Practical Example 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import the relevant libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Price</th>\n",
       "      <th>Body</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineV</th>\n",
       "      <th>Engine Type</th>\n",
       "      <th>Registration</th>\n",
       "      <th>Year</th>\n",
       "      <th>Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BMW</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>277</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>1991</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>7900.0</td>\n",
       "      <td>van</td>\n",
       "      <td>427</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>1999</td>\n",
       "      <td>Sprinter 212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>13300.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>358</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Gas</td>\n",
       "      <td>yes</td>\n",
       "      <td>2003</td>\n",
       "      <td>S 500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>crossover</td>\n",
       "      <td>240</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2007</td>\n",
       "      <td>Q7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>18300.0</td>\n",
       "      <td>crossover</td>\n",
       "      <td>120</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2011</td>\n",
       "      <td>Rav 4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Brand    Price       Body  Mileage  EngineV Engine Type  \\\n",
       "0            BMW   4200.0      sedan      277      2.0      Petrol   \n",
       "1  Mercedes-Benz   7900.0        van      427      2.9      Diesel   \n",
       "2  Mercedes-Benz  13300.0      sedan      358      5.0         Gas   \n",
       "3           Audi  23000.0  crossover      240      4.2      Petrol   \n",
       "4         Toyota  18300.0  crossover      120      2.0      Petrol   \n",
       "\n",
       "  Registration  Year         Model  \n",
       "0          yes  1991           320  \n",
       "1          yes  1999  Sprinter 212  \n",
       "2          yes  2003         S 500  \n",
       "3          yes  2007            Q7  \n",
       "4          yes  2011         Rav 4  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv(\"1.04. Real-life example.csv\")\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring the descriptive statistics of the variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Price</th>\n",
       "      <th>Body</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineV</th>\n",
       "      <th>Engine Type</th>\n",
       "      <th>Registration</th>\n",
       "      <th>Year</th>\n",
       "      <th>Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4345</td>\n",
       "      <td>4173.000000</td>\n",
       "      <td>4345</td>\n",
       "      <td>4345.000000</td>\n",
       "      <td>4195.000000</td>\n",
       "      <td>4345</td>\n",
       "      <td>4345</td>\n",
       "      <td>4345.000000</td>\n",
       "      <td>4345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E-Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>936</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1649</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "      <td>3947</td>\n",
       "      <td>NaN</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>19418.746935</td>\n",
       "      <td>NaN</td>\n",
       "      <td>161.237284</td>\n",
       "      <td>2.790734</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006.550058</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25584.242620</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.705797</td>\n",
       "      <td>5.066437</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.719097</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1969.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6999.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21700.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>980.000000</td>\n",
       "      <td>99.990000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Brand          Price   Body      Mileage      EngineV  \\\n",
       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
       "unique           7            NaN      6          NaN          NaN   \n",
       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
       "freq           936            NaN   1649          NaN          NaN   \n",
       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
       "\n",
       "       Engine Type Registration         Year    Model  \n",
       "count         4345         4345  4345.000000     4345  \n",
       "unique           4            2          NaN      312  \n",
       "top         Diesel          yes          NaN  E-Class  \n",
       "freq          2019         3947          NaN      199  \n",
       "mean           NaN          NaN  2006.550058      NaN  \n",
       "std            NaN          NaN     6.719097      NaN  \n",
       "min            NaN          NaN  1969.000000      NaN  \n",
       "25%            NaN          NaN  2003.000000      NaN  \n",
       "50%            NaN          NaN  2008.000000      NaN  \n",
       "75%            NaN          NaN  2012.000000      NaN  \n",
       "max            NaN          NaN  2016.000000      NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Determining the variables of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Price</th>\n",
       "      <th>Body</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineV</th>\n",
       "      <th>Engine Type</th>\n",
       "      <th>Registration</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4345</td>\n",
       "      <td>4173.000000</td>\n",
       "      <td>4345</td>\n",
       "      <td>4345.000000</td>\n",
       "      <td>4195.000000</td>\n",
       "      <td>4345</td>\n",
       "      <td>4345</td>\n",
       "      <td>4345.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>936</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1649</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "      <td>3947</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>19418.746935</td>\n",
       "      <td>NaN</td>\n",
       "      <td>161.237284</td>\n",
       "      <td>2.790734</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006.550058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25584.242620</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.705797</td>\n",
       "      <td>5.066437</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.719097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1969.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6999.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21700.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>980.000000</td>\n",
       "      <td>99.990000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Brand          Price   Body      Mileage      EngineV  \\\n",
       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
       "unique           7            NaN      6          NaN          NaN   \n",
       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
       "freq           936            NaN   1649          NaN          NaN   \n",
       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
       "\n",
       "       Engine Type Registration         Year  \n",
       "count         4345         4345  4345.000000  \n",
       "unique           4            2          NaN  \n",
       "top         Diesel          yes          NaN  \n",
       "freq          2019         3947          NaN  \n",
       "mean           NaN          NaN  2006.550058  \n",
       "std            NaN          NaN     6.719097  \n",
       "min            NaN          NaN  1969.000000  \n",
       "25%            NaN          NaN  2003.000000  \n",
       "50%            NaN          NaN  2008.000000  \n",
       "75%            NaN          NaN  2012.000000  \n",
       "max            NaN          NaN  2016.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = raw_data.drop([\"Model\"], axis=1)\n",
    "data.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dealing with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brand             0\n",
       "Price           172\n",
       "Body              0\n",
       "Mileage           0\n",
       "EngineV         150\n",
       "Engine Type       0\n",
       "Registration      0\n",
       "Year              0\n",
       "Model             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Price</th>\n",
       "      <th>Body</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineV</th>\n",
       "      <th>Engine Type</th>\n",
       "      <th>Registration</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4025</td>\n",
       "      <td>4025.000000</td>\n",
       "      <td>4025</td>\n",
       "      <td>4025.000000</td>\n",
       "      <td>4025.000000</td>\n",
       "      <td>4025</td>\n",
       "      <td>4025</td>\n",
       "      <td>4025.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>880</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1534</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1861</td>\n",
       "      <td>3654</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>19552.308065</td>\n",
       "      <td>NaN</td>\n",
       "      <td>163.572174</td>\n",
       "      <td>2.764586</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006.379627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25815.734988</td>\n",
       "      <td>NaN</td>\n",
       "      <td>103.394703</td>\n",
       "      <td>4.935941</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.695595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1969.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6999.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21900.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>980.000000</td>\n",
       "      <td>99.990000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Brand          Price   Body      Mileage      EngineV  \\\n",
       "count         4025    4025.000000   4025  4025.000000  4025.000000   \n",
       "unique           7            NaN      6          NaN          NaN   \n",
       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
       "freq           880            NaN   1534          NaN          NaN   \n",
       "mean           NaN   19552.308065    NaN   163.572174     2.764586   \n",
       "std            NaN   25815.734988    NaN   103.394703     4.935941   \n",
       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
       "25%            NaN    6999.000000    NaN    90.000000     1.800000   \n",
       "50%            NaN   11500.000000    NaN   158.000000     2.200000   \n",
       "75%            NaN   21900.000000    NaN   230.000000     3.000000   \n",
       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
       "\n",
       "       Engine Type Registration         Year  \n",
       "count         4025         4025  4025.000000  \n",
       "unique           4            2          NaN  \n",
       "top         Diesel          yes          NaN  \n",
       "freq          1861         3654          NaN  \n",
       "mean           NaN          NaN  2006.379627  \n",
       "std            NaN          NaN     6.695595  \n",
       "min            NaN          NaN  1969.000000  \n",
       "25%            NaN          NaN  2003.000000  \n",
       "50%            NaN          NaN  2007.000000  \n",
       "75%            NaN          NaN  2012.000000  \n",
       "max            NaN          NaN  2016.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_no_mv = data.dropna(axis=0)\n",
    "data_no_mv.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring the Probability Density Functions (PDFs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Price', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_no_mv[\"Price\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dealing with outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Price</th>\n",
       "      <th>Body</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineV</th>\n",
       "      <th>Engine Type</th>\n",
       "      <th>Registration</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3984</td>\n",
       "      <td>3984.000000</td>\n",
       "      <td>3984</td>\n",
       "      <td>3984.000000</td>\n",
       "      <td>3984.000000</td>\n",
       "      <td>3984</td>\n",
       "      <td>3984</td>\n",
       "      <td>3984.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>880</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1528</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1853</td>\n",
       "      <td>3613</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>17837.117460</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.116466</td>\n",
       "      <td>2.743770</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006.292922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>18976.268315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.766126</td>\n",
       "      <td>4.956057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.672745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1969.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6980.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2002.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11400.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>129222.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>980.000000</td>\n",
       "      <td>99.990000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Brand          Price   Body      Mileage      EngineV  \\\n",
       "count         3984    3984.000000   3984  3984.000000  3984.000000   \n",
       "unique           7            NaN      6          NaN          NaN   \n",
       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
       "freq           880            NaN   1528          NaN          NaN   \n",
       "mean           NaN   17837.117460    NaN   165.116466     2.743770   \n",
       "std            NaN   18976.268315    NaN   102.766126     4.956057   \n",
       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
       "25%            NaN    6980.000000    NaN    93.000000     1.800000   \n",
       "50%            NaN   11400.000000    NaN   160.000000     2.200000   \n",
       "75%            NaN   21000.000000    NaN   230.000000     3.000000   \n",
       "max            NaN  129222.000000    NaN   980.000000    99.990000   \n",
       "\n",
       "       Engine Type Registration         Year  \n",
       "count         3984         3984  3984.000000  \n",
       "unique           4            2          NaN  \n",
       "top         Diesel          yes          NaN  \n",
       "freq          1853         3613          NaN  \n",
       "mean           NaN          NaN  2006.292922  \n",
       "std            NaN          NaN     6.672745  \n",
       "min            NaN          NaN  1969.000000  \n",
       "25%            NaN          NaN  2002.750000  \n",
       "50%            NaN          NaN  2007.000000  \n",
       "75%            NaN          NaN  2011.000000  \n",
       "max            NaN          NaN  2016.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q = data_no_mv[\"Price\"].quantile(0.99)\n",
    "data_1 = data_no_mv[data_no_mv[\"Price\"]<q]\n",
    "data_1.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Price', ylabel='Density'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_1[\"Price\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Mileage', ylabel='Density'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_no_mv[\"Mileage\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "q = data_1[\"Mileage\"].quantile(0.99)\n",
    "data_2 = data_1[data_1[\"Mileage\"]<q]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2858ed6a1c0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data_2[\"Mileage\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='EngineV', ylabel='Density'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_no_mv[\"EngineV\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_3 = data_2[data_2[\"EngineV\"]<6.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='EngineV', ylabel='Density'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_3[\"EngineV\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='Density'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_no_mv[\"Year\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "q = data_3[\"Year\"].quantile(0.01)\n",
    "data_4 = data_3[data_3[\"Year\"]>q]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='Density'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_4[\"Year\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_cleaned = data_4.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Price</th>\n",
       "      <th>Body</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineV</th>\n",
       "      <th>Engine Type</th>\n",
       "      <th>Registration</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3867</td>\n",
       "      <td>3867.000000</td>\n",
       "      <td>3867</td>\n",
       "      <td>3867.000000</td>\n",
       "      <td>3867.000000</td>\n",
       "      <td>3867</td>\n",
       "      <td>3867</td>\n",
       "      <td>3867.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>848</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1467</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1807</td>\n",
       "      <td>3505</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>18194.455679</td>\n",
       "      <td>NaN</td>\n",
       "      <td>160.542539</td>\n",
       "      <td>2.450440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006.709853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>19085.855165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.633291</td>\n",
       "      <td>0.949366</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.103870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1988.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7200.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11700.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>157.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21700.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>129222.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Brand          Price   Body      Mileage      EngineV  \\\n",
       "count         3867    3867.000000   3867  3867.000000  3867.000000   \n",
       "unique           7            NaN      6          NaN          NaN   \n",
       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
       "freq           848            NaN   1467          NaN          NaN   \n",
       "mean           NaN   18194.455679    NaN   160.542539     2.450440   \n",
       "std            NaN   19085.855165    NaN    95.633291     0.949366   \n",
       "min            NaN     800.000000    NaN     0.000000     0.600000   \n",
       "25%            NaN    7200.000000    NaN    91.000000     1.800000   \n",
       "50%            NaN   11700.000000    NaN   157.000000     2.200000   \n",
       "75%            NaN   21700.000000    NaN   225.000000     3.000000   \n",
       "max            NaN  129222.000000    NaN   435.000000     6.300000   \n",
       "\n",
       "       Engine Type Registration         Year  \n",
       "count         3867         3867  3867.000000  \n",
       "unique           4            2          NaN  \n",
       "top         Diesel          yes          NaN  \n",
       "freq          1807         3505          NaN  \n",
       "mean           NaN          NaN  2006.709853  \n",
       "std            NaN          NaN     6.103870  \n",
       "min            NaN          NaN  1988.000000  \n",
       "25%            NaN          NaN  2003.000000  \n",
       "50%            NaN          NaN  2008.000000  \n",
       "75%            NaN          NaN  2012.000000  \n",
       "max            NaN          NaN  2016.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cleaned.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking the OLS assumptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(15, 3))\n",
    "ax1.scatter(data_cleaned[\"Year\"], data_cleaned[\"Price\"])\n",
    "ax1.set_title(\"Price and Year\")\n",
    "ax2.scatter(data_cleaned[\"EngineV\"], data_cleaned[\"Price\"])\n",
    "ax2.set_title(\"Price and EngineV\")\n",
    "ax3.scatter(data_cleaned[\"Mileage\"], data_cleaned[\"Price\"])\n",
    "ax3.set_title(\"Price and Mileage\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2858efadb20>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data_cleaned[\"Price\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Relaxing the assumptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Price</th>\n",
       "      <th>Body</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineV</th>\n",
       "      <th>Engine Type</th>\n",
       "      <th>Registration</th>\n",
       "      <th>Year</th>\n",
       "      <th>log_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BMW</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>277</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>1991</td>\n",
       "      <td>8.342840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>7900.0</td>\n",
       "      <td>van</td>\n",
       "      <td>427</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>1999</td>\n",
       "      <td>8.974618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>13300.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>358</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Gas</td>\n",
       "      <td>yes</td>\n",
       "      <td>2003</td>\n",
       "      <td>9.495519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>crossover</td>\n",
       "      <td>240</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2007</td>\n",
       "      <td>10.043249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>18300.0</td>\n",
       "      <td>crossover</td>\n",
       "      <td>120</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2011</td>\n",
       "      <td>9.814656</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Brand    Price       Body  Mileage  EngineV Engine Type  \\\n",
       "0            BMW   4200.0      sedan      277      2.0      Petrol   \n",
       "1  Mercedes-Benz   7900.0        van      427      2.9      Diesel   \n",
       "2  Mercedes-Benz  13300.0      sedan      358      5.0         Gas   \n",
       "3           Audi  23000.0  crossover      240      4.2      Petrol   \n",
       "4         Toyota  18300.0  crossover      120      2.0      Petrol   \n",
       "\n",
       "  Registration  Year  log_price  \n",
       "0          yes  1991   8.342840  \n",
       "1          yes  1999   8.974618  \n",
       "2          yes  2003   9.495519  \n",
       "3          yes  2007  10.043249  \n",
       "4          yes  2011   9.814656  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_price = np.log(data_cleaned[\"Price\"])\n",
    "data_cleaned[\"log_price\"] = log_price\n",
    "data_cleaned.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='log_price', ylabel='Density'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_cleaned[\"log_price\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(15, 3))\n",
    "ax1.scatter(data_cleaned[\"Year\"], data_cleaned[\"log_price\"])\n",
    "ax1.set_title(\"Log Price and Year\")\n",
    "ax2.scatter(data_cleaned[\"EngineV\"], data_cleaned[\"log_price\"])\n",
    "ax2.set_title(\"Log Price and EngineV\")\n",
    "ax3.scatter(data_cleaned[\"Mileage\"], data_cleaned[\"log_price\"])\n",
    "ax3.set_title(\"Log Price and Mileage\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Body</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineV</th>\n",
       "      <th>Engine Type</th>\n",
       "      <th>Registration</th>\n",
       "      <th>Year</th>\n",
       "      <th>log_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BMW</td>\n",
       "      <td>sedan</td>\n",
       "      <td>277</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>1991</td>\n",
       "      <td>8.342840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>van</td>\n",
       "      <td>427</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>1999</td>\n",
       "      <td>8.974618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>sedan</td>\n",
       "      <td>358</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Gas</td>\n",
       "      <td>yes</td>\n",
       "      <td>2003</td>\n",
       "      <td>9.495519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>crossover</td>\n",
       "      <td>240</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2007</td>\n",
       "      <td>10.043249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>crossover</td>\n",
       "      <td>120</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2011</td>\n",
       "      <td>9.814656</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Brand       Body  Mileage  EngineV Engine Type Registration  Year  \\\n",
       "0            BMW      sedan      277      2.0      Petrol          yes  1991   \n",
       "1  Mercedes-Benz        van      427      2.9      Diesel          yes  1999   \n",
       "2  Mercedes-Benz      sedan      358      5.0         Gas          yes  2003   \n",
       "3           Audi  crossover      240      4.2      Petrol          yes  2007   \n",
       "4         Toyota  crossover      120      2.0      Petrol          yes  2011   \n",
       "\n",
       "   log_price  \n",
       "0   8.342840  \n",
       "1   8.974618  \n",
       "2   9.495519  \n",
       "3  10.043249  \n",
       "4   9.814656  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cleaned = data_cleaned.drop([\"Price\"], axis=1)\n",
    "data_cleaned.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multicollinearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Brand', 'Body', 'Mileage', 'EngineV', 'Engine Type',\n",
       "       'Registration', 'Year', 'log_price'], dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cleaned.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "variables =data_cleaned[[\"Mileage\", \"Year\", \"EngineV\"]]\n",
    "vif = pd.DataFrame()\n",
    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range (variables.shape[1])]\n",
    "vif[\"features\"] = variables.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIF</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.791584</td>\n",
       "      <td>Mileage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.354854</td>\n",
       "      <td>Year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.662068</td>\n",
       "      <td>EngineV</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         VIF features\n",
       "0   3.791584  Mileage\n",
       "1  10.354854     Year\n",
       "2   7.662068  EngineV"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Body</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineV</th>\n",
       "      <th>Engine Type</th>\n",
       "      <th>Registration</th>\n",
       "      <th>log_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BMW</td>\n",
       "      <td>sedan</td>\n",
       "      <td>277</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>8.342840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>van</td>\n",
       "      <td>427</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>8.974618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>sedan</td>\n",
       "      <td>358</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Gas</td>\n",
       "      <td>yes</td>\n",
       "      <td>9.495519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>crossover</td>\n",
       "      <td>240</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>10.043249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>crossover</td>\n",
