housing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import tarfile\n",
    "from six.moves import urllib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml/master/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "HOUSING_PATH = os.path.join(\"datasets\",\"housing\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "HOUSING_URL = DOWNLOAD_ROOT+ \"datasets/housing/housing.tgz\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fetch_housing_data(housing_url=HOUSING_URL,housing_path=HOUSING_PATH):\n",
    "    if not os.path.isdir(housing_path):\n",
    "        os.makedirs(housing_path)\n",
    "    tgz_path = os.path.join(housing_path,\"housing.tgz\")\n",
    "    urllib.request.urlretrieve(housing_url,tgz_path)\n",
    "    housing_tgz = tarfile.open(tgz_path)\n",
    "    housing_tgz.extractall(path=housing_path)\n",
    "    housing_tgz.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#fetch_housing_data(HOUSING_URL,HOUSING_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_housing_data(housing_path=HOUSING_PATH):\n",
    "    csv_path = os.path.join(housing_path, \"housing.csv\")\n",
    "    return pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "housing = load_housing_data(HOUSING_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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": 10,
   "metadata": {},
   "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",
       "    </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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fdff8f73d68>,\n",
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       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fdff8ec4978>,\n",
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       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fdff8cc6320>]], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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tuqFn+a4t5w05EkmSJB0Jk0iSJEnLwFxJPEmSpPmyO5skSZIkSZL6MokkSZIk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\na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SNgsX8HTf6sHzb3DxpFqarF21jy08DaqvqF9vzVwA9U1b+ZtNxGYGN7+gLg3nls7jnA\nl48g7jB1ORt0O1+Xs0G383U5G3Q730Jn++6qeu4Cru+Y4r5kWqOUFcw7bKOUd5SyQnfyui85Asf4\nvqRLebqUBcwziHlm1qU8s80y531JJy+sXVVbga1Hso4kd1bV6gWKtKC6nA26na/L2aDb+bqcDbqd\nr8vZNL2jfV8y2ShlBfMO2yjlHaWsMHp5dWSOxn1Jl/J0KQuYZxDzzKxLeYaZ5UnDWOkM9gGn9j1f\n0dokSZot9yWSpCPlvkSS5mGxi0gfBVYlOS3JU4D1wI5FziBJGm3uSyRJR8p9iSTNw6IOZ6uqQ0n+\nDfA39G6l+daq2jWkzR3RaadD1uVs0O18Xc4G3c7X5WzQ7XxdznbMcV8yrVHKCuYdtlHKO0pZYfTy\nagrH+L6kS3m6lAXMM4h5ZtalPEPLsqgX1pYkSZIkSdJoWuzhbJIkSZIkSRpBFpEkSZIkSZI00FFX\nREqyNsm9SXYn2bSI292TZGeSTyS5s7WdkuSWJPe1vyf3Lb+5Zbw3yfl97We39exOck2SzDPPW5Mc\nSPLpvrYFy5PkhCTvbu23J1l5hNlel2Rfe/8+keRlS5Gt9T81yYeT3J1kV5Iru/L+zZCtE+9fkqcm\nuSPJJ1u+3+rQezddtk68d+qWLNG+ZIocQ/0sX+CsQ//sXOC8Q/+8GkLm45J8PMn7RiBrp74XzSLv\ns5P8VZLPJLknyQ92Oa9Gw2LuS5b6/1w69NtjmixL9n0vHfttMUOeJXmP0qHfDzNkWdLfCxni/n8+\neaiqo+ZB76J4nwW+B3gK8Eng9EXa9h7gOZPafh/Y1KY3Ab/Xpk9v2U4ATmuZj2vz7gDOBQLcDPzk\nPPP8CPD9wKeHkQe4HPjTNr0eePcRZnsd8KtTLLuo2VqfZcD3t+lnAv9fy7Hk798M2Trx/rV1PaNN\nPxm4vW2jC+/ddNk68d756M6DJdyXTJFlqJ/lC5x16J+dC5x36J9XQ8j8b4F3Au/r8r+Ftp09dOh7\n0SzybgN+oU0/BXh2l/P66P6DRd6XLPX/OTr022OaLK9jib7v0bHfFjPkWZL3iA79fpghy5L9+2nL\nDW3/P688C/Gh1ZUH8IPA3/Q93wxsXqRt7+HwD+57gWVtehlw71S56N0V4gfbMjkn740AAAUFSURB\nVJ/pa/9Z4M+OINNKvv3Dc8HyTCzTpo8Hvky7UPs8s033H3PRs02R4UbgpV16/6bI1rn3D3ga8DHg\nB7r23k3K1rn3zsfSPljCfck0eVYypM/yIede8M/OIWYdyufVAmdcAdwK/BhPfInsZNa27j107HvR\nDFmfBdw/+fO6q3l9jMaDRd6XdOH/HB367TFFltfRke97dOy3BR36PUGHfj/Qkd8LDHn/P5/35mgb\nzrYc+ELf872tbTEU8MEkdyXZ2NrGqmp/m34QGGvT0+Vc3qYnty+UhczzrT5VdQj4GvAdR5jvF5N8\nKr1TUCdOyVvSbO10vhfTq0J36v2blA068v610y0/ARwAbqmqzrx302SDjrx36oyl3JfMRtf2LYcZ\n4mfnQucc9ufVQvpD4DXAN/vaupoVRuN70YTTgC8Bb2vDBd6c5OkdzqvRsNj7ki7+n+vE978+S/59\nr2u/Lbrye6JLvx86+Hth2Pv/Of/bOdqKSEvph6vqRcBPAlck+ZH+mdUr7dWSJJtC1/IA19I73fdF\nwH7g9UsbB5I8A/hr4Jer6pH+eUv9/k2RrTPvX1U93v4vrADOSXLmpPlL9t5Nk60z7500V0v9WTSV\nLn92Ttblz6t+SV4OHKiqu6ZbpitZ+4zS96Lj6Q19ubaqXgx8g97wgG/pWF5pKp3+P7fU26cD3/e6\ntn/s0u+JLu2Pu/R7oav7/6OtiLQPOLXv+YrWNnRVta/9PQC8FzgHeCjJMoD298CAnPva9OT2hbKQ\neb7VJ8nx9E4Ff3i+warqofYf9pvAn9N7/5YsW5In0/tQfUdVvac1d+L9mypb196/lumrwIeBtXTk\nvZsqWxffOy25JduXzFLX9i3fsgifnUMxxM+rhfIS4BVJ9gA3AD+W5O0dzQqMzPeiCXuBvX1Hm/+K\nXlGpq3k1GhZ1X9LR/3Od+f631N/3uvbboqu/J7r0+6EjvxcWY/8/5/fmaCsifRRYleS0JE+hd2Go\nHcPeaJKnJ3nmxDTwE8Cn27Y3tMU20BtvSmtf366EfhqwCrijnZL2SJJz29XSL+7rsxAWMk//un4a\n+FCrgs7LxH+C5l/Se/+WJFtb31uAe6rqDX2zlvz9my5bV96/JM9N8uw2fSK98dWfoRvv3ZTZuvLe\nqVOWZF8yB13btwCL9tm5kHkX4/NqQVTV5qpaUVUr6f17/FBVvaqLWWGkvhcBUFUPAl9I8oLWdB5w\nd1fzamQs2r6kw//nlvz734Sl/L7Xtd8WXfs90aXfD137vbBI+/+5/9+qWV4AbFQewMvoXWH+s8Bv\nLNI2v4feVdA/Ceya2C69sYS3AvcBHwRO6evzGy3jvfTd9QBY3f5Rfhb4Y+Z5UV7gXfROtXuM3hG2\nSxcyD/BU4C+B3fSu9P49R5jtL4CdwKfaP+RlS5Gt9f9heqcEfgr4RHu8rAvv3wzZOvH+Af8c+HjL\n8WngPyz0/4UjeO+my9aJ985Htx4swb5kmhxD/Sxf4KxD/+xc4LxD/7wa0r+JNTxxYc1OZqWD34tm\nkflFwJ3t38N/BU7ucl4fo/FgkfYlXfg/R4d+e0yTZcm+79Gx3xYz5FmS94gO/X6YIcuS/15gSPv/\n+eSZ6ChJkiRJkiRN62gbziZJkiRJkqQhsIgkSZIkSZKkgSwiSZIkSZIkaSCLSJIkSZIkSRrIIpIk\nSZIkSZIGsogkSZIkSZKkgSwiSZIkSZIkaaD/H1pnesv/bV1XAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe02aa08978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "housing.hist(bins=50,figsize=(20,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_ratio = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import hashlib\n",
