python-api-covid19-pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Uso de la API del COVID19 con Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Base de datos de interés, como URL y en formato JSON\n",
    "http://api.covid19api.com/countries "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instalar las librerías con las que vamos a trabajar \n",
    "\n",
    "Pandas es una librería estándar de python para el manejo de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instalamos pandas con el comando `!pip install`, que es lenguaje de python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "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": [
    "## Configuración de Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importamos la librería pandas y la renombramos como \"pd\" para utilizarla en el resto del notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creación de variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creamos una variable que contiene la base de datos que vamos a usar mediante `=`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://api.covid19api.com/countries'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creación del Dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creamos un dataframe de pandas leyendo el JSON de la base de datos\n",
    "\n",
    "Según la [documentación oficial de pandas](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html), un dataframe es una estructura de datos de dos dimensiones \"de tamaño variable y potencialmente heterogéneos\".\n",
    "\n",
    "Un JSON, según hemos visto en la asignatura, es un tipo de familia de datos. Concretamente, JSON es una notación de objetos en Javascript. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_json(url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mostramos la tabla\n",
    "\n",
    "Introduciendo df (que es como hemos nombrado al datafrmae), mostramos la tabla "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Slug</th>\n",
       "      <th>ISO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Honduras</td>\n",
       "      <td>honduras</td>\n",
       "      <td>HN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>nicaragua</td>\n",
       "      <td>NI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>tunisia</td>\n",
       "      <td>TN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gibraltar</td>\n",
       "      <td>gibraltar</td>\n",
       "      <td>GI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Georgia and the South Sandwich Islands</td>\n",
       "      <td>south-georgia-and-the-south-sandwich-islands</td>\n",
       "      <td>GS</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>Holy See (Vatican City State)</td>\n",
       "      <td>holy-see-vatican-city-state</td>\n",
       "      <td>VA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>Lesotho</td>\n",
       "      <td>lesotho</td>\n",
       "      <td>LS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>Norfolk Island</td>\n",
       "      <td>norfolk-island</td>\n",
       "      <td>NF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>Palau</td>\n",
       "      <td>palau</td>\n",
       "      <td>PW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>Wallis and Futuna Islands</td>\n",
       "      <td>wallis-and-futuna-islands</td>\n",
       "      <td>WF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>248 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Country  \\\n",
       "0                                        Honduras   \n",
       "1                                       Nicaragua   \n",
       "2                                         Tunisia   \n",
       "3                                       Gibraltar   \n",
       "4    South Georgia and the South Sandwich Islands   \n",
       "..                                            ...   \n",
       "243                 Holy See (Vatican City State)   \n",
       "244                                       Lesotho   \n",
       "245                                Norfolk Island   \n",
       "246                                         Palau   \n",
       "247                     Wallis and Futuna Islands   \n",
       "\n",
       "                                             Slug ISO2  \n",
       "0                                        honduras   HN  \n",
       "1                                       nicaragua   NI  \n",
       "2                                         tunisia   TN  \n",
       "3                                       gibraltar   GI  \n",
       "4    south-georgia-and-the-south-sandwich-islands   GS  \n",
       "..                                            ...  ...  \n",
       "243                   holy-see-vatican-city-state   VA  \n",
       "244                                       lesotho   LS  \n",
       "245                                norfolk-island   NF  \n",
       "246                                         palau   PW  \n",
       "247                     wallis-and-futuna-islands   WF  \n",
       "\n",
       "[248 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploración de la tabla"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cabecera\n",
    "\n",
    "Mediante `df.head()` se muestran las primeras entradas de la tabla (cabecera o head), entre paréntesis se pueden indicar cuántas entradas se quieren mostrar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "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>Honduras</td>\n",
       "      <td>honduras</td>\n",
       "      <td>HN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>nicaragua</td>\n",
       "      <td>NI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>tunisia</td>\n",
       "      <td>TN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gibraltar</td>\n",
       "      <td>gibraltar</td>\n",
       "      <td>GI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Georgia and the South Sandwich Islands</td>\n",
       "      <td>south-georgia-and-the-south-sandwich-islands</td>\n",
       "      <td>GS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Country  \\\n",
       "0                                      Honduras   \n",
       "1                                     Nicaragua   \n",
       "2                                       Tunisia   \n",
