 00:00:00+00:00  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rt_es.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b6ca596",
   "metadata": {},
   "source": [
    "#### Casos de España de la parte inferior de la tabla de datos\n",
    "Esta visualización se logra colocando df_rt_es.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0501cfb9",
   "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>Country</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>Province</th>\n",
       "      <th>City</th>\n",
       "      <th>CityCode</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Status</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>12818184</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-07-03 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>12818184</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-07-04 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>12890002</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-07-05 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>12890002</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-07-06 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>12890002</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-07-07 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country CountryCode Province City CityCode    Lat   Lon     Cases  \\\n",
       "893   Spain          ES                         40.46 -3.75  12818184   \n",
       "894   Spain          ES                         40.46 -3.75  12818184   \n",
       "895   Spain          ES                         40.46 -3.75  12890002   \n",
       "896   Spain          ES                         40.46 -3.75  12890002   \n",
       "897   Spain          ES                         40.46 -3.75  12890002   \n",
       "\n",
       "        Status                      Date  \n",
       "893  confirmed 2022-07-03 00:00:00+00:00  \n",
       "894  confirmed 2022-07-04 00:00:00+00:00  \n",
       "895  confirmed 2022-07-05 00:00:00+00:00  \n",
       "896  confirmed 2022-07-06 00:00:00+00:00  \n",
       "897  confirmed 2022-07-07 00:00:00+00:00  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rt_es.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e46f4a3",
   "metadata": {},
   "source": [
    "### Visualización de los casos de covid-19 en España\n",
    "Para visualizar los casos y fechas de España en una gráfica convierto a fecha en columna de control, estableciendo el tiempo específico que deseo saber, en este caso del 1 de enero de 2020 al 30 de junio de 2022. En esta etapa uso la variable plot_rt_es."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a352ff78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Casos de Covid19 en España desde 20/1/2020 hasta 30/6/2022'}, xlabel='Date'>"
      ]
     },
     "execution_count": 12,
     "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": [
    "df_rt_es.set_index('Date')['Cases'].plot(title='Casos de Covid19 en España desde 20/1/2020 hasta 30/6/2022')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f3012a1a",
   "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>Country</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>Province</th>\n",
       "      <th>City</th>\n",
       "      <th>CityCode</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Status</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-22 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-23 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-24 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-25 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-26 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>12818184</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-07-03 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>12818184</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-07-04 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>12890002</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-07-05 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>12890002</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-07-06 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>12890002</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-07-07 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>898 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country CountryCode Province City CityCode    Lat   Lon     Cases  \\\n",
       "0     Spain          ES                         40.46 -3.75         0   \n",
       "1     Spain          ES                         40.46 -3.75         0   \n",
       "2     Spain          ES                         40.46 -3.75         0   \n",
       "3     Spain          ES                         40.46 -3.75         0   \n",
       "4     Spain          ES                         40.46 -3.75         0   \n",
       "..      ...         ...      ...  ...      ...    ...   ...       ...   \n",
       "893   Spain          ES                         40.46 -3.75  12818184   \n",
       "894   Spain          ES                         40.46 -3.75  12818184   \n",
       "895   Spain          ES                         40.46 -3.75  12890002   \n",
       "896   Spain          ES                         40.46 -3.75  12890002   \n",
       "897   Spain          ES                         40.46 -3.75  12890002   \n",
       "\n",
       "        Status                      Date  \n",
       "0    confirmed 2020-01-22 00:00:00+00:00  \n",
       "1    confirmed 2020-01-23 00:00:00+00:00  \n",
       "2    confirmed 2020-01-24 00:00:00+00:00  \n",
       "3    confirmed 2020-01-25 00:00:00+00:00  \n",
       "4    confirmed 2020-01-26 00:00:00+00:00  \n",
       "..         ...                       ...  \n",
       "893  confirmed 2022-07-03 00:00:00+00:00  \n",
       "894  confirmed 2022-07-04 00:00:00+00:00  \n",
       "895  confirmed 2022-07-05 00:00:00+00:00  \n",
       "896  confirmed 2022-07-06 00:00:00+00:00  \n",
       "897  confirmed 2022-07-07 00:00:00+00:00  \n",
       "\n",
       "[898 rows x 10 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url_es = 'https://api.covid19api.com/country/spain/status/confirmed/live'\n",
    "df_es = pd.read_json(url_es)\n",
    "df_es"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c7e185f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Casos de Covid19 en España'}, xlabel='Date'>"
      ]
     },
     "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": [
    "casos_es = df_es.set_index('Date')['Cases']\n",
    "casos_es.plot(title='Casos de Covid19 en España')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b8c1033",
   "metadata": {},
   "source": [
    "### Casos de Covid19 de Panamá\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a0ec900",
   "metadata": {},
   "source": [
    "Repetimos el mismo proceso, solo que debemos tener cuidado que los datos sean los de Panamá."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c3953911",
   "metadata": {},
   "outputs": [],
   "source": [
    "url_rt_pa = 'https://api.covid19api.com/country/panama/status/confirmed/live'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4c688444",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rt_pa = pd.read_json(url_rt_pa)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6533100c",
   "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>Country</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>Province</th>\n",
       "      <th>City</th>\n",
       "      <th>CityCode</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Status</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Panama</td>\n",
       "      <td>PA</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>8.54</td>\n",
       "      <td>-80.78</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-22 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Panama</td>\n",
       "      <td>PA</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>8.54</td>\n",
       "      <td>-80.78</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-23 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Panama</td>\n",
       "      <td>PA</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>8.54</td>\n",