    "def test_set_check(identfier, test_ratio, hash):\n",
    "    return has(np.int64(identifier)).digest() [-1] <256*test_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing[\"income_cat\"] = np.ceil(housing['median_income']/1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing[\"income_cat\"].where(housing[\"income_cat\"] < 5 , 5.0 , inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stratified_split = StratifiedShuffleSplit(n_splits=1,test_size=0.2,random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for train_index, test_index in  stratified_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": 20,
   "metadata": {},
   "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>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5241</th>\n",
       "      <td>-118.39</td>\n",
       "      <td>34.12</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6447.0</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>2184.0</td>\n",
       "      <td>960.0</td>\n",
       "      <td>8.2816</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10970</th>\n",
       "      <td>-117.86</td>\n",
       "      <td>33.77</td>\n",
       "      <td>39.0</td>\n",
       "      <td>4159.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>1669.0</td>\n",
       "      <td>651.0</td>\n",
       "      <td>4.6111</td>\n",
       "      <td>240300.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20351</th>\n",
       "      <td>-119.05</td>\n",
       "      <td>34.21</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4357.0</td>\n",
       "      <td>926.0</td>\n",
       "      <td>2110.0</td>\n",
       "      <td>876.0</td>\n",
       "      <td>3.0119</td>\n",
       "      <td>218200.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6568</th>\n",
       "      <td>-118.15</td>\n",
       "      <td>34.20</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1786.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>4.1518</td>\n",
       "      <td>182100.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13285</th>\n",
       "      <td>-117.68</td>\n",
       "      <td>34.07</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1775.0</td>\n",
       "      <td>314.0</td>\n",
       "      <td>1067.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>4.0375</td>\n",
       "      <td>121300.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20552</th>\n",
       "      <td>-121.80</td>\n",
       "      <td>38.68</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3851.0</td>\n",
       "      <td>892.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>747.0</td>\n",
       "      <td>3.4331</td>\n",
       "      <td>120600.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19989</th>\n",
       "      <td>-119.32</td>\n",
       "      <td>36.19</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1281.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>861.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>1.0962</td>\n",
       "      <td>72300.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17049</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.46</td>\n",
       "      <td>26.0</td>\n",
       "      <td>5067.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>728.0</td>\n",
       "      <td>7.0001</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13692</th>\n",
       "      <td>-117.24</td>\n",
       "      <td>34.13</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3774.0</td>\n",
       "      <td>716.0</td>\n",
       "      <td>1913.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>3.3534</td>\n",
       "      <td>98900.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13916</th>\n",
       "      <td>-116.15</td>\n",
       "      <td>34.14</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3312.0</td>\n",
       "      <td>705.0</td>\n",
       "      <td>1251.0</td>\n",
       "      <td>512.0</td>\n",
       "      <td>3.0139</td>\n",
       "      <td>82600.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20374</th>\n",
       "      <td>-118.84</td>\n",
       "      <td>34.16</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6075.0</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2571.0</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>5.2200</td>\n",
       "      <td>399400.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18776</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>40.46</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2633.0</td>\n",
       "      <td>530.0</td>\n",
       "      <td>1324.0</td>\n",
       "      <td>513.0</td>\n",
       "      <td>2.2768</td>\n",
       "      <td>78600.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16531</th>\n",
       "      <td>-121.20</td>\n",
       "      <td>37.81</td>\n",
       "      <td>26.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>7.3718</td>\n",
