python-api-covid19-pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Uso de la API del Covid19 con Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La URL https://api.covid19api.com/countries\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instalacion librerias\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instalamos gracias a `!pip` la libreria de pandas, una bilbioteca de software escrita para manipular y analizar datos en Python"
   ]
  },
  {
   "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: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (1.21.1)\r\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2.8.1)\r\n",
      "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2020.4)\r\n",
      "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7.3->pandas) (1.14.0)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuracion de Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Configuramos panda como pd a la hora de importar la libreria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crear variable\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Asignamos a la variable URL, la dirección de la que queremos extraer los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://api.covid19api.com/countries\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crear el dataframe\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creamos el dataframe que nos va a servir para visualizar la información del lugar del cual extraemos los datos\n",
    "\n",
    "Por tanto decimos que el dataframe sea igual a la lectura que va a hacer pandas (que previamente lo hemos importado como pd), que \n",
    "lea los datos de la url con JSON (Java Script Object Notation)\n",
    "\n",
    "Y decimos que nos muestre el dataframe poniendo df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_json(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por defecto al haber tantos datos, nos saldran los cinco primeros y los cinco últimos aunque se puede cambiar utilizando \n",
    "`df.head`o `df.tail` junto con el numero de resultado que queremos que salgan en parentesis (n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploración de la tabla"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>246</th>\n",
       "      <td>Pitcairn</td>\n",
       "      <td>pitcairn</td>\n",
       "      <td>PN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>Tanzania, United Republic of</td>\n",
       "      <td>tanzania</td>\n",
       "      <td>TZ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Country      Slug ISO2\n",
       "246                      Pitcairn  pitcairn   PN\n",
       "247  Tanzania, United Republic of  tanzania   TZ"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para mostrar la información del dataframe utilizamos `df.info()` O para buscar en este caso el nombre en concreto de la lista `df[\"Country\"][\"N\"]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Somalia'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Country\"][66]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tiempo real \n",
    "https://api.covid19api.com/country/spain/status/confirmed/live"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora vamos a hacer el mismo ejercicio pero con un país en especifico\n",
    "\n",
    "Creamos la variable de España en este caso tanto de los datos como del dataframe (url_es y df_es)\n",
    "\n",
    "Igualamos la url a la dirección de la que queremos extraer los datos y el data frame a la lectura que queremos que haga pandas (previamente importado como pd)\n",
    "de la dirección de la que queremos extraer los datos gracias a JSON\n",
    "\n",
    "Ponemos df_es para que nos muestre la visualizacion de los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>798</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>11508309</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-03-30 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>11508309</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-03-31 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>11551574</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-01 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>11551574</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-02 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>11551574</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-03 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>803 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country CountryCode Province City CityCode    Lat   Lon     Cases  \\\n",
       "0     Spain          ES                         40.46 -3.75         0   \n",
       "1     Spain          ES                         40.46 -3.75         0   \n",
       "2     Spain          ES                         40.46 -3.75         0   \n",
       "3     Spain          ES                         40.46 -3.75         0   \n",
       "4     Spain          ES                         40.46 -3.75         0   \n",
       "..      ...         ...      ...  ...      ...    ...   ...       ...   \n",
       "798   Spain          ES                         40.46 -3.75  11508309   \n",
       "799   Spain          ES                         40.46 -3.75  11508309   \n",
       "800   Spain          ES                         40.46 -3.75  11551574   \n",
