casas_california.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importamos las librerias."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importamos los datos (dataframe)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-114.31</td>\n",
       "      <td>34.19</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5612.0</td>\n",
       "      <td>1283.0</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>1.4936</td>\n",
       "      <td>66900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-114.47</td>\n",
       "      <td>34.40</td>\n",
       "      <td>19.0</td>\n",
       "      <td>7650.0</td>\n",
       "      <td>1901.0</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>463.0</td>\n",
       "      <td>1.8200</td>\n",
       "      <td>80100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-114.56</td>\n",
       "      <td>33.69</td>\n",
       "      <td>17.0</td>\n",
       "      <td>720.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>1.6509</td>\n",
       "      <td>85700.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-114.57</td>\n",
       "      <td>33.64</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1501.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>3.1917</td>\n",
       "      <td>73400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-114.57</td>\n",
       "      <td>33.57</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1454.0</td>\n",
       "      <td>326.0</td>\n",
       "      <td>624.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>1.9250</td>\n",
       "      <td>65500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -114.31     34.19                15.0       5612.0          1283.0   \n",
       "1    -114.47     34.40                19.0       7650.0          1901.0   \n",
       "2    -114.56     33.69                17.0        720.0           174.0   \n",
       "3    -114.57     33.64                14.0       1501.0           337.0   \n",
       "4    -114.57     33.57                20.0       1454.0           326.0   \n",
       "\n",
       "   population  households  median_income  median_house_value  \n",
       "0      1015.0       472.0         1.4936             66900.0  \n",
       "1      1129.0       463.0         1.8200             80100.0  \n",
       "2       333.0       117.0         1.6509             85700.0  \n",
       "3       515.0       226.0         3.1917             73400.0  \n",
       "4       624.0       262.0         1.9250             65500.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "california_housing_dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tomamos los datos que nos van a ser utiles a partir del dataframe importado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_median_age = california_housing_dataframe['housing_median_age']\n",
    "population = california_housing_dataframe['population']\n",
    "total_rooms = california_housing_dataframe['total_rooms']\n",
    "households = california_housing_dataframe['households']\n",
    "median_house_value = california_housing_dataframe['median_house_value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creamos un dataframe con esos datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    " casas_california = pd.DataFrame({'Edad Media':housing_median_age, 'Poblacion':population, 'Habitaciones':total_rooms, 'Hogares':households, 'Valor Medio':median_house_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Edad Media</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Habitaciones</th>\n",
       "      <th>Hogares</th>\n",
       "      <th>Valor Medio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15.0</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>5612.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>66900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.0</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>7650.0</td>\n",
       "      <td>463.0</td>\n",
       "      <td>80100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>720.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>85700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>1501.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>73400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.0</td>\n",
       "      <td>624.0</td>\n",
       "      <td>1454.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>65500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Edad Media  Poblacion  Habitaciones  Hogares  Valor Medio\n",
       "0        15.0     1015.0        5612.0    472.0      66900.0\n",
       "1        19.0     1129.0        7650.0    463.0      80100.0\n",
       "2        17.0      333.0         720.0    117.0      85700.0\n",
       "3        14.0      515.0        1501.0    226.0      73400.0\n",
       "4        20.0      624.0        1454.0    262.0      65500.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casas_california.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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