python-api-covid19-pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Uso de la API del Covid 19 con Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para realizar esta práctica utilizamos **Python**, un lenguaje de programación de alto nivel para el desarrollo de software. A diferencia de otros lenguajes como Java, se trata de un **lenguaje interpretado**, es decir, cuyo código ya es legible y no requiere traducción. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este cuaderno Python trabajaremos la representación de los datos de la API del Covid-19 por países con la librería **Pandas**. Como sus siglas indican, Pandas es la librería de Python para el anális de datos (*Python Data Analysis Library*) que nos es muy útil para la manipulación y análisis de datos en el lenguaje de progrmación de Python. Es una biblioteca de software escrita como extensión de NumPy que ofrece estructuras de datos y funcicones para manipular tablas numéricas y series temporales."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La Url con la que vampos a trabajar es la siguiente: https://api.covid19api.com/countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Antes de poder empezar a trabajar con los datos necesitamos realizar tres pasos previos: \n",
    "1. Preparar librerías\n",
    "    1.1 Instalar\n",
    "    1.2 Importar\n",
    "2. Definir las variables con las que vamos a trabajar, en este caso la API del Covid-19\n",
    "3. Crear del dataframe para que Python muestre los datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparación de librería"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Requiere de dos pasos: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instalación"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Necesitamos instalar la librería para que Python pueda trabajar a partir de ella para la leer los datos y trabajar con ellos. Una vez instalada permanecerá en la consola. Lo hacemos con la sintaxis `!pip install ()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2.8.1)\r\n",
      "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (1.21.1)\r\n",
      "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2020.4)\r\n",
      "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7.3->pandas) (1.14.0)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El *f* nos indica que no estamos ejecutando Python, sino Bash. Pandas es una librería de Python especializada en manejar análisis y estructuras de datos. En clases anteriores efectivamente estuvimos viendo el tipo de estructura de datos en temas, donde los tipos de datos que podemos encontrar son: cadenas de datos, numbers o datos binarios v(erdadero / falso). Hoy veremos la estructura de datos. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configuración de Pandas "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Que hayamos instalado la librería no significa que podamos trabajar con ella. Tenemos que configurarla, importarla al presente notebook para hacer uso de ella. Puede compararse a cuando nos compramos un libro: necesitamos abrirlo para poder leerlo.\n",
    "Lo hacemos con el comando `import()` y con su abreviación típica:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Así abreviamos la importación de pandas como pd.\n",
    "Ya tenemos el libro comprado y abierto. Ahora falta leerlo. Para ello tenemos que crear un *dataframe*, la forma en la que Python a través de Pandas leerá los datos de la API. Pero para ello primero debemos decirle qué datos queremos que lea. Para ello creamos una variable que lo indique. lo hacemos con `=` "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crear Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://api.covid19api.com/countries\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Así definimos la variable url que pusimos antes y que Python vea lo que es. Para ello le explicamos a Python que url es `=` a una cadena de de caracreres, motivo por el que van \"entre comillas\". La referencia nos sirve para el resto de las cajas del cuaderno."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crear Dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por último, ahora que Pyton ya tiene la librería (Pandas) y los datos de la API con la variable `url` definida es el momento de que lea los datos. De Pandas hay una función que se llama *read_json*; esto es, una función para leer JSON (*Java Script Object Notation* o notación de objetos en java script. Dentro de la función *read_json* ponemos *:* para poner lo que queremos que lea, La url con los datos está es JSON: son los datos en este lenguaje."