       "      <td>120</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>9.814656</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Brand       Body  Mileage  EngineV Engine Type Registration  \\\n",
       "0            BMW      sedan      277      2.0      Petrol          yes   \n",
       "1  Mercedes-Benz        van      427      2.9      Diesel          yes   \n",
       "2  Mercedes-Benz      sedan      358      5.0         Gas          yes   \n",
       "3           Audi  crossover      240      4.2      Petrol          yes   \n",
       "4         Toyota  crossover      120      2.0      Petrol          yes   \n",
       "\n",
       "   log_price  \n",
       "0   8.342840  \n",
       "1   8.974618  \n",
       "2   9.495519  \n",
       "3  10.043249  \n",
       "4   9.814656  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_no_multicollinearity = data_cleaned.drop([\"Year\"], axis=1)\n",
    "data_no_multicollinearity.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recheck for multicollinearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "variables =data_cleaned[[\"Mileage\", \"EngineV\"]]\n",
    "vif = pd.DataFrame()\n",
    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range (variables.shape[1])]\n",
    "vif[\"features\"] = variables.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIF</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.805214</td>\n",
       "      <td>Mileage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.805214</td>\n",
       "      <td>EngineV</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        VIF features\n",
       "0  2.805214  Mileage\n",
       "1  2.805214  EngineV"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineV</th>\n",
       "      <th>log_price</th>\n",
       "      <th>Brand_BMW</th>\n",
       "      <th>Brand_Mercedes-Benz</th>\n",
       "      <th>Brand_Mitsubishi</th>\n",
       "      <th>Brand_Renault</th>\n",
       "      <th>Brand_Toyota</th>\n",
       "      <th>Brand_Volkswagen</th>\n",
       "      <th>Body_hatch</th>\n",
       "      <th>Body_other</th>\n",
       "      <th>Body_sedan</th>\n",
       "      <th>Body_vagon</th>\n",
       "      <th>Body_van</th>\n",
       "      <th>Engine Type_Gas</th>\n",
       "      <th>Engine Type_Other</th>\n",
       "      <th>Engine Type_Petrol</th>\n",
       "      <th>Registration_yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>277</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.342840</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>427</td>\n",
       "      <td>2.9</td>\n",
       "      <td>8.974618</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>358</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.495519</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>240</td>\n",
       "      <td>4.2</td>\n",
       "      <td>10.043249</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>120</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.814656</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Mileage  EngineV  log_price  Brand_BMW  Brand_Mercedes-Benz  \\\n",
       "0      277      2.0   8.342840          1                    0   \n",
       "1      427      2.9   8.974618          0                    1   \n",
       "2      358      5.0   9.495519          0                    1   \n",
       "3      240      4.2  10.043249          0                    0   \n",
       "4      120      2.0   9.814656          0                    0   \n",
       "\n",
       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
       "0                 0              0             0                 0   \n",
       "1                 0              0             0                 0   \n",
       "2                 0              0             0                 0   \n",
       "3                 0              0             0                 0   \n",
       "4                 0              0             1                 0   \n",
       "\n",
       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
       "0           0           0           1           0         0                0   \n",
       "1           0           0           0           0         1                0   \n",
       "2           0           0           1           0         0                1   \n",
       "3           0           0           0           0         0                0   \n",
       "4           0           0           0           0         0                0   \n",
       "\n",
       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
       "0                  0                   1                 1  \n",
       "1                  0                   0                 1  \n",
       "2                  0                   0                 1  \n",
       "3                  0                   1                 1  \n",
       "4                  0                   1                 1  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_with_dummies = pd.get_dummies(data_no_multicollinearity, drop_first=True)\n",
    "data_with_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rearrange a bit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mileage', 'EngineV', 'log_price', 'Brand_BMW',\n",
       "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
       "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
       "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
       "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_with_dummies.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['log_price', 'Mileage', 'EngineV', 'Brand_BMW',\n",
    "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
    "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
    "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
    "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>log_price</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineV</th>\n",
       "      <th>Brand_BMW</th>\n",
       "      <th>Brand_Mercedes-Benz</th>\n",
       "      <th>Brand_Mitsubishi</th>\n",
       "      <th>Brand_Renault</th>\n",
       "      <th>Brand_Toyota</th>\n",
       "      <th>Brand_Volkswagen</th>\n",
       "      <th>Body_hatch</th>\n",
       "      <th>Body_other</th>\n",
       "      <th>Body_sedan</th>\n",
       "      <th>Body_vagon</th>\n",
       "      <th>Body_van</th>\n",
       "      <th>Engine Type_Gas</th>\n",
       "      <th>Engine Type_Other</th>\n",
       "      <th>Engine Type_Petrol</th>\n",
       "      <th>Registration_yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.342840</td>\n",
       "      <td>277</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.974618</td>\n",
       "      <td>427</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.495519</td>\n",
       "      <td>358</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.043249</td>\n",
       "      <td>240</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.814656</td>\n",
       "      <td>120</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   log_price  Mileage  EngineV  Brand_BMW  Brand_Mercedes-Benz  \\\n",
       "0   8.342840      277      2.0          1                    0   \n",
       "1   8.974618      427      2.9          0                    1   \n",
       "2   9.495519      358      5.0          0                    1   \n",
       "3  10.043249      240      4.2          0                    0   \n",
       "4   9.814656      120      2.0          0                    0   \n",
       "\n",
       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
       "0                 0              0             0                 0   \n",
       "1                 0              0             0                 0   \n",
       "2                 0              0             0                 0   \n",
       "3                 0              0             0                 0   \n",
       "4                 0              0             1                 0   \n",
       "\n",
       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
       "0           0           0           1           0         0                0   \n",
       "1           0           0           0           0         1                0   \n",
       "2           0           0           1           0         0                1   \n",
       "3           0           0           0           0         0                0   \n",
       "4           0           0           0           0         0                0   \n",
       "\n",
       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
       "0                  0                   1                 1  \n",
       "1                  0                   0                 1  \n",
       "2                  0                   0                 1  \n",
       "3                  0                   1                 1  \n",
       "4                  0                   1                 1  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_preprocessed = data_with_dummies[cols]\n",
    "data_preprocessed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear regression model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Declare the inputs and targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "targets = data_preprocessed[\"log_price\"]\n",
    "inputs = data_preprocessed.drop([\"log_price\"], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scale the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "scaler.fit(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs_scaled = scaler.transform(inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(inputs_scaled, targets, test_size = 0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create the regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = LinearRegression()\n",
    "\n",
    "reg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat = reg.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_train, y_hat)\n",
    "plt.xlabel(\"Targets (y_train)\", size = 18)\n",
    "plt.ylabel(\"Predictions (y_hat)\", size = 18)\n",
    "plt.xlim(6, 13)\n",
    "plt.ylim(6, 13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Residuals PDF')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_train - y_hat)\n",
    "plt.title(\"Residuals PDF\", size=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7546487921881216"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find the weights and bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.421003614148074"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Bias\n",
    "\n",
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.46815598,  0.21526817,  0.01105151,  0.00569157, -0.14247174,\n",
       "       -0.18769337, -0.06529696, -0.09898873, -0.13980763, -0.09384461,\n",
       "       -0.17892753, -0.11457574, -0.15668036, -0.11685263, -0.02572253,\n",
       "       -0.15106183,  0.31442123])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Weight\n",
    "\n",
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>Weights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mileage</td>\n",
       "      <td>-0.468156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EngineV</td>\n",
       "      <td>0.215268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brand_BMW</td>\n",
       "      <td>0.011052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brand_Mercedes-Benz</td>\n",
       "      <td>0.005692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brand_Mitsubishi</td>\n",
       "      <td>-0.142472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Brand_Renault</td>\n",
       "      <td>-0.187693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Brand_Toyota</td>\n",
       "      <td>-0.065297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Brand_Volkswagen</td>\n",
       "      <td>-0.098989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Body_hatch</td>\n",
       "      <td>-0.139808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Body_other</td>\n",
       "      <td>-0.093845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Body_sedan</td>\n",
       "      <td>-0.178928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Body_vagon</td>\n",
       "      <td>-0.114576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Body_van</td>\n",
       "      <td>-0.156680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Engine Type_Gas</td>\n",
       "      <td>-0.116853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Engine Type_Other</td>\n",
       "      <td>-0.025723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Engine Type_Petrol</td>\n",
       "      <td>-0.151062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Registration_yes</td>\n",
       "      <td>0.314421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Features   Weights\n",
       "0               Mileage -0.468156\n",
       "1               EngineV  0.215268\n",
       "2             Brand_BMW  0.011052\n",
       "3   Brand_Mercedes-Benz  0.005692\n",
       "4      Brand_Mitsubishi -0.142472\n",
       "5         Brand_Renault -0.187693\n",
       "6          Brand_Toyota -0.065297\n",
       "7      Brand_Volkswagen -0.098989\n",
       "8            Body_hatch -0.139808\n",
       "9            Body_other -0.093845\n",
       "10           Body_sedan -0.178928\n",
       "11           Body_vagon -0.114576\n",
       "12             Body_van -0.156680\n",
       "13      Engine Type_Gas -0.116853\n",
       "14    Engine Type_Other -0.025723\n",
       "15   Engine Type_Petrol -0.151062\n",
       "16     Registration_yes  0.314421"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_summary = pd.DataFrame(inputs.columns.values, columns=[\"Features\"])\n",
    "reg_summary[\"Weights\"] = reg.coef_\n",
    "reg_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['BMW', 'Mercedes-Benz', 'Audi', 'Toyota', 'Renault', 'Volkswagen',\n",
       "       'Mitsubishi'], dtype=object)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cleaned[\"Brand\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat_test = reg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_hat_test, alpha=0.2)\n",
    "plt.xlabel(\"Targets (t_test)\", size = 18)\n",
    "plt.ylabel(\"Predictions (y_hat_test)\", size = 18)\n",
    "plt.xlim(6, 13)\n",
    "plt.ylim(6, 13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40419.308971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7956.895960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5757.284408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9765.810605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7027.363814</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Predictions\n",
       "0  40419.308971\n",
       "1   7956.895960\n",
       "2   5757.284408\n",
       "3   9765.810605\n",
       "4   7027.363814"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pf = pd.DataFrame(np.exp(y_hat_test), columns=[\"Predictions\"])\n",
    "df_pf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predictions</th>\n",
       "      <th>Targets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40419.308971</td>\n",
       "      <td>4200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7956.895960</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5757.284408</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9765.810605</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7027.363814</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Predictions  Targets\n",
       "0  40419.308971   4200.0\n",
       "1   7956.895960      NaN\n",
       "2   5757.284408      NaN\n",
       "3   9765.810605      NaN\n",
       "4   7027.363814      NaN"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pf[\"Targets\"] = np.exp(y_test)\n",
    "df_pf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    9.966462\n",
       "1    9.093807\n",
       "2    8.086410\n",
       "3    9.392662\n",
       "4    8.594154\n",
       "Name: log_price, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test = y_test.reset_index(drop=True)\n",
    "y_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predictions</th>\n",
       "      <th>Targets</th>\n",
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       "  <tbody>\n",
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       "      <td>7956.895960</td>\n",
       "      <td>8900.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5757.284408</td>\n",
       "      <td>3250.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9765.810605</td>\n",
       "      <td>12000.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7027.363814</td>\n",
       "      <td>5400.0</td>\n",
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       "      <th>...</th>\n",
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       "      <th>769</th>\n",
       "      <td>4699.586013</td>\n",
       "      <td>4200.0</td>\n",
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       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>9123.067372</td>\n",
       "      <td>8800.0</td>\n",
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       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>6267.803094</td>\n",
       "      <td>4850.0</td>\n",
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       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>20906.128600</td>\n",
       "      <td>27000.0</td>\n",
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       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>9244.000735</td>\n",
       "      <td>10900.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>774 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Predictions  Targets\n",
       "0    40419.308971  21300.0\n",
       "1     7956.895960   8900.0\n",
       "2     5757.284408   3250.0\n",
       "3     9765.810605  12000.0\n",
       "4     7027.363814   5400.0\n",
       "..            ...      ...\n",
       "769   4699.586013   4200.0\n",
       "770   9123.067372   8800.0\n",
       "771   6267.803094   4850.0\n",
       "772  20906.128600  27000.0\n",
       "773   9244.000735  10900.0\n",
       "\n",
       "[774 rows x 2 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pf[\"Targets\"] = np.exp(y_test)\n",
    "df_pf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pf[\"Residuals\"] = df_pf[\"Targets\"] - df_pf[\"Predictions\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pf[\"Difference%\"] = np.absolute(df_pf[\"Residuals\"]/df_pf[\"Targets\"]*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Predictions</th>\n",
       "      <th>Targets</th>\n",
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       "      <td>89.762014</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7956.895960</td>\n",
       "      <td>8900.0</td>\n",
       "      <td>943.104040</td>\n",
       "      <td>10.596675</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5757.284408</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>-2507.284408</td>\n",
       "      <td>77.147213</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9765.810605</td>\n",
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       "      <td>18.618245</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7027.363814</td>\n",
       "      <td>5400.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>769</th>\n",
       "      <td>4699.586013</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>-499.586013</td>\n",
       "      <td>11.894905</td>\n",
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       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>9123.067372</td>\n",
       "      <td>8800.0</td>\n",
       "      <td>-323.067372</td>\n",
       "      <td>3.671220</td>\n",
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       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>6267.803094</td>\n",
       "      <td>4850.0</td>\n",
       "      <td>-1417.803094</td>\n",
       "      <td>29.233053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>20906.128600</td>\n",
       "      <td>27000.0</td>\n",
       "      <td>6093.871400</td>\n",
       "      <td>22.569894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>9244.000735</td>\n",
       "      <td>10900.0</td>\n",
       "      <td>1655.999265</td>\n",
       "      <td>15.192654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>774 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Predictions  Targets     Residuals  Difference%\n",
       "0    40419.308971  21300.0 -19119.308971    89.762014\n",
       "1     7956.895960   8900.0    943.104040    10.596675\n",
       "2     5757.284408   3250.0  -2507.284408    77.147213\n",
       "3     9765.810605  12000.0   2234.189395    18.618245\n",
       "4     7027.363814   5400.0  -1627.363814    30.136367\n",
       "..            ...      ...           ...          ...\n",
       "769   4699.586013   4200.0   -499.586013    11.894905\n",
       "770   9123.067372   8800.0   -323.067372     3.671220\n",
       "771   6267.803094   4850.0  -1417.803094    29.233053\n",
       "772  20906.128600  27000.0   6093.871400    22.569894\n",
       "773   9244.000735  10900.0   1655.999265    15.192654\n",
       "\n",
       "[774 rows x 4 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predictions</th>\n",
       "      <th>Targets</th>\n",
       "      <th>Residuals</th>\n",
       "      <th>Difference%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>774.000000</td>\n",
       "      <td>774.000000</td>\n",
       "      <td>774.000000</td>\n",
       "      <td>774.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16113.011638</td>\n",
       "      <td>17799.988992</td>\n",
       "      <td>1686.977355</td>\n",
       "      <td>42.085282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13326.229797</td>\n",
       "      <td>19723.434542</td>\n",
       "      <td>11192.614496</td>\n",
       "      <td>96.169928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>967.030415</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>-42859.020603</td>\n",
       "      <td>0.020246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7467.591694</td>\n",
       "      <td>6700.000000</td>\n",
       "      <td>-2246.354353</td>\n",
       "      <td>11.024941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11914.358989</td>\n",
       "      <td>11050.000000</td>\n",
       "      <td>-38.697776</td>\n",
       "      <td>24.086183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>20249.505640</td>\n",
       "      <td>20475.000000</td>\n",
       "      <td>3004.524837</td>\n",
       "      <td>42.381716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>79026.348020</td>\n",
       "      <td>125000.000000</td>\n",
       "      <td>81051.158885</td>\n",
       "      <td>1785.792525</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Predictions        Targets     Residuals  Difference%\n",
       "count    774.000000     774.000000    774.000000   774.000000\n",
       "mean   16113.011638   17799.988992   1686.977355    42.085282\n",
       "std    13326.229797   19723.434542  11192.614496    96.169928\n",
       "min      967.030415     999.000000 -42859.020603     0.020246\n",
       "25%     7467.591694    6700.000000  -2246.354353    11.024941\n",
       "50%    11914.358989   11050.000000    -38.697776    24.086183\n",
       "75%    20249.505640   20475.000000   3004.524837    42.381716\n",
       "max    79026.348020  125000.000000  81051.158885  1785.792525"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predictions</th>\n",
       "      <th>Targets</th>\n",
       "      <th>Residuals</th>\n",
       "      <th>Difference%</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>13602.75</td>\n",
       "      <td>13600.00</td>\n",
       "      <td>-2.75</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>28889.06</td>\n",
       "      <td>28900.00</td>\n",
       "      <td>10.94</td>\n",
       "      <td>0.04</td>\n",
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       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>35530.21</td>\n",
       "      <td>35500.00</td>\n",
       "      <td>-30.21</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>11313.27</td>\n",
       "      <td>11300.00</td>\n",
       "      <td>-13.27</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>2995.68</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>4.32</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>52180.11</td>\n",
       "      <td>52055.25</td>\n",
       "      <td>-124.86</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>3107.51</td>\n",
       "      <td>3100.00</td>\n",
       "      <td>-7.51</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>9675.57</td>\n",
       "      <td>9700.00</td>\n",
       "      <td>24.43</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>21407.59</td>\n",
       "      <td>21335.00</td>\n",
       "      <td>-72.59</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>9865.72</td>\n",
       "      <td>9900.00</td>\n",
       "      <td>34.28</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>12257.04</td>\n",
       "      <td>12300.00</td>\n",
       "      <td>42.96</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>9040.90</td>\n",
       "      <td>9000.00</td>\n",
       "      <td>-40.90</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>11554.52</td>\n",
       "      <td>11500.00</td>\n",
       "      <td>-54.52</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>42728.70</td>\n",
       "      <td>42500.00</td>\n",
       "      <td>-228.70</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>6736.49</td>\n",
       "      <td>6700.00</td>\n",
       "      <td>-36.49</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>3878.09</td>\n",
       "      <td>3900.00</td>\n",
       "      <td>21.91</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>8151.45</td>\n",
       "      <td>8200.00</td>\n",
       "      <td>48.55</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>5033.58</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>-33.58</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>32158.13</td>\n",
       "      <td>32400.00</td>\n",
       "      <td>241.87</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>18543.19</td>\n",
       "      <td>18700.00</td>\n",
       "      <td>156.81</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>18888.88</td>\n",
       "      <td>18700.00</td>\n",
       "      <td>-188.88</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>16820.70</td>\n",
       "      <td>17000.00</td>\n",
       "      <td>179.30</td>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>5460.40</td>\n",
       "      <td>5400.00</td>\n",
       "      <td>-60.40</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>7077.79</td>\n",
       "      <td>6999.00</td>\n",
       "      <td>-78.79</td>\n",
       "      <td>1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>1530.62</td>\n",
       "      <td>1550.00</td>\n",
       "      <td>19.38</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>12358.46</td>\n",
       "      <td>12200.00</td>\n",
       "      <td>-158.46</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>37798.37</td>\n",
       "      <td>38300.00</td>\n",
       "      <td>501.63</td>\n",
       "      <td>1.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>16772.48</td>\n",
       "      <td>16999.00</td>\n",
       "      <td>226.52</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>12626.83</td>\n",
       "      <td>12800.00</td>\n",
       "      <td>173.17</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>17742.21</td>\n",
       "      <td>17500.00</td>\n",
       "      <td>-242.21</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>12318.70</td>\n",
       "      <td>12500.00</td>\n",
       "      <td>181.30</td>\n",
       "      <td>1.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>6497.81</td>\n",
       "      <td>6400.00</td>\n",