       "      <td>212500.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14690</th>\n",
       "      <td>-117.09</td>\n",
       "      <td>32.80</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2163.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>915.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>4.7188</td>\n",
       "      <td>174100.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17812</th>\n",
       "      <td>-121.86</td>\n",
       "      <td>37.39</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1777.0</td>\n",
       "      <td>328.0</td>\n",
       "      <td>1235.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>5.4225</td>\n",
       "      <td>258100.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16570</th>\n",
       "      <td>-121.42</td>\n",
       "      <td>37.75</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>1061.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>3.5242</td>\n",
       "      <td>121400.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5814</th>\n",
       "      <td>-118.25</td>\n",
       "      <td>34.14</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3487.0</td>\n",
       "      <td>1131.0</td>\n",
       "      <td>3749.0</td>\n",
       "      <td>1072.0</td>\n",
       "      <td>2.1602</td>\n",
       "      <td>221900.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5473</th>\n",
       "      <td>-118.46</td>\n",
       "      <td>33.99</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1158.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>528.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>3.5234</td>\n",
       "      <td>334700.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7888</th>\n",
       "      <td>-118.06</td>\n",
       "      <td>33.88</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7187.0</td>\n",
       "      <td>1073.0</td>\n",
       "      <td>3844.0</td>\n",
       "      <td>1068.0</td>\n",
       "      <td>6.5901</td>\n",
       "      <td>337400.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5376</th>\n",
       "      <td>-118.39</td>\n",
       "      <td>34.04</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1492.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>4.7386</td>\n",
       "      <td>340400.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10383</th>\n",
       "      <td>-117.64</td>\n",
       "      <td>33.62</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3970.0</td>\n",
       "      <td>771.0</td>\n",
       "      <td>1202.0</td>\n",
       "      <td>734.0</td>\n",
       "      <td>3.4115</td>\n",
       "      <td>184800.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16815</th>\n",
       "      <td>-122.43</td>\n",
       "      <td>37.64</td>\n",
       "      <td>42.0</td>\n",
       "      <td>4091.0</td>\n",
       "      <td>757.0</td>\n",
       "      <td>1861.0</td>\n",
       "      <td>771.0</td>\n",
       "      <td>4.2070</td>\n",
       "      <td>272700.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11142</th>\n",
       "      <td>-117.95</td>\n",
       "      <td>33.84</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1229.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>1035.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>3.5455</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2554</th>\n",
       "      <td>-124.18</td>\n",
       "      <td>40.78</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1076.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>656.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>2.5096</td>\n",
       "      <td>72200.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14472</th>\n",
       "      <td>-117.22</td>\n",
       "      <td>32.85</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1647.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>694.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>4.6875</td>\n",
       "      <td>274400.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5986</th>\n",
       "      <td>-117.71</td>\n",
       "      <td>34.10</td>\n",
       "      <td>52.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>2688.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>1.8750</td>\n",
       "      <td>212500.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15825</th>\n",
       "      <td>-122.42</td>\n",
       "      <td>37.75</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2112.0</td>\n",
       "      <td>528.0</td>\n",
       "      <td>1227.0</td>\n",
       "      <td>513.0</td>\n",
       "      <td>3.5536</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6472</th>\n",
       "      <td>-118.06</td>\n",
       "      <td>34.09</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>389.0</td>\n",
       "      <td>1116.0</td>\n",
       "      <td>378.0</td>\n",
       "      <td>4.2898</td>\n",
       "      <td>251600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4309</th>\n",
       "      <td>-118.32</td>\n",
       "      <td>34.09</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2666.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2297.0</td>\n",
