Milestone01-Salinee_Kingbaisomboon.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capstone - Project Definition\n",
    "## Author - Salinee Kingbaisomboon\n",
    "### UW NetID: 1950831"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this capstone project, we will use the **SECOM** manufacturing Data Set from the UCI Machine Learning Repository. The set is originally for semiconductor manufacturing, but in our case, we will assume that it is for the diaper manufacturing process.\n",
    "\n",
    "The dataset consists of two files:\n",
    "1. A dataset file SECOM containing 1567 examples, each with 591 features, presented in a 1567 x 591 matrix\n",
    "2. A labels file listing the classifications and date time stamp for each example\n",
    "\n",
    "## Instructions\n",
    "Assume the 591 attributes represent different sensors readings across the manufacturing process.\n",
    "\n",
    "1. Refine your data understanding and prepare a DFD of solving the manufacturing quality problem\n",
    "2. Read and merge data\n",
    "3. Clean and prepare data\n",
    "4. Visually explore data\n",
    "5. Handle class imbalance problem\n",
    "6. Apply feature selection techniques to reduce dimensionality of data\n",
    "7. Describe your initial findings in a summary section\n",
    "8. Solicit specific feedback on your code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Understandins and Data Flow Diagram (DFD)\n",
    "The **SECOM manufacturing Data Set** is originally for semiconductor manufacturing, but in our case, we will assume that it is for the diaper manufacturing process.\n",
    "\n",
    "Based on https://archive.ics.uci.edu/ml/machine-learning-databases/secom/secom.names, this data set has <font color=red>**missing values**</font>. Therefore, we need to clean data as well.\n",
    "\n",
    "Also, these data set doesn't come with header definition. Therefore, we have to create pseudo features names for all attribues in order to be able to perform data analysis.\n",
    "\n",
    "Moreover, not all of these signals are equally valuable  in a specific monitoring system. The measured signals contain a combination of useful information, irrelevant information as well as noise. We have to perform some <font color=blue>**feature engineering**</font> to reduce dimensionality of data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://i.ibb.co/PMZznZM/DFD.png)\n",
    "\n",
    "**Link: https://i.ibb.co/PMZznZM/DFD.png**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from collections import Counter\n",
    "\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.feature_selection import RFE # Recursive Feature Elimination (for backward model selection)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\") # To suppress warning\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "pd.options.display.max_rows = None\n",
    "pd.options.display.max_columns = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Declare Functions used in this milestone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to replace missing numeric values\n",
    "def replace_missing_value(x, col):\n",
    "    # We determine the locations of NaN\n",
    "    HasNan = np.isnan(x.loc[:, col]) \n",
    "    # If there are NaN\n",
    "    if sum(HasNan) > 0:\n",
    "        # Calculate the Median for current column without Nan\n",
    "        Median = np.nanmedian(x.loc[:, col])\n",
    "        # Replace the missing value with Median\n",
    "        x.loc[HasNan, col] = Median\n",
    "    return x.loc[:, col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data and label\n",
    "dataFilename = 'https://archive.ics.uci.edu/ml/machine-learning-databases/secom/secom.data'\n",
    "data_df = pd.read_csv(dataFilename, header=None, delim_whitespace=True)\n",
    "\n",
    "labelFilename = 'https://archive.ics.uci.edu/ml/machine-learning-databases/secom/secom_labels.data'\n",
    "label_df = pd.read_csv(labelFilename, header=None, delim_whitespace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set pseudo names for input data\n",
    "for col in data_df.columns:\n",
    "    data_df.rename(columns = {col:'feature_'+ str(col)}, inplace = True)\n",
    "    \n",
    "# Set pseudo name for label data\n",
    "label_df.rename(columns = {0:'HouseLineTestingResult'}, inplace = True)\n",
    "label_df.rename(columns = {1:'TimeStamp'}, inplace = True)\n",
    "\n",
    "# Since we didn't do the time series analysis, we don't need the timestamp column\n",
    "# Drop the unused columns\n",
    "label_df.drop(['TimeStamp'],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean input data\n",
    "for col in data_df.columns:\n",
    "    # Replace missing values with Median for current column without Nan\n",
    "    data_df.loc[:, col] = replace_missing_value(data_df, col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge input and label data into one\n",
    "df = pd.concat([data_df,label_df],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_0</th>\n",
       "      <th>feature_1</th>\n",
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       "      <th>feature_8</th>\n",
       "      <th>feature_9</th>\n",
       "      <th>...</th>\n",
       "      <th>feature_581</th>\n",
       "      <th>feature_582</th>\n",
       "      <th>feature_583</th>\n",
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       "      <th>feature_587</th>\n",
       "      <th>feature_588</th>\n",
       "      <th>feature_589</th>\n",
       "      <th>HouseLineTestingResult</th>\n",
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       "      <th>0</th>\n",
       "      <td>3030.93</td>\n",
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       "      <td>0.1242</td>\n",
       "      <td>1.5005</td>\n",
       "      <td>0.0162</td>\n",
       "      <td>...</td>\n",
       "      <td>72.2889</td>\n",
       "      <td>0.5005</td>\n",
       "      <td>0.0118</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>2.3630</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0148</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>71.9005</td>\n",
       "      <td>-1</td>\n",
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       "      <td>3095.78</td>\n",
       "      <td>2465.14</td>\n",
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       "      <td>1463.6606</td>\n",
       "      <td>0.8294</td>\n",
       "      <td>100.0</td>\n",
       "      <td>102.3433</td>\n",
       "      <td>0.1247</td>\n",
       "      <td>1.4966</td>\n",
       "      <td>-0.0005</td>\n",
       "      <td>...</td>\n",
       "      <td>208.2045</td>\n",
       "      <td>0.5019</td>\n",
       "      <td>0.0223</td>\n",
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       "      <td>4.4447</td>\n",
       "      <td>0.0096</td>\n",
       "      <td>0.0201</td>\n",
       "      <td>0.0060</td>\n",
       "      <td>208.2045</td>\n",
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       "    <tr>\n",
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       "      <td>2559.94</td>\n",
       "      <td>2186.4111</td>\n",
       "      <td>1698.0172</td>\n",
       "      <td>1.5102</td>\n",
       "      <td>100.0</td>\n",
       "      <td>95.4878</td>\n",
       "      <td>0.1241</td>\n",
       "      <td>1.4436</td>\n",
       "      <td>0.0041</td>\n",
       "      <td>...</td>\n",
       "      <td>82.8602</td>\n",
       "      <td>0.4958</td>\n",
       "      <td>0.0157</td>\n",
       "      <td>0.0039</td>\n",
       "      <td>3.1745</td>\n",
       "      <td>0.0584</td>\n",
       "      <td>0.0484</td>\n",
       "      <td>0.0148</td>\n",
       "      <td>82.8602</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2988.72</td>\n",
       "      <td>2479.90</td>\n",
       "      <td>2199.0333</td>\n",
       "      <td>909.7926</td>\n",
       "      <td>1.3204</td>\n",
       "      <td>100.0</td>\n",
       "      <td>104.2367</td>\n",
       "      <td>0.1217</td>\n",
       "      <td>1.4882</td>\n",
       "      <td>-0.0124</td>\n",
       "      <td>...</td>\n",
       "      <td>73.8432</td>\n",
       "      <td>0.4990</td>\n",
       "      <td>0.0103</td>\n",
       "      <td>0.0025</td>\n",
       "      <td>2.0544</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>0.0149</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>73.8432</td>\n",
       "      <td>-1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3032.24</td>\n",
       "      <td>2502.87</td>\n",
       "      <td>2233.3667</td>\n",
       "      <td>1326.5200</td>\n",
       "      <td>1.5334</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.3967</td>\n",
       "      <td>0.1235</td>\n",
       "      <td>1.5031</td>\n",
       "      <td>-0.0031</td>\n",
       "      <td>...</td>\n",
       "      <td>72.2889</td>\n",
       "      <td>0.4800</td>\n",
       "      <td>0.4766</td>\n",
       "      <td>0.1045</td>\n",
       "      <td>99.3032</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>0.0149</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>73.8432</td>\n",
       "      <td>-1</td>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 591 columns</p>\n",
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      "text/plain": [
       "   feature_0  feature_1  feature_2  feature_3  feature_4  feature_5  \\\n",
       "0    3030.93    2564.00  2187.7333  1411.1265     1.3602      100.0   \n",
       "1    3095.78    2465.14  2230.4222  1463.6606     0.8294      100.0   \n",
       "2    2932.61    2559.94  2186.4111  1698.0172     1.5102      100.0   \n",
       "3    2988.72    2479.90  2199.0333   909.7926     1.3204      100.0   \n",
       "4    3032.24    2502.87  2233.3667  1326.5200     1.5334      100.0   \n",
       "\n",
       "   feature_6  feature_7  feature_8  feature_9  ...  feature_581  feature_582  \\\n",
       "0    97.6133     0.1242     1.5005     0.0162  ...      72.2889       0.5005   \n",
       "1   102.3433     0.1247     1.4966    -0.0005  ...     208.2045       0.5019   \n",
       "2    95.4878     0.1241     1.4436     0.0041  ...      82.8602       0.4958   \n",
       "3   104.2367     0.1217     1.4882    -0.0124  ...      73.8432       0.4990   \n",
       "4   100.3967     0.1235     1.5031    -0.0031  ...      72.2889       0.4800   \n",
       "\n",
       "   feature_583  feature_584  feature_585  feature_586  feature_587  \\\n",
       "0       0.0118       0.0035       2.3630       0.0205       0.0148   \n",
       "1       0.0223       0.0055       4.4447       0.0096       0.0201   \n",
       "2       0.0157       0.0039       3.1745       0.0584       0.0484   \n",
       "3       0.0103       0.0025       2.0544       0.0202       0.0149   \n",
       "4       0.4766       0.1045      99.3032       0.0202       0.0149   \n",
       "\n",
       "   feature_588  feature_589  HouseLineTestingResult  \n",
       "0       0.0046      71.9005                      -1  \n",
       "1       0.0060     208.2045                      -1  \n",
       "2       0.0148      82.8602                       1  \n",
       "3       0.0044      73.8432                      -1  \n",
       "4       0.0044      73.8432                      -1  \n",
       "\n",
       "[5 rows x 591 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View first 5 rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "feature_185                      float64\n",
      "feature_186                      float64\n",
      "feature_187                      float64\n",
      "feature_188                      float64\n",
      "feature_189                      float64\n",
      "feature_190                      float64\n",
      "feature_191                      float64\n",
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      "feature_307                      float64\n",
      "feature_308                      float64\n",
      "feature_309                      float64\n",
      "feature_310                      float64\n",
      "feature_311                      float64\n",
      "feature_312                      float64\n",
      "feature_313                      float64\n",
      "feature_314                      float64\n",
      "feature_315                      float64\n",
      "feature_316                      float64\n",
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      "feature_318                      float64\n",
      "feature_319                      float64\n",
      "feature_320                      float64\n",
      "feature_321                      float64\n",
      "feature_322                      float64\n",
      "feature_323                      float64\n",
      "feature_324                      float64\n",
      "feature_325                      float64\n",
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      "feature_327                      float64\n",
      "feature_328                      float64\n",
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      "feature_350                      float64\n",
      "feature_351                      float64\n",
      "feature_352                      float64\n",
      "feature_353                      float64\n",
      "feature_354                      float64\n",
      "feature_355                      float64\n",
      "feature_356                      float64\n",
      "feature_357                      float64\n",
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      "feature_359                      float64\n",
      "feature_360                      float64\n",
      "feature_361                      float64\n",
      "feature_362                      float64\n",
      "feature_363                      float64\n",
      "feature_364                      float64\n",
      "feature_365                      float64\n",
      "feature_366                      float64\n",
      "feature_367                      float64\n",
      "feature_368                      float64\n",
      "feature_369                      float64\n",
      "feature_370                      float64\n",
      "feature_371                      float64\n",
      "feature_372                      float64\n",
      "feature_373                      float64\n",
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      "feature_375                      float64\n",
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      "feature_382                      float64\n",
      "feature_383                      float64\n",
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      "feature_385                      float64\n",
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      "feature_387                      float64\n",
      "feature_388                      float64\n",
      "feature_389                      float64\n",
      "feature_390                      float64\n",
      "feature_391                      float64\n",
      "feature_392                      float64\n",
      "feature_393                      float64\n",
      "feature_394                      float64\n",
      "feature_395                      float64\n",
      "feature_396                      float64\n",
      "feature_397                      float64\n",
      "feature_398                      float64\n",
      "feature_399                      float64\n",
      "feature_400                      float64\n",
      "feature_401                      float64\n",
      "feature_402                      float64\n",
      "feature_403                      float64\n",
      "feature_404                      float64\n",
      "feature_405                      float64\n",
      "feature_406                      float64\n",
      "feature_407                      float64\n",
      "feature_408                      float64\n",
      "feature_409                      float64\n",
      "feature_410                      float64\n",
      "feature_411                      float64\n",
      "feature_412                      float64\n",
      "feature_413                      float64\n",
      "feature_414                      float64\n",
      "feature_415                      float64\n",
      "feature_416                      float64\n",
      "feature_417                      float64\n",
      "feature_418                      float64\n",
      "feature_419                      float64\n",
      "feature_420                      float64\n",
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      "feature_428                      float64\n",
      "feature_429                      float64\n",
      "feature_430                      float64\n",
      "feature_431                      float64\n",
      "feature_432                      float64\n",
      "feature_433                      float64\n",
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      "feature_435                      float64\n",
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      "feature_437                      float64\n",
      "feature_438                      float64\n",
      "feature_439                      float64\n",
      "feature_440                      float64\n",
      "feature_441                      float64\n",
      "feature_442                      float64\n",
      "feature_443                      float64\n",
      "feature_444                      float64\n",
      "feature_445                      float64\n",
      "feature_446                      float64\n",
      "feature_447                      float64\n",
      "feature_448                      float64\n",
      "feature_449                      float64\n",
      "feature_450                      float64\n",
      "feature_451                      float64\n",
      "feature_452                      float64\n",
      "feature_453                      float64\n",
      "feature_454                      float64\n",
      "feature_455                      float64\n",
      "feature_456                      float64\n",
      "feature_457                      float64\n",
      "feature_458                      float64\n",
      "feature_459                      float64\n",
      "feature_460                      float64\n",
      "feature_461                      float64\n",
      "feature_462                      float64\n",
      "feature_463                      float64\n",
      "feature_464                      float64\n",
      "feature_465                      float64\n",
      "feature_466                      float64\n",
      "feature_467                      float64\n",
      "feature_468                      float64\n",
      "feature_469                      float64\n",
      "feature_470                      float64\n",
      "feature_471                      float64\n",
      "feature_472                      float64\n",
      "feature_473                      float64\n",
      "feature_474                      float64\n",
      "feature_475                      float64\n",
      "feature_476                      float64\n",
      "feature_477                      float64\n",
      "feature_478                      float64\n",
      "feature_479                      float64\n",
      "feature_480                      float64\n",
      "feature_481                      float64\n",
      "feature_482                      float64\n",
      "feature_483                      float64\n",
      "feature_484                      float64\n",
      "feature_485                      float64\n",
      "feature_486                      float64\n",
      "feature_487                      float64\n",
      "feature_488                      float64\n",
      "feature_489                      float64\n",
      "feature_490                      float64\n",
      "feature_491                      float64\n",
      "feature_492                      float64\n",
      "feature_493                      float64\n",
      "feature_494                      float64\n",
      "feature_495                      float64\n",
      "feature_496                      float64\n",
      "feature_497                      float64\n",
      "feature_498                      float64\n",
      "feature_499                      float64\n",
      "feature_500                      float64\n",
      "feature_501                      float64\n",
      "feature_502                      float64\n",
      "feature_503                      float64\n",
      "feature_504                      float64\n",
      "feature_505                      float64\n",
      "feature_506                      float64\n",
      "feature_507                      float64\n",
      "feature_508                      float64\n",
      "feature_509                      float64\n",
      "feature_510                      float64\n",
      "feature_511                      float64\n",
      "feature_512                      float64\n",
      "feature_513                      float64\n",
      "feature_514                      float64\n",
      "feature_515                      float64\n",
      "feature_516                      float64\n",
      "feature_517                      float64\n",
      "feature_518                      float64\n",
      "feature_519                      float64\n",
      "feature_520                      float64\n",
      "feature_521                      float64\n",
      "feature_522                      float64\n",
      "feature_523                      float64\n",
      "feature_524                      float64\n",
      "feature_525                      float64\n",
      "feature_526                      float64\n",
      "feature_527                      float64\n",
      "feature_528                      float64\n",
      "feature_529                      float64\n",
      "feature_530                      float64\n",
      "feature_531                      float64\n",
      "feature_532                      float64\n",
      "feature_533                      float64\n",
      "feature_534                      float64\n",
      "feature_535                      float64\n",
      "feature_536                      float64\n",
      "feature_537                      float64\n",
      "feature_538                      float64\n",
      "feature_539                      float64\n",
      "feature_540                      float64\n",
      "feature_541                      float64\n",
      "feature_542                      float64\n",
      "feature_543                      float64\n",
      "feature_544                      float64\n",
      "feature_545                      float64\n",
      "feature_546                      float64\n",
      "feature_547                      float64\n",
      "feature_548                      float64\n",
      "feature_549                      float64\n",
      "feature_550                      float64\n",
      "feature_551                      float64\n",
      "feature_552                      float64\n",
      "feature_553                      float64\n",
      "feature_554                      float64\n",
      "feature_555                      float64\n",
      "feature_556                      float64\n",
      "feature_557                      float64\n",
      "feature_558                      float64\n",
      "feature_559                      float64\n",
      "feature_560                      float64\n",
      "feature_561                      float64\n",
      "feature_562                      float64\n",
      "feature_563                      float64\n",
      "feature_564                      float64\n",
      "feature_565                      float64\n",
      "feature_566                      float64\n",
      "feature_567                      float64\n",
      "feature_568                      float64\n",
      "feature_569                      float64\n",
      "feature_570                      float64\n",
      "feature_571                      float64\n",
      "feature_572                      float64\n",
      "feature_573                      float64\n",
      "feature_574                      float64\n",
      "feature_575                      float64\n",
      "feature_576                      float64\n",
      "feature_577                      float64\n",
      "feature_578                      float64\n",
      "feature_579                      float64\n",
      "feature_580                      float64\n",
      "feature_581                      float64\n",
      "feature_582                      float64\n",
      "feature_583                      float64\n",
      "feature_584                      float64\n",
      "feature_585                      float64\n",
      "feature_586                      float64\n",
      "feature_587                      float64\n",
      "feature_588                      float64\n",
      "feature_589                      float64\n",
      "HouseLineTestingResult             int64\n",
      "TimeStamp                 datetime64[ns]\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.shape) # Print DataFrame's size\n",
    "print(df.dtypes) # Print DataFrame's data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['feature_0', 'feature_1', 'feature_2', 'feature_3', 'feature_4',\n",
       "       'feature_5', 'feature_6', 'feature_7', 'feature_8', 'feature_9',\n",
       "       ...\n",
       "       'feature_582', 'feature_583', 'feature_584', 'feature_585',\n",
       "       'feature_586', 'feature_587', 'feature_588', 'feature_589',\n",
       "       'HouseLineTestingResult', 'TimeStamp'],\n",
       "      dtype='object', length=592)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View data frame columns\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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_0</th>\n",
       "      <th>feature_1</th>\n",
       "      <th>feature_2</th>\n",
       "      <th>feature_3</th>\n",
       "      <th>feature_4</th>\n",
       "      <th>feature_5</th>\n",
       "      <th>feature_6</th>\n",
       "      <th>feature_7</th>\n",
       "      <th>feature_8</th>\n",
       "      <th>feature_9</th>\n",
       "      <th>feature_10</th>\n",
       "      <th>feature_11</th>\n",
       "      <th>feature_12</th>\n",
       "      <th>feature_13</th>\n",
       "      <th>feature_14</th>\n",
       "      <th>feature_15</th>\n",
       "      <th>feature_16</th>\n",
       "      <th>feature_17</th>\n",
       "      <th>feature_18</th>\n",
       "      <th>feature_19</th>\n",
       "      <th>feature_20</th>\n",
       "      <th>feature_21</th>\n",
       "      <th>feature_22</th>\n",
       "      <th>feature_23</th>\n",
       "      <th>feature_24</th>\n",
       "      <th>feature_25</th>\n",
       "      <th>feature_26</th>\n",
       "      <th>feature_27</th>\n",
       "      <th>feature_28</th>\n",
       "      <th>feature_29</th>\n",
       "      <th>feature_30</th>\n",
       "      <th>feature_31</th>\n",
       "      <th>feature_32</th>\n",
       "      <th>feature_33</th>\n",
       "      <th>feature_34</th>\n",
       "      <th>feature_35</th>\n",
       "      <th>feature_36</th>\n",
       "      <th>feature_37</th>\n",
       "      <th>feature_38</th>\n",
       "      <th>feature_39</th>\n",
       "      <th>feature_40</th>\n",
       "      <th>feature_41</th>\n",
       "      <th>feature_42</th>\n",
       "      <th>feature_43</th>\n",
       "      <th>feature_44</th>\n",
       "      <th>feature_45</th>\n",
       "      <th>feature_46</th>\n",
       "      <th>feature_47</th>\n",
       "      <th>feature_48</th>\n",
       "      <th>feature_49</th>\n",
       "      <th>feature_50</th>\n",
       "      <th>feature_51</th>\n",
       "      <th>feature_52</th>\n",
       "      <th>feature_53</th>\n",
       "      <th>feature_54</th>\n",
       "      <th>feature_55</th>\n",
       "      <th>feature_56</th>\n",
       "      <th>feature_57</th>\n",
       "      <th>feature_58</th>\n",
       "      <th>feature_59</th>\n",
       "      <th>feature_60</th>\n",
       "      <th>feature_61</th>\n",
       "      <th>feature_62</th>\n",
       "      <th>feature_63</th>\n",
       "      <th>feature_64</th>\n",
       "      <th>feature_65</th>\n",
       "      <th>feature_66</th>\n",
       "      <th>feature_67</th>\n",
       "      <th>feature_68</th>\n",
       "      <th>feature_69</th>\n",
       "      <th>feature_70</th>\n",
       "      <th>feature_71</th>\n",
       "      <th>feature_72</th>\n",
       "      <th>feature_73</th>\n",
       "      <th>feature_74</th>\n",
       "      <th>feature_75</th>\n",
       "      <th>feature_76</th>\n",
       "      <th>feature_77</th>\n",
       "      <th>feature_78</th>\n",
       "      <th>feature_79</th>\n",
       "      <th>feature_80</th>\n",
       "      <th>feature_81</th>\n",
       "      <th>feature_82</th>\n",
       "      <th>feature_83</th>\n",
       "      <th>feature_84</th>\n",
       "      <th>feature_85</th>\n",
       "      <th>feature_86</th>\n",
       "      <th>feature_87</th>\n",
       "      <th>feature_88</th>\n",
       "      <th>feature_89</th>\n",
       "      <th>feature_90</th>\n",
       "      <th>feature_91</th>\n",
       "      <th>feature_92</th>\n",
       "      <th>feature_93</th>\n",
       "      <th>feature_94</th>\n",
       "      <th>feature_95</th>\n",
       "      <th>feature_96</th>\n",
       "      <th>feature_97</th>\n",
       "      <th>feature_98</th>\n",
       "      <th>feature_99</th>\n",
       "      <th>feature_100</th>\n",
       "      <th>feature_101</th>\n",
       "      <th>feature_102</th>\n",
       "      <th>feature_103</th>\n",
       "      <th>feature_104</th>\n",
       "      <th>feature_105</th>\n",
       "      <th>feature_106</th>\n",
       "      <th>feature_107</th>\n",
       "      <th>feature_108</th>\n",
       "      <th>feature_109</th>\n",
       "      <th>feature_110</th>\n",
       "      <th>feature_111</th>\n",
       "      <th>feature_112</th>\n",
       "      <th>feature_113</th>\n",
       "      <th>feature_114</th>\n",
       "      <th>feature_115</th>\n",
       "      <th>feature_116</th>\n",
       "      <th>feature_117</th>\n",
       "      <th>feature_118</th>\n",
       "      <th>feature_119</th>\n",
       "      <th>feature_120</th>\n",
       "      <th>feature_121</th>\n",
       "      <th>feature_122</th>\n",
       "      <th>feature_123</th>\n",
       "      <th>feature_124</th>\n",
       "      <th>feature_125</th>\n",