       "801   Spain          ES                         40.46 -3.75  11551574   \n",
       "802   Spain          ES                         40.46 -3.75  11551574   \n",
       "\n",
       "        Status                      Date  \n",
       "0    confirmed 2020-01-22 00:00:00+00:00  \n",
       "1    confirmed 2020-01-23 00:00:00+00:00  \n",
       "2    confirmed 2020-01-24 00:00:00+00:00  \n",
       "3    confirmed 2020-01-25 00:00:00+00:00  \n",
       "4    confirmed 2020-01-26 00:00:00+00:00  \n",
       "..         ...                       ...  \n",
       "798  confirmed 2022-03-30 00:00:00+00:00  \n",
       "799  confirmed 2022-03-31 00:00:00+00:00  \n",
       "800  confirmed 2022-04-01 00:00:00+00:00  \n",
       "801  confirmed 2022-04-02 00:00:00+00:00  \n",
       "802  confirmed 2022-04-03 00:00:00+00:00  \n",
       "\n",
       "[803 rows x 10 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url_es = \"https://api.covid19api.com/country/spain/status/confirmed/live\"\n",
    "df_es = pd.read_json(url_es)\n",
    "df_es"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Indagamos en la propia visualización de los datos investigando que tipo de datos nos muestra poniendo el df_es seguido de .columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'CountryCode', 'Province', 'City', 'CityCode', 'Lat', 'Lon',\n",
       "       'Cases', 'Status', 'Date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2020-01-22 00:00:00+00:00\n",
       "Name: Date, dtype: datetime64[ns, UTC]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es[\"Date\"].head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "802   2022-04-03 00:00:00+00:00\n",
       "Name: Date, dtype: datetime64[ns, UTC]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es[\"Date\"].tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 803 entries, 0 to 802\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype              \n",
      "---  ------       --------------  -----              \n",
      " 0   Country      803 non-null    object             \n",
      " 1   CountryCode  803 non-null    object             \n",
      " 2   Province     803 non-null    object             \n",
      " 3   City         803 non-null    object             \n",
      " 4   CityCode     803 non-null    object             \n",
      " 5   Lat          803 non-null    float64            \n",
      " 6   Lon          803 non-null    float64            \n",
      " 7   Cases        803 non-null    int64              \n",
      " 8   Status       803 non-null    object             \n",
      " 9   Date         803 non-null    datetime64[ns, UTC]\n",
      "dtypes: datetime64[ns, UTC](1), float64(2), int64(1), object(6)\n",
      "memory usage: 62.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_es.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este caso le pedimos que el dataframe haga una descripción de los datos "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.030000e+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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este caso le decimos que nos muestre los datos haciendo un índice de los datos clasificados por la fecha"
   ]
  },
  {
   "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",
       "    }\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>2022-03-30 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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.set_index(\"Date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este caso solo queremos que nos saque solamente la fecha y los casos que se ubican en los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.set_index(\"Date\")[\"Cases\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Teniendo en cuenta las funciones que hemos visto para ordenar y buscar los datos hacemos que se nos muestre el dataframe a través de un gráfico.\n",
    "Lo primero le decimos que nos saque las fechas y los casos de los datos leídos. Utilzamos `plot`para designar el gráfico y \n",
    "entre paréntesis indicamos el nombre de dicho grafico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Casos de Covid19 en España'}, xlabel='Date'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_es.set_index(\"Date\")[\"Cases\"].plot(title=\"Casos de Covid19 en España\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora repetimos el mismo procedimiento con dos países más para hacer una comparación "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Otro País"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>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>Italy</td>\n",
       "      <td>IT</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>41.87</td>\n",
       "      <td>12.57</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>Italy</td>\n",
       "      <td>IT</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>41.87</td>\n",
       "      <td>12.57</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>Italy</td>\n",
       "      <td>IT</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>41.87</td>\n",
       "      <td>12.57</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>Italy</td>\n",
       "      <td>IT</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>41.87</td>\n",