       "      <td>-97.81</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>26438.30</td>\n",
       "      <td>25999.00</td>\n",
       "      <td>-439.30</td>\n",
       "      <td>1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>10311.04</td>\n",
       "      <td>10500.00</td>\n",
       "      <td>188.96</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>5203.59</td>\n",
       "      <td>5300.00</td>\n",
       "      <td>96.41</td>\n",
       "      <td>1.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>22290.65</td>\n",
       "      <td>22711.65</td>\n",
       "      <td>421.00</td>\n",
       "      <td>1.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>6571.82</td>\n",
       "      <td>6699.00</td>\n",
       "      <td>127.18</td>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>75324.97</td>\n",
       "      <td>73900.00</td>\n",
       "      <td>-1424.97</td>\n",
       "      <td>1.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>7353.63</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>146.37</td>\n",
       "      <td>1.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>12645.69</td>\n",
       "      <td>12900.00</td>\n",
       "      <td>254.31</td>\n",
       "      <td>1.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>12770.59</td>\n",
       "      <td>12499.00</td>\n",
       "      <td>-271.59</td>\n",
       "      <td>2.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>41965.41</td>\n",
       "      <td>42900.00</td>\n",
       "      <td>934.59</td>\n",
       "      <td>2.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>19026.21</td>\n",
       "      <td>19500.00</td>\n",
       "      <td>473.79</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>8560.18</td>\n",
       "      <td>8350.00</td>\n",
       "      <td>-210.18</td>\n",
       "      <td>2.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>16978.04</td>\n",
       "      <td>16555.00</td>\n",
       "      <td>-423.04</td>\n",
       "      <td>2.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>8939.67</td>\n",
       "      <td>8700.00</td>\n",
       "      <td>-239.67</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6474.90</td>\n",
       "      <td>6300.00</td>\n",
       "      <td>-174.90</td>\n",
       "      <td>2.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>9038.89</td>\n",
       "      <td>9300.00</td>\n",
       "      <td>261.11</td>\n",
       "      <td>2.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>6785.47</td>\n",
       "      <td>6600.00</td>\n",
       "      <td>-185.47</td>\n",
       "      <td>2.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>6891.01</td>\n",
       "      <td>6700.00</td>\n",
       "      <td>-191.01</td>\n",
       "      <td>2.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>22335.41</td>\n",
       "      <td>23000.00</td>\n",
       "      <td>664.59</td>\n",
       "      <td>2.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>2815.34</td>\n",
       "      <td>2900.00</td>\n",
       "      <td>84.66</td>\n",
       "      <td>2.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>13899.37</td>\n",
       "      <td>13499.00</td>\n",
       "      <td>-400.37</td>\n",
       "      <td>2.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>4325.31</td>\n",
       "      <td>4200.00</td>\n",
       "      <td>-125.31</td>\n",
       "      <td>2.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>12506.72</td>\n",
       "      <td>12900.00</td>\n",
       "      <td>393.28</td>\n",
       "      <td>3.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>4067.27</td>\n",
       "      <td>4200.00</td>\n",
       "      <td>132.73</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>11148.59</td>\n",
       "      <td>10800.00</td>\n",
       "      <td>-348.59</td>\n",
       "      <td>3.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>29841.46</td>\n",
       "      <td>28900.00</td>\n",
       "      <td>-941.46</td>\n",
       "      <td>3.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>13630.20</td>\n",
       "      <td>13200.00</td>\n",
       "      <td>-430.20</td>\n",
       "      <td>3.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>9296.69</td>\n",
       "      <td>9000.00</td>\n",
       "      <td>-296.69</td>\n",
       "      <td>3.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>18270.67</td>\n",
       "      <td>18900.00</td>\n",
       "      <td>629.33</td>\n",
       "      <td>3.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>7246.61</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>253.39</td>\n",
       "      <td>3.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>6722.21</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>-222.21</td>\n",
       "      <td>3.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>18346.18</td>\n",
       "      <td>18999.00</td>\n",
       "      <td>652.82</td>\n",
       "      <td>3.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>5785.33</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>214.67</td>\n",
       "      <td>3.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>21107.44</td>\n",
       "      <td>21900.00</td>\n",
       "      <td>792.56</td>\n",
       "      <td>3.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>19687.35</td>\n",
       "      <td>18999.00</td>\n",
       "      <td>-688.35</td>\n",
       "      <td>3.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>45597.17</td>\n",
       "      <td>44000.00</td>\n",
       "      <td>-1597.17</td>\n",
       "      <td>3.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>9123.07</td>\n",
       "      <td>8800.00</td>\n",
       "      <td>-323.07</td>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>25818.91</td>\n",
       "      <td>24900.00</td>\n",
       "      <td>-918.91</td>\n",
       "      <td>3.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>10062.08</td>\n",
       "      <td>9700.00</td>\n",
       "      <td>-362.08</td>\n",
       "      <td>3.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>7698.56</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>301.44</td>\n",
       "      <td>3.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>18578.60</td>\n",
       "      <td>17900.00</td>\n",
       "      <td>-678.60</td>\n",
       "      <td>3.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>23081.50</td>\n",
       "      <td>24000.00</td>\n",
       "      <td>918.50</td>\n",
       "      <td>3.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>43510.26</td>\n",
       "      <td>41900.00</td>\n",
       "      <td>-1610.26</td>\n",
       "      <td>3.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>12982.56</td>\n",
       "      <td>12500.00</td>\n",
       "      <td>-482.56</td>\n",
       "      <td>3.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>27394.78</td>\n",
       "      <td>28500.00</td>\n",
       "      <td>1105.22</td>\n",
       "      <td>3.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>12872.08</td>\n",
       "      <td>13400.00</td>\n",
       "      <td>527.92</td>\n",
       "      <td>3.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>5759.79</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>240.21</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>9986.72</td>\n",
       "      <td>9599.00</td>\n",
       "      <td>-387.72</td>\n",
       "      <td>4.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>2654.86</td>\n",
       "      <td>2550.00</td>\n",
       "      <td>-104.86</td>\n",
       "      <td>4.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>3978.93</td>\n",
       "      <td>4150.00</td>\n",
       "      <td>171.07</td>\n",
       "      <td>4.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>35102.95</td>\n",
       "      <td>33700.00</td>\n",
       "      <td>-1402.95</td>\n",
       "      <td>4.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>24725.89</td>\n",
       "      <td>25800.00</td>\n",
       "      <td>1074.11</td>\n",
       "      <td>4.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>57292.95</td>\n",
       "      <td>55000.00</td>\n",
       "      <td>-2292.95</td>\n",
       "      <td>4.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>4896.50</td>\n",
       "      <td>4700.00</td>\n",
       "      <td>-196.50</td>\n",
       "      <td>4.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>9533.79</td>\n",
       "      <td>9950.00</td>\n",
       "      <td>416.21</td>\n",
       "      <td>4.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>54120.02</td>\n",
       "      <td>56500.00</td>\n",
       "      <td>2379.98</td>\n",
       "      <td>4.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>7712.65</td>\n",
       "      <td>7400.00</td>\n",
       "      <td>-312.65</td>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>13305.00</td>\n",
       "      <td>13900.00</td>\n",
       "      <td>595.00</td>\n",
       "      <td>4.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>7547.39</td>\n",
       "      <td>7899.00</td>\n",
       "      <td>351.61</td>\n",
       "      <td>4.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>16742.87</td>\n",
       "      <td>16000.00</td>\n",
       "      <td>-742.87</td>\n",
       "      <td>4.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>41862.20</td>\n",
       "      <td>39999.00</td>\n",
       "      <td>-1863.20</td>\n",
       "      <td>4.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>13441.93</td>\n",
       "      <td>14100.00</td>\n",
       "      <td>658.07</td>\n",
       "      <td>4.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>21877.35</td>\n",
       "      <td>20900.00</td>\n",
       "      <td>-977.35</td>\n",
       "      <td>4.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>14105.89</td>\n",
       "      <td>14800.00</td>\n",
       "      <td>694.11</td>\n",
       "      <td>4.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>6956.59</td>\n",
       "      <td>7300.00</td>\n",
       "      <td>343.41</td>\n",
       "      <td>4.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>3979.88</td>\n",
       "      <td>3800.00</td>\n",
       "      <td>-179.88</td>\n",
       "      <td>4.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>4285.09</td>\n",
       "      <td>4500.00</td>\n",
       "      <td>214.91</td>\n",
       "      <td>4.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>17126.96</td>\n",
       "      <td>18000.00</td>\n",
       "      <td>873.04</td>\n",
       "      <td>4.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>29490.39</td>\n",
       "      <td>31000.00</td>\n",
       "      <td>1509.61</td>\n",
       "      <td>4.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>36047.48</td>\n",
       "      <td>37900.00</td>\n",
       "      <td>1852.52</td>\n",
       "      <td>4.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>11122.47</td>\n",
       "      <td>10600.00</td>\n",
       "      <td>-522.47</td>\n",
       "      <td>4.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>6558.66</td>\n",
       "      <td>6899.00</td>\n",
       "      <td>340.34</td>\n",
       "      <td>4.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>20800.89</td>\n",
       "      <td>21900.00</td>\n",
       "      <td>1099.11</td>\n",
       "      <td>5.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>24173.42</td>\n",
       "      <td>23000.00</td>\n",
       "      <td>-1173.42</td>\n",
       "      <td>5.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>14928.85</td>\n",
       "      <td>14200.00</td>\n",
       "      <td>-728.85</td>\n",
       "      <td>5.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>9884.36</td>\n",
       "      <td>9400.00</td>\n",
       "      <td>-484.36</td>\n",
       "      <td>5.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>9358.79</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>-458.79</td>\n",
       "      <td>5.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>16120.81</td>\n",
       "      <td>16999.00</td>\n",
       "      <td>878.19</td>\n",
       "      <td>5.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>14126.94</td>\n",
       "      <td>14900.00</td>\n",
       "      <td>773.06</td>\n",
       "      <td>5.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>12586.88</td>\n",
       "      <td>11960.00</td>\n",
       "      <td>-626.88</td>\n",
       "      <td>5.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>29841.46</td>\n",
       "      <td>31500.00</td>\n",
       "      <td>1658.54</td>\n",
       "      <td>5.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>53508.88</td>\n",
       "      <td>56500.00</td>\n",
       "      <td>2991.12</td>\n",
       "      <td>5.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>11371.78</td>\n",
       "      <td>10800.00</td>\n",
       "      <td>-571.78</td>\n",
       "      <td>5.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>23665.06</td>\n",
       "      <td>24999.00</td>\n",
       "      <td>1333.94</td>\n",
       "      <td>5.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>41579.85</td>\n",
       "      <td>44000.00</td>\n",
       "      <td>2420.15</td>\n",
       "      <td>5.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>22166.00</td>\n",
       "      <td>21000.00</td>\n",
       "      <td>-1166.00</td>\n",
       "      <td>5.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>23026.53</td>\n",
       "      <td>21800.00</td>\n",
       "      <td>-1226.53</td>\n",
       "      <td>5.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>20274.26</td>\n",
       "      <td>21500.00</td>\n",
       "      <td>1225.74</td>\n",
       "      <td>5.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>52776.07</td>\n",
       "      <td>56000.00</td>\n",
       "      <td>3223.93</td>\n",
       "      <td>5.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>19588.09</td>\n",
       "      <td>18500.00</td>\n",
       "      <td>-1088.09</td>\n",
       "      <td>5.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>13355.89</td>\n",
       "      <td>12600.00</td>\n",
       "      <td>-755.89</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>18418.31</td>\n",
       "      <td>19600.00</td>\n",
       "      <td>1181.69</td>\n",
       "      <td>6.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>9438.41</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>-538.41</td>\n",
       "      <td>6.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>42995.33</td>\n",
       "      <td>40500.00</td>\n",
       "      <td>-2495.33</td>\n",
       "      <td>6.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>45344.25</td>\n",
       "      <td>42700.00</td>\n",
       "      <td>-2644.25</td>\n",
       "      <td>6.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>7500.49</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>499.51</td>\n",
       "      <td>6.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>6006.61</td>\n",
       "      <td>5650.00</td>\n",
       "      <td>-356.61</td>\n",
       "      <td>6.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>17795.63</td>\n",
       "      <td>19000.00</td>\n",
       "      <td>1204.37</td>\n",
       "      <td>6.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>22452.40</td>\n",
       "      <td>24000.00</td>\n",
       "      <td>1547.60</td>\n",
       "      <td>6.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>10184.22</td>\n",
       "      <td>10900.00</td>\n",
       "      <td>715.78</td>\n",
       "      <td>6.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>8527.59</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>-527.59</td>\n",
       "      <td>6.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>8873.27</td>\n",
       "      <td>9500.00</td>\n",
       "      <td>626.73</td>\n",
       "      <td>6.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>17365.29</td>\n",
       "      <td>18600.00</td>\n",
       "      <td>1234.71</td>\n",
       "      <td>6.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>16003.49</td>\n",
       "      <td>14999.00</td>\n",
       "      <td>-1004.49</td>\n",
       "      <td>6.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>13900.52</td>\n",
       "      <td>14900.00</td>\n",
       "      <td>999.48</td>\n",
       "      <td>6.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>20175.24</td>\n",
       "      <td>18900.00</td>\n",
       "      <td>-1275.24</td>\n",
       "      <td>6.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>17842.82</td>\n",
       "      <td>16700.00</td>\n",
       "      <td>-1142.82</td>\n",
       "      <td>6.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>4282.13</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>-282.13</td>\n",
       "      <td>7.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>5357.57</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>-357.57</td>\n",
       "      <td>7.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>11787.08</td>\n",
       "      <td>11000.00</td>\n",
       "      <td>-787.08</td>\n",
       "      <td>7.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>10514.46</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>-714.46</td>\n",
       "      <td>7.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>5280.18</td>\n",
       "      <td>5700.00</td>\n",
       "      <td>419.82</td>\n",
       "      <td>7.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>7859.03</td>\n",
       "      <td>7300.00</td>\n",
       "      <td>-559.03</td>\n",
       "      <td>7.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>25851.88</td>\n",
       "      <td>24000.00</td>\n",
       "      <td>-1851.88</td>\n",
       "      <td>7.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>8014.08</td>\n",
       "      <td>8700.00</td>\n",
       "      <td>685.92</td>\n",
       "      <td>7.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>8195.24</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>704.76</td>\n",
       "      <td>7.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6719.36</td>\n",
       "      <td>7300.00</td>\n",
       "      <td>580.64</td>\n",
       "      <td>7.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>33376.16</td>\n",
       "      <td>30900.00</td>\n",
       "      <td>-2476.16</td>\n",
       "      <td>8.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>8969.71</td>\n",
       "      <td>8300.00</td>\n",
       "      <td>-669.71</td>\n",
       "      <td>8.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>13301.82</td>\n",
       "      <td>14490.00</td>\n",
       "      <td>1188.18</td>\n",
       "      <td>8.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>8889.26</td>\n",
       "      <td>9700.00</td>\n",
       "      <td>810.74</td>\n",
       "      <td>8.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>9211.40</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>-711.40</td>\n",
       "      <td>8.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16586.98</td>\n",
       "      <td>15300.00</td>\n",
       "      <td>-1286.98</td>\n",
       "      <td>8.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>6864.69</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>635.31</td>\n",
       "      <td>8.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>11719.69</td>\n",
       "      <td>10800.00</td>\n",
       "      <td>-919.69</td>\n",
       "      <td>8.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>24176.66</td>\n",
       "      <td>26500.00</td>\n",
       "      <td>2323.34</td>\n",
       "      <td>8.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>15669.90</td>\n",
       "      <td>17200.00</td>\n",
       "      <td>1530.10</td>\n",
       "      <td>8.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>690</th>\n",
       "      <td>43216.46</td>\n",
       "      <td>47500.00</td>\n",
       "      <td>4283.54</td>\n",
       "      <td>9.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>21810.04</td>\n",
       "      <td>20000.00</td>\n",
       "      <td>-1810.04</td>\n",
       "      <td>9.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693</th>\n",
       "      <td>2545.91</td>\n",
       "      <td>2800.00</td>\n",
       "      <td>254.09</td>\n",
       "      <td>9.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>9258.05</td>\n",
       "      <td>10200.00</td>\n",
       "      <td>941.95</td>\n",
       "      <td>9.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>10923.58</td>\n",
       "      <td>9999.00</td>\n",
       "      <td>-924.58</td>\n",
       "      <td>9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>19485.38</td>\n",
       "      <td>21500.00</td>\n",
       "      <td>2014.62</td>\n",
       "      <td>9.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>32503.84</td>\n",
       "      <td>35900.00</td>\n",
       "      <td>3396.16</td>\n",
       "      <td>9.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>6105.45</td>\n",
       "      <td>6750.00</td>\n",
       "      <td>644.55</td>\n",
       "      <td>9.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>9943.62</td>\n",
       "      <td>10999.00</td>\n",
       "      <td>1055.38</td>\n",
       "      <td>9.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>10194.96</td>\n",
       "      <td>9300.00</td>\n",
       "      <td>-894.96</td>\n",
       "      <td>9.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>14821.23</td>\n",
       "      <td>13500.00</td>\n",
       "      <td>-1321.23</td>\n",
       "      <td>9.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>17673.56</td>\n",
       "      <td>19600.00</td>\n",
       "      <td>1926.44</td>\n",
       "      <td>9.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>10547.68</td>\n",
       "      <td>11700.00</td>\n",
       "      <td>1152.32</td>\n",
       "      <td>9.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>25686.36</td>\n",
       "      <td>28500.00</td>\n",
       "      <td>2813.64</td>\n",
       "      <td>9.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>6705.44</td>\n",
       "      <td>6100.00</td>\n",
       "      <td>-605.44</td>\n",
       "      <td>9.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>13746.52</td>\n",
       "      <td>12500.00</td>\n",
       "      <td>-1246.52</td>\n",
       "      <td>9.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>8468.54</td>\n",
       "      <td>7700.00</td>\n",
       "      <td>-768.54</td>\n",
       "      <td>9.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>13701.64</td>\n",
       "      <td>15250.00</td>\n",
       "      <td>1548.36</td>\n",
       "      <td>10.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>11787.08</td>\n",
       "      <td>10700.00</td>\n",
       "      <td>-1087.08</td>\n",
       "      <td>10.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>18941.10</td>\n",
       "      <td>17191.20</td>\n",
       "      <td>-1749.90</td>\n",
       "      <td>10.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>30527.47</td>\n",
       "      <td>34000.00</td>\n",
       "      <td>3472.53</td>\n",
       "      <td>10.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>5386.17</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>613.83</td>\n",
       "      <td>10.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>5831.36</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>668.64</td>\n",
       "      <td>10.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>16742.87</td>\n",
       "      <td>18700.00</td>\n",
       "      <td>1957.13</td>\n",
       "      <td>10.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7956.90</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>943.10</td>\n",
       "      <td>10.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>34173.14</td>\n",
       "      <td>38233.00</td>\n",
       "      <td>4059.86</td>\n",
       "      <td>10.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>7966.77</td>\n",
       "      <td>7200.00</td>\n",
       "      <td>-766.77</td>\n",
       "      <td>10.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>16601.37</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>-1601.37</td>\n",
       "      <td>10.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>35698.51</td>\n",
       "      <td>40000.00</td>\n",
       "      <td>4301.49</td>\n",
       "      <td>10.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>8640.42</td>\n",
       "      <td>7800.00</td>\n",
       "      <td>-840.42</td>\n",
       "      <td>10.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>57076.55</td>\n",
       "      <td>51500.00</td>\n",
       "      <td>-5576.55</td>\n",
       "      <td>10.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>4324.27</td>\n",
       "      <td>3900.00</td>\n",
       "      <td>-424.27</td>\n",
       "      <td>10.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>3162.93</td>\n",
       "      <td>2850.00</td>\n",
       "      <td>-312.93</td>\n",
       "      <td>10.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>22201.06</td>\n",
       "      <td>19999.00</td>\n",
       "      <td>-2202.06</td>\n",
       "      <td>11.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>8898.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>1102.00</td>\n",
       "      <td>11.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>19654.05</td>\n",
       "      <td>17700.00</td>\n",
       "      <td>-1954.05</td>\n",
       "      <td>11.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>59933.58</td>\n",
       "      <td>67500.00</td>\n",
       "      <td>7566.42</td>\n",
       "      <td>11.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>7901.16</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>998.84</td>\n",
       "      <td>11.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>1779.94</td>\n",
       "      <td>1600.00</td>\n",
       "      <td>-179.94</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>17746.12</td>\n",
       "      <td>20000.00</td>\n",
       "      <td>2253.88</td>\n",
       "      <td>11.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>22605.01</td>\n",
       "      <td>25500.00</td>\n",
       "      <td>2894.99</td>\n",
       "      <td>11.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>57356.68</td>\n",
       "      <td>51500.00</td>\n",
       "      <td>-5856.68</td>\n",
       "      <td>11.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8150.20</td>\n",
       "      <td>9200.00</td>\n",
       "      <td>1049.80</td>\n",
       "      <td>11.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>53508.88</td>\n",
       "      <td>60500.00</td>\n",
       "      <td>6991.12</td>\n",
       "      <td>11.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>14589.79</td>\n",
       "      <td>16500.00</td>\n",
       "      <td>1910.21</td>\n",
       "      <td>11.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>6538.61</td>\n",
       "      <td>7400.00</td>\n",
       "      <td>861.39</td>\n",
       "      <td>11.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>19326.96</td>\n",
       "      <td>17300.00</td>\n",
       "      <td>-2026.96</td>\n",
       "      <td>11.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>6479.57</td>\n",
       "      <td>5800.00</td>\n",
       "      <td>-679.57</td>\n",
       "      <td>11.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>11911.57</td>\n",
       "      <td>13500.00</td>\n",
       "      <td>1588.43</td>\n",
       "      <td>11.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>9881.64</td>\n",
       "      <td>11200.00</td>\n",
