api-covid-pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Uso de la API del Covid19 con Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "API es la abreviatura para \"Application Programming Interfaces\" (su traducción al español sería interfaz de programación de aplicaciones). Se trata de un conjunto de definiciones y protocolos que se emplea para desarrollar e integrar el software de las aplicaciones. Esto nos permite la comunicación entre dos aplicaciones de software por medio de un conjunto de reglas. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas por su parte es la libería de Python especializada en el manejo y análisis de estructuras de datos. Esta librería dispone de tres estructuras de datos diferentes: \n",
    "- Series: estrucutura de una dimensión.\n",
    "- DataFrame: Estructura de dos dimensiones (tablas).\n",
    "- Panel: Estructura de tres dimensiones (cubos).\n",
    "\n",
    "En el presente trabajo se analizán datos extraídos de Panda sobre el Covid-19"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La URL:  https://api.covid19api.com/countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instalación de liberías"
   ]
  },
  {
   "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: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2020.4)\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: 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": [
    "Para instalar la libería hemos utilizado la función \"!pip\". **Importante** cuando se utiliza el símbolo (!) quiere decir que no estamos ejecutando en Python sino bash"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuración de Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usamos 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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este paso vamos a crear la variable asignando la dirección a la url del principio con el fin de obtener los datos requeridos para llevar a cabo la práctica. Las variables se asignan utilizando el símbolo \"=\" seguido en este caso de la variable entrecomillada porque se trata de una cadena de caracteres (string)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://api.covid19api.com/countries'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crear el Dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La abreviatura para dataframe es \"df aunque nosotras vamos a poner pd proque es como hemos nombrado a Pandas. El Dataframe utiliza Pandas y usamos una función que lee un json, dentro de dicha función localizaremos el valor de lo que queremos que lea. Es decir, la url de la que queremos extraer los valores. "
   ]
  },
  {
   "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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       "    }\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>Tonga</td>\n",
       "      <td>tonga</td>\n",
       "      <td>TO</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>Nauru</td>\n",
       "      <td>nauru</td>\n",
       "      <td>NR</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>Tajikistan</td>\n",
       "      <td>tajikistan</td>\n",
       "      <td>TJ</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>Vanuatu</td>\n",
       "      <td>vanuatu</td>\n",
       "      <td>VU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>India</td>\n",
       "      <td>india</td>\n",
       "      <td>IN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>Libya</td>\n",
       "      <td>libya</td>\n",
       "      <td>LY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>rwanda</td>\n",
       "      <td>RW</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>248 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Country                   Slug ISO2\n",
       "0                    Tonga                  tonga   TO\n",
       "1                Indonesia              indonesia   ID\n",
       "2                    Nauru                  nauru   NR\n",
       "3    Sao Tome and Principe  sao-tome-and-principe   ST\n",
       "4               Tajikistan             tajikistan   TJ\n",
       "..                     ...                    ...  ...\n",
       "243                Vanuatu                vanuatu   VU\n",