       "      <th>feature_126</th>\n",
       "      <th>feature_127</th>\n",
       "      <th>feature_128</th>\n",
       "      <th>feature_129</th>\n",
       "      <th>feature_130</th>\n",
       "      <th>feature_131</th>\n",
       "      <th>feature_132</th>\n",
       "      <th>feature_133</th>\n",
       "      <th>feature_134</th>\n",
       "      <th>feature_135</th>\n",
       "      <th>feature_136</th>\n",
       "      <th>feature_137</th>\n",
       "      <th>feature_138</th>\n",
       "      <th>feature_139</th>\n",
       "      <th>feature_140</th>\n",
       "      <th>feature_141</th>\n",
       "      <th>feature_142</th>\n",
       "      <th>feature_143</th>\n",
       "      <th>feature_144</th>\n",
       "      <th>feature_145</th>\n",
       "      <th>feature_146</th>\n",
       "      <th>feature_147</th>\n",
       "      <th>feature_148</th>\n",
       "      <th>feature_149</th>\n",
       "      <th>feature_150</th>\n",
       "      <th>feature_151</th>\n",
       "      <th>feature_152</th>\n",
       "      <th>feature_153</th>\n",
       "      <th>feature_154</th>\n",
       "      <th>feature_155</th>\n",
       "      <th>feature_156</th>\n",
       "      <th>feature_157</th>\n",
       "      <th>feature_158</th>\n",
       "      <th>feature_159</th>\n",
       "      <th>feature_160</th>\n",
       "      <th>feature_161</th>\n",
       "      <th>feature_162</th>\n",
       "      <th>feature_163</th>\n",
       "      <th>feature_164</th>\n",
       "      <th>feature_165</th>\n",
       "      <th>feature_166</th>\n",
       "      <th>feature_167</th>\n",
       "      <th>feature_168</th>\n",
       "      <th>feature_169</th>\n",
       "      <th>feature_170</th>\n",
       "      <th>feature_171</th>\n",
       "      <th>feature_172</th>\n",
       "      <th>feature_173</th>\n",
       "      <th>feature_174</th>\n",
       "      <th>feature_175</th>\n",
       "      <th>feature_176</th>\n",
       "      <th>feature_177</th>\n",
       "      <th>feature_178</th>\n",
       "      <th>feature_179</th>\n",
       "      <th>feature_180</th>\n",
       "      <th>feature_181</th>\n",
       "      <th>feature_182</th>\n",
       "      <th>feature_183</th>\n",
       "      <th>feature_184</th>\n",
       "      <th>feature_185</th>\n",
       "      <th>feature_186</th>\n",
       "      <th>feature_187</th>\n",
       "      <th>feature_188</th>\n",
       "      <th>feature_189</th>\n",
       "      <th>feature_190</th>\n",
       "      <th>feature_191</th>\n",
       "      <th>feature_192</th>\n",
       "      <th>feature_193</th>\n",
       "      <th>feature_194</th>\n",
       "      <th>feature_195</th>\n",
       "      <th>feature_196</th>\n",
       "      <th>feature_197</th>\n",
       "      <th>feature_198</th>\n",
       "      <th>feature_199</th>\n",
       "      <th>feature_200</th>\n",
       "      <th>feature_201</th>\n",
       "      <th>feature_202</th>\n",
       "      <th>feature_203</th>\n",
       "      <th>feature_204</th>\n",
       "      <th>feature_205</th>\n",
       "      <th>feature_206</th>\n",
       "      <th>feature_207</th>\n",
       "      <th>feature_208</th>\n",
       "      <th>feature_209</th>\n",
       "      <th>feature_210</th>\n",
       "      <th>feature_211</th>\n",
       "      <th>feature_212</th>\n",
       "      <th>feature_213</th>\n",
       "      <th>feature_214</th>\n",
       "      <th>feature_215</th>\n",
       "      <th>feature_216</th>\n",
       "      <th>feature_217</th>\n",
       "      <th>feature_218</th>\n",
       "      <th>feature_219</th>\n",
       "      <th>feature_220</th>\n",
       "      <th>feature_221</th>\n",
       "      <th>feature_222</th>\n",
       "      <th>feature_223</th>\n",
       "      <th>feature_224</th>\n",
       "      <th>feature_225</th>\n",
       "      <th>feature_226</th>\n",
       "      <th>feature_227</th>\n",
       "      <th>feature_228</th>\n",
       "      <th>feature_229</th>\n",
       "      <th>feature_230</th>\n",
       "      <th>feature_231</th>\n",
       "      <th>feature_232</th>\n",
       "      <th>feature_233</th>\n",
       "      <th>feature_234</th>\n",
       "      <th>feature_235</th>\n",
       "      <th>feature_236</th>\n",
       "      <th>feature_237</th>\n",
       "      <th>feature_238</th>\n",
       "      <th>feature_239</th>\n",
       "      <th>feature_240</th>\n",
       "      <th>feature_241</th>\n",
       "      <th>feature_242</th>\n",
       "      <th>feature_243</th>\n",
       "      <th>feature_244</th>\n",
       "      <th>feature_245</th>\n",
       "      <th>feature_246</th>\n",
       "      <th>feature_247</th>\n",
       "      <th>feature_248</th>\n",
       "      <th>feature_249</th>\n",
       "      <th>feature_250</th>\n",
       "      <th>feature_251</th>\n",
       "      <th>feature_252</th>\n",
       "      <th>feature_253</th>\n",
       "      <th>feature_254</th>\n",
       "      <th>feature_255</th>\n",
       "      <th>feature_256</th>\n",
       "      <th>feature_257</th>\n",
       "      <th>feature_258</th>\n",
       "      <th>feature_259</th>\n",
       "      <th>feature_260</th>\n",
       "      <th>feature_261</th>\n",
       "      <th>feature_262</th>\n",
       "      <th>feature_263</th>\n",
       "      <th>feature_264</th>\n",
       "      <th>feature_265</th>\n",
       "      <th>feature_266</th>\n",
       "      <th>feature_267</th>\n",
       "      <th>feature_268</th>\n",
       "      <th>feature_269</th>\n",
       "      <th>feature_270</th>\n",
       "      <th>feature_271</th>\n",
       "      <th>feature_272</th>\n",
       "      <th>feature_273</th>\n",
       "      <th>feature_274</th>\n",
       "      <th>feature_275</th>\n",
       "      <th>feature_276</th>\n",
       "      <th>feature_277</th>\n",
       "      <th>feature_278</th>\n",
       "      <th>feature_279</th>\n",
       "      <th>feature_280</th>\n",
       "      <th>feature_281</th>\n",
       "      <th>feature_282</th>\n",
       "      <th>feature_283</th>\n",
       "      <th>feature_284</th>\n",
       "      <th>feature_285</th>\n",
       "      <th>feature_286</th>\n",
       "      <th>feature_287</th>\n",
       "      <th>feature_288</th>\n",
       "      <th>feature_289</th>\n",
       "      <th>feature_290</th>\n",
       "      <th>feature_291</th>\n",
       "      <th>feature_292</th>\n",
       "      <th>feature_293</th>\n",
       "      <th>feature_294</th>\n",
       "      <th>feature_295</th>\n",
       "      <th>feature_296</th>\n",
       "      <th>feature_297</th>\n",
       "      <th>feature_298</th>\n",
       "      <th>feature_299</th>\n",
       "      <th>feature_300</th>\n",
       "      <th>feature_301</th>\n",
       "      <th>feature_302</th>\n",
       "      <th>feature_303</th>\n",
       "      <th>feature_304</th>\n",
       "      <th>feature_305</th>\n",
       "      <th>feature_306</th>\n",
       "      <th>feature_307</th>\n",
       "      <th>feature_308</th>\n",
       "      <th>feature_309</th>\n",
       "      <th>feature_310</th>\n",
       "      <th>feature_311</th>\n",
       "      <th>feature_312</th>\n",
       "      <th>feature_313</th>\n",
       "      <th>feature_314</th>\n",
       "      <th>feature_315</th>\n",
       "      <th>feature_316</th>\n",
       "      <th>feature_317</th>\n",
       "      <th>feature_318</th>\n",
       "      <th>feature_319</th>\n",
       "      <th>feature_320</th>\n",
       "      <th>feature_321</th>\n",
       "      <th>feature_322</th>\n",
       "      <th>feature_323</th>\n",
       "      <th>feature_324</th>\n",
       "      <th>feature_325</th>\n",
       "      <th>feature_326</th>\n",
       "      <th>feature_327</th>\n",
       "      <th>feature_328</th>\n",
       "      <th>feature_329</th>\n",
       "      <th>feature_330</th>\n",
       "      <th>feature_331</th>\n",
       "      <th>feature_332</th>\n",
       "      <th>feature_333</th>\n",
       "      <th>feature_334</th>\n",
       "      <th>feature_335</th>\n",
       "      <th>feature_336</th>\n",
       "      <th>feature_337</th>\n",
       "      <th>feature_338</th>\n",
       "      <th>feature_339</th>\n",
       "      <th>feature_340</th>\n",
       "      <th>feature_341</th>\n",
       "      <th>feature_342</th>\n",
       "      <th>feature_343</th>\n",
       "      <th>feature_344</th>\n",
       "      <th>feature_345</th>\n",
       "      <th>feature_346</th>\n",
       "      <th>feature_347</th>\n",
       "      <th>feature_348</th>\n",
       "      <th>feature_349</th>\n",
       "      <th>feature_350</th>\n",
       "      <th>feature_351</th>\n",
       "      <th>feature_352</th>\n",
       "      <th>feature_353</th>\n",
       "      <th>feature_354</th>\n",
       "      <th>feature_355</th>\n",
       "      <th>feature_356</th>\n",
       "      <th>feature_357</th>\n",
       "      <th>feature_358</th>\n",
       "      <th>feature_359</th>\n",
       "      <th>feature_360</th>\n",
       "      <th>feature_361</th>\n",
       "      <th>feature_362</th>\n",
       "      <th>feature_363</th>\n",
       "      <th>feature_364</th>\n",
       "      <th>feature_365</th>\n",
       "      <th>feature_366</th>\n",
       "      <th>feature_367</th>\n",
       "      <th>feature_368</th>\n",
       "      <th>feature_369</th>\n",
       "      <th>feature_370</th>\n",
       "      <th>feature_371</th>\n",
       "      <th>feature_372</th>\n",
       "      <th>feature_373</th>\n",
       "      <th>feature_374</th>\n",
       "      <th>feature_375</th>\n",
       "      <th>feature_376</th>\n",
       "      <th>feature_377</th>\n",
       "      <th>feature_378</th>\n",
       "      <th>feature_379</th>\n",
       "      <th>feature_380</th>\n",
       "      <th>feature_381</th>\n",
       "      <th>feature_382</th>\n",
       "      <th>feature_383</th>\n",
       "      <th>feature_384</th>\n",
       "      <th>feature_385</th>\n",
       "      <th>feature_386</th>\n",
       "      <th>feature_387</th>\n",
       "      <th>feature_388</th>\n",
       "      <th>feature_389</th>\n",
       "      <th>feature_390</th>\n",
       "      <th>feature_391</th>\n",
       "      <th>feature_392</th>\n",
       "      <th>feature_393</th>\n",
       "      <th>feature_394</th>\n",
       "      <th>feature_395</th>\n",
       "      <th>feature_396</th>\n",
       "      <th>feature_397</th>\n",
       "      <th>feature_398</th>\n",
       "      <th>feature_399</th>\n",
       "      <th>feature_400</th>\n",
       "      <th>feature_401</th>\n",
       "      <th>feature_402</th>\n",
       "      <th>feature_403</th>\n",
       "      <th>feature_404</th>\n",
       "      <th>feature_405</th>\n",
       "      <th>feature_406</th>\n",
       "      <th>feature_407</th>\n",
       "      <th>feature_408</th>\n",
       "      <th>feature_409</th>\n",
       "      <th>feature_410</th>\n",
       "      <th>feature_411</th>\n",
       "      <th>feature_412</th>\n",
       "      <th>feature_413</th>\n",
       "      <th>feature_414</th>\n",
       "      <th>feature_415</th>\n",
       "      <th>feature_416</th>\n",
       "      <th>feature_417</th>\n",
       "      <th>feature_418</th>\n",
       "      <th>feature_419</th>\n",
       "      <th>feature_420</th>\n",
       "      <th>feature_421</th>\n",
       "      <th>feature_422</th>\n",
       "      <th>feature_423</th>\n",
       "      <th>feature_424</th>\n",
       "      <th>feature_425</th>\n",
       "      <th>feature_426</th>\n",
       "      <th>feature_427</th>\n",
       "      <th>feature_428</th>\n",
       "      <th>feature_429</th>\n",
       "      <th>feature_430</th>\n",
       "      <th>feature_431</th>\n",
       "      <th>feature_432</th>\n",
       "      <th>feature_433</th>\n",
       "      <th>feature_434</th>\n",
       "      <th>feature_435</th>\n",
       "      <th>feature_436</th>\n",
       "      <th>feature_437</th>\n",
       "      <th>feature_438</th>\n",
       "      <th>feature_439</th>\n",
       "      <th>feature_440</th>\n",
       "      <th>feature_441</th>\n",
       "      <th>feature_442</th>\n",
       "      <th>feature_443</th>\n",
       "      <th>feature_444</th>\n",
       "      <th>feature_445</th>\n",
       "      <th>feature_446</th>\n",
       "      <th>feature_447</th>\n",
       "      <th>feature_448</th>\n",
       "      <th>feature_449</th>\n",
       "      <th>feature_450</th>\n",
       "      <th>feature_451</th>\n",
       "      <th>feature_452</th>\n",
       "      <th>feature_453</th>\n",
       "      <th>feature_454</th>\n",
       "      <th>feature_455</th>\n",
       "      <th>feature_456</th>\n",
       "      <th>feature_457</th>\n",
       "      <th>feature_458</th>\n",
       "      <th>feature_459</th>\n",
       "      <th>feature_460</th>\n",
       "      <th>feature_461</th>\n",
       "      <th>feature_462</th>\n",
       "      <th>feature_463</th>\n",
       "      <th>feature_464</th>\n",
       "      <th>feature_465</th>\n",
       "      <th>feature_466</th>\n",
       "      <th>feature_467</th>\n",
       "      <th>feature_468</th>\n",
       "      <th>feature_469</th>\n",
       "      <th>feature_470</th>\n",
       "      <th>feature_471</th>\n",
       "      <th>feature_472</th>\n",
       "      <th>feature_473</th>\n",
       "      <th>feature_474</th>\n",
       "      <th>feature_475</th>\n",
       "      <th>feature_476</th>\n",
       "      <th>feature_477</th>\n",
       "      <th>feature_478</th>\n",
       "      <th>feature_479</th>\n",
       "      <th>feature_480</th>\n",
       "      <th>feature_481</th>\n",
       "      <th>feature_482</th>\n",
       "      <th>feature_483</th>\n",
       "      <th>feature_484</th>\n",
       "      <th>feature_485</th>\n",
       "      <th>feature_486</th>\n",
       "      <th>feature_487</th>\n",
       "      <th>feature_488</th>\n",
       "      <th>feature_489</th>\n",
       "      <th>feature_490</th>\n",
       "      <th>feature_491</th>\n",
       "      <th>feature_492</th>\n",
       "      <th>feature_493</th>\n",
       "      <th>feature_494</th>\n",
       "      <th>feature_495</th>\n",
       "      <th>feature_496</th>\n",
       "      <th>feature_497</th>\n",
       "      <th>feature_498</th>\n",
       "      <th>feature_499</th>\n",
       "      <th>feature_500</th>\n",
       "      <th>feature_501</th>\n",
       "      <th>feature_502</th>\n",
       "      <th>feature_503</th>\n",
       "      <th>feature_504</th>\n",
       "      <th>feature_505</th>\n",
       "      <th>feature_506</th>\n",
       "      <th>feature_507</th>\n",
       "      <th>feature_508</th>\n",
       "      <th>feature_509</th>\n",
       "      <th>feature_510</th>\n",
       "      <th>feature_511</th>\n",
       "      <th>feature_512</th>\n",
       "      <th>feature_513</th>\n",
       "      <th>feature_514</th>\n",
       "      <th>feature_515</th>\n",
       "      <th>feature_516</th>\n",
       "      <th>feature_517</th>\n",
       "      <th>feature_518</th>\n",
       "      <th>feature_519</th>\n",
       "      <th>feature_520</th>\n",
       "      <th>feature_521</th>\n",
       "      <th>feature_522</th>\n",
       "      <th>feature_523</th>\n",
       "      <th>feature_524</th>\n",
       "      <th>feature_525</th>\n",
       "      <th>feature_526</th>\n",
       "      <th>feature_527</th>\n",
       "      <th>feature_528</th>\n",
       "      <th>feature_529</th>\n",
       "      <th>feature_530</th>\n",
       "      <th>feature_531</th>\n",
       "      <th>feature_532</th>\n",
       "      <th>feature_533</th>\n",
       "      <th>feature_534</th>\n",
       "      <th>feature_535</th>\n",
       "      <th>feature_536</th>\n",
       "      <th>feature_537</th>\n",
       "      <th>feature_538</th>\n",
       "      <th>feature_539</th>\n",
       "      <th>feature_540</th>\n",
       "      <th>feature_541</th>\n",
       "      <th>feature_542</th>\n",
       "      <th>feature_543</th>\n",
       "      <th>feature_544</th>\n",
       "      <th>feature_545</th>\n",
       "      <th>feature_546</th>\n",
       "      <th>feature_547</th>\n",
       "      <th>feature_548</th>\n",
       "      <th>feature_549</th>\n",
       "      <th>feature_550</th>\n",
       "      <th>feature_551</th>\n",
       "      <th>feature_552</th>\n",
       "      <th>feature_553</th>\n",
       "      <th>feature_554</th>\n",
       "      <th>feature_555</th>\n",
       "      <th>feature_556</th>\n",
       "      <th>feature_557</th>\n",
       "      <th>feature_558</th>\n",
       "      <th>feature_559</th>\n",
       "      <th>feature_560</th>\n",
       "      <th>feature_561</th>\n",
       "      <th>feature_562</th>\n",
       "      <th>feature_563</th>\n",
       "      <th>feature_564</th>\n",
       "      <th>feature_565</th>\n",
       "      <th>feature_566</th>\n",
       "      <th>feature_567</th>\n",
       "      <th>feature_568</th>\n",
       "      <th>feature_569</th>\n",
       "      <th>feature_570</th>\n",
       "      <th>feature_571</th>\n",
       "      <th>feature_572</th>\n",
       "      <th>feature_573</th>\n",
       "      <th>feature_574</th>\n",
       "      <th>feature_575</th>\n",
       "      <th>feature_576</th>\n",
       "      <th>feature_577</th>\n",
       "      <th>feature_578</th>\n",
       "      <th>feature_579</th>\n",
       "      <th>feature_580</th>\n",
       "      <th>feature_581</th>\n",
       "      <th>feature_582</th>\n",
       "      <th>feature_583</th>\n",
       "      <th>feature_584</th>\n",
       "      <th>feature_585</th>\n",
       "      <th>feature_586</th>\n",
       "      <th>feature_587</th>\n",
       "      <th>feature_588</th>\n",
       "      <th>feature_589</th>\n",
       "      <th>HouseLineTestingResult</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.00000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.00000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
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       "      <td>1567.0</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>1567.00000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3014.441551</td>\n",
       "      <td>2495.866110</td>\n",
       "      <td>2200.551958</td>\n",
       "      <td>1395.383474</td>\n",
       "      <td>4.171281</td>\n",
       "      <td>100.0</td>\n",
       "      <td>101.116476</td>\n",
       "      <td>0.121825</td>\n",
       "      <td>1.462860</td>\n",
       "      <td>-0.000842</td>\n",
       "      <td>0.000146</td>\n",
       "      <td>0.964355</td>\n",
       "      <td>199.956272</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.005297</td>\n",
       "      <td>413.084376</td>\n",
       "      <td>9.907496</td>\n",
       "      <td>0.971446</td>\n",
       "      <td>190.046620</td>\n",
       "      <td>12.481152</td>\n",
       "      <td>1.405054</td>\n",
       "      <td>-5618.272176</td>\n",
       "      <td>2699.333280</td>\n",
       "      <td>-3806.318177</td>\n",
       "      <td>-298.317538</td>\n",
       "      <td>1.203946</td>\n",
       "      <td>1.938538</td>\n",
       "      <td>6.639427</td>\n",
       "      <td>69.499093</td>\n",
       "      <td>2.366212</td>\n",
       "      <td>0.184162</td>\n",
       "      <td>3.67288</td>\n",
       "      <td>85.337340</td>\n",
       "      <td>8.960157</td>\n",
       "      <td>50.58252</td>\n",
       "      <td>64.555538</td>\n",
       "      <td>49.417489</td>\n",
       "      <td>66.221280</td>\n",
       "      <td>86.836566</td>\n",
       "      <td>118.679376</td>\n",
       "      <td>68.063966</td>\n",
       "      <td>3.348792</td>\n",
       "      <td>70.0</td>\n",
       "      <td>355.537743</td>\n",
       "      <td>10.031167</td>\n",
       "      <td>136.742841</td>\n",
       "      <td>733.672669</td>\n",
       "      <td>1.178005</td>\n",
       "      <td>139.972254</td>\n",
       "      <td>1.0</td>\n",
       "      <td>632.253633</td>\n",
       "      <td>157.437518</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.592979</td>\n",
       "      <td>4.838534</td>\n",
       "      <td>2856.166560</td>\n",
       "      <td>0.928855</td>\n",
       "      <td>0.949215</td>\n",
       "      <td>4.593259</td>\n",
       "      <td>2.951249</td>\n",
       "      <td>355.153887</td>\n",
       "      <td>10.423195</td>\n",
       "      <td>116.501216</td>\n",
       "      <td>13.986604</td>\n",
       "      <td>20.539783</td>\n",
       "      <td>27.127928</td>\n",
       "      <td>706.667700</td>\n",
       "      <td>16.655186</td>\n",
       "      <td>147.438189</td>\n",
       "      <td>1.0</td>\n",
       "      <td>619.101422</td>\n",
       "      <td>104.322429</td>\n",
       "      <td>151.342346</td>\n",
       "      <td>467.038061</td>\n",
       "      <td>0.002677</td>\n",
       "      <td>-0.006894</td>\n",
       "      <td>-0.029383</td>\n",
       "      <td>-0.007085</td>\n",
       "      <td>-0.013625</td>\n",
       "      <td>0.003415</td>\n",
       "      <td>-0.018380</td>\n",
       "      <td>-0.021130</td>\n",
       "      <td>0.006079</td>\n",
       "      <td>7.452076</td>\n",
       "      <td>0.133107</td>\n",
       "      <td>0.113439</td>\n",
       "      <td>2.401872</td>\n",
       "      <td>0.982420</td>\n",
       "      <td>1807.815021</td>\n",
       "      <td>0.188749</td>\n",
       "      <td>8827.468461</td>\n",
       "      <td>0.002431</td>\n",
       "      <td>0.000507</td>\n",
       "      <td>-0.000540</td>\n",
       "      <td>-0.000029</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.017076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.018073</td>\n",
       "      <td>0.001534</td>\n",
       "      <td>-0.000021</td>\n",
       "      <td>-0.000007</td>\n",
       "      <td>0.001110</td>\n",
       "      <td>-0.009789</td>\n",
       "      <td>-0.000015</td>\n",
       "      <td>-0.000497</td>\n",
       "      <td>0.000538</td>\n",
       "      <td>-0.001759</td>\n",
       "      <td>-0.010790</td>\n",
       "      <td>0.980647</td>\n",
       "      <td>101.424436</td>\n",
       "      <td>231.417611</td>\n",
       "      <td>0.459962</td>\n",
       "      <td>0.945424</td>\n",
       "      <td>0.000123</td>\n",
       "      <td>747.383792</td>\n",
       "      <td>0.987130</td>\n",
       "      <td>58.625908</td>\n",
       "      <td>0.598421</td>\n",
       "      <td>0.970777</td>\n",
       "      <td>6.310863</td>\n",
       "      <td>15.796388</td>\n",
       "      <td>3.898267</td>\n",
       "      <td>15.829662</td>\n",
       "      <td>15.794620</td>\n",
       "      <td>1.184720</td>\n",
       "      <td>2.750638</td>\n",
       "      <td>0.648509</td>\n",
       "      <td>3.192198</td>\n",
       "      <td>-0.551860</td>\n",
       "      <td>0.745056</td>\n",
       "      <td>0.997808</td>\n",
       "      <td>2.318513</td>\n",
       "      <td>1004.043129</td>\n",
       "      <td>39.389480</td>\n",
       "      <td>117.932355</td>\n",
       "      <td>138.180983</td>\n",
       "      <td>122.670645</td>\n",
       "      <td>57.587810</td>\n",
       "      <td>416.077185</td>\n",
       "      <td>25.847024</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.638156</td>\n",
       "      <td>0.004168</td>\n",
       "      <td>0.119992</td>\n",
       "      <td>0.063615</td>\n",
       "      <td>0.055004</td>\n",
       "      <td>0.017409</td>\n",
       "      <td>8.470601</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.812615</td>\n",
       "      <td>14.041556</td>\n",
       "      <td>1.195339</td>\n",
       "      <td>0.011925</td>\n",
       "      <td>7.697616</td>\n",
       "      <td>0.505977</td>\n",
       "      <td>0.058089</td>\n",
       "      <td>0.036476</td>\n",
       "      <td>1022.004195</td>\n",
       "      <td>882.349075</td>\n",
       "      <td>555.196554</td>\n",
       "      <td>4064.996171</td>\n",
       "      <td>4793.308870</td>\n",
       "      <td>0.140179</td>\n",
       "      <td>0.127893</td>\n",
       "      <td>0.251939</td>\n",
       "      <td>2.788641</td>\n",
       "      <td>1.235737</td>\n",
       "      <td>0.124390</td>\n",
       "      <td>0.400468</td>\n",
       "      <td>0.684331</td>\n",
       "      <td>0.120059</td>\n",
       "      <td>0.320115</td>\n",
       "      <td>0.576193</td>\n",
       "      <td>0.320115</td>\n",
       "      <td>0.778037</td>\n",
       "      <td>0.244717</td>\n",
       "      <td>0.394699</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.013050</td>\n",
       "      <td>0.546756</td>\n",
       "      <td>10.780153</td>\n",
       "      <td>26.661515</td>\n",
       "      <td>0.144808</td>\n",
       "      <td>7.365338</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.936244</td>\n",
       "      <td>43.209503</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.287013</td>\n",
       "      <td>8.679962</td>\n",
       "      <td>20.089943</td>\n",
       "      <td>0.556848</td>\n",
       "      <td>11.520715</td>\n",
       "      <td>17.598561</td>\n",
       "      <td>7.834537</td>\n",
       "      <td>10.162831</td>\n",
       "      <td>30.073234</td>\n",
       "      <td>32.095412</td>\n",
       "      <td>9.045105</td>\n",
       "      <td>0.001276</td>\n",
       "      <td>20.373663</td>\n",
       "      <td>73.264254</td>\n",
       "      <td>0.029451</td>\n",
       "      <td>0.088725</td>\n",
       "      <td>0.056701</td>\n",
       "      <td>0.051281</td>\n",
       "      <td>0.060280</td>\n",
       "      <td>0.083148</td>\n",
       "      <td>0.081097</td>\n",
       "      <td>0.083501</td>\n",
       "      <td>0.071483</td>\n",
       "      <td>3.771376</td>\n",
       "      <td>0.003252</td>\n",
       "      <td>0.008988</td>\n",
       "      <td>0.060718</td>\n",
       "      <td>0.008821</td>\n",
       "      <td>122.846571</td>\n",
       "      <td>0.058734</td>\n",
       "      <td>1038.656080</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.019122</td>\n",
       "      <td>0.017841</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004791</td>\n",
       "      <td>0.004575</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003121</td>\n",
       "      <td>1.375909</td>\n",
       "      <td>3.839677</td>\n",
       "      <td>0.041340</td>\n",
       "      <td>0.025171</td>\n",
       "      <td>0.001065</td>\n",
       "      <td>109.650967</td>\n",
       "      <td>0.004285</td>\n",
       "      <td>4.645115</td>\n",
       "      <td>0.033179</td>\n",
       "      <td>0.013943</td>\n",
       "      <td>0.403848</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.070587</td>\n",
       "      <td>19.496877</td>\n",
       "      <td>3.777486</td>\n",
       "      <td>29.258737</td>\n",
       "      <td>46.055143</td>\n",
       "      <td>41.292434</td>\n",
       "      <td>20.175883</td>\n",
       "      <td>136.062895</td>\n",
       "      <td>8.616246</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.209604</td>\n",
       "      <td>0.001117</td>\n",
       "      <td>0.041052</td>\n",
       "      <td>0.018032</td>\n",
       "      <td>0.015093</td>\n",
       "      <td>0.005769</td>\n",
       "      <td>2.803798</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.119327</td>\n",
       "      <td>4.258295</td>\n",
       "      <td>0.367092</td>\n",
       "      <td>0.003923</td>\n",
       "      <td>2.578539</td>\n",
       "      <td>0.123171</td>\n",
       "      <td>0.019926</td>\n",
       "      <td>0.011307</td>\n",
       "      <td>319.424471</td>\n",
       "      <td>401.657580</td>\n",
       "      <td>252.926146</td>\n",
       "      <td>1878.364532</td>\n",
       "      <td>2340.883483</td>\n",
       "      <td>0.063790</td>\n",
       "      <td>0.060241</td>\n",
       "      <td>0.118341</td>\n",
       "      <td>0.910082</td>\n",
       "      <td>0.403314</td>\n",
       "      <td>0.040342</td>\n",
       "      <td>0.132083</td>\n",
       "      <td>0.264916</td>\n",
       "      <td>0.048621</td>\n",
       "      <td>0.128921</td>\n",
       "      <td>0.218415</td>\n",
       "      <td>0.128922</td>\n",
       "      <td>0.304751</td>\n",
       "      <td>0.097345</td>\n",
       "      <td>0.160026</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.976884</td>\n",
       "      <td>0.172623</td>\n",
       "      <td>3.188586</td>\n",
       "      <td>7.916338</td>\n",
       "      <td>0.043103</td>\n",
       "      <td>2.263609</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.393462</td>\n",
       "      <td>13.331644</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.083236</td>\n",
       "      <td>2.591113</td>\n",
       "      <td>6.214963</td>\n",
       "      <td>0.168215</td>\n",
       "      <td>3.423428</td>\n",
       "      <td>9.733425</td>\n",
       "      <td>2.326262</td>\n",
       "      <td>3.035450</td>\n",
       "      <td>9.329514</td>\n",
       "      <td>14.617500</td>\n",
       "      <td>2.730735</td>\n",
       "      <td>0.000285</td>\n",
       "      <td>6.197770</td>\n",
       "      <td>23.217137</td>\n",
       "      <td>6.746664</td>\n",
       "      <td>4.390054</td>\n",
       "      <td>0.008880</td>\n",
       "      <td>0.024673</td>\n",
       "      <td>0.025233</td>\n",
       "      <td>0.023135</td>\n",
       "      <td>0.027549</td>\n",
       "      <td>0.023335</td>\n",
       "      <td>0.040358</td>\n",
       "      <td>0.041956</td>\n",
       "      <td>0.034464</td>\n",
       "      <td>1.298601</td>\n",
       "      <td>0.000998</td>\n",
       "      <td>0.002406</td>\n",
       "      <td>0.019840</td>\n",
       "      <td>0.002945</td>\n",
       "      <td>39.936406</td>\n",
       "      <td>0.018191</td>\n",
       "      <td>332.555156</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005198</td>\n",
       "      <td>0.004813</td>\n",
       "      <td>0.003771</td>\n",
       "      <td>0.003170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001601</td>\n",
       "      <td>0.001571</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000964</td>\n",
       "      <td>0.431613</td>\n",
       "      <td>1.169065</td>\n",
