{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uso de la API del Covid-19 con Pandas\n",
    "En esta práctica vamos a utilizar \"Pandas\", la librería de Phyton, para trabajar sobre los datos de la Covid-19 y analizarlos.\n",
    "\n",
    "La URL: https://api.covid19api.com/countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instalción de librerías:\n",
    "Para instalar la librería utilizaremos la función \"!pip\". La exclamación es necesaria porque no estamos ejecutando Python, sino bash."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (1.3.1)\n",
      "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2020.4)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2.8.1)\n",
      "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (1.21.1)\n",
      "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7.3->pandas) (1.14.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configuración de Pandas\n",
    "\n",
    "Para importar utilizaremos la convención \"pd\" como abrebiatura para llamar a la librería. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Crear variable\n",
    "Las variables se asignan con el símbolo \"=\" y los escribimos entrecomillados por ser una cadena de caracteres."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://api.covid19api.com/countries\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Crear dataframe\n",
    "La abreviatura que se emplea para los dataframe es \"df\". Existe una función \"read_json()\" que lo que hace es leer el formato \"json\". Dentro del paréntesis indicaremos el valor de lo que queremos que lea, en este caso la url. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_json(url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para visualizar los datos llamamos al objeto. Observaremos una tabla que contiene tres columnas y una columna de control de Pandas, que identifica cada una de las entradas del dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Slug</th>\n",
       "      <th>ISO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gambia</td>\n",
       "      <td>gambia</td>\n",
       "      <td>GM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Paraguay</td>\n",
       "      <td>paraguay</td>\n",
       "      <td>PY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>trinidad-and-tobago</td>\n",
       "      <td>TT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>austria</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>Cape Verde</td>\n",
       "      <td>cape-verde</td>\n",
       "      <td>CV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>Israel</td>\n",
       "      <td>israel</td>\n",
       "      <td>IL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>Bolivia</td>\n",
       "      <td>bolivia</td>\n",
       "      <td>BO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>Latvia</td>\n",
       "      <td>latvia</td>\n",
       "      <td>LV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>papua-new-guinea</td>\n",
       "      <td>PG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>248 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Country                   Slug ISO2\n",
       "0                   Gambia                 gambia   GM\n",
       "1                 Paraguay               paraguay   PY\n",
       "2      Trinidad and Tobago    trinidad-and-tobago   TT\n",
       "3                  Austria                austria   AT\n",
       "4    Sao Tome and Principe  sao-tome-and-principe   ST\n",
       "..                     ...                    ...  ...\n",
       "243             Cape Verde             cape-verde   CV\n",
       "244                 Israel                 israel   IL\n",
       "245                Bolivia                bolivia   BO\n",