       "244               Zimbabwe               zimbabwe   ZW\n",
       "245                  India                  india   IN\n",
       "246                  Libya                  libya   LY\n",
       "247                 Rwanda                 rwanda   RW\n",
       "\n",
       "[248 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Surge una tabla por la que visualizamos lo datos a través de tres columnas de valores y una de control de Pandas que identifica cada una de las entradas del dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploración de la tabla"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como hay un número muy alto de valores la tabla no nos los muestra todos. Para poder ver el conjunto de ellos que queramos se utilzia la función  head que funciona con 'int'y que nos devuelve en número de filas el valor del entero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Tonga</td>\n",
       "      <td>tonga</td>\n",
       "      <td>TO</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>Nauru</td>\n",
       "      <td>nauru</td>\n",
       "      <td>NR</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>Tajikistan</td>\n",
       "      <td>tajikistan</td>\n",
       "      <td>TJ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Country                   Slug ISO2\n",
       "0                  Tonga                  tonga   TO\n",
       "1              Indonesia              indonesia   ID\n",
       "2                  Nauru                  nauru   NR\n",
       "3  Sao Tome and Principe  sao-tome-and-principe   ST\n",
       "4             Tajikistan             tajikistan   TJ"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si queremos ver los 6 primeros valores por ejemplo, escribiremos este número entre los paréntesis de la función \"df.head()\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Slug</th>\n",
       "      <th>ISO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>tonga</td>\n",
       "      <td>TO</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>Nauru</td>\n",
       "      <td>nauru</td>\n",
       "      <td>NR</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>Tajikistan</td>\n",
       "      <td>tajikistan</td>\n",
       "      <td>TJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dominica</td>\n",
       "      <td>dominica</td>\n",
       "      <td>DM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Country                   Slug ISO2\n",
       "0                  Tonga                  tonga   TO\n",
       "1              Indonesia              indonesia   ID\n",
       "2                  Nauru                  nauru   NR\n",
       "3  Sao Tome and Principe  sao-tome-and-principe   ST\n",
       "4             Tajikistan             tajikistan   TJ\n",
       "5               Dominica               dominica   DM"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si lo que queremos es conocer los últimos valores de la cola utilizaremos la función \"df.tail()\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>246</th>\n",
       "      <td>Libya</td>\n",
       "      <td>libya</td>\n",
       "      <td>LY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>rwanda</td>\n",
       "      <td>RW</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country    Slug ISO2\n",
       "246   Libya   libya   LY\n",
       "247  Rwanda  rwanda   RW"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para conocer la información general sobre las variables que contiene el dataframe utilizamos la función \"df.info\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                      Tonga\n",
       "1                  Indonesia\n",
       "2                      Nauru\n",
       "3      Sao Tome and Principe\n",
       "4                 Tajikistan\n",
       "               ...          \n",
       "243                  Vanuatu\n",
       "244                 Zimbabwe\n",
       "245                    India\n",
       "246                    Libya\n",