       "      <td>-80.78</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-24 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Panama</td>\n",
       "      <td>PA</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>8.54</td>\n",
       "      <td>-80.78</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-25 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Panama</td>\n",
       "      <td>PA</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>8.54</td>\n",
       "      <td>-80.78</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-26 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>Panama</td>\n",
       "      <td>PA</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>8.54</td>\n",
       "      <td>-80.78</td>\n",
       "      <td>925254</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-07-04 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>Panama</td>\n",
       "      <td>PA</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>8.54</td>\n",
       "      <td>-80.78</td>\n",
       "      <td>925254</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-07-05 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>Panama</td>\n",
       "      <td>PA</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>8.54</td>\n",
       "      <td>-80.78</td>\n",
       "      <td>925254</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-07-06 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>Panama</td>\n",
       "      <td>PA</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>8.54</td>\n",
       "      <td>-80.78</td>\n",
       "      <td>925254</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-07-07 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>Panama</td>\n",
       "      <td>PA</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>8.54</td>\n",
       "      <td>-80.78</td>\n",
       "      <td>925254</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-07-08 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>899 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country CountryCode Province City CityCode   Lat    Lon   Cases  \\\n",
       "0    Panama          PA                         8.54 -80.78       0   \n",
       "1    Panama          PA                         8.54 -80.78       0   \n",
       "2    Panama          PA                         8.54 -80.78       0   \n",
       "3    Panama          PA                         8.54 -80.78       0   \n",
       "4    Panama          PA                         8.54 -80.78       0   \n",
       "..      ...         ...      ...  ...      ...   ...    ...     ...   \n",
       "894  Panama          PA                         8.54 -80.78  925254   \n",
       "895  Panama          PA                         8.54 -80.78  925254   \n",
       "896  Panama          PA                         8.54 -80.78  925254   \n",
       "897  Panama          PA                         8.54 -80.78  925254   \n",
       "898  Panama          PA                         8.54 -80.78  925254   \n",
       "\n",
       "        Status                      Date  \n",
       "0    confirmed 2020-01-22 00:00:00+00:00  \n",
       "1    confirmed 2020-01-23 00:00:00+00:00  \n",
       "2    confirmed 2020-01-24 00:00:00+00:00  \n",
       "3    confirmed 2020-01-25 00:00:00+00:00  \n",
       "4    confirmed 2020-01-26 00:00:00+00:00  \n",
       "..         ...                       ...  \n",
       "894  confirmed 2022-07-04 00:00:00+00:00  \n",
       "895  confirmed 2022-07-05 00:00:00+00:00  \n",
       "896  confirmed 2022-07-06 00:00:00+00:00  \n",
       "897  confirmed 2022-07-07 00:00:00+00:00  \n",
       "898  confirmed 2022-07-08 00:00:00+00:00  \n",
       "\n",
       "[899 rows x 10 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rt_pa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4a637b13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Casos de Covid19 en Panamá'}, xlabel='Date'>"
      ]
     },
     "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": [
    "casos_pa = df_rt_pa.set_index('Date')['Cases']\n",
    "casos_pa.plot(title='Casos de Covid19 en Panamá')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2bd35b1",
   "metadata": {},
   "source": [
    "### Ploteamos los países\n",
    "Es necesario colocar las variables que ya hemos definido para no tener que repetir todo el proceso."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1d5130a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cases</th>\n",
       "      <th>Cases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>2022-07-05 00:00:00+00:00</th>\n",
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       "      <th>2022-07-07 00:00:00+00:00</th>\n",
       "      <td>12890002.0</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-08 00:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>899 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Cases   Cases\n",
       "Date                                         \n",
       "2020-01-22 00:00:00+00:00         0.0       0\n",
       "2020-01-23 00:00:00+00:00         0.0       0\n",
       "2020-01-24 00:00:00+00:00         0.0       0\n",
       "2020-01-25 00:00:00+00:00         0.0       0\n",
       "2020-01-26 00:00:00+00:00         0.0       0\n",
       "...                               ...     ...\n",
       "2022-07-04 00:00:00+00:00  12818184.0  925254\n",
       "2022-07-05 00:00:00+00:00  12890002.0  925254\n",
       "2022-07-06 00:00:00+00:00  12890002.0  925254\n",
       "2022-07-07 00:00:00+00:00  12890002.0  925254\n",
       "2022-07-08 00:00:00+00:00         NaN  925254\n",
       "\n",
       "[899 rows x 2 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pa_vs_es = pd.concat([casos_es,casos_pa],axis=1)\n",
    "pa_vs_es"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "37a6a4e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "                               España  Panamá\n",
       "Date                                         \n",
       "2020-01-22 00:00:00+00:00         0.0       0\n",
       "2020-01-23 00:00:00+00:00         0.0       0\n",
       "2020-01-24 00:00:00+00:00         0.0       0\n",
       "2020-01-25 00:00:00+00:00         0.0       0\n",
       "2020-01-26 00:00:00+00:00         0.0       0\n",
       "...                               ...     ...\n",
       "2022-07-04 00:00:00+00:00  12818184.0  925254\n",
       "2022-07-05 00:00:00+00:00  12890002.0  925254\n",
       "2022-07-06 00:00:00+00:00  12890002.0  925254\n",
       "2022-07-07 00:00:00+00:00  12890002.0  925254\n",
       "2022-07-08 00:00:00+00:00         NaN  925254\n",
       "\n",
       "[899 rows x 2 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pa_vs_es.columns = ['España','Panamá']\n",
    "pa_vs_es"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1104db47",
   "metadata": {},
   "source": [
    "### Gráfica comparativa de los casos de Covid19 en España y Panamá\n",
    "Ya hemos ploteado los indicadores de ambos países, en primera instacia colocando los casos y luego estableciendo el nombre. Con ellos las líneas de cada país se pueden indentificar con mayor facilidad."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "44c2e05b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Comparativa Covid19 España y Panamá'}, xlabel='Date'>"
      ]
     },
     "execution_count": 53,
     "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": [
    "pa_vs_es.plot(title='Comparativa Covid19 España y Panamá')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "150198f6",
   "metadata": {},
   "source": [
    "### Comparativa de casos entre Costa Rica y Panamá\n",
    "Repetimos nuevamente los pasos, pero en esta ocasión con Costa Rica, con la finalidad de comparar el comportamiento de la pandemia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f29be41b",
   "metadata": {},
   "outputs": [],
   "source": [
    "url_rt_cr = 'https://api.covid19api.com/country/costa-rica/status/confirmed/live'\n",
    "df_rt_cr = pd.read_json(url_rt_cr)\n",
    "casos_cr = df_rt_cr.set_index('Date')['Cases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9af04c2b",
   "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>Cases</th>\n",