       "      <td>0.009316</td>\n",
       "      <td>0.008281</td>\n",
       "      <td>0.000339</td>\n",
       "      <td>35.155091</td>\n",
       "      <td>0.001338</td>\n",
       "      <td>1.431868</td>\n",
       "      <td>0.010944</td>\n",
       "      <td>0.004533</td>\n",
       "      <td>0.133990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.024206</td>\n",
       "      <td>6.726477</td>\n",
       "      <td>1.232037</td>\n",
       "      <td>5.339597</td>\n",
       "      <td>4.580083</td>\n",
       "      <td>4.928466</td>\n",
       "      <td>2.615479</td>\n",
       "      <td>30.868839</td>\n",
       "      <td>25.564824</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.62656</td>\n",
       "      <td>3.403370</td>\n",
       "      <td>8.189890</td>\n",
       "      <td>320.236157</td>\n",
       "      <td>309.014570</td>\n",
       "      <td>1.821037</td>\n",
       "      <td>4.174228</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77.645599</td>\n",
       "      <td>3.314227</td>\n",
       "      <td>6.792422</td>\n",
       "      <td>1.233669</td>\n",
       "      <td>4.058277</td>\n",
       "      <td>4.209984</td>\n",
       "      <td>4.171844</td>\n",
       "      <td>18.412262</td>\n",
       "      <td>22.350676</td>\n",
       "      <td>99.314795</td>\n",
       "      <td>205.449868</td>\n",
       "      <td>14.728156</td>\n",
       "      <td>9.364514</td>\n",
       "      <td>7.507205</td>\n",
       "      <td>4.016484</td>\n",
       "      <td>54.693892</td>\n",
       "      <td>70.637297</td>\n",
       "      <td>11.527330</td>\n",
       "      <td>0.802084</td>\n",
       "      <td>1.345207</td>\n",
       "      <td>0.633947</td>\n",
       "      <td>0.895048</td>\n",
       "      <td>0.647092</td>\n",
       "      <td>1.174996</td>\n",
       "      <td>0.281894</td>\n",
       "      <td>0.332218</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.346767</td>\n",
       "      <td>5.460822</td>\n",
       "      <td>7.883449</td>\n",
       "      <td>3.636690</td>\n",
       "      <td>12.325063</td>\n",
       "      <td>5.263366</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.838375</td>\n",
       "      <td>29.19548</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.250541</td>\n",
       "      <td>223.843226</td>\n",
       "      <td>5.661566</td>\n",
       "      <td>5.362749</td>\n",
       "      <td>9.630209</td>\n",
       "      <td>137.890044</td>\n",
       "      <td>39.403707</td>\n",
       "      <td>37.615532</td>\n",
       "      <td>4.262626</td>\n",
       "      <td>20.116232</td>\n",
       "      <td>6.254032</td>\n",
       "      <td>0.127632</td>\n",
       "      <td>3.283016</td>\n",
       "      <td>75.518589</td>\n",
       "      <td>0.0</td>\n",
       "      <td>318.037084</td>\n",
       "      <td>205.672096</td>\n",
       "      <td>214.117076</td>\n",
       "      <td>199.761505</td>\n",
       "      <td>301.700889</td>\n",
       "      <td>237.507454</td>\n",
       "      <td>352.553880</td>\n",
       "      <td>271.362828</td>\n",
       "      <td>51.352261</td>\n",
       "      <td>2.441204</td>\n",
       "      <td>8.032313</td>\n",
       "      <td>2.530046</td>\n",
       "      <td>0.956442</td>\n",
       "      <td>6.807826</td>\n",
       "      <td>29.611163</td>\n",
       "      <td>11.791267</td>\n",
       "      <td>0.0</td>\n",
       "      <td>262.859941</td>\n",
       "      <td>240.673807</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.752306</td>\n",
       "      <td>275.627217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.351346</td>\n",
       "      <td>1.359116</td>\n",
       "      <td>1.665187</td>\n",
       "      <td>9.038411</td>\n",
       "      <td>2.695999</td>\n",
       "      <td>11.610080</td>\n",
       "      <td>14.728866</td>\n",
       "      <td>0.453896</td>\n",
       "      <td>5.687782</td>\n",
       "      <td>5.553870</td>\n",
       "      <td>1.443457</td>\n",
       "      <td>6.395717</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.03434</td>\n",
       "      <td>1.942025</td>\n",
       "      <td>9.610850</td>\n",
       "      <td>0.111206</td>\n",
       "      <td>0.008470</td>\n",
       "      <td>0.002509</td>\n",
       "      <td>7.610771</td>\n",
       "      <td>1.018304</td>\n",
       "      <td>403.476047</td>\n",
       "      <td>75.415081</td>\n",
       "      <td>0.631357</td>\n",
       "      <td>16.901595</td>\n",
       "      <td>1.217320</td>\n",
       "      <td>0.263615</td>\n",
       "      <td>7.658083</td>\n",
       "      <td>0.499550</td>\n",
       "      <td>57.198192</td>\n",
       "      <td>4.192049</td>\n",
       "      <td>1.607594</td>\n",
       "      <td>0.994995</td>\n",
       "      <td>0.325686</td>\n",
       "      <td>0.072435</td>\n",
       "      <td>32.283326</td>\n",
       "      <td>262.998383</td>\n",
       "      <td>0.674651</td>\n",
       "      <td>6.221117</td>\n",
       "      <td>0.141069</td>\n",
       "      <td>2.530656</td>\n",
       "      <td>0.058094</td>\n",
       "      <td>2.373545</td>\n",
       "      <td>20.398270</td>\n",
       "      <td>530.523623</td>\n",
       "      <td>2.101836</td>\n",
       "      <td>28.450165</td>\n",
       "      <td>0.345636</td>\n",
       "      <td>9.162315</td>\n",
       "      <td>0.104729</td>\n",
       "      <td>5.563747</td>\n",
       "      <td>16.642363</td>\n",
       "      <td>0.020879</td>\n",
       "      <td>0.015600</td>\n",
       "      <td>0.004974</td>\n",
       "      <td>82.403069</td>\n",
       "      <td>0.500096</td>\n",
       "      <td>0.015317</td>\n",
       "      <td>0.003846</td>\n",
       "      <td>3.067628</td>\n",
       "      <td>0.021458</td>\n",
       "      <td>0.016474</td>\n",
       "      <td>0.005283</td>\n",
       "      <td>99.652345</td>\n",
       "      <td>-0.867262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>73.480841</td>\n",
       "      <td>80.228143</td>\n",
       "      <td>29.380973</td>\n",
       "      <td>439.837330</td>\n",
       "      <td>56.103721</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.209385</td>\n",
       "      <td>0.008936</td>\n",
       "      <td>0.073849</td>\n",
       "      <td>0.015107</td>\n",
       "      <td>0.009296</td>\n",
       "      <td>0.012444</td>\n",
       "      <td>3.255230</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.793916</td>\n",
       "      <td>17.204633</td>\n",
       "      <td>2.401564</td>\n",
       "      <td>0.012051</td>\n",
       "      <td>2.778426</td>\n",
       "      <td>0.217273</td>\n",
       "      <td>0.016737</td>\n",
       "      <td>626.430997</td>\n",
       "      <td>295.312481</td>\n",
       "      <td>1379.280633</td>\n",
       "      <td>2900.846582</td>\n",
       "      <td>0.177510</td>\n",
       "      <td>0.189382</td>\n",
       "      <td>1.243655</td>\n",
       "      <td>3.458992</td>\n",
       "      <td>0.408433</td>\n",
       "      <td>0.032923</td>\n",
       "      <td>0.53505</td>\n",
       "      <td>2.025908</td>\n",
       "      <td>1.344036</td>\n",
       "      <td>1.18225</td>\n",
       "      <td>2.573945</td>\n",
       "      <td>1.182251</td>\n",
       "      <td>0.304044</td>\n",
       "      <td>0.446613</td>\n",
       "      <td>1.806658</td>\n",
       "      <td>23.911897</td>\n",
       "      <td>2.342518</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.232884</td>\n",
       "      <td>0.174982</td>\n",
       "      <td>7.846746</td>\n",
       "      <td>12.166430</td>\n",
       "      <td>0.189585</td>\n",
       "      <td>4.522806</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.641254</td>\n",
       "      <td>60.909166</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.054880</td>\n",
       "      <td>0.059505</td>\n",
       "      <td>25.716644</td>\n",
       "      <td>0.006800</td>\n",
       "      <td>0.004171</td>\n",
       "      <td>0.084992</td>\n",
       "      <td>9.511839</td>\n",
       "      <td>6.016917</td>\n",
       "      <td>0.274351</td>\n",
       "      <td>8.612494</td>\n",
       "      <td>7.104106</td>\n",
       "      <td>4.966452</td>\n",
       "      <td>7.106006</td>\n",
       "      <td>11.600798</td>\n",
       "      <td>306.914183</td>\n",
       "      <td>4.231976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.520899</td>\n",
       "      <td>31.591384</td>\n",
       "      <td>12.947203</td>\n",
       "      <td>12.416250</td>\n",
       "      <td>0.105986</td>\n",
       "      <td>0.022121</td>\n",
       "      <td>0.032948</td>\n",
       "      <td>0.031129</td>\n",
       "      <td>0.047504</td>\n",
       "      <td>0.022905</td>\n",
       "      <td>0.048862</td>\n",
       "      <td>0.016891</td>\n",
       "      <td>0.035797</td>\n",
       "      <td>0.516087</td>\n",
       "      <td>0.005032</td>\n",
       "      <td>0.001142</td>\n",
       "      <td>0.037332</td>\n",
       "      <td>0.012848</td>\n",
       "      <td>53.537262</td>\n",
       "      <td>0.051514</td>\n",
       "      <td>389.807042</td>\n",
       "      <td>0.087515</td>\n",
       "      <td>0.003229</td>\n",
       "      <td>0.003008</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>0.219159</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.426292</td>\n",
       "      <td>0.062620</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>0.062847</td>\n",
       "      <td>0.003063</td>\n",
       "      <td>0.000850</td>\n",
       "      <td>0.003196</td>\n",
       "      <td>0.002982</td>\n",
       "      <td>0.087307</td>\n",
       "      <td>0.086591</td>\n",
       "      <td>0.005166</td>\n",
       "      <td>1.114905</td>\n",
       "      <td>1.279821</td>\n",
       "      <td>0.036174</td>\n",
       "      <td>0.012133</td>\n",
       "      <td>0.001668</td>\n",
       "      <td>48.949250</td>\n",
       "      <td>0.009497</td>\n",
       "      <td>6.485174</td>\n",
       "      <td>0.008040</td>\n",
       "      <td>0.008949</td>\n",
       "      <td>0.124304</td>\n",
       "      <td>0.099332</td>\n",
       "      <td>0.901520</td>\n",
       "      <td>0.108003</td>\n",
       "      <td>0.113821</td>\n",
       "      <td>0.279764</td>\n",
       "      <td>0.252744</td>\n",
       "      <td>0.135020</td>\n",
       "      <td>0.263414</td>\n",
       "      <td>1.217366</td>\n",
       "      <td>0.082300</td>\n",
       "      <td>0.002244</td>\n",
       "      <td>0.053047</td>\n",
       "      <td>6.520981</td>\n",
       "      <td>2.983032</td>\n",
       "      <td>57.454912</td>\n",
       "      <td>53.806875</td>\n",
       "      <td>52.137163</td>\n",
       "      <td>12.291096</td>\n",
       "      <td>262.221743</td>\n",
       "      <td>504.656946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.536522</td>\n",
       "      <td>0.001278</td>\n",
       "      <td>0.061305</td>\n",
       "      <td>0.026524</td>\n",
       "      <td>0.021831</td>\n",
       "      <td>0.027106</td>\n",
       "      <td>18.728671</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.238956</td>\n",
       "      <td>30.973119</td>\n",
       "      <td>23.341694</td>\n",
       "      <td>0.009337</td>\n",
       "      <td>5.234204</td>\n",
       "      <td>1.118937</td>\n",
       "      <td>0.079174</td>\n",
       "      <td>0.012152</td>\n",
       "      <td>120.461488</td>\n",
       "      <td>982.458855</td>\n",
       "      <td>574.456703</td>\n",
       "      <td>4236.854877</td>\n",
       "      <td>6550.267099</td>\n",
       "      <td>0.121913</td>\n",
       "      <td>0.242383</td>\n",
       "      <td>0.407076</td>\n",
       "      <td>1.119061</td>\n",
       "      <td>0.632364</td>\n",
       "      <td>0.047609</td>\n",
       "      <td>0.197792</td>\n",
       "      <td>0.157418</td>\n",
       "      <td>0.060766</td>\n",
       "      <td>0.071220</td>\n",
       "      <td>0.095703</td>\n",
       "      <td>0.071224</td>\n",
       "      <td>0.116285</td>\n",
       "      <td>0.074894</td>\n",
       "      <td>0.282823</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>3.310585</td>\n",
       "      <td>0.224331</td>\n",
       "      <td>4.162749</td>\n",
       "      <td>6.833931</td>\n",
       "      <td>0.110163</td>\n",
       "      <td>7.186442</td>\n",
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       "      <td>8.607162</td>\n",
       "      <td>21.705075</td>\n",
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       "      <td>0.394684</td>\n",
       "      <td>15.686267</td>\n",
       "      <td>10.532022</td>\n",
       "      <td>0.536737</td>\n",
       "      <td>16.415041</td>\n",
       "      <td>8.671307</td>\n",
       "      <td>5.093583</td>\n",
       "      <td>14.590631</td>\n",
       "      <td>17.428314</td>\n",
       "      <td>564.021108</td>\n",
       "      <td>11.519237</td>\n",
       "      <td>0.050524</td>\n",
       "      <td>17.464051</td>\n",
       "      <td>28.013323</td>\n",
       "      <td>1.165835</td>\n",
       "      <td>0.041756</td>\n",
       "      <td>0.024817</td>\n",
       "      <td>0.031358</td>\n",
       "      <td>0.052624</td>\n",
       "      <td>0.056030</td>\n",
       "      <td>0.030203</td>\n",
       "      <td>0.025566</td>\n",
       "      <td>0.045944</td>\n",
       "      <td>1.170068</td>\n",
       "      <td>0.001640</td>\n",
       "      <td>0.000760</td>\n",
       "      <td>0.023305</td>\n",
       "      <td>0.055937</td>\n",
       "      <td>55.156003</td>\n",
       "      <td>0.070128</td>\n",
       "      <td>426.259257</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.010749</td>\n",
       "      <td>0.010738</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001697</td>\n",
       "      <td>0.001440</td>\n",
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       "      <td>0.050093</td>\n",
       "      <td>2.577886</td>\n",
       "      <td>5.946545</td>\n",
       "      <td>0.051023</td>\n",
       "      <td>0.049235</td>\n",
       "      <td>0.015771</td>\n",
       "      <td>54.597274</td>\n",
       "      <td>0.037472</td>\n",
       "      <td>64.354756</td>\n",
       "      <td>0.022255</td>\n",
       "      <td>0.009132</td>\n",
       "      <td>0.120334</td>\n",
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       "      <td>7.326430</td>\n",
       "      <td>1.149394</td>\n",
       "      <td>8.388634</td>\n",
       "      <td>17.832193</td>\n",
       "      <td>17.698031</td>\n",
       "      <td>3.813721</td>\n",
       "      <td>85.258562</td>\n",
       "      <td>168.194470</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191138</td>\n",
       "      <td>0.000339</td>\n",
       "      <td>0.020277</td>\n",
       "      <td>0.006479</td>\n",
       "      <td>0.005542</td>\n",
       "      <td>0.008545</td>\n",
       "      <td>5.860580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.962059</td>\n",
       "      <td>9.754552</td>\n",
       "      <td>7.379271</td>\n",
       "      <td>0.002933</td>\n",
       "      <td>1.615444</td>\n",
       "      <td>0.270139</td>\n",
       "      <td>0.025549</td>\n",
       "      <td>0.003540</td>\n",
       "      <td>41.033649</td>\n",
       "      <td>476.765634</td>\n",
       "      <td>283.356947</td>\n",
       "      <td>1973.997717</td>\n",
       "      <td>3225.321542</td>\n",
       "      <td>0.064185</td>\n",
       "      <td>0.130744</td>\n",
       "      <td>0.219011</td>\n",
       "      <td>0.331774</td>\n",
       "      <td>0.197389</td>\n",
       "      <td>0.014501</td>\n",
       "      <td>0.064826</td>\n",
       "      <td>0.057369</td>\n",
       "      <td>0.025392</td>\n",
       "      <td>0.027459</td>\n",
       "      <td>0.033582</td>\n",
       "      <td>0.027461</td>\n",
       "      <td>0.043446</td>\n",
       "      <td>0.028787</td>\n",
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       "      <td>1.018311</td>\n",
       "      <td>0.072370</td>\n",
       "      <td>1.215564</td>\n",
       "      <td>2.178396</td>\n",
       "      <td>0.031875</td>\n",
       "      <td>2.116324</td>\n",
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       "      <td>2.518056</td>\n",
       "      <td>6.613770</td>\n",
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       "      <td>0.063344</td>\n",
       "      <td>5.632706</td>\n",
       "      <td>3.396952</td>\n",
       "      <td>0.172568</td>\n",
       "      <td>5.770748</td>\n",
       "      <td>7.539354</td>\n",
       "      <td>1.695730</td>\n",
       "      <td>5.632481</td>\n",
       "      <td>6.063060</td>\n",
       "      <td>261.238119</td>\n",
       "      <td>3.660934</td>\n",
       "      <td>0.011297</td>\n",
       "      <td>5.361538</td>\n",
       "      <td>8.878164</td>\n",
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       "      <td>0.351511</td>\n",
       "      <td>0.011774</td>\n",
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       "      <td>0.013057</td>\n",
       "      <td>0.015382</td>\n",
       "      <td>0.012970</td>\n",
       "      <td>0.022144</td>\n",
       "      <td>0.386796</td>\n",
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       "      <td>0.000150</td>\n",
       "      <td>0.007136</td>\n",
       "      <td>0.020003</td>\n",
       "      <td>17.056304</td>\n",
       "      <td>0.021314</td>\n",
       "      <td>136.586680</td>\n",
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       "      <td>1.876245</td>\n",
       "      <td>0.010838</td>\n",
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       "      <td>0.004989</td>\n",
       "      <td>17.227003</td>\n",
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       "      <td>20.326415</td>\n",
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       "      <td>1.773445</td>\n",
       "      <td>2.118268</td>\n",
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       "      <td>18.336590</td>\n",
       "      <td>47.099220</td>\n",
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       "      <td>3.94087</td>\n",
       "      <td>1.032534</td>\n",
       "      <td>4.052025</td>\n",
       "      <td>287.521430</td>\n",
       "      <td>325.243132</td>\n",
       "      <td>3.055746</td>\n",
       "      <td>6.909444</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.567460</td>\n",
       "      <td>6.319367</td>\n",
       "      <td>23.235597</td>\n",
       "      <td>0.994675</td>\n",
       "      <td>3.039233</td>\n",
       "      <td>10.599579</td>\n",
       "      <td>6.435390</td>\n",
       "      <td>36.037997</td>\n",
       "      <td>36.372786</td>\n",
       "      <td>126.116776</td>\n",
       "      <td>225.643014</td>\n",
       "      <td>34.087451</td>\n",
       "      <td>34.348266</td>\n",
       "      <td>34.536146</td>\n",
       "      <td>1.610266</td>\n",
       "      <td>34.086853</td>\n",
       "      <td>38.352115</td>\n",
       "      <td>6.165562</td>\n",
       "      <td>0.184154</td>\n",
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       "      <td>0.155472</td>\n",
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       "      <td>0.176102</td>\n",
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       "      <td>3.058705</td>\n",
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       "      <td>8.123318</td>\n",
       "      <td>4.536303</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>8.662693</td>\n",
       "      <td>230.303247</td>\n",
       "      <td>3.145663</td>\n",
       "      <td>4.974466</td>\n",
       "      <td>10.155553</td>\n",
       "      <td>47.591323</td>\n",
       "      <td>22.409521</td>\n",
       "      <td>24.769461</td>\n",
       "      <td>2.606167</td>\n",
       "      <td>14.913155</td>\n",
       "      <td>10.165690</td>\n",
       "      <td>5.052374</td>\n",
       "      <td>2.633556</td>\n",
       "      <td>35.685329</td>\n",
       "      <td>0.0</td>\n",
       "      <td>278.866442</td>\n",
       "      <td>191.514533</td>\n",
       "      <td>211.695946</td>\n",
       "      <td>217.277824</td>\n",
       "      <td>285.226689</td>\n",
       "      <td>262.273846</td>\n",
       "      <td>250.105428</td>\n",
       "      <td>226.384996</td>\n",
       "      <td>18.042987</td>\n",
       "      <td>1.219698</td>\n",
       "      <td>0.669272</td>\n",
       "      <td>0.973948</td>\n",
       "      <td>6.615200</td>\n",
       "      <td>3.260019</td>\n",
       "      <td>24.257158</td>\n",
       "      <td>4.877969</td>\n",
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       "      <td>324.699975</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>37.668964</td>\n",
       "      <td>329.601505</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.383784</td>\n",
       "      <td>2.906512</td>\n",
       "      <td>2.791631</td>\n",
       "      <td>12.022316</td>\n",
       "      <td>5.702366</td>\n",
       "      <td>103.122996</td>\n",
       "      <td>7.104435</td>\n",
       "      <td>4.147581</td>\n",
       "      <td>20.663414</td>\n",
       "      <td>3.890565</td>\n",
       "      <td>0.958428</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.24971</td>\n",
       "      <td>0.730143</td>\n",
       "      <td>2.888989</td>\n",
       "      <td>0.002736</td>\n",
       "      <td>0.001533</td>\n",
       "      <td>0.000295</td>\n",
       "      <td>1.314823</td>\n",
       "      <td>0.358508</td>\n",
       "      <td>4.627143</td>\n",
       "      <td>3.152734</td>\n",
       "      <td>0.619061</td>\n",
       "      <td>4.542840</td>\n",
       "      <td>1.243363</td>\n",
       "      <td>0.253958</td>\n",
       "      <td>2.005003</td>\n",
       "      <td>0.546962</td>\n",
       "      <td>32.175775</td>\n",
       "      <td>1.170259</td>\n",
       "      <td>1.708471</td>\n",
       "      <td>0.083835</td>\n",
       "      <td>0.201329</td>\n",
       "      <td>0.051562</td>\n",
       "      <td>19.020115</td>\n",
       "      <td>6.958289</td>\n",
       "      <td>0.111169</td>\n",
       "      <td>2.442203</td>\n",
       "      <td>0.074373</td>\n",
       "      <td>0.954554</td>\n",
       "      <td>0.030083</td>\n",
       "      <td>0.921761</td>\n",
       "      <td>9.411775</td>\n",
       "      <td>17.499736</td>\n",
       "      <td>0.275112</td>\n",
       "      <td>86.304681</td>\n",
       "      <td>0.248478</td>\n",
       "      <td>26.920150</td>\n",
       "      <td>0.067791</td>\n",
       "      <td>16.921369</td>\n",
       "      <td>12.485267</td>\n",
       "      <td>0.007387</td>\n",
       "      <td>0.006132</td>\n",
       "      <td>0.001985</td>\n",
       "      <td>56.348694</td>\n",
       "      <td>0.003403</td>\n",
       "      <td>0.017174</td>\n",
       "      <td>0.003719</td>\n",
       "      <td>3.576899</td>\n",
       "      <td>0.012354</td>\n",
       "      <td>0.008805</td>\n",
       "      <td>0.002866</td>\n",
       "      <td>93.864558</td>\n",
       "      <td>0.498010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2743.240000</td>\n",
       "      <td>2158.750000</td>\n",
       "      <td>2060.660000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.681500</td>\n",
       "      <td>100.0</td>\n",
       "      <td>82.131100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.191000</td>\n",
       "      <td>-0.053400</td>\n",
       "      <td>-0.034900</td>\n",
       "      <td>0.655400</td>\n",
       "      <td>182.094000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.249300</td>\n",
       "      <td>333.448600</td>\n",
       "      <td>4.469600</td>\n",
       "      <td>0.579400</td>\n",
       "      <td>169.177400</td>\n",
       "      <td>9.877300</td>\n",
       "      <td>1.179700</td>\n",
       "      <td>-7150.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-9986.750000</td>\n",
       "      <td>-14804.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>59.400000</td>\n",
       "      <td>0.666700</td>\n",
       "      <td>0.034100</td>\n",
       "      <td>2.06980</td>\n",
       "      <td>83.182900</td>\n",
       "      <td>7.603200</td>\n",
       "      <td>49.83480</td>\n",
       "      <td>63.677400</td>\n",
       "      <td>40.228900</td>\n",
       "      <td>64.919300</td>\n",
       "      <td>84.732700</td>\n",
       "      <td>111.712800</td>\n",
       "      <td>1.434000</td>\n",
       "      <td>-0.075900</td>\n",
       "      <td>70.0</td>\n",
       "      <td>342.754500</td>\n",
       "      <td>9.464000</td>\n",
       "      <td>108.846400</td>\n",
       "      <td>699.813900</td>\n",
       "      <td>0.496700</td>\n",
       "      <td>125.798200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>607.392700</td>\n",
       "      <td>40.261400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.706000</td>\n",
       "      <td>3.932000</td>\n",
       "      <td>2801.000000</td>\n",
       "      <td>0.875500</td>\n",
       "      <td>0.931900</td>\n",
       "      <td>4.219900</td>\n",
       "      <td>-28.988200</td>\n",
       "      <td>324.714500</td>\n",
       "      <td>9.461100</td>\n",
       "      <td>81.490000</td>\n",
       "      <td>1.659100</td>\n",
       "      <td>6.448200</td>\n",
       "      <td>4.308000</td>\n",
       "      <td>632.422600</td>\n",
       "      <td>0.413700</td>\n",
       "      <td>87.025500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>581.777300</td>\n",
       "      <td>21.433200</td>\n",
       "      <td>-59.477700</td>\n",
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       "      <td>-0.186200</td>\n",
       "      <td>-0.104600</td>\n",
       "      <td>-0.348200</td>\n",
       "      <td>-0.056800</td>\n",
       "      <td>-0.143700</td>\n",
       "      <td>-0.098200</td>\n",
       "      <td>-0.212900</td>\n",
       "      <td>5.825700</td>\n",
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       "      <td>-0.002000</td>\n",
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       "      <td>-5.271700</td>\n",
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       "      <td>-0.522600</td>\n",
       "      <td>-0.345400</td>\n",
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       "      <td>544.025400</td>\n",
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       "      <td>52.806800</td>\n",
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       "      <td>5.125900</td>\n",
       "      <td>15.460000</td>\n",
       "      <td>1.671000</td>\n",
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       "      <td>15.430000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.033000</td>\n",
       "      <td>0.046000</td>\n",
       "      <td>0.297900</td>\n",
       "      <td>0.008900</td>\n",
       "      <td>0.128700</td>\n",
       "      <td>0.253800</td>\n",
       "      <td>0.128700</td>\n",
       "      <td>0.461600</td>\n",
       "      <td>0.073500</td>\n",
       "      <td>0.047000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>0.093000</td>\n",
       "      <td>3.170000</td>\n",
       "      <td>5.014000</td>\n",
       "      <td>0.029700</td>\n",
       "      <td>1.940000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.220000</td>\n",
       "      <td>6.613000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>9.220000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>2.770000</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.728000</td>\n",
       "      <td>0.042900</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.010000</td>\n",
       "      <td>5.359000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.031900</td>\n",
       "      <td>0.002200</td>\n",
       "      <td>0.007100</td>\n",
       "      <td>0.003700</td>\n",
       "      <td>0.019300</td>\n",
       "      <td>0.005900</td>\n",
       "      <td>0.009700</td>\n",
       "      <td>0.007900</td>\n",
       "      <td>1.034000</td>\n",
       "      <td>0.000700</td>\n",
       "      <td>0.005700</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>32.263700</td>\n",
       "      <td>0.009300</td>\n",
       "      <td>168.799800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006200</td>\n",
       "      <td>0.007200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001300</td>\n",
       "      <td>0.001400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.291400</td>\n",
       "      <td>1.102200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.010700</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.767300</td>\n",
       "      <td>0.009400</td>\n",
       "      <td>0.001700</td>\n",
       "      <td>0.126900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.019800</td>\n",
       "      <td>6.098000</td>\n",
       "      <td>1.301700</td>\n",
       "      <td>15.547100</td>\n",
       "      <td>10.401500</td>\n",
       "      <td>6.943100</td>\n",
       "      <td>8.651200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.561500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010700</td>\n",
       "      <td>0.007300</td>\n",
       "      <td>0.006900</td>\n",
       "      <td>0.001600</td>\n",
       "      <td>0.505000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.461100</td>\n",
       "      <td>0.728000</td>\n",
       "      <td>0.051300</td>\n",
       "      <td>0.001200</td>\n",
       "      <td>0.396000</td>\n",
       "      <td>0.041600</td>\n",
       "      <td>0.003800</td>\n",
       "      <td>0.004100</td>\n",
       "      <td>82.323300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>0.111800</td>\n",
       "      <td>0.010800</td>\n",
       "      <td>0.013800</td>\n",
       "      <td>0.117100</td>\n",
       "      <td>0.003400</td>\n",
       "      <td>0.054900</td>\n",
       "      <td>0.091300</td>\n",
       "      <td>0.054900</td>\n",
       "      <td>0.180900</td>\n",
       "      <td>0.032800</td>\n",
       "      <td>0.022400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.788200</td>\n",
       "      <td>0.028300</td>\n",
       "      <td>0.984800</td>\n",
       "      <td>1.657400</td>\n",
       "      <td>0.008400</td>\n",