       "247                   Rwanda\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 el valor concreto de una columna determinada ponenemos entre corchetes el valor de 'Country' (no entre paréntesis porque no estamos es una lista, si usáranos comillas accederíamos al Index 66). La diferencia entre corchetes y paréntesis radica en el matiz de los diccionarios que son las llaves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Benin'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country'][66]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tiempo real "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La URL que vamos a utilizar de ahora en adelante nos ofrece datos a tiempo real en España en materia del COVID19: https://api.covid19api.com/country/spain/status/confirmed/live\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A continuación ejecutaremos las mismas funciones aprendidas previamente pero aplicadas a una región en concreto. Para ello guardaeremos los datos de la misma forma que lo hicimos antes pero como en este caso se trata únicamente de valores relacioandos con España añadiremos \"df_es\", la abrebiatura que nos indica que este dataframe es de este país. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "    </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>821</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>11786036</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-22 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</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>11786036</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-23 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>823</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>11786036</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-24 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</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>11786036</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-25 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</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>11833457</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-26 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>826 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",
       "821   Spain          ES                         40.46 -3.75  11786036   \n",
       "822   Spain          ES                         40.46 -3.75  11786036   \n",
       "823   Spain          ES                         40.46 -3.75  11786036   \n",
       "824   Spain          ES                         40.46 -3.75  11786036   \n",
       "825   Spain          ES                         40.46 -3.75  11833457   \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",
       "821  confirmed 2022-04-22 00:00:00+00:00  \n",
       "822  confirmed 2022-04-23 00:00:00+00:00  \n",
       "823  confirmed 2022-04-24 00:00:00+00:00  \n",
       "824  confirmed 2022-04-25 00:00:00+00:00  \n",
       "825  confirmed 2022-04-26 00:00:00+00:00  \n",
       "\n",
       "[826 rows x 10 columns]"
      ]
     },
     "execution_count": 12,
     "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cabeceras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>Province</th>\n",
       "      <th>City</th>\n",
       "      <th>CityCode</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Status</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-22 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-23 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-24 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-25 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-26 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "  </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",
       "2   Spain          ES                         40.46 -3.75      0  confirmed   \n",
       "3   Spain          ES                         40.46 -3.75      0  confirmed   \n",
       "4   Spain          ES                         40.46 -3.75      0  confirmed   \n",
       "\n",
       "                       Date  \n",
       "0 2020-01-22 00:00:00+00:00  \n",
       "1 2020-01-23 00:00:00+00:00  \n",
       "2 2020-01-24 00:00:00+00:00  \n",
       "3 2020-01-25 00:00:00+00:00  \n",
       "4 2020-01-26 00:00:00+00:00  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2020-01-22 00:00:00+00:00\n",
