6_TratamientoDatos.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tratamiento de Datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identificar Nulos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conteo por Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'isa'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/2d/p3zg32ds4p598pyxblpsp5ph0000gn/T/ipykernel_2676/1660468150.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# .isna = es nulo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_excel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/Users/jimenacambronero/Desktop/Proyectos para Portfolio/PythonPracticas/Datos/Frutos_Secos.xlsx'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparse_dates\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Fecha Venta'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misa\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5573\u001b[0m         ):\n\u001b[1;32m   5574\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5575\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5576\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5577\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'isa'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# .isna = es nulo\n",
    "df = pd.read_excel('/Users/jimenacambronero/Desktop/Proyectos para Portfolio/PythonPracticas/Datos/Frutos_Secos.xlsx', index_col='id', parse_dates=['Fecha Venta'])\n",
    "df.isa()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nombre Cliente           0\n",
       "Tipo Producto            0\n",
       "Precio (€/kg)            0\n",
       "Cantidad Vendida (kg)    0\n",
       "Fecha Venta              0\n",
       "Provincia Cliente        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# conteo de nulos\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "37  38  Nombre38   NaN         NaN\n",
       "38  39  Nombre39  30.0         NaN\n",
       "39  40  Nombre40  30.0         NaN\n",
       "40  41  Nombre41  30.0        90.0\n",
       "41  42  Nombre42   NaN        90.0\n",
       "42  43  Nombre43  30.0         NaN\n",
       "43  44  Nombre44  30.0         NaN\n",
       "44  45  Nombre45  25.0        95.0\n",
       "45  46  Nombre46   NaN        95.0\n",
       "46  47  Nombre47  25.0        95.0\n",
       "47  48  Nombre48   NaN        90.0\n",
       "48  49  Nombre49  25.0        95.0\n",
       "49  50  Nombre50  30.0        90.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cargamos otro archivo con registros nulos\n",
    "df_nulos= pd.read_csv('/Users/jimenacambronero/Desktop/Proyectos para Portfolio/PythonPracticas/Datos/registros_nulos.csv')\n",
    "df_nulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID             0\n",
       "Nombre         0\n",
       "Edad          15\n",
       "Puntuacion    20\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nulos.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Puntuacion    20\n",
       "Edad          15\n",
       "ID             0\n",
       "Nombre         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nulos.isna().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Porcentaje por Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Puntuacion    40.0\n",
       "Edad          30.0\n",
       "ID             0.0\n",
       "Nombre         0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nulos.isna().mean().sort_values(ascending= False)*100\n",
    "#obtendremos el porcentaje de nulos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conteo de Duplicados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_duplicados= pd.read_excel('/Users/jimenacambronero/Desktop/Proyectos para Portfolio/PythonPracticas/Datos/Frutos_Secos_duplicados.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Saber cuantos duplicados hay\n",
    "df_duplicados.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Localizar Duplicados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Cacahuetes</td>\n",
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       "      <td>1626</td>\n",
       "      <td>5/2/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>389955</td>\n",
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       "      <td>Caprabo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>6.24</td>\n",
       "      <td>1626</td>\n",
       "      <td>5/2/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>389955</td>\n",
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       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>Caprabo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>6.24</td>\n",
       "      <td>1626</td>\n",
       "      <td>5/2/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>389955</td>\n",
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       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>Caprabo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>6.24</td>\n",
       "      <td>1626</td>\n",
       "      <td>5/2/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>389955</td>\n",
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       "      <th>321</th>\n",
       "      <td>Caprabo</td>\n",
       "      <td>Cacahuetes</td>\n",
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       "      <td>1626</td>\n",
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      ],
      "text/plain": [
       "    Nombre Cliente Tipo Producto  Precio (€/kg)  Cantidad Vendida (kg)  \\\n",
       "300        Caprabo    Cacahuetes           6.24                   1626   \n",
       "301        Caprabo    Cacahuetes           6.24                   1626   \n",
       "302        Caprabo    Cacahuetes           6.24                   1626   \n",
       "303        Caprabo    Cacahuetes           6.24                   1626   \n",
       "304        Caprabo    Cacahuetes           6.24                   1626   \n",
       "305        Caprabo    Cacahuetes           6.24                   1626   \n",