       "      <td>0.611400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.710100</td>\n",
       "      <td>2.234500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022400</td>\n",
       "      <td>0.537300</td>\n",
       "      <td>2.837200</td>\n",
       "      <td>0.028200</td>\n",
       "      <td>0.789900</td>\n",
       "      <td>5.215100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.200100</td>\n",
       "      <td>0.013100</td>\n",
       "      <td>0.574100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.256500</td>\n",
       "      <td>2.056000</td>\n",
       "      <td>1.769400</td>\n",
       "      <td>1.017700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010300</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.002900</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.005600</td>\n",
       "      <td>0.002600</td>\n",
       "      <td>0.004000</td>\n",
       "      <td>0.003800</td>\n",
       "      <td>0.379600</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.001700</td>\n",
       "      <td>0.007600</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>10.720400</td>\n",
       "      <td>0.002800</td>\n",
       "      <td>60.988200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001700</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.087500</td>\n",
       "      <td>0.338300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.310100</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.304600</td>\n",
       "      <td>0.003100</td>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.034200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006200</td>\n",
       "      <td>2.054500</td>\n",
       "      <td>0.424000</td>\n",
       "      <td>2.737800</td>\n",
       "      <td>1.216300</td>\n",
       "      <td>0.734200</td>\n",
       "      <td>0.960900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.041600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.53400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.153100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.441100</td>\n",
       "      <td>0.721700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.020000</td>\n",
       "      <td>0.486600</td>\n",
       "      <td>1.466600</td>\n",
       "      <td>0.363200</td>\n",
       "      <td>0.663700</td>\n",
       "      <td>1.119800</td>\n",
       "      <td>0.783700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.156800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.120600</td>\n",
       "      <td>1.097300</td>\n",
       "      <td>0.351200</td>\n",
       "      <td>0.097400</td>\n",
       "      <td>0.216900</td>\n",
       "      <td>0.333600</td>\n",
       "      <td>0.308600</td>\n",
       "      <td>0.696800</td>\n",
       "      <td>0.084600</td>\n",
       "      <td>0.039900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.670900</td>\n",
       "      <td>0.903700</td>\n",
       "      <td>2.329400</td>\n",
       "      <td>0.694800</td>\n",
       "      <td>3.048900</td>\n",
       "      <td>1.442800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.991000</td>\n",
       "      <td>7.95340</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.716300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.600900</td>\n",
       "      <td>0.832500</td>\n",
       "      <td>2.402600</td>\n",
       "      <td>11.499700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.101100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.687200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.645900</td>\n",
       "      <td>8.840600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.722500</td>\n",
       "      <td>0.555800</td>\n",
       "      <td>4.888200</td>\n",
       "      <td>0.833000</td>\n",
       "      <td>0.034200</td>\n",
       "      <td>1.772000</td>\n",
       "      <td>4.813500</td>\n",
       "      <td>1.949600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.028700</td>\n",
       "      <td>0.288000</td>\n",
       "      <td>0.467400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.312100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.681100</td>\n",
       "      <td>0.025800</td>\n",
       "      <td>1.310400</td>\n",
       "      <td>1.540000</td>\n",
       "      <td>0.170500</td>\n",
       "      <td>2.170000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85160</td>\n",
       "      <td>0.614400</td>\n",
       "      <td>3.276100</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.005100</td>\n",
       "      <td>0.001600</td>\n",
       "      <td>4.429400</td>\n",
       "      <td>0.444400</td>\n",
       "      <td>372.822000</td>\n",
       "      <td>71.038000</td>\n",
       "      <td>0.044600</td>\n",
       "      <td>6.110000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.018700</td>\n",
       "      <td>2.786000</td>\n",
       "      <td>0.052000</td>\n",
       "      <td>4.826900</td>\n",
       "      <td>1.496700</td>\n",
       "      <td>0.164600</td>\n",
       "      <td>0.891900</td>\n",
       "      <td>0.069900</td>\n",
       "      <td>0.017700</td>\n",
       "      <td>7.236900</td>\n",
       "      <td>242.286000</td>\n",
       "      <td>0.304900</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>0.022400</td>\n",
       "      <td>0.412200</td>\n",
       "      <td>0.009100</td>\n",
       "      <td>0.370600</td>\n",
       "      <td>3.250400</td>\n",
       "      <td>317.196400</td>\n",
       "      <td>0.980200</td>\n",
       "      <td>3.540000</td>\n",
       "      <td>0.066700</td>\n",
       "      <td>1.039500</td>\n",
       "      <td>0.023000</td>\n",
       "      <td>0.663600</td>\n",
       "      <td>4.582000</td>\n",
       "      <td>-0.016900</td>\n",
       "      <td>0.003200</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.477800</td>\n",
       "      <td>0.006000</td>\n",
       "      <td>0.001700</td>\n",
       "      <td>1.197500</td>\n",
       "      <td>-0.016900</td>\n",
       "      <td>0.003200</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2966.665000</td>\n",
       "      <td>2452.885000</td>\n",
       "      <td>2181.099950</td>\n",
       "      <td>1083.885800</td>\n",
       "      <td>1.017700</td>\n",
       "      <td>100.0</td>\n",
       "      <td>97.937800</td>\n",
       "      <td>0.121100</td>\n",
       "      <td>1.411250</td>\n",
       "      <td>-0.010800</td>\n",
       "      <td>-0.005600</td>\n",
       "      <td>0.958100</td>\n",
       "      <td>198.130950</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.096750</td>\n",
       "      <td>406.131000</td>\n",
       "      <td>9.568550</td>\n",
       "      <td>0.968250</td>\n",
       "      <td>188.300650</td>\n",
       "      <td>12.460000</td>\n",
       "      <td>1.396500</td>\n",
       "      <td>-5932.625000</td>\n",
       "      <td>2578.125000</td>\n",
       "      <td>-4370.625000</td>\n",
       "      <td>-1474.375000</td>\n",
       "      <td>1.094900</td>\n",
       "      <td>1.906750</td>\n",
       "      <td>5.267350</td>\n",
       "      <td>67.383350</td>\n",
       "      <td>2.088900</td>\n",
       "      <td>0.161800</td>\n",
       "      <td>3.36270</td>\n",
       "      <td>84.490500</td>\n",
       "      <td>8.580000</td>\n",
       "      <td>50.25290</td>\n",
       "      <td>64.024800</td>\n",
       "      <td>49.421900</td>\n",
       "      <td>66.040800</td>\n",
       "      <td>86.578300</td>\n",
       "      <td>118.015600</td>\n",
       "      <td>74.840000</td>\n",
       "      <td>2.699000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>350.802250</td>\n",
       "      <td>9.925450</td>\n",
       "      <td>130.730450</td>\n",
       "      <td>724.444000</td>\n",
       "      <td>0.985000</td>\n",
       "      <td>136.930000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>625.928650</td>\n",
       "      <td>115.537450</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.574000</td>\n",
       "      <td>4.816000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.88990</td>\n",
       "      <td>1.390600</td>\n",
       "      <td>7.504200</td>\n",
       "      <td>0.109600</td>\n",
       "      <td>0.007800</td>\n",
       "      <td>0.002400</td>\n",
       "      <td>7.116000</td>\n",
       "      <td>0.814100</td>\n",
       "      <td>400.814000</td>\n",
       "      <td>73.254000</td>\n",
       "      <td>0.289600</td>\n",
       "      <td>14.820000</td>\n",
       "      <td>0.910000</td>\n",
       "      <td>0.116700</td>\n",
       "      <td>6.738100</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>34.147100</td>\n",
       "      <td>3.654100</td>\n",
       "      <td>1.224200</td>\n",
       "      <td>0.955200</td>\n",
       "      <td>0.149950</td>\n",
       "      <td>0.036200</td>\n",
       "      <td>15.766900</td>\n",
       "      <td>262.101000</td>\n",
       "      <td>0.567100</td>\n",
       "      <td>4.980000</td>\n",
       "      <td>0.087700</td>\n",
       "      <td>2.090200</td>\n",
       "      <td>0.038200</td>\n",
       "      <td>1.884400</td>\n",
       "      <td>15.466200</td>\n",
       "      <td>530.702700</td>\n",
       "      <td>1.982900</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.242250</td>\n",
       "      <td>2.567850</td>\n",
       "      <td>0.075100</td>\n",
       "      <td>1.408450</td>\n",
       "      <td>11.501550</td>\n",
       "      <td>0.020400</td>\n",
       "      <td>0.014800</td>\n",
       "      <td>0.004700</td>\n",
       "      <td>72.288900</td>\n",
       "      <td>0.497900</td>\n",
       "      <td>0.011600</td>\n",
       "      <td>0.003100</td>\n",
       "      <td>2.306500</td>\n",
       "      <td>0.013450</td>\n",
       "      <td>0.010600</td>\n",
       "      <td>0.003300</td>\n",
       "      <td>44.368600</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3011.490000</td>\n",
       "      <td>2499.405000</td>\n",
       "      <td>2201.066700</td>\n",
       "      <td>1285.214400</td>\n",
       "      <td>1.316800</td>\n",
       "      <td>100.0</td>\n",
       "      <td>101.512200</td>\n",
       "      <td>0.122400</td>\n",
       "      <td>1.461600</td>\n",
       "      <td>-0.001300</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.965800</td>\n",
       "      <td>199.535600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.967000</td>\n",
       "      <td>412.219100</td>\n",
       "      <td>9.851750</td>\n",
       "      <td>0.972600</td>\n",
       "      <td>189.664200</td>\n",
       "      <td>12.499600</td>\n",
       "      <td>1.406000</td>\n",
       "      <td>-5523.250000</td>\n",
       "      <td>2664.000000</td>\n",
       "      <td>-3820.750000</td>\n",
       "      <td>-78.750000</td>\n",
       "      <td>1.283000</td>\n",
       "      <td>1.986500</td>\n",
       "      <td>7.264700</td>\n",
       "      <td>69.155600</td>\n",
       "      <td>2.377800</td>\n",
       "      <td>0.186700</td>\n",
       "      <td>3.43100</td>\n",
       "      <td>85.135450</td>\n",
       "      <td>8.769800</td>\n",
       "      <td>50.39640</td>\n",
       "      <td>64.165800</td>\n",
       "      <td>49.603600</td>\n",
       "      <td>66.231800</td>\n",
       "      <td>86.820700</td>\n",
       "      <td>118.399300</td>\n",
       "      <td>78.290000</td>\n",
       "      <td>3.074000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>353.720900</td>\n",
       "      <td>10.034850</td>\n",
       "      <td>136.400000</td>\n",
       "      <td>733.450000</td>\n",
       "      <td>1.251050</td>\n",
       "      <td>140.007750</td>\n",
       "      <td>1.0</td>\n",
       "      <td>631.370900</td>\n",
       "      <td>183.318150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.596000</td>\n",
       "      <td>4.843000</td>\n",
       "      <td>2854.000000</td>\n",
       "      <td>0.931000</td>\n",
       "      <td>0.949300</td>\n",
       "      <td>4.572700</td>\n",
       "      <td>0.947250</td>\n",
       "      <td>353.799100</td>\n",
       "      <td>10.436700</td>\n",
       "      <td>116.211800</td>\n",
       "      <td>13.246050</td>\n",
       "      <td>20.021350</td>\n",
       "      <td>26.261450</td>\n",
       "      <td>706.453600</td>\n",
       "      <td>0.978300</td>\n",
       "      <td>147.597300</td>\n",
       "      <td>1.0</td>\n",
       "      <td>619.032700</td>\n",
       "      <td>102.604300</td>\n",
       "      <td>152.297200</td>\n",
       "      <td>466.081700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.006300</td>\n",
       "      <td>-0.028900</td>\n",
       "      <td>-0.009900</td>\n",
       "      <td>-0.012500</td>\n",
       "      <td>0.000600</td>\n",
       "      <td>-0.008700</td>\n",
       "      <td>-0.019600</td>\n",
       "      <td>0.007600</td>\n",
       "      <td>7.467450</td>\n",
       "      <td>0.133000</td>\n",
       "      <td>0.113550</td>\n",
       "      <td>2.403900</td>\n",
       "      <td>0.987400</td>\n",
       "      <td>1809.249200</td>\n",
       "      <td>0.190100</td>\n",
       "      <td>8825.435100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>-0.000200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.010100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000200</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.011200</td>\n",
       "      <td>0.981000</td>\n",
       "      <td>101.481700</td>\n",
       "      <td>231.201200</td>\n",
       "      <td>0.462850</td>\n",
       "      <td>0.946400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>750.861400</td>\n",
       "      <td>0.990500</td>\n",
       "      <td>58.549100</td>\n",
       "      <td>0.599000</td>\n",
       "      <td>0.969400</td>\n",
       "      <td>6.313600</td>\n",
       "      <td>15.790000</td>\n",
       "      <td>3.877000</td>\n",
       "      <td>15.830000</td>\n",
       "      <td>15.780000</td>\n",
       "      <td>1.144000</td>\n",
       "      <td>2.735000</td>\n",
       "      <td>0.653900</td>\n",
       "      <td>3.195000</td>\n",
       "      <td>-0.141900</td>\n",
       "      <td>0.758750</td>\n",
       "      <td>0.997750</td>\n",
       "      <td>2.312400</td>\n",
       "      <td>1004.050000</td>\n",
       "      <td>38.902600</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>134.600000</td>\n",
       "      <td>117.700000</td>\n",
       "      <td>55.900100</td>\n",
       "      <td>339.561000</td>\n",
       "      <td>0.235800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.260000</td>\n",
       "      <td>0.003900</td>\n",
       "      <td>0.107500</td>\n",
       "      <td>0.058600</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.015900</td>\n",
       "      <td>7.917300</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.951000</td>\n",
       "      <td>10.993500</td>\n",
       "      <td>0.468700</td>\n",
       "      <td>0.011100</td>\n",
       "      <td>7.512700</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.048700</td>\n",
       "      <td>0.035450</td>\n",
       "      <td>1020.300050</td>\n",
       "      <td>623.000000</td>\n",
       "      <td>438.000000</td>\n",
       "      <td>2614.000000</td>\n",
       "      <td>1784.000000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.089000</td>\n",
       "      <td>0.184000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.119000</td>\n",
       "      <td>0.412000</td>\n",
       "      <td>0.686000</td>\n",
       "      <td>0.112500</td>\n",
       "      <td>0.323850</td>\n",
       "      <td>0.577600</td>\n",
       "      <td>0.323850</td>\n",
       "      <td>0.768200</td>\n",
       "      <td>0.242900</td>\n",
       "      <td>0.299000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.690000</td>\n",
       "      <td>0.524000</td>\n",
       "      <td>10.170000</td>\n",
       "      <td>27.200500</td>\n",
       "      <td>0.132600</td>\n",
       "      <td>6.735000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.865000</td>\n",
       "      <td>40.209500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.259000</td>\n",
       "      <td>6.780000</td>\n",
       "      <td>19.370000</td>\n",
       "      <td>0.424000</td>\n",
       "      <td>8.570000</td>\n",
       "      <td>17.235000</td>\n",
       "      <td>6.760000</td>\n",
       "      <td>8.462000</td>\n",
       "      <td>30.097000</td>\n",
       "      <td>0.158200</td>\n",
       "      <td>7.740000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.720000</td>\n",
       "      <td>73.248000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.079700</td>\n",
       "      <td>0.053200</td>\n",
       "      <td>0.041600</td>\n",
       "      <td>0.056000</td>\n",
       "      <td>0.075400</td>\n",
       "      <td>0.082500</td>\n",
       "      <td>0.084600</td>\n",
       "      <td>0.061700</td>\n",
       "      <td>3.630750</td>\n",
       "      <td>0.003000</td>\n",
       "      <td>0.008950</td>\n",
       "      <td>0.060900</td>\n",
       "      <td>0.002300</td>\n",
       "      <td>119.436000</td>\n",
       "      <td>0.039800</td>\n",
       "      <td>967.299800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.016500</td>\n",
       "      <td>0.015500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004600</td>\n",
       "      <td>0.004400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001700</td>\n",
       "      <td>1.185100</td>\n",
       "      <td>3.673000</td>\n",
       "      <td>0.027000</td>\n",
       "      <td>0.021000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>103.093600</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>2.864600</td>\n",
       "      <td>0.030800</td>\n",
       "      <td>0.015000</td>\n",
       "      <td>0.405100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.070600</td>\n",
       "      <td>17.977000</td>\n",
       "      <td>3.703500</td>\n",
       "      <td>28.773500</td>\n",
       "      <td>45.676500</td>\n",
       "      <td>40.019250</td>\n",
       "      <td>19.580900</td>\n",
       "      <td>110.601400</td>\n",
       "      <td>0.078400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.083100</td>\n",
       "      <td>0.001100</td>\n",
       "      <td>0.037200</td>\n",
       "      <td>0.016900</td>\n",
       "      <td>0.013900</td>\n",
       "      <td>0.005300</td>\n",
       "      <td>2.658000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.875150</td>\n",
       "      <td>3.360050</td>\n",
       "      <td>0.138950</td>\n",
       "      <td>0.003600</td>\n",
       "      <td>2.549000</td>\n",
       "      <td>0.083300</td>\n",
       "      <td>0.016900</td>\n",
       "      <td>0.011000</td>\n",
       "      <td>317.867100</td>\n",
       "      <td>278.671900</td>\n",
       "      <td>195.825600</td>\n",
       "      <td>1202.412100</td>\n",
       "      <td>820.098800</td>\n",
       "      <td>0.052800</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.082800</td>\n",
       "      <td>0.860400</td>\n",
       "      <td>0.380800</td>\n",
       "      <td>0.038800</td>\n",
       "      <td>0.137200</td>\n",
       "      <td>0.264300</td>\n",
       "      <td>0.044800</td>\n",
       "      <td>0.129500</td>\n",
       "      <td>0.219450</td>\n",
       "      <td>0.129500</td>\n",
       "      <td>0.302900</td>\n",
       "      <td>0.097700</td>\n",
       "      <td>0.121500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.831500</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>2.898900</td>\n",
       "      <td>8.388800</td>\n",
       "      <td>0.039850</td>\n",
       "      <td>2.077650</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.458800</td>\n",
       "      <td>12.504500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.084800</td>\n",
       "      <td>2.062700</td>\n",
       "      <td>5.980100</td>\n",
       "      <td>0.129400</td>\n",
       "      <td>2.513500</td>\n",
       "      <td>9.073550</td>\n",
       "      <td>2.054450</td>\n",
       "      <td>2.560850</td>\n",
       "      <td>9.474200</td>\n",
       "      <td>0.046400</td>\n",
       "      <td>2.377300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.005600</td>\n",
       "      <td>23.214700</td>\n",
       "      <td>5.567000</td>\n",
       "      <td>3.046400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022600</td>\n",
       "      <td>0.024000</td>\n",
       "      <td>0.018800</td>\n",
       "      <td>0.025300</td>\n",
       "      <td>0.022000</td>\n",
       "      <td>0.042100</td>\n",
       "      <td>0.044200</td>\n",
       "      <td>0.029400</td>\n",
       "      <td>1.255300</td>\n",
       "      <td>0.000900</td>\n",
       "      <td>0.002400</td>\n",
       "      <td>0.019600</td>\n",
       "      <td>0.000700</td>\n",
       "      <td>39.696100</td>\n",
       "      <td>0.012500</td>\n",
       "      <td>309.831650</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004600</td>\n",
       "      <td>0.004300</td>\n",
       "      <td>0.003200</td>\n",
       "      <td>0.002800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001600</td>\n",
       "      <td>0.001500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.372600</td>\n",
       "      <td>1.106300</td>\n",
       "      <td>0.006800</td>\n",
       "      <td>0.006800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.530700</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.877300</td>\n",
       "      <td>0.010200</td>\n",
       "      <td>0.004900</td>\n",
       "      <td>0.133900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.023900</td>\n",
       "      <td>5.920100</td>\n",
       "      <td>1.239700</td>\n",
       "      <td>4.922450</td>\n",
       "      <td>4.489700</td>\n",
       "      <td>4.732750</td>\n",
       "      <td>2.548100</td>\n",
       "      <td>26.156900</td>\n",
       "      <td>20.255100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.17660</td>\n",
       "      <td>3.234000</td>\n",
       "      <td>7.395600</td>\n",
       "      <td>302.177600</td>\n",
       "      <td>272.448700</td>\n",
       "      <td>1.645100</td>\n",
       "      <td>3.943100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.905450</td>\n",
       "      <td>2.667100</td>\n",
       "      <td>4.764400</td>\n",
       "      <td>1.135300</td>\n",
       "      <td>3.941450</td>\n",
       "      <td>2.534100</td>\n",
       "      <td>3.453800</td>\n",
       "      <td>11.105600</td>\n",
       "      <td>16.381000</td>\n",
       "      <td>57.969300</td>\n",
       "      <td>151.115600</td>\n",
       "      <td>10.197700</td>\n",
       "      <td>4.551100</td>\n",
       "      <td>2.764300</td>\n",
       "      <td>3.780900</td>\n",
       "      <td>49.090900</td>\n",
       "      <td>65.437800</td>\n",
       "      <td>12.085900</td>\n",
       "      <td>0.807600</td>\n",
       "      <td>1.264550</td>\n",
       "      <td>0.643500</td>\n",
       "      <td>0.902700</td>\n",
       "      <td>0.651100</td>\n",
       "      <td>1.163800</td>\n",
       "      <td>0.279700</td>\n",
       "      <td>0.251200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.271450</td>\n",
       "      <td>5.227100</td>\n",
       "      <td>7.424900</td>\n",
       "      <td>3.724500</td>\n",
       "      <td>11.350900</td>\n",
       "      <td>4.793350</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.830350</td>\n",
       "      <td>26.16785</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.645000</td>\n",
       "      <td>150.340100</td>\n",
       "      <td>5.472400</td>\n",
       "      <td>4.061100</td>\n",
       "      <td>7.396000</td>\n",
       "      <td>138.255150</td>\n",
       "      <td>34.246750</td>\n",
       "      <td>32.820050</td>\n",
       "      <td>4.276200</td>\n",
       "      <td>15.973800</td>\n",
       "      <td>5.242200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.184500</td>\n",
       "      <td>70.434500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>293.518500</td>\n",
       "      <td>148.317500</td>\n",
       "      <td>138.775500</td>\n",
       "      <td>112.953400</td>\n",
       "      <td>249.927000</td>\n",
       "      <td>112.275500</td>\n",
       "      <td>348.529400</td>\n",
       "      <td>219.487200</td>\n",
       "      <td>48.557450</td>\n",
       "      <td>2.250800</td>\n",
       "      <td>8.008950</td>\n",
       "      <td>2.529100</td>\n",
       "      <td>0.232500</td>\n",
       "      <td>6.607900</td>\n",
       "      <td>22.039100</td>\n",
       "      <td>10.906550</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.986100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.174700</td>\n",
       "      <td>1.154300</td>\n",
       "      <td>1.589100</td>\n",
       "      <td>5.832950</td>\n",
       "      <td>2.221000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.742600</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>4.877100</td>\n",
       "      <td>5.134200</td>\n",
       "      <td>1.550100</td>\n",
       "      <td>6.410800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.05480</td>\n",
       "      <td>1.785500</td>\n",
       "      <td>9.459300</td>\n",
       "      <td>0.109600</td>\n",
       "      <td>0.007800</td>\n",
       "      <td>0.002600</td>\n",
       "      <td>7.116000</td>\n",
       "      <td>0.911100</td>\n",
       "      <td>403.122000</td>\n",
       "      <td>74.084000</td>\n",
       "      <td>0.471000</td>\n",
       "      <td>16.340000</td>\n",
       "      <td>1.150000</td>\n",
       "      <td>0.197900</td>\n",
       "      <td>7.427900</td>\n",
       "      <td>0.478900</td>\n",
       "      <td>54.441700</td>\n",
       "      <td>4.067100</td>\n",
       "      <td>1.529800</td>\n",
       "      <td>0.972700</td>\n",
       "      <td>0.290900</td>\n",
       "      <td>0.059200</td>\n",
       "      <td>29.731150</td>\n",
       "      <td>264.272000</td>\n",
       "      <td>0.651000</td>\n",
       "      <td>5.160000</td>\n",
       "      <td>0.119550</td>\n",
       "      <td>2.150450</td>\n",
       "      <td>0.048650</td>\n",
       "      <td>1.999700</td>\n",
       "      <td>16.988350</td>\n",
       "      <td>532.398200</td>\n",
       "      <td>2.118600</td>\n",
       "      <td>8.650000</td>\n",
       "      <td>0.293400</td>\n",
       "      <td>2.975800</td>\n",
       "      <td>0.089500</td>\n",
       "      <td>1.624500</td>\n",
       "      <td>13.817900</td>\n",
       "      <td>0.020400</td>\n",
       "      <td>0.014800</td>\n",
       "      <td>0.004700</td>\n",
       "      <td>72.288900</td>\n",
       "      <td>0.500200</td>\n",
       "      <td>0.013800</td>\n",
       "      <td>0.003600</td>\n",
       "      <td>2.757650</td>\n",
       "      <td>0.020500</td>\n",
       "      <td>0.014800</td>\n",
       "      <td>0.004600</td>\n",
       "      <td>71.900500</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3056.540000</td>\n",
       "      <td>2538.745000</td>\n",
       "      <td>2218.055500</td>\n",
       "      <td>1590.169900</td>\n",
       "      <td>1.518800</td>\n",
       "      <td>100.0</td>\n",
       "      <td>104.530000</td>\n",
       "      <td>0.123800</td>\n",
       "      <td>1.516850</td>\n",
       "      <td>0.008400</td>\n",
       "      <td>0.005900</td>\n",
       "      <td>0.971300</td>\n",
       "      <td>202.006750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.858700</td>\n",
       "      <td>419.082800</td>\n",
       "      <td>10.127750</td>\n",
       "      <td>0.976800</td>\n",
       "      <td>192.178900</td>\n",
       "      <td>12.547100</td>\n",
       "      <td>1.415000</td>\n",
       "      <td>-5356.625000</td>\n",
       "      <td>2840.625000</td>\n",
       "      <td>-3356.375000</td>\n",
       "      <td>1376.250000</td>\n",
       "      <td>1.304300</td>\n",
       "      <td>2.003200</td>\n",
       "      <td>7.329600</td>\n",
       "      <td>72.255550</td>\n",
       "      <td>2.655600</td>\n",
       "      <td>0.207000</td>\n",
       "      <td>3.53125</td>\n",
       "      <td>85.741900</td>\n",
       "      <td>9.060600</td>\n",
       "      <td>50.57810</td>\n",
       "      <td>64.344700</td>\n",
       "      <td>49.747100</td>\n",
       "      <td>66.343050</td>\n",
       "      <td>87.002400</td>\n",
       "      <td>118.939600</td>\n",
       "      <td>80.180000</td>\n",
       "      <td>3.515000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>360.771800</td>\n",
       "      <td>10.152450</td>\n",
       "      <td>142.090950</td>\n",
       "      <td>741.449500</td>\n",
       "      <td>1.340200</td>\n",
       "      <td>143.194100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>638.128150</td>\n",
       "      <td>206.976700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.617000</td>\n",
       "      <td>4.869000</td>\n",
       "      <td>2874.000000</td>\n",
       "      <td>0.933100</td>\n",
       "      <td>0.952000</td>\n",
       "      <td>4.668600</td>\n",
       "      <td>4.337700</td>\n",
       "      <td>359.665450</td>\n",
       "      <td>10.590550</td>\n",
       "      <td>120.918200</td>\n",
       "      <td>16.325500</td>\n",
       "      <td>22.799550</td>\n",
       "      <td>29.907350</td>\n",
       "      <td>714.521050</td>\n",
       "      <td>1.064900</td>\n",
       "      <td>149.935900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>625.160450</td>\n",
       "      <td>115.439600</td>\n",
       "      <td>152.297200</td>\n",
       "      <td>466.081700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006600</td>\n",
       "      <td>-0.006900</td>\n",
       "      <td>0.008900</td>\n",
       "      <td>0.012050</td>\n",
       "      <td>0.012800</td>\n",
       "      <td>0.008700</td>\n",
       "      <td>-0.012150</td>\n",
       "      <td>0.026800</td>\n",
       "      <td>7.807350</td>\n",
       "      <td>0.136300</td>\n",
       "      <td>0.113550</td>\n",
       "      <td>2.428600</td>\n",
       "      <td>0.989700</td>\n",
       "      <td>1841.873000</td>\n",
       "      <td>0.200150</td>\n",
       "      <td>9055.260000</td>\n",