       "      <td>726.0</td>\n",
       "      <td>1.6760</td>\n",
       "      <td>208800.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20066</th>\n",
       "      <td>-120.39</td>\n",
       "      <td>38.00</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2177.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>383.0</td>\n",
       "      <td>3.2120</td>\n",
       "      <td>105200.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2735</th>\n",
       "      <td>-115.56</td>\n",
       "      <td>32.79</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2372.0</td>\n",
       "      <td>835.0</td>\n",
       "      <td>2283.0</td>\n",
       "      <td>767.0</td>\n",
       "      <td>1.1707</td>\n",
       "      <td>62500.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>-121.95</td>\n",
       "      <td>37.52</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3994.0</td>\n",
       "      <td>764.0</td>\n",
       "      <td>2721.0</td>\n",
       "      <td>763.0</td>\n",
       "      <td>5.2308</td>\n",
       "      <td>196900.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2343</th>\n",
       "      <td>-119.69</td>\n",
       "      <td>36.82</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3303.0</td>\n",
       "      <td>512.0</td>\n",
       "      <td>1687.0</td>\n",
       "      <td>505.0</td>\n",
       "      <td>4.8100</td>\n",
       "      <td>93600.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9989</th>\n",
       "      <td>-121.07</td>\n",
       "      <td>39.15</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6828.0</td>\n",
       "      <td>1319.0</td>\n",
       "      <td>3002.0</td>\n",
       "      <td>1318.0</td>\n",
       "      <td>2.4726</td>\n",
       "      <td>143400.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7560</th>\n",
       "      <td>-118.20</td>\n",
       "      <td>33.91</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2283.0</td>\n",
       "      <td>499.0</td>\n",
       "      <td>1836.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8793</td>\n",
       "      <td>118100.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2286</th>\n",
       "      <td>-119.76</td>\n",
       "      <td>36.79</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2463.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>3.3281</td>\n",
       "      <td>75100.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11342</th>\n",
       "      <td>-117.93</td>\n",
       "      <td>33.76</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3341.0</td>\n",
       "      <td>803.0</td>\n",
       "      <td>3381.0</td>\n",
       "      <td>825.0</td>\n",
       "      <td>3.3710</td>\n",
       "      <td>161800.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11438</th>\n",
       "      <td>-117.96</td>\n",
       "      <td>33.65</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3603.0</td>\n",
       "      <td>879.0</td>\n",
       "      <td>1549.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>4.0229</td>\n",
       "      <td>363100.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15190</th>\n",
       "      <td>-117.06</td>\n",
       "      <td>33.02</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2635.0</td>\n",
       "      <td>389.0</td>\n",
       "      <td>994.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>5.8966</td>\n",
       "      <td>261500.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1075</th>\n",
       "      <td>-121.82</td>\n",
       "      <td>39.75</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2236.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>974.0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>3.2016</td>\n",
       "      <td>97000.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17934</th>\n",
       "      <td>-121.95</td>\n",
       "      <td>37.35</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1246.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>3.6118</td>\n",
       "      <td>235500.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9498</th>\n",
       "      <td>-123.73</td>\n",
       "      <td>39.17</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4620.0</td>\n",
       "      <td>1042.0</td>\n",
       "      <td>1745.0</td>\n",
       "      <td>794.0</td>\n",
       "      <td>2.3750</td>\n",
       "      <td>158800.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13279</th>\n",
       "      <td>-117.65</td>\n",
       "      <td>34.10</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1526.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>831.0</td>\n",
       "      <td>326.0</td>\n",
       "      <td>3.0284</td>\n",
       "      <td>115800.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2344</th>\n",
       "      <td>-119.68</td>\n",
       "      <td>36.81</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2668.0</td>\n",
       "      <td>454.0</td>\n",
       "      <td>1536.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>3.9792</td>\n",
