python-api-covid19-pandas 1 (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Uso de la API del COVID19 con Pandas ¶\n",
    "Una API (Application Programming Interface o Interfaz de Programación de Aplicaciones) es el \"conjunto de funciones y procedimientos que ofrece una biblioteca para ser utilizadas por otro software\", en este caso por Jupyter. \n",
    "Fuente: https://web.archive.org/web/20190215165536/https://blog.pandorafms.org/es/para-que-sirve-una-api/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La base de datos que es de nuestro interés para la realización de este *notebook* son los datos de la COVID-19 clasificado por países. http://api.covid19api.com/countries\n",
    "\n",
    "Los datos están presentados como URL, que según la escuela EDIX significa Uniform Resource Locator que es la \"dirección única y específica de cada página que existe en la web\". Además, podemos encontrar estos datos en formato JSON (JavaScript Object Notation o  Notación de Objeto Javascript), que según la página \"Desarrollo web\" es un lenguaje para el intercambio de datos entre sistemas.  \n",
    "Fuentes:  \n",
    "1. https://www.edix.com/es/instituto/que-es-url/\n",
    "2. https://desarrolloweb.com/home/json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instalamos las librerías que vamos a necesitar \n",
    "\n",
    "Para proceder al análisis de esta base de datos, antes necesitamos instalar  pandas, que según la página oficial es una herramienta de análisis de código abierto que está construida sobre el lenguaje de programación Phyton. \n",
    "\n",
    "Fuente: https://pandas.pydata.org/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por tanto, usamos el comando \"!pip install\", lenguaje perteneciente a Phyton y que permite instalar librerías. Como en nuestro caso, queremos instalar la librería pandas, añadimos al comando \"!pip install\" la palabra \"pandas\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (1.3.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2.8.1)\n",
      "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2020.4)\n",
      "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (1.21.1)\n",
      "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7.3->pandas) (1.14.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuración de Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para poder importar la librería ya instalada en el paso anterior, pandas y poder usarla en todo nuestro *notebook*, la llamamos \"pd\". Esto significa que a lo largo de nuestro *notebook^* para nombrar a una función de pandas no hace falta que escribamos pandas, sino pd que es el nuevo nombre que le hemos proporcionado.\n",
    "Fuente: https://datacarpentry.org/python-ecology-lesson-es/02-starting-with-data/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crear variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Establecemos una variable que la incluye nuestra base de datos usada con \"=\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://api.covid19api.com/countries'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creamos un dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Un dataframe, [según la página oficial de Pandas](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html), son estructuras de datos de dos dimensiones  que incluye datos de muchos tipos, por tanto se les considera heterogéneas. Esta tabla de datos se muestra en JSON, ya definido anteriormente. Por ello, en el siguiente comando \"df\" corresponde a dataframe y después del igual todo aquello que queremos representar en la tabla: pd la librería de pandas, \"read\" es la función que permite leer los datos y json es el lenguaje de programación el cual está presentado. Entre paréntesis ponemos \"url\" porque es donde se contienen todos los datos. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_json(url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Se muestra la tabla\n",
    "Con el comando df (que es como hemos llamado al dataframe), podemos visualizar la tabla."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>Chad</td>\n",
       "      <td>chad</td>\n",
       "      <td>TD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Guinea-Bissau</td>\n",
       "      <td>guinea-bissau</td>\n",
       "      <td>GW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hong Kong, SAR China</td>\n",
       "      <td>hong-kong-sar-china</td>\n",
       "      <td>HK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New Caledonia</td>\n",
       "      <td>new-caledonia</td>\n",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>nigeria</td>\n",
       "      <td>NG</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>Pakistan</td>\n",
       "      <td>pakistan</td>\n",
       "      <td>PK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>algeria</td>\n",
       "      <td>DZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>Bermuda</td>\n",
       "      <td>bermuda</td>\n",
       "      <td>BM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>Gambia</td>\n",
       "      <td>gambia</td>\n",
       "      <td>GM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>russia</td>\n",
       "      <td>RU</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>248 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Country                 Slug ISO2\n",
       "0                    Chad                 chad   TD\n",
       "1           Guinea-Bissau        guinea-bissau   GW\n",
       "2    Hong Kong, SAR China  hong-kong-sar-china   HK\n",