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_json(url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La función *read_jason* lee un JSON y entre paréntesis () la función de lo que queremos que lea, en este caso una url. Es decir, si ponemos en el navegador esta función, me devuelve un JSON, una lista de diccionarios. Al crear un Dataframe creamos una tabla que nos lee esto en JSON. Una vez creada la variable de *dataframe* la ejecutamos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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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       "</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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploración de la tabla "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora que ya Python ha abierto los datos es el momendo de explorarlos. A la variable definida como `df` podemos añadirle funciones. En este caso la función `.head` sirve para mostrarnos los datos a la cabeza del conjunto. Para ver los últimos valores (que se corresponden en este caso con los países con menos certeza de casos covid), la función es `.tail`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para determinar el número de países que queremos que nos muestre en ambas funciones, lo especificamos dentro del paréntseis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head (6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>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",
       "245           Bolivia           bolivia   BO\n",
       "246            Latvia            latvia   LV\n",
       "247  Papua New Guinea  papua-new-guinea   PG"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail (3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La función `.info` nos explica qué hay en el dataframe *(df)*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Honduras'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country'] [66]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para centrarnos en una columna concreta ponemos los corechetes y dentro de comillas simples *['']* el nombre de la columna que nos interese explorar. Para seleccionar un número concreto de la lista abrimos a continuación, tras un espacio, otros corchetes con el número de la fila que queramos dentro."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Caso de España: eje temporal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a centrarnos en los patos de España de la Api del covid-19 con el objetico de representar en un gráfico la evolución de la pandemia en este país. Para ello creamos un dataframe con estos datos definiendo previamente la url."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La url: https://api.covid19api.com/country/spain/status/confirmed/live"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "url_es = 'https://api.covid19api.com/country/spain/status/confirmed/live'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Repetimos el proceso con la nueva url y la función de Pandas `.read_json()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_es = pd.read_json(url_es)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>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>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",
       "    <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>11736893</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-22 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>822 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",
       "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",
       "821   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",
       "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",
       "821  confirmed 2022-04-22 00:00:00+00:00  \n",
       "\n",
       "[822 rows x 10 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La función `.columns` dice el nombre de las distintas columnas que hay."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'CountryCode', 'Province', 'City', 'CityCode', 'Lat', 'Lon',\n",
       "       'Cases', 'Status', 'Date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al ver las columnas de esta manera resulta mucho más facil detectar qué categorías de datos tenemos que usar para nuestro objetivo. Como queremos representar la evolución en el tiempo de la pandemia en España nos interesan las categorías \"Cases\" y \"Date\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 822 entries, 0 to 821\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype              \n",
      "---  ------       --------------  -----              \n",