       "      <th>Cases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-04 00:00:00+00:00</th>\n",
       "      <td>904934</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-05 00:00:00+00:00</th>\n",
       "      <td>904934</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-06 00:00:00+00:00</th>\n",
       "      <td>904934</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-07 00:00:00+00:00</th>\n",
       "      <td>904934</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-08 00:00:00+00:00</th>\n",
       "      <td>904934</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>899 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Cases   Cases\n",
       "Date                                     \n",
       "2020-01-22 00:00:00+00:00       0       0\n",
       "2020-01-23 00:00:00+00:00       0       0\n",
       "2020-01-24 00:00:00+00:00       0       0\n",
       "2020-01-25 00:00:00+00:00       0       0\n",
       "2020-01-26 00:00:00+00:00       0       0\n",
       "...                           ...     ...\n",
       "2022-07-04 00:00:00+00:00  904934  925254\n",
       "2022-07-05 00:00:00+00:00  904934  925254\n",
       "2022-07-06 00:00:00+00:00  904934  925254\n",
       "2022-07-07 00:00:00+00:00  904934  925254\n",
       "2022-07-08 00:00:00+00:00  904934  925254\n",
       "\n",
       "[899 rows x 2 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pa_vs_cr = pd.concat([casos_cr,casos_pa],axis=1)\n",
    "pa_vs_cr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6fa0f3d1",
   "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>Costa Rica</th>\n",
       "      <th>Panamá</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-04 00:00:00+00:00</th>\n",
       "      <td>904934</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-05 00:00:00+00:00</th>\n",
       "      <td>904934</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-06 00:00:00+00:00</th>\n",
       "      <td>904934</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-07 00:00:00+00:00</th>\n",
       "      <td>904934</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-08 00:00:00+00:00</th>\n",
       "      <td>904934</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>899 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Costa Rica  Panamá\n",
       "Date                                         \n",
       "2020-01-22 00:00:00+00:00           0       0\n",
       "2020-01-23 00:00:00+00:00           0       0\n",
       "2020-01-24 00:00:00+00:00           0       0\n",
       "2020-01-25 00:00:00+00:00           0       0\n",
       "2020-01-26 00:00:00+00:00           0       0\n",
       "...                               ...     ...\n",
       "2022-07-04 00:00:00+00:00      904934  925254\n",
       "2022-07-05 00:00:00+00:00      904934  925254\n",
       "2022-07-06 00:00:00+00:00      904934  925254\n",
       "2022-07-07 00:00:00+00:00      904934  925254\n",
       "2022-07-08 00:00:00+00:00      904934  925254\n",
       "\n",
       "[899 rows x 2 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pa_vs_cr.columns = ['Costa Rica','Panamá']\n",
    "pa_vs_cr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "aba6e80e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Comparativa Covid19 Costa Rica y Panamá'}, xlabel='Date'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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++uknli5dyrRp07j33nu5/vrrzyizatUqvv/+e1avXk1wcDAjR44s9tLXRYsW0atXLx544AFuv/12Pvnkk7PiKOoqqDvuuIO//e1vTJgwgVWrVjFr1iw31lgp5Qmr9yfx895EBkQ1JMDvdOtD1+zN3J38GDkSxL8azeVEQBsCy7mMWpc0POHCCy/koYce4s033+Tmm28GYN26dWRlZTF8+HD++9//Mn36dJKTk/npp594+umnOXz4MJGRkdx8881kZmayceNGrr/+evz9/cnLy8Pf35+0tDQaNmxIcHAwu3btYs2aNSXG4e/vz+zZs2nXrh07d+6kS5fTV0yMGjWKyZMnc/fddxMeHk5ycjJhYWGkpaURGRkJwPz58ytvIymlzllsyikA3v2/QQT6+VoDE3bBW7MhohXB133MnPotipz2vZvdW4YmjSogInz66afMnDmTuXPnEhQURFRUFC+88ALDhw9n9erV9OrVCxHhqaeeomnTpsyfP5+nn34af39/QkJCWLBgAQAzZsygZ8+e9O3bl3nz5vH666/Ts2dPOnXqxODBg0uNpU6dOtxzzz0888wzvP32238M79atGw8//DAjRozA19eXPn368M477zBr1iyuuuoqIiMjGTx4MAcPHqy07aSUKru4tFPc9f5mTuXlcyz1FE1CA08njNj1sHgq+AfBdR9DMQmjLGrcM8L79+9vCj9Po/BRtap4uo2V8ozPNh1l5gebGdIunCB/X4a2j+DGoW2sy2pfGwK+/jDlfWjavcT5iMgGY0z/0panNQ2llKrGYpKzAJh3wwCC/H1Pj/jxKTgZDzevLDVhlIUmDaWUqoaWbo1j1pc7yMjOo3G9wDMTRtJ+WPcW9L0emveu0OVq0lBKqWroh10JZOflc0XfFgxsE3Z6xKlUeO9P4F8HRj5Y4cvVpKGUUtVEXNopPtl4FGMMvx9KokdkfZ6c3OPMQr88b9U0blgK9ZpWeAyaNJRSqpp486eDzPv19BWMf+rX8swCWcnw+5vQ/QqIOr9SYtCkUUu99tprTJ06ldDQUE+HopQqxcpdCazYFc/KXSfo3bIBH916HgD+voW6D1zzKuRlwvC/V1osmjSqiK+vLz169MDhcNClSxfmz59PcHCwR2JZsmQJx44d04ShlBfLyM5j8e8x5OY7eee3Q2Rk51E3wI8JvZqfnSzAOiW19r/QZQI0rrzL3zVpVJE6der80dHg1KlTef311/nb3/7mkVhOnTrFY4895pFlK6VK9snGWHYcS2dPfAY/77V6oPYReO26foztVkwbhTGw5M/g4wejZ1VqfJo0PGDYsGFs3bqVL7/8ktmzZ5Obm0t4eDiLFi2iSZMmzJo1iyNHjnDgwAGOHDnCzJkzufPOOwGrK/SYmBiys7O56667mDFjBgAhISHcfvvtfP/99zRs2JA5c+Zw3333ceTIEV544QUmTJjAoUOHmDZtGpmZmTz//PO8/PLLDBkyxJObQqlaK99p2Jdwkrx8J3//aAtHU0+R63CS43ACEBLox+V9I/nX5T3wESm6dlFg73KI2wITX4HwdpUad+27I/ybB+D4topdaNMecPHcEouEhIRw8uRJHA4HV1xxBePGjWPKlCk0aNDgj27Hd+7cybPPPsusWbP47rvvWLlyJRkZGXTq1Injx4/j7+//R59QBV2h//jjj388//zrr7/m4osvZvLkyWRmZrJ06VKio6OZPn06mzdvJisrCx8fH4KCgti1axdTp05lw4YNFbIJ9I5wpdyz4XAKr/+4n62xqcSnn37M6rAOEbRvHEKjeoHcNLTN6a5A3DFvHKTGwJ2bwC+gXHHpHeFepuDhSWDVNG666SZ2795dbLfjl1xyCYGBgQQGBtK4cWPi4+Np0aJFsV2hBwQEMG7cOAB69OhBYGAg/v7+9OjRg0OHDgHgcDi4++672bVrF/7+/uzatatKt4FStVXiyRz+++N+Nh5JZcPhFMLqBtC5aT2mDW5N+8YhhAT6c3778CJ7mi7V4dVwZDWM+3e5E0ZZ1L6kUUqNoLK4tmkUKKnb8cDA0x0X+/r64nA4SuwK3d/f/48dzsfH54/pfXx8cDgcADz//PM0atSIt99+G4fDQVBQUCWusVJq9/EM3vz5AEu3xpHjyKd/6zAu6tqEh8d3ISqi7rkvICcDvrkXgsOtu7+rQO1LGl6krN2Ol7Ur9MJSUlJo2dK6rnvhwoXk55/9oBal1Lk7lnqKt34+yPzVh/D3Ffq2asgTk7rTrlFIxS7ox6cgfgdc+yEEVM3VmJo0PKis3Y6PGzeuzF2hu7rtttu48sor+eijj7jggguoW7cCjnSUUmf4cH0M//xsOzkOJ+N7NOXJST1oWLcSThudPGH1L9XjKuhwUcXPvxi1ryFcVQrdxqq2O56WzZyvd/LFlmMMaRfOnaM6MDAqDB+fcrRTuOPHp2DlHPjrOojocM6zc7chvIRruM6Y2d0iskNEtovI+yISJCJhIrJcRPbafxu6lH9QRPaJyG4RGesyvJ+IbLPHvST2SXgRCRSRD+zha0UkymWa6fYy9orI9DJtBaWUqgIJGdlc9d/f+GLLMS7s3JgFNw5kcNvwyksY2Wnw09PQrGeFJIyyKDVpiEgkcCfQ3xjTHfAFpgAPACuMMR2AFfZnRKSrPb4bMA54VUQKrh17DZgBdLBf4+zhNwEpxpj2wPPAv+15hQGPAoOAgcCjrslJKaU8zek03LxgA4kZuXx4y3m8Pb0/fiXdU3HuC4QPpkF+LnS7vPKWUwx318wPqCMifkAwcAyYCBS03s4HJtnvJwKLjTE5xpiDwD5goIg0A0KNMauNdU5sQaFpCua1BBhl10LGAsuNMcnGmBRgOacTjVJKeVRyZi5jX/iJLTGpPHpZVwa2CSvfZbNlEf0pHPwRLnkOhs6s3GUVodSkYYw5CjwDHAHigDRjzHdAE2NMnF0mDmhsTxIJxLjMItYeFmm/Lzz8jGmMMQ4gDQgvYV5nEJEZIrJeRNafOHGiuPUobVVVOem2VbXVU9/uYm/CSa4Z2JJJfc76aap4+Xnww2xo3A363VD5yyuCO6enGmLVBNoAzYG6InJdSZMUMcyUMLy805weYMwbxpj+xpj+jRo1OmuCoKAgkpKS9MetEhhjSEpK0ns+VK2z63g6H66P4cbz2/Cvy3ue+eS8yrJpISQfgFGPgE8VLK8I7lxyOxo4aIw5ASAinwBDgHgRaWaMibNPPSXY5WMB107eW2Cdzoq13xce7jpNrH0KrD6QbA8fWWiaVe6u3B8TtWhBbGwsxdVC1LkJCgqiRYsWpRdUqgaZ98tBggP8uHNU+6pZ4NEN8N0/oeVg6Di29PKVxJ2kcQQYLCLBwClgFLAeyASmA3Ptv5/b5b8A3hOR57BqJh2A340x+SKSISKDgbXA9cB/XKaZDqwGrgR+MMYYEVkGzHFp/B4DlPn5hf7+/md00aGUUufi7V8O8uH6WMZ2a0KD4MrvuoO0o/D+tRAcBle8BZXdblKCUpOGMWatiCwBNgIOYBPwBhACfCgiN2Ellqvs8jtE5EMg2i5/uzGm4Nbj24B3gDrAN/YL4G1goYjsw6phTLHnlSwiTwDr7HKPG2OSz2mNlVLqHGTmOHh++R6CA3y58fxKPhh15MKmBfDj05CbCdM+hQYtS5+uEtWKm/uUUqqiLFp7mIc/3c4nfxlC31aVcAeAMZC0D/atsJ7El3oYWp0HY+dAZN+KX55Ne7lVSqkKZoxh4erDdG0WSp+WDc59hqdSIXk/pB+DI2usZ2Ic3wbZqdb4Zr3gkmeh/WiPnpJypUlDKaXctOt4BruOZ/Dk5O7u34+RdwqSD1q1h6R91mNZC95nJZ4u5xsITbtDt0nQrDe0GV7pD1QqD00aSinlBmMMX2yxLvg8r2342QUcOZAQDXFbrdpC8gFI2ms9HMn1ToGQphDeHjpfYiWF8PZQrxlEdITACu4FtxJo0lBKKTes3J3Aa6v2U8ffl9bhdg/RCTth4wI48CMk7gan9ewaAupZCaHFQOh93enkEN4OAut5biUqgCYNpZRywzfbjgPw0a3n4RuzGlbNtbrz8PGHNsOseyea9YSmPaFhG/CpxP6nPEiThlJKlcLpNKzcncCEnk3ovuMZ+O0lqw3ivL/C0LuhboSnQ6wymjSUUqoUW2JTOXUyjcdOPAZ7tkH3K+GyF6tFG0RF06ShlFKlWLEzgWcC/kuD1GgY/4zVWaCvv6fD8ghNGkopVYqsHV9zsc/vcOGjMPBmT4fjUTWzpUYppSqIyUzikfRZpPs3giF3eDocj9OkoZRSJdjx9i0A7G47vdaeknKlSUMppYqRfvwg3ZOXc8yEETZqpqfD8QqaNJRSqhiHNy4DIHHCIto1rt435VUUTRpKKVUMs3cF+Ubo1L2Pp0PxGpo0lFKqKNnp9Ez5jhS/CAID63g6Gq+hSUMppYqQsf59ALa3u8XDkXgXTRpKKVWYMx//n58CIGLAlR4Oxrto0lBKqcKObSYoJ5FH826gY5vWno7Gq2jSUEqpQvIO/QbAlnrDCPDTn0lX2o2IUkoVcmDjCoKdjQhvqrWMwjSFKqWUK2NolLKZbb6defqqXp6Oxuto0lBKKRfpcfsIMykEtR1CWN0AT4fjdTRpKKWUiwM71gHQuOMgD0finTRpKKWUi5jYIwC0b9few5F4J00aSinlIi/NehZ4UP0mHo7EO2nSUEopF37ZiWRJXfAP8nQoXkmThlJKFTCG9jnRpAc08nQkXkuThlJKAalZuTzx1By6mv0cbTjQ0+F4LU0aSikFfL8zgeD0AwBEXPJPD0fjvTRpKKUU8H10PFGBGZg6DWndspWnw/FamjSUUrVedl4+P+09wWjfjUhIU0+H49U0aSilar3VB5JonBdL/bwTEKiPdS2JJg2lVK23clcCPfzjrA9jZns2GC+nSUMpVetFH0tnUGiK9aFxF88G4+XcShoi0kBElojILhHZKSLniUiYiCwXkb3234Yu5R8UkX0isltExroM7yci2+xxL4mI2MMDReQDe/haEYlymWa6vYy9IjK9AtddKaV4d81h1h9OoXPAcQhpAkGhng7Jq7lb03gR+NYY0xnoBewEHgBWGGM6ACvsz4hIV2AK0A0YB7wqIr72fF4DZgAd7Nc4e/hNQIoxpj3wPPBve15hwKPAIGAg8KhrclJKqXORl+/k6WW7AejoFw/h2t9UaUpNGiISCgwH3gYwxuQaY1KBicB8u9h8YJL9fiKw2BiTY4w5COwDBopIMyDUGLPaGGOABYWmKZjXEmCUXQsZCyw3xiQbY1KA5ZxONEopdU6+j44n7VQe/7thAKGZhyG8nadD8nru1DTaAieA/4nIJhF5S0TqAk2MMXEA9t/GdvlIIMZl+lh7WKT9vvDwM6YxxjiANCC8hHmdQURmiMh6EVl/4sQJN1ZJKaVg0dojRDaow/BW/pB5AsI7eDokr+dO0vAD+gKvGWP6AJnYp6KKIUUMMyUML+80pwcY84Yxpr8xpn+jRtpnjFKqdEeSsvhlXyLXDGyJb7J1J7ieniqdO0kjFog1xqy1Py/BSiLx9ikn7L8JLuVbukzfAjhmD29RxPAzphERP6A+kFzCvJRSqtycTsPT31ltGZf2bA5Je60REVrTKE2pScMYcxyIEZFO9qBRQDTwBVBwNdN04HP7/RfAFPuKqDZYDd6/26ewMkRksN1ecX2haQrmdSXwg93usQwYIyIN7QbwMfYwpZQqtxdW7OXLLcdoGVaH1uHBkLQPxBcatPZ0aF7Pz81ydwCLRCQAOAD8GSvhfCgiNwFHgKsAjDE7RORDrMTiAG43xuTb87kNeAeoA3xjv8BqZF8oIvuwahhT7Hkli8gTwDq73OPGmORyrqtSSnHgxEle/3E/57cP5+Vr+iIicHSDdWrKT58JXhqxDuhrjv79+5v169d7OgyllJdZdyiZExk5fLrpKD/vPcFP915A49AgyM2Cua1g0C0w9klPh+kxIrLBGNO/tHLu1jSUUspjjDGkZztISM/meHo2JzJyyMh2kH4qj5SsPFKycjmZ4yAr10FWbj4Z2Q6ychzkOJzkOJxk5+XjcJ4+QJ7Yu7mVMADiNoMzD6KGemblqhlNGkopr5LvNGyOSWXTkRQOJGayMy6d3cczyMrNL7J83QBfGtYNoF6QP8EBvoQE+tGsfhB1A/wI9Pch0M8XPx8hIiSQ89tH4OsjVjtGgaMbrL+RpR5kKzRpKKW8QHJmLt9Hx7Po9yNsiUn9Y3i9ID+6NA3lyn4taNkwmCb1g2hcL5AmoUGEBvlRL8ifAL9z7EIvdj00aAUherm+OzRpKKU8IivXwU97TvDe7zH8ui+RfKehU5N63DayHR2bhDCsQyMiQgIrP5CUg9Coc+Uvp4bQpKGUqnLf7TjOQ59uJ/FkDo3qBXLL8LYM69CIQW3C8PEp6p7eSnQyAZr2rNplVmOaNJRSVeZgYiazv4pmxa4EujYL5Z+XdmFkx8bUD/b3TEBOp5U0QhqXXlYBmjSUUlVk45EUbnpnHZk5+VzSoxnP/qkXQf6+pU9YmXZ+ASbf6hJduUWThlKq0n234zi3vLuBlg2D+fQv5xMVUdfTIVm2f2z97TDGs3FUI5o0lFKV6uMNsdzz0RYAHp/YzXsSRmYS7Pse+t0AYW08HU21oUlDKVVpNsekcv/HWxnSLpx5Nwzw/OkoV2tfh7xTMOg2T0dSregzwpVSleKnPSeY9Mqv1Anw5fVp/bwrYTjzYdO70OEiaKyX25aFJg2lVIVLPJnDXYs3ERLox4MXdyE0yENXRxXnwErIOAa9p3o6kmpHT08ppSrco5/vIDMnn6V3DqVDk3qeDudsWz6AOg2h08WejqTa0ZqGUqrCGGP4eEMsS7fFceuItt6ZMABi1kDbkeBXBXec1zCaNJRSFWbZjvg/rpS6qn/LUkp7SNJ+SD2id4GXkyYNpVSFyMxx8NiXO2gTUZe1D42iZVhw6RNVtfXz4PWh4BsI3SZ7OppqSZOGUuqcGWP4Zvtx4tKymT2pO00KnlXhTY5ugK/uhsh+MGOl3ptRTtoQrpQ6J8YYZizcwPLoeEIC/RjYJszTIZ0p7ah1T8ba/4L4wPhn9DLbc6BJQyl1Tr7aGsfy6HiuHdSKy3o2x9/Xgycw8h2QdgSSDsDhX+DEbti73HoyX+dLYdQj0KiT5+KrATRpKKXKLTPHweyl0XSPDOWJid3xrexuzZ35cDIe0mIhLQbS4yAjDpL2WQ3cKYesBAHg4wdh7aDvNOg7HZr3rtzYaglNGkqpcsl3Gh7/Mpr49Byev7r3uSUMYyA7FTLi4eTxQn/jIeO4nSSOnU4KBfyCrOTQuDN0vgTC20N4O2jcxboXQ1UoTRpKqXJ55rvdfLA+hmb1gxjUJrzoQk4nZCUVSgTHTycC17+O7LOn9w+2nnUR0hRaDoT6LaB+S+vVoCXUawpBDUCq+MFNtZgmDaVUme0+nsGbPx1gdJcmPHtVL3xxQtIh6/RQ7DpI3GO/9oHj1NkzCKpvJYJ6TaDlIOtvSFMrCYQ0Of03sJ4mBC+jSUMpVWb/+nIzwwL38lKL7QR/MhdifoecNHusQINWENERooZDw9ZnJoJ6TcG/jkfjV+WnSUMp5Z6cDJx7lrP5uwW8nL6aEMmGn4GITtB9MrQYYJ0+iuwPgSGejlZVEk0aSqniGWOdblo/D3Z8io8jm5YmlNXBF9B/9J9o2HkE1C2mPUPVSJo0lFJny8+DrR/A6lchYQcEhJDW8Upu29qO4HZDePOGQYi2NdRKmjSUUqc5cmDrh/DzM1ajdpMecOkL7IwYw7R3oznlk8+PV/XRhFGLadJQSkFeNqx9Dda8Zl3+GtYWrlkMHccRk3KKqa/8SnJmLrdf0I6IEO1OvDbTpKFUbebMh+jPYMXjVs2i3YUw4WX7WRMB5OU7mbFwA3n5Tn64ZwRtG2kDd22nSUOp2ur4NvjyLqv314hOcP3nVrKwbT+axjVvriEj28Er1/bVhKEATRpK1T6pMbDqX7DlfagTBpPfgO5XgO/pn4OY5Cz+/M46/H19+PuYjlzcvakHA1beRJOGUrWFIxe2vAffPgROBwy6DYb/HYLP7Mp845EUblm4gZy8fD75yxDaN/bSR7Yqj3C7D2MR8RWRTSLylf05TESWi8he+29Dl7IPisg+EdktImNdhvcTkW32uJfEvgRDRAJF5AN7+FoRiXKZZrq9jL0iMr1C1lqp2ubIGnh1kHU6qmkP+Os6GDfnrITx054TXP3f1ZzIyOGeMZ00YaizlKXj+7uAnS6fHwBWGGM6ACvsz4hIV2AK0A0YB7wqIr72NK8BM4AO9mucPfwmIMUY0x54Hvi3Pa8w4FFgEDAQeNQ1OSmlSnEyAb64A+aNs2oX13wAN35rde3h4lRuPt9si+OuxZto1yiEtQ+NYvqQKM/ErLyaW0lDRFoAlwBvuQyeCMy3388HJrkMX2yMyTHGHAT2AQNFpBkQaoxZbYwxwIJC0xTMawkwyq6FjAWWG2OSjTEpwHJOJxqlVHHyHbD+f/Byf9iyGAbOgNt+g07jzuoAMDUrl2veXMNtizaSkpXHYxO6eefjWpVXcLdN4wXgPsC1rtrEGBMHYIyJE5HG9vBIYI1LuVh7WJ79vvDwgmli7Hk5RCQNCHcdXsQ0SqmiHPrFehZ24h5oNQQuexEadTyrWK7DyZs/H2DRmsMknszlxSm9GRAVRvMG2pmgKl6pSUNELgUSjDEbRGSkG/Ms6lZRU8Lw8k7jGuMMrNNetGrVyo0QlaqBjm6wnoO99QNoGAVXv2s94rSIu7dTs3K5ZeEG1h5MJiIkkJkXdWBibz0eU6Vzp6ZxPjBBRMYDQUCoiLwLxItIM7uW0QxIsMvHAi1dpm8BHLOHtyhiuOs0sSLiB9QHku3hIwtNs6pwgMaYN4A3APr3739WUlGqRju6EVa/AtuXQEAIDLkDRj4EAcFFFv9s01Ee/yqajOw8XpzSW5OFKpNS2zSMMQ8aY1oYY6KwGrh/MMZcB3wBFFzNNB343H7/BTDFviKqDVaD9+/2qawMERlst1dcX2iagnldaS/DAMuAMSLS0G4AH2MPU6p2y3fAzi/hnUvhzQtg99cw7B64ZxeMmV1swpj/2yFmfrCZID8f/n1FT00YqszO5T6NucCHInITcAS4CsAYs0NEPgSiAQdwuzEm357mNuAdoA7wjf0CeBtYKCL7sGoYU+x5JYvIE8A6u9zjxpjkc4hZqeotaT9s/wQ2/A/Sj0JoC7jocej3ZwgKLXayQ4mZPL1sN6t2JzC0fQT/+/MA/H3LcvGkUhaxDuhrjv79+5v169d7OgylKkbeKTj8Kxz4EfYuhxP2Ve/tLoQB/wcdxp5xJ3dRtsSk8ud31pGX76RTk3o8cllXerZoUPmxq2pFRDYYY/qXVk7vCFfKmzidkBANB1bCvhVw+DfIzwEff2g1GPrNhU7jz7rPoij5TsNzy3fzysr9tGhYhwU3nqf9R6lzpklDKU9xOiH1ECTshPho6wl5MWsg237WdkQnGHATtBsFrc+DgLpuzzrfabhvyVY+3hhLzxb1eXJSD00YqkJo0lCqKpw8YT0BL2EnxO+wahMJuyAv83SZ8A7QdaJ1b0WbYdbztsvhUGImD36yjdUHkrh7dEfuGt2hglZCKU0aSlUsYyD1iFVrOL7VShDHt1kPNioQHA6Nu0Lfadbfxl2hcWcIPPd+nvYlZHDNm2s5kZHDuG5NuXNU+3Oep1KuNGkodS6MgRO7rIbqo+vh8GpItzs+8A2wTjG1vcDqJLBJN+sV0rjkeZbTr/sSuWXhBoL8fVl+93A6NNHOBlXF06ShVFllJlkN1ftXwv4fIMO+R7VeM6uxuvVM62+jzuDrXyUhvfXzAWYvta6semt6f00YqtJo0lCqNM58iFkL+763ksSxzYCBoAbWk+7aXQjtLoAGVd+FjTGG93+PYfbSnYzp2oR/XtqVlmFF39inVEXQpKFUcU7shvXzYNtHkJUE4gstBsAFD1mJonkf8PEtfT6VxJHv5Pb3NrJsRzy9Wjbglal99YY9Vek0aSjlKi0Wti2BnV9YHQD6+EOXS6HLBGg/CoLqezpCwKph/PPzHSzbEc81A1tyy/B2mjBUldCkoZQx1k10696ykoXTAc16w+jHoPe1ldZwXV5ZuQ4e/zKaxetiuHVEOx64uLOnQ1K1iCYNVbslH4Rv7oe9y6xaxKBbYeDNVtfiXsgYw71LtrJ0axxR4cHM1HswVBXTpKFqp7xs+OEJ6/kTfoFw0RNWX07F9A7rLeb/doilW+O4f1xnbhvZztPhqFpIk4aqfbKSYfG1cGQ19JkGFzwMoc08HVWpvtkWx6wvozm/fTi3jmjr6XBULaVJQ9UuCbtg4STraqgr50H3KzwdkVt+3nuC2xZtBOC2Ee2RIp7Gp1RV0KShao/kA7BgImDgxm8hsp+nI3JL0skc7v5gCx0ah/DFX4dSJ8Bzl/kqpUlD1Q4Zx2HhZKub8T9/A427eDoit2TlOrhz8SYST+aw8KaBmjCUx2nSUDVfbha8e6XV0+z0L6pNwjDGcMd7m/h1XxKD24bRpVnxT+ZTqqpo0lA13w+zIX4bTF0CLUp9MJnX+GLLMVbsSuCWEW25e3RHT4ejFKBJQ9V0cVtgzav2o1Ev8nQ0bsvLd/LYl9H0adWA+8Z2xtdHG76Vd9B+B1TNtmouBIXCqEc8HYnb0rPzuPSlX0jOzOXWEe00YSivoklD1VxbPoDdX8OQO72mzyh3vPT9XnbHZzChV3NGdGzk6XCUOoOenlI1kyMXvp9l9Up7/kxPR+O26GPpvPPbIa7u35J/X9nT0+EodRataaiaKfoz6+FII+4H3+pxbOTId/KPz7YREuTHfeM6eTocpYqkSUPVTBsXQMM20H60pyNxiyPfyZQ31rDxSCoXd29KeEigp0NSqkiaNFTNk3wADv0MvadCNelu47nle1h/OIWbh7XhvrHa1bnyXtWj3q6Uu5xO+PZB8A2EPtd5Ohq3/LY/kVdX7efyPpE8fElXT4ejVIk0aaiaZc0rsOdbuPipatFz7dKtcdz+3kb8fIRZE7t5OhylSqWnp1TNkREPKx6HzpfCwBmejqZUsSlZPPDJVqLCg3lzen9Cg/w9HZJSpdKahqo5Ni+C/FwYPcvr2zJiU7K44X/rMAYW3jSIlmHe/fAnpQpo0lA1g9MJG+dD1DCI8O5HoO4/cZJr3lhDQkYOL13TRxOGqlY0aaiaYe3rkHIIRj3q6UiKlZGdx2ebj/HUt7sI9PNh6Z1D6da8+typrhRo0lA1wfHtsPwR6DQeuk32dDRF2ngkhZvnrycpM5c2EXVZcONArWGoakmThqrejIHvHobAEJj4ile1ZeQ7De//foT//LCX+PQcAvx8eOmaPlzYuTEhgfrVU9WT7rmqetu3Ag6sgnFzITjMo6GkZOby/c54NhxO4XBSFoeSMolLy6aOvy+3jGjLDUOiaFa/jkdjVOpclZo0RKQlsABoCjiBN4wxL4pIGPABEAUcAv5kjEmxp3kQuAnIB+40xiyzh/cD3gHqAF8DdxljjIgE2svoByQBVxtjDtnTTAf+YYcz2xgz/5zXWtUMWcnw5V1WdyH9b6rSRRtjiE/PYeORFH7ee4LouAy2xKQCVmWnW/NQWjYM5rJezfnbRR0J8tfHtKqawZ2ahgO4xxizUUTqARtEZDlwA7DCGDNXRB4AHgDuF5GuwBSgG9Ac+F5EOhpj8oHXgBnAGqykMQ74BivBpBhj2ovIFODfwNV2YnoU6A8Ye9lfFCQnVcutmmt1Svh/34NfQKUtxuk0xKVns+5gMnsTMtifkMn6wykknsz5o0yXZqFcO6gVY7s1pXfLBtSvo/dcqJqp1KRhjIkD4uz3GSKyE4gEJgIj7WLzgVXA/fbwxcaYHOCgiOwDBorIISDUGLMaQEQWAJOwksZEYJY9ryXAyyIiwFhguTEm2Z5mOVaief8c1lnVBCf2wLq3oN+fIbJfhc46NSuXNQeS2BN/krUHk9h0JJWs3HwA/HyEBsEB9GpRn/5RYbSJqMt57cI1Sahao0xtGiISBfQB1gJN7ISCMSZORBrbxSKxahIFYu1hefb7wsMLpomx5+UQkTQg3HV4EdOo2soY+PZ+CKgLIx88p1nlOpzsjEtn+7E0Vu46wdqDSWRkO/4Y3zo8mKv6taBd4xD6tmpIp6b18PfVjhRU7eV20hCREOBjYKYxJl2Kv0qlqBGmhOHlncY1thlYp71o1apVcXGpmuLXF2D/DzD+GQgp+5Pt0rPz+GFnAit3J/B9dDyZdi2iSWggl/ZsRmSDOpzXLoIuzeoRHKDXiijlyq1vhIj4YyWMRcaYT+zB8SLSzK5lNAMS7OGxQEuXyVsAx+zhLYoY7jpNrIj4AfWBZHv4yELTrCocnzHmDeANgP79+5+VVFQNErve6l+q68QyNX4bY/hhVwJv/nyAjUdSyXU4Ca8bwLAOjZjQuzndm9cnsmEdfR63UqVw5+opAd4GdhpjnnMZ9QUwHZhr//3cZfh7IvIcVkN4B+B3Y0y+iGSIyGCs01vXA/8pNK/VwJXAD/ZVVcuAOSLS0C43Bji38xGq+ko/Bh9cB6GRcNlL4FP6aaK8fCcLVh9m0ZrDHEjMJLJBHSb3jmRSn0gGtQnDR5OEUmXiTk3jfGAasE1ENtvDHsJKFh+KyE3AEeAqAGPMDhH5EIjGuvLqdvvKKYDbOH3J7Tf2C6yktNBuNE/GuvoKY0yyiDwBrLPLPV7QKK5qmZyT8N7V1t+blkGdBqVOEn0snTsXb2Jfwkm6R4Zy79hOzBjeVtsklDoHYkzNOpvTv39/s379ek+HoSqSMx8WT4W9y+DaD6HDRSUWT8/O45llu3l3zWH8fHx46sqeTOzdnBLa4ZSq9URkgzGmf2nltJVPeb/v/gl7vrEavktIGDmOfJZsiOU/K/YRn5HN9YNbc/sF7WkcGlSFwSpVs2nSUN7LkQtf3wMbF8CgW2HgzUUWy8t3smJnAk98Fc3R1FNEhAQy9/IeXD1Ar6RTqqJp0lDeKTsdPpxm9Ss19G9w4T/OKpLrcPLxxlj+s2Ivx+w+nubd0J8LOjXWU1FKVRJNGsr7HNsMn94