       "306        Caprabo    Cacahuetes           6.24                   1626   \n",
       "307        Caprabo    Cacahuetes           6.24                   1626   \n",
       "308        Caprabo    Cacahuetes           6.24                   1626   \n",
       "309        Caprabo    Cacahuetes           6.24                   1626   \n",
       "310        Caprabo    Cacahuetes           6.24                   1626   \n",
       "311        Caprabo    Cacahuetes           6.24                   1626   \n",
       "312        Caprabo    Cacahuetes           6.24                   1626   \n",
       "313        Caprabo    Cacahuetes           6.24                   1626   \n",
       "314        Caprabo    Cacahuetes           6.24                   1626   \n",
       "315        Caprabo    Cacahuetes           6.24                   1626   \n",
       "316        Caprabo    Cacahuetes           6.24                   1626   \n",
       "317        Caprabo    Cacahuetes           6.24                   1626   \n",
       "318        Caprabo    Cacahuetes           6.24                   1626   \n",
       "319        Caprabo    Cacahuetes           6.24                   1626   \n",
       "320        Caprabo    Cacahuetes           6.24                   1626   \n",
       "321        Caprabo    Cacahuetes           6.24                   1626   \n",
       "322        Caprabo    Cacahuetes           6.24                   1626   \n",
       "\n",
       "    Fecha Venta Provincia Cliente      id  \n",
       "300      5/2/23          Valencia  389955  \n",
       "301      5/2/23          Valencia  389955  \n",
       "302      5/2/23          Valencia  389955  \n",
       "303      5/2/23          Valencia  389955  \n",
       "304      5/2/23          Valencia  389955  \n",
       "305      5/2/23          Valencia  389955  \n",
       "306      5/2/23          Valencia  389955  \n",
       "307      5/2/23          Valencia  389955  \n",
       "308      5/2/23          Valencia  389955  \n",
       "309      5/2/23          Valencia  389955  \n",
       "310      5/2/23          Valencia  389955  \n",
       "311      5/2/23          Valencia  389955  \n",
       "312      5/2/23          Valencia  389955  \n",
       "313      5/2/23          Valencia  389955  \n",
       "314      5/2/23          Valencia  389955  \n",
       "315      5/2/23          Valencia  389955  \n",
       "316      5/2/23          Valencia  389955  \n",
       "317      5/2/23          Valencia  389955  \n",
       "318      5/2/23          Valencia  389955  \n",
       "319      5/2/23          Valencia  389955  \n",
       "320      5/2/23          Valencia  389955  \n",
       "321      5/2/23          Valencia  389955  \n",
       "322      5/2/23          Valencia  389955  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_duplicados[df_duplicados.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Cacahuetes</td>\n",
       "      <td>6.24</td>\n",
       "      <td>1626</td>\n",
       "      <td>5/2/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>389955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>Caprabo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>6.24</td>\n",
       "      <td>1626</td>\n",
       "      <td>5/2/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>389955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>Caprabo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>6.24</td>\n",
       "      <td>1626</td>\n",
       "      <td>5/2/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>389955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>Caprabo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>6.24</td>\n",
       "      <td>1626</td>\n",
       "      <td>5/2/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>389955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>Caprabo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>6.24</td>\n",
       "      <td>1626</td>\n",
       "      <td>5/2/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>389955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>Caprabo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>6.24</td>\n",
       "      <td>1626</td>\n",
       "      <td>5/2/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>389955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>Caprabo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>6.24</td>\n",
       "      <td>1626</td>\n",
       "      <td>5/2/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>389955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>Caprabo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>6.24</td>\n",
       "      <td>1626</td>\n",
       "      <td>5/2/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>389955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>Caprabo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>6.24</td>\n",
       "      <td>1626</td>\n",
       "      <td>5/2/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>389955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>Caprabo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>6.24</td>\n",
       "      <td>1626</td>\n",
       "      <td>5/2/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>389955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>Caprabo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>6.24</td>\n",
       "      <td>1626</td>\n",
       "      <td>5/2/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>389955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>Caprabo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>6.24</td>\n",
       "      <td>1626</td>\n",
       "      <td>5/2/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>389955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>Caprabo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>6.24</td>\n",
       "      <td>1626</td>\n",