      " 0   Country      822 non-null    object             \n",
      " 1   CountryCode  822 non-null    object             \n",
      " 2   Province     822 non-null    object             \n",
      " 3   City         822 non-null    object             \n",
      " 4   CityCode     822 non-null    object             \n",
      " 5   Lat          822 non-null    float64            \n",
      " 6   Lon          822 non-null    float64            \n",
      " 7   Cases        822 non-null    int64              \n",
      " 8   Status       822 non-null    object             \n",
      " 9   Date         822 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": [
    "Con `.info` obtenemos la información básica del dataframe.  La función `.describe` se centra en valores numéricos. Hace un conteo de los valores, la media, la mínima, la desviación típica..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.220000e+02</td>\n",
       "      <td>822.00</td>\n",
       "      <td>8.220000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>3.436625e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.109753e-15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.323205e+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>3.181958e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>4.956001e+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  8.220000e+02  822.00  8.220000e+02\n",
       "mean   4.046000e+01   -3.75  3.436625e+06\n",
       "std    7.109753e-15    0.00  3.323205e+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  3.181958e+06\n",
       "75%    4.046000e+01   -3.75  4.956001e+06\n",
       "max    4.046000e+01   -3.75  1.173689e+07"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>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",
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       "      <td>...</td>\n",
       "      <td>...</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",
       "    <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>11736893</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-22 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>822 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",
       "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",
       "821   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",
       "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",
       "821  confirmed 2022-04-22 00:00:00+00:00  \n",
       "\n",
       "[822 rows x 10 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Definimos los ejes**: El valor de la gráfica es el número de casos (**eje Y**) y las fechas (**eje X**)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Con la función `set_index`, ponemos la columna *Dates* la primera para organizar los datos a partir de la misma. Puesto que el eje X son las fechas comenzamos por esta categoría."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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",
       "    </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>2022-04-18 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",
       "    <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>11736893</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>822 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",
       "2022-04-18 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",
       "2022-04-22 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",
       "2022-04-18 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",
       "2022-04-22 00:00:00+00:00 -3.75  11736893  confirmed  \n",
       "\n",
       "[822 rows x 9 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.set_index('Date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pero además del número de casos confirmados los demás datos no nos interesan. Queremos cruzar el datos de las fechas con los de casos confirmados para lograr un gráfico que represente la evolución de la pandemia en España. Por lo que añadimos *[ ]* en el nombre de la otra columna:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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    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",
       "2022-04-22 00:00:00+00:00    11736893\n",
