9_UnionIntegracioDeDatos.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unión e Integración de Datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imagina que tenemos un conjunto de datos (DataFrame) y queremos hacer ejemplos prácticos para aprender. Para hacer esto, necesitamos crear nuevos archivos a partir de nuestros datos originales, pero solo con una parte de la información.\n",
    "\n",
    "**Ejemplos de Apilar**:\n",
    "\n",
    "1. **variables_izq**: Será un archivo con la mitad izquierda de las columnas del conjunto de datos original, pero sin una identificación única para cada fila.\n",
    "\n",
    "2. **variables_drc**: Este archivo contendrá la mitad derecha de las columnas del conjunto de datos original, pero también sin una identificación única.\n",
    "\n",
    "3. **registros_sup**: Será un archivo con la mitad superior de las filas del conjunto de datos original. Al igual que los anteriores, no tendrá una identificación única.\n",
    "\n",
    "4. **registros_inf**: Este archivo contendrá la mitad inferior de las filas del conjunto de datos original, sin identificación única.\n",
    "\n",
    "**Ejemplos de Cruzar**:\n",
    "\n",
    "5. **fichero1**: En este caso, será un archivo con la mitad izquierda de las columnas del conjunto de datos original, pero con una identificación única para cada fila.\n",
    "\n",
    "6. **fichero2**: Contendrá la mitad derecha de las columnas del conjunto de datos original y también tendrá una identificación única para cada fila.\n",
    "\n",
    "Estos nuevos archivos nos permitirán practicar diferentes operaciones, como apilar o combinar la información de acuerdo a lo que necesitemos aprender. Recuerda que se están creando solo para propósitos de ejemplificación y práctica, y no contienen datos reales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>Nombre Cliente</th>\n",
       "      <th>Tipo Producto</th>\n",
       "      <th>Precio (€/kg)</th>\n",
       "      <th>Cantidad Vendida (kg)</th>\n",
       "      <th>Fecha Venta</th>\n",
       "      <th>Provincia Cliente</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Almendras</td>\n",
       "      <td>4.54</td>\n",
       "      <td>1212</td>\n",
       "      <td>29/3/23</td>\n",
       "      <td>Cádiz</td>\n",
       "      <td>389932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mercadona</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>5.84</td>\n",
       "      <td>591</td>\n",
       "      <td>5/12/23</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>389933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alcampo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>2.69</td>\n",
       "      <td>2831</td>\n",
       "      <td>26/7/23</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>389934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>6.70</td>\n",
       "      <td>516</td>\n",
       "      <td>7/10/23</td>\n",
       "      <td>Alicante</td>\n",
       "      <td>389935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lidl</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>6.14</td>\n",
       "      <td>1283</td>\n",
       "      <td>12/5/23</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>389936</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>Caprabo</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>9.75</td>\n",
       "      <td>2367</td>\n",
       "      <td>15/9/23</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>390227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>Alcampo</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>3.82</td>\n",
       "      <td>1951</td>\n",
       "      <td>19/9/23</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>390228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>5.22</td>\n",
       "      <td>2811</td>\n",
       "      <td>4/5/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>390229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>8.07</td>\n",
       "      <td>473</td>\n",
       "      <td>18/1/23</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>390230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Aldi</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>5.80</td>\n",
       "      <td>1081</td>\n",
       "      <td>23/3/23</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>390231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Nombre Cliente Tipo Producto  Precio (€/kg)  Cantidad Vendida (kg)  \\\n",
       "0        Mas y Mas     Almendras           4.54                   1212   \n",
       "1        Mercadona        Nueces           5.84                    591   \n",
       "2          Alcampo    Cacahuetes           2.69                   2831   \n",
       "3        Mas y Mas     Pistachos           6.70                    516   \n",
       "4             Lidl        Nueces           6.14                   1283   \n",
       "..             ...           ...            ...                    ...   \n",
       "295        Caprabo        Nueces           9.75                   2367   \n",
       "296        Alcampo     Pistachos           3.82                   1951   \n",
       "297      Mas y Mas     Pistachos           5.22                   2811   \n",
       "298      Mas y Mas    Cacahuetes           8.07                    473   \n",
       "299           Aldi        Nueces           5.80                   1081   \n",
       "\n",
       "    Fecha Venta Provincia Cliente      id  \n",
       "0       29/3/23             Cádiz  389932  \n",
       "1       5/12/23           Sevilla  389933  \n",
       "2       26/7/23            Madrid  389934  \n",
       "3       7/10/23          Alicante  389935  \n",
       "4       12/5/23         Barcelona  389936  \n",
       "..          ...               ...     ...  \n",
       "295     15/9/23           Sevilla  390227  \n",
       "296     19/9/23            Madrid  390228  \n",
       "297      4/5/23          Valencia  390229  \n",
       "298     18/1/23            Madrid  390230  \n",
       "299     23/3/23           Sevilla  390231  \n",