       "      <td>1318.36</td>\n",
       "      <td>11.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>10061.69</td>\n",
       "      <td>9000.00</td>\n",
       "      <td>-1061.69</td>\n",
       "      <td>11.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>40974.12</td>\n",
       "      <td>46500.00</td>\n",
       "      <td>5525.88</td>\n",
       "      <td>11.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>12952.43</td>\n",
       "      <td>14700.00</td>\n",
       "      <td>1747.57</td>\n",
       "      <td>11.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>769</th>\n",
       "      <td>4699.59</td>\n",
       "      <td>4200.00</td>\n",
       "      <td>-499.59</td>\n",
       "      <td>11.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>30215.35</td>\n",
       "      <td>26999.00</td>\n",
       "      <td>-3216.35</td>\n",
       "      <td>11.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>6869.97</td>\n",
       "      <td>7800.00</td>\n",
       "      <td>930.03</td>\n",
       "      <td>11.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>2742.63</td>\n",
       "      <td>2450.00</td>\n",
       "      <td>-292.63</td>\n",
       "      <td>11.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>14783.38</td>\n",
       "      <td>13200.00</td>\n",
       "      <td>-1583.38</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>7840.41</td>\n",
       "      <td>6999.00</td>\n",
       "      <td>-841.41</td>\n",
       "      <td>12.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2804.12</td>\n",
       "      <td>2500.00</td>\n",
       "      <td>-304.12</td>\n",
       "      <td>12.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2368.86</td>\n",
       "      <td>2700.00</td>\n",
       "      <td>331.14</td>\n",
       "      <td>12.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>7858.74</td>\n",
       "      <td>7000.00</td>\n",
       "      <td>-858.74</td>\n",
       "      <td>12.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>8059.79</td>\n",
       "      <td>9200.00</td>\n",
       "      <td>1140.21</td>\n",
       "      <td>12.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>15126.06</td>\n",
       "      <td>17300.00</td>\n",
       "      <td>2173.94</td>\n",
       "      <td>12.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>9519.93</td>\n",
       "      <td>10900.00</td>\n",
       "      <td>1380.07</td>\n",
       "      <td>12.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>10597.08</td>\n",
       "      <td>9400.00</td>\n",
       "      <td>-1197.08</td>\n",
       "      <td>12.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>2922.53</td>\n",
       "      <td>3350.00</td>\n",
       "      <td>427.47</td>\n",
       "      <td>12.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>13066.91</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>1933.09</td>\n",
       "      <td>12.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>7574.29</td>\n",
       "      <td>8700.00</td>\n",
       "      <td>1125.71</td>\n",
       "      <td>12.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>13334.46</td>\n",
       "      <td>11800.00</td>\n",
       "      <td>-1534.46</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>9381.12</td>\n",
       "      <td>8300.00</td>\n",
       "      <td>-1081.12</td>\n",
       "      <td>13.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>21618.71</td>\n",
       "      <td>19100.00</td>\n",
       "      <td>-2518.71</td>\n",
       "      <td>13.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>2491.59</td>\n",
       "      <td>2200.00</td>\n",
       "      <td>-291.59</td>\n",
       "      <td>13.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>12551.44</td>\n",
       "      <td>14500.00</td>\n",
       "      <td>1948.56</td>\n",
       "      <td>13.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>7788.25</td>\n",
       "      <td>9000.00</td>\n",
       "      <td>1211.75</td>\n",
       "      <td>13.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>12149.99</td>\n",
       "      <td>10700.00</td>\n",
       "      <td>-1449.99</td>\n",
       "      <td>13.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>32524.17</td>\n",
       "      <td>28600.00</td>\n",
       "      <td>-3924.17</td>\n",
       "      <td>13.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>10604.69</td>\n",
       "      <td>12300.00</td>\n",
       "      <td>1695.31</td>\n",
       "      <td>13.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>34834.36</td>\n",
       "      <td>40500.00</td>\n",
       "      <td>5665.64</td>\n",
       "      <td>13.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>7466.24</td>\n",
       "      <td>8700.00</td>\n",
       "      <td>1233.76</td>\n",
       "      <td>14.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>8276.70</td>\n",
       "      <td>7245.00</td>\n",
       "      <td>-1031.70</td>\n",
       "      <td>14.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>8830.01</td>\n",
       "      <td>10299.00</td>\n",
       "      <td>1468.99</td>\n",
       "      <td>14.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>19495.26</td>\n",
       "      <td>22800.00</td>\n",
       "      <td>3304.74</td>\n",
       "      <td>14.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>4354.13</td>\n",
       "      <td>5100.00</td>\n",
       "      <td>745.87</td>\n",
       "      <td>14.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>6307.69</td>\n",
       "      <td>5500.00</td>\n",
       "      <td>-807.69</td>\n",
       "      <td>14.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>12455.28</td>\n",
       "      <td>14600.00</td>\n",
       "      <td>2144.72</td>\n",
       "      <td>14.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>10236.99</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>1763.01</td>\n",
       "      <td>14.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>13552.84</td>\n",
       "      <td>11800.00</td>\n",
       "      <td>-1752.84</td>\n",
       "      <td>14.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>8385.53</td>\n",
       "      <td>7300.00</td>\n",
       "      <td>-1085.53</td>\n",
       "      <td>14.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>4939.63</td>\n",
       "      <td>4300.00</td>\n",
       "      <td>-639.63</td>\n",
       "      <td>14.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>6969.39</td>\n",
       "      <td>8200.00</td>\n",
       "      <td>1230.61</td>\n",
       "      <td>15.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>7939.74</td>\n",
       "      <td>6900.00</td>\n",
       "      <td>-1039.74</td>\n",
       "      <td>15.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>9244.00</td>\n",
       "      <td>10900.00</td>\n",
       "      <td>1656.00</td>\n",
       "      <td>15.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>26384.26</td>\n",
       "      <td>31111.00</td>\n",
       "      <td>4726.74</td>\n",
       "      <td>15.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>22840.15</td>\n",
       "      <td>19800.00</td>\n",
       "      <td>-3040.15</td>\n",
       "      <td>15.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>11584.18</td>\n",
       "      <td>13708.50</td>\n",
       "      <td>2124.32</td>\n",
       "      <td>15.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>8026.52</td>\n",
       "      <td>9500.00</td>\n",
       "      <td>1473.48</td>\n",
       "      <td>15.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>28899.37</td>\n",
       "      <td>25000.00</td>\n",
       "      <td>-3899.37</td>\n",
       "      <td>15.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>18563.77</td>\n",
       "      <td>21999.00</td>\n",
       "      <td>3435.23</td>\n",
       "      <td>15.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>7758.12</td>\n",
       "      <td>9200.00</td>\n",
       "      <td>1441.88</td>\n",
       "      <td>15.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>8431.47</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>1568.53</td>\n",
       "      <td>15.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>31179.56</td>\n",
       "      <td>37000.00</td>\n",
       "      <td>5820.44</td>\n",
       "      <td>15.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>8840.69</td>\n",
       "      <td>10500.00</td>\n",
       "      <td>1659.31</td>\n",
       "      <td>15.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>31873.48</td>\n",
       "      <td>27500.00</td>\n",
       "      <td>-4373.48</td>\n",
       "      <td>15.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>2774.95</td>\n",
       "      <td>3300.00</td>\n",
       "      <td>525.05</td>\n",
       "      <td>15.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>2840.83</td>\n",
       "      <td>2450.00</td>\n",
       "      <td>-390.83</td>\n",
       "      <td>15.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>8656.08</td>\n",
       "      <td>10300.00</td>\n",
       "      <td>1643.92</td>\n",
       "      <td>15.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>17686.53</td>\n",
       "      <td>15250.00</td>\n",
       "      <td>-2436.53</td>\n",
       "      <td>15.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>40490.79</td>\n",
       "      <td>34900.00</td>\n",
       "      <td>-5590.79</td>\n",
       "      <td>16.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>20397.29</td>\n",
       "      <td>24300.00</td>\n",
       "      <td>3902.71</td>\n",
       "      <td>16.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2138.99</td>\n",
       "      <td>2550.00</td>\n",
       "      <td>411.01</td>\n",
       "      <td>16.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>2904.84</td>\n",
       "      <td>2500.00</td>\n",
       "      <td>-404.84</td>\n",
       "      <td>16.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>14890.32</td>\n",
       "      <td>17800.00</td>\n",
       "      <td>2909.68</td>\n",
       "      <td>16.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>26665.38</td>\n",
       "      <td>22900.00</td>\n",
       "      <td>-3765.38</td>\n",
       "      <td>16.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>8334.59</td>\n",
       "      <td>7150.00</td>\n",
       "      <td>-1184.59</td>\n",
       "      <td>16.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>16229.18</td>\n",
       "      <td>13900.00</td>\n",
       "      <td>-2329.18</td>\n",
       "      <td>16.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>62376.39</td>\n",
       "      <td>75000.00</td>\n",
       "      <td>12623.61</td>\n",
       "      <td>16.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>48958.22</td>\n",
       "      <td>41900.00</td>\n",
       "      <td>-7058.22</td>\n",
       "      <td>16.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>6319.31</td>\n",
       "      <td>7600.00</td>\n",
       "      <td>1280.69</td>\n",
       "      <td>16.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>33323.71</td>\n",
       "      <td>28500.00</td>\n",
       "      <td>-4823.71</td>\n",
       "      <td>16.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>10014.72</td>\n",
       "      <td>12090.00</td>\n",
       "      <td>2075.28</td>\n",
       "      <td>17.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>4553.35</td>\n",
       "      <td>5500.00</td>\n",
       "      <td>946.65</td>\n",
       "      <td>17.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>15196.84</td>\n",
       "      <td>12950.00</td>\n",
       "      <td>-2246.84</td>\n",
       "      <td>17.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>18139.19</td>\n",
       "      <td>21950.00</td>\n",
       "      <td>3810.81</td>\n",
       "      <td>17.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>6196.82</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>1303.18</td>\n",
       "      <td>17.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>3521.38</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>-521.38</td>\n",
       "      <td>17.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>53771.50</td>\n",
       "      <td>65099.00</td>\n",
       "      <td>11327.50</td>\n",
       "      <td>17.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>4128.62</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>871.38</td>\n",
       "      <td>17.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>20640.25</td>\n",
       "      <td>25000.00</td>\n",
       "      <td>4359.75</td>\n",
       "      <td>17.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>13466.18</td>\n",
       "      <td>11450.00</td>\n",
       "      <td>-2016.18</td>\n",
       "      <td>17.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>9057.98</td>\n",
       "      <td>7700.00</td>\n",
       "      <td>-1357.98</td>\n",
       "      <td>17.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>7983.18</td>\n",
       "      <td>9700.00</td>\n",
       "      <td>1716.82</td>\n",
       "      <td>17.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>5918.73</td>\n",
       "      <td>7200.00</td>\n",
       "      <td>1281.27</td>\n",
       "      <td>17.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>15323.65</td>\n",
       "      <td>13000.00</td>\n",
       "      <td>-2323.65</td>\n",
       "      <td>17.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>9608.71</td>\n",
       "      <td>11700.00</td>\n",
       "      <td>2091.29</td>\n",
       "      <td>17.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>15208.55</td>\n",
       "      <td>12900.00</td>\n",
       "      <td>-2308.55</td>\n",
       "      <td>17.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>7899.67</td>\n",
       "      <td>6700.00</td>\n",
       "      <td>-1199.67</td>\n",
       "      <td>17.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>20999.37</td>\n",
       "      <td>17800.00</td>\n",
       "      <td>-3199.37</td>\n",
       "      <td>17.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>19836.68</td>\n",
       "      <td>16800.00</td>\n",
       "      <td>-3036.68</td>\n",
       "      <td>18.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>11049.53</td>\n",
       "      <td>13500.00</td>\n",
       "      <td>2450.47</td>\n",
       "      <td>18.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>28555.16</td>\n",
       "      <td>34900.00</td>\n",
       "      <td>6344.84</td>\n",
       "      <td>18.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>8917.37</td>\n",
       "      <td>10900.00</td>\n",
       "      <td>1982.63</td>\n",
       "      <td>18.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>1633.01</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>366.99</td>\n",
       "      <td>18.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>15103.04</td>\n",
       "      <td>18500.00</td>\n",
       "      <td>3396.96</td>\n",
       "      <td>18.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>4323.82</td>\n",
       "      <td>5300.00</td>\n",
       "      <td>976.18</td>\n",
       "      <td>18.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>2446.44</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>553.56</td>\n",
       "      <td>18.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>3341.48</td>\n",
       "      <td>4100.00</td>\n",
       "      <td>758.52</td>\n",
       "      <td>18.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>10668.76</td>\n",
       "      <td>8999.00</td>\n",
       "      <td>-1669.76</td>\n",
       "      <td>18.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>10015.66</td>\n",
       "      <td>12300.00</td>\n",
       "      <td>2284.34</td>\n",
       "      <td>18.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>47322.46</td>\n",
       "      <td>39900.00</td>\n",
       "      <td>-7422.46</td>\n",
       "      <td>18.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>10378.24</td>\n",
       "      <td>8750.00</td>\n",
       "      <td>-1628.24</td>\n",
       "      <td>18.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9765.81</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>2234.19</td>\n",
       "      <td>18.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>5931.31</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>-931.31</td>\n",
       "      <td>18.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>10618.43</td>\n",
       "      <td>8950.00</td>\n",
       "      <td>-1668.43</td>\n",
       "      <td>18.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>10679.28</td>\n",
       "      <td>9000.00</td>\n",
       "      <td>-1679.28</td>\n",
       "      <td>18.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>11036.95</td>\n",
       "      <td>9300.00</td>\n",
       "      <td>-1736.95</td>\n",
       "      <td>18.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>11543.90</td>\n",
       "      <td>14200.00</td>\n",
       "      <td>2656.10</td>\n",
       "      <td>18.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>23337.70</td>\n",
       "      <td>28713.75</td>\n",
       "      <td>5376.05</td>\n",
       "      <td>18.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>6661.85</td>\n",
       "      <td>8200.00</td>\n",
       "      <td>1538.15</td>\n",
       "      <td>18.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>11768.26</td>\n",
       "      <td>9900.00</td>\n",
       "      <td>-1868.26</td>\n",
       "      <td>18.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>12371.38</td>\n",
       "      <td>10400.00</td>\n",
       "      <td>-1971.38</td>\n",
       "      <td>18.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>5916.22</td>\n",
       "      <td>7300.00</td>\n",
       "      <td>1383.78</td>\n",
       "      <td>18.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>8925.91</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>-1425.91</td>\n",
       "      <td>19.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>16425.19</td>\n",
       "      <td>20300.00</td>\n",
       "      <td>3874.81</td>\n",
       "      <td>19.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>2586.93</td>\n",
       "      <td>3200.00</td>\n",
       "      <td>613.07</td>\n",
       "      <td>19.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>10488.85</td>\n",
       "      <td>8800.00</td>\n",
       "      <td>-1688.85</td>\n",
       "      <td>19.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>3150.92</td>\n",
       "      <td>3900.00</td>\n",
       "      <td>749.08</td>\n",
       "      <td>19.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>22539.73</td>\n",
       "      <td>27900.00</td>\n",
       "      <td>5360.27</td>\n",
       "      <td>19.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>17770.44</td>\n",
       "      <td>22000.00</td>\n",
       "      <td>4229.56</td>\n",
       "      <td>19.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>53771.50</td>\n",
       "      <td>66666.00</td>\n",
       "      <td>12894.50</td>\n",
       "      <td>19.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>11681.84</td>\n",
       "      <td>14500.00</td>\n",
       "      <td>2818.16</td>\n",
       "      <td>19.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>10997.23</td>\n",
       "      <td>9200.00</td>\n",
       "      <td>-1797.23</td>\n",
       "      <td>19.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>4906.83</td>\n",
       "      <td>4100.00</td>\n",
       "      <td>-806.83</td>\n",
       "      <td>19.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>23588.84</td>\n",
       "      <td>19700.00</td>\n",
       "      <td>-3888.84</td>\n",
       "      <td>19.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>4648.32</td>\n",
       "      <td>5800.00</td>\n",
       "      <td>1151.68</td>\n",
       "      <td>19.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>4562.65</td>\n",
       "      <td>5700.00</td>\n",
       "      <td>1137.35</td>\n",
       "      <td>19.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3588.56</td>\n",
       "      <td>2990.00</td>\n",
       "      <td>-598.56</td>\n",
       "      <td>20.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>5761.38</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>-961.38</td>\n",
       "      <td>20.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>11914.81</td>\n",
       "      <td>14899.00</td>\n",
       "      <td>2984.19</td>\n",
       "      <td>20.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>7752.06</td>\n",
       "      <td>9700.00</td>\n",
       "      <td>1947.94</td>\n",
       "      <td>20.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>2764.13</td>\n",
       "      <td>2300.00</td>\n",
       "      <td>-464.13</td>\n",
       "      <td>20.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>14123.09</td>\n",
       "      <td>11750.00</td>\n",
       "      <td>-2373.09</td>\n",
       "      <td>20.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>12045.24</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>-2045.24</td>\n",
       "      <td>20.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>65166.43</td>\n",
       "      <td>81999.00</td>\n",
       "      <td>16832.57</td>\n",
       "      <td>20.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>8053.79</td>\n",
       "      <td>10200.00</td>\n",
       "      <td>2146.21</td>\n",
       "      <td>21.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>22452.40</td>\n",
       "      <td>28500.00</td>\n",
       "      <td>6047.60</td>\n",
       "      <td>21.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>7471.65</td>\n",
       "      <td>9500.00</td>\n",
       "      <td>2028.35</td>\n",
       "      <td>21.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>6919.67</td>\n",
       "      <td>5700.00</td>\n",
       "      <td>-1219.67</td>\n",
       "      <td>21.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>22400.05</td>\n",
       "      <td>28500.00</td>\n",
       "      <td>6099.95</td>\n",
       "      <td>21.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>8625.18</td>\n",
       "      <td>7100.00</td>\n",
       "      <td>-1525.18</td>\n",
       "      <td>21.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>18442.10</td>\n",
       "      <td>23500.00</td>\n",
       "      <td>5057.90</td>\n",
       "      <td>21.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>50926.23</td>\n",
       "      <td>65000.00</td>\n",
       "      <td>14073.77</td>\n",
       "      <td>21.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>26404.64</td>\n",
       "      <td>21700.00</td>\n",
       "      <td>-4704.64</td>\n",
       "      <td>21.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>37188.32</td>\n",
       "      <td>47500.00</td>\n",
       "      <td>10311.68</td>\n",
       "      <td>21.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>43418.52</td>\n",
       "      <td>55500.00</td>\n",
       "      <td>12081.48</td>\n",
       "      <td>21.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2618.11</td>\n",
       "      <td>2150.00</td>\n",
       "      <td>-468.11</td>\n",
       "      <td>21.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>11936.30</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>-2136.30</td>\n",
       "      <td>21.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>10061.21</td>\n",
       "      <td>8260.00</td>\n",
       "      <td>-1801.21</td>\n",
       "      <td>21.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>16400.49</td>\n",
       "      <td>20999.00</td>\n",
       "      <td>4598.51</td>\n",
       "      <td>21.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>8197.59</td>\n",
       "      <td>10500.00</td>\n",
       "      <td>2302.41</td>\n",
       "      <td>21.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>14642.59</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>-2642.59</td>\n",
       "      <td>22.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>45597.17</td>\n",
       "      <td>58500.00</td>\n",
       "      <td>12902.83</td>\n",
       "      <td>22.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>3099.74</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>900.26</td>\n",
       "      <td>22.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>7718.67</td>\n",
       "      <td>6300.00</td>\n",
       "      <td>-1418.67</td>\n",
       "      <td>22.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>20906.13</td>\n",
       "      <td>27000.00</td>\n",
       "      <td>6093.87</td>\n",
       "      <td>22.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>3248.04</td>\n",
       "      <td>4200.00</td>\n",
       "      <td>951.96</td>\n",
       "      <td>22.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>8580.60</td>\n",
       "      <td>11100.00</td>\n",
       "      <td>2519.40</td>\n",
       "      <td>22.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>13507.61</td>\n",
       "      <td>10999.00</td>\n",
       "      <td>-2508.61</td>\n",
       "      <td>22.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>2699.49</td>\n",
       "      <td>3500.00</td>\n",
       "      <td>800.51</td>\n",
       "      <td>22.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>11440.36</td>\n",
       "      <td>9300.00</td>\n",
       "      <td>-2140.36</td>\n",
       "      <td>23.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>11994.88</td>\n",
       "      <td>9750.00</td>\n",
       "      <td>-2244.88</td>\n",
       "      <td>23.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>7875.05</td>\n",
       "      <td>6400.00</td>\n",
       "      <td>-1475.05</td>\n",
       "      <td>23.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>4799.48</td>\n",
       "      <td>3900.00</td>\n",
       "      <td>-899.48</td>\n",
       "      <td>23.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>26705.21</td>\n",
       "      <td>21700.00</td>\n",
       "      <td>-5005.21</td>\n",
       "      <td>23.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>21524.61</td>\n",
       "      <td>28000.00</td>\n",
       "      <td>6475.39</td>\n",
       "      <td>23.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>30661.17</td>\n",
       "      <td>39900.00</td>\n",
       "      <td>9238.83</td>\n",
       "      <td>23.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>10372.36</td>\n",
       "      <td>13500.00</td>\n",
       "      <td>3127.64</td>\n",
       "      <td>23.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>6506.25</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>1993.75</td>\n",
       "      <td>23.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>10090.75</td>\n",
       "      <td>13200.00</td>\n",
       "      <td>3109.25</td>\n",
       "      <td>23.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>4963.93</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>1536.07</td>\n",
       "      <td>23.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>13341.93</td>\n",
       "      <td>17499.00</td>\n",
       "      <td>4157.07</td>\n",
       "      <td>23.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>51534.01</td>\n",
       "      <td>67622.55</td>\n",
       "      <td>16088.54</td>\n",
       "      <td>23.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6321.33</td>\n",
       "      <td>8300.00</td>\n",
       "      <td>1978.67</td>\n",
       "      <td>23.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>15992.34</td>\n",
       "      <td>21000.00</td>\n",
       "      <td>5007.66</td>\n",