       "      <td>5/2/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>389955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>Caprabo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>6.24</td>\n",
       "      <td>1626</td>\n",
       "      <td>5/2/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>389955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>Caprabo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>6.24</td>\n",
       "      <td>1626</td>\n",
       "      <td>5/2/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>389955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>Caprabo</td>\n",
       "      <td>Cacahuetes</td>\n",
       "      <td>6.24</td>\n",
       "      <td>1626</td>\n",
       "      <td>5/2/23</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>389955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Nombre Cliente Tipo Producto  Precio (€/kg)  Cantidad Vendida (kg)  \\\n",
       "23         Caprabo    Cacahuetes           6.24                   1626   \n",
       "300        Caprabo    Cacahuetes           6.24                   1626   \n",
       "301        Caprabo    Cacahuetes           6.24                   1626   \n",
       "302        Caprabo    Cacahuetes           6.24                   1626   \n",
       "303        Caprabo    Cacahuetes           6.24                   1626   \n",
       "304        Caprabo    Cacahuetes           6.24                   1626   \n",
       "305        Caprabo    Cacahuetes           6.24                   1626   \n",
       "306        Caprabo    Cacahuetes           6.24                   1626   \n",
       "307        Caprabo    Cacahuetes           6.24                   1626   \n",
       "308        Caprabo    Cacahuetes           6.24                   1626   \n",
       "309        Caprabo    Cacahuetes           6.24                   1626   \n",
       "310        Caprabo    Cacahuetes           6.24                   1626   \n",
       "311        Caprabo    Cacahuetes           6.24                   1626   \n",
       "312        Caprabo    Cacahuetes           6.24                   1626   \n",
       "313        Caprabo    Cacahuetes           6.24                   1626   \n",
       "314        Caprabo    Cacahuetes           6.24                   1626   \n",
       "315        Caprabo    Cacahuetes           6.24                   1626   \n",
       "316        Caprabo    Cacahuetes           6.24                   1626   \n",
       "317        Caprabo    Cacahuetes           6.24                   1626   \n",
       "318        Caprabo    Cacahuetes           6.24                   1626   \n",
       "319        Caprabo    Cacahuetes           6.24                   1626   \n",
       "320        Caprabo    Cacahuetes           6.24                   1626   \n",
       "321        Caprabo    Cacahuetes           6.24                   1626   \n",
       "322        Caprabo    Cacahuetes           6.24                   1626   \n",
       "\n",
       "    Fecha Venta Provincia Cliente      id  \n",
       "23       5/2/23          Valencia  389955  \n",
       "300      5/2/23          Valencia  389955  \n",
       "301      5/2/23          Valencia  389955  \n",
       "302      5/2/23          Valencia  389955  \n",
       "303      5/2/23          Valencia  389955  \n",
       "304      5/2/23          Valencia  389955  \n",
       "305      5/2/23          Valencia  389955  \n",
       "306      5/2/23          Valencia  389955  \n",
       "307      5/2/23          Valencia  389955  \n",
       "308      5/2/23          Valencia  389955  \n",
       "309      5/2/23          Valencia  389955  \n",
       "310      5/2/23          Valencia  389955  \n",
       "311      5/2/23          Valencia  389955  \n",
       "312      5/2/23          Valencia  389955  \n",
       "313      5/2/23          Valencia  389955  \n",
       "314      5/2/23          Valencia  389955  \n",
       "315      5/2/23          Valencia  389955  \n",
       "316      5/2/23          Valencia  389955  \n",
       "317      5/2/23          Valencia  389955  \n",
       "318      5/2/23          Valencia  389955  \n",
       "319      5/2/23          Valencia  389955  \n",
       "320      5/2/23          Valencia  389955  \n",
       "321      5/2/23          Valencia  389955  \n",
       "322      5/2/23          Valencia  389955  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ver duplicados de otra manera\n",
    "df_duplicados[df_duplicados.duplicated(keep=False)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Análisis de Valores Únicos\n",
    "Si una variable solo tiene un valor no te aporta nada en tu análisis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Número e Valores Únicos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('/Users/jimenacambronero/Desktop/Proyectos para Portfolio/PythonPracticas/Datos/Frutos_Secos_unicos.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nombre Cliente            11\n",
       "Tipo Producto              4\n",
       "Precio (€/kg)            258\n",
       "Cantidad Vendida (kg)    284\n",
       "Fecha Venta              204\n",
       "Provincia Cliente          6\n",
       "id                       300\n",
       "Completado                 1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Si\n",
       "1      Si\n",
       "2      Si\n",
       "3      Si\n",
       "4      Si\n",
       "       ..\n",
       "295    Si\n",
       "296    Si\n",
       "297    Si\n",
       "298    Si\n",
       "299    Si\n",
       "Name: Completado, Length: 300, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # puede eliminarse porque es una constante"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Valores Únicos Diferentes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Cádiz', 'Sevilla', 'Madrid', 'Alicante', 'Barcelona', 'Valencia'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Provincia Cliente'].unique()"
   ]
  }
 ],
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