Linear regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practical Example Of Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Raw Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data = pd.read_csv(\"1.04. Real-life example.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "      <td>count</td>\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",
       "      <td>unique</td>\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",
       "      <td>top</td>\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",
       "      <td>freq</td>\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",
       "      <td>mean</td>\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",
       "      <td>std</td>\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",
       "      <td>min</td>\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",
       "      <td>25%</td>\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",
       "      <td>50%</td>\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",
       "      <td>75%</td>\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",
       "      <td>max</td>\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Detrmine The Variable Of Intrest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = raw_data.drop(['Model'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <td>count</td>\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",
       "      <td>unique</td>\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",
       "      <td>top</td>\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",
       "      <td>freq</td>\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",
       "      <td>mean</td>\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",
       "      <td>std</td>\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",
       "      <td>min</td>\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",
       "      <td>25%</td>\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",
       "      <td>50%</td>\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",
       "      <td>75%</td>\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",
       "      <td>max</td>\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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dealing With Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_no_mv = data.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
       "      <td>count</td>\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",
       "      <td>unique</td>\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",
       "      <td>top</td>\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",
       "      <td>freq</td>\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",
       "      <td>mean</td>\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",
       "      <td>std</td>\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",
       "      <td>min</td>\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",
       "      <td>25%</td>\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",
       "      <td>50%</td>\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",
       "      <td>75%</td>\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",
       "      <td>max</td>\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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_no_mv.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brand           0\n",
       "Price           0\n",
       "Body            0\n",
       "Mileage         0\n",
       "EngineV         0\n",
       "Engine Type     0\n",
       "Registration    0\n",
       "Year            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_no_mv.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring the PDF's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ce20d289c8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_no_mv['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
       "      <td>count</td>\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",
       "      <td>unique</td>\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",
       "      <td>top</td>\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",
       "      <td>freq</td>\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",
       "      <td>mean</td>\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",
       "      <td>std</td>\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",
       "      <td>min</td>\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",
       "      <td>25%</td>\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",
       "      <td>50%</td>\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",
       "      <td>75%</td>\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",
       "      <td>max</td>\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": 12,
     "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ce210dae48>"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_1['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ce211d5dc8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_no_mv['Mileage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ce212cfe08>"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "q = data_no_mv['Mileage'].quantile(0.99)\n",
    "data_2 = data_no_mv[data_no_mv['Mileage']<q]\n",
    "sns.distplot(data_2['Mileage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ce21376dc8>"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_no_mv['EngineV'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_3 = data_2[data_2['EngineV']<6.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ce21474c08>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TBT4P337hfLZDEVnTUkrsxpgPGGNOGmPOGGM+uch27zDG6P/qVXDy4lWmpmPcunl1pg9IRam/gEf2NnKwtYf27pFshyOyZi2Z2I0x9cAXgPuA3cDHjTE75tmuFvh9IDeqjw53+EwvRQWeFZ+aN11v29eIv9DLt5/X9V0kW1KpsT8MPGutHbDWjgFPAI/Ns91Xgc9lMjiZXywe53BbP7duqsK7giNNl8Nf5ONtdzVxuK2P1ot6upJINqQypUAdMLurQxewb/YGxphfBV4DXl5OEFVV6d/8C4WCyzlVVmUq5taLAwyPTfHGOxrx+wsJBorm3c7vLyRU6Z+3LD4wPu9+Pp/32vq55bPLFjvX+9++nZdOdPNXT1t++9/fvaz4lqNngb9pJc6VSWv5u7ya1lLMqSR2NzB7TlYXcG0UijHmFuC9wENAw3KC6O8fJRZLfdrXUChIb29+teFmMubnDrTjdrnYEPIzPh5mZHRy3u3Gx8P0RqPzl4Uj8+43PZ1YHwwU3VA+U5bKuT70lhZ+7+9e5x+fsTTVzn/hXiy+ZfF4lvVeZNNa/y6vFqfF7Ha7Fq0Qp/I7vgNYP2t5HXB51vL7kuUHgSeBOmPM8ykcV5bp9TN9mKZySopW/mEay7WtuYIH99Tzw9c76bk6nu1wRNaUVBL7M8BDxpiQMcZPonb+1EyhtfYz1toWa+1u4FHgsrX2/pUJVwaGJ7ncN8Ztm6uyHcqS3vfAZipKC3nx2BUiUU01ILJalkzs1tpO4FPAfuAw8Li19lVjzJPGmL0rHaBcr7U9cUNye3NFliNZWlGBl/c/3MLw+DRH2vqyHY7ImpHSfOzW2seBx+ese3Se7S4AGzIRmMyv9eIgJUVeGmqyP9o0FduaK9jaUMbJ81dprg1SXV6c7ZBEHC+3+srJklrbr2KaKnC78me4wB0mRHGRlx8f1+yPIqtBiT2P9A1N0Dc0uaKPv1sJBT4P9+ysZWh0iqNteoSeyEpTYs8jtn0QgG1Nud++Pld9KMDmulKOnx+gf2j+Lok3IxaLE57Ove6MItmgZ57mkdaLVwkU+6gPlWQ7lGXZu72Gy/1jvHj8Co/e05yRYz5/9DKvnurhbOcQbreLh/c2UB4ozMixRfKVaux5pLV9ENNYnlft67MV+jzcvXMdV0fCnLpw8w/kePVUN19/spWB4Unu3rkOr8fNs4c6mQhHMhCtSP5SYs8TfYMT9A9Psi0PujkuprEmQEOohGNnBxgZn1r2cbr6x/j691rZUl/G5/79Pv7dWw2/+m92MxGOsP+1TvWblzVNiT1PtCbb1/Ptxul87jA1RGIxnnzp4rL2D09H+fK3j+PzuPnF9+y8NhHaxroy7t+1nr6hSU1AJmuaEnueONOR6L9eV52f7euzlQUKME3l/PhYF529o2nv/92XLtLRO8bH37WDytLrJ/1qqg1SW1HM6UtDxOOpzz8k4iRK7HmirXOIzfVledu+Ptdtm6spKvDyD/vb0tqvf2iSf321nbt21HLLpvmnVWhpKmd0YprLfZqjRtYmJfY8MDoxTVf/OFvqs//Q6kwpKvDwtrubOH5ugGPnUu/b/sQPzwLw2Js2L7hNU22QogIPpy8N3nScIvlIiT0PtHUOAbC1wTmJHeCNu+qoqSjmH55tS2lE6tnOIV452c1b9zVSVTb/vOsAHreLLfVldPSMMjYxncmQRfKCEnseaOsYwuN2sWF9abZDySivx837HtjC5b4xfnSka9FtI9EYf/P0acpKCnj07qX7wG9tLCMOnOkYylC0IvlDiT0PtHUM0lQboNDnyXYoGbenpRrTWM63nz/H6CK166deaedi9wgffKSFooKlx9UF/QXUVfs5d3lYN1FlzVFiz3GRaIzzV0bYUp//3Rzn43K5eP/DW5kIR/g//3R83iaZjt5R/umF89y5rYa922pSPnZTTZDRiWmGxpbfX14kHymx57iL3SNMR2KOa1+frak2yIfeYjh54Srf3H/2urLxyQhf++4p/EVePviWlrSO21CT6Bp6qSf9LpUi+UxzxeS4s8k24s0O6hEzn/t31XGpZ5SnD1wiGovzhlvXMTEZ4WtPnmJwZIpf+ulbKPUXpHVMf5GPqtIiOpTYZY1RYs9xZzqHqC4roiLo/ImtfvahLYxNRnju9U5+cKgDgNqKYn7rQ3ewqW55N44bako40tbPyPgUJYX6usvaoG96DovH47R1DLF9Q37PD5Mqj9vNx961g/c/vJXXT/cyOjnNm29voLBg+TeNG2sCHGnr58T5AdZV+DMYrUjuUmLPYX1DkwyNTTlqYFIqAsU+7t9Vl5FjVQQL8Rd5OXaun4f2NGTkmCK5TjdPc1hbsn19rSX2THK5XDTWBGi9cJXpiB7EIWuDEnsOO9M5RFGBh4ZQfjy4Olc1hAJMRWLXZsgUcTol9hzW1jHE5rpS3G5nTPyVLbWVxfg87rTmpBHJZ0rsOWp8MkJn7yhbGpw5MGk1eT1utjSWcezczT+1SSQfKLHnqHOXh4gDWxrKiMRgLByZ919Mo+VTsmNDJd0D4/QMTmQ7FJEVp14xOaqtcwiXCzatLyU8HeHAqe55t9vVElrlyPLTjg2V/F/OcvxcP29W7xhxONXYc9SZjiEaQwGKNagmI0LlRYTKizh2Vu3s4nxK7DkoGotx7vIwmx08P8xqc7lc3LqpilPt6vYozpdSddAY8wHg04AP+KK19ktzyt8DfA5wAeeBj1hr9TThZeroGSM8HWXrGu6/HolBeDoyb1mhz4t3GVWSWzdV8exrnZy+NMTOjZU3GaFI7loysRtj6oEvAHcAYeBFY8x+a+3JZHkp8GXgTmttpzHm88Bngf+4YlE7XGt74prY0rh2e8Qsdl/hzu21eJfRRLWtqQKvx83Rs/1K7OJoqdR7HgaetdYOWGvHgCeAx2aV+4BPWms7k8tHgabMhrm2tF68Sk1FMZWlCz/+TdJXWOBhx4YKXj/Tq4dviKOlUu2pA2Y/t6wL2DezYK3tB74FYIwpBn4T+NN0gqiqSn9kZSgUTHufbEsl5mg0xpnOIe7fXX9t+/jAOMHA/Ene5/MuWOb3FxKqnH/iq4WOOft4c8uXc67FYl9OfIvt15PCPm/c08CfffMIY5E4G5c5Y2SmOfW7nGvWUsypJHY3MLt64wJueMyNMaaMRII/Yq39RjpB9PePEkujQ3YoFKS3dySdU2RdqjGf7xpmfDJCc03Jte3HwxFGRifn3X56euGy8fEwvdH5bxQudMyZ4wUDRTeUL+dci8W+nPgW3c/jWXKfzeuCuIBnX7nIu+/bOO+2q8nJ3+Vc4rSY3W7XohXiVJpiOoD1s5bXAZdnb2CMWQ88T6IZ5qMpHFMWMNO+vq1pbUzVu9rKSgrY3FDGa2d6sx2KyIpJJbE/AzxkjAkZY/zAe4GnZgqNMR7gn4F/tNb+J2utGi9vQuvFQdZV+ikPOP/BGtmyZ2uI9u5R+oY0ClWcacnEnrwp+ilgP3AYeNxa+6ox5kljzF7g3cAe4DFjzOHkv6+uaNQOFYnGON0xyLZm1dZX0u1bqwF4/UxfliMRWRkp9Rmz1j4OPD5n3aPJlwfRQKeMuNg9QngqyramtdvNcTXUVvqpry7h9dO9PLK3MdvhiGScEnIOab2YaF83al9fcXtaQthLgwwMz3+zVSSfKbHnkOPnBqgPlVBWUpDtUBzvDbetJx6HF452LbrdQjNrRm7oFyaSOzTDVI4Ym5zmTMcQb79bY7tWQ015MTs3VPCjo5d5570bFnyYyUIjYJc7+lVkNajGniOOne0nFo+za0t1tkNZM960u56B4TDHz+sBHOIsSuw54nBbH6V+H5vW58ZoyLVg99Zqgn4fPzzcufTGInlEiT0HRKIxjp0b4LbN1Xq+6QpxuV03tJOHIzHu2lHLkbZ+BkfD2Q5RJGPUSJgDzlwaZCIcUTPMCgpPRzly+sbRpoFiH3HiPPnyRT7wcEsWIhPJPNXYc8Dhtn68Hjc7N6qb42orLSngnp3r2P9aJ139Y9kORyQjlNizLB6Pc6Stj+3NFRQV6AdUNrzj3g34vG6+uf9stkMRyQgl9iw7d3mYnsEJ9rSoGSZbSksKeOe9Gzjc1seJC+ohI/lPiT3Lnj96mQKfm33ba7Mdypr2yN4GqsuK+Mb3Whkem8p2OCI3RYk9iyanIrxyqoc7TQ3Fhd4FRzmmMVW9LJPP6+ET79nJ8NgUf/zEUcJTK/PA67mfcc/AuEazSsapUTeLDrT2EJ6Kcv+uOmDhUY67WkKrHdqatLmujE+8eyd/9q1jfOU7J/gPP3VLxs8x9zOe/UATjWaVTFGNPYueP9pFbaWfrQ1l2Q5Fkm5vCfHBR1o43NbH7/71IXoHNWf7SlnoF6p+vdw8VQ+ypKt/jLaOIR57YDMulwYl5ZI372mgIljIX3z3FL/3t6+xe2s1WxrKcGfpc4rEEjX9+RT6vHjztHq20C9U0K+Xm6V3Lkv+9dV2vB4X996yLtuhyDxu3xrisx8J8pXvnODlE920XrzK3m011FWXrHosiyXAfTvXEZ6e/yaMz+tlOnLjBWG5F4OZC0x8YJzx8PXHzecLjBMpsWfBlYFxXjh6hTfvqdcj8HJYVVkRv/q+2/i/z53lkO3lmYMd1IdKuMOs7j2P8HSUqyNhRiemGR2fZjwcYSIcYWo6yksnrjA0OoXX48Ln9eAv9FIWKKAiUMgDexs4cbb/huMttzY8c4GZ70HnqmHnFn0SWfDt58/h9bp4x70bsh2KLMHlctG8LkhDTQmtFwc5eraff/7xBXoHJ/mZ+zdRVVaUsXNFojHau0e42D3Cpe5R2ntGuTIwfkP3S7fLRXGhhwKfB6/XjcsFU9MxRicidPSMEk12o/rBoQ6qyoqoD5XQVBMk4PdlLFbJbUrsq6y9e4RXT/Xwjnua9UCNPOJxu9m5sZLN9aUcOzvAgVPdHDjVzQO763nHvRuW9VnG43H6Bic4fXGAzr4x/vbp09eScoHPTWNNgNs2V1EeLGRoNEyw2EfA76PQ57l2X2ZXS+i6OXDi8ThjExH6hyeJxuMcO9vPwdZeDrb2UhEspKk2wPrqElrqy3Rvx8GU2FdRPB7nm/vb8Bd6eftdeqBGPioq8HLn9hp+7uGt/ODgJZ59rZMfHb3MQ3c08KZdddRU+Bfdf3IqQnv3CJ29Y3T2jl1rq64sLeTBPfVsqS+jqTZITXnxtZk+x8ILt7HP5XK5CPgTF4BdLSE2rS9lZHyK9u5RLvWMcqStnyNt/VSXFbGnJcStm6vYUldGYYHn5t4YySlK7Kvo6QOXOHHhKv/2LS34i/SzOJ9Vlhbx4bdv5+13NfPtF87z1MvtfO/ldrbUl7GtuYK6Kj/lgUKmIlHGJiK094xw/vIwZy8PE43F8XncrK/2s6WxgspAAf4iL3dur6VkBdqpg/4Cdm6sZOfGSibCEbweNyfOD/Dsax08feASHreLDeuCNNYGaawJ0BgKUB8qoVht5nlLn9wqOd1+lSeeO8uelhAP3l6f7XAybma+87mcPmq2ttLPJ969k8fetJlXTnXzyslunnzpIrH49X+41+OmqTbAA7fX43G7CFUU43G75r0RuZKKCxMXkEf2NjI5FeFMxxCt7Vc52zHEKye7ee71nzx0pKa8mKbaAM3rgjTVBqkuL161OOXmKLGvguHxKX7vb16jPFDARx7d5si2zYXmO18ro2aryop49O5mHr27melIjJ6riZueBQUeigu81FQU4/W402pWWWlFBV5u3VTFrZuqgERTYf/wJB09Y1zqGaG9Z5SL3SMctD/5XIN+HxvryqgpL6K2shiPW30cc5ES+wobGJ7k9//+MFdHwvzGz+2mRE0wN22hXweeHHlrfV439aEA9Tl6TVvo/QMoDxZTXVbM7q0/mW10fHKa9u5RznQO8erJbk6c6+doLI7X42J9VQkNNSWYpoq0mpHCU1HaOoY4cX6AqyNhwlNRwtNRPB4XxYVeuvrH2dFcwdaGcvxFSlPp0ju2grr6x/jDfzjMeDjC5z9+DzVB9YLJhIV+HezduT4L0eSfhd4/WGDAk8tF47og9bVBgn4fRUUFtLUP0NE7Rmdv4qbsy8e7aV4XZGtDOZvrSwmVF1MeKMTlglcN1g8AAArMSURBVIlwhOGxKTp6E78Eznclbh7PNFf5C73Xum9GonH6hya51N3B9w9cwuWCloZy9m6r4Q4T0riPFCmxr4BwJMZTr1zkyRcvUljg4Vfeexuh8mLGxsMaoTfLYjXHXG+bXyz2fP6MF0v6M81qPq+bhpoADTUB4vEaro6EicWhrWOI5w538v2DlxY8fkmRlw3rS9m9pZq6UAkDw5Pz3qTdtaWaK/3jnLo4wGun+/jb75/m8e+fZmtDWTLJ11ARzEySd+KUDUrsGRSNxThke/nnFy/Q2TtGY02Au3bUcmVgnLGpHkZGJzVCb5ZUkkiuWiz2tfQZu1wuKkuLrvXoiURjdPaOcXUkzOBomDhQXOAh4PdRXx2gPFBw7R7TYvcbCnwetjdXsL25gp9542Y6+8Y41NrDAdvD48+c4fFnzrCloYxbNlayqa6UDetKKSnyLnj/amR8atFKxKFWZ81Zk1LExpgPAJ8GfMAXrbVfmlO+G/gqUAr8CPhFa+3876LDxOJxzncN8/rpPl45eYX+4TCh8mLeuLuO5tpAVm+U5nONOJ+t5ffd63HTvC5I87pgRo9bX11C/X0befd9G+nqH+Og7eVQaw//9Px5Zt7SogIPVWVFlBR6KSzw4nG7iMXjxGJxYsDA0CTxeJxYPHGjOB6HOHEKfR7GwxHcLhcFXje+5L8Cn4f+4UnqqkqoLiuiuqyYskBB1iaDS8eSid0YUw98AbgDCAMvGmP2W2tPztrsb4CPWmtfNsZ8DfgY8OWVCDgb4vF4csj2NIOjYXquTtA1MMaFrhHOdw0zNhnB43axramcDzzSwpbGcg619mQ77LyuEeczve8rK1RRwpvvKOTNdzQwEY5w8coIl/vGGBqb4urwJBPhCCPjU0SicTxuF243uN1uYvE4LsDjduFyuXC7XLhcUBYsxDc2RSwWZzoSY2wywnQkxlQkim0fvO7cXo+L6rJiQuXF1JQXE6pI/re8iFB5MQW+3BjolUqN/WHgWWvtAIAx5gngMeDzyeVmoNha+3Jy+78EPkdqid0DXBthl45ILM6h1h4mp6Nw7eqbuAIT59qNmZn1xLl2hSae2D8ajTEdjRGNxYlEYkSiscS6WJxoNM5UJMZEOMLoxDTR6PUTRLtcsK6yhAf3NLCpvpTtTRXX2gonpqI3DEAqLvQSjfjw+jyEF5hs2u12zztwyeuZf/1KlM2sn4k3U8dbndhdyz7eQt/Blf67Zr/PC8WxUp/xco8373djge91gdeDZ4E26uV+JtFIlBPnr382bW2ln7fsa6J4gRG0cY+HV45dnrds+8ZKTp2f/1m32zdUEJ6KMjAc5urwJAMjYQaGJ+kfmuR0xyDHzl8/yVpxgRd/kRd/kS/5Xy9et+va3+N1u/F4Ehcbn9fDXhMi6F+4U8VC78Gs9fP+wa54fPHfhsaY/waUWGs/nVz+KLDPWvvx5PI9wP+y1t6XXN4CPGmtbVn0wAn3Ac+nsJ2IiNzofuCFuStTqbG7gdnZ3wXE0ihfzIFkYF3AyjxkUkTEeTzAehI59AapJPYOEsl3xjrg8pzy9YuULybMPFcbERFZ0tmFClLpofkM8JAxJmSM8QPvBZ6aKbTWXgQmjTFvSK76EPC9mwhWRERuwpKJ3VrbCXwK2A8cBh631r5qjHnSGLM3udkHgT8yxrQCAeBPVipgERFZ3JI3T0VEJL/k4WBZERFZjBK7iIjDKLGLiDiMEruIiMMosYuIOExezUe51CyTucoYUwq8CLzTWnshy+EsyRjzGeDfJBe/a639r9mMJxXGmM+TmMMoDnzNWvuHWQ4pJcaY3weqrbUfznYsqTDG7AdqgOnkqk9Ya1/JYkiLMsa8C/gMUAI8ba39j1kOaVHJKVt+edaqjcBfW2t/eYFd5pU33R2Ts0y+wKxZJoH3z5llMucYY+4C/hzYBrTkemI3xjxMYhK3B0kkyaeAP7PWfiurgS3CGPMmEjOQPkDion8SeJu11mYzrqUYYx4C/p7ExfPDWQ5nScYYF4mR5s35MC23MWYTibmo7gK6gWeB37XW5sUASmPMTuDbwD3W2r509s2npphrs0xaa8eAmVkmc93HgE+S+jQL2dYF/Gdr7ZS1dho4BTRlOaZFWWt/CDyYTDY1JH6JjmU3qsUZYypJXIx+N9uxpMEk//u0MeaIMSatWmQW/DTwD9bajuR3+WeBnP11MY8vA7+VblKH/GqKqSORdGZ0AfuyFEvKrLUfBTDGLLVpTrDWnph5bYzZSqJJ5g0L75EbrLXTxpjPAb8BfBPozHJIS/kKiRHdjdkOJA0VwA+AXyHxy+g5Y4y11n4/u2EtaAswZYz5DonKyb8A/z27IaUm+cu52Fr7zeXsn0819puZRVLSlPwZ+H3gv1hrz2Q7nlRYaz8DhEgky49lOZwFJdtRL1lrf5DtWNJhrX3JWvvvrLVDyVrk14BHsx3XIrwkfun/AnAPiSaZn89qRKn7BLDs+0T5lNhvZhZJSUNyQrcfAL9prf1GtuNZijFmW/LxjFhrx4H/B9yW3agW9bPAW4wxh0k8sObdxpg/ynJMSzLG3Je8LzDDxU9uouaiK8Az1tpea+0E8C3y4Fe+MaYAeBPwneUeI5+aYp4BPmuMCZFoP30v8PHshuQ8xphGEjdsftZa+2y240nRJuBzxpj7SPyqew/wF9kNaWHW2kdmXhtjPgw8YK39texFlLJy4PPGmHtJNMX8PPCL2Q1pUf8CfMMYUw6MAG8n8d3OdbcBp5P3Epclb2rsC80ymd2oHOk3gCLgD40xh5P/cvl/Xqy1TwLfBV4HDgEvWmv/PrtROY+19l+4/n3+C2vtS9mNamHJbpi/R6I33UngIvD1rAaVmk0kWiiWLW+6O4qISGrypsYuIiKpUWIXEXEYJXYREYdRYhcRcRgldhERh8mnfuwiCzLGxIHjQHRO0U8td+K15ACiB6y1g8vY9zMkJm9625z1e0hMrNZorQ0vJy6Rpai7ozhCMrGHljNh0kowxqwHzgNbrbWXZq3/CtBvrf2trAUnjqcauzieMeYBEjMpngNuITFq8hPW2h8nRzJ/HdgM9JMYhn7cWvvZmYsF8E4SMwXGgK3AOPDz1tpTxpgy4I+BW5PH/QGJ+XW6kpNPfRj4nWQcARKTqu1ajb9b1i61sYuT7J81WvawMWb2HPJ3AX9grb2dRCKfmS73T4AT1trtwPuAexc49puAX7HW3kJi6tffTK7/I+CQtfYO4HagGvj1ZNn/Bj6SnMcc4P3Ac9ba9pv+S0UWoRq7OMmDizTFXLTWHk6+fo1ETRoSsxPuAUjWsp9YYP9D1tqZYd6vAT+TfP1OYJ8x5heSy8UzO1hrnzPGjJN4aMmzJOY2mrkgiKwYJXZZKyZmvY6TmJkQIDLrNdx483Wp/T3A+6y1pwCSE07NvnH1ZeAXjDEDQCDfpuqV/KSmGFnrvktivm6MMVUk2tLT6VHwr8CvGWNcxphCElOtzn6y0F8BbwZ+CciLZ/RK/lONXZxkvzFmbo37t0jc7FzIrwFfNcYcI3Hz9OIS28/1qyRunh4jcfP0GRIzCgJgrR0xxvw/4EMkZs4UWXHq7ihrmjHml4DXrbUvJWvczwOfyZcHHovMRzV2WetOAn9qjPEABcA3ldQl36nGLiLiMLp5KiLiMErsIiIOo8QuIuIwSuwiIg6jxC4i4jD/H9WVXQhpr8mpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_3['EngineV'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ce2152ed08>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xfz5yKXfesHhS73dJfTk+f3BStfeTXVQLY62163CmZCLbbox43MQY0zXWWksMc/YikhpauwY41X2Gd1wa3Tz5RPJzM/mL25fT0j5AraeAyspZk74+saC2mJzsDJr2t9O4sGJK+pcstNNWRKbc8HLMJXWja99PVobbzZzKwkmXTR6WlenmkovKaNrfnna3VFTCF5Ept+NQJ1Vl+VSUTH7+fjo1Lqygu2+II62puUJrPEr4IjKlhnwB9hzpYtlFZfHuyrguqS8HnAqb6UQJX0Sm1J6jXQz5g6xI4Pnx4nBtnXS7cKuELyJTqml/BzlZGRPWtom3hrpS9jWfxudPn2JqSvgiMmVCoRBNB9pZelEZWQl6s1eX20W/1099TTE+f5Cdhzvp9/pJh7yfmD8REUlKzW39dPZ4WbGgPN5dGZfXF+D13a309HtxARu3Huf13a3j3qg8lSjhi8iUadrvlCxYnsAJf1h2Vgblxbmc7OiPd1dmjBK+iEyZpgPtXDS7KKb6NvE0uzyf9tODaTOPr4QvIlOiZ2CIg8d7WLEgcVfnjFRdnk8oBK2dA/HuyoxQwheRKdG0v50QsHxh4k/nDPOU5OF2u2jpUMIXEYna5t2n8JTkUleVPDeUycxwU1mSx0mN8EVEotPTP8Tuw12saai64Fo3M212eT5dvV56B4bi3ZVpp4QvIhfsTXuKYCjE2yLuSpUsqsudWxTuO3Y6zj2Zfkr4InLBXtvVSm1FAXMqk+c+1MPKZ+WSlelm77HUL7OghC8iF6SzZ5C9zadZ0xDb3acShdvtoqo0b8z74qYaJXwRuSCv7zkFcM5NxpPN7PIC2roH6Tg99s3KU4USvohMWigU4uW3WphfXURVWX68uzNpw/P4u46kdrlkJXwRmbS9x7ppbuvn2pVTcyvDeCkpzKYoPyvlyyUr4YvIpD33ZjMFuZlcviR5p3MAXC4XF88tYfeRrpS+7aESvohMSmfPIFv3tnPNihqyszLi3Z0LZuaWcLpviBPtqVtMTQlfRCZl49bjhAjxziSfzhnWMN+5JeNbB1N3Hl8JX0RiNuQL8LttJ2hcWJGwNyqPVWlRDrWeAt462BHvrkwbJXwRidnzW5rpO+PjPavnxrsrU2pZfTl7j3VzxpuaN0NRwheRmAwM+lj/6hGW1Zdj5pXGuztTall9OYFgiD0pulpHCV9EYrJ+01EGBv3c+o76eHdlyl08p5ic7IyUndbJjOYkY8wdwP1AFvCgtfbhEccbgUeAWcALwL3WWn/E8ZXAJmttctwGR0TG1NXr5bdvHONtS6uYl0RlkKOVmeFmSV0pbx3sIBQKJV3lz4lMOMI3xtQCXwauAhqBe4wxS0ac9jjwKWvtIsAF3B3x+nzg60D2VHVaROLjpxv3EwyG+ODVqTe6H7ZsQTkdPV5OpOBNUaKZ0rke2GCt7bTW9gNPALcNHzTG1AF51tpN4aZHgdsjXv9V4MGp6a6IxMuWvW1s2tXKTVfMx5MiK3PGsrzeuWPXWwdSb1onmimdGqAl4nkLsGaC43MAjDE3A/nW2ieMMTF3rrw8eUqtejyp9/U2UirHp9gm1jswxOO/3Ut9TTF33nwJmRnRXf4LdQ5QVJg7qj0rK3PM9vz8HDxR1uQZji3Wz5josz2eIupritl+sIOP3rR03PePtb+xmo7fy2gSvhuI3GvsAoITHTfGVOPM+18/2c51dPQRDCb+NmePp4i2tt54d2PapHJ8im1ioVCI7zy1i97+IT5z23K6OqPfiTrg9dPbN7oCpc83dvvAgJe2QGDC942MLdbPiOazVyws5+cvHGTvwXayszPGPD+W/sZqsj87t9t13oFyNH+mm4HZEc+rgRNRHL8JKAdeMMZsAzDGbDPGpO5wSiQFbdhynE27WvmDK+ePe6HWH4R+r3/UvyQYr41plfEAzjRWKolmhP8c8EVjjAfoB24F7hk+aK09YowZNMZcaa19Gfgo8Iy19hGclTsAGGNC1trGqe2+iEynPUe6+OFz+2hcWMFNV8wf9zyvz8/ru1tHta9Y5JnG3k2f2eUF1FQU8KY9xeWXVMe7O1NmwhG+tfY4cB+wEdgGrLPWbjbGrDfGrAqfthZ4wBizBygEHpquDovIzDjVNcA3frGDqrI87v6DJbhTbIniRC5b5MEe606pm5tHtQ7fWrsOWDei7caIx02ceyF3rPdIr98WkSR2qvsM//rDrQD8+a3LycuJKlVMK3/Q+SYBzoXagXD5g+maNrrMeHjqlcNs399BdlZq7FGN/09RRBJKW/cZvrJuC96hAH/1kZVUJ8idrCKnjYoKc89eSJ2uaaO5lYVUluSxbV8ba5K83v+w1PizJSJTYn/zaf7pv95kcCjAZz+8MiV300bL5XKxanFlShVTU8IXEUKhEL/bdpx/WbeFnKwMPr/2Uuqq0zfZD7t8aRXBEBw+mRpLdzWlI5Lm2k+f4fHf7GX7gQ6WXlTGvbcspSA3K97dSghzPIXUVBRw6EQPDXXJXxlUCV8kTQ0O+fntG8386tXDuHDxkesu5rrL5uB2j7++IvLCaaRkXW8fjdUNlTz54iF6+oeYVZDcJcGU8EXSjM8f5HfbjvP0K4fpGfCx8uIK7rh+EeXFY5cPiJRq6+2jcZlxEv6hlh5WLKyId3cuiBK+SJoIhkK8uuMkT750iPbTgyyeV8Kfv2MBC2qLzzlvvFG88x4z0dPEUlqUQ3VZPgdP9LB8QXlSl0xWwhdJQOdLujlZmWTGuNyitXOA763fzb7m09RVF/Gx9xmWzi8bM3mNN4qH1B7Ju9wu+sdYjRMMwUU1Rby6o5WO04NJfQ9fJXyRBHS+pLu6oYrMGDZCbdx6nB89v4+sDDefuHExVy6bnXa7ZqPh9QVoGqN2zopFHuqqinht1ykOnuhRwheR+Bv5rSDQ0c8Pf7uXDW82c0l9GZ+4oYHSIt10bjKyszKY4yng8MleVi2uPO+F7USmhC+SIiK/FQSDITbtOsX+5m6uaazhY+8xSZukEkV9zSyOtvbR0tFPrSd57tURSRuvRFJMKBTi1Z0n2d/czaWLKrjt2gVK9lOg1lNAdqabgyd64t2VSVPCF0kxTfs7OHC8h1UNVVxSn9yrShJJhttNXXURx0714fMHJ35BAlLCF0khB46fZvuBDhbWFp8t+DW8+mTkvyTNWXFVXzMLfyDEsVN98e7KpGgOXyRFnOoa4LVdrVSV5nH50qqzI/vxVp/EutpHoLI0j4LcTA619FBfMyve3YmZftoiKcDnD/Lo+j243S6uXjE7aefsx1sLD4mx6cvlclFXXcSeI10M+ab+XrbTTQlfJAX8/IWDHDvVx7Ura8iPsvDZ+TYaxct430YgcTZ91VUXsetwV1JO6yjhiyS5HYc6eHbzUa5aPjum+vXn22gk46soziU/N5MjrcmX8HXRViSJ9fQP8cjTu6mpKOCD19THuztpweVyUVdVxIn2/qS7MYoSvkiSCoVCfG/9bgYG/dx781KyszLi3aW0UVddSDAYYuehznh3JSZK+CJJ6rk3m9l+oIMPvWshcyqTc+dnsvKU5JGXk8HWfWNfb0hUSvgiSehoay8/3bifFQvKedeltfHuTtpxuVzMqypi96EuBoeSZ1pHCV8kyQz5AnzrlzspyMviE+9v0E7aOJlXVYgvEGTX4a54dyVqSvgiSeZn/3OAkx0D3HXTEmblJ/ct95JZVWk+eTkZbNvfHu+uRE3LMkWSyMETp3llx0nevXou82fPOmcdfSJsTEonbreLhroyth/oIBgKJcU9BpTwRZJEd5+XTTtbqa+ZRVVp3qgbpGj9/My7pL6MLXvbONTSw4Ka4olfEGdRJXxjzB3A/UAW8KC19uERxxuBR4BZwAvAvdZavzHmSuABIBvoAD5prT0yhf0XSQvOjcdPkJnh5o/fZzicxCV6U8mS+WW4XE6F0mRI+BPO4RtjaoEvA1cBjcA9xpglI057HPiUtXYR4ALuDrf/ALjLWtsYfvzQVHVcJF0EQyFebDpBT/8QV6+YTXGh7lqVKArysri4tpimJJnHj+ai7fXABmttp7W2H3gCuG34oDGmDsiz1m4KNz0K3G6MyQHut9ZuD7dvB+ZNWc9F0sTWve00t/WzenEls8sL4t0dGWHFxRUcO9VHx+nBeHdlQtFM6dQALRHPW4A1ExyfY6314oz8Mca4gS8Cv4ilc+XlybOZxOOJvoZJMkrl+BIxtlDnAEWFuew61MHOQ51cUl/O6qWzAcjKyqSoMHfUa8ZqLyrMjen887VP5jXT+dnDj+PZp/z8HN65uo6fbjzAgdY+Fi+cuuso0/F7GU3CdwOR1/9dQDDa48aYbOCx8Gf9Uyyd6+joI5gESw88niLa2nrj3Y1pk8rxJWpsA14/Ow+08butJ5hdnk/jwnJ6+5wRpM/nP/s40sj2osJcevsGoz5/ovbJvGa6Pns4tnj3aWDAS352BpWleby0rZk1iyrGPC9Wk/29dLtd5x0oRzOl0wzMjnheDZyI5rgxphB4FifZ32Kt9UXXbZH0tq+5mxeaWigvzuXalbVJW98+HbhcLhoXVrDnSOLvuo0m4T8HXGeM8Rhj8oFbcZI4AOFVN4PhFTkAHwWeCT9+HNgPfCg8xSMiEzhw/DTffnInRXlZvOuyOWRlan9koluxsAJ/IJTwu24n/E2y1h4H7gM2AtuAddbazcaY9caYVeHT1gIPGGP2AIXAQ8aYlcAtwJXAFmPMNmPM+mmJQiRFHGrp4Ws/2UZhXhbvXj2H3GxVwEwGF88pJi8nM+F33Ua1Dt9auw5YN6LtxojHTZx7IRdgK858vohE4VBLD1/90TYKcrP49O3L2d98Ot5dkihlZrhZVp/4u271XVEkAexr7ubffrSV/NxMPnfHSkqLxl4VIolrxcIKevqHONSSuJvilPBF4mzPkS6+9uMmZuVn8/m1l1JRnBfvLskkLKsvx+1yJfQmLCV8kTjacaiDB37aRHlxLn+99lLKZmlkn6wK87JYOKeYpv0d8e7KuJTwReJk2752HnpiO7PL8vncHSspUcmEpNe4MLF33Srhi8TBqztO8vDP32JuZSGf/chK1bVPESsWlgPQdCAxp3WU8EVm2LOvHeU7T+/i4jnFfOaPVuJyu+j3+s/5lwQbzGUM1WX5VJbmJezyTNXDF5khwVCIJzYe4NnNR1m1uJK7b1rCUCA4qq49qLZ9shredbthSzODQ35ysxMrxWqELzID/IEg3316N89uPsq7Lq3l3puXagdtikrkXbf6jROZZgODPh56Yjuv7jzJB6+pZ+27F6k2TgpL5F23ifV9QyTFnOwc4KEnttPWfYY7b1jMNStq4t0lmWaJvOtWI3yRabJlbxv/+Ngb9J3x8dkPNyrZp5HG8K7bgwl2K0qN8EWmmM8f5Ccb9/P8m83UVRXxZx+8hIoS7Z5NJ8sXVJCV6WbTzpMsrE2ce90q4YtMof3HT/PYM3s43t7Pu1fN5bZrF5CV6cYfBK9vdK10Lb9MTfm5may8uILXdrXy4esuJjMjMSZTlPAlaY2XRHOyMpnpBTA9/UM8+dIh/mfrcUpn5fAXty1nxcLf3/3I6/Nr+WWaueKS2WzefYrtBzq4NEF+zkr4krTGS6KrG6rIzJmZX+2e/iGee/MYv329mSF/gOsum8MHr6knb4Y+XxLX0otKmVWQzSs7TirhiyQrnz/A7iPdvPxWC1v2thEIhljTUMkHrq6noiQfr8/ZLRtJUzfpJ8Pt5vIlVTz/ZjN9Z3wU5mXFu0tK+CIT6R0Y4uipPg6d6GH/8dPsOdLFkD9IQW4m77p0Du9orKGmogCAfq+mbuT3rlw2m9+8fozXdrVy3WVz4t0dJXwRfxDODPno7vVysnOAk50DtHaE/7frDP1nfGfPrS7L5+rlNSxbUE5DXQlZmboFoYxvbmUh86oK2bClmXcmwM3olfAlbQRDIbp7vbSfHqSt+wxt3WfoOePnQHM3LR39+AO/n3fJycqguDCbxoXlzK0sotZTwEXVReTnxv9ruSSXGy+v45tP7uQNe4o1DVVx7YsSvqSUQDDEqa4BevuHaO06w6nwv7buM7SfHsQfCJ491wVUlOZRPiuXi+eUUFyQTXGh82+46NXqhioKdKJ9wRYAAAu4SURBVAFWLsAqU8ns8kM8/cphVi2ujOvOW/0mS9IKhUL0DfjOjtbbugfp7B0kFHGBNDc7g8qSPGo9BTReXIGnJA9PSS6e4jzKi3OZXV3M4eauMefdgbOli0fKyszE59e6epmY2+3ipivm852ndrF1bzuXmfhdz1HCl6Ti9QXYfaSL7fvb2ba/ne6+IQAyM1yUz8plyfwyLjUeKopz8ZTkUZiXhSs8oprM+nyvL0DT3rZR7SsWecZtFxlpTUMlv3zpEE+9fIiViyriNspXwpeE19XrpWl/O03729kdXiGTk53B4nmlLMpy4ynJo7Qo5+wFseFkPPI2c2uWVuP1nTsED3UOaFQu0y7D7eaWqy7i20/t4tnXjnLj5XVx6YcSviScYCjEkZO9bNvXTtOBdo629gFQUZzL1StqaFxYwaK5JePePGQ8Y43Wiwpzqa8pmtL+i4zlbUuq2LKvnf/+3UEWzS2JS40dJXxJCGe8fnYd7mL7gXa2H+jgdP8QLhcsrC3mtmsXsGJhBTXl+WenZwCGIi7AiiQ6l8vFne8zHG7p4VtP7uRLn1w946u+lPAlLoKhEEdbe9lxsJMdBzvYf6KHYDBEXk4GDXVlXFJfRsP8Mgrzss5eIB0YCox4jzh1XmSS8nOz+F+3LOWfH9/CP/9gK5++ddmMVlKNKuEbY+4A7geygAettQ+PON4IPALMAl4A7rXW+o0x84DHgUrAAmuttX1T2H9JEv2DvrM7VQ+e6OHgiR4Gwqtf5lUVct1lc3C5oLIk7+xc/O7DnYAukEpqWVBTzKdvW843n9zJ3z/2BvfcvISl88vO+fY6XSZM+MaYWuDLwGWAF3jFGLPRWrsr4rTHgbustZuMMd8F7gb+A/gG8A1r7Y+MMV8AvgD89VQHIdEJhUL4/EH6BgP0DHjx+gJ4hwIMDgXw+YNkZbrJiPidc7lcZLhdlHWeob93kIwMFxluN5kZTntGhhu324XPH8Q7FDj7fv2DPjqGNzeF/7er1+u8J1DrKWDV4krM3BKWXFRGcUH2uCUJRFLRsvpyvvDxVXz9Z9v52o+buGh2EdddNocl88soKcyZts+NZoR/PbDBWtsJYIx5ArgN+Pvw8zogz1q7KXz+o8CXjDGPANcAH4ho/x3RJfwMYFLbkP2BIP+z7ThnvAEyXC7cGS4nQUX89Rxepz1ivcYYj37/JLItFDr3lXl5OZw54x3nfcf8iFF9cQ6Hxjw3NGbbuf31+QIM+YP4fEG8gQA+X9B57g/g9TkJecgXIBiauXmQ4sIcyopyWFA7i4riPOZVFVJTUYR7xNJIrz+I2+0edz4zM2PsY1PRnpeTOe750/3Zk2mP5TV5OZkE/Flx+eypbh95bDi2ROjTZMsl1FQU8KU/eRtv7jnFS2+18MuXD/PLlw9TXJjDR95jqK8qjPk9I/oyZs0P18jkNZIx5m+AAmvt/eHndwFrrLX3hJ+/HfiKtfaq8POFwHrgHcDr1to54fZMYMBamx1Fv68CXoziPBERGe1q4KWRjdGM8N2cOzZ1AcEojo9sZ8Trzud1nA63AIEJzhUREUcGMBsnh44STcJvxkm+w6qBEyOOzx7j+Cmg2BiTYa0NhM+JfN35eBnjr5OIiEzowHgHotlo/hxwnTHGY4zJB24Fnh0+aK09AgwaY64MN30UeMZa68OZlvlQuP1jwDOT6LyIiEyBCRO+tfY4cB+wEdgGrLPWbjbGrDfGrAqfthZ4wBizBygEHgq3/ylwjzFmF863hPunOgAREYnOhBdtRUQkNcRYO1BERJKVEr6ISJpQwhcRSRNK+CIiaULVMs/DGDMLeAW4yVp72BhzJ/A5nM1gG4D/C5QBv4l4WTHgsdYWGmNKgB8A9UAb8EfW2pMzGMK4ooktXABvPvB9nMJ43cDHrbVHjDHZwHeBVcAZ4A5r7Z6Zj2RsMcS3BngYyAGO4tSEOpmo8Rlj/g74o/DTX1lrP2eMuR74GpAH/DhiV3zSFTWMJb6I13wfp/zLo+HnCRlfjD+7W4Av4WxkPQR8wlrbdaGxaYQ/DmPM23A2fy0KPzfAPwLXWWuX4VQO/bS19pS1ttFa2whcChwG7gm/zT8CL1prG4DvAP8+s1GMLdrYwqf/A/DDcHw/wymkR/h4fzi2z+DUSkoI0cZnjHEBTwCfs9Yux/nD9u3w2yRcfOHk8B5gJdAIXGaM+QjwPeAWoAFYbYy5IfySx4FPWWsX4SSOu8Ptw0UNFwNv4BQ1jLtY4zPG1BhjnsKp7RUp4eKLJbbwYOU/gPdba1cA24Evht/qgmJTwh/f3cCf8fvdwcuBV621LeHnT/P7wnDDPoFTL2hd+Pn7cUb4AD8EbjDGzOwdD8YWS2wZOCNEgAKc0S5ExGatfQHwhEcfiSDa+CpwCv9tjGh/nzEmh8SMrwXnm8lQeGPjbpw/avustYestX6cJH/7OEUNbw///l2D84fubPsMxnA+UccXPn8t8CTwk+E3SOD4YoktC/iz8B4ocBL+vKmITVM647DW3gXgDA4BaAK+ZoyZi5NIbsMpI0H4vAycDWq3RLxNDc4PmvBX6R7AQ/QlJqZFjLF9Aack9qeBbODt4fazsYW1AHNwpkXiKob42oF+Y8x7rLW/AT6M83+2chIwPmvtzuHHxpiLcaYHvs7Y/Ryv/xVATzjBRLbHXYzxYa39SvjcqyKOJ2R8scRmre0Afh4+Nw/4fPjcC45NI/woWWv34vyH/yVOyYjtwFDEKe/D+Wv9VkTbyLqpIwvPJYQJYnsMuMdaWwvcC/w8PBUyUVG9hDFefNbaEE6pkL81xmwFSoAOnNgTNj5jzFLgt8BfAQeJrnjhVBQ1nBFRxjeehI4vltiMMcXAr4Ama+1jTEFsSvhRMsbkAputtSuttVcAxzm3SNEHgB+NeNlxwiPlcHnoIpyEklDGi80Y4wEWW2ufBLDW/gwnngrGL5qXcCb42fmstddaa1fifKXOADpJ0PjCNaueBz4fTgLj9XPCoobh9liKGk67GOIbT8LGF0tsxpjZ/H5wclf4+AXHpoQfvQLgeWNMUXgFx58DP444/nZG1/Bfj1M0Dpwici+G5+8SzXixteMUxrsazv7C9lpr24iILfyVetBaG/fpnHGc72f3n8aY1eHHfwn81FobJAHjC09J/QJnxdDw4OI155BZGE4Ed+AUL0y6ooaxxDfeeyRqfLHEFn78FPATa+1nwt9EpyQ2zeFHyVrbYYz5ErAJZ553XcTFWXCWXjaPeNkXgEeNMTtxljSunZHOxuh8sRlj/hD4engusRdnCgScOcVvhWPz4iSUhDTBz+5/48SRjzOa+pNweyLG91kgF+d6xHDbN4E7cVZQ5eL8oRq+qLcW+E541ccWzi1q+Jgx5n6caxIfmYnORyHW+MaTiPHFEtsHcFb8ZRpjhlcgvRG+NnVBsal4mohImtCUjohImlDCFxFJE0r4IiJpQglfRCRNKOGLiKQJLcuUtGeMeRi4DLjSWhsIt2XgVJjcOLI6o0iy0ghfxClzXQD8TUTb3+CUUv67uPRIZBpoHb4IYIxZBryMU43QhVPDZLW19rgx5gPA3+Js2urHqXr4Wnj7+7dwCuJV45TGvt1a226MacYp0bwCp/zyUzMdk8hIGuGLAOGid/fh3DDke8CfhJP9YpwbUbw3XG/nT3EKyOXibIV/wVr7dmAB4OPc3dRN1toGJXtJFEr4ImHW2q/jjOA3WWuHa5S8B6gFNhpjtuHcJCUELLDWfhXYbIz5S5y7ZjUAhRFvObK2kkhc6aKtyLkOcW4V1Azg19basyP3cCGs48aYr+JM2TwKbMS5TV1kSey431ZPJJJG+CLn9zzOncqGb5d4M7ANp9jVe4EHrLWP41QWvQ7nD4RIQlLCFzkPa+12nIqaPzHGNAH/D7jZWjuAM7f/78aY7Th3KHoRWBi3zopMQKt0RETShEb4IiJpQglfRCRNKOGLiKQJJXwRkTShhC8ikiaU8EVE0oQSvohImlDCFxFJE/8frz3ssrofjrUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_no_mv['Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "q = data_no_mv['Year'].quantile(0.01)\n",