KiXtg4qvQZ+oZo40xLNkQy7++2UVyZi49Iuvz0CVd6Ne6oXYIqFQl06ShvIcjF35+Fn5+BoIj4Lol0O7CM4psjU1l9lc7+f1QMn1bNeDesZ2Y2Lu53oSnVBXRb5ryDjHr4KuZEL8del59VlfniSdzePjTbSzbEU+9QD/uuLA9d4/uqPdZKFXFNGkoz0o+CN8/CtGfQ0hTmPI+dB7/x+j07DxeXbmf13/cD8CM4W2548L21AvSDgKV8gRNGqrqGQOx62DrB9aVUT5+MOIBGHKH9QQ+wJHv5LVV+3nz5wOkZzuY3CeSy/tGMrR9hDZyK+VBmjRU1clOh+0fw/q34fg28A2AHn+yrowKbfZHscNJmdz27kai49Lp0iyUf1/Ynot7NCthxkqpqqJJQ1UuZz4c/s2qVWz/GPKyoEl3uPQF6HHVHzULgMwcB098Fc3idTH4+QivXNuXS3pqslDKm2jSUJXjxB7YvMhKFhlx4F8Xul8B/W6wHprkcorJke9k3q8Heevng5w4mcN1g1sxuU8L+rVuWPz8lVIeoUlDVZy8U7D9E9jwP6vNQnyhwxgYO8f661KrAOsxqj/uPcFz3+1h29E0+rRqwEvX9GFw23APrYBSqjSaNNS5McZ6xsXmRVbCyEmDiI4wZrbVXlGvSZGT7TiWxj8+286mI6mE1w3g1al9Ga/tFkp5PU0aqnzSjlqnnra8b9257VcHuk6APtMgaugZp59c7Us4ySOfb+e3/UlEhATwr8t7cGnPZnoJrVLVhCYN5b78PNjzrXWZ7L7vwTih5WCY8B/oOgmCQoudND49m3m/HOR/vx2ijr8vt1/QjhnD2lE/WJOFUtWJJg1VuqT9sHE+bH4fMhOgXjOrE8E+UyGsbamTr9gZz71LtpKcmcu4bk15fFI3GtfT7sqVqo40aaii5Z2C6C+sWsXhX6xG7Y7joO/10H40+Ja+6+yNz+D+j7ey8UgqnZvW472bB9G5afG1EaWU99Okoc50Yjf8/iZs/dBq1G7YBkY9Cr2vhXpN3ZqF02lYuOYwc77eSZ0AX/4+piM3D29LoJ9vJQevlKpsmjSUdQPesc2w+V3Y8A74+FuN2n2vh9ZDwcf9Z2ofSszkr+9vZPvRdIa2j+C5q3vpqSilahBNGrWZMbBtCXz3MJyMt05BDbgZRtwPdct2r4Qj38k7vx3iueV78Pf14YlJ3ZkyoCX+vu4nHKWU99OkUVsd2wzf3A8xa6B5HxjzJLS7AOpGlHlWG4+k8OjnO9h2NI3eLRvwytS+RDbQhyEpVRNp0qhtMhNhxeNWA3dwuHW5bO/rynQKqkByZi7//Gw7S7fF0bheIE9f2ZPL+7bAV59xoVSNpUmjtsh3wPp5sHI25GbC4L/AiPugToNyzW7NgSTuWryJ5Mxcpg5qxYPjuxASqLuTUjWdfstrg5jf4au/Qfw2aDsSLn4KGnUq16yy8/J55PPtfLg+lqjwYD79y/l0j6xfsfEqpbyWJo2aLOWQ9cztjQsgNBL+tAC6TCi2i4/S/LI3kdlLo9l1PINpg1szc3QHwkMCKzZmpZRX06RRE+VkwKq5sOY1K0Gc91cY+QAE1ivX7Bz5Tp5atps3fjpAw2B/3rq+P6O7Ft0RoVKqZtOkUdPs/ga+uhsyjlv3WYx8AEKbl3t2W2JSefLrnfx+MJlrB7Xi72M6EVY3oAIDVkpVJ5o0aoqMeFjxmNVFeZMecPUiaNGv3LNLyczlqWW7WbzuCH4+wtzLezBlYKsKDFgpVR1p0qjucjNh9SvwywuQn2t1JDjyAfArX1uDI9/JB+tjeHrZbjKyHdx4fhvuuLA9DYK1dqGU0qRRfeU7YMt7sHKO9TjVLhNg9CwIb1eu2Z3McfDBuhj+9+tBYlNOMahNGI9P7E6npuVrB1FK1UyaNKqbvGzY+QX8+BQk7YXI/nDl/6D1eeWaXXx6Nm//cpD31x4hI8fBgKiG/PPSrozp2gQp51VWSqmaS5NGdXHyhPWUvNUvW/1ENeoMV78LnS8t8yW0Tqfhxz0n+GprHF9uPUa+0zC+RzNuGtqG3i0bVE78SqkaQZOGN3Pmw8Ef4bf/wP4frGFtRsCk16DtBWXq+sPpNGyJTWXZjni+3R7HoaQs6gX5cUXfSP4ysj0tw4IraSWUUjWJJg1v4siFuM1w+Fc49CvErIWcdKgTBkPuhF5ToEk392eX7+T3g8l8u+M43+2I53h6Nn4+wuC24dx9UUcu7t6MAD/thVYp5b5qkTREZBzwIuALvGWMmevhkMrOGDiVAmmxp1/pLu/TjkLGMeu52wARnaD7FdB2BHQa7/bVUAnp2aw+kMRPexJZsSue1Kw8gvx9GN6hEfd178Sozk30udxKqXLz+qQhIr7AK8BFQCywTkS+MMZEezYyF04nZCVaN9SdjLf+ph8tlCCOQl7WmdP5Bljde9RvAW2GWe+b9YRWQyCkUbGLy3Hkk5Ht4GS2g9iUU2w/lsb2o2nsOJbOwcRMAEKD/BjVpQljuzVlRMdG1AnQp+Yppc6d1ycNYCCwzxhzAEBEFgMTgSKTRmZyHGsWPYYYgwHryN0Ya6QxgBN7xOmjemPsz4X+/vGeP8r652cRnJdivRypBOelUMeRhg/Os2LJ8AsnNaAxqf6RpIb2Idm/Mcm+TUj2a0SSX2PSpT4OIziNwZluyE815B8Gs/oA+c795DsNTmPIcTjJyHaQkZ1HeraDXMfZy4psUIduzUOZMqAlQ9pF0LV5qHZRrpSqcNUhaUQCMS6fY4FBrgVEZAYwA6BfMx8G733unBfqNGKnDbFfAEIWgSQTyglCSTbhJNOGFEJJpj6JEsYJGpIkDUmUMBxOf3xyBJ88wUfAVwQfH8HXR/ARwUdOury3h/ucWdbPx4e6df1oHV6XekF+1AvyIzTIn5BA632T0CC6NQ/Vm++UUlWiOiSNog6XzRkfjHkDeAOgT+/eJm3mCkQEAcTHB0EQH0HwQXwECn0WBEQQ8bGmE/Ap5jLWAKAB0LYi11AppaqJ6pA0YoGWLp9bAMeKK+zr50f9BmV7vrVSSin3VIfrLdcBHUSkjYgEAFOALzwck1JK1UpeX9MwxjhE5K/AMqxLbucZY3Z4OCyllKqVvD5pABhjvga+9nQcSilV21WH01NKKaW8hCYNpZRSbtOkoZRSym2aNJRSSrlNjDGll6pGRCQD2O0yqD6Q5ubkZSkLEAEkVsK8y1K2LDGUdd6VGYu3xFGWeVfH/aMy4yhr+eoYR1nm7S3/l/LG0ckYU/qjOo0xNeoFrC/0+Y0yTOt22aKWVVHzLmNZt2Mo5zpWSizeEkdZ5l0d94/q+n/xljhq0/7hbjy14fTUl5VUtqyqYxzlKV9Z8/WGbeINMZS1bFl5y//FW+Ioy7y9IYayli2zmnh6ar0xpn9NW5Y3x1DAW2LRODQOjaPscbgbT02sabxRQ5dVHG+IoYC3xKJxnEnjOJPGcaY3Cv0tUY2raSillKo8NbGmoZRSqpJo0lBKKeU2TRqlEJHJImJEpLOnYwEQkZOljF8lIpXWuCYiLUTkcxHZKyL7ReRFu8v64srPFJHgSoqlxG1RFXT/OGv+un+cGUON2z80aZTuGuAXrOd4uE1EfCsnHM8REQE+AT4zxnQAOgIhwJMlTDYTqJQfBS+h+4dN948i1bj9Q5NGCUQkBDgfuAn7ny4iI0XkJxH5VESiReR1EfGxx50UkcdFZC1wXiXGNVJEvnL5/LKI3FBZy3NxIZBtjPkfgDEmH7gbuFFE6orIMyKyTUS2isgdInIn0BxYKSIrKyMgEQkRkRUistFe9kR7eJSI7BSRN0Vkh4h8JyJ1KnrZ6P7hSvePQsumBu4fmjRKNgn41hizB0gWkb728IHAPUAPoB1wuT28LrDdGDPIGPNLVQdbBboBG1wHGGPSgSPA/wFtgD7GmJ7AImPMS1iP5r3AGHNBJcWUDUw2xvQFLgCetY94AToArxhjugGpwBUVvOxJ6P7hSvePM02iBu4fmjRKdg2w2H6/2P4M8Lsx5oB9JPU+MNQeng98XLUhVikBirpGW4DhwOvGGAeAMSa5CmOaIyJbge+BSKCJPe6gMWaz/X4DEFXBy9b940y6f5ypRu4f1eLJfZ4gIuFY1e3uImKwHjVrsJ4gWPiLUfA5294RKpuDMxN+UBUsE2AHhY7GRCQUaAkcoOgfjMo2FWgE9DPG5InIIU5vjxyXcvlAhZ1+0P2jSLp/2Gry/qE1jeJdCSwwxrQ2xkQZY1oCB7GOCgaKSBv7XOTVWA1dVekw0FVEAkWkPjCqipa7AggWkevhj8a6Z4F3gO+AW0XEzx4XZk+TAZTec2b51QcS7B+EC4DWlbgsV7p/nE33j9Nq7P6hSaN41wCfFhr2MXAtsBqYC2zH2hEKl6sU9hcuxxgTA3wIbAUWAZuqYvnG6j5gMnCViOwF9mCdM34IeAvr3PVWEdmCtZ3A6prgm4pu6CzYFljr319E1mMdVe6qyOWUQPePQnT/OEON3T+0G5EyEpGRwN+NMZd6YNm9gDeNMQOretnexlu3he4f3sFbt0VN2D+0plFNiMitWI1m//B0LJ6m2+Jsuk1O021xtorcJlrTUEop5TataSivJyItRWSlfTPWDhG5yx4eJiLLxeqyYrmINLSHXyQiG+ybuTaIyIUu83pSRGLEC7qYUBWjovYPEQkWkaUissuez1xPrpe30pqG8noi0gxoZozZKCL1sK6pnwTcACQbY+aKyANAQ2PM/SLSB4g3xhwTke7AMmNMpD2vwVhXj+w1xoR4Yn1Uxaqo/UOsPrAGGWNWitVf1gpgjjHmG4+smJfSpKGqHRH5HHjZfo00xsTZPxyrjDGdCpUVIBFobozJcRl+UpNGzVQR+4c97kWsO7TfrKLQqwU9PaWqFRGJAvoAa4Emxpg4APtv4yImuQLYVPgHQdVMFbV/iEgD4DKs2oZyoXeEq2pDrA7gPgZmGmPS5Y8uhIot3w34NzCmCsJTHlZR+4d9P8P7wEvGmAOVFG61pTUNVS2IiD/WD8IiY8wn9uB4+7RDwXntBJfyLbBumrreGLO/quNVVauC9483sNq8Xqj0wKshTRrK69nnnd8GdhpjnnMZ9QUw3X4/HfjcLt8AWAo8aIz5tQpDVR5QkfuHiMzG6npkZuVGXX1pQ7jyeiIyFPgZ2AY47cEPYZ23/hBohdVFxVXGmGQR+QfwILDXZTZjjDEJIvIUVlcOzbG65X7LGDOrSlZEVYqK2j+AACAGq6uRgjaOl40xb1X6SlQjmjSUUkq5TU9PKaWUcpsmDaWUUm7TpKGUUsptmjSUUkq5TZOGUkopt2nSUOociUi+iGy2e0bdIiJ/sx/lWdI0USJybUlllPJGmjSUOnenjDG9jTHdgIuA8cCjpUwTxelHnipVbeh9Gkqdo8I95opIW2AdEAG0BhYCde3RfzXG/CYia4AuWM+Ing+8hPXc6JFAIPCKMea/VbYSSrlJk4ZS56iobtZFJAXoDGQATmNMtoh0AN43xvQv/KxoEZkBNDbGzBaRQOBXrDuYD1bluihVGu3lVqnKUdDFqj/wsoj0BvKBjsWUHwP0FJEr7c/1gQ5YNRGlvIYmDaUqmH16Kh+rV9VHgXigF1YbYnZxkwF3GGOWVUmQSpWTNoQrVYFEpBHwOlZHdwarxhBnjHEC0wBfu2gGUM9l0mXAbXYX34hIRxGpi1JeRmsaSp27OiKyGetUlAOr4bugi+5XgY9F5CpgJZBpD98KOERkC/AO8CLWFVUb7a6+T2A951opr6IN4Uoppdymp6eUUkq5TZOGUkopt2nSUEop5TZNGkoppdymSUMppZTbNGkopZRymyYNpZRSbtOkoZRSym3/D+ZPf8nquUFFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pa_vs_cr.plot(title='Comparativa Covid19 Costa Rica y Panamá')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eafa2a55",
   "metadata": {},
   "source": [
    "### Comparativa con Centroamérica\n",
    "Los pasos vuelven a ponerse en práctica, pero en esta ocasión con el resto de los países de la región. Es importante cambiar los países para no colocar el de otros y que haya errores.\n",
    "\n",
    "-Panamá\n",
    "\n",
    "-Costa Rica\n",
    "\n",
    "-Nicaragua\n",
    "\n",
    "-Guatemala\n",
    "\n",
    "-El Salvador\n",
    "\n",
    "-Honduras"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d85e9e2",
   "metadata": {},
   "source": [
    "#### Gráficas de Panamá vs. el resto de América Central"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb81b541",
   "metadata": {},
   "source": [
    "##### vs. Honduras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5e0acfa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "url_rt_hon = 'https://api.covid19api.com/country/honduras/status/confirmed/live'\n",
    "df_rt_hon = pd.read_json(url_rt_hon)\n",
    "casos_hon = df_rt_hon.set_index('Date')['Cases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c442d85e",
   "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>Cases</th>\n",
       "      <th>Cases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-04 00:00:00+00:00</th>\n",
       "      <td>427718</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-05 00:00:00+00:00</th>\n",
       "      <td>427718</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-06 00:00:00+00:00</th>\n",
       "      <td>427718</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-07 00:00:00+00:00</th>\n",
       "      <td>427718</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-08 00:00:00+00:00</th>\n",
       "      <td>427718</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>899 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Cases   Cases\n",
       "Date                                     \n",
       "2020-01-22 00:00:00+00:00       0       0\n",
       "2020-01-23 00:00:00+00:00       0       0\n",
       "2020-01-24 00:00:00+00:00       0       0\n",
       "2020-01-25 00:00:00+00:00       0       0\n",
       "2020-01-26 00:00:00+00:00       0       0\n",
       "...                           ...     ...\n",