       "      <td>12.57</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>Italy</td>\n",
       "      <td>IT</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>41.87</td>\n",
       "      <td>12.57</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-26 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>Italy</td>\n",
       "      <td>IT</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>41.87</td>\n",
       "      <td>12.57</td>\n",
       "      <td>14567990</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-03-30 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>Italy</td>\n",
       "      <td>IT</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>41.87</td>\n",
       "      <td>12.57</td>\n",
       "      <td>14642354</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-03-31 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>Italy</td>\n",
       "      <td>IT</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>41.87</td>\n",
       "      <td>12.57</td>\n",
       "      <td>14719394</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-01 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>Italy</td>\n",
       "      <td>IT</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>41.87</td>\n",
       "      <td>12.57</td>\n",
       "      <td>14790806</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-02 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>Italy</td>\n",
       "      <td>IT</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>41.87</td>\n",
       "      <td>12.57</td>\n",
       "      <td>14845815</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-03 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>803 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country CountryCode Province City CityCode    Lat    Lon     Cases  \\\n",
       "0     Italy          IT                         41.87  12.57         0   \n",
       "1     Italy          IT                         41.87  12.57         0   \n",
       "2     Italy          IT                         41.87  12.57         0   \n",
       "3     Italy          IT                         41.87  12.57         0   \n",
       "4     Italy          IT                         41.87  12.57         0   \n",
       "..      ...         ...      ...  ...      ...    ...    ...       ...   \n",
       "798   Italy          IT                         41.87  12.57  14567990   \n",
       "799   Italy          IT                         41.87  12.57  14642354   \n",
       "800   Italy          IT                         41.87  12.57  14719394   \n",
       "801   Italy          IT                         41.87  12.57  14790806   \n",
       "802   Italy          IT                         41.87  12.57  14845815   \n",
       "\n",
       "        Status                      Date  \n",
       "0    confirmed 2020-01-22 00:00:00+00:00  \n",
       "1    confirmed 2020-01-23 00:00:00+00:00  \n",
       "2    confirmed 2020-01-24 00:00:00+00:00  \n",
       "3    confirmed 2020-01-25 00:00:00+00:00  \n",
       "4    confirmed 2020-01-26 00:00:00+00:00  \n",
       "..         ...                       ...  \n",
       "798  confirmed 2022-03-30 00:00:00+00:00  \n",
       "799  confirmed 2022-03-31 00:00:00+00:00  \n",
       "800  confirmed 2022-04-01 00:00:00+00:00  \n",
       "801  confirmed 2022-04-02 00:00:00+00:00  \n",
       "802  confirmed 2022-04-03 00:00:00+00:00  \n",
       "\n",
       "[803 rows x 10 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url_it = \"https://api.covid19api.com/country/italy/status/confirmed/live\"\n",
    "df_it = pd.read_json(url_it)\n",
    "df_it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Casos de Covid19 en Italia'}, xlabel='Date'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_it.set_index(\"Date\")[\"Cases\"].plot(title=\"Casos de Covid19 en Italia\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Otro nuevo caso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Casos de Covid19 en Alemania'}, xlabel='Date'>"
      ]
     },
     "execution_count": 30,
     "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_ger = \"https://api.covid19api.com/country/germany/status/confirmed/live\"\n",
    "df_ger = pd.read_json(url_ger)\n",
    "df_ger.set_index(\"Date\")[\"Cases\"].plot(title=\"Casos de Covid19 en Alemania\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparacion \n",
    "Compararemos estos tres países en un solo gráfico"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lo primero creamos una variable para cada uno de los dataframe de los tres países que hemos escogido, en este caso España, Italia y Alemania\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "casos_es = df_es.set_index(\"Date\")[\"Cases\"]\n",
    "casos_it = df_it.set_index(\"Date\")[\"Cases\"]\n",
    "casos_ger = df_ger.set_index(\"Date\")[\"Cases\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creamos la variable de vs para visualizar los datos que igualamos a la concatenación de los dataframe de cada pais que hacemos gracias a panda por ello `pd.concat`\n",
    "Finalmente ponemos `axis=1`para ordenar los países en una sola fila\n",
    "Escribimos vs para mostranos la concatenacion de los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "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>2022-03-30 00:00:00+00:00</th>\n",