    "data_4 = data_3[data_3['Year']>q]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ce215fff88>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_4['Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_cleaned = data_4.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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",
       "      <td>count</td>\n",
       "      <td>3907</td>\n",
       "      <td>3907.000000</td>\n",
       "      <td>3907</td>\n",
       "      <td>3907.000000</td>\n",
       "      <td>3907.000000</td>\n",
       "      <td>3907</td>\n",
       "      <td>3907</td>\n",
       "      <td>3907.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>unique</td>\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",
       "      <td>top</td>\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",
       "      <td>freq</td>\n",
       "      <td>848</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1472</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1815</td>\n",
       "      <td>3545</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19960.692119</td>\n",
       "      <td>NaN</td>\n",
       "      <td>158.929358</td>\n",
       "      <td>2.474771</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006.798055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26048.838086</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.242696</td>\n",
       "      <td>0.978819</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.127291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\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",
       "      <td>25%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7274.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.500000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11800.000000</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",
       "      <td>75%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22000.000000</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",
       "      <td>max</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>433.000000</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         3907    3907.000000   3907  3907.000000  3907.000000   \n",
       "unique           7            NaN      6          NaN          NaN   \n",
       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
       "freq           848            NaN   1472          NaN          NaN   \n",
       "mean           NaN   19960.692119    NaN   158.929358     2.474771   \n",
       "std            NaN   26048.838086    NaN    96.242696     0.978819   \n",
       "min            NaN     800.000000    NaN     0.000000     0.600000   \n",
       "25%            NaN    7274.500000    NaN    89.500000     1.800000   \n",
       "50%            NaN   11800.000000    NaN   155.000000     2.200000   \n",
       "75%            NaN   22000.000000    NaN   225.000000     3.000000   \n",
       "max            NaN  300000.000000    NaN   433.000000     6.300000   \n",
       "\n",
       "       Engine Type Registration         Year  \n",
       "count         3907         3907  3907.000000  \n",
       "unique           4            2          NaN  \n",
       "top         Diesel          yes          NaN  \n",
       "freq          1815         3545          NaN  \n",
       "mean           NaN          NaN  2006.798055  \n",
       "std            NaN          NaN     6.127291  \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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cleaned.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### Checking Ols Assumption "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ce226b7348>"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_cleaned['Price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relaxing the assumptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3902</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>van</td>\n",
       "      <td>163</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2008</td>\n",
       "      <td>9.350102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3903</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>17900.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>35</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2014</td>\n",
       "      <td>9.792556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3904</td>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>125000.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2014</td>\n",
       "      <td>11.736069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3905</td>\n",
       "      <td>BMW</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>1999</td>\n",
       "      <td>8.779557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3906</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>van</td>\n",
       "      <td>124</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2013</td>\n",
       "      <td>9.510445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3907 rows × 9 columns</p>\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",
       "3902     Volkswagen   11500.0        van      163      2.5      Diesel   \n",
       "3903         Toyota   17900.0      sedan       35      1.6      Petrol   \n",
       "3904  Mercedes-Benz  125000.0      sedan        9      3.0      Diesel   \n",
       "3905            BMW    6500.0      sedan        1      3.5      Petrol   \n",
       "3906     Volkswagen   13500.0        van      124      2.0      Diesel   \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  \n",
       "...           ...   ...        ...  \n",
       "3902          yes  2008   9.350102  \n",
       "3903          yes  2014   9.792556  \n",
       "3904          yes  2014  11.736069  \n",
       "3905          yes  1999   8.779557  \n",
       "3906          yes  2013   9.510445  \n",
       "\n",
       "[3907 rows x 9 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_price = np.log(data_cleaned['Price'])\n",
    "data_cleaned['log_price'] = log_price\n",
    "data_cleaned"
   ]
  },
  {
   "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": {
      "needs_background": "light"
     },
     "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",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multicolinatrity\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Brand', 'Price', 'Body', 'Mileage', 'EngineV', 'Engine Type',\n",
       "       'Registration', 'Year', 'log_price'], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cleaned.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "variables = data_cleaned[['Mileage','Price','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": 30,
   "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",
       "      <td>0</td>\n",
       "      <td>3.706109</td>\n",
       "      <td>Mileage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2.710930</td>\n",
       "      <td>Price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6.480095</td>\n",
       "      <td>EngineV</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        VIF Features\n",
       "0  3.706109  Mileage\n",
       "1  2.710930    Price\n",
       "2  6.480095  EngineV"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>log_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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>8.342840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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>8.974618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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>9.495519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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>10.043249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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>9.814656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3902</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>van</td>\n",
       "      <td>163</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>9.350102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3903</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>17900.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>35</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>9.792556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3904</td>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>125000.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>11.736069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3905</td>\n",
       "      <td>BMW</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>8.779557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3906</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>van</td>\n",
       "      <td>124</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>9.510445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3907 rows × 8 columns</p>\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",
       "3902     Volkswagen   11500.0        van      163      2.5      Diesel   \n",
       "3903         Toyota   17900.0      sedan       35      1.6      Petrol   \n",
       "3904  Mercedes-Benz  125000.0      sedan        9      3.0      Diesel   \n",
       "3905            BMW    6500.0      sedan        1      3.5      Petrol   \n",
       "3906     Volkswagen   13500.0        van      124      2.0      Diesel   \n",
       "\n",
       "     Registration  log_price  \n",
       "0             yes   8.342840  \n",
       "1             yes   8.974618  \n",
       "2             yes   9.495519  \n",
       "3             yes  10.043249  \n",
       "4             yes   9.814656  \n",
       "...           ...        ...  \n",
       "3902          yes   9.350102  \n",
       "3903          yes   9.792556  \n",
       "3904          yes  11.736069  \n",
       "3905          yes   8.779557  \n",
       "3906          yes   9.510445  \n",
       "\n",
       "[3907 rows x 8 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_no_multicollinerity= data_cleaned.drop(['Year'],axis=1)\n",
    "data_no_multicollinerity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_with_dummy = pd.get_dummies(data_no_multicollinerity,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>Price</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",
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       "      <td>4200.0</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>240</td>\n",
       "      <td>4.2</td>\n",
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       "      <td>4</td>\n",
       "      <td>18300.0</td>\n",
       "      <td>120</td>\n",
       "      <td>2.0</td>\n",
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      "text/plain": [
       "     Price  Mileage  EngineV  log_price  Brand_BMW  Brand_Mercedes-Benz  \\\n",
       "0   4200.0      277      2.0   8.342840          1                    0   \n",
       "1   7900.0      427      2.9   8.974618          0                    1   \n",
       "2  13300.0      358      5.0   9.495519          0                    1   \n",
       "3  23000.0      240      4.2  10.043249          0                    0   \n",
       "4  18300.0      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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_with_dummy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rearrange a bit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Price', '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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_with_dummy.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['Price', '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']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_prerocessed = data_with_dummy[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3904</td>\n",
       "      <td>125000.0</td>\n",
       "      <td>9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.736069</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3905</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.779557</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>1</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",
       "      <td>3906</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>124</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.510445</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>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>3907 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Price  Mileage  EngineV  log_price  Brand_BMW  Brand_Mercedes-Benz  \\\n",
       "0       4200.0      277      2.0   8.342840          1                    0   \n",
       "1       7900.0      427      2.9   8.974618          0                    1   \n",
       "2      13300.0      358      5.0   9.495519          0                    1   \n",
       "3      23000.0      240      4.2  10.043249          0                    0   \n",
       "4      18300.0      120      2.0   9.814656          0                    0   \n",
       "...        ...      ...      ...        ...        ...                  ...   \n",
       "3902   11500.0      163      2.5   9.350102          0                    0   \n",
       "3903   17900.0       35      1.6   9.792556          0                    0   \n",
       "3904  125000.0        9      3.0  11.736069          0                    1   \n",
       "3905    6500.0        1      3.5   8.779557          1                    0   \n",
       "3906   13500.0      124      2.0   9.510445          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",
       "3902                 0              0             0                 1   \n",
       "3903                 0              0             1                 0   \n",
       "3904                 0              0             0                 0   \n",
       "3905                 0              0             0                 0   \n",
       "3906                 0              0             0                 1   \n",
       "\n",
       "      Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  \\\n",
       "0              0           0           1           0         0   \n",
       "1              0           0           0           0         1   \n",
       "2              0           0           1           0         0   \n",
       "3              0           0           0           0         0   \n",
       "4              0           0           0           0         0   \n",
       "...          ...         ...         ...         ...       ...   \n",
       "3902           0           0           0           0         1   \n",
       "3903           0           0           1           0         0   \n",
       "3904           0           0           1           0         0   \n",
       "3905           0           0           1           0         0   \n",
       "3906           0           0           0           0         1   \n",
       "\n",
       "      Engine Type_Gas  Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
       "0                   0                  0                   1                 1  \n",
       "1                   0                  0                   0                 1  \n",
       "2                   1                  0                   0                 1  \n",
       "3                   0                  0                   1                 1  \n",
       "4                   0                  0                   1                 1  \n",
       "...               ...                ...                 ...               ...  \n",
       "3902                0                  0                   0                 1  \n",
       "3903                0                  0                   1                 1  \n",
       "3904                0                  0                   0                 1  \n",
       "3905                0                  0                   1                 1  \n",
       "3906                0                  0                   0                 1  \n",
       "\n",
       "[3907 rows x 19 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_prerocessed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Declear inputs and targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "targets = data_prerocessed['log_price']\n",
    "inputs = data_prerocessed.drop(['log_price'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scale data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 50,
     "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": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs_scale = scaler.transform(inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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_scale,targets,test_size=0.2,random_state = 101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = LinearRegression()\n",
    "\n",
    "reg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat = reg.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_train,y_hat)\n",
    "plt.xlabel('Targets(Y_train)',size=18)\n",
    "plt.ylabel('Prediction(y_hat)',size=18)\n",
    "plt.xlim(6,13)\n",
    "plt.ylim(6,13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Residuals_pdf')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_train - y_hat)\n",
    "plt.title('Residuals_pdf',size =18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8479973108092679"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding weights and bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.440896222437043"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.37556998, -0.30519251,  0.12348859,  0.01002892, -0.02054452,\n",
       "       -0.09378764, -0.14420675, -0.02586401, -0.06415709, -0.10785229,\n",
       "       -0.07036688, -0.13027784, -0.08735168, -0.11377903, -0.07996661,\n",
       "       -0.02144992, -0.11514994,  0.28173925])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "req_summary = pd.DataFrame(inputs.columns.values,columns=['Feature'])\n",
    "req_summary['Weights'] = reg.coef_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>Feature</th>\n",
       "      <th>Weights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Price</td>\n",
       "      <td>0.375570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Mileage</td>\n",
       "      <td>-0.305193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>EngineV</td>\n",
       "      <td>0.123489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Brand_BMW</td>\n",
       "      <td>0.010029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Brand_Mercedes-Benz</td>\n",
       "      <td>-0.020545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Brand_Mitsubishi</td>\n",
       "      <td>-0.093788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Brand_Renault</td>\n",
       "      <td>-0.144207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Brand_Toyota</td>\n",
       "      <td>-0.025864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Brand_Volkswagen</td>\n",
       "      <td>-0.064157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Body_hatch</td>\n",
       "      <td>-0.107852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Body_other</td>\n",
       "      <td>-0.070367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Body_sedan</td>\n",
       "      <td>-0.130278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Body_vagon</td>\n",