       "2022-07-04 00:00:00+00:00  427718  925254\n",
       "2022-07-05 00:00:00+00:00  427718  925254\n",
       "2022-07-06 00:00:00+00:00  427718  925254\n",
       "2022-07-07 00:00:00+00:00  427718  925254\n",
       "2022-07-08 00:00:00+00:00  427718  925254\n",
       "\n",
       "[899 rows x 2 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pa_vs_hon = pd.concat([casos_hon,casos_pa],axis=1)\n",
    "pa_vs_hon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "34dfe849",
   "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>Honduras</th>\n",
       "      <th>Panamá</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-26 00:00:00+00:00</th>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-04 00:00:00+00:00</th>\n",
       "      <td>427718</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-05 00:00:00+00:00</th>\n",
       "      <td>427718</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-06 00:00:00+00:00</th>\n",
       "      <td>427718</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-07 00:00:00+00:00</th>\n",
       "      <td>427718</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-08 00:00:00+00:00</th>\n",
       "      <td>427718</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>899 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Honduras  Panamá\n",
       "Date                                       \n",
       "2020-01-22 00:00:00+00:00         0       0\n",
       "2020-01-23 00:00:00+00:00         0       0\n",
       "2020-01-24 00:00:00+00:00         0       0\n",
       "2020-01-25 00:00:00+00:00         0       0\n",
       "2020-01-26 00:00:00+00:00         0       0\n",
       "...                             ...     ...\n",
       "2022-07-04 00:00:00+00:00    427718  925254\n",
       "2022-07-05 00:00:00+00:00    427718  925254\n",
       "2022-07-06 00:00:00+00:00    427718  925254\n",
       "2022-07-07 00:00:00+00:00    427718  925254\n",
       "2022-07-08 00:00:00+00:00    427718  925254\n",
       "\n",
       "[899 rows x 2 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pa_vs_hon.columns = ['Honduras','Panamá']\n",
    "pa_vs_hon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "799dfad8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Comparativa Covid19 Honduras y Panamá'}, xlabel='Date'>"
      ]
     },
     "execution_count": 51,
     "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": [
    "pa_vs_hon.plot(title='Comparativa Covid19 Honduras y Panamá')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fb6a0b2",
   "metadata": {},
   "source": [
    "##### vs. Nicaragua"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2a742a9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "url_rt_ni = 'https://api.covid19api.com/country/nicaragua/status/confirmed/live'\n",
    "df_rt_ni = pd.read_json(url_rt_ni)\n",
    "casos_ni = df_rt_ni.set_index('Date')['Cases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "30cd6134",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cases</th>\n",
       "      <th>Cases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2020-01-22 00:00:00+00:00</th>\n",
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       "    <tr>\n",
       "      <th>2020-01-23 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-25 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-26 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-04 00:00:00+00:00</th>\n",
       "      <td>14690</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-05 00:00:00+00:00</th>\n",
       "      <td>14690</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-06 00:00:00+00:00</th>\n",
       "      <td>14721</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-07 00:00:00+00:00</th>\n",
       "      <td>14721</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-08 00:00:00+00:00</th>\n",
       "      <td>14721</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>899 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Cases   Cases\n",
       "Date                                    \n",
       "2020-01-22 00:00:00+00:00      0       0\n",
       "2020-01-23 00:00:00+00:00      0       0\n",
       "2020-01-24 00:00:00+00:00      0       0\n",
       "2020-01-25 00:00:00+00:00      0       0\n",
       "2020-01-26 00:00:00+00:00      0       0\n",
       "...                          ...     ...\n",
       "2022-07-04 00:00:00+00:00  14690  925254\n",
       "2022-07-05 00:00:00+00:00  14690  925254\n",
       "2022-07-06 00:00:00+00:00  14721  925254\n",
       "2022-07-07 00:00:00+00:00  14721  925254\n",
       "2022-07-08 00:00:00+00:00  14721  925254\n",
       "\n",
       "[899 rows x 2 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pa_vs_ni = pd.concat([casos_ni,casos_pa],axis=1)\n",
    "pa_vs_ni"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ddabecfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nicaragua</th>\n",
       "      <th>Panamá</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>2020-01-25 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-04 00:00:00+00:00</th>\n",
       "      <td>14690</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-05 00:00:00+00:00</th>\n",
       "      <td>14690</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-06 00:00:00+00:00</th>\n",
       "      <td>14721</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-07 00:00:00+00:00</th>\n",
       "      <td>14721</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-08 00:00:00+00:00</th>\n",
       "      <td>14721</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>899 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Nicaragua  Panamá\n",
       "Date                                        \n",
       "2020-01-22 00:00:00+00:00          0       0\n",
       "2020-01-23 00:00:00+00:00          0       0\n",
       "2020-01-24 00:00:00+00:00          0       0\n",
       "2020-01-25 00:00:00+00:00          0       0\n",
       "2020-01-26 00:00:00+00:00          0       0\n",
       "...                              ...     ...\n",
       "2022-07-04 00:00:00+00:00      14690  925254\n",
       "2022-07-05 00:00:00+00:00      14690  925254\n",
       "2022-07-06 00:00:00+00:00      14721  925254\n",
       "2022-07-07 00:00:00+00:00      14721  925254\n",
       "2022-07-08 00:00:00+00:00      14721  925254\n",
       "\n",
       "[899 rows x 2 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pa_vs_ni.columns = ['Nicaragua','Panamá']\n",
    "pa_vs_ni"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "be537d93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Comparativa Covid19 Nicaragua y Panamá'}, xlabel='Date'>"
      ]
     },
     "execution_count": 57,
     "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": [
    "pa_vs_ni.plot(title='Comparativa Covid19 Nicaragua y Panamá')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc766c10",
   "metadata": {},
   "source": [
    "##### vs. Guatemala"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "53a2514b",
   "metadata": {},
   "outputs": [],
   "source": [
    "url_rt_gua = 'https://api.covid19api.com/country/guatemala/status/confirmed/live'\n",
    "df_rt_gua = pd.read_json(url_rt_gua)\n",
    "casos_gua = df_rt_gua.set_index('Date')['Cases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ee6318bc",
   "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>Cases</th>\n",
       "      <th>Cases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-04 00:00:00+00:00</th>\n",
       "      <td>921146</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-05 00:00:00+00:00</th>\n",
       "      <td>922340</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-06 00:00:00+00:00</th>\n",
       "      <td>927473</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-07 00:00:00+00:00</th>\n",
       "      <td>933259</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-08 00:00:00+00:00</th>\n",
       "      <td>933259</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>899 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Cases   Cases\n",
       "Date                                     \n",
       "2020-01-22 00:00:00+00:00       0       0\n",
       "2020-01-23 00:00:00+00:00       0       0\n",
