pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "housing = pd.read_csv(\"housing.csv\") #(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      "longitude             20640 non-null float64\n",
      "latitude              20640 non-null float64\n",
      "housing_median_age    20640 non-null float64\n",
      "total_rooms           20640 non-null float64\n",
      "total_bedrooms        20433 non-null float64\n",
      "population            20640 non-null float64\n",
      "households            20640 non-null float64\n",
      "median_income         20640 non-null float64\n",
      "median_house_value    20640 non-null float64\n",
      "ocean_proximity       20640 non-null object\n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"ocean_proximity\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# to make this notebook's output identical at every run\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# For illustration only. Sklearn has train_test_split()\n",
    "def split_train_test(data, test_ratio):\n",
    "    shuffled_indices = np.random.permutation(len(data))\n",
    "    test_set_size = int(len(data) * test_ratio)\n",
    "    test_indices = shuffled_indices[:test_set_size]\n",
    "    train_indices = shuffled_indices[test_set_size:]\n",
    "    return data.iloc[train_indices], data.iloc[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16512 train + 4128 test\n"
     ]
    }
   ],
   "source": [
    "train_set, test_set = split_train_test(housing, 0.2)\n",
    "print(len(train_set), \"train +\", len(test_set), \"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import hashlib\n",
    "\n",
    "def test_set_check(identifier, test_ratio, hash):\n",
    "    return hash(np.int64(identifier)).digest()[-1] < 256 * test_ratio\n",
    "\n",
    "def split_train_test_by_id(data, test_ratio, id_column, hash=hashlib.md5):\n",
    "    ids = data[id_column]\n",
    "    in_test_set = ids.apply(lambda id_: test_set_check(id_, test_ratio, hash))\n",
    "    return data.loc[~in_test_set], data.loc[in_test_set]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# This version supports both Python 2 and Python 3, instead of just Python 3.\n",
    "def test_set_check(identifier, test_ratio, hash):\n",
    "    return bytearray(hash(np.int64(identifier)).digest())[-1] < 256 * test_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing_with_id = housing.reset_index()   # adds an `index` column\n",
    "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, \"index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing_with_id[\"id\"] = housing[\"longitude\"] * 1000 + housing[\"latitude\"]\n",
    "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, \"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</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",
       "      <th>ocean_proximity</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.84</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2555.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>595.0</td>\n",
       "      <td>2.0804</td>\n",
       "      <td>226700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2202.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>910.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>3.2031</td>\n",
       "      <td>281500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3503.0</td>\n",
       "      <td>752.0</td>\n",
       "      <td>1504.0</td>\n",
       "      <td>734.0</td>\n",
       "      <td>3.2705</td>\n",
       "      <td>241800.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2491.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>3.0750</td>\n",
       "      <td>213500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>2.6736</td>\n",
       "      <td>191300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "8       8    -122.26     37.84                42.0       2555.0   \n",
       "10     10    -122.26     37.85                52.0       2202.0   \n",
       "11     11    -122.26     37.85                52.0       3503.0   \n",
       "12     12    -122.26     37.85                52.0       2491.0   \n",
       "13     13    -122.26     37.84                52.0        696.0   \n",
       "\n",
       "    total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "8            665.0      1206.0       595.0         2.0804            226700.0   \n",
       "10           434.0       910.0       402.0         3.2031            281500.0   \n",
       "11           752.0      1504.0       734.0         3.2705            241800.0   \n",
       "12           474.0      1098.0       468.0         3.0750            213500.0   \n",
       "13           191.0       345.0       174.0         2.6736            191300.0   \n",
       "\n",
       "   ocean_proximity         id  \n",
       "8         NEAR BAY -122222.16  \n",
       "10        NEAR BAY -122222.15  \n",
       "11        NEAR BAY -122222.15  \n",
       "12        NEAR BAY -122222.15  \n",
       "13        NEAR BAY -122222.16  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20046</th>\n",
       "      <td>-119.01</td>\n",