       "      <td>-0.087352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>Body_van</td>\n",
       "      <td>-0.113779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>Engine Type_Gas</td>\n",
       "      <td>-0.079967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Engine Type_Other</td>\n",
       "      <td>-0.021450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>Engine Type_Petrol</td>\n",
       "      <td>-0.115150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>Registration_yes</td>\n",
       "      <td>0.281739</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Feature   Weights\n",
       "0                 Price  0.375570\n",
       "1               Mileage -0.305193\n",
       "2               EngineV  0.123489\n",
       "3             Brand_BMW  0.010029\n",
       "4   Brand_Mercedes-Benz -0.020545\n",
       "5      Brand_Mitsubishi -0.093788\n",
       "6         Brand_Renault -0.144207\n",
       "7          Brand_Toyota -0.025864\n",
       "8      Brand_Volkswagen -0.064157\n",
       "9            Body_hatch -0.107852\n",
       "10           Body_other -0.070367\n",
       "11           Body_sedan -0.130278\n",
       "12           Body_vagon -0.087352\n",
       "13             Body_van -0.113779\n",
       "14      Engine Type_Gas -0.079967\n",
       "15    Engine Type_Other -0.021450\n",
       "16   Engine Type_Petrol -0.115150\n",
       "17     Registration_yes  0.281739"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "req_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['BMW', 'Mercedes-Benz', 'Audi', 'Toyota', 'Renault', 'Volkswagen',\n",
       "       'Mitsubishi'], dtype=object)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cleaned['Brand'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat_test = reg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_train,y_hat,alpha =0.2)\n",
    "plt.xlabel('Targets(Y_train)',size=18)\n",
    "plt.ylabel('Prediction(y_hat)',size=18)\n",
    "plt.xlim(6,13)\n",
    "plt.ylim(6,13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pf = pd.DataFrame(y_hat_test,columns=['Prediction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of      Prediction   Target\n",
       "0      8.890196      NaN\n",
       "1      9.717387      NaN\n",
       "2      9.415209      NaN\n",
       "3      9.045553      NaN\n",
       "4      9.654880  18300.0\n",
       "..          ...      ...\n",
       "777    9.797037      NaN\n",
       "778   10.414669      NaN\n",
       "779    9.699657      NaN\n",
       "780    8.996825      NaN\n",
       "781    8.891791   8900.0\n",
       "\n",
       "[782 rows x 2 columns]>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pf['Target'] = np.exp(y_test)\n",
    "df_pf.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = y_test.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    9.210240\n",
       "1    9.952230\n",
       "2    9.384294\n",
       "3    8.070906\n",
       "4    9.539194\n",
       "Name: log_price, dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pf['Residuals']=df_pf['Target']-df_pf['Prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pf['Difference%']=np.absolute(df_pf['Residuals']/df_pf['Target']*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>Prediction</th>\n",
       "      <th>Target</th>\n",
       "      <th>Residuals</th>\n",
       "      <th>Difference%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>782.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>9.409337</td>\n",
       "      <td>20964.795724</td>\n",
       "      <td>20955.433819</td>\n",
       "      <td>99.900226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.854736</td>\n",
       "      <td>27106.078841</td>\n",
       "      <td>27106.099621</td>\n",
       "      <td>0.093056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>7.447382</td>\n",
       "      <td>1650.000000</td>\n",
       "      <td>1640.928132</td>\n",
       "      <td>99.450190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>8.893701</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>7990.809814</td>\n",
       "      <td>99.885217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>9.300806</td>\n",
       "      <td>12475.000000</td>\n",
       "      <td>12465.001610</td>\n",
       "      <td>99.924477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>9.893958</td>\n",
       "      <td>20150.000000</td>\n",
       "      <td>20141.146890</td>\n",
       "      <td>99.957209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>13.203846</td>\n",
       "      <td>199999.000000</td>\n",
       "      <td>199989.360044</td>\n",
       "      <td>99.995180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Prediction         Target      Residuals  Difference%\n",
       "count  782.000000     152.000000     152.000000   152.000000\n",
       "mean     9.409337   20964.795724   20955.433819    99.900226\n",
       "std      0.854736   27106.078841   27106.099621     0.093056\n",
       "min      7.447382    1650.000000    1640.928132    99.450190\n",
       "25%      8.893701    8000.000000    7990.809814    99.885217\n",
       "50%      9.300806   12475.000000   12465.001610    99.924477\n",
       "75%      9.893958   20150.000000   20141.146890    99.957209\n",
       "max     13.203846  199999.000000  199989.360044    99.995180"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>Prediction</th>\n",
       "      <th>Target</th>\n",
       "      <th>Residuals</th>\n",
       "      <th>Difference%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>9.07</td>\n",
       "      <td>1650.00</td>\n",
       "      <td>1640.93</td>\n",
       "      <td>99.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>366</td>\n",
       "      <td>8.40</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>1691.60</td>\n",
       "      <td>99.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>415</td>\n",
       "      <td>11.45</td>\n",
       "      <td>2450.00</td>\n",
       "      <td>2438.55</td>\n",
       "      <td>99.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>586</td>\n",
       "      <td>11.29</td>\n",
       "      <td>2800.00</td>\n",
       "      <td>2788.71</td>\n",
       "      <td>99.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>587</td>\n",
       "      <td>8.34</td>\n",
       "      <td>2200.00</td>\n",
       "      <td>2191.66</td>\n",
       "      <td>99.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>376</td>\n",
       "      <td>9.31</td>\n",
       "      <td>2900.00</td>\n",
       "      <td>2890.69</td>\n",
       "      <td>99.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>9.35</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>2990.65</td>\n",
       "      <td>99.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>184</td>\n",
       "      <td>9.18</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>2990.82</td>\n",
       "      <td>99.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>555</td>\n",
       "      <td>8.99</td>\n",
       "      <td>3100.00</td>\n",
       "      <td>3091.01</td>\n",
       "      <td>99.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>429</td>\n",
       "      <td>8.64</td>\n",
       "      <td>3149.25</td>\n",
       "      <td>3140.61</td>\n",
       "      <td>99.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>304</td>\n",
       "      <td>8.09</td>\n",
       "      <td>2999.00</td>\n",
       "      <td>2990.91</td>\n",
       "      <td>99.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>196</td>\n",
       "      <td>9.47</td>\n",
       "      <td>3800.00</td>\n",
       "      <td>3790.53</td>\n",
       "      <td>99.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>759</td>\n",
       "      <td>9.45</td>\n",
       "      <td>4400.00</td>\n",
       "      <td>4390.55</td>\n",
       "      <td>99.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>203</td>\n",
       "      <td>10.08</td>\n",
       "      <td>4700.00</td>\n",
       "      <td>4689.92</td>\n",
       "      <td>99.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>423</td>\n",
       "      <td>10.24</td>\n",
       "      <td>5300.00</td>\n",
       "      <td>5289.76</td>\n",
       "      <td>99.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>204</td>\n",
       "      <td>9.28</td>\n",
       "      <td>4999.00</td>\n",
       "      <td>4989.72</td>\n",
       "      <td>99.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>629</td>\n",
       "      <td>9.43</td>\n",
       "      <td>5100.00</td>\n",
       "      <td>5090.57</td>\n",
       "      <td>99.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>576</td>\n",
       "      <td>9.38</td>\n",
       "      <td>5100.00</td>\n",
       "      <td>5090.62</td>\n",
       "      <td>99.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>669</td>\n",
       "      <td>8.69</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>4791.31</td>\n",
       "      <td>99.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>607</td>\n",
       "      <td>10.50</td>\n",
       "      <td>5900.00</td>\n",
       "      <td>5889.50</td>\n",
       "      <td>99.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>603</td>\n",
       "      <td>10.07</td>\n",
       "      <td>5900.00</td>\n",
       "      <td>5889.93</td>\n",
       "      <td>99.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>252</td>\n",
       "      <td>9.52</td>\n",
       "      <td>5900.00</td>\n",
       "      <td>5890.48</td>\n",
       "      <td>99.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>773</td>\n",
       "      <td>8.99</td>\n",
       "      <td>5700.00</td>\n",
       "      <td>5691.01</td>\n",
       "      <td>99.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>12.44</td>\n",
       "      <td>7900.00</td>\n",
       "      <td>7887.56</td>\n",
       "      <td>99.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>497</td>\n",
       "      <td>10.20</td>\n",
       "      <td>6799.00</td>\n",
       "      <td>6788.80</td>\n",
       "      <td>99.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>621</td>\n",
       "      <td>9.26</td>\n",
       "      <td>6200.00</td>\n",
       "      <td>6190.74</td>\n",
       "      <td>99.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>10.11</td>\n",
       "      <td>6999.00</td>\n",
       "      <td>6988.89</td>\n",
       "      <td>99.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>9.19</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>6490.81</td>\n",
       "      <td>99.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>328</td>\n",
       "      <td>8.75</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>6491.25</td>\n",
       "      <td>99.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>247</td>\n",
       "      <td>10.16</td>\n",
       "      <td>7700.00</td>\n",
       "      <td>7689.84</td>\n",
       "      <td>99.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>212</td>\n",
       "      <td>11.45</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>8888.55</td>\n",
       "      <td>99.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>8.76</td>\n",
       "      <td>6900.00</td>\n",
       "      <td>6891.24</td>\n",
       "      <td>99.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>12.87</td>\n",
       "      <td>10300.00</td>\n",
       "      <td>10287.13</td>\n",
       "      <td>99.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>289</td>\n",
       "      <td>9.37</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>7490.63</td>\n",
       "      <td>99.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>420</td>\n",
       "      <td>8.71</td>\n",
       "      <td>7000.00</td>\n",
       "      <td>6991.29</td>\n",
       "      <td>99.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>617</td>\n",
       "      <td>11.10</td>\n",
       "      <td>9000.00</td>\n",
       "      <td>8988.90</td>\n",
       "      <td>99.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>112</td>\n",
       "      <td>9.26</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>7990.74</td>\n",
       "      <td>99.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>332</td>\n",
       "      <td>9.34</td>\n",
       "      <td>8100.00</td>\n",
       "      <td>8090.66</td>\n",
       "      <td>99.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>496</td>\n",
       "      <td>9.17</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>7990.83</td>\n",
       "      <td>99.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>164</td>\n",
       "      <td>9.11</td>\n",
       "      <td>8100.00</td>\n",
       "      <td>8090.89</td>\n",
       "      <td>99.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>8.57</td>\n",
       "      <td>7700.00</td>\n",
       "      <td>7691.43</td>\n",
       "      <td>99.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>9.14</td>\n",
       "      <td>8250.00</td>\n",
       "      <td>8240.86</td>\n",
       "      <td>99.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>742</td>\n",
       "      <td>8.10</td>\n",
       "      <td>7400.00</td>\n",
       "      <td>7391.90</td>\n",
       "      <td>99.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104</td>\n",
       "      <td>8.76</td>\n",
       "      <td>8200.00</td>\n",
       "      <td>8191.24</td>\n",
       "      <td>99.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>384</td>\n",
       "      <td>10.22</td>\n",
       "      <td>9600.00</td>\n",
       "      <td>9589.78</td>\n",
       "      <td>99.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>323</td>\n",
       "      <td>8.12</td>\n",
       "      <td>7700.00</td>\n",
       "      <td>7691.88</td>\n",
       "      <td>99.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>9.03</td>\n",
       "      <td>8700.00</td>\n",
       "      <td>8690.97</td>\n",
       "      <td>99.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>123</td>\n",
       "      <td>9.14</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>8890.86</td>\n",
       "      <td>99.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>568</td>\n",
       "      <td>9.56</td>\n",
       "      <td>9400.00</td>\n",
       "      <td>9390.44</td>\n",
       "      <td>99.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>666</td>\n",
       "      <td>9.33</td>\n",
       "      <td>9200.00</td>\n",
       "      <td>9190.67</td>\n",
       "      <td>99.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>341</td>\n",
       "      <td>9.11</td>\n",
       "      <td>9000.00</td>\n",
       "      <td>8990.89</td>\n",
       "      <td>99.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>510</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>8491.42</td>\n",
       "      <td>99.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>484</td>\n",
       "      <td>7.69</td>\n",
       "      <td>7650.00</td>\n",
       "      <td>7642.31</td>\n",
       "      <td>99.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>781</td>\n",
       "      <td>8.89</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>8891.11</td>\n",
       "      <td>99.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>569</td>\n",
       "      <td>9.96</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>9990.04</td>\n",
       "      <td>99.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>8.96</td>\n",
       "      <td>9200.00</td>\n",
       "      <td>9191.04</td>\n",
       "      <td>99.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>199</td>\n",
       "      <td>9.41</td>\n",
       "      <td>9700.00</td>\n",
       "      <td>9690.59</td>\n",
       "      <td>99.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>257</td>\n",
       "      <td>9.57</td>\n",
       "      <td>9999.00</td>\n",
       "      <td>9989.43</td>\n",
       "      <td>99.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>291</td>\n",
       "      <td>8.71</td>\n",
       "      <td>9100.00</td>\n",
       "      <td>9091.29</td>\n",
       "      <td>99.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>9.94</td>\n",
       "      <td>10799.00</td>\n",
       "      <td>10789.06</td>\n",
       "      <td>99.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>407</td>\n",
       "      <td>9.16</td>\n",
       "      <td>9999.00</td>\n",
       "      <td>9989.84</td>\n",
       "      <td>99.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>439</td>\n",
       "      <td>9.66</td>\n",
       "      <td>10600.00</td>\n",
       "      <td>10590.34</td>\n",
       "      <td>99.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>221</td>\n",
       "      <td>8.95</td>\n",
       "      <td>9900.00</td>\n",
       "      <td>9891.05</td>\n",
       "      <td>99.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>256</td>\n",
       "      <td>9.03</td>\n",
       "      <td>9999.00</td>\n",
       "      <td>9989.97</td>\n",
       "      <td>99.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>209</td>\n",
       "      <td>9.10</td>\n",
       "      <td>10100.00</td>\n",
       "      <td>10090.90</td>\n",
       "      <td>99.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>182</td>\n",
       "      <td>9.26</td>\n",
       "      <td>10650.00</td>\n",
       "      <td>10640.74</td>\n",
       "      <td>99.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>325</td>\n",
       "      <td>8.26</td>\n",
       "      <td>9500.00</td>\n",
       "      <td>9491.74</td>\n",
       "      <td>99.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>442</td>\n",
       "      <td>10.38</td>\n",
       "      <td>12250.00</td>\n",
       "      <td>12239.62</td>\n",
       "      <td>99.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>9.94</td>\n",
       "      <td>11950.00</td>\n",
       "      <td>11940.06</td>\n",
       "      <td>99.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>639</td>\n",
       "      <td>11.09</td>\n",
       "      <td>13700.00</td>\n",
       "      <td>13688.91</td>\n",
       "      <td>99.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>9.59</td>\n",
       "      <td>11990.00</td>\n",
       "      <td>11980.41</td>\n",
       "      <td>99.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>358</td>\n",
       "      <td>9.19</td>\n",
       "      <td>11500.00</td>\n",
       "      <td>11490.81</td>\n",
       "      <td>99.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>330</td>\n",
       "      <td>9.48</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>11990.52</td>\n",
       "      <td>99.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>214</td>\n",
       "      <td>9.05</td>\n",
       "      <td>11500.00</td>\n",
       "      <td>11490.95</td>\n",
       "      <td>99.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>155</td>\n",
       "      <td>7.86</td>\n",
       "      <td>10299.00</td>\n",
       "      <td>10291.14</td>\n",
       "      <td>99.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>111</td>\n",
       "      <td>9.62</td>\n",
       "      <td>12700.00</td>\n",
       "      <td>12690.38</td>\n",
       "      <td>99.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>670</td>\n",
       "      <td>10.17</td>\n",
       "      <td>13500.00</td>\n",
       "      <td>13489.83</td>\n",
       "      <td>99.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>692</td>\n",
       "      <td>9.30</td>\n",
       "      <td>12700.00</td>\n",
       "      <td>12690.70</td>\n",
       "      <td>99.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>585</td>\n",
       "      <td>8.62</td>\n",
       "      <td>11900.00</td>\n",
       "      <td>11891.38</td>\n",
       "      <td>99.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>379</td>\n",
       "      <td>11.72</td>\n",
       "      <td>16300.00</td>\n",
       "      <td>16288.28</td>\n",
       "      <td>99.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>126</td>\n",
       "      <td>9.21</td>\n",
       "      <td>13000.00</td>\n",
       "      <td>12990.79</td>\n",
       "      <td>99.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>268</td>\n",
       "      <td>9.12</td>\n",
       "      <td>13000.00</td>\n",
       "      <td>12990.88</td>\n",
       "      <td>99.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>188</td>\n",
       "      <td>9.92</td>\n",
       "      <td>14200.00</td>\n",
       "      <td>14190.08</td>\n",
       "      <td>99.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>312</td>\n",
       "      <td>7.75</td>\n",
       "      <td>11100.00</td>\n",
       "      <td>11092.25</td>\n",
       "      <td>99.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>292</td>\n",
       "      <td>9.22</td>\n",
       "      <td>13300.00</td>\n",
       "      <td>13290.78</td>\n",
       "      <td>99.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>189</td>\n",
       "      <td>9.47</td>\n",
       "      <td>13700.00</td>\n",
       "      <td>13690.53</td>\n",
       "      <td>99.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>177</td>\n",
       "      <td>10.00</td>\n",
       "      <td>14500.00</td>\n",
       "      <td>14490.00</td>\n",
       "      <td>99.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>372</td>\n",
       "      <td>8.81</td>\n",
       "      <td>12800.00</td>\n",
       "      <td>12791.19</td>\n",
       "      <td>99.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>563</td>\n",
       "      <td>9.08</td>\n",
       "      <td>13500.00</td>\n",
       "      <td>13490.92</td>\n",
       "      <td>99.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>747</td>\n",
       "      <td>9.83</td>\n",
       "      <td>14700.00</td>\n",
       "      <td>14690.17</td>\n",
       "      <td>99.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>231</td>\n",
       "      <td>9.16</td>\n",
       "      <td>13700.00</td>\n",
       "      <td>13690.84</td>\n",
       "      <td>99.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>9.45</td>\n",
       "      <td>14200.00</td>\n",
       "      <td>14190.55</td>\n",
       "      <td>99.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>8.96</td>\n",
       "      <td>13566.00</td>\n",
       "      <td>13557.04</td>\n",
       "      <td>99.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>744</td>\n",
       "      <td>9.80</td>\n",
       "      <td>15500.00</td>\n",
