TIPs_2A - Con respuestas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Preguntas teóricas**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) ¿Ante qué tipo de operaciones se puede ejecutar un Trigger en SQL?\n",
    "\n",
    "*El trigger se ejecuta ante las operaciones Insert, Update o Delete*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) ¿Cuál es la diferencia entre una vista y una vista materializada?\n",
    "\n",
    "Las vistas son objetos dinamicos, ejecutan una query y muestran el resultado en tiempo real\n",
    "Mientras que las vistas materializadas son una \"foto\" de un momento en particular del resultado de la query\n",
    "La gran diferencia es que la vista materializada guarda el resultado en una tabla, y persiste mas alla de los cambios que se hagan en las demas tablas de la query?\n",
    "\n",
    "Que pro y contra le ven? para que usarian una tabla materializada?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) ¿Cómo harías para quedarte con las primeras tres letras de una palabra en SQL?\n",
    "\n",
    "*SELECT SUBSTRING(\"palabra\", 1,3);*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Preguntas prácticas**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset disponible en : https://www.kaggle.com/datasets/franvaluch/properati-dataset\n",
    "\n",
    "En el siguiente link, podrán encontrar toda la información correspondiente a los datos: https://www.properati.com.ar/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data = pd.read_csv(r\"C:\\Users\\leand\\Downloads\\archive\\DS_Proyecto_01_Datos_Properati.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_date</th>\n",
       "      <th>end_date</th>\n",
       "      <th>created_on</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>l1</th>\n",
       "      <th>l2</th>\n",
       "      <th>l3</th>\n",
       "      <th>rooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>surface_total</th>\n",
       "      <th>surface_covered</th>\n",
       "      <th>price</th>\n",
       "      <th>currency</th>\n",
       "      <th>title</th>\n",
       "      <th>description</th>\n",
       "      <th>property_type</th>\n",
       "      <th>operation_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-10-17</td>\n",
       "      <td>2019-12-23</td>\n",
       "      <td>2019-10-17</td>\n",
       "      <td>-34.605880</td>\n",
       "      <td>-58.384949</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>San Cristobal</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>153000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>***Venta semipiso centro, ideal hostel*****</td>\n",
       "      <td>DESCRIPCION DE LA PROPIEDAD: Departamento de 1...</td>\n",
       "      <td>Departamento</td>\n",
       "      <td>Venta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-10-17</td>\n",
       "      <td>2019-11-21</td>\n",
       "      <td>2019-10-17</td>\n",
       "      <td>-34.624056</td>\n",
       "      <td>-58.412110</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>Boedo</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>159000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>Espectacular PH reciclado en Boedo sin expensas.</td>\n",
       "      <td>PH reciclado en Boedo a una cuadra de la plaz...</td>\n",
       "      <td>PH</td>\n",
       "      <td>Venta</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   start_date    end_date  created_on        lat        lon         l1  \\\n",
       "0  2019-10-17  2019-12-23  2019-10-17 -34.605880 -58.384949  Argentina   \n",
       "1  2019-10-17  2019-11-21  2019-10-17 -34.624056 -58.412110  Argentina   \n",
       "\n",
       "                l2             l3  rooms  bedrooms  bathrooms  surface_total  \\\n",
       "0  Capital Federal  San Cristobal    7.0       7.0        2.0          140.0   \n",
       "1  Capital Federal          Boedo    2.0       1.0        2.0           70.0   \n",
       "\n",
       "   surface_covered     price currency  \\\n",
       "0            140.0  153000.0      USD   \n",
       "1             58.0  159000.0      USD   \n",
       "\n",
       "                                              title  \\\n",
       "0       ***Venta semipiso centro, ideal hostel*****   \n",
       "1  Espectacular PH reciclado en Boedo sin expensas.   \n",
       "\n",
       "                                         description property_type  \\\n",
       "0  DESCRIPCION DE LA PROPIEDAD: Departamento de 1...  Departamento   \n",
       "1   PH reciclado en Boedo a una cuadra de la plaz...            PH   \n",
       "\n",
       "  operation_type  \n",
       "0          Venta  \n",