       "2020-01-24 00:00:00+00:00       0       0\n",
       "2020-01-25 00:00:00+00:00       0       0\n",
       "2020-01-26 00:00:00+00:00       0       0\n",
       "...                           ...     ...\n",
       "2022-07-04 00:00:00+00:00  921146  925254\n",
       "2022-07-05 00:00:00+00:00  922340  925254\n",
       "2022-07-06 00:00:00+00:00  927473  925254\n",
       "2022-07-07 00:00:00+00:00  933259  925254\n",
       "2022-07-08 00:00:00+00:00  933259  925254\n",
       "\n",
       "[899 rows x 2 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pa_vs_gua = pd.concat([casos_gua,casos_pa],axis=1)\n",
    "pa_vs_gua"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "54705ead",
   "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>Guatemala</th>\n",
       "      <th>Panamá</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-04 00:00:00+00:00</th>\n",
       "      <td>921146</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-05 00:00:00+00:00</th>\n",
       "      <td>922340</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-06 00:00:00+00:00</th>\n",
       "      <td>927473</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-07 00:00:00+00:00</th>\n",
       "      <td>933259</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-08 00:00:00+00:00</th>\n",
       "      <td>933259</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>899 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Guatemala  Panamá\n",
       "Date                                        \n",
       "2020-01-22 00:00:00+00:00          0       0\n",
       "2020-01-23 00:00:00+00:00          0       0\n",
       "2020-01-24 00:00:00+00:00          0       0\n",
       "2020-01-25 00:00:00+00:00          0       0\n",
       "2020-01-26 00:00:00+00:00          0       0\n",
       "...                              ...     ...\n",
       "2022-07-04 00:00:00+00:00     921146  925254\n",
       "2022-07-05 00:00:00+00:00     922340  925254\n",
       "2022-07-06 00:00:00+00:00     927473  925254\n",
       "2022-07-07 00:00:00+00:00     933259  925254\n",
       "2022-07-08 00:00:00+00:00     933259  925254\n",
       "\n",
       "[899 rows x 2 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pa_vs_gua.columns = ['Guatemala','Panamá']\n",
    "pa_vs_gua"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1fa19351",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Comparativa Covid19 Guatemala y Panamá'}, xlabel='Date'>"
      ]
     },
     "execution_count": 61,
     "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": [
    "pa_vs_gua.plot(title='Comparativa Covid19 Guatemala y Panamá')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19fd90bf",
   "metadata": {},
   "source": [
    "##### vs. El Salvador"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8d326041",
   "metadata": {},
   "outputs": [],
   "source": [
    "url_rt_sal = 'https://api.covid19api.com/country/el-salvador/status/confirmed/live'\n",
    "df_rt_sal = pd.read_json(url_rt_sal)\n",
    "casos_sal = df_rt_sal.set_index('Date')['Cases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "43aeba02",
   "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>Cases</th>\n",
       "      <th>Cases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-04 00:00:00+00:00</th>\n",
       "      <td>169646</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-05 00:00:00+00:00</th>\n",
       "      <td>169646</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-06 00:00:00+00:00</th>\n",
       "      <td>169646</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-07 00:00:00+00:00</th>\n",
       "      <td>169646</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-08 00:00:00+00:00</th>\n",
       "      <td>169646</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>899 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Cases   Cases\n",
       "Date                                     \n",
       "2020-01-22 00:00:00+00:00       0       0\n",
       "2020-01-23 00:00:00+00:00       0       0\n",
       "2020-01-24 00:00:00+00:00       0       0\n",
       "2020-01-25 00:00:00+00:00       0       0\n",
       "2020-01-26 00:00:00+00:00       0       0\n",
       "...                           ...     ...\n",
       "2022-07-04 00:00:00+00:00  169646  925254\n",
       "2022-07-05 00:00:00+00:00  169646  925254\n",
       "2022-07-06 00:00:00+00:00  169646  925254\n",
       "2022-07-07 00:00:00+00:00  169646  925254\n",
       "2022-07-08 00:00:00+00:00  169646  925254\n",
       "\n",
       "[899 rows x 2 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pa_vs_sal = pd.concat([casos_sal,casos_pa],axis=1)\n",
    "pa_vs_sal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "aebe1404",
   "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>El Salvador</th>\n",
       "      <th>Panamá</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-04 00:00:00+00:00</th>\n",
       "      <td>169646</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-05 00:00:00+00:00</th>\n",
       "      <td>169646</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-06 00:00:00+00:00</th>\n",
       "      <td>169646</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-07 00:00:00+00:00</th>\n",
       "      <td>169646</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-08 00:00:00+00:00</th>\n",
       "      <td>169646</td>\n",
       "      <td>925254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>899 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           El Salvador  Panamá\n",
       "Date                                          \n",
       "2020-01-22 00:00:00+00:00            0       0\n",
       "2020-01-23 00:00:00+00:00            0       0\n",
       "2020-01-24 00:00:00+00:00            0       0\n",
       "2020-01-25 00:00:00+00:00            0       0\n",
       "2020-01-26 00:00:00+00:00            0       0\n",
       "...                                ...     ...\n",
       "2022-07-04 00:00:00+00:00       169646  925254\n",
       "2022-07-05 00:00:00+00:00       169646  925254\n",
       "2022-07-06 00:00:00+00:00       169646  925254\n",
       "2022-07-07 00:00:00+00:00       169646  925254\n",
       "2022-07-08 00:00:00+00:00       169646  925254\n",
       "\n",
       "[899 rows x 2 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pa_vs_sal.columns = ['El Salvador','Panamá']\n",
    "pa_vs_sal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "0d841320",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Comparativa Covid19 El Salvador y Panamá'}, xlabel='Date'>"
      ]
     },
     "execution_count": 65,
     "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": [
    "pa_vs_sal.plot(title='Comparativa Covid19 El Salvador y Panamá')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e59b66c",
   "metadata": {},
   "source": [
    "#### Comportamiento de la Covid19 en Centro América\n",
    "Adicional a repetir los pasos, al momento de generar la gráfica final es importante agrupar los contenidos con concatenación y darles un nombre sencillo. En este caso **ca** incluye los seis países."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "298b7751",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Cases</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Cases</th>\n",
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       "    <tr>\n",
       "      <th>2020-01-26 00:00:00+00:00</th>\n",
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       "      <th>2022-07-06 00:00:00+00:00</th>\n",
       "      <td>925254</td>\n",
       "      <td>904934</td>\n",
       "      <td>427718</td>\n",
       "      <td>14721</td>\n",
       "      <td>927473</td>\n",
       "      <td>169646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-07 00:00:00+00:00</th>\n",
       "      <td>925254</td>\n",
       "      <td>904934</td>\n",
       "      <td>427718</td>\n",
       "      <td>14721</td>\n",
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       "    <tr>\n",
       "      <th>2022-07-08 00:00:00+00:00</th>\n",
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       "      <td>427718</td>\n",
       "      <td>14721</td>\n",
