python-api-covid19-pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uso de la API del Covid19 con Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a hacer un ejercicio a partir de la siguiente url: https://api.covid19api.com/countries, que nos aporta datos sobre Covid según distintos países del mundo.\n",
    "\n",
    "El término API es una abreviatura de **Application Programming Interfaces**, que en español significa Interfaz de Programación de Aplicaciones. El concepto hace referencia a los procesos, las funciones y los métodos que aporta una determinada biblioteca de programación para que sea empleada por otro programa informático, permitiendo la comunicación entre dos aplicaciones de software a través de un conjunto de reglas.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instalación librerías"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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: 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: 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": [
    "Recordemos que el símbolo de exclamación hacia abajo `!` significa que no estamos ejecutando Python sino bash"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuración de Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Pandas** es una librería de Phtyon especializada en el manejo y análisis de estructuras de datos, que incluye distintos tipos de estructuras de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para utilizar esta librería, usamos la convención de pandas como `pd`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crear variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a crear la variable asignando la dirección a la url del principio. Para ello, lo hacemos a través del signo = e incluimos la dirección del url entre comillas, porque es una cadena de caracteres (string)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://api.covid19api.com/countries'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crear DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los **DataFrame** por convención se definen como `df`. El Dataframe utiliza pandas y usamos una función que lee un json y dentro de ella, el valor de lo que queremos que lea (la url que he definido antes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_json(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Slug</th>\n",
       "      <th>ISO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>French Polynesia</td>\n",
       "      <td>french-polynesia</td>\n",
       "      <td>PF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>indonesia</td>\n",
       "      <td>ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Malta</td>\n",
       "      <td>malta</td>\n",
       "      <td>MT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>sao-tome-and-principe</td>\n",
       "      <td>ST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>haiti</td>\n",
       "      <td>HT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>myanmar</td>\n",
       "      <td>MM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>turkey</td>\n",
       "      <td>TR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>western-sahara</td>\n",
       "      <td>EH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>Pitcairn</td>\n",
       "      <td>pitcairn</td>\n",
       "      <td>PN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>Tanzania, United Republic of</td>\n",
       "      <td>tanzania</td>\n",
       "      <td>TZ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>248 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Country                   Slug ISO2\n",
       "0                French Polynesia       french-polynesia   PF\n",
       "1                       Indonesia              indonesia   ID\n",
       "2                           Malta                  malta   MT\n",
       "3           Sao Tome and Principe  sao-tome-and-principe   ST\n",
       "4                           Haiti                  haiti   HT\n",
       "..                            ...                    ...  ...\n",
       "243                       Myanmar                myanmar   MM\n",
       "244                        Turkey                 turkey   TR\n",
       "245                Western Sahara         western-sahara   EH\n",
       "246                      Pitcairn               pitcairn   PN\n",
       "247  Tanzania, United Republic of               tanzania   TZ\n",
       "\n",
       "[248 rows x 3 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploración de la tabla"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La función `head` funciona con 'int', por lo que devuelve en número de filas el valor del entero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>ISO2</th>\n",
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       "      <td>French Polynesia</td>\n",
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       "      <td>PF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>indonesia</td>\n",
       "      <td>ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Malta</td>\n",
       "      <td>malta</td>\n",
       "      <td>MT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>sao-tome-and-principe</td>\n",
       "      <td>ST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>haiti</td>\n",
       "      <td>HT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                 Country                   Slug ISO2\n",
       "0       French Polynesia       french-polynesia   PF\n",
       "1              Indonesia              indonesia   ID\n",
       "2                  Malta                  malta   MT\n",
       "3  Sao Tome and Principe  sao-tome-and-principe   ST\n",
       "4                  Haiti                  haiti   HT"
      ]
     },
     "execution_count": 10,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Slug</th>\n",
       "      <th>ISO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>French Polynesia</td>\n",
       "      <td>french-polynesia</td>\n",
       "      <td>PF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>indonesia</td>\n",