       "      <td>88900.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3118</th>\n",
       "      <td>-117.81</td>\n",
       "      <td>35.65</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>598.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>1.8984</td>\n",
       "      <td>54300.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11782</th>\n",
       "      <td>-121.24</td>\n",
       "      <td>38.78</td>\n",
       "      <td>18.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>0.8691</td>\n",
       "      <td>136500.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11298</th>\n",
       "      <td>-117.91</td>\n",
       "      <td>33.78</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2729.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>2223.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>4.0362</td>\n",
       "      <td>177900.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15643</th>\n",
       "      <td>-122.42</td>\n",
       "      <td>37.80</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>2122.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>2.5603</td>\n",
       "      <td>287500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.88</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2604.0</td>\n",
       "      <td>837.0</td>\n",
       "      <td>1798.0</td>\n",
       "      <td>769.0</td>\n",
       "      <td>1.7250</td>\n",
       "      <td>287500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4038</th>\n",
       "      <td>-118.51</td>\n",
       "      <td>34.16</td>\n",
       "      <td>23.0</td>\n",
       "      <td>11154.0</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>4076.0</td>\n",
       "      <td>1809.0</td>\n",
       "      <td>5.4609</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5353</th>\n",
       "      <td>-118.40</td>\n",
       "      <td>34.03</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1831.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>871.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>8.1484</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15763</th>\n",
       "      <td>-122.43</td>\n",
       "      <td>37.77</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2514.0</td>\n",
       "      <td>729.0</td>\n",
       "      <td>1428.0</td>\n",
       "      <td>597.0</td>\n",
       "      <td>2.3977</td>\n",
       "      <td>412500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3239</th>\n",
       "      <td>-119.56</td>\n",
       "      <td>36.09</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1648.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>792.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>3.2847</td>\n",
       "      <td>64700.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13114</th>\n",
       "      <td>-121.36</td>\n",
       "      <td>38.39</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5121.0</td>\n",
       "      <td>763.0</td>\n",
       "      <td>2568.0</td>\n",
       "      <td>758.0</td>\n",
       "      <td>5.2447</td>\n",
       "      <td>148100.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3115</th>\n",
       "      <td>-117.66</td>\n",
       "      <td>35.60</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1740.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>2.5812</td>\n",
       "      <td>91700.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20519</th>\n",
       "      <td>-121.53</td>\n",
       "      <td>38.58</td>\n",
       "      <td>33.0</td>\n",
       "      <td>4988.0</td>\n",
       "      <td>1169.0</td>\n",
       "      <td>2414.0</td>\n",
       "      <td>1075.0</td>\n",
       "      <td>1.9728</td>\n",
       "      <td>76400.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17430</th>\n",
       "      <td>-120.44</td>\n",
       "      <td>34.65</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2265.0</td>\n",
       "      <td>512.0</td>\n",
       "      <td>1402.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>1.9750</td>\n",
       "      <td>134000.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4019</th>\n",
       "      <td>-118.49</td>\n",
       "      <td>34.18</td>\n",
       "      <td>31.0</td>\n",
       "      <td>3073.0</td>\n",
       "      <td>674.0</td>\n",
       "      <td>1486.0</td>\n",
       "      <td>684.0</td>\n",
       "      <td>4.8984</td>\n",
       "      <td>311700.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12107</th>\n",
       "      <td>-117.32</td>\n",
       "      <td>33.99</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5464.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>836.0</td>\n",
       "      <td>4.7110</td>\n",
       "      <td>133500.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2398</th>\n",
       "      <td>-118.91</td>\n",
       "      <td>36.79</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1616.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.7857</td>\n",