       "      <td>36.06</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1505.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1392.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>1.6812</td>\n",
       "      <td>47700.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3024</th>\n",
       "      <td>-119.46</td>\n",
       "      <td>35.14</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2943.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1565.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>2.5313</td>\n",
       "      <td>45800.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15663</th>\n",
       "      <td>-122.44</td>\n",
       "      <td>37.80</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3830.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>963.0</td>\n",
       "      <td>3.4801</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20484</th>\n",
       "      <td>-118.72</td>\n",
       "      <td>34.28</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3051.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1705.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>5.7376</td>\n",
       "      <td>218600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9814</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>36.62</td>\n",
       "      <td>34.0</td>\n",
       "      <td>2351.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1063.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>3.7250</td>\n",
       "      <td>278000.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "20046    -119.01     36.06                25.0       1505.0             NaN   \n",
       "3024     -119.46     35.14                30.0       2943.0             NaN   \n",
       "15663    -122.44     37.80                52.0       3830.0             NaN   \n",
       "20484    -118.72     34.28                17.0       3051.0             NaN   \n",
       "9814     -121.93     36.62                34.0       2351.0             NaN   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "20046      1392.0       359.0         1.6812             47700.0   \n",
       "3024       1565.0       584.0         2.5313             45800.0   \n",
       "15663      1310.0       963.0         3.4801            500001.0   \n",
       "20484      1705.0       495.0         5.7376            218600.0   \n",
       "9814       1063.0       428.0         3.7250            278000.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "20046          INLAND  \n",
       "3024           INLAND  \n",
       "15663        NEAR BAY  \n",
       "20484       <1H OCEAN  \n",
       "9814       NEAR OCEAN  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "for train_index, test_index in split.split(housing, housing[\"income_cat\"]):\n",
    "    strat_train_set = housing.loc[train_index]\n",
    "    strat_test_set = housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    0.350581\n",
       "2.0    0.318847\n",
       "4.0    0.176308\n",
       "5.0    0.114438\n",
       "1.0    0.039826\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"income_cat\"].value_counts() / len(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def income_cat_proportions(data):\n",
    "    return data[\"income_cat\"].value_counts() / len(data)\n",
    "\n",
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)\n",
    "\n",
    "compare_props = pd.DataFrame({\n",
    "    \"Overall\": income_cat_proportions(housing),\n",
    "    \"Stratified\": income_cat_proportions(strat_test_set),\n",
    "    \"Random\": income_cat_proportions(test_set),\n",
    "}).sort_index()\n",
    "compare_props[\"Rand. %error\"] = 100 * compare_props[\"Random\"] / compare_props[\"Overall\"] - 100\n",
    "compare_props[\"Strat. %error\"] = 100 * compare_props[\"Stratified\"] / compare_props[\"Overall\"] - 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Overall</th>\n",
       "      <th>Random</th>\n",
       "      <th>Stratified</th>\n",
       "      <th>Rand. %error</th>\n",
       "      <th>Strat. %error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.039826</td>\n",
       "      <td>0.040213</td>\n",
       "      <td>0.039729</td>\n",
       "      <td>0.973236</td>\n",
       "      <td>-0.243309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.318847</td>\n",
       "      <td>0.324370</td>\n",
       "      <td>0.318798</td>\n",
       "      <td>1.732260</td>\n",
       "      <td>-0.015195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.350581</td>\n",
       "      <td>0.358527</td>\n",
       "      <td>0.350533</td>\n",
       "      <td>2.266446</td>\n",
       "      <td>-0.013820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.176308</td>\n",
       "      <td>0.167393</td>\n",
       "      <td>0.176357</td>\n",
       "      <td>-5.056334</td>\n",
       "      <td>0.027480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>0.114438</td>\n",
       "      <td>0.109496</td>\n",
       "      <td>0.114583</td>\n",
       "      <td>-4.318374</td>\n",
       "      <td>0.127011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Overall    Random  Stratified  Rand. %error  Strat. %error\n",
       "1.0  0.039826  0.040213    0.039729      0.973236      -0.243309\n",
       "2.0  0.318847  0.324370    0.318798      1.732260      -0.015195\n",