       "Name: Cases, Length: 822, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.set_index('Date')['Cases']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Así vemos que ya solo nos muestra la información que necesitamos. A continuación, utilizamos la función `.plot()` al final de la sintaxis anterior para pintar la gráfica. Esta función es una de las funciones de Python por defecto."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_es.set_index('Date')['Cases'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La función `.plot(title='...')` sirve para titular la gráfica:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Casos de COVID19 en España'}, xlabel='Date'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_es.set_index('Date')['Cases'].plot(title=\"Casos de COVID19 en España\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Así tenemos la representación gráfica que queríamos partiendo de unos datos. Podemos tratar el gráfico como imagen y gruardarlo como archivo independiente para poder utilizarlo donde nos interese. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La librería Pandas es muy útil tambien para comparar datos. Vamos a repetir este proceso con otro país, Italia por ejemplo, para luego comparar las curvas de ambos países."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Caso de Italia: eje temporal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Repetimos el proceso esta vez en un sólo paso porque ya sabemos hacerlo: \n",
    "1. Definimos una variable (url) con los datos de la Api del covid-19 para Italia\n",
    "2. Definimos una variable (dataframe) con la función de Pandas `.read_json()`\n",
    "3. Sobre la variable anterior aplicamos la función `.set:index()[]` con las columnas de los datos que queremos curzar para su representación.\n",
    "    - Al final de la sintaxis añadimos la función de Python `.plot(title='...')` para que nos muestre directamente estos datos representados en un gráfico titulado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Casos de COVID19 en Italia'}, xlabel='Date'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z/IfhEBFJUj96bgtDsjL42sdmJTqUmFroDwALu1i+EzjX3U8BvgMsiUNcIiJJ7zer9/DU2/v4+7OnMKKPbh7qSixD0C03s8ldLF8R9XIl0DtPbhcRSSJV9S38x7KNnDF5BDee1/vPaYlFvPvQrwee6myhmS02s9VmtrqioiLOuxYR6TsPvrKLw/Ut3PKJk8nKSI7TkXGLwszOI0jo/9pZGXdf4u7F7l5cUFAQr12LiPSpxpY2Hlyxi4+cUMjs8cMSHc4Rcbls0cxOBe4BLnT3g/HYpohIsnp+YzkH65oTdgNRZ467hW5mRcCTwNXuvuX4QxIRSW6/fb2E0XnZfHBafqJDeY9uW+hm9giwAMg3sxLgFiATwN3vAr4FjALuDO+OanX34t4KWEQkkV7dcZDnNpbz5fOmkZ6WmDtCOxPLVS6Xd7P8BuCGuEUkIpLE7n15J6OGZnHTR2YkOpT3SY5TsyIi/UBVQwsvbi5n0dzxDMpMT3Q476OELiISo2fX76OlzfnEnLGJDuWolNBFRGL0/MZyxg4bxNyJwxMdylEpoYuIxKC1LcKK7Qf48IyChD0etztK6CIiMVhXWkV1Yytnz0iuSxWjKaGLiMTg5a0HMIMPTVdCFxHptyIR5+m39zF73DBGJsFTFTujhC4i0o2VOw+yoayaqz8wKdGhdEkJXUSkG3/esJ/sjDQ+MWdcokPpkhK6iEg3Vu06xLxJIxiclXw3E0VTQhcR6UJdUysby2qYNymx44XGQgldRKQLb5ZU0hZxTldCFxHp39buPgzA6ROV0EVE+rW171QyozCHYUMyEx1Kt5TQRUS68FZpFXOS9NktHXWb0M3sPjMrN7O3O1luZvYTM9tmZuvM7PT4hyki0vcO1TVTUdPECWNyEx1KTGJpoT8ALOxi+YXAjPBnMfDz4w9LRCTxtuyvAWDm6BRJ6O6+HDjURZFFwEMeWAkMN7PkfFiwiEgPbN4XJPRZKdRC7854YE/U65JwnohIv7Z+bxXDBmdSmJud6FBi0qcnRc1ssZmtNrPVFRUVfblrEZEeaWxpY9lb+/jICYVJ+/zzjuKR0EuBiVGvJ4Tz3sfdl7h7sbsXFxQUxGHXIiK9463SKmqbWrlw9phEhxKzeCT0pcA14dUuHwCq3L0sDtsVEUmY9huKiiePTHAkscvoroCZPQIsAPLNrAS4BcgEcPe7gGXARcA2oB74fG8FKyLSVzaUVTNu2KCkfv55R90mdHe/vJvlDnw5bhGJiCSBTWU1nDA2L9Fh9IjuFBUR6aCptY3tFbWcOLZ/XK7YTgldRKSDrftraY04J4xRC11EpF/bVl4L9J8bitopoYuIdLCtvJb0NGPyqKGJDqVHlNBFRDrYXlHLpJFDyMroXymyf0UrItIHtlfUMrUgJ9Fh9JgSuohIlNa2CDsP1DGtsH91t4ASuojIe+w53EBLmzNdLXQRkf5te3iFy7RCJXQRkX5t075qAKaphS4i0r/9cV0ZpxUNZ9jg5B8UuiMldBGRUF1TK5v21bBgZmGiQzkmSugiIqHN4Rii/e0ZLu2U0EVEQpvK2hN6/3qGSzsldBGR0KZ91eRkZzB++OBEh3JMlNBFREKbymqYNSaXtLT+MYZoRzEldDNbaGabzWybmX39KMuLzOxFM3vdzNaZ2UXxD1VEpPe4O5v2Vffb/nOIIaGbWTpwB3AhcBJwuZmd1KHYN4HH3P004DLgzngHKiLSm8qqGqlubGVWP3sGerRYWujzgW3uvsPdm4FHgUUdyjjQfhSGAXvjF6KISO/bEl7hMmt0/22hdzumKDAe2BP1ugQ4s0OZbwPPmtlNwFDg/LhEJyLSR9oHtZjRD2/5bxevk6KXAw+4+wTgIuBhM3vfts1ssZmtNrPVFRUVcdq1iMjx27q/lvycLEYMzUp0KMcsloReCkyMej0hnBfteuAxAHd/BRgE5HfckLsvcfdidy8uKCg4tohFRHrB1vIapvfj1jnEltBXATPMbIqZZRGc9Fzaocw7wEcBzOxEgoSuJriI9Avuztb9tcwo7L/95xBDQnf3VuBG4BlgI8HVLOvN7FYz+2RY7GbgC2b2JvAIcJ27e28FLSIST/urm6hpamXm6P7dQo/lpCjuvgxY1mHet6KmNwAfim9oIiJ9Y2t5cIXL9FRvoYuIpLpVOw+RZnBSP32GSzsldBEZ8FZsP8icicMZNqT/PQM9mhK6iAxokYizoayaOROGJzqU46aELiID2u5D9dQ3t3HSuP7d3QJK6CIywG3YG4wh2t/7z0EJXUQGuLf3VpGRZszo55csghK6iAxwy7dUMHficLIz0hMdynFTQheRAau+uZX1e6s5e8b7nlTSLymhi8iAtfNAHUC/v+W/nRK6iAxYOyqChD4lf2iCI4kPJXQRGbC27K8hPc2YWqCELiLSr20sq2Zq/lAGZfb/E6KghC4iA9jGshpOTIHrz9spoYvIgFRV30JpZQMnjE2NE6KghC4iA9Tzm/YDMDcFnuHSTgldRAak375eytT8oZw1bVSiQ4mbmBK6mS00s81mts3Mvt5Jmc+a2QYzW29mv45vmCIi8dPQ3MarOw/xkRMKMbNEhxM33Y5YZGbpwB3ABUAJsMrMloajFLWXmQH8H+BD7n7YzAp7K2ARkeO1cudBmlsjfHhmag1WH0sLfT6wzd13uHsz8CiwqEOZLwB3uPthAHcvj2+YIiLx85fNFWRnpDF/yshEhxJXsST08cCeqNcl4bxoM4GZZvY3M1tpZguPtiEzW2xmq81sdUVFxbFFLCJynJZvreADU0elzPXn7eJ1UjQDmAEsAC4HfmFmwzsWcvcl7l7s7sUFBan1VUdE+oc9h+rZUVGXct0tEFtCLwUmRr2eEM6LVgIsdfcWd98JbCFI8CIiSeUP6/YCcP6JqXeqL5aEvgqYYWZTzCwLuAxY2qHM7wha55hZPkEXzI74hSkicvyaWyM8vrqE+ZNHMmlUajy/JVq3Cd3dW4EbgWeAjcBj7r7ezG41s0+GxZ4BDprZBuBF4J/d/WBvBS0i0lPuzuKHV7PjQB3XfWhyosPpFd1etgjg7suAZR3mfStq2oGvhj8iIknnkdf28NLmCm75xElcdMrYRIfTK3SnqIikvDW7D/PdP23g7On5XHvW5ESH02tiaqGLiPRHkYhz78s7+d7Tmxg/YjD//dk5pKWlzp2hHSmhi0hK2nmgjq8/sY5Xdx5i4clj+M7FsynIzU50WL1KCV1EUkpLW4T7/7aTHzy7hayMNG7/zKlcWjwhpZ7Z0hkldBFJCeU1jTzy6h5+9epuymuauOCk0Xz34tmMzhuU6ND6jBK6iPRb7s7reyp5cMUulr1VRkub8+GZBXzvksksmFkwIFrl0ZTQRaRfcXe2ldfyly0VLH1zL+tKqsjJzuDKMydxzVmTmFqQk+gQE0YJXUSSXmV9My9vO8DyLRX8desByqoaAZg1OpdbF53Mp0+fQE620pmOgIgknLtT3dhKRU0j+6ubKK9pZM+hBjaWVbOhrJrdB+sByB2UwdnT8/mHjxZwzox8JowYkuDIk4sSuoj0mraIc7CuiQM1zRyobaKiponymiBhl4eJuz2BN7ZE3rf+5FFDOHlcHpfOm8BZ0/KZM2EYGem6H7IzSugicszcnf3VTeyoqGV7RS3bK+p451A9+6sbqahp4kBtExF//3o52RkU5mZTmJfN3InDGZ2XTWHuIAqjfo8dNoghWUpRPaGjJSLdcnf2VjXydmkVm/fVhAm8jh0VtdQ1tx0pNyQrncmjhjI6L5vZ44ZRmJdNQW42BTnZ5Odmk5+TTWFuNkPV390rdFRF5D1a2yJsq6hlY1k1m8pqWL+3mrf3VlFZ33KkzPjhg5laMJRLiycyrWAoUwtymFaQw+i87AF3qWAyUUIXGcCq6lvYWl7D5v1B4l5fWsWmfTU0tQb92Vnpacwak8uFs8dw0rhhzB6Xx6wxueoKSVL6q4ikAHenNeI0t0ZoaGmjobntyO/65jYaWlqpqGmi9HADpZWNlFYGw7CV1zQd2UbuoAxmjxvGNWdNYvb4YZw0No/J+UPJ1EnIfiOmhB4O+vxjIB24x91v66TcZ4DHgTPcfXXcohQZACIR50BdE3srGymrbKC0soH91Y1U1rdQ3dhCdUMrVQ0tNLS00dwaoak1QnNrG81tEZpbI0c9+dhRmsHovEGMHz6Yc2YUMHN0DjNG5zCjMJcJIwaru6Sf6zahm1k6cAdwAcHYoavMbKm7b+hQLhf4R+DV3ghUJJVU1jezatdh3txTycayarZV1FJW2Uhz23sv3RuUmcaIIVnkDcokb3BGcOVHdgZZ6WlkZaSRnRH8jn49JCudQZnpDM5Kf3c6M538nGzGDBukFncKi6WFPh/Y5u47AMzsUWARsKFDue8A3wP+Oa4RiqSIbeW1PLN+H8u3VLB692HaIk56mjGtYCizxw9j4ewxjB8+mHHDBjNu+GDGDx9M3uAMtZolZrEk9PHAnqjXJcCZ0QXM7HRgorv/ycyU0EVCh+qaWfZWGU+uLWHtO5UAnDAmly+dO41zZxUwe9wwBmelJzZISRnHfVLUzNKA/waui6HsYmAxQFFR0fHuWiQpuTtr3znMQ6/sPvIEwOmFOXzjohO4eO54CgfQ41ylb8WS0EuBiVGvJ4Tz2uUCs4GXwq+GY4ClZvbJjidG3X0JsASguLg4hlM4Iv1Hc2uE375ewpLlO9heUUdudgZXfWASl86byIljc9V1Ir0uloS+CphhZlMIEvllwBXtC929Cshvf21mLwFf01UuMlDUN7fy6Gt7WLJ8B/uqG5k9Po/bLzmVi04ZqycASp/qtra5e6uZ3Qg8Q3DZ4n3uvt7MbgVWu/vS3g5SJBntrWzgh3/ewh/XldHQ0sb8KSO5/ZJTOWdGvlrjkhAxNR/cfRmwrMO8b3VSdsHxhyWSvJpbIzy+poT/XLaRlkiET502nkvmTWDepJGJDk0GOH0fFOmBte8c5ubH3mTngTrmTxnJ9y+ZQ9EoPZNbkoMSukgMmlsj/OT5rdz50jbGDhvMvdcWc96sQtLS1LUiyUMJXaQbW/bX8JX/eYP1e6u5ZN4EbvnESeQOykx0WCLvo4Qu0onGljbu+esOfvLCNnKzM7j76nl8/OQxiQ5LpFNK6CJHsXV/DV/+9Vq27K9l4clj+O6nZpOfk53osES6pIQuEqWlLcKS5Tv48fNbyc3O4IHPn8GCWYWJDkskJkroIqH1e6v4l8fXsX5vNRedMoZvf/JkCnN1m770H0roMuCVVjbwk+e28sTaEoYPyeKuq05n4eyxiQ5LpMeU0GXAqqhp4p6Xd/Dgil24w5VnFvHVC2YxbIiuYJH+SQldBpTWtggvbzvAb9aU8Oz6fbRFnE/OGcfXPj6LCSN0g5D0b0roMiC8XVrFY6v3sOytMg7UNjNscCbXnDWZK88sYmpBTqLDE4kLJXRJWfuqGvndG6X87vVSNu2rISsjjQtOHM0n5ozjvBMKyM7QwBKSWpTQJaXUNbXyhzf38rs3Snl15yHc4bSi4Xxn0cl8cs549Y9LSlNCl36vLeK8tvMQT64tYdlbZdQ1tzG1YCj/+NEZXDx3PJPzhyY6RJE+oYQu/VZlfTMPvbKbX67cTXlNE0Oz0rnolLFcNr+I04uG65nkMuAooUu/s7+6kZ++sJUn1pTS0NLGebMK+ObpEzj/xEKGZKlKy8AVU+03s4XAjwlGLLrH3W/rsPyrwA1AK1AB/L27745zrDLAVdW38NAru/j5X7bT2uYsmjuO68+Zwglj8hIdmkhS6Dahm1k6cAdwAVACrDKzpe6+IarY60Cxu9eb2ZeA24HP9UbAMvBUN7bwvac28fiaEppaI3z85NF846ITmTRKfeMi0WJpoc8Htrn7DgAzexRYBBxJ6O7+YlT5lcBV8QxSBqZdB+p4YMUuHl9TQn1zK587o4grzyxi9vhhiQ5NJCnFktDHA3uiXpcAZ3ZR/nrgqeMJSgYud+dv2w5y/9928sLmcjLSjP/vlLHccM5UJXKRbsT1DJKZXQUUA+d2snwxsBigqKgonruWfq6ptY0n15Zy/992smV/Lfk5Wdz0kRlcdWYRhXl64qFILGJJ6KXAxKjXE8J572Fm5wP/Bpzr7k1H25C7LwGWABQXF3uPo5WUU9/cymOr9nD38h2UVTVy8rg8vn/pHP7u1LEMytSdnCI9EUtCXwXMMLMpBIn8MuCK6AJmdhpwN7DQ3cvjHqWknEjE+dVr7/Dfz27mcH0LZ0wewe2XnMrZ0/N1/bjIMeo2obt7q5ndCDxDcNnife6+3sxuBVa7+1Lgv4Ac4DfhP+M77v7JXoxb+rFN+6q55ffreXXnIT40fRRfOX8mxZNHJjoskX4vpj50d18GLOsw71tR0+fHOS5JQW0R56cvbOUnz28lb3Amt3/mVC4tnqAWuUic6LY66ROV9c38w6NvsHxLBZ86bTy3fOIkhg/JSnRYIilFCV163b6qRq74xUpKDjfwH586hcvnT1SrXKQXKKFLryqtbOCKX6zkYG0zv/rCmZyhvnKRXqOELr1m14E6rrr3VaoaWnj4+vmcVjQi0SGJpDQldOkVuw/Wccldr9AWifDrGz7AKRN0l6dIb1NCl7g7WNvEdfevojUS4fEvnsX0wtxEhyQyIKQlOgBJLYfrmrn2/tfYW9nAvdcWK5mL9CG10CVuDtc187klr7DrYD13Xz2PeZN0AlSkLymhS1wcrG3i2vtfY9fBeh647gw+OD0/0SGJDDhK6HLcSisbuPreVyk93MDdV81TMhdJECV0OS7bymu45t7XqGlq5Zc36DpzkURSQpdj9tyG/Xzt8TfJSEvj0cUf4ORxujRRJJGU0KXH9lY2cOsfNvD0+n2cMCaXJVcXUzRqSKLDEhnwlNAlJgdqm3h1xyGeXFvCS1sqyEgz/mXhLL5wzlQy03X1q0gyUEIfoNoiTl1zK3VNwU9tU1v4u5XqhhbKa5oor26krKqR9XurKa1sAGB0XjaLPzyVK+YXMXGkWuUiyUQJvQvNrRGaWttoao2E05Ej85rD6daIE/Hgpy1CMB1x2txpizjuQfKMeDAdccfhyGt3JxL9m2A0n4gH2wim393G0fZzpIw7rW0RGlsi1De30djSRkNLGw3R0+HrptZIt+8/d1AGo/MGcVrRcK774GTmTBzOvEkjSE/TkxJFklFMCd3MFgI/Jhix6B53v63D8mzgIWAecBD4nLvvim+o8dHcGqG0soHSww2U1zSyv7qJ8ppGysPfh+tbqGoIfppjSHp9yQzSzUgzIy0tetpITzPSDNLTjCFZGQzKTGdwZhqDs9IZMSQzfJ3OkKx0BmWlMygjnZzsDHIGZTA0O4Oc7HSGZgXTeYMyKcjNZnCWxvQU6U+6Tehmlg7cAVwAlACrzGypu2+IKnY9cNjdp5vZZcD3gM/1RsCxaIs4ew7Vs628ll0H69h9sP7I79LKBtoi7x2fOic7g8K8bApzs5lRmMOwwZkMG5xJTnaQGLMz08hKTwt/p5OdkUZW+JORZpgFCTU9TLRp1p5g30207YnXCKbNggSdZh3mESbrcP3obegZ4iLSlVha6POBbe6+A8DMHgUWAdEJfRHw7XD6ceBnZmbu/t7MGeVAbRP3/HVH0O1Ae3fEu9MQ3R3x7nwPuyyi59U1tXKwrpnD9c0cqGlm58G697SucwdlMCV/KHMmDmfR3HFMGjWU8cMHMzovm8K8QeRkq+dJRPq/WDLZeGBP1OsS4MzOyoSDSlcBo4AD0YXMbDGwGCBrzHS++6eNxxR00JINWqwGDMlKZ1RONiOHZlE0agjnzipgemEO0wpymJo/lOFDMtW6FZGU16dNU3dfAiwBOO30ef6Xb3/sSGJOC7sb2vNudHeEHfmtbgcRkc7EktBLgYlRryeE845WpsTMMoBhBCdHO5WeZuQNyuxBqCIi0pVY7ghZBcwwsylmlgVcBiztUGYpcG04fQnwQlf95yIiEn/dttDDPvEbgWcILlu8z93Xm9mtwGp3XwrcCzxsZtuAQwRJX0RE+lBMfejuvgxY1mHet6KmG4FL4xuaiIj0hB7CISKSIpTQRURShBK6iEiKUEIXEUkRlqirC82sBtgcNWsYUNWDTfS0PEA+He5e7YV99HSdVIjpWPbRF3+/nu4nGetUMsZ0LOukSp3q7ZhiiWeWu+cedYm7J+SH4JLH6NdLerh+j8ofbZ+9tI+evo9+H1Oy/v16up9krFPJGNMxvo+UqFO9HVMs8XRVJpm6XP7Qy+WPxbHso7fjSsaYjmUffRFTT/czUI+T6lTv7aOv6jmQ2C6X1e5enOr77I5iil0yxqWYYpOMMUHyxRVLPF2VSWQLfckA2Wd3FFPskjEuxRSbZIwJki+uWOLptEzCWugiIhJfydSHLiIix0EJXUQkRaRcQjezi83MzeyERMfSkZnVdrP8JTPrkxM0ZjbBzH5vZlvNbLuZ/Th8PHJn5f/JzIb0UWxdHqe+pjoVcyyqUzHqrTqVcgkduBx4Ofwds3Aw7AHBgmGfngR+5+4zgJlADvDvXaz2T0Cf/PMlIdWpbqhO9Vjv1KmeXoifzD8EFaiUoDJtDuctAJYDfyK4M/UuIC1cVgv8AHgTOLsP4qsN4/lj1LyfAdeF0y8BxX0Qx0eB5R3m5RGMMjUU+D7wNrAOuAn4B6AZeAt4sY+OUw7wPLA23O+icNlkYCPwC2A98CwwWHVKdUp1KrluLIqHRcDT7r4FOGhm88L58wkq0UnANODT4fyhwKvuPsfdX+7zaBPnZGBN9Ax3rwbeAW4gqOBz3f1U4Ffu/hNgL3Ceu5/XRzE2Ap9y99OB84Af2LsDys4A7nD3k4FK4DO9GIfqVGxUp2LXa3Uq1RL65cCj4fSjvPt15jV33+HubcAjwNnh/Dbgib4NMektAO5291YAdz+UoDgM+A8zWwc8B4wHRofLdrr7G+H0GoJk0VtUp47fAlSnovVanYppxKL+wMxGAh8BTjEzJxguzwm+wnS82L79dWN48PpSK+/9IB3Ux/sH2EAw9usRZpYHFAG7EhDP0VwJFADz3L3FzHbx7rFqiirXBgzujQBUp3pEdSoGvV2nUqmFfgnwsLtPcvfJ7j4R2AmcA8wPB7lOAz5HcDIiUXYDJ5lZtpkNJ+h77GvPA0PM7Bo4cqLlB8ADBGPH/i8zywiXjQzXqQGO/oS33jEMKA//8c4DJvXhvtupTsVOdSo2vVqnUimhXw78tsO8J8L5qwhOFG0kOHgdy/W6sDI3ufse4DGCE0SPAa/3dSwenGn5FHCpmW0FthD0L34DuIeg33Odmb0JXBGutgR42sxe7M3Y2o8T8Cug2MzeAq4BNvXmfjuhOhUj1amY9WqdSvlb/81sAfA1d/+7BMcxB/iFu89PZBzJrj8cJ9Wp/qU/HKd41alUaqEnLTP7IsFJjm8mOpZkpuMUOx2r2Ay045TyLXQRkYFCLXRJGDObaGYvmtkGM1tvZv8Yzh9pZn8ObyH/s5mNCOdfaWbrzOwtM1sRfpVu39Z9ZlZuZm8n6v1I4sWrTnW2nWSnFrokjJmNBca6+1ozyyW4/vdi4DrgkLvfZmZfB0a4+7+a2QeBje5+2MwuBL7t7meG2/owwR11D7n77ES8H0m8eNWpzrbj7hsS8sZipBa6JIy7l7n72nC6huDs/niCO+keDIs9SPAPibuvcPfD4fyVwISobS0HEnXDiiSJeNWpLraT1JTQJSmY2WTgNOBVYLS7l4WL9vHu3XzRrgee6pvopD+KV53qsJ2kljJ3ikr/ZWY5BNfi/pO7V7/7eI3g+ubwjrro8ucR/POdjchRxKtOddxOrwd+nNRCl4Qys0yCf5hfufuT4ez9YR9me59oeVT5UwluVFnk7gf7Ol5JfvGqU51sJ6kpoUvCWNBsupfgpNR/Ry1aClwbTl8L/D4sX0TwzO2rwyfVibxHvOpUF9tJarrKRRLGzM4G/krwbOpIOPsbBH2VjxE82Gk38Fl3P2Rm9xA81nR3WLbV3YvDbT1C8FS/fGA/cIu739tHb0WSRLzqVGfbcfdlffNOjo0SuohIilCXi4hIilBCFxFJEUroIiIpQgldRCRFKKGLiKQIJXQZMMyszczeCJ+e96aZ3RwO99XVOpPN7IquyogkCyV0GUga3H2uu58MXABcCNzSzTqTeXfINJGkpuvQZcAws1p3z4l6PZVgHMd8ggGDHwaGhotvdPcVZrYSOJFgjMcHgZ8AtxHcxJQN3OHud/fZmxDpghK6DBgdE3o4rxKYRTACfcTdG81sBvBIeMfgAqLGejSzxUChu3/XzLKBvwGXuvvOPnwrIkelpy2KBDKBn5nZXKANmNlJuY8Bp5rZJeHrYcAMgha8SEIpocuAFXa5tBE8ee8WgmfAzCE4t9TY2WrATe7+TJ8EKdIDOikqA5KZFQB3AT/zoN9xGFDm7hHgaiA9LFoD5Eat+gzwpfDRqpjZTDMbikgSUAtdBpLBZvYGQfdKK8FJ0PZHo94JPGFm1wBPA3Xh/HVAm5m9CTwA/Jjgype14SNWKwiHMxNJNJ0UFRFJEepyERFJEUroIiIpQgldRCRFKKGLiKQIJXQRkRShhC4ikiKU0EVEUoQSuohIivj/ATNDAYyIueCOAAAAAElFTkSuQmCC\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 en Italia\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Así obtenemos el gráfico resultante. Una vez seguido el procedimiento podemos utilizar los tres tipos de códigos empleados arriba para conocer la gráfica de otros países. Escojo Francia por su cercanía."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Caso de Francia: eje temporal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Casos de COVID19 en Francia'}, xlabel='Date'>"
      ]
     },
     "execution_count": 49,
     "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_fr = 'https://api.covid19api.com/country/france/status/confirmed/live'\n",
    "df_fr = pd.read_json(url_fr)\n",
    "df_fr.set_index('Date')['Cases'].plot(title=\"Casos de COVID19 en Francia\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparación de países: España - Italia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora que tenemos la evolución de la pandemia en tres países, España, Francia e Italia, los comparamos en un sólo gráfico. Primero comparamos solo Italia y España. Para ello buscaremos concatenar las columnas de los casos de los distitntos países, definiendo primero qué incluimos en esta categoría: columna *Date*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definimos las variables a concatenar. Lo hacemos como anteriormente, con la función `.set_index()[]` sobre el dataframe correspondiente en cada caso:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "casos_es = df_es.set_index('Date')['Cases']\n",