       "      <td>15490.20</td>\n",
       "      <td>99.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>233</td>\n",
       "      <td>8.77</td>\n",
       "      <td>13999.00</td>\n",
       "      <td>13990.23</td>\n",
       "      <td>99.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>368</td>\n",
       "      <td>8.63</td>\n",
       "      <td>13893.75</td>\n",
       "      <td>13885.12</td>\n",
       "      <td>99.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>218</td>\n",
       "      <td>10.09</td>\n",
       "      <td>16999.00</td>\n",
       "      <td>16988.91</td>\n",
       "      <td>99.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>490</td>\n",
       "      <td>8.71</td>\n",
       "      <td>14899.00</td>\n",
       "      <td>14890.29</td>\n",
       "      <td>99.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>422</td>\n",
       "      <td>9.62</td>\n",
       "      <td>16500.00</td>\n",
       "      <td>16490.38</td>\n",
       "      <td>99.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>536</td>\n",
       "      <td>9.97</td>\n",
       "      <td>17200.00</td>\n",
       "      <td>17190.03</td>\n",
       "      <td>99.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>508</td>\n",
       "      <td>10.96</td>\n",
       "      <td>18999.00</td>\n",
       "      <td>18988.04</td>\n",
       "      <td>99.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>677</td>\n",
       "      <td>10.62</td>\n",
       "      <td>18700.00</td>\n",
       "      <td>18689.38</td>\n",
       "      <td>99.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>137</td>\n",
       "      <td>9.41</td>\n",
       "      <td>16900.00</td>\n",
       "      <td>16890.59</td>\n",
       "      <td>99.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>403</td>\n",
       "      <td>9.79</td>\n",
       "      <td>18100.00</td>\n",
       "      <td>18090.21</td>\n",
       "      <td>99.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>633</td>\n",
       "      <td>9.68</td>\n",
       "      <td>17900.00</td>\n",
       "      <td>17890.32</td>\n",
       "      <td>99.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>141</td>\n",
       "      <td>9.24</td>\n",
       "      <td>17300.00</td>\n",
       "      <td>17290.76</td>\n",
       "      <td>99.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>9.65</td>\n",
       "      <td>18300.00</td>\n",
       "      <td>18290.35</td>\n",
       "      <td>99.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>260</td>\n",
       "      <td>9.27</td>\n",
       "      <td>17700.00</td>\n",
       "      <td>17690.73</td>\n",
       "      <td>99.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>726</td>\n",
       "      <td>8.93</td>\n",
       "      <td>17977.00</td>\n",
       "      <td>17968.07</td>\n",
       "      <td>99.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>647</td>\n",
       "      <td>9.15</td>\n",
       "      <td>19000.00</td>\n",
       "      <td>18990.85</td>\n",
       "      <td>99.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>588</td>\n",
       "      <td>8.39</td>\n",
       "      <td>17500.00</td>\n",
       "      <td>17491.61</td>\n",
       "      <td>99.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>768</td>\n",
       "      <td>8.88</td>\n",
       "      <td>20100.00</td>\n",
       "      <td>20091.12</td>\n",
       "      <td>99.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>183</td>\n",
       "      <td>8.79</td>\n",
       "      <td>20300.00</td>\n",
       "      <td>20291.21</td>\n",
       "      <td>99.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>480</td>\n",
       "      <td>8.57</td>\n",
       "      <td>19990.00</td>\n",
       "      <td>19981.43</td>\n",
       "      <td>99.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>339</td>\n",
       "      <td>8.17</td>\n",
       "      <td>19200.00</td>\n",
       "      <td>19191.83</td>\n",
       "      <td>99.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>668</td>\n",
       "      <td>9.80</td>\n",
       "      <td>23500.00</td>\n",
       "      <td>23490.20</td>\n",
       "      <td>99.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>657</td>\n",
       "      <td>8.88</td>\n",
       "      <td>21500.00</td>\n",
       "      <td>21491.12</td>\n",
       "      <td>99.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>459</td>\n",
       "      <td>9.00</td>\n",
       "      <td>21900.00</td>\n",
       "      <td>21891.00</td>\n",
       "      <td>99.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>180</td>\n",
       "      <td>8.24</td>\n",
       "      <td>20500.00</td>\n",
       "      <td>20491.76</td>\n",
       "      <td>99.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>200</td>\n",
       "      <td>8.68</td>\n",
       "      <td>25342.20</td>\n",
       "      <td>25333.52</td>\n",
       "      <td>99.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>9.62</td>\n",
       "      <td>28500.00</td>\n",
       "      <td>28490.38</td>\n",
       "      <td>99.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>9.52</td>\n",
       "      <td>28500.00</td>\n",
       "      <td>28490.48</td>\n",
       "      <td>99.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>458</td>\n",
       "      <td>8.85</td>\n",
       "      <td>26850.00</td>\n",
       "      <td>26841.15</td>\n",
       "      <td>99.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>498</td>\n",
       "      <td>8.42</td>\n",
       "      <td>25749.75</td>\n",
       "      <td>25741.33</td>\n",
       "      <td>99.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>522</td>\n",
       "      <td>10.02</td>\n",
       "      <td>33700.00</td>\n",
       "      <td>33689.98</td>\n",
       "      <td>99.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>8.92</td>\n",
       "      <td>31500.00</td>\n",
       "      <td>31491.08</td>\n",
       "      <td>99.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>721</td>\n",
       "      <td>9.58</td>\n",
       "      <td>34900.00</td>\n",
       "      <td>34890.42</td>\n",
       "      <td>99.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>246</td>\n",
       "      <td>9.09</td>\n",
       "      <td>34900.00</td>\n",
       "      <td>34890.91</td>\n",
       "      <td>99.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>400</td>\n",
       "      <td>9.29</td>\n",
       "      <td>36900.00</td>\n",
       "      <td>36890.71</td>\n",
       "      <td>99.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>215</td>\n",
       "      <td>9.28</td>\n",
       "      <td>36900.00</td>\n",
       "      <td>36890.72</td>\n",
       "      <td>99.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>8.21</td>\n",
       "      <td>32900.00</td>\n",
       "      <td>32891.79</td>\n",
       "      <td>99.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>649</td>\n",
       "      <td>9.52</td>\n",
       "      <td>39999.00</td>\n",
       "      <td>39989.48</td>\n",
       "      <td>99.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>572</td>\n",
       "      <td>8.97</td>\n",
       "      <td>38600.00</td>\n",
       "      <td>38591.03</td>\n",
       "      <td>99.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>534</td>\n",
       "      <td>9.17</td>\n",
       "      <td>39500.00</td>\n",
       "      <td>39490.83</td>\n",
       "      <td>99.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>316</td>\n",
       "      <td>9.20</td>\n",
       "      <td>39900.00</td>\n",
       "      <td>39890.80</td>\n",
       "      <td>99.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>741</td>\n",
       "      <td>8.91</td>\n",
       "      <td>39900.00</td>\n",
       "      <td>39891.09</td>\n",
       "      <td>99.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>8.48</td>\n",
       "      <td>38233.00</td>\n",
       "      <td>38224.52</td>\n",
       "      <td>99.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>589</td>\n",
       "      <td>10.89</td>\n",
       "      <td>49900.00</td>\n",
       "      <td>49889.11</td>\n",
       "      <td>99.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>681</td>\n",
       "      <td>10.10</td>\n",
       "      <td>47300.00</td>\n",
       "      <td>47289.90</td>\n",
       "      <td>99.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>9.47</td>\n",
       "      <td>50000.00</td>\n",
       "      <td>49990.53</td>\n",
       "      <td>99.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>520</td>\n",
       "      <td>8.96</td>\n",
       "      <td>51500.00</td>\n",
       "      <td>51491.04</td>\n",
       "      <td>99.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>556</td>\n",
       "      <td>10.20</td>\n",
       "      <td>59900.00</td>\n",
       "      <td>59889.80</td>\n",
       "      <td>99.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>665</td>\n",
       "      <td>8.87</td>\n",
       "      <td>53999.00</td>\n",
       "      <td>53990.13</td>\n",
       "      <td>99.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>258</td>\n",
       "      <td>9.83</td>\n",
       "      <td>61000.00</td>\n",
       "      <td>60990.17</td>\n",
       "      <td>99.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>9.61</td>\n",
       "      <td>69999.00</td>\n",
       "      <td>69989.39</td>\n",
       "      <td>99.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>557</td>\n",
       "      <td>8.55</td>\n",
       "      <td>63000.00</td>\n",
       "      <td>62991.45</td>\n",
       "      <td>99.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129</td>\n",
       "      <td>8.99</td>\n",
       "      <td>87777.00</td>\n",
       "      <td>87768.01</td>\n",
       "      <td>99.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>172</td>\n",
       "      <td>9.02</td>\n",
       "      <td>99999.00</td>\n",
       "      <td>99989.98</td>\n",
       "      <td>99.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>9.68</td>\n",
       "      <td>133000.00</td>\n",
       "      <td>132990.32</td>\n",
       "      <td>99.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>770</td>\n",
       "      <td>8.88</td>\n",
       "      <td>129999.00</td>\n",
       "      <td>129990.12</td>\n",
       "      <td>99.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>198</td>\n",
       "      <td>8.94</td>\n",
       "      <td>135000.00</td>\n",
       "      <td>134991.06</td>\n",
       "      <td>99.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>314</td>\n",
       "      <td>9.64</td>\n",
       "      <td>199999.00</td>\n",
       "      <td>199989.36</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>8.89</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>9.72</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>9.42</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>9.05</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>9.32</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>9.26</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>8.99</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>11.07</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>10.10</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>9.07</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>9.91</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>9.01</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>9.82</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>9.39</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>9.99</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>10.02</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>8.10</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>11.83</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>8.14</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>9.33</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>8.96</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>10.52</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>9.97</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>8.28</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>9.06</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>8.28</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>7.89</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>9.51</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>8.73</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>10.19</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>8.88</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>9.01</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>8.73</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>9.31</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>8.56</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>8.99</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>9.92</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>8.26</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>7.45</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>10.18</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>11.85</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>8.19</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>9.91</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>8.94</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>9.01</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>9.03</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>8.64</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>10.24</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>9.22</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>9.17</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>9.37</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>8.88</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>9.36</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>9.24</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>9.89</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>9.38</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>10.45</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>8.72</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>8.84</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>8.03</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>8.90</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>9.83</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>10.21</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>9.15</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>11.83</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>7.97</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>9.36</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>13.20</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>9.21</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>9.71</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>9.33</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>10.17</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>9.27</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>10.70</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>9.26</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>10.14</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>8.66</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>8.19</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>9.02</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>9.12</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>11.71</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>9.22</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>9.16</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>10.88</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>106</td>\n",
       "      <td>10.52</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107</td>\n",
       "      <td>9.54</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>9.36</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109</td>\n",
       "      <td>9.45</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>110</td>\n",
       "      <td>10.04</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113</td>\n",
       "      <td>9.97</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>114</td>\n",
       "      <td>8.99</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>115</td>\n",
       "      <td>10.04</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>9.67</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>117</td>\n",
       "      <td>7.96</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>8.32</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>10.19</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>9.34</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>8.71</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>124</td>\n",
       "      <td>10.38</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125</td>\n",
       "      <td>9.34</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>127</td>\n",
       "      <td>10.59</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>128</td>\n",
       "      <td>8.81</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>130</td>\n",
       "      <td>8.45</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>131</td>\n",
       "      <td>12.58</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>132</td>\n",
       "      <td>9.66</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>133</td>\n",
       "      <td>9.08</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>134</td>\n",
       "      <td>9.94</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>135</td>\n",
       "      <td>9.17</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>136</td>\n",
       "      <td>10.97</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>138</td>\n",
       "      <td>8.10</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>139</td>\n",
       "      <td>9.09</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>140</td>\n",
       "      <td>9.42</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>142</td>\n",
       "      <td>9.10</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>143</td>\n",
       "      <td>9.91</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>144</td>\n",
       "      <td>8.99</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>8.98</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>9.97</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>8.66</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>151</td>\n",
       "      <td>8.63</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>152</td>\n",
       "      <td>8.93</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>153</td>\n",
       "      <td>8.95</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>154</td>\n",
       "      <td>8.85</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>156</td>\n",
       "      <td>9.12</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>157</td>\n",
       "      <td>8.40</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>158</td>\n",
       "      <td>9.12</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>159</td>\n",
       "      <td>10.42</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>160</td>\n",
       "      <td>8.95</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>161</td>\n",
       "      <td>9.71</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>162</td>\n",
       "      <td>9.51</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>163</td>\n",
       "      <td>8.55</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>165</td>\n",
       "      <td>8.55</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>166</td>\n",
       "      <td>8.46</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>167</td>\n",
       "      <td>9.16</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>168</td>\n",
       "      <td>9.45</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>169</td>\n",
       "      <td>9.24</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>170</td>\n",
       "      <td>8.89</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>171</td>\n",
       "      <td>9.02</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>173</td>\n",
       "      <td>10.13</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>174</td>\n",
       "      <td>10.60</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>175</td>\n",
       "      <td>9.53</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>176</td>\n",
       "      <td>7.68</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>178</td>\n",
       "      <td>8.96</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>179</td>\n",
       "      <td>10.01</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>181</td>\n",
       "      <td>9.12</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>185</td>\n",
       "      <td>9.72</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>186</td>\n",
       "      <td>9.76</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>187</td>\n",
       "      <td>9.76</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>190</td>\n",
       "      <td>10.20</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>191</td>\n",
       "      <td>10.44</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>192</td>\n",
       "      <td>8.57</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>193</td>\n",
       "      <td>10.29</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>194</td>\n",
       "      <td>8.01</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>195</td>\n",
       "      <td>8.87</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>197</td>\n",
       "      <td>9.02</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>201</td>\n",
       "      <td>9.12</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>202</td>\n",
       "      <td>8.71</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>205</td>\n",
       "      <td>11.63</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>206</td>\n",
       "      <td>9.22</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>207</td>\n",
       "      <td>9.29</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>208</td>\n",
       "      <td>8.94</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>210</td>\n",
       "      <td>9.24</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>211</td>\n",
       "      <td>9.36</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>213</td>\n",