       "\n",
       "[300 rows x 7 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_excel('/Users/jimenacambronero/Desktop/Proyectos para Portfolio/PythonPracticas/Datos/Frutos_Secos.xlsx')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Que deseamos apilar\n",
    "variable_izq = df.iloc[:,:3] #Hasta Precio\n",
    "variable_drc = df.iloc[:,3:] #Cantidad a id\n",
    "\n",
    "registro_sup = df.iloc[:150,:] #Los primeros 150 registros\n",
    "registro_inf = df.iloc[150:,:] #Del registro 151 en adelante"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creamos los que vamos a cruzar\n",
    "# Primero debemos crear un campo Clave\n",
    "import numpy as np\n",
    "df2 = df.copy()\n",
    "df2.insert(0,'Clave', np.arange(0,len(df)))\n",
    "\n",
    "#Ahora crearemos los ficheros\n",
    "fichero1 = df2.copy().drop(df2.columns[8:], axis = 1)\n",
    "fichero2 = df2.copy().drop(df2.columns[1:8],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Clave</th>\n",
       "      <th>Nombre Cliente</th>\n",
       "      <th>Tipo Producto</th>\n",
       "      <th>Precio (€/kg)</th>\n",
       "      <th>Cantidad Vendida (kg)</th>\n",
       "      <th>Fecha Venta</th>\n",
       "      <th>Provincia Cliente</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Almendras</td>\n",
       "      <td>4.54</td>\n",
       "      <td>1212</td>\n",
       "      <td>29/3/23</td>\n",
       "      <td>Cádiz</td>\n",
       "      <td>389932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Mercadona</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>5.84</td>\n",
       "      <td>591</td>\n",
       "      <td>5/12/23</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>389933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Alcampo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>2.69</td>\n",
       "      <td>2831</td>\n",
       "      <td>26/7/23</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>389934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>6.70</td>\n",
       "      <td>516</td>\n",
       "      <td>7/10/23</td>\n",
       "      <td>Alicante</td>\n",
       "      <td>389935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Lidl</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>6.14</td>\n",
       "      <td>1283</td>\n",
       "      <td>12/5/23</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>389936</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>295</td>\n",
       "      <td>Caprabo</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>9.75</td>\n",
       "      <td>2367</td>\n",
       "      <td>15/9/23</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>390227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>296</td>\n",
       "      <td>Alcampo</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>3.82</td>\n",
       "      <td>1951</td>\n",
       "      <td>19/9/23</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>390228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>297</td>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>5.22</td>\n",
       "      <td>2811</td>\n",
       "      <td>4/5/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>390229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>298</td>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>8.07</td>\n",
       "      <td>473</td>\n",
       "      <td>18/1/23</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>390230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>299</td>\n",
       "      <td>Aldi</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>5.80</td>\n",
       "      <td>1081</td>\n",
       "      <td>23/3/23</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>390231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Clave Nombre Cliente Tipo Producto  Precio (€/kg)  Cantidad Vendida (kg)  \\\n",
       "0        0      Mas y Mas     Almendras           4.54                   1212   \n",
       "1        1      Mercadona        Nueces           5.84                    591   \n",
       "2        2        Alcampo    Cacahuetes           2.69                   2831   \n",
       "3        3      Mas y Mas     Pistachos           6.70                    516   \n",
       "4        4           Lidl        Nueces           6.14                   1283   \n",
       "..     ...            ...           ...            ...                    ...   \n",
       "295    295        Caprabo        Nueces           9.75                   2367   \n",
       "296    296        Alcampo     Pistachos           3.82                   1951   \n",
       "297    297      Mas y Mas     Pistachos           5.22                   2811   \n",
       "298    298      Mas y Mas    Cacahuetes           8.07                    473   \n",
       "299    299           Aldi        Nueces           5.80                   1081   \n",
       "\n",
       "    Fecha Venta Provincia Cliente      id  \n",
       "0       29/3/23             Cádiz  389932  \n",
       "1       5/12/23           Sevilla  389933  \n",
       "2       26/7/23            Madrid  389934  \n",
       "3       7/10/23          Alicante  389935  \n",
       "4       12/5/23         Barcelona  389936  \n",
       "..          ...               ...     ...  \n",
       "295     15/9/23           Sevilla  390227  \n",
       "296     19/9/23            Madrid  390228  \n",
       "297      4/5/23          Valencia  390229  \n",
       "298     18/1/23            Madrid  390230  \n",
       "299     23/3/23           Sevilla  390231  \n",
       "\n",
       "[300 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fichero1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Clave</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Clave\n",
       "0        0\n",
       "1        1\n",
       "2        2\n",
       "3        3\n",
       "4        4\n",
       "..     ...\n",
       "295    295\n",
       "296    296\n",
       "297    297\n",
       "298    298\n",
       "299    299\n",
       "\n",