       "3           New Caledonia        new-caledonia   NC\n",
       "4                 Nigeria              nigeria   NG\n",
       "..                    ...                  ...  ...\n",
       "243              Pakistan             pakistan   PK\n",
       "244               Algeria              algeria   DZ\n",
       "245               Bermuda              bermuda   BM\n",
       "246                Gambia               gambia   GM\n",
       "247    Russian Federation               russia   RU\n",
       "\n",
       "[248 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploración de la tabla\n",
    "\n",
    "### Cabecera"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se muestran las primeras filas de la tabla (cabecera o head) mediante el comando df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Slug</th>\n",
       "      <th>ISO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chad</td>\n",
       "      <td>chad</td>\n",
       "      <td>TD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Guinea-Bissau</td>\n",
       "      <td>guinea-bissau</td>\n",
       "      <td>GW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hong Kong, SAR China</td>\n",
       "      <td>hong-kong-sar-china</td>\n",
       "      <td>HK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New Caledonia</td>\n",
       "      <td>new-caledonia</td>\n",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>nigeria</td>\n",
       "      <td>NG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Country                 Slug ISO2\n",
       "0                  Chad                 chad   TD\n",
       "1         Guinea-Bissau        guinea-bissau   GW\n",
       "2  Hong Kong, SAR China  hong-kong-sar-china   HK\n",
       "3         New Caledonia        new-caledonia   NC\n",
       "4               Nigeria              nigeria   NG"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si queremos ver solo un número determinado de entradas de la cabecera, indicamos el número en el paréntesis, es decir, escribimos el comando: \"df.head(6)\""
   ]
  },
  {
   "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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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Slug</th>\n",
       "      <th>ISO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chad</td>\n",
       "      <td>chad</td>\n",
       "      <td>TD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Guinea-Bissau</td>\n",
       "      <td>guinea-bissau</td>\n",
       "      <td>GW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hong Kong, SAR China</td>\n",
       "      <td>hong-kong-sar-china</td>\n",
       "      <td>HK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New Caledonia</td>\n",
       "      <td>new-caledonia</td>\n",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>nigeria</td>\n",
       "      <td>NG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>sierra-leone</td>\n",
       "      <td>SL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Country                 Slug ISO2\n",
       "0                  Chad                 chad   TD\n",
       "1         Guinea-Bissau        guinea-bissau   GW\n",
       "2  Hong Kong, SAR China  hong-kong-sar-china   HK\n",
       "3         New Caledonia        new-caledonia   NC\n",
       "4               Nigeria              nigeria   NG\n",
       "5          Sierra Leone         sierra-leone   SL"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final de la tabla\n",
    "Se muestran las últimas entradas de la tabla (tail); el 2 indica cuántas filas mostramos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>243</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>pakistan</td>\n",
       "      <td>PK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>algeria</td>\n",
       "      <td>DZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>Bermuda</td>\n",
       "      <td>bermuda</td>\n",
       "      <td>BM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>Gambia</td>\n",
       "      <td>gambia</td>\n",
       "      <td>GM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>russia</td>\n",
       "      <td>RU</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Country      Slug ISO2\n",
       "243            Pakistan  pakistan   PK\n",
       "244             Algeria   algeria   DZ\n",
       "245             Bermuda   bermuda   BM\n",
       "246              Gambia    gambia   GM\n",
       "247  Russian Federation    russia   RU"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Slug</th>\n",
       "      <th>ISO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>Gambia</td>\n",
       "      <td>gambia</td>\n",
       "      <td>GM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>russia</td>\n",
       "      <td>RU</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Country    Slug ISO2\n",
       "246              Gambia  gambia   GM\n",
       "247  Russian Federation  russia   RU"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Presentamos la información de la tabla como objeto de pandas\n",
    "\n",
    "Con el comando \"df.info()\" se muestra la 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. [Toda la información ha sido obtenida de la página oficial de Pandas](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "## Presentamos la columna Country\n",
    "Con el comando \"df[]\" nos permite mostrar una columna en específico que queramos de nuestro dataframe. Para este ejemplo hemos elegido la columna llamada \"Country\". Para ello, hemos puesto la palabra entre corchetes y comillas simples después del comando df. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                      Chad\n",