       "      <td>23.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>7691.17</td>\n",
       "      <td>10100.00</td>\n",
       "      <td>2408.83</td>\n",
       "      <td>23.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>12868.90</td>\n",
       "      <td>16900.00</td>\n",
       "      <td>4031.10</td>\n",
       "      <td>23.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>79026.35</td>\n",
       "      <td>104000.00</td>\n",
       "      <td>24973.65</td>\n",
       "      <td>24.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>9057.98</td>\n",
       "      <td>7300.00</td>\n",
       "      <td>-1757.98</td>\n",
       "      <td>24.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10670.54</td>\n",
       "      <td>8599.00</td>\n",
       "      <td>-2071.54</td>\n",
       "      <td>24.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>5312.04</td>\n",
       "      <td>6999.00</td>\n",
       "      <td>1686.96</td>\n",
       "      <td>24.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>36037.78</td>\n",
       "      <td>47600.00</td>\n",
       "      <td>11562.22</td>\n",
       "      <td>24.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>29121.98</td>\n",
       "      <td>38500.00</td>\n",
       "      <td>9378.02</td>\n",
       "      <td>24.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>8713.76</td>\n",
       "      <td>7000.00</td>\n",
       "      <td>-1713.76</td>\n",
       "      <td>24.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>53771.50</td>\n",
       "      <td>43163.25</td>\n",
       "      <td>-10608.25</td>\n",
       "      <td>24.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>12050.92</td>\n",
       "      <td>9658.94</td>\n",
       "      <td>-2391.98</td>\n",
       "      <td>24.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>7492.94</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>-1492.94</td>\n",
       "      <td>24.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>15488.91</td>\n",
       "      <td>12400.00</td>\n",
       "      <td>-3088.91</td>\n",
       "      <td>24.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>27923.11</td>\n",
       "      <td>37200.00</td>\n",
       "      <td>9276.89</td>\n",
       "      <td>24.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>5375.96</td>\n",
       "      <td>4300.00</td>\n",
       "      <td>-1075.96</td>\n",
       "      <td>25.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>15264.36</td>\n",
       "      <td>12200.00</td>\n",
       "      <td>-3064.36</td>\n",
       "      <td>25.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>14222.59</td>\n",
       "      <td>18999.00</td>\n",
       "      <td>4776.41</td>\n",
       "      <td>25.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>23825.56</td>\n",
       "      <td>31900.00</td>\n",
       "      <td>8074.44</td>\n",
       "      <td>25.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>2164.01</td>\n",
       "      <td>2900.00</td>\n",
       "      <td>735.99</td>\n",
       "      <td>25.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>2508.03</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>-508.03</td>\n",
       "      <td>25.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>6631.54</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>2268.46</td>\n",
       "      <td>25.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>5146.54</td>\n",
       "      <td>4100.00</td>\n",
       "      <td>-1046.54</td>\n",
       "      <td>25.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>13396.77</td>\n",
       "      <td>17999.00</td>\n",
       "      <td>4602.23</td>\n",
       "      <td>25.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>3419.97</td>\n",
       "      <td>4600.00</td>\n",
       "      <td>1180.03</td>\n",
       "      <td>25.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>15937.99</td>\n",
       "      <td>21500.00</td>\n",
       "      <td>5562.01</td>\n",
       "      <td>25.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>24420.21</td>\n",
       "      <td>33000.00</td>\n",
       "      <td>8579.79</td>\n",
       "      <td>26.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>27094.57</td>\n",
       "      <td>21500.00</td>\n",
       "      <td>-5594.57</td>\n",
       "      <td>26.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>15518.70</td>\n",
       "      <td>21000.00</td>\n",
       "      <td>5481.30</td>\n",
       "      <td>26.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>49198.51</td>\n",
       "      <td>39000.00</td>\n",
       "      <td>-10198.51</td>\n",
       "      <td>26.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>9463.00</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>-1963.00</td>\n",
       "      <td>26.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>20141.11</td>\n",
       "      <td>27300.00</td>\n",
       "      <td>7158.89</td>\n",
       "      <td>26.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>40941.00</td>\n",
       "      <td>55500.00</td>\n",
       "      <td>14559.00</td>\n",
       "      <td>26.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>16355.31</td>\n",
       "      <td>12950.00</td>\n",
       "      <td>-3405.31</td>\n",
       "      <td>26.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>11566.75</td>\n",
       "      <td>15700.00</td>\n",
       "      <td>4133.25</td>\n",
       "      <td>26.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>7201.08</td>\n",
       "      <td>5700.00</td>\n",
       "      <td>-1501.08</td>\n",
       "      <td>26.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>64222.22</td>\n",
       "      <td>50800.00</td>\n",
       "      <td>-13422.22</td>\n",
       "      <td>26.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>11401.08</td>\n",
       "      <td>9000.00</td>\n",
       "      <td>-2401.08</td>\n",
       "      <td>26.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>11275.10</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>-2375.10</td>\n",
       "      <td>26.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>12094.72</td>\n",
       "      <td>16500.00</td>\n",
       "      <td>4405.28</td>\n",
       "      <td>26.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>18982.69</td>\n",
       "      <td>25900.00</td>\n",
       "      <td>6917.31</td>\n",
       "      <td>26.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>13286.22</td>\n",
       "      <td>18200.00</td>\n",
       "      <td>4913.78</td>\n",
       "      <td>27.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>8757.05</td>\n",
       "      <td>11999.00</td>\n",
       "      <td>3241.95</td>\n",
       "      <td>27.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>16903.25</td>\n",
       "      <td>13300.00</td>\n",
       "      <td>-3603.25</td>\n",
       "      <td>27.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>12390.75</td>\n",
       "      <td>17000.00</td>\n",
       "      <td>4609.25</td>\n",
       "      <td>27.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>23913.48</td>\n",
       "      <td>18800.00</td>\n",
       "      <td>-5113.48</td>\n",
       "      <td>27.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>75694.66</td>\n",
       "      <td>103999.00</td>\n",
       "      <td>28304.34</td>\n",
       "      <td>27.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>28409.24</td>\n",
       "      <td>39040.00</td>\n",
       "      <td>10630.76</td>\n",
       "      <td>27.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>15909.92</td>\n",
       "      <td>12500.00</td>\n",
       "      <td>-3409.92</td>\n",
       "      <td>27.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>20998.45</td>\n",
       "      <td>28950.00</td>\n",
       "      <td>7951.55</td>\n",
       "      <td>27.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>10844.33</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>-2344.33</td>\n",
       "      <td>27.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>9380.78</td>\n",
       "      <td>12999.00</td>\n",
       "      <td>3618.22</td>\n",
       "      <td>27.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>9380.78</td>\n",
       "      <td>12999.00</td>\n",
       "      <td>3618.22</td>\n",
       "      <td>27.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>15149.74</td>\n",
       "      <td>11850.00</td>\n",
       "      <td>-3299.74</td>\n",
       "      <td>27.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>11463.14</td>\n",
       "      <td>15900.00</td>\n",
       "      <td>4436.86</td>\n",
       "      <td>27.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>24119.99</td>\n",
       "      <td>33500.00</td>\n",
       "      <td>9380.01</td>\n",
       "      <td>28.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>11525.14</td>\n",
       "      <td>8999.00</td>\n",
       "      <td>-2526.14</td>\n",
       "      <td>28.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>21791.60</td>\n",
       "      <td>17000.00</td>\n",
       "      <td>-4791.60</td>\n",
       "      <td>28.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>12558.19</td>\n",
       "      <td>17499.00</td>\n",
       "      <td>4940.81</td>\n",
       "      <td>28.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>12310.96</td>\n",
       "      <td>9599.00</td>\n",
       "      <td>-2711.96</td>\n",
       "      <td>28.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>2366.96</td>\n",
       "      <td>3300.00</td>\n",
       "      <td>933.04</td>\n",
       "      <td>28.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>18279.26</td>\n",
       "      <td>25500.00</td>\n",
       "      <td>7220.74</td>\n",
       "      <td>28.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>3223.18</td>\n",
       "      <td>4500.00</td>\n",
       "      <td>1276.82</td>\n",
       "      <td>28.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>27148.61</td>\n",
       "      <td>37976.25</td>\n",
       "      <td>10827.64</td>\n",
       "      <td>28.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>7582.58</td>\n",
       "      <td>5900.00</td>\n",
       "      <td>-1682.58</td>\n",
       "      <td>28.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>8877.70</td>\n",
       "      <td>6900.00</td>\n",
       "      <td>-1977.70</td>\n",
       "      <td>28.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>8541.14</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>3458.86</td>\n",
       "      <td>28.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>14852.57</td>\n",
       "      <td>20900.00</td>\n",
       "      <td>6047.43</td>\n",
       "      <td>28.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>11153.46</td>\n",
       "      <td>15700.00</td>\n",
       "      <td>4546.54</td>\n",
       "      <td>28.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>3544.57</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>1455.43</td>\n",
       "      <td>29.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2303.65</td>\n",
       "      <td>3250.00</td>\n",
       "      <td>946.35</td>\n",
       "      <td>29.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>16990.24</td>\n",
       "      <td>24000.00</td>\n",
       "      <td>7009.76</td>\n",
       "      <td>29.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>6267.80</td>\n",
       "      <td>4850.00</td>\n",
       "      <td>-1417.80</td>\n",
       "      <td>29.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>6924.83</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>2875.17</td>\n",
       "      <td>29.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>13778.33</td>\n",
       "      <td>10650.00</td>\n",
       "      <td>-3128.33</td>\n",
       "      <td>29.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>23290.66</td>\n",
       "      <td>18000.00</td>\n",
       "      <td>-5290.66</td>\n",
       "      <td>29.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>2462.88</td>\n",
       "      <td>3500.00</td>\n",
       "      <td>1037.12</td>\n",
       "      <td>29.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>16094.42</td>\n",
       "      <td>22900.00</td>\n",
       "      <td>6805.58</td>\n",
       "      <td>29.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>9339.75</td>\n",
       "      <td>7200.00</td>\n",
       "      <td>-2139.75</td>\n",
       "      <td>29.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>23308.62</td>\n",
       "      <td>33200.00</td>\n",
       "      <td>9891.38</td>\n",
       "      <td>29.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>39941.61</td>\n",
       "      <td>57000.00</td>\n",
       "      <td>17058.39</td>\n",
       "      <td>29.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>17294.18</td>\n",
       "      <td>13300.00</td>\n",
       "      <td>-3994.18</td>\n",
       "      <td>30.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>18337.09</td>\n",
       "      <td>14100.00</td>\n",
       "      <td>-4237.09</td>\n",
       "      <td>30.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7027.36</td>\n",
       "      <td>5400.00</td>\n",
       "      <td>-1627.36</td>\n",
       "      <td>30.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>16681.64</td>\n",
       "      <td>12800.00</td>\n",
       "      <td>-3881.64</td>\n",
       "      <td>30.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>8150.20</td>\n",
       "      <td>11700.00</td>\n",
       "      <td>3549.80</td>\n",
       "      <td>30.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>10726.51</td>\n",
       "      <td>15400.00</td>\n",
       "      <td>4673.49</td>\n",
       "      <td>30.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>13037.51</td>\n",
       "      <td>9999.00</td>\n",
       "      <td>-3038.51</td>\n",
       "      <td>30.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>6891.01</td>\n",
       "      <td>9900.00</td>\n",
       "      <td>3008.99</td>\n",
       "      <td>30.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>8589.77</td>\n",
       "      <td>12350.00</td>\n",
       "      <td>3760.23</td>\n",
       "      <td>30.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>47603.85</td>\n",
       "      <td>68500.00</td>\n",
       "      <td>20896.15</td>\n",
       "      <td>30.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>12429.59</td>\n",
       "      <td>17900.00</td>\n",
       "      <td>5470.41</td>\n",
       "      <td>30.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>8397.38</td>\n",
       "      <td>12100.00</td>\n",
       "      <td>3702.62</td>\n",
       "      <td>30.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>7760.78</td>\n",
       "      <td>11200.00</td>\n",
       "      <td>3439.22</td>\n",
       "      <td>30.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>13992.60</td>\n",
       "      <td>10700.00</td>\n",
       "      <td>-3292.60</td>\n",
       "      <td>30.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>51534.01</td>\n",
       "      <td>74500.00</td>\n",
       "      <td>22965.99</td>\n",
       "      <td>30.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>11759.18</td>\n",
       "      <td>17000.00</td>\n",
       "      <td>5240.82</td>\n",
       "      <td>30.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>23040.18</td>\n",
       "      <td>33333.00</td>\n",
       "      <td>10292.82</td>\n",
       "      <td>30.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>37302.87</td>\n",
       "      <td>28500.00</td>\n",
       "      <td>-8802.87</td>\n",
       "      <td>30.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>24534.38</td>\n",
       "      <td>35500.00</td>\n",
       "      <td>10965.62</td>\n",
       "      <td>30.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>19994.54</td>\n",
       "      <td>28999.00</td>\n",
       "      <td>9004.46</td>\n",
       "      <td>31.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>3737.66</td>\n",
       "      <td>2850.00</td>\n",
       "      <td>-887.66</td>\n",
       "      <td>31.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>16161.36</td>\n",
       "      <td>23500.00</td>\n",
       "      <td>7338.64</td>\n",
       "      <td>31.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>6246.23</td>\n",
       "      <td>4750.00</td>\n",
       "      <td>-1496.23</td>\n",
       "      <td>31.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>18031.99</td>\n",
       "      <td>13700.00</td>\n",
       "      <td>-4331.99</td>\n",
       "      <td>31.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>9217.98</td>\n",
       "      <td>6999.00</td>\n",
       "      <td>-2218.98</td>\n",
       "      <td>31.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>23453.42</td>\n",
       "      <td>17800.00</td>\n",
       "      <td>-5653.42</td>\n",
       "      <td>31.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>12915.52</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>-3115.52</td>\n",
       "      <td>31.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>7668.98</td>\n",
       "      <td>5800.00</td>\n",
       "      <td>-1868.98</td>\n",
       "      <td>32.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>47371.35</td>\n",
       "      <td>69900.00</td>\n",
       "      <td>22528.65</td>\n",
       "      <td>32.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>17194.11</td>\n",
       "      <td>12999.00</td>\n",
       "      <td>-4195.11</td>\n",
       "      <td>32.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>24980.48</td>\n",
       "      <td>36900.00</td>\n",
       "      <td>11919.52</td>\n",
       "      <td>32.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>13257.43</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>-3257.43</td>\n",
       "      <td>32.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>7346.16</td>\n",
       "      <td>10900.00</td>\n",
       "      <td>3553.84</td>\n",
       "      <td>32.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>14591.48</td>\n",
       "      <td>21700.00</td>\n",
       "      <td>7108.52</td>\n",
       "      <td>32.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>14141.88</td>\n",
       "      <td>10650.00</td>\n",
       "      <td>-3491.88</td>\n",
       "      <td>32.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>4519.07</td>\n",
       "      <td>3400.00</td>\n",
       "      <td>-1119.07</td>\n",
       "      <td>32.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>3721.61</td>\n",
       "      <td>2800.00</td>\n",
       "      <td>-921.61</td>\n",
       "      <td>32.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>7036.87</td>\n",
       "      <td>10500.00</td>\n",
       "      <td>3463.13</td>\n",
       "      <td>32.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>23279.41</td>\n",
       "      <td>17500.00</td>\n",
       "      <td>-5779.41</td>\n",
       "      <td>33.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>7760.78</td>\n",
       "      <td>11600.00</td>\n",
       "      <td>3839.22</td>\n",
       "      <td>33.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>30276.28</td>\n",
       "      <td>45500.00</td>\n",
       "      <td>15223.72</td>\n",
       "      <td>33.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>2335.84</td>\n",
       "      <td>1750.00</td>\n",
       "      <td>-585.84</td>\n",
       "      <td>33.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>53481.60</td>\n",
       "      <td>40000.00</td>\n",
       "      <td>-13481.60</td>\n",
       "      <td>33.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>8802.32</td>\n",
       "      <td>13300.00</td>\n",
       "      <td>4497.68</td>\n",
       "      <td>33.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2777.76</td>\n",
       "      <td>4200.00</td>\n",
       "      <td>1422.24</td>\n",
       "      <td>33.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>7211.26</td>\n",
       "      <td>10950.00</td>\n",
       "      <td>3738.74</td>\n",
       "      <td>34.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>7573.11</td>\n",
       "      <td>11500.00</td>\n",
       "      <td>3926.89</td>\n",
       "      <td>34.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>7768.52</td>\n",
       "      <td>11800.00</td>\n",
       "      <td>4031.48</td>\n",
       "      <td>34.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>7173.57</td>\n",
       "      <td>10900.00</td>\n",
       "      <td>3726.43</td>\n",
       "      <td>34.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>4468.72</td>\n",
       "      <td>6800.00</td>\n",
       "      <td>2331.28</td>\n",
       "      <td>34.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>3901.31</td>\n",
       "      <td>2900.00</td>\n",
       "      <td>-1001.31</td>\n",
       "      <td>34.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>4320.55</td>\n",
       "      <td>6600.00</td>\n",
       "      <td>2279.45</td>\n",
       "      <td>34.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>7146.52</td>\n",
       "      <td>5300.00</td>\n",
       "      <td>-1846.52</td>\n",
       "      <td>34.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>18561.05</td>\n",
       "      <td>28500.00</td>\n",
       "      <td>9938.95</td>\n",
       "      <td>34.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>19154.03</td>\n",
       "      <td>14200.00</td>\n",
       "      <td>-4954.03</td>\n",
       "      <td>34.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>15942.89</td>\n",
       "      <td>24500.00</td>\n",
       "      <td>8557.11</td>\n",
       "      <td>34.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>11415.51</td>\n",
       "      <td>17595.00</td>\n",
       "      <td>6179.49</td>\n",
       "      <td>35.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>23016.58</td>\n",
       "      <td>35500.00</td>\n",
       "      <td>12483.42</td>\n",
       "      <td>35.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>23016.58</td>\n",
       "      <td>35500.00</td>\n",
       "      <td>12483.42</td>\n",
       "      <td>35.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>15869.43</td>\n",
       "      <td>24500.00</td>\n",
       "      <td>8630.57</td>\n",
       "      <td>35.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>15824.71</td>\n",
       "      <td>11700.00</td>\n",
       "      <td>-4124.71</td>\n",
       "      <td>35.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>9707.85</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>5292.15</td>\n",
       "      <td>35.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>3180.11</td>\n",
       "      <td>2350.00</td>\n",
       "      <td>-830.11</td>\n",
       "      <td>35.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>8712.34</td>\n",
       "      <td>13500.00</td>\n",
       "      <td>4787.66</td>\n",
       "      <td>35.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>3590.16</td>\n",
       "      <td>2650.00</td>\n",
       "      <td>-940.16</td>\n",
       "      <td>35.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>35486.64</td>\n",
       "      <td>55000.00</td>\n",
       "      <td>19513.36</td>\n",
       "      <td>35.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>11829.98</td>\n",
       "      <td>8700.00</td>\n",
       "      <td>-3129.98</td>\n",
       "      <td>35.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>38795.93</td>\n",
       "      <td>28500.00</td>\n",
       "      <td>-10295.93</td>\n",
       "      <td>36.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>8712.34</td>\n",
       "      <td>6400.00</td>\n",
       "      <td>-2312.34</td>\n",
       "      <td>36.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>16066.47</td>\n",
       "      <td>11800.00</td>\n",
       "      <td>-4266.47</td>\n",
       "      <td>36.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>66801.82</td>\n",
       "      <td>104999.00</td>\n",
       "      <td>38197.18</td>\n",
       "      <td>36.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>12697.81</td>\n",
       "      <td>19999.00</td>\n",
       "      <td>7301.19</td>\n",
       "      <td>36.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>7865.18</td>\n",
       "      <td>12399.00</td>\n",
       "      <td>4533.82</td>\n",
       "      <td>36.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>12705.80</td>\n",
       "      <td>9300.00</td>\n",
       "      <td>-3405.80</td>\n",
       "      <td>36.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>8865.99</td>\n",
       "      <td>13999.00</td>\n",
       "      <td>5133.01</td>\n",
       "      <td>36.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>13391.21</td>\n",
       "      <td>21200.00</td>\n",
       "      <td>7808.79</td>\n",
       "      <td>36.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>14037.00</td>\n",
       "      <td>10250.00</td>\n",
       "      <td>-3787.00</td>\n",
       "      <td>36.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>4657.79</td>\n",
       "      <td>7400.00</td>\n",
       "      <td>2742.21</td>\n",
       "      <td>37.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3761.89</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>2238.11</td>\n",
       "      <td>37.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>3711.42</td>\n",
       "      <td>2700.00</td>\n",
       "      <td>-1011.42</td>\n",
       "      <td>37.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>9369.46</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>5630.54</td>\n",
       "      <td>37.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>3989.54</td>\n",
       "      <td>2900.00</td>\n",
       "      <td>-1089.54</td>\n",
       "      <td>37.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>12311.11</td>\n",
       "      <td>19900.00</td>\n",
       "      <td>7588.89</td>\n",
       "      <td>38.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>14653.87</td>\n",
       "      <td>23700.00</td>\n",
       "      <td>9046.13</td>\n",
       "      <td>38.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>6790.78</td>\n",
       "      <td>10999.00</td>\n",
       "      <td>4208.22</td>\n",
       "      <td>38.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>20037.81</td>\n",
       "      <td>32500.00</td>\n",
       "      <td>12462.19</td>\n",
       "      <td>38.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>9616.26</td>\n",
       "      <td>6950.00</td>\n",
       "      <td>-2666.26</td>\n",
       "      <td>38.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>43139.46</td>\n",
       "      <td>70000.00</td>\n",
       "      <td>26860.54</td>\n",
       "      <td>38.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>66409.41</td>\n",
       "      <td>107777.00</td>\n",
       "      <td>41367.59</td>\n",
       "      <td>38.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>14152.95</td>\n",
       "      <td>10200.00</td>\n",
       "      <td>-3952.95</td>\n",
       "      <td>38.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>7517.66</td>\n",
       "      <td>12350.00</td>\n",
       "      <td>4832.34</td>\n",
       "      <td>39.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>9461.10</td>\n",
       "      <td>6800.00</td>\n",
       "      <td>-2661.10</td>\n",
       "      <td>39.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>17820.44</td>\n",
       "      <td>12800.00</td>\n",
       "      <td>-5020.44</td>\n",
       "      <td>39.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>8189.34</td>\n",