       "246                 Latvia                 latvia   LV\n",
       "247       Papua New Guinea       papua-new-guinea   PG\n",
       "\n",
       "[248 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploración de la tabla\n",
    "Para ver las primeras entradas de la tabla utilizaremos la siguiente función. Como metemos el número 6 veremos las seis primeras observaciones. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Slug</th>\n",
       "      <th>ISO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gambia</td>\n",
       "      <td>gambia</td>\n",
       "      <td>GM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Paraguay</td>\n",
       "      <td>paraguay</td>\n",
       "      <td>PY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>trinidad-and-tobago</td>\n",
       "      <td>TT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>austria</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</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>5</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>timor-leste</td>\n",
       "      <td>TL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Country                   Slug ISO2\n",
       "0                 Gambia                 gambia   GM\n",
       "1               Paraguay               paraguay   PY\n",
       "2    Trinidad and Tobago    trinidad-and-tobago   TT\n",
       "3                Austria                austria   AT\n",
       "4  Sao Tome and Principe  sao-tome-and-principe   ST\n",
       "5            Timor-Leste            timor-leste   TL"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para ver las 6 últimas entradas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Slug</th>\n",
       "      <th>ISO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>Barbados</td>\n",
       "      <td>barbados</td>\n",
       "      <td>BB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>Cape Verde</td>\n",
       "      <td>cape-verde</td>\n",
       "      <td>CV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>Israel</td>\n",
       "      <td>israel</td>\n",
       "      <td>IL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>Bolivia</td>\n",
       "      <td>bolivia</td>\n",
       "      <td>BO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>Latvia</td>\n",
       "      <td>latvia</td>\n",
       "      <td>LV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>papua-new-guinea</td>\n",
       "      <td>PG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Country              Slug ISO2\n",
       "242          Barbados          barbados   BB\n",
       "243        Cape Verde        cape-verde   CV\n",
       "244            Israel            israel   IL\n",
       "245           Bolivia           bolivia   BO\n",
       "246            Latvia            latvia   LV\n",
       "247  Papua New Guinea  papua-new-guinea   PG"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para ver las información sobre las variables que contiene el dataframe utilizamos la siguiente función:"
   ]
  },
  {
   "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": [
    "Para visualizar solo una de las variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                     Gambia\n",
       "1                   Paraguay\n",
       "2        Trinidad and Tobago\n",
       "3                    Austria\n",
       "4      Sao Tome and Principe\n",
       "               ...          \n",
       "243               Cape Verde\n",
       "244                   Israel\n",
       "245                  Bolivia\n",
       "246                   Latvia\n",
       "247         Papua New Guinea\n",