       "      <td>8.50</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>216</td>\n",
       "      <td>9.13</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>217</td>\n",
       "      <td>9.88</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>219</td>\n",
       "      <td>9.50</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>220</td>\n",
       "      <td>9.41</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>222</td>\n",
       "      <td>8.93</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>223</td>\n",
       "      <td>9.14</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>224</td>\n",
       "      <td>8.99</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>225</td>\n",
       "      <td>9.42</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>226</td>\n",
       "      <td>9.29</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>227</td>\n",
       "      <td>9.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>228</td>\n",
       "      <td>9.79</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>229</td>\n",
       "      <td>9.17</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>230</td>\n",
       "      <td>7.99</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>232</td>\n",
       "      <td>9.77</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>234</td>\n",
       "      <td>9.46</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>235</td>\n",
       "      <td>9.15</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>236</td>\n",
       "      <td>9.11</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>237</td>\n",
       "      <td>8.98</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>238</td>\n",
       "      <td>9.20</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>239</td>\n",
       "      <td>10.02</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>240</td>\n",
       "      <td>8.99</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>241</td>\n",
       "      <td>11.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>242</td>\n",
       "      <td>10.45</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>243</td>\n",
       "      <td>9.24</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>244</td>\n",
       "      <td>9.40</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>245</td>\n",
       "      <td>8.82</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>248</td>\n",
       "      <td>11.23</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>249</td>\n",
       "      <td>10.44</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>250</td>\n",
       "      <td>10.76</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>251</td>\n",
       "      <td>11.50</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>253</td>\n",
       "      <td>9.49</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>254</td>\n",
       "      <td>8.79</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>255</td>\n",
       "      <td>10.25</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>259</td>\n",
       "      <td>10.99</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>261</td>\n",
       "      <td>9.94</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>262</td>\n",
       "      <td>8.95</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>263</td>\n",
       "      <td>9.66</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>264</td>\n",
       "      <td>9.31</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>265</td>\n",
       "      <td>9.26</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>266</td>\n",
       "      <td>8.84</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>267</td>\n",
       "      <td>9.02</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>269</td>\n",
       "      <td>8.45</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>270</td>\n",
       "      <td>9.25</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>271</td>\n",
       "      <td>9.79</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>272</td>\n",
       "      <td>11.98</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>273</td>\n",
       "      <td>8.87</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>274</td>\n",
       "      <td>9.24</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>275</td>\n",
       "      <td>9.78</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>276</td>\n",
       "      <td>8.52</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>277</td>\n",
       "      <td>8.19</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>278</td>\n",
       "      <td>8.38</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>279</td>\n",
       "      <td>9.85</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>280</td>\n",
       "      <td>10.11</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>281</td>\n",
       "      <td>10.75</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>282</td>\n",
       "      <td>8.95</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>283</td>\n",
       "      <td>9.58</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>284</td>\n",
       "      <td>8.85</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>285</td>\n",
       "      <td>8.85</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>286</td>\n",
       "      <td>8.94</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>287</td>\n",
       "      <td>9.37</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>288</td>\n",
       "      <td>8.93</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>290</td>\n",
       "      <td>10.18</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>293</td>\n",
       "      <td>9.53</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>294</td>\n",
       "      <td>9.06</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>295</td>\n",
       "      <td>7.72</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>296</td>\n",
       "      <td>10.20</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>297</td>\n",
       "      <td>10.28</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>298</td>\n",
       "      <td>9.23</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>299</td>\n",
       "      <td>8.98</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>300</td>\n",
       "      <td>10.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>301</td>\n",
       "      <td>10.23</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>302</td>\n",
       "      <td>9.39</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>303</td>\n",
       "      <td>9.53</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>305</td>\n",
       "      <td>9.34</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>306</td>\n",
       "      <td>9.06</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>307</td>\n",
       "      <td>7.66</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>308</td>\n",
       "      <td>9.63</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>309</td>\n",
       "      <td>9.34</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>310</td>\n",
       "      <td>8.18</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>311</td>\n",
       "      <td>8.77</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>313</td>\n",
       "      <td>9.07</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>315</td>\n",
       "      <td>8.56</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>317</td>\n",
       "      <td>8.72</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>318</td>\n",
       "      <td>8.81</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>319</td>\n",
       "      <td>9.22</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>320</td>\n",
       "      <td>9.89</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>321</td>\n",
       "      <td>8.76</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>322</td>\n",
       "      <td>10.14</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>324</td>\n",
       "      <td>9.48</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>326</td>\n",
       "      <td>9.02</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>327</td>\n",
       "      <td>10.46</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>329</td>\n",
       "      <td>8.99</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>331</td>\n",
       "      <td>8.58</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>333</td>\n",
       "      <td>9.03</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>334</td>\n",
       "      <td>9.93</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>335</td>\n",
       "      <td>9.14</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>336</td>\n",
       "      <td>9.87</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>337</td>\n",
       "      <td>10.39</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>338</td>\n",
       "      <td>11.58</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>340</td>\n",
       "      <td>8.89</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>342</td>\n",
       "      <td>9.82</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>343</td>\n",
       "      <td>10.71</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>344</td>\n",
       "      <td>7.89</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>345</td>\n",
       "      <td>8.91</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>346</td>\n",
       "      <td>9.54</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>347</td>\n",
       "      <td>9.53</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>348</td>\n",
       "      <td>8.37</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>349</td>\n",
       "      <td>9.29</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>350</td>\n",
       "      <td>8.48</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>351</td>\n",
       "      <td>9.40</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>352</td>\n",
       "      <td>10.57</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>353</td>\n",
       "      <td>9.77</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>354</td>\n",
       "      <td>9.45</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>355</td>\n",
       "      <td>8.57</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>356</td>\n",
       "      <td>12.23</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>357</td>\n",
       "      <td>9.14</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>359</td>\n",
       "      <td>10.72</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>360</td>\n",
       "      <td>10.18</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>361</td>\n",
       "      <td>8.04</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>362</td>\n",
       "      <td>10.25</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>363</td>\n",
       "      <td>10.92</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>364</td>\n",
       "      <td>8.63</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>365</td>\n",
       "      <td>10.02</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>367</td>\n",
       "      <td>9.97</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>369</td>\n",
       "      <td>9.65</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>370</td>\n",
       "      <td>8.83</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371</td>\n",
       "      <td>7.82</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>373</td>\n",
       "      <td>9.51</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>374</td>\n",
       "      <td>10.07</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>375</td>\n",
       "      <td>9.24</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>377</td>\n",
       "      <td>9.57</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>378</td>\n",
       "      <td>7.99</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>380</td>\n",
       "      <td>10.87</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>381</td>\n",
       "      <td>9.36</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>382</td>\n",
       "      <td>9.63</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>383</td>\n",
       "      <td>9.59</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>385</td>\n",
       "      <td>9.37</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>386</td>\n",
       "      <td>8.95</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>387</td>\n",
       "      <td>8.28</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>388</td>\n",
       "      <td>10.22</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>389</td>\n",
       "      <td>8.23</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>390</td>\n",
       "      <td>10.44</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>391</td>\n",
       "      <td>10.55</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>392</td>\n",
       "      <td>9.20</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>393</td>\n",
       "      <td>8.74</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>394</td>\n",
       "      <td>9.30</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>395</td>\n",
       "      <td>9.65</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>396</td>\n",
       "      <td>9.81</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>397</td>\n",
       "      <td>9.37</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>398</td>\n",
       "      <td>9.50</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>399</td>\n",
       "      <td>9.28</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>401</td>\n",
       "      <td>9.92</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>402</td>\n",
       "      <td>9.71</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>404</td>\n",
       "      <td>8.75</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>405</td>\n",
       "      <td>8.85</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>406</td>\n",
       "      <td>8.21</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>408</td>\n",
       "      <td>8.56</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>409</td>\n",
       "      <td>9.31</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>410</td>\n",
       "      <td>9.75</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>411</td>\n",
       "      <td>9.12</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>412</td>\n",
       "      <td>9.38</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>413</td>\n",
       "      <td>10.78</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>414</td>\n",
       "      <td>10.45</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>416</td>\n",
       "      <td>9.45</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>417</td>\n",
       "      <td>7.93</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>418</td>\n",
       "      <td>8.49</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>419</td>\n",
       "      <td>9.19</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>421</td>\n",
       "      <td>10.64</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>424</td>\n",
       "      <td>10.09</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>425</td>\n",
       "      <td>9.85</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>426</td>\n",
       "      <td>10.44</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>427</td>\n",
       "      <td>10.13</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>428</td>\n",
       "      <td>7.87</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>430</td>\n",
       "      <td>9.16</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>431</td>\n",
       "      <td>10.06</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>432</td>\n",
       "      <td>9.39</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>433</td>\n",
       "      <td>9.60</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>434</td>\n",
       "      <td>9.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>435</td>\n",
       "      <td>8.10</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>436</td>\n",
       "      <td>9.89</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>437</td>\n",
       "      <td>9.23</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>438</td>\n",
       "      <td>10.79</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>440</td>\n",
       "      <td>8.15</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>441</td>\n",
       "      <td>9.57</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>443</td>\n",
       "      <td>9.61</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>444</td>\n",
       "      <td>9.65</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>445</td>\n",
       "      <td>9.66</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>446</td>\n",
       "      <td>9.83</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>447</td>\n",
       "      <td>10.14</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>448</td>\n",
       "      <td>8.91</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>449</td>\n",
       "      <td>8.91</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>450</td>\n",
       "      <td>10.36</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>451</td>\n",
       "      <td>8.78</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>452</td>\n",
       "      <td>12.16</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>453</td>\n",
       "      <td>10.14</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>454</td>\n",
       "      <td>9.21</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>455</td>\n",
       "      <td>11.02</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>456</td>\n",
       "      <td>9.41</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>457</td>\n",
       "      <td>11.49</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>460</td>\n",
       "      <td>9.39</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>461</td>\n",
       "      <td>8.56</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>462</td>\n",
       "      <td>10.58</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>463</td>\n",
       "      <td>10.18</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>464</td>\n",
       "      <td>10.73</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>465</td>\n",
       "      <td>9.63</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>466</td>\n",
       "      <td>10.28</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>467</td>\n",
       "      <td>8.83</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>468</td>\n",
       "      <td>9.65</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>469</td>\n",
       "      <td>9.64</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>470</td>\n",
       "      <td>10.84</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>471</td>\n",
       "      <td>10.77</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>472</td>\n",
       "      <td>9.20</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>473</td>\n",
       "      <td>9.28</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>474</td>\n",
       "      <td>12.35</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>475</td>\n",
       "      <td>8.02</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>476</td>\n",
       "      <td>9.87</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>477</td>\n",
       "      <td>9.66</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>478</td>\n",
       "      <td>9.45</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>479</td>\n",
       "      <td>9.81</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>481</td>\n",
       "      <td>8.99</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>482</td>\n",
       "      <td>9.13</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>483</td>\n",
       "      <td>9.43</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>485</td>\n",
       "      <td>8.83</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>486</td>\n",
       "      <td>9.44</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>487</td>\n",
       "      <td>9.11</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>488</td>\n",
       "      <td>9.50</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>489</td>\n",
       "      <td>10.06</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>491</td>\n",
       "      <td>8.04</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>492</td>\n",
       "      <td>10.40</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>493</td>\n",
       "      <td>8.84</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>494</td>\n",
       "      <td>10.36</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>495</td>\n",
       "      <td>11.22</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>499</td>\n",
       "      <td>8.92</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>10.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>501</td>\n",
       "      <td>9.50</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>502</td>\n",
       "      <td>9.17</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>503</td>\n",
       "      <td>9.83</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>504</td>\n",
       "      <td>8.89</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>505</td>\n",
       "      <td>11.17</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>506</td>\n",
       "      <td>8.82</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>507</td>\n",
       "      <td>9.72</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>509</td>\n",
       "      <td>8.62</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>511</td>\n",
       "      <td>9.15</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>512</td>\n",
       "      <td>9.11</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>513</td>\n",
       "      <td>9.10</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>514</td>\n",
       "      <td>10.01</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>515</td>\n",
       "      <td>9.48</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>516</td>\n",
       "      <td>10.25</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>517</td>\n",
       "      <td>8.90</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>518</td>\n",
       "      <td>10.47</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>519</td>\n",