       "      <td>13499.00</td>\n",
       "      <td>5309.66</td>\n",
       "      <td>39.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>9088.28</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>5911.72</td>\n",
       "      <td>39.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>27228.47</td>\n",
       "      <td>19500.00</td>\n",
       "      <td>-7728.47</td>\n",
       "      <td>39.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>24580.95</td>\n",
       "      <td>17600.00</td>\n",
       "      <td>-6980.95</td>\n",
       "      <td>39.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>5311.50</td>\n",
       "      <td>3800.00</td>\n",
       "      <td>-1511.50</td>\n",
       "      <td>39.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>4895.80</td>\n",
       "      <td>3500.00</td>\n",
       "      <td>-1395.80</td>\n",
       "      <td>39.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>4195.28</td>\n",
       "      <td>7000.00</td>\n",
       "      <td>2804.72</td>\n",
       "      <td>40.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>36723.90</td>\n",
       "      <td>61500.00</td>\n",
       "      <td>24776.10</td>\n",
       "      <td>40.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>9684.49</td>\n",
       "      <td>6900.00</td>\n",
       "      <td>-2784.49</td>\n",
       "      <td>40.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>14277.16</td>\n",
       "      <td>24000.00</td>\n",
       "      <td>9722.84</td>\n",
       "      <td>40.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>5035.44</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>3464.56</td>\n",
       "      <td>40.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>27842.47</td>\n",
       "      <td>47000.00</td>\n",
       "      <td>19157.53</td>\n",
       "      <td>40.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>2046.25</td>\n",
       "      <td>1450.00</td>\n",
       "      <td>-596.25</td>\n",
       "      <td>41.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>4708.42</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>3291.58</td>\n",
       "      <td>41.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>2471.54</td>\n",
       "      <td>4200.00</td>\n",
       "      <td>1728.46</td>\n",
       "      <td>41.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>12432.10</td>\n",
       "      <td>8800.00</td>\n",
       "      <td>-3632.10</td>\n",
       "      <td>41.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>5279.18</td>\n",
       "      <td>9000.00</td>\n",
       "      <td>3720.82</td>\n",
       "      <td>41.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>44252.81</td>\n",
       "      <td>75500.00</td>\n",
       "      <td>31247.19</td>\n",
       "      <td>41.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>11593.77</td>\n",
       "      <td>8200.00</td>\n",
       "      <td>-3393.77</td>\n",
       "      <td>41.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>20332.54</td>\n",
       "      <td>34899.14</td>\n",
       "      <td>14566.60</td>\n",
       "      <td>41.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>8375.19</td>\n",
       "      <td>14400.00</td>\n",
       "      <td>6024.81</td>\n",
       "      <td>41.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>704</th>\n",
       "      <td>6892.92</td>\n",
       "      <td>11900.00</td>\n",
       "      <td>5007.08</td>\n",
       "      <td>42.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>16178.48</td>\n",
       "      <td>28000.00</td>\n",
       "      <td>11821.52</td>\n",
       "      <td>42.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>18183.57</td>\n",
       "      <td>31500.00</td>\n",
       "      <td>13316.43</td>\n",
       "      <td>42.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>7399.35</td>\n",
       "      <td>12850.00</td>\n",
       "      <td>5450.65</td>\n",
       "      <td>42.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>3103.80</td>\n",
       "      <td>5400.00</td>\n",
       "      <td>2296.20</td>\n",
       "      <td>42.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>7798.87</td>\n",
       "      <td>13600.00</td>\n",
       "      <td>5801.13</td>\n",
       "      <td>42.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>23967.67</td>\n",
       "      <td>16800.00</td>\n",
       "      <td>-7167.67</td>\n",
       "      <td>42.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>8296.06</td>\n",
       "      <td>14500.00</td>\n",
       "      <td>6203.94</td>\n",
       "      <td>42.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>66150.89</td>\n",
       "      <td>115800.00</td>\n",
       "      <td>49649.11</td>\n",
       "      <td>42.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>721</th>\n",
       "      <td>8293.88</td>\n",
       "      <td>5800.00</td>\n",
       "      <td>-2493.88</td>\n",
       "      <td>43.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>7223.06</td>\n",
       "      <td>5050.00</td>\n",
       "      <td>-2173.06</td>\n",
       "      <td>43.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>4497.51</td>\n",
       "      <td>7900.00</td>\n",
       "      <td>3402.49</td>\n",
       "      <td>43.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>47139.99</td>\n",
       "      <td>83000.00</td>\n",
       "      <td>35860.01</td>\n",
       "      <td>43.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>6589.04</td>\n",
       "      <td>4599.00</td>\n",
       "      <td>-1990.04</td>\n",
       "      <td>43.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>15294.35</td>\n",
       "      <td>27000.00</td>\n",
       "      <td>11705.65</td>\n",
       "      <td>43.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>25856.26</td>\n",
       "      <td>46000.00</td>\n",
       "      <td>20143.74</td>\n",
       "      <td>43.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>21585.14</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>-6585.14</td>\n",
       "      <td>43.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>45704.37</td>\n",
       "      <td>81500.00</td>\n",
       "      <td>35795.63</td>\n",
       "      <td>43.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>20700.02</td>\n",
       "      <td>37000.00</td>\n",
       "      <td>16299.98</td>\n",
       "      <td>44.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>39063.12</td>\n",
       "      <td>69999.00</td>\n",
       "      <td>30935.88</td>\n",
       "      <td>44.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>12119.09</td>\n",
       "      <td>8400.00</td>\n",
       "      <td>-3719.09</td>\n",
       "      <td>44.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>13712.02</td>\n",
       "      <td>9500.00</td>\n",
       "      <td>-4212.02</td>\n",
       "      <td>44.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>10825.86</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>-3325.86</td>\n",
       "      <td>44.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>6355.66</td>\n",
       "      <td>4400.00</td>\n",
       "      <td>-1955.66</td>\n",
       "      <td>44.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>9409.51</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>-2909.51</td>\n",
       "      <td>44.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>11460.58</td>\n",
       "      <td>7900.00</td>\n",
       "      <td>-3560.58</td>\n",
       "      <td>45.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>10087.83</td>\n",
       "      <td>6950.00</td>\n",
       "      <td>-3137.83</td>\n",
       "      <td>45.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>24477.28</td>\n",
       "      <td>44700.00</td>\n",
       "      <td>20222.72</td>\n",
       "      <td>45.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>3781.29</td>\n",
       "      <td>2600.00</td>\n",
       "      <td>-1181.29</td>\n",
       "      <td>45.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>14276.79</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>-4476.79</td>\n",
       "      <td>45.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>11650.42</td>\n",
       "      <td>7990.00</td>\n",
       "      <td>-3660.42</td>\n",
       "      <td>45.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>3210.72</td>\n",
       "      <td>2200.00</td>\n",
       "      <td>-1010.72</td>\n",
       "      <td>45.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>2408.79</td>\n",
       "      <td>1650.00</td>\n",
       "      <td>-758.79</td>\n",
       "      <td>45.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>15643.71</td>\n",
       "      <td>29000.00</td>\n",
       "      <td>13356.29</td>\n",
       "      <td>46.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>18733.64</td>\n",
       "      <td>34900.00</td>\n",
       "      <td>16166.36</td>\n",
       "      <td>46.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>12091.55</td>\n",
       "      <td>8250.00</td>\n",
       "      <td>-3841.55</td>\n",
       "      <td>46.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>21074.26</td>\n",
       "      <td>39500.00</td>\n",
       "      <td>18425.74</td>\n",
       "      <td>46.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>4210.43</td>\n",
       "      <td>7900.00</td>\n",
       "      <td>3689.57</td>\n",
       "      <td>46.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2611.28</td>\n",
       "      <td>4900.00</td>\n",
       "      <td>2288.72</td>\n",
       "      <td>46.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>43880.55</td>\n",
       "      <td>29900.00</td>\n",
       "      <td>-13980.55</td>\n",
       "      <td>46.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>66409.41</td>\n",
       "      <td>124999.00</td>\n",
       "      <td>58589.59</td>\n",
       "      <td>46.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>5879.66</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>-1879.66</td>\n",
       "      <td>46.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>2428.27</td>\n",
       "      <td>1650.00</td>\n",
       "      <td>-778.27</td>\n",
       "      <td>47.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>6622.96</td>\n",
       "      <td>4500.00</td>\n",
       "      <td>-2122.96</td>\n",
       "      <td>47.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>20616.50</td>\n",
       "      <td>14000.00</td>\n",
       "      <td>-6616.50</td>\n",
       "      <td>47.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>8804.41</td>\n",
       "      <td>16700.00</td>\n",
       "      <td>7895.59</td>\n",
       "      <td>47.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>1369.29</td>\n",
       "      <td>2600.00</td>\n",
       "      <td>1230.71</td>\n",
       "      <td>47.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>17460.93</td>\n",
       "      <td>11800.00</td>\n",
       "      <td>-5660.93</td>\n",
       "      <td>47.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>44425.51</td>\n",
       "      <td>85555.00</td>\n",
       "      <td>41129.49</td>\n",
       "      <td>48.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>5636.42</td>\n",
       "      <td>3800.00</td>\n",
       "      <td>-1836.42</td>\n",
       "      <td>48.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>11348.91</td>\n",
       "      <td>22000.00</td>\n",
       "      <td>10651.09</td>\n",
       "      <td>48.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>22574.68</td>\n",
       "      <td>15200.00</td>\n",
       "      <td>-7374.68</td>\n",
       "      <td>48.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>15130.36</td>\n",
       "      <td>29500.00</td>\n",
       "      <td>14369.64</td>\n",
       "      <td>48.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>9163.29</td>\n",
       "      <td>18000.00</td>\n",
       "      <td>8836.71</td>\n",
       "      <td>49.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>56748.58</td>\n",
       "      <td>37700.00</td>\n",
       "      <td>-19048.58</td>\n",
       "      <td>50.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>6909.10</td>\n",
       "      <td>14000.00</td>\n",
       "      <td>7090.90</td>\n",
       "      <td>50.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>3207.45</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>3292.55</td>\n",
       "      <td>50.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>5032.02</td>\n",
       "      <td>10200.00</td>\n",
       "      <td>5167.98</td>\n",
       "      <td>50.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>22617.64</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>-7617.64</td>\n",
       "      <td>50.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>60821.82</td>\n",
       "      <td>124000.00</td>\n",
       "      <td>63178.18</td>\n",
       "      <td>50.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5849.20</td>\n",
       "      <td>11999.00</td>\n",
       "      <td>6149.80</td>\n",
       "      <td>51.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>21938.58</td>\n",
       "      <td>14500.00</td>\n",
       "      <td>-7438.58</td>\n",
       "      <td>51.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>31647.69</td>\n",
       "      <td>64999.00</td>\n",
       "      <td>33351.31</td>\n",
       "      <td>51.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>9920.69</td>\n",
       "      <td>20400.00</td>\n",
       "      <td>10479.31</td>\n",
       "      <td>51.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>2953.85</td>\n",
       "      <td>1950.00</td>\n",
       "      <td>-1003.85</td>\n",
       "      <td>51.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>5254.91</td>\n",
       "      <td>10900.00</td>\n",
       "      <td>5645.09</td>\n",
       "      <td>51.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>30340.56</td>\n",
       "      <td>63000.00</td>\n",
       "      <td>32659.44</td>\n",
       "      <td>51.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>3494.61</td>\n",
       "      <td>2300.00</td>\n",
       "      <td>-1194.61</td>\n",
       "      <td>51.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>22827.39</td>\n",
       "      <td>47777.00</td>\n",
       "      <td>24949.61</td>\n",
       "      <td>52.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>17202.44</td>\n",
       "      <td>11300.00</td>\n",
       "      <td>-5902.44</td>\n",
       "      <td>52.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>10968.71</td>\n",
       "      <td>7200.00</td>\n",
       "      <td>-3768.71</td>\n",
       "      <td>52.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>19098.43</td>\n",
       "      <td>12500.00</td>\n",
       "      <td>-6598.43</td>\n",
       "      <td>52.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>7573.11</td>\n",
       "      <td>4950.00</td>\n",
       "      <td>-2623.11</td>\n",
       "      <td>52.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>2296.58</td>\n",
       "      <td>4900.00</td>\n",
       "      <td>2603.42</td>\n",
       "      <td>53.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>7661.36</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>-2661.36</td>\n",
       "      <td>53.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>7056.33</td>\n",
       "      <td>4600.00</td>\n",
       "      <td>-2456.33</td>\n",
       "      <td>53.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>18275.16</td>\n",
       "      <td>11900.00</td>\n",
       "      <td>-6375.16</td>\n",
       "      <td>53.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>14315.96</td>\n",
       "      <td>30900.00</td>\n",
       "      <td>16584.04</td>\n",
       "      <td>53.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>23855.51</td>\n",
       "      <td>15500.00</td>\n",
       "      <td>-8355.51</td>\n",
       "      <td>53.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>2438.38</td>\n",
       "      <td>5400.00</td>\n",
       "      <td>2961.62</td>\n",
       "      <td>54.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>25652.67</td>\n",
       "      <td>57000.00</td>\n",
       "      <td>31347.33</td>\n",
       "      <td>55.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>13971.79</td>\n",
       "      <td>9000.00</td>\n",
       "      <td>-4971.79</td>\n",
       "      <td>55.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>4816.05</td>\n",
       "      <td>3100.00</td>\n",
       "      <td>-1716.05</td>\n",
       "      <td>55.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>33650.38</td>\n",
       "      <td>75500.00</td>\n",
       "      <td>41849.62</td>\n",
       "      <td>55.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>19739.81</td>\n",
       "      <td>12700.00</td>\n",
       "      <td>-7039.81</td>\n",
       "      <td>55.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>9025.30</td>\n",
       "      <td>5800.00</td>\n",
       "      <td>-3225.30</td>\n",
       "      <td>55.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>7858.74</td>\n",
       "      <td>17777.00</td>\n",
       "      <td>9918.26</td>\n",
       "      <td>55.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>7858.74</td>\n",
       "      <td>17777.00</td>\n",
       "      <td>9918.26</td>\n",
       "      <td>55.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>9509.57</td>\n",
       "      <td>6100.00</td>\n",
       "      <td>-3409.57</td>\n",
       "      <td>55.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>38986.34</td>\n",
       "      <td>24990.00</td>\n",
       "      <td>-13996.34</td>\n",
       "      <td>56.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>7233.14</td>\n",
       "      <td>4599.00</td>\n",
       "      <td>-2634.14</td>\n",
       "      <td>57.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>35512.33</td>\n",
       "      <td>85000.00</td>\n",
       "      <td>49487.67</td>\n",
       "      <td>58.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>18941.10</td>\n",
       "      <td>11962.00</td>\n",
       "      <td>-6979.10</td>\n",
       "      <td>58.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>14764.47</td>\n",
       "      <td>35500.00</td>\n",
       "      <td>20735.53</td>\n",
       "      <td>58.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>5230.69</td>\n",
       "      <td>3300.00</td>\n",
       "      <td>-1930.69</td>\n",
       "      <td>58.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>31591.02</td>\n",
       "      <td>19900.00</td>\n",
       "      <td>-11691.02</td>\n",
       "      <td>58.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>37323.42</td>\n",
       "      <td>23500.00</td>\n",
       "      <td>-13823.42</td>\n",
       "      <td>58.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>967.03</td>\n",
       "      <td>2350.00</td>\n",
       "      <td>1382.97</td>\n",
       "      <td>58.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>22303.95</td>\n",
       "      <td>13999.00</td>\n",
       "      <td>-8304.95</td>\n",
       "      <td>59.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>20422.49</td>\n",
       "      <td>12800.00</td>\n",
       "      <td>-7622.49</td>\n",
       "      <td>59.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>4790.15</td>\n",
       "      <td>2999.00</td>\n",
       "      <td>-1791.15</td>\n",
       "      <td>59.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>20020.44</td>\n",
       "      <td>49999.00</td>\n",
       "      <td>29978.56</td>\n",
       "      <td>59.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>2306.08</td>\n",
       "      <td>5900.00</td>\n",
       "      <td>3593.92</td>\n",
       "      <td>60.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>12397.83</td>\n",
       "      <td>32000.00</td>\n",
       "      <td>19602.17</td>\n",
       "      <td>61.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>10890.61</td>\n",
       "      <td>6750.00</td>\n",
       "      <td>-4140.61</td>\n",
       "      <td>61.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>43139.46</td>\n",
       "      <td>112000.00</td>\n",
       "      <td>68860.54</td>\n",
       "      <td>61.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>15708.19</td>\n",
       "      <td>9700.00</td>\n",
       "      <td>-6008.19</td>\n",
       "      <td>61.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>27879.65</td>\n",
       "      <td>17200.00</td>\n",
       "      <td>-10679.65</td>\n",
       "      <td>62.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>20922.34</td>\n",
       "      <td>55555.00</td>\n",
       "      <td>34632.66</td>\n",
       "      <td>62.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>15596.87</td>\n",
       "      <td>9600.00</td>\n",
       "      <td>-5996.87</td>\n",
       "      <td>62.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>8457.60</td>\n",
       "      <td>5200.00</td>\n",
       "      <td>-3257.60</td>\n",
       "      <td>62.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>13056.85</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>-5056.85</td>\n",
       "      <td>63.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>19632.18</td>\n",
       "      <td>55555.00</td>\n",
       "      <td>35922.82</td>\n",
       "      <td>64.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>43948.84</td>\n",
       "      <td>125000.00</td>\n",
       "      <td>81051.16</td>\n",
       "      <td>64.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>36037.78</td>\n",
       "      <td>102800.00</td>\n",
       "      <td>66762.22</td>\n",
       "      <td>64.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>8048.15</td>\n",
       "      <td>4850.00</td>\n",
       "      <td>-3198.15</td>\n",
       "      <td>65.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>5476.42</td>\n",
       "      <td>3300.00</td>\n",
       "      <td>-2176.42</td>\n",
       "      <td>65.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>41238.77</td>\n",
       "      <td>121500.00</td>\n",
       "      <td>80261.23</td>\n",
       "      <td>66.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2779.20</td>\n",
       "      <td>8200.00</td>\n",
       "      <td>5420.80</td>\n",
       "      <td>66.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>40241.51</td>\n",
       "      <td>119000.00</td>\n",
       "      <td>78758.49</td>\n",
       "      <td>66.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>11552.21</td>\n",
       "      <td>6950.00</td>\n",
       "      <td>-4602.21</td>\n",
       "      <td>66.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>38785.36</td>\n",
       "      <td>115000.00</td>\n",
       "      <td>76214.64</td>\n",
       "      <td>66.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>14820.49</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>-5920.49</td>\n",
       "      <td>66.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>6994.65</td>\n",
       "      <td>4200.00</td>\n",
       "      <td>-2794.65</td>\n",
       "      <td>66.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>8440.68</td>\n",
       "      <td>4999.00</td>\n",
       "      <td>-3441.68</td>\n",
       "      <td>68.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>12270.14</td>\n",
       "      <td>7200.00</td>\n",
       "      <td>-5070.14</td>\n",
       "      <td>70.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>6494.68</td>\n",
       "      <td>3799.00</td>\n",
       "      <td>-2695.68</td>\n",
       "      <td>70.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>4038.29</td>\n",
       "      <td>15500.00</td>\n",
       "      <td>11461.71</td>\n",
       "      <td>73.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>24194.96</td>\n",
       "      <td>13900.00</td>\n",
       "      <td>-10294.96</td>\n",
       "      <td>74.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>1741.53</td>\n",
       "      <td>999.00</td>\n",
       "      <td>-742.53</td>\n",
       "      <td>74.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6803.03</td>\n",
       "      <td>3900.00</td>\n",
       "      <td>-2903.03</td>\n",
       "      <td>74.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>4447.98</td>\n",
       "      <td>18000.00</td>\n",
       "      <td>13552.02</td>\n",
       "      <td>75.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2391.07</td>\n",
       "      <td>1350.00</td>\n",
       "      <td>-1041.07</td>\n",
       "      <td>77.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5757.28</td>\n",
       "      <td>3250.00</td>\n",
       "      <td>-2507.28</td>\n",
       "      <td>77.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>6743.11</td>\n",
       "      <td>3800.00</td>\n",
       "      <td>-2943.11</td>\n",
       "      <td>77.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>11071.15</td>\n",
       "      <td>6200.00</td>\n",
       "      <td>-4871.15</td>\n",
       "      <td>78.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>2515.64</td>\n",
       "      <td>1400.00</td>\n",
       "      <td>-1115.64</td>\n",
       "      <td>79.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>15358.78</td>\n",
       "      <td>8300.00</td>\n",
       "      <td>-7058.78</td>\n",
       "      <td>85.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>37006.69</td>\n",
       "      <td>19990.00</td>\n",
       "      <td>-17016.69</td>\n",
       "      <td>85.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>14684.85</td>\n",
       "      <td>7900.00</td>\n",
       "      <td>-6784.85</td>\n",
       "      <td>85.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>2982.74</td>\n",
       "      <td>1600.00</td>\n",
       "      <td>-1382.74</td>\n",
       "      <td>86.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40419.31</td>\n",
       "      <td>21300.00</td>\n",
       "      <td>-19119.31</td>\n",
       "      <td>89.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>11128.82</td>\n",
       "      <td>5850.00</td>\n",
       "      <td>-5278.82</td>\n",
       "      <td>90.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>20063.44</td>\n",
       "      <td>10500.00</td>\n",
       "      <td>-9563.44</td>\n",
       "      <td>91.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>12091.55</td>\n",
       "      <td>6299.00</td>\n",
       "      <td>-5792.55</td>\n",
       "      <td>91.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>20983.51</td>\n",
       "      <td>10900.00</td>\n",
       "      <td>-10083.51</td>\n",
       "      <td>92.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>16665.92</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>-8165.92</td>\n",
       "      <td>96.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>7485.91</td>\n",
       "      <td>3800.00</td>\n",
       "      <td>-3685.91</td>\n",
       "      <td>97.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>4143.08</td>\n",
       "      <td>2100.00</td>\n",
       "      <td>-2043.08</td>\n",
       "      <td>97.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>3061.88</td>\n",
       "      <td>1550.00</td>\n",
       "      <td>-1511.88</td>\n",
       "      <td>97.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>5255.20</td>\n",
       "      <td>2650.00</td>\n",
       "      <td>-2605.20</td>\n",
       "      <td>98.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>12577.96</td>\n",
       "      <td>6300.00</td>\n",
       "      <td>-6277.96</td>\n",