       "      <td>78600.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4128 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "5241     -118.39     34.12                29.0       6447.0          1012.0   \n",
       "10970    -117.86     33.77                39.0       4159.0           655.0   \n",
       "20351    -119.05     34.21                27.0       4357.0           926.0   \n",
       "6568     -118.15     34.20                52.0       1786.0           306.0   \n",
       "13285    -117.68     34.07                32.0       1775.0           314.0   \n",
       "20552    -121.80     38.68                11.0       3851.0           892.0   \n",
       "19989    -119.32     36.19                11.0       1281.0           291.0   \n",
       "17049    -122.26     37.46                26.0       5067.0           750.0   \n",
       "13692    -117.24     34.13                26.0       3774.0           716.0   \n",
       "13916    -116.15     34.14                18.0       3312.0           705.0   \n",
       "20374    -118.84     34.16                18.0       6075.0          1056.0   \n",
       "18776    -122.27     40.46                14.0       2633.0           530.0   \n",
       "16531    -121.20     37.81                26.0        395.0            74.0   \n",
       "14690    -117.09     32.80                36.0       2163.0           367.0   \n",
       "17812    -121.86     37.39                17.0       1777.0           328.0   \n",
       "16570    -121.42     37.75                33.0       1999.0           368.0   \n",
       "5814     -118.25     34.14                13.0       3487.0          1131.0   \n",
       "5473     -118.46     33.99                52.0       1158.0           253.0   \n",
       "7888     -118.06     33.88                17.0       7187.0          1073.0   \n",
       "5376     -118.39     34.04                52.0       1492.0           277.0   \n",
       "10383    -117.64     33.62                16.0       3970.0           771.0   \n",
       "16815    -122.43     37.64                42.0       4091.0           757.0   \n",
       "11142    -117.95     33.84                34.0       1229.0           215.0   \n",
       "2554     -124.18     40.78                33.0       1076.0           222.0   \n",
       "14472    -117.22     32.85                26.0       1647.0           261.0   \n",
       "5986     -117.71     34.10                52.0        567.0           152.0   \n",
       "15825    -122.42     37.75                52.0       2112.0           528.0   \n",
       "6472     -118.06     34.09                40.0       1975.0           389.0   \n",
       "4309     -118.32     34.09                44.0       2666.0             NaN   \n",
       "20066    -120.39     38.00                33.0       2177.0           404.0   \n",
       "...          ...       ...                 ...          ...             ...   \n",
       "2735     -115.56     32.79                20.0       2372.0           835.0   \n",
       "918      -121.95     37.52                33.0       3994.0           764.0   \n",
       "2343     -119.69     36.82                15.0       3303.0           512.0   \n",
       "9989     -121.07     39.15                15.0       6828.0          1319.0   \n",
       "7560     -118.20     33.91                36.0       2283.0           499.0   \n",
       "2286     -119.76     36.79                32.0       2463.0           468.0   \n",
       "11342    -117.93     33.76                17.0       3341.0           803.0   \n",
       "11438    -117.96     33.65                18.0       3603.0           879.0   \n",
       "15190    -117.06     33.02                17.0       2635.0           389.0   \n",
       "1075     -121.82     39.75                37.0       2236.0           372.0   \n",
       "17934    -121.95     37.35                48.0       1246.0           294.0   \n",
       "9498     -123.73     39.17                20.0       4620.0          1042.0   \n",
       "13279    -117.65     34.10                44.0       1526.0           337.0   \n",
       "2344     -119.68     36.81                16.0       2668.0           454.0   \n",
       "3118     -117.81     35.65                19.0       1124.0           290.0   \n",
       "11782    -121.24     38.78                18.0        549.0           143.0   \n",
       "11298    -117.91     33.78                33.0       2729.0           549.0   \n",
       "15643    -122.42     37.80                52.0       3067.0           870.0   \n",
       "455      -122.26     37.88                52.0       2604.0           837.0   \n",
       "4038     -118.51     34.16                23.0      11154.0          1995.0   \n",
       "5353     -118.40     34.03                36.0       1831.0           296.0   \n",
       "15763    -122.43     37.77                52.0       2514.0           729.0   \n",
       "3239     -119.56     36.09                35.0       1648.0           285.0   \n",
       "13114    -121.36     38.39                10.0       5121.0           763.0   \n",
       "3115     -117.66     35.60                14.0       1740.0           391.0   \n",