       "Name: Date, dtype: datetime64[ns, UTC]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es['Date'].head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por columnas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'CountryCode', 'Province', 'City', 'CityCode', 'Lat', 'Lon',\n",
       "       'Cases', 'Status', 'Date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cola"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "825   2022-04-26 00:00:00+00:00\n",
       "Name: Date, dtype: datetime64[ns, UTC]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es['Date'].tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Información sobre la variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 826 entries, 0 to 825\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype              \n",
      "---  ------       --------------  -----              \n",
      " 0   Country      826 non-null    object             \n",
      " 1   CountryCode  826 non-null    object             \n",
      " 2   Province     826 non-null    object             \n",
      " 3   City         826 non-null    object             \n",
      " 4   CityCode     826 non-null    object             \n",
      " 5   Lat          826 non-null    float64            \n",
      " 6   Lon          826 non-null    float64            \n",
      " 7   Cases        826 non-null    int64              \n",
      " 8   Status       826 non-null    object             \n",
      " 9   Date         826 non-null    datetime64[ns, UTC]\n",
      "dtypes: datetime64[ns, UTC](1), float64(2), int64(1), object(6)\n",
      "memory usage: 64.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df_es.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.260000e+02</td>\n",
       "      <td>826.00</td>\n",
       "      <td>8.260000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>3.477175e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.421946e-14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.365777e+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>3.183704e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>4.961128e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>1.183346e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Lat     Lon         Cases\n",
       "count  8.260000e+02  826.00  8.260000e+02\n",
       "mean   4.046000e+01   -3.75  3.477175e+06\n",
       "std    1.421946e-14    0.00  3.365777e+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  3.183704e+06\n",
       "75%    4.046000e+01   -3.75  4.961128e+06\n",
       "max    4.046000e+01   -3.75  1.183346e+07"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Últimos valores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "825   2022-04-26 00:00:00+00:00\n",
       "Name: Date, dtype: datetime64[ns, UTC]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es['Date'].tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creación de gráficos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para crear nuestra gráfica elegiremos las columnas que nos sean útiles. \n",
    "En el eje de la \"x\" aparecerán el número de casos y en el de la \"y\" las fechas. El valor de la gráfica es el número de casos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "X= casos confirmados\n",
    "Y= fechas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esta es la función que nos llevará a hacer la siguiente tabla donde podremos visualizar los datos. Par ayudarnos a hacer este gráfico sobre los datos escogemos un dato Índice (de ahí la función set index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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>2022-04-22 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>11786036</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-23 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>11786036</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-24 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>11786036</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-25 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>11786036</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-26 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>11833457</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>826 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",