       "      <td>0.050350</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.121200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.188650</td>\n",
       "      <td>0.029800</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.033600</td>\n",
       "      <td>-0.008200</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.001100</td>\n",
       "      <td>0.001550</td>\n",
       "      <td>0.048600</td>\n",
       "      <td>0.037850</td>\n",
       "      <td>0.981000</td>\n",
       "      <td>101.481700</td>\n",
       "      <td>231.201200</td>\n",
       "      <td>0.463400</td>\n",
       "      <td>0.952300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>776.781850</td>\n",
       "      <td>0.990900</td>\n",
       "      <td>59.133900</td>\n",
       "      <td>0.603300</td>\n",
       "      <td>0.978300</td>\n",
       "      <td>6.375850</td>\n",
       "      <td>15.860000</td>\n",
       "      <td>4.392000</td>\n",
       "      <td>15.900000</td>\n",
       "      <td>15.870000</td>\n",
       "      <td>1.338000</td>\n",
       "      <td>2.873000</td>\n",
       "      <td>0.712400</td>\n",
       "      <td>3.311000</td>\n",
       "      <td>0.047300</td>\n",
       "      <td>0.814500</td>\n",
       "      <td>0.998900</td>\n",
       "      <td>2.358300</td>\n",
       "      <td>1008.670600</td>\n",
       "      <td>40.804600</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>180.900000</td>\n",
       "      <td>161.600000</td>\n",
       "      <td>62.900100</td>\n",
       "      <td>494.806000</td>\n",
       "      <td>0.434800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.004900</td>\n",
       "      <td>0.132650</td>\n",
       "      <td>0.071800</td>\n",
       "      <td>0.061500</td>\n",
       "      <td>0.021300</td>\n",
       "      <td>9.581850</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.269500</td>\n",
       "      <td>14.342000</td>\n",
       "      <td>0.677500</td>\n",
       "      <td>0.014900</td>\n",
       "      <td>9.050950</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.066700</td>\n",
       "      <td>0.035450</td>\n",
       "      <td>1020.300050</td>\n",
       "      <td>963.500000</td>\n",
       "      <td>624.500000</td>\n",
       "      <td>5033.000000</td>\n",
       "      <td>6376.000000</td>\n",
       "      <td>0.154000</td>\n",
       "      <td>0.116000</td>\n",
       "      <td>0.254500</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.150500</td>\n",
       "      <td>0.536000</td>\n",
       "      <td>0.797300</td>\n",
       "      <td>0.140300</td>\n",
       "      <td>0.370200</td>\n",
       "      <td>0.634500</td>\n",
       "      <td>0.370200</td>\n",
       "      <td>0.843900</td>\n",
       "      <td>0.293750</td>\n",
       "      <td>0.423000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.965000</td>\n",
       "      <td>0.688500</td>\n",
       "      <td>13.335000</td>\n",
       "      <td>31.687000</td>\n",
       "      <td>0.169100</td>\n",
       "      <td>8.450000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.860000</td>\n",
       "      <td>57.672500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.296000</td>\n",
       "      <td>9.510000</td>\n",
       "      <td>21.450000</td>\n",
       "      <td>0.724500</td>\n",
       "      <td>11.425000</td>\n",
       "      <td>20.160000</td>\n",
       "      <td>9.485000</td>\n",
       "      <td>11.931950</td>\n",
       "      <td>33.504500</td>\n",
       "      <td>0.230600</td>\n",
       "      <td>9.930000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.365000</td>\n",
       "      <td>90.452500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.099050</td>\n",
       "      <td>0.064100</td>\n",
       "      <td>0.062100</td>\n",
       "      <td>0.073400</td>\n",
       "      <td>0.093250</td>\n",
       "      <td>0.098100</td>\n",
       "      <td>0.097300</td>\n",
       "      <td>0.086050</td>\n",
       "      <td>4.403400</td>\n",
       "      <td>0.003800</td>\n",
       "      <td>0.008950</td>\n",
       "      <td>0.076500</td>\n",
       "      <td>0.005500</td>\n",
       "      <td>144.502800</td>\n",
       "      <td>0.059200</td>\n",
       "      <td>1252.399650</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.021200</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005700</td>\n",
       "      <td>0.005300</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001700</td>\n",
       "      <td>1.185100</td>\n",
       "      <td>3.673000</td>\n",
       "      <td>0.030100</td>\n",
       "      <td>0.027300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>131.758400</td>\n",
       "      <td>0.001300</td>\n",
       "      <td>3.795050</td>\n",
       "      <td>0.037700</td>\n",
       "      <td>0.021300</td>\n",
       "      <td>0.480950</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.091600</td>\n",
       "      <td>24.653000</td>\n",
       "      <td>4.376800</td>\n",
       "      <td>31.691650</td>\n",
       "      <td>59.484150</td>\n",
       "      <td>54.166500</td>\n",
       "      <td>22.089100</td>\n",
       "      <td>161.979100</td>\n",
       "      <td>0.144900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.513100</td>\n",
       "      <td>0.001300</td>\n",
       "      <td>0.045750</td>\n",
       "      <td>0.020700</td>\n",
       "      <td>0.016600</td>\n",
       "      <td>0.007100</td>\n",
       "      <td>3.145700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.605900</td>\n",
       "      <td>4.306600</td>\n",
       "      <td>0.198400</td>\n",
       "      <td>0.004900</td>\n",
       "      <td>3.023800</td>\n",
       "      <td>0.117550</td>\n",
       "      <td>0.023600</td>\n",
       "      <td>0.011000</td>\n",
       "      <td>317.867100</td>\n",
       "      <td>428.515800</td>\n",
       "      <td>273.865150</td>\n",
       "      <td>2337.950650</td>\n",
       "      <td>3188.190750</td>\n",
       "      <td>0.069200</td>\n",
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       "      <td>0.115350</td>\n",
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       "      <td>0.476700</td>\n",
       "      <td>0.048600</td>\n",
       "      <td>0.178500</td>\n",
       "      <td>0.307300</td>\n",
       "      <td>0.055100</td>\n",
       "      <td>0.147600</td>\n",
       "      <td>0.237900</td>\n",
       "      <td>0.147600</td>\n",
       "      <td>0.331900</td>\n",
       "      <td>0.115900</td>\n",
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       "      <td>6.546900</td>\n",
       "      <td>0.218100</td>\n",
       "      <td>4.021200</td>\n",
       "      <td>9.480800</td>\n",
       "      <td>0.050200</td>\n",
       "      <td>2.632900</td>\n",
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       "      <td>2.789300</td>\n",
       "      <td>6.544500</td>\n",
       "      <td>0.210150</td>\n",
       "      <td>3.355500</td>\n",
       "      <td>10.034700</td>\n",
       "      <td>2.779950</td>\n",
       "      <td>3.397900</td>\n",
       "      <td>10.438250</td>\n",
       "      <td>0.066800</td>\n",
       "      <td>2.984400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.881050</td>\n",
       "      <td>28.852650</td>\n",
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       "      <td>0.028500</td>\n",
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       "      <td>0.041900</td>\n",
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       "      <td>0.001800</td>\n",
       "      <td>47.079200</td>\n",
       "      <td>0.018300</td>\n",
       "      <td>409.116550</td>\n",
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       "      <td>582.803100</td>\n",
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       "      <td>3.609000</td>\n",
       "      <td>4.755800</td>\n",
       "      <td>17.402050</td>\n",
       "      <td>21.739650</td>\n",
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       "      <td>4.678250</td>\n",
       "      <td>66.666700</td>\n",
       "      <td>84.944500</td>\n",
       "      <td>15.795850</td>\n",
       "      <td>0.927600</td>\n",
       "      <td>1.577550</td>\n",
       "      <td>0.733250</td>\n",
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       "      <td>9.576750</td>\n",
       "      <td>4.341750</td>\n",
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       "      <td>334.674000</td>\n",
       "      <td>6.003200</td>\n",
       "      <td>6.991150</td>\n",
       "      <td>9.707900</td>\n",
       "      <td>168.270250</td>\n",
       "      <td>47.690150</td>\n",
       "      <td>45.160350</td>\n",
       "      <td>4.741050</td>\n",
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       "      <td>6.699750</td>\n",
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       "      <td>3.624550</td>\n",
       "      <td>92.911650</td>\n",
       "      <td>0.0</td>\n",
       "      <td>512.390750</td>\n",
       "      <td>260.079000</td>\n",
       "      <td>288.918450</td>\n",
       "      <td>283.289000</td>\n",
       "      <td>497.384500</td>\n",
       "      <td>391.277500</td>\n",
       "      <td>507.497050</td>\n",
       "      <td>372.341900</td>\n",
       "      <td>61.489950</td>\n",
       "      <td>2.830000</td>\n",
       "      <td>8.008950</td>\n",
       "      <td>3.199100</td>\n",
       "      <td>0.563000</td>\n",
       "      <td>7.897200</td>\n",
       "      <td>31.961500</td>\n",
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       "      <td>1.154300</td>\n",
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       "      <td>2.903700</td>\n",
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       "      <td>6.450650</td>\n",
       "      <td>6.301850</td>\n",
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       "      <td>0.002600</td>\n",
       "      <td>8.008950</td>\n",
       "      <td>1.216500</td>\n",
       "      <td>406.763000</td>\n",
       "      <td>76.960000</td>\n",
       "      <td>0.773400</td>\n",
       "      <td>18.405000</td>\n",
       "      <td>1.330000</td>\n",
       "      <td>0.321750</td>\n",
       "      <td>8.316650</td>\n",
       "      <td>0.533550</td>\n",
       "      <td>69.630650</td>\n",
       "      <td>4.564950</td>\n",
       "      <td>1.815600</td>\n",
       "      <td>1.000800</td>\n",
       "      <td>0.443600</td>\n",
       "      <td>0.089000</td>\n",
       "      <td>44.113400</td>\n",
       "      <td>264.733000</td>\n",
       "      <td>0.738250</td>\n",
       "      <td>7.310000</td>\n",
       "      <td>0.166850</td>\n",
       "      <td>2.909150</td>\n",
       "      <td>0.068100</td>\n",
       "      <td>2.807750</td>\n",
       "      <td>23.035200</td>\n",
       "      <td>534.356400</td>\n",
       "      <td>2.290650</td>\n",
       "      <td>10.130000</td>\n",
       "      <td>0.366900</td>\n",
       "      <td>3.492500</td>\n",
       "      <td>0.112150</td>\n",
       "      <td>1.902000</td>\n",
       "      <td>17.080900</td>\n",
       "      <td>0.020400</td>\n",
       "      <td>0.014800</td>\n",
       "      <td>0.004700</td>\n",
       "      <td>72.288900</td>\n",
       "      <td>0.502350</td>\n",
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       "      <td>114.749700</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3356.350000</td>\n",
       "      <td>2846.440000</td>\n",
       "      <td>2315.266700</td>\n",
       "      <td>3715.041700</td>\n",
       "      <td>1114.536600</td>\n",
       "      <td>100.0</td>\n",
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       "      <td>0.128600</td>\n",
       "      <td>1.656400</td>\n",
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       "      <td>0.053000</td>\n",
       "      <td>0.984800</td>\n",
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       "      <td>19.546500</td>\n",
       "      <td>824.927100</td>\n",
       "      <td>102.867700</td>\n",
       "      <td>0.984800</td>\n",
       "      <td>215.597700</td>\n",
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       "      <td>2363.000000</td>\n",
       "      <td>14106.000000</td>\n",
       "      <td>1.382800</td>\n",
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       "      <td>7.658800</td>\n",
       "      <td>77.900000</td>\n",
       "      <td>3.511100</td>\n",
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       "      <td>4.80440</td>\n",
       "      <td>105.603800</td>\n",
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       "      <td>59.77110</td>\n",
       "      <td>94.264100</td>\n",
       "      <td>50.165200</td>\n",
       "      <td>67.958600</td>\n",
       "      <td>88.418800</td>\n",
       "      <td>133.389800</td>\n",
       "      <td>86.120000</td>\n",
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       "      <td>70.0</td>\n",
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       "      <td>176.313600</td>\n",
       "      <td>789.752300</td>\n",
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       "      <td>770.608400</td>\n",
       "      <td>7272.828300</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.864300</td>\n",
       "      <td>0.548400</td>\n",
       "      <td>1.172000</td>\n",
       "      <td>0.441100</td>\n",
       "      <td>1.858000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.670000</td>\n",
       "      <td>3.573000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>72.947000</td>\n",
       "      <td>3.228300</td>\n",
       "      <td>267.910000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>307.930000</td>\n",
       "      <td>191.830000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.838000</td>\n",
       "      <td>396.110000</td>\n",
       "      <td>252.870000</td>\n",
       "      <td>10.017000</td>\n",
       "      <td>390.120000</td>\n",
       "      <td>199.620000</td>\n",
       "      <td>126.530000</td>\n",
       "      <td>490.561000</td>\n",
       "      <td>500.349000</td>\n",
       "      <td>9998.448300</td>\n",
       "      <td>320.050000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>457.650000</td>\n",
       "      <td>172.349000</td>\n",
       "      <td>46.150000</td>\n",
       "      <td>0.516400</td>\n",
       "      <td>0.322700</td>\n",
       "      <td>0.594100</td>\n",
       "      <td>1.283700</td>\n",
       "      <td>0.761500</td>\n",
       "      <td>0.342900</td>\n",
       "      <td>0.282800</td>\n",
       "      <td>0.674400</td>\n",
       "      <td>8.801500</td>\n",
       "      <td>0.016300</td>\n",
       "      <td>0.024000</td>\n",
       "      <td>0.230500</td>\n",
       "      <td>0.991100</td>\n",
       "      <td>1768.880200</td>\n",
       "      <td>1.436100</td>\n",
       "      <td>3601.299800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.154100</td>\n",
       "      <td>0.213300</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.024400</td>\n",
       "      <td>0.023600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.984400</td>\n",
       "      <td>99.902200</td>\n",
       "      <td>237.183700</td>\n",
       "      <td>0.491400</td>\n",
       "      <td>0.973200</td>\n",
       "      <td>0.413800</td>\n",
       "      <td>1119.704200</td>\n",
       "      <td>0.990900</td>\n",
       "      <td>2549.988500</td>\n",
       "      <td>0.451700</td>\n",
       "      <td>0.078700</td>\n",
       "      <td>0.925500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.157800</td>\n",
       "      <td>40.855000</td>\n",
       "      <td>10.152900</td>\n",
       "      <td>158.526000</td>\n",
       "      <td>132.647900</td>\n",
       "      <td>122.117400</td>\n",
       "      <td>43.573700</td>\n",
       "      <td>659.169600</td>\n",
       "      <td>3332.596400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.170900</td>\n",
       "      <td>0.003400</td>\n",
       "      <td>0.188400</td>\n",
       "      <td>0.075500</td>\n",
       "      <td>0.059700</td>\n",
       "      <td>0.308300</td>\n",
       "      <td>232.804900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.869800</td>\n",
       "      <td>207.016100</td>\n",
       "      <td>292.227400</td>\n",
       "      <td>0.074900</td>\n",
       "      <td>59.518700</td>\n",
       "      <td>4.420300</td>\n",
       "      <td>0.691500</td>\n",
       "      <td>0.083100</td>\n",
       "      <td>879.226000</td>\n",
       "      <td>3933.755000</td>\n",
       "      <td>2005.874400</td>\n",
       "      <td>15559.952500</td>\n",
       "      <td>18520.468300</td>\n",
       "      <td>0.526400</td>\n",
       "      <td>1.031200</td>\n",
       "      <td>1.812300</td>\n",
       "      <td>5.711000</td>\n",
       "      <td>5.154900</td>\n",
       "      <td>0.225800</td>\n",
       "      <td>0.333700</td>\n",
       "      <td>0.475000</td>\n",
       "      <td>0.224600</td>\n",
       "      <td>0.211200</td>\n",
       "      <td>0.323900</td>\n",
       "      <td>0.211200</td>\n",
       "      <td>0.443800</td>\n",
       "      <td>0.178400</td>\n",
       "      <td>0.754900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.095800</td>\n",
       "      <td>1.003400</td>\n",
       "      <td>15.893400</td>\n",
       "      <td>20.045500</td>\n",
       "      <td>0.947400</td>\n",
       "      <td>79.151500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89.191700</td>\n",
       "      <td>51.867800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.095900</td>\n",
       "      <td>174.894400</td>\n",
       "      <td>90.515900</td>\n",
       "      <td>3.412500</td>\n",
       "      <td>172.711900</td>\n",
       "      <td>214.862800</td>\n",
       "      <td>38.899500</td>\n",
       "      <td>196.688000</td>\n",
       "      <td>197.498800</td>\n",
       "      <td>5043.878900</td>\n",
       "      <td>97.708900</td>\n",
       "      <td>0.447200</td>\n",
       "      <td>156.336000</td>\n",
       "      <td>59.324100</td>\n",
       "      <td>257.010600</td>\n",
       "      <td>187.758900</td>\n",
       "      <td>13.914700</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.133900</td>\n",
       "      <td>0.291400</td>\n",
       "      <td>0.618800</td>\n",
       "      <td>0.142900</td>\n",
       "      <td>0.153500</td>\n",
       "      <td>0.134400</td>\n",
       "      <td>0.278900</td>\n",
       "      <td>2.834800</td>\n",
       "      <td>0.005200</td>\n",
       "      <td>0.004700</td>\n",
       "      <td>0.088800</td>\n",
       "      <td>0.409000</td>\n",
       "      <td>547.172200</td>\n",
       "      <td>0.416300</td>\n",
       "      <td>1072.203100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.036800</td>\n",
       "      <td>0.039200</td>\n",
       "      <td>0.035700</td>\n",
       "      <td>0.033400</td>\n",
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       "      <td>0.008200</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.627100</td>\n",
       "      <td>30.998200</td>\n",
       "      <td>74.844500</td>\n",
       "      <td>0.207300</td>\n",
       "      <td>0.306800</td>\n",
       "      <td>0.130900</td>\n",
       "      <td>348.829300</td>\n",
       "      <td>0.312700</td>\n",
       "      <td>805.393600</td>\n",
       "      <td>0.137500</td>\n",
       "      <td>0.022900</td>\n",
       "      <td>0.299400</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.051400</td>\n",
       "      <td>14.727700</td>\n",
       "      <td>3.312800</td>\n",
       "      <td>44.310000</td>\n",
       "      <td>9.576500</td>\n",
       "      <td>13.807100</td>\n",
       "      <td>6.215000</td>\n",
       "      <td>128.281600</td>\n",
       "      <td>899.119000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>116.86150</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>39.037600</td>\n",
       "      <td>999.316000</td>\n",
       "      <td>998.681300</td>\n",
       "      <td>111.495600</td>\n",
       "      <td>273.095200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>424.215200</td>\n",
       "      <td>103.180900</td>\n",
       "      <td>898.608500</td>\n",
       "      <td>24.990400</td>\n",
       "      <td>113.223000</td>\n",
       "      <td>118.753300</td>\n",
       "      <td>186.616400</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>994.285700</td>\n",
       "      <td>995.744700</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>32.274000</td>\n",
       "      <td>851.612900</td>\n",
       "      <td>657.762100</td>\n",
       "      <td>33.058000</td>\n",
       "      <td>1.277100</td>\n",
       "      <td>5.131700</td>\n",
       "      <td>1.085100</td>\n",
       "      <td>1.351100</td>\n",
       "      <td>1.108700</td>\n",
       "      <td>1.763900</td>\n",
       "      <td>0.508500</td>\n",
       "      <td>1.475400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.977600</td>\n",
       "      <td>34.490200</td>\n",
       "      <td>42.070300</td>\n",
       "      <td>10.184000</td>\n",
       "      <td>232.125800</td>\n",
       "      <td>164.109300</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.777200</td>\n",
       "      <td>149.38510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>109.007400</td>\n",
       "      <td>999.877000</td>\n",
       "      <td>77.800700</td>\n",
       "      <td>87.134700</td>\n",
       "      <td>212.655700</td>\n",
       "      <td>492.771800</td>\n",
       "      <td>358.950400</td>\n",
       "      <td>415.435500</td>\n",
       "      <td>79.116200</td>\n",
       "      <td>274.887100</td>\n",
       "      <td>289.826400</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>63.333600</td>\n",
       "      <td>221.974700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>999.413500</td>\n",
       "      <td>989.473700</td>\n",
       "      <td>996.858600</td>\n",
       "      <td>994.000000</td>\n",
       "      <td>999.491100</td>\n",
       "      <td>995.744700</td>\n",
       "      <td>997.518600</td>\n",
       "      <td>994.003500</td>\n",
       "      <td>142.843600</td>\n",
       "      <td>12.769800</td>\n",
       "      <td>21.044300</td>\n",
       "      <td>9.402400</td>\n",
       "      <td>127.572800</td>\n",
       "      <td>107.692600</td>\n",
       "      <td>219.643600</td>\n",
       "      <td>40.281800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>999.233700</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451.485100</td>\n",
       "      <td>1000.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>252.860400</td>\n",
       "      <td>113.275800</td>\n",
       "      <td>111.349500</td>\n",
       "      <td>184.348800</td>\n",
       "      <td>111.736500</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>137.983800</td>\n",
       "      <td>111.333000</td>\n",
       "      <td>818.000500</td>\n",
       "      <td>80.040600</td>\n",
       "      <td>8.203700</td>\n",
       "      <td>14.447900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.58030</td>\n",
       "      <td>4.082500</td>\n",
       "      <td>25.779200</td>\n",
       "      <td>0.118400</td>\n",
       "      <td>0.024000</td>\n",
       "      <td>0.004700</td>\n",
       "      <td>21.044300</td>\n",
       "      <td>3.978600</td>\n",
       "      <td>421.702000</td>\n",
       "      <td>83.720000</td>\n",
       "      <td>7.065600</td>\n",
       "      <td>131.680000</td>\n",
       "      <td>39.330000</td>\n",
       "      <td>2.718200</td>\n",
       "      <td>56.930300</td>\n",
       "      <td>17.478100</td>\n",
       "      <td>303.550000</td>\n",
       "      <td>35.319800</td>\n",
       "      <td>54.291700</td>\n",
       "      <td>1.512100</td>\n",
       "      <td>1.073700</td>\n",
       "      <td>0.445700</td>\n",
       "      <td>101.114600</td>\n",
       "      <td>311.404000</td>\n",
       "      <td>1.298800</td>\n",
       "      <td>32.580000</td>\n",
       "      <td>0.689200</td>\n",
       "      <td>14.014100</td>\n",
       "      <td>0.293200</td>\n",
       "      <td>12.746200</td>\n",
       "      <td>84.802400</td>\n",
       "      <td>589.508200</td>\n",
       "      <td>2.739500</td>\n",
       "      <td>454.560000</td>\n",
       "      <td>2.196700</td>\n",
       "      <td>170.020400</td>\n",
       "      <td>0.550200</td>\n",
       "      <td>90.423500</td>\n",
       "      <td>96.960100</td>\n",
       "      <td>0.102800</td>\n",
       "      <td>0.079900</td>\n",
       "      <td>0.028600</td>\n",
       "      <td>737.304800</td>\n",
       "      <td>0.509800</td>\n",
       "      <td>0.476600</td>\n",
       "      <td>0.104500</td>\n",
       "      <td>99.303200</td>\n",
       "      <td>0.102800</td>\n",
       "      <td>0.079900</td>\n",
       "      <td>0.028600</td>\n",
       "      <td>737.304800</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         feature_0    feature_1    feature_2    feature_3    feature_4  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean   3014.441551  2495.866110  2200.551958  1395.383474     4.171281   \n",
       "std      73.480841    80.228143    29.380973   439.837330    56.103721   \n",
       "min    2743.240000  2158.750000  2060.660000     0.000000     0.681500   \n",
       "25%    2966.665000  2452.885000  2181.099950  1083.885800     1.017700   \n",
       "50%    3011.490000  2499.405000  2201.066700  1285.214400     1.316800   \n",
       "75%    3056.540000  2538.745000  2218.055500  1590.169900     1.518800   \n",
       "max    3356.350000  2846.440000  2315.266700  3715.041700  1114.536600   \n",
       "\n",
       "       feature_5    feature_6    feature_7    feature_8    feature_9  \\\n",
       "count     1567.0  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean       100.0   101.116476     0.121825     1.462860    -0.000842   \n",
       "std          0.0     6.209385     0.008936     0.073849     0.015107   \n",
       "min        100.0    82.131100     0.000000     1.191000    -0.053400   \n",
       "25%        100.0    97.937800     0.121100     1.411250    -0.010800   \n",
       "50%        100.0   101.512200     0.122400     1.461600    -0.001300   \n",
       "75%        100.0   104.530000     0.123800     1.516850     0.008400   \n",
       "max        100.0   129.252200     0.128600     1.656400     0.074900   \n",
       "\n",
       "        feature_10   feature_11   feature_12  feature_13   feature_14  \\\n",
       "count  1567.000000  1567.000000  1567.000000      1567.0  1567.000000   \n",
       "mean      0.000146     0.964355   199.956272         0.0     9.005297   \n",
       "std       0.009296     0.012444     3.255230         0.0     2.793916   \n",
       "min      -0.034900     0.655400   182.094000         0.0     2.249300   \n",
       "25%      -0.005600     0.958100   198.130950         0.0     7.096750   \n",
       "50%       0.000400     0.965800   199.535600         0.0     8.967000   \n",
       "75%       0.005900     0.971300   202.006750         0.0    10.858700   \n",
       "max       0.053000     0.984800   272.045100         0.0    19.546500   \n",
       "\n",
       "        feature_15   feature_16   feature_17   feature_18   feature_19  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean    413.084376     9.907496     0.971446   190.046620    12.481152   \n",
       "std      17.204633     2.401564     0.012051     2.778426     0.217273   \n",
       "min     333.448600     4.469600     0.579400   169.177400     9.877300   \n",
       "25%     406.131000     9.568550     0.968250   188.300650    12.460000   \n",
       "50%     412.219100     9.851750     0.972600   189.664200    12.499600   \n",
       "75%     419.082800    10.127750     0.976800   192.178900    12.547100   \n",
       "max     824.927100   102.867700     0.984800   215.597700    12.989800   \n",
       "\n",
       "        feature_20   feature_21   feature_22   feature_23    feature_24  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000   1567.000000   \n",
       "mean      1.405054 -5618.272176  2699.333280 -3806.318177   -298.317538   \n",
       "std       0.016737   626.430997   295.312481  1379.280633   2900.846582   \n",
       "min       1.179700 -7150.250000     0.000000 -9986.750000 -14804.500000   \n",
       "25%       1.396500 -5932.625000  2578.125000 -4370.625000  -1474.375000   \n",
       "50%       1.406000 -5523.250000  2664.000000 -3820.750000    -78.750000   \n",
       "75%       1.415000 -5356.625000  2840.625000 -3356.375000   1376.250000   \n",
       "max       1.453400     0.000000  3656.250000  2363.000000  14106.000000   \n",
       "\n",
       "        feature_25   feature_26   feature_27   feature_28   feature_29  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      1.203946     1.938538     6.639427    69.499093     2.366212   \n",
       "std       0.177510     0.189382     1.243655     3.458992     0.408433   \n",
       "min       0.000000     0.000000     0.000000    59.400000     0.666700   \n",
       "25%       1.094900     1.906750     5.267350    67.383350     2.088900   \n",
       "50%       1.283000     1.986500     7.264700    69.155600     2.377800   \n",
       "75%       1.304300     2.003200     7.329600    72.255550     2.655600   \n",
       "max       1.382800     2.052800     7.658800    77.900000     3.511100   \n",
       "\n",
       "        feature_30  feature_31   feature_32   feature_33  feature_34  \\\n",
       "count  1567.000000  1567.00000  1567.000000  1567.000000  1567.00000   \n",
       "mean      0.184162     3.67288    85.337340     8.960157    50.58252   \n",
       "std       0.032923     0.53505     2.025908     1.344036     1.18225   \n",
       "min       0.034100     2.06980    83.182900     7.603200    49.83480   \n",
       "25%       0.161800     3.36270    84.490500     8.580000    50.25290   \n",
       "50%       0.186700     3.43100    85.135450     8.769800    50.39640   \n",
       "75%       0.207000     3.53125    85.741900     9.060600    50.57810   \n",
       "max       0.285100     4.80440   105.603800    23.345300    59.77110   \n",
       "\n",
       "        feature_35   feature_36   feature_37   feature_38   feature_39  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean     64.555538    49.417489    66.221280    86.836566   118.679376   \n",