    "casos_it = df_it.set_index('Date')['Cases']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora concatenamos las variables recién definidas con la función `.concat` para crear una serie con ambos países y poder compararlos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cases</th>\n",
       "      <th>Cases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>822 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Cases     Cases\n",
       "Date                                         \n",
       "2020-01-22 00:00:00+00:00         0         0\n",
       "2020-01-23 00:00:00+00:00         0         0\n",
       "2020-01-24 00:00:00+00:00         0         0\n",
       "2020-01-25 00:00:00+00:00         0         0\n",
       "2020-01-26 00:00:00+00:00         0         0\n",
       "...                             ...       ...\n",
       "2022-04-18 00:00:00+00:00  11662214  15730676\n",
       "2022-04-19 00:00:00+00:00  11736893  15758002\n",
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       "2022-04-22 00:00:00+00:00  11736893  16008181\n",
       "\n",
       "[822 rows x 2 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([casos_es,casos_it],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pero con los datos presentados de esta manera no rsulta intuitivo inferir qué columna es para cada país. Necesitamos nombrarlas. Para ello creamos una variable (\"vs\") con la sintaxis anterior para nombrar de esta forma la primera fila y poder renombrar sus categorías. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "                              Cases     Cases\n",
       "Date                                         \n",
       "2020-01-22 00:00:00+00:00         0         0\n",
       "2020-01-23 00:00:00+00:00         0         0\n",
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       "2020-01-25 00:00:00+00:00         0         0\n",
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       "...                             ...       ...\n",
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       "2022-04-22 00:00:00+00:00  11736893  16008181\n",
       "\n",
       "[822 rows x 2 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs = pd.concat([casos_es,casos_it],axis=1)\n",
    "vs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A  esta variable creada le añadimos la función `.columns` indiccándole las categorías."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "vs.columns = ['España', 'Italia']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lo comprobamos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['España', 'Italia'], dtype='object')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lo ejecutamos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "                             España    Italia\n",
       "Date                                         \n",
       "2020-01-22 00:00:00+00:00         0         0\n",
       "2020-01-23 00:00:00+00:00         0         0\n",
       "2020-01-24 00:00:00+00:00         0         0\n",
       "2020-01-25 00:00:00+00:00         0         0\n",
       "2020-01-26 00:00:00+00:00         0         0\n",
       "...                             ...       ...\n",
       "2022-04-18 00:00:00+00:00  11662214  15730676\n",
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       "2022-04-22 00:00:00+00:00  11736893  16008181\n",
       "\n",
       "[822 rows x 2 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora que ya tenemos los datos a pintar respresentados de esta manera clara aplicamos la función `.plot(title=\"...\")` sobre la variable anterior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Comparación de la evolución de la pandemia en España e Italia'}, xlabel='Date'>"
      ]
     },
     "execution_count": 83,
     "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=\"Comparación de la evolución de la pandemia en España e Italia\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ya tenemos el resultado. Ahora puede ser interesante incluir el país restante."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparación de los tres países: España - Italia - Francia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Repetimos el proceso anterior creando la variable para los casos de Francia. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "casos_es = df_es.set_index('Date')['Cases']\n",
    "casos_it = df_it.set_index('Date')['Cases']\n",
    "casos_fr = df_fr.set_index('Date')['Cases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "                                Cases       Cases     Cases\n",