       "3.0  0.350581  0.358527    0.350533      2.266446      -0.013820\n",
       "4.0  0.176308  0.167393    0.176357     -5.056334       0.027480\n",
       "5.0  0.114438  0.109496    0.114583     -4.318374       0.127011"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_props"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for set_ in (strat_train_set, strat_test_set):\n",
    "    set_.drop(\"income_cat\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discover and visualize the data to gain insights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nbuser/anaconda3_410/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n",
      "/home/nbuser/anaconda3_410/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    },
    {
     "data": {
      "image/png": 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LFejaXM2uxQvdi9l4Z6tr8Upb1bd93v60V5/6Av3rj1SfUdMBNGKoxwz7rSo4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\nbgT+y5o43kFzxtTVEfG3NKfGHrNO3JI0b/4AOCkiTmwH8Q9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rIZds9EyK/X/n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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe61a3102e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "housing.hist(bins=50, figsize=(20,15))\n",
    "#save_fig(\"attribute_histogram_plots\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe619691898>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yS8CNs+y3Edg4w/g+YEXnMc3MbD74s4O65J6AmdWNPzvIzMw64iLQkyJ3gARF7gBJmrLm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6196766d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing[\"median_income\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Divide by 1.5 to limit the number of income categories\n",
    "housing[\"income_cat\"] = np.ceil(housing[\"median_income\"] / 1.5)\n",
    "# Label those above 5 as 5\n",
    "housing[\"income_cat\"].where(housing[\"income_cat\"] < 5, 5.0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    7236\n",
       "2.0    6581\n",
       "4.0    3639\n",
       "5.0    2362\n",
       "1.0     822\n",
       "Name: income_cat, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"income_cat\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe6186af0f0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uO585v+nc4hefnSbfF5/94/oS7KuSrkwZiKQpOlcwBxZ9Kqt8niNOyCCfdYng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\nnDcCM7Mx543AzGzMeSMwMxtz/x9b0w3YqneNTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe614be94a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing[\"income_cat\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'strat_train_set' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-e5edbf14ad6c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhousing\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstrat_train_set\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'strat_train_set' is not defined"
     ]
    }
   ],
   "source": [
    "housing = strat_train_set.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\")\n",
    "save_fig(\"bad_visualization_plot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.1)\n",
    "save_fig(\"better_visualization_plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The argument `sharex=False` fixes a display bug (the x-axis values and legend were not displayed). This is a temporary fix (see: https://github.com/pandas-dev/pandas/issues/10611). Thanks to Wilmer Arellano for pointing it out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n",
    "    s=housing[\"population\"]/100, label=\"population\", figsize=(10,7),\n",
    "    c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=True,\n",
    "    sharex=False)\n",
    "plt.legend()\n",
    "save_fig(\"housing_prices_scatterplot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "corr_matrix = housing.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# from pandas.tools.plotting import scatter_matrix # For older versions of Pandas\n",
    "from pandas.plotting import scatter_matrix\n",
    "\n",
    "attributes = [\"median_house_value\", \"median_income\", \"total_rooms\",\n",
    "              \"housing_median_age\"]\n",
    "scatter_matrix(housing[attributes], figsize=(12, 8))\n",
    "save_fig(\"scatter_matrix_plot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"median_income\", y=\"median_house_value\",\n",
    "             alpha=0.1)\n",
    "plt.axis([0, 16, 0, 550000])\n",
    "save_fig(\"income_vs_house_value_scatterplot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing[\"rooms_per_household\"] = housing[\"total_rooms\"]/housing[\"households\"]\n",
    "housing[\"bedrooms_per_room\"] = housing[\"total_bedrooms\"]/housing[\"total_rooms\"]\n",
    "housing[\"population_per_household\"]=housing[\"population\"]/housing[\"households\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: there was a bug in the previous cell, in the definition of the `rooms_per_household` attribute. This explains why the correlation value below differs slightly from the value in the book (unless you are reading the latest version)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "corr_matrix = housing.corr()\n",