       "std       2.573945     1.182251     0.304044     0.446613     1.806658   \n",
       "min      63.677400    40.228900    64.919300    84.732700   111.712800   \n",
       "25%      64.024800    49.421900    66.040800    86.578300   118.015600   \n",
       "50%      64.165800    49.603600    66.231800    86.820700   118.399300   \n",
       "75%      64.344700    49.747100    66.343050    87.002400   118.939600   \n",
       "max      94.264100    50.165200    67.958600    88.418800   133.389800   \n",
       "\n",
       "        feature_40   feature_41  feature_42   feature_43   feature_44  \\\n",
       "count  1567.000000  1567.000000      1567.0  1567.000000  1567.000000   \n",
       "mean     68.063966     3.348792        70.0   355.537743    10.031167   \n",
       "std      23.911897     2.342518         0.0     6.232884     0.174982   \n",
       "min       1.434000    -0.075900        70.0   342.754500     9.464000   \n",
       "25%      74.840000     2.699000        70.0   350.802250     9.925450   \n",
       "50%      78.290000     3.074000        70.0   353.720900    10.034850   \n",
       "75%      80.180000     3.515000        70.0   360.771800    10.152450   \n",
       "max      86.120000    37.880000        70.0   377.297300    11.053000   \n",
       "\n",
       "        feature_45   feature_46   feature_47   feature_48  feature_49  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000      1567.0   \n",
       "mean    136.742841   733.672669     1.178005   139.972254         1.0   \n",
       "std       7.846746    12.166430     0.189585     4.522806         0.0   \n",
       "min     108.846400   699.813900     0.496700   125.798200         1.0   \n",
       "25%     130.730450   724.444000     0.985000   136.930000         1.0   \n",
       "50%     136.400000   733.450000     1.251050   140.007750         1.0   \n",
       "75%     142.090950   741.449500     1.340200   143.194100         1.0   \n",
       "max     176.313600   789.752300     1.511100   163.250900         1.0   \n",
       "\n",
       "        feature_50   feature_51  feature_52   feature_53   feature_54  \\\n",
       "count  1567.000000  1567.000000      1567.0  1567.000000  1567.000000   \n",
       "mean    632.253633   157.437518         0.0     4.592979     4.838534   \n",
       "std       8.641254    60.909166         0.0     0.054880     0.059505   \n",
       "min     607.392700    40.261400         0.0     3.706000     3.932000   \n",
       "25%     625.928650   115.537450         0.0     4.574000     4.816000   \n",
       "50%     631.370900   183.318150         0.0     4.596000     4.843000   \n",
       "75%     638.128150   206.976700         0.0     4.617000     4.869000   \n",
       "max     667.741800   258.543200         0.0     4.764000     5.011000   \n",
       "\n",
       "        feature_55   feature_56   feature_57   feature_58   feature_59  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean   2856.166560     0.928855     0.949215     4.593259     2.951249   \n",
       "std      25.716644     0.006800     0.004171     0.084992     9.511839   \n",
       "min    2801.000000     0.875500     0.931900     4.219900   -28.988200   \n",
       "25%    2836.000000     0.925500     0.946700     4.531900    -1.855450   \n",
       "50%    2854.000000     0.931000     0.949300     4.572700     0.947250   \n",
       "75%    2874.000000     0.933100     0.952000     4.668600     4.337700   \n",
       "max    2936.000000     0.937800     0.959800     4.847500   168.145500   \n",
       "\n",
       "        feature_60   feature_61   feature_62   feature_63   feature_64  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean    355.153887    10.423195   116.501216    13.986604    20.539783   \n",
       "std       6.016917     0.274351     8.612494     7.104106     4.966452   \n",
       "min     324.714500     9.461100    81.490000     1.659100     6.448200   \n",
       "25%     350.606850    10.284050   112.055450    10.383650    17.377300   \n",
       "50%     353.799100    10.436700   116.211800    13.246050    20.021350   \n",
       "75%     359.665450    10.590550   120.918200    16.325500    22.799550   \n",
       "max     373.866400    11.784900   287.150900   188.092300    48.988200   \n",
       "\n",
       "        feature_65   feature_66   feature_67   feature_68  feature_69  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000      1567.0   \n",
       "mean     27.127928   706.667700    16.655186   147.438189         1.0   \n",
       "std       7.106006    11.600798   306.914183     4.231976         0.0   \n",
       "min       4.308000   632.422600     0.413700    87.025500         1.0   \n",
       "25%      23.074450   698.796550     0.891500   145.242300         1.0   \n",
       "50%      26.261450   706.453600     0.978300   147.597300         1.0   \n",
       "75%      29.907350   714.521050     1.064900   149.935900         1.0   \n",
       "max     118.083600   770.608400  7272.828300   167.830900         1.0   \n",
       "\n",
       "        feature_70   feature_71   feature_72   feature_73   feature_74  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean    619.101422   104.322429   151.342346   467.038061     0.002677   \n",
       "std       9.520899    31.591384    12.947203    12.416250     0.105986   \n",
       "min     581.777300    21.433200   -59.477700   456.044700     0.000000   \n",
       "25%     612.783650    87.584600   152.297200   466.081700     0.000000   \n",
       "50%     619.032700   102.604300   152.297200   466.081700     0.000000   \n",
       "75%     625.160450   115.439600   152.297200   466.081700     0.000000   \n",
       "max     722.601800   238.477500   175.413200   692.425600     4.195500   \n",
       "\n",
       "        feature_75   feature_76   feature_77   feature_78   feature_79  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean     -0.006894    -0.029383    -0.007085    -0.013625     0.003415   \n",
       "std       0.022121     0.032948     0.031129     0.047504     0.022905   \n",
       "min      -0.104900    -0.186200    -0.104600    -0.348200    -0.056800   \n",
       "25%      -0.019200    -0.051350    -0.029400    -0.047300    -0.010700   \n",
       "50%      -0.006300    -0.028900    -0.009900    -0.012500     0.000600   \n",
       "75%       0.006600    -0.006900     0.008900     0.012050     0.012800   \n",
       "max       0.231500     0.072300     0.133100     0.249200     0.101300   \n",
       "\n",
       "        feature_80   feature_81   feature_82   feature_83   feature_84  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean     -0.018380    -0.021130     0.006079     7.452076     0.133107   \n",
       "std       0.048862     0.016891     0.035797     0.516087     0.005032   \n",
       "min      -0.143700    -0.098200    -0.212900     5.825700     0.117400   \n",
       "25%      -0.042950    -0.027100    -0.017350     7.104350     0.129800   \n",
       "50%      -0.008700    -0.019600     0.007600     7.467450     0.133000   \n",
       "75%       0.008700    -0.012150     0.026800     7.807350     0.136300   \n",
       "max       0.118600     0.058400     0.143700     8.990400     0.150500   \n",
       "\n",
       "        feature_85   feature_86   feature_87   feature_88   feature_89  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.113439     2.401872     0.982420  1807.815021     0.188749   \n",
       "std       0.001142     0.037332     0.012848    53.537262     0.051514   \n",
       "min       0.105300     2.242500     0.774900  1627.471400     0.111300   \n",
       "25%       0.113550     2.376850     0.975800  1777.470300     0.169750   \n",
       "50%       0.113550     2.403900     0.987400  1809.249200     0.190100   \n",
       "75%       0.113550     2.428600     0.989700  1841.873000     0.200150   \n",
       "max       0.118400     2.555500     0.993500  2105.182300     1.472700   \n",
       "\n",
       "         feature_90   feature_91   feature_92   feature_93   feature_94  \\\n",
       "count   1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean    8827.468461     0.002431     0.000507    -0.000540    -0.000029   \n",
       "std      389.807042     0.087515     0.003229     0.003008     0.000174   \n",
       "min     7397.310000    -0.357000    -0.012600    -0.017100    -0.002000   \n",
       "25%     8578.569950    -0.042650    -0.001150    -0.001600    -0.000100   \n",
       "50%     8825.435100     0.000000     0.000400    -0.000200     0.000000   \n",
       "75%     9055.260000     0.050350     0.002000     0.001000     0.000100   \n",
       "max    10746.600000     0.362700     0.028100     0.013300     0.001100   \n",
       "\n",
       "        feature_95   feature_96  feature_97   feature_98   feature_99  \\\n",
       "count  1567.000000  1567.000000      1567.0  1567.000000  1567.000000   \n",
       "mean      0.000060     0.017076         0.0    -0.018073     0.001534   \n",
       "std       0.000104     0.219159         0.0     0.426292     0.062620   \n",
       "min      -0.000900    -1.480300         0.0    -5.271700    -0.528300   \n",
       "25%       0.000000    -0.088400         0.0    -0.217300    -0.029800   \n",
       "50%       0.000000     0.003900         0.0     0.000000     0.000000   \n",
       "75%       0.000100     0.121200         0.0     0.188650     0.029800   \n",
       "max       0.000900     2.509300         0.0     2.569800     0.885400   \n",
       "\n",
       "       feature_100  feature_101  feature_102  feature_103  feature_104  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean     -0.000021    -0.000007     0.001110    -0.009789    -0.000015   \n",
       "std       0.000355     0.000220     0.062847     0.003063     0.000850   \n",
       "min      -0.003000    -0.002400    -0.535300    -0.032900    -0.011900   \n",
       "25%      -0.000200    -0.000100    -0.035300    -0.011800    -0.000400   \n",
       "50%       0.000000     0.000000     0.000000    -0.010100     0.000000   \n",
       "75%       0.000200     0.000100     0.033600    -0.008200     0.000400   \n",
       "max       0.002300     0.001700     0.297900     0.020300     0.007100   \n",
       "\n",
       "       feature_105  feature_106  feature_107  feature_108  feature_109  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean     -0.000497     0.000538    -0.001759    -0.010790     0.980647   \n",
       "std       0.003196     0.002982     0.087307     0.086591     0.005166   \n",
       "min      -0.028100    -0.013300    -0.522600    -0.345400     0.784800   \n",
       "25%      -0.001900    -0.001000    -0.048350    -0.064400     0.981000   \n",
       "50%      -0.000200     0.000200     0.000000    -0.011200     0.981000   \n",
       "75%       0.001100     0.001550     0.048600     0.037850     0.981000   \n",
       "max       0.012700     0.017200     0.485600     0.393800     0.984200   \n",
       "\n",
       "       feature_110  feature_111  feature_112  feature_113  feature_114  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean    101.424436   231.417611     0.459962     0.945424     0.000123   \n",
       "std       1.114905     1.279821     0.036174     0.012133     0.001668   \n",
       "min      88.193800   213.008300     0.000000     0.853400     0.000000   \n",
       "25%     101.481700   231.201200     0.462400     0.938600     0.000000   \n",
       "50%     101.481700   231.201200     0.462850     0.946400     0.000000   \n",
       "75%     101.481700   231.201200     0.463400     0.952300     0.000000   \n",
       "max     106.922700   236.954600     0.488500     0.976300     0.041400   \n",
       "\n",
       "       feature_115  feature_116  feature_117  feature_118  feature_119  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean    747.383792     0.987130    58.625908     0.598421     0.970777   \n",
       "std      48.949250     0.009497     6.485174     0.008040     0.008949   \n",
       "min     544.025400     0.890000    52.806800     0.527400     0.841100   \n",
       "25%     721.023000     0.989500    57.978300     0.594200     0.964800   \n",
       "50%     750.861400     0.990500    58.549100     0.599000     0.969400   \n",
       "75%     776.781850     0.990900    59.133900     0.603300     0.978300   \n",
       "max     924.531800     0.992400   311.734400     0.624500     0.982700   \n",
       "\n",
       "       feature_120  feature_121  feature_122  feature_123  feature_124  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      6.310863    15.796388     3.898267    15.829662    15.794620   \n",
       "std       0.124304     0.099332     0.901520     0.108003     0.113821   \n",
       "min       5.125900    15.460000     1.671000    15.170000    15.430000   \n",
       "25%       6.246400    15.730000     3.202000    15.770000    15.730000   \n",
       "50%       6.313600    15.790000     3.877000    15.830000    15.780000   \n",
       "75%       6.375850    15.860000     4.392000    15.900000    15.870000   \n",
       "max       7.522000    16.070000     6.889000    16.100000    16.100000   \n",
       "\n",
       "       feature_125  feature_126  feature_127  feature_128  feature_129  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      1.184720     2.750638     0.648509     3.192198    -0.551860   \n",
       "std       0.279764     0.252744     0.135020     0.263414     1.217366   \n",
       "min       0.312200     2.340000     0.316100     0.000000    -3.779000   \n",
       "25%       0.974400     2.574000     0.548900     3.076000    -0.898800   \n",
       "50%       1.144000     2.735000     0.653900     3.195000    -0.141900   \n",
       "75%       1.338000     2.873000     0.712400     3.311000     0.047300   \n",
       "max       2.465000     3.991000     1.175000     3.895000     2.458000   \n",
       "\n",
       "       feature_130  feature_131  feature_132  feature_133  feature_134  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.745056     0.997808     2.318513  1004.043129    39.389480   \n",
       "std       0.082300     0.002244     0.053047     6.520981     2.983032   \n",
       "min       0.419900     0.993600     2.191100   980.451000    33.365800   \n",
       "25%       0.688700     0.996400     2.277300  1000.045450    37.368900   \n",
       "50%       0.758750     0.997750     2.312400  1004.050000    38.902600   \n",
       "75%       0.814500     0.998900     2.358300  1008.670600    40.804600   \n",
       "max       0.888400     1.019000     2.472300  1020.994400    64.128700   \n",
       "\n",
       "       feature_135  feature_136  feature_137  feature_138  feature_139  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean    117.932355   138.180983   122.670645    57.587810   416.077185   \n",
       "std      57.454912    53.806875    52.137163    12.291096   262.221743   \n",
       "min      58.000000    36.100000    19.200000    19.800000     0.000000   \n",
       "25%      92.000000    90.150000    81.400000    51.000000   243.841100   \n",
       "50%     109.000000   134.600000   117.700000    55.900100   339.561000   \n",
       "75%     127.000000   180.900000   161.600000    62.900100   494.806000   \n",
       "max     994.000000   295.800000   334.700000   141.799800  1770.690900   \n",
       "\n",
       "       feature_140  feature_141  feature_142  feature_143  feature_144  \\\n",
       "count  1567.000000       1567.0  1567.000000  1567.000000  1567.000000   \n",
       "mean     25.847024          0.0     6.638156     0.004168     0.119992   \n",
       "std     504.656946          0.0     3.536522     0.001278     0.061305   \n",
       "min       0.031900          0.0     1.740000     0.000000     0.032400   \n",
       "25%       0.131800          0.0     5.110000     0.003300     0.083900   \n",
       "50%       0.235800          0.0     6.260000     0.003900     0.107500   \n",
       "75%       0.434800          0.0     7.500000     0.004900     0.132650   \n",
       "max    9998.894400          0.0   103.390000     0.012100     0.625300   \n",
       "\n",
       "       feature_145  feature_146  feature_147  feature_148  feature_149  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000       1567.0   \n",
       "mean      0.063615     0.055004     0.017409     8.470601          0.0   \n",
       "std       0.026524     0.021831     0.027106    18.728671          0.0   \n",
       "min       0.021400     0.022700     0.004300     1.420800          0.0   \n",
       "25%       0.048050     0.042350     0.010000     6.362800          0.0   \n",
       "50%       0.058600     0.050000     0.015900     7.917300          0.0   \n",
       "75%       0.071800     0.061500     0.021300     9.581850          0.0   \n",
       "max       0.250700     0.247900     0.978300   742.942100          0.0   \n",
       "\n",
       "       feature_150  feature_151  feature_152  feature_153  feature_154  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      6.812615    14.041556     1.195339     0.011925     7.697616   \n",
       "std       3.238956    30.973119    23.341694     0.009337     5.234204   \n",
       "min       1.337000     2.020000     0.154400     0.003600     1.243800   \n",
       "25%       4.465500     8.096000     0.373850     0.007300     5.931600   \n",
       "50%       5.951000    10.993500     0.468700     0.011100     7.512700   \n",
       "75%       8.269500    14.342000     0.677500     0.014900     9.050950   \n",
       "max      22.318000   536.564000   924.378000     0.238900   191.547800   \n",
       "\n",
       "       feature_155  feature_156  feature_157  feature_158  feature_159  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.505977     0.058089     0.036476  1022.004195   882.349075   \n",
       "std       1.118937     0.079174     0.012152   120.461488   982.458855   \n",
       "min       0.140000     0.011100     0.011800   234.099600     0.000000   \n",
       "25%       0.240000     0.036250     0.035450  1020.300050   411.500000   \n",
       "50%       0.320000     0.048700     0.035450  1020.300050   623.000000   \n",
       "75%       0.450000     0.066700     0.035450  1020.300050   963.500000   \n",
       "max      12.710000     2.201600     0.287600  2505.299800  7791.000000   \n",
       "\n",
       "       feature_160   feature_161   feature_162  feature_163  feature_164  \\\n",
       "count  1567.000000   1567.000000   1567.000000  1567.000000  1567.000000   \n",
       "mean    555.196554   4064.996171   4793.308870     0.140179     0.127893   \n",
       "std     574.456703   4236.854877   6550.267099     0.121913     0.242383   \n",
       "min       0.000000      0.000000      0.000000     0.000000     0.000000   \n",
       "25%     295.000000   1322.500000    451.000000     0.091000     0.068500   \n",
       "50%     438.000000   2614.000000   1784.000000     0.120000     0.089000   \n",
       "75%     624.500000   5033.000000   6376.000000     0.154000     0.116000   \n",
       "max    4170.000000  37943.000000  36871.000000     0.957000     1.817000   \n",
       "\n",
       "       feature_165  feature_166  feature_167  feature_168  feature_169  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.251939     2.788641     1.235737     0.124390     0.400468   \n",
       "std       0.407076     1.119061     0.632364     0.047609     0.197792   \n",
       "min       0.000000     0.800000     0.300000     0.033000     0.046000   \n",
       "25%       0.132000     2.100000     0.900000     0.090000     0.230500   \n",
       "50%       0.184000     2.600000     1.200000     0.119000     0.412000   \n",
       "75%       0.254500     3.200000     1.500000     0.150500     0.536000   \n",
       "max       3.286000    21.100000    16.300000     0.725000     1.143000   \n",
       "\n",
       "       feature_170  feature_171  feature_172  feature_173  feature_174  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.684331     0.120059     0.320115     0.576193     0.320115   \n",
       "std       0.157418     0.060766     0.071220     0.095703     0.071224   \n",
       "min       0.297900     0.008900     0.128700     0.253800     0.128700   \n",
       "25%       0.575600     0.079800     0.276600     0.516900     0.276500   \n",
       "50%       0.686000     0.112500     0.323850     0.577600     0.323850   \n",
       "75%       0.797300     0.140300     0.370200     0.634500     0.370200   \n",
       "max       1.153000     0.494000     0.548400     0.864300     0.548400   \n",
       "\n",
       "       feature_175  feature_176  feature_177  feature_178  feature_179  \\\n",
       "count  1567.000000  1567.000000  1567.000000       1567.0       1567.0   \n",
       "mean      0.778037     0.244717     0.394699          0.0          0.0   \n",
       "std       0.116285     0.074894     0.282823          0.0          0.0   \n",
       "min       0.461600     0.073500     0.047000          0.0          0.0   \n",
       "25%       0.692200     0.196300     0.222000          0.0          0.0   \n",
       "50%       0.768200     0.242900     0.299000          0.0          0.0   \n",
       "75%       0.843900     0.293750     0.423000          0.0          0.0   \n",
       "max       1.172000     0.441100     1.858000          0.0          0.0   \n",
       "\n",
       "       feature_180  feature_181  feature_182  feature_183  feature_184  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean     19.013050     0.546756    10.780153    26.661515     0.144808   \n",
       "std       3.310585     0.224331     4.162749     6.833931     0.110163   \n",
       "min       9.400000     0.093000     3.170000     5.014000     0.029700   \n",
       "25%      16.850000     0.378000     7.735000    21.172000     0.102200   \n",
       "50%      18.690000     0.524000    10.170000    27.200500     0.132600   \n",
       "75%      20.965000     0.688500    13.335000    31.687000     0.169100   \n",
       "max      48.670000     3.573000    55.000000    72.947000     3.228300   \n",
       "\n",
       "       feature_185  feature_186  feature_187  feature_188  feature_189  \\\n",
       "count  1567.000000       1567.0  1567.000000  1567.000000       1567.0   \n",
       "mean      7.365338          0.0    17.936244    43.209503          0.0   \n",
       "std       7.186442          0.0     8.607162    21.705075          0.0   \n",
       "min       1.940000          0.0     6.220000     6.613000          0.0   \n",
       "25%       5.390000          0.0    14.510000    24.714000          0.0   \n",
       "50%       6.735000          0.0    17.865000    40.209500          0.0   \n",
       "75%       8.450000          0.0    20.860000    57.672500          0.0   \n",
       "max     267.910000          0.0   307.930000   191.830000          0.0   \n",
       "\n",
       "       feature_190  feature_191  feature_192  feature_193  feature_194  \\\n",
       "count       1567.0       1567.0       1567.0       1567.0       1567.0   \n",
       "mean           0.0          0.0          0.0          0.0          0.0   \n",
       "std            0.0          0.0          0.0          0.0          0.0   \n",
       "min            0.0          0.0          0.0          0.0          0.0   \n",
       "25%            0.0          0.0          0.0          0.0          0.0   \n",
       "50%            0.0          0.0          0.0          0.0          0.0   \n",
       "75%            0.0          0.0          0.0          0.0          0.0   \n",
       "max            0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "       feature_195  feature_196  feature_197  feature_198  feature_199  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.287013     8.679962    20.089943     0.556848    11.520715   \n",
       "std       0.394684    15.686267    10.532022     0.536737    16.415041   \n",
       "min       0.080000     1.750000     9.220000     0.090000     2.770000   \n",
       "25%       0.219500     5.045000    17.150000     0.296000     6.745000   \n",
       "50%       0.259000     6.780000    19.370000     0.424000     8.570000   \n",
       "75%       0.296000     9.510000    21.450000     0.724500    11.425000   \n",
       "max       4.838000   396.110000   252.870000    10.017000   390.120000   \n",
       "\n",
       "       feature_200  feature_201  feature_202  feature_203  feature_204  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean     17.598561     7.834537    10.162831    30.073234    32.095412   \n",
       "std       8.671307     5.093583    14.590631    17.428314   564.021108   \n",
       "min       3.210000     0.000000     0.000000     7.728000     0.042900   \n",
       "25%      14.175000     5.025000     6.101500    24.667000     0.114300   \n",
       "50%      17.235000     6.760000     8.462000    30.097000     0.158200   \n",
       "75%      20.160000     9.485000    11.931950    33.504500     0.230600   \n",
       "max     199.620000   126.530000   490.561000   500.349000  9998.448300   \n",
       "\n",
       "       feature_205  feature_206  feature_207  feature_208  feature_209  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      9.045105     0.001276    20.373663    73.264254     0.029451   \n",
       "std      11.519237     0.050524    17.464051    28.013323     1.165835   \n",
       "min       2.300000     0.000000     4.010000     5.359000     0.000000   \n",
       "25%       6.050000     0.000000    16.365000    56.220500     0.000000   \n",
       "50%       7.740000     0.000000    19.720000    73.248000     0.000000   \n",
       "75%       9.930000     0.000000    22.365000    90.452500     0.000000   \n",
       "max     320.050000     2.000000   457.650000   172.349000    46.150000   \n",
       "\n",
       "       feature_210  feature_211  feature_212  feature_213  feature_214  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.088725     0.056701     0.051281     0.060280     0.083148   \n",
       "std       0.041756     0.024817     0.031358     0.052624     0.056030   \n",
       "min       0.031900     0.002200     0.007100     0.003700     0.019300   \n",
       "25%       0.065900     0.043950     0.032600     0.036950     0.057050   \n",
       "50%       0.079700     0.053200     0.041600     0.056000     0.075400   \n",
       "75%       0.099050     0.064100     0.062100     0.073400     0.093250   \n",
       "max       0.516400     0.322700     0.594100     1.283700     0.761500   \n",
       "\n",
       "       feature_215  feature_216  feature_217  feature_218  feature_219  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.081097     0.083501     0.071483     3.771376     0.003252   \n",
       "std       0.030203     0.025566     0.045944     1.170068     0.001640   \n",
       "min       0.005900     0.009700     0.007900     1.034000     0.000700   \n",
       "25%       0.063600     0.069800     0.046100     2.946200     0.002300   \n",
       "50%       0.082500     0.084600     0.061700     3.630750     0.003000   \n",
       "75%       0.098100     0.097300     0.086050     4.403400     0.003800   \n",
       "max       0.342900     0.282800     0.674400     8.801500     0.016300   \n",
       "\n",
       "       feature_220  feature_221  feature_222  feature_223  feature_224  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.008988     0.060718     0.008821   122.846571     0.058734   \n",
       "std       0.000760     0.023305     0.055937    55.156003     0.070128   \n",
       "min       0.005700     0.020000     0.000300    32.263700     0.009300   \n",
       "25%       0.008950     0.040200     0.001400    95.147350     0.030050   \n",
       "50%       0.008950     0.060900     0.002300   119.436000     0.039800   \n",
       "75%       0.008950     0.076500     0.005500   144.502800     0.059200   \n",
       "max       0.024000     0.230500     0.991100  1768.880200     1.436100   \n",
       "\n",
       "       feature_225  feature_226  feature_227  feature_228  feature_229  \\\n",
       "count  1567.000000       1567.0  1567.000000  1567.000000       1567.0   \n",
       "mean   1038.656080          0.0     0.019122     0.017841          0.0   \n",
       "std     426.259257          0.0     0.010749     0.010738          0.0   \n",
       "min     168.799800          0.0     0.006200     0.007200          0.0   \n",
       "25%     726.500000          0.0     0.013250     0.012650          0.0   \n",
       "50%     967.299800          0.0     0.016500     0.015500          0.0   \n",
       "75%    1252.399650          0.0     0.021200     0.020000          0.0   \n",
       "max    3601.299800          0.0     0.154100     0.213300          0.0   \n",
       "\n",
       "       feature_230  feature_231  feature_232  feature_233  feature_234  \\\n",
       "count       1567.0       1567.0       1567.0       1567.0       1567.0   \n",
       "mean           0.0          0.0          0.0          0.0          0.0   \n",
       "std            0.0          0.0          0.0          0.0          0.0   \n",
       "min            0.0          0.0          0.0          0.0          0.0   \n",
       "25%            0.0          0.0          0.0          0.0          0.0   \n",
       "50%            0.0          0.0          0.0          0.0          0.0   \n",
       "75%            0.0          0.0          0.0          0.0          0.0   \n",
       "max            0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "       feature_235  feature_236  feature_237  feature_238  feature_239  \\\n",
       "count       1567.0       1567.0       1567.0  1567.000000  1567.000000   \n",
       "mean           0.0          0.0          0.0     0.004791     0.004575   \n",
       "std            0.0          0.0          0.0     0.001697     0.001440   \n",
       "min            0.0          0.0          0.0     0.001300     0.001400   \n",
       "25%            0.0          0.0          0.0     0.003700     0.003600   \n",
       "50%            0.0          0.0          0.0     0.004600     0.004400   \n",
       "75%            0.0          0.0          0.0     0.005700     0.005300   \n",
       "max            0.0          0.0          0.0     0.024400     0.023600   \n",
       "\n",
       "       feature_240  feature_241  feature_242  feature_243  feature_244  \\\n",