       "[300 rows x 1 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fichero2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apilar Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cuando hablamos de \"apilar variables\", nos referimos a combinar dos archivos que tienen los mismos registros (filas) pero diferentes columnas, y queremos juntar esas columnas en un solo conjunto de datos. Es como si tuviéramos dos piezas de un rompecabezas que encajan perfectamente.\n",
    "\n",
    "Sin embargo, en este proceso, no tenemos un campo clave para emparejar los registros, por lo que es esencial que ambos archivos tengan exactamente los mismos registros en el mismo orden para que coincidan correctamente.\n",
    "\n",
    "Para lograr esto en Pandas, utilizamos la función `concat()`, especificando el parámetro `axis` como 'columns', lo que significa que estamos apilando (uniendo) las variables o columnas de ambos archivos uno al lado del otro.\n",
    "\n",
    "Los parámetros más importantes son:\n",
    "\n",
    "1. **objs:** Aquí proporcionamos los archivos que queremos unir como una lista. Estos archivos deben tener los mismos registros en el mismo orden.\n",
    "\n",
    "2. **axis:** Configuramos este parámetro en 'columns' para indicar que estamos apilando las columnas.\n",
    "\n",
    "En resumen, apilar variables significa combinar dos archivos que tienen los mismos registros pero diferentes columnas en uno solo. Lo hacemos con `concat()` y aseguramos que los registros coincidan en el mismo orden en ambos archivos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Nombre Cliente</th>\n",
       "      <th>Tipo Producto</th>\n",
       "      <th>Precio (€/kg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Almendras</td>\n",
       "      <td>4.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mercadona</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>5.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alcampo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>2.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>6.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lidl</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>6.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>Caprabo</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>9.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>Alcampo</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>3.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>5.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>8.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Aldi</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>5.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Nombre Cliente Tipo Producto  Precio (€/kg)\n",
       "0        Mas y Mas     Almendras           4.54\n",
       "1        Mercadona        Nueces           5.84\n",
       "2          Alcampo    Cacahuetes           2.69\n",
       "3        Mas y Mas     Pistachos           6.70\n",
       "4             Lidl        Nueces           6.14\n",
       "..             ...           ...            ...\n",
       "295        Caprabo        Nueces           9.75\n",
       "296        Alcampo     Pistachos           3.82\n",
       "297      Mas y Mas     Pistachos           5.22\n",
       "298      Mas y Mas    Cacahuetes           8.07\n",
       "299           Aldi        Nueces           5.80\n",
       "\n",
       "[300 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variable_izq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Cantidad Vendida (kg)</th>\n",
       "      <th>Fecha Venta</th>\n",
       "      <th>Provincia Cliente</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1212</td>\n",
       "      <td>29/3/23</td>\n",
       "      <td>Cádiz</td>\n",
       "      <td>389932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>591</td>\n",
       "      <td>5/12/23</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>389933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2831</td>\n",
       "      <td>26/7/23</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>389934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>516</td>\n",
       "      <td>7/10/23</td>\n",
       "      <td>Alicante</td>\n",
       "      <td>389935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1283</td>\n",
       "      <td>12/5/23</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>389936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>2367</td>\n",
       "      <td>15/9/23</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>390227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>1951</td>\n",
       "      <td>19/9/23</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>390228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>2811</td>\n",
       "      <td>4/5/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>390229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>473</td>\n",
       "      <td>18/1/23</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>390230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>1081</td>\n",
       "      <td>23/3/23</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>390231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Cantidad Vendida (kg) Fecha Venta Provincia Cliente      id\n",
       "0                     1212     29/3/23             Cádiz  389932\n",
       "1                      591     5/12/23           Sevilla  389933\n",
       "2                     2831     26/7/23            Madrid  389934\n",
       "3                      516     7/10/23          Alicante  389935\n",
       "4                     1283     12/5/23         Barcelona  389936\n",
       "..                     ...         ...               ...     ...\n",
       "295                   2367     15/9/23           Sevilla  390227\n",
       "296                   1951     19/9/23            Madrid  390228\n",
       "297                   2811      4/5/23          Valencia  390229\n",
       "298                    473     18/1/23            Madrid  390230\n",