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RDk+l0fGB2DBeXmlwfa3Ncq3DuekSlin2jIe9txhvyn1+LkvLC0DEhswcmkfP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\nMBj2HP8fo2qc4h2jUWwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f26c3f10b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"rooms_per_household\", y=\"median_house_value\",\n",
    "             alpha=0.2)\n",
    "plt.axis([0, 5, 0, 520000])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<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",
       "      <th>rooms_per_household</th>\n",
       "      <th>bedrooms_per_room</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16512.000000</td>\n",
       "      <td>16512.000000</td>\n",
       "      <td>16512.000000</td>\n",
       "      <td>16512.000000</td>\n",
       "      <td>16354.000000</td>\n",
       "      <td>16512.000000</td>\n",
       "      <td>16512.000000</td>\n",
       "      <td>16512.000000</td>\n",
       "      <td>16512.000000</td>\n",
       "      <td>16512.000000</td>\n",
       "      <td>16354.000000</td>\n",
       "      <td>16512.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.575834</td>\n",
       "      <td>35.639577</td>\n",
       "      <td>28.653101</td>\n",
       "      <td>2622.728319</td>\n",
       "      <td>534.973890</td>\n",
       "      <td>1419.790819</td>\n",
       "      <td>497.060380</td>\n",
       "      <td>3.875589</td>\n",
       "      <td>206990.920724</td>\n",
       "      <td>5.440341</td>\n",
       "      <td>0.212878</td>\n",
       "      <td>3.096437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.001860</td>\n",
       "      <td>2.138058</td>\n",
       "      <td>12.574726</td>\n",
       "      <td>2138.458419</td>\n",
       "      <td>412.699041</td>\n",
       "      <td>1115.686241</td>\n",
       "      <td>375.720845</td>\n",
       "      <td>1.904950</td>\n",
       "      <td>115703.014830</td>\n",
       "      <td>2.611712</td>\n",
       "      <td>0.057379</td>\n",
       "      <td>11.584826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>1.130435</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.940000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1443.000000</td>\n",
       "      <td>295.000000</td>\n",
       "      <td>784.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>2.566775</td>\n",
       "      <td>119800.000000</td>\n",
       "      <td>4.442040</td>\n",
       "      <td>0.175304</td>\n",
       "      <td>2.431287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.510000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2119.500000</td>\n",
       "      <td>433.000000</td>\n",
       "      <td>1164.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>3.540900</td>\n",
       "      <td>179500.000000</td>\n",
       "      <td>5.232284</td>\n",
       "      <td>0.203031</td>\n",
       "      <td>2.817653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.720000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3141.000000</td>\n",
       "      <td>644.000000</td>\n",
       "      <td>1719.250000</td>\n",
       "      <td>602.000000</td>\n",
       "      <td>4.744475</td>\n",
       "      <td>263900.000000</td>\n",
       "      <td>6.056361</td>\n",
       "      <td>0.239831</td>\n",
       "      <td>3.281420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6210.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>5358.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "      <td>141.909091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1243.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  16512.000000  16512.000000        16512.000000  16512.000000   \n",
       "mean    -119.575834     35.639577           28.653101   2622.728319   \n",
       "std        2.001860      2.138058           12.574726   2138.458419   \n",
       "min     -124.350000     32.540000            1.000000      6.000000   \n",
       "25%     -121.800000     33.940000           18.000000   1443.000000   \n",
       "50%     -118.510000     34.260000           29.000000   2119.500000   \n",
       "75%     -118.010000     37.720000           37.000000   3141.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    16354.000000  16512.000000  16512.000000   16512.000000   \n",
       "mean       534.973890   1419.790819    497.060380       3.875589   \n",
       "std        412.699041   1115.686241    375.720845       1.904950   \n",
       "min          2.000000      3.000000      2.000000       0.499900   \n",
       "25%        295.000000    784.000000    279.000000       2.566775   \n",
       "50%        433.000000   1164.000000    408.000000       3.540900   \n",
       "75%        644.000000   1719.250000    602.000000       4.744475   \n",
       "max       6210.000000  35682.000000   5358.000000      15.000100   \n",
       "\n",
       "       median_house_value  rooms_per_household  bedrooms_per_room  \\\n",
       "count        16512.000000         16512.000000       16354.000000   \n",
       "mean        206990.920724             5.440341           0.212878   \n",
       "std         115703.014830             2.611712           0.057379   \n",
       "min          14999.000000             1.130435           0.100000   \n",