       "1             Guinea-Bissau\n",
       "2      Hong Kong, SAR China\n",
       "3             New Caledonia\n",
       "4                   Nigeria\n",
       "               ...         \n",
       "243                Pakistan\n",
       "244                 Algeria\n",
       "245                 Bermuda\n",
       "246                  Gambia\n",
       "247      Russian Federation\n",
       "Name: Country, Length: 248, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Presentamos la columna Country y la fila 66"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Republic of Kosovo'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country'][66]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tiempo real\n",
    "Para conocer los datos en tiempo real, usamos la siguiente base de datos: https://api.covid19api.com/country/spain/status/confirmed/live"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Definimos la variable \n",
    "Como ya hemos explicado anteriormente para definir esta nueva variable con el símbolo \"=\". El cambio es que ahora nuestra nueva variable será \"url_es\" correspondiendo esa iniciales de \"es\" a España porque van a ser los datos que vamos a usar en este nuevo dataframe. La variable df_es que indica que vamos a crear un nuevo dataframe que los datos de España. Para poder mostrar la tabla en la línea de abajo ponemos \"df_es\", y asi es como podremos visualizar los datos. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>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>11662214</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>11662214</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>11662214</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-18 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>11736893</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-19 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>11736893</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-20 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>820 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",
       "815   Spain          ES                         40.46 -3.75  11662214   \n",
       "816   Spain          ES                         40.46 -3.75  11662214   \n",
       "817   Spain          ES                         40.46 -3.75  11662214   \n",
       "818   Spain          ES                         40.46 -3.75  11736893   \n",
       "819   Spain          ES                         40.46 -3.75  11736893   \n",
       "\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",
       "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",
       "819  confirmed 2022-04-20 00:00:00+00:00  \n",
       "\n",
       "[820 rows x 10 columns]"
      ]
     },
     "execution_count": 13,
     "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": [
    "## Se presentan los nombres de la columna de la tabla\n",
    "Con la función \".columns\" podemos extraer los nombres de las columnas de esta misma tabla. Para ello, hay que poner primero el nombre del dataframe que como antes hemos renombrado se llama \"df_es\". Así que para visualizar las columnas sería la siguiente función 'df_es.columns'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'CountryCode', 'Province', 'City', 'CityCode', 'Lat', 'Lon',\n",
       "       'Cases', 'Status', 'Date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualización columna date \n",
    "\n",
    "A continuación podemos visualizar solo una columna en específico. En este caso, hemos escogido la columna llamada \"Date\" y la fila 1. Para ello, se usará la función \"df_es['Date'].head(1)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2020-01-22 00:00:00+00:00\n",
       "Name: Date, dtype: datetime64[ns, UTC]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es['Date'].head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora solo queremos mostrar la última fila de nuestro data frame y es igual cambiando el final de dicha función, es decir, \"df_es['Date'].tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "819   2022-04-20 00:00:00+00:00\n",
       "Name: Date, dtype: datetime64[ns, UTC]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es['Date'].tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mostramos la información sobre el dataframe \n",
    "Como ya se ha explicado anteriormente, la función \"df_es.info() muestra 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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 820 entries, 0 to 819\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype              \n",
      "---  ------       --------------  -----              \n",
      " 0   Country      820 non-null    object             \n",
      " 1   CountryCode  820 non-null    object             \n",
      " 2   Province     820 non-null    object             \n",
      " 3   City         820 non-null    object             \n",
      " 4   CityCode     820 non-null    object             \n",
      " 5   Lat          820 non-null    float64            \n",
      " 6   Lon          820 non-null    float64            \n",
      " 7   Cases        820 non-null    int64              \n",
      " 8   Status       820 non-null    object             \n",
      " 9   Date         820 non-null    datetime64[ns, UTC]\n",
      "dtypes: datetime64[ns, UTC](1), float64(2), int64(1), object(6)\n",