       "299                   1081     23/3/23           Sevilla  390231\n",
       "\n",
       "[300 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variable_drc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Nombre Cliente</th>\n",
       "      <th>Tipo Producto</th>\n",
       "      <th>Precio (€/kg)</th>\n",
       "      <th>Cantidad Vendida (kg)</th>\n",
       "      <th>Fecha Venta</th>\n",
       "      <th>Provincia Cliente</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Almendras</td>\n",
       "      <td>4.54</td>\n",
       "      <td>1212</td>\n",
       "      <td>29/3/23</td>\n",
       "      <td>Cádiz</td>\n",
       "      <td>389932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mercadona</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>5.84</td>\n",
       "      <td>591</td>\n",
       "      <td>5/12/23</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>389933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alcampo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>2.69</td>\n",
       "      <td>2831</td>\n",
       "      <td>26/7/23</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>389934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>6.70</td>\n",
       "      <td>516</td>\n",
       "      <td>7/10/23</td>\n",
       "      <td>Alicante</td>\n",
       "      <td>389935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lidl</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>6.14</td>\n",
       "      <td>1283</td>\n",
       "      <td>12/5/23</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>389936</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>Caprabo</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>9.75</td>\n",
       "      <td>2367</td>\n",
       "      <td>15/9/23</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>390227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>Alcampo</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>3.82</td>\n",
       "      <td>1951</td>\n",
       "      <td>19/9/23</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>390228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>5.22</td>\n",
       "      <td>2811</td>\n",
       "      <td>4/5/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>390229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>8.07</td>\n",
       "      <td>473</td>\n",
       "      <td>18/1/23</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>390230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Aldi</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>5.80</td>\n",
       "      <td>1081</td>\n",
       "      <td>23/3/23</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>390231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Nombre Cliente Tipo Producto  Precio (€/kg)  Cantidad Vendida (kg)  \\\n",
       "0        Mas y Mas     Almendras           4.54                   1212   \n",
       "1        Mercadona        Nueces           5.84                    591   \n",
       "2          Alcampo    Cacahuetes           2.69                   2831   \n",
       "3        Mas y Mas     Pistachos           6.70                    516   \n",
       "4             Lidl        Nueces           6.14                   1283   \n",
       "..             ...           ...            ...                    ...   \n",
       "295        Caprabo        Nueces           9.75                   2367   \n",
       "296        Alcampo     Pistachos           3.82                   1951   \n",
       "297      Mas y Mas     Pistachos           5.22                   2811   \n",
       "298      Mas y Mas    Cacahuetes           8.07                    473   \n",
       "299           Aldi        Nueces           5.80                   1081   \n",
       "\n",
       "    Fecha Venta Provincia Cliente      id  \n",
       "0       29/3/23             Cádiz  389932  \n",
       "1       5/12/23           Sevilla  389933  \n",
       "2       26/7/23            Madrid  389934  \n",
       "3       7/10/23          Alicante  389935  \n",
       "4       12/5/23         Barcelona  389936  \n",
       "..          ...               ...     ...  \n",
       "295     15/9/23           Sevilla  390227  \n",
       "296     19/9/23            Madrid  390228  \n",
       "297      4/5/23          Valencia  390229  \n",
       "298     18/1/23            Madrid  390230  \n",
       "299     23/3/23           Sevilla  390231  \n",
       "\n",
       "[300 rows x 7 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([variable_izq,variable_drc],axis='columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apilar Registros"
   ]
  },
  {
   "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>Nombre Cliente</th>\n",
       "      <th>Tipo Producto</th>\n",
       "      <th>Precio (€/kg)</th>\n",
       "      <th>Cantidad Vendida (kg)</th>\n",
       "      <th>Fecha Venta</th>\n",
       "      <th>Provincia Cliente</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Almendras</td>\n",
       "      <td>4.54</td>\n",
       "      <td>1212</td>\n",
       "      <td>29/3/23</td>\n",
       "      <td>Cádiz</td>\n",
       "      <td>389932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mercadona</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>5.84</td>\n",
       "      <td>591</td>\n",
       "      <td>5/12/23</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>389933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alcampo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>2.69</td>\n",
       "      <td>2831</td>\n",
       "      <td>26/7/23</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>389934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>6.70</td>\n",
       "      <td>516</td>\n",
       "      <td>7/10/23</td>\n",
       "      <td>Alicante</td>\n",
       "      <td>389935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lidl</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>6.14</td>\n",
       "      <td>1283</td>\n",