       "Name: Country, Length: 248, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para ver un valor concreto de una de las variables:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Honduras'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country'][66]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tiempo real\n",
    "La URL que utilizaremos ahora es la siguiente: https://api.covid19.api.com/country/spain/status/confirmed/live\n",
    "\n",
    "Guardaremos los datos de la misma manera que lo hicimos anteriormente, pero añadiendo \"df_es\" (abreviatura de España) para identificar que este dataframe es de este país concreto.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>816</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>11662214</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-17 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</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>11662214</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-18 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</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>11736893</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-19 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</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>11736893</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-20 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</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>11736893</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-21 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>821 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",
       "816   Spain          ES                         40.46 -3.75  11662214   \n",
       "817   Spain          ES                         40.46 -3.75  11662214   \n",
       "818   Spain          ES                         40.46 -3.75  11736893   \n",
       "819   Spain          ES                         40.46 -3.75  11736893   \n",
       "820   Spain          ES                         40.46 -3.75  11736893   \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",
       "816  confirmed 2022-04-17 00:00:00+00:00  \n",
       "817  confirmed 2022-04-18 00:00:00+00:00  \n",
       "818  confirmed 2022-04-19 00:00:00+00:00  \n",
       "819  confirmed 2022-04-20 00:00:00+00:00  \n",
       "820  confirmed 2022-04-21 00:00:00+00:00  \n",
       "\n",
       "[821 rows x 10 columns]"
      ]
     },
     "execution_count": 14,
     "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": [
    "### Exploración de datos:\n",
    "\n",
    "Por columnas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'CountryCode', 'Province', 'City', 'CityCode', 'Lat', 'Lon',\n",
       "       'Cases', 'Status', 'Date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cabecera"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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",
       "\n",
       "                       Date  \n",
       "0 2020-01-22 00:00:00+00:00  \n",
       "1 2020-01-23 00:00:00+00:00  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cola"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>819</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>11736893</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-20 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</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>11736893</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-21 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",
       "819   Spain          ES                         40.46 -3.75  11736893   \n",
       "820   Spain          ES                         40.46 -3.75  11736893   \n",
       "\n",
       "        Status                      Date  \n",
       "819  confirmed 2022-04-20 00:00:00+00:00  \n",