       "      <td>ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Malta</td>\n",
       "      <td>malta</td>\n",
       "      <td>MT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>sao-tome-and-principe</td>\n",
       "      <td>ST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>haiti</td>\n",
       "      <td>HT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Poland</td>\n",
       "      <td>poland</td>\n",
       "      <td>PL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Country                   Slug ISO2\n",
       "0       French Polynesia       french-polynesia   PF\n",
       "1              Indonesia              indonesia   ID\n",
       "2                  Malta                  malta   MT\n",
       "3  Sao Tome and Principe  sao-tome-and-principe   ST\n",
       "4                  Haiti                  haiti   HT\n",
       "5                 Poland                 poland   PL"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al utilizar la función `df.head()` e introducir el número 6 dentro de los paréntesis, nos devuelve los seis primeros valores de la tabla (la cabecera)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lo mismo ocurre si usamos `df.tail()`, pero en este caso nos devuelve los últimos valores (cola)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>Country</th>\n",
       "      <th>Slug</th>\n",
       "      <th>ISO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>Pitcairn</td>\n",
       "      <td>pitcairn</td>\n",
       "      <td>PN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>Tanzania, United Republic of</td>\n",
       "      <td>tanzania</td>\n",
       "      <td>TZ</td>\n",
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      "text/plain": [
       "                          Country      Slug ISO2\n",
       "246                      Pitcairn  pitcairn   PN\n",
       "247  Tanzania, United Republic of  tanzania   TZ"
      ]
     },
     "execution_count": 13,
     "metadata": {},
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   ],
   "source": [
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La función `df.info()` nos proporciona información general sobre el DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                  French Polynesia\n",
       "1                         Indonesia\n",
       "2                             Malta\n",
       "3             Sao Tome and Principe\n",
       "4                             Haiti\n",
       "                   ...             \n",
       "243                         Myanmar\n",
       "244                          Turkey\n",
       "245                  Western Sahara\n",
       "246                        Pitcairn\n",
       "247    Tanzania, United Republic of\n",
       "Name: Country, Length: 248, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si queremos saber un valor en concreto dentro de toda la columna, introducimos su número entre paréntesis y nos dará el valor correspondiente. En este caso accedemos al Index 66."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Somalia'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country'][66]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este caso ponemos corchetes y no paréntesis porque es una lista. Lo hacemos para diferenciarlo con corchetes, llaves y paréntesis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tiempo real\n",
    "La URL que vamos a utilizar ahora es la siguiente, que ofrece datos a tiempo real para España:\n",
    "https://api.covid19api.com/country/spain/status/confirmed/live \n",
    "\n",
    "Dado que durante todo este ejercicio solo emplearemos datos de España, llamaremos a esta dirección url_es (lo mismo haremos con df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>Province</th>\n",
       "      <th>City</th>\n",
       "      <th>CityCode</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Status</th>\n",
       "      <th>Date</th>\n",
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       "    <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>814</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>11627487</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-15 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</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>11627487</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-16 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>11627487</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-17 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>11627487</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>11627487</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-19 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>819 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",
       "814   Spain          ES                         40.46 -3.75  11627487   \n",
       "815   Spain          ES                         40.46 -3.75  11627487   \n",
       "816   Spain          ES                         40.46 -3.75  11627487   \n",
       "817   Spain          ES                         40.46 -3.75  11627487   \n",
       "818   Spain          ES                         40.46 -3.75  11627487   \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",
       "814  confirmed 2022-04-15 00:00:00+00:00  \n",
       "815  confirmed 2022-04-16 00:00:00+00:00  \n",
       "816  confirmed 2022-04-17 00:00:00+00:00  \n",
       "817  confirmed 2022-04-18 00:00:00+00:00  \n",
       "818  confirmed 2022-04-19 00:00:00+00:00  \n",
       "\n",
       "[819 rows x 10 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url_es = 'https://api.covid19api.com/country/spain/status/confirmed/live'\n",
    "df_es = pd.read_json(url_es)\n",
    "df_es"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>Province</th>\n",
       "      <th>City</th>\n",
       "      <th>CityCode</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Cases</th>\n",
       "      <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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'CountryCode', 'Province', 'City', 'CityCode', 'Lat', 'Lon',\n",