       "25%         119800.000000             4.442040           0.175304   \n",
       "50%         179500.000000             5.232284           0.203031   \n",
       "75%         263900.000000             6.056361           0.239831   \n",
       "max         500001.000000           141.909091           1.000000   \n",
       "\n",
       "       population_per_household  \n",
       "count              16512.000000  \n",
       "mean                   3.096437  \n",
       "std                   11.584826  \n",
       "min                    0.692308  \n",
       "25%                    2.431287  \n",
       "50%                    2.817653  \n",
       "75%                    3.281420  \n",
       "max                 1243.333333  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare the data for Machine Learning algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing = strat_train_set.drop(\"median_house_value\", axis=1) # drop labels for training set\n",
    "housing_labels = strat_train_set[\"median_house_value\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4629</th>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.07</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3759.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3296.0</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>2.2708</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6068</th>\n",
       "      <td>-117.86</td>\n",
       "      <td>34.01</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>5.1762</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17923</th>\n",
       "      <td>-121.97</td>\n",
       "      <td>37.35</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>999.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>4.6328</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13656</th>\n",
       "      <td>-117.30</td>\n",
       "      <td>34.05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2155.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>1.6675</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19252</th>\n",
       "      <td>-122.79</td>\n",
       "      <td>38.48</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6837.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3468.0</td>\n",
       "      <td>1405.0</td>\n",
       "      <td>3.1662</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "4629     -118.30     34.07                18.0       3759.0             NaN   \n",
       "6068     -117.86     34.01                16.0       4632.0             NaN   \n",
       "17923    -121.97     37.35                30.0       1955.0             NaN   \n",
       "13656    -117.30     34.05                 6.0       2155.0             NaN   \n",
       "19252    -122.79     38.48                 7.0       6837.0             NaN   \n",
       "\n",
       "       population  households  median_income ocean_proximity  \n",
       "4629       3296.0      1462.0         2.2708       <1H OCEAN  \n",
       "6068       3038.0       727.0         5.1762       <1H OCEAN  \n",
       "17923       999.0       386.0         4.6328       <1H OCEAN  \n",
       "13656      1039.0       391.0         1.6675          INLAND  \n",
       "19252      3468.0      1405.0         3.1662       <1H OCEAN  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_incomplete_rows = housing[housing.isnull().any(axis=1)].head()\n",
    "sample_incomplete_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income, ocean_proximity]\n",
       "Index: []"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_incomplete_rows.dropna(subset=[\"total_bedrooms\"])    # option 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4629</th>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.07</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3759.0</td>\n",
       "      <td>3296.0</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>2.2708</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6068</th>\n",
       "      <td>-117.86</td>\n",
       "      <td>34.01</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>5.1762</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17923</th>\n",
       "      <td>-121.97</td>\n",
       "      <td>37.35</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>4.6328</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13656</th>\n",
       "      <td>-117.30</td>\n",
       "      <td>34.05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2155.0</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>1.6675</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19252</th>\n",
       "      <td>-122.79</td>\n",
       "      <td>38.48</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6837.0</td>\n",
       "      <td>3468.0</td>\n",
       "      <td>1405.0</td>\n",
       "      <td>3.1662</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  population  \\\n",
       "4629     -118.30     34.07                18.0       3759.0      3296.0   \n",
       "6068     -117.86     34.01                16.0       4632.0      3038.0   \n",
       "17923    -121.97     37.35                30.0       1955.0       999.0   \n",