       "      <td>12/5/23</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>389936</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Carrefour</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>7.04</td>\n",
       "      <td>877</td>\n",
       "      <td>7/3/23</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>390077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Mercadona</td>\n",
       "      <td>Almendras</td>\n",
       "      <td>9.13</td>\n",
       "      <td>1132</td>\n",
       "      <td>22/10/23</td>\n",
       "      <td>Alicante</td>\n",
       "      <td>390078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Family Cash</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>10.99</td>\n",
       "      <td>277</td>\n",
       "      <td>6/3/23</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>390079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Family Cash</td>\n",
       "      <td>Almendras</td>\n",
       "      <td>2.78</td>\n",
       "      <td>2150</td>\n",
       "      <td>1/9/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>390080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Consum</td>\n",
       "      <td>Almendras</td>\n",
       "      <td>4.08</td>\n",
       "      <td>937</td>\n",
       "      <td>2/7/23</td>\n",
       "      <td>Alicante</td>\n",
       "      <td>390081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Nombre Cliente Tipo Producto  Precio (€/kg)  Cantidad Vendida (kg)  \\\n",
       "0        Mas y Mas     Almendras           4.54                   1212   \n",
       "1        Mercadona        Nueces           5.84                    591   \n",
       "2          Alcampo    Cacahuetes           2.69                   2831   \n",
       "3        Mas y Mas     Pistachos           6.70                    516   \n",
       "4             Lidl        Nueces           6.14                   1283   \n",
       "..             ...           ...            ...                    ...   \n",
       "145      Carrefour    Cacahuetes           7.04                    877   \n",
       "146      Mercadona     Almendras           9.13                   1132   \n",
       "147    Family Cash     Pistachos          10.99                    277   \n",
       "148    Family Cash     Almendras           2.78                   2150   \n",
       "149         Consum     Almendras           4.08                    937   \n",
       "\n",
       "    Fecha Venta Provincia Cliente      id  \n",
       "0       29/3/23             Cádiz  389932  \n",
       "1       5/12/23           Sevilla  389933  \n",
       "2       26/7/23            Madrid  389934  \n",
       "3       7/10/23          Alicante  389935  \n",
       "4       12/5/23         Barcelona  389936  \n",
       "..          ...               ...     ...  \n",
       "145      7/3/23           Sevilla  390077  \n",
       "146    22/10/23          Alicante  390078  \n",
       "147      6/3/23            Madrid  390079  \n",
       "148      1/9/23          Valencia  390080  \n",
       "149      2/7/23          Alicante  390081  \n",
       "\n",
       "[150 rows x 7 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "registro_sup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Nombre Cliente</th>\n",
       "      <th>Tipo Producto</th>\n",
       "      <th>Precio (€/kg)</th>\n",
       "      <th>Cantidad Vendida (kg)</th>\n",
       "      <th>Fecha Venta</th>\n",
       "      <th>Provincia Cliente</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Aldi</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>6.92</td>\n",
       "      <td>977</td>\n",
       "      <td>8/2/23</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>390082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Alcampo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>9.86</td>\n",
       "      <td>601</td>\n",
       "      <td>10/11/23</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>390083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Family Cash</td>\n",
       "      <td>Almendras</td>\n",
       "      <td>2.57</td>\n",
       "      <td>1326</td>\n",
       "      <td>15/9/23</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>390084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Alcampo</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>8.54</td>\n",
       "      <td>2607</td>\n",
       "      <td>20/8/23</td>\n",
       "      <td>Cádiz</td>\n",
       "      <td>390085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Aldi</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>10.48</td>\n",
       "      <td>1081</td>\n",
       "      <td>16/8/23</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>390086</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>Caprabo</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>9.75</td>\n",
       "      <td>2367</td>\n",
       "      <td>15/9/23</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>390227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>Alcampo</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>3.82</td>\n",
       "      <td>1951</td>\n",
       "      <td>19/9/23</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>390228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>5.22</td>\n",
       "      <td>2811</td>\n",
       "      <td>4/5/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>390229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>8.07</td>\n",
       "      <td>473</td>\n",
       "      <td>18/1/23</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>390230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Aldi</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>5.80</td>\n",
       "      <td>1081</td>\n",
       "      <td>23/3/23</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>390231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Nombre Cliente Tipo Producto  Precio (€/kg)  Cantidad Vendida (kg)  \\\n",
       "150           Aldi     Pistachos           6.92                    977   \n",
       "151        Alcampo    Cacahuetes           9.86                    601   \n",