       "       'Cases', 'Status', 'Date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2020-01-22 00:00:00+00:00\n",
       "Name: Date, dtype: datetime64[ns, UTC]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es['Date'].head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "802   2022-04-03 00:00:00+00:00\n",
       "Name: Date, dtype: datetime64[ns, UTC]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es['Date'].tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.030000e+02</td>\n",
       "      <td>803.00</td>\n",
       "      <td>8.030000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>3.242248e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.109856e-15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.109442e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>3.186710e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>3.149012e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>4.920388e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>1.155157e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Lat     Lon         Cases\n",
       "count  8.030000e+02  803.00  8.030000e+02\n",
       "mean   4.046000e+01   -3.75  3.242248e+06\n",
       "std    7.109856e-15    0.00  3.109442e+06\n",
       "min    4.046000e+01   -3.75  0.000000e+00\n",
       "25%    4.046000e+01   -3.75  3.186710e+05\n",
       "50%    4.046000e+01   -3.75  3.149012e+06\n",
       "75%    4.046000e+01   -3.75  4.920388e+06\n",
       "max    4.046000e+01   -3.75  1.155157e+07"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El valor de la gráfica es el número de casos. Para hacer un gráfico pondremos en el eje de la \"x\" el número de casos y en el de la \"y\" las fechas. Para ayudarnos a elaborar el gráfico sobre estos datos, vamos a coger un dato que podemos utilizar como índice, utilizando para este propósito la función `set_index`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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",
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       "      <th>2020-01-22 00:00:00+00:00</th>\n",
       "      <td>Spain</td>\n",
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       "    <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",
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       "    <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",
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       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
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       "    <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",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-30 00:00:00+00:00</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>11508309</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-31 00:00:00+00:00</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>11508309</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-01 00:00:00+00:00</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>11551574</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-02 00:00:00+00:00</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>11551574</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-03 00:00:00+00:00</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>11551574</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>803 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Country CountryCode Province City CityCode    Lat  \\\n",
       "Date                                                                          \n",
       "2020-01-22 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2020-01-23 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2020-01-24 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2020-01-25 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2020-01-26 00:00:00+00:00   Spain          ES                         40.46   \n",
       "...                           ...         ...      ...  ...      ...    ...   \n",
       "2022-03-30 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2022-03-31 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2022-04-01 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2022-04-02 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2022-04-03 00:00:00+00:00   Spain          ES                         40.46   \n",
       "\n",
       "                            Lon     Cases     Status  \n",
       "Date                                                  \n",
       "2020-01-22 00:00:00+00:00 -3.75         0  confirmed  \n",
       "2020-01-23 00:00:00+00:00 -3.75         0  confirmed  \n",
       "2020-01-24 00:00:00+00:00 -3.75         0  confirmed  \n",
       "2020-01-25 00:00:00+00:00 -3.75         0  confirmed  \n",
       "2020-01-26 00:00:00+00:00 -3.75         0  confirmed  \n",
       "...                         ...       ...        ...  \n",
       "2022-03-30 00:00:00+00:00 -3.75  11508309  confirmed  \n",
       "2022-03-31 00:00:00+00:00 -3.75  11508309  confirmed  \n",
       "2022-04-01 00:00:00+00:00 -3.75  11551574  confirmed  \n",
       "2022-04-02 00:00:00+00:00 -3.75  11551574  confirmed  \n",
       "2022-04-03 00:00:00+00:00 -3.75  11551574  confirmed  \n",
       "\n",
       "[803 rows x 9 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.set_index('Date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "He convertido la columna de \"fecha\" en la columna de índice. La otra columna que quiero tener es la de \"casos confirmados\". Es decir, quiero una tabla que incluya ambos datos"
   ]
  },
  {
   "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",
       "2022-03-30 00:00:00+00:00    11508309\n",
       "2022-03-31 00:00:00+00:00    11508309\n",
       "2022-04-01 00:00:00+00:00    11551574\n",
       "2022-04-02 00:00:00+00:00    11551574\n",
       "2022-04-03 00:00:00+00:00    11551574\n",
       "Name: Cases, Length: 803, dtype: int64"
      ]
     },
     "execution_count": 30,
     "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)\n",
    "\n",
    "Sin embargo, aquí solo podemos observar 10 primeras filas sobre los datos de origen. \n",
    "\n",