      "memory usage: 64.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_es.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Se presentan las variables estadísticas de la tabla (es decir, las de las columnas numéricas)\n",
    "Con \"df_es.describe\" nos presenta variables estadísticas de la tabla como media, meadiana, máx, mín, std y conteos, entre otros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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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>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.200000e+02</td>\n",
       "      <td>820.00</td>\n",
       "      <td>8.200000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>3.416380e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.109764e-15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.301819e+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>3.414432e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>3.179284e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>4.952212e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.046000e+01</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>1.173689e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Lat     Lon         Cases\n",
       "count  8.200000e+02  820.00  8.200000e+02\n",
       "mean   4.046000e+01   -3.75  3.416380e+06\n",
       "std    7.109764e-15    0.00  3.301819e+06\n",
       "min    4.046000e+01   -3.75  0.000000e+00\n",
       "25%    4.046000e+01   -3.75  3.414432e+05\n",
       "50%    4.046000e+01   -3.75  3.179284e+06\n",
       "75%    4.046000e+01   -3.75  4.952212e+06\n",
       "max    4.046000e+01   -3.75  1.173689e+07"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se presentan la tabla otra vez con la función df_es"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>Province</th>\n",
       "      <th>City</th>\n",
       "      <th>CityCode</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Status</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-22 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-23 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-24 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-25 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2020-01-26 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <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>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>11662214</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>11662214</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>11662214</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-18 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>11736893</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-19 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>11736893</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>2022-04-20 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>820 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",
       "815   Spain          ES                         40.46 -3.75  11662214   \n",
       "816   Spain          ES                         40.46 -3.75  11662214   \n",
       "817   Spain          ES                         40.46 -3.75  11662214   \n",
       "818   Spain          ES                         40.46 -3.75  11736893   \n",
       "819   Spain          ES                         40.46 -3.75  11736893   \n",
       "\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",
       "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",
       "819  confirmed 2022-04-20 00:00:00+00:00  \n",
       "\n",
       "[820 rows x 10 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indexamos el dataframe mostrando solo una columna\n",
    "A continuación, ordenamos los datos solo mostrando una columna. Esto lo realizamos con \"df_es.set_index('')\". En este caso, solo queremos ver la columna \"Date\", así que ponemos en el paréntesis ese dato. "
   ]
  },
  {
   "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",
       "    </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>2022-04-16 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>11662214</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>11662214</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>11662214</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-19 00:00:00+00:00</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>11736893</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-20 00:00:00+00:00</th>\n",
       "      <td>Spain</td>\n",
       "      <td>ES</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>40.46</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>11736893</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>820 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",
       "2022-04-16 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",
       "2022-04-20 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",
       "2022-04-16 00:00:00+00:00 -3.75  11662214  confirmed  \n",
       "2022-04-17 00:00:00+00:00 -3.75  11662214  confirmed  \n",
       "2022-04-18 00:00:00+00:00 -3.75  11662214  confirmed  \n",
       "2022-04-19 00:00:00+00:00 -3.75  11736893  confirmed  \n",
       "2022-04-20 00:00:00+00:00 -3.75  11736893  confirmed  \n",
       "\n",
       "[820 rows x 9 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.set_index('Date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se presenta la columna llamada cases una vez mostrada la tabla por date con la función \"df_es.set_index('Date')['Cases']\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2020-01-22 00:00:00+00:00           0\n",