       "1          Venta  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) ¿Qué porcentaje del total de propiedades publicadas corresponden a la zona sur del Gran Buenos Aires?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El porcentaje de propiedades publicadas en el sur del GBA es: 11.71 %\n"
     ]
    }
   ],
   "source": [
    "print('El porcentaje de propiedades publicadas en el sur del GBA es:', round((data.l2.value_counts()[2] / data.shape[0]) * 100, 2), '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) ¿En qué barrio se encuentra la propiedad más cara?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Palermo'], dtype=object)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['l3'][data.price == data.price.max()].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) Reagrupe las propiedades por el precio promedio de cada barrio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l3</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>José C Paz</th>\n",
       "      <td>91501.874564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Constitución</th>\n",
       "      <td>104534.073840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Villa Soldati</th>\n",
       "      <td>122472.463768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avellaneda</th>\n",
       "      <td>127272.403650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boca</th>\n",
       "      <td>128428.273504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgrano</th>\n",
       "      <td>376293.661283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recoleta</th>\n",
       "      <td>410252.230184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Las Cañitas</th>\n",
       "      <td>469045.204056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Villa Riachuelo</th>\n",
       "      <td>535988.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puerto Madero</th>\n",
       "      <td>984679.031303</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         price\n",
       "l3                            \n",
       "José C Paz        91501.874564\n",
       "Constitución     104534.073840\n",
       "Villa Soldati    122472.463768\n",
       "Avellaneda       127272.403650\n",
       "Boca             128428.273504\n",
       "...                        ...\n",
       "Belgrano         376293.661283\n",
       "Recoleta         410252.230184\n",
       "Las Cañitas      469045.204056\n",
       "Villa Riachuelo  535988.071429\n",
       "Puerto Madero    984679.031303\n",
       "\n",
       "[89 rows x 1 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['l3', 'price']].groupby('l3').mean().sort_values('price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4) Señale los 5 barrios con la mayor media de precios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Puerto Madero', 'Villa Riachuelo', 'Las Cañitas', 'Recoleta', 'Belgrano']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "barrios = data.groupby('l3').mean().sort_values('price', ascending=False).head(5)\n",
    "list(barrios.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5) Graficar un histograma que muestre la distribución del precio de las casas ubicadas en el barrio de Almagro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (10,6))\n",
    "sns.histplot(x = 'price', data = data[(data.property_type == 'Casa') & (data.l3 == 'Almagro')] , kde = True, bins = 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6) Realizar un diagrama de barras para la cantidad de habitaciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,6))\n",
    "sns.countplot(x = 'bedrooms', data = data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7) Considerando la técnica del rango intercuartílico, ¿cuántos outliers tiene la variable `surface_total`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La cantidad de outliers para el atributo surface_total es : 13452\n"
     ]
    }
   ],
   "source": [
    "Q1 = data['surface_total'].quantile(0.25)\n",
    "Q3 = data['surface_total'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "BI = Q1 - 1.5*IQR\n",
    "BS = Q3 + 1.5*IQR\n",
    "out = (data['surface_total']<BI) | (data['surface_total']>BS)\n",
    "print('La cantidad de outliers para el atributo surface_total es :', out.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8. ¿Cuál es el precio promedio del m2 (metro cuadrado) en barrio Palermo?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['price/m2'] = data.price / data.surface_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "palermo = data[data['l3'] == 'Palermo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "En promedio, el metro cuadrado en barrio Palermo vale : 3528 USD\n"
     ]
    }
   ],
   "source": [
    "print('En promedio, el metro cuadrado en barrio Palermo vale :', int(palermo['price/m2'].mean()) , 'USD')"
   ]
  }
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