       "152    Family Cash     Almendras           2.57                   1326   \n",
       "153        Alcampo     Pistachos           8.54                   2607   \n",
       "154           Aldi    Cacahuetes          10.48                   1081   \n",
       "..             ...           ...            ...                    ...   \n",
       "295        Caprabo        Nueces           9.75                   2367   \n",
       "296        Alcampo     Pistachos           3.82                   1951   \n",
       "297      Mas y Mas     Pistachos           5.22                   2811   \n",
       "298      Mas y Mas    Cacahuetes           8.07                    473   \n",
       "299           Aldi        Nueces           5.80                   1081   \n",
       "\n",
       "    Fecha Venta Provincia Cliente      id  \n",
       "150      8/2/23         Barcelona  390082  \n",
       "151    10/11/23         Barcelona  390083  \n",
       "152     15/9/23            Madrid  390084  \n",
       "153     20/8/23             Cádiz  390085  \n",
       "154     16/8/23         Barcelona  390086  \n",
       "..          ...               ...     ...  \n",
       "295     15/9/23           Sevilla  390227  \n",
       "296     19/9/23            Madrid  390228  \n",
       "297      4/5/23          Valencia  390229  \n",
       "298     18/1/23            Madrid  390230  \n",
       "299     23/3/23           Sevilla  390231  \n",
       "\n",
       "[150 rows x 7 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "registro_inf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Nombre Cliente</th>\n",
       "      <th>Tipo Producto</th>\n",
       "      <th>Precio (€/kg)</th>\n",
       "      <th>Cantidad Vendida (kg)</th>\n",
       "      <th>Fecha Venta</th>\n",
       "      <th>Provincia Cliente</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Almendras</td>\n",
       "      <td>4.54</td>\n",
       "      <td>1212</td>\n",
       "      <td>29/3/23</td>\n",
       "      <td>Cádiz</td>\n",
       "      <td>389932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mercadona</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>5.84</td>\n",
       "      <td>591</td>\n",
       "      <td>5/12/23</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>389933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alcampo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>2.69</td>\n",
       "      <td>2831</td>\n",
       "      <td>26/7/23</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>389934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>6.70</td>\n",
       "      <td>516</td>\n",
       "      <td>7/10/23</td>\n",
       "      <td>Alicante</td>\n",
       "      <td>389935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lidl</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>6.14</td>\n",
       "      <td>1283</td>\n",
       "      <td>12/5/23</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>389936</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>Caprabo</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>9.75</td>\n",
       "      <td>2367</td>\n",
       "      <td>15/9/23</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>390227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>Alcampo</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>3.82</td>\n",
       "      <td>1951</td>\n",
       "      <td>19/9/23</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>390228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>5.22</td>\n",
       "      <td>2811</td>\n",
       "      <td>4/5/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>390229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>8.07</td>\n",
       "      <td>473</td>\n",
       "      <td>18/1/23</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>390230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Aldi</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>5.80</td>\n",
       "      <td>1081</td>\n",
       "      <td>23/3/23</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>390231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Nombre Cliente Tipo Producto  Precio (€/kg)  Cantidad Vendida (kg)  \\\n",
       "0        Mas y Mas     Almendras           4.54                   1212   \n",
       "1        Mercadona        Nueces           5.84                    591   \n",
       "2          Alcampo    Cacahuetes           2.69                   2831   \n",
       "3        Mas y Mas     Pistachos           6.70                    516   \n",
       "4             Lidl        Nueces           6.14                   1283   \n",
       "..             ...           ...            ...                    ...   \n",
       "295        Caprabo        Nueces           9.75                   2367   \n",
       "296        Alcampo     Pistachos           3.82                   1951   \n",
       "297      Mas y Mas     Pistachos           5.22                   2811   \n",
       "298      Mas y Mas    Cacahuetes           8.07                    473   \n",
       "299           Aldi        Nueces           5.80                   1081   \n",
       "\n",
       "    Fecha Venta Provincia Cliente      id  \n",
       "0       29/3/23             Cádiz  389932  \n",
       "1       5/12/23           Sevilla  389933  \n",
       "2       26/7/23            Madrid  389934  \n",
       "3       7/10/23          Alicante  389935  \n",
       "4       12/5/23         Barcelona  389936  \n",
       "..          ...               ...     ...  \n",
       "295     15/9/23           Sevilla  390227  \n",
       "296     19/9/23            Madrid  390228  \n",
       "297      4/5/23          Valencia  390229  \n",
       "298     18/1/23            Madrid  390230  \n",
       "299     23/3/23           Sevilla  390231  \n",
       "\n",