       "2020-01-23 00:00:00+00:00           0\n",
       "2020-01-24 00:00:00+00:00           0\n",
       "2020-01-25 00:00:00+00:00           0\n",
       "2020-01-26 00:00:00+00:00           0\n",
       "                               ...   \n",
       "2022-04-16 00:00:00+00:00    11662214\n",
       "2022-04-17 00:00:00+00:00    11662214\n",
       "2022-04-18 00:00:00+00:00    11662214\n",
       "2022-04-19 00:00:00+00:00    11736893\n",
       "2022-04-20 00:00:00+00:00    11736893\n",
       "Name: Cases, Length: 820, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_es.set_index('Date')['Cases']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creamos el gráfico \n",
    "Como ya hemos indexado, es decir, hemos ordenado los datos según la columna \"Date\" y \"Cases\" vamos a crear un gráfico con la función .plot y le damos el siguiente título \"Casos de Covid19 en tiempo real\" con la función title. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Casos de Covid19 en tiempo real'}, xlabel='Date'>"
      ]
     },
     "execution_count": 22,
     "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Definimos las nuevas variables, ahora con los datos del país Italia y, a continuación, creamos el nuevo dataframe y el gráfico. El gráfico se creará, a partir de el nuevo dataframe y con la función .plot. El resto de funciones ya las hemos ido explicando a lo largo de este *notebook*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 23,
     "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",
    "Comparemos estos dos países en un solo gráfico para observar la diferencia entre los casos y fechas de la pandemia COVID-19 en estos dos países elegidos: España e Italia. Es necesario volver a definir las variables mediante \"casos_es\" correspodiente a los casos de España y \"casos_it\" para los datos de Italia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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 los casos \n",
    "Antes de crear el nuevo dataframe, concatenamos los datos de los dos países (España e Italia) con \"vs\" para ver los datos de ambos visualizados en una misma tabla"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>2022-04-16 00:00:00+00:00</th>\n",
       "      <td>15659835</td>\n",
       "      <td>11662214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-17 00:00:00+00:00</th>\n",
       "      <td>15712088</td>\n",
       "      <td>11662214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-18 00:00:00+00:00</th>\n",
       "      <td>15730676</td>\n",
       "      <td>11662214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-19 00:00:00+00:00</th>\n",
       "      <td>15758002</td>\n",
       "      <td>11736893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-20 00:00:00+00:00</th>\n",
       "      <td>15858442</td>\n",
       "      <td>11736893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>820 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",
       "2022-04-16 00:00:00+00:00  15659835  11662214\n",
       "2022-04-17 00:00:00+00:00  15712088  11662214\n",
       "2022-04-18 00:00:00+00:00  15730676  11662214\n",
       "2022-04-19 00:00:00+00:00  15758002  11736893\n",
       "2022-04-20 00:00:00+00:00  15858442  11736893\n",
       "\n",
       "[820 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs = pd.concat([casos_it,casos_es],axis=1)\n",
    "vs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mostramos el gráfico\n",
    "Para que pueda entender que hemos concatenado los datos de Italia y España, ponemos \"vs.columns\" y luego creamos el gráfico con la función \"vs.plot\". Además, le damos nombre al gráfico con la función \"title=\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'España vs Italia'}, 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": [
    "vs.columns =['España','Italia']\n",
    "vs.plot(title=\"España vs Italia\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creamos y guardamos en archivo CSV\n",
    "Ahora para poder guardar en nuestro Jupyter todo el trabajo realizado, lo guardaremos en formato csv con la función vs.to_csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "vs.to_csv('esvit.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora comprobamos que todo se ha guardado correctamente con el comando ls, que permite ver todo lo que tienes guardado en tu unidad de Jupyter; y le añadimos \"!\" que es lo que hace que Jupyter nos entienda y permita usar un comando de la terminal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " api-pandas-folium.ipynb\t       presupuesto.png\r\n",
      " escit.png\t\t\t      'probando con r.ipynb'\r\n",
      " esvit.csv\t\t\t      'python-api-covid19-pandas 1.ipynb'\r\n",
      " esvsit.csv\t\t\t       python-api-covid19-pandas.ipynb\r\n",
      " evsit.png\t\t\t       seaborn-data\r\n",
      " phyton-pruebas.ipynb\t\t       Shared_Resources\r\n",
      " presupuesto.madrid\t\t       Untitled.ipynb\r\n",
      " presupuesto-madrid-csv-pandas.ipynb\r\n"
     ]
    }
   ],
   "source": [
    " !ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Guardamos el archivo matplotlib.pyplot y lo renombramos como plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vs.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para finalizar la práctica, guardamos el gráfico ya creado con \"plt.savefig()\" y nos lo guardará en formato png, que es un tipo de archivo de imagen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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
}