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       "      <td>169646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>899 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Cases   Cases   Cases  Cases   Cases   Cases\n",
       "Date                                                                    \n",
       "2020-01-22 00:00:00+00:00       0       0       0      0       0       0\n",
       "2020-01-23 00:00:00+00:00       0       0       0      0       0       0\n",
       "2020-01-24 00:00:00+00:00       0       0       0      0       0       0\n",
       "2020-01-25 00:00:00+00:00       0       0       0      0       0       0\n",
       "2020-01-26 00:00:00+00:00       0       0       0      0       0       0\n",
       "...                           ...     ...     ...    ...     ...     ...\n",
       "2022-07-04 00:00:00+00:00  925254  904934  427718  14690  921146  169646\n",
       "2022-07-05 00:00:00+00:00  925254  904934  427718  14690  922340  169646\n",
       "2022-07-06 00:00:00+00:00  925254  904934  427718  14721  927473  169646\n",
       "2022-07-07 00:00:00+00:00  925254  904934  427718  14721  933259  169646\n",
       "2022-07-08 00:00:00+00:00  925254  904934  427718  14721  933259  169646\n",
       "\n",
       "[899 rows x 6 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ca = pd.concat([casos_pa,casos_cr,casos_hon,casos_ni,casos_gua,casos_sal],axis=1)\n",
    "df_ca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "32cd4aec",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Panamá</th>\n",
       "      <th>Costa Rica</th>\n",
       "      <th>Honduras</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24 00:00:00+00:00</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25 00:00:00+00:00</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26 00:00:00+00:00</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-04 00:00:00+00:00</th>\n",
       "      <td>925254</td>\n",
       "      <td>904934</td>\n",
       "      <td>427718</td>\n",
       "      <td>14690</td>\n",
       "      <td>921146</td>\n",
       "      <td>169646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-05 00:00:00+00:00</th>\n",
       "      <td>925254</td>\n",
       "      <td>904934</td>\n",
       "      <td>427718</td>\n",
       "      <td>14690</td>\n",
       "      <td>922340</td>\n",
       "      <td>169646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-06 00:00:00+00:00</th>\n",
       "      <td>925254</td>\n",
       "      <td>904934</td>\n",
       "      <td>427718</td>\n",
       "      <td>14721</td>\n",
       "      <td>927473</td>\n",
       "      <td>169646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-07 00:00:00+00:00</th>\n",
       "      <td>925254</td>\n",
       "      <td>904934</td>\n",
       "      <td>427718</td>\n",
       "      <td>14721</td>\n",
       "      <td>933259</td>\n",
       "      <td>169646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-08 00:00:00+00:00</th>\n",
       "      <td>925254</td>\n",
       "      <td>904934</td>\n",
       "      <td>427718</td>\n",
       "      <td>14721</td>\n",
       "      <td>933259</td>\n",
       "      <td>169646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>899 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Panamá  Costa Rica  Honduras  Nicaragua  Guatemala  \\\n",
       "Date                                                                            \n",
       "2020-01-22 00:00:00+00:00       0           0         0          0          0   \n",
       "2020-01-23 00:00:00+00:00       0           0         0          0          0   \n",
       "2020-01-24 00:00:00+00:00       0           0         0          0          0   \n",
       "2020-01-25 00:00:00+00:00       0           0         0          0          0   \n",
       "2020-01-26 00:00:00+00:00       0           0         0          0          0   \n",
       "...                           ...         ...       ...        ...        ...   \n",
       "2022-07-04 00:00:00+00:00  925254      904934    427718      14690     921146   \n",
       "2022-07-05 00:00:00+00:00  925254      904934    427718      14690     922340   \n",
       "2022-07-06 00:00:00+00:00  925254      904934    427718      14721     927473   \n",
       "2022-07-07 00:00:00+00:00  925254      904934    427718      14721     933259   \n",
       "2022-07-08 00:00:00+00:00  925254      904934    427718      14721     933259   \n",
       "\n",
       "                           El Salvador  \n",
       "Date                                    \n",
       "2020-01-22 00:00:00+00:00            0  \n",
       "2020-01-23 00:00:00+00:00            0  \n",
       "2020-01-24 00:00:00+00:00            0  \n",
       "2020-01-25 00:00:00+00:00            0  \n",
       "2020-01-26 00:00:00+00:00            0  \n",
       "...                                ...  \n",
       "2022-07-04 00:00:00+00:00       169646  \n",
       "2022-07-05 00:00:00+00:00       169646  \n",
       "2022-07-06 00:00:00+00:00       169646  \n",
       "2022-07-07 00:00:00+00:00       169646  \n",
       "2022-07-08 00:00:00+00:00       169646  \n",
       "\n",
       "[899 rows x 6 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ca.columns = ['Panamá','Costa Rica','Honduras','Nicaragua','Guatemala','El Salvador']\n",
    "df_ca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "785866f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Comparativa de casos de Covid19 en América Central'}, xlabel='Date'>"
      ]
     },
     "execution_count": 68,
     "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": [
    "df_ca.plot(title='Comparativa de casos de Covid19 en América Central',logy=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cfc4581",
   "metadata": {},
   "source": [
    "### ¿Cómo ha sido el comportamiento de la covid-19 en América Central?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7ffd850",
   "metadata": {},
   "source": [
    "Para entender el comportamiento de la covid19 en **Centroamérica** hay que tomar en cuenta el factor político. Este es el caso de Nicaragua, cuyas curvas indican que es el país con menos casos en la región. Sin embargo, hay que considerar  que su gobierno fue señalado por esconder los casos y también por no brindar los reportes diarios.\n",
    "\n",
    "Situación contraria ocurre con **Costa Rica y Panamá**, en los cuales las cifras se han manejado con mayor transparencia a lo largo de toda la pandemia. Con respecto a los números en Panamá, los contagios han aumentado  durante el último año debido a la relajación conferida por la vacunación. La tierra del Canal empezó con la inmunización en enero de 2021 y desde entonces las estrictas medidas se flexibilizaron y la población también comenzó reducir las medidas de cuidado.\n",
    "\n",
    "Entre los meses de mayo y junio, de acuerdo con datos de la [Organización Panamericana de la Salud](https://www.bing.com/search?q=los+paises+de+las+americas+deben+redoblar+los+esfueros+para+alcanzar+la+meta+de+70%25&cvid=d0fbbcfcff70410d8e96483482391392&aqs=edge..69i57.23481j0j4&FORM=ANAB01&PC=U531), Centroamérica fue la única subregión que reportó una tendencia a la baja, con una reducción del **32%** de los casos de covid-19 y un descenso del **36%** de las muertes.\n",
    "\n",
    "En este último punto, la estrategia de vacunación ha sido importante debido a que desde que se implementó, el número de fallecimientos se ha reducido considerablemente.\n",
    "\n",
    "No obstante, los encargados de las instituciones sanitarias de la región recuerdan que la pandemia todavía no se ha terminado y es importante continuar con las medidas de bioseguridad.\n",
    "\n",
    "#### Cifras de vacunación en América Central por cada 100,000 habitantes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6aa966f",
   "metadata": {},
   "source": [
    "1. Costa Rica     209,94\n",
    "2. Panamá         190,77\n",
    "3. Nicaragua      169,21\n",
    "4. El Salvador    167,11\n",
    "5. Honduras       143,14\n",
    "6. Guatemala      109,84"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3351c734",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}