python-api-covid19-pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Uso de la API del Covid19 con pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "API como una especificación formal que se establece cómo un módulo de un software, es decir, se comunica o interactúa con otro para cumplir una o muchas funciones. En este práctica se hará uso de la librería de código abierto de Python, **pandas**, que es una librería de código abierto con la que podemos analizar datos desde diferentes tipos de archivos (html, json, csv...), los modemos manipular y cargar en nuestro notebook de Jupyter. \n",
    "\n",
    "La URL que vamos a utilizar para importar nuestros datos es la siguiente: https://api.covid19api.com/countries\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instalar pandas\n",
    "\n",
    "A continuación procederemos a instalar pandas a través de: `!pip install pandas`. Cabe destacar en este apartado debemos emplear un signo de exclamación de cierre al principio de la instalación para informar que no estamos ejecutando Python, sino bash (que es otra librería de visualización de adtos de los lenguajes de programación)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (1.3.1)\r\n",
      "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (1.21.1)\r\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2.8.1)\r\n",
      "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2020.4)\r\n",
      "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7.3->pandas) (1.14.0)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuración de pandas\n",
    "\n",
    "En la configuración de la librería pandas se usa la convención de pandas como `pd`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Crear Variable\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se asignan con el igual `=` y se escriben entre comillas `' '` porque es una cadena de caracteres, lo que se denomina string. Lo que hacemos en este apartado es básicamente definir la variable URL. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://api.covid19api.com/countries'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Crear Dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se define DataFrame a través de `df`, se trata de una herramienta por la cual podemos podemos organizar y almacenar una estructura de datos. Del mismo modo con anterioridad hemos importado la biblioteca ya instalada pandasa través de la convección `pd`. En pandas hay una función que se denomina como `pd.read_json(url)` y lo que hace efectivamente es leer los datos que se encuentran dentro de un archivo json.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_json(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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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>French Polynesia</td>\n",
       "      <td>french-polynesia</td>\n",
       "      <td>PF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>indonesia</td>\n",
       "      <td>ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Malta</td>\n",
       "      <td>malta</td>\n",
       "      <td>MT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>sao-tome-and-principe</td>\n",
       "      <td>ST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>haiti</td>\n",
       "      <td>HT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>myanmar</td>\n",
       "      <td>MM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>turkey</td>\n",
       "      <td>TR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>western-sahara</td>\n",
       "      <td>EH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>Pitcairn</td>\n",
       "      <td>pitcairn</td>\n",
       "      <td>PN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>Tanzania, United Republic of</td>\n",
       "      <td>tanzania</td>\n",
       "      <td>TZ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>248 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Country                   Slug ISO2\n",
       "0                French Polynesia       french-polynesia   PF\n",
       "1                       Indonesia              indonesia   ID\n",
       "2                           Malta                  malta   MT\n",
       "3           Sao Tome and Principe  sao-tome-and-principe   ST\n",
       "4                           Haiti                  haiti   HT\n",
       "..                            ...                    ...  ...\n",
       "243                       Myanmar                myanmar   MM\n",
       "244                        Turkey                 turkey   TR\n",
       "245                Western Sahara         western-sahara   EH\n",
       "246                      Pitcairn               pitcairn   PN\n",
       "247  Tanzania, United Republic of               tanzania   TZ\n",
       "\n",
       "[248 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lo que vemos aquí es una tabla, un DataFrame de Python, que viene de unos datos de un archivo de json desde una URL."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploración de la tabla "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al DataFrame le aplicamos diferentes funciones que se hacen poniendo `df.head` `df.ail` etc. Además, para poner más cosas y ver que podemos hacer podemos consultar en la ayuda dando al tabulador en los paréntesis. Se pone entreparéntesis la cantidad de, en este caso países, que queremos ver. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Df.head` = se usa esta función para ver la cabecera en parénteis escogemos el número que queremos. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>French Polynesia</td>\n",
       "      <td>french-polynesia</td>\n",
       "      <td>PF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>indonesia</td>\n",
       "      <td>ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Malta</td>\n",
       "      <td>malta</td>\n",
       "      <td>MT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>sao-tome-and-principe</td>\n",
       "      <td>ST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>haiti</td>\n",
       "      <td>HT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Poland</td>\n",
       "      <td>poland</td>\n",
       "      <td>PL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Country                   Slug ISO2\n",
       "0       French Polynesia       french-polynesia   PF\n",
       "1              Indonesia              indonesia   ID\n",
       "2                  Malta                  malta   MT\n",
       "3  Sao Tome and Principe  sao-tome-and-principe   ST\n",
       "4                  Haiti                  haiti   HT\n",
       "5                 Poland                 poland   PL"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Df.tail` = se usa esta función para ver la cola o los últimos del listado. De nuevo en parénteis escogemos el número que queremos ver. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>246</th>\n",
       "      <td>Pitcairn</td>\n",
       "      <td>pitcairn</td>\n",
       "      <td>PN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>Tanzania, United Republic of</td>\n",
       "      <td>tanzania</td>\n",
       "      <td>TZ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Country      Slug ISO2\n",
       "246                      Pitcairn  pitcairn   PN\n",
       "247  Tanzania, United Republic of  tanzania   TZ"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Df.info` = nos da información sobre el dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 248 entries, 0 to 247\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   Country  248 non-null    object\n",
