7_EstadisticaBasicaPandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estadisticos Básicos Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variables Categóricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_excel('/Users/jimenacambronero/Desktop/Proyectos para Portfolio/PythonPracticas/Datos/Frutos_Secos_unicos.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cacahuetes    100\n",
       "Almendras      69\n",
       "Pistachos      66\n",
       "Nueces         65\n",
       "Name: Tipo Producto, dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Conteo de frecuencias\n",
    "df['Tipo Producto'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cacahuetes    33.333333\n",
       "Almendras     23.000000\n",
       "Pistachos     22.000000\n",
       "Nueces        21.666667\n",
       "Name: Tipo Producto, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# En porcentaje\n",
    "df['Tipo Producto'].value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Mercadona\n",
       "Name: Nombre Cliente, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Moda (Valor más frecuente)\n",
    "df['Nombre Cliente'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Cacahuetes\n",
       "Name: Tipo Producto, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Moda (Valor más Frecuente)\n",
    "df['Tipo Producto'].mode()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variables Continuas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.598333333333333"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Media\n",
    "df['Precio (€/kg)'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.475"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mediana\n",
    "df['Precio (€/kg)'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.57"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Máximo\n",
    "df['Precio (€/kg)'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mínimo\n",
    "df['Precio (€/kg)'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Localizar el Indice del valor máximo\n",
    "df['Precio (€/kg)'].idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nombre Cliente                 Aldi\n",
       "Tipo Producto            Cacahuetes\n",
       "Precio (€/kg)                 11.57\n",
       "Cantidad Vendida (kg)          1650\n",
       "Fecha Venta                 8/12/23\n",
       "Provincia Cliente           Sevilla\n",
       "id                           389944\n",
       "Completado                       Si\n",
       "Name: 12, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "206"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Localizar el Indice del valor mínimo\n",
    "df['Precio (€/kg)'].idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nombre Cliente           Mas y Mas\n",
       "Tipo Producto            Pistachos\n",
       "Precio (€/kg)                 0.97\n",
       "Cantidad Vendida (kg)          316\n",
       "Fecha Venta                26/1/23\n",
       "Provincia Cliente            Cádiz\n",
       "id                          390138\n",
       "Completado                      Si\n",
       "Name: 206, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[206]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlación"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variables Continuas\n",
    "\n",
    "Una variables continua es una caraceristica que puede tomar cualquier valor dentro de un rango específico. No se limita a valores discretos y puede tener infinitos valores posibles. Por ejemplo, la altura de las personas es una variable continua, ya que puede ser cualquier valor en el rango entre la estatura más corta y la más alta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_numericos = df.select_dtypes(include=['float64','int64'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Precio (€/kg)</th>\n",
       "      <th>Cantidad Vendida (kg)</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Precio (€/kg)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.114999</td>\n",
       "      <td>-0.059525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cantidad Vendida (kg)</th>\n",
       "      <td>-0.114999</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.080907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>-0.059525</td>\n",
       "      <td>-0.080907</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                       Precio (€/kg)  Cantidad Vendida (kg)        id\n",
       "Precio (€/kg)               1.000000              -0.114999 -0.059525\n",
       "Cantidad Vendida (kg)      -0.114999               1.000000 -0.080907\n",
       "id                         -0.059525              -0.080907  1.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correlación \n",
    "df_numericos.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Precio (€/kg)</th>\n",
       "      <th>Cantidad Vendida (kg)</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Precio (€/kg)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.114999</td>\n",
       "      <td>-0.059525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cantidad Vendida (kg)</th>\n",
       "      <td>-0.114999</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.080907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>-0.059525</td>\n",
       "      <td>-0.080907</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Precio (€/kg)  Cantidad Vendida (kg)        id\n",
       "Precio (€/kg)               1.000000              -0.114999 -0.059525\n",
       "Cantidad Vendida (kg)      -0.114999               1.000000 -0.080907\n",
       "id                         -0.059525              -0.080907  1.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_numericos.corr(method='pearson') #método por defecto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.11499876970258154"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ver la correlación solo entre dos variables\n",
    "df_numericos['Precio (€/kg)'].corr(df_numericos['Cantidad Vendida (kg)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Año</th>\n",
       "      <th>Beneficios</th>\n",
       "      <th>Valor en Bolsa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>109.934283</td>\n",
       "      <td>92.923789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>97.234714</td>\n",
       "      <td>106.034024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>112.953771</td>\n",
       "      <td>101.523421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>130.460597</td>\n",
       "      <td>106.837428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>95.316933</td>\n",
       "      <td>94.010688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Año  Beneficios  Valor en Bolsa\n",
       "0    1  109.934283       92.923789\n",
       "1    2   97.234714      106.034024\n",
       "2    3  112.953771      101.523421\n",
       "3    4  130.460597      106.837428\n",
       "4    5   95.316933       94.010688"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_corre = pd.read_excel('/Users/jimenacambronero/Desktop/Proyectos para Portfolio/PythonPracticas/Datos/datos_correlacionados.xlsx')\n",