       "[300 rows x 7 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([registro_sup,registro_inf],axis='index')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cruzar Ficheros"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cuando trabajas en entornos profesionales con datos que a menudo provienen de bases de datos, es común necesitar combinar dos tablas de datos basadas en un campo clave. Esto se hace con la función merge() en Pandas.\n",
    "\n",
    "Puedes realizar diferentes tipos de combinaciones (cruces) según cuál de las dos tablas quieras que \"mande\" en la operación:\n",
    "\n",
    "Left Join (Unión Izquierda): La tabla de la izquierda \"manda\". Esto significa que se incluirán todos los registros de la tabla de la izquierda, independientemente de si tienen una coincidencia en la tabla de la derecha.\n",
    "\n",
    "Right Join (Unión Derecha): La tabla de la derecha \"manda\". Esto significa que se incluirán todos los registros de la tabla de la derecha, independientemente de si tienen una coincidencia en la tabla de la izquierda.\n",
    "\n",
    "Inner Join (Unión Interna): En este caso, solamente se incluirán los registros que tienen una coincidencia en ambas tablas. Los registros que no tienen coincidencias se excluyen.\n",
    "\n",
    "Full Join (Unión Completa): En este tipo de unión, se incluirán todos los registros tanto de la tabla de la izquierda como de la tabla de la derecha. Si hay coincidencias, se mostrarán, y si no las hay, se llenarán con valores nulos.\n",
    "\n",
    "Los parámetros más importantes para la función merge() son:\n",
    "\n",
    "left: La tabla de la izquierda que deseas combinar.\n",
    "right: La tabla de la derecha que deseas combinar.\n",
    "how: El tipo de unión que deseas realizar ('left', 'right', 'inner' o 'outer' según las descripciones anteriores).\n",
    "on: El nombre del campo clave que se utilizará para unir las dos tablas si ambos tienen el mismo nombre.\n",
    "left_on: El nombre del campo clave en la tabla de la izquierda.\n",
    "right_on: El nombre del campo clave en la tabla de la derecha.\n",
    "En resumen, merge() es útil cuando deseas combinar dos tablas de datos en función de un campo clave y tienes control sobre cuál de las dos tablas debe \"mandar\" en la operación de combinación. Los parámetros te permiten especificar cómo deseas realizar la combinación y qué campos clave utilizar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>Clave</th>\n",
       "      <th>Nombre Cliente</th>\n",
       "      <th>Tipo Producto</th>\n",
       "      <th>Precio (€/kg)</th>\n",
       "      <th>Cantidad Vendida (kg)</th>\n",
       "      <th>Fecha Venta</th>\n",
       "      <th>Provincia Cliente</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Almendras</td>\n",
       "      <td>4.54</td>\n",
       "      <td>1212</td>\n",
       "      <td>29/3/23</td>\n",
       "      <td>Cádiz</td>\n",
       "      <td>389932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Mercadona</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>5.84</td>\n",
       "      <td>591</td>\n",
       "      <td>5/12/23</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>389933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Alcampo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>2.69</td>\n",
       "      <td>2831</td>\n",
       "      <td>26/7/23</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>389934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>6.70</td>\n",
       "      <td>516</td>\n",
       "      <td>7/10/23</td>\n",
       "      <td>Alicante</td>\n",
       "      <td>389935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Lidl</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>6.14</td>\n",
       "      <td>1283</td>\n",
       "      <td>12/5/23</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>389936</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>295</td>\n",
       "      <td>Caprabo</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>9.75</td>\n",
       "      <td>2367</td>\n",
       "      <td>15/9/23</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>390227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>296</td>\n",
       "      <td>Alcampo</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>3.82</td>\n",
       "      <td>1951</td>\n",
       "      <td>19/9/23</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>390228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>297</td>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>5.22</td>\n",
       "      <td>2811</td>\n",
       "      <td>4/5/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>390229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>298</td>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>8.07</td>\n",
       "      <td>473</td>\n",
       "      <td>18/1/23</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>390230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>299</td>\n",
       "      <td>Aldi</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>5.80</td>\n",
       "      <td>1081</td>\n",
       "      <td>23/3/23</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>390231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Clave Nombre Cliente Tipo Producto  Precio (€/kg)  Cantidad Vendida (kg)  \\\n",
       "0        0      Mas y Mas     Almendras           4.54                   1212   \n",
       "1        1      Mercadona        Nueces           5.84                    591   \n",
       "2        2        Alcampo    Cacahuetes           2.69                   2831   \n",
       "3        3      Mas y Mas     Pistachos           6.70                    516   \n",
       "4        4           Lidl        Nueces           6.14                   1283   \n",
       "..     ...            ...           ...            ...                    ...   \n",
       "295    295        Caprabo        Nueces           9.75                   2367   \n",
       "296    296        Alcampo     Pistachos           3.82                   1951   \n",
       "297    297      Mas y Mas     Pistachos           5.22                   2811   \n",
       "298    298      Mas y Mas    Cacahuetes           8.07                    473   \n",