       "count       1567.0       1567.0       1567.0       1567.0  1567.000000   \n",
       "mean           0.0          0.0          0.0          0.0     0.003121   \n",
       "std            0.0          0.0          0.0          0.0     0.050093   \n",
       "min            0.0          0.0          0.0          0.0     0.000300   \n",
       "25%            0.0          0.0          0.0          0.0     0.001700   \n",
       "50%            0.0          0.0          0.0          0.0     0.001700   \n",
       "75%            0.0          0.0          0.0          0.0     0.001700   \n",
       "max            0.0          0.0          0.0          0.0     1.984400   \n",
       "\n",
       "       feature_245  feature_246  feature_247  feature_248  feature_249  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      1.375909     3.839677     0.041340     0.025171     0.001065   \n",
       "std       2.577886     5.946545     0.051023     0.049235     0.015771   \n",
       "min       0.291400     1.102200     0.000000     0.003000     0.000000   \n",
       "25%       1.185100     3.673000     0.025150     0.014700     0.000000   \n",
       "50%       1.185100     3.673000     0.027000     0.021000     0.000000   \n",
       "75%       1.185100     3.673000     0.030100     0.027300     0.000000   \n",
       "max      99.902200   237.183700     0.491400     0.973200     0.413800   \n",
       "\n",
       "       feature_250  feature_251  feature_252  feature_253  feature_254  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean    109.650967     0.004285     4.645115     0.033179     0.013943   \n",
       "std      54.597274     0.037472    64.354756     0.022255     0.009132   \n",
       "min      21.010700     0.000300     0.767300     0.009400     0.001700   \n",
       "25%      76.132150     0.000700     2.205650     0.024600     0.004700   \n",
       "50%     103.093600     0.001000     2.864600     0.030800     0.015000   \n",
       "75%     131.758400     0.001300     3.795050     0.037700     0.021300   \n",
       "max    1119.704200     0.990900  2549.988500     0.451700     0.078700   \n",
       "\n",
       "       feature_255  feature_256  feature_257  feature_258  feature_259  \\\n",
       "count  1567.000000       1567.0       1567.0       1567.0       1567.0   \n",
       "mean      0.403848          0.0          0.0          0.0          0.0   \n",
       "std       0.120334          0.0          0.0          0.0          0.0   \n",
       "min       0.126900          0.0          0.0          0.0          0.0   \n",
       "25%       0.307600          0.0          0.0          0.0          0.0   \n",
       "50%       0.405100          0.0          0.0          0.0          0.0   \n",
       "75%       0.480950          0.0          0.0          0.0          0.0   \n",
       "max       0.925500          0.0          0.0          0.0          0.0   \n",
       "\n",
       "       feature_260  feature_261  feature_262  feature_263  feature_264  \\\n",
       "count       1567.0       1567.0       1567.0       1567.0       1567.0   \n",
       "mean           0.0          0.0          0.0          0.0          0.0   \n",
       "std            0.0          0.0          0.0          0.0          0.0   \n",
       "min            0.0          0.0          0.0          0.0          0.0   \n",
       "25%            0.0          0.0          0.0          0.0          0.0   \n",
       "50%            0.0          0.0          0.0          0.0          0.0   \n",
       "75%            0.0          0.0          0.0          0.0          0.0   \n",
       "max            0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "       feature_265  feature_266  feature_267  feature_268  feature_269  \\\n",
       "count       1567.0       1567.0  1567.000000  1567.000000  1567.000000   \n",
       "mean           0.0          0.0     0.070587    19.496877     3.777486   \n",
       "std            0.0          0.0     0.029573     7.326430     1.149394   \n",
       "min            0.0          0.0     0.019800     6.098000     1.301700   \n",
       "25%            0.0          0.0     0.044000    13.828000     2.962400   \n",
       "50%            0.0          0.0     0.070600    17.977000     3.703500   \n",
       "75%            0.0          0.0     0.091600    24.653000     4.376800   \n",
       "max            0.0          0.0     0.157800    40.855000    10.152900   \n",
       "\n",
       "       feature_270  feature_271  feature_272  feature_273  feature_274  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean     29.258737    46.055143    41.292434    20.175883   136.062895   \n",
       "std       8.388634    17.832193    17.698031     3.813721    85.258562   \n",
       "min      15.547100    10.401500     6.943100     8.651200     0.000000   \n",
       "25%      24.982300    30.043050    27.101500    18.279250    81.247400   \n",
       "50%      28.773500    45.676500    40.019250    19.580900   110.601400   \n",
       "75%      31.691650    59.484150    54.166500    22.089100   161.979100   \n",
       "max     158.526000   132.647900   122.117400    43.573700   659.169600   \n",
       "\n",
       "       feature_275  feature_276  feature_277  feature_278  feature_279  \\\n",
       "count  1567.000000       1567.0  1567.000000  1567.000000  1567.000000   \n",
       "mean      8.616246          0.0     2.209604     0.001117     0.041052   \n",
       "std     168.194470          0.0     1.191138     0.000339     0.020277   \n",
       "min       0.011100          0.0     0.561500     0.000000     0.010700   \n",
       "25%       0.044800          0.0     1.698550     0.000900     0.028300   \n",
       "50%       0.078400          0.0     2.083100     0.001100     0.037200   \n",
       "75%       0.144900          0.0     2.513100     0.001300     0.045750   \n",
       "max    3332.596400          0.0    32.170900     0.003400     0.188400   \n",
       "\n",
       "       feature_280  feature_281  feature_282  feature_283  feature_284  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000       1567.0   \n",
       "mean      0.018032     0.015093     0.005769     2.803798          0.0   \n",
       "std       0.006479     0.005542     0.008545     5.860580          0.0   \n",
       "min       0.007300     0.006900     0.001600     0.505000          0.0   \n",
       "25%       0.014200     0.011900     0.003300     2.211500          0.0   \n",
       "50%       0.016900     0.013900     0.005300     2.658000          0.0   \n",
       "75%       0.020700     0.016600     0.007100     3.145700          0.0   \n",
       "max       0.075500     0.059700     0.308300   232.804900          0.0   \n",
       "\n",
       "       feature_285  feature_286  feature_287  feature_288  feature_289  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      2.119327     4.258295     0.367092     0.003923     2.578539   \n",
       "std       0.962059     9.754552     7.379271     0.002933     1.615444   \n",
       "min       0.461100     0.728000     0.051300     0.001200     0.396000   \n",
       "25%       1.440150     2.467600     0.114950     0.002400     2.092950   \n",
       "50%       1.875150     3.360050     0.138950     0.003600     2.549000   \n",
       "75%       2.605900     4.306600     0.198400     0.004900     3.023800   \n",
       "max       6.869800   207.016100   292.227400     0.074900    59.518700   \n",
       "\n",
       "       feature_290  feature_291  feature_292  feature_293  feature_294  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.123171     0.019926     0.011307   319.424471   401.657580   \n",
       "std       0.270139     0.025549     0.003540    41.033649   476.765634   \n",
       "min       0.041600     0.003800     0.004100    82.323300     0.000000   \n",
       "25%       0.065200     0.012500     0.011000   317.867100   185.111450   \n",
       "50%       0.083300     0.016900     0.011000   317.867100   278.671900   \n",
       "75%       0.117550     0.023600     0.011000   317.867100   428.515800   \n",
       "max       4.420300     0.691500     0.083100   879.226000  3933.755000   \n",
       "\n",
       "       feature_295   feature_296   feature_297  feature_298  feature_299  \\\n",
       "count  1567.000000   1567.000000   1567.000000  1567.000000  1567.000000   \n",
       "mean    252.926146   1878.364532   2340.883483     0.063790     0.060241   \n",
       "std     283.356947   1973.997717   3225.321542     0.064185     0.130744   \n",
       "min       0.000000      0.000000      0.000000     0.000000     0.000000   \n",
       "25%     130.263200    603.290800    211.254550     0.040700     0.030200   \n",
       "50%     195.825600   1202.412100    820.098800     0.052800     0.040000   \n",
       "75%     273.865150   2337.950650   3188.190750     0.069200     0.052000   \n",
       "max    2005.874400  15559.952500  18520.468300     0.526400     1.031200   \n",
       "\n",
       "       feature_300  feature_301  feature_302  feature_303  feature_304  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.118341     0.910082     0.403314     0.040342     0.132083   \n",
       "std       0.219011     0.331774     0.197389     0.014501     0.064826   \n",
       "min       0.000000     0.310000     0.111800     0.010800     0.013800   \n",
       "25%       0.058950     0.717400     0.295800     0.030000     0.072800   \n",
       "50%       0.082800     0.860400     0.380800     0.038800     0.137200   \n",
       "75%       0.115350     1.046400     0.476700     0.048600     0.178500   \n",
       "max       1.812300     5.711000     5.154900     0.225800     0.333700   \n",
       "\n",
       "       feature_305  feature_306  feature_307  feature_308  feature_309  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.264916     0.048621     0.128921     0.218415     0.128922   \n",
       "std       0.057369     0.025392     0.027459     0.033582     0.027461   \n",
       "min       0.117100     0.003400     0.054900     0.091300     0.054900   \n",
       "25%       0.225000     0.033100     0.113700     0.197600     0.113700   \n",
       "50%       0.264300     0.044800     0.129500     0.219450     0.129500   \n",
       "75%       0.307300     0.055100     0.147600     0.237900     0.147600   \n",
       "max       0.475000     0.224600     0.211200     0.323900     0.211200   \n",
       "\n",
       "       feature_310  feature_311  feature_312  feature_313  feature_314  \\\n",
       "count  1567.000000  1567.000000  1567.000000       1567.0       1567.0   \n",
       "mean      0.304751     0.097345     0.160026          0.0          0.0   \n",
       "std       0.043446     0.028787     0.117282          0.0          0.0   \n",
       "min       0.180900     0.032800     0.022400          0.0          0.0   \n",
       "25%       0.278600     0.077600     0.091500          0.0          0.0   \n",
       "50%       0.302900     0.097700     0.121500          0.0          0.0   \n",
       "75%       0.331900     0.115900     0.160150          0.0          0.0   \n",
       "max       0.443800     0.178400     0.754900          0.0          0.0   \n",
       "\n",
       "       feature_315  feature_316  feature_317  feature_318  feature_319  \\\n",
       "count       1567.0  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean           0.0     5.976884     0.172623     3.188586     7.916338   \n",
       "std            0.0     1.018311     0.072370     1.215564     2.178396   \n",
       "min            0.0     2.788200     0.028300     0.984800     1.657400   \n",
       "25%            0.0     5.302150     0.117450     2.320050     6.247800   \n",
       "50%            0.0     5.831500     0.163400     2.898900     8.388800   \n",
       "75%            0.0     6.546900     0.218100     4.021200     9.480800   \n",
       "max            0.0    13.095800     1.003400    15.893400    20.045500   \n",
       "\n",
       "       feature_320  feature_321  feature_322  feature_323  feature_324  \\\n",
       "count  1567.000000  1567.000000       1567.0  1567.000000  1567.000000   \n",
       "mean      0.043103     2.263609          0.0     5.393462    13.331644   \n",
       "std       0.031875     2.116324          0.0     2.518056     6.613770   \n",
       "min       0.008400     0.611400          0.0     1.710100     2.234500   \n",
       "25%       0.031200     1.670350          0.0     4.273400     7.578800   \n",
       "50%       0.039850     2.077650          0.0     5.458800    12.504500   \n",
       "75%       0.050200     2.632900          0.0     6.344350    17.925150   \n",
       "max       0.947400    79.151500          0.0    89.191700    51.867800   \n",
       "\n",
       "       feature_325  feature_326  feature_327  feature_328  feature_329  \\\n",
       "count       1567.0       1567.0       1567.0       1567.0       1567.0   \n",
       "mean           0.0          0.0          0.0          0.0          0.0   \n",
       "std            0.0          0.0          0.0          0.0          0.0   \n",
       "min            0.0          0.0          0.0          0.0          0.0   \n",
       "25%            0.0          0.0          0.0          0.0          0.0   \n",
       "50%            0.0          0.0          0.0          0.0          0.0   \n",
       "75%            0.0          0.0          0.0          0.0          0.0   \n",
       "max            0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "       feature_330  feature_331  feature_332  feature_333  feature_334  \\\n",
       "count       1567.0  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean           0.0     0.083236     2.591113     6.214963     0.168215   \n",
       "std            0.0     0.063344     5.632706     3.396952     0.172568   \n",
       "min            0.0     0.022400     0.537300     2.837200     0.028200   \n",
       "25%            0.0     0.068800     1.546800     5.456600     0.089700   \n",
       "50%            0.0     0.084800     2.062700     5.980100     0.129400   \n",
       "75%            0.0     0.095600     2.789300     6.544500     0.210150   \n",
       "max            0.0     1.095900   174.894400    90.515900     3.412500   \n",
       "\n",
       "       feature_335  feature_336  feature_337  feature_338  feature_339  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      3.423428     9.733425     2.326262     3.035450     9.329514   \n",
       "std       5.770748     7.539354     1.695730     5.632481     6.063060   \n",
       "min       0.789900     5.215100     0.000000     0.000000     2.200100   \n",
       "25%       2.036500     8.292150     1.544400     1.902800     7.593400   \n",
       "50%       2.513500     9.073550     2.054450     2.560850     9.474200   \n",
       "75%       3.355500    10.034700     2.779950     3.397900    10.438250   \n",
       "max     172.711900   214.862800    38.899500   196.688000   197.498800   \n",
       "\n",
       "       feature_340  feature_341  feature_342  feature_343  feature_344  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean     14.617500     2.730735     0.000285     6.197770    23.217137   \n",
       "std     261.238119     3.660934     0.011297     5.361538     8.878164   \n",
       "min       0.013100     0.574100     0.000000     1.256500     2.056000   \n",
       "25%       0.034750     1.914250     0.000000     5.007700    17.941050   \n",
       "50%       0.046400     2.377300     0.000000     6.005600    23.214700   \n",
       "75%       0.066800     2.984400     0.000000     6.881050    28.852650   \n",
       "max    5043.878900    97.708900     0.447200   156.336000    59.324100   \n",
       "\n",
       "       feature_345  feature_346  feature_347  feature_348  feature_349  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      6.746664     4.390054     0.008880     0.024673     0.025233   \n",
       "std      12.354268    12.067624     0.351511     0.011774     0.010523   \n",
       "min       1.769400     1.017700     0.000000     0.010300     0.001000   \n",
       "25%       5.567000     3.046400     0.000000     0.018200     0.019700   \n",
       "50%       5.567000     3.046400     0.000000     0.022600     0.024000   \n",
       "75%       5.567000     3.046400     0.000000     0.027300     0.028500   \n",
       "max     257.010600   187.758900    13.914700     0.220000     0.133900   \n",
       "\n",
       "       feature_350  feature_351  feature_352  feature_353  feature_354  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.023135     0.027549     0.023335     0.040358     0.041956   \n",
       "std       0.014226     0.024376     0.013057     0.015382     0.012970   \n",
       "min       0.002900     0.002000     0.005600     0.002600     0.004000   \n",
       "25%       0.014600     0.016600     0.016100     0.030500     0.035300   \n",
       "50%       0.018800     0.025300     0.022000     0.042100     0.044200   \n",
       "75%       0.028100     0.033600     0.026800     0.050100     0.049900   \n",
       "max       0.291400     0.618800     0.142900     0.153500     0.134400   \n",
       "\n",
       "       feature_355  feature_356  feature_357  feature_358  feature_359  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.034464     1.298601     0.000998     0.002406     0.019840   \n",
       "std       0.022144     0.386796     0.000499     0.000150     0.007136   \n",
       "min       0.003800     0.379600     0.000300     0.001700     0.007600   \n",
       "25%       0.021400     1.025650     0.000700     0.002400     0.013800   \n",
       "50%       0.029400     1.255300     0.000900     0.002400     0.019600   \n",
       "75%       0.041900     1.533250     0.001100     0.002400     0.025000   \n",
       "max       0.278900     2.834800     0.005200     0.004700     0.088800   \n",
       "\n",
       "       feature_360  feature_361  feature_362  feature_363  feature_364  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000       1567.0   \n",
       "mean      0.002945    39.936406     0.018191   332.555156          0.0   \n",
       "std       0.020003    17.056304     0.021314   136.586680          0.0   \n",
       "min       0.000100    10.720400     0.002800    60.988200          0.0   \n",
       "25%       0.000400    32.168700     0.009500   230.073100          0.0   \n",
       "50%       0.000700    39.696100     0.012500   309.831650          0.0   \n",
       "75%       0.001800    47.079200     0.018300   409.116550          0.0   \n",
       "max       0.409000   547.172200     0.416300  1072.203100          0.0   \n",
       "\n",
       "       feature_365  feature_366  feature_367  feature_368  feature_369  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000       1567.0   \n",
       "mean      0.005198     0.004813     0.003771     0.003170          0.0   \n",
       "std       0.002654     0.002381     0.002694     0.002103          0.0   \n",
       "min       0.001700     0.002000     0.000000     0.000000          0.0   \n",
       "25%       0.003800     0.003550     0.002600     0.002200          0.0   \n",
       "50%       0.004600     0.004300     0.003200     0.002800          0.0   \n",
       "75%       0.005800     0.005400     0.004200     0.003600          0.0   \n",
       "max       0.036800     0.039200     0.035700     0.033400          0.0   \n",
       "\n",
       "       feature_370  feature_371  feature_372  feature_373  feature_374  \\\n",
       "count       1567.0       1567.0       1567.0       1567.0       1567.0   \n",
       "mean           0.0          0.0          0.0          0.0          0.0   \n",
       "std            0.0          0.0          0.0          0.0          0.0   \n",
       "min            0.0          0.0          0.0          0.0          0.0   \n",
       "25%            0.0          0.0          0.0          0.0          0.0   \n",
       "50%            0.0          0.0          0.0          0.0          0.0   \n",
       "75%            0.0          0.0          0.0          0.0          0.0   \n",
       "max            0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "       feature_375  feature_376  feature_377  feature_378  feature_379  \\\n",
       "count       1567.0  1567.000000  1567.000000       1567.0       1567.0   \n",
       "mean           0.0     0.001601     0.001571          0.0          0.0   \n",
       "std            0.0     0.000534     0.000466          0.0          0.0   \n",
       "min            0.0     0.000400     0.000400          0.0          0.0   \n",
       "25%            0.0     0.001300     0.001300          0.0          0.0   \n",
       "50%            0.0     0.001600     0.001500          0.0          0.0   \n",
       "75%            0.0     0.001900     0.001800          0.0          0.0   \n",
       "max            0.0     0.008200     0.007700          0.0          0.0   \n",
       "\n",
       "       feature_380  feature_381  feature_382  feature_383  feature_384  \\\n",
       "count       1567.0       1567.0  1567.000000  1567.000000  1567.000000   \n",
       "mean           0.0          0.0     0.000964     0.431613     1.169065   \n",
       "std            0.0          0.0     0.015831     0.797348     1.876245   \n",
       "min            0.0          0.0     0.000100     0.087500     0.338300   \n",
       "25%            0.0          0.0     0.000500     0.372600     1.106300   \n",
       "50%            0.0          0.0     0.000500     0.372600     1.106300   \n",
       "75%            0.0          0.0     0.000500     0.372600     1.106300   \n",
       "max            0.0          0.0     0.627100    30.998200    74.844500   \n",
       "\n",
       "       feature_385  feature_386  feature_387  feature_388  feature_389  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.009316     0.008281     0.000339    35.155091     0.001338   \n",
       "std       0.010838     0.015488     0.004989    17.227003     0.011816   \n",
       "min       0.000000     0.000800     0.000000     6.310100     0.000100   \n",
       "25%       0.006500     0.004800     0.000000    24.386550     0.000200   \n",
       "50%       0.006800     0.006800     0.000000    32.530700     0.000300   \n",
       "75%       0.007150     0.009300     0.000000    42.652450     0.000400   \n",
       "max       0.207300     0.306800     0.130900   348.829300     0.312700   \n",
       "\n",
       "       feature_390  feature_391  feature_392  feature_393  feature_394  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000       1567.0   \n",
       "mean      1.431868     0.010944     0.004533     0.133990          0.0   \n",
       "std      20.326415     0.006687     0.002956     0.038408          0.0   \n",
       "min       0.304600     0.003100     0.000500     0.034200          0.0   \n",
       "25%       0.675150     0.008350     0.001500     0.104400          0.0   \n",
       "50%       0.877300     0.010200     0.004900     0.133900          0.0   \n",
       "75%       1.148200     0.012400     0.006900     0.160400          0.0   \n",
       "max     805.393600     0.137500     0.022900     0.299400          0.0   \n",
       "\n",
       "       feature_395  feature_396  feature_397  feature_398  feature_399  \\\n",
       "count       1567.0       1567.0       1567.0       1567.0       1567.0   \n",
       "mean           0.0          0.0          0.0          0.0          0.0   \n",
       "std            0.0          0.0          0.0          0.0          0.0   \n",
       "min            0.0          0.0          0.0          0.0          0.0   \n",
       "25%            0.0          0.0          0.0          0.0          0.0   \n",
       "50%            0.0          0.0          0.0          0.0          0.0   \n",
       "75%            0.0          0.0          0.0          0.0          0.0   \n",
       "max            0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "       feature_400  feature_401  feature_402  feature_403  feature_404  \\\n",
       "count       1567.0       1567.0       1567.0       1567.0       1567.0   \n",
       "mean           0.0          0.0          0.0          0.0          0.0   \n",
       "std            0.0          0.0          0.0          0.0          0.0   \n",
       "min            0.0          0.0          0.0          0.0          0.0   \n",
       "25%            0.0          0.0          0.0          0.0          0.0   \n",
       "50%            0.0          0.0          0.0          0.0          0.0   \n",
       "75%            0.0          0.0          0.0          0.0          0.0   \n",
       "max            0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "       feature_405  feature_406  feature_407  feature_408  feature_409  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.024206     6.726477     1.232037     5.339597     4.580083   \n",
       "std       0.010701     2.822938     0.363779     2.574107     1.773445   \n",
       "min       0.006200     2.054500     0.424000     2.737800     1.216300   \n",
       "25%       0.014100     4.559800     0.966500     4.127800     3.019000   \n",
       "50%       0.023900     5.920100     1.239700     4.922450     4.489700   \n",
       "75%       0.032300     8.563000     1.416700     5.787100     5.929950   \n",
       "max       0.051400    14.727700     3.312800    44.310000     9.576500   \n",
       "\n",
       "       feature_410  feature_411  feature_412  feature_413  feature_414  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000       1567.0   \n",
       "mean      4.928466     2.615479    30.868839    25.564824          0.0   \n",
       "std       2.118268     0.549041    18.336590    47.099220          0.0   \n",
       "min       0.734200     0.960900     0.000000     4.041600          0.0   \n",
       "25%       3.269450     2.325300    18.470300    11.474550          0.0   \n",
       "50%       4.732750     2.548100    26.156900    20.255100          0.0   \n",
       "75%       6.453800     2.848450    38.026900    29.307300          0.0   \n",
       "max      13.807100     6.215000   128.281600   899.119000          0.0   \n",
       "\n",
       "       feature_415  feature_416  feature_417  feature_418  feature_419  \\\n",
       "count   1567.00000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean       6.62656     3.403370     8.189890   320.236157   309.014570   \n",
       "std        3.94087     1.032534     4.052025   287.521430   325.243132   \n",
       "min        1.53400     0.000000     2.153100     0.000000     0.000000   \n",
       "25%        4.92880     2.667850     5.767150     0.000000     0.000000   \n",
       "50%        6.17660     3.234000     7.395600   302.177600   272.448700   \n",
       "75%        7.54530     4.010700     9.167850   523.624450   582.803100   \n",
       "max      116.86150     9.690000    39.037600   999.316000   998.681300   \n",
       "\n",
       "       feature_420  feature_421  feature_422  feature_423  feature_424  \\\n",
       "count  1567.000000  1567.000000       1567.0  1567.000000  1567.000000   \n",
       "mean      1.821037     4.174228          0.0    77.645599     3.314227   \n",
       "std       3.055746     6.909444          0.0    32.567460     6.319367   \n",
       "min       0.441100     0.721700          0.0    23.020000     0.486600   \n",
       "25%       1.030950     3.184850          0.0    56.008200     1.965450   \n",
       "50%       1.645100     3.943100          0.0    69.905450     2.667100   \n",
       "75%       2.214600     4.783200          0.0    92.832750     3.469200   \n",
       "max     111.495600   273.095200          0.0   424.215200   103.180900   \n",
       "\n",
       "       feature_425  feature_426  feature_427  feature_428  feature_429  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      6.792422     1.233669     4.058277     4.209984     4.171844   \n",
       "std      23.235597     0.994675     3.039233    10.599579     6.435390   \n",
       "min       1.466600     0.363200     0.663700     1.119800     0.783700   \n",
       "25%       3.767150     0.744200     3.113950     1.936500     2.571400   \n",
       "50%       4.764400     1.135300     3.941450     2.534100     3.453800   \n",
       "75%       6.873850     1.538850     4.767950     3.609000     4.755800   \n",
       "max     898.608500    24.990400   113.223000   118.753300   186.616400   \n",
       "\n",
       "       feature_430  feature_431  feature_432  feature_433  feature_434  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean     18.412262    22.350676    99.314795   205.449868    14.728156   \n",
       "std      36.037997    36.372786   126.116776   225.643014    34.087451   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       7.006200    11.069050    31.033850    10.047450     7.551150   \n",
       "50%      11.105600    16.381000    57.969300   151.115600    10.197700   \n",
       "75%      17.402050    21.739650   120.136900   304.541800    12.752800   \n",
       "max     400.000000   400.000000   994.285700   995.744700   400.000000   \n",
       "\n",
       "       feature_435  feature_436  feature_437  feature_438  feature_439  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      9.364514     7.507205     4.016484    54.693892    70.637297   \n",
       "std      34.348266    34.536146     1.610266    34.086853    38.352115   \n",
       "min       0.000000     0.000000     1.156800     0.000000    14.120600   \n",
       "25%       3.495000     1.951700     3.071700    36.343700    48.177350   \n",
       "50%       4.551100     2.764300     3.780900    49.090900    65.437800   \n",
       "75%       5.822150     3.821250     4.678250    66.666700    84.944500   \n",
       "max     400.000000   400.000000    32.274000   851.612900   657.762100   \n",
       "\n",
       "       feature_440  feature_441  feature_442  feature_443  feature_444  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean     11.527330     0.802084     1.345207     0.633947     0.895048   \n",
       "std       6.165562     0.184154     0.658987     0.143506     0.155472   \n",
       "min       1.097300     0.351200     0.097400     0.216900     0.333600   \n",
       "25%       5.418350     0.679600     0.908800     0.550500     0.804800   \n",
       "50%      12.085900     0.807600     1.264550     0.643500     0.902700   \n",