       "      <td>8.37</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>521</td>\n",
       "      <td>9.05</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>523</td>\n",
       "      <td>9.94</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>524</td>\n",
       "      <td>9.29</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>525</td>\n",
       "      <td>11.21</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>526</td>\n",
       "      <td>8.15</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>527</td>\n",
       "      <td>8.94</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>528</td>\n",
       "      <td>7.91</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>529</td>\n",
       "      <td>10.03</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>530</td>\n",
       "      <td>8.43</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>531</td>\n",
       "      <td>7.83</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>532</td>\n",
       "      <td>9.38</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>533</td>\n",
       "      <td>9.95</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>535</td>\n",
       "      <td>10.20</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>537</td>\n",
       "      <td>8.77</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>538</td>\n",
       "      <td>9.24</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>539</td>\n",
       "      <td>10.46</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>540</td>\n",
       "      <td>9.94</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>541</td>\n",
       "      <td>8.88</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>542</td>\n",
       "      <td>8.94</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>543</td>\n",
       "      <td>9.30</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>544</td>\n",
       "      <td>10.84</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>545</td>\n",
       "      <td>10.15</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>546</td>\n",
       "      <td>8.24</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>547</td>\n",
       "      <td>11.27</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>548</td>\n",
       "      <td>8.88</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>549</td>\n",
       "      <td>9.25</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>550</td>\n",
       "      <td>8.71</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>551</td>\n",
       "      <td>8.19</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>552</td>\n",
       "      <td>11.01</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>553</td>\n",
       "      <td>10.76</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>554</td>\n",
       "      <td>9.74</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>558</td>\n",
       "      <td>9.82</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>559</td>\n",
       "      <td>9.51</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>560</td>\n",
       "      <td>7.65</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>561</td>\n",
       "      <td>9.39</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>562</td>\n",
       "      <td>8.36</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>564</td>\n",
       "      <td>9.45</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>565</td>\n",
       "      <td>8.85</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>566</td>\n",
       "      <td>9.76</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>567</td>\n",
       "      <td>9.34</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>570</td>\n",
       "      <td>9.09</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>571</td>\n",
       "      <td>7.51</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>573</td>\n",
       "      <td>8.51</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>574</td>\n",
       "      <td>9.13</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>575</td>\n",
       "      <td>9.21</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>577</td>\n",
       "      <td>9.72</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>578</td>\n",
       "      <td>8.79</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>579</td>\n",
       "      <td>10.41</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>580</td>\n",
       "      <td>9.86</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>581</td>\n",
       "      <td>9.03</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>582</td>\n",
       "      <td>8.82</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>583</td>\n",
       "      <td>8.99</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>584</td>\n",
       "      <td>10.52</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>590</td>\n",
       "      <td>8.98</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>591</td>\n",
       "      <td>9.91</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>592</td>\n",
       "      <td>8.02</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>593</td>\n",
       "      <td>7.86</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>594</td>\n",
       "      <td>7.90</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>595</td>\n",
       "      <td>8.85</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>596</td>\n",
       "      <td>8.79</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>597</td>\n",
       "      <td>9.30</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>598</td>\n",
       "      <td>9.55</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>599</td>\n",
       "      <td>9.49</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>600</td>\n",
       "      <td>9.42</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>601</td>\n",
       "      <td>7.88</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>602</td>\n",
       "      <td>8.83</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>604</td>\n",
       "      <td>8.83</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>605</td>\n",
       "      <td>8.77</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>606</td>\n",
       "      <td>8.86</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>608</td>\n",
       "      <td>9.28</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>609</td>\n",
       "      <td>7.93</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>610</td>\n",
       "      <td>8.25</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>611</td>\n",
       "      <td>7.74</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>612</td>\n",
       "      <td>9.20</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>613</td>\n",
       "      <td>10.25</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>614</td>\n",
       "      <td>10.61</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>615</td>\n",
       "      <td>9.44</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>616</td>\n",
       "      <td>11.22</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>618</td>\n",
       "      <td>9.97</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>619</td>\n",
       "      <td>10.34</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>620</td>\n",
       "      <td>9.30</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>622</td>\n",
       "      <td>11.47</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>623</td>\n",
       "      <td>9.31</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>624</td>\n",
       "      <td>9.76</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>625</td>\n",
       "      <td>9.97</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>626</td>\n",
       "      <td>10.82</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>627</td>\n",
       "      <td>10.44</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>628</td>\n",
       "      <td>7.85</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>630</td>\n",
       "      <td>8.08</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>631</td>\n",
       "      <td>9.41</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>632</td>\n",
       "      <td>9.75</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>634</td>\n",
       "      <td>10.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>635</td>\n",
       "      <td>9.97</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>636</td>\n",
       "      <td>9.12</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>637</td>\n",
       "      <td>9.42</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>638</td>\n",
       "      <td>8.58</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>640</td>\n",
       "      <td>7.67</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>641</td>\n",
       "      <td>9.39</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>642</td>\n",
       "      <td>9.13</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>643</td>\n",
       "      <td>9.89</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>644</td>\n",
       "      <td>8.80</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>645</td>\n",
       "      <td>8.34</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>646</td>\n",
       "      <td>8.82</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>648</td>\n",
       "      <td>8.16</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>650</td>\n",
       "      <td>8.32</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>651</td>\n",
       "      <td>9.11</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>652</td>\n",
       "      <td>9.19</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>653</td>\n",
       "      <td>10.17</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>654</td>\n",
       "      <td>8.99</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>655</td>\n",
       "      <td>8.91</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>656</td>\n",
       "      <td>9.07</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>658</td>\n",
       "      <td>10.07</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>659</td>\n",
       "      <td>10.60</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>660</td>\n",
       "      <td>9.12</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>661</td>\n",
       "      <td>9.06</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>662</td>\n",
       "      <td>9.66</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>663</td>\n",
       "      <td>10.02</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>664</td>\n",
       "      <td>9.68</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>667</td>\n",
       "      <td>7.66</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>671</td>\n",
       "      <td>8.91</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>672</td>\n",
       "      <td>8.74</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>673</td>\n",
       "      <td>9.06</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>674</td>\n",
       "      <td>8.02</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>675</td>\n",
       "      <td>9.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>676</td>\n",
       "      <td>9.52</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>678</td>\n",
       "      <td>8.15</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>679</td>\n",
       "      <td>9.22</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>680</td>\n",
       "      <td>9.35</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>682</td>\n",
       "      <td>10.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>683</td>\n",
       "      <td>9.44</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>684</td>\n",
       "      <td>8.84</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>685</td>\n",
       "      <td>9.47</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>686</td>\n",
       "      <td>10.87</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>687</td>\n",
       "      <td>8.92</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>688</td>\n",
       "      <td>8.85</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>689</td>\n",
       "      <td>10.28</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>690</td>\n",
       "      <td>8.92</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>691</td>\n",
       "      <td>8.06</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>693</td>\n",
       "      <td>11.60</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>694</td>\n",
       "      <td>9.17</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>695</td>\n",
       "      <td>8.77</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>696</td>\n",
       "      <td>8.38</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>697</td>\n",
       "      <td>9.24</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>698</td>\n",
       "      <td>8.86</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>699</td>\n",
       "      <td>8.81</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>700</td>\n",
       "      <td>8.91</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>701</td>\n",
       "      <td>9.64</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>702</td>\n",
       "      <td>8.02</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>703</td>\n",
       "      <td>9.69</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>704</td>\n",
       "      <td>9.44</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>705</td>\n",
       "      <td>8.30</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>706</td>\n",
       "      <td>9.78</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>707</td>\n",
       "      <td>8.04</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>708</td>\n",
       "      <td>10.28</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>709</td>\n",
       "      <td>11.35</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>710</td>\n",
       "      <td>9.52</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>711</td>\n",
       "      <td>7.98</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>712</td>\n",
       "      <td>10.45</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>713</td>\n",
       "      <td>10.04</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>714</td>\n",
       "      <td>9.36</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>715</td>\n",
       "      <td>9.39</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>716</td>\n",
       "      <td>8.57</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>717</td>\n",
       "      <td>10.67</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>718</td>\n",
       "      <td>10.05</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>719</td>\n",
       "      <td>9.39</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>720</td>\n",
       "      <td>9.64</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>722</td>\n",
       "      <td>9.07</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>723</td>\n",
       "      <td>9.50</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>724</td>\n",
       "      <td>9.20</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>725</td>\n",
       "      <td>10.18</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>727</td>\n",
       "      <td>11.20</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>728</td>\n",
       "      <td>9.67</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>729</td>\n",
       "      <td>8.81</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>730</td>\n",
       "      <td>9.07</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>731</td>\n",
       "      <td>9.76</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>732</td>\n",
       "      <td>10.42</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>733</td>\n",
       "      <td>9.57</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>734</td>\n",
       "      <td>10.36</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>735</td>\n",
       "      <td>7.88</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>736</td>\n",
       "      <td>9.17</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>737</td>\n",
       "      <td>10.03</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>738</td>\n",
       "      <td>9.82</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>739</td>\n",
       "      <td>11.50</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>740</td>\n",
       "      <td>9.28</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>743</td>\n",
       "      <td>9.44</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>745</td>\n",
       "      <td>9.35</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>746</td>\n",
       "      <td>9.33</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>748</td>\n",
       "      <td>9.50</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>749</td>\n",
       "      <td>8.09</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>750</td>\n",
       "      <td>10.92</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>751</td>\n",
       "      <td>10.27</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>752</td>\n",
       "      <td>8.68</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>753</td>\n",
       "      <td>9.99</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>754</td>\n",
       "      <td>8.72</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>755</td>\n",
       "      <td>10.22</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>756</td>\n",
       "      <td>11.22</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>757</td>\n",
       "      <td>9.39</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>758</td>\n",
       "      <td>8.55</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>760</td>\n",
       "      <td>8.89</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>761</td>\n",
       "      <td>9.01</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>762</td>\n",
       "      <td>9.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>763</td>\n",
       "      <td>9.50</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>764</td>\n",
       "      <td>9.46</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>765</td>\n",
       "      <td>9.68</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>766</td>\n",
       "      <td>9.69</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>767</td>\n",
       "      <td>8.82</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>769</td>\n",
       "      <td>9.55</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>771</td>\n",
       "      <td>9.18</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>772</td>\n",
       "      <td>9.84</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>774</td>\n",
       "      <td>10.02</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>775</td>\n",
       "      <td>9.48</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>776</td>\n",
       "      <td>8.98</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>777</td>\n",
       "      <td>9.80</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>778</td>\n",
       "      <td>10.41</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>779</td>\n",
       "      <td>9.70</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>780</td>\n",
       "      <td>9.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Prediction    Target  Residuals  Difference%\n",
       "55         9.07   1650.00    1640.93        99.45\n",
       "366        8.40   1700.00    1691.60        99.51\n",
       "415       11.45   2450.00    2438.55        99.53\n",
       "586       11.29   2800.00    2788.71        99.60\n",
       "587        8.34   2200.00    2191.66        99.62\n",
       "376        9.31   2900.00    2890.69        99.68\n",
       "62         9.35   3000.00    2990.65        99.69\n",
       "184        9.18   3000.00    2990.82        99.69\n",
       "555        8.99   3100.00    3091.01        99.71\n",
       "429        8.64   3149.25    3140.61        99.73\n",
       "304        8.09   2999.00    2990.91        99.73\n",
       "196        9.47   3800.00    3790.53        99.75\n",
       "759        9.45   4400.00    4390.55        99.79\n",
       "203       10.08   4700.00    4689.92        99.79\n",
       "423       10.24   5300.00    5289.76        99.81\n",
       "204        9.28   4999.00    4989.72        99.81\n",
       "629        9.43   5100.00    5090.57        99.82\n",
       "576        9.38   5100.00    5090.62        99.82\n",
       "669        8.69   4800.00    4791.31        99.82\n",
       "607       10.50   5900.00    5889.50        99.82\n",
       "603       10.07   5900.00    5889.93        99.83\n",
       "252        9.52   5900.00    5890.48        99.84\n",
       "773        8.99   5700.00    5691.01        99.84\n",
       "146       12.44   7900.00    7887.56        99.84\n",
       "497       10.20   6799.00    6788.80        99.85\n",
       "621        9.26   6200.00    6190.74        99.85\n",
       "77        10.11   6999.00    6988.89        99.86\n",
       "60         9.19   6500.00    6490.81        99.86\n",
       "328        8.75   6500.00    6491.25        99.87\n",
       "247       10.16   7700.00    7689.84        99.87\n",
       "212       11.45   8900.00    8888.55        99.87\n",
       "148        8.76   6900.00    6891.24        99.87\n",
       "105       12.87  10300.00   10287.13        99.88\n",
       "289        9.37   7500.00    7490.63        99.88\n",
       "420        8.71   7000.00    6991.29        99.88\n",
       "617       11.10   9000.00    8988.90        99.88\n",
       "112        9.26   8000.00    7990.74        99.88\n",
       "332        9.34   8100.00    8090.66        99.88\n",
       "496        9.17   8000.00    7990.83        99.89\n",
       "164        9.11   8100.00    8090.89        99.89\n",
       "38         8.57   7700.00    7691.43        99.89\n",
       "45         9.14   8250.00    8240.86        99.89\n",
       "742        8.10   7400.00    7391.90        99.89\n",
       "104        8.76   8200.00    8191.24        99.89\n",
       "384       10.22   9600.00    9589.78        99.89\n",
       "323        8.12   7700.00    7691.88        99.89\n",
       "63         9.03   8700.00    8690.97        99.90\n",
       "123        9.14   8900.00    8890.86        99.90\n",