       "820  confirmed 2022-04-21 00:00:00+00:00  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Información sobre las variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 821 entries, 0 to 820\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype              \n",
      "---  ------       --------------  -----              \n",
      " 0   Country      821 non-null    object             \n",
      " 1   CountryCode  821 non-null    object             \n",
      " 2   Province     821 non-null    object             \n",
      " 3   City         821 non-null    object             \n",
      " 4   CityCode     821 non-null    object             \n",
      " 5   Lat          821 non-null    float64            \n",
      " 6   Lon          821 non-null    float64            \n",
      " 7   Cases        821 non-null    int64              \n",
      " 8   Status       821 non-null    object             \n",
      " 9   Date         821 non-null    datetime64[ns, UTC]\n",
      "dtypes: datetime64[ns, UTC](1), float64(2), int64(1), object(6)\n",
      "memory usage: 64.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df_es.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para obtener una descripción estadística de las variables numéricas del dataframe (obtenemos el número total, la media, la desviación, el mínimo y máximo y los cuartiles):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.210000e+02</td>\n",
       "      <td>821.00</td>\n",
       "      <td>8.210000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>3.426515e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.421952e-14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.312558e+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>3.428130e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>3.180212e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>4.953930e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>1.173689e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Lat     Lon         Cases\n",
       "count  8.210000e+02  821.00  8.210000e+02\n",
       "mean   4.046000e+01   -3.75  3.426515e+06\n",
       "std    1.421952e-14    0.00  3.312558e+06\n",
       "min    4.046000e+01   -3.75  0.000000e+00\n",
       "25%    4.046000e+01   -3.75  3.428130e+05\n",
       "50%    4.046000e+01   -3.75  3.180212e+06\n",
       "75%    4.046000e+01   -3.75  4.953930e+06\n",
       "max    4.046000e+01   -3.75  1.173689e+07"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Elaboración de un gráfico\n",
    "Para elaborar el gráfico en el eje X la fecha y en el eje Y los casos confirmados.\n",
    "\n",
    "Para ello vamos a establecer la fecha como índice de la función. Esto hace que nuestras entradas se identifiquen por la fecha y no por el índice que le había dado Pandas en un inicio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
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       "      <th></th>\n",
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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>2022-04-17 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>11662214</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-18 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>11662214</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-19 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>11736893</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-20 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>11736893</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-21 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>11736893</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>821 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",