       "3                                     Gibraltar   \n",
       "4  South Georgia and the South Sandwich Islands   \n",
       "\n",
       "                                           Slug ISO2  \n",
       "0                                      honduras   HN  \n",
       "1                                     nicaragua   NI  \n",
       "2                                       tunisia   TN  \n",
       "3                                     gibraltar   GI  \n",
       "4  south-georgia-and-the-south-sandwich-islands   GS  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tail de la tabla\n",
    "\n",
    "Ahora se muestran las últimas entradas de la tabla (tail) mediante `df.tail()`\n",
    "\n",
    "De nuevo, entre paréntesis se puede espeficiar el número de entradas que se quieren mostrar. En este caso se muestran dos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>246</th>\n",
       "      <td>Palau</td>\n",
       "      <td>palau</td>\n",
       "      <td>PW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>Wallis and Futuna Islands</td>\n",
       "      <td>wallis-and-futuna-islands</td>\n",
       "      <td>WF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                       Country                       Slug ISO2\n",
       "246                      Palau                      palau   PW\n",
       "247  Wallis and Futuna Islands  wallis-and-futuna-islands   WF"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mostramos la información de la tabla como objeto de pandas\n",
    "\n",
    "Mediante `df.info()` se muestra la información de la tabla\n",
    "\n",
    "Según la [página web de pandas](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.info.html), muestra información sobre el dataframe entre la que incluye el índice (RangeIndex) con el número de entradas, el número de columnas que hay (Data Columns), el nombre de las columnas (Colum), la cantidad de datos nulos que posee la tabla (Non-Null Count), el tipo de variable de las columnas (Dtype) y el tamaño (memory usage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 248 entries, 0 to 247\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   Country  248 non-null    object\n",
      " 1   Slug     248 non-null    object\n",
      " 2   ISO2     248 non-null    object\n",
      "dtypes: object(3)\n",
      "memory usage: 5.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mostramos la columna Country\n",
    "\n",
    "`df[]` permite mostrar una columna del datafram en concreto. En este caso se ha elegido la columna de los países, \"Country\": "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                          Honduras\n",
       "1                                         Nicaragua\n",
       "2                                           Tunisia\n",
       "3                                         Gibraltar\n",
       "4      South Georgia and the South Sandwich Islands\n",
       "                           ...                     \n",
       "243                   Holy See (Vatican City State)\n",
       "244                                         Lesotho\n",
       "245                                  Norfolk Island\n",
       "246                                           Palau\n",
       "247                       Wallis and Futuna Islands\n",
       "Name: Country, Length: 248, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mostramos la fila 66 de la fila Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Western Sahara'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country'][66]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tiempo real\n",
    "\n",
    "Empleamos la base de datos siguiente \n",
    "https://api.covid19api.com/country/spain/status/confirmed/live\n",
    "\n",
    "Como se puede ver, se trata de una API, que como hemos visto en clase es una interfaz de programación de acceso o Assist Programming Interface. Una APII muy reconocida es http. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Definimos variables\n",
    "\n",
    "Como ya hemos hecho en otras ocasiones, a continuación definimos unas nuevas variables mediante `=`, para la que se necesita la base de datos que se acaba de mencionar. \n",
    "\n",
    "Definimos la varibale `url_es`, en la que se encuentran los datos para España, y la variable `df_es` que hace referencia al nuevo dataframe que se va a crear para los datos de España\n",
    "\n",
    "A continuación, mediante  mostramos el dataframe o tabla poniendo `df_es`, esta vez sin el igual "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>Province</th>\n",
       "      <th>City</th>\n",
       "      <th>CityCode</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Cases</th>\n",
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       "      <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",
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       "      <th>1</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
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       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-23 00:00:00+00:00</td>\n",
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       "    <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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url_es = 'https://api.covid19api.com/country/spain/status/confirmed/live'\n",
    "df_es = pd.read_json(url_es)\n",
    "df_es "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Nombres de las columnas de la tabla\n",
    "\n",
    "Se puede mostrar el nombre de las columnas de la tabla mediante `.columns` y añadiendo delante el nombre del data frame (que es `df_es`). Por lo tanto, para mostrar los datos de este dataframe se debe poner `df_es.columns`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'CountryCode', 'Province', 'City', 'CityCode', 'Lat', 'Lon',\n",
       "       'Cases', 'Status', 'Date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Columna Date\n",
    "\n",
    "También se pueden mostrar columnas en concreto mediante `df['nombre de la columna']`\n",
    "\n",
    "A continuación se ejemplifica con la columna \"Date\", aunque únicamente se muestra la primera fila (1) por la cabecera (head) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2020-01-22 00:00:00+00:00\n",