       "20519    -121.53     38.58                33.0       4988.0          1169.0   \n",
       "17430    -120.44     34.65                30.0       2265.0           512.0   \n",
       "4019     -118.49     34.18                31.0       3073.0           674.0   \n",
       "12107    -117.32     33.99                27.0       5464.0           850.0   \n",
       "2398     -118.91     36.79                19.0       1616.0           324.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "5241       2184.0       960.0         8.2816            500001.0   \n",
       "10970      1669.0       651.0         4.6111            240300.0   \n",
       "20351      2110.0       876.0         3.0119            218200.0   \n",
       "6568       1018.0       322.0         4.1518            182100.0   \n",
       "13285      1067.0       302.0         4.0375            121300.0   \n",
       "20552      1847.0       747.0         3.4331            120600.0   \n",
       "19989       861.0       313.0         1.0962             72300.0   \n",
       "17049      1996.0       728.0         7.0001            500001.0   \n",
       "13692      1913.0       620.0         3.3534             98900.0   \n",
       "13916      1251.0       512.0         3.0139             82600.0   \n",
       "20374      2571.0      1018.0         5.2200            399400.0   \n",
       "18776      1324.0       513.0         2.2768             78600.0   \n",
       "16531       193.0        72.0         7.3718            212500.0   \n",
       "14690       915.0       360.0         4.7188            174100.0   \n",
       "17812      1235.0       329.0         5.4225            258100.0   \n",
       "16570      1061.0       390.0         3.5242            121400.0   \n",
       "5814       3749.0      1072.0         2.1602            221900.0   \n",
       "5473        528.0       253.0         3.5234            334700.0   \n",
       "7888       3844.0      1068.0         6.5901            337400.0   \n",
       "5376        666.0       289.0         4.7386            340400.0   \n",
       "10383      1202.0       734.0         3.4115            184800.0   \n",
       "16815      1861.0       771.0         4.2070            272700.0   \n",
       "11142      1035.0       218.0         3.5455            180000.0   \n",
       "2554        656.0       236.0         2.5096             72200.0   \n",
       "14472       694.0       259.0         4.6875            274400.0   \n",
       "5986       2688.0       126.0         1.8750            212500.0   \n",
       "15825      1227.0       513.0         3.5536            400000.0   \n",
       "6472       1116.0       378.0         4.2898            251600.0   \n",
       "4309       2297.0       726.0         1.6760            208800.0   \n",
       "20066       891.0       383.0         3.2120            105200.0   \n",
       "...           ...         ...            ...                 ...   \n",
       "2735       2283.0       767.0         1.1707             62500.0   \n",
       "918        2721.0       763.0         5.2308            196900.0   \n",
       "2343       1687.0       505.0         4.8100             93600.0   \n",
       "9989       3002.0      1318.0         2.4726            143400.0   \n",
       "7560       1836.0       462.0         2.8793            118100.0   \n",
       "2286       1261.0       486.0         3.3281             75100.0   \n",
       "11342      3381.0       825.0         3.3710            161800.0   \n",
       "11438      1549.0       756.0         4.0229            363100.0   \n",
       "15190       994.0       359.0         5.8966            261500.0   \n",
       "1075        974.0       379.0         3.2016             97000.0   \n",
       "17934       697.0       284.0         3.6118            235500.0   \n",
       "9498       1745.0       794.0         2.3750            158800.0   \n",
       "13279       831.0       326.0         3.0284            115800.0   \n",
       "2344       1536.0       457.0         3.9792             88900.0   \n",
       "3118        598.0       261.0         1.8984             54300.0   \n",
       "11782       249.0       136.0         0.8691            136500.0   \n",
       "11298      2223.0       535.0         4.0362            177900.0   \n",
       "15643      2122.0       850.0         2.5603            287500.0   \n",
       "455        1798.0       769.0         1.7250            287500.0   \n",
       "4038       4076.0      1809.0         5.4609            500001.0   \n",
       "5353        871.0       269.0         8.1484            500001.0   \n",
       "15763      1428.0       597.0         2.3977            412500.0   \n",
       "3239        792.0       265.0         3.2847             64700.0   \n",
       "13114      2568.0       758.0         5.2447            148100.0   \n",
       "3115        850.0       317.0         2.5812             91700.0   \n",
       "20519      2414.0      1075.0         1.9728             76400.0   \n",
       "17430      1402.0       471.0         1.9750            134000.0   \n",
       "4019       1486.0       684.0         4.8984            311700.0   \n",