       "2022-04-17 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2022-04-18 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2022-04-19 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2022-04-20 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2022-04-21 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",
       "2022-04-17 00:00:00+00:00 -3.75  11662214  confirmed  \n",
       "2022-04-18 00:00:00+00:00 -3.75  11662214  confirmed  \n",
       "2022-04-19 00:00:00+00:00 -3.75  11736893  confirmed  \n",
       "2022-04-20 00:00:00+00:00 -3.75  11736893  confirmed  \n",
       "2022-04-21 00:00:00+00:00 -3.75  11736893  confirmed  \n",
       "\n",
       "[821 rows x 9 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.set_index('Date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como necesito una tabla que también refleje los casos confirmados crearemos una vista en la que ver el número de casos confirmados por fecha. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
       "2022-04-17 00:00:00+00:00    11662214\n",
       "2022-04-18 00:00:00+00:00    11662214\n",
       "2022-04-19 00:00:00+00:00    11736893\n",
       "2022-04-20 00:00:00+00:00    11736893\n",
       "2022-04-21 00:00:00+00:00    11736893\n",
       "Name: Cases, Length: 821, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.set_index('Date')['Cases']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sobre este vista creamos el gráfico a través de la función \"plot\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7Kzhcc4qxw4bwietmMq0wl5vnjSMjPfWu+VCgi0hcamkPUNnQSlVjK1WNbVQ3tnK8voUX9lWz6XAt7QEnKyONxdMLeMuCCXx0yfSkewJRXynQRWRQnGrroLqxjeqmNhpbOmhoaaeqsZXKcFifDu6qxlaqGlppagt0u50Lxg9jxWXFTBw5lLe/biL5KTas0hsFuoj0S0t7gLrmdvZVNFJW20z5yWZO1LecCefqplaqG9s41UNAm8HInCwK87IozMtm3sQRZ14X5WVTEH5dmJ/NyJxMcrIUWz3RNyMifXKsrpnn91SxrbyOTUdOUnq0no6gn1lvBgW52WdCubggh4JOwTwqJ4v8IRnkD8mkMC+LUblZKTnePRAU6CJyVtuP1vHwy4fZeOgku080EAg6uVnpnDduGB+6ehpjhw1hSkEuUwtzGTNsCFkZCuhYUKCLSLfaOoI8sv4wv9pYxpayOrIz0rh0WgFXzSzkbQsnMnN0HmlpyX0rfaJRoIvIa9Q1t/PL9Yf52brDHK45xZxxw7h9cTGfvn4Ww3N0AjKeKdBF5IwX9lbxmUc3c7y+hYLcLO5fUcK1541O+kmtkoUCXUSobmzl67/fweObyplelMtvP3Y58yYM15BKglGgi6S4g1VNrPjvlzle18JVs4r4xtsuZPyI5HzmZrJToIuksO1H63jP/S/j7jy88lIWTh4Z65KkHxToIinqye3H+fDPNuIO33z7hQrzJKBAF0lB6w/W8PGHNzFvwnD+7W3zOH9cfqxLkihQoIukmGd2VfDhn22kKC+bB9+3iJG5WbEuSaJEgS6SQp7bU8n7H1xP0OHOa2cozJNMRPfnmtlSM9tlZnvN7HO9tHu7mbmZlUSvRBGJhsPVp7jzF5uYNSaf7f/39SxfNDnWJUmUnTXQzSwduAe4CZgDLDezOd20ywc+CbwU7SJFpH92HKvntvvW0dYRemBybrb+cZ6MIjlCXwTsdff97t4GPAIs66bdV4FvAi1RrE9E+ulYXTO3/XgdZbXN3HbJZCaNyol1STJAIvnf9ATgSKf3ZcAlnRuY2UJgkrv/3sw+G8X6RKQfOgJBPvHwJlo7gvzpM1cxY7SuZklm/f53l5mlAd8Bbo+g7UpgJcDkyRq/ExlIqzcf5bt/3M2Bqib+450XKcxTQCRDLuXApE7vJ4aXnZYPzAWeMbODwKXA6u5OjLr7KncvcfeSoqKic69aRHrU0h7gk49s4hMPb6KtI8hnXz+btyyYEOuyZBBEcoS+HphpZlMJBfmtwG2nV7p7HVB4+r2ZPQP8k7tviG6pItIdd6eqsY1t5XU8tO4Qf95ZAcAnr5vJndfOIFNPA0oZZw10d+8wszuBJ4F04AF3325mXwE2uPvqgS5SRKA9EGTDwVoqG1vZeayeI7XNnKhrofRYPY2tHQAU5mVzxxVTubh4FEvnjo1xxTLYIhpDd/c1wJouy77UQ9sl/S9LJLXVNrVxoLqJbeV17DzewJGaU2w+cpL6llBwZ6QZE0YOpSgvm7cumMD0olwmjszhqllFevxbCtPFqCIx5O5UNLRSerSe7UdD4f3qkZOU1TafaTMiJ5NJI3NYOncsV80qYsboPKYW5pKdkR7DyiUeKdBFBlFrR4CNh2pZf6CWreV1bDpcS3VT25n1k0YNZfaYfFZcVszUwlxmjsljSkFuDCuWRKJAFxlgdc3tPL3jBH/ZVcna3ZXUNbcDUJiXxbXnjeaC8cO4YMJwzhubT/4QPbNTzp0CXWQAtAeCPLOrkh8/t58tZSdpaQ9SlJ/NZdMKeNvCCVw2vUDhLVGnQBeJko5AkKd3VvDYhjI2HKrh5Kl2JowYyhsvHM8tJRO5uHiUntEpA0qBLtIP7YEg6/ZX86uNZbywr5rKhtYzR+JXzSrirQsmMCRTJy9lcCjQRfqgoaWd9QdreHFfNev217DreANtgSDDh2ZycfFIbpwzlrctnECGbuaRGFCgS8oLBJ3tR+sAmDdxxJnl7YEgu080sONYA/srG9lwsJaNh2sJBJ2s9DQWTB7BbZdMpqR4JNefP0ZH4hJzCnRJScGg88K+ah7beIS1uyupPRW68uS775zP9vJ61h+sYcfxBto6ggCkpxlj8rP5yNXTWTy9gIVTRirAJe4o0CWlNLcFWLV2P49uOEL5yWbyh2Rww/ljCLrz21eP8ulfbsYMLp4yitsXFzN3wnDmjBvG1MJc0nVCU+KcAl1Swqm2Dr71xC5+/tIh2gPOlTML+fQNs7j2vNGMys3C3Xn76yZSmJfN1MJcHX1LQlKgS9KrbWrj7T96gf2VTbzxwnHcPG8cN1047jVtzIwrZ2pKZ0lsCnRJao+uP8L/+fUWAB66Y5FCW5KaAl2S1ssHavjCb7Zy3th8PnrNDIW5JD0FuiSllvYAH/35K0welcMvP3QZw4fqNntJfrr7QZJObVMbKx54marGVr548xyFuaQMBboknbtWb+elAzWUTBnJ4hkFsS5HZNBoyEWSyp9KT7B681E+c8MsPnHdzFiXIzKodIQuScPd+ervS5k9Jp8PXz091uWIDDoFuiSFQND55hO7OFR9indfNkXP1ZSUpF4vSeHXr5Txo2f3MSQzjWtm6/JESU0aQ5eEV9/Szref3MWCySN4/COLMdOcK5KaFOiS8L7z1G6qGlu5b0WJwlxSmoZcJKFtOlzLgy8c5M3zx79mLnORVKRAl4S1tayOt/7wBQDe0GWyLZFUFFGgm9lSM9tlZnvN7HPdrP+MmZWa2RYze9rMpkS/VJG/aQ8E+dzjWyjIzeKpT1/FjReMjXVJIjF31kA3s3TgHuAmYA6w3MzmdGm2CShx93nAr4BvRbtQkc7ufmo324/W8/W3zmXWmPxYlyMSFyI5KboI2Ovu+wHM7BFgGVB6uoG7/6VT+3XAu6NZpMhpTa0dfOAnG3hxfzVvvHAcS+dqqEXktEgCfQJwpNP7MuCSXtrfAfyhP0WJdKe6sZVb7n2RfZVNLF80mY/oblCR14jqZYtm9m6gBLi6h/UrgZUAkydPjuauJYm9criW7z+9h2d3V5JmxhdvnsMdV0yNdVkicSeSQC8HJnV6PzG87DXM7HrgX4Cr3b21uw25+ypgFUBJSYn3uVpJGcfqmvnNpnKe3VXJSwdqGJWbxa0XT2LZRRO4dJpmUBTpTiSBvh6YaWZTCQX5rcBtnRuY2QLgXmCpu1dEvUpJCR2BIM/tqeJXr5TxxLbjBILOtMJcbl9czGdfP5vcbN0HJ9Kbs/6GuHuHmd0JPAmkAw+4+3Yz+wqwwd1XA/8O5AGPhe/UO+zubx7AuiVJtLQH2Hiolj/tOMET245zrK6FETmZvHn+eD51/UymFOTGukSRhBHRIY+7rwHWdFn2pU6vr49yXZLE6k6182Tpcf68o4K1eyo51RYgOyONqYW53PWmOVx73hjNlihyDvRvWBkUgaCzZusxfvLCQbaW19HaEWTc8CFcd/4YbpgzhhvnjGFIZnqsyxRJaAp0GVCBoPOHbcf4/tN72H2ikWlFuSy7aDzvvHgSCyeP1GRaIlGkQJcBs628jk/98lX2VjQydtgQvv7WuSy/eDJpaQpxkYGgQJeoO3mqjY8/vInn9lRRmJfND25bwE1zx5GuIBcZUAp0iaojNad4570vUtXYxsqrpnFLySRmjM6LdVkiKUGBLlHz0xcP8uXV2wk6fO/Wi1h20YRYlySSUhTo0m/uzjee2Mm9z+5n/sThLF80WWEuEgMKdOmX+pZ2/m3NTh5++TDvumQyX1k2V2PlIjGiQJdzdryuhfc/uJ7SY/VMHpXDl998gcJcJIYU6HJOapraWHbP8zS2dHDPbQu57vzRZKbr7k6RWFKgS58Eg869a/fzzSd2AvDIyks1+6FInFCgS8T2VjTy+ce3sP5gLTfOGcOb5o9XmIvEEQW6nNXWsjq+9/Qe/rTjBABfeMN5fPDKabptXyTOKNClW20dQR7beIT7nzvA/qomhg3J4NPXz2LJ7CLmTxoR6/JEpBsKdHmN3ScauO+5/Ty3p4pjdS1cNGkEH10yndsvL2Z0/pBYlycivVCgC4Gg88fSEzz+ShlPlZ4gNyuduROG869vnMMbLhyroRWRBKFAT2HNbQGe3nmCu5/azYGqJgBumDOGr71lLmOG6WhcJNEo0FNQRyDIvWv38+ALB6lsaKUwL4t7blvI0rljdWOQSAJToKeQDQdr+PUr5ax+tZymtgBXzizkizfPYfH0AgrzsmNdnoj0kwI9yR2uPsWabcf4Y+kJNh6qJScrnWvPG83rLxjLm+aPj3V5IhJFCvQkdKyumTVbj/Ps7krW7q4E4Pxxw/jglVP55PWzyMvWf3aRZKTf7CRQVnuKnccaeH5vFTuO1bPpyEnaOoJMK8rl1osn8ZEl05lSkBvrMkVkgCnQE0BLe4DqpjaqG1upbmpj57EGdh2v51DNKQ5Xn6K6qQ2AIZlpzBk3jFsvnsR7LyvWk4JEUowCvRN3x8xwd1o7gpxqC3CqrYPmtgCn2gI0tHTQ0h6gPRCkLRCkPeB0BILh9057IEh7R5COoNMRDNIR8NDrQHhZwGkPBgmEX7+mTTC0vUCn9s1tASoaWmls7fi7WieMGMqUghxuvGAss8fkUVyYy8XFo8jVcIpIykrZ3/6apja2lddReqyejYdq2V/ZyJHaZvKzM2ho7aCtI9iv7aenGRmn/6SnkZlu4WVpZKSfXhd+nZ52pu2QzDQysjPCr9O5alY2RfnZFORmUZCXzajcTCaNytFdmyLydyIKdDNbCnwPSAfuc/dvdFmfDfwUeB1QDbzT3Q9Gt9T+OVJziqdKT7D+QA1by+soP9l8Zt20olxmjs7j8hmFNLcFGJmbxfChmeRmpZOTlcHQrHRystLJy84gJyuDjHQjMz2NrPQ0MjNCrzPDoZ2RFvpbd1eKyGA7a6CbWTpwD3ADUAasN7PV7l7aqdkdQK27zzCzW4FvAu8ciIIjFQg6O47V8/SOCp7cfpzSY/UATCnIYcHkEaxYPIW5E4Yze0w+BboGW0SSQCRH6IuAve6+H8DMHgGWAZ0DfRnw5fDrXwE/MDNzd+9po1WNrdz33H7cwXGCzpnX7qHxbHdCyzsvgx4/0x4IUtXYRkV9C6VH62lo7cAMFk4eyRfecB6vv2CsrvYQkaQVSaBPAI50el8GXNJTG3fvMLM6oACo6tzIzFYCKwGyxs7ga7/f0eeCzcCANLPw6/DfBhlpaRTkZTE6P5ub54/nkqmjWDy9gNGal0REUsCgnhR191XAKoAFC1/nz375xh7D2TDSDMwMI7xM49IiIj2KJNDLgUmd3k8ML+uuTZmZZQDDCZ0c7VF6mjFsSGYfShURkd5E8pj29cBMM5tqZlnArcDqLm1WAyvCr/8B+HNv4+ciIhJ9Zz1CD4+J3wk8SeiyxQfcfbuZfQXY4O6rgfuBh8xsL1BDKPRFRGQQRTSG7u5rgDVdln2p0+sW4B3RLU1ERPoikiEXERFJAAp0EZEkoUAXEUkSCnQRkSRhsbq60MwagF2dFg0H6vqwib62Byiky92rA7CPvn6mrzWdyz6SoSYY+LrUpwauPQz8d5UqfWq2u+d3uyY0Z8rg/yF0yWPn96v6+Pk+te9unwO0j77+HH2qaTC+q3isaTDqUp+Kr/9+8VhTX/czEDX1tj6ehlz+d4Dbn4tz2Uc81pWqNfV1P/H4PZ3rZwZ6H6laU1/3M1g1AbEdctng7iXJvs+zUU2Ri8e6VFPk4rGuRKypt/WxPEJflSL7PBvVFLl4rEs1RS4e60rEmnpcH7MjdBERia54GkMXEZF+UKCLiCSJpAt0M3uLmbmZnRfrWroys8azrH/GzAblBI2ZTTSz/zGzPWa2z8y+F54euaf2nzKznEGqrdfvabCpT0Vci/pUhAaqTyVdoAPLgefDf0cs/DDslGChRz89DvzW3WcCs4A84Ou9fOxTwKD88sUh9amzUJ/qs4HpU3296D2e/xDqQOWEOtOu8LIlwFrg94TuTP0RkBZe1wjcDWwGrhiE+hrD9fyu07IfALeHXz8DlAxCHdcBa7ssG0boKVO5wLeBbcAW4OPAJ4A2YCvwl0H6nvKAp4FXwvtdFl5XDOwAfgxsB54ChqpPqU+pT8XXjUXRsAx4wt13A9Vm9rrw8kWEOtEcYDrwtvDyXOAld5