       "299    299           Aldi        Nueces           5.80                   1081   \n",
       "\n",
       "    Fecha Venta Provincia Cliente      id  \n",
       "0       29/3/23             Cádiz  389932  \n",
       "1       5/12/23           Sevilla  389933  \n",
       "2       26/7/23            Madrid  389934  \n",
       "3       7/10/23          Alicante  389935  \n",
       "4       12/5/23         Barcelona  389936  \n",
       "..          ...               ...     ...  \n",
       "295     15/9/23           Sevilla  390227  \n",
       "296     19/9/23            Madrid  390228  \n",
       "297      4/5/23          Valencia  390229  \n",
       "298     18/1/23            Madrid  390230  \n",
       "299     23/3/23           Sevilla  390231  \n",
       "\n",
       "[300 rows x 8 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cruzamos los ficheron con los mismos registros\n",
    "pd.merge(left=fichero1,\n",
    "         right=fichero2,\n",
    "         how='inner',\n",
    "         on='Clave')"
   ]
  },
  {
   "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",
       "        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>Clave</th>\n",
       "      <th>Nombre Cliente</th>\n",
       "      <th>Tipo Producto</th>\n",
       "      <th>Precio (€/kg)</th>\n",
       "      <th>Cantidad Vendida (kg)</th>\n",
       "      <th>Fecha Venta</th>\n",
       "      <th>Provincia Cliente</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Almendras</td>\n",
       "      <td>4.54</td>\n",
       "      <td>1212</td>\n",
       "      <td>29/3/23</td>\n",
       "      <td>Cádiz</td>\n",
       "      <td>389932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Mercadona</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>5.84</td>\n",
       "      <td>591</td>\n",
       "      <td>5/12/23</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>389933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Alcampo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>2.69</td>\n",
       "      <td>2831</td>\n",
       "      <td>26/7/23</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>389934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>6.70</td>\n",
       "      <td>516</td>\n",
       "      <td>7/10/23</td>\n",
       "      <td>Alicante</td>\n",
       "      <td>389935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Lidl</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>6.14</td>\n",
       "      <td>1283</td>\n",
       "      <td>12/5/23</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>389936</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>295</td>\n",
       "      <td>Caprabo</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>9.75</td>\n",
       "      <td>2367</td>\n",
       "      <td>15/9/23</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>390227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>296</td>\n",
       "      <td>Alcampo</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>3.82</td>\n",
       "      <td>1951</td>\n",
       "      <td>19/9/23</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>390228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>297</td>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Pistachos</td>\n",
       "      <td>5.22</td>\n",
       "      <td>2811</td>\n",
       "      <td>4/5/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>390229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>298</td>\n",
       "      <td>Mas y Mas</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>8.07</td>\n",
       "      <td>473</td>\n",
       "      <td>18/1/23</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>390230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>299</td>\n",
       "      <td>Aldi</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>5.80</td>\n",
       "      <td>1081</td>\n",
       "      <td>23/3/23</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>390231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Clave Nombre Cliente Tipo Producto  Precio (€/kg)  Cantidad Vendida (kg)  \\\n",
       "0        0      Mas y Mas     Almendras           4.54                   1212   \n",
       "1        1      Mercadona        Nueces           5.84                    591   \n",
       "2        2        Alcampo    Cacahuetes           2.69                   2831   \n",
       "3        3      Mas y Mas     Pistachos           6.70                    516   \n",
       "4        4           Lidl        Nueces           6.14                   1283   \n",
       "..     ...            ...           ...            ...                    ...   \n",
       "295    295        Caprabo        Nueces           9.75                   2367   \n",
       "296    296        Alcampo     Pistachos           3.82                   1951   \n",
       "297    297      Mas y Mas     Pistachos           5.22                   2811   \n",
       "298    298      Mas y Mas    Cacahuetes           8.07                    473   \n",
       "299    299           Aldi        Nueces           5.80                   1081   \n",
       "\n",
       "    Fecha Venta Provincia Cliente      id  \n",
       "0       29/3/23             Cádiz  389932  \n",
       "1       5/12/23           Sevilla  389933  \n",
       "2       26/7/23            Madrid  389934  \n",
       "3       7/10/23          Alicante  389935  \n",
       "4       12/5/23         Barcelona  389936  \n",
       "..          ...               ...     ...  \n",
       "295     15/9/23           Sevilla  390227  \n",
       "296     19/9/23            Madrid  390228  \n",
       "297      4/5/23          Valencia  390229  \n",
       "298     18/1/23            Madrid  390230  \n",
       "299     23/3/23           Sevilla  390231  \n",
       "\n",
       "[300 rows x 8 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tambien podemos aplicar merge directamente sobre el fichero\n",
    "fichero1.merge(right=fichero2,how='inner',on='Clave')"
   ]
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}