       "568        9.56   9400.00    9390.44        99.90\n",
       "666        9.33   9200.00    9190.67        99.90\n",
       "341        9.11   9000.00    8990.89        99.90\n",
       "510        8.58   8500.00    8491.42        99.90\n",
       "484        7.69   7650.00    7642.31        99.90\n",
       "781        8.89   8900.00    8891.11        99.90\n",
       "569        9.96  10000.00    9990.04        99.90\n",
       "48         8.96   9200.00    9191.04        99.90\n",
       "199        9.41   9700.00    9690.59        99.90\n",
       "257        9.57   9999.00    9989.43        99.90\n",
       "291        8.71   9100.00    9091.29        99.90\n",
       "7          9.94  10799.00   10789.06        99.91\n",
       "407        9.16   9999.00    9989.84        99.91\n",
       "439        9.66  10600.00   10590.34        99.91\n",
       "221        8.95   9900.00    9891.05        99.91\n",
       "256        9.03   9999.00    9989.97        99.91\n",
       "209        9.10  10100.00   10090.90        99.91\n",
       "182        9.26  10650.00   10640.74        99.91\n",
       "325        8.26   9500.00    9491.74        99.91\n",
       "442       10.38  12250.00   12239.62        99.92\n",
       "9          9.94  11950.00   11940.06        99.92\n",
       "639       11.09  13700.00   13688.91        99.92\n",
       "74         9.59  11990.00   11980.41        99.92\n",
       "358        9.19  11500.00   11490.81        99.92\n",
       "330        9.48  12000.00   11990.52        99.92\n",
       "214        9.05  11500.00   11490.95        99.92\n",
       "155        7.86  10299.00   10291.14        99.92\n",
       "111        9.62  12700.00   12690.38        99.92\n",
       "670       10.17  13500.00   13489.83        99.92\n",
       "692        9.30  12700.00   12690.70        99.93\n",
       "585        8.62  11900.00   11891.38        99.93\n",
       "379       11.72  16300.00   16288.28        99.93\n",
       "126        9.21  13000.00   12990.79        99.93\n",
       "268        9.12  13000.00   12990.88        99.93\n",
       "188        9.92  14200.00   14190.08        99.93\n",
       "312        7.75  11100.00   11092.25        99.93\n",
       "292        9.22  13300.00   13290.78        99.93\n",
       "189        9.47  13700.00   13690.53        99.93\n",
       "177       10.00  14500.00   14490.00        99.93\n",
       "372        8.81  12800.00   12791.19        99.93\n",
       "563        9.08  13500.00   13490.92        99.93\n",
       "747        9.83  14700.00   14690.17        99.93\n",
       "231        9.16  13700.00   13690.84        99.93\n",
       "6          9.45  14200.00   14190.55        99.93\n",
       "25         8.96  13566.00   13557.04        99.93\n",
       "744        9.80  15500.00   15490.20        99.94\n",
       "233        8.77  13999.00   13990.23        99.94\n",
       "368        8.63  13893.75   13885.12        99.94\n",
       "218       10.09  16999.00   16988.91        99.94\n",
       "490        8.71  14899.00   14890.29        99.94\n",
       "422        9.62  16500.00   16490.38        99.94\n",
       "536        9.97  17200.00   17190.03        99.94\n",
       "508       10.96  18999.00   18988.04        99.94\n",
       "677       10.62  18700.00   18689.38        99.94\n",
       "137        9.41  16900.00   16890.59        99.94\n",
       "403        9.79  18100.00   18090.21        99.95\n",
       "633        9.68  17900.00   17890.32        99.95\n",
       "141        9.24  17300.00   17290.76        99.95\n",
       "4          9.65  18300.00   18290.35        99.95\n",
       "260        9.27  17700.00   17690.73        99.95\n",
       "726        8.93  17977.00   17968.07        99.95\n",
       "647        9.15  19000.00   18990.85        99.95\n",
       "588        8.39  17500.00   17491.61        99.95\n",
       "768        8.88  20100.00   20091.12        99.96\n",
       "183        8.79  20300.00   20291.21        99.96\n",
       "480        8.57  19990.00   19981.43        99.96\n",
       "339        8.17  19200.00   19191.83        99.96\n",
       "668        9.80  23500.00   23490.20        99.96\n",
       "657        8.88  21500.00   21491.12        99.96\n",
       "459        9.00  21900.00   21891.00        99.96\n",
       "180        8.24  20500.00   20491.76        99.96\n",
       "200        8.68  25342.20   25333.52        99.97\n",
       "81         9.62  28500.00   28490.38        99.97\n",
       "147        9.52  28500.00   28490.48        99.97\n",
       "458        8.85  26850.00   26841.15        99.97\n",
       "498        8.42  25749.75   25741.33        99.97\n",
       "522       10.02  33700.00   33689.98        99.97\n",
       "90         8.92  31500.00   31491.08        99.97\n",
       "721        9.58  34900.00   34890.42        99.97\n",
       "246        9.09  34900.00   34890.91        99.97\n",
       "400        9.29  36900.00   36890.71        99.97\n",
       "215        9.28  36900.00   36890.72        99.97\n",
       "121        8.21  32900.00   32891.79        99.98\n",
       "649        9.52  39999.00   39989.48        99.98\n",
       "572        8.97  38600.00   38591.03        99.98\n",
       "534        9.17  39500.00   39490.83        99.98\n",
       "316        9.20  39900.00   39890.80        99.98\n",
       "741        8.91  39900.00   39891.09        99.98\n",
       "18         8.48  38233.00   38224.52        99.98\n",
       "589       10.89  49900.00   49889.11        99.98\n",
       "681       10.10  47300.00   47289.90        99.98\n",
       "56         9.47  50000.00   49990.53        99.98\n",
       "520        8.96  51500.00   51491.04        99.98\n",
       "556       10.20  59900.00   59889.80        99.98\n",
       "665        8.87  53999.00   53990.13        99.98\n",
       "258        9.83  61000.00   60990.17        99.98\n",
       "59         9.61  69999.00   69989.39        99.99\n",
       "557        8.55  63000.00   62991.45        99.99\n",
       "129        8.99  87777.00   87768.01        99.99\n",
       "172        9.02  99999.00   99989.98        99.99\n",
       "54         9.68 133000.00  132990.32        99.99\n",
       "770        8.88 129999.00  129990.12        99.99\n",
       "198        8.94 135000.00  134991.06        99.99\n",
       "314        9.64 199999.00  199989.36       100.00\n",
       "0          8.89       nan        nan          nan\n",
       "1          9.72       nan        nan          nan\n",
       "2          9.42       nan        nan          nan\n",
       "3          9.05       nan        nan          nan\n",
       "5          9.32       nan        nan          nan\n",
       "8          9.26       nan        nan          nan\n",
       "10         8.99       nan        nan          nan\n",
       "11        11.07       nan        nan          nan\n",
       "12        10.10       nan        nan          nan\n",
       "13         9.07       nan        nan          nan\n",
       "14         9.91       nan        nan          nan\n",
       "15         9.01       nan        nan          nan\n",
       "16         9.82       nan        nan          nan\n",
       "17         9.39       nan        nan          nan\n",
       "19         9.99       nan        nan          nan\n",
       "20        10.02       nan        nan          nan\n",
       "21         8.10       nan        nan          nan\n",
       "22        11.83       nan        nan          nan\n",
       "23         8.14       nan        nan          nan\n",
       "24         9.33       nan        nan          nan\n",
       "26         8.96       nan        nan          nan\n",
       "27        10.52       nan        nan          nan\n",
       "28         9.97       nan        nan          nan\n",
       "29         8.28       nan        nan          nan\n",
       "30         9.06       nan        nan          nan\n",
       "31         8.28       nan        nan          nan\n",
       "32         7.89       nan        nan          nan\n",
       "33         9.51       nan        nan          nan\n",
       "34         8.73       nan        nan          nan\n",
       "35        10.19       nan        nan          nan\n",
       "36         8.88       nan        nan          nan\n",
       "37         9.01       nan        nan          nan\n",
       "39         8.73       nan        nan          nan\n",
       "40         9.31       nan        nan          nan\n",
       "41         8.56       nan        nan          nan\n",
       "42         8.99       nan        nan          nan\n",
       "43         9.92       nan        nan          nan\n",
       "44         8.26       nan        nan          nan\n",
       "46         7.45       nan        nan          nan\n",
       "47        10.18       nan        nan          nan\n",
       "49        11.85       nan        nan          nan\n",
       "50         8.19       nan        nan          nan\n",
       "51         9.91       nan        nan          nan\n",
       "52         8.94       nan        nan          nan\n",
       "53         9.01       nan        nan          nan\n",
       "57         9.03       nan        nan          nan\n",
       "58         8.64       nan        nan          nan\n",
       "61        10.24       nan        nan          nan\n",
       "64         9.22       nan        nan          nan\n",
       "65         9.17       nan        nan          nan\n",
       "66         9.37       nan        nan          nan\n",
       "67         8.88       nan        nan          nan\n",
       "68         9.36       nan        nan          nan\n",
       "69         9.24       nan        nan          nan\n",
       "70         9.89       nan        nan          nan\n",
       "71         9.38       nan        nan          nan\n",
       "72        10.45       nan        nan          nan\n",
       "73         8.72       nan        nan          nan\n",
       "75         8.84       nan        nan          nan\n",
       "76         8.03       nan        nan          nan\n",
       "78         8.90       nan        nan          nan\n",
       "79         9.83       nan        nan          nan\n",
       "80        10.21       nan        nan          nan\n",
       "82         9.15       nan        nan          nan\n",
       "83        11.83       nan        nan          nan\n",
       "84         7.97       nan        nan          nan\n",
       "85         9.36       nan        nan          nan\n",
       "86        13.20       nan        nan          nan\n",
       "87         9.21       nan        nan          nan\n",
       "88         9.71       nan        nan          nan\n",
       "89         9.33       nan        nan          nan\n",
       "91        10.17       nan        nan          nan\n",
       "92         9.27       nan        nan          nan\n",
       "93        10.70       nan        nan          nan\n",
       "94         9.26       nan        nan          nan\n",
       "95        10.14       nan        nan          nan\n",
       "96         8.66       nan        nan          nan\n",
       "97         8.19       nan        nan          nan\n",
       "98         9.02       nan        nan          nan\n",
       "99         9.12       nan        nan          nan\n",
       "100       11.71       nan        nan          nan\n",
       "101        9.22       nan        nan          nan\n",
       "102        9.16       nan        nan          nan\n",
       "103       10.88       nan        nan          nan\n",
       "106       10.52       nan        nan          nan\n",
       "107        9.54       nan        nan          nan\n",
       "108        9.36       nan        nan          nan\n",
       "109        9.45       nan        nan          nan\n",
       "110       10.04       nan        nan          nan\n",
       "113        9.97       nan        nan          nan\n",
       "114        8.99       nan        nan          nan\n",
       "115       10.04       nan        nan          nan\n",
       "116        9.67       nan        nan          nan\n",
       "117        7.96       nan        nan          nan\n",
       "118        8.32       nan        nan          nan\n",
       "119       10.19       nan        nan          nan\n",
       "120        9.34       nan        nan          nan\n",
       "122        8.71       nan        nan          nan\n",
       "124       10.38       nan        nan          nan\n",
       "125        9.34       nan        nan          nan\n",
       "127       10.59       nan        nan          nan\n",
       "128        8.81       nan        nan          nan\n",
       "130        8.45       nan        nan          nan\n",
       "131       12.58       nan        nan          nan\n",
       "132        9.66       nan        nan          nan\n",
       "133        9.08       nan        nan          nan\n",
       "134        9.94       nan        nan          nan\n",
       "135        9.17       nan        nan          nan\n",
       "136       10.97       nan        nan          nan\n",
       "138        8.10       nan        nan          nan\n",
       "139        9.09       nan        nan          nan\n",
       "140        9.42       nan        nan          nan\n",
       "142        9.10       nan        nan          nan\n",
       "143        9.91       nan        nan          nan\n",
       "144        8.99       nan        nan          nan\n",
       "145        8.98       nan        nan          nan\n",
       "149        9.97       nan        nan          nan\n",
       "150        8.66       nan        nan          nan\n",
       "151        8.63       nan        nan          nan\n",
       "152        8.93       nan        nan          nan\n",
       "153        8.95       nan        nan          nan\n",
       "154        8.85       nan        nan          nan\n",
       "156        9.12       nan        nan          nan\n",
       "157        8.40       nan        nan          nan\n",
       "158        9.12       nan        nan          nan\n",
       "159       10.42       nan        nan          nan\n",
       "160        8.95       nan        nan          nan\n",
       "161        9.71       nan        nan          nan\n",
       "162        9.51       nan        nan          nan\n",
       "163        8.55       nan        nan          nan\n",
       "165        8.55       nan        nan          nan\n",
       "166        8.46       nan        nan          nan\n",
       "167        9.16       nan        nan          nan\n",
       "168        9.45       nan        nan          nan\n",
       "169        9.24       nan        nan          nan\n",
       "170        8.89       nan        nan          nan\n",
       "171        9.02       nan        nan          nan\n",
       "173       10.13       nan        nan          nan\n",
       "174       10.60       nan        nan          nan\n",
       "175        9.53       nan        nan          nan\n",
       "176        7.68       nan        nan          nan\n",
       "178        8.96       nan        nan          nan\n",
       "179       10.01       nan        nan          nan\n",
       "181        9.12       nan        nan          nan\n",
       "185        9.72       nan        nan          nan\n",
       "186        9.76       nan        nan          nan\n",
       "187        9.76       nan        nan          nan\n",
       "190       10.20       nan        nan          nan\n",
       "191       10.44       nan        nan          nan\n",
       "192        8.57       nan        nan          nan\n",
       "193       10.29       nan        nan          nan\n",
       "194        8.01       nan        nan          nan\n",
       "195        8.87       nan        nan          nan\n",
       "197        9.02       nan        nan          nan\n",
       "201        9.12       nan        nan          nan\n",
       "202        8.71       nan        nan          nan\n",
       "205       11.63       nan        nan          nan\n",
       "206        9.22       nan        nan          nan\n",
       "207        9.29       nan        nan          nan\n",
       "208        8.94       nan        nan          nan\n",
       "210        9.24       nan        nan          nan\n",
       "211        9.36       nan        nan          nan\n",
       "213        8.50       nan        nan          nan\n",
       "216        9.13       nan        nan          nan\n",
       "217        9.88       nan        nan          nan\n",
       "219        9.50       nan        nan          nan\n",
       "220        9.41       nan        nan          nan\n",
       "222        8.93       nan        nan          nan\n",
       "223        9.14       nan        nan          nan\n",
       "224        8.99       nan        nan          nan\n",
       "225        9.42       nan        nan          nan\n",
       "226        9.29       nan        nan          nan\n",
       "227        9.00       nan        nan          nan\n",
       "228        9.79       nan        nan          nan\n",
       "229        9.17       nan        nan          nan\n",
       "230        7.99       nan        nan          nan\n",
       "232        9.77       nan        nan          nan\n",
       "234        9.46       nan        nan          nan\n",
       "235        9.15       nan        nan          nan\n",
       "236        9.11       nan        nan          nan\n",
       "237        8.98       nan        nan          nan\n",
       "238        9.20       nan        nan          nan\n",
       "239       10.02       nan        nan          nan\n",
       "240        8.99       nan        nan          nan\n",
       "241       11.00       nan        nan          nan\n",
       "242       10.45       nan        nan          nan\n",
       "243        9.24       nan        nan          nan\n",
       "244        9.40       nan        nan          nan\n",
       "245        8.82       nan        nan          nan\n",
       "248       11.23       nan        nan          nan\n",
       "249       10.44       nan        nan          nan\n",
       "250       10.76       nan        nan          nan\n",
       "251       11.50       nan        nan          nan\n",
       "253        9.49       nan        nan          nan\n",
       "254        8.79       nan        nan          nan\n",
       "255       10.25       nan        nan          nan\n",
       "259       10.99       nan        nan          nan\n",
       "261        9.94       nan        nan          nan\n",
       "262        8.95       nan        nan          nan\n",
       "263        9.66       nan        nan          nan\n",
       "264        9.31       nan        nan          nan\n",
       "265        9.26       nan        nan          nan\n",
       "266        8.84       nan        nan          nan\n",
       "267        9.02       nan        nan          nan\n",
       "269        8.45       nan        nan          nan\n",
       "270        9.25       nan        nan          nan\n",
       "271        9.79       nan        nan          nan\n",
       "272       11.98       nan        nan          nan\n",
       "273        8.87       nan        nan          nan\n",
       "274        9.24       nan        nan          nan\n",
       "275        9.78       nan        nan          nan\n",
       "276        8.52       nan        nan          nan\n",
       "277        8.19       nan        nan          nan\n",
       "278        8.38       nan        nan          nan\n",
       "279        9.85       nan        nan          nan\n",
       "280       10.11       nan        nan          nan\n",
       "281       10.75       nan        nan          nan\n",
       "282        8.95       nan        nan          nan\n",
       "283        9.58       nan        nan          nan\n",
       "284        8.85       nan        nan          nan\n",
       "285        8.85       nan        nan          nan\n",
       "286        8.94       nan        nan          nan\n",
       "287        9.37       nan        nan          nan\n",
       "288        8.93       nan        nan          nan\n",
       "290       10.18       nan        nan          nan\n",
       "293        9.53       nan        nan          nan\n",
       "294        9.06       nan        nan          nan\n",
       "295        7.72       nan        nan          nan\n",
       "296       10.20       nan        nan          nan\n",
       "297       10.28       nan        nan          nan\n",
       "298        9.23       nan        nan          nan\n",
       "299        8.98       nan        nan          nan\n",
       "300       10.00       nan        nan          nan\n",
       "301       10.23       nan        nan          nan\n",
       "302        9.39       nan        nan          nan\n",
       "303        9.53       nan        nan          nan\n",
       "305        9.34       nan        nan          nan\n",
       "306        9.06       nan        nan          nan\n",
       "307        7.66       nan        nan          nan\n",
       "308        9.63       nan        nan          nan\n",
       "309        9.34       nan        nan          nan\n",
       "310        8.18       nan        nan          nan\n",
       "311        8.77       nan        nan          nan\n",
       "313        9.07       nan        nan          nan\n",
       "315        8.56       nan        nan          nan\n",
       "317        8.72       nan        nan          nan\n",
       "318        8.81       nan        nan          nan\n",
       "319        9.22       nan        nan          nan\n",
       "320        9.89       nan        nan          nan\n",
       "321        8.76       nan        nan          nan\n",
       "322       10.14       nan        nan          nan\n",
       "324        9.48       nan        nan          nan\n",
       "326        9.02       nan        nan          nan\n",
       "327       10.46       nan        nan          nan\n",
       "329        8.99       nan        nan          nan\n",
       "331        8.58       nan        nan          nan\n",
       "333        9.03       nan        nan          nan\n",
       "334        9.93       nan        nan          nan\n",
       "335        9.14       nan        nan          nan\n",
       "336        9.87       nan        nan          nan\n",
       "337       10.39       nan        nan          nan\n",
       "338       11.58       nan        nan          nan\n",
       "340        8.89       nan        nan          nan\n",
       "342        9.82       nan        nan          nan\n",