/v7s8PerWxcwGwsfMCd68HDgMfINTBL3L3ecDP3f37wFHgGne/ZpBqbAHe6u4LgWuAu+1vD5WdCdzj7hcAJ4G3D2Ad6lORUZ+K3ID1qWQL9OXAI+HXj/C3f8687O773T0APAxcEV4eAH49uCXGvSXAve7eAeDuNTGqw4D/Z2ZbgD8BE4Ax4XUH3P3V8OuNhMJioKhP9d8S1Kc6G7A+FdETixKBmY0CrgUuNDMn9Lg8J/RPmK4X259+3xL+8gZTB6/9H+mQQd4/QCmhZ7+eYWbDgMnAwRjU0513AUXA69y93cwO8rfvqrVTuwAwdCAKUJ/qE/WpCAx0n0qmI/R/AB5y9ynuXuzuk4ADwJXAovBDrtOAdxI6GRErh4A5ZpZtZiMIjT0OtqeBHDN7L5w50XI38CChZ8d+yMwywutGhT/TAHQ/w9vAGA5UhH/xrgGmDOK+T1Ofipz6VGQGtE8lU6AvB37TZdmvw8vXEzpRtIPQl9e13YALd+ZWdz8CPEroBNGjwKbBrsVDZ1reCrzDzPYAuwmNL34BuI/QuOcWM9sM3Bb+2CrgCTP7y0DWdvp7An4OlJjZVuC9wM6B3G8P1KcipD4VsQHtU0l/67+ZLQH+yd1vjnEd84Efu/uiWNYR7xLhe1KfSiyJ8D1Fq08l0xF63DKzDxM6yfGvsa4lnul7ipy+q8ik2veU9EfoIiKpQkfoEjNmNsnM/mJmpWa23cw+GV4+ysz+GL6F/I9mNjK8/F1mtsXMtprZC+F/Sp/e1gNmVmFm22L180jsRatP9bSdeKcjdIkZMxsHjHP3V8wsn9D1v28Bbgdq3P0bZvY5YKS7/7OZLQZ2uHutmd0EfNndLwlv6ypCd9T91N3nxuLnkdiLVp/qaTvuXhqTHyxCOkKXmHH3Y+7+Svh1A6Gz+xMI3Un3k3CznxD6hcTdX3D32vDydcDETttaC8TqhhWJE9HqU71sJ64p0CUumFkxsAB4CRjj7sfCq47zt7v5OrsD+MPgVCeJKFp9qst24lrS3CkqicvM8ghdi/spd6//2/Qaoeubw3fUdW5/DaFfvisQ6Ua0+lTX7Qx44f2kI3SJKTPLJPQL83N3fzy8+ER4DPP0mGhFp/bzCN2osszdqwe7Xol/0epTPWwnrinQJWYsdNh0P6GTUt/ptGo1sCL8egXwP+H2kwnNuf2e8Ex1Iq8RrT7Vy3bimq5ykZgxsyuA5wjNTR0ML/4CobHKRwlN7HQIuMXda8zsPkLTmh4Kt+1w95Lwth4mNKtfIXACuMvd7x+kH0XiRLT6VE/bcfc1g/OTnBsFuohIktCQi4hIklCgi4gkCQW6iEiSUKCLiCQJBbqISJJQoEvKMLOAmb0anj1vs5n9Y/hxX719ptjMbuutjUi8UKBLKml294vc/QLgBuAm4K6zfKaYvz0yTSSu6Tp0SRlm1ujueZ3eTyP0HMdCQg8MfgjIDa++091fMLN1wPmEnvH4E+D7wDcI3cSUDdzj7vcO2g8h0gsFuqSMroEeXnYSmE3oCfRBd28xs5nAw+E7BpfQ6VmPZrYSGO3uXzOzbOCvwDvc/cAg/igi3dJsiyIhmcAPzOwiIADM6qHdjcA8M/uH8PvhwExCR/AiMaVAl5QVHnIJEJp57y5Cc8DMJ3RuqaWnjwEfd/cnB6VIkT7QSVFJSWZWBPwI+IGHxh2HA8fcPQi8B0gPN20A8jt99EngI+GpVTGzWWaWi0gc0BG6pJKhZvYqoeGVDkInQU9PjfpD4Ndm9l7gCaApvHwLEDCzzcCDwPcIXfnySniK1UrCjzMTiTWdFBURSRIachERSRIKdBGRJKFAFxFJEgp0EZEkoUAXEUkSCnQRkSShQBcRSRIKdBGRJPH/AR8X4vC8YnZjAAAAAElFTkSuQmCC\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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nombraremos nuestro gráfico con el atributo title:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Casos de Covid19 en España'}, xlabel='Date'>"
      ]
     },
     "execution_count": 23,
     "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": [
    "Repetimos la operación con un nuevo país, Italia. Lo identificamos con _it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Casos de Covid19 Italia'}, xlabel='Date'>"
      ]
     },
     "execution_count": 25,
     "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(title= \"Casos de Covid19 Italia\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora con Uruguay (_ur):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Casos de Covid19 Uruguay'}, xlabel='Date'>"
      ]
     },
     "execution_count": 26,
     "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_ur = 'https://api.covid19api.com/country/uruguay/status/confirmed/live'\n",
    "df_ur = pd.read_json(url_ur)\n",
    "df_ur.set_index('Date')['Cases'].plot(title= \"Casos de Covid19 Uruguay\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparación\n",
    "Las vistas creadas antes las vamos a guardar como objetos para poder agruparlos y compararlos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "casos_es = df_es.set_index('Date')['Cases']\n",
    "casos_it = df_it.set_index('Date')['Cases']\n",
    "casos_ur = df_ur.set_index('Date')['Cases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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",
       "2022-04-18 00:00:00+00:00    895775\n",
       "2022-04-19 00:00:00+00:00    895775\n",
       "2022-04-20 00:00:00+00:00    895775\n",
       "2022-04-21 00:00:00+00:00    895775\n",
       "2022-04-22 00:00:00+00:00    895775\n",
       "Name: Cases, Length: 822, dtype: int64"
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     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casos_ur"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a concatenar los daots con la función pd.concat. Así los uniremos en el mismo dataframe. Es importante hacer la concatenación en el eje 1 (axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "                                Cases       Cases   Cases\n",
       "Date                                                     \n",
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       "\n",
       "[822 rows x 3 columns]"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([casos_es,casos_it,casos_ur],axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para comparar los datos de los países vamos a crear un objeto llamado \"vs\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "vs = pd.concat([casos_es,casos_it,casos_ur],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "                                Cases       Cases   Cases\n",
       "Date                                                     \n",
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       "2022-04-22 00:00:00+00:00         NaN         NaN  895775\n",
       "\n",
       "[822 rows x 3 columns]"
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     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora crearemos el gráfico. Para ello vamos a nombrar las variables, los casos de cada país, con el nombre del país para la leyenda del gráfico:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "vs.columns = ['España', 'Italia', 'Uruguay']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "                               España      Italia  Uruguay\n",
       "Date                                                      \n",
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       "...                               ...         ...      ...\n",
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       "2022-04-22 00:00:00+00:00         NaN         NaN   895775\n",
       "\n",
       "[822 rows x 3 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como antes para visualizar el gráfico utilizaremos la función \"plot\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'España vs Italia vs Uruguay'}, xlabel='Date'>"
      ]
     },
     "execution_count": 37,
     "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 Uruguay\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a elegir el tipo que gráfico que queremos, un gráfico de áreas, con el atributo \"kind\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'España vs Italia vs Uruguay'}, xlabel='Date'>"
      ]
     },
     "execution_count": 38,
     "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": [
    "vs.plot(title=\"España vs Italia vs Uruguay\", kind='area')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Guardar el gráfico\n",
    "Vamos a guardar el dataframe \"vs\" como csv de la siguiente manera:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "vs.to_csv('esvsit.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para ver dónde lo hemos guardado:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "api-pandas-folium.ipynb  probando-con-r.ipynb\t\tShared_Resources\r\n",
      "esvsit.csv\t\t python-api-covid-pandas.ipynb\tUntitled.ipynb\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Guardar la imagen:\n",
    "Importamos la librería matplotlib para guardarlo porque es aquella que permite visualizar los datos en Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "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": [
    "import matplotlib.pyplot as plt\n",
    "vs.plot()\n",
    "plt.savefig('esvsit.png')"
   ]
  }
 ],
 "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}