       "      <td>11508309</td>\n",
       "      <td>14567990</td>\n",
       "      <td>20829608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-31 00:00:00+00:00</th>\n",
       "      <td>11508309</td>\n",
       "      <td>14642354</td>\n",
       "      <td>21394747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-01 00:00:00+00:00</th>\n",
       "      <td>11551574</td>\n",
       "      <td>14719394</td>\n",
       "      <td>21394691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-02 00:00:00+00:00</th>\n",
       "      <td>11551574</td>\n",
       "      <td>14790806</td>\n",
       "      <td>21591147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-03 00:00:00+00:00</th>\n",
       "      <td>11551574</td>\n",
       "      <td>14845815</td>\n",
       "      <td>21706329</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>803 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",
       "2022-03-30 00:00:00+00:00  11508309  14567990  20829608\n",
       "2022-03-31 00:00:00+00:00  11508309  14642354  21394747\n",
       "2022-04-01 00:00:00+00:00  11551574  14719394  21394691\n",
       "2022-04-02 00:00:00+00:00  11551574  14790806  21591147\n",
       "2022-04-03 00:00:00+00:00  11551574  14845815  21706329\n",
       "\n",
       "[803 rows x 3 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs = pd.concat([casos_es,casos_it,casos_ger],axis=1)\n",
    "vs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora las nombramos cada una de las columnas de vs como los nombres de los países"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>España</th>\n",
       "      <th>Italia</th>\n",
       "      <th>Alemania</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>2022-03-30 00:00:00+00:00</th>\n",
       "      <td>11508309</td>\n",
       "      <td>14567990</td>\n",
       "      <td>20829608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-31 00:00:00+00:00</th>\n",
       "      <td>11508309</td>\n",
       "      <td>14642354</td>\n",
       "      <td>21394747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-01 00:00:00+00:00</th>\n",
       "      <td>11551574</td>\n",
       "      <td>14719394</td>\n",
       "      <td>21394691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-02 00:00:00+00:00</th>\n",
       "      <td>11551574</td>\n",
       "      <td>14790806</td>\n",
       "      <td>21591147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-03 00:00:00+00:00</th>\n",
       "      <td>11551574</td>\n",
       "      <td>14845815</td>\n",
       "      <td>21706329</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>803 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             España    Italia  Alemania\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",
       "2022-03-30 00:00:00+00:00  11508309  14567990  20829608\n",
       "2022-03-31 00:00:00+00:00  11508309  14642354  21394747\n",
       "2022-04-01 00:00:00+00:00  11551574  14719394  21394691\n",
       "2022-04-02 00:00:00+00:00  11551574  14790806  21591147\n",
       "2022-04-03 00:00:00+00:00  11551574  14845815  21706329\n",
       "\n",
       "[803 rows x 3 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs.columns = [\"España\",\"Italia\",\"Alemania\"]\n",
    "vs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para mostranos la visualizacion a través de un grafico utilizamos `plot`con la variable vs. Le ponemos titulo e indicamos el tipo de gráfico que queremos mostrar,\n",
    "en este caso un gráfico en el que se aprecie el área de cada uno de los países."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Casos de Covid19 en España, Italia y Alemania'}, xlabel='Date'>"
      ]
     },
     "execution_count": 41,
     "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=\"Casos de Covid19 en España, Italia y Alemania\", kind=\"area\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Guardar el gráfico \n",
    "Lo primero transformamos el grafico al formato de datos de csv y entre parentesis indicamos el nombre del archivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "vs.to_csv(\"esvsit.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizamos la funcion `!ls`para comprobar si se ha guardado correctamente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "esvsit.csv\t\t\t python-prueba.ipynb  Shared_Resources\r\n",
      "python-api-covid19-pandas.ipynb  seaborn-data\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizamos el mismo gráfico, pero ahora vamos a guardarlo como una imagen en PNG.\n",
    "Importamos la libreria que nos permite guardar imagenes en pyhon `matplotlib.pypplot`y lo importamos como plt.\n",
    "Hacemos que aparezca el gráfico y utilizamos plt para guardar la imagen del gráfico con el nombre \"esvsit.png\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "vs.plot()\n",
    "plt.savefig(\"esvsit.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comprobamos una última vez que todo esta guardado correctamente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "esvsit.csv  python-api-covid19-pandas.ipynb  seaborn-data\r\n",
      "esvsit.png  python-prueba.ipynb\t\t     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
}