      " 1   Slug     248 non-null    object\n",
      " 2   ISO2     248 non-null    object\n",
      "dtypes: object(3)\n",
      "memory usage: 5.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A continuación ponemos `df[ ]` esto es así porque queremos **ver solo una de las columnas**, utilizaremos esta función entrecorchetes y entre comillas dentro el nombre de la columna en cuestión. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                  French Polynesia\n",
       "1                         Indonesia\n",
       "2                             Malta\n",
       "3             Sao Tome and Principe\n",
       "4                             Haiti\n",
       "                   ...             \n",
       "243                         Myanmar\n",
       "244                          Turkey\n",
       "245                  Western Sahara\n",
       "246                        Pitcairn\n",
       "247    Tanzania, United Republic of\n",
       "Name: Country, Length: 248, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Conseguir un valor concreto**: Si quisieramos ver un valor concreto pondríamos depués un número dentro de la lista que hemos creado, accedemos al index 66. Es entre corchetes porque es una lista, se diferencia el matiz de los diccionarios que son las llaves. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Somalia'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country'][66]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tiempo real\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La URL: https://api.covid19api.com/country/spain/status/confirmed/live\n",
    "\n",
    "Lo nombramos así para crear una nueva URL porque se puede.No podría nombrarlo con guion medio que es un caracter reservado que significa menos. Por ello lo denominamos `url_es` porque cambiamos el país en este caso. En este caso lo hemos ejecutado todo a la vez, definiendo cada cosa: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
       "      <th>Cases</th>\n",
       "      <th>Status</th>\n",
       "      <th>Date</th>\n",
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       "  <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",
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       "      <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",
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       "    <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",
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       "      <th>...</th>\n",
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       "      <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>798</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>11508309</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-03-30 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</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>11508309</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-03-31 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</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>11551574</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-01 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</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>11551574</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-02 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</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>11551574</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-03 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>803 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",
       "798   Spain          ES                         40.46 -3.75  11508309   \n",
       "799   Spain          ES                         40.46 -3.75  11508309   \n",
       "800   Spain          ES                         40.46 -3.75  11551574   \n",
       "801   Spain          ES                         40.46 -3.75  11551574   \n",
       "802   Spain          ES                         40.46 -3.75  11551574   \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",
       "798  confirmed 2022-03-30 00:00:00+00:00  \n",
       "799  confirmed 2022-03-31 00:00:00+00:00  \n",
       "800  confirmed 2022-04-01 00:00:00+00:00  \n",
       "801  confirmed 2022-04-02 00:00:00+00:00  \n",
       "802  confirmed 2022-04-03 00:00:00+00:00  \n",
       "\n",
       "[803 rows x 10 columns]"
      ]
     },
     "execution_count": 11,
     "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": "markdown",
   "metadata": {},
   "source": [
    "## Explorar\n",
    "¿Como podríamos saber cual es el día de inicio y cual es el día del fin?. Lo primero es pedir información sobre las columnas y lo que podemos saber de los datos a través de la función `df_es.columns`. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'CountryCode', 'Province', 'City', 'CityCode', 'Lat', 'Lon',\n",
       "       'Cases', 'Status', 'Date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2020-01-22 00:00:00+00:00\n",
       "Name: Date, dtype: datetime64[ns, UTC]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es['Date'].head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "802   2022-04-03 00:00:00+00:00\n",
       "Name: Date, dtype: datetime64[ns, UTC]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es['Date'].tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 803 entries, 0 to 802\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype              \n",
      "---  ------       --------------  -----              \n",
      " 0   Country      803 non-null    object             \n",
      " 1   CountryCode  803 non-null    object             \n",
      " 2   Province     803 non-null    object             \n",
      " 3   City         803 non-null    object             \n",
      " 4   CityCode     803 non-null    object             \n",
      " 5   Lat          803 non-null    float64            \n",
      " 6   Lon          803 non-null    float64            \n",
      " 7   Cases        803 non-null    int64              \n",
      " 8   Status       803 non-null    object             \n",
      " 9   Date         803 non-null    datetime64[ns, UTC]\n",
      "dtypes: datetime64[ns, UTC](1), float64(2), int64(1), object(6)\n",
      "memory usage: 62.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_es.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dtypes son tipos de datos y nos dice la cantidad de datos, entonces como hay valores numéricos, vamos a emplear también otra función que es la de describe que se usa sobre todo para valores numéricos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Lat</th>\n",
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       "      <th>Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.030000e+02</td>\n",
       "      <td>803.00</td>\n",
       "      <td>8.030000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>3.242248e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.109856e-15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.109442e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>3.186710e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>3.149012e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>4.920388e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>1.155157e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Lat     Lon         Cases\n",
       "count  8.030000e+02  803.00  8.030000e+02\n",