       "343       10.71       nan        nan          nan\n",
       "344        7.89       nan        nan          nan\n",
       "345        8.91       nan        nan          nan\n",
       "346        9.54       nan        nan          nan\n",
       "347        9.53       nan        nan          nan\n",
       "348        8.37       nan        nan          nan\n",
       "349        9.29       nan        nan          nan\n",
       "350        8.48       nan        nan          nan\n",
       "351        9.40       nan        nan          nan\n",
       "352       10.57       nan        nan          nan\n",
       "353        9.77       nan        nan          nan\n",
       "354        9.45       nan        nan          nan\n",
       "355        8.57       nan        nan          nan\n",
       "356       12.23       nan        nan          nan\n",
       "357        9.14       nan        nan          nan\n",
       "359       10.72       nan        nan          nan\n",
       "360       10.18       nan        nan          nan\n",
       "361        8.04       nan        nan          nan\n",
       "362       10.25       nan        nan          nan\n",
       "363       10.92       nan        nan          nan\n",
       "364        8.63       nan        nan          nan\n",
       "365       10.02       nan        nan          nan\n",
       "367        9.97       nan        nan          nan\n",
       "369        9.65       nan        nan          nan\n",
       "370        8.83       nan        nan          nan\n",
       "371        7.82       nan        nan          nan\n",
       "373        9.51       nan        nan          nan\n",
       "374       10.07       nan        nan          nan\n",
       "375        9.24       nan        nan          nan\n",
       "377        9.57       nan        nan          nan\n",
       "378        7.99       nan        nan          nan\n",
       "380       10.87       nan        nan          nan\n",
       "381        9.36       nan        nan          nan\n",
       "382        9.63       nan        nan          nan\n",
       "383        9.59       nan        nan          nan\n",
       "385        9.37       nan        nan          nan\n",
       "386        8.95       nan        nan          nan\n",
       "387        8.28       nan        nan          nan\n",
       "388       10.22       nan        nan          nan\n",
       "389        8.23       nan        nan          nan\n",
       "390       10.44       nan        nan          nan\n",
       "391       10.55       nan        nan          nan\n",
       "392        9.20       nan        nan          nan\n",
       "393        8.74       nan        nan          nan\n",
       "394        9.30       nan        nan          nan\n",
       "395        9.65       nan        nan          nan\n",
       "396        9.81       nan        nan          nan\n",
       "397        9.37       nan        nan          nan\n",
       "398        9.50       nan        nan          nan\n",
       "399        9.28       nan        nan          nan\n",
       "401        9.92       nan        nan          nan\n",
       "402        9.71       nan        nan          nan\n",
       "404        8.75       nan        nan          nan\n",
       "405        8.85       nan        nan          nan\n",
       "406        8.21       nan        nan          nan\n",
       "408        8.56       nan        nan          nan\n",
       "409        9.31       nan        nan          nan\n",
       "410        9.75       nan        nan          nan\n",
       "411        9.12       nan        nan          nan\n",
       "412        9.38       nan        nan          nan\n",
       "413       10.78       nan        nan          nan\n",
       "414       10.45       nan        nan          nan\n",
       "416        9.45       nan        nan          nan\n",
       "417        7.93       nan        nan          nan\n",
       "418        8.49       nan        nan          nan\n",
       "419        9.19       nan        nan          nan\n",
       "421       10.64       nan        nan          nan\n",
       "424       10.09       nan        nan          nan\n",
       "425        9.85       nan        nan          nan\n",
       "426       10.44       nan        nan          nan\n",
       "427       10.13       nan        nan          nan\n",
       "428        7.87       nan        nan          nan\n",
       "430        9.16       nan        nan          nan\n",
       "431       10.06       nan        nan          nan\n",
       "432        9.39       nan        nan          nan\n",
       "433        9.60       nan        nan          nan\n",
       "434        9.00       nan        nan          nan\n",
       "435        8.10       nan        nan          nan\n",
       "436        9.89       nan        nan          nan\n",
       "437        9.23       nan        nan          nan\n",
       "438       10.79       nan        nan          nan\n",
       "440        8.15       nan        nan          nan\n",
       "441        9.57       nan        nan          nan\n",
       "443        9.61       nan        nan          nan\n",
       "444        9.65       nan        nan          nan\n",
       "445        9.66       nan        nan          nan\n",
       "446        9.83       nan        nan          nan\n",
       "447       10.14       nan        nan          nan\n",
       "448        8.91       nan        nan          nan\n",
       "449        8.91       nan        nan          nan\n",
       "450       10.36       nan        nan          nan\n",
       "451        8.78       nan        nan          nan\n",
       "452       12.16       nan        nan          nan\n",
       "453       10.14       nan        nan          nan\n",
       "454        9.21       nan        nan          nan\n",
       "455       11.02       nan        nan          nan\n",
       "456        9.41       nan        nan          nan\n",
       "457       11.49       nan        nan          nan\n",
       "460        9.39       nan        nan          nan\n",
       "461        8.56       nan        nan          nan\n",
       "462       10.58       nan        nan          nan\n",
       "463       10.18       nan        nan          nan\n",
       "464       10.73       nan        nan          nan\n",
       "465        9.63       nan        nan          nan\n",
       "466       10.28       nan        nan          nan\n",
       "467        8.83       nan        nan          nan\n",
       "468        9.65       nan        nan          nan\n",
       "469        9.64       nan        nan          nan\n",
       "470       10.84       nan        nan          nan\n",
       "471       10.77       nan        nan          nan\n",
       "472        9.20       nan        nan          nan\n",
       "473        9.28       nan        nan          nan\n",
       "474       12.35       nan        nan          nan\n",
       "475        8.02       nan        nan          nan\n",
       "476        9.87       nan        nan          nan\n",
       "477        9.66       nan        nan          nan\n",
       "478        9.45       nan        nan          nan\n",
       "479        9.81       nan        nan          nan\n",
       "481        8.99       nan        nan          nan\n",
       "482        9.13       nan        nan          nan\n",
       "483        9.43       nan        nan          nan\n",
       "485        8.83       nan        nan          nan\n",
       "486        9.44       nan        nan          nan\n",
       "487        9.11       nan        nan          nan\n",
       "488        9.50       nan        nan          nan\n",
       "489       10.06       nan        nan          nan\n",
       "491        8.04       nan        nan          nan\n",
       "492       10.40       nan        nan          nan\n",
       "493        8.84       nan        nan          nan\n",
       "494       10.36       nan        nan          nan\n",
       "495       11.22       nan        nan          nan\n",
       "499        8.92       nan        nan          nan\n",
       "500       10.00       nan        nan          nan\n",
       "501        9.50       nan        nan          nan\n",
       "502        9.17       nan        nan          nan\n",
       "503        9.83       nan        nan          nan\n",
       "504        8.89       nan        nan          nan\n",
       "505       11.17       nan        nan          nan\n",
       "506        8.82       nan        nan          nan\n",
       "507        9.72       nan        nan          nan\n",
       "509        8.62       nan        nan          nan\n",
       "511        9.15       nan        nan          nan\n",
       "512        9.11       nan        nan          nan\n",
       "513        9.10       nan        nan          nan\n",
       "514       10.01       nan        nan          nan\n",
       "515        9.48       nan        nan          nan\n",
       "516       10.25       nan        nan          nan\n",
       "517        8.90       nan        nan          nan\n",
       "518       10.47       nan        nan          nan\n",
       "519        8.37       nan        nan          nan\n",
       "521        9.05       nan        nan          nan\n",
       "523        9.94       nan        nan          nan\n",
       "524        9.29       nan        nan          nan\n",
       "525       11.21       nan        nan          nan\n",
       "526        8.15       nan        nan          nan\n",
       "527        8.94       nan        nan          nan\n",
       "528        7.91       nan        nan          nan\n",
       "529       10.03       nan        nan          nan\n",
       "530        8.43       nan        nan          nan\n",
       "531        7.83       nan        nan          nan\n",
       "532        9.38       nan        nan          nan\n",
       "533        9.95       nan        nan          nan\n",
       "535       10.20       nan        nan          nan\n",
       "537        8.77       nan        nan          nan\n",
       "538        9.24       nan        nan          nan\n",
       "539       10.46       nan        nan          nan\n",
       "540        9.94       nan        nan          nan\n",
       "541        8.88       nan        nan          nan\n",
       "542        8.94       nan        nan          nan\n",
       "543        9.30       nan        nan          nan\n",
       "544       10.84       nan        nan          nan\n",
       "545       10.15       nan        nan          nan\n",
       "546        8.24       nan        nan          nan\n",
       "547       11.27       nan        nan          nan\n",
       "548        8.88       nan        nan          nan\n",
       "549        9.25       nan        nan          nan\n",
       "550        8.71       nan        nan          nan\n",
       "551        8.19       nan        nan          nan\n",
       "552       11.01       nan        nan          nan\n",
       "553       10.76       nan        nan          nan\n",
       "554        9.74       nan        nan          nan\n",
       "558        9.82       nan        nan          nan\n",
       "559        9.51       nan        nan          nan\n",
       "560        7.65       nan        nan          nan\n",
       "561        9.39       nan        nan          nan\n",
       "562        8.36       nan        nan          nan\n",
       "564        9.45       nan        nan          nan\n",
       "565        8.85       nan        nan          nan\n",
       "566        9.76       nan        nan          nan\n",
       "567        9.34       nan        nan          nan\n",
       "570        9.09       nan        nan          nan\n",
       "571        7.51       nan        nan          nan\n",
       "573        8.51       nan        nan          nan\n",
       "574        9.13       nan        nan          nan\n",
       "575        9.21       nan        nan          nan\n",
       "577        9.72       nan        nan          nan\n",
       "578        8.79       nan        nan          nan\n",
       "579       10.41       nan        nan          nan\n",
       "580        9.86       nan        nan          nan\n",
       "581        9.03       nan        nan          nan\n",
       "582        8.82       nan        nan          nan\n",
       "583        8.99       nan        nan          nan\n",
       "584       10.52       nan        nan          nan\n",
       "590        8.98       nan        nan          nan\n",
       "591        9.91       nan        nan          nan\n",
       "592        8.02       nan        nan          nan\n",
       "593        7.86       nan        nan          nan\n",
       "594        7.90       nan        nan          nan\n",
       "595        8.85       nan        nan          nan\n",
       "596        8.79       nan        nan          nan\n",
       "597        9.30       nan        nan          nan\n",
       "598        9.55       nan        nan          nan\n",
       "599        9.49       nan        nan          nan\n",
       "600        9.42       nan        nan          nan\n",
       "601        7.88       nan        nan          nan\n",
       "602        8.83       nan        nan          nan\n",
       "604        8.83       nan        nan          nan\n",
       "605        8.77       nan        nan          nan\n",
       "606        8.86       nan        nan          nan\n",
       "608        9.28       nan        nan          nan\n",
       "609        7.93       nan        nan          nan\n",
       "610        8.25       nan        nan          nan\n",
       "611        7.74       nan        nan          nan\n",
       "612        9.20       nan        nan          nan\n",
       "613       10.25       nan        nan          nan\n",
       "614       10.61       nan        nan          nan\n",
       "615        9.44       nan        nan          nan\n",
       "616       11.22       nan        nan          nan\n",
       "618        9.97       nan        nan          nan\n",
       "619       10.34       nan        nan          nan\n",
       "620        9.30       nan        nan          nan\n",
       "622       11.47       nan        nan          nan\n",
       "623        9.31       nan        nan          nan\n",
       "624        9.76       nan        nan          nan\n",
       "625        9.97       nan        nan          nan\n",
       "626       10.82       nan        nan          nan\n",
       "627       10.44       nan        nan          nan\n",
       "628        7.85       nan        nan          nan\n",
       "630        8.08       nan        nan          nan\n",
       "631        9.41       nan        nan          nan\n",
       "632        9.75       nan        nan          nan\n",
       "634       10.00       nan        nan          nan\n",
       "635        9.97       nan        nan          nan\n",
       "636        9.12       nan        nan          nan\n",
       "637        9.42       nan        nan          nan\n",
       "638        8.58       nan        nan          nan\n",
       "640        7.67       nan        nan          nan\n",
       "641        9.39       nan        nan          nan\n",
       "642        9.13       nan        nan          nan\n",
       "643        9.89       nan        nan          nan\n",
       "644        8.80       nan        nan          nan\n",
       "645        8.34       nan        nan          nan\n",
       "646        8.82       nan        nan          nan\n",
       "648        8.16       nan        nan          nan\n",
       "650        8.32       nan        nan          nan\n",
       "651        9.11       nan        nan          nan\n",
       "652        9.19       nan        nan          nan\n",
       "653       10.17       nan        nan          nan\n",
       "654        8.99       nan        nan          nan\n",
       "655        8.91       nan        nan          nan\n",
       "656        9.07       nan        nan          nan\n",
       "658       10.07       nan        nan          nan\n",
       "659       10.60       nan        nan          nan\n",
       "660        9.12       nan        nan          nan\n",
       "661        9.06       nan        nan          nan\n",
       "662        9.66       nan        nan          nan\n",
       "663       10.02       nan        nan          nan\n",
       "664        9.68       nan        nan          nan\n",
       "667        7.66       nan        nan          nan\n",
       "671        8.91       nan        nan          nan\n",
       "672        8.74       nan        nan          nan\n",
       "673        9.06       nan        nan          nan\n",
       "674        8.02       nan        nan          nan\n",
       "675        9.00       nan        nan          nan\n",
       "676        9.52       nan        nan          nan\n",
       "678        8.15       nan        nan          nan\n",
       "679        9.22       nan        nan          nan\n",
       "680        9.35       nan        nan          nan\n",
       "682       10.00       nan        nan          nan\n",
       "683        9.44       nan        nan          nan\n",
       "684        8.84       nan        nan          nan\n",
       "685        9.47       nan        nan          nan\n",
       "686       10.87       nan        nan          nan\n",
       "687        8.92       nan        nan          nan\n",
       "688        8.85       nan        nan          nan\n",
       "689       10.28       nan        nan          nan\n",
       "690        8.92       nan        nan          nan\n",
       "691        8.06       nan        nan          nan\n",
       "693       11.60       nan        nan          nan\n",
       "694        9.17       nan        nan          nan\n",
       "695        8.77       nan        nan          nan\n",
       "696        8.38       nan        nan          nan\n",
       "697        9.24       nan        nan          nan\n",
       "698        8.86       nan        nan          nan\n",
       "699        8.81       nan        nan          nan\n",
       "700        8.91       nan        nan          nan\n",
       "701        9.64       nan        nan          nan\n",
       "702        8.02       nan        nan          nan\n",
       "703        9.69       nan        nan          nan\n",
       "704        9.44       nan        nan          nan\n",
       "705        8.30       nan        nan          nan\n",
       "706        9.78       nan        nan          nan\n",
       "707        8.04       nan        nan          nan\n",
       "708       10.28       nan        nan          nan\n",
       "709       11.35       nan        nan          nan\n",
       "710        9.52       nan        nan          nan\n",
       "711        7.98       nan        nan          nan\n",
       "712       10.45       nan        nan          nan\n",
       "713       10.04       nan        nan          nan\n",
       "714        9.36       nan        nan          nan\n",
       "715        9.39       nan        nan          nan\n",
       "716        8.57       nan        nan          nan\n",
       "717       10.67       nan        nan          nan\n",
       "718       10.05       nan        nan          nan\n",
       "719        9.39       nan        nan          nan\n",
       "720        9.64       nan        nan          nan\n",
       "722        9.07       nan        nan          nan\n",
       "723        9.50       nan        nan          nan\n",
       "724        9.20       nan        nan          nan\n",
       "725       10.18       nan        nan          nan\n",
       "727       11.20       nan        nan          nan\n",
       "728        9.67       nan        nan          nan\n",
       "729        8.81       nan        nan          nan\n",
       "730        9.07       nan        nan          nan\n",
       "731        9.76       nan        nan          nan\n",
       "732       10.42       nan        nan          nan\n",
       "733        9.57       nan        nan          nan\n",
       "734       10.36       nan        nan          nan\n",
       "735        7.88       nan        nan          nan\n",
       "736        9.17       nan        nan          nan\n",
       "737       10.03       nan        nan          nan\n",
       "738        9.82       nan        nan          nan\n",
       "739       11.50       nan        nan          nan\n",
       "740        9.28       nan        nan          nan\n",
       "743        9.44       nan        nan          nan\n",
       "745        9.35       nan        nan          nan\n",
       "746        9.33       nan        nan          nan\n",
       "748        9.50       nan        nan          nan\n",
       "749        8.09       nan        nan          nan\n",
       "750       10.92       nan        nan          nan\n",
       "751       10.27       nan        nan          nan\n",
       "752        8.68       nan        nan          nan\n",
       "753        9.99       nan        nan          nan\n",
       "754        8.72       nan        nan          nan\n",
       "755       10.22       nan        nan          nan\n",
       "756       11.22       nan        nan          nan\n",
       "757        9.39       nan        nan          nan\n",
       "758        8.55       nan        nan          nan\n",
       "760        8.89       nan        nan          nan\n",
       "761        9.01       nan        nan          nan\n",
       "762        9.00       nan        nan          nan\n",
       "763        9.50       nan        nan          nan\n",
       "764        9.46       nan        nan          nan\n",
       "765        9.68       nan        nan          nan\n",
       "766        9.69       nan        nan          nan\n",
       "767        8.82       nan        nan          nan\n",
       "769        9.55       nan        nan          nan\n",
       "771        9.18       nan        nan          nan\n",
       "772        9.84       nan        nan          nan\n",
       "774       10.02       nan        nan          nan\n",
       "775        9.48       nan        nan          nan\n",
       "776        8.98       nan        nan          nan\n",
       "777        9.80       nan        nan          nan\n",
       "778       10.41       nan        nan          nan\n",
       "779        9.70       nan        nan          nan\n",
       "780        9.00       nan        nan          nan"
      ]
     },
     "execution_count": 91,
     "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": []
  }
 ],
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