       "13656    -117.30     34.05                 6.0       2155.0      1039.0   \n",
       "19252    -122.79     38.48                 7.0       6837.0      3468.0   \n",
       "\n",
       "       households  median_income ocean_proximity  \n",
       "4629       1462.0         2.2708       <1H OCEAN  \n",
       "6068        727.0         5.1762       <1H OCEAN  \n",
       "17923       386.0         4.6328       <1H OCEAN  \n",
       "13656       391.0         1.6675          INLAND  \n",
       "19252      1405.0         3.1662       <1H OCEAN  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_incomplete_rows.drop(\"total_bedrooms\", axis=1)       # option 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4629</th>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.07</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3759.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>3296.0</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>2.2708</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6068</th>\n",
       "      <td>-117.86</td>\n",
       "      <td>34.01</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>5.1762</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17923</th>\n",
       "      <td>-121.97</td>\n",
       "      <td>37.35</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>4.6328</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13656</th>\n",
       "      <td>-117.30</td>\n",
       "      <td>34.05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2155.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>1.6675</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19252</th>\n",
       "      <td>-122.79</td>\n",
       "      <td>38.48</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6837.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>3468.0</td>\n",
       "      <td>1405.0</td>\n",
       "      <td>3.1662</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "4629     -118.30     34.07                18.0       3759.0           433.0   \n",
       "6068     -117.86     34.01                16.0       4632.0           433.0   \n",
       "17923    -121.97     37.35                30.0       1955.0           433.0   \n",
       "13656    -117.30     34.05                 6.0       2155.0           433.0   \n",
       "19252    -122.79     38.48                 7.0       6837.0           433.0   \n",
       "\n",
       "       population  households  median_income ocean_proximity  \n",
       "4629       3296.0      1462.0         2.2708       <1H OCEAN  \n",
       "6068       3038.0       727.0         5.1762       <1H OCEAN  \n",
       "17923       999.0       386.0         4.6328       <1H OCEAN  \n",
       "13656      1039.0       391.0         1.6675          INLAND  \n",
       "19252      3468.0      1405.0         3.1662       <1H OCEAN  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median = housing[\"total_bedrooms\"].median()\n",
    "sample_incomplete_rows[\"total_bedrooms\"].fillna(median, inplace=True) # option 3\n",
    "sample_incomplete_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Imputer\n",
    "\n",
    "imputer = Imputer(strategy=\"median\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove the text attribute because median can only be calculated on numerical attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing_num = housing.drop(\"ocean_proximity\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Imputer(axis=0, copy=True, missing_values='NaN', strategy='median', verbose=0)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.fit(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -118.51  ,    34.26  ,    29.    ,  2119.5   ,   433.    ,\n",
       "        1164.    ,   408.    ,     3.5409])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that this is the same as manually computing the median of each attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -118.51  ,    34.26  ,    29.    ,  2119.5   ,   433.    ,\n",
       "        1164.    ,   408.    ,     3.5409])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num.median().values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = imputer.transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing_tr = pd.DataFrame(X, columns=housing_num.columns,\n",
    "                          index = list(housing.index.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4629</th>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.07</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3759.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>3296.0</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>2.2708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6068</th>\n",
       "      <td>-117.86</td>\n",
       "      <td>34.01</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>5.1762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17923</th>\n",
       "      <td>-121.97</td>\n",
       "      <td>37.35</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>4.6328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13656</th>\n",
       "      <td>-117.30</td>\n",
       "      <td>34.05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2155.0</td>\n",
       "      <td>433.0</td>\n