    "Para poder visualizar los datos en su totalidad, acudimos a la creación de un gráfico. Para ello, a lo anterior le digo que me lo pinte. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Casos de Covid19 en España'}, xlabel='Date'>"
      ]
     },
     "execution_count": 33,
     "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 añadir lo último (title=...), le pongo el título que yo quiero al gráfico."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Segundo país: Italia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Casos de Covid19 en Italia'}, xlabel='Date'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "url_it = 'https://api.covid19api.com/country/italy/status/confirmed/live'\n",
    "df_it = pd.read_json(url_it)\n",
    "df_it.set_index('Date')['Cases'].plot(title='Casos de Covid19 en Italia')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tercer país: México"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Casos de Covid19 en México'}, xlabel='Date'>"
      ]
     },
     "execution_count": 32,
     "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_mx = 'https://api.covid19api.com/country/mexico/status/confirmed/live'\n",
    "df_mx = pd.read_json(url_mx)\n",
    "df_mx.set_index('Date')['Cases'].plot(title='Casos de Covid19 en México')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparación entre los tres países"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para obtener una información más completa, comparamos los tres países en un solo gráfico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "casos_es = df_es.set_index('Date')['Cases']\n",
    "casos_it = df_it.set_index('Date')['Cases']\n",
    "casos_mx = df_mx.set_index('Date')['Cases']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a usar la función `.concat` para crear una serie con los tres países y poder compararlos entre sí"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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",
       "      <th>Cases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>0</td>\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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-15 00:00:00+00:00</th>\n",
       "      <td>11627487</td>\n",
       "      <td>15595302</td>\n",
       "      <td>5725476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-16 00:00:00+00:00</th>\n",
       "      <td>11627487</td>\n",
       "      <td>15659835</td>\n",
       "      <td>5726668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-17 00:00:00+00:00</th>\n",
       "      <td>11627487</td>\n",
       "      <td>15712088</td>\n",
       "      <td>5727668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-18 00:00:00+00:00</th>\n",
       "      <td>11627487</td>\n",
       "      <td>15730676</td>\n",
       "      <td>5727668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-19 00:00:00+00:00</th>\n",
       "      <td>11627487</td>\n",
       "      <td>15758002</td>\n",
       "      <td>5729270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>819 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\n",
       "2020-01-23 00:00:00+00:00         0         0        0\n",
       "2020-01-24 00:00:00+00:00         0         0        0\n",
       "2020-01-25 00:00:00+00:00         0         0        0\n",
       "2020-01-26 00:00:00+00:00         0         0        0\n",
       "...                             ...       ...      ...\n",
       "2022-04-15 00:00:00+00:00  11627487  15595302  5725476\n",
       "2022-04-16 00:00:00+00:00  11627487  15659835  5726668\n",
       "2022-04-17 00:00:00+00:00  11627487  15712088  5727668\n",
       "2022-04-18 00:00:00+00:00  11627487  15730676  5727668\n",
       "2022-04-19 00:00:00+00:00  11627487  15758002  5729270\n",
       "\n",
       "[819 rows x 3 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs = pd.concat([casos_es,casos_it,casos_mx],axis=1)\n",
    "vs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'España vs Italia vs México'}, xlabel='Date'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vs.columns = ['España', 'Italia', 'México']\n",
    "vs.plot(title='España vs Italia vs México')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'España vs Italia vs México'}, xlabel='Date'>"
      ]
     },
     "execution_count": 40,
     "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.columns = ['España', 'Italia', 'México']\n",
    "vs.plot(title=\"España vs Italia vs México\", kind='area')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Guardar el progreso"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Después guardamos este gráfico en csv. Nuestro DataFrame, que había sido denominado como vs, se guardará en nuestro perfil de Jupyter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "vs.to_csv('esvsitvmx.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Con el `!`, como explicamos previamente, indicamos que estamos ejecutando Bash y no Python. Mediante la función ls listamos y comprobamos si se ha guardado correctamente y dónde se ha guardado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "api-pandas-folium.ipynb  probando-con-r.ipynb\t\t  Shared_Resources\r\n",
      "esvsitvmx.csv\t\t python-api-covid19-pandas.ipynb  Untitled.ipynb\r\n",
      "esvsitvmx.png\t\t python-pruebas.ipynb\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Previamente, con csv, hemos guardado los datos. Sin embargo, también queremos guardar la gráfica como imagen. Por tanto, usamos el comando `matplotlib` (una librería que permite visualizar datos en Jupyter) y de ahí, guardamos en png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "vs.plot()\n",
    "plt.savefig('esvsitvmx.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "api-pandas-folium.ipynb  probando-con-r.ipynb\t\t  Shared_Resources\r\n",
      "esvsitvmx.csv\t\t python-api-covid19-pandas.ipynb  Untitled.ipynb\r\n",
      "esvsitvmx.png\t\t python-pruebas.ipynb\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por último, comprobamos dónde se ha guardado y si hemos ejecutado correctamente el último comando"
   ]
  }
 ],
 "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
}