       "      <td>99.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>13929.91</td>\n",
       "      <td>6900.00</td>\n",
       "      <td>-7029.91</td>\n",
       "      <td>101.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>6120.60</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>-3120.60</td>\n",
       "      <td>104.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>10912.99</td>\n",
       "      <td>5300.00</td>\n",
       "      <td>-5612.99</td>\n",
       "      <td>105.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>19852.93</td>\n",
       "      <td>9500.00</td>\n",
       "      <td>-10352.93</td>\n",
       "      <td>108.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>10547.04</td>\n",
       "      <td>4999.00</td>\n",
       "      <td>-5548.04</td>\n",
       "      <td>110.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>14570.59</td>\n",
       "      <td>6799.00</td>\n",
       "      <td>-7771.59</td>\n",
       "      <td>114.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>14051.83</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>-7551.83</td>\n",
       "      <td>116.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>4892.64</td>\n",
       "      <td>2200.00</td>\n",
       "      <td>-2692.64</td>\n",
       "      <td>122.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>46693.89</td>\n",
       "      <td>20500.00</td>\n",
       "      <td>-26193.89</td>\n",
       "      <td>127.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>16622.29</td>\n",
       "      <td>7200.00</td>\n",
       "      <td>-9422.29</td>\n",
       "      <td>130.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>10740.12</td>\n",
       "      <td>4500.00</td>\n",
       "      <td>-6240.12</td>\n",
       "      <td>138.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>21855.86</td>\n",
       "      <td>9000.00</td>\n",
       "      <td>-12855.86</td>\n",
       "      <td>142.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>9559.08</td>\n",
       "      <td>3799.00</td>\n",
       "      <td>-5760.08</td>\n",
       "      <td>151.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>22752.26</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>-13852.26</td>\n",
       "      <td>155.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>19919.18</td>\n",
       "      <td>7700.00</td>\n",
       "      <td>-12219.18</td>\n",
       "      <td>158.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>7319.10</td>\n",
       "      <td>2800.00</td>\n",
       "      <td>-4519.10</td>\n",
       "      <td>161.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>6544.84</td>\n",
       "      <td>2450.00</td>\n",
       "      <td>-4094.84</td>\n",
       "      <td>167.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>6152.48</td>\n",
       "      <td>2300.00</td>\n",
       "      <td>-3852.48</td>\n",
       "      <td>167.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>16171.29</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>-10171.29</td>\n",
       "      <td>169.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>30472.10</td>\n",
       "      <td>11299.00</td>\n",
       "      <td>-19173.10</td>\n",
       "      <td>169.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>7290.89</td>\n",
       "      <td>2600.00</td>\n",
       "      <td>-4690.89</td>\n",
       "      <td>180.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>9818.75</td>\n",
       "      <td>3500.00</td>\n",
       "      <td>-6318.75</td>\n",
       "      <td>180.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>10134.87</td>\n",
       "      <td>3500.00</td>\n",
       "      <td>-6634.87</td>\n",
       "      <td>189.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>9795.81</td>\n",
       "      <td>3350.00</td>\n",
       "      <td>-6445.81</td>\n",
       "      <td>192.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>18200.62</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>-12200.62</td>\n",
       "      <td>203.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>12684.91</td>\n",
       "      <td>4100.00</td>\n",
       "      <td>-8584.91</td>\n",
       "      <td>209.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>7238.80</td>\n",
       "      <td>2200.00</td>\n",
       "      <td>-5038.80</td>\n",
       "      <td>229.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>7910.62</td>\n",
       "      <td>2400.00</td>\n",
       "      <td>-5510.62</td>\n",
       "      <td>229.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>12616.19</td>\n",
       "      <td>3700.00</td>\n",
       "      <td>-8916.19</td>\n",
       "      <td>240.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4124.58</td>\n",
       "      <td>1200.00</td>\n",
       "      <td>-2924.58</td>\n",
       "      <td>243.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>12327.86</td>\n",
       "      <td>3100.00</td>\n",
       "      <td>-9227.86</td>\n",
       "      <td>297.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18012.80</td>\n",
       "      <td>4200.00</td>\n",
       "      <td>-13812.80</td>\n",
       "      <td>328.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>46426.82</td>\n",
       "      <td>10600.00</td>\n",
       "      <td>-35826.82</td>\n",
       "      <td>337.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>16422.31</td>\n",
       "      <td>3600.00</td>\n",
       "      <td>-12822.31</td>\n",
       "      <td>356.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>11410.67</td>\n",
       "      <td>2500.00</td>\n",
       "      <td>-8910.67</td>\n",
       "      <td>356.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>10572.99</td>\n",
       "      <td>2300.00</td>\n",
       "      <td>-8272.99</td>\n",
       "      <td>359.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>31429.04</td>\n",
       "      <td>6800.00</td>\n",
       "      <td>-24629.04</td>\n",
       "      <td>362.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>11913.91</td>\n",
       "      <td>2300.00</td>\n",
       "      <td>-9613.91</td>\n",
       "      <td>418.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>33893.01</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>-27393.01</td>\n",
       "      <td>421.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>20887.21</td>\n",
       "      <td>3500.00</td>\n",
       "      <td>-17387.21</td>\n",
       "      <td>496.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>13941.93</td>\n",
       "      <td>2200.00</td>\n",
       "      <td>-11741.93</td>\n",
       "      <td>533.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>39469.51</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>-34669.51</td>\n",
       "      <td>722.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>27632.40</td>\n",
       "      <td>2900.00</td>\n",
       "      <td>-24732.40</td>\n",
       "      <td>852.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>30076.59</td>\n",
       "      <td>3150.00</td>\n",
       "      <td>-26926.59</td>\n",
       "      <td>854.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>45259.02</td>\n",
       "      <td>2400.00</td>\n",
       "      <td>-42859.02</td>\n",
       "      <td>1785.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Predictions   Targets  Residuals  Difference%\n",
       "387     13602.75  13600.00      -2.75         0.02\n",
       "74      28889.06  28900.00      10.94         0.04\n",
       "314     35530.21  35500.00     -30.21         0.09\n",
       "336     11313.27  11300.00     -13.27         0.12\n",
       "425      2995.68   3000.00       4.32         0.14\n",
       "262     52180.11  52055.25    -124.86         0.24\n",
       "674      3107.51   3100.00      -7.51         0.24\n",
       "648      9675.57   9700.00      24.43         0.25\n",
       "79      21407.59  21335.00     -72.59         0.34\n",
       "313      9865.72   9900.00      34.28         0.35\n",
       "16      12257.04  12300.00      42.96         0.35\n",
       "549      9040.90   9000.00     -40.90         0.45\n",
       "581     11554.52  11500.00     -54.52         0.47\n",
       "671     42728.70  42500.00    -228.70         0.54\n",
       "250      6736.49   6700.00     -36.49         0.54\n",
       "456      3878.09   3900.00      21.91         0.56\n",
       "563      8151.45   8200.00      48.55         0.59\n",
       "113      5033.58   5000.00     -33.58         0.67\n",
       "529     32158.13  32400.00     241.87         0.75\n",
       "145     18543.19  18700.00     156.81         0.84\n",
       "480     18888.88  18700.00    -188.88         1.01\n",
       "263     16820.70  17000.00     179.30         1.05\n",
       "520      5460.40   5400.00     -60.40         1.12\n",
       "534      7077.79   6999.00     -78.79         1.13\n",
       "436      1530.62   1550.00      19.38         1.25\n",
       "453     12358.46  12200.00    -158.46         1.30\n",
       "667     37798.37  38300.00     501.63         1.31\n",
       "102     16772.48  16999.00     226.52         1.33\n",
       "291     12626.83  12800.00     173.17         1.35\n",
       "753     17742.21  17500.00    -242.21         1.38\n",
       "464     12318.70  12500.00     181.30         1.45\n",
       "756      6497.81   6400.00     -97.81         1.53\n",
       "22      26438.30  25999.00    -439.30         1.69\n",
       "177     10311.04  10500.00     188.96         1.80\n",
       "130      5203.59   5300.00      96.41         1.82\n",
       "349     22290.65  22711.65     421.00         1.85\n",
       "50       6571.82   6699.00     127.18         1.90\n",
       "726     75324.97  73900.00   -1424.97         1.93\n",
       "95       7353.63   7500.00     146.37         1.95\n",
       "359     12645.69  12900.00     254.31         1.97\n",
       "476     12770.59  12499.00    -271.59         2.17\n",
       "509     41965.41  42900.00     934.59         2.18\n",
       "302     19026.21  19500.00     473.79         2.43\n",
       "727      8560.18   8350.00    -210.18         2.52\n",
       "149     16978.04  16555.00    -423.04         2.56\n",
       "264      8939.67   8700.00    -239.67         2.75\n",
       "133      6474.90   6300.00    -174.90         2.78\n",
       "572      9038.89   9300.00     261.11         2.81\n",
       "468      6785.47   6600.00    -185.47         2.81\n",
       "191      6891.01   6700.00    -191.01         2.85\n",
       "715     22335.41  23000.00     664.59         2.89\n",
       "278      2815.34   2900.00      84.66         2.92\n",
       "766     13899.37  13499.00    -400.37         2.97\n",
       "487      4325.31   4200.00    -125.31         2.98\n",
       "744     12506.72  12900.00     393.28         3.05\n",
       "658      4067.27   4200.00     132.73         3.16\n",
       "224     11148.59  10800.00    -348.59         3.23\n",
       "481     29841.46  28900.00    -941.46         3.26\n",
       "49      13630.20  13200.00    -430.20         3.26\n",
       "553      9296.69   9000.00    -296.69         3.30\n",
       "281     18270.67  18900.00     629.33         3.33\n",
       "156      7246.61   7500.00     253.39         3.38\n",
       "409      6722.21   6500.00    -222.21         3.42\n",
       "657     18346.18  18999.00     652.82         3.44\n",
       "492      5785.33   6000.00     214.67         3.58\n",
       "590     21107.44  21900.00     792.56         3.62\n",
       "226     19687.35  18999.00    -688.35         3.62\n",
       "577     45597.17  44000.00   -1597.17         3.63\n",
       "770      9123.07   8800.00    -323.07         3.67\n",
       "92      25818.91  24900.00    -918.91         3.69\n",
       "27      10062.08   9700.00    -362.08         3.73\n",
       "89       7698.56   8000.00     301.44         3.77\n",
       "85      18578.60  17900.00    -678.60         3.79\n",
       "438     23081.50  24000.00     918.50         3.83\n",
       "60      43510.26  41900.00   -1610.26         3.84\n",
       "421     12982.56  12500.00    -482.56         3.86\n",
       "358     27394.78  28500.00    1105.22         3.88\n",
       "151     12872.08  13400.00     527.92         3.94\n",
       "39       5759.79   6000.00     240.21         4.00\n",
       "122      9986.72   9599.00    -387.72         4.04\n",
       "544      2654.86   2550.00    -104.86         4.11\n",
       "478      3978.93   4150.00     171.07         4.12\n",
       "423     35102.95  33700.00   -1402.95         4.16\n",
       "422     24725.89  25800.00    1074.11         4.16\n",
       "732     57292.95  55000.00   -2292.95         4.17\n",
       "624      4896.50   4700.00    -196.50         4.18\n",
       "47       9533.79   9950.00     416.21         4.18\n",
       "569     54120.02  56500.00    2379.98         4.21\n",
       "441      7712.65   7400.00    -312.65         4.22\n",
       "725     13305.00  13900.00     595.00         4.28\n",
       "116      7547.39   7899.00     351.61         4.45\n",
       "305     16742.87  16000.00    -742.87         4.64\n",
       "147     41862.20  39999.00   -1863.20         4.66\n",
       "69      13441.93  14100.00     658.07         4.67\n",
       "603     21877.35  20900.00    -977.35         4.68\n",
       "461     14105.89  14800.00     694.11         4.69\n",
       "301      6956.59   7300.00     343.41         4.70\n",
       "181      3979.88   3800.00    -179.88         4.73\n",
       "497      4285.09   4500.00     214.91         4.78\n",
       "532     17126.96  18000.00     873.04         4.85\n",
       "379     29490.39  31000.00    1509.61         4.87\n",
       "160     36047.48  37900.00    1852.52         4.89\n",
       "170     11122.47  10600.00    -522.47         4.93\n",
       "75       6558.66   6899.00     340.34         4.93\n",
       "457     20800.89  21900.00    1099.11         5.02\n",
       "596     24173.42  23000.00   -1173.42         5.10\n",
       "55      14928.85  14200.00    -728.85         5.13\n",
       "48       9884.36   9400.00    -484.36         5.15\n",
       "288      9358.79   8900.00    -458.79         5.15\n",
       "561     16120.81  16999.00     878.19         5.17\n",
       "639     14126.94  14900.00     773.06         5.19\n",
       "424     12586.88  11960.00    -626.88         5.24\n",
       "680     29841.46  31500.00    1658.54         5.27\n",
       "344     53508.88  56500.00    2991.12         5.29\n",
       "111     11371.78  10800.00    -571.78         5.29\n",
       "323     23665.06  24999.00    1333.94         5.34\n",
       "350     41579.85  44000.00    2420.15         5.50\n",
       "96      22166.00  21000.00   -1166.00         5.55\n",
       "543     23026.53  21800.00   -1226.53         5.63\n",
       "692     20274.26  21500.00    1225.74         5.70\n",
       "162     52776.07  56000.00    3223.93         5.76\n",
       "644     19588.09  18500.00   -1088.09         5.88\n",
       "609     13355.89  12600.00    -755.89         6.00\n",
       "33      18418.31  19600.00    1181.69         6.03\n",
       "200      9438.41   8900.00    -538.41         6.05\n",
       "735     42995.33  40500.00   -2495.33         6.16\n",
       "293     45344.25  42700.00   -2644.25         6.19\n",
       "723      7500.49   8000.00     499.51         6.24\n",
       "616      6006.61   5650.00    -356.61         6.31\n",
       "24      17795.63  19000.00    1204.37         6.34\n",
       "126     22452.40  24000.00    1547.60         6.45\n",
       "182     10184.22  10900.00     715.78         6.57\n",
       "722      8527.59   8000.00    -527.59         6.59\n",
       "608      8873.27   9500.00     626.73         6.60\n",
       "670     17365.29  18600.00    1234.71         6.64\n",
       "369     16003.49  14999.00   -1004.49         6.70\n",
       "689     13900.52  14900.00     999.48         6.71\n",
       "215     20175.24  18900.00   -1275.24         6.75\n",
       "760     17842.82  16700.00   -1142.82         6.84\n",
       "505      4282.13   4000.00    -282.13         7.05\n",
       "659      5357.57   5000.00    -357.57         7.15\n",
       "270     11787.08  11000.00    -787.08         7.16\n",
       "622     10514.46   9800.00    -714.46         7.29\n",
       "527      5280.18   5700.00     419.82         7.37\n",
       "512      7859.03   7300.00    -559.03         7.66\n",
       "245     25851.88  24000.00   -1851.88         7.72\n",
       "697      8014.08   8700.00     685.92         7.88\n",
       "184      8195.24   8900.00     704.76         7.92\n",
       "14       6719.36   7300.00     580.64         7.95\n",
       "625     33376.16  30900.00   -2476.16         8.01\n",
       "677      8969.71   8300.00    -669.71         8.07\n",
       "125     13301.82  14490.00    1188.18         8.20\n",
       "440      8889.26   9700.00     810.74         8.36\n",
       "597      9211.40   8500.00    -711.40         8.37\n",
       "5       16586.98  15300.00   -1286.98         8.41\n",
       "586      6864.69   7500.00     635.31         8.47\n",
       "611     11719.69  10800.00    -919.69         8.52\n",
       "471     24176.66  26500.00    2323.34         8.77\n",
       "229     15669.90  17200.00    1530.10         8.90\n",
       "690     43216.46  47500.00    4283.54         9.02\n",
       "541     21810.04  20000.00   -1810.04         9.05\n",
       "693      2545.91   2800.00     254.09         9.07\n",
       "634      9258.05  10200.00     941.95         9.23\n",
       "637     10923.58   9999.00    -924.58         9.25\n",
       "632     19485.38  21500.00    2014.62         9.37\n",
       "84      32503.84  35900.00    3396.16         9.46\n",
       "410      6105.45   6750.00     644.55         9.55\n",
       "641      9943.62  10999.00    1055.38         9.60\n",
       "99      10194.96   9300.00    -894.96         9.62\n",
       "594     14821.23  13500.00   -1321.23         9.79\n",
       "171     17673.56  19600.00    1926.44         9.83\n",
       "602     10547.68  11700.00    1152.32         9.85\n",
       "57      25686.36  28500.00    2813.64         9.87\n",
       "104      6705.44   6100.00    -605.44         9.93\n",
       "447     13746.52  12500.00   -1246.52         9.97\n",
       "393      8468.54   7700.00    -768.54         9.98\n",
       "473     13701.64  15250.00    1548.36        10.15\n",
       "493     11787.08  10700.00   -1087.08        10.16\n",
       "121     18941.10  17191.20   -1749.90        10.18\n",
       "268     30527.47  34000.00    3472.53        10.21\n",
       "300      5386.17   6000.00     613.83        10.23\n",
       "317      5831.36   6500.00     668.64        10.29\n",
       "610     16742.87  18700.00    1957.13        10.47\n",
       "1        7956.90   8900.00     943.10        10.60\n",
       "166     34173.14  38233.00    4059.86        10.62\n",
       "322      7966.77   7200.00    -766.77        10.65\n",
       "651     16601.37  15000.00   -1601.37        10.68\n",
       "326     35698.51  40000.00    4301.49        10.75\n",
       "701      8640.42   7800.00    -840.42        10.77\n",
       "724     57076.55  51500.00   -5576.55        10.83\n",
       "518      4324.27   3900.00    -424.27        10.88\n",
       "269      3162.93   2850.00    -312.93        10.98\n",
       "642     22201.06  19999.00   -2202.06        11.01\n",
       "415      8898.00  10000.00    1102.00        11.02\n",
       "494     19654.05  17700.00   -1954.05        11.04\n",
       "252     59933.58  67500.00    7566.42        11.21\n",
       "195      7901.16   8900.00     998.84        11.22\n",
       "762      1779.94   1600.00    -179.94        11.25\n",
       "172     17746.12  20000.00    2253.88        11.27\n",
       "214     22605.01  25500.00    2894.99        11.35\n",
       "213     57356.68  51500.00   -5856.68        11.37\n",
       "7        8150.20   9200.00    1049.80        11.41\n",
       "742     53508.88  60500.00    6991.12        11.56\n",
       "558     14589.79  16500.00    1910.21        11.58\n",
       "407      6538.61   7400.00     861.39        11.64\n",
       "548     19326.96  17300.00   -2026.96        11.72\n",
       "479      6479.57   5800.00    -679.57        11.72\n",
       "390     11911.57  13500.00    1588.43        11.77\n",
       "576      9881.64  11200.00    1318.36        11.77\n",
       "324     10061.69   9000.00   -1061.69        11.80\n",
       "165     40974.12  46500.00    5525.88        11.88\n",
       "662     12952.43  14700.00    1747.57        11.89\n",
       "769      4699.59   4200.00    -499.59        11.89\n",
       "44      30215.35  26999.00   -3216.35        11.91\n",
       "217      6869.97   7800.00     930.03        11.92\n",
       "633      2742.63   2450.00    -292.63        11.94\n",
       "287     14783.38  13200.00   -1583.38        12.00\n",
       "583      7840.41   6999.00    -841.41        12.02\n",
       "66       2804.12   2500.00    -304.12        12.16\n",
       "67       2368.86   2700.00     331.14        12.26\n",
       "352      7858.74   7000.00    -858.74        12.27\n",
       "575      8059.79   9200.00    1140.21        12.39\n",
       "389     15126.06  17300.00    2173.94        12.57\n",
       "631      9519.93  10900.00    1380.07        12.66\n",
       "399     10597.08   9400.00   -1197.08        12.73\n",
       "620      2922.53   3350.00     427.47        12.76\n",
       "454     13066.91  15000.00    1933.09        12.89\n",
       "364      7574.29   8700.00    1125.71        12.94\n",
       "516     13334.46  11800.00   -1534.46        13.00\n",
       "451      9381.12   8300.00   -1081.12        13.03\n",
       "757     21618.71  19100.00   -2518.71        13.19\n",
       "745      2491.59   2200.00    -291.59        13.25\n",
       "73      12551.44  14500.00    1948.56        13.44\n",
       "472      7788.25   9000.00    1211.75        13.46\n",
       "328     12149.99  10700.00   -1449.99        13.55\n",
       "554     32524.17  28600.00   -3924.17        13.72\n",
       "31      10604.69  12300.00    1695.31        13.78\n",
       "702     34834.36  40500.00    5665.64        13.99\n",
       "475      7466.24   8700.00    1233.76        14.18\n",
       "466      8276.70   7245.00   -1031.70        14.24\n",
       "559      8830.01  10299.00    1468.99        14.26\n",
       "71      19495.26  22800.00    3304.74        14.49\n",
       "530      4354.13   5100.00     745.87        14.62\n",
       "216      6307.69   5500.00    -807.69        14.69\n",
       "299     12455.28  14600.00    2144.72        14.69\n",
       "256     10236.99  12000.00    1763.01        14.69\n",
       "123     13552.84  11800.00   -1752.84        14.85\n",
       "167      8385.53   7300.00   -1085.53        14.87\n",
       "416      4939.63   4300.00    -639.63        14.88\n",
       "444      6969.39   8200.00    1230.61        15.01\n",
       "600      7939.74   6900.00   -1039.74        15.07\n",
       "773      9244.00  10900.00    1656.00        15.19\n",
       "148     26384.26  31111.00    4726.74        15.19\n",
       "719     22840.15  19800.00   -3040.15        15.35\n",
       "198     11584.18  13708.50    2124.32        15.50\n",
       "398      8026.52   9500.00    1473.48        15.51\n",
       "259     28899.37  25000.00   -3899.37        15.60\n",
       "309     18563.77  21999.00    3435.23        15.62\n",
       "117      7758.12   9200.00    1441.88        15.67\n",
       "736      8431.47  10000.00    1568.53        15.69\n",
       "362     31179.56  37000.00    5820.44        15.73\n",
       "752      8840.69  10500.00    1659.31        15.80\n",
       "206     31873.48  27500.00   -4373.48        15.90\n",
       "606      2774.95   3300.00     525.05        15.91\n",
       "296      2840.83   2450.00    -390.83        15.95\n",
       "374      8656.08  10300.00    1643.92        15.96\n",
       "500     17686.53  15250.00   -2436.53        15.98\n",
       "489     40490.79  34900.00   -5590.79        16.02\n",
       "54      20397.29  24300.00    3902.71        16.06\n",
       "15       2138.99   2550.00     411.01        16.12\n",
       "683      2904.84   2500.00    -404.84        16.19\n",
       "94      14890.32  17800.00    2909.68        16.35\n",
       "68      26665.38  22900.00   -3765.38        16.44\n",
       "51       8334.59   7150.00   -1184.59        16.57\n",
       "743     16229.18  13900.00   -2329.18        16.76\n",
       "591     62376.39  75000.00   12623.61        16.83\n",
       "395     48958.22  41900.00   -7058.22        16.85\n",
       "411      6319.31   7600.00    1280.69        16.85\n",
       "507     33323.71  28500.00   -4823.71        16.93\n",
       "101     10014.72  12090.00    2075.28        17.17\n",
       "135      4553.35   5500.00     946.65        17.21\n",
       "97      15196.84  12950.00   -2246.84        17.35\n",
       "348     18139.19  21950.00    3810.81        17.36\n",
       "413      6196.82   7500.00    1303.18        17.38\n",
       "329      3521.38   3000.00    -521.38        17.38\n",
       "334     53771.50  65099.00   11327.50        17.40\n",
       "373      4128.62   5000.00     871.38        17.43\n",
       "428     20640.25  25000.00    4359.75        17.44\n",
       "619     13466.18  11450.00   -2016.18        17.61\n",
       "645      9057.98   7700.00   -1357.98        17.64\n",
       "699      7983.18   9700.00    1716.82        17.70\n",
       "676      5918.73   7200.00    1281.27        17.80\n",
       "283     15323.65  13000.00   -2323.65        17.87\n",
       "696      9608.71  11700.00    2091.29        17.87\n",
       "612     15208.55  12900.00   -2308.55        17.90\n",
       "169      7899.67   6700.00   -1199.67        17.91\n",
       "81      20999.37  17800.00   -3199.37        17.97\n",
       "653     19836.68  16800.00   -3036.68        18.08\n",
       "566     11049.53  13500.00    2450.47        18.15\n",
       "178     28555.16  34900.00    6344.84        18.18\n",
       "86       8917.37  10900.00    1982.63        18.19\n",
       "426      1633.01   2000.00     366.99        18.35\n",
       "711     15103.04  18500.00    3396.96        18.36\n",
       "218      4323.82   5300.00     976.18        18.42\n",
       "227      2446.44   3000.00     553.56        18.45\n",
       "346      3341.48   4100.00     758.52        18.50\n",
       "419     10668.76   8999.00   -1669.76        18.56\n",
       "712     10015.66  12300.00    2284.34        18.57\n",
       "137     47322.46  39900.00   -7422.46        18.60\n",
       "367     10378.24   8750.00   -1628.24        18.61\n",
       "3        9765.81  12000.00    2234.19        18.62\n",
       "370      5931.31   5000.00    -931.31        18.63\n",
       "321     10618.43   8950.00   -1668.43        18.64\n",
       "443     10679.28   9000.00   -1679.28        18.66\n",
       "187     11036.95   9300.00   -1736.95        18.68\n",
       "418     11543.90  14200.00    2656.10        18.70\n",
       "751     23337.70  28713.75    5376.05        18.72\n",
       "201      6661.85   8200.00    1538.15        18.76\n",
       "730     11768.26   9900.00   -1868.26        18.87\n",
       "682     12371.38  10400.00   -1971.38        18.96\n",
       "408      5916.22   7300.00    1383.78        18.96\n",
       "687      8925.91   7500.00   -1425.91        19.01\n",
       "124     16425.19  20300.00    3874.81        19.09\n",
       "332      2586.93   3200.00     613.07        19.16\n",
       "320     10488.85   8800.00   -1688.85        19.19\n",
       "560      3150.92   3900.00     749.08        19.21\n",
       "593     22539.73  27900.00    5360.27        19.21\n",
       "231     17770.44  22000.00    4229.56        19.23\n",
       "199     53771.50  66666.00   12894.50        19.34\n",
       "311     11681.84  14500.00    2818.16        19.44\n",