       "Name: Date, dtype: datetime64[ns, UTC]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es['Date'].head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mostramos la primera fila (1) por el final de tabla (tail) de la columna Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "818   2022-04-19 00:00:00+00:00\n",
       "Name: Date, dtype: datetime64[ns, UTC]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es['Date'].tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mostramos información sobre la tabla\n",
    "\n",
    "COmo ya se ha mencionado antes, mediante `df_es.info()` se muestra la información sobre este dataframe. En ella se incluye el índice (RangeIndex) con el número de entradas, el número de columnas que hay (Data Columns), el nombre de las columnas (Colum), la cantidad de datos nulos que posee la tabla (Non-Null Count), el tipo de variable de las columnas (Dtype) y el tamaño (memory usage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 819 entries, 0 to 818\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype              \n",
      "---  ------       --------------  -----              \n",
      " 0   Country      819 non-null    object             \n",
      " 1   CountryCode  819 non-null    object             \n",
      " 2   Province     819 non-null    object             \n",
      " 3   City         819 non-null    object             \n",
      " 4   CityCode     819 non-null    object             \n",
      " 5   Lat          819 non-null    float64            \n",
      " 6   Lon          819 non-null    float64            \n",
      " 7   Cases        819 non-null    int64              \n",
      " 8   Status       819 non-null    object             \n",
      " 9   Date         819 non-null    datetime64[ns, UTC]\n",
      "dtypes: datetime64[ns, UTC](1), float64(2), int64(1), object(6)\n",
      "memory usage: 64.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_es.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Muestra variables estadísticas de la tabla (de las columnas numéricas)\n",
    "\n",
    "Mediante `df_es.describe()` se muestran las variables estadísticas del dataframe, algo que no se muestra normalmente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.190000e+02</td>\n",
       "      <td>819.00</td>\n",
       "      <td>8.190000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>3.405791e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.421954e-14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.289907e+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>3.400735e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>3.178356e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>4.949120e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>1.162749e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Lat     Lon         Cases\n",
       "count  8.190000e+02  819.00  8.190000e+02\n",
       "mean   4.046000e+01   -3.75  3.405791e+06\n",
       "std    1.421954e-14    0.00  3.289907e+06\n",
       "min    4.046000e+01   -3.75  0.000000e+00\n",
       "25%    4.046000e+01   -3.75  3.400735e+05\n",
       "50%    4.046000e+01   -3.75  3.178356e+06\n",
       "75%    4.046000e+01   -3.75  4.949120e+06\n",
       "max    4.046000e+01   -3.75  1.162749e+07"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mostramos el dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Indexar el dataframe según una columna\n",
    "\n",
    "También es posible indexar -ordenar los datos- en función de una columna mediante `df.set_index('')`\n",
    "\n",
    "En este caso, se indexa el dataframe según la columna \"Date\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>Province</th>\n",
       "      <th>City</th>\n",
       "      <th>CityCode</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22 00:00:00+00:00</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23 00:00:00+00:00</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24 00:00:00+00:00</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25 00:00:00+00:00</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26 00:00:00+00:00</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-15 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>11627487</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-16 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>11627487</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-17 00:00:00+00:00</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>11627487</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-18 00:00:00+00:00</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>11627487</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-19 00:00:00+00:00</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>11627487</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>819 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Country CountryCode Province City CityCode    Lat  \\\n",
       "Date                                                                          \n",
       "2020-01-22 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2020-01-23 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2020-01-24 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2020-01-25 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2020-01-26 00:00:00+00:00   Spain          ES                         40.46   \n",
       "...                           ...         ...      ...  ...      ...    ...   \n",
       "2022-04-15 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2022-04-16 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2022-04-17 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2022-04-18 00:00:00+00:00   Spain          ES                         40.46   \n",
       "2022-04-19 00:00:00+00:00   Spain          ES                         40.46   \n",
       "\n",
       "                            Lon     Cases     Status  \n",
       "Date                                                  \n",
       "2020-01-22 00:00:00+00:00 -3.75         0  confirmed  \n",
       "2020-01-23 00:00:00+00:00 -3.75         0  confirmed  \n",
       "2020-01-24 00:00:00+00:00 -3.75         0  confirmed  \n",
       "2020-01-25 00:00:00+00:00 -3.75         0  confirmed  \n",
       "2020-01-26 00:00:00+00:00 -3.75         0  confirmed  \n",