       "12107      2400.0       836.0         4.7110            133500.0   \n",
       "2398        187.0        80.0         3.7857             78600.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "5241        <1H OCEAN  \n",
       "10970       <1H OCEAN  \n",
       "20351       <1H OCEAN  \n",
       "6568           INLAND  \n",
       "13285          INLAND  \n",
       "20552          INLAND  \n",
       "19989          INLAND  \n",
       "17049      NEAR OCEAN  \n",
       "13692          INLAND  \n",
       "13916          INLAND  \n",
       "20374       <1H OCEAN  \n",
       "18776          INLAND  \n",
       "16531          INLAND  \n",
       "14690      NEAR OCEAN  \n",
       "17812       <1H OCEAN  \n",
       "16570          INLAND  \n",
       "5814        <1H OCEAN  \n",
       "5473        <1H OCEAN  \n",
       "7888        <1H OCEAN  \n",
       "5376        <1H OCEAN  \n",
       "10383       <1H OCEAN  \n",
       "16815      NEAR OCEAN  \n",
       "11142       <1H OCEAN  \n",
       "2554       NEAR OCEAN  \n",
       "14472      NEAR OCEAN  \n",
       "5986           INLAND  \n",
       "15825        NEAR BAY  \n",
       "6472        <1H OCEAN  \n",
       "4309        <1H OCEAN  \n",
       "20066          INLAND  \n",
       "...               ...  \n",
       "2735           INLAND  \n",
       "918         <1H OCEAN  \n",
       "2343           INLAND  \n",
       "9989           INLAND  \n",
       "7560        <1H OCEAN  \n",
       "2286           INLAND  \n",
       "11342       <1H OCEAN  \n",
       "11438       <1H OCEAN  \n",
       "15190       <1H OCEAN  \n",
       "1075           INLAND  \n",
       "17934       <1H OCEAN  \n",
       "9498       NEAR OCEAN  \n",
       "13279          INLAND  \n",
       "2344           INLAND  \n",
       "3118           INLAND  \n",
       "11782          INLAND  \n",
       "11298       <1H OCEAN  \n",
       "15643        NEAR BAY  \n",
       "455          NEAR BAY  \n",
       "4038        <1H OCEAN  \n",
       "5353        <1H OCEAN  \n",
       "15763        NEAR BAY  \n",
       "3239           INLAND  \n",
       "13114          INLAND  \n",
       "3115           INLAND  \n",
       "20519          INLAND  \n",
       "17430      NEAR OCEAN  \n",
       "4019        <1H OCEAN  \n",
       "12107          INLAND  \n",
       "2398           INLAND  \n",
       "\n",
       "[4128 rows x 10 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_train_set.drop(\"income_cat\",axis=1)\n",
    "strat_test_set.drop(\"income_cat\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator,TransformerMixin\n",
    "rooms_ix,bedrooms_ix,population_ix,household_ix = 3,4,5,6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CombinedAttributesAdder(BaseEstimator,TransformerMixin):\n",
    "    def __init__(self,add_bedrooms_per_room =True):\n",
    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
    "    def fit(self,X,y=None):\n",
    "        return self\n",
    "    def transform(self,X,y=None):\n",
    "        rooms_per_household = X[:,rooms_ix]/X[:,household_ix]\n",
    "        population_per_household = X[:,population_ix]/X[:,household_ix]\n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = X[:,bedrooms_ix]/X[:,rooms_ix]\n",
    "            return np.c_[X,rooms_per_household,population_per_household,bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[X,rooms_per_household,population_per_household]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing_cat = housing[\"ocean_proximity\"]\n",
    "housing_num = housing.drop(\"ocean_proximity\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler,Imputer\n",
    "num_pipeline = Pipeline([('imputer',Imputer(strategy=\"median\")),\n",
    "                         ('attribs_adder',CombinedAttributesAdder()),\n",
    "                         ('std_scaler',StandardScaler()),\n",
    "                          ])\n",
    "housing_num_tr = num_pipeline.fit_transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataFrameSelector(BaseEstimator,TransformerMixin):\n",
    "    def __init__(self,attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "        def fit(self,X,y= None):\n",
    "            return self\n",
    "        def transform(self,X):\n",
    "            return X[self.attribute_names].values\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Ca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_attribs = list(housing_num)\n",
    "cat_attribs = [\"ocean_proximity\"]\n",
    "num_pipeline = Pipeline([('selector',DataFrameSelector(num_attribs)),\n",
    "                         ('imputer',Imputer(strategy=\"median\")),\n",
    "                         ('attribs_adder',CombinedAttributesAdder()),\n",
    "                         ('std_scaler',StandardScaler()),\n",
    "                        ])\n",
    "cat_pipeline = Pipeline([('selector',DataFrameSelector(cat_attribs)),\n",
    "                         'cat_encoder',CategoricalEncoder(encoding='onehot-dense\")),\n",
    "                                                          "
   ]
  }
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