       "486     10997.23   9200.00   -1797.23        19.54\n",
       "115      4906.83   4100.00    -806.83        19.68\n",
       "118     23588.84  19700.00   -3888.84        19.74\n",
       "758      4648.32   5800.00    1151.68        19.86\n",
       "397      4562.65   5700.00    1137.35        19.95\n",
       "25       3588.56   2990.00    -598.56        20.02\n",
       "120      5761.38   4800.00    -961.38        20.03\n",
       "255     11914.81  14899.00    2984.19        20.03\n",
       "501      7752.06   9700.00    1947.94        20.08\n",
       "539      2764.13   2300.00    -464.13        20.18\n",
       "46      14123.09  11750.00   -2373.09        20.20\n",
       "108     12045.24  10000.00   -2045.24        20.45\n",
       "174     65166.43  81999.00   16832.57        20.53\n",
       "607      8053.79  10200.00    2146.21        21.04\n",
       "220     22452.40  28500.00    6047.60        21.22\n",
       "43       7471.65   9500.00    2028.35        21.35\n",
       "378      6919.67   5700.00   -1219.67        21.40\n",
       "98      22400.05  28500.00    6099.95        21.40\n",
       "114      8625.18   7100.00   -1525.18        21.48\n",
       "132     18442.10  23500.00    5057.90        21.52\n",
       "754     50926.23  65000.00   14073.77        21.65\n",
       "688     26404.64  21700.00   -4704.64        21.68\n",
       "290     37188.32  47500.00   10311.68        21.71\n",
       "535     43418.52  55500.00   12081.48        21.77\n",
       "64       2618.11   2150.00    -468.11        21.77\n",
       "450     11936.30   9800.00   -2136.30        21.80\n",
       "517     10061.21   8260.00   -1801.21        21.81\n",
       "746     16400.49  20999.00    4598.51        21.90\n",
       "498      8197.59  10500.00    2302.41        21.93\n",
       "656     14642.59  12000.00   -2642.59        22.02\n",
       "212     45597.17  58500.00   12902.83        22.06\n",
       "506      3099.74   4000.00     900.26        22.51\n",
       "412      7718.67   6300.00   -1418.67        22.52\n",
       "772     20906.13  27000.00    6093.87        22.57\n",
       "458      3248.04   4200.00     951.96        22.67\n",
       "557      8580.60  11100.00    2519.40        22.70\n",
       "649     13507.61  10999.00   -2508.61        22.81\n",
       "186      2699.49   3500.00     800.51        22.87\n",
       "173     11440.36   9300.00   -2140.36        23.01\n",
       "571     11994.88   9750.00   -2244.88        23.02\n",
       "80       7875.05   6400.00   -1475.05        23.05\n",
       "728      4799.48   3900.00    -899.48        23.06\n",
       "360     26705.21  21700.00   -5005.21        23.07\n",
       "380     21524.61  28000.00    6475.39        23.13\n",
       "233     30661.17  39900.00    9238.83        23.15\n",
       "112     10372.36  13500.00    3127.64        23.17\n",
       "401      6506.25   8500.00    1993.75        23.46\n",
       "190     10090.75  13200.00    3109.25        23.55\n",
       "242      4963.93   6500.00    1536.07        23.63\n",
       "463     13341.93  17499.00    4157.07        23.76\n",
       "158     51534.01  67622.55   16088.54        23.79\n",
       "144      6321.33   8300.00    1978.67        23.84\n",
       "698     15992.34  21000.00    5007.66        23.85\n",
       "604      7691.17  10100.00    2408.83        23.85\n",
       "279     12868.90  16900.00    4031.10        23.85\n",
       "9       79026.35 104000.00   24973.65        24.01\n",
       "714      9057.98   7300.00   -1757.98        24.08\n",
       "19      10670.54   8599.00   -2071.54        24.09\n",
       "511      5312.04   6999.00    1686.96        24.10\n",
       "241     36037.78  47600.00   11562.22        24.29\n",
       "275     29121.98  38500.00    9378.02        24.36\n",
       "189      8713.76   7000.00   -1713.76        24.48\n",
       "433     53771.50  43163.25  -10608.25        24.58\n",
       "312     12050.92   9658.94   -2391.98        24.76\n",
       "18       7492.94   6000.00   -1492.94        24.88\n",
       "703     15488.91  12400.00   -3088.91        24.91\n",
       "524     27923.11  37200.00    9276.89        24.94\n",
       "307      5375.96   4300.00   -1075.96        25.02\n",
       "538     15264.36  12200.00   -3064.36        25.12\n",
       "377     14222.59  18999.00    4776.41        25.14\n",
       "238     23825.56  31900.00    8074.44        25.31\n",
       "672      2164.01   2900.00     735.99        25.38\n",
       "685      2508.03   2000.00    -508.03        25.40\n",
       "523      6631.54   8900.00    2268.46        25.49\n",
       "38       5146.54   4100.00   -1046.54        25.53\n",
       "330     13396.77  17999.00    4602.23        25.57\n",
       "207      3419.97   4600.00    1180.03        25.65\n",
       "435     15937.99  21500.00    5562.01        25.87\n",
       "627     24420.21  33000.00    8579.79        26.00\n",
       "203     27094.57  21500.00   -5594.57        26.02\n",
       "77      15518.70  21000.00    5481.30        26.10\n",
       "437     49198.51  39000.00  -10198.51        26.15\n",
       "469      9463.00   7500.00   -1963.00        26.17\n",
       "768     20141.11  27300.00    7158.89        26.22\n",
       "8       40941.00  55500.00   14559.00        26.23\n",
       "445     16355.31  12950.00   -3405.31        26.30\n",
       "109     11566.75  15700.00    4133.25        26.33\n",
       "150      7201.08   5700.00   -1501.08        26.33\n",
       "185     64222.22  50800.00  -13422.22        26.42\n",
       "274     11401.08   9000.00   -2401.08        26.68\n",
       "488     11275.10   8900.00   -2375.10        26.69\n",
       "26      12094.72  16500.00    4405.28        26.70\n",
       "257     18982.69  25900.00    6917.31        26.71\n",
       "405     13286.22  18200.00    4913.78        27.00\n",
       "718      8757.05  11999.00    3241.95        27.02\n",
       "271     16903.25  13300.00   -3603.25        27.09\n",
       "567     12390.75  17000.00    4609.25        27.11\n",
       "351     23913.48  18800.00   -5113.48        27.20\n",
       "341     75694.66 103999.00   28304.34        27.22\n",
       "222     28409.24  39040.00   10630.76        27.23\n",
       "396     15909.92  12500.00   -3409.92        27.28\n",
       "131     20998.45  28950.00    7951.55        27.47\n",
       "508     10844.33   8500.00   -2344.33        27.58\n",
       "37       9380.78  12999.00    3618.22        27.83\n",
       "298      9380.78  12999.00    3618.22        27.83\n",
       "536     15149.74  11850.00   -3299.74        27.85\n",
       "654     11463.14  15900.00    4436.86        27.90\n",
       "183     24119.99  33500.00    9380.01        28.00\n",
       "194     11525.14   8999.00   -2526.14        28.07\n",
       "531     21791.60  17000.00   -4791.60        28.19\n",
       "202     12558.19  17499.00    4940.81        28.23\n",
       "205     12310.96   9599.00   -2711.96        28.25\n",
       "574      2366.96   3300.00     933.04        28.27\n",
       "709     18279.26  25500.00    7220.74        28.32\n",
       "372      3223.18   4500.00    1276.82        28.37\n",
       "598     27148.61  37976.25   10827.64        28.51\n",
       "755      7582.58   5900.00   -1682.58        28.52\n",
       "595      8877.70   6900.00   -1977.70        28.66\n",
       "646      8541.14  12000.00    3458.86        28.82\n",
       "310     14852.57  20900.00    6047.43        28.94\n",
       "164     11153.46  15700.00    4546.54        28.96\n",
       "432      3544.57   5000.00    1455.43        29.11\n",
       "28       2303.65   3250.00     946.35        29.12\n",
       "316     16990.24  24000.00    7009.76        29.21\n",
       "771      6267.80   4850.00   -1417.80        29.23\n",
       "61       6924.83   9800.00    2875.17        29.34\n",
       "42      13778.33  10650.00   -3128.33        29.37\n",
       "315     23290.66  18000.00   -5290.66        29.39\n",
       "129      2462.88   3500.00    1037.12        29.63\n",
       "737     16094.42  22900.00    6805.58        29.72\n",
       "510      9339.75   7200.00   -2139.75        29.72\n",
       "136     23308.62  33200.00    9891.38        29.79\n",
       "82      39941.61  57000.00   17058.39        29.93\n",
       "235     17294.18  13300.00   -3994.18        30.03\n",
       "392     18337.09  14100.00   -4237.09        30.05\n",
       "4        7027.36   5400.00   -1627.36        30.14\n",
       "455     16681.64  12800.00   -3881.64        30.33\n",
       "152      8150.20  11700.00    3549.80        30.34\n",
       "733     10726.51  15400.00    4673.49        30.35\n",
       "371     13037.51   9999.00   -3038.51        30.39\n",
       "21       6891.01   9900.00    3008.99        30.39\n",
       "403      8589.77  12350.00    3760.23        30.45\n",
       "587     47603.85  68500.00   20896.15        30.51\n",
       "356     12429.59  17900.00    5470.41        30.56\n",
       "503      8397.38  12100.00    3702.62        30.60\n",
       "427      7760.78  11200.00    3439.22        30.71\n",
       "579     13992.60  10700.00   -3292.60        30.77\n",
       "545     51534.01  74500.00   22965.99        30.83\n",
       "519     11759.18  17000.00    5240.82        30.83\n",
       "337     23040.18  33333.00   10292.82        30.88\n",
       "45      37302.87  28500.00   -8802.87        30.89\n",
       "211     24534.38  35500.00   10965.62        30.89\n",
       "605     19994.54  28999.00    9004.46        31.05\n",
       "284      3737.66   2850.00    -887.66        31.15\n",
       "40      16161.36  23500.00    7338.64        31.23\n",
       "63       6246.23   4750.00   -1496.23        31.50\n",
       "34      18031.99  13700.00   -4331.99        31.62\n",
       "679      9217.98   6999.00   -2218.98        31.70\n",
       "705     23453.42  17800.00   -5653.42        31.76\n",
       "366     12915.52   9800.00   -3115.52        31.79\n",
       "708      7668.98   5800.00   -1868.98        32.22\n",
       "276     47371.35  69900.00   22528.65        32.23\n",
       "253     17194.11  12999.00   -4195.11        32.27\n",
       "289     24980.48  36900.00   11919.52        32.30\n",
       "297     13257.43  10000.00   -3257.43        32.57\n",
       "430      7346.16  10900.00    3553.84        32.60\n",
       "266     14591.48  21700.00    7108.52        32.76\n",
       "628     14141.88  10650.00   -3491.88        32.79\n",
       "394      4519.07   3400.00   -1119.07        32.91\n",
       "223      3721.61   2800.00    -921.61        32.91\n",
       "640      7036.87  10500.00    3463.13        32.98\n",
       "319     23279.41  17500.00   -5779.41        33.03\n",
       "542      7760.78  11600.00    3839.22        33.10\n",
       "647     30276.28  45500.00   15223.72        33.46\n",
       "308      2335.84   1750.00    -585.84        33.48\n",
       "515     53481.60  40000.00  -13481.60        33.70\n",
       "564      8802.32  13300.00    4497.68        33.82\n",
       "248      2777.76   4200.00    1422.24        33.86\n",
       "570      7211.26  10950.00    3738.74        34.14\n",
       "429      7573.11  11500.00    3926.89        34.15\n",
       "197      7768.52  11800.00    4031.48        34.17\n",
       "107      7173.57  10900.00    3726.43        34.19\n",
       "338      4468.72   6800.00    2331.28        34.28\n",
       "761      3901.31   2900.00   -1001.31        34.53\n",
       "483      4320.55   6600.00    2279.45        34.54\n",
       "72       7146.52   5300.00   -1846.52        34.84\n",
       "504     18561.05  28500.00    9938.95        34.87\n",
       "65      19154.03  14200.00   -4954.03        34.89\n",
       "470     15942.89  24500.00    8557.11        34.93\n",
       "141     11415.51  17595.00    6179.49        35.12\n",
       "76      23016.58  35500.00   12483.42        35.16\n",
       "582     23016.58  35500.00   12483.42        35.16\n",
       "106     15869.43  24500.00    8630.57        35.23\n",
       "91      15824.71  11700.00   -4124.71        35.25\n",
       "382      9707.85  15000.00    5292.15        35.28\n",
       "484      3180.11   2350.00    -830.11        35.32\n",
       "434      8712.34  13500.00    4787.66        35.46\n",
       "176      3590.16   2650.00    -940.16        35.48\n",
       "477     35486.64  55000.00   19513.36        35.48\n",
       "664     11829.98   8700.00   -3129.98        35.98\n",
       "78      38795.93  28500.00  -10295.93        36.13\n",
       "286      8712.34   6400.00   -2312.34        36.13\n",
       "442     16066.47  11800.00   -4266.47        36.16\n",
       "675     66801.82 104999.00   38197.18        36.38\n",
       "230     12697.81  19999.00    7301.19        36.51\n",
       "196      7865.18  12399.00    4533.82        36.57\n",
       "110     12705.80   9300.00   -3405.80        36.62\n",
       "431      8865.99  13999.00    5133.01        36.67\n",
       "555     13391.21  21200.00    7808.79        36.83\n",
       "103     14037.00  10250.00   -3787.00        36.95\n",
       "83       4657.79   7400.00    2742.21        37.06\n",
       "13       3761.89   6000.00    2238.11        37.30\n",
       "127      3711.42   2700.00   -1011.42        37.46\n",
       "365      9369.46  15000.00    5630.54        37.54\n",
       "546      3989.54   2900.00   -1089.54        37.57\n",
       "578     12311.11  19900.00    7588.89        38.14\n",
       "355     14653.87  23700.00    9046.13        38.17\n",
       "490      6790.78  10999.00    4208.22        38.26\n",
       "460     20037.81  32500.00   12462.19        38.35\n",
       "306      9616.26   6950.00   -2666.26        38.36\n",
       "347     43139.46  70000.00   26860.54        38.37\n",
       "695     66409.41 107777.00   41367.59        38.38\n",
       "146     14152.95  10200.00   -3952.95        38.75\n",
       "159      7517.66  12350.00    4832.34        39.13\n",
       "765      9461.10   6800.00   -2661.10        39.13\n",
       "357     17820.44  12800.00   -5020.44        39.22\n",
       "384      8189.34  13499.00    5309.66        39.33\n",
       "449      9088.28  15000.00    5911.72        39.41\n",
       "340     27228.47  19500.00   -7728.47        39.63\n",
       "342     24580.95  17600.00   -6980.95        39.66\n",
       "400      5311.50   3800.00   -1511.50        39.78\n",
       "155      4895.80   3500.00   -1395.80        39.88\n",
       "240      4195.28   7000.00    2804.72        40.07\n",
       "661     36723.90  61500.00   24776.10        40.29\n",
       "741      9684.49   6900.00   -2784.49        40.35\n",
       "232     14277.16  24000.00    9722.84        40.51\n",
       "526      5035.44   8500.00    3464.56        40.76\n",
       "540     27842.47  47000.00   19157.53        40.76\n",
       "142      2046.25   1450.00    -596.25        41.12\n",
       "139      4708.42   8000.00    3291.58        41.14\n",
       "660      2471.54   4200.00    1728.46        41.15\n",
       "128     12432.10   8800.00   -3632.10        41.27\n",
       "93       5279.18   9000.00    3720.82        41.34\n",
       "385     44252.81  75500.00   31247.19        41.39\n",
       "168     11593.77   8200.00   -3393.77        41.39\n",
       "588     20332.54  34899.14   14566.60        41.74\n",
       "228      8375.19  14400.00    6024.81        41.84\n",
       "704      6892.92  11900.00    5007.08        42.08\n",
       "749     16178.48  28000.00   11821.52        42.22\n",
       "673     18183.57  31500.00   13316.43        42.27\n",
       "630      7399.35  12850.00    5450.65        42.42\n",
       "303      3103.80   5400.00    2296.20        42.52\n",
       "134      7798.87  13600.00    5801.13        42.66\n",
       "550     23967.67  16800.00   -7167.67        42.66\n",
       "759      8296.06  14500.00    6203.94        42.79\n",
       "614     66150.89 115800.00   49649.11        42.87\n",
       "721      8293.88   5800.00   -2493.88        43.00\n",
       "496      7223.06   5050.00   -2173.06        43.03\n",
       "446      4497.51   7900.00    3402.49        43.07\n",
       "295     47139.99  83000.00   35860.01        43.20\n",
       "294      6589.04   4599.00   -1990.04        43.27\n",
       "764     15294.35  27000.00   11705.65        43.35\n",
       "368     25856.26  46000.00   20143.74        43.79\n",
       "589     21585.14  15000.00   -6585.14        43.90\n",
       "485     45704.37  81500.00   35795.63        43.92\n",
       "462     20700.02  37000.00   16299.98        44.05\n",
       "193     39063.12  69999.00   30935.88        44.19\n",
       "20      12119.09   8400.00   -3719.09        44.27\n",
       "686     13712.02   9500.00   -4212.02        44.34\n",
       "499     10825.86   7500.00   -3325.86        44.34\n",
       "402      6355.66   4400.00   -1955.66        44.45\n",
       "52       9409.51   6500.00   -2909.51        44.76\n",
       "154     11460.58   7900.00   -3560.58        45.07\n",
       "247     10087.83   6950.00   -3137.83        45.15\n",
       "448     24477.28  44700.00   20222.72        45.24\n",
       "763      3781.29   2600.00   -1181.29        45.43\n",
       "467     14276.79   9800.00   -4476.79        45.68\n",
       "533     11650.42   7990.00   -3660.42        45.81\n",
       "706      3210.72   2200.00   -1010.72        45.94\n",
       "353      2408.79   1650.00    -758.79        45.99\n",
       "580     15643.71  29000.00   13356.29        46.06\n",
       "618     18733.64  34900.00   16166.36        46.32\n",
       "439     12091.55   8250.00   -3841.55        46.56\n",
       "32      21074.26  39500.00   18425.74        46.65\n",
       "613      4210.43   7900.00    3689.57        46.70\n",
       "35       2611.28   4900.00    2288.72        46.71\n",
       "565     43880.55  29900.00  -13980.55        46.76\n",
       "663     66409.41 124999.00   58589.59        46.87\n",
       "599      5879.66   4000.00   -1879.66        46.99\n",
       "668      2428.27   1650.00    -778.27        47.17\n",
       "738      6622.96   4500.00   -2122.96        47.18\n",
       "138     20616.50  14000.00   -6616.50        47.26\n",
       "225      8804.41  16700.00    7895.59        47.28\n",
       "652      1369.29   2600.00    1230.71        47.34\n",
       "56      17460.93  11800.00   -5660.93        47.97\n",
       "643     44425.51  85555.00   41129.49        48.07\n",
       "474      5636.42   3800.00   -1836.42        48.33\n",
       "694     11348.91  22000.00   10651.09        48.41\n",
       "617     22574.68  15200.00   -7374.68        48.52\n",
       "325     15130.36  29500.00   14369.64        48.71\n",
       "273      9163.29  18000.00    8836.71        49.09\n",
       "584     56748.58  37700.00  -19048.58        50.53\n",
       "292      6909.10  14000.00    7090.90        50.65\n",
       "716      3207.45   6500.00    3292.55        50.65\n",
       "237      5032.02  10200.00    5167.98        50.67\n",
       "258     22617.64  15000.00   -7617.64        50.78\n",
       "29      60821.82 124000.00   63178.18        50.95\n",
       "23       5849.20  11999.00    6149.80        51.25\n",
       "638     21938.58  14500.00   -7438.58        51.30\n",
       "251     31647.69  64999.00   33351.31        51.31\n",
       "459      9920.69  20400.00   10479.31        51.37\n",
       "495      2953.85   1950.00   -1003.85        51.48\n",
       "333      5254.91  10900.00    5645.09        51.79\n",
       "244     30340.56  63000.00   32659.44        51.84\n",
       "650      3494.61   2300.00   -1194.61        51.94\n",
       "282     22827.39  47777.00   24949.61        52.22\n",
       "404     17202.44  11300.00   -5902.44        52.23\n",
       "277     10968.71   7200.00   -3768.71        52.34\n",
       "521     19098.43  12500.00   -6598.43        52.79\n",
       "381      7573.11   4950.00   -2623.11        52.99\n",
       "731      2296.58   4900.00    2603.42        53.13\n",
       "552      7661.36   5000.00   -2661.36        53.23\n",
       "209      7056.33   4600.00   -2456.33        53.40\n",
       "53      18275.16  11900.00   -6375.16        53.57\n",
       "655     14315.96  30900.00   16584.04        53.67\n",
       "192     23855.51  15500.00   -8355.51        53.91\n",
       "210      2438.38   5400.00    2961.62        54.84\n",
       "621     25652.67  57000.00   31347.33        55.00\n",
       "87      13971.79   9000.00   -4971.79        55.24\n",
       "707      4816.05   3100.00   -1716.05        55.36\n",
       "243     33650.38  75500.00   41849.62        55.43\n",
       "188     19739.81  12700.00   -7039.81        55.43\n",
       "119      9025.30   5800.00   -3225.30        55.61\n",
       "417      7858.74  17777.00    9918.26        55.79\n",
       "318      7858.74  17777.00    9918.26        55.79\n",
       "740      9509.57   6100.00   -3409.57        55.89\n",
       "204     38986.34  24990.00  -13996.34        56.01\n",
       "304      7233.14   4599.00   -2634.14        57.28\n",
       "62      35512.33  85000.00   49487.67        58.22\n",
       "601     18941.10  11962.00   -6979.10        58.34\n",
       "354     14764.47  35500.00   20735.53        58.41\n",
       "547      5230.69   3300.00   -1930.69        58.51\n",
       "375     31591.02  19900.00  -11691.02        58.75\n",
       "175     37323.42  23500.00  -13823.42        58.82\n",
       "615       967.03   2350.00    1382.97        58.85\n",
       "537     22303.95  13999.00   -8304.95        59.33\n",
       "734     20422.49  12800.00   -7622.49        59.55\n",
       "383      4790.15   2999.00   -1791.15        59.72\n",
       "105     20020.44  49999.00   29978.56        59.96\n",
       "514      2306.08   5900.00    3593.92        60.91\n",
       "254     12397.83  32000.00   19602.17        61.26\n",
       "208     10890.61   6750.00   -4140.61        61.34\n",
       "573     43139.46 112000.00   68860.54        61.48\n",
       "452     15708.19   9700.00   -6008.19        61.94\n",
       "261     27879.65  17200.00  -10679.65        62.09\n",
       "265     20922.34  55555.00   34632.66        62.34\n",
       "90      15596.87   9600.00   -5996.87        62.47\n",
       "41       8457.60   5200.00   -3257.60        62.65\n",
       "767     13056.85   8000.00   -5056.85        63.21\n",
       "100     19632.18  55555.00   35922.82        64.66\n",
       "267     43948.84 125000.00   81051.16        64.84\n",
       "88      36037.78 102800.00   66762.22        64.94\n",
       "246      8048.15   4850.00   -3198.15        65.94\n",
       "414      5476.42   3300.00   -2176.42        65.95\n",
       "153     41238.77 121500.00   80261.23        66.06\n",
       "12       2779.20   8200.00    5420.80        66.11\n",
       "140     40241.51 119000.00   78758.49        66.18\n",
       "684     11552.21   6950.00   -4602.21        66.22\n",
       "739     38785.36 115000.00   76214.64        66.27\n",
       "157     14820.49   8900.00   -5920.49        66.52\n",
       "748      6994.65   4200.00   -2794.65        66.54\n",
       "420      8440.68   4999.00   -3441.68        68.85\n",
       "236     12270.14   7200.00   -5070.14        70.42\n",
       "568      6494.68   3799.00   -2695.68        70.96\n",
       "635      4038.29  15500.00   11461.71        73.95\n",
       "376     24194.96  13900.00  -10294.96        74.06\n",
       "525      1741.53    999.00    -742.53        74.33\n",
       "143      6803.03   3900.00   -2903.03        74.44\n",
       "180      4447.98  18000.00   13552.02        75.29\n",
       "17       2391.07   1350.00   -1041.07        77.12\n",
       "2        5757.28   3250.00   -2507.28        77.15\n",
       "592      6743.11   3800.00   -2943.11        77.45\n",
       "522     11071.15   6200.00   -4871.15        78.57\n",
       "335      2515.64   1400.00   -1115.64        79.69\n",
       "665     15358.78   8300.00   -7058.78        85.05\n",
       "386     37006.69  19990.00  -17016.69        85.13\n",
       "327     14684.85   7900.00   -6784.85        85.88\n",
       "234      2982.74   1600.00   -1382.74        86.42\n",
       "0       40419.31  21300.00  -19119.31        89.76\n",
       "482     11128.82   5850.00   -5278.82        90.24\n",
       "720     20063.44  10500.00   -9563.44        91.08\n",
       "678     12091.55   6299.00   -5792.55        91.96\n",
       "666     20983.51  10900.00  -10083.51        92.51\n",
       "221     16665.92   8500.00   -8165.92        96.07\n",
       "391      7485.91   3800.00   -3685.91        97.00\n",
       "59       4143.08   2100.00   -2043.08        97.29\n",
       "551      3061.88   1550.00   -1511.88        97.54\n",
       "36       5255.20   2650.00   -2605.20        98.31\n",
       "491     12577.96   6300.00   -6277.96        99.65\n",
       "179     13929.91   6900.00   -7029.91       101.88\n",
       "361      6120.60   3000.00   -3120.60       104.02\n",
       "626     10912.99   5300.00   -5612.99       105.91\n",
       "502     19852.93   9500.00  -10352.93       108.98\n",
       "406     10547.04   4999.00   -5548.04       110.98\n",
       "285     14570.59   6799.00   -7771.59       114.30\n",
       "70      14051.83   6500.00   -7551.83       116.18\n",
       "343      4892.64   2200.00   -2692.64       122.39\n",
       "239     46693.89  20500.00  -26193.89       127.78\n",
       "363     16622.29   7200.00   -9422.29       130.87\n",
       "629     10740.12   4500.00   -6240.12       138.67\n",
       "623     21855.86   9000.00  -12855.86       142.84\n",
       "345      9559.08   3799.00   -5760.08       151.62\n",
       "11      22752.26   8900.00  -13852.26       155.64\n",
       "717     19919.18   7700.00  -12219.18       158.69\n",
       "161      7319.10   2800.00   -4519.10       161.40\n",
       "465      6544.84   2450.00   -4094.84       167.14\n",
       "249      6152.48   2300.00   -3852.48       167.50\n",
       "272     16171.29   6000.00  -10171.29       169.52\n",
       "528     30472.10  11299.00  -19173.10       169.69\n",
       "513      7290.89   2600.00   -4690.89       180.42\n",
       "669      9818.75   3500.00   -6318.75       180.54\n",
       "163     10134.87   3500.00   -6634.87       189.57\n",
       "636      9795.81   3350.00   -6445.81       192.41\n",
       "691     18200.62   6000.00  -12200.62       203.34\n",
       "710     12684.91   4100.00   -8584.91       209.39\n",
       "331      7238.80   2200.00   -5038.80       229.04\n",
       "30       7910.62   2400.00   -5510.62       229.61\n",
       "729     12616.19   3700.00   -8916.19       240.98\n",
       "10       4124.58   1200.00   -2924.58       243.71\n",
       "585     12327.86   3100.00   -9227.86       297.67\n",
       "6       18012.80   4200.00  -13812.80       328.88\n",
       "388     46426.82  10600.00  -35826.82       337.99\n",
       "700     16422.31   3600.00  -12822.31       356.18\n",
       "339     11410.67   2500.00   -8910.67       356.43\n",
       "747     10572.99   2300.00   -8272.99       359.70\n",
       "562     31429.04   6800.00  -24629.04       362.19\n",
       "58      11913.91   2300.00   -9613.91       418.00\n",
       "260     33893.01   6500.00  -27393.01       421.43\n",
       "556     20887.21   3500.00  -17387.21       496.78\n",
       "713     13941.93   2200.00  -11741.93       533.72\n",
       "280     39469.51   4800.00  -34669.51       722.28\n",
       "219     27632.40   2900.00  -24732.40       852.84\n",
       "750     30076.59   3150.00  -26926.59       854.81\n",
       "681     45259.02   2400.00  -42859.02      1785.79"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_rows=999\n",
    "pd.set_option(\"display.float_format\", lambda x: \"%.2f\" % x)\n",
    "df_pf.sort_values(by=[\"Difference%\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
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 },
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