       "Date                                                       \n",
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       "2020-01-22 00:00:00+00:00         0.0         0.0         0\n",
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       "2020-01-22 00:00:00+00:00         0.0         0.0         0\n",
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       "\n",
       "[9865 rows x 3 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([casos_es,casos_it,casos_fr],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>2022-04-22 00:00:00+00:00</th>\n",
       "      <td>11736893.0</td>\n",
       "      <td>16008181.0</td>\n",
       "      <td>37038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-23 00:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27416764</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9865 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Cases       Cases     Cases\n",
       "Date                                                       \n",
       "2020-01-22 00:00:00+00:00         0.0         0.0         0\n",
       "2020-01-22 00:00:00+00:00         0.0         0.0         0\n",
       "2020-01-22 00:00:00+00:00         0.0         0.0         0\n",
       "2020-01-22 00:00:00+00:00         0.0         0.0         0\n",
       "2020-01-22 00:00:00+00:00         0.0         0.0         0\n",
       "...                               ...         ...       ...\n",
       "2022-04-22 00:00:00+00:00  11736893.0  16008181.0      2641\n",
       "2022-04-22 00:00:00+00:00  11736893.0  16008181.0       454\n",
       "2022-04-22 00:00:00+00:00  11736893.0  16008181.0     72648\n",
       "2022-04-22 00:00:00+00:00  11736893.0  16008181.0     37038\n",
       "2022-04-23 00:00:00+00:00         NaN         NaN  27416764\n",
       "\n",
       "[9865 rows x 3 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs = pd.concat([casos_es,casos_it,casos_fr],axis=1)\n",
    "vs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "vs.columns = ['España', 'Italia', 'Francia']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para que no nos pinte la franja con los casos acumulados de los tres países sino que distinga la de cada país añadimos `kind='area'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'España vs Italia vs Francia'}, xlabel='Date'>"
      ]
     },
     "execution_count": 109,
     "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 Francia\", kind='area')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Guardar los datos (csv) y gráfico (png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al pinchar con el boton derecho encima de cada gráfico puedo gurardalo en el ordenador local como imagen. Pero también puedo hacerlo en Jupyter. Eso es últil para utilizar el archivo en otro cuaderno y para tener los datos representados en una hoja aparte. Para ello indicamos el nombre de la variable de nuestro gráfico añadiendo la función `.to_csv('...')`. Queremos guardarlo como un archivo csv para que guarde los datos tabulados. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "vs.to_csv('esvsit.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comprobamos que esté el archivo con un `ls`precedido de `!` porque es comando de Bash."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Api-pandas-folium.ipynb   Python-api-covid19-pandas.ipynb   \u001b[0m\u001b[01;36mShared_Resources\u001b[0m@\r\n",
      " esvsit.csv                Python-prueba.ipynb\r\n",
      "'Probando con R.ipynb'     \u001b[01;34mseaborn-data\u001b[0m/\r\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Api-pandas-folium.ipynb   Python-api-covid19-pandas.ipynb   Shared_Resources\r\n",
      " esvsit.csv\t\t   Python-prueba.ipynb\r\n",
      "'Probando con R.ipynb'\t   seaborn-data\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para guardarlo como imagen  necesitamos importar la librería Matplolib.pyplot Lo hacemos con su abreviación plt y utilizamos su función `.savefig`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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(title='Evolución pandemia: España vs Italia')\n",
    "plt.savefig('esvsitvsmx.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " api-pandas-folium.ipynb  'Probando con R.ipynb'\r\n",
      "'Borrador p.4.ipynb'\t   python-api-covid19-pandas.ipynb\r\n",
      " esvsit.csv\t\t   python-csv-covid19-pandas.ipynb\r\n",
      " esvsitvsmx.png\t\t   Python-prueba.ipynb\r\n",
      " practica-4\t\t   seaborn-data\r\n",
      " practica-4-grafico.png    Shared_Resources\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}