       "75%      15.795850     0.927600     1.577550     0.733250     0.988800   \n",
       "max      33.058000     1.277100     5.131700     1.085100     1.351100   \n",
       "\n",
       "       feature_445  feature_446  feature_447  feature_448  feature_449  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000       1567.0   \n",
       "mean      0.647092     1.174996     0.281894     0.332218          0.0   \n",
       "std       0.141207     0.176102     0.086433     0.236209          0.0   \n",
       "min       0.308600     0.696800     0.084600     0.039900          0.0   \n",
       "25%       0.555800     1.046800     0.226100     0.187700          0.0   \n",
       "50%       0.651100     1.163800     0.279700     0.251200          0.0   \n",
       "75%       0.748400     1.272300     0.338650     0.351100          0.0   \n",
       "max       1.108700     1.763900     0.508500     1.475400          0.0   \n",
       "\n",
       "       feature_450  feature_451  feature_452  feature_453  feature_454  \\\n",
       "count       1567.0       1567.0  1567.000000  1567.000000  1567.000000   \n",
       "mean           0.0          0.0     5.346767     5.460822     7.883449   \n",
       "std            0.0          0.0     0.918905     2.250093     3.058705   \n",
       "min            0.0          0.0     2.670900     0.903700     2.329400   \n",
       "25%            0.0          0.0     4.765400     3.748750     5.808150   \n",
       "50%            0.0          0.0     5.271450     5.227100     7.424900   \n",
       "75%            0.0          0.0     5.912900     6.898750     9.576750   \n",
       "max            0.0          0.0    13.977600    34.490200    42.070300   \n",
       "\n",
       "       feature_455  feature_456  feature_457  feature_458  feature_459  \\\n",
       "count  1567.000000  1567.000000  1567.000000       1567.0  1567.000000   \n",
       "mean      3.636690    12.325063     5.263366          0.0     2.838375   \n",
       "std       0.938075     8.123318     4.536303          0.0     1.345147   \n",
       "min       0.694800     3.048900     1.442800          0.0     0.991000   \n",
       "25%       2.899750     8.817150     3.828650          0.0     2.291350   \n",
       "50%       3.724500    11.350900     4.793350          0.0     2.830350   \n",
       "75%       4.341750    14.387400     6.089400          0.0     3.308950   \n",
       "max      10.184000   232.125800   164.109300          0.0    47.777200   \n",
       "\n",
       "       feature_460  feature_461  feature_462  feature_463  feature_464  \\\n",
       "count   1567.00000       1567.0       1567.0       1567.0       1567.0   \n",
       "mean      29.19548          0.0          0.0          0.0          0.0   \n",
       "std       13.33115          0.0          0.0          0.0          0.0   \n",
       "min        7.95340          0.0          0.0          0.0          0.0   \n",
       "25%       20.22410          0.0          0.0          0.0          0.0   \n",
       "50%       26.16785          0.0          0.0          0.0          0.0   \n",
       "75%       35.26840          0.0          0.0          0.0          0.0   \n",
       "max      149.38510          0.0          0.0          0.0          0.0   \n",
       "\n",
       "       feature_465  feature_466  feature_467  feature_468  feature_469  \\\n",
       "count       1567.0       1567.0  1567.000000  1567.000000  1567.000000   \n",
       "mean           0.0          0.0     6.250541   223.843226     5.661566   \n",
       "std            0.0          0.0     8.662693   230.303247     3.145663   \n",
       "min            0.0          0.0     1.716300     0.000000     2.600900   \n",
       "25%            0.0          0.0     4.697500    38.882650     4.848500   \n",
       "50%            0.0          0.0     5.645000   150.340100     5.472400   \n",
       "75%            0.0          0.0     6.386900   334.674000     6.003200   \n",
       "max            0.0          0.0   109.007400   999.877000    77.800700   \n",
       "\n",
       "       feature_470  feature_471  feature_472  feature_473  feature_474  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      5.362749     9.630209   137.890044    39.403707    37.615532   \n",
       "std       4.974466    10.155553    47.591323    22.409521    24.769461   \n",
       "min       0.832500     2.402600    11.499700     0.000000     0.000000   \n",
       "25%       2.824000     5.821600   105.622150    24.933350    23.185450   \n",
       "50%       4.061100     7.396000   138.255150    34.246750    32.820050   \n",
       "75%       6.991150     9.707900   168.270250    47.690150    45.160350   \n",
       "max      87.134700   212.655700   492.771800   358.950400   415.435500   \n",
       "\n",
       "       feature_475  feature_476  feature_477  feature_478  feature_479  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      4.262626    20.116232     6.254032     0.127632     3.283016   \n",
       "std       2.606167    14.913155    10.165690     5.052374     2.633556   \n",
       "min       1.101100     0.000000     1.687200     0.000000     0.645900   \n",
       "25%       3.496850    11.585350     4.111500     0.000000     2.629900   \n",
       "50%       4.276200    15.973800     5.242200     0.000000     3.184500   \n",
       "75%       4.741050    23.682400     6.699750     0.000000     3.624550   \n",
       "max      79.116200   274.887100   289.826400   200.000000    63.333600   \n",
       "\n",
       "       feature_480  feature_481  feature_482  feature_483  feature_484  \\\n",
       "count  1567.000000       1567.0  1567.000000  1567.000000  1567.000000   \n",
       "mean     75.518589          0.0   318.037084   205.672096   214.117076   \n",
       "std      35.685329          0.0   278.866442   191.514533   211.695946   \n",
       "min       8.840600          0.0     0.000000     0.000000     0.000000   \n",
       "25%      52.971050          0.0     0.000000    82.410150    77.011800   \n",
       "50%      70.434500          0.0   293.518500   148.317500   138.775500   \n",
       "75%      92.911650          0.0   512.390750   260.079000   288.918450   \n",
       "max     221.974700          0.0   999.413500   989.473700   996.858600   \n",
       "\n",
       "       feature_485  feature_486  feature_487  feature_488  feature_489  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean    199.761505   301.700889   237.507454   352.553880   271.362828   \n",
       "std     217.277824   285.226689   262.273846   250.105428   226.384996   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%      51.188500     0.000000    57.316900   145.156850   113.806650   \n",
       "50%     112.953400   249.927000   112.275500   348.529400   219.487200   \n",
       "75%     283.289000   497.384500   391.277500   507.497050   372.341900   \n",
       "max     994.000000   999.491100   995.744700   997.518600   994.003500   \n",
       "\n",
       "       feature_490  feature_491  feature_492  feature_493  feature_494  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean     51.352261     2.441204     8.032313     2.530046     0.956442   \n",
       "std      18.042987     1.219698     0.669272     0.973948     6.615200   \n",
       "min      13.722500     0.555800     4.888200     0.833000     0.034200   \n",
       "25%      38.393700     1.747100     8.008950     1.663750     0.139000   \n",
       "50%      48.557450     2.250800     8.008950     2.529100     0.232500   \n",
       "75%      61.489950     2.830000     8.008950     3.199100     0.563000   \n",
       "max     142.843600    12.769800    21.044300     9.402400   127.572800   \n",
       "\n",
       "       feature_495  feature_496  feature_497  feature_498  feature_499  \\\n",
       "count  1567.000000  1567.000000  1567.000000       1567.0  1567.000000   \n",
       "mean      6.807826    29.611163    11.791267          0.0   262.859941   \n",
       "std       3.260019    24.257158     4.877969          0.0   324.699975   \n",
       "min       1.772000     4.813500     1.949600          0.0     0.000000   \n",
       "25%       5.274600    16.486550     8.272500          0.0     0.000000   \n",
       "50%       6.607900    22.039100    10.906550          0.0     0.000000   \n",
       "75%       7.897200    31.961500    14.337300          0.0   536.122600   \n",
       "max     107.692600   219.643600    40.281800          0.0  1000.000000   \n",
       "\n",
       "       feature_500  feature_501  feature_502  feature_503  feature_504  \\\n",
       "count  1567.000000       1567.0       1567.0       1567.0       1567.0   \n",
       "mean    240.673807          0.0          0.0          0.0          0.0   \n",
       "std     322.911797          0.0          0.0          0.0          0.0   \n",
       "min       0.000000          0.0          0.0          0.0          0.0   \n",
       "25%       0.000000          0.0          0.0          0.0          0.0   \n",
       "50%       0.000000          0.0          0.0          0.0          0.0   \n",
       "75%     505.225750          0.0          0.0          0.0          0.0   \n",
       "max     999.233700          0.0          0.0          0.0          0.0   \n",
       "\n",
       "       feature_505  feature_506  feature_507  feature_508  feature_509  \\\n",
       "count       1567.0       1567.0       1567.0       1567.0       1567.0   \n",
       "mean           0.0          0.0          0.0          0.0          0.0   \n",
       "std            0.0          0.0          0.0          0.0          0.0   \n",
       "min            0.0          0.0          0.0          0.0          0.0   \n",
       "25%            0.0          0.0          0.0          0.0          0.0   \n",
       "50%            0.0          0.0          0.0          0.0          0.0   \n",
       "75%            0.0          0.0          0.0          0.0          0.0   \n",
       "max            0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "       feature_510  feature_511  feature_512  feature_513  feature_514  \\\n",
       "count  1567.000000  1567.000000       1567.0       1567.0       1567.0   \n",
       "mean     55.752306   275.627217          0.0          0.0          0.0   \n",
       "std      37.668964   329.601505          0.0          0.0          0.0   \n",
       "min       0.000000     0.000000          0.0          0.0          0.0   \n",
       "25%      35.324400     0.000000          0.0          0.0          0.0   \n",
       "50%      46.986100     0.000000          0.0          0.0          0.0   \n",
       "75%      64.228450   554.010700          0.0          0.0          0.0   \n",
       "max     451.485100  1000.000000          0.0          0.0          0.0   \n",
       "\n",
       "       feature_515  feature_516  feature_517  feature_518  feature_519  \\\n",
       "count       1567.0  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean           0.0     0.351346     1.359116     1.665187     9.038411   \n",
       "std            0.0     6.383784     2.906512     2.791631    12.022316   \n",
       "min            0.0     0.028700     0.288000     0.467400     0.000000   \n",
       "25%            0.0     0.174700     1.154300     1.589100     5.430050   \n",
       "50%            0.0     0.174700     1.154300     1.589100     5.832950   \n",
       "75%            0.0     0.174700     1.154300     1.589100     6.468900   \n",
       "max            0.0   252.860400   113.275800   111.349500   184.348800   \n",
       "\n",
       "       feature_520  feature_521  feature_522  feature_523  feature_524  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      2.695999    11.610080    14.728866     0.453896     5.687782   \n",
       "std       5.702366   103.122996     7.104435     4.147581    20.663414   \n",
       "min       0.312100     0.000000     2.681100     0.025800     1.310400   \n",
       "25%       1.552150     0.000000    10.182800     0.073050     3.769650   \n",
       "50%       2.221000     0.000000    13.742600     0.100000     4.877100   \n",
       "75%       2.903700     0.000000    17.808950     0.133200     6.450650   \n",
       "max     111.736500  1000.000000   137.983800   111.333000   818.000500   \n",
       "\n",
       "       feature_525  feature_526  feature_527  feature_528  feature_529  \\\n",
       "count  1567.000000  1567.000000  1567.000000       1567.0       1567.0   \n",
       "mean      5.553870     1.443457     6.395717          0.0          0.0   \n",
       "std       3.890565     0.958428     1.888698          0.0          0.0   \n",
       "min       1.540000     0.170500     2.170000          0.0          0.0   \n",
       "25%       4.117100     0.484200     4.895450          0.0          0.0   \n",
       "50%       5.134200     1.550100     6.410800          0.0          0.0   \n",
       "75%       6.301850     2.211650     7.594250          0.0          0.0   \n",
       "max      80.040600     8.203700    14.447900          0.0          0.0   \n",
       "\n",
       "       feature_530  feature_531  feature_532  feature_533  feature_534  \\\n",
       "count       1567.0       1567.0       1567.0       1567.0       1567.0   \n",
       "mean           0.0          0.0          0.0          0.0          0.0   \n",
       "std            0.0          0.0          0.0          0.0          0.0   \n",
       "min            0.0          0.0          0.0          0.0          0.0   \n",
       "25%            0.0          0.0          0.0          0.0          0.0   \n",
       "50%            0.0          0.0          0.0          0.0          0.0   \n",
       "75%            0.0          0.0          0.0          0.0          0.0   \n",
       "max            0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "       feature_535  feature_536  feature_537  feature_538  feature_539  \\\n",
       "count       1567.0       1567.0       1567.0       1567.0   1567.00000   \n",
       "mean           0.0          0.0          0.0          0.0      3.03434   \n",
       "std            0.0          0.0          0.0          0.0      1.24971   \n",
       "min            0.0          0.0          0.0          0.0      0.85160   \n",
       "25%            0.0          0.0          0.0          0.0      1.88990   \n",
       "50%            0.0          0.0          0.0          0.0      3.05480   \n",
       "75%            0.0          0.0          0.0          0.0      3.94180   \n",
       "max            0.0          0.0          0.0          0.0      6.58030   \n",
       "\n",
       "       feature_540  feature_541  feature_542  feature_543  feature_544  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      1.942025     9.610850     0.111206     0.008470     0.002509   \n",
       "std       0.730143     2.888989     0.002736     0.001533     0.000295   \n",
       "min       0.614400     3.276100     0.105300     0.005100     0.001600   \n",
       "25%       1.390600     7.504200     0.109600     0.007800     0.002400   \n",
       "50%       1.785500     9.459300     0.109600     0.007800     0.002600   \n",
       "75%       2.454500    11.200850     0.113400     0.009000     0.002600   \n",
       "max       4.082500    25.779200     0.118400     0.024000     0.004700   \n",
       "\n",
       "       feature_545  feature_546  feature_547  feature_548  feature_549  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      7.610771     1.018304   403.476047    75.415081     0.631357   \n",
       "std       1.314823     0.358508     4.627143     3.152734     0.619061   \n",
       "min       4.429400     0.444400   372.822000    71.038000     0.044600   \n",
       "25%       7.116000     0.814100   400.814000    73.254000     0.289600   \n",
       "50%       7.116000     0.911100   403.122000    74.084000     0.471000   \n",
       "75%       8.008950     1.216500   406.763000    76.960000     0.773400   \n",
       "max      21.044300     3.978600   421.702000    83.720000     7.065600   \n",
       "\n",
       "       feature_550  feature_551  feature_552  feature_553  feature_554  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean     16.901595     1.217320     0.263615     7.658083     0.499550   \n",
       "std       4.542840     1.243363     0.253958     2.005003     0.546962   \n",
       "min       6.110000     0.120000     0.018700     2.786000     0.052000   \n",
       "25%      14.820000     0.910000     0.116700     6.738100     0.375000   \n",
       "50%      16.340000     1.150000     0.197900     7.427900     0.478900   \n",
       "75%      18.405000     1.330000     0.321750     8.316650     0.533550   \n",
       "max     131.680000    39.330000     2.718200    56.930300    17.478100   \n",
       "\n",
       "       feature_555  feature_556  feature_557  feature_558  feature_559  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean     57.198192     4.192049     1.607594     0.994995     0.325686   \n",
       "std      32.175775     1.170259     1.708471     0.083835     0.201329   \n",
       "min       4.826900     1.496700     0.164600     0.891900     0.069900   \n",
       "25%      34.147100     3.654100     1.224200     0.955200     0.149950   \n",
       "50%      54.441700     4.067100     1.529800     0.972700     0.290900   \n",
       "75%      69.630650     4.564950     1.815600     1.000800     0.443600   \n",
       "max     303.550000    35.319800    54.291700     1.512100     1.073700   \n",
       "\n",
       "       feature_560  feature_561  feature_562  feature_563  feature_564  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.072435    32.283326   262.998383     0.674651     6.221117   \n",
       "std       0.051562    19.020115     6.958289     0.111169     2.442203   \n",
       "min       0.017700     7.236900   242.286000     0.304900     0.970000   \n",
       "25%       0.036200    15.766900   262.101000     0.567100     4.980000   \n",
       "50%       0.059200    29.731150   264.272000     0.651000     5.160000   \n",
       "75%       0.089000    44.113400   264.733000     0.738250     7.310000   \n",
       "max       0.445700   101.114600   311.404000     1.298800    32.580000   \n",
       "\n",
       "       feature_565  feature_566  feature_567  feature_568  feature_569  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.141069     2.530656     0.058094     2.373545    20.398270   \n",
       "std       0.074373     0.954554     0.030083     0.921761     9.411775   \n",
       "min       0.022400     0.412200     0.009100     0.370600     3.250400   \n",
       "25%       0.087700     2.090200     0.038200     1.884400    15.466200   \n",
       "50%       0.119550     2.150450     0.048650     1.999700    16.988350   \n",
       "75%       0.166850     2.909150     0.068100     2.807750    23.035200   \n",
       "max       0.689200    14.014100     0.293200    12.746200    84.802400   \n",
       "\n",
       "       feature_570  feature_571  feature_572  feature_573  feature_574  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean    530.523623     2.101836    28.450165     0.345636     9.162315   \n",
       "std      17.499736     0.275112    86.304681     0.248478    26.920150   \n",
       "min     317.196400     0.980200     3.540000     0.066700     1.039500   \n",
       "25%     530.702700     1.982900     7.500000     0.242250     2.567850   \n",
       "50%     532.398200     2.118600     8.650000     0.293400     2.975800   \n",
       "75%     534.356400     2.290650    10.130000     0.366900     3.492500   \n",
       "max     589.508200     2.739500   454.560000     2.196700   170.020400   \n",
       "\n",
       "       feature_575  feature_576  feature_577  feature_578  feature_579  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.104729     5.563747    16.642363     0.020879     0.015600   \n",
       "std       0.067791    16.921369    12.485267     0.007387     0.006132   \n",
       "min       0.023000     0.663600     4.582000    -0.016900     0.003200   \n",
       "25%       0.075100     1.408450    11.501550     0.020400     0.014800   \n",
       "50%       0.089500     1.624500    13.817900     0.020400     0.014800   \n",
       "75%       0.112150     1.902000    17.080900     0.020400     0.014800   \n",
       "max       0.550200    90.423500    96.960100     0.102800     0.079900   \n",
       "\n",
       "       feature_580  feature_581  feature_582  feature_583  feature_584  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.004974    82.403069     0.500096     0.015317     0.003846   \n",
       "std       0.001985    56.348694     0.003403     0.017174     0.003719   \n",
       "min       0.001000     0.000000     0.477800     0.006000     0.001700   \n",
       "25%       0.004700    72.288900     0.497900     0.011600     0.003100   \n",
       "50%       0.004700    72.288900     0.500200     0.013800     0.003600   \n",
       "75%       0.004700    72.288900     0.502350     0.016500     0.004100   \n",
       "max       0.028600   737.304800     0.509800     0.476600     0.104500   \n",
       "\n",
       "       feature_585  feature_586  feature_587  feature_588  feature_589  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      3.067628     0.021458     0.016474     0.005283    99.652345   \n",
       "std       3.576899     0.012354     0.008805     0.002866    93.864558   \n",
       "min       1.197500    -0.016900     0.003200     0.001000     0.000000   \n",
       "25%       2.306500     0.013450     0.010600     0.003300    44.368600   \n",
       "50%       2.757650     0.020500     0.014800     0.004600    71.900500   \n",
       "75%       3.294950     0.027600     0.020300     0.006400   114.749700   \n",
       "max      99.303200     0.102800     0.079900     0.028600   737.304800   \n",
       "\n",
       "       HouseLineTestingResult  \n",
       "count             1567.000000  \n",
       "mean                -0.867262  \n",
       "std                  0.498010  \n",
       "min                 -1.000000  \n",
       "25%                 -1.000000  \n",
       "50%                 -1.000000  \n",
       "75%                 -1.000000  \n",
       "max                  1.000000  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View Statistic Summary\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handle Class Imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split test(90%) and train (10%) data\n",
    "targetOutcome = pd.DataFrame(df,columns=['HouseLineTestingResult'])\n",
    "allInputs = pd.DataFrame(df,columns=df.columns.difference(['HouseLineTestingResult']))\n",
    "X, XX, Y, YY = train_test_split(allInputs, targetOutcome, test_size = 0.1, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original training targets shape Counter({-1: 1463, 1: 104})\n",
      "Resampled dataset shape Counter({-1: 1463, 1: 1463})\n"
     ]
    }
   ],
   "source": [
    "# Modify data by handling class imbalance using SMOTE method\n",
    "print('Original training targets shape {}'.format(Counter(targetOutcome.values.ravel())))\n",
    "sm = SMOTE(random_state=0)\n",
    "allInputs_resample, targetOutcome_resample = sm.fit_sample(allInputs, targetOutcome.values.ravel())\n",
    "print('Resampled dataset shape {}'.format(Counter(targetOutcome_resample)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection\n",
    "I decide to use <font color=blue>**Backward Stepwise Model Selection**</font> which mean we start the model with all features included and we removes one feature on each iteration. The variables that can minimally increase the **Residual Sum of Squares (RSS)** on the data is chosen as the feature to be removed from the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False False False False  True False False False False False False False\n",
      " False False False False False False  True False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False  True False\n",
      "  True False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False  True\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False  True False  True False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      "  True False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False  True False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False  True False False\n",
      " False False]\n",
      "[416 426 114  69   1 130  15  14   5  59 395 222  77  84 350 421 167 144\n",
      "   1 406 111 255 150  44 208 228  49 237  50 218 240 171 363 362 221 466\n",
      " 311  31  74 367 273 425 415 422 283 438 394 215 468 277  19 116 126 286\n",
      "   7 204 467 398 345 459 101  95 205 186  97  43 428 424 399 452 442 463\n",
      " 135 134 302 164 251 104 266 306  61  88   1 169   1  37  91 196 489 469\n",
      " 207 391 172 246 195 170 386 473 274 352 472 211 471 485 487 474 470 152\n",
      " 318 309 223 248 343  76 360 319 347 230 314 263 379 243 436 312 444  57\n",
      "  46  89  54 110  86  27 177 336  51 443   3  33  48 349 278 405 506 151\n",
      "  30 495 465 475 491 476 477 501 492 488 493  16  12 464 490 481 494 484\n",
      "  83 109 106  67 257   1 124 372 250 184  71  92  79 479 499 478 500 125\n",
      " 498 486 483 529 510 482 496  73 148 217 282 328 364 358 329 411 214 502\n",
      " 276  39 115 291  56  34   6 180 480 344 376 100 157 357 175 185  96  41\n",
      " 451 414 420 441 462 133 226 439 163 325 356 249  94 212  64  87   1 244\n",
      "   1 280 310  90 160 504 517 497 232 131 234 284 202 292 353 509 261 351\n",
      " 505 503 539 521 515 259 524 149 317 231 209 324 293 224 227 305 161 313\n",
      " 262 393 316 396 322 321 315  42  45 404 138  53 220  85  26 193 371 153\n",
      "   1  32 162  47 384 119 400 532  20  29  21  28 511 118 512 508 519 548\n",
      " 526 516  13  11 525 528 281 507 534 146 359 136  65  55   1 369 112 268\n",
      " 183  70  40  78 536 513 514 531 549 522 342 385 553 545 527 533 518 269\n",
      " 295 213 327 361 373 355 216 410 387 530 388 229 413 448 457 520  98 206\n",
      " 523 392 375 323  99 181 256 287 197 412 418 454 431 432 307 308 165 408\n",
      " 378 192 377  62 258  24 238  25  38 141 201 543 198 535 538 233 173 235\n",
      " 194 390 354 541 260 279 374 542 556 537 561 547 552 239 460 331 264 242\n",
      " 247 389 320 383 225 382 271 297 241 200 401 550 458 456 449 433 446 453\n",
      " 429 455 540 338 122 298  81 168 348 380 368 571 450 551 199 437 544 565\n",
      " 546 564 555 569 557 563 560 403 417 435 558 562 559 554 107 108 105 304\n",
      " 578 299 445 370 332 285 182  93 166 579 568   9 580 581 573 574 576 572\n",
      " 577 575 566  75  10 147 272 127  22   2 128 236 326 419 189 407 179 142\n",
      " 188 430 294 434 178 143 187 253  52 337 254 333 158 330 174 155 102 154\n",
      " 334   8 366 190 365 145 341 129 340 339 219  18 245  17 427 275  36  35\n",
      " 117 103  58 210 440 300 409 288 191 270 381 265 267 296 447 290 570 176\n",
      " 289 397 346 402 335  68 113 156 252 123 203 303  63 137 301 121  23  82\n",
      " 132 423 120  80 461 159   4  66  72   1 140 567 139  60]\n",
      "\u001b[1m\u001b[4mSeledted features from Backward Stepwise Model Selection are: \u001b[0m\n",
      "feature_4\n",
      "feature_18\n",
      "feature_82\n",
      "feature_84\n",
      "feature_167\n",
      "feature_232\n",
      "feature_234\n",
      "feature_288\n",
      "feature_320\n",
      "feature_585\n"
     ]
    }
   ],
   "source": [
    "# Create linear regression model as the estimator\n",
    "estimator = LinearRegression()\n",
    "# From the Filter method, we select 10 features -- this number is arbitary (need to find the optimal number with further analysis)\n",
    "selector = RFE(estimator, 10, step=1)\n",
    "# Learn from this dataset\n",
    "selector = selector.fit(allInputs, targetOutcome)\n",
    "# The mask of selected features (which variables are selected)\n",
    "print(selector.support_)\n",
    "# Selected features are ranked 1. The variable with the highest rank is the one that is removed first\n",
    "print(selector.ranking_)\n",
    "# Print the name of selected features\n",
    "f = selector.get_support(True) # the most important features\n",
    "print('\\033[1m\\033[4mSeledted features from Backward Stepwise Model Selection are: \\033[0m')\n",
    "for f_index in f:\n",
    "    print(df.columns[f_index])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**Summary:** Initial finding are:\n",
    "\n",
    "- Based on **Statistic Summary**, we found that this data set has a wide range of values amoung each features. Therefore, we need to perform the **Normalization** for further analysis.\n",
    "- Some features act like the constant and are not provide any benefit to the analysis at all such as \n",
    "    - **feature_5** which has constant value = 100\n",
    "    - **feature_13** which has constant value = 0\n",
    "    - **feature_42** which has constant value = 70\n",
    "    - **feature_49** which has constant value = 1\n",
    "- Based on an feature selection method using **Backward Stepwise Model Selection**, the model has select first ten important features:\n",
    "    - feature_4\n",
    "    - feature_18\n",
    "    - feature_82\n",
    "    - feature_84\n",
    "    - feature_167\n",
    "    - feature_232\n",
    "    - feature_234\n",
    "    - feature_288\n",
    "    - feature_320\n",
    "    - feature_585\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**Solicit Feedback:**  \n",
    "\n",
    "- I decide to drop the **TimeStamp** features from the data set since I stated that we didn't perform the **Times Series** analysis. My question is \"Is it possible to perform the Time Series Analysis\" to measure the manufacturing quality (Quality Control)? Is there any real scenario that there is a trend in the quality control of the production line? I feel like time of the year should not be related when the product will pass or failed.\n",
    "- For **feature selection** using **Backward Stepwise Model**, I did **select 10 features** by arbitary. I did stated that I need to try to find the optimal number of this n features. Is the **Accuracy Measurement (AUC, Recall, Precision)** the right way (select the n features from the model that yeild the best accuracy)?\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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