    "\n",
    "df_corre.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_corre['Beneficios'].corr([df_corre['Valor en Bolsa']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Gráfico de dispersión con una linea de regresión\n",
    "sns.regplot(x='Beneficios', y='Valor en Bolsa', data=df_corre)\n",
    "plt.title('Correlación entre Beneficios y Valor en Bolsa')\n",
    "plt.xlabel('Beneficios')\n",
    "plt.ylabel('Valor en Bolsa')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlación postiva. A mayor Valor en Bolsa mayor Beneficio. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gráfico de lineas\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(df_corre['Año'], df_corre['Beneficios'], label= 'Beneficios')\n",
    "plt.plot(df_corre['Año'], df_corre['Valor en Bolsa'], label='Valor en Bolsa')\n",
    "\n",
    "# Etiquetas y leyenda\n",
    "plt.title('Evaloción de Beneficios y Valor en Bolsa a lo largo de los años')\n",
    "plt.xlabel('Año')\n",
    "plt.ylabel('Cantidad')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ordinales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si las caracteristicas que estamos comparando no son números que pueden tomar cualquier valor( como la edad o la temperatura), sino que tienen un orden (como la clasificacion de productos de peor a mejor), entonces usas métodos como la tau e Kendall o la Rho de Spearman."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Precio (€/kg)</th>\n",
       "      <th>Cantidad Vendida (kg)</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Precio (€/kg)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.073944</td>\n",
       "      <td>-0.036675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cantidad Vendida (kg)</th>\n",
       "      <td>-0.073944</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.055171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>-0.036675</td>\n",
       "      <td>-0.055171</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Precio (€/kg)  Cantidad Vendida (kg)        id\n",
       "Precio (€/kg)               1.000000              -0.073944 -0.036675\n",
       "Cantidad Vendida (kg)      -0.073944               1.000000 -0.055171\n",
       "id                         -0.036675              -0.055171  1.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_numericos.corr(method='kendall')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Precio (€/kg)</th>\n",
       "      <th>Cantidad Vendida (kg)</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Precio (€/kg)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.109109</td>\n",
       "      <td>-0.057322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cantidad Vendida (kg)</th>\n",
       "      <td>-0.109109</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.080302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>-0.057322</td>\n",
       "      <td>-0.080302</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Precio (€/kg)  Cantidad Vendida (kg)        id\n",
       "Precio (€/kg)               1.000000              -0.109109 -0.057322\n",
       "Cantidad Vendida (kg)      -0.109109               1.000000 -0.080302\n",
       "id                         -0.057322              -0.080302  1.000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_numericos.corr(method='spearman') # no sigue una correlacion lineal (ejemplo edad - compra de tecnología)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.07394350955380263"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_numericos['Precio (€/kg)'].corr(df_numericos['Cantidad Vendida (kg)'], method='kendall')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seleccionar Variables según su Tipología"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_excel('/Users/jimenacambronero/Desktop/Proyectos para Portfolio/PythonPracticas/Datos/Frutos_Secos_unicos.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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",
       "      <th>Completado</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",
       "      <td>Si</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",
       "      <td>Si</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",
       "      <td>Si</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "\n",
       "  Fecha Venta Provincia Cliente      id Completado  \n",
       "0     29/3/23             Cádiz  389932         Si  \n",
       "1     5/12/23           Sevilla  389933         Si  \n",
       "2     26/7/23            Madrid  389934         Si  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Precio (€/kg)</th>\n",
       "      <th>Cantidad Vendida (kg)</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.54</td>\n",
       "      <td>1212</td>\n",
       "      <td>389932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.84</td>\n",
       "      <td>591</td>\n",
       "      <td>389933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.69</td>\n",
       "      <td>2831</td>\n",
       "      <td>389934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.70</td>\n",
       "      <td>516</td>\n",
       "      <td>389935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.14</td>\n",
       "      <td>1283</td>\n",
       "      <td>389936</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>9.75</td>\n",
       "      <td>2367</td>\n",
       "      <td>390227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>3.82</td>\n",
       "      <td>1951</td>\n",
       "      <td>390228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>5.22</td>\n",
       "      <td>2811</td>\n",
       "      <td>390229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>8.07</td>\n",
       "      <td>473</td>\n",
       "      <td>390230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>5.80</td>\n",
       "      <td>1081</td>\n",
       "      <td>390231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Precio (€/kg)  Cantidad Vendida (kg)      id\n",
       "0             4.54                   1212  389932\n",
       "1             5.84                    591  389933\n",
       "2             2.69                   2831  389934\n",
       "3             6.70                    516  389935\n",
       "4             6.14                   1283  389936\n",
       "..             ...                    ...     ...\n",
       "295           9.75                   2367  390227\n",
       "296           3.82                   1951  390228\n",
       "297           5.22                   2811  390229\n",
       "298           8.07                    473  390230\n",
       "299           5.80                   1081  390231\n",
       "\n",
       "[300 rows x 3 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes('number')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Precio (€/kg)                 5.598333\n",
       "Cantidad Vendida (kg)      1616.950000\n",
       "id                       390081.500000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes('number').mean()"
   ]
  }
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