       "mean   4.046000e+01   -3.75  3.242248e+06\n",
       "std    7.109856e-15    0.00  3.109442e+06\n",
       "min    4.046000e+01   -3.75  0.000000e+00\n",
       "25%    4.046000e+01   -3.75  3.186710e+05\n",
       "50%    4.046000e+01   -3.75  3.149012e+06\n",
       "75%    4.046000e+01   -3.75  4.920388e+06\n",
       "max    4.046000e+01   -3.75  1.155157e+07"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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",
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       "      <th>Date</th>\n",
       "    </tr>\n",
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       "  <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",
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       "      <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",
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       "      <td></td>\n",
       "      <td>40.46</td>\n",
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       "      <td>2020-01-23 00:00:00+00:00</td>\n",
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       "    <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>798</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>11508309</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-03-30 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</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>11508309</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-03-31 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</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>11551574</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-01 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</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>11551574</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-02 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</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>11551574</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-03 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>803 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",
       "798   Spain          ES                         40.46 -3.75  11508309   \n",
       "799   Spain          ES                         40.46 -3.75  11508309   \n",
       "800   Spain          ES                         40.46 -3.75  11551574   \n",
       "801   Spain          ES                         40.46 -3.75  11551574   \n",
       "802   Spain          ES                         40.46 -3.75  11551574   \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",
       "798  confirmed 2022-03-30 00:00:00+00:00  \n",
       "799  confirmed 2022-03-31 00:00:00+00:00  \n",
       "800  confirmed 2022-04-01 00:00:00+00:00  \n",
       "801  confirmed 2022-04-02 00:00:00+00:00  \n",
       "802  confirmed 2022-04-03 00:00:00+00:00  \n",
       "\n",
       "[803 rows x 10 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creamos un gráfico \n",
    "\n",
    "Vamos a hacer una gráfica a partir de una tabla. \n",
    "\n",
    "### ¿Qué Columnas pueden sernos útiles a la hora elaborar una gráfica?\n",
    "- Eje X = casos confirmados\n",
    "- Eje Y = fechas\n",
    "\n",
    "\n",
    "Para hacer un gráfico pondríamos en el eje de la X el número de casos covid y en el de las Y las fechas. Para ayudarnos para hacer este gráfico sobre los datos vamos a coger un dato que será usado como índice y sirviéndonos de la siguiente función: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22 00:00:00+00:00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23 00:00:00+00:00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24 00:00:00+00:00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25 00:00:00+00:00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26 00:00:00+00:00</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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-30 00:00:00+00:00</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>11508309</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-31 00:00:00+00:00</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>11508309</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-01 00:00:00+00:00</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>11551574</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-02 00:00:00+00:00</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>11551574</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-03 00:00:00+00:00</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>11551574</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>803 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Country CountryCode Province City CityCode    Lat  \\\n",
       "Date                                                                          \n",
       "2020-01-22 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2020-01-23 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2020-01-24 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2020-01-25 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2020-01-26 00:00:00+00:00   Spain          ES                         40.46   \n",
       "...                           ...         ...      ...  ...      ...    ...   \n",
       "2022-03-30 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2022-03-31 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2022-04-01 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2022-04-02 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2022-04-03 00:00:00+00:00   Spain          ES                         40.46   \n",
       "\n",
       "                            Lon     Cases     Status  \n",
       "Date                                                  \n",
       "2020-01-22 00:00:00+00:00 -3.75         0  confirmed  \n",
       "2020-01-23 00:00:00+00:00 -3.75         0  confirmed  \n",
       "2020-01-24 00:00:00+00:00 -3.75         0  confirmed  \n",
       "2020-01-25 00:00:00+00:00 -3.75         0  confirmed  \n",
       "2020-01-26 00:00:00+00:00 -3.75         0  confirmed  \n",
       "...                         ...       ...        ...  \n",
       "2022-03-30 00:00:00+00:00 -3.75  11508309  confirmed  \n",
       "2022-03-31 00:00:00+00:00 -3.75  11508309  confirmed  \n",
       "2022-04-01 00:00:00+00:00 -3.75  11551574  confirmed  \n",
       "2022-04-02 00:00:00+00:00 -3.75  11551574  confirmed  \n",
       "2022-04-03 00:00:00+00:00 -3.75  11551574  confirmed  \n",
       "\n",
       "[803 rows x 9 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.set_index('Date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al hacer esto, `Date` se traslada al índice. Es decir, la columna de fecha se convierte en la columna que queríamos que fuera el índice. No obstante, necesitamos una **tabla con dos columnas** para poder hacer el gráfico y en la que se reflejen los casos confirmados. Por ello haremos una nueva celda a través de la siguiente función:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "2022-03-30 00:00:00+00:00    11508309\n",
       "2022-03-31 00:00:00+00:00    11508309\n",
       "2022-04-01 00:00:00+00:00    11551574\n",
       "2022-04-02 00:00:00+00:00    11551574\n",
       "2022-04-03 00:00:00+00:00    11551574\n",
       "Name: Cases, Length: 803, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.set_index('Date')['Cases']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Es importante mencionar que yo no estoy cambiando nada de los datos de origen, solamente cambio la manera de ver los datos que se presentan*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Crear una gráfico\n",