       "...                         ...       ...        ...  \n",
       "2022-04-15 00:00:00+00:00 -3.75  11627487  confirmed  \n",
       "2022-04-16 00:00:00+00:00 -3.75  11627487  confirmed  \n",
       "2022-04-17 00:00:00+00:00 -3.75  11627487  confirmed  \n",
       "2022-04-18 00:00:00+00:00 -3.75  11627487  confirmed  \n",
       "2022-04-19 00:00:00+00:00 -3.75  11627487  confirmed  \n",
       "\n",
       "[819 rows x 9 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.set_index('Date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Muestra la columna Cases una vez se ha indexado la tabla por Date, y muestra esta columna junto a su índice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2020-01-22 00:00:00+00:00           0\n",
       "2020-01-23 00:00:00+00:00           0\n",
       "2020-01-24 00:00:00+00:00           0\n",
       "2020-01-25 00:00:00+00:00           0\n",
       "2020-01-26 00:00:00+00:00           0\n",
       "                               ...   \n",
       "2022-04-15 00:00:00+00:00    11627487\n",
       "2022-04-16 00:00:00+00:00    11627487\n",
       "2022-04-17 00:00:00+00:00    11627487\n",
       "2022-04-18 00:00:00+00:00    11627487\n",
       "2022-04-19 00:00:00+00:00    11627487\n",
       "Name: Cases, Length: 819, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.set_index('Date')['Cases']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Nombrar y crear el gráfico a partir del dataframe \n",
    "\n",
    "Una vez indexados los datos según \"Date\" y \"Cases\", mediante `.plot` se muestran los datos en un gráfico\n",
    "\n",
    "Añadiendo `title=` como argumento de .plot se pone un título al gráfico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Casos de Covid19 en tiempo real'}, xlabel='Date'>"
      ]
     },
     "execution_count": 31,
     "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 tiempo real\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Otro país\n",
    "\n",
    "Definimos nuevas variables, esta vez para el país Italia, y creamos un dataframe para este país\n",
    "\n",
    "A continuación creamos un gráfico mediante `.plot` a partir del dataframe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "url_it = 'https://api.covid19api.com/country/italy/status/confirmed/live'\n",
    "df_it = pd.read_json(url_it)\n",
    "df_it.set_index('Date')['Cases'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparación\n",
    "\n",
    "Comparamos estos dos países en un solo gráfico para poder observar las diferencias entre los casos y las fechas de COVID-19 en los dos países (España e Italia)\n",
    "\n",
    "Para ello definimos dos variables mediante `casos_es` para los datos de España, y `casos_it` para los datos italianos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "casos_es = df_es.set_index('Date')['Cases']\n",
    "casos_it = df_it.set_index('Date')['Cases']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Concatenamos ambos casos \n",
    "\n",
    "Antes de crear un dataframe y un gráfico con estos datos, es necesario concatearlos mediante 'vs' para que se muestren en una sola tabla"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cases</th>\n",
       "      <th>Cases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26 00:00:00+00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-16 00:00:00+00:00</th>\n",
       "      <td>11627487</td>\n",
       "      <td>15659835</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-18 00:00:00+00:00</th>\n",
       "      <td>11627487</td>\n",
       "      <td>15730676</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>819 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Cases     Cases\n",
       "Date                                         \n",
       "2020-01-22 00:00:00+00:00         0         0\n",
       "2020-01-23 00:00:00+00:00         0         0\n",
       "2020-01-24 00:00:00+00:00         0         0\n",
       "2020-01-25 00:00:00+00:00         0         0\n",
       "2020-01-26 00:00:00+00:00         0         0\n",
       "...                             ...       ...\n",
       "2022-04-15 00:00:00+00:00  11627487  15595302\n",
       "2022-04-16 00:00:00+00:00  11627487  15659835\n",
       "2022-04-17 00:00:00+00:00  11627487  15712088\n",
       "2022-04-18 00:00:00+00:00  11627487  15730676\n",
       "2022-04-19 00:00:00+00:00  11627487  15758002\n",
       "\n",
       "[819 rows x 2 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs = pd.concat([casos_es,casos_it],axis=1)\n",
    "vs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creamos el gráfico\n",
    "\n",
    "Creamos un gráfico que una los dos casos\n",
    "\n",
    "Para ello necesitamos marcar la concatenación de los datos de España e Italia mediante `vs.columns` y a continuación creamos la tabla a partir de esta concatenación `vs.plot`. Aquí también definimos el título de la tabla como \"España vs Itlia\" mediante el argumento `title`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'España vs Italia'}, xlabel='Date'>"
      ]
     },
     "execution_count": 27,
     "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']\n",
    "vs.plot(title=\"España vs Italia\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Guardamos en un csv\n",
    "\n",
    "Para guardar todo lo realizado en un csv, es necesario poner `vs.to_csv`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "vs.to_csv('esvit.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comprobamos que el archivo se ha creado \n",
    "\n",
    "Para ello, necesitamos usar `!` que nos permite emplear en Jupyter un comando de la terminal, concretamente el comando `ls`, que sirve para saber qué tienes en un lugar concreto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "api-pandas-folium.ipynb  probando-con-r.ipynb\t\t  seaborn-data\r\n",
      "escit.png\t\t python-api-covid19-pandas.ipynb  Shared_Resources\r\n",
      "esvit.csv\t\t python-pruebas.ipynb\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importamos matplotlib.pyplot y lo redefinimos como plt\n",
    "\n",
    "Por último, guardamos el gráfico mediante `plt.savefig()` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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('escit.png')"
   ]
  }
 ],
 "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
}