       "2022-04-22 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2022-04-23 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2022-04-24 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2022-04-25 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2022-04-26 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",
       "2022-04-22 00:00:00+00:00 -3.75  11786036  confirmed  \n",
       "2022-04-23 00:00:00+00:00 -3.75  11786036  confirmed  \n",
       "2022-04-24 00:00:00+00:00 -3.75  11786036  confirmed  \n",
       "2022-04-25 00:00:00+00:00 -3.75  11786036  confirmed  \n",
       "2022-04-26 00:00:00+00:00 -3.75  11833457  confirmed  \n",
       "\n",
       "[826 rows x 9 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.set_index('Date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A continuacuón creamos una tabla en la que se incluyan tanto la columna de la \"fecha\" como también la de \"casos confirmados\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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",
       "2022-04-22 00:00:00+00:00    11786036\n",
       "2022-04-23 00:00:00+00:00    11786036\n",
       "2022-04-24 00:00:00+00:00    11786036\n",
       "2022-04-25 00:00:00+00:00    11786036\n",
       "2022-04-26 00:00:00+00:00    11833457\n",
       "Name: Cases, Length: 826, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.set_index('Date')['Cases']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se muestra también el número de casos (pero la tabla original no se ha tocado). Sin embargo, aquí solo podemos observar las 10 primeras filas sobre los datos de origen. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creamos la tabla "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para poder visualizar los datos en su totalidad creamos un gráfico. Pintamos la función por plor, la forma de visualización de datos más sencilla de Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Casos de Covid19 en España'}, xlabel='Date'>"
      ]
     },
     "execution_count": 24,
     "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": [
    "Al añadirle \"title=...\" le puedo poner el nombre que yo quiera a la tabla"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Otro país: Italia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este apartado de la tarea simplemente cambiamos los datos de España a Italia y vemos qué ocurre en cada función"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uruguay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Casos de Covid19 Uruguay'}, xlabel='Date'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QWsX0UYMxS6/HlbalJCEi0sNaWpwNu6rSftAalCRERHrcmpJKquqb0vb24ImUJEREetjS1/cCcPpxw2KO5OiUJEREeti6XVUMHZjH6IL+cYdyVEoSIiI9bN2uKmZkwKA1KEmIiPSolhZnQ2lV2l8f0UpJQkSkB22vqKW2oTnt79nUSklCRKQHrcuQK61bKUmIiPSg9SFJZMI1EqAkISLSo9bvqmLSsHwG9suJO5SUKEmIiPSgtbsqmZEhvQhQkhAR6TF1jc28vqcmYwatIcUkYWaFZvaAma0zs7VmdoaZDTWzp8xsY/hdFOqamd1oZpvM7FUzm5ewnitD/Y1mdmVC+almtjIsc6OFk4eTtSEikok27a6mxWHG6IK4Q0lZqj2JnwGPu/tM4GRgLXAt8LS7TwOeDq8BLgCmhZ+rgZ9DtMMHrgNOAxYA1yXs9H8OfDxhuYWhPFkbIiIZJ9PObIIUkoSZDQHeBtwG4O4N7r4PWATcGardCVwcphcBd3nkRaDQzMYA5wNPufted68AngIWhnkF7v6iuztwV5t1tdeGiEjGWVdSSV5OFpOH5ccdSspS6UlMAcqA/zWzl83sV2Y2EBjl7iWhzi5gVJgeB2xPWL44lHVUXtxOOR20ISKScdaXVjF91CBysjNnODiVSHOAecDP3X0uUEObwz6hB+BdH15qbZjZ1Wa21MyWlpWVdWcYIiJviLuzZmclMzNoPAJSSxLFQLG7LwmvHyBKGqXhUBHh9+4wfwcwIWH58aGso/Lx7ZTTQRuHcfdb3X2+u88fMWJECm9JRKRnlVbWU17TwJyxvSxJuPsuYLuZzQhF5wBrgMVA6xlKVwIPh+nFwBXhLKfTgf3hkNETwHlmVhQGrM8DngjzKs3s9HBW0xVt1tVeGyIiGWX1zv0AzB43JOZIOifVS/4+C9xtZnnAa8BHiRLM/WZ2FbAV+ECo+yhwIbAJqA11cfe9ZvZ94B+h3vfcfW+Y/hRwBzAAeCz8APwoSRsiIhll1Y5KzOCEMZnVk0gpSbj7CmB+O7POaaeuA59Osp7bgdvbKV8KzGmnvLy9NkREMs26XZVMGprPoAy5HUerzBliFxHJYFv21HD8iEFxh9FpShIiIt2spcXZsqeGKcMHxh1KpylJiIh0s5LKOuqbWpgyQklCRETa2FJWA8Bxw3W4SURE2tiypxqA49STEBGRtjaX1ZCfl83Iwf3iDqXTlCRERLpZ66B1eApCRlGSEBHpZpvLqjPyzCZQkhAR6VaVdY0UVxzIuCutWylJiIh0o3Ul0YOGZilJiIhIW2tLKoHMu2dTKyUJEZFutLakkqL8XEYVZN6ZTaAkISLSrdaUVHLCmIKMPLMJlCRERLpNU3ML63dVZeyhJlCSEBHpNq+X11Df1KIkISIiR1qT4Wc2gZKEiEi3WVtSSW62MXVk5t3Yr5WShIhIN1lbUsnxIwaRl5O5u9rMjVxEJM2tLanM6ENNoCQhItItyqvrKa2sz+hBa1CSEBHpFmvDoLWShIiIHOHQ7TgGxxzJsVGSEBHpBmtLKhlV0I9hgzLzdhytlCRERLpB6+04Mp