    "\n",
    "Hemos creado un gráfico con la función `plot` que es *píntamelo* con la que podremos visualizar el gráfico. Como vemos tras `plot` aplicamos entre paréntesis la función tittle con la que podemos nombrar el gráfico:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Casos de Covid19 en España'}, xlabel='Date'>"
      ]
     },
     "execution_count": 20,
     "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_es.set_index('Date')['Cases'].plot(title=\"Casos de Covid19 en España\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Otro país \n",
    "Para insertar un nuevo país debemos cambiar de nuevo los nombres,  cambiar la url y poner it y cambiar el país. \n",
    "Es importante recordar en este apartado que los valores nulos no son valores que no hayan, como se ve en la tabla. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 21,
     "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": [
    "url_it = 'https://api.covid19api.com/country/italy/status/confirmed/live'\n",
    "df_it = pd.read_json(url_it)\n",
    "df_it.set_index('Date')['Cases'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Casos de Covid19 en Italia'}, xlabel='Date'>"
      ]
     },
     "execution_count": 22,
     "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_it.set_index('Date')['Cases'].plot(title=\"Casos de Covid19 en Italia\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Otro país más "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Casos de Covid19 en Mexico'}, xlabel='Date'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FKq2oYuXOAgb36MjIY9LiXU6jKchFpMVasGU/ZZXV/OC8UbRqZfEup9EU5CLSYs1dn0tKa+OEQcl1kazaFOQi0iIVlVUyc94WJg7oSoe2beJdzlFRkItIi+Pu3DBzAWWV1Uwb2Sve5Ry1iIPczFqb2Udm9nI0CxIRibZl2fnM37yfW88ayvWnDIx3OUetIS3y24DV0SpERCRWHn1/Kx3atubG0wYm9U7OQyIKcjPrB5wHPBjdckREomtzbjEvLtnBFZMz6ZyaEu9ymkSkPfz3AN8BOkevFBGR6HF3Zi3czp9nb6Rt61bcPGVwvEtqMkcMcjM7H8hx90VmNuUw000HpgP079+/qeoTETkqewpK2ZxbzDtrc3jg35sYmNGR310xnp6dk++WbvWJpEV+CvBZM/sMkAqkmdnj7n51zYncfQYwA2DSpEne5JWKiBxB3sFyNu4t4oNN+9m6r5hNe4tZtO0AHibSp0f34k9XTaR1M+gXr+mIQe7u3wO+BxC2yO+oHeIiIrF2sLyS+Zv3s2ZXIdv2F7NwywHW5xR9PD6jU1sGZnTkq1MGc9KgDPp1bU9WRvLd/ScSyX0UvIi0GPuLy1mWncdTH25j1a4Csg+UfNzS7tIhhXH9unDxcX0Z2L0jJw/OIL1D89iRGYkGBbm7zwZmR6USEZHQwfJKFm09wLo9Razcmc/8TfvZkVcCQHr7FE4f1oNLj8tkdJ80Jmd1a1GhXRe1yEUkrtyd7ftL2LyvmOXZecxZn8virQeorA6a2xmd2jFxQBeuPD6TcZldGJfZhbRmcthgU1GQi0jMVVRV8+76vWzJPcishdtZs7vw43Gj+6Rxw2kDGdevC5Oyujaro0uiRUEuIjFxKLzfXp3DGyt3k1tUDkCf9FTuumAUY/qmk9W9Iz06t4tzpclHQS4iUVVYWsGLS3Zy/+yN7MgroWPb1pwyJIMrJmcyPrMLXTu0bRanyceTglxEouYvczbx89dWU+0wtl86d10wiinDe9K2jS682pQU5CLSpApKK/jl62t4e3UOu/JLOW1oBl+dMoQTB3XDTC3vaFCQi0iT+XDzfu54dik78ko4Y1gPbjxtEFefOEAt8ChTkIvIUcs7WM4dzy7jn6v30Ds9lWemn8ikrOS+fVoyUZCLyFHZW1jGNQ/NZ83uQq4+sT/fOWeEjvOOMQW5iDRKeWU1sxZu5/9eW0NFdTWP33ACpw7NiHdZLZKCXEQabH9xOVfO+IC1ewrpk57KA9dM4th+6fEuq8VSkItIg7yzNodvzVpKcVkl933hOM4a2ZPUlNbxLqtFU5CLSMQ+2LSPLz2ygIEZHXnwukkc179rvEsSGnbzZRFpwXbmlfDVJxbTrWNbfnnpWIV4AlGLXESOaO76XL725GIqq6p56ZZTGdyjU7xLkhrUIheRw8otKuP2Zz6iZ+d2/O3mkxXiCUgtchGpV2VVNd99bjkFpZU88eUTGX5M53iXJHVQkItInXbll/CVxxaxNDufH54/SiGewBTkIvJf3J1vP7uM9TlF/PHKCZw/tne8S5LDUB+5iPyXvy/ZydwNuXzv3BFcMK6PrlqY4BTkIvIfCksr+OmrqxnXL52rThgQ73IkAupaEZGP7cov4UuPLCC3qIwHr52kO/ckCbXIReRjP3llNVv2FTPjmkmMy+wS73IkQgpyEQFgeXY+ryzbxU2nD+ZTo3rFuxxpAAW5iADwwJyNdG7XhhtOGxjvUqSBFOQiLZy788h7m3l52S6uOnGAbgqRhI4Y5GaWamYfmtlSM1tpZv8vFoWJSGy8vGwX/+8fqxjWqxO3Txsa73KkESI5aqUMmOruRWaWAsw1s9fc/YMo1yYiUVZV7dzzz3Uck5bK3792qq4rnqSO2CL3QFH4NCX88ahWJSIx8Y+lO9m4t5gfnj+K9m0V4skqoj5yM2ttZkuAHOAtd58f1apEJOoOllfyqzfWMqp3GueOOSbe5chRiCjI3b3K3ccD/YDjzWxM7WnMbLqZLTSzhXv37m3iMkWkKe0tLOOce95lZ34JPzx/lE78SXINOmrF3fOAd4Bz6hg3w90nufukHj16NFF5IhINv3lzLbvyS3j8hhM4aXD3eJcjRymSo1Z6mFmX8HF74FPAmijXJSJRsjw7n2cWbue6k7I4ZUhGvMuRJhDJUSu9gb+aWWuC4J/l7i9HtywRiYbqaueul1bQvWNbbtWhhs3GEYPc3ZcBE2JQi4hEUXFZJVc9OJ8l2/P45aVjdeJPM6KrH4q0EL9+cy1Ls/P41aVjuXRiv3iXI01IQS7SAizZnsfMeVu4+oQBXDYpM97lSBPTtVZEmrmcglK++9wyenVO5TvnDI93ORIFapGLNGMHyyv5zB/mknewnBnXTqSz+sWbJQW5SDNVVlnF5/78PrlFZTx6/fGcPkzndzRX6loRaYZKyqv4/gsrWL2rgOmnD+K0oTpevDlTi1ykmdm+/yCfn/EBO/JKuGXqEL51tvrFmzsFuUgzkn0gCPGiskoevf54tcRbCAW5SDNQUVXNH/+1gSfnb6W8sponvnwix/ZLj3dZEiMKcpEktyW3mG/OWsLibXlMGd6DO84ezpi+CvGWREEukoTcnfc37mPGu5uYvXYvndu14Y9XTuCCcX3iXZrEgYJcJIls2lvEs4uyeXnZTrbvLyGjU1tunTqEyyZlktmtQ7zLkzhRkIsksKKyShZu2c87a3KYvW4vW/cdpHUr47ShGXxtyhAumtBX99kUBblIonB39hSUsXxHPsuz85i7IZel2flUVTupKa04ZXAG156UxQVje9MzLTXe5UoCUZCLxEllVTXbD5SwLDuPZdn5zF2fy9o9hQC0Mji2XxduPmMwJwzqxuSsbmp5S70U5CIxUFpRxcqdBSzeeoA1uwtZt6eQtXsKKa+sBiA1pRUje6fxw/NHMT4znRHHpNGxnT6eEhltKSJNrKyyio+25bFkex7Ld+SzdHse2QdKPh7fK60dQ3p24osnZzGkRydG9Ulj+DGdSWmtK2ZI4yjIRY5SVbWzeNsB3ly5m2XZ+SzNzqO0Imhp9+3SnvGZXbh8UibDenXmuAFd6NlZ/dvStBTkIo2QX1LBW6v28OJHO1i87QAHy6to27oVo/um8fnJ/Tl5cHcmZ3Wja8e28S5VWgAFuUiESsqrmLN+L09/uI1/r9tLtUP/bh24bGI/JmZ148zhPXS9b4kLBblIPaqrnQ17i/jXmhxe/GgH6/YUUu1BH/dNZwxm2sieHNe/K2YW71KlhVOQi9SSW1TGayt289C7m9iy7yAAk7O68vWpQ5nQvwunDcmgjXZMSgJRkIsQdJss3Lqfv7y7mTnr9gIwrl86//e5YzlxUHcGdO8Y5wpF6qcglxZtd34pv35zLX9fsoOKKqdbx7bcPm0oU0f05Ni+6eo2kaSgIJcW6cPN+3n8g628uWo31dVw5fH9mTK8BycO6k6HtvpYSHLRFisthruzZnchD83dzN8WZdO9Y1vOO7YPt08bqisHSlI7YpCbWSbwKNALcGCGu/8+2oWJNKWcwlLufH45/1ydQ5tWxs1TBnPL1CFqfUuzEMlWXAl8y90Xm1lnYJGZveXuq6Jcm8hR21NQygP/3sTzH2VTUl7F/5wzggvH96FPl/bxLk2kyRwxyN19F7ArfFxoZquBvoCCXBLa6yt28d3nl3OwvIqTB3fnB+eNYkjPTvEuS6TJNeh7pZllAROA+VGpRqQJrNiRzx3PLmXN7kKO7ZvO7z8/nkE9FODSfEUc5GbWCXgOuN3dC+oYPx2YDtC/f/8mK1CkId5evYdbnvqILu1T+NGFo/n85P60baOTd6R5iyjIzSyFIMSfcPfn65rG3WcAMwAmTZrkTVahSAQOllfy7WeX8cryXYzuk8YjX5ysu+hIixHJUSsGPASsdvffRr8kkYapqnZue3oJb6/ewzemDeOmMwbpbjrSokTSIj8FuAZYbmZLwmF3uvurUatKJEJLtufx81dXM3/zfi6f1I/bpg2Nd0kiMRfJUStzAZ2nLAnno20HuPT+9+nSPoU7zh7G9acOjHdJInGhsyEkKRWXVfKNZ5ZwTFoqr956GukddB1wabkU5JJ0qqud77+wnK37D/L0jScqxKXF03FZknT+96UVvLhkJ9+cNowTBnWPdzkicacgl6Ty73V7efyDbdx42kC+PnVIvMsRSQgKckkaFVXV/OgfK8nq3oE7Pj1c1woXCSnIJWk8+v5WNu4t5ofnj6JdGx0nLnKIglySwgeb9nHPW+s4fVgPpo7oGe9yRBKKglwSXk5hKTfMXEDPtHb87OIx6lIRqUVBLgnv56+uobyqmgevm0y/rrqTj0htCnJJaM8s2MYLH+3g5ilDGJihO9mL1EVBLglr1sLtfO/55ZwypDu3naVrqIjUR0EuCemdNTlhiGdw/9UTad1K/eIi9dEp+pJwFm7Zz9eeXMyIYzrz56sn0qmdNlORw9EnRBLGwfJKfvSPVTy7KJsB3TrwyJcmK8RFIqBPiSSEg+WVfOmRBSzYsp9rT8rilqlD6N6pXbzLEkkKCnKJu3+t2cOv3ljH2t0F/O6K8Vw4vm+8SxJJKtrZKXH1p9kbuH7mQorLKrn/6okKcZFGUItc4iK/pILfvbWOmfO28NlxffjN5eNIaa12hUhjKMgl5nbklXDNQ/PZnFvMtScN4K4LRuvwQpGjoCCXmFq/p5BrHvqQ4vJKnr7xRN0YQqQJKMglZhZvO8D1MxeQ0roVs246iZG90+JdkkizoCCXmJi9NoebH19Mz7R2PHb9CfTvrotfiTQVBblElbvz13lb+MkrqxnWqzN/vf54enTW8eEiTUlBLlFTUVXN919YzqyF2ZwxrAd//MIE0lJ1x3uRpqYgl6jIO1jOrU8vYc66vdw6dQjf+NQw3RBCJEoU5NLkXl2+i/95bhkl5VX83+eO5YrJ/eNdkkizdsQgN7OHgfOBHHcfE/2SJFm5Ow/M2cQvXlvDhP5d+NnFx+rIFJEYiKRFPhO4F3g0uqVIMttTUMoPX1zBm6v2cP7Y3vz6snGkpuhO9yKxcMQgd/c5ZpYVg1okyRSWVvDikp28tWoP8zftA+D7nxnJl08bqP5wkRhqsj5yM5sOTAfo3199os1ZQWkFj72/lftnb6SwrJJBPTpy5fH9+dIpWQzorvtqisRakwW5u88AZgBMmjTJm2q5kjhyi8p4eO5mHnt/K4VllZw1oie3njWUcZld4l2aSIumo1bkiNbvKeTh9zbz/OIdlFdVc+6YY7j5jCEc2y893qWJCApyqYe78/bqHB6Zt5n3NuyjXZtWXDyhLzeePojBPTrFuzwRqSGSww+fAqYAGWaWDdzl7g9FuzCJj6pqZ876vdz7rw0s2nqAPumpfPvTw7ny+P5069g23uWJSB0iOWrlylgUIvFVUFrB3z/awYx3N7F9fwk9O7fjF5ccy6UT+9FGN3wQSWjqWmnBqqqd9zbk8rdF2byxcjdlldWM65fOd88ZybRRPWnXRseBiyQDBXkL5e5c8cD7LNx6gPT2KVw+KZNLJ/ZjbL90HQMukmQU5C1MWWUVb6/O4ZkF21m49QB3nD2MG08fpNa3SBJTkLcA7s6y7Hz+tiibl5buJL+kgt7pqdx21lC+csZg9YGLJDkFeTOWU1DKCx/t4G+LslmfU0S7Nq04Z8wxXDqxHycPztANj0WaCQV5M+PuLNhygJnzNvPGyj1UVTsTB3Tl55ccy3lje+vGDiLNkIK8mSitqOKlpTuZ+d4WVu0qIL19CjecOpArJmfqBB6RZk5BnuR25pXw+AdbeXrBdvYXlzO8V2d+fsmxXDS+L+3bagemSEugIE9C7s7CrQeY+d4WXl+5G3dn2shefPGULE4a1F2HD4q0MAryJFJZVc2rK3bz0LubWJqdT1pqG244dSDXnDiAzG4d4l2eiMSJgjwJFJRWMPO9LTw5fxu7C0oZmNGRH180hs8d15cObfUnFGnplAIJrLC0gkfe28KD726ioLSSM4b14McXjeGsET1ppUMHRSSkIE9AhaUV/HXeFv7y7mbySyr41Khe3HbWUMb01fW/ReS/KcgTSFW18+T8rfz2rXUcOFjBtJE9uX3aMAW4iByWgjwBuDvvrM3hV2+sY/WuAk4a1J3vfWYEY/t1iXdpIpIEFORxtmJHPt9/YTlLs/Pp17U9f7rqOM4dc4wOIRSRiCnI42RHXgn3vLWO5xZn071TO3556VguntCXFF3ASkQaSEEeY/uKyvjT7I089v5WAL50ykBunTqU9A66BoqINI6CPEbyD1Zw/5yNzHxvC2WVVVw6sR+3TRtG3y7t412aiCQ5BXmU5ZcEJ/M8NHcThWWVfHZcH26ZOpQhPXUhKxFpGgryKMkpLOWZD7fzl/Bknmkje/HNTw1jVJ+0eJcmIs2MgryJLdmex/2zN/LW6uBa4FNH9ORbZw9jdB8dCy4i0aEgbwKVVdX8c/UeHn1/K/M27iO9fQpfPnUgl+ta4CISAwryo7BpbxEvfrSDWQuz2V1QSt8u7fnuuSO4+sQBdGqnt1ZEYkNp0wDV1c6KnfnMXruXt1fvYWl2Pq0MTh0aXMxq6oieug+miMScgvwI8g9WMGf9Xt5Zm8OcdXvJLSrHDMb2TefOz4zgwvF96ZWWGu8yRaQFiyjIzewc4PdAa+BBd/9FVKuKk8qqarbsK2bVrkJW7yrgw837+WjbAaodunRI4fShPZgyvAenD+tBRqd28S5XRASIIMjNrDVwH/ApIBtYYGYvufuqaBcXDRVV1eQWlbGnoIw9BaXsOFDCmt0FrN5VyLo9hZRVVgPQppUxqk8aXz9zCGcM78n4zC7qNhGRhBRJi/x4YIO7bwIws6eBC4F6gzy3qIwH390EgDs4Hv4OnsMnw4Jp/nO84zXmDR54rWUFy6gx/cfzO2WV1ZSUV3GwoorS8ioOlldRUFrBnoIy9hWXfTz/Id07tmVk7zSuPWkAI45JY2TvNIb07ETbNrruiYgkvkiCvC+wvcbzbOCE2hOZ2XRgOkDbY4bwk1dWN0mBnywfLFhP+DscTjDCPp7GaJfSig4prUlt25oObVvTPqU1vdJSGdsvnZ6dU+mVlkqvtHb0SkvlmPRUundsq6sNikjSarKdne4+A5gBMOG4iT77rrPrDF/DqJmZNYfVGdQKWBGRw4okyHcAmTWe9wuH1at1KyO9va7mJyISC5F0Ai8AhprZQDNrC3weeCm6ZYmISKSO2CJ390oz+zrwBsHhhw+7+8qoVyYiIhGJqI/c3V8FXo1yLSIi0gg6vk5EJMkpyEVEkpyCXEQkySnIRUSSnHnt89WbYqFmhcDaGoPSgfwGLKKh0wNkALlRXkcs6mrseho6T6LWBdGvLRFrauw8iVoXxKa25lTXGHdv3KVUg+ucNO0PsLDW8xkNnL9B09e1ziitI+p1xaq2RK0rFrUlYk36Ozb69TenuoobOs+hn1h1rfwjytM3RmPWEYu6GrsevWfRmfZoJOr7lah1NWY9Lb0uIHpdKwvdfVKTLzjB1hkJ1dVwiVhbItYEiVsXJG5tCVxXsbt3bMy80WqRz4jSchNtnZFQXQ2XiLUlYk2QuHVB4taWqHU939gZo9IiFxGR2NHhhyIiSU5BLiKS5JIqyM3sIjNzMxsR71rqYmZFRxg/28xitpPFzPqZ2d/NbL2ZbTSz34eXIq5v+tvNrEOMajvsexUv2sYaXI+2sQaI1vaVVEEOXAnMDX9HLLyBdItiwa2VngdedPehwDCgE/DTw8x2OxCTD1kC0zYWIW1jjRKd7auxB6DH+odgA9lBsLGsDYdNAeYArxCcSXo/0CocVwT8BlgKnBqjGovCml6uMexe4Ivh49nApBjVchYwp9awNGAf0BH4NbACWAbcAtwKlAPLgXdi9F51At4GFofrvTAclwWsBv4CrATeBNprG9M2lszbWDS3r2RqkV8IvO7u64B9ZjYxHH48wUYyChgMXBIO7wjMd/dx7j435tXG32hgUc0B7l4AbAO+TLAhj3f3scAT7v4HYCdwprufGaMaS4GL3f044EzgN/bJTVqHAve5+2ggD/hcDOrRNtYw2sYaJmrbVzIF+ZXA0+Hjp/nkq8mH7r7J3auAp4BTw+FVwHOxLTFpTAEecPdKAHffH6c6DPiZmS0D/gn0BXqF4za7+5Lw8SKCUIg2bWNNZwraxmqL2vYV0R2C4s3MugFTgWPNzAluOecEX0dqHwh/6Hlp+MbEWiX/+Q+ycRfBOXqrgEtrDjCzNKA/sCUeBdXhKqAHMNHdK8xsC5+8X2U1pqsC2kezEG1jjaJtLELR3r6SpUV+KfCYuw9w9yx3zwQ2A6cBx4c3hm4FXEGwIyGetgKjzKydmXUh6EeMh7eBDmZ2LXy8s+Q3wEyC+6/eZGZtwnHdwnkKgc4xrDEdyAk/YGcCA2K47tq0jTWctrHIRXX7SpYgvxJ4odaw58LhCwh29qwmeGNqTxcT4QZb5u7bgVkEO3lmAR/Fox4P9pZcDFxmZuuBdQT9hXcCDxL0Yy4zs6XAF8LZZgCvm9k70azt0HsFPAFMMrPlwLXAmmiu9wi0jTWQtrEGier2ldSn6JvZFOAOdz8/zqVgZuOAv7j78fGuJdEl03ulbSw5Jct71VTbV7K0yBOamX2FYCfFD+JdS6LTe9U4et8i1xLfq6RukYuIiFrkEgNmlmlm75jZKjNbaWa3hcO7mdlb4endb5lZ13D4VWa2zMyWm9m88GvyoWU9bGY5ZrYiXq9HEktTbV/1LScZqEUuUWdmvYHe7r7YzDoTHLN7EfBFYL+7/8LMvgt0dff/MbOTgdXufsDMzgXudvcTwmWdTnDG26PuPiYer0cSS1NtX/Utx91XxeWFNYBa5BJ17r7L3ReHjwsJ9s73JTjT7a/hZH8l+PDh7vPc/UA4/AOgX41lzQHidXKJJKCm2r4Os5yEpyCXmDKzLGACMB/o5e67wlG7+eSMu5puAF6LTXWS7Jpq+6q1nISXFGd2SvNgZp0Ijp293d0LPrnkRXBMcnjGW83pzyT4oJ2KyBE01fZVezlRL7wJqEUuMWFmKQQfjifc/dC9CfeE/ZKH+jlzakw/luCkkgvdfV+s65Xk0lTbVz3LSXgKcok6C5pGDxHsYPptjVEvAdeFj68D/h5O35/gOtfXhFeKE6lXU21fh1lOwtNRKxJ1ZnYq8C7B9aCrw8F3EvQ/ziK4yNJW4HJ3329mDxJcUnRrOG2lu08Kl/UUwZX1MoA9wF3u/lCMXookoKbavupbjru/GptX0ngKchGRJKeuFRGRJKcgFxFJcgpyEZEkpyAXEUlyCnIRkSSnIJekZ2ZVZrYkvGLdUjP7VnjbrMPNk2VmXzjcNCLJQkEuzUGJu49399HAp4BzgbuOME8Wn9x+TCSp6ThySXpmVuTunWo8H0RwH8QMgpvtPgZ0DEd/3d3nmdkHwEiCeyT+FfgD8AuCk43aAfe5+wMxexEiR0FBLkmvdpCHw/KA4QR3ba9291IzGwo8FZ7FN4Ua90o0s+lAT3f/iZm1A94DLnP3zTF8KSKNoqsfSnOXAtxrZuOBKmBYPdOdDYw1s0vD5+nAUIIWu0hCU5BLsxN2rVQRXO3uLoJrsowj2CdUWt9swC3u/kZMihRpQtrZKc2KmfUA7gfu9aDfMB3Y5e7VwDVA63DSQqBzjVnfAG4OL2OKmQ0zs46IJAG1yKU5aG9mSwi6USoJdm4eugzpn4DnzOxa4HWgOBy+DKgys6XATOD3BEeyLA4vZ7qX8NZgIolOOztFRJKculZERJKcglxEJMkpyEVEkpyCXEQkySnIRUSSnIJcRCTJKchFRJKcglxEJMn9f9nLP/203GxlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "url_mx = 'https://api.covid19api.com/country/mexico/status/confirmed/live'\n",
    "df_mx = pd.read_json(url_mx)\n",
    "df_mx.set_index('Date')['Cases'].plot(title=\"Casos de Covid19 en Mexico\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparación de los tres gráficos creados con anterioridad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compararemos estos tres países en un solo gráfico\n",
    "\n",
    "- Vamos a concatenar estos dataframe que tienen la misma estructura\n",
    "- Ahora solamente poniendo `casos_es` nos pone todo esto que antes ocupaba mucho más"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "casos_it = df_it.set_index('Date')['Cases']\n",
    "casos_es = df_es.set_index('Date')['Cases']\n",
    "casos_mx = df_mx.set_index('Date')['Cases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "2022-03-30 00:00:00+00:00    11508309\n",
       "2022-03-31 00:00:00+00:00    11508309\n",
       "2022-04-01 00:00:00+00:00    11551574\n",
       "2022-04-02 00:00:00+00:00    11551574\n",
       "2022-04-03 00:00:00+00:00    11551574\n",
       "Name: Cases, Length: 803, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casos_es"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concatenar los datos\n",
    "Vamos a utilizar ahora la función concaternar `pd.concat`, y dentro de la función concat ponemos una serie donde están los casos. Esto es importante: se debe hacer en el eje 1 (`axis=1`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                              Cases     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",
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       "...                             ...       ...      ...\n",
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       "2022-04-03 00:00:00+00:00  11551574  14845815  5666215\n",
       "\n",
       "[803 rows x 3 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([casos_es,casos_it,casos_mx], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VS es como definimos este dataframe\n",
    "\n",
    "En el antiguo caso se concatena lo que ya vemos, pero si queremos crearlo ponemos *algo es igual a algo* (`=`) y podemos poner después versus, comparación etc. Se puede poner lo que sea, en el caso del inicio el import pandas pd si que teníamos que ponerlo así, pero en este caso no es necesario. Si llamamos a esta selección o concatenación nos sale el resultado, pero aquí estamos a esta selección llamándola `vs`,  por eso como se ve abajo no se cambia nada después, solamente lo hemos creado con un nombre. En conclusión, `VS` es como definimos este dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "vs = pd.concat([casos_es,casos_it,casos_mx], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
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       "                                Cases       Cases     Cases\n",
       "Date                                                       \n",
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       "...                               ...         ...       ...\n",
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       "\n",
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   "source": [
    "vs"
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  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "vs.columns = ['España','Italia','Mexico']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "                             España    Italia   Mexico\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",
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       "...                             ...       ...      ...\n",
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       "2022-04-03 00:00:00+00:00  11551574  14845815  5666215\n",
       "\n",
       "[803 rows x 3 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creamos el gráfico\n",
    "\n",
    "De nuevo volvemos a crear un gráfico a través de la función `plot` y con la función `vs.plot(title=\"\")` denominamos el gráfico. \n",
    "\n",
    "> RECORDAMOS: Cabe señalar en este punto que vs.plot nos da los datos de los tres países porque hemos denominado con antelación que vs sea la concatenación de los datos de los tres países `vs = pd.concat([casos_es,casos_it,casos_mx], axis=1)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'España vs Italia vs Mexico'}, xlabel='Date'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vs.plot(title=\"España vs Italia vs Mexico\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'España vs Italia vs Mexico'}, xlabel='Date'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gYREJkPpweEpVXxKR24D5qvoC8BDwqIisAWqAS3ss4l5y++23c8cdd7SMUd/U1NThsMXtOeyww3j33XcJBoO8+eab/OQnP+GZZ57prfCNMTmqKlrFd9/+LlsiWwg6QeJevFfOu8dkr6qLgaPbWf7TVo9jwMWZDS23JJPJDoctbk99fT1f/epXWb16NSJCMtnzn9zGmNy2rm4dV752JXXxOhxxei3Rg4162Wm7G7a4PTfffDOnnXYaS5cu5cUXX7Qhi40Z4Hz1ufm9mwknwxkbybIrLNl3oLS0lHA43PK8q8MW19fXM2bMGMCGVTDGwNOrnmZx1WIEIam9/03fkn0HJk2aRCAQYPLkydx11118+9vf5uGHH2by5MmsWLFij8MW//CHP+Smm27i6KOPxnUzN76FMabv+demf3H73Nt77WZse3J6iONsNr3MVTbEsTF9y/r69Vzy4iV46mVkcLN+OcRxX0/Mxhjz63m/xlUXUcnoKJZdZdU4xhjTQxbuWMiszbNAyUo9fWuW7I0xpge4vsvtc28n5ISyWqJvZsneGGN6wJ0L7mRZ9bKMzB+bCZbsjTEmw2pjtTy54kkccbJefdPMkr0xxmTYkyufJOEnEHJnWg9L9m2ICF/+8pdbnruuy4gRI1oGOOuq++67j0ceeSRT4Rljclzci/PY8sdwcHq9l+zu5HTTy+lPTqc6tvcjJTcrKyhj5hdn7nab4uJili5dSjQapbCwkBkzZrT0hO2Ob37zm93e1xjT97yy7hVq47XkOXk9PmxxV+R0yT6Tib4rxzv33HN5+eWXAXj88ce57LLLWtY1Njbyta99jWnTpnH00Ufz/PPPA3D99ddz2223AfD6669z6qmn4vs+t956K3fccQcAa9as4YwzzmDy5Mkcc8wxrF27FlXlxhtvZOLEiRx11FE8+eSTmXzJxphe9o81/yAowZxK9JDjyT5bLr30Up544glisRiLFy/m+OOPb1n3y1/+ktNPP525c+fy9ttvc+ONN9LY2Mj//M//8OSTT/L2229z3XXX8X//9384zs5v7+WXX853vvMdFi1axOzZs9l333159tlnWbhwIYsWLeLNN9/kxhtvZOvWrb39ko0xGVAbq2Vx5WI0V5rgtGLJvh2TJk1iw4YNPP7445x77rk7rXvjjTe4/fbbmTJlCtOnTycWi7Fx40aKiop44IEHOPPMM7n22ms56KCDdtovHA6zefNmLrzwQgAKCgooKipi1qxZXHbZZQQCAUaOHMmnP/1p5s2b12uv1RiTOfcuvBdffQISyHYou8jpOvtsOv/887nhhhuYOXMm1dWfVP+oKs888wyHHnroLvssWbKEsrIytmzZ0puhGmNyQCQR4fm1zyMiOVeFA1ay79DXvvY1brnlFo466qidln/2s5/lD3/4A80DyH300UcAlJeX89vf/paPPvqIV199lQ8++GCn/UpLSxk7dizPPfccAPF4nKamJk455RSefPJJPM+jsrKSd955h2nTpvX8CzTGZNQb5W8QdaM9Wqov9fxu72vJvgNjx47luuuu22X5zTffTDKZZNKkSRx55JHcfPPNqCpXXXUVd9xxB6NHj+ahhx7i61//+i4Tljz66KPcfffdTJo0iZNOOolt27Zx4YUXMmnSJCZPnszpp5/Or3/9a0aNGtVbL9MYkyGzNs8iIIEeG8J4kOdx/7bujwKc00McZ6PpZa6zIY6NyT2u73LKE6fQmGzssZuzd26v5NNNUfb5S5y6DbH+NcRxX0/MxpiBYVn1MiLJSI+1rf9SfZgzm6LcN2QQByS711rPqnGMMWYvzd4yG6BHEv2YpMt3a+t4r7CACfEEgW7WxliyN8aYvTR782wcyXw6HeW6/N/W7TgoswqL+Ew0tuedOpBzyT5b9xD6AntvjMk9WyNbWVy1GKcH0un3auoY7Pv8d1kZ19TV7dWx9hidiOwnIm+LyMciskxErm9nm+kiUi8iC9M/P+1OMAUFBVRXV1tSa4eqUl1dTUFBQbZDMca08tflf+2RnDW9sYlzG5t4urSEG6prGOp3v9kldO4GrQv8QFU/FJFSYIGIzFDVj9ts966qdm9oyLSxY8dSUVFBZWXl3hym3yooKGDs2LHZDsMY08qM8hkImZ1ftsT3ubm6lpV5IfJUKc3Ah8kek72qbgW2ph+HRWQ5MAZom+z3WigU4oADDsj0YY0xpkfUx+vZ2riVkBPC38uSd7Ni3+eBrTvYx/N4YMhgflJdk5HjdqmSSUTGA0cDH7Sz+kQRWSQir4rIkZkIzhhjctmq2lUZP+Y1dfUcnkhwW9kwvlLfkLHpTzrdzl5ESoBngO+qakOb1R8C+6tqRETOBZ4DJrRzjGuAawDGjRvX3ZiNMSYnfFydquDIVK/ZKbE4V9SHeb24iK/V1zPWzdzkJ50q2YtIiFSi/5uqPtt2vao2qGok/fgVICQiw9vZ7n5VnaqqU0eMGLGXoRtjTHYtq16WsbFwylyPn1dWsz0YZH0oL6OJHjrXGkeAh4DlqnpnB9uMSm+HiExLHzezM48YY0yOWVa1DF/3vq5+QiLBM5u3Mtp1eWDIIL61l80s29OZapxPAVcAS0RkYXrZT4BxAKp6H3AR8C0RcYEocKla+0ljTD8WToTZGN5IyAntVTXOSNflT9t24Inw67Iyvl9T2yMdoDrTGmcW7P4egareA9yTqaCMMSbXra5dDYDsxS3UMs/j7u2VFPnKr8qGcktVNT01QHJOD4RmjDG5ak3dGqD74+EcFYvzh+2VlPo+vygr48fVNT2W6CEHh0swxpi+YG3d2m6X6k+Ixvjzth1EHYc7ysr4Xm0tRT1c820le2OM6Ya1dWsRkS4NlRBQ5Rt19Xy9roFNoSCvFpdyU3V1xtrS744le2OM6SJVZXXd6i6PifODmjquaAjzz6JCPiwo5Iaaml5J9GDJ3hhjumxV7SpqYjXkB/KJe/FO7fP5cIQrGsI8XlrClHicz9RkZhiEzrJkb4wxXfRG+RsIQsLr3M3ZL9WH+WFNLR8U5DPSdTk80TPz1O6O3aA1xpgueqfiHRxxOjXf7OmNTdxUU8s7RYUsLCjk9L2YgGRvWLI3xpgucH230y1xhnoeP62qYUVeiLAI3+iBnrGdZcneGGO6YHNkM0k/ScDZQ6t4Vf6rqoZS3+fFklLOb2zqnQA7YMneGGO6oLkzlevvfrKS79fWcVZTlAeHDNrrKQUzwZK9McZ0wbq6dQB42vGolCc1RfmP+jBPlxQzJRpncIYmNtkbluyNMaYL1tav3eOwxtfUNbA1ECCoyknxzjXN7GmW7I0xpgvW1a3b7bDGk2Nxjo3Heaa0hPOyXE/fmiV7Y4zpJF991tevx5GOU+flDWHqHYdRrptTHZks2RtjTCdtb9xOzIsRlPbTeECVT0WjvFVUyLk5VKoHS/bGGNNp6+pTN2c76kx1ZX0Dg3xlfV5ej49i2VWW7I0xppPW168H2h/D/pBEgu/U1jOjqJDPhiO9HdoeWbI3xphOWle/rsP6+ksaIrgibAiFODLZ+2Pf7Ikle2OM6aR19evaHdZYVDm9qYlZhQV8IQdL9WDJ3hhjOiXhJVhZs7LddYcnEozwfFbm5zMsBzpQtceSvTHGdMKcrXOIJCPkOXm7rPt0UxQfGJWD1TfNLNkbY0wnLNi+AEGI+7v2iP10U4wl+XmcGMvO8MWdYcneGGM6YUnVEkR2HdZ4QiLBkYkEcwsKGON2PF5Otu0x2YvIfiLytoh8LCLLROT6drYREblbRNaIyGIROaZnwjXGmOxYU7um3Zuz/1lbT4PjkO/nVrv6tjpTsneBH6jqEcAJwHdE5Ig225wDTEj/XAP8MaNRGmNMFtXGaqmN1xJyQjstz/d9Tm6K8kJJEV+MhLMUXefsMdmr6lZV/TD9OAwsB8a02ewC4BFNmQMMEZF9Mx6tMcZkQXPP2bbDGh+ZSBACGp0g+bldsO9anb2IjAeOBj5os2oMsKnV8wp2/UAwxpg+qaNkPyWW6kk7Modb4TTrdLIXkRLgGeC7qtrQnZOJyDUiMl9E5ldWVnbnEMYY0+vW1a1rd87ZKfE45cEgRyR2HT4h13Qq2YtIiFSi/5uqPtvOJpuB/Vo9H5tethNVvV9Vp6rq1BEjRnQnXmOM6XVr69buulCVybE4iwryOKg/lOwl1dboIWC5qt7ZwWYvAF9Jt8o5AahX1a0ZjNMYY7Jmdd3qXUr2Y12XYb7P1mCIPUw9nhM6M7b+p4ArgCUisjC97CfAOABVvQ94BTgXWAM0Af+R8UiNMSYLIokIVdEq8py8nUa7PCyRKs0X5niTy2Z7TPaqOgvaqazaeRsFvpOpoIwxJld0NIb9IYkEHjDKzf0qHLAetMYYs1vN9fVJf+ekfmgiycZgkEMSluyNMabPW1fffkucQxIJVuWF2M91sxBV11myN8aY3Vhbt3aXMXEOiScY63qsz8vrEzdnwZK9McZ0KObG+GjHR7uMiXNxOEJcYJCXuwOftWXJ3hhjOjB321wiycguY+KcEo3ybmEh5zY2ZSmyrrNkb4wxHVhcuRjYeYLxQZ7PGNdjUyjEkBydlao9luyNMaYDSyqX7HJz9pCWoRF22yI951iyN8aYdvjqs7hq8S43Zw9NJ/uyPtK+vpkle2OMaUd5Q3m79fWHJZJUOw7j+kiTy2aW7I0xph1LqpYAkPB2HtHy0ESCFXl5HNxHOlM1s2RvjDHtWFy5GEF2GiYhqMpBiSTloSAl7UxRmMss2RtjTDuWVC7ZZdn4ZJI8IBLozBiSucWSvTHGtBFzY6ysXUlAdu4fOyFddTPY61v19WDJ3hhjdrGiZgWeejiyc4qckEjiAqP7wGQlbVmyN8aYNlbXrQZ27kwFMCGRoDwU5MA+1hIHLNkbY8wuyuvLd+lMJaocnkiyOi+Pfd2+MyZOM0v2xhjTiq8+C7Yv2KUK55hYnJGex8q8vD6ZOPtizMYY02PerXiXpdVLd1l+XqSRRhEOTCTa2Sv3WbI3xphWnlj5BEEJ4unOVTXHxuPMLSjg09FoliLbO5bsjTEmLZwI897m93aZb7bA9xmXdNkYCjKoj0ww3pYle2OMSVtStQRFd6mvPziZJAA0BfrKvFS7smRvjDFpc7bOQZBdJhdvnlR8SLLvNblsZsneGGMAVeWf5f/cZUhjSI1h3yjCmD42rHFrluyNMQZY37CejeGNBGXXcW8OTiRZFwpxSH8u2YvIn0Vkh4js2hYptX66iNSLyML0z08zH6YxxvSsJ1c8CezaaxZSyX5tXoiRfWiC8bY6M3TbX4B7gEd2s827qnpeRiIyxphedu/Ce3lsxWM4OPjsPK/sMM+jzPepDgT72ESEO9tjyV5V3wFqeiEWY4zpde9veZ8/LvojAQnskugBDkrfnA3QN5tcNstUnf2JIrJIRF4VkSM72khErhGR+SIyv7KyMkOnNsaY7mlKNnHr+7dSECjA110TPaSaXQIM64ODn7WWiWT/IbC/qk4G/gA819GGqnq/qk5V1akjRozIwKmNMaZ7Nkc2c9XrV7ElsgUff5eOVM0OTiRpcBzG9OGbs5CBZK+qDaoaST9+BQiJyPC9jswYY3rIvG3zuOTFS1hes5yQhHaZZ7a1QxMJ1oRCfXJY49b2OtmLyChJN0wVkWnpY1bv7XGNMSbTKpsq+fW8X3PNG9cQdaMEJEBSO247PybpMjmeYEFBPkP99qt5+oo9tsYRkceB6cBwEakAbgFCAKp6H3AR8C0RcYEocKlqH5uJ1xjTr21q2MQL617gkWWPEHNjIIBPuzdkWzsxGgMg0Kfb4aTsMdmr6mV7WH8PqaaZxhiTM5J+knc2vcNTq55i9pbZADjiEHACuwyH0JGj4zFqHIeTmhp7MtRe0femSDfGmA746rOyZiUzymfw7OpnqY5Vk+fkEZAAqoqvfoetbtpzdCzBwoJ8pjf1zWGNW7Nkb4zps5JektV1q/m4+mMW7ljIrM2zqI6lbhk64lAQKCDmxbp17BGuy36uy4ziIhws2RtjTI9LeAkqwhVsDG9kc2Qzq2tTCX517WpcTbWSccRBVckP5JPwEvjqdzvRAxwdT7XQCfaTO5CW7I0xOSHhJdjYsJHyhnLKw+VsiWxhU3gTG+o3sK1x2043U5sTuyMO+YF84l68pXom7sUzEs/RsRgxEQ6Ld/8DI5dYsjfG9CpVZUPDBhZVLmJlzUrWN6ynvL6czZHNO3Vsap5ARFUJSICQE2pJ5M2J3VMPr4cGJzsmFmdxfh5HxPvmnLNtWbI3xvSoSCLC4qrFLKpcxEfbP2JJ1RIiyQgAguCIg68+QSeIIw5JL4nPzjdSXXVxvd7r1FTk+xyaSPK3QaVMi2Xmm0K2WbI3xmTcmto1vLjuReZuncvHNR+3JO6ABFBS9erNSb15Yu/ONofsDUfFEwSAqNN3pyFsy5K9MSYjNkc28/Sqp3m34l1W1q5sKbULQsgJkfSTLYk9U/XqPaV5pMt9kv2jCgcs2Rtj9oLne7y96W1eXvcyb216C1VFZNfk3vy7rxifTBIRYd8+Ph5Oa5bsjTFdlvSTvLzuZR5a8hAbGjakpvLTVDWNq26XOi7lmkLf59RolPJQiP3dvvUhtTuW7I0xXTJr8yx+/v7P2dK4hfxAfkspHujTSb7ZheFGxrgeD5QN5qfV/WfeJkv2xphOWbB9AU+seILXNrxGyAmR7+TnfN17l6lyRUMDC/PzuDASydjsTrnAkr0xZrfiXpw759/JYyseQ5DUsMA51HImk8a5LmNdj+dLSvlOXV22w8koS/bGmHapKq+uf5V7F91LeUM5AQmkOjH1sZutXXFMuk19cR8fu749luyNMbsIJ8LcMvsWZpTPICAB8pw8En7/aYbYkdOaouwIBDgo0c+qp7Bkb4xpxVefv378Vx5b8RhbIlsGRGm+WYHvc2I0xgslxVwYjmQ7nF0kNUC5jgRWdWt/S/bGGACakk386J0fMbNiJo44hAK7n5u1vzk+FqNQlfpAkLxsBwOowmodw8veCXyoE1joH0yYIoZzfreOZ8nemAGuuW7+7o/uZktkC0EJ4qo7oBL9SNfl63UNREQ4OEujXMY0RBWDme0dydv+FJbpeDbqSABKaOJUZzEHOVv5gw7u1vEt2RszgEXdKLfOvpVX1r9CUIKEnNCAqJtvbZDn8diW7ZT6Pr8bNoRra+p65by+Cmt1NHP8w3naO5VFenDLuiKiHCIVnBWYz0injjfdY5jlT+QV/wS28q9unc+SvTEDUNJP8vSqp7l/8f1URataer7STybq6IofV9cy1PO4Zfhw/qu6miLtuTehVkt4yTuBtTqaF7yTqGEQAIdJOVcHXiYpQQo0QZWW8oZ/HIu9g/C9zLT2t2RvzACS9JPM2TKH3y74LWvr1iLIgGlpswtVrmgI87nGJh4YPIir6+t6JNEnNcBMfzLv+pN42juVJgoAOFmWMCmwnnotZLk3jge8cwHJ+PmbWbI3pp9LeAlW1qzkzY1v8tya56iJ1RByQi1JfiAm+lLP57aqas5oivJhfj7DXI8Dkpkd9MxVh7f9Kfy3eznrdV8Aznfe4yinnNneYWxnGPe63bvZ2h2W7I3pB3z12da4jc2Rzexo2sHGho2sr1/Puvp1rKtf19Lj1cEZuCV5AFWmxeLcXFXDGNflD0MGc1w0yhcaGzN6mn96R/OD5Depo5SDZDM/DT7CUn88H+oEXnA/ldFzddYek72I/Bk4D9ihqhPbWS/A74FzgSbgSlX9MNOBGmOgJlbDurpUAt8U3sSm8CbW16+nIlyxSwJvniikdVWNjz8gE31QlVOaolxV38DkeIIax+Hnw8u4rraW4V5me8s+7p7GT9yrOFw28uXAmzRoMbe5V9CTVTSd0ZmS/V+Ae4BHOlh/DjAh/XM88Mf0b2NMN1VHq1lZs5J19etYW7+WdXXrWFO3hoZEQ8s2baf0yw/k4/puu2PID4ROUW2JKsfE4pzb2MRnG5sY7PtsDQT4/dDBjE8k+a+q6oy2pw9rIS95J3CnexHHyGqOdtZyj3thBs+wd/aY7FX1HREZv5tNLgAeUVUF5ojIEBHZV1W3ZipIY/q7bY3bWLB9AfO3z2fetnmUN5S3rHNwUBRHUlUwvvq46qJoTk7pl02lns/EeJwTYzHOiTQxyvOIijCzsJDyvDxGuEmura0n05MNuurwreR3meUfBcA3Ai/xC/eKDJ9l72Sizn4MsKnV84r0Mkv2xnTA9V3e3/I+M8pnMHfbXDZHNgOp0jqwU1L3SVUzDJRhCzojpMoo12W06zE+meSoeIJJ8XjLTVYXmF1YwP8VDWKI53NhOMI5TU09Fs893ueZ5R/F/wv+lQ+9CTzknttj5+quXr1BKyLXANcAjBs3rjdPbUxOWFS5iBfXvsib5W9SHatO1aurttSpa7qh+0CsV3dUKfV9Bvs+gz2fIS2PPUZ6HqNdL53gXUa0qWevcRyW5ufxRnERMSfAqGSCk6MxTq3u+d6wf3Q/x+/ci5juLOR57ySW6oE9fs7uyESy3wzs1+r52PSyXajq/cD9AFOnTh2A3TfMQFQfr+f1Da/zyvpXWLB9AY6kOsm0bhXTX5N7se8zxnXZx/UY7nmUeanfQ71UIk/99hjk+wzyO04JcWB7MMiWYIB3CwupCQSIOg4gDHOT7JdMcmAyySnRWK/eBp3rH8qv3Msoo57PBD7i5uR/9OLZuyYTyf4F4FoReYLUjdl6q683BlbXruaJFU/w/NrniXtxgk6wZdwZgIT2nwRf5nockkgwIZnkkESSgxJJxrouQ9oZF75RhNqAQ50ToCbgsDEUpEkckiIkHMFHCKoSUJ8CXylSn2LfZ4Tnc1giyXGxeMbr3LtDFe5IXsJw6jgv8D63JL+a7ZB2qzNNLx8HpgPDRaQCuAUIAajqfcArpJpdriHV9DJ3P9qM6WFJL8lbm97i76v+zgdbP2gpxQedIK6f2U47WaPKQckkk+IJTm6KMimeYJT3yb2EqoDDmlCIt4sKqQ8EcMWh2HMpVJ9iz2eE7zPI99nPdTky4We5QWL3zfaPZK4ezg8Dj/M77yL8HJ/EsDOtcS7bw3oFvpOxiIzpg8KJMH/9+K88vuJxauO1hJwQQQniq4+P3+cn4i7xfU5uinJcLM7pTU0tbdO3BwJ8WJDP5mAIAca4SfZLuhyRSHBCrP9NANLas97JDCbCZh1OIlX+zWnWg9aYvVARruDBJQ/y8rqXiXkxHJzURNx+3090eb7y6WiUcyKNTG+KEgKiIswqLGBtXj5DvST7J5Kc0djUB1JdZr3tTeYZ/9Oc5nzEC352esR2lSV7Y7qhsqmSPy/9M0+sfCJVatdPbrj29UR/RDzBWY1NXBwOM8hXwiI8U1pCfSDIxFiUY2NxzmyKZjvMrGnUfK5L/icAJznLeNs/OssRdY4le2O6YHvjdh5a+hB/X/l3PPUQERwcXNw+36LmkHiCG2rqODGWaq44q7CAhQUFHBONck66B6qBZ71TCFPEL4MP8Uev9wYy21uW7I3phA31G7hn4T3MKJ+BqiIiLWPAN3d66otElZOjMa6oD3NiLEad4/C/QwYz1POYFItzbW1dtkPMKb4KD3tnMVHW85T7aSoYke2QOs2SvTG7sTWylQeXPMjTq59u6d3aH5J8ke9zeUOYsxqbOCyRZHsgwKODSqlzAlxTX0e+9YJp17P+yazRsdwcfISf59hwCHtiyd6YNhZVLuKFNS+wvmE9H23/qKW6RhA89fpmkldlH89jSjzBlFiccyONlPk+q0Ihfj90MEfE4ny5Idxnm0H2ltnekQynjve8I8n2KJZdZcneGFKTbs/ZOocHlzzI3G1zW5Y3t5NvbkKZi0pb9UId7PsM8vx0j1SfMs/nwGSSw+IJytJ17gngo4J8/lxUzPnhMN+prbdE0An3up/jWf9UTnM+Yq5/eLbD6TL7G5sBzVefmZtm8sCSB1hatZQ8J4+ABFoGHMt2+/igKmOTLmNcl31dj1Gey6j0GDHDPJ8RntduL9VmjSJUBIO8W1TIhlCIYl85LB5jf9fjxpqaXnwlfVtYC/mDeyHFRDkrML/PtMBpzZK9GZCSfpKnVj7FXz/+KxWRCoISJM/JI+knWwYj61WqjHNdjownOCiZ5MBEkoOSScYl3Z3+SV2gKhBgWzBAeSjI0vw8GgIBPIQgSqHnEUDTwwwoBaqMcj0+F2nMiSEG+qqXvBOIUsCPgk9wU/LqbIfTLZbszYCzpnYNN717EytqVxB0goScUGo8+F7O8cNdj+NjMU6Ixjg+GmPf9JADLlARDLIuL8QHBQVEnQBBVYZ4LoM9nxG+x/iky+R4oo/VGvddT3incbBUMMPteyX6ZpbszYAR9+L8bPbPeGX9K4hI78/FqsqUeIIzG5s4MRpjQjI14Ui94zCvIJ/XQnmI+uyfTHJoMsmpTVH7B80BC/wJLNKD+UHgKX7rXZztcLrNriUzIGwKb+L7M7/PipoVOOIgKr0z6qQqhyWSfKapielNUQ5LJFvdIB3EEM9jv3Riz2Pg9krNZf/0jiGIyw4dTF9rgdOaJXvT7y3csZAfzPwBNfGanSbe7kllnse/RRr5fLiRCckkHrA0P4/fDx3CKDfJ8dE4x8ca9ngck12Puadzr3cBE2Uds3RStsPZK5bsTb/23ub3uP7t63F9l4AEerTaJs9Xzmps4oJIhGNjcULAkrw87ho6hJFJlxPiMa63Hql9RkxD/Nz9MgBfCczgh+41WY5o71iyN/3We5vf49p/XtsyMXdPzd86NpnkknCEz4cbGer7bAwG+dugUmKOwwnRKN+trevDX/4Hrl+6lxOlgO8GnuaH7jeyHc5es2Rv+qWnVj7F7xb8DhHB74EBvAKqnNoU5ZJwhJOjMVxgZlEha/LyOKGpia80hHN8KguzO1t0GE94pzPdWchi/4Bsh5MRluxNvzOjfAY/n/NzAhIgIIGM1s+XuR5fCEe4KBxhX89jRyDAw4NKKfU9TmuKccYAHvq3P5npTSFJkOMCq/hN8pJsh5MRluxNv7Jwx0J+9M6PWtrOZ6TqRpWpsThfDEf4THqijjkF+Tw2eBDHNTVxeUPY/pH6kS06jDn+4RQRY657SLbDyRi7Rk2/8Y/V/+D2ubcDqbFu9sY+rosrwtmRJi4ORzg4maTecXiqtISaYIgLwmF+UFObibBNDqnTYs6I30ETBUyT5azS/bIdUsZYsjf9wmvrX+Ons3/aUnWT1GSXj1Hq+Zzd2MjnI41Min/SamdpXh6/GTaE8YkkF4cj5GUycJNT/uyeTRMF3Bh4kioGMdc7LNshZYwle9PnvbHhDW569yZCTgjXd7tUdRNS5fTGJj4faeT4aIwQsDoU4rmSYgRYm5fHZyMRbqixFjX93UZ/H/7oXcC/Oe/zgn8iK3VctkPKKEv2ps+KulF+MecXvLD2BYISxPO9Tg9idkAiyRfCEc6PpJpLbg0EeGxQKdtDIU6NRLgg0phO7o09+RJMjnjTO4anvE+TJMhhTgUvuydmO6SMs2Rv+qQ1tWu4adZNrKxZ2TJzVGccEU/wrdo6pkdjJEk1l/ywoJCjo018qSFMqGfDNjlGFe7zPsev3MsA+JSzlFe947IcVc/oVLIXkbOB3wMB4EFVvb3N+iuB3wCb04vuUdUHMxinMQBURau4+8O7eW7NcwQkQMgJdapXbJnn8b2aOi6INBIV4aHBpRT4yoWRRs605pIDTr0W8ao3jSe801ioEzjH+YDRVDHPP5SPGZ/t8HrEHpO9iASA/wXOBCqAeSLygqp+3GbTJ1X12h6I0RjW1K7h6dVP89ya52hKNuGIg69+p0r0JzdFuaWqhjLP49FBJQz1fL4QbtztpB+m/1CFHQyhXEcyxz+CBf4EPvAPJ0Y++8s2rgm8yAZ/FA/pv2U71B7VmZL9NGCNqq4DEJEngAuAtsnemIzbEtnCg0se5JlVz6QGHFQIOsHU+PN7UOz7/Ki6lgsjjWwOBrh1+DB+UFPHMEvy/YKnQiMFRCikXoup0UFs0TK2UMYWHc4WLWNz+neM/Jb9DpYKTnMWMsVZy/PuSdzvfS6Lr6L3dCbZjwE2tXpeARzfznZfEJFTgVXA91R1UzvbGNMptbFa7lt0H0+tfApPPRxxUFV8/E4Nf3BsNMYPa2o5JJHk4UGlHBqP8/OqGhvCoIe56lDDIGq0lAiFhLWIBgqJaj5x8oiRR4wQcU3/Jo8EQZIaJEGIBAEShIhrKPWbEAmCqe3S2zQvc3eTvoZTx75SwyFSwcnOEvLFJakhQiSZ5U/kn/4xvOq3l8b6r0zdoH0ReFxV4yLyDeBh4PS2G4nINcA1AOPG9a9mTSYzEl6Cx1c8zn2L7qMx2YgjTpduwKLKlxoi/LCmlqgIvxk2hP+oDzPK65lB0AYaVSjXkazXfanQ4Xys49mqw9iqw9ihQ6mltFPHCeKST5J8kuSRJE9c8nAJ4ZJHkgISlBBlqITJwyWIR0A8gnjkiYegOKKESD0G8DU1UqVLgGoGU64jme0fSQNF9OVx6DOlM8l+M9C6G9lYPrkRC4CqVrd6+iDw6/YOpKr3A/cDTJ06NQsTfZpc5four214jXsX3sum8CaCTpC8QB5xL97pYwx3Pb5dV8/F4QjvFBawOi+Pb9U1WN38XmjSfOb5h/KeP5HFegBr/LFUMbhl/RDCjJEq9pMdTJa15IlHoSRQoNHPR0VIaoAYefgqxAkRIw+PIE3kpx87RDWfRgpwCZAgVYL3cEAtSWdKZ5L9PGCCiBxAKslfCnyp9QYisq+qbk0/PR9YntEoTb82d+tcbptzG+UN5akkn55gxKWTpXlgajTGb3dUMcz3eam4iELf56p6mxykO9b4o3nfP4IXvRP5SCeQJEgeSQ6Xck5yljLe2U6tllCnxVTqEFbqfizT8VjpObftMdmrqisi1wKvk2p6+WdVXSYitwHzVfUF4DoROZ/UXMk1wJU9GLPpJ+pidfxm/m9SnaJaJfmuKPF9LmsI853aeiqCQe4ZOpQLGxo4Ktn14RIGMlcd3vSP5QH3XBbooQDsL9v4UuCfOKLE/SAz/Kks0oPp4Um+TA+RvR0wqrumTp2q8+fPz8q5Tfa9Wf4mt71/G3XxupZmlJ3t/QqAKqc3Rbmxppaxrsf7BQUsLsjn6rp6uwnbSaqwhTL+7J7D895JVDGEfanmssBbVDGYrf5QZuhUrMSeW7Y+/F3iW1d3+Y9iPWhNrwonwvx8zs95df2rLYOWdfrmKzDI8/hSQ4RzGhs5MOmyORjgF2XDODsS4Rt19T0Yef/hqfCSfwJ3u//OWh1DEJfTnI/YXyqJaYjfeV/At4/MfseSvek1K2tW8r2Z36MiXEFQgp1O8oW+z79FmjgxGuVT0RjFqnxQkM+LJSUcHI/z4+oau5DbEdcgVQymUodQpYP50J/AHP9wlup4EuRxsFRwTeAl9pF6/uT+GzPon8MEmBT7HzG94vUNr/Nfs/4LT73ODXGgyqR4ggsjEc5qbGKQr2wNBHirqJCPCwr4Yn3DgJi821chTCH1WkIdxdRpCXWUUK/FO/2u02LqtYR6imnUAsIU0UDxTscK4DFJ1vHvgVmMkx28603kfu/fsGqagcGSvelRvvrcOf9OHv74YUJOCBQS2nGiL/M8zg83cn6kkYOTSRpFeLeokCX5BZzaGOGspiY+19jUi68g81RhO0Op0BFs1WFsS7dTr9bBLQm9Pp3AGyjebZVKETEGE2GwNDKUCONlG0USJ58kJRJlkDShOGzTwSQ1yPv+kXzkTejFV2tyhSV702NUlTvm38GjHz9KQAJ4vtfufLCiyrRYnIvDEU5PT/u3KD+Pu4YOYXwywRmNUc7uQwneV6GGUmq1lA06kg06iq06jHIdxUodS5UO3qn7PqSSdpnUM5hGhhJmrFRRLDHyxCVfUi2LUr1KHTwN0KgFeAQISxFbdRiVOoS1jCGp9i9t2mdXhukRST/Jbe/f1jI6ZXsTiuT7PheHI1zaEGF/122Z9m97KMTpjY1cX1vX67cJkxpgi5ZRQymHy0YK0ok2qQHqSI2/kqoqKWSbDmU7Q9mhQ9ihzb+HUMXgXbryFxBntFQxSdYx2GlkiDSRIEidX4hHgEqajzGUZYxHtZOv3Lommk6yZG8yLpwI872Z3+ODrR+0m+iDqlwUjnB1XQP7eB4L8/N4sXQwk6MxLglHemVMeU+FtTqapXoAi/0D2aCj2KCjqNDhLYm6gDgjpJ46LSFMUYfHGkqYEVLHSGo52NnCYIlQKElimoei7PAHs0WHU08JM/ypJO3fzmSBXXUmoyqbKvnmm99kTe2adlvcHBeN8ePqWg5JJvkwP5/HBw1ielMj19b2fLPJei3m796pLPAP4R1/Eo0UAlBIjPGyncOlnNOchRRIgm06jCodxGAaKXGi5EsSB8jDpUnziGg+IVzqtYRKhtBAEfP0UGKa33EAVgo3WWTJ3mTM8url3PTuTZQ3lBNwAjsNQxxQ5draer5W38C2YIBflg3lvHCE62trezyuTf4I/u6dyoPev9FEAYNo5CxnPvs49VT7JTRqAR/qISzX/Xs8FmOyxZK9yYjXNrzW0rRSRHZK9KNcl59V1nBSLMazJcU4qvyourbHL76wFvJb92Ie9c7EI8A0Wc5ZgQXM8I7lHX8S1f7gPR/EmH7Ckr3ZK5FEhDsX3MnfV/29pWllUj9J9EfHYty7rZIQym+GDeFzkUYOS/TsuDUxDXG/dx6PumdQxWC+6LzNEGlitb8vv3C/3KPnNiZXWbI33fZuxbv87P2fsaNpB0HZdfaocyON3FJVw45AgL8OHsR3ausY2oPDDXsqfOAfzk/cr7NBRzFBKvh+4Gnu9c5nkz+yx85rTF9gyd50SSQRYWbFTF5c+yKzt8wm5IR2GXc+qMoPamr5ckOERfl5/KuomP9XXdNj/TRV4RX/eH7tfpFyHcUIavlN6D4eds/ip+5/WOsXY7BkbzqhJlbDzE0zebP8TeZsnUPST7YMYta2ND/U87hrexXHxuM8WVpCQOG6DN+EVQVFmK+H8Ko3jTf9Y9mk+3CQbOb64LMExeXHyavxCGT0vMb0ZZbszS5UlU3hTczZOocF2xfw+obX8dQj6ATxfZ/8QH67M0gdHYvx06paxrout5UN4/KGBg5Kdn5Ey7ZcddjGMDb6+7Bcx1GlgynXUczyJ7aM++Lgc7ws59LAW6zw9+Nu9/OojdhozC4s2RsAqqPVzN02lzlb5/D+lvfZ2piaeKx5ou+gE8T1U4m7baIPqHJ1XQPfqKunOhDgF2VD+X5NHcP2UD+f1ACVDGa1P5ZtOowaStmiZWzVYWzR4azSsTv1RA3iMpwGznTmU+wkGEqYHf5g3vKP5n3vyAy/I8b0L5bsB6i6WB0Lti9g/vb5zNs2j5W1KwEISABffUJOCFVt6RTVnOjbOiIe5/s1dRwfi/NaURFbQkFuqarZpRdsjZbygX8Yy/39Wav7stA/mK2U7TLI1yAaGSU17EMdlwfeJCSpD4wav4SN/j4kJMTz/qdwfbt0jekK+48ZIMKJMO9veZ952+Yxf/t81tStAVIld4CQE8IRp6XU3rYuvrWAKsfE4ny9roGTYjHCItwxbAhnNDZxdlNqwDJXHeb6h/GGP5VXvOPZwdDU+fAZSQ1TnLWcIR+mkrn67NAhNGo+NQymQoeznn2Z5R2168mtF6ox3WLJvh+Ke3FW1qxkSdUSllUtY0nVEjaGN+KrjyMOghB0ggifdH7yteMqF1FlQiLJ1FicY2MxpsXiDPF9qh2HPw4ZxAjX45u19Th+Hq/6x/Gc9ylm+xNbxpM5w5nPaKlhhNSxxBvPGsYy059MlIJeeT+MMZbs+zTXd9kY3sj6uvWsq1/H6trVrKpdxfqG9S3Ju7laJugEccT5pDqmgxKyo8oY12VSPMHEeJzxSZcj4omW+vdtgQDvFhZQkZfHsGSSz9bkMc+fyDX+Scz3DyVBiOHU81lnHkOcRkSVF70TeJOpvfGWGGM6YMk+xyW9JJsjm9kU3kRFpIJN4U1sCm9iY8NGNoY37lSX3jzCZEAC5Dl5JPxEy4iT7VXL5Ps+45MuByaTHJhMcmI0xmGJBPnpD4KYCGtDQeYUFrA+VEAiPoxIYhx+/SjW+ONY7o+jiiEAjJFKLg28RVCURj+PZ/1TSVq9ujE5w/4bs8RXn9pYLZXRSiqbKqmMVrKjaQeVTZXsiKZ+b2/aTnW0Gm1VDBcER5xUU0gJkhfII+GlZn5qTuyeei2PRZV9PI/9ki7jXJcxSZcxrsvkeJzRrtdye9QD1oRCPFM8iIiWst0dzY74gbiNZWzyR7NCx7W0jMkjycGymVOcJYxxqqnUQTT4RTzqnWnNHo3JUZbsM8DzPSLJCA3xBuoT9dTF66iPf/K75XGinrpYHZXRSqqj1e1O6BGQVEeg5mqY1tUvnnooivouJaoU+zFKfJ8SXxns+5R5XsvPGDeV4Me6LgX6yYeFB1QHAizML2BG/mB2+GVEEiPZkdyPjyMTqQiPbEnYQVxGSi2jqebLgTcZJFGKJMZqbzQrdT/e8yeywx/a82+wMWavWbJPU1Wqw5vZWrmUuBsjHKshHKulIVZHOFFPQyJCQzJC2IvRkP4Je3Ea/ARNuvuOQ4W+UqxKke9T7Psc6PtM9X2GeT7DPI9hrkeZ51LmeRQoBFAC6d+hNs+DCkW6+yYpERG2BYOUO0W8VzCISr+MHf4+VCZHs9kdR2V8FLWNQ3baZwhhDpeNnBlYiAg46rPZL2MF41ijo5nnHba3b7ExJos6lexF5Gzg90AAeFBVb2+zPh94BDgWqAa+qKobMhvq3vM9l/LNs1m/ZT4VtWvY3LiZimg1FW6EzeISl45Hbyn2fQb5PqVe6vfY5ufpnyJfKfZ9in2lRJUSL7WsUAEEVwQXBx/BRfDSyzwcXEK4ks8OBE8cXBy89DqP1G/FwcPBxyFOiCgFNPqFxLWIqBYS8Uuo9YdS6Zex1RtDgw7aKX4Hn32oZZzsYLIsTN08FfCBzd5wVjGWpXqAdU4ypp/aY7IXkQDwv8CZQAUwT0ReUNWPW212FVCrqgeLyKXAr4Av9kTAndUY2cba8n+xZus8llcvZ3nTVlaRIOp8ktCLfZ99ksogN8AUtxCSpSTdYfh+AZ5bjOuVknAHEfMH0aQl1GoBOwjiEcDFwSWIm07E2VJEjGJiFEuMUpoYRhMT5SOGBiIUSxwUKv1BNGkeGxnFCt2PuXp41uI1xmRHZ0r204A1qroOQESeAC4AWif7C4Bb04+fBu4REVHtuL5hU9UqvvvgmYCS2kpbbkRq82MFlfRSbbuenbZVlDgJGiVJveOxI/hJAi7yfcbEAxwWH4obG0td7AB2JPYnrMVAgpjGKCZOIQnyxKWEOAUkCODjSBhH6nFEcVAQEFKPJf2Tum2aeqytHrfeRgEH0tunSPoVkF4uqZeJL6ljuATwNFW6T2gIlwAuAeKEcDX1uIZBNFJAlHzKdSQNFIGmi+zGGJPWmWQ/BtjU6nkFcHxH26iqKyL1QBlQ1XojEbkGuAZAgsKffjmrZZ3f5OMUdb6E3JXth3o+tZ5DbUTIL1pBMl0iTydT9RF11Wn3g0njTSL5RV3qt9nVffxogziFg3r0HN3dpzdiG8hx9ae/Y3f2ydW4eiO27sbkxyLdq0pQ1d3+ABeRqqdvfn4FcE+bbZYCY1s9XwsM38Nx57d5fv+eYtmb7ds7Zw+ep6uvJSfj6q3YBnJc/env2M33OCfj6o3YuhtTd+JS7Vxl82Zgv1bPx6aXtbuNiASBwaRu1HbFiz28fXd15zy9EVuuxtWd8wzkuPrT37G7+/TGOXLxPeutmACQ9KdFxxukkvcq4DOkkvo84EuquqzVNt8BjlLVb6Zv0P67ql6yh+POV9Ve7UOfjXN2Rq7GBbkbm8XVNRZX1+VibCIyH6A7ce2xzl5TdfDXAq+Tanr5Z1VdJiK3kfo68QLwEPCoiKwBaoBLO3Hu+7sabAZk45ydkatxQe7GZnF1jcXVdbkYW7dj2mPJ3hhjTN9nA5kYY8wAYMneGGMGgH6Z7EXk8yKiIpKTA7qISGQP62eKSK/dGBKRsSLyvIisFpG1IvJ7EcnbzfbfFZGiXoptt+9VNtj11eV47Prqop64xvplsgcuA2alf3daemiIAUVEBHgWeE5VJwCHACXAL3ez23eBXvlnzFF2fXWSXV/dlvlrrDuN83P5h9SFtJnURbUyvWw68A7wMrASuA9w0usiwG+BRcDJvRRjJB3TS62W3QNcmX48E5jaS7F8BninzbJBpPpJFAN3kOo0txj4T+A6IAEsAd7upfeqBPgn8GH6vBek140HlgMPAMuAN4BCu77s+uqr11dPXmP9sWR/AfCaqq4CqkXk2PTyaaQupiOAg4B/Ty8vBj5Q1cmqOmuXo/V/RwILWi9Q1QZgI/B1Uhf8FFWdBPxNVe8GtgCnqeppvRRjDLhQVY8BTgN+my4xAkwA/ldVjwTqgC/0cCx2fXWNXV9d1yPXWH9M9pcBT6QfP8EnX4Pmquo6VfWAx4GT08s94JneDbHPmA78STU1YL+q1mQpDgH+W0QWA2+SGotpZHrdelVdmH68gFTy6El2fWXOdOz6ak+PXGP9avISERkGnA4cJSJKqhOYkvrq07ZDQfPzWPrN620uO3/YFmQhBkiNXnpR6wUiMggYB2zIRkDtuBwYARyrqkkR2cAn71e81XYeUNhTQdj11S12fXVBT15j/a1kfxHwqKrur6rjVXU/YD1wCjBNRA4QEYfUWPvZ/kpdDhwhIvkiMoRU3WY2/BMoEpGvQMsNnt8CfyHVa/ob6SEzmi9EgDBQ2osxDgZ2pP8RTwP278Vzt2bXV9fZ9dU1PXaN9bdkfxnwjzbLnkkvn0fqJtVyUm9e2+16RfrCjqvqJuApUjenngI+ykY8mrrDcyFwsYisJjUOUgz4CfAgqbrVxSKyCPhSerf7gddE5O2ejK35vQL+BkwVkSXAV4AVPXne3bDrq4vs+uqyHrvGBsRwCSIyHbhBVc/LciiIyGTgAVWdlu1Ycl1fea/s+uqb+tJ7lYlrrL+V7HOaiHyT1I2V/8p2LLnO3quus/es8wbiezUgSvbGGDPQWcne5AQR2U9E3haRj0VkmYhcn14+TERmpLvazxCRoenll4vIYhFZIiKz01/Jm4/1ZxHZISJLs/V6TO7J1DXW0XFynZXsTU4QkX2BfVX1QxEpJdWm+fPAlUCNqt4uIj8Ghqrqj0TkJGC5qtaKyDnArap6fPpYp5LqVfiIqk7MxusxuSdT11hHx1HVj7PywjrJSvYmJ6jqVlX9MP04TKrFwRhSvQkfTm/2MKl/TlR1tqrWppfPITVdZvOx3iE1iY4xLTJ1je3mODnNkr3JOSIyHjga+AAYqapb06u28UnPxtauAl7tnehMf5Cpa6zNcXJav+pBa/o+ESkh1a74u6ra8MkQJak22+leha23P43UP+LJGNMJmbrG2h6nxwPfS1ayNzlDREKk/nn+pqrPphdvT9eRNte57mi1/SRSHXMuUNXq3o7X9D2ZusY6OE5Os2RvcoKkilcPkbohdmerVS8AX00//irwfHr7caTGSb8iPTqgMbuVqWtsN8fJadYax+QEETkZeJfUeOJ+evFPSNWFPkVq4Kxy4BJVrRGRB0kNN1ue3tZV1anpYz1OakTF4cB24BZVfaiXXorJUZm6xjo6jqq+0juvpHss2RtjzABg1TjGGDMAWLI3xpgBwJK9McYMAJbsjTFmALBkb4wxA4AlezNgiIgnIgvTIxUuEpEfpKd4290+40XkS7vbxpi+wJK9GUiiqjpFVY8EzgTOAW7Zwz7j+WS6PGP6LGtnbwYMEYmoakmr5weSmtdzOKlJph8FitOrr1XV2SIyBzic1JyfDwN3A7eT6rSVD/yvqv6p116EMd1kyd4MGG2TfXpZHXAoEAZ8VY2JyATg8XRvyem0mvtTRK4B9lHVX4hIPvAecLGqru/Fl2JMl9mol8akhIB7RGQK4AGHdLDdWcAkEbko/XwwMIFUyd+YnGXJ3gxY6Wocj9Qoh7eQGkdnMql7WbGOdgP+U1Vf75UgjckQu0FrBiQRGQHcB9yjqbrMwcBWVfWBK4BAetMwUNpq19eBb6WHuEVEDhGRYozJcVayNwNJoYgsJFVl45K6Ids8RO29wDMi8hXgNaAxvXwx4InIIuAvwO9JtdD5MD3UbSXpaeyMyWV2g9YYYwYAq8YxxpgBwJK9McYMAJbsjTFmALBkb4wxA4Ale2OMGQAs2RtjzABgyd4YYwYAS/bGGDMA/H+tzXYamngrrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vs.plot(title=\"España vs Italia vs Mexico\", kind='area')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Como guardar los datos en csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "vs.to_csv('esvsit.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "esvsit.csv\t\t\t python-pruebas.ipynb  Untitled.ipynb\r\n",
      "probando-con-r.ipynb\t\t Shared_Resources\r\n",
      "python-api-covid19-pandas.ipynb  Untitled1.ipynb\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Guardar la imagen \n",
    "\n",
    "Usamos el comando `import matplotlib.pyplot as plt` para guardar la imagen. Importamos por ello la libreria matplotlib con la que nos permite ver los fatos en Jupyter.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "dsw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ¿Dónde está guardado? \n",
    "\n",
    "Para ver donde están guardadas las cosas, o simplemente si se nos han guardado bien utilizaremos el comando `!ls`, para listar lo que tenemos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "esvsit.csv\t      python-api-covid19-pandas.ipynb  Untitled1.ipynb\r\n",
      "esvsti.png\t      python-pruebas.ipynb\t       Untitled.ipynb\r\n",
      "probando-con-r.ipynb  Shared_Resources\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}