2ShIhIF2toamFzWbWShIiIHOnlbRU0Njsnjy+MO5RjpiQhItLFnttYRnaW8eapw+IO5ZgpSYiIdLG/bCjj1IlFFPTPjTuUY6YkISLShcqq6lm1o5K3TR8edyhdQklCRKQLPb+pDICzpo+MOZKuoSQhItKF/ry+jGED85g9NvPPbAIlCRGRLtPQ1MIz63Zz9oyRZGVl5uNK21KSEBHpIi9s3kNVXRMXnjg67lC6jJKEiEgXeXzVLgb1y+HMab1j0BqUJEREukRFTQOPrdrFO2aOpF9OdtzhdJmUk4SZZZvZy2b2SHg9xcyWmNkmM/utmeWF8n7h9aYwf3LCOr4Wyteb2fkJ5QtD2SYzuzahvN02RETSyZLXyrnopuepbWjik2cdH3c4XaozPYlrgLUJr/8d+Km7TwUqgKtC+VVARSj/aaiHmc0CLgVmAwuBW0LiyQZuBi4AZgGXhbodtSEiEqum5hZWFu/n+kfWcNkvXyQ3O4t7Pn46s3rJWU2tclKpZGbjgXcBPwC+YGYGvAO4PFS5E/gO8HNgUZgGeAC4KdRfBNzn7vXAFjPbBCwI9Ta5+2uhrfuARWa2toM2RER6xP4DjWzfW8v2vbVs21vL9opaNpZW82rxfg40NpNl8E/zxnPde2YzqF9Ku9SMkuo7+i/gK0Dr0zOGAfvcvSm8LgbGhelxwHYAd28ys/2h/jjgxYR1Ji6zvU35aUdpQ0TkmLW0OBW1DZRW1lNaVUdxxQGKE5LBtvJaKuuaDltmyIBcJg8fyD+/aQLzJxexYPJQRhb0j+kddL+jJgkzezew292XmdnZ3R7RG2BmVwNXA0ycODHmaEQkHTSHBLCj4gDbK2oprjjArv11lFbWsauyjtL9dZRV19PY7Ictl5edxfihA5hQlM/cCUVMGDqAiUPzGV+Uz4Sh+QwZkPn3Y+qMVHoSbwHeY2YXAv2BAuBnQKGZ5YRv+uOBHaH+DmACUGxmOcAQoDyhvFXiMu2Vl3fQxmHc/VbgVoD58+d7e3VEJHPVNzWzv7aRitpGKmob2FfbQEVtI3trDk1X1DSEeY3srW1g/4FGvM3eYHC/HEYW9GP0kP6cfvwwRhX0Z9Tgfowq6M/Ign6MK8xn5OB+veZCuK5w1CTh7l8DvgYQehJfcvcPmtnvgPcB9wFXAg+HRRaH138P859xdzezxcA9ZvYTYCwwDXgJMGCamU0hSgKXApeHZZ5N0oaIZBB3p76phcoDjVTWNbL/QBOVdY1U1TUdLKsMZfsPJoJG9tU2sO9AI7UNzUnX3T83i6H5eRTm51E0MJexhQMoys+jKD+XoQPzGFeUz/iiAYwrGtAr7sra045llOWrwH1mdj3wMnBbKL8N+HUYmN5LtNPH3Veb2f3AGqAJ+LS7NwOY2WeAJ4Bs4HZ3X32UNkQkDdQ2NLFlTw2v76llx75ayqsbKK9poLy6nn0HGtl/IOz8DzTS0NzS4bpys40hA3IZMiCXovw8xhb254QxBRTl51KYn0thfh6F+dG8wpAAivLz6J/be65JSEfmbftjGW7+/Pm+dOnSuMMQ6XW27Knh2XW72bi7ii17atiyp4bSyvrD6uRlZzF8UB5DB0U78IL+uRQMyKVgQM6h6f454XcuQxLK++VkEZ0IKXEws2XuPr9tee87X0tEusz+A43cs2QbD6/YwbpdVQAMHZjH5GH5nDl1BFOG5zNl+CAmD48GdQf3y9GOvpdRkhCRI7g7Dy3fwf97bC17qhuYN7GQ6y6axbknjGLC0Py4w5MepCQhIocpr67ns/e+zAuby5k3sZD//cgCThw/JO6wJCZKEiJy0M59B/jQbUvYUXGA6y+ew+ULJup00D5OSUJEANi+t5bLf/Ui+2oa+fVVp7FgytC4Q5I0oCQhIlTUNHDprS9SXd/Eb/7lNE6eUBh3SJIm9DwJkT7O3fnGH1ayu6qOuz62QAlCDqMkIdLHPbh8B4+u3MXnzp2uBCFHUJIQ6cNW7djP13+/kgVThva6h+VI11CSEOmj3J3rFq9myIBc/udDp5Kts5ikHUoSIn3UE6tLWba1gi+8czpDB+rJwNI+JQmRPqilxfnpUxs4fsRA3n/q+LjDkTSmJCHSBz2ysoT1pVV85h1TycnWbkCS09Yh0sfUNTbz3cWrmTOugItOGht3OJLmlCRE+pgHlhVTXtPAN981S70IOSptISJ9SF1jMzc/u4lTJhRymm67ISlQkhDpQ+584XVK9tfx1YUz9dwHSYmShEgfsa+2gZuf3cTZM0ZwxvHD4g5HMoSShEgfccufN1NV38RXF86MOxTJIEoSIn3A3poG7njhdd47dzwnjCmIOxzJIEoSIn3A4hU7aGhq4SNvnhx3KJJhlCREernKukZ+/OQG5k4sZM449SKkc5QkRHq5B5YWU13fxPfeM0dnNEmnKUmI9GK1DU384rnNzJ9UxInjh8QdjmQgJQmRXuyXz22htLKeay/QGU3yxihJiPRS+w80cutzm1k4ezTzJ+vqanljlCREeqnfLd1OTUMzn3771LhDkQymJCHSCzW3OHe88DoLJg/VWIQcEyUJkV7o7iVbKa44wFVvnRJ3KJLhlCREepnSyjr+4/H1vHXacM6bNSrucCTDKUmI9DLf/eNqGptbuP5iXRchx05JQqQXeXptKY+u3MW/nTONScMGxh2O9AJKEiK9RG1DE99+eDXTRg7i4289Lu5wpJfIiTsAEekaNzy5gR37DvC7T55BXo6+/0nX0JYk0gv8ef1ubnt+Cx86fSJv0oVz0oWUJEQyXGVdI197aCXTRg7iW++eFXc40sscNUmY2QQze9bM1pjZajO7JpQPNbOnzGxj+F0Uys3MbjSzTWb2qpnNS1jXlaH+RjO7MqH8VDNbGZa50cIpGcnaEJFDrn9kDaWVdfz4/SfTLyc77nCkl0mlJ9EEfNHdZwGnA582s1nAtcDT7j4NeDq8BrgAmBZ+rgZ+DtEOH7gOOA1YAFyXsNP/OfDxhOUWhvJkbYgI8My6Uu5fWswnzjqekycUxh2O9EJHTRLuXuLuy8N0FbAWGAcsAu4M1e4ELg7Ti4C7PPIiUGhmY4Dzgafcfa+7VwBPAQvDvAJ3f9HdHbirzbraa0Okz/vbpj188jfLmTl6MNecMy3ucKSX6tSYhJlNBuYCS4BR7l4SZu0CWi/tHAdsT1isOJR1VF7cTjkdtNE2rqvNbKmZLS0rK+vMWxLJSHtrGvjcb1cweVg+93z8dPrn6jCTdI+Uk4SZDQIeBD7n7pWJ80IPwLs4tsN01Ia73+ru8919/ogRI7ozDJHYuTvf+P1K9tc28rNL5zJ0YF7cIUkvllKSMLNcogRxt7s/FIpLw6Eiwu/doXwHMCFh8fGhrKPy8e2Ud9SGSJ+1+JWdPLZqF59/53ROGKNnVkv3SuXsJgNuA9a6+08SZi0GWs9QuhJ4OKH8inCW0+nA/nDI6AngPDMrCgPW5wFPhHmVZnZ6aOuKNutqrw2RPmlfbQPfWbyauRMLufptuqpaul8qV1y/BfgwsNLMVoSyrwM/Au43s6uArcAHwrxHgQuBTUAt8FEAd99rZt8H/hHqfc/d94bpTwF3AAOAx8IPHbQh0ifd8OQGKuua+OElJ5KdpZv3Sfc7apJw9+eBZFvjOe3Ud+DTSdZ1O3B7O+VLgTntlJe314ZIX7RmZyV3L9nKFWdM1mEm6TG64lokAzQ2t/Cth1dRmJ/H58+dHnc40ocoSYhkgP94fB3LtlZw3UWzGJKfG3c40ocoSYikuSWvlfPLv0Y371t0yrijLyDShZQkRNJYc4tz7UMrmTg0n69feELc4UgfpCQhksYWv7KDLXtq+OrCmeTn6fEv0vOUJETS1LbyWr79h+iaiPNnt3tHGpFupyQhkqa++fAqMPjvy+aSk61/VYmHtjyRNLTktXKe21DGNedMY3xRftzhSB+mJCGShv60tpS87Cw+eNqkuEORPk5JQiTNNDW38OSaUk6dVMSAPN0CXOKlJCGSZh5avoOt5bV89C2T4w5FRElCJJ3sq23gp3/awMnjh/DOWTqjSeKnE69F0sj1/7eWPdX13PLBeUR3zheJl3oSImli2da9PLCsmKvOPI65E4viDkcEUJIQSQtNzS186w+rGTOkP599x9S4wxE5SElCJA3c/rctrCmp5FvvnsXAfjoKLOlDSUIkZqt27Oc/n1jP+bNHccGc0XGHI3IYJQmRGNU1NvP5365g6MA8/v2fTtJgtaQd9WtFYlLf1My/3fsyG3dXc9fHFlCYnxd3SCJHUE9CJAbuzhfvf4Un15TynYtm8bbpI+IOSaRdShIiPexAQzPX3LeCR14t4cvnz+Ajb5kSd0giSelwk0gPWrVjP5/77Qo27a7mKwtn8K9nHR93SCIdUpIQ6QHNLc6tz73GT55az9CBefzmqtM4c9rwuMMSOSolCZFu1NLiPLqqhBuf3siG0mredeIYfnDJHA1SS8ZQkhDpBjX1TSx+ZSe3P7+FjburmTpyEDdfPo8LTxyt01wloyhJiHQRd+fV4v38btl2/vDyTqrrm5g5ejD/fdlcLjxxDNlZSg6SeZQkRI5BY3MLy7dW8LfN5Ty2soSNu6vJy8ni3SeO4fLTJnLqpCL1HCSjKUmIdNL2vbU8t7GMv6wv44XN5VTXN5FlMHdiET+85ETeddIYhgzIjTtMkS6hJCFyFC0tzorifTy5upSn1uxic1kNAOMKB/CeU8Zy1vQRnHH8MAr6KzFI76MkIZLE+l1V3PvSNh5dWcLuqnpysozTjxvG5adN4qzpIzh+xEAdSpJeT0lCJEFzi/N/K0u4Z8lWlmzZS152FmfPGMEFc8bw9hkjGZKv3oL0LUoSIkTJ4ZFXd3Lj0xvZXFbDlOED+fy507nijEm6pkH6NCUJ6dO27Knh4RU7+MPLO3i9vJaZowdzywfnsXD2aLJ0yqqIkoT0Le7Opt3V/Gntbh5fVcIrxfsxg9OnDONL58/gwjljlBxEEihJSK/X0NTCki3lPL12N0+vK2X73gMAzB5bwNcvnMlFJ49lzJABMUcpkp7SPkmY2ULgZ0A28Ct3/1HMIUkaq29qprjiAFvLa1ixfT/Ltu7l5W37qG1opl9OFm+ZOpxPvO14zjlhpBKDSArSOkmYWTZwM/BOoBj4h5ktdvc18UYmx8LdaW5xGpudxpYWGptaounmlvBz+HRTcwsNCeXV9U1U1TVRVddIVV0T+2obKa6oZfveWkoq63CP2skymDG6gH+aN56zpo/gLVOHMyAvO943L5Jh0jpJAAuATe7+GoCZ3QcsApImiT3V9fzqr68B4A6OJ0wfWRb99oPzE5dLLMP9sOUPLpu4zsPKojotbeq3HGzLo3keLRHV9cPjPNhm9LsloQxPYV2HvW8/LPbD2zp8uqX1rRxsN8zvaF2JMSV8Js3NTkOz05SYDFpaDn4exyo/L5uC/rmMLxrA6ccNY8LQfCYOzWfisHxmjh7MYF3gJnJM0j1JjAO2J7wuBk5rW8nMrgauBsgbPZXr/29ttwRjBha1F36HcqIZbcuy7FBdDLLMDq6jdRqi31kWLXNYG9baZpJ1hfayQqOt87OykqwrTLe21RpzdlbWwbaOWJcd/n4Pm8YOttU2pta2srOM3Ows8rKNnOysI6Zzsy387ng6JzuLvOwscnOMgXk5DO6fw6B+OeRk6+GKIt0p3ZNEStz9VuBWgLnzTvU/X3feETt0OLRjS7xINrGstT5w2E5RRKSvSvcksQOYkPB6fChLKjvLdHM1EZEuku599X8A08xsipnlAZcCi2OOSUSkz0jrnoS7N5nZZ4AniE6Bvd3dV8cclohIn5HWSQLA3R8FHo07DhGRvijdDzeJiEiMlCRERCQpJQkREUlKSUJERJIy76r7I6QJM6sC1icUDQH2d2IVna0/HNjTjet/I8t0NqY30kZviAm6/++n7al76qdjTJDZ29MMdx98RGl0H57e8wMsbfP61k4u39n6S7tz/T0RUzp+Tj0RU0/8/bQ99Z2YeuLv152fU7K6feFw0x+7uX5nvZH1d3dMb6QNxdQ99TtL21P3tdFXYzpMbzzctNTd5/fW9lKhmFKXbnGlWzygmDoj3eLqTDzJ6vbGnsStvby9VCim1KVbXOkWDyimzki3uDoTT7t1e11PQkREuk5v7EmIiEgXUZIQEZGklCRSZGYXm5mb2cy4Y2nLzKqPMv/PZtbtg2lmNt7MHjazjWa22cx+Fm7xnqz+58wsvwfi6vDziUO6bk/psi2FtrQ9pag7tyclidRdBjwffqfMzLK7J5z0YtEj/B4C/uDu04DpwCDgBx0s9jmg2/+p05S2pw5oe+q07tueOnuxSF/8Ido4dxBtqOtD2dnAc8D/EV3h/T9AVphXDdwAvAKc2QPxVYd4Hkkouwn4SJj+MzC/m2M4B3iuTVkBUA4MBH4MrAJeBT4L/BvQAKwEnu2Bz2cQ8DSwPLS5KMybDKwFfgmsBp4EBvTV7SkdtiVtT+m1PaknkZpFwOPuvgEoN7NTQ/kCog10FnA88N5QPhBY4u4nu/vzPR5tPGYDyxIL3L0S2Ab8C9E/zynufhJwt7vfCOwE3u7ub++B+OqAS9x9HvB24AY79ADzacDN7j4b2Af8UzfHou3p6LQ9pa5btyclidRcBtwXpu/jUJfuJXd/zd2bgXuBM0N5M/Bgz4aY1s4GfuHuTQDuvjeGGAz4oZm9CvwJGAeMCvO2uPuKML2MaAfUnbQ9HZuz0faUqFu3p7R/Ml3czGwo8A7gRDNzoseoOlE3ru1FJq2v68Ifpic1cXjS79/D7a8B3pdYYGYFwETg9R6OpT0fBEYAp7p7o5m9zqHPqD6hXjMwoLuCyJDtKe5tCbQ9paQntif1JI7ufcCv3X2Su0929wnAFuCtwAIzm2JmWcA/Ew0cxWUrMMvM+plZIdEx3Z70NJBvZlfAwQGxG4A7iJ5R/gkzywnzhoZlqoAj7zrZPYYAu8M/9NuBST3UbluZsD3FvS2BtqdUdfv2pCRxdJcBv29T9mAo/wfRoN5aoj9M23rdLvyj1Lv7duB+osG8+4GXezIOj0bELgHeb2YbgQ1Ex22/DvyK6Fjyq2b2CnB5WOxW4HEze7a74mr9fIC7gflmthK4AljXXW0eRdpuT+myLYG2p07o9u1Jt+V4g8zsbOBL7v7umOM4Gfiluy+IM450lSmfTzpsT5nyWcUpUz6jrtye1JPIYGb2SaIBqW/GHUs60ueTOn1WR9dXPyP1JEREJCn1JKRXMbMJZvasma0xs9Vmdk0oH2pmT4VbPDxlZkWh/INm9qqZrTSzF8LhhNZ13W5mu81sVVzvR+LVVdtTsvVkAvUkpFcxszHAGHdfbmaDic5Tvxj4CLDX3X9kZtcCRe7+VTN7M7DW3SvM7ALgO+5+WljX24iuTr3L3efE8X4kXl21PSVbj7uvieWNdYJ6EtKruHuJuy8P01VEZ3aMI7oq9c5Q7U6if3Tc/QV3rwjlLwLjE9b1HBDHhVqSJrpqe+pgPWlPSUJ6LTObDMwFlgCj3L0kzNrFoatjE10FPNYz0Umm6artqc160p6uuJZeycwGEZ0v/jl3rzx0W53oHPxwdWpi/bcT/VOfiUgbXbU9tV1PtwfeBdSTkF7HzHKJ/hHvdveHQnFpOC7cepx5d0L9k4gu0Frk7uU9Ha+kt67anpKsJ+0pSUivYtFXvNuIBg9/kjBrMXBlmL4SeDjUn0j03IIPh7toihzUVdtTB+tJezq7SXoVMzsT+CvRPf5bQvHXiY7/3k90g7itwAfcfa+Z/YroVs5bQ90md58f1nUv0R1HhwOlwHXuflsPvRVJA121PSVbj7s/2jPv5I1TkhARkaR0uElERJJSkhARkaSUJEREJCklCRERSUpJQkREklKSEDkGZtZsZivCnT1fMbMvhsdFdrTMZDO7vKM6IulCSULk2Bxw91PcfTbwTuAC4LqjLDOZQ4/cFElruk5C5BiYWbW7D0p4fRzRs4WHA5OAXwMDw+zPuPsLZvYicALRc4fvBG4EfkR04V4/4GZ3/0WPvQmRDihJiByDtkkilO0DZgBVQIu715nZNODecPXt2SQ8f9jMrgZGuvv1ZtYP+Bvwfnff0oNvRaRdugusSPfJBW4ys1OAZmB6knrnASeZ2fvC6yHANKKehkislCREulA43NRMdFfQ64ju+XQy0fhfXbLFgM+6+xM9EqRIJ2jgWqSLmNkI4H+Amzw6jjsEKHH3FuDDQHaoWgUMTlj0CeBfw62kMbPpZjYQkTSgnoTIsRlgZiuIDi01EQ1Ut94K+hbgQTO7AngcqAnlrwLNZvYKcAfwM6IznpaHW0qXER6HKRI3DVyLiEhSOtwkIiJJKUmIiEhSShIiIpKUkoSIiCSlJCEiIkkpSYiISFJKEiIikpSShIiIJPX/AVAeNXf0GQxAAAAAAElFTkSuQmCC\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estas vistas creadas las vamos a guardar como objetos para poder agruparlas y compararlas. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 30,
   "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",
       "2022-04-23 00:00:00+00:00    895775\n",
       "2022-04-24 00:00:00+00:00    895775\n",
       "2022-04-25 00:00:00+00:00    895775\n",
       "2022-04-26 00:00:00+00:00    895775\n",
       "2022-04-27 00:00:00+00:00    895775\n",
       "Name: Cases, Length: 827, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
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    "casos_ur"
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   "execution_count": 31,
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       "                                Cases       Cases   Cases\n",
       "Date                                                     \n",
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     "metadata": {},
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    "pd.concat([casos_es,casos_it,casos_ur],axis=1)"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para poder comparar los datos de ambos países vamos a crear un objeto al que llamaremos \"vs\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "vs = pd.concat([casos_es,casos_it,casos_ur],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
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       "                                Cases       Cases   Cases\n",
       "Date                                                     \n",
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       "\n",
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     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "vs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para crear el gráfico vamos a nombrar las variables: los casos de cada país con el correspondiente nombre de cada uno como leyenda de la gráfica. "
   ]
  },
  {
   "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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       "                               España      Italia  Uruguay\n",
       "Date                                                      \n",
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       "\n",
       "[827 rows x 3 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Siguiendo los mismos pasos que 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": [
    "El gráfico que queremos utilizar para representar nuestros valores es un gráfico de áreas, para lo que utilizaremos 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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a guardar el gráfico en CVS. De esta forma nuestro dataframe que había sido denominado como \"vs\" será guardado en nuestro perfil de Jupyter. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "vs.to_csv('esvsit.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para saber dónde lo hemos guardado utilizamos el comando de bash \"ls\"=listado (recordamos que como vamos a trabajar en lenguaje bash tenemos que añadir !): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "esvsit.csv\t      pyhton-primeros-pasos.ipynb    Untitled.ipynb\r\n",
      "probando-con-r.ipynb  python-api-covid-pandas.ipynb\r\n",
      "Prueba-con-r.ipynb    Shared_Resources\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Guardar la imágen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Acabamos de guardar nuestro gráfico y nuestros datos con cvs. Para guardar la gráfica también como imagen utilizamos el comando \"matplotlib\", una librería que nos permite visualizar los datos en Jupyter y desde ahí mismo guardarlos en png."
   ]
  },
  {
   "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('esvsitvmx.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para comprobar que hemos hecho los pasos correctos y se ha guardado debidamente  ejecutamos el comando de listado en bash otra vez."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "esvsit.csv\t      Prueba-con-r.ipynb\t     Shared_Resources\r\n",
      "esvsitvmx.png\t      pyhton-primeros-pasos.ipynb    Untitled.ipynb\r\n",
      "probando-con-r.ipynb  